Bump up the version to v0.2.0 (#1212)

Co-authored-by: timlacroix <t@mistral.ai>

.github/workflows/publish.yml

@@ -0,0 +1,101 @@
+# This workflow will upload a Python Package to Release asset
+# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions
+
+name: Create Release
+
+on:
+  push:
+    tags:
+      - v*
+
+# Needed to create release and upload assets
+permissions:
+  contents: write
+
+jobs:
+  release:
+    # Retrieve tag and create release
+    name: Create Release
+    runs-on: ubuntu-latest
+    outputs:
+      upload_url: ${{ steps.create_release.outputs.upload_url }}
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Extract branch info
+        shell: bash
+        run: |
+          echo "release_tag=${GITHUB_REF#refs/*/}" >> $GITHUB_ENV
+
+      - name: Create Release
+        id: create_release
+        uses: "actions/github-script@v6"
+        env:
+          RELEASE_TAG: ${{ env.release_tag }}
+        with:
+          github-token: "${{ secrets.GITHUB_TOKEN }}"
+          script: |
+            const script = require('.github/workflows/scripts/create_release.js')
+            await script(github, context, core)
+
+  wheel:
+    name: Build Wheel
+    runs-on: ${{ matrix.os }}
+    needs: release
+    
+    strategy:
+      fail-fast: false
+      matrix:
+          os: ['ubuntu-20.04']
+          python-version: ['3.8', '3.9', '3.10', '3.11']
+          cuda-version: ['11.8'] # Github runner can't build anything older than 11.8
+
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Set up Linux Env
+        if: ${{ runner.os == 'Linux' }}
+        run: |
+          bash -x .github/workflows/scripts/env.sh
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+            python-version: ${{ matrix.python-version }}
+
+      - name: Install CUDA ${{ matrix.cuda-version }}
+        run: |
+          bash -x .github/workflows/scripts/cuda-install.sh ${{ matrix.cuda-version }} ${{ matrix.os }}
+
+      - name: Install PyTorch-cu${{ matrix.cuda-version }}
+        run: |
+          bash -x .github/workflows/scripts/pytorch-install.sh ${{ matrix.python-version }} ${{ matrix.cuda-version }}
+
+      - name: Build wheel
+        shell: bash
+        run: |
+          bash -x .github/workflows/scripts/build.sh ${{ matrix.python-version }} ${{ matrix.cuda-version }}
+          wheel_name=$(ls dist/*whl | xargs -n 1 basename)
+          asset_name=${wheel_name//"linux"/"manylinux1"}
+          echo "wheel_name=${wheel_name}" >> $GITHUB_ENV
+          echo "asset_name=${asset_name}" >> $GITHUB_ENV
+      
+      - name: Upload Release Asset
+        uses: actions/upload-release-asset@v1
+        env:
+          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        with:
+          upload_url: ${{ needs.release.outputs.upload_url }}
+          asset_path: ./dist/${{ env.wheel_name }}
+          asset_name: ${{ env.asset_name }}
+          asset_content_type: application/*
+
+      # (Danielkinz): This last step will publish the .whl to pypi. Warning: untested
+      # - name: Publish package
+      #   uses: pypa/gh-action-pypi-publish@release/v1.8
+      #   with:
+      #     repository-url: https://test.pypi.org/legacy/
+      #     password: ${{ secrets.PYPI_API_TOKEN }}
+      #     skip-existing: true

.github/workflows/scripts/build.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+
+python_executable=python$1
+cuda_home=/usr/local/cuda-$2
+
+# Update paths
+PATH=${cuda_home}/bin:$PATH
+LD_LIBRARY_PATH=${cuda_home}/lib64:$LD_LIBRARY_PATH
+
+# Install requirements
+$python_executable -m pip install wheel packaging
+$python_executable -m pip install -r requirements.txt
+
+# Build
+$python_executable setup.py bdist_wheel --dist-dir=dist

.github/workflows/scripts/create_release.js

@@ -0,0 +1,20 @@
+// Uses Github's API to create the release and wait for result.
+// We use a JS script since github CLI doesn't provide a way to wait for the release's creation and returns immediately.
+
+module.exports = async (github, context, core) => {
+	try {
+		const response = await github.rest.repos.createRelease({
+			draft: false,
+			generate_release_notes: true,
+			name: process.env.RELEASE_TAG,
+			owner: context.repo.owner,
+			prerelease: false,
+			repo: context.repo.repo,
+			tag_name: process.env.RELEASE_TAG,
+		});
+
+		core.setOutput('upload_url', response.data.upload_url);
+	} catch (error) {
+		core.setFailed(error.message);
+	}
+}

.github/workflows/scripts/cuda-install.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+# Replace '.' with '-' ex: 11.8 -> 11-8
+cuda_version=$(echo $1 | tr "." "-")
+# Removes '-' and '.' ex: ubuntu-20.04 -> ubuntu2004
+OS=$(echo $2 | tr -d ".\-")
+
+# Installs CUDA
+wget -nv https://developer.download.nvidia.com/compute/cuda/repos/${OS}/x86_64/cuda-keyring_1.1-1_all.deb
+sudo dpkg -i cuda-keyring_1.1-1_all.deb
+rm cuda-keyring_1.1-1_all.deb
+sudo apt -qq update
+sudo apt -y install cuda-${cuda_version} cuda-nvcc-${cuda_version} cuda-libraries-dev-${cuda_version}
+sudo apt clean
+
+# Test nvcc
+PATH=/usr/local/cuda-$1/bin:${PATH}
+nvcc --version

.github/workflows/scripts/env.sh

@@ -0,0 +1,56 @@
+#!/bin/bash
+
+# This file installs common linux environment tools
+
+export LANG C.UTF-8
+
+# python_version=$1
+
+sudo    apt-get update && \
+sudo    apt-get install -y --no-install-recommends \
+        software-properties-common \
+
+sudo    apt-get install -y --no-install-recommends \
+        build-essential \
+        apt-utils \
+        ca-certificates \
+        wget \
+        git \
+        vim \
+        libssl-dev \
+        curl \
+        unzip \
+        unrar \
+        cmake \
+        net-tools \
+        sudo \
+        autotools-dev \
+        rsync \
+        jq \
+        openssh-server \
+        tmux \
+        screen \
+        htop \
+        pdsh \
+        openssh-client \
+        lshw \
+        dmidecode \
+        util-linux \
+        automake \
+        autoconf \
+        libtool \
+        net-tools \
+        pciutils \
+        libpci-dev \
+        libaio-dev \
+        libcap2 \
+        libtinfo5 \
+        fakeroot \
+        devscripts \
+        debhelper \
+        nfs-common
+
+# Remove github bloat files to free up disk space
+sudo rm -rf "/usr/local/share/boost"
+sudo rm -rf "$AGENT_TOOLSDIRECTORY"
+sudo rm -rf "/usr/share/dotnet"

.github/workflows/scripts/pytorch-install.sh

@@ -0,0 +1,14 @@
+#!/bin/bash
+
+python_executable=python$1
+cuda_version=$2
+
+# Install torch
+$python_executable -m pip install numpy pyyaml scipy ipython mkl mkl-include ninja cython typing pandas typing-extensions dataclasses setuptools && conda clean -ya
+$python_executable -m pip install torch -f https://download.pytorch.org/whl/cu${cuda_version//./}/torch_stable.html
+
+# Print version information
+$python_executable --version
+$python_executable -c "import torch; print('PyTorch:', torch.__version__)"
+$python_executable -c "import torch; print('CUDA:', torch.version.cuda)"
+$python_executable -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)"

.gitignore

@@ -173,3 +173,7 @@ cython_debug/
 
 # Sphinx documentation
 _build/
+
+# vim swap files
+*.swo
+*.swp

README.md

@@ -10,13 +10,26 @@ Easy, fast, and cheap LLM serving for everyone
 </h3>
 
 <p align="center">
-| <a href="https://vllm.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://github.com/vllm-project/vllm/discussions"><b>Discussions</b></a> |
+| <a href="https://vllm.readthedocs.io/en/latest/"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://discord.gg/jz7wjKhh6g"><b>Discord</b></a> |
 
 </p>
 
 ---
 
+**The First vLLM Bay Area Meetup (Oct 5th 6pm-8pm PT)**
+
+We are excited to invite you to the first vLLM meetup!
+The vLLM team will share recent updates and roadmap.
+We will also have vLLM users and contributors coming up to the stage to share their experiences.
+Please register [here](https://lu.ma/first-vllm-meetup) and join us!
+
+---
+
 *Latest News* 🔥
+- [2023/09] We created our [Discord server](https://discord.gg/jz7wjKhh6g)! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.
+- [2023/09] We released our [PagedAttention paper](https://arxiv.org/abs/2309.06180) on arXiv!
+- [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (a16z) for providing a generous grant to support the open-source development and research of vLLM.
+- [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!
 - [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click [example](https://github.com/skypilot-org/skypilot/blob/master/llm/vllm) to start the vLLM demo, and the [blog post](https://blog.skypilot.co/serving-llm-24x-faster-on-the-cloud-with-vllm-and-skypilot/) for the story behind vLLM development on the clouds.
 - [2023/06] We officially released vLLM! FastChat-vLLM integration has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai).
 
@@ -33,21 +46,28 @@ vLLM is fast with:
 
 vLLM is flexible and easy to use with:
 
-- Seamless integration with popular HuggingFace models
+- Seamless integration with popular Hugging Face models
 - High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
 - Tensor parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
 
-vLLM seamlessly supports many Huggingface models, including the following architectures:
+vLLM seamlessly supports many Hugging Face models, including the following architectures:
 
+- Aquila (`BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc.)
+- Baichuan (`baichuan-inc/Baichuan-7B`, `baichuan-inc/Baichuan-13B-Chat`, etc.)
 - BLOOM (`bigscience/bloom`, `bigscience/bloomz`, etc.)
+- Falcon (`tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc.)
 - GPT-2 (`gpt2`, `gpt2-xl`, etc.)
 - GPT BigCode (`bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, etc.)
+- GPT-J (`EleutherAI/gpt-j-6b`, `nomic-ai/gpt4all-j`, etc.)
 - GPT-NeoX (`EleutherAI/gpt-neox-20b`, `databricks/dolly-v2-12b`, `stabilityai/stablelm-tuned-alpha-7b`, etc.)
-- LLaMA (`lmsys/vicuna-13b-v1.3`, `young-geng/koala`, `openlm-research/open_llama_13b`, etc.)
+- InternLM (`internlm/internlm-7b`, `internlm/internlm-chat-7b`, etc.)
+- LLaMA & LLaMA-2 (`meta-llama/Llama-2-70b-hf`, `lmsys/vicuna-13b-v1.3`, `young-geng/koala`, `openlm-research/open_llama_13b`, etc.)
+- Mistral (`mistralai/Mistral-7B-v0.1`, `mistralai/Mistral-7B-Instruct-v0.1`, etc.)
 - MPT (`mosaicml/mpt-7b`, `mosaicml/mpt-30b`, etc.)
 - OPT (`facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc.)
+- Qwen (`Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc.)
 
 Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):
 
@@ -64,7 +84,7 @@ Visit our [documentation](https://vllm.readthedocs.io/en/latest/) to get started
 
 ## Performance
 
-vLLM outperforms HuggingFace Transformers (HF) by up to 24x and Text Generation Inference (TGI) by up to 3.5x, in terms of throughput.
+vLLM outperforms Hugging Face Transformers (HF) by up to 24x and Text Generation Inference (TGI) by up to 3.5x, in terms of throughput.
 For details, check out our [blog post](https://vllm.ai).
 
 <p align="center">
@@ -96,3 +116,15 @@ For details, check out our [blog post](https://vllm.ai).
 
 We welcome and value any contributions and collaborations.
 Please check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.
+
+## Citation
+
+If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs/2309.06180):
+```bibtex
+@inproceedings{kwon2023efficient,
+  title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
+  author={Woosuk Kwon and Zhuohan Li and Siyuan Zhuang and Ying Sheng and Lianmin Zheng and Cody Hao Yu and Joseph E. Gonzalez and Hao Zhang and Ion Stoica},
+  booktitle={Proceedings of the ACM SIGOPS 29th Symposium on Operating Systems Principles},
+  year={2023}
+}
+```

benchmarks/benchmark_latency.py

@@ -18,9 +18,11 @@ def main(args: argparse.Namespace):
     llm = LLM(
         model=args.model,
         tokenizer=args.tokenizer,
+        quantization=args.quantization,
         tensor_parallel_size=args.tensor_parallel_size,
         max_num_seqs=args.batch_size,
         max_num_batched_tokens=args.batch_size * args.input_len,
+        trust_remote_code=args.trust_remote_code,
     )
 
     sampling_params = SamplingParams(
@@ -62,17 +64,28 @@ def main(args: argparse.Namespace):
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(
         description='Benchmark the latency of processing a single batch of '
-                    'requests till completion.')
+        'requests till completion.')
     parser.add_argument('--model', type=str, default='facebook/opt-125m')
     parser.add_argument('--tokenizer', type=str, default=None)
+    parser.add_argument('--quantization',
+                        '-q',
+                        choices=['awq', None],
+                        default=None)
     parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
     parser.add_argument('--input-len', type=int, default=32)
     parser.add_argument('--output-len', type=int, default=128)
     parser.add_argument('--batch-size', type=int, default=8)
-    parser.add_argument('--n', type=int, default=1,
+    parser.add_argument('--n',
+                        type=int,
+                        default=1,
                         help='Number of generated sequences per prompt.')
     parser.add_argument('--use-beam-search', action='store_true')
-    parser.add_argument('--num-iters', type=int, default=3,
+    parser.add_argument('--num-iters',
+                        type=int,
+                        default=3,
                         help='Number of iterations to run.')
+    parser.add_argument('--trust-remote-code',
+                        action='store_true',
+                        help='trust remote code from huggingface')
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_serving.py

@@ -177,7 +177,7 @@ def main(args: argparse.Namespace):
     np.random.seed(args.seed)
 
     api_url = f"http://{args.host}:{args.port}/generate"
-    tokenizer = get_tokenizer(args.tokenizer)
+    tokenizer = get_tokenizer(args.tokenizer, trust_remote_code=args.trust_remote_code)
     input_requests = sample_requests(args.dataset, args.num_prompts, tokenizer)
 
     benchmark_start_time = time.time()
@@ -227,5 +227,7 @@ if __name__ == "__main__":
                              "Otherwise, we use Poisson process to synthesize "
                              "the request arrival times.")
     parser.add_argument("--seed", type=int, default=0)
+    parser.add_argument('--trust-remote-code', action='store_true',
+                        help='trust remote code from huggingface')
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_throughput.py

@@ -3,7 +3,7 @@ import argparse
 import json
 import random
 import time
-from typing import List, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 from transformers import AutoModelForCausalLM, PreTrainedTokenizerBase
@@ -22,15 +22,10 @@ def sample_requests(
     with open(dataset_path) as f:
         dataset = json.load(f)
     # Filter out the conversations with less than 2 turns.
-    dataset = [
-        data for data in dataset
-        if len(data["conversations"]) >= 2
-    ]
+    dataset = [data for data in dataset if len(data["conversations"]) >= 2]
     # Only keep the first two turns of each conversation.
-    dataset = [
-        (data["conversations"][0]["value"], data["conversations"][1]["value"])
-        for data in dataset
-    ]
+    dataset = [(data["conversations"][0]["value"],
+                data["conversations"][1]["value"]) for data in dataset]
 
     # Tokenize the prompts and completions.
     prompts = [prompt for prompt, _ in dataset]
@@ -63,16 +58,20 @@ def run_vllm(
     requests: List[Tuple[str, int, int]],
     model: str,
     tokenizer: str,
+    quantization: Optional[str],
     tensor_parallel_size: int,
     seed: int,
     n: int,
     use_beam_search: bool,
+    trust_remote_code: bool,
 ) -> float:
     llm = LLM(
         model=model,
         tokenizer=tokenizer,
+        quantization=quantization,
         tensor_parallel_size=tensor_parallel_size,
         seed=seed,
+        trust_remote_code=trust_remote_code,
     )
 
     # Add the requests to the engine.
@@ -106,9 +105,11 @@ def run_hf(
     n: int,
     use_beam_search: bool,
     max_batch_size: int,
+    trust_remote_code: bool,
 ) -> float:
     assert not use_beam_search
-    llm = AutoModelForCausalLM.from_pretrained(model, torch_dtype=torch.float16)
+    llm = AutoModelForCausalLM.from_pretrained(
+        model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
     if llm.config.model_type == "llama":
         # To enable padding in the HF backend.
         tokenizer.pad_token = tokenizer.eos_token
@@ -128,13 +129,14 @@ def run_hf(
         if len(batch) < max_batch_size and i != len(requests) - 1:
             # Check if we can add more requests to the batch.
             _, next_prompt_len, next_output_len = requests[i + 1]
-            if (max(max_prompt_len, next_prompt_len) + max(
-                max_output_len, next_output_len)) <= 2048:
+            if (max(max_prompt_len, next_prompt_len) +
+                    max(max_output_len, next_output_len)) <= 2048:
                 # We can add more requests to the batch.
                 continue
 
         # Generate the sequences.
-        input_ids = tokenizer(batch, return_tensors="pt", padding=True).input_ids
+        input_ids = tokenizer(batch, return_tensors="pt",
+                              padding=True).input_ids
         llm_outputs = llm.generate(
             input_ids=input_ids.cuda(),
             do_sample=not use_beam_search,
@@ -161,44 +163,62 @@ def main(args: argparse.Namespace):
     random.seed(args.seed)
 
     # Sample the requests.
-    tokenizer = get_tokenizer(args.tokenizer)
+    tokenizer = get_tokenizer(args.tokenizer,
+                              trust_remote_code=args.trust_remote_code)
     requests = sample_requests(args.dataset, args.num_prompts, tokenizer)
 
     if args.backend == "vllm":
-        elapsed_time = run_vllm(
-            requests, args.model, args.tokenizer, args.tensor_parallel_size,
-            args.seed, args.n, args.use_beam_search)
+        elapsed_time = run_vllm(requests, args.model, args.tokenizer,
+                                args.quantization, args.tensor_parallel_size,
+                                args.seed, args.n, args.use_beam_search,
+                                args.trust_remote_code)
     elif args.backend == "hf":
         assert args.tensor_parallel_size == 1
         elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
-                              args.use_beam_search, args.hf_max_batch_size)
+                              args.use_beam_search, args.hf_max_batch_size,
+                              args.trust_remote_code)
     else:
         raise ValueError(f"Unknown backend: {args.backend}")
-    total_num_tokens = sum(
-        prompt_len + output_len
-        for _, prompt_len, output_len in requests
-    )
+    total_num_tokens = sum(prompt_len + output_len
+                           for _, prompt_len, output_len in requests)
     print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
           f"{total_num_tokens / elapsed_time:.2f} tokens/s")
 
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Benchmark the throughput.")
-    parser.add_argument("--backend", type=str, choices=["vllm", "hf"],
+    parser.add_argument("--backend",
+                        type=str,
+                        choices=["vllm", "hf"],
                         default="vllm")
-    parser.add_argument("--dataset", type=str, required=True,
+    parser.add_argument("--dataset",
+                        type=str,
+                        required=True,
                         help="Path to the dataset.")
     parser.add_argument("--model", type=str, default="facebook/opt-125m")
     parser.add_argument("--tokenizer", type=str, default=None)
+    parser.add_argument('--quantization',
+                        '-q',
+                        choices=['awq', None],
+                        default=None)
     parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
-    parser.add_argument("--n", type=int, default=1,
+    parser.add_argument("--n",
+                        type=int,
+                        default=1,
                         help="Number of generated sequences per prompt.")
     parser.add_argument("--use-beam-search", action="store_true")
-    parser.add_argument("--num-prompts", type=int, default=1000,
+    parser.add_argument("--num-prompts",
+                        type=int,
+                        default=1000,
                         help="Number of prompts to process.")
     parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument("--hf-max-batch-size", type=int, default=None,
+    parser.add_argument("--hf-max-batch-size",
+                        type=int,
+                        default=None,
                         help="Maximum batch size for HF backend.")
+    parser.add_argument('--trust-remote-code',
+                        action='store_true',
+                        help='trust remote code from huggingface')
     args = parser.parse_args()
 
     if args.backend == "vllm":
@@ -207,6 +227,8 @@ if __name__ == "__main__":
     elif args.backend == "hf":
         if args.hf_max_batch_size is None:
             raise ValueError("HF max batch size is required for HF backend.")
+        if args.quantization is not None:
+            raise ValueError("Quantization is only for vLLM backend.")
     if args.tokenizer is None:
         args.tokenizer = args.model
 

csrc/activation.cpp

@@ -4,9 +4,25 @@ void silu_and_mul(
   torch::Tensor& out,
   torch::Tensor& input);
 
+void gelu_new(
+  torch::Tensor& out,
+  torch::Tensor& input);
+
+void gelu_fast(
+  torch::Tensor& out,
+  torch::Tensor& input);
+
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def(
     "silu_and_mul",
     &silu_and_mul,
     "Activation function used in SwiGLU.");
+  m.def(
+    "gelu_new",
+    &gelu_new,
+    "GELU implementation used in GPT-2.");
+  m.def(
+    "gelu_fast",
+    &gelu_fast,
+    "Approximate GELU implementation.");
 }

csrc/activation_kernels.cu

@@ -1,6 +1,8 @@
 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
 
+#include "dispatch_utils.h"
+
 namespace vllm {
 
 template<typename T>
@@ -34,9 +36,7 @@ void silu_and_mul(
   dim3 grid(num_tokens);
   dim3 block(std::min(d, 1024));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     input.scalar_type(),
     "silu_and_mul_kernel",
     [&] {
@@ -46,3 +46,69 @@ void silu_and_mul(
         d);
     });
 }
+
+namespace vllm {
+
+// Element-wise activation kernel template.
+template<typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
+__global__ void activation_kernel(
+  scalar_t* __restrict__ out,               // [num_tokens, d]
+  const scalar_t* __restrict__ input,       // [num_tokens, d]
+  const int d) {
+  const int token_idx = blockIdx.x;
+  for (int idx = threadIdx.x; idx < d; idx += blockDim.x) {
+    const scalar_t x = __ldg(&input[token_idx * d + idx]);
+    out[token_idx * d + idx] = ACT_FN(x);
+  }
+}
+
+} // namespace vllm
+
+// Launch element-wise activation kernel.
+#define LAUNCH_ACTIVATION_KERNEL(KERNEL)                                                  \
+  int num_tokens = input.size(0);                                                         \
+  int d = input.size(1);                                                                  \
+  dim3 grid(num_tokens);                                                                  \
+  dim3 block(std::min(d, 1024));                                                          \
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();                           \
+  VLLM_DISPATCH_FLOATING_TYPES(                                                           \
+    input.scalar_type(),                                                                  \
+    "activation_kernel",                                                                  \
+    [&] {                                                                                 \
+      vllm::activation_kernel<scalar_t, KERNEL<scalar_t>><<<grid, block, 0, stream>>>(    \
+        out.data_ptr<scalar_t>(),                                                         \
+        input.data_ptr<scalar_t>(),                                                       \
+        d);                                                                               \
+    });
+
+namespace vllm {
+
+template<typename T>
+__device__ __forceinline__ T gelu_new_kernel(const T& x) {
+  const float x3 = (float) (x * x * x);
+  const T t = (T) tanhf((T) (0.79788456f * (float) (x + (T) (0.044715f * x3))));
+  return ((T) 0.5) * x * (((T) 1.0) + t);
+}
+
+template<typename T>
+__device__ __forceinline__ T gelu_fast_kernel(const T& x) {
+  const float f = (float) x;
+  const T t = (T) tanhf(((T) (f * 0.79788456f)) * (((T) 1.0) + (T) (0.044715f * f) * x));
+  return ((T) 0.5) * x * (((T) 1.0) + t);
+}
+
+} // namespace vllm
+
+void gelu_new(
+  torch::Tensor& out,     // [num_tokens, d]
+  torch::Tensor& input)   // [num_tokens, d]
+{
+  LAUNCH_ACTIVATION_KERNEL(vllm::gelu_new_kernel);
+}
+
+void gelu_fast(
+  torch::Tensor& out,     // [num_tokens, d]
+  torch::Tensor& input)   // [num_tokens, d]
+{
+  LAUNCH_ACTIVATION_KERNEL(vllm::gelu_fast_kernel);
+}

csrc/attention.cpp

@@ -6,6 +6,7 @@ void single_query_cached_kv_attention(
   torch::Tensor& query,
   torch::Tensor& key_cache,
   torch::Tensor& value_cache,
+  torch::Tensor& head_mapping,
   float scale,
   torch::Tensor& block_tables,
   torch::Tensor& context_lens,

csrc/attention/attention_kernels.cu

@@ -74,15 +74,20 @@ template<
 __global__ void single_query_cached_kv_attention_kernel(
   scalar_t* __restrict__ out,             // [num_seqs, num_heads, head_size]
   const scalar_t* __restrict__ q,         // [num_seqs, num_heads, head_size]
-  const scalar_t* __restrict__ k_cache,   // [num_blocks, num_heads, head_size/x, block_size, x]
-  const scalar_t* __restrict__ v_cache,   // [num_blocks, num_heads, head_size, block_size]
+  const scalar_t* __restrict__ k_cache,   // [num_blocks, num_kv_heads, head_size/x, block_size, x]
+  const scalar_t* __restrict__ v_cache,   // [num_blocks, num_kv_heads, head_size, block_size]
+  const int* __restrict__ head_mapping,   // [num_heads]
   const float scale,
   const int* __restrict__ block_tables,   // [num_seqs, max_num_blocks_per_seq]
   const int* __restrict__ context_lens,   // [num_seqs]
   const int max_num_blocks_per_seq,
   const float* __restrict__ alibi_slopes, // [num_heads]
-  const int q_stride) {
+  const int q_stride,
+  const int kv_block_stride,
+  const int kv_head_stride) {
   constexpr int THREAD_GROUP_SIZE = MAX(WARP_SIZE / BLOCK_SIZE, 1);
+  constexpr int NUM_THREAD_GROUPS = NUM_THREADS / THREAD_GROUP_SIZE; // Note: This assumes THREAD_GROUP_SIZE divides NUM_THREADS
+  assert(NUM_THREADS % THREAD_GROUP_SIZE == 0);
   constexpr int NUM_TOKENS_PER_THREAD_GROUP = (BLOCK_SIZE + WARP_SIZE - 1) / WARP_SIZE;
   constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
   const int thread_idx = threadIdx.x;
@@ -91,6 +96,7 @@ __global__ void single_query_cached_kv_attention_kernel(
 
   const int head_idx = blockIdx.x;
   const int num_heads = gridDim.x;
+  const int kv_head_idx = head_mapping[head_idx];
   const int seq_idx = blockIdx.y;
   const float alibi_slope = alibi_slopes == nullptr ? 0.f : alibi_slopes[head_idx];
 
@@ -116,12 +122,13 @@ __global__ void single_query_cached_kv_attention_kernel(
   // th vectors of the query, and so on.
   // NOTE(woosuk): Because q is split from a qkv tensor, it may not be contiguous.
   const scalar_t* q_ptr = q + seq_idx * q_stride + head_idx * HEAD_SIZE;
-  Q_vec q_vecs[NUM_VECS_PER_THREAD];
+  __shared__ Q_vec q_vecs[THREAD_GROUP_SIZE][NUM_VECS_PER_THREAD];
 #pragma unroll
-  for (int i = 0; i < NUM_VECS_PER_THREAD; i++) {
+  for (int i = thread_group_idx; i < NUM_VECS_PER_THREAD; i += NUM_THREAD_GROUPS) {
     const int vec_idx = thread_group_offset + i * THREAD_GROUP_SIZE;
-    q_vecs[i] = *reinterpret_cast<const Q_vec*>(q_ptr + vec_idx * VEC_SIZE);
+    q_vecs[thread_group_offset][i] = *reinterpret_cast<const Q_vec*>(q_ptr + vec_idx * VEC_SIZE);
   }
+  __syncthreads(); // TODO(naed90): possible speedup if this is replaced with a memory wall right before we use q_vecs
 
   // Memory planning.
   extern __shared__ char shared_mem[];
@@ -158,8 +165,8 @@ __global__ void single_query_cached_kv_attention_kernel(
 
 #pragma unroll
       for (int j = 0; j < NUM_VECS_PER_THREAD; j++) {
-        const scalar_t* k_ptr = k_cache + physical_block_number * num_heads * HEAD_SIZE * BLOCK_SIZE
-                                        + head_idx * HEAD_SIZE * BLOCK_SIZE
+        const scalar_t* k_ptr = k_cache + physical_block_number * kv_block_stride
+                                        + kv_head_idx * kv_head_stride
                                         + physical_block_offset * x;
         const int vec_idx = thread_group_offset + j * THREAD_GROUP_SIZE;
         const int offset1 = (vec_idx * VEC_SIZE) / x;
@@ -169,9 +176,9 @@ __global__ void single_query_cached_kv_attention_kernel(
 
       // Compute dot product.
       // This includes a reduction across the threads in the same thread group.
-      float qk = scale * Qk_dot<scalar_t, THREAD_GROUP_SIZE>::dot(q_vecs, k_vecs);
+      float qk = scale * Qk_dot<scalar_t, THREAD_GROUP_SIZE>::dot(q_vecs[thread_group_offset], k_vecs);
       // Add the ALiBi bias if slopes are given.
-      qk += (alibi_slope != 0) ? alibi_slope * (token_idx - context_len) : 0;
+      qk += (alibi_slope != 0) ? alibi_slope * (token_idx - context_len + 1) : 0;
 
       if (thread_group_offset == 0) {
         // Store the partial reductions to shared memory.
@@ -239,6 +246,8 @@ __global__ void single_query_cached_kv_attention_kernel(
     accs[i] = 0.f;
   }
 
+  scalar_t zero_value;
+  zero(zero_value);
   for (int block_idx = warp_idx; block_idx < num_blocks; block_idx += NUM_WARPS) {
     const int physical_block_number = block_table[block_idx];
     const int physical_block_offset = (lane % NUM_V_VECS_PER_ROW) * V_VEC_SIZE;
@@ -246,14 +255,24 @@ __global__ void single_query_cached_kv_attention_kernel(
     L_vec logits_vec;
     from_float(logits_vec, *reinterpret_cast<Float_L_vec*>(logits + token_idx));
 
-    const scalar_t* v_ptr = v_cache + physical_block_number * num_heads * HEAD_SIZE * BLOCK_SIZE
-                                    + head_idx * HEAD_SIZE * BLOCK_SIZE;
+    const scalar_t* v_ptr = v_cache + physical_block_number * kv_block_stride
+                                    + kv_head_idx * kv_head_stride;
 #pragma unroll
     for (int i = 0; i < NUM_ROWS_PER_THREAD; i++) {
       const int row_idx = lane / NUM_V_VECS_PER_ROW + i * NUM_ROWS_PER_ITER;
       if (row_idx < HEAD_SIZE) {
         const int offset = row_idx * BLOCK_SIZE + physical_block_offset;
         V_vec v_vec = *reinterpret_cast<const V_vec*>(v_ptr + offset);
+        if (block_idx == num_blocks - 1) {
+          // NOTE(woosuk): When v_vec contains the tokens that are out of the context,
+          // we should explicitly zero out the values since they may contain NaNs.
+          // See https://github.com/vllm-project/vllm/issues/641#issuecomment-1682544472
+          scalar_t* v_vec_ptr = reinterpret_cast<scalar_t*>(&v_vec);
+#pragma unroll
+          for (int j = 0; j <= V_VEC_SIZE; j++) {
+            v_vec_ptr[j] = token_idx + j < context_len ? v_vec_ptr[j] : zero_value;
+          }
+        }
         accs[i] += dot(logits_vec, v_vec);
       }
     }
@@ -322,18 +341,24 @@ __global__ void single_query_cached_kv_attention_kernel(
 } // namespace vllm
 
 #define LAUNCH_ATTENTION_KERNEL(T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS)                        \
+  cudaFuncSetAttribute(                                                                       \
+      vllm::single_query_cached_kv_attention_kernel<T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS>,   \
+      cudaFuncAttributeMaxDynamicSharedMemorySize, shared_mem_size);                          \
   vllm::single_query_cached_kv_attention_kernel<T, HEAD_SIZE, BLOCK_SIZE, NUM_THREADS>        \
   <<<grid, block, shared_mem_size, stream>>>(                                                 \
     out_ptr,                                                                                  \
     query_ptr,                                                                                \
     key_cache_ptr,                                                                            \
     value_cache_ptr,                                                                          \
+    head_mapping_ptr,                                                                         \
     scale,                                                                                    \
     block_tables_ptr,                                                                         \
     context_lens_ptr,                                                                         \
     max_num_blocks_per_seq,                                                                   \
     alibi_slopes_ptr,                                                                         \
-    query_stride);
+    q_stride,                                                                                 \
+    kv_block_stride,                                                                          \
+    kv_head_stride);
 
 // TODO(woosuk): Tune NUM_THREADS.
 template<
@@ -345,6 +370,7 @@ void single_query_cached_kv_attention_launcher(
   torch::Tensor& query,
   torch::Tensor& key_cache,
   torch::Tensor& value_cache,
+  torch::Tensor& head_mapping,
   float scale,
   torch::Tensor& block_tables,
   torch::Tensor& context_lens,
@@ -354,7 +380,9 @@ void single_query_cached_kv_attention_launcher(
   int num_heads = query.size(1);
   int head_size = query.size(2);
   int max_num_blocks_per_seq = block_tables.size(1);
-  int query_stride = query.stride(0);
+  int q_stride = query.stride(0);
+  int kv_block_stride = key_cache.stride(0);
+  int kv_head_stride = key_cache.stride(1);
 
   int thread_group_size = MAX(WARP_SIZE / BLOCK_SIZE, 1);
   assert(head_size % thread_group_size == 0);
@@ -368,6 +396,7 @@ void single_query_cached_kv_attention_launcher(
   T* query_ptr = reinterpret_cast<T*>(query.data_ptr());
   T* key_cache_ptr = reinterpret_cast<T*>(key_cache.data_ptr());
   T* value_cache_ptr = reinterpret_cast<T*>(value_cache.data_ptr());
+  int* head_mapping_ptr = reinterpret_cast<int*>(head_mapping.data_ptr());
   int* block_tables_ptr = block_tables.data_ptr<int>();
   int* context_lens_ptr = context_lens.data_ptr<int>();
 
@@ -375,6 +404,8 @@ void single_query_cached_kv_attention_launcher(
   int padded_max_context_len = ((max_context_len + BLOCK_SIZE - 1) / BLOCK_SIZE) * BLOCK_SIZE;
   int logits_size = padded_max_context_len * sizeof(float);
   int outputs_size = (NUM_WARPS / 2) * head_size * sizeof(float);
+  // Python-side check in vllm.worker.worker._check_if_can_support_max_seq_len
+  // Keep that in sync with the logic here!
   int shared_mem_size = std::max(logits_size, outputs_size);
 
   dim3 grid(num_heads, num_seqs);
@@ -382,7 +413,7 @@ void single_query_cached_kv_attention_launcher(
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   switch (head_size) {
     // NOTE(woosuk): To reduce the compilation time, we omitted head sizes
-    // 32, 160, 192, 256.
+    // 32, 160, 192.
     // case 32:
     //   LAUNCH_ATTENTION_KERNEL(T, 32, BLOCK_SIZE, NUM_THREADS);
     //   break;
@@ -407,9 +438,9 @@ void single_query_cached_kv_attention_launcher(
     // case 192:
     //   LAUNCH_ATTENTION_KERNEL(T, 192, BLOCK_SIZE, NUM_THREADS);
     //   break;
-    // case 256:
-    //   LAUNCH_ATTENTION_KERNEL(T, 256, BLOCK_SIZE, NUM_THREADS);
-    //   break;
+    case 256:
+      LAUNCH_ATTENTION_KERNEL(T, 256, BLOCK_SIZE, NUM_THREADS);
+      break;
     default:
       TORCH_CHECK(false, "Unsupported head size: ", head_size);
       break;
@@ -422,6 +453,7 @@ void single_query_cached_kv_attention_launcher(
     query,                                                          \
     key_cache,                                                      \
     value_cache,                                                    \
+    head_mapping,                                                   \
     scale,                                                          \
     block_tables,                                                   \
     context_lens,                                                   \
@@ -469,6 +501,7 @@ void single_query_cached_kv_attention(
   torch::Tensor& query,           // [num_seqs, num_heads, head_size]
   torch::Tensor& key_cache,       // [num_blocks, num_heads, head_size/x, block_size, x]
   torch::Tensor& value_cache,     // [num_blocks, num_heads, head_size, block_size]
+  torch::Tensor& head_mapping,    // [num_heads]
   float scale,
   torch::Tensor& block_tables,    // [num_seqs, max_num_blocks_per_seq]
   torch::Tensor& context_lens,    // [num_seqs]

csrc/attention/dtype_bfloat16.cuh

@@ -420,4 +420,14 @@ inline __device__ void from_float(bf16_8_t& dst, Float8_ src) {
 #endif
 }
 
+// Zero-out a variable.
+inline __device__ void zero(__nv_bfloat16& dst) {
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
+  assert(false);
+#else
+  // Same as CUDART_ZERO_BF16 introduced in CUDA 12.2.
+  dst = __ushort_as_bfloat16((unsigned short)0x0000U);
+#endif
+}
+
 } // namespace vllm

csrc/attention/dtype_float16.cuh

@@ -390,11 +390,6 @@ inline __device__ float sum(uint4 v) {
   return sum(c);
 }
 
-// Zero-out a vector.
-inline __device__ void zero(uint16_t& dst) {
-  dst = uint16_t(0);
-}
-
 // From float32 to float16.
 inline __device__ void from_float(uint16_t& dst, float src) {
   dst = float_to_half(src);
@@ -441,4 +436,9 @@ inline __device__ Float8_ to_float(uint4 u) {
   return tmp;
 }
 
+// Zero-out a variable.
+inline __device__ void zero(uint16_t& dst) {
+  dst = uint16_t(0);
+}
+
 } // namespace vllm

csrc/attention/dtype_float32.cuh

@@ -265,4 +265,9 @@ inline __device__ Float8_ to_float(Float8_ u) {
   return u;
 }
 
+// Zero-out a variable.
+inline __device__ void zero(float& dst) {
+  dst = 0.f;
+}
+
 } // namespace vllm

csrc/cache_kernels.cu

@@ -1,6 +1,8 @@
 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
 
+#include "dispatch_utils.h"
+
 #include <algorithm>
 #include <cassert>
 #include <map>
@@ -125,9 +127,7 @@ void copy_blocks(
   dim3 grid(num_layers, num_pairs);
   dim3 block(std::min(1024, numel_per_block));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     key_caches[0].scalar_type(), "copy_blocks_kernel", ([&] {
       vllm::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
         key_cache_ptrs_tensor.data_ptr<int64_t>(),
@@ -202,9 +202,7 @@ void reshape_and_cache(
   dim3 grid(num_tokens);
   dim3 block(std::min(num_heads * head_size, 512));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     key.scalar_type(),
     "reshape_and_cache_kernel",
     [&] {
@@ -364,9 +362,7 @@ void gather_cached_kv(
   dim3 grid(num_tokens);
   dim3 block(std::min(num_heads * head_size, 512));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     key.scalar_type(),
     "gather_cached_kv_kernel_optimized",
     [&] {

csrc/cuda_utils.cpp

@@ -0,0 +1,13 @@
+#include <torch/extension.h>
+
+int get_device_attribute(
+    int attribute,
+    int device_id);
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def(
+    "get_device_attribute",
+    &get_device_attribute,
+    "Gets the specified device attribute.");
+}
+

csrc/cuda_utils_kernels.cu

@@ -0,0 +1,14 @@
+int get_device_attribute(
+    int attribute,
+    int device_id)
+{
+    int device, value;
+    if (device_id < 0) {
+        cudaGetDevice(&device);
+    }
+    else {
+        device = device_id;
+    }
+    cudaDeviceGetAttribute(&value, static_cast<cudaDeviceAttr>(attribute), device);
+    return value;
+}

csrc/dispatch_utils.h

@@ -0,0 +1,14 @@
+/*
+ * Adapted from
+ * https://github.com/pytorch/pytorch/blob/v2.0.1/aten/src/ATen/Dispatch.h
+ */
+#include <torch/extension.h>
+
+#define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)              \
+  AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)      \
+  AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)       \
+  AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__)
+
+#define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...)             \
+  AT_DISPATCH_SWITCH(                                             \
+    TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))

csrc/layernorm_kernels.cu

@@ -1,6 +1,7 @@
 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
 
+#include "dispatch_utils.h"
 #include "reduction_utils.cuh"
 
 namespace vllm {
@@ -46,9 +47,7 @@ void rms_norm(
   dim3 grid(num_tokens);
   dim3 block(std::min(hidden_size, 1024));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     input.scalar_type(),
     "rms_norm_kernel",
     [&] {

csrc/pos_encoding.cpp

@@ -1,15 +1,16 @@
 #include <torch/extension.h>
 
-void rotary_embedding_neox(
+void rotary_embedding(
   torch::Tensor& positions,
   torch::Tensor& query,
   torch::Tensor& key,
   int head_size,
-  torch::Tensor& cos_sin_cache);
+  torch::Tensor& cos_sin_cache,
+  bool is_neox);
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def(
-    "rotary_embedding_neox",
-    &rotary_embedding_neox,
-    "Apply GPT-NeoX style rotary embedding to query and key");
+    "rotary_embedding",
+    &rotary_embedding,
+    "Apply GPT-NeoX or GPT-J style rotary embedding to query and key");
 }

csrc/pos_encoding_kernels.cu

@@ -1,17 +1,51 @@
 #include <torch/extension.h>
 #include <ATen/cuda/CUDAContext.h>
 
+#include "dispatch_utils.h"
+
 namespace vllm {
 
-template<typename scalar_t>
-__global__ void rotary_embedding_neox_kernel(
+template<typename scalar_t, bool IS_NEOX>
+inline __device__ void apply_rotary_embedding(
+  scalar_t* __restrict__ arr,
+  const scalar_t* __restrict__ cos_ptr,
+  const scalar_t* __restrict__ sin_ptr,
+  int rot_offset,
+  int embed_dim)
+{
+  int x_index, y_index;
+  scalar_t cos, sin;
+  if (IS_NEOX) {
+    // GPT-NeoX style rotary embedding.
+    x_index = rot_offset;
+    y_index = embed_dim + rot_offset;
+    cos = __ldg(cos_ptr + x_index);
+    sin = __ldg(sin_ptr + x_index);
+  } else {
+    // GPT-J style rotary embedding.
+    x_index = 2 * rot_offset;
+    y_index = 2 * rot_offset + 1;
+    cos = __ldg(cos_ptr + x_index / 2);
+    sin = __ldg(sin_ptr + x_index / 2);
+  }
+
+  const scalar_t x = arr[x_index];
+  const scalar_t y = arr[y_index];
+  arr[x_index] = x * cos - y * sin;
+  arr[y_index] = y * cos + x * sin;
+}
+
+template<typename scalar_t, bool IS_NEOX>
+__global__ void rotary_embedding_kernel(
   const int64_t* __restrict__ positions,        // [num_tokens]
   scalar_t* __restrict__ query,                 // [num_tokens, num_heads, head_size]
-  scalar_t* __restrict__ key,                   // [num_tokens, num_heads, head_size]
+  scalar_t* __restrict__ key,                   // [num_tokens, num_kv_heads, head_size]
   const scalar_t* __restrict__ cos_sin_cache,   // [max_position, 2, rot_dim // 2]
   const int rot_dim,
-  const int stride,
+  const int query_stride,
+  const int key_stride,
   const int num_heads,
+  const int num_kv_heads,
   const int head_size) {
   // Each thread block is responsible for one token.
   const int token_idx = blockIdx.x;
@@ -19,65 +53,75 @@ __global__ void rotary_embedding_neox_kernel(
   const scalar_t* cache_ptr = cos_sin_cache + pos * rot_dim;
 
   const int embed_dim = rot_dim / 2;
-  const int n = num_heads * embed_dim;
-  for (int i = threadIdx.x; i < n; i += blockDim.x) {
+  const scalar_t* cos_ptr = cache_ptr;
+  const scalar_t* sin_ptr = cache_ptr + embed_dim;
+
+  const int nq = num_heads * embed_dim;
+  for (int i = threadIdx.x; i < nq; i += blockDim.x) {
     const int head_idx = i / embed_dim;
-    const int token_head = token_idx * stride + head_idx * head_size;
-
+    const int token_head = token_idx * query_stride + head_idx * head_size;
     const int rot_offset = i % embed_dim;
-    const int x_index = rot_offset;
-    const int y_index = embed_dim + rot_offset;
+    apply_rotary_embedding<scalar_t, IS_NEOX>(query + token_head, cos_ptr,
+                                              sin_ptr, rot_offset, embed_dim);
+  }
 
-    const int out_x = token_idx * stride + head_idx * head_size + x_index;
-    const int out_y = token_idx * stride + head_idx * head_size + y_index;
-
-    const scalar_t cos = __ldg(cache_ptr + x_index);
-    const scalar_t sin = __ldg(cache_ptr + y_index);
-
-    const scalar_t q_x = query[token_head + x_index];
-    const scalar_t q_y = query[token_head + y_index];
-    query[out_x] = q_x * cos - q_y * sin;
-    query[out_y] = q_y * cos + q_x * sin;
-
-    const scalar_t k_x = key[token_head + x_index];
-    const scalar_t k_y = key[token_head + y_index];
-    key[out_x] = k_x * cos - k_y * sin;
-    key[out_y] = k_y * cos + k_x * sin;
+  const int nk = num_kv_heads * embed_dim;
+  for (int i = threadIdx.x; i < nk; i += blockDim.x) {
+    const int head_idx = i / embed_dim;
+    const int token_head = token_idx * key_stride + head_idx * head_size;
+    const int rot_offset = i % embed_dim;
+    apply_rotary_embedding<scalar_t, IS_NEOX>(key + token_head, cos_ptr,
+                                              sin_ptr, rot_offset, embed_dim);
   }
 }
 
 } // namespace vllm
 
-void rotary_embedding_neox(
+void rotary_embedding(
   torch::Tensor& positions,         // [num_tokens]
   torch::Tensor& query,             // [num_tokens, num_heads * head_size]
-  torch::Tensor& key,               // [num_tokens, num_heads * head_size]
+  torch::Tensor& key,               // [num_tokens, num_kv_heads * head_size]
   int head_size,
-  torch::Tensor& cos_sin_cache)     // [max_position, rot_dim]
-{
+  torch::Tensor& cos_sin_cache,     // [max_position, rot_dim]
+  bool is_neox) {
   int num_tokens = query.size(0);
   int rot_dim = cos_sin_cache.size(1);
   int num_heads = query.size(1) / head_size;
-  int stride = query.stride(0);
-  TORCH_CHECK(stride == key.stride(0));
+  int num_kv_heads = key.size(1) / head_size;
+  int query_stride = query.stride(0);
+  int key_stride = key.stride(0);
 
   dim3 grid(num_tokens);
   dim3 block(std::min(num_heads * rot_dim / 2, 512));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  AT_DISPATCH_FLOATING_TYPES_AND2(
-    at::ScalarType::Half,
-    at::ScalarType::BFloat16,
+  VLLM_DISPATCH_FLOATING_TYPES(
     query.scalar_type(),
-    "rotary_embedding_neox",
+    "rotary_embedding",
     [&] {
-      vllm::rotary_embedding_neox_kernel<scalar_t><<<grid, block, 0, stream>>>(
-        positions.data_ptr<int64_t>(),
-        query.data_ptr<scalar_t>(),
-        key.data_ptr<scalar_t>(),
-        cos_sin_cache.data_ptr<scalar_t>(),
-        rot_dim,
-        stride,
-        num_heads,
-        head_size);
+      if (is_neox) {
+        vllm::rotary_embedding_kernel<scalar_t, true><<<grid, block, 0, stream>>>(
+          positions.data_ptr<int64_t>(),
+          query.data_ptr<scalar_t>(),
+          key.data_ptr<scalar_t>(),
+          cos_sin_cache.data_ptr<scalar_t>(),
+          rot_dim,
+          query_stride,
+          key_stride,
+          num_heads,
+          num_kv_heads,
+          head_size);
+      } else {
+        vllm::rotary_embedding_kernel<scalar_t, false><<<grid, block, 0, stream>>>(
+          positions.data_ptr<int64_t>(),
+          query.data_ptr<scalar_t>(),
+          key.data_ptr<scalar_t>(),
+          cos_sin_cache.data_ptr<scalar_t>(),
+          rot_dim,
+          query_stride,
+          key_stride,
+          num_heads,
+          num_kv_heads,
+          head_size);
+      }
     });
 }

csrc/quantization.cpp

@@ -0,0 +1,15 @@
+#include <torch/extension.h>
+
+torch::Tensor awq_gemm(
+  torch::Tensor _in_feats,
+  torch::Tensor _kernel,
+  torch::Tensor _scaling_factors,
+  torch::Tensor _zeros,
+  int split_k_iters);
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def(
+    "awq_gemm",
+    &awq_gemm,
+    "Quantized GEMM for AWQ");
+}

csrc/quantization/awq/dequantize.cuh

@@ -0,0 +1,87 @@
+/*
+Adapted from https://github.com/mit-han-lab/llm-awq
+Modified from NVIDIA FasterTransformer: https://github.com/NVIDIA/FasterTransformer/blob/main/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h
+@article{lin2023awq,
+  title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
+  author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
+  journal={arXiv},
+  year={2023}
+}
+*/
+
+#pragma once
+
+namespace vllm {
+namespace awq {
+
+__device__ uint4 dequantize_s4_to_fp16x2(uint32_t const& source)
+{
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
+  assert(false);
+#else
+    uint4 result;
+
+    uint32_t*      h   = reinterpret_cast<uint32_t*>(&result);
+    uint32_t const i4s = reinterpret_cast<uint32_t const&>(source);
+
+    // First, we extract the i4s and construct an intermediate fp16 number.
+    static constexpr uint32_t immLut                = (0xf0 & 0xcc) | 0xaa;
+    static constexpr uint32_t BOTTOM_MASK           = 0x000f000f;
+    static constexpr uint32_t TOP_MASK              = 0x00f000f0;
+    static constexpr uint32_t I4s_TO_F16s_MAGIC_NUM = 0x64006400;
+
+    // Note that the entire sequence only requires 1 shift instruction. This is thanks to the register packing
+    // format and the fact that we force our integers to be unsigned, and account for this in the fp16 subtractions.
+    // In addition, I exploit the fact that sub and fma have the same throughput in order to convert elt_23 and
+    // elt_67 to fp16 without having to shift them to the bottom bits before hand.
+
+    // Shift right by 8 to now consider elt_45 and elt_67. Issue first to hide RAW dependency if we issue
+    // immediately before required.
+    const uint32_t top_i4s = i4s >> 8;
+    // Extract elt_01 - (i4s & 0x000f000f) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                    : "=r"(h[0])
+                    : "r"(i4s), "n"(BOTTOM_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
+    // Extract elt_23 (i4s & 0x00f000f0) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                    : "=r"(h[1])
+                    : "r"(i4s), "n"(TOP_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
+    // Extract elt_45 (top_i4s & 0x000f000f) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                    : "=r"(h[2])
+                    : "r"(top_i4s), "n"(BOTTOM_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
+    // Extract elt_67 (top_i4s & 0x00f000f0) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                    : "=r"(h[3])
+                    : "r"(top_i4s), "n"(TOP_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
+
+    // I use inline PTX below because I am not sure if the compiler will emit float2half instructions if I use the
+    // half2 ctor. In this case, I chose performance reliability over code readability.
+
+    // This is the half2 {1032, 1032} represented as an integer.
+    // static constexpr uint32_t FP16_TOP_MAGIC_NUM = 0x64086408;
+    // Haotian: subtract {1024, 1024} instead, we do not need to map to [-8, 7]
+    static constexpr uint32_t FP16_TOP_MAGIC_NUM = 0x64006400;
+    // This is the half2 {1 / 16, 1 / 16} represented as an integer.
+    static constexpr uint32_t ONE_SIXTEENTH = 0x2c002c00;
+    // This is the half2 {-72, -72} represented as an integer.
+    // static constexpr uint32_t NEG_72 = 0xd480d480;
+    // Haotian: Let's use {-64, -64}.
+    static constexpr uint32_t NEG_64 = 0xd400d400;
+
+    // Finally, we construct the output numbers.
+    // Convert elt_01
+    asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(h[0]) : "r"(h[0]), "r"(FP16_TOP_MAGIC_NUM));
+    // Convert elt_23
+    asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(h[1]) : "r"(h[1]), "r"(ONE_SIXTEENTH), "r"(NEG_64));
+    // Convert elt_45
+    asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(h[2]) : "r"(h[2]), "r"(FP16_TOP_MAGIC_NUM));
+    // Convert elt_67
+    asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(h[3]) : "r"(h[3]), "r"(ONE_SIXTEENTH), "r"(NEG_64));
+
+    return result;
+#endif
+}
+
+} // namespace awq
+} // namespace vllm

csrc/quantization/awq/gemm_kernels.cu

@@ -0,0 +1,491 @@
+/*
+Adapted from https://github.com/mit-han-lab/llm-awq
+@article{lin2023awq,
+  title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
+  author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
+  journal={arXiv},
+  year={2023}
+}
+ */
+
+
+#include <torch/extension.h>
+#include <c10/cuda/CUDAGuard.h>
+
+#include "dequantize.cuh"
+
+#include <cuda_fp16.h>
+
+namespace vllm {
+namespace awq {
+
+// Pack two half values.
+static inline __device__ __host__ unsigned
+__pack_half2(const half x, const half y) {
+  unsigned v0 = *((unsigned short *)&x);
+  unsigned v1 = *((unsigned short *)&y);
+  return (v1 << 16) | v0;
+}
+
+__global__ void __launch_bounds__(64) gemm_forward_4bit_cuda_m16n128k32(int G, int split_k_iters, half* __restrict__ A, int* __restrict__ B, half* __restrict__ scaling_factors, int* __restrict__ zeros, int M, int IC, int OC, half* __restrict__ C) 
+{
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
+  assert(false);
+#else
+  static constexpr uint32_t ZERO = 0x0;
+  float C_warp[32];
+  __shared__ half A_shared[16 * (32 + 8)];
+  __shared__ half B_shared[32 * (128 + 8)];
+  
+  __shared__ half scaling_factors_shared[128];
+  __shared__ half zeros_shared[128];
+
+  int j_factors1 = ((OC + 128 - 1) / 128);
+  int blockIdx_x = 0;
+  int blockIdx_y = blockIdx.x % ((M + 16 - 1) / 16 * j_factors1);
+  int blockIdx_z = blockIdx.x / ((M + 16 - 1) / 16 * j_factors1);
+
+  half A_shared_warp[8];
+  half B_shared_warp[32];
+  for (int j_0_4_init = 0; j_0_4_init < 4; ++j_0_4_init) {
+    for (int i = 0; i < 8; ++i) {
+      C_warp[(j_0_4_init * 8) + i] = 0.0;
+    }
+  }
+
+  static constexpr int row_stride_warp = 32 * 8 / 32;
+  static constexpr int row_stride = 2 * 32 * 8 / 128;
+  bool ld_zero_flag = (threadIdx.y * 32 + threadIdx.x) * 8 < 128;
+  // TODO: Haotian: blockIdx_y / j_factors1 in A loading to support bsz > 16
+  bool ld_A_flag = (blockIdx_y / j_factors1 * 16 + threadIdx.y * row_stride_warp + threadIdx.x * 8 / 32) < M;     // threadIdx.y is warp_id
+  // bool wb_C_flag = (threadIdx.x / 4) < M;
+
+  half* A_ptr = A 
+                + (((int)blockIdx_y) / j_factors1 * 16 + (((int)threadIdx.y) * row_stride_warp) + ((int)threadIdx.x) / (32 / 8)) * IC
+                + (((int)threadIdx.x) % (32 / 8)) * 8;
+  
+  int* B_ptr = B
+            + ((int)threadIdx.y) * (OC / 8) * 2
+            + (((int)threadIdx.x) / (128 / 8)) * (OC / 8)
+            + (((int)blockIdx_y) % j_factors1) * (128 / 8)
+            + (((int)threadIdx.x) % (128 / 8)) * 1;
+// Why * 1 in the above line?
+                        
+  half* A_shared_ptr = A_shared 
+                    + ((int)threadIdx.y) * row_stride_warp * (32 + 8) 
+                    + (((int)threadIdx.x) / (32 / 8)) * (32 + 8)
+                    + (((int)threadIdx.x) % (32 / 8) ) * 8;
+
+  half* B_shared_ptr = B_shared
+                    + ((int)threadIdx.y) * (row_stride / 2) * (128 + 8)
+                    + (((int)threadIdx.x) / (128 / 8)) * (128 + 8)
+                    + (((int)threadIdx.x) % (128 / 8)) * 8;
+  
+  int* zeros_ptr = zeros
+                + (((int)blockIdx_y) % j_factors1) * (128 / 8)
+                + ((int)threadIdx.x) % (128 / 8);
+  
+  half* scaling_factors_ptr = scaling_factors
+                            + (((int)blockIdx_y) % j_factors1) * (128) 
+                            + (((int)threadIdx.x) % (128 / 8)) * 8;
+
+  half* C_ptr = C 
+              + blockIdx_z * M * OC        // blockIdz.x -> split_k dim
+              + (((int)blockIdx_y) % j_factors1) * 128
+              + ((int)threadIdx.y) * 64
+              + (((int)threadIdx.x) % 4) * 2;
+
+  // preload s.f. and zeros
+  int k_bound = (IC / 32 + split_k_iters - 1) / split_k_iters;
+  if ((k_bound - 1) * split_k_iters * 32 + blockIdx_z * 32 >= IC) k_bound -= 1;
+  for (int _k_0_0 = 0; _k_0_0 < k_bound; ++_k_0_0) {
+    int k_0_0 = _k_0_0 * split_k_iters + blockIdx_z;
+    __syncthreads();
+    // TODO: Haotian: blockIdx_y / j_factors1 in A loading to support bsz > 16
+    if (ld_A_flag)
+    {
+      *(uint4*)(A_shared_ptr) = *(uint4*)(A_ptr + (k_0_0 * 32));
+    }
+    else
+    {
+      *(uint4*)(A_shared_ptr) = make_uint4(0, 0, 0, 0);
+    }
+
+    // for (int ax0_ax1_fused_0 = 0; ax0_ax1_fused_0 < 2; ++ax0_ax1_fused_0) {
+    uint32_t zeros_loaded = *(uint32_t*)(zeros_ptr + k_0_0 * 32 / G * (OC / 8));
+    uint4 B_loaded_zero = dequantize_s4_to_fp16x2(zeros_loaded);
+    uint4 B_loaded_scale = *(uint4*)(scaling_factors_ptr + k_0_0 * 32 / G * (OC));
+    /*
+    if (blockIdx_z == 0 && blockIdx_y == 0 && k_0_0 == 0 && threadIdx.x == 0 && threadIdx.y == 0){
+      printf("%x %x %x %x %x %x %x %x\n", B_loaded_scale.x, B_loaded_scale.y, B_loaded_scale.z, B_loaded_scale.w, B_loaded_zero.x, B_loaded_zero.y, B_loaded_zero.z, B_loaded_zero.w);
+    }
+    */
+    // uint4 B_loaded_scale = make_uint4(0, 0, 0, 0);
+    int* B_ptr_local = B_ptr + k_0_0 * 32 * (OC / 8);
+
+    for (int ax0_ax1_fused_0 = 0; ax0_ax1_fused_0 < 8; ++ax0_ax1_fused_0) {
+
+      // B: 32 x 136 (128+8) float16
+      // each warp: 32 x 4
+      // each thr: read 32 bit -> convert to 8xFP16 (a UINT4) -> scale and minus zero -> WB UINT4
+      // *(uint4*)(B_shared + ((((ax0_ax1_fused_0 * 544) + (((int)threadIdx.y) * 272)) + ((((int)threadIdx.x) >> 4) * 136)) + ((((int)threadIdx.x) & 15) * 8))) = *(uint4*)(B + ((((((k_0_0 * 163840) + (ax0_ax1_fused_0 * 20480)) + (((int)threadIdx.y) * 10240)) + ((((int)threadIdx.x) >> 4) * 5120)) + (((int)blockIdx_y) * 128)) + ((((int)threadIdx.x) & 15) * 8)));
+      // row stride in shared memory: (NWARPS * 32 * 8 / cta_N) 
+      uint32_t B_loaded = *(uint32_t*)(B_ptr_local + ax0_ax1_fused_0 * row_stride * (OC / 8));
+      uint4 B_loaded_fp16 = dequantize_s4_to_fp16x2(B_loaded);
+      //uint4 B_loaded_zero = *(uint4*)(zeros_shared + (threadIdx.x % (cta_N / 8)) * 8);
+
+      // uint4 B_loaded_scale = *(uint4*)(scaling_factors_shared + (threadIdx.x % (cta_N / 8)) * 8);
+      // - zero and * scale
+      // TODO (Haotian): can save 4 assembly instructions if sormulate as deq = q * scale - zero * scale.
+      asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(B_loaded_fp16.x) : "r"(B_loaded_fp16.x), "r"(B_loaded_zero.x));
+      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(B_loaded_fp16.x) : "r"(B_loaded_fp16.x), "r"(B_loaded_scale.x), "r"(ZERO));
+      asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(B_loaded_fp16.y) : "r"(B_loaded_fp16.y), "r"(B_loaded_zero.y));
+      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(B_loaded_fp16.y) : "r"(B_loaded_fp16.y), "r"(B_loaded_scale.y), "r"(ZERO));
+      asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(B_loaded_fp16.z) : "r"(B_loaded_fp16.z), "r"(B_loaded_zero.z));
+      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(B_loaded_fp16.z) : "r"(B_loaded_fp16.z), "r"(B_loaded_scale.z), "r"(ZERO));
+      asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(B_loaded_fp16.w) : "r"(B_loaded_fp16.w), "r"(B_loaded_zero.w));
+      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(B_loaded_fp16.w) : "r"(B_loaded_fp16.w), "r"(B_loaded_scale.w), "r"(ZERO));
+      /*
+      if (ax0_ax1_fused_0 == 0 && blockIdx_z == 0 && blockIdx_y == 0 && k_0_0 == 0 && threadIdx.x == 17 && threadIdx.y == 0){
+        printf("[x] %X %X %X %X\n", B_loaded_fp16.x, B_loaded_fp16.y, B_loaded_fp16.z, B_loaded_fp16.w);
+      }
+      */
+
+      // write back
+      *(uint4*)(B_shared_ptr + ax0_ax1_fused_0 * row_stride * (128 + 8)) = B_loaded_fp16;
+    }
+    __syncthreads();
+
+    for (int k_0_1 = 0; k_0_1 < 2; ++k_0_1) {
+      {
+        unsigned int addr;
+        __asm__ __volatile__(
+          "{ .reg .u64 addr; cvta.to.shared.u64 addr, %1; cvt.u32.u64 %0, addr; }\n"
+          : "=r"(addr)
+          : "l"((void *)((&(A_shared[(k_0_1 * 16)])) + (((((int)threadIdx.x) & 15) * 40) + ((((int)threadIdx.x) >> 4) * 8))))
+        );
+
+
+        __asm__ __volatile__(
+          "ldmatrix.sync.aligned.m8n8.x4.shared.b16"
+          "{%0, %1, %2, %3}, [%4];\n"
+          : "=r"(((unsigned *)(A_shared_warp + 0))[0]), "=r"(((unsigned *)(A_shared_warp + 0))[1]), "=r"(((unsigned *)(A_shared_warp + 0))[2]), "=r"(((unsigned *)(A_shared_warp + 0))[3])
+          : "r"(addr)
+        );
+      }
+
+      for (int ax1_0 = 0; ax1_0 < 4; ++ax1_0) {
+        {
+          unsigned int addr;
+          __asm__ __volatile__(
+            "{ .reg .u64 addr; cvta.to.shared.u64 addr, %1; cvt.u32.u64 %0, addr; }\n"
+            : "=r"(addr)
+            : "l"((void *)((&(B_shared[(((k_0_1 * 2176) + (((int)threadIdx.y) * 64)) + (ax1_0 * 16))])) + (((((int)threadIdx.x) & 15) * 136) + ((((int)threadIdx.x) >> 4) * 8))))
+          );
+          __asm__ __volatile__(
+            "ldmatrix.sync.aligned.m8n8.x4.trans.shared.b16"
+            "{%0, %1, %2, %3}, [%4];\n"
+            : "=r"(((unsigned *)(B_shared_warp + (ax1_0 * 8)))[0]), "=r"(((unsigned *)(B_shared_warp + (ax1_0 * 8)))[1]), "=r"(((unsigned *)(B_shared_warp + (ax1_0 * 8)))[2]), "=r"(((unsigned *)(B_shared_warp + (ax1_0 * 8)))[3])
+            : "r"(addr)
+          );
+        }
+      }
+      for (int j_0_4 = 0; j_0_4 < 4; ++j_0_4) {
+        {
+          __asm__ __volatile__(
+            "mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32"
+            "{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%10, %11, %12, %13};\n"
+            :  "=f"(((float *)(C_warp + (j_0_4 * 8)))[0]), "=f"(((float *)(C_warp + (j_0_4 * 8)))[1]), "=f"(((float *)(C_warp + (j_0_4 * 8)))[2]), "=f"(((float *)(C_warp + (j_0_4 * 8)))[3])
+            : "r"(((unsigned *)(A_shared_warp + 0))[0]), "r"(((unsigned *)(A_shared_warp + 0))[1]), "r"(((unsigned *)(A_shared_warp + 0))[2]), "r"(((unsigned *)(A_shared_warp + 0))[3]), "r"(((unsigned *)(B_shared_warp + (j_0_4 * 8)))[0]), "r"(((unsigned *)(B_shared_warp + (j_0_4 * 8)))[1]), "f"(((float *)(C_warp + (j_0_4 * 8)))[0]), "f"(((float *)(C_warp + (j_0_4 * 8)))[1]), "f"(((float *)(C_warp + (j_0_4 * 8)))[2]), "f"(((float *)(C_warp + (j_0_4 * 8)))[3]));
+        }
+
+        {
+          __asm__ __volatile__(
+            "mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32"
+            "{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%10, %11, %12, %13};\n"
+            :  "=f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[0]), "=f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[1]), "=f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[2]), "=f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[3])
+            : "r"(((unsigned *)(A_shared_warp + 0))[0]), "r"(((unsigned *)(A_shared_warp + 0))[1]), "r"(((unsigned *)(A_shared_warp + 0))[2]), "r"(((unsigned *)(A_shared_warp + 0))[3]), "r"(((unsigned *)(B_shared_warp + ((j_0_4 * 8) + 4)))[0]), "r"(((unsigned *)(B_shared_warp + ((j_0_4 * 8) + 4)))[1]), "f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[0]), "f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[1]), "f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[2]), "f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[3]));
+        }
+      }
+    }
+  }
+
+// TODO: Shang: Hoist loop invariance.
+  for (int ax1_0_1 = 0; ax1_0_1 < 4; ++ax1_0_1) {
+    for (int local_id = 0; local_id < 8; ++local_id) {
+      int row_offset = (((int)blockIdx_y) / j_factors1) * 16 + ((int)threadIdx.x) / 4 + (local_id % 4) / 2 * 8;
+      if (row_offset < M)
+      {
+        *(C_ptr + ax1_0_1 * 16 + row_offset * OC + (local_id / 4) * 8 + local_id % 2) = __float2half(C_warp[(ax1_0_1 * 8) + local_id]);
+      }
+    }
+  }
+#endif
+}
+
+
+__global__ void __launch_bounds__(64) gemm_forward_4bit_cuda_m16n64k32(int G, int split_k_iters, half* __restrict__ A, int* __restrict__ B, half* __restrict__ scaling_factors, int* __restrict__ zeros, int M, int IC, int OC, half* __restrict__ C) 
+{
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
+  assert(false);
+#else
+  static constexpr uint32_t ZERO = 0x0;
+  float C_warp[32];
+  __shared__ half A_shared[16 * (32 + 8)];
+  __shared__ half B_shared[32 * (64 + 8)];
+  
+  __shared__ half scaling_factors_shared[64];
+  __shared__ half zeros_shared[64];
+
+  int j_factors1 = ((OC + 64 - 1) / 64);
+
+  int blockIdx_x = 0;
+  int blockIdx_y = blockIdx.x % ((M + 16 - 1) / 16 * j_factors1);
+  int blockIdx_z = blockIdx.x / ((M + 16 - 1) / 16 * j_factors1);
+
+  half A_shared_warp[8];
+  half B_shared_warp[16];
+  for (int j_0_4_init = 0; j_0_4_init < 2; ++j_0_4_init) {
+    for (int i = 0; i < 8; ++i) {
+      C_warp[(j_0_4_init * 8) + i] = 0.0;
+    }
+  }
+
+  static constexpr int row_stride_warp = 32 * 8 / 32;
+  static constexpr int row_stride = 2 * 32 * 8 / 64;
+  bool ld_zero_flag = (threadIdx.y * 32 + threadIdx.x) * 8 < 64;
+  // TODO: Haotian: blockIdx_y / j_factors1 in A loading to support bsz > 16
+  bool ld_A_flag = (blockIdx_y / j_factors1 * 16 + threadIdx.y * row_stride_warp + threadIdx.x * 8 / 32) < M;     // threadIdx.y is warp_id
+  // bool wb_C_flag = (threadIdx.x / 4) < M;
+
+  half* A_ptr = A 
+                + (((int)blockIdx_y) / j_factors1 * 16 + (((int)threadIdx.y) * row_stride_warp) + ((int)threadIdx.x) / (32 / 8)) * IC
+                + (((int)threadIdx.x) % (32 / 8)) * 8;
+  
+  int* B_ptr = B
+            + ((int)threadIdx.y) * (OC / 8) * 4
+            + (((int)threadIdx.x) / (64 / 8)) * (OC / 8)
+            + (((int)blockIdx_y) % j_factors1) * (64 / 8)
+            + (((int)threadIdx.x) % (64 / 8)) * 1;
+// Why * 1 in the above line?
+                        
+  half* A_shared_ptr = A_shared 
+                    + ((int)threadIdx.y) * row_stride_warp * (32 + 8) 
+                    + (((int)threadIdx.x) / (32 / 8)) * (32 + 8)
+                    + (((int)threadIdx.x) % (32 / 8) ) * 8;
+
+  half* B_shared_ptr = B_shared
+                    + ((int)threadIdx.y) * (row_stride / 2) * (64 + 8)
+                    + (((int)threadIdx.x) / (64 / 8)) * (64 + 8)
+                    + (((int)threadIdx.x) % (64 / 8)) * 8;
+  
+  int* zeros_ptr = zeros
+                + (((int)blockIdx_y) % j_factors1) * (64 / 8)
+                + ((int)threadIdx.x) % (64 / 8);
+  
+  half* scaling_factors_ptr = scaling_factors
+                            + (((int)blockIdx_y) % j_factors1) * (64) 
+                            + (((int)threadIdx.x) % (64 / 8)) * 8;
+
+  half* C_ptr = C 
+              + blockIdx_z * M * OC        // blockIdz.x -> split_k dim
+              + (((int)blockIdx_y) % j_factors1) * 64
+              + ((int)threadIdx.y) * 32
+              + (((int)threadIdx.x) % 4) * 2;
+
+  // preload s.f. and zeros
+  int k_bound = (IC / 32 + split_k_iters - 1) / split_k_iters;
+  if ((k_bound - 1) * split_k_iters * 32 + blockIdx_z * 32 >= IC) k_bound -= 1;
+  for (int _k_0_0 = 0; _k_0_0 < k_bound; ++_k_0_0) {
+    int k_0_0 = _k_0_0 * split_k_iters + blockIdx_z;
+    __syncthreads();
+    // TODO: Haotian: blockIdx_y / j_factors1 in A loading to support bsz > 16
+    if (ld_A_flag)
+    {
+      *(uint4*)(A_shared_ptr) = *(uint4*)(A_ptr + (k_0_0 * 32));
+    }
+    else
+    {
+      *(uint4*)(A_shared_ptr) = make_uint4(0, 0, 0, 0);
+    }
+
+    // for (int ax0_ax1_fused_0 = 0; ax0_ax1_fused_0 < 2; ++ax0_ax1_fused_0) {
+    uint32_t zeros_loaded = *(uint32_t*)(zeros_ptr + k_0_0 * 32 / G * (OC / 8));
+    uint4 B_loaded_zero = dequantize_s4_to_fp16x2(zeros_loaded);
+    uint4 B_loaded_scale = *(uint4*)(scaling_factors_ptr + k_0_0 * 32 / G * (OC));
+    /*
+    if (blockIdx_z == 0 && blockIdx_y == 0 && k_0_0 == 0 && threadIdx.x == 0 && threadIdx.y == 0){
+      printf("%x %x %x %x %x %x %x %x\n", B_loaded_scale.x, B_loaded_scale.y, B_loaded_scale.z, B_loaded_scale.w, B_loaded_zero.x, B_loaded_zero.y, B_loaded_zero.z, B_loaded_zero.w);
+    }
+    */
+    // uint4 B_loaded_scale = make_uint4(0, 0, 0, 0);
+    int* B_ptr_local = B_ptr + k_0_0 * 32 * (OC / 8);
+
+    for (int ax0_ax1_fused_0 = 0; ax0_ax1_fused_0 < 4; ++ax0_ax1_fused_0) {
+
+      // B: 32 x 136 (128+8) float16
+      // each warp: 32 x 4
+      // each thr: read 32 bit -> convert to 8xFP16 (a UINT4) -> scale and minus zero -> WB UINT4
+      // *(uint4*)(B_shared + ((((ax0_ax1_fused_0 * 544) + (((int)threadIdx.y) * 272)) + ((((int)threadIdx.x) >> 4) * 136)) + ((((int)threadIdx.x) & 15) * 8))) = *(uint4*)(B + ((((((k_0_0 * 163840) + (ax0_ax1_fused_0 * 20480)) + (((int)threadIdx.y) * 10240)) + ((((int)threadIdx.x) >> 4) * 5120)) + (((int)blockIdx_y) * 128)) + ((((int)threadIdx.x) & 15) * 8)));
+      // row stride in shared memory: (NWARPS * 32 * 8 / cta_N) 
+      uint32_t B_loaded = *(uint32_t*)(B_ptr_local + ax0_ax1_fused_0 * row_stride * (OC / 8));
+      uint4 B_loaded_fp16 = dequantize_s4_to_fp16x2(B_loaded);
+      //uint4 B_loaded_zero = *(uint4*)(zeros_shared + (threadIdx.x % (cta_N / 8)) * 8);
+
+      // uint4 B_loaded_scale = *(uint4*)(scaling_factors_shared + (threadIdx.x % (cta_N / 8)) * 8);
+      // - zero and * scale
+      // TODO (Haotian): can save 4 assembly instructions if sormulate as deq = q * scale - zero * scale.
+      asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(B_loaded_fp16.x) : "r"(B_loaded_fp16.x), "r"(B_loaded_zero.x));
+      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(B_loaded_fp16.x) : "r"(B_loaded_fp16.x), "r"(B_loaded_scale.x), "r"(ZERO));
+      asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(B_loaded_fp16.y) : "r"(B_loaded_fp16.y), "r"(B_loaded_zero.y));
+      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(B_loaded_fp16.y) : "r"(B_loaded_fp16.y), "r"(B_loaded_scale.y), "r"(ZERO));
+      asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(B_loaded_fp16.z) : "r"(B_loaded_fp16.z), "r"(B_loaded_zero.z));
+      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(B_loaded_fp16.z) : "r"(B_loaded_fp16.z), "r"(B_loaded_scale.z), "r"(ZERO));
+      asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(B_loaded_fp16.w) : "r"(B_loaded_fp16.w), "r"(B_loaded_zero.w));
+      asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(B_loaded_fp16.w) : "r"(B_loaded_fp16.w), "r"(B_loaded_scale.w), "r"(ZERO));
+      /*
+      if (ax0_ax1_fused_0 == 0 && blockIdx_z == 0 && blockIdx_y == 0 && k_0_0 == 0 && threadIdx.x == 17 && threadIdx.y == 0){
+        printf("[x] %X %X %X %X\n", B_loaded_fp16.x, B_loaded_fp16.y, B_loaded_fp16.z, B_loaded_fp16.w);
+      }
+      */
+
+      // write back
+      *(uint4*)(B_shared_ptr + ax0_ax1_fused_0 * row_stride * (64 + 8)) = B_loaded_fp16;
+    }
+    __syncthreads();
+
+    for (int k_0_1 = 0; k_0_1 < 2; ++k_0_1) 
+    {
+      {
+        unsigned int addr;
+        __asm__ __volatile__(
+          "{ .reg .u64 addr; cvta.to.shared.u64 addr, %1; cvt.u32.u64 %0, addr; }\n"
+          : "=r"(addr)
+          : "l"((void *)((&(A_shared[(k_0_1 * 16)])) + (((((int)threadIdx.x) & 15) * 40) + ((((int)threadIdx.x) >> 4) * 8))))
+        );
+        __asm__ __volatile__(
+          "ldmatrix.sync.aligned.m8n8.x4.shared.b16"
+          "{%0, %1, %2, %3}, [%4];\n"
+          : "=r"(((unsigned *)(A_shared_warp + 0))[0]), "=r"(((unsigned *)(A_shared_warp + 0))[1]), "=r"(((unsigned *)(A_shared_warp + 0))[2]), "=r"(((unsigned *)(A_shared_warp + 0))[3])
+          : "r"(addr)
+        );
+      }
+        
+
+      for (int ax1_0 = 0; ax1_0 < 2; ++ax1_0) 
+      {
+        {
+          unsigned int addr;
+          __asm__ __volatile__(
+            "{ .reg .u64 addr; cvta.to.shared.u64 addr, %1; cvt.u32.u64 %0, addr; }\n"
+            : "=r"(addr)
+            : "l"((void *)((&(B_shared[(((k_0_1 * 1152) + (((int)threadIdx.y) * 32)) + (ax1_0 * 16))])) + (((((int)threadIdx.x) & 15) * 72) + ((((int)threadIdx.x) >> 4) * 8))))
+          );
+          __asm__ __volatile__(
+            "ldmatrix.sync.aligned.m8n8.x4.trans.shared.b16"
+            "{%0, %1, %2, %3}, [%4];\n"
+            : "=r"(((unsigned *)(B_shared_warp + (ax1_0 * 8)))[0]), "=r"(((unsigned *)(B_shared_warp + (ax1_0 * 8)))[1]), "=r"(((unsigned *)(B_shared_warp + (ax1_0 * 8)))[2]), "=r"(((unsigned *)(B_shared_warp + (ax1_0 * 8)))[3])
+            : "r"(addr)
+          );
+        }
+      }
+      
+      for (int j_0_4 = 0; j_0_4 < 2; ++j_0_4) 
+      {
+
+        {
+          __asm__ __volatile__(
+            "mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32"
+            "{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%10, %11, %12, %13};\n"
+            :  "=f"(((float *)(C_warp + (j_0_4 * 8)))[0]), "=f"(((float *)(C_warp + (j_0_4 * 8)))[1]), "=f"(((float *)(C_warp + (j_0_4 * 8)))[2]), "=f"(((float *)(C_warp + (j_0_4 * 8)))[3])
+            : "r"(((unsigned *)(A_shared_warp + 0))[0]), "r"(((unsigned *)(A_shared_warp + 0))[1]), "r"(((unsigned *)(A_shared_warp + 0))[2]), "r"(((unsigned *)(A_shared_warp + 0))[3]), "r"(((unsigned *)(B_shared_warp + (j_0_4 * 8)))[0]), "r"(((unsigned *)(B_shared_warp + (j_0_4 * 8)))[1]), "f"(((float *)(C_warp + (j_0_4 * 8)))[0]), "f"(((float *)(C_warp + (j_0_4 * 8)))[1]), "f"(((float *)(C_warp + (j_0_4 * 8)))[2]), "f"(((float *)(C_warp + (j_0_4 * 8)))[3]));
+        }
+
+        {
+          __asm__ __volatile__(
+            "mma.sync.aligned.m16n8k16.row.col.f32.f16.f16.f32"
+            "{%0, %1, %2, %3}, {%4, %5, %6, %7}, {%8, %9}, {%10, %11, %12, %13};\n"
+            :  "=f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[0]), "=f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[1]), "=f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[2]), "=f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[3])
+            : "r"(((unsigned *)(A_shared_warp + 0))[0]), "r"(((unsigned *)(A_shared_warp + 0))[1]), "r"(((unsigned *)(A_shared_warp + 0))[2]), "r"(((unsigned *)(A_shared_warp + 0))[3]), "r"(((unsigned *)(B_shared_warp + ((j_0_4 * 8) + 4)))[0]), "r"(((unsigned *)(B_shared_warp + ((j_0_4 * 8) + 4)))[1]), "f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[0]), "f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[1]), "f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[2]), "f"(((float *)(C_warp + ((j_0_4 * 8) + 4)))[3]));
+        }
+      }
+    }
+  }
+
+// TODO: Shang: Hoist loop invariance.
+  for (int ax1_0_1 = 0; ax1_0_1 < 2; ++ax1_0_1) {
+    for (int local_id = 0; local_id < 8; ++local_id) {
+      int row_offset = (((int)blockIdx_y) / j_factors1) * 16 + ((int)threadIdx.x) / 4 + (local_id % 4) / 2 * 8;
+      if (row_offset < M)
+      {
+        *(C_ptr + ax1_0_1 * 16 + row_offset * OC + (local_id / 4) * 8 + local_id % 2) = __float2half(C_warp[(ax1_0_1 * 8) + local_id]);
+      }
+    }
+  }
+#endif
+}
+
+} // namespace awq
+} // namespace vllm
+
+// in_feats: M, IC [float16]
+// kernel: IC, OC // 8 [int32] -> cast to IC, OC [uint4b]
+// scaling_factors: IC // G, OC [float16]
+// zeros: IC // G, OC // 8 [int32] -> cast to IC // G, OC [uint4b]
+// assume that batch_size < 16 for now
+
+torch::Tensor awq_gemm(
+    torch::Tensor _in_feats,
+    torch::Tensor _kernel,
+    torch::Tensor _scaling_factors,
+    torch::Tensor _zeros,
+    int split_k_iters)
+{
+    int num_in_feats = _in_feats.size(0);
+    int num_in_channels = _in_feats.size(1);
+    const at::cuda::OptionalCUDAGuard device_guard(device_of(_in_feats));
+
+    auto options = torch::TensorOptions().dtype(_in_feats.dtype()).device(_in_feats.device());
+    at::Tensor _out_feats = torch::empty({split_k_iters, num_in_feats, _kernel.size(1) * 8}, options);
+    int num_out_feats = _out_feats.size(-2);
+    int num_out_channels = _out_feats.size(-1);
+
+    auto in_feats = reinterpret_cast<half*>(_in_feats.data_ptr<at::Half>());
+    auto kernel = reinterpret_cast<int*>(_kernel.data_ptr<int>());
+    auto out_feats = reinterpret_cast<half*>(_out_feats.data_ptr<at::Half>());
+    auto scaling_factors = reinterpret_cast<half*>(_scaling_factors.data_ptr<at::Half>());
+    auto zeros = reinterpret_cast<int*>(_zeros.data_ptr<int>());
+    int group_size = num_in_channels / _scaling_factors.size(0);
+
+    if (num_out_channels % 64 != 0)
+        throw std::invalid_argument("OC is not multiple of cta_N = 64");
+    if (num_out_channels % 8 != 0)
+        throw std::invalid_argument("OC is not multiple of pack_num = 8");
+    if (group_size % 32 != 0)
+	      throw std::invalid_argument("Group size should be a multiple of 32");
+    if (num_out_channels % group_size != 0)
+        throw std::invalid_argument("OC is not multiple of Group size");
+
+    if (num_out_channels % 128 == 0)
+    {
+        int j_factors1 = num_out_channels / 128 / 1;
+        dim3 num_blocks((num_out_feats + 16 - 1) / 16 * j_factors1 * split_k_iters);
+        // threadIdx.x: 32
+        // threadIdx.y: i_factors[2] * j_factors[2]
+        dim3 threads_per_block(32, 2);
+        vllm::awq::gemm_forward_4bit_cuda_m16n128k32<<<num_blocks, threads_per_block>>>(
+            group_size, split_k_iters, in_feats, kernel, scaling_factors, zeros, num_in_feats, num_in_channels, num_out_channels, out_feats);
+    }
+    else if (num_out_channels % 64 == 0)
+    {
+	int j_factors1 = num_out_channels / 64 / 1;
+        dim3 num_blocks(1 * (num_out_feats + 16 - 1) / 16 * j_factors1 * split_k_iters);
+    
+        // threadIdx.x: 32
+        // threadIdx.y: i_factors[2] * j_factors[2]
+        dim3 threads_per_block(32, 2);
+        vllm::awq::gemm_forward_4bit_cuda_m16n64k32<<<num_blocks, threads_per_block>>>(
+            group_size, split_k_iters, in_feats, kernel, scaling_factors, zeros, num_in_feats, num_in_channels, num_out_channels, out_feats);
+    }
+    return _out_feats.sum(0);
+}

docs/source/getting_started/installation.rst

@@ -3,31 +3,15 @@
 Installation
 ============
 
-vLLM is a Python library that also contains some C++ and CUDA code.
-This additional code requires compilation on the user's machine.
+vLLM is a Python library that also contains pre-compiled C++ and CUDA (11.8) binaries.
 
 Requirements
 ------------
 
 * OS: Linux
-* Python: 3.8 or higher
-* CUDA: 11.0 -- 11.8
+* Python: 3.8 -- 3.11
 * GPU: compute capability 7.0 or higher (e.g., V100, T4, RTX20xx, A100, L4, etc.)
 
-.. note::
-    As of now, vLLM does not support CUDA 12.
-    If you are using Hopper or Lovelace GPUs, please use CUDA 11.8 instead of CUDA 12.
-
-.. tip::
-    If you have trouble installing vLLM, we recommend using the NVIDIA PyTorch Docker image.
-
-    .. code-block:: console
-
-        $ # Pull the Docker image with CUDA 11.8.
-        $ docker run --gpus all -it --rm --shm-size=8g nvcr.io/nvidia/pytorch:22.12-py3
-
-    Inside the Docker container, please execute :code:`pip uninstall torch` before installing vLLM.
-
 Install with pip
 ----------------
 
@@ -40,7 +24,7 @@ You can install vLLM using pip:
     $ conda activate myenv
 
     $ # Install vLLM.
-    $ pip install vllm  # This may take 5-10 minutes.
+    $ pip install vllm
 
 
 .. _build_from_source:
@@ -55,3 +39,12 @@ You can also build and install vLLM from source:
     $ git clone https://github.com/vllm-project/vllm.git
     $ cd vllm
     $ pip install -e .  # This may take 5-10 minutes.
+
+.. tip::
+    If you have trouble building vLLM, we recommend using the NVIDIA PyTorch Docker image.
+
+    .. code-block:: console
+
+        $ # Pull the Docker image with CUDA 11.8.
+        $ # Use `--ipc=host` to make sure the shared memory is large enough.
+        $ docker run --gpus all -it --rm --ipc=host nvcr.io/nvidia/pytorch:22.12-py3

docs/source/getting_started/quickstart.rst

@@ -128,4 +128,4 @@ Since this server is compatible with OpenAI API, you can use it as a drop-in rep
                                           prompt="San Francisco is a")
     print("Completion result:", completion)
 
-For a more detailed client example, refer to `examples/openai_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_client.py>`_.
+For a more detailed client example, refer to `examples/openai_completion_client.py <https://github.com/vllm-project/vllm/blob/main/examples/openai_completion_client.py>`_.

docs/source/index.rst

@@ -43,6 +43,7 @@ vLLM is flexible and easy to use with:
 For more information, check out the following:
 
 * `vLLM announcing blog post <https://vllm.ai>`_ (intro to PagedAttention)
+* `vLLM paper <https://arxiv.org/abs/2309.06180>`_ (SOSP 2023)
 * `How continuous batching enables 23x throughput in LLM inference while reducing p50 latency <https://www.anyscale.com/blog/continuous-batching-llm-inference>`_ by Cade Daniel et al.
 
 
@@ -62,6 +63,8 @@ Documentation
    :caption: Serving
 
    serving/distributed_serving
+   serving/run_on_sky
+   serving/deploying_with_triton
 
 .. toctree::
    :maxdepth: 1

docs/source/models/adding_model.rst

@@ -59,7 +59,7 @@ Next, you need to rewrite the :code:`forward` methods of your model by following
     +    kv_caches: List[KVCache],
     +    input_metadata: InputMetadata,
     +    cache_events: Optional[List[torch.cuda.Event]],
-    +) -> Dict[int, SequenceOutputs]:
+    +) -> SamplerOutput:
 
 3. Update the code by considering that :code:`input_ids` and :code:`positions` are now flattened tensors.
 4. Replace the attention operation with either :code:`GPTPagedAttention` or :code:`GPTNeoXPagedAttention`, depending on the model's architecture.

docs/source/models/supported_models.rst

@@ -14,27 +14,48 @@ Alongside each architecture, we include some popular models that use it.
   * - Architecture
     - Models
     - Example HuggingFace Models
+  * - :code:`AquilaForCausalLM`
+    - Aquila
+    - :code:`BAAI/Aquila-7B`, :code:`BAAI/AquilaChat-7B`, etc.
+  * - :code:`BaiChuanForCausalLM`
+    - Baichuan
+    - :code:`baichuan-inc/Baichuan-7B`, :code:`baichuan-inc/Baichuan-13B-Chat`, etc.
   * - :code:`BloomForCausalLM`
     - BLOOM, BLOOMZ, BLOOMChat
     - :code:`bigscience/bloom`, :code:`bigscience/bloomz`, etc.
+  * - :code:`FalconForCausalLM`
+    - Falcon
+    - :code:`tiiuae/falcon-7b``, :code:`tiiuae/falcon-40b`, :code:`tiiuae/falcon-rw-7b`, etc.
   * - :code:`GPT2LMHeadModel`
     - GPT-2
     - :code:`gpt2`, :code:`gpt2-xl`, etc.
   * - :code:`GPTBigCodeForCausalLM`
     - StarCoder, SantaCoder, WizardCoder
     - :code:`bigcode/starcoder`, :code:`bigcode/gpt_bigcode-santacoder`, :code:`WizardLM/WizardCoder-15B-V1.0`, etc.
+  * - :code:`GPTJForCausalLM`
+    - GPT-J
+    - :code:`EleutherAI/gpt-j-6b`, :code:`nomic-ai/gpt4all-j`, etc.
   * - :code:`GPTNeoXForCausalLM`
     - GPT-NeoX, Pythia, OpenAssistant, Dolly V2, StableLM
     - :code:`EleutherAI/gpt-neox-20b`, :code:`EleutherAI/pythia-12b`, :code:`OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5`, :code:`databricks/dolly-v2-12b`, :code:`stabilityai/stablelm-tuned-alpha-7b`, etc.
+  * - :code:`InternLMForCausalLM`
+    - InternLM
+    - :code:`internlm/internlm-7b`, :code:`internlm/internlm-chat-7b`, etc.
   * - :code:`LlamaForCausalLM`
-    - LLaMA, Vicuna, Alpaca, Koala, Guanaco
-    - :code:`openlm-research/open_llama_13b`, :code:`lmsys/vicuna-13b-v1.3`, :code:`young-geng/koala`, :code:`JosephusCheung/Guanaco`, etc.
+    - LLaMA, LLaMA-2, Vicuna, Alpaca, Koala, Guanaco
+    - :code:`meta-llama/Llama-2-13b-hf`, :code:`meta-llama/Llama-2-70b-hf`, :code:`openlm-research/open_llama_13b`, :code:`lmsys/vicuna-13b-v1.3`, :code:`young-geng/koala`, etc.
+  * - :code:`MistralForCausalLM`
+    - Mistral, Mistral-Instruct
+    - :code:`mistralai/Mistral-7B-v0.1`, :code:`mistralai/Mistral-7B-Instruct-v0.1`, etc.
   * - :code:`MPTForCausalLM`
     - MPT, MPT-Instruct, MPT-Chat, MPT-StoryWriter
     - :code:`mosaicml/mpt-7b`, :code:`mosaicml/mpt-7b-storywriter`, :code:`mosaicml/mpt-30b`, etc.
   * - :code:`OPTForCausalLM`
     - OPT, OPT-IML
     - :code:`facebook/opt-66b`, :code:`facebook/opt-iml-max-30b`, etc.
+  * - :code:`QWenLMHeadModel`
+    - Qwen
+    - :code:`Qwen/Qwen-7B`, :code:`Qwen/Qwen-7B-Chat`, etc.
 
 If your model uses one of the above model architectures, you can seamlessly run your model with vLLM.
 Otherwise, please refer to :ref:`Adding a New Model <adding_a_new_model>` for instructions on how to implement support for your model.

docs/source/serving/deploying_with_triton.rst

@@ -0,0 +1,6 @@
+.. _deploying_with_triton:
+
+Deploying with NVIDIA Triton
+============================
+
+The `Triton Inference Server <https://github.com/triton-inference-server>`_ hosts a tutorial demonstrating how to quickly deploy a simple `facebook/opt-125m <https://huggingface.co/facebook/opt-125m>`_ model using vLLM. Please see `Deploying a vLLM model in Triton <https://github.com/triton-inference-server/tutorials/blob/main/Quick_Deploy/vLLM/README.md#deploying-a-vllm-model-in-triton>`_ for more details.

docs/source/serving/run_on_sky.rst

@@ -0,0 +1,69 @@
+.. _on_cloud:
+
+Running on clouds with SkyPilot
+===============================
+
+.. raw:: html
+
+    <p align="center">
+        <img src="https://imgur.com/yxtzPEu.png" alt="vLLM"/>
+    </p>
+
+vLLM can be run on the cloud to scale to multiple GPUs with `SkyPilot <https://github.com/skypilot-org/skypilot>`__, an open-source framework for running LLMs on any cloud.
+
+To install SkyPilot and setup your cloud credentials, run:
+
+.. code-block:: console
+
+    $ pip install skypilot
+    $ sky check
+
+See the vLLM SkyPilot YAML for serving, `serving.yaml <https://github.com/skypilot-org/skypilot/blob/master/llm/vllm/serve.yaml>`__.
+
+.. code-block:: yaml
+
+    resources:
+        accelerators: A100
+
+    envs:
+        MODEL_NAME: decapoda-research/llama-13b-hf
+        TOKENIZER: hf-internal-testing/llama-tokenizer
+
+    setup: |
+        conda create -n vllm python=3.9 -y
+        conda activate vllm
+        git clone https://github.com/vllm-project/vllm.git
+        cd vllm
+        pip install .
+        pip install gradio
+
+    run: |
+        conda activate vllm
+        echo 'Starting vllm api server...'
+        python -u -m vllm.entrypoints.api_server \
+                        --model $MODEL_NAME \
+                        --tensor-parallel-size $SKYPILOT_NUM_GPUS_PER_NODE \
+                        --tokenizer $TOKENIZER 2>&1 | tee api_server.log &
+        echo 'Waiting for vllm api server to start...'
+        while ! `cat api_server.log | grep -q 'Uvicorn running on'`; do sleep 1; done
+        echo 'Starting gradio server...'
+        python vllm/examples/gradio_webserver.py
+
+Start the serving the LLaMA-13B model on an A100 GPU:
+
+.. code-block:: console
+
+    $ sky launch serving.yaml
+
+Check the output of the command. There will be a sharable gradio link (like the last line of the following). Open it in your browser to use the LLaMA model to do the text completion.
+
+.. code-block:: console
+
+    (task, pid=7431) Running on public URL: https://<gradio-hash>.gradio.live
+
+**Optional**: Serve the 65B model instead of the default 13B and use more GPU:
+
+.. code-block:: console
+
+    sky launch -c vllm-serve-new -s serve.yaml --gpus A100:8 --env MODEL_NAME=decapoda-research/llama-65b-hf
+

examples/llm_engine_example.py

@@ -10,7 +10,8 @@ def main(args: argparse.Namespace):
 
     # Test the following prompts.
     test_prompts = [
-        ("A robot may not injure a human being", SamplingParams()),
+        ("A robot may not injure a human being",
+         SamplingParams(temperature=0.0)),
         ("To be or not to be,",
          SamplingParams(temperature=0.8, top_k=5, presence_penalty=0.2)),
         ("What is the meaning of life?",
@@ -27,7 +28,7 @@ def main(args: argparse.Namespace):
     # Run the engine by calling `engine.step()` manually.
     request_id = 0
     while True:
-        # To test iteration-level scheduling, we add one request at each step.
+        # To test continuous batching, we add one request at each step.
         if test_prompts:
             prompt, sampling_params = test_prompts.pop(0)
             engine.add_request(str(request_id), prompt, sampling_params)

examples/openai_chatcompletion_client.py

@@ -0,0 +1,33 @@
+import openai
+
+# Modify OpenAI's API key and API base to use vLLM's API server.
+openai.api_key = "EMPTY"
+openai.api_base = "http://localhost:8000/v1"
+
+# List models API
+models = openai.Model.list()
+print("Models:", models)
+
+model = models["data"][0]["id"]
+
+# Chat completion API
+chat_completion = openai.ChatCompletion.create(
+    model=model,
+    messages=[{
+        "role": "system",
+        "content": "You are a helpful assistant."
+    }, {
+        "role": "user",
+        "content": "Who won the world series in 2020?"
+    }, {
+        "role":
+        "assistant",
+        "content":
+        "The Los Angeles Dodgers won the World Series in 2020."
+    }, {
+        "role": "user",
+        "content": "Where was it played?"
+    }])
+
+print("Chat completion results:")
+print(chat_completion)

examples/openai_completion_client.py

@@ -3,26 +3,26 @@ import openai
 # Modify OpenAI's API key and API base to use vLLM's API server.
 openai.api_key = "EMPTY"
 openai.api_base = "http://localhost:8000/v1"
-model = "facebook/opt-125m"
 
-# Test list models API
+# List models API
 models = openai.Model.list()
 print("Models:", models)
 
-# Test completion API
-stream = True
+model = models["data"][0]["id"]
+
+# Completion API
+stream = False
 completion = openai.Completion.create(
     model=model,
     prompt="A robot may not injure a human being",
     echo=False,
     n=2,
-    best_of=3,
     stream=stream,
     logprobs=3)
 
-# print the completion
+print("Completion results:")
 if stream:
     for c in completion:
         print(c)
 else:
-    print("Completion result:", completion)
+    print(completion)

requirements-dev.txt

@@ -10,3 +10,5 @@ types-setuptools
 
 # testing
 pytest
+pytest-forked
+pytest-asyncio

requirements.txt

@@ -1,12 +1,13 @@
 ninja  # For faster builds.
 psutil
-ray
+ray >= 2.5.1
+pandas  # Required for Ray data.
+pyarrow  # Required for Ray data.
 sentencepiece  # Required for LLaMA tokenizer.
 numpy
 torch >= 2.0.0
-transformers >= 4.28.0  # Required for LLaMA.
-xformers >= 0.0.19
+transformers >= 4.33.1  # Required for Code Llama.
+xformers >= 0.0.22
 fastapi
-uvicorn
-pydantic  # Required for OpenAI server.
-fschat  # Required for OpenAI ChatCompletion Endpoint.
+uvicorn[standard]
+pydantic < 2  # Required for OpenAI server.

setup.py

@@ -3,6 +3,7 @@ import os
 import re
 import subprocess
 from typing import List, Set
+import warnings
 
 from packaging.version import parse, Version
 import setuptools
@@ -11,6 +12,9 @@ from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME
 
 ROOT_DIR = os.path.dirname(__file__)
 
+# Supported NVIDIA GPU architectures.
+SUPPORTED_ARCHS = ["7.0", "7.5", "8.0", "8.6", "8.9", "9.0"]
+
 # Compiler flags.
 CXX_FLAGS = ["-g", "-O2", "-std=c++17"]
 # TODO(woosuk): Should we use -O3?
@@ -22,7 +26,7 @@ NVCC_FLAGS += [f"-D_GLIBCXX_USE_CXX11_ABI={ABI}"]
 
 if CUDA_HOME is None:
     raise RuntimeError(
-        f"Cannot find CUDA_HOME. CUDA must be available in order to build the package.")
+        "Cannot find CUDA_HOME. CUDA must be available to build the package.")
 
 
 def get_nvcc_cuda_version(cuda_dir: str) -> Version:
@@ -38,32 +42,82 @@ def get_nvcc_cuda_version(cuda_dir: str) -> Version:
     return nvcc_cuda_version
 
 
-# Collect the compute capabilities of all available GPUs.
-device_count = torch.cuda.device_count()
-compute_capabilities: Set[int] = set()
-for i in range(device_count):
-    major, minor = torch.cuda.get_device_capability(i)
-    if major < 7:
-        raise RuntimeError(
-            "GPUs with compute capability less than 7.0 are not supported.")
-    compute_capabilities.add(major * 10 + minor)
-# If no GPU is available, add all supported compute capabilities.
+def get_torch_arch_list() -> Set[str]:
+    # TORCH_CUDA_ARCH_LIST can have one or more architectures,
+    # e.g. "8.0" or "7.5,8.0,8.6+PTX". Here, the "8.6+PTX" option asks the
+    # compiler to additionally include PTX code that can be runtime-compiled
+    # and executed on the 8.6 or newer architectures. While the PTX code will
+    # not give the best performance on the newer architectures, it provides
+    # forward compatibility.
+    valid_arch_strs = SUPPORTED_ARCHS + [s + "+PTX" for s in SUPPORTED_ARCHS]
+    arch_list = os.environ.get("TORCH_CUDA_ARCH_LIST", None)
+    if arch_list is None:
+        return set()
+
+    # List are separated by ; or space.
+    arch_list = arch_list.replace(" ", ";").split(";")
+    for arch in arch_list:
+        if arch not in valid_arch_strs:
+            raise ValueError(
+                f"Unsupported CUDA arch ({arch}). "
+                f"Valid CUDA arch strings are: {valid_arch_strs}.")
+    return set(arch_list)
+
+
+# First, check the TORCH_CUDA_ARCH_LIST environment variable.
+compute_capabilities = get_torch_arch_list()
 if not compute_capabilities:
-    compute_capabilities = {70, 75, 80, 86, 90}
-# Add target compute capabilities to NVCC flags.
-for capability in compute_capabilities:
-    NVCC_FLAGS += ["-gencode", f"arch=compute_{capability},code=sm_{capability}"]
+    # If TORCH_CUDA_ARCH_LIST is not defined or empty, target all available
+    # GPUs on the current machine.
+    device_count = torch.cuda.device_count()
+    for i in range(device_count):
+        major, minor = torch.cuda.get_device_capability(i)
+        if major < 7:
+            raise RuntimeError(
+                "GPUs with compute capability below 7.0 are not supported.")
+        compute_capabilities.add(f"{major}.{minor}")
+
+nvcc_cuda_version = get_nvcc_cuda_version(CUDA_HOME)
+if not compute_capabilities:
+    # If no GPU is specified nor available, add all supported architectures
+    # based on the NVCC CUDA version.
+    compute_capabilities = set(SUPPORTED_ARCHS)
+    if nvcc_cuda_version < Version("11.1"):
+        compute_capabilities.remove("8.6")
+    if nvcc_cuda_version < Version("11.8"):
+        compute_capabilities.remove("8.9")
+        compute_capabilities.remove("9.0")
 
 # Validate the NVCC CUDA version.
-nvcc_cuda_version = get_nvcc_cuda_version(CUDA_HOME)
 if nvcc_cuda_version < Version("11.0"):
     raise RuntimeError("CUDA 11.0 or higher is required to build the package.")
-if 86 in compute_capabilities and nvcc_cuda_version < Version("11.1"):
-    raise RuntimeError(
-        "CUDA 11.1 or higher is required for GPUs with compute capability 8.6.")
-if 90 in compute_capabilities and nvcc_cuda_version < Version("11.8"):
-    raise RuntimeError(
-        "CUDA 11.8 or higher is required for GPUs with compute capability 9.0.")
+if nvcc_cuda_version < Version("11.1"):
+    if any(cc.startswith("8.6") for cc in compute_capabilities):
+        raise RuntimeError(
+            "CUDA 11.1 or higher is required for compute capability 8.6.")
+if nvcc_cuda_version < Version("11.8"):
+    if any(cc.startswith("8.9") for cc in compute_capabilities):
+        # CUDA 11.8 is required to generate the code targeting compute capability 8.9.
+        # However, GPUs with compute capability 8.9 can also run the code generated by
+        # the previous versions of CUDA 11 and targeting compute capability 8.0.
+        # Therefore, if CUDA 11.8 is not available, we target compute capability 8.0
+        # instead of 8.9.
+        warnings.warn(
+            "CUDA 11.8 or higher is required for compute capability 8.9. "
+            "Targeting compute capability 8.0 instead.")
+        compute_capabilities = set(cc for cc in compute_capabilities
+                                   if not cc.startswith("8.9"))
+        compute_capabilities.add("8.0+PTX")
+    if any(cc.startswith("9.0") for cc in compute_capabilities):
+        raise RuntimeError(
+            "CUDA 11.8 or higher is required for compute capability 9.0.")
+
+# Add target compute capabilities to NVCC flags.
+for capability in compute_capabilities:
+    num = capability[0] + capability[2]
+    NVCC_FLAGS += ["-gencode", f"arch=compute_{num},code=sm_{num}"]
+    if capability.endswith("+PTX"):
+        NVCC_FLAGS += ["-gencode", f"arch=compute_{num},code=compute_{num}"]
 
 # Use NVCC threads to parallelize the build.
 if nvcc_cuda_version >= Version("11.2"):
@@ -76,7 +130,10 @@ ext_modules = []
 cache_extension = CUDAExtension(
     name="vllm.cache_ops",
     sources=["csrc/cache.cpp", "csrc/cache_kernels.cu"],
-    extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS},
+    extra_compile_args={
+        "cxx": CXX_FLAGS,
+        "nvcc": NVCC_FLAGS,
+    },
 )
 ext_modules.append(cache_extension)
 
@@ -84,7 +141,10 @@ ext_modules.append(cache_extension)
 attention_extension = CUDAExtension(
     name="vllm.attention_ops",
     sources=["csrc/attention.cpp", "csrc/attention/attention_kernels.cu"],
-    extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS},
+    extra_compile_args={
+        "cxx": CXX_FLAGS,
+        "nvcc": NVCC_FLAGS,
+    },
 )
 ext_modules.append(attention_extension)
 
@@ -92,7 +152,10 @@ ext_modules.append(attention_extension)
 positional_encoding_extension = CUDAExtension(
     name="vllm.pos_encoding_ops",
     sources=["csrc/pos_encoding.cpp", "csrc/pos_encoding_kernels.cu"],
-    extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS},
+    extra_compile_args={
+        "cxx": CXX_FLAGS,
+        "nvcc": NVCC_FLAGS,
+    },
 )
 ext_modules.append(positional_encoding_extension)
 
@@ -100,7 +163,10 @@ ext_modules.append(positional_encoding_extension)
 layernorm_extension = CUDAExtension(
     name="vllm.layernorm_ops",
     sources=["csrc/layernorm.cpp", "csrc/layernorm_kernels.cu"],
-    extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS},
+    extra_compile_args={
+        "cxx": CXX_FLAGS,
+        "nvcc": NVCC_FLAGS,
+    },
 )
 ext_modules.append(layernorm_extension)
 
@@ -108,10 +174,38 @@ ext_modules.append(layernorm_extension)
 activation_extension = CUDAExtension(
     name="vllm.activation_ops",
     sources=["csrc/activation.cpp", "csrc/activation_kernels.cu"],
-    extra_compile_args={"cxx": CXX_FLAGS, "nvcc": NVCC_FLAGS},
+    extra_compile_args={
+        "cxx": CXX_FLAGS,
+        "nvcc": NVCC_FLAGS,
+    },
 )
 ext_modules.append(activation_extension)
 
+# Quantization kernels.
+quantization_extension = CUDAExtension(
+    name="vllm.quantization_ops",
+    sources=[
+        "csrc/quantization.cpp",
+        "csrc/quantization/awq/gemm_kernels.cu",
+    ],
+    extra_compile_args={
+        "cxx": CXX_FLAGS,
+        "nvcc": NVCC_FLAGS,
+    },
+)
+ext_modules.append(quantization_extension)
+
+# Misc. CUDA utils.
+cuda_utils_extension = CUDAExtension(
+    name="vllm.cuda_utils",
+    sources=["csrc/cuda_utils.cpp", "csrc/cuda_utils_kernels.cu"],
+    extra_compile_args={
+        "cxx": CXX_FLAGS,
+        "nvcc": NVCC_FLAGS,
+    },
+)
+ext_modules.append(cuda_utils_extension)
+
 
 def get_path(*filepath) -> str:
     return os.path.join(ROOT_DIR, *filepath)
@@ -123,8 +217,8 @@ def find_version(filepath: str):
     Adapted from https://github.com/ray-project/ray/blob/0b190ee1160eeca9796bc091e07eaebf4c85b511/python/setup.py
     """
     with open(filepath) as fp:
-        version_match = re.search(
-            r"^__version__ = ['\"]([^'\"]*)['\"]", fp.read(), re.M)
+        version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]",
+                                  fp.read(), re.M)
         if version_match:
             return version_match.group(1)
         raise RuntimeError("Unable to find version string.")
@@ -147,7 +241,8 @@ setuptools.setup(
     version=find_version(get_path("vllm", "__init__.py")),
     author="vLLM Team",
     license="Apache 2.0",
-    description="A high-throughput and memory-efficient inference and serving engine for LLMs",
+    description=("A high-throughput and memory-efficient inference and "
+                 "serving engine for LLMs"),
     long_description=read_readme(),
     long_description_content_type="text/markdown",
     url="https://github.com/vllm-project/vllm",
@@ -159,11 +254,12 @@ setuptools.setup(
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
         "License :: OSI Approved :: Apache Software License",
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
     ],
-    packages=setuptools.find_packages(
-        exclude=("assets", "benchmarks", "csrc", "docs", "examples", "tests")),
+    packages=setuptools.find_packages(exclude=("benchmarks", "csrc", "docs",
+                                               "examples", "tests")),
     python_requires=">=3.8",
     install_requires=get_requirements(),
     ext_modules=ext_modules,

tests/async_engine/api_server_async_engine.py

@@ -0,0 +1,50 @@
+"""vllm.entrypoints.api_server with some extra logging for testing."""
+import argparse
+from typing import Any, Dict
+
+import uvicorn
+from fastapi.responses import JSONResponse, Response
+
+import vllm.entrypoints.api_server
+from vllm.engine.arg_utils import AsyncEngineArgs
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+
+app = vllm.entrypoints.api_server.app
+
+
+class AsyncLLMEngineWithStats(AsyncLLMEngine):
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self._num_aborts = 0
+
+    async def abort(self, request_id: str) -> None:
+        await super().abort(request_id)
+        self._num_aborts += 1
+
+    def testing_stats(self) -> Dict[str, Any]:
+        return {"num_aborted_requests": self._num_aborts}
+
+
+@app.get("/stats")
+def stats() -> Response:
+    """Get the statistics of the engine."""
+    return JSONResponse(engine.testing_stats())
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--host", type=str, default="localhost")
+    parser.add_argument("--port", type=int, default=8000)
+    parser = AsyncEngineArgs.add_cli_args(parser)
+    args = parser.parse_args()
+
+    engine_args = AsyncEngineArgs.from_cli_args(args)
+    engine = AsyncLLMEngineWithStats.from_engine_args(engine_args)
+    vllm.entrypoints.api_server.engine = engine
+    uvicorn.run(
+        app,
+        host=args.host,
+        port=args.port,
+        log_level="debug",
+        timeout_keep_alive=vllm.entrypoints.api_server.TIMEOUT_KEEP_ALIVE)

tests/async_engine/test_api_server.py

@@ -0,0 +1,86 @@
+import subprocess
+import sys
+import time
+from multiprocessing import Pool
+from pathlib import Path
+
+import pytest
+import requests
+
+
+def _query_server(prompt: str) -> dict:
+    response = requests.post("http://localhost:8000/generate",
+                             json={
+                                 "prompt": prompt,
+                                 "max_tokens": 100,
+                                 "temperature": 0,
+                                 "ignore_eos": True
+                             })
+    response.raise_for_status()
+    return response.json()
+
+
+@pytest.fixture
+def api_server():
+    script_path = Path(__file__).parent.joinpath(
+        "api_server_async_engine.py").absolute()
+    uvicorn_process = subprocess.Popen([
+        sys.executable, "-u",
+        str(script_path), "--model", "facebook/opt-125m"
+    ])
+    yield
+    uvicorn_process.terminate()
+
+
+def test_api_server(api_server):
+    """
+    Run the API server and test it.
+
+    We run both the server and requests in separate processes.
+
+    We test that the server can handle incoming requests, including
+    multiple requests at the same time, and that it can handle requests
+    being cancelled without crashing.
+    """
+    with Pool(32) as pool:
+        # Wait until the server is ready
+        prompts = ["Hello world"] * 1
+        result = None
+        while not result:
+            try:
+                for result in pool.map(_query_server, prompts):
+                    break
+            except:
+                time.sleep(1)
+
+        # Actual tests start here
+        # Try with 1 prompt
+        for result in pool.map(_query_server, prompts):
+            assert result
+
+        num_aborted_requests = requests.get(
+            "http://localhost:8000/stats").json()["num_aborted_requests"]
+        assert num_aborted_requests == 0
+
+        # Try with 100 prompts
+        prompts = ["Hello world"] * 100
+        for result in pool.map(_query_server, prompts):
+            assert result
+
+        # Cancel requests
+        pool.map_async(_query_server, prompts)
+        time.sleep(0.01)
+        pool.terminate()
+        pool.join()
+
+        # check cancellation stats
+        num_aborted_requests = requests.get(
+            "http://localhost:8000/stats").json()["num_aborted_requests"]
+        assert num_aborted_requests > 0
+
+    # check that server still runs after cancellations
+    with Pool(32) as pool:
+        # Try with 100 prompts
+        prompts = ["Hello world"] * 100
+        for result in pool.map(_query_server, prompts):
+            assert result

tests/async_engine/test_async_llm_engine.py

@@ -0,0 +1,80 @@
+import asyncio
+from dataclasses import dataclass
+
+import pytest
+
+from vllm.engine.async_llm_engine import AsyncLLMEngine
+
+
+@dataclass
+class RequestOutput:
+    request_id: int
+    finished: bool = False
+
+
+class MockEngine:
+
+    def __init__(self):
+        self.step_calls = 0
+        self.add_request_calls = 0
+        self.abort_request_calls = 0
+        self.request_id = None
+
+    async def step_async(self):
+        self.step_calls += 1
+        return [RequestOutput(
+            request_id=self.request_id)] if self.request_id else []
+
+    def generate(self, request_id):
+        self.request_id = request_id
+
+    def stop_generating(self):
+        self.request_id = None
+
+    def add_request(self, **kwargs):
+        self.add_request_calls += 1
+        return
+
+    def abort_request(self, request_id):
+        self.abort_request_calls += 1
+        return
+
+
+class MockAsyncLLMEngine(AsyncLLMEngine):
+
+    def _init_engine(self, *args, **kwargs):
+        return MockEngine()
+
+
+@pytest.mark.asyncio
+async def test_new_requests_event():
+    engine = MockAsyncLLMEngine(worker_use_ray=False, engine_use_ray=False)
+    engine.start_background_loop()
+    await asyncio.sleep(0.01)
+    assert engine.engine.step_calls == 0
+
+    await engine.add_request("1", "", None)
+    await asyncio.sleep(0.01)
+    assert engine.engine.add_request_calls == 1
+    assert engine.engine.step_calls == 1
+
+    await engine.add_request("2", "", None)
+    engine.engine.generate("2")
+    await asyncio.sleep(0)
+    assert engine.engine.add_request_calls == 2
+    assert engine.engine.step_calls == 2
+    await asyncio.sleep(0)
+    assert engine.engine.step_calls == 3
+    engine.engine.stop_generating()
+    await asyncio.sleep(0)
+    assert engine.engine.step_calls == 4
+    await asyncio.sleep(0)
+    assert engine.engine.step_calls == 4
+
+    await engine.add_request("3", "", None)
+    await asyncio.sleep(0.01)
+    assert engine.engine.add_request_calls == 3
+    assert engine.engine.step_calls == 5
+    await asyncio.sleep(0.01)
+    assert engine.engine.add_request_calls == 3
+    assert engine.engine.step_calls == 5

tests/async_engine/test_request_tracker.py

@@ -0,0 +1,75 @@
+import pytest
+
+from vllm.engine.async_llm_engine import RequestTracker
+from vllm.outputs import RequestOutput
+
+
+class DummyEvent:
+
+    def __init__(self):
+        self._flag = False
+
+    def set(self):
+        self._flag = True
+
+    def clear(self):
+        self._flag = False
+
+
+def test_request_tracker():
+    tracker = RequestTracker()
+    tracker.new_requests_event = DummyEvent()
+    stream_1 = tracker.add_request("1")
+    assert tracker.new_requests_event._flag
+    new, finished = tracker.get_new_and_finished_requests()
+    assert not tracker.new_requests_event._flag
+    assert len(new) == 1
+    assert new[0]["request_id"] == "1"
+    assert not finished
+    assert not stream_1.finished
+
+    stream_2 = tracker.add_request("2")
+    stream_3 = tracker.add_request("3")
+    assert tracker.new_requests_event._flag
+    new, finished = tracker.get_new_and_finished_requests()
+    assert not tracker.new_requests_event._flag
+    assert len(new) == 2
+    assert new[0]["request_id"] == "2"
+    assert new[1]["request_id"] == "3"
+    assert not finished
+    assert not stream_2.finished
+    assert not stream_3.finished
+
+    # request_ids must be unique
+    with pytest.raises(KeyError):
+        tracker.add_request("1")
+    assert not tracker.new_requests_event._flag
+
+    tracker.abort_request("1")
+    new, finished = tracker.get_new_and_finished_requests()
+    assert len(finished) == 1
+    assert "1" in finished
+    assert not new
+    assert stream_1.finished
+
+    stream_4 = tracker.add_request("4")
+    tracker.abort_request("4")
+    assert tracker.new_requests_event._flag
+    new, finished = tracker.get_new_and_finished_requests()
+    assert len(finished) == 1
+    assert "4" in finished
+    assert not new
+    assert stream_4.finished
+
+    stream_5 = tracker.add_request("5")
+    assert tracker.new_requests_event._flag
+    tracker.process_request_output(
+        RequestOutput("2", "output", [], [], finished=True))
+    new, finished = tracker.get_new_and_finished_requests()
+    assert not tracker.new_requests_event._flag
+    assert len(finished) == 1
+    assert "2" in finished
+    assert len(new) == 1
+    assert new[0]["request_id"] == "5"
+    assert stream_2.finished
+    assert not stream_5.finished

tests/conftest.py

@@ -0,0 +1,178 @@
+from typing import List, Optional, Tuple
+
+import pytest
+import torch
+from transformers import AutoModelForCausalLM
+
+from vllm import LLM, SamplingParams
+from vllm.transformers_utils.tokenizer import get_tokenizer
+
+_TEST_PROMPTS = [
+    "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs.",
+    "Briefly describe the major milestones in the development of artificial intelligence from 1950 to 2020.",
+    "Compare and contrast artificial intelligence with human intelligence in terms of processing information.",
+    "Describe the basic components of a neural network and how it can be trained.",
+    "Write a short story about a robot that dreams for the first time.",
+    "Analyze the impact of the COVID-19 pandemic on global economic structures and future business models.",
+    "Explain the cultural significance of the Mona Lisa painting, and how its perception might vary in Western versus Eastern societies.",
+    "Translate the following English sentence into Japanese, French, and Swahili: 'The early bird catches the worm.'",
+]
+
+
+@pytest.fixture
+def example_prompts() -> List[str]:
+    return _TEST_PROMPTS
+
+
+_STR_DTYPE_TO_TORCH_DTYPE = {
+    "half": torch.half,
+    "bfloat16": torch.bfloat16,
+    "float": torch.float,
+}
+
+
+class HfRunner:
+
+    def __init__(
+        self,
+        model_name: str,
+        tokenizer_name: Optional[str] = None,
+        dtype: str = "half",
+    ) -> None:
+        assert dtype in _STR_DTYPE_TO_TORCH_DTYPE
+        torch_dtype = _STR_DTYPE_TO_TORCH_DTYPE[dtype]
+        self.model = AutoModelForCausalLM.from_pretrained(
+            model_name,
+            torch_dtype=torch_dtype,
+            trust_remote_code=True,
+        ).cuda()
+        if tokenizer_name is None:
+            tokenizer_name = model_name
+        self.tokenizer = get_tokenizer(tokenizer_name, trust_remote_code=True)
+
+    def generate(
+        self,
+        prompts: List[str],
+        **kwargs,
+    ) -> List[Tuple[List[int], str]]:
+        outputs: List[Tuple[List[int], str]] = []
+        for prompt in prompts:
+            input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids
+            output_ids = self.model.generate(
+                input_ids.cuda(),
+                use_cache=True,
+                **kwargs,
+            )
+            output_str = self.tokenizer.batch_decode(
+                output_ids,
+                skip_special_tokens=True,
+                clean_up_tokenization_spaces=False,
+            )
+            output_ids = output_ids.cpu().tolist()
+            outputs.append((output_ids, output_str))
+        return outputs
+
+    def generate_greedy(
+        self,
+        prompts: List[str],
+        max_tokens: int,
+    ) -> List[Tuple[List[int], str]]:
+        outputs = self.generate(prompts,
+                                do_sample=False,
+                                max_new_tokens=max_tokens)
+        for i in range(len(outputs)):
+            output_ids, output_str = outputs[i]
+            outputs[i] = (output_ids[0], output_str[0])
+        return outputs
+
+    def generate_beam_search(
+        self,
+        prompts: List[str],
+        beam_width: int,
+        max_tokens: int,
+    ) -> List[Tuple[List[int], str]]:
+        outputs = self.generate(prompts,
+                                do_sample=False,
+                                max_new_tokens=max_tokens,
+                                num_beams=beam_width,
+                                num_return_sequences=beam_width)
+        for i in range(len(outputs)):
+            output_ids, output_str = outputs[i]
+            for j in range(len(output_ids)):
+                output_ids[j] = [
+                    x for x in output_ids[j]
+                    if x != self.tokenizer.pad_token_id
+                ]
+            outputs[i] = (output_ids, output_str)
+        return outputs
+
+
+@pytest.fixture
+def hf_runner():
+    return HfRunner
+
+
+class VllmRunner:
+
+    def __init__(
+        self,
+        model_name: str,
+        tokenizer_name: Optional[str] = None,
+        dtype: str = "half",
+    ) -> None:
+        self.model = LLM(
+            model=model_name,
+            tokenizer=tokenizer_name,
+            trust_remote_code=True,
+            dtype=dtype,
+            swap_space=0,
+        )
+
+    def generate(
+        self,
+        prompts: List[str],
+        sampling_params: SamplingParams,
+    ) -> List[Tuple[List[int], str]]:
+        req_outputs = self.model.generate(prompts,
+                                          sampling_params=sampling_params)
+        outputs = []
+        for req_output in req_outputs:
+            prompt_str = req_output.prompt
+            prompt_ids = req_output.prompt_token_ids
+            req_sample_output_ids = []
+            req_sample_output_strs = []
+            for sample in req_output.outputs:
+                output_str = sample.text
+                output_ids = sample.token_ids
+                req_sample_output_ids.append(prompt_ids + output_ids)
+                req_sample_output_strs.append(prompt_str + output_str)
+            outputs.append((req_sample_output_ids, req_sample_output_strs))
+        return outputs
+
+    def generate_greedy(
+        self,
+        prompts: List[str],
+        max_tokens: int,
+    ) -> List[Tuple[List[int], str]]:
+        greedy_params = SamplingParams(temperature=0.0, max_tokens=max_tokens)
+        outputs = self.generate(prompts, greedy_params)
+        return [(output_ids[0], output_str[0])
+                for output_ids, output_str in outputs]
+
+    def generate_beam_search(
+        self,
+        prompts: List[str],
+        beam_width: int,
+        max_tokens: int,
+    ) -> List[Tuple[List[int], str]]:
+        beam_search_params = SamplingParams(n=beam_width,
+                                            use_beam_search=True,
+                                            temperature=0.0,
+                                            max_tokens=max_tokens)
+        outputs = self.generate(prompts, beam_search_params)
+        return outputs
+
+
+@pytest.fixture
+def vllm_runner():
+    return VllmRunner

tests/engine/test_detokenize.py

@@ -0,0 +1,62 @@
+import pytest
+
+from transformers import AutoTokenizer
+
+from vllm.transformers_utils.tokenizer import detokenize_incrementally
+
+TRUTH = [
+    "Hello here, this is a simple test",
+    "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be used in production environments, where inference and serving",
+    "我很感谢你的热情"
+]
+TOKENIZERS = [
+    "facebook/opt-125m",
+    "gpt2",
+    "bigcode/tiny_starcoder_py",
+    "EleutherAI/gpt-j-6b",
+    "EleutherAI/pythia-70m",
+    "bigscience/bloom-560m",
+    "mosaicml/mpt-7b",
+    "tiiuae/falcon-7b",
+    "meta-llama/Llama-2-7b-hf",
+    "codellama/CodeLlama-7b-hf",
+]
+
+
+def _run_incremental_decode(tokenizer, all_input_ids,
+                            skip_special_tokens: bool):
+    decoded_text = ""
+    offset = 0
+    token_offset = 0
+    prev_tokens = None
+    for i in range(len(all_input_ids)):
+        new_tokens, text, offset, token_offset = detokenize_incrementally(
+            tokenizer,
+            all_input_ids[:i + 1],
+            prev_tokens,
+            offset,
+            token_offset,
+            skip_special_tokens=skip_special_tokens)
+        decoded_text += text
+        if prev_tokens is None:
+            prev_tokens = new_tokens
+        else:
+            prev_tokens += new_tokens
+    return decoded_text
+
+
+@pytest.mark.parametrize("truth", TRUTH)
+@pytest.mark.parametrize("tokenizer_id", TOKENIZERS)
+@pytest.mark.parametrize("skip_special_tokens", (True, False))
+def test_decode_streaming(tokenizer_id, truth, skip_special_tokens):
+    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)
+    all_input_ids = tokenizer(truth, add_special_tokens=False)["input_ids"]
+    if skip_special_tokens:
+        all_input_ids = ([tokenizer.bos_token_id]
+                         if tokenizer.bos_token_id is not None else
+                         []) + all_input_ids + [tokenizer.eos_token_id]
+
+    decoded_text = _run_incremental_decode(
+        tokenizer, all_input_ids, skip_special_tokens=skip_special_tokens)
+
+    assert decoded_text == truth

tests/kernels/conftest.py

@@ -0,0 +1,43 @@
+from typing import List, Tuple
+
+import pytest
+import torch
+
+
+def create_kv_caches(
+    num_blocks: int,
+    block_size: int,
+    num_layers: int,
+    num_heads: int,
+    head_size: int,
+    dtype: torch.dtype,
+    seed: int,
+) -> Tuple[List[torch.Tensor], List[torch.Tensor]]:
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+
+    scale = head_size**-0.5
+    x = 16 // torch.tensor([], dtype=dtype).element_size()
+    key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
+    key_caches = []
+    for _ in range(num_layers):
+        key_cache = torch.empty(size=key_cache_shape,
+                                dtype=dtype,
+                                device='cuda')
+        key_cache.uniform_(-scale, scale)
+        key_caches.append(key_cache)
+
+    value_cache_shape = (num_blocks, num_heads, head_size, block_size)
+    value_caches = []
+    for _ in range(num_layers):
+        value_cache = torch.empty(size=value_cache_shape,
+                                  dtype=dtype,
+                                  device='cuda')
+        value_cache.uniform_(-scale, scale)
+        value_caches.append(value_cache)
+    return key_caches, value_caches
+
+
+@pytest.fixture()
+def kv_cache_factory():
+    return create_kv_caches

tests/kernels/test_activation.py

@@ -1,20 +1,34 @@
+import pytest
 import torch
 import torch.nn.functional as F
+from transformers.activations import get_activation
 
 from vllm import activation_ops
 
+DTYPES = [torch.half, torch.bfloat16, torch.float]
+NUM_TOKENS = [7, 83, 2048]  # Arbitrary values for testing
+D = [512, 4096, 5120, 13824]  # Arbitrary values for testing
+SEEDS = [0]
+
 
 def ref_silu_and_mul(x: torch.Tensor) -> torch.Tensor:
     x1, x2 = x.chunk(chunks=2, dim=1)
     return F.silu(x1) * x2
 
 
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("d", D)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
 @torch.inference_mode()
-def run_silu_and_mul(
+def test_silu_and_mul(
     num_tokens: int,
     d: int,
     dtype: torch.dtype,
+    seed: int,
 ) -> None:
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
     x = torch.randn(num_tokens, 2 * d, dtype=dtype, device='cuda')
     out = torch.empty(num_tokens, d, dtype=dtype, device='cuda')
     activation_ops.silu_and_mul(out, x)
@@ -22,9 +36,40 @@ def run_silu_and_mul(
     assert torch.allclose(out, ref_out, atol=1e-5, rtol=1e-5)
 
 
-def test_silu_and_mul() -> None:
-    for dtype in [torch.half, torch.bfloat16, torch.float]:
-        for num_tokens in [7, 83, 2048]:
-            for d in [512, 4096, 5120, 13824]:
-                print(f'Testing dtype={dtype}, num_tokens={num_tokens}, d={d}')
-                run_silu_and_mul(num_tokens, d, dtype)
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("d", D)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@torch.inference_mode()
+def test_gelu_new(
+    num_tokens: int,
+    d: int,
+    dtype: torch.dtype,
+    seed: int,
+) -> None:
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    x = torch.randn(num_tokens, d, dtype=dtype, device='cuda')
+    out = torch.empty(num_tokens, d, dtype=dtype, device='cuda')
+    activation_ops.gelu_new(out, x)
+    ref_out = get_activation("gelu_new")(x)
+    assert torch.allclose(out, ref_out, atol=1e-5, rtol=1e-5)
+
+
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("d", D)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+def test_gelu_fast(
+    num_tokens: int,
+    d: int,
+    dtype: torch.dtype,
+    seed: int,
+) -> None:
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    x = torch.randn(num_tokens, d, dtype=dtype, device='cuda')
+    out = torch.empty(num_tokens, d, dtype=dtype, device='cuda')
+    activation_ops.gelu_fast(out, x)
+    ref_out = get_activation("gelu_fast")(x)
+    assert torch.allclose(out, ref_out, atol=1e-5, rtol=1e-5)

tests/kernels/test_attention.py

@@ -1,14 +1,28 @@
 import random
-from typing import List, Optional
+from typing import List, Optional, Tuple
 
+import pytest
 import torch
 from xformers import ops as xops
 from xformers.ops.fmha.attn_bias import BlockDiagonalCausalMask
 
 from vllm import attention_ops
+from vllm.utils import get_max_shared_memory_bytes
 
-MAX_SEQ_LEN = 4096
-TEST_SEED = 0
+FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
+# This will change depending on the compute capability.
+# - 512 as a buffer
+MAX_SEQ_LEN = get_max_shared_memory_bytes() // FLOAT32_BYTES - 512
+NUM_BLOCKS = 128  # Arbitrary values for testing
+
+DTYPES = [torch.half, torch.bfloat16, torch.float]
+NUM_GEN_SEQS = [7]  # Arbitrary values for testing
+NUM_PREFILL_SEQS = [1, 3, 7]  # Arbitrary values for testing
+NUM_HEADS = [(40, 40), (64, 8)]  # Arbitrary values for testing
+HEAD_SIZES = [64, 80, 96, 112, 128, 256]
+BLOCK_SIZES = [8, 16, 32]
+USE_ALIBI = [False, True]
+SEEDS = [0]
 
 
 def ref_masked_attention(
@@ -18,29 +32,34 @@ def ref_masked_attention(
     scale: float,
     attn_mask: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
-    query = query * scale
-    attn = torch.einsum('qhd,khd->hqk', query, key)
+    attn_weights = scale * torch.einsum("qhd,khd->hqk", query, key).float()
     if attn_mask is not None:
-        attn = attn + attn_mask
-    attn = torch.softmax(attn, dim=-1)
-    out = torch.einsum('hqk,khd->qhd', attn, value)
+        attn_weights = attn_weights + attn_mask.float()
+    attn_weights = torch.softmax(attn_weights, dim=-1).to(value.dtype)
+    out = torch.einsum("hqk,khd->qhd", attn_weights, value)
     return out
 
 
 def ref_single_query_cached_kv_attention(
     output: torch.Tensor,
     query: torch.Tensor,
+    num_queries_per_kv: int,
     key_cache: torch.Tensor,
     value_cache: torch.Tensor,
     block_tables: torch.Tensor,
     context_lens: torch.Tensor,
+    scale: float,
+    alibi_slopes: Optional[torch.Tensor],
 ) -> None:
-    num_heads = value_cache.shape[1]
+    num_query_heads = query.shape[1]
+    num_kv_heads = value_cache.shape[1]
     head_size = value_cache.shape[2]
     block_size = value_cache.shape[3]
+    num_seqs = query.shape[0]
 
-    num_input_tokens = query.shape[0]
-    for i in range(num_input_tokens):
+    block_tables = block_tables.cpu().tolist()
+    context_lens = context_lens.cpu().tolist()
+    for i in range(num_seqs):
         q = query[i].unsqueeze(0)
         block_table = block_tables[i]
         context_len = int(context_lens[i])
@@ -52,30 +71,139 @@ def ref_single_query_cached_kv_attention(
             block_offset = j % block_size
 
             k = key_cache[block_number, :, :, block_offset, :]
-            k = k.reshape(num_heads, head_size)
+            k = k.reshape(num_kv_heads, head_size)
             keys.append(k)
 
             v = value_cache[block_number, :, :, block_offset]
             values.append(v)
         keys = torch.stack(keys, dim=0)
         values = torch.stack(values, dim=0)
+        if num_queries_per_kv > 1:
+            # Handle MQA and GQA
+            keys = torch.repeat_interleave(keys, num_queries_per_kv, dim=1)
+            values = torch.repeat_interleave(values, num_queries_per_kv, dim=1)
 
-        scale = 1.0 / (head_size**0.5)
-        out = ref_masked_attention(q, keys, values, scale)
-        out = out.view(num_heads, head_size)
+        alibi_bias = None
+        if alibi_slopes is not None:
+            # Create the ALiBi bias used in the paged attention kernel.
+            position_ids = torch.arange(context_len, device="cuda").int()
+            alibi_bias = (position_ids - context_len + 1).float()
+            alibi_bias = alibi_slopes.view(-1, 1, 1) * alibi_bias.view(
+                1, 1, -1)
+
+        out = ref_masked_attention(q, keys, values, scale, alibi_bias)
+        out = out.view(num_query_heads, head_size)
         output[i].copy_(out, non_blocking=True)
 
 
+@pytest.mark.parametrize("num_seqs", NUM_GEN_SEQS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("use_alibi", USE_ALIBI)
+@pytest.mark.parametrize("block_size", BLOCK_SIZES)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
+@torch.inference_mode()
+def test_single_query_cached_kv_attention(
+    kv_cache_factory,
+    num_seqs: int,
+    num_heads: Tuple[int, int],
+    head_size: int,
+    use_alibi: bool,
+    block_size: int,
+    dtype: torch.dtype,
+    seed: int,
+) -> None:
+    random.seed(seed)
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+
+    scale = float(1.0 / (head_size**0.5))
+    num_query_heads, num_kv_heads = num_heads
+    query = torch.empty(num_seqs,
+                        num_query_heads,
+                        head_size,
+                        dtype=dtype,
+                        device="cuda")
+    query.uniform_(-scale, scale)
+
+    assert num_query_heads % num_kv_heads == 0
+    num_queries_per_kv = num_query_heads // num_kv_heads
+    head_mapping = torch.repeat_interleave(
+        torch.arange(num_kv_heads, dtype=torch.int32, device="cuda"),
+        num_queries_per_kv)
+    alibi_slopes = None
+    if use_alibi:
+        alibi_slopes = torch.randn(num_query_heads,
+                                   dtype=torch.float,
+                                   device="cuda")
+
+    context_lens = [random.randint(1, MAX_SEQ_LEN) for _ in range(num_seqs)]
+    context_lens[-1] = MAX_SEQ_LEN
+    max_context_len = max(context_lens)
+    context_lens = torch.tensor(context_lens, dtype=torch.int, device="cuda")
+
+    # Create the block tables.
+    max_num_blocks_per_seq = (max_context_len + block_size - 1) // block_size
+    block_tables = []
+    for _ in range(num_seqs):
+        block_table = [
+            random.randint(0, NUM_BLOCKS - 1)
+            for _ in range(max_num_blocks_per_seq)
+        ]
+        block_tables.append(block_table)
+    block_tables = torch.tensor(block_tables, dtype=torch.int, device="cuda")
+
+    # Create the KV caches.
+    key_caches, value_caches = kv_cache_factory(NUM_BLOCKS, block_size, 1,
+                                                num_kv_heads, head_size, dtype,
+                                                seed)
+    key_cache, value_cache = key_caches[0], value_caches[0]
+
+    # Call the paged attention kernel.
+    output = torch.empty_like(query)
+    attention_ops.single_query_cached_kv_attention(
+        output,
+        query,
+        key_cache,
+        value_cache,
+        head_mapping,
+        scale,
+        block_tables,
+        context_lens,
+        block_size,
+        max_context_len,
+        alibi_slopes,
+    )
+
+    # Run the reference implementation.
+    ref_output = torch.empty_like(query)
+    ref_single_query_cached_kv_attention(
+        ref_output,
+        query,
+        num_queries_per_kv,
+        key_cache,
+        value_cache,
+        block_tables,
+        context_lens,
+        scale,
+        alibi_slopes,
+    )
+
+    # NOTE(woosuk): Due to the kernel-level differences in the two
+    # implementations, there is a small numerical difference in the two
+    # outputs. Thus, we use a relaxed tolerance for the test.
+    assert torch.allclose(output, ref_output, atol=1e-3, rtol=1e-5)
+
+
 def ref_multi_query_kv_attention(
     cu_seq_lens: List[int],
     query: torch.Tensor,
     key: torch.Tensor,
     value: torch.Tensor,
+    scale: float,
     dtype: torch.dtype,
 ) -> torch.Tensor:
-    head_size = query.shape[-1]
-    scale = 1.0 / (head_size**0.5)
-
     num_seqs = len(cu_seq_lens) - 1
     ref_outputs = []
     for i in range(num_seqs):
@@ -87,7 +215,7 @@ def ref_multi_query_kv_attention(
         attn_mask = torch.triu(torch.ones(seq_len, seq_len, dtype=dtype),
                                diagonal=1)
         attn_mask = attn_mask * torch.finfo(dtype).min
-        attn_mask = attn_mask.to(dtype=dtype, device='cuda')
+        attn_mask = attn_mask.to(dtype=dtype, device="cuda")
 
         ref_output = ref_masked_attention(
             query[start_idx:end_idx],
@@ -101,161 +229,47 @@ def ref_multi_query_kv_attention(
     return ref_output
 
 
-def ref_multi_query_cached_kv_attention(
-    cu_query_lens: List[int],
-    query: torch.Tensor,
-    key_cache: torch.Tensor,
-    value_cache: torch.Tensor,
-    block_tables: torch.Tensor,
-    context_lens: torch.Tensor,
-    dtype: torch.dtype,
-) -> torch.Tensor:
-    num_heads = value_cache.shape[1]
-    head_size = value_cache.shape[2]
-    block_size = value_cache.shape[3]
-    scale = 1.0 / (head_size**0.5)
-
-    num_queries = len(cu_query_lens) - 1
-    ref_outputs = []
-    for i in range(num_queries):
-        start_idx = cu_query_lens[i]
-        end_idx = cu_query_lens[i + 1]
-        query_len = end_idx - start_idx
-        context_len = int(context_lens[i])
-        block_table = block_tables[i]
-
-        # Create attention mask
-        attn_mask = torch.triu(torch.ones(query_len, context_len),
-                               diagonal=context_len - query_len + 1) * -1e5
-        attn_mask = attn_mask.to(dtype=dtype, device='cuda')
-
-        keys = []
-        values = []
-        for j in range(context_len):
-            block_number = int(block_table[j // block_size])
-            block_offset = j % block_size
-
-            k = key_cache[block_number, :, :, block_offset, :]
-            k = k.reshape(num_heads, head_size)
-            keys.append(k)
-
-            v = value_cache[block_number, :, :, block_offset]
-            values.append(v)
-        keys = torch.stack(keys, dim=0)
-        values = torch.stack(values, dim=0)
-
-        ref_output = ref_masked_attention(
-            query[start_idx:end_idx],
-            keys,
-            values,
-            scale,
-            attn_mask=attn_mask,
-        )
-        ref_outputs.append(ref_output)
-    ref_output = torch.cat(ref_outputs, dim=0)
-    return ref_output
-
-
+# TODO(woosuk): Add tests for USE_ALIBI=True.
+@pytest.mark.parametrize("num_seqs", NUM_PREFILL_SEQS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
 @torch.inference_mode()
-def run_single_query_cached_kv_attention(
-    num_tokens: int,
-    num_heads: int,
-    head_size: int,
-    block_size: int,
-    num_blocks: int,
-    dtype: torch.dtype,
-) -> None:
-    qkv = torch.empty(num_tokens,
-                      3,
-                      num_heads,
-                      head_size,
-                      dtype=dtype,
-                      device='cuda')
-    qkv.uniform_(-1e-3, 1e-3)
-    query, _, _ = qkv.unbind(dim=1)
-
-    x = 16 // torch.tensor([], dtype=dtype).element_size()
-    key_block_shape = (num_heads, head_size // x, block_size, x)
-    key_cache = torch.empty(size=(num_blocks, *key_block_shape),
-                            dtype=dtype,
-                            device='cuda')
-    key_cache.uniform_(-1e-3, 1e-3)
-    value_block_shape = (num_heads, head_size, block_size)
-    value_cache = torch.empty(size=(num_blocks, *value_block_shape),
-                              dtype=dtype,
-                              device='cuda')
-    value_cache.uniform_(-1e-3, 1e-3)
-
-    context_lens = [random.randint(1, MAX_SEQ_LEN) for _ in range(num_tokens)]
-    max_context_len = max(context_lens)
-    context_lens = torch.tensor(context_lens, dtype=torch.int, device='cuda')
-
-    max_num_blocks_per_seq = (max_context_len + block_size - 1) // block_size
-    block_tables = []
-    for _ in range(num_tokens):
-        block_table = [
-            random.randint(0, num_blocks - 1)
-            for _ in range(max_num_blocks_per_seq)
-        ]
-        block_tables.append(block_table)
-    block_tables = torch.tensor(block_tables, dtype=torch.int, device='cuda')
-
-    scale = float(1.0 / (head_size**0.5))
-    output = torch.empty(num_tokens,
-                         num_heads,
-                         head_size,
-                         dtype=dtype,
-                         device='cuda')
-    attention_ops.single_query_cached_kv_attention(
-        output,
-        query,
-        key_cache,
-        value_cache,
-        scale,
-        block_tables,
-        context_lens,
-        block_size,
-        max_context_len,
-        None,  # ALiBi slopes.
-    )
-
-    ref_output = torch.empty_like(query)
-    ref_single_query_cached_kv_attention(
-        ref_output,
-        query,
-        key_cache,
-        value_cache,
-        block_tables,
-        context_lens,
-    )
-    # NOTE(woosuk): Due to the difference in the data types the two
-    # implementations use for attention softmax logits and accumulation,
-    # there is a small difference in the final outputs.
-    # We should use a relaxed tolerance for the test.
-    assert torch.allclose(output, ref_output, atol=1e-3, rtol=1e-5)
-
-
-@torch.inference_mode()
-def run_multi_query_kv_attention(
+def test_multi_query_kv_attention(
     num_seqs: int,
-    num_heads: int,
+    num_heads: Tuple[int, int],
     head_size: int,
     dtype: torch.dtype,
+    seed: int,
 ) -> None:
-    seq_lens = random.sample(range(1, MAX_SEQ_LEN), num_seqs)
+    random.seed(seed)
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+
+    # MAX_SEQ_LEN sometimes causes OOM in the reference implementation.
+    # As the xformers library is already tested with its own tests, we can use
+    # a smaller MAX_SEQ_LEN here.
+    max_len = min(MAX_SEQ_LEN, 4096)
+    seq_lens = random.sample(range(1, max_len), num_seqs)
     num_tokens = sum(seq_lens)
 
     scale = float(1.0 / (head_size**0.5))
+    num_query_heads, num_kv_heads = num_heads
     qkv = torch.empty(num_tokens,
-                      3,
-                      num_heads,
+                      num_query_heads + 2 * num_kv_heads,
                       head_size,
                       dtype=dtype,
-                      device='cuda')
-    qkv.uniform_(-1e-3, 1e-3)
-    query, key, value = qkv.unbind(dim=1)
+                      device="cuda")
+    qkv.uniform_(-scale, scale)
+    query, key, value = qkv.split(
+        [num_query_heads, num_kv_heads, num_kv_heads], dim=1)
 
-    attn_op = xops.fmha.cutlass.FwOp()
+    num_queries_per_kv = num_query_heads // num_kv_heads
+    if num_queries_per_kv > 1:
+        # Handle MQA and GQA
+        key = torch.repeat_interleave(key, num_queries_per_kv, dim=1)
+        value = torch.repeat_interleave(value, num_queries_per_kv, dim=1)
     attn_bias = BlockDiagonalCausalMask.from_seqlens(seq_lens)
     output = xops.memory_efficient_attention_forward(
         query.unsqueeze(0),
@@ -264,7 +278,6 @@ def run_multi_query_kv_attention(
         attn_bias=attn_bias,
         p=0.0,
         scale=scale,
-        op=attn_op,
     )
     output = output.squeeze(0)
 
@@ -276,40 +289,7 @@ def run_multi_query_kv_attention(
         query,
         key,
         value,
+        scale,
         dtype,
     )
     assert torch.allclose(output, ref_output, atol=1e-3, rtol=1e-5)
-
-
-def test_single_query_cached_kv_attention() -> None:
-    torch.random.manual_seed(TEST_SEED)
-    torch.cuda.manual_seed(TEST_SEED)
-    for dtype in [torch.half, torch.bfloat16, torch.float]:
-        for block_size in [8, 16, 32]:
-            for head_size in [64, 80, 96, 128]:
-                print(f'Testing single_query_cached_kv_attention with '
-                      f'dtype={dtype}, block_size={block_size}, '
-                      f'head_size={head_size}')
-                run_single_query_cached_kv_attention(
-                    num_tokens=37,
-                    num_heads=3,
-                    head_size=head_size,
-                    block_size=block_size,
-                    num_blocks=1024,
-                    dtype=dtype,
-                )
-
-
-def test_multi_query_kv_attention() -> None:
-    torch.random.manual_seed(TEST_SEED)
-    torch.cuda.manual_seed(TEST_SEED)
-    for dtype in [torch.half, torch.bfloat16, torch.float]:
-        for head_size in [64, 80, 96, 128]:
-            print(f'Testing multi_query_kv_attention with dtype={dtype}, '
-                  f'head_size={head_size}')
-            run_multi_query_kv_attention(
-                num_seqs=5,
-                num_heads=3,
-                head_size=head_size,
-                dtype=dtype,
-            )

tests/kernels/test_cache.py

@@ -1,12 +1,32 @@
 import random
 
+import pytest
 import torch
 
 from vllm import cache_ops
 
+DTYPES = [torch.half, torch.bfloat16, torch.float]
+NUM_TOKENS = [7, 83, 2048]  # Arbitrary values for testing
+NUM_LAYERS = [5]  # Arbitrary values for testing
+NUM_HEADS = [8]  # Arbitrary values for testing
+HEAD_SIZES = [64, 80, 96, 112, 128, 256]
+BLOCK_SIZES = [8, 16, 32]
+NUM_BLOCKS = [1024]  # Arbitrary values for testing
+NUM_MAPPINGS = [32, 256]  # Arbitrary values for testing
+SEEDS = [0]
 
+
+@pytest.mark.parametrize("num_mappings", NUM_MAPPINGS)
+@pytest.mark.parametrize("num_layers", NUM_LAYERS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("block_size", BLOCK_SIZES)
+@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
 @torch.inference_mode()
-def run_copy_blocks(
+def test_copy_blocks(
+    kv_cache_factory,
     num_mappings: int,
     num_layers: int,
     num_heads: int,
@@ -14,48 +34,43 @@ def run_copy_blocks(
     block_size: int,
     num_blocks: int,
     dtype: torch.dtype,
+    seed: int,
 ) -> None:
-    # Generate random block mappings.
+    random.seed(seed)
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+
+    # Generate random block mappings where each source block is mapped to two
+    # destination blocks.
+    assert 2 * num_mappings <= num_blocks
     src_blocks = random.sample(range(num_blocks), num_mappings)
     remainig_blocks = list(set(range(num_blocks)) - set(src_blocks))
-    dst_blocks = random.sample(remainig_blocks, num_mappings)
-    block_mapping = {src: [dst] for src, dst in zip(src_blocks, dst_blocks)}
+    dst_blocks = random.sample(remainig_blocks, 2 * num_mappings)
+    block_mapping = {}
+    for i in range(num_mappings):
+        src = src_blocks[i]
+        dst1 = dst_blocks[2 * i]
+        dst2 = dst_blocks[2 * i + 1]
+        block_mapping[src] = [dst1, dst2]
 
-    # Create the KV cache.
-    x = 16 // torch.tensor([], dtype=dtype).element_size()
-    key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
-    key_caches = []
-    for _ in range(num_layers):
-        key_cache = torch.randn(size=key_cache_shape,
-                                dtype=dtype,
-                                device='cuda')
-        key_caches.append(key_cache)
-    cloned_key_caches = []
-    for key_cache in key_caches:
-        cloned_key_caches.append(key_cache.clone())
+    # Create the KV caches.
+    key_caches, value_caches = kv_cache_factory(num_blocks, block_size,
+                                                num_layers, num_heads,
+                                                head_size, dtype, seed)
 
-    value_cache_shape = (num_blocks, num_heads, head_size, block_size)
-    value_caches = []
-    for _ in range(num_layers):
-        value_cache = torch.randn(size=value_cache_shape,
-                                  dtype=dtype,
-                                  device='cuda')
-        value_caches.append(value_cache)
-    cloned_value_caches = []
-    for value_cache in value_caches:
-        cloned_value_caches.append(value_cache.clone())
+    # Clone the KV caches.
+    cloned_key_caches = [key_cache.clone() for key_cache in key_caches]
+    cloned_value_caches = [value_cache.clone() for value_cache in value_caches]
 
     # Call the copy blocks kernel.
     cache_ops.copy_blocks(key_caches, value_caches, block_mapping)
 
-    # Reference implementation.
+    # Run the reference implementation.
     for src, dsts in block_mapping.items():
         for dst in dsts:
-            for key_cache, cloned_key_cache in zip(key_caches,
-                                                   cloned_key_caches):
+            for cloned_key_cache in cloned_key_caches:
                 cloned_key_cache[dst] = cloned_key_cache[src]
-            for value_cache, cloned_value_cache in zip(value_caches,
-                                                       cloned_value_caches):
+            for cloned_value_cache in cloned_value_caches:
                 cloned_value_cache[dst] = cloned_value_cache[src]
 
     # Compare the results.
@@ -66,15 +81,29 @@ def run_copy_blocks(
         assert torch.allclose(value_cache, cloned_value_cache)
 
 
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("block_size", BLOCK_SIZES)
+@pytest.mark.parametrize("num_blocks", NUM_BLOCKS)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
 @torch.inference_mode()
-def run_reshape_and_cache(
+def test_reshape_and_cache(
+    kv_cache_factory,
     num_tokens: int,
     num_heads: int,
     head_size: int,
     block_size: int,
     num_blocks: int,
     dtype: torch.dtype,
+    seed: int,
 ) -> None:
+    random.seed(seed)
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+
+    # Create a random slot mapping.
     num_slots = block_size * num_blocks
     slot_mapping = random.sample(range(num_slots), num_tokens)
     slot_mapping = torch.tensor(slot_mapping, dtype=torch.int, device='cuda')
@@ -87,110 +116,31 @@ def run_reshape_and_cache(
                       device='cuda')
     _, key, value = qkv.unbind(dim=1)
 
-    x = 16 // torch.tensor([], dtype=dtype).element_size()
-    key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
-    key_cache = torch.randn(size=key_cache_shape, dtype=dtype, device='cuda')
-    cloned_key_cache = key_cache.clone()
+    # Create the KV caches.
+    key_caches, value_caches = kv_cache_factory(num_blocks, block_size, 1,
+                                                num_heads, head_size, dtype,
+                                                seed)
+    key_cache, value_cache = key_caches[0], value_caches[0]
 
-    value_cache_shape = (num_blocks, num_heads, head_size, block_size)
-    value_cache = torch.randn(size=value_cache_shape,
-                              dtype=dtype,
-                              device='cuda')
+    # Clone the KV caches.
+    cloned_key_cache = key_cache.clone()
     cloned_value_cache = value_cache.clone()
 
+    # Call the reshape_and_cache kernel.
     cache_ops.reshape_and_cache(key, value, key_cache, value_cache,
                                 slot_mapping)
 
+    # Run the reference implementation.
+    reshaped_key = key.reshape(num_tokens, *key_cache[0, :, :, 0, :].shape)
+    block_indicies = torch.div(slot_mapping, block_size, rounding_mode='floor')
+    block_indicies = block_indicies.cpu().tolist()
+    block_offsets = slot_mapping % block_size
+    block_offsets = block_offsets.cpu().tolist()
     for i in range(num_tokens):
-        reshaped_key = key.reshape(num_tokens, num_heads, head_size // x, x)
-        block_idx = torch.div(slot_mapping[i],
-                              block_size,
-                              rounding_mode='floor')
-        block_offset = slot_mapping[i] % block_size
+        block_idx = block_indicies[i]
+        block_offset = block_offsets[i]
         cloned_key_cache[block_idx, :, :, block_offset, :] = reshaped_key[i]
         cloned_value_cache[block_idx, :, :, block_offset] = value[i]
 
     assert torch.allclose(key_cache, cloned_key_cache)
     assert torch.allclose(value_cache, cloned_value_cache)
-
-
-@torch.inference_mode()
-def run_gather_cached_kv(
-    num_tokens: int,
-    num_heads: int,
-    head_size: int,
-    block_size: int,
-    num_blocks: int,
-    dtype: torch.dtype,
-) -> None:
-    num_slots = block_size * num_blocks
-    slot_mapping = random.sample(range(num_slots), num_tokens)
-    slot_mapping = torch.tensor(slot_mapping, dtype=torch.int, device='cuda')
-
-    qkv = torch.randn(num_tokens,
-                      3,
-                      num_heads,
-                      head_size,
-                      dtype=dtype,
-                      device='cuda')
-    _, key, value = qkv.unbind(dim=1)
-
-    qkv_clone = qkv.clone()
-    _, cloned_key, cloned_value = qkv_clone.unbind(dim=1)
-
-    x = 16 // torch.tensor([], dtype=dtype).element_size()
-    key_cache_shape = (num_blocks, num_heads, head_size // x, block_size, x)
-    key_cache = torch.randn(size=key_cache_shape, dtype=dtype, device='cuda')
-
-    value_cache_shape = (num_blocks, num_heads, head_size, block_size)
-    value_cache = torch.randn(size=value_cache_shape,
-                              dtype=dtype,
-                              device='cuda')
-
-    cache_ops.gather_cached_kv(key, value, key_cache, value_cache,
-                               slot_mapping)
-
-    # Reference implementation.
-    for i in range(num_tokens):
-        reshaped_key = cloned_key.reshape(num_tokens, num_heads,
-                                          head_size // x, x)
-        block_idx = torch.div(slot_mapping[i],
-                              block_size,
-                              rounding_mode='floor')
-        block_offset = slot_mapping[i] % block_size
-        reshaped_key[i] = key_cache[block_idx, :, :, block_offset, :]
-        cloned_value[i] = value_cache[block_idx, :, :, block_offset]
-
-    assert torch.allclose(key, cloned_key)
-    assert torch.allclose(value, cloned_value)
-
-
-def test_copy_blocks() -> None:
-    for dtype in [torch.half, torch.bfloat16, torch.float]:
-        run_copy_blocks(num_mappings=23,
-                        num_layers=7,
-                        num_heads=17,
-                        head_size=16,
-                        block_size=8,
-                        num_blocks=1024,
-                        dtype=dtype)
-
-
-def test_reshape_and_cache() -> None:
-    for dtype in [torch.half, torch.bfloat16, torch.float]:
-        run_reshape_and_cache(num_tokens=3,
-                              num_heads=2,
-                              head_size=16,
-                              block_size=8,
-                              num_blocks=2,
-                              dtype=dtype)
-
-
-def test_gather_cached_kv() -> None:
-    for dtype in [torch.half, torch.bfloat16, torch.float]:
-        run_gather_cached_kv(num_tokens=3,
-                             num_heads=2,
-                             head_size=16,
-                             block_size=8,
-                             num_blocks=2,
-                             dtype=dtype)

tests/kernels/test_layernorm.py

@@ -1,35 +1,50 @@
+import pytest
 import torch
 import torch.nn as nn
 
 from vllm import layernorm_ops
 
+DTYPES = [torch.half, torch.bfloat16, torch.float]
+HIDDEN_SIZES = [67, 768, 2048, 5120, 8192]  # Arbitrary values for testing
+NUM_TOKENS = [7, 83, 4096]  # Arbitrary values for testing
+SEEDS = [0]
+
 
 class RefRMSNorm(nn.Module):
 
     def __init__(self, hidden_size, eps=1e-6):
         super().__init__()
         weight = torch.empty(hidden_size)
-        weight.uniform_(-1e-3, 1e-3)
+        weight.normal_(mean=1.0, std=0.1)
         self.weight = nn.Parameter(weight)
         self.variance_epsilon = eps
 
     def forward(self, hidden_states):
-        variance = hidden_states.to(torch.float32).pow(2).mean(-1,
-                                                               keepdim=True)
+        input_dtype = hidden_states.dtype
+        hidden_states = hidden_states.to(torch.float32)
+        variance = hidden_states.pow(2).mean(-1, keepdim=True)
         hidden_states = hidden_states * torch.rsqrt(variance +
                                                     self.variance_epsilon)
-        if self.weight.dtype in [torch.half, torch.float16, torch.bfloat16]:
-            hidden_states = hidden_states.to(self.weight.dtype)
-        return self.weight * hidden_states
+        return self.weight * hidden_states.to(input_dtype)
 
 
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("hidden_size", HIDDEN_SIZES)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
 @torch.inference_mode()
-def run_rms_norm(
+def test_rms_norm(
     num_tokens: int,
     hidden_size: int,
     dtype: torch.dtype,
+    seed: int,
 ) -> None:
-    x = torch.randn(num_tokens, hidden_size, dtype=dtype, device='cuda')
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+
+    scale = float(hidden_size**-0.5)
+    x = torch.empty(num_tokens, hidden_size, dtype=dtype, device="cuda")
+    x.uniform_(-scale, scale)
     ref = RefRMSNorm(hidden_size).to(dtype).cuda()
 
     out = torch.empty_like(x)
@@ -40,17 +55,4 @@ def run_rms_norm(
         ref.variance_epsilon,
     )
     ref_out = ref(x)
-    assert torch.allclose(out, ref_out, atol=1e-3, rtol=1e-5)
-
-
-def test_rms_norm() -> None:
-    for dtype in [torch.half, torch.bfloat16, torch.float]:
-        for num_tokens in [7, 128, 2048]:
-            for hidden_size in [13, 64, 1024, 5120]:
-                print(f'Testing RMS kernel with dtype={dtype}, num_tokens='
-                      f'{num_tokens}, hidden_size={hidden_size}')
-                run_rms_norm(
-                    num_tokens=num_tokens,
-                    hidden_size=hidden_size,
-                    dtype=dtype,
-                )
+    assert torch.allclose(out, ref_out, atol=1e-2, rtol=1e-5)

tests/kernels/test_pos_encoding.py

@@ -1,47 +1,70 @@
-from typing import Tuple
+from typing import Optional, Tuple
 
+import pytest
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
 from vllm import pos_encoding_ops
 
+IS_NEOX_STYLE = [True, False]
+DTYPES = [torch.half, torch.bfloat16, torch.float]
+HEAD_SIZES = [64, 80, 96, 112, 128, 256]
+ROTARY_DIMS = [None, 32]  # None means rotary dim == head size
+NUM_HEADS = [7, 12, 40, 52]  # Arbitrary values for testing
+NUM_TOKENS = [11, 83, 2048]  # Arbitrary values for testing
+SEEDS = [0]
 
-def rotate_half(x: torch.Tensor) -> torch.Tensor:
+
+def rotate_neox(x: torch.Tensor) -> torch.Tensor:
     x1 = x[..., :x.shape[-1] // 2]
     x2 = x[..., x.shape[-1] // 2:]
     return torch.cat((-x2, x1), dim=-1)
 
 
-def apply_rotary_pos_emb(
+def rotate_gptj(x: torch.Tensor) -> torch.Tensor:
+    x1 = x[..., ::2]
+    x2 = x[..., 1::2]
+    x = torch.stack((-x2, x1), dim=-1)
+    return x.flatten(-2)
+
+
+def apply_rope(
     q: torch.Tensor,
     k: torch.Tensor,
     cos: torch.Tensor,
     sin: torch.Tensor,
+    is_neox_style: bool,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
-    q_embed = (q * cos) + (rotate_half(q) * sin)
-    k_embed = (k * cos) + (rotate_half(k) * sin)
+    rotate_fn = rotate_neox if is_neox_style else rotate_gptj
+    q_embed = (q * cos) + (rotate_fn(q) * sin)
+    k_embed = (k * cos) + (rotate_fn(k) * sin)
     return q_embed, k_embed
 
 
-class RefRotaryEmbeddingNeox(nn.Module):
-    """Reference implementation of the GPT-NeoX style rotary embedding."""
+class RefRotaryEmbedding(nn.Module):
+    """Reference implementation of rotary embedding."""
 
     def __init__(
         self,
         dim: int,
-        max_position_embeddings: int = 2048,
+        is_neox_style: bool,
+        max_position_embeddings: int = 8192,
         base: int = 10000,
     ) -> None:
         super().__init__()
         self.rotary_dim = dim
+        self.is_neox_style = is_neox_style
         self.max_position_embeddings = max_position_embeddings
 
         # Create cos and sin embeddings.
         inv_freq = 1.0 / (base**(torch.arange(0, dim, 2) / dim))
         t = torch.arange(max_position_embeddings).float()
         freqs = torch.einsum("i,j->ij", t, inv_freq.float())
-        emb = torch.cat((freqs, freqs), dim=-1)
+        if is_neox_style:
+            emb = torch.cat((freqs, freqs), dim=-1)
+        else:
+            emb = torch.repeat_interleave(freqs, 2, -1)
         cos = emb.cos().to(dtype=inv_freq.dtype)
         sin = emb.sin().to(dtype=inv_freq.dtype)
         self.register_buffer("cos_cached", cos, persistent=False)
@@ -53,7 +76,6 @@ class RefRotaryEmbeddingNeox(nn.Module):
         query: torch.Tensor,  # [num_tokens, num_heads, head_size]
         key: torch.Tensor,  # [num_tokens, num_heads, head_size]
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-
         query_rot = query[..., :self.rotary_dim]
         query_pass = query[..., self.rotary_dim:]
         key_rot = key[..., :self.rotary_dim]
@@ -63,7 +85,9 @@ class RefRotaryEmbeddingNeox(nn.Module):
         key_rot = key_rot.transpose(0, 1)
         cos = F.embedding(positions, self.cos_cached)
         sin = F.embedding(positions, self.sin_cached)
-        query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin)
+
+        query_rot, key_rot = apply_rope(query_rot, key_rot, cos, sin,
+                                        self.is_neox_style)
         query_rot = query_rot.transpose(0, 1).contiguous()
         key_rot = key_rot.transpose(0, 1).contiguous()
 
@@ -74,30 +98,45 @@ class RefRotaryEmbeddingNeox(nn.Module):
         return query, key
 
 
+@pytest.mark.parametrize("is_neox_style", IS_NEOX_STYLE)
+@pytest.mark.parametrize("num_tokens", NUM_TOKENS)
+@pytest.mark.parametrize("num_heads", NUM_HEADS)
+@pytest.mark.parametrize("head_size", HEAD_SIZES)
+@pytest.mark.parametrize("rotary_dim", ROTARY_DIMS)
+@pytest.mark.parametrize("dtype", DTYPES)
+@pytest.mark.parametrize("seed", SEEDS)
 @torch.inference_mode()
-def run_rotary_embedding_neox(
+def test_rotary_embedding(
+    is_neox_style: bool,
     num_tokens: int,
     num_heads: int,
     head_size: int,
-    max_position: int,
-    rotary_dim: int,
+    rotary_dim: Optional[int],
     dtype: torch.dtype,
+    seed: int,
+    max_position: int = 8192,
     base: int = 10000,
 ) -> None:
-    positions = torch.randint(0, max_position, (num_tokens, ), device='cuda')
+    if rotary_dim is None:
+        rotary_dim = head_size
+    torch.random.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+
+    positions = torch.randint(0, max_position, (num_tokens, ), device="cuda")
     query = torch.randn(num_tokens,
                         num_heads * head_size,
                         dtype=dtype,
-                        device='cuda')
+                        device="cuda")
     key = torch.randn(num_tokens,
                       num_heads * head_size,
                       dtype=dtype,
-                      device='cuda')
+                      device="cuda")
 
     # Create the rotary embedding.
-    inv_freq = 1.0 / (base**(torch.arange(0, rotary_dim, 2) / rotary_dim))
+    inv_freq = 1.0 / (base**(
+        torch.arange(0, rotary_dim, 2, dtype=torch.float) / rotary_dim))
     t = torch.arange(max_position).float()
-    freqs = torch.einsum('i,j -> ij', t, inv_freq.float())
+    freqs = torch.einsum("i,j -> ij", t, inv_freq)
     cos = freqs.cos()
     sin = freqs.sin()
     cos_sin_cache = torch.cat((cos, sin), dim=-1)
@@ -106,20 +145,22 @@ def run_rotary_embedding_neox(
     # Run the kernel. The kernel is in-place, so we need to clone the inputs.
     out_query = query.clone()
     out_key = key.clone()
-    pos_encoding_ops.rotary_embedding_neox(
+    pos_encoding_ops.rotary_embedding(
         positions,
         out_query,
         out_key,
         head_size,
         cos_sin_cache,
+        is_neox_style,
     )
 
     # Run the reference implementation.
-    ref_rotary_embedding = RefRotaryEmbeddingNeox(
+    ref_rotary_embedding = RefRotaryEmbedding(
         dim=rotary_dim,
+        is_neox_style=is_neox_style,
         max_position_embeddings=max_position,
         base=base,
-    ).to(dtype=dtype, device='cuda')
+    ).to(dtype=dtype, device="cuda")
     ref_query, ref_key = ref_rotary_embedding(
         positions,
         query.view(num_tokens, num_heads, head_size),
@@ -129,19 +170,5 @@ def run_rotary_embedding_neox(
     ref_key = ref_key.view(num_tokens, num_heads * head_size)
 
     # Compare the results.
-    assert torch.allclose(out_query, ref_query, atol=1e-3, rtol=1e-5)
-    assert torch.allclose(out_key, ref_key, atol=1e-3, rtol=1e-5)
-
-
-def test_rotary_embedding_neox() -> None:
-    for dtype in [torch.half, torch.bfloat16, torch.float]:
-        for head_size in [32, 64, 80, 96, 128, 160, 192, 256]:
-            print(f'Running tests for head_size={head_size} and dtype={dtype}')
-            run_rotary_embedding_neox(
-                num_tokens=2145,
-                num_heads=5,
-                head_size=head_size,
-                max_position=8192,
-                rotary_dim=head_size,
-                dtype=dtype,
-            )
+    assert torch.allclose(out_query, ref_query, atol=1e-5, rtol=1e-5)
+    assert torch.allclose(out_key, ref_key, atol=1e-5, rtol=1e-5)

tests/models/test_models.py

@@ -0,0 +1,45 @@
+"""Compare the outputs of HF and vLLM when using greedy sampling.
+
+Run `pytest tests/models/test_models.py --forked`.
+"""
+import pytest
+
+MODELS = [
+    "facebook/opt-125m",
+    "gpt2",
+    "bigcode/tiny_starcoder_py",
+    "EleutherAI/gpt-j-6b",
+    "EleutherAI/pythia-70m",
+    "bigscience/bloom-560m",
+    "mosaicml/mpt-7b",
+    "tiiuae/falcon-7b",
+    "meta-llama/Llama-2-7b-hf",
+]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", [128])
+def test_models(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+) -> None:
+    hf_model = hf_runner(model, dtype=dtype)
+    hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens)
+    del hf_model
+
+    vllm_model = vllm_runner(model, dtype=dtype)
+    vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens)
+    del vllm_model
+
+    for i in range(len(example_prompts)):
+        hf_output_ids, hf_output_str = hf_outputs[i]
+        vllm_output_ids, vllm_output_str = vllm_outputs[i]
+        assert hf_output_str == vllm_output_str, (
+            f"Test{i}:\nHF: {hf_output_str!r}\nvLLM: {vllm_output_str!r}")
+        assert hf_output_ids == vllm_output_ids, (
+            f"Test{i}:\nHF: {hf_output_ids}\nvLLM: {vllm_output_ids}")

tests/samplers/test_beam_search.py

@@ -0,0 +1,46 @@
+"""Compare the outputs of HF and vLLM when using beam search.
+
+Run `pytest tests/samplers/test_beam_search.py --forked`.
+"""
+import pytest
+
+# FIXME(zhuohan): The test can not pass if we:
+#   1. Increase max_tokens to 256.
+#   2. Increase beam_width to 8.
+#   3. Use the model "huggyllama/llama-7b".
+MAX_TOKENS = [128]
+BEAM_WIDTHS = [4]
+MODELS = ["facebook/opt-125m"]
+
+
+@pytest.mark.parametrize("model", MODELS)
+@pytest.mark.parametrize("dtype", ["half"])
+@pytest.mark.parametrize("max_tokens", MAX_TOKENS)
+@pytest.mark.parametrize("beam_width", BEAM_WIDTHS)
+def test_beam_search_single_input(
+    hf_runner,
+    vllm_runner,
+    example_prompts,
+    model: str,
+    dtype: str,
+    max_tokens: int,
+    beam_width: int,
+) -> None:
+    hf_model = hf_runner(model, dtype=dtype)
+    hf_outputs = hf_model.generate_beam_search(example_prompts, beam_width,
+                                               max_tokens)
+    del hf_model
+
+    vllm_model = vllm_runner(model, dtype=dtype)
+    vllm_outputs = vllm_model.generate_beam_search(example_prompts, beam_width,
+                                                   max_tokens)
+    del vllm_model
+
+    for i in range(len(example_prompts)):
+        hf_output_ids, _ = hf_outputs[i]
+        vllm_output_ids, _ = vllm_outputs[i]
+        assert len(hf_output_ids) == len(vllm_output_ids)
+        for j in range(len(hf_output_ids)):
+            assert hf_output_ids[j] == vllm_output_ids[j], (
+                f"Test{i} output{j}:\nHF: {hf_output_ids}\n"
+                f"vLLM: {vllm_output_ids}")

tests/samplers/test_sampler.py

@@ -0,0 +1,184 @@
+import pytest
+import random
+from typing import Tuple
+from unittest.mock import patch
+
+import torch
+
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.utils import set_random_seed
+from vllm.sequence import SamplingParams, SequenceData, SequenceGroupMetadata
+from vllm.worker.worker import Worker
+
+
+class MockLogitsSampler(Sampler):
+
+    def __init__(self, vocab_size: int, fake_logits: torch.Tensor):
+        super().__init__(vocab_size=vocab_size)
+        self.fake_logits = fake_logits
+
+    def forward(self, *args, **kwargs):
+        with patch("vllm.model_executor.layers.sampler._prune_hidden_states",
+                   lambda x, y: x):
+            with patch("vllm.model_executor.layers.sampler._get_logits",
+                       lambda *args, **kwargs: self.fake_logits):
+                return super().forward(*args, **kwargs)
+
+
+def _prepare_test(
+    batch_size: int
+) -> Tuple[torch.Tensor, torch.Tensor, MockLogitsSampler, Worker]:
+    vocab_size = 32000
+    input_tensor = torch.rand((batch_size, 1024),
+                              device="cuda",
+                              dtype=torch.float16)
+    fake_logits = torch.full((batch_size, vocab_size),
+                             1e-2,
+                             device=input_tensor.device,
+                             dtype=input_tensor.dtype)
+    sampler = MockLogitsSampler(32000, fake_logits)
+    worker = Worker(None, None, None)
+    worker.block_size = 16
+    return input_tensor, fake_logits, sampler, worker
+
+
+RANDOM_SEEDS = list(range(128))
+
+
+@pytest.mark.parametrize("seed", RANDOM_SEEDS)
+def test_sampler_all_greedy(seed: int):
+    set_random_seed(seed)
+    batch_size = random.randint(1, 256)
+    input_tensor, fake_logits, sampler, worker = _prepare_test(batch_size)
+
+    seq_group_metadata_list = []
+    for i in range(batch_size):
+        seq_group_metadata_list.append(
+            SequenceGroupMetadata(
+                request_id=f"test_{i}",
+                is_prompt=True,
+                seq_data={0: SequenceData([1, 2, 3])},
+                sampling_params=SamplingParams(temperature=0, ),
+                block_tables={0: [1]},
+            ))
+
+    _, _, input_metadata = worker._prepare_inputs(seq_group_metadata_list)
+    sampler_output = sampler(embedding=None,
+                             hidden_states=input_tensor,
+                             input_metadata=input_metadata)
+    expected = torch.argmax(fake_logits, dim=-1)
+    for i, sequence_output in enumerate(sampler_output):
+        for nth_output in sequence_output:
+            assert nth_output.output_token == expected[i].item()
+
+
+@pytest.mark.parametrize("seed", RANDOM_SEEDS)
+def test_sampler_all_random(seed: int):
+    set_random_seed(seed)
+    batch_size = random.randint(1, 256)
+    input_tensor, fake_logits, sampler, worker = _prepare_test(batch_size)
+
+    for i in range(batch_size):
+        fake_logits[i, i] = 1e2
+
+    seq_group_metadata_list = []
+    for i in range(batch_size):
+        seq_group_metadata_list.append(
+            SequenceGroupMetadata(
+                request_id=f"test_{i}",
+                is_prompt=True,
+                seq_data={0: SequenceData([1, 2, 3])},
+                sampling_params=SamplingParams(
+                    temperature=1.0,
+                    n=random.randint(1, 10),
+                ),
+                block_tables={0: [1]},
+            ))
+
+    _, _, input_metadata = worker._prepare_inputs(seq_group_metadata_list)
+    sampler_output = sampler(embedding=None,
+                             hidden_states=input_tensor,
+                             input_metadata=input_metadata)
+    for i, sequence_output in enumerate(sampler_output):
+        for nth_output in sequence_output:
+            assert nth_output.output_token == i
+
+
+@pytest.mark.parametrize("seed", RANDOM_SEEDS)
+def test_sampler_all_beam(seed: int):
+    set_random_seed(seed)
+    batch_size = random.randint(1, 256)
+    input_tensor, fake_logits, sampler, worker = _prepare_test(batch_size)
+
+    seq_group_metadata_list = []
+    for i in range(batch_size):
+        seq_group_metadata_list.append(
+            SequenceGroupMetadata(
+                request_id=f"test_{i}",
+                is_prompt=True,
+                seq_data={0: SequenceData([1, 2, 3])},
+                sampling_params=SamplingParams(
+                    temperature=0,
+                    best_of=2,
+                    use_beam_search=True,
+                ),
+                block_tables={0: [1]},
+            ))
+
+    _, _, input_metadata = worker._prepare_inputs(seq_group_metadata_list)
+    sampler(embedding=None,
+            hidden_states=input_tensor,
+            input_metadata=input_metadata)
+    # no assertion here as I am not sure how to determine whether
+    # the outputs are expected - in other words, this just tests
+    # whether there are no exceptions in the sampler
+    # when handling an all-beam search case.
+
+
+@pytest.mark.parametrize("seed", RANDOM_SEEDS)
+def test_sampler_mixed(seed: int):
+    set_random_seed(seed)
+    batch_size = random.randint(1, 256)
+    input_tensor, fake_logits, sampler, worker = _prepare_test(batch_size)
+
+    seq_group_metadata_list = []
+    expected_tokens = []
+    for i in range(batch_size):
+        n = 1
+        sampling_type = random.randint(0, 2)
+        if sampling_type == 0:
+            sampling_params = SamplingParams(temperature=0)
+        elif sampling_type == 1:
+            n = random.randint(1, 10)
+            sampling_params = SamplingParams(
+                temperature=random.random() + 0.1,
+                top_p=min(random.random() + 0.1, 1),
+                top_k=random.randint(0, 10) or -1,
+                n=n,
+                presence_penalty=random.randint(0, 1),
+            )
+        else:
+            sampling_params = SamplingParams(temperature=0,
+                                             use_beam_search=True,
+                                             best_of=2)
+        for idx in range(n):
+            fake_logits[i, i + idx] = 1e2
+            expected_tokens.append(i + idx)
+        seq_group_metadata_list.append(
+            SequenceGroupMetadata(
+                request_id=f"test_{i}",
+                is_prompt=True,
+                seq_data={0: SequenceData([1, 2, 3])},
+                sampling_params=sampling_params,
+                block_tables={0: [1]},
+            ))
+
+    _, _, input_metadata = worker._prepare_inputs(seq_group_metadata_list)
+    sampler_output = sampler(embedding=None,
+                             hidden_states=input_tensor,
+                             input_metadata=input_metadata)
+    for i, sequence_output in enumerate(sampler_output):
+        if seq_group_metadata_list[i].sampling_params.use_beam_search:
+            continue
+        for nth_output in sequence_output:
+            assert nth_output.output_token in expected_tokens

vllm/__init__.py

@@ -8,7 +8,7 @@ from vllm.entrypoints.llm import LLM
 from vllm.outputs import CompletionOutput, RequestOutput
 from vllm.sampling_params import SamplingParams
 
-__version__ = "0.1.2"
+__version__ = "0.2.0"
 
 __all__ = [
     "LLM",

vllm/config.py

@@ -20,15 +20,31 @@ class ModelConfig:
         tokenizer: Name or path of the huggingface tokenizer to use.
         tokenizer_mode: Tokenizer mode. "auto" will use the fast tokenizer if
             available, and "slow" will always use the slow tokenizer.
+        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
+            downloading the model and tokenizer.
         download_dir: Directory to download and load the weights, default to the
             default cache directory of huggingface.
-        use_np_weights: Save a numpy copy of model weights for faster loading.
-            This can increase the disk usage by up to 2x.
-        use_dummy_weights: Use dummy values for model weights (for profiling).
+        load_format: The format of the model weights to load:
+            "auto" will try to load the weights in the safetensors format and
+                fall back to the pytorch bin format if safetensors format is
+                not available.
+            "pt" will load the weights in the pytorch bin format.
+            "safetensors" will load the weights in the safetensors format.
+            "npcache" will load the weights in pytorch format and store
+                a numpy cache to speed up the loading.
+            "dummy" will initialize the weights with random values, which is
+                mainly for profiling.
         dtype: Data type for model weights and activations. The "auto" option
             will use FP16 precision for FP32 and FP16 models, and BF16 precision
             for BF16 models.
         seed: Random seed for reproducibility.
+        revision: The specific model version to use. It can be a branch name,
+            a tag name, or a commit id. If unspecified, will use the default
+            version.
+        max_model_len: Maximum length of a sequence (including prompt and
+            output). If None, will be derived from the model.
+        quantization: Quantization method that was used to quantize the model
+            weights. If None, we assume the model weights are not quantized.
     """
 
     def __init__(
@@ -36,23 +52,42 @@ class ModelConfig:
         model: str,
         tokenizer: str,
         tokenizer_mode: str,
+        trust_remote_code: bool,
         download_dir: Optional[str],
-        use_np_weights: bool,
-        use_dummy_weights: bool,
+        load_format: str,
         dtype: str,
         seed: int,
+        revision: Optional[str] = None,
+        max_model_len: Optional[int] = None,
+        quantization: Optional[str] = None,
     ) -> None:
         self.model = model
         self.tokenizer = tokenizer
         self.tokenizer_mode = tokenizer_mode
+        self.trust_remote_code = trust_remote_code
         self.download_dir = download_dir
-        self.use_np_weights = use_np_weights
-        self.use_dummy_weights = use_dummy_weights
+        self.load_format = load_format
         self.seed = seed
+        self.revision = revision
+        self.quantization = quantization
 
-        self.hf_config = get_config(model)
+        self.hf_config = get_config(model, trust_remote_code, revision)
         self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
+        self.max_model_len = _get_and_verify_max_len(self.hf_config,
+                                                     max_model_len)
+        self._verify_load_format()
         self._verify_tokenizer_mode()
+        self._verify_quantization()
+
+    def _verify_load_format(self) -> None:
+        load_format = self.load_format.lower()
+        if load_format not in [
+                "auto", "pt", "safetensors", "npcache", "dummy"
+        ]:
+            raise ValueError(
+                f"Unknown load format: {self.load_format}. Must be one of "
+                "'auto', 'pt', 'safetensors', 'npcache', or 'dummy'.")
+        self.load_format = load_format
 
     def _verify_tokenizer_mode(self) -> None:
         tokenizer_mode = self.tokenizer_mode.lower()
@@ -62,6 +97,17 @@ class ModelConfig:
                 "either 'auto' or 'slow'.")
         self.tokenizer_mode = tokenizer_mode
 
+    def _verify_quantization(self) -> None:
+        supported_quantization = ["awq"]
+        if self.quantization is None:
+            return
+        quantization = self.quantization.lower()
+        if quantization not in supported_quantization:
+            raise ValueError(
+                f"Unknown quantization: {self.quantization}. Must be one of "
+                f"{supported_quantization}.")
+        self.quantization = quantization
+
     def verify_with_parallel_config(
         self,
         parallel_config: "ParallelConfig",
@@ -89,7 +135,32 @@ class ModelConfig:
         # FIXME(woosuk): This may not be true for all models.
         return self.hf_config.hidden_size // self.hf_config.num_attention_heads
 
-    def get_num_heads(self, parallel_config: "ParallelConfig") -> int:
+    def get_num_kv_heads(self, parallel_config: "ParallelConfig") -> int:
+        """Returns the number of KV heads per GPU worker."""
+        # For GPTBigCode & Falcon:
+        # Note: for falcon, when new_decoder_architecture is True, the
+        # multi_query flag is ignored and we use n_head_kv for the number of
+        # KV heads.
+        falcon_model_types = ["falcon", "RefinedWeb", "RefinedWebModel"]
+        new_decoder_arch_falcon = (
+            self.hf_config.model_type in falcon_model_types
+            and getattr(self.hf_config, "new_decoder_architecture", False))
+        if not new_decoder_arch_falcon and getattr(self.hf_config,
+                                                   "multi_query", False):
+            # Multi-query attention, only one KV head.
+            # Currently, tensor parallelism is not supported in this case.
+            return 1
+        # For Falcon:
+        if getattr(self.hf_config, "n_head_kv", None) is not None:
+            return (self.hf_config.n_head_kv //
+                    parallel_config.tensor_parallel_size)
+        if getattr(self.hf_config, "num_kv_heads", None) is not None:
+            return (self.hf_config.num_kv_heads //
+                    parallel_config.tensor_parallel_size)
+        # For LLaMA-2:
+        if getattr(self.hf_config, "num_key_value_heads", None) is not None:
+            return (self.hf_config.num_key_value_heads //
+                    parallel_config.tensor_parallel_size)
         total_num_attention_heads = self.hf_config.num_attention_heads
         return total_num_attention_heads // parallel_config.tensor_parallel_size
 
@@ -113,10 +184,12 @@ class CacheConfig:
         block_size: int,
         gpu_memory_utilization: float,
         swap_space: int,
+        sliding_window: Optional[int] = None,
     ) -> None:
         self.block_size = block_size
         self.gpu_memory_utilization = gpu_memory_utilization
         self.swap_space_bytes = swap_space * _GB
+        self.sliding_window = sliding_window
         self._verify_args()
 
         # Will be set after profiling.
@@ -188,15 +261,40 @@ class SchedulerConfig:
             a single iteration.
         max_num_seqs: Maximum number of sequences to be processed in a single
             iteration.
-        max_seq_len: Maximum length of a sequence (including prompt
+        max_model_len: Maximum length of a sequence (including prompt
             and generated text).
     """
 
-    def __init__(self, max_num_batched_tokens: int, max_num_seqs: int,
-                 max_seq_len: int) -> None:
-        self.max_num_batched_tokens = max_num_batched_tokens
+    def __init__(
+        self,
+        max_num_batched_tokens: Optional[int],
+        max_num_seqs: int,
+        max_model_len: int,
+    ) -> None:
+        if max_num_batched_tokens is not None:
+            self.max_num_batched_tokens = max_num_batched_tokens
+        else:
+            # If max_model_len is too short, use 2048 as the default value for
+            # higher throughput.
+            self.max_num_batched_tokens = max(max_model_len, 2048)
         self.max_num_seqs = max_num_seqs
-        self.max_seq_len = max_seq_len
+        self.max_model_len = max_model_len
+        self._verify_args()
+
+    def _verify_args(self) -> None:
+        if self.max_num_batched_tokens < self.max_model_len:
+            raise ValueError(
+                f"max_num_batched_tokens ({self.max_num_batched_tokens}) is "
+                f"smaller than max_model_len ({self.max_model_len}). "
+                "This effectively limits the maximum sequence length to "
+                "max_num_batched_tokens and makes vLLM reject longer "
+                "sequences. Please increase max_num_batched_tokens or "
+                "decrease max_model_len.")
+        if self.max_num_batched_tokens < self.max_num_seqs:
+            raise ValueError(
+                f"max_num_batched_tokens ({self.max_num_batched_tokens}) must "
+                "be greater than or equal to max_num_seqs "
+                f"({self.max_num_seqs}).")
 
 
 _STR_DTYPE_TO_TORCH_DTYPE = {
@@ -252,3 +350,56 @@ def _get_and_verify_dtype(
                 f"of at least 8.0. Your {gpu_name} GPU has compute capability "
                 f"{compute_capability[0]}.{compute_capability[1]}.")
     return torch_dtype
+
+
+def _get_and_verify_max_len(
+    hf_config: PretrainedConfig,
+    max_model_len: Optional[int],
+) -> int:
+    """Get and verify the model's maximum length."""
+    derived_max_model_len = float("inf")
+    possible_keys = [
+        # OPT
+        "max_position_embeddings",
+        # GPT-2
+        "n_positions",
+        # MPT
+        "max_seq_len",
+        # Others
+        "max_sequence_length",
+        "max_seq_length",
+        "seq_len",
+    ]
+    for key in possible_keys:
+        max_len_key = getattr(hf_config, key, None)
+        if max_len_key is not None:
+            derived_max_model_len = min(derived_max_model_len, max_len_key)
+    if derived_max_model_len == float("inf"):
+        if max_model_len is not None:
+            # If max_model_len is specified, we use it.
+            return max_model_len
+
+        default_max_len = 2048
+        logger.warning(
+            "The model's config.json does not contain any of the following "
+            "keys to determine the original maximum length of the model: "
+            f"{possible_keys}. Assuming the model's maximum length is "
+            f"{default_max_len}.")
+        derived_max_model_len = default_max_len
+
+    rope_scaling = getattr(hf_config, "rope_scaling", None)
+    if rope_scaling is not None:
+        assert "factor" in rope_scaling
+        scaling_factor = rope_scaling["factor"]
+        derived_max_model_len *= scaling_factor
+
+    if max_model_len is None:
+        max_model_len = derived_max_model_len
+    elif max_model_len > derived_max_model_len:
+        raise ValueError(
+            f"User-specified max_model_len ({max_model_len}) is greater than "
+            f"the derived max_model_len ({max_len_key}={derived_max_model_len}"
+            " in model's config.json). This may lead to incorrect model "
+            "outputs or CUDA errors. Make sure the value is correct and "
+            "within the model context size.")
+    return int(max_model_len)

vllm/core/block_manager.py

@@ -63,10 +63,18 @@ class BlockSpaceManager:
         num_gpu_blocks: int,
         num_cpu_blocks: int,
         watermark: float = 0.01,
+        sliding_window: Optional[int] = None,
     ) -> None:
         self.block_size = block_size
         self.num_total_gpu_blocks = num_gpu_blocks
         self.num_total_cpu_blocks = num_cpu_blocks
+
+        self.block_sliding_window = None
+        if sliding_window is not None:
+            assert sliding_window % block_size == 0, (sliding_window,
+                                                      block_size)
+            self.block_sliding_window = sliding_window // block_size
+
         self.watermark = watermark
         assert watermark >= 0.0
 
@@ -83,6 +91,9 @@ class BlockSpaceManager:
         # the same prompt. This may not be true for preempted sequences.
         seq = seq_group.get_seqs()[0]
         num_required_blocks = len(seq.logical_token_blocks)
+        if self.block_sliding_window is not None:
+            num_required_blocks = min(num_required_blocks,
+                                      self.block_sliding_window)
         num_free_gpu_blocks = self.gpu_allocator.get_num_free_blocks()
         # Use watermark to avoid frequent cache eviction.
         return (num_free_gpu_blocks - num_required_blocks >=
@@ -95,8 +106,12 @@ class BlockSpaceManager:
 
         # Allocate new physical token blocks that will store the prompt tokens.
         block_table: BlockTable = []
-        for _ in range(len(seq.logical_token_blocks)):
-            block = self.gpu_allocator.allocate()
+        for logical_idx in range(len(seq.logical_token_blocks)):
+            if (self.block_sliding_window is not None
+                    and logical_idx >= self.block_sliding_window):
+                block = block_table[logical_idx % self.block_sliding_window]
+            else:
+                block = self.gpu_allocator.allocate()
             # Set the reference counts of the token blocks.
             block.ref_count = seq_group.num_seqs()
             block_table.append(block)
@@ -118,11 +133,17 @@ class BlockSpaceManager:
         block_table = self.block_tables[seq.seq_id]
 
         if len(block_table) < len(logical_blocks):
-            # The sequence has a new logical block.
-            # Allocate a new physical block.
-            block = self.gpu_allocator.allocate()
-            block_table.append(block)
-            return None
+            if (self.block_sliding_window
+                    and len(block_table) >= self.block_sliding_window):
+                # re-use a block
+                block_table.append(block_table[len(block_table) %
+                                               self.block_sliding_window])
+            else:
+                # The sequence has a new logical block.
+                # Allocate a new physical block.
+                block = self.gpu_allocator.allocate()
+                block_table.append(block)
+                return None
 
         # We want to append the token to the last physical block.
         last_block = block_table[-1]
@@ -154,9 +175,7 @@ class BlockSpaceManager:
         for seq in seq_group.get_seqs():
             if seq.is_finished():
                 continue
-            block_table = self.block_tables[seq.seq_id]
-            for block in block_table:
-                blocks.add(block)
+            blocks.update(self.block_tables[seq.seq_id])
         return list(blocks)
 
     def can_swap_in(self, seq_group: SequenceGroup) -> bool:
@@ -172,9 +191,7 @@ class BlockSpaceManager:
     def swap_in(self, seq_group: SequenceGroup) -> Dict[int, int]:
         # CPU block -> GPU block.
         mapping: Dict[PhysicalTokenBlock, PhysicalTokenBlock] = {}
-        for seq in seq_group.get_seqs():
-            if seq.is_finished():
-                continue
+        for seq in seq_group.get_seqs(status=SequenceStatus.SWAPPED):
             new_block_table: BlockTable = []
             block_table = self.block_tables[seq.seq_id]
 
@@ -203,9 +220,7 @@ class BlockSpaceManager:
     def swap_out(self, seq_group: SequenceGroup) -> Dict[int, int]:
         # GPU block -> CPU block.
         mapping: Dict[PhysicalTokenBlock, PhysicalTokenBlock] = {}
-        for seq in seq_group.get_seqs():
-            if seq.is_finished():
-                continue
+        for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
             new_block_table: BlockTable = []
             block_table = self.block_tables[seq.seq_id]
 
@@ -228,7 +243,7 @@ class BlockSpaceManager:
         return block_number_mapping
 
     def _free_block_table(self, block_table: BlockTable) -> None:
-        for block in block_table:
+        for block in set(block_table):
             if block.device == Device.GPU:
                 self.gpu_allocator.free(block)
             else:

vllm/core/scheduler.py

@@ -1,19 +1,16 @@
 import enum
 import time
-from typing import Dict, List, Optional, Tuple
+from typing import Dict, Iterable, List, Optional, Tuple, Union
 
 from vllm.config import CacheConfig, SchedulerConfig
 from vllm.core.block_manager import BlockSpaceManager
 from vllm.core.policy import PolicyFactory
 from vllm.logger import init_logger
 from vllm.sequence import (Sequence, SequenceData, SequenceGroup,
-                           SequenceGroupMetadata, SequenceOutputs,
-                           SequenceStatus)
+                           SequenceGroupMetadata, SequenceStatus)
 
 logger = init_logger(__name__)
 
-_LOGGING_INTERVAL_SEC = 5
-
 
 class PreemptionMode(enum.Enum):
     """Preemption modes.
@@ -32,19 +29,28 @@ class SchedulerOutputs:
 
     def __init__(
         self,
+        scheduled_seq_groups: List[SequenceGroup],
+        prompt_run: bool,
+        num_batched_tokens: int,
         blocks_to_swap_in: Dict[int, int],
         blocks_to_swap_out: Dict[int, int],
         blocks_to_copy: Dict[int, List[int]],
+        ignored_seq_groups: List[SequenceGroup],
     ) -> None:
+        self.scheduled_seq_groups = scheduled_seq_groups
+        self.prompt_run = prompt_run
+        self.num_batched_tokens = num_batched_tokens
         self.blocks_to_swap_in = blocks_to_swap_in
         self.blocks_to_swap_out = blocks_to_swap_out
         self.blocks_to_copy = blocks_to_copy
         # Swap in and swap out should never happen at the same time.
         assert not (blocks_to_swap_in and blocks_to_swap_out)
+        self.ignored_seq_groups = ignored_seq_groups
 
     def is_empty(self) -> bool:
-        return (not self.blocks_to_swap_in and not self.blocks_to_swap_out
-                and not self.blocks_to_copy)
+        # NOTE: We do not consider the ignored sequence groups.
+        return (not self.scheduled_seq_groups and not self.blocks_to_swap_in
+                and not self.blocks_to_swap_out and not self.blocks_to_copy)
 
 
 class Scheduler:
@@ -53,11 +59,12 @@ class Scheduler:
         self,
         scheduler_config: SchedulerConfig,
         cache_config: CacheConfig,
-        log_stats: bool,
     ) -> None:
         self.scheduler_config = scheduler_config
         self.cache_config = cache_config
-        self.log_stats = log_stats
+
+        self.prompt_limit = min(self.scheduler_config.max_model_len,
+                                self.scheduler_config.max_num_batched_tokens)
 
         # Instantiate the scheduling policy.
         self.policy = PolicyFactory.get_policy(policy_name="fcfs")
@@ -66,8 +73,9 @@ class Scheduler:
             block_size=self.cache_config.block_size,
             num_gpu_blocks=self.cache_config.num_gpu_blocks,
             num_cpu_blocks=self.cache_config.num_cpu_blocks,
-        )
+            sliding_window=self.cache_config.sliding_window)
 
+        # TODO(zhuohan): Use deque instead of list for better performance.
         # Sequence groups in the WAITING state.
         self.waiting: List[SequenceGroup] = []
         # Sequence groups in the RUNNING state.
@@ -75,25 +83,30 @@ class Scheduler:
         # Sequence groups in the SWAPPED state.
         self.swapped: List[SequenceGroup] = []
 
-        self.last_logging_time: float = 0.0
-        # List[timestamp, num_tokens]
-        self.num_input_tokens: List[Tuple[float, int]] = []
-
     def add_seq_group(self, seq_group: SequenceGroup) -> None:
         # Add sequence groups to the waiting queue.
         self.waiting.append(seq_group)
 
-    def abort_seq_group(self, request_id: str) -> None:
+    def abort_seq_group(self, request_id: Union[str, Iterable[str]]) -> None:
+        if isinstance(request_id, str):
+            request_id = (request_id, )
+        request_ids = set(request_id)
         for state_queue in [self.waiting, self.running, self.swapped]:
-            for seq_group in state_queue:
-                if seq_group.request_id == request_id:
+            # We need to reverse the list as we are removing elements
+            # from it as we iterate over it. If we don't do it,
+            # indices will get messed up and we will skip over elements.
+            for seq_group in reversed(state_queue):
+                if seq_group.request_id in request_ids:
                     # Remove the sequence group from the state queue.
                     state_queue.remove(seq_group)
-                    for seq in seq_group.seqs:
+                    for seq in seq_group.get_seqs():
                         if seq.is_finished():
                             continue
-                        self.free_seq(seq, SequenceStatus.FINISHED_ABORTED)
-                    return
+                        seq.status = SequenceStatus.FINISHED_ABORTED
+                        self.free_seq(seq)
+                    request_ids.remove(seq_group.request_id)
+                    if not request_ids:
+                        return
 
     def has_unfinished_seqs(self) -> bool:
         return self.waiting or self.running or self.swapped
@@ -101,21 +114,81 @@ class Scheduler:
     def get_num_unfinished_seq_groups(self) -> int:
         return len(self.waiting) + len(self.running) + len(self.swapped)
 
-    def _schedule(
-            self) -> Tuple[SchedulerOutputs, List[str], List[SequenceGroup]]:
+    def _schedule(self) -> SchedulerOutputs:
         # Blocks that need to be swaped or copied before model execution.
         blocks_to_swap_in: Dict[int, int] = {}
         blocks_to_swap_out: Dict[int, int] = {}
         blocks_to_copy: Dict[int, List[int]] = {}
-        ignored_seq_groups: List[SequenceGroup] = []
 
         # Fix the current time.
         now = time.time()
 
-        # NOTE(woosuk): We prioritize the sequence groups in the RUNNING state
-        # in order to minimize the preemption overheads.
-        # Preemption happens only when there is no available slot to keep all
-        # the sequence groups in the RUNNING state.
+        # Join waiting sequences if possible.
+        if not self.swapped:
+            ignored_seq_groups: List[SequenceGroup] = []
+            scheduled: List[SequenceGroup] = []
+            # The total number of sequences on the fly, including the
+            # requests in the generation phase.
+            num_curr_seqs = sum(seq_group.get_max_num_running_seqs()
+                                for seq_group in self.running)
+            num_batched_tokens = 0
+            # Optimization: We do not sort the waiting queue since the preempted
+            # sequence groups are added to the front and the new sequence groups
+            # are added to the back.
+            while self.waiting:
+                seq_group = self.waiting[0]
+
+                assert seq_group.num_seqs() == 1, (
+                    "Waiting sequence group should have only one prompt "
+                    "sequence.")
+                num_prompt_tokens = seq_group.get_seqs()[0].get_len()
+                if num_prompt_tokens > self.prompt_limit:
+                    logger.warning(
+                        f"Input prompt ({num_prompt_tokens} tokens) is too long"
+                        f" and exceeds limit of {self.prompt_limit}")
+                    for seq in seq_group.get_seqs():
+                        seq.status = SequenceStatus.FINISHED_IGNORED
+                    ignored_seq_groups.append(seq_group)
+                    self.waiting.pop(0)
+                    continue
+
+                # If the sequence group cannot be allocated, stop.
+                if not self.block_manager.can_allocate(seq_group):
+                    break
+
+                # If the number of batched tokens exceeds the limit, stop.
+                if (num_batched_tokens + num_prompt_tokens >
+                        self.scheduler_config.max_num_batched_tokens):
+                    break
+
+                # The total number of sequences in the RUNNING state should not
+                # exceed the maximum number of sequences.
+                num_new_seqs = seq_group.get_max_num_running_seqs()
+                if (num_curr_seqs + num_new_seqs >
+                        self.scheduler_config.max_num_seqs):
+                    break
+
+                seq_group = self.waiting.pop(0)
+                self._allocate(seq_group)
+                self.running.append(seq_group)
+                num_batched_tokens += num_prompt_tokens
+                num_curr_seqs += num_new_seqs
+                scheduled.append(seq_group)
+
+            if scheduled or ignored_seq_groups:
+                scheduler_outputs = SchedulerOutputs(
+                    scheduled_seq_groups=scheduled,
+                    prompt_run=True,
+                    num_batched_tokens=num_batched_tokens,
+                    blocks_to_swap_in=blocks_to_swap_in,
+                    blocks_to_swap_out=blocks_to_swap_out,
+                    blocks_to_copy=blocks_to_copy,
+                    ignored_seq_groups=ignored_seq_groups,
+                )
+                return scheduler_outputs
+
+        # NOTE(woosuk): Preemption happens only when there is no available slot
+        # to keep all the sequence groups in the RUNNING state.
         # In this case, the policy is responsible for deciding which sequence
         # groups to preempt.
         self.running = self.policy.sort_by_priority(now, self.running)
@@ -145,150 +218,56 @@ class Scheduler:
 
         # Swap in the sequence groups in the SWAPPED state if possible.
         self.swapped = self.policy.sort_by_priority(now, self.swapped)
-        while self.swapped and not blocks_to_swap_out:
-            seq_group = self.swapped[0]
-            # If the sequence group has been preempted in this step, stop.
-            if seq_group in preempted:
-                break
-            # If the sequence group cannot be swapped in, stop.
-            if not self.block_manager.can_swap_in(seq_group):
-                break
+        if not preempted:
+            num_curr_seqs = sum(seq_group.get_max_num_running_seqs()
+                                for seq_group in self.running)
 
-            # The total number of sequences in the RUNNING state should not
-            # exceed the maximum number of sequences.
-            num_new_seqs = seq_group.num_seqs(status=SequenceStatus.SWAPPED)
-            num_curr_seqs = sum(
-                seq_group.num_seqs(status=SequenceStatus.RUNNING)
-                for seq_group in self.running)
-            if (num_curr_seqs + num_new_seqs >
-                    self.scheduler_config.max_num_seqs):
-                break
-
-            seq_group = self.swapped.pop(0)
-            self._swap_in(seq_group, blocks_to_swap_in)
-            self._append_slot(seq_group, blocks_to_copy)
-            self.running.append(seq_group)
-
-        num_batched_tokens = sum(
-            seq_group.num_seqs(status=SequenceStatus.RUNNING)
-            for seq_group in self.running)
-
-        # Join waiting sequences if possible.
-        prompt_group_ids: List[str] = []
-        # NOTE(woosuk): The sequence groups in the SWAPPED state are strictly
-        # prioritized over the sequence groups in the WAITING state.
-        # This is because we want to bound the amount of CPU memory taken by
-        # the swapped sequence groups.
-        if not self.swapped:
-            # Optimization: We do not sort the waiting queue since the preempted
-            # sequence groups are added to the front and the new sequence groups
-            # are added to the back.
-            while self.waiting:
-                seq_group = self.waiting[0]
-                # If the sequence group has been preempted in this step, stop.
-                if seq_group in preempted:
-                    break
-
-                num_prompt_tokens = seq_group.get_seqs()[0].get_len()
-                if num_prompt_tokens >= self.scheduler_config.max_seq_len:
-                    logger.warning(
-                        f"Input prompt ({num_prompt_tokens} tokens) is too long"
-                        " and exceeds limit of "
-                        f"{self.scheduler_config.max_seq_len}")
-                    for seq in seq_group.get_seqs():
-                        seq.status = SequenceStatus.FINISHED_IGNORED
-                    ignored_seq_groups.append(seq_group)
-                    self.waiting.pop(0)
-                    break
-
-                # If the sequence group cannot be allocated, stop.
-                if not self.block_manager.can_allocate(seq_group):
-                    break
-
-                # If the number of batched tokens exceeds the limit, stop.
-                if (num_batched_tokens + num_prompt_tokens >
-                        self.scheduler_config.max_num_batched_tokens):
+            while self.swapped:
+                seq_group = self.swapped[0]
+                # If the sequence group cannot be swapped in, stop.
+                if not self.block_manager.can_swap_in(seq_group):
                     break
 
                 # The total number of sequences in the RUNNING state should not
                 # exceed the maximum number of sequences.
-                num_new_seqs = seq_group.num_seqs(
-                    status=SequenceStatus.WAITING)
-                num_curr_seqs = sum(
-                    seq_group.num_seqs(status=SequenceStatus.RUNNING)
-                    for seq_group in self.running)
+                num_new_seqs = seq_group.get_max_num_running_seqs()
                 if (num_curr_seqs + num_new_seqs >
                         self.scheduler_config.max_num_seqs):
                     break
 
-                seq_group = self.waiting.pop(0)
-                self._allocate(seq_group)
+                seq_group = self.swapped.pop(0)
+                self._swap_in(seq_group, blocks_to_swap_in)
+                self._append_slot(seq_group, blocks_to_copy)
+                num_curr_seqs += num_new_seqs
                 self.running.append(seq_group)
-                num_batched_tokens += num_prompt_tokens
-                prompt_group_ids.append(seq_group.request_id)
+
+        # Each sequence in the generation phase only takes one token slot.
+        # Therefore, the number of batched tokens is equal to the number of
+        # sequences in the RUNNING state.
+        num_batched_tokens = sum(
+            seq_group.num_seqs(status=SequenceStatus.RUNNING)
+            for seq_group in self.running)
 
         scheduler_outputs = SchedulerOutputs(
+            scheduled_seq_groups=self.running,
+            prompt_run=False,
+            num_batched_tokens=num_batched_tokens,
             blocks_to_swap_in=blocks_to_swap_in,
             blocks_to_swap_out=blocks_to_swap_out,
             blocks_to_copy=blocks_to_copy,
+            ignored_seq_groups=[],
         )
-        if not self.log_stats:
-            return scheduler_outputs, prompt_group_ids, ignored_seq_groups
+        return scheduler_outputs
 
-        # TODO(woosuk): Move the below code to the engine.
-        now = time.time()
-        if num_batched_tokens > 0:
-            self.num_input_tokens.append((now, num_batched_tokens))
-        elapsed_time = now - self.last_logging_time
-        if elapsed_time > _LOGGING_INTERVAL_SEC:
-            self.last_logging_time = now
-            self.num_input_tokens = [(t, n) for t, n in self.num_input_tokens
-                                     if now - t < _LOGGING_INTERVAL_SEC]
-            if len(self.num_input_tokens) > 1:
-                total_num_tokens = sum(n
-                                       for _, n in self.num_input_tokens[:-1])
-                window = now - self.num_input_tokens[0][0]
-                avg_throughput = total_num_tokens / window
-            else:
-                avg_throughput = 0.0
-
-            total_num_gpu_blocks = self.cache_config.num_gpu_blocks
-            num_free_gpu_blocks = self.block_manager.get_num_free_gpu_blocks()
-            num_used_gpu_blocks = total_num_gpu_blocks - num_free_gpu_blocks
-            gpu_cache_usage = num_used_gpu_blocks / total_num_gpu_blocks
-
-            total_num_cpu_blocks = self.cache_config.num_cpu_blocks
-            if total_num_cpu_blocks > 0:
-                num_free_cpu_blocks = (
-                    self.block_manager.get_num_free_cpu_blocks())
-                num_used_cpu_blocks = total_num_cpu_blocks - num_free_cpu_blocks
-                cpu_cache_usage = num_used_cpu_blocks / total_num_cpu_blocks
-            else:
-                cpu_cache_usage = 0.0
-
-            logger.info(f"Throughput: {avg_throughput:.1f} tokens/s, "
-                        f"Running: {len(self.running)} reqs, "
-                        f"Swapped: {len(self.swapped)} reqs, "
-                        f"Pending: {len(self.waiting)} reqs, "
-                        f"GPU KV cache usage: {gpu_cache_usage * 100:.1f}%, "
-                        f"CPU KV cache usage: {cpu_cache_usage * 100:.1f}%")
-        return scheduler_outputs, prompt_group_ids, ignored_seq_groups
-
-    def schedule(
-        self
-    ) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs,
-               List[SequenceGroup]]:
+    def schedule(self) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs]:
         # Schedule sequence groups.
         # This function call changes the internal states of the scheduler
         # such as self.running, self.swapped, and self.waiting.
-        (scheduler_outputs, prompt_group_ids,
-         ignored_seq_groups) = self._schedule()
+        scheduler_outputs = self._schedule()
 
         # Create input data structures.
         seq_group_metadata_list: List[SequenceGroupMetadata] = []
-        for seq_group in self.running:
-            is_prompt = seq_group.request_id in prompt_group_ids
-
+        for seq_group in scheduler_outputs.scheduled_seq_groups:
             seq_data: Dict[int, List[SequenceData]] = {}
             block_tables: Dict[int, List[int]] = {}
             for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
@@ -298,43 +277,18 @@ class Scheduler:
 
             seq_group_metadata = SequenceGroupMetadata(
                 request_id=seq_group.request_id,
-                is_prompt=is_prompt,
+                is_prompt=scheduler_outputs.prompt_run,
                 seq_data=seq_data,
                 sampling_params=seq_group.sampling_params,
                 block_tables=block_tables,
             )
             seq_group_metadata_list.append(seq_group_metadata)
-        return seq_group_metadata_list, scheduler_outputs, ignored_seq_groups
+        return seq_group_metadata_list, scheduler_outputs
 
-    def update(
-        self,
-        seq_outputs: Dict[int, SequenceOutputs],
-    ) -> List[SequenceGroup]:
-        # Update the running sequences and free blocks.
-        for seq_group in self.running:
-            # Process beam search results before processing the new tokens.
-            for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
-                output = seq_outputs[seq.seq_id]
-                if seq.seq_id != output.parent_seq_id:
-                    # The sequence is a fork of the parent sequence (beam
-                    # search). Free the current sequence.
-                    self.block_manager.free(seq)
-                    # Fork the parent sequence.
-                    parent_seq = seq_group.find(output.parent_seq_id)
-                    parent_seq.fork(seq)
-                    self.block_manager.fork(parent_seq, seq)
+    def fork_seq(self, parent_seq: Sequence, child_seq: Sequence) -> None:
+        self.block_manager.fork(parent_seq, child_seq)
 
-            # Process the new tokens.
-            for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
-                # Append a new token to the sequence.
-                output = seq_outputs[seq.seq_id]
-                seq.append_token_id(output.output_token, output.logprobs)
-        # Return a shallow copy of the running queue to prevent the queue
-        # from being modified by the caller.
-        return self.running.copy()
-
-    def free_seq(self, seq: Sequence, finish_status: SequenceStatus) -> None:
-        seq.status = finish_status
+    def free_seq(self, seq: Sequence) -> None:
         self.block_manager.free(seq)
 
     def free_finished_seq_groups(self) -> None:
@@ -371,8 +325,8 @@ class Scheduler:
         # If preemption mode is not specified, we determine the mode as follows:
         # We use recomputation by default since it incurs lower overhead than
         # swapping. However, when the sequence group has multiple sequences
-        # (e.g., beam search), recomputation is not supported. In such a case,
-        # we use swapping instead.
+        # (e.g., beam search), recomputation is not currently supported. In
+        # such a case, we use swapping instead.
         # FIXME(woosuk): This makes our scheduling policy a bit bizarre.
         # As swapped sequences are prioritized over waiting sequences,
         # sequence groups with multiple sequences are implicitly prioritized
@@ -380,8 +334,7 @@ class Scheduler:
         # TODO(woosuk): Support recomputation for sequence groups with multiple
         # sequences. This may require a more sophisticated CUDA kernel.
         if preemption_mode is None:
-            seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
-            if len(seqs) == 1:
+            if seq_group.get_max_num_running_seqs() == 1:
                 preemption_mode = PreemptionMode.RECOMPUTE
             else:
                 preemption_mode = PreemptionMode.SWAP
@@ -410,9 +363,6 @@ class Scheduler:
         seq_group: SequenceGroup,
         blocks_to_swap_out: Dict[int, int],
     ) -> None:
-        seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
-        for seq in seqs:
-            seq.status = SequenceStatus.SWAPPED
         self._swap_out(seq_group, blocks_to_swap_out)
         self.swapped.append(seq_group)
 

vllm/engine/arg_utils.py

@@ -13,25 +13,27 @@ class EngineArgs:
     model: str
     tokenizer: Optional[str] = None
     tokenizer_mode: str = 'auto'
+    trust_remote_code: bool = False
     download_dir: Optional[str] = None
-    use_np_weights: bool = False
-    use_dummy_weights: bool = False
+    load_format: str = 'auto'
     dtype: str = 'auto'
     seed: int = 0
+    max_model_len: Optional[int] = None
     worker_use_ray: bool = False
     pipeline_parallel_size: int = 1
     tensor_parallel_size: int = 1
     block_size: int = 16
     swap_space: int = 4  # GiB
     gpu_memory_utilization: float = 0.90
-    max_num_batched_tokens: int = 2560
+    max_num_batched_tokens: Optional[int] = None
     max_num_seqs: int = 256
     disable_log_stats: bool = False
+    revision: Optional[str] = None
+    quantization: Optional[str] = None
 
     def __post_init__(self):
         if self.tokenizer is None:
             self.tokenizer = self.model
-        self.max_num_seqs = min(self.max_num_seqs, self.max_num_batched_tokens)
 
     @staticmethod
     def add_cli_args(
@@ -48,6 +50,13 @@ class EngineArgs:
             type=str,
             default=EngineArgs.tokenizer,
             help='name or path of the huggingface tokenizer to use')
+        parser.add_argument(
+            '--revision',
+            type=str,
+            default=None,
+            help='the specific model version to use. It can be a branch '
+            'name, a tag name, or a commit id. If unspecified, will use '
+            'the default version.')
         parser.add_argument('--tokenizer-mode',
                             type=str,
                             default=EngineArgs.tokenizer_mode,
@@ -55,30 +64,46 @@ class EngineArgs:
                             help='tokenizer mode. "auto" will use the fast '
                             'tokenizer if available, and "slow" will '
                             'always use the slow tokenizer.')
+        parser.add_argument('--trust-remote-code',
+                            action='store_true',
+                            help='trust remote code from huggingface')
         parser.add_argument('--download-dir',
                             type=str,
                             default=EngineArgs.download_dir,
                             help='directory to download and load the weights, '
                             'default to the default cache dir of '
                             'huggingface')
-        parser.add_argument('--use-np-weights',
-                            action='store_true',
-                            help='save a numpy copy of model weights for '
-                            'faster loading. This can increase the disk '
-                            'usage by up to 2x.')
-        parser.add_argument('--use-dummy-weights',
-                            action='store_true',
-                            help='use dummy values for model weights')
-        # TODO(woosuk): Support FP32.
+        parser.add_argument(
+            '--load-format',
+            type=str,
+            default=EngineArgs.load_format,
+            choices=['auto', 'pt', 'safetensors', 'npcache', 'dummy'],
+            help='The format of the model weights to load. '
+            '"auto" will try to load the weights in the safetensors format '
+            'and fall back to the pytorch bin format if safetensors format '
+            'is not available. '
+            '"pt" will load the weights in the pytorch bin format. '
+            '"safetensors" will load the weights in the safetensors format. '
+            '"npcache" will load the weights in pytorch format and store '
+            'a numpy cache to speed up the loading. '
+            '"dummy" will initialize the weights with random values, '
+            'which is mainly for profiling.')
         parser.add_argument(
             '--dtype',
             type=str,
             default=EngineArgs.dtype,
-            choices=['auto', 'half', 'bfloat16', 'float'],
+            choices=[
+                'auto', 'half', 'float16', 'bfloat16', 'float', 'float32'
+            ],
             help='data type for model weights and activations. '
             'The "auto" option will use FP16 precision '
             'for FP32 and FP16 models, and BF16 precision '
             'for BF16 models.')
+        parser.add_argument('--max-model-len',
+                            type=int,
+                            default=None,
+                            help='model context length. If unspecified, '
+                            'will be automatically derived from the model.')
         # Parallel arguments
         parser.add_argument('--worker-use-ray',
                             action='store_true',
@@ -126,6 +151,13 @@ class EngineArgs:
         parser.add_argument('--disable-log-stats',
                             action='store_true',
                             help='disable logging statistics')
+        # Quantization settings.
+        parser.add_argument('--quantization',
+                            '-q',
+                            type=str,
+                            choices=['awq', None],
+                            default=None,
+                            help='Method used to quantize the weights')
         return parser
 
     @classmethod
@@ -139,22 +171,20 @@ class EngineArgs:
     def create_engine_configs(
         self,
     ) -> Tuple[ModelConfig, CacheConfig, ParallelConfig, SchedulerConfig]:
-        # Initialize the configs.
         model_config = ModelConfig(self.model, self.tokenizer,
-                                   self.tokenizer_mode, self.download_dir,
-                                   self.use_np_weights, self.use_dummy_weights,
-                                   self.dtype, self.seed)
-        cache_config = CacheConfig(self.block_size,
-                                   self.gpu_memory_utilization,
-                                   self.swap_space)
+                                   self.tokenizer_mode, self.trust_remote_code,
+                                   self.download_dir, self.load_format,
+                                   self.dtype, self.seed, self.revision,
+                                   self.max_model_len, self.quantization)
+        cache_config = CacheConfig(
+            self.block_size, self.gpu_memory_utilization, self.swap_space,
+            getattr(model_config.hf_config, 'sliding_window', None))
         parallel_config = ParallelConfig(self.pipeline_parallel_size,
                                          self.tensor_parallel_size,
                                          self.worker_use_ray)
-        model_max_len = getattr(model_config.hf_config,
-                                'max_position_embeddings', float('inf'))
-        max_seq_len = min(self.max_num_batched_tokens, model_max_len)
         scheduler_config = SchedulerConfig(self.max_num_batched_tokens,
-                                           self.max_num_seqs, max_seq_len)
+                                           self.max_num_seqs,
+                                           model_config.max_model_len)
         return model_config, cache_config, parallel_config, scheduler_config
 
 
@@ -163,6 +193,7 @@ class AsyncEngineArgs(EngineArgs):
     """Arguments for asynchronous vLLM engine."""
     engine_use_ray: bool = False
     disable_log_requests: bool = False
+    max_log_len: Optional[int] = None
 
     @staticmethod
     def add_cli_args(
@@ -175,4 +206,10 @@ class AsyncEngineArgs(EngineArgs):
         parser.add_argument('--disable-log-requests',
                             action='store_true',
                             help='disable logging requests')
+        parser.add_argument('--max-log-len',
+                            type=int,
+                            default=None,
+                            help='max number of prompt characters or prompt '
+                            'ID numbers being printed in log. '
+                            'Default: unlimited.')
         return parser

vllm/engine/async_llm_engine.py

@@ -1,6 +1,8 @@
 import asyncio
 import time
-from typing import Dict, List, Optional
+from functools import partial
+from typing import (Any, Dict, Iterable, List, Optional, Set, Tuple, Type,
+                    Union)
 
 from vllm.config import ModelConfig
 from vllm.engine.arg_utils import AsyncEngineArgs
@@ -12,7 +14,219 @@ from vllm.sampling_params import SamplingParams
 
 logger = init_logger(__name__)
 
-TIMEOUT_TO_PREVENT_DEADLOCK = 1  # seconds
+
+class AsyncEngineDeadError(RuntimeError):
+    pass
+
+
+def _raise_exception_on_finish(task: asyncio.Task,
+                               request_tracker: "RequestTracker") -> None:
+    msg = ("Task finished unexpectedly. This should never happen! "
+           "Please open an issue on Github.")
+    try:
+        try:
+            task.result()
+        except asyncio.CancelledError:
+            return
+        except Exception as exc:
+            raise AsyncEngineDeadError(
+                msg + " See stack trace above for the actual cause.") from exc
+        raise AsyncEngineDeadError(msg)
+    except Exception as exc:
+        request_tracker.propagate_exception(exc)
+        raise exc
+
+
+class AsyncStream:
+    """A stream of RequestOutputs for a request that can be
+    iterated over asynchronously."""
+
+    def __init__(self, request_id: str) -> None:
+        self.request_id = request_id
+        self._queue = asyncio.Queue()
+        self._finished = False
+
+    def put(self, item: RequestOutput) -> None:
+        if self._finished:
+            return
+        self._queue.put_nowait(item)
+
+    def finish(self) -> None:
+        self._queue.put_nowait(StopIteration)
+        self._finished = True
+
+    @property
+    def finished(self) -> bool:
+        return self._finished
+
+    def __aiter__(self):
+        return self
+
+    async def __anext__(self) -> RequestOutput:
+        result = await self._queue.get()
+        if result is StopIteration:
+            raise StopAsyncIteration
+        elif isinstance(result, Exception):
+            raise result
+        return result
+
+
+class RequestTracker:
+    """Synchronous abstraction for tracking requests."""
+
+    def __init__(self) -> None:
+        self._request_streams: Dict[str, AsyncStream] = {}
+        self._finished_requests: asyncio.Queue[str] = asyncio.Queue()
+        self._new_requests: asyncio.Queue[Tuple[AsyncStream,
+                                                dict]] = asyncio.Queue()
+        self.new_requests_event = None
+
+    def __contains__(self, item):
+        return item in self._request_streams
+
+    def init_event(self):
+        self.new_requests_event = asyncio.Event()
+
+    def propagate_exception(self,
+                            exc: Exception,
+                            request_id: Optional[str] = None) -> None:
+        """Propagate an exception to request streams
+        (all if request_id is None)."""
+        if request_id is not None:
+            self._request_streams[request_id].put(exc)
+        else:
+            for stream in self._request_streams.values():
+                stream.put(exc)
+
+    def process_request_output(self,
+                               request_output: RequestOutput,
+                               *,
+                               verbose: bool = False) -> None:
+        """Process a request output from the engine."""
+        request_id = request_output.request_id
+
+        self._request_streams[request_id].put(request_output)
+        if request_output.finished:
+            if verbose:
+                logger.info(f"Finished request {request_id}.")
+            self.abort_request(request_id)
+
+    def add_request(self, request_id: str,
+                    **engine_add_request_kwargs) -> AsyncStream:
+        """Add a request to be sent to the engine on the next background
+        loop iteration."""
+        if request_id in self._request_streams:
+            raise KeyError(f"Request {request_id} already exists.")
+
+        stream = AsyncStream(request_id)
+        self._new_requests.put_nowait((stream, {
+            "request_id": request_id,
+            **engine_add_request_kwargs
+        }))
+
+        self.new_requests_event.set()
+
+        return stream
+
+    def abort_request(self, request_id: str, *, verbose: bool = False) -> None:
+        """Abort a request during next background loop iteration."""
+        if verbose:
+            logger.info(f"Aborted request {request_id}.")
+
+        self._finished_requests.put_nowait(request_id)
+
+        if request_id not in self._request_streams or self._request_streams[
+                request_id].finished:
+            # The request has already finished or been aborted.
+            return
+
+        self._request_streams[request_id].finish()
+
+    def get_new_and_finished_requests(self) -> Tuple[List[dict], Set[str]]:
+        """Get the new requests and finished requests to be
+        sent to the engine."""
+        new_requests: List[dict] = []
+        finished_requests: Set[str] = set()
+
+        while not self._finished_requests.empty():
+            request_id = self._finished_requests.get_nowait()
+            finished_requests.add(request_id)
+            self._request_streams.pop(request_id, None)
+
+        while not self._new_requests.empty():
+            stream, new_request = self._new_requests.get_nowait()
+            if stream.request_id in finished_requests:
+                # The request has already been aborted.
+                stream.finish()
+                continue
+            self._request_streams[stream.request_id] = stream
+            new_requests.append(new_request)
+
+        self.new_requests_event.clear()
+
+        return new_requests, finished_requests
+
+    async def wait_for_new_requests(self):
+        await self.new_requests_event.wait()
+
+
+class _AsyncLLMEngine(LLMEngine):
+    """Extension of LLMEngine to add async methods."""
+
+    async def step_async(self) -> List[RequestOutput]:
+        """Performs one decoding iteration and returns newly generated results.
+        The workers are ran asynchronously if possible.
+
+        This function performs one decoding iteration of the engine. It first
+        schedules the sequences to be executed in the next iteration and the
+        token blocks to be swapped in/out/copy. Then, it executes the model
+        and updates the scheduler with the model outputs. Finally, it decodes
+        the sequences and returns the newly generated results.
+        """
+        seq_group_metadata_list, scheduler_outputs, ignored = self._schedule()
+        if scheduler_outputs.is_empty():
+            return ignored
+
+        # Execute the model.
+        output = await self._run_workers_async(
+            "execute_model",
+            seq_group_metadata_list=seq_group_metadata_list,
+            blocks_to_swap_in=scheduler_outputs.blocks_to_swap_in,
+            blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
+            blocks_to_copy=scheduler_outputs.blocks_to_copy,
+        )
+
+        return self._process_model_outputs(output, scheduler_outputs) + ignored
+
+    async def _run_workers_async(
+        self,
+        method: str,
+        *args,
+        get_all_outputs: bool = False,
+        **kwargs,
+    ) -> Any:
+        """Runs the given method on all workers."""
+        all_outputs = []
+        for worker in self.workers:
+            if self.parallel_config.worker_use_ray:
+                executor = partial(worker.execute_method.remote, method)
+            else:
+                executor = getattr(worker, method)
+
+            output = executor(*args, **kwargs)
+            all_outputs.append(output)
+
+        if self.parallel_config.worker_use_ray:
+            all_outputs = await asyncio.gather(*all_outputs)
+
+        if get_all_outputs:
+            return all_outputs
+
+        # Make sure all workers have the same results.
+        output = all_outputs[0]
+        for other_output in all_outputs[1:]:
+            assert output == other_output
+        return output
 
 
 class AsyncLLMEngine:
@@ -34,52 +248,149 @@ class AsyncLLMEngine:
             async frontend will be executed in a separate process as the
             model workers.
         log_requests: Whether to log the requests.
+        start_engine_loop: If True, the background task to run the engine
+            will be automatically started in the generate call.
         *args, *kwargs: Arguments for LLMEngine.
     """
 
+    _engine_class: Type[_AsyncLLMEngine] = _AsyncLLMEngine
+
     def __init__(self,
                  worker_use_ray: bool,
                  engine_use_ray: bool,
                  *args,
                  log_requests: bool = True,
+                 max_log_len: Optional[int] = None,
+                 start_engine_loop: bool = True,
                  **kwargs) -> None:
         self.worker_use_ray = worker_use_ray
         self.engine_use_ray = engine_use_ray
         self.log_requests = log_requests
-        if not self.engine_use_ray:
-            engine_class = LLMEngine
-        elif self.worker_use_ray:
-            engine_class = ray.remote(num_cpus=0)(LLMEngine).remote
-        else:
-            engine_class = ray.remote(num_gpus=1)(LLMEngine).remote
-        self.engine = engine_class(*args, **kwargs)
-        # Request id -> request output.
-        self.request_outputs: Dict[str, RequestOutput] = {}
-        # Request id -> event to notify that there is new output.
-        self.request_events: Dict[str, asyncio.Event] = {}
-        self.is_engine_running = False
-        self.kicking_request_id: Optional[str] = None
+        self.max_log_len = max_log_len
+        self.engine = self._init_engine(*args, **kwargs)
+
+        self.background_loop = None
+        # We need to keep a reference to unshielded
+        # task as well to prevent it from being garbage
+        # collected
+        self._background_loop_unshielded = None
+        self.start_engine_loop = start_engine_loop
+        self._request_tracker = RequestTracker()
+
+    @property
+    def is_running(self) -> bool:
+        return (self.background_loop is not None
+                and not self.background_loop.done())
+
+    def start_background_loop(self) -> None:
+        """Start the background loop."""
+        if self.is_running:
+            raise RuntimeError("Background loop is already running.")
+        self._request_tracker.init_event()
+
+        self._background_loop_unshielded = asyncio.get_event_loop(
+        ).create_task(self.run_engine_loop())
+        self._background_loop_unshielded.add_done_callback(
+            partial(_raise_exception_on_finish,
+                    request_tracker=self._request_tracker))
+        self.background_loop = asyncio.shield(self._background_loop_unshielded)
+
+    def _init_engine(self, *args,
+                     **kwargs) -> Union[_AsyncLLMEngine, "ray.ObjectRef"]:
+        if not self.engine_use_ray:
+            engine_class = self._engine_class
+        elif self.worker_use_ray:
+            engine_class = ray.remote(num_cpus=0)(self._engine_class).remote
+        else:
+            engine_class = ray.remote(num_gpus=1)(self._engine_class).remote
+        return engine_class(*args, **kwargs)
+
+    async def engine_step(self) -> bool:
+        """Kick the engine to process the waiting requests.
+
+        Returns True if there are in-progress requests."""
+
+        new_requests, finished_requests = (
+            self._request_tracker.get_new_and_finished_requests())
+
+        for new_request in new_requests:
+            # Add the request into the vLLM engine's waiting queue.
+            # TODO: Maybe add add_request_batch to reduce Ray overhead
+            if self.engine_use_ray:
+                await self.engine.add_request.remote(**new_request)
+            else:
+                self.engine.add_request(**new_request)
+
+        if finished_requests:
+            await self._engine_abort(finished_requests)
 
-    async def engine_step(self, kicking_request_id: Optional[str] = None):
-        """Kick the engine to process the waiting requests."""
-        self.is_engine_running = True
-        self.kicking_request_id = kicking_request_id
         if self.engine_use_ray:
             request_outputs = await self.engine.step.remote()
         else:
-            # Yield to the event loop to allow other coroutines to run
-            # while is_engine_running is True. This let the engine to add new
-            # requests into the queue.
-            await asyncio.sleep(0)
-            request_outputs = self.engine.step()
-        self.is_engine_running = False
-        self.kicking_request_id = None
+            request_outputs = await self.engine.step_async()
 
-        # Notify the waiting coroutines that there are new outputs ready.
+        # Put the outputs into the corresponding streams.
         for request_output in request_outputs:
-            request_id = request_output.request_id
-            self.request_outputs[request_id] = request_output
-            self.request_events[request_id].set()
+            self._request_tracker.process_request_output(
+                request_output, verbose=self.log_requests)
+
+        return len(request_outputs) > 0
+
+    async def _engine_abort(self, request_ids: Iterable[str]):
+        if self.engine_use_ray:
+            await self.engine.abort_request.remote(request_ids)
+        else:
+            self.engine.abort_request(request_ids)
+
+    async def run_engine_loop(self):
+        # Initialize the RequestTracker here so it uses the right event loop.
+        has_requests_in_progress = False
+        while True:
+            if not has_requests_in_progress:
+                await self._request_tracker.wait_for_new_requests()
+            has_requests_in_progress = await self.engine_step()
+            await asyncio.sleep(0)
+
+    async def add_request(
+        self,
+        request_id: str,
+        prompt: Optional[str],
+        sampling_params: SamplingParams,
+        prompt_token_ids: Optional[List[int]] = None,
+        arrival_time: Optional[float] = None,
+    ) -> AsyncStream:
+        if self.log_requests:
+            shortened_prompt = prompt
+            shortened_token_ids = prompt_token_ids
+            if self.max_log_len is not None:
+                if shortened_prompt is not None:
+                    shortened_prompt = shortened_prompt[:self.max_log_len]
+                if shortened_token_ids is not None:
+                    shortened_token_ids = shortened_token_ids[:self.
+                                                              max_log_len]
+            logger.info(f"Received request {request_id}: "
+                        f"prompt: {shortened_prompt!r}, "
+                        f"sampling params: {sampling_params}, "
+                        f"prompt token ids: {shortened_token_ids}.")
+
+        if not self.is_running:
+            if self.start_engine_loop:
+                self.start_background_loop()
+            else:
+                raise AsyncEngineDeadError(
+                    "Background loop is not running. If it was running, "
+                    "inspect the output to find the stacktrace of the "
+                    "error that caused the background loop to stop "
+                    "(AsyncEngineDeadError).")
+
+        stream = self._request_tracker.add_request(
+            request_id,
+            prompt=prompt,
+            sampling_params=sampling_params,
+            prompt_token_ids=prompt_token_ids,
+            arrival_time=arrival_time)
+
+        return stream
 
     async def generate(
             self,
@@ -108,76 +419,20 @@ class AsyncLLMEngine:
         # Preprocess the request.
         arrival_time = time.time()
 
-        # Create an event to notify us that there is new output from the
-        # vLLM engine.
-        request_event = asyncio.Event()
-        self.request_events[request_id] = request_event
+        try:
+            stream = await self.add_request(request_id,
+                                            prompt,
+                                            sampling_params,
+                                            prompt_token_ids=prompt_token_ids,
+                                            arrival_time=arrival_time)
 
-        if self.log_requests:
-            logger.info(f"Received request {request_id}: "
-                        f"prompt: {prompt!r}, "
-                        f"sampling params: {sampling_params}, "
-                        f"prompt token ids: {prompt_token_ids}.")
-
-        # Add the request into the vLLM engine's waiting queue.
-        if self.engine_use_ray:
-            await self.engine.add_request.remote(
-                request_id,
-                prompt,
-                sampling_params,
-                prompt_token_ids=prompt_token_ids,
-                arrival_time=arrival_time)
-        else:
-            self.engine.add_request(request_id,
-                                    prompt,
-                                    sampling_params,
-                                    prompt_token_ids=prompt_token_ids,
-                                    arrival_time=arrival_time)
-
-        # The vLLM engine does not have a background loop that keeps
-        # processing incoming requests. Therefore, we need to keep kicking
-        # the engine to process the requests.
-        while True:
-            if request_id not in self.request_events:
-                # The request has been aborted.
-                return
-
-            # Kick the engine if the engine is not running.
-            if not self.is_engine_running:
-                try:
-                    await self.engine_step(request_id)
-                except RuntimeError as e:
-                    await self.abort(request_id)
-                    raise e
-
-            # Wait for new output. The group_event will be set in engine_step
-            # when there is new output available for the sequence group.
-            # Added a timeout to prevent deadlock.
-            try:
-                await asyncio.wait_for(request_event.wait(),
-                                       timeout=TIMEOUT_TO_PREVENT_DEADLOCK)
-            except asyncio.TimeoutError:
-                continue
-            # Reset the event to wait for the next output.
-            request_event.clear()
-
-            # Decode and return new outputs.
-            request_output = self.request_outputs[request_id]
-            yield request_output
-
-            # Once finished, release the resources of the sequence group.
-            if request_output.finished:
-                if self.log_requests:
-                    logger.info(f"Finished request {request_id}.")
-
-                del self.request_outputs[request_id]
-                del self.request_events[request_id]
-                # Kick the engine if the engine is not running. This is to
-                # prevent that there are still requests in engine's waiting
-                # queue to be executed.
-                if not self.is_engine_running:
-                    await self.engine_step()
-                break
+            async for request_output in stream:
+                yield request_output
+        except (Exception, asyncio.CancelledError) as e:
+            # If there is an exception or coroutine is cancelled, abort the
+            # request.
+            self._abort(request_id)
+            raise e
 
     async def abort(self, request_id: str) -> None:
         """Abort a request.
@@ -188,28 +443,26 @@ class AsyncLLMEngine:
         Args:
             request_id: The unique id of the request.
         """
-        if request_id not in self.request_events:
-            # The request has already finished or been aborted.
-            return
+        if not self.is_running:
+            raise AsyncEngineDeadError(
+                "Background loop is not running. If it was running, "
+                "inspect the output to find the stacktrace of the "
+                "error that caused the background loop to stop "
+                "(AsyncEngineDeadError).")
 
-        if self.log_requests:
-            logger.info(f"Aborted request {request_id}.")
+        return self._abort(request_id)
 
-        if self.engine_use_ray:
-            await self.engine.abort_request.remote(request_id)
-        else:
-            self.engine.abort_request(request_id)
+    def _abort(self, request_id: str) -> None:
+        """Abort a request.
 
-        if request_id in self.request_events:
-            del self.request_events[request_id]
-        if request_id in self.request_outputs:
-            del self.request_outputs[request_id]
+        Abort a submitted request. If the request is finished or not found,
+        this method will be a no-op.
 
-        # To prevent deadlock when a request is aborted while the engine is
-        # running.
-        if self.kicking_request_id == request_id:
-            self.is_engine_running = False
-            self.kicking_request_id = None
+        Args:
+            request_id: The unique id of the request.
+        """
+        self._request_tracker.abort_request(request_id,
+                                            verbose=self.log_requests)
 
     async def get_model_config(self) -> ModelConfig:
         """Get the model configuration of the vLLM engine."""
@@ -220,20 +473,23 @@ class AsyncLLMEngine:
 
     @classmethod
     def from_engine_args(cls,
-                         engine_args: AsyncEngineArgs) -> "AsyncLLMEngine":
+                         engine_args: AsyncEngineArgs,
+                         start_engine_loop: bool = True) -> "AsyncLLMEngine":
         """Creates an async LLM engine from the engine arguments."""
         # Create the engine configs.
         engine_configs = engine_args.create_engine_configs()
         parallel_config = engine_configs[2]
         # Initialize the cluster.
-        distributed_init_method, devices = initialize_cluster(
+        distributed_init_method, placement_group = initialize_cluster(
             parallel_config, engine_args.engine_use_ray)
         # Create the async LLM engine.
         engine = cls(engine_args.worker_use_ray,
                      engine_args.engine_use_ray,
                      *engine_configs,
                      distributed_init_method,
-                     devices,
+                     placement_group,
                      log_requests=not engine_args.disable_log_requests,
-                     log_stats=not engine_args.disable_log_stats)
+                     log_stats=not engine_args.disable_log_stats,
+                     max_log_len=engine_args.max_log_len,
+                     start_engine_loop=start_engine_loop)
         return engine

vllm/engine/llm_engine.py

@@ -1,22 +1,34 @@
+import copy
 import time
-from typing import Any, List, Optional
+from functools import partial
+from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Tuple, Union
 
 from vllm.config import (CacheConfig, ModelConfig, ParallelConfig,
                          SchedulerConfig)
-from vllm.core.scheduler import Scheduler
+from vllm.core.scheduler import Scheduler, SchedulerOutputs
 from vllm.engine.arg_utils import EngineArgs
-from vllm.engine.ray_utils import DeviceID, initialize_cluster, ray
+from vllm.engine.ray_utils import RayWorker, initialize_cluster, ray
 from vllm.logger import init_logger
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import SamplingParams
-from vllm.sequence import Sequence, SequenceGroup, SequenceStatus
+from vllm.sequence import (SamplerOutput, Sequence, SequenceGroup,
+                           SequenceGroupMetadata, SequenceOutputs,
+                           SequenceStatus)
 from vllm.transformers_utils.tokenizer import (detokenize_incrementally,
                                                get_tokenizer)
 from vllm.utils import Counter
-from vllm.worker.worker import Worker
+
+if ray:
+    from ray.air.util.torch_dist import init_torch_dist_process_group
+    from ray.util.scheduling_strategies import PlacementGroupSchedulingStrategy
+
+if TYPE_CHECKING:
+    from ray.util.placement_group import PlacementGroup
 
 logger = init_logger(__name__)
 
+_LOGGING_INTERVAL_SEC = 5
+
 
 class LLMEngine:
     """An LLM engine that receives requests and generates texts.
@@ -42,8 +54,8 @@ class LLMEngine:
         scheduler_config: The configuration related to the request scheduler.
         distributed_init_method: The initialization method for distributed
             execution. See `torch.distributed.init_process_group` for details.
-        stage_devices: The list of devices for each stage. Each stage is a list
-            of (rank, node_resource, device) tuples.
+        placement_group: Ray placement group for distributed execution.
+            Required for distributed execution.
         log_stats: Whether to log statistics.
     """
 
@@ -54,7 +66,7 @@ class LLMEngine:
         parallel_config: ParallelConfig,
         scheduler_config: SchedulerConfig,
         distributed_init_method: str,
-        stage_devices: List[List[DeviceID]],
+        placement_group: Optional["PlacementGroup"],
         log_stats: bool,
     ) -> None:
         logger.info(
@@ -62,50 +74,112 @@ class LLMEngine:
             f"model={model_config.model!r}, "
             f"tokenizer={model_config.tokenizer!r}, "
             f"tokenizer_mode={model_config.tokenizer_mode}, "
+            f"revision={model_config.revision}, "
+            f"trust_remote_code={model_config.trust_remote_code}, "
             f"dtype={model_config.dtype}, "
-            f"use_dummy_weights={model_config.use_dummy_weights}, "
+            f"max_seq_len={model_config.max_model_len}, "
             f"download_dir={model_config.download_dir!r}, "
-            f"use_np_weights={model_config.use_np_weights}, "
+            f"load_format={model_config.load_format}, "
             f"tensor_parallel_size={parallel_config.tensor_parallel_size}, "
+            f"quantization={model_config.quantization}, "
             f"seed={model_config.seed})")
         # TODO(woosuk): Print more configs in debug mode.
 
         self.model_config = model_config
         self.cache_config = cache_config
+        assert self.cache_config.sliding_window == getattr(
+            self.model_config.hf_config, "sliding_window", None)
         self.parallel_config = parallel_config
         self.scheduler_config = scheduler_config
         self.log_stats = log_stats
         self._verify_args()
 
         self.tokenizer = get_tokenizer(
-            model_config.tokenizer, tokenizer_mode=model_config.tokenizer_mode)
+            model_config.tokenizer,
+            tokenizer_mode=model_config.tokenizer_mode,
+            trust_remote_code=model_config.trust_remote_code,
+            revision=model_config.revision)
         self.seq_counter = Counter()
 
         # Create the parallel GPU workers.
-        self.workers: List[Worker] = []
-        assert len(stage_devices) == 1, "Only support one stage for now."
-        for rank, node_resource, _ in stage_devices[0]:
-            worker_cls = Worker
-            if self.parallel_config.worker_use_ray:
-                worker_cls = ray.remote(
-                    num_cpus=0,
-                    num_gpus=1,
-                    resources={node_resource: 1e-3},
-                )(worker_cls).remote
+        if self.parallel_config.worker_use_ray:
+            self._init_workers_ray(placement_group)
+        else:
+            self._init_workers(distributed_init_method)
 
-            worker = worker_cls(
-                model_config,
-                parallel_config,
-                scheduler_config,
-                rank,
-                distributed_init_method,
-            )
-            self.workers.append(worker)
         # Profile the memory usage and initialize the cache.
         self._init_cache()
 
         # Create the scheduler.
-        self.scheduler = Scheduler(scheduler_config, cache_config, log_stats)
+        self.scheduler = Scheduler(scheduler_config, cache_config)
+
+        # Logging.
+        self.last_logging_time = 0.0
+        # List of (timestamp, num_tokens)
+        self.num_prompt_tokens: List[Tuple[float, int]] = []
+        # List of (timestamp, num_tokens)
+        self.num_generation_tokens: List[Tuple[float, int]] = []
+
+    def _init_workers(self, distributed_init_method: str):
+        # Lazy import the Worker to avoid importing torch.cuda/xformers
+        # before CUDA_VISIBLE_DEVICES is set in the Worker
+        from vllm.worker.worker import Worker  # pylint: disable=import-outside-toplevel
+
+        assert self.parallel_config.world_size == 1, (
+            "Ray is required if parallel_config.world_size > 1.")
+
+        self.workers: List[Worker] = []
+        worker = Worker(
+            self.model_config,
+            self.parallel_config,
+            self.scheduler_config,
+            0,
+            distributed_init_method,
+        )
+        self.workers.append(worker)
+        self._run_workers(
+            "init_model",
+            get_all_outputs=True,
+        )
+
+    def _init_workers_ray(self, placement_group: "PlacementGroup",
+                          **ray_remote_kwargs):
+        # Lazy import the Worker to avoid importing torch.cuda/xformers
+        # before CUDA_VISIBLE_DEVICES is set in the Worker
+        from vllm.worker.worker import Worker  # pylint: disable=import-outside-toplevel
+
+        self.workers: List[Worker] = []
+        for bundle in placement_group.bundle_specs:
+            if not bundle.get("GPU", 0):
+                continue
+            worker = ray.remote(
+                num_cpus=0,
+                num_gpus=1,
+                scheduling_strategy=PlacementGroupSchedulingStrategy(
+                    placement_group=placement_group,
+                    placement_group_capture_child_tasks=True),
+                **ray_remote_kwargs,
+            )(RayWorker).remote(self.model_config.trust_remote_code)
+            self.workers.append(worker)
+
+        # Initialize torch distributed process group for the workers.
+        init_torch_dist_process_group(self.workers, backend="nccl")
+        model_config = copy.deepcopy(self.model_config)
+        parallel_config = copy.deepcopy(self.parallel_config)
+        scheduler_config = copy.deepcopy(self.scheduler_config)
+        self._run_workers("init_worker",
+                          get_all_outputs=True,
+                          worker_init_fn=lambda: Worker(
+                              model_config,
+                              parallel_config,
+                              scheduler_config,
+                              None,
+                              None,
+                          ))
+        self._run_workers(
+            "init_model",
+            get_all_outputs=True,
+        )
 
     def _verify_args(self) -> None:
         self.model_config.verify_with_parallel_config(self.parallel_config)
@@ -149,11 +223,12 @@ class LLMEngine:
         engine_configs = engine_args.create_engine_configs()
         parallel_config = engine_configs[2]
         # Initialize the cluster.
-        distributed_init_method, devices = initialize_cluster(parallel_config)
+        distributed_init_method, placement_group = initialize_cluster(
+            parallel_config)
         # Create the LLM engine.
         engine = cls(*engine_configs,
                      distributed_init_method,
-                     devices,
+                     placement_group,
                      log_stats=not engine_args.disable_log_stats)
         return engine
 
@@ -189,24 +264,21 @@ class LLMEngine:
 
         # Create the sequences.
         block_size = self.cache_config.block_size
-        seqs: List[Sequence] = []
-        for _ in range(sampling_params.best_of):
-            seq_id = next(self.seq_counter)
-            seq = Sequence(seq_id, prompt, prompt_token_ids, block_size)
-            seqs.append(seq)
+        seq_id = next(self.seq_counter)
+        seq = Sequence(seq_id, prompt, prompt_token_ids, block_size)
 
         # Create the sequence group.
-        seq_group = SequenceGroup(request_id, seqs, sampling_params,
+        seq_group = SequenceGroup(request_id, [seq], sampling_params,
                                   arrival_time)
 
         # Add the sequence group to the scheduler.
         self.scheduler.add_seq_group(seq_group)
 
-    def abort_request(self, request_id: str) -> None:
-        """Aborts a request with the given ID.
+    def abort_request(self, request_id: Union[str, Iterable[str]]) -> None:
+        """Aborts a request(s) with the given ID.
 
         Args:
-            request_id: The ID of the request to abort.
+            request_id: The ID(s) of the request to abort.
         """
         self.scheduler.abort_seq_group(request_id)
 
@@ -222,6 +294,249 @@ class LLMEngine:
         """Returns True if there are unfinished requests."""
         return self.scheduler.has_unfinished_seqs()
 
+    def _schedule(
+        self
+    ) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs,
+               List[RequestOutput]]:
+        seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule()
+        return seq_group_metadata_list, scheduler_outputs, [
+            RequestOutput.from_seq_group(seq_group)
+            for seq_group in scheduler_outputs.ignored_seq_groups
+        ]
+
+    def _check_beam_search_early_stopping(
+        self,
+        early_stopping: Union[bool, str],
+        sampling_params: SamplingParams,
+        best_running_seq: Sequence,
+        current_worst_seq: Sequence,
+    ) -> bool:
+        assert sampling_params.use_beam_search
+        length_penalty = sampling_params.length_penalty
+        if early_stopping is True:
+            return True
+
+        current_worst_score = (current_worst_seq.get_beam_search_score(
+            length_penalty=length_penalty,
+            eos_token_id=self.tokenizer.eos_token_id))
+        if early_stopping is False:
+            highest_attainable_score = (best_running_seq.get_beam_search_score(
+                length_penalty=length_penalty,
+                eos_token_id=self.tokenizer.eos_token_id))
+        else:
+            assert early_stopping == "never"
+            if length_penalty > 0.0:
+                # If length_penalty > 0.0, beam search will prefer longer
+                # sequences. The highest attainable score calculation is
+                # based on the longest possible sequence length in this case.
+                max_possible_length = max(
+                    best_running_seq.get_prompt_len() +
+                    sampling_params.max_tokens,
+                    self.scheduler_config.max_model_len)
+                highest_attainable_score = (
+                    best_running_seq.get_beam_search_score(
+                        length_penalty=length_penalty,
+                        eos_token_id=self.tokenizer.eos_token_id,
+                        seq_len=max_possible_length))
+            else:
+                # Otherwise, beam search will prefer shorter sequences. The
+                # highest attainable score calculation is based on the current
+                # sequence length.
+                highest_attainable_score = (
+                    best_running_seq.get_beam_search_score(
+                        length_penalty=length_penalty,
+                        eos_token_id=self.tokenizer.eos_token_id))
+        return current_worst_score >= highest_attainable_score
+
+    def _process_sequence_group_samples(
+            self, seq_group: SequenceGroup,
+            samples: List[SequenceOutputs]) -> None:
+        parent_seqs = seq_group.get_seqs(status=SequenceStatus.RUNNING)
+        existing_finished_seqs = seq_group.get_finished_seqs()
+        parent_child_dict = {
+            parent_seq.seq_id: []
+            for parent_seq in parent_seqs
+        }
+        for sample in samples:
+            parent_child_dict[sample.parent_seq_id].append(sample)
+        # List of (child, parent)
+        child_seqs: List[Tuple[Sequence, Sequence]] = []
+
+        # Process the child samples for each parent sequence
+        for parent in parent_seqs:
+            child_samples: List[SequenceOutputs] = parent_child_dict[
+                parent.seq_id]
+            if len(child_samples) == 0:
+                # This parent sequence has no children samples. Remove
+                # the parent sequence from the sequence group since it will
+                # not be used in the future iterations.
+                parent.status = SequenceStatus.FINISHED_ABORTED
+                seq_group.remove(parent.seq_id)
+                self.scheduler.free_seq(parent)
+                continue
+            # Fork the parent sequence if there are multiple child samples.
+            for child_sample in child_samples[:-1]:
+                new_child_seq_id = next(self.seq_counter)
+                child = parent.fork(new_child_seq_id)
+                child.append_token_id(child_sample.output_token,
+                                      child_sample.logprobs)
+                child_seqs.append((child, parent))
+            # Continue the parent sequence for the last child sample.
+            # We reuse the parent sequence here to reduce redundant memory
+            # copies, especially when using non-beam search sampling methods.
+            last_child_sample = child_samples[-1]
+            parent.append_token_id(last_child_sample.output_token,
+                                   last_child_sample.logprobs)
+            child_seqs.append((parent, parent))
+
+        for seq, _ in child_seqs:
+            self._decode_sequence(seq, seq_group.sampling_params)
+            self._check_stop(seq, seq_group.sampling_params)
+
+        # Non-beam search case
+        if not seq_group.sampling_params.use_beam_search:
+            # For newly created child sequences, add them to the sequence group
+            # and fork them in block manager if they are not finished.
+            for seq, parent in child_seqs:
+                if seq is not parent:
+                    seq_group.add(seq)
+                    if not seq.is_finished():
+                        self.scheduler.fork_seq(parent, seq)
+
+            # Free the finished and selected parent sequences' memory in block
+            # manager. Keep them in the sequence group as candidate output.
+            # NOTE: we need to fork the new sequences before freeing the
+            # old sequences.
+            for seq, parent in child_seqs:
+                if seq is parent and seq.is_finished():
+                    self.scheduler.free_seq(seq)
+            return
+
+        # Beam search case
+        # Select the child sequences to keep in the sequence group.
+        selected_child_seqs = []
+        unselected_child_seqs = []
+        beam_width = seq_group.sampling_params.best_of
+        length_penalty = seq_group.sampling_params.length_penalty
+
+        # Select the newly finished sequences with the highest scores
+        # to replace existing finished sequences.
+        # Tuple of (seq, parent, is_new)
+        existing_finished_seqs = [(seq, None, False)
+                                  for seq in existing_finished_seqs]
+        new_finished_seqs = [(seq, parent, True) for seq, parent in child_seqs
+                             if seq.is_finished()]
+        all_finished_seqs = existing_finished_seqs + new_finished_seqs
+        # Sort the finished sequences by their scores.
+        all_finished_seqs.sort(key=lambda x: x[0].get_beam_search_score(
+            length_penalty=length_penalty,
+            eos_token_id=self.tokenizer.eos_token_id),
+                               reverse=True)
+        for seq, parent, is_new in all_finished_seqs[:beam_width]:
+            if is_new:
+                # A newly generated child sequence finishes and has a high
+                # score, so we will add it into the sequence group.
+                selected_child_seqs.append((seq, parent))
+        for seq, parent, is_new in all_finished_seqs[beam_width:]:
+            if is_new:
+                # A newly generated child sequence finishes but has a low
+                # score, so we will not add it into the sequence group.
+                # Additionally, if this sequence is a continuation of a
+                # parent sequence, we will need remove the parent sequence
+                # from the sequence group.
+                unselected_child_seqs.append((seq, parent))
+            else:
+                # An existing finished sequence has a low score, so we will
+                # remove it from the sequence group.
+                seq_group.remove(seq.seq_id)
+
+        # select the top beam_width sequences from the running
+        # sequences for the next iteration to continue the beam
+        # search.
+        running_child_seqs = [(seq, parent) for seq, parent in child_seqs
+                              if not seq.is_finished()]
+        # Sort the running sequences by their scores.
+        running_child_seqs.sort(key=lambda x: x[0].get_beam_search_score(
+            length_penalty=length_penalty,
+            eos_token_id=self.tokenizer.eos_token_id),
+                                reverse=True)
+
+        # Check if we can stop the beam search.
+        if len(running_child_seqs) == 0:
+            # No running sequences, stop the beam search.
+            stop_beam_search = True
+        elif len(all_finished_seqs) < beam_width:
+            # Not enough finished sequences, continue the beam search.
+            stop_beam_search = False
+        else:
+            # Check the early stopping criteria
+            best_running_seq = running_child_seqs[0][0]
+            current_worst_seq = all_finished_seqs[beam_width - 1][0]
+            stop_beam_search = self._check_beam_search_early_stopping(
+                seq_group.sampling_params.early_stopping,
+                seq_group.sampling_params, best_running_seq, current_worst_seq)
+
+        if stop_beam_search:
+            # Stop the beam search and remove all the running sequences from
+            # the sequence group.
+            unselected_child_seqs.extend(running_child_seqs)
+        else:
+            # Continue the beam search and select the top beam_width sequences
+            # to continue the beam search.
+            selected_child_seqs.extend(running_child_seqs[:beam_width])
+            # The remaining running sequences will not be used in the next
+            # iteration. Again, if these sequences are continuations of
+            # parent sequences, we will need to remove the parent sequences
+            # from the sequence group.
+            unselected_child_seqs.extend(running_child_seqs[beam_width:])
+
+        # For newly created child sequences, add them to the sequence group
+        # and fork them in block manager if they are not finished.
+        for seq, parent in selected_child_seqs:
+            if seq is not parent:
+                seq_group.add(seq)
+                if not seq.is_finished():
+                    self.scheduler.fork_seq(parent, seq)
+
+        # Free the finished and selected parent sequences' memory in block
+        # manager. Keep them in the sequence group as candidate output.
+        for seq, parent in selected_child_seqs:
+            if seq is parent and seq.is_finished():
+                self.scheduler.free_seq(seq)
+
+        # Remove the unselected parent sequences from the sequence group and
+        # free their memory in block manager.
+        for seq, parent in unselected_child_seqs:
+            if seq is parent:
+                # Remove the parent sequence if it is not selected for next
+                # iteration
+                seq_group.remove(seq.seq_id)
+                self.scheduler.free_seq(seq)
+
+    def _process_model_outputs(
+            self, output: SamplerOutput,
+            scheduler_outputs: SchedulerOutputs) -> List[RequestOutput]:
+        # Update the scheduled sequence groups with the model outputs.
+        scheduled_seq_groups = scheduler_outputs.scheduled_seq_groups
+        for seq_group, samples in zip(scheduled_seq_groups, output):
+            self._process_sequence_group_samples(seq_group, samples)
+
+        # Free the finished sequence groups.
+        self.scheduler.free_finished_seq_groups()
+
+        # Create the outputs.
+        request_outputs: List[RequestOutput] = []
+        for seq_group in (scheduled_seq_groups +
+                          scheduler_outputs.ignored_seq_groups):
+            request_output = RequestOutput.from_seq_group(seq_group)
+            request_outputs.append(request_output)
+
+        if self.log_stats:
+            # Log the system stats.
+            self._log_system_stats(scheduler_outputs.prompt_run,
+                                   scheduler_outputs.num_batched_tokens)
+        return request_outputs
+
     def step(self) -> List[RequestOutput]:
         """Performs one decoding iteration and returns newly generated results.
 
@@ -231,12 +546,9 @@ class LLMEngine:
         and updates the scheduler with the model outputs. Finally, it decodes
         the sequences and returns the newly generated results.
         """
-        (seq_group_metadata_list, scheduler_outputs,
-         ignored_seq_groups) = self.scheduler.schedule()
-        if ((not seq_group_metadata_list) and scheduler_outputs.is_empty()
-                and (not ignored_seq_groups)):
-            # Nothing to do.
-            return []
+        seq_group_metadata_list, scheduler_outputs, ignored = self._schedule()
+        if scheduler_outputs.is_empty():
+            return ignored
 
         # Execute the model.
         output = self._run_workers(
@@ -246,72 +558,121 @@ class LLMEngine:
             blocks_to_swap_out=scheduler_outputs.blocks_to_swap_out,
             blocks_to_copy=scheduler_outputs.blocks_to_copy,
         )
-        # Update the scheduler with the model outputs.
-        seq_groups = self.scheduler.update(output)
 
-        # Decode the sequences.
-        self._decode_sequences(seq_groups)
-        # Stop the sequences that meet the stopping criteria.
-        self._stop_sequences(seq_groups)
-        # Free the finished sequence groups.
-        self.scheduler.free_finished_seq_groups()
+        return self._process_model_outputs(output, scheduler_outputs) + ignored
 
-        # Create the outputs.
-        request_outputs: List[RequestOutput] = []
-        for seq_group in seq_groups + ignored_seq_groups:
-            request_output = RequestOutput.from_seq_group(seq_group)
-            request_outputs.append(request_output)
-        return request_outputs
+    def _log_system_stats(
+        self,
+        prompt_run: bool,
+        num_batched_tokens: int,
+    ) -> None:
+        now = time.time()
+        # Log the number of batched input tokens.
+        if prompt_run:
+            self.num_prompt_tokens.append((now, num_batched_tokens))
+        else:
+            self.num_generation_tokens.append((now, num_batched_tokens))
 
-    def _decode_sequences(self, seq_groups: List[SequenceGroup]) -> None:
-        """Decodes the sequence outputs."""
-        for seq_group in seq_groups:
-            for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
-                new_token, new_output_text = detokenize_incrementally(
-                    self.tokenizer,
-                    seq.output_tokens,
-                    seq.get_last_token_id(),
-                    skip_special_tokens=True,
-                )
-                seq.output_tokens.append(new_token)
-                seq.output_text = new_output_text
+        elapsed_time = now - self.last_logging_time
+        if elapsed_time < _LOGGING_INTERVAL_SEC:
+            return
 
-    def _stop_sequences(self, seq_groups: List[SequenceGroup]) -> None:
+        # Discard the old stats.
+        self.num_prompt_tokens = [(t, n) for t, n in self.num_prompt_tokens
+                                  if now - t < _LOGGING_INTERVAL_SEC]
+        self.num_generation_tokens = [(t, n)
+                                      for t, n in self.num_generation_tokens
+                                      if now - t < _LOGGING_INTERVAL_SEC]
+
+        if len(self.num_prompt_tokens) > 1:
+            total_num_tokens = sum(n for _, n in self.num_prompt_tokens[:-1])
+            window = now - self.num_prompt_tokens[0][0]
+            avg_prompt_throughput = total_num_tokens / window
+        else:
+            avg_prompt_throughput = 0.0
+        if len(self.num_generation_tokens) > 1:
+            total_num_tokens = sum(n
+                                   for _, n in self.num_generation_tokens[:-1])
+            window = now - self.num_generation_tokens[0][0]
+            avg_generation_throughput = total_num_tokens / window
+        else:
+            avg_generation_throughput = 0.0
+
+        total_num_gpu_blocks = self.cache_config.num_gpu_blocks
+        num_free_gpu_blocks = (
+            self.scheduler.block_manager.get_num_free_gpu_blocks())
+        num_used_gpu_blocks = total_num_gpu_blocks - num_free_gpu_blocks
+        gpu_cache_usage = num_used_gpu_blocks / total_num_gpu_blocks
+
+        total_num_cpu_blocks = self.cache_config.num_cpu_blocks
+        if total_num_cpu_blocks > 0:
+            num_free_cpu_blocks = (
+                self.scheduler.block_manager.get_num_free_cpu_blocks())
+            num_used_cpu_blocks = total_num_cpu_blocks - num_free_cpu_blocks
+            cpu_cache_usage = num_used_cpu_blocks / total_num_cpu_blocks
+        else:
+            cpu_cache_usage = 0.0
+
+        logger.info("Avg prompt throughput: "
+                    f"{avg_prompt_throughput:.1f} tokens/s, "
+                    "Avg generation throughput: "
+                    f"{avg_generation_throughput:.1f} tokens/s, "
+                    f"Running: {len(self.scheduler.running)} reqs, "
+                    f"Swapped: {len(self.scheduler.swapped)} reqs, "
+                    f"Pending: {len(self.scheduler.waiting)} reqs, "
+                    f"GPU KV cache usage: {gpu_cache_usage * 100:.1f}%, "
+                    f"CPU KV cache usage: {cpu_cache_usage * 100:.1f}%")
+        self.last_logging_time = now
+
+    def _decode_sequence(self, seq: Sequence,
+                         sampling_params: SamplingParams) -> None:
+        """Decodes the new token for a sequence."""
+        (new_tokens, new_output_text, prefix_offset,
+         read_offset) = detokenize_incrementally(
+             self.tokenizer,
+             all_input_ids=seq.get_token_ids(),
+             prev_tokens=seq.tokens,
+             prefix_offset=seq.prefix_offset,
+             read_offset=seq.read_offset,
+             skip_special_tokens=sampling_params.skip_special_tokens,
+         )
+        if seq.tokens is None:
+            seq.tokens = new_tokens
+        else:
+            seq.tokens.extend(new_tokens)
+        seq.prefix_offset = prefix_offset
+        seq.read_offset = read_offset
+        seq.output_text += new_output_text
+
+    def _check_stop(self, seq: Sequence,
+                    sampling_params: SamplingParams) -> None:
         """Stop the finished sequences."""
-        for seq_group in seq_groups:
-            sampling_params = seq_group.sampling_params
-            for seq in seq_group.get_seqs(status=SequenceStatus.RUNNING):
-                # Check if the sequence has generated a stop string.
-                stopped = False
-                for stop_str in sampling_params.stop:
-                    if seq.output_text.endswith(stop_str):
-                        # Truncate the output text so that the stop string is
-                        # not included in the output.
-                        seq.output_text = seq.output_text[:-len(stop_str)]
-                        self.scheduler.free_seq(
-                            seq, SequenceStatus.FINISHED_STOPPED)
-                        stopped = True
-                        break
-                if stopped:
-                    continue
+        for stop_str in sampling_params.stop:
+            if seq.output_text.endswith(stop_str):
+                # Truncate the output text so that the stop string is
+                # not included in the output.
+                seq.output_text = seq.output_text[:-len(stop_str)]
+                seq.status = SequenceStatus.FINISHED_STOPPED
+                return
+        if seq.get_last_token_id() in sampling_params.stop_token_ids:
+            seq.status = SequenceStatus.FINISHED_STOPPED
+            return
 
-                # Check if the sequence has reached max_seq_len.
-                if (seq.get_len() >=
-                        self.scheduler.scheduler_config.max_seq_len):
-                    self.scheduler.free_seq(
-                        seq, SequenceStatus.FINISHED_LENGTH_CAPPED)
-                    continue
-                # Check if the sequence has reached max_tokens.
-                if seq.get_output_len() == sampling_params.max_tokens:
-                    self.scheduler.free_seq(
-                        seq, SequenceStatus.FINISHED_LENGTH_CAPPED)
-                    continue
-                # Check if the sequence has generated the EOS token.
-                if not sampling_params.ignore_eos:
-                    if seq.get_last_token_id() == self.tokenizer.eos_token_id:
-                        self.scheduler.free_seq(
-                            seq, SequenceStatus.FINISHED_STOPPED)
-                        continue
+        # Check if the sequence has reached max_model_len.
+        if seq.get_len() > self.scheduler_config.max_model_len:
+            seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
+            return
+
+        # Check if the sequence has reached max_tokens.
+        if seq.get_output_len() == sampling_params.max_tokens:
+            seq.status = SequenceStatus.FINISHED_LENGTH_CAPPED
+            return
+
+        # Check if the sequence has generated the EOS token.
+        if ((not sampling_params.ignore_eos)
+                and seq.get_last_token_id() == self.tokenizer.eos_token_id):
+            seq.status = SequenceStatus.FINISHED_STOPPED
+            return
 
     def _run_workers(
         self,
@@ -323,9 +684,10 @@ class LLMEngine:
         """Runs the given method on all workers."""
         all_outputs = []
         for worker in self.workers:
-            executor = getattr(worker, method)
             if self.parallel_config.worker_use_ray:
-                executor = executor.remote
+                executor = partial(worker.execute_method.remote, method)
+            else:
+                executor = getattr(worker, method)
 
             output = executor(*args, **kwargs)
             all_outputs.append(output)

vllm/engine/ray_utils.py

@@ -1,22 +1,59 @@
-import random
-from typing import List, Optional, Tuple
+import socket
+from typing import Optional, Tuple, TYPE_CHECKING
+
+from vllm.config import ParallelConfig
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
 
 try:
     import ray
-except ImportError:
+    from ray.air.util.torch_dist import TorchDistributedWorker
+
+    class RayWorker(TorchDistributedWorker):
+        """Ray wrapper for vllm.worker.Worker, allowing Worker to be
+        lazliy initialized after Ray sets CUDA_VISIBLE_DEVICES."""
+
+        def __init__(self, init_cached_hf_modules=False) -> None:
+            if init_cached_hf_modules:
+                # pylint: disable=import-outside-toplevel
+                from transformers.dynamic_module_utils import init_hf_modules
+                init_hf_modules()
+            self.worker = None
+
+        def init_worker(self, worker_init_fn):
+            self.worker = worker_init_fn()
+
+        def __getattr__(self, name):
+            return getattr(self.worker, name)
+
+        def execute_method(self, method, *args, **kwargs):
+            executor = getattr(self, method)
+            return executor(*args, **kwargs)
+
+except ImportError as e:
+    logger.warning(f"Failed to import Ray with {e!r}. "
+                   "For distributed inference, please install Ray with "
+                   "`pip install ray pandas pyarrow`.")
     ray = None
+    TorchDistributedWorker = None
+    RayWorker = None  # pylint: disable=invalid-name
 
-from vllm.config import ParallelConfig
+if TYPE_CHECKING:
+    from ray.util.placement_group import PlacementGroup
 
-# rank, node resource (node IP), device id
-DeviceID = Tuple[int, Optional[str], int]
+
+def get_open_port():
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
 
 
 def initialize_cluster(
     parallel_config: ParallelConfig,
     engine_use_ray: bool = False,
     ray_address: Optional[str] = None,
-) -> Tuple[str, List[List[DeviceID]]]:
+) -> Tuple[str, Optional["PlacementGroup"]]:
     """Initialize the distributed cluster probably with Ray.
 
     Args:
@@ -26,11 +63,10 @@ def initialize_cluster(
             the default Ray cluster address.
 
     Returns:
-        A tuple of (`distributed_init_method`, `all_stage_devices`). The
+        A tuple of (`distributed_init_method`, `placement_group`). The
         `distributed_init_method` is the address for initializing the
-        distributed backend. `all_stage_devices` includes device IDs for
-        each worker in each pipeline stage. Each device ID is a tuple of
-        (rank, node resource, device id).
+        distributed backend. `placement_group` includes the specification
+        of the resources for each distributed worker.
     """
     if parallel_config.worker_use_ray or engine_use_ray:
         if ray is None:
@@ -38,71 +74,46 @@ def initialize_cluster(
                 "Ray is not installed. Please install Ray to use distributed "
                 "serving.")
         # Connect to a ray cluster.
-        ray.init(address=ray_address)
+        ray.init(address=ray_address, ignore_reinit_error=True)
 
     if not parallel_config.worker_use_ray:
         # Initialize cluster locally.
-        port = random.randint(10000, 20000)
+        port = get_open_port()
         # We need to setup the distributed init method to make sure
         # the distributed megatron code (e.g., get world size) works correctly.
         distributed_init_method = f"tcp://localhost:{port}"
-        all_stage_devices = [[(0, None, 0)]]
-        return distributed_init_method, all_stage_devices
+        return distributed_init_method, None
 
-    # Assume we have a uniform cluster that each node has the same number of
-    # GPUs for now.
-    valid_node_resources = []
-    num_devices_per_node = None
-    for node in ray.nodes():
-        if (not node["Alive"]) or node["Resources"]["GPU"] <= 0:
-            continue
-        if num_devices_per_node is None:
-            num_devices_per_node = node["Resources"]["GPU"]
-        else:
-            assert num_devices_per_node == node["Resources"]["GPU"], (
-                "The number of GPUs per node is not uniform.")
-        for key in node["Resources"]:
-            if key.startswith("node:"):
-                valid_node_resources.append(key)
-
-    # Verify the parallel config.
-    num_nodes = len(valid_node_resources)
-    if parallel_config.world_size > num_nodes * num_devices_per_node:
-        raise ValueError(
-            "The number of required GPUs exceeds the total number of "
-            "available GPUs.")
-    if parallel_config.tensor_parallel_size >= num_devices_per_node:
-        if parallel_config.tensor_parallel_size % num_devices_per_node != 0:
+    current_placement_group = ray.util.get_current_placement_group()
+    if current_placement_group:
+        # We are in a placement group
+        bundles = current_placement_group.bundle_specs
+        # Verify that we can use the placement group.
+        gpu_bundles = 0
+        for bundle in bundles:
+            bundle_gpus = bundle.get("GPU", 0)
+            if bundle_gpus > 1:
+                raise ValueError(
+                    "Placement group bundle cannot have more than 1 GPU.")
+            if bundle_gpus:
+                gpu_bundles += 1
+        if parallel_config.world_size > gpu_bundles:
             raise ValueError(
-                "The number of tensor parallelism is not divisible by the "
-                "number of GPUs per node.")
+                "The number of required GPUs exceeds the total number of "
+                "available GPUs in the placement group.")
     else:
-        if num_devices_per_node % parallel_config.tensor_parallel_size != 0:
+        num_gpus_in_cluster = ray.cluster_resources().get("GPU", 0)
+        if parallel_config.world_size > num_gpus_in_cluster:
             raise ValueError(
-                "The number of GPUs per node is not divisible by the number "
-                "of tensor parallelism.")
+                "The number of required GPUs exceeds the total number of "
+                "available GPUs in the cluster.")
+        # Create a new placement group
+        current_placement_group = ray.util.placement_group([{
+            "GPU": 1
+        }] * parallel_config.world_size)
+        # Wait until PG is ready - this will block until all
+        # requested resources are available, and will timeout
+        # if they cannot be provisioned.
+        ray.get(current_placement_group.ready(), timeout=1800)
 
-    # Assign GPUs to pipeline stages.
-    rank = 0
-    current_node_id = 0
-    current_device_id = 0
-    distributed_init_method = None
-    all_stage_devices = []
-
-    for _ in range(parallel_config.pipeline_parallel_size):
-        stage_devices = []
-        for _ in range(parallel_config.tensor_parallel_size):
-            node_resource = valid_node_resources[current_node_id]
-            stage_devices.append((rank, node_resource, current_device_id))
-            if distributed_init_method is None:
-                ip = node_resource.split("node:")[-1]
-                port = random.randint(10000, 20000)
-                distributed_init_method = f"tcp://{ip}:{port}"
-            rank += 1
-            current_device_id += 1
-            if current_device_id >= num_devices_per_node:
-                current_node_id += 1
-                current_device_id = 0
-        all_stage_devices.append(stage_devices)
-
-    return distributed_init_method, all_stage_devices
+    return None, current_placement_group

vllm/entrypoints/api_server.py

@@ -2,8 +2,8 @@ import argparse
 import json
 from typing import AsyncGenerator
 
-from fastapi import BackgroundTasks, FastAPI, Request
-from fastapi.responses import Response, StreamingResponse
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse, Response, StreamingResponse
 import uvicorn
 
 from vllm.engine.arg_utils import AsyncEngineArgs
@@ -14,6 +14,7 @@ from vllm.utils import random_uuid
 TIMEOUT_KEEP_ALIVE = 5  # seconds.
 TIMEOUT_TO_PREVENT_DEADLOCK = 1  # seconds.
 app = FastAPI()
+engine = None
 
 
 @app.post("/generate")
@@ -30,6 +31,7 @@ async def generate(request: Request) -> Response:
     stream = request_dict.pop("stream", False)
     sampling_params = SamplingParams(**request_dict)
     request_id = random_uuid()
+
     results_generator = engine.generate(prompt, sampling_params, request_id)
 
     # Streaming case
@@ -42,14 +44,8 @@ async def generate(request: Request) -> Response:
             ret = {"text": text_outputs}
             yield (json.dumps(ret) + "\0").encode("utf-8")
 
-    async def abort_request() -> None:
-        await engine.abort(request_id)
-
     if stream:
-        background_tasks = BackgroundTasks()
-        # Abort the request if the client disconnects.
-        background_tasks.add_task(abort_request)
-        return StreamingResponse(stream_results(), background=background_tasks)
+        return StreamingResponse(stream_results())
 
     # Non-streaming case
     final_output = None
@@ -64,7 +60,7 @@ async def generate(request: Request) -> Response:
     prompt = final_output.prompt
     text_outputs = [prompt + output.text for output in final_output.outputs]
     ret = {"text": text_outputs}
-    return Response(content=json.dumps(ret))
+    return JSONResponse(ret)
 
 
 if __name__ == "__main__":

vllm/entrypoints/llm.py

@@ -28,6 +28,8 @@ class LLM:
         tokenizer: The name or path of a HuggingFace Transformers tokenizer.
         tokenizer_mode: The tokenizer mode. "auto" will use the fast tokenizer
             if available, and "slow" will always use the slow tokenizer.
+        trust_remote_code: Trust remote code (e.g., from HuggingFace) when
+            downloading the model and tokenizer.
         tensor_parallel_size: The number of GPUs to use for distributed
             execution with tensor parallelism.
         dtype: The data type for the model weights and activations. Currently,
@@ -35,7 +37,22 @@ class LLM:
             the `torch_dtype` attribute specified in the model config file.
             However, if the `torch_dtype` in the config is `float32`, we will
             use `float16` instead.
+        quantization: The method used to quantize the model weights. Currently,
+            we support "awq". If None, we assume the model weights are not
+            quantized and use `dtype` to determine the data type of the weights.
+        revision: The specific model version to use. It can be a branch name,
+            a tag name, or a commit id.
         seed: The seed to initialize the random number generator for sampling.
+        gpu_memory_utilization: The ratio (between 0 and 1) of GPU memory to
+            reserve for the model weights, activations, and KV cache. Higher
+            values will increase the KV cache size and thus improve the model's
+            throughput. However, if the value is too high, it may cause out-of-
+            memory (OOM) errors.
+        swap_space: The size (GiB) of CPU memory per GPU to use as swap space.
+            This can be used for temporarily storing the states of the requests
+            when their `best_of` sampling parameters are larger than 1. If all
+            requests will have `best_of=1`, you can safely set this to 0.
+            Otherwise, too small values may cause out-of-memory (OOM) errors.
     """
 
     def __init__(
@@ -43,9 +60,14 @@ class LLM:
         model: str,
         tokenizer: Optional[str] = None,
         tokenizer_mode: str = "auto",
+        trust_remote_code: bool = False,
         tensor_parallel_size: int = 1,
         dtype: str = "auto",
+        quantization: Optional[str] = None,
+        revision: Optional[str] = None,
         seed: int = 0,
+        gpu_memory_utilization: float = 0.9,
+        swap_space: int = 4,
         **kwargs,
     ) -> None:
         if "disable_log_stats" not in kwargs:
@@ -54,9 +76,14 @@ class LLM:
             model=model,
             tokenizer=tokenizer,
             tokenizer_mode=tokenizer_mode,
+            trust_remote_code=trust_remote_code,
             tensor_parallel_size=tensor_parallel_size,
             dtype=dtype,
+            quantization=quantization,
+            revision=revision,
             seed=seed,
+            gpu_memory_utilization=gpu_memory_utilization,
+            swap_space=swap_space,
             **kwargs,
         )
         self.llm_engine = LLMEngine.from_engine_args(engine_args)
@@ -151,4 +178,8 @@ class LLM:
                         pbar.update(1)
         if use_tqdm:
             pbar.close()
+        # Sort the outputs by request ID.
+        # This is necessary because some requests may be finished earlier than
+        # its previous requests.
+        outputs = sorted(outputs, key=lambda x: int(x.request_id))
         return outputs

vllm/entrypoints/openai/api_server.py

@@ -3,20 +3,18 @@
 
 import argparse
 import asyncio
-from http import HTTPStatus
 import json
 import time
-from typing import AsyncGenerator, Dict, List, Optional
+from http import HTTPStatus
+from typing import AsyncGenerator, Dict, List, Optional, Tuple, Union
 
 import fastapi
-from fastapi import BackgroundTasks, Request
+import uvicorn
+from fastapi import Request
 from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, StreamingResponse
-from fastchat.conversation import Conversation, SeparatorStyle
-from fastchat.model.model_adapter import get_conversation_template
-
-import uvicorn
+from packaging import version
 
 from vllm.engine.arg_utils import AsyncEngineArgs
 from vllm.engine.async_llm_engine import AsyncLLMEngine
@@ -33,11 +31,20 @@ from vllm.sampling_params import SamplingParams
 from vllm.transformers_utils.tokenizer import get_tokenizer
 from vllm.utils import random_uuid
 
+try:
+    import fastchat
+    from fastchat.conversation import Conversation, SeparatorStyle
+    from fastchat.model.model_adapter import get_conversation_template
+    _fastchat_available = True
+except ImportError:
+    _fastchat_available = False
+
 TIMEOUT_KEEP_ALIVE = 5  # seconds
 
 logger = init_logger(__name__)
 served_model = None
 app = fastapi.FastAPI()
+engine = None
 
 
 def create_error_response(status_code: HTTPStatus,
@@ -63,10 +70,21 @@ async def check_model(request) -> Optional[JSONResponse]:
 
 
 async def get_gen_prompt(request) -> str:
+    if not _fastchat_available:
+        raise ModuleNotFoundError(
+            "fastchat is not installed. Please install fastchat to use "
+            "the chat completion and conversation APIs: `$ pip install fschat`"
+        )
+    if version.parse(fastchat.__version__) < version.parse("0.2.23"):
+        raise ImportError(
+            f"fastchat version is low. Current version: {fastchat.__version__} "
+            "Please upgrade fastchat to use: `$ pip install -U fschat`")
+
     conv = get_conversation_template(request.model)
     conv = Conversation(
         name=conv.name,
-        system=conv.system,
+        system_template=conv.system_template,
+        system_message=conv.system_message,
         roles=conv.roles,
         messages=list(conv.messages),  # prevent in-place modification
         offset=conv.offset,
@@ -83,7 +101,7 @@ async def get_gen_prompt(request) -> str:
         for message in request.messages:
             msg_role = message["role"]
             if msg_role == "system":
-                conv.system = message["content"]
+                conv.system_message = message["content"]
             elif msg_role == "user":
                 conv.append_message(conv.roles[0], message["content"])
             elif msg_role == "assistant":
@@ -98,32 +116,33 @@ async def get_gen_prompt(request) -> str:
     return prompt
 
 
-async def check_length(request, prompt, model_config):
-    if hasattr(model_config.hf_config, "max_sequence_length"):
-        context_len = model_config.hf_config.max_sequence_length
-    elif hasattr(model_config.hf_config, "seq_length"):
-        context_len = model_config.hf_config.seq_length
-    elif hasattr(model_config.hf_config, "max_position_embeddings"):
-        context_len = model_config.hf_config.max_position_embeddings
-    elif hasattr(model_config.hf_config, "seq_length"):
-        context_len = model_config.hf_config.seq_length
+async def check_length(
+    request: Union[ChatCompletionRequest, CompletionRequest],
+    prompt: Optional[str] = None,
+    prompt_ids: Optional[List[int]] = None
+) -> Tuple[List[int], Optional[JSONResponse]]:
+    assert (not (prompt is None and prompt_ids is None)
+            and not (prompt is not None and prompt_ids is not None)
+            ), "Either prompt or prompt_ids should be provided."
+    if prompt_ids is not None:
+        input_ids = prompt_ids
     else:
-        context_len = 2048
-
-    input_ids = tokenizer(prompt).input_ids
+        input_ids = tokenizer(prompt).input_ids
     token_num = len(input_ids)
 
-    if token_num + request.max_tokens > context_len:
-        return create_error_response(
+    if request.max_tokens is None:
+        request.max_tokens = max_model_len - token_num
+    if token_num + request.max_tokens > max_model_len:
+        return input_ids, create_error_response(
             HTTPStatus.BAD_REQUEST,
-            f"This model's maximum context length is {context_len} tokens. "
+            f"This model's maximum context length is {max_model_len} tokens. "
             f"However, you requested {request.max_tokens + token_num} tokens "
             f"({token_num} in the messages, "
             f"{request.max_tokens} in the completion). "
             f"Please reduce the length of the messages or completion.",
         )
     else:
-        return None
+        return input_ids, None
 
 
 @app.get("/v1/models")
@@ -162,7 +181,8 @@ def create_logprobs(token_ids: List[int],
 
 
 @app.post("/v1/chat/completions")
-async def create_chat_completion(raw_request: Request):
+async def create_chat_completion(request: ChatCompletionRequest,
+                                 raw_request: Request):
     """Completion API similar to OpenAI's API.
 
     See  https://platform.openai.com/docs/api-reference/chat/create
@@ -172,20 +192,19 @@ async def create_chat_completion(raw_request: Request):
         - function_call (Users should implement this by themselves)
         - logit_bias (to be supported by vLLM engine)
     """
-    request = ChatCompletionRequest(**await raw_request.json())
     logger.info(f"Received chat completion request: {request}")
 
     error_check_ret = await check_model(request)
     if error_check_ret is not None:
         return error_check_ret
 
-    if request.logit_bias is not None:
+    if request.logit_bias is not None and len(request.logit_bias) > 0:
         # TODO: support logit_bias in vLLM engine.
         return create_error_response(HTTPStatus.BAD_REQUEST,
                                      "logit_bias is not currently supported")
 
     prompt = await get_gen_prompt(request)
-    error_check_ret = await check_length(request, prompt, engine_model_config)
+    token_ids, error_check_ret = await check_length(request, prompt=prompt)
     if error_check_ret is not None:
         return error_check_ret
 
@@ -200,19 +219,19 @@ async def create_chat_completion(raw_request: Request):
             temperature=request.temperature,
             top_p=request.top_p,
             stop=request.stop,
+            stop_token_ids=request.stop_token_ids,
             max_tokens=request.max_tokens,
             best_of=request.best_of,
             top_k=request.top_k,
             ignore_eos=request.ignore_eos,
             use_beam_search=request.use_beam_search,
+            skip_special_tokens=request.skip_special_tokens,
         )
     except ValueError as e:
         return create_error_response(HTTPStatus.BAD_REQUEST, str(e))
 
-    result_generator = engine.generate(prompt, sampling_params, request_id)
-
-    async def abort_request() -> None:
-        await engine.abort(request_id)
+    result_generator = engine.generate(prompt, sampling_params, request_id,
+                                       token_ids)
 
     def create_stream_response_json(
         index: int,
@@ -269,23 +288,19 @@ async def create_chat_completion(raw_request: Request):
                         finish_reason=output.finish_reason,
                     )
                     yield f"data: {response_json}\n\n"
-            yield "data: [DONE]\n\n"
+        yield "data: [DONE]\n\n"
 
     # Streaming response
     if request.stream:
-        background_tasks = BackgroundTasks()
-        # Abort the request if the client disconnects.
-        background_tasks.add_task(abort_request)
         return StreamingResponse(completion_stream_generator(),
-                                 media_type="text/event-stream",
-                                 background=background_tasks)
+                                 media_type="text/event-stream")
 
     # Non-streaming response
     final_res: RequestOutput = None
     async for res in result_generator:
         if await raw_request.is_disconnected():
             # Abort the request if the client disconnects.
-            await abort_request()
+            await engine.abort(request_id)
             return create_error_response(HTTPStatus.BAD_REQUEST,
                                          "Client disconnected")
         final_res = res
@@ -331,7 +346,7 @@ async def create_chat_completion(raw_request: Request):
 
 
 @app.post("/v1/completions")
-async def create_completion(raw_request: Request):
+async def create_completion(request: CompletionRequest, raw_request: Request):
     """Completion API similar to OpenAI's API.
 
     See https://platform.openai.com/docs/api-reference/completions/create
@@ -344,7 +359,6 @@ async def create_completion(raw_request: Request):
           suffix)
         - logit_bias (to be supported by vLLM engine)
     """
-    request = CompletionRequest(**await raw_request.json())
     logger.info(f"Received completion request: {request}")
 
     error_check_ret = await check_model(request)
@@ -362,24 +376,41 @@ async def create_completion(raw_request: Request):
         return create_error_response(HTTPStatus.BAD_REQUEST,
                                      "suffix is not currently supported")
 
-    if request.logit_bias is not None:
+    if request.logit_bias is not None and len(request.logit_bias) > 0:
         # TODO: support logit_bias in vLLM engine.
         return create_error_response(HTTPStatus.BAD_REQUEST,
                                      "logit_bias is not currently supported")
 
     model_name = request.model
     request_id = f"cmpl-{random_uuid()}"
+
+    use_token_ids = False
     if isinstance(request.prompt, list):
         if len(request.prompt) == 0:
             return create_error_response(HTTPStatus.BAD_REQUEST,
                                          "please provide at least one prompt")
-        if len(request.prompt) > 1:
-            return create_error_response(
-                HTTPStatus.BAD_REQUEST,
-                "multiple prompts in a batch is not currently supported")
-        prompt = request.prompt[0]
+        first_element = request.prompt[0]
+        if isinstance(first_element, int):
+            use_token_ids = True
+            prompt = request.prompt
+        elif isinstance(first_element, (str, list)):
+            # TODO: handles multiple prompt case in list[list[int]]
+            if len(request.prompt) > 1:
+                return create_error_response(
+                    HTTPStatus.BAD_REQUEST,
+                    "multiple prompts in a batch is not currently supported")
+            use_token_ids = not isinstance(first_element, str)
+            prompt = request.prompt[0]
     else:
         prompt = request.prompt
+
+    if use_token_ids:
+        _, error_check_ret = await check_length(request, prompt_ids=prompt)
+    else:
+        token_ids, error_check_ret = await check_length(request, prompt=prompt)
+    if error_check_ret is not None:
+        return error_check_ret
+
     created_time = int(time.time())
     try:
         sampling_params = SamplingParams(
@@ -391,15 +422,24 @@ async def create_completion(raw_request: Request):
             top_p=request.top_p,
             top_k=request.top_k,
             stop=request.stop,
+            stop_token_ids=request.stop_token_ids,
             ignore_eos=request.ignore_eos,
             max_tokens=request.max_tokens,
             logprobs=request.logprobs,
             use_beam_search=request.use_beam_search,
+            skip_special_tokens=request.skip_special_tokens,
         )
     except ValueError as e:
         return create_error_response(HTTPStatus.BAD_REQUEST, str(e))
 
-    result_generator = engine.generate(prompt, sampling_params, request_id)
+    if use_token_ids:
+        result_generator = engine.generate(None,
+                                           sampling_params,
+                                           request_id,
+                                           prompt_token_ids=prompt)
+    else:
+        result_generator = engine.generate(prompt, sampling_params, request_id,
+                                           token_ids)
 
     # Similar to the OpenAI API, when n != best_of, we do not stream the
     # results. In addition, we do not stream the results when use beam search.
@@ -407,9 +447,6 @@ async def create_completion(raw_request: Request):
               and (request.best_of is None or request.n == request.best_of)
               and not request.use_beam_search)
 
-    async def abort_request() -> None:
-        await engine.abort(request_id)
-
     def create_stream_response_json(
         index: int,
         text: str,
@@ -465,23 +502,19 @@ async def create_completion(raw_request: Request):
                         finish_reason=output.finish_reason,
                     )
                     yield f"data: {response_json}\n\n"
-            yield "data: [DONE]\n\n"
+        yield "data: [DONE]\n\n"
 
     # Streaming response
     if stream:
-        background_tasks = BackgroundTasks()
-        # Abort the request if the client disconnects.
-        background_tasks.add_task(abort_request)
         return StreamingResponse(completion_stream_generator(),
-                                 media_type="text/event-stream",
-                                 background=background_tasks)
+                                 media_type="text/event-stream")
 
     # Non-streaming response
     final_res: RequestOutput = None
     async for res in result_generator:
         if await raw_request.is_disconnected():
             # Abort the request if the client disconnects.
-            await abort_request()
+            await engine.abort(request_id)
             return create_error_response(HTTPStatus.BAD_REQUEST,
                                          "Client disconnected")
         final_res = res
@@ -582,10 +615,12 @@ if __name__ == "__main__":
     engine_args = AsyncEngineArgs.from_cli_args(args)
     engine = AsyncLLMEngine.from_engine_args(engine_args)
     engine_model_config = asyncio.run(engine.get_model_config())
+    max_model_len = engine_model_config.max_model_len
 
     # A separate tokenizer to map token IDs to strings.
     tokenizer = get_tokenizer(engine_args.tokenizer,
-                              tokenizer_mode=engine_args.tokenizer_mode)
+                              tokenizer_mode=engine_args.tokenizer_mode,
+                              trust_remote_code=engine_args.trust_remote_code)
 
     uvicorn.run(app,
                 host=args.host,

vllm/entrypoints/openai/protocol.py

@@ -58,7 +58,7 @@ class ChatCompletionRequest(BaseModel):
     temperature: Optional[float] = 0.7
     top_p: Optional[float] = 1.0
     n: Optional[int] = 1
-    max_tokens: Optional[int] = 16
+    max_tokens: Optional[int] = None
     stop: Optional[Union[str, List[str]]] = Field(default_factory=list)
     stream: Optional[bool] = False
     presence_penalty: Optional[float] = 0.0
@@ -70,11 +70,14 @@ class ChatCompletionRequest(BaseModel):
     top_k: Optional[int] = -1
     ignore_eos: Optional[bool] = False
     use_beam_search: Optional[bool] = False
+    stop_token_ids: Optional[List[int]] = Field(default_factory=list)
+    skip_special_tokens: Optional[bool] = True
 
 
 class CompletionRequest(BaseModel):
     model: str
-    prompt: Union[str, List[str]]
+    # a string, array of strings, array of tokens, or array of token arrays
+    prompt: Union[List[int], List[List[int]], str, List[str]]
     suffix: Optional[str] = None
     max_tokens: Optional[int] = 16
     temperature: Optional[float] = 1.0
@@ -93,6 +96,8 @@ class CompletionRequest(BaseModel):
     top_k: Optional[int] = -1
     ignore_eos: Optional[bool] = False
     use_beam_search: Optional[bool] = False
+    stop_token_ids: Optional[List[int]] = Field(default_factory=list)
+    skip_special_tokens: Optional[bool] = True
 
 
 class LogProbs(BaseModel):

vllm/model_executor/input_metadata.py

@@ -1,4 +1,4 @@
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import torch
 from xformers.ops import AttentionBias
@@ -29,6 +29,7 @@ class InputMetadata:
         context_lens: torch.Tensor,
         max_context_len: int,
         block_tables: torch.Tensor,
+        sliding_window: Optional[int] = None,
     ) -> None:
         self.seq_groups = seq_groups
         self.seq_data = seq_data
@@ -38,6 +39,24 @@ class InputMetadata:
         self.max_context_len = max_context_len
         self.block_tables = block_tables
 
+        self.to_cache = None
+        if sliding_window is not None:
+            # We need to keep the positions of sliding windows within
+            # the key / value tables, this is helpful to know which
+            # elements we need to cache and where
+            to_cache, start_idx = [], 0
+            for prompt_len in self.prompt_lens:
+                to_cache.extend(
+                    range(
+                        start_idx + max(0, prompt_len - sliding_window),
+                        start_idx + prompt_len,
+                    ))
+                start_idx += prompt_len
+            to_cache.extend(range(start_idx, slot_mapping.shape[0]))
+            self.to_cache = torch.tensor(to_cache,
+                                         dtype=torch.int32,
+                                         device=self.slot_mapping.device)
+
         self.num_prompts = len(prompt_lens)
         self.num_prompt_tokens = sum(prompt_lens)
         self.num_generation_tokens = context_lens.shape[0]

vllm/model_executor/layers/activation.py

@@ -4,23 +4,6 @@ import torch.nn as nn
 
 from vllm import activation_ops
 
-_ACTIVATION_REGISTRY = {
-    "gelu": nn.GELU(),
-    # NOTE: The following GELU functions may introduce small rounding errors.
-    "gelu_new": nn.GELU(approximate="tanh"),
-    "gelu_fast": nn.GELU(approximate="tanh"),
-    "gelu_pytorch_tanh": nn.GELU(approximate="tanh"),
-    "relu": nn.ReLU(),
-}
-
-
-def get_act_fn(act_fn: str) -> nn.Module:
-    """Get an activation function by name."""
-    act_fn = act_fn.lower()
-    if act_fn in _ACTIVATION_REGISTRY:
-        return _ACTIVATION_REGISTRY[act_fn]
-    raise ValueError(f"Activation function {act_fn!r} is not supported.")
-
 
 class SiluAndMul(nn.Module):
     """An activation function for SwiGLU.
@@ -38,3 +21,40 @@ class SiluAndMul(nn.Module):
         out = torch.empty(num_tokens, d, dtype=x.dtype, device=x.device)
         activation_ops.silu_and_mul(out, x)
         return out
+
+
+class NewGELU(nn.Module):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        num_tokens = x.shape[0]
+        d = x.shape[1]
+        out = torch.empty(num_tokens, d, dtype=x.dtype, device=x.device)
+        activation_ops.gelu_new(out, x)
+        return out
+
+
+class FastGELU(nn.Module):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        num_tokens = x.shape[0]
+        d = x.shape[1]
+        out = torch.empty(num_tokens, d, dtype=x.dtype, device=x.device)
+        activation_ops.gelu_fast(out, x)
+        return out
+
+
+_ACTIVATION_REGISTRY = {
+    "gelu": nn.GELU(),
+    "gelu_fast": FastGELU(),
+    "gelu_new": NewGELU(),
+    "gelu_pytorch_tanh": nn.GELU(approximate="tanh"),
+    "relu": nn.ReLU(),
+}
+
+
+def get_act_fn(act_fn: str) -> nn.Module:
+    """Get an activation function by name."""
+    act_fn = act_fn.lower()
+    if act_fn in _ACTIVATION_REGISTRY:
+        return _ACTIVATION_REGISTRY[act_fn]
+    raise ValueError(f"Activation function {act_fn!r} is not supported.")

vllm/model_executor/layers/attention.py

@@ -1,5 +1,5 @@
 """Multi-head attention."""
-from typing import List, Optional
+from typing import Any, Dict, List, Optional
 
 import torch
 import torch.nn as nn
@@ -9,10 +9,12 @@ from xformers.ops.fmha.attn_bias import (BlockDiagonalCausalMask,
 
 from vllm import attention_ops
 from vllm import cache_ops
-from vllm import pos_encoding_ops
 from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.rotary_embedding import (
+    DynamicNTKScalingRotaryEmbedding, LinearScalingRotaryEmbedding,
+    RotaryEmbedding)
 
-_SUPPORTED_HEAD_SIZES = [64, 80, 96, 112, 128]
+_SUPPORTED_HEAD_SIZES = [64, 80, 96, 112, 128, 256]
 
 
 class PagedAttention(nn.Module):
@@ -20,12 +22,20 @@ class PagedAttention(nn.Module):
     """GPT-style multi-head PagedAttention.
 
     This class takes flattened 1D query, key, and value tensors as input. The
-    input 1D tensors can be split into three parts: the prompt tokens, the
-    generation tokens, and the paddings.
+    input 1D tensors can either contain prompt tokens or generation tokens, in
+    addition to paddings.
 
-    |<------------------------------------- num_valid_tokens ------------------------------------->|
-    |<--------------- num_prompt_tokens -------------->|<------- num_generation_tokens (M) ------->|
-    |<--prompt_0-->|<--prompt_1-->|...|<--prompt_N-1-->|<--generation_0-->|...|<--generation_M-1-->|<--padding-->|
+    If the input tensors contain prompt tokens, the layout is as follows:
+
+    |<---------------------- num_valid_tokens ---------------------->|
+    |<--------------- num_prompt_tokens -------------->|
+    |<--prompt_0-->|<--prompt_1-->|...|<--prompt_N-1-->|<--padding-->|
+
+    Otherwise, the layout is as follows:
+
+    |<------------------ num_valid_tokens ------------------->|
+    |<------- num_generation_tokens (M) ------->|
+    |<--generation_0-->|...|<--generation_M-1-->|<--padding-->|
 
     The prompts might have different lengths, while the generation tokens always
     have length 1. The paddings are appended to make the input length a multiple
@@ -44,23 +54,42 @@ class PagedAttention(nn.Module):
     5. Output a flattened 1D tensor.
     """
 
-    def __init__(self, num_heads: int, head_size: int, scale: float) -> None:
+    def __init__(self,
+                 num_heads: int,
+                 head_size: int,
+                 scale: float,
+                 num_kv_heads: Optional[int] = None,
+                 sliding_window: Optional[int] = None) -> None:
         super().__init__()
         self.num_heads = num_heads
         self.head_size = head_size
         self.scale = float(scale)
-        self.attn_op = xops.fmha.cutlass.FwOp()
+        self.num_kv_heads = num_heads if num_kv_heads is None else num_kv_heads
+        self.sliding_window = sliding_window
+
+        assert self.num_heads % self.num_kv_heads == 0
+        self.num_queries_per_kv = self.num_heads // self.num_kv_heads
+        self.head_mapping = torch.repeat_interleave(
+            torch.arange(self.num_kv_heads, dtype=torch.int32, device="cuda"),
+            self.num_queries_per_kv)
 
         if self.head_size not in _SUPPORTED_HEAD_SIZES:
             raise ValueError(f"head_size ({self.head_size}) is not supported. "
                              f"Supported head sizes: {_SUPPORTED_HEAD_SIZES}.")
 
-    def set_attn_bias(self, input_metadata: InputMetadata) -> None:
+    def set_attn_bias(
+        self,
+        input_metadata: InputMetadata,
+        dtype: torch.dtype,
+    ) -> None:
+        del dtype  # Unused.
         if input_metadata.attn_bias:
             # Already set by a previous layer.
             return
         prompt_lens = input_metadata.prompt_lens
         attn_bias = BlockDiagonalCausalMask.from_seqlens(prompt_lens)
+        if self.sliding_window is not None:
+            attn_bias = attn_bias.make_local_attention(self.sliding_window)
         input_metadata.attn_bias.append(attn_bias)
 
     def multi_query_kv_attention(
@@ -76,10 +105,18 @@ class PagedAttention(nn.Module):
         Args:
             output: shape = [num_prompt_tokens, num_heads, head_size]
             query: shape = [num_prompt_tokens, num_heads, head_size]
-            key: shape = [num_prompt_tokens, num_heads, head_size]
-            value: shape = [num_prompt_tokens, num_heads, head_size]
+            key: shape = [num_prompt_tokens, num_kv_heads, head_size]
+            value: shape = [num_prompt_tokens, num_kv_heads, head_size]
             input_metadata: metadata for paged attention.
         """
+
+        if self.num_kv_heads != self.num_heads:
+            # Project the key and value tensors to the desired number of heads.
+            key = torch.repeat_interleave(key, self.num_queries_per_kv, dim=1)
+            value = torch.repeat_interleave(value,
+                                            self.num_queries_per_kv,
+                                            dim=1)
+
         # TODO(woosuk): The unsqueeze op may incur some CPU overhead. Optimize.
         out = xops.memory_efficient_attention_forward(
             query.unsqueeze(0),
@@ -88,7 +125,6 @@ class PagedAttention(nn.Module):
             attn_bias=input_metadata.attn_bias[0],
             p=0.0,
             scale=self.scale,
-            op=self.attn_op,
         )
         # TODO(woosuk): Unnecessary copy. Optimize.
         output.copy_(out.squeeze(0))
@@ -107,9 +143,10 @@ class PagedAttention(nn.Module):
         Args:
             output: shape = [num_generation_tokens, num_heads, head_size]
             query: shape = [num_generation_tokens, num_heads, head_size]
-            key_cache: shape = [num_blocks, num_heads, head_size/x,
+            key_cache: shape = [num_blocks, num_kv_heads, head_size/x,
                 block_size, x]
-            value_cache: shape = [num_blocks, num_heads, head_size, block_size]
+            value_cache: shape = [num_blocks, num_kv_heads, head_size,
+                block_size]
             input_metadata: metadata for paged attention.
         """
         block_size = value_cache.shape[3]
@@ -118,6 +155,7 @@ class PagedAttention(nn.Module):
             query,
             key_cache,
             value_cache,
+            self.head_mapping,
             self.scale,
             input_metadata.block_tables,
             input_metadata.context_lens,
@@ -143,11 +181,12 @@ class PagedAttention(nn.Module):
 
         Args:
             query: shape = [num_tokens, num_heads * head_size]
-            key: shape = [num_tokens, num_heads * head_size]
-            value: shape = [num_tokens, num_heads * head_size]
-            key_cache: shape = [num_blocks, num_heads, head_size/x,
+            key: shape = [num_tokens, num_kv_heads * head_size]
+            value: shape = [num_tokens, num_kv_heads * head_size]
+            key_cache: shape = [num_blocks, num_kv_heads, head_size/x,
                 block_size, x]
-            value_cache: shape = [num_blocks, num_heads, head_size, block_size]
+            value_cache: shape = [num_blocks, num_kv_heads, head_size,
+                block_size]
             input_metadata: metadata for paged attention.
             cache_event: event to wait for the cache operations to finish.
 
@@ -157,8 +196,8 @@ class PagedAttention(nn.Module):
 
         # Reshape the query, key, and value tensors.
         query = query.view(-1, self.num_heads, self.head_size)
-        key = key.view(-1, self.num_heads, self.head_size)
-        value = value.view(-1, self.num_heads, self.head_size)
+        key = key.view(-1, self.num_kv_heads, self.head_size)
+        value = value.view(-1, self.num_kv_heads, self.head_size)
 
         # Pre-allocate the output tensor.
         output = torch.empty_like(query)
@@ -166,7 +205,9 @@ class PagedAttention(nn.Module):
         # Compute the attention op for prompts.
         num_prompt_tokens = input_metadata.num_prompt_tokens
         if num_prompt_tokens > 0:
-            self.set_attn_bias(input_metadata)
+            # Prompt run.
+            assert input_metadata.num_generation_tokens == 0
+            self.set_attn_bias(input_metadata, dtype=query.dtype)
             self.multi_query_kv_attention(
                 output[:num_prompt_tokens],
                 query[:num_prompt_tokens],
@@ -186,15 +227,25 @@ class PagedAttention(nn.Module):
         if (num_valid_tokens > 0 and key_cache is not None
                 and value_cache is not None):
             # The stride is 3 because the key and value are sliced from qkv.
+            key_to_cache = key[:num_valid_tokens]
+            value_to_cache = value[:num_valid_tokens]
+            slot_mapping = input_metadata.slot_mapping
+            if input_metadata.to_cache is not None:
+                key_to_cache = key_to_cache[input_metadata.to_cache]
+                value_to_cache = value_to_cache[input_metadata.to_cache]
+                slot_mapping = slot_mapping[input_metadata.to_cache]
+
             cache_ops.reshape_and_cache(
-                key[:num_valid_tokens],
-                value[:num_valid_tokens],
+                key_to_cache,
+                value_to_cache,
                 key_cache,
                 value_cache,
-                input_metadata.slot_mapping,
+                slot_mapping,
             )
 
         if input_metadata.num_generation_tokens > 0:
+            # Decoding run.
+            assert input_metadata.num_prompt_tokens == 0
             assert key_cache is not None and value_cache is not None, (
                 "key_cache and value_cache must be provided when "
                 "generating tokens.")
@@ -210,7 +261,7 @@ class PagedAttention(nn.Module):
 
 
 class PagedAttentionWithRoPE(PagedAttention):
-    """PagedAttention with GPT-NeoX style rotary embedding."""
+    """PagedAttention with rotary positional embedding."""
 
     def __init__(
         self,
@@ -220,24 +271,33 @@ class PagedAttentionWithRoPE(PagedAttention):
         rotary_dim: int,
         max_position: int = 8192,
         base: int = 10000,
+        num_kv_heads: Optional[int] = None,
+        is_neox_style: bool = True,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        sliding_window: Optional[int] = None,
     ) -> None:
-        super().__init__(num_heads, head_size, scale)
-
-        # Create the cos and sin cache.
-        inv_freq = 1.0 / (base**(torch.arange(0, rotary_dim, 2) / rotary_dim))
-        t = torch.arange(max_position).float()
-        freqs = torch.einsum("i,j -> ij", t, inv_freq.float())
-        cos = freqs.cos()
-        sin = freqs.sin()
-        cache = torch.cat((cos, sin), dim=-1)
-
-        # FIXME(woosuk): This assumes that we configure the default dtype when
-        # initializing the model.
-        # TODO(woosuk): Make it more robust.
-        torch_dtype = torch.get_default_dtype()
-        cache = cache.to(torch_dtype)
-        # Embedding size: [max_position, rotary_dim]
-        self.register_buffer("cos_sin_cache", cache, persistent=False)
+        super().__init__(num_heads,
+                         head_size,
+                         scale,
+                         num_kv_heads,
+                         sliding_window=sliding_window)
+        if rope_scaling is None:
+            self.rotary_emb = RotaryEmbedding(head_size, rotary_dim,
+                                              max_position, base,
+                                              is_neox_style)
+        else:
+            scaling_type = rope_scaling["type"]
+            scaling_factor = rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = LinearScalingRotaryEmbedding(
+                    head_size, rotary_dim, max_position, base, is_neox_style,
+                    scaling_factor)
+            elif scaling_type == "dynamic":
+                self.rotary_emb = DynamicNTKScalingRotaryEmbedding(
+                    head_size, rotary_dim, max_position, base, is_neox_style,
+                    scaling_factor)
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
 
     def forward(
         self,
@@ -254,12 +314,13 @@ class PagedAttentionWithRoPE(PagedAttention):
 
         Args:
             positions: shape = [num_tokens]
-                        query: shape = [num_tokens, num_heads * head_size]
-            key: shape = [num_tokens, num_heads * head_size]
-            value: shape = [num_tokens, num_heads * head_size]
-            key_cache: shape = [num_blocks, num_heads, head_size/x,
+            query: shape = [num_tokens, num_heads * head_size]
+            key: shape = [num_tokens, num_kv_heads * head_size]
+            value: shape = [num_tokens, num_kv_heads * head_size]
+            key_cache: shape = [num_blocks, num_kv_heads, head_size/x,
                 block_size, x]
-            value_cache: shape = [num_blocks, num_heads, head_size, block_size]
+            value_cache: shape = [num_blocks, num_kv_heads, head_size,
+                block_size]
             input_metadata: metadata for paged attention.
             cache_event: event to wait for the cache operations to finish.
 
@@ -269,13 +330,7 @@ class PagedAttentionWithRoPE(PagedAttention):
 
         # Apply rotary embedding to the query and key before passing them
         # to the attention op.
-        pos_encoding_ops.rotary_embedding_neox(
-            positions,
-            query,
-            key,
-            self.head_size,
-            self.cos_sin_cache,
-        )
+        query, key = self.rotary_emb(positions, query, key)
         return super().forward(
             query,
             key,
@@ -290,26 +345,31 @@ class PagedAttentionWithRoPE(PagedAttention):
 class PagedAttentionWithALiBi(PagedAttention):
     """PagedAttention with ALiBi attention bias."""
 
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        scale: float,
-        slopes: List[float],
-    ) -> None:
-        super().__init__(num_heads, head_size, scale)
+    def __init__(self,
+                 num_heads: int,
+                 head_size: int,
+                 scale: float,
+                 slopes: List[float],
+                 num_kv_heads: Optional[int] = None) -> None:
+        super().__init__(num_heads, head_size, scale, num_kv_heads)
         assert len(slopes) == num_heads
 
         slopes = torch.tensor(slopes, dtype=torch.float32)
         self.register_buffer("alibi_slopes", slopes, persistent=False)
 
-    def set_attn_bias(self, input_metadata: InputMetadata) -> None:
+    def set_attn_bias(self, input_metadata: InputMetadata,
+                      dtype: torch.dtype) -> None:
         if input_metadata.attn_bias:
             # Already set by a previous layer.
             return
         # Generates ALiBi mask for each prompt.
         for prompt_len in input_metadata.prompt_lens:
-            bias = torch.arange(prompt_len)
+            bias = torch.arange(prompt_len, dtype=dtype)
+            # Note(zhuohan): HF uses
+            #     `bias = bias[None, :].repeat(prompt_len, 1)`
+            # here. We find that both biases give the same results, but
+            # the bias below more accurately follows the original ALiBi
+            # paper.
             bias = bias[None, :] - bias[:, None]
             bias = bias.to(self.alibi_slopes.device)
 
@@ -317,11 +377,13 @@ class PagedAttentionWithALiBi(PagedAttention):
             # be sliced from a tensor whose length is a multiple of 8.
             padded_len = (prompt_len + 7) // 8 * 8
             bias = torch.empty(
+                1,  # batch_size
                 self.num_heads,
-                padded_len,
+                prompt_len,
                 padded_len,
                 device=self.alibi_slopes.device,
-            )[:, :prompt_len, :prompt_len].copy_(bias)
+                dtype=dtype,
+            )[:, :, :, :prompt_len].copy_(bias)
             bias.mul_(self.alibi_slopes[:, None, None])
             attn_bias = LowerTriangularMaskWithTensorBias(bias)
             input_metadata.attn_bias.append(attn_bias)
@@ -339,10 +401,17 @@ class PagedAttentionWithALiBi(PagedAttention):
         Args:
             output: shape = [num_prompt_tokens, num_heads, head_size]
             query: shape = [num_prompt_tokens, num_heads, head_size]
-            key: shape = [num_prompt_tokens, num_heads, head_size]
-            value: shape = [num_prompt_tokens, num_heads, head_size]
+            key: shape = [num_prompt_tokens, num_kv_heads, head_size]
+            value: shape = [num_prompt_tokens, num_kv_heads, head_size]
             input_metadata: metadata for paged attention.
         """
+        if self.num_kv_heads != self.num_heads:
+            # Project the key and value tensors to the desired number of heads.
+            key = torch.repeat_interleave(key, self.num_queries_per_kv, dim=1)
+            value = torch.repeat_interleave(value,
+                                            self.num_queries_per_kv,
+                                            dim=1)
+
         # FIXME(woosuk): Because xformers does not support dynamic sequence
         # lengths with custom attention bias, we process each prompt one by
         # one. This is inefficient, especially when we have many short prompts.
@@ -356,7 +425,6 @@ class PagedAttentionWithALiBi(PagedAttention):
                 attn_bias=input_metadata.attn_bias[i],
                 p=0.0,
                 scale=self.scale,
-                op=self.attn_op,
             )
             # TODO(woosuk): Unnecessary copy. Optimize.
             output[start:end].copy_(out.squeeze(0))
@@ -376,9 +444,10 @@ class PagedAttentionWithALiBi(PagedAttention):
         Args:
             output: shape = [num_generation_tokens, num_heads, head_size]
             query: shape = [num_generation_tokens, num_heads, head_size]
-            key_cache: shape = [num_blocks, num_heads, head_size/x,
+            key_cache: shape = [num_blocks, num_kv_heads, head_size/x,
                 block_size, x]
-            value_cache: shape = [num_blocks, num_heads, head_size, block_size]
+            value_cache: shape = [num_blocks, num_kv_heads, head_size,
+                block_size]
             input_metadata: metadata for paged attention.
         """
         block_size = value_cache.shape[3]
@@ -387,6 +456,7 @@ class PagedAttentionWithALiBi(PagedAttention):
             query,
             key_cache,
             value_cache,
+            self.head_mapping,
             self.scale,
             input_metadata.block_tables,
             input_metadata.context_lens,

vllm/model_executor/layers/quantized_linear/__init__.py

@@ -0,0 +1,37 @@
+from vllm.model_executor.layers.quantized_linear.awq import (
+    AWQColumnParallelLinear, AWQRowParallelLinear)
+from vllm.model_executor.parallel_utils.tensor_parallel import (
+    ColumnParallelLinear, RowParallelLinear)
+
+_QUANTIZED_LINEAR_REGISTRY = {
+    "awq": (AWQColumnParallelLinear, AWQRowParallelLinear),
+}
+
+
+class ParallelLinear:
+
+    @classmethod
+    def column(cls, *args, **kwargs) -> ColumnParallelLinear:
+        quant_config = kwargs.get("quant_config", None)
+        if quant_config is None:
+            return ColumnParallelLinear(*args, **kwargs)
+
+        name = quant_config.get_name()
+        if name not in _QUANTIZED_LINEAR_REGISTRY:
+            raise ValueError(f"No quantized linear is found for {name}")
+
+        quant_linear_cls = _QUANTIZED_LINEAR_REGISTRY[name][0]
+        return quant_linear_cls(*args, **kwargs)
+
+    @classmethod
+    def row(cls, *args, **kwargs) -> RowParallelLinear:
+        quant_config = kwargs.get("quant_config", None)
+        if quant_config is None:
+            return RowParallelLinear(*args, **kwargs)
+
+        name = quant_config.get_name()
+        if name not in _QUANTIZED_LINEAR_REGISTRY:
+            raise ValueError(f"No quantized linear is found for {name}")
+
+        quant_linear_cls = _QUANTIZED_LINEAR_REGISTRY[name][1]
+        return quant_linear_cls(*args, **kwargs)

vllm/model_executor/layers/quantized_linear/awq.py

@@ -0,0 +1,102 @@
+from typing import Optional
+
+import torch
+from torch.nn.parameter import Parameter
+
+from vllm import quantization_ops
+from vllm.model_executor.parallel_utils.tensor_parallel.layers import (
+    ColumnParallelLinear, RowParallelLinear)
+
+
+class AWQColumnParallelLinear(ColumnParallelLinear):
+
+    def create_weights(self, dtype: torch.dtype) -> None:
+        assert self.input_size % self.quant_config.weight_bits == 0
+        assert (self.output_size_per_partition %
+                self.quant_config.pack_factor == 0)
+        self.qweight = Parameter(
+            torch.empty(
+                self.input_size,
+                self.output_size_per_partition //
+                self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        self.qzeros = Parameter(
+            torch.empty(
+                self.input_size // self.quant_config.group_size,
+                self.output_size_per_partition //
+                self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        self.scales = Parameter(
+            torch.empty(
+                self.input_size // self.quant_config.group_size,
+                self.output_size_per_partition,
+                device="cuda",
+                dtype=dtype,
+            ),
+            requires_grad=False,
+        )
+
+    def apply_weights(
+        self,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        pack_factor = self.quant_config.pack_factor
+        out_shape = (x.shape[-2], self.qweight.shape[-1] * pack_factor)
+        reshaped_x = x.reshape(-1, x.shape[-1])
+        out = quantization_ops.awq_gemm(reshaped_x, self.qweight, self.scales,
+                                        self.qzeros, pack_factor)
+        if bias is not None:
+            out = out + bias
+        return out.reshape(out_shape)
+
+
+class AWQRowParallelLinear(RowParallelLinear):
+
+    def create_weights(self, dtype: torch.dtype) -> None:
+        assert (self.input_size_per_partition %
+                self.quant_config.weight_bits == 0)
+        assert self.output_size % self.quant_config.pack_factor == 0
+        self.qweight = Parameter(
+            torch.empty(
+                self.input_size_per_partition,
+                self.output_size // self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        self.qzeros = Parameter(
+            torch.empty(
+                self.input_size_per_partition // self.quant_config.group_size,
+                self.output_size // self.quant_config.pack_factor,
+                device="cuda",
+                dtype=torch.int32,
+            ),
+            requires_grad=False,
+        )
+        self.scales = Parameter(
+            torch.empty(
+                self.input_size_per_partition // self.quant_config.group_size,
+                self.output_size,
+                device="cuda",
+                dtype=dtype,
+            ),
+            requires_grad=False,
+        )
+
+    def apply_weights(self, x: torch.Tensor) -> torch.Tensor:
+        pack_factor = self.quant_config.pack_factor
+        out_shape = (x.shape[-2], self.qweight.shape[-1] * pack_factor)
+        reshaped_x = x.reshape(-1, x.shape[-1])
+        out = quantization_ops.awq_gemm(reshaped_x, self.qweight, self.scales,
+                                        self.qzeros, pack_factor)
+        return out.reshape(out_shape)

vllm/model_executor/layers/rotary_embedding.py

@@ -0,0 +1,169 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.33.2/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Rotary Positional Embeddings."""
+from typing import Tuple, Union
+
+import torch
+import torch.nn as nn
+
+from vllm import pos_encoding_ops
+
+
+class RotaryEmbedding(nn.Module):
+    """Original rotary positional embedding."""
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+    ) -> None:
+        super().__init__()
+        self.head_size = head_size
+        self.rotary_dim = rotary_dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        self.is_neox_style = is_neox_style
+
+        cache = self._compute_cos_sin_cache()
+        cache = cache.to(torch.get_default_dtype())
+        self.register_buffer("cos_sin_cache", cache, persistent=False)
+
+    def _compute_inv_freq(self, base: Union[int, float]) -> torch.Tensor:
+        """Compute the inverse frequency."""
+        # NOTE(woosuk): The HF implementation uses `torch.arange(...).float()`.
+        # However, we use `torch.arange(..., dtype=torch.float)` instead to
+        # avoid numerical issues with large base values (e.g., 10000000).
+        # This may cause a slight numerical difference between the HF
+        # implementation and ours.
+        # NOTE(woosuk): To exactly match the HF implementation, we need to
+        # use CPU to compute the cache and then move it to GPU. However, we
+        # create the cache on GPU for faster initialization. This may cause
+        # a slight numerical difference between the HF implementation and ours.
+        inv_freq = 1.0 / (base**(torch.arange(
+            0, self.rotary_dim, 2, dtype=torch.float, device="cuda") /
+                                 self.rotary_dim))
+        return inv_freq
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        """Compute the cos and sin cache."""
+        inv_freq = self._compute_inv_freq(self.base)
+        t = torch.arange(self.max_position_embeddings,
+                         dtype=torch.float,
+                         device="cuda")
+
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos()
+        sin = freqs.sin()
+        cache = torch.cat((cos, sin), dim=-1)
+        return cache
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        query: torch.Tensor,
+        key: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # pos_encoding_ops.rotary_embedding() is an in-place operation that
+        # updates the query and key tensors.
+        pos_encoding_ops.rotary_embedding(positions, query, key,
+                                          self.head_size, self.cos_sin_cache,
+                                          self.is_neox_style)
+        return query, key
+
+
+class LinearScalingRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with linear scaling.
+
+    Credits to the Reddit user /u/kaiokendev
+    """
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        scaling_factor: float,
+    ) -> None:
+        self.scaling_factor = scaling_factor
+        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
+                         is_neox_style)
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        inv_freq = self._compute_inv_freq(self.base)
+        # NOTE(woosuk): self.max_position_embeddings is the original
+        # maximum length before applying the rope scaling.
+        # Thus, the maximum length after applying the rope scaling is
+        # self.max_position_embeddings * self.scaling_factor.
+        max_len = self.max_position_embeddings * self.scaling_factor
+        t = torch.arange(max_len, dtype=torch.float, device="cuda")
+        t = t / self.scaling_factor
+
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos()
+        sin = freqs.sin()
+        cache = torch.cat((cos, sin), dim=-1)
+        return cache
+
+
+class DynamicNTKScalingRotaryEmbedding(RotaryEmbedding):
+    """RotaryEmbedding extended with Dynamic NTK scaling.
+
+    Credits to the Reddit users /u/bloc97 and /u/emozilla
+    """
+
+    def __init__(
+        self,
+        head_size: int,
+        rotary_dim: int,
+        max_position_embeddings: int,
+        base: int,
+        is_neox_style: bool,
+        scaling_factor: float,
+    ) -> None:
+        self.scaling_factor = scaling_factor
+        super().__init__(head_size, rotary_dim, max_position_embeddings, base,
+                         is_neox_style)
+
+    def _compute_cos_sin_cache(self) -> torch.Tensor:
+        # NOTE(woosuk): self.max_position_embeddings is the original
+        # maximum length before applying the rope scaling.
+        # Thus, the maximum length after applying the rope scaling is
+        # self.max_position_embeddings * self.scaling_factor.
+        max_len = self.max_position_embeddings * self.scaling_factor
+        base = self.base * (
+            (self.scaling_factor * max_len / self.max_position_embeddings) -
+            (self.scaling_factor - 1))**(self.rotary_dim /
+                                         (self.rotary_dim - 2))
+        inv_freq = self._compute_inv_freq(base)
+        t = torch.arange(max_len, dtype=torch.float, device="cuda")
+
+        freqs = torch.einsum("i,j -> ij", t, inv_freq)
+        cos = freqs.cos()
+        sin = freqs.sin()
+        cache = torch.cat((cos, sin), dim=-1)
+        return cache

vllm/model_executor/layers/sampler.py

@@ -1,15 +1,14 @@
 """A layer that samples the next tokens from the model's outputs."""
-from typing import Dict, List, Tuple, Optional
+from typing import Dict, List, Optional, Tuple
 
-import numpy as np
 import torch
 import torch.nn as nn
 
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.parallel_utils.tensor_parallel import (
     gather_from_tensor_model_parallel_region)
-from vllm.sampling_params import SamplingParams
-from vllm.sequence import SequenceOutputs
+from vllm.sampling_params import SamplingParams, SamplingType
+from vllm.sequence import SamplerOutput, SequenceData, SequenceOutputs
 
 _SAMPLING_EPS = 1e-5
 
@@ -38,15 +37,14 @@ class Sampler(nn.Module):
         embedding: torch.Tensor,
         hidden_states: torch.Tensor,
         input_metadata: InputMetadata,
-    ) -> Dict[int, SequenceOutputs]:
+        embedding_bias: Optional[torch.Tensor] = None,
+    ) -> SamplerOutput:
         # Get the hidden states that we use for sampling.
         hidden_states = _prune_hidden_states(hidden_states, input_metadata)
 
         # Get the logits for the next tokens.
-        logits = torch.matmul(hidden_states, embedding.t())
-        logits = gather_from_tensor_model_parallel_region(logits)
-        # Remove paddings in vocab (if any).
-        logits = logits[:, :self.vocab_size]
+        logits = _get_logits(hidden_states, embedding, embedding_bias,
+                             self.vocab_size)
 
         # Apply presence and frequency penalties.
         output_tokens = _get_output_tokens(input_metadata)
@@ -56,7 +54,7 @@ class Sampler(nn.Module):
         assert len(presence_penalties) == logits.shape[0]
         assert len(frequency_penalties) == logits.shape[0]
         logits = _apply_penalties(logits, output_tokens, presence_penalties,
-                                  frequency_penalties, self.vocab_size)
+                                  frequency_penalties)
 
         # Apply temperature scaling.
         temperatures = _get_temperatures(input_metadata)
@@ -68,36 +66,66 @@ class Sampler(nn.Module):
             # Use in-place division to avoid creating a new tensor.
             logits.div_(t.unsqueeze(dim=1))
 
-        # We use float32 for probabilities and log probabilities.
-        # Compute the probabilities.
-        probs = torch.softmax(logits, dim=-1, dtype=torch.float)
-        # Compute the log probabilities (before applying top-p and top-k).
-        logprobs = torch.log(probs)
-
         # Apply top-p and top-k truncation.
         top_ps, top_ks = _get_top_p_top_k(input_metadata, self.vocab_size)
-        assert len(top_ps) == len(top_ks) == probs.shape[0]
+        assert len(top_ps) == len(top_ks) == logits.shape[0]
         do_top_p = any(p < 1.0 - _SAMPLING_EPS for p in top_ps)
         do_top_k = any(k != self.vocab_size for k in top_ks)
         if do_top_p or do_top_k:
-            probs = _apply_top_p_top_k(probs, top_ps, top_ks)
+            logits = _apply_top_p_top_k(logits, top_ps, top_ks)
+
+        # We use float32 for probabilities and log probabilities.
+        # Compute the probabilities.
+        probs = torch.softmax(logits, dim=-1, dtype=torch.float)
+        # Compute the log probabilities.
+        # Use log_softmax to ensure numerical stability.
+        logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float)
 
         # Sample the next tokens.
         return _sample(probs, logprobs, input_metadata)
 
 
+def _get_logits(hidden_states: torch.Tensor, embedding: torch.Tensor,
+                embedding_bias: Optional[torch.Tensor],
+                vocab_size: int) -> torch.Tensor:
+    # Get the logits for the next tokens.
+    logits = torch.matmul(hidden_states, embedding.t())
+    if embedding_bias is not None:
+        logits += embedding_bias
+    logits = gather_from_tensor_model_parallel_region(logits)
+    # Remove paddings in vocab (if any).
+    logits = logits[:, :vocab_size]
+    return logits
+
+
 def _prune_hidden_states(
     hidden_states: torch.Tensor,
     input_metadata: InputMetadata,
 ) -> torch.Tensor:
+    last_token_indices = {t: [] for t in SamplingType}
     start_idx = 0
-    last_token_indicies: List[int] = []
-    for prompt_len in input_metadata.prompt_lens:
-        last_token_indicies.append(start_idx + prompt_len - 1)
-        start_idx += prompt_len
-    last_token_indicies.extend(
-        range(start_idx, start_idx + input_metadata.num_generation_tokens))
-    return hidden_states[last_token_indicies]
+    for i, seq_group in enumerate(input_metadata.seq_groups):
+        seq_ids, sampling_params = seq_group
+        sampling_type = sampling_params.sampling_type
+        if i < input_metadata.num_prompts:
+            assert len(seq_ids) == 1, "Prompt input should have only one seq."
+            prompt_len = input_metadata.prompt_lens[i]
+            last_token_indices[sampling_type].append(start_idx + prompt_len -
+                                                     1)
+            start_idx += prompt_len
+        else:
+            num_seqs = len(seq_ids)
+            last_token_indices[sampling_type].extend(
+                range(start_idx, start_idx + num_seqs))
+            start_idx += num_seqs
+
+    all_last_token_indices = []
+    for sampling_type in SamplingType:
+        all_last_token_indices.extend(last_token_indices[sampling_type])
+    all_last_token_indices = torch.tensor(all_last_token_indices,
+                                          dtype=torch.long,
+                                          device=hidden_states.device)
+    return hidden_states.index_select(0, all_last_token_indices)
 
 
 def _get_penalties(
@@ -105,37 +133,22 @@ def _get_penalties(
     # Collect the presence and frequency penalties.
     presence_penalties: List[float] = []
     frequency_penalties: List[float] = []
-    for i, seq_group in enumerate(input_metadata.seq_groups):
+    for seq_group in input_metadata.seq_groups:
         seq_ids, sampling_params = seq_group
         p = sampling_params.presence_penalty
         f = sampling_params.frequency_penalty
-        if i < input_metadata.num_prompts:
-            # A prompt input.
-            presence_penalties.append(p)
-            frequency_penalties.append(f)
-        else:
-            # A generation token.
-            presence_penalties += [p] * len(seq_ids)
-            frequency_penalties += [f] * len(seq_ids)
+        presence_penalties += [p] * len(seq_ids)
+        frequency_penalties += [f] * len(seq_ids)
     return presence_penalties, frequency_penalties
 
 
 def _get_output_tokens(input_metadata: InputMetadata) -> List[List[int]]:
     output_tokens: List[List[int]] = []
-    for i, seq_group in enumerate(input_metadata.seq_groups):
+    for seq_group in input_metadata.seq_groups:
         seq_ids, _ = seq_group
-        if i < input_metadata.num_prompts:
-            # A prompt input.
-            # NOTE: While the prompt input usually has no output tokens,
-            # it may have output tokens in the case of recomputation.
-            seq_id = seq_ids[0]
+        for seq_id in seq_ids:
             seq_data = input_metadata.seq_data[seq_id]
             output_tokens.append(seq_data.output_token_ids)
-        else:
-            # A generation token.
-            for seq_id in seq_ids:
-                seq_data = input_metadata.seq_data[seq_id]
-                output_tokens.append(seq_data.output_token_ids)
     return output_tokens
 
 
@@ -144,52 +157,56 @@ def _apply_penalties(
     output_tokens: List[List[int]],
     presence_penalties: List[float],
     frequency_penalties: List[float],
-    vocab_size: int,
 ) -> torch.Tensor:
-    num_seqs = logits.shape[0]
-    # Collect the indices of sequences that have non-zero penalties.
-    indices = []
+    num_seqs, vocab_size = logits.shape
     for i in range(num_seqs):
         if not output_tokens[i]:
             continue
         p = presence_penalties[i]
         f = frequency_penalties[i]
-        if p < _SAMPLING_EPS and f < _SAMPLING_EPS:
+        if abs(p) < _SAMPLING_EPS and abs(f) < _SAMPLING_EPS:
             continue
-        indices.append(i)
-
-    # Return early if all sequences have zero penalties.
-    if not indices:
+        break
+    else:
+        # Return early if all sequences have zero penalties.
         return logits
 
-    bin_counts = []
-    for i in indices:
-        bin_counts.append(np.bincount(output_tokens[i], minlength=vocab_size))
-    bin_counts = np.stack(bin_counts, axis=0)
-    bin_counts = torch.from_numpy(bin_counts).to(dtype=logits.dtype,
-                                                 device=logits.device)
+    max_output_len = max(len(tokens) for tokens in output_tokens)
+    padded_output_tokens = [
+        tokens + [vocab_size] * (max_output_len - len(tokens))
+        for tokens in output_tokens
+    ]
+    output_tokens_tensor = torch.tensor(padded_output_tokens,
+                                        dtype=torch.long,
+                                        device=logits.device)
+
+    # Compute the bin counts for the output tokens.
+    # vocab_size + 1 for padding.
+    bin_counts = torch.zeros((num_seqs, vocab_size + 1),
+                             dtype=torch.long,
+                             device=logits.device)
+    bin_counts.scatter_add_(1, output_tokens_tensor,
+                            torch.ones_like(output_tokens_tensor))
+    bin_counts = bin_counts[:, :vocab_size]  # Remove the padding bin.
 
-    frequency_penalties = [frequency_penalties[i] for i in indices]
     frequency_penalties = torch.tensor(frequency_penalties,
                                        dtype=logits.dtype,
                                        device=logits.device)
-    presence_penalties = [presence_penalties[i] for i in indices]
     presence_penalties = torch.tensor(presence_penalties,
                                       dtype=logits.dtype,
                                       device=logits.device)
 
     # We follow the definition in OpenAI API.
     # Refer to https://platform.openai.com/docs/api-reference/parameter-details
-    logits[indices] -= frequency_penalties.unsqueeze(dim=1) * bin_counts
-    presence_mask = (bin_counts > 0.0).to(dtype=logits.dtype)
-    logits[indices] -= presence_penalties.unsqueeze(dim=1) * presence_mask
+    logits -= frequency_penalties.unsqueeze(dim=1) * bin_counts
+    logits -= presence_penalties.unsqueeze(dim=1) * (bin_counts > 0)
     return logits
 
 
 def _get_temperatures(input_metadata: InputMetadata) -> List[float]:
     # Collect the temperatures for the logits.
     temperatures: List[float] = []
-    for i, seq_group in enumerate(input_metadata.seq_groups):
+    for seq_group in input_metadata.seq_groups:
         seq_ids, sampling_params = seq_group
         temperature = sampling_params.temperature
         if temperature < _SAMPLING_EPS:
@@ -197,13 +214,7 @@ def _get_temperatures(input_metadata: InputMetadata) -> List[float]:
             # (i.e., greedy sampling or beam search).
             # Set the temperature to 1 to avoid division by zero.
             temperature = 1.0
-
-        if i < input_metadata.num_prompts:
-            # A prompt input.
-            temperatures.append(temperature)
-        else:
-            # A generation token.
-            temperatures += [temperature] * len(seq_ids)
+        temperatures += [temperature] * len(seq_ids)
     return temperatures
 
 
@@ -213,221 +224,279 @@ def _get_top_p_top_k(
 ) -> Tuple[List[float], List[int]]:
     top_ps: List[float] = []
     top_ks: List[int] = []
-    for i, seq_group in enumerate(input_metadata.seq_groups):
+    for seq_group in input_metadata.seq_groups:
         seq_ids, sampling_params = seq_group
         top_p = sampling_params.top_p
         # k should not be greater than the vocab size.
         top_k = min(sampling_params.top_k, vocab_size)
         # k=-1 means no truncation.
         top_k = vocab_size if top_k == -1 else top_k
-        if i < input_metadata.num_prompts:
-            # A prompt input.
-            top_ps.append(top_p)
-            top_ks.append(top_k)
-        else:
-            # A generation token.
-            top_ps += [top_p] * len(seq_ids)
-            top_ks += [top_k] * len(seq_ids)
+        top_ps += [top_p] * len(seq_ids)
+        top_ks += [top_k] * len(seq_ids)
     return top_ps, top_ks
 
 
 def _apply_top_p_top_k(
-    probs: torch.Tensor,
+    logits: torch.Tensor,
     top_ps: List[float],
     top_ks: List[int],
 ) -> torch.Tensor:
-    p = torch.tensor(top_ps, dtype=probs.dtype, device=probs.device)
-    k = torch.tensor(top_ks, dtype=torch.int, device=probs.device)
-    probs_sort, probs_idx = probs.sort(dim=-1, descending=True)
+    p = torch.tensor(top_ps, dtype=logits.dtype, device=logits.device)
+    k = torch.tensor(top_ks, dtype=torch.int, device=logits.device)
+    logits_sort, logits_idx = logits.sort(dim=-1, descending=True)
 
     # Apply top-p.
-    probs_sum = torch.cumsum(probs_sort, dim=-1)
+    probs_sort = logits_sort.softmax(dim=-1)
+    probs_sum = probs_sort.cumsum(dim=-1)
     top_p_mask = (probs_sum - probs_sort) > p.unsqueeze(dim=1)
-    probs_sort[top_p_mask] = 0.0
+    logits_sort[top_p_mask] = -float("inf")
 
     # Apply top-k.
     # Create a mask for the top-k elements.
-    top_k_mask = torch.arange(probs_idx.shape[-1], device=probs_idx.device)
-    top_k_mask = top_k_mask.expand(probs_idx.shape[0], -1)
+    top_k_mask = torch.arange(logits_idx.shape[-1], device=logits_idx.device)
+    top_k_mask = top_k_mask.expand(logits_idx.shape[0], -1)
     top_k_mask = top_k_mask >= k.unsqueeze(dim=1)
-    probs_sort[top_k_mask] = 0.0
+    logits_sort[top_k_mask] = -float("inf")
 
     # Re-sort the probabilities.
-    probs = torch.gather(probs_sort,
-                         dim=-1,
-                         index=torch.argsort(probs_idx, dim=-1))
-    return probs
+    logits = torch.gather(logits_sort,
+                          dim=-1,
+                          index=torch.argsort(logits_idx, dim=-1))
+    return logits
 
 
 def _get_topk_logprobs(
     logprobs: torch.Tensor,
     num_logprobs: Optional[int],
-) -> Dict[int, float]:
+) -> List[Dict[int, float]]:
+    num_seqs = logprobs.size(0)
     if num_logprobs is None or num_logprobs == 0:
-        return {}
+        return [{} for _ in range(num_seqs)]
 
-    topk_logprobs, topk_ids = torch.topk(logprobs, num_logprobs)
-    if num_logprobs == 1:
-        topk_logprobs = [topk_logprobs.item()]
-        topk_ids = [topk_ids.item()]
-    else:
-        topk_logprobs = topk_logprobs.tolist()
-        topk_ids = topk_ids.tolist()
-
-    token_to_logprob: Dict[int, float] = {}
-    for token_id, logprob in zip(topk_ids, topk_logprobs):
-        token_to_logprob[token_id] = logprob
-    return token_to_logprob
+    all_topk_logprobs, all_topk_ids = torch.topk(logprobs,
+                                                 num_logprobs,
+                                                 dim=-1)
+    all_topk_logprobs = all_topk_logprobs.cpu()
+    all_topk_ids = all_topk_ids.cpu()
+    all_token_to_logprob = []
+    for topk_logprobs, topk_ids in zip(all_topk_logprobs, all_topk_ids):
+        token_to_logprob: Dict[int, float] = {}
+        for token_id, logprob in zip(topk_ids, topk_logprobs):
+            token_to_logprob[token_id.item()] = logprob.item()
+        all_token_to_logprob.append(token_to_logprob)
+    return all_token_to_logprob
 
 
-def _sample_from_prompt(
-    prob: torch.Tensor,
-    sampling_params: SamplingParams,
-) -> List[int]:
-    if sampling_params.use_beam_search:
-        # Beam search.
-        beam_width = sampling_params.best_of
-        _, next_token_ids = torch.topk(prob, beam_width)
-        next_token_ids = next_token_ids.tolist()
-    elif sampling_params.temperature < _SAMPLING_EPS:
-        # Greedy sampling.
-        assert sampling_params.best_of == 1
-        next_token_id = torch.argmax(prob)
-        next_token_ids = [next_token_id.item()]
-    else:
-        # Random sampling.
-        # Sample `best_of` tokens for the prompt.
-        num_seqs = sampling_params.best_of
-        next_token_ids = torch.multinomial(prob,
-                                           num_samples=num_seqs,
-                                           replacement=True)
-        next_token_ids = next_token_ids.tolist()
-    return next_token_ids
+def _build_sequence_outputs(
+    parent_ids: List[int],
+    next_token_ids: List[int],
+    selected_token_logprobs: torch.Tensor,
+    parent_seq_ids: List[int],
+    parent_logprobs: torch.Tensor,
+    num_output_logprobs: Optional[int],
+) -> List[SequenceOutputs]:
+    # Get top-k log probabilities for the next tokens.
+    next_logprobs = _get_topk_logprobs(parent_logprobs, num_output_logprobs)
+    seq_outputs: List[SequenceOutputs] = []
+    for parent_id, next_token_id, token_logprob in zip(
+            parent_ids, next_token_ids, selected_token_logprobs):
+        output_logprobs = next_logprobs[parent_id].copy()
+        output_logprobs[next_token_id] = token_logprob
+        seq_outputs.append(
+            SequenceOutputs(parent_seq_ids[parent_id], next_token_id,
+                            output_logprobs))
+    return seq_outputs
 
 
-def _sample_from_generation_tokens(
-    seq_ids: List[int],
-    probs: torch.Tensor,
+def _greedy_sample(
+    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
     logprobs: torch.Tensor,
-    seq_logprobs: List[float],
-    sampling_params: SamplingParams,
-) -> Tuple[List[int], List[int]]:
-    # NOTE(woosuk): sampling_params.best_of can be greater than
-    # len(seq_ids) because some sequences in the group might have
-    # been already terminated.
-    if sampling_params.use_beam_search:
-        # Beam search.
-        # Add cumulative logprobs for the sequences in the group.
-        seq_logprobs = torch.tensor(seq_logprobs,
-                                    dtype=torch.float,
-                                    device=logprobs.device)
-        logprobs = logprobs + seq_logprobs.unsqueeze(dim=1)
+) -> List[Tuple[List[int], List[int]]]:
+    samples = torch.argmax(logprobs, dim=-1).cpu()
+    sample_idx = 0
+    results = []
+    for seq_group in selected_seq_groups:
+        seq_ids, _ = seq_group
+        num_parent_seqs = len(seq_ids)
+        assert num_parent_seqs == 1, (
+            "Greedy sampling should have only one seq.")
+        parent_ids = list(range(num_parent_seqs))
+        next_token_ids = [samples[sample_idx].item()]
+        results.append((next_token_ids, parent_ids))
+        sample_idx += num_parent_seqs
+    assert sample_idx == logprobs.size(0)
+    return results
 
-        vocab_size = logprobs.size(-1)
-        beam_width = len(seq_ids)
-        _, topk_ids = torch.topk(logprobs.flatten(), beam_width)
-        topk_ids = topk_ids.tolist()
-        seq_idx = [i // vocab_size for i in topk_ids]
-        beam_seq_ids = [seq_ids[i] for i in seq_idx]
-        token_ids = [i % vocab_size for i in topk_ids]
 
-        beam_outputs: Dict[int, Tuple[int, int]] = {}
-        outstanding_beams: List[Tuple[int, int]] = []
-        # If a beam survives, continue with it.
-        for seq_id, token_id in zip(beam_seq_ids, token_ids):
-            if seq_id not in beam_outputs:
-                beam_outputs[seq_id] = (seq_id, token_id)
-            else:
-                outstanding_beams.append((seq_id, token_id))
+def _random_sample(
+    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
+    is_prompts: List[bool],
+    probs: torch.Tensor,
+) -> List[Tuple[List[int], List[int]]]:
+    # Find the maximum best_of value of the prompt phase requests.
+    max_best_of = 1
+    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
+        if is_prompt:
+            seq_ids, sampling_params = seq_group
+            max_best_of = max(max_best_of, sampling_params.best_of)
+    random_samples = torch.multinomial(probs,
+                                       num_samples=max_best_of,
+                                       replacement=True).cpu()
+    sample_idx = 0
+    results = []
+    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
+        seq_ids, sampling_params = seq_group
+        num_parent_seqs = len(seq_ids)
+        if is_prompt:
+            # Prompt phase.
+            assert num_parent_seqs == 1, (
+                "Prompt input should have only one seq.")
+            parent_ids = [0] * sampling_params.best_of
+            next_token_ids = random_samples[
+                sample_idx, :sampling_params.best_of].tolist()
+        else:
+            # Generation phase.
+            parent_ids = list(range(num_parent_seqs))
+            next_token_ids = random_samples[sample_idx:sample_idx +
+                                            num_parent_seqs, 0].tolist()
+        results.append((next_token_ids, parent_ids))
+        sample_idx += num_parent_seqs
+    assert sample_idx == probs.size(0)
+    return results
 
-        # If a beam is discarded, fork another beam.
-        for seq_id in seq_ids:
-            if seq_id not in beam_outputs:
-                beam_outputs[seq_id] = outstanding_beams.pop()
-        assert not outstanding_beams
 
-        parent_seq_ids = [beam_outputs[seq_id][0] for seq_id in seq_ids]
-        next_token_ids = [beam_outputs[seq_id][1] for seq_id in seq_ids]
-    elif sampling_params.temperature < _SAMPLING_EPS:
-        # Greedy sampling.
-        assert len(seq_ids) == 1
-        next_token_id = torch.argmax(probs, dim=-1)
-        next_token_ids = [int(next_token_id.item())]
-        parent_seq_ids = seq_ids
-    else:
-        # Random sampling.
-        # Sample 1 token for each sequence in the group.
-        next_token_ids = torch.multinomial(probs,
-                                           num_samples=1,
-                                           replacement=True)
-        next_token_ids = next_token_ids.squeeze(dim=-1).tolist()
-        parent_seq_ids = seq_ids
-    return parent_seq_ids, next_token_ids
+def _beam_search_sample(
+    selected_seq_groups: List[Tuple[List[int], SamplingParams]],
+    is_prompts: List[bool],
+    seq_data: Dict[int, SequenceData],
+    logprobs: torch.Tensor,
+) -> List[Tuple[List[int], List[int]]]:
+    # We sample 2 * beam_width candidates to make sure that with high
+    # probability we can get `beam_width` candidates in addition to
+    # the finished sequences for the next iteration. See
+    # https://github.com/tensorflow/tensor2tensor/blob/bafdc1b67730430d38d6ab802cbd51f9d053ba2e/tensor2tensor/utils/beam_search.py#L557-L563
+    # for details. See also HF reference:
+    # https://github.com/huggingface/transformers/blob/a4dd53d88e4852f023332d284ff07a01afcd5681/src/transformers/generation/utils.py#L3063-L3065
+    #
+    # Note: Beam search is not vectorized, so its speed can be slower than
+    # other sampling methods.
+    sample_idx = 0
+    results = []
+    for seq_group, is_prompt in zip(selected_seq_groups, is_prompts):
+        seq_ids, sampling_params = seq_group
+        num_parent_seqs = len(seq_ids)
+        beam_width = sampling_params.best_of
+        seq_group_logprobs = logprobs[sample_idx:sample_idx + num_parent_seqs]
+        if is_prompt:
+            # Prompt phase.
+            assert num_parent_seqs == 1, (
+                "Prompt input should have only one seq.")
+            parent_ids = [0] * (2 * beam_width)
+            _, next_token_ids = torch.topk(seq_group_logprobs[0],
+                                           2 * beam_width)
+            next_token_ids = next_token_ids.tolist()
+        else:
+            # Generation phase.
+            cumulative_logprobs = [
+                seq_data[seq_id].cumulative_logprob for seq_id in seq_ids
+            ]
+            cumulative_logprobs = torch.tensor(
+                cumulative_logprobs,
+                dtype=torch.float,
+                device=seq_group_logprobs.device)
+            seq_group_logprobs = (seq_group_logprobs +
+                                  cumulative_logprobs.unsqueeze(dim=1))
+            _, topk_ids = torch.topk(seq_group_logprobs.flatten(),
+                                     2 * beam_width)
+            topk_ids = topk_ids.tolist()
+            vocab_size = seq_group_logprobs.size(-1)
+            parent_ids = [i // vocab_size for i in topk_ids]
+            next_token_ids = [i % vocab_size for i in topk_ids]
+        results.append((next_token_ids, parent_ids))
+        sample_idx += num_parent_seqs
+    assert sample_idx == logprobs.size(0)
+    return results
 
 
 def _sample(
     probs: torch.Tensor,
     logprobs: torch.Tensor,
     input_metadata: InputMetadata,
-) -> Dict[int, SequenceOutputs]:
-    seq_outputs: Dict[int, SequenceOutputs] = {}
-
-    # TODO(woosuk): Optimize.
-    idx = 0
+) -> SamplerOutput:
+    categorized_seq_group_ids = {t: [] for t in SamplingType}
+    category_num_tokens = {t: 0 for t in SamplingType}
     for i, seq_group in enumerate(input_metadata.seq_groups):
         seq_ids, sampling_params = seq_group
-        if i < input_metadata.num_prompts:
-            # Generate the next tokens for a prompt input.
-            assert len(seq_ids) == sampling_params.best_of
-            prob = probs[idx]
-            logprob = logprobs[idx]
-            idx += 1
+        sampling_type = sampling_params.sampling_type
+        categorized_seq_group_ids[sampling_type].append(i)
+        num_seqs = len(seq_ids)
+        category_num_tokens[sampling_type] += num_seqs
 
-            # Sample the next tokens.
-            next_token_ids = _sample_from_prompt(prob, sampling_params)
-            # Get top-k log probabilities for the next tokens.
-            next_logprobs = _get_topk_logprobs(logprob,
-                                               sampling_params.logprobs)
-
-            # Build the output.
-            for seq_id, next_token_id in zip(seq_ids, next_token_ids):
-                output_logprobs = next_logprobs.copy()
-                output_logprobs[next_token_id] = logprob[next_token_id].item()
-                seq_outputs[seq_id] = SequenceOutputs(seq_id, seq_id,
-                                                      next_token_id,
-                                                      output_logprobs)
+    seq_outputs_dict: Dict[int, List[SequenceOutputs]] = {}
+    category_start_idx = 0
+    for sampling_type in SamplingType:
+        seq_group_ids = categorized_seq_group_ids[sampling_type]
+        seq_groups = [input_metadata.seq_groups[i] for i in seq_group_ids]
+        is_prompts = [i < input_metadata.num_prompts for i in seq_group_ids]
+        num_tokens = category_num_tokens[sampling_type]
+        if num_tokens == 0:
+            continue
+        category_logprobs = logprobs[category_start_idx:category_start_idx +
+                                     num_tokens]
+        category_probs = probs[category_start_idx:category_start_idx +
+                               num_tokens]
+        if sampling_type == SamplingType.GREEDY:
+            sample_results = _greedy_sample(seq_groups, category_logprobs)
+        elif sampling_type == SamplingType.RANDOM:
+            sample_results = _random_sample(seq_groups, is_prompts,
+                                            category_probs)
+        elif sampling_type == SamplingType.BEAM:
+            sample_results = _beam_search_sample(seq_groups, is_prompts,
+                                                 input_metadata.seq_data,
+                                                 category_logprobs)
         else:
-            # Generate the next tokens for generation tokens.
-            prob = probs[idx:idx + len(seq_ids)]
-            logprob = logprobs[idx:idx + len(seq_ids)]
-            idx += len(seq_ids)
+            raise ValueError(f"Unsupported sampling type: {sampling_type}")
 
-            # Sample the next tokens.
-            seq_logprobs = [
-                input_metadata.seq_data[seq_id].cumulative_logprob
-                for seq_id in seq_ids
-            ]
-            parent_seq_ids, next_token_ids = _sample_from_generation_tokens(
-                seq_ids, prob, logprob, seq_logprobs, sampling_params)
+        # Batched query for logprobs of selected token
+        batched_logprobs_query_seq_indices: List[int] = []
+        batched_logprobs_query_token_indices: List[int] = []
+        sample_idx = 0
+        for seq_group_id, seq_group, sample_result in zip(
+                seq_group_ids, seq_groups, sample_results):
+            seq_ids, sampling_params = seq_group
+            next_token_ids, parent_ids = sample_result
+            num_parent_seqs = len(seq_ids)
+            batched_logprobs_query_seq_indices.extend(
+                [sample_idx + parent_id for parent_id in parent_ids])
+            batched_logprobs_query_token_indices.extend(next_token_ids)
+            sample_idx += num_parent_seqs
+        assert sample_idx == num_tokens
+        batched_logprobs_query_result = category_logprobs[[
+            batched_logprobs_query_seq_indices,
+            batched_logprobs_query_token_indices
+        ]].tolist()
 
-            # Get top-k log probabilities for the next tokens.
-            next_logprobs: Dict[int, Dict[int, float]] = {}
-            for j, seq_id in enumerate(seq_ids):
-                next_logprobs[seq_id] = _get_topk_logprobs(
-                    logprob[j], sampling_params.logprobs)
+        # Build the sequence outputs.
+        sample_idx = 0
+        result_idx = 0
+        for seq_group_id, seq_group, sample_result in zip(
+                seq_group_ids, seq_groups, sample_results):
+            seq_ids, sampling_params = seq_group
+            next_token_ids, parent_ids = sample_result
+            num_results = len(next_token_ids)
+            num_parent_seqs = len(seq_ids)
+            parent_logprobs = category_logprobs[sample_idx:sample_idx +
+                                                num_parent_seqs]
+            selected_token_logprobs = batched_logprobs_query_result[
+                result_idx:result_idx + num_results]
+            seq_output = _build_sequence_outputs(parent_ids, next_token_ids,
+                                                 selected_token_logprobs,
+                                                 seq_ids, parent_logprobs,
+                                                 sampling_params.logprobs)
+            seq_outputs_dict[seq_group_id] = seq_output
+            sample_idx += num_parent_seqs
+            result_idx += num_results
+        assert sample_idx == num_tokens
+        category_start_idx += num_tokens
 
-            # Build the output.
-            for seq_id, parent_seq_id, next_token_id in zip(
-                    seq_ids, parent_seq_ids, next_token_ids):
-                j = seq_ids.index(parent_seq_id)
-                output_logprobs = next_logprobs[parent_seq_id].copy()
-                output_logprobs[next_token_id] = logprob[j,
-                                                         next_token_id].item()
-                seq_outputs[seq_id] = SequenceOutputs(
-                    seq_id,
-                    parent_seq_id,
-                    next_token_id,
-                    output_logprobs,
-                )
-
-    return seq_outputs
+    return [seq_outputs_dict[i] for i in range(len(input_metadata.seq_groups))]

vllm/model_executor/model_loader.py

@@ -1,4 +1,5 @@
 """Utilities for selecting and loading models."""
+import contextlib
 from typing import Type
 
 import torch
@@ -7,20 +8,44 @@ from transformers import PretrainedConfig
 
 from vllm.config import ModelConfig
 from vllm.model_executor.models import *  # pylint: disable=wildcard-import
-from vllm.model_executor.weight_utils import initialize_dummy_weights
+from vllm.model_executor.weight_utils import (get_quant_config,
+                                              initialize_dummy_weights)
 
 # TODO(woosuk): Lazy-load the model classes.
 _MODEL_REGISTRY = {
+    "AquilaModel": AquilaForCausalLM,
+    "BaiChuanForCausalLM": BaiChuanForCausalLM,  # baichuan-7b
+    "BaichuanForCausalLM": BaichuanForCausalLM,  # baichuan-13b
     "BloomForCausalLM": BloomForCausalLM,
+    "FalconForCausalLM": FalconForCausalLM,
     "GPT2LMHeadModel": GPT2LMHeadModel,
     "GPTBigCodeForCausalLM": GPTBigCodeForCausalLM,
+    "GPTJForCausalLM": GPTJForCausalLM,
     "GPTNeoXForCausalLM": GPTNeoXForCausalLM,
+    "InternLMForCausalLM": InternLMForCausalLM,
     "LlamaForCausalLM": LlamaForCausalLM,
     "LLaMAForCausalLM": LlamaForCausalLM,  # For decapoda-research/llama-*
+    "MistralForCausalLM": MistralForCausalLM,
     "MPTForCausalLM": MPTForCausalLM,
     "OPTForCausalLM": OPTForCausalLM,
+    "QWenLMHeadModel": QWenLMHeadModel,
+    "RWForCausalLM": FalconForCausalLM,
 }
 
+# FIXME(woosuk): Remove this once all models support quantization.
+_MODEL_CLASSES_SUPPORT_QUANTIZATION = [
+    LlamaForCausalLM,
+]
+
+
+@contextlib.contextmanager
+def _set_default_torch_dtype(dtype: torch.dtype):
+    """Sets the default torch dtype to the given dtype."""
+    old_dtype = torch.get_default_dtype()
+    torch.set_default_dtype(dtype)
+    yield
+    torch.set_default_dtype(old_dtype)
+
 
 def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
     architectures = getattr(config, "architectures", [])
@@ -34,19 +59,46 @@ def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
 
 def get_model(model_config: ModelConfig) -> nn.Module:
     model_class = _get_model_architecture(model_config.hf_config)
-    torch.set_default_dtype(model_config.dtype)
 
-    # Create a model instance.
-    # The weights will be initialized as empty tensors.
-    model = model_class(model_config.hf_config)
-    if model_config.use_dummy_weights:
-        model = model.cuda()
-        # NOTE(woosuk): For accurate performance evaluation, we assign
-        # random values to the weights.
-        initialize_dummy_weights(model)
-    else:
-        # Load the weights from the cached or downloaded files.
-        model.load_weights(model_config.model, model_config.download_dir,
-                           model_config.use_np_weights)
-        model = model.cuda()
+    # Get the quantization config.
+    quant_config = None
+    if model_config.quantization is not None:
+        if model_class not in _MODEL_CLASSES_SUPPORT_QUANTIZATION:
+            raise ValueError(
+                f"Quantization is not supported for {model_class}.")
+        quant_config = get_quant_config(model_config.quantization,
+                                        model_config.model,
+                                        model_config.download_dir)
+        capability = torch.cuda.get_device_capability()
+        capability = capability[0] * 10 + capability[1]
+        if capability < quant_config.get_min_capability():
+            raise ValueError(
+                f"The quantization method {model_config.quantization} is not "
+                "supported for the current GPU. "
+                f"Minimum capability: {quant_config.get_min_capability()}. "
+                f"Current capability: {capability}.")
+        supported_dtypes = quant_config.get_supported_act_dtypes()
+        if model_config.dtype not in supported_dtypes:
+            raise ValueError(
+                f"{model_config.dtype} is not supported for quantization "
+                f"method {model_config.quantization}. Supported dtypes: "
+                f"{supported_dtypes}")
+
+    with _set_default_torch_dtype(model_config.dtype):
+        # Create a model instance.
+        # The weights will be initialized as empty tensors.
+        if model_class in _MODEL_CLASSES_SUPPORT_QUANTIZATION:
+            model = model_class(model_config.hf_config, quant_config)
+        else:
+            model = model_class(model_config.hf_config)
+        if model_config.load_format == "dummy":
+            model = model.cuda()
+            # NOTE(woosuk): For accurate performance evaluation, we assign
+            # random values to the weights.
+            initialize_dummy_weights(model)
+        else:
+            # Load the weights from the cached or downloaded files.
+            model.load_weights(model_config.model, model_config.download_dir,
+                               model_config.load_format, model_config.revision)
+            model = model.cuda()
     return model.eval()

vllm/model_executor/models/__init__.py

@@ -1,17 +1,33 @@
+from vllm.model_executor.models.aquila import AquilaForCausalLM
+from vllm.model_executor.models.baichuan import (BaiChuanForCausalLM,
+                                                 BaichuanForCausalLM)
 from vllm.model_executor.models.bloom import BloomForCausalLM
+from vllm.model_executor.models.falcon import FalconForCausalLM
 from vllm.model_executor.models.gpt2 import GPT2LMHeadModel
 from vllm.model_executor.models.gpt_bigcode import GPTBigCodeForCausalLM
+from vllm.model_executor.models.gpt_j import GPTJForCausalLM
 from vllm.model_executor.models.gpt_neox import GPTNeoXForCausalLM
+from vllm.model_executor.models.internlm import InternLMForCausalLM
 from vllm.model_executor.models.llama import LlamaForCausalLM
 from vllm.model_executor.models.mpt import MPTForCausalLM
 from vllm.model_executor.models.opt import OPTForCausalLM
+from vllm.model_executor.models.qwen import QWenLMHeadModel
+from vllm.model_executor.models.mistral import MistralForCausalLM
 
 __all__ = [
+    "AquilaForCausalLM",
+    "BaiChuanForCausalLM",
+    "BaichuanForCausalLM",
     "BloomForCausalLM",
+    "FalconForCausalLM",
     "GPT2LMHeadModel",
     "GPTBigCodeForCausalLM",
+    "GPTJForCausalLM",
     "GPTNeoXForCausalLM",
+    "InternLMForCausalLM",
     "LlamaForCausalLM",
     "MPTForCausalLM",
     "OPTForCausalLM",
+    "QWenLMHeadModel",
+    "MistralForCausalLM",
 ]

vllm/model_executor/models/aquila.py

@@ -0,0 +1,369 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only LLaMA model compatible with HuggingFace weights.
+
+The input of the model is flattened to a 1D tensor of tokens. The model uses
+InputMetadata to extract the original 2D shape of the input.
+"""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (
+    hf_model_weights_iterator, load_padded_tensor_parallel_vocab,
+    load_tensor_parallel_weights)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
+from vllm.sequence import SamplerOutput
+from vllm.transformers_utils.configs.aquila import AquilaConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class AquilaMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+    ):
+        super().__init__()
+        self.gate_up_proj = ColumnParallelLinear(hidden_size,
+                                                 2 * intermediate_size,
+                                                 bias=False,
+                                                 gather_output=False,
+                                                 perform_initialization=False)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           input_is_parallel=True,
+                                           perform_initialization=False)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class AquilaRMSNorm(nn.Module):
+
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        AquilaRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1,
+                                                               keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance +
+                                                    self.variance_epsilon)
+
+        return (self.weight * hidden_states).to(input_dtype)
+
+
+class AquilaAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        max_position_embeddings: int = 8192,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        assert self.total_num_kv_heads % tp_size == 0
+        self.num_kv_heads = self.total_num_kv_heads // tp_size
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = ColumnParallelLinear(
+            hidden_size,
+            (self.total_num_heads + 2 * self.total_num_kv_heads) *
+            self.head_dim,
+            bias=False,
+            gather_output=False,
+            perform_initialization=False,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            perform_initialization=False,
+        )
+        self.attn = PagedAttentionWithRoPE(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            rotary_dim=self.head_dim,
+            base=self.rope_theta,
+            max_position=self.max_position_embeddings,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
+                                input_metadata, cache_event)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class AquilaDecoderLayer(nn.Module):
+
+    def __init__(self, config: AquilaConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = AquilaAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_attention_heads,
+            rope_theta=rope_theta,
+            max_position_embeddings=max_position_embeddings,
+        )
+        self.mlp = AquilaMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+        )
+        self.input_layernorm = AquilaRMSNorm(config.hidden_size,
+                                             eps=config.rms_norm_eps)
+        self.post_attention_layernorm = AquilaRMSNorm(config.hidden_size,
+                                                      eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+            cache_event=cache_event,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class AquilaModel(nn.Module):
+
+    def __init__(self, config: AquilaConfig):
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        #vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            perform_initialization=False)
+        self.layers = nn.ModuleList([
+            AquilaDecoderLayer(config) for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = AquilaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        for i in range(len(self.layers)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            layer = self.layers[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+            )
+        hidden_states = self.norm(hidden_states)
+
+        return hidden_states
+
+
+class AquilaForCausalLM(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.model = AquilaModel(config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.lm_head = ColumnParallelLinear(config.hidden_size,
+                                            vocab_size,
+                                            bias=False,
+                                            gather_output=False,
+                                            perform_initialization=False)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> SamplerOutput:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   input_metadata)
+        return next_tokens
+
+    _column_parallel_weights = [
+        "qkv_proj.weight", "gate_proj.weight", "up_proj.weight"
+    ]
+    _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        tp_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
+        q_proj_shard_size = (self.config.hidden_size // tp_size)
+        kv_proj_shard_size = (self.config.hidden_size //
+                              self.config.num_attention_heads *
+                              self.config.num_attention_heads // tp_size)
+        attention_weight_specs = [
+            # (weight_name, shard_size, offset)
+            ("q_proj", q_proj_shard_size, 0),
+            ("k_proj", kv_proj_shard_size, q_proj_shard_size),
+            ("v_proj", kv_proj_shard_size,
+             q_proj_shard_size + kv_proj_shard_size),
+        ]
+        state_dict = self.state_dict()
+
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            is_attention_weight = False
+            for weight_name, shard_size, offset in attention_weight_specs:
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "qkv_proj")]
+
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[offset:offset + shard_size]
+                assert param_slice.shape == loaded_weight.shape
+
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
+                break
+            if is_attention_weight:
+                continue
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
+                break
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
+
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)

vllm/model_executor/models/baichuan.py

@@ -0,0 +1,387 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only BaiChuan model compatible with HuggingFace weights.
+
+The input of the model is flattened to a 1D tensor of tokens. The model uses
+InputMetadata to extract the original 2D shape of the input.
+"""
+import math
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.attention import (PagedAttentionWithRoPE,
+                                                  PagedAttentionWithALiBi)
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor, hf_model_weights_iterator,
+    load_padded_tensor_parallel_vocab, load_tensor_parallel_weights)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
+from vllm.sequence import SamplerOutput
+from vllm.transformers_utils.configs.baichuan import BaiChuanConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
+    closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
+    base = torch.tensor(
+        2**(-(2**-(math.log2(closest_power_of_2) - 3))),
+        dtype=torch.float32,
+    )
+    powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
+    slopes = torch.pow(base, powers)
+
+    if closest_power_of_2 != total_num_heads:
+        extra_base = torch.tensor(
+            2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
+            dtype=torch.float32,
+        )
+        num_remaining_heads = min(closest_power_of_2,
+                                  total_num_heads - closest_power_of_2)
+        extra_powers = torch.arange(start=1,
+                                    end=1 + 2 * num_remaining_heads,
+                                    step=2,
+                                    dtype=torch.int32)
+        slopes = torch.cat(
+            [slopes, torch.pow(extra_base, extra_powers)], dim=0)
+    return slopes
+
+
+class BaiChuanMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+    ):
+        super().__init__()
+        self.gate_up_proj = ColumnParallelLinear(hidden_size,
+                                                 2 * intermediate_size,
+                                                 bias=False,
+                                                 gather_output=False,
+                                                 perform_initialization=False)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           input_is_parallel=True,
+                                           perform_initialization=False)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class BaiChuanAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        position_embedding: str,
+        rope_theta: float = 10000,
+        max_position_embeddings: int = 8192,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
+        )
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.postion_embedding = position_embedding
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        # pylint: disable=invalid-name
+        self.W_pack = ColumnParallelLinear(
+            hidden_size,
+            3 * hidden_size,
+            bias=False,
+            gather_output=False,
+            perform_initialization=False,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            perform_initialization=False,
+        )
+        # Create the alibi slopes and slice them.
+        if self.postion_embedding == "ALIBI":
+            tp_rank = get_tensor_model_parallel_rank()
+            head_start = tp_rank * self.num_heads
+            head_end = (tp_rank + 1) * self.num_heads
+            alibi_slopes = _get_alibi_slopes(self.total_num_heads)
+            alibi_slopes = alibi_slopes[head_start:head_end].tolist()
+
+            scaling = self.head_dim**-0.5
+            self.attn = PagedAttentionWithALiBi(self.num_heads, self.head_dim,
+                                                scaling, alibi_slopes)
+        else:
+            self.scaling = self.head_dim**-0.5
+            self.attn = PagedAttentionWithRoPE(
+                self.num_heads,
+                self.head_dim,
+                self.scaling,
+                rotary_dim=self.head_dim,
+                base=self.rope_theta,
+                max_position=self.max_position_embeddings)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        qkv, _ = self.W_pack(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        k_cache, v_cache = kv_cache
+        if self.postion_embedding == "ALIBI":
+            attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata,
+                                    cache_event)
+        else:
+            attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
+                                    input_metadata, cache_event)
+
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class BaiChuanDecoderLayer(nn.Module):
+
+    def __init__(self, config: BaiChuanConfig, position_embedding: str):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = BaiChuanAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            position_embedding=position_embedding,
+            rope_theta=rope_theta,
+            max_position_embeddings=max_position_embeddings,
+        )
+        self.mlp = BaiChuanMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+            cache_event=cache_event,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class BaiChuanModel(nn.Module):
+
+    def __init__(self, config: BaiChuanConfig, position_embedding: str):
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            perform_initialization=False)
+        self.layers = nn.ModuleList([
+            BaiChuanDecoderLayer(config, position_embedding)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        for i in range(len(self.layers)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            layer = self.layers[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+            )
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class BaiChuanBaseForCausalLM(nn.Module):
+
+    def __init__(self, config, position_embedding: str):
+        super().__init__()
+        self.config = config
+        self.model = BaiChuanModel(config, position_embedding)
+        self.lm_head = ColumnParallelLinear(config.hidden_size,
+                                            config.vocab_size,
+                                            bias=False,
+                                            gather_output=False,
+                                            perform_initialization=False)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> SamplerOutput:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   input_metadata)
+        return next_tokens
+
+    _column_parallel_weights = []
+    _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        tp_world_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        state_dict = self.state_dict()
+
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+
+            if "W_pack" in name:
+                total_num_heads = self.config.num_attention_heads
+                hidden_size = self.config.hidden_size
+                head_size = hidden_size // total_num_heads
+                num_heads = total_num_heads // tp_world_size
+                head_start = tp_rank * num_heads
+                head_end = (tp_rank + 1) * num_heads
+
+                loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                   head_size, hidden_size)
+                loaded_weight = loaded_weight[:, head_start:head_end, :, :]
+                loaded_weight = loaded_weight.reshape(-1, hidden_size)
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[shard_size * tp_rank:shard_size *
+                                              (tp_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
+                break
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tp_rank)
+                continue
+
+            load_tensor_parallel_weights(
+                param,
+                loaded_weight,
+                name,
+                self._column_parallel_weights,
+                self._row_parallel_weights,
+                tp_rank,
+            )
+
+
+class BaichuanForCausalLM(BaiChuanBaseForCausalLM):  # baichuan 13b
+
+    def __init__(self, config):
+        super().__init__(config, "ALIBI")
+
+
+class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):  # baichuan 7b
+
+    def __init__(self, config):
+        super().__init__(config, "ROPE")

vllm/model_executor/models/bloom.py

@@ -21,7 +21,7 @@ The input of the model is flattened to a 1D tensor of tokens. The model uses
 InputMetadata to extract the original 2D shape of the input.
 """
 import math
-from typing import Dict, List, Optional, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 from torch import nn
@@ -37,7 +37,7 @@ from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.model_executor.parallel_utils.tensor_parallel import (
     VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
-from vllm.sequence import SequenceOutputs
+from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -264,7 +264,7 @@ class BloomForCausalLM(nn.Module):
         kv_caches: List[KVCache],
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
-    ) -> Dict[int, SequenceOutputs]:
+    ) -> SamplerOutput:
         hidden_states = self.transformer(input_ids, positions, kv_caches,
                                          input_metadata, cache_events)
         next_tokens = self.sampler(self.lm_head_weight, hidden_states,
@@ -279,15 +279,23 @@ class BloomForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     use_np_cache: bool = False):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tp_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, use_np_cache):
-            if not name.startswith("transformer."):
-                name = "transformer." + name
+                model_name_or_path, cache_dir, load_format, revision):
+            if name == "lm_head.weight":
+                # Since hidden_states are parallelized, we need to
+                # load lm_head.weight in parallel.
+                self._column_parallel_weights.append(name)
+                # If lm_head is provided, use it instead.
+                param = self.lm_head_weight
+            else:
+                if not name.startswith("transformer."):
+                    name = "transformer." + name
+                param = state_dict[name]
 
-            param = state_dict[name]
             if "query_key_value" in name:
                 # NOTE(woosuk): BLOOM's fused QKV has the shape of
                 # [num_heads * 3 * head_size, hidden_size], while the

vllm/model_executor/models/falcon.py

@@ -0,0 +1,504 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/a5cc30d72ae2dc19af534e4b35c986cc28db1275/src/transformers/models/falcon/modeling_falcon.py
+# Copyright 2023 The vLLM team.
+# Copyright 2023 the Falcon authors and HuggingFace Inc. team.  All rights
+# reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""PyTorch Falcon model."""
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn import LayerNorm
+from transformers import FalconConfig as HF_FalconConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.attention import (PagedAttention,
+                                                  PagedAttentionWithALiBi,
+                                                  PagedAttentionWithRoPE)
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (convert_pyslice_to_tensor,
+                                              hf_model_weights_iterator,
+                                              load_tensor_parallel_weights)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear,
+    reduce_from_tensor_model_parallel_region)
+from vllm.sequence import SamplerOutput
+from vllm.transformers_utils.configs import RWConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+FalconConfig = Union[HF_FalconConfig, RWConfig]
+
+
+# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during
+# training, this means that there's one additional quantization to bfloat16
+# between the operations. In order not to degrade the quality of our HF-port,
+# we keep these characteristics in the final model.
+class FalconLinear(nn.Linear):
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        hidden_states = x @ self.weight.T
+        if self.bias is None:
+            return hidden_states
+        return hidden_states + self.bias
+
+
+def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
+    closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
+    base = torch.tensor(2**(-(2**-(math.log2(closest_power_of_2) - 3))),
+                        dtype=torch.float32)
+    powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
+    slopes = torch.pow(base, powers)
+
+    if closest_power_of_2 != total_num_heads:
+        extra_base = torch.tensor(
+            2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
+            dtype=torch.float32)
+        num_remaining_heads = min(closest_power_of_2,
+                                  total_num_heads - closest_power_of_2)
+        extra_powers = torch.arange(1,
+                                    1 + 2 * num_remaining_heads,
+                                    2,
+                                    dtype=torch.int32)
+        slopes = torch.cat(
+            [slopes, torch.pow(extra_base, extra_powers)], dim=0)
+
+    return slopes
+
+
+class FalconAttention(nn.Module):
+
+    def __init__(self, config: FalconConfig):
+        super().__init__()
+
+        self.hidden_size = config.hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+
+        self.total_num_heads = config.num_attention_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.head_dim = self.hidden_size // self.total_num_heads
+        assert self.head_dim * self.total_num_heads == self.hidden_size
+
+        self.new_decoder_architecture = config.new_decoder_architecture
+        self.multi_query = config.multi_query
+
+        if self.new_decoder_architecture:
+            self.total_num_kv_heads = config.num_kv_heads
+            assert self.total_num_heads % tp_size == 0
+            self.num_kv_heads = self.total_num_kv_heads // tp_size
+            self.query_key_value = ColumnParallelLinear(
+                self.hidden_size,
+                (self.total_num_heads + 2 * self.total_num_kv_heads) *
+                self.head_dim,
+                bias=config.bias,
+                gather_output=False,
+                perform_initialization=False,
+                skip_bias_add=True,
+            )
+        elif self.multi_query:
+            self.total_num_kv_heads = 1
+            self.num_kv_heads = 1
+            self.query = ColumnParallelLinear(
+                self.hidden_size,
+                self.total_num_heads * self.head_dim,
+                bias=config.bias,
+                gather_output=False,
+                perform_initialization=False,
+                skip_bias_add=True,
+            )
+            self.key_value = FalconLinear(self.hidden_size,
+                                          2 * self.head_dim,
+                                          bias=config.bias)
+        else:
+            self.total_num_kv_heads = self.total_num_heads
+            self.num_kv_heads = self.num_heads
+            self.query_key_value = ColumnParallelLinear(
+                self.hidden_size,
+                (self.total_num_heads + 2 * self.total_num_kv_heads) *
+                self.head_dim,
+                bias=config.bias,
+                gather_output=False,
+                perform_initialization=False,
+                skip_bias_add=True,
+            )
+
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+
+        # Layer-wise attention scaling
+        self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
+        self.reduce_row_parallel_results = not (config.new_decoder_architecture
+                                                or config.parallel_attn)
+        self.dense = RowParallelLinear(
+            self.hidden_size,
+            self.hidden_size,
+            bias=config.bias,
+            input_is_parallel=True,
+            perform_initialization=False,
+            skip_bias_add=True,
+            reduce_results=self.reduce_row_parallel_results)
+
+        self.use_rotary = config.rotary
+        self.use_alibi = config.alibi
+        assert not (self.use_rotary and self.use_alibi), (
+            "Rotary and alibi are mutually exclusive.")
+
+        if self.use_rotary:
+            rope_theta = getattr(config, "rope_theta", 10000)
+            max_position_embeddings = getattr(config,
+                                              "max_position_embeddings", 8192)
+            self.attn = PagedAttentionWithRoPE(
+                self.num_heads,
+                self.head_dim,
+                self.inv_norm_factor,
+                base=rope_theta,
+                max_position=max_position_embeddings,
+                rotary_dim=self.head_dim,
+                num_kv_heads=self.num_kv_heads)
+        elif self.use_alibi:
+            tp_rank = get_tensor_model_parallel_rank()
+            head_start = tp_rank * self.num_heads
+            head_end = (tp_rank + 1) * self.num_heads
+            alibi_slopes = (_get_alibi_slopes(self.total_num_heads) *
+                            self.inv_norm_factor)
+            alibi_slopes = alibi_slopes[head_start:head_end].tolist()
+            self.attn = PagedAttentionWithALiBi(self.num_heads,
+                                                self.head_dim,
+                                                self.inv_norm_factor,
+                                                alibi_slopes,
+                                                num_kv_heads=self.num_kv_heads)
+        else:
+            self.attn = PagedAttention(self.num_heads,
+                                       self.head_dim,
+                                       scale=self.inv_norm_factor,
+                                       num_kv_heads=self.num_kv_heads)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        if not self.new_decoder_architecture and self.multi_query:
+            q, bias = self.query(hidden_states)
+            if bias is not None:
+                q += bias
+            kv = self.key_value(hidden_states)
+            k, v = kv.split([self.kv_size, self.kv_size], dim=-1)
+        else:
+            qkv, bias = self.query_key_value(hidden_states)
+            if bias is not None:
+                qkv += bias
+            q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size],
+                                dim=-1)
+        k_cache, v_cache = kv_cache
+        if self.use_rotary:
+            attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
+                                    input_metadata, cache_event)
+        else:
+            attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata,
+                                    cache_event)
+        attn_output, bias = self.dense(attn_output)
+        return attn_output, bias
+
+
+class FalconMLP(nn.Module):
+
+    def __init__(self, config: FalconConfig):
+        super().__init__()
+        hidden_size = config.hidden_size
+
+        self.dense_h_to_4h = ColumnParallelLinear(hidden_size,
+                                                  4 * hidden_size,
+                                                  bias=config.bias,
+                                                  gather_output=False,
+                                                  perform_initialization=False,
+                                                  skip_bias_add=True)
+        self.act = nn.GELU()
+        self.reduce_row_parallel_results = not (config.new_decoder_architecture
+                                                or config.parallel_attn)
+        self.dense_4h_to_h = RowParallelLinear(
+            4 * hidden_size,
+            hidden_size,
+            bias=config.bias,
+            input_is_parallel=True,
+            perform_initialization=False,
+            skip_bias_add=True,
+            reduce_results=self.reduce_row_parallel_results)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # NOTE(zhuohan): Following huggingface, we do not fuse bias add here.
+        x, bias = self.dense_h_to_4h(x)
+        if bias is not None:
+            x += bias
+        x = self.act(x)
+        x, bias = self.dense_4h_to_h(x)
+        return x, bias
+
+
+class FalconDecoderLayer(nn.Module):
+
+    def __init__(self, config: FalconConfig):
+        super().__init__()
+        hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.self_attention = FalconAttention(config)
+        self.mlp = FalconMLP(config)
+        self.config = config
+
+        if config.new_decoder_architecture:
+            # The layer norm before self-attention
+            self.ln_attn = LayerNorm(hidden_size,
+                                     eps=config.layer_norm_epsilon)
+            # The layer norm before the MLP
+            self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
+        else:
+            self.input_layernorm = LayerNorm(hidden_size,
+                                             eps=config.layer_norm_epsilon)
+            if not config.parallel_attn:
+                self.post_attention_layernorm = LayerNorm(
+                    hidden_size, eps=config.layer_norm_epsilon)
+
+        self.reduce_row_parallel_results = not (config.new_decoder_architecture
+                                                or config.parallel_attn)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ):
+        residual = hidden_states
+
+        if self.config.new_decoder_architecture:
+            attention_layernorm_out = self.ln_attn(hidden_states)
+            mlp_layernorm_out = self.ln_mlp(hidden_states)
+        else:
+            attention_layernorm_out = self.input_layernorm(hidden_states)
+
+        # Self attention.
+        attention_output, attention_bias = self.self_attention(
+            positions=positions,
+            hidden_states=attention_layernorm_out,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+            cache_event=cache_event,
+        )
+        if self.reduce_row_parallel_results and attention_bias is not None:
+            attention_output += attention_bias
+
+        if not self.config.new_decoder_architecture:
+            if self.config.parallel_attn:
+                mlp_layernorm_out = attention_layernorm_out
+            else:
+                residual += attention_output
+                mlp_layernorm_out = self.post_attention_layernorm(residual)
+
+        # MLP.
+        mlp_output, mlp_bias = self.mlp(mlp_layernorm_out)
+        if self.reduce_row_parallel_results and mlp_bias is not None:
+            mlp_output += mlp_bias
+
+        if not self.reduce_row_parallel_results:
+            # When MLP and Attention layers are parallel, we can use
+            # only one all-reduce operator to reduce the results from
+            # both MLP and Attention layers.
+            mlp_output += attention_output
+            mlp_output = reduce_from_tensor_model_parallel_region(mlp_output)
+            if attention_bias is not None:
+                mlp_output += attention_bias
+            if mlp_bias is not None:
+                mlp_output += mlp_bias
+
+        output = mlp_output + residual
+
+        return output
+
+
+class FalconModel(nn.Module):
+
+    def __init__(self, config: FalconConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.use_alibi = config.alibi
+
+        # Embedding + LN Embedding
+        self.word_embeddings = VocabParallelEmbedding(
+            config.vocab_size, self.embed_dim, perform_initialization=False)
+
+        # Transformer blocks
+        self.h = nn.ModuleList([
+            FalconDecoderLayer(config) for _ in range(config.num_hidden_layers)
+        ])
+
+        # Final Layer Norm
+        self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.word_embeddings(input_ids)
+        for i in range(len(self.h)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            layer = self.h[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+            )
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class FalconForCausalLM(nn.Module):
+
+    def __init__(self, config: FalconConfig):
+        super().__init__()
+        self.config = config
+        self.transformer = FalconModel(config)
+        self.lm_head = ColumnParallelLinear(config.hidden_size,
+                                            config.vocab_size,
+                                            bias=False,
+                                            gather_output=False,
+                                            perform_initialization=False)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.LongTensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> SamplerOutput:
+        hidden_states = self.transformer(
+            input_ids,
+            positions,
+            kv_caches,
+            input_metadata,
+            cache_events,
+        )
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   input_metadata)
+
+        return next_tokens
+
+    _column_parallel_weights = [
+        "word_embeddings.weight", "lm_head.weight", "dense_h_to_4h.weight",
+        "dense_h_to_4h.bias"
+    ]
+    _row_parallel_weights = ["dense.weight", "dense_4h_to_h.weight"]
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        tp_size = (get_tensor_model_parallel_world_size())
+        tp_rank = get_tensor_model_parallel_rank()
+
+        hidden_size = self.config.hidden_size
+        total_num_heads = self.config.num_attention_heads
+        num_heads = total_num_heads // tp_size
+        head_size = hidden_size // total_num_heads
+        head_start = tp_rank * num_heads
+        head_end = (tp_rank + 1) * num_heads
+        if self.config.new_decoder_architecture:
+            total_num_kv_heads = self.config.num_kv_heads
+            num_kv_heads = total_num_kv_heads // tp_size
+            separated_q_kv = False
+            kv_head_start = tp_rank * num_kv_heads
+            kv_head_end = (tp_rank + 1) * num_kv_heads
+        elif self.config.multi_query:
+            total_num_kv_heads = 1
+            num_kv_heads = 1
+            separated_q_kv = True
+            kv_head_start = 0
+            kv_head_end = 1
+        else:
+            total_num_kv_heads = total_num_heads
+            num_kv_heads = total_num_kv_heads // tp_size
+            separated_q_kv = False
+            kv_head_start = tp_rank * num_kv_heads
+            kv_head_end = (tp_rank + 1) * num_kv_heads
+        num_query_heads_per_kv_head = total_num_heads // total_num_kv_heads
+        state_dict = self.state_dict()
+
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "query_key_value" in name:
+                loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+                loaded_weight_size = loaded_weight.size()
+                loaded_weight = loaded_weight.view(
+                    total_num_kv_heads, num_query_heads_per_kv_head + 2,
+                    head_size, *loaded_weight_size[1:])
+
+                wq = loaded_weight[:, :-2].reshape(-1, *loaded_weight_size[1:])
+                wk = loaded_weight[:, [-2]].reshape(-1,
+                                                    *loaded_weight_size[1:])
+                wv = loaded_weight[:, [-1]].reshape(-1,
+                                                    *loaded_weight_size[1:])
+
+                wq = wq[head_size * head_start:head_size * head_end]
+                wk = wk[head_size * kv_head_start:head_size * kv_head_end]
+                wv = wv[head_size * kv_head_start:head_size * kv_head_end]
+
+                if separated_q_kv:
+                    loaded_weight_q = wq
+                    loaded_weight_kv = torch.cat([wk, wv], dim=0)
+                    q_weight_name = name.replace("query_key_value", "query")
+                    kv_weight_name = name.replace("query_key_value",
+                                                  "key_value")
+                    load_tensor_parallel_weights(state_dict[q_weight_name],
+                                                 loaded_weight_q,
+                                                 q_weight_name,
+                                                 self._column_parallel_weights,
+                                                 self._row_parallel_weights,
+                                                 tp_rank)
+                    load_tensor_parallel_weights(state_dict[kv_weight_name],
+                                                 loaded_weight_kv,
+                                                 kv_weight_name,
+                                                 self._column_parallel_weights,
+                                                 self._row_parallel_weights,
+                                                 tp_rank)
+                    continue
+                else:
+                    loaded_weight = torch.cat([wq, wk, wv], dim=0)
+
+            param = state_dict[name]
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights, tp_rank)

vllm/model_executor/models/gpt2.py

@@ -21,7 +21,7 @@
 The input of the model is flattened to a 1D tensor of tokens. The model uses
 InputMetadata to extract the original 2D shape of the input.
 """
-from typing import Dict, List, Optional, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 from torch import nn
@@ -31,13 +31,14 @@ from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.attention import PagedAttention
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
-                                              load_tensor_parallel_weights)
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor, hf_model_weights_iterator,
+    load_padded_tensor_parallel_vocab, load_tensor_parallel_weights)
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.model_executor.parallel_utils.tensor_parallel import (
     VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
-from vllm.sequence import SequenceOutputs
+from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -217,27 +218,28 @@ class GPT2LMHeadModel(nn.Module):
         kv_caches: List[KVCache],
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
-    ) -> Dict[int, SequenceOutputs]:
+    ) -> SamplerOutput:
         hidden_states = self.transformer(input_ids, positions, kv_caches,
                                          input_metadata, cache_events)
         next_tokens = self.sampler(self.lm_head_weight, hidden_states,
                                    input_metadata)
         return next_tokens
 
-    _column_parallel_weights = ["wte.weight", "c_fc.weight", "c_fc.bias"]
+    _column_parallel_weights = ["c_fc.weight", "c_fc.bias"]
     _row_parallel_weights = ["c_proj.weight"]
 
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     use_np_cache: bool = False):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tensor_model_parallel_world_size = (
             get_tensor_model_parallel_world_size())
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, load_format, revision):
             if "lm_head.weight" in name:
                 # GPT-2 ties the weights of the embedding layer and the final
                 # linear layer.
@@ -250,6 +252,8 @@ class GPT2LMHeadModel(nn.Module):
             if not name.startswith("transformer."):
                 name = "transformer." + name
 
+            loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+
             # The HF's GPT-2 implementation uses Conv1D instead of Linear.
             # Because of this, we need to transpose the weights.
             for conv1d_weight_name in ["c_attn", "c_proj", "c_fc"]:
@@ -261,14 +265,9 @@ class GPT2LMHeadModel(nn.Module):
             param = state_dict[name]
 
             if name == "transformer.wte.weight":
-                # Consider padding in the vocab size.
-                padded_vocab_size = (param.shape[0] *
-                                     tensor_model_parallel_world_size)
-                num_extra_rows = padded_vocab_size - self.config.vocab_size
-                extra_rows = torch.empty(num_extra_rows,
-                                         loaded_weight.shape[1])
-                extra_rows = extra_rows.to(loaded_weight)
-                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
 
             # For the fused QKV linear layer, manually shard the weights.
             if "c_attn" in name:

vllm/model_executor/models/gpt_bigcode.py

@@ -22,24 +22,24 @@
 The input of the model is flattened to a 1D tensor of tokens. The model uses
 InputMetadata to extract the original 2D shape of the input.
 """
-from typing import Dict, List, Optional, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 from torch import nn
-import numpy as np
 from transformers import GPTBigCodeConfig
 
 from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.attention import PagedAttention
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
-                                              load_tensor_parallel_weights)
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor, hf_model_weights_iterator,
+    load_padded_tensor_parallel_vocab, load_tensor_parallel_weights)
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.model_executor.parallel_utils.tensor_parallel import (
     VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
-from vllm.sequence import SequenceOutputs
+from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -50,18 +50,36 @@ class GPTBigCodeAttention(nn.Module):
         super().__init__()
         self.hidden_size = config.hidden_size
         total_num_heads = config.num_attention_heads
-        tensor_model_parallel_world_size = (
+        self.tensor_model_parallel_world_size = (
             get_tensor_model_parallel_world_size())
-        assert total_num_heads % tensor_model_parallel_world_size == 0
-        self.num_heads = total_num_heads // tensor_model_parallel_world_size
+        assert total_num_heads % self.tensor_model_parallel_world_size == 0
+        self.num_heads = (total_num_heads //
+                          self.tensor_model_parallel_world_size)
         self.head_dim = self.hidden_size // total_num_heads
         self.scale = self.head_dim**-0.5
 
-        self.c_attn = ColumnParallelLinear(self.hidden_size,
-                                           3 * self.hidden_size,
-                                           bias=True,
-                                           gather_output=False,
-                                           perform_initialization=False)
+        self.multi_query = config.multi_query
+        if self.multi_query:
+            self.num_kv_heads = 1
+            self.kv_dim = self.head_dim
+            self.c_attn_q = ColumnParallelLinear(self.hidden_size,
+                                                 self.hidden_size,
+                                                 bias=True,
+                                                 gather_output=False,
+                                                 perform_initialization=False)
+            self.c_attn_kv = nn.Linear(self.hidden_size,
+                                       2 * self.kv_dim,
+                                       bias=True)
+        else:
+            self.num_kv_heads = self.num_heads
+            self.kv_dim = self.num_kv_heads * self.head_dim
+            self.c_attn = ColumnParallelLinear(self.hidden_size,
+                                               self.hidden_size +
+                                               2 * self.kv_dim,
+                                               bias=True,
+                                               gather_output=False,
+                                               perform_initialization=False)
+
         self.c_proj = RowParallelLinear(self.hidden_size,
                                         self.hidden_size,
                                         bias=True,
@@ -69,7 +87,8 @@ class GPTBigCodeAttention(nn.Module):
                                         perform_initialization=False)
         self.attn = PagedAttention(self.num_heads,
                                    self.head_dim,
-                                   scale=self.scale)
+                                   scale=self.scale,
+                                   num_kv_heads=self.num_kv_heads)
 
     def forward(
         self,
@@ -78,8 +97,17 @@ class GPTBigCodeAttention(nn.Module):
         input_metadata: InputMetadata,
         cache_event: Optional[torch.cuda.Event],
     ) -> torch.Tensor:
-        qkv, _ = self.c_attn(hidden_states)
-        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        if self.multi_query:
+            q, _ = self.c_attn_q(hidden_states)
+            kv = self.c_attn_kv(hidden_states)
+            k, v = kv.split([self.kv_dim, self.kv_dim], dim=-1)
+        else:
+            qkv, _ = self.c_attn(hidden_states)
+            q, k, v = qkv.split([
+                self.hidden_size // self.tensor_model_parallel_world_size,
+                self.kv_dim, self.kv_dim
+            ],
+                                dim=-1)
         key_cache, value_cache = kv_cache
         attn_output = self.attn(q, k, v, key_cache, value_cache,
                                 input_metadata, cache_event)
@@ -218,27 +246,28 @@ class GPTBigCodeForCausalLM(nn.Module):
         kv_caches: List[KVCache],
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
-    ) -> Dict[int, SequenceOutputs]:
+    ) -> SamplerOutput:
         hidden_states = self.transformer(input_ids, positions, kv_caches,
                                          input_metadata, cache_events)
         next_tokens = self.sampler(self.lm_head_weight, hidden_states,
                                    input_metadata)
         return next_tokens
 
-    _column_parallel_weights = ["wte.weight", "c_fc.weight", "c_fc.bias"]
+    _column_parallel_weights = ["c_fc.weight", "c_fc.bias"]
     _row_parallel_weights = ["c_proj.weight"]
 
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     use_np_cache: bool = False):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tensor_model_parallel_world_size = (
             get_tensor_model_parallel_world_size())
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, load_format, revision):
             if "lm_head.weight" in name:
                 # GPT-2 ties the weights of the embedding layer and the final
                 # linear layer.
@@ -248,51 +277,9 @@ class GPTBigCodeForCausalLM(nn.Module):
                 # NOTE: "c_attn.bias" should not be skipped.
                 continue
 
-            param = state_dict[name]
-
             if not name.startswith("transformer."):
                 name = "transformer." + name
 
-            if name == "transformer.wte.weight":
-                # Consider padding in the vocab size.
-                padded_vocab_size = param.shape[
-                    0] * tensor_model_parallel_world_size
-                num_extra_rows = padded_vocab_size - self.config.vocab_size
-                extra_rows = torch.empty(num_extra_rows,
-                                         loaded_weight.shape[1])
-                extra_rows = extra_rows.to(loaded_weight)
-                loaded_weight = torch.cat([loaded_weight, extra_rows], dim=0)
-
-            def _expand_mqa_mha(qkv_array, n_head, head_dim):
-                """manipulates along axis=0 from MQA to MHA
-                inputs: qkv_array.shape=((n_heads + 2) * head_dim, hidden_dim)
-                    with n_heads for q, then 1 for k, 1 for 1 v, times head dim
-                return: qkv_array.shape=(3 * n_heads * head_dim, hidden_dim)
-
-                TODO: this function is no longer needed once vllm supports MQA.
-                """
-                qkv_array = qkv_array.numpy()
-
-                dims_q = n_head * head_dim
-                # pylint: disable=unbalanced-tuple-unpacking
-                q, k, v = np.split(qkv_array, (dims_q, dims_q + head_dim),
-                                   axis=0)
-                # q is fine, but k & v have not replicated shape along the first
-                # axis as long as MQA is not nativly supported, increase memory
-                # and replicated (head_dim, hidden_dim) to
-                # (n_heads * head_dim, hidden_dim)
-                if k.ndim == 2 and v.ndim == 2:
-                    replication = (n_head, 1)  # weights
-                else:
-                    replication = n_head  # biases
-                # replicate n_head times for q, v
-                k, v = np.tile(k, replication), np.tile(v, replication)
-                # concat q, k, v along the first axis
-                # (n_heads * head_dim, hidden_dim)
-                # to (3 * n_heads * head_dim, hidden_dim)
-                qkv_array = np.concatenate((q, k, v), axis=0)
-                return torch.from_numpy(qkv_array)
-
             # For the fused QKV linear layer, manually shard the weights.
             if "c_attn" in name:
                 # GPT-2's fused QKV has the shape of
@@ -300,30 +287,53 @@ class GPTBigCodeForCausalLM(nn.Module):
                 # When tensor parallelism is used, we shard the weights along
                 # the head dimension.
                 total_num_heads = self.config.num_attention_heads
+                total_num_kv_heads = (1 if self.config.multi_query else
+                                      total_num_heads)
                 hidden_size = self.config.hidden_size
                 head_size = hidden_size // total_num_heads
+                total_kv_size = head_size * total_num_kv_heads
                 num_heads = total_num_heads // tensor_model_parallel_world_size
                 head_start = tensor_model_parallel_rank * num_heads
                 head_end = (tensor_model_parallel_rank + 1) * num_heads
 
-                if name.endswith(".weight"):
-                    loaded_weight = _expand_mqa_mha(loaded_weight,
-                                                    n_head=total_num_heads,
-                                                    head_dim=head_size)
-                    loaded_weight = loaded_weight.view(3, total_num_heads,
-                                                       head_size, hidden_size)
-                    loaded_weight = loaded_weight[:, head_start:head_end, :, :]
-                    loaded_weight = loaded_weight.reshape(-1, hidden_size)
-                elif name.endswith(".bias"):
-                    loaded_weight = _expand_mqa_mha(loaded_weight,
-                                                    n_head=total_num_heads,
-                                                    head_dim=head_size)
-                    loaded_weight = loaded_weight.view(3, total_num_heads,
-                                                       head_size)
-                    loaded_weight = loaded_weight[:, head_start:head_end, :]
-                    loaded_weight = loaded_weight.reshape(-1)
+                loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+                wq, wk, wv = torch.split(
+                    loaded_weight, [hidden_size, total_kv_size, total_kv_size],
+                    dim=0)
+
+                wq = wq[head_size * head_start:head_size * head_end]
+                if not self.config.multi_query:
+                    # Split the heads when using normal multi-head attention
+                    wk = wk[head_size * head_start:head_size * head_end]
+                    wv = wv[head_size * head_start:head_size * head_end]
+                    loaded_weight = torch.cat([wq, wk, wv], dim=0)
                 else:
-                    raise ValueError(f"Unexpected parameter name {name}")
+                    # For multi-query attention, we split the query
+                    # but replicate the key and value.
+                    loaded_weight_q = wq
+                    loaded_weight_kv = torch.cat([wk, wv], dim=0)
+                    q_weight_name = name.replace("c_attn", "c_attn_q")
+                    kv_weight_name = name.replace("c_attn", "c_attn_kv")
+                    load_tensor_parallel_weights(state_dict[q_weight_name],
+                                                 loaded_weight_q,
+                                                 q_weight_name,
+                                                 self._column_parallel_weights,
+                                                 self._row_parallel_weights,
+                                                 tensor_model_parallel_rank)
+                    load_tensor_parallel_weights(state_dict[kv_weight_name],
+                                                 loaded_weight_kv,
+                                                 kv_weight_name,
+                                                 self._column_parallel_weights,
+                                                 self._row_parallel_weights,
+                                                 tensor_model_parallel_rank)
+                    continue
+
+            param = state_dict[name]
+
+            if name == "transformer.wte.weight":
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
 
             load_tensor_parallel_weights(param, loaded_weight, name,
                                          self._column_parallel_weights,

vllm/model_executor/models/gpt_j.py

@@ -0,0 +1,261 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gptj/modeling_gptj.py
+# Copyright 2023 The vLLM team.
+# Copyright 2021 The EleutherAI and HuggingFace Teams. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only GPT-J model compatible with HuggingFace weights.
+
+The input of the model is flattened to a 1D tensor of tokens. The model uses
+InputMetadata to extract the original 2D shape of the input.
+"""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import GPTJConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import get_act_fn
+from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+                                              load_tensor_parallel_weights)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class GPTJAttention(nn.Module):
+
+    def __init__(self, config: GPTJConfig):
+        super().__init__()
+        self.total_num_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_size = self.hidden_size // self.total_num_heads
+
+        self.qkv_proj = ColumnParallelLinear(config.hidden_size,
+                                             3 * config.hidden_size,
+                                             bias=False,
+                                             gather_output=False,
+                                             perform_initialization=False)
+        self.out_proj = RowParallelLinear(config.hidden_size,
+                                          config.hidden_size,
+                                          bias=False,
+                                          input_is_parallel=True,
+                                          perform_initialization=False)
+
+        tp_world_size = get_tensor_model_parallel_world_size()
+        assert self.total_num_heads % tp_world_size == 0
+        self.num_heads = self.total_num_heads // tp_world_size
+
+        scaling = self.head_size**-0.5
+        assert getattr(config, "rotary", True)
+        assert config.rotary_dim % 2 == 0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.attn = PagedAttentionWithRoPE(
+            self.num_heads,
+            self.head_size,
+            scaling,
+            config.rotary_dim,
+            base=rope_theta,
+            max_position=max_position_embeddings,
+            is_neox_style=False)
+        self.warmup = False
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(position_ids, q, k, v, k_cache, v_cache,
+                                input_metadata, cache_event)
+        attn_output, _ = self.out_proj(attn_output)
+        return attn_output
+
+
+class GPTJMLP(nn.Module):
+
+    def __init__(self, intermediate_size: int, config: GPTJConfig):
+        super().__init__()
+        hidden_size = config.n_embd
+        self.fc_in = ColumnParallelLinear(hidden_size,
+                                          intermediate_size,
+                                          gather_output=False,
+                                          perform_initialization=False)
+        self.fc_out = RowParallelLinear(intermediate_size,
+                                        hidden_size,
+                                        input_is_parallel=True,
+                                        perform_initialization=False)
+        self.act = get_act_fn(config.activation_function)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states, _ = self.fc_in(hidden_states)
+        hidden_states = self.act(hidden_states)
+        hidden_states, _ = self.fc_out(hidden_states)
+        return hidden_states
+
+
+class GPTJBlock(nn.Module):
+
+    def __init__(self, config: GPTJConfig):
+        super().__init__()
+        if config.n_inner is None:
+            inner_dim = 4 * config.n_embd
+        else:
+            inner_dim = config.n_inner
+        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.attn = GPTJAttention(config)
+        self.mlp = GPTJMLP(inner_dim, config)
+
+    def forward(
+        self,
+        position_ids: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        attn_output = self.attn(
+            position_ids=position_ids,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+            cache_event=cache_event,
+        )
+        mlp_output = self.mlp(hidden_states)
+        hidden_states = attn_output + mlp_output + residual
+        return hidden_states
+
+
+class GPTJModel(nn.Module):
+
+    def __init__(self, config: GPTJConfig):
+        super().__init__()
+        self.config = config
+        self.embed_dim = config.n_embd
+        self.wte = VocabParallelEmbedding(config.vocab_size,
+                                          self.embed_dim,
+                                          perform_initialization=False)
+        self.h = nn.ModuleList(
+            [GPTJBlock(config) for _ in range(config.n_layer)])
+        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        position_ids: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.wte(input_ids)
+        for i in range(len(self.h)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            layer = self.h[i]
+            hidden_states = layer(
+                position_ids,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+            )
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class GPTJForCausalLM(nn.Module):
+
+    def __init__(self, config: GPTJConfig):
+        super().__init__()
+        self.config = config
+        assert not config.tie_word_embeddings
+        self.transformer = GPTJModel(config)
+        self.lm_head = ColumnParallelLinear(config.n_embd,
+                                            config.vocab_size,
+                                            gather_output=False,
+                                            perform_initialization=False)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> SamplerOutput:
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   input_metadata, self.lm_head.bias)
+        return next_tokens
+
+    _column_parallel_weights = [
+        "wte.weight", "fc_in.weight", "fc_in.bias", "lm_head.weight",
+        "lm_head.bias"
+    ]
+    _row_parallel_weights = ["out_proj.weight", "fc_out.weight"]
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        tp_rank = get_tensor_model_parallel_rank()
+        state_dict = self.state_dict()
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "attn.bias" in name or "attn.masked_bias" in name:
+                continue
+
+            is_attention_weight = False
+            for stride_id, att_weight_name in enumerate(
+                ["q_proj", "k_proj", "v_proj"]):
+                if att_weight_name not in name:
+                    continue
+                param = state_dict[name.replace(att_weight_name, "qkv_proj")]
+                shard_size = param.shape[1]
+                loaded_weight = loaded_weight[shard_size * tp_rank:shard_size *
+                                              (tp_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
+                break
+            if is_attention_weight:
+                continue
+
+            param = state_dict[name]
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights, tp_rank)

vllm/model_executor/models/gpt_neox.py

@@ -20,7 +20,7 @@
 The input of the model is flattened to a 1D tensor of tokens. The model uses
 InputMetadata to extract the original 2D shape of the input.
 """
-from typing import Dict, List, Optional, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 from torch import nn
@@ -36,7 +36,7 @@ from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.model_executor.parallel_utils.tensor_parallel import (
     VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
-from vllm.sequence import SequenceOutputs
+from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -68,8 +68,16 @@ class GPTNeoXAttention(nn.Module):
         scaling = self.head_size**-0.5
         rotary_dim = int(self.head_size * config.rotary_pct)
         assert rotary_dim % 2 == 0
-        self.attn = PagedAttentionWithRoPE(self.num_heads, self.head_size,
-                                           scaling, rotary_dim)
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.attn = PagedAttentionWithRoPE(
+            self.num_heads,
+            self.head_size,
+            scaling,
+            rotary_dim,
+            base=rope_theta,
+            max_position=max_position_embeddings)
 
     def forward(
         self,
@@ -215,7 +223,7 @@ class GPTNeoXForCausalLM(nn.Module):
         kv_caches: List[KVCache],
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
-    ) -> Dict[int, SequenceOutputs]:
+    ) -> SamplerOutput:
         hidden_states = self.gpt_neox(input_ids, positions, kv_caches,
                                       input_metadata, cache_events)
         next_tokens = self.sampler(self.embed_out.weight, hidden_states,
@@ -231,11 +239,12 @@ class GPTNeoXForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     use_np_cache: bool = False):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, load_format, revision):
             if ("attention.bias" in name or "attention.masked_bias" in name
                     or "rotary_emb.inv_freq" in name):
                 continue

vllm/model_executor/models/internlm.py

@@ -0,0 +1,305 @@
+# -*- coding: utf-8 -*-
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+from transformers import LlamaConfig
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.tensor_parallel import (
+    ColumnParallelLinear, RowParallelLinear, VocabParallelEmbedding)
+from vllm.model_executor.weight_utils import (
+    hf_model_weights_iterator, load_padded_tensor_parallel_vocab,
+    load_tensor_parallel_weights)
+from vllm.sequence import SamplerOutput
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class InternLMMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+    ):
+        super().__init__()
+        self.gate_up_proj = ColumnParallelLinear(hidden_size,
+                                                 2 * intermediate_size,
+                                                 bias=False,
+                                                 gather_output=False,
+                                                 perform_initialization=False)
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           input_is_parallel=True,
+                                           perform_initialization=False)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class InternLMAttention(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        rope_theta: float = 10000,
+        max_position_embeddings: int = 8192,
+    ):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tensor_model_parallel_world_size = (
+            get_tensor_model_parallel_world_size())
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
+        self.head_dim = hidden_size // self.total_num_heads
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        self.qkv_proj = ColumnParallelLinear(
+            hidden_size,
+            3 * self.total_num_heads * self.head_dim,
+            bias=True,
+            gather_output=False,
+            perform_initialization=False,
+        )
+        self.o_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=True,
+            input_is_parallel=True,
+            perform_initialization=False,
+        )
+        self.attn = PagedAttentionWithRoPE(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            base=self.rope_theta,
+            max_position=self.max_position_embeddings,
+            rotary_dim=self.head_dim)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
+                                input_metadata, cache_event)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class InternLMDecoderLayer(nn.Module):
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
+        self.self_attn = InternLMAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            rope_theta=rope_theta,
+            max_position_embeddings=max_position_embeddings,
+        )
+        self.mlp = InternLMMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+            cache_event=cache_event,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class InternLMModel(nn.Module):
+
+    def __init__(self, config: LlamaConfig):
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.embed_tokens = VocabParallelEmbedding(
+            vocab_size, config.hidden_size, perform_initialization=False)
+        self.layers = nn.ModuleList([
+            InternLMDecoderLayer(config)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        for i in range(len(self.layers)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            layer = self.layers[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+            )
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class InternLMForCausalLM(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.model = InternLMModel(config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.lm_head = ColumnParallelLinear(config.hidden_size,
+                                            vocab_size,
+                                            bias=False,
+                                            gather_output=False,
+                                            perform_initialization=False)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> SamplerOutput:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   input_metadata)
+        return next_tokens
+
+    _column_parallel_weights = [
+        "qkv_proj.weight", "gate_proj.weight", "up_proj.weight"
+    ]
+    _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
+        state_dict = self.state_dict()
+
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            if "embed_tokens" in name or "lm_head" in name:
+                param = state_dict[name]
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
+
+            is_attention_weight = False
+            for stride_id, att_weight_name in enumerate(
+                ["q_proj", "k_proj", "v_proj"]):
+                if att_weight_name not in name:
+                    continue
+                param = state_dict[name.replace(att_weight_name, "qkv_proj")]
+                shard_size = param.shape[0] // 3
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
+                break
+            if is_attention_weight:
+                continue
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
+                break
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         self._column_parallel_weights,
+                                         self._row_parallel_weights,
+                                         tensor_model_parallel_rank)

vllm/model_executor/models/llama.py

@@ -25,7 +25,7 @@
 The input of the model is flattened to a 1D tensor of tokens. The model uses
 InputMetadata to extract the original 2D shape of the input.
 """
-from typing import Dict, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import torch
 from torch import nn
@@ -36,13 +36,16 @@ from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
-                                              load_tensor_parallel_weights)
+from vllm.model_executor.layers.quantized_linear import ParallelLinear
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.model_executor.parallel_utils.tensor_parallel import (
-    VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
-from vllm.sequence import SequenceOutputs
+    VocabParallelEmbedding)
+from vllm.model_executor.quantization_utils import QuantizationConfig
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor, hf_model_weights_iterator,
+    load_tensor_parallel_weights, load_padded_tensor_parallel_vocab)
+from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -54,18 +57,21 @@ class LlamaMLP(nn.Module):
         hidden_size: int,
         intermediate_size: int,
         hidden_act: str,
-    ):
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
         super().__init__()
-        self.gate_up_proj = ColumnParallelLinear(hidden_size,
-                                                 2 * intermediate_size,
-                                                 bias=False,
-                                                 gather_output=False,
-                                                 perform_initialization=False)
-        self.down_proj = RowParallelLinear(intermediate_size,
-                                           hidden_size,
-                                           bias=False,
-                                           input_is_parallel=True,
-                                           perform_initialization=False)
+        self.gate_up_proj = ParallelLinear.column(hidden_size,
+                                                  2 * intermediate_size,
+                                                  bias=False,
+                                                  gather_output=False,
+                                                  perform_initialization=False,
+                                                  quant_config=quant_config)
+        self.down_proj = ParallelLinear.row(intermediate_size,
+                                            hidden_size,
+                                            bias=False,
+                                            input_is_parallel=True,
+                                            perform_initialization=False,
+                                            quant_config=quant_config)
         if hidden_act != "silu":
             raise ValueError(f"Unsupported activation: {hidden_act}. "
                              "Only silu is supported for now.")
@@ -84,36 +90,54 @@ class LlamaAttention(nn.Module):
         self,
         hidden_size: int,
         num_heads: int,
-    ):
+        num_kv_heads: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
         super().__init__()
         self.hidden_size = hidden_size
-        tensor_model_parallel_world_size = (
-            get_tensor_model_parallel_world_size())
+        tp_size = get_tensor_model_parallel_world_size()
         self.total_num_heads = num_heads
-        assert self.total_num_heads % tensor_model_parallel_world_size == 0
-        self.num_heads = (self.total_num_heads //
-                          tensor_model_parallel_world_size)
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        assert self.total_num_kv_heads % tp_size == 0
+        self.num_kv_heads = self.total_num_kv_heads // tp_size
         self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
         self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
 
-        self.qkv_proj = ColumnParallelLinear(
+        self.qkv_proj = ParallelLinear.column(
             hidden_size,
-            3 * self.total_num_heads * self.head_dim,
+            (self.total_num_heads + 2 * self.total_num_kv_heads) *
+            self.head_dim,
             bias=False,
             gather_output=False,
             perform_initialization=False,
+            quant_config=quant_config,
         )
-        self.o_proj = RowParallelLinear(
+        self.o_proj = ParallelLinear.row(
             self.total_num_heads * self.head_dim,
             hidden_size,
             bias=False,
             input_is_parallel=True,
             perform_initialization=False,
+            quant_config=quant_config,
         )
-        self.attn = PagedAttentionWithRoPE(self.num_heads,
-                                           self.head_dim,
-                                           self.scaling,
-                                           rotary_dim=self.head_dim)
+        self.attn = PagedAttentionWithRoPE(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            base=self.rope_theta,
+            max_position=self.max_position_embeddings,
+            rotary_dim=self.head_dim,
+            num_kv_heads=self.num_kv_heads,
+            rope_scaling=rope_scaling)
 
     def forward(
         self,
@@ -124,7 +148,7 @@ class LlamaAttention(nn.Module):
         cache_event: Optional[torch.cuda.Event],
     ) -> torch.Tensor:
         qkv, _ = self.qkv_proj(hidden_states)
-        q, k, v = qkv.chunk(chunks=3, dim=-1)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
         k_cache, v_cache = kv_cache
         attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
                                 input_metadata, cache_event)
@@ -134,17 +158,32 @@ class LlamaAttention(nn.Module):
 
 class LlamaDecoderLayer(nn.Module):
 
-    def __init__(self, config: LlamaConfig):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
         super().__init__()
         self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings",
+                                          8192)
         self.self_attn = LlamaAttention(
             hidden_size=self.hidden_size,
             num_heads=config.num_attention_heads,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            rope_scaling=rope_scaling,
+            max_position_embeddings=max_position_embeddings,
+            quant_config=quant_config,
         )
         self.mlp = LlamaMLP(
             hidden_size=self.hidden_size,
             intermediate_size=config.intermediate_size,
             hidden_act=config.hidden_act,
+            quant_config=quant_config,
         )
         self.input_layernorm = RMSNorm(config.hidden_size,
                                        eps=config.rms_norm_eps)
@@ -181,18 +220,22 @@ class LlamaDecoderLayer(nn.Module):
 
 class LlamaModel(nn.Module):
 
-    def __init__(self, config: LlamaConfig):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
         super().__init__()
         self.config = config
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
 
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.embed_tokens = VocabParallelEmbedding(
-            config.vocab_size,
-            config.hidden_size,
-            perform_initialization=False)
+            vocab_size, config.hidden_size, perform_initialization=False)
         self.layers = nn.ModuleList([
-            LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)
+            LlamaDecoderLayer(config, quant_config)
+            for _ in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
@@ -224,15 +267,23 @@ class LlamaModel(nn.Module):
 
 class LlamaForCausalLM(nn.Module):
 
-    def __init__(self, config):
+    def __init__(
+        self,
+        config: LlamaConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
         super().__init__()
         self.config = config
-        self.model = LlamaModel(config)
-        self.lm_head = ColumnParallelLinear(config.hidden_size,
-                                            config.vocab_size,
-                                            bias=False,
-                                            gather_output=False,
-                                            perform_initialization=False)
+        self.quant_config = quant_config
+        self.model = LlamaModel(config, quant_config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        # NOTE: The LM head is not quantized.
+        self.lm_head = ParallelLinear.column(config.hidden_size,
+                                             vocab_size,
+                                             bias=False,
+                                             gather_output=False,
+                                             perform_initialization=False,
+                                             quant_config=None)
         self.sampler = Sampler(config.vocab_size)
 
     def forward(
@@ -242,44 +293,82 @@ class LlamaForCausalLM(nn.Module):
         kv_caches: List[KVCache],
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
-    ) -> Dict[int, SequenceOutputs]:
+    ) -> SamplerOutput:
         hidden_states = self.model(input_ids, positions, kv_caches,
                                    input_metadata, cache_events)
         next_tokens = self.sampler(self.lm_head.weight, hidden_states,
                                    input_metadata)
         return next_tokens
 
-    _column_parallel_weights = [
-        "embed_tokens.weight", "lm_head.weight", "qkv_proj.weight",
-        "gate_proj.weight", "up_proj.weight"
-    ]
-    _row_parallel_weights = ["o_proj.weight", "down_proj.weight"]
+    _column_parallel_layers = []
+    _row_parallel_layers = ["o_proj", "down_proj"]
 
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     use_np_cache: bool = False):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        if self.quant_config is None:
+            weight_suffixes = ["weight"]
+        else:
+            weight_suffixes = self.quant_config.get_tp_tensor_names()
+
+        column_parallel_weights: List[str] = []
+        for layer in self._column_parallel_layers:
+            for suffix in weight_suffixes:
+                column_parallel_weights.append(f"{layer}.{suffix}")
+        row_parallel_weights: List[str] = []
+        for layer in self._row_parallel_layers:
+            for suffix in weight_suffixes:
+                row_parallel_weights.append(f"{layer}.{suffix}")
+
+        tp_size = get_tensor_model_parallel_world_size()
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
+        q_proj_shard_size = (self.config.hidden_size // tp_size)
+        kv_proj_shard_size = (self.config.hidden_size //
+                              self.config.num_attention_heads *
+                              self.config.num_key_value_heads // tp_size)
+        attention_weight_specs = [
+            # (weight_name, shard_size, offset)
+            ("q_proj", q_proj_shard_size, 0),
+            ("k_proj", kv_proj_shard_size, q_proj_shard_size),
+            ("v_proj", kv_proj_shard_size,
+             q_proj_shard_size + kv_proj_shard_size),
+        ]
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
 
+            is_packed = False
+            is_transposed = False
+            if self.quant_config is not None:
+                is_packed = self.quant_config.is_packed(name)
+                is_transposed = self.quant_config.is_transposed(name)
+            if is_transposed:
+                loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+                loaded_weight = loaded_weight.T
+
             is_attention_weight = False
-            for stride_id, att_weight_name in enumerate(
-                ["q_proj", "k_proj", "v_proj"]):
-                if att_weight_name not in name:
+            for weight_name, shard_size, offset in attention_weight_specs:
+                if weight_name not in name:
                     continue
-                param = state_dict[name.replace(att_weight_name, "qkv_proj")]
-                shard_size = param.shape[0] // 3
+                param = state_dict[name.replace(weight_name, "qkv_proj")]
+                if is_transposed:
+                    param = param.T
+
+                if is_packed:
+                    shard_size //= self.quant_config.pack_factor
+                    offset //= self.quant_config.pack_factor
+
                 loaded_weight = loaded_weight[
                     shard_size * tensor_model_parallel_rank:shard_size *
                     (tensor_model_parallel_rank + 1)]
-                param_slice = param.data[shard_size * stride_id:shard_size *
-                                         (stride_id + 1)]
+                param_slice = param.data[offset:offset + shard_size]
                 assert param_slice.shape == loaded_weight.shape
+
                 param_slice.copy_(loaded_weight)
                 is_attention_weight = True
                 break
@@ -291,6 +380,9 @@ class LlamaForCausalLM(nn.Module):
                 if weight_name not in name:
                     continue
                 param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                if is_transposed:
+                    param = param.T
+
                 shard_size = param.shape[0] // 2
                 loaded_weight = loaded_weight[
                     shard_size * tensor_model_parallel_rank:shard_size *
@@ -305,7 +397,15 @@ class LlamaForCausalLM(nn.Module):
                 continue
 
             param = state_dict[name]
+            if is_transposed:
+                param = param.T
+
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
+
             load_tensor_parallel_weights(param, loaded_weight, name,
-                                         self._column_parallel_weights,
-                                         self._row_parallel_weights,
+                                         column_parallel_weights,
+                                         row_parallel_weights,
                                          tensor_model_parallel_rank)

vllm/model_executor/models/mistral.py

@@ -0,0 +1,404 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Inference-only LLaMA model compatible with HuggingFace weights.
+
+The input of the model is flattened to a 1D tensor of tokens. The model uses
+InputMetadata to extract the original 2D shape of the input.
+"""
+from typing import List, Optional, Tuple
+
+import torch
+from torch import nn
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.layers.quantized_linear import ParallelLinear
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
+from vllm.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding)
+from vllm.model_executor.quantization_utils import QuantizationConfig
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor, hf_model_weights_iterator,
+    load_tensor_parallel_weights, load_padded_tensor_parallel_vocab)
+from vllm.sequence import SamplerOutput
+from vllm.transformers_utils.configs.mistral import MistralConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class MistralMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = ParallelLinear.column(hidden_size,
+                                                  2 * intermediate_size,
+                                                  bias=False,
+                                                  gather_output=False,
+                                                  perform_initialization=False,
+                                                  quant_config=quant_config)
+        self.down_proj = ParallelLinear.row(intermediate_size,
+                                            hidden_size,
+                                            bias=False,
+                                            input_is_parallel=True,
+                                            perform_initialization=False,
+                                            quant_config=quant_config)
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class MistralAttention(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 num_heads: int,
+                 num_kv_heads: int,
+                 max_position: int = 4096 * 32,
+                 rope_theta: float = 10000,
+                 quant_config: Optional[QuantizationConfig] = None,
+                 sliding_window: Optional[int] = None) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        tp_size = get_tensor_model_parallel_world_size()
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        self.total_num_kv_heads = num_kv_heads
+        assert self.total_num_kv_heads % tp_size == 0
+        self.num_kv_heads = self.total_num_kv_heads // tp_size
+        self.head_dim = hidden_size // self.total_num_heads
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.sliding_window = sliding_window
+
+        self.qkv_proj = ParallelLinear.column(
+            hidden_size,
+            (self.total_num_heads + 2 * self.total_num_kv_heads) *
+            self.head_dim,
+            bias=False,
+            gather_output=False,
+            perform_initialization=False,
+            quant_config=quant_config,
+        )
+        self.o_proj = ParallelLinear.row(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            perform_initialization=False,
+            quant_config=quant_config,
+        )
+        self.attn = PagedAttentionWithRoPE(self.num_heads,
+                                           self.head_dim,
+                                           self.scaling,
+                                           base=self.rope_theta,
+                                           max_position=max_position,
+                                           rotary_dim=self.head_dim,
+                                           num_kv_heads=self.num_kv_heads,
+                                           sliding_window=self.sliding_window)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        qkv, _ = self.qkv_proj(hidden_states)
+        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
+                                input_metadata, cache_event)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MistralDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: MistralConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        # Requires transformers > 4.32.0
+        rope_theta = getattr(config, "rope_theta", 10000)
+        self.self_attn = MistralAttention(
+            hidden_size=self.hidden_size,
+            num_heads=config.num_attention_heads,
+            max_position=config.max_position_embeddings,
+            num_kv_heads=config.num_key_value_heads,
+            rope_theta=rope_theta,
+            quant_config=quant_config,
+            sliding_window=config.sliding_window)
+        self.mlp = MistralMLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+        )
+        self.input_layernorm = RMSNorm(config.hidden_size,
+                                       eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size,
+                                                eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+            cache_event=cache_event,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class MistralModel(nn.Module):
+
+    def __init__(
+        self,
+        config: MistralConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.embed_tokens = VocabParallelEmbedding(
+            vocab_size, config.hidden_size, perform_initialization=False)
+        self.layers = nn.ModuleList([
+            MistralDecoderLayer(config, quant_config)
+            for _ in range(config.num_hidden_layers)
+        ])
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.embed_tokens(input_ids)
+        for i in range(len(self.layers)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            layer = self.layers[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+            )
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class MistralForCausalLM(nn.Module):
+
+    def __init__(
+        self,
+        config: MistralConfig,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.quant_config = quant_config
+        self.model = MistralModel(config, quant_config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        # NOTE: The LM head is not quantized.
+        self.lm_head = ParallelLinear.column(config.hidden_size,
+                                             vocab_size,
+                                             bias=False,
+                                             gather_output=False,
+                                             perform_initialization=False,
+                                             quant_config=None)
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> SamplerOutput:
+        hidden_states = self.model(input_ids, positions, kv_caches,
+                                   input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   input_metadata)
+        return next_tokens
+
+    _column_parallel_layers = []
+    _row_parallel_layers = ["o_proj", "down_proj"]
+
+    def load_weights(self,
+                     model_name_or_path: str,
+                     cache_dir: Optional[str] = None,
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
+        if self.quant_config is None:
+            weight_suffixes = ["weight"]
+        else:
+            weight_suffixes = self.quant_config.get_tp_tensor_names()
+
+        column_parallel_weights: List[str] = []
+        for layer in self._column_parallel_layers:
+            for suffix in weight_suffixes:
+                column_parallel_weights.append(f"{layer}.{suffix}")
+        row_parallel_weights: List[str] = []
+        for layer in self._row_parallel_layers:
+            for suffix in weight_suffixes:
+                row_parallel_weights.append(f"{layer}.{suffix}")
+
+        tp_size = get_tensor_model_parallel_world_size()
+        tensor_model_parallel_rank = get_tensor_model_parallel_rank()
+        q_proj_shard_size = (self.config.hidden_size // tp_size)
+        kv_proj_shard_size = (self.config.hidden_size //
+                              self.config.num_attention_heads *
+                              self.config.num_key_value_heads // tp_size)
+        attention_weight_specs = [
+            # (weight_name, shard_size, offset)
+            ("q_proj", q_proj_shard_size, 0),
+            ("k_proj", kv_proj_shard_size, q_proj_shard_size),
+            ("v_proj", kv_proj_shard_size,
+             q_proj_shard_size + kv_proj_shard_size),
+        ]
+        state_dict = self.state_dict()
+
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            is_packed = False
+            is_transposed = False
+            if self.quant_config is not None:
+                is_packed = self.quant_config.is_packed(name)
+                is_transposed = self.quant_config.is_transposed(name)
+            if is_transposed:
+                loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+                loaded_weight = loaded_weight.T
+
+            is_attention_weight = False
+            for weight_name, shard_size, offset in attention_weight_specs:
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "qkv_proj")]
+                if is_transposed:
+                    param = param.T
+
+                if is_packed:
+                    shard_size //= self.quant_config.pack_factor
+                    offset //= self.quant_config.pack_factor
+
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[offset:offset + shard_size]
+                assert param_slice.shape == loaded_weight.shape
+
+                param_slice.copy_(loaded_weight)
+                is_attention_weight = True
+                break
+            if is_attention_weight:
+                continue
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["gate_proj", "up_proj"]):
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                if is_transposed:
+                    param = param.T
+
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[
+                    shard_size * tensor_model_parallel_rank:shard_size *
+                    (tensor_model_parallel_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
+                break
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+            if is_transposed:
+                param = param.T
+
+            if "embed_tokens" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tensor_model_parallel_rank)
+                continue
+
+            load_tensor_parallel_weights(param, loaded_weight, name,
+                                         column_parallel_weights,
+                                         row_parallel_weights,
+                                         tensor_model_parallel_rank)

vllm/model_executor/models/mpt.py

@@ -1,6 +1,7 @@
+# coding=utf-8
 # Adapted from https://huggingface.co/mosaicml/mpt-7b/tree/main
 import math
-from typing import Dict, List, Optional, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 import torch.nn as nn
@@ -9,13 +10,14 @@ from vllm.model_executor.input_metadata import InputMetadata
 from vllm.model_executor.layers.activation import get_act_fn
 from vllm.model_executor.layers.attention import PagedAttentionWithALiBi
 from vllm.model_executor.layers.sampler import Sampler
-from vllm.model_executor.weight_utils import (hf_model_weights_iterator,
+from vllm.model_executor.weight_utils import (convert_pyslice_to_tensor,
+                                              hf_model_weights_iterator,
                                               load_tensor_parallel_weights)
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.model_executor.parallel_utils.tensor_parallel import (
     VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
-from vllm.sequence import SequenceOutputs
+from vllm.sequence import SamplerOutput
 from vllm.transformers_utils.configs.mpt import MPTConfig
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
@@ -229,7 +231,7 @@ class MPTForCausalLM(nn.Module):
         kv_caches: List[KVCache],
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
-    ) -> Dict[int, SequenceOutputs]:
+    ) -> SamplerOutput:
         hidden_states = self.transformer(input_ids, positions, kv_caches,
                                          input_metadata, cache_events)
         next_tokens = self.sampler(self.lm_head_weight, hidden_states,
@@ -242,12 +244,13 @@ class MPTForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     use_np_cache: bool = False):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tp_world_size = get_tensor_model_parallel_world_size()
         tp_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, load_format, revision):
             if "Wqkv" in name:
                 # NOTE(woosuk): MPT's fused QKV has the shape of
                 # [3 * num_heads * head_size, hidden_size].
@@ -259,7 +262,7 @@ class MPTForCausalLM(nn.Module):
                 num_heads = total_num_heads // tp_world_size
                 head_start = tp_rank * num_heads
                 head_end = (tp_rank + 1) * num_heads
-
+                loaded_weight = convert_pyslice_to_tensor(loaded_weight)
                 if name.endswith(".weight"):
                     loaded_weight = loaded_weight.view(3, total_num_heads,
                                                        head_size, hidden_size)

vllm/model_executor/models/opt.py

@@ -21,7 +21,7 @@
 The input of the model is flattened to a 1D tensor of tokens. The model uses
 InputMetadata to extract the original 2D shape of the input.
 """
-from typing import Dict, List, Optional, Tuple
+from typing import List, Optional, Tuple
 
 import torch
 from torch import nn
@@ -37,7 +37,7 @@ from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
 from vllm.model_executor.parallel_utils.tensor_parallel import (
     VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
-from vllm.sequence import SequenceOutputs
+from vllm.sequence import SamplerOutput
 
 KVCache = Tuple[torch.Tensor, torch.Tensor]
 
@@ -282,7 +282,7 @@ class OPTForCausalLM(nn.Module):
         kv_caches: List[KVCache],
         input_metadata: InputMetadata,
         cache_events: Optional[List[torch.cuda.Event]],
-    ) -> Dict[int, SequenceOutputs]:
+    ) -> SamplerOutput:
         hidden_states = self.model(input_ids, positions, kv_caches,
                                    input_metadata, cache_events)
         next_tokens = self.sampler(self.lm_head_weight, hidden_states,
@@ -297,12 +297,13 @@ class OPTForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     use_np_cache: bool = False):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tensor_model_parallel_rank = get_tensor_model_parallel_rank()
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, use_np_cache):
+                model_name_or_path, cache_dir, load_format, revision):
             if "lm_head.weight" in name:
                 continue
 

vllm/model_executor/models/qwen.py

@@ -0,0 +1,327 @@
+# coding=utf-8
+# Adapted from
+# https://huggingface.co/Qwen/Qwen-7B/blob/main/modeling_qwen.py
+# Copyright (c) Alibaba Cloud.
+# LICENSE: https://huggingface.co/Qwen/Qwen-7B/blob/main/LICENSE
+"""Inference-only QWen model compatible with HuggingFace weights.
+
+The input of the model is flattened to a 1D tensor of tokens. The model uses
+InputMetadata to extract the original 2D shape of the input.
+"""
+from typing import Any, Dict, List, Optional, Tuple
+
+import torch
+from torch import nn
+
+from vllm.model_executor.input_metadata import InputMetadata
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.attention import PagedAttentionWithRoPE
+from vllm.model_executor.layers.sampler import Sampler
+from vllm.model_executor.weight_utils import (
+    convert_pyslice_to_tensor,
+    hf_model_weights_iterator,
+    load_padded_tensor_parallel_vocab,
+    load_tensor_parallel_weights,
+)
+from vllm.model_executor.parallel_utils.parallel_state import (
+    get_tensor_model_parallel_rank,
+    get_tensor_model_parallel_world_size,
+)
+from vllm.model_executor.parallel_utils.tensor_parallel import (
+    VocabParallelEmbedding,
+    ColumnParallelLinear,
+    RowParallelLinear,
+)
+from vllm.sequence import SamplerOutput
+from vllm.transformers_utils.configs.qwen import QWenConfig
+
+KVCache = Tuple[torch.Tensor, torch.Tensor]
+
+
+class QWenMLP(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str = "silu",
+    ):
+        super().__init__()
+        self.gate_up_proj = ColumnParallelLinear(
+            hidden_size,
+            2 * intermediate_size,
+            bias=False,
+            gather_output=False,
+            perform_initialization=False,
+        )
+        self.c_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            perform_initialization=False,
+        )
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.c_proj(x)
+        return x
+
+
+class QWenAttention(nn.Module):
+
+    def __init__(self,
+                 hidden_size: int,
+                 num_heads: int,
+                 max_position_embeddings: int,
+                 rope_theta: float = 10000,
+                 rope_scaling: Optional[Dict[str, Any]] = None):
+        super().__init__()
+        self.hidden_size = hidden_size
+        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
+        )
+        self.total_num_heads = num_heads
+        assert self.total_num_heads % tensor_model_parallel_world_size == 0
+        self.num_heads = (self.total_num_heads //
+                          tensor_model_parallel_world_size)
+        self.head_dim = hidden_size // self.total_num_heads
+
+        # pylint: disable=invalid-name
+        self.c_attn = ColumnParallelLinear(
+            hidden_size,
+            3 * hidden_size,
+            bias=True,
+            gather_output=False,
+            perform_initialization=False,
+        )
+        self.c_proj = RowParallelLinear(
+            self.total_num_heads * self.head_dim,
+            hidden_size,
+            bias=False,
+            input_is_parallel=True,
+            perform_initialization=False,
+        )
+        self.scaling = self.head_dim**-0.5
+        self.attn = PagedAttentionWithRoPE(
+            self.num_heads,
+            self.head_dim,
+            self.scaling,
+            rotary_dim=self.head_dim,
+            base=rope_theta,
+            max_position=max_position_embeddings,
+            rope_scaling=rope_scaling)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        qkv, _ = self.c_attn(hidden_states)
+        q, k, v = qkv.chunk(chunks=3, dim=-1)
+
+        k_cache, v_cache = kv_cache
+        attn_output = self.attn(positions, q, k, v, k_cache, v_cache,
+                                input_metadata, cache_event)
+
+        output, _ = self.c_proj(attn_output)
+        return output
+
+
+class QWenBlock(nn.Module):
+
+    def __init__(self, config: QWenConfig):
+        super().__init__()
+        self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        self.attn = QWenAttention(config.hidden_size,
+                                  config.num_attention_heads,
+                                  config.max_position_embeddings,
+                                  rope_theta=rope_theta,
+                                  rope_scaling=rope_scaling)
+
+        self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+        self.mlp = QWenMLP(config.hidden_size, config.intermediate_size // 2)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: KVCache,
+        input_metadata: InputMetadata,
+        cache_event: Optional[torch.cuda.Event],
+    ) -> torch.Tensor:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.ln_1(hidden_states)
+        hidden_states = self.attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            kv_cache=kv_cache,
+            input_metadata=input_metadata,
+            cache_event=cache_event,
+        )
+        hidden_states = residual + hidden_states
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.ln_2(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        return hidden_states
+
+
+class QWenModel(nn.Module):
+
+    def __init__(self, config: QWenConfig):
+        super().__init__()
+        self.config = config
+        self.vocab_size = config.vocab_size
+
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.wte = VocabParallelEmbedding(vocab_size,
+                                          config.hidden_size,
+                                          perform_initialization=False)
+        self.h = nn.ModuleList(
+            [QWenBlock(config) for _ in range(config.num_hidden_layers)])
+        self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> torch.Tensor:
+        hidden_states = self.wte(input_ids)
+        for i in range(len(self.h)):
+            if cache_events is None:
+                cache_event = None
+            else:
+                cache_event = cache_events[i]
+            layer = self.h[i]
+            hidden_states = layer(
+                positions,
+                hidden_states,
+                kv_caches[i],
+                input_metadata,
+                cache_event,
+            )
+        hidden_states = self.ln_f(hidden_states)
+        return hidden_states
+
+
+class QWenLMHeadModel(nn.Module):
+
+    def __init__(self, config: QWenConfig):
+        super().__init__()
+        self.config = config
+        self.transformer = QWenModel(config)
+        vocab_size = ((config.vocab_size + 63) // 64) * 64
+        self.lm_head = ColumnParallelLinear(
+            config.hidden_size,
+            vocab_size,
+            bias=False,
+            gather_output=False,
+            perform_initialization=False,
+        )
+        self.sampler = Sampler(config.vocab_size)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        kv_caches: List[KVCache],
+        input_metadata: InputMetadata,
+        cache_events: Optional[List[torch.cuda.Event]],
+    ) -> SamplerOutput:
+        hidden_states = self.transformer(input_ids, positions, kv_caches,
+                                         input_metadata, cache_events)
+        next_tokens = self.sampler(self.lm_head.weight, hidden_states,
+                                   input_metadata)
+        return next_tokens
+
+    _column_parallel_weights = []
+    _row_parallel_weights = ["c_proj.weight"]
+
+    def load_weights(
+        self,
+        model_name_or_path: str,
+        cache_dir: Optional[str] = None,
+        load_format: str = "auto",
+        revision: Optional[str] = None,
+    ):
+        tp_world_size = get_tensor_model_parallel_world_size()
+        tp_rank = get_tensor_model_parallel_rank()
+        state_dict = self.state_dict()
+
+        for name, loaded_weight in hf_model_weights_iterator(
+                model_name_or_path, cache_dir, load_format, revision):
+            if "rotary_emb.inv_freq" in name:
+                continue
+
+            loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+
+            if "c_attn" in name:
+                total_num_heads = self.config.num_attention_heads
+                hidden_size = self.config.hidden_size
+                head_size = hidden_size // total_num_heads
+                num_heads = total_num_heads // tp_world_size
+                head_start = tp_rank * num_heads
+                head_end = (tp_rank + 1) * num_heads
+
+                if "weight" in name:
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size, hidden_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :, :]
+                    loaded_weight = loaded_weight.reshape(-1, hidden_size)
+                elif "bias" in name:
+                    loaded_weight = loaded_weight.view(3, total_num_heads,
+                                                       head_size)
+                    loaded_weight = loaded_weight[:, head_start:head_end, :]
+                    loaded_weight = loaded_weight.reshape(-1)
+
+            is_gate_up_weight = False
+            for stride_id, weight_name in enumerate(["w2", "w1"]):
+                if weight_name not in name:
+                    continue
+                param = state_dict[name.replace(weight_name, "gate_up_proj")]
+                shard_size = param.shape[0] // 2
+                loaded_weight = loaded_weight[shard_size * tp_rank:shard_size *
+                                              (tp_rank + 1)]
+                param_slice = param.data[shard_size * stride_id:shard_size *
+                                         (stride_id + 1)]
+                assert param_slice.shape == loaded_weight.shape
+                param_slice.copy_(loaded_weight)
+                is_gate_up_weight = True
+                break
+            if is_gate_up_weight:
+                continue
+
+            param = state_dict[name]
+
+            if "wte" in name or "lm_head" in name:
+                load_padded_tensor_parallel_vocab(param, loaded_weight,
+                                                  tp_rank)
+                continue
+
+            load_tensor_parallel_weights(
+                param,
+                loaded_weight,
+                name,
+                self._column_parallel_weights,
+                self._row_parallel_weights,
+                tp_rank,
+            )

vllm/model_executor/parallel_utils/__init__.py

@@ -1,9 +1,6 @@
 import vllm.model_executor.parallel_utils.parallel_state
 import vllm.model_executor.parallel_utils.tensor_parallel
 
-# Alias parallel_state as mpu, its legacy name
-mpu = parallel_state
-
 __all__ = [
     "parallel_state",
     "tensor_parallel",

vllm/model_executor/parallel_utils/parallel_state.py

@@ -44,7 +44,6 @@ _PIPELINE_GLOBAL_RANKS = None
 # rank when broadcasting weights from src to all other data parallel ranks
 _DATA_PARALLEL_GLOBAL_RANKS = None
 
-_ALL_REDUCE_LAUNCHER: Optional['GraphAllReduce'] = None
 
 def initialize_model_parallel(
     tensor_model_parallel_size: int = 1,
@@ -196,20 +195,6 @@ def initialize_model_parallel(
         if rank in ranks:
             _POSITION_EMBEDDING_GLOBAL_RANKS = position_embedding_ranks
 
-def initialize_all_reduce_launcher(
-    max_num_tokens: int,
-    hidden_size: int,
-    dtype: torch.dtype,
-    disable_graph: bool = False,
-) -> None:
-    global _ALL_REDUCE_LAUNCHER
-    _ALL_REDUCE_LAUNCHER = GraphAllReduce(
-        max_num_tokens=max_num_tokens,
-        hidden_size=hidden_size,
-        dtype=dtype,
-        disable_graph=disable_graph,
-    )
-
 def model_parallel_is_initialized():
     """Check if model and data parallel groups are initialized."""
     if _TENSOR_MODEL_PARALLEL_GROUP is None or \
@@ -458,6 +443,7 @@ def get_pipeline_model_parallel_last_rank():
     last_rank_local = get_pipeline_model_parallel_world_size() - 1
     return _PIPELINE_GLOBAL_RANKS[last_rank_local]
 
+
 def get_pipeline_model_parallel_next_rank():
     """Return the global rank that follows the caller in the pipeline"""
     assert _PIPELINE_GLOBAL_RANKS is not None, \
@@ -485,10 +471,6 @@ def get_data_parallel_rank():
     """Return my rank for the data parallel group."""
     return torch.distributed.get_rank(group=get_data_parallel_group())
 
-def get_all_reduce_launcher() -> 'GraphAllReduce':
-    assert _ALL_REDUCE_LAUNCHER is not None, 'all reduce launcher is not initialized'
-    return _ALL_REDUCE_LAUNCHER
-
 def destroy_model_parallel():
     """Set the groups to none."""
     global _MODEL_PARALLEL_GROUP
@@ -515,56 +497,3 @@ def destroy_model_parallel():
     _MPU_TENSOR_MODEL_PARALLEL_RANK = None
     global _MPU_PIPELINE_MODEL_PARALLEL_RANK
     _MPU_PIPELINE_MODEL_PARALLEL_RANK = None
-
-
-class GraphAllReduce:
-
-    def __init__(
-        self,
-        max_num_tokens: int,
-        hidden_size: int,
-        dtype: torch.dtype,
-        disable_graph: bool = False,
-    ) -> None:
-        self.max_num_tokens = max_num_tokens
-        self.hidden_size = hidden_size
-        self.disable_graph = disable_graph
-
-        tp_world_size = get_tensor_model_parallel_world_size()
-        if tp_world_size == 1:
-            return
-
-        self.group = get_tensor_model_parallel_group()
-        self.buffer = torch.empty(
-            size=(max_num_tokens, hidden_size),
-            dtype=dtype,
-            device='cuda',
-        )
-
-        # Build graphs for different number of tokens.
-        if not self.disable_graph:
-            self.graphs = {}
-            for num_tokens in range(8, max_num_tokens + 1, 8):
-                self.graphs[num_tokens] = self._build_graph(num_tokens)
-
-    def _build_graph(self, num_tokens: int) -> torch.cuda.CUDAGraph:
-        # Warm up.
-        torch.distributed.all_reduce(self.buffer[:num_tokens], group=self.group)
-        torch.cuda.synchronize()
-
-        # Build graph.
-        graph = torch.cuda.CUDAGraph()
-        with torch.cuda.graph(graph):
-            torch.distributed.all_reduce(
-                self.buffer[:num_tokens], group=self.group)
-        torch.cuda.synchronize()
-        return graph
-
-    def launch(self, x: torch.Tensor) -> torch.Tensor:
-        # NOTE: x must be a slice of self.buffer.
-        num_tokens = x.shape[0]
-        if self.disable_graph:
-            torch.distributed.all_reduce(x, group=self.group)
-        else:
-            self.graphs[num_tokens].replay()
-        return x

vllm/model_executor/parallel_utils/tensor_parallel/__init__.py

@@ -12,6 +12,7 @@ from .mappings import (
     copy_to_tensor_model_parallel_region,
     gather_from_tensor_model_parallel_region,
     gather_from_sequence_parallel_region,
+    reduce_from_tensor_model_parallel_region,
     scatter_to_tensor_model_parallel_region,
     scatter_to_sequence_parallel_region,
 )
@@ -38,7 +39,7 @@ __all__ = [
     "copy_to_tensor_model_parallel_region",
     "gather_from_tensor_model_parallel_region",
     "gather_from_sequence_parallel_region",
-#    "reduce_from_tensor_model_parallel_region",
+    "reduce_from_tensor_model_parallel_region",
     "scatter_to_tensor_model_parallel_region",
     "scatter_to_sequence_parallel_region",
     # random.py

vllm/model_executor/parallel_utils/tensor_parallel/layers.py

@@ -4,7 +4,7 @@
 
 # Parts of the code here are adapted from PyTorch
 # repo: https://github.com/pytorch/pytorch
-
+from typing import Optional
 
 import torch
 import torch.nn.functional as F
@@ -14,16 +14,13 @@ from torch.nn.parameter import Parameter
 from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_rank,
     get_tensor_model_parallel_world_size,
-    get_all_reduce_launcher,
 )
 from .mappings import (
-    copy_to_tensor_model_parallel_region,
     gather_from_tensor_model_parallel_region,
     reduce_from_tensor_model_parallel_region,
     scatter_to_tensor_model_parallel_region,
 )
 
-from .random import get_cuda_rng_tracker
 from .utils import (
     divide,
     VocabUtility,
@@ -66,59 +63,6 @@ def copy_tensor_model_parallel_attributes(destination_tensor, source_tensor):
         maybe_copy(attribute)
 
 
-def _initialize_affine_weight_gpu(weight, init_method,
-                                  partition_dim, stride=1):
-    """Initialize affine weight for model parallel on GPU."""
-
-    set_tensor_model_parallel_attributes(tensor=weight,
-                                         is_parallel=True,
-                                         dim=partition_dim,
-                                         stride=stride)
-
-    with get_cuda_rng_tracker().fork():
-        init_method(weight)
-
-
-def _initialize_affine_weight_cpu(weight, output_size, input_size,
-                                  per_partition_size, partition_dim,
-                                  init_method, stride=1,
-                                  return_master_weight=False,
-                                  *, params_dtype=None):
-    """Initialize affine weight for model parallel.
-
-    Build the master weight on all processes and scatter
-    the relevant chunk."""
-
-    set_tensor_model_parallel_attributes(tensor=weight,
-                                         is_parallel=True,
-                                         dim=partition_dim,
-                                         stride=stride)
-
-    if params_dtype is None:
-        params_dtype = torch.get_default_dtype()
-
-    # Initialize master weight
-    master_weight = torch.empty(output_size, input_size,
-                                dtype=torch.float,
-                                requires_grad=False)
-    init_method(master_weight)
-    master_weight = master_weight.to(dtype=params_dtype)
-
-    # Split and copy
-    per_partition_per_stride_size = divide(per_partition_size, stride)
-    weight_list = torch.split(master_weight, per_partition_per_stride_size,
-                              dim=partition_dim)
-    rank = get_tensor_model_parallel_rank()
-    world_size = get_tensor_model_parallel_world_size()
-    my_weight_list = weight_list[rank::world_size]
-
-    with torch.no_grad():
-        torch.cat(my_weight_list, dim=partition_dim, out=weight)
-    if return_master_weight:
-        return master_weight
-    return None
-
-
 class VocabParallelEmbedding(torch.nn.Module):
     """Embedding parallelized in the vocabulary dimension.
 
@@ -139,8 +83,11 @@ class VocabParallelEmbedding(torch.nn.Module):
                  init_method=init.xavier_normal_,
                  params_dtype: torch.dtype=None,
                  use_cpu_initialization: bool=False,
-                 perform_initialization: bool=True):
+                 perform_initialization: bool=False):
         super(VocabParallelEmbedding, self).__init__()
+        assert not perform_initialization
+        assert not use_cpu_initialization
+
         # Keep the input dimensions.
         self.num_embeddings = num_embeddings
         self.embedding_dim = embedding_dim
@@ -163,23 +110,9 @@ class VocabParallelEmbedding(torch.nn.Module):
         self.num_embeddings_per_partition = self.vocab_end_index - \
             self.vocab_start_index
 
-        # Allocate weights and initialize.
-        if use_cpu_initialization:
-            self.weight = Parameter(torch.empty(
-                self.num_embeddings_per_partition, self.embedding_dim,
-                dtype=params_dtype))
-            if perform_initialization:
-                _initialize_affine_weight_cpu(
-                    self.weight, self.num_embeddings, self.embedding_dim,
-                    self.num_embeddings_per_partition, 0, init_method,
-                    params_dtype=params_dtype)
-        else:
-            self.weight = Parameter(torch.empty(
-                self.num_embeddings_per_partition, self.embedding_dim,
-                device=torch.cuda.current_device(), dtype=params_dtype))
-            if perform_initialization:
-                _initialize_affine_weight_gpu(self.weight, init_method,
-                                              partition_dim=0, stride=1)
+        self.weight = Parameter(torch.empty(
+            self.num_embeddings_per_partition, self.embedding_dim,
+            device=torch.cuda.current_device(), dtype=params_dtype))
 
     def forward(self, input_):
         if self.tensor_model_parallel_size > 1:
@@ -239,18 +172,22 @@ class ColumnParallelLinear(torch.nn.Module):
                  skip_bias_add=False,
                  params_dtype=None,
                  use_cpu_initialization=False,
-                 perform_initialization=True,
+                 perform_initialization=False,
+                 quant_config=None,
                  ):
         super(ColumnParallelLinear, self).__init__()
+        assert not perform_initialization
+        assert not use_cpu_initialization
 
         # Keep input parameters
         self.input_size = input_size
         self.output_size = output_size
         self.gather_output = gather_output
         # Divide the weight matrix along the last dimension.
-        world_size = get_tensor_model_parallel_world_size()
-        self.output_size_per_partition = divide(output_size, world_size)
+        self.world_size = get_tensor_model_parallel_world_size()
+        self.output_size_per_partition = divide(output_size, self.world_size)
         self.skip_bias_add = skip_bias_add
+        self.quant_config = quant_config
 
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
@@ -258,33 +195,13 @@ class ColumnParallelLinear(torch.nn.Module):
         # Parameters.
         # Note: torch.nn.functional.linear performs XA^T + b and as a result
         # we allocate the transpose.
-        # Initialize weight.
-        if use_cpu_initialization:
-            self.weight = Parameter(torch.empty(self.output_size_per_partition,
-                                                self.input_size,
-                                                dtype=params_dtype))
-            if perform_initialization:
-                self.master_weight = _initialize_affine_weight_cpu(
-                    self.weight, self.output_size, self.input_size,
-                    self.output_size_per_partition, 0, init_method,
-                    stride=stride, return_master_weight=keep_master_weight_for_test)
-        else:
-            self.weight = Parameter(torch.empty(
-                self.output_size_per_partition, self.input_size,
-                device=torch.cuda.current_device(), dtype=params_dtype))
-            if perform_initialization:
-                _initialize_affine_weight_gpu(self.weight, init_method,
-                                              partition_dim=0, stride=stride)
+        self.create_weights(params_dtype)
 
         if bias:
-            if use_cpu_initialization:
-                self.bias = Parameter(torch.empty(
-                    self.output_size_per_partition, dtype=params_dtype))
-            else:
-                self.bias = Parameter(torch.empty(
-                    self.output_size_per_partition,
-                    device=torch.cuda.current_device(),
-                    dtype=params_dtype))
+            self.bias = Parameter(torch.empty(
+                self.output_size_per_partition,
+                device=torch.cuda.current_device(),
+                dtype=params_dtype))
             set_tensor_model_parallel_attributes(self.bias, True, 0, stride)
             # Always initialize bias to zero.
             with torch.no_grad():
@@ -292,6 +209,17 @@ class ColumnParallelLinear(torch.nn.Module):
         else:
             self.register_parameter('bias', None)
 
+    def create_weights(self, dtype: torch.dtype) -> None:
+        self.weight = Parameter(torch.empty(
+            self.output_size_per_partition, self.input_size,
+            device=torch.cuda.current_device(), dtype=dtype))
+
+    def apply_weights(
+        self,
+        x: torch.Tensor,
+        bias: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        return F.linear(x, self.weight, bias)
 
     def forward(self, input_):
         """Forward of ColumnParallelLinear
@@ -307,7 +235,7 @@ class ColumnParallelLinear(torch.nn.Module):
 
         input_parallel = input_
         # Matrix multiply.
-        output_parallel = F.linear(input_parallel, self.weight, bias)
+        output_parallel = self.apply_weights(input_parallel, bias)
         if self.gather_output:
             # All-gather across the partitions.
             output = gather_from_tensor_model_parallel_region(output_parallel)
@@ -350,6 +278,7 @@ class RowParallelLinear(torch.nn.Module):
         params_dtype:
         use_cpu_initialization:
         perform_initialization:
+        reduce_results:
     """
 
     def __init__(self, input_size, output_size, *,
@@ -359,58 +288,52 @@ class RowParallelLinear(torch.nn.Module):
                  skip_bias_add=False,
                  params_dtype=None,
                  use_cpu_initialization=False,
-                 perform_initialization=True,
+                 perform_initialization=False,
+                 reduce_results=True,
+                 quant_config=None,
                  ):
         super(RowParallelLinear, self).__init__()
+        assert not perform_initialization
+        assert not use_cpu_initialization
 
         # Keep input parameters
         self.input_size = input_size
         self.output_size = output_size
         self.input_is_parallel = input_is_parallel
+        self.reduce_results = reduce_results
         if params_dtype is None:
             params_dtype = torch.get_default_dtype()
 
         # Divide the weight matrix along the last dimension.
-        world_size = get_tensor_model_parallel_world_size()
-        self.input_size_per_partition = divide(input_size, world_size)
+        self.world_size = get_tensor_model_parallel_world_size()
+        self.input_size_per_partition = divide(input_size, self.world_size)
         self.skip_bias_add = skip_bias_add
+        self.quant_config = quant_config
+
+        self.create_weights(params_dtype)
+
+        if not reduce_results and (bias and not skip_bias_add):
+            raise ValueError("When not reduce the results, adding bias to the "
+                             "results can lead to incorrect results")
 
-        # Parameters.
-        # Note: torch.nn.functional.linear performs XA^T + b and as a result
-        # we allocate the transpose.
-        # Initialize weight.
-        if use_cpu_initialization:
-            self.weight = Parameter(torch.empty(self.output_size,
-                                                self.input_size_per_partition,
-                                                dtype=params_dtype))
-            if perform_initialization:
-                self.master_weight = _initialize_affine_weight_cpu(
-                    self.weight, self.output_size, self.input_size,
-                    self.input_size_per_partition, 1, init_method,
-                    stride=stride, return_master_weight=keep_master_weight_for_test,
-                    params_dtype=params_dtype)
-        else:
-            self.weight = Parameter(torch.empty(
-                self.output_size, self.input_size_per_partition,
-                device=torch.cuda.current_device(), dtype=params_dtype))
-            if perform_initialization:
-                _initialize_affine_weight_gpu(self.weight, init_method,
-                                              partition_dim=1, stride=stride)
         if bias:
-            if use_cpu_initialization:
-                self.bias = Parameter(torch.empty(self.output_size,
-                                                  dtype=params_dtype))
-            else:
-                self.bias = Parameter(torch.empty(
-                    self.output_size, device=torch.cuda.current_device(),
-                    dtype=params_dtype))
+            self.bias = Parameter(torch.empty(
+                self.output_size, device=torch.cuda.current_device(),
+                dtype=params_dtype))
 
             # Always initialize bias to zero.
             with torch.no_grad():
                 self.bias.zero_()
         else:
             self.register_parameter('bias', None)
-        self.weight_t = self.weight.t()
+
+    def create_weights(self, dtype: torch.dtype) -> None:
+        self.weight = Parameter(torch.empty(
+                self.output_size, self.input_size_per_partition,
+                device=torch.cuda.current_device(), dtype=dtype))
+
+    def apply_weights(self, x: torch.Tensor) -> torch.Tensor:
+        return F.linear(x, self.weight)
 
     def forward(self, input_):
         """Forward of RowParallelLinear
@@ -427,17 +350,12 @@ class RowParallelLinear(torch.nn.Module):
             input_parallel = input_
         else:
             input_parallel = scatter_to_tensor_model_parallel_region(input_)
-        if get_tensor_model_parallel_world_size() == 1:
-            # Matrix multiply.
-            output_ = F.linear(input_parallel, self.weight)
+        # Matrix multiply.
+        output_parallel = self.apply_weights(input_parallel)
+        if self.reduce_results and self.world_size > 1:
+            output_ = reduce_from_tensor_model_parallel_region(output_parallel)
         else:
-            # Matrix multiply.
-            all_reduce_launcher = get_all_reduce_launcher()
-            num_tokens = input_parallel.shape[0]
-            output_buffer = all_reduce_launcher.buffer[:num_tokens]
-            torch.matmul(input_parallel, self.weight_t, out=output_buffer)
-            # All-reduce across all the partitions.
-            output_ = all_reduce_launcher.launch(output_buffer)
+            output_ = output_parallel
 
         if not self.skip_bias_add:
             output = output_ + self.bias if self.bias is not None else output_

vllm/model_executor/quantization_utils/__init__.py

@@ -0,0 +1,20 @@
+from typing import Type
+
+from vllm.model_executor.quantization_utils.awq import AWQConfig
+from vllm.model_executor.quantization_utils.base import QuantizationConfig
+
+_QUANTIZATION_REGISTRY = {
+    "awq": AWQConfig,
+}
+
+
+def get_quant_class(quantization: str) -> Type[QuantizationConfig]:
+    if quantization not in _QUANTIZATION_REGISTRY:
+        raise ValueError(f"Invalid quantization method: {quantization}")
+    return _QUANTIZATION_REGISTRY[quantization]
+
+
+__all__ = [
+    "QuantizationConfig",
+    "get_quant_class",
+]

vllm/model_executor/quantization_utils/awq.py

@@ -0,0 +1,72 @@
+from typing import Any, Dict, List
+
+import torch
+
+from vllm.model_executor.quantization_utils.base import QuantizationConfig
+
+
+class AWQConfig(QuantizationConfig):
+    """Config class for AWQ.
+
+    Reference: https://arxiv.org/abs/2306.00978
+    """
+
+    def __init__(
+        self,
+        weight_bits: int,
+        group_size: int,
+        zero_point: bool,
+    ) -> None:
+        self.weight_bits = weight_bits
+        self.group_size = group_size
+        self.zero_point = zero_point
+
+        if self.weight_bits != 4:
+            raise ValueError(
+                "Currently, only 4-bit weight quantization is supported for "
+                f"AWQ, but got {self.weight_bits} bits.")
+        self.pack_factor = 32 // self.weight_bits
+
+    def __repr__(self) -> str:
+        return (f"AWQConfig(weight_bits={self.weight_bits}, "
+                f"group_size={self.group_size}, "
+                f"zero_point={self.zero_point})")
+
+    @classmethod
+    def get_name(cls) -> str:
+        return "awq"
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        return [torch.half]
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        # The AWQ kernel only supports Ampere or newer GPUs.
+        return 80
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        return [
+            "quant_config.json",  # E.g., casperhansen/vicuna-7b-v1.5-awq
+            "quantize_config.json",  # E.g., abhinavkulkarni/mosaicml-mpt-7b-instruct-w4-g128-awq  # pylint: disable=line-too-long
+        ]
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "AWQConfig":
+        weight_bits = cls.get_from_keys(config, ["w_bit", "bits"])
+        group_size = cls.get_from_keys(config, ["q_group_size", "group_size"])
+        zero_point = cls.get_from_keys(config, ["zero_point"])
+        return cls(weight_bits, group_size, zero_point)
+
+    @classmethod
+    def get_packed_tensor_names(cls) -> List[str]:
+        return ["qweight", "qzeros"]
+
+    @classmethod
+    def get_transposed_tensor_names(cls) -> List[str]:
+        return ["qweight", "qzeros", "scales"]
+
+    @classmethod
+    def get_tp_tensor_names(cls) -> List[str]:
+        return ["qweight", "qzeros", "scales"]

vllm/model_executor/quantization_utils/base.py

@@ -0,0 +1,75 @@
+from typing import Any, Dict, List
+
+import torch
+
+
+class QuantizationConfig:
+
+    @classmethod
+    def get_name(cls) -> str:
+        """Name of the quantization method."""
+        raise NotImplementedError
+
+    @classmethod
+    def get_supported_act_dtypes(cls) -> List[torch.dtype]:
+        """List of supported activation dtypes."""
+        raise NotImplementedError
+
+    @classmethod
+    def get_min_capability(cls) -> int:
+        """Minimum GPU capability to support the quantization method.
+
+        E.g., 70 for Volta, 75 for Turing, 80 for Ampere.
+        This requirement is due to the custom CUDA kernels used by the
+        quantization method.
+        """
+        raise NotImplementedError
+
+    @classmethod
+    def get_config_filenames(cls) -> List[str]:
+        """List of filenames to search for in the model directory."""
+        raise NotImplementedError
+
+    @classmethod
+    def from_config(cls, config: Dict[str, Any]) -> "QuantizationConfig":
+        """Create a config class from the model's quantization config."""
+        raise NotImplementedError
+
+    @staticmethod
+    def get_from_keys(config: Dict[str, Any], keys: List[str]) -> Any:
+        """Get a value from the model's quantization config."""
+        for key in keys:
+            if key in config:
+                return config[key]
+        raise ValueError(f"Cannot find any of {keys} in the model's "
+                         "quantization config.")
+
+    @classmethod
+    def get_packed_tensor_names(cls) -> List[str]:
+        raise NotImplementedError
+
+    @classmethod
+    def is_packed(cls, tensor_name: str) -> bool:
+        """Returns True if a tensor is packed.
+
+        A tensor is considered packed if each element in the tensor is a
+        packed representation of multiple elements in the original tensor.
+        For example, an INT32 element in the tensor may represent 8 INT4
+        elements in the original tensor.
+        """
+        return any(tag in tensor_name for tag in cls.get_packed_tensor_names())
+
+    @classmethod
+    def get_transposed_tensor_names(cls) -> List[str]:
+        raise NotImplementedError
+
+    @classmethod
+    def is_transposed(cls, tensor_name: str) -> bool:
+        """Returns True if a tensor is transposed relative to nn.Linear.weight.
+        """
+        return any(tag in tensor_name
+                   for tag in cls.get_transposed_tensor_names())
+
+    @classmethod
+    def get_tp_tensor_names(cls) -> List[str]:
+        raise NotImplementedError

vllm/model_executor/weight_utils.py

@@ -3,13 +3,21 @@ import filelock
 import glob
 import json
 import os
-from typing import Iterator, List, Optional, Tuple
+from collections import defaultdict
+from typing import Any, Iterator, List, Optional, Tuple
 
 from huggingface_hub import snapshot_download
+from safetensors.torch import load_file, save_file, safe_open
 import numpy as np
 import torch
 from tqdm.auto import tqdm
 
+from vllm.logger import init_logger
+from vllm.model_executor.quantization_utils import get_quant_class
+from vllm.model_executor.quantization_utils.base import QuantizationConfig
+
+logger = init_logger(__name__)
+
 
 class Disabledtqdm(tqdm):
 
@@ -17,40 +25,186 @@ class Disabledtqdm(tqdm):
         super().__init__(*args, **kwargs, disable=True)
 
 
-def hf_model_weights_iterator(
-    model_name_or_path: str,
-    cache_dir: Optional[str] = None,
-    use_np_cache: bool = False,
-) -> Iterator[Tuple[str, torch.Tensor]]:
-    # Prepare file lock directory to prevent multiple processes from
-    # downloading the same model weights at the same time.
+def get_lock(model_name_or_path: str, cache_dir: Optional[str] = None):
     lock_dir = cache_dir if cache_dir is not None else "/tmp"
     lock_file_name = model_name_or_path.replace("/", "-") + ".lock"
     lock = filelock.FileLock(os.path.join(lock_dir, lock_file_name))
+    return lock
 
-    # Download model weights from huggingface.
+
+def _shared_pointers(tensors):
+    ptrs = defaultdict(list)
+    for k, v in tensors.items():
+        ptrs[v.data_ptr()].append(k)
+    failing = []
+    for _, names in ptrs.items():
+        if len(names) > 1:
+            failing.append(names)
+    return failing
+
+
+def convert_bin_to_safetensor_file(
+    pt_filename: str,
+    sf_filename: str,
+) -> None:
+    loaded = torch.load(pt_filename, map_location="cpu")
+    if "state_dict" in loaded:
+        loaded = loaded["state_dict"]
+    shared = _shared_pointers(loaded)
+    for shared_weights in shared:
+        for name in shared_weights[1:]:
+            loaded.pop(name)
+
+    # For tensors to be contiguous
+    loaded = {k: v.contiguous() for k, v in loaded.items()}
+
+    dirname = os.path.dirname(sf_filename)
+    os.makedirs(dirname, exist_ok=True)
+    save_file(loaded, sf_filename, metadata={"format": "pt"})
+
+    # check file size
+    sf_size = os.stat(sf_filename).st_size
+    pt_size = os.stat(pt_filename).st_size
+    if (sf_size - pt_size) / pt_size > 0.01:
+        raise RuntimeError(f"""The file size different is more than 1%:
+         - {sf_filename}: {sf_size}
+         - {pt_filename}: {pt_size}
+         """)
+
+    # check if the tensors are the same
+    reloaded = load_file(sf_filename)
+    for k in loaded:
+        pt_tensor = loaded[k]
+        sf_tensor = reloaded[k]
+        if not torch.equal(pt_tensor, sf_tensor):
+            raise RuntimeError(f"The output tensors do not match for key {k}")
+
+
+# TODO(woosuk): Move this to other place.
+def get_quant_config(
+    quantization: str,
+    model_name_or_path: str,
+    cache_dir: Optional[str] = None,
+) -> QuantizationConfig:
     is_local = os.path.isdir(model_name_or_path)
     if not is_local:
-        with lock:
+        # Download the config files.
+        with get_lock(model_name_or_path, cache_dir):
             hf_folder = snapshot_download(model_name_or_path,
-                                          allow_patterns="*.bin",
+                                          allow_patterns="*.json",
                                           cache_dir=cache_dir,
                                           tqdm_class=Disabledtqdm)
     else:
         hf_folder = model_name_or_path
+    config_files = glob.glob(os.path.join(hf_folder, "*.json"))
 
-    hf_bin_files = glob.glob(os.path.join(hf_folder, "*.bin"))
+    quant_cls = get_quant_class(quantization)
+    quant_config_files = [
+        f for f in config_files if any(
+            f.endswith(x) for x in quant_cls.get_config_filenames())
+    ]
+    if len(quant_config_files) == 0:
+        raise ValueError(f"Cannot find the config file for {quantization}")
+    if len(quant_config_files) > 1:
+        raise ValueError(f"Found multiple config files for {quantization}: "
+                         f"{quant_config_files}")
+
+    quant_config_file = quant_config_files[0]
+    with open(quant_config_file, "r") as f:
+        config = json.load(f)
+    return quant_cls.from_config(config)
+
+
+def prepare_hf_model_weights(
+    model_name_or_path: str,
+    cache_dir: Optional[str] = None,
+    use_safetensors: bool = False,
+    fall_back_to_pt: bool = True,
+    revision: Optional[str] = None,
+) -> Tuple[str, List[str], bool]:
+    # Download model weights from huggingface.
+    is_local = os.path.isdir(model_name_or_path)
+    if use_safetensors:
+        allow_patterns = ["*.safetensors"]
+    else:
+        # Some quantized models use .pt files for storing the weights.
+        allow_patterns = ["*.bin", "*.pt"]
+    if not is_local:
+        # Use file lock to prevent multiple processes from
+        # downloading the same model weights at the same time.
+        with get_lock(model_name_or_path, cache_dir):
+            hf_folder = snapshot_download(model_name_or_path,
+                                          allow_patterns=allow_patterns,
+                                          cache_dir=cache_dir,
+                                          tqdm_class=Disabledtqdm,
+                                          revision=revision)
+    else:
+        hf_folder = model_name_or_path
+    hf_weights_files: List[str] = []
+    for pattern in allow_patterns:
+        hf_weights_files += glob.glob(os.path.join(hf_folder, pattern))
+    if not use_safetensors:
+        hf_weights_files = [
+            x for x in hf_weights_files if not x.endswith("training_args.bin")
+        ]
+
+    if len(hf_weights_files) == 0 and use_safetensors and fall_back_to_pt:
+        return prepare_hf_model_weights(model_name_or_path,
+                                        cache_dir=cache_dir,
+                                        use_safetensors=False,
+                                        fall_back_to_pt=False,
+                                        revision=revision)
+
+    if len(hf_weights_files) == 0:
+        raise RuntimeError(
+            f"Cannot find any model weights with `{model_name_or_path}`")
+
+    return hf_folder, hf_weights_files, use_safetensors
+
+
+def hf_model_weights_iterator(
+    model_name_or_path: str,
+    cache_dir: Optional[str] = None,
+    load_format: str = "auto",
+    revision: Optional[str] = None,
+) -> Iterator[Tuple[str, torch.Tensor]]:
+    use_safetensors = False
+    use_np_cache = False
+    fall_back_to_pt = False
+    if load_format == "auto":
+        use_safetensors = True
+        fall_back_to_pt = True
+    elif load_format == "safetensors":
+        use_safetensors = True
+    elif load_format == "pt":
+        pass
+    elif load_format == "npcache":
+        use_np_cache = True
+    else:
+        raise ValueError(f"Unknown load_format: {load_format}")
+
+    hf_folder, hf_weights_files, use_safetensors = prepare_hf_model_weights(
+        model_name_or_path,
+        cache_dir=cache_dir,
+        use_safetensors=use_safetensors,
+        fall_back_to_pt=fall_back_to_pt,
+        revision=revision)
 
     if use_np_cache:
+        # Currently np_cache only support *.bin checkpoints
+        assert use_safetensors is False
+
         # Convert the model weights from torch tensors to numpy arrays for
         # faster loading.
         np_folder = os.path.join(hf_folder, "np")
         os.makedirs(np_folder, exist_ok=True)
         weight_names_file = os.path.join(np_folder, "weight_names.json")
-        with lock:
+        # Use file lock to prevent multiple processes from
+        # dumping the same model weights to numpy at the same time.
+        with get_lock(model_name_or_path, cache_dir):
             if not os.path.exists(weight_names_file):
                 weight_names = []
-                for bin_file in hf_bin_files:
+                for bin_file in hf_weights_files:
                     state = torch.load(bin_file, map_location="cpu")
                     for name, param in state.items():
                         param_path = os.path.join(np_folder, name)
@@ -68,16 +222,52 @@ def hf_model_weights_iterator(
             with open(param_path, "rb") as f:
                 param = np.load(f)
             yield name, torch.from_numpy(param)
+    elif use_safetensors:
+        for st_file in hf_weights_files:
+            with safe_open(st_file, framework="pt") as f:
+                for name in f.keys():
+                    param = f.get_slice(name)
+                    yield name, param
     else:
-        for bin_file in hf_bin_files:
+        for bin_file in hf_weights_files:
             state = torch.load(bin_file, map_location="cpu")
             for name, param in state.items():
                 yield name, param
+            del state
+            torch.cuda.empty_cache()
+
+
+def convert_pyslice_to_tensor(x: Any) -> torch.Tensor:
+    """convert PySafeSlice object from safetensors to torch.Tensor
+
+    PySafeSlice object supports indexing, which is done before loading the
+    actual tensor and can reduce the amount of memory being read into the
+    memory. However, it does not support more advanced functionalities
+    like `.view()` or `.t()`. Therefore, if we need to modify the loaded
+    tensor with these more complicated operators, we need to convert to
+    tensor first.
+    """
+    if not isinstance(x, torch.Tensor):
+        x = x[:]
+    return x
+
+
+def load_padded_tensor_parallel_vocab(
+    param: torch.Tensor,
+    loaded_weight: Any,  # `torch.Tensor` or `PySafeSlice`
+    tensor_model_parallel_rank: int,
+) -> None:
+    shard_size = param.shape[0]
+    start_idx = tensor_model_parallel_rank * shard_size
+    end_idx = (tensor_model_parallel_rank + 1) * shard_size
+    loaded_weight = loaded_weight[start_idx:end_idx]
+    loaded_weight = convert_pyslice_to_tensor(loaded_weight)
+    param[:loaded_weight.shape[0]].copy_(loaded_weight)
 
 
 def load_tensor_parallel_weights(
     param: torch.Tensor,
-    loaded_weight: torch.Tensor,
+    loaded_weight: Any,  # `torch.Tensor` or `PySafeSlice`
     param_name: str,
     column_parallel_weight_names: List[str],
     row_parallel_weight_names: List[str],
@@ -97,6 +287,8 @@ def load_tensor_parallel_weights(
             end_idx = (tensor_model_parallel_rank + 1) * shard_size
             loaded_weight = loaded_weight[:, start_idx:end_idx]
             break
+
+    loaded_weight = convert_pyslice_to_tensor(loaded_weight)
     assert param.shape == loaded_weight.shape, (
         f"{param_name} shape mismatch between model and checkpoint: "
         f"{param.shape} != {loaded_weight.shape}")

vllm/outputs.py

@@ -75,10 +75,12 @@ class RequestOutput:
         # Get the top-n sequences.
         n = seq_group.sampling_params.n
         seqs = seq_group.get_seqs()
-        assert n <= len(seqs)
-        sorted_seqs = sorted(seqs,
-                             key=lambda seq: seq.get_cumulative_logprob(),
-                             reverse=True)
+        if seq_group.sampling_params.use_beam_search:
+            sorting_key = lambda seq: seq.get_beam_search_score(
+                seq_group.sampling_params.length_penalty)
+        else:
+            sorting_key = lambda seq: seq.get_cumulative_logprob()
+        sorted_seqs = sorted(seqs, key=sorting_key, reverse=True)
         top_n_seqs = sorted_seqs[:n]
 
         # Create the outputs.