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

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

.gitignore

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

README.md

@@ -10,13 +10,24 @@ 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.
@@ -35,13 +46,13 @@ 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.)
@@ -53,6 +64,7 @@ vLLM seamlessly supports many Huggingface models, including the following archit
 - GPT-NeoX (`EleutherAI/gpt-neox-20b`, `databricks/dolly-v2-12b`, `stabilityai/stablelm-tuned-alpha-7b`, 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.)
@@ -72,7 +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">
@@ -104,3 +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

@@ -18,6 +18,7 @@ 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,
@@ -63,19 +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',
+    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]
@@ -63,6 +58,7 @@ def run_vllm(
     requests: List[Tuple[str, int, int]],
     model: str,
     tokenizer: str,
+    quantization: Optional[str],
     tensor_parallel_size: int,
     seed: int,
     n: int,
@@ -72,6 +68,7 @@ def run_vllm(
     llm = LLM(
         model=model,
         tokenizer=tokenizer,
+        quantization=quantization,
         tensor_parallel_size=tensor_parallel_size,
         seed=seed,
         trust_remote_code=trust_remote_code,
@@ -111,8 +108,8 @@ def run_hf(
     trust_remote_code: bool,
 ) -> float:
     assert not use_beam_search
-    llm = AutoModelForCausalLM.from_pretrained(model,
-        torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
+    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
@@ -132,13 +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,
@@ -165,44 +163,58 @@ def main(args: argparse.Namespace):
     random.seed(args.seed)
 
     # Sample the requests.
-    tokenizer = get_tokenizer(args.tokenizer, trust_remote_code=args.trust_remote_code)
+    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, args.trust_remote_code)
+        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.trust_remote_code)
+        elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
+                              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',
@@ -215,6 +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/attention/attention_kernels.cu

@@ -341,6 +341,9 @@ __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,                                                                                  \
@@ -401,6 +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);

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/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.
 
 
@@ -63,6 +64,7 @@ Documentation
 
    serving/distributed_serving
    serving/run_on_sky
+   serving/deploying_with_triton
 
 .. toctree::
    :maxdepth: 1

docs/source/models/supported_models.rst

@@ -44,6 +44,9 @@ Alongside each architecture, we include some popular models that use it.
   * - :code:`LlamaForCausalLM`
     - 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.

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.

requirements-dev.txt

@@ -11,3 +11,4 @@ types-setuptools
 # testing
 pytest
 pytest-forked
+pytest-asyncio

requirements.txt

@@ -1,11 +1,13 @@
 ninja  # For faster builds.
 psutil
 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 >= 4.33.1  # Required for Code Llama.
-xformers >= 0.0.21
+xformers >= 0.0.22
 fastapi
-uvicorn
+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 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:
@@ -38,47 +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)
+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:
+    # 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 89 in compute_capabilities and nvcc_cuda_version < Version("11.8"):
-    # 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.
-    compute_capabilities.remove(89)
-    compute_capabilities.add(80)
-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 no GPU is available, add all supported compute capabilities.
-if not compute_capabilities:
-    compute_capabilities = {70, 75, 80}
-    if nvcc_cuda_version >= Version("11.1"):
-        compute_capabilities.add(86)
-    if nvcc_cuda_version >= Version("11.8"):
-        compute_capabilities.add(89)
-        compute_capabilities.add(90)
+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:
-    NVCC_FLAGS += ["-gencode", f"arch=compute_{capability},code=sm_{capability}"]
+    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"):
@@ -91,7 +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)
 
@@ -99,7 +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)
 
@@ -107,7 +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)
 
@@ -115,7 +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)
 
@@ -123,10 +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)
@@ -138,8 +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.")
@@ -162,7 +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",
@@ -174,11 +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

@@ -40,8 +40,7 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     engine_args = AsyncEngineArgs.from_cli_args(args)
-    engine = AsyncLLMEngineWithStats.from_engine_args(engine_args,
-                                                      start_engine_loop=False)
+    engine = AsyncLLMEngineWithStats.from_engine_args(engine_args)
     vllm.entrypoints.api_server.engine = engine
     uvicorn.run(
         app,

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

@@ -4,10 +4,25 @@ 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
@@ -15,7 +30,9 @@ def test_request_tracker():
 
     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"
@@ -26,6 +43,7 @@ def test_request_tracker():
     # 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()
@@ -36,6 +54,7 @@ def test_request_tracker():
 
     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
@@ -43,9 +62,11 @@ def test_request_tracker():
     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

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/test_attention.py

@@ -7,8 +7,12 @@ 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 = 8192
+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]
@@ -135,6 +139,7 @@ def test_single_query_cached_kv_attention(
                                    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")
 
@@ -242,7 +247,11 @@ def test_multi_query_kv_attention(
     torch.random.manual_seed(seed)
     torch.cuda.manual_seed(seed)
 
-    seq_lens = random.sample(range(1, MAX_SEQ_LEN), num_seqs)
+    # 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))

tests/kernels/test_pos_encoding.py

@@ -133,9 +133,10 @@ def test_rotary_embedding(
                       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)

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__ = "0.1.5"
+__version__ = "0.2.0"
 
 __all__ = [
     "LLM",

vllm/config.py

@@ -38,6 +38,13 @@ class ModelConfig:
             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__(
@@ -50,6 +57,9 @@ class ModelConfig:
         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
@@ -58,11 +68,16 @@ class ModelConfig:
         self.download_dir = download_dir
         self.load_format = load_format
         self.seed = seed
+        self.revision = revision
+        self.quantization = quantization
 
-        self.hf_config = get_config(model, trust_remote_code)
+        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()
@@ -82,6 +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",
@@ -109,22 +135,28 @@ 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 == "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 //
@@ -132,26 +164,6 @@ class ModelConfig:
         total_num_attention_heads = self.hf_config.num_attention_heads
         return total_num_attention_heads // parallel_config.tensor_parallel_size
 
-    def get_max_model_len(self) -> int:
-        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(self.hf_config, key, None)
-            if max_len_key is not None:
-                max_model_len = min(max_model_len, max_len_key)
-        return max_model_len
-
     def get_num_layers(self, parallel_config: "ParallelConfig") -> int:
         total_num_hidden_layers = self.hf_config.num_hidden_layers
         return total_num_hidden_layers // parallel_config.pipeline_parallel_size
@@ -172,10 +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.
@@ -251,11 +265,36 @@ class SchedulerConfig:
             and generated text).
     """
 
-    def __init__(self, max_num_batched_tokens: int, max_num_seqs: int,
-                 max_model_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_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 = {
@@ -311,3 +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:
@@ -224,7 +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

@@ -73,7 +73,7 @@ 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.
@@ -175,7 +175,7 @@ class Scheduler:
                 num_curr_seqs += num_new_seqs
                 scheduled.append(seq_group)
 
-            if scheduled:
+            if scheduled or ignored_seq_groups:
                 scheduler_outputs = SchedulerOutputs(
                     scheduled_seq_groups=scheduled,
                     prompt_run=True,

vllm/engine/arg_utils.py

@@ -18,20 +18,22 @@ class EngineArgs:
     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,
@@ -79,16 +88,22 @@ class EngineArgs:
             'a numpy cache to speed up the loading. '
             '"dummy" will initialize the weights with random values, '
             'which is mainly for profiling.')
-        # TODO(woosuk): Support FP32.
         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',
@@ -136,6 +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
@@ -149,20 +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.trust_remote_code,
                                    self.download_dir, self.load_format,
-                                   self.dtype, self.seed)
-        cache_config = CacheConfig(self.block_size,
-                                   self.gpu_memory_utilization,
-                                   self.swap_space)
+                                   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)
         scheduler_config = SchedulerConfig(self.max_num_batched_tokens,
                                            self.max_num_seqs,
-                                           model_config.get_max_model_len())
+                                           model_config.max_model_len)
         return model_config, cache_config, parallel_config, scheduler_config
 
 
@@ -171,6 +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(
@@ -183,4 +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

@@ -1,7 +1,8 @@
 import asyncio
 import time
 from functools import partial
-from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Type, Union
+from typing import (Any, Dict, Iterable, List, Optional, Set, Tuple, Type,
+                    Union)
 
 from vllm.config import ModelConfig
 from vllm.engine.arg_utils import AsyncEngineArgs
@@ -78,14 +79,24 @@ class RequestTracker:
         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 propagate_exception(self, exc: Exception) -> None:
-        """Propagate an exception to all request streams."""
-        for stream in self._request_streams.values():
-            stream.put(exc)
+    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,
@@ -112,6 +123,9 @@ class RequestTracker:
             "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:
@@ -148,8 +162,13 @@ class RequestTracker:
             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."""
@@ -164,10 +183,9 @@ class _AsyncLLMEngine(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,
-         early_return) = self._schedule()
-        if early_return is not None:
-            return early_return
+        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(
@@ -178,7 +196,7 @@ class _AsyncLLMEngine(LLMEngine):
             blocks_to_copy=scheduler_outputs.blocks_to_copy,
         )
 
-        return self._process_model_outputs(output, scheduler_outputs)
+        return self._process_model_outputs(output, scheduler_outputs) + ignored
 
     async def _run_workers_async(
         self,
@@ -230,6 +248,8 @@ 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.
     """
 
@@ -240,17 +260,22 @@ class AsyncLLMEngine:
                  engine_use_ray: bool,
                  *args,
                  log_requests: bool = True,
-                 start_engine_loop: bool = False,
+                 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
+        self.max_log_len = max_log_len
         self.engine = self._init_engine(*args, **kwargs)
 
-        self.request_tracker: RequestTracker = RequestTracker()
         self.background_loop = None
-        if start_engine_loop:
-            self.start_background_loop()
+        # 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:
@@ -261,11 +286,14 @@ class AsyncLLMEngine:
         """Start the background loop."""
         if self.is_running:
             raise RuntimeError("Background loop is already running.")
-        self.background_loop = asyncio.get_event_loop().create_task(
-            self.run_engine_loop())
-        self.background_loop.add_done_callback(
+        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))
+                    request_tracker=self._request_tracker))
+        self.background_loop = asyncio.shield(self._background_loop_unshielded)
 
     def _init_engine(self, *args,
                      **kwargs) -> Union[_AsyncLLMEngine, "ray.ObjectRef"]:
@@ -277,11 +305,13 @@ class AsyncLLMEngine:
             engine_class = ray.remote(num_gpus=1)(self._engine_class).remote
         return engine_class(*args, **kwargs)
 
-    async def engine_step(self):
-        """Kick the engine to process the waiting requests."""
+    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())
+            self._request_tracker.get_new_and_finished_requests())
 
         for new_request in new_requests:
             # Add the request into the vLLM engine's waiting queue.
@@ -301,9 +331,11 @@ class AsyncLLMEngine:
 
         # Put the outputs into the corresponding streams.
         for request_output in request_outputs:
-            self.request_tracker.process_request_output(
+            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)
@@ -311,8 +343,12 @@ class AsyncLLMEngine:
             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:
-            await self.engine_step()
+            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(
@@ -324,19 +360,30 @@ class AsyncLLMEngine:
         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: {prompt!r}, "
+                        f"prompt: {shortened_prompt!r}, "
                         f"sampling params: {sampling_params}, "
-                        f"prompt token ids: {prompt_token_ids}.")
+                        f"prompt token ids: {shortened_token_ids}.")
 
         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.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(
+        stream = self._request_tracker.add_request(
             request_id,
             prompt=prompt,
             sampling_params=sampling_params,
@@ -381,8 +428,9 @@ class AsyncLLMEngine:
 
             async for request_output in stream:
                 yield request_output
-        except Exception as e:
-            # If there is an exception, abort the request.
+        except (Exception, asyncio.CancelledError) as e:
+            # If there is an exception or coroutine is cancelled, abort the
+            # request.
             self._abort(request_id)
             raise e
 
@@ -413,8 +461,8 @@ class AsyncLLMEngine:
         Args:
             request_id: The unique id of the request.
         """
-        self.request_tracker.abort_request(request_id,
-                                           verbose=self.log_requests)
+        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."""
@@ -426,7 +474,7 @@ class AsyncLLMEngine:
     @classmethod
     def from_engine_args(cls,
                          engine_args: AsyncEngineArgs,
-                         start_engine_loop: bool = False) -> "AsyncLLMEngine":
+                         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()
@@ -442,5 +490,6 @@ class AsyncLLMEngine:
                      placement_group,
                      log_requests=not engine_args.disable_log_requests,
                      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

@@ -54,8 +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.
     """
 
@@ -74,16 +74,21 @@ 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"max_seq_len={model_config.max_model_len}, "
             f"download_dir={model_config.download_dir!r}, "
             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
@@ -92,7 +97,8 @@ class LLMEngine:
         self.tokenizer = get_tokenizer(
             model_config.tokenizer,
             tokenizer_mode=model_config.tokenizer_mode,
-            trust_remote_code=model_config.trust_remote_code)
+            trust_remote_code=model_config.trust_remote_code,
+            revision=model_config.revision)
         self.seq_counter = Counter()
 
         # Create the parallel GPU workers.
@@ -153,7 +159,7 @@ class LLMEngine:
                     placement_group=placement_group,
                     placement_group_capture_child_tasks=True),
                 **ray_remote_kwargs,
-            )(RayWorker).remote()
+            )(RayWorker).remote(self.model_config.trust_remote_code)
             self.workers.append(worker)
 
         # Initialize torch distributed process group for the workers.
@@ -291,14 +297,12 @@ class LLMEngine:
     def _schedule(
         self
     ) -> Tuple[List[SequenceGroupMetadata], SchedulerOutputs,
-               Optional[List[RequestOutput]]]:
+               List[RequestOutput]]:
         seq_group_metadata_list, scheduler_outputs = self.scheduler.schedule()
-        if scheduler_outputs.is_empty():
-            return seq_group_metadata_list, scheduler_outputs, [
-                RequestOutput.from_seq_group(seq_group)
-                for seq_group in scheduler_outputs.ignored_seq_groups
-            ]
-        return seq_group_metadata_list, scheduler_outputs, None
+        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,
@@ -386,7 +390,7 @@ class LLMEngine:
             child_seqs.append((parent, parent))
 
         for seq, _ in child_seqs:
-            self._decode_sequence(seq)
+            self._decode_sequence(seq, seq_group.sampling_params)
             self._check_stop(seq, seq_group.sampling_params)
 
         # Non-beam search case
@@ -542,10 +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,
-         early_return) = self._schedule()
-        if early_return is not None:
-            return early_return
+        seq_group_metadata_list, scheduler_outputs, ignored = self._schedule()
+        if scheduler_outputs.is_empty():
+            return ignored
 
         # Execute the model.
         output = self._run_workers(
@@ -556,7 +559,7 @@ class LLMEngine:
             blocks_to_copy=scheduler_outputs.blocks_to_copy,
         )
 
-        return self._process_model_outputs(output, scheduler_outputs)
+        return self._process_model_outputs(output, scheduler_outputs) + ignored
 
     def _log_system_stats(
         self,
@@ -621,17 +624,25 @@ class LLMEngine:
                     f"CPU KV cache usage: {cpu_cache_usage * 100:.1f}%")
         self.last_logging_time = now
 
-    def _decode_sequence(self, seq: Sequence) -> None:
+    def _decode_sequence(self, seq: Sequence,
+                         sampling_params: SamplingParams) -> None:
         """Decodes the new token for a sequence."""
-        new_token, new_output_text = detokenize_incrementally(
-            self.tokenizer,
-            seq.output_tokens,
-            seq.get_last_token_id(),
-            skip_special_tokens=True,
-        )
-        if new_token is not None:
-            seq.output_tokens.append(new_token)
-            seq.output_text = new_output_text
+        (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:
@@ -643,6 +654,9 @@ class LLMEngine:
                 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_model_len.
         if seq.get_len() > self.scheduler_config.max_model_len:

vllm/engine/ray_utils.py

@@ -2,6 +2,9 @@ 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
@@ -11,7 +14,11 @@ try:
         """Ray wrapper for vllm.worker.Worker, allowing Worker to be
         lazliy initialized after Ray sets CUDA_VISIBLE_DEVICES."""
 
-        def __init__(self) -> None:
+        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):
@@ -24,7 +31,10 @@ try:
             executor = getattr(self, method)
             return executor(*args, **kwargs)
 
-except ImportError:
+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
@@ -53,11 +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:

vllm/entrypoints/api_server.py

@@ -2,7 +2,7 @@ import argparse
 import json
 from typing import AsyncGenerator
 
-from fastapi import BackgroundTasks, FastAPI, Request
+from fastapi import FastAPI, Request
 from fastapi.responses import JSONResponse, Response, StreamingResponse
 import uvicorn
 
@@ -32,9 +32,6 @@ async def generate(request: Request) -> Response:
     sampling_params = SamplingParams(**request_dict)
     request_id = random_uuid()
 
-    if not engine.is_running:
-        engine.start_background_loop()
-
     results_generator = engine.generate(prompt, sampling_params, request_id)
 
     # Streaming case
@@ -47,14 +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
@@ -80,8 +71,7 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     engine_args = AsyncEngineArgs.from_cli_args(args)
-    engine = AsyncLLMEngine.from_engine_args(engine_args,
-                                             start_engine_loop=False)
+    engine = AsyncLLMEngine.from_engine_args(engine_args)
 
     uvicorn.run(app,
                 host=args.host,

vllm/entrypoints/llm.py

@@ -37,7 +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__(
@@ -48,7 +63,11 @@ class LLM:
         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:
@@ -60,7 +79,11 @@ class LLM:
             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)

vllm/entrypoints/openai/api_server.py

@@ -10,7 +10,7 @@ from typing import AsyncGenerator, Dict, List, Optional, Tuple, Union
 
 import fastapi
 import uvicorn
-from fastapi import BackgroundTasks, Request
+from fastapi import Request
 from fastapi.exceptions import RequestValidationError
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, StreamingResponse
@@ -130,6 +130,8 @@ async def check_length(
         input_ids = tokenizer(prompt).input_ids
     token_num = len(input_ids)
 
+    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,
@@ -192,14 +194,11 @@ async def create_chat_completion(request: ChatCompletionRequest,
     """
     logger.info(f"Received chat completion request: {request}")
 
-    if not engine.is_running:
-        engine.start_background_loop()
-
     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")
@@ -220,11 +219,13 @@ async def create_chat_completion(request: ChatCompletionRequest,
             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))
@@ -232,9 +233,6 @@ async def create_chat_completion(request: ChatCompletionRequest,
     result_generator = engine.generate(prompt, sampling_params, request_id,
                                        token_ids)
 
-    async def abort_request() -> None:
-        await engine.abort(request_id)
-
     def create_stream_response_json(
         index: int,
         text: str,
@@ -294,19 +292,15 @@ async def create_chat_completion(request: ChatCompletionRequest,
 
     # 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
@@ -367,9 +361,6 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
     """
     logger.info(f"Received completion request: {request}")
 
-    if not engine.is_running:
-        engine.start_background_loop()
-
     error_check_ret = await check_model(request)
     if error_check_ret is not None:
         return error_check_ret
@@ -385,7 +376,7 @@ async def create_completion(request: CompletionRequest, 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")
@@ -431,10 +422,12 @@ async def create_completion(request: CompletionRequest, 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))
@@ -454,9 +447,6 @@ async def create_completion(request: CompletionRequest, 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,
@@ -516,19 +506,15 @@ async def create_completion(request: CompletionRequest, raw_request: Request):
 
     # 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
@@ -627,10 +613,9 @@ if __name__ == "__main__":
         served_model = args.model
 
     engine_args = AsyncEngineArgs.from_cli_args(args)
-    engine = AsyncLLMEngine.from_engine_args(engine_args,
-                                             start_engine_loop=False)
+    engine = AsyncLLMEngine.from_engine_args(engine_args)
     engine_model_config = asyncio.run(engine.get_model_config())
-    max_model_len = engine_model_config.get_max_model_len()
+    max_model_len = engine_model_config.max_model_len
 
     # A separate tokenizer to map token IDs to strings.
     tokenizer = get_tokenizer(engine_args.tokenizer,

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
@@ -70,6 +70,8 @@ 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):
@@ -94,6 +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/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
@@ -9,8 +9,10 @@ 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, 256]
 
@@ -56,12 +58,14 @@ class PagedAttention(nn.Module):
                  num_heads: int,
                  head_size: int,
                  scale: float,
-                 num_kv_heads: Optional[int] = None) -> None:
+                 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.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
@@ -73,12 +77,19 @@ class PagedAttention(nn.Module):
             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(
@@ -196,7 +207,7 @@ class PagedAttention(nn.Module):
         if num_prompt_tokens > 0:
             # Prompt run.
             assert input_metadata.num_generation_tokens == 0
-            self.set_attn_bias(input_metadata)
+            self.set_attn_bias(input_metadata, dtype=query.dtype)
             self.multi_query_kv_attention(
                 output[:num_prompt_tokens],
                 query[:num_prompt_tokens],
@@ -216,12 +227,20 @@ 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:
@@ -242,7 +261,7 @@ class PagedAttention(nn.Module):
 
 
 class PagedAttentionWithRoPE(PagedAttention):
-    """PagedAttention with rotary embedding."""
+    """PagedAttention with rotary positional embedding."""
 
     def __init__(
         self,
@@ -254,25 +273,31 @@ class PagedAttentionWithRoPE(PagedAttention):
         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, num_kv_heads)
-        self.is_neox_style = is_neox_style
-
-        # 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,
@@ -289,7 +314,7 @@ class PagedAttentionWithRoPE(PagedAttention):
 
         Args:
             positions: shape = [num_tokens]
-                        query: shape = [num_tokens, num_heads * head_size]
+            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,
@@ -305,14 +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(
-            positions,
-            query,
-            key,
-            self.head_size,
-            self.cos_sin_cache,
-            self.is_neox_style,
-        )
+        query, key = self.rotary_emb(positions, query, key)
         return super().forward(
             query,
             key,
@@ -339,13 +357,14 @@ class PagedAttentionWithALiBi(PagedAttention):
         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
@@ -363,6 +382,7 @@ class PagedAttentionWithALiBi(PagedAttention):
                 prompt_len,
                 padded_len,
                 device=self.alibi_slopes.device,
+                dtype=dtype,
             )[:, :, :, :prompt_len].copy_(bias)
             bias.mul_(self.alibi_slopes[:, None, None])
             attn_bias = LowerTriangularMaskWithTensorBias(bias)

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 SamplerOutput, SequenceOutputs
+from vllm.sampling_params import SamplingParams, SamplingType
+from vllm.sequence import SamplerOutput, SequenceData, SequenceOutputs
 
 _SAMPLING_EPS = 1e-5
 
@@ -44,12 +43,8 @@ class Sampler(nn.Module):
         hidden_states = _prune_hidden_states(hidden_states, input_metadata)
 
         # 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[:, :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)
@@ -59,7 +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)
@@ -82,26 +77,55 @@ class Sampler(nn.Module):
         # 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)
+        # 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.index_select(
-        0, torch.tensor(last_token_indicies, device=hidden_states.device))
+    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(
@@ -109,37 +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
 
 
@@ -148,11 +157,8 @@ 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
@@ -160,40 +166,47 @@ def _apply_penalties(
         f = frequency_penalties[i]
         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:
@@ -201,13 +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
 
 
@@ -217,21 +224,15 @@ 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
 
 
@@ -267,95 +268,154 @@ def _apply_top_p_top_k(
 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
-        # 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
-        _, next_token_ids = torch.topk(prob, 2 * 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(), 2 * beam_width)
-        topk_ids = topk_ids.tolist()
-        seq_idx = [i // vocab_size for i in topk_ids]
-        parent_seq_ids = [seq_ids[i] for i in seq_idx]
-        next_token_ids = [i % vocab_size for i in topk_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 _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
+
+
+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(
@@ -363,65 +423,80 @@ def _sample(
     logprobs: torch.Tensor,
     input_metadata: InputMetadata,
 ) -> SamplerOutput:
-    seq_outputs: SamplerOutput = []
-
-    # TODO(woosuk): Optimize.
-    idx = 0
+    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_group_outputs: List[SequenceOutputs] = []
         seq_ids, sampling_params = seq_group
-        if i < input_metadata.num_prompts:
-            # Generate the next tokens for a prompt input.
-            assert len(seq_ids) == 1, "Prompt input should have only one seq."
-            parent_seq_id = seq_ids[0]
-            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 next_token_id in next_token_ids:
-                output_logprobs = next_logprobs.copy()
-                output_logprobs[next_token_id] = logprob[next_token_id].item()
-                seq_group_outputs.append(
-                    SequenceOutputs(parent_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.
+            raise ValueError(f"Unsupported sampling type: {sampling_type}")
+
+        # 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)
-            prob = probs[idx:idx + num_parent_seqs]
-            logprob = logprobs[idx:idx + num_parent_seqs]
-            idx += num_parent_seqs
+            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()
 
-            # 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)
+        # 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
 
-            # 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 output.
-            for parent_seq_id, next_token_id in zip(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_group_outputs.append(
-                    SequenceOutputs(parent_seq_id, next_token_id,
-                                    output_logprobs))
-        seq_outputs.append(seq_group_outputs)
-
-    return seq_outputs
+    return [seq_outputs_dict[i] for i in range(len(input_metadata.seq_groups))]

vllm/model_executor/model_loader.py

@@ -8,7 +8,8 @@ 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 = {
@@ -24,12 +25,18 @@ _MODEL_REGISTRY = {
     "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):
@@ -52,10 +59,38 @@ 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)
+
+    # 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.
-        model = model_class(model_config.hf_config)
+        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
@@ -64,6 +99,6 @@ def get_model(model_config: ModelConfig) -> nn.Module:
         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.load_format, model_config.revision)
             model = model.cuda()
     return model.eval()

vllm/model_executor/models/__init__.py

@@ -12,6 +12,7 @@ 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",
@@ -28,4 +29,5 @@ __all__ = [
     "MPTForCausalLM",
     "OPTForCausalLM",
     "QWenLMHeadModel",
+    "MistralForCausalLM",
 ]

vllm/model_executor/models/aquila.py

@@ -105,6 +105,8 @@ class AquilaAttention(nn.Module):
         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
@@ -119,6 +121,8 @@ class AquilaAttention(nn.Module):
         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,
@@ -140,6 +144,8 @@ class AquilaAttention(nn.Module):
             self.head_dim,
             self.scaling,
             rotary_dim=self.head_dim,
+            base=self.rope_theta,
+            max_position=self.max_position_embeddings,
         )
 
     def forward(
@@ -164,10 +170,15 @@ 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,
@@ -288,7 +299,8 @@ class AquilaForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     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)
@@ -305,7 +317,7 @@ class AquilaForCausalLM(nn.Module):
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
 

vllm/model_executor/models/baichuan.py

@@ -111,6 +111,8 @@ class BaiChuanAttention(nn.Module):
         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
@@ -122,6 +124,8 @@ class BaiChuanAttention(nn.Module):
                           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(
@@ -151,10 +155,13 @@ class BaiChuanAttention(nn.Module):
                                                 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)
+            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,
@@ -183,10 +190,15 @@ 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,
@@ -303,13 +315,14 @@ class BaiChuanBaseForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     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):
+                model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
 

vllm/model_executor/models/bloom.py

@@ -279,11 +279,12 @@ class BloomForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     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):
+                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.

vllm/model_executor/models/falcon.py

@@ -161,12 +161,17 @@ class FalconAttention(nn.Module):
             "Rotary and alibi are mutually exclusive.")
 
         if self.use_rotary:
-            # TODO(zhuohan): Pass in correct `max_position``
-            self.attn = PagedAttentionWithRoPE(self.num_heads,
-                                               self.head_dim,
-                                               self.inv_norm_factor,
-                                               rotary_dim=self.head_dim,
-                                               num_kv_heads=self.num_kv_heads)
+            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
@@ -420,7 +425,8 @@ class FalconForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     load_format: str = "auto",
+                     revision: Optional[str] = None):
         tp_size = (get_tensor_model_parallel_world_size())
         tp_rank = get_tensor_model_parallel_rank()
 
@@ -452,7 +458,7 @@ class FalconForCausalLM(nn.Module):
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                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()

vllm/model_executor/models/gpt2.py

@@ -231,14 +231,15 @@ class GPT2LMHeadModel(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     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, load_format):
+                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.

vllm/model_executor/models/gpt_bigcode.py

@@ -259,14 +259,15 @@ class GPTBigCodeForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     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, load_format):
+                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.

vllm/model_executor/models/gpt_j.py

@@ -67,11 +67,17 @@ class GPTJAttention(nn.Module):
         scaling = self.head_size**-0.5
         assert getattr(config, "rotary", True)
         assert config.rotary_dim % 2 == 0
-        self.attn = PagedAttentionWithRoPE(self.num_heads,
-                                           self.head_size,
-                                           scaling,
-                                           config.rotary_dim,
-                                           is_neox_style=False)
+        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(
@@ -222,11 +228,12 @@ class GPTJForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     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):
+                model_name_or_path, cache_dir, load_format, revision):
             if "attn.bias" in name or "attn.masked_bias" in name:
                 continue
 

vllm/model_executor/models/gpt_neox.py

@@ -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,
@@ -231,11 +239,12 @@ class GPTNeoXForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     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):
+                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

@@ -59,6 +59,8 @@ class InternLMAttention(nn.Module):
         self,
         hidden_size: int,
         num_heads: int,
+        rope_theta: float = 10000,
+        max_position_embeddings: int = 8192,
     ):
         super().__init__()
         self.hidden_size = hidden_size
@@ -70,6 +72,8 @@ class InternLMAttention(nn.Module):
                           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,
@@ -85,10 +89,13 @@ class InternLMAttention(nn.Module):
             input_is_parallel=True,
             perform_initialization=False,
         )
-        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)
 
     def forward(
         self,
@@ -112,9 +119,14 @@ 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,
@@ -233,12 +245,13 @@ class InternLMForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     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):
+                model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
 

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 List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import torch
 from torch import nn
@@ -36,13 +36,15 @@ 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 (
-    load_tensor_parallel_weights, load_padded_tensor_parallel_vocab,
-    hf_model_weights_iterator)
+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)
+    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]
@@ -55,18 +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.")
@@ -87,7 +92,10 @@ class LlamaAttention(nn.Module):
         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
         tp_size = get_tensor_model_parallel_world_size()
@@ -102,28 +110,34 @@ class LlamaAttention(nn.Module):
         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,
             (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,
-                                           base=self.rope_theta,
-                                           rotary_dim=self.head_dim,
-                                           num_kv_heads=self.num_kv_heads)
+        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,
@@ -144,21 +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)
@@ -195,7 +220,11 @@ 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
@@ -205,7 +234,8 @@ class LlamaModel(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(
             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)
 
@@ -237,16 +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.quant_config = quant_config
+        self.model = LlamaModel(config, quant_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)
+        # 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(
@@ -263,15 +300,28 @@ class LlamaForCausalLM(nn.Module):
                                    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"]
+    _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"):
+                     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)
@@ -288,15 +338,30 @@ class LlamaForCausalLM(nn.Module):
         state_dict = self.state_dict()
 
         for name, loaded_weight in hf_model_weights_iterator(
-                model_name_or_path, cache_dir, load_format):
+                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 *
@@ -315,6 +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 *
@@ -329,6 +397,8 @@ 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,
@@ -336,6 +406,6 @@ class LlamaForCausalLM(nn.Module):
                 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

@@ -244,12 +244,13 @@ class MPTForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     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):
+                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].

vllm/model_executor/models/opt.py

@@ -297,12 +297,13 @@ class OPTForCausalLM(nn.Module):
     def load_weights(self,
                      model_name_or_path: str,
                      cache_dir: Optional[str] = None,
-                     load_format: str = "auto"):
+                     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):
+                model_name_or_path, cache_dir, load_format, revision):
             if "lm_head.weight" in name:
                 continue
 

vllm/model_executor/models/qwen.py

@@ -8,7 +8,7 @@
 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 List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import torch
 from torch import nn
@@ -76,8 +76,12 @@ class QWenMLP(nn.Module):
 
 class QWenAttention(nn.Module):
 
-    def __init__(self, hidden_size: int, num_heads: int,
-                 max_position_embeddings: int):
+    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(
@@ -109,8 +113,9 @@ class QWenAttention(nn.Module):
             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,
@@ -135,14 +140,19 @@ class QWenBlock(nn.Module):
 
     def __init__(self, config: QWenConfig):
         super().__init__()
-        self.ln_1 = RMSNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.ln_1 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
-        self.attn = QWenAttention(config.n_embd, config.num_attention_heads,
-                                  config.max_position_embeddings)
+        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.n_embd, eps=config.layer_norm_epsilon)
+        self.ln_2 = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
-        self.mlp = QWenMLP(config.n_embd, config.ffn_hidden_size // 2)
+        self.mlp = QWenMLP(config.hidden_size, config.intermediate_size // 2)
 
     def forward(
         self,
@@ -181,11 +191,11 @@ class QWenModel(nn.Module):
 
         vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.wte = VocabParallelEmbedding(vocab_size,
-                                          config.n_embd,
+                                          config.hidden_size,
                                           perform_initialization=False)
         self.h = nn.ModuleList(
             [QWenBlock(config) for _ in range(config.num_hidden_layers)])
-        self.ln_f = RMSNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        self.ln_f = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)
 
     def forward(
         self,
@@ -221,7 +231,7 @@ class QWenLMHeadModel(nn.Module):
         self.transformer = QWenModel(config)
         vocab_size = ((config.vocab_size + 63) // 64) * 64
         self.lm_head = ColumnParallelLinear(
-            config.n_embd,
+            config.hidden_size,
             vocab_size,
             bias=False,
             gather_output=False,
@@ -251,13 +261,14 @@ class QWenLMHeadModel(nn.Module):
         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):
+                model_name_or_path, cache_dir, load_format, revision):
             if "rotary_emb.inv_freq" in name:
                 continue
 

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
@@ -16,13 +16,11 @@ from vllm.model_executor.parallel_utils.parallel_state import (
     get_tensor_model_parallel_world_size,
 )
 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,
@@ -65,59 +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.
 
@@ -138,8 +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
@@ -162,23 +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:
@@ -238,9 +172,12 @@ 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
@@ -250,6 +187,7 @@ class ColumnParallelLinear(torch.nn.Module):
         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()
@@ -257,33 +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():
@@ -291,6 +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
@@ -306,7 +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)
@@ -359,10 +288,13 @@ 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
@@ -376,47 +308,32 @@ class RowParallelLinear(torch.nn.Module):
         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
@@ -434,7 +351,7 @@ class RowParallelLinear(torch.nn.Module):
         else:
             input_parallel = scatter_to_tensor_model_parallel_region(input_)
         # Matrix multiply.
-        output_parallel = F.linear(input_parallel, self.weight)
+        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:

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

@@ -4,7 +4,7 @@ import glob
 import json
 import os
 from collections import defaultdict
-from typing import Iterator, List, Optional, Tuple, Any
+from typing import Any, Iterator, List, Optional, Tuple
 
 from huggingface_hub import snapshot_download
 from safetensors.torch import load_file, save_file, safe_open
@@ -13,6 +13,8 @@ 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__)
 
@@ -44,7 +46,7 @@ def _shared_pointers(tensors):
 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"]
@@ -78,15 +80,55 @@ def convert_bin_to_safetensor_file(
             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:
+        # Download the config files.
+        with get_lock(model_name_or_path, cache_dir):
+            hf_folder = snapshot_download(model_name_or_path,
+                                          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"))
+
+    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)
-    allow_patterns = "*.safetensors" if use_safetensors else "*.bin"
+    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.
@@ -94,10 +136,13 @@ def prepare_hf_model_weights(
             hf_folder = snapshot_download(model_name_or_path,
                                           allow_patterns=allow_patterns,
                                           cache_dir=cache_dir,
-                                          tqdm_class=Disabledtqdm)
+                                          tqdm_class=Disabledtqdm,
+                                          revision=revision)
     else:
         hf_folder = model_name_or_path
-    hf_weights_files = glob.glob(os.path.join(hf_folder, allow_patterns))
+    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")
@@ -107,7 +152,8 @@ def prepare_hf_model_weights(
         return prepare_hf_model_weights(model_name_or_path,
                                         cache_dir=cache_dir,
                                         use_safetensors=False,
-                                        fall_back_to_pt=False)
+                                        fall_back_to_pt=False,
+                                        revision=revision)
 
     if len(hf_weights_files) == 0:
         raise RuntimeError(
@@ -120,6 +166,7 @@ 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
@@ -140,7 +187,8 @@ def hf_model_weights_iterator(
         model_name_or_path,
         cache_dir=cache_dir,
         use_safetensors=use_safetensors,
-        fall_back_to_pt=fall_back_to_pt)
+        fall_back_to_pt=fall_back_to_pt,
+        revision=revision)
 
     if use_np_cache:
         # Currently np_cache only support *.bin checkpoints

vllm/sampling_params.py

@@ -1,9 +1,17 @@
 """Sampling parameters for text generation."""
+from enum import IntEnum
+from functools import cached_property
 from typing import List, Optional, Union
 
 _SAMPLING_EPS = 1e-5
 
 
+class SamplingType(IntEnum):
+    GREEDY = 0
+    RANDOM = 1
+    BEAM = 2
+
+
 class SamplingParams:
     """Sampling parameters for text generation.
 
@@ -45,10 +53,15 @@ class SamplingParams:
             (canonical beam search algorithm).
         stop: List of strings that stop the generation when they are generated.
             The returned output will not contain the stop strings.
+        stop_token_ids: List of tokens that stop the generation when they are
+            generated. The returned output will contain the stop tokens unless
+            the stop tokens are sepcial tokens.
         ignore_eos: Whether to ignore the EOS token and continue generating
             tokens after the EOS token is generated.
         max_tokens: Maximum number of tokens to generate per output sequence.
         logprobs: Number of log probabilities to return per output token.
+        skip_special_tokens: Whether to skip special tokens in the output.
+            Defaults to true.
     """
 
     def __init__(
@@ -64,9 +77,11 @@ class SamplingParams:
         length_penalty: float = 1.0,
         early_stopping: Union[bool, str] = False,
         stop: Union[None, str, List[str]] = None,
+        stop_token_ids: List[int] = None,
         ignore_eos: bool = False,
         max_tokens: int = 16,
         logprobs: Optional[int] = None,
+        skip_special_tokens: bool = True,
     ) -> None:
         self.n = n
         self.best_of = best_of if best_of is not None else n
@@ -84,9 +99,14 @@ class SamplingParams:
             self.stop = [stop]
         else:
             self.stop = list(stop)
+        if stop_token_ids is None:
+            self.stop_token_ids = []
+        else:
+            self.stop_token_ids = list(stop_token_ids)
         self.ignore_eos = ignore_eos
         self.max_tokens = max_tokens
         self.logprobs = logprobs
+        self.skip_special_tokens = skip_special_tokens
 
         self._verify_args()
         if self.use_beam_search:
@@ -158,6 +178,14 @@ class SamplingParams:
         if self.top_k != -1:
             raise ValueError("top_k must be -1 when using greedy sampling.")
 
+    @cached_property
+    def sampling_type(self) -> SamplingType:
+        if self.use_beam_search:
+            return SamplingType.BEAM
+        if self.temperature < _SAMPLING_EPS:
+            return SamplingType.GREEDY
+        return SamplingType.RANDOM
+
     def __repr__(self) -> str:
         return (f"SamplingParams(n={self.n}, "
                 f"best_of={self.best_of}, "
@@ -172,4 +200,5 @@ class SamplingParams:
                 f"stop={self.stop}, "
                 f"ignore_eos={self.ignore_eos}, "
                 f"max_tokens={self.max_tokens}, "
-                f"logprobs={self.logprobs})")
+                f"logprobs={self.logprobs}, "
+                f"skip_special_tokens={self.skip_special_tokens})")

vllm/sequence.py

@@ -114,7 +114,6 @@ class Sequence:
 
         self.data = SequenceData(prompt_token_ids)
         self.output_logprobs: List[Dict[int, float]] = []
-        self.output_tokens: List[str] = []
         self.output_text = ""
 
         self.logical_token_blocks: List[LogicalTokenBlock] = []
@@ -122,6 +121,12 @@ class Sequence:
         self._append_tokens_to_blocks(prompt_token_ids)
         self.status = SequenceStatus.WAITING
 
+        # Used for incremental detokenization
+        self.prefix_offset = 0
+        self.read_offset = 0
+        # Input + output tokens
+        self.tokens: Optional[List[str]] = None
+
     def _append_logical_block(self) -> None:
         block = LogicalTokenBlock(
             block_number=len(self.logical_token_blocks),
@@ -245,8 +250,8 @@ class SequenceGroup:
                 # generation stage, we will have `best_of` sequences running.
                 return self.sampling_params.best_of
             # At sampling stages, return the number of actual sequences
-            # running.
-            return self.num_seqs(status=SequenceStatus.RUNNING)
+            # that are not finished yet.
+            return self.num_unfinished_seqs()
 
     def get_seqs(
         self,
@@ -259,12 +264,23 @@ class SequenceGroup:
                 seq for seq in self.seqs_dict.values() if seq.status == status
             ]
 
+    def get_unfinished_seqs(self) -> List[Sequence]:
+        return [
+            seq for seq in self.seqs_dict.values() if not seq.is_finished()
+        ]
+
     def get_finished_seqs(self) -> List[Sequence]:
         return [seq for seq in self.seqs_dict.values() if seq.is_finished()]
 
     def num_seqs(self, status: Optional[SequenceStatus] = None) -> int:
         return len(self.get_seqs(status))
 
+    def num_unfinished_seqs(self) -> int:
+        return len(self.get_unfinished_seqs())
+
+    def num_finished_seqs(self) -> int:
+        return len(self.get_finished_seqs())
+
     def find(self, seq_id: int) -> Sequence:
         if seq_id not in self.seqs_dict:
             raise ValueError(f"Sequence {seq_id} not found.")
@@ -345,7 +361,7 @@ class SequenceOutputs:
 
     def __eq__(self, other: object) -> bool:
         if not isinstance(other, SequenceOutputs):
-            return NotImplementedError()
+            raise NotImplementedError()
         return (self.parent_seq_id == other.parent_seq_id
                 and self.output_token == other.output_token
                 and self.logprobs == other.logprobs)

vllm/transformers_utils/config.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 from transformers import AutoConfig, PretrainedConfig
 
 from vllm.transformers_utils.configs import *  # pylint: disable=wildcard-import
@@ -12,10 +14,21 @@ _CONFIG_REGISTRY = {
 }
 
 
-def get_config(model: str, trust_remote_code: bool) -> PretrainedConfig:
+def get_config(model: str,
+               trust_remote_code: bool,
+               revision: Optional[str] = None) -> PretrainedConfig:
+    # NOTE: Because the Mistral model in HF hub does not have
+    # `configuration_mistral.py`, we cannot use `AutoConfig` to load the
+    # config. Instead, we use `MistralConfig` directly.
+    # NOTE: This is a hack. This does not work for local models.
+    # FIXME: Remove this once the Mistral model is available in the stable
+    # version of HF transformers.
+    if "mistral" in model.lower():
+        return MistralConfig.from_pretrained(model, revision=revision)
+
     try:
         config = AutoConfig.from_pretrained(
-            model, trust_remote_code=trust_remote_code)
+            model, trust_remote_code=trust_remote_code, revision=revision)
     except ValueError as e:
         if (not trust_remote_code and
                 "requires you to execute the configuration file" in str(e)):
@@ -29,5 +42,5 @@ def get_config(model: str, trust_remote_code: bool) -> PretrainedConfig:
             raise e
     if config.model_type in _CONFIG_REGISTRY:
         config_class = _CONFIG_REGISTRY[config.model_type]
-        config = config_class.from_pretrained(model)
+        config = config_class.from_pretrained(model, revision=revision)
     return config

vllm/transformers_utils/configs/__init__.py

@@ -6,6 +6,7 @@ from vllm.transformers_utils.configs.qwen import QWenConfig
 # tiiuae/falcon-7b(-instruct) models. Newer Falcon models will use the
 # `FalconConfig` class from the official HuggingFace transformers library.
 from vllm.transformers_utils.configs.falcon import RWConfig
+from vllm.transformers_utils.configs.mistral import MistralConfig
 
 __all__ = [
     "MPTConfig",
@@ -13,4 +14,5 @@ __all__ = [
     "AquilaConfig",
     "QWenConfig",
     "RWConfig",
+    "MistralConfig",
 ]

vllm/transformers_utils/configs/mistral.py

@@ -0,0 +1,66 @@
+# 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.
+"""Mistral-7B-v0.1 configuration"""
+from transformers.configuration_utils import PretrainedConfig
+
+
+class MistralConfig(PretrainedConfig):
+    model_type = "mistral"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=14336,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=8,
+        hidden_act="silu",
+        max_position_embeddings=4096 * 32,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        sliding_window=4096,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.sliding_window = sliding_window
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )

vllm/transformers_utils/configs/qwen.py

@@ -7,65 +7,54 @@ from transformers import PretrainedConfig
 class QWenConfig(PretrainedConfig):
     model_type = "qwen"
     keys_to_ignore_at_inference = ["past_key_values"]
-    attribute_map = {
-        "hidden_size": "n_embd",
-        "num_attention_heads": "n_head",
-        "max_position_embeddings": "n_positions",
-        "num_hidden_layers": "n_layer",
-    }
 
     def __init__(
         self,
-        vocab_size=151851,
-        n_embd=4096,
-        n_layer=32,
-        n_head=32,
-        n_inner=None,
-        embd_pdrop=0.0,
-        attn_pdrop=0.0,
-        layer_norm_epsilon=1e-5,
+        vocab_size=151936,
+        hidden_size=4096,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        emb_dropout_prob=0.0,
+        attn_dropout_prob=0.0,
+        layer_norm_epsilon=1e-6,
         initializer_range=0.02,
+        max_position_embeddings=8192,
         scale_attn_weights=True,
         use_cache=True,
-        eos_token_id=151643,
-        apply_residual_connection_post_layernorm=False,
-        bf16=True,
+        bf16=False,
+        fp16=False,
+        fp32=False,
         kv_channels=128,
         rotary_pct=1.0,
         rotary_emb_base=10000,
-        use_dynamic_ntk=False,
-        use_logn_attn=False,
-        use_flash_attn=True,
-        ffn_hidden_size=22016,
+        use_dynamic_ntk=True,
+        use_logn_attn=True,
+        use_flash_attn="auto",
+        intermediate_size=22016,
         no_bias=True,
         tie_word_embeddings=False,
         **kwargs,
     ):
-        self.eos_token_id = eos_token_id
-        super().__init__(eos_token_id=eos_token_id,
-                         tie_word_embeddings=tie_word_embeddings,
-                         **kwargs)
-
         self.vocab_size = vocab_size
-        self.n_embd = n_embd
-        self.n_layer = n_layer
-        self.n_head = n_head
-        self.n_inner = n_inner
-        self.embd_pdrop = embd_pdrop
-        self.attn_pdrop = attn_pdrop
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.emb_dropout_prob = emb_dropout_prob
+        self.attn_dropout_prob = attn_dropout_prob
         self.layer_norm_epsilon = layer_norm_epsilon
         self.initializer_range = initializer_range
         self.scale_attn_weights = scale_attn_weights
         self.use_cache = use_cache
-        self.apply_residual_connection_post_layernorm = (
-            apply_residual_connection_post_layernorm)
+        self.max_position_embeddings = max_position_embeddings
         self.bf16 = bf16
+        self.fp16 = fp16
+        self.fp32 = fp32
         self.kv_channels = kv_channels
         self.rotary_pct = rotary_pct
         self.rotary_emb_base = rotary_emb_base
         self.use_dynamic_ntk = use_dynamic_ntk
         self.use_logn_attn = use_logn_attn
         self.use_flash_attn = use_flash_attn
-        self.ffn_hidden_size = ffn_hidden_size
         self.no_bias = no_bias
-        self.tie_word_embeddings = tie_word_embeddings
+        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)

vllm/transformers_utils/tokenizer.py

@@ -1,4 +1,4 @@
-from typing import List, Tuple, Union
+from typing import List, Optional, Tuple, Union
 
 from transformers import (AutoTokenizer, PreTrainedTokenizer,
                           PreTrainedTokenizerFast)
@@ -28,8 +28,8 @@ def get_tokenizer(
     if ("llama" in tokenizer_name.lower() and kwargs.get("use_fast", True)
             and tokenizer_name != _FAST_LLAMA_TOKENIZER):
         logger.info(
-            "For some LLaMA-based models, initializing the fast tokenizer may "
-            "take a long time. To eliminate the initialization time, consider "
+            "For some LLaMA V1 models, initializing the fast tokenizer may "
+            "take a long time. To reduce the initialization time, consider "
             f"using '{_FAST_LLAMA_TOKENIZER}' instead of the original "
             "tokenizer.")
     try:
@@ -41,9 +41,9 @@ def get_tokenizer(
     except TypeError as e:
         # The LLaMA tokenizer causes a protobuf error in some environments.
         err_msg = (
-            "Failed to load the tokenizer. If you are using a LLaMA-based "
-            f"model, use '{_FAST_LLAMA_TOKENIZER}' instead of the original "
-            "tokenizer.")
+            "Failed to load the tokenizer. If you are using a LLaMA V1 model "
+            f"consider using '{_FAST_LLAMA_TOKENIZER}' instead of the "
+            "original tokenizer.")
         raise RuntimeError(err_msg) from e
     except ValueError as e:
         # If the error pertains to the tokenizer class not existing or not
@@ -67,33 +67,11 @@ def get_tokenizer(
     return tokenizer
 
 
-def detokenize_incrementally(
+def _convert_tokens_to_string_with_added_encoders(
     tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
-    prev_output_tokens: List[str],
-    new_token_id: int,
+    output_tokens: List[str],
     skip_special_tokens: bool,
-) -> Tuple[str, str]:
-    """Detokenizes the new token in conjunction with the previous output tokens.
-
-    NOTE: This function does not update prev_output_tokens.
-
-    Returns:
-        new_token: The new token as a string.
-        output_text: The new output text as a string.
-    """
-    if skip_special_tokens and (new_token_id in tokenizer.all_special_ids):
-        return None, prev_output_tokens
-    new_token = tokenizer.convert_ids_to_tokens(
-        new_token_id, skip_special_tokens=skip_special_tokens)
-    output_tokens = prev_output_tokens + [new_token]
-
-    # Convert the tokens to a string.
-    # Optimization: If the tokenizer does not have `added_tokens_encoder`,
-    # then we can directly use `convert_tokens_to_string`.
-    if not getattr(tokenizer, "added_tokens_encoder", {}):
-        output_text = tokenizer.convert_tokens_to_string(output_tokens)
-        return new_token, output_text
-
+) -> str:
     # Adapted from
     # https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/tokenization_utils.py#L921
     # NOTE(woosuk): The following code is slow because it runs a for loop over
@@ -115,5 +93,61 @@ def detokenize_incrementally(
     if current_sub_text:
         sub_text = tokenizer.convert_tokens_to_string(current_sub_text)
         sub_texts.append(sub_text)
-    output_text = " ".join(sub_texts)
-    return new_token, output_text
+    return " ".join(sub_texts)
+
+
+# Based on
+# https://github.com/huggingface/text-generation-inference/blob/v0.9.4/server/text_generation_server/models/model.py#L62C9-L62C15
+# under Apache 2.0 license
+def detokenize_incrementally(
+    tokenizer: Union[PreTrainedTokenizer, PreTrainedTokenizerFast],
+    all_input_ids: List[int],
+    prev_tokens: Optional[List[str]],
+    prefix_offset: int = 0,
+    read_offset: int = 0,
+    skip_special_tokens: bool = False,
+) -> Tuple[List[str], str, int, int]:
+    new_token_id = all_input_ids[-1]
+    # This is the first iteration for this sequence
+    if prev_tokens is None:
+        new_tokens = tokenizer.convert_ids_to_tokens(
+            all_input_ids, skip_special_tokens=skip_special_tokens)
+        output_tokens = new_tokens
+        # 5 is an arbitrary value that should work for all
+        # tokenizers (bigger = more conservative).
+        # Subtract 1 extra to account for the generated token.
+        prefix_offset = max(len(output_tokens) - 6, 0)
+        read_offset = max(len(output_tokens) - 1, 0)
+    else:
+        # Put new_token_id in a list so skip_special_tokens is respected
+        new_tokens = tokenizer.convert_ids_to_tokens(
+            [new_token_id], skip_special_tokens=skip_special_tokens)
+        output_tokens = prev_tokens + new_tokens
+
+    # The prefix text is necessary only to defeat cleanup algorithms in
+    # the decode which decide to add a space or not depending on the
+    # surrounding ids.
+    if not getattr(tokenizer, "added_tokens_encoder", {}):
+        prefix_text = tokenizer.convert_tokens_to_string(
+            output_tokens[prefix_offset:read_offset])
+        new_text = tokenizer.convert_tokens_to_string(
+            output_tokens[prefix_offset:])
+    else:
+        prefix_text = _convert_tokens_to_string_with_added_encoders(
+            tokenizer,
+            output_tokens[prefix_offset:read_offset],
+            skip_special_tokens=skip_special_tokens)
+        new_text = _convert_tokens_to_string_with_added_encoders(
+            tokenizer,
+            output_tokens[prefix_offset:],
+            skip_special_tokens=skip_special_tokens)
+
+    if len(new_text) > len(prefix_text) and not new_text.endswith("<EFBFBD>"):
+        # utf-8 char at the end means it's a potential unfinished byte sequence
+        # from byte fallback tokenization.
+        # If it's in the middle, it's probably a real invalid id generated
+        # by the model
+        new_text = new_text[len(prefix_text):]
+        return new_tokens, new_text, read_offset, len(output_tokens)
+    else:
+        return new_tokens, "", prefix_offset, read_offset

vllm/utils.py

@@ -1,10 +1,12 @@
 import enum
-from platform import uname
 import uuid
+from platform import uname
 
 import psutil
 import torch
 
+from vllm import cuda_utils
+
 
 class Device(enum.Enum):
     GPU = enum.auto()
@@ -25,6 +27,15 @@ class Counter:
         self.counter = 0
 
 
+def get_max_shared_memory_bytes(gpu: int = 0) -> int:
+    """Returns the maximum shared memory per thread block in bytes."""
+    # https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html
+    cudaDevAttrMaxSharedMemoryPerBlockOptin = 97  # pylint: disable=invalid-name
+    max_shared_mem = cuda_utils.get_device_attribute(
+        cudaDevAttrMaxSharedMemoryPerBlockOptin, gpu)
+    return int(max_shared_mem)
+
+
 def get_gpu_memory(gpu: int = 0) -> int:
     """Returns the total memory of the GPU in bytes."""
     return torch.cuda.get_device_properties(gpu).total_memory

vllm/worker/cache_engine.py

@@ -33,7 +33,7 @@ class CacheEngine:
 
         self.head_size = model_config.get_head_size()
         self.num_layers = model_config.get_num_layers(parallel_config)
-        self.num_heads = model_config.get_num_heads(parallel_config)
+        self.num_heads = model_config.get_num_kv_heads(parallel_config)
         self.dtype = model_config.dtype
 
         self.block_size = cache_config.block_size
@@ -146,7 +146,7 @@ class CacheEngine:
         parallel_config: ParallelConfig,
     ) -> int:
         head_size = model_config.get_head_size()
-        num_heads = model_config.get_num_heads(parallel_config)
+        num_heads = model_config.get_num_kv_heads(parallel_config)
         num_layers = model_config.get_num_layers(parallel_config)
 
         key_cache_block = block_size * num_heads * head_size

vllm/worker/worker.py

@@ -13,7 +13,7 @@ from vllm.model_executor.parallel_utils.parallel_state import (
 from vllm.sampling_params import SamplingParams
 from vllm.sequence import SamplerOutput, SequenceData, SequenceGroupMetadata
 from vllm.worker.cache_engine import CacheEngine
-from vllm.utils import get_gpu_memory
+from vllm.utils import get_gpu_memory, get_max_shared_memory_bytes
 
 
 class Worker:
@@ -42,6 +42,7 @@ class Worker:
         # self.init_cache_engine().
         self.cache_config = None
         self.block_size = None
+        self.sliding_window = None
         self.cache_engine = None
         self.cache_events = None
         self.gpu_cache = None
@@ -136,6 +137,15 @@ class Worker:
     def init_cache_engine(self, cache_config: CacheConfig) -> None:
         self.cache_config = cache_config
         self.block_size = cache_config.block_size
+        self.sliding_window = cache_config.sliding_window
+
+        if self.sliding_window is None:
+            max_seq_len = self.scheduler_config.max_model_len
+        else:
+            max_seq_len = min(self.scheduler_config.max_model_len,
+                              self.sliding_window)
+        _check_if_can_support_max_seq_len(max_seq_len, self.block_size)
+
         self.cache_engine = CacheEngine(self.cache_config, self.model_config,
                                         self.parallel_config)
         self.cache_events = self.cache_engine.events
@@ -207,10 +217,11 @@ class Worker:
 
                 context_len = seq_data.get_len()
                 position = context_len - 1
+                if self.sliding_window is not None:
+                    context_len = min(context_len, self.sliding_window)
                 input_positions.append(position)
 
                 block_table = seq_group_metadata.block_tables[seq_id]
-                generation_block_tables.append(block_table)
 
                 max_context_len = max(max_context_len, context_len)
                 max_num_blocks_per_seq = max(max_num_blocks_per_seq,
@@ -222,21 +233,37 @@ class Worker:
                 slot = block_number * self.block_size + block_offset
                 slot_mapping.append(slot)
 
+                if self.sliding_window is not None:
+                    sliding_window_blocks = (self.sliding_window //
+                                             self.block_size)
+                    block_table = block_table[-sliding_window_blocks:]
+                generation_block_tables.append(block_table)
+
         # Optimization: Pad the input length to be a multiple of 8.
         # This is required for utilizing the Tensor Cores in NVIDIA GPUs.
         input_tokens = _pad_to_alignment(input_tokens, multiple_of=8)
         input_positions = _pad_to_alignment(input_positions, multiple_of=8)
 
         # Convert to tensors.
-        tokens_tensor = torch.cuda.LongTensor(input_tokens)
-        positions_tensor = torch.cuda.LongTensor(input_positions)
-        slot_mapping_tensor = torch.cuda.IntTensor(slot_mapping)
-        context_lens_tensor = torch.cuda.IntTensor(context_lens)
+        tokens_tensor = torch.tensor(input_tokens,
+                                     dtype=torch.long,
+                                     device="cuda")
+        positions_tensor = torch.tensor(input_positions,
+                                        dtype=torch.long,
+                                        device="cuda")
+        slot_mapping_tensor = torch.tensor(slot_mapping,
+                                           dtype=torch.int,
+                                           device="cuda")
+        context_lens_tensor = torch.tensor(context_lens,
+                                           dtype=torch.int,
+                                           device="cuda")
         padded_block_tables = [
             _pad_to_max(block_table, max_num_blocks_per_seq)
             for block_table in generation_block_tables
         ]
-        block_tables_tensor = torch.cuda.IntTensor(padded_block_tables)
+        block_tables_tensor = torch.tensor(padded_block_tables,
+                                           dtype=torch.int,
+                                           device="cuda")
 
         seq_data: Dict[int, SequenceData] = {}
         for seq_group_metadata in seq_group_metadata_list:
@@ -250,6 +277,7 @@ class Worker:
             context_lens=context_lens_tensor,
             max_context_len=max_context_len,
             block_tables=block_tables_tensor,
+            sliding_window=self.sliding_window,
         )
         return tokens_tensor, positions_tensor, input_metadata
 
@@ -337,3 +365,23 @@ def _pad_to_alignment(x: List[int], multiple_of: int) -> List[int]:
 
 def _pad_to_max(x: List[int], max_len: int) -> List[int]:
     return x + [0] * (max_len - len(x))
+
+
+def _check_if_can_support_max_seq_len(max_seq_len: int,
+                                      block_size: int) -> None:
+    # Follows the logic in
+    # attention_kernels.cu::single_query_cached_kv_attention_launcher
+    max_shared_mem = get_max_shared_memory_bytes()
+    float32_bytes = torch.finfo(torch.float).bits // 8
+    padded_max_seq_len = (
+        (max_seq_len + block_size - 1) / block_size) * block_size
+    # padded_max_seq_len + extra buffer
+    required_shared_mem = (padded_max_seq_len + 512) * float32_bytes
+    if padded_max_seq_len * float32_bytes > max_shared_mem:
+        raise RuntimeError(
+            f"vLLM cannot currently support max_model_len={max_seq_len} "
+            f"with block_size={block_size} on GPU with compute "
+            f"capability {torch.cuda.get_device_capability()} "
+            f"(required shared memory {required_shared_mem} > "
+            f"available shared memory {max_shared_mem}). "
+            "This will be fixed in a future release.")