fix precommit

.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh

@@ -46,6 +46,6 @@ while getopts "m:b:l:f:t:" OPT; do
 done
 
 lm_eval --model vllm \
-  --model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,add_bos_token=true,trust_remote_code=true,max_model_len=4096" \
+  --model_args "pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,distributed_executor_backend=ray,trust_remote_code=true,max_model_len=4096" \
   --tasks gsm8k --num_fewshot "$FEWSHOT" --limit "$LIMIT" \
   --batch_size "$BATCH_SIZE"

.buildkite/lm-eval-harness/test_lm_eval_correctness.py

@@ -18,14 +18,12 @@ RTOL = 0.08
 
 def launch_lm_eval(eval_config, tp_size):
     trust_remote_code = eval_config.get("trust_remote_code", False)
-    max_model_len = eval_config.get("max_model_len", 4096)
     model_args = (
         f"pretrained={eval_config['model_name']},"
         f"tensor_parallel_size={tp_size},"
         f"enforce_eager=true,"
         f"add_bos_token=true,"
-        f"trust_remote_code={trust_remote_code},"
-        f"max_model_len={max_model_len}"
+        f"trust_remote_code={trust_remote_code}"
     )
     results = lm_eval.simple_evaluate(
         model="vllm",

.buildkite/nightly-benchmarks/README.md

@@ -11,7 +11,7 @@ See [vLLM performance dashboard](https://perf.vllm.ai) for the latest performanc
 
 ## Performance benchmark quick overview
 
-**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) and Intel® Xeon® Processors, with different models.
+**Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!), with different models.
 
 **Benchmarking Duration**: about 1hr.
 
@@ -28,34 +28,16 @@ See [vLLM performance dashboard](https://perf.vllm.ai) for the latest performanc
 ## Trigger the benchmark
 
 Performance benchmark will be triggered when:
-
 - A PR being merged into vllm.
 - Every commit for those PRs with `perf-benchmarks` label AND `ready` label.
 
-Manually Trigger the benchmark
-
-```bash
-bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
-```
-
-Runtime environment variables:
-
-- `ON_CPU`: set the value to '1' on Intel® Xeon® Processors. Default value is 0.
-- `SERVING_JSON`: JSON file to use for the serving tests. Default value is empty string (use default file).
-- `LATENCY_JSON`: JSON file to use for the latency tests. Default value is empty string (use default file).
-- `THROUGHPUT_JSON`: JSON file to use for the throughout tests. Default value is empty string (use default file).
-- `REMOTE_HOST`: IP for the remote vLLM service to benchmark. Default value is empty string.
-- `REMOTE_PORT`: Port for the remote vLLM service to benchmark. Default value is empty string.
-
 Nightly benchmark will be triggered when:
-
 - Every commit for those PRs with `perf-benchmarks` label and `nightly-benchmarks` label.
 
 ## Performance benchmark details
 
 See [performance-benchmarks-descriptions.md](performance-benchmarks-descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
-> NOTE: For Intel® Xeon® Processors, use `tests/latency-tests-cpu.json`, `tests/throughput-tests-cpu.json`, `tests/serving-tests-cpu.json` instead.
->
+
 ### Latency test
 
 Here is an example of one test inside `latency-tests.json`:
@@ -78,7 +60,7 @@ Here is an example of one test inside `latency-tests.json`:
 In this example:
 
 - The `test_name` attributes is a unique identifier for the test. In `latency-tests.json`, it must start with `latency_`.
-- The `parameters` attribute control the command line arguments to be used for `vllm bench latency`. Note that please use underline `_` instead of the dash `-` when specifying the command line arguments, and `run-performance-benchmarks.sh` will convert the underline to dash when feeding the arguments to `vllm bench latency`. For example, the corresponding command line arguments for `vllm bench latency` will be `--model meta-llama/Meta-Llama-3-8B --tensor-parallel-size 1 --load-format dummy --num-iters-warmup 5 --num-iters 15`
+- The `parameters` attribute control the command line arguments to be used for `benchmark_latency.py`. Note that please use underline `_` instead of the dash `-` when specifying the command line arguments, and `run-performance-benchmarks.sh` will convert the underline to dash when feeding the arguments to `benchmark_latency.py`. For example, the corresponding command line arguments for `benchmark_latency.py` will be `--model meta-llama/Meta-Llama-3-8B --tensor-parallel-size 1 --load-format dummy --num-iters-warmup 5 --num-iters 15`
 
 Note that the performance numbers are highly sensitive to the value of the parameters. Please make sure the parameters are set correctly.
 
@@ -86,13 +68,13 @@ WARNING: The benchmarking script will save json results by itself, so please do
 
 ### Throughput test
 
-The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `vllm bench throughput`.
+The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `benchmark_throughput.py`.
 
 The number of this test is also stable -- a slight change on the value of this number might vary the performance numbers by a lot.
 
 ### Serving test
 
-We test the throughput by using `vllm bench serve` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
+We test the throughput by using `benchmark_serving.py` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
 
 ```json
 [
@@ -104,6 +86,7 @@ We test the throughput by using `vllm bench serve` with request rate = inf to co
             "tensor_parallel_size": 1,
             "swap_space": 16,
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "load_format": "dummy"
         },
         "client_parameters": {
@@ -121,8 +104,8 @@ Inside this example:
 
 - The `test_name` attribute is also a unique identifier for the test. It must start with `serving_`.
 - The `server-parameters` includes the command line arguments for vLLM server.
-- The `client-parameters` includes the command line arguments for `vllm bench serve`.
-- The `qps_list` controls the list of qps for test. It will be used to configure the `--request-rate` parameter in `vllm bench serve`
+- The `client-parameters` includes the command line arguments for `benchmark_serving.py`.
+- The `qps_list` controls the list of qps for test. It will be used to configure the `--request-rate` parameter in `benchmark_serving.py`
 
 The number of this test is less stable compared to the delay and latency benchmarks (due to randomized sharegpt dataset sampling inside `benchmark_serving.py`), but a large change on this number (e.g. 5% change) still vary the output greatly.
 
@@ -136,31 +119,6 @@ If you do not see the table, please wait till the benchmark finish running.
 The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
 The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
 
-The `compare-json-results.py` helps to compare benchmark results JSON files converted using `convert-results-json-to-markdown.py`.
-When run, benchmark script generates results under `benchmark/results` folder, along with the `benchmark_results.md` and `benchmark_results.json`.
-`compare-json-results.py` compares two `benchmark_results.json` files and provides performance ratio e.g. for Output Tput, Median TTFT and Median TPOT.
-
-Here is an example using the script to compare result_a and result_b without detail test name.
-`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json --ignore_test_name`
-
-|    | results_a/benchmark_results.json | results_b/benchmark_results.json | perf_ratio        |
-|----|----------------------------------------|----------------------------------------|----------|
-| 0  | 142.633982                             | 156.526018                             | 1.097396 |
-| 1  | 241.620334                             | 294.018783                             | 1.216863 |
-| 2  | 218.298905                             | 262.664916                             | 1.203235 |
-| 3  | 242.743860                             | 299.816190                             | 1.235113 |
-
-Here is an example using the script to compare result_a and result_b with detail test name.
-`python3 compare-json-results.py -f results_a/benchmark_results.json -f results_b/benchmark_results.json`
-
-|   | results_a/benchmark_results.json_name | results_a/benchmark_results.json | results_b/benchmark_results.json_name | results_b/benchmark_results.json | perf_ratio        |
-|---|---------------------------------------------|----------------------------------------|---------------------------------------------|----------------------------------------|----------|
-| 0 | serving_llama8B_tp1_sharegpt_qps_1          | 142.633982                             | serving_llama8B_tp1_sharegpt_qps_1          | 156.526018                             | 1.097396 |
-| 1 | serving_llama8B_tp1_sharegpt_qps_16         | 241.620334                             | serving_llama8B_tp1_sharegpt_qps_16         | 294.018783                             | 1.216863 |
-| 2 | serving_llama8B_tp1_sharegpt_qps_4          | 218.298905                             | serving_llama8B_tp1_sharegpt_qps_4          | 262.664916                             | 1.203235 |
-| 3 | serving_llama8B_tp1_sharegpt_qps_inf        | 242.743860                             | serving_llama8B_tp1_sharegpt_qps_inf        | 299.816190                             | 1.235113 |
-| 4 | serving_llama8B_tp2_random_1024_128_qps_1   | 96.613390                              | serving_llama8B_tp4_random_1024_128_qps_1   | 108.404853                             | 1.122048 |
-
 ## Nightly test details
 
 See [nightly-descriptions.md](nightly-descriptions.md) for the detailed description on test workload, models and docker containers of benchmarking other llm engines.
@@ -168,9 +126,9 @@ See [nightly-descriptions.md](nightly-descriptions.md) for the detailed descript
 ### Workflow
 
 - The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines.
-- Inside each container, we run [scripts/run-nightly-benchmarks.sh](scripts/run-nightly-benchmarks.sh), which will probe the serving engine of the current container.
-- The `scripts/run-nightly-benchmarks.sh` will parse the workload described in [nightly-tests.json](tests/nightly-tests.json) and launch the right benchmark for the specified serving engine via `scripts/launch-server.sh`.
-- At last, we run [scripts/summary-nightly-results.py](scripts/summary-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
+- Inside each container, we run [run-nightly-suite.sh](run-nightly-suite.sh), which will probe the serving engine of the current container.
+- The `run-nightly-suite.sh` will redirect the request to `tests/run-[llm serving engine name]-nightly.sh`, which parses the workload described in [nightly-tests.json](tests/nightly-tests.json) and performs the benchmark.
+- At last, we run [scripts/plot-nightly-results.py](scripts/plot-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
 
 ### Nightly tests
 
@@ -180,6 +138,6 @@ In [nightly-tests.json](tests/nightly-tests.json), we include the command line a
 
 The docker containers for benchmarking are specified in `nightly-pipeline.yaml`.
 
-WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `scripts/run-nightly-benchmarks.sh` and `scripts/launch-server.sh`.
+WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `tests/run-[llm serving engine name]-nightly.sh`.
 
 WARNING: populating `trt-llm` to latest version is not easy, as it requires updating several protobuf files in [tensorrt-demo](https://github.com/neuralmagic/tensorrt-demo.git).

.buildkite/nightly-benchmarks/nightly-annotation.md

@@ -1,4 +1,3 @@
-# Nightly benchmark annotation
 
 ## Description
 
@@ -14,15 +13,15 @@ Please download the visualization scripts in the post
 
 - Find the docker we use in `benchmarking pipeline`
 - Deploy the docker, and inside the docker:
-    - Download `nightly-benchmarks.zip`.
-    - In the same folder, run the following code:
+  - Download `nightly-benchmarks.zip`.
+  - In the same folder, run the following code:
 
-    ```bash
-    export HF_TOKEN=<your HF token>
-    apt update
-    apt install -y git
-    unzip nightly-benchmarks.zip
-    VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
-    ```
+  ```console
+  export HF_TOKEN=<your HF token>
+  apt update
+  apt install -y git
+  unzip nightly-benchmarks.zip
+  VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
+  ```
 
 And the results will be inside `./benchmarks/results`.

.buildkite/nightly-benchmarks/nightly-descriptions.md

@@ -13,25 +13,25 @@ Latest reproduction guilde: [github issue link](https://github.com/vllm-project/
 ## Setup
 
 - Docker images:
-    - vLLM: `vllm/vllm-openai:v0.6.2`
-    - SGLang: `lmsysorg/sglang:v0.3.2-cu121`
-    - LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
-    - TensorRT-LLM: `nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3`
-        - *NOTE: we uses r24.07 as the current implementation only works for this version. We are going to bump this up.*
-    - Check [nightly-pipeline.yaml](nightly-pipeline.yaml) for the concrete docker images, specs and commands we use for the benchmark.
+  - vLLM: `vllm/vllm-openai:v0.6.2`
+  - SGLang: `lmsysorg/sglang:v0.3.2-cu121`
+  - LMDeploy: `openmmlab/lmdeploy:v0.6.1-cu12`
+  - TensorRT-LLM: `nvcr.io/nvidia/tritonserver:24.07-trtllm-python-py3`
+    - *NOTE: we uses r24.07 as the current implementation only works for this version. We are going to bump this up.*
+  - Check [nightly-pipeline.yaml](nightly-pipeline.yaml) for the concrete docker images, specs and commands we use for the benchmark.
 - Hardware
-    - 8x Nvidia A100 GPUs
+  - 8x Nvidia A100 GPUs
 - Workload:
-    - Dataset
-        - ShareGPT dataset
-        - Prefill-heavy dataset (in average 462 input tokens, 16 tokens as output)
-        - Decode-heavy dataset (in average 462 input tokens, 256 output tokens)
-        - Check [nightly-tests.json](tests/nightly-tests.json) for the concrete configuration of datasets we use.
-    - Models: llama-3 8B, llama-3 70B.
-        - We do not use llama 3.1 as it is incompatible with trt-llm r24.07. ([issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105)).
-    - Average QPS (query per second): 2, 4, 8, 16, 32 and inf.
-        - Queries are randomly sampled, and arrival patterns are determined via Poisson process, but all with fixed random seed.
-    - Evaluation metrics: Throughput (higher the better), TTFT (time to the first token, lower the better), ITL (inter-token latency, lower the better).
+  - Dataset
+    - ShareGPT dataset
+    - Prefill-heavy dataset (in average 462 input tokens, 16 tokens as output)
+    - Decode-heavy dataset (in average 462 input tokens, 256 output tokens)
+    - Check [nightly-tests.json](tests/nightly-tests.json) for the concrete configuration of datasets we use.
+  - Models: llama-3 8B, llama-3 70B.
+    - We do not use llama 3.1 as it is incompatible with trt-llm r24.07. ([issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105)).
+  - Average QPS (query per second): 2, 4, 8, 16, 32 and inf.
+    - Queries are randomly sampled, and arrival patterns are determined via Poisson process, but all with fixed random seed.
+  - Evaluation metrics: Throughput (higher the better), TTFT (time to the first token, lower the better), ITL (inter-token latency, lower the better).
 
 ## Known issues
 

.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md

@@ -1,12 +1,10 @@
-# Performance benchmarks descriptions
 
 ## Latency tests
 
 - Input length: 32 tokens.
 - Output length: 128 tokens.
 - Batch size: fixed (8).
-- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
-- CPU Models: llama-3.1 8B.
+- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
 - Evaluation metrics: end-to-end latency (mean, median, p99).
 
 {latency_tests_markdown_table}
@@ -16,8 +14,7 @@
 - Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
 - Output length: the corresponding output length of these 200 prompts.
 - Batch size: dynamically determined by vllm to achieve maximum throughput.
-- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
-- CPU Models: llama-3.1 8B.
+- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
 - Evaluation metrics: throughput.
 
 {throughput_tests_markdown_table}
@@ -28,18 +25,12 @@
 - Output length: the corresponding output length of these 200 prompts.
 - Batch size: dynamically determined by vllm and the arrival pattern of the requests.
 - **Average QPS (query per second)**: 1, 4, 16 and inf. QPS = inf means all requests come at once. For other QPS values, the arrival time of each query is determined using a random Poisson process (with fixed random seed).
-- GPU Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
-- We also added a speculative decoding test for llama-3 70B on GPU, under QPS 2
-- CPU Models: llama-3.1 8B.
+- Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
+- We also added a speculative decoding test for llama-3 70B, under QPS 2
 - Evaluation metrics: throughput, TTFT (time to the first token, with mean, median and p99), ITL (inter-token latency, with mean, median and p99).
-- For CPU, we added random dataset tests to benchmark fixed input/output length with 100 prompts.
 
 {serving_tests_markdown_table}
 
-## Platform Information
-
-{platform_markdown_table}
-
 ## json version of the benchmarking tables
 
 This section contains the data of the markdown tables above in JSON format.

.buildkite/nightly-benchmarks/scripts/compare-json-results.py

@@ -1,66 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import argparse
-
-import pandas as pd
-
-
-def compare_data_columns(
-    files, name_column, data_column, drop_column, ignore_test_name=False
-):
-    print("\ncompare_data_column: " + data_column)
-    frames = []
-    compare_frames = []
-    for file in files:
-        data_df = pd.read_json(file)
-        serving_df = data_df.dropna(subset=[drop_column], ignore_index=True)
-        if ignore_test_name is False:
-            serving_df = serving_df.rename(columns={name_column: file + "_name"})
-            frames.append(serving_df[file + "_name"])
-        serving_df = serving_df.rename(columns={data_column: file})
-        frames.append(serving_df[file])
-        compare_frames.append(serving_df[file])
-        if len(compare_frames) >= 2:
-            # Compare numbers among two files
-            ratio_df = compare_frames[1] / compare_frames[0]
-            frames.append(ratio_df)
-            compare_frames.pop(1)
-
-    concat_df = pd.concat(frames, axis=1)
-    return concat_df
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "-f", "--file", action="append", type=str, help="input file name"
-    )
-    parser.add_argument(
-        "--ignore_test_name", action="store_true", help="ignore_test_name or not"
-    )
-    args = parser.parse_args()
-    files = args.file
-    print("comparing : " + ", ".join(files))
-
-    drop_column = "P99"
-    name_column = "Test name"
-    data_cols_to_compare = ["Output Tput (tok/s)", "Median TTFT (ms)", "Median"]
-    html_msgs_for_data_cols = [
-        "Compare Output Tokens /n",
-        "Median TTFT /n",
-        "Median TPOT /n",
-    ]
-    ignore_test_name = args.ignore_test_name
-    with open("perf_comparison.html", "w") as text_file:
-        for i in range(len(data_cols_to_compare)):
-            output_df = compare_data_columns(
-                files,
-                name_column,
-                data_cols_to_compare[i],
-                drop_column,
-                ignore_test_name=ignore_test_name,
-            )
-            print(output_df)
-            html = output_df.to_html()
-            text_file.write(html_msgs_for_data_cols[i])
-            text_file.write(html)

.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py

@@ -3,11 +3,9 @@
 
 import json
 import os
-from importlib import util
 from pathlib import Path
 
 import pandas as pd
-import psutil
 from tabulate import tabulate
 
 results_folder = Path("results/")
@@ -31,11 +29,11 @@ throughput_results = []
 throughput_results_column_mapping = {
     "test_name": "Test name",
     "gpu_type": "GPU",
-    "num_requests": "# of req.",
-    "total_num_tokens": "Total # of tokens",
-    "elapsed_time": "Elapsed time (s)",
+    # "num_requests": "# of req.",
+    # "total_num_tokens": "Total # of tokens",
+    # "elapsed_time": "Elapsed time (s)",
     "requests_per_second": "Tput (req/s)",
-    "tokens_per_second": "Tput (tok/s)",
+    # "tokens_per_second": "Tput (tok/s)",
 }
 
 # serving results and the keys that will be printed into markdown
@@ -43,19 +41,16 @@ serving_results = []
 serving_column_mapping = {
     "test_name": "Test name",
     "gpu_type": "GPU",
-    "completed": "# of req.",
-    "max_concurrency": "# of max concurrency.",
+    # "completed": "# of req.",
     "request_throughput": "Tput (req/s)",
-    "total_token_throughput": "Total Token Tput (tok/s)",
-    "output_throughput": "Output Tput (tok/s)",
-    "total_input_tokens": "Total input tokens",
-    "total_output_tokens": "Total output tokens",
+    # "input_throughput": "Input Tput (tok/s)",
+    # "output_throughput": "Output Tput (tok/s)",
     "mean_ttft_ms": "Mean TTFT (ms)",
     "median_ttft_ms": "Median TTFT (ms)",
     "p99_ttft_ms": "P99 TTFT (ms)",
-    "mean_tpot_ms": "Mean TPOT (ms)",
-    "median_tpot_ms": "Median",
-    "p99_tpot_ms": "P99",
+    # "mean_tpot_ms": "Mean TPOT (ms)",
+    # "median_tpot_ms": "Median",
+    # "p99_tpot_ms": "P99",
     "mean_itl_ms": "Mean ITL (ms)",
     "median_itl_ms": "Median ITL (ms)",
     "p99_itl_ms": "P99 ITL (ms)",
@@ -80,20 +75,6 @@ def results_to_json(latency, throughput, serving):
     )
 
 
-def get_size_with_unit(bytes, suffix="B"):
-    """
-    Scale bytes to its proper format
-    e.g:
-        1253656 => '1.20MB'
-        1253656678 => '1.17GB'
-    """
-    factor = 1024
-    for unit in ["", "K", "M", "G", "T", "P"]:
-        if bytes < factor:
-            return f"{bytes:.2f}{unit}{suffix}"
-        bytes /= factor
-
-
 if __name__ == "__main__":
     # collect results
     for test_file in results_folder.glob("*.json"):
@@ -101,7 +82,7 @@ if __name__ == "__main__":
             raw_result = json.loads(f.read())
 
         if "serving" in str(test_file):
-            # this result is generated via `vllm bench serve` command
+            # this result is generated via `benchmark_serving.py`
 
             # attach the benchmarking command to raw_result
             try:
@@ -121,7 +102,7 @@ if __name__ == "__main__":
             continue
 
         elif "latency" in f.name:
-            # this result is generated via `vllm bench latency` command
+            # this result is generated via `benchmark_latency.py`
 
             # attach the benchmarking command to raw_result
             try:
@@ -149,7 +130,7 @@ if __name__ == "__main__":
             continue
 
         elif "throughput" in f.name:
-            # this result is generated via `vllm bench throughput` command
+            # this result is generated via `benchmark_throughput.py`
 
             # attach the benchmarking command to raw_result
             try:
@@ -174,27 +155,6 @@ if __name__ == "__main__":
     serving_results = pd.DataFrame.from_dict(serving_results)
     throughput_results = pd.DataFrame.from_dict(throughput_results)
 
-    svmem = psutil.virtual_memory()
-    platform_data = {
-        "Physical cores": [psutil.cpu_count(logical=False)],
-        "Total cores": [psutil.cpu_count(logical=True)],
-        "Total Memory": [get_size_with_unit(svmem.total)],
-    }
-
-    if util.find_spec("numa") is not None:
-        from numa import info
-
-        platform_data["Total NUMA nodes"] = [info.get_num_configured_nodes()]
-
-    if util.find_spec("cpuinfo") is not None:
-        from cpuinfo import get_cpu_info
-
-        platform_data["CPU Brand"] = [get_cpu_info()["brand_raw"]]
-
-    platform_results = pd.DataFrame.from_dict(
-        platform_data, orient="index", columns=["Platform Info"]
-    )
-
     raw_results_json = results_to_json(
         latency_results, throughput_results, serving_results
     )
@@ -240,9 +200,6 @@ if __name__ == "__main__":
     throughput_md_table = tabulate(
         throughput_results, headers="keys", tablefmt="pipe", showindex=False
     )
-    platform_md_table = tabulate(
-        platform_results, headers="keys", tablefmt="pipe", showindex=True
-    )
 
     # document the result
     with open(results_folder / "benchmark_results.md", "w") as f:
@@ -254,7 +211,6 @@ if __name__ == "__main__":
             latency_tests_markdown_table=latency_md_table,
             throughput_tests_markdown_table=throughput_md_table,
             serving_tests_markdown_table=serving_md_table,
-            platform_markdown_table=platform_md_table,
             benchmarking_results_in_json_string=processed_results_json,
         )
         f.write(results)

.buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh

@@ -73,7 +73,7 @@ get_current_llm_serving_engine() {
     echo "Container: vllm"
     # move to a completely irrelevant directory, to avoid import vllm from current folder
     export CURRENT_LLM_SERVING_ENGINE=vllm
-
+    
     return
   fi
 }
@@ -95,14 +95,12 @@ json2args() {
 }
 
 kill_gpu_processes() {
-  pkill -f '[p]ython'
-  pkill -f '[p]ython3'
-  pkill -f '[t]ritonserver'
-  pkill -f '[p]t_main_thread'
-  pkill -f '[t]ext-generation'
-  pkill -f '[l]mdeploy'
-  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
-  pkill -f '[V]LLM'
+  pkill -f python
+  pkill -f python3
+  pkill -f tritonserver
+  pkill -f pt_main_thread
+  pkill -f text-generation
+  pkill -f lmdeploy
 
   while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
     sleep 1
@@ -127,7 +125,7 @@ ensure_installed() {
 }
 
 run_serving_tests() {
-  # run serving tests using `vllm bench serve` command
+  # run serving tests using `benchmark_serving.py`
   # $1: a json file specifying serving test cases
 
   local serving_test_file
@@ -227,7 +225,7 @@ run_serving_tests() {
 
       if [[ "$dataset_name" = "sharegpt" ]]; then
 
-        client_command="vllm bench serve \
+        client_command="python3 benchmark_serving.py \
           --backend $backend \
           --tokenizer /tokenizer_cache \
           --model $model \
@@ -248,7 +246,7 @@ run_serving_tests() {
         sonnet_output_len=$(echo "$common_params" | jq -r '.sonnet_output_len')
         sonnet_prefix_len=$(echo "$common_params" | jq -r '.sonnet_prefix_len')
 
-        client_command="vllm bench serve \
+        client_command="python3 benchmark_serving.py \
           --backend $backend \
           --tokenizer /tokenizer_cache \
           --model $model \
@@ -267,13 +265,13 @@ run_serving_tests() {
           $client_args"
 
       else
-
+  
         echo "The dataset name must be either 'sharegpt' or 'sonnet'. Got $dataset_name."
         exit 1
 
       fi
 
-
+        
 
       echo "Running test case $test_name with qps $qps"
       echo "Client command: $client_command"
@@ -304,7 +302,7 @@ run_serving_tests() {
 }
 
 run_genai_perf_tests() {
-  # run genai-perf tests
+  # run genai-perf tests 
 
   # $1: a json file specifying genai-perf test cases
   local genai_perf_test_file
@@ -313,14 +311,14 @@ run_genai_perf_tests() {
   # Iterate over genai-perf tests
   jq -c '.[]' "$genai_perf_test_file" | while read -r params; do
     # get the test name, and append the GPU type back to it.
-    test_name=$(echo "$params" | jq -r '.test_name')
-
+    test_name=$(echo "$params" | jq -r '.test_name')    
+    
     # if TEST_SELECTOR is set, only run the test cases that match the selector
     if [[ -n "$TEST_SELECTOR" ]] && [[ ! "$test_name" =~ $TEST_SELECTOR ]]; then
       echo "Skip test case $test_name."
       continue
     fi
-
+    
     # prepend the current serving engine to the test name
     test_name=${CURRENT_LLM_SERVING_ENGINE}_${test_name}
 
@@ -371,10 +369,10 @@ run_genai_perf_tests() {
         qps=$num_prompts
         echo "now qps is $qps"
       fi
-
+    
       new_test_name=$test_name"_qps_"$qps
       backend=$CURRENT_LLM_SERVING_ENGINE
-
+      
       if [[ "$backend" == *"vllm"* ]]; then
         backend="vllm"
       fi
@@ -415,7 +413,7 @@ prepare_dataset() {
   do
     cat sonnet.txt >> sonnet_4x.txt
   done
-
+  
 }
 
 main() {

.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh

@@ -31,20 +31,6 @@ check_gpus() {
   echo "GPU type is $gpu_type"
 }
 
-check_cpus() {
-  # check the number of CPUs and NUMA Node and GPU type.
-  declare -g numa_count=$(lscpu | grep "NUMA node(s):" | awk '{print $3}')
-  if [[ $numa_count -gt 0 ]]; then
-    echo "NUMA found."
-    echo $numa_count
-  else
-    echo "Need at least 1 NUMA to run benchmarking."
-    exit 1
-  fi
-  declare -g gpu_type="cpu"
-  echo "GPU type is $gpu_type"
-}
-
 check_hf_token() {
   # check if HF_TOKEN is available and valid
   if [[ -z "$HF_TOKEN" ]]; then
@@ -83,22 +69,6 @@ json2args() {
   echo "$args"
 }
 
-json2envs() {
-  # transforms the JSON string to environment variables.
-  # example:
-  # input: { "VLLM_CPU_KVCACHE_SPACE": 5 }
-  # output: VLLM_CPU_KVCACHE_SPACE=5
-  local json_string=$1
-  local args=$(
-    echo "$json_string" | jq -r '
-      to_entries |
-      map((.key ) + "=" + (.value | tostring)) |
-      join(" ")
-    '
-  )
-  echo "$args"
-}
-
 wait_for_server() {
   # wait for vllm server to start
   # return 1 if vllm server crashes
@@ -126,8 +96,7 @@ kill_gpu_processes() {
   ps -aux
   lsof -t -i:8000 | xargs -r kill -9
   pgrep python3 | xargs -r kill -9
-  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
-  pgrep VLLM | xargs -r kill -9
+
 
   # wait until GPU memory usage smaller than 1GB
   if command -v nvidia-smi; then
@@ -165,7 +134,7 @@ upload_to_buildkite() {
 }
 
 run_latency_tests() {
-  # run latency tests using `vllm bench latency` command
+  # run latency tests using `benchmark_latency.py`
   # $1: a json file specifying latency test cases
 
   local latency_test_file
@@ -189,24 +158,15 @@ run_latency_tests() {
     # get arguments
     latency_params=$(echo "$params" | jq -r '.parameters')
     latency_args=$(json2args "$latency_params")
-    latency_environment_variables=$(echo "$params" | jq -r '.environment_variables')
-    latency_envs=$(json2envs "$latency_environment_variables")
 
     # check if there is enough GPU to run the test
     tp=$(echo "$latency_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
-        continue
-      fi
-    else
-      if [[ $gpu_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
-        continue
-      fi
+    if [[ $gpu_count -lt $tp ]]; then
+      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
+      continue
     fi
 
-    latency_command=" $latency_envs vllm bench latency \
+    latency_command="python3 benchmark_latency.py \
       --output-json $RESULTS_FOLDER/${test_name}.json \
       $latency_args"
 
@@ -232,7 +192,7 @@ run_latency_tests() {
 }
 
 run_throughput_tests() {
-  # run throughput tests using `vllm bench throughput`
+  # run throughput tests using `benchmark_throughput.py`
   # $1: a json file specifying throughput test cases
 
   local throughput_test_file
@@ -256,24 +216,15 @@ run_throughput_tests() {
     # get arguments
     throughput_params=$(echo "$params" | jq -r '.parameters')
     throughput_args=$(json2args "$throughput_params")
-    throughput_environment_variables=$(echo "$params" | jq -r '.environment_variables')
-    throughput_envs=$(json2envs "$throughput_environment_variables")
 
     # check if there is enough GPU to run the test
     tp=$(echo "$throughput_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
-        continue
-      fi
-    else
-      if [[ $gpu_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
-        continue
-      fi
+    if [[ $gpu_count -lt $tp ]]; then
+      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
+      continue
     fi
 
-    throughput_command=" $throughput_envs vllm bench throughput \
+    throughput_command="python3 benchmark_throughput.py \
       --output-json $RESULTS_FOLDER/${test_name}.json \
       $throughput_args"
 
@@ -298,7 +249,7 @@ run_throughput_tests() {
 }
 
 run_serving_tests() {
-  # run serving tests using `vllm bench serve` command
+  # run serving tests using `benchmark_serving.py`
   # $1: a json file specifying serving test cases
 
   local serving_test_file
@@ -321,27 +272,18 @@ run_serving_tests() {
 
     # get client and server arguments
     server_params=$(echo "$params" | jq -r '.server_parameters')
-    server_envs=$(echo "$params" | jq -r '.server_environment_variables')
     client_params=$(echo "$params" | jq -r '.client_parameters')
     server_args=$(json2args "$server_params")
-    server_envs=$(json2envs "$server_envs")
     client_args=$(json2args "$client_params")
     qps_list=$(echo "$params" | jq -r '.qps_list')
     qps_list=$(echo "$qps_list" | jq -r '.[] | @sh')
     echo "Running over qps list $qps_list"
 
-    # check if there is enough resources to run the test
+    # check if there is enough GPU to run the test
     tp=$(echo "$server_params" | jq -r '.tensor_parallel_size')
-    if [ "$ON_CPU" == "1" ];then
-      if [[ $numa_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $numa_count NUMA nodes found. Skip testcase $test_name."
-        continue
-      fi
-    else
-      if [[ $gpu_count -lt $tp ]]; then
-        echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
-        continue
-      fi
+    if [[ $gpu_count -lt $tp ]]; then
+      echo "Required tensor-parallel-size $tp but only $gpu_count GPU found. Skip testcase $test_name."
+      continue
     fi
 
     # check if server model and client model is aligned
@@ -352,33 +294,23 @@ run_serving_tests() {
       continue
     fi
 
-    server_command="$server_envs python3 \
+    server_command="python3 \
       -m vllm.entrypoints.openai.api_server \
       $server_args"
 
     # run the server
     echo "Running test case $test_name"
     echo "Server command: $server_command"
-    # support remote vllm server
-    client_remote_args=""
-    if [[ -z "${REMOTE_HOST}" ]]; then
-      bash -c "$server_command" &
-      server_pid=$!
-      # wait until the server is alive
-      if wait_for_server; then
-        echo ""
-        echo "vLLM server is up and running."
-      else
-        echo ""
-        echo "vLLM failed to start within the timeout period."
-      fi
+    bash -c "$server_command" &
+    server_pid=$!
+
+    # wait until the server is alive
+    if wait_for_server; then
+      echo ""
+      echo "vllm server is up and running."
     else
-      server_command="Using Remote Server $REMOTE_HOST $REMOTE_PORT"
-      if [[ ${REMOTE_PORT} ]]; then
-        client_remote_args=" --host=$REMOTE_HOST --port=$REMOTE_PORT "
-      else
-        client_remote_args=" --host=$REMOTE_HOST "
-      fi
+      echo ""
+      echo "vllm failed to start within the timeout period."
     fi
 
     # iterate over different QPS
@@ -394,13 +326,13 @@ run_serving_tests() {
 
       # pass the tensor parallel size to the client so that it can be displayed
       # on the benchmark dashboard
-      client_command="vllm bench serve \
+      client_command="python3 benchmark_serving.py \
         --save-result \
         --result-dir $RESULTS_FOLDER \
         --result-filename ${new_test_name}.json \
         --request-rate $qps \
         --metadata "tensor_parallel_size=$tp" \
-        $client_args $client_remote_args "
+        $client_args"
 
       echo "Running test case $test_name with qps $qps"
       echo "Client command: $client_command"
@@ -428,14 +360,7 @@ run_serving_tests() {
 }
 
 main() {
-  local ARCH
-  ARCH=''
-  if [ "$ON_CPU" == "1" ];then
-     check_cpus
-     ARCH='-cpu'
-  else
-     check_gpus
-  fi
+  check_gpus
   check_hf_token
 
   # Set to v1 to run v1 benchmark
@@ -448,7 +373,7 @@ main() {
   (which jq) || (apt-get update && apt-get -y install jq)
   (which lsof) || (apt-get update && apt-get install -y lsof)
 
-  # get the current IP address, required by `vllm bench serve` command
+  # get the current IP address, required by benchmark_serving.py
   export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
   # turn of the reporting of the status of each request, to clean up the terminal output
   export VLLM_LOGGING_LEVEL="WARNING"
@@ -461,9 +386,9 @@ main() {
   QUICK_BENCHMARK_ROOT=../.buildkite/nightly-benchmarks/
 
   # benchmarking
-  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/"${SERVING_JSON:-serving-tests$ARCH.json}"
-  run_latency_tests $QUICK_BENCHMARK_ROOT/tests/"${LATENCY_JSON:-latency-tests$ARCH.json}"
-  run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/"${THROUGHPUT_JSON:-throughput-tests$ARCH.json}"
+  run_serving_tests $QUICK_BENCHMARK_ROOT/tests/serving-tests.json
+  run_latency_tests $QUICK_BENCHMARK_ROOT/tests/latency-tests.json
+  run_throughput_tests $QUICK_BENCHMARK_ROOT/tests/throughput-tests.json
 
   # postprocess benchmarking results
   pip install tabulate pandas

.buildkite/nightly-benchmarks/tests/genai-perf-tests.json

@@ -11,6 +11,7 @@
         },
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
             "num_scheduler_steps": 10,
             "max_num_seqs": 512,

.buildkite/nightly-benchmarks/tests/latency-tests-cpu.json

@@ -1,30 +0,0 @@
-[
-    {
-        "test_name": "latency_llama8B_tp1",
-        "environment_variables": {
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-            "load_format": "dummy",
-            "num_iters_warmup": 5,
-            "num_iters": 15
-        }
-    },
-    {
-        "test_name": "latency_llama8B_tp4",
-        "environment_variables": {
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-            "load_format": "dummy",
-            "num_iters_warmup": 5,
-            "num_iters": 15
-        }
-    }
-]

.buildkite/nightly-benchmarks/tests/nightly-tests.json

@@ -35,6 +35,7 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
             "num_scheduler_steps": 10,
             "max_num_seqs": 512,
@@ -89,6 +90,7 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
             "num_scheduler_steps": 10,
             "max_num_seqs": 512,
@@ -143,6 +145,7 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
             "num_scheduler_steps": 10,
             "max_num_seqs": 512,
@@ -194,6 +197,7 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
             "num_scheduler_steps": 10,
             "max_num_seqs": 512,
@@ -247,6 +251,7 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
             "num_scheduler_steps": 10,
             "max_num_seqs": 512,
@@ -300,6 +305,7 @@
         }, 
         "vllm_server_parameters": {
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "gpu_memory_utilization": 0.9,
             "num_scheduler_steps": 10,
             "max_num_seqs": 512,

.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc2.json

@@ -1,203 +0,0 @@
-[
-    {
-        "test_name": "serving_llama8B_tp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp4_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp1_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp4_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    }
-]

.buildkite/nightly-benchmarks/tests/serving-tests-cpu-snc3.json

@@ -1,205 +0,0 @@
-[
-    {
-        "test_name": "serving_llama8B_pp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "pipeline_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_pp3_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "pipeline_parallel_size": 3,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2pp6_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-            "pipeline_parallel_size": 3,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_pp1_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "pipeline_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    },
-    {
-        "test_name": "serving_llama8B_pp3_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL:": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "pipeline_parallel_size": 3,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2pp3_random_128_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-            "pipeline_parallel_size": 3,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 128,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 1000,
-            "num_prompts": 1000
-        }
-    }
-]

.buildkite/nightly-benchmarks/tests/serving-tests-cpu.json

@@ -1,168 +0,0 @@
-[
-    {
-        "test_name": "serving_llama8B_tp1_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp2_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 2,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp4_sharegpt",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "sharegpt",
-            "dataset_path": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-	    "max_concurrency": 60,
-            "num_prompts": 200
-        }
-    },
-    {
-        "test_name": "serving_llama8B_tp4_random_1024_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 1024,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 100,
-            "num_prompts": 100
-        }
-    },
-    {
-        "test_name": "serving_llama8B_pp6_random_1024_128",
-        "qps_list": [1, 4, 16, "inf"],
-        "server_environment_variables": {
-            "VLLM_RPC_TIMEOUT": 100000,
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_ENGINE_ITERATION_TIMEOUT_S": 120,
-	    "VLLM_CPU_SGL_KERNEL": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "pipeline_parallel_size": 6,
-	    "dtype": "bfloat16",
-	    "distributed_executor_backend": "mp",
-	    "block_size": 128,
-	    "trust_remote_code": "",
-	    "enable_chunked_prefill": "",
-            "disable_log_stats": "",
-	    "enforce_eager": "",
-	    "max_num_batched_tokens": 2048,
-	    "max_num_seqs": 256,
-            "load_format": "dummy"
-        },
-        "client_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "backend": "vllm",
-            "dataset_name": "random",
-	    "random-input-len": 1024,
-	    "random-output-len": 128,
-	    "ignore-eos": "",
-	    "max_concurrency": 100,
-            "num_prompts": 100
-        }
-    }
-]

.buildkite/nightly-benchmarks/tests/serving-tests.json

@@ -7,6 +7,7 @@
             "tensor_parallel_size": 1,
             "swap_space": 16,
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "load_format": "dummy"
         },
         "client_parameters": {
@@ -25,6 +26,7 @@
             "tensor_parallel_size": 4,
             "swap_space": 16,
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "load_format": "dummy"
         },
         "client_parameters": {
@@ -43,6 +45,7 @@
             "tensor_parallel_size": 2,
             "swap_space": 16,
             "disable_log_stats": "",
+            "disable_log_requests": "",
             "load_format": "dummy"
         },
         "client_parameters": {
@@ -57,7 +60,8 @@
         "test_name": "serving_llama70B_tp4_sharegpt_specdecode",
         "qps_list": [2],
         "server_parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct", 
+            "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
+            "disable_log_requests": "", 
             "tensor_parallel_size": 4,
             "swap_space": 16,
             "speculative_config": {

.buildkite/nightly-benchmarks/tests/throughput-tests-cpu.json

@@ -1,32 +0,0 @@
-[
-    {
-        "test_name": "throughput_llama8B_tp1",
-        "environment_variables": {
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 1,
-            "load_format": "dummy",
-            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200,
-            "backend": "vllm"
-        }
-    },
-    {
-        "test_name": "throughput_llama8B_tp4",
-        "environment_variables": {
-	    "VLLM_ALLOW_LONG_MAX_MODEL_LEN": 1,
-	    "VLLM_CPU_KVCACHE_SPACE": 40
-        },
-        "parameters": {
-            "model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
-            "tensor_parallel_size": 4,
-            "load_format": "dummy",
-            "dataset": "./ShareGPT_V3_unfiltered_cleaned_split.json",
-            "num_prompts": 200,
-            "backend": "vllm"
-        }
-    }
-]

.buildkite/release-pipeline.yaml

@@ -52,7 +52,7 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
   - label: "Annotate release workflow"
@@ -101,8 +101,7 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_AVX512BF16=true --build-arg VLLM_CPU_AVX512VNNI=true --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
     env:
       DOCKER_BUILDKIT: "1"
@@ -118,7 +117,6 @@ steps:
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest --progress plain -f docker/Dockerfile.neuron ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest"
       - "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version)"
     env:
       DOCKER_BUILDKIT: "1"

.buildkite/scripts/hardware_ci/run-amd-test.sh

@@ -107,8 +107,10 @@ fi
 
 if [[ $commands == *" kernels/attention"* ]]; then
   commands="${commands} \
-  --ignore=kernels/attention/test_attention_selector.py \
+  --ignore=kernels/attention/stest_attention_selector.py \
+  --ignore=kernels/attention/test_blocksparse_attention.py \
   --ignore=kernels/attention/test_encoder_decoder_attn.py \
+  --ignore=kernels/attention/test_attention_selector.py \
   --ignore=kernels/attention/test_flash_attn.py \
   --ignore=kernels/attention/test_flashinfer.py \
   --ignore=kernels/attention/test_prefix_prefill.py \

.buildkite/scripts/hardware_ci/run-cpu-test.sh

@@ -6,16 +6,15 @@ set -ex
 
 # allow to bind to different cores
 CORE_RANGE=${CORE_RANGE:-48-95}
-# used for TP/PP E2E test
 OMP_CORE_RANGE=${OMP_CORE_RANGE:-48-95}
 NUMA_NODE=${NUMA_NODE:-1}
 
 export CMAKE_BUILD_PARALLEL_LEVEL=32
 
 # Setup cleanup
-remove_docker_container() {
-    set -e;
-    docker rm -f cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"-avx2 || true;
+remove_docker_container() { 
+    set -e; 
+    docker rm -f cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"-avx2 || true; 
 }
 trap remove_docker_container EXIT
 remove_docker_container
@@ -25,8 +24,8 @@ numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$NUMA_NODE
 numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$NUMA_NODE"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
 
 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_CI_ENV=1 -e E2E_OMP_THREADS="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE"-avx2 cpu-test-"$NUMA_NODE"-avx2
+docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_OMP_THREADS_BIND="$OMP_CORE_RANGE" --env VLLM_CPU_CI_ENV=1 --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
+docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_OMP_THREADS_BIND="$OMP_CORE_RANGE" --env VLLM_CPU_CI_ENV=1 --shm-size=4g --name cpu-test-"$NUMA_NODE"-avx2 cpu-test-"$NUMA_NODE"-avx2
 
 function cpu_tests() {
   set -e
@@ -49,16 +48,9 @@ function cpu_tests() {
   # Run basic model test
   docker exec cpu-test-"$NUMA_NODE" bash -c "
     set -e
-    # Note: disable until supports V1
-    # pytest -v -s tests/kernels/attention/test_cache.py -m cpu_model
-    # pytest -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model
-
-    # Note: disable Bart until supports V1
-    pytest -v -s tests/models/language/generation -m cpu_model \
-                --ignore=tests/models/language/generation/test_bart.py
-    VLLM_CPU_SGL_KERNEL=1 pytest -v -s tests/models/language/generation -m cpu_model \
-                --ignore=tests/models/language/generation/test_bart.py
-
+    pytest -v -s tests/kernels/attention/test_cache.py -m cpu_model
+    pytest -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model
+    pytest -v -s tests/models/language/generation -m cpu_model
     pytest -v -s tests/models/language/pooling -m cpu_model
     pytest -v -s tests/models/multimodal/generation \
                 --ignore=tests/models/multimodal/generation/test_mllama.py \
@@ -69,34 +61,41 @@ function cpu_tests() {
   docker exec cpu-test-"$NUMA_NODE" bash -c "
     set -e
     pytest -s -v \
-    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs[False-10-32-neuralmagic/Llama-3.2-1B-quantized.w8a8]"
+    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_static_setup \
+    tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_dynamic_per_token"
 
-  # Note: disable it until supports V1
   # Run AWQ test
-  # docker exec cpu-test-"$NUMA_NODE" bash -c "
-  #   set -e
-  #   VLLM_USE_V1=0 pytest -s -v \
-  #   tests/quantization/test_ipex_quant.py"
+  docker exec cpu-test-"$NUMA_NODE" bash -c "
+    set -e
+    VLLM_USE_V1=0 pytest -s -v \
+    tests/quantization/test_ipex_quant.py"
+
+  # Run chunked-prefill and prefix-cache test
+  docker exec cpu-test-"$NUMA_NODE" bash -c "
+    set -e
+    pytest -s -v -k cpu_model \
+    tests/basic_correctness/test_chunked_prefill.py"  
+
+  # online serving
+  docker exec cpu-test-"$NUMA_NODE" bash -c "
+    set -e
+    python3 -m vllm.entrypoints.openai.api_server --model facebook/opt-125m --dtype half & 
+    timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
+    VLLM_CPU_CI_ENV=0 python3 benchmarks/benchmark_serving.py \
+      --backend vllm \
+      --dataset-name random \
+      --model facebook/opt-125m \
+      --num-prompts 20 \
+      --endpoint /v1/completions \
+      --tokenizer facebook/opt-125m"
 
   # Run multi-lora tests
   docker exec cpu-test-"$NUMA_NODE" bash -c "
     set -e
     pytest -s -v \
     tests/lora/test_qwen2vl.py"
-
-  # online serving
-  docker exec cpu-test-"$NUMA_NODE" bash -c '
-    set -e
-    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS VLLM_CPU_SGL_KERNEL=1 vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 &
-    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
-    vllm bench serve \
-      --backend vllm \
-      --dataset-name random \
-      --model meta-llama/Llama-3.2-3B-Instruct \
-      --num-prompts 20 \
-      --endpoint /v1/completions'
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.
 export -f cpu_tests
-timeout 1.5h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
+timeout 1h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"

.buildkite/scripts/hardware_ci/run-gh200-test.sh

@@ -16,7 +16,8 @@ DOCKER_BUILDKIT=1 docker build . \
   --build-arg max_jobs=66 \
   --build-arg nvcc_threads=2 \
   --build-arg RUN_WHEEL_CHECK=false \
-  --build-arg torch_cuda_arch_list="9.0+PTX"
+  --build-arg torch_cuda_arch_list="9.0+PTX" \
+  --build-arg vllm_fa_cmake_gpu_arches="90-real"
 
 # Setup cleanup
 remove_docker_container() { docker rm -f gh200-test || true; }

.buildkite/scripts/hardware_ci/run-hpu-test.sh

@@ -2,32 +2,10 @@
 
 # This script build the CPU docker image and run the offline inference inside the container.
 # It serves a sanity check for compilation and basic model usage.
-set -exuo pipefail
+set -ex
 
 # Try building the docker image
-cat <<EOF | docker build -t hpu-plugin-v1-test-env -f - .
-FROM gaudi-base-image:latest
-
-COPY ./ /workspace/vllm
-
-WORKDIR /workspace/vllm
-
-ENV no_proxy=localhost,127.0.0.1
-ENV PT_HPU_ENABLE_LAZY_COLLECTIVES=true
-
-RUN VLLM_TARGET_DEVICE=empty pip install .
-RUN pip install git+https://github.com/vllm-project/vllm-gaudi.git
-
-# install development dependencies (for testing)
-RUN python3 -m pip install -e tests/vllm_test_utils
-
-WORKDIR /workspace/
-
-RUN git clone https://github.com/vllm-project/vllm-gaudi.git
-
-RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
-
-EOF
+docker build -t hpu-test-env -f docker/Dockerfile.hpu .
 
 # Setup cleanup
 # certain versions of HPU software stack have a bug that can
@@ -36,21 +14,13 @@ EOF
 # functions, while other platforms only need one remove_docker_container
 # function.
 EXITCODE=1
-remove_docker_containers() { docker rm -f hpu-plugin-v1-test || true; }
-trap 'remove_docker_containers; exit $EXITCODE;' EXIT
+remove_docker_containers() { docker rm -f hpu-test || true; docker rm -f hpu-test-tp2 || true; }
+remove_docker_containers_and_exit() { remove_docker_containers; exit $EXITCODE; }
+trap remove_docker_containers_and_exit EXIT
 remove_docker_containers
 
-echo "Running HPU plugin v1 test"
-docker run --rm --runtime=habana --name=hpu-plugin-v1-test --network=host \
-  -e HABANA_VISIBLE_DEVICES=all \
-  hpu-plugin-v1-test-env \
-  /bin/bash "/workspace/vllm-gaudi/tests/upstream_tests/ci_tests.sh"
+# Run the image and launch offline inference
+docker run --runtime=habana --name=hpu-test --network=host -e HABANA_VISIBLE_DEVICES=all -e VLLM_SKIP_WARMUP=true --entrypoint="" hpu-test-env python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
+docker run --runtime=habana --name=hpu-test-tp2 --network=host -e HABANA_VISIBLE_DEVICES=all -e VLLM_SKIP_WARMUP=true --entrypoint="" hpu-test-env python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --tensor-parallel-size 2
 
 EXITCODE=$?
-if [ $EXITCODE -eq 0 ]; then
-  echo "Test with basic model passed"
-else
-  echo "Test with basic model FAILED with exit code: $EXITCODE" >&2
-fi
-
-# The trap will handle the container removal and final exit.

.buildkite/scripts/hardware_ci/run-neuron-test.sh

@@ -54,11 +54,10 @@ docker run --rm -it --device=/dev/neuron0 --network bridge \
        --name "${container_name}" \
        ${image_name} \
        /bin/bash -c "
-            set -e; # Exit on first error
             python3 /workspace/vllm/examples/offline_inference/neuron.py;
             python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys;
             for f in /workspace/vllm/tests/neuron/2_core/*.py; do
-                echo \"Running test file: \$f\";
+                echo 'Running test file: '$f;
                 python3 -m pytest \$f -v --capture=tee-sys;
             done
        "

.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh

@@ -1,167 +0,0 @@
-#!/bin/bash
-
-set -xu
-
-
-remove_docker_container() { 
-    docker rm -f tpu-test || true;
-}
-
-trap remove_docker_container EXIT
-
-# Remove the container that might not be cleaned up in the previous run.
-remove_docker_container
-
-# Build the docker image.
-docker build -f docker/Dockerfile.tpu -t vllm-tpu .
-
-# Set up cleanup.
-cleanup_docker() {
-  # Get Docker's root directory
-  docker_root=$(docker info -f '{{.DockerRootDir}}')
-  if [ -z "$docker_root" ]; then
-    echo "Failed to determine Docker root directory."
-    exit 1
-  fi
-  echo "Docker root directory: $docker_root"
-  # Check disk usage of the filesystem where Docker's root directory is located
-  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
-  # Define the threshold
-  threshold=70
-  if [ "$disk_usage" -gt "$threshold" ]; then
-    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
-    # Remove dangling images (those that are not tagged and not used by any container)
-    docker image prune -f
-    # Remove unused volumes / force the system prune for old images as well.
-    docker volume prune -f && docker system prune --force --filter "until=72h" --all
-    echo "Docker images and volumes cleanup completed."
-  else
-    echo "Disk usage is below $threshold%. No cleanup needed."
-  fi
-}
-cleanup_docker
-
-# For HF_TOKEN.
-source /etc/environment
-
-docker run --privileged --net host --shm-size=16G -it \
-    -e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
-    vllm-tpu /bin/bash -c '
-set -e # Exit immediately if a command exits with a non-zero status.
-set -u # Treat unset variables as an error.
-
-echo "--- Starting script inside Docker container ---"
-
-# Create results directory
-RESULTS_DIR=$(mktemp -d)
-# If mktemp fails, set -e will cause the script to exit.
-echo "Results will be stored in: $RESULTS_DIR"
-
-# Install dependencies
-echo "--- Installing Python dependencies ---"
-python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
-    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4 \
-    && python3 -m pip install --progress-bar off hf-transfer
-echo "--- Python dependencies installed ---"
-export VLLM_USE_V1=1
-export VLLM_XLA_CHECK_RECOMPILATION=1
-export VLLM_XLA_CACHE_PATH=
-echo "Using VLLM V1"
-
-echo "--- Hardware Information ---"
-# tpu-info
-echo "--- Starting Tests ---"
-set +e
-overall_script_exit_code=0
-
-# --- Test Definitions ---
-# If a test fails, this function will print logs and will not cause the main script to exit.
-run_test() {
-    local test_num=$1
-    local test_name=$2
-    local test_command=$3
-    local log_file="$RESULTS_DIR/test_${test_num}.log"
-    local actual_exit_code
-
-    echo "--- TEST_$test_num: Running $test_name ---"
-    
-    # Execute the test command.
-    eval "$test_command" > >(tee -a "$log_file") 2> >(tee -a "$log_file" >&2)
-    actual_exit_code=$?
-
-    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" # This goes to main log
-    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" >> "$log_file" # Also to per-test log
-
-    if [ "$actual_exit_code" -ne 0 ]; then
-        echo "TEST_$test_num ($test_name) FAILED with exit code $actual_exit_code." >&2
-        echo "--- Log for failed TEST_$test_num ($test_name) ---" >&2
-        if [ -f "$log_file" ]; then
-            cat "$log_file" >&2
-        else
-            echo "Log file $log_file not found for TEST_$test_num ($test_name)." >&2
-        fi
-        echo "--- End of log for TEST_$test_num ($test_name) ---" >&2
-        return "$actual_exit_code" # Return the failure code
-    else
-        echo "TEST_$test_num ($test_name) PASSED."
-        return 0 # Return success
-    fi
-}
-
-# Helper function to call run_test and update the overall script exit code
-run_and_track_test() {
-    local test_num_arg="$1"
-    local test_name_arg="$2"
-    local test_command_arg="$3"
-
-    # Run the test
-    run_test "$test_num_arg" "$test_name_arg" "$test_command_arg"
-    local test_specific_exit_code=$?
-
-    # If the test failed, set the overall script exit code to 1
-    if [ "$test_specific_exit_code" -ne 0 ]; then
-        # No need for extra echo here, run_test already logged the failure.
-        overall_script_exit_code=1
-    fi
-}
-
-# --- Actual Test Execution ---
-run_and_track_test 1 "test_struct_output_generate.py" \
-    "HF_HUB_DISABLE_XET=1 python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
-run_and_track_test 2 "test_moe_pallas.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
-run_and_track_test 3 "test_lora.py" \
-    "VLLM_XLA_CHECK_RECOMPILATION=0 python3 -m pytest -s -v /workspace/vllm/tests/tpu/lora/test_lora.py"
-run_and_track_test 4 "test_tpu_qkv_linear.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_qkv_linear.py"
-run_and_track_test 5 "test_spmd_model_weight_loading.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_spmd_model_weight_loading.py"
-run_and_track_test 6 "test_kv_cache_update_kernel.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_kv_cache_update_kernel.py"
-run_and_track_test 7 "test_tpu_int8.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_int8.py"
-
-# After all tests have been attempted, exit with the overall status.
-if [ "$overall_script_exit_code" -ne 0 ]; then
-    echo "--- One or more tests FAILED. Overall script exiting with failure code 1. ---"
-else
-    echo "--- All tests have completed and PASSED. Overall script exiting with success code 0. ---"
-fi
-exit "$overall_script_exit_code"
-' # IMPORTANT: This is the closing single quote for the bash -c "..." command. Ensure it is present and correct.
-
-# Capture the exit code of the docker run command
-DOCKER_RUN_EXIT_CODE=$?
-
-# The trap will run for cleanup.
-# Exit the main script with the Docker run command's exit code.
-if [ "$DOCKER_RUN_EXIT_CODE" -ne 0 ]; then
-    echo "Docker run command failed with exit code $DOCKER_RUN_EXIT_CODE."
-    exit "$DOCKER_RUN_EXIT_CODE"
-else
-    echo "Docker run command completed successfully."
-    exit 0
-fi
-# TODO: This test fails because it uses RANDOM_SEED sampling
-# pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \

.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh

@@ -5,6 +5,7 @@ set -xu
 
 remove_docker_container() { 
     docker rm -f tpu-test || true; 
+    docker rm -f vllm-tpu || true;
 }
 
 trap remove_docker_container EXIT
@@ -61,8 +62,7 @@ echo "Results will be stored in: $RESULTS_DIR"
 echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
     && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4 \
-    && python3 -m pip install --progress-bar off hf-transfer
+    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4
 echo "--- Python dependencies installed ---"
 export VLLM_USE_V1=1
 export VLLM_XLA_CHECK_RECOMPILATION=1
@@ -70,7 +70,7 @@ export VLLM_XLA_CACHE_PATH=
 echo "Using VLLM V1"
 
 echo "--- Hardware Information ---"
-# tpu-info
+tpu-info
 echo "--- Starting Tests ---"
 set +e
 overall_script_exit_code=0
@@ -134,7 +134,7 @@ run_and_track_test 1 "test_compilation.py" \
 run_and_track_test 2 "test_basic.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py"
 run_and_track_test 3 "test_accuracy.py::test_lm_eval_accuracy_v1_engine" \
-    "HF_HUB_DISABLE_XET=1 python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
+    "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
 run_and_track_test 4 "test_quantization_accuracy.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py"
 run_and_track_test 5 "examples/offline_inference/tpu.py" \
@@ -149,6 +149,16 @@ run_and_track_test 9 "test_multimodal.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_multimodal.py"
 run_and_track_test 10 "test_pallas.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py"
+run_and_track_test 11 "test_struct_output_generate.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
+run_and_track_test 12 "test_moe_pallas.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
+run_and_track_test 13 "test_lora.py" \
+    "VLLM_XLA_CHECK_RECOMPILATION=0 python3 -m pytest -s -v /workspace/vllm/tests/tpu/lora/test_lora.py"
+run_and_track_test 14 "test_tpu_qkv_linear.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_qkv_linear.py"
+run_and_track_test 15 "test_spmd_model_weight_loading.py" \
+    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_spmd_model_weight_loading.py"
 
 # After all tests have been attempted, exit with the overall status.
 if [ "$overall_script_exit_code" -ne 0 ]; then

.buildkite/scripts/hardware_ci/run-xpu-test.sh

@@ -11,8 +11,8 @@ container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head
 docker build -t ${image_name} -f docker/Dockerfile.xpu .
 
 # Setup cleanup
-remove_docker_container() {
-  docker rm -f "${container_name}" || true;
+remove_docker_container() { 
+  docker rm -f "${container_name}" || true; 
   docker image rm -f "${image_name}" || true;
   docker system prune -f || true;
 }
@@ -26,18 +26,6 @@ docker run \
     --name "${container_name}" \
     "${image_name}" \
     sh -c '
-    VLLM_USE_V1=1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
-    VLLM_USE_V1=1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
-    VLLM_USE_V1=1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
-    cd tests
-    pytest -v -s v1/core
-    pytest -v -s v1/engine
-    pytest -v -s v1/sample --ignore=v1/sample/test_logprobs.py --ignore=v1/sample/test_logprobs_e2e.py
-    pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py
-    pytest -v -s v1/structured_output
-    pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_eagle.py
-    pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_nixl_connector.py
-    pytest -v -s v1/test_serial_utils.py
-    pytest -v -s v1/test_utils.py
-    pytest -v -s v1/test_metrics_reader.py
+    VLLM_USE_V1=0 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
+    VLLM_USE_V1=0 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m -tp 2
 '

.buildkite/scripts/run-benchmarks.sh

@@ -11,10 +11,10 @@ cd "$(dirname "${BASH_SOURCE[0]}")/../.."
 (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
 
 # run python-based benchmarks and upload the result to buildkite
-vllm bench latency --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
+python3 benchmarks/benchmark_latency.py --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
 bench_latency_exit_code=$?
 
-vllm bench throughput --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
+python3 benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
 bench_throughput_exit_code=$?
 
 # run server-based benchmarks and upload the result to buildkite
@@ -24,7 +24,7 @@ wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/r
 
 # wait for server to start, timeout after 600 seconds
 timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
-vllm bench serve \
+python3 benchmarks/benchmark_serving.py \
     --backend vllm \
     --dataset-name sharegpt \
     --dataset-path ./ShareGPT_V3_unfiltered_cleaned_split.json \

.buildkite/scripts/tpu/config_v6e_1.env

@@ -1,11 +1,11 @@
 # Environment config
 TEST_NAME=llama8b
-CONTAINER_NAME=tpu-test
+CONTAINER_NAME=vllm-tpu
 
 # vllm config
 MODEL=meta-llama/Llama-3.1-8B-Instruct
-MAX_NUM_SEQS=256
-MAX_NUM_BATCHED_TOKENS=1024
+MAX_NUM_SEQS=512
+MAX_NUM_BATCHED_TOKENS=512
 TENSOR_PARALLEL_SIZE=1
 MAX_MODEL_LEN=2048
 DOWNLOAD_DIR=/mnt/disks/persist

.buildkite/scripts/tpu/docker_run_bm.sh

@@ -12,6 +12,8 @@ source /etc/environment
 source $ENV_FILE
 
 remove_docker_container() { 
+    docker rm -f tpu-test || true; 
+    docker rm -f vllm-tpu || true;
     docker rm -f $CONTAINER_NAME || true;
 }
 
@@ -20,6 +22,16 @@ trap remove_docker_container EXIT
 # Remove the container that might not be cleaned up in the previous run.
 remove_docker_container
 
+# Build docker image.
+# TODO: build the image outside the script and share the image with other
+# tpu test if building time is too long.
+DOCKER_BUILDKIT=1 docker build \
+  --build-arg max_jobs=16 \
+  --build-arg USE_SCCACHE=1 \
+  --build-arg GIT_REPO_CHECK=0 \
+  --tag vllm/vllm-tpu-bm \
+  --progress plain -f docker/Dockerfile.tpu .
+
 LOG_ROOT=$(mktemp -d)
 # If mktemp fails, set -e will cause the script to exit.
 echo "Results will be stored in: $LOG_ROOT"
@@ -56,7 +68,7 @@ docker run \
 
 echo "run script..."
 echo
-docker exec "$CONTAINER_NAME" /bin/bash -c ".buildkite/scripts/tpu/run_bm.sh"
+docker exec "$CONTAINER_NAME" /bin/bash -c ".buildkite/scripts/hardware_ci/run_bm.sh"
 
 echo "copy result back..."
 VLLM_LOG="$LOG_ROOT/$TEST_NAME"_vllm_log.txt

.buildkite/scripts/tpu/quantized_v6e_1.env

@@ -1,14 +0,0 @@
-# Environment config
-TEST_NAME=llama8bw8a8
-CONTAINER_NAME=tpu-test
-
-# vllm config
-MODEL=RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8
-MAX_NUM_SEQS=128
-MAX_NUM_BATCHED_TOKENS=1024
-TENSOR_PARALLEL_SIZE=1
-MAX_MODEL_LEN=2048
-DOWNLOAD_DIR=/mnt/disks/persist
-EXPECTED_THROUGHPUT=10.0
-INPUT_LEN=1800
-OUTPUT_LEN=128

.buildkite/scripts/tpu/run_bm.sh

@@ -44,6 +44,7 @@ echo
 
 VLLM_USE_V1=1 vllm serve $MODEL \
  --seed 42 \
+ --disable-log-requests \
  --max-num-seqs $MAX_NUM_SEQS \
  --max-num-batched-tokens $MAX_NUM_BATCHED_TOKENS \
  --tensor-parallel-size $TENSOR_PARALLEL_SIZE \
@@ -76,7 +77,7 @@ done
 echo "run benchmark test..."
 echo "logging to $BM_LOG"
 echo
-vllm bench serve \
+python benchmarks/benchmark_serving.py \
     --backend vllm \
     --model $MODEL  \
     --dataset-name sonnet \

.buildkite/test-pipeline.yaml

@@ -41,26 +41,15 @@ steps:
   # TODO: add `--strict` once warnings in docstrings are fixed
   - mkdocs build
 
-- label: Pytorch Nightly Dependency Override Check # 2min
-  # if this test fails, it means the nightly torch version is not compatible with some
-  # of the dependencies. Please check the error message and add the package to whitelist
-  # in /vllm/tools/generate_nightly_torch_test.py
-  soft_fail: true
-  source_file_dependencies:
-  - requirements/nightly_torch_test.txt
-  commands:
-  - bash standalone_tests/pytorch_nightly_dependency.sh
-
 - label: Async Engine, Inputs, Utils, Worker Test # 24min
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/
   - tests/mq_llm_engine
   - tests/async_engine
-  - tests/test_inputs.py
-  - tests/test_outputs.py
+  - tests/test_inputs
   - tests/multimodal
-  - tests/utils_
+  - tests/test_utils
   - tests/worker
   - tests/standalone_tests/lazy_imports.py
   commands:
@@ -71,7 +60,7 @@ steps:
   - pytest -v -s test_inputs.py
   - pytest -v -s test_outputs.py
   - pytest -v -s multimodal
-  - pytest -v -s utils_ # Utils
+  - pytest -v -s test_utils.py # Utils
   - pytest -v -s worker # Worker
 
 - label: Python-only Installation Test
@@ -83,7 +72,7 @@ steps:
   - bash standalone_tests/python_only_compile.sh
 
 - label: Basic Correctness Test # 30min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   fast_check: true
   torch_nightly: true
   source_file_dependencies:
@@ -109,7 +98,7 @@ steps:
   - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_chunked_prefill.py
 
 - label: Core Test # 10min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   fast_check: true
   source_file_dependencies:
   - vllm/core
@@ -118,7 +107,7 @@ steps:
   commands:
   - pytest -v -s core
 
-- label: Entrypoints Test (LLM) # 40min
+- label: Entrypoints Test # 40min
   mirror_hardwares: [amdexperimental]
   working_dir: "/vllm-workspace/tests"
   fast_check: true
@@ -126,28 +115,19 @@ steps:
   source_file_dependencies:
   - vllm/
   - tests/entrypoints/llm
+  - tests/entrypoints/openai
+  - tests/entrypoints/test_chat_utils
   - tests/entrypoints/offline_mode
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_generate_multiple_loras.py --ignore=entrypoints/llm/test_collective_rpc.py
+  - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_generate_multiple_loras.py --ignore=entrypoints/llm/test_guided_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
   - pytest -v -s entrypoints/llm/test_lazy_outlines.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_generate_multiple_loras.py # it needs a clean process
-  - VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
-
-- label: Entrypoints Test (API Server) # 40min
-  mirror_hardwares: [amdexperimental]
-  working_dir: "/vllm-workspace/tests"
-  fast_check: true
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/
-  - tests/entrypoints/openai
-  - tests/entrypoints/test_chat_utils
-  commands:
-  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - VLLM_USE_V1=0 pytest -v -s entrypoints/llm/test_guided_generate.py # it needs a clean process
   - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/test_tensorizer_entrypoint.py --ignore=entrypoints/openai/correctness/
   - pytest -v -s entrypoints/test_chat_utils.py
+  - VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
 - label: Distributed Tests (4 GPUs) # 10min
   mirror_hardwares: [amdexperimental]
@@ -159,14 +139,12 @@ steps:
   - tests/distributed/test_utils
   - tests/distributed/test_pynccl
   - tests/distributed/test_events
+  - tests/spec_decode/e2e/test_integration_dist_tp4
   - tests/compile/test_basic_correctness
   - examples/offline_inference/rlhf.py
   - examples/offline_inference/rlhf_colocate.py
   - tests/examples/offline_inference/data_parallel.py
   - tests/v1/test_async_llm_dp.py
-  - tests/v1/test_external_lb_dp.py
-  - tests/v1/test_internal_lb_dp.py
-  - tests/v1/test_hybrid_lb_dp.py
   - tests/v1/engine/test_engine_core_client.py
   commands:
   # test with tp=2 and external_dp=2
@@ -175,16 +153,14 @@ steps:
   # test with tp=2 and pp=2
   - PP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
   # test with internal dp
-  - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
+  - python3 ../examples/offline_inference/data_parallel.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
-  - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/test_internal_lb_dp.py
-  - TP_SIZE=1 DP_SIZE=4 pytest -v -s v1/test_hybrid_lb_dp.py
   - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
   - pytest -v -s distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
   - pytest -v -s distributed/test_events.py
+  - pytest -v -s spec_decode/e2e/test_integration_dist_tp4.py
   # TODO: create a dedicated test section for multi-GPU example tests
   # when we have multiple distributed example tests
   - pushd ../examples/offline_inference
@@ -192,25 +168,8 @@ steps:
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
   - popd
 
-- label: EPLB Algorithm Test
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/eplb
-  - tests/distributed/test_eplb_algo.py
-  commands:
-  - pytest -v -s distributed/test_eplb_algo.py
-
-- label: EPLB Execution Test # 5min
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  source_file_dependencies:
-  - vllm/distributed/eplb
-  - tests/distributed/test_eplb_execute.py
-  commands:
-  - pytest -v -s distributed/test_eplb_execute.py
-
 - label: Metrics, Tracing Test # 10min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   num_gpus: 2
   source_file_dependencies:
   - vllm/
@@ -229,7 +188,7 @@ steps:
 #####  1 GPU test  #####
 
 - label: Regression Test # 5min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - vllm/
   - tests/test_regression
@@ -239,7 +198,7 @@ steps:
   working_dir: "/vllm-workspace/tests" # optional
 
 - label: Engine Test # 10min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - vllm/
   - tests/engine
@@ -268,7 +227,6 @@ steps:
     - pytest -v -s v1/structured_output
     - pytest -v -s v1/spec_decode
     - pytest -v -s v1/kv_connector/unit
-    - pytest -v -s v1/metrics
     - pytest -v -s v1/test_serial_utils.py
     - pytest -v -s v1/test_utils.py
     - pytest -v -s v1/test_oracle.py
@@ -277,7 +235,7 @@ steps:
     # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
     - pytest -v -s v1/e2e
     # Integration test for streaming correctness (requires special branch).
-    - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
+    - pip install -U git+https://github.com/robertgshaw2-neuralmagic/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
 - label: Examples Test # 25min
@@ -295,7 +253,7 @@ steps:
     - python3 offline_inference/llm_engine_example.py
     - python3 offline_inference/audio_language.py --seed 0
     - python3 offline_inference/vision_language.py --seed 0
-    - python3 offline_inference/vision_language_pooling.py --seed 0
+    - python3 offline_inference/vision_language_embedding.py --seed 0
     - python3 offline_inference/vision_language_multi_image.py --seed 0
     - VLLM_USE_V1=0 python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
     - python3 offline_inference/encoder_decoder.py
@@ -306,22 +264,13 @@ steps:
     - VLLM_USE_V1=0 python3 offline_inference/profiling.py --model facebook/opt-125m run_num_steps --num-steps 2
 
 - label: Prefix Caching Test # 9min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - vllm/
   - tests/prefix_caching
   commands:
     - pytest -v -s prefix_caching
 
-
-- label: Platform Tests (CUDA)
-  mirror_hardwares: [amdexperimental]
-  source_file_dependencies:
-  - vllm/
-  - tests/cuda
-  commands:
-    - pytest -v -s cuda/test_cuda_context.py
-
 - label: Samplers Test # 36min
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:
@@ -333,16 +282,27 @@ steps:
     - pytest -v -s samplers
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
 
-- label: LoRA Test %N # 15min each
+- label: Speculative decoding tests # 40min
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:
+  - vllm/spec_decode
+  - tests/spec_decode
+  - vllm/model_executor/models/eagle.py
+  commands:
+    - pytest -v -s spec_decode/e2e/test_multistep_correctness.py
+    - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s spec_decode --ignore=spec_decode/e2e/test_multistep_correctness.py --ignore=spec_decode/e2e/test_mtp_correctness.py
+    - pytest -v -s spec_decode/e2e/test_eagle_correctness.py
+
+- label: LoRA Test %N # 15min each
+  mirror_hardwares: [amdexperimental, amdproduction]
+  source_file_dependencies:
   - vllm/lora
   - tests/lora
   command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py
   parallelism: 4
 
 - label: PyTorch Compilation Unit Tests
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   torch_nightly: true
   source_file_dependencies:
     - vllm/
@@ -354,10 +314,9 @@ steps:
     - pytest -v -s compile/test_silu_mul_quant_fusion.py
     - pytest -v -s compile/test_sequence_parallelism.py
     - pytest -v -s compile/test_async_tp.py
-    - pytest -v -s compile/test_fusion_all_reduce.py
 
 - label: PyTorch Fullgraph Smoke Test # 9min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -370,7 +329,7 @@ steps:
   - pytest -v -s compile/piecewise/test_full_cudagraph.py
 
 - label: PyTorch Fullgraph Test # 18min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -379,7 +338,7 @@ steps:
   - pytest -v -s compile/test_full_graph.py
 
 - label: Kernels Core Operation Test
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - csrc/
   - tests/kernels/core
@@ -387,7 +346,7 @@ steps:
     - pytest -v -s kernels/core
 
 - label: Kernels Attention Test %N
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - csrc/attention/
   - vllm/attention
@@ -398,24 +357,23 @@ steps:
   parallelism: 2
 
 - label: Kernels Quantization Test %N
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - csrc/quantization/
   - vllm/model_executor/layers/quantization
   - tests/kernels/quantization
   commands:
-    - pytest -v -s kernels/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+    - pytest -v -s kernels/quantization  --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   parallelism: 2
 
-- label: Kernels MoE Test %N
+- label: Kernels MoE Test
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - csrc/moe/
   - tests/kernels/moe
   - vllm/model_executor/layers/fused_moe/
   commands:
-    - pytest -v -s kernels/moe --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 2
+    - pytest -v -s kernels/moe
 
 - label: Kernels Mamba Test
   mirror_hardwares: [amdexperimental]
@@ -426,7 +384,8 @@ steps:
     - pytest -v -s kernels/mamba
 
 - label: Tensorizer Test # 11min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
+  soft_fail: true
   source_file_dependencies:
   - vllm/model_executor/model_loader
   - tests/tensorizer_loader
@@ -438,7 +397,8 @@ steps:
     - pytest -v -s entrypoints/openai/test_tensorizer_entrypoint.py
 
 - label: Model Executor Test
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
+  soft_fail: true
   source_file_dependencies:
   - vllm/model_executor
   - tests/model_executor
@@ -448,7 +408,7 @@ steps:
     - pytest -v -s model_executor
 
 - label: Benchmarks # 9min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   working_dir: "/vllm-workspace/.buildkite"
   source_file_dependencies:
   - benchmarks/
@@ -456,7 +416,7 @@ steps:
   - bash scripts/run-benchmarks.sh
 
 - label: Benchmarks CLI Test # 10min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - vllm/
   - tests/benchmarks/
@@ -516,7 +476,7 @@ steps:
 #####  models test  #####
 
 - label: Basic Models Test # 24min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   torch_nightly: true
   source_file_dependencies:
   - vllm/
@@ -535,22 +495,11 @@ steps:
   - vllm/
   - tests/models/language
   commands:
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
     - pip freeze | grep -E 'torch'
     - pytest -v -s models/language -m core_model
 
-- label: Language Models Test (Hybrid) # 35 min
-  mirror_hardwares: [amdexperimental]
-  torch_nightly: true
-  source_file_dependencies:
-  - vllm/
-  - tests/models/language/generation
-  commands:
-    # Install fast path packages for testing against transformers
-    # Note: also needed to run plamo2 model in vLLM
-    - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.2.5'
-    - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.2'
-    - pytest -v -s models/language/generation -m hybrid_model
-
 - label: Language Models Test (Extended Generation) # 1hr20min
   mirror_hardwares: [amdexperimental]
   optional: true
@@ -560,7 +509,7 @@ steps:
   commands:
     # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
     - pip install 'git+https://github.com/Dao-AILab/causal-conv1d@v1.5.0.post8'
-    - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
+    - pytest -v -s models/language/generation -m 'not core_model'
 
 - label: Language Models Test (Extended Pooling)  # 36min
   mirror_hardwares: [amdexperimental]
@@ -581,8 +530,7 @@ steps:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pip freeze | grep -E 'torch'
     - pytest -v -s models/multimodal/processing
-    - pytest -v -s --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/test_tensor_schema.py models/multimodal -m core_model
-    - pytest -v -s models/multimodal/test_tensor_schema.py -m core_model  # Needs mp_method="spawn"
+    - pytest -v -s --ignore models/multimodal/generation/test_whisper.py models/multimodal -m core_model
     - cd .. && pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
 
 - label: Multi-Modal Models Test (Extended) 1
@@ -606,7 +554,7 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
 
 - label: Multi-Modal Models Test (Extended) 3
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   optional: true
   source_file_dependencies:
   - vllm/
@@ -616,7 +564,7 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 
 - label: Quantized Models Test
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   source_file_dependencies:
   - vllm/model_executor/layers/quantization
   - tests/models/quantization
@@ -625,7 +573,7 @@ steps:
 
 # This test is used only in PR development phase to test individual models and should never run on main
 - label: Custom Models Test
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   optional: true
   commands:
     - echo 'Testing custom models...'
@@ -633,52 +581,11 @@ steps:
     # e.g. pytest -v -s models/encoder_decoder/vision_language/test_mllama.py
     # *To avoid merge conflicts, remember to REMOVE (not just comment out) them before merging the PR*
 
-- label: Transformers Nightly Models Test
-  working_dir: "/vllm-workspace/"
-  optional: true
-  commands:
-    - pip install --upgrade git+https://github.com/huggingface/transformers
-    - pytest -v -s tests/models/test_initialization.py
-    - pytest -v -s tests/models/multimodal/processing/
-    - pytest -v -s tests/models/multimodal/test_mapping.py
-    - python3 examples/offline_inference/basic/chat.py
-    - python3 examples/offline_inference/audio_language.py --model-type whisper
-    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
-
-- label: Blackwell Test
-  working_dir: "/vllm-workspace/"
-  gpu: b200
-  # optional: true
-  source_file_dependencies:
-  - csrc/quantization/fp4/
-  - csrc/attention/mla/
-  - csrc/quantization/cutlass_w8a8/moe/
-  - vllm/model_executor/layers/fused_moe/cutlass_moe.py
-  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_moe.py
-  - vllm/model_executor/layers/fused_moe/flashinfer_cutlass_prepare_finalize.py
-  - vllm/v1/attention/backends/flashinfer.py
-  - vllm/compilation/fusion.py
-  commands:
-    - nvidia-smi
-    - python3 examples/offline_inference/basic/chat.py
-    # Attention
-    # num_heads2 broken by https://github.com/flashinfer-ai/flashinfer/issues/1353
-    - pytest -v -s tests/kernels/attention/test_flashinfer.py -k 'not num_heads2'
-    - pytest -v -s tests/kernels/attention/test_flashinfer_trtllm_attention.py
-    - pytest -v -s tests/kernels/test_cutlass_mla_decode.py
-    # Quantization
-    - pytest -v -s tests/kernels/quantization/test_cutlass_scaled_mm.py -k 'fp8'
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_quant.py
-    - pytest -v -s tests/kernels/quantization/test_nvfp4_scaled_mm.py
-    - pytest -v -s tests/kernels/moe/test_nvfp4_moe.py
-    # Fusion
-    - pytest -v -s tests/compile/test_fusion_all_reduce.py
-
 #####  1 GPU test  #####
 #####  multi gpus test  #####
 
 - label: Distributed Comm Ops Test # 7min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
@@ -699,18 +606,13 @@ steps:
   - vllm/executor/
   - vllm/model_executor/models/
   - tests/distributed/
-  - tests/examples/offline_inference/data_parallel.py
   commands:
   - # the following commands are for the first node, with ip 192.168.10.10 (ray environment already set up)
     - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed'
-    - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
-    - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=0 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
     - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py
     - VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py
   - # the following commands are for the second node, with ip 192.168.10.11 (ray environment already set up)
     - VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed'
-    - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
-    - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
 
 - label: Distributed Tests (2 GPUs) # 40min
   mirror_hardwares: [amdexperimental]
@@ -728,12 +630,10 @@ steps:
   - vllm/worker/model_runner.py
   - entrypoints/llm/test_collective_rpc.py
   - tests/v1/test_async_llm_dp.py
-  - tests/v1/test_external_lb_dp.py
   - tests/v1/entrypoints/openai/test_multi_api_servers.py
   - vllm/v1/engine/
   commands:
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py
   - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
   - pytest -v -s entrypoints/llm/test_collective_rpc.py
   - pytest -v -s ./compile/test_basic_correctness.py
@@ -748,9 +648,10 @@ steps:
   - pytest -v -s distributed/test_sequence_parallel.py
   # this test fails consistently.
   # TODO: investigate and fix
+  # - pytest -v -s spec_decode/e2e/test_integration_dist_tp2.py
   - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
+  - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s kv_transfer/test_disagg.py
   - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
-  - pytest -v -s models/multimodal/generation/test_maverick.py
 
 - label: Plugin Tests (2 GPUs) # 40min
   mirror_hardwares: [amdexperimental]
@@ -774,7 +675,7 @@ steps:
   - pytest -v -s plugins/lora_resolvers # unit tests for in-tree lora resolver plugins
 
 - label: Multi-step Tests (4 GPUs) # 36min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 4
   source_file_dependencies:
@@ -795,7 +696,7 @@ steps:
   - pytest -v -s multi_step/test_correctness_llm.py
 
 - label: Pipeline Parallelism Test # 45min
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 4
   source_file_dependencies:
@@ -809,7 +710,7 @@ steps:
   - pytest -v -s distributed/test_pipeline_parallel.py
 
 - label: LoRA TP Test (Distributed)
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental, amdproduction]
   num_gpus: 4
   source_file_dependencies:
   - vllm/lora
@@ -822,7 +723,6 @@ steps:
     # requires multi-GPU testing for validation.
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
-    - pytest -v -s -x lora/test_multi_loras_with_tp.py
 
 
 - label: Weight Loading Multiple GPU Test  # 33min
@@ -836,7 +736,7 @@ steps:
     - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models.txt
 
 - label: Weight Loading Multiple GPU Test - Large Models # optional
-  mirror_hardwares: [amdexperimental]
+  mirror_hardwares: [amdexperimental] 
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   gpu: a100

.gemini/config.yaml

@@ -1,6 +0,0 @@
-# https://developers.google.com/gemini-code-assist/docs/customize-gemini-behavior-github
-have_fun: false  # Just review the code
-code_review:
-  comment_severity_threshold: HIGH  # Reduce quantity of comments
-  pull_request_opened:
-    summary: false  # Don't summarize the PR in a separate comment

.github/CODEOWNERS

@@ -10,17 +10,13 @@
 /vllm/worker/worker.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth
+/vllm/model_executor/guided_decoding @mgoin @russellb @aarnphm
 /vllm/multimodal @DarkLight1337 @ywang96
 /vllm/vllm_flash_attn @LucasWilkinson
 /vllm/lora @jeejeelee
 /vllm/reasoning @aarnphm
 /vllm/entrypoints @aarnphm
-/vllm/compilation @zou3519 @youkaichao @ProExpertProg
-CMakeLists.txt @tlrmchlsmth @LucasWilkinson
-
-# Any change to the VllmConfig changes can have a large user-facing impact,
-# so spam a lot of people
-/vllm/config @simon-mo @WoosukKwon @youkaichao @robertgshaw2-redhat @mgoin @tlrmchlsmth @houseroad @hmellor
+CMakeLists.txt @tlrmchlsmth
 
 # vLLM V1
 /vllm/v1 @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat
@@ -34,12 +30,15 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/distributed/test_pipeline_parallel.py @youkaichao
 /tests/distributed/test_same_node.py @youkaichao
 /tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo @aarnphm
+/tests/entrypoints/llm/test_guided_generate.py @mgoin @russellb @aarnphm
 /tests/kernels @tlrmchlsmth @WoosukKwon
+/tests/model_executor/test_guided_processors.py @mgoin @russellb
 /tests/models @DarkLight1337 @ywang96
 /tests/multi_step @alexm-redhat @comaniac
 /tests/multimodal @DarkLight1337 @ywang96
 /tests/prefix_caching @comaniac @KuntaiDu
 /tests/quantization @mgoin @robertgshaw2-redhat
+/tests/spec_decode @njhill @LiuXiaoxuanPKU
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
@@ -49,27 +48,3 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 # Docs
 /docs @hmellor
 mkdocs.yaml @hmellor
-
-# CPU
-/vllm/v1/worker/^cpu @bigPYJ1151
-/csrc/cpu @bigPYJ1151
-/vllm/platforms/cpu.py @bigPYJ1151
-/cmake/cpu_extension.cmake @bigPYJ1151
-/docker/Dockerfile.cpu @bigPYJ1151
-
-# Intel GPU
-/vllm/v1/worker/^xpu @jikunshang
-/vllm/platforms/xpu.py @jikunshang
-/docker/Dockerfile.xpu @jikunshang
-
-# Qwen-specific files
-/vllm/attention/backends/dual_chunk_flash_attn.py @sighingnow
-/vllm/model_executor/models/qwen* @sighingnow
-
-# Mistral-specific files
-/vllm/model_executor/models/mistral*.py @patrickvonplaten
-/vllm/model_executor/models/mixtral*.py @patrickvonplaten
-/vllm/model_executor/models/voxtral*.py @patrickvonplaten
-/vllm/model_executor/models/pixtral*.py @patrickvonplaten
-/vllm/transformers_utils/configs/mistral.py @patrickvonplaten
-/vllm/transformers_utils/tokenizers/mistral.py @patrickvonplaten

.github/ISSUE_TEMPLATE/750-RFC.yml

@@ -46,7 +46,7 @@ body:
 - type: markdown
   attributes:
     value: >
-      Thanks for contributing 🎉! The vLLM core team hosts a biweekly RFC review session at 9:30AM Pacific Time, while most RFCs can be discussed online, you can optionally sign up for a slot to discuss your RFC online [here](https://docs.google.com/document/d/1CiLVBZeIVfR7_PNAKVSusxpceywkoOOB78qoWqHvSZc/edit).
+      Thanks for contributing 🎉!
 - type: checkboxes
   id: askllm
   attributes:

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,5 +1,4 @@
-# Essential Elements of an Effective PR Description Checklist
-
+## Essential Elements of an Effective PR Description Checklist
 - [ ] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
 - [ ] The test plan, such as providing test command.
 - [ ] The test results, such as pasting the results comparison before and after, or e2e results
@@ -15,4 +14,5 @@ PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS ABOVE HAVE B
 
 ## (Optional) Documentation Update
 
+<!--- pyml disable-next-line no-emphasis-as-heading -->
 **BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing>** (anything written below this line will be removed by GitHub Actions)

.github/mergify.yml

@@ -27,22 +27,6 @@ pull_request_rules:
       add:
         - ci/build
 
-- name: label-deepseek
-  description: Automatically apply deepseek label
-  conditions:
-    - or:
-      - files~=^examples/.*deepseek.*\.py
-      - files~=^tests/.*deepseek.*\.py
-      - files~=^vllm/entrypoints/openai/tool_parsers/.*deepseek.*\.py
-      - files~=^vllm/model_executor/models/.*deepseek.*\.py
-      - files~=^vllm/reasoning/.*deepseek.*\.py
-      - files~=^vllm/transformers_utils/.*deepseek.*\.py
-      - title~=(?i)DeepSeek
-  actions:
-    label:
-      add:
-        - deepseek
-
 - name: label-frontend
   description: Automatically apply frontend label
   conditions:
@@ -61,7 +45,6 @@ pull_request_rules:
       - files~=^vllm/entrypoints/openai/tool_parsers/llama.*\.py
       - files~=^vllm/model_executor/models/.*llama.*\.py
       - files~=^vllm/transformers_utils/configs/.*llama.*\.py
-      - title~=(?i)llama
   actions:
     label:
       add:
@@ -74,36 +57,14 @@ pull_request_rules:
       - files~=^vllm/multimodal/
       - files~=^tests/multimodal/
       - files~=^tests/models/multimodal/
+      - files~=^tests/models/*/audio_language/
+      - files~=^tests/models/*/vision_language/
       - files=tests/models/test_vision.py
   actions:
     label:
       add:
         - multi-modality
 
-- name: label-new-model
-  description: Automatically apply new-model label
-  conditions:
-    - and:
-      - files~=^vllm/model_executor/models/
-      - files=vllm/model_executor/models/registry.py
-  actions:
-    label:
-      add:
-        - new-model
-
-- name: label-performance
-  description: Automatically apply performance label
-  conditions:
-    - or:
-      - files~=^benchmarks/
-      - files~=^vllm/benchmarks/
-      - files~=^tests/benchmarks/
-      - files~=^\.buildkite/nightly-benchmarks/
-  actions:
-    label:
-      add:
-        - performance
-
 - name: label-qwen
   description: Automatically apply qwen label
   conditions:
@@ -113,25 +74,12 @@ pull_request_rules:
       - files~=^vllm/model_executor/models/.*qwen.*\.py
       - files~=^vllm/reasoning/.*qwen.*\.py
       - title~=(?i)Qwen
+      - body~=(?i)Qwen
   actions:
     label:
       add:
         - qwen
 
-- name: label-gpt-oss
-  description: Automatically apply gpt-oss label
-  conditions:
-    - or:
-      - files~=^examples/.*gpt[-_]?oss.*\.py
-      - files~=^tests/.*gpt[-_]?oss.*\.py
-      - files~=^vllm/model_executor/models/.*gpt[-_]?oss.*\.py
-      - files~=^vllm/model_executor/layers/.*gpt[-_]?oss.*\.py
-      - title~=(?i)gpt[-_]?oss
-  actions:
-    label:
-      add:
-        - gpt-oss
-
 - name: label-rocm
   description: Automatically apply rocm label
   conditions:
@@ -163,6 +111,9 @@ pull_request_rules:
       - files=examples/offline_inference/structured_outputs.py
       - files=examples/online_serving/openai_chat_completion_structured_outputs.py
       - files=examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py
+      - files~=^vllm/model_executor/guided_decoding/
+      - files=tests/model_executor/test_guided_processors.py
+      - files=tests/entrypoints/llm/test_guided_generate.py
       - files~=^tests/v1/structured_output/
       - files=tests/v1/entrypoints/llm/test_guided_generate.py
       - files~=^vllm/v1/structured_output/
@@ -175,12 +126,9 @@ pull_request_rules:
   description: Automatically apply speculative-decoding label
   conditions:
     - or:
-      - files~=^vllm/v1/spec_decode/
-      - files~=^tests/v1/spec_decode/
-      - files~=^examples/.*(spec_decode|mlpspeculator|eagle|speculation).*\.py
-      - files~=^vllm/model_executor/models/.*eagle.*\.py
-      - files=vllm/model_executor/models/mlp_speculator.py
-      - files~=^vllm/transformers_utils/configs/(eagle|medusa|mlp_speculator)\.py
+      - files~=^vllm/spec_decode/
+      - files=vllm/model_executor/layers/spec_decode_base_sampler.py
+      - files~=^tests/spec_decode/
   actions:
     label:
       add:

.github/workflows/lint-and-deploy.yaml

@@ -2,10 +2,6 @@ name: Lint and Deploy Charts
 
 on: pull_request
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: true
-
 permissions:
   contents: read
 
@@ -72,7 +68,7 @@ jobs:
           export AWS_ACCESS_KEY_ID=minioadmin
           export AWS_SECRET_ACCESS_KEY=minioadmin
           sleep 30 && kubectl -n ns-vllm logs -f "$(kubectl -n ns-vllm get pods | awk '/deployment/ {print $1;exit}')" &
-          helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/online_serving/chart-helm -f examples/online_serving/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set image.env[2].name=VLLM_CPU_CI_ENV --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string image.env[2].value="1" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
+          helm install --wait --wait-for-jobs --timeout 5m0s --debug --create-namespace --namespace=ns-vllm test-vllm examples/online_serving/chart-helm -f examples/online_serving/chart-helm/values.yaml --set secrets.s3endpoint=http://minio:9000 --set secrets.s3bucketname=testbucket --set secrets.s3accesskeyid=$AWS_ACCESS_KEY_ID --set secrets.s3accesskey=$AWS_SECRET_ACCESS_KEY --set resources.requests.cpu=1 --set resources.requests.memory=4Gi --set resources.limits.cpu=2 --set resources.limits.memory=5Gi --set image.env[0].name=VLLM_CPU_KVCACHE_SPACE --set image.env[1].name=VLLM_LOGGING_LEVEL --set-string image.env[0].value="1" --set-string image.env[1].value="DEBUG" --set-string extraInit.s3modelpath="opt-125m/" --set-string 'resources.limits.nvidia\.com/gpu=0' --set-string 'resources.requests.nvidia\.com/gpu=0' --set-string image.repository="vllm-cpu-env"
 
       - name: curl test
         run: |

.github/workflows/matchers/markdownlint.json

@@ -1,17 +0,0 @@
-{
-  "problemMatcher": [
-    {
-      "owner": "markdownlint",
-      "pattern": [
-        {
-          "regexp": "^([^:]*):(\\d+):?(\\d+)?\\s([\\w-\\/]*)\\s(.*)$",
-          "file": 1,
-          "line": 2,
-          "column": 3,
-          "code": 4,
-          "message": 5
-        }
-      ]
-    }
-  ]
-}

.github/workflows/pre-commit.yml

@@ -5,10 +5,6 @@ on:
   push:
     branches: [main]
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}
-  cancel-in-progress: ${{ github.event_name == 'pull_request' }}
-
 permissions:
   contents: read
 
@@ -21,7 +17,6 @@ jobs:
       with:
         python-version: "3.12"
     - run: echo "::add-matcher::.github/workflows/matchers/actionlint.json"
-    - run: echo "::add-matcher::.github/workflows/matchers/markdownlint.json"
     - run: echo "::add-matcher::.github/workflows/matchers/mypy.json"
     - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
       with:

.github/workflows/scripts/build.sh

@@ -15,6 +15,7 @@ $python_executable -m pip install -r requirements/build.txt -r requirements/cuda
 export MAX_JOBS=1
 # Make sure release wheels are built for the following architectures
 export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX"
+export VLLM_FA_CMAKE_GPU_ARCHES="80-real;90-real"
 
 bash tools/check_repo.sh
 

.gitignore

@@ -4,9 +4,6 @@
 # vllm-flash-attn built from source
 vllm/vllm_flash_attn/*
 
-# triton jit 
-.triton
-
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -149,7 +146,6 @@ venv.bak/
 
 # mkdocs documentation
 /site
-docs/argparse
 docs/examples
 
 # mypy

.markdownlint.yaml

@@ -1,13 +0,0 @@
-MD007:
-  indent: 4
-MD013: false
-MD024:
-  siblings_only: true
-MD033: false
-MD042: false
-MD045: false
-MD046: false
-MD051: false
-MD052: false
-MD053: false
-MD059: false

.pre-commit-config.yaml

@@ -21,7 +21,7 @@ repos:
   - id: ruff-format
     files: ^(.buildkite|benchmarks|examples)/.*
 - repo: https://github.com/crate-ci/typos
-  rev: v1.34.0
+  rev: v1.32.0
   hooks:
   - id: typos
 - repo: https://github.com/PyCQA/isort
@@ -35,12 +35,12 @@ repos:
     exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))|vllm/third_party/.*'
     types_or: [c++, cuda]
     args: [--style=file, --verbose]
-- repo: https://github.com/igorshubovych/markdownlint-cli
-  rev: v0.45.0
+- repo: https://github.com/jackdewinter/pymarkdown
+  rev: v0.9.29
   hooks:
-  - id: markdownlint
+  - id: pymarkdown
     exclude: '.*\.inc\.md'
-    stages: [manual] # Only run in CI
+    args: [fix]
 - repo: https://github.com/rhysd/actionlint
   rev: v1.7.7
   hooks:
@@ -53,11 +53,6 @@ repos:
       files: ^requirements/test\.(in|txt)$
 - repo: local
   hooks:
-  - id: format-torch-nightly-test
-    name: reformat nightly_torch_test.txt to be in sync with test.in
-    language: python
-    entry: python tools/generate_nightly_torch_test.py
-    files: ^requirements/test\.(in|txt)$
   - id: mypy-local
     name: Run mypy for local Python installation
     entry: tools/mypy.sh 0 "local"
@@ -120,11 +115,6 @@ repos:
     entry: python tools/check_spdx_header.py
     language: python
     types: [python]
-  - id: check-root-lazy-imports
-    name: Check root lazy imports
-    entry: python tools/check_init_lazy_imports.py
-    language: python
-    types: [python]
   - id: check-filenames
     name: Check for spaces in all filenames
     entry: bash
@@ -160,17 +150,10 @@ repos:
     types: [python]
     pass_filenames: false
     additional_dependencies: [pathspec, regex]
-  - id: validate-config
-    name: Validate configuration has default values and that each field has a docstring
-    entry: python tools/validate_config.py
-    language: python
-    types: [python]
-    pass_filenames: true
-    files: vllm/config.py|tests/test_config.py|vllm/entrypoints/openai/cli_args.py
   # Keep `suggestion` last
   - id: suggestion
     name: Suggestion
-    entry: bash -c 'echo "To bypass all the pre-commit hooks, add --no-verify to git commit. To skip a specific hook, prefix the commit command with SKIP=<hook-id>."'
+    entry: bash -c 'echo "To bypass pre-commit hooks, add --no-verify to git commit."'
     language: system
     verbose: true
     pass_filenames: false

.readthedocs.yaml

@@ -7,9 +7,6 @@ build:
   os: ubuntu-22.04
   tools:
     python: "3.12"
-  jobs:
-    post_checkout:
-      - git fetch --unshallow || true
 
 mkdocs:
   configuration: mkdocs.yaml

CMakeLists.txt

@@ -45,7 +45,7 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from docker/Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.7.1")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.7.0")
 set(TORCH_SUPPORTED_VERSION_ROCM "2.7.0")
 
 #
@@ -171,6 +171,7 @@ if(NVCC_THREADS AND VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_GPU_FLAGS "--threads=${NVCC_THREADS}")
 endif()
 
+
 #
 # Use FetchContent for C++ dependencies that are compiled as part of vLLM's build process.
 # setup.py will override FETCHCONTENT_BASE_DIR to play nicely with sccache.
@@ -231,6 +232,7 @@ endif()
 
 set(VLLM_EXT_SRC
   "csrc/mamba/mamba_ssm/selective_scan_fwd.cu"
+  "csrc/mamba/causal_conv1d/causal_conv1d.cu"
   "csrc/cache_kernels.cu"
   "csrc/attention/paged_attention_v1.cu"
   "csrc/attention/paged_attention_v2.cu"
@@ -257,7 +259,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
 
   # Set CUTLASS_REVISION. Used for FetchContent. Also fixes some bogus messages when building.
-  set(CUTLASS_REVISION "v4.0.0" CACHE STRING "CUTLASS revision to use")
+  set(CUTLASS_REVISION "v3.9.2" CACHE STRING "CUTLASS revision to use")
 
   # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
   if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
@@ -296,8 +298,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     "csrc/quantization/fp4/nvfp4_blockwise_moe_kernel.cu"
     "csrc/sparse/cutlass/sparse_scaled_mm_entry.cu"
     "csrc/cutlass_extensions/common.cpp"
-    "csrc/attention/mla/cutlass_mla_entry.cu"
-    "csrc/quantization/fp8/per_token_group_quant.cu")
+    "csrc/attention/mla/cutlass_mla_entry.cu")
 
   set_gencode_flags_for_srcs(
     SRCS "${VLLM_EXT_SRC}"
@@ -392,7 +393,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # The cutlass_scaled_mm kernels for Hopper (c3x, i.e. CUTLASS 3.x) require
   # CUDA 12.0 or later
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND SCALED_MM_ARCHS)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_ARCHS)
     set(SRCS
        "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm90.cu"
        "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8.cu"
@@ -408,7 +409,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
     message(STATUS "Building scaled_mm_c3x_sm90 for archs: ${SCALED_MM_ARCHS}")
   else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND SCALED_MM_ARCHS)
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_ARCHS)
       message(STATUS "Not building scaled_mm_c3x_sm90 as CUDA Compiler version is "
                      "not >= 12.0, we recommend upgrading to CUDA 12.0 or "
                      "later if you intend on running FP8 quantized models on "
@@ -419,41 +420,10 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
   endif()
 
-
-  # The cutlass_scaled_mm kernels for Geforce Blackwell SM120 (c3x, i.e. CUTLASS 3.x) require
-  # CUDA 12.8 or later
-  cuda_archs_loose_intersection(SCALED_MM_ARCHS "12.0;12.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-    set(SRCS
-      "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm120.cu"
-      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm120_fp8.cu"
-      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm120_fp8.cu"
-    )
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${SCALED_MM_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_SCALED_MM_SM120=1")
-    # Let scaled_mm_c2x know it doesn't need to build these arches
-    list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
-    message(STATUS "Building scaled_mm_c3x_sm120 for archs: ${SCALED_MM_ARCHS}")
-  else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-      message(STATUS "Not building scaled_mm_c3x_sm120 as CUDA Compiler version is "
-                     "not >= 12.8, we recommend upgrading to CUDA 12.8 or "
-                     "later if you intend on running FP8 quantized models on "
-                     "Blackwell.")
-    else()
-      message(STATUS "Not building scaled_mm_c3x_120 as no compatible archs found "
-                     "in CUDA target architectures")
-    endif()
-  endif()
-
-
   # The cutlass_scaled_mm kernels for Blackwell SM100 (c3x, i.e. CUTLASS 3.x)
   # require CUDA 12.8 or later
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND SCALED_MM_ARCHS)
     set(SRCS
       "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm100.cu"
       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8.cu"
@@ -468,7 +438,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
     message(STATUS "Building scaled_mm_c3x_sm100 for archs: ${SCALED_MM_ARCHS}")
   else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND SCALED_MM_ARCHS)
       message(STATUS "Not building scaled_mm_c3x_sm100 as CUDA Compiler version is "
                      "not >= 12.8, we recommend upgrading to CUDA 12.8 or "
                      "later if you intend on running FP8 quantized models on "
@@ -511,7 +481,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # The 2:4 sparse kernels cutlass_scaled_sparse_mm and cutlass_compressor
   # require CUDA 12.2 or later (and only work on Hopper).
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.2 AND SCALED_MM_ARCHS)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_ARCHS)
     set(SRCS "csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
@@ -520,7 +490,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_SPARSE_SCALED_MM_C3X=1")
     message(STATUS "Building sparse_scaled_mm_c3x for archs: ${SCALED_MM_ARCHS}")
   else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.2 AND SCALED_MM_ARCHS)
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_ARCHS)
       message(STATUS "Not building sparse_scaled_mm_c3x kernels as CUDA Compiler version is "
                      "not >= 12.2, we recommend upgrading to CUDA 12.2 or later "
                      "if you intend on running FP8 sparse quantized models on Hopper.")
@@ -530,28 +500,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
   endif()
 
-  # The nvfp4_scaled_mm_sm120 kernels for Geforce Blackwell SM120 require
-  # CUDA 12.8 or later
-  cuda_archs_loose_intersection(FP4_ARCHS "12.0;12.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS)
-    set(SRCS
-      "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
-      "csrc/quantization/fp4/nvfp4_scaled_mm_sm120_kernels.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${FP4_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM120=1")
-    message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
-  else()
-    message(STATUS "Not building NVFP4 as no compatible archs were found.")
-    # clear FP4_ARCHS
-    set(FP4_ARCHS)
-  endif()
-
   # FP4 Archs and flags
   cuda_archs_loose_intersection(FP4_ARCHS "10.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND FP4_ARCHS)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND FP4_ARCHS)
     set(SRCS
       "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
       "csrc/quantization/fp4/nvfp4_experts_quant.cu"
@@ -561,8 +512,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
       SRCS "${SRCS}"
       CUDA_ARCHS "${FP4_ARCHS}")
     list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4_SM100=1")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4=1")
     message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
   else()
     message(STATUS "Not building NVFP4 as no compatible archs were found.")
@@ -572,10 +522,9 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
   # CUTLASS MLA Archs and flags
   cuda_archs_loose_intersection(MLA_ARCHS "10.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND MLA_ARCHS)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND MLA_ARCHS)
     set(SRCS
-      "csrc/attention/mla/cutlass_mla_kernels.cu"
-      "csrc/attention/mla/sm100_cutlass_mla_kernel.cu")
+      "csrc/attention/mla/cutlass_mla_kernels.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${MLA_ARCHS}")
@@ -598,7 +547,8 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # if it's possible to compile MoE kernels that use its output.
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm90.cu")
+    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu"
+             "csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${SCALED_MM_ARCHS}")
@@ -612,66 +562,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
                      "if you intend on running FP8 quantized MoE models on Hopper.")
     else()
       message(STATUS "Not building grouped_mm_c3x as no compatible archs found "
-                     "in CUDA target architectures.")
-    endif()
-  endif()
-
-  cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x_sm100.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${SCALED_MM_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
-    message(STATUS "Building grouped_mm_c3x for archs: ${SCALED_MM_ARCHS}")
-  else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-      message(STATUS "Not building grouped_mm_c3x kernels as CUDA Compiler version is "
-                     "not >= 12.8, we recommend upgrading to CUDA 12.8 or later "
-                     "if you intend on running FP8 quantized MoE models on Blackwell.")
-    else()
-      message(STATUS "Not building grouped_mm_c3x as no compatible archs found "
-                     "in CUDA target architectures.")
-    endif()
-  endif()
-
-  # moe_data.cu is used by all CUTLASS MoE kernels.
-  cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND CUTLASS_MOE_DATA_ARCHS)
-    set(SRCS "csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${CUTLASS_MOE_DATA_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    message(STATUS "Building moe_data for archs: ${CUTLASS_MOE_DATA_ARCHS}")
-  else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND CUTLASS_MOE_DATA_ARCHS)
-      message(STATUS "Not building moe_data as CUDA Compiler version is "
-                     "not >= 12.3, we recommend upgrading to CUDA 12.3 or later "
-                     "if you intend on running FP8 quantized MoE models on Hopper or Blackwell.")
-    else()
-      message(STATUS "Not building moe_data as no compatible archs found "
-                     "in CUDA target architectures.")
-    endif()
-  endif()
-
-  cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/cutlass_w8a8/moe/blockwise_scaled_group_mm_sm100.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${SCALED_MM_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM100=1")
-    message(STATUS "Building blockwise_scaled_group_mm_sm100 for archs: ${SCALED_MM_ARCHS}")
-  else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8 AND SCALED_MM_ARCHS)
-      message(STATUS "Not building blockwise_scaled_group_mm_sm100 kernels as CUDA Compiler version is "
-                     "not >= 12.8, we recommend upgrading to CUDA 12.8 or later "
-                     "if you intend on running FP8 quantized MoE models on Blackwell.")
-    else()
-      message(STATUS "Not building blockwise_scaled_group_mm_sm100 as no compatible archs found "
                      "in CUDA target architectures")
     endif()
   endif()
@@ -682,7 +572,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # The machete kernels only work on hopper and require CUDA 12.0 or later.
   # Only build Machete kernels if we are building for something compatible with sm90a
   cuda_archs_loose_intersection(MACHETE_ARCHS "9.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0 AND MACHETE_ARCHS)
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND MACHETE_ARCHS)
     #
     # For the Machete kernels we automatically generate sources for various
     # preselected input type pairs and schedules.
@@ -734,7 +624,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
     message(STATUS "Building Machete kernels for archs: ${MACHETE_ARCHS}")
   else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.0
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0
         AND MACHETE_ARCHS)
       message(STATUS "Not building Machete kernels as CUDA Compiler version is "
                      "not >= 12.0, we recommend upgrading to CUDA 12.0 or "
@@ -748,14 +638,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 # if CUDA endif
 endif()
 
-if (VLLM_GPU_LANG STREQUAL "HIP")
-  # Add QuickReduce kernels
-  list(APPEND VLLM_EXT_SRC
-    "csrc/custom_quickreduce.cu"
-  )
-# if ROCM endif
-endif()
-
 message(STATUS "Enabling C extension.")
 define_gpu_extension_target(
   _C
@@ -788,14 +670,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_MOE_EXT_SRC "csrc/moe/moe_wna16.cu")
 endif()
 
-if(VLLM_GPU_LANG STREQUAL "CUDA")
-  set(MOE_PERMUTE_SRC
-      "csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu"
-      "csrc/moe/moe_permute_unpermute_op.cu")
-
-  list(APPEND VLLM_MOE_EXT_SRC "${MOE_PERMUTE_SRC}")
-endif()
-
 set_gencode_flags_for_srcs(
   SRCS "${VLLM_MOE_EXT_SRC}"
   CUDA_ARCHS "${CUDA_ARCHS}")
@@ -864,6 +738,17 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   endif()
 endif()
 
+if(VLLM_GPU_LANG STREQUAL "CUDA")
+  set(MOE_PERMUTE_SRC
+      "csrc/moe/permute_unpermute_kernels/moe_permute_unpermute_kernel.cu"
+      "csrc/moe/moe_permute_unpermute_op.cu")
+
+  set_gencode_flags_for_srcs(
+    SRCS "${MARLIN_PERMUTE_SRC}"
+    CUDA_ARCHS "${MOE_PERMUTE_ARCHS}")
+
+  list(APPEND VLLM_MOE_EXT_SRC "${MOE_PERMUTE_SRC}")
+endif()
 message(STATUS "Enabling moe extension.")
 define_gpu_extension_target(
   _moe_C

README.md

@@ -1,4 +1,3 @@
-<!-- markdownlint-disable MD001 MD041 -->
 <p align="center">
   <picture>
     <source media="(prefers-color-scheme: dark)" srcset="https://raw.githubusercontent.com/vllm-project/vllm/main/docs/assets/logos/vllm-logo-text-dark.png">
@@ -17,16 +16,14 @@ Easy, fast, and cheap LLM serving for everyone
 ---
 
 *Latest News* 🔥
-
-- [2025/08] We hosted [vLLM Beijing Meetup](https://mp.weixin.qq.com/s/dgkWg1WFpWGO2jCdTqQHxA) focusing on large-scale LLM deployment! Please find the meetup slides [here](https://drive.google.com/drive/folders/1Pid6NSFLU43DZRi0EaTcPgXsAzDvbBqF) and the recording [here](https://www.chaspark.com/#/live/1166916873711665152).
 - [2025/05] We hosted [NYC vLLM Meetup](https://lu.ma/c1rqyf1f)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1_q_aW_ioMJWUImf1s1YM-ZhjXz8cUeL0IJvaquOYBeA/edit?usp=sharing).
 - [2025/05] vLLM is now a hosted project under PyTorch Foundation! Please find the announcement [here](https://pytorch.org/blog/pytorch-foundation-welcomes-vllm/).
+- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
 - [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
 
 <details>
 <summary>Previous News</summary>
 
-- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
 - [2025/03] We hosted [vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).
 - [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
 - [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
@@ -49,7 +46,6 @@ Easy, fast, and cheap LLM serving for everyone
 </details>
 
 ---
-
 ## About
 
 vLLM is a fast and easy-to-use library for LLM inference and serving.
@@ -67,11 +63,13 @@ vLLM is fast with:
 - Speculative decoding
 - Chunked prefill
 
+**Performance benchmark**: We include a performance benchmark at the end of [our blog post](https://blog.vllm.ai/2024/09/05/perf-update.html). It compares the performance of vLLM against other LLM serving engines ([TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM), [SGLang](https://github.com/sgl-project/sglang) and [LMDeploy](https://github.com/InternLM/lmdeploy)). The implementation is under [nightly-benchmarks folder](.buildkite/nightly-benchmarks/) and you can [reproduce](https://github.com/vllm-project/vllm/issues/8176) this benchmark using our one-click runnable script.
+
 vLLM is flexible and easy to use with:
 
 - Seamless integration with popular Hugging Face models
 - High-throughput serving with various decoding algorithms, including *parallel sampling*, *beam search*, and more
-- Tensor, pipeline, data and expert parallelism support for distributed inference
+- Tensor parallelism and pipeline parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
 - Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron
@@ -79,7 +77,6 @@ vLLM is flexible and easy to use with:
 - Multi-LoRA support
 
 vLLM seamlessly supports most popular open-source models on HuggingFace, including:
-
 - Transformer-like LLMs (e.g., Llama)
 - Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
 - Embedding Models (e.g., E5-Mistral)
@@ -96,7 +93,6 @@ pip install vllm
 ```
 
 Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more.
-
 - [Installation](https://docs.vllm.ai/en/latest/getting_started/installation.html)
 - [Quickstart](https://docs.vllm.ai/en/latest/getting_started/quickstart.html)
 - [List of Supported Models](https://docs.vllm.ai/en/latest/models/supported_models.html)
@@ -113,7 +109,6 @@ vLLM is a community project. Our compute resources for development and testing a
 <!-- Note: Please sort them in alphabetical order. -->
 <!-- Note: Please keep these consistent with docs/community/sponsors.md -->
 Cash Donations:
-
 - a16z
 - Dropbox
 - Sequoia Capital
@@ -121,8 +116,6 @@ Cash Donations:
 - ZhenFund
 
 Compute Resources:
-
-- Alibaba Cloud
 - AMD
 - Anyscale
 - AWS
@@ -161,13 +154,11 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 
 ## Contact Us
 
-<!-- --8<-- [start:contact-us] -->
 - For technical questions and feature requests, please use GitHub [Issues](https://github.com/vllm-project/vllm/issues) or [Discussions](https://github.com/vllm-project/vllm/discussions)
 - For discussing with fellow users, please use the [vLLM Forum](https://discuss.vllm.ai)
 - For coordinating contributions and development, please use [Slack](https://slack.vllm.ai)
 - For security disclosures, please use GitHub's [Security Advisories](https://github.com/vllm-project/vllm/security/advisories) feature
 - For collaborations and partnerships, please contact us at [vllm-questions@lists.berkeley.edu](mailto:vllm-questions@lists.berkeley.edu)
-<!-- --8<-- [end:contact-us] -->
 
 ## Media Kit
 

RELEASE.md

@@ -52,39 +52,3 @@ After branch cut, we approach finalizing the release branch with clear criteria
 * Release branch specific changes (e.g. change version identifiers or CI fixes)
 
 Please note: **No feature work allowed for cherry picks**. All PRs that are considered for cherry-picks need to be merged on trunk, the only exception are Release branch specific changes.
-
-## Manual validations
-
-### E2E Performance Validation
-
-Before each release, we perform end-to-end performance validation to ensure no regressions are introduced. This validation uses the [vllm-benchmark workflow](https://github.com/pytorch/pytorch-integration-testing/actions/workflows/vllm-benchmark.yml) on PyTorch CI.
-
-**Current Coverage:**
-
-* Models: Llama3, Llama4, and Mixtral
-* Hardware: NVIDIA H100 and AMD MI300x
-* _Note: Coverage may change based on new model releases and hardware availability_
-
-**Performance Validation Process:**
-
-**Step 1: Get Access**
-Request write access to the [pytorch/pytorch-integration-testing](https://github.com/pytorch/pytorch-integration-testing) repository to run the benchmark workflow.
-
-**Step 2: Review Benchmark Setup**
-Familiarize yourself with the benchmark configurations:
-
-* [CUDA setup](https://github.com/pytorch/pytorch-integration-testing/tree/main/vllm-benchmarks/benchmarks/cuda)
-* [ROCm setup](https://github.com/pytorch/pytorch-integration-testing/tree/main/vllm-benchmarks/benchmarks/rocm)
-
-**Step 3: Run the Benchmark**
-Navigate to the [vllm-benchmark workflow](https://github.com/pytorch/pytorch-integration-testing/actions/workflows/vllm-benchmark.yml) and configure:
-
-* **vLLM branch**: Set to the release branch (e.g., `releases/v0.9.2`)
-* **vLLM commit**: Set to the RC commit hash
-
-**Step 4: Review Results**
-Once the workflow completes, benchmark results will be available on the [vLLM benchmark dashboard](https://hud.pytorch.org/benchmark/llms?repoName=vllm-project%2Fvllm) under the corresponding branch and commit.
-
-**Step 5: Performance Comparison**
-Compare the current results against the previous release to verify no performance regressions have occurred. Here is an
-example of [v0.9.1 vs v0.9.2](https://hud.pytorch.org/benchmark/llms?startTime=Thu%2C%2017%20Apr%202025%2021%3A43%3A50%20GMT&stopTime=Wed%2C%2016%20Jul%202025%2021%3A43%3A50%20GMT&granularity=week&lBranch=releases/v0.9.1&lCommit=b6553be1bc75f046b00046a4ad7576364d03c835&rBranch=releases/v0.9.2&rCommit=a5dd03c1ebc5e4f56f3c9d3dc0436e9c582c978f&repoName=vllm-project%2Fvllm&benchmarkName=&modelName=All%20Models&backendName=All%20Backends&modeName=All%20Modes&dtypeName=All%20DType&deviceName=All%20Devices&archName=All%20Platforms).

SECURITY.md

@@ -1,45 +1,13 @@
 # Security Policy
 
-## Reporting security issues
+## Reporting a Vulnerability
 
-Please report security issues privately using [the vulnerability submission form](https://github.com/vllm-project/vllm/security/advisories/new).
+If you believe you have found a security vulnerability in vLLM, we encourage you to let us know right away. We will investigate all legitimate reports and do our best to quickly fix the problem.
 
-## Issue triage
+Please report security issues privately using [the vulnerability submission form](https://github.com/vllm-project/vllm/security/advisories/new). Reports will then be triaged by the [vulnerability management team](https://docs.vllm.ai/en/latest/contributing/vulnerability_management.html).
 
-Reports will then be triaged by the [vulnerability management team](https://docs.vllm.ai/en/latest/contributing/vulnerability_management.html).
-
-## Threat model
+---
 
 Please see the [Security Guide in the vLLM documentation](https://docs.vllm.ai/en/latest/usage/security.html) for more information on vLLM's security assumptions and recommendations.
 
 Please see [PyTorch's Security Policy](https://github.com/pytorch/pytorch/blob/main/SECURITY.md) for more information and recommendations on how to securely interact with models.
-
-## Issue severity
-
-We will determine the risk of each issue, taking into account our experience dealing with past issues, versions affected, common defaults, and use cases. We use the following severity categories:
-
-### CRITICAL Severity
-
-Vulnerabilities that allow remote attackers to execute arbitrary code, take full control of the system, or significantly compromise confidentiality, integrity, or availability without any interaction or privileges needed, examples include remote code execution via network, deserialization issues that allow exploit chains. Generally those issues which are rated as CVSS  ≥ 9.0.
-
-### HIGH Severity
-
-Serious security flaws that allow elevated impact—like RCE in specific, limited contexts or significant data loss—but require advanced conditions or some trust, examples include RCE in advanced deployment modes (e.g. multi-node), or high impact issues where some sort of privileged network access is required. These issues typically have CVSS scores between 7.0 and 8.9
-
-### MODERATE Severity
-
-Vulnerabilities that cause denial of service or partial disruption, but do not allow arbitrary code execution or data breach and have limited impact. These issues have a CVSS rating between 4.0 and 6.9
-
-### LOW Severity
-
-Minor issues such as informational disclosures, logging errors, non-exploitable flaws, or weaknesses that require local or high-privilege access and offer negligible impact. Examples include side channel attacks or hash collisions. These issues often have CVSS scores less than 4.0
-
-## Prenotification policy
-
-For certain security issues of CRITICAL, HIGH, or MODERATE severity level, we may prenotify certain organizations or vendors that ship vLLM. The purpose of this prenotification is to allow for a coordinated release of fixes for severe issues.
-
-* This prenotification will be in the form of a private email notification. It may also include adding security contacts to the GitHub security advisory, typically a few days before release.
-
-* If you wish to be added to the prenotification group, please send an email copying all the members of the [vulnerability management team](https://docs.vllm.ai/en/latest/contributing/vulnerability_management.html). Each vendor contact will be analyzed on a case-by-case basis.
-
-* We may withdraw organizations from receiving future prenotifications if they release fixes or any other information about issues before they are public. Group membership may also change based on policy refinements for who may be included.

benchmarks/README.md

@@ -81,17 +81,13 @@ become available.
 
 **Note**: HuggingFace dataset's `dataset-name` should be set to `hf`
 
-## 🚀 Example - Online Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
+---
+## Example - Online Benchmark
 
 First start serving your model
 
 ```bash
-vllm serve NousResearch/Hermes-3-Llama-3.1-8B
+vllm serve NousResearch/Hermes-3-Llama-3.1-8B --disable-log-requests
 ```
 
 Then run the benchmarking script
@@ -99,7 +95,7 @@ Then run the benchmarking script
 ```bash
 # download dataset
 # wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
   --backend vllm \
   --model NousResearch/Hermes-3-Llama-3.1-8B \
   --endpoint /v1/completions \
@@ -110,48 +106,47 @@ vllm bench serve \
 
 If successful, you will see the following output
 
-```text
+```
 ============ Serving Benchmark Result ============
-Successful requests:                     10
-Benchmark duration (s):                  5.78
-Total input tokens:                      1369
-Total generated tokens:                  2212
-Request throughput (req/s):              1.73
-Output token throughput (tok/s):         382.89
-Total Token throughput (tok/s):          619.85
+Successful requests:                     10        
+Benchmark duration (s):                  5.78      
+Total input tokens:                      1369      
+Total generated tokens:                  2212      
+Request throughput (req/s):              1.73      
+Output token throughput (tok/s):         382.89    
+Total Token throughput (tok/s):          619.85    
 ---------------Time to First Token----------------
-Mean TTFT (ms):                          71.54
-Median TTFT (ms):                        73.88
-P99 TTFT (ms):                           79.49
+Mean TTFT (ms):                          71.54     
+Median TTFT (ms):                        73.88     
+P99 TTFT (ms):                           79.49     
 -----Time per Output Token (excl. 1st token)------
-Mean TPOT (ms):                          7.91
-Median TPOT (ms):                        7.96
-P99 TPOT (ms):                           8.03
+Mean TPOT (ms):                          7.91      
+Median TPOT (ms):                        7.96      
+P99 TPOT (ms):                           8.03      
 ---------------Inter-token Latency----------------
-Mean ITL (ms):                           7.74
-Median ITL (ms):                         7.70
-P99 ITL (ms):                            8.39
+Mean ITL (ms):                           7.74      
+Median ITL (ms):                         7.70      
+P99 ITL (ms):                            8.39      
 ==================================================
 ```
 
 ### Custom Dataset
-
 If the dataset you want to benchmark is not supported yet in vLLM, even then you can benchmark on it using `CustomDataset`. Your data needs to be in `.jsonl` format and needs to have "prompt" field per entry, e.g., data.jsonl
 
-```json
+```
 {"prompt": "What is the capital of India?"}
 {"prompt": "What is the capital of Iran?"}
 {"prompt": "What is the capital of China?"}
-```
+``` 
 
 ```bash
 # start server
-VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct
+VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct --disable-log-requests
 ```
 
 ```bash
 # run benchmarking script
-vllm bench serve --port 9001 --save-result --save-detailed \
+python3 benchmarks/benchmark_serving.py --port 9001 --save-result --save-detailed \
   --backend vllm \
   --model meta-llama/Llama-3.1-8B-Instruct \
   --endpoint /v1/completions \
@@ -171,11 +166,11 @@ You can skip applying chat template if your data already has it by using `--cust
 
 ```bash
 # need a model with vision capability here
-vllm serve Qwen/Qwen2-VL-7B-Instruct
+vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
 ```
 
 ```bash
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
   --backend openai-chat \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --endpoint /v1/chat/completions \
@@ -195,7 +190,7 @@ VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
 ```
 
 ``` bash
-vllm bench serve \
+python3 benchmarks/benchmark_serving.py \
     --model meta-llama/Meta-Llama-3-8B-Instruct \
     --dataset-name hf \
     --dataset-path likaixin/InstructCoder \
@@ -205,13 +200,13 @@ vllm bench serve \
 ### Other HuggingFaceDataset Examples
 
 ```bash
-vllm serve Qwen/Qwen2-VL-7B-Instruct
+vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
 ```
 
-`lmms-lab/LLaVA-OneVision-Data`:
+**`lmms-lab/LLaVA-OneVision-Data`**
 
 ```bash
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
   --backend openai-chat \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --endpoint /v1/chat/completions \
@@ -222,10 +217,10 @@ vllm bench serve \
   --num-prompts 10
 ```
 
-`Aeala/ShareGPT_Vicuna_unfiltered`:
+**`Aeala/ShareGPT_Vicuna_unfiltered`**
 
 ```bash
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
   --backend openai-chat \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --endpoint /v1/chat/completions \
@@ -235,10 +230,10 @@ vllm bench serve \
   --num-prompts 10
 ```
 
-`AI-MO/aimo-validation-aime`:
+**`AI-MO/aimo-validation-aime`**
 
 ``` bash
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
     --model Qwen/QwQ-32B \
     --dataset-name hf \
     --dataset-path AI-MO/aimo-validation-aime \
@@ -246,10 +241,10 @@ vllm bench serve \
     --seed 42
 ```
 
-`philschmid/mt-bench`:
+**`philschmid/mt-bench`**
 
 ``` bash
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
     --model Qwen/QwQ-32B \
     --dataset-name hf \
     --dataset-path philschmid/mt-bench \
@@ -262,7 +257,7 @@ When using OpenAI-compatible backends such as `vllm`, optional sampling
 parameters can be specified. Example client command:
 
 ```bash
-vllm bench serve \
+python3 vllm/benchmarks/benchmark_serving.py \
   --backend vllm \
   --model NousResearch/Hermes-3-Llama-3.1-8B \
   --endpoint /v1/completions \
@@ -274,34 +269,11 @@ vllm bench serve \
   --num-prompts 10
 ```
 
-### Running With Ramp-Up Request Rate
-
-The benchmark tool also supports ramping up the request rate over the
-duration of the benchmark run. This can be useful for stress testing the
-server or finding the maximum throughput that it can handle, given some latency budget.
-
-Two ramp-up strategies are supported:
-
-- `linear`: Increases the request rate linearly from a start value to an end value.
-- `exponential`: Increases the request rate exponentially.
-
-The following arguments can be used to control the ramp-up:
-
-- `--ramp-up-strategy`: The ramp-up strategy to use (`linear` or `exponential`).
-- `--ramp-up-start-rps`: The request rate at the beginning of the benchmark.
-- `--ramp-up-end-rps`: The request rate at the end of the benchmark.
-
-</details>
-
-## 📈 Example - Offline Throughput Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
+---
+## Example - Offline Throughput Benchmark
 
 ```bash
-vllm bench throughput \
+python3 vllm/benchmarks/benchmark_throughput.py \
   --model NousResearch/Hermes-3-Llama-3.1-8B \
   --dataset-name sonnet \
   --dataset-path vllm/benchmarks/sonnet.txt \
@@ -310,7 +282,7 @@ vllm bench throughput \
 
 If successful, you will see the following output
 
-```text
+```
 Throughput: 7.15 requests/s, 4656.00 total tokens/s, 1072.15 output tokens/s
 Total num prompt tokens:  5014
 Total num output tokens:  1500
@@ -318,8 +290,8 @@ Total num output tokens:  1500
 
 ### VisionArena Benchmark for Vision Language Models
 
-```bash
-vllm bench throughput \
+``` bash
+python3 vllm/benchmarks/benchmark_throughput.py \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --backend vllm-chat \
   --dataset-name hf \
@@ -330,7 +302,7 @@ vllm bench throughput \
 
 The `num prompt tokens` now includes image token counts
 
-```text
+```
 Throughput: 2.55 requests/s, 4036.92 total tokens/s, 326.90 output tokens/s
 Total num prompt tokens:  14527
 Total num output tokens:  1280
@@ -341,7 +313,7 @@ Total num output tokens:  1280
 ``` bash
 VLLM_WORKER_MULTIPROC_METHOD=spawn \
 VLLM_USE_V1=1 \
-vllm bench throughput \
+python3 vllm/benchmarks/benchmark_throughput.py \
     --dataset-name=hf \
     --dataset-path=likaixin/InstructCoder \
     --model=meta-llama/Meta-Llama-3-8B-Instruct \
@@ -354,7 +326,7 @@ vllm bench throughput \
     "prompt_lookup_min": 2}'
 ```
 
-```text
+```
 Throughput: 104.77 requests/s, 23836.22 total tokens/s, 10477.10 output tokens/s
 Total num prompt tokens:  261136
 Total num output tokens:  204800
@@ -362,10 +334,10 @@ Total num output tokens:  204800
 
 ### Other HuggingFaceDataset Examples
 
-`lmms-lab/LLaVA-OneVision-Data`:
+**`lmms-lab/LLaVA-OneVision-Data`**
 
 ```bash
-vllm bench throughput \
+python3 vllm/benchmarks/benchmark_throughput.py \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --backend vllm-chat \
   --dataset-name hf \
@@ -375,10 +347,10 @@ vllm bench throughput \
   --num-prompts 10
 ```
 
-`Aeala/ShareGPT_Vicuna_unfiltered`:
+**`Aeala/ShareGPT_Vicuna_unfiltered`**
 
 ```bash
-vllm bench throughput \
+python3 vllm/benchmarks/benchmark_throughput.py \
   --model Qwen/Qwen2-VL-7B-Instruct \
   --backend vllm-chat \
   --dataset-name hf \
@@ -387,10 +359,10 @@ vllm bench throughput \
   --num-prompts 10
 ```
 
-`AI-MO/aimo-validation-aime`:
+**`AI-MO/aimo-validation-aime`**
 
 ```bash
-vllm bench throughput \
+python3 benchmarks/benchmark_throughput.py \
   --model Qwen/QwQ-32B \
   --backend vllm \
   --dataset-name hf \
@@ -399,12 +371,12 @@ vllm bench throughput \
   --num-prompts 10
 ```
 
-Benchmark with LoRA adapters:
+### Benchmark with LoRA Adapters
 
 ``` bash
 # download dataset
 # wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-vllm bench throughput \
+python3 vllm/benchmarks/benchmark_throughput.py \
   --model meta-llama/Llama-2-7b-hf \
   --backend vllm \
   --dataset_path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
@@ -415,204 +387,3 @@ vllm bench throughput \
   --enable-lora \
   --lora-path yard1/llama-2-7b-sql-lora-test
   ```
-
-</details>
-
-## 🛠️ Example - Structured Output Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
-
-Benchmark the performance of structured output generation (JSON, grammar, regex).
-
-### Server Setup
-
-```bash
-vllm serve NousResearch/Hermes-3-Llama-3.1-8B
-```
-
-### JSON Schema Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset json \
-  --structured-output-ratio 1.0 \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### Grammar-based Generation Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset grammar \
-  --structure-type grammar \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### Regex-based Generation Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset regex \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### Choice-based Generation Benchmark
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset choice \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-### XGrammar Benchmark Dataset
-
-```bash
-python3 benchmarks/benchmark_serving_structured_output.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset xgrammar_bench \
-  --request-rate 10 \
-  --num-prompts 1000
-```
-
-</details>
-
-## 📚 Example - Long Document QA Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
-
-Benchmark the performance of long document question-answering with prefix caching.
-
-### Basic Long Document QA Test
-
-```bash
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 16 \
-  --document-length 2000 \
-  --output-len 50 \
-  --repeat-count 5
-```
-
-### Different Repeat Modes
-
-```bash
-# Random mode (default) - shuffle prompts randomly
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 8 \
-  --document-length 3000 \
-  --repeat-count 3 \
-  --repeat-mode random
-
-# Tile mode - repeat entire prompt list in sequence
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 8 \
-  --document-length 3000 \
-  --repeat-count 3 \
-  --repeat-mode tile
-
-# Interleave mode - repeat each prompt consecutively
-python3 benchmarks/benchmark_long_document_qa_throughput.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-documents 8 \
-  --document-length 3000 \
-  --repeat-count 3 \
-  --repeat-mode interleave
-```
-
-</details>
-
-## 🗂️ Example - Prefix Caching Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
-
-Benchmark the efficiency of automatic prefix caching.
-
-### Fixed Prompt with Prefix Caching
-
-```bash
-python3 benchmarks/benchmark_prefix_caching.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --enable-prefix-caching \
-  --num-prompts 1 \
-  --repeat-count 100 \
-  --input-length-range 128:256
-```
-
-### ShareGPT Dataset with Prefix Caching
-
-```bash
-# download dataset
-# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-
-python3 benchmarks/benchmark_prefix_caching.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --dataset-path /path/ShareGPT_V3_unfiltered_cleaned_split.json \
-  --enable-prefix-caching \
-  --num-prompts 20 \
-  --repeat-count 5 \
-  --input-length-range 128:256
-```
-
-</details>
-
-## ⚡ Example - Request Prioritization Benchmark
-
-<details>
-<summary>Show more</summary>
-
-<br/>
-
-Benchmark the performance of request prioritization in vLLM.
-
-### Basic Prioritization Test
-
-```bash
-python3 benchmarks/benchmark_prioritization.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --input-len 128 \
-  --output-len 64 \
-  --num-prompts 100 \
-  --scheduling-policy priority
-```
-
-### Multiple Sequences per Prompt
-
-```bash
-python3 benchmarks/benchmark_prioritization.py \
-  --model meta-llama/Llama-2-7b-chat-hf \
-  --input-len 128 \
-  --output-len 64 \
-  --num-prompts 100 \
-  --scheduling-policy priority \
-  --n 2
-```
-
-</details>

benchmarks/auto_tune.sh

@@ -1,18 +1,43 @@
 #!/bin/bash
 
-# This script aims to tune the best server parameter combinations to maximize throughput for given requirement.
-# See details in README (benchmarks/auto_tune/README.md).
+# This script aims to tune the best server parameter combinations to maximize throughput for given requirement. 
+# The current server parameter combination is  max_num_seqs and max_num_batched_tokens
+# It also supports additional requirement: e2e latency and prefix cache. 
+
+# Pre-requisite:
+# 1. Checkout to your branch, install/ update the correct running env. For TPU, activate conda env and install the corresponding torch, xla version. 
+# 2. If the model is customized, replace the MODEL's config with the customized config.
+# 3. Set variables (ALL REQUIRED)
+#   BASE: your directory for vllm repo
+#   MODEL: the model served by vllm
+#   TP: ways of tensor parallelism
+#   DOWNLOAD_DIR: directory to download and load model weights.
+#   INPUT_LEN: request input len
+#   OUTPUT_LEN: request output len
+#   MIN_CACHE_HIT_PCT: prefix cache rate
+#   MAX_LATENCY_ALLOWED_MS: (e2e) latency requirement. If there's no latency requirement, set it to a large number like 1000000000
+#   NUM_SEQS_LIST: a list of `max-num-seqs` you want to loop with.
+#   NUM_BATCHED_TOKENS_LIST: a list of `max-num-batched-tokens` you want to loop with.
+#   Note that the default NUM_SEQS_LIST and NUM_BATCHED_TOKENS_LIST are set for medium size input/output len, for extra short context (such as 20:20), you might need to include larger numbers in NUM_SEQS_LIST.
+# 4. Run the script, it might take a long time, you can use tmux to avoid the script stop if disconnection happens.
+# 5. The final result will be saved in RESULT file. 
+
+
+# Example use cases 
+# 1. Given input_len=1800, output_len=20, what's the best max_num_seqs and max_num_batched_tokens to get highest throughput?
+# Use INPUT_LEN=1800,  OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=0, MAX_LATENCY_ALLOWED_MS=100000000000
+# 2. If we have latency requirement to be lower than 500ms, what's the best server parameter?
+# Use INPUT_LEN=1800,  OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=0, MAX_LATENCY_ALLOWED_MS=500
+# 3. If we want to reach 60% prefix cache, what's the best server parameter? 
+# Use INPUT_LEN=1800,  OUTPUT_LEN=20, MIN_CACHE_HIT_PCT=60, MAX_LATENCY_ALLOWED_MS=500
 
 TAG=$(date +"%Y_%m_%d_%H_%M")
-SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
-BASE="$SCRIPT_DIR/../../.."
+BASE=""
 MODEL="meta-llama/Llama-3.1-8B-Instruct"
-SYSTEM="TPU"
 TP=1
 DOWNLOAD_DIR=""
 INPUT_LEN=4000
 OUTPUT_LEN=16
-MAX_MODEL_LEN=4096
 MIN_CACHE_HIT_PCT=0
 MAX_LATENCY_ALLOWED_MS=100000000000
 NUM_SEQS_LIST="128 256"
@@ -20,15 +45,12 @@ NUM_BATCHED_TOKENS_LIST="512 1024 2048 4096"
 
 LOG_FOLDER="$BASE/auto-benchmark/$TAG"
 RESULT="$LOG_FOLDER/result.txt"
-PROFILE_PATH="$LOG_FOLDER/profile"
 
 echo "result file: $RESULT"
 echo "model: $MODEL"
 
 rm -rf $LOG_FOLDER
-rm -rf $PROFILE_PATH
 mkdir -p $LOG_FOLDER
-mkdir -p $PROFILE_PATH
 
 cd "$BASE/vllm"
 
@@ -38,61 +60,36 @@ current_hash=$(git rev-parse HEAD)
 echo "hash:$current_hash" >> "$RESULT"
 echo "current_hash: $current_hash"
 
-TOTAL_LEN=$((INPUT_LEN + OUTPUT_LEN))
-RED='\033[0;31m'
-if (( TOTAL_LEN > MAX_MODEL_LEN )); then
-    echo -e "${RED}FAILED: INPUT_LEN($INPUT_LEN) + OUTPUT_LEN($OUTPUT_LEN) = $TOTAL_LEN, which is > MAX_MODEL_LEN = $MAX_MODEL_LEN.\033[0m" >&2
-    exit 1
-fi
-
 best_throughput=0
 best_max_num_seqs=0
 best_num_batched_tokens=0
 best_goodput=0
-best_request_rate=0
 
 start_server() {
     local gpu_memory_utilization=$1
     local max_num_seqs=$2
     local max_num_batched_tokens=$3
     local vllm_log=$4
-    local profile_dir=$5
+    
+    pkill -f vllm
 
-    pkill -if vllm
-
-    # Define the common arguments as a bash array.
-    # Each argument and its value are separate elements.
-    local common_args_array=(
-        "$MODEL"
-        "--disable-log-requests"
-        "--port" "8004"
-        "--gpu-memory-utilization" "$gpu_memory_utilization"
-        "--max-num-seqs" "$max_num_seqs"
-        "--max-num-batched-tokens" "$max_num_batched_tokens"
-        "--tensor-parallel-size" "$TP"
-        "--enable-prefix-caching"
-        "--load-format" "dummy"
-        "--download-dir" "$DOWNLOAD_DIR"
-        "--max-model-len" "$MAX_MODEL_LEN"
-    )
-
-    # Use the array expansion "${common_args_array[@]}"
-    # This correctly passes each element as a separate argument.
-    if [[ -n "$profile_dir" ]]; then
-        # Start server with profiling enabled
-        VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 VLLM_TORCH_PROFILER_DIR=$profile_dir \
-            vllm serve "${common_args_array[@]}" > "$vllm_log" 2>&1 &
-    else
-        # Start server without profiling
-        VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 \
-            vllm serve "${common_args_array[@]}" > "$vllm_log" 2>&1 &
-    fi
+    VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 vllm serve $MODEL \
+        --disable-log-requests \
+        --port 8004 \
+        --gpu-memory-utilization $gpu_memory_utilization \
+        --max-num-seqs $max_num_seqs \
+        --max-num-batched-tokens $max_num_batched_tokens \
+        --tensor-parallel-size $TP \
+        --enable-prefix-caching \
+        --load-format dummy \
+        --download-dir "$DOWNLOAD_DIR" \
+        --max-model-len $(( INPUT_LEN+OUTPUT_LEN )) > "$vllm_log" 2>&1 &
 
     # wait for 10 minutes...
     server_started=0
-    for i in {1..60}; do
+    for i in {1..60}; do  
         RESPONSE=$(curl -s -X GET "http://0.0.0.0:8004/health" -w "%{http_code}" -o /dev/stdout)
-        STATUS_CODE=$(echo "$RESPONSE" | tail -n 1)
+        STATUS_CODE=$(echo "$RESPONSE" | tail -n 1) 
         if [[ "$STATUS_CODE" -eq 200 ]]; then
             server_started=1
             break
@@ -100,7 +97,6 @@ start_server() {
             sleep 10
         fi
     done
-
     if (( ! server_started )); then
         echo "server did not start within 10 minutes. Please check server log at $vllm_log".
         return 1
@@ -118,11 +114,10 @@ run_benchmark() {
     echo "vllm_log: $vllm_log"
     echo
     rm -f $vllm_log
-    pkill -if vllm
+    pkill -f vllm
 
     echo "starting server..."
-    # Call start_server without a profile_dir to avoid profiling overhead
-    start_server $gpu_memory_utilization $max_num_seqs $max_num_batched_tokens $vllm_log ""
+    start_server $gpu_memory_utilization $max_num_seqs $max_num_batched_tokens $vllm_log
     result=$?
     if [[ "$result" -eq 1 ]]; then
         echo "server failed to start. gpu_memory_utilization:$gpu_memory_utilization, max_num_seqs:$max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
@@ -130,19 +125,17 @@ run_benchmark() {
         echo "server started."
     fi
     echo
-
+    
     echo "run benchmark test..."
     meet_latency_requirement=0
     # get a basic qps by using request-rate inf
     bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_inf.txt"
     prefix_len=$(( INPUT_LEN * MIN_CACHE_HIT_PCT / 100 ))
-    adjusted_input_len=$(( INPUT_LEN - prefix_len ))
-    # --profile flag is removed from this call
-    vllm bench serve \
+    python benchmarks/benchmark_serving.py \
         --backend vllm \
         --model $MODEL  \
         --dataset-name random \
-        --random-input-len $adjusted_input_len \
+        --random-input-len $INPUT_LEN \
         --random-output-len $OUTPUT_LEN \
         --ignore-eos \
         --disable-tqdm \
@@ -169,11 +162,11 @@ run_benchmark() {
             curl -X POST http://0.0.0.0:8004/reset_prefix_cache
             sleep 5
             bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_${request_rate}.txt"
-            vllm bench serve \
+            python benchmarks/benchmark_serving.py \
                 --backend vllm \
                 --model $MODEL  \
                 --dataset-name random \
-                --random-input-len $adjusted_input_len \
+                --random-input-len $INPUT_LEN \
                 --random-output-len $OUTPUT_LEN \
                 --ignore-eos \
                 --disable-tqdm \
@@ -202,7 +195,6 @@ run_benchmark() {
             best_max_num_seqs=$max_num_seqs
             best_num_batched_tokens=$max_num_batched_tokens
             best_goodput=$goodput
-            best_request_rate=$request_rate
         fi
     else
         echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens does not meet latency requirement ${MAX_LATENCY_ALLOWED_MS}"
@@ -211,7 +203,7 @@ run_benchmark() {
 
     echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"
 
-    pkill -if vllm
+    pkill vllm
     sleep 10
     printf '=%.0s' $(seq 1 20)
     return 0
@@ -224,8 +216,7 @@ read -r -a num_batched_tokens_list <<< "$NUM_BATCHED_TOKENS_LIST"
 gpu_memory_utilization=0.98
 find_gpu_memory_utilization=0
 while (( $(echo "$gpu_memory_utilization >= 0.9" | bc -l) )); do
-    # Pass empty string for profile_dir argument
-    start_server $gpu_memory_utilization "${num_seqs_list[-1]}" "${num_batched_tokens_list[-1]}" "$LOG_FOLDER/vllm_log_gpu_memory_utilization_$gpu_memory_utilization.log" ""
+    start_server $gpu_memory_utilization "${num_seqs_list[-1]}" "${num_batched_tokens_list[-1]}" "$LOG_FOLDER/vllm_log_gpu_memory_utilization_$gpu_memory_utilization.log"
     result=$?
     if [[ "$result" -eq 0 ]]; then
         find_gpu_memory_utilization=1
@@ -248,45 +239,6 @@ for num_seqs in "${num_seqs_list[@]}"; do
     done
 done
 echo "finish permutations"
+echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"
+echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput" >> "$RESULT"
 
-# =================================================================================
-# FINAL PROFILING RUN FOR THE BEST CONFIGURATION
-# =================================================================================
-if (( $(echo "$best_throughput > 0" | bc -l) )); then
-    echo
-    echo "Benchmark tuning finished. Now running profiling on the best configuration found..."
-    echo "Best config: max_num_seqs: $best_max_num_seqs, max_num_batched_tokens: $best_num_batched_tokens, throughput: $best_throughput"
-    echo
-
-    vllm_log="$LOG_FOLDER/vllm_log_BEST_PROFILE.txt"
-    bm_log="$LOG_FOLDER/bm_log_BEST_PROFILE.txt"
-
-    # Start server with the best params and profiling ENABLED
-    echo "Starting server for profiling..."
-    start_server $gpu_memory_utilization $best_max_num_seqs $best_num_batched_tokens "$vllm_log" "$PROFILE_PATH"
-
-    # Run benchmark with the best params and the --profile flag
-    echo "Running benchmark with profiling..."
-    prefix_len=$(( INPUT_LEN * MIN_CACHE_HIT_PCT / 100 ))
-    adjusted_input_len=$(( INPUT_LEN - prefix_len ))
-    vllm bench serve \
-        --backend vllm \
-        --model $MODEL \
-        --dataset-name random \
-        --random-input-len $adjusted_input_len \
-        --random-output-len $OUTPUT_LEN \
-        --ignore-eos \
-        --disable-tqdm \
-        --request-rate $best_request_rate \
-        --percentile-metrics ttft,tpot,itl,e2el \
-        --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
-        --num-prompts 100 \
-        --random-prefix-len $prefix_len \
-        --port 8004 \
-        --profile &> "$bm_log"
-else
-    echo "No configuration met the latency requirements. Skipping final profiling run."
-fi
-pkill -if vllm
-echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput, profile saved in: $PROFILE_PATH"
-echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput, profile saved in: $PROFILE_PATH" >> "$RESULT"

benchmarks/auto_tune/README.md

@@ -1,145 +0,0 @@
-# Automated vLLM Server Parameter Tuning
-
-This script automates the process of finding the optimal server parameter combination (`max-num-seqs` and `max-num-batched-tokens`) to maximize throughput for a vLLM server. It also supports additional constraints such as E2E latency and prefix cache hit rate.
-
-## Table of Contents
-
-- [Prerequisites](#prerequisites)
-- [Configuration](#configuration)
-- [How to Run](#how-to-run)
-- [Example Use Cases](#example-use-cases)
-- [Output](#output)
-- [How It Works](#how-it-works)
-
-## Prerequisites
-
-Before running the script, please ensure the following steps are completed:
-
-1. **Clone vLLM & Set Up Branch**: Clone the vLLM repository and check out to your desired branch.
-
-```bash
-git clone https://github.com/vllm-project/vllm.git
-cd vllm
-# git checkout <your-branch>
-```
-
-1. **Install Environment**: Install or update the correct running environment. For TPU usage, activate your `conda` environment and install the corresponding `torch` and `torch_xla` versions.
-
-2. **Model Configuration**: If you are using a customized model, ensure its configuration files are correctly placed and accessible.
-
-## Configuration
-
-You must set the following variables at the top of the script before execution.
-
-| Variable | Description | Example Value |
-| --- | --- | --- |
-| `BASE` | **Required.** The absolute path to the parent directory of your vLLM repository directory. | `"$HOME"` |
-| `MODEL` | **Required.** The Hugging Face model identifier to be served by vllm. | `"meta-llama/Llama-3.1-8B-Instruct"` |
-| `SYSTEM`| **Required.** The hardware you are running on. Choices: `TPU` or `GPU`. (For other systems, it might not support saving profiles) | `"TPU"` |
-| `TP` | **Required.** The tensor-parallelism size. | `1` |
-| `DOWNLOAD_DIR` | **Required.** Directory to download and load model weights from. | `""` (default download path) |
-| `INPUT_LEN` | **Required.** Request input length. | `4000` |
-| `OUTPUT_LEN` | **Required.** Request output length. | `16` |
-| `MAX_MODEL_LEN` | **Required.** Max model length. | `4096` |
-| `MIN_CACHE_HIT_PCT` | Prefix cache hit rate in percentage (0-100). Set to `0` to disable. | `60` |
-| `MAX_LATENCY_ALLOWED_MS` | The maximum allowed P99 end-to-end latency in milliseconds. Set to a very large number (e.g., `100000000000`) to effectively ignore the latency constraint. | `500` |
-| `NUM_SEQS_LIST` | A space-separated string of `max-num-seqs` values to test. | `"128 256"` |
-| `NUM_BATCHED_TOKENS_LIST` | A space-separated string of `max-num-batched-tokens` values to test. | `"1024 2048 4096"` |
-
-**Note**: The default `NUM_SEQS_LIST` and `NUM_BATCHED_TOKENS_LIST` are set for medium-sized inputs/outputs. For very short contexts (e.g., 20 input, 20 output tokens), you may need to test larger values for `max-num-seqs`.
-
-## How to Run
-
-1. **Configure**: Edit the script and set the variables in the [Configuration](#configuration) section.
-2. **Execute**: Run the script. Since the process can take a long time, it is highly recommended to use a terminal multiplexer like `tmux` or `screen` to prevent the script from stopping if your connection is lost.
-
-```bash
-cd <FOLDER_OF_THIS_SCRIPT>
-bash auto_tune.sh
-```
-
-    Please note that the `bash auto_tune.sh` command cannot contain full or partial path with keyword `vllm`, otherwise `pkill -f vllm` command will also kill this script itself.
-
-## Example Use Cases
-
-Here are a few examples of how to configure the script for different goals:
-
-### 1. Maximize Throughput (No Latency Constraint)
-
-- **Goal**: Find the best `max-num-seqs` and `max-num-batched-tokens` to get the highest possible throughput for 1800 input tokens and 20 output tokens.
-- **Configuration**:
-
-```bash
-INPUT_LEN=1800
-OUTPUT_LEN=20
-MAX_MODEL_LEN=2048
-MIN_CACHE_HIT_PCT=0
-MAX_LATENCY_ALLOWED_MS=100000000000 # A very large number
-```
-
-#### 2. Maximize Throughput with a Latency Requirement
-
-- **Goal**: Find the best server parameters when P99 end-to-end latency must be below 500ms.
-- **Configuration**:
-
-```bash
-INPUT_LEN=1800
-OUTPUT_LEN=20
-MAX_MODEL_LEN=2048
-MIN_CACHE_HIT_PCT=0
-MAX_LATENCY_ALLOWED_MS=500
-```
-
-#### 3. Maximize Throughput with Prefix Caching and Latency Requirements
-
-- **Goal**: Find the best server parameters assuming a 60% prefix cache hit rate and a latency requirement of 500ms.
-- **Configuration**:
-
-```bash
-INPUT_LEN=1800
-OUTPUT_LEN=20
-MAX_MODEL_LEN=2048
-MIN_CACHE_HIT_PCT=60
-MAX_LATENCY_ALLOWED_MS=500
-```
-
-## Output
-
-After the script finishes, you will find the results in a new, timestamped directory created inside `$BASE/auto-benchmark/`.
-
-- **Log Files**: The directory (`$BASE/auto-benchmark/YYYY_MM_DD_HH_MM/`) contains detailed logs for each run:
-    - `vllm_log_...txt`: The log output from the vLLM server for each parameter combination.
-    - `bm_log_...txt`: The log output from the `vllm bench serve` command for each benchmark run.
-
-- **Final Result Summary**: A file named `result.txt` is created in the log directory. It contains a summary of each tested combination and concludes with the overall best parameters found.
-
-```text
-# Example result.txt content
-hash:a1b2c3d4...
-max_num_seqs: 128, max_num_batched_tokens: 2048, request_rate: 10.0, e2el: 450.5, throughput: 9.8, goodput: 9.8
-max_num_seqs: 128, max_num_batched_tokens: 4096 does not meet latency requirement 500
-...
-best_max_num_seqs: 256, best_num_batched_tokens: 2048, best_throughput: 12.5, profile saved in: /home/user/vllm/auto-benchmark/2024_08_01_10_30/profile
-```
-
-  If it cannot find the best parameters, the final row will be `best_max_num_seqs: 0, best_num_batched_tokens: 0, best_throughput: 0`. This can be due to either the server not starting properly, or the latency requirement being too strict.
-
-- **Profiler Trace**: A directory named `profile` is created inside the log directory. It contains the profiler trace file (e.g., `.xplane.pb` for TPU or a `.json` trace for GPU) from the single best-performing run.
-
-## How It Works
-
-The script follows a systematic process to find the optimal parameters:
-
-1. **Find Max GPU Memory Utilization**: The script first determines the highest safe `gpu-memory-utilization` (starting from 0.98 and decreasing) that does not cause an Out-Of-Memory (OOM) error when launching the server. This ensures the benchmark runs use the maximum available memory without crashing.
-
-2. **Iterate and Benchmark**: It then enters a nested loop, iterating through every combination of `max-num-seqs` and `max-num-batched-tokens` provided in the configuration lists.
-
-3. **Latency-Aware Throughput Search**: For each parameter combination:
-    - The vLLM server is started.
-    - A benchmark is first run with an infinite request rate (`--request-rate inf`).
-    - If the resulting P99 E2E latency is within the `MAX_LATENCY_ALLOWED_MS` limit, this throughput is considered the maximum for this configuration.
-    - If the latency is too high, the script performs a search by iteratively decreasing the request rate until the latency constraint is met. This finds the highest sustainable throughput for the given parameters and latency requirement.
-
-4. **Track Best Result**: Throughout the process, the script tracks the parameter combination that has yielded the highest valid throughput so far.
-
-5. **Profile Collection**: For the best-performing run, the script saves the vLLM profiler output, which can be used for deep-dive performance analysis with tools like TensorBoard.

benchmarks/backend_request_func.py

@@ -31,7 +31,7 @@ class RequestFuncInput:
     model_name: Optional[str] = None
     logprobs: Optional[int] = None
     extra_body: Optional[dict] = None
-    multi_modal_content: Optional[dict | list[dict]] = None
+    multi_modal_content: Optional[dict] = None
     ignore_eos: bool = False
     language: Optional[str] = None
 
@@ -364,15 +364,7 @@ async def async_request_openai_chat_completions(
     ) as session:
         content = [{"type": "text", "text": request_func_input.prompt}]
         if request_func_input.multi_modal_content:
-            mm_content = request_func_input.multi_modal_content
-            if isinstance(mm_content, list):
-                content.extend(mm_content)
-            elif isinstance(mm_content, dict):
-                content.append(mm_content)
-            else:
-                raise TypeError(
-                    "multi_modal_content must be a dict or list[dict] for openai-chat"
-                )
+            content.append(request_func_input.multi_modal_content)
         payload = {
             "model": request_func_input.model_name
             if request_func_input.model_name
@@ -412,14 +404,8 @@ async def async_request_openai_chat_completions(
                         chunk_bytes = chunk_bytes.strip()
                         if not chunk_bytes:
                             continue
-                        chunk_bytes = chunk_bytes.decode("utf-8")
-                        # NOTE: SSE comments (often used as pings) start with a colon.
-                        # These are not JSON data payload and should be skipped.
-                        if chunk_bytes.startswith(":"):
-                            continue
-
-                        chunk = chunk_bytes.removeprefix("data: ")
 
+                        chunk = chunk_bytes.decode("utf-8").removeprefix("data: ")
                         if chunk != "[DONE]":
                             timestamp = time.perf_counter()
                             data = json.loads(chunk)
@@ -499,10 +485,7 @@ async def async_request_openai_audio(
             buffer.seek(0)
             return buffer
 
-        mm_audio = request_func_input.multi_modal_content
-        if not isinstance(mm_audio, dict) or "audio" not in mm_audio:
-            raise TypeError("multi_modal_content must be a dict containing 'audio'")
-        with to_bytes(*mm_audio["audio"]) as f:
+        with to_bytes(*request_func_input.multi_modal_content["audio"]) as f:
             form = aiohttp.FormData()
             form.add_field("file", f, content_type="audio/wav")
             for key, value in payload.items():

benchmarks/benchmark_dataset.py

@@ -52,7 +52,7 @@ class SampleRequest:
     prompt: Union[str, Any]
     prompt_len: int
     expected_output_len: int
-    multi_modal_data: Optional[Union[MultiModalDataDict, dict, list[dict]]] = None
+    multi_modal_data: Optional[Union[MultiModalDataDict, dict]] = None
     lora_request: Optional[LoRARequest] = None
 
 
@@ -324,9 +324,6 @@ class RandomDataset(BenchmarkDataset):
         input_low = int(real_input_len * (1 - range_ratio))
         input_high = int(real_input_len * (1 + range_ratio))
         output_low = int(output_len * (1 - range_ratio))
-        # Ensure the lower bound for output length is at least 1 to prevent
-        # sampling 0 tokens, which can cause request failures.
-        output_low = max(output_low, 1)
         output_high = int(output_len * (1 + range_ratio))
 
         # Add logging for debugging
@@ -352,12 +349,11 @@ class RandomDataset(BenchmarkDataset):
             # [1650, 939, 486] -> ['Ġcall', 'sh', 'ere']
             # To avoid uncontrolled change of the prompt length,
             # the encoded sequence is truncated before being decode again.
-            total_input_len = prefix_len + int(input_lens[i])
             re_encoded_sequence = tokenizer.encode(prompt, add_special_tokens=False)[
-                :total_input_len
+                : input_lens[i]
             ]
             prompt = tokenizer.decode(re_encoded_sequence)
-            total_input_len = len(re_encoded_sequence)
+            total_input_len = prefix_len + int(input_lens[i])
             requests.append(
                 SampleRequest(
                     prompt=prompt,
@@ -704,7 +700,6 @@ class HuggingFaceDataset(BenchmarkDataset):
         self,
         dataset_path: str,
         dataset_split: str,
-        no_stream: bool = False,
         dataset_subset: Optional[str] = None,
         **kwargs,
     ) -> None:
@@ -712,7 +707,6 @@ class HuggingFaceDataset(BenchmarkDataset):
 
         self.dataset_split = dataset_split
         self.dataset_subset = dataset_subset
-        self.load_stream = not no_stream
         self.load_data()
 
     def load_data(self) -> None:
@@ -721,7 +715,7 @@ class HuggingFaceDataset(BenchmarkDataset):
             self.dataset_path,
             name=self.dataset_subset,
             split=self.dataset_split,
-            streaming=self.load_stream,
+            streaming=True,
         )
         self.data = self.data.shuffle(seed=self.random_seed)
 

benchmarks/benchmark_latency.py

@@ -11,7 +11,6 @@ from typing import Any, Optional
 
 import numpy as np
 from tqdm import tqdm
-from typing_extensions import deprecated
 
 import vllm.envs as envs
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
@@ -35,10 +34,6 @@ def save_to_pytorch_benchmark_format(
         write_to_json(pt_file, pt_records)
 
 
-@deprecated(
-    "benchmark_latency.py is deprecated and will be removed in a "
-    "future version. Please use 'vllm bench latency' instead.",
-)
 def main(args: argparse.Namespace):
     print(args)
 

benchmarks/benchmark_serving.py

@@ -5,7 +5,8 @@ r"""Benchmark online serving throughput.
 On the server side, run one of the following commands:
     vLLM OpenAI API server
     vllm serve <your_model> \
-        --swap-space 16
+        --swap-space 16 \
+        --disable-log-requests
 
 On the client side, run:
     python benchmarks/benchmark_serving.py \
@@ -29,15 +30,14 @@ import os
 import random
 import time
 import warnings
-from collections.abc import Iterable
+from collections.abc import AsyncGenerator, Iterable
 from dataclasses import dataclass
 from datetime import datetime
-from typing import Any, Literal, Optional
+from typing import Any, Optional
 
 import numpy as np
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase
-from typing_extensions import deprecated
 
 from backend_request_func import (
     ASYNC_REQUEST_FUNCS,
@@ -73,7 +73,6 @@ from benchmark_dataset import (
     VisionArenaDataset,
 )
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
-from vllm.benchmarks.serve import get_request
 
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
 
@@ -108,6 +107,51 @@ class BenchmarkMetrics:
     percentiles_e2el_ms: list[tuple[float, float]]
 
 
+async def get_request(
+    input_requests: list[SampleRequest],
+    request_rate: float,
+    burstiness: float = 1.0,
+) -> AsyncGenerator[SampleRequest, None]:
+    """
+    Asynchronously generates requests at a specified rate
+    with OPTIONAL burstiness.
+
+    Args:
+        input_requests:
+            A list of input requests, each represented as a SampleRequest.
+        request_rate:
+            The rate at which requests are generated (requests/s).
+        burstiness (optional):
+            The burstiness factor of the request generation.
+            Only takes effect when request_rate is not inf.
+            Default value is 1, which follows a Poisson process.
+            Otherwise, the request intervals follow a gamma distribution.
+            A lower burstiness value (0 < burstiness < 1) results
+            in more bursty requests, while a higher burstiness value
+            (burstiness > 1) results in a more uniform arrival of requests.
+    """
+    input_requests: Iterable[SampleRequest] = iter(input_requests)
+
+    # Calculate scale parameter theta to maintain the desired request_rate.
+    assert burstiness > 0, (
+        f"A positive burstiness factor is expected, but given {burstiness}."
+    )
+    theta = 1.0 / (request_rate * burstiness)
+
+    for request in input_requests:
+        yield request
+
+        if request_rate == float("inf"):
+            # If the request rate is infinity, then we don't need to wait.
+            continue
+
+        # Sample the request interval from the gamma distribution.
+        # If burstiness is 1, it follows exponential distribution.
+        interval = np.random.gamma(shape=burstiness, scale=theta)
+        # The next request will be sent after the interval.
+        await asyncio.sleep(interval)
+
+
 def calculate_metrics(
     input_requests: list[SampleRequest],
     outputs: list[RequestFuncOutput],
@@ -246,9 +290,6 @@ async def benchmark(
     max_concurrency: Optional[int],
     lora_modules: Optional[Iterable[str]],
     extra_body: Optional[dict],
-    ramp_up_strategy: Optional[Literal["linear", "exponential"]] = None,
-    ramp_up_start_rps: Optional[int] = None,
-    ramp_up_end_rps: Optional[int] = None,
 ):
     if backend in ASYNC_REQUEST_FUNCS:
         request_func = ASYNC_REQUEST_FUNCS[backend]
@@ -263,14 +304,7 @@ async def benchmark(
         input_requests[0].multi_modal_data,
     )
 
-    assert (
-        test_mm_content is None
-        or isinstance(test_mm_content, dict)
-        or (
-            isinstance(test_mm_content, list)
-            and all(isinstance(item, dict) for item in test_mm_content)
-        )
-    ), "multi_modal_data must be a dict or list[dict]"
+    assert test_mm_content is None or isinstance(test_mm_content, dict)
     test_input = RequestFuncInput(
         model=model_id,
         model_name=model_name,
@@ -319,15 +353,7 @@ async def benchmark(
 
     distribution = "Poisson process" if burstiness == 1.0 else "Gamma distribution"
 
-    if ramp_up_strategy is not None:
-        print(
-            f"Traffic ramp-up strategy: {ramp_up_strategy}. Will increase "
-            f"RPS from {ramp_up_start_rps} to {ramp_up_end_rps} RPS over "
-            "the duration of the benchmark."
-        )
-    else:
-        print(f"Traffic request rate: {request_rate} RPS.")
-
+    print(f"Traffic request rate: {request_rate}")
     print(f"Burstiness factor: {burstiness} ({distribution})")
     print(f"Maximum request concurrency: {max_concurrency}")
 
@@ -347,34 +373,7 @@ async def benchmark(
 
     benchmark_start_time = time.perf_counter()
     tasks: list[asyncio.Task] = []
-
-    rps_change_events = []
-    last_int_rps = -1
-    if ramp_up_strategy is not None and ramp_up_start_rps is not None:
-        last_int_rps = ramp_up_start_rps
-        rps_change_events.append(
-            {
-                "rps": last_int_rps,
-                "timestamp": datetime.now().isoformat(),
-            }
-        )
-
-    async for request, current_request_rate in get_request(
-        input_requests,
-        request_rate,
-        burstiness,
-        ramp_up_strategy,
-        ramp_up_start_rps,
-        ramp_up_end_rps,
-    ):
-        if ramp_up_strategy is not None:
-            current_int_rps = int(current_request_rate)
-            if current_int_rps > last_int_rps:
-                timestamp = datetime.now().isoformat()
-                for rps_val in range(last_int_rps + 1, current_int_rps + 1):
-                    rps_change_events.append({"rps": rps_val, "timestamp": timestamp})
-                last_int_rps = current_int_rps
-
+    async for request in get_request(input_requests, request_rate, burstiness):
         prompt, prompt_len, output_len, mm_content = (
             request.prompt,
             request.prompt_len,
@@ -398,10 +397,27 @@ async def benchmark(
             ignore_eos=ignore_eos,
             extra_body=extra_body,
         )
-        task = limited_request_func(request_func_input=request_func_input, pbar=pbar)
-        tasks.append(asyncio.create_task(task))
+        tasks.append(
+            asyncio.create_task(
+                limited_request_func(request_func_input=request_func_input, pbar=pbar)
+            )
+        )
     outputs: list[RequestFuncOutput] = await asyncio.gather(*tasks)
 
+    if profile:
+        print("Stopping profiler...")
+        profile_input = RequestFuncInput(
+            model=model_id,
+            prompt=test_prompt,
+            api_url=base_url + "/stop_profile",
+            prompt_len=test_prompt_len,
+            output_len=test_output_len,
+            logprobs=logprobs,
+        )
+        profile_output = await request_func(request_func_input=profile_input)
+        if profile_output.success:
+            print("Profiler stopped")
+
     if pbar is not None:
         pbar.close()
 
@@ -419,10 +435,6 @@ async def benchmark(
 
     print("{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=50, c="="))
     print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
-    if max_concurrency is not None:
-        print("{:<40} {:<10}".format("Maximum request concurrency:", max_concurrency))
-    if request_rate != float("inf"):
-        print("{:<40} {:<10.2f}".format("Request rate configured (RPS):", request_rate))
     print("{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration))
     print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
     print("{:<40} {:<10}".format("Total generated tokens:", metrics.total_output))
@@ -454,7 +466,7 @@ async def benchmark(
         "total_input_tokens": metrics.total_input,
         "total_output_tokens": metrics.total_output,
         "request_throughput": metrics.request_throughput,
-        "request_goodput": metrics.request_goodput if goodput_config_dict else None,
+        "request_goodput:": metrics.request_goodput if goodput_config_dict else None,
         "output_throughput": metrics.output_throughput,
         "total_token_throughput": metrics.total_token_throughput,
         "input_lens": [output.prompt_len for output in outputs],
@@ -465,9 +477,6 @@ async def benchmark(
         "errors": [output.error for output in outputs],
     }
 
-    if rps_change_events:
-        result["rps_change_events"] = rps_change_events
-
     def process_one_metric(
         # E.g., "ttft"
         metric_attribute_name: str,
@@ -514,20 +523,6 @@ async def benchmark(
 
     print("=" * 50)
 
-    if profile:
-        print("Stopping profiler...")
-        profile_input = RequestFuncInput(
-            model=model_id,
-            prompt=test_prompt,
-            api_url=base_url + "/stop_profile",
-            prompt_len=test_prompt_len,
-            output_len=test_output_len,
-            logprobs=logprobs,
-        )
-        profile_output = await request_func(request_func_input=profile_input)
-        if profile_output.success:
-            print("Profiler stopped")
-
     return result
 
 
@@ -604,10 +599,6 @@ def save_to_pytorch_benchmark_format(
         write_to_json(pt_file, pt_records)
 
 
-@deprecated(
-    "benchmark_serving.py is deprecated and will be removed in a future "
-    "version. Please use 'vllm bench serve' instead.",
-)
 def main(args: argparse.Namespace):
     print(args)
     random.seed(args.seed)
@@ -619,26 +610,6 @@ def main(args: argparse.Namespace):
     tokenizer_id = args.tokenizer if args.tokenizer is not None else args.model
     tokenizer_mode = args.tokenizer_mode
 
-    # Validate ramp-up arguments
-    if args.ramp_up_strategy is not None:
-        if args.request_rate != float("inf"):
-            raise ValueError(
-                "When using ramp-up, do not specify --request-rate. "
-                "The request rate will be controlled by ramp-up parameters. "
-                "Please remove the --request-rate argument."
-            )
-        if args.ramp_up_start_rps is None or args.ramp_up_end_rps is None:
-            raise ValueError(
-                "When using --ramp-up-strategy, both --ramp-up-start-rps and "
-                "--ramp-up-end-rps must be specified"
-            )
-        if args.ramp_up_start_rps < 0 or args.ramp_up_end_rps < 0:
-            raise ValueError("Ramp-up start and end RPS must be non-negative")
-        if args.ramp_up_start_rps > args.ramp_up_end_rps:
-            raise ValueError("Ramp-up start RPS must be less than end RPS")
-        if args.ramp_up_strategy == "exponential" and args.ramp_up_start_rps == 0:
-            raise ValueError("For exponential ramp-up, the start RPS cannot be 0.")
-
     if args.base_url is not None:
         api_url = f"{args.base_url}{args.endpoint}"
         base_url = f"{args.base_url}"
@@ -746,7 +717,6 @@ def main(args: argparse.Namespace):
             dataset_subset=args.hf_subset,
             dataset_split=args.hf_split,
             random_seed=args.seed,
-            no_stream=args.no_stream,
         ).sample(
             num_requests=args.num_prompts,
             tokenizer=tokenizer,
@@ -832,9 +802,6 @@ def main(args: argparse.Namespace):
             max_concurrency=args.max_concurrency,
             lora_modules=args.lora_modules,
             extra_body=sampling_params,
-            ramp_up_strategy=args.ramp_up_strategy,
-            ramp_up_start_rps=args.ramp_up_start_rps,
-            ramp_up_end_rps=args.ramp_up_end_rps,
         )
     )
 
@@ -867,11 +834,6 @@ def main(args: argparse.Namespace):
         result_json["burstiness"] = args.burstiness
         result_json["max_concurrency"] = args.max_concurrency
 
-        if args.ramp_up_strategy is not None:
-            result_json["ramp_up_strategy"] = args.ramp_up_strategy
-            result_json["ramp_up_start_rps"] = args.ramp_up_start_rps
-            result_json["ramp_up_end_rps"] = args.ramp_up_end_rps
-
         # Merge with benchmark result
         result_json = {**result_json, **benchmark_result}
 
@@ -897,10 +859,7 @@ def main(args: argparse.Namespace):
             if args.max_concurrency is not None
             else ""
         )
-        if args.ramp_up_strategy is not None:
-            file_name = f"{backend}-ramp-up-{args.ramp_up_strategy}-{args.ramp_up_start_rps}qps-{args.ramp_up_end_rps}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json"  # noqa
-        else:
-            file_name = f"{backend}-{args.request_rate}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json"  # noqa
+        file_name = f"{backend}-{args.request_rate}qps{max_concurrency_str}-{base_model_id}-{current_dt}.json"  # noqa
         if args.result_filename:
             file_name = args.result_filename
         if args.result_dir:
@@ -955,11 +914,6 @@ def create_argument_parser():
         help="Path to the sharegpt/sonnet dataset. "
         "Or the huggingface dataset ID if using HF dataset.",
     )
-    parser.add_argument(
-        "--no-stream",
-        action="store_true",
-        help="Do not load the dataset in streaming mode.",
-    )
     parser.add_argument(
         "--max-concurrency",
         type=int,
@@ -1271,31 +1225,6 @@ def create_argument_parser():
         "script chooses a LoRA module at random.",
     )
 
-    parser.add_argument(
-        "--ramp-up-strategy",
-        type=str,
-        default=None,
-        choices=["linear", "exponential"],
-        help="The ramp-up strategy. This would be used to "
-        "ramp up the request rate from initial RPS to final "
-        "RPS rate (specified by --ramp-up-start-rps and --ramp-up-end-rps). "
-        "over the duration of the benchmark.",
-    )
-    parser.add_argument(
-        "--ramp-up-start-rps",
-        type=int,
-        default=None,
-        help="The starting request rate for ramp-up (RPS). "
-        "Needs to be specified when --ramp-up-strategy is used.",
-    )
-    parser.add_argument(
-        "--ramp-up-end-rps",
-        type=int,
-        default=None,
-        help="The ending request rate for ramp-up (RPS). "
-        "Needs to be specified when --ramp-up-strategy is used.",
-    )
-
     return parser
 
 

benchmarks/benchmark_serving_structured_output.py

@@ -4,7 +4,7 @@ r"""Benchmark online serving throughput with structured outputs.
 
 On the server side, run one of the following commands:
     (vLLM OpenAI API server)
-    vllm serve <your_model>
+    vllm serve <your_model> --disable-log-requests
 
 On the client side, run:
     python benchmarks/benchmark_serving_structured_output.py \
@@ -538,6 +538,20 @@ async def benchmark(
         )
     outputs: list[RequestFuncOutput] = await asyncio.gather(*tasks)
 
+    if profile:
+        print("Stopping profiler...")
+        profile_input = RequestFuncInput(
+            model=model_id,
+            prompt=test_request.prompt,
+            api_url=base_url + "/stop_profile",
+            prompt_len=test_request.prompt_len,
+            output_len=test_request.expected_output_len,
+            extra_body={test_request.structure_type: test_request.schema},
+        )
+        profile_output = await request_func(request_func_input=profile_input)
+        if profile_output.success:
+            print("Profiler stopped")
+
     if pbar is not None:
         pbar.close()
 
@@ -555,10 +569,6 @@ async def benchmark(
 
     print("{s:{c}^{n}}".format(s=" Serving Benchmark Result ", n=50, c="="))
     print("{:<40} {:<10}".format("Successful requests:", metrics.completed))
-    if max_concurrency is not None:
-        print("{:<40} {:<10}".format("Maximum request concurrency:", max_concurrency))
-    if request_rate != float("inf"):
-        print("{:<40} {:<10.2f}".format("Request rate configured (RPS):", request_rate))
     print("{:<40} {:<10.2f}".format("Benchmark duration (s):", benchmark_duration))
     print("{:<40} {:<10}".format("Total input tokens:", metrics.total_input))
     print("{:<40} {:<10}".format("Total generated tokens:", metrics.total_output))
@@ -656,20 +666,6 @@ async def benchmark(
 
     print("=" * 50)
 
-    if profile:
-        print("Stopping profiler...")
-        profile_input = RequestFuncInput(
-            model=model_id,
-            prompt=test_request.prompt,
-            api_url=base_url + "/stop_profile",
-            prompt_len=test_request.prompt_len,
-            output_len=test_request.expected_output_len,
-            extra_body={test_request.structure_type: test_request.schema},
-        )
-        profile_output = await request_func(request_func_input=profile_input)
-        if profile_output.success:
-            print("Profiler stopped")
-
     return result, ret
 
 

benchmarks/benchmark_throughput.py

@@ -15,7 +15,6 @@ import torch
 import uvloop
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerBase
-from typing_extensions import deprecated
 
 from benchmark_dataset import (
     AIMODataset,
@@ -98,7 +97,7 @@ def run_vllm(
         assert lora_requests is None, "BeamSearch API does not support LoRA"
         prompts = [request.prompt for request in requests]
         # output_len should be the same for all requests.
-        output_len = requests[0].expected_output_len
+        output_len = requests[0][2]
         for request in requests:
             assert request.expected_output_len == output_len
         start = time.perf_counter()
@@ -168,8 +167,7 @@ async def run_vllm_async(
     from vllm import SamplingParams
 
     async with build_async_engine_client_from_engine_args(
-        engine_args,
-        disable_frontend_multiprocessing=disable_frontend_multiprocessing,
+        engine_args, disable_frontend_multiprocessing
     ) as llm:
         model_config = await llm.get_model_config()
         assert all(
@@ -358,7 +356,6 @@ def get_requests(args, tokenizer):
     elif args.dataset_name == "burstgpt":
         dataset_cls = BurstGPTDataset
     elif args.dataset_name == "hf":
-        common_kwargs["no_stream"] = args.no_stream
         if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
             dataset_cls = VisionArenaDataset
             common_kwargs["dataset_subset"] = None
@@ -383,10 +380,6 @@ def get_requests(args, tokenizer):
     return dataset_cls(**common_kwargs).sample(**sample_kwargs)
 
 
-@deprecated(
-    "benchmark_throughput.py is deprecated and will be removed in a "
-    "future version. Please use 'vllm bench throughput' instead.",
-)
 def main(args: argparse.Namespace):
     if args.seed is None:
         args.seed = 0
@@ -617,11 +610,6 @@ def create_argument_parser():
         help="Name of the dataset to benchmark on.",
         default="sharegpt",
     )
-    parser.add_argument(
-        "--no-stream",
-        action="store_true",
-        help="Do not load the dataset in streaming mode.",
-    )
     parser.add_argument(
         "--dataset",
         type=str,

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -19,7 +19,7 @@ from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     w8a8_block_fp8_matmul,
 )
-from vllm.utils import FlexibleArgumentParser, cdiv
+from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
 DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]
@@ -117,9 +117,14 @@ def bench_fp8(
     scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
     scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
 
-    block_scale_a = torch.rand((m, cdiv(k, 128)), device="cuda", dtype=torch.float32)
+    def ceil_div(x: int, y: int) -> int:
+        return (x + y - 1) // y
+
+    block_scale_a = torch.rand(
+        (m, ceil_div(k, 128)), device="cuda", dtype=torch.float32
+    )
     block_scale_b = torch.rand(
-        cdiv(k, 128), cdiv(n, 128), device="cuda", dtype=torch.float32
+        ceil_div(k, 128), ceil_div(n, 128), device="cuda", dtype=torch.float32
     )
     block_scale_a_M_major = block_scale_a.t().contiguous().t()
     block_scale_b_K_major = block_scale_b.t().contiguous().t()

benchmarks/disagg_benchmarks/disagg_overhead_benchmark.sh

@@ -3,7 +3,7 @@
 # benchmark the overhead of disaggregated prefill.
 # methodology:
 # - send all request to prefill vLLM instance. It will buffer KV cache.
-# - then send all request to decode instance.
+# - then send all request to decode instance. 
 # - The TTFT of decode instance is the overhead.
 
 set -ex
@@ -12,8 +12,6 @@ kill_gpu_processes() {
   # kill all processes on GPU.
   pgrep pt_main_thread | xargs -r kill -9
   pgrep python3 | xargs -r kill -9
-  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
-  pgrep VLLM | xargs -r kill -9
   sleep 10
 
   # remove vllm config file
@@ -63,7 +61,7 @@ benchmark() {
     --gpu-memory-utilization 0.6 \
     --kv-transfer-config \
     '{"kv_connector":"PyNcclConnector","kv_role":"kv_producer","kv_rank":0,"kv_parallel_size":2,"kv_buffer_size":5e9}' &
-
+    
 
   CUDA_VISIBLE_DEVICES=1 python3 \
     -m vllm.entrypoints.openai.api_server \
@@ -78,38 +76,38 @@ benchmark() {
   wait_for_server 8200
 
   # let the prefill instance finish prefill
-  vllm bench serve \
-    --backend vllm \
-    --model $model \
-    --dataset-name $dataset_name \
-    --dataset-path $dataset_path \
-    --sonnet-input-len $input_len \
-    --sonnet-output-len "$output_len" \
-    --sonnet-prefix-len $prefix_len \
-    --num-prompts $num_prompts \
-    --port 8100 \
-    --save-result \
-    --result-dir $results_folder \
-    --result-filename disagg_prefill_tp1.json \
-    --request-rate "inf"
+  python3 ../benchmark_serving.py \
+          --backend vllm \
+          --model $model \
+          --dataset-name $dataset_name \
+          --dataset-path $dataset_path \
+          --sonnet-input-len $input_len \
+          --sonnet-output-len "$output_len" \
+          --sonnet-prefix-len $prefix_len \
+          --num-prompts $num_prompts \
+          --port 8100 \
+          --save-result \
+          --result-dir $results_folder \
+          --result-filename disagg_prefill_tp1.json \
+          --request-rate "inf"
 
 
   # send the request to decode.
   # The TTFT of this command will be the overhead of disagg prefill impl.
-  vllm bench serve \
-    --backend vllm \
-    --model $model \
-    --dataset-name $dataset_name \
-    --dataset-path $dataset_path \
-    --sonnet-input-len $input_len \
-    --sonnet-output-len "$output_len" \
-    --sonnet-prefix-len $prefix_len \
-    --num-prompts $num_prompts \
-    --port 8200 \
-    --save-result \
-    --result-dir $results_folder \
-    --result-filename disagg_prefill_tp1_overhead.json \
-    --request-rate "$qps"
+  python3 ../benchmark_serving.py \
+          --backend vllm \
+          --model $model \
+          --dataset-name $dataset_name \
+          --dataset-path $dataset_path \
+          --sonnet-input-len $input_len \
+          --sonnet-output-len "$output_len" \
+          --sonnet-prefix-len $prefix_len \
+          --num-prompts $num_prompts \
+          --port 8200 \
+          --save-result \
+          --result-dir $results_folder \
+          --result-filename disagg_prefill_tp1_overhead.json \
+          --request-rate "$qps"
   kill_gpu_processes
 
 }

benchmarks/disagg_benchmarks/disagg_performance_benchmark.sh

@@ -18,8 +18,6 @@ kill_gpu_processes() {
   # kill all processes on GPU.
   pgrep pt_main_thread | xargs -r kill -9
   pgrep python3 | xargs -r kill -9
-  # vLLM now names the process with VLLM prefix after https://github.com/vllm-project/vllm/pull/21445
-  pgrep VLLM | xargs -r kill -9
   for port in 8000 8100 8200; do lsof -t -i:$port | xargs -r kill -9; done
   sleep 1
 }
@@ -60,7 +58,7 @@ launch_chunked_prefill() {
 
 
 launch_disagg_prefill() {
-  model="meta-llama/Meta-Llama-3.1-8B-Instruct"
+  model="meta-llama/Meta-Llama-3.1-8B-Instruct" 
   # disagg prefill
   CUDA_VISIBLE_DEVICES=0 python3 \
     -m vllm.entrypoints.openai.api_server \
@@ -99,20 +97,20 @@ benchmark() {
   output_len=$2
   tag=$3
 
-  vllm bench serve \
-    --backend vllm \
-    --model $model \
-    --dataset-name $dataset_name \
-    --dataset-path $dataset_path \
-    --sonnet-input-len $input_len \
-    --sonnet-output-len "$output_len" \
-    --sonnet-prefix-len $prefix_len \
-    --num-prompts $num_prompts \
-    --port 8000 \
-    --save-result \
-    --result-dir $results_folder \
-    --result-filename "$tag"-qps-"$qps".json \
-    --request-rate "$qps"
+  python3 ../benchmark_serving.py \
+          --backend vllm \
+          --model $model \
+          --dataset-name $dataset_name \
+          --dataset-path $dataset_path \
+          --sonnet-input-len $input_len \
+          --sonnet-output-len "$output_len" \
+          --sonnet-prefix-len $prefix_len \
+          --num-prompts $num_prompts \
+          --port 8000 \
+          --save-result \
+          --result-dir $results_folder \
+          --result-filename "$tag"-qps-"$qps".json \
+          --request-rate "$qps"
 
   sleep 2
 }

benchmarks/kernels/bench_fp8_gemm.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import argparse
 import copy
 import itertools

benchmarks/kernels/bench_nvfp4_gemm.py

@@ -1,141 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import argparse
-import copy
-import itertools
-
-import torch
-from weight_shapes import WEIGHT_SHAPES
-
-from vllm import _custom_ops as ops
-from vllm.platforms import current_platform
-from vllm.scalar_type import scalar_types
-from vllm.triton_utils import triton
-
-if not current_platform.has_device_capability(100):
-    raise RuntimeError("NVFP4 requires compute capability of 10.0 (Blackwell)")
-
-
-FLOAT4_E2M1_MAX = scalar_types.float4_e2m1f.max()
-FLOAT8_E4M3_MAX = torch.finfo(torch.float8_e4m3fn).max
-
-PROVIDER_CFGS = {
-    "torch-bf16": dict(enabled=True),
-    "nvfp4": dict(no_a_quant=False, enabled=True),
-    "nvfp4-noquant": dict(no_a_quant=True, enabled=True),
-}
-
-_enabled = [k for k, v in PROVIDER_CFGS.items() if v["enabled"]]
-
-
-def _quant_weight_nvfp4(b: torch.Tensor, device: str):
-    # Compute global scale for weight
-    b_amax = torch.abs(b).max().to(torch.float32)
-    b_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / b_amax
-    b_fp4, scale_b_fp4 = ops.scaled_fp4_quant(b, b_global_scale)
-    return b_fp4, scale_b_fp4, b_global_scale
-
-
-def build_nvfp4_runner(cfg, a, b, dtype, device):
-    b_fp4, scale_b_fp4, b_global_scale = _quant_weight_nvfp4(b, device)
-
-    # Compute global scale for activation
-    # NOTE: This is generally provided ahead-of-time by the model checkpoint.
-    a_amax = torch.abs(a).max().to(torch.float32)
-    a_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / a_amax
-
-    # Alpha for the GEMM operation
-    alpha = 1.0 / (a_global_scale * b_global_scale)
-
-    if cfg["no_a_quant"]:
-        # Pre-quantize activation
-        a_fp4, scale_a_fp4 = ops.scaled_fp4_quant(a, a_global_scale)
-
-        def run():
-            return ops.cutlass_scaled_fp4_mm(
-                a_fp4, b_fp4, scale_a_fp4, scale_b_fp4, alpha, dtype
-            )
-
-        return run
-
-    # Quantize activation on-the-fly
-    def run():
-        a_fp4, scale_a_fp4 = ops.scaled_fp4_quant(a, a_global_scale)
-        return ops.cutlass_scaled_fp4_mm(
-            a_fp4, b_fp4, scale_a_fp4, scale_b_fp4, alpha, dtype
-        )
-
-    return run
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=["batch_size"],
-        x_vals=[1, 16, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384],
-        x_log=False,
-        line_arg="provider",
-        line_vals=_enabled,
-        line_names=_enabled,
-        ylabel="TFLOP/s (larger is better)",
-        plot_name="BF16 vs NVFP4 GEMMs",
-        args={},
-    )
-)
-def benchmark(batch_size, provider, N, K):
-    M = batch_size
-    device = "cuda"
-    dtype = torch.bfloat16
-
-    a = torch.randn((M, K), device=device, dtype=dtype)
-    b = torch.randn((N, K), device=device, dtype=dtype)
-
-    quantiles = [0.5, 0.2, 0.8]
-
-    if provider == "torch-bf16":
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: torch.nn.functional.linear(a, b), quantiles=quantiles
-        )
-    else:
-        cfg = PROVIDER_CFGS[provider]
-        run_quant = build_nvfp4_runner(cfg, a, b, dtype, device)
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: run_quant(), quantiles=quantiles
-        )
-
-    to_tflops = lambda t_ms: (2 * M * N * K) * 1e-12 / (t_ms * 1e-3)
-    return to_tflops(ms), to_tflops(max_ms), to_tflops(min_ms)
-
-
-def prepare_shapes(args):
-    out = []
-    for model, tp_size in itertools.product(args.models, args.tp_sizes):
-        for KN, tp_dim in copy.deepcopy(WEIGHT_SHAPES[model]):
-            KN[tp_dim] //= tp_size
-            KN.append(model)
-            out.append(KN)
-    return out
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=["meta-llama/Llama-3.1-8B-Instruct"],
-        choices=list(WEIGHT_SHAPES.keys()),
-    )
-    parser.add_argument("--tp-sizes", nargs="+", type=int, default=[1])
-    args = parser.parse_args()
-
-    for K, N, model in prepare_shapes(args):
-        print(f"{model}, N={N} K={K}, BF16 vs NVFP4 GEMMs TFLOP/s:")
-        benchmark.run(
-            print_data=True,
-            show_plots=True,
-            save_path=f"bench_nvfp4_res_n{N}_k{K}",
-            N=N,
-            K=K,
-        )
-
-    print("Benchmark finished!")

benchmarks/kernels/bench_per_token_quant_fp8.py

@@ -1,98 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import itertools
-from typing import Callable
-
-import torch
-
-from vllm import _custom_ops as ops
-from vllm.config import CompilationConfig, VllmConfig, set_current_vllm_config
-from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
-from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
-from vllm.triton_utils import triton
-
-
-# TODO(luka): use standalone_compile utility
-def with_dyn_arg(fn: Callable, arg_index: int, dim_index: int):
-    def inner(*args):
-        torch._dynamo.mark_dynamic(args[arg_index], dim_index)
-        return fn(*args)
-
-    return inner
-
-
-torch._dynamo.config.recompile_limit = 8888
-compilation_config = CompilationConfig(custom_ops=["none"])
-with set_current_vllm_config(VllmConfig(compilation_config=compilation_config)):
-    torch_per_token_quant_fp8 = torch.compile(
-        QuantFP8(False, GroupShape.PER_TOKEN),
-        fullgraph=True,
-        dynamic=False,  # recompile for different shapes
-    )
-
-    # First dim is explicitly dynamic to simulate vLLM usage
-    torch_per_token_quant_fp8 = with_dyn_arg(torch_per_token_quant_fp8, 0, 0)
-
-
-def cuda_per_token_quant_fp8(
-    input: torch.Tensor,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    return ops.scaled_fp8_quant(input)
-
-
-def calculate_diff(batch_size: int, seq_len: int):
-    """Calculate difference between Triton and CUDA implementations."""
-    device = torch.device("cuda")
-    x = torch.rand((batch_size * seq_len, 4096), dtype=torch.float16, device=device)
-
-    torch_out, torch_scale = torch_per_token_quant_fp8(x)
-    cuda_out, cuda_scale = cuda_per_token_quant_fp8(x)
-
-    if torch.allclose(
-        cuda_out.to(torch.float32), torch_out.to(torch.float32), rtol=1e-3, atol=1e-5
-    ) and torch.allclose(cuda_scale, torch_scale, rtol=1e-3, atol=1e-5):
-        print("✅ All implementations match")
-    else:
-        print("❌ Implementations differ")
-
-
-batch_size_range = [1, 16, 32, 64, 128]
-seq_len_range = [1, 16, 64, 128, 256, 512, 1024, 2048, 4096]
-
-configs = list(itertools.product(batch_size_range, seq_len_range))
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=["batch_size", "seq_len"],
-        x_vals=configs,
-        line_arg="provider",
-        line_vals=["torch", "cuda"],
-        line_names=["Torch", "CUDA"],
-        styles=[("blue", "-"), ("green", "-")],
-        ylabel="us",
-        plot_name="per-token-dynamic-quant-fp8-performance",
-        args={},
-    )
-)
-def benchmark_quantization(batch_size, seq_len, provider):
-    dtype = torch.float16
-    device = torch.device("cuda")
-
-    x = torch.randn(batch_size * seq_len, 4096, device=device, dtype=dtype)
-
-    quantiles = [0.5, 0.2, 0.8]
-
-    if provider == "torch":
-        fn = lambda: torch_per_token_quant_fp8(x.clone())
-    elif provider == "cuda":
-        fn = lambda: cuda_per_token_quant_fp8(x.clone())
-
-    ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(fn, quantiles=quantiles)
-
-    return 1000 * ms, 1000 * max_ms, 1000 * min_ms
-
-
-if __name__ == "__main__":
-    calculate_diff(batch_size=4, seq_len=4096)
-    benchmark_quantization.run(print_data=True)

benchmarks/kernels/benchmark_bitblas.py

@@ -3,8 +3,6 @@
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 
-from packaging import version
-
 from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
     MINIMUM_BITBLAS_VERSION,
 )
@@ -12,7 +10,7 @@ from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
 try:
     import bitblas
 
-    if version.parse(bitblas.__version__) < version.parse(MINIMUM_BITBLAS_VERSION):
+    if bitblas.__version__ < MINIMUM_BITBLAS_VERSION:
         raise ImportError(
             "bitblas version is wrong. Please "
             f"install bitblas>={MINIMUM_BITBLAS_VERSION}"

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -113,7 +113,6 @@ def bench_run(
         w2_scale: torch.Tensor,
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
-        per_act_token: bool,
         num_repeats: int,
     ):
         for _ in range(num_repeats):
@@ -125,8 +124,7 @@ def bench_run(
                 topk_ids,
                 w1_scale,
                 w2_scale,
-                per_act_token,
-                a1_scale=None,
+                a1_scale=a_scale,
             )
 
     def run_cutlass_from_graph(
@@ -150,8 +148,7 @@ def bench_run(
                 topk_ids,
                 w1_scale,
                 w2_scale,
-                per_act_token,
-                a1_scale=None,
+                a1_scale=a_scale,
             )
 
     def run_triton_from_graph(
@@ -230,7 +227,6 @@ def bench_run(
         "w2_q": w2_q,
         "w1_scale": w1_scale,
         "w2_scale": w2_scale,
-        "per_act_token": per_act_token,
         # cuda graph params
         "cutlass_graph": cutlass_graph,
         "triton_graph": triton_graph,
@@ -291,13 +287,12 @@ def bench_run(
         w2_scale,
         topk_weights,
         topk_ids,
-        per_act_token,
         num_warmup,
     )
 
     results.append(
         benchmark.Timer(
-            stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, per_act_token, num_runs)",  # noqa: E501
+            stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, num_runs)",  # noqa: E501
             globals=globals,
             label=label,
             sub_label=sub_label,

benchmarks/kernels/benchmark_machete.py

@@ -234,10 +234,8 @@ def marlin_create_bench_fn(bt: BenchmarkTensors) -> Callable:
 
         fn = lambda: ops.gptq_marlin_gemm(
             a=bt.a,
-            c=None,
             b_q_weight=w_q,
             b_scales=w_s,
-            global_scale=None,
             b_zeros=w_zp,
             g_idx=g_idx,
             perm=sort_indices,

benchmarks/kernels/benchmark_marlin.py

@@ -22,16 +22,8 @@ from vllm.model_executor.layers.quantization.utils.marlin_utils import (
     MARLIN_SUPPORTED_GROUP_SIZES,
     query_marlin_supported_quant_types,
 )
-from vllm.model_executor.layers.quantization.utils.marlin_utils_fp4 import (
-    FP4_MARLIN_SUPPORTED_GROUP_SIZES,
-    rand_marlin_weight_fp4_like,
-)
-from vllm.model_executor.layers.quantization.utils.marlin_utils_fp8 import (
-    marlin_quant_fp8_torch,
-)
 from vllm.model_executor.layers.quantization.utils.marlin_utils_test import (
     MarlinWorkspace,
-    awq_marlin_quantize,
     marlin_quantize,
 )
 from vllm.model_executor.layers.quantization.utils.marlin_utils_test_24 import (
@@ -43,7 +35,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
     quantize_weights,
     sort_weights,
 )
-from vllm.scalar_type import ScalarType, scalar_types
+from vllm.scalar_type import ScalarType
 from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = ["meta-llama/Llama-2-7b-hf/TP1"]
@@ -65,144 +57,80 @@ def bench_run(
     size_n: int,
 ):
     label = "Quant Matmul"
+
     sub_label = "{}, act={} k_full={}, q={}, g={}, MKN=({}x{}x{})".format(
         model, act_order, is_k_full, str(quant_type), group_size, size_m, size_k, size_n
     )
+
     print(f"Testing: {sub_label}")
 
     a = torch.randn(size_m, size_k).to(torch.half).cuda()
     b = torch.rand(size_k, size_n).to(torch.half).cuda()
-    has_zp = quant_type in [scalar_types.uint4, scalar_types.uint8]
-    if act_order and (group_size == -1 or group_size == size_k or has_zp):
-        return
-    if size_k % group_size != 0:
-        return
 
-    marlin_24_supported = (
-        quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
-        and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES
-    )
-    repack_supported = (
-        quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
-        and group_size in MARLIN_SUPPORTED_GROUP_SIZES
-    )
-    allspark_supported = (
-        quant_type in ALLSPARK_SUPPORTED_QUANT_TYPES
-        and group_size == -1
-        and not act_order
-        and is_k_full
-    )
-
-    def gen_marlin_params():
-        # Marlin quant
-        marlin_g_idx = marlin_sort_indices = marlin_zp = marlin_s2 = None
-        if quant_type == scalar_types.float4_e2m1f:
-            if group_size != 16 or act_order:
-                return
-            marlin_w_ref, marlin_q_w, marlin_s, marlin_s2 = rand_marlin_weight_fp4_like(
-                b.T, group_size
-            )
-        elif quant_type == scalar_types.float8_e4m3fn:
-            if group_size not in [-1, 128] or act_order:
-                return
-            marlin_w_ref, marlin_q_w, marlin_s = marlin_quant_fp8_torch(b.T, group_size)
-        elif group_size == 16:
-            return
-        elif has_zp:
-            marlin_w_ref, marlin_q_w, marlin_s, marlin_zp = awq_marlin_quantize(
-                b, quant_type, group_size
-            )
-        else:
-            marlin_w_ref, marlin_q_w, marlin_s, marlin_g_idx, marlin_sort_indices, _ = (
-                marlin_quantize(b, quant_type, group_size, act_order)
-            )
-        return (
-            marlin_w_ref,
-            marlin_q_w,
-            marlin_s,
-            marlin_s2,
-            marlin_zp,
-            marlin_g_idx,
-            marlin_sort_indices,
-        )
-
-    def gen_marlin_24_params():
-        marlin_24_w_ref = marlin_24_q_w_comp = marlin_24_meta = marlin_24_s = None
-        if marlin_24_supported:
-            (marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s) = (
-                marlin_24_quantize(b, quant_type, group_size)
-            )
-        return (marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s)
-
-    def gen_repack_params():
-        q_w_gptq = None
-        repack_sort_indices = None
-        if repack_supported:
-            (w_ref, q_w, s, g_idx, rand_perm) = gptq_quantize_weights(
-                b, quant_type, group_size, act_order
-            )
-            q_w_gptq = gptq_pack(q_w, quant_type.size_bits, size_k, size_n)
-
-            # For act_order, sort the "weights" and "g_idx"
-            # so that group ids are increasing
-            repack_sort_indices = torch.empty(0, dtype=torch.int, device=b.device)
-            if act_order:
-                (q_w, g_idx, repack_sort_indices) = sort_weights(q_w, g_idx)
-        return q_w_gptq, repack_sort_indices
-
-    def gen_allspark_params():
-        qw_reorder = s_reorder = zp_reorder = sm_count = sm_version = (
-            CUBLAS_M_THRESHOLD
-        ) = None
-        nonlocal allspark_supported
-        if allspark_supported:
-            properties = torch.cuda.get_device_properties(b.device.index)
-            sm_count = properties.multi_processor_count
-            sm_version = properties.major * 10 + properties.minor
-
-            supported_arch = sm_version >= 80 and sm_version < 90
-            allspark_supported = allspark_supported and supported_arch
-            if supported_arch:
-                w_ref, qw, s, zp = quantize_weights(b, quant_type, group_size, has_zp)
-                qw = qw.to(torch.uint8)
-
-                qw_reorder, s_reorder, zp_reorder = ops.allspark_repack_weight(
-                    qw, s, zp, has_zp
-                )
-                CUBLAS_M_THRESHOLD = ALLSPARK_AMPERE_M_CUBLAS_THRESHOLD
-        return (
-            qw_reorder,
-            s_reorder,
-            zp_reorder,
-            sm_count,
-            sm_version,
-            CUBLAS_M_THRESHOLD,
-        )
+    a_tmp = torch.zeros(size_m, size_k).to(torch.half).cuda()
 
+    # Marlin quant
     (
         marlin_w_ref,
         marlin_q_w,
         marlin_s,
-        marlin_s2,
-        marlin_zp,
         marlin_g_idx,
         marlin_sort_indices,
-    ) = gen_marlin_params()
-    marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s = (
-        gen_marlin_24_params()
+        marlin_rand_perm,
+    ) = marlin_quantize(b, quant_type, group_size, act_order)
+
+    # Marlin_24 quant
+    (marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s) = (
+        marlin_24_quantize(b, quant_type, group_size)
     )
-    q_w_gptq, repack_sort_indices = gen_repack_params()
-    qw_reorder, s_reorder, zp_reorder, sm_count, sm_version, CUBLAS_M_THRESHOLD = (
-        gen_allspark_params()
+
+    marlin_zp = torch.empty(0, dtype=torch.int, device=b.device)
+
+    # GPTQ quant
+    (w_ref, q_w, s, g_idx, rand_perm) = gptq_quantize_weights(
+        b, quant_type, group_size, act_order
     )
+    q_w_gptq = gptq_pack(q_w, quant_type.size_bits, size_k, size_n)
+
+    # For act_order, sort the "weights" and "g_idx"
+    # so that group ids are increasing
+    repack_sort_indices = torch.empty(0, dtype=torch.int, device=b.device)
+    if act_order:
+        (q_w, g_idx, repack_sort_indices) = sort_weights(q_w, g_idx)
 
     # Prepare
     marlin_workspace = MarlinWorkspace(
         size_n, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL
     )
+
     marlin_24_workspace = MarlinWorkspace(
         size_n, GPTQ_MARLIN_24_MIN_THREAD_N, GPTQ_MARLIN_24_MAX_PARALLEL
     )
+    marlin_zp = torch.zeros_like(marlin_s, dtype=torch.int)
+
+    # AllSpark W8A16 quant
+    as_supported_case = (
+        quant_type in ALLSPARK_SUPPORTED_QUANT_TYPES
+        and group_size == -1
+        and not act_order
+        and is_k_full
+    )
+    if as_supported_case:
+        properties = torch.cuda.get_device_properties(b.device.index)
+        sm_count = properties.multi_processor_count
+        sm_version = properties.major * 10 + properties.minor
+
+        supported_arch = sm_version >= 80 and sm_version < 90
+        as_supported_case = as_supported_case and supported_arch
+        if supported_arch:
+            has_zp = False
+            w_ref, qw, s, zp = quantize_weights(b, quant_type, group_size, has_zp)
+            qw = qw.to(torch.uint8)
+
+            qw_reorder, s_reorder, zp_reorder = ops.allspark_repack_weight(
+                qw, s, zp, has_zp
+            )
+            CUBLAS_M_THRESHOLD = ALLSPARK_AMPERE_M_CUBLAS_THRESHOLD
 
     globals = {
         # Gen params
@@ -212,14 +140,15 @@ def bench_run(
         "size_n": size_n,
         "size_k": size_k,
         "a": a,
+        "a_tmp": a_tmp,
         # Marlin params
         "marlin_w_ref": marlin_w_ref,
         "marlin_q_w": marlin_q_w,
         "marlin_s": marlin_s,
-        "marlin_s2": marlin_s2,
         "marlin_zp": marlin_zp,
         "marlin_g_idx": marlin_g_idx,
         "marlin_sort_indices": marlin_sort_indices,
+        "marlin_rand_perm": marlin_rand_perm,
         "marlin_workspace": marlin_workspace,
         "is_k_full": is_k_full,
         # Marlin_24 params
@@ -232,12 +161,12 @@ def bench_run(
         "q_w_gptq": q_w_gptq,
         "repack_sort_indices": repack_sort_indices,
         # AllSpark W8A16 params
-        "qw_reorder": qw_reorder,
-        "s_reorder": s_reorder,
-        "zp_reorder": zp_reorder,
-        "sm_count": sm_count,
-        "sm_version": sm_version,
-        "CUBLAS_M_THRESHOLD": CUBLAS_M_THRESHOLD,
+        "qw_reorder": qw_reorder if as_supported_case else None,
+        "s_reorder": s_reorder if as_supported_case else None,
+        "zp_reorder": zp_reorder if as_supported_case else None,
+        "sm_count": sm_count if as_supported_case else None,
+        "sm_version": sm_version if as_supported_case else None,
+        "CUBLAS_M_THRESHOLD": CUBLAS_M_THRESHOLD if as_supported_case else None,
         # Kernels
         "gptq_marlin_gemm": ops.gptq_marlin_gemm,
         "gptq_marlin_24_gemm": ops.gptq_marlin_24_gemm,
@@ -248,7 +177,7 @@ def bench_run(
     min_run_time = 1
 
     # Warmup pytorch
-    for _ in range(5):
+    for i in range(5):
         torch.matmul(a, marlin_w_ref)
 
     results.append(
@@ -263,17 +192,17 @@ def bench_run(
 
     results.append(
         benchmark.Timer(
-            stmt="output = gptq_marlin_gemm(a, None, marlin_q_w, marlin_s, marlin_s2, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, False, False)",  # noqa: E501
+            stmt="output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, False, False)",  # noqa: E501
             globals=globals,
             label=label,
             sub_label=sub_label,
-            description="gptq_marlin_gemm",
+            description="gptq_marlin_gemm_fp16",
         ).blocked_autorange(min_run_time=min_run_time)
     )
 
     results.append(
         benchmark.Timer(
-            stmt="output = gptq_marlin_gemm(a, None, marlin_q_w, marlin_s, marlin_s2, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, True, False)",  # noqa: E501
+            stmt="output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_zp, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, quant_type, size_m, size_n, size_k, is_k_full, False, True, False)",  # noqa: E501
             globals=globals,
             label=label,
             sub_label=sub_label,
@@ -281,7 +210,10 @@ def bench_run(
         ).blocked_autorange(min_run_time=min_run_time)
     )
 
-    if marlin_24_supported:
+    if (
+        quant_type in GPTQ_MARLIN_24_SUPPORTED_QUANT_TYPES
+        and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES
+    ):
         results.append(
             benchmark.Timer(
                 stmt="output = gptq_marlin_24_gemm(a, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s, marlin_24_workspace.scratch, quant_type, size_m, size_n, size_k)",  # noqa: E501
@@ -292,18 +224,17 @@ def bench_run(
             ).blocked_autorange(min_run_time=min_run_time)
         )
 
-    if repack_supported:
-        results.append(
-            benchmark.Timer(
-                stmt="q_res = gptq_marlin_repack(q_w_gptq, repack_sort_indices, size_k, size_n, quant_type.size_bits)",  # noqa: E501
-                globals=globals,
-                label=label,
-                sub_label=sub_label,
-                description="gptq_marlin_repack",
-            ).blocked_autorange(min_run_time=min_run_time)
-        )
+    results.append(
+        benchmark.Timer(
+            stmt="q_res = gptq_marlin_repack(q_w_gptq, repack_sort_indices, size_k, size_n, quant_type.size_bits)",  # noqa: E501
+            globals=globals,
+            label=label,
+            sub_label=sub_label,
+            description="gptq_marlin_repack",
+        ).blocked_autorange(min_run_time=min_run_time)
+    )
 
-    if allspark_supported:
+    if as_supported_case:
         results.append(
             benchmark.Timer(
                 stmt="output = allspark_w8a16_gemm(a, qw_reorder, s_reorder, zp_reorder, size_n, group_size, sm_count, sm_version, CUBLAS_M_THRESHOLD, False, True)",  # noqa: E501
@@ -319,6 +250,7 @@ def main(args):
     print("Benchmarking models:")
     for i, model in enumerate(args.models):
         print(f"[{i}]  {model}")
+
     results: list[benchmark.Measurement] = []
 
     for model in args.models:
@@ -346,17 +278,14 @@ def main(args):
                     ):
                         continue
 
-                    for quant_type in query_marlin_supported_quant_types():
+                    for quant_type in query_marlin_supported_quant_types(False):
                         if (
                             len(args.limit_num_bits) > 0
                             and quant_type.size_bits not in args.limit_num_bits
                         ):
                             continue
 
-                        for group_size in (
-                            MARLIN_SUPPORTED_GROUP_SIZES
-                            + FP4_MARLIN_SUPPORTED_GROUP_SIZES
-                        ):
+                        for group_size in MARLIN_SUPPORTED_GROUP_SIZES:
                             if (
                                 len(args.limit_group_size) > 0
                                 and group_size not in args.limit_group_size

benchmarks/kernels/benchmark_moe.py

@@ -22,13 +22,6 @@ from vllm.utils import FlexibleArgumentParser
 FP8_DTYPE = current_platform.fp8_dtype()
 
 
-def ensure_divisibility(numerator, denominator, text):
-    """Ensure that numerator is divisible by the denominator."""
-    assert numerator % denominator == 0, "{} {} is not divisible by tp {}.".format(
-        text, numerator, denominator
-    )
-
-
 class BenchmarkConfig(TypedDict):
     BLOCK_SIZE_M: int
     BLOCK_SIZE_N: int
@@ -93,9 +86,6 @@ def benchmark_config(
             (num_experts, 2 * shard_intermediate_size), dtype=torch.float32
         )
         w2_scale = torch.randn((hidden_size, num_experts), dtype=torch.float32)
-    if use_deep_gemm:
-        # we use the default block shape for deepgemm
-        block_quant_shape = [128, 128]
     if use_fp8_w8a8:
         if block_quant_shape:
             block_n, block_k = block_quant_shape[0], block_quant_shape[1]
@@ -577,26 +567,22 @@ def main(args: argparse.Namespace):
         E = config.ffn_config.moe_num_experts
         topk = config.ffn_config.moe_top_k
         intermediate_size = config.ffn_config.ffn_hidden_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
     elif config.architectures[0] == "JambaForCausalLM":
         E = config.num_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.intermediate_size
-    elif config.architectures[0] in (
-        "DeepseekV3ForCausalLM",
-        "DeepseekV2ForCausalLM",
-        "Glm4MoeForCausalLM",
-    ):
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
+    elif config.architectures[0] in ("DeepseekV3ForCausalLM", "DeepseekV2ForCausalLM"):
         E = config.n_routed_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
     elif config.architectures[0] in ("Qwen2MoeForCausalLM", "Qwen3MoeForCausalLM"):
         E = config.num_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
-    elif config.architectures[0] in ("HunYuanMoEV1ForCausalLM"):
-        E = config.num_experts
-        topk = config.moe_topk[0]
-        intermediate_size = config.moe_intermediate_size[0]
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
     else:
         # Support for llama4
         config = config.get_text_config()
@@ -604,14 +590,8 @@ def main(args: argparse.Namespace):
         E = config.num_local_experts
         topk = config.num_experts_per_tok
         intermediate_size = config.intermediate_size
-    enable_ep = bool(args.enable_expert_parallel)
-    if enable_ep:
-        ensure_divisibility(E, args.tp_size, "Number of experts")
-        E = E // args.tp_size
-        shard_intermediate_size = 2 * intermediate_size
-    else:
-        ensure_divisibility(intermediate_size, args.tp_size, "intermediate_size")
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
+
     hidden_size = config.hidden_size
     dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
     use_fp8_w8a8 = args.dtype == "fp8_w8a8"
@@ -640,7 +620,7 @@ def main(args: argparse.Namespace):
             4096,
         ]
     else:
-        batch_sizes = args.batch_size
+        batch_sizes = [args.batch_size]
 
     use_deep_gemm = bool(args.use_deep_gemm)
 
@@ -743,13 +723,12 @@ if __name__ == "__main__":
     parser.add_argument(
         "--tp-size", "-tp", "--tensor-parallel-size", type=int, default=2
     )
-    parser.add_argument("--enable-expert-parallel", "-enable-ep", action="store_true")
     parser.add_argument(
         "--dtype", type=str, choices=["auto", "fp8_w8a8", "int8_w8a16"], default="auto"
     )
     parser.add_argument("--use-deep-gemm", action="store_true")
     parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument("--batch-size", type=int, nargs="+", required=False)
+    parser.add_argument("--batch-size", type=int, required=False)
     parser.add_argument("--tune", action="store_true")
     parser.add_argument("--trust-remote-code", action="store_true")
     parser.add_argument("--model-prefix", type=str, required=False)

benchmarks/kernels/benchmark_moe_align_block_size.py

@@ -5,8 +5,9 @@ import itertools
 
 import torch
 
+from vllm import _custom_ops as ops
 from vllm.model_executor.layers.fused_moe.moe_align_block_size import (
-    moe_align_block_size,
+    moe_align_block_size_triton,
 )
 from vllm.triton_utils import triton
 
@@ -20,6 +21,62 @@ def get_topk_ids(num_tokens: int, num_experts: int, topk: int) -> torch.Tensor:
     )
 
 
+def check_correctness(num_tokens, num_experts=256, block_size=256, topk=8):
+    """
+    Verifies vllm vs. Triton
+    """
+    topk_ids = get_topk_ids(num_tokens, num_experts, topk)
+
+    # 1. malloc space for triton and vllm
+    # malloc enough space (max_num_tokens_padded) for the sorted ids
+    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
+    sorted_ids_triton = torch.empty(
+        (max_num_tokens_padded,), dtype=torch.int32, device="cuda"
+    )
+    sorted_ids_triton.fill_(topk_ids.numel())  # fill with sentinel value
+    expert_ids_triton = torch.zeros(
+        (max_num_tokens_padded // block_size,), dtype=torch.int32, device="cuda"
+    )
+    num_tokens_post_pad_triton = torch.empty((1,), dtype=torch.int32, device="cuda")
+
+    sorted_ids_vllm = torch.empty_like(sorted_ids_triton)
+    sorted_ids_vllm.fill_(topk_ids.numel())
+    expert_ids_vllm = torch.zeros_like(expert_ids_triton)
+    num_tokens_post_pad_vllm = torch.empty_like(num_tokens_post_pad_triton)
+
+    # 2. run implementations
+    moe_align_block_size_triton(
+        topk_ids,
+        num_experts,
+        block_size,
+        sorted_ids_triton,
+        expert_ids_triton,
+        num_tokens_post_pad_triton,
+    )
+
+    ops.moe_align_block_size(
+        topk_ids,
+        num_experts,
+        block_size,
+        sorted_ids_vllm,
+        expert_ids_vllm,
+        num_tokens_post_pad_vllm,
+    )
+    print(f"✅ VLLM implementation works with {num_experts} experts!")
+
+    # 3. compare results
+    if torch.allclose(expert_ids_triton, expert_ids_vllm) and torch.allclose(
+        num_tokens_post_pad_triton, num_tokens_post_pad_vllm
+    ):
+        print("✅ Triton and VLLM implementations match.")
+    else:
+        print("❌ Triton and VLLM implementations DO NOT match.")
+        print("Triton expert_ids:", expert_ids_triton)
+        print("VLLM expert_ids:", expert_ids_vllm)
+        print("Triton num_tokens_post_pad:", num_tokens_post_pad_triton)
+        print("VLLM num_tokens_post_pad:", num_tokens_post_pad_vllm)
+
+
 # test configurations
 num_tokens_range = [1, 16, 256, 4096]
 num_experts_range = [16, 64, 224, 256, 280, 512]
@@ -32,8 +89,8 @@ configs = list(itertools.product(num_tokens_range, num_experts_range, topk_range
         x_names=["num_tokens", "num_experts", "topk"],
         x_vals=configs,
         line_arg="provider",
-        line_vals=["vllm"],
-        line_names=["vLLM"],
+        line_vals=["vllm", "triton"],  # "triton"
+        line_names=["VLLM", "Triton"],  # "Triton"
         plot_name="moe-align-block-size-performance",
         args={},
     )
@@ -43,11 +100,37 @@ def benchmark(num_tokens, num_experts, topk, provider):
     block_size = 256
     topk_ids = get_topk_ids(num_tokens, num_experts, topk)
 
+    max_num_tokens_padded = topk_ids.numel() + num_experts * (block_size - 1)
+    sorted_ids = torch.empty((max_num_tokens_padded,), dtype=torch.int32, device="cuda")
+    sorted_ids.fill_(topk_ids.numel())
+    max_num_m_blocks = max_num_tokens_padded // block_size
+    expert_ids = torch.empty((max_num_m_blocks,), dtype=torch.int32, device="cuda")
+    num_tokens_post_pad = torch.empty((1,), dtype=torch.int32, device="cuda")
+
     quantiles = [0.5, 0.2, 0.8]
 
     if provider == "vllm":
         ms, min_ms, max_ms = triton.testing.do_bench(
-            lambda: moe_align_block_size(topk_ids, block_size, num_experts),
+            lambda: ops.moe_align_block_size(
+                topk_ids,
+                num_experts,
+                block_size,
+                sorted_ids.clone(),
+                expert_ids.clone(),
+                num_tokens_post_pad.clone(),
+            ),
+            quantiles=quantiles,
+        )
+    elif provider == "triton":
+        ms, min_ms, max_ms = triton.testing.do_bench(
+            lambda: moe_align_block_size_triton(
+                topk_ids,
+                num_experts,
+                block_size,
+                sorted_ids.clone(),
+                expert_ids.clone(),
+                num_tokens_post_pad.clone(),
+            ),
             quantiles=quantiles,
         )
 
@@ -71,4 +154,6 @@ if __name__ == "__main__":
     )
     args = parser.parse_args()
 
+    print("Running correctness check...")
+    check_correctness(num_tokens=1024, num_experts=args.num_experts, topk=args.topk)
     benchmark.run(print_data=True, show_plots=True)

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -8,13 +8,12 @@ import ray
 import torch
 from transformers import AutoConfig
 
-from vllm.model_executor.layers.fused_moe.fused_moe import *
-from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import (
+from vllm.model_executor.layers.fused_moe.deep_gemm_moe import (
     _moe_permute,
     _moe_unpermute_and_reduce,
-    moe_permute,
-    moe_unpermute,
 )
+from vllm.model_executor.layers.fused_moe.fused_moe import *
+from vllm.model_executor.layers.fused_moe.moe_permute_unpermute import *
 from vllm.model_executor.layers.fused_moe.utils import _fp8_quantize
 from vllm.platforms import current_platform
 from vllm.utils import FlexibleArgumentParser
@@ -64,19 +63,18 @@ def benchmark_permute(
 
     def run():
         if use_customized_permute:
-            (
-                permuted_hidden_states,
-                a1q_scale,
-                first_token_off,
-                inv_perm_idx,
-                m_indices,
-            ) = moe_permute(
-                qhidden_states,
-                a1q_scale=None,
-                topk_ids=topk_ids,
-                n_expert=num_experts,
-                expert_map=None,
-                align_block_size=align_block_size,
+            (permuted_hidden_states, first_token_off, inv_perm_idx, m_indices) = (
+                moe_permute(
+                    qhidden_states,
+                    topk_weights=topk_weights,
+                    topk_ids=topk_ids,
+                    token_expert_indices=token_expert_indices,
+                    topk=topk,
+                    n_expert=num_experts,
+                    n_local_expert=num_experts,
+                    expert_map=None,
+                    align_block_size=align_block_size,
+                )
             )
         else:
             (
@@ -152,19 +150,18 @@ def benchmark_unpermute(
 
     def prepare():
         if use_customized_permute:
-            (
-                permuted_hidden_states,
-                a1q_scale,
-                first_token_off,
-                inv_perm_idx,
-                m_indices,
-            ) = moe_permute(
-                qhidden_states,
-                a1q_scale=None,
-                topk_ids=topk_ids,
-                n_expert=num_experts,
-                expert_map=None,
-                align_block_size=align_block_size,
+            (permuted_hidden_states, first_token_off, inv_perm_idx, m_indices) = (
+                moe_permute(
+                    qhidden_states,
+                    topk_weights=topk_weights,
+                    topk_ids=topk_ids,
+                    token_expert_indices=token_expert_indices,
+                    topk=topk,
+                    n_expert=num_experts,
+                    n_local_expert=num_experts,
+                    expert_map=None,
+                    align_block_size=align_block_size,
+                )
             )
             # convert to fp16/bf16 as gemm output
             return (
@@ -194,19 +191,16 @@ def benchmark_unpermute(
 
     def run(input: tuple):
         if use_customized_permute:
-            (
-                permuted_hidden_states,
-                first_token_off,
-                inv_perm_idx,
-                m_indices,
-            ) = input
-            output = torch.empty_like(hidden_states)
+            (permuted_hidden_states, first_token_off, inv_perm_idx, m_indices) = input
             moe_unpermute(
-                output,
                 permuted_hidden_states,
                 topk_weights,
+                topk_ids,
                 inv_perm_idx,
                 first_token_off,
+                topk,
+                num_experts,
+                num_experts,
             )
         else:
             (
@@ -217,11 +211,7 @@ def benchmark_unpermute(
                 inv_perm,
             ) = input
             _moe_unpermute_and_reduce(
-                output_hidden_states,
-                permuted_hidden_states,
-                inv_perm,
-                topk_weights,
-                True,
+                output_hidden_states, permuted_hidden_states, inv_perm, topk_weights
             )
 
     # JIT compilation & warmup
@@ -328,7 +318,6 @@ def main(args: argparse.Namespace):
     elif (
         config.architectures[0] == "DeepseekV3ForCausalLM"
         or config.architectures[0] == "DeepseekV2ForCausalLM"
-        or config.architectures[0] == "Glm4MoeForCausalLM"
     ):
         E = config.n_routed_experts
         topk = config.num_experts_per_tok

benchmarks/kernels/benchmark_mrope.py

@@ -1,328 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# This script benchmarks the mrope kernel (mainly for Qwen2VL and Qwen2.5VL models).
-# It generates test data, runs benchmarks, and saves results to a CSV file.
-#
-# The CSV file (named with current date/time) contains these columns:
-# model_name, tp_size, num_tokens, num_heads, num_kv_heads, head_dim, max_position,
-# rope_theta, is_neox_style, rope_scaling, dtype, torch_mean, torch_median, torch_p99,
-# torch_min, torch_max, triton_mean, triton_median, triton_p99, triton_min, triton_max,
-# speedup
-#
-# == Usage Examples ==
-#
-# Single model benchmark:
-# python3 benchmark_mrope.py --model-name Qwen/Qwen2-VL-7B-Instruct --tp-size 1 \
-#   --warmup-iter 10 --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
-#
-# All models benchmark:
-# python3 benchmark_mrope.py --model-name "" --tp-size 1 --warmup-iter 10 \
-#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
-#
-# All models with different TP sizes:
-# python3 benchmark_mrope.py --model-name "" --tp-size 1 2 4 8 --warmup-iter 10 \
-#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024
-#
-# All models with different token counts:
-# python3 benchmark_mrope.py --model-name "" --tp-size 1 --warmup-iter 10 \
-#   --benchmark-iter 100 --dtype bfloat16 --seed 0 --num-tokens 1024 4096 16384
-import csv
-import os
-import time
-from datetime import datetime
-from typing import Any
-
-import numpy as np
-import torch
-
-from vllm.model_executor.layers.rotary_embedding import get_rope
-from vllm.platforms import current_platform
-from vllm.transformers_utils.config import get_config
-from vllm.utils import FlexibleArgumentParser
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-
-def generate_test_data(
-    num_tokens: int,
-    num_q_heads: int,
-    num_kv_heads: int,
-    head_size: int,
-    max_position_embeddings: int,
-    dtype: torch.dtype,
-    device: torch.device,
-):
-    """Generate test data for given configuration."""
-    # Create 2D positions (3, num_tokens) for multimodal case
-    positions = torch.randint(
-        0, max_position_embeddings // 4, (3, num_tokens), device=device
-    )
-
-    # Create query and key tensors
-    query = torch.randn(num_tokens, num_q_heads * head_size, dtype=dtype, device=device)
-    key = torch.randn(num_tokens, num_kv_heads * head_size, dtype=dtype, device=device)
-
-    return positions, query, key
-
-
-def calculate_stats(times: list[float]) -> dict[str, float]:
-    """Calculate statistics from a list of times."""
-    times_array = np.array(times)
-    return {
-        "mean": np.mean(times_array),
-        "median": np.median(times_array),
-        "p99": np.percentile(times_array, 99),
-        "min": np.min(times_array),
-        "max": np.max(times_array),
-    }
-
-
-def benchmark_mrope(
-    model_name: str,
-    num_tokens: int,
-    head_dim: int,
-    tp_size: int,
-    num_heads: int,
-    num_kv_heads: int,
-    max_position: int = 8192,
-    rope_theta: float = 10000,
-    is_neox_style: bool = True,
-    rope_scaling: dict[str, Any] = None,
-    dtype: torch.dtype = torch.bfloat16,
-    seed: int = 0,
-    warmup_iter: int = 10,
-    benchmark_iter: int = 100,
-    csv_writer=None,
-):
-    current_platform.seed_everything(seed)
-    torch.set_default_device(device)
-    # the parameters to compute the q k v size based on tp_size
-    mrope_helper_class = get_rope(
-        head_size=head_dim,
-        rotary_dim=head_dim,
-        max_position=max_position,
-        base=rope_theta,
-        is_neox_style=is_neox_style,
-        rope_scaling=rope_scaling,
-        dtype=dtype,
-    ).to(device=device)
-
-    print(80 * "=")
-    print(
-        f"Evaluating model: {model_name} "
-        f"with tp_size: {tp_size} "
-        f"and num_tokens: {num_tokens}, "
-        f"dtype: {dtype}"
-    )
-
-    # create q k v input tensors
-    # create rotary pos emb input tensors
-    positions, query, key = generate_test_data(
-        num_tokens, num_heads, num_kv_heads, head_dim, max_position, dtype, device
-    )
-
-    # Warm up
-    for _ in range(warmup_iter):
-        mrope_helper_class.forward_native(
-            positions,
-            query.clone(),
-            key.clone(),
-        )
-
-        mrope_helper_class.forward_cuda(
-            positions,
-            query.clone(),
-            key.clone(),
-        )
-
-    torch.cuda.synchronize()
-
-    # Time reference implementation
-    torch_times = []
-    for _ in range(benchmark_iter):
-        query_clone = query.clone()
-        key_clone = key.clone()
-        torch.cuda.synchronize()
-        start_time = time.time()
-
-        mrope_helper_class.forward_native(
-            positions,
-            query_clone,
-            key_clone,
-        )
-
-        torch.cuda.synchronize()
-        torch_times.append(time.time() - start_time)
-
-    # Time triton kernel implementation
-    triton_times = []
-    for _ in range(benchmark_iter):
-        query_clone = query.clone()
-        key_clone = key.clone()
-        torch.cuda.synchronize()
-        start_time = time.time()
-        mrope_helper_class.forward_cuda(
-            positions,
-            query_clone,
-            key_clone,
-        )
-        torch.cuda.synchronize()
-        triton_times.append(time.time() - start_time)
-
-    # Calculate statistics
-    torch_stats = calculate_stats(torch_times)
-    triton_stats = calculate_stats(triton_times)
-    print(f"\nPerformance for config ({num_tokens}, {num_heads}, {num_kv_heads}):")
-
-    print(
-        f"Torch implementation: "
-        f"mean={torch_stats['mean']:.8f}s, "
-        f"median={torch_stats['median']:.8f}s, "
-        f"p99={torch_stats['p99']:.8f}s"
-    )
-
-    print(
-        f"Triton implementation: "
-        f"mean={triton_stats['mean']:.8f}s, "
-        f"median={triton_stats['median']:.8f}s, "
-        f"p99={triton_stats['p99']:.8f}s"
-    )
-
-    print(
-        f"Triton Speedup over Torch: {torch_stats['mean'] / triton_stats['mean']:.8f}x"
-    )
-
-    # Write to CSV
-    if csv_writer:
-        row = [
-            model_name,
-            tp_size,
-            num_tokens,
-            num_heads,
-            num_kv_heads,
-            head_dim,
-            max_position,
-            rope_theta,
-            is_neox_style,
-            str(rope_scaling),
-            str(dtype).split(".")[-1],
-            torch_stats["mean"],
-            torch_stats["median"],
-            torch_stats["p99"],
-            torch_stats["min"],
-            torch_stats["max"],
-            triton_stats["mean"],
-            triton_stats["median"],
-            triton_stats["p99"],
-            triton_stats["min"],
-            triton_stats["max"],
-            torch_stats["mean"] / triton_stats["mean"],  # speedup
-        ]
-        csv_writer.writerow(row)
-
-    return torch_stats, triton_stats
-
-
-if __name__ == "__main__":
-    parser = FlexibleArgumentParser(
-        description="Benchmark the rotary embedding kernels."
-    )
-    parser.add_argument("--model-name", type=str, default="")
-    parser.add_argument("--tp-size", type=int, default=1)
-    parser.add_argument("--warmup-iter", type=int, default=10)
-    parser.add_argument("--benchmark-iter", type=int, default=100)
-    parser.add_argument("--dtype", type=str, choices=["bfloat16"], default="bfloat16")
-    parser.add_argument("--seed", type=int, default=0)
-    parser.add_argument("--num-tokens", type=int, nargs="+", required=False)
-    parser.add_argument("--trust-remote-code", action="store_true")
-    parser.add_argument("--output-csv", type=str, default="mrope_benchmark_results.csv")
-    args = parser.parse_args()
-    print(args)
-
-    # Create CSV file for results
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    csv_filename = f"{os.path.splitext(args.output_csv)[0]}_{timestamp}.csv"
-
-    with open(csv_filename, "w", newline="") as csvfile:
-        csv_writer = csv.writer(csvfile)
-        # Write header
-        header = [
-            "model_name",
-            "tp_size",
-            "num_tokens",
-            "num_heads",
-            "num_kv_heads",
-            "head_dim",
-            "max_position",
-            "rope_theta",
-            "is_neox_style",
-            "rope_scaling",
-            "dtype",
-            "torch_mean",
-            "torch_median",
-            "torch_p99",
-            "torch_min",
-            "torch_max",
-            "triton_mean",
-            "triton_median",
-            "triton_p99",
-            "triton_min",
-            "triton_max",
-            "speedup",
-        ]
-        csv_writer.writerow(header)
-
-        model_tp_dict = {}
-        if args.model_name == "":
-            model_tp_dict = {
-                "Qwen/Qwen2-VL-2B-Instruct": [1],
-                "Qwen/Qwen2-VL-7B-Instruct": [1],
-                "Qwen/Qwen2-VL-72B-Instruct": [2, 4, 8],
-                "Qwen/Qwen2.5-VL-3B-Instruct": [1, 2, 4, 8],
-                "Qwen/Qwen2.5-VL-7B-Instruct": [1, 2, 4, 8],
-                "Qwen/Qwen2.5-VL-72B-Instruct": [2, 4, 8],
-            }
-        else:
-            model_tp_dict[args.model_name] = [args.tp_size]
-
-        if args.num_tokens is None:
-            num_tokens_list = [2**i for i in range(0, 18)]
-        else:
-            num_tokens_list = args.num_tokens
-
-        for model_name, tp_list in model_tp_dict.items():
-            config = get_config(model_name, trust_remote_code=args.trust_remote_code)
-            for tp_size in tp_list:
-                # get the model config
-                total_num_kv_heads = config.num_key_value_heads
-                total_num_heads = config.num_attention_heads
-                num_heads = total_num_heads // tp_size
-                num_kv_heads = max(1, total_num_kv_heads // tp_size)
-                head_dim = config.hidden_size // total_num_heads
-                q_size = num_heads * head_dim
-                kv_size = num_kv_heads * head_dim
-                is_neox_style = True
-                rope_theta = config.rope_theta
-                max_position = config.max_position_embeddings
-
-                for num_tokens in num_tokens_list:
-                    benchmark_mrope(
-                        model_name=model_name,
-                        num_tokens=num_tokens,
-                        head_dim=head_dim,
-                        tp_size=tp_size,
-                        num_heads=num_heads,
-                        num_kv_heads=num_kv_heads,
-                        max_position=max_position,
-                        rope_theta=rope_theta,
-                        is_neox_style=is_neox_style,
-                        rope_scaling=config.rope_scaling,
-                        dtype=getattr(torch, args.dtype),
-                        seed=args.seed,
-                        warmup_iter=args.warmup_iter,
-                        benchmark_iter=args.benchmark_iter,
-                        csv_writer=csv_writer,
-                    )
-
-    print(f"Benchmark results saved to {csv_filename}")

benchmarks/kernels/benchmark_per_token_group_quant.py

@@ -1,159 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import argparse
-import math
-from contextlib import contextmanager
-from typing import Callable
-from unittest.mock import patch
-
-import torch
-
-from vllm.model_executor.layers.quantization.utils import fp8_utils, int8_utils
-from vllm.platforms import current_platform
-
-
-@contextmanager
-def _triton_mode():
-    """Temporarily force the Triton fallback path"""
-    with patch("vllm.platforms.current_platform.is_cuda", return_value=False):
-        yield
-
-
-def _time_cuda(
-    fn: Callable[[], tuple[torch.Tensor, torch.Tensor]],
-    warmup_iters: int,
-    bench_iters: int,
-) -> float:
-    # warmup
-    for _ in range(warmup_iters):
-        fn()
-    torch.cuda.synchronize()
-
-    start = torch.cuda.Event(enable_timing=True)
-    end = torch.cuda.Event(enable_timing=True)
-
-    start.record()
-    for _ in range(bench_iters):
-        fn()
-    end.record()
-    torch.cuda.synchronize()
-
-    return start.elapsed_time(end) / bench_iters  # ms/iter
-
-
-def _run_single(
-    shape: tuple[int, int],
-    group_size: int,
-    dtype: str,
-    *,
-    column_major: bool = False,
-    scale_ue8m0: bool = False,
-    warmup_iters: int,
-    bench_iters: int,
-) -> None:
-    num_tokens, hidden_dim = shape
-
-    device = torch.device("cuda")
-    torch.manual_seed(42)
-    x = torch.randn(num_tokens, hidden_dim, device=device, dtype=torch.bfloat16) * 8
-
-    if dtype == "fp8":
-
-        def cuda_impl():
-            return fp8_utils.per_token_group_quant_fp8(
-                x,
-                group_size,
-                column_major_scales=column_major,
-                use_ue8m0=scale_ue8m0,
-            )
-
-        def triton_impl():
-            with _triton_mode():
-                return fp8_utils.per_token_group_quant_fp8(
-                    x,
-                    group_size,
-                    column_major_scales=column_major,
-                    use_ue8m0=scale_ue8m0,
-                )
-    elif dtype == "int8":
-
-        def cuda_impl():
-            return int8_utils.per_token_group_quant_int8(x, group_size)
-
-        def triton_impl():
-            with _triton_mode():
-                return int8_utils.per_token_group_quant_int8(x, group_size)
-    else:
-        raise ValueError("dtype must be 'fp8' or 'int8'")
-
-    cuda_ms = _time_cuda(cuda_impl, warmup_iters, bench_iters)
-    triton_ms = _time_cuda(triton_impl, warmup_iters, bench_iters)
-
-    speedup = triton_ms / cuda_ms if cuda_ms else math.inf
-
-    cfg_desc = (
-        f"shape={shape}  gs={group_size:<3}  col_major={column_major:<5}  "
-        f"ue8m0={scale_ue8m0:<5}  dtype={dtype}"
-    )
-    print(
-        f"{cfg_desc:55} | CUDA {cuda_ms:7.3f} ms  | Triton {triton_ms:7.3f} ms  | "
-        f"speed-up ×{speedup:5.2f}"
-    )
-
-
-def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--warmup-iters", type=int, default=10)
-    parser.add_argument("--bench-iters", type=int, default=100)
-    parser.add_argument("--dtype", choices=["fp8", "int8", "both"], default="both")
-    return parser.parse_args()
-
-
-if __name__ == "__main__":
-    if not current_platform.is_cuda():
-        raise RuntimeError("CUDA device is required to run this benchmark.")
-
-    args = parse_args()
-    warmup_iters, bench_iters = args.warmup_iters, args.bench_iters
-
-    shapes = [(32, 128), (64, 256), (16, 512)]
-    group_sizes = [64, 128]
-
-    dtypes = ["fp8", "int8"] if args.dtype == "both" else [args.dtype]
-
-    header = (
-        "Configuration".ljust(55)
-        + " | "
-        + "CUDA (ms)".center(12)
-        + " | "
-        + "Triton (ms)".center(13)
-        + " | "
-        + "Speed-up"
-    )
-    print(header)
-    print("-" * len(header))
-
-    for dtype in dtypes:
-        for shape in shapes:
-            for gs in group_sizes:
-                if dtype == "fp8":
-                    for col_major in (False, True):
-                        for ue8m0 in (False, True):
-                            _run_single(
-                                shape,
-                                gs,
-                                dtype,
-                                column_major=col_major,
-                                scale_ue8m0=ue8m0,
-                                warmup_iters=warmup_iters,
-                                bench_iters=bench_iters,
-                            )
-                else:  # INT8 has no col-major / ue8m0 switches
-                    _run_single(
-                        shape,
-                        gs,
-                        dtype,
-                        warmup_iters=warmup_iters,
-                        bench_iters=bench_iters,
-                    )

benchmarks/kernels/benchmark_reshape_and_cache_flash.py

@@ -1,156 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from __future__ import annotations
-
-import random
-import time
-
-import torch
-from tabulate import tabulate
-
-from vllm import _custom_ops as ops
-from vllm.logger import init_logger
-from vllm.platforms import current_platform
-from vllm.utils import (
-    STR_DTYPE_TO_TORCH_DTYPE,
-    FlexibleArgumentParser,
-    create_kv_caches_with_random_flash,
-)
-
-logger = init_logger(__name__)
-
-
-@torch.inference_mode()
-def run_benchmark(
-    num_tokens: int,
-    num_heads: int,
-    head_size: int,
-    block_size: int,
-    num_blocks: int,
-    dtype: torch.dtype,
-    kv_cache_dtype: str,
-    kv_cache_layout: str,
-    num_iters: int,
-    device: str = "cuda",
-) -> float:
-    """Return latency (seconds) for given num_tokens."""
-
-    if kv_cache_dtype == "fp8" and head_size % 16:
-        raise ValueError("fp8 kv-cache requires head_size to be a multiple of 16.")
-
-    current_platform.seed_everything(42)
-    torch.set_default_device(device)
-
-    # create random key / value tensors [T, H, D].
-    key = torch.randn(num_tokens, num_heads, head_size, dtype=dtype, device=device)
-    value = torch.randn_like(key)
-
-    # prepare the slot mapping.
-    # each token is assigned a unique slot in the KV-cache.
-    num_slots = block_size * num_blocks
-    if num_tokens > num_slots:
-        raise ValueError("num_tokens cannot exceed the total number of cache slots")
-    slot_mapping_lst = random.sample(range(num_slots), num_tokens)
-    slot_mapping = torch.tensor(slot_mapping_lst, dtype=torch.long, device=device)
-
-    key_caches, value_caches = create_kv_caches_with_random_flash(
-        num_blocks,
-        block_size,
-        1,  # num_layers
-        num_heads,
-        head_size,
-        kv_cache_dtype,
-        dtype,
-        device=device,
-        cache_layout=kv_cache_layout,
-    )
-    key_cache, value_cache = key_caches[0], value_caches[0]
-
-    # compute per-kernel scaling factors for fp8 conversion (if used).
-    k_scale = (key.amax() / 64.0).to(torch.float32)
-    v_scale = (value.amax() / 64.0).to(torch.float32)
-
-    def run_cuda_benchmark(n_iters: int) -> float:
-        nonlocal key, value, key_cache, value_cache, slot_mapping
-        torch.cuda.synchronize()
-        start = time.perf_counter()
-        for _ in range(n_iters):
-            ops.reshape_and_cache_flash(
-                key,
-                value,
-                key_cache,
-                value_cache,
-                slot_mapping,
-                kv_cache_dtype,
-                k_scale,
-                v_scale,
-            )
-        torch.cuda.synchronize()
-        end = time.perf_counter()
-        return (end - start) / n_iters
-
-    # warm-up
-    run_cuda_benchmark(3)
-
-    lat = run_cuda_benchmark(num_iters)
-
-    # free tensors to mitigate OOM when sweeping
-    del key, value, key_cache, value_cache, slot_mapping
-    torch.cuda.empty_cache()
-
-    return lat
-
-
-def main(args):
-    rows = []
-    for layout in ["NHD", "HND"]:
-        for exp in range(1, 17):
-            n_tok = 2**exp
-            lat = run_benchmark(
-                num_tokens=n_tok,
-                num_heads=args.num_heads,
-                head_size=args.head_size,
-                block_size=args.block_size,
-                num_blocks=args.num_blocks,
-                dtype=STR_DTYPE_TO_TORCH_DTYPE[args.dtype],
-                kv_cache_dtype=args.kv_cache_dtype,
-                kv_cache_layout=layout,
-                num_iters=args.iters,
-                device="cuda",
-            )
-            rows.append([n_tok, layout, f"{lat * 1e6:.3f}"])
-
-    print(tabulate(rows, headers=["num_tokens", "layout", "latency (µs)"]))
-
-
-if __name__ == "__main__":
-    parser = FlexibleArgumentParser()
-
-    parser.add_argument("--num-heads", type=int, default=128)
-    parser.add_argument(
-        "--head-size",
-        type=int,
-        choices=[64, 80, 96, 112, 120, 128, 192, 256],
-        default=128,
-    )
-    parser.add_argument("--block-size", type=int, choices=[16, 32], default=16)
-    parser.add_argument("--num-blocks", type=int, default=128 * 512)
-
-    parser.add_argument(
-        "--dtype",
-        type=str,
-        choices=["half", "bfloat16", "float"],
-        default="bfloat16",
-    )
-
-    parser.add_argument(
-        "--kv-cache-dtype",
-        type=str,
-        choices=["auto", "fp8"],
-        default="auto",
-    )
-
-    parser.add_argument("--iters", type=int, default=100)
-    args = parser.parse_args()
-
-    main(args)

benchmarks/kernels/benchmark_trtllm_decode_attention.py

@@ -1,253 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import csv
-import os
-import random
-from datetime import datetime
-
-import flashinfer
-import torch
-
-FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
-
-# KV Cache Layout for TRT-LLM
-# kv_cache_shape = (num_blocks, 2, num_kv_heads, page_size, head_dim)
-
-
-def to_float8(x, dtype=torch.float8_e4m3fn):
-    finfo = torch.finfo(dtype)
-    min_val, max_val = x.aminmax()
-    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
-    scale = finfo.max / amax * 0.1
-    x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
-    return x_scl_sat.to(dtype), scale.float().reciprocal()
-
-
-@torch.no_grad()
-def benchmark_decode(
-    num_seqs,
-    max_seq_len,
-    page_size=16,
-    dtype=torch.bfloat16,
-    kv_layout="HND",
-    num_kv_heads=8,
-    kv_cache_dtype="auto",
-    head_dim=128,
-    warmup=10,
-    trials=20,
-):
-    torch.set_default_device("cuda")
-    device = "cuda"
-    torch.manual_seed(0)
-
-    HEAD_GRP_SIZE = 8
-    MAX_SEQ_LEN = max_seq_len
-
-    # large number to reduce kv_cache reuse
-    NUM_BLOCKS = int(256000 / page_size)
-
-    workspace_buffer = torch.empty(1024 * 1024 * 1024, dtype=torch.int8, device=device)
-
-    # For decode, batch_size is num_decode_token
-    num_qo_heads = num_kv_heads * HEAD_GRP_SIZE
-    sm_scale = float(1.0 / (head_dim**0.5))
-    q = torch.randn(num_seqs, num_qo_heads, head_dim, device=device, dtype=dtype)
-    kv_lens = [random.randint(1, MAX_SEQ_LEN) for _ in range(num_seqs)]
-
-    max_kv_len = max(kv_lens)
-    kv_lens_tensor = torch.tensor(kv_lens, dtype=torch.int, device=device)
-    max_num_blocks_per_seq = (max_kv_len + page_size - 1) // page_size
-
-    block_tables = torch.randint(
-        0, NUM_BLOCKS, (num_seqs, max_num_blocks_per_seq), dtype=torch.int32
-    )
-
-    kv_cache_shape = (NUM_BLOCKS, 2, num_kv_heads, page_size, head_dim)
-    kv_cache = torch.randn(size=kv_cache_shape, device=device, dtype=dtype)
-    k_scale = v_scale = 1.0
-
-    if kv_cache_dtype.startswith("fp8"):
-        kv_cache, _ = to_float8(kv_cache)
-
-    output_trtllm = torch.empty(q.shape, dtype=dtype)
-
-    # Benchmark TRT decode
-    def trt_decode():
-        return flashinfer.decode.trtllm_batch_decode_with_kv_cache(
-            q,
-            kv_cache,
-            workspace_buffer,
-            block_tables,
-            kv_lens_tensor,
-            max_kv_len,
-            bmm1_scale=k_scale * sm_scale,
-            bmm2_scale=v_scale,
-            out=output_trtllm,
-        )
-
-    def time_fn(fn, warmup=10, trials=20):
-        torch.cuda.synchronize()
-        start = torch.cuda.Event(enable_timing=True)
-        end = torch.cuda.Event(enable_timing=True)
-        times = []
-        for i in range(warmup):
-            fn()
-        for i in range(trials):
-            start.record()
-            fn()
-            end.record()
-            torch.cuda.synchronize()
-            times.append(start.elapsed_time(end))  # ms
-        return sum(times) / len(times), torch.std(torch.tensor(times))
-
-    # TRT Decode
-    trt_mean, trt_std = time_fn(trt_decode)
-
-    kv_indptr = [0]
-    kv_indices = []
-    kv_last_page_lens = []
-    for i in range(num_seqs):
-        seq_len = kv_lens[i]
-        assert seq_len > 0
-        num_blocks = (seq_len + page_size - 1) // page_size
-        kv_indices.extend(block_tables[i, :num_blocks])
-        kv_indptr.append(kv_indptr[-1] + num_blocks)
-        kv_last_page_len = seq_len % page_size
-        if kv_last_page_len == 0:
-            kv_last_page_len = page_size
-        kv_last_page_lens.append(kv_last_page_len)
-
-    kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
-    kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
-    kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
-
-    output_baseline = torch.empty(q.shape, dtype=dtype)
-
-    wrapper = flashinfer.BatchDecodeWithPagedKVCacheWrapper(
-        workspace_buffer,
-        kv_layout,
-        use_tensor_cores=((num_qo_heads // num_kv_heads) > 4),
-    )
-
-    wrapper.plan(
-        kv_indptr,
-        kv_indices,
-        kv_last_page_lens,
-        num_qo_heads,
-        num_kv_heads,
-        head_dim,
-        page_size,
-        "NONE",
-        q_data_type=dtype,
-        kv_data_type=torch.float8_e4m3fn if kv_cache_dtype.startswith("fp8") else dtype,
-    )
-
-    def baseline_decode():
-        return wrapper.run(q, kv_cache, sm_scale, k_scale, v_scale, output_baseline)
-
-    baseline_mean, baseline_std = time_fn(baseline_decode)
-
-    # Calculate percentage speedup (positive means TRT is faster)
-    speedup_percent = (baseline_mean - trt_mean) / baseline_mean
-
-    print(
-        f"\t{num_seqs}\t{max_seq_len}\t{trt_mean:.3f}\t{trt_std.item():.3f}"
-        f"\t{baseline_mean:.3f}\t{baseline_std.item():.3f}\t{speedup_percent:.3f}"
-    )
-
-    # Return results for CSV writing
-    return {
-        "num_seqs": num_seqs,
-        "trt_mean": trt_mean,
-        "trt_std": trt_std.item(),
-        "baseline_mean": baseline_mean,
-        "baseline_std": baseline_std.item(),
-        "speedup_percent": speedup_percent,
-        "q_dtype": str(dtype),
-        "kv_cache_dtype": kv_cache_dtype,
-        "page_size": page_size,
-        "num_kv_heads": num_kv_heads,
-        "head_dim": head_dim,
-        "max_seq_len": max_seq_len,
-    }
-
-
-def write_results_to_csv(results, filename=None):
-    """Write benchmark results to CSV file."""
-    if filename is None:
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        filename = f"flashinfer_trtllm_benchmark_{timestamp}.csv"
-
-    fieldnames = [
-        "num_seqs",
-        "trt_mean",
-        "trt_std",
-        "baseline_mean",
-        "baseline_std",
-        "speedup_percent",
-        "q_dtype",
-        "kv_cache_dtype",
-        "page_size",
-        "num_kv_heads",
-        "head_dim",
-        "max_seq_len",
-    ]
-
-    file_exists = os.path.exists(filename)
-
-    with open(filename, "a", newline="") as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
-
-        if not file_exists:
-            writer.writeheader()
-
-        for result in results:
-            writer.writerow(result)
-
-    print(f"Results written to {filename}")
-
-
-if __name__ == "__main__":
-    num_seqs = [1, 4, 8, 16, 32, 64, 128, 256]
-    max_seq_lens = [1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072]
-    all_results = []
-
-    print(
-        "Running benchmark for q_dtype = bfloat16, kv_cache_dtype: bfloat16, "
-        "output_dtype: bfloat16"
-    )
-    print(
-        "\tnum_seqs\tmax_seq_len\ttrt_mean\ttrt_std\tbaseline_mean\t"
-        "baseline_std\tspeedup_percent"
-    )
-    for max_seq_len in max_seq_lens:
-        for bs in num_seqs:
-            result = benchmark_decode(
-                bs,
-                max_seq_len,
-                dtype=torch.bfloat16,
-                kv_cache_dtype="auto",
-            )
-            all_results.append(result)
-
-    print(
-        "Running benchmark for q_dtype = bfloat16, kv_cache_dtype: fp8, "
-        "output_dtype: bfloat16"
-    )
-    print(
-        "\tnum_seqs\tmax_seq_len\ttrt_mean\ttrt_std\tbaseline_mean\t"
-        "baseline_std\tspeedup_percent"
-    )
-    for max_seq_len in max_seq_lens:
-        for bs in num_seqs:
-            result = benchmark_decode(
-                bs,
-                max_seq_len,
-                dtype=torch.bfloat16,
-                kv_cache_dtype="fp8",
-            )
-            all_results.append(result)
-
-    # Write all results to CSV
-    write_results_to_csv(all_results)

benchmarks/kernels/benchmark_trtllm_prefill_attention.py

@@ -1,250 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import csv
-import os
-import random
-from datetime import datetime
-
-import flashinfer
-import torch
-
-FLOAT32_BYTES = torch.finfo(torch.float).bits // 8
-
-# KV Cache Layout for TRT-LLM
-# kv_cache_shape = (num_blocks, 2, num_kv_heads, page_size, head_dim)
-
-
-def to_float8(x, dtype=torch.float8_e4m3fn):
-    finfo = torch.finfo(dtype)
-    min_val, max_val = x.aminmax()
-    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
-    scale = finfo.max / amax * 0.1
-    x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
-    return x_scl_sat.to(dtype), scale.float().reciprocal()
-
-
-@torch.no_grad()
-def benchmark_prefill(
-    num_seqs,
-    max_seq_len,
-    page_size=16,
-    dtype=torch.bfloat16,
-    kv_layout="HND",
-    num_kv_heads=8,
-    kv_cache_dtype="auto",
-    head_dim=128,
-    warmup=10,
-    trials=20,
-):
-    torch.set_default_device("cuda")
-    torch.manual_seed(0)
-
-    HEAD_GRP_SIZE = 8
-    MAX_SEQ_LEN = max_seq_len
-
-    # large number to reduce kv_cache reuse
-    NUM_BLOCKS = int(256000 / page_size)
-
-    workspace_buffer = torch.empty(1024 * 1024 * 1024, dtype=torch.int8)
-
-    num_qo_heads = num_kv_heads * HEAD_GRP_SIZE
-    sm_scale = float(1.0 / (head_dim**0.5))
-
-    q_lens = [random.randint(1, MAX_SEQ_LEN) for _ in range(num_seqs)]
-    q_lens[-1] = MAX_SEQ_LEN
-    max_q_len = max(q_lens)
-    q_indptr = torch.cat(
-        [
-            torch.tensor([0], dtype=torch.int32),
-            torch.cumsum(
-                torch.tensor(q_lens, dtype=torch.int32), dim=0, dtype=torch.int32
-            ),
-        ]
-    )
-    q = torch.randn(sum(q_lens), num_qo_heads, head_dim, dtype=dtype)
-
-    kv_lens = [random.randint(0, MAX_SEQ_LEN) for _ in range(num_seqs)]
-    kv_lens[-1] = MAX_SEQ_LEN
-
-    seq_lens = [q_len + kv_len for q_len, kv_len in zip(q_lens, kv_lens)]
-    max_seq_len = max(seq_lens)
-    seq_lens_tensor = torch.tensor(seq_lens, dtype=torch.int32)
-
-    max_num_blocks_per_seq = (max_seq_len + page_size - 1) // page_size
-    block_tables = torch.randint(
-        0, NUM_BLOCKS, (num_seqs, max_num_blocks_per_seq), dtype=torch.int32
-    )
-
-    kv_cache_shape = (NUM_BLOCKS, 2, num_kv_heads, page_size, head_dim)
-    kv_cache = torch.randn(size=kv_cache_shape, dtype=dtype)
-    k_scale = v_scale = 1.0
-
-    if kv_cache_dtype.startswith("fp8"):
-        kv_cache, _ = to_float8(kv_cache)
-
-    output_trtllm = torch.empty(q.shape, dtype=dtype)
-
-    kv_indptr = [0]
-    kv_indices = []
-    kv_last_page_lens = []
-    for i in range(num_seqs):
-        seq_len = seq_lens[i]
-        assert seq_len > 0
-        num_blocks = (seq_len + page_size - 1) // page_size
-        kv_indices.extend(block_tables[i, :num_blocks])
-        kv_indptr.append(kv_indptr[-1] + num_blocks)
-        kv_last_page_len = seq_len % page_size
-        if kv_last_page_len == 0:
-            kv_last_page_len = page_size
-        kv_last_page_lens.append(kv_last_page_len)
-
-    kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32)
-    kv_indices = torch.tensor(kv_indices, dtype=torch.int32)
-    kv_last_page_lens = torch.tensor(kv_last_page_lens, dtype=torch.int32)
-
-    output_baseline = torch.empty(q.shape, dtype=dtype)
-
-    wrapper = flashinfer.BatchPrefillWithPagedKVCacheWrapper(
-        workspace_buffer, kv_layout
-    )
-    wrapper.plan(
-        q_indptr,
-        kv_indptr,
-        kv_indices,
-        kv_last_page_lens,
-        num_qo_heads,
-        num_kv_heads,
-        head_dim,
-        page_size,
-        causal=True,
-        sm_scale=sm_scale,
-        q_data_type=dtype,
-        kv_data_type=kv_cache.dtype,
-    )
-
-    def time_fn(fn, warmup=10, trials=20):
-        torch.cuda.synchronize()
-        start = torch.cuda.Event(enable_timing=True)
-        end = torch.cuda.Event(enable_timing=True)
-        times = []
-        for i in range(warmup):
-            fn()
-        for i in range(trials):
-            start.record()
-            fn()
-            end.record()
-            torch.cuda.synchronize()
-            times.append(start.elapsed_time(end))  # ms
-        return sum(times) / len(times), torch.std(torch.tensor(times))
-
-    def baseline_prefill():
-        return wrapper.run(
-            q, kv_cache, k_scale=k_scale, v_scale=v_scale, out=output_baseline
-        )
-
-    def trt_prefill():
-        return flashinfer.prefill.trtllm_batch_context_with_kv_cache(
-            query=q,
-            kv_cache=kv_cache,
-            workspace_buffer=workspace_buffer,
-            block_tables=block_tables,
-            seq_lens=seq_lens_tensor,
-            max_q_len=max_q_len,
-            max_kv_len=max_seq_len,
-            bmm1_scale=k_scale * sm_scale,
-            bmm2_scale=v_scale,
-            batch_size=num_seqs,
-            cum_seq_lens_q=q_indptr,
-            cum_seq_lens_kv=kv_indptr,
-            out=output_trtllm,
-        )
-
-    trt_mean, trt_std = time_fn(trt_prefill)
-    baseline_mean, baseline_std = time_fn(baseline_prefill)
-
-    # Calculate percentage speedup (positive means TRT is faster)
-    speedup_percent = (baseline_mean - trt_mean) / baseline_mean
-
-    print(
-        f"\t{num_seqs}\t{max_seq_len}\t{trt_mean:.5f}\t{trt_std.item():.5f}"
-        f"\t{baseline_mean:.5f}\t{baseline_std.item():.5f}\t{speedup_percent:.5f}"
-    )
-
-    # Return results for CSV writing
-    return {
-        "num_seqs": num_seqs,
-        "trt_mean": trt_mean,
-        "trt_std": trt_std.item(),
-        "baseline_mean": baseline_mean,
-        "baseline_std": baseline_std.item(),
-        "speedup_percent": speedup_percent,
-        "q_dtype": str(dtype),
-        "kv_cache_dtype": kv_cache_dtype,
-        "page_size": page_size,
-        "num_kv_heads": num_kv_heads,
-        "head_dim": head_dim,
-        "max_seq_len": max_seq_len,
-    }
-
-
-def write_results_to_csv(results, filename=None):
-    """Write benchmark results to CSV file."""
-    if filename is None:
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-        filename = f"flashinfer_trtllm_benchmark_{timestamp}.csv"
-
-    fieldnames = [
-        "num_seqs",
-        "trt_mean",
-        "trt_std",
-        "baseline_mean",
-        "baseline_std",
-        "speedup_percent",
-        "q_dtype",
-        "kv_cache_dtype",
-        "page_size",
-        "num_kv_heads",
-        "head_dim",
-        "max_seq_len",
-    ]
-
-    file_exists = os.path.exists(filename)
-
-    with open(filename, "a", newline="") as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
-
-        if not file_exists:
-            writer.writeheader()
-
-        for result in results:
-            writer.writerow(result)
-
-    print(f"Results written to {filename}")
-
-
-if __name__ == "__main__":
-    num_seqs = [1, 4, 8, 16, 32, 64, 128, 256]
-    max_seq_lens = [1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072]
-    all_results = []
-
-    print(
-        "Running benchmark for q_dtype = bfloat16, kv_cache_dtype: bfloat16, "
-        "output_dtype: bfloat16"
-    )
-    print(
-        "\tnum_seqs\tmax_seq_len\ttrt_mean\ttrt_std\tbaseline_mean\t"
-        "baseline_std\tspeedup_percent"
-    )
-    for max_seq_len in max_seq_lens:
-        for bs in num_seqs:
-            result = benchmark_prefill(
-                bs,
-                max_seq_len,
-                dtype=torch.bfloat16,
-                kv_cache_dtype="auto",
-            )
-            all_results.append(result)
-
-    # Write all results to CSV
-    write_results_to_csv(all_results)

benchmarks/kernels/deepgemm/README.md

@@ -8,7 +8,7 @@ Currently this just includes dense GEMMs and only works on Hopper GPUs.
 
 You need to install vLLM in your usual fashion, then install DeepGEMM from source in its own directory:
 
-```bash
+```
 git clone --recursive https://github.com/deepseek-ai/DeepGEMM
 cd DeepGEMM
 python setup.py install
@@ -17,7 +17,7 @@ uv pip install -e .
 
 ## Usage
 
-```console
+```
 python benchmark_fp8_block_dense_gemm.py
 INFO 02-26 21:55:13 [__init__.py:207] Automatically detected platform cuda.
 ===== STARTING FP8 GEMM BENCHMARK =====

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

@@ -4,16 +4,49 @@
 # ruff: noqa: E501
 import time
 
+# Import DeepGEMM functions
+import deep_gemm
 import torch
+from deep_gemm import calc_diff, ceil_div, get_col_major_tma_aligned_tensor
 
+# Import vLLM functions
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    get_col_major_tma_aligned_tensor,
     per_token_group_quant_fp8,
     w8a8_block_fp8_matmul,
 )
 from vllm.triton_utils import triton
-from vllm.utils.deep_gemm import calc_diff, fp8_gemm_nt, per_block_cast_to_fp8
+
+
+# Copied from
+# https://github.com/deepseek-ai/DeepGEMM/blob/78cacf70d41d15d688bd493ebc85845f7f2a3d5d/tests/test_core.py#L9
+def per_token_cast_to_fp8(
+        x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """Convert tensor to FP8 format with per-token scaling."""
+    assert x.dim() == 2 and x.size(1) % 128 == 0
+    m, n = x.shape
+    x_view = x.view(m, -1, 128)
+    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
+    return (x_view * (448.0 / x_amax.unsqueeze(2))).to(
+        torch.float8_e4m3fn).view(m, n), (x_amax / 448.0).view(m, -1)
+
+
+# Copied from
+# https://github.com/deepseek-ai/DeepGEMM/blob/78cacf70d41d15d688bd493ebc85845f7f2a3d5d/tests/test_core.py#L17
+def per_block_cast_to_fp8(
+        x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """Convert tensor to FP8 format with per-block scaling."""
+    assert x.dim() == 2
+    m, n = x.shape
+    x_padded = torch.zeros((ceil_div(m, 128) * 128, ceil_div(n, 128) * 128),
+                           dtype=x.dtype,
+                           device=x.device)
+    x_padded[:m, :n] = x
+    x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
+    x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
+    x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
+    return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (
+        x_amax / 448.0).view(x_view.size(0), x_view.size(2))
 
 
 def benchmark_shape(m: int,
@@ -36,14 +69,14 @@ def benchmark_shape(m: int,
 
     # Pre-quantize B for all implementations
     # (weights can be pre-quantized offline)
-    B_deepgemm, B_scale_deepgemm = per_block_cast_to_fp8(B, [128, 128], use_ue8m0=True)
-    B_vllm, B_scale_vllm = per_block_cast_to_fp8(B, [128, 128], use_ue8m0=True)
+    B_deepgemm, B_scale_deepgemm = per_block_cast_to_fp8(B)
+    B_vllm, B_scale_vllm = per_block_cast_to_fp8(B)
 
     # Block size configuration
     block_size = [128, 128]
 
     # Pre-quantize A for all implementations
-    A_deepgemm, A_scale_deepgemm = per_token_group_quant_fp8(A, block_size[1])
+    A_deepgemm, A_scale_deepgemm = per_token_cast_to_fp8(A)
     A_scale_deepgemm = get_col_major_tma_aligned_tensor(A_scale_deepgemm)
     C_deepgemm = torch.empty((m, n), device='cuda', dtype=torch.bfloat16)
     A_vllm, A_scale_vllm = per_token_group_quant_fp8(A, block_size[1])
@@ -52,13 +85,21 @@ def benchmark_shape(m: int,
 
     # === DeepGEMM Implementation ===
     def deepgemm_gemm():
-        fp8_gemm_nt((A_deepgemm, A_scale_deepgemm),
+        # A quantization is inside the loop as it depends on activations
+        # A_deepgemm, A_scale_deepgemm = per_token_cast_to_fp8(A)
+        # A_deepgemm, A_scale_deepgemm = per_token_group_quant_fp8(
+        #     A, block_size[1])
+        # A_scale_aligned = get_col_major_tma_aligned_tensor(A_scale_deepgemm)
+        # C_deepgemm = torch.empty((m, n), device='cuda', dtype=torch.bfloat16)
+        deep_gemm.gemm_fp8_fp8_bf16_nt((A_deepgemm, A_scale_deepgemm),
                                        (B_deepgemm, B_scale_deepgemm),
                                        C_deepgemm)
         return C_deepgemm
 
     # === vLLM Triton Implementation ===
     def vllm_triton_gemm():
+        # A quantization is inside the loop as it depends on activations
+        # A_vllm, A_scale_vllm = per_token_group_quant_fp8(A, block_size[1])
         return w8a8_block_fp8_matmul(A_vllm,
                                      B_vllm,
                                      A_scale_vllm,
@@ -68,6 +109,9 @@ def benchmark_shape(m: int,
 
     # === vLLM CUTLASS Implementation ===
     def vllm_cutlass_gemm():
+        # A quantization is inside the loop as it depends on activations
+        # A_vllm_cutlass, A_scale_vllm_cutlass = per_token_group_quant_fp8(
+        #     A, block_size[1], column_major_scales=True)
         return ops.cutlass_scaled_mm(A_vllm_cutlass,
                                      B_vllm.T,
                                      scale_a=A_scale_vllm_cutlass,

benchmarks/kv_cache/benchmark_block_pool.py

@@ -1,108 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import gc
-import time
-from typing import Optional
-
-from tabulate import tabulate
-
-from vllm.utils import FlexibleArgumentParser
-from vllm.v1.core.block_pool import BlockPool
-
-
-class Metric:
-    def __init__(self) -> None:
-        self.cnt: int = 0
-        self.sum_v: int = 0
-        self.max_v: Optional[int] = None
-
-    def update(self, v: int) -> None:
-        self.cnt += 1
-        self.sum_v += v
-        if self.max_v is None:
-            self.max_v = v
-        else:
-            self.max_v = max(self.max_v, v)
-
-    def avg_v(self) -> float:
-        return self.sum_v * 1.0 / self.cnt
-
-
-def main(args):
-    rows = []
-    for allocate_block in args.allocate_blocks:
-        # Enforce a GC collect ahead to minimize the impact among runs
-        gc.collect()
-        block_pool = BlockPool(num_gpu_blocks=args.num_gpu_blocks, enable_caching=True)
-
-        get_blocks_metric: Metric = Metric()
-        free_blocks_metric: Metric = Metric()
-        for _ in range(args.num_iteration):
-            t1 = time.monotonic_ns()
-            blocks = block_pool.get_new_blocks(allocate_block)
-            t2 = time.monotonic_ns()
-            block_pool.free_blocks(blocks)
-            t3 = time.monotonic_ns()
-            get_blocks_metric.update(t2 - t1)
-            free_blocks_metric.update(t3 - t2)
-
-        if get_blocks_metric.max_v is not None and free_blocks_metric.max_v is not None:
-            rows.append(
-                [
-                    get_blocks_metric.cnt,
-                    args.num_gpu_blocks,
-                    allocate_block,
-                    get_blocks_metric.avg_v() / 1000000,
-                    get_blocks_metric.max_v / 1000000.0,
-                    free_blocks_metric.avg_v() / 1000000,
-                    free_blocks_metric.max_v / 1000000.0,
-                ]
-            )
-        else:
-            print(
-                "No valid metrics found."
-                f" {get_blocks_metric.max_v=} {free_blocks_metric.max_v=}"
-            )
-
-    print(
-        tabulate(
-            rows,
-            headers=[
-                "Iterations",
-                "Total\nBlocks",
-                "Allocated\nBlocks",
-                "Get Blocks\nAvg (ms)",
-                "Get Blocks\nMax (ms)",
-                "Free Blocks\nAvg (ms)",
-                "Free Blocks\nMax (ms)",
-            ],
-            tablefmt="grid",
-            floatfmt=".6f",
-        )
-    )
-
-
-def invoke_main() -> None:
-    parser = FlexibleArgumentParser(
-        description="Benchmark the performance of BlockPool for KV Cache."
-    )
-    parser.add_argument("--num-gpu-blocks", type=int, default=100000)
-    parser.add_argument(
-        "--num-iteration",
-        type=int,
-        default=1000,
-        help="Number of iterations to run to stablize final data readings",
-    )
-    parser.add_argument(
-        "--allocate-blocks",
-        type=int,
-        nargs="*",
-        default=[10, 50, 100, 500, 1000],
-        help="Number of blocks to allocate",
-    )
-    args = parser.parse_args()
-    main(args)
-
-
-if __name__ == "__main__":
-    invoke_main()  # pragma: no cover

benchmarks/multi_turn/README.md

@@ -1,71 +0,0 @@
-# Benchmark KV Cache Offloading with Multi-Turn Conversations
-
-The requirements (pip) for `benchmark_serving_multi_turn.py` can be found in `requirements.txt`
-
-First start serving your model
-
-```bash
-export MODEL_NAME=/models/meta-llama/Meta-Llama-3.1-8B-Instruct/
-
-vllm serve $MODEL_NAME --disable-log-requests
-```
-
-## Synthetic Multi-Turn Conversations
-
-Download the following text file (used for generation of synthetic conversations)
-
-```bash
-wget https://www.gutenberg.org/ebooks/1184.txt.utf-8
-mv 1184.txt.utf-8 pg1184.txt
-```
-
-The filename `pg1184.txt` is used in `generate_multi_turn.json` (see `"text_files"`).
-
-But you may use other text files if you prefer (using this specific file is not required).
-
-Then run the benchmarking script
-
-```bash
-export MODEL_NAME=/models/meta-llama/Meta-Llama-3.1-8B-Instruct/
-
-python benchmark_serving_multi_turn.py --model $MODEL_NAME --input-file generate_multi_turn.json \
---num-clients 2 --max-active-conversations 6
-```
-
-You can edit the file `generate_multi_turn.json` to change the conversation parameters (number of turns, etc.).
-
-If successful, you will see the following output
-
-```bash
-----------------------------------------------------------------------------------------------------
-Statistics summary:
-runtime_sec = 215.810
-requests_per_sec = 0.769
-----------------------------------------------------------------------------------------------------
-                   count     mean     std      min      25%      50%      75%      90%      99%      max
-ttft_ms            166.0    78.22   67.63    45.91    59.94    62.26    64.43    69.66   353.18   567.54
-tpot_ms            166.0    25.37    0.57    24.40    25.07    25.31    25.50    25.84    27.50    28.05
-latency_ms         166.0  2591.07  326.90  1998.53  2341.62  2573.01  2860.10  3003.50  3268.46  3862.94
-input_num_turns    166.0     7.43    4.57     1.00     3.00     7.00    11.00    13.00    17.00    17.00
-input_num_tokens   166.0  2006.20  893.56   522.00  1247.75  2019.00  2718.00  3233.00  3736.45  3899.00
-output_num_tokens  166.0   100.01   11.80    80.00    91.00    99.00   109.75   116.00   120.00   120.00
-output_num_chunks  166.0    99.01   11.80    79.00    90.00    98.00   108.75   115.00   119.00   119.00
-----------------------------------------------------------------------------------------------------
-```
-
-## ShareGPT Conversations
-
-To run with the ShareGPT data, download the following ShareGPT dataset:
-`https://huggingface.co/datasets/philschmid/sharegpt-raw/blob/main/sharegpt_20230401_clean_lang_split.json`
-
-Use the `convert_sharegpt_to_openai.py` script to convert the dataset to a format supported by `benchmark_serving_multi_turn.py`
-
-```bash
-python convert_sharegpt_to_openai.py sharegpt_20230401_clean_lang_split.json sharegpt_conv_128.json --seed=99 --max-items=128
-```
-
-The script will convert the ShareGPT dataset to a dataset with the standard user/assistant roles.
-
-The flag `--max-items=128` is used to sample 128 conversations from the original dataset (change as needed).
-
-Use the output JSON file `sharegpt_conv_128.json` as the `--input-file` for `benchmark_serving_multi_turn.py`.

benchmarks/multi_turn/bench_dataset.py

@@ -1,493 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-from abc import ABC, abstractmethod
-from statistics import mean
-from typing import Any, NamedTuple, Optional, Union
-
-import numpy as np  # type: ignore
-import pandas as pd  # type: ignore
-from bench_utils import (
-    TEXT_SEPARATOR,
-    Color,
-    logger,
-)
-from transformers import AutoTokenizer  # type: ignore
-
-# Conversation ID is a string (e.g: "UzTK34D")
-ConvId = str
-
-# A list of dicts (dicts with keys "id" and "messages")
-ShareGptConversations = list[dict[str, Any]]
-
-# A list of dicts (dicts with keys "role" and "content")
-MessagesList = list[dict[str, str]]
-
-# Map conversation ID to conversation messages
-ConversationsMap = list[ConvId, MessagesList]
-
-
-class Distribution(ABC):
-    @abstractmethod
-    def sample(self, size: int = 1) -> np.ndarray:
-        pass
-
-
-class UniformDistribution(Distribution):
-    def __init__(
-        self,
-        min_val: Union[int, float],
-        max_val: Union[int, float],
-        is_integer: bool = True,
-    ) -> None:
-        self.min_val = min_val
-        self.max_val = max_val
-        self.is_integer = is_integer
-
-    def sample(self, size: int = 1) -> np.ndarray:
-        if self.is_integer:
-            return np.random.randint(
-                int(self.min_val), int(self.max_val + 1), size=size
-            )
-        else:
-            return np.random.uniform(self.min_val, self.max_val, size=size)
-
-    def __repr__(self) -> str:
-        return f"UniformDistribution[{self.min_val}, {self.max_val}]"
-
-
-class ConstantDistribution(Distribution):
-    def __init__(self, value: Union[int, float]) -> None:
-        self.value = value
-        self.max_val = value
-
-    def sample(self, size: int = 1) -> np.ndarray:
-        return np.full(shape=size, fill_value=self.value)
-
-    def __repr__(self) -> str:
-        return f"Constant[{self.value}]"
-
-
-class ZipfDistribution(Distribution):
-    def __init__(self, alpha: float, max_val: Optional[int] = None) -> None:
-        self.alpha = alpha
-        self.max_val = max_val
-
-    def sample(self, size: int = 1) -> np.ndarray:
-        samples = np.random.zipf(self.alpha, size=size)
-        if self.max_val:
-            samples = np.minimum(samples, self.max_val)
-        return samples
-
-    def __repr__(self) -> str:
-        return f"ZipfDistribution[{self.alpha}]"
-
-
-class PoissonDistribution(Distribution):
-    def __init__(self, alpha: float, max_val: Optional[int] = None) -> None:
-        self.alpha = alpha
-        self.max_val = max_val
-
-    def sample(self, size: int = 1) -> np.ndarray:
-        samples = np.random.poisson(self.alpha, size=size)
-        if self.max_val:
-            samples = np.minimum(samples, self.max_val)
-        return samples
-
-    def __repr__(self) -> str:
-        return f"PoissonDistribution[{self.alpha}]"
-
-
-class LognormalDistribution(Distribution):
-    def __init__(
-        self, mean: float, sigma: float, max_val: Optional[int] = None
-    ) -> None:
-        self.mean = mean
-        self.sigma = sigma
-        self.max_val = max_val
-
-    def sample(self, size: int = 1) -> np.ndarray:
-        samples = np.random.lognormal(mean=self.mean, sigma=self.sigma, size=size)
-        if self.max_val:
-            samples = np.minimum(samples, self.max_val)
-
-        return np.round(samples).astype(int)
-
-    def __repr__(self) -> str:
-        return f"LognormalDistribution[{self.mean}, {self.sigma}]"
-
-
-class GenConvArgs(NamedTuple):
-    num_conversations: int
-    text_files: list[str]
-    input_num_turns: Distribution
-    input_common_prefix_num_tokens: Distribution
-    input_prefix_num_tokens: Distribution
-    input_num_tokens: Distribution
-    output_num_tokens: Distribution
-    print_stats: bool
-
-
-def verify_field_exists(
-    conf: dict, field_name: str, section: str, subsection: str
-) -> None:
-    if field_name not in conf:
-        raise ValueError(
-            f"Missing field '{field_name}' in {section=} and {subsection=}"
-        )
-
-
-def get_random_distribution(
-    conf: dict, section: str, subsection: str, optional: bool = False
-) -> Distribution:
-    # section can be "prompt_input" or "prompt_output" (both required)
-    conf = conf[section]
-
-    if optional and subsection not in conf:
-        # Optional subsection, if not found assume the value is always 0
-        return ConstantDistribution(0)
-
-    # subsection can be "num_turns", "num_tokens" or "prefix_num_tokens"
-    if subsection not in conf:
-        raise ValueError(f"Missing subsection {subsection} in section {section}")
-
-    conf = conf[subsection]
-
-    distribution = conf.get("distribution")
-    if distribution is None:
-        raise ValueError(
-            f"Missing field 'distribution' in {section=} and {subsection=}"
-        )
-
-    if distribution == "constant":
-        verify_field_exists(conf, "value", section, subsection)
-        return ConstantDistribution(conf["value"])
-
-    elif distribution == "zipf":
-        verify_field_exists(conf, "alpha", section, subsection)
-        max_val = conf.get("max", None)
-        return ZipfDistribution(conf["alpha"], max_val=max_val)
-
-    elif distribution == "poisson":
-        verify_field_exists(conf, "alpha", section, subsection)
-        max_val = conf.get("max", None)
-        return PoissonDistribution(conf["alpha"], max_val=max_val)
-
-    elif distribution == "lognormal":
-        verify_field_exists(conf, "mean", section, subsection)
-        verify_field_exists(conf, "sigma", section, subsection)
-        max_val = conf.get("max", None)
-        return LognormalDistribution(conf["mean"], conf["sigma"], max_val=max_val)
-
-    elif distribution == "uniform":
-        verify_field_exists(conf, "min", section, subsection)
-        verify_field_exists(conf, "max", section, subsection)
-
-        min_value = conf["min"]
-        max_value = conf["max"]
-
-        assert min_value > 0
-        assert min_value <= max_value
-
-        is_integer = isinstance(min_value, int) and isinstance(max_value, int)
-        return UniformDistribution(min_value, max_value, is_integer)
-    else:
-        raise ValueError(f"Unknown distribution: {distribution}")
-
-
-def parse_input_json_file(conf: dict) -> GenConvArgs:
-    # Validate the input file
-    assert isinstance(conf, dict)
-    required_fields = [
-        "filetype",
-        "num_conversations",
-        "text_files",
-        "prompt_input",
-        "prompt_output",
-    ]
-    for field in required_fields:
-        assert field in conf, f"Missing field {field} in input {conf}"
-
-    assert conf["filetype"] == "generate_conversations"
-
-    assert conf["num_conversations"] > 0, "num_conversations should be larger than zero"
-
-    text_files = conf["text_files"]
-
-    assert isinstance(text_files, list), "Field 'text_files' should be a list"
-    assert len(text_files) > 0, (
-        "Field 'text_files' should be a list with at least one file"
-    )
-
-    # Parse the parameters for the prompt input/output workload
-    input_num_turns = get_random_distribution(conf, "prompt_input", "num_turns")
-    input_num_tokens = get_random_distribution(conf, "prompt_input", "num_tokens")
-    input_common_prefix_num_tokens = get_random_distribution(
-        conf, "prompt_input", "common_prefix_num_tokens", optional=True
-    )
-    input_prefix_num_tokens = get_random_distribution(
-        conf, "prompt_input", "prefix_num_tokens"
-    )
-    output_num_tokens = get_random_distribution(conf, "prompt_output", "num_tokens")
-
-    print_stats: bool = conf.get("print_stats", False)
-    assert isinstance(print_stats, bool), (
-        "Field 'print_stats' should be either 'true' or 'false'"
-    )
-
-    args = GenConvArgs(
-        num_conversations=conf["num_conversations"],
-        text_files=text_files,
-        input_num_turns=input_num_turns,
-        input_common_prefix_num_tokens=input_common_prefix_num_tokens,
-        input_prefix_num_tokens=input_prefix_num_tokens,
-        input_num_tokens=input_num_tokens,
-        output_num_tokens=output_num_tokens,
-        print_stats=print_stats,
-    )
-    return args
-
-
-def print_conv_stats(conversations: ConversationsMap, tokenizer: AutoTokenizer) -> None:
-    # Collect statistics
-    conv_stats: list[dict[Any, Any]] = []
-    req_stats: list[int] = []
-
-    print("\nCollecting statistics...")
-    for messages in conversations.values():
-        # messages is a list of dicts
-        user_tokens: list[int] = []
-        assistant_tokens: list[int] = []
-        request_tokens: list[int] = []
-
-        req_tokens = 0
-        for m in messages:
-            content = m["content"]
-            num_tokens = len(tokenizer(content).input_ids)
-
-            if m["role"] == "user":
-                user_tokens.append(num_tokens)
-                # New user prompt including all chat history
-                req_tokens += num_tokens
-                request_tokens.append(req_tokens)
-
-            elif m["role"] == "assistant":
-                assistant_tokens.append(num_tokens)
-                # Update assistant answer
-                # (will be part of chat history for the next user prompt)
-                req_tokens += num_tokens
-
-        item_stats = {
-            "conversation_turns": len(messages),
-            "user_tokens": mean(user_tokens),
-            "assistant_tokens": mean(assistant_tokens),
-        }
-
-        conv_stats.append(item_stats)
-        req_stats.extend(request_tokens)
-
-    # Print statistics
-    percentiles = [0.25, 0.5, 0.75, 0.9, 0.99]
-
-    print(TEXT_SEPARATOR)
-    print(f"{Color.YELLOW}Conversations statistics:{Color.RESET}")
-    print(TEXT_SEPARATOR)
-    df = pd.DataFrame(conv_stats)
-    print(df.describe(percentiles=percentiles).transpose())
-    print(TEXT_SEPARATOR)
-    print(f"{Color.YELLOW}Request statistics:{Color.RESET}")
-    print(TEXT_SEPARATOR)
-    df = pd.DataFrame(req_stats, columns=["request_tokens"])
-    print(df.describe(percentiles=percentiles).transpose())
-    print(TEXT_SEPARATOR)
-
-
-def generate_conversations(
-    args: GenConvArgs, tokenizer: AutoTokenizer
-) -> ConversationsMap:
-    # Text for all user prompts
-    # (text from the input text files will be appended to this line)
-    base_prompt_text = "Please rewrite the following text and add more content: "
-    base_prompt_token_count = len(
-        tokenizer.encode(base_prompt_text, add_special_tokens=False)
-    )
-
-    logger.info(f"{Color.PURPLE}Generating conversations...{Color.RESET}")
-    logger.info(args)
-
-    list_of_tokens = []
-
-    for filename in args.text_files:
-        # Load text file that will be used to generate prompts
-        with open(filename) as file:
-            data = file.read()
-            tokens_in_file = tokenizer.encode(data, add_special_tokens=False)
-            list_of_tokens.extend(tokens_in_file)
-
-    conversations: ConversationsMap = {}
-    conv_id = 0
-
-    # Generate number of turns for every conversation
-    turn_count: np.ndarray = args.input_num_turns.sample(args.num_conversations)
-
-    # Turn count should be at least 2 (one user prompt and one assistant answer)
-    turn_count = np.maximum(turn_count, 2)
-
-    # Round up to an even number (every user prompt should have an answer)
-    turn_count = turn_count + (turn_count % 2)
-
-    # Generate number of prefix tokens for every conversation
-    conv_prefix_tokens: np.ndarray = args.input_prefix_num_tokens.sample(
-        args.num_conversations
-    )
-
-    # Used to reduce shared text between conversations
-    # (jump/skip over text sections between conversations)
-    base_offset = 0
-
-    # Common prefix size for all conversations (only 1 sample required)
-    common_prefix_text = ""
-    common_prefix_tokens: int = args.input_common_prefix_num_tokens.sample(1)[0]
-    if common_prefix_tokens > 0:
-        # Using "." at the end to separate sentences
-        common_prefix_text = (
-            tokenizer.decode(list_of_tokens[: common_prefix_tokens - 2]) + "."
-        )
-        base_offset += common_prefix_tokens
-
-    for conv_id in range(args.num_conversations):
-        # Generate a single conversation
-        messages: MessagesList = []
-
-        nturns = turn_count[conv_id]
-
-        # User prompt token count per turn (with lower limit)
-        input_token_count: np.ndarray = args.input_num_tokens.sample(nturns)
-        input_token_count = np.maximum(input_token_count, base_prompt_token_count)
-
-        # Assistant answer token count per turn (with lower limit)
-        output_token_count: np.ndarray = args.output_num_tokens.sample(nturns)
-        output_token_count = np.maximum(output_token_count, 1)
-
-        user_turn = True
-        for turn_id in range(nturns):
-            if user_turn:
-                role = "user"
-                num_tokens = input_token_count[turn_id]
-
-                # Generate the user prompt,
-                # use a unique prefix (the conv_id) for each conversation
-                # (to avoid shared prefix between conversations)
-                content = f"{conv_id} is a nice number... "
-
-                if len(common_prefix_text) > 0 and turn_id == 0:
-                    content = common_prefix_text + content
-
-                # Update the number of tokens left for the content
-                num_tokens -= len(tokenizer.encode(content, add_special_tokens=False))
-
-                if turn_id == 0:
-                    prefix_num_tokens = conv_prefix_tokens[conv_id]
-                    if prefix_num_tokens > 0:
-                        # Add prefix text (context) to the first turn
-                        start_offset = base_offset
-                        end_offset = start_offset + prefix_num_tokens
-                        assert len(list_of_tokens) > end_offset, (
-                            "Not enough input text to generate "
-                            f"{prefix_num_tokens} tokens for the "
-                            f"prefix text ({start_offset=}, {end_offset=})"
-                        )
-
-                        content += f"{conv_id}, " + tokenizer.decode(
-                            list_of_tokens[start_offset:end_offset]
-                        )
-                        base_offset += prefix_num_tokens
-
-                # Add the actual user prompt/question after the prefix text
-                content += base_prompt_text
-                num_tokens -= base_prompt_token_count
-
-                if num_tokens > 0:
-                    # Add text from the input file (to reach the desired token count)
-                    start_offset = base_offset + turn_id * input_token_count.max()
-                    end_offset = start_offset + num_tokens
-                    assert len(list_of_tokens) > end_offset, (
-                        f"Not enough input text to generate {num_tokens} tokens "
-                        f"for the prompt ({start_offset=}, {end_offset=})"
-                    )
-
-                    # Convert tokens back to text
-                    content += tokenizer.decode(list_of_tokens[start_offset:end_offset])
-            else:
-                role = "assistant"
-                # This content will not be used as input to the LLM server
-                # (actual answers will be used instead).
-                # Content is only required to determine the min_tokens/max_tokens
-                # (inputs to the LLM server).
-                num_tokens = output_token_count[turn_id]
-                assert len(list_of_tokens) > num_tokens, (
-                    f"Not enough input text to generate {num_tokens} "
-                    "tokens for assistant content"
-                )
-                content = tokenizer.decode(list_of_tokens[:num_tokens])
-
-            # Append the user/assistant message to the list of messages
-            messages.append({"role": role, "content": content})
-            user_turn = not user_turn
-
-        # Add the new conversation
-        conversations[f"CONV_ID_{conv_id}"] = messages
-
-        # Increase base offset for the next conversation
-        base_offset += nturns
-
-    if args.print_stats:
-        print_conv_stats(conversations, tokenizer)
-
-    return conversations
-
-
-def conversations_list_to_dict(input_list: ShareGptConversations) -> ConversationsMap:
-    conversations: ConversationsMap = {}
-
-    for item in input_list:
-        conv_id: str = item["id"]
-        assert isinstance(conv_id, str)
-
-        assert conv_id not in conversations, (
-            f"Conversation ID {conv_id} found more than once in the input"
-        )
-
-        messages: MessagesList = item["messages"]
-        assert isinstance(messages, list), (
-            f"Conversation messages should be a list (ID: {conv_id})"
-        )
-        assert len(messages) > 0, f"Conversation with no messages (ID: {conv_id})"
-
-        conversations[conv_id] = messages
-
-    logger.info(f"Using {len(conversations)} unique conversations (IDs)")
-    assert len(conversations) == len(input_list)
-
-    # Print statistics about the selected conversations
-    stats: list[dict[str, Any]] = []
-    for conv_data in conversations.values():
-        stats.append({"num_turns": len(conv_data)})
-
-    print(TEXT_SEPARATOR)
-    print(f"{Color.YELLOW}Conversations statistics:{Color.RESET}")
-    print(TEXT_SEPARATOR)
-    percentiles = [0.25, 0.5, 0.75, 0.9, 0.99, 0.999, 0.9999]
-    conv_stats = pd.DataFrame(stats).describe(percentiles=percentiles)
-    print(conv_stats.transpose())
-    print(TEXT_SEPARATOR)
-
-    return conversations
-
-
-def conversations_dict_to_list(input_dict: ConversationsMap) -> ShareGptConversations:
-    output: ShareGptConversations = []
-    for conv_id, conv_data in input_dict.items():
-        new_item = {"id": conv_id, "messages": conv_data}
-        output.append(new_item)
-
-    return output

benchmarks/multi_turn/bench_utils.py

@@ -1,25 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import logging
-from enum import Enum
-
-
-class Color(str, Enum):
-    RED = "\033[91m"
-    GREEN = "\033[92m"
-    BLUE = "\033[94m"
-    PURPLE = "\033[95m"
-    CYAN = "\033[96m"
-    YELLOW = "\033[93m"
-    RESET = "\033[0m"
-
-
-TEXT_SEPARATOR = "-" * 100
-
-# Configure the logger
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] - %(message)s",
-    datefmt="%d-%m-%Y %H:%M:%S",
-)
-logger = logging.getLogger(__name__)

benchmarks/multi_turn/convert_sharegpt_to_openai.py

@@ -1,354 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""
-Download dataset from:
-https://huggingface.co/datasets/philschmid/sharegpt-raw/blob/main/sharegpt_20230401_clean_lang_split.json
-
-Convert to OpenAI API:
-export INPUT_FILE=sharegpt_20230401_clean_lang_split.json
-python convert_sharegpt_to_openai.py $INPUT_FILE sharegpt_conv_128.json --max-items=128
-"""
-
-import argparse
-import json
-import random
-from statistics import mean
-from typing import Any, Optional
-
-import pandas as pd  # type: ignore
-import tqdm  # type: ignore
-from transformers import AutoTokenizer  # type: ignore
-
-
-def has_non_english_chars(text: str) -> bool:
-    return not text.isascii()
-
-
-def content_is_valid(
-    content: str, min_content_len: Optional[int], max_content_len: Optional[int]
-) -> bool:
-    if min_content_len and len(content) < min_content_len:
-        return False
-
-    if max_content_len and len(content) > max_content_len:
-        return False
-
-    return has_non_english_chars(content)
-
-
-def print_stats(
-    conversations: "list[dict[Any, Any]]", tokenizer: Optional[AutoTokenizer] = None
-) -> None:
-    # Collect statistics
-    stats = []
-
-    print("\nCollecting statistics...")
-    for item in tqdm.tqdm(conversations):
-        # item has "id" and "messages"
-        messages = item["messages"]
-
-        user_turns = 0
-        assistant_turns = 0
-        user_words = 0
-        assistant_words = 0
-        conv_chars = 0
-
-        user_tokens: list[int] = []
-        assistant_tokens: list[int] = []
-
-        for m in messages:
-            content = m["content"]
-            conv_chars += len(content)
-            content_num_words = content.count(" ") + 1
-
-            num_tokens = 0
-            if tokenizer:
-                num_tokens = len(tokenizer(m["content"]).input_ids)
-
-            if m["role"] == "user":
-                user_turns += 1
-                user_words += content_num_words
-                if tokenizer:
-                    user_tokens.append(num_tokens)
-
-            elif m["role"] == "assistant":
-                assistant_turns += 1
-                assistant_words += content_num_words
-                if tokenizer:
-                    assistant_tokens.append(num_tokens)
-
-        # assert user_turns == assistant_turns, \
-        # f"Invalid conversation ID {item['id']}"
-
-        conv_words = user_words + assistant_words
-        item_stats = {
-            "user_turns": user_turns,
-            "assistant_turns": assistant_turns,
-            "user_words": user_words,
-            "assistant_words": assistant_words,
-            "conv_turns": len(messages),
-            "conv_words": conv_words,
-            "conv_characters": conv_chars,
-        }
-
-        if len(user_tokens) > 0:
-            item_stats["user_tokens"] = int(mean(user_tokens))
-
-        if len(assistant_tokens) > 0:
-            item_stats["assistant_tokens"] = int(mean(assistant_tokens))
-
-        stats.append(item_stats)
-
-    print("\nStatistics:")
-    percentiles = [0.25, 0.5, 0.75, 0.9, 0.99, 0.999, 0.9999]
-    df = pd.DataFrame(stats)
-    print(df.describe(percentiles=percentiles).transpose())
-
-
-def convert_sharegpt_to_openai(
-    seed: int,
-    input_file: str,
-    output_file: str,
-    max_items: Optional[int],
-    min_content_len: Optional[int] = None,
-    max_content_len: Optional[int] = None,
-    min_turns: Optional[int] = None,
-    max_turns: Optional[int] = None,
-    model: Optional[str] = None,
-) -> None:
-    if min_turns and max_turns:
-        assert min_turns <= max_turns
-
-    if min_content_len and max_content_len:
-        # Verify that min is not larger than max if both were given
-        assert min_content_len <= max_content_len
-
-    print(
-        f"Input parameters:\n{seed=}, {max_items=}, {min_content_len=},"
-        f" {max_content_len=}, {min_turns=}, {max_turns=}\n"
-    )
-
-    random.seed(seed)
-
-    tokenizer = None
-    if model is not None:
-        print(f"Loading tokenizer from: {model}")
-        tokenizer = AutoTokenizer.from_pretrained(model)
-
-    # Read the ShareGPT JSON file
-    print(f"Reading file: {input_file}")
-    with open(input_file, encoding="utf-8") as f:
-        # Should be a list of dicts
-        # Each dict should have "id" (string) and "conversations" (list of dicts)
-        sharegpt_data = json.load(f)
-
-    assert isinstance(sharegpt_data, list), "Input file should contain a list of dicts"
-
-    print(f"Total items in input file: {len(sharegpt_data):,}")
-
-    print(f"Shuffling dataset with seed {seed}")
-    random.shuffle(sharegpt_data)
-
-    # Map conversation ID to the all the messages
-    conversation_parts: dict[str, list[Any]] = {}
-
-    for item in tqdm.tqdm(sharegpt_data):
-        assert "id" in item, "Missing key 'id'"
-        assert "conversations" in item, "Missing key 'conversations'"
-
-        # Conversation ID (e.g: "hiWPlMD") and part/session (0, 1, 2, etc.)
-        conv_id, _ = item["id"].split("_")
-        new_turns = item["conversations"]
-
-        if conv_id not in conversation_parts:
-            # Start new conversation
-            conversation_parts[conv_id] = []
-        elif len(conversation_parts[conv_id]) > 0 and len(new_turns) > 0:
-            prev_turns = conversation_parts[conv_id][-1]
-            if prev_turns[-1]["from"] == new_turns[0]["from"]:
-                new_turns = new_turns[1:]
-
-        if len(new_turns) > 0:
-            # We assume that parts are in order in the ShareGPT dataset
-            conversation_parts[conv_id].append(new_turns)
-
-    dataset: list[dict[str, Any]] = []
-    for conv_id, conv_parts in conversation_parts.items():
-        new_item = {"id": conv_id}
-
-        conversations: list[dict[str, str]] = []
-
-        # Merge all parts
-        for conv_part in conv_parts:
-            conversations.extend(conv_part)
-
-        if len(conversations) > 0:
-            new_item["conversations"] = conversations
-            dataset.append(new_item)
-
-    print(f"Total unique conversations (IDs) in input file: {len(dataset):,}")
-
-    # Final output data
-    final_openai_dataset: list[dict] = []
-
-    # Filter conversations from the ShareGPT dataset and convert to OpenAI format
-    for item in tqdm.tqdm(dataset):
-        messages: list[dict] = []
-
-        assert "id" in item, "Missing key 'id'"
-        assert "conversations" in item, "Missing key 'conversations'"
-
-        conv_id = item["id"]
-        conversations = item["conversations"]
-
-        if min_turns is not None and len(conversations) < min_turns:
-            # Skip short conversations
-            continue
-
-        # Convert each message in the conversation, up to max_turns if specified
-        for i, turn in enumerate(conversations):
-            assert "from" in turn and "value" in turn, (
-                f"Invalid conversation ID {conv_id} - missing 'from' or 'value'"
-            )
-
-            role = None
-            turn_from = turn["from"]
-
-            if turn_from in {"human", "user"}:
-                role = "user"
-            elif turn_from in {"gpt", "bing", "chatgpt", "bard"}:
-                role = "assistant"
-            elif turn_from == "system":
-                role = "system"
-
-            assert role is not None, (
-                f"Invalid conversation ID {conv_id} - 'from'='{turn_from}' is invalid"
-            )
-
-            if i == 0 and role != "user":
-                # If the first message is from assistant (gpt), skip it.
-                # this happens when the conversation is a follow-up
-                # to a previous conversation (from the same user).
-                continue
-
-            if max_turns is not None and i >= max_turns:
-                break
-
-            # Convert message to OpenAI format (with "role" and "content")
-            content = turn["value"]
-            messages.append({"role": role, "content": content})
-
-        # Add the converted conversation to the OpenAI format
-        if len(messages) > 0:
-            valid_messages = True
-
-            # First turn should always be from the user
-            user_turn = True
-
-            for m in messages:
-                # Make sure that turns alternate between user and assistant
-                if (user_turn and m["role"] != "user") or (
-                    not user_turn and m["role"] != "assistant"
-                ):
-                    valid_messages = False
-                    break
-
-                user_turn = not user_turn
-
-                content = m["content"]
-                valid_messages = content_is_valid(
-                    content, min_content_len, max_content_len
-                )
-                if not valid_messages:
-                    break
-
-            if valid_messages is True:
-                final_openai_dataset.append({"id": conv_id, "messages": messages})
-
-    assert len(final_openai_dataset) > 0, "Final number of conversations is zero"
-
-    print_stats(final_openai_dataset)
-
-    print_stats_again = False
-    if max_items is not None and len(final_openai_dataset) > max_items:
-        print(f"\n\nSampling {max_items} items from the dataset...")
-        print_stats_again = True
-        final_openai_dataset = random.sample(final_openai_dataset, max_items)
-
-    if print_stats_again:
-        # Print stats after the dataset changed
-        print_stats(final_openai_dataset, tokenizer)
-
-    # Write the converted data to a new JSON file
-    final_size = len(final_openai_dataset)
-    print(f"\nTotal conversations converted (after filtering): {final_size:,}")
-    print(f"\nWriting file: {output_file}")
-    with open(output_file, "w", encoding="utf-8") as f:
-        json.dump(final_openai_dataset, f, ensure_ascii=False, indent=2)
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(
-        description="Convert ShareGPT dataset to OpenAI API format"
-    )
-    parser.add_argument("input_file", help="Path to the input ShareGPT JSON file")
-    parser.add_argument(
-        "output_file", help="Path to the output OpenAI format JSON file"
-    )
-    parser.add_argument(
-        "--seed", type=int, default=0, help="Seed for random number generators"
-    )
-    parser.add_argument(
-        "--max-items",
-        type=int,
-        default=None,
-        help="Maximum number of items in the output file",
-    )
-    parser.add_argument(
-        "--min-turns",
-        type=int,
-        default=None,
-        help="Minimum number of turns per conversation",
-    )
-    parser.add_argument(
-        "--max-turns",
-        type=int,
-        default=None,
-        help="Maximum number of turns per conversation",
-    )
-    parser.add_argument(
-        "--min-content-len",
-        type=int,
-        default=None,
-        help="Min number of characters in the messages' content",
-    )
-    parser.add_argument(
-        "--max-content-len",
-        type=int,
-        default=None,
-        help="Max number of characters in the messages' content",
-    )
-    parser.add_argument(
-        "--model",
-        type=str,
-        default=None,
-        help="LLM model, only the tokenizer will be used",
-    )
-
-    args = parser.parse_args()
-
-    convert_sharegpt_to_openai(
-        args.seed,
-        args.input_file,
-        args.output_file,
-        args.max_items,
-        args.min_content_len,
-        args.max_content_len,
-        args.min_turns,
-        args.max_turns,
-        args.model,
-    )
-
-
-if __name__ == "__main__":
-    main()

benchmarks/multi_turn/generate_multi_turn.json

@@ -1,35 +0,0 @@
-{
-    "filetype": "generate_conversations",
-    "num_conversations": 24,
-    "text_files": ["pg1184.txt"],
-    "print_stats": false,
-    "prompt_input": {
-        "num_turns": {
-            "distribution": "uniform",
-            "min": 12,
-            "max": 18
-        },
-        "common_prefix_num_tokens": {
-            "distribution": "constant",
-            "value": 500
-        },
-        "prefix_num_tokens": {
-            "distribution": "lognormal",
-            "mean": 6,
-            "sigma": 4,
-            "max": 1500
-        },
-        "num_tokens": {
-            "distribution": "uniform",
-            "min": 120,
-            "max": 160
-        }
-    },
-    "prompt_output": {
-        "num_tokens": {
-            "distribution": "uniform",
-            "min": 80,
-            "max": 120
-        }
-    }
-}

benchmarks/multi_turn/requirements.txt

@@ -1,5 +0,0 @@
-numpy>=1.24
-pandas>=2.0.0
-aiohttp>=3.10
-transformers>=4.46
-xlsxwriter>=3.2.1

cmake/cpu_extension.cmake

@@ -12,8 +12,9 @@ endif()
 #
 # Define environment variables for special configurations
 #
-set(ENABLE_AVX512BF16 $ENV{VLLM_CPU_AVX512BF16})
-set(ENABLE_AVX512VNNI $ENV{VLLM_CPU_AVX512VNNI})
+if(DEFINED ENV{VLLM_CPU_AVX512BF16})
+    set(ENABLE_AVX512BF16 ON)
+endif()
 
 include_directories("${CMAKE_SOURCE_DIR}/csrc")
 
@@ -58,22 +59,6 @@ function (find_isa CPUINFO TARGET OUT)
     endif()
 endfunction()
 
-
-function(check_sysctl TARGET OUT)
-    execute_process(COMMAND sysctl -n "${TARGET}"
-                    RESULT_VARIABLE SYSCTL_RET
-                    OUTPUT_VARIABLE SYSCTL_INFO
-                    ERROR_QUIET
-                    OUTPUT_STRIP_TRAILING_WHITESPACE)
-    if(SYSCTL_RET EQUAL 0 AND
-      (SYSCTL_INFO STREQUAL "1" OR SYSCTL_INFO GREATER 0))
-        set(${OUT} ON PARENT_SCOPE)
-    else()
-        set(${OUT} OFF PARENT_SCOPE)
-    endif()
-endfunction()
-
-
 function (is_avx512_disabled OUT)
     set(DISABLE_AVX512 $ENV{VLLM_CPU_DISABLE_AVX512})
     if(DISABLE_AVX512 AND DISABLE_AVX512 STREQUAL "true")
@@ -86,10 +71,7 @@ endfunction()
 is_avx512_disabled(AVX512_DISABLED)
 
 if (MACOSX_FOUND AND CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64")
-    message(STATUS "Apple Silicon Detected")
-    set(ENABLE_NUMA OFF)
-    check_sysctl(hw.optional.neon ASIMD_FOUND)
-    check_sysctl(hw.optional.arm.FEAT_BF16 ARM_BF16_FOUND)
+    set(APPLE_SILICON_FOUND TRUE)
 else()
     find_isa(${CPUINFO} "avx2" AVX2_FOUND)
     find_isa(${CPUINFO} "avx512f" AVX512_FOUND)
@@ -101,6 +83,7 @@ else()
     find_isa(${CPUINFO} "S390" S390_FOUND)
 endif()
 
+
 if (AVX512_FOUND AND NOT AVX512_DISABLED)
     list(APPEND CXX_COMPILE_FLAGS
         "-mavx512f"
@@ -113,30 +96,12 @@ if (AVX512_FOUND AND NOT AVX512_DISABLED)
         if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND
             CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 12.3)
             list(APPEND CXX_COMPILE_FLAGS "-mavx512bf16")
-            set(ENABLE_AVX512BF16 ON)
         else()
-            set(ENABLE_AVX512BF16 OFF)
             message(WARNING "Disable AVX512-BF16 ISA support, requires gcc/g++ >= 12.3")
         endif()
     else()
-        set(ENABLE_AVX512BF16 OFF)
         message(WARNING "Disable AVX512-BF16 ISA support, no avx512_bf16 found in local CPU flags." " If cross-compilation is required, please set env VLLM_CPU_AVX512BF16=1.")
     endif()
-
-    find_isa(${CPUINFO} "avx512_vnni" AVX512VNNI_FOUND)
-    if (AVX512VNNI_FOUND OR ENABLE_AVX512VNNI)
-        if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND
-            CMAKE_CXX_COMPILER_VERSION VERSION_GREATER_EQUAL 12.3)
-            list(APPEND CXX_COMPILE_FLAGS "-mavx512vnni")
-            set(ENABLE_AVX512VNNI ON)
-        else()
-            set(ENABLE_AVX512VNNI OFF)
-            message(WARNING "Disable AVX512-VNNI ISA support, requires gcc/g++ >= 12.3")
-        endif()
-    else()
-        set(ENABLE_AVX512VNNI OFF)
-        message(WARNING "Disable AVX512-VNNI ISA support, no avx512_vnni found in local CPU flags." " If cross-compilation is required, please set env VLLM_CPU_AVX512VNNI=1.")
-    endif()
     
 elseif (AVX2_FOUND)
     list(APPEND CXX_COMPILE_FLAGS "-mavx2")
@@ -167,6 +132,9 @@ elseif (ASIMD_FOUND)
         set(MARCH_FLAGS "-march=armv8.2-a+dotprod+fp16")  
     endif()
     list(APPEND CXX_COMPILE_FLAGS ${MARCH_FLAGS})     
+elseif(APPLE_SILICON_FOUND)
+    message(STATUS "Apple Silicon Detected")
+    set(ENABLE_NUMA OFF)
 elseif (S390_FOUND)
     message(STATUS "S390 detected")
     # Check for S390 VXE support
@@ -180,32 +148,17 @@ else()
 endif()
 
 #
-# Build oneDNN for W8A8 GEMM kernels (only for x86-AVX512 /ARM platforms)
-# Flag to enable ACL kernels for AARCH64 platforms
-if ( VLLM_BUILD_ACL STREQUAL "ON")
-    set(USE_ACL ON)
-else()
-    set(USE_ACL OFF)
-endif()
-
-if ((AVX512_FOUND AND NOT AVX512_DISABLED) OR ASIMD_FOUND)
+# Build oneDNN for W8A8 GEMM kernels (only for x86-AVX512 platforms)
+#
+if (AVX512_FOUND AND NOT AVX512_DISABLED)
     FetchContent_Declare(
         oneDNN
         GIT_REPOSITORY https://github.com/oneapi-src/oneDNN.git
-        GIT_TAG  v3.8.1
+        GIT_TAG  v3.7.1
         GIT_PROGRESS TRUE
         GIT_SHALLOW TRUE
     )
 
-    if(USE_ACL)
-        find_library(ARM_COMPUTE_LIBRARY NAMES arm_compute PATHS $ENV{ACL_ROOT_DIR}/build/)
-        if(NOT ARM_COMPUTE_LIBRARY)
-            message(FATAL_ERROR "Could not find ARM Compute Library: please set ACL_ROOT_DIR")
-        endif()
-        set(ONEDNN_AARCH64_USE_ACL "ON")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wl,-rpath,$ENV{ACL_ROOT_DIR}/build/")
-        endif()
-
     set(ONEDNN_LIBRARY_TYPE "STATIC")
     set(ONEDNN_BUILD_DOC "OFF")
     set(ONEDNN_BUILD_EXAMPLES "OFF")
@@ -278,29 +231,11 @@ if (AVX512_FOUND AND NOT AVX512_DISABLED)
         "csrc/cpu/quant.cpp"
         "csrc/cpu/shm.cpp"
         ${VLLM_EXT_SRC})
-    if (ENABLE_AVX512BF16 AND ENABLE_AVX512VNNI)
-        set(VLLM_EXT_SRC
-            "csrc/cpu/sgl-kernels/gemm.cpp"
-            "csrc/cpu/sgl-kernels/gemm_int8.cpp"
-            "csrc/cpu/sgl-kernels/gemm_fp8.cpp"
-            "csrc/cpu/sgl-kernels/moe.cpp"
-            "csrc/cpu/sgl-kernels/moe_int8.cpp"
-            "csrc/cpu/sgl-kernels/moe_fp8.cpp"
-            ${VLLM_EXT_SRC})
-        add_compile_definitions(-DCPU_CAPABILITY_AVX512)
-    endif()
 elseif(POWER10_FOUND)
     set(VLLM_EXT_SRC
         "csrc/cpu/quant.cpp"
         ${VLLM_EXT_SRC})
 endif()
-if (ASIMD_FOUND)
-    set(VLLM_EXT_SRC
-        "csrc/cpu/quant.cpp"
-        ${VLLM_EXT_SRC})
-endif()
-
-message(STATUS "CPU extension source files: ${VLLM_EXT_SRC}")
 
 #
 # Define extension targets

cmake/external_projects/flashmla.cmake

@@ -19,7 +19,7 @@ else()
   FetchContent_Declare(
         flashmla
         GIT_REPOSITORY https://github.com/vllm-project/FlashMLA.git
-        GIT_TAG 0e43e774597682284358ff2c54530757b654b8d1
+        GIT_TAG 575f7724b9762f265bbee5889df9c7d630801845
         GIT_PROGRESS TRUE
         CONFIGURE_COMMAND ""
         BUILD_COMMAND ""
@@ -37,9 +37,9 @@ cuda_archs_loose_intersection(FLASH_MLA_ARCHS "9.0a" "${CUDA_ARCHS}")
 if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.3 AND FLASH_MLA_ARCHS)
     set(FlashMLA_SOURCES
         ${flashmla_SOURCE_DIR}/csrc/flash_api.cpp
-        ${flashmla_SOURCE_DIR}/csrc/kernels/splitkv_mla.cu
-        ${flashmla_SOURCE_DIR}/csrc/kernels/mla_combine.cu
-        ${flashmla_SOURCE_DIR}/csrc/kernels/get_mla_metadata.cu)
+        ${flashmla_SOURCE_DIR}/csrc/flash_fwd_mla_bf16_sm90.cu
+        ${flashmla_SOURCE_DIR}/csrc/flash_fwd_mla_fp16_sm90.cu
+        ${flashmla_SOURCE_DIR}/csrc/flash_fwd_mla_metadata.cu)
 
     set(FlashMLA_INCLUDES
         ${flashmla_SOURCE_DIR}/csrc/cutlass/include

cmake/external_projects/vllm_flash_attn.cmake

@@ -38,7 +38,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 93cf5a08f421a3efd0c4a7e005ef8f742b578ce0
+          GIT_TAG 763ad155a1c826f71ff318f41edb1e4e5e376ddb
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

cmake/utils.cmake

@@ -265,8 +265,8 @@ macro(set_gencode_flags_for_srcs)
 endmacro()
 
 #
-# For the given `SRC_CUDA_ARCHS` list of gencode versions in the form
-#  `<major>.<minor>[letter]` compute the "loose intersection" with the
+# For the given `SRC_CUDA_ARCHS` list of gencode versions in the form 
+#  `<major>.<minor>[letter]` compute the "loose intersection" with the 
 #  `TGT_CUDA_ARCHS` list of gencodes. We also support the `+PTX` suffix in
 #  `SRC_CUDA_ARCHS` which indicates that the PTX code should be built when there
 #  is a CUDA_ARCH in `TGT_CUDA_ARCHS` that is equal to or larger than the
@@ -278,7 +278,7 @@ endmacro()
 #  in `SRC_CUDA_ARCHS` that is less or equal to the version in `TGT_CUDA_ARCHS`.
 # We have special handling for x.0a, if x.0a is in `SRC_CUDA_ARCHS` and x.0 is
 #  in `TGT_CUDA_ARCHS` then we should remove x.0a from `SRC_CUDA_ARCHS` and add
-#  x.0a to the result (and remove x.0 from TGT_CUDA_ARCHS).
+#  x.0a to the result (and remove x.0 from TGT_CUDA_ARCHS). 
 # The result is stored in `OUT_CUDA_ARCHS`.
 #
 # Example:
@@ -313,16 +313,21 @@ function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_AR
   # if x.0a is in SRC_CUDA_ARCHS and x.0 is in CUDA_ARCHS then we should
   # remove x.0a from SRC_CUDA_ARCHS and add x.0a to _CUDA_ARCHS
   set(_CUDA_ARCHS)
-  foreach(_arch ${_SRC_CUDA_ARCHS})
-    if(_arch MATCHES "\\a$")
-      list(REMOVE_ITEM _SRC_CUDA_ARCHS "${_arch}")
-      string(REPLACE "a" "" _base "${_arch}")
-      if ("${_base}" IN_LIST TGT_CUDA_ARCHS)
-        list(REMOVE_ITEM _TGT_CUDA_ARCHS "${_base}")
-        list(APPEND _CUDA_ARCHS "${_arch}")
-      endif()
+  if ("9.0a" IN_LIST _SRC_CUDA_ARCHS)
+    list(REMOVE_ITEM _SRC_CUDA_ARCHS "9.0a")
+    if ("9.0" IN_LIST TGT_CUDA_ARCHS)
+      list(REMOVE_ITEM _TGT_CUDA_ARCHS "9.0")
+      set(_CUDA_ARCHS "9.0a")
     endif()
-  endforeach()
+  endif()
+
+  if ("10.0a" IN_LIST _SRC_CUDA_ARCHS)
+    list(REMOVE_ITEM _SRC_CUDA_ARCHS "10.0a")
+    if ("10.0" IN_LIST TGT_CUDA_ARCHS)
+      list(REMOVE_ITEM _TGT_CUDA_ARCHS "10.0")
+      set(_CUDA_ARCHS "10.0a")
+    endif()
+  endif()
 
   list(SORT _SRC_CUDA_ARCHS COMPARE NATURAL ORDER ASCENDING)
 
@@ -354,7 +359,7 @@ function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_AR
   endforeach()
 
   list(REMOVE_DUPLICATES _CUDA_ARCHS)
-
+  
   # reapply +PTX suffix to architectures that requested PTX
   set(_FINAL_ARCHS)
   foreach(_arch ${_CUDA_ARCHS})
@@ -365,7 +370,7 @@ function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_AR
     endif()
   endforeach()
   set(_CUDA_ARCHS ${_FINAL_ARCHS})
-
+  
   set(${OUT_CUDA_ARCHS} ${_CUDA_ARCHS} PARENT_SCOPE)
 endfunction()
 
@@ -467,12 +472,6 @@ function (define_gpu_extension_target GPU_MOD_NAME)
   if (GPU_LANGUAGE STREQUAL "HIP")
     # Make this target dependent on the hipify preprocessor step.
     add_dependencies(${GPU_MOD_NAME} hipify${GPU_MOD_NAME})
-    # Make sure we include the hipified versions of the headers, and avoid conflicts with the ones in the original source folder
-    target_include_directories(${GPU_MOD_NAME} PRIVATE ${CMAKE_CURRENT_BINARY_DIR}/csrc
-      ${GPU_INCLUDE_DIRECTORIES})
-  else()
-    target_include_directories(${GPU_MOD_NAME} PRIVATE csrc
-      ${GPU_INCLUDE_DIRECTORIES})
   endif()
 
   if (GPU_ARCHITECTURES)
@@ -488,6 +487,8 @@ function (define_gpu_extension_target GPU_MOD_NAME)
   target_compile_definitions(${GPU_MOD_NAME} PRIVATE
     "-DTORCH_EXTENSION_NAME=${GPU_MOD_NAME}")
 
+  target_include_directories(${GPU_MOD_NAME} PRIVATE csrc
+    ${GPU_INCLUDE_DIRECTORIES})
 
   target_link_libraries(${GPU_MOD_NAME} PRIVATE torch ${GPU_LIBRARIES})
 

csrc/attention/attention_kernels.cuh

@@ -24,7 +24,6 @@
 
 #include "attention_dtypes.h"
 #include "attention_utils.cuh"
-#include "../cuda_compat.h"
 
 #ifdef USE_ROCM
   #include <hip/hip_bf16.h>
@@ -34,6 +33,12 @@ typedef __hip_bfloat16 __nv_bfloat16;
   #include "../quantization/fp8/nvidia/quant_utils.cuh"
 #endif
 
+#ifndef USE_ROCM
+  #define WARP_SIZE 32
+#else
+  #define WARP_SIZE warpSize
+#endif
+
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
 #define DIVIDE_ROUND_UP(a, b) (((a) + (b) - 1) / (b))
@@ -665,6 +670,7 @@ __global__ void paged_attention_v2_reduce_kernel(
 
 }  // namespace vllm
 
+#undef WARP_SIZE
 #undef MAX
 #undef MIN
 #undef DIVIDE_ROUND_UP

csrc/attention/mla/cutlass_mla_kernels.cu

@@ -207,7 +207,7 @@ void cutlass_mla_decode_sm100a(torch::Tensor const& out,
               "page_table must be a 32-bit integer tensor");
 
   auto in_dtype = q_nope.dtype();
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(q_nope));
+  at::cuda::CUDAGuard device_guard{(char)q_nope.get_device()};
   const cudaStream_t stream =
       at::cuda::getCurrentCUDAStream(q_nope.get_device());
   if (in_dtype == at::ScalarType::Half) {

csrc/attention/mla/cutlass_sm100_mla/device/sm100_mla.hpp

@@ -1,372 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- * 1. Redistributions of source code must retain the above copyright notice,
- *this list of conditions and the following disclaimer.
- *
- * 2. Redistributions in binary form must reproduce the above copyright notice,
- * this list of conditions and the following disclaimer in the documentation
- * and/or other materials provided with the distribution.
- *
- * 3. Neither the name of the copyright holder nor the names of its
- * contributors may be used to endorse or promote products derived from
- * this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
- *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
- *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
- *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
- *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
- *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
- *POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*
- * Taken from SGLANG PR https://github.com/sgl-project/sglang/pull/6929
- * by Alcanderian JieXin Liang
- */
-
-/*!
- \file
- \brief An universal device layer for cutlass 3.x-style kernels.
-*/
-
-// clang-format off
-#pragma once
-
-// common
-#include "cutlass/cutlass.h"
-#include "cutlass/device_kernel.h"
-
-#if !defined(__CUDACC_RTC__)
-#include "cutlass/cluster_launch.hpp"
-#include "cutlass/trace.h"
-#endif // !defined(__CUDACC_RTC__)
-
-#include "../kernel/sm100_fmha_mla_tma_warpspecialized.hpp"
-#include "../kernel/sm100_fmha_mla_reduction.hpp"
-
-////////////////////////////////////////////////////////////////////////////////
-
-namespace cutlass::fmha::device {
-
-using namespace cute;
-using namespace cutlass::fmha::kernel;
-
-
-////////////////////////////////////////////////////////////////////////////////
-////////////////////////////// CUTLASS 3.x API /////////////////////////////////
-////////////////////////////////////////////////////////////////////////////////
-
-template<
-    class Kernel_
->
-class MLA {
-public:
-
-  using Kernel = Kernel_;
-
-  using ReductionKernel = cutlass::fmha::kernel::Sm100FmhaMlaReductionKernel<
-      typename Kernel::ElementOut,
-      typename Kernel::ElementAcc,
-      typename Kernel::ElementAcc,
-      Kernel::TileShapeH::value,
-      Kernel::TileShapeL::value,
-      256 /*Max split*/
-  >;
-
-  /// Argument structure: User API
-  using KernelArguments = typename Kernel::Arguments;
-  using ReductionArguments = typename ReductionKernel::Arguments;
-
-  using Arguments = KernelArguments;
-
-  /// Argument structure: Kernel API
-  using KernelParams = typename Kernel::Params;
-  using ReductionParams = typename ReductionKernel::Params;
-  struct Params {
-    KernelParams fmha_params;
-    ReductionParams reduction_params;
-  };
-
-private:
-
-  /// Kernel API parameters object
-  Params params_;
-
-  bool is_initialized(bool set = false) {
-    static bool initialized = false;
-    if (set) initialized = true;
-    return initialized;
-  }
-
-  static ReductionArguments to_reduction_args(Arguments const& args) {
-    auto [H, K, D, B] = args.problem_shape;
-    return ReductionArguments{
-      nullptr, args.epilogue.ptr_o, nullptr, args.epilogue.ptr_lse,
-      args.mainloop.softmax_scale, B, args.split_kv, K, args.mainloop.ptr_seq,
-      args.ptr_split_kv, Kernel::TileShapeS::value
-    };
-  }
-
-public:
-
-  /// Access the Params structure
-  Params const& params() const {
-    return params_;
-  }
-
-  static void set_split_kv (KernelArguments& args) {
-    // printf("set_split_kv start");
-    if (args.split_kv >= 1) return;
-    auto [H, K, D, B] = args.problem_shape;
-    // std::cout << H << " " << K << " " << D << " " << B << "\n";      
-    int sm_count = args.hw_info.sm_count;
-    // printf("    sm_count = %d\n", sm_count);
-    int max_splits = ceil_div(K, 128);
-    max_splits = min(16, max_splits);
-    // printf("    max_splits = %d\n", max_splits);
-    int sms_per_batch = max(1, sm_count / B);
-    // printf("    sms_per_batch = %d\n", sms_per_batch);
-    int split_heur = min(max_splits, sms_per_batch);
-    int waves = ceil_div(B * split_heur, sm_count);
-    int k_waves = ceil_div(max_splits, split_heur);
-    int split_wave_aware = ceil_div(max_splits, k_waves);
-    args.split_kv = split_wave_aware;
-    // printf("    args.split_kv = %d\n", args.split_kv);
-
-  }
-
-  /// Determines whether the GEMM can execute the given problem.
-  static Status
-  can_implement(Arguments const& args) {
-    if (! Kernel::can_implement(args)) {
-      return Status::kInvalid;
-    }
-    if (! ReductionKernel::can_implement(to_reduction_args(args))) {
-      return Status::kInvalid;
-    }
-    return Status::kSuccess;
-  }
-
-  /// Gets the workspace size
-  static size_t
-  get_workspace_size(Arguments const& args) {
-    size_t workspace_bytes = 0;
-    workspace_bytes += Kernel::get_workspace_size(args);
-    workspace_bytes += ReductionKernel::get_workspace_size(to_reduction_args(args));
-    return workspace_bytes;
-  }
-
-  /// Computes the maximum number of active blocks per multiprocessor
-  static int maximum_active_blocks(int /* smem_capacity */ = -1) {
-    CUTLASS_TRACE_HOST("MLA::maximum_active_blocks()");
-    int max_active_blocks = -1;
-    int smem_size = Kernel::SharedStorageSize;
-
-    // first, account for dynamic smem capacity if needed
-    cudaError_t result;
-    if (smem_size >= (48 << 10)) {
-      CUTLASS_TRACE_HOST("  Setting smem size to " << smem_size);
-      result = cudaFuncSetAttribute(
-          device_kernel<Kernel>,
-          cudaFuncAttributeMaxDynamicSharedMemorySize,
-          smem_size);
-      if (cudaSuccess != result) {
-        result = cudaGetLastError(); // to clear the error bit
-        CUTLASS_TRACE_HOST(
-          "  cudaFuncSetAttribute() returned error: "
-          << cudaGetErrorString(result));
-        return -1;
-      }
-    }
-
-    // query occupancy after setting smem size
-    result = cudaOccupancyMaxActiveBlocksPerMultiprocessor(
-        &max_active_blocks,
-        device_kernel<Kernel>,
-        Kernel::MaxThreadsPerBlock,
-        smem_size);
-
-    if (cudaSuccess != result) {
-      result = cudaGetLastError(); // to clear the error bit
-      CUTLASS_TRACE_HOST(
-        "  cudaOccupancyMaxActiveBlocksPerMultiprocessor() returned error: "
-        << cudaGetErrorString(result));
-      return -1;
-    }
-
-    CUTLASS_TRACE_HOST("  max_active_blocks: " << max_active_blocks);
-    return max_active_blocks;
-  }
-
-  /// Initializes GEMM state from arguments.
-  Status
-  initialize(Arguments const& args, void* workspace = nullptr, cudaStream_t stream = nullptr) {
-    CUTLASS_TRACE_HOST("MLA::initialize() - workspace "
-      << workspace << ", stream: " << (stream ? "non-null" : "null"));
-
-    // Initialize the workspace
-    Status status = Kernel::initialize_workspace(args, workspace, stream);
-    if (status != Status::kSuccess) {
-      return status;
-    }
-    status = ReductionKernel::initialize_workspace(to_reduction_args(args), workspace, stream);
-    if (status != Status::kSuccess) {
-      return status;
-    }
-    KernelParams kernel_params = Kernel::to_underlying_arguments(args, workspace);
-
-    ReductionArguments reduction_args = to_reduction_args(args);
-    if (reduction_args.split_kv > 1) {
-      reduction_args.ptr_oaccum   = kernel_params.epilogue.ptr_o_acc;
-      reduction_args.ptr_lseaccum = kernel_params.epilogue.ptr_lse_acc;
-    }
-    ReductionParams reduction_params = ReductionKernel::to_underlying_arguments(reduction_args, workspace);
-    // Initialize the Params structure
-    params_ = Params {kernel_params, reduction_params};
-
-    if (is_initialized()) return Status::kSuccess;
-
-    // account for dynamic smem capacity if needed
-    // no dynamic smem is needed for reduction kernel
-    int smem_size = Kernel::SharedStorageSize;
-    if (smem_size >= (48 << 10)) {
-      CUTLASS_TRACE_HOST("  Setting smem size to " << smem_size);
-      cudaError_t result = cudaFuncSetAttribute(
-          device_kernel<Kernel>,
-          cudaFuncAttributeMaxDynamicSharedMemorySize,
-          smem_size);
-      if (cudaSuccess != result) {
-        result = cudaGetLastError(); // to clear the error bit
-        CUTLASS_TRACE_HOST("  cudaFuncSetAttribute() returned error: " << cudaGetErrorString(result));
-        return Status::kErrorInternal;
-      }
-    }
-
-    is_initialized(true);
-
-    return Status::kSuccess;
-  }
-
-  /// Update API is preserved in 3.0, but does not guarantee a lightweight update of params.
-  Status
-  update(Arguments const& args, void* workspace = nullptr) {
-    CUTLASS_TRACE_HOST("MLA()::update() - workspace: " << workspace);
-
-    size_t workspace_bytes = get_workspace_size(args);
-    if (workspace_bytes > 0 && nullptr == workspace) {
-      return Status::kErrorWorkspaceNull;
-    }
-
-    auto fmha_params = Kernel::to_underlying_arguments(args, workspace);
-
-    ReductionArguments reduction_args = to_reduction_args(args);
-    if (reduction_args.split_kv > 1) {
-      reduction_args.ptr_oaccum   = fmha_params.epilogue.ptr_o_acc;
-      reduction_args.ptr_lseaccum = fmha_params.epilogue.ptr_lse_acc;
-    }
-    ReductionParams reduction_params = ReductionKernel::to_underlying_arguments(reduction_args, workspace);
-    // Initialize the Params structure
-    params_ = Params {fmha_params, reduction_params};
-
-    return Status::kSuccess;
-  }
-
-  /// Primary run() entry point API that is static allowing users to create and manage their own params.
-  /// Supplied params struct must be construct by calling Kernel::to_underling_arguments()
-  static Status
-  run(Params& params, cudaStream_t stream = nullptr) {
-    CUTLASS_TRACE_HOST("MLA::run()");
-    dim3 const block = Kernel::get_block_shape();
-    dim3 const grid = Kernel::get_grid_shape(params.fmha_params);
-
-    // configure smem size and carveout
-    int smem_size = Kernel::SharedStorageSize;
-
-    Status launch_result;
-    // Use extended launch API only for mainloops that use it
-    if constexpr(Kernel::ArchTag::kMinComputeCapability >= 90) {
-      dim3 cluster(cute::size<0>(typename Kernel::ClusterShape{}),
-                   cute::size<1>(typename Kernel::ClusterShape{}),
-                   cute::size<2>(typename Kernel::ClusterShape{}));
-      void const* kernel = (void const*) device_kernel<Kernel>;
-      void* kernel_params[] = {&params.fmha_params};
-      launch_result = ClusterLauncher::launch(grid, cluster, block, smem_size, stream, kernel, kernel_params);
-    }
-    else {
-      launch_result = Status::kSuccess;
-      device_kernel<Kernel><<<grid, block, smem_size, stream>>>(params.fmha_params);
-    }
-
-    cudaError_t result = cudaGetLastError();
-    if (cudaSuccess != result or Status::kSuccess != launch_result) {
-      //return Status::kSuccess;
-      CUTLASS_TRACE_HOST("  Kernel launch failed. Reason: " << result);
-      return Status::kErrorInternal;
-    }
-    if (params.reduction_params.split_kv > 1) {
-      // launch reduction kernel
-      dim3 const block = ReductionKernel::get_block_shape();
-      dim3 const grid  = ReductionKernel::get_grid_shape(params.reduction_params);
-      device_kernel<ReductionKernel><<<grid, block, 0, stream>>>(params.reduction_params);
-      cudaError_t result = cudaGetLastError();
-      if (cudaSuccess == result) {
-        return Status::kSuccess;
-      }
-      else {
-        CUTLASS_TRACE_HOST("  Kernel launch failed. Reason: " << result);
-        return Status::kErrorInternal;
-      }
-    }
-    else {
-      return Status::kSuccess;
-    }
-  }
-
-  //
-  // Non-static launch overloads that first create and set the internal params struct of this kernel handle.
-  //
-
-  /// Launches the kernel after first constructing Params internal state from supplied arguments.
-  Status
-  run(Arguments const& args, void* workspace = nullptr, cudaStream_t stream = nullptr) {
-    Status status = initialize(args, workspace, stream);
-    if (Status::kSuccess == status) {
-      status = run(params_, stream);
-    }
-    return status;
-  }
-
-  /// Launches the kernel after first constructing Params internal state from supplied arguments.
-  Status
-  operator()(Arguments const& args, void* workspace = nullptr, cudaStream_t stream = nullptr) {
-    return run(args, workspace, stream);
-  }
-
-  /// Overload that allows a user to re-launch the same kernel without updating internal params struct.
-  Status
-  run(cudaStream_t stream = nullptr) {
-    return run(params_, stream);
-  }
-
-  /// Overload that allows a user to re-launch the same kernel without updating internal params struct.
-  Status
-  operator()(cudaStream_t stream = nullptr) {
-    return run(params_, stream);
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////
-
-} // namespace cutlass::fmha::device
-
-////////////////////////////////////////////////////////////////////////////////

csrc/attention/mla/cutlass_sm100_mla/kernel/sm100_fmha_mla_reduction.hpp

@@ -1,203 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2024 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights
- *reserved. SPDX-License-Identifier: BSD-3-Clause
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- * 1. Redistributions of source code must retain the above copyright notice,
- *this list of conditions and the following disclaimer.
- *
- * 2. Redistributions in binary form must reproduce the above copyright notice,
- * this list of conditions and the following disclaimer in the documentation
- * and/or other materials provided with the distribution.
- *
- * 3. Neither the name of the copyright holder nor the names of its
- * contributors may be used to endorse or promote products derived from
- * this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
- *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
- *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
- *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
- *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
- *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
- *POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*
- * Taken from SGLANG PR https://github.com/sgl-project/sglang/pull/6929
- * by Alcanderian JieXin Liang
- */
-
-// clang-format off
-#pragma once
-
-#include "cutlass/cutlass.h"
-#include "cutlass/arch/arch.h"
-#include "cute/tensor.hpp"
-
-namespace cutlass::fmha::kernel {
-
-using namespace cute;
-template<
-    class ElementOut,
-    class ElementAcc,
-    class ElementScale,
-    size_t kNumHeads,
-    size_t kHeadDimLatent,
-    int kMaxSplits
->
-struct Sm100FmhaMlaReductionKernel {
-
-  static const int SharedStorageSize = 0;
-  static const int MaxThreadsPerBlock = 128;
-  static const int MinBlocksPerMultiprocessor = 1;
-
-  using ArchTag = cutlass::arch::Sm100;
-
-  static_assert(kHeadDimLatent % MaxThreadsPerBlock == 0);
-  struct Arguments {
-    ElementAcc* ptr_oaccum = nullptr;
-    ElementOut* ptr_o = nullptr;
-    ElementAcc* ptr_lseaccum = nullptr;
-    ElementAcc* ptr_lse = nullptr;
-    ElementScale scale = 1.f;
-    int num_batches = 0;
-    int split_kv = -1;
-    int dim_k = -1;
-    int* ptr_seq = nullptr;
-    int* ptr_split_kv = nullptr;
-    int tile_shape_s = 128;
-  };
-  using Params = Arguments;
-
-  static Params to_underlying_arguments(Arguments const& args, void* workspace) {
-    return {args.ptr_oaccum, args.ptr_o, args.ptr_lseaccum, args.ptr_lse,
-	    args.scale, args.num_batches, args.split_kv, args.dim_k, args.ptr_seq,
-	    args.ptr_split_kv, args.tile_shape_s};
-  }
-
-  static size_t get_workspace_size(Arguments const& /*args*/) {
-    return 0;
-  }
-
-  static Status initialize_workspace(
-      Arguments const& /*args*/, void* /*ws*/, cudaStream_t /*stream*/) {
-    return Status::kSuccess;
-  }
-
-  static dim3 get_grid_shape(Params const& params) {
-    return dim3(kNumHeads, 1, params.num_batches);
-  }
-
-  static dim3 get_block_shape() {
-    return dim3(MaxThreadsPerBlock, 1, 1);
-  }
-
-  static bool can_implement(Arguments const& args) {
-    if (args.num_batches <= 0) return false;
-    if (args.split_kv <= 0) return false;
-    return true;
-  }
-
-  CUTLASS_DEVICE void operator() (Params const& params, char* smem_raw) {
-    if (params.split_kv <= 1) return;
-    auto blk_coord = make_coord(blockIdx.x, _0{}, blockIdx.z);
-
-    __shared__ ElementAcc sLseScale[kMaxSplits];
-    const size_t offset_lseaccum = get<0>(blk_coord) + kNumHeads * params.split_kv * get<2>(blk_coord);
-    const size_t offset_lse = get<0>(blk_coord) + kNumHeads * get<2>(blk_coord);
-
-    Tensor gLSEaccum = make_tensor(make_gmem_ptr(params.ptr_lseaccum + offset_lseaccum),
-                                   make_shape(params.split_kv), Stride<Int<kNumHeads>>{});
-
-    Tensor gLSE = make_tensor(make_gmem_ptr(params.ptr_lse + offset_lse),
-                              Shape<_1>{}, Stride<_1>{});
-
-    auto dim_k = params.ptr_seq == nullptr ?  params.dim_k : params.ptr_seq[get<2>(blk_coord)];
-    auto local_split_kv = params.ptr_split_kv == nullptr ? params.split_kv : params.ptr_split_kv[get<2>(blk_coord)];
-    auto k_tile_total = ceil_div(dim_k, params.tile_shape_s);
-    auto k_tile_per_cta = ceil_div(k_tile_total, local_split_kv);
-    local_split_kv = ceil_div(k_tile_total, k_tile_per_cta);
-
-    int warp_idx = cutlass::canonical_warp_idx_sync();
-    if (warp_idx == 0) {
-      constexpr int kNLsePerThread = cute::ceil_div(kMaxSplits, 32);
-
-      ElementAcc local_lse[kNLsePerThread];
-
-      CUTLASS_PRAGMA_UNROLL
-      for (int i = 0; i < kNLsePerThread; ++i) {
-        const int split = i * 32 + threadIdx.x;
-        local_lse[i] = split < local_split_kv ? gLSEaccum(split) : -std::numeric_limits<ElementAcc>::infinity();
-      }
-
-      ElementAcc lse_max = -std::numeric_limits<ElementAcc>::infinity();
-      CUTLASS_PRAGMA_UNROLL
-      for (int i = 0; i < kNLsePerThread; ++i) {
-        lse_max = max(lse_max, local_lse[i]);
-      }
-      CUTLASS_PRAGMA_UNROLL
-      for (int offset = 16; offset >= 1; offset /= 2) {
-        lse_max = max(lse_max, __shfl_xor_sync(0xffffffff, lse_max, offset));
-      }
-      lse_max = lse_max == -std::numeric_limits<ElementAcc>::infinity() ? 0.0f : lse_max;  // In case all local LSEs are -inf
-      lse_max = __shfl_sync(0xffffffff, lse_max, 0);
-
-      ElementAcc sum_lse = 0;
-      CUTLASS_PRAGMA_UNROLL
-      for (int i = 0; i < kNLsePerThread; ++i) {
-        sum_lse = sum_lse + expf(local_lse[i] - lse_max);
-      }
-
-      CUTLASS_PRAGMA_UNROLL
-      for (int offset = 16; offset >= 1; offset /= 2) {
-        sum_lse = sum_lse + __shfl_xor_sync(0xffffffff, sum_lse, offset);
-      }
-
-      sum_lse = __shfl_sync(0xffffffff, sum_lse, 0);
-
-      ElementAcc global_lse = (sum_lse == 0.f || sum_lse != sum_lse) ? std::numeric_limits<ElementAcc>::infinity() : logf(sum_lse) + lse_max;
-      if (threadIdx.x == 0 and params.ptr_lse != nullptr) {
-        gLSE(0) = global_lse;
-      }
-
-      CUTLASS_PRAGMA_UNROLL
-      for (int i = 0; i < kNLsePerThread; ++i) {
-        const int split = i * 32 + threadIdx.x;
-        if (split < local_split_kv) {
-          sLseScale[split] = expf(local_lse[i] - global_lse);
-        }
-      }
-    }
-    __syncthreads();
-
-    constexpr int Elements = kHeadDimLatent / MaxThreadsPerBlock;
-    const size_t offset_oaccum = kHeadDimLatent * params.split_kv * (get<0>(blk_coord) + kNumHeads * get<2>(blk_coord));
-    Tensor gOaccum = make_tensor(make_gmem_ptr(params.ptr_oaccum + offset_oaccum),
-                               Shape<Int<kHeadDimLatent>>{}, Stride<_1>{});
-    ElementAcc local_val[Elements] = {0};
-    for (int split = 0; split < local_split_kv; ++split) {
-      ElementAcc lse_scale = sLseScale[split];
-      CUTLASS_PRAGMA_UNROLL
-      for(int i = 0; i < Elements; ++i) {
-        local_val[i] += lse_scale * gOaccum(threadIdx.x + MaxThreadsPerBlock * i);
-      }
-      gOaccum.data() = gOaccum.data() + kHeadDimLatent;
-    }
-    auto ptr_o_local = params.ptr_o + (get<0>(blk_coord) + get<2>(blk_coord) * kNumHeads) * kHeadDimLatent;
-    Tensor gO = make_tensor(make_gmem_ptr(ptr_o_local), Shape<Int<kHeadDimLatent>>{}, Stride<_1>{});
-
-    CUTLASS_PRAGMA_UNROLL
-    for(int i = 0; i < Elements; ++i) {
-      gO(threadIdx.x + MaxThreadsPerBlock * i) = static_cast<ElementOut>(local_val[i]);
-    }
-  }
-};
-
-}  // namespace cutlass::fmha::kernel

csrc/attention/mla/cutlass_sm100_mla/kernel/sm100_mla_tile_scheduler.hpp

@@ -1,165 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2024 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights
- *reserved. SPDX-License-Identifier: BSD-3-Clause
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- * 1. Redistributions of source code must retain the above copyright notice,
- *this list of conditions and the following disclaimer.
- *
- * 2. Redistributions in binary form must reproduce the above copyright notice,
- * this list of conditions and the following disclaimer in the documentation
- * and/or other materials provided with the distribution.
- *
- * 3. Neither the name of the copyright holder nor the names of its
- * contributors may be used to endorse or promote products derived from
- * this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
- *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
- *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
- *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
- *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
- *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
- *POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*
- * Taken from SGLANG PR https://github.com/sgl-project/sglang/pull/6929
- * by Alcanderian JieXin Liang
- */
-
-// clang-format off
-#pragma once
-
-#include "cutlass/cutlass.h"
-#include "cutlass/fast_math.h"
-#include "cutlass/kernel_hardware_info.h"
-
-namespace cutlass::fmha::kernel {
-
-////////////////////////////////////////////////////////////////////////////////
-
-struct Sm100MlaIndividualTileScheduler {
-
-  struct Params {
-    dim3 grid;
-  };
-
-  bool valid_ = true;
-
-  CUTLASS_DEVICE
-  Sm100MlaIndividualTileScheduler(Params const&) {}
-
-  template<class ProblemShape, class ClusterShape>
-  static Params to_underlying_arguments(
-      ProblemShape const& problem_shape, KernelHardwareInfo hw_info,
-      ClusterShape const& cluster_shape, int const& split_kv) {
-    using namespace cute;
-    dim3 grid(get<0>(cluster_shape), get<3>(problem_shape) /* Batch */, split_kv /*Maximum Split KV*/);
-    return Params{ grid };
-  }
-
-  static dim3 get_grid_shape(Params const& params) {
-    return params.grid;
-  }
-
-  CUTLASS_DEVICE
-  bool is_valid() {
-    return valid_;
-  }
-
-  CUTLASS_DEVICE
-  auto get_block_coord() {
-    using namespace cute;
-    return make_coord(blockIdx.x, _0{}, blockIdx.y, blockIdx.z);
-  }
-
-  CUTLASS_DEVICE
-  Sm100MlaIndividualTileScheduler& operator++() {
-    valid_ = false;
-    return *this;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////
-
-struct Sm100MlaPersistentTileScheduler {
-
-  struct Params {
-    int num_blocks;
-    FastDivmod divmod_m_block;
-    FastDivmod divmod_b;
-    FastDivmod divmod_split_kv;
-    KernelHardwareInfo hw_info;
-  };
-
-  int block_idx = 0;
-  Params params;
-
-  CUTLASS_DEVICE
-  Sm100MlaPersistentTileScheduler(Params const& params) : block_idx(blockIdx.x), params(params) {}
-
-  template<class ProblemShape, class ClusterShape>
-  static Params to_underlying_arguments(
-      ProblemShape const& problem_shape, KernelHardwareInfo hw_info,
-      ClusterShape const& cluster_shape, int const& split_kv) {
-    using namespace cute;
-    // Get SM count if needed, otherwise use user supplied SM count
-    int sm_count = hw_info.sm_count;
-    if (sm_count <= 1 || sm_count % size<0>(cluster_shape) != 0) {
-      CUTLASS_TRACE_HOST("  WARNING: Arguments do not include a valid SM count.\n"
-          "  For optimal performance, populate the arguments KernelHardwareInfo struct with the SM count.");
-      sm_count = KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
-    }
-
-    CUTLASS_TRACE_HOST("to_underlying_arguments(): Setting persistent grid SM count to " << sm_count);
-    hw_info.sm_count = sm_count;
-
-    int num_m_blocks = size<0>(cluster_shape);
-    int num_blocks = num_m_blocks * get<3>(problem_shape)  /* Batch */;
-    num_blocks *= split_kv; /* Maximum Split KV*/
-
-    return Params {
-      num_blocks,
-      { num_m_blocks}, { get<3>(problem_shape) }, {split_kv},
-      hw_info
-    };
-  }
-
-  static dim3 get_grid_shape(Params const& params) {
-    dim3 grid(std::min(params.num_blocks, params.hw_info.sm_count), 1, 1);
-    return grid;
-  }
-
-  CUTLASS_DEVICE
-  bool is_valid() {
-    return block_idx < params.num_blocks;
-  }
-
-  CUTLASS_DEVICE
-  auto get_block_coord() {
-    using namespace cute;
-    int block_decode = block_idx;
-    int m_block, bidb, n_split_kv;
-    params.divmod_m_block(block_decode, m_block, block_decode);
-    params.divmod_b(block_decode, bidb, block_decode);
-    params.divmod_split_kv(block_decode, n_split_kv, block_decode);
-    return make_coord(m_block, _0{}, bidb, n_split_kv);
-  }
-
-  CUTLASS_DEVICE
-  Sm100MlaPersistentTileScheduler& operator++() {
-    block_idx += gridDim.x;
-    return *this;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////
-
-} // namespace cutlass::fmha::kernel

csrc/attention/mla/sm100_cutlass_mla_kernel.cu

@@ -1,283 +0,0 @@
-/*
-Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
-Copyright 2025 SGLang Team. All Rights Reserved.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-    http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an "AS IS" BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-See the License for the specific language governing permissions and
-limitations under the License.
-==============================================================================*/
-/*
- * Taken from SGLANG PR https://github.com/sgl-project/sglang/pull/6929
- * by Alcanderian JieXin Liang
- */
-#include "core/registration.h"
-
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include <cutlass/cutlass.h>
-#include <cutlass/kernel_hardware_info.h>
-#include <torch/all.h>
-
-#include <cute/tensor.hpp>
-#include <iostream>
-
-#include "cutlass_sm100_mla/device/sm100_mla.hpp"
-#include "cutlass_sm100_mla/kernel/sm100_mla_tile_scheduler.hpp"
-
-// clang-format off
-#if !defined(CUDA_VERSION) || CUDA_VERSION < 12040
-void sm100_cutlass_mla_decode(
-    torch::Tensor const& out,
-    torch::Tensor const& q_nope,
-    torch::Tensor const& q_pe,
-    torch::Tensor const& kv_c_and_k_pe_cache,
-    torch::Tensor const& seq_lens,
-    torch::Tensor const& page_table,
-    torch::Tensor const& workspace,
-    int64_t num_kv_splits) {
-  TORCH_CHECK(false, "CUDA version must be >= 12.4 for cutlass_mla_decode");
-}
-int64_t sm100_cutlass_mla_get_workspace_size(int64_t max_seq_len, int64_t num_batches, int64_t sm_count, int64_t num_kv_splits) {
-  TORCH_CHECK(false, "CUDA version must be >= 12.4 for cutlass_mla_get_workspace_size");
-}
-#else
-
-#define CUTLASS_CHECK(status)                                                       \
-  {                                                                                 \
-    cutlass::Status error = status;                                                 \
-    TORCH_CHECK(error == cutlass::Status::kSuccess, cutlassGetStatusString(error)); \
-  }
-
-using namespace cute;
-using namespace cutlass::fmha::kernel;
-
-template <bool v>
-struct IsPersistent {
-  static const bool value = v;
-};
-
-template <typename T, bool IsPaged128, typename PersistenceOption = IsPersistent<true>>
-struct MlaSm100 {
-  using Element = T;
-  using ElementAcc = float;
-  using ElementOut = T;
-
-  using TileShape = Shape<_128, _128, Shape<_512, _64>>;
-  using TileShapeH = cute::tuple_element_t<0, TileShape>;
-  using TileShapeD = cute::tuple_element_t<2, TileShape>;
-
-  // H K (D_latent D_rope) B
-  using ProblemShape = cute::tuple<TileShapeH, int, TileShapeD, int>;
-
-  using StrideQ = cute::tuple<int64_t, _1, int64_t>;  // H D B
-  using StrideK = cute::tuple<int64_t, _1, int64_t>;  // K D B
-  using StrideO = StrideK;                            // H D B
-  using StrideLSE = cute::tuple<_1, int>;             // H B
-
-  using TileScheduler =
-      std::conditional_t<PersistenceOption::value, Sm100MlaPersistentTileScheduler, Sm100MlaIndividualTileScheduler>;
-
-  using FmhaKernel = cutlass::fmha::kernel::Sm100FmhaMlaKernelTmaWarpspecialized<
-      TileShape,
-      Element,
-      ElementAcc,
-      ElementOut,
-      ElementAcc,
-      TileScheduler,
-      /*kIsCpAsync=*/!IsPaged128>;
-  using Fmha = cutlass::fmha::device::MLA<FmhaKernel>;
-};
-
-template <typename T>
-typename T::Fmha::Arguments args_from_options(
-    at::Tensor const& out,
-    at::Tensor const& q_nope,
-    at::Tensor const& q_pe,
-    at::Tensor const& kv_c_and_k_pe_cache,
-    at::Tensor const& seq_lens,
-    at::Tensor const& page_table,
-    double sm_scale,
-    int64_t num_kv_splits) {
-  cutlass::KernelHardwareInfo hw_info;
-  hw_info.device_id = q_nope.device().index();
-  hw_info.sm_count = cutlass::KernelHardwareInfo::query_device_multiprocessor_count(hw_info.device_id);
-
-  int batches = q_nope.sizes()[0];
-  int page_count_per_seq = page_table.sizes()[1];
-  int page_count_total = kv_c_and_k_pe_cache.sizes()[0];
-  int page_size = kv_c_and_k_pe_cache.sizes()[1];
-  int max_seq_len = page_size * page_count_per_seq;
-  using TileShapeH = typename T::TileShapeH;
-  using TileShapeD = typename T::TileShapeD;
-  auto problem_shape = cute::make_tuple(TileShapeH{}, max_seq_len, TileShapeD{}, batches);
-
-  auto [H, K, D, B] = problem_shape;
-  auto [D_latent, D_rope] = D;
-
-  float scale = float(sm_scale);
-
-  using StrideQ = typename T::StrideQ;
-  using StrideK = typename T::StrideK;
-  using StrideO = typename T::StrideO;
-  using StrideLSE = typename T::StrideLSE;
-
-  StrideQ stride_Q_nope = cute::make_tuple(
-      static_cast<int64_t>(q_nope.stride(1)), _1{}, static_cast<int64_t>(q_nope.stride(0)));
-  StrideQ stride_Q_pe = cute::make_tuple(
-      static_cast<int64_t>(q_pe.stride(1)), _1{}, static_cast<int64_t>(q_pe.stride(0)));
-
-  StrideK stride_C = cute::make_tuple(
-      static_cast<int64_t>(0 + D_latent + D_rope), _1{}, static_cast<int64_t>(page_size * (D_latent + D_rope)));
-  StrideLSE stride_PT = cute::make_stride(_1{}, page_count_per_seq);
-  StrideLSE stride_LSE = cute::make_tuple(_1{}, 0 + H);
-  StrideO stride_O = cute::make_tuple(static_cast<int64_t>(0 + D_latent), _1{}, static_cast<int64_t>(0 + H * D_latent));
-
-  using Element = typename T::Element;
-  using ElementOut = typename T::ElementOut;
-  using ElementAcc = typename T::ElementAcc;
-  auto Q_nope_ptr = static_cast<Element*>(q_nope.data_ptr());
-  auto Q_pe_ptr = static_cast<Element*>(q_pe.data_ptr());
-  auto C_ptr = static_cast<Element*>(kv_c_and_k_pe_cache.data_ptr());
-  typename T::Fmha::Arguments arguments{
-      problem_shape,
-      {scale,
-       Q_nope_ptr,
-       stride_Q_nope,
-       Q_pe_ptr,
-       stride_Q_pe,
-       C_ptr,
-       stride_C,
-       C_ptr + D_latent,
-       stride_C,
-       static_cast<int*>(seq_lens.data_ptr()),
-       static_cast<int*>(page_table.data_ptr()),
-       stride_PT,
-       page_count_total,
-       page_size},
-      {static_cast<ElementOut*>(out.data_ptr()), stride_O, static_cast<ElementAcc*>(nullptr), stride_LSE},
-      hw_info,
-      // TODO(trevor-m): Change split_kv back to -1 when
-      // https://github.com/NVIDIA/cutlass/issues/2274 is fixed. Split_kv=1 will
-      // perform worse with larger context length and smaller batch sizes.
-      num_kv_splits, // split_kv
-      nullptr,       // is_var_split_kv
-  };
-  // TODO(kaixih@nvidia): When split_kv=-1 and is_var_split_kv=false, we compute
-  // split_kv automatically based on batch size and sequence length to balance
-  // workload across available SMs. Consider using var_split_kv for manual
-  // control if needed.
-  T::Fmha::set_split_kv(arguments);
-  return arguments;
-}
-
-template <typename Element, bool IsPaged128, typename PersistenceOption>
-void runMla(
-    at::Tensor const& out,
-    at::Tensor const& q_nope,
-    at::Tensor const& q_pe,
-    at::Tensor const& kv_c_and_k_pe_cache,
-    at::Tensor const& seq_lens,
-    at::Tensor const& page_table,
-    at::Tensor const& workspace,
-    double sm_scale,
-    int64_t num_kv_splits,
-    cudaStream_t stream) {
-  using MlaSm100Type = MlaSm100<Element, IsPaged128, PersistenceOption>;
-  typename MlaSm100Type::Fmha fmha;
-  auto arguments = args_from_options<MlaSm100Type>(out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, sm_scale, num_kv_splits);
-
-  CUTLASS_CHECK(fmha.can_implement(arguments));
-
-  CUTLASS_CHECK(fmha.initialize(arguments, workspace.data_ptr(), stream));
-
-  CUTLASS_CHECK(fmha.run(arguments, workspace.data_ptr(), stream));
-}
-
-#define DISPATCH_BOOL(expr, const_expr, ...) \
-  [&]() -> bool {                            \
-    if (expr) {                              \
-      constexpr bool const_expr = true;      \
-      return __VA_ARGS__();                  \
-    } else {                                 \
-      constexpr bool const_expr = false;     \
-      return __VA_ARGS__();                  \
-    }                                        \
-  }()
-
-void sm100_cutlass_mla_decode(
-    torch::Tensor const& out,
-    torch::Tensor const& q_nope,
-    torch::Tensor const& q_pe,
-    torch::Tensor const& kv_c_and_k_pe_cache,
-    torch::Tensor const& seq_lens,
-    torch::Tensor const& page_table,
-    torch::Tensor const& workspace,
-    double sm_scale,
-    int64_t num_kv_splits) {
-  auto in_dtype = q_nope.dtype();
-  at::cuda::CUDAGuard device_guard{(char)q_nope.get_device()};
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream(q_nope.get_device());
-  const int page_size = kv_c_and_k_pe_cache.sizes()[1];
-  
-  // NOTE(alcanderian): IsPersistent has bug with manual split_kv.
-  // Kernel will hang if batch is too large with large num_kv_splits. (for example bs=8, num_kv_splits=8)
-  // Maybe per batch split kv will fix this.
-  DISPATCH_BOOL(page_size == 128, IsPaged128, [&] {
-    DISPATCH_BOOL(num_kv_splits <= 1, NotManualSplitKV, [&] {
-      if (in_dtype == at::ScalarType::Half) {
-        runMla<cutlass::half_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
-          out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
-      } else if (in_dtype == at::ScalarType::BFloat16) {
-        runMla<cutlass::bfloat16_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
-          out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
-      } else if (in_dtype == at::ScalarType::Float8_e4m3fn) {
-        runMla<cutlass::float_e4m3_t, IsPaged128, IsPersistent<NotManualSplitKV>>(
-          out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, workspace, sm_scale, num_kv_splits, stream);
-      } else {
-        TORCH_CHECK(false, "Unsupported input data type of MLA");
-      }
-      return true;
-    });
-    return true;
-  });
-}
-
-int64_t sm100_cutlass_mla_get_workspace_size(int64_t max_seq_len, int64_t num_batches, int64_t sm_count, int64_t num_kv_splits) {
-  // Workspace size depends on ElementAcc and ElementLSE (same as ElementAcc)
-  // which are float, so Element type here doesn't matter.
-  using MlaSm100Type = MlaSm100<cutlass::half_t, true>;
-
-  // Get split kv. Requires problem shape and sm_count only.
-  typename MlaSm100Type::Fmha::Arguments arguments;
-  using TileShapeH = typename MlaSm100Type::TileShapeH;
-  using TileShapeD = typename MlaSm100Type::TileShapeD;
-  arguments.problem_shape =
-      cute::make_tuple(TileShapeH{}, static_cast<int>(max_seq_len), TileShapeD{}, static_cast<int>(num_batches));
-  // Assumes device 0 when getting sm_count.
-  arguments.hw_info.sm_count =
-      sm_count <= 0 ? cutlass::KernelHardwareInfo::query_device_multiprocessor_count(/*device_id=*/0) : sm_count;
-  arguments.split_kv = num_kv_splits;
-  MlaSm100Type::Fmha::set_split_kv(arguments);
-
-  return MlaSm100Type::Fmha::get_workspace_size(arguments);
-}
-
-#endif
-
-TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CUDA, m) {
-  m.impl("sm100_cutlass_mla_decode", &sm100_cutlass_mla_decode);
-}
-
-TORCH_LIBRARY_IMPL_EXPAND(TORCH_EXTENSION_NAME, CatchAll, m) {
-  m.impl("sm100_cutlass_mla_get_workspace_size", &sm100_cutlass_mla_get_workspace_size);
-}
-
-// clang-format on

csrc/attention/paged_attention_v1.cu

@@ -16,8 +16,14 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+
 #include "attention_kernels.cuh"
-#include "../cuda_compat.h"
+
+#ifndef USE_ROCM
+  #define WARP_SIZE 32
+#else
+  #define WARP_SIZE warpSize
+#endif
 
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
@@ -74,7 +80,7 @@ void paged_attention_v1_launcher(
   const float* k_scale_ptr = reinterpret_cast<const float*>(k_scale.data_ptr());
   const float* v_scale_ptr = reinterpret_cast<const float*>(v_scale.data_ptr());
 
-  const int NUM_WARPS = NUM_THREADS / WARP_SIZE;
+  constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
   int padded_max_seq_len =
       DIVIDE_ROUND_UP(max_seq_len, BLOCK_SIZE) * BLOCK_SIZE;
   int logits_size = padded_max_seq_len * sizeof(float);
@@ -181,6 +187,7 @@ void paged_attention_v1(
                              CALL_V1_LAUNCHER_BLOCK_SIZE)
 }
 
+#undef WARP_SIZE
 #undef MAX
 #undef MIN
 #undef DIVIDE_ROUND_UP

csrc/attention/paged_attention_v2.cu

@@ -16,8 +16,14 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
+
 #include "attention_kernels.cuh"
-#include "../cuda_compat.h"
+
+#ifndef USE_ROCM
+  #define WARP_SIZE 32
+#else
+  #define WARP_SIZE warpSize
+#endif
 
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
@@ -78,7 +84,7 @@ void paged_attention_v2_launcher(
   const float* k_scale_ptr = reinterpret_cast<const float*>(k_scale.data_ptr());
   const float* v_scale_ptr = reinterpret_cast<const float*>(v_scale.data_ptr());
 
-  const int NUM_WARPS = NUM_THREADS / WARP_SIZE;
+  constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
   int max_num_partitions = DIVIDE_ROUND_UP(max_seq_len, PARTITION_SIZE);
   int logits_size = PARTITION_SIZE * sizeof(float);
   int outputs_size = (NUM_WARPS / 2) * head_size * sizeof(float);
@@ -191,6 +197,7 @@ void paged_attention_v2(
                              CALL_V2_LAUNCHER_BLOCK_SIZE)
 }
 
+#undef WARP_SIZE
 #undef MAX
 #undef MIN
 #undef DIVIDE_ROUND_UP