[Feature] Integrate SM100 DeepGEMM support #20087

yewentao256 · 2025-06-25T17:55:57Z

Purpose

DeepGemm is updating to v2.0, which includes a new implementation for SM100 that expects block FP8 scales in E8M0 format deepseek-ai/DeepGEMM#112

Previous context: #19820

We add a wrapper to support both Hopper (1.x) and Blackwell (2.x) interfaces

Test

Unit Test

Acc Test

Qwen3

VLLM_USE_DEEP_GEMM=1 lm_eval   --model vllm   --model_args "pretrained=Qwen/Qwen3-30B-A3B-FP8,max_model_len=32768,enforce_eager=True"   --trust_remote_code   --tasks gsm8k   --num_fewshot 5   --batch_size auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8567|±  |0.0097|
|     |       |strict-match    |     5|exact_match|↑  |0.8893|±  |0.0086|

# No deepgemm
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8256|±  |0.0105|
|     |       |strict-match    |     5|exact_match|↑  |0.8878|±  |0.0087|

Qwen3 (DP+EP)

export VLLM_ALL2ALL_BACKEND="deepep_high_throughput"
VLLM_USE_DEEP_GEMM=1 lm_eval   --model vllm   --model_args "pretrained=Qwen/Qwen3-30B-A3B-FP8,data_parallel_size=2,max_model_len=32768,enable_expert_parallel=True,enforce_eager=True"   --trust_remote_code   --tasks gsm8k   --num_fewshot 5   --batch_size auto
|Tasks|Version|   Filter   |n-shot| Metric  |  |Value |  |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|   3|flexible-extract|   5|exact_match|↑ |0.8302|± |0.0103|
|   |    |strict-match  |   5|exact_match|↑ |0.8294|± |0.0104|
# No deepgemm
|Tasks|Version|   Filter   |n-shot| Metric  |  |Value |  |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|   3|flexible-extract|   5|exact_match|↑ |0.8150|± |0.0107|
|   |    |strict-match  |   5|exact_match|↑ |0.8848|± |0.0088|

R1(TP)

VLLM_USE_DEEP_GEMM=1 lm_eval  --model vllm  --model_args "pretrained=deepseek-ai/DeepSeek-R1,tensor_parallel_size=8,max_model_len=16384,enforce_eager=True"  --tasks gsm8k  --num_fewshot 5  --batch_size auto  --trust_remote_code
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9477|±  |0.0061|
|     |       |strict-match    |     5|exact_match|↑  |0.9484|±  |0.0061|
# No deepgemm
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9500|±  |0.0060|
|     |       |strict-match    |     5|exact_match|↑  |0.9477|±  |0.0061|

R1 DP+EP

export VLLM_ALL2ALL_BACKEND="deepep_high_throughput"
VLLM_USE_DEEP_GEMM=1 lm_eval   --model vllm   --model_args "pretrained=deepseek-ai/DeepSeek-R1,data_parallel_size=8,gpu_memory_utilization=0.95,max_model_len=16384,enable_expert_parallel=True"   --tasks gsm8k   --batch_size auto   --num_fewshot 5
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9492|±  | 0.006|
|     |       |strict-match    |     5|exact_match|↑  |0.9492|±  | 0.006|

# Nodeepgemm
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.9530|±  |0.0058|
|     |       |strict-match    |     5|exact_match|↑  |0.9522|±  |0.0059|

Performance

Kernel

python benchmark_moe.py --use-deep-gemm --dtype fp8_w8a8
Batch size: 1, config: {'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1}
Kernel time: 114.16 us
Batch size: 2, config: {'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1}
Kernel time: 142.56 us
Batch size: 4, config: {'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1}
Kernel time: 182.89 us
Batch size: 8, config: {'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1}
Kernel time: 223.89 us
Batch size: 16, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 702.21 us
Batch size: 24, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 706.32 us
Batch size: 32, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 712.53 us
Batch size: 48, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 715.12 us
Batch size: 64, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 719.69 us
Batch size: 96, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 727.46 us
Batch size: 128, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 540.07 us
Batch size: 256, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 584.94 us
Batch size: 512, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 680.25 us
Batch size: 1024, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 913.30 us
Batch size: 1536, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 1143.87 us
Batch size: 2048, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 1432.49 us
Batch size: 3072, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 1881.03 us
Batch size: 4096, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 2346.28 us

# No deepgemm
Batch size: 1, config: {'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1}
Kernel time: 106.60 us
Batch size: 2, config: {'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1}
Kernel time: 132.80 us
Batch size: 4, config: {'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1}
Kernel time: 163.22 us
Batch size: 8, config: {'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 1}
Kernel time: 188.97 us
Batch size: 16, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 234.29 us
Batch size: 24, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 237.68 us
Batch size: 32, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 243.71 us
Batch size: 48, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 247.41 us
Batch size: 64, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 251.17 us
Batch size: 96, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 261.22 us
Batch size: 128, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 269.07 us
Batch size: 256, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 399.63 us
Batch size: 512, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 574.25 us
Batch size: 1024, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 951.36 us
Batch size: 1536, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 1334.19 us
Batch size: 2048, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 1712.87 us
Batch size: 3072, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 2492.51 us
Batch size: 4096, config: {'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}
Kernel time: 3247.75 us

Qwen3

VLLM_USE_DEEP_GEMM=1 vllm bench throughput  --model Qwen/Qwen3-30B-A3B-FP8 --load-format dummy --input-len 1000 --output-len 100 --trust_remote_code --enforce-eager --enable-expert-parallel --quantization fp8
Throughput: 23.87 requests/s, 26204.10 total tokens/s, 2387.13 output tokens/s

# No deepgemm
Throughput: 40.88 requests/s, 44870.57 total tokens/s, 4087.60 output tokens/s

R1(TP)

VLLM_USE_DEEP_GEMM=1 vllm bench throughput  --model deepseek-ai/DeepSeek-R1 --load-format dummy --input-len 1000 --output-len 100 --trust_remote_code --enforce-eager --enable-expert-parallel  -tp 8
Throughput: 5.94 requests/s, 6523.81 total tokens/s, 593.93 output tokens/s
# NO deepgemm
Throughput: 8.52 requests/s, 9361.67 total tokens/s, 852.29 output tokens/s

R1 DP+EP

export VLLM_USE_DEEP_GEMM=1 
export VLLM_ALL2ALL_BACKEND="deepep_high_throughput"
export VLLM_RANDOMIZE_DP_DUMMY_INPUTS=1
vllm serve deepseek-ai/DeepSeek-R1        --load-format dummy        --trust-remote-code        --enforce-eager        --enable-expert-parallel        --data-parallel-size 8

vllm bench serve        --model deepseek-ai/DeepSeek-R1        --dataset-name random        --random-input-len 256        --random-output-len 100        --num-prompts 1000        --request-rate inf
============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  39.66     
Total input tokens:                      254242    
Total generated tokens:                  100000    
Request throughput (req/s):              25.21     
Output token throughput (tok/s):         2521.12   
Total Token throughput (tok/s):          8930.87   
---------------Time to First Token----------------
Mean TTFT (ms):                          3046.61   
Median TTFT (ms):                        3329.41   
P99 TTFT (ms):                           4115.32   
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          365.50    
Median TPOT (ms):                        363.06    
P99 TPOT (ms):                           374.11    
---------------Inter-token Latency----------------
Mean ITL (ms):                           365.50    
Median ITL (ms):                         355.88    
P99 ITL (ms):                            787.11    
==================================================

# No deepgemm
============ Serving Benchmark Result ============
Successful requests:                     1000      
Benchmark duration (s):                  75.61     
Total input tokens:                      254242    
Total generated tokens:                  100000    
Request throughput (req/s):              13.23     
Output token throughput (tok/s):         1322.57   
Total Token throughput (tok/s):          4685.11   
---------------Time to First Token----------------
Mean TTFT (ms):                          7876.30   
Median TTFT (ms):                        10187.52  
P99 TTFT (ms):                           11462.69  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          680.14    
Median TPOT (ms):                        658.94    
P99 TPOT (ms):                           739.41    
---------------Inter-token Latency----------------
Mean ITL (ms):                           680.14    
Median ITL (ms):                         646.89    
P99 ITL (ms):                            1273.78   
==================================================

Acc on H100

VLLM_USE_DEEP_GEMM=1 lm_eval   --model vllm   --model_args "pretrained=Qwen/Qwen3-30B-A3B-FP8,max_model_len=32768,enforce_eager=True"   --trust_remote_code   --tasks gsm8k   --num_fewshot 5   --batch_size auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8317|±  |0.0103|
|     |       |strict-match    |     5|exact_match|↑  |0.8961|±  |0.0084|
# without deepgemm
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8271|±  |0.0104|
|     |       |strict-match    |     5|exact_match|↑  |0.8999|±  |0.0083|

LLM_USE_DEEP_GEMM=1 lm_eval   --model vllm   --model_args "pretrained=Qwen/Qwen3-30B-A3B-FP8,data_parallel_size=2,max_model_len=32768,enable_expert_parallel=True,enforce_eager=True"   --trust_remote_code   --tasks gsm8k   --num_fewshot 5   --batch_size auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8180|±  |0.0106|
|     |       |strict-match    |     5|exact_match|↑  |0.8946|±  |0.0085|
# No deepgemm
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8143|±  |0.0107|
|     |       |strict-match    |     5|exact_match|↑  |0.8870|±  |0.0087|

Conclusion:

DeepGemm is good only for Deepseek models.
Integration of Deepgemm make great benefits for deepseek models. (1.8x E2E performance improvement)

Follow up TODOs:

Currently every shape goes to DeepGemm branch because they only support E8M0 scale on B200, so some smaller shape falling back to Triton would cause trouble to accuracy. This could be optimized if they support E4M3 in the future
Document about the best recipe for using MOE on B200
Current quant is just using Deepgemm's torch version, which could be slow, we can optimize it further.

Signed-off-by: yewentao256 <[email protected]>

…pgemm Signed-off-by: yewentao256 <[email protected]>

github-actions · 2025-06-25T17:56:06Z

👋 Hi! Thank you for contributing to the vLLM project.

💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels.

Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run fastcheck CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your fastcheck build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping simon-mo or khluu to add you in our Buildkite org.

Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.

To run CI, PR reviewers can either: Add ready label to the PR or enable auto-merge.

🚀

gemini-code-assist

Summary of Changes

Hello @yewentao256, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request is a significant migration effort to integrate the breaking API changes introduced in DeepGEMM v2.0 into the vLLM codebase. The primary goal is to maintain compatibility with the latest version of the DeepGEMM library, which is crucial for optimized FP8 GEMM operations. The changes involve updating function calls across various modules, adjusting import paths for DeepGEMM utilities, and temporarily incorporating some DeepGEMM helper functions directly into vLLM until they are officially re-exposed in DeepGEMM's public API.

Highlights

DeepGEMM API Migration: Updated all usages of the DeepGEMM library to its new v2.0 API, reflecting significant interface changes. This includes renaming core GEMM functions (e.g., gemm_fp8_fp8_bf16_nt to fp8_gemm_nt) and grouped GEMM functions (e.g., m_grouped_gemm_fp8_fp8_bf16_nt_masked to fp8_m_grouped_gemm_nt_masked).
Utility Function Relocation: Adjusted imports for DeepGEMM utility functions (like per_token_cast_to_fp8 and per_block_cast_to_fp8) to use their new locations within the deep_gemm.utils.math module. Additionally, some previously DeepGEMM-provided utilities (ceil_div, get_tma_aligned_size, get_col_major_tma_aligned_tensor) were copied locally into vLLM's fp8_utils.py as a temporary measure, with a TODO to remove them once DeepGEMM re-exposes them publicly.
Benchmark and Test Updates: Modified existing benchmarks (benchmark_fp8_block_dense_gemm.py, benchmark_moe.py) and unit tests (test_deepep_deepgemm_moe.py, test_block_fp8.py) to align with the new DeepGEMM API. This ensures continued correctness and performance evaluation of FP8 operations within vLLM.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point in your pull request via creating an issue comment (i.e. comment on the pull request page) using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands.

Feature	Command	Description
Code Review	`/gemini review`	Performs a code review for the current pull request in its current state.
Pull Request Summary	`/gemini summary`	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in issue comments and review comments.
Help	`/gemini help`	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist is currently in preview and may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments to provide feedback.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

gemini-code-assist

Code Review

This pull request integrates the new DeepGEMM library, updating the API interfaces and migrating to the new version. The changes include modifications to benchmark scripts and test files to accommodate the new DeepGEMM API. The code has been reviewed and suggestions have been provided to improve code clarity and efficiency.

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

mergify · 2025-07-02T14:24:03Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @yewentao256.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

…pgemm Signed-off-by: yewentao256 <[email protected]>

Signed-off-by: yewentao256 <[email protected]>

mergify · 2025-07-10T19:51:21Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @yewentao256.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

Signed-off-by: yewentao256 <[email protected]>

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm_b200.py

vllm/utils/deep_gemm.py

Signed-off-by: yewentao256 <[email protected]>

mgoin

LGTM, thank you for iterating on this

Signed-off-by: Patrick von Platen <[email protected]>

Signed-off-by: avigny <[email protected]>

yewentao256 and others added 10 commits June 18, 2025 16:45

integrate new deepgemm

5bd848f

Signed-off-by: yewentao256 <[email protected]>

update unit test

c78b3ca

Signed-off-by: yewentao256 <[email protected]>

update benchmark for dense gemm

3ec1674

Signed-off-by: yewentao256 <[email protected]>

add bench moe

e24cd09

Signed-off-by: yewentao256 <[email protected]>

add comments back

a6541d1

Signed-off-by: yewentao256 <[email protected]>

add deprecate

0a2005a

Signed-off-by: yewentao256 <[email protected]>

Merge branch 'vllm-project:main' into wye-integrate-new-deepgemm

de687b1

Merge branch 'vllm-project:main' into wye-integrate-new-deepgemm

eb9611f

Merge branch 'vllm-project:main' into wye-integrate-new-deepgemm

bd52201

Merge remote-tracking branch 'origin/main' into wye-integrate-new-dee…

78a10ec

…pgemm Signed-off-by: yewentao256 <[email protected]>

yewentao256 requested review from tlrmchlsmth, WoosukKwon, mgoin and robertgshaw2-redhat as code owners June 25, 2025 17:55

mergify bot added the performance Performance-related issues label Jun 25, 2025

gemini-code-assist bot reviewed Jun 25, 2025

View reviewed changes

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py Outdated Show resolved Hide resolved

Merge branch 'main' into wye-integrate-new-deepgemm

a6c8b9c

mergify bot added the needs-rebase label Jul 2, 2025

Merge remote-tracking branch 'origin/main' into wye-integrate-new-dee…

fb959c5

…pgemm Signed-off-by: yewentao256 <[email protected]>

mergify bot removed the needs-rebase label Jul 2, 2025

yewentao256 added 5 commits July 2, 2025 18:08

update to global diff

9fdf2c8

Signed-off-by: yewentao256 <[email protected]>

wrapper for deepgemm

43dc3d8

Signed-off-by: yewentao256 <[email protected]>

per block fp8

4bba04b

Signed-off-by: yewentao256 <[email protected]>

update

d9fd43e

Signed-off-by: yewentao256 <[email protected]>

add is new deep geem api

b2a740b

Signed-off-by: yewentao256 <[email protected]>

yewentao256 marked this pull request as draft July 3, 2025 21:20

integration of weights

07c1ad5

Signed-off-by: yewentao256 <[email protected]>

yewentao256 requested a review from aarnphm as a code owner July 10, 2025 19:28

mergify bot added the frontend label Jul 10, 2025

mergify bot added the needs-rebase label Jul 10, 2025

yewentao256 changed the title ~~[Do Not Merge Now] Integrate new deepgemm~~ [Feature] Integrate new deepgemm Jul 10, 2025

delete b200 benchmark for another pr

d9fcdb3

Signed-off-by: yewentao256 <[email protected]>

mgoin reviewed Jul 10, 2025

View reviewed changes

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm_b200.py Outdated Show resolved Hide resolved

vllm/utils/deep_gemm.py Outdated Show resolved Hide resolved

vllm/utils/deep_gemm.py Outdated Show resolved Hide resolved

vllm/utils/deep_gemm.py Outdated Show resolved Hide resolved

mgoin changed the title ~~[Feature] Integrate new deepgemm~~ [Feature] Integrate SM100 DeepGEMM support Jul 10, 2025

yewentao256 added 4 commits July 10, 2025 18:27

add device check

400232b

Signed-off-by: yewentao256 <[email protected]>

add is_blackwell_deep_gemm_used check

8e0548f

Signed-off-by: yewentao256 <[email protected]>

add comments for calc_diff

beaa23d

Signed-off-by: yewentao256 <[email protected]>

Merge branch 'main' into wye-integrate-new-deepgemm

a1fc38d

Signed-off-by: yewentao256 <[email protected]>

mergify bot removed the needs-rebase label Jul 10, 2025

mgoin approved these changes Jul 10, 2025

View reviewed changes

mgoin enabled auto-merge (squash) July 10, 2025 23:01

github-actions bot added the ready ONLY add when PR is ready to merge/full CI is needed label Jul 10, 2025

mgoin added quantization deepseek Related to DeepSeek models ready ONLY add when PR is ready to merge/full CI is needed and removed ready ONLY add when PR is ready to merge/full CI is needed labels Jul 10, 2025

Merge branch 'main' into wye-integrate-new-deepgemm

f490831

simon-mo merged commit e2de455 into vllm-project:main Jul 11, 2025
63 of 71 checks passed

yewentao256 mentioned this pull request Jul 11, 2025

[Perf] Use Triton instead of Torch for DeepGEMM Per Token Group Quant #20841

Merged

Chen-zexi pushed a commit to Chen-zexi/vllm that referenced this pull request Jul 13, 2025

[Feature] Integrate SM100 DeepGEMM support (vllm-project#20087)

1ede4f1

yewentao256 deleted the wye-integrate-new-deepgemm branch July 14, 2025 15:13

patrickvonplaten pushed a commit to patrickvonplaten/vllm that referenced this pull request Jul 15, 2025

[Feature] Integrate SM100 DeepGEMM support (vllm-project#20087)

cfbdc49

Signed-off-by: Patrick von Platen <[email protected]>

LyrisZhong pushed a commit to LyrisZhong/vllm that referenced this pull request Jul 23, 2025

[Feature] Integrate SM100 DeepGEMM support (vllm-project#20087)

77b1fe7

avigny pushed a commit to avigny/vllm that referenced this pull request Jul 31, 2025

[Feature] Integrate SM100 DeepGEMM support (vllm-project#20087)

d956e54

Signed-off-by: avigny <[email protected]>

Pradyun92 pushed a commit to Pradyun92/vllm that referenced this pull request Aug 6, 2025

[Feature] Integrate SM100 DeepGEMM support (vllm-project#20087)

6289a89

npanpaliya pushed a commit to odh-on-pz/vllm-upstream that referenced this pull request Aug 6, 2025

[Feature] Integrate SM100 DeepGEMM support (vllm-project#20087)

3eaecba

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Uh oh!

Uh oh!

[Feature] Integrate SM100 DeepGEMM support #20087

[Feature] Integrate SM100 DeepGEMM support #20087

Uh oh!

yewentao256 commented Jun 25, 2025 •

edited by github-actions bot

Loading

Uh oh!

github-actions bot commented Jun 25, 2025

Uh oh!

gemini-code-assist bot left a comment

Uh oh!

gemini-code-assist bot left a comment

Uh oh!

Uh oh!

mergify bot commented Jul 2, 2025

Uh oh!

mergify bot commented Jul 10, 2025

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

mgoin left a comment

Uh oh!

Uh oh!

Uh oh!

Uh oh!

[Feature] Integrate SM100 DeepGEMM support #20087

[Feature] Integrate SM100 DeepGEMM support #20087

Uh oh!

Conversation

yewentao256 commented Jun 25, 2025 • edited by github-actions bot Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Purpose

Test

Unit Test

Acc Test

Qwen3

Qwen3 (DP+EP)

R1(TP)

R1 DP+EP

Performance

Kernel

Qwen3

R1(TP)

R1 DP+EP

Acc on H100

Uh oh!

github-actions bot commented Jun 25, 2025

Uh oh!

gemini-code-assist bot left a comment

Choose a reason for hiding this comment

Summary of Changes

Highlights

Footnotes

Uh oh!

gemini-code-assist bot left a comment

Choose a reason for hiding this comment

Code Review

Uh oh!

Uh oh!

mergify bot commented Jul 2, 2025

Uh oh!

mergify bot commented Jul 10, 2025

Uh oh!

Uh oh!

Uh oh!

Uh oh!

Uh oh!

mgoin left a comment

Choose a reason for hiding this comment

Uh oh!

Uh oh!

Uh oh!

yewentao256 commented Jun 25, 2025 •

edited by github-actions bot

Loading