[Kernel] DP + EP : GPTOSS + DeepEP-HighThroughput#22907
[Kernel] DP + EP : GPTOSS + DeepEP-HighThroughput#22907varun-sundar-rabindranath wants to merge 8 commits intovllm-project:mainfrom
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Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com> Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com >
Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com> Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com >
Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com>
Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com> Signed-off-by: Varun Sundar Rabindranath <vsundarr@redhat.com >
varun-sundar-rabindranath
requested review from
mgoin,
robertgshaw2-redhat,
tlrmchlsmth and
yewentao256
as code owners
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Code Review
This pull request integrates GPT-OSS with DeepEPHT, introducing a new path for Mixture-of-Experts (MoE) layers using flashinfer kernels, specifically for data and expert parallelism. It adds support for mxfp8 quantization and a new trtllm_moe layer. The changes are quite extensive. My review has identified some leftover debugging code and comments in vllm/model_executor/layers/quantization/mxfp4.py which should be removed before this pull request is merged. Given that the pull request description indicates debugging is still in progress, these findings serve as a reminder for cleanup.
| if False: | ||
| # TODO(varun) : remove before landing | ||
| return self._route_and_experts_example( | ||
| layer, x, router_logits, top_k, renormalize, use_grouped_topk, | ||
| topk_group, num_expert_group, global_num_experts, expert_map, | ||
| custom_routing_function, scoring_func, e_score_correction_bias, | ||
| apply_router_weight_on_input, activation, enable_eplb, | ||
| expert_load_view, logical_to_physical_map, | ||
| logical_replica_count) |
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This block of code is disabled with if False: and contains a TODO to remove it before landing. This debugging code should be removed from the final version of the pull request.
| else: | ||
| #pass | ||
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| if (envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8 | ||
| or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16): | ||
| # B200 code ?? | ||
| # Quant config shouldn't be None !! | ||
| return TrtLlmGenExperts(moe) | ||
| else: | ||
| # H100 code ?? | ||
| # you use matmul_ogs kernel here! | ||
| raise NotImplementedError( | ||
| "Mxfp4 does not support non-batched experts format for EP") |
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This else block contains leftover debugging comments and a #pass statement. These should be removed for production code to improve clarity and maintainability.
else:
if (envs.VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8
or envs.VLLM_USE_FLASHINFER_MOE_MXFP4_BF16):
return TrtLlmGenExperts(moe)
else:
raise NotImplementedError(
"Mxfp4 does not support non-batched experts format for EP")
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Also a side note, please use the evaluation strategy in the recipe instead of |
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This pull request has merge conflicts that must be resolved before it can be |
| logical_replica_count: Optional[torch.Tensor] = None | ||
| ) -> torch.Tensor: | ||
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| topk_weights, topk_ids = FusedMoE.select_experts( |
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topk_ids and topk_weights needs to be local and non local experts' id should be -1.
Or use global topk_ids and topk_weights, and provide local_expert_offset and local_num_experts.
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The topk_ids and topk_weights gets processed into,
use global topk_ids and topk_weights, and provide local_expert_offset and local_num_experts
in the all2alls. I verified that this is correct.
| None, | ||
| "tile_tokens_dim": | ||
| self._get_tile_tokens_dim(x_quant, topk, local_num_experts), | ||
| "routing_method_type": |
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routing_method_type is hardcoded to renormalize. Maybe add assertion above to make sure it's not using a different routing method.
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Update on debugging:
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| "MX-FP8 quantization. Please install it with" \ | ||
| "`pip install flashinfer`") from err | ||
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| return mxfp8_quantize(x, is_sf_swizzled_layout=False) |
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@IwakuraRein is this the right way to quantize bf16 activations to fp8 ? Thanks.
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Yes, when you are using mxfp4 x mxfp8 path.
mgoin
changed the title
If torch.compile is using garbage values to initialize |
3 participants
Purpose
Integrate GPTOSS with DeepEPHTPrepareFinalize
Commands:
server command:
VLLM_ALL2ALL_BACKEND="deepep_high_throughput" vllm serve openai/gpt-oss-20b --port 9010 --data-parallel-size 2 --enable-expert-parallel --no-enable-prefix-cachinglm_eval command:
lm_eval --model local-completions --tasks gsm8k --model_args model=openai/gpt-oss-20b,base_url=http://127.0.0.1:9010/v1/completions,num_concurrent=30,max_retries=3 --limit 100Issue: The server some times hangs / reports IMA. When the server runs through the lm_eval outputs are good. They look like
and match main TP.
Debugging:
This PR uses
trtllm_fp4_block_scale_routed_moefrom flashinfer. I narrowed the issue down to the flashinfer kernel.mPtrExpertCountsis not big enough.I am still debugging this.
Test Plan
Test Result
(Optional) Documentation Update
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.