[feat] Add P/D attention select for draft model#9755
[feat] Add P/D attention select for draft model#9755zhyncs merged 11 commits intosgl-project:mainfrom
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Summary of Changes
Hello @Ximingwang-09, 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 completes the implementation for selecting distinct attention backends for the draft model within speculative decoding, specifically allowing separate configurations for its prefill (extend) and decode phases. This enhances the flexibility and performance tuning capabilities for the draft model's attention mechanisms.
Highlights
- Granular Attention Backend Control: Enables independent selection of attention backends for the draft model's prefill (draft_extend_attn_backend) and decode (draft_attn_backend) operations, moving beyond a single global attention_backend setting.
- Code Refactoring for Clarity: The init_attention_backend method has been refactored into smaller, more manageable private helper methods (_create_decode_backend, _create_draft_extend_backend, and specific create*_backend functions for each attention type), improving code organization and maintainability.
- Dynamic Backend Initialization: Implements a dynamic mapping system to initialize the correct attention backend based on the decode_attention_backend and prefill_attention_backend arguments provided to the server.
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Code Review
This pull request refactors the attention backend initialization for the draft model, allowing separate backends for prefill and decode modes. The changes significantly improve code structure and maintainability by breaking down a large if/elif block into smaller, more focused methods. My review includes a couple of suggestions to further reduce code duplication and improve clarity.
| def _create_trtllm_mla_decode_backend(self): | ||
| if not global_server_args_dict["use_mla_backend"]: | ||
| raise ValueError( | ||
| "trtllm_mla backend requires MLA model (use_mla_backend=True)." | ||
| ) | ||
| self.draft_extend_attn_backend = TRTLLMMLABackend( | ||
| self.draft_model_runner, | ||
| skip_prefill=False, | ||
|
|
||
| from sglang.srt.layers.attention.trtllm_mla_backend import ( | ||
| TRTLLMMLAMultiStepDraftBackend, | ||
| ) | ||
|
|
||
| self.has_prefill_wrapper_verify = True | ||
| return TRTLLMMLAMultiStepDraftBackend( | ||
| self.draft_model_runner, self.topk, self.speculative_num_steps | ||
| ) |
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The check for use_mla_backend is duplicated in _create_trtllm_mla_decode_backend and _create_trtllm_mla_prefill_backend. To improve maintainability, you can extract this logic into a helper method.
For example, you can add this helper method to the class:
def _check_mla_backend(self):
if not global_server_args_dict["use_mla_backend"]:
raise ValueError(
"trtllm_mla backend requires MLA model (use_mla_backend=True)."
)Then you can call self._check_mla_backend() here and in _create_trtllm_mla_prefill_backend.
def _create_trtllm_mla_decode_backend(self):
self._check_mla_backend()
from sglang.srt.layers.attention.trtllm_mla_backend import (
TRTLLMMLAMultiStepDraftBackend,
)
self.has_prefill_wrapper_verify = True
return TRTLLMMLAMultiStepDraftBackend(
self.draft_model_runner, self.topk, self.speculative_num_steps
)
Qiaolin-Yu
left a comment
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For the multiple _create_xxx_decode_backend functions, would writing it following the code here makes the implementation simpler?
Thanks for the suggestion! I think multiple |
…mingwang-09/sglang into pd_attention_select_for_draft
hmm. I think maybe using if/else here could make it easier to read, but just a nit. |
Co-authored-by: 纬杭 <ximing.wxm@antgroup.com>
Motivation
#9573 has already enabled compatibility of Hybrid Attention in Speculative Decoding. However, for the draft model, the functionality to select the attention backend separately for the extend mode and decode mode has not been fully implemented—this PR supplements and completes the implementation of this feature.
Modifications
Accuracy Tests
Launch the server
Test accuracy
Accuracy: 0.901
Benchmarking and Profiling
Checklist