83a4065b4b
### What this PR does / why we need it?
See https://github.com/vllm-project/vllm-ascend/pull/7402, pre-commit
hook will forbid init_logger(__name__) in vllm_ascend patch modules
- vLLM version: v0.17.0
- vLLM main:
8a680463fa
---------
Signed-off-by: wangli <wangli858794774@gmail.com>
69 lines
2.7 KiB
Python
69 lines
2.7 KiB
Python
import torch
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import torch.nn as nn
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from typing_extensions import override
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from vllm.config import VllmConfig
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from vllm.model_executor.model_loader import get_model
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from vllm.v1.spec_decode.utils import create_vllm_config_for_draft_model
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from vllm_ascend.spec_decode.eagle_proposer import SpecDecodeBaseProposer
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class AscendDraftModelProposer(SpecDecodeBaseProposer):
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def __init__(
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self,
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vllm_config: VllmConfig,
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device: torch.device,
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runner=None,
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):
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super().__init__(
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vllm_config=vllm_config,
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device=device,
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pass_hidden_states_to_model=False,
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runner=runner,
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)
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self._raise_if_vocab_size_mismatch()
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self._raise_if_draft_tp_mismatch()
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def _raise_if_vocab_size_mismatch(self):
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self.speculative_config.verify_equal_vocab_size_if_draft_model()
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def _raise_if_draft_tp_mismatch(self):
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# Note(Tomas Ruiz) If we run the target model with TP > 1 and
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# the draft model with TP = 1, then the different TP ranks collide.
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# Specifically when all ranks compile the draft model on rank 0
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# (because TP=1), then the torch compile cache is overwritten and corrupted.
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# We need a mechanism like this: https://github.com/vllm-project/vllm/pull/5414
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# To prevent this error, we assert that both TP sizes must be the same.
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spec_cfg = self.speculative_config
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tgt_tp = spec_cfg.target_parallel_config.tensor_parallel_size
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draft_tp = spec_cfg.draft_parallel_config.tensor_parallel_size
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if draft_tp != tgt_tp:
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raise ValueError(
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f"Currently, 'draft_tensor_parallel_size' and 'tensor_parallel_size' "
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f"must be the same. Got {draft_tp} and {tgt_tp}. "
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"Please pass 'draft_tensor_parallel_size' in the speculative_config."
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)
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def _get_model(self) -> nn.Module:
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# Draft models may be quantized or on different parallelism,
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# so we load them with a modified vllm config
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from vllm.compilation.backends import set_model_tag
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temp_vllm_config = create_vllm_config_for_draft_model(self.vllm_config)
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with set_model_tag("draft_model"):
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model = get_model(
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vllm_config=temp_vllm_config,
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prefix="draft_model",
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)
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return model
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@override
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def _maybe_share_embeddings(self, target_language_model: nn.Module) -> None:
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# Draft models don't share embeddings with the target model
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pass
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@override
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def _maybe_share_lm_head(self, target_language_model: nn.Module) -> None:
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# Draft models don't share lm_head with the target model
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pass
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