130 lines
5.0 KiB
Python
130 lines
5.0 KiB
Python
#
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# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""
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Patch: fix target_layer_num for Eagle3 draft models under Pipeline Parallelism.
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Upstream Eagle3 draft models (Eagle3LlamaForCausalLM, Eagle3DeepseekV2ForCausalLM)
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compute ``target_layer_num`` via ``model_config.get_num_layers(parallel_config)``
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which, under PP, returns the **per-PP-stage** count. This value feeds into the
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draft model's ``start_layer_id`` (used to build parameter name prefixes like
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``model.layers.<start_layer_id + i>``). With PP>1 the prefixes collide with
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the checkpoint (e.g. a 61-layer target + 2-way PP builds prefixes 31..34 while
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the checkpoint expects 61..64), breaking weight loading. Additionally,
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``config.target_layer_count`` (used to index ``layer_types`` for draft
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attention) ends up wrong.
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Fix: use ``get_total_num_hidden_layers()`` instead. This matches the
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checkpoint's global layer indices and keeps ``target_layer_count`` correct.
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Currently patches:
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- Eagle3LlamaForCausalLM (Qwen, LLaMA-based Eagle3 targets)
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- Eagle3DeepseekV2ForCausalLM / Eagle3DeepseekV3ForCausalLM (DeepSeek-V2/V3,
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Kimi K2/K2.6)
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"""
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import logging
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import torch
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import torch.nn as nn
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead
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from vllm.model_executor.models.deepseek_eagle3 import (
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DeepseekV2Eagle3Model,
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Eagle3DeepseekV2ForCausalLM,
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)
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from vllm.model_executor.models.llama_eagle3 import (
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Eagle3LlamaForCausalLM,
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LlamaModel,
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get_draft_quant_config,
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)
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from vllm.model_executor.models.utils import maybe_prefix
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logger = logging.getLogger(__name__)
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def _patched_eagle3_llama_init(self, *, vllm_config, prefix: str = ""):
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nn.Module.__init__(self)
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self.config = vllm_config.speculative_config.draft_model_config.hf_config
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if getattr(self.config, "draft_vocab_size", None) is None:
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base_vocab_size = getattr(self.config, "vocab_size", None)
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self.config.draft_vocab_size = base_vocab_size
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target_layer_num = vllm_config.model_config.get_total_num_hidden_layers()
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self.config.target_layer_count = target_layer_num
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self.model = LlamaModel(vllm_config=vllm_config, prefix="model", start_layer_id=target_layer_num)
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logit_scale = getattr(self.config, "logit_scale", 1.0)
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self.lm_head = ParallelLMHead(
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self.config.draft_vocab_size,
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self.config.hidden_size,
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quant_config=get_draft_quant_config(vllm_config),
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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self.logits_processor = LogitsProcessor(self.config.draft_vocab_size, scale=logit_scale)
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self.draft_id_to_target_id = nn.Parameter(
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torch.zeros(self.config.draft_vocab_size, dtype=torch.long),
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requires_grad=False,
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)
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self.use_parallel_drafting = vllm_config.speculative_config.parallel_drafting
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if self.use_parallel_drafting:
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self.register_buffer(
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"mask_hidden",
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torch.zeros(
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1,
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(3 if self.model.use_aux_hidden_state else 1) * self.config.hidden_size,
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),
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persistent=False,
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)
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def _patched_eagle3_deepseek_v2_init(self, *, vllm_config, prefix: str = ""):
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nn.Module.__init__(self)
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self.config = vllm_config.speculative_config.draft_model_config.hf_config
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if getattr(self.config, "draft_vocab_size", None) is None:
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base_vocab_size = getattr(self.config, "vocab_size", None)
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self.config.draft_vocab_size = base_vocab_size
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target_layer_num = vllm_config.model_config.get_total_num_hidden_layers()
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self.config.target_layer_count = target_layer_num
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self.model = DeepseekV2Eagle3Model(vllm_config=vllm_config, prefix="model", start_layer_id=target_layer_num)
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logit_scale = getattr(self.config, "logit_scale", 1.0)
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self.lm_head = ParallelLMHead(
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self.config.draft_vocab_size,
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self.config.hidden_size,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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self.logits_processor = LogitsProcessor(self.config.draft_vocab_size, scale=logit_scale)
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self.draft_id_to_target_id = nn.Parameter(
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torch.zeros(self.config.draft_vocab_size, dtype=torch.long),
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requires_grad=False,
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)
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Eagle3LlamaForCausalLM.__init__ = _patched_eagle3_llama_init
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Eagle3DeepseekV2ForCausalLM.__init__ = _patched_eagle3_deepseek_v2_init
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logger.info(
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"Patched Eagle3LlamaForCausalLM and Eagle3DeepseekV2ForCausalLM "
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"__init__ to use get_total_num_hidden_layers() for target_layer_num."
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)
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