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e94aabe03d
Signed-off-by: Tyler Michael Smith <tlrmchlsmth@gmail.com> Signed-off-by: yewentao256 <zhyanwentao@126.com>
275 lines
11 KiB
Python
275 lines
11 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Copyright 2025 The Baidu team.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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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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"""Inference-only Ernie-MTP model."""
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from collections.abc import Iterable
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from typing import Optional
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import torch
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import torch.nn as nn
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from transformers import PretrainedConfig
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from vllm.config import VllmConfig
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from vllm.model_executor.layers.layernorm import RMSNorm
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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 (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsPP
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from .llama import LlamaDecoderLayer
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from .utils import is_pp_missing_parameter, maybe_prefix
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class ErnieMultiTokenPredictorLayer(nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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prefix: str,
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) -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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self.mtp_emb_norm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.mtp_hidden_norm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.mtp_linear_proj = nn.Linear(config.hidden_size * 2,
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config.hidden_size,
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bias=False)
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self.mtp_block = LlamaDecoderLayer(vllm_config, prefix)
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def forward(
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self,
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inputs_embeds: torch.Tensor,
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positions: torch.Tensor,
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previous_hidden_states: torch.Tensor,
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spec_step_index: int = 0,
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) -> torch.Tensor:
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assert inputs_embeds is not None
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# masking inputs at position 0, as not needed by MTP
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inputs_embeds[positions == 0] = 0
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inputs_embeds = self.mtp_emb_norm(inputs_embeds)
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previous_hidden_states = self.mtp_hidden_norm(previous_hidden_states)
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hidden_states = self.mtp_linear_proj(
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torch.cat([inputs_embeds, previous_hidden_states], dim=-1))
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hidden_states, residual = self.mtp_block(positions=positions,
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hidden_states=hidden_states,
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residual=None)
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hidden_states = residual + hidden_states
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return hidden_states
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class ErnieMultiTokenPredictor(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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config = vllm_config.model_config.hf_config
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self.mtp_start_layer_idx = config.num_hidden_layers
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self.num_mtp_layers = config.num_nextn_predict_layers
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# to map the exact layer index from weights
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self.layers = torch.nn.ModuleDict({
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str(idx):
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ErnieMultiTokenPredictorLayer(
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vllm_config,
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f"{prefix}.layers.{idx}",
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)
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for idx in range(self.mtp_start_layer_idx,
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self.mtp_start_layer_idx + self.num_mtp_layers)
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})
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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previous_hidden_states: torch.Tensor,
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inputs_embeds: Optional[torch.Tensor] = None,
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spec_step_idx: int = 0,
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) -> torch.Tensor:
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if inputs_embeds is None:
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inputs_embeds = self.embed_tokens(input_ids)
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return self.layers[str(self.mtp_start_layer_idx + spec_step_idx)](
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inputs_embeds,
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positions,
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previous_hidden_states,
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spec_step_idx,
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)
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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lm_head: ParallelLMHead,
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spec_step_idx: int = 0,
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) -> torch.Tensor:
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self.layers[str(self.mtp_start_layer_idx + spec_step_idx)]
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logits = self.logits_processor(lm_head, hidden_states)
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return logits
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class ErnieMTP(nn.Module, SupportsPP):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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super().__init__()
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self.config = vllm_config.model_config.hf_config
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self.model = ErnieMultiTokenPredictor(vllm_config=vllm_config,
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prefix=maybe_prefix(
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prefix, "model"))
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self.lm_head = ParallelLMHead(self.config.vocab_size,
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self.config.hidden_size,
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prefix=maybe_prefix(prefix, "lm_head"))
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if self.config.tie_word_embeddings:
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self.lm_head.weight = self.model.embed_tokens.weight
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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intermediate_tensors: Optional[IntermediateTensors] = None,
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inputs_embeds: Optional[torch.Tensor] = None,
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spec_step_idx: int = 0,
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) -> torch.Tensor:
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assert spec_step_idx == 0, "ernie_mtp only support predict one token"
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hidden_states = self.model(input_ids, positions, hidden_states,
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inputs_embeds, spec_step_idx)
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return hidden_states
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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spec_step_idx: int = 0,
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) -> Optional[torch.Tensor]:
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return self.model.compute_logits(hidden_states, self.lm_head,
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spec_step_idx)
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def load_weights(self, weights: Iterable[tuple[str,
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torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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if self.config.tie_word_embeddings and name.endswith(
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"lm_head.weight"):
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continue
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if "rotary_emb.inv_freq" in name:
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continue
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if "mtp" in name:
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name = self._rewrite_spec_layer_name(self.config, name)
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for (param_name, weight_name, shard_id) in stacked_params_mapping:
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# Skip non-stacked layers and experts (experts handled below).
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if weight_name not in name:
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continue
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if "mtp" not in name:
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continue
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# We have mlp.experts[0].gate_proj in the checkpoint.
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# Since we handle the experts below in expert_params_mapping,
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# we need to skip here BEFORE we update the name, otherwise
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# name will be updated to mlp.experts[0].gate_up_proj, which
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# will then be updated below in expert_params_mapping
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# for mlp.experts[0].gate_gate_up_proj, which breaks load.
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if (("mlp.experts." in name) and name not in params_dict):
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if ((name.endswith(".bias") or name.endswith("_bias"))
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and name not in params_dict):
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continue
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# Skip layers on other devices.
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if ((name.endswith(".bias") or name.endswith("_bias"))
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and name not in params_dict):
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continue
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# Skip layers on other devices.
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if is_pp_missing_parameter(name, self):
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continue
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# According to DeepSeek-V3 Technical Report, MTP modules
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# shares embedding layer. We only load the first weights.
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if "mtp_" not in name and ("embed_tokens" not in name
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and "lm_head" not in name):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader",
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default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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def _rewrite_spec_layer_name(self, config: PretrainedConfig,
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name: str) -> str:
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"""
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Rewrite the weight name to match the format of the original model.
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"""
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spec_layer_weight_names = [
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"embed_tokens", "mtp_emb_norm", "mtp_hidden_norm",
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"mtp_linear_proj"
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]
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layer_idx = config.num_hidden_layers
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for weight_name in spec_layer_weight_names:
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if weight_name in name:
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name = name.replace(
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f"model.{weight_name}.0.",
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f"model.layers.{layer_idx}.{weight_name}.")
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return name
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name = name.replace("model.mtp_block.0.",
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f"model.layers.{layer_idx}.mtp_block.")
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return name
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