wehub-resource-sync
223 lines
8.1 KiB
Python
223 lines
8.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/model_executor/models/deepseek_mtp.py
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# Copyright 2025 Xiaomi Corporation.
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# Copyright 2023 The vLLM team.
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# Copyright 2024 DeepSeek-AI team.
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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 MiMo-MTP model."""
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from collections.abc import Iterable
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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 CacheConfig, ModelConfig, 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.quantization import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.models.qwen2 import Qwen2DecoderLayer
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from vllm.sequence import IntermediateTensors
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from .utils import AutoWeightsLoader, WeightsMapper, maybe_prefix
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class MiMoMultiTokenPredictorLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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prefix: str,
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model_config: ModelConfig,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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) -> None:
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super().__init__()
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self.token_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.hidden_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.input_proj = nn.Linear(
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config.hidden_size * 2, config.hidden_size, bias=False
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)
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self.mtp_block = Qwen2DecoderLayer(
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix,
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)
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self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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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.token_layernorm(inputs_embeds)
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previous_hidden_states = self.hidden_layernorm(previous_hidden_states)
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hidden_states = self.input_proj(
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torch.cat([previous_hidden_states, inputs_embeds], dim=-1)
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)
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hidden_states, residual = self.mtp_block(
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positions=positions, hidden_states=hidden_states, residual=None
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)
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hidden_states = residual + hidden_states
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return self.final_layernorm(hidden_states)
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class MiMoMultiTokenPredictor(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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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.mtp_layers = torch.nn.ModuleDict(
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{
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str(idx): MiMoMultiTokenPredictorLayer(
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config,
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f"{prefix}.layers.{idx}",
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model_config=vllm_config.model_config,
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cache_config=vllm_config.cache_config,
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quant_config=vllm_config.quant_config,
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)
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for idx in range(
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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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}
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)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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def embed_input_ids(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: torch.Tensor | None = 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.mtp_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.mtp_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 MiMoMTP(nn.Module):
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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 = MiMoMultiTokenPredictor(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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)
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self.lm_head = ParallelLMHead(
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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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)
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# Checkpoint stores MTP layers 0-indexed and without the `mtp_block`
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# wrapper around the transformer block; remap onto the offset index.
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start = self.config.num_hidden_layers
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self.hf_to_vllm_mapper = WeightsMapper(
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orig_to_new_substr={
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".self_attn.": ".mtp_block.self_attn.",
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".mlp.": ".mtp_block.mlp.",
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".input_layernorm.": ".mtp_block.input_layernorm.",
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".post_attention_layernorm.": ".mtp_block.post_attention_layernorm.",
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},
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orig_to_new_stacked={
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".q_proj": (".qkv_proj", "q"),
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".k_proj": (".qkv_proj", "k"),
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".v_proj": (".qkv_proj", "v"),
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".gate_proj": (".gate_up_proj", 0),
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".up_proj": (".gate_up_proj", 1),
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},
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orig_to_new_prefix={
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f"model.mtp_layers.{i}.": f"model.mtp_layers.{i + start}."
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for i in range(self.config.num_nextn_predict_layers)
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},
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)
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.model.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None = None,
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inputs_embeds: torch.Tensor | None = 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, "mimo_mtp only support predict one token now"
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hidden_states = self.model(
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input_ids, positions, hidden_states, inputs_embeds, spec_step_idx
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)
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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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) -> torch.Tensor | None:
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return self.model.compute_logits(hidden_states, self.lm_head, spec_step_idx)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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# The checkpoint carries the full model; keep only the MTP layers and
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# the shared embedding/head.
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def mtp_weights():
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for name, weight in weights:
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if "mtp_layers" in name or "embed_tokens" in name or "lm_head" in name:
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yield name, weight
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loader = AutoWeightsLoader(self)
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return loader.load_weights(mtp_weights(), mapper=self.hf_to_vllm_mapper)
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