v1.0
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349
model_executor/models/longcat_flash_mtp.py
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349
model_executor/models/longcat_flash_mtp.py
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# 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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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 VllmConfig
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import ReplicatedLinear
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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.quantization.utils.int8_utils import block_dequant
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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.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.models.longcat_flash import FlashConfig
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from vllm.sequence import IntermediateTensors
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from .deepseek_v2 import DeepseekV2DecoderLayer
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from .interfaces import SupportsPP
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from .utils import maybe_prefix
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class LongCatMultiTokenPredictorLayer(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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vllm_config: VllmConfig,
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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.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.eh_proj = ReplicatedLinear(
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2 * config.hidden_size,
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config.hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix="eh_proj",
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)
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self.mtp_block = DeepseekV2DecoderLayer(vllm_config, prefix)
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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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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_index: int = 0,
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) -> torch.Tensor:
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assert inputs_embeds is not None
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inputs_embeds = self.enorm(inputs_embeds)
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previous_hidden_states = self.hnorm(previous_hidden_states)
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hidden_states, _ = self.eh_proj(
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torch.cat([inputs_embeds, previous_hidden_states], 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, _ = self.final_layernorm(hidden_states, residual)
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return hidden_states
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class LongCatMultiTokenPredictor(nn.Module):
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def __init__(
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self,
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*,
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vllm_config: VllmConfig,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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config = FlashConfig(**vllm_config.model_config.hf_config.__dict__)
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vllm_config.model_config.hf_config.intermediate_size = config.intermediate_size
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self.mtp_start_layer_idx = config.num_hidden_layers * 2
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self.num_mtp_layers = 1
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self.layers = torch.nn.ModuleDict(
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{
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str(idx): LongCatMultiTokenPredictorLayer(
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config,
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prefix=f"{prefix}.layers.{idx}",
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vllm_config=vllm_config,
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quant_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.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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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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current_step_idx = spec_step_idx % self.num_mtp_layers
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return self.layers[str(self.mtp_start_layer_idx + current_step_idx)](
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input_ids,
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positions,
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previous_hidden_states,
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inputs_embeds,
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current_step_idx,
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)
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class LongCatFlashMTP(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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# LongCat MTP without MoE layers
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vllm_config.model_config.hf_config.n_routed_experts = None
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self.config = FlashConfig(**vllm_config.model_config.hf_config.__dict__)
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self.quant_config = (
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None
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if "mtp" in getattr(self.config, "disable_quant_module", [])
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else vllm_config.quant_config
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)
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self.model = LongCatMultiTokenPredictor(
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vllm_config=vllm_config,
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quant_config=self.quant_config,
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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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quant_config=self.quant_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.vocab_size)
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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: 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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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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logits = self.logits_processor(self.lm_head, hidden_states)
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return logits
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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("fused_qkv_a_proj", "q_a_proj", 0),
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("fused_qkv_a_proj", "kv_a_proj_with_mqa", 1),
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]
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new_to_old_names_mapping = {
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"model.mtp.embed_tokens.weight": "model.layers.0.embed_tokens.weight",
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"model.mtp.layers.0.eh_proj.weight": "eh_proj.weight",
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"model.mtp.layers.0.eh_proj.weight_scale_inv": "eh_proj.weight_scale_inv",
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"model.mtp.layers.0.enorm.m.weight": "enorm.weight",
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"model.mtp.layers.0.hnorm.m.weight": "hnorm.weight",
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"model.mtp.layers.0.input_layernorm.weight": "model.layers.0.input_layernorm.weight", # noqa: E501
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"model.mtp.layers.0.post_attention_layernorm.weight": "model.layers.0.post_attention_layernorm.weight", # noqa: E501
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"model.mtp.layers.0.self_attn.kv_a_layernorm.weight": "model.layers.0.self_attn.kv_a_layernorm.weight", # noqa: E501
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"model.mtp.layers.0.self_attn.kv_a_proj_with_mqa.weight": "model.layers.0.self_attn.kv_a_proj_with_mqa.weight", # noqa: E501
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"model.mtp.layers.0.self_attn.kv_a_proj_with_mqa.weight_scale_inv": "model.layers.0.self_attn.kv_a_proj_with_mqa.weight_scale_inv", # noqa: E501
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"model.mtp.layers.0.self_attn.kv_b_proj.weight": "model.layers.0.self_attn.kv_b_proj.weight", # noqa: E501
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"model.mtp.layers.0.self_attn.kv_b_proj.weight_scale_inv": "model.layers.0.self_attn.kv_b_proj.weight_scale_inv", # noqa: E501
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"model.mtp.layers.0.self_attn.o_proj.weight": "model.layers.0.self_attn.o_proj.weight", # noqa: E501
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"model.mtp.layers.0.self_attn.o_proj.weight_scale_inv": "model.layers.0.self_attn.o_proj.weight_scale_inv", # noqa: E501
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"model.mtp.layers.0.self_attn.q_a_layernorm.weight": "model.layers.0.self_attn.q_a_layernorm.weight", # noqa: E501
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"model.mtp.layers.0.self_attn.q_a_proj.weight": "model.layers.0.self_attn.q_a_proj.weight", # noqa: E501
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"model.mtp.layers.0.self_attn.q_a_proj.weight_scale_inv": "model.layers.0.self_attn.q_a_proj.weight_scale_inv", # noqa: E501
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"model.mtp.layers.0.self_attn.q_b_proj.weight": "model.layers.0.self_attn.q_b_proj.weight", # noqa: E501
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"model.mtp.layers.0.self_attn.q_b_proj.weight_scale_inv": "model.layers.0.self_attn.q_b_proj.weight_scale_inv", # noqa: E501
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"model.mtp.layers.0.transformer_layer.mlp.down_proj.weight": "model.layers.0.mlp.down_proj.weight", # noqa: E501
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"model.mtp.layers.0.transformer_layer.mlp.down_proj.weight_scale_inv": "model.layers.0.mlp.down_proj.weight_scale_inv", # noqa: E501
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"model.mtp.layers.0.transformer_layer.mlp.gate_proj.weight": "model.layers.0.mlp.gate_proj.weight", # noqa: E501
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"model.mtp.layers.0.transformer_layer.mlp.gate_proj.weight_scale_inv": "model.layers.0.mlp.gate_proj.weight_scale_inv", # noqa: E501
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"model.mtp.layers.0.transformer_layer.mlp.up_proj.weight": "model.layers.0.mlp.up_proj.weight", # noqa: E501
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"model.mtp.layers.0.transformer_layer.mlp.up_proj.weight_scale_inv": "model.layers.0.mlp.up_proj.weight_scale_inv", # noqa: E501
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"model.mtp.norm.weight": "final_layernorm.weight",
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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 "rotary_emb.inv_freq" in name:
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continue
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spec_layer = self.get_spec_layer_idx_from_weight_name(self.config, name)
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if spec_layer is None:
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continue
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name = self._rewrite_spec_layer_name(
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spec_layer, name, new_to_old_names_mapping
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)
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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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# 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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# QKV fusion is optional, fall back to normal
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# weight loading if it's not enabled
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if (param_name == "fused_qkv_a_proj") and name not in params_dict:
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continue
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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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") and name not in params_dict:
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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 (
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spec_layer != self.model.mtp_start_layer_idx
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and ".layers" not in name
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):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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spec_layer_id = self.config.num_hidden_layers * 2
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self_attn = self.model.layers[str(spec_layer_id)].mtp_block.self_attn
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if hasattr(
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self.quant_config, "weight_block_size"
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) and self_attn.kv_b_proj.weight.dtype in (
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torch.float8_e4m3fn,
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torch.float8_e4m3fnuz,
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):
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weight_block_size = self.quant_config.weight_block_size
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if weight_block_size is not None:
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dtype = torch.get_default_dtype()
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w = block_dequant(
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self_attn.kv_b_proj.weight,
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self_attn.kv_b_proj.weight_scale_inv,
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weight_block_size,
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).to(dtype)
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else:
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w = self_attn.kv_b_proj.weight
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else:
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w = self_attn.kv_b_proj.weight
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w_kc, w_vc = w.unflatten(
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0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
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).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
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self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
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self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
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if self.config.mla_scale_q_lora:
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self_attn.q_a_layernorm.weight.data *= (
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self.config.hidden_size / self.config.q_lora_rank
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) ** 0.5
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if self.config.mla_scale_kv_lora:
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self_attn.kv_a_layernorm.weight.data *= (
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self.config.hidden_size / self.config.kv_lora_rank
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) ** 0.5
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return loaded_params
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def _rewrite_spec_layer_name(
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self, spec_layer: int, name: str, new_to_old_names_mapping: dict
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) -> 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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Add .mtp_block for modules in transformer layer block for spec layer
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and rename shared layer weights to be top level.
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"""
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if name in new_to_old_names_mapping:
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name = new_to_old_names_mapping[name]
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spec_layer_weight_names = [
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"embed_tokens",
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"enorm",
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"hnorm",
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"eh_proj",
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"shared_head",
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]
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if (
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name.startswith("enorm")
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or name.startswith("hnorm")
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or name.startswith("eh_proj")
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or name.startswith("final_layernorm")
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):
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name = "model.layers." + str(spec_layer) + "." + name
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shared_weight_names = ["embed_tokens"]
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spec_layer_weight = False
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shared_weight = False
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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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spec_layer_weight = True
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if weight_name in shared_weight_names:
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shared_weight = True
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break
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if not spec_layer_weight:
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# treat rest weights as weights for transformer layer block
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name = name.replace(
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"model.layers.0.", f"model.layers.{spec_layer}.mtp_block."
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)
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elif shared_weight:
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# treat shared weights as top level weights
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name = name.replace("model.layers.0.", "model.")
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return name
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def get_spec_layer_idx_from_weight_name(
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self, config: PretrainedConfig, weight_name: str
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) -> int | None:
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if "model.mtp" in weight_name:
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return config.num_hidden_layers * 2
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return None
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