500 lines
20 KiB
Python
500 lines
20 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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"""MTP draft model for DeepSeek V4 (internal codename: DeepseekV4).
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Split from ``deepseek_mtp.py`` because the V4 architecture introduces several
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pieces that have no analogue in V3/V32:
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* separate ``e_proj`` / ``h_proj`` with fp8 linear quantization (instead of
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the fused ``eh_proj``);
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* ``hc_head`` hypercompressed vocab projection applied in ``compute_logits``;
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* ``DeepseekV4DecoderLayer`` with its own aux-stream management;
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* V4-specific checkpoint weight-name remapping in ``load_weights``.
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"""
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import typing
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from collections.abc import Callable, Iterable
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import regex as re
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import torch
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import torch.nn as nn
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import VllmConfig
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from vllm.distributed import (
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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)
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from vllm.logger import init_logger
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from vllm.model_executor.layers.fused_moe import FusedMoE
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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.vocab_parallel_embedding import (
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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.platforms import current_platform
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from vllm.sequence import IntermediateTensors
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from .deepseek_mtp import SharedHead
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from .deepseek_v2 import get_spec_layer_idx_from_weight_name
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from .deepseek_v4 import (
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DeepseekV4DecoderLayer,
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hc_head,
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make_deepseek_v4_expert_params_mapping,
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)
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from .utils import maybe_prefix
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logger = init_logger(__name__)
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# MoE expert scales are fused into per-layer w13/w2 tensors. The exact
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# parameter suffix depends on which FusedMoE method handles the experts:
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# - fp4 experts (Mxfp4MoEMethod) register ``w{1,2,3}_weight_scale``;
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# - fp8 experts (Fp8MoEMethod with block_quant=True) register
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# ``w{1,2,3}_weight_scale_inv``.
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# Other FP8 linear scales (including shared experts) always use
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# ``.weight_scale_inv``. Mirrors the per-instance mapper built by
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# ``_make_deepseek_v4_weights_mapper`` in deepseek_v4.py.
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_EXPERT_SCALE_RE = re.compile(r"\.experts\.\d+\.w[123]\.scale$")
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class DeepSeekV4MultiTokenPredictorLayer(nn.Module):
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def __init__(
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self,
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vllm_config: VllmConfig,
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topk_indices_buffer: torch.Tensor,
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prefix: str,
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aux_stream_list: list[torch.cuda.Stream] | None = None,
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) -> None:
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super().__init__()
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config = vllm_config.speculative_config.draft_model_config.hf_config
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self.config = config
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quant_config = vllm_config.quant_config
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self.rms_norm_eps = config.rms_norm_eps
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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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# V4 keeps e_ and h_ proj separate (with fp8 linear quant) rather than
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# fusing them the way V3 does with eh_proj.
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self.e_proj = ReplicatedLinear(
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config.hidden_size,
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config.hidden_size,
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bias=False,
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return_bias=False,
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quant_config=quant_config,
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)
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self.h_proj = ReplicatedLinear(
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config.hidden_size,
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config.hidden_size,
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bias=False,
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return_bias=False,
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quant_config=quant_config,
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)
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self.hc_eps = config.hc_eps
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self.hc_mult = config.hc_mult
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self.hc_dim = self.hc_mult * config.hidden_size
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self.hc_head_fn = nn.Parameter(
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torch.empty(self.hc_mult, self.hc_dim, dtype=torch.float32),
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requires_grad=False,
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)
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self.hc_head_base = nn.Parameter(
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torch.empty(self.hc_mult, dtype=torch.float32),
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requires_grad=False,
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)
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self.hc_head_scale = nn.Parameter(
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torch.empty(1, dtype=torch.float32),
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requires_grad=False,
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)
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self.shared_head = SharedHead(
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config=config, prefix=prefix, quant_config=quant_config
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)
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self.mtp_block = DeepseekV4DecoderLayer(
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vllm_config,
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prefix,
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topk_indices_buffer=topk_indices_buffer,
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aux_stream_list=aux_stream_list,
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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_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 = torch.where(positions.unsqueeze(-1) == 0, 0, inputs_embeds)
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inputs_embeds = self.enorm(inputs_embeds)
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# Target stashes pre-hc_head residual as flat (T, hc_mult * D);
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# reshape to (T, hc_mult, D) — the training-time layout.
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previous_hidden_states = previous_hidden_states.view(
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-1, self.hc_mult, self.config.hidden_size
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)
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previous_hidden_states = self.hnorm(previous_hidden_states)
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hidden_states = self.h_proj(previous_hidden_states) + self.e_proj(
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inputs_embeds
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).unsqueeze(-2)
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hidden_states = self.mtp_block(
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positions=positions, x=hidden_states, input_ids=None
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)
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# Return the flat pre-hc_head residual so it can be re-fed as the
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# next spec step's `previous_hidden_states` when
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# num_speculative_tokens > 1. hc_head is deferred to compute_logits.
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return hidden_states.flatten(1)
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class DeepSeekV4MultiTokenPredictor(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.device = current_platform.device_type
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topk_tokens = config.index_topk
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self.topk_indices_buffer = torch.empty(
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vllm_config.scheduler_config.max_num_batched_tokens,
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topk_tokens,
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dtype=torch.int32,
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device=self.device,
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)
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# Three aux streams shared across all MTP layers, mirroring
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# DeepseekV4Model.
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aux_stream_list = [torch.cuda.Stream() for _ in range(3)]
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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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{
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str(idx): DeepSeekV4MultiTokenPredictorLayer(
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vllm_config,
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self.topk_indices_buffer,
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f"{prefix}.layers.{idx}",
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aux_stream_list=aux_stream_list,
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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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prefix=maybe_prefix(prefix, "embed_tokens"),
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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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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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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:
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current_step_idx = spec_step_idx % self.num_mtp_layers
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mtp_layer = self.layers[str(self.mtp_start_layer_idx + current_step_idx)]
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# MTP forward returns the pre-hc_head residual (T, hc_mult * D); apply
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# hc_head here so logits are computed from the dense hidden state.
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hidden_states = hidden_states.view(
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-1, mtp_layer.hc_mult, mtp_layer.config.hidden_size
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)
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hidden_states = hc_head(
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hidden_states,
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mtp_layer.hc_head_fn,
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mtp_layer.hc_head_scale,
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mtp_layer.hc_head_base,
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mtp_layer.rms_norm_eps,
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mtp_layer.hc_eps,
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)
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logits = self.logits_processor(
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mtp_layer.shared_head.head, mtp_layer.shared_head(hidden_states)
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)
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return logits
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@support_torch_compile
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class DeepSeekV4MTP(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.quant_config = vllm_config.quant_config
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self.model = DeepSeekV4MultiTokenPredictor(
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vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")
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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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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, spec_step_idx)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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# Weight name remapping for checkpoint compatibility.
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# Maps checkpoint weight paths to model parameter paths.
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WEIGHT_NAME_REMAPPING: dict[str, str] = {
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".emb.tok_emb.weight": ".embed_tokens.weight",
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".head.weight": ".shared_head.head.weight",
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".norm.weight": ".shared_head.norm.weight",
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}
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def _remap_weight_name(name: str) -> str:
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"""Remap checkpoint weight names to model parameter names."""
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for old_pattern, new_pattern in WEIGHT_NAME_REMAPPING.items():
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if old_pattern in name:
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name = name.replace(old_pattern, new_pattern)
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return name
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def _find_mtp_layer_idx(name: str) -> int:
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subnames = name.split(".")
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for subname in subnames:
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try:
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# we return the first encountered integer
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return int(subname)
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except ValueError:
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continue
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return 0
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("gate_up_proj", "w1", 0),
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("gate_up_proj", "w3", 1),
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("attn.fused_wqa_wkv", "attn.wq_a", 0),
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("attn.fused_wqa_wkv", "attn.wkv", 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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# TP for attention
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tp_size = get_tensor_model_parallel_world_size()
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tp_rank = get_tensor_model_parallel_rank()
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n_head = self.config.num_attention_heads
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n_local_head = n_head // tp_size
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head_rank_start = n_local_head * tp_rank
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head_rank_end = n_local_head * (tp_rank + 1)
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# Pre-compute expert mapping ONCE.
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first_layer = next(iter(self.model.layers.values()))
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if first_layer.mtp_block.ffn.use_mega_moe:
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expert_mapping = make_deepseek_v4_expert_params_mapping(
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self.config.n_routed_experts
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)
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else:
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expert_mapping = FusedMoE.make_expert_params_mapping(
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self,
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ckpt_gate_proj_name="w1",
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ckpt_down_proj_name="w2",
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ckpt_up_proj_name="w3",
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num_experts=self.config.n_routed_experts,
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)
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# FP8 experts register ``..._weight_scale_inv`` (block_quant) while
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# FP4/MXFP4 experts register ``..._weight_scale``. Choose the suffix
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# for the rename below based on the model's expert dtype.
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expert_scale_suffix = (
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".weight_scale"
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if getattr(self.config, "expert_dtype", "fp4") == "fp4"
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else ".weight_scale_inv"
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)
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for name, loaded_weight in weights:
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mtp_layer_idx = _find_mtp_layer_idx(name)
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# V4 checkpoints store MTP weights as `mtp.{i}.*`; remap to
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# `model.layers.{num_hidden_layers + i}.*` so that
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# get_spec_layer_idx_from_weight_name can identify them.
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name = name.replace(
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f"mtp.{mtp_layer_idx}.",
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f"model.layers.{self.config.num_hidden_layers + mtp_layer_idx}.",
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)
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spec_layer = 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 = _remap_weight_name(name)
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name = self._rewrite_spec_layer_name(spec_layer, name)
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if spec_layer != self.model.mtp_start_layer_idx and ".layers" not in name:
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continue
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if name.endswith(".scale"):
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suffix = (
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expert_scale_suffix
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if _EXPERT_SCALE_RE.search(name)
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else ".weight_scale_inv"
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)
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name = name.removesuffix(".scale") + suffix
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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 ".experts." in name:
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continue
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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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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loaded_params.add(name)
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break
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else:
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if ".experts." in name:
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# Reinterpret E8M0 scales as uint8 to preserve raw
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# exponent bytes; numeric copy_() would zero them.
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# Mirrors the main DeepseekV4 loader.
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if (
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"weight_scale" in name
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and loaded_weight.dtype == torch.float8_e8m0fnu
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):
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loaded_weight = loaded_weight.view(torch.uint8)
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for mapping in expert_mapping:
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param_name, weight_name, expert_id, shard_id = mapping
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if weight_name not in name:
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continue
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name_mapped = name.replace(weight_name, param_name)
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param = params_dict[name_mapped]
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# We should ask the weight loader to return success or not
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# here since otherwise we may skip experts with other
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# available replicas.
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weight_loader = typing.cast(
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Callable[..., bool], param.weight_loader
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)
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success = weight_loader(
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param,
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loaded_weight,
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name_mapped,
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shard_id=shard_id,
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expert_id=expert_id,
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return_success=True,
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)
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if success:
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name = name_mapped
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loaded_params.add(name_mapped)
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break
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continue
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elif "attn_sink" in name:
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narrow_weight = loaded_weight[head_rank_start:head_rank_end]
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n = narrow_weight.shape[0]
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params_dict[name][:n].copy_(narrow_weight)
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loaded_params.add(name)
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continue
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else:
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if ".shared_experts.w2" in name:
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name = name.replace(
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".shared_experts.w2", ".shared_experts.down_proj"
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)
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if name.endswith(".ffn.gate.bias"):
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name = name.replace(".bias", ".e_score_correction_bias")
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param = params_dict[name]
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weight_loader = getattr(
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param, "weight_loader", default_weight_loader
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)
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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continue
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loaded_layers: set[int] = set()
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for param_name in loaded_params:
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spec_layer = get_spec_layer_idx_from_weight_name(self.config, param_name)
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if spec_layer is not None:
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loaded_layers.add(spec_layer)
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for layer_idx in range(
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self.model.mtp_start_layer_idx,
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self.model.mtp_start_layer_idx + self.model.num_mtp_layers,
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):
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if layer_idx not in loaded_layers:
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raise ValueError(
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f"MTP speculative decoding layer {layer_idx} weights "
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f"missing from checkpoint. The checkpoint may have "
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f"been quantized without including the MTP layers. "
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f"Use a checkpoint that includes MTP layer weights, "
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f"or disable speculative decoding."
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)
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self.finalize_mega_moe_weights()
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logger.info_once("MTP draft model loaded: %d params", len(loaded_params))
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return loaded_params
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def finalize_mega_moe_weights(self) -> None:
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for layer in self.model.layers.values():
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layer.mtp_block.ffn.finalize_mega_moe_weights()
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def _rewrite_spec_layer_name(self, spec_layer: int, name: str) -> str:
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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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|
"""
|
|
spec_layer_weight_names = [
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|
"embed_tokens",
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|
"enorm",
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|
"hnorm",
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|
"h_proj",
|
|
"e_proj",
|
|
"shared_head",
|
|
"hc_head_fn",
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|
"hc_head_base",
|
|
"hc_head_scale",
|
|
]
|
|
shared_weight_names = ["embed_tokens"]
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|
spec_layer_weight = False
|
|
shared_weight = False
|
|
for weight_name in spec_layer_weight_names:
|
|
if weight_name in name:
|
|
spec_layer_weight = True
|
|
if weight_name in shared_weight_names:
|
|
shared_weight = True
|
|
break
|
|
if not spec_layer_weight:
|
|
# treat rest weights as weights for transformer layer block
|
|
name = name.replace(
|
|
f"model.layers.{spec_layer}.", f"model.layers.{spec_layer}.mtp_block."
|
|
)
|
|
elif shared_weight:
|
|
# treat shared weights as top level weights
|
|
name = name.replace(f"model.layers.{spec_layer}.", "model.")
|
|
return name
|