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vllm/model_executor/models/step3p5_mtp.py
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vllm/model_executor/models/step3p5_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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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.logger import init_logger
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from vllm.model_executor.layers.layernorm import GemmaRMSNorm
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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.model_loader.weight_utils import default_weight_loader
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from vllm.sequence import IntermediateTensors
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from .step3p5 import Step3p5DecoderLayer, get_spec_layer_idx_from_weight_name
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from .utils import maybe_prefix
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logger = init_logger(__name__)
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class SharedHead(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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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.norm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
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self.head = ParallelLMHead(
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config.vocab_size, config.hidden_size, quant_config=quant_config
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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return self.norm(hidden_states)
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class Step3p5AMultiTokenPredictorLayer(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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quant_config = vllm_config.quant_config
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self.enorm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
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self.hnorm = GemmaRMSNorm(config.hidden_size, config.rms_norm_eps)
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self.eh_proj = nn.Linear(config.hidden_size * 2, config.hidden_size, bias=False)
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self.shared_head = SharedHead(config=config, quant_config=quant_config)
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self.mtp_block = Step3p5DecoderLayer(
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vllm_config,
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prefix=f"{prefix}.mtp_block",
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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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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 = self.mtp_block(positions=positions, hidden_states=hidden_states)
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return hidden_states
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class Step3p5AMultiTokenPredictor(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.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_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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{
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str(idx): Step3p5AMultiTokenPredictorLayer(
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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(
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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 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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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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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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class Step3p5MTP(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.vllm_config = vllm_config
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self.model = Step3p5AMultiTokenPredictor(
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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,
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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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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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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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expert_params_mapping = [
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(".moe.experts.w13_weight", ".moe.gate_proj.weight", "w1"),
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(".moe.experts.w13_weight", ".moe.up_proj.weight", "w3"),
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(".moe.experts.w2_weight", ".moe.down_proj.weight", "w2"),
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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 = get_spec_layer_idx_from_weight_name(self.config, name)
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if "embed_tokens" not in name and spec_layer is None:
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continue
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name = self._rewrite_spec_layer_name(spec_layer, 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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# 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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if "experts" in name or "moe" in name:
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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") 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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for mapping in expert_params_mapping:
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param_name, weight_name, shard_id = mapping
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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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# Skip loading extra bias for GPTQ models.
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if (
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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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param = params_dict[name]
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weight_loader = param.weight_loader
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for expert_id in range(loaded_weight.shape[0]):
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loaded_weight_expert = loaded_weight[expert_id]
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weight_loader(
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param,
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loaded_weight_expert,
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name,
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shard_id=shard_id,
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expert_id=expert_id,
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)
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loaded_params.add(name)
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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 (
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name.endswith(".bias")
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and name not in params_dict
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or "tok_embeddings" in name
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):
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continue
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if spec_layer is not None and ".transformer." in name:
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name = name.replace(".transformer.", ".")
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if "shared_head" in name:
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name = name.replace("shared_head.output", "shared_head.head")
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if "embed_tokens" in name:
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assert (
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hasattr(self.config, "num_nextn_predict_layers")
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and self.config.num_nextn_predict_layers > 0
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)
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name = "model.embed_tokens.weight"
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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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params_need_to_load = set(params_dict.keys())
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# Some KV cache scales are optional: checkpoints may omit them and vLLM
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# will fall back to default scales during initialization.
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optional_params = {
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name
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for name, param in params_dict.items()
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if name.endswith((".k_scale", ".v_scale", ".q_scale", ".prob_scale"))
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and getattr(param, "numel", lambda: 0)() == 1
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and getattr(param, "requires_grad", False) is False
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}
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params_need_to_load -= optional_params
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if params_need_to_load != loaded_params:
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missing_params = list(params_need_to_load - loaded_params)
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param_name_example = missing_params[0]
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raise RuntimeError(
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"Some parameters like "
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f"{param_name_example} are not in the checkpoint and will falsely "
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"use random initialization"
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)
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return loaded_params
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def _rewrite_spec_layer_name(self, spec_layer: int, 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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Add .mtp_block for modules in transformer layer block for spec layer
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"""
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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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spec_layer_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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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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f"model.layers.{spec_layer}.", f"model.layers.{spec_layer}.mtp_block."
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)
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return name
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