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342 lines
12 KiB
Python
342 lines
12 KiB
Python
# 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 LlamaConfig
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import VllmConfig, get_current_vllm_config
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from vllm.logger import init_logger
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import QKVParallelLinear
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE,
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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.llama import LlamaDecoderLayer, LlamaForCausalLM
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from vllm.multimodal import MULTIMODAL_REGISTRY
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from vllm.multimodal.inputs import NestedTensors
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from .utils import AutoWeightsLoader, maybe_prefix
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logger = init_logger(__name__)
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class LlamaDecoderLayer(LlamaDecoderLayer):
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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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config: LlamaConfig | None = None,
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layer_idx: int = 0,
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) -> None:
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super().__init__(vllm_config, prefix=prefix, config=config)
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config = config or vllm_config.model_config.hf_config
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quant_config = self.get_quant_config(vllm_config)
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# First layer uses 2*hidden_size (embeds + hidden_states concatenated)
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# Subsequent layers use hidden_size (only hidden_states, no embeds)
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qkv_input_size = 2 * self.hidden_size if layer_idx == 0 else self.hidden_size
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# override qkv
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self.self_attn.qkv_proj = QKVParallelLinear(
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qkv_input_size,
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self.self_attn.head_dim,
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self.self_attn.total_num_heads,
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self.self_attn.total_num_kv_heads,
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bias=False,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "qkv_proj"),
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)
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self.hidden_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.layer_idx = layer_idx
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if getattr(config, "norm_before_residual", False):
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self._residual_norm = self._norm_before_residual
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else:
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self._residual_norm = self._norm_after_residual
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def get_quant_config(self, vllm_config: VllmConfig) -> QuantizationConfig | None:
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"""Use drafter's quantization config instead of verifier's."""
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draft_model_config = vllm_config.speculative_config.draft_model_config
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draft_load_config = vllm_config.load_config
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return (
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VllmConfig.get_quantization_config(draft_model_config, draft_load_config)
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if draft_model_config
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else None
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)
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def _norm_before_residual(
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self, hidden_states: torch.Tensor
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) -> tuple[torch.Tensor, torch.Tensor]:
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hidden_states = self.hidden_norm(hidden_states)
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residual = hidden_states
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return hidden_states, residual
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def _norm_after_residual(
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self, hidden_states: torch.Tensor
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) -> tuple[torch.Tensor, torch.Tensor]:
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residual = hidden_states
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hidden_states = self.hidden_norm(hidden_states)
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return hidden_states, residual
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def forward(
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self,
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positions: torch.Tensor,
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embeds: torch.Tensor,
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hidden_states: torch.Tensor,
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residual: torch.Tensor | None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if self.layer_idx == 0:
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# First layer: concatenate embeds with hidden_states
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embeds = self.input_layernorm(embeds)
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hidden_states, residual = self._residual_norm(hidden_states=hidden_states)
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hidden_states = torch.cat([embeds, hidden_states], dim=-1)
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else:
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# Subsequent layers: process hidden_states and residuals only
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hidden_states, residual = self.input_layernorm(hidden_states, residual)
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# Self Attention
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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)
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hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
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# Fully Connected
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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@support_torch_compile(
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# torch.compile is disabled for multimodal EAGLE3 models due to constraint
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# violations with dynamic shapes during tensor concatenation operations.
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# See: https://github.com/vllm-project/vllm/pull/22872/files#r2362028132
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# Non-multimodal EAGLE3 models can still use torch.compile safely.
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enable_if=lambda vllm_config: not MULTIMODAL_REGISTRY.supports_multimodal_inputs(
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vllm_config.model_config
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),
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)
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class LlamaModel(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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start_layer_id: int = 0,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = vllm_config.speculative_config.draft_model_config.hf_config
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self.vocab_size = self.config.vocab_size
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current_vllm_config = get_current_vllm_config()
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self.embed_tokens = VocabParallelEmbedding(
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self.config.vocab_size,
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self.config.hidden_size,
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prefix=maybe_prefix(prefix, "embed_tokens"),
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)
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self.layers = nn.ModuleList(
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[
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LlamaDecoderLayer(
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current_vllm_config,
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prefix=maybe_prefix(prefix, f"layers.{layer_idx + start_layer_id}"),
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config=self.config,
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layer_idx=layer_idx,
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)
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for layer_idx in range(self.config.num_hidden_layers)
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]
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)
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if hasattr(self.config, "target_hidden_size"):
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self.fc = torch.nn.Linear(
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self.config.target_hidden_size * 3, self.config.hidden_size, bias=False
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)
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else:
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self.fc = torch.nn.Linear(
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self.config.hidden_size * 3, self.config.hidden_size, bias=False
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)
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self.norm = RMSNorm(
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self.config.hidden_size,
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eps=self.config.rms_norm_eps,
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)
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def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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input_embeds: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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if input_embeds is None:
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input_embeds = self.get_input_embeddings(input_ids)
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assert hidden_states.shape[-1] == input_embeds.shape[-1]
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residual = None
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for layer in self.layers:
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hidden_states, residual = layer(
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positions=positions,
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embeds=input_embeds,
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hidden_states=hidden_states,
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residual=residual,
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)
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hidden_states, hidden_prenorm = self.norm(hidden_states, residual)
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return hidden_states, hidden_prenorm
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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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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 "midlayer." in name:
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name = name.replace("midlayer.", "layers.0.")
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for param_name, weight_name, shard_id in stacked_params_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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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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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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return loaded_params
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class Eagle3LlamaForCausalLM(LlamaForCausalLM):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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nn.Module.__init__(self)
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self.config = vllm_config.speculative_config.draft_model_config.hf_config
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# Ensure draft_vocab_size is set
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# default to the base vocab size when absent
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if getattr(self.config, "draft_vocab_size", None) is None:
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base_vocab_size = getattr(self.config, "vocab_size", None)
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self.config.draft_vocab_size = base_vocab_size
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target_layer_num = vllm_config.model_config.get_num_layers(
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vllm_config.parallel_config
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)
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# Store target layer count in draft config for
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# proper layer_types indexing in draft models
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self.config.target_layer_count = target_layer_num
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self.model = LlamaModel(
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vllm_config=vllm_config, prefix="model", start_layer_id=target_layer_num
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)
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logit_scale = getattr(self.config, "logit_scale", 1.0)
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self.lm_head = ParallelLMHead(
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self.config.draft_vocab_size,
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self.config.hidden_size,
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org_num_embeddings=self.config.draft_vocab_size,
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padding_size=(DEFAULT_VOCAB_PADDING_SIZE),
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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self.logits_processor = LogitsProcessor(
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self.config.draft_vocab_size, scale=logit_scale
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)
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self.draft_id_to_target_id = nn.Parameter(
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torch.zeros(self.config.draft_vocab_size, dtype=torch.long),
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requires_grad=False,
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)
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def get_input_embeddings(
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self,
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input_ids: torch.Tensor,
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multimodal_embeddings: NestedTensors | None = None,
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is_multimodal: torch.Tensor | None = None,
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) -> torch.Tensor:
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return self.model.get_input_embeddings(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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inputs_embeds: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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return self.model(input_ids, positions, hidden_states, inputs_embeds)
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor | None:
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logits = self.logits_processor(self.lm_head, hidden_states)
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if self.draft_id_to_target_id is None:
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assert logits.shape[1] == self.config.vocab_size, (
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"Expected logits to have shape "
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f"(*, {self.config.vocab_size}), but got {logits.shape}"
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)
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return logits
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base = torch.arange(self.config.draft_vocab_size, device=logits.device)
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targets = base + self.draft_id_to_target_id
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logits_new = logits.new_full(
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(
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logits.shape[0],
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self.config.vocab_size,
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),
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float("-inf"),
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)
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logits_new[:, targets] = logits
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return logits_new
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def combine_hidden_states(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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# combine multiple auxiliary hidden states returned by eagle3
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return self.model.fc(hidden_states)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]):
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model_weights = {}
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includes_draft_id_mapping = False
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includes_embed_tokens = False
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for name, loaded_weight in weights:
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if "t2d" in name:
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continue
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if "d2t" in name:
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name = name.replace("d2t", "draft_id_to_target_id")
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includes_draft_id_mapping = True
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elif "lm_head" not in name:
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name = "model." + name
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if "embed_tokens" in name:
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includes_embed_tokens = True
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model_weights[name] = loaded_weight
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skip_substrs = []
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if not includes_draft_id_mapping:
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skip_substrs.append("draft_id_to_target_id")
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if not includes_embed_tokens:
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skip_substrs.append("embed_tokens")
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loader = AutoWeightsLoader(
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self,
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skip_prefixes=None,
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skip_substrs=skip_substrs,
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)
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loader.load_weights(model_weights.items())
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