[V1][Spec Decode] EAGLE-3 Support (#16937)
Signed-off-by: Bryan Lu <yuzhelu@amazon.com> Signed-off-by: Benjamin Chislett <benjamin.chislett@centml.ai> Co-authored-by: Bryan Lu <yuzhelu@amazon.com>
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vllm/model_executor/models/llama_eagle3.py
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232
vllm/model_executor/models/llama_eagle3.py
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# SPDX-License-Identifier: Apache-2.0
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from typing import Iterable, Optional, Set, Tuple
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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.config import ModelConfig
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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 (
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QuantizationConfig)
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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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,
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LlamaForCausalLM)
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from vllm.v1.sample.metadata import SamplingMetadata
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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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config: LlamaConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__(config, quant_config=quant_config, prefix=prefix)
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# override qkv
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self.self_attn.qkv_proj = QKVParallelLinear(
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2 * self.hidden_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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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: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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residual = hidden_states
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embeds = self.input_layernorm(embeds)
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hidden_states = self.hidden_norm(hidden_states)
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hidden_states = torch.cat([embeds, hidden_states], dim=-1)
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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(
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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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class LlamaModel(nn.Module):
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def __init__(
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self,
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*,
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model_config: ModelConfig,
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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 = model_config.hf_config
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self.vocab_size = self.config.vocab_size
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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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LlamaDecoderLayer(
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self.config,
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prefix=maybe_prefix(prefix, f"layers.{start_layer_id}"),
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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(self.config.target_hidden_size * 3,
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self.config.hidden_size,
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bias=False)
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else:
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self.fc = torch.nn.Linear(self.config.hidden_size * 3,
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self.config.hidden_size,
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bias=False)
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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 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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) -> torch.Tensor:
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input_embeds = self.embed_tokens(input_ids)
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if (hidden_states.shape[-1] != input_embeds.shape[-1]):
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hidden_states = self.fc(hidden_states)
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residual = None
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hidden_states, residual = self.layers[0](
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positions,
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input_embeds,
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hidden_states,
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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,
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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",
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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, *, model_config: ModelConfig, start_layer_id: int = 0):
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nn.Module.__init__(self)
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self.config = model_config.hf_config
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self.model = LlamaModel(model_config=model_config,
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start_layer_id=start_layer_id,
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prefix="model")
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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="")
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self.logits_processor = LogitsProcessor(self.config.draft_vocab_size,
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scale=logit_scale)
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self.draft_id_to_target_id = nn.Parameter(
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torch.zeros((self.config.draft_vocab_size),
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dtype=torch.long).type(torch.LongTensor),
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requires_grad=False,
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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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hidden_states: torch.Tensor,
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) -> torch.Tensor:
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return self.model(input_ids, positions, 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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sampling_metadata: SamplingMetadata,
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) -> Optional[torch.Tensor]:
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logits = self.logits_processor(self.lm_head, hidden_states,
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sampling_metadata)
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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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logits.shape[0],
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self.config.vocab_size,
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), float('-inf'))
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logits_new[:, targets] = logits
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return logits_new
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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loader = AutoWeightsLoader(
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self,
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skip_prefixes=None,
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)
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model_weights = {}
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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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elif "lm_head" not in name:
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name = "model." + name
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model_weights[name] = loaded_weight
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return loader.load_weights(model_weights.items())
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