mirror of
https://github.com/vllm-project/vllm.git
synced 2025-10-20 14:53:52 +08:00
[Feature] minicpm eagle support (#18943)
Signed-off-by: huangyuxiang03 <huangyx0321@gmail.com> Co-authored-by: huangyuxiang03 <huangyx0321@gmail.com>
This commit is contained in:
Shawn Huang
GitHub
@ -434,6 +434,11 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
|
||||
trust_remote_code=True,
|
||||
speculative_model="yuhuili/EAGLE3-LLaMA3.1-Instruct-8B",
|
||||
tokenizer="meta-llama/Llama-3.1-8B-Instruct"),
|
||||
"EagleMiniCPMForCausalLM": _HfExamplesInfo("openbmb/MiniCPM-1B-sft-bf16",
|
||||
trust_remote_code=True,
|
||||
is_available_online=False,
|
||||
speculative_model="openbmb/MiniCPM-2B-sft-bf16",
|
||||
tokenizer="openbmb/MiniCPM-2B-sft-bf16"),
|
||||
"MiMoMTPModel": _HfExamplesInfo("XiaomiMiMo/MiMo-7B-RL",
|
||||
trust_remote_code=True,
|
||||
speculative_model="XiaomiMiMo/MiMo-7B-RL")
|
||||
|
||||
@ -242,6 +242,7 @@ class MiniCPMAttention(nn.Module):
|
||||
base=rope_theta,
|
||||
rope_scaling=rope_scaling,
|
||||
)
|
||||
|
||||
self.attn = Attention(self.num_heads,
|
||||
self.head_dim,
|
||||
self.scaling,
|
||||
@ -444,6 +445,7 @@ class MiniCPMModel(nn.Module):
|
||||
for weight_name in ["w1", "w2", "w3"]
|
||||
]
|
||||
params_dict = dict(self.named_parameters())
|
||||
|
||||
loaded_params: set[str] = set()
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
@ -567,7 +569,7 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
) -> Union[torch.Tensor, IntermediateTensors]:
|
||||
hidden_states = self.model(input_ids, positions, intermediate_tensors,
|
||||
inputs_embeds)
|
||||
inputs_embeds) / self.scale_width
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(
|
||||
@ -575,7 +577,6 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
|
||||
hidden_states: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> Optional[torch.Tensor]:
|
||||
hidden_states = hidden_states / self.scale_width
|
||||
logits = self.logits_processor(self.lm_head, hidden_states,
|
||||
sampling_metadata)
|
||||
return logits
|
||||
|
||||
390
vllm/model_executor/models/minicpm_eagle.py
Normal file
390
vllm/model_executor/models/minicpm_eagle.py
Normal file
@ -0,0 +1,390 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
# Adapted from
|
||||
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
|
||||
# Copyright 2023 The vLLM team.
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Inference-only EagleMiniCPM model compatible with HuggingFace weights."""
|
||||
import math
|
||||
from collections.abc import Iterable
|
||||
from typing import Optional, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from transformers import PretrainedConfig
|
||||
|
||||
from vllm.compilation.decorators import support_torch_compile
|
||||
from vllm.config import CacheConfig, VllmConfig
|
||||
from vllm.model_executor.layers.layernorm import RMSNorm
|
||||
from vllm.model_executor.layers.logits_processor import LogitsProcessor
|
||||
from vllm.model_executor.layers.quantization import QuantizationConfig
|
||||
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
||||
DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
|
||||
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
|
||||
from vllm.model_executor.sampling_metadata import SamplingMetadata
|
||||
from vllm.sequence import IntermediateTensors
|
||||
|
||||
from .interfaces import SupportsLoRA, SupportsPP
|
||||
from .minicpm import MiniCPMAttention as EagleMiniCPMAttention
|
||||
from .minicpm import MiniCPMMLP as EagleMiniCPMMLP
|
||||
from .minicpm import MiniCPMMoE as EagleMiniCPMMoE
|
||||
from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
|
||||
make_empty_intermediate_tensors_factory, maybe_prefix)
|
||||
|
||||
|
||||
class EagleMiniCPMDecoderLayer(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.cache_config = cache_config
|
||||
self.quant_config = quant_config
|
||||
self.hidden_size = config.hidden_size
|
||||
self.rope_theta = getattr(config, "rope_theta", 10000)
|
||||
self.rope_scaling = getattr(config, "rope_scaling", None)
|
||||
self.max_position_embeddings = getattr(config,
|
||||
"max_position_embeddings", 8192)
|
||||
self.prefix = prefix
|
||||
self._init_attn_block()
|
||||
self._init_ffn_block()
|
||||
|
||||
def _init_attn_block(self):
|
||||
self.input_layernorm = RMSNorm(self.config.hidden_size,
|
||||
eps=self.config.rms_norm_eps)
|
||||
self.self_attn = EagleMiniCPMAttention(
|
||||
hidden_size=self.hidden_size,
|
||||
num_heads=self.config.num_attention_heads,
|
||||
num_kv_heads=self.config.num_key_value_heads,
|
||||
rope_theta=self.rope_theta,
|
||||
rope_scaling=self.rope_scaling,
|
||||
max_position_embeddings=self.max_position_embeddings,
|
||||
cache_config=self.cache_config,
|
||||
quant_config=self.quant_config,
|
||||
prefix=f"{self.prefix}.self_attn",
|
||||
)
|
||||
|
||||
def _init_ffn_block(self):
|
||||
self.post_attention_layernorm = RMSNorm(self.config.hidden_size,
|
||||
eps=self.config.rms_norm_eps)
|
||||
self.num_experts = getattr(self.config, "num_experts", 0)
|
||||
if self.num_experts == 0:
|
||||
self.mlp = EagleMiniCPMMLP(
|
||||
hidden_size=self.hidden_size,
|
||||
intermediate_size=self.config.intermediate_size,
|
||||
hidden_act=self.config.hidden_act,
|
||||
hidden_act_param=getattr(self.config, "hidden_act_param", 0.),
|
||||
quant_config=self.quant_config,
|
||||
)
|
||||
else:
|
||||
self.mlp = EagleMiniCPMMoE(
|
||||
num_experts=self.config.num_experts,
|
||||
top_k=self.config.num_experts_per_tok,
|
||||
hidden_size=self.config.hidden_size,
|
||||
intermediate_size=self.config.intermediate_size)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
residual: Optional[torch.Tensor],
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
# Self Attention
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
hidden_states = self.self_attn(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
)
|
||||
hidden_states = residual + hidden_states * \
|
||||
(self.config.scale_depth / math.sqrt(self.config.mup_denominator))
|
||||
|
||||
# Fully Connected
|
||||
residual = hidden_states
|
||||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states * \
|
||||
(self.config.scale_depth / math.sqrt(self.config.mup_denominator))
|
||||
|
||||
return hidden_states, None
|
||||
|
||||
|
||||
@support_torch_compile
|
||||
class EagleMiniCPMModel(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
vllm_config: VllmConfig,
|
||||
prefix: str = "",
|
||||
start_layer: int = 0):
|
||||
super().__init__()
|
||||
|
||||
config = vllm_config.speculative_config.draft_model_config.hf_config
|
||||
cache_config = vllm_config.cache_config
|
||||
quant_config = vllm_config.quant_config
|
||||
lora_config = vllm_config.lora_config
|
||||
|
||||
self.config = config
|
||||
self.cache_config = cache_config
|
||||
self.quant_config = quant_config
|
||||
lora_vocab = (lora_config.lora_extra_vocab_size *
|
||||
(lora_config.max_loras or 1)) if lora_config else 0
|
||||
self.vocab_size = config.vocab_size + lora_vocab
|
||||
self.org_vocab_size = config.vocab_size
|
||||
self.fc = torch.nn.Linear(self.config.hidden_size * 2,
|
||||
self.config.hidden_size,
|
||||
bias=False)
|
||||
self.input_norm1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.input_norm2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
self.vocab_size,
|
||||
config.hidden_size,
|
||||
org_num_embeddings=config.vocab_size,
|
||||
)
|
||||
self.num_experts = getattr(self.config, "num_experts", 0)
|
||||
self._init_layers(prefix, config, cache_config, quant_config,
|
||||
start_layer)
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.make_empty_intermediate_tensors = (
|
||||
make_empty_intermediate_tensors_factory(
|
||||
["hidden_states", "residual"], self.config.hidden_size))
|
||||
|
||||
def _init_layers(
|
||||
self,
|
||||
prefix: str,
|
||||
config: PretrainedConfig,
|
||||
cache_config: Optional[CacheConfig],
|
||||
quant_config: Optional[QuantizationConfig],
|
||||
start_layer: int,
|
||||
):
|
||||
self.eagle_layers = nn.ModuleList([
|
||||
EagleMiniCPMDecoderLayer(
|
||||
config,
|
||||
cache_config,
|
||||
quant_config,
|
||||
f"{prefix}.eagle_layers.{i + start_layer}",
|
||||
) for i in range(self.config.num_hidden_layers)
|
||||
])
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
embedding = self.embed_tokens(input_ids)
|
||||
return embedding * self.config.scale_emb
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> Union[torch.Tensor, IntermediateTensors]:
|
||||
input_embeds = self.get_input_embeddings(input_ids)
|
||||
input_embeds = self.input_norm1(input_embeds)
|
||||
hidden_states = self.input_norm2(hidden_states)
|
||||
|
||||
hidden_states = self.fc(
|
||||
torch.cat((input_embeds, hidden_states), dim=-1))
|
||||
residual = None
|
||||
for layer in self.eagle_layers:
|
||||
hidden_states, residual = layer(
|
||||
positions,
|
||||
hidden_states,
|
||||
residual,
|
||||
)
|
||||
|
||||
return hidden_states, hidden_states
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str,
|
||||
torch.Tensor]]) -> set[str]:
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
expert_params_mapping = [
|
||||
# (param_name, weight_name, expert_id)
|
||||
("ws" if weight_name in ["w1", "w3"] else "w2s",
|
||||
f"experts.{expert_id}.{weight_name}.weight", expert_id)
|
||||
for expert_id in range(self.num_experts)
|
||||
for weight_name in ["w1", "w2", "w3"]
|
||||
]
|
||||
params_dict = dict(self.named_parameters())
|
||||
|
||||
loaded_params: set[str] = set()
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
if ("rotary_emb.cos_cached" in name
|
||||
or "rotary_emb.sin_cached" in name):
|
||||
# Models trained using ColossalAI may include these tensors in
|
||||
# the checkpoint. Skip them.
|
||||
continue
|
||||
for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
for param_name, weight_name, expert_id in expert_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param,
|
||||
loaded_weight,
|
||||
weight_name,
|
||||
expert_id=expert_id)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
|
||||
class EagleMiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": [
|
||||
"q_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
],
|
||||
"gate_up_proj": [
|
||||
"gate_proj",
|
||||
"up_proj",
|
||||
],
|
||||
}
|
||||
|
||||
# LoRA specific attributes
|
||||
embedding_modules = {
|
||||
"embed_tokens": "input_embeddings",
|
||||
"lm_head": "output_embeddings",
|
||||
}
|
||||
embedding_padding_modules = ["lm_head"]
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
config = vllm_config.speculative_config.draft_model_config.hf_config
|
||||
cache_config = vllm_config.cache_config
|
||||
quant_config = vllm_config.quant_config
|
||||
lora_config = vllm_config.lora_config
|
||||
|
||||
self.prefix = prefix
|
||||
self.vllm_config = vllm_config
|
||||
self.config = config
|
||||
self.lora_config = lora_config
|
||||
self.cache_config = cache_config
|
||||
self.quant_config = quant_config
|
||||
|
||||
target_layer_num = vllm_config.model_config.get_num_layers(
|
||||
vllm_config.parallel_config)
|
||||
|
||||
self.model = self._init_model(vllm_config=vllm_config,
|
||||
prefix=maybe_prefix(prefix, "model"),
|
||||
start_layer=target_layer_num)
|
||||
|
||||
unpadded_vocab_size = config.vocab_size
|
||||
if lora_config:
|
||||
unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
||||
self.lm_head = ParallelLMHead(
|
||||
unpadded_vocab_size,
|
||||
config.hidden_size,
|
||||
org_num_embeddings=config.vocab_size,
|
||||
padding_size=DEFAULT_VOCAB_PADDING_SIZE
|
||||
# We need bigger padding if using lora for kernel
|
||||
# compatibility
|
||||
if not lora_config else lora_config.lora_vocab_padding_size,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
if config.tie_word_embeddings:
|
||||
self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
|
||||
self.scale_width = self.config.hidden_size / self.config.dim_model_base
|
||||
|
||||
self.logits_processor = LogitsProcessor(unpadded_vocab_size,
|
||||
config.vocab_size)
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.model.make_empty_intermediate_tensors)
|
||||
|
||||
def _init_model(self,
|
||||
*,
|
||||
vllm_config: VllmConfig,
|
||||
prefix: str = "",
|
||||
start_layer: int = 0):
|
||||
return EagleMiniCPMModel(vllm_config=vllm_config,
|
||||
prefix=prefix,
|
||||
start_layer=start_layer)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.get_input_embeddings(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
hidden_states, hidden_states2 = self.model(input_ids, positions,
|
||||
hidden_states)
|
||||
hidden_states = hidden_states / self.scale_width
|
||||
hidden_states2 = hidden_states2 / self.scale_width
|
||||
return hidden_states, hidden_states2
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> Optional[torch.Tensor]:
|
||||
logits = self.logits_processor(self.lm_head, hidden_states,
|
||||
sampling_metadata)
|
||||
return logits
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str,
|
||||
torch.Tensor]]) -> set[str]:
|
||||
loader = AutoWeightsLoader(
|
||||
self,
|
||||
skip_prefixes=(["lm_head."]
|
||||
if self.config.tie_word_embeddings else None),
|
||||
)
|
||||
return loader.load_weights(weights)
|
||||
@ -223,6 +223,7 @@ _SPECULATIVE_DECODING_MODELS = {
|
||||
"MiMoMTPModel": ("mimo_mtp", "MiMoMTP"),
|
||||
"EAGLEModel": ("eagle", "EAGLE"),
|
||||
"EagleLlamaForCausalLM": ("llama_eagle", "EagleLlamaForCausalLM"),
|
||||
"EagleMiniCPMForCausalLM": ("minicpm_eagle", "EagleMiniCPMForCausalLM"),
|
||||
"Eagle3LlamaForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
|
||||
"DeepSeekMTPModel": ("deepseek_mtp", "DeepSeekMTP"),
|
||||
"MedusaModel": ("medusa", "Medusa"),
|
||||
|
||||
Reference in New Issue
Block a user