mirror of
https://github.com/vllm-project/vllm.git
synced 2025-10-20 14:53:52 +08:00
Add quantized mixtral support (#2673)
This commit is contained in:
Woosuk Kwon
GitHub
@ -4,7 +4,6 @@ from typing import Optional, Type
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from transformers import PretrainedConfig
|
||||
|
||||
from vllm.config import ModelConfig, LoRAConfig
|
||||
from vllm.model_executor.models import ModelRegistry
|
||||
@ -21,8 +20,14 @@ def _set_default_torch_dtype(dtype: torch.dtype):
|
||||
torch.set_default_dtype(old_dtype)
|
||||
|
||||
|
||||
def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
|
||||
architectures = getattr(config, "architectures", [])
|
||||
def _get_model_architecture(model_config: ModelConfig) -> Type[nn.Module]:
|
||||
architectures = getattr(model_config.hf_config, "architectures", [])
|
||||
# Special handling for quantized Mixtral.
|
||||
# FIXME(woosuk): This is a temporary hack.
|
||||
if (model_config.quantization is not None
|
||||
and "MixtralForCausalLM" in architectures):
|
||||
architectures = ["QuantMixtralForCausalLM"]
|
||||
|
||||
for arch in architectures:
|
||||
model_cls = ModelRegistry.load_model_cls(arch)
|
||||
if model_cls is not None:
|
||||
@ -34,7 +39,7 @@ def _get_model_architecture(config: PretrainedConfig) -> Type[nn.Module]:
|
||||
|
||||
def get_model(model_config: ModelConfig,
|
||||
lora_config: Optional[LoRAConfig] = None) -> nn.Module:
|
||||
model_class = _get_model_architecture(model_config.hf_config)
|
||||
model_class = _get_model_architecture(model_config)
|
||||
|
||||
# Get the (maybe quantized) linear method.
|
||||
linear_method = None
|
||||
|
||||
@ -30,6 +30,7 @@ _MODELS = {
|
||||
"LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
|
||||
"MistralForCausalLM": ("mistral", "MistralForCausalLM"),
|
||||
"MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"),
|
||||
"QuantMixtralForCausalLM": ("mixtral_quant", "MixtralForCausalLM"),
|
||||
# transformers's mpt class has lower case
|
||||
"MptForCausalLM": ("mpt", "MPTForCausalLM"),
|
||||
"MPTForCausalLM": ("mpt", "MPTForCausalLM"),
|
||||
|
||||
412
vllm/model_executor/models/mixtral_quant.py
Normal file
412
vllm/model_executor/models/mixtral_quant.py
Normal file
@ -0,0 +1,412 @@
|
||||
# coding=utf-8
|
||||
# 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 Mixtral model."""
|
||||
from typing import List, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
|
||||
from torch import nn
|
||||
from transformers import MixtralConfig
|
||||
|
||||
from vllm.model_executor.input_metadata import InputMetadata
|
||||
from vllm.model_executor.layers.attention import PagedAttention
|
||||
from vllm.model_executor.layers.layernorm import RMSNorm
|
||||
from vllm.model_executor.layers.linear import (LinearMethodBase,
|
||||
ReplicatedLinear,
|
||||
QKVParallelLinear,
|
||||
RowParallelLinear)
|
||||
from vllm.model_executor.layers.rotary_embedding import get_rope
|
||||
from vllm.model_executor.layers.sampler import Sampler
|
||||
from vllm.model_executor.layers.vocab_parallel_embedding import (
|
||||
VocabParallelEmbedding, ParallelLMHead)
|
||||
from vllm.model_executor.parallel_utils.communication_op import (
|
||||
tensor_model_parallel_all_reduce)
|
||||
from vllm.model_executor.parallel_utils.parallel_state import (
|
||||
get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
|
||||
from vllm.model_executor.sampling_metadata import SamplingMetadata
|
||||
from vllm.model_executor.weight_utils import (default_weight_loader,
|
||||
hf_model_weights_iterator)
|
||||
from vllm.sequence import SamplerOutput
|
||||
|
||||
KVCache = Tuple[torch.Tensor, torch.Tensor]
|
||||
|
||||
|
||||
class MixtralMLP(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
num_experts: int,
|
||||
hidden_size: int,
|
||||
intermediate_size: int,
|
||||
linear_method: Optional[LinearMethodBase] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.num_experts = num_experts
|
||||
self.ffn_dim = intermediate_size
|
||||
self.hidden_dim = hidden_size
|
||||
|
||||
self.w1 = ReplicatedLinear(self.hidden_dim,
|
||||
self.ffn_dim,
|
||||
bias=False,
|
||||
linear_method=linear_method)
|
||||
self.w2 = ReplicatedLinear(self.ffn_dim,
|
||||
self.hidden_dim,
|
||||
bias=False,
|
||||
linear_method=linear_method)
|
||||
self.w3 = ReplicatedLinear(self.hidden_dim,
|
||||
self.ffn_dim,
|
||||
bias=False,
|
||||
linear_method=linear_method)
|
||||
|
||||
# TODO: Use vllm's SiluAndMul
|
||||
self.act_fn = nn.SiLU()
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
w1_out, _ = self.w1(hidden_states)
|
||||
w1_out = self.act_fn(w1_out)
|
||||
w3_out, _ = self.w3(hidden_states)
|
||||
current_hidden_states = w1_out * w3_out
|
||||
current_hidden_states, _ = self.w2(current_hidden_states)
|
||||
return current_hidden_states
|
||||
|
||||
|
||||
class MixtralMoE(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: MixtralConfig,
|
||||
linear_method: Optional[LinearMethodBase] = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.rank = get_tensor_model_parallel_rank()
|
||||
self.tp_size = get_tensor_model_parallel_world_size()
|
||||
self.num_total_experts = config.num_local_experts
|
||||
self.top_k = config.num_experts_per_tok
|
||||
if self.tp_size > self.num_total_experts:
|
||||
raise ValueError(
|
||||
f"Tensor parallel size {self.tp_size} is greater than "
|
||||
f"the number of experts {self.num_total_experts}.")
|
||||
# Split experts equally between ranks
|
||||
self.expert_indicies = np.array_split(range(
|
||||
self.num_total_experts), self.tp_size)[self.rank].tolist()
|
||||
if not self.expert_indicies:
|
||||
raise ValueError(
|
||||
f"Rank {self.rank} has no experts assigned to it.")
|
||||
|
||||
self.experts = nn.ModuleList([
|
||||
MixtralMLP(self.num_total_experts,
|
||||
config.hidden_size,
|
||||
config.intermediate_size,
|
||||
linear_method=linear_method)
|
||||
if idx in self.expert_indicies else None
|
||||
for idx in range(self.num_total_experts)
|
||||
])
|
||||
self.gate = ReplicatedLinear(config.hidden_size,
|
||||
self.num_total_experts,
|
||||
bias=False,
|
||||
linear_method=None)
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
batch_size, sequence_length, hidden_dim = hidden_states.shape
|
||||
hidden_states = hidden_states.view(-1, hidden_dim)
|
||||
# router_logits: (batch * sequence_length, n_experts)
|
||||
router_logits, _ = self.gate(hidden_states)
|
||||
|
||||
routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
|
||||
routing_weights, selected_experts = torch.topk(routing_weights,
|
||||
self.top_k,
|
||||
dim=-1)
|
||||
routing_weights /= routing_weights.sum(dim=-1, keepdim=True)
|
||||
|
||||
final_hidden_states = None
|
||||
for expert_idx in self.expert_indicies:
|
||||
expert_layer = self.experts[expert_idx]
|
||||
expert_mask = (selected_experts == expert_idx)
|
||||
expert_weights = (routing_weights * expert_mask).sum(dim=-1,
|
||||
keepdim=True)
|
||||
|
||||
current_hidden_states = expert_layer(hidden_states).mul_(
|
||||
expert_weights)
|
||||
if final_hidden_states is None:
|
||||
final_hidden_states = current_hidden_states
|
||||
else:
|
||||
final_hidden_states.add_(current_hidden_states)
|
||||
|
||||
return tensor_model_parallel_all_reduce(final_hidden_states).view(
|
||||
batch_size, sequence_length, hidden_dim)
|
||||
|
||||
|
||||
class MixtralAttention(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
hidden_size: int,
|
||||
num_heads: int,
|
||||
num_kv_heads: int,
|
||||
max_position: int = 4096 * 32,
|
||||
rope_theta: float = 10000,
|
||||
linear_method: Optional[LinearMethodBase] = None,
|
||||
sliding_window: Optional[int] = None) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = hidden_size
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
self.total_num_heads = num_heads
|
||||
assert self.total_num_heads % tp_size == 0
|
||||
self.num_heads = self.total_num_heads // tp_size
|
||||
self.total_num_kv_heads = num_kv_heads
|
||||
if self.total_num_kv_heads >= tp_size:
|
||||
# Number of KV heads is greater than TP size, so we partition
|
||||
# the KV heads across multiple tensor parallel GPUs.
|
||||
assert self.total_num_kv_heads % tp_size == 0
|
||||
else:
|
||||
# Number of KV heads is less than TP size, so we replicate
|
||||
# the KV heads across multiple tensor parallel GPUs.
|
||||
assert tp_size % self.total_num_kv_heads == 0
|
||||
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
||||
self.head_dim = hidden_size // self.total_num_heads
|
||||
self.q_size = self.num_heads * self.head_dim
|
||||
self.kv_size = self.num_kv_heads * self.head_dim
|
||||
self.scaling = self.head_dim**-0.5
|
||||
self.rope_theta = rope_theta
|
||||
self.sliding_window = sliding_window
|
||||
|
||||
self.qkv_proj = QKVParallelLinear(
|
||||
hidden_size,
|
||||
self.head_dim,
|
||||
self.total_num_heads,
|
||||
self.total_num_kv_heads,
|
||||
bias=False,
|
||||
linear_method=linear_method,
|
||||
)
|
||||
self.o_proj = RowParallelLinear(
|
||||
self.total_num_heads * self.head_dim,
|
||||
hidden_size,
|
||||
bias=False,
|
||||
linear_method=linear_method,
|
||||
)
|
||||
self.rotary_emb = get_rope(
|
||||
self.head_dim,
|
||||
rotary_dim=self.head_dim,
|
||||
max_position=max_position,
|
||||
base=int(self.rope_theta),
|
||||
is_neox_style=True,
|
||||
)
|
||||
self.attn = PagedAttention(
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
self.scaling,
|
||||
num_kv_heads=self.num_kv_heads,
|
||||
sliding_window=self.sliding_window,
|
||||
)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
kv_cache: KVCache,
|
||||
input_metadata: InputMetadata,
|
||||
) -> torch.Tensor:
|
||||
qkv, _ = self.qkv_proj(hidden_states)
|
||||
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
||||
q, k = self.rotary_emb(positions, q, k)
|
||||
k_cache, v_cache = kv_cache
|
||||
attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
|
||||
output, _ = self.o_proj(attn_output)
|
||||
return output
|
||||
|
||||
|
||||
class MixtralDecoderLayer(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: MixtralConfig,
|
||||
linear_method: Optional[LinearMethodBase] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
# Requires transformers > 4.32.0
|
||||
rope_theta = getattr(config, "rope_theta", 10000)
|
||||
self.self_attn = MixtralAttention(
|
||||
hidden_size=self.hidden_size,
|
||||
num_heads=config.num_attention_heads,
|
||||
max_position=config.max_position_embeddings,
|
||||
num_kv_heads=config.num_key_value_heads,
|
||||
rope_theta=rope_theta,
|
||||
sliding_window=config.sliding_window,
|
||||
linear_method=linear_method)
|
||||
self.block_sparse_moe = MixtralMoE(config=config,
|
||||
linear_method=linear_method)
|
||||
self.input_layernorm = RMSNorm(config.hidden_size,
|
||||
eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = RMSNorm(config.hidden_size,
|
||||
eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
kv_cache: KVCache,
|
||||
input_metadata: InputMetadata,
|
||||
residual: Optional[torch.Tensor],
|
||||
) -> torch.Tensor:
|
||||
# Self Attention
|
||||
if residual is None:
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
else:
|
||||
hidden_states, residual = self.input_layernorm(
|
||||
hidden_states, residual)
|
||||
hidden_states = self.self_attn(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
kv_cache=kv_cache,
|
||||
input_metadata=input_metadata,
|
||||
)
|
||||
|
||||
# Fully Connected
|
||||
hidden_states, residual = self.post_attention_layernorm(
|
||||
hidden_states, residual)
|
||||
hidden_states = self.block_sparse_moe(hidden_states)
|
||||
return hidden_states, residual
|
||||
|
||||
|
||||
class MixtralModel(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: MixtralConfig,
|
||||
linear_method: Optional[LinearMethodBase] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
)
|
||||
self.layers = nn.ModuleList([
|
||||
MixtralDecoderLayer(config, linear_method=linear_method)
|
||||
for _ in range(config.num_hidden_layers)
|
||||
])
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
kv_caches: List[KVCache],
|
||||
input_metadata: InputMetadata,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
residual = None
|
||||
for i in range(len(self.layers)):
|
||||
layer = self.layers[i]
|
||||
hidden_states, residual = layer(positions, hidden_states,
|
||||
kv_caches[i], input_metadata,
|
||||
residual)
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class MixtralForCausalLM(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: MixtralConfig,
|
||||
linear_method: Optional[LinearMethodBase] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.linear_method = linear_method
|
||||
self.model = MixtralModel(config, linear_method)
|
||||
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
|
||||
self.sampler = Sampler(config.vocab_size)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
kv_caches: List[KVCache],
|
||||
input_metadata: InputMetadata,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.model(input_ids, positions, kv_caches,
|
||||
input_metadata)
|
||||
return hidden_states
|
||||
|
||||
def sample(
|
||||
self,
|
||||
hidden_states: Optional[torch.Tensor],
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> Optional[SamplerOutput]:
|
||||
next_tokens = self.sampler(self.lm_head.weight, hidden_states,
|
||||
sampling_metadata)
|
||||
return next_tokens
|
||||
|
||||
def load_weights(self,
|
||||
model_name_or_path: str,
|
||||
cache_dir: Optional[str] = None,
|
||||
load_format: str = "auto",
|
||||
revision: Optional[str] = None):
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
]
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
for name, loaded_weight in hf_model_weights_iterator(
|
||||
model_name_or_path,
|
||||
cache_dir,
|
||||
load_format,
|
||||
revision,
|
||||
fall_back_to_pt=False):
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
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
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
# Skip experts that are not assigned to this worker.
|
||||
if ("block_sparse_moe.experts." in name
|
||||
and name not in params_dict):
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
Reference in New Issue
Block a user