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Unverified Commit 344020c9 authored by Roy's avatar Roy Committed by GitHub
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Migrate MistralForCausalLM to LlamaForCausalLM (#2868)

parent 5574081c
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vllm/model_executor/models/__init__.py
View file @ 344020c9
...@@ -30,7 +30,7 @@ _MODELS = { ...@@ -30,7 +30,7 @@ _MODELS = {
"LlamaForCausalLM": ("llama", "LlamaForCausalLM"), "LlamaForCausalLM": ("llama", "LlamaForCausalLM"),
# For decapoda-research/llama-* # For decapoda-research/llama-*
"LLaMAForCausalLM": ("llama", "LlamaForCausalLM"), "LLaMAForCausalLM": ("llama", "LlamaForCausalLM"),
"MistralForCausalLM": ("mistral", "MistralForCausalLM"), "MistralForCausalLM": ("llama", "LlamaForCausalLM"),
"MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"), "MixtralForCausalLM": ("mixtral", "MixtralForCausalLM"),
"QuantMixtralForCausalLM": ("mixtral_quant", "MixtralForCausalLM"), "QuantMixtralForCausalLM": ("mixtral_quant", "MixtralForCausalLM"),
# transformers's mpt class has lower case # transformers's mpt class has lower case
......
vllm/model_executor/models/llama.py
View file @ 344020c9
...@@ -92,6 +92,7 @@ class LlamaAttention(nn.Module): ...@@ -92,6 +92,7 @@ class LlamaAttention(nn.Module):
max_position_embeddings: int = 8192, max_position_embeddings: int = 8192,
linear_method: Optional[LinearMethodBase] = None, linear_method: Optional[LinearMethodBase] = None,
bias: bool = False, bias: bool = False,
sliding_window: Optional[int] = None,
) -> None: ) -> None:
super().__init__() super().__init__()
self.hidden_size = hidden_size self.hidden_size = hidden_size
...@@ -141,7 +142,8 @@ class LlamaAttention(nn.Module): ...@@ -141,7 +142,8 @@ class LlamaAttention(nn.Module):
self.attn = PagedAttention(self.num_heads, self.attn = PagedAttention(self.num_heads,
self.head_dim, self.head_dim,
self.scaling, self.scaling,
num_kv_heads=self.num_kv_heads) num_kv_heads=self.num_kv_heads,
sliding_window=sliding_window)
def forward( def forward(
self, self,
...@@ -172,6 +174,7 @@ class LlamaDecoderLayer(nn.Module): ...@@ -172,6 +174,7 @@ class LlamaDecoderLayer(nn.Module):
rope_scaling = getattr(config, "rope_scaling", None) rope_scaling = getattr(config, "rope_scaling", None)
max_position_embeddings = getattr(config, "max_position_embeddings", max_position_embeddings = getattr(config, "max_position_embeddings",
8192) 8192)
sliding_window = getattr(config, "sliding_window", None)
self.self_attn = LlamaAttention( self.self_attn = LlamaAttention(
hidden_size=self.hidden_size, hidden_size=self.hidden_size,
num_heads=config.num_attention_heads, num_heads=config.num_attention_heads,
...@@ -182,6 +185,7 @@ class LlamaDecoderLayer(nn.Module): ...@@ -182,6 +185,7 @@ class LlamaDecoderLayer(nn.Module):
max_position_embeddings=max_position_embeddings, max_position_embeddings=max_position_embeddings,
linear_method=linear_method, linear_method=linear_method,
bias=getattr(config, "bias", False), bias=getattr(config, "bias", False),
sliding_window=sliding_window,
) )
self.mlp = LlamaMLP( self.mlp = LlamaMLP(
hidden_size=self.hidden_size, hidden_size=self.hidden_size,
......
vllm/model_executor/models/mistral.py deleted 100644 → 0
View file @ 5574081c
# 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 Mistral model compatible with HuggingFace weights."""
from typing import List, Optional, Tuple
import torch
from torch import nn
from transformers import MistralConfig
from vllm.model_executor.input_metadata import InputMetadata
from vllm.model_executor.layers.activation import SiluAndMul
from vllm.model_executor.layers.attention import PagedAttention
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (LinearMethodBase,
MergedColumnParallelLinear,
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, DEFAULT_VOCAB_PADDING_SIZE)
from vllm.model_executor.parallel_utils.parallel_state import (
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
from vllm.config import LoRAConfig
KVCache = Tuple[torch.Tensor, torch.Tensor]
class MistralMLP(nn.Module):
def __init__(
self,
hidden_size: int,
intermediate_size: int,
hidden_act: str,
linear_method: Optional[LinearMethodBase] = None,
) -> None:
super().__init__()
self.gate_up_proj = MergedColumnParallelLinear(
hidden_size, [intermediate_size] * 2,
bias=False,
linear_method=linear_method)
self.down_proj = RowParallelLinear(intermediate_size,
hidden_size,
bias=False,
linear_method=linear_method)
if hidden_act != "silu":
raise ValueError(f"Unsupported activation: {hidden_act}. "
"Only silu is supported for now.")
self.act_fn = SiluAndMul()
def forward(self, x):
gate_up, _ = self.gate_up_proj(x)
x = self.act_fn(gate_up)
x, _ = self.down_proj(x)
return x
class MistralAttention(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=self.rope_theta,
)
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 MistralDecoderLayer(nn.Module):
def __init__(
self,
config: MistralConfig,
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 = MistralAttention(
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,
linear_method=linear_method,
sliding_window=config.sliding_window)
self.mlp = MistralMLP(
hidden_size=self.hidden_size,
intermediate_size=config.intermediate_size,
hidden_act=config.hidden_act,
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],
) -> Tuple[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.mlp(hidden_states)
return hidden_states, residual
class MistralModel(nn.Module):
def __init__(
self,
config: MistralConfig,
linear_method: Optional[LinearMethodBase] = None,
lora_config: Optional[LoRAConfig] = None,
) -> None:
super().__init__()
self.config = config
self.padding_idx = config.pad_token_id
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.embed_tokens = VocabParallelEmbedding(
self.vocab_size,
config.hidden_size,
org_num_embeddings=config.vocab_size,
)
self.layers = nn.ModuleList([
MistralDecoderLayer(config, 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 MistralForCausalLM(nn.Module):
packed_modules_mapping = {
"qkv_proj": [
"q_proj",
"k_proj",
"v_proj",
],
"gate_up_proj": [
"gate_proj",
"up_proj",
],
}
# LoRA specific attributes
supported_lora_modules = [
"qkv_proj",
"o_proj",
"gate_up_proj",
"down_proj",
"embed_tokens",
"lm_head",
]
embedding_modules = {
"embed_tokens": "input_embeddings",
"lm_head": "output_embeddings",
}
embedding_padding_modules = ["lm_head"]
def __init__(
self,
config: MistralConfig,
linear_method: Optional[LinearMethodBase] = None,
lora_config: Optional[LoRAConfig] = None,
) -> None:
super().__init__()
self.config = config
self.linear_method = linear_method
self.model = MistralModel(config,
linear_method,
lora_config=lora_config)
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,
)
self.sampler = Sampler(unpadded_vocab_size, 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: 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"),
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
]
params_dict = dict(self.named_parameters())
for name, loaded_weight in hf_model_weights_iterator(
model_name_or_path, cache_dir, load_format, revision):
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
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
weight_loader(param, loaded_weight)
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