[gpt-oss] Add gpt-oss bf16 support
This commit is contained in:
412
vllm/model_executor/models/opt.py
Normal file
412
vllm/model_executor/models/opt.py
Normal file
@@ -0,0 +1,412 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
# Adapted from
|
||||
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/opt/modeling_opt.py
|
||||
# Copyright 2023 The vLLM team.
|
||||
# Copyright 2022 The Fairseq Authors and The HuggingFace Inc. team. All rights
|
||||
# reserved.
|
||||
#
|
||||
# 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 OPT model compatible with HuggingFace weights."""
|
||||
from collections.abc import Iterable
|
||||
from typing import Optional, Union
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from transformers import OPTConfig
|
||||
|
||||
from vllm.attention import Attention
|
||||
from vllm.compilation.decorators import support_torch_compile
|
||||
from vllm.config import CacheConfig, VllmConfig
|
||||
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
|
||||
from vllm.model_executor.layers.activation import get_act_fn
|
||||
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
|
||||
QKVParallelLinear,
|
||||
ReplicatedLinear,
|
||||
RowParallelLinear)
|
||||
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 (
|
||||
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 SupportsPP
|
||||
from .utils import (AutoWeightsLoader, WeightsMapper, is_pp_missing_parameter,
|
||||
make_empty_intermediate_tensors_factory, make_layers,
|
||||
maybe_prefix)
|
||||
|
||||
|
||||
class OPTLearnedPositionalEmbedding(nn.Embedding):
|
||||
|
||||
def __init__(self, num_embeddings: int, embedding_dim: int):
|
||||
# OPT is set up so that if padding_idx is specified then offset the
|
||||
# embedding ids by 2 and adjust num_embeddings appropriately. Other
|
||||
# models don't have this hack
|
||||
self.offset = 2
|
||||
super().__init__(num_embeddings + self.offset, embedding_dim)
|
||||
|
||||
def forward(self, positions: torch.Tensor):
|
||||
return super().forward(positions + self.offset)
|
||||
|
||||
|
||||
class OPTAttention(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
embed_dim: int,
|
||||
num_heads: int,
|
||||
bias: bool = True,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.embed_dim = embed_dim
|
||||
tensor_model_parallel_world_size = (
|
||||
get_tensor_model_parallel_world_size())
|
||||
total_num_heads = num_heads
|
||||
assert num_heads % tensor_model_parallel_world_size == 0
|
||||
self.num_heads = total_num_heads // tensor_model_parallel_world_size
|
||||
self.head_dim = embed_dim // total_num_heads
|
||||
self.scaling = self.head_dim**-0.5
|
||||
|
||||
self.qkv_proj = QKVParallelLinear(
|
||||
embed_dim,
|
||||
self.head_dim,
|
||||
total_num_heads,
|
||||
bias=bias,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.qkv_proj",
|
||||
)
|
||||
self.out_proj = RowParallelLinear(
|
||||
embed_dim,
|
||||
embed_dim,
|
||||
bias=bias,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.out_proj",
|
||||
)
|
||||
self.attn = Attention(self.num_heads,
|
||||
self.head_dim,
|
||||
scale=self.scaling,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.attn")
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
qkv, _ = self.qkv_proj(hidden_states)
|
||||
q, k, v = qkv.chunk(chunks=3, dim=-1)
|
||||
attn_output = self.attn(q, k, v)
|
||||
output, _ = self.out_proj(attn_output)
|
||||
return output
|
||||
|
||||
|
||||
class OPTDecoderLayer(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: OPTConfig,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.embed_dim = config.hidden_size
|
||||
self.self_attn = OPTAttention(
|
||||
embed_dim=self.embed_dim,
|
||||
num_heads=config.num_attention_heads,
|
||||
bias=config.enable_bias,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.self_attn",
|
||||
)
|
||||
self.do_layer_norm_before = config.do_layer_norm_before
|
||||
|
||||
self.self_attn_layer_norm = nn.LayerNorm(
|
||||
self.embed_dim,
|
||||
elementwise_affine=config.layer_norm_elementwise_affine)
|
||||
self.fc1 = ColumnParallelLinear(
|
||||
self.embed_dim,
|
||||
config.ffn_dim,
|
||||
bias=config.enable_bias,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.fc1",
|
||||
)
|
||||
self.activation_fn = get_act_fn(config.activation_function)
|
||||
self.fc2 = RowParallelLinear(
|
||||
config.ffn_dim,
|
||||
self.embed_dim,
|
||||
bias=config.enable_bias,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.fc2",
|
||||
)
|
||||
self.final_layer_norm = nn.LayerNorm(
|
||||
self.embed_dim,
|
||||
elementwise_affine=config.layer_norm_elementwise_affine)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor:
|
||||
# Self Attention
|
||||
residual = hidden_states
|
||||
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
|
||||
if self.do_layer_norm_before:
|
||||
hidden_states = self.self_attn_layer_norm(hidden_states)
|
||||
hidden_states = self.self_attn(hidden_states=hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
# 350m applies layer norm AFTER attention
|
||||
if not self.do_layer_norm_before:
|
||||
hidden_states = self.self_attn_layer_norm(hidden_states)
|
||||
|
||||
# Fully Connected
|
||||
residual = hidden_states
|
||||
# 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
|
||||
if self.do_layer_norm_before:
|
||||
hidden_states = self.final_layer_norm(hidden_states)
|
||||
hidden_states, _ = self.fc1(hidden_states)
|
||||
hidden_states = self.activation_fn(hidden_states)
|
||||
hidden_states, _ = self.fc2(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
# 350m applies layer norm AFTER attention
|
||||
if not self.do_layer_norm_before:
|
||||
hidden_states = self.final_layer_norm(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class OPTDecoder(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: OPTConfig,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.max_target_positions = config.max_position_embeddings
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
config.vocab_size,
|
||||
config.word_embed_proj_dim,
|
||||
)
|
||||
# Positional embeddings are replicated (not sharded).
|
||||
self.embed_positions = OPTLearnedPositionalEmbedding(
|
||||
config.max_position_embeddings, config.hidden_size)
|
||||
|
||||
# Project out & in will be replicated if they exist.
|
||||
if config.word_embed_proj_dim != config.hidden_size:
|
||||
self.project_out = ReplicatedLinear(config.hidden_size,
|
||||
config.word_embed_proj_dim,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.project_out")
|
||||
else:
|
||||
self.project_out = None
|
||||
|
||||
if config.word_embed_proj_dim != config.hidden_size:
|
||||
self.project_in = ReplicatedLinear(config.word_embed_proj_dim,
|
||||
config.hidden_size,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.project_in")
|
||||
else:
|
||||
self.project_in = None
|
||||
|
||||
# Note that the only purpose of `config._remove_final_layer_norm` is to
|
||||
# keep backward compatibility with checkpoints that have been fine-tuned
|
||||
# before transformers v4.20.1
|
||||
# see https://github.com/facebookresearch/metaseq/pull/164
|
||||
if config.do_layer_norm_before and not config._remove_final_layer_norm:
|
||||
self.final_layer_norm = nn.LayerNorm(
|
||||
config.hidden_size,
|
||||
elementwise_affine=config.layer_norm_elementwise_affine)
|
||||
else:
|
||||
self.final_layer_norm = None
|
||||
|
||||
self.start_layer, self.end_layer, self.layers = make_layers(
|
||||
config.num_hidden_layers,
|
||||
lambda prefix: OPTDecoderLayer(
|
||||
config, cache_config, quant_config, prefix=prefix),
|
||||
prefix=f"{prefix}.layers")
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embed_tokens(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: Optional[IntermediateTensors],
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
) -> Union[torch.Tensor, IntermediateTensors]:
|
||||
if get_pp_group().is_first_rank:
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.get_input_embeddings(input_ids)
|
||||
pos_embeds = self.embed_positions(positions)
|
||||
if self.project_in is not None:
|
||||
inputs_embeds, _ = self.project_in(inputs_embeds)
|
||||
hidden_states = inputs_embeds + pos_embeds
|
||||
else:
|
||||
assert intermediate_tensors is not None
|
||||
hidden_states = intermediate_tensors["hidden_states"]
|
||||
|
||||
for layer in self.layers[self.start_layer:self.end_layer]:
|
||||
hidden_states = layer(hidden_states)
|
||||
|
||||
if not get_pp_group().is_last_rank:
|
||||
return IntermediateTensors({"hidden_states": hidden_states})
|
||||
if self.final_layer_norm is not None:
|
||||
hidden_states = self.final_layer_norm(hidden_states)
|
||||
if self.project_out is not None:
|
||||
hidden_states, _ = self.project_out(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
@support_torch_compile
|
||||
class OPTModel(nn.Module):
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
|
||||
config = vllm_config.model_config.hf_config
|
||||
cache_config = vllm_config.cache_config
|
||||
quant_config = vllm_config.quant_config
|
||||
|
||||
self.decoder = OPTDecoder(config,
|
||||
cache_config,
|
||||
quant_config,
|
||||
prefix=f"{prefix}.decoder")
|
||||
self.make_empty_intermediate_tensors = (
|
||||
make_empty_intermediate_tensors_factory(["hidden_states"],
|
||||
config.hidden_size))
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.decoder.get_input_embeddings(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: Optional[IntermediateTensors],
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
) -> Union[torch.Tensor, IntermediateTensors]:
|
||||
return self.decoder(input_ids,
|
||||
positions,
|
||||
intermediate_tensors,
|
||||
inputs_embeds=inputs_embeds)
|
||||
|
||||
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"),
|
||||
]
|
||||
params_dict = dict(self.named_parameters(remove_duplicate=False))
|
||||
loaded_params: set[str] = set()
|
||||
for name, loaded_weight in weights:
|
||||
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:
|
||||
# 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 OPTForCausalLM(nn.Module, SupportsPP):
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": ["q_proj", "k_proj", "v_proj"],
|
||||
"gate_up_proj": ["gate_proj", "up_proj"]
|
||||
}
|
||||
|
||||
hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={
|
||||
"decoder.": "model.decoder.",
|
||||
})
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
self.model = OPTModel(vllm_config=vllm_config,
|
||||
prefix=maybe_prefix(prefix, "model"))
|
||||
if self.config.tie_word_embeddings:
|
||||
self.lm_head = self.model.decoder.embed_tokens
|
||||
else:
|
||||
self.lm_head = ParallelLMHead(config.vocab_size,
|
||||
config.word_embed_proj_dim)
|
||||
self.logits_processor = LogitsProcessor(config.vocab_size)
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.model.make_empty_intermediate_tensors)
|
||||
|
||||
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,
|
||||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
) -> Union[torch.Tensor, IntermediateTensors]:
|
||||
hidden_states = self.model(input_ids, positions, intermediate_tensors,
|
||||
inputs_embeds)
|
||||
return hidden_states
|
||||
|
||||
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.weight"]
|
||||
if self.config.tie_word_embeddings else None),
|
||||
)
|
||||
return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)
|
||||
Reference in New Issue
Block a user