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# 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/gpt2/modeling_gpt2.py
# Copyright 2023 The vLLM team.
# Copyright 2023 CTranslate2, and Michael Feil
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. 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 GPTBigCode model compatible with HuggingFace weights."""
from collections.abc import Iterable
from typing import Optional, Union
import torch
from torch import nn
from transformers import GPTBigCodeConfig
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,
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 SupportsLoRA, SupportsPP
from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
make_empty_intermediate_tensors_factory, make_layers)
class GPTBigCodeAttention(nn.Module):
def __init__(
self,
config: GPTBigCodeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
self.hidden_size = config.hidden_size
total_num_heads = config.num_attention_heads
self.tensor_model_parallel_world_size = (
get_tensor_model_parallel_world_size())
assert total_num_heads % self.tensor_model_parallel_world_size == 0
self.num_heads = (total_num_heads //
self.tensor_model_parallel_world_size)
self.head_dim = self.hidden_size // total_num_heads
self.scale = self.head_dim**-0.5
self.multi_query = config.multi_query
if self.multi_query:
total_num_kv_heads = 1
self.num_kv_heads = 1
else:
total_num_kv_heads = total_num_heads
self.num_kv_heads = self.num_heads
self.kv_dim = self.head_dim * self.num_kv_heads
self.c_attn = QKVParallelLinear(
self.hidden_size,
self.head_dim,
total_num_heads,
total_num_kv_heads,
bias=True,
quant_config=quant_config,
)
self.c_proj = RowParallelLinear(
self.hidden_size,
self.hidden_size,
bias=True,
quant_config=quant_config,
)
self.attn = Attention(self.num_heads,
self.head_dim,
scale=self.scale,
num_kv_heads=self.num_kv_heads,
cache_config=cache_config,
quant_config=quant_config,
prefix=f"{prefix}.attn")
def forward(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
qkv, _ = self.c_attn(hidden_states)
q, k, v = qkv.split(
[
self.hidden_size // self.tensor_model_parallel_world_size,
self.kv_dim, self.kv_dim
],
dim=-1,
)
attn_output = self.attn(q, k, v)
attn_output, _ = self.c_proj(attn_output)
return attn_output
class GPTBigMLP(nn.Module):
def __init__(
self,
intermediate_size: int,
config: GPTBigCodeConfig,
quant_config: Optional[QuantizationConfig] = None,
):
super().__init__()
hidden_size = config.hidden_size
self.c_fc = ColumnParallelLinear(
hidden_size,
intermediate_size,
bias=True,
quant_config=quant_config,
)
self.c_proj = RowParallelLinear(
intermediate_size,
hidden_size,
bias=True,
quant_config=quant_config,
)
self.act = get_act_fn(config.activation_function)
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
hidden_states, _ = self.c_fc(hidden_states)
hidden_states = self.act(hidden_states)
hidden_states, _ = self.c_proj(hidden_states)
return hidden_states
class GPTBigCodeBlock(nn.Module):
def __init__(
self,
config: GPTBigCodeConfig,
cache_config: Optional[CacheConfig] = None,
quant_config: Optional[QuantizationConfig] = None,
prefix: str = "",
):
super().__init__()
hidden_size = config.hidden_size
inner_dim = (config.n_inner if config.n_inner is not None else 4 *
hidden_size)
self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.attn = GPTBigCodeAttention(config,
cache_config,
quant_config,
prefix=f"{prefix}.attn")
self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
self.mlp = GPTBigMLP(inner_dim, config, quant_config)
def forward(
self,
hidden_states: torch.Tensor,
) -> torch.Tensor:
residual = hidden_states
hidden_states = self.ln_1(hidden_states)
attn_output = self.attn(hidden_states=hidden_states, )
# residual connection
hidden_states = attn_output + residual
residual = hidden_states
hidden_states = self.ln_2(hidden_states)
feed_forward_hidden_states = self.mlp(hidden_states)
# residual connection
hidden_states = residual + feed_forward_hidden_states
return hidden_states
@support_torch_compile
class GPTBigCodeModel(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
lora_config = vllm_config.lora_config
self.config = config
assert not config.add_cross_attention
self.embed_dim = config.hidden_size
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.wte = VocabParallelEmbedding(self.vocab_size,
self.embed_dim,
org_num_embeddings=config.vocab_size)
self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
self.start_layer, self.end_layer, self.h = make_layers(
config.num_hidden_layers,
lambda prefix: GPTBigCodeBlock(
config, cache_config, quant_config, prefix=prefix),
prefix=f"{prefix}.h",
)
self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
self.make_empty_intermediate_tensors = (
make_empty_intermediate_tensors_factory(["hidden_states"],
config.n_embd))
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.wte(input_ids)
def forward(
self,
input_ids: torch.Tensor,
position_ids: 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)
hidden_states = inputs_embeds + self.wpe(position_ids)
else:
hidden_states = intermediate_tensors["hidden_states"]
for layer in self.h[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})
hidden_states = self.ln_f(hidden_states)
return hidden_states
def load_weights(self, weights: Iterable[tuple[str,
torch.Tensor]]) -> set[str]:
params_dict = dict(self.named_parameters(remove_duplicate=False))
loaded_params: set[str] = set()
for name, loaded_weight in weights:
if ".attn.bias" in name:
# Skip attention mask.
# NOTE: "c_attn.bias" should not be skipped.
continue
if is_pp_missing_parameter(name, self):
continue
param = params_dict[name]
weight_loader = getattr(param, "weight_loader",
default_weight_loader)
# TODO (@robertgshaw2-neuralmagic): move to fp8 linear method
if "c_attn.input_scale" in name or "c_attn.weight_scale" in name:
weight_loader(param, loaded_weight, 'q')
weight_loader(param, loaded_weight, 'k')
weight_loader(param, loaded_weight, 'v')
else:
weight_loader(param, loaded_weight)
loaded_params.add(name)
return loaded_params
class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
packed_modules_mapping = {"c_attn": ["c_attn"]}
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
super().__init__()
config = vllm_config.model_config.hf_config
quant_config = vllm_config.quant_config
lora_config = vllm_config.lora_config
self.config = config
self.lora_config = lora_config
self.quant_config = quant_config
self.transformer = GPTBigCodeModel(vllm_config=vllm_config,
prefix=prefix)
if self.config.tie_word_embeddings:
self.lm_head = self.transformer.wte
else:
self.lm_head = ParallelLMHead(
self.transformer.vocab_size,
self.transformer.embed_dim,
org_num_embeddings=self.config.vocab_size)
self.unpadded_vocab_size = config.vocab_size
if lora_config:
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
config.vocab_size)
self.make_empty_intermediate_tensors = (
self.transformer.make_empty_intermediate_tensors)
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
return self.transformer.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.transformer(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]:
skip_prefixes = None
if self.config.tie_word_embeddings:
skip_prefixes = ["lm_head."]
loader = AutoWeightsLoader(
self,
skip_prefixes=skip_prefixes,
)
return loader.load_weights(weights)
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