# 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 itertools import islice import torch from torch import nn from transformers import GPTBigCodeConfig 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.attention import Attention 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.sequence import IntermediateTensors from .interfaces import SupportsLoRA, SupportsPP from .utils import ( AutoWeightsLoader, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix, ) class GPTBigCodeAttention(nn.Module): def __init__( self, config: GPTBigCodeConfig, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = 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, prefix=f"{prefix}.c_attn", ) self.c_proj = RowParallelLinear( self.hidden_size, self.hidden_size, bias=True, quant_config=quant_config, prefix=f"{prefix}.c_proj", ) 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: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() hidden_size = config.hidden_size self.c_fc = ColumnParallelLinear( hidden_size, intermediate_size, bias=True, quant_config=quant_config, prefix=f"{prefix}.c_fc", ) self.c_proj = RowParallelLinear( intermediate_size, hidden_size, bias=True, quant_config=quant_config, prefix=f"{prefix}.c_proj", ) 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: CacheConfig | None = None, quant_config: QuantizationConfig | None = 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, prefix=f"{prefix}.mlp") 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 self.config = config assert not config.add_cross_attention self.embed_dim = config.hidden_size self.vocab_size = config.vocab_size 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 embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.wte(input_ids) def forward( self, input_ids: torch.Tensor | None, position_ids: torch.Tensor, intermediate_tensors: IntermediateTensors | None, inputs_embeds: torch.Tensor | None = None, ) -> torch.Tensor | IntermediateTensors: if get_pp_group().is_first_rank: if inputs_embeds is None: inputs_embeds = self.embed_input_ids(input_ids) hidden_states = inputs_embeds + self.wpe(position_ids) else: hidden_states = intermediate_tensors["hidden_states"] for layer in islice(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: 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 self.config = config self.quant_config = quant_config self.transformer = GPTBigCodeModel( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "transformer") ) 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, prefix=maybe_prefix(prefix, "lm_head"), ) self.logits_processor = LogitsProcessor(config.vocab_size) self.make_empty_intermediate_tensors = ( self.transformer.make_empty_intermediate_tensors ) def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor: return self.transformer.embed_input_ids(input_ids) def forward( self, input_ids: torch.Tensor | None, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None = None, inputs_embeds: torch.Tensor | None = None, ) -> 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, ) -> torch.Tensor | None: logits = self.logits_processor(self.lm_head, hidden_states) 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)