gpt_bigcode.py 13.3 KB
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# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt2/modeling_gpt2.py
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# 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.
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"""Inference-only GPTBigCode model compatible with HuggingFace weights."""
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from typing import Iterable, List, Optional, Set, Tuple, Union
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import torch
from torch import nn
from transformers import GPTBigCodeConfig

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from vllm.attention import Attention, AttentionMetadata
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import get_act_fn
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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    ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
from .utils import (is_pp_missing_parameter,
                    make_empty_intermediate_tensors_factory, make_layers)
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class GPTBigCodeAttention(nn.Module):

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    def __init__(
        self,
        config: GPTBigCodeConfig,
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        cache_config: Optional[CacheConfig] = None,
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        quant_config: Optional[QuantizationConfig] = None,
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    ):
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        super().__init__()
        self.hidden_size = config.hidden_size
        total_num_heads = config.num_attention_heads
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        self.tensor_model_parallel_world_size = (
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            get_tensor_model_parallel_world_size())
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        assert total_num_heads % self.tensor_model_parallel_world_size == 0
        self.num_heads = (total_num_heads //
                          self.tensor_model_parallel_world_size)
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        self.head_dim = self.hidden_size // total_num_heads
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        self.scale = self.head_dim**-0.5
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        self.multi_query = config.multi_query
        if self.multi_query:
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            total_num_kv_heads = 1
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            self.num_kv_heads = 1
        else:
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            total_num_kv_heads = total_num_heads
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            self.num_kv_heads = self.num_heads
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        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,
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            quant_config=quant_config,
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        )
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        self.c_proj = RowParallelLinear(
            self.hidden_size,
            self.hidden_size,
            bias=True,
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            quant_config=quant_config,
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        )
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        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              scale=self.scale,
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                              num_kv_heads=self.num_kv_heads,
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                              cache_config=cache_config,
                              quant_config=quant_config)
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    def forward(
        self,
        hidden_states: torch.Tensor,
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        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
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    ) -> torch.Tensor:
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        qkv, _ = self.c_attn(hidden_states)
        q, k, v = qkv.split(
            [
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                self.hidden_size // self.tensor_model_parallel_world_size,
                self.kv_dim, self.kv_dim
            ],
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            dim=-1,
        )
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        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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        attn_output, _ = self.c_proj(attn_output)
        return attn_output


class GPTBigMLP(nn.Module):

    def __init__(
        self,
        intermediate_size: int,
        config: GPTBigCodeConfig,
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        quant_config: Optional[QuantizationConfig] = None,
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    ):
        super().__init__()
        hidden_size = config.hidden_size
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        self.c_fc = ColumnParallelLinear(
            hidden_size,
            intermediate_size,
            bias=True,
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            quant_config=quant_config,
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        )
        self.c_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=True,
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            quant_config=quant_config,
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        )
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        self.act = get_act_fn(config.activation_function)
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    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):

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    def __init__(
        self,
        config: GPTBigCodeConfig,
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        cache_config: Optional[CacheConfig] = None,
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        quant_config: Optional[QuantizationConfig] = None,
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    ):
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        super().__init__()
        hidden_size = config.hidden_size
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        inner_dim = (config.n_inner if config.n_inner is not None else 4 *
                     hidden_size)
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        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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        self.attn = GPTBigCodeAttention(config, cache_config, quant_config)
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        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
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        self.mlp = GPTBigMLP(inner_dim, config, quant_config)
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    def forward(
        self,
        hidden_states: torch.Tensor,
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        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
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    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_output = self.attn(
            hidden_states=hidden_states,
            kv_cache=kv_cache,
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            attn_metadata=attn_metadata,
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        )
        # 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


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@support_torch_compile
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class GPTBigCodeModel(nn.Module):

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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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        super().__init__()
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        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

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        self.config = config
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        assert not config.add_cross_attention
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        self.embed_dim = config.hidden_size
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        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)
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        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
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        self.start_layer, self.end_layer, self.h = make_layers(
            config.num_hidden_layers,
            lambda prefix: GPTBigCodeBlock(config, cache_config, quant_config),
            prefix=f"{prefix}.h",
        )
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        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
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        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(["hidden_states"],
                                                    config.n_embd))
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    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.wte(input_ids)

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    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
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        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
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        intermediate_tensors: Optional[IntermediateTensors],
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        inputs_embeds: Optional[torch.Tensor] = None,
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    ) -> Union[torch.Tensor, IntermediateTensors]:
        if get_pp_group().is_first_rank:
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            if inputs_embeds is None:
                inputs_embeds = self.get_input_embeddings(input_ids)
            hidden_states = inputs_embeds + self.wpe(position_ids)
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        else:
            hidden_states = intermediate_tensors["hidden_states"]
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        for i in range(self.start_layer, self.end_layer):
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            layer = self.h[i]
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            hidden_states = layer(hidden_states,
                                  kv_caches[i - self.start_layer],
                                  attn_metadata)
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        if not get_pp_group().is_last_rank:
            return IntermediateTensors({"hidden_states": hidden_states})
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        hidden_states = self.ln_f(hidden_states)
        return hidden_states


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class GPTBigCodeForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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    packed_modules_mapping = {"c_attn": ["c_attn"]}

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    supported_lora_modules = ["c_fc", "c_proj", "wte", "c_attn"]
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    embedding_modules = {
        "wte": "input_embeddings",
        "lm_head": "output_embeddings",
    }

    embedding_padding_modules = []
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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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        super().__init__()
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        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config
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        self.config = config
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        self.lora_config = lora_config

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        self.quant_config = quant_config
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        self.transformer = GPTBigCodeModel(vllm_config=vllm_config,
                                           prefix=prefix)
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        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)
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        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)
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        self.sampler = get_sampler()
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        self.make_empty_intermediate_tensors = (
            self.transformer.make_empty_intermediate_tensors)
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    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.transformer.get_input_embeddings(input_ids)

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    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
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        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
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        intermediate_tensors: Optional[IntermediateTensors] = None,
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        inputs_embeds: Optional[torch.Tensor] = None,
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    ) -> Union[torch.Tensor, IntermediateTensors]:
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        hidden_states = self.transformer(input_ids, positions, kv_caches,
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                                         attn_metadata, intermediate_tensors,
                                         inputs_embeds)
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        return hidden_states

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    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
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        logits = self.logits_processor(self.lm_head, hidden_states,
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                                       sampling_metadata)
        return logits

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    def sample(
        self,
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        logits: torch.Tensor,
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        sampling_metadata: SamplingMetadata,
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    ) -> Optional[SamplerOutput]:
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        next_tokens = self.sampler(logits, sampling_metadata)
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        return next_tokens

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    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
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        params_dict = dict(self.named_parameters(remove_duplicate=False))
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        loaded_params: Set[str] = set()
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        for name, loaded_weight in weights:
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            if "lm_head.weight" in name:
                continue
            if ".attn.bias" in name:
                # Skip attention mask.
                # NOTE: "c_attn.bias" should not be skipped.
                continue
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            if is_pp_missing_parameter(name, self):
                continue
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            param = params_dict[name]
            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
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            # 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)
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            loaded_params.add(name)
        return loaded_params