gpt_j.py

# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gptj/modeling_gptj.py
# Copyright 2023 The vLLM team.
# Copyright 2021 The EleutherAI and HuggingFace Teams. 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 GPT-J model compatible with HuggingFace weights."""
from typing import List, Optional, Tuple

import torch
from torch import nn
from transformers import GPTJConfig

from vllm.model_executor.input_metadata import InputMetadata
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.attention import PagedAttention
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               LinearMethodBase,
                                               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)
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

KVCache = Tuple[torch.Tensor, torch.Tensor]


class GPTJAttention(nn.Module):

    def __init__(
        self,
        config: GPTJConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.total_num_heads = config.num_attention_heads
        self.hidden_size = config.hidden_size
        self.head_size = self.hidden_size // self.total_num_heads

        self.qkv_proj = QKVParallelLinear(
            config.hidden_size,
            self.head_size,
            self.total_num_heads,
            bias=False,
            linear_method=linear_method,
        )
        self.out_proj = RowParallelLinear(
            config.hidden_size,
            config.hidden_size,
            bias=False,
            linear_method=linear_method,
        )

        tp_world_size = get_tensor_model_parallel_world_size()
        assert self.total_num_heads % tp_world_size == 0
        self.num_heads = self.total_num_heads // tp_world_size

        scaling = self.head_size**-0.5
        assert getattr(config, "rotary", True)
        assert config.rotary_dim % 2 == 0
        rope_theta = getattr(config, "rope_theta", 10000)
        max_position_embeddings = getattr(config, "max_position_embeddings",
                                          8192)
        self.rotary_emb = get_rope(
            self.head_size,
            rotary_dim=config.rotary_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
            is_neox_style=False,
        )
        self.attn = PagedAttention(self.num_heads, self.head_size, scaling)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        q, k = self.rotary_emb(position_ids, q, k)
        k_cache, v_cache = kv_cache
        attn_output = self.attn(q, k, v, k_cache, v_cache, input_metadata)
        attn_output, _ = self.out_proj(attn_output)
        return attn_output


class GPTJMLP(nn.Module):

    def __init__(
        self,
        intermediate_size: int,
        config: GPTJConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        hidden_size = config.n_embd
        self.fc_in = ColumnParallelLinear(
            hidden_size,
            intermediate_size,
            linear_method=linear_method,
        )
        self.fc_out = RowParallelLinear(
            intermediate_size,
            hidden_size,
            linear_method=linear_method,
        )
        quant_config = getattr(linear_method, "quant_config", None)
        self.act = get_act_fn(config.activation_function, quant_config,
                              intermediate_size)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states, _ = self.fc_in(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states, _ = self.fc_out(hidden_states)
        return hidden_states


class GPTJBlock(nn.Module):

    def __init__(
        self,
        config: GPTJConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        inner_dim = 4 * config.n_embd if config.n_inner is None else config.n_inner
        self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
        self.attn = GPTJAttention(config, linear_method)
        self.mlp = GPTJMLP(inner_dim, config, linear_method)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_output = self.attn(
            position_ids=position_ids,
            hidden_states=hidden_states,
            kv_cache=kv_cache,
            input_metadata=input_metadata,
        )
        mlp_output = self.mlp(hidden_states)
        hidden_states = attn_output + mlp_output + residual
        return hidden_states


class GPTJModel(nn.Module):

    def __init__(
        self,
        config: GPTJConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.config = config
        self.embed_dim = config.n_embd
        self.wte = VocabParallelEmbedding(
            config.vocab_size,
            self.embed_dim,
        )
        self.h = nn.ModuleList(
            [GPTJBlock(config, linear_method) for _ in range(config.n_layer)])
        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
    ) -> torch.Tensor:
        hidden_states = self.wte(input_ids)
        for i in range(len(self.h)):
            layer = self.h[i]
            hidden_states = layer(
                position_ids,
                hidden_states,
                kv_caches[i],
                input_metadata,
            )
        hidden_states = self.ln_f(hidden_states)
        return hidden_states


class GPTJForCausalLM(nn.Module):

    def __init__(
        self,
        config: GPTJConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.config = config
        self.linear_method = linear_method
        assert not config.tie_word_embeddings
        self.transformer = GPTJModel(config, linear_method)
        self.lm_head = ParallelLMHead(
            config.vocab_size,
            config.n_embd,
            bias=True,
        )
        self.sampler = Sampler(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.transformer(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, self.lm_head.bias)
        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 "attn.bias" in name or "attn.masked_bias" 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)