aphrodite-engine/aphrodite/modeling/models/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 Iterable, List, Optional, Tuple

import torch
from torch import nn
from transformers import GPTJConfig

from aphrodite.attention import Attention, AttentionMetadata
from aphrodite.common.sequence import SamplerOutput
from aphrodite.distributed import get_tensor_model_parallel_world_size
from aphrodite.modeling.layers.activation import get_act_fn
from aphrodite.modeling.layers.linear import (ColumnParallelLinear,
                                              QKVParallelLinear,
                                              RowParallelLinear)
from aphrodite.modeling.layers.logits_processor import LogitsProcessor
from aphrodite.modeling.layers.rotary_embedding import get_rope
from aphrodite.modeling.layers.sampler import Sampler
from aphrodite.modeling.layers.vocab_parallel_embedding import (
    ParallelLMHead, VocabParallelEmbedding)
from aphrodite.modeling.model_loader.weight_utils import default_weight_loader
from aphrodite.modeling.sampling_metadata import SamplingMetadata
from aphrodite.quantization.base_config import QuantizationConfig


class GPTJAttention(nn.Module):

    def __init__(
        self,
        config: GPTJConfig,
        quant_config: Optional[QuantizationConfig] = 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,
            quant_config=quant_config,
        )
        self.out_proj = RowParallelLinear(
            config.hidden_size,
            config.hidden_size,
            bias=False,
            quant_config=quant_config,
        )

        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 = Attention(self.num_heads, self.head_size, scaling)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> 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)
        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
        attn_output, _ = self.out_proj(attn_output)
        return attn_output


class GPTJMLP(nn.Module):

    def __init__(
        self,
        intermediate_size: int,
        config: GPTJConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        hidden_size = config.n_embd
        self.fc_in = ColumnParallelLinear(
            hidden_size,
            intermediate_size,
            quant_config=quant_config,
        )
        self.fc_out = RowParallelLinear(
            intermediate_size,
            hidden_size,
            quant_config=quant_config,
        )
        quant_config = getattr(quant_config, "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,
        quant_config: Optional[QuantizationConfig] = 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, quant_config)
        self.mlp = GPTJMLP(inner_dim, config, quant_config)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> 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,
            attn_metadata=attn_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,
        quant_config: Optional[QuantizationConfig] = 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, quant_config) 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[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> 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],
                attn_metadata,
            )
        hidden_states = self.ln_f(hidden_states)
        return hidden_states


class GPTJForCausalLM(nn.Module):

    def __init__(
        self,
        config: GPTJConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.config = config
        self.quant_config = quant_config
        assert not config.tie_word_embeddings
        self.transformer = GPTJModel(config, quant_config)
        self.lm_head = ParallelLMHead(
            config.vocab_size,
            config.n_embd,
            bias=True,
        )
        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.sampler = Sampler()

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        hidden_states = self.transformer(input_ids, positions, kv_caches,
                                         attn_metadata)
        return hidden_states

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

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

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        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 weights:
            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)