gpt_neox.py

# coding=utf-8
# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt_neox/modeling_gpt_neox.py
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI The HuggingFace Inc. team. 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-NeoX model compatible with HuggingFace weights.

The input of the model is flattened to a 1D tensor of tokens. The model uses
InputMetadata to extract the original 2D shape of the input.
"""
from typing import List, Optional, Tuple

import torch
from torch import nn
from transformers import GPTNeoXConfig

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 PagedAttentionWithRoPE
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               LinearMethodBase,
                                               QKVParallelLinear,
                                               RowParallelLinear)
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.weight_utils import (default_weight_loader,
                                              hf_model_weights_iterator)
from vllm.sequence import SamplerOutput

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


class GPTNeoXAttention(nn.Module):

    def __init__(
        self,
        config: GPTNeoXConfig,
        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

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

        self.query_key_value = QKVParallelLinear(
            config.hidden_size,
            self.head_size,
            self.total_num_heads,
            linear_method=linear_method,
        )
        self.dense = RowParallelLinear(
            config.hidden_size,
            config.hidden_size,
            linear_method=linear_method,
        )

        scaling = self.head_size**-0.5
        rotary_dim = int(self.head_size * config.rotary_pct)
        assert rotary_dim % 2 == 0
        rope_theta = getattr(config, "rope_theta", 10000)
        max_position_embeddings = getattr(config, "max_position_embeddings",
                                          8192)
        self.attn = PagedAttentionWithRoPE(
            self.num_heads,
            self.head_size,
            scaling,
            rotary_dim,
            base=rope_theta,
            max_position=max_position_embeddings)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
        cache_event: Optional[torch.cuda.Event],
    ) -> torch.Tensor:
        qkv, _ = self.query_key_value(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        k_cache, v_cache = kv_cache
        attn_output = self.attn(position_ids, q, k, v, k_cache, v_cache,
                                input_metadata, cache_event)
        output, _ = self.dense(attn_output)
        return output


class GPTNeoXMLP(nn.Module):

    def __init__(
        self,
        config: GPTNeoXConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.dense_h_to_4h = ColumnParallelLinear(
            config.hidden_size,
            config.intermediate_size,
            linear_method=linear_method,
        )
        self.dense_4h_to_h = RowParallelLinear(
            config.intermediate_size,
            config.hidden_size,
            linear_method=linear_method,
        )
        quant_config = getattr(linear_method, "quant_config", None)
        self.act = get_act_fn(config.hidden_act, quant_config,
                              config.intermediate_size)

    def forward(self, hidden_states):
        hidden_states, _ = self.dense_h_to_4h(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states, _ = self.dense_4h_to_h(hidden_states)
        return hidden_states


class GPTNeoXLayer(nn.Module):

    def __init__(
        self,
        config: GPTNeoXConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.use_parallel_residual = config.use_parallel_residual
        self.input_layernorm = nn.LayerNorm(config.hidden_size,
                                            eps=config.layer_norm_eps)
        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
                                                     eps=config.layer_norm_eps)
        self.attention = GPTNeoXAttention(config, linear_method)
        self.mlp = GPTNeoXMLP(config, linear_method)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: KVCache,
        input_metadata: InputMetadata,
        cache_event: Optional[torch.cuda.Event],
    ) -> torch.Tensor:
        attn_input = self.input_layernorm(hidden_states)
        attn_output = self.attention(
            position_ids=position_ids,
            hidden_states=attn_input,
            kv_cache=kv_cache,
            input_metadata=input_metadata,
            cache_event=cache_event,
        )

        if self.use_parallel_residual:
            # pseudocode:
            # x = x + attn(ln1(x)) + mlp(ln2(x))
            mlp_input = self.post_attention_layernorm(hidden_states)
            mlp_output = self.mlp(mlp_input)
            hidden_states = mlp_output + attn_output + hidden_states
        else:
            # pseudocode:
            # x = x + attn(ln1(x))
            # x = x + mlp(ln2(x))
            attn_output = attn_output + hidden_states
            mlp_input = self.post_attention_layernorm(attn_output)
            mlp_output = self.mlp(mlp_input)
            hidden_states = mlp_output + attn_output
        return hidden_states


class GPTNeoXModel(nn.Module):

    def __init__(
        self,
        config: GPTNeoXConfig,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.config = config

        self.embed_in = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )
        self.layers = nn.ModuleList([
            GPTNeoXLayer(config, linear_method)
            for _ in range(config.num_hidden_layers)
        ])
        self.final_layer_norm = nn.LayerNorm(config.hidden_size,
                                             eps=config.layer_norm_eps)

    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> torch.Tensor:
        hidden_states = self.embed_in(input_ids)
        for i in range(len(self.layers)):
            cache_event = None if cache_events is None else cache_events[i]
            layer = self.layers[i]
            hidden_states = layer(
                position_ids,
                hidden_states,
                kv_caches[i],
                input_metadata,
                cache_event,
            )
        hidden_states = self.final_layer_norm(hidden_states)
        return hidden_states


class GPTNeoXForCausalLM(nn.Module):

    def __init__(
        self,
        config,
        linear_method: Optional[LinearMethodBase] = None,
    ):
        super().__init__()
        self.config = config
        self.linear_method = linear_method
        self.gpt_neox = GPTNeoXModel(config, linear_method)
        self.embed_out = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
        )
        self.sampler = Sampler(config.vocab_size)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        kv_caches: List[KVCache],
        input_metadata: InputMetadata,
        cache_events: Optional[List[torch.cuda.Event]],
    ) -> SamplerOutput:
        hidden_states = self.gpt_neox(input_ids, positions, kv_caches,
                                      input_metadata, cache_events)
        next_tokens = self.sampler(self.embed_out.weight, hidden_states,
                                   input_metadata)
        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):
        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 ("attention.bias" in name or "attention.masked_bias" in name
                    or "rotary_emb.inv_freq" in name):
                continue
            param = params_dict[name]

            if "query_key_value" in name:
                # NOTE: GPT-NeoX's fused QKV's output_dim has the shape of
                # (num_heads * 3 * head_size), while the
                # required shape is (3 * num_heads * head_size).
                # Thus, we need weight conversion.
                output_dim = getattr(param, "output_dim", None)
                num_heads = self.config.num_attention_heads
                if output_dim is not None:
                    loaded_weight_shape = loaded_weight.shape
                    loaded_weight = loaded_weight.view(
                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
                        loaded_weight_shape[output_dim + 1:])
                    loaded_weight = loaded_weight.transpose(
                        output_dim, output_dim + 1)
                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)

            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)