aphrodite-engine/aphrodite/modeling/models/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."""
from typing import Iterable, List, Optional, Tuple

import torch
from torch import nn
from transformers import GPTNeoXConfig

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 GPTNeoXAttention(nn.Module):

    def __init__(
        self,
        config: GPTNeoXConfig,
        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.bias = getattr(config, "attention_bias", True)

        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,
            bias=self.bias,
            quant_config=quant_config,
        )
        self.dense = RowParallelLinear(
            config.hidden_size,
            config.hidden_size,
            bias=self.bias,
            quant_config=quant_config,
        )
        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.rotary_emb = get_rope(
            self.head_size,
            rotary_dim=rotary_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
        )
        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.query_key_value(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)
        output, _ = self.dense(attn_output)
        return output


class GPTNeoXMLP(nn.Module):

    def __init__(
        self,
        config: GPTNeoXConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.dense_h_to_4h = ColumnParallelLinear(
            config.hidden_size,
            config.intermediate_size,
            quant_config=quant_config,
        )
        self.dense_4h_to_h = RowParallelLinear(
            config.intermediate_size,
            config.hidden_size,
            quant_config=quant_config,
        )
        quant_config = getattr(quant_config, "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,
        quant_config: Optional[QuantizationConfig] = 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, quant_config)
        self.mlp = GPTNeoXMLP(config, quant_config)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> 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,
            attn_metadata=attn_metadata,
        )

        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,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.config = config

        self.embed_in = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )
        self.layers = nn.ModuleList([
            GPTNeoXLayer(config, quant_config)
            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[torch.Tensor],
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        hidden_states = self.embed_in(input_ids)
        for i in range(len(self.layers)):
            layer = self.layers[i]
            hidden_states = layer(
                position_ids,
                hidden_states,
                kv_caches[i],
                attn_metadata,
            )
        hidden_states = self.final_layer_norm(hidden_states)
        return hidden_states


class GPTNeoXForCausalLM(nn.Module):

    def __init__(
        self,
        config,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.config = config
        self.quant_config = quant_config
        self.gpt_neox = GPTNeoXModel(config, quant_config)
        self.embed_out = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
        )
        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.gpt_neox(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.embed_out.weight, hidden_states,
                                       sampling_metadata)
        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]]):
        params_dict = dict(self.named_parameters())
        for name, loaded_weight in weights:
            if ("attention.bias" in name or "attention.masked_bias" in name
                    or "rotary_emb.inv_freq" in name):
                continue
            if ("rotary_emb.cos_cached" in name
                    or "rotary_emb.sin_cached" in name):
                # Models trained using OpenRLHF may include
                # these tensors in the checkpoint. Skip them.
                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)