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# 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 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 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 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
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,
linear_method=linear_method,
)
self.dense = RowParallelLinear(
config.hidden_size,
config.hidden_size,
bias=self.bias,
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.rotary_emb = get_rope(
self.head_size,
rotary_dim=rotary_dim,
max_position=max_position_embeddings,
base=rope_theta,
)
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.query_key_value(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)
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,
) -> 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,
)
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,
) -> 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],
input_metadata,
)
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,
) -> torch.Tensor:
hidden_states = self.gpt_neox(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.embed_out.weight, hidden_states,
sampling_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)
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