232 lines
9.9 KiB
Python
232 lines
9.9 KiB
Python
"""1D GPT-NeoX model compatible with HuggingFace weights."""
|
|
from typing import Dict, List, Optional, Tuple
|
|
|
|
import torch
|
|
from torch import nn
|
|
from transformers import GPTNeoXConfig
|
|
|
|
from cacheflow.model_executor.input_metadata import InputMetadata
|
|
from cacheflow.model_executor.layers.attention import GPTNeoXCacheFlowAttention
|
|
from cacheflow.model_executor.layers.sampler import Sampler
|
|
from cacheflow.model_executor.weight_utils import (hf_model_weights_iterator,
|
|
load_tensor_parallel_weights)
|
|
from cacheflow.model_executor.parallel_utils.parallel_state import (
|
|
get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size)
|
|
from cacheflow.model_executor.parallel_utils.tensor_parallel import (
|
|
VocabParallelEmbedding, ColumnParallelLinear, RowParallelLinear)
|
|
from cacheflow.sequence import SequenceOutputs
|
|
|
|
KVCache = Tuple[torch.Tensor, torch.Tensor]
|
|
|
|
|
|
class GPTNeoXAttention(nn.Module):
|
|
|
|
def __init__(self, config: GPTNeoXConfig):
|
|
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 = ColumnParallelLinear(config.hidden_size,
|
|
3 * config.hidden_size,
|
|
gather_output=False,
|
|
perform_initialization=False)
|
|
self.dense = RowParallelLinear(config.hidden_size, config.hidden_size,
|
|
input_is_parallel=True,
|
|
perform_initialization=False)
|
|
|
|
scaling = self.head_size ** -0.5
|
|
rotary_dim = int(self.head_size * config.rotary_pct)
|
|
assert rotary_dim % 2 == 0
|
|
self.attn = GPTNeoXCacheFlowAttention(scaling, rotary_dim)
|
|
|
|
def forward(
|
|
self,
|
|
position_ids: torch.LongTensor,
|
|
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):
|
|
super().__init__()
|
|
self.dense_h_to_4h = ColumnParallelLinear(config.hidden_size,
|
|
config.intermediate_size,
|
|
gather_output=False,
|
|
perform_initialization=False)
|
|
self.dense_4h_to_h = RowParallelLinear(config.intermediate_size, config.hidden_size,
|
|
input_is_parallel=True,
|
|
perform_initialization=False)
|
|
if config.hidden_act != 'gelu':
|
|
raise ValueError(f'Unsupported activation: {config.hidden_act}. '
|
|
'Only gelu is supported for now.')
|
|
self.act = torch.nn.GELU()
|
|
|
|
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):
|
|
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)
|
|
self.mlp = GPTNeoXMLP(config)
|
|
|
|
def forward(
|
|
self,
|
|
position_ids: torch.LongTensor,
|
|
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):
|
|
super().__init__()
|
|
self.config = config
|
|
|
|
self.embed_in = VocabParallelEmbedding(config.vocab_size, config.hidden_size,
|
|
perform_initialization=False)
|
|
self.layers = nn.ModuleList([GPTNeoXLayer(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.LongTensor,
|
|
position_ids: torch.LongTensor,
|
|
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)):
|
|
if cache_events is None:
|
|
cache_event = None
|
|
else:
|
|
cache_event = 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):
|
|
super().__init__()
|
|
self.config = config
|
|
self.gpt_neox = GPTNeoXModel(config)
|
|
self.embed_out = ColumnParallelLinear(config.hidden_size, config.vocab_size,
|
|
bias=False, gather_output=False,
|
|
perform_initialization=False)
|
|
self.sampler = Sampler(config.vocab_size)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.LongTensor,
|
|
positions: torch.LongTensor,
|
|
kv_caches: List[KVCache],
|
|
input_metadata: InputMetadata,
|
|
cache_events: Optional[List[torch.cuda.Event]],
|
|
) -> Dict[int, SequenceOutputs]:
|
|
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
|
|
|
|
_column_parallel_weights = ["embed_in.weight", "embed_out.weight", "dense_h_to_4h.weight", "dense_h_to_4h.bias"]
|
|
_row_parallel_weights = ["dense.weight", "dense_4h_to_h.weight"]
|
|
|
|
def load_weights(self, model_name_or_path: str,
|
|
cache_dir: Optional[str] = None,
|
|
use_np_cache: bool = False):
|
|
tensor_model_parallel_rank = get_tensor_model_parallel_rank()
|
|
state_dict = self.state_dict()
|
|
for name, loaded_weight in hf_model_weights_iterator(
|
|
model_name_or_path, cache_dir, use_np_cache):
|
|
if ("attention.bias" in name or "attention.masked_bias" in name
|
|
or "rotary_emb.inv_freq" in name):
|
|
continue
|
|
param = state_dict[name]
|
|
if "query_key_value" in name:
|
|
# NOTE(woosuk): GPT-NeoX's fused QKV has the shape of
|
|
# [num_heads * 3 * head_size, hidden_size], while the
|
|
# required shape is [3 * num_heads * head_size, hidden_size].
|
|
# Thus, we need weight conversion.
|
|
shard_size = param.shape[0]
|
|
loaded_weight = loaded_weight[shard_size * tensor_model_parallel_rank
|
|
:shard_size * (tensor_model_parallel_rank + 1)]
|
|
|
|
num_heads = self.config.num_attention_heads
|
|
hidden_size = self.config.hidden_size
|
|
head_size = hidden_size // num_heads
|
|
if 'query_key_value.weight' in name:
|
|
loaded_weight = loaded_weight.view(-1, 3, head_size, hidden_size)
|
|
loaded_weight = loaded_weight.transpose(0, 1)
|
|
loaded_weight = loaded_weight.reshape(-1, hidden_size)
|
|
elif 'query_key_value.bias' in name:
|
|
loaded_weight = loaded_weight.view(-1, 3, head_size)
|
|
loaded_weight = loaded_weight.transpose(0, 1)
|
|
loaded_weight = loaded_weight.reshape(-1)
|
|
else:
|
|
raise ValueError(f"Unexpected weight name: {name}")
|
|
load_tensor_parallel_weights(param, loaded_weight, name,
|
|
self._column_parallel_weights,
|
|
self._row_parallel_weights,
|
|
tensor_model_parallel_rank)
|