795 lines
30 KiB
Python
795 lines
30 KiB
Python
# coding=utf-8
|
|
# 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.
|
|
""" PyTorch GPTNeoX model."""
|
|
|
|
from typing import Optional, Tuple, Union
|
|
|
|
import os
|
|
import torch
|
|
import torch.distributed
|
|
import torch.utils.checkpoint
|
|
from torch import nn
|
|
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
|
|
|
from transformers.activations import ACT2FN
|
|
from transformers.file_utils import (
|
|
add_code_sample_docstrings,
|
|
add_start_docstrings,
|
|
add_start_docstrings_to_model_forward,
|
|
replace_return_docstrings,
|
|
)
|
|
from transformers.modeling_outputs import (
|
|
BaseModelOutputWithPast,
|
|
CausalLMOutputWithPast,
|
|
QuestionAnsweringModelOutput,
|
|
SequenceClassifierOutputWithPast,
|
|
TokenClassifierOutput,
|
|
)
|
|
from transformers.modeling_utils import PreTrainedModel
|
|
from transformers import GPTNeoXConfig
|
|
from loguru import logger
|
|
from text_generation_server.utils.layers import (
|
|
TensorParallelColumnLinear,
|
|
TensorParallelEmbedding,
|
|
TensorParallelRowLinear,
|
|
TensorParallelHead,
|
|
)
|
|
|
|
|
|
CUSTOM_KERNELS_ENABLED = False
|
|
if not os.environ.get("DISABLE_CUSTOM_KERNELS", "False") == "True":
|
|
try:
|
|
from custom_kernels import fused_attention_cuda
|
|
|
|
CUSTOM_KERNELS_ENABLED = True
|
|
except ImportError:
|
|
pass
|
|
|
|
if not CUSTOM_KERNELS_ENABLED:
|
|
logger.warning("We're not using custom kernels.")
|
|
|
|
|
|
def make_causal_mask(
|
|
input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
|
|
) -> torch.BoolTensor:
|
|
"""
|
|
Make causal mask used for self-attention.
|
|
"""
|
|
batch_size, target_length = input_ids_shape
|
|
mask = torch.ones(
|
|
(target_length, target_length + past_key_values_length),
|
|
dtype=torch.bool,
|
|
device=device,
|
|
)
|
|
mask = mask.triu(1 + past_key_values_length)
|
|
|
|
expanded_mask = mask.unsqueeze(0).expand(
|
|
batch_size, target_length, target_length + past_key_values_length
|
|
)
|
|
return expanded_mask
|
|
|
|
|
|
def expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor:
|
|
"""
|
|
Expands attention_mask from `[batch_size, src_length]` to `[batch_size, 1, tgt_length, src_length]`.
|
|
"""
|
|
batch_size, src_length = mask.shape
|
|
tgt_length = tgt_length if tgt_length is not None else src_length
|
|
|
|
expanded_mask = ~(mask[:, None, :].to(torch.bool))
|
|
return expanded_mask.expand(batch_size, tgt_length, src_length)
|
|
|
|
|
|
def prepare_attn_mask(
|
|
attention_mask: torch.Tensor,
|
|
input_shape: Tuple[int, int],
|
|
past_key_values_length: int,
|
|
) -> torch.BoolTensor:
|
|
# create causal mask
|
|
# [batch_size, seq_length] -> [batch_size, tgt_length, src_length]
|
|
combined_attention_mask = None
|
|
device = attention_mask.device
|
|
_, src_length = input_shape
|
|
|
|
if src_length > 1:
|
|
combined_attention_mask = make_causal_mask(
|
|
input_shape, device=device, past_key_values_length=past_key_values_length
|
|
)
|
|
|
|
# [batch_size, seq_length] -> [batch_size, tgt_length, src_length]
|
|
expanded_attn_mask = expand_mask(attention_mask, tgt_length=src_length)
|
|
combined_attention_mask = (
|
|
expanded_attn_mask
|
|
if combined_attention_mask is None
|
|
else expanded_attn_mask | combined_attention_mask
|
|
)
|
|
|
|
return combined_attention_mask
|
|
|
|
|
|
class GPTNeoXPreTrainedModel(PreTrainedModel):
|
|
"""
|
|
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
|
models.
|
|
"""
|
|
|
|
|
|
class GPTNeoXAttention(nn.Module):
|
|
def __init__(self, config, prefix, weights):
|
|
super().__init__()
|
|
self.num_attention_heads = config.num_attention_heads
|
|
self.hidden_size = config.hidden_size
|
|
self.head_size = self.hidden_size // self.num_attention_heads
|
|
self.rotary_ndims = int(self.head_size * config.rotary_pct)
|
|
max_positions = config.max_position_embeddings
|
|
# ??? TODO
|
|
# self.register_buffer(
|
|
# "bias",
|
|
# torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view(
|
|
# 1, 1, max_positions, max_positions
|
|
# ),
|
|
# )
|
|
# self.register_buffer("masked_bias", torch.tensor(-1e9))
|
|
self.rotary_emb = RotaryEmbedding(
|
|
self.rotary_ndims,
|
|
config.max_position_embeddings,
|
|
base=config.rotary_emb_base,
|
|
)
|
|
self.rotary_emb.inv_freq = nn.Parameter(
|
|
weights.get_tensor(f"{prefix}.rotary_emb.inv_freq")
|
|
)
|
|
self.inv_norm_factor = 1.0 / torch.sqrt(
|
|
torch.tensor(self.head_size, dtype=torch.float32)
|
|
).to(torch.get_default_dtype())
|
|
|
|
assert self.num_attention_heads % weights.process_group.size() == 0
|
|
self.num_attention_heads = (
|
|
self.num_attention_heads // weights.process_group.size()
|
|
)
|
|
self.query_key_value = TensorParallelColumnLinear.load(
|
|
config, prefix=f"{prefix}.query_key_value", weights=weights, bias=True
|
|
)
|
|
self.dense = TensorParallelRowLinear.load(
|
|
config, prefix=f"{prefix}.dense", weights=weights, bias=True
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
position_ids,
|
|
attention_mask,
|
|
head_mask=None,
|
|
layer_past=None,
|
|
use_cache=False,
|
|
output_attentions=False,
|
|
):
|
|
has_layer_past = layer_past is not None
|
|
|
|
# Compute QKV
|
|
# Attention heads [batch, seq_len, hidden_size]
|
|
# --> [batch, seq_len, (np * 3 * head_size)]
|
|
qkv = self.query_key_value(hidden_states)
|
|
|
|
# [batch, seq_len, (num_heads * 3 * head_size)]
|
|
# --> [batch, seq_len, num_heads, 3 * head_size]
|
|
new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size)
|
|
qkv = qkv.view(*new_qkv_shape).permute(0, 2, 1, 3)
|
|
# [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size]
|
|
query, key, value = qkv.split(self.head_size, -1)
|
|
|
|
# Compute token offset for rotary embeddings (when decoding)
|
|
seq_len = key.shape[-2]
|
|
if has_layer_past:
|
|
seq_len += layer_past[0].shape[-2]
|
|
|
|
# Compute rotary embeddings on rotary_ndims
|
|
query_rot = query[..., : self.rotary_ndims]
|
|
key_rot = key[..., : self.rotary_ndims]
|
|
|
|
query_rot, key_rot = self.rotary_emb(query_rot, key_rot, position_ids, seq_len)
|
|
|
|
query[..., : self.rotary_ndims] = query_rot
|
|
key[..., : self.rotary_ndims] = key_rot
|
|
|
|
if CUSTOM_KERNELS_ENABLED:
|
|
attn_output, present, attn_weights = fused_attention_cuda.forward(
|
|
query,
|
|
key,
|
|
value,
|
|
layer_past,
|
|
attention_mask,
|
|
head_mask,
|
|
self.inv_norm_factor,
|
|
self.num_attention_heads,
|
|
use_cache,
|
|
)
|
|
else:
|
|
# Cache QKV values
|
|
if has_layer_past:
|
|
past_key = layer_past[0]
|
|
past_value = layer_past[1]
|
|
key = torch.cat((past_key, key), dim=-2)
|
|
value = torch.cat((past_value, value), dim=-2)
|
|
present = (key, value) if use_cache else None
|
|
|
|
# Compute attention
|
|
attn_output, attn_weights = self._attn(
|
|
query, key, value, attention_mask, head_mask
|
|
)
|
|
|
|
# Reshape outputs
|
|
attn_output = self._merge_heads(
|
|
attn_output, self.num_attention_heads, self.head_size
|
|
)
|
|
|
|
attn_output = self.dense(attn_output)
|
|
|
|
outputs = (attn_output, present)
|
|
if output_attentions:
|
|
outputs += (attn_weights,)
|
|
|
|
return outputs
|
|
|
|
@classmethod
|
|
def _split_heads(cls, tensor, num_attention_heads, attn_head_size):
|
|
"""
|
|
Splits hidden dim into attn_head_size and num_attention_heads
|
|
"""
|
|
# tensor: [bs, seq_len, hidden_size]
|
|
new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size)
|
|
# -> [bs, seq_len, num_attention_heads, attn_head_size]
|
|
tensor = tensor.view(new_shape)
|
|
# -> [bs, num_attention_heads, seq_len, attn_head_size]
|
|
tensor = tensor.permute(0, 2, 1, 3)
|
|
return tensor
|
|
|
|
@classmethod
|
|
def _merge_heads(cls, tensor, num_attention_heads, attn_head_size):
|
|
"""
|
|
Merges attn_head_size dim and num_attn_heads dim into hidden dim
|
|
"""
|
|
# tensor [bs, num_attention_heads, seq_len, attn_head_size]
|
|
tensor = tensor.permute(0, 2, 1, 3).contiguous()
|
|
# -> [bs, seq_len, num_attention_heads, attn_head_size]
|
|
tensor = tensor.view(
|
|
tensor.size(0), tensor.size(1), num_attention_heads * attn_head_size
|
|
)
|
|
# -> [bs, seq_len, hidden_size]
|
|
return tensor
|
|
|
|
def _attn(self, query, key, value, attention_mask=None, head_mask=None):
|
|
# q, k, v: [bs, num_attention_heads, seq_len, attn_head_size]
|
|
# compute causal mask from causal mask buffer
|
|
batch_size, num_attention_heads, query_length, attn_head_size = query.size()
|
|
key_length = key.size(-2)
|
|
|
|
query = query.view(
|
|
batch_size * num_attention_heads, query_length, attn_head_size
|
|
)
|
|
key = key.view(batch_size * num_attention_heads, key_length, attn_head_size)
|
|
attn_scores = torch.zeros(
|
|
1,
|
|
dtype=query.dtype,
|
|
device=key.device,
|
|
).expand(batch_size * num_attention_heads, query_length, key_length)
|
|
attn_scores = torch.baddbmm(
|
|
attn_scores,
|
|
query,
|
|
key.transpose(1, 2),
|
|
beta=1.0,
|
|
alpha=self.inv_norm_factor,
|
|
)
|
|
|
|
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
|
|
input_dtype = attn_scores.dtype
|
|
if input_dtype in [torch.float16, torch.bfloat16]:
|
|
attn_scores = attn_scores.to(torch.float)
|
|
attn_scores = torch.where(
|
|
attention_mask, torch.finfo(attn_scores.dtype).min, attn_scores
|
|
)
|
|
attn_scores = attn_scores.view(
|
|
batch_size, num_attention_heads, query_length, key_length
|
|
)
|
|
|
|
attn_weights = nn.functional.softmax(attn_scores, dim=-1)
|
|
attn_weights = attn_weights.to(value.dtype)
|
|
|
|
# Mask heads if we want to
|
|
if head_mask is not None:
|
|
attn_weights = attn_weights * head_mask
|
|
|
|
attn_output = torch.matmul(attn_weights, value)
|
|
return attn_output, attn_weights
|
|
|
|
|
|
class RotaryEmbedding(torch.nn.Module):
|
|
def __init__(self, dim, max_position_embeddings, base=10000, device=None):
|
|
super().__init__()
|
|
self.true_inv_freq = 1.0 / (
|
|
base ** (torch.arange(0, dim, 2).float().to(device) / dim)
|
|
)
|
|
self.register_buffer("inv_freq", self.true_inv_freq)
|
|
|
|
# Build here to make `torch.jit.trace` work.
|
|
self.max_seq_len_cached = max_position_embeddings
|
|
self.cos_cached = None
|
|
self.sin_cached = None
|
|
|
|
@staticmethod
|
|
def rotate_half(x):
|
|
"""Rotates half the hidden dims of the input."""
|
|
x1 = x[..., : x.shape[-1] // 2]
|
|
x2 = x[..., x.shape[-1] // 2 :]
|
|
return torch.cat((-x2, x1), dim=-1)
|
|
|
|
@staticmethod
|
|
def _create_cos_sin(inv_freq, max_position_embeddings, dtype, device):
|
|
t = torch.arange(
|
|
max_position_embeddings, device=inv_freq.device, dtype=inv_freq.dtype
|
|
)
|
|
freqs = torch.einsum("i,j->ij", t, inv_freq)
|
|
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
|
emb = torch.cat((freqs, freqs), dim=-1)
|
|
return emb.cos().to(device).to(dtype), emb.sin().to(device).to(dtype)
|
|
|
|
def forward(self, q, k, position_ids, seq_len=None):
|
|
# x: [bs, num_attention_heads, seq_len, head_size]
|
|
if (
|
|
seq_len > self.max_seq_len_cached
|
|
or self.cos_cached is None
|
|
or self.sin_cached is None
|
|
):
|
|
if seq_len > self.max_seq_len_cached:
|
|
self.max_seq_len_cached = seq_len
|
|
self.cos_cached, self.sin_cached = self._create_cos_sin(
|
|
self.true_inv_freq, self.max_seq_len_cached, q.dtype, q.device
|
|
)
|
|
return rotary_forward(q, k, self.cos_cached, self.sin_cached, position_ids)
|
|
|
|
|
|
@torch.jit.script
|
|
def rotary_forward(q, k, cos, sin, position_ids):
|
|
cos = cos[position_ids].unsqueeze(1)
|
|
sin = sin[position_ids].unsqueeze(1)
|
|
|
|
chunk_size = q.shape[-1] // 2
|
|
q1, q2 = q.split(chunk_size, -1)
|
|
q_rotated = torch.cat((-q2, q1), dim=-1)
|
|
k1, k2 = k.split(chunk_size, -1)
|
|
k_rotated = torch.cat((-k2, k1), dim=-1)
|
|
|
|
q_embed = (q * cos) + (q_rotated * sin)
|
|
k_embed = (k * cos) + (k_rotated * sin)
|
|
return q_embed, k_embed
|
|
|
|
|
|
class GPTNeoXMLP(nn.Module):
|
|
def __init__(self, config, prefix, weights):
|
|
super().__init__()
|
|
self.act = (
|
|
ACT2FN[config.hidden_act]
|
|
if "gelu_fast" not in config.hidden_act
|
|
else lambda x: torch.nn.functional.gelu(x, approximate="tanh")
|
|
)
|
|
|
|
self.dense_h_to_4h = TensorParallelColumnLinear.load(
|
|
config, prefix=f"{prefix}.dense_h_to_4h", weights=weights, bias=True
|
|
)
|
|
self.dense_4h_to_h = TensorParallelRowLinear.load(
|
|
config, prefix=f"{prefix}.dense_4h_to_h", weights=weights, bias=True
|
|
)
|
|
|
|
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, layer_id, config, weights):
|
|
super().__init__()
|
|
self.use_parallel_residual = config.use_parallel_residual
|
|
self.input_layernorm = nn.LayerNorm.load(
|
|
prefix=f"gpt_neox.layers.{layer_id}.input_layernorm",
|
|
weights=weights,
|
|
eps=config.layer_norm_eps,
|
|
)
|
|
self.post_attention_layernorm = nn.LayerNorm.load(
|
|
prefix=f"gpt_neox.layers.{layer_id}.post_attention_layernorm",
|
|
weights=weights,
|
|
eps=config.layer_norm_eps,
|
|
)
|
|
self.attention = GPTNeoXAttention(
|
|
config, prefix=f"gpt_neox.layers.{layer_id}.attention", weights=weights
|
|
)
|
|
self.mlp = GPTNeoXMLP(
|
|
config, prefix=f"gpt_neox.layers.{layer_id}.mlp", weights=weights
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
position_ids,
|
|
attention_mask=None,
|
|
head_mask=None,
|
|
use_cache=False,
|
|
layer_past=None,
|
|
output_attentions=False,
|
|
):
|
|
attention_layer_outputs = self.attention(
|
|
self.input_layernorm(hidden_states),
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
layer_past=layer_past,
|
|
head_mask=head_mask,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
)
|
|
attn_output = attention_layer_outputs[
|
|
0
|
|
] # output_attn: attn_output, present, (attn_weights)
|
|
outputs = attention_layer_outputs[1:]
|
|
|
|
if self.use_parallel_residual:
|
|
# pseudocode:
|
|
# x = x + attn(ln1(x)) + mlp(ln2(x))
|
|
mlp_output = self.mlp(self.post_attention_layernorm(hidden_states))
|
|
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_output = self.mlp(self.post_attention_layernorm(attn_output))
|
|
hidden_states = mlp_output + attn_output
|
|
|
|
if use_cache:
|
|
outputs = (
|
|
hidden_states,
|
|
) + outputs # hidden_states, present, (attn_weights)
|
|
else:
|
|
outputs = (hidden_states,) + outputs[1:] # hidden_states, (attn_weights)
|
|
|
|
return outputs
|
|
|
|
|
|
class GPTNeoXModel(GPTNeoXPreTrainedModel):
|
|
def __init__(self, config, weights):
|
|
super().__init__(config)
|
|
self.config = config
|
|
|
|
self.num_attention_heads = config.num_attention_heads
|
|
|
|
self.embed_in = TensorParallelEmbedding(
|
|
prefix="gpt_neox.embed_in", weights=weights
|
|
)
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
GPTNeoXLayer(layer_id, config, weights)
|
|
for layer_id in range(config.num_hidden_layers)
|
|
]
|
|
)
|
|
self.final_layer_norm = nn.LayerNorm.load(
|
|
prefix="gpt_neox.final_layer_norm",
|
|
weights=weights,
|
|
eps=config.layer_norm_eps,
|
|
)
|
|
self.tp_world_size = weights.process_group.size()
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
position_ids=None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, BaseModelOutputWithPast]:
|
|
r"""
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
|
|
Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
|
`past_key_values`).
|
|
"""
|
|
output_attentions = (
|
|
output_attentions
|
|
if output_attentions is not None
|
|
else self.config.output_attentions
|
|
)
|
|
output_hidden_states = (
|
|
output_hidden_states
|
|
if output_hidden_states is not None
|
|
else self.config.output_hidden_states
|
|
)
|
|
return_dict = (
|
|
return_dict if return_dict is not None else self.config.use_return_dict
|
|
)
|
|
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
|
|
|
if input_ids is not None and inputs_embeds is not None:
|
|
raise ValueError(
|
|
"You cannot specify both input_ids and inputs_embeds at the same time"
|
|
)
|
|
elif input_ids is not None:
|
|
input_shape = input_ids.size()
|
|
elif inputs_embeds is not None:
|
|
input_shape = inputs_embeds.size()[:-1]
|
|
else:
|
|
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
|
|
|
batch_size, seq_length = input_shape
|
|
|
|
if past_key_values is None:
|
|
past_length = 0
|
|
past_key_values = tuple([None] * self.config.num_hidden_layers)
|
|
else:
|
|
past_length = past_key_values[0][0].size(-2)
|
|
|
|
if position_ids is None:
|
|
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
|
position_ids = torch.arange(
|
|
past_length, seq_length + past_length, dtype=torch.long, device=device
|
|
)
|
|
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
|
else:
|
|
position_ids = position_ids.view(-1, seq_length).long()
|
|
|
|
if inputs_embeds is None:
|
|
inputs_embeds = self.embed_in(input_ids)
|
|
|
|
hidden_states = inputs_embeds
|
|
|
|
# Attention mask.
|
|
seq_length_with_past = seq_length
|
|
past_key_values_length = 0
|
|
if past_key_values[0] is not None:
|
|
past_key_values_length = past_key_values[0][0].shape[-1]
|
|
seq_length_with_past = seq_length_with_past + past_key_values_length
|
|
if attention_mask is None:
|
|
attention_mask = torch.ones(
|
|
(batch_size, seq_length_with_past), device=hidden_states.device
|
|
)
|
|
else:
|
|
attention_mask = attention_mask.to(hidden_states.device)
|
|
|
|
causal_mask = prepare_attn_mask(
|
|
attention_mask,
|
|
input_shape=(batch_size, seq_length),
|
|
past_key_values_length=past_key_values_length,
|
|
)
|
|
|
|
assert self.num_attention_heads % self.tp_world_size == 0
|
|
block_size = self.num_attention_heads // self.tp_world_size
|
|
causal_mask = torch.repeat_interleave(causal_mask, block_size, dim=0)
|
|
|
|
# Prepare head mask if needed
|
|
# 1.0 in head_mask indicate we keep the head
|
|
# attention_probs has shape bsz x n_heads x N x N
|
|
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
|
|
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
|
|
head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
|
|
|
|
presents = () if use_cache else None
|
|
all_attentions = () if output_attentions else None
|
|
all_hidden_states = () if output_hidden_states else None
|
|
for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)):
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
outputs = layer(
|
|
hidden_states,
|
|
position_ids=position_ids,
|
|
attention_mask=causal_mask,
|
|
head_mask=head_mask[i],
|
|
layer_past=layer_past,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
)
|
|
hidden_states = outputs[0]
|
|
if use_cache is True:
|
|
presents = presents + (outputs[1],)
|
|
if output_attentions:
|
|
all_attentions = all_attentions + (outputs[2 if use_cache else 1],)
|
|
|
|
hidden_states = self.final_layer_norm(hidden_states)
|
|
# Add last hidden state
|
|
if output_hidden_states:
|
|
all_hidden_states = all_hidden_states + (hidden_states,)
|
|
|
|
if not return_dict:
|
|
return tuple(
|
|
v
|
|
for v in [hidden_states, presents, all_hidden_states, all_attentions]
|
|
if v is not None
|
|
)
|
|
|
|
return BaseModelOutputWithPast(
|
|
last_hidden_state=hidden_states,
|
|
past_key_values=presents,
|
|
hidden_states=all_hidden_states,
|
|
attentions=all_attentions,
|
|
)
|
|
|
|
|
|
class GPTNeoxForCausalLM(GPTNeoXPreTrainedModel):
|
|
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
|
|
|
|
def __init__(self, config, weights):
|
|
super().__init__(config)
|
|
self.gpt_neox = GPTNeoXModel(config, weights)
|
|
self.embed_out = TensorParallelHead.load(
|
|
config, prefix="embed_out", weights=weights
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: Optional[torch.LongTensor] = None,
|
|
attention_mask: Optional[torch.FloatTensor] = None,
|
|
position_ids: Optional[torch.LongTensor] = None,
|
|
inputs_embeds: Optional[torch.FloatTensor] = None,
|
|
head_mask: Optional[torch.FloatTensor] = None,
|
|
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
|
|
labels: Optional[torch.LongTensor] = None,
|
|
use_cache: Optional[bool] = None,
|
|
output_attentions: Optional[bool] = None,
|
|
output_hidden_states: Optional[bool] = None,
|
|
return_dict: Optional[bool] = None,
|
|
) -> Union[Tuple, CausalLMOutputWithPast]:
|
|
r"""
|
|
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
|
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
|
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
|
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`. The two additional tensors are
|
|
only required when the model is used as a decoder in a Sequence to Sequence model.
|
|
|
|
Contains pre-computed hidden-states (key and values in the self-attention blocks that can be used (see
|
|
`past_key_values` input) to speed up sequential decoding.
|
|
|
|
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
|
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
|
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
|
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
|
Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
|
|
`[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
|
|
ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.
|
|
use_cache (`bool`, *optional*):
|
|
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
|
`past_key_values`).
|
|
|
|
Returns:
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import AutoTokenizer, GPTNeoXForCausalLM, GPTNeoXConfig
|
|
>>> import torch
|
|
|
|
>>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
|
|
>>> config = GPTNeoXConfig.from_pretrained("EleutherAI/gpt-neox-20b")
|
|
>>> config.is_decoder = True
|
|
>>> model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", config=config)
|
|
|
|
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
|
|
>>> outputs = model(**inputs)
|
|
|
|
>>> prediction_logits = outputs.logits
|
|
```"""
|
|
return_dict = (
|
|
return_dict if return_dict is not None else self.config.use_return_dict
|
|
)
|
|
|
|
outputs = self.gpt_neox(
|
|
input_ids,
|
|
attention_mask=attention_mask,
|
|
position_ids=position_ids,
|
|
head_mask=head_mask,
|
|
inputs_embeds=inputs_embeds,
|
|
past_key_values=past_key_values,
|
|
use_cache=use_cache,
|
|
output_attentions=output_attentions,
|
|
output_hidden_states=output_hidden_states,
|
|
return_dict=return_dict,
|
|
)
|
|
|
|
hidden_states = outputs[0]
|
|
lm_logits = self.embed_out(hidden_states)
|
|
|
|
lm_loss = None
|
|
if labels is not None:
|
|
# move labels to correct device to enable model parallelism
|
|
labels = labels.to(lm_logits.device)
|
|
# we are doing next-token prediction; shift prediction scores and input ids by one
|
|
shift_logits = lm_logits[:, :-1, :].contiguous()
|
|
labels = labels[:, 1:].contiguous()
|
|
loss_fct = CrossEntropyLoss()
|
|
lm_loss = loss_fct(
|
|
shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)
|
|
)
|
|
|
|
if not return_dict:
|
|
output = (lm_logits,) + outputs[1:]
|
|
return ((lm_loss,) + output) if lm_loss is not None else output
|
|
|
|
return CausalLMOutputWithPast(
|
|
loss=lm_loss,
|
|
logits=lm_logits,
|
|
past_key_values=outputs.past_key_values,
|
|
hidden_states=outputs.hidden_states,
|
|
attentions=outputs.attentions,
|
|
)
|
|
|
|
def prepare_inputs_for_generation(
|
|
self,
|
|
input_ids,
|
|
past_key_values=None,
|
|
attention_mask=None,
|
|
inputs_embeds=None,
|
|
**kwargs,
|
|
):
|
|
input_shape = input_ids.shape
|
|
|
|
# cut decoder_input_ids if past is used
|
|
if past_key_values and past_key_values[0] is not None:
|
|
input_ids = input_ids[:, -1:]
|
|
|
|
position_ids = kwargs.get("position_ids", None)
|
|
if attention_mask is not None and position_ids is None:
|
|
# create position_ids on the fly for batch generation
|
|
position_ids = attention_mask.long().cumsum(-1) - 1
|
|
position_ids.masked_fill_(attention_mask == 0, 1)
|
|
if past_key_values:
|
|
position_ids = position_ids[:, -1].unsqueeze(-1)
|
|
|
|
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
|
|
if attention_mask is None:
|
|
attention_mask = input_ids.new_ones(input_shape)
|
|
|
|
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
|
if inputs_embeds is not None and past_key_values is None:
|
|
model_inputs = {"inputs_embeds": inputs_embeds}
|
|
else:
|
|
model_inputs = {"input_ids": input_ids}
|
|
|
|
model_inputs.update(
|
|
{
|
|
"attention_mask": attention_mask,
|
|
"past_key_values": past_key_values,
|
|
"position_ids": position_ids,
|
|
}
|
|
)
|
|
|
|
return model_inputs
|
|
|
|
def _reorder_cache(self, past_key_values, beam_idx):
|
|
reordered_past = ()
|
|
for layer_past in past_key_values:
|
|
reordered_past += (
|
|
tuple(
|
|
past_state.index_select(0, beam_idx)
|
|
for past_state in layer_past[:2]
|
|
)
|
|
+ layer_past[2:],
|
|
)
|
|
return reordered_past
|