Spaces:
Runtime error
Runtime error
File size: 9,767 Bytes
5bdad4f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 |
"""
Bloom intermediate layer
Based on https://github.com/huggingface/transformers/commit/ca2a55e9dfb245527b5e1c954fec6ffbb7aef07b
See commit history for authorship.
"""
import math
import torch
import torch.nn as nn
import torch.nn.quantized.dynamic.modules.linear
from src.bloom.ops import (BloomGelu, BloomScaledSoftmax, attention_mask_func, build_alibi_tensor, dropout_add,
pre_process_alibi_for_pad, split_tensor_along_last_dim)
class BloomAttention(nn.Module):
def __init__(self, config, layer_number=None):
super().__init__()
self.hidden_size = config.hidden_size
self.num_heads = config.n_head
self.head_dim = self.hidden_size // self.num_heads
self.split_size = self.hidden_size
self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
self.masked_softmax_fusion = config.masked_softmax_fusion
self.hidden_dropout = config.hidden_dropout
if self.head_dim * self.num_heads != self.hidden_size:
raise ValueError(
f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
f" {self.num_heads})."
)
# Layer-wise attention scaling
self.layer_number = max(1, layer_number)
self.norm_factor = math.sqrt(self.head_dim) * self.layer_number
# Scaled Softmax
self.scale_mask_softmax = BloomScaledSoftmax(
self.masked_softmax_fusion,
attention_mask_func,
self.attention_softmax_in_fp32,
self.layer_number,
)
self.query_key_value = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=True)
self.dense = nn.Linear(self.hidden_size, self.hidden_size)
self.attention_dropout = nn.Dropout(config.attention_dropout)
def forward(
self,
hidden_states,
residual,
layer_past=None,
attention_mask=None,
alibi=None,
head_mask=None,
use_cache=False,
output_attentions=False,
):
if alibi is None:
current_sequence_length = hidden_states.shape[1] + (0 if layer_past is None else layer_past[0].shape[1])
alibi = build_alibi_tensor(
current_sequence_length, n_head=self.num_heads, dtype=hidden_states.dtype, device=hidden_states.device
)
# hidden_states: [batch_size, seq_length, hidden_size]
# apply preprocessing if the input is padded
if attention_mask is not None:
alibi = pre_process_alibi_for_pad(alibi, attention_mask)
# otherwise repeat alibi tensor with the batch size
else:
alibi = alibi.repeat(hidden_states.shape[0], 1, 1)
mixed_x_layer = self.query_key_value(hidden_states)
# [batch_size, seq_length, 3 x hidden_size] --> [batch_size, seq_length, num_heads, 3 x head_dim]
new_tensor_shape = mixed_x_layer.size()[:-1] + (self.num_heads, 3 * self.head_dim)
mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
# [batch_size, seq_length, num_heads, 3 x head_dim] --> 3 [batch_size, seq_length, num_heads, head_dim]
(query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
if layer_past is not None:
past_key, past_value = layer_past
key_layer = torch.cat((past_key.type_as(key_layer), key_layer), dim=1)
value_layer = torch.cat((past_value.type_as(value_layer), value_layer), dim=1)
if use_cache is True:
present = (key_layer, value_layer)
else:
present = None
# [batch_size, head_dim, q_length, k_length]
output_size = (query_layer.size(0), query_layer.size(2), query_layer.size(1), key_layer.size(1))
# [batch_size, q_length, num_heads, head_dim] -> [q_length, batch_size * num_heads, head_dim]
query_layer = query_layer.transpose(1, 0).reshape(output_size[2], output_size[0] * output_size[1], -1)
# [batch_size, k_length, num_heads, head_dim] -> [k_length, batch_size * num_heads, head_dim]
key_layer = key_layer.transpose(1, 0).reshape(output_size[3], output_size[0] * output_size[1], -1)
# Raw attention scores. [batch_size * num_heads, q_length, k_length]
beta = 1.0 / self.layer_number
matmul_result = torch.baddbmm(
alibi,
query_layer.transpose(1, 0),
key_layer.transpose(1, 0).transpose(1, 2),
beta=beta,
alpha=(1.0 / self.norm_factor),
)
# change view to [batch_size, num_heads, q_length, k_length]
attention_scores = matmul_result.view(*output_size)
# attention scores and attention mask [b, np, sq, sk]
max_positions = max(attention_scores.shape[-1], attention_scores.shape[-2])
attention_probs = self.scale_mask_softmax(attention_scores, attention_mask, max_positions).to(value_layer.dtype)
attention_probs = self.attention_dropout(attention_probs)
if head_mask is not None:
attention_probs = attention_probs * head_mask
# context layer shape: [batch_size, num_heads, q_length, head_dim]
output_size = (value_layer.size(0), value_layer.size(2), query_layer.size(0), value_layer.size(3))
# change view [k_length, batch_size x num_heads, head_dim]
value_layer = value_layer.transpose(1, 0).reshape(value_layer.size(1), output_size[0] * output_size[1], -1)
# change view [batch_size x num_heads, q_length, k_length]
attention_probs_reshaped = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
# matmul: [batch_size * num_heads, q_length, head_dim]
context_layer = torch.bmm(attention_probs_reshaped, value_layer.transpose(0, 1))
# change view [batch_size, num_heads, q_length, head_dim]
context_layer = context_layer.view(*output_size)
# [batchs_size, num_heads, q_length, head_dim] --> [q_length, batch_size, num_heads, head_dim]
context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
# [q_length, batch_size, num_heads, head_dim] --> [q_length, batch_size, hidden_size]
new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size,)
context_layer = context_layer.view(*new_context_layer_shape)
# Output. [q_length, batch_size, hidden_size]
# aggregate results across tp ranks. See here: https://github.com/pytorch/pytorch/issues/76232
output_tensor = self.dense(context_layer)
output = output_tensor.transpose(1, 0)
output = dropout_add(output, residual, self.hidden_dropout, self.training)
outputs = (output, present)
if output_attentions:
outputs += (attention_probs,)
return outputs
class BloomMLP(nn.Module):
def __init__(self, config):
super().__init__()
self.hidden_size = config.hidden_size
self.dense_h_to_4h = nn.Linear(self.hidden_size, 4 * self.hidden_size)
self.dense_4h_to_h = nn.Linear(4 * self.hidden_size, self.hidden_size)
self.hidden_dropout = config.hidden_dropout
self.gelu_impl = BloomGelu()
def forward(self, hidden_states, residual):
hidden_states = self.gelu_impl(self.dense_h_to_4h(hidden_states))
intermediate_output = self.dense_4h_to_h(hidden_states)
output = dropout_add(intermediate_output, residual, self.hidden_dropout, self.training)
return output
class BloomBlock(nn.Module):
def __init__(self, config, layer_number=None):
super().__init__()
self.hidden_size = config.hidden_size
self.input_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_epsilon)
self.n_head = config.n_head
self.self_attention = BloomAttention(config, layer_number=layer_number)
self.post_attention_layernorm = nn.LayerNorm(self.hidden_size, eps=config.layer_norm_epsilon)
self.mlp = BloomMLP(config)
self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
self.hidden_dropout = config.hidden_dropout
def forward(
self,
hidden_states,
layer_past=None,
attention_mask=None,
head_mask=None,
use_cache=False,
output_attentions=False,
alibi=None,
):
# hidden_states: [batch_size, seq_length, hidden_size]
# Layer norm at the beginning of the transformer layer.
layernorm_output = self.input_layernorm(hidden_states)
# Layer norm post the self attention.
if self.apply_residual_connection_post_layernorm:
residual = layernorm_output
else:
residual = hidden_states
# Self attention.
attn_outputs = self.self_attention(
layernorm_output,
residual,
layer_past=layer_past,
attention_mask=attention_mask,
alibi=alibi,
head_mask=head_mask,
use_cache=use_cache,
output_attentions=output_attentions,
)
attention_output = attn_outputs[0]
outputs = attn_outputs[1:]
layernorm_output = self.post_attention_layernorm(attention_output)
# Get residual
if self.apply_residual_connection_post_layernorm:
residual = layernorm_output
else:
residual = attention_output
# MLP.
output = self.mlp(layernorm_output, residual)
if use_cache:
outputs = (output,) + outputs
else:
outputs = (output,) + outputs[1:]
return outputs # hidden_states, present, attentions
|