Commit
•
14a2ab2
1
Parent(s):
b150840
Create modelling_RW.py
Browse files- modelling_RW.py +1095 -0
modelling_RW.py
ADDED
@@ -0,0 +1,1095 @@
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1 |
+
# port of models described in RW
|
2 |
+
# We use the bloom model as a starting point for these model.
|
3 |
+
# Please refer to the bloom models for usage instructions.
|
4 |
+
|
5 |
+
import math
|
6 |
+
import warnings
|
7 |
+
from typing import Optional, Tuple, Union
|
8 |
+
|
9 |
+
import torch
|
10 |
+
import torch.utils.checkpoint
|
11 |
+
from torch import nn
|
12 |
+
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
|
13 |
+
from torch.nn import functional as F
|
14 |
+
|
15 |
+
from transformers.modeling_outputs import (
|
16 |
+
BaseModelOutputWithPastAndCrossAttentions,
|
17 |
+
CausalLMOutputWithCrossAttentions,
|
18 |
+
QuestionAnsweringModelOutput,
|
19 |
+
SequenceClassifierOutputWithPast,
|
20 |
+
TokenClassifierOutput,
|
21 |
+
)
|
22 |
+
from transformers.modeling_utils import PreTrainedModel
|
23 |
+
from transformers.utils import logging
|
24 |
+
from .configuration_RW import RWConfig
|
25 |
+
|
26 |
+
logger = logging.get_logger(__name__)
|
27 |
+
|
28 |
+
# NOTE(Hesslow): Unfortunately we did not fuse matmul and bias during training, this means that there's one additional quantization to bfloat16 between the operations.
|
29 |
+
# In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
|
30 |
+
class Linear(nn.Linear):
|
31 |
+
def forward(self, input: torch.Tensor) -> torch.Tensor:
|
32 |
+
ret = input @ self.weight.T
|
33 |
+
if self.bias is None:
|
34 |
+
return ret
|
35 |
+
else:
|
36 |
+
return ret + self.bias
|
37 |
+
|
38 |
+
|
39 |
+
from einops import rearrange
|
40 |
+
|
41 |
+
# rotary pos emb helpers (torch.jit.script does not seem to support staticmethod...)
|
42 |
+
def rotate_half(x):
|
43 |
+
x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
|
44 |
+
return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in torch < 1.8.0
|
45 |
+
|
46 |
+
|
47 |
+
class RotaryEmbedding(torch.nn.Module):
|
48 |
+
"""Implementation of RotaryEmbedding from GPT-NeoX.
|
49 |
+
This implementation is design to operate on queries and keys that are compatible with
|
50 |
+
[batch_size, n_heads_per_partition, seq_len, head_dim] (e.g. MinGPTAttention format).
|
51 |
+
"""
|
52 |
+
|
53 |
+
def __init__(
|
54 |
+
self,
|
55 |
+
head_dim: int,
|
56 |
+
base=10000,
|
57 |
+
):
|
58 |
+
super().__init__()
|
59 |
+
inv_freq = 1.0 / (base ** (torch.arange(0, head_dim, 2).float() / head_dim))
|
60 |
+
self.register_buffer("inv_freq", inv_freq, persistent=False)
|
61 |
+
self.head_dim = head_dim
|
62 |
+
self.seq_len_cached = None
|
63 |
+
self.batch_size_cached = None
|
64 |
+
self.cos_cached: torch.Tensor | None = None
|
65 |
+
self.sin_cached: torch.Tensor | None = None
|
66 |
+
|
67 |
+
def cos_sin(
|
68 |
+
self,
|
69 |
+
seq_len: int,
|
70 |
+
device="cuda",
|
71 |
+
dtype=torch.bfloat16,
|
72 |
+
) -> torch.Tensor:
|
73 |
+
if seq_len != self.seq_len_cached:
|
74 |
+
self.seq_len_cached = seq_len
|
75 |
+
t = torch.arange(seq_len, device=device).type_as(self.inv_freq)
|
76 |
+
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
77 |
+
emb = torch.cat((freqs, freqs), dim=-1).to(device)
|
78 |
+
|
79 |
+
if dtype in [torch.float16, torch.bfloat16]:
|
80 |
+
emb = emb.float()
|
81 |
+
|
82 |
+
self.cos_cached = emb.cos()[None, :, :]
|
83 |
+
self.sin_cached = emb.sin()[None, :, :]
|
84 |
+
|
85 |
+
self.cos_cached = self.cos_cached.type(dtype)
|
86 |
+
self.sin_cached = self.sin_cached.type(dtype)
|
87 |
+
|
88 |
+
return self.cos_cached, self.sin_cached
|
89 |
+
|
90 |
+
def forward(self, q, k):
|
91 |
+
batch, seq_len, head_dim = q.shape
|
92 |
+
cos, sin = self.cos_sin(seq_len, q.device, q.dtype)
|
93 |
+
return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
|
94 |
+
|
95 |
+
|
96 |
+
def _make_causal_mask(
|
97 |
+
input_ids_shape: torch.Size, device: torch.device, past_key_values_length: int
|
98 |
+
) -> torch.BoolTensor:
|
99 |
+
batch_size, target_length = input_ids_shape
|
100 |
+
mask = torch.empty((target_length, target_length + past_key_values_length), dtype=torch.bool, device=device)
|
101 |
+
# ONNX doesn't support `torch.Tensor.triu` properly, thus we use this workaround
|
102 |
+
seq_ids = torch.arange(target_length, device=device)
|
103 |
+
mask[:, past_key_values_length:] = seq_ids[:, None] < seq_ids[None, :]
|
104 |
+
|
105 |
+
if past_key_values_length > 0:
|
106 |
+
mask[:, :past_key_values_length] = False
|
107 |
+
|
108 |
+
expanded_mask = mask[None, None, :, :].expand(batch_size, 1, target_length, target_length + past_key_values_length)
|
109 |
+
return expanded_mask
|
110 |
+
|
111 |
+
|
112 |
+
def _expand_mask(mask: torch.Tensor, tgt_length: int) -> torch.BoolTensor:
|
113 |
+
batch_size, src_length = mask.shape
|
114 |
+
tgt_length = tgt_length if tgt_length is not None else src_length
|
115 |
+
|
116 |
+
expanded_mask = ~(mask[:, None, None, :].to(torch.bool))
|
117 |
+
return expanded_mask.expand(batch_size, 1, tgt_length, src_length)
|
118 |
+
|
119 |
+
|
120 |
+
def build_alibi_tensor(attention_mask: torch.Tensor, num_heads: int, dtype: torch.dtype) -> torch.Tensor:
|
121 |
+
batch_size, seq_length = attention_mask.shape
|
122 |
+
closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
|
123 |
+
base = torch.tensor(
|
124 |
+
2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
|
125 |
+
)
|
126 |
+
powers = torch.arange(1, 1 + closest_power_of_2, device=attention_mask.device, dtype=torch.int32)
|
127 |
+
slopes = torch.pow(base, powers)
|
128 |
+
|
129 |
+
if closest_power_of_2 != num_heads:
|
130 |
+
extra_base = torch.tensor(
|
131 |
+
2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))), device=attention_mask.device, dtype=torch.float32
|
132 |
+
)
|
133 |
+
num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
|
134 |
+
extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=attention_mask.device, dtype=torch.int32)
|
135 |
+
slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
|
136 |
+
|
137 |
+
# Note: alibi will added to the attention bias that will be applied to the query, key product of attention
|
138 |
+
# => therefore alibi will have to be of shape (batch_size, num_heads, query_length, key_length)
|
139 |
+
# => here we set (batch_size=1, num_heads=num_heads, query_length=1, key_length=max_length)
|
140 |
+
# => the query_length dimension will then be broadcasted correctly
|
141 |
+
# This is more or less identical to T5's relative position bias:
|
142 |
+
# https://github.com/huggingface/transformers/blob/f681437203baa7671de3174b0fa583c349d9d5e1/src/transformers/models/t5/modeling_t5.py#L527
|
143 |
+
arange_tensor = ((attention_mask.cumsum(dim=-1) - 1) * attention_mask)[:, None, :]
|
144 |
+
alibi = slopes[..., None].bfloat16() * arange_tensor
|
145 |
+
return alibi.reshape(batch_size * num_heads, 1, seq_length).to(dtype)
|
146 |
+
|
147 |
+
|
148 |
+
def dropout_add(x: torch.Tensor, residual: torch.Tensor, prob: float, training: bool) -> torch.Tensor:
|
149 |
+
out = F.dropout(x, p=prob, training=training)
|
150 |
+
out = residual + out
|
151 |
+
return out
|
152 |
+
|
153 |
+
|
154 |
+
class Attention(nn.Module):
|
155 |
+
def __init__(self, config: RWConfig):
|
156 |
+
super().__init__()
|
157 |
+
|
158 |
+
self.hidden_size = config.hidden_size
|
159 |
+
self.num_heads = config.n_head
|
160 |
+
self.head_dim = self.hidden_size // self.num_heads
|
161 |
+
self.split_size = self.hidden_size
|
162 |
+
self.hidden_dropout = config.hidden_dropout
|
163 |
+
|
164 |
+
if self.head_dim * self.num_heads != self.hidden_size:
|
165 |
+
raise ValueError(
|
166 |
+
f"`hidden_size` must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`:"
|
167 |
+
f" {self.num_heads})."
|
168 |
+
)
|
169 |
+
|
170 |
+
self.maybe_rotary = RotaryEmbedding(config.head_dim) if config.rotary else lambda q, k: (q, k)
|
171 |
+
|
172 |
+
# Layer-wise attention scaling
|
173 |
+
self.inv_norm_factor = 1.0 / math.sqrt(self.head_dim)
|
174 |
+
self.beta = self.inv_norm_factor
|
175 |
+
|
176 |
+
self.query_key_value = Linear(
|
177 |
+
self.hidden_size,
|
178 |
+
3 * self.hidden_size if not config.multi_query else (self.hidden_size + 2 * self.head_dim),
|
179 |
+
bias=config.bias,
|
180 |
+
)
|
181 |
+
self.multi_query = config.multi_query
|
182 |
+
self.dense = Linear(self.hidden_size, self.hidden_size, bias=config.bias)
|
183 |
+
self.attention_dropout = nn.Dropout(config.attention_dropout)
|
184 |
+
self.num_kv = config.n_head if not self.multi_query else 1
|
185 |
+
|
186 |
+
def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
|
187 |
+
"""
|
188 |
+
Split the last dimension into (num_heads, head_dim) without making any copies, results share same memory
|
189 |
+
storage as `fused_qkv`
|
190 |
+
Args:
|
191 |
+
fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
|
192 |
+
Returns:
|
193 |
+
query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
|
194 |
+
value: [batch_size, seq_length, num_heads, head_dim]
|
195 |
+
"""
|
196 |
+
if not self.multi_query:
|
197 |
+
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
|
198 |
+
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
|
199 |
+
return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
|
200 |
+
else:
|
201 |
+
batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
|
202 |
+
fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
|
203 |
+
return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
|
204 |
+
|
205 |
+
def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
|
206 |
+
"""
|
207 |
+
Merge heads together over the last dimenstion
|
208 |
+
Args:
|
209 |
+
x: (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]
|
210 |
+
Returns:
|
211 |
+
torch.tensor: [batch_size, seq_length, num_heads * head_dim]
|
212 |
+
"""
|
213 |
+
# What we want to achieve is:
|
214 |
+
# batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
|
215 |
+
batch_size_and_num_heads, seq_length, _ = x.shape
|
216 |
+
batch_size = batch_size_and_num_heads // self.num_heads
|
217 |
+
|
218 |
+
# First view to decompose the batch size
|
219 |
+
# batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
|
220 |
+
x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
|
221 |
+
|
222 |
+
# batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
|
223 |
+
x = x.permute(0, 2, 1, 3)
|
224 |
+
|
225 |
+
# batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
|
226 |
+
return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
|
227 |
+
|
228 |
+
def forward(
|
229 |
+
self,
|
230 |
+
hidden_states: torch.Tensor,
|
231 |
+
alibi: torch.Tensor,
|
232 |
+
attention_mask: torch.Tensor,
|
233 |
+
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
234 |
+
head_mask: Optional[torch.Tensor] = None,
|
235 |
+
use_cache: bool = False,
|
236 |
+
output_attentions: bool = False,
|
237 |
+
):
|
238 |
+
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
|
239 |
+
|
240 |
+
# 3 x [batch_size, seq_length, num_heads, head_dim]
|
241 |
+
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
|
242 |
+
|
243 |
+
batch_size, q_length, _, _ = query_layer.shape
|
244 |
+
|
245 |
+
query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
|
246 |
+
key_layer = key_layer.transpose(1, 2).reshape(
|
247 |
+
batch_size * self.num_kv,
|
248 |
+
q_length,
|
249 |
+
self.head_dim,
|
250 |
+
)
|
251 |
+
value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_kv, q_length, self.head_dim)
|
252 |
+
|
253 |
+
query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
|
254 |
+
|
255 |
+
if layer_past is not None:
|
256 |
+
past_key, past_value = layer_past
|
257 |
+
# concatenate along seq_length dimension:
|
258 |
+
# - key: [batch_size * self.num_heads, head_dim, kv_length]
|
259 |
+
# - value: [batch_size * self.num_heads, kv_length, head_dim]
|
260 |
+
key_layer = torch.cat((past_key, key_layer), dim=1)
|
261 |
+
value_layer = torch.cat((past_value, value_layer), dim=1)
|
262 |
+
|
263 |
+
_, kv_length, _ = key_layer.shape
|
264 |
+
|
265 |
+
if use_cache is True:
|
266 |
+
present = (key_layer, value_layer)
|
267 |
+
else:
|
268 |
+
present = None
|
269 |
+
|
270 |
+
if alibi is None:
|
271 |
+
query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
|
272 |
+
key_layer_ = key_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
|
273 |
+
value_layer_ = value_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
|
274 |
+
|
275 |
+
attn_output = F.scaled_dot_product_attention(
|
276 |
+
query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
|
277 |
+
)
|
278 |
+
|
279 |
+
x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
|
280 |
+
x = x.permute(0, 2, 1, 3)
|
281 |
+
attn_output = x.reshape(batch_size, q_length, self.num_heads * self.head_dim)
|
282 |
+
|
283 |
+
output_tensor = self.dense(attn_output)
|
284 |
+
|
285 |
+
outputs = (output_tensor, present)
|
286 |
+
assert not output_attentions # not supported.
|
287 |
+
return outputs
|
288 |
+
else:
|
289 |
+
attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, -1e9).to(torch.bfloat16)
|
290 |
+
matmul_result = query_layer @ key_layer.transpose(-1, -2)
|
291 |
+
|
292 |
+
# change view to [batch_size, num_heads, q_length, kv_length]
|
293 |
+
attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
|
294 |
+
|
295 |
+
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
|
296 |
+
input_dtype = attention_scores.dtype
|
297 |
+
# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
|
298 |
+
if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
|
299 |
+
attention_scores = attention_scores.to(torch.float32)
|
300 |
+
# attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
|
301 |
+
attention_probs = F.softmax(
|
302 |
+
(attention_scores + alibi) * self.inv_norm_factor + attention_mask_float,
|
303 |
+
dim=-1,
|
304 |
+
dtype=hidden_states.dtype,
|
305 |
+
)
|
306 |
+
# [batch_size, num_heads, q_length, kv_length]
|
307 |
+
attention_probs = self.attention_dropout(attention_probs)
|
308 |
+
|
309 |
+
if head_mask is not None:
|
310 |
+
attention_probs = attention_probs * head_mask
|
311 |
+
|
312 |
+
# change view [batch_size x num_heads, q_length, kv_length]
|
313 |
+
attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length)
|
314 |
+
|
315 |
+
# matmul: [batch_size * num_heads, q_length, head_dim]
|
316 |
+
context_layer = attention_probs_reshaped @ value_layer
|
317 |
+
|
318 |
+
# change view [batch_size, num_heads, q_length, head_dim]
|
319 |
+
context_layer = self._merge_heads(context_layer)
|
320 |
+
|
321 |
+
output_tensor = self.dense(context_layer)
|
322 |
+
|
323 |
+
outputs = (output_tensor, present)
|
324 |
+
if output_attentions:
|
325 |
+
outputs += (attention_probs,)
|
326 |
+
|
327 |
+
return outputs
|
328 |
+
|
329 |
+
|
330 |
+
class MLP(nn.Module):
|
331 |
+
def __init__(self, config: RWConfig):
|
332 |
+
super().__init__()
|
333 |
+
hidden_size = config.hidden_size
|
334 |
+
|
335 |
+
self.dense_h_to_4h = Linear(hidden_size, 4 * hidden_size, bias=config.bias)
|
336 |
+
self.act = nn.GELU()
|
337 |
+
self.dense_4h_to_h = Linear(4 * hidden_size, hidden_size, bias=config.bias)
|
338 |
+
self.hidden_dropout = config.hidden_dropout
|
339 |
+
|
340 |
+
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
341 |
+
x = self.act(self.dense_h_to_4h(x))
|
342 |
+
x = self.dense_4h_to_h(x)
|
343 |
+
return x
|
344 |
+
|
345 |
+
|
346 |
+
class DecoderLayer(nn.Module):
|
347 |
+
def __init__(self, config: RWConfig):
|
348 |
+
super().__init__()
|
349 |
+
hidden_size = config.hidden_size
|
350 |
+
|
351 |
+
self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
352 |
+
self.num_heads = config.n_head
|
353 |
+
self.self_attention = Attention(config)
|
354 |
+
|
355 |
+
if not config.parallel_attn:
|
356 |
+
# unused if parallel attn
|
357 |
+
self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
|
358 |
+
|
359 |
+
self.mlp = MLP(config)
|
360 |
+
|
361 |
+
self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
|
362 |
+
self.hidden_dropout = config.hidden_dropout
|
363 |
+
|
364 |
+
self.config = config
|
365 |
+
|
366 |
+
def forward(
|
367 |
+
self,
|
368 |
+
hidden_states: torch.Tensor,
|
369 |
+
alibi: torch.Tensor,
|
370 |
+
attention_mask: torch.Tensor,
|
371 |
+
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
|
372 |
+
head_mask: Optional[torch.Tensor] = None,
|
373 |
+
use_cache: bool = False,
|
374 |
+
output_attentions: bool = False,
|
375 |
+
):
|
376 |
+
|
377 |
+
layernorm_output = self.input_layernorm(hidden_states)
|
378 |
+
residual = hidden_states
|
379 |
+
|
380 |
+
# Self attention.
|
381 |
+
attn_outputs = self.self_attention(
|
382 |
+
layernorm_output,
|
383 |
+
layer_past=layer_past,
|
384 |
+
attention_mask=attention_mask,
|
385 |
+
alibi=alibi,
|
386 |
+
head_mask=head_mask,
|
387 |
+
use_cache=use_cache,
|
388 |
+
output_attentions=output_attentions,
|
389 |
+
)
|
390 |
+
|
391 |
+
attention_output = attn_outputs[0]
|
392 |
+
|
393 |
+
if not self.config.parallel_attn:
|
394 |
+
residual = dropout_add(attention_output, residual, self.config.attention_dropout, training=self.training)
|
395 |
+
layernorm_output = self.post_attention_layernorm(residual)
|
396 |
+
|
397 |
+
outputs = attn_outputs[1:]
|
398 |
+
|
399 |
+
# MLP.
|
400 |
+
mlp_output = self.mlp(layernorm_output)
|
401 |
+
|
402 |
+
if self.config.parallel_attn:
|
403 |
+
mlp_output += attention_output
|
404 |
+
|
405 |
+
output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training)
|
406 |
+
|
407 |
+
if use_cache:
|
408 |
+
outputs = (output,) + outputs
|
409 |
+
else:
|
410 |
+
outputs = (output,) + outputs[1:]
|
411 |
+
|
412 |
+
return outputs # hidden_states, present, attentions
|
413 |
+
|
414 |
+
|
415 |
+
class RWPreTrainedModel(PreTrainedModel):
|
416 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
417 |
+
"""
|
418 |
+
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
|
419 |
+
models.
|
420 |
+
"""
|
421 |
+
|
422 |
+
config_class = RWConfig
|
423 |
+
base_model_prefix = "transformer"
|
424 |
+
supports_gradient_checkpointing = True
|
425 |
+
_no_split_modules = ["DecoderLayer"]
|
426 |
+
|
427 |
+
def __init__(self, *inputs, **kwargs):
|
428 |
+
super().__init__(*inputs, **kwargs)
|
429 |
+
|
430 |
+
def _init_weights(self, module: nn.Module):
|
431 |
+
"""Initialize the weights."""
|
432 |
+
if isinstance(module, nn.Linear) or isinstance(module, Linear):
|
433 |
+
# Slightly different from the TF version which uses truncated_normal for initialization
|
434 |
+
# cf https://github.com/pytorch/pytorch/pull/5617
|
435 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
436 |
+
if module.bias is not None:
|
437 |
+
module.bias.data.zero_()
|
438 |
+
elif isinstance(module, nn.Embedding):
|
439 |
+
module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
|
440 |
+
if module.padding_idx is not None:
|
441 |
+
module.weight.data[module.padding_idx].zero_()
|
442 |
+
elif isinstance(module, LayerNorm):
|
443 |
+
module.bias.data.zero_()
|
444 |
+
module.weight.data.fill_(1.0)
|
445 |
+
|
446 |
+
def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
|
447 |
+
if isinstance(module, RWModel):
|
448 |
+
module.gradient_checkpointing = value
|
449 |
+
|
450 |
+
@staticmethod
|
451 |
+
def _convert_to_standard_cache(
|
452 |
+
past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
|
453 |
+
) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
|
454 |
+
"""
|
455 |
+
Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
|
456 |
+
num_heads, ...]))
|
457 |
+
"""
|
458 |
+
batch_size_times_num_heads, head_dim, seq_length = past_key_value[0][0].shape
|
459 |
+
num_heads = batch_size_times_num_heads // batch_size
|
460 |
+
# key: [batch_size * num_heads, head_dim, seq_length] -> [batch_size, num_heads, head_dim, seq_length]
|
461 |
+
# value: [batch_size * num_heads, seq_length, head_dim] -> [batch_size, num_heads, seq_length, head_dim]
|
462 |
+
return tuple(
|
463 |
+
(
|
464 |
+
layer_past[0].view(batch_size, num_heads, head_dim, seq_length),
|
465 |
+
layer_past[1].view(batch_size, num_heads, seq_length, head_dim),
|
466 |
+
)
|
467 |
+
for layer_past in past_key_value
|
468 |
+
)
|
469 |
+
|
470 |
+
@staticmethod
|
471 |
+
def _convert_to_rw_cache(
|
472 |
+
past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
|
473 |
+
) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
|
474 |
+
batch_size, num_heads, head_dim, seq_length = past_key_value[0][0].shape
|
475 |
+
batch_size_times_num_heads = batch_size * num_heads
|
476 |
+
# key: [batch_size, num_heads, head_dim, seq_length] -> [batch_size * num_heads, head_dim, seq_length]
|
477 |
+
# value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size * num_heads, seq_length, head_dim]
|
478 |
+
return tuple(
|
479 |
+
(
|
480 |
+
layer_past[0].view(batch_size_times_num_heads, head_dim, seq_length),
|
481 |
+
layer_past[1].view(batch_size_times_num_heads, seq_length, head_dim),
|
482 |
+
)
|
483 |
+
for layer_past in past_key_value
|
484 |
+
)
|
485 |
+
|
486 |
+
|
487 |
+
class RWModel(RWPreTrainedModel):
|
488 |
+
def __init__(self, config: RWConfig):
|
489 |
+
super().__init__(config)
|
490 |
+
|
491 |
+
self.embed_dim = config.hidden_size
|
492 |
+
self.num_heads = config.n_head
|
493 |
+
self.alibi = config.alibi
|
494 |
+
|
495 |
+
# Embedding + LN Embedding
|
496 |
+
self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
|
497 |
+
|
498 |
+
# Transformer blocks
|
499 |
+
self.h = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
500 |
+
|
501 |
+
# Final Layer Norm
|
502 |
+
self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
|
503 |
+
|
504 |
+
self.gradient_checkpointing = False
|
505 |
+
|
506 |
+
# Initialize weights and apply final processing
|
507 |
+
self.post_init()
|
508 |
+
|
509 |
+
def get_input_embeddings(self):
|
510 |
+
return self.word_embeddings
|
511 |
+
|
512 |
+
def _prepare_attn_mask(
|
513 |
+
self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
|
514 |
+
) -> torch.BoolTensor:
|
515 |
+
# create causal mask
|
516 |
+
# [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
|
517 |
+
combined_attention_mask = None
|
518 |
+
device = attention_mask.device
|
519 |
+
_, src_length = input_shape
|
520 |
+
|
521 |
+
if src_length > 1:
|
522 |
+
combined_attention_mask = _make_causal_mask(
|
523 |
+
input_shape, device=device, past_key_values_length=past_key_values_length
|
524 |
+
)
|
525 |
+
|
526 |
+
# [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
|
527 |
+
expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
|
528 |
+
combined_attention_mask = (
|
529 |
+
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
|
530 |
+
)
|
531 |
+
|
532 |
+
return combined_attention_mask
|
533 |
+
|
534 |
+
def set_input_embeddings(self, new_embeddings: torch.Tensor):
|
535 |
+
self.word_embeddings = new_embeddings
|
536 |
+
|
537 |
+
def forward(
|
538 |
+
self,
|
539 |
+
input_ids: Optional[torch.LongTensor] = None,
|
540 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
541 |
+
attention_mask: Optional[torch.Tensor] = None,
|
542 |
+
head_mask: Optional[torch.LongTensor] = None,
|
543 |
+
inputs_embeds: Optional[torch.LongTensor] = None,
|
544 |
+
use_cache: Optional[bool] = None,
|
545 |
+
output_attentions: Optional[bool] = None,
|
546 |
+
output_hidden_states: Optional[bool] = None,
|
547 |
+
return_dict: Optional[bool] = None,
|
548 |
+
**deprecated_arguments,
|
549 |
+
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
|
550 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
551 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
552 |
+
warnings.warn(
|
553 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
554 |
+
" passing `position_ids`.",
|
555 |
+
FutureWarning,
|
556 |
+
)
|
557 |
+
if len(deprecated_arguments) > 0:
|
558 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
559 |
+
|
560 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
561 |
+
output_hidden_states = (
|
562 |
+
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
563 |
+
)
|
564 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
565 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
566 |
+
|
567 |
+
if input_ids is not None and inputs_embeds is not None:
|
568 |
+
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
|
569 |
+
elif input_ids is not None:
|
570 |
+
batch_size, seq_length = input_ids.shape
|
571 |
+
elif inputs_embeds is not None:
|
572 |
+
batch_size, seq_length, _ = inputs_embeds.shape
|
573 |
+
else:
|
574 |
+
raise ValueError("You have to specify either input_ids or inputs_embeds")
|
575 |
+
|
576 |
+
if past_key_values is None:
|
577 |
+
past_key_values = tuple([None] * len(self.h))
|
578 |
+
|
579 |
+
# Prepare head mask if needed
|
580 |
+
# 1.0 in head_mask indicate we keep the head
|
581 |
+
# attention_probs has shape batch_size x num_heads x N x N
|
582 |
+
# head_mask has shape n_layer x batch x num_heads x N x N
|
583 |
+
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
584 |
+
|
585 |
+
if inputs_embeds is None:
|
586 |
+
inputs_embeds = self.word_embeddings(input_ids)
|
587 |
+
|
588 |
+
hidden_states = inputs_embeds
|
589 |
+
|
590 |
+
presents = () if use_cache else None
|
591 |
+
all_self_attentions = () if output_attentions else None
|
592 |
+
all_hidden_states = () if output_hidden_states else None
|
593 |
+
|
594 |
+
# Compute alibi tensor: check build_alibi_tensor documentation
|
595 |
+
seq_length_with_past = seq_length
|
596 |
+
past_key_values_length = 0
|
597 |
+
if past_key_values[0] is not None:
|
598 |
+
past_key_values_length = past_key_values[0][0].shape[2]
|
599 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
600 |
+
if attention_mask is None:
|
601 |
+
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
|
602 |
+
else:
|
603 |
+
attention_mask = attention_mask.to(hidden_states.device)
|
604 |
+
|
605 |
+
if self.alibi:
|
606 |
+
alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
|
607 |
+
else:
|
608 |
+
alibi = None
|
609 |
+
|
610 |
+
causal_mask = self._prepare_attn_mask(
|
611 |
+
attention_mask,
|
612 |
+
input_shape=(batch_size, seq_length),
|
613 |
+
past_key_values_length=past_key_values_length,
|
614 |
+
)
|
615 |
+
|
616 |
+
for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
|
617 |
+
|
618 |
+
if output_hidden_states:
|
619 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
620 |
+
|
621 |
+
if self.gradient_checkpointing and self.training:
|
622 |
+
|
623 |
+
if use_cache:
|
624 |
+
logger.warning(
|
625 |
+
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
626 |
+
)
|
627 |
+
use_cache = False
|
628 |
+
|
629 |
+
def create_custom_forward(module):
|
630 |
+
def custom_forward(*inputs):
|
631 |
+
# None for past_key_value
|
632 |
+
return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
|
633 |
+
|
634 |
+
return custom_forward
|
635 |
+
|
636 |
+
outputs = torch.utils.checkpoint.checkpoint(
|
637 |
+
create_custom_forward(block),
|
638 |
+
hidden_states,
|
639 |
+
alibi,
|
640 |
+
causal_mask,
|
641 |
+
head_mask[i],
|
642 |
+
)
|
643 |
+
else:
|
644 |
+
outputs = block(
|
645 |
+
hidden_states,
|
646 |
+
layer_past=layer_past,
|
647 |
+
attention_mask=causal_mask,
|
648 |
+
head_mask=head_mask[i],
|
649 |
+
use_cache=use_cache,
|
650 |
+
output_attentions=output_attentions,
|
651 |
+
alibi=alibi,
|
652 |
+
)
|
653 |
+
|
654 |
+
hidden_states = outputs[0]
|
655 |
+
if use_cache is True:
|
656 |
+
presents = presents + (outputs[1],)
|
657 |
+
|
658 |
+
if output_attentions:
|
659 |
+
all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
|
660 |
+
|
661 |
+
# Add last hidden state
|
662 |
+
hidden_states = self.ln_f(hidden_states)
|
663 |
+
|
664 |
+
if output_hidden_states:
|
665 |
+
all_hidden_states = all_hidden_states + (hidden_states,)
|
666 |
+
|
667 |
+
if not return_dict:
|
668 |
+
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
|
669 |
+
|
670 |
+
return BaseModelOutputWithPastAndCrossAttentions(
|
671 |
+
last_hidden_state=hidden_states,
|
672 |
+
past_key_values=presents,
|
673 |
+
hidden_states=all_hidden_states,
|
674 |
+
attentions=all_self_attentions,
|
675 |
+
)
|
676 |
+
|
677 |
+
|
678 |
+
class RWForCausalLM(RWPreTrainedModel):
|
679 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
680 |
+
|
681 |
+
def __init__(self, config: RWConfig):
|
682 |
+
super().__init__(config)
|
683 |
+
self.transformer = RWModel(config)
|
684 |
+
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
685 |
+
|
686 |
+
# Initialize weights and apply final processing
|
687 |
+
self.post_init()
|
688 |
+
|
689 |
+
def get_output_embeddings(self):
|
690 |
+
return self.lm_head
|
691 |
+
|
692 |
+
def set_output_embeddings(self, new_embeddings: torch.Tensor):
|
693 |
+
self.lm_head = new_embeddings
|
694 |
+
|
695 |
+
def prepare_inputs_for_generation(
|
696 |
+
self,
|
697 |
+
input_ids: torch.LongTensor,
|
698 |
+
past: Optional[torch.Tensor] = None,
|
699 |
+
attention_mask: Optional[torch.Tensor] = None,
|
700 |
+
**kwargs,
|
701 |
+
) -> dict:
|
702 |
+
# only last token for input_ids if past is not None
|
703 |
+
if past:
|
704 |
+
input_ids = input_ids[:, -1].unsqueeze(-1)
|
705 |
+
|
706 |
+
# the cache may be in the stardard format (e.g. in contrastive search), convert to our's format if needed
|
707 |
+
if past[0][0].shape[0] == input_ids.shape[0]:
|
708 |
+
past = self._convert_to_rw_cache(past)
|
709 |
+
|
710 |
+
return {
|
711 |
+
"input_ids": input_ids,
|
712 |
+
"past_key_values": past,
|
713 |
+
"use_cache": kwargs.get("use_cache"),
|
714 |
+
"attention_mask": attention_mask,
|
715 |
+
}
|
716 |
+
|
717 |
+
def forward(
|
718 |
+
self,
|
719 |
+
input_ids: Optional[torch.LongTensor] = None,
|
720 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
721 |
+
attention_mask: Optional[torch.Tensor] = None,
|
722 |
+
head_mask: Optional[torch.Tensor] = None,
|
723 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
724 |
+
labels: Optional[torch.Tensor] = None,
|
725 |
+
use_cache: Optional[bool] = None,
|
726 |
+
output_attentions: Optional[bool] = None,
|
727 |
+
output_hidden_states: Optional[bool] = None,
|
728 |
+
return_dict: Optional[bool] = None,
|
729 |
+
**deprecated_arguments,
|
730 |
+
) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
731 |
+
r"""
|
732 |
+
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
733 |
+
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
|
734 |
+
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
|
735 |
+
are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
|
736 |
+
"""
|
737 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
738 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
739 |
+
warnings.warn(
|
740 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
741 |
+
" passing `position_ids`.",
|
742 |
+
FutureWarning,
|
743 |
+
)
|
744 |
+
if len(deprecated_arguments) > 0:
|
745 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
746 |
+
|
747 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
748 |
+
|
749 |
+
transformer_outputs = self.transformer(
|
750 |
+
input_ids,
|
751 |
+
past_key_values=past_key_values,
|
752 |
+
attention_mask=attention_mask,
|
753 |
+
head_mask=head_mask,
|
754 |
+
inputs_embeds=inputs_embeds,
|
755 |
+
use_cache=use_cache,
|
756 |
+
output_attentions=output_attentions,
|
757 |
+
output_hidden_states=output_hidden_states,
|
758 |
+
return_dict=return_dict,
|
759 |
+
)
|
760 |
+
hidden_states = transformer_outputs[0]
|
761 |
+
|
762 |
+
lm_logits = self.lm_head(hidden_states)
|
763 |
+
|
764 |
+
loss = None
|
765 |
+
if labels is not None:
|
766 |
+
# Shift so that tokens < n predict n
|
767 |
+
shift_logits = lm_logits[..., :-1, :].contiguous()
|
768 |
+
shift_labels = labels[..., 1:].contiguous()
|
769 |
+
batch_size, seq_length, vocab_size = shift_logits.shape
|
770 |
+
# Flatten the tokens
|
771 |
+
loss_fct = CrossEntropyLoss()
|
772 |
+
loss = loss_fct(
|
773 |
+
shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
|
774 |
+
)
|
775 |
+
|
776 |
+
if not return_dict:
|
777 |
+
output = (lm_logits,) + transformer_outputs[1:]
|
778 |
+
return ((loss,) + output) if loss is not None else output
|
779 |
+
|
780 |
+
return CausalLMOutputWithCrossAttentions(
|
781 |
+
loss=loss,
|
782 |
+
logits=lm_logits,
|
783 |
+
past_key_values=transformer_outputs.past_key_values,
|
784 |
+
hidden_states=transformer_outputs.hidden_states,
|
785 |
+
attentions=transformer_outputs.attentions,
|
786 |
+
)
|
787 |
+
|
788 |
+
def _reorder_cache(
|
789 |
+
self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
|
790 |
+
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
|
791 |
+
"""
|
792 |
+
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
|
793 |
+
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
|
794 |
+
beam_idx at every generation step.
|
795 |
+
Output shares the same memory storage as `past`.
|
796 |
+
"""
|
797 |
+
standardized_past = self._convert_to_standard_cache(past, batch_size=len(beam_idx))
|
798 |
+
|
799 |
+
# Get a copy of `beam_idx` on all the devices where we need those indices.
|
800 |
+
device_to_beam_idx = {
|
801 |
+
past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
|
802 |
+
}
|
803 |
+
reordered_past = tuple(
|
804 |
+
(
|
805 |
+
layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
|
806 |
+
layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
|
807 |
+
)
|
808 |
+
for layer_past in standardized_past
|
809 |
+
)
|
810 |
+
return self._convert_to_rw_cache(reordered_past)
|
811 |
+
|
812 |
+
|
813 |
+
class RWForSequenceClassification(RWPreTrainedModel):
|
814 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
815 |
+
|
816 |
+
def __init__(self, config: RWConfig):
|
817 |
+
super().__init__(config)
|
818 |
+
self.num_labels = config.num_labels
|
819 |
+
self.transformer = RWModel(config)
|
820 |
+
self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
|
821 |
+
|
822 |
+
# Initialize weights and apply final processing
|
823 |
+
self.post_init()
|
824 |
+
|
825 |
+
def forward(
|
826 |
+
self,
|
827 |
+
input_ids: Optional[torch.LongTensor] = None,
|
828 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
829 |
+
attention_mask: Optional[torch.Tensor] = None,
|
830 |
+
head_mask: Optional[torch.Tensor] = None,
|
831 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
832 |
+
labels: Optional[torch.Tensor] = None,
|
833 |
+
use_cache: Optional[bool] = None,
|
834 |
+
output_attentions: Optional[bool] = None,
|
835 |
+
output_hidden_states: Optional[bool] = None,
|
836 |
+
return_dict: Optional[bool] = None,
|
837 |
+
**deprecated_arguments,
|
838 |
+
) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
|
839 |
+
r"""
|
840 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
841 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
842 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
843 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
844 |
+
"""
|
845 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
846 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
847 |
+
warnings.warn(
|
848 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
849 |
+
" passing `position_ids`.",
|
850 |
+
FutureWarning,
|
851 |
+
)
|
852 |
+
if len(deprecated_arguments) > 0:
|
853 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
854 |
+
|
855 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
856 |
+
|
857 |
+
transformer_outputs = self.transformer(
|
858 |
+
input_ids,
|
859 |
+
past_key_values=past_key_values,
|
860 |
+
attention_mask=attention_mask,
|
861 |
+
head_mask=head_mask,
|
862 |
+
inputs_embeds=inputs_embeds,
|
863 |
+
use_cache=use_cache,
|
864 |
+
output_attentions=output_attentions,
|
865 |
+
output_hidden_states=output_hidden_states,
|
866 |
+
return_dict=return_dict,
|
867 |
+
)
|
868 |
+
|
869 |
+
hidden_states = transformer_outputs[0]
|
870 |
+
logits = self.score(hidden_states)
|
871 |
+
|
872 |
+
if input_ids is not None:
|
873 |
+
batch_size = input_ids.shape[0]
|
874 |
+
else:
|
875 |
+
batch_size = inputs_embeds.shape[0]
|
876 |
+
|
877 |
+
if self.config.pad_token_id is None and batch_size != 1:
|
878 |
+
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
879 |
+
if self.config.pad_token_id is None:
|
880 |
+
sequence_lengths = -1
|
881 |
+
else:
|
882 |
+
if input_ids is not None:
|
883 |
+
sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1
|
884 |
+
else:
|
885 |
+
sequence_lengths = -1
|
886 |
+
logger.warning(
|
887 |
+
f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
|
888 |
+
"unexpected if using padding tokens in conjunction with `inputs_embeds.`"
|
889 |
+
)
|
890 |
+
|
891 |
+
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
892 |
+
|
893 |
+
loss = None
|
894 |
+
if labels is not None:
|
895 |
+
if self.config.problem_type is None:
|
896 |
+
if self.num_labels == 1:
|
897 |
+
self.config.problem_type = "regression"
|
898 |
+
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
899 |
+
self.config.problem_type = "single_label_classification"
|
900 |
+
else:
|
901 |
+
self.config.problem_type = "multi_label_classification"
|
902 |
+
|
903 |
+
if self.config.problem_type == "regression":
|
904 |
+
loss_fct = MSELoss()
|
905 |
+
if self.num_labels == 1:
|
906 |
+
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
907 |
+
else:
|
908 |
+
loss = loss_fct(pooled_logits, labels)
|
909 |
+
elif self.config.problem_type == "single_label_classification":
|
910 |
+
loss_fct = CrossEntropyLoss()
|
911 |
+
loss = loss_fct(pooled_logits, labels)
|
912 |
+
elif self.config.problem_type == "multi_label_classification":
|
913 |
+
loss_fct = BCEWithLogitsLoss()
|
914 |
+
loss = loss_fct(pooled_logits, labels)
|
915 |
+
if not return_dict:
|
916 |
+
output = (pooled_logits,) + transformer_outputs[1:]
|
917 |
+
return ((loss,) + output) if loss is not None else output
|
918 |
+
|
919 |
+
return SequenceClassifierOutputWithPast(
|
920 |
+
loss=loss,
|
921 |
+
logits=pooled_logits,
|
922 |
+
past_key_values=transformer_outputs.past_key_values,
|
923 |
+
hidden_states=transformer_outputs.hidden_states,
|
924 |
+
attentions=transformer_outputs.attentions,
|
925 |
+
)
|
926 |
+
|
927 |
+
|
928 |
+
class RWForTokenClassification(RWPreTrainedModel):
|
929 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
930 |
+
|
931 |
+
def __init__(self, config: RWConfig):
|
932 |
+
super().__init__(config)
|
933 |
+
self.num_labels = config.num_labels
|
934 |
+
|
935 |
+
self.transformer = RWModel(config)
|
936 |
+
if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
|
937 |
+
classifier_dropout = config.classifier_dropout
|
938 |
+
elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
|
939 |
+
classifier_dropout = config.hidden_dropout
|
940 |
+
else:
|
941 |
+
classifier_dropout = 0.1
|
942 |
+
self.dropout = nn.Dropout(classifier_dropout)
|
943 |
+
self.classifier = nn.Linear(config.hidden_size, config.num_labels)
|
944 |
+
|
945 |
+
# Initialize weights and apply final processing
|
946 |
+
self.post_init()
|
947 |
+
|
948 |
+
def forward(
|
949 |
+
self,
|
950 |
+
input_ids: Optional[torch.LongTensor] = None,
|
951 |
+
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
|
952 |
+
attention_mask: Optional[torch.Tensor] = None,
|
953 |
+
head_mask: Optional[torch.Tensor] = None,
|
954 |
+
inputs_embeds: Optional[torch.Tensor] = None,
|
955 |
+
labels: Optional[torch.Tensor] = None,
|
956 |
+
use_cache: Optional[bool] = None,
|
957 |
+
output_attentions: Optional[bool] = None,
|
958 |
+
output_hidden_states: Optional[bool] = None,
|
959 |
+
return_dict: Optional[bool] = None,
|
960 |
+
**deprecated_arguments,
|
961 |
+
) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
|
962 |
+
r"""
|
963 |
+
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
964 |
+
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
965 |
+
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
966 |
+
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
967 |
+
"""
|
968 |
+
if deprecated_arguments.pop("position_ids", False) is not False:
|
969 |
+
# `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
|
970 |
+
warnings.warn(
|
971 |
+
"`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
|
972 |
+
" passing `position_ids`.",
|
973 |
+
FutureWarning,
|
974 |
+
)
|
975 |
+
if len(deprecated_arguments) > 0:
|
976 |
+
raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
|
977 |
+
|
978 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
979 |
+
|
980 |
+
transformer_outputs = self.transformer(
|
981 |
+
input_ids,
|
982 |
+
past_key_values=past_key_values,
|
983 |
+
attention_mask=attention_mask,
|
984 |
+
head_mask=head_mask,
|
985 |
+
inputs_embeds=inputs_embeds,
|
986 |
+
use_cache=use_cache,
|
987 |
+
output_attentions=output_attentions,
|
988 |
+
output_hidden_states=output_hidden_states,
|
989 |
+
return_dict=return_dict,
|
990 |
+
)
|
991 |
+
|
992 |
+
hidden_states = transformer_outputs[0]
|
993 |
+
hidden_states = self.dropout(hidden_states)
|
994 |
+
logits = self.classifier(hidden_states)
|
995 |
+
|
996 |
+
loss = None
|
997 |
+
if labels is not None:
|
998 |
+
batch_size, seq_length = labels.shape
|
999 |
+
loss_fct = CrossEntropyLoss()
|
1000 |
+
loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length))
|
1001 |
+
|
1002 |
+
if not return_dict:
|
1003 |
+
output = (logits,) + transformer_outputs[2:]
|
1004 |
+
return ((loss,) + output) if loss is not None else output
|
1005 |
+
|
1006 |
+
return TokenClassifierOutput(
|
1007 |
+
loss=loss,
|
1008 |
+
logits=logits,
|
1009 |
+
hidden_states=transformer_outputs.hidden_states,
|
1010 |
+
attentions=transformer_outputs.attentions,
|
1011 |
+
)
|
1012 |
+
|
1013 |
+
|
1014 |
+
class RWForQuestionAnswering(RWPreTrainedModel):
|
1015 |
+
_keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
|
1016 |
+
|
1017 |
+
def __init__(self, config):
|
1018 |
+
super().__init__(config)
|
1019 |
+
self.transformer = RWModel(config)
|
1020 |
+
self.qa_outputs = nn.Linear(config.hidden_size, 2)
|
1021 |
+
|
1022 |
+
# Initialize weights and apply final processing
|
1023 |
+
self.post_init()
|
1024 |
+
|
1025 |
+
def forward(
|
1026 |
+
self,
|
1027 |
+
input_ids: Optional[torch.LongTensor] = None,
|
1028 |
+
attention_mask: Optional[torch.FloatTensor] = None,
|
1029 |
+
position_ids: Optional[torch.LongTensor] = None,
|
1030 |
+
head_mask: Optional[torch.FloatTensor] = None,
|
1031 |
+
inputs_embeds: Optional[torch.FloatTensor] = None,
|
1032 |
+
start_positions: Optional[torch.LongTensor] = None,
|
1033 |
+
end_positions: Optional[torch.LongTensor] = None,
|
1034 |
+
output_attentions: Optional[bool] = None,
|
1035 |
+
output_hidden_states: Optional[bool] = None,
|
1036 |
+
return_dict: Optional[bool] = None,
|
1037 |
+
) -> Union[Tuple, QuestionAnsweringModelOutput]:
|
1038 |
+
r"""
|
1039 |
+
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1040 |
+
Labels for position (index) of the start of the labelled span for computing the token classification loss.
|
1041 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1042 |
+
are not taken into account for computing the loss.
|
1043 |
+
end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
1044 |
+
Labels for position (index) of the end of the labelled span for computing the token classification loss.
|
1045 |
+
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
|
1046 |
+
are not taken into account for computing the loss.
|
1047 |
+
"""
|
1048 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
1049 |
+
|
1050 |
+
outputs = self.transformer(
|
1051 |
+
input_ids,
|
1052 |
+
attention_mask=attention_mask,
|
1053 |
+
position_ids=position_ids,
|
1054 |
+
head_mask=head_mask,
|
1055 |
+
inputs_embeds=inputs_embeds,
|
1056 |
+
output_attentions=output_attentions,
|
1057 |
+
output_hidden_states=output_hidden_states,
|
1058 |
+
return_dict=return_dict,
|
1059 |
+
)
|
1060 |
+
|
1061 |
+
sequence_output = outputs[0]
|
1062 |
+
|
1063 |
+
logits = self.qa_outputs(sequence_output)
|
1064 |
+
start_logits, end_logits = logits.split(1, dim=-1)
|
1065 |
+
start_logits = start_logits.squeeze(-1).contiguous()
|
1066 |
+
end_logits = end_logits.squeeze(-1).contiguous()
|
1067 |
+
|
1068 |
+
total_loss = None
|
1069 |
+
if start_positions is not None and end_positions is not None:
|
1070 |
+
# If we are on multi-GPU, split add a dimension
|
1071 |
+
if len(start_positions.size()) > 1:
|
1072 |
+
start_positions = start_positions.squeeze(-1)
|
1073 |
+
if len(end_positions.size()) > 1:
|
1074 |
+
end_positions = end_positions.squeeze(-1)
|
1075 |
+
# sometimes the start/end positions are outside our model inputs, we ignore these terms
|
1076 |
+
ignored_index = start_logits.size(1)
|
1077 |
+
start_positions = start_positions.clamp(0, ignored_index)
|
1078 |
+
end_positions = end_positions.clamp(0, ignored_index)
|
1079 |
+
|
1080 |
+
loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
|
1081 |
+
start_loss = loss_fct(start_logits, start_positions)
|
1082 |
+
end_loss = loss_fct(end_logits, end_positions)
|
1083 |
+
total_loss = (start_loss + end_loss) / 2
|
1084 |
+
|
1085 |
+
if not return_dict:
|
1086 |
+
output = (start_logits, end_logits) + outputs[2:]
|
1087 |
+
return ((total_loss,) + output) if total_loss is not None else output
|
1088 |
+
|
1089 |
+
return QuestionAnsweringModelOutput(
|
1090 |
+
loss=total_loss,
|
1091 |
+
start_logits=start_logits,
|
1092 |
+
end_logits=end_logits,
|
1093 |
+
hidden_states=outputs.hidden_states,
|
1094 |
+
attentions=outputs.attentions,
|
1095 |
+
)
|