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Delete modelling_RW.py

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- # port of models described in RW
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- # We use the bloom model as a starting point for these model.
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- # Please refer to the bloom models for usage instructions.
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-
5
- import math
6
- import warnings
7
- from typing import Optional, Tuple, Union
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-
9
- import torch
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- import torch.utils.checkpoint
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- from torch import nn
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- from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, LayerNorm, MSELoss
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- from torch.nn import functional as F
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-
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- from transformers.modeling_outputs import (
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- BaseModelOutputWithPastAndCrossAttentions,
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- CausalLMOutputWithCrossAttentions,
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- QuestionAnsweringModelOutput,
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- SequenceClassifierOutputWithPast,
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- TokenClassifierOutput,
21
- )
22
- from transformers.modeling_utils import PreTrainedModel
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- from transformers.utils import logging
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- from .configuration_RW import RWConfig
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-
26
- logger = logging.get_logger(__name__)
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-
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- # 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.
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- # In order not to degrade the quality of our HF-port, we keep these characteristics in the final model.
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- class Linear(nn.Linear):
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- def forward(self, input: torch.Tensor) -> torch.Tensor:
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- ret = input @ self.weight.T
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- if self.bias is None:
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- return ret
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- else:
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- return ret + self.bias
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-
38
-
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- from einops import rearrange
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-
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- # rotary pos emb helpers (torch.jit.script does not seem to support staticmethod...)
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- def rotate_half(x):
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- x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
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- return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in torch < 1.8.0
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-
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-
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- class RotaryEmbedding(torch.nn.Module):
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- """Implementation of RotaryEmbedding from GPT-NeoX.
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- 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))
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- self.register_buffer("inv_freq", inv_freq, persistent=False)
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- self.head_dim = head_dim
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- self.seq_len_cached = None
63
- self.batch_size_cached = None
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- self.cos_cached: torch.Tensor | None = None
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- self.sin_cached: torch.Tensor | None = None
66
-
67
- def cos_sin(
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- self,
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- seq_len: int,
70
- device="cuda",
71
- dtype=torch.bfloat16,
72
- ) -> torch.Tensor:
73
- if seq_len != self.seq_len_cached:
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- self.seq_len_cached = seq_len
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- t = torch.arange(seq_len, device=device).type_as(self.inv_freq)
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- freqs = torch.einsum("i,j->ij", t, self.inv_freq)
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- 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
-
191
- Args:
192
- fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
193
-
194
- Returns:
195
- query: [batch_size, seq_length, num_heads, head_dim] key: [batch_size, seq_length, num_heads, head_dim]
196
- value: [batch_size, seq_length, num_heads, head_dim]
197
- """
198
- if not self.multi_query:
199
- batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
200
- fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads, 3, self.head_dim)
201
- return fused_qkv[..., 0, :], fused_qkv[..., 1, :], fused_qkv[..., 2, :]
202
- else:
203
- batch_size, seq_length, three_times_hidden_size = fused_qkv.shape
204
- fused_qkv = fused_qkv.view(batch_size, seq_length, self.num_heads + 2, self.head_dim)
205
- return fused_qkv[..., :-2, :], fused_qkv[..., [-2], :], fused_qkv[..., [-1], :]
206
-
207
- def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
208
- """
209
- Merge heads together over the last dimenstion
210
-
211
- Args:
212
- x: (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]
213
-
214
- Returns:
215
- torch.tensor: [batch_size, seq_length, num_heads * head_dim]
216
- """
217
- # What we want to achieve is:
218
- # batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
219
- batch_size_and_num_heads, seq_length, _ = x.shape
220
- batch_size = batch_size_and_num_heads // self.num_heads
221
-
222
- # First view to decompose the batch size
223
- # batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
224
- x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
225
-
226
- # batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
227
- x = x.permute(0, 2, 1, 3)
228
-
229
- # batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
230
- return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
231
-
232
- def forward(
233
- self,
234
- hidden_states: torch.Tensor,
235
- alibi: torch.Tensor,
236
- attention_mask: torch.Tensor,
237
- layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
238
- head_mask: Optional[torch.Tensor] = None,
239
- use_cache: bool = False,
240
- output_attentions: bool = False,
241
- ):
242
- fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
243
-
244
- # 3 x [batch_size, seq_length, num_heads, head_dim]
245
- (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
246
-
247
- batch_size, q_length, _, _ = query_layer.shape
248
-
249
- query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
250
- key_layer = key_layer.transpose(1, 2).reshape(
251
- batch_size * self.num_kv,
252
- q_length,
253
- self.head_dim,
254
- )
255
- value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_kv, q_length, self.head_dim)
256
-
257
- query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
258
-
259
- if layer_past is not None:
260
- past_key, past_value = layer_past
261
- # concatenate along seq_length dimension:
262
- # - key: [batch_size * self.num_heads, head_dim, kv_length]
263
- # - value: [batch_size * self.num_heads, kv_length, head_dim]
264
- key_layer = torch.cat((past_key, key_layer), dim=1)
265
- value_layer = torch.cat((past_value, value_layer), dim=1)
266
-
267
- _, kv_length, _ = key_layer.shape
268
-
269
- if use_cache is True:
270
- present = (key_layer, value_layer)
271
- else:
272
- present = None
273
-
274
- if alibi is None:
275
- query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
276
- key_layer_ = key_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
277
- value_layer_ = value_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
278
-
279
- attn_output = F.scaled_dot_product_attention(
280
- query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
281
- )
282
-
283
- x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
284
- x = x.permute(0, 2, 1, 3)
285
- attn_output = x.reshape(batch_size, q_length, self.num_heads * self.head_dim)
286
-
287
- output_tensor = self.dense(attn_output)
288
-
289
- outputs = (output_tensor, present)
290
- assert not output_attentions # not supported.
291
- return outputs
292
- else:
293
- attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, -1e9).to(torch.bfloat16)
294
- matmul_result = query_layer @ key_layer.transpose(-1, -2)
295
-
296
- # change view to [batch_size, num_heads, q_length, kv_length]
297
- attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
298
-
299
- # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
300
- input_dtype = attention_scores.dtype
301
- # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
302
- if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
303
- attention_scores = attention_scores.to(torch.float32)
304
- # attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
305
- attention_probs = F.softmax(
306
- (attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)) * self.inv_norm_factor + attention_mask_float,
307
- dim=-1,
308
- dtype=hidden_states.dtype,
309
- )
310
- # [batch_size, num_heads, q_length, kv_length]
311
- attention_probs = self.attention_dropout(attention_probs)
312
-
313
- if head_mask is not None:
314
- attention_probs = attention_probs * head_mask
315
-
316
- # change view [batch_size x num_heads, q_length, kv_length]
317
- attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length)
318
-
319
- # matmul: [batch_size * num_heads, q_length, head_dim]
320
- context_layer = attention_probs_reshaped @ value_layer
321
-
322
- # change view [batch_size, num_heads, q_length, head_dim]
323
- context_layer = self._merge_heads(context_layer)
324
-
325
- output_tensor = self.dense(context_layer)
326
-
327
- outputs = (output_tensor, present)
328
- if output_attentions:
329
- outputs += (attention_probs,)
330
-
331
- return outputs
332
-
333
-
334
- class MLP(nn.Module):
335
- def __init__(self, config: RWConfig):
336
- super().__init__()
337
- hidden_size = config.hidden_size
338
-
339
- self.dense_h_to_4h = Linear(hidden_size, 4 * hidden_size, bias=config.bias)
340
- self.act = nn.GELU()
341
- self.dense_4h_to_h = Linear(4 * hidden_size, hidden_size, bias=config.bias)
342
- self.hidden_dropout = config.hidden_dropout
343
-
344
- def forward(self, x: torch.Tensor) -> torch.Tensor:
345
- x = self.act(self.dense_h_to_4h(x))
346
- x = self.dense_4h_to_h(x)
347
- return x
348
-
349
-
350
- class DecoderLayer(nn.Module):
351
- def __init__(self, config: RWConfig):
352
- super().__init__()
353
- hidden_size = config.hidden_size
354
-
355
- self.input_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
356
- self.num_heads = config.n_head
357
- self.self_attention = Attention(config)
358
-
359
- if not config.parallel_attn:
360
- # unused if parallel attn
361
- self.post_attention_layernorm = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
362
-
363
- self.mlp = MLP(config)
364
-
365
- self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
366
- self.hidden_dropout = config.hidden_dropout
367
-
368
- self.config = config
369
-
370
- def forward(
371
- self,
372
- hidden_states: torch.Tensor,
373
- alibi: torch.Tensor,
374
- attention_mask: torch.Tensor,
375
- layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
376
- head_mask: Optional[torch.Tensor] = None,
377
- use_cache: bool = False,
378
- output_attentions: bool = False,
379
- ):
380
-
381
- layernorm_output = self.input_layernorm(hidden_states)
382
- residual = hidden_states
383
-
384
- # Self attention.
385
- attn_outputs = self.self_attention(
386
- layernorm_output,
387
- layer_past=layer_past,
388
- attention_mask=attention_mask,
389
- alibi=alibi,
390
- head_mask=head_mask,
391
- use_cache=use_cache,
392
- output_attentions=output_attentions,
393
- )
394
-
395
- attention_output = attn_outputs[0]
396
-
397
- if not self.config.parallel_attn:
398
- residual = dropout_add(attention_output, residual, self.config.attention_dropout, training=self.training)
399
- layernorm_output = self.post_attention_layernorm(residual)
400
-
401
- outputs = attn_outputs[1:]
402
-
403
- # MLP.
404
- mlp_output = self.mlp(layernorm_output)
405
-
406
- if self.config.parallel_attn:
407
- mlp_output += attention_output
408
-
409
- output = dropout_add(mlp_output, residual, self.config.hidden_dropout, training=self.training)
410
-
411
- if use_cache:
412
- outputs = (output,) + outputs
413
- else:
414
- outputs = (output,) + outputs[1:]
415
-
416
- return outputs # hidden_states, present, attentions
417
-
418
-
419
- class RWPreTrainedModel(PreTrainedModel):
420
- _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
421
- """
422
- An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
423
- models.
424
- """
425
-
426
- config_class = RWConfig
427
- base_model_prefix = "transformer"
428
- supports_gradient_checkpointing = True
429
- _no_split_modules = ["DecoderLayer"]
430
-
431
- def __init__(self, *inputs, **kwargs):
432
- super().__init__(*inputs, **kwargs)
433
-
434
- def _init_weights(self, module: nn.Module):
435
- """Initialize the weights."""
436
- if isinstance(module, nn.Linear) or isinstance(module, Linear):
437
- # Slightly different from the TF version which uses truncated_normal for initialization
438
- # cf https://github.com/pytorch/pytorch/pull/5617
439
- module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
440
- if module.bias is not None:
441
- module.bias.data.zero_()
442
- elif isinstance(module, nn.Embedding):
443
- module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
444
- if module.padding_idx is not None:
445
- module.weight.data[module.padding_idx].zero_()
446
- elif isinstance(module, LayerNorm):
447
- module.bias.data.zero_()
448
- module.weight.data.fill_(1.0)
449
-
450
- def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
451
- if isinstance(module, RWModel):
452
- module.gradient_checkpointing = value
453
-
454
- @staticmethod
455
- def _convert_to_standard_cache(
456
- past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
457
- ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
458
- """
459
- Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
460
- num_heads, ...]))
461
- """
462
- batch_size_times_num_heads, head_dim, seq_length = past_key_value[0][0].shape
463
- num_heads = batch_size_times_num_heads // batch_size
464
- # key: [batch_size * num_heads, head_dim, seq_length] -> [batch_size, num_heads, head_dim, seq_length]
465
- # value: [batch_size * num_heads, seq_length, head_dim] -> [batch_size, num_heads, seq_length, head_dim]
466
- return tuple(
467
- (
468
- layer_past[0].view(batch_size, num_heads, head_dim, seq_length),
469
- layer_past[1].view(batch_size, num_heads, seq_length, head_dim),
470
- )
471
- for layer_past in past_key_value
472
- )
473
-
474
- @staticmethod
475
- def _convert_to_rw_cache(
476
- past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
477
- ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
478
- batch_size, num_heads, head_dim, seq_length = past_key_value[0][0].shape
479
- batch_size_times_num_heads = batch_size * num_heads
480
- # key: [batch_size, num_heads, head_dim, seq_length] -> [batch_size * num_heads, head_dim, seq_length]
481
- # value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size * num_heads, seq_length, head_dim]
482
- return tuple(
483
- (
484
- layer_past[0].view(batch_size_times_num_heads, head_dim, seq_length),
485
- layer_past[1].view(batch_size_times_num_heads, seq_length, head_dim),
486
- )
487
- for layer_past in past_key_value
488
- )
489
-
490
-
491
- class RWModel(RWPreTrainedModel):
492
- def __init__(self, config: RWConfig):
493
- super().__init__(config)
494
-
495
- self.embed_dim = config.hidden_size
496
- self.num_heads = config.n_head
497
- self.alibi = config.alibi
498
-
499
- # Embedding + LN Embedding
500
- self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
501
-
502
- # Transformer blocks
503
- self.h = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
504
-
505
- # Final Layer Norm
506
- self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
507
-
508
- self.gradient_checkpointing = False
509
-
510
- # Initialize weights and apply final processing
511
- self.post_init()
512
-
513
- def get_input_embeddings(self):
514
- return self.word_embeddings
515
-
516
- def _prepare_attn_mask(
517
- self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
518
- ) -> torch.BoolTensor:
519
- # create causal mask
520
- # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
521
- combined_attention_mask = None
522
- device = attention_mask.device
523
- _, src_length = input_shape
524
-
525
- if src_length > 1:
526
- combined_attention_mask = _make_causal_mask(
527
- input_shape, device=device, past_key_values_length=past_key_values_length
528
- )
529
-
530
- # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
531
- expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
532
- combined_attention_mask = (
533
- expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
534
- )
535
-
536
- return combined_attention_mask
537
-
538
- def set_input_embeddings(self, new_embeddings: torch.Tensor):
539
- self.word_embeddings = new_embeddings
540
-
541
- def forward(
542
- self,
543
- input_ids: Optional[torch.LongTensor] = None,
544
- past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
545
- attention_mask: Optional[torch.Tensor] = None,
546
- head_mask: Optional[torch.LongTensor] = None,
547
- inputs_embeds: Optional[torch.LongTensor] = None,
548
- use_cache: Optional[bool] = None,
549
- output_attentions: Optional[bool] = None,
550
- output_hidden_states: Optional[bool] = None,
551
- return_dict: Optional[bool] = None,
552
- **deprecated_arguments,
553
- ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
554
- if deprecated_arguments.pop("position_ids", False) is not False:
555
- # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
556
- warnings.warn(
557
- "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
558
- " passing `position_ids`.",
559
- FutureWarning,
560
- )
561
- if len(deprecated_arguments) > 0:
562
- raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
563
-
564
- output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
565
- output_hidden_states = (
566
- output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
567
- )
568
- use_cache = use_cache if use_cache is not None else self.config.use_cache
569
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
570
-
571
- if input_ids is not None and inputs_embeds is not None:
572
- raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
573
- elif input_ids is not None:
574
- batch_size, seq_length = input_ids.shape
575
- elif inputs_embeds is not None:
576
- batch_size, seq_length, _ = inputs_embeds.shape
577
- else:
578
- raise ValueError("You have to specify either input_ids or inputs_embeds")
579
-
580
- if past_key_values is None:
581
- past_key_values = tuple([None] * len(self.h))
582
-
583
- # Prepare head mask if needed
584
- # 1.0 in head_mask indicate we keep the head
585
- # attention_probs has shape batch_size x num_heads x N x N
586
- # head_mask has shape n_layer x batch x num_heads x N x N
587
- head_mask = self.get_head_mask(head_mask, self.config.n_layer)
588
-
589
- if inputs_embeds is None:
590
- inputs_embeds = self.word_embeddings(input_ids)
591
-
592
- hidden_states = inputs_embeds
593
-
594
- presents = () if use_cache else None
595
- all_self_attentions = () if output_attentions else None
596
- all_hidden_states = () if output_hidden_states else None
597
-
598
- # Compute alibi tensor: check build_alibi_tensor documentation
599
- seq_length_with_past = seq_length
600
- past_key_values_length = 0
601
- if past_key_values[0] is not None:
602
- past_key_values_length = past_key_values[0][0].shape[2]
603
- seq_length_with_past = seq_length_with_past + past_key_values_length
604
- if attention_mask is None:
605
- attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
606
- else:
607
- attention_mask = attention_mask.to(hidden_states.device)
608
-
609
- if self.alibi:
610
- alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
611
- else:
612
- alibi = None
613
-
614
- causal_mask = self._prepare_attn_mask(
615
- attention_mask,
616
- input_shape=(batch_size, seq_length),
617
- past_key_values_length=past_key_values_length,
618
- )
619
-
620
- for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
621
-
622
- if output_hidden_states:
623
- all_hidden_states = all_hidden_states + (hidden_states,)
624
-
625
- if self.gradient_checkpointing and self.training:
626
-
627
- if use_cache:
628
- logger.warning(
629
- "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
630
- )
631
- use_cache = False
632
-
633
- def create_custom_forward(module):
634
- def custom_forward(*inputs):
635
- # None for past_key_value
636
- return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
637
-
638
- return custom_forward
639
-
640
- outputs = torch.utils.checkpoint.checkpoint(
641
- create_custom_forward(block),
642
- hidden_states,
643
- alibi,
644
- causal_mask,
645
- head_mask[i],
646
- )
647
- else:
648
- outputs = block(
649
- hidden_states,
650
- layer_past=layer_past,
651
- attention_mask=causal_mask,
652
- head_mask=head_mask[i],
653
- use_cache=use_cache,
654
- output_attentions=output_attentions,
655
- alibi=alibi,
656
- )
657
-
658
- hidden_states = outputs[0]
659
- if use_cache is True:
660
- presents = presents + (outputs[1],)
661
-
662
- if output_attentions:
663
- all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
664
-
665
- # Add last hidden state
666
- hidden_states = self.ln_f(hidden_states)
667
-
668
- if output_hidden_states:
669
- all_hidden_states = all_hidden_states + (hidden_states,)
670
-
671
- if not return_dict:
672
- return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
673
-
674
- return BaseModelOutputWithPastAndCrossAttentions(
675
- last_hidden_state=hidden_states,
676
- past_key_values=presents,
677
- hidden_states=all_hidden_states,
678
- attentions=all_self_attentions,
679
- )
680
-
681
-
682
- class RWForCausalLM(RWPreTrainedModel):
683
- _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
684
-
685
- def __init__(self, config: RWConfig):
686
- super().__init__(config)
687
- self.transformer = RWModel(config)
688
- self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
689
-
690
- # Initialize weights and apply final processing
691
- self.post_init()
692
-
693
- def get_output_embeddings(self):
694
- return self.lm_head
695
-
696
- def set_output_embeddings(self, new_embeddings: torch.Tensor):
697
- self.lm_head = new_embeddings
698
-
699
- def prepare_inputs_for_generation(
700
- self,
701
- input_ids: torch.LongTensor,
702
- past: Optional[torch.Tensor] = None,
703
- attention_mask: Optional[torch.Tensor] = None,
704
- **kwargs,
705
- ) -> dict:
706
- # only last token for input_ids if past is not None
707
- if past:
708
- input_ids = input_ids[:, -1].unsqueeze(-1)
709
-
710
- # the cache may be in the stardard format (e.g. in contrastive search), convert to our's format if needed
711
- if past[0][0].shape[0] == input_ids.shape[0]:
712
- past = self._convert_to_rw_cache(past)
713
-
714
- return {
715
- "input_ids": input_ids,
716
- "past_key_values": past,
717
- "use_cache": kwargs.get("use_cache"),
718
- "attention_mask": attention_mask,
719
- }
720
-
721
- def forward(
722
- self,
723
- input_ids: Optional[torch.LongTensor] = None,
724
- past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
725
- attention_mask: Optional[torch.Tensor] = None,
726
- head_mask: Optional[torch.Tensor] = None,
727
- inputs_embeds: Optional[torch.Tensor] = None,
728
- labels: Optional[torch.Tensor] = None,
729
- use_cache: Optional[bool] = None,
730
- output_attentions: Optional[bool] = None,
731
- output_hidden_states: Optional[bool] = None,
732
- return_dict: Optional[bool] = None,
733
- **deprecated_arguments,
734
- ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
735
- r"""
736
- labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
737
- Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
738
- `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
739
- are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
740
- """
741
- if deprecated_arguments.pop("position_ids", False) is not False:
742
- # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
743
- warnings.warn(
744
- "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
745
- " passing `position_ids`.",
746
- FutureWarning,
747
- )
748
- if len(deprecated_arguments) > 0:
749
- raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
750
-
751
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
752
-
753
- transformer_outputs = self.transformer(
754
- input_ids,
755
- past_key_values=past_key_values,
756
- attention_mask=attention_mask,
757
- head_mask=head_mask,
758
- inputs_embeds=inputs_embeds,
759
- use_cache=use_cache,
760
- output_attentions=output_attentions,
761
- output_hidden_states=output_hidden_states,
762
- return_dict=return_dict,
763
- )
764
- hidden_states = transformer_outputs[0]
765
-
766
- lm_logits = self.lm_head(hidden_states)
767
-
768
- loss = None
769
- if labels is not None:
770
- # Shift so that tokens < n predict n
771
- shift_logits = lm_logits[..., :-1, :].contiguous()
772
- shift_labels = labels[..., 1:].contiguous()
773
- batch_size, seq_length, vocab_size = shift_logits.shape
774
- # Flatten the tokens
775
- loss_fct = CrossEntropyLoss()
776
- loss = loss_fct(
777
- shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
778
- )
779
-
780
- if not return_dict:
781
- output = (lm_logits,) + transformer_outputs[1:]
782
- return ((loss,) + output) if loss is not None else output
783
-
784
- return CausalLMOutputWithCrossAttentions(
785
- loss=loss,
786
- logits=lm_logits,
787
- past_key_values=transformer_outputs.past_key_values,
788
- hidden_states=transformer_outputs.hidden_states,
789
- attentions=transformer_outputs.attentions,
790
- )
791
-
792
- def _reorder_cache(
793
- self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
794
- ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
795
- """
796
- This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
797
- [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
798
- beam_idx at every generation step.
799
-
800
- Output shares the same memory storage as `past`.
801
- """
802
- standardized_past = self._convert_to_standard_cache(past, batch_size=len(beam_idx))
803
-
804
- # Get a copy of `beam_idx` on all the devices where we need those indices.
805
- device_to_beam_idx = {
806
- past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
807
- }
808
- reordered_past = tuple(
809
- (
810
- layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
811
- layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
812
- )
813
- for layer_past in standardized_past
814
- )
815
- return self._convert_to_rw_cache(reordered_past)
816
-
817
-
818
- class RWForSequenceClassification(RWPreTrainedModel):
819
- _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
820
-
821
- def __init__(self, config: RWConfig):
822
- super().__init__(config)
823
- self.num_labels = config.num_labels
824
- self.transformer = RWModel(config)
825
- self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
826
-
827
- # Initialize weights and apply final processing
828
- self.post_init()
829
-
830
- def forward(
831
- self,
832
- input_ids: Optional[torch.LongTensor] = None,
833
- past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
834
- attention_mask: Optional[torch.Tensor] = None,
835
- head_mask: Optional[torch.Tensor] = None,
836
- inputs_embeds: Optional[torch.Tensor] = None,
837
- labels: Optional[torch.Tensor] = None,
838
- use_cache: Optional[bool] = None,
839
- output_attentions: Optional[bool] = None,
840
- output_hidden_states: Optional[bool] = None,
841
- return_dict: Optional[bool] = None,
842
- **deprecated_arguments,
843
- ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
844
- r"""
845
- labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
846
- Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
847
- config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
848
- `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
849
- """
850
- if deprecated_arguments.pop("position_ids", False) is not False:
851
- # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
852
- warnings.warn(
853
- "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
854
- " passing `position_ids`.",
855
- FutureWarning,
856
- )
857
- if len(deprecated_arguments) > 0:
858
- raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
859
-
860
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
861
-
862
- transformer_outputs = self.transformer(
863
- input_ids,
864
- past_key_values=past_key_values,
865
- attention_mask=attention_mask,
866
- head_mask=head_mask,
867
- inputs_embeds=inputs_embeds,
868
- use_cache=use_cache,
869
- output_attentions=output_attentions,
870
- output_hidden_states=output_hidden_states,
871
- return_dict=return_dict,
872
- )
873
-
874
- hidden_states = transformer_outputs[0]
875
- logits = self.score(hidden_states)
876
-
877
- if input_ids is not None:
878
- batch_size = input_ids.shape[0]
879
- else:
880
- batch_size = inputs_embeds.shape[0]
881
-
882
- if self.config.pad_token_id is None and batch_size != 1:
883
- raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
884
- if self.config.pad_token_id is None:
885
- sequence_lengths = -1
886
- else:
887
- if input_ids is not None:
888
- sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1
889
- else:
890
- sequence_lengths = -1
891
- logger.warning(
892
- f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
893
- "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
894
- )
895
-
896
- pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
897
-
898
- loss = None
899
- if labels is not None:
900
- if self.config.problem_type is None:
901
- if self.num_labels == 1:
902
- self.config.problem_type = "regression"
903
- elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
904
- self.config.problem_type = "single_label_classification"
905
- else:
906
- self.config.problem_type = "multi_label_classification"
907
-
908
- if self.config.problem_type == "regression":
909
- loss_fct = MSELoss()
910
- if self.num_labels == 1:
911
- loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
912
- else:
913
- loss = loss_fct(pooled_logits, labels)
914
- elif self.config.problem_type == "single_label_classification":
915
- loss_fct = CrossEntropyLoss()
916
- loss = loss_fct(pooled_logits, labels)
917
- elif self.config.problem_type == "multi_label_classification":
918
- loss_fct = BCEWithLogitsLoss()
919
- loss = loss_fct(pooled_logits, labels)
920
- if not return_dict:
921
- output = (pooled_logits,) + transformer_outputs[1:]
922
- return ((loss,) + output) if loss is not None else output
923
-
924
- return SequenceClassifierOutputWithPast(
925
- loss=loss,
926
- logits=pooled_logits,
927
- past_key_values=transformer_outputs.past_key_values,
928
- hidden_states=transformer_outputs.hidden_states,
929
- attentions=transformer_outputs.attentions,
930
- )
931
-
932
-
933
- class RWForTokenClassification(RWPreTrainedModel):
934
- _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
935
-
936
- def __init__(self, config: RWConfig):
937
- super().__init__(config)
938
- self.num_labels = config.num_labels
939
-
940
- self.transformer = RWModel(config)
941
- if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
942
- classifier_dropout = config.classifier_dropout
943
- elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
944
- classifier_dropout = config.hidden_dropout
945
- else:
946
- classifier_dropout = 0.1
947
- self.dropout = nn.Dropout(classifier_dropout)
948
- self.classifier = nn.Linear(config.hidden_size, config.num_labels)
949
-
950
- # Initialize weights and apply final processing
951
- self.post_init()
952
-
953
- def forward(
954
- self,
955
- input_ids: Optional[torch.LongTensor] = None,
956
- past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
957
- attention_mask: Optional[torch.Tensor] = None,
958
- head_mask: Optional[torch.Tensor] = None,
959
- inputs_embeds: Optional[torch.Tensor] = None,
960
- labels: Optional[torch.Tensor] = None,
961
- use_cache: Optional[bool] = None,
962
- output_attentions: Optional[bool] = None,
963
- output_hidden_states: Optional[bool] = None,
964
- return_dict: Optional[bool] = None,
965
- **deprecated_arguments,
966
- ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
967
- r"""
968
- labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
969
- Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
970
- config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
971
- `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
972
- """
973
- if deprecated_arguments.pop("position_ids", False) is not False:
974
- # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
975
- warnings.warn(
976
- "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
977
- " passing `position_ids`.",
978
- FutureWarning,
979
- )
980
- if len(deprecated_arguments) > 0:
981
- raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
982
-
983
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
984
-
985
- transformer_outputs = self.transformer(
986
- input_ids,
987
- past_key_values=past_key_values,
988
- attention_mask=attention_mask,
989
- head_mask=head_mask,
990
- inputs_embeds=inputs_embeds,
991
- use_cache=use_cache,
992
- output_attentions=output_attentions,
993
- output_hidden_states=output_hidden_states,
994
- return_dict=return_dict,
995
- )
996
-
997
- hidden_states = transformer_outputs[0]
998
- hidden_states = self.dropout(hidden_states)
999
- logits = self.classifier(hidden_states)
1000
-
1001
- loss = None
1002
- if labels is not None:
1003
- batch_size, seq_length = labels.shape
1004
- loss_fct = CrossEntropyLoss()
1005
- loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length))
1006
-
1007
- if not return_dict:
1008
- output = (logits,) + transformer_outputs[2:]
1009
- return ((loss,) + output) if loss is not None else output
1010
-
1011
- return TokenClassifierOutput(
1012
- loss=loss,
1013
- logits=logits,
1014
- hidden_states=transformer_outputs.hidden_states,
1015
- attentions=transformer_outputs.attentions,
1016
- )
1017
-
1018
-
1019
- class RWForQuestionAnswering(RWPreTrainedModel):
1020
- _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
1021
-
1022
- def __init__(self, config):
1023
- super().__init__(config)
1024
- self.transformer = RWModel(config)
1025
- self.qa_outputs = nn.Linear(config.hidden_size, 2)
1026
-
1027
- # Initialize weights and apply final processing
1028
- self.post_init()
1029
-
1030
- def forward(
1031
- self,
1032
- input_ids: Optional[torch.LongTensor] = None,
1033
- attention_mask: Optional[torch.FloatTensor] = None,
1034
- position_ids: Optional[torch.LongTensor] = None,
1035
- head_mask: Optional[torch.FloatTensor] = None,
1036
- inputs_embeds: Optional[torch.FloatTensor] = None,
1037
- start_positions: Optional[torch.LongTensor] = None,
1038
- end_positions: Optional[torch.LongTensor] = None,
1039
- output_attentions: Optional[bool] = None,
1040
- output_hidden_states: Optional[bool] = None,
1041
- return_dict: Optional[bool] = None,
1042
- ) -> Union[Tuple, QuestionAnsweringModelOutput]:
1043
- r"""
1044
- start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1045
- Labels for position (index) of the start of the labelled span for computing the token classification loss.
1046
- Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1047
- are not taken into account for computing the loss.
1048
- end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1049
- Labels for position (index) of the end of the labelled span for computing the token classification loss.
1050
- Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1051
- are not taken into account for computing the loss.
1052
- """
1053
- return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1054
-
1055
- outputs = self.transformer(
1056
- input_ids,
1057
- attention_mask=attention_mask,
1058
- position_ids=position_ids,
1059
- head_mask=head_mask,
1060
- inputs_embeds=inputs_embeds,
1061
- output_attentions=output_attentions,
1062
- output_hidden_states=output_hidden_states,
1063
- return_dict=return_dict,
1064
- )
1065
-
1066
- sequence_output = outputs[0]
1067
-
1068
- logits = self.qa_outputs(sequence_output)
1069
- start_logits, end_logits = logits.split(1, dim=-1)
1070
- start_logits = start_logits.squeeze(-1).contiguous()
1071
- end_logits = end_logits.squeeze(-1).contiguous()
1072
-
1073
- total_loss = None
1074
- if start_positions is not None and end_positions is not None:
1075
- # If we are on multi-GPU, split add a dimension
1076
- if len(start_positions.size()) > 1:
1077
- start_positions = start_positions.squeeze(-1)
1078
- if len(end_positions.size()) > 1:
1079
- end_positions = end_positions.squeeze(-1)
1080
- # sometimes the start/end positions are outside our model inputs, we ignore these terms
1081
- ignored_index = start_logits.size(1)
1082
- start_positions = start_positions.clamp(0, ignored_index)
1083
- end_positions = end_positions.clamp(0, ignored_index)
1084
-
1085
- loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
1086
- start_loss = loss_fct(start_logits, start_positions)
1087
- end_loss = loss_fct(end_logits, end_positions)
1088
- total_loss = (start_loss + end_loss) / 2
1089
-
1090
- if not return_dict:
1091
- output = (start_logits, end_logits) + outputs[2:]
1092
- return ((total_loss,) + output) if total_loss is not None else output
1093
-
1094
- return QuestionAnsweringModelOutput(
1095
- loss=total_loss,
1096
- start_logits=start_logits,
1097
- end_logits=end_logits,
1098
- hidden_states=outputs.hidden_states,
1099
- attentions=outputs.attentions,
1100
- )