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configuration_RW.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2022 the Big Science Workshop and HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """ Bloom configuration"""
16
+ from transformers.configuration_utils import PretrainedConfig
17
+ from transformers.utils import logging
18
+
19
+
20
+ logger = logging.get_logger(__name__)
21
+
22
+
23
+ class RWConfig(PretrainedConfig):
24
+ model_type = "RefinedWeb"
25
+ keys_to_ignore_at_inference = ["past_key_values"]
26
+ attribute_map = {
27
+ "num_hidden_layers": "n_layer",
28
+ "num_attention_heads": "n_head",
29
+ }
30
+
31
+ def __init__(
32
+ self,
33
+ vocab_size=250880,
34
+ hidden_size=64,
35
+ n_layer=2,
36
+ n_head=8,
37
+ layer_norm_epsilon=1e-5,
38
+ initializer_range=0.02,
39
+ use_cache=True,
40
+ bos_token_id=1,
41
+ eos_token_id=2,
42
+ apply_residual_connection_post_layernorm=False,
43
+ hidden_dropout=0.0,
44
+ attention_dropout=0.0,
45
+ n_head_kv=None,
46
+ alibi=False,
47
+ **kwargs,
48
+ ):
49
+ self.vocab_size = vocab_size
50
+ # Backward compatibility with n_embed kwarg
51
+ n_embed = kwargs.pop("n_embed", None)
52
+ self.hidden_size = hidden_size if n_embed is None else n_embed
53
+ self.n_layer = n_layer
54
+ self.n_head = n_head
55
+ self.layer_norm_epsilon = layer_norm_epsilon
56
+ self.initializer_range = initializer_range
57
+ self.use_cache = use_cache
58
+ self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
59
+ self.hidden_dropout = hidden_dropout
60
+ self.attention_dropout = attention_dropout
61
+
62
+ self.bos_token_id = bos_token_id
63
+ self.eos_token_id = eos_token_id
64
+ self.n_head_kv = n_head if n_head_kv is None else n_head_kv
65
+ self.alibi = alibi
66
+
67
+ super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
68
+
69
+ @property
70
+ def head_dim(self):
71
+ return self.hidden_size // self.n_head
72
+
73
+ @property
74
+ def rotary(self):
75
+ return not self.alibi
generation_config.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "eos_token_id": 2,
5
+ "transformers_version": "4.27.4"
6
+ }
modelling_RW.py ADDED
@@ -0,0 +1,1106 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ (config.n_head_kv * 2 + config.n_head) * self.head_dim,
179
+ bias=config.bias,
180
+ )
181
+ self.dense = Linear(self.hidden_size, self.hidden_size, bias=config.bias)
182
+ self.attention_dropout = nn.Dropout(config.attention_dropout)
183
+ self.num_kv = config.n_head_kv
184
+
185
+ def _split_heads(self, fused_qkv: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
186
+ """
187
+ Split the last dimension into (num_heads, head_dim), results share same memory
188
+ storage as `fused_qkv`
189
+
190
+ Args:
191
+ fused_qkv (`torch.tensor`, *required*): [batch_size, seq_length, num_heads * 3 * head_dim]
192
+
193
+ Returns:
194
+ query: [batch_size, seq_length, num_heads, head_dim]
195
+ key: [batch_size, seq_length, num_heads, head_dim]
196
+ value: [batch_size, seq_length, num_heads, head_dim]
197
+ """
198
+ batch, seq_len, _ = fused_qkv.shape
199
+ qkv = fused_qkv.view(batch, seq_len, -1, self.num_heads // self.num_kv + 2, 64)
200
+ q = qkv[:, :, :, :-2]
201
+ k = qkv[:, :, :, [-2]]
202
+ v = qkv[:, :, :, [-1]]
203
+ k = torch.broadcast_to(k, q.shape)
204
+ v = torch.broadcast_to(v, q.shape)
205
+
206
+ q, k, v = [
207
+ rearrange(
208
+ x,
209
+ "batch seq_len group num_heads head_dim ->\
210
+ batch seq_len (group num_heads) head_dim",
211
+ head_dim=self.head_dim,
212
+ )
213
+ for x in [q, k, v]
214
+ ]
215
+ return q, k, v
216
+
217
+ def _merge_heads(self, x: torch.Tensor) -> torch.Tensor:
218
+ """
219
+ Merge heads together over the last dimenstion
220
+
221
+ Args:
222
+ x: (`torch.tensor`, *required*): [batch_size * num_heads, seq_length, head_dim]
223
+
224
+ Returns:
225
+ torch.tensor: [batch_size, seq_length, num_heads * head_dim]
226
+ """
227
+ # What we want to achieve is:
228
+ # batch_size * num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads * head_dim
229
+ batch_size_and_num_heads, seq_length, _ = x.shape
230
+ batch_size = batch_size_and_num_heads // self.num_heads
231
+
232
+ # First view to decompose the batch size
233
+ # batch_size * num_heads, seq_length, head_dim -> batch_size, num_heads, seq_length, head_dim
234
+ x = x.view(batch_size, self.num_heads, seq_length, self.head_dim)
235
+
236
+ # batch_size, num_heads, seq_length, head_dim -> batch_size, seq_length, num_heads, head_dim
237
+ x = x.permute(0, 2, 1, 3)
238
+
239
+ # batch_size, seq_length, num_heads, head_dim -> batch_size, seq_length, num_heads * head_dim
240
+ return x.reshape(batch_size, seq_length, self.num_heads * self.head_dim)
241
+
242
+ def forward(
243
+ self,
244
+ hidden_states: torch.Tensor,
245
+ alibi: torch.Tensor,
246
+ attention_mask: torch.Tensor,
247
+ layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
248
+ head_mask: Optional[torch.Tensor] = None,
249
+ use_cache: bool = False,
250
+ output_attentions: bool = False,
251
+ ):
252
+ fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
253
+
254
+ # 3 x [batch_size, seq_length, num_heads, head_dim]
255
+ (query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
256
+
257
+ batch_size, q_length, _, _ = query_layer.shape
258
+
259
+ query_layer = query_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
260
+ key_layer = key_layer.transpose(1, 2).reshape(
261
+ batch_size * self.num_heads,
262
+ q_length,
263
+ self.head_dim,
264
+ )
265
+ value_layer = value_layer.transpose(1, 2).reshape(batch_size * self.num_heads, q_length, self.head_dim)
266
+
267
+ query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
268
+
269
+ if layer_past is not None:
270
+ past_key, past_value = layer_past
271
+ # concatenate along seq_length dimension:
272
+ # - key: [batch_size * self.num_heads, head_dim, kv_length]
273
+ # - value: [batch_size * self.num_heads, kv_length, head_dim]
274
+ key_layer = torch.cat((past_key, key_layer), dim=1)
275
+ value_layer = torch.cat((past_value, value_layer), dim=1)
276
+
277
+ _, kv_length, _ = key_layer.shape
278
+
279
+ if use_cache is True:
280
+ present = (key_layer, value_layer)
281
+ else:
282
+ present = None
283
+
284
+ if alibi is None:
285
+ query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
286
+ key_layer_ = key_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
287
+ value_layer_ = value_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
288
+
289
+ attn_output = F.scaled_dot_product_attention(
290
+ query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
291
+ )
292
+
293
+ x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
294
+ x = x.permute(0, 2, 1, 3)
295
+ attn_output = x.reshape(batch_size, q_length, self.num_heads * self.head_dim)
296
+
297
+ output_tensor = self.dense(attn_output)
298
+
299
+ outputs = (output_tensor, present)
300
+ assert not output_attentions # not supported.
301
+ return outputs
302
+ else:
303
+ attention_mask_float = (attention_mask * 1.0).masked_fill(attention_mask, -1e9).to(torch.bfloat16)
304
+ matmul_result = query_layer @ key_layer.transpose(-1, -2)
305
+
306
+ # change view to [batch_size, num_heads, q_length, kv_length]
307
+ attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
308
+
309
+ # cast attention scores to fp32, compute scaled softmax and cast back to initial dtype - [batch_size, num_heads, q_length, kv_length]
310
+ input_dtype = attention_scores.dtype
311
+ # `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
312
+ if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
313
+ attention_scores = attention_scores.to(torch.float32)
314
+ # attn_weights = torch.masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
315
+ attention_probs = F.softmax(
316
+ (attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)) * self.inv_norm_factor
317
+ + attention_mask_float,
318
+ dim=-1,
319
+ dtype=hidden_states.dtype,
320
+ )
321
+ # [batch_size, num_heads, q_length, kv_length]
322
+ attention_probs = self.attention_dropout(attention_probs)
323
+
324
+ if head_mask is not None:
325
+ attention_probs = attention_probs * head_mask
326
+
327
+ # change view [batch_size x num_heads, q_length, kv_length]
328
+ attention_probs_reshaped = attention_probs.view(batch_size * self.num_heads, q_length, kv_length)
329
+
330
+ # matmul: [batch_size * num_heads, q_length, head_dim]
331
+ context_layer = attention_probs_reshaped @ value_layer
332
+
333
+ # change view [batch_size, num_heads, q_length, head_dim]
334
+ context_layer = self._merge_heads(context_layer)
335
+
336
+ output_tensor = self.dense(context_layer)
337
+
338
+ outputs = (output_tensor, present)
339
+ if output_attentions:
340
+ outputs += (attention_probs,)
341
+
342
+ return outputs
343
+
344
+
345
+ class MLP(nn.Module):
346
+ def __init__(self, config: RWConfig):
347
+ super().__init__()
348
+ hidden_size = config.hidden_size
349
+
350
+ self.dense_h_to_4h = Linear(hidden_size, 4 * hidden_size, bias=config.bias)
351
+ self.act = nn.GELU()
352
+ self.dense_4h_to_h = Linear(4 * hidden_size, hidden_size, bias=config.bias)
353
+ self.hidden_dropout = config.hidden_dropout
354
+
355
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
356
+ x = self.act(self.dense_h_to_4h(x))
357
+ x = self.dense_4h_to_h(x)
358
+ return x
359
+
360
+
361
+ class DecoderLayer(nn.Module):
362
+ def __init__(self, config: RWConfig):
363
+ super().__init__()
364
+ hidden_size = config.hidden_size
365
+
366
+ self.ln_attn = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
367
+ self.ln_mlp = LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
368
+
369
+ self.num_heads = config.n_head
370
+ self.self_attention = Attention(config)
371
+
372
+ self.mlp = MLP(config)
373
+
374
+ self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
375
+ self.hidden_dropout = config.hidden_dropout
376
+
377
+ self.config = config
378
+
379
+ def forward(
380
+ self,
381
+ hidden_states: torch.Tensor,
382
+ alibi: torch.Tensor,
383
+ attention_mask: torch.Tensor,
384
+ layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
385
+ head_mask: Optional[torch.Tensor] = None,
386
+ use_cache: bool = False,
387
+ output_attentions: bool = False,
388
+ ):
389
+
390
+ ln_attn = self.ln_attn(hidden_states)
391
+ ln_mlp = self.ln_mlp(hidden_states)
392
+
393
+ residual = hidden_states
394
+
395
+ # Self attention.
396
+ attn_outputs = self.self_attention(
397
+ ln_attn,
398
+ layer_past=layer_past,
399
+ attention_mask=attention_mask,
400
+ alibi=alibi,
401
+ head_mask=head_mask,
402
+ use_cache=use_cache,
403
+ output_attentions=output_attentions,
404
+ )
405
+
406
+ attention_output = attn_outputs[0]
407
+
408
+ outputs = attn_outputs[1:]
409
+
410
+ # MLP.
411
+ mlp_output = self.mlp(ln_mlp)
412
+
413
+ output = dropout_add(
414
+ mlp_output + attention_output, residual, self.config.hidden_dropout, training=self.training
415
+ )
416
+
417
+ if use_cache:
418
+ outputs = (output,) + outputs
419
+ else:
420
+ outputs = (output,) + outputs[1:]
421
+
422
+ return outputs # hidden_states, present, attentions
423
+
424
+
425
+ class RWPreTrainedModel(PreTrainedModel):
426
+ _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
427
+ """
428
+ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
429
+ models.
430
+ """
431
+
432
+ config_class = RWConfig
433
+ base_model_prefix = "transformer"
434
+ supports_gradient_checkpointing = True
435
+ _no_split_modules = ["DecoderLayer"]
436
+
437
+ def __init__(self, *inputs, **kwargs):
438
+ super().__init__(*inputs, **kwargs)
439
+
440
+ def _init_weights(self, module: nn.Module):
441
+ """Initialize the weights."""
442
+ if isinstance(module, nn.Linear) or isinstance(module, Linear):
443
+ # Slightly different from the TF version which uses truncated_normal for initialization
444
+ # cf https://github.com/pytorch/pytorch/pull/5617
445
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
446
+ if module.bias is not None:
447
+ module.bias.data.zero_()
448
+ elif isinstance(module, nn.Embedding):
449
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
450
+ if module.padding_idx is not None:
451
+ module.weight.data[module.padding_idx].zero_()
452
+ elif isinstance(module, LayerNorm):
453
+ module.bias.data.zero_()
454
+ module.weight.data.fill_(1.0)
455
+
456
+ def _set_gradient_checkpointing(self, module: nn.Module, value: bool = False):
457
+ if isinstance(module, RWModel):
458
+ module.gradient_checkpointing = value
459
+
460
+ @staticmethod
461
+ def _convert_to_standard_cache(
462
+ past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
463
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
464
+ """
465
+ Standardizes the format of the cache so as to match most implementations, i.e. to tuple(tuple([batch_size,
466
+ num_heads, ...]))
467
+ """
468
+ batch_size_times_num_heads, head_dim, seq_length = past_key_value[0][0].shape
469
+ num_heads = batch_size_times_num_heads // batch_size
470
+ # key: [batch_size * num_heads, head_dim, seq_length] -> [batch_size, num_heads, head_dim, seq_length]
471
+ # value: [batch_size * num_heads, seq_length, head_dim] -> [batch_size, num_heads, seq_length, head_dim]
472
+ return tuple(
473
+ (
474
+ layer_past[0].view(batch_size, num_heads, head_dim, seq_length),
475
+ layer_past[1].view(batch_size, num_heads, seq_length, head_dim),
476
+ )
477
+ for layer_past in past_key_value
478
+ )
479
+
480
+ @staticmethod
481
+ def _convert_to_rw_cache(
482
+ past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]]
483
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
484
+ batch_size, num_heads, head_dim, seq_length = past_key_value[0][0].shape
485
+ batch_size_times_num_heads = batch_size * num_heads
486
+ # key: [batch_size, num_heads, head_dim, seq_length] -> [batch_size * num_heads, head_dim, seq_length]
487
+ # value: [batch_size, num_heads, seq_length, head_dim] -> [batch_size * num_heads, seq_length, head_dim]
488
+ return tuple(
489
+ (
490
+ layer_past[0].view(batch_size_times_num_heads, head_dim, seq_length),
491
+ layer_past[1].view(batch_size_times_num_heads, seq_length, head_dim),
492
+ )
493
+ for layer_past in past_key_value
494
+ )
495
+
496
+
497
+ class RWModel(RWPreTrainedModel):
498
+ def __init__(self, config: RWConfig):
499
+ super().__init__(config)
500
+
501
+ self.embed_dim = config.hidden_size
502
+ self.num_heads = config.n_head
503
+ self.alibi = config.alibi
504
+
505
+ # Embedding + LN Embedding
506
+ self.word_embeddings = nn.Embedding(config.vocab_size, self.embed_dim)
507
+
508
+ # Transformer blocks
509
+ self.h = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
510
+
511
+ # Final Layer Norm
512
+ self.ln_f = LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)
513
+
514
+ self.gradient_checkpointing = False
515
+
516
+ # Initialize weights and apply final processing
517
+ self.post_init()
518
+
519
+ def get_input_embeddings(self):
520
+ return self.word_embeddings
521
+
522
+ def _prepare_attn_mask(
523
+ self, attention_mask: torch.Tensor, input_shape: Tuple[int, int], past_key_values_length: int
524
+ ) -> torch.BoolTensor:
525
+ # create causal mask
526
+ # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
527
+ combined_attention_mask = None
528
+ device = attention_mask.device
529
+ _, src_length = input_shape
530
+
531
+ if src_length > 1:
532
+ combined_attention_mask = _make_causal_mask(
533
+ input_shape, device=device, past_key_values_length=past_key_values_length
534
+ )
535
+
536
+ # [batch_size, seq_length] -> [batch_size, 1, tgt_length, src_length]
537
+ expanded_attn_mask = _expand_mask(attention_mask, tgt_length=src_length)
538
+ combined_attention_mask = (
539
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
540
+ )
541
+
542
+ return combined_attention_mask
543
+
544
+ def set_input_embeddings(self, new_embeddings: torch.Tensor):
545
+ self.word_embeddings = new_embeddings
546
+
547
+ def forward(
548
+ self,
549
+ input_ids: Optional[torch.LongTensor] = None,
550
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
551
+ attention_mask: Optional[torch.Tensor] = None,
552
+ head_mask: Optional[torch.LongTensor] = None,
553
+ inputs_embeds: Optional[torch.LongTensor] = None,
554
+ use_cache: Optional[bool] = None,
555
+ output_attentions: Optional[bool] = None,
556
+ output_hidden_states: Optional[bool] = None,
557
+ return_dict: Optional[bool] = None,
558
+ **deprecated_arguments,
559
+ ) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
560
+ if deprecated_arguments.pop("position_ids", False) is not False:
561
+ # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
562
+ warnings.warn(
563
+ "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
564
+ " passing `position_ids`.",
565
+ FutureWarning,
566
+ )
567
+ if len(deprecated_arguments) > 0:
568
+ raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
569
+
570
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
571
+ output_hidden_states = (
572
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
573
+ )
574
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
575
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
576
+
577
+ if input_ids is not None and inputs_embeds is not None:
578
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
579
+ elif input_ids is not None:
580
+ batch_size, seq_length = input_ids.shape
581
+ elif inputs_embeds is not None:
582
+ batch_size, seq_length, _ = inputs_embeds.shape
583
+ else:
584
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
585
+
586
+ if past_key_values is None:
587
+ past_key_values = tuple([None] * len(self.h))
588
+
589
+ # Prepare head mask if needed
590
+ # 1.0 in head_mask indicate we keep the head
591
+ # attention_probs has shape batch_size x num_heads x N x N
592
+ # head_mask has shape n_layer x batch x num_heads x N x N
593
+ head_mask = self.get_head_mask(head_mask, self.config.n_layer)
594
+
595
+ if inputs_embeds is None:
596
+ inputs_embeds = self.word_embeddings(input_ids)
597
+
598
+ hidden_states = inputs_embeds
599
+
600
+ presents = () if use_cache else None
601
+ all_self_attentions = () if output_attentions else None
602
+ all_hidden_states = () if output_hidden_states else None
603
+
604
+ # Compute alibi tensor: check build_alibi_tensor documentation
605
+ seq_length_with_past = seq_length
606
+ past_key_values_length = 0
607
+ if past_key_values[0] is not None:
608
+ past_key_values_length = past_key_values[0][0].shape[2]
609
+ seq_length_with_past = seq_length_with_past + past_key_values_length
610
+ if attention_mask is None:
611
+ attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
612
+ else:
613
+ attention_mask = attention_mask.to(hidden_states.device)
614
+
615
+ if self.alibi:
616
+ alibi = build_alibi_tensor(attention_mask, self.num_heads, dtype=hidden_states.dtype)
617
+ else:
618
+ alibi = None
619
+
620
+ causal_mask = self._prepare_attn_mask(
621
+ attention_mask,
622
+ input_shape=(batch_size, seq_length),
623
+ past_key_values_length=past_key_values_length,
624
+ )
625
+
626
+ for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
627
+
628
+ if output_hidden_states:
629
+ all_hidden_states = all_hidden_states + (hidden_states,)
630
+
631
+ if self.gradient_checkpointing and self.training:
632
+
633
+ if use_cache:
634
+ logger.warning(
635
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
636
+ )
637
+ use_cache = False
638
+
639
+ def create_custom_forward(module):
640
+ def custom_forward(*inputs):
641
+ # None for past_key_value
642
+ return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
643
+
644
+ return custom_forward
645
+
646
+ outputs = torch.utils.checkpoint.checkpoint(
647
+ create_custom_forward(block),
648
+ hidden_states,
649
+ alibi,
650
+ causal_mask,
651
+ head_mask[i],
652
+ )
653
+ else:
654
+ outputs = block(
655
+ hidden_states,
656
+ layer_past=layer_past,
657
+ attention_mask=causal_mask,
658
+ head_mask=head_mask[i],
659
+ use_cache=use_cache,
660
+ output_attentions=output_attentions,
661
+ alibi=alibi,
662
+ )
663
+
664
+ hidden_states = outputs[0]
665
+ if use_cache is True:
666
+ presents = presents + (outputs[1],)
667
+
668
+ if output_attentions:
669
+ all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
670
+
671
+ # Add last hidden state
672
+ hidden_states = self.ln_f(hidden_states)
673
+
674
+ if output_hidden_states:
675
+ all_hidden_states = all_hidden_states + (hidden_states,)
676
+
677
+ if not return_dict:
678
+ return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
679
+
680
+ return BaseModelOutputWithPastAndCrossAttentions(
681
+ last_hidden_state=hidden_states,
682
+ past_key_values=presents,
683
+ hidden_states=all_hidden_states,
684
+ attentions=all_self_attentions,
685
+ )
686
+
687
+
688
+ class RWForCausalLM(RWPreTrainedModel):
689
+ _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
690
+
691
+ def __init__(self, config: RWConfig):
692
+ super().__init__(config)
693
+ self.transformer = RWModel(config)
694
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
695
+
696
+ # Initialize weights and apply final processing
697
+ self.post_init()
698
+
699
+ def get_output_embeddings(self):
700
+ return self.lm_head
701
+
702
+ def set_output_embeddings(self, new_embeddings: torch.Tensor):
703
+ self.lm_head = new_embeddings
704
+
705
+ def prepare_inputs_for_generation(
706
+ self,
707
+ input_ids: torch.LongTensor,
708
+ past: Optional[torch.Tensor] = None,
709
+ attention_mask: Optional[torch.Tensor] = None,
710
+ **kwargs,
711
+ ) -> dict:
712
+ # only last token for input_ids if past is not None
713
+ if past:
714
+ input_ids = input_ids[:, -1].unsqueeze(-1)
715
+
716
+ # the cache may be in the stardard format (e.g. in contrastive search), convert to our's format if needed
717
+ if past[0][0].shape[0] == input_ids.shape[0]:
718
+ past = self._convert_to_rw_cache(past)
719
+
720
+ return {
721
+ "input_ids": input_ids,
722
+ "past_key_values": past,
723
+ "use_cache": kwargs.get("use_cache"),
724
+ "attention_mask": attention_mask,
725
+ }
726
+
727
+ def forward(
728
+ self,
729
+ input_ids: Optional[torch.LongTensor] = None,
730
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
731
+ attention_mask: Optional[torch.Tensor] = None,
732
+ head_mask: Optional[torch.Tensor] = None,
733
+ inputs_embeds: Optional[torch.Tensor] = None,
734
+ labels: Optional[torch.Tensor] = None,
735
+ use_cache: Optional[bool] = None,
736
+ output_attentions: Optional[bool] = None,
737
+ output_hidden_states: Optional[bool] = None,
738
+ return_dict: Optional[bool] = None,
739
+ **deprecated_arguments,
740
+ ) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
741
+ r"""
742
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
743
+ Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
744
+ `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100`
745
+ are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]`
746
+ """
747
+ if deprecated_arguments.pop("position_ids", False) is not False:
748
+ # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
749
+ warnings.warn(
750
+ "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
751
+ " passing `position_ids`.",
752
+ FutureWarning,
753
+ )
754
+ if len(deprecated_arguments) > 0:
755
+ raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
756
+
757
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
758
+
759
+ transformer_outputs = self.transformer(
760
+ input_ids,
761
+ past_key_values=past_key_values,
762
+ attention_mask=attention_mask,
763
+ head_mask=head_mask,
764
+ inputs_embeds=inputs_embeds,
765
+ use_cache=use_cache,
766
+ output_attentions=output_attentions,
767
+ output_hidden_states=output_hidden_states,
768
+ return_dict=return_dict,
769
+ )
770
+ hidden_states = transformer_outputs[0]
771
+
772
+ lm_logits = self.lm_head(hidden_states)
773
+
774
+ loss = None
775
+ if labels is not None:
776
+ # Shift so that tokens < n predict n
777
+ shift_logits = lm_logits[..., :-1, :].contiguous()
778
+ shift_labels = labels[..., 1:].contiguous()
779
+ batch_size, seq_length, vocab_size = shift_logits.shape
780
+ # Flatten the tokens
781
+ loss_fct = CrossEntropyLoss()
782
+ loss = loss_fct(
783
+ shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length)
784
+ )
785
+
786
+ if not return_dict:
787
+ output = (lm_logits,) + transformer_outputs[1:]
788
+ return ((loss,) + output) if loss is not None else output
789
+
790
+ return CausalLMOutputWithCrossAttentions(
791
+ loss=loss,
792
+ logits=lm_logits,
793
+ past_key_values=transformer_outputs.past_key_values,
794
+ hidden_states=transformer_outputs.hidden_states,
795
+ attentions=transformer_outputs.attentions,
796
+ )
797
+
798
+ def _reorder_cache(
799
+ self, past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
800
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
801
+ """
802
+ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
803
+ [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
804
+ beam_idx at every generation step.
805
+
806
+ Output shares the same memory storage as `past`.
807
+ """
808
+ standardized_past = self._convert_to_standard_cache(past, batch_size=len(beam_idx))
809
+
810
+ # Get a copy of `beam_idx` on all the devices where we need those indices.
811
+ device_to_beam_idx = {
812
+ past_state.device: beam_idx.to(past_state.device) for layer_past in past for past_state in layer_past
813
+ }
814
+ reordered_past = tuple(
815
+ (
816
+ layer_past[0].index_select(0, device_to_beam_idx[layer_past[0].device]),
817
+ layer_past[1].index_select(0, device_to_beam_idx[layer_past[0].device]),
818
+ )
819
+ for layer_past in standardized_past
820
+ )
821
+ return self._convert_to_rw_cache(reordered_past)
822
+
823
+
824
+ class RWForSequenceClassification(RWPreTrainedModel):
825
+ _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
826
+
827
+ def __init__(self, config: RWConfig):
828
+ super().__init__(config)
829
+ self.num_labels = config.num_labels
830
+ self.transformer = RWModel(config)
831
+ self.score = nn.Linear(config.hidden_size, config.num_labels, bias=False)
832
+
833
+ # Initialize weights and apply final processing
834
+ self.post_init()
835
+
836
+ def forward(
837
+ self,
838
+ input_ids: Optional[torch.LongTensor] = None,
839
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
840
+ attention_mask: Optional[torch.Tensor] = None,
841
+ head_mask: Optional[torch.Tensor] = None,
842
+ inputs_embeds: Optional[torch.Tensor] = None,
843
+ labels: Optional[torch.Tensor] = None,
844
+ use_cache: Optional[bool] = None,
845
+ output_attentions: Optional[bool] = None,
846
+ output_hidden_states: Optional[bool] = None,
847
+ return_dict: Optional[bool] = None,
848
+ **deprecated_arguments,
849
+ ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]:
850
+ r"""
851
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
852
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
853
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
854
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
855
+ """
856
+ if deprecated_arguments.pop("position_ids", False) is not False:
857
+ # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
858
+ warnings.warn(
859
+ "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
860
+ " passing `position_ids`.",
861
+ FutureWarning,
862
+ )
863
+ if len(deprecated_arguments) > 0:
864
+ raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
865
+
866
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
867
+
868
+ transformer_outputs = self.transformer(
869
+ input_ids,
870
+ past_key_values=past_key_values,
871
+ attention_mask=attention_mask,
872
+ head_mask=head_mask,
873
+ inputs_embeds=inputs_embeds,
874
+ use_cache=use_cache,
875
+ output_attentions=output_attentions,
876
+ output_hidden_states=output_hidden_states,
877
+ return_dict=return_dict,
878
+ )
879
+
880
+ hidden_states = transformer_outputs[0]
881
+ logits = self.score(hidden_states)
882
+
883
+ if input_ids is not None:
884
+ batch_size = input_ids.shape[0]
885
+ else:
886
+ batch_size = inputs_embeds.shape[0]
887
+
888
+ if self.config.pad_token_id is None and batch_size != 1:
889
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
890
+ if self.config.pad_token_id is None:
891
+ sequence_lengths = -1
892
+ else:
893
+ if input_ids is not None:
894
+ sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(dim=-1) - 1
895
+ else:
896
+ sequence_lengths = -1
897
+ logger.warning(
898
+ f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be "
899
+ "unexpected if using padding tokens in conjunction with `inputs_embeds.`"
900
+ )
901
+
902
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
903
+
904
+ loss = None
905
+ if labels is not None:
906
+ if self.config.problem_type is None:
907
+ if self.num_labels == 1:
908
+ self.config.problem_type = "regression"
909
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
910
+ self.config.problem_type = "single_label_classification"
911
+ else:
912
+ self.config.problem_type = "multi_label_classification"
913
+
914
+ if self.config.problem_type == "regression":
915
+ loss_fct = MSELoss()
916
+ if self.num_labels == 1:
917
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
918
+ else:
919
+ loss = loss_fct(pooled_logits, labels)
920
+ elif self.config.problem_type == "single_label_classification":
921
+ loss_fct = CrossEntropyLoss()
922
+ loss = loss_fct(pooled_logits, labels)
923
+ elif self.config.problem_type == "multi_label_classification":
924
+ loss_fct = BCEWithLogitsLoss()
925
+ loss = loss_fct(pooled_logits, labels)
926
+ if not return_dict:
927
+ output = (pooled_logits,) + transformer_outputs[1:]
928
+ return ((loss,) + output) if loss is not None else output
929
+
930
+ return SequenceClassifierOutputWithPast(
931
+ loss=loss,
932
+ logits=pooled_logits,
933
+ past_key_values=transformer_outputs.past_key_values,
934
+ hidden_states=transformer_outputs.hidden_states,
935
+ attentions=transformer_outputs.attentions,
936
+ )
937
+
938
+
939
+ class RWForTokenClassification(RWPreTrainedModel):
940
+ _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
941
+
942
+ def __init__(self, config: RWConfig):
943
+ super().__init__(config)
944
+ self.num_labels = config.num_labels
945
+
946
+ self.transformer = RWModel(config)
947
+ if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
948
+ classifier_dropout = config.classifier_dropout
949
+ elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
950
+ classifier_dropout = config.hidden_dropout
951
+ else:
952
+ classifier_dropout = 0.1
953
+ self.dropout = nn.Dropout(classifier_dropout)
954
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
955
+
956
+ # Initialize weights and apply final processing
957
+ self.post_init()
958
+
959
+ def forward(
960
+ self,
961
+ input_ids: Optional[torch.LongTensor] = None,
962
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
963
+ attention_mask: Optional[torch.Tensor] = None,
964
+ head_mask: Optional[torch.Tensor] = None,
965
+ inputs_embeds: Optional[torch.Tensor] = None,
966
+ labels: Optional[torch.Tensor] = None,
967
+ use_cache: Optional[bool] = None,
968
+ output_attentions: Optional[bool] = None,
969
+ output_hidden_states: Optional[bool] = None,
970
+ return_dict: Optional[bool] = None,
971
+ **deprecated_arguments,
972
+ ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
973
+ r"""
974
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
975
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
976
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
977
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
978
+ """
979
+ if deprecated_arguments.pop("position_ids", False) is not False:
980
+ # `position_ids` could have been `torch.Tensor` or `None` so defaulting pop to `False` allows to detect if users were passing explicitly `None`
981
+ warnings.warn(
982
+ "`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. You can safely ignore"
983
+ " passing `position_ids`.",
984
+ FutureWarning,
985
+ )
986
+ if len(deprecated_arguments) > 0:
987
+ raise ValueError(f"Got unexpected arguments: {deprecated_arguments}")
988
+
989
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
990
+
991
+ transformer_outputs = self.transformer(
992
+ input_ids,
993
+ past_key_values=past_key_values,
994
+ attention_mask=attention_mask,
995
+ head_mask=head_mask,
996
+ inputs_embeds=inputs_embeds,
997
+ use_cache=use_cache,
998
+ output_attentions=output_attentions,
999
+ output_hidden_states=output_hidden_states,
1000
+ return_dict=return_dict,
1001
+ )
1002
+
1003
+ hidden_states = transformer_outputs[0]
1004
+ hidden_states = self.dropout(hidden_states)
1005
+ logits = self.classifier(hidden_states)
1006
+
1007
+ loss = None
1008
+ if labels is not None:
1009
+ batch_size, seq_length = labels.shape
1010
+ loss_fct = CrossEntropyLoss()
1011
+ loss = loss_fct(logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length))
1012
+
1013
+ if not return_dict:
1014
+ output = (logits,) + transformer_outputs[2:]
1015
+ return ((loss,) + output) if loss is not None else output
1016
+
1017
+ return TokenClassifierOutput(
1018
+ loss=loss,
1019
+ logits=logits,
1020
+ hidden_states=transformer_outputs.hidden_states,
1021
+ attentions=transformer_outputs.attentions,
1022
+ )
1023
+
1024
+
1025
+ class RWForQuestionAnswering(RWPreTrainedModel):
1026
+ _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"]
1027
+
1028
+ def __init__(self, config):
1029
+ super().__init__(config)
1030
+ self.transformer = RWModel(config)
1031
+ self.qa_outputs = nn.Linear(config.hidden_size, 2)
1032
+
1033
+ # Initialize weights and apply final processing
1034
+ self.post_init()
1035
+
1036
+ def forward(
1037
+ self,
1038
+ input_ids: Optional[torch.LongTensor] = None,
1039
+ attention_mask: Optional[torch.FloatTensor] = None,
1040
+ position_ids: Optional[torch.LongTensor] = None,
1041
+ head_mask: Optional[torch.FloatTensor] = None,
1042
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1043
+ start_positions: Optional[torch.LongTensor] = None,
1044
+ end_positions: Optional[torch.LongTensor] = None,
1045
+ output_attentions: Optional[bool] = None,
1046
+ output_hidden_states: Optional[bool] = None,
1047
+ return_dict: Optional[bool] = None,
1048
+ ) -> Union[Tuple, QuestionAnsweringModelOutput]:
1049
+ r"""
1050
+ start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1051
+ Labels for position (index) of the start of the labelled span for computing the token classification loss.
1052
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1053
+ are not taken into account for computing the loss.
1054
+ end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1055
+ Labels for position (index) of the end of the labelled span for computing the token classification loss.
1056
+ Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence
1057
+ are not taken into account for computing the loss.
1058
+ """
1059
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1060
+
1061
+ outputs = self.transformer(
1062
+ input_ids,
1063
+ attention_mask=attention_mask,
1064
+ position_ids=position_ids,
1065
+ head_mask=head_mask,
1066
+ inputs_embeds=inputs_embeds,
1067
+ output_attentions=output_attentions,
1068
+ output_hidden_states=output_hidden_states,
1069
+ return_dict=return_dict,
1070
+ )
1071
+
1072
+ sequence_output = outputs[0]
1073
+
1074
+ logits = self.qa_outputs(sequence_output)
1075
+ start_logits, end_logits = logits.split(1, dim=-1)
1076
+ start_logits = start_logits.squeeze(-1).contiguous()
1077
+ end_logits = end_logits.squeeze(-1).contiguous()
1078
+
1079
+ total_loss = None
1080
+ if start_positions is not None and end_positions is not None:
1081
+ # If we are on multi-GPU, split add a dimension
1082
+ if len(start_positions.size()) > 1:
1083
+ start_positions = start_positions.squeeze(-1)
1084
+ if len(end_positions.size()) > 1:
1085
+ end_positions = end_positions.squeeze(-1)
1086
+ # sometimes the start/end positions are outside our model inputs, we ignore these terms
1087
+ ignored_index = start_logits.size(1)
1088
+ start_positions = start_positions.clamp(0, ignored_index)
1089
+ end_positions = end_positions.clamp(0, ignored_index)
1090
+
1091
+ loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
1092
+ start_loss = loss_fct(start_logits, start_positions)
1093
+ end_loss = loss_fct(end_logits, end_positions)
1094
+ total_loss = (start_loss + end_loss) / 2
1095
+
1096
+ if not return_dict:
1097
+ output = (start_logits, end_logits) + outputs[2:]
1098
+ return ((total_loss,) + output) if total_loss is not None else output
1099
+
1100
+ return QuestionAnsweringModelOutput(
1101
+ loss=total_loss,
1102
+ start_logits=start_logits,
1103
+ end_logits=end_logits,
1104
+ hidden_states=outputs.hidden_states,
1105
+ attentions=outputs.attentions,
1106
+ )
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