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1
+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ PyTorch MiniCPM model."""
21
+ import math
22
+ import warnings
23
+ from typing import List, Optional, Tuple, Union, Dict
24
+
25
+ import torch
26
+ import torch.nn.functional as F
27
+ import torch.utils.checkpoint
28
+ from torch import nn
29
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
30
+
31
+ from transformers.activations import ACT2FN
32
+ from transformers.cache_utils import Cache, DynamicCache
33
+ from transformers.modeling_attn_mask_utils import (
34
+ AttentionMaskConverter,
35
+ _prepare_4d_attention_mask,
36
+ _prepare_4d_causal_attention_mask,
37
+ _prepare_4d_causal_attention_mask_for_sdpa,
38
+ )
39
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
40
+ from transformers.modeling_utils import PreTrainedModel
41
+ from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
42
+ from transformers.utils import (
43
+ add_start_docstrings,
44
+ add_start_docstrings_to_model_forward,
45
+ is_flash_attn_2_available,
46
+ is_flash_attn_greater_or_equal_2_10,
47
+ logging,
48
+ replace_return_docstrings,
49
+ )
50
+ from transformers.utils.import_utils import is_torch_fx_available
51
+ from .configuration_minicpm import MiniCPMConfig
52
+ import re
53
+
54
+ try:
55
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
56
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
57
+ except:
58
+ pass
59
+
60
+
61
+ # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
62
+ # It means that the function will not be traced through and simply appear as a node in the graph.
63
+ if is_torch_fx_available():
64
+ if not is_torch_greater_or_equal_than_1_13:
65
+ import torch.fx
66
+
67
+ _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
68
+
69
+
70
+ logger = logging.get_logger(__name__)
71
+
72
+ _CONFIG_FOR_DOC = "MiniCPMConfig"
73
+
74
+
75
+ def _get_unpad_data(attention_mask):
76
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
77
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
78
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
79
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
80
+ return (
81
+ indices,
82
+ cu_seqlens,
83
+ max_seqlen_in_batch,
84
+ )
85
+
86
+
87
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
88
+ warnings.warn(
89
+ "Calling `transformers.models.minicpm.modeling_minicpm._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
90
+ )
91
+ return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
92
+
93
+
94
+ def _make_causal_mask(
95
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
96
+ ):
97
+ warnings.warn(
98
+ "Calling `transformers.models.minicpm.modeling_minicpm._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.minicpm.modeling_minicpm.AttentionMaskConverter._make_causal_mask"
99
+ )
100
+ return AttentionMaskConverter._make_causal_mask(
101
+ input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
102
+ )
103
+
104
+ # @torch.jit.script # type: ignore
105
+ def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):
106
+ old_dtype = hidden.dtype
107
+ variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
108
+ hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)
109
+ return hidden * weight
110
+
111
+
112
+ class MiniCPMRMSNorm(nn.Module):
113
+ def __init__(self, hidden_size, eps=1e-6):
114
+ """
115
+ MiniCPMRMSNorm is equivalent to T5LayerNorm
116
+ """
117
+ super().__init__()
118
+ self.weight = nn.Parameter(torch.ones(hidden_size))
119
+ self.variance_epsilon = eps
120
+
121
+ def forward(self, hidden_states):
122
+ return rms_layernorm(hidden_states, self.weight, self.variance_epsilon)
123
+
124
+
125
+ ALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm)
126
+
127
+
128
+ class MiniCPMRotaryEmbedding(nn.Module):
129
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
130
+ super().__init__()
131
+
132
+ self.dim = dim
133
+ self.max_position_embeddings = max_position_embeddings
134
+ self.base = base
135
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
136
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
137
+
138
+ # Build here to make `torch.jit.trace` work.
139
+ self._set_cos_sin_cache(
140
+ # seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
141
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32
142
+ )
143
+
144
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
145
+ self.max_seq_len_cached = seq_len
146
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
147
+ freqs = torch.outer(t, self.inv_freq)
148
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
149
+ emb = torch.cat((freqs, freqs), dim=-1)
150
+
151
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
152
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
153
+
154
+ def forward(self, x, seq_len=None):
155
+ # x: [bs, num_attention_heads, seq_len, head_size]
156
+ if seq_len > self.max_seq_len_cached:
157
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
158
+
159
+ return (
160
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
161
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
162
+ )
163
+
164
+
165
+ class MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
166
+ """MiniCPMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
167
+
168
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
169
+ self.scaling_factor = scaling_factor
170
+ super().__init__(dim, max_position_embeddings, base, device)
171
+
172
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
173
+ self.max_seq_len_cached = seq_len
174
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
175
+ t = t / self.scaling_factor
176
+
177
+ freqs = torch.outer(t, self.inv_freq)
178
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
179
+ emb = torch.cat((freqs, freqs), dim=-1)
180
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
181
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
182
+
183
+
184
+ class MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
185
+ """MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
186
+
187
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
188
+ self.scaling_factor = scaling_factor
189
+ super().__init__(dim, max_position_embeddings, base, device)
190
+
191
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
192
+ self.max_seq_len_cached = seq_len
193
+
194
+ if seq_len > self.max_position_embeddings:
195
+ base = self.base * (
196
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
197
+ ) ** (self.dim / (self.dim - 2))
198
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
199
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
200
+
201
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
202
+
203
+ freqs = torch.outer(t, self.inv_freq)
204
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
205
+ emb = torch.cat((freqs, freqs), dim=-1)
206
+
207
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
208
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
209
+
210
+
211
+ def rotate_half(x):
212
+ """Rotates half the hidden dims of the input."""
213
+ x1 = x[..., : x.shape[-1] // 2]
214
+ x2 = x[..., x.shape[-1] // 2 :]
215
+ return torch.cat((-x2, x1), dim=-1)
216
+
217
+
218
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
219
+ """Applies Rotary Position Embedding to the query and key tensors.
220
+
221
+ Args:
222
+ q (`torch.Tensor`): The query tensor.
223
+ k (`torch.Tensor`): The key tensor.
224
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
225
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
226
+ position_ids (`torch.Tensor`):
227
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
228
+ used to pass offsetted position ids when working with a KV-cache.
229
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
230
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
231
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
232
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
233
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
234
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
235
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
236
+ Returns:
237
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
238
+ """
239
+ # cos = cos[position_ids].unsqueeze(unsqueeze_dim)
240
+ # sin = sin[position_ids].unsqueeze(unsqueeze_dim)
241
+ # q_embed = (q * cos) + (rotate_half(q) * sin)
242
+ # k_embed = (k * cos) + (rotate_half(k) * sin)
243
+ orig_dtype = k.dtype
244
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]
245
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim) # [bs, 1, seq_len, dim]
246
+ q_fp32 = q.to(dtype=torch.float32, device=q.device)
247
+ k_fp32 = k.to(dtype=torch.float32, device=k.device)
248
+ q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin)
249
+ k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin)
250
+ return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype)
251
+
252
+ class MiniCPMMLP(nn.Module):
253
+ def __init__(self, config):
254
+ super().__init__()
255
+ self.config = config
256
+ self.hidden_size = config.hidden_size
257
+ self.intermediate_size = config.intermediate_size
258
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
259
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
260
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
261
+ self.act_fn = ACT2FN[config.hidden_act]
262
+
263
+ def forward(self, x):
264
+ if self.config.pretraining_tp > 1:
265
+ slice = self.intermediate_size // self.config.pretraining_tp
266
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
267
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
268
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
269
+
270
+ gate_proj = torch.cat(
271
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
272
+ )
273
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
274
+
275
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
276
+ down_proj = [
277
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
278
+ ]
279
+ down_proj = sum(down_proj)
280
+ else:
281
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
282
+
283
+ return down_proj
284
+
285
+
286
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
287
+ """
288
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
289
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
290
+ """
291
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
292
+ if n_rep == 1:
293
+ return hidden_states
294
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
295
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
296
+
297
+
298
+
299
+ class MiniCPMAttention(nn.Module):
300
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
301
+
302
+ def __init__(self, config: MiniCPMConfig, layer_idx: Optional[int] = None):
303
+ super().__init__()
304
+ self.config = config
305
+ self.layer_idx = layer_idx
306
+ if layer_idx is None:
307
+ logger.warning_once(
308
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
309
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
310
+ "when creating this class."
311
+ )
312
+
313
+ self.attention_dropout = config.attention_dropout
314
+ self.hidden_size = config.hidden_size
315
+ self.num_heads = config.num_attention_heads
316
+ self.head_dim = self.hidden_size // self.num_heads
317
+ self.num_key_value_heads = config.num_key_value_heads
318
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
319
+ self.max_position_embeddings = config.max_position_embeddings
320
+ self.rope_theta = config.rope_theta
321
+ self.is_causal = True
322
+
323
+ if (self.head_dim * self.num_heads) != self.hidden_size:
324
+ raise ValueError(
325
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
326
+ f" and `num_heads`: {self.num_heads})."
327
+ )
328
+
329
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
330
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
331
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
332
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
333
+ self._init_rope()
334
+
335
+ def _init_rope(self):
336
+ if self.config.rope_scaling is None:
337
+ self.rotary_emb = MiniCPMRotaryEmbedding(
338
+ self.head_dim,
339
+ max_position_embeddings=self.max_position_embeddings,
340
+ base=self.rope_theta,
341
+ )
342
+ else:
343
+ scaling_type = self.config.rope_scaling["type"]
344
+ scaling_factor = self.config.rope_scaling["factor"]
345
+ if scaling_type == "linear":
346
+ self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding(
347
+ self.head_dim,
348
+ max_position_embeddings=self.max_position_embeddings,
349
+ scaling_factor=scaling_factor,
350
+ base=self.rope_theta,
351
+ )
352
+ elif scaling_type == "dynamic":
353
+ self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding(
354
+ self.head_dim,
355
+ max_position_embeddings=self.max_position_embeddings,
356
+ scaling_factor=scaling_factor,
357
+ base=self.rope_theta,
358
+ )
359
+ else:
360
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
361
+
362
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
363
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
364
+
365
+ def forward(
366
+ self,
367
+ hidden_states: torch.Tensor,
368
+ attention_mask: Optional[torch.Tensor] = None,
369
+ position_ids: Optional[torch.LongTensor] = None,
370
+ past_key_value: Optional[Cache] = None,
371
+ output_attentions: bool = False,
372
+ use_cache: bool = False,
373
+ **kwargs,
374
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
375
+ if "padding_mask" in kwargs:
376
+ warnings.warn(
377
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
378
+ )
379
+
380
+ bsz, q_len, _ = hidden_states.size()
381
+
382
+ if self.config.pretraining_tp > 1:
383
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
384
+ query_slices = self.q_proj.weight.split(
385
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
386
+ )
387
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
388
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
389
+
390
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
391
+ query_states = torch.cat(query_states, dim=-1)
392
+
393
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
394
+ key_states = torch.cat(key_states, dim=-1)
395
+
396
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
397
+ value_states = torch.cat(value_states, dim=-1)
398
+
399
+ else:
400
+ query_states = self.q_proj(hidden_states)
401
+ key_states = self.k_proj(hidden_states)
402
+ value_states = self.v_proj(hidden_states)
403
+
404
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
405
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
406
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
407
+
408
+ kv_seq_len = key_states.shape[-2]
409
+ if past_key_value is not None:
410
+ if self.layer_idx is None:
411
+ raise ValueError(
412
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
413
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
414
+ "with a layer index."
415
+ )
416
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
417
+ cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
418
+
419
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
420
+
421
+ if past_key_value is not None:
422
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
423
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
424
+
425
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
426
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
427
+
428
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
429
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
430
+ raise ValueError(
431
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
432
+ f" {attn_weights.size()}"
433
+ )
434
+
435
+ if attention_mask is not None:
436
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
437
+ raise ValueError(
438
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
439
+ )
440
+ attn_weights = attn_weights + attention_mask
441
+
442
+ # upcast attention to fp32
443
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
444
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
445
+ attn_output = torch.matmul(attn_weights, value_states)
446
+
447
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
448
+ raise ValueError(
449
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
450
+ f" {attn_output.size()}"
451
+ )
452
+
453
+ attn_output = attn_output.transpose(1, 2).contiguous()
454
+
455
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
456
+
457
+ if self.config.pretraining_tp > 1:
458
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
459
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
460
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
461
+ else:
462
+ attn_output = self.o_proj(attn_output)
463
+
464
+ if not output_attentions:
465
+ attn_weights = None
466
+
467
+ return attn_output, attn_weights, past_key_value
468
+
469
+
470
+ class MiniCPMFlashAttention2(MiniCPMAttention):
471
+ """
472
+ MiniCPM flash attention module. This module inherits from `MiniCPMAttention` as the weights of the module stays
473
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
474
+ flash attention and deal with padding tokens in case the input contains any of them.
475
+ """
476
+
477
+ def __init__(self, *args, **kwargs):
478
+ super().__init__(*args, **kwargs)
479
+
480
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
481
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
482
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
483
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
484
+
485
+ def forward(
486
+ self,
487
+ hidden_states: torch.Tensor,
488
+ attention_mask: Optional[torch.LongTensor] = None,
489
+ position_ids: Optional[torch.LongTensor] = None,
490
+ past_key_value: Optional[Cache] = None,
491
+ output_attentions: bool = False,
492
+ use_cache: bool = False,
493
+ **kwargs,
494
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
495
+ # MiniCPMFlashAttention2 attention does not support output_attentions
496
+ if "padding_mask" in kwargs:
497
+ warnings.warn(
498
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
499
+ )
500
+
501
+ # overwrite attention_mask with padding_mask
502
+ attention_mask = kwargs.pop("padding_mask")
503
+
504
+ output_attentions = False
505
+
506
+ bsz, q_len, _ = hidden_states.size()
507
+
508
+ query_states = self.q_proj(hidden_states)
509
+ key_states = self.k_proj(hidden_states)
510
+ value_states = self.v_proj(hidden_states)
511
+
512
+ # Flash attention requires the input to have the shape
513
+ # batch_size x seq_length x head_dim x hidden_dim
514
+ # therefore we just need to keep the original shape
515
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
516
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
517
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
518
+
519
+ kv_seq_len = key_states.shape[-2]
520
+ if past_key_value is not None:
521
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
522
+ cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
523
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
524
+
525
+ if past_key_value is not None:
526
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
527
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
528
+
529
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
530
+ # to be able to avoid many of these transpose/reshape/view.
531
+ query_states = query_states.transpose(1, 2)
532
+ key_states = key_states.transpose(1, 2)
533
+ value_states = value_states.transpose(1, 2)
534
+
535
+ dropout_rate = self.attention_dropout if self.training else 0.0
536
+
537
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
538
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
539
+ # cast them back in the correct dtype just to be sure everything works as expected.
540
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
541
+ # in fp32. (MiniCPMRMSNorm handles it correctly)
542
+
543
+ input_dtype = query_states.dtype
544
+ if input_dtype == torch.float32:
545
+ # Handle the case where the model is quantized
546
+ if hasattr(self.config, "_pre_quantization_dtype"):
547
+ target_dtype = self.config._pre_quantization_dtype
548
+ else:
549
+ target_dtype = self.q_proj.weight.dtype
550
+
551
+ logger.warning_once(
552
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
553
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
554
+ f" {target_dtype}."
555
+ )
556
+
557
+ query_states = query_states.to(target_dtype)
558
+ key_states = key_states.to(target_dtype)
559
+ value_states = value_states.to(target_dtype)
560
+
561
+ attn_output = self._flash_attention_forward(
562
+ query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
563
+ )
564
+
565
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
566
+ attn_output = self.o_proj(attn_output)
567
+
568
+ if not output_attentions:
569
+ attn_weights = None
570
+
571
+ return attn_output, attn_weights, past_key_value
572
+
573
+ def _flash_attention_forward(
574
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
575
+ ):
576
+ """
577
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
578
+ first unpad the input, then computes the attention scores and pad the final attention scores.
579
+
580
+ Args:
581
+ query_states (`torch.Tensor`):
582
+ Input query states to be passed to Flash Attention API
583
+ key_states (`torch.Tensor`):
584
+ Input key states to be passed to Flash Attention API
585
+ value_states (`torch.Tensor`):
586
+ Input value states to be passed to Flash Attention API
587
+ attention_mask (`torch.Tensor`):
588
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
589
+ position of padding tokens and 1 for the position of non-padding tokens.
590
+ dropout (`int`, *optional*):
591
+ Attention dropout
592
+ softmax_scale (`float`, *optional*):
593
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
594
+ """
595
+ if not self._flash_attn_uses_top_left_mask:
596
+ causal = self.is_causal
597
+ else:
598
+ # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__.
599
+ causal = self.is_causal and query_length != 1
600
+ # Contains at least one padding token in the sequence
601
+ if attention_mask is not None:
602
+ batch_size = query_states.shape[0]
603
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
604
+ query_states, key_states, value_states, attention_mask, query_length
605
+ )
606
+
607
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
608
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
609
+ attn_output_unpad = flash_attn_varlen_func(
610
+ query_states,
611
+ key_states,
612
+ value_states,
613
+ cu_seqlens_q=cu_seqlens_q,
614
+ cu_seqlens_k=cu_seqlens_k,
615
+ max_seqlen_q=max_seqlen_in_batch_q,
616
+ max_seqlen_k=max_seqlen_in_batch_k,
617
+ dropout_p=dropout,
618
+ softmax_scale=softmax_scale,
619
+ causal=causal,
620
+ )
621
+
622
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
623
+ else:
624
+ attn_output = flash_attn_func(
625
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
626
+ )
627
+
628
+ return attn_output
629
+
630
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
631
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
632
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
633
+
634
+ key_layer = index_first_axis(
635
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
636
+ )
637
+ value_layer = index_first_axis(
638
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
639
+ )
640
+ if query_length == kv_seq_len:
641
+ query_layer = index_first_axis(
642
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
643
+ )
644
+ cu_seqlens_q = cu_seqlens_k
645
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
646
+ indices_q = indices_k
647
+ elif query_length == 1:
648
+ max_seqlen_in_batch_q = 1
649
+ cu_seqlens_q = torch.arange(
650
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
651
+ ) # There is a memcpy here, that is very bad.
652
+ indices_q = cu_seqlens_q[:-1]
653
+ query_layer = query_layer.squeeze(1)
654
+ else:
655
+ # The -q_len: slice assumes left padding.
656
+ attention_mask = attention_mask[:, -query_length:]
657
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
658
+
659
+ return (
660
+ query_layer,
661
+ key_layer,
662
+ value_layer,
663
+ indices_q,
664
+ (cu_seqlens_q, cu_seqlens_k),
665
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
666
+ )
667
+
668
+
669
+ class MiniCPMSdpaAttention(MiniCPMAttention):
670
+ """
671
+ MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
672
+ `MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
673
+ SDPA API.
674
+ """
675
+
676
+ # Adapted from MiniCPMAttention.forward
677
+ def forward(
678
+ self,
679
+ hidden_states: torch.Tensor,
680
+ attention_mask: Optional[torch.Tensor] = None,
681
+ position_ids: Optional[torch.LongTensor] = None,
682
+ past_key_value: Optional[Cache] = None,
683
+ output_attentions: bool = False,
684
+ use_cache: bool = False,
685
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
686
+ if output_attentions:
687
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
688
+ logger.warning_once(
689
+ "MiniCPMModel is using MiniCPMSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
690
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
691
+ )
692
+ return super().forward(
693
+ hidden_states=hidden_states,
694
+ attention_mask=attention_mask,
695
+ position_ids=position_ids,
696
+ past_key_value=past_key_value,
697
+ output_attentions=output_attentions,
698
+ use_cache=use_cache,
699
+ )
700
+
701
+ bsz, q_len, _ = hidden_states.size()
702
+
703
+ query_states = self.q_proj(hidden_states)
704
+ key_states = self.k_proj(hidden_states)
705
+ value_states = self.v_proj(hidden_states)
706
+
707
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
708
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
709
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
710
+
711
+ kv_seq_len = key_states.shape[-2]
712
+ if past_key_value is not None:
713
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
714
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
715
+
716
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
717
+
718
+ if past_key_value is not None:
719
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
720
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
721
+
722
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
723
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
724
+
725
+ if attention_mask is not None:
726
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
727
+ raise ValueError(
728
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
729
+ )
730
+
731
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
732
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
733
+ if query_states.device.type == "cuda" and attention_mask is not None:
734
+ query_states = query_states.contiguous()
735
+ key_states = key_states.contiguous()
736
+ value_states = value_states.contiguous()
737
+
738
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
739
+ query_states,
740
+ key_states,
741
+ value_states,
742
+ attn_mask=attention_mask,
743
+ dropout_p=self.attention_dropout if self.training else 0.0,
744
+ # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
745
+ is_causal=self.is_causal and attention_mask is None and q_len > 1,
746
+ )
747
+
748
+ attn_output = attn_output.transpose(1, 2).contiguous()
749
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
750
+
751
+ attn_output = self.o_proj(attn_output)
752
+
753
+ return attn_output, None, past_key_value
754
+
755
+
756
+ MINICPM_ATTENTION_CLASSES = {
757
+ "eager": MiniCPMAttention,
758
+ "flash_attention_2": MiniCPMFlashAttention2,
759
+ "sdpa": MiniCPMSdpaAttention,
760
+ }
761
+
762
+
763
+ class MiniCPMDecoderLayer(nn.Module):
764
+ def __init__(self, config: MiniCPMConfig, layer_idx: int):
765
+ super().__init__()
766
+ self.hidden_size = config.hidden_size
767
+ self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
768
+
769
+ self.mlp = MiniCPMMLP(config)
770
+ self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
771
+ self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
772
+
773
+ self.scale_depth = config.scale_depth
774
+ self.num_hidden_layers = config.num_hidden_layers
775
+
776
+ def forward(
777
+ self,
778
+ hidden_states: torch.Tensor,
779
+ attention_mask: Optional[torch.Tensor] = None,
780
+ position_ids: Optional[torch.LongTensor] = None,
781
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
782
+ output_attentions: Optional[bool] = False,
783
+ use_cache: Optional[bool] = False,
784
+ **kwargs,
785
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
786
+ """
787
+ Args:
788
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
789
+ attention_mask (`torch.FloatTensor`, *optional*):
790
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
791
+ query_sequence_length, key_sequence_length)` if default attention is used.
792
+ output_attentions (`bool`, *optional*):
793
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
794
+ returned tensors for more detail.
795
+ use_cache (`bool`, *optional*):
796
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
797
+ (see `past_key_values`).
798
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
799
+ """
800
+ if "padding_mask" in kwargs:
801
+ warnings.warn(
802
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
803
+ )
804
+
805
+ residual = hidden_states
806
+ hidden_states = self.input_layernorm(hidden_states)
807
+ # Self Attention
808
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
809
+ hidden_states=hidden_states,
810
+ attention_mask=attention_mask,
811
+ position_ids=position_ids,
812
+ past_key_value=past_key_value,
813
+ output_attentions=output_attentions,
814
+ use_cache=use_cache,
815
+ **kwargs,
816
+ )
817
+
818
+ hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
819
+
820
+ # Fully Connected
821
+ residual = hidden_states
822
+ hidden_states = self.post_attention_layernorm(hidden_states)
823
+
824
+ hidden_states = self.mlp(hidden_states)
825
+ hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
826
+
827
+ outputs = (hidden_states,)
828
+
829
+ if output_attentions:
830
+ outputs += (self_attn_weights,)
831
+
832
+ if use_cache:
833
+ outputs += (present_key_value,)
834
+
835
+ return outputs
836
+
837
+
838
+ MINICPM_START_DOCSTRING = r"""
839
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
840
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
841
+ etc.)
842
+
843
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
844
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
845
+ and behavior.
846
+
847
+ Parameters:
848
+ config ([`MiniCPMConfig`]):
849
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
850
+ load the weights associated with the model, only the configuration. Check out the
851
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
852
+ """
853
+
854
+
855
+ @add_start_docstrings(
856
+ "The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
857
+ MINICPM_START_DOCSTRING,
858
+ )
859
+ class MiniCPMPreTrainedModel(PreTrainedModel):
860
+ config_class = MiniCPMConfig
861
+ base_model_prefix = "model"
862
+ supports_gradient_checkpointing = True
863
+ _no_split_modules = ["MiniCPMDecoderLayer"]
864
+ _skip_keys_device_placement = "past_key_values"
865
+ _supports_flash_attn_2 = True
866
+ _supports_sdpa = True
867
+ _supports_cache_class = True
868
+
869
+ def _init_weights(self, module):
870
+ std = self.config.initializer_range
871
+ if isinstance(module, nn.Linear):
872
+ module.weight.data.normal_(mean=0.0, std=std)
873
+ if module.bias is not None:
874
+ module.bias.data.zero_()
875
+ elif isinstance(module, nn.Embedding):
876
+ module.weight.data.normal_(mean=0.0, std=std)
877
+ if module.padding_idx is not None:
878
+ module.weight.data[module.padding_idx].zero_()
879
+
880
+
881
+ MINICPM_INPUTS_DOCSTRING = r"""
882
+ Args:
883
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
884
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
885
+ it.
886
+
887
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
888
+ [`PreTrainedTokenizer.__call__`] for details.
889
+
890
+ [What are input IDs?](../glossary#input-ids)
891
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
892
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
893
+
894
+ - 1 for tokens that are **not masked**,
895
+ - 0 for tokens that are **masked**.
896
+
897
+ [What are attention masks?](../glossary#attention-mask)
898
+
899
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
900
+ [`PreTrainedTokenizer.__call__`] for details.
901
+
902
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
903
+ `past_key_values`).
904
+
905
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
906
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
907
+ information on the default strategy.
908
+
909
+ - 1 indicates the head is **not masked**,
910
+ - 0 indicates the head is **masked**.
911
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
912
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
913
+ config.n_positions - 1]`.
914
+
915
+ [What are position IDs?](../glossary#position-ids)
916
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
917
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
918
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
919
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
920
+
921
+ Two formats are allowed:
922
+ - a [`~cache_utils.Cache`] instance;
923
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
924
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
925
+ cache format.
926
+
927
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
928
+ legacy cache format will be returned.
929
+
930
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
931
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
932
+ of shape `(batch_size, sequence_length)`.
933
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
934
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
935
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
936
+ model's internal embedding lookup matrix.
937
+ use_cache (`bool`, *optional*):
938
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
939
+ `past_key_values`).
940
+ output_attentions (`bool`, *optional*):
941
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
942
+ tensors for more detail.
943
+ output_hidden_states (`bool`, *optional*):
944
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
945
+ more detail.
946
+ return_dict (`bool`, *optional*):
947
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
948
+ """
949
+
950
+
951
+ @add_start_docstrings(
952
+ "The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
953
+ MINICPM_START_DOCSTRING,
954
+ )
955
+ class MiniCPMModel(MiniCPMPreTrainedModel):
956
+ """
957
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]
958
+
959
+ Args:
960
+ config: MiniCPMConfig
961
+ """
962
+
963
+ def __init__(self, config: MiniCPMConfig):
964
+ super().__init__(config)
965
+ self.padding_idx = config.pad_token_id
966
+ self.vocab_size = config.vocab_size
967
+
968
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
969
+ self.layers = nn.ModuleList(
970
+ [MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
971
+ )
972
+ self._use_sdpa = config._attn_implementation == "sdpa"
973
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
974
+
975
+ self.norm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
976
+
977
+ self.gradient_checkpointing = False
978
+ # Initialize weights and apply final processing
979
+ self.post_init()
980
+
981
+ def get_input_embeddings(self):
982
+ return self.embed_tokens
983
+
984
+ def set_input_embeddings(self, value):
985
+ self.embed_tokens = value
986
+
987
+ @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
988
+ def forward(
989
+ self,
990
+ input_ids: torch.LongTensor = None,
991
+ attention_mask: Optional[torch.Tensor] = None,
992
+ position_ids: Optional[torch.LongTensor] = None,
993
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
994
+ inputs_embeds: Optional[torch.FloatTensor] = None,
995
+ use_cache: Optional[bool] = None,
996
+ output_attentions: Optional[bool] = None,
997
+ output_hidden_states: Optional[bool] = None,
998
+ return_dict: Optional[bool] = None,
999
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
1000
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1001
+ output_hidden_states = (
1002
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1003
+ )
1004
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
1005
+
1006
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1007
+
1008
+ # retrieve input_ids and inputs_embeds
1009
+ if input_ids is not None and inputs_embeds is not None:
1010
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
1011
+ elif input_ids is not None:
1012
+ batch_size, seq_length = input_ids.shape[:2]
1013
+ elif inputs_embeds is not None:
1014
+ batch_size, seq_length = inputs_embeds.shape[:2]
1015
+ else:
1016
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
1017
+
1018
+ if self.gradient_checkpointing and self.training:
1019
+ if use_cache:
1020
+ logger.warning_once(
1021
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
1022
+ )
1023
+ use_cache = False
1024
+
1025
+ past_key_values_length = 0
1026
+ if use_cache:
1027
+ use_legacy_cache = not isinstance(past_key_values, Cache)
1028
+ if use_legacy_cache:
1029
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
1030
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
1031
+
1032
+ if position_ids is None:
1033
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
1034
+ position_ids = torch.arange(
1035
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
1036
+ )
1037
+ position_ids = position_ids.unsqueeze(0)
1038
+
1039
+ if inputs_embeds is None:
1040
+ inputs_embeds = self.embed_tokens(input_ids) * self.config.scale_emb
1041
+
1042
+ if self._use_flash_attention_2:
1043
+ # 2d mask is passed through the layers
1044
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
1045
+ elif self._use_sdpa and not output_attentions:
1046
+ # output_attentions=True can not be supported when using SDPA, and we fall back on
1047
+ # the manual implementation that requires a 4D causal mask in all cases.
1048
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
1049
+ attention_mask,
1050
+ (batch_size, seq_length),
1051
+ inputs_embeds,
1052
+ past_key_values_length,
1053
+ )
1054
+ else:
1055
+ # 4d mask is passed through the layers
1056
+ attention_mask = _prepare_4d_causal_attention_mask(
1057
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
1058
+ )
1059
+
1060
+ # embed positions
1061
+ hidden_states = inputs_embeds
1062
+
1063
+ # decoder layers
1064
+ all_hidden_states = () if output_hidden_states else None
1065
+ all_self_attns = () if output_attentions else None
1066
+ next_decoder_cache = None
1067
+
1068
+ for decoder_layer in self.layers:
1069
+ if output_hidden_states:
1070
+ all_hidden_states += (hidden_states,)
1071
+
1072
+ if self.gradient_checkpointing and self.training:
1073
+ layer_outputs = self._gradient_checkpointing_func(
1074
+ decoder_layer.__call__,
1075
+ hidden_states,
1076
+ attention_mask,
1077
+ position_ids,
1078
+ past_key_values,
1079
+ output_attentions,
1080
+ use_cache,
1081
+ )
1082
+ else:
1083
+ layer_outputs = decoder_layer(
1084
+ hidden_states,
1085
+ attention_mask=attention_mask,
1086
+ position_ids=position_ids,
1087
+ past_key_value=past_key_values,
1088
+ output_attentions=output_attentions,
1089
+ use_cache=use_cache,
1090
+ )
1091
+
1092
+ hidden_states = layer_outputs[0]
1093
+
1094
+ if use_cache:
1095
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1096
+
1097
+ if output_attentions:
1098
+ all_self_attns += (layer_outputs[1],)
1099
+
1100
+ hidden_states = self.norm(hidden_states)
1101
+
1102
+ # add hidden states from the last decoder layer
1103
+ if output_hidden_states:
1104
+ all_hidden_states += (hidden_states,)
1105
+
1106
+ next_cache = None
1107
+ if use_cache:
1108
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
1109
+ if not return_dict:
1110
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
1111
+ return BaseModelOutputWithPast(
1112
+ last_hidden_state=hidden_states,
1113
+ past_key_values=next_cache,
1114
+ hidden_states=all_hidden_states,
1115
+ attentions=all_self_attns,
1116
+ )
1117
+
1118
+
1119
+ class MiniCPMForCausalLM(MiniCPMPreTrainedModel):
1120
+ _tied_weights_keys = ["lm_head.weight"]
1121
+
1122
+ def __init__(self, config):
1123
+ super().__init__(config)
1124
+ self.model = MiniCPMModel(config)
1125
+ self.vocab_size = config.vocab_size
1126
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1127
+
1128
+ # Initialize weights and apply final processing
1129
+ self.post_init()
1130
+
1131
+ def get_input_embeddings(self):
1132
+ return self.model.embed_tokens
1133
+
1134
+ def set_input_embeddings(self, value):
1135
+ self.model.embed_tokens = value
1136
+
1137
+ def get_output_embeddings(self):
1138
+ return self.lm_head
1139
+
1140
+ def set_output_embeddings(self, new_embeddings):
1141
+ self.lm_head = new_embeddings
1142
+
1143
+ def set_decoder(self, decoder):
1144
+ self.model = decoder
1145
+
1146
+ def get_decoder(self):
1147
+ return self.model
1148
+
1149
+ @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
1150
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1151
+ def forward(
1152
+ self,
1153
+ input_ids: torch.LongTensor = None,
1154
+ attention_mask: Optional[torch.Tensor] = None,
1155
+ position_ids: Optional[torch.LongTensor] = None,
1156
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1157
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1158
+ labels: Optional[torch.LongTensor] = None,
1159
+ use_cache: Optional[bool] = None,
1160
+ output_attentions: Optional[bool] = None,
1161
+ output_hidden_states: Optional[bool] = None,
1162
+ return_dict: Optional[bool] = None,
1163
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1164
+ r"""
1165
+ Args:
1166
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1167
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1168
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1169
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1170
+
1171
+ Returns:
1172
+
1173
+ Example:
1174
+
1175
+ ```python
1176
+ >>> from transformers import AutoTokenizer, MiniCPMForCausalLM
1177
+
1178
+ >>> model = MiniCPMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1179
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1180
+
1181
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1182
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1183
+
1184
+ >>> # Generate
1185
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1186
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1187
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1188
+ ```"""
1189
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1190
+ output_hidden_states = (
1191
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1192
+ )
1193
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1194
+
1195
+
1196
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1197
+ outputs = self.model(
1198
+ input_ids=input_ids,
1199
+ attention_mask=attention_mask,
1200
+ position_ids=position_ids,
1201
+ past_key_values=past_key_values,
1202
+ inputs_embeds=inputs_embeds,
1203
+ use_cache=use_cache,
1204
+ output_attentions=output_attentions,
1205
+ output_hidden_states=output_hidden_states,
1206
+ return_dict=return_dict,
1207
+ )
1208
+
1209
+ hidden_states = outputs[0]
1210
+ if self.config.pretraining_tp > 1:
1211
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
1212
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
1213
+ logits = torch.cat(logits, dim=-1)
1214
+ else:
1215
+ logits = self.lm_head(hidden_states / (self.config.hidden_size / self.config.dim_model_base))
1216
+ logits = logits.float()
1217
+
1218
+ loss = None
1219
+ if labels is not None:
1220
+ # Shift so that tokens < n predict n
1221
+ shift_logits = logits[..., :-1, :].contiguous()
1222
+ shift_labels = labels[..., 1:].contiguous()
1223
+ # Flatten the tokens
1224
+ loss_fct = CrossEntropyLoss()
1225
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1226
+ shift_labels = shift_labels.view(-1)
1227
+ # Enable model parallelism
1228
+ shift_labels = shift_labels.to(shift_logits.device)
1229
+ loss = loss_fct(shift_logits, shift_labels)
1230
+
1231
+ if not return_dict:
1232
+ output = (logits,) + outputs[1:]
1233
+ return (loss,) + output if loss is not None else output
1234
+
1235
+ return CausalLMOutputWithPast(
1236
+ loss=loss,
1237
+ logits=logits,
1238
+ past_key_values=outputs.past_key_values,
1239
+ hidden_states=outputs.hidden_states,
1240
+ attentions=outputs.attentions,
1241
+ )
1242
+
1243
+ def prepare_inputs_for_generation(
1244
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1245
+ ):
1246
+ if past_key_values is not None:
1247
+ if isinstance(past_key_values, Cache):
1248
+ cache_length = past_key_values.get_seq_length()
1249
+ past_length = past_key_values.seen_tokens
1250
+ max_cache_length = past_key_values.get_max_length()
1251
+ else:
1252
+ cache_length = past_length = past_key_values[0][0].shape[2]
1253
+ max_cache_length = None
1254
+
1255
+ # Keep only the unprocessed tokens:
1256
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
1257
+ # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
1258
+ # input)
1259
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
1260
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
1261
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
1262
+ # input_ids based on the past_length.
1263
+ elif past_length < input_ids.shape[1]:
1264
+ input_ids = input_ids[:, past_length:]
1265
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
1266
+
1267
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
1268
+ if (
1269
+ max_cache_length is not None
1270
+ and attention_mask is not None
1271
+ and cache_length + input_ids.shape[1] > max_cache_length
1272
+ ):
1273
+ attention_mask = attention_mask[:, -max_cache_length:]
1274
+
1275
+ position_ids = kwargs.get("position_ids", None)
1276
+ if attention_mask is not None and position_ids is None:
1277
+ # create position_ids on the fly for batch generation
1278
+ position_ids = attention_mask.long().cumsum(-1) - 1
1279
+ position_ids.masked_fill_(attention_mask == 0, 1)
1280
+ if past_key_values:
1281
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1282
+
1283
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1284
+ if inputs_embeds is not None and past_key_values is None:
1285
+ model_inputs = {"inputs_embeds": inputs_embeds}
1286
+ else:
1287
+ model_inputs = {"input_ids": input_ids}
1288
+
1289
+ model_inputs.update(
1290
+ {
1291
+ "position_ids": position_ids,
1292
+ "past_key_values": past_key_values,
1293
+ "use_cache": kwargs.get("use_cache"),
1294
+ "attention_mask": attention_mask,
1295
+ }
1296
+ )
1297
+ return model_inputs
1298
+
1299
+ @staticmethod
1300
+ def _reorder_cache(past_key_values, beam_idx):
1301
+ reordered_past = ()
1302
+ for layer_past in past_key_values:
1303
+ reordered_past += (
1304
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1305
+ )
1306
+ return reordered_past
1307
+
1308
+ @torch.inference_mode()
1309
+ def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user",
1310
+ max_length: int = 4096, num_beams=1, do_sample=True, top_p=0.8, temperature=0.3, logits_processor=None,
1311
+ **kwargs):
1312
+ if history is None:
1313
+ history = []
1314
+ if logits_processor:
1315
+ gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
1316
+ "temperature": temperature, "logits_processor": logits_processor, **kwargs}
1317
+ else:
1318
+ gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
1319
+ "temperature": temperature, "logits_processor": logits_processor, **kwargs}
1320
+
1321
+ history.append({"role": role, "content": query})
1322
+ history_str = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=False)
1323
+ inputs = tokenizer(history_str, return_tensors='pt').to(self.device)
1324
+ outputs = self.generate(**inputs, **gen_kwargs)
1325
+ outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
1326
+ response = tokenizer.decode(outputs)
1327
+ pattern = re.compile(r".*?(?=<AI>|<用户>)", re.DOTALL)
1328
+ matches = pattern.findall(response)
1329
+ if len(matches) > 0:
1330
+ response = matches[0]
1331
+ history.append({"role": "assistant", "content": response})
1332
+ return response, history
1333
+
1334
+
1335
+ @add_start_docstrings(
1336
+ """
1337
+ The MiniCPM Model transformer with a sequence classification head on top (linear layer).
1338
+
1339
+ [`MiniCPMForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1340
+ (e.g. GPT-2) do.
1341
+
1342
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1343
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1344
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1345
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1346
+ each row of the batch).
1347
+ """,
1348
+ MINICPM_START_DOCSTRING,
1349
+ )
1350
+ class MiniCPMForSequenceClassification(MiniCPMPreTrainedModel):
1351
+ def __init__(self, config):
1352
+ super().__init__(config)
1353
+ self.num_labels = config.num_labels
1354
+ self.model = MiniCPMModel(config)
1355
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1356
+
1357
+ # Initialize weights and apply final processing
1358
+ self.post_init()
1359
+
1360
+ def get_input_embeddings(self):
1361
+ return self.model.embed_tokens
1362
+
1363
+ def set_input_embeddings(self, value):
1364
+ self.model.embed_tokens = value
1365
+
1366
+ @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
1367
+ def forward(
1368
+ self,
1369
+ input_ids: torch.LongTensor = None,
1370
+ attention_mask: Optional[torch.Tensor] = None,
1371
+ position_ids: Optional[torch.LongTensor] = None,
1372
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1373
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1374
+ labels: Optional[torch.LongTensor] = None,
1375
+ use_cache: Optional[bool] = None,
1376
+ output_attentions: Optional[bool] = None,
1377
+ output_hidden_states: Optional[bool] = None,
1378
+ return_dict: Optional[bool] = None,
1379
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1380
+ r"""
1381
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1382
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1383
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1384
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1385
+ """
1386
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1387
+
1388
+ transformer_outputs = self.model(
1389
+ input_ids,
1390
+ attention_mask=attention_mask,
1391
+ position_ids=position_ids,
1392
+ past_key_values=past_key_values,
1393
+ inputs_embeds=inputs_embeds,
1394
+ use_cache=use_cache,
1395
+ output_attentions=output_attentions,
1396
+ output_hidden_states=output_hidden_states,
1397
+ return_dict=return_dict,
1398
+ )
1399
+ hidden_states = transformer_outputs[0]
1400
+ logits = self.score(hidden_states)
1401
+
1402
+ if input_ids is not None:
1403
+ batch_size = input_ids.shape[0]
1404
+ else:
1405
+ batch_size = inputs_embeds.shape[0]
1406
+
1407
+ if self.config.pad_token_id is None and batch_size != 1:
1408
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1409
+ if self.config.pad_token_id is None:
1410
+ sequence_lengths = -1
1411
+ else:
1412
+ if input_ids is not None:
1413
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
1414
+ logits.device
1415
+ )
1416
+ else:
1417
+ sequence_lengths = -1
1418
+
1419
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1420
+
1421
+ loss = None
1422
+ if labels is not None:
1423
+ labels = labels.to(logits.device)
1424
+ if self.config.problem_type is None:
1425
+ if self.num_labels == 1:
1426
+ self.config.problem_type = "regression"
1427
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1428
+ self.config.problem_type = "single_label_classification"
1429
+ else:
1430
+ self.config.problem_type = "multi_label_classification"
1431
+
1432
+ if self.config.problem_type == "regression":
1433
+ loss_fct = MSELoss()
1434
+ if self.num_labels == 1:
1435
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1436
+ else:
1437
+ loss = loss_fct(pooled_logits, labels)
1438
+ elif self.config.problem_type == "single_label_classification":
1439
+ loss_fct = CrossEntropyLoss()
1440
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1441
+ elif self.config.problem_type == "multi_label_classification":
1442
+ loss_fct = BCEWithLogitsLoss()
1443
+ loss = loss_fct(pooled_logits, labels)
1444
+ if not return_dict:
1445
+ output = (pooled_logits,) + transformer_outputs[1:]
1446
+ return ((loss,) + output) if loss is not None else output
1447
+
1448
+ return SequenceClassifierOutputWithPast(
1449
+ loss=loss,
1450
+ logits=pooled_logits,
1451
+ past_key_values=transformer_outputs.past_key_values,
1452
+ hidden_states=transformer_outputs.hidden_states,
1453
+ attentions=transformer_outputs.attentions,
1454
+ )