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added_tokens.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
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+ {
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+ "<|endoftext|>": 151643,
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+ "<|im_end|>": 151645,
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+ "<|im_start|>": 151644
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+ }
config_sentence_transformers.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "__version__": {
3
+ "sentence_transformers": "2.7.0",
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+ "transformers": "4.39.3",
5
+ "pytorch": "2.1.0+cu121"
6
+ },
7
+ "prompts": {
8
+ "query": "Instruct: Given a web search query, retrieve relevant passages that answer the query\nQuery: "
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+ },
10
+ "default_prompt_name": null
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+ }
generation_config.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
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+ {
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+ "bos_token_id": 151643,
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+ "eos_token_id": 151643,
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+ "max_new_tokens": 2048,
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+ "transformers_version": "4.41.2"
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+ }
modeling_qwen.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2024 The Qwen team, Alibaba Group 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 Qwen2 model."""
21
+ from transformers import Qwen2Config
22
+ import inspect
23
+ import math
24
+ import os
25
+ import warnings
26
+ from typing import List, Optional, Tuple, Union
27
+
28
+ import torch
29
+ import torch.nn.functional as F
30
+ import torch.utils.checkpoint
31
+ from torch import nn
32
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
33
+
34
+ from transformers.activations import ACT2FN
35
+ from transformers.cache_utils import Cache, DynamicCache
36
+ from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask, _prepare_4d_causal_attention_mask_for_sdpa, _prepare_4d_attention_mask, _prepare_4d_attention_mask_for_sdpa
37
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
38
+ from transformers.modeling_utils import PreTrainedModel
39
+ from transformers.utils import (
40
+ add_start_docstrings,
41
+ add_start_docstrings_to_model_forward,
42
+ is_flash_attn_2_available,
43
+ is_flash_attn_greater_or_equal_2_10,
44
+ logging,
45
+ replace_return_docstrings,
46
+ )
47
+
48
+
49
+ if is_flash_attn_2_available():
50
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
51
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
52
+
53
+ _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
54
+
55
+
56
+ logger = logging.get_logger(__name__)
57
+
58
+
59
+ _CHECKPOINT_FOR_DOC = "Qwen/Qwen2-7B-beta"
60
+ _CONFIG_FOR_DOC = "Qwen2Config"
61
+
62
+ QWEN2_PRETRAINED_MODEL_ARCHIVE_LIST = [
63
+ "Qwen/Qwen2-7B-beta",
64
+ # See all Qwen2 models at https://huggingface.co/models?filter=qwen2
65
+ ]
66
+
67
+
68
+ # Copied from transformers.models.llama.modeling_llama._get_unpad_data
69
+ def _get_unpad_data(attention_mask):
70
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
71
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
72
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
73
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
74
+ return (
75
+ indices,
76
+ cu_seqlens,
77
+ max_seqlen_in_batch,
78
+ )
79
+
80
+
81
+ # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Qwen2
82
+ class Qwen2RMSNorm(nn.Module):
83
+ def __init__(self, hidden_size, eps=1e-6):
84
+ """
85
+ Qwen2RMSNorm is equivalent to T5LayerNorm
86
+ """
87
+ super().__init__()
88
+ self.weight = nn.Parameter(torch.ones(hidden_size))
89
+ self.variance_epsilon = eps
90
+
91
+ def forward(self, hidden_states):
92
+ input_dtype = hidden_states.dtype
93
+ hidden_states = hidden_states.to(torch.float32)
94
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
95
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
96
+ return self.weight * hidden_states.to(input_dtype)
97
+
98
+
99
+ # Copied from transformers.models.mistral.modeling_mistral.MistralRotaryEmbedding with Mistral->Qwen2
100
+ class Qwen2RotaryEmbedding(nn.Module):
101
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
102
+ super().__init__()
103
+
104
+ self.dim = dim
105
+ self.max_position_embeddings = max_position_embeddings
106
+ self.base = base
107
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
108
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
109
+
110
+ # Build here to make `torch.jit.trace` work.
111
+ self._set_cos_sin_cache(
112
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
113
+ )
114
+
115
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
116
+ self.max_seq_len_cached = seq_len
117
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq)
118
+
119
+ freqs = torch.outer(t, self.inv_freq)
120
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
121
+ emb = torch.cat((freqs, freqs), dim=-1)
122
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
123
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
124
+
125
+ def forward(self, x, seq_len=None):
126
+ # x: [bs, num_attention_heads, seq_len, head_size]
127
+ if seq_len > self.max_seq_len_cached:
128
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
129
+
130
+ return (
131
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
132
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
133
+ )
134
+
135
+
136
+ # Copied from transformers.models.llama.modeling_llama.rotate_half
137
+ def rotate_half(x):
138
+ """Rotates half the hidden dims of the input."""
139
+ x1 = x[..., : x.shape[-1] // 2]
140
+ x2 = x[..., x.shape[-1] // 2 :]
141
+ return torch.cat((-x2, x1), dim=-1)
142
+
143
+
144
+ # Copied from transformers.models.mistral.modeling_mistral.apply_rotary_pos_emb
145
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
146
+ """Applies Rotary Position Embedding to the query and key tensors.
147
+
148
+ Args:
149
+ q (`torch.Tensor`): The query tensor.
150
+ k (`torch.Tensor`): The key tensor.
151
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
152
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
153
+ position_ids (`torch.Tensor`):
154
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
155
+ used to pass offsetted position ids when working with a KV-cache.
156
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
157
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
158
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
159
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
160
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
161
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
162
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
163
+ Returns:
164
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
165
+ """
166
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
167
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
168
+ q_embed = (q * cos) + (rotate_half(q) * sin)
169
+ k_embed = (k * cos) + (rotate_half(k) * sin)
170
+ return q_embed, k_embed
171
+
172
+
173
+ # Copied from transformers.models.mistral.modeling_mistral.MistralMLP with Mistral->Qwen2
174
+ class Qwen2MLP(nn.Module):
175
+ def __init__(self, config):
176
+ super().__init__()
177
+ self.config = config
178
+ self.hidden_size = config.hidden_size
179
+ self.intermediate_size = config.intermediate_size
180
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
181
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
182
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
183
+ self.act_fn = ACT2FN[config.hidden_act]
184
+
185
+ def forward(self, x):
186
+ return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
187
+
188
+
189
+ # Copied from transformers.models.llama.modeling_llama.repeat_kv
190
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
191
+ """
192
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
193
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
194
+ """
195
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
196
+ if n_rep == 1:
197
+ return hidden_states
198
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
199
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
200
+
201
+
202
+ class Qwen2Attention(nn.Module):
203
+ """
204
+ Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
205
+ and "Generating Long Sequences with Sparse Transformers".
206
+ """
207
+
208
+ def __init__(self, config: Qwen2Config, layer_idx: Optional[int] = None):
209
+ super().__init__()
210
+ self.config = config
211
+ self.layer_idx = layer_idx
212
+ if layer_idx is None:
213
+ logger.warning_once(
214
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
215
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
216
+ "when creating this class."
217
+ )
218
+
219
+ self.hidden_size = config.hidden_size
220
+ self.num_heads = config.num_attention_heads
221
+ self.head_dim = self.hidden_size // self.num_heads
222
+ self.num_key_value_heads = config.num_key_value_heads
223
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
224
+ self.max_position_embeddings = config.max_position_embeddings
225
+ self.rope_theta = config.rope_theta
226
+ self.is_causal = True
227
+ self.attention_dropout = config.attention_dropout
228
+
229
+ if (self.head_dim * self.num_heads) != self.hidden_size:
230
+ raise ValueError(
231
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
232
+ f" and `num_heads`: {self.num_heads})."
233
+ )
234
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
235
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
236
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
237
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
238
+
239
+ self.rotary_emb = Qwen2RotaryEmbedding(
240
+ self.head_dim,
241
+ max_position_embeddings=self.max_position_embeddings,
242
+ base=self.rope_theta,
243
+ )
244
+
245
+ def forward(
246
+ self,
247
+ hidden_states: torch.Tensor,
248
+ attention_mask: Optional[torch.Tensor] = None,
249
+ position_ids: Optional[torch.LongTensor] = None,
250
+ past_key_value: Optional[Cache] = None,
251
+ output_attentions: bool = False,
252
+ use_cache: bool = False,
253
+ **kwargs,
254
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
255
+ if "padding_mask" in kwargs:
256
+ warnings.warn(
257
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
258
+ )
259
+ bsz, q_len, _ = hidden_states.size()
260
+
261
+ query_states = self.q_proj(hidden_states)
262
+ key_states = self.k_proj(hidden_states)
263
+ value_states = self.v_proj(hidden_states)
264
+
265
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
266
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
267
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
268
+
269
+ kv_seq_len = key_states.shape[-2]
270
+ if past_key_value is not None:
271
+ if self.layer_idx is None:
272
+ raise ValueError(
273
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
274
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
275
+ "with a layer index."
276
+ )
277
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
278
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
279
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
280
+
281
+ if past_key_value is not None:
282
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
283
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
284
+
285
+ # repeat k/v heads if n_kv_heads < n_heads
286
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
287
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
288
+
289
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
290
+
291
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
292
+ raise ValueError(
293
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
294
+ f" {attn_weights.size()}"
295
+ )
296
+
297
+ if attention_mask is not None:
298
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
299
+ raise ValueError(
300
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
301
+ )
302
+
303
+ attn_weights = attn_weights + attention_mask
304
+
305
+ # upcast attention to fp32
306
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
307
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
308
+ attn_output = torch.matmul(attn_weights, value_states)
309
+
310
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
311
+ raise ValueError(
312
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
313
+ f" {attn_output.size()}"
314
+ )
315
+
316
+ attn_output = attn_output.transpose(1, 2).contiguous()
317
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
318
+
319
+ attn_output = self.o_proj(attn_output)
320
+
321
+ if not output_attentions:
322
+ attn_weights = None
323
+
324
+ return attn_output, attn_weights, past_key_value
325
+
326
+
327
+ class Qwen2FlashAttention2(Qwen2Attention):
328
+ """
329
+ Qwen2 flash attention module, following Qwen2 attention module. This module inherits from `Qwen2Attention`
330
+ as the weights of the module stays untouched. The only required change would be on the forward pass
331
+ where it needs to correctly call the public API of flash attention and deal with padding tokens
332
+ in case the input contains any of them. Additionally, for sliding window attention, we apply SWA only to the bottom
333
+ config.max_window_layers layers.
334
+ """
335
+
336
+ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
337
+ def __init__(self, *args, **kwargs):
338
+ super().__init__(*args, **kwargs)
339
+
340
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
341
+ # 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.
342
+ # 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).
343
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
344
+
345
+ def forward(
346
+ self,
347
+ hidden_states: torch.Tensor,
348
+ attention_mask: Optional[torch.Tensor] = None,
349
+ position_ids: Optional[torch.LongTensor] = None,
350
+ past_key_value: Optional[Cache] = None,
351
+ output_attentions: bool = False,
352
+ use_cache: bool = False,
353
+ is_causal: bool = True,
354
+ **kwargs,
355
+ ):
356
+ if "padding_mask" in kwargs:
357
+ warnings.warn(
358
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
359
+ )
360
+
361
+ # overwrite attention_mask with padding_mask
362
+ attention_mask = kwargs.pop("padding_mask")
363
+ bsz, q_len, _ = hidden_states.size()
364
+
365
+ query_states = self.q_proj(hidden_states)
366
+ key_states = self.k_proj(hidden_states)
367
+ value_states = self.v_proj(hidden_states)
368
+
369
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
370
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
371
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
372
+
373
+ kv_seq_len = key_states.shape[-2]
374
+ if past_key_value is not None:
375
+ if self.layer_idx is None:
376
+ raise ValueError(
377
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
378
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
379
+ "with a layer index."
380
+ )
381
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
382
+
383
+ # Because the input can be padded, the absolute sequence length depends on the max position id.
384
+ rotary_seq_len = max(kv_seq_len, position_ids[:, -1].max().item()) + 1
385
+ cos, sin = self.rotary_emb(value_states, seq_len=rotary_seq_len)
386
+
387
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
388
+
389
+ use_sliding_windows = (
390
+ _flash_supports_window_size
391
+ and getattr(self.config, "sliding_window", None) is not None
392
+ and kv_seq_len > self.config.sliding_window
393
+ and self.config.use_sliding_window
394
+ )
395
+
396
+ if not _flash_supports_window_size:
397
+ logger.warning_once(
398
+ "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
399
+ " make sure to upgrade flash-attn library."
400
+ )
401
+
402
+ if past_key_value is not None:
403
+ # Activate slicing cache only if the config has a value `sliding_windows` attribute
404
+ cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
405
+ if (
406
+ getattr(self.config, "sliding_window", None) is not None
407
+ and kv_seq_len > self.config.sliding_window
408
+ and cache_has_contents
409
+ ):
410
+ slicing_tokens = 1 - self.config.sliding_window
411
+
412
+ past_key = past_key_value[self.layer_idx][0]
413
+ past_value = past_key_value[self.layer_idx][1]
414
+
415
+ past_key = past_key[:, :, slicing_tokens:, :].contiguous()
416
+ past_value = past_value[:, :, slicing_tokens:, :].contiguous()
417
+
418
+ if past_key.shape[-2] != self.config.sliding_window - 1:
419
+ raise ValueError(
420
+ f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
421
+ f" {past_key.shape}"
422
+ )
423
+
424
+ if attention_mask is not None:
425
+ attention_mask = attention_mask[:, slicing_tokens:]
426
+ attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
427
+
428
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
429
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
430
+
431
+ # repeat k/v heads if n_kv_heads < n_heads
432
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
433
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
434
+ dropout_rate = 0.0 if not self.training else self.attention_dropout
435
+
436
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
437
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
438
+ # cast them back in float16 just to be sure everything works as expected.
439
+ input_dtype = query_states.dtype
440
+ if input_dtype == torch.float32:
441
+ if torch.is_autocast_enabled():
442
+ target_dtype = torch.get_autocast_gpu_dtype()
443
+ # Handle the case where the model is quantized
444
+ elif hasattr(self.config, "_pre_quantization_dtype"):
445
+ target_dtype = self.config._pre_quantization_dtype
446
+ else:
447
+ target_dtype = self.q_proj.weight.dtype
448
+
449
+ logger.warning_once(
450
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
451
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
452
+ f" {target_dtype}."
453
+ )
454
+
455
+ query_states = query_states.to(target_dtype)
456
+ key_states = key_states.to(target_dtype)
457
+ value_states = value_states.to(target_dtype)
458
+
459
+ # Reashape to the expected shape for Flash Attention
460
+ query_states = query_states.transpose(1, 2)
461
+ key_states = key_states.transpose(1, 2)
462
+ value_states = value_states.transpose(1, 2)
463
+
464
+ attn_output = self._flash_attention_forward(
465
+ query_states,
466
+ key_states,
467
+ value_states,
468
+ attention_mask,
469
+ q_len,
470
+ dropout=dropout_rate,
471
+ use_sliding_windows=use_sliding_windows,
472
+ is_causal=is_causal
473
+ )
474
+
475
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
476
+ attn_output = self.o_proj(attn_output)
477
+
478
+ if not output_attentions:
479
+ attn_weights = None
480
+
481
+ return attn_output, attn_weights, past_key_value
482
+
483
+ def _flash_attention_forward(
484
+ self,
485
+ query_states,
486
+ key_states,
487
+ value_states,
488
+ attention_mask,
489
+ query_length,
490
+ dropout=0.0,
491
+ softmax_scale=None,
492
+ use_sliding_windows=False,
493
+ is_causal=True,
494
+ ):
495
+ """
496
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
497
+ first unpad the input, then computes the attention scores and pad the final attention scores.
498
+
499
+ Args:
500
+ query_states (`torch.Tensor`):
501
+ Input query states to be passed to Flash Attention API
502
+ key_states (`torch.Tensor`):
503
+ Input key states to be passed to Flash Attention API
504
+ value_states (`torch.Tensor`):
505
+ Input value states to be passed to Flash Attention API
506
+ attention_mask (`torch.Tensor`):
507
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
508
+ position of padding tokens and 1 for the position of non-padding tokens.
509
+ dropout (`int`, *optional*):
510
+ Attention dropout
511
+ softmax_scale (`float`, *optional*):
512
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
513
+ use_sliding_windows (`bool`, *optional*):
514
+ Whether to activate sliding window attention.
515
+ """
516
+ if not self._flash_attn_uses_top_left_mask:
517
+ causal = is_causal
518
+ else:
519
+ # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
520
+ causal = is_causal and query_length != 1
521
+
522
+ # Decide whether to use SWA or not by layer index.
523
+ if use_sliding_windows and self.layer_idx >= self.config.max_window_layers:
524
+ use_sliding_windows = False
525
+
526
+ # Contains at least one padding token in the sequence
527
+ if attention_mask is not None:
528
+ batch_size = query_states.shape[0]
529
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
530
+ query_states, key_states, value_states, attention_mask, query_length
531
+ )
532
+
533
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
534
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
535
+
536
+ if not use_sliding_windows:
537
+ attn_output_unpad = flash_attn_varlen_func(
538
+ query_states,
539
+ key_states,
540
+ value_states,
541
+ cu_seqlens_q=cu_seqlens_q,
542
+ cu_seqlens_k=cu_seqlens_k,
543
+ max_seqlen_q=max_seqlen_in_batch_q,
544
+ max_seqlen_k=max_seqlen_in_batch_k,
545
+ dropout_p=dropout,
546
+ softmax_scale=softmax_scale,
547
+ causal=causal,
548
+ )
549
+ else:
550
+ attn_output_unpad = flash_attn_varlen_func(
551
+ query_states,
552
+ key_states,
553
+ value_states,
554
+ cu_seqlens_q=cu_seqlens_q,
555
+ cu_seqlens_k=cu_seqlens_k,
556
+ max_seqlen_q=max_seqlen_in_batch_q,
557
+ max_seqlen_k=max_seqlen_in_batch_k,
558
+ dropout_p=dropout,
559
+ softmax_scale=softmax_scale,
560
+ causal=causal,
561
+ window_size=(self.config.sliding_window, self.config.sliding_window),
562
+ )
563
+
564
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
565
+ else:
566
+ if not use_sliding_windows:
567
+ attn_output = flash_attn_func(
568
+ query_states,
569
+ key_states,
570
+ value_states,
571
+ dropout,
572
+ softmax_scale=softmax_scale,
573
+ causal=causal,
574
+ )
575
+ else:
576
+ attn_output = flash_attn_func(
577
+ query_states,
578
+ key_states,
579
+ value_states,
580
+ dropout,
581
+ softmax_scale=softmax_scale,
582
+ causal=causal,
583
+ window_size=(self.config.sliding_window, self.config.sliding_window),
584
+ )
585
+
586
+ return attn_output
587
+
588
+ # Copied from transformers.models.mistral.modeling_mistral.MistralFlashAttention2._upad_input
589
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
590
+ batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
591
+
592
+ # On the first iteration we need to properly re-create the padding mask
593
+ # by slicing it on the proper place
594
+ if kv_seq_len != attention_mask.shape[-1]:
595
+ attention_mask_num_tokens = attention_mask.shape[-1]
596
+ attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
597
+
598
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
599
+
600
+ key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
601
+ value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
602
+
603
+ if query_length == kv_seq_len:
604
+ query_layer = index_first_axis(
605
+ query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
606
+ )
607
+ cu_seqlens_q = cu_seqlens_k
608
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
609
+ indices_q = indices_k
610
+ elif query_length == 1:
611
+ max_seqlen_in_batch_q = 1
612
+ cu_seqlens_q = torch.arange(
613
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
614
+ ) # There is a memcpy here, that is very bad.
615
+ indices_q = cu_seqlens_q[:-1]
616
+ query_layer = query_layer.squeeze(1)
617
+ else:
618
+ # The -q_len: slice assumes left padding.
619
+ attention_mask = attention_mask[:, -query_length:]
620
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
621
+
622
+ return (
623
+ query_layer,
624
+ key_layer,
625
+ value_layer,
626
+ indices_q,
627
+ (cu_seqlens_q, cu_seqlens_k),
628
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
629
+ )
630
+
631
+
632
+ # Copied from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Qwen2
633
+ class Qwen2SdpaAttention(Qwen2Attention):
634
+ """
635
+ Qwen2 attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
636
+ `Qwen2Attention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
637
+ SDPA API.
638
+ """
639
+
640
+ # Adapted from Qwen2Attention.forward
641
+ def forward(
642
+ self,
643
+ hidden_states: torch.Tensor,
644
+ attention_mask: Optional[torch.Tensor] = None,
645
+ position_ids: Optional[torch.LongTensor] = None,
646
+ past_key_value: Optional[Cache] = None,
647
+ output_attentions: bool = False,
648
+ use_cache: bool = False,
649
+ is_causal: bool = False,
650
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
651
+ if output_attentions:
652
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
653
+ logger.warning_once(
654
+ "Qwen2Model is using Qwen2SdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
655
+ '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.'
656
+ )
657
+ return super().forward(
658
+ hidden_states=hidden_states,
659
+ attention_mask=attention_mask,
660
+ position_ids=position_ids,
661
+ past_key_value=past_key_value,
662
+ output_attentions=output_attentions,
663
+ use_cache=use_cache,
664
+ is_causal=is_causal
665
+ )
666
+
667
+ bsz, q_len, _ = hidden_states.size()
668
+
669
+ query_states = self.q_proj(hidden_states)
670
+ key_states = self.k_proj(hidden_states)
671
+ value_states = self.v_proj(hidden_states)
672
+
673
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
674
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
675
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
676
+
677
+ kv_seq_len = key_states.shape[-2]
678
+ if past_key_value is not None:
679
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
680
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
681
+
682
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
683
+
684
+ if past_key_value is not None:
685
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
686
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
687
+
688
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
689
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
690
+
691
+ if attention_mask is not None:
692
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
693
+ raise ValueError(
694
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
695
+ )
696
+
697
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
698
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
699
+ if query_states.device.type == "cuda" and attention_mask is not None:
700
+ query_states = query_states.contiguous()
701
+ key_states = key_states.contiguous()
702
+ value_states = value_states.contiguous()
703
+
704
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
705
+ query_states,
706
+ key_states,
707
+ value_states,
708
+ attn_mask=attention_mask,
709
+ dropout_p=self.attention_dropout if self.training else 0.0,
710
+ # 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.
711
+ is_causal=is_causal and attention_mask is None and q_len > 1,
712
+ )
713
+
714
+ attn_output = attn_output.transpose(1, 2).contiguous()
715
+ attn_output = attn_output.view(bsz, q_len, self.hidden_size)
716
+
717
+ attn_output = self.o_proj(attn_output)
718
+
719
+ return attn_output, None, past_key_value
720
+
721
+
722
+ QWEN2_ATTENTION_CLASSES = {
723
+ "eager": Qwen2Attention,
724
+ "flash_attention_2": Qwen2FlashAttention2,
725
+ "sdpa": Qwen2SdpaAttention,
726
+ }
727
+
728
+
729
+ class Qwen2DecoderLayer(nn.Module):
730
+ def __init__(self, config: Qwen2Config, layer_idx: int):
731
+ super().__init__()
732
+ self.hidden_size = config.hidden_size
733
+
734
+ if config.use_sliding_window and config._attn_implementation != "flash_attention_2":
735
+ logger.warning_once(
736
+ f"Sliding Window Attention is enabled but not implemented for `{config._attn_implementation}`; "
737
+ "unexpected results may be encountered."
738
+ )
739
+ self.self_attn = QWEN2_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
740
+
741
+ self.mlp = Qwen2MLP(config)
742
+ self.input_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
743
+ self.post_attention_layernorm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
744
+
745
+ def forward(
746
+ self,
747
+ hidden_states: torch.Tensor,
748
+ attention_mask: Optional[torch.Tensor] = None,
749
+ position_ids: Optional[torch.LongTensor] = None,
750
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
751
+ output_attentions: Optional[bool] = False,
752
+ use_cache: Optional[bool] = False,
753
+ is_causal: Optional[bool] = True,
754
+ **kwargs,
755
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
756
+ if "padding_mask" in kwargs:
757
+ warnings.warn(
758
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. "
759
+ "Please make sure use `attention_mask` instead.`"
760
+ )
761
+ """
762
+ Args:
763
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
764
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
765
+ `(batch, sequence_length)` where padding elements are indicated by 0.
766
+ output_attentions (`bool`, *optional*):
767
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
768
+ returned tensors for more detail.
769
+ use_cache (`bool`, *optional*):
770
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
771
+ (see `past_key_values`).
772
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
773
+ """
774
+
775
+ residual = hidden_states
776
+
777
+ hidden_states = self.input_layernorm(hidden_states)
778
+
779
+ # Self Attention
780
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
781
+ hidden_states=hidden_states,
782
+ attention_mask=attention_mask,
783
+ position_ids=position_ids,
784
+ past_key_value=past_key_value,
785
+ output_attentions=output_attentions,
786
+ use_cache=use_cache,
787
+ is_causal=is_causal,
788
+ )
789
+ hidden_states = residual + hidden_states
790
+
791
+ # Fully Connected
792
+ residual = hidden_states
793
+ hidden_states = self.post_attention_layernorm(hidden_states)
794
+ hidden_states = self.mlp(hidden_states)
795
+ hidden_states = residual + hidden_states
796
+
797
+ outputs = (hidden_states,)
798
+
799
+ if output_attentions:
800
+ outputs += (self_attn_weights,)
801
+
802
+ if use_cache:
803
+ outputs += (present_key_value,)
804
+
805
+ return outputs
806
+
807
+
808
+ QWEN2_START_DOCSTRING = r"""
809
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
810
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
811
+ etc.)
812
+
813
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
814
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
815
+ and behavior.
816
+
817
+ Parameters:
818
+ config ([`Qwen2Config`]):
819
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
820
+ load the weights associated with the model, only the configuration. Check out the
821
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
822
+ """
823
+
824
+
825
+ @add_start_docstrings(
826
+ "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
827
+ QWEN2_START_DOCSTRING,
828
+ )
829
+ class Qwen2PreTrainedModel(PreTrainedModel):
830
+ config_class = Qwen2Config
831
+ base_model_prefix = "model"
832
+ supports_gradient_checkpointing = True
833
+ _no_split_modules = ["Qwen2DecoderLayer"]
834
+ _skip_keys_device_placement = "past_key_values"
835
+ _supports_flash_attn_2 = True
836
+ _supports_sdpa = True
837
+ _supports_cache_class = True
838
+
839
+ def _init_weights(self, module):
840
+ std = self.config.initializer_range
841
+ if isinstance(module, nn.Linear):
842
+ module.weight.data.normal_(mean=0.0, std=std)
843
+ if module.bias is not None:
844
+ module.bias.data.zero_()
845
+ elif isinstance(module, nn.Embedding):
846
+ module.weight.data.normal_(mean=0.0, std=std)
847
+ if module.padding_idx is not None:
848
+ module.weight.data[module.padding_idx].zero_()
849
+
850
+
851
+ QWEN2_INPUTS_DOCSTRING = r"""
852
+ Args:
853
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
854
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
855
+ it.
856
+
857
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
858
+ [`PreTrainedTokenizer.__call__`] for details.
859
+
860
+ [What are input IDs?](../glossary#input-ids)
861
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
862
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
863
+
864
+ - 1 for tokens that are **not masked**,
865
+ - 0 for tokens that are **masked**.
866
+
867
+ [What are attention masks?](../glossary#attention-mask)
868
+
869
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
870
+ [`PreTrainedTokenizer.__call__`] for details.
871
+
872
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
873
+ `past_key_values`).
874
+
875
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
876
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
877
+ information on the default strategy.
878
+
879
+ - 1 indicates the head is **not masked**,
880
+ - 0 indicates the head is **masked**.
881
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
882
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
883
+ config.n_positions - 1]`.
884
+
885
+ [What are position IDs?](../glossary#position-ids)
886
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
887
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
888
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
889
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
890
+
891
+ Two formats are allowed:
892
+ - a [`~cache_utils.Cache`] instance;
893
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
894
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
895
+ cache format.
896
+
897
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
898
+ legacy cache format will be returned.
899
+
900
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
901
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
902
+ of shape `(batch_size, sequence_length)`.
903
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
904
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
905
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
906
+ model's internal embedding lookup matrix.
907
+ use_cache (`bool`, *optional*):
908
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
909
+ `past_key_values`).
910
+ output_attentions (`bool`, *optional*):
911
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
912
+ tensors for more detail.
913
+ output_hidden_states (`bool`, *optional*):
914
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
915
+ more detail.
916
+ return_dict (`bool`, *optional*):
917
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
918
+ """
919
+
920
+
921
+ @add_start_docstrings(
922
+ "The bare Qwen2 Model outputting raw hidden-states without any specific head on top.",
923
+ QWEN2_START_DOCSTRING,
924
+ )
925
+ class Qwen2Model(Qwen2PreTrainedModel):
926
+ """
927
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`Qwen2DecoderLayer`]
928
+
929
+ Args:
930
+ config: Qwen2Config
931
+ """
932
+
933
+ def __init__(self, config: Qwen2Config):
934
+ super().__init__(config)
935
+ self.padding_idx = config.pad_token_id
936
+ self.vocab_size = config.vocab_size
937
+
938
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
939
+ self.layers = nn.ModuleList(
940
+ [Qwen2DecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
941
+ )
942
+ self._attn_implementation = config._attn_implementation
943
+ self.norm = Qwen2RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
944
+
945
+ self.gradient_checkpointing = False
946
+ # Initialize weights and apply final processing
947
+ self.post_init()
948
+
949
+ def get_input_embeddings(self):
950
+ return self.embed_tokens
951
+
952
+ def set_input_embeddings(self, value):
953
+ self.embed_tokens = value
954
+
955
+ @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
956
+ def forward(
957
+ self,
958
+ input_ids: torch.LongTensor = None,
959
+ attention_mask: Optional[torch.Tensor] = None,
960
+ position_ids: Optional[torch.LongTensor] = None,
961
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
962
+ inputs_embeds: Optional[torch.FloatTensor] = None,
963
+ use_cache: Optional[bool] = None,
964
+ output_attentions: Optional[bool] = None,
965
+ output_hidden_states: Optional[bool] = None,
966
+ return_dict: Optional[bool] = None,
967
+ labels: Optional[torch.LongTensor] = None,
968
+ is_causal: Optional[bool] = True,
969
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
970
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
971
+ output_hidden_states = (
972
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
973
+ )
974
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
975
+
976
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
977
+
978
+ # retrieve input_ids and inputs_embeds
979
+ if input_ids is not None and inputs_embeds is not None:
980
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
981
+ elif input_ids is not None:
982
+ batch_size, seq_length = input_ids.shape
983
+ elif inputs_embeds is not None:
984
+ batch_size, seq_length, _ = inputs_embeds.shape
985
+ else:
986
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
987
+
988
+ if self.gradient_checkpointing and self.training:
989
+ if use_cache:
990
+ logger.warning_once(
991
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
992
+ )
993
+ use_cache = False
994
+
995
+ past_key_values_length = 0
996
+
997
+ if use_cache:
998
+ use_legacy_cache = not isinstance(past_key_values, Cache)
999
+ if use_legacy_cache:
1000
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
1001
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
1002
+
1003
+ if position_ids is None:
1004
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
1005
+ position_ids = torch.arange(
1006
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
1007
+ )
1008
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
1009
+ else:
1010
+ position_ids = position_ids.view(-1, seq_length).long()
1011
+
1012
+ if inputs_embeds is None:
1013
+ inputs_embeds = self.embed_tokens(input_ids)
1014
+
1015
+ if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
1016
+ is_padding_right = attention_mask[:, -1].sum().item() != batch_size
1017
+ if is_padding_right:
1018
+ raise ValueError(
1019
+ "You are attempting to perform batched generation with padding_side='right'"
1020
+ " this may lead to unexpected behaviour for Flash Attention version of Qwen2. Make sure to "
1021
+ " call `tokenizer.padding_side = 'left'` before tokenizing the input. "
1022
+ )
1023
+
1024
+ if self._attn_implementation == "flash_attention_2":
1025
+ # 2d mask is passed through the layers
1026
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
1027
+ elif self._attn_implementation == "sdpa" and not output_attentions:
1028
+ # output_attentions=True can not be supported when using SDPA, and we fall back on
1029
+ # the manual implementation that requires a 4D causal mask in all cases.
1030
+ if is_causal:
1031
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
1032
+ attention_mask,
1033
+ (batch_size, seq_length),
1034
+ inputs_embeds,
1035
+ past_key_values_length,
1036
+ )
1037
+ else:
1038
+ attention_mask = _prepare_4d_attention_mask_for_sdpa(
1039
+ attention_mask, inputs_embeds.dtype
1040
+ )
1041
+ else:
1042
+ # 4d mask is passed through the layers
1043
+ if is_causal:
1044
+ # Causal mask with -3.3895e+38 where no attention should be
1045
+ attention_mask = _prepare_4d_causal_attention_mask(
1046
+ attention_mask,
1047
+ (batch_size, seq_length),
1048
+ inputs_embeds,
1049
+ past_key_values_length,
1050
+ sliding_window=self.config.sliding_window,
1051
+ )
1052
+ else:
1053
+ # Shape: batch_size, 1, query_length, key_value_length
1054
+ attention_mask = _prepare_4d_attention_mask(
1055
+ attention_mask, inputs_embeds.dtype
1056
+ )
1057
+
1058
+ hidden_states = inputs_embeds
1059
+
1060
+ # decoder layers
1061
+ all_hidden_states = () if output_hidden_states else None
1062
+ all_self_attns = () if output_attentions else None
1063
+ next_decoder_cache = None
1064
+
1065
+ for decoder_layer in self.layers:
1066
+ if output_hidden_states:
1067
+ all_hidden_states += (hidden_states,)
1068
+
1069
+ if self.gradient_checkpointing and self.training:
1070
+ layer_outputs = self._gradient_checkpointing_func(
1071
+ decoder_layer.__call__,
1072
+ hidden_states,
1073
+ attention_mask,
1074
+ position_ids,
1075
+ past_key_values,
1076
+ output_attentions,
1077
+ use_cache,
1078
+ is_causal,
1079
+ )
1080
+ else:
1081
+ layer_outputs = decoder_layer(
1082
+ hidden_states,
1083
+ attention_mask=attention_mask,
1084
+ position_ids=position_ids,
1085
+ past_key_value=past_key_values,
1086
+ output_attentions=output_attentions,
1087
+ use_cache=use_cache,
1088
+ is_causal=is_causal,
1089
+ )
1090
+
1091
+ hidden_states = layer_outputs[0]
1092
+
1093
+ if use_cache:
1094
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1095
+
1096
+ if output_attentions:
1097
+ all_self_attns += (layer_outputs[1],)
1098
+
1099
+ hidden_states = self.norm(hidden_states)
1100
+
1101
+ # add hidden states from the last decoder layer
1102
+ if output_hidden_states:
1103
+ all_hidden_states += (hidden_states,)
1104
+
1105
+ next_cache = None
1106
+ if use_cache:
1107
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
1108
+
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 Qwen2ForCausalLM(Qwen2PreTrainedModel):
1120
+ _tied_weights_keys = ["lm_head.weight"]
1121
+
1122
+ def __init__(self, config):
1123
+ super().__init__(config)
1124
+ self.model = Qwen2Model(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(QWEN2_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
+ is_causal: Optional[bool] = True,
1164
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1165
+ r"""
1166
+ Args:
1167
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1168
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1169
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1170
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1171
+
1172
+ Returns:
1173
+
1174
+ Example:
1175
+
1176
+ ```python
1177
+ >>> from transformers import AutoTokenizer, Qwen2ForCausalLM
1178
+
1179
+ >>> model = Qwen2ForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1180
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1181
+
1182
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1183
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1184
+
1185
+ >>> # Generate
1186
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1187
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1188
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1189
+ ```"""
1190
+
1191
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1192
+ output_hidden_states = (
1193
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1194
+ )
1195
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1196
+
1197
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1198
+ outputs = self.model(
1199
+ input_ids=input_ids,
1200
+ attention_mask=attention_mask,
1201
+ position_ids=position_ids,
1202
+ past_key_values=past_key_values,
1203
+ inputs_embeds=inputs_embeds,
1204
+ use_cache=use_cache,
1205
+ output_attentions=output_attentions,
1206
+ output_hidden_states=output_hidden_states,
1207
+ return_dict=return_dict,
1208
+ is_causal=is_causal,
1209
+ )
1210
+
1211
+ hidden_states = outputs[0]
1212
+ logits = self.lm_head(hidden_states)
1213
+ logits = logits.float()
1214
+
1215
+ loss = None
1216
+ if labels is not None:
1217
+ # Shift so that tokens < n predict n
1218
+ shift_logits = logits[..., :-1, :].contiguous()
1219
+ shift_labels = labels[..., 1:].contiguous()
1220
+ # Flatten the tokens
1221
+ loss_fct = CrossEntropyLoss()
1222
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1223
+ shift_labels = shift_labels.view(-1)
1224
+ # Enable model parallelism
1225
+ shift_labels = shift_labels.to(shift_logits.device)
1226
+ loss = loss_fct(shift_logits, shift_labels)
1227
+
1228
+ if not return_dict:
1229
+ output = (logits,) + outputs[1:]
1230
+ return (loss,) + output if loss is not None else output
1231
+
1232
+ return CausalLMOutputWithPast(
1233
+ loss=loss,
1234
+ logits=logits,
1235
+ past_key_values=outputs.past_key_values,
1236
+ hidden_states=outputs.hidden_states,
1237
+ attentions=outputs.attentions,
1238
+ )
1239
+
1240
+ def prepare_inputs_for_generation(
1241
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1242
+ ):
1243
+ # Omit tokens covered by past_key_values
1244
+ if past_key_values is not None:
1245
+ if isinstance(past_key_values, Cache):
1246
+ cache_length = past_key_values.get_seq_length()
1247
+ past_length = past_key_values.seen_tokens
1248
+ max_cache_length = past_key_values.get_max_length()
1249
+ else:
1250
+ cache_length = past_length = past_key_values[0][0].shape[2]
1251
+ max_cache_length = None
1252
+
1253
+ # Keep only the unprocessed tokens:
1254
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
1255
+ # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
1256
+ # input)
1257
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
1258
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
1259
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
1260
+ # input_ids based on the past_length.
1261
+ elif past_length < input_ids.shape[1]:
1262
+ input_ids = input_ids[:, past_length:]
1263
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
1264
+
1265
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
1266
+ if (
1267
+ max_cache_length is not None
1268
+ and attention_mask is not None
1269
+ and cache_length + input_ids.shape[1] > max_cache_length
1270
+ ):
1271
+ attention_mask = attention_mask[:, -max_cache_length:]
1272
+
1273
+ position_ids = kwargs.get("position_ids", None)
1274
+ if attention_mask is not None and position_ids is None:
1275
+ # create position_ids on the fly for batch generation
1276
+ position_ids = attention_mask.long().cumsum(-1) - 1
1277
+ position_ids.masked_fill_(attention_mask == 0, 1)
1278
+ if past_key_values:
1279
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1280
+
1281
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1282
+ if inputs_embeds is not None and past_key_values is None:
1283
+ model_inputs = {"inputs_embeds": inputs_embeds}
1284
+ else:
1285
+ model_inputs = {"input_ids": input_ids}
1286
+
1287
+ model_inputs.update(
1288
+ {
1289
+ "position_ids": position_ids,
1290
+ "past_key_values": past_key_values,
1291
+ "use_cache": kwargs.get("use_cache"),
1292
+ "attention_mask": attention_mask,
1293
+ }
1294
+ )
1295
+ return model_inputs
1296
+
1297
+ @staticmethod
1298
+ def _reorder_cache(past_key_values, beam_idx):
1299
+ reordered_past = ()
1300
+ for layer_past in past_key_values:
1301
+ reordered_past += (
1302
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1303
+ )
1304
+ return reordered_past
1305
+
1306
+
1307
+ @add_start_docstrings(
1308
+ """
1309
+ The Qwen2 Model transformer with a sequence classification head on top (linear layer).
1310
+
1311
+ [`Qwen2ForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1312
+ (e.g. GPT-2) do.
1313
+
1314
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1315
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1316
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1317
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1318
+ each row of the batch).
1319
+ """,
1320
+ QWEN2_START_DOCSTRING,
1321
+ )
1322
+ class Qwen2ForSequenceClassification(Qwen2PreTrainedModel):
1323
+ def __init__(self, config):
1324
+ super().__init__(config)
1325
+ self.num_labels = config.num_labels
1326
+ self.model = Qwen2Model(config)
1327
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1328
+
1329
+ # Initialize weights and apply final processing
1330
+ self.post_init()
1331
+
1332
+ def get_input_embeddings(self):
1333
+ return self.model.embed_tokens
1334
+
1335
+ def set_input_embeddings(self, value):
1336
+ self.model.embed_tokens = value
1337
+
1338
+ @add_start_docstrings_to_model_forward(QWEN2_INPUTS_DOCSTRING)
1339
+ def forward(
1340
+ self,
1341
+ input_ids: torch.LongTensor = None,
1342
+ attention_mask: Optional[torch.Tensor] = None,
1343
+ position_ids: Optional[torch.LongTensor] = None,
1344
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1345
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1346
+ labels: Optional[torch.LongTensor] = None,
1347
+ use_cache: Optional[bool] = None,
1348
+ output_attentions: Optional[bool] = None,
1349
+ output_hidden_states: Optional[bool] = None,
1350
+ return_dict: Optional[bool] = None,
1351
+ is_causal: Optional[bool] = True,
1352
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1353
+ r"""
1354
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1355
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1356
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1357
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1358
+ """
1359
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1360
+
1361
+ transformer_outputs = self.model(
1362
+ input_ids,
1363
+ attention_mask=attention_mask,
1364
+ position_ids=position_ids,
1365
+ past_key_values=past_key_values,
1366
+ inputs_embeds=inputs_embeds,
1367
+ use_cache=use_cache,
1368
+ output_attentions=output_attentions,
1369
+ output_hidden_states=output_hidden_states,
1370
+ return_dict=return_dict,
1371
+ is_causal=is_causal,
1372
+ )
1373
+ hidden_states = transformer_outputs[0]
1374
+ logits = self.score(hidden_states)
1375
+
1376
+ if input_ids is not None:
1377
+ batch_size = input_ids.shape[0]
1378
+ else:
1379
+ batch_size = inputs_embeds.shape[0]
1380
+
1381
+ if self.config.pad_token_id is None and batch_size != 1:
1382
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1383
+ if self.config.pad_token_id is None:
1384
+ sequence_lengths = -1
1385
+ else:
1386
+ if input_ids is not None:
1387
+ # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
1388
+ sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
1389
+ sequence_lengths = sequence_lengths % input_ids.shape[-1]
1390
+ sequence_lengths = sequence_lengths.to(logits.device)
1391
+ else:
1392
+ sequence_lengths = -1
1393
+
1394
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1395
+
1396
+ loss = None
1397
+ if labels is not None:
1398
+ labels = labels.to(logits.device)
1399
+ if self.config.problem_type is None:
1400
+ if self.num_labels == 1:
1401
+ self.config.problem_type = "regression"
1402
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1403
+ self.config.problem_type = "single_label_classification"
1404
+ else:
1405
+ self.config.problem_type = "multi_label_classification"
1406
+
1407
+ if self.config.problem_type == "regression":
1408
+ loss_fct = MSELoss()
1409
+ if self.num_labels == 1:
1410
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1411
+ else:
1412
+ loss = loss_fct(pooled_logits, labels)
1413
+ elif self.config.problem_type == "single_label_classification":
1414
+ loss_fct = CrossEntropyLoss()
1415
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1416
+ elif self.config.problem_type == "multi_label_classification":
1417
+ loss_fct = BCEWithLogitsLoss()
1418
+ loss = loss_fct(pooled_logits, labels)
1419
+ if not return_dict:
1420
+ output = (pooled_logits,) + transformer_outputs[1:]
1421
+ return ((loss,) + output) if loss is not None else output
1422
+
1423
+ return SequenceClassifierOutputWithPast(
1424
+ loss=loss,
1425
+ logits=pooled_logits,
1426
+ past_key_values=transformer_outputs.past_key_values,
1427
+ hidden_states=transformer_outputs.hidden_states,
1428
+ attentions=transformer_outputs.attentions,
1429
+ )
modules.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [
2
+ {
3
+ "idx": 0,
4
+ "name": "0",
5
+ "path": "",
6
+ "type": "sentence_transformers.models.Transformer"
7
+ },
8
+ {
9
+ "idx": 1,
10
+ "name": "1",
11
+ "path": "1_Pooling",
12
+ "type": "sentence_transformers.models.Pooling"
13
+ },
14
+ {
15
+ "idx": 2,
16
+ "name": "2",
17
+ "path": "2_Normalize",
18
+ "type": "sentence_transformers.models.Normalize"
19
+ }
20
+ ]
sentence_bert_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "max_seq_length": 32768,
3
+ "do_lower_case": false
4
+ }
special_tokens_map.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|im_start|>",
4
+ "<|im_end|>"
5
+ ],
6
+ "eos_token": {
7
+ "content": "<|endoftext|>",
8
+ "lstrip": false,
9
+ "normalized": false,
10
+ "rstrip": false,
11
+ "single_word": false
12
+ },
13
+ "pad_token": {
14
+ "content": "<|endoftext|>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false
19
+ }
20
+ }
tokenization_qwen.py ADDED
@@ -0,0 +1,267 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from typing import List, Optional
3
+ from transformers.models.qwen2.tokenization_qwen2 import Qwen2Tokenizer as OriginalQwen2Tokenizer
4
+ from transformers.models.qwen2.tokenization_qwen2_fast import Qwen2TokenizerFast as OriginalQwen2TokenizerFast
5
+ from tokenizers import processors
6
+
7
+ VOCAB_FILES_NAMES = {
8
+ "vocab_file": "vocab.json",
9
+ "merges_file": "merges.txt",
10
+ "tokenizer_file": "tokenizer.json",
11
+ }
12
+
13
+ class Qwen2Tokenizer(OriginalQwen2Tokenizer):
14
+ """
15
+ Construct a Qwen2 tokenizer. Based on byte-level Byte-Pair-Encoding.
16
+
17
+ Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
18
+ be encoded differently whether it is at the beginning of the sentence (without space) or not:
19
+
20
+ ```python
21
+ >>> from transformers import Qwen2Tokenizer
22
+
23
+ >>> tokenizer = Qwen2Tokenizer.from_pretrained("Qwen/Qwen-tokenizer")
24
+ >>> tokenizer("Hello world")["input_ids"]
25
+ [9707, 1879]
26
+
27
+ >>> tokenizer(" Hello world")["input_ids"]
28
+ [21927, 1879]
29
+ ```
30
+ This is expected.
31
+
32
+ You should not use GPT2Tokenizer instead, because of the different pretokenization rules.
33
+
34
+ This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
35
+ this superclass for more information regarding those methods.
36
+
37
+ Args:
38
+ vocab_file (`str`):
39
+ Path to the vocabulary file.
40
+ merges_file (`str`):
41
+ Path to the merges file.
42
+ errors (`str`, *optional*, defaults to `"replace"`):
43
+ Paradigm to follow when decoding bytes to UTF-8. See
44
+ [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
45
+ unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
46
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
47
+ token instead.
48
+ bos_token (`str`, *optional*):
49
+ The beginning of sequence token. Not applicable for this tokenizer.
50
+ eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
51
+ The end of sequence token.
52
+ pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
53
+ The token used for padding, for example when batching sequences of different lengths.
54
+ clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
55
+ Whether or not the model should cleanup the spaces that were added when splitting the input text during the
56
+ tokenization process. Not applicable to this tokenizer, since tokenization does not add spaces.
57
+ split_special_tokens (`bool`, *optional*, defaults to `False`):
58
+ Whether or not the special tokens should be split during the tokenization process. The default behavior is
59
+ to not split special tokens. This means that if `<|endoftext|>` is the `eos_token`, then `tokenizer.tokenize("<|endoftext|>") =
60
+ ['<|endoftext|>`]. Otherwise, if `split_special_tokens=True`, then `tokenizer.tokenize("<|endoftext|>")` will be give `['<',
61
+ '|', 'endo', 'ft', 'ext', '|', '>']`. This argument is only supported for `slow` tokenizers for the moment.
62
+ add_eos_token (`bool`, *optional*, defaults to `False`):
63
+ Whether or not to add an `eos_token` at the end of sequences.
64
+ """
65
+
66
+ def __init__(
67
+ self,
68
+ vocab_file,
69
+ merges_file,
70
+ errors="replace",
71
+ unk_token="<|endoftext|>",
72
+ bos_token=None,
73
+ eos_token="<|endoftext|>",
74
+ pad_token="<|endoftext|>",
75
+ clean_up_tokenization_spaces=False,
76
+ split_special_tokens=False,
77
+ add_eos_token=False,
78
+ **kwargs,
79
+ ):
80
+ # The add_eos_token code was inspired by the LlamaTokenizer
81
+ self.add_eos_token = add_eos_token
82
+
83
+ super().__init__(
84
+ vocab_file=vocab_file,
85
+ merges_file=merges_file,
86
+ errors=errors,
87
+ unk_token=unk_token,
88
+ bos_token=bos_token,
89
+ eos_token=eos_token,
90
+ pad_token=pad_token,
91
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
92
+ split_special_tokens=split_special_tokens,
93
+ add_eos_token=add_eos_token,
94
+ **kwargs,
95
+ )
96
+
97
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
98
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
99
+
100
+ output = token_ids_0 + eos_token_id
101
+
102
+ if token_ids_1 is not None:
103
+ output = output + token_ids_1 + eos_token_id
104
+
105
+ return output
106
+
107
+ def get_special_tokens_mask(
108
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
109
+ ) -> List[int]:
110
+ """
111
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
112
+ special tokens using the tokenizer `prepare_for_model` method.
113
+
114
+ Args:
115
+ token_ids_0 (`List[int]`):
116
+ List of IDs.
117
+ token_ids_1 (`List[int]`, *optional*):
118
+ Optional second list of IDs for sequence pairs.
119
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
120
+ Whether or not the token list is already formatted with special tokens for the model.
121
+
122
+ Returns:
123
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
124
+ """
125
+ if already_has_special_tokens:
126
+ return super().get_special_tokens_mask(
127
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
128
+ )
129
+
130
+ eos_token_id = [1] if self.add_eos_token else []
131
+
132
+ if token_ids_1 is None:
133
+ return ([0] * len(token_ids_0)) + eos_token_id
134
+ return (
135
+ ([0] * len(token_ids_0))
136
+ + eos_token_id
137
+ + ([0] * len(token_ids_1))
138
+ + eos_token_id
139
+ )
140
+
141
+ def create_token_type_ids_from_sequences(
142
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
143
+ ) -> List[int]:
144
+ """
145
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
146
+ sequence pair mask has the following format:
147
+
148
+ ```
149
+ 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
150
+ | first sequence | second sequence |
151
+ ```
152
+
153
+ if token_ids_1 is None, only returns the first portion of the mask (0s).
154
+
155
+ Args:
156
+ token_ids_0 (`List[int]`):
157
+ List of ids.
158
+ token_ids_1 (`List[int]`, *optional*):
159
+ Optional second list of IDs for sequence pairs.
160
+
161
+ Returns:
162
+ `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
163
+ """
164
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
165
+
166
+ output = [0] * len(token_ids_0 + eos_token_id)
167
+
168
+ if token_ids_1 is not None:
169
+ output += [1] * len(token_ids_1 + eos_token_id)
170
+
171
+ return output
172
+
173
+ class Qwen2TokenizerFast(OriginalQwen2TokenizerFast):
174
+ """
175
+ Construct a "fast" Qwen2 tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
176
+ Byte-Pair-Encoding.
177
+
178
+ Same with GPT2Tokenizer, this tokenizer has been trained to treat spaces like parts of the tokens so a word will
179
+ be encoded differently whether it is at the beginning of the sentence (without space) or not:
180
+
181
+ ```python
182
+ >>> from transformers import Qwen2TokenizerFast
183
+
184
+ >>> tokenizer = Qwen2TokenizerFast.from_pretrained("Qwen/Qwen-tokenizer")
185
+ >>> tokenizer("Hello world")["input_ids"]
186
+ [9707, 1879]
187
+
188
+ >>> tokenizer(" Hello world")["input_ids"]
189
+ [21927, 1879]
190
+ ```
191
+ This is expected.
192
+
193
+ This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
194
+ refer to this superclass for more information regarding those methods.
195
+
196
+ Args:
197
+ vocab_file (`str`, *optional*):
198
+ Path to the vocabulary file.
199
+ merges_file (`str`, *optional*):
200
+ Path to the merges file.
201
+ tokenizer_file (`str`, *optional*):
202
+ Path to [tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that
203
+ contains everything needed to load the tokenizer.
204
+ unk_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
205
+ The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
206
+ token instead. Not applicable to this tokenizer.
207
+ bos_token (`str`, *optional*):
208
+ The beginning of sequence token. Not applicable for this tokenizer.
209
+ eos_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
210
+ The end of sequence token.
211
+ pad_token (`str`, *optional*, defaults to `"<|endoftext|>"`):
212
+ The token used for padding, for example when batching sequences of different lengths.
213
+ add_eos_token (`bool`, *optional*, defaults to `False`):
214
+ Whether or not to add an `eos_token` at the end of sequences.
215
+ """
216
+
217
+ slow_tokenizer_class = Qwen2Tokenizer
218
+ padding_side = "left"
219
+
220
+ def __init__(
221
+ self,
222
+ vocab_file=None,
223
+ merges_file=None,
224
+ tokenizer_file=None,
225
+ unk_token="<|endoftext|>",
226
+ bos_token=None,
227
+ eos_token="<|endoftext|>",
228
+ pad_token="<|endoftext|>",
229
+ add_eos_token=False,
230
+ **kwargs,
231
+ ):
232
+ super().__init__(
233
+ vocab_file=vocab_file,
234
+ merges_file=merges_file,
235
+ tokenizer_file=tokenizer_file,
236
+ unk_token=unk_token,
237
+ bos_token=bos_token,
238
+ eos_token=eos_token,
239
+ pad_token=pad_token,
240
+ **kwargs,
241
+ )
242
+
243
+ self._add_eos_token = add_eos_token
244
+ self.update_post_processor()
245
+
246
+ def update_post_processor(self):
247
+ """
248
+ Updates the underlying post processor with the current `eos_token`.
249
+ """
250
+ eos = self.eos_token
251
+ eos_token_id = self.eos_token_id
252
+ if eos is None and self.add_eos_token:
253
+ raise ValueError("add_eos_token = True but eos_token = None")
254
+
255
+ single = f"$A:0{(' '+eos+':0') if self.add_eos_token else ''}"
256
+ pair = f"{single} $B:1{(' '+eos+':1') if self.add_eos_token else ''}"
257
+
258
+ special_tokens = []
259
+ if self.add_eos_token:
260
+ special_tokens.append((eos, eos_token_id))
261
+ self._tokenizer.post_processor = processors.TemplateProcessing(
262
+ single=single, pair=pair, special_tokens=special_tokens
263
+ )
264
+
265
+ @property
266
+ def add_eos_token(self):
267
+ return self._add_eos_token
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,47 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_eos_token": true,
3
+ "add_prefix_space": false,
4
+ "added_tokens_decoder": {
5
+ "151643": {
6
+ "content": "<|endoftext|>",
7
+ "lstrip": false,
8
+ "normalized": false,
9
+ "rstrip": false,
10
+ "single_word": false,
11
+ "special": true
12
+ },
13
+ "151644": {
14
+ "content": "<|im_start|>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false,
19
+ "special": true
20
+ },
21
+ "151645": {
22
+ "content": "<|im_end|>",
23
+ "lstrip": false,
24
+ "normalized": false,
25
+ "rstrip": false,
26
+ "single_word": false,
27
+ "special": true
28
+ }
29
+ },
30
+ "additional_special_tokens": [
31
+ "<|im_start|>",
32
+ "<|im_end|>"
33
+ ],
34
+ "auto_map": {
35
+ "AutoTokenizer": ["tokenization_qwen.Qwen2Tokenizer", "tokenization_qwen.Qwen2TokenizerFast"]
36
+ },
37
+ "bos_token": null,
38
+ "chat_template": "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
39
+ "clean_up_tokenization_spaces": false,
40
+ "eos_token": "<|endoftext|>",
41
+ "errors": "replace",
42
+ "model_max_length": 32768,
43
+ "pad_token": "<|endoftext|>",
44
+ "split_special_tokens": false,
45
+ "tokenizer_class": "Qwen2Tokenizer",
46
+ "unk_token": null
47
+ }
vocab.json ADDED
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