JosephusCheung commited on
Commit
26bd0aa
1 Parent(s): 7a48db5

Upload folder using huggingface_hub

Browse files
config.json ADDED
@@ -0,0 +1,26 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "Pwen-VL-Chat",
3
+ "architectures": [
4
+ "LlamaForCausalLM"
5
+ ],
6
+ "bos_token_id": 151643,
7
+ "eos_token_id": 151643,
8
+ "hidden_act": "silu",
9
+ "hidden_size": 4096,
10
+ "initializer_range": 0.02,
11
+ "intermediate_size": 11008,
12
+ "max_position_embeddings": 8192,
13
+ "model_type": "llama",
14
+ "num_attention_heads": 32,
15
+ "num_hidden_layers": 32,
16
+ "num_key_value_heads": 32,
17
+ "pad_token_id": 151643,
18
+ "pretraining_tp": 1,
19
+ "rms_norm_eps": 1e-05,
20
+ "rope_scaling": null,
21
+ "tie_word_embeddings": false,
22
+ "torch_dtype": "bfloat16",
23
+ "transformers_version": "4.32.0.dev0",
24
+ "use_cache": false,
25
+ "vocab_size": 151936
26
+ }
generation_config.json ADDED
@@ -0,0 +1,11 @@
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "chat_format": "chatml",
3
+ "do_sample": true,
4
+ "eos_token_id": 151643,
5
+ "max_new_tokens": 512,
6
+ "max_window_size": 6144,
7
+ "pad_token_id": 151643,
8
+ "top_k": 0,
9
+ "top_p": 0.5,
10
+ "transformers_version": "4.32.0.dev0"
11
+ }
modeling_llama.py ADDED
@@ -0,0 +1,1013 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 LLaMA model."""
21
+ import math
22
+ from typing import List, Optional, Tuple, Union
23
+
24
+ import torch
25
+ import torch.nn.functional as F
26
+ import torch.utils.checkpoint
27
+ from torch import nn
28
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
29
+
30
+ from ...activations import ACT2FN
31
+ from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
32
+ from ...modeling_utils import PreTrainedModel
33
+ from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
34
+ from .configuration_llama import LlamaConfig
35
+
36
+
37
+ logger = logging.get_logger(__name__)
38
+
39
+ _CONFIG_FOR_DOC = "LlamaConfig"
40
+
41
+
42
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
43
+ def _make_causal_mask(
44
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
45
+ ):
46
+ """
47
+ Make causal mask used for bi-directional self-attention.
48
+ """
49
+ bsz, tgt_len = input_ids_shape
50
+ mask = torch.full((tgt_len, tgt_len), torch.finfo(dtype).min, device=device)
51
+ mask_cond = torch.arange(mask.size(-1), device=device)
52
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
53
+ mask = mask.to(dtype)
54
+
55
+ if past_key_values_length > 0:
56
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
57
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
58
+
59
+
60
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
61
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
62
+ """
63
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
64
+ """
65
+ bsz, src_len = mask.size()
66
+ tgt_len = tgt_len if tgt_len is not None else src_len
67
+
68
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
69
+
70
+ inverted_mask = 1.0 - expanded_mask
71
+
72
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
73
+
74
+
75
+ class LlamaRMSNorm(nn.Module):
76
+ def __init__(self, hidden_size, eps=1e-6):
77
+ """
78
+ LlamaRMSNorm is equivalent to T5LayerNorm
79
+ """
80
+ super().__init__()
81
+ self.weight = nn.Parameter(torch.ones(hidden_size))
82
+ self.variance_epsilon = eps
83
+
84
+ def forward(self, hidden_states):
85
+ input_dtype = hidden_states.dtype
86
+ hidden_states = hidden_states.to(torch.float32)
87
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
88
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
89
+ return self.weight * hidden_states.to(input_dtype)
90
+
91
+
92
+ class LlamaRotaryEmbedding(torch.nn.Module):
93
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
94
+ super().__init__()
95
+
96
+ self.dim = dim
97
+ self.max_position_embeddings = max_position_embeddings
98
+ self.base = base
99
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
100
+ self.register_buffer("inv_freq", inv_freq)
101
+
102
+ # Build here to make `torch.jit.trace` work.
103
+ self._set_cos_sin_cache(
104
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
105
+ )
106
+
107
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
108
+ self.max_seq_len_cached = seq_len
109
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.float32)
110
+
111
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
112
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
113
+ emb = torch.cat((freqs, freqs), dim=-1)
114
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
115
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
116
+
117
+ def forward(self, x, seq_len=None):
118
+ # x: [bs, num_attention_heads, seq_len, head_size]
119
+ if seq_len > self.max_seq_len_cached:
120
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
121
+
122
+ return (
123
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
124
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
125
+ )
126
+
127
+
128
+ class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
129
+ """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
130
+
131
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
132
+ self.scaling_factor = scaling_factor
133
+ super().__init__(dim, max_position_embeddings, base, device)
134
+
135
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
136
+ self.max_seq_len_cached = seq_len
137
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
138
+ t = t / self.scaling_factor
139
+
140
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
141
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
142
+ emb = torch.cat((freqs, freqs), dim=-1)
143
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
144
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
145
+
146
+
147
+ class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
148
+ """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
149
+
150
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
151
+ self.scaling_factor = scaling_factor
152
+ super().__init__(dim, max_position_embeddings, base, device)
153
+
154
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
155
+ self.max_seq_len_cached = seq_len
156
+
157
+ if seq_len > self.max_position_embeddings:
158
+ base = self.base * (
159
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
160
+ ) ** (self.dim / (self.dim - 2))
161
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
162
+ self.register_buffer("inv_freq", inv_freq)
163
+
164
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
165
+
166
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
167
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
168
+ emb = torch.cat((freqs, freqs), dim=-1)
169
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
170
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
171
+
172
+
173
+ def rotate_half(x):
174
+ """Rotates half the hidden dims of the input."""
175
+ x1 = x[..., : x.shape[-1] // 2]
176
+ x2 = x[..., x.shape[-1] // 2 :]
177
+ return torch.cat((-x2, x1), dim=-1)
178
+
179
+
180
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
181
+ # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
182
+ cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
183
+ sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
184
+ cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
185
+ sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
186
+ q_embed = (q * cos) + (rotate_half(q) * sin)
187
+ k_embed = (k * cos) + (rotate_half(k) * sin)
188
+ return q_embed, k_embed
189
+
190
+
191
+ class LlamaMLP(nn.Module):
192
+ def __init__(self, config):
193
+ super().__init__()
194
+ self.config = config
195
+ self.hidden_size = config.hidden_size
196
+ self.intermediate_size = config.intermediate_size
197
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
198
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
199
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
200
+ self.act_fn = ACT2FN[config.hidden_act]
201
+
202
+ def forward(self, x):
203
+ if self.config.pretraining_tp > 1:
204
+ slice = self.intermediate_size // self.config.pretraining_tp
205
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
206
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
207
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
208
+
209
+ gate_proj = torch.cat(
210
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
211
+ )
212
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
213
+
214
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
215
+ down_proj = [
216
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
217
+ ]
218
+ down_proj = sum(down_proj)
219
+ else:
220
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
221
+
222
+ return down_proj
223
+
224
+
225
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
226
+ """
227
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
228
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
229
+ """
230
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
231
+ if n_rep == 1:
232
+ return hidden_states
233
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
234
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
235
+
236
+
237
+ class LlamaAttention(nn.Module):
238
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
239
+
240
+ def __init__(self, config: LlamaConfig):
241
+ super().__init__()
242
+ self.config = config
243
+ self.hidden_size = config.hidden_size
244
+ self.num_heads = config.num_attention_heads
245
+ self.head_dim = self.hidden_size // self.num_heads
246
+ self.num_key_value_heads = config.num_key_value_heads
247
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
248
+ self.max_position_embeddings = config.max_position_embeddings
249
+
250
+ if (self.head_dim * self.num_heads) != self.hidden_size:
251
+ raise ValueError(
252
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
253
+ f" and `num_heads`: {self.num_heads})."
254
+ )
255
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
256
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
257
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
258
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
259
+ self._init_rope()
260
+
261
+ def _init_rope(self):
262
+ if self.config.rope_scaling is None:
263
+ self.rotary_emb = LlamaRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
264
+ else:
265
+ scaling_type = self.config.rope_scaling["type"]
266
+ scaling_factor = self.config.rope_scaling["factor"]
267
+ if scaling_type == "linear":
268
+ self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
269
+ self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
270
+ )
271
+ elif scaling_type == "dynamic":
272
+ self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
273
+ self.head_dim, max_position_embeddings=self.max_position_embeddings, scaling_factor=scaling_factor
274
+ )
275
+ else:
276
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
277
+
278
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
279
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
280
+
281
+ def forward(
282
+ self,
283
+ hidden_states: torch.Tensor,
284
+ attention_mask: Optional[torch.Tensor] = None,
285
+ position_ids: Optional[torch.LongTensor] = None,
286
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
287
+ output_attentions: bool = False,
288
+ use_cache: bool = False,
289
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
290
+ bsz, q_len, _ = hidden_states.size()
291
+
292
+ if self.config.pretraining_tp > 1:
293
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
294
+ query_slices = self.q_proj.weight.split(
295
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
296
+ )
297
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
298
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
299
+
300
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
301
+ query_states = torch.cat(query_states, dim=-1)
302
+
303
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
304
+ key_states = torch.cat(key_states, dim=-1)
305
+
306
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
307
+ value_states = torch.cat(value_states, dim=-1)
308
+
309
+ else:
310
+ query_states = self.q_proj(hidden_states)
311
+ key_states = self.k_proj(hidden_states)
312
+ value_states = self.v_proj(hidden_states)
313
+
314
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
315
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
316
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
317
+
318
+ kv_seq_len = key_states.shape[-2]
319
+ if past_key_value is not None:
320
+ kv_seq_len += past_key_value[0].shape[-2]
321
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
322
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
323
+
324
+ if past_key_value is not None:
325
+ # reuse k, v, self_attention
326
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
327
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
328
+
329
+ past_key_value = (key_states, value_states) if use_cache else None
330
+
331
+ # repeat k/v heads if n_kv_heads < n_heads
332
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
333
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
334
+
335
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
336
+
337
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
338
+ raise ValueError(
339
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
340
+ f" {attn_weights.size()}"
341
+ )
342
+
343
+ if attention_mask is not None:
344
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
345
+ raise ValueError(
346
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
347
+ )
348
+ attn_weights = attn_weights + attention_mask
349
+
350
+ # upcast attention to fp32
351
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
352
+ attn_output = torch.matmul(attn_weights, value_states)
353
+
354
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
355
+ raise ValueError(
356
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
357
+ f" {attn_output.size()}"
358
+ )
359
+
360
+ attn_output = attn_output.transpose(1, 2).contiguous()
361
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
362
+
363
+ if self.config.pretraining_tp > 1:
364
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
365
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
366
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
367
+ else:
368
+ attn_output = self.o_proj(attn_output)
369
+
370
+ if not output_attentions:
371
+ attn_weights = None
372
+
373
+ return attn_output, attn_weights, past_key_value
374
+
375
+
376
+ class LlamaDecoderLayer(nn.Module):
377
+ def __init__(self, config: LlamaConfig):
378
+ super().__init__()
379
+ self.hidden_size = config.hidden_size
380
+ self.self_attn = LlamaAttention(config=config)
381
+ self.mlp = LlamaMLP(config)
382
+ self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
383
+ self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
384
+
385
+ def forward(
386
+ self,
387
+ hidden_states: torch.Tensor,
388
+ attention_mask: Optional[torch.Tensor] = None,
389
+ position_ids: Optional[torch.LongTensor] = None,
390
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
391
+ output_attentions: Optional[bool] = False,
392
+ use_cache: Optional[bool] = False,
393
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
394
+ """
395
+ Args:
396
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
397
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
398
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
399
+ output_attentions (`bool`, *optional*):
400
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
401
+ returned tensors for more detail.
402
+ use_cache (`bool`, *optional*):
403
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
404
+ (see `past_key_values`).
405
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
406
+ """
407
+
408
+ residual = hidden_states
409
+
410
+ hidden_states = self.input_layernorm(hidden_states)
411
+
412
+ # Self Attention
413
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
414
+ hidden_states=hidden_states,
415
+ attention_mask=attention_mask,
416
+ position_ids=position_ids,
417
+ past_key_value=past_key_value,
418
+ output_attentions=output_attentions,
419
+ use_cache=use_cache,
420
+ )
421
+ hidden_states = residual + hidden_states
422
+
423
+ # Fully Connected
424
+ residual = hidden_states
425
+ hidden_states = self.post_attention_layernorm(hidden_states)
426
+ hidden_states = self.mlp(hidden_states)
427
+ hidden_states = residual + hidden_states
428
+
429
+ outputs = (hidden_states,)
430
+
431
+ if output_attentions:
432
+ outputs += (self_attn_weights,)
433
+
434
+ if use_cache:
435
+ outputs += (present_key_value,)
436
+
437
+ return outputs
438
+
439
+
440
+ LLAMA_START_DOCSTRING = r"""
441
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
442
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
443
+ etc.)
444
+
445
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
446
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
447
+ and behavior.
448
+
449
+ Parameters:
450
+ config ([`LlamaConfig`]):
451
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
452
+ load the weights associated with the model, only the configuration. Check out the
453
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
454
+ """
455
+
456
+
457
+ @add_start_docstrings(
458
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
459
+ LLAMA_START_DOCSTRING,
460
+ )
461
+ class LlamaPreTrainedModel(PreTrainedModel):
462
+ config_class = LlamaConfig
463
+ base_model_prefix = "model"
464
+ supports_gradient_checkpointing = True
465
+ _no_split_modules = ["LlamaDecoderLayer"]
466
+ _skip_keys_device_placement = "past_key_values"
467
+
468
+ def _init_weights(self, module):
469
+ std = self.config.initializer_range
470
+ if isinstance(module, nn.Linear):
471
+ module.weight.data.normal_(mean=0.0, std=std)
472
+ if module.bias is not None:
473
+ module.bias.data.zero_()
474
+ elif isinstance(module, nn.Embedding):
475
+ module.weight.data.normal_(mean=0.0, std=std)
476
+ if module.padding_idx is not None:
477
+ module.weight.data[module.padding_idx].zero_()
478
+
479
+ def _set_gradient_checkpointing(self, module, value=False):
480
+ if isinstance(module, LlamaModel):
481
+ module.gradient_checkpointing = value
482
+
483
+
484
+ LLAMA_INPUTS_DOCSTRING = r"""
485
+ Args:
486
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
487
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
488
+ it.
489
+
490
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
491
+ [`PreTrainedTokenizer.__call__`] for details.
492
+
493
+ [What are input IDs?](../glossary#input-ids)
494
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
495
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
496
+
497
+ - 1 for tokens that are **not masked**,
498
+ - 0 for tokens that are **masked**.
499
+
500
+ [What are attention masks?](../glossary#attention-mask)
501
+
502
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
503
+ [`PreTrainedTokenizer.__call__`] for details.
504
+
505
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
506
+ `past_key_values`).
507
+
508
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
509
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
510
+ information on the default strategy.
511
+
512
+ - 1 indicates the head is **not masked**,
513
+ - 0 indicates the head is **masked**.
514
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
515
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
516
+ config.n_positions - 1]`.
517
+
518
+ [What are position IDs?](../glossary#position-ids)
519
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
520
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
521
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
522
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
523
+
524
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
525
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
526
+
527
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
528
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
529
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
530
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
531
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
532
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
533
+ model's internal embedding lookup matrix.
534
+ use_cache (`bool`, *optional*):
535
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
536
+ `past_key_values`).
537
+ output_attentions (`bool`, *optional*):
538
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
539
+ tensors for more detail.
540
+ output_hidden_states (`bool`, *optional*):
541
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
542
+ more detail.
543
+ return_dict (`bool`, *optional*):
544
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
545
+ """
546
+
547
+
548
+ @add_start_docstrings(
549
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
550
+ LLAMA_START_DOCSTRING,
551
+ )
552
+ class LlamaModel(LlamaPreTrainedModel):
553
+ """
554
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
555
+
556
+ Args:
557
+ config: LlamaConfig
558
+ """
559
+
560
+ def __init__(self, config: LlamaConfig):
561
+ super().__init__(config)
562
+ self.padding_idx = config.pad_token_id
563
+ self.vocab_size = config.vocab_size
564
+
565
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
566
+ self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
567
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
568
+
569
+ self.gradient_checkpointing = False
570
+ # Initialize weights and apply final processing
571
+ self.post_init()
572
+
573
+ def get_input_embeddings(self):
574
+ return self.embed_tokens
575
+
576
+ def set_input_embeddings(self, value):
577
+ self.embed_tokens = value
578
+
579
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
580
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
581
+ # create causal mask
582
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
583
+ combined_attention_mask = None
584
+ if input_shape[-1] > 1:
585
+ combined_attention_mask = _make_causal_mask(
586
+ input_shape,
587
+ inputs_embeds.dtype,
588
+ device=inputs_embeds.device,
589
+ past_key_values_length=past_key_values_length,
590
+ )
591
+
592
+ if attention_mask is not None:
593
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
594
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
595
+ inputs_embeds.device
596
+ )
597
+ combined_attention_mask = (
598
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
599
+ )
600
+
601
+ return combined_attention_mask
602
+
603
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
604
+ def forward(
605
+ self,
606
+ input_ids: torch.LongTensor = None,
607
+ attention_mask: Optional[torch.Tensor] = None,
608
+ position_ids: Optional[torch.LongTensor] = None,
609
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
610
+ inputs_embeds: Optional[torch.FloatTensor] = None,
611
+ use_cache: Optional[bool] = None,
612
+ output_attentions: Optional[bool] = None,
613
+ output_hidden_states: Optional[bool] = None,
614
+ return_dict: Optional[bool] = None,
615
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
616
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
617
+ output_hidden_states = (
618
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
619
+ )
620
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
621
+
622
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
623
+
624
+ # retrieve input_ids and inputs_embeds
625
+ if input_ids is not None and inputs_embeds is not None:
626
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
627
+ elif input_ids is not None:
628
+ batch_size, seq_length = input_ids.shape
629
+ elif inputs_embeds is not None:
630
+ batch_size, seq_length, _ = inputs_embeds.shape
631
+ else:
632
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
633
+
634
+ seq_length_with_past = seq_length
635
+ past_key_values_length = 0
636
+
637
+ if past_key_values is not None:
638
+ past_key_values_length = past_key_values[0][0].shape[2]
639
+ seq_length_with_past = seq_length_with_past + past_key_values_length
640
+
641
+ if position_ids is None:
642
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
643
+ position_ids = torch.arange(
644
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
645
+ )
646
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
647
+ else:
648
+ position_ids = position_ids.view(-1, seq_length).long()
649
+
650
+ if inputs_embeds is None:
651
+ inputs_embeds = self.embed_tokens(input_ids)
652
+ # embed positions
653
+ if attention_mask is None:
654
+ attention_mask = torch.ones(
655
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
656
+ )
657
+ attention_mask = self._prepare_decoder_attention_mask(
658
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
659
+ )
660
+
661
+ hidden_states = inputs_embeds
662
+
663
+ if self.gradient_checkpointing and self.training:
664
+ if use_cache:
665
+ logger.warning_once(
666
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
667
+ )
668
+ use_cache = False
669
+
670
+ # decoder layers
671
+ all_hidden_states = () if output_hidden_states else None
672
+ all_self_attns = () if output_attentions else None
673
+ next_decoder_cache = () if use_cache else None
674
+
675
+ for idx, decoder_layer in enumerate(self.layers):
676
+ if output_hidden_states:
677
+ all_hidden_states += (hidden_states,)
678
+
679
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
680
+
681
+ if self.gradient_checkpointing and self.training:
682
+
683
+ def create_custom_forward(module):
684
+ def custom_forward(*inputs):
685
+ # None for past_key_value
686
+ return module(*inputs, output_attentions, None)
687
+
688
+ return custom_forward
689
+
690
+ layer_outputs = torch.utils.checkpoint.checkpoint(
691
+ create_custom_forward(decoder_layer),
692
+ hidden_states,
693
+ attention_mask,
694
+ position_ids,
695
+ None,
696
+ )
697
+ else:
698
+ layer_outputs = decoder_layer(
699
+ hidden_states,
700
+ attention_mask=attention_mask,
701
+ position_ids=position_ids,
702
+ past_key_value=past_key_value,
703
+ output_attentions=output_attentions,
704
+ use_cache=use_cache,
705
+ )
706
+
707
+ hidden_states = layer_outputs[0]
708
+
709
+ if use_cache:
710
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
711
+
712
+ if output_attentions:
713
+ all_self_attns += (layer_outputs[1],)
714
+
715
+ hidden_states = self.norm(hidden_states)
716
+
717
+ # add hidden states from the last decoder layer
718
+ if output_hidden_states:
719
+ all_hidden_states += (hidden_states,)
720
+
721
+ next_cache = next_decoder_cache if use_cache else None
722
+ if not return_dict:
723
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
724
+ return BaseModelOutputWithPast(
725
+ last_hidden_state=hidden_states,
726
+ past_key_values=next_cache,
727
+ hidden_states=all_hidden_states,
728
+ attentions=all_self_attns,
729
+ )
730
+
731
+
732
+ class LlamaForCausalLM(LlamaPreTrainedModel):
733
+ _tied_weights_keys = ["lm_head.weight"]
734
+
735
+ def __init__(self, config):
736
+ super().__init__(config)
737
+ self.model = LlamaModel(config)
738
+ self.vocab_size = config.vocab_size
739
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
740
+
741
+ # Initialize weights and apply final processing
742
+ self.post_init()
743
+
744
+ def get_input_embeddings(self):
745
+ return self.model.embed_tokens
746
+
747
+ def set_input_embeddings(self, value):
748
+ self.model.embed_tokens = value
749
+
750
+ def get_output_embeddings(self):
751
+ return self.lm_head
752
+
753
+ def set_output_embeddings(self, new_embeddings):
754
+ self.lm_head = new_embeddings
755
+
756
+ def set_decoder(self, decoder):
757
+ self.model = decoder
758
+
759
+ def get_decoder(self):
760
+ return self.model
761
+
762
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
763
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
764
+ def forward(
765
+ self,
766
+ input_ids: torch.LongTensor = None,
767
+ attention_mask: Optional[torch.Tensor] = None,
768
+ position_ids: Optional[torch.LongTensor] = None,
769
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
770
+ inputs_embeds: Optional[torch.FloatTensor] = None,
771
+ labels: Optional[torch.LongTensor] = None,
772
+ use_cache: Optional[bool] = None,
773
+ output_attentions: Optional[bool] = None,
774
+ output_hidden_states: Optional[bool] = None,
775
+ return_dict: Optional[bool] = None,
776
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
777
+ r"""
778
+ Args:
779
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
780
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
781
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
782
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
783
+
784
+ Returns:
785
+
786
+ Example:
787
+
788
+ ```python
789
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
790
+
791
+ >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
792
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
793
+
794
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
795
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
796
+
797
+ >>> # Generate
798
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
799
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
800
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
801
+ ```"""
802
+
803
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
804
+ output_hidden_states = (
805
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
806
+ )
807
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
808
+
809
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
810
+ outputs = self.model(
811
+ input_ids=input_ids,
812
+ attention_mask=attention_mask,
813
+ position_ids=position_ids,
814
+ past_key_values=past_key_values,
815
+ inputs_embeds=inputs_embeds,
816
+ use_cache=use_cache,
817
+ output_attentions=output_attentions,
818
+ output_hidden_states=output_hidden_states,
819
+ return_dict=return_dict,
820
+ )
821
+
822
+ hidden_states = outputs[0]
823
+ if self.config.pretraining_tp > 1:
824
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
825
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
826
+ logits = torch.cat(logits, dim=-1)
827
+ else:
828
+ logits = self.lm_head(hidden_states)
829
+ logits = logits.float()
830
+
831
+ loss = None
832
+ if labels is not None:
833
+ # Shift so that tokens < n predict n
834
+ shift_logits = logits[..., :-1, :].contiguous()
835
+ shift_labels = labels[..., 1:].contiguous()
836
+ # Flatten the tokens
837
+ loss_fct = CrossEntropyLoss()
838
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
839
+ shift_labels = shift_labels.view(-1)
840
+ # Enable model parallelism
841
+ shift_labels = shift_labels.to(shift_logits.device)
842
+ loss = loss_fct(shift_logits, shift_labels)
843
+
844
+ if not return_dict:
845
+ output = (logits,) + outputs[1:]
846
+ return (loss,) + output if loss is not None else output
847
+
848
+ return CausalLMOutputWithPast(
849
+ loss=loss,
850
+ logits=logits,
851
+ past_key_values=outputs.past_key_values,
852
+ hidden_states=outputs.hidden_states,
853
+ attentions=outputs.attentions,
854
+ )
855
+
856
+ def prepare_inputs_for_generation(
857
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
858
+ ):
859
+ if past_key_values:
860
+ input_ids = input_ids[:, -1:]
861
+
862
+ position_ids = kwargs.get("position_ids", None)
863
+ if attention_mask is not None and position_ids is None:
864
+ # create position_ids on the fly for batch generation
865
+ position_ids = attention_mask.long().cumsum(-1) - 1
866
+ position_ids.masked_fill_(attention_mask == 0, 1)
867
+ if past_key_values:
868
+ position_ids = position_ids[:, -1].unsqueeze(-1)
869
+
870
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
871
+ if inputs_embeds is not None and past_key_values is None:
872
+ model_inputs = {"inputs_embeds": inputs_embeds}
873
+ else:
874
+ model_inputs = {"input_ids": input_ids}
875
+
876
+ model_inputs.update(
877
+ {
878
+ "position_ids": position_ids,
879
+ "past_key_values": past_key_values,
880
+ "use_cache": kwargs.get("use_cache"),
881
+ "attention_mask": attention_mask,
882
+ }
883
+ )
884
+ return model_inputs
885
+
886
+ @staticmethod
887
+ def _reorder_cache(past_key_values, beam_idx):
888
+ reordered_past = ()
889
+ for layer_past in past_key_values:
890
+ reordered_past += (
891
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
892
+ )
893
+ return reordered_past
894
+
895
+
896
+ @add_start_docstrings(
897
+ """
898
+ The LLaMa Model transformer with a sequence classification head on top (linear layer).
899
+
900
+ [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
901
+ (e.g. GPT-2) do.
902
+
903
+ Since it does classification on the last token, it requires to know the position of the last token. If a
904
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
905
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
906
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
907
+ each row of the batch).
908
+ """,
909
+ LLAMA_START_DOCSTRING,
910
+ )
911
+ class LlamaForSequenceClassification(LlamaPreTrainedModel):
912
+ def __init__(self, config):
913
+ super().__init__(config)
914
+ self.num_labels = config.num_labels
915
+ self.model = LlamaModel(config)
916
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
917
+
918
+ # Initialize weights and apply final processing
919
+ self.post_init()
920
+
921
+ def get_input_embeddings(self):
922
+ return self.model.embed_tokens
923
+
924
+ def set_input_embeddings(self, value):
925
+ self.model.embed_tokens = value
926
+
927
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
928
+ def forward(
929
+ self,
930
+ input_ids: torch.LongTensor = None,
931
+ attention_mask: Optional[torch.Tensor] = None,
932
+ position_ids: Optional[torch.LongTensor] = None,
933
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
934
+ inputs_embeds: Optional[torch.FloatTensor] = None,
935
+ labels: Optional[torch.LongTensor] = None,
936
+ use_cache: Optional[bool] = None,
937
+ output_attentions: Optional[bool] = None,
938
+ output_hidden_states: Optional[bool] = None,
939
+ return_dict: Optional[bool] = None,
940
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
941
+ r"""
942
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
943
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
944
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
945
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
946
+ """
947
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
948
+
949
+ transformer_outputs = self.model(
950
+ input_ids,
951
+ attention_mask=attention_mask,
952
+ position_ids=position_ids,
953
+ past_key_values=past_key_values,
954
+ inputs_embeds=inputs_embeds,
955
+ use_cache=use_cache,
956
+ output_attentions=output_attentions,
957
+ output_hidden_states=output_hidden_states,
958
+ return_dict=return_dict,
959
+ )
960
+ hidden_states = transformer_outputs[0]
961
+ logits = self.score(hidden_states)
962
+
963
+ if input_ids is not None:
964
+ batch_size = input_ids.shape[0]
965
+ else:
966
+ batch_size = inputs_embeds.shape[0]
967
+
968
+ if self.config.pad_token_id is None and batch_size != 1:
969
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
970
+ if self.config.pad_token_id is None:
971
+ sequence_lengths = -1
972
+ else:
973
+ if input_ids is not None:
974
+ sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
975
+ else:
976
+ sequence_lengths = -1
977
+
978
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
979
+
980
+ loss = None
981
+ if labels is not None:
982
+ labels = labels.to(logits.device)
983
+ if self.config.problem_type is None:
984
+ if self.num_labels == 1:
985
+ self.config.problem_type = "regression"
986
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
987
+ self.config.problem_type = "single_label_classification"
988
+ else:
989
+ self.config.problem_type = "multi_label_classification"
990
+
991
+ if self.config.problem_type == "regression":
992
+ loss_fct = MSELoss()
993
+ if self.num_labels == 1:
994
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
995
+ else:
996
+ loss = loss_fct(pooled_logits, labels)
997
+ elif self.config.problem_type == "single_label_classification":
998
+ loss_fct = CrossEntropyLoss()
999
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1000
+ elif self.config.problem_type == "multi_label_classification":
1001
+ loss_fct = BCEWithLogitsLoss()
1002
+ loss = loss_fct(pooled_logits, labels)
1003
+ if not return_dict:
1004
+ output = (pooled_logits,) + transformer_outputs[1:]
1005
+ return ((loss,) + output) if loss is not None else output
1006
+
1007
+ return SequenceClassifierOutputWithPast(
1008
+ loss=loss,
1009
+ logits=pooled_logits,
1010
+ past_key_values=transformer_outputs.past_key_values,
1011
+ hidden_states=transformer_outputs.hidden_states,
1012
+ attentions=transformer_outputs.attentions,
1013
+ )
pytorch_model-00001-of-00002.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:cf344aaaa2e54a7beaa969d8b9cb358ffed7f5042c17c84abc42e0932ea58fb6
3
+ size 9969236702
pytorch_model-00002-of-00002.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5c2c7d31e3104c2ecbdc9c544632c718043a335ccf9ca56427ba344bb526cd5a
3
+ size 5472745157
pytorch_model.bin.index.json ADDED
@@ -0,0 +1,330 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "metadata": {
3
+ "total_size": 15441870848
4
+ },
5
+ "weight_map": {
6
+ "lm_head.weight": "pytorch_model-00002-of-00002.bin",
7
+ "model.embed_tokens.weight": "pytorch_model-00001-of-00002.bin",
8
+ "model.layers.0.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
9
+ "model.layers.0.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
10
+ "model.layers.0.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
11
+ "model.layers.0.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
12
+ "model.layers.0.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
13
+ "model.layers.0.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
14
+ "model.layers.0.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
15
+ "model.layers.0.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
16
+ "model.layers.0.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
17
+ "model.layers.0.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
18
+ "model.layers.1.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
19
+ "model.layers.1.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
20
+ "model.layers.1.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
21
+ "model.layers.1.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
22
+ "model.layers.1.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
23
+ "model.layers.1.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
24
+ "model.layers.1.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
25
+ "model.layers.1.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
26
+ "model.layers.1.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
27
+ "model.layers.1.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
28
+ "model.layers.10.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
29
+ "model.layers.10.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
30
+ "model.layers.10.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
31
+ "model.layers.10.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
32
+ "model.layers.10.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
33
+ "model.layers.10.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
34
+ "model.layers.10.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
35
+ "model.layers.10.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
36
+ "model.layers.10.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
37
+ "model.layers.10.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
38
+ "model.layers.11.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
39
+ "model.layers.11.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
40
+ "model.layers.11.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
41
+ "model.layers.11.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
42
+ "model.layers.11.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
43
+ "model.layers.11.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
44
+ "model.layers.11.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
45
+ "model.layers.11.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
46
+ "model.layers.11.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
47
+ "model.layers.11.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
48
+ "model.layers.12.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
49
+ "model.layers.12.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
50
+ "model.layers.12.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
51
+ "model.layers.12.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
52
+ "model.layers.12.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
53
+ "model.layers.12.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
54
+ "model.layers.12.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
55
+ "model.layers.12.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
56
+ "model.layers.12.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
57
+ "model.layers.12.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
58
+ "model.layers.13.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
59
+ "model.layers.13.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
60
+ "model.layers.13.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
61
+ "model.layers.13.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
62
+ "model.layers.13.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
63
+ "model.layers.13.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
64
+ "model.layers.13.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
65
+ "model.layers.13.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
66
+ "model.layers.13.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
67
+ "model.layers.13.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
68
+ "model.layers.14.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
69
+ "model.layers.14.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
70
+ "model.layers.14.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
71
+ "model.layers.14.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
72
+ "model.layers.14.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
73
+ "model.layers.14.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
74
+ "model.layers.14.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
75
+ "model.layers.14.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
76
+ "model.layers.14.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
77
+ "model.layers.14.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
78
+ "model.layers.15.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
79
+ "model.layers.15.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
80
+ "model.layers.15.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
81
+ "model.layers.15.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
82
+ "model.layers.15.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
83
+ "model.layers.15.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
84
+ "model.layers.15.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
85
+ "model.layers.15.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
86
+ "model.layers.15.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
87
+ "model.layers.15.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
88
+ "model.layers.16.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
89
+ "model.layers.16.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
90
+ "model.layers.16.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
91
+ "model.layers.16.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
92
+ "model.layers.16.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
93
+ "model.layers.16.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
94
+ "model.layers.16.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
95
+ "model.layers.16.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
96
+ "model.layers.16.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
97
+ "model.layers.16.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
98
+ "model.layers.17.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
99
+ "model.layers.17.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
100
+ "model.layers.17.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
101
+ "model.layers.17.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
102
+ "model.layers.17.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
103
+ "model.layers.17.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
104
+ "model.layers.17.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
105
+ "model.layers.17.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
106
+ "model.layers.17.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
107
+ "model.layers.17.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
108
+ "model.layers.18.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
109
+ "model.layers.18.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
110
+ "model.layers.18.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
111
+ "model.layers.18.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
112
+ "model.layers.18.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
113
+ "model.layers.18.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
114
+ "model.layers.18.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
115
+ "model.layers.18.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
116
+ "model.layers.18.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
117
+ "model.layers.18.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
118
+ "model.layers.19.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
119
+ "model.layers.19.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
120
+ "model.layers.19.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
121
+ "model.layers.19.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
122
+ "model.layers.19.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
123
+ "model.layers.19.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
124
+ "model.layers.19.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
125
+ "model.layers.19.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
126
+ "model.layers.19.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
127
+ "model.layers.19.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
128
+ "model.layers.2.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
129
+ "model.layers.2.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
130
+ "model.layers.2.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
131
+ "model.layers.2.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
132
+ "model.layers.2.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
133
+ "model.layers.2.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
134
+ "model.layers.2.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
135
+ "model.layers.2.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
136
+ "model.layers.2.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
137
+ "model.layers.2.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
138
+ "model.layers.20.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
139
+ "model.layers.20.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
140
+ "model.layers.20.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
141
+ "model.layers.20.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
142
+ "model.layers.20.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
143
+ "model.layers.20.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
144
+ "model.layers.20.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
145
+ "model.layers.20.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
146
+ "model.layers.20.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
147
+ "model.layers.20.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
148
+ "model.layers.21.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
149
+ "model.layers.21.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
150
+ "model.layers.21.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
151
+ "model.layers.21.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
152
+ "model.layers.21.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
153
+ "model.layers.21.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
154
+ "model.layers.21.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
155
+ "model.layers.21.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
156
+ "model.layers.21.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
157
+ "model.layers.21.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
158
+ "model.layers.22.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
159
+ "model.layers.22.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
160
+ "model.layers.22.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
161
+ "model.layers.22.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
162
+ "model.layers.22.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
163
+ "model.layers.22.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
164
+ "model.layers.22.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
165
+ "model.layers.22.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
166
+ "model.layers.22.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
167
+ "model.layers.22.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
168
+ "model.layers.23.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
169
+ "model.layers.23.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
170
+ "model.layers.23.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
171
+ "model.layers.23.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
172
+ "model.layers.23.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
173
+ "model.layers.23.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
174
+ "model.layers.23.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
175
+ "model.layers.23.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
176
+ "model.layers.23.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
177
+ "model.layers.23.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
178
+ "model.layers.24.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
179
+ "model.layers.24.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
180
+ "model.layers.24.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
181
+ "model.layers.24.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
182
+ "model.layers.24.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
183
+ "model.layers.24.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
184
+ "model.layers.24.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
185
+ "model.layers.24.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
186
+ "model.layers.24.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
187
+ "model.layers.24.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
188
+ "model.layers.25.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
189
+ "model.layers.25.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
190
+ "model.layers.25.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
191
+ "model.layers.25.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
192
+ "model.layers.25.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
193
+ "model.layers.25.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
194
+ "model.layers.25.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
195
+ "model.layers.25.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
196
+ "model.layers.25.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
197
+ "model.layers.25.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
198
+ "model.layers.26.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
199
+ "model.layers.26.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
200
+ "model.layers.26.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
201
+ "model.layers.26.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
202
+ "model.layers.26.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
203
+ "model.layers.26.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
204
+ "model.layers.26.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
205
+ "model.layers.26.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
206
+ "model.layers.26.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
207
+ "model.layers.26.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
208
+ "model.layers.27.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
209
+ "model.layers.27.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
210
+ "model.layers.27.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
211
+ "model.layers.27.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
212
+ "model.layers.27.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
213
+ "model.layers.27.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
214
+ "model.layers.27.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
215
+ "model.layers.27.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
216
+ "model.layers.27.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
217
+ "model.layers.27.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
218
+ "model.layers.28.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
219
+ "model.layers.28.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
220
+ "model.layers.28.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
221
+ "model.layers.28.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
222
+ "model.layers.28.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
223
+ "model.layers.28.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
224
+ "model.layers.28.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
225
+ "model.layers.28.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
226
+ "model.layers.28.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
227
+ "model.layers.28.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
228
+ "model.layers.29.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
229
+ "model.layers.29.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
230
+ "model.layers.29.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
231
+ "model.layers.29.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
232
+ "model.layers.29.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
233
+ "model.layers.29.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
234
+ "model.layers.29.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
235
+ "model.layers.29.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
236
+ "model.layers.29.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
237
+ "model.layers.29.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
238
+ "model.layers.3.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
239
+ "model.layers.3.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
240
+ "model.layers.3.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
241
+ "model.layers.3.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
242
+ "model.layers.3.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
243
+ "model.layers.3.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
244
+ "model.layers.3.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
245
+ "model.layers.3.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
246
+ "model.layers.3.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
247
+ "model.layers.3.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
248
+ "model.layers.30.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
249
+ "model.layers.30.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
250
+ "model.layers.30.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
251
+ "model.layers.30.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
252
+ "model.layers.30.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
253
+ "model.layers.30.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
254
+ "model.layers.30.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
255
+ "model.layers.30.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
256
+ "model.layers.30.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
257
+ "model.layers.30.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
258
+ "model.layers.31.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
259
+ "model.layers.31.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
260
+ "model.layers.31.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
261
+ "model.layers.31.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
262
+ "model.layers.31.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
263
+ "model.layers.31.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
264
+ "model.layers.31.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
265
+ "model.layers.31.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
266
+ "model.layers.31.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
267
+ "model.layers.31.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
268
+ "model.layers.4.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
269
+ "model.layers.4.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
270
+ "model.layers.4.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
271
+ "model.layers.4.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
272
+ "model.layers.4.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
273
+ "model.layers.4.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
274
+ "model.layers.4.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
275
+ "model.layers.4.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
276
+ "model.layers.4.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
277
+ "model.layers.4.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
278
+ "model.layers.5.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
279
+ "model.layers.5.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
280
+ "model.layers.5.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
281
+ "model.layers.5.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
282
+ "model.layers.5.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
283
+ "model.layers.5.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
284
+ "model.layers.5.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
285
+ "model.layers.5.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
286
+ "model.layers.5.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
287
+ "model.layers.5.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
288
+ "model.layers.6.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
289
+ "model.layers.6.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
290
+ "model.layers.6.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
291
+ "model.layers.6.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
292
+ "model.layers.6.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
293
+ "model.layers.6.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
294
+ "model.layers.6.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
295
+ "model.layers.6.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
296
+ "model.layers.6.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
297
+ "model.layers.6.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
298
+ "model.layers.7.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
299
+ "model.layers.7.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
300
+ "model.layers.7.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
301
+ "model.layers.7.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
302
+ "model.layers.7.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
303
+ "model.layers.7.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
304
+ "model.layers.7.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
305
+ "model.layers.7.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
306
+ "model.layers.7.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
307
+ "model.layers.7.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
308
+ "model.layers.8.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
309
+ "model.layers.8.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
310
+ "model.layers.8.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
311
+ "model.layers.8.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
312
+ "model.layers.8.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
313
+ "model.layers.8.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
314
+ "model.layers.8.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
315
+ "model.layers.8.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
316
+ "model.layers.8.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
317
+ "model.layers.8.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
318
+ "model.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
319
+ "model.layers.9.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
320
+ "model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
321
+ "model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
322
+ "model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
323
+ "model.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
324
+ "model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
325
+ "model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
326
+ "model.layers.9.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
327
+ "model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
328
+ "model.norm.weight": "pytorch_model-00002-of-00002.bin"
329
+ }
330
+ }
qwen.tiktoken ADDED
The diff for this file is too large to render. See raw diff
 
special_tokens_map.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
2
+ "bos_token": "<|endoftext|>",
3
+ "eos_token": "<|endoftext|>",
4
+ "unk_token": "<|endoftext|>"
5
+ }
tokenization_qwen.py ADDED
@@ -0,0 +1,230 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Alibaba Cloud.
2
+ #
3
+ # This source code is licensed under the license found in the
4
+ # LICENSE file in the root directory of this source tree.
5
+
6
+ """Tokenization classes for QWen."""
7
+
8
+ import base64
9
+ import logging
10
+ import os
11
+ import unicodedata
12
+ from typing import Collection, Dict, List, Set, Tuple, Union
13
+
14
+ import tiktoken
15
+ from transformers import PreTrainedTokenizer, AddedToken
16
+
17
+ logger = logging.getLogger(__name__)
18
+
19
+
20
+ VOCAB_FILES_NAMES = {"vocab_file": "qwen.tiktoken"}
21
+
22
+ PAT_STR = r"""(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+"""
23
+ ENDOFTEXT = "<|endoftext|>"
24
+ IMSTART = "<|im_start|>"
25
+ IMEND = "<|im_end|>"
26
+ # as the default behavior is changed to allow special tokens in
27
+ # regular texts, the surface forms of special tokens need to be
28
+ # as different as possible to minimize the impact
29
+ EXTRAS = tuple((f"<|extra_{i}|>" for i in range(205)))
30
+ SPECIAL_TOKENS = (
31
+ ENDOFTEXT,
32
+ IMSTART,
33
+ IMEND,
34
+ ) + EXTRAS
35
+
36
+
37
+ def _load_tiktoken_bpe(tiktoken_bpe_file: str) -> Dict[bytes, int]:
38
+ with open(tiktoken_bpe_file, "rb") as f:
39
+ contents = f.read()
40
+ return {
41
+ base64.b64decode(token): int(rank)
42
+ for token, rank in (line.split() for line in contents.splitlines() if line)
43
+ }
44
+
45
+ class QWenTokenizer(PreTrainedTokenizer):
46
+ """QWen tokenizer."""
47
+
48
+ vocab_files_names = VOCAB_FILES_NAMES
49
+
50
+ def __init__(
51
+ self,
52
+ vocab_file,
53
+ errors="replace",
54
+ **kwargs,
55
+ ):
56
+ super().__init__(**kwargs)
57
+
58
+ self.errors = errors # how to handle errors in decoding
59
+
60
+ self.mergeable_ranks = _load_tiktoken_bpe(vocab_file) # type: dict[bytes, int]
61
+ self.special_tokens = {
62
+ token: index
63
+ for index, token in enumerate(
64
+ SPECIAL_TOKENS, start=len(self.mergeable_ranks)
65
+ )
66
+ }
67
+
68
+ enc = tiktoken.Encoding(
69
+ "Qwen",
70
+ pat_str=PAT_STR,
71
+ mergeable_ranks=self.mergeable_ranks,
72
+ special_tokens=self.special_tokens,
73
+ )
74
+ assert (
75
+ len(self.mergeable_ranks) + len(self.special_tokens) == enc.n_vocab
76
+ ), f"{len(self.mergeable_ranks) + len(self.special_tokens)} != {enc.n_vocab} in encoding"
77
+
78
+ self.decoder = {
79
+ v: k for k, v in self.mergeable_ranks.items()
80
+ } # type: dict[int, bytes|str]
81
+ self.decoder.update({v: k for k, v in self.special_tokens.items()})
82
+
83
+ self.tokenizer = enc # type: tiktoken.Encoding
84
+
85
+ self.eod_id = self.tokenizer.eot_token
86
+ self.im_start_id = self.special_tokens[IMSTART]
87
+ self.im_end_id = self.special_tokens[IMEND]
88
+
89
+ def __len__(self) -> int:
90
+ return self.tokenizer.n_vocab
91
+
92
+ def get_vocab(self) -> Dict[bytes, int]:
93
+ return self.mergeable_ranks
94
+
95
+ def convert_tokens_to_ids(
96
+ self, tokens: Union[bytes, str, List[Union[bytes, str]]]
97
+ ) -> List[int]:
98
+ ids = []
99
+ if isinstance(tokens, (str, bytes)):
100
+ if tokens in self.special_tokens:
101
+ return self.special_tokens[tokens]
102
+ else:
103
+ return self.mergeable_ranks.get(tokens)
104
+ for token in tokens:
105
+ if token in self.special_tokens:
106
+ ids.append(self.special_tokens[token])
107
+ else:
108
+ ids.append(self.mergeable_ranks.get(token))
109
+ return ids
110
+
111
+ def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
112
+ if not special_tokens and new_tokens:
113
+ raise ValueError('Adding regular tokens is not supported')
114
+ for token in new_tokens:
115
+ surface_form = token.content if isinstance(token, AddedToken) else token
116
+ if surface_form not in SPECIAL_TOKENS:
117
+ raise ValueError('Adding unknown special tokens is not supported')
118
+ return 0
119
+
120
+ def save_vocabulary(self, save_directory: str, **kwargs) -> Tuple[str]:
121
+ """
122
+ Save only the vocabulary of the tokenizer (vocabulary).
123
+
124
+ Returns:
125
+ `Tuple(str)`: Paths to the files saved.
126
+ """
127
+ file_path = os.path.join(save_directory, "qwen.tiktoken")
128
+ with open(file_path, "w", encoding="utf8") as w:
129
+ for k, v in self.mergeable_ranks.items():
130
+ line = base64.b64encode(k).decode("utf8") + " " + str(v) + "\n"
131
+ w.write(line)
132
+ return (file_path,)
133
+
134
+ def tokenize(
135
+ self,
136
+ text: str,
137
+ allowed_special: Union[Set, str] = "all",
138
+ disallowed_special: Union[Collection, str] = (),
139
+ **kwargs,
140
+ ) -> List[Union[bytes, str]]:
141
+ """
142
+ Converts a string in a sequence of tokens.
143
+
144
+ Args:
145
+ text (`str`):
146
+ The sequence to be encoded.
147
+ allowed_special (`Literal["all"]` or `set`):
148
+ The surface forms of the tokens to be encoded as special tokens in regular texts.
149
+ Default to "all".
150
+ disallowed_special (`Literal["all"]` or `Collection`):
151
+ The surface forms of the tokens that should not be in regular texts and trigger errors.
152
+ Default to an empty tuple.
153
+
154
+ kwargs (additional keyword arguments, *optional*):
155
+ Will be passed to the underlying model specific encode method.
156
+
157
+ Returns:
158
+ `List[bytes|str]`: The list of tokens.
159
+ """
160
+
161
+
162
+ tokens = []
163
+ text = unicodedata.normalize("NFC", text)
164
+
165
+ # this implementation takes a detour: text -> token id -> token surface forms
166
+ for t in self.tokenizer.encode(
167
+ text, allowed_special=allowed_special, disallowed_special=disallowed_special
168
+ ):
169
+ tokens.append(self.decoder[t])
170
+ return tokens
171
+
172
+ def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
173
+ """
174
+ Converts a sequence of tokens in a single string.
175
+ """
176
+ text = ""
177
+ temp = b""
178
+ for t in tokens:
179
+ if isinstance(t, str):
180
+ if temp:
181
+ text += temp.decode("utf-8", errors=self.errors)
182
+ temp = b""
183
+ text += t
184
+ elif isinstance(t, bytes):
185
+ temp += t
186
+ else:
187
+ raise TypeError("token should only be of type types or str")
188
+ if temp:
189
+ text += temp.decode("utf-8", errors=self.errors)
190
+ return text
191
+
192
+ @property
193
+ def vocab_size(self):
194
+ return self.tokenizer.n_vocab
195
+
196
+ def _convert_id_to_token(self, index: int) -> Union[bytes, str]:
197
+ """Converts an id to a token, special tokens included"""
198
+ if index in self.decoder:
199
+ return self.decoder[index]
200
+ raise ValueError("unknown ids")
201
+
202
+ def _convert_token_to_id(self, token: Union[bytes, str]) -> int:
203
+ """Converts a token to an id using the vocab, special tokens included"""
204
+ if token in self.special_tokens:
205
+ return self.special_tokens[token]
206
+ if token in self.mergeable_ranks:
207
+ return self.mergeable_ranks[token]
208
+ raise ValueError("unknown token")
209
+
210
+ def _tokenize(self, text: str, **kwargs):
211
+ """
212
+ Converts a string in a sequence of tokens (string), using the tokenizer. Split in words for word-based
213
+ vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
214
+
215
+ Do NOT take care of added tokens.
216
+ """
217
+ raise NotImplementedError
218
+
219
+ def _decode(
220
+ self,
221
+ token_ids: Union[int, List[int]],
222
+ skip_special_tokens: bool = False,
223
+ errors: str = None,
224
+ **kwargs,
225
+ ) -> str:
226
+ if isinstance(token_ids, int):
227
+ token_ids = [token_ids]
228
+ if skip_special_tokens:
229
+ token_ids = [i for i in token_ids if i < self.eod_id]
230
+ return self.tokenizer.decode(token_ids, errors=errors or self.errors)
tokenizer_config.json ADDED
@@ -0,0 +1,10 @@
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "model_max_length": 999999999999999999,
3
+ "tokenizer_class": "QWenTokenizer",
4
+ "auto_map": {
5
+ "AutoTokenizer": [
6
+ "tokenization_qwen.QWenTokenizer",
7
+ null
8
+ ]
9
+ }
10
+ }
visual.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:219ab65695072fc179a29903587e9744b124aee1bd4d09ec960d22d81f207450
3
+ size 3871401097