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Initial GPTQ model commit

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  1. modeling_llama.py +1020 -0
modeling_llama.py ADDED
@@ -0,0 +1,1020 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 transformers.activations import ACT2FN
31
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
32
+ from transformers.modeling_utils import PreTrainedModel
33
+ from transformers.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, persistent=False)
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=self.inv_freq.dtype)
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, persistent=False)
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
+ self.rope_theta = config.rope_theta
250
+
251
+ if (self.head_dim * self.num_heads) != self.hidden_size:
252
+ raise ValueError(
253
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
254
+ f" and `num_heads`: {self.num_heads})."
255
+ )
256
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
257
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
258
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
259
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
260
+ self._init_rope()
261
+
262
+ def _init_rope(self):
263
+ if self.config.rope_scaling is None:
264
+ self.rotary_emb = LlamaRotaryEmbedding(
265
+ self.head_dim, max_position_embeddings=self.max_position_embeddings,
266
+ base=self.rope_theta
267
+ )
268
+ else:
269
+ scaling_type = self.config.rope_scaling["type"]
270
+ scaling_factor = self.config.rope_scaling["factor"]
271
+ if scaling_type == "linear":
272
+ self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
273
+ self.head_dim, max_position_embeddings=self.max_position_embeddings,
274
+ base=self.rope_theta, scaling_factor=scaling_factor
275
+ )
276
+ elif scaling_type == "dynamic":
277
+ self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
278
+ self.head_dim, max_position_embeddings=self.max_position_embeddings,
279
+ base=self.rope_theta, scaling_factor=scaling_factor
280
+ )
281
+ else:
282
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
283
+
284
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
285
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
286
+
287
+ def forward(
288
+ self,
289
+ hidden_states: torch.Tensor,
290
+ attention_mask: Optional[torch.Tensor] = None,
291
+ position_ids: Optional[torch.LongTensor] = None,
292
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
293
+ output_attentions: bool = False,
294
+ use_cache: bool = False,
295
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
296
+ bsz, q_len, _ = hidden_states.size()
297
+
298
+ if self.config.pretraining_tp > 1:
299
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
300
+ query_slices = self.q_proj.weight.split(
301
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
302
+ )
303
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
304
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
305
+
306
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
307
+ query_states = torch.cat(query_states, dim=-1)
308
+
309
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
310
+ key_states = torch.cat(key_states, dim=-1)
311
+
312
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
313
+ value_states = torch.cat(value_states, dim=-1)
314
+
315
+ else:
316
+ query_states = self.q_proj(hidden_states)
317
+ key_states = self.k_proj(hidden_states)
318
+ value_states = self.v_proj(hidden_states)
319
+
320
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
321
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
322
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
323
+
324
+ kv_seq_len = key_states.shape[-2]
325
+ if past_key_value is not None:
326
+ kv_seq_len += past_key_value[0].shape[-2]
327
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
328
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
329
+
330
+ if past_key_value is not None:
331
+ # reuse k, v, self_attention
332
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
333
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
334
+
335
+ past_key_value = (key_states, value_states) if use_cache else None
336
+
337
+ # repeat k/v heads if n_kv_heads < n_heads
338
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
339
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
340
+
341
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
342
+
343
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
344
+ raise ValueError(
345
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
346
+ f" {attn_weights.size()}"
347
+ )
348
+
349
+ if attention_mask is not None:
350
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
351
+ raise ValueError(
352
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
353
+ )
354
+ attn_weights = attn_weights + attention_mask
355
+
356
+ # upcast attention to fp32
357
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
358
+ attn_output = torch.matmul(attn_weights, value_states)
359
+
360
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
361
+ raise ValueError(
362
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
363
+ f" {attn_output.size()}"
364
+ )
365
+
366
+ attn_output = attn_output.transpose(1, 2).contiguous()
367
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
368
+
369
+ if self.config.pretraining_tp > 1:
370
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
371
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
372
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
373
+ else:
374
+ attn_output = self.o_proj(attn_output)
375
+
376
+ if not output_attentions:
377
+ attn_weights = None
378
+
379
+ return attn_output, attn_weights, past_key_value
380
+
381
+
382
+ class LlamaDecoderLayer(nn.Module):
383
+ def __init__(self, config: LlamaConfig):
384
+ super().__init__()
385
+ self.hidden_size = config.hidden_size
386
+ self.self_attn = LlamaAttention(config=config)
387
+ self.mlp = LlamaMLP(config)
388
+ self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
389
+ self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
390
+
391
+ def forward(
392
+ self,
393
+ hidden_states: torch.Tensor,
394
+ attention_mask: Optional[torch.Tensor] = None,
395
+ position_ids: Optional[torch.LongTensor] = None,
396
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
397
+ output_attentions: Optional[bool] = False,
398
+ use_cache: Optional[bool] = False,
399
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
400
+ """
401
+ Args:
402
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
403
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
404
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
405
+ output_attentions (`bool`, *optional*):
406
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
407
+ returned tensors for more detail.
408
+ use_cache (`bool`, *optional*):
409
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
410
+ (see `past_key_values`).
411
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
412
+ """
413
+
414
+ residual = hidden_states
415
+
416
+ hidden_states = self.input_layernorm(hidden_states)
417
+
418
+ # Self Attention
419
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
420
+ hidden_states=hidden_states,
421
+ attention_mask=attention_mask,
422
+ position_ids=position_ids,
423
+ past_key_value=past_key_value,
424
+ output_attentions=output_attentions,
425
+ use_cache=use_cache,
426
+ )
427
+ hidden_states = residual + hidden_states
428
+
429
+ # Fully Connected
430
+ residual = hidden_states
431
+ hidden_states = self.post_attention_layernorm(hidden_states)
432
+ hidden_states = self.mlp(hidden_states)
433
+ hidden_states = residual + hidden_states
434
+
435
+ outputs = (hidden_states,)
436
+
437
+ if output_attentions:
438
+ outputs += (self_attn_weights,)
439
+
440
+ if use_cache:
441
+ outputs += (present_key_value,)
442
+
443
+ return outputs
444
+
445
+
446
+ LLAMA_START_DOCSTRING = r"""
447
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
448
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
449
+ etc.)
450
+
451
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
452
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
453
+ and behavior.
454
+
455
+ Parameters:
456
+ config ([`LlamaConfig`]):
457
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
458
+ load the weights associated with the model, only the configuration. Check out the
459
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
460
+ """
461
+
462
+
463
+ @add_start_docstrings(
464
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
465
+ LLAMA_START_DOCSTRING,
466
+ )
467
+ class LlamaPreTrainedModel(PreTrainedModel):
468
+ config_class = LlamaConfig
469
+ base_model_prefix = "model"
470
+ supports_gradient_checkpointing = True
471
+ _no_split_modules = ["LlamaDecoderLayer"]
472
+ _skip_keys_device_placement = "past_key_values"
473
+
474
+ def _init_weights(self, module):
475
+ std = self.config.initializer_range
476
+ if isinstance(module, nn.Linear):
477
+ module.weight.data.normal_(mean=0.0, std=std)
478
+ if module.bias is not None:
479
+ module.bias.data.zero_()
480
+ elif isinstance(module, nn.Embedding):
481
+ module.weight.data.normal_(mean=0.0, std=std)
482
+ if module.padding_idx is not None:
483
+ module.weight.data[module.padding_idx].zero_()
484
+
485
+ def _set_gradient_checkpointing(self, module, value=False):
486
+ if isinstance(module, LlamaModel):
487
+ module.gradient_checkpointing = value
488
+
489
+
490
+ LLAMA_INPUTS_DOCSTRING = r"""
491
+ Args:
492
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
493
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
494
+ it.
495
+
496
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
497
+ [`PreTrainedTokenizer.__call__`] for details.
498
+
499
+ [What are input IDs?](../glossary#input-ids)
500
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
501
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
502
+
503
+ - 1 for tokens that are **not masked**,
504
+ - 0 for tokens that are **masked**.
505
+
506
+ [What are attention masks?](../glossary#attention-mask)
507
+
508
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
509
+ [`PreTrainedTokenizer.__call__`] for details.
510
+
511
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
512
+ `past_key_values`).
513
+
514
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
515
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
516
+ information on the default strategy.
517
+
518
+ - 1 indicates the head is **not masked**,
519
+ - 0 indicates the head is **masked**.
520
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
521
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
522
+ config.n_positions - 1]`.
523
+
524
+ [What are position IDs?](../glossary#position-ids)
525
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
526
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
527
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
528
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
529
+
530
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
531
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
532
+
533
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
534
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
535
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
536
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
537
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
538
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
539
+ model's internal embedding lookup matrix.
540
+ use_cache (`bool`, *optional*):
541
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
542
+ `past_key_values`).
543
+ output_attentions (`bool`, *optional*):
544
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
545
+ tensors for more detail.
546
+ output_hidden_states (`bool`, *optional*):
547
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
548
+ more detail.
549
+ return_dict (`bool`, *optional*):
550
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
551
+ """
552
+
553
+
554
+ @add_start_docstrings(
555
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
556
+ LLAMA_START_DOCSTRING,
557
+ )
558
+ class LlamaModel(LlamaPreTrainedModel):
559
+ """
560
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
561
+
562
+ Args:
563
+ config: LlamaConfig
564
+ """
565
+
566
+ def __init__(self, config: LlamaConfig):
567
+ super().__init__(config)
568
+ self.padding_idx = config.pad_token_id
569
+ self.vocab_size = config.vocab_size
570
+
571
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
572
+ self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
573
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
574
+
575
+ self.gradient_checkpointing = False
576
+ # Initialize weights and apply final processing
577
+ self.post_init()
578
+
579
+ def get_input_embeddings(self):
580
+ return self.embed_tokens
581
+
582
+ def set_input_embeddings(self, value):
583
+ self.embed_tokens = value
584
+
585
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
586
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
587
+ # create causal mask
588
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
589
+ combined_attention_mask = None
590
+ if input_shape[-1] > 1:
591
+ combined_attention_mask = _make_causal_mask(
592
+ input_shape,
593
+ inputs_embeds.dtype,
594
+ device=inputs_embeds.device,
595
+ past_key_values_length=past_key_values_length,
596
+ )
597
+
598
+ if attention_mask is not None:
599
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
600
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
601
+ inputs_embeds.device
602
+ )
603
+ combined_attention_mask = (
604
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
605
+ )
606
+
607
+ return combined_attention_mask
608
+
609
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
610
+ def forward(
611
+ self,
612
+ input_ids: torch.LongTensor = None,
613
+ attention_mask: Optional[torch.Tensor] = None,
614
+ position_ids: Optional[torch.LongTensor] = None,
615
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
616
+ inputs_embeds: Optional[torch.FloatTensor] = None,
617
+ use_cache: Optional[bool] = None,
618
+ output_attentions: Optional[bool] = None,
619
+ output_hidden_states: Optional[bool] = None,
620
+ return_dict: Optional[bool] = None,
621
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
622
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
623
+ output_hidden_states = (
624
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
625
+ )
626
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
627
+
628
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
629
+
630
+ # retrieve input_ids and inputs_embeds
631
+ if input_ids is not None and inputs_embeds is not None:
632
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
633
+ elif input_ids is not None:
634
+ batch_size, seq_length = input_ids.shape
635
+ elif inputs_embeds is not None:
636
+ batch_size, seq_length, _ = inputs_embeds.shape
637
+ else:
638
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
639
+
640
+ seq_length_with_past = seq_length
641
+ past_key_values_length = 0
642
+
643
+ if past_key_values is not None:
644
+ past_key_values_length = past_key_values[0][0].shape[2]
645
+ seq_length_with_past = seq_length_with_past + past_key_values_length
646
+
647
+ if position_ids is None:
648
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
649
+ position_ids = torch.arange(
650
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
651
+ )
652
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
653
+ else:
654
+ position_ids = position_ids.view(-1, seq_length).long()
655
+
656
+ if inputs_embeds is None:
657
+ inputs_embeds = self.embed_tokens(input_ids)
658
+ # embed positions
659
+ if attention_mask is None:
660
+ attention_mask = torch.ones(
661
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
662
+ )
663
+ attention_mask = self._prepare_decoder_attention_mask(
664
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
665
+ )
666
+
667
+ hidden_states = inputs_embeds
668
+
669
+ if self.gradient_checkpointing and self.training:
670
+ if use_cache:
671
+ logger.warning_once(
672
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
673
+ )
674
+ use_cache = False
675
+
676
+ # decoder layers
677
+ all_hidden_states = () if output_hidden_states else None
678
+ all_self_attns = () if output_attentions else None
679
+ next_decoder_cache = () if use_cache else None
680
+
681
+ for idx, decoder_layer in enumerate(self.layers):
682
+ if output_hidden_states:
683
+ all_hidden_states += (hidden_states,)
684
+
685
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
686
+
687
+ if self.gradient_checkpointing and self.training:
688
+
689
+ def create_custom_forward(module):
690
+ def custom_forward(*inputs):
691
+ # None for past_key_value
692
+ return module(*inputs, past_key_value, output_attentions)
693
+
694
+ return custom_forward
695
+
696
+ layer_outputs = torch.utils.checkpoint.checkpoint(
697
+ create_custom_forward(decoder_layer),
698
+ hidden_states,
699
+ attention_mask,
700
+ position_ids,
701
+ )
702
+ else:
703
+ layer_outputs = decoder_layer(
704
+ hidden_states,
705
+ attention_mask=attention_mask,
706
+ position_ids=position_ids,
707
+ past_key_value=past_key_value,
708
+ output_attentions=output_attentions,
709
+ use_cache=use_cache,
710
+ )
711
+
712
+ hidden_states = layer_outputs[0]
713
+
714
+ if use_cache:
715
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
716
+
717
+ if output_attentions:
718
+ all_self_attns += (layer_outputs[1],)
719
+
720
+ hidden_states = self.norm(hidden_states)
721
+
722
+ # add hidden states from the last decoder layer
723
+ if output_hidden_states:
724
+ all_hidden_states += (hidden_states,)
725
+
726
+ next_cache = next_decoder_cache if use_cache else None
727
+ if not return_dict:
728
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
729
+ return BaseModelOutputWithPast(
730
+ last_hidden_state=hidden_states,
731
+ past_key_values=next_cache,
732
+ hidden_states=all_hidden_states,
733
+ attentions=all_self_attns,
734
+ )
735
+
736
+
737
+ class LlamaForCausalLM(LlamaPreTrainedModel):
738
+ _tied_weights_keys = ["lm_head.weight"]
739
+
740
+ def __init__(self, config):
741
+ super().__init__(config)
742
+ self.model = LlamaModel(config)
743
+ self.vocab_size = config.vocab_size
744
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
745
+
746
+ # Initialize weights and apply final processing
747
+ self.post_init()
748
+
749
+ def get_input_embeddings(self):
750
+ return self.model.embed_tokens
751
+
752
+ def set_input_embeddings(self, value):
753
+ self.model.embed_tokens = value
754
+
755
+ def get_output_embeddings(self):
756
+ return self.lm_head
757
+
758
+ def set_output_embeddings(self, new_embeddings):
759
+ self.lm_head = new_embeddings
760
+
761
+ def set_decoder(self, decoder):
762
+ self.model = decoder
763
+
764
+ def get_decoder(self):
765
+ return self.model
766
+
767
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
768
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
769
+ def forward(
770
+ self,
771
+ input_ids: torch.LongTensor = None,
772
+ attention_mask: Optional[torch.Tensor] = None,
773
+ position_ids: Optional[torch.LongTensor] = None,
774
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
775
+ inputs_embeds: Optional[torch.FloatTensor] = None,
776
+ labels: Optional[torch.LongTensor] = None,
777
+ use_cache: Optional[bool] = None,
778
+ output_attentions: Optional[bool] = None,
779
+ output_hidden_states: Optional[bool] = None,
780
+ return_dict: Optional[bool] = None,
781
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
782
+ r"""
783
+ Args:
784
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
785
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
786
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
787
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
788
+
789
+ Returns:
790
+
791
+ Example:
792
+
793
+ ```python
794
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
795
+
796
+ >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
797
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
798
+
799
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
800
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
801
+
802
+ >>> # Generate
803
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
804
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
805
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
806
+ ```"""
807
+
808
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
809
+ output_hidden_states = (
810
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
811
+ )
812
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
813
+
814
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
815
+ outputs = self.model(
816
+ input_ids=input_ids,
817
+ attention_mask=attention_mask,
818
+ position_ids=position_ids,
819
+ past_key_values=past_key_values,
820
+ inputs_embeds=inputs_embeds,
821
+ use_cache=use_cache,
822
+ output_attentions=output_attentions,
823
+ output_hidden_states=output_hidden_states,
824
+ return_dict=return_dict,
825
+ )
826
+
827
+ hidden_states = outputs[0]
828
+ if self.config.pretraining_tp > 1:
829
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
830
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
831
+ logits = torch.cat(logits, dim=-1)
832
+ else:
833
+ logits = self.lm_head(hidden_states)
834
+ logits = logits.float()
835
+
836
+ loss = None
837
+ if labels is not None:
838
+ # Shift so that tokens < n predict n
839
+ shift_logits = logits[..., :-1, :].contiguous()
840
+ shift_labels = labels[..., 1:].contiguous()
841
+ # Flatten the tokens
842
+ loss_fct = CrossEntropyLoss()
843
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
844
+ shift_labels = shift_labels.view(-1)
845
+ # Enable model parallelism
846
+ shift_labels = shift_labels.to(shift_logits.device)
847
+ loss = loss_fct(shift_logits, shift_labels)
848
+
849
+ if not return_dict:
850
+ output = (logits,) + outputs[1:]
851
+ return (loss,) + output if loss is not None else output
852
+
853
+ return CausalLMOutputWithPast(
854
+ loss=loss,
855
+ logits=logits,
856
+ past_key_values=outputs.past_key_values,
857
+ hidden_states=outputs.hidden_states,
858
+ attentions=outputs.attentions,
859
+ )
860
+
861
+ def prepare_inputs_for_generation(
862
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
863
+ ):
864
+ if past_key_values:
865
+ input_ids = input_ids[:, -1:]
866
+
867
+ position_ids = kwargs.get("position_ids", None)
868
+ if attention_mask is not None and position_ids is None:
869
+ # create position_ids on the fly for batch generation
870
+ position_ids = attention_mask.long().cumsum(-1) - 1
871
+ position_ids.masked_fill_(attention_mask == 0, 1)
872
+ if past_key_values:
873
+ position_ids = position_ids[:, -1].unsqueeze(-1)
874
+
875
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
876
+ if inputs_embeds is not None and past_key_values is None:
877
+ model_inputs = {"inputs_embeds": inputs_embeds}
878
+ else:
879
+ model_inputs = {"input_ids": input_ids}
880
+
881
+ model_inputs.update(
882
+ {
883
+ "position_ids": position_ids,
884
+ "past_key_values": past_key_values,
885
+ "use_cache": kwargs.get("use_cache"),
886
+ "attention_mask": attention_mask,
887
+ }
888
+ )
889
+ return model_inputs
890
+
891
+ @staticmethod
892
+ def _reorder_cache(past_key_values, beam_idx):
893
+ reordered_past = ()
894
+ for layer_past in past_key_values:
895
+ reordered_past += (
896
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
897
+ )
898
+ return reordered_past
899
+
900
+
901
+ @add_start_docstrings(
902
+ """
903
+ The LLaMa Model transformer with a sequence classification head on top (linear layer).
904
+
905
+ [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
906
+ (e.g. GPT-2) do.
907
+
908
+ Since it does classification on the last token, it requires to know the position of the last token. If a
909
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
910
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
911
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
912
+ each row of the batch).
913
+ """,
914
+ LLAMA_START_DOCSTRING,
915
+ )
916
+ class LlamaForSequenceClassification(LlamaPreTrainedModel):
917
+ def __init__(self, config):
918
+ super().__init__(config)
919
+ self.num_labels = config.num_labels
920
+ self.model = LlamaModel(config)
921
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
922
+
923
+ # Initialize weights and apply final processing
924
+ self.post_init()
925
+
926
+ def get_input_embeddings(self):
927
+ return self.model.embed_tokens
928
+
929
+ def set_input_embeddings(self, value):
930
+ self.model.embed_tokens = value
931
+
932
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
933
+ def forward(
934
+ self,
935
+ input_ids: torch.LongTensor = None,
936
+ attention_mask: Optional[torch.Tensor] = None,
937
+ position_ids: Optional[torch.LongTensor] = None,
938
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
939
+ inputs_embeds: Optional[torch.FloatTensor] = None,
940
+ labels: Optional[torch.LongTensor] = None,
941
+ use_cache: Optional[bool] = None,
942
+ output_attentions: Optional[bool] = None,
943
+ output_hidden_states: Optional[bool] = None,
944
+ return_dict: Optional[bool] = None,
945
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
946
+ r"""
947
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
948
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
949
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
950
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
951
+ """
952
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
953
+
954
+ transformer_outputs = self.model(
955
+ input_ids,
956
+ attention_mask=attention_mask,
957
+ position_ids=position_ids,
958
+ past_key_values=past_key_values,
959
+ inputs_embeds=inputs_embeds,
960
+ use_cache=use_cache,
961
+ output_attentions=output_attentions,
962
+ output_hidden_states=output_hidden_states,
963
+ return_dict=return_dict,
964
+ )
965
+ hidden_states = transformer_outputs[0]
966
+ logits = self.score(hidden_states)
967
+
968
+ if input_ids is not None:
969
+ batch_size = input_ids.shape[0]
970
+ else:
971
+ batch_size = inputs_embeds.shape[0]
972
+
973
+ if self.config.pad_token_id is None and batch_size != 1:
974
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
975
+ if self.config.pad_token_id is None:
976
+ sequence_lengths = -1
977
+ else:
978
+ if input_ids is not None:
979
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to(
980
+ logits.device
981
+ )
982
+ else:
983
+ sequence_lengths = -1
984
+
985
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
986
+
987
+ loss = None
988
+ if labels is not None:
989
+ labels = labels.to(logits.device)
990
+ if self.config.problem_type is None:
991
+ if self.num_labels == 1:
992
+ self.config.problem_type = "regression"
993
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
994
+ self.config.problem_type = "single_label_classification"
995
+ else:
996
+ self.config.problem_type = "multi_label_classification"
997
+
998
+ if self.config.problem_type == "regression":
999
+ loss_fct = MSELoss()
1000
+ if self.num_labels == 1:
1001
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1002
+ else:
1003
+ loss = loss_fct(pooled_logits, labels)
1004
+ elif self.config.problem_type == "single_label_classification":
1005
+ loss_fct = CrossEntropyLoss()
1006
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1007
+ elif self.config.problem_type == "multi_label_classification":
1008
+ loss_fct = BCEWithLogitsLoss()
1009
+ loss = loss_fct(pooled_logits, labels)
1010
+ if not return_dict:
1011
+ output = (pooled_logits,) + transformer_outputs[1:]
1012
+ return ((loss,) + output) if loss is not None else output
1013
+
1014
+ return SequenceClassifierOutputWithPast(
1015
+ loss=loss,
1016
+ logits=pooled_logits,
1017
+ past_key_values=transformer_outputs.past_key_values,
1018
+ hidden_states=transformer_outputs.hidden_states,
1019
+ attentions=transformer_outputs.attentions,
1020
+ )