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Initial GPTQ model commit

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  1. modeling_llama.py +991 -0
modeling_llama.py ADDED
@@ -0,0 +1,991 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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)
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)
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
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
445
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
446
+ and behavior.
447
+ Parameters:
448
+ config ([`LlamaConfig`]):
449
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
450
+ load the weights associated with the model, only the configuration. Check out the
451
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
452
+ """
453
+
454
+
455
+ @add_start_docstrings(
456
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
457
+ LLAMA_START_DOCSTRING,
458
+ )
459
+ class LlamaPreTrainedModel(PreTrainedModel):
460
+ config_class = LlamaConfig
461
+ base_model_prefix = "model"
462
+ supports_gradient_checkpointing = True
463
+ _no_split_modules = ["LlamaDecoderLayer"]
464
+ _skip_keys_device_placement = "past_key_values"
465
+
466
+ def _init_weights(self, module):
467
+ std = self.config.initializer_range
468
+ if isinstance(module, nn.Linear):
469
+ module.weight.data.normal_(mean=0.0, std=std)
470
+ if module.bias is not None:
471
+ module.bias.data.zero_()
472
+ elif isinstance(module, nn.Embedding):
473
+ module.weight.data.normal_(mean=0.0, std=std)
474
+ if module.padding_idx is not None:
475
+ module.weight.data[module.padding_idx].zero_()
476
+
477
+ def _set_gradient_checkpointing(self, module, value=False):
478
+ if isinstance(module, LlamaModel):
479
+ module.gradient_checkpointing = value
480
+
481
+
482
+ LLAMA_INPUTS_DOCSTRING = r"""
483
+ Args:
484
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
485
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
486
+ it.
487
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
488
+ [`PreTrainedTokenizer.__call__`] for details.
489
+ [What are input IDs?](../glossary#input-ids)
490
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
491
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
492
+ - 1 for tokens that are **not masked**,
493
+ - 0 for tokens that are **masked**.
494
+ [What are attention masks?](../glossary#attention-mask)
495
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
496
+ [`PreTrainedTokenizer.__call__`] for details.
497
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
498
+ `past_key_values`).
499
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
500
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
501
+ information on the default strategy.
502
+ - 1 indicates the head is **not masked**,
503
+ - 0 indicates the head is **masked**.
504
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
505
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
506
+ config.n_positions - 1]`.
507
+ [What are position IDs?](../glossary#position-ids)
508
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
509
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
510
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
511
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
512
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
513
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
514
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
515
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
516
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
517
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
518
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
519
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
520
+ model's internal embedding lookup matrix.
521
+ use_cache (`bool`, *optional*):
522
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
523
+ `past_key_values`).
524
+ output_attentions (`bool`, *optional*):
525
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
526
+ tensors for more detail.
527
+ output_hidden_states (`bool`, *optional*):
528
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
529
+ more detail.
530
+ return_dict (`bool`, *optional*):
531
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
532
+ """
533
+
534
+
535
+ @add_start_docstrings(
536
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
537
+ LLAMA_START_DOCSTRING,
538
+ )
539
+ class LlamaModel(LlamaPreTrainedModel):
540
+ """
541
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
542
+ Args:
543
+ config: LlamaConfig
544
+ """
545
+
546
+ def __init__(self, config: LlamaConfig):
547
+ super().__init__(config)
548
+ self.padding_idx = config.pad_token_id
549
+ self.vocab_size = config.vocab_size
550
+
551
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
552
+ self.layers = nn.ModuleList([LlamaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
553
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
554
+
555
+ self.gradient_checkpointing = False
556
+ # Initialize weights and apply final processing
557
+ self.post_init()
558
+
559
+ def get_input_embeddings(self):
560
+ return self.embed_tokens
561
+
562
+ def set_input_embeddings(self, value):
563
+ self.embed_tokens = value
564
+
565
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
566
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
567
+ # create causal mask
568
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
569
+ combined_attention_mask = None
570
+ if input_shape[-1] > 1:
571
+ combined_attention_mask = _make_causal_mask(
572
+ input_shape,
573
+ inputs_embeds.dtype,
574
+ device=inputs_embeds.device,
575
+ past_key_values_length=past_key_values_length,
576
+ )
577
+
578
+ if attention_mask is not None:
579
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
580
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
581
+ inputs_embeds.device
582
+ )
583
+ combined_attention_mask = (
584
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
585
+ )
586
+
587
+ return combined_attention_mask
588
+
589
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
590
+ def forward(
591
+ self,
592
+ input_ids: torch.LongTensor = None,
593
+ attention_mask: Optional[torch.Tensor] = None,
594
+ position_ids: Optional[torch.LongTensor] = None,
595
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
596
+ inputs_embeds: Optional[torch.FloatTensor] = None,
597
+ use_cache: Optional[bool] = None,
598
+ output_attentions: Optional[bool] = None,
599
+ output_hidden_states: Optional[bool] = None,
600
+ return_dict: Optional[bool] = None,
601
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
602
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
603
+ output_hidden_states = (
604
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
605
+ )
606
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
607
+
608
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
609
+
610
+ # retrieve input_ids and inputs_embeds
611
+ if input_ids is not None and inputs_embeds is not None:
612
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
613
+ elif input_ids is not None:
614
+ batch_size, seq_length = input_ids.shape
615
+ elif inputs_embeds is not None:
616
+ batch_size, seq_length, _ = inputs_embeds.shape
617
+ else:
618
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
619
+
620
+ seq_length_with_past = seq_length
621
+ past_key_values_length = 0
622
+
623
+ if past_key_values is not None:
624
+ past_key_values_length = past_key_values[0][0].shape[2]
625
+ seq_length_with_past = seq_length_with_past + past_key_values_length
626
+
627
+ if position_ids is None:
628
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
629
+ position_ids = torch.arange(
630
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
631
+ )
632
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
633
+ else:
634
+ position_ids = position_ids.view(-1, seq_length).long()
635
+
636
+ if inputs_embeds is None:
637
+ inputs_embeds = self.embed_tokens(input_ids)
638
+ # embed positions
639
+ if attention_mask is None:
640
+ attention_mask = torch.ones(
641
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
642
+ )
643
+ attention_mask = self._prepare_decoder_attention_mask(
644
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
645
+ )
646
+
647
+ hidden_states = inputs_embeds
648
+
649
+ if self.gradient_checkpointing and self.training:
650
+ if use_cache:
651
+ logger.warning_once(
652
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
653
+ )
654
+ use_cache = False
655
+
656
+ # decoder layers
657
+ all_hidden_states = () if output_hidden_states else None
658
+ all_self_attns = () if output_attentions else None
659
+ next_decoder_cache = () if use_cache else None
660
+
661
+ for idx, decoder_layer in enumerate(self.layers):
662
+ if output_hidden_states:
663
+ all_hidden_states += (hidden_states,)
664
+
665
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
666
+
667
+ if self.gradient_checkpointing and self.training:
668
+
669
+ def create_custom_forward(module):
670
+ def custom_forward(*inputs):
671
+ # None for past_key_value
672
+ return module(*inputs, output_attentions, None)
673
+
674
+ return custom_forward
675
+
676
+ layer_outputs = torch.utils.checkpoint.checkpoint(
677
+ create_custom_forward(decoder_layer),
678
+ hidden_states,
679
+ attention_mask,
680
+ position_ids,
681
+ None,
682
+ )
683
+ else:
684
+ layer_outputs = decoder_layer(
685
+ hidden_states,
686
+ attention_mask=attention_mask,
687
+ position_ids=position_ids,
688
+ past_key_value=past_key_value,
689
+ output_attentions=output_attentions,
690
+ use_cache=use_cache,
691
+ )
692
+
693
+ hidden_states = layer_outputs[0]
694
+
695
+ if use_cache:
696
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
697
+
698
+ if output_attentions:
699
+ all_self_attns += (layer_outputs[1],)
700
+
701
+ hidden_states = self.norm(hidden_states)
702
+
703
+ # add hidden states from the last decoder layer
704
+ if output_hidden_states:
705
+ all_hidden_states += (hidden_states,)
706
+
707
+ next_cache = next_decoder_cache if use_cache else None
708
+ if not return_dict:
709
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
710
+ return BaseModelOutputWithPast(
711
+ last_hidden_state=hidden_states,
712
+ past_key_values=next_cache,
713
+ hidden_states=all_hidden_states,
714
+ attentions=all_self_attns,
715
+ )
716
+
717
+
718
+ class LlamaForCausalLM(LlamaPreTrainedModel):
719
+ _tied_weights_keys = ["lm_head.weight"]
720
+
721
+ def __init__(self, config):
722
+ super().__init__(config)
723
+ self.model = LlamaModel(config)
724
+ self.vocab_size = config.vocab_size
725
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
726
+
727
+ # Initialize weights and apply final processing
728
+ self.post_init()
729
+
730
+ def get_input_embeddings(self):
731
+ return self.model.embed_tokens
732
+
733
+ def set_input_embeddings(self, value):
734
+ self.model.embed_tokens = value
735
+
736
+ def get_output_embeddings(self):
737
+ return self.lm_head
738
+
739
+ def set_output_embeddings(self, new_embeddings):
740
+ self.lm_head = new_embeddings
741
+
742
+ def set_decoder(self, decoder):
743
+ self.model = decoder
744
+
745
+ def get_decoder(self):
746
+ return self.model
747
+
748
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
749
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
750
+ def forward(
751
+ self,
752
+ input_ids: torch.LongTensor = None,
753
+ attention_mask: Optional[torch.Tensor] = None,
754
+ position_ids: Optional[torch.LongTensor] = None,
755
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
756
+ inputs_embeds: Optional[torch.FloatTensor] = None,
757
+ labels: Optional[torch.LongTensor] = None,
758
+ use_cache: Optional[bool] = None,
759
+ output_attentions: Optional[bool] = None,
760
+ output_hidden_states: Optional[bool] = None,
761
+ return_dict: Optional[bool] = None,
762
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
763
+ r"""
764
+ Args:
765
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
766
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
767
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
768
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
769
+ Returns:
770
+ Example:
771
+ ```python
772
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
773
+ >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
774
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
775
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
776
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
777
+ >>> # Generate
778
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
779
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
780
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
781
+ ```"""
782
+
783
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
784
+ output_hidden_states = (
785
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
786
+ )
787
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
788
+
789
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
790
+ outputs = self.model(
791
+ input_ids=input_ids,
792
+ attention_mask=attention_mask,
793
+ position_ids=position_ids,
794
+ past_key_values=past_key_values,
795
+ inputs_embeds=inputs_embeds,
796
+ use_cache=use_cache,
797
+ output_attentions=output_attentions,
798
+ output_hidden_states=output_hidden_states,
799
+ return_dict=return_dict,
800
+ )
801
+
802
+ hidden_states = outputs[0]
803
+ if self.config.pretraining_tp > 1:
804
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
805
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
806
+ logits = torch.cat(logits, dim=-1)
807
+ else:
808
+ logits = self.lm_head(hidden_states)
809
+ logits = logits.float()
810
+
811
+ loss = None
812
+ if labels is not None:
813
+ # Shift so that tokens < n predict n
814
+ shift_logits = logits[..., :-1, :].contiguous()
815
+ shift_labels = labels[..., 1:].contiguous()
816
+ # Flatten the tokens
817
+ loss_fct = CrossEntropyLoss()
818
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
819
+ shift_labels = shift_labels.view(-1)
820
+ # Enable model parallelism
821
+ shift_labels = shift_labels.to(shift_logits.device)
822
+ loss = loss_fct(shift_logits, shift_labels)
823
+
824
+ if not return_dict:
825
+ output = (logits,) + outputs[1:]
826
+ return (loss,) + output if loss is not None else output
827
+
828
+ return CausalLMOutputWithPast(
829
+ loss=loss,
830
+ logits=logits,
831
+ past_key_values=outputs.past_key_values,
832
+ hidden_states=outputs.hidden_states,
833
+ attentions=outputs.attentions,
834
+ )
835
+
836
+ def prepare_inputs_for_generation(
837
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
838
+ ):
839
+ if past_key_values:
840
+ input_ids = input_ids[:, -1:]
841
+
842
+ position_ids = kwargs.get("position_ids", None)
843
+ if attention_mask is not None and position_ids is None:
844
+ # create position_ids on the fly for batch generation
845
+ position_ids = attention_mask.long().cumsum(-1) - 1
846
+ position_ids.masked_fill_(attention_mask == 0, 1)
847
+ if past_key_values:
848
+ position_ids = position_ids[:, -1].unsqueeze(-1)
849
+
850
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
851
+ if inputs_embeds is not None and past_key_values is None:
852
+ model_inputs = {"inputs_embeds": inputs_embeds}
853
+ else:
854
+ model_inputs = {"input_ids": input_ids}
855
+
856
+ model_inputs.update(
857
+ {
858
+ "position_ids": position_ids,
859
+ "past_key_values": past_key_values,
860
+ "use_cache": kwargs.get("use_cache"),
861
+ "attention_mask": attention_mask,
862
+ }
863
+ )
864
+ return model_inputs
865
+
866
+ @staticmethod
867
+ def _reorder_cache(past_key_values, beam_idx):
868
+ reordered_past = ()
869
+ for layer_past in past_key_values:
870
+ reordered_past += (
871
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
872
+ )
873
+ return reordered_past
874
+
875
+
876
+ @add_start_docstrings(
877
+ """
878
+ The LLaMa Model transformer with a sequence classification head on top (linear layer).
879
+ [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
880
+ (e.g. GPT-2) do.
881
+ Since it does classification on the last token, it requires to know the position of the last token. If a
882
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
883
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
884
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
885
+ each row of the batch).
886
+ """,
887
+ LLAMA_START_DOCSTRING,
888
+ )
889
+ class LlamaForSequenceClassification(LlamaPreTrainedModel):
890
+ def __init__(self, config):
891
+ super().__init__(config)
892
+ self.num_labels = config.num_labels
893
+ self.model = LlamaModel(config)
894
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
895
+
896
+ # Initialize weights and apply final processing
897
+ self.post_init()
898
+
899
+ def get_input_embeddings(self):
900
+ return self.model.embed_tokens
901
+
902
+ def set_input_embeddings(self, value):
903
+ self.model.embed_tokens = value
904
+
905
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
906
+ def forward(
907
+ self,
908
+ input_ids: torch.LongTensor = None,
909
+ attention_mask: Optional[torch.Tensor] = None,
910
+ position_ids: Optional[torch.LongTensor] = None,
911
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
912
+ inputs_embeds: Optional[torch.FloatTensor] = None,
913
+ labels: Optional[torch.LongTensor] = None,
914
+ use_cache: Optional[bool] = None,
915
+ output_attentions: Optional[bool] = None,
916
+ output_hidden_states: Optional[bool] = None,
917
+ return_dict: Optional[bool] = None,
918
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
919
+ r"""
920
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
921
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
922
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
923
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
924
+ """
925
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
926
+
927
+ transformer_outputs = self.model(
928
+ input_ids,
929
+ attention_mask=attention_mask,
930
+ position_ids=position_ids,
931
+ past_key_values=past_key_values,
932
+ inputs_embeds=inputs_embeds,
933
+ use_cache=use_cache,
934
+ output_attentions=output_attentions,
935
+ output_hidden_states=output_hidden_states,
936
+ return_dict=return_dict,
937
+ )
938
+ hidden_states = transformer_outputs[0]
939
+ logits = self.score(hidden_states)
940
+
941
+ if input_ids is not None:
942
+ batch_size = input_ids.shape[0]
943
+ else:
944
+ batch_size = inputs_embeds.shape[0]
945
+
946
+ if self.config.pad_token_id is None and batch_size != 1:
947
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
948
+ if self.config.pad_token_id is None:
949
+ sequence_lengths = -1
950
+ else:
951
+ if input_ids is not None:
952
+ sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
953
+ else:
954
+ sequence_lengths = -1
955
+
956
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
957
+
958
+ loss = None
959
+ if labels is not None:
960
+ labels = labels.to(logits.device)
961
+ if self.config.problem_type is None:
962
+ if self.num_labels == 1:
963
+ self.config.problem_type = "regression"
964
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
965
+ self.config.problem_type = "single_label_classification"
966
+ else:
967
+ self.config.problem_type = "multi_label_classification"
968
+
969
+ if self.config.problem_type == "regression":
970
+ loss_fct = MSELoss()
971
+ if self.num_labels == 1:
972
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
973
+ else:
974
+ loss = loss_fct(pooled_logits, labels)
975
+ elif self.config.problem_type == "single_label_classification":
976
+ loss_fct = CrossEntropyLoss()
977
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
978
+ elif self.config.problem_type == "multi_label_classification":
979
+ loss_fct = BCEWithLogitsLoss()
980
+ loss = loss_fct(pooled_logits, labels)
981
+ if not return_dict:
982
+ output = (pooled_logits,) + transformer_outputs[1:]
983
+ return ((loss,) + output) if loss is not None else output
984
+
985
+ return SequenceClassifierOutputWithPast(
986
+ loss=loss,
987
+ logits=pooled_logits,
988
+ past_key_values=transformer_outputs.past_key_values,
989
+ hidden_states=transformer_outputs.hidden_states,
990
+ attentions=transformer_outputs.attentions,
991
+ )