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llava-v1.5-13b-llava-cut-layer-20

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+ }
modeling_llama.py ADDED
@@ -0,0 +1,1476 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
+ import warnings
23
+ from typing import List, Optional, Tuple, Union
24
+
25
+ import torch
26
+ import torch.nn.functional as F
27
+ import torch.utils.checkpoint
28
+ from torch import nn
29
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
30
+
31
+ from ...activations import ACT2FN
32
+ from ...cache_utils import Cache, DynamicCache
33
+ from ...modeling_attn_mask_utils import (
34
+ AttentionMaskConverter,
35
+ _prepare_4d_attention_mask,
36
+ _prepare_4d_causal_attention_mask,
37
+ _prepare_4d_causal_attention_mask_for_sdpa,
38
+ )
39
+ from ...modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
40
+ from ...modeling_utils import PreTrainedModel
41
+ from ...pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
42
+ from ...utils import (
43
+ add_start_docstrings,
44
+ add_start_docstrings_to_model_forward,
45
+ is_flash_attn_2_available,
46
+ is_flash_attn_greater_or_equal_2_10,
47
+ logging,
48
+ replace_return_docstrings,
49
+ )
50
+ from ...utils.import_utils import is_torch_fx_available
51
+ from .configuration_llama import LlamaConfig
52
+
53
+
54
+ if is_flash_attn_2_available():
55
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
56
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
57
+
58
+
59
+ # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
60
+ # It means that the function will not be traced through and simply appear as a node in the graph.
61
+ if is_torch_fx_available():
62
+ if not is_torch_greater_or_equal_than_1_13:
63
+ import torch.fx
64
+
65
+ _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
66
+
67
+
68
+ logger = logging.get_logger(__name__)
69
+
70
+ _CONFIG_FOR_DOC = "LlamaConfig"
71
+
72
+
73
+ def _get_unpad_data(attention_mask):
74
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
75
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
76
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
77
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
78
+ return (
79
+ indices,
80
+ cu_seqlens,
81
+ max_seqlen_in_batch,
82
+ )
83
+
84
+
85
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
86
+ warnings.warn(
87
+ "Calling `transformers.models.llama.modeling_llama._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
88
+ )
89
+ return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
90
+
91
+
92
+ def _make_causal_mask(
93
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
94
+ ):
95
+ warnings.warn(
96
+ "Calling `transformers.models.llama.modeling_llama._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.llama.modeling_llama.AttentionMaskConverter._make_causal_mask"
97
+ )
98
+ return AttentionMaskConverter._make_causal_mask(
99
+ input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
100
+ )
101
+
102
+
103
+ class LlamaRMSNorm(nn.Module):
104
+ def __init__(self, hidden_size, eps=1e-6):
105
+ """
106
+ LlamaRMSNorm is equivalent to T5LayerNorm
107
+ """
108
+ super().__init__()
109
+ self.weight = nn.Parameter(torch.ones(hidden_size))
110
+ self.variance_epsilon = eps
111
+
112
+ def forward(self, hidden_states):
113
+ input_dtype = hidden_states.dtype
114
+ hidden_states = hidden_states.to(torch.float32)
115
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
116
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
117
+ return self.weight * hidden_states.to(input_dtype)
118
+
119
+
120
+ ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
121
+
122
+
123
+ class LlamaRotaryEmbedding(nn.Module):
124
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
125
+ super().__init__()
126
+
127
+ self.dim = dim
128
+ self.max_position_embeddings = max_position_embeddings
129
+ self.base = base
130
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
131
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
132
+
133
+ # Build here to make `torch.jit.trace` work.
134
+ self._set_cos_sin_cache(
135
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
136
+ )
137
+
138
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
139
+ self.max_seq_len_cached = seq_len
140
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
141
+
142
+ freqs = torch.outer(t, self.inv_freq)
143
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
144
+ emb = torch.cat((freqs, freqs), dim=-1)
145
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
146
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
147
+
148
+ def forward(self, x, seq_len=None):
149
+ # x: [bs, num_attention_heads, seq_len, head_size]
150
+ if seq_len > self.max_seq_len_cached:
151
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
152
+
153
+ return (
154
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
155
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
156
+ )
157
+
158
+
159
+ class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
160
+ """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
161
+
162
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
163
+ self.scaling_factor = scaling_factor
164
+ super().__init__(dim, max_position_embeddings, base, device)
165
+
166
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
167
+ self.max_seq_len_cached = seq_len
168
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
169
+ t = t / self.scaling_factor
170
+
171
+ freqs = torch.outer(t, self.inv_freq)
172
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
173
+ emb = torch.cat((freqs, freqs), dim=-1)
174
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
175
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
176
+
177
+
178
+ class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
179
+ """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
180
+
181
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
182
+ self.scaling_factor = scaling_factor
183
+ super().__init__(dim, max_position_embeddings, base, device)
184
+
185
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
186
+ self.max_seq_len_cached = seq_len
187
+
188
+ if seq_len > self.max_position_embeddings:
189
+ base = self.base * (
190
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
191
+ ) ** (self.dim / (self.dim - 2))
192
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
193
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
194
+
195
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
196
+
197
+ freqs = torch.outer(t, self.inv_freq)
198
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
199
+ emb = torch.cat((freqs, freqs), dim=-1)
200
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
201
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
202
+
203
+
204
+ def rotate_half(x):
205
+ """Rotates half the hidden dims of the input."""
206
+ x1 = x[..., : x.shape[-1] // 2]
207
+ x2 = x[..., x.shape[-1] // 2 :]
208
+ return torch.cat((-x2, x1), dim=-1)
209
+
210
+
211
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
212
+ """Applies Rotary Position Embedding to the query and key tensors.
213
+
214
+ Args:
215
+ q (`torch.Tensor`): The query tensor.
216
+ k (`torch.Tensor`): The key tensor.
217
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
218
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
219
+ position_ids (`torch.Tensor`):
220
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
221
+ used to pass offsetted position ids when working with a KV-cache.
222
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
223
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
224
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
225
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
226
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
227
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
228
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
229
+ Returns:
230
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
231
+ """
232
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
233
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
234
+ q_embed = (q * cos) + (rotate_half(q) * sin)
235
+ k_embed = (k * cos) + (rotate_half(k) * sin)
236
+ return q_embed, k_embed
237
+
238
+
239
+ class LlamaMLP(nn.Module):
240
+ def __init__(self, config):
241
+ super().__init__()
242
+ self.config = config
243
+ self.hidden_size = config.hidden_size
244
+ self.intermediate_size = config.intermediate_size
245
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
246
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
247
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
248
+ self.act_fn = ACT2FN[config.hidden_act]
249
+
250
+ def forward(self, x):
251
+ if self.config.pretraining_tp > 1:
252
+ slice = self.intermediate_size // self.config.pretraining_tp
253
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
254
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
255
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
256
+
257
+ gate_proj = torch.cat(
258
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
259
+ )
260
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
261
+
262
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
263
+ down_proj = [
264
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
265
+ ]
266
+ down_proj = sum(down_proj)
267
+ else:
268
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
269
+
270
+ return down_proj
271
+
272
+
273
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
274
+ """
275
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
276
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
277
+ """
278
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
279
+ if n_rep == 1:
280
+ return hidden_states
281
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
282
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
283
+
284
+
285
+ class LlamaAttention(nn.Module):
286
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
287
+
288
+ def __init__(self, config: LlamaConfig, layer_idx: Optional[int] = None):
289
+ super().__init__()
290
+ self.config = config
291
+ self.layer_idx = layer_idx
292
+ if layer_idx is None:
293
+ logger.warning_once(
294
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
295
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
296
+ "when creating this class."
297
+ )
298
+
299
+ self.attention_dropout = config.attention_dropout
300
+ self.hidden_size = config.hidden_size
301
+ self.num_heads = config.num_attention_heads
302
+ self.head_dim = self.hidden_size // self.num_heads
303
+ self.num_key_value_heads = config.num_key_value_heads
304
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
305
+ self.max_position_embeddings = config.max_position_embeddings
306
+ self.rope_theta = config.rope_theta
307
+ self.is_causal = True
308
+
309
+ if (self.head_dim * self.num_heads) != self.hidden_size:
310
+ raise ValueError(
311
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
312
+ f" and `num_heads`: {self.num_heads})."
313
+ )
314
+
315
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
316
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
317
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
318
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
319
+ self._init_rope()
320
+
321
+ def _init_rope(self):
322
+ if self.config.rope_scaling is None:
323
+ self.rotary_emb = LlamaRotaryEmbedding(
324
+ self.head_dim,
325
+ max_position_embeddings=self.max_position_embeddings,
326
+ base=self.rope_theta,
327
+ )
328
+ else:
329
+ scaling_type = self.config.rope_scaling["type"]
330
+ scaling_factor = self.config.rope_scaling["factor"]
331
+ if scaling_type == "linear":
332
+ self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
333
+ self.head_dim,
334
+ max_position_embeddings=self.max_position_embeddings,
335
+ scaling_factor=scaling_factor,
336
+ base=self.rope_theta,
337
+ )
338
+ elif scaling_type == "dynamic":
339
+ self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
340
+ self.head_dim,
341
+ max_position_embeddings=self.max_position_embeddings,
342
+ scaling_factor=scaling_factor,
343
+ base=self.rope_theta,
344
+ )
345
+ else:
346
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
347
+
348
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
349
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
350
+
351
+ def forward(
352
+ self,
353
+ hidden_states: torch.Tensor,
354
+ attention_mask: Optional[torch.Tensor] = None,
355
+ position_ids: Optional[torch.LongTensor] = None,
356
+ past_key_value: Optional[Cache] = None,
357
+ output_attentions: bool = False,
358
+ use_cache: bool = False,
359
+ **kwargs,
360
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
361
+ if "padding_mask" in kwargs:
362
+ warnings.warn(
363
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
364
+ )
365
+
366
+ bsz, q_len, _ = hidden_states.size()
367
+
368
+ if self.config.pretraining_tp > 1:
369
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
370
+ query_slices = self.q_proj.weight.split(
371
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
372
+ )
373
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
374
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
375
+
376
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
377
+ query_states = torch.cat(query_states, dim=-1)
378
+
379
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
380
+ key_states = torch.cat(key_states, dim=-1)
381
+
382
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
383
+ value_states = torch.cat(value_states, dim=-1)
384
+
385
+ else:
386
+ query_states = self.q_proj(hidden_states)
387
+ key_states = self.k_proj(hidden_states)
388
+ value_states = self.v_proj(hidden_states)
389
+
390
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
391
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
392
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
393
+
394
+ kv_seq_len = key_states.shape[-2]
395
+ if past_key_value is not None:
396
+ if self.layer_idx is None:
397
+ raise ValueError(
398
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
399
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
400
+ "with a layer index."
401
+ )
402
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
403
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
404
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
405
+
406
+ if past_key_value is not None:
407
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
408
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
409
+
410
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
411
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
412
+ # import pdb; pdb.set_trace()
413
+ #attn = "local"
414
+ attn = "original"
415
+ # print("layer_index",self.layer_idx)
416
+ if self.layer_idx>20: # > 37
417
+ attention_mask = attention_mask.clone()
418
+ attention_mask[:,:,577:,:577]=-65504.0
419
+ attention_mask = attention_mask.clone()
420
+ # print(value_states.shape)
421
+ if attn == "original":
422
+ import time
423
+ start = time.time()
424
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) #torch.Size([16, 40, 1752, 1752])
425
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
426
+ raise ValueError(
427
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
428
+ f" {attn_weights.size()}"
429
+ )
430
+
431
+ if attention_mask is not None:
432
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
433
+ raise ValueError(
434
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
435
+ )
436
+ attn_weights = attn_weights + attention_mask
437
+
438
+ # upcast attention to fp32
439
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
440
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
441
+ attn_output = torch.matmul(attn_weights, value_states)
442
+ end = time.time()
443
+ #print(end-start)
444
+
445
+
446
+ # import pdb; pdb.set_trace()
447
+ #print("1")
448
+ if attn == "local":
449
+ import time
450
+ start = time.time()
451
+ num_feats = 3
452
+ query_states_t = query_states[:,:,1+576*num_feats:,:]
453
+ key_states_v = key_states[:,:,:1+576*num_feats,:]
454
+ key_states_t = key_states[:,:,1+576*num_feats:,:]
455
+ attn_weights_t_t = torch.matmul(query_states_t, key_states_t.transpose(2, 3)) / math.sqrt(self.head_dim)
456
+ attn_weights_t_v = torch.matmul(query_states_t, key_states_v.transpose(2, 3)) / math.sqrt(self.head_dim)
457
+ attn_weights_t_v_t = torch.cat([attn_weights_t_v,attn_weights_t_t],dim=-1)
458
+
459
+ # attn_weights = torch.empty((bsz, self.num_heads, q_len, q_len), device=query_states.device,dtype=torch.bfloat16).fill_(float('-inf')) # float('-inf')
460
+
461
+ #print("6")
462
+ #attn_weights[:,:,1+576*num_feats:,1+576*num_feats:]=attn_weights_t
463
+ #attn_weights[:,:,1+576*num_feats:,:1+576*num_feats]=attn_weights_t_v
464
+
465
+ # diag_mask = torch.eye(1 + 576 * num_feats, device=attn_weights.device, dtype=attn_weights.dtype).unsqueeze(0).unsqueeze(0)
466
+ #attn_weights[:,:,:1+576*num_feats,:1+576*num_feats] = torch.where(diag_mask.bool(), torch.tensor(1.0, dtype=attn_weights.dtype, device=attn_weights.device), attn_weights[:,:,:1+576*num_feats,:1+576*num_feats])
467
+
468
+ #print(query_states.shape)
469
+ #print("7")
470
+
471
+ # attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) #torch.Size([16, 40, 1752, 1752])
472
+ # import pdb; pdb.set_trace()
473
+
474
+
475
+ # upcast attention to fp32
476
+ #import pdb; pdb.set_trace()
477
+ attn_weights_t_v_t = attn_weights_t_v_t + attention_mask[:,:,1+576*num_feats:,:]
478
+ attn_weights_t_v_t = nn.functional.softmax(attn_weights_t_v_t, dim=-1, dtype=torch.float32).to(query_states.dtype)
479
+ attn_weights_t_v_t = nn.functional.dropout(attn_weights_t_v_t, p=self.attention_dropout, training=self.training)
480
+ # import pdb; pdb.set_trace()
481
+ attn_output_t = torch.matmul(attn_weights_t_v_t, value_states)
482
+ value_states_v = value_states[:,:,:1+576*num_feats,:]
483
+ attn_output = torch.cat([value_states_v,attn_output_t],dim=2)
484
+ end = time.time()
485
+ #print(end-start)
486
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
487
+ raise ValueError(
488
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
489
+ f" {attn_output.size()}"
490
+ )
491
+
492
+ attn_output = attn_output.transpose(1, 2).contiguous()
493
+
494
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
495
+
496
+ if self.config.pretraining_tp > 1:
497
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
498
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
499
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
500
+ else:
501
+ attn_output = self.o_proj(attn_output)
502
+
503
+ if not output_attentions:
504
+ attn_weights = None
505
+
506
+ return attn_output, attn_weights, past_key_value
507
+
508
+
509
+ class LlamaFlashAttention2(LlamaAttention):
510
+ """
511
+ Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
512
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
513
+ flash attention and deal with padding tokens in case the input contains any of them.
514
+ """
515
+
516
+ def __init__(self, *args, **kwargs):
517
+ super().__init__(*args, **kwargs)
518
+
519
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
520
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
521
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
522
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
523
+
524
+ def forward(
525
+ self,
526
+ hidden_states: torch.Tensor,
527
+ attention_mask: Optional[torch.LongTensor] = None,
528
+ position_ids: Optional[torch.LongTensor] = None,
529
+ past_key_value: Optional[Cache] = None,
530
+ output_attentions: bool = False,
531
+ use_cache: bool = False,
532
+ **kwargs,
533
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
534
+ # LlamaFlashAttention2 attention does not support output_attentions
535
+ if "padding_mask" in kwargs:
536
+ warnings.warn(
537
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
538
+ )
539
+
540
+ # overwrite attention_mask with padding_mask
541
+ attention_mask = kwargs.pop("padding_mask")
542
+
543
+ output_attentions = False
544
+
545
+ bsz, q_len, _ = hidden_states.size()
546
+
547
+ query_states = self.q_proj(hidden_states)
548
+ key_states = self.k_proj(hidden_states)
549
+ value_states = self.v_proj(hidden_states)
550
+
551
+ # Flash attention requires the input to have the shape
552
+ # batch_size x seq_length x head_dim x hidden_dim
553
+ # therefore we just need to keep the original shape
554
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
555
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
556
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
557
+
558
+ kv_seq_len = key_states.shape[-2]
559
+ if past_key_value is not None:
560
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
561
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
562
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
563
+
564
+ if past_key_value is not None:
565
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
566
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
567
+
568
+ # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
569
+ # to be able to avoid many of these transpose/reshape/view.
570
+ query_states = query_states.transpose(1, 2)
571
+ key_states = key_states.transpose(1, 2)
572
+ value_states = value_states.transpose(1, 2)
573
+
574
+ dropout_rate = self.attention_dropout if self.training else 0.0
575
+
576
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
577
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
578
+ # cast them back in the correct dtype just to be sure everything works as expected.
579
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
580
+ # in fp32. (LlamaRMSNorm handles it correctly)
581
+
582
+ input_dtype = query_states.dtype
583
+ if input_dtype == torch.float32:
584
+ # Handle the case where the model is quantized
585
+ if hasattr(self.config, "_pre_quantization_dtype"):
586
+ target_dtype = self.config._pre_quantization_dtype
587
+ else:
588
+ target_dtype = self.q_proj.weight.dtype
589
+
590
+ logger.warning_once(
591
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
592
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
593
+ f" {target_dtype}."
594
+ )
595
+
596
+ query_states = query_states.to(target_dtype)
597
+ key_states = key_states.to(target_dtype)
598
+ value_states = value_states.to(target_dtype)
599
+
600
+ attn_output = self._flash_attention_forward(
601
+ query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
602
+ )
603
+
604
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
605
+ attn_output = self.o_proj(attn_output)
606
+
607
+ if not output_attentions:
608
+ attn_weights = None
609
+
610
+ return attn_output, attn_weights, past_key_value
611
+
612
+ def _flash_attention_forward(
613
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
614
+ ):
615
+ """
616
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
617
+ first unpad the input, then computes the attention scores and pad the final attention scores.
618
+
619
+ Args:
620
+ query_states (`torch.Tensor`):
621
+ Input query states to be passed to Flash Attention API
622
+ key_states (`torch.Tensor`):
623
+ Input key states to be passed to Flash Attention API
624
+ value_states (`torch.Tensor`):
625
+ Input value states to be passed to Flash Attention API
626
+ attention_mask (`torch.Tensor`):
627
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
628
+ position of padding tokens and 1 for the position of non-padding tokens.
629
+ dropout (`int`, *optional*):
630
+ Attention dropout
631
+ softmax_scale (`float`, *optional*):
632
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
633
+ """
634
+ if not self._flash_attn_uses_top_left_mask:
635
+ causal = self.is_causal
636
+ else:
637
+ # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
638
+ causal = self.is_causal and query_length != 1
639
+
640
+ # Contains at least one padding token in the sequence
641
+ if attention_mask is not None:
642
+ batch_size = query_states.shape[0]
643
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
644
+ query_states, key_states, value_states, attention_mask, query_length
645
+ )
646
+
647
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
648
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
649
+
650
+ attn_output_unpad = flash_attn_varlen_func(
651
+ query_states,
652
+ key_states,
653
+ value_states,
654
+ cu_seqlens_q=cu_seqlens_q,
655
+ cu_seqlens_k=cu_seqlens_k,
656
+ max_seqlen_q=max_seqlen_in_batch_q,
657
+ max_seqlen_k=max_seqlen_in_batch_k,
658
+ dropout_p=dropout,
659
+ softmax_scale=softmax_scale,
660
+ causal=causal,
661
+ )
662
+
663
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
664
+ else:
665
+ attn_output = flash_attn_func(
666
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
667
+ )
668
+
669
+ return attn_output
670
+
671
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
672
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
673
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
674
+
675
+ key_layer = index_first_axis(
676
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
677
+ )
678
+ value_layer = index_first_axis(
679
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
680
+ )
681
+ if query_length == kv_seq_len:
682
+ query_layer = index_first_axis(
683
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
684
+ )
685
+ cu_seqlens_q = cu_seqlens_k
686
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
687
+ indices_q = indices_k
688
+ elif query_length == 1:
689
+ max_seqlen_in_batch_q = 1
690
+ cu_seqlens_q = torch.arange(
691
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
692
+ ) # There is a memcpy here, that is very bad.
693
+ indices_q = cu_seqlens_q[:-1]
694
+ query_layer = query_layer.squeeze(1)
695
+ else:
696
+ # The -q_len: slice assumes left padding.
697
+ attention_mask = attention_mask[:, -query_length:]
698
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
699
+
700
+ return (
701
+ query_layer,
702
+ key_layer,
703
+ value_layer,
704
+ indices_q,
705
+ (cu_seqlens_q, cu_seqlens_k),
706
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
707
+ )
708
+
709
+
710
+ class LlamaSdpaAttention(LlamaAttention):
711
+ """
712
+ Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
713
+ `LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
714
+ SDPA API.
715
+ """
716
+
717
+ # Adapted from LlamaAttention.forward
718
+ def forward(
719
+ self,
720
+ hidden_states: torch.Tensor,
721
+ attention_mask: Optional[torch.Tensor] = None,
722
+ position_ids: Optional[torch.LongTensor] = None,
723
+ past_key_value: Optional[Cache] = None,
724
+ output_attentions: bool = False,
725
+ use_cache: bool = False,
726
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
727
+ if output_attentions:
728
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
729
+ logger.warning_once(
730
+ "LlamaModel is using LlamaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
731
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
732
+ )
733
+ return super().forward(
734
+ hidden_states=hidden_states,
735
+ attention_mask=attention_mask,
736
+ position_ids=position_ids,
737
+ past_key_value=past_key_value,
738
+ output_attentions=output_attentions,
739
+ use_cache=use_cache,
740
+ )
741
+
742
+ bsz, q_len, _ = hidden_states.size()
743
+
744
+ query_states = self.q_proj(hidden_states)
745
+ key_states = self.k_proj(hidden_states)
746
+ value_states = self.v_proj(hidden_states)
747
+
748
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
749
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
750
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
751
+
752
+ kv_seq_len = key_states.shape[-2]
753
+ if past_key_value is not None:
754
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
755
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
756
+
757
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
758
+
759
+ if past_key_value is not None:
760
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
761
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
762
+
763
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
764
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
765
+
766
+ if attention_mask is not None:
767
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
768
+ raise ValueError(
769
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
770
+ )
771
+
772
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
773
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
774
+ if query_states.device.type == "cuda" and attention_mask is not None:
775
+ query_states = query_states.contiguous()
776
+ key_states = key_states.contiguous()
777
+ value_states = value_states.contiguous()
778
+
779
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
780
+ query_states,
781
+ key_states,
782
+ value_states,
783
+ attn_mask=attention_mask,
784
+ dropout_p=self.attention_dropout if self.training else 0.0,
785
+ # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
786
+ is_causal=self.is_causal and attention_mask is None and q_len > 1,
787
+ )
788
+
789
+ attn_output = attn_output.transpose(1, 2).contiguous()
790
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
791
+
792
+ attn_output = self.o_proj(attn_output)
793
+
794
+ return attn_output, None, past_key_value
795
+
796
+
797
+ LLAMA_ATTENTION_CLASSES = {
798
+ "eager": LlamaAttention,
799
+ "flash_attention_2": LlamaFlashAttention2,
800
+ "sdpa": LlamaSdpaAttention,
801
+ }
802
+
803
+
804
+ class LlamaDecoderLayer(nn.Module):
805
+ def __init__(self, config: LlamaConfig, layer_idx: int):
806
+ super().__init__()
807
+ self.hidden_size = config.hidden_size
808
+ config._attn_implementation="eager"
809
+ self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
810
+
811
+ self.mlp = LlamaMLP(config)
812
+ self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
813
+ self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
814
+ def forward(
815
+ self,
816
+ hidden_states: torch.Tensor,
817
+ attention_mask: Optional[torch.Tensor] = None,
818
+ position_ids: Optional[torch.LongTensor] = None,
819
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
820
+ output_attentions: Optional[bool] = False,
821
+ use_cache: Optional[bool] = False,
822
+ **kwargs,
823
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
824
+ """
825
+ Args:
826
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
827
+ attention_mask (`torch.FloatTensor`, *optional*):
828
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
829
+ query_sequence_length, key_sequence_length)` if default attention is used.
830
+ output_attentions (`bool`, *optional*):
831
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
832
+ returned tensors for more detail.
833
+ use_cache (`bool`, *optional*):
834
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
835
+ (see `past_key_values`).
836
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
837
+ """
838
+ if "padding_mask" in kwargs:
839
+ warnings.warn(
840
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
841
+ )
842
+ residual = hidden_states
843
+
844
+ hidden_states = self.input_layernorm(hidden_states)
845
+ '''
846
+ attention_mask = attention_mask.clone()
847
+ if attention_mask.shape[2]!=1:
848
+ attention_mask[:,:,:576,:576]= -65504.0 # -1000000000000.0 -65504.0
849
+
850
+ for i in range(576):
851
+ if attention_mask.shape[2]!=1:
852
+ attention_mask[:,:,i,i]=0.0
853
+
854
+ # import pdb; pdb.set_trace()
855
+ attention_mask=attention_mask.clone()
856
+ '''
857
+ # Self Attention
858
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
859
+ hidden_states=hidden_states,
860
+ attention_mask=attention_mask,
861
+ position_ids=position_ids,
862
+ past_key_value=past_key_value,
863
+ output_attentions=output_attentions,
864
+ use_cache=use_cache,
865
+ **kwargs,
866
+ )
867
+ hidden_states = residual + hidden_states
868
+
869
+ # Fully Connected
870
+ residual = hidden_states
871
+ hidden_states = self.post_attention_layernorm(hidden_states)
872
+ hidden_states = self.mlp(hidden_states)
873
+ hidden_states = residual + hidden_states
874
+
875
+ outputs = (hidden_states,)
876
+
877
+ if output_attentions:
878
+ outputs += (self_attn_weights,)
879
+
880
+ if use_cache:
881
+ outputs += (present_key_value,)
882
+
883
+ return outputs
884
+
885
+
886
+ LLAMA_START_DOCSTRING = r"""
887
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
888
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
889
+ etc.)
890
+
891
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
892
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
893
+ and behavior.
894
+
895
+ Parameters:
896
+ config ([`LlamaConfig`]):
897
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
898
+ load the weights associated with the model, only the configuration. Check out the
899
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
900
+ """
901
+
902
+
903
+ @add_start_docstrings(
904
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
905
+ LLAMA_START_DOCSTRING,
906
+ )
907
+ class LlamaPreTrainedModel(PreTrainedModel):
908
+ config_class = LlamaConfig
909
+ base_model_prefix = "model"
910
+ supports_gradient_checkpointing = True
911
+ _no_split_modules = ["LlamaDecoderLayer"]
912
+ _skip_keys_device_placement = "past_key_values"
913
+ _supports_flash_attn_2 = True
914
+ _supports_sdpa = True
915
+ _supports_cache_class = True
916
+
917
+ def _init_weights(self, module):
918
+ std = self.config.initializer_range
919
+ if isinstance(module, nn.Linear):
920
+ module.weight.data.normal_(mean=0.0, std=std)
921
+ if module.bias is not None:
922
+ module.bias.data.zero_()
923
+ elif isinstance(module, nn.Embedding):
924
+ module.weight.data.normal_(mean=0.0, std=std)
925
+ if module.padding_idx is not None:
926
+ module.weight.data[module.padding_idx].zero_()
927
+
928
+
929
+ LLAMA_INPUTS_DOCSTRING = r"""
930
+ Args:
931
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
932
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
933
+ it.
934
+
935
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
936
+ [`PreTrainedTokenizer.__call__`] for details.
937
+
938
+ [What are input IDs?](../glossary#input-ids)
939
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
940
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
941
+
942
+ - 1 for tokens that are **not masked**,
943
+ - 0 for tokens that are **masked**.
944
+
945
+ [What are attention masks?](../glossary#attention-mask)
946
+
947
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
948
+ [`PreTrainedTokenizer.__call__`] for details.
949
+
950
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
951
+ `past_key_values`).
952
+
953
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
954
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
955
+ information on the default strategy.
956
+
957
+ - 1 indicates the head is **not masked**,
958
+ - 0 indicates the head is **masked**.
959
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
960
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
961
+ config.n_positions - 1]`.
962
+
963
+ [What are position IDs?](../glossary#position-ids)
964
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
965
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
966
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
967
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
968
+
969
+ Two formats are allowed:
970
+ - a [`~cache_utils.Cache`] instance;
971
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
972
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
973
+ cache format.
974
+
975
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
976
+ legacy cache format will be returned.
977
+
978
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
979
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
980
+ of shape `(batch_size, sequence_length)`.
981
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
982
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
983
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
984
+ model's internal embedding lookup matrix.
985
+ use_cache (`bool`, *optional*):
986
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
987
+ `past_key_values`).
988
+ output_attentions (`bool`, *optional*):
989
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
990
+ tensors for more detail.
991
+ output_hidden_states (`bool`, *optional*):
992
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
993
+ more detail.
994
+ return_dict (`bool`, *optional*):
995
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
996
+ """
997
+
998
+
999
+ @add_start_docstrings(
1000
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
1001
+ LLAMA_START_DOCSTRING,
1002
+ )
1003
+ class LlamaModel(LlamaPreTrainedModel):
1004
+ """
1005
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
1006
+
1007
+ Args:
1008
+ config: LlamaConfig
1009
+ """
1010
+
1011
+ def __init__(self, config: LlamaConfig):
1012
+ super().__init__(config)
1013
+ self.padding_idx = config.pad_token_id
1014
+ self.vocab_size = config.vocab_size
1015
+
1016
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
1017
+ self.layers = nn.ModuleList(
1018
+ [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
1019
+ )
1020
+ self._use_sdpa = config._attn_implementation == "sdpa"
1021
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
1022
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
1023
+
1024
+ self.gradient_checkpointing = False
1025
+ # Initialize weights and apply final processing
1026
+ self.post_init()
1027
+
1028
+ def get_input_embeddings(self):
1029
+ return self.embed_tokens
1030
+
1031
+ def set_input_embeddings(self, value):
1032
+ self.embed_tokens = value
1033
+
1034
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1035
+ def forward(
1036
+ self,
1037
+ input_ids: torch.LongTensor = None,
1038
+ attention_mask: Optional[torch.Tensor] = None,
1039
+ position_ids: Optional[torch.LongTensor] = None,
1040
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1041
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1042
+ use_cache: Optional[bool] = None,
1043
+ output_attentions: Optional[bool] = None,
1044
+ output_hidden_states: Optional[bool] = None,
1045
+ return_dict: Optional[bool] = None,
1046
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
1047
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1048
+ output_hidden_states = (
1049
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1050
+ )
1051
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
1052
+
1053
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1054
+
1055
+ # retrieve input_ids and inputs_embeds
1056
+ if input_ids is not None and inputs_embeds is not None:
1057
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
1058
+ elif input_ids is not None:
1059
+ batch_size, seq_length = input_ids.shape[:2]
1060
+ elif inputs_embeds is not None:
1061
+ batch_size, seq_length = inputs_embeds.shape[:2]
1062
+ else:
1063
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
1064
+
1065
+ if self.gradient_checkpointing and self.training:
1066
+ if use_cache:
1067
+ logger.warning_once(
1068
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
1069
+ )
1070
+ use_cache = False
1071
+
1072
+ past_key_values_length = 0
1073
+ if use_cache:
1074
+ use_legacy_cache = not isinstance(past_key_values, Cache)
1075
+ if use_legacy_cache:
1076
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
1077
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
1078
+
1079
+ if position_ids is None:
1080
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
1081
+ position_ids = torch.arange(
1082
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
1083
+ )
1084
+ position_ids = position_ids.unsqueeze(0)
1085
+
1086
+ if inputs_embeds is None:
1087
+ inputs_embeds = self.embed_tokens(input_ids)
1088
+
1089
+ if self._use_flash_attention_2:
1090
+ # 2d mask is passed through the layers
1091
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
1092
+ elif self._use_sdpa and not output_attentions:
1093
+ # output_attentions=True can not be supported when using SDPA, and we fall back on
1094
+ # the manual implementation that requires a 4D causal mask in all cases.
1095
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
1096
+ attention_mask,
1097
+ (batch_size, seq_length),
1098
+ inputs_embeds,
1099
+ past_key_values_length,
1100
+ )
1101
+ else:
1102
+ # 4d mask is passed through the layers
1103
+ attention_mask = _prepare_4d_causal_attention_mask(
1104
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
1105
+ )
1106
+
1107
+ # embed positions
1108
+ hidden_states = inputs_embeds
1109
+
1110
+ # decoder layers
1111
+ all_hidden_states = () if output_hidden_states else None
1112
+ all_self_attns = () if output_attentions else None
1113
+ next_decoder_cache = None
1114
+ import time
1115
+ start = time.time()
1116
+ for i, decoder_layer in enumerate(self.layers):
1117
+ if output_hidden_states:
1118
+ all_hidden_states += (hidden_states,)
1119
+ #import pdb; pdb.set_trace()
1120
+ if self.gradient_checkpointing and self.training:
1121
+ layer_outputs = self._gradient_checkpointing_func(
1122
+ decoder_layer.__call__,
1123
+ hidden_states,
1124
+ attention_mask,
1125
+ position_ids,
1126
+ past_key_values,
1127
+ output_attentions,
1128
+ use_cache,
1129
+ )
1130
+ else:
1131
+ layer_outputs = decoder_layer(
1132
+ hidden_states,
1133
+ attention_mask=attention_mask,
1134
+ position_ids=position_ids,
1135
+ past_key_value=past_key_values,
1136
+ output_attentions=output_attentions,
1137
+ use_cache=use_cache,
1138
+ )
1139
+
1140
+ hidden_states = layer_outputs[0]
1141
+
1142
+ if use_cache:
1143
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1144
+
1145
+ if output_attentions:
1146
+ all_self_attns += (layer_outputs[1],)
1147
+ end = time.time()
1148
+ #print(end-start, len(self.layers))
1149
+ hidden_states = self.norm(hidden_states)
1150
+
1151
+ # add hidden states from the last decoder layer
1152
+ if output_hidden_states:
1153
+ all_hidden_states += (hidden_states,)
1154
+
1155
+ next_cache = None
1156
+ if use_cache:
1157
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
1158
+ if not return_dict:
1159
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
1160
+ return BaseModelOutputWithPast(
1161
+ last_hidden_state=hidden_states,
1162
+ past_key_values=next_cache,
1163
+ hidden_states=all_hidden_states,
1164
+ attentions=all_self_attns,
1165
+ )
1166
+
1167
+
1168
+ class LlamaForCausalLM(LlamaPreTrainedModel):
1169
+ _tied_weights_keys = ["lm_head.weight"]
1170
+
1171
+ def __init__(self, config):
1172
+ super().__init__(config)
1173
+ self.model = LlamaModel(config)
1174
+ self.vocab_size = config.vocab_size
1175
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1176
+
1177
+ # Initialize weights and apply final processing
1178
+ self.post_init()
1179
+
1180
+ def get_input_embeddings(self):
1181
+ return self.model.embed_tokens
1182
+
1183
+ def set_input_embeddings(self, value):
1184
+ self.model.embed_tokens = value
1185
+
1186
+ def get_output_embeddings(self):
1187
+ return self.lm_head
1188
+
1189
+ def set_output_embeddings(self, new_embeddings):
1190
+ self.lm_head = new_embeddings
1191
+
1192
+ def set_decoder(self, decoder):
1193
+ self.model = decoder
1194
+
1195
+ def get_decoder(self):
1196
+ return self.model
1197
+
1198
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1199
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1200
+ def forward(
1201
+ self,
1202
+ input_ids: torch.LongTensor = None,
1203
+ attention_mask: Optional[torch.Tensor] = None,
1204
+ position_ids: Optional[torch.LongTensor] = None,
1205
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1206
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1207
+ labels: Optional[torch.LongTensor] = None,
1208
+ use_cache: Optional[bool] = None,
1209
+ output_attentions: Optional[bool] = None,
1210
+ output_hidden_states: Optional[bool] = None,
1211
+ return_dict: Optional[bool] = None,
1212
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1213
+ r"""
1214
+ Args:
1215
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1216
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1217
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1218
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1219
+
1220
+ Returns:
1221
+
1222
+ Example:
1223
+
1224
+ ```python
1225
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
1226
+
1227
+ >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1228
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1229
+
1230
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1231
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1232
+
1233
+ >>> # Generate
1234
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1235
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1236
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1237
+ ```"""
1238
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1239
+ output_hidden_states = (
1240
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1241
+ )
1242
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1243
+
1244
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1245
+ outputs = self.model(
1246
+ input_ids=input_ids,
1247
+ attention_mask=attention_mask,
1248
+ position_ids=position_ids,
1249
+ past_key_values=past_key_values,
1250
+ inputs_embeds=inputs_embeds,
1251
+ use_cache=use_cache,
1252
+ output_attentions=output_attentions,
1253
+ output_hidden_states=output_hidden_states,
1254
+ return_dict=return_dict,
1255
+ )
1256
+
1257
+ hidden_states = outputs[0]
1258
+ if self.config.pretraining_tp > 1:
1259
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
1260
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
1261
+ logits = torch.cat(logits, dim=-1)
1262
+ else:
1263
+ logits = self.lm_head(hidden_states)
1264
+ logits = logits.float()
1265
+
1266
+ loss = None
1267
+ if labels is not None:
1268
+ # Shift so that tokens < n predict n
1269
+ shift_logits = logits[..., :-1, :].contiguous()
1270
+ shift_labels = labels[..., 1:].contiguous()
1271
+ # Flatten the tokens
1272
+ loss_fct = CrossEntropyLoss()
1273
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1274
+ shift_labels = shift_labels.view(-1)
1275
+ # Enable model parallelism
1276
+ shift_labels = shift_labels.to(shift_logits.device)
1277
+ loss = loss_fct(shift_logits, shift_labels)
1278
+
1279
+ if not return_dict:
1280
+ output = (logits,) + outputs[1:]
1281
+ return (loss,) + output if loss is not None else output
1282
+
1283
+ return CausalLMOutputWithPast(
1284
+ loss=loss,
1285
+ logits=logits,
1286
+ past_key_values=outputs.past_key_values,
1287
+ hidden_states=outputs.hidden_states,
1288
+ attentions=outputs.attentions,
1289
+ )
1290
+
1291
+ def prepare_inputs_for_generation(
1292
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1293
+ ):
1294
+ if past_key_values is not None:
1295
+ if isinstance(past_key_values, Cache):
1296
+ cache_length = past_key_values.get_seq_length()
1297
+ past_length = past_key_values.seen_tokens
1298
+ max_cache_length = past_key_values.get_max_length()
1299
+ else:
1300
+ cache_length = past_length = past_key_values[0][0].shape[2]
1301
+ max_cache_length = None
1302
+
1303
+ # Keep only the unprocessed tokens:
1304
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
1305
+ # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
1306
+ # input)
1307
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
1308
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
1309
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
1310
+ # input_ids based on the past_length.
1311
+ elif past_length < input_ids.shape[1]:
1312
+ input_ids = input_ids[:, past_length:]
1313
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
1314
+
1315
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
1316
+ if (
1317
+ max_cache_length is not None
1318
+ and attention_mask is not None
1319
+ and cache_length + input_ids.shape[1] > max_cache_length
1320
+ ):
1321
+ attention_mask = attention_mask[:, -max_cache_length:]
1322
+
1323
+ position_ids = kwargs.get("position_ids", None)
1324
+ if attention_mask is not None and position_ids is None:
1325
+ # create position_ids on the fly for batch generation
1326
+ position_ids = attention_mask.long().cumsum(-1) - 1
1327
+ position_ids.masked_fill_(attention_mask == 0, 1)
1328
+ if past_key_values:
1329
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1330
+
1331
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1332
+ if inputs_embeds is not None and past_key_values is None:
1333
+ model_inputs = {"inputs_embeds": inputs_embeds}
1334
+ else:
1335
+ model_inputs = {"input_ids": input_ids}
1336
+
1337
+ model_inputs.update(
1338
+ {
1339
+ "position_ids": position_ids,
1340
+ "past_key_values": past_key_values,
1341
+ "use_cache": kwargs.get("use_cache"),
1342
+ "attention_mask": attention_mask,
1343
+ }
1344
+ )
1345
+ return model_inputs
1346
+
1347
+ @staticmethod
1348
+ def _reorder_cache(past_key_values, beam_idx):
1349
+ reordered_past = ()
1350
+ for layer_past in past_key_values:
1351
+ reordered_past += (
1352
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1353
+ )
1354
+ return reordered_past
1355
+
1356
+
1357
+ @add_start_docstrings(
1358
+ """
1359
+ The LLaMa Model transformer with a sequence classification head on top (linear layer).
1360
+
1361
+ [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1362
+ (e.g. GPT-2) do.
1363
+
1364
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1365
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1366
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1367
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1368
+ each row of the batch).
1369
+ """,
1370
+ LLAMA_START_DOCSTRING,
1371
+ )
1372
+ class LlamaForSequenceClassification(LlamaPreTrainedModel):
1373
+ def __init__(self, config):
1374
+ super().__init__(config)
1375
+ self.num_labels = config.num_labels
1376
+ self.model = LlamaModel(config)
1377
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1378
+
1379
+ # Initialize weights and apply final processing
1380
+ self.post_init()
1381
+
1382
+ def get_input_embeddings(self):
1383
+ return self.model.embed_tokens
1384
+
1385
+ def set_input_embeddings(self, value):
1386
+ self.model.embed_tokens = value
1387
+
1388
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1389
+ def forward(
1390
+ self,
1391
+ input_ids: torch.LongTensor = None,
1392
+ attention_mask: Optional[torch.Tensor] = None,
1393
+ position_ids: Optional[torch.LongTensor] = None,
1394
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1395
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1396
+ labels: Optional[torch.LongTensor] = None,
1397
+ use_cache: Optional[bool] = None,
1398
+ output_attentions: Optional[bool] = None,
1399
+ output_hidden_states: Optional[bool] = None,
1400
+ return_dict: Optional[bool] = None,
1401
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1402
+ r"""
1403
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1404
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1405
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1406
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1407
+ """
1408
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1409
+
1410
+ transformer_outputs = self.model(
1411
+ input_ids,
1412
+ attention_mask=attention_mask,
1413
+ position_ids=position_ids,
1414
+ past_key_values=past_key_values,
1415
+ inputs_embeds=inputs_embeds,
1416
+ use_cache=use_cache,
1417
+ output_attentions=output_attentions,
1418
+ output_hidden_states=output_hidden_states,
1419
+ return_dict=return_dict,
1420
+ )
1421
+ hidden_states = transformer_outputs[0]
1422
+ logits = self.score(hidden_states)
1423
+
1424
+ if input_ids is not None:
1425
+ batch_size = input_ids.shape[0]
1426
+ else:
1427
+ batch_size = inputs_embeds.shape[0]
1428
+
1429
+ if self.config.pad_token_id is None and batch_size != 1:
1430
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1431
+ if self.config.pad_token_id is None:
1432
+ sequence_lengths = -1
1433
+ else:
1434
+ if input_ids is not None:
1435
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
1436
+ logits.device
1437
+ )
1438
+ else:
1439
+ sequence_lengths = -1
1440
+
1441
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1442
+
1443
+ loss = None
1444
+ if labels is not None:
1445
+ labels = labels.to(logits.device)
1446
+ if self.config.problem_type is None:
1447
+ if self.num_labels == 1:
1448
+ self.config.problem_type = "regression"
1449
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1450
+ self.config.problem_type = "single_label_classification"
1451
+ else:
1452
+ self.config.problem_type = "multi_label_classification"
1453
+
1454
+ if self.config.problem_type == "regression":
1455
+ loss_fct = MSELoss()
1456
+ if self.num_labels == 1:
1457
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1458
+ else:
1459
+ loss = loss_fct(pooled_logits, labels)
1460
+ elif self.config.problem_type == "single_label_classification":
1461
+ loss_fct = CrossEntropyLoss()
1462
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1463
+ elif self.config.problem_type == "multi_label_classification":
1464
+ loss_fct = BCEWithLogitsLoss()
1465
+ loss = loss_fct(pooled_logits, labels)
1466
+ if not return_dict:
1467
+ output = (pooled_logits,) + transformer_outputs[1:]
1468
+ return ((loss,) + output) if loss is not None else output
1469
+
1470
+ return SequenceClassifierOutputWithPast(
1471
+ loss=loss,
1472
+ logits=pooled_logits,
1473
+ past_key_values=transformer_outputs.past_key_values,
1474
+ hidden_states=transformer_outputs.hidden_states,
1475
+ attentions=transformer_outputs.attentions,
1476
+ )
special_tokens_map.json ADDED
@@ -0,0 +1,24 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "bos_token": {
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+ "content": "<s>",
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+ "lstrip": false,
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+ "rstrip": false,
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+ },
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+ "eos_token": {
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+ "content": "</s>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
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+ },
16
+ "pad_token": "<unk>",
17
+ "unk_token": {
18
+ "content": "<unk>",
19
+ "lstrip": false,
20
+ "normalized": false,
21
+ "rstrip": false,
22
+ "single_word": false
23
+ }
24
+ }
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
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+ size 499723
tokenizer_config.json ADDED
@@ -0,0 +1,42 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "add_bos_token": true,
3
+ "add_eos_token": false,
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+ "added_tokens_decoder": {
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+ "0": {
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+ "content": "<unk>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "1": {
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+ "content": "<s>",
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ },
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+ "2": {
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+ "content": "</s>",
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+ "lstrip": false,
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+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false,
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+ "special": true
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+ }
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+ },
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+ "bos_token": "<s>",
31
+ "clean_up_tokenization_spaces": false,
32
+ "eos_token": "</s>",
33
+ "legacy": false,
34
+ "model_max_length": 1560,
35
+ "pad_token": "<unk>",
36
+ "padding_side": "right",
37
+ "sp_model_kwargs": {},
38
+ "spaces_between_special_tokens": false,
39
+ "tokenizer_class": "LlamaTokenizer",
40
+ "unk_token": "<unk>",
41
+ "use_default_system_prompt": false
42
+ }
trainer_state.json ADDED
The diff for this file is too large to render. See raw diff
 
training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:0c50fa35d72c68b5daeda4801b4e1cc2ae0dfbd60322b532927ede0c338e85f5
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+ size 6776