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README.md ADDED
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+ ---
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+ library_name: transformers
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+ <!-- Provide a quick summary of what the model is/does. -->
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+ #### Summary
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+ ## Model Examination [optional]
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+ ## Environmental Impact
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+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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+ ## Technical Specifications [optional]
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+
config.json ADDED
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+ {
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+ "_name_or_path": "/extra_disk_1/blacksamorez/miqu-1-70b-AQLM-2Bit-1x16-hf/",
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+ "aqlm": {
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+ "in_group_size": 8,
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+ "nbits_per_codebook": 16,
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+ "num_codebooks": 1,
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+ "out_group_size": 1
8
+ },
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+ "architectures": [
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+ "LlamaForCausalLM"
11
+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "auto_map": {
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+ "AutoConfig": "configuration_llama_aqlm.LlamaConfig",
16
+ "AutoModelForCausalLM": "modeling_llama_aqlm.LlamaForCausalLM"
17
+ },
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 8192,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 28672,
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+ "max_position_embeddings": 32764,
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+ "model_type": "llama_aqlm",
26
+ "num_attention_heads": 64,
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+ "num_hidden_layers": 80,
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+ "num_key_value_heads": 8,
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+ "pad_token_id": 0,
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+ "pretraining_tp": 1,
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+ "rms_norm_eps": 1e-05,
32
+ "rope_scaling": null,
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+ "rope_theta": 1000000,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.38.0.dev0",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
configuration_llama_aqlm.py ADDED
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+ from transformers import LlamaConfig as OrigLlamaConfig
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+
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+
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+ class LlamaConfig(OrigLlamaConfig):
5
+ model_type = "llama_aqlm"
6
+
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+ def __init__(
8
+ self,
9
+ aqlm: dict[str, int] = {
10
+ "nbits_per_codebook": 16,
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+ "num_codebooks": 1,
12
+ "out_group_size": 8,
13
+ "in_group_size": 1,
14
+ },
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+ **kwargs,
16
+ ):
17
+ super().__init__(**kwargs)
18
+ self.aqlm = aqlm
generation_config.json ADDED
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+ {
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+ "_from_model_config": true,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "pad_token_id": 0,
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+ "transformers_version": "4.38.0.dev0"
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+ }
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modeling_llama_aqlm.py ADDED
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1
+ # coding=utf-8
2
+ # This code is a modification of transformers/models/llama/modeling_llama.py , which is has the following copyright:
3
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
4
+ #
5
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
6
+ # and OPT implementations in this library. It has been modified from its
7
+ # original forms to accommodate minor architectural differences compared
8
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
9
+ #
10
+ # Licensed under the Apache License, Version 2.0 (the "License");
11
+ # you may not use this file except in compliance with the License.
12
+ # You may obtain a copy of the License at
13
+ #
14
+ # http://www.apache.org/licenses/LICENSE-2.0
15
+ #
16
+ # Unless required by applicable law or agreed to in writing, software
17
+ # distributed under the License is distributed on an "AS IS" BASIS,
18
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
19
+ # See the License for the specific language governing permissions and
20
+ # limitations under the License.
21
+ """ PyTorch LLaMA model."""
22
+ import math
23
+ import warnings
24
+ from typing import List, Optional, Tuple, Union
25
+
26
+ import torch
27
+ import torch.nn.functional as F
28
+ import torch.utils.checkpoint
29
+ from aqlm import QuantizedLinear
30
+ from torch import nn
31
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
32
+ from transformers.activations import ACT2FN
33
+ from transformers.cache_utils import Cache, DynamicCache
34
+ from transformers.modeling_attn_mask_utils import (
35
+ AttentionMaskConverter,
36
+ _prepare_4d_attention_mask,
37
+ _prepare_4d_causal_attention_mask,
38
+ _prepare_4d_causal_attention_mask_for_sdpa,
39
+ )
40
+ from transformers.modeling_outputs import (
41
+ BaseModelOutputWithPast,
42
+ CausalLMOutputWithPast,
43
+ SequenceClassifierOutputWithPast,
44
+ )
45
+ from transformers.modeling_utils import PreTrainedModel
46
+ from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
47
+ from transformers.utils import (
48
+ add_start_docstrings,
49
+ add_start_docstrings_to_model_forward,
50
+ is_flash_attn_2_available,
51
+ is_flash_attn_greater_or_equal_2_10,
52
+ logging,
53
+ replace_return_docstrings,
54
+ )
55
+ from transformers.utils.import_utils import is_torch_fx_available
56
+
57
+ from .configuration_llama_aqlm import LlamaConfig
58
+
59
+ if is_flash_attn_2_available():
60
+ try:
61
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
62
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
63
+ except:
64
+ pass
65
+
66
+
67
+ # This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
68
+ # It means that the function will not be traced through and simply appear as a node in the graph.
69
+ if is_torch_fx_available():
70
+ if not is_torch_greater_or_equal_than_1_13:
71
+ import torch.fx
72
+
73
+ _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
74
+
75
+
76
+ logger = logging.get_logger(__name__)
77
+
78
+ _CONFIG_FOR_DOC = "LlamaConfig"
79
+
80
+
81
+ def _get_unpad_data(attention_mask):
82
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
83
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
84
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
85
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
86
+ return (
87
+ indices,
88
+ cu_seqlens,
89
+ max_seqlen_in_batch,
90
+ )
91
+
92
+
93
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
94
+ warnings.warn(
95
+ "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"
96
+ )
97
+ return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
98
+
99
+
100
+ def _make_causal_mask(
101
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
102
+ ):
103
+ warnings.warn(
104
+ "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"
105
+ )
106
+ return AttentionMaskConverter._make_causal_mask(
107
+ input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
108
+ )
109
+
110
+
111
+ class LlamaRMSNorm(nn.Module):
112
+ def __init__(self, hidden_size, eps=1e-6):
113
+ """
114
+ LlamaRMSNorm is equivalent to T5LayerNorm
115
+ """
116
+ super().__init__()
117
+ self.weight = nn.Parameter(torch.ones(hidden_size))
118
+ self.variance_epsilon = eps
119
+
120
+ def forward(self, hidden_states):
121
+ input_dtype = hidden_states.dtype
122
+ hidden_states = hidden_states.to(torch.float32)
123
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
124
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
125
+ return self.weight * hidden_states.to(input_dtype)
126
+
127
+
128
+ ALL_LAYERNORM_LAYERS.append(LlamaRMSNorm)
129
+
130
+
131
+ class LlamaRotaryEmbedding(nn.Module):
132
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
133
+ super().__init__()
134
+
135
+ self.dim = dim
136
+ self.max_position_embeddings = max_position_embeddings
137
+ self.base = base
138
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
139
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
140
+
141
+ # Build here to make `torch.jit.trace` work.
142
+ self._set_cos_sin_cache(
143
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
144
+ )
145
+
146
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
147
+ self.max_seq_len_cached = seq_len
148
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
149
+
150
+ freqs = torch.outer(t, self.inv_freq)
151
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
152
+ emb = torch.cat((freqs, freqs), dim=-1)
153
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
154
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
155
+
156
+ def forward(self, x, seq_len=None):
157
+ # x: [bs, num_attention_heads, seq_len, head_size]
158
+ if seq_len > self.max_seq_len_cached:
159
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
160
+
161
+ return (
162
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
163
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
164
+ )
165
+
166
+
167
+ class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
168
+ """LlamaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
169
+
170
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
171
+ self.scaling_factor = scaling_factor
172
+ super().__init__(dim, max_position_embeddings, base, device)
173
+
174
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
175
+ self.max_seq_len_cached = seq_len
176
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
177
+ t = t / self.scaling_factor
178
+
179
+ freqs = torch.outer(t, self.inv_freq)
180
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
181
+ emb = torch.cat((freqs, freqs), dim=-1)
182
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
183
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
184
+
185
+
186
+ class LlamaDynamicNTKScalingRotaryEmbedding(LlamaRotaryEmbedding):
187
+ """LlamaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
188
+
189
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
190
+ self.scaling_factor = scaling_factor
191
+ super().__init__(dim, max_position_embeddings, base, device)
192
+
193
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
194
+ self.max_seq_len_cached = seq_len
195
+
196
+ if seq_len > self.max_position_embeddings:
197
+ base = self.base * (
198
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
199
+ ) ** (self.dim / (self.dim - 2))
200
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
201
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
202
+
203
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
204
+
205
+ freqs = torch.outer(t, self.inv_freq)
206
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
207
+ emb = torch.cat((freqs, freqs), dim=-1)
208
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
209
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
210
+
211
+
212
+ def rotate_half(x):
213
+ """Rotates half the hidden dims of the input."""
214
+ x1 = x[..., : x.shape[-1] // 2]
215
+ x2 = x[..., x.shape[-1] // 2 :]
216
+ return torch.cat((-x2, x1), dim=-1)
217
+
218
+
219
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
220
+ """Applies Rotary Position Embedding to the query and key tensors.
221
+
222
+ Args:
223
+ q (`torch.Tensor`): The query tensor.
224
+ k (`torch.Tensor`): The key tensor.
225
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
226
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
227
+ position_ids (`torch.Tensor`):
228
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
229
+ used to pass offsetted position ids when working with a KV-cache.
230
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
231
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
232
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
233
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
234
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
235
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
236
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
237
+ Returns:
238
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
239
+ """
240
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
241
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
242
+ q_embed = (q * cos) + (rotate_half(q) * sin)
243
+ k_embed = (k * cos) + (rotate_half(k) * sin)
244
+ return q_embed, k_embed
245
+
246
+
247
+ class LlamaMLP(nn.Module):
248
+ def __init__(self, config):
249
+ super().__init__()
250
+ self.config = config
251
+ self.hidden_size = config.hidden_size
252
+ self.intermediate_size = config.intermediate_size
253
+ self.gate_proj = QuantizedLinear(self.hidden_size, self.intermediate_size, bias=False, **config.aqlm)
254
+ self.up_proj = QuantizedLinear(self.hidden_size, self.intermediate_size, bias=False, **config.aqlm)
255
+ self.down_proj = QuantizedLinear(self.intermediate_size, self.hidden_size, bias=False, **config.aqlm)
256
+ self.act_fn = ACT2FN[config.hidden_act]
257
+
258
+ def forward(self, x):
259
+ if self.config.pretraining_tp > 1:
260
+ slice = self.intermediate_size // self.config.pretraining_tp
261
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
262
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
263
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
264
+
265
+ gate_proj = torch.cat([F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
266
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
267
+
268
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
269
+ down_proj = [
270
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
271
+ ]
272
+ down_proj = sum(down_proj)
273
+ else:
274
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
275
+
276
+ return down_proj
277
+
278
+
279
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
280
+ """
281
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
282
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
283
+ """
284
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
285
+ if n_rep == 1:
286
+ return hidden_states
287
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
288
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
289
+
290
+
291
+ class LlamaAttention(nn.Module):
292
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
293
+
294
+ def __init__(self, config: LlamaConfig, layer_idx: Optional[int] = None):
295
+ super().__init__()
296
+ self.config = config
297
+ self.layer_idx = layer_idx
298
+ if layer_idx is None:
299
+ logger.warning_once(
300
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
301
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
302
+ "when creating this class."
303
+ )
304
+
305
+ self.attention_dropout = config.attention_dropout
306
+ self.hidden_size = config.hidden_size
307
+ self.num_heads = config.num_attention_heads
308
+ self.head_dim = self.hidden_size // self.num_heads
309
+ self.num_key_value_heads = config.num_key_value_heads
310
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
311
+ self.max_position_embeddings = config.max_position_embeddings
312
+ self.rope_theta = config.rope_theta
313
+ self.is_causal = True
314
+
315
+ if (self.head_dim * self.num_heads) != self.hidden_size:
316
+ raise ValueError(
317
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
318
+ f" and `num_heads`: {self.num_heads})."
319
+ )
320
+
321
+ self.q_proj = QuantizedLinear(
322
+ self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias, **config.aqlm
323
+ )
324
+ self.k_proj = QuantizedLinear(
325
+ self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias, **config.aqlm
326
+ )
327
+ self.v_proj = QuantizedLinear(
328
+ self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias, **config.aqlm
329
+ )
330
+ self.o_proj = QuantizedLinear(
331
+ self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias, **config.aqlm
332
+ )
333
+ self._init_rope()
334
+
335
+ def _init_rope(self):
336
+ if self.config.rope_scaling is None:
337
+ self.rotary_emb = LlamaRotaryEmbedding(
338
+ self.head_dim,
339
+ max_position_embeddings=self.max_position_embeddings,
340
+ base=self.rope_theta,
341
+ )
342
+ else:
343
+ scaling_type = self.config.rope_scaling["type"]
344
+ scaling_factor = self.config.rope_scaling["factor"]
345
+ if scaling_type == "linear":
346
+ self.rotary_emb = LlamaLinearScalingRotaryEmbedding(
347
+ self.head_dim,
348
+ max_position_embeddings=self.max_position_embeddings,
349
+ scaling_factor=scaling_factor,
350
+ base=self.rope_theta,
351
+ )
352
+ elif scaling_type == "dynamic":
353
+ self.rotary_emb = LlamaDynamicNTKScalingRotaryEmbedding(
354
+ self.head_dim,
355
+ max_position_embeddings=self.max_position_embeddings,
356
+ scaling_factor=scaling_factor,
357
+ base=self.rope_theta,
358
+ )
359
+ else:
360
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
361
+
362
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
363
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
364
+
365
+ def forward(
366
+ self,
367
+ hidden_states: torch.Tensor,
368
+ attention_mask: Optional[torch.Tensor] = None,
369
+ position_ids: Optional[torch.LongTensor] = None,
370
+ past_key_value: Optional[Cache] = None,
371
+ output_attentions: bool = False,
372
+ use_cache: bool = False,
373
+ **kwargs,
374
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
375
+ if "padding_mask" in kwargs:
376
+ warnings.warn(
377
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
378
+ )
379
+
380
+ bsz, q_len, _ = hidden_states.size()
381
+
382
+ if self.config.pretraining_tp > 1:
383
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
384
+ query_slices = self.q_proj.weight.split(
385
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
386
+ )
387
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
388
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
389
+
390
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
391
+ query_states = torch.cat(query_states, dim=-1)
392
+
393
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
394
+ key_states = torch.cat(key_states, dim=-1)
395
+
396
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
397
+ value_states = torch.cat(value_states, dim=-1)
398
+
399
+ else:
400
+ query_states = self.q_proj(hidden_states)
401
+ key_states = self.k_proj(hidden_states)
402
+ value_states = self.v_proj(hidden_states)
403
+
404
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
405
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
406
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
407
+
408
+ kv_seq_len = key_states.shape[-2]
409
+ if past_key_value is not None:
410
+ if self.layer_idx is None:
411
+ raise ValueError(
412
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
413
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
414
+ "with a layer index."
415
+ )
416
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
417
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
418
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
419
+
420
+ if past_key_value is not None:
421
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
422
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
423
+
424
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
425
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
426
+
427
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
428
+
429
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
430
+ raise ValueError(
431
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
432
+ f" {attn_weights.size()}"
433
+ )
434
+
435
+ if attention_mask is not None:
436
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
437
+ raise ValueError(
438
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
439
+ )
440
+ attn_weights = attn_weights + attention_mask
441
+
442
+ # upcast attention to fp32
443
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
444
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
445
+ attn_output = torch.matmul(attn_weights, value_states)
446
+
447
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
448
+ raise ValueError(
449
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
450
+ f" {attn_output.size()}"
451
+ )
452
+
453
+ attn_output = attn_output.transpose(1, 2).contiguous()
454
+
455
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
456
+
457
+ if self.config.pretraining_tp > 1:
458
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
459
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
460
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
461
+ else:
462
+ attn_output = self.o_proj(attn_output)
463
+
464
+ if not output_attentions:
465
+ attn_weights = None
466
+
467
+ return attn_output, attn_weights, past_key_value
468
+
469
+
470
+ class LlamaFlashAttention2(LlamaAttention):
471
+ """
472
+ Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays
473
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
474
+ flash attention and deal with padding tokens in case the input contains any of them.
475
+ """
476
+
477
+ def __init__(self, *args, **kwargs):
478
+ super().__init__(*args, **kwargs)
479
+
480
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
481
+ # 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.
482
+ # 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).
483
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
484
+
485
+ def forward(
486
+ self,
487
+ hidden_states: torch.Tensor,
488
+ attention_mask: Optional[torch.LongTensor] = None,
489
+ position_ids: Optional[torch.LongTensor] = None,
490
+ past_key_value: Optional[Cache] = None,
491
+ output_attentions: bool = False,
492
+ use_cache: bool = False,
493
+ **kwargs,
494
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
495
+ # LlamaFlashAttention2 attention does not support output_attentions
496
+ if "padding_mask" in kwargs:
497
+ warnings.warn(
498
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
499
+ )
500
+
501
+ # overwrite attention_mask with padding_mask
502
+ attention_mask = kwargs.pop("padding_mask")
503
+
504
+ output_attentions = False
505
+
506
+ bsz, q_len, _ = hidden_states.size()
507
+
508
+ query_states = self.q_proj(hidden_states)
509
+ key_states = self.k_proj(hidden_states)
510
+ value_states = self.v_proj(hidden_states)
511
+
512
+ # Flash attention requires the input to have the shape
513
+ # batch_size x seq_length x head_dim x hidden_dim
514
+ # therefore we just need to keep the original shape
515
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
516
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
517
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
518
+
519
+ kv_seq_len = key_states.shape[-2]
520
+ if past_key_value is not None:
521
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
522
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
523
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
524
+
525
+ if past_key_value is not None:
526
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
527
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
528
+
529
+ # 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
530
+ # to be able to avoid many of these transpose/reshape/view.
531
+ query_states = query_states.transpose(1, 2)
532
+ key_states = key_states.transpose(1, 2)
533
+ value_states = value_states.transpose(1, 2)
534
+
535
+ dropout_rate = self.attention_dropout if self.training else 0.0
536
+
537
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
538
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
539
+ # cast them back in the correct dtype just to be sure everything works as expected.
540
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
541
+ # in fp32. (LlamaRMSNorm handles it correctly)
542
+
543
+ input_dtype = query_states.dtype
544
+ if input_dtype == torch.float32:
545
+ # Handle the case where the model is quantized
546
+ if hasattr(self.config, "_pre_quantization_dtype"):
547
+ target_dtype = self.config._pre_quantization_dtype
548
+ else:
549
+ target_dtype = self.q_proj.weight.dtype
550
+
551
+ logger.warning_once(
552
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
553
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
554
+ f" {target_dtype}."
555
+ )
556
+
557
+ query_states = query_states.to(target_dtype)
558
+ key_states = key_states.to(target_dtype)
559
+ value_states = value_states.to(target_dtype)
560
+
561
+ attn_output = self._flash_attention_forward(
562
+ query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
563
+ )
564
+
565
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
566
+ attn_output = self.o_proj(attn_output)
567
+
568
+ if not output_attentions:
569
+ attn_weights = None
570
+
571
+ return attn_output, attn_weights, past_key_value
572
+
573
+ def _flash_attention_forward(
574
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
575
+ ):
576
+ """
577
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
578
+ first unpad the input, then computes the attention scores and pad the final attention scores.
579
+
580
+ Args:
581
+ query_states (`torch.Tensor`):
582
+ Input query states to be passed to Flash Attention API
583
+ key_states (`torch.Tensor`):
584
+ Input key states to be passed to Flash Attention API
585
+ value_states (`torch.Tensor`):
586
+ Input value states to be passed to Flash Attention API
587
+ attention_mask (`torch.Tensor`):
588
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
589
+ position of padding tokens and 1 for the position of non-padding tokens.
590
+ dropout (`int`, *optional*):
591
+ Attention dropout
592
+ softmax_scale (`float`, *optional*):
593
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
594
+ """
595
+ if not self._flash_attn_uses_top_left_mask:
596
+ causal = self.is_causal
597
+ else:
598
+ # 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__.
599
+ causal = self.is_causal and query_length != 1
600
+
601
+ # Contains at least one padding token in the sequence
602
+ if attention_mask is not None:
603
+ batch_size = query_states.shape[0]
604
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
605
+ query_states, key_states, value_states, attention_mask, query_length
606
+ )
607
+
608
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
609
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
610
+
611
+ attn_output_unpad = flash_attn_varlen_func(
612
+ query_states,
613
+ key_states,
614
+ value_states,
615
+ cu_seqlens_q=cu_seqlens_q,
616
+ cu_seqlens_k=cu_seqlens_k,
617
+ max_seqlen_q=max_seqlen_in_batch_q,
618
+ max_seqlen_k=max_seqlen_in_batch_k,
619
+ dropout_p=dropout,
620
+ softmax_scale=softmax_scale,
621
+ causal=causal,
622
+ )
623
+
624
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
625
+ else:
626
+ attn_output = flash_attn_func(
627
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
628
+ )
629
+
630
+ return attn_output
631
+
632
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
633
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
634
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
635
+
636
+ key_layer = index_first_axis(
637
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
638
+ )
639
+ value_layer = index_first_axis(
640
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
641
+ )
642
+ if query_length == kv_seq_len:
643
+ query_layer = index_first_axis(
644
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
645
+ )
646
+ cu_seqlens_q = cu_seqlens_k
647
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
648
+ indices_q = indices_k
649
+ elif query_length == 1:
650
+ max_seqlen_in_batch_q = 1
651
+ cu_seqlens_q = torch.arange(
652
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
653
+ ) # There is a memcpy here, that is very bad.
654
+ indices_q = cu_seqlens_q[:-1]
655
+ query_layer = query_layer.squeeze(1)
656
+ else:
657
+ # The -q_len: slice assumes left padding.
658
+ attention_mask = attention_mask[:, -query_length:]
659
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
660
+
661
+ return (
662
+ query_layer,
663
+ key_layer,
664
+ value_layer,
665
+ indices_q,
666
+ (cu_seqlens_q, cu_seqlens_k),
667
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
668
+ )
669
+
670
+
671
+ class LlamaSdpaAttention(LlamaAttention):
672
+ """
673
+ Llama attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
674
+ `LlamaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
675
+ SDPA API.
676
+ """
677
+
678
+ # Adapted from LlamaAttention.forward
679
+ def forward(
680
+ self,
681
+ hidden_states: torch.Tensor,
682
+ attention_mask: Optional[torch.Tensor] = None,
683
+ position_ids: Optional[torch.LongTensor] = None,
684
+ past_key_value: Optional[Cache] = None,
685
+ output_attentions: bool = False,
686
+ use_cache: bool = False,
687
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
688
+ if output_attentions:
689
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
690
+ logger.warning_once(
691
+ "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, "
692
+ '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.'
693
+ )
694
+ return super().forward(
695
+ hidden_states=hidden_states,
696
+ attention_mask=attention_mask,
697
+ position_ids=position_ids,
698
+ past_key_value=past_key_value,
699
+ output_attentions=output_attentions,
700
+ use_cache=use_cache,
701
+ )
702
+
703
+ bsz, q_len, _ = hidden_states.size()
704
+
705
+ query_states = self.q_proj(hidden_states)
706
+ key_states = self.k_proj(hidden_states)
707
+ value_states = self.v_proj(hidden_states)
708
+
709
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
710
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
711
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
712
+
713
+ kv_seq_len = key_states.shape[-2]
714
+ if past_key_value is not None:
715
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
716
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
717
+
718
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
719
+
720
+ if past_key_value is not None:
721
+ cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models
722
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
723
+
724
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
725
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
726
+
727
+ if attention_mask is not None:
728
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
729
+ raise ValueError(
730
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
731
+ )
732
+
733
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
734
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
735
+ if query_states.device.type == "cuda" and attention_mask is not None:
736
+ query_states = query_states.contiguous()
737
+ key_states = key_states.contiguous()
738
+ value_states = value_states.contiguous()
739
+
740
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
741
+ query_states,
742
+ key_states,
743
+ value_states,
744
+ attn_mask=attention_mask,
745
+ dropout_p=self.attention_dropout if self.training else 0.0,
746
+ # 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.
747
+ is_causal=self.is_causal and attention_mask is None and q_len > 1,
748
+ )
749
+
750
+ attn_output = attn_output.transpose(1, 2).contiguous()
751
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
752
+
753
+ attn_output = self.o_proj(attn_output)
754
+
755
+ return attn_output, None, past_key_value
756
+
757
+
758
+ LLAMA_ATTENTION_CLASSES = {
759
+ "eager": LlamaAttention,
760
+ "flash_attention_2": LlamaFlashAttention2,
761
+ "sdpa": LlamaSdpaAttention,
762
+ }
763
+
764
+
765
+ class LlamaDecoderLayer(nn.Module):
766
+ def __init__(self, config: LlamaConfig, layer_idx: int):
767
+ super().__init__()
768
+ self.hidden_size = config.hidden_size
769
+
770
+ self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
771
+
772
+ self.mlp = LlamaMLP(config)
773
+ self.input_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
774
+ self.post_attention_layernorm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
775
+
776
+ def forward(
777
+ self,
778
+ hidden_states: torch.Tensor,
779
+ attention_mask: Optional[torch.Tensor] = None,
780
+ position_ids: Optional[torch.LongTensor] = None,
781
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
782
+ output_attentions: Optional[bool] = False,
783
+ use_cache: Optional[bool] = False,
784
+ **kwargs,
785
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
786
+ """
787
+ Args:
788
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
789
+ attention_mask (`torch.FloatTensor`, *optional*):
790
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
791
+ query_sequence_length, key_sequence_length)` if default attention is used.
792
+ output_attentions (`bool`, *optional*):
793
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
794
+ returned tensors for more detail.
795
+ use_cache (`bool`, *optional*):
796
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
797
+ (see `past_key_values`).
798
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
799
+ """
800
+ if "padding_mask" in kwargs:
801
+ warnings.warn(
802
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
803
+ )
804
+
805
+ residual = hidden_states
806
+
807
+ hidden_states = self.input_layernorm(hidden_states)
808
+
809
+ # Self Attention
810
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
811
+ hidden_states=hidden_states,
812
+ attention_mask=attention_mask,
813
+ position_ids=position_ids,
814
+ past_key_value=past_key_value,
815
+ output_attentions=output_attentions,
816
+ use_cache=use_cache,
817
+ **kwargs,
818
+ )
819
+ hidden_states = residual + hidden_states
820
+
821
+ # Fully Connected
822
+ residual = hidden_states
823
+ hidden_states = self.post_attention_layernorm(hidden_states)
824
+ hidden_states = self.mlp(hidden_states)
825
+ hidden_states = residual + hidden_states
826
+
827
+ outputs = (hidden_states,)
828
+
829
+ if output_attentions:
830
+ outputs += (self_attn_weights,)
831
+
832
+ if use_cache:
833
+ outputs += (present_key_value,)
834
+
835
+ return outputs
836
+
837
+
838
+ LLAMA_START_DOCSTRING = r"""
839
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
840
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
841
+ etc.)
842
+
843
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
844
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
845
+ and behavior.
846
+
847
+ Parameters:
848
+ config ([`LlamaConfig`]):
849
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
850
+ load the weights associated with the model, only the configuration. Check out the
851
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
852
+ """
853
+
854
+
855
+ @add_start_docstrings(
856
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
857
+ LLAMA_START_DOCSTRING,
858
+ )
859
+ class LlamaPreTrainedModel(PreTrainedModel):
860
+ config_class = LlamaConfig
861
+ base_model_prefix = "model"
862
+ supports_gradient_checkpointing = True
863
+ _no_split_modules = ["LlamaDecoderLayer"]
864
+ _skip_keys_device_placement = "past_key_values"
865
+ _supports_flash_attn_2 = True
866
+ _supports_sdpa = True
867
+ _supports_cache_class = True
868
+
869
+ def _init_weights(self, module):
870
+ std = self.config.initializer_range
871
+ if isinstance(module, nn.Linear):
872
+ module.weight.data.normal_(mean=0.0, std=std)
873
+ if module.bias is not None:
874
+ module.bias.data.zero_()
875
+ elif isinstance(module, nn.Embedding):
876
+ module.weight.data.normal_(mean=0.0, std=std)
877
+ if module.padding_idx is not None:
878
+ module.weight.data[module.padding_idx].zero_()
879
+
880
+
881
+ LLAMA_INPUTS_DOCSTRING = r"""
882
+ Args:
883
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
884
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
885
+ it.
886
+
887
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
888
+ [`PreTrainedTokenizer.__call__`] for details.
889
+
890
+ [What are input IDs?](../glossary#input-ids)
891
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
892
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
893
+
894
+ - 1 for tokens that are **not masked**,
895
+ - 0 for tokens that are **masked**.
896
+
897
+ [What are attention masks?](../glossary#attention-mask)
898
+
899
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
900
+ [`PreTrainedTokenizer.__call__`] for details.
901
+
902
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
903
+ `past_key_values`).
904
+
905
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
906
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
907
+ information on the default strategy.
908
+
909
+ - 1 indicates the head is **not masked**,
910
+ - 0 indicates the head is **masked**.
911
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
912
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
913
+ config.n_positions - 1]`.
914
+
915
+ [What are position IDs?](../glossary#position-ids)
916
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
917
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
918
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
919
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
920
+
921
+ Two formats are allowed:
922
+ - a [`~cache_utils.Cache`] instance;
923
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
924
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
925
+ cache format.
926
+
927
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
928
+ legacy cache format will be returned.
929
+
930
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
931
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
932
+ of shape `(batch_size, sequence_length)`.
933
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
934
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
935
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
936
+ model's internal embedding lookup matrix.
937
+ use_cache (`bool`, *optional*):
938
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
939
+ `past_key_values`).
940
+ output_attentions (`bool`, *optional*):
941
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
942
+ tensors for more detail.
943
+ output_hidden_states (`bool`, *optional*):
944
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
945
+ more detail.
946
+ return_dict (`bool`, *optional*):
947
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
948
+ """
949
+
950
+
951
+ @add_start_docstrings(
952
+ "The bare LLaMA Model outputting raw hidden-states without any specific head on top.",
953
+ LLAMA_START_DOCSTRING,
954
+ )
955
+ class LlamaModel(LlamaPreTrainedModel):
956
+ """
957
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`LlamaDecoderLayer`]
958
+
959
+ Args:
960
+ config: LlamaConfig
961
+ """
962
+
963
+ def __init__(self, config: LlamaConfig):
964
+ super().__init__(config)
965
+ self.padding_idx = config.pad_token_id
966
+ self.vocab_size = config.vocab_size
967
+
968
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
969
+ self.layers = nn.ModuleList(
970
+ [LlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
971
+ )
972
+ self._use_sdpa = config._attn_implementation == "sdpa"
973
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
974
+ self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
975
+
976
+ self.gradient_checkpointing = False
977
+ # Initialize weights and apply final processing
978
+ self.post_init()
979
+
980
+ def get_input_embeddings(self):
981
+ return self.embed_tokens
982
+
983
+ def set_input_embeddings(self, value):
984
+ self.embed_tokens = value
985
+
986
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
987
+ def forward(
988
+ self,
989
+ input_ids: torch.LongTensor = None,
990
+ attention_mask: Optional[torch.Tensor] = None,
991
+ position_ids: Optional[torch.LongTensor] = None,
992
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
993
+ inputs_embeds: Optional[torch.FloatTensor] = None,
994
+ use_cache: Optional[bool] = None,
995
+ output_attentions: Optional[bool] = None,
996
+ output_hidden_states: Optional[bool] = None,
997
+ return_dict: Optional[bool] = None,
998
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
999
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1000
+ output_hidden_states = (
1001
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1002
+ )
1003
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
1004
+
1005
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1006
+
1007
+ # retrieve input_ids and inputs_embeds
1008
+ if input_ids is not None and inputs_embeds is not None:
1009
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
1010
+ elif input_ids is not None:
1011
+ batch_size, seq_length = input_ids.shape[:2]
1012
+ elif inputs_embeds is not None:
1013
+ batch_size, seq_length = inputs_embeds.shape[:2]
1014
+ else:
1015
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
1016
+
1017
+ if self.gradient_checkpointing and self.training:
1018
+ if use_cache:
1019
+ logger.warning_once(
1020
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
1021
+ )
1022
+ use_cache = False
1023
+
1024
+ past_key_values_length = 0
1025
+ if use_cache:
1026
+ use_legacy_cache = not isinstance(past_key_values, Cache)
1027
+ if use_legacy_cache:
1028
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
1029
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
1030
+
1031
+ if position_ids is None:
1032
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
1033
+ position_ids = torch.arange(
1034
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
1035
+ )
1036
+ position_ids = position_ids.unsqueeze(0)
1037
+
1038
+ if inputs_embeds is None:
1039
+ inputs_embeds = self.embed_tokens(input_ids)
1040
+
1041
+ if self._use_flash_attention_2:
1042
+ # 2d mask is passed through the layers
1043
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
1044
+ elif self._use_sdpa and not output_attentions:
1045
+ # output_attentions=True can not be supported when using SDPA, and we fall back on
1046
+ # the manual implementation that requires a 4D causal mask in all cases.
1047
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
1048
+ attention_mask,
1049
+ (batch_size, seq_length),
1050
+ inputs_embeds,
1051
+ past_key_values_length,
1052
+ )
1053
+ else:
1054
+ # 4d mask is passed through the layers
1055
+ attention_mask = _prepare_4d_causal_attention_mask(
1056
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
1057
+ )
1058
+
1059
+ # embed positions
1060
+ hidden_states = inputs_embeds
1061
+
1062
+ # decoder layers
1063
+ all_hidden_states = () if output_hidden_states else None
1064
+ all_self_attns = () if output_attentions else None
1065
+ next_decoder_cache = None
1066
+
1067
+ for decoder_layer in self.layers:
1068
+ if output_hidden_states:
1069
+ all_hidden_states += (hidden_states,)
1070
+
1071
+ if self.gradient_checkpointing and self.training:
1072
+ layer_outputs = self._gradient_checkpointing_func(
1073
+ decoder_layer.__call__,
1074
+ hidden_states,
1075
+ attention_mask,
1076
+ position_ids,
1077
+ past_key_values,
1078
+ output_attentions,
1079
+ use_cache,
1080
+ )
1081
+ else:
1082
+ layer_outputs = decoder_layer(
1083
+ hidden_states,
1084
+ attention_mask=attention_mask,
1085
+ position_ids=position_ids,
1086
+ past_key_value=past_key_values,
1087
+ output_attentions=output_attentions,
1088
+ use_cache=use_cache,
1089
+ )
1090
+
1091
+ hidden_states = layer_outputs[0]
1092
+
1093
+ if use_cache:
1094
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1095
+
1096
+ if output_attentions:
1097
+ all_self_attns += (layer_outputs[1],)
1098
+
1099
+ hidden_states = self.norm(hidden_states)
1100
+
1101
+ # add hidden states from the last decoder layer
1102
+ if output_hidden_states:
1103
+ all_hidden_states += (hidden_states,)
1104
+
1105
+ next_cache = None
1106
+ if use_cache:
1107
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
1108
+ if not return_dict:
1109
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
1110
+ return BaseModelOutputWithPast(
1111
+ last_hidden_state=hidden_states,
1112
+ past_key_values=next_cache,
1113
+ hidden_states=all_hidden_states,
1114
+ attentions=all_self_attns,
1115
+ )
1116
+
1117
+
1118
+ class LlamaForCausalLM(LlamaPreTrainedModel):
1119
+ _tied_weights_keys = ["lm_head.weight"]
1120
+
1121
+ def __init__(self, config):
1122
+ super().__init__(config)
1123
+ self.model = LlamaModel(config)
1124
+ self.vocab_size = config.vocab_size
1125
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
1126
+
1127
+ # Initialize weights and apply final processing
1128
+ self.post_init()
1129
+
1130
+ def get_input_embeddings(self):
1131
+ return self.model.embed_tokens
1132
+
1133
+ def set_input_embeddings(self, value):
1134
+ self.model.embed_tokens = value
1135
+
1136
+ def get_output_embeddings(self):
1137
+ return self.lm_head
1138
+
1139
+ def set_output_embeddings(self, new_embeddings):
1140
+ self.lm_head = new_embeddings
1141
+
1142
+ def set_decoder(self, decoder):
1143
+ self.model = decoder
1144
+
1145
+ def get_decoder(self):
1146
+ return self.model
1147
+
1148
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1149
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1150
+ def forward(
1151
+ self,
1152
+ input_ids: torch.LongTensor = None,
1153
+ attention_mask: Optional[torch.Tensor] = None,
1154
+ position_ids: Optional[torch.LongTensor] = None,
1155
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1156
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1157
+ labels: Optional[torch.LongTensor] = None,
1158
+ use_cache: Optional[bool] = None,
1159
+ output_attentions: Optional[bool] = None,
1160
+ output_hidden_states: Optional[bool] = None,
1161
+ return_dict: Optional[bool] = None,
1162
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1163
+ r"""
1164
+ Args:
1165
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1166
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1167
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1168
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1169
+
1170
+ Returns:
1171
+
1172
+ Example:
1173
+
1174
+ ```python
1175
+ >>> from transformers import AutoTokenizer, LlamaForCausalLM
1176
+
1177
+ >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
1178
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
1179
+
1180
+ >>> prompt = "Hey, are you conscious? Can you talk to me?"
1181
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1182
+
1183
+ >>> # Generate
1184
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1185
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1186
+ "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
1187
+ ```"""
1188
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1189
+ output_hidden_states = (
1190
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1191
+ )
1192
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1193
+
1194
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1195
+ outputs = self.model(
1196
+ input_ids=input_ids,
1197
+ attention_mask=attention_mask,
1198
+ position_ids=position_ids,
1199
+ past_key_values=past_key_values,
1200
+ inputs_embeds=inputs_embeds,
1201
+ use_cache=use_cache,
1202
+ output_attentions=output_attentions,
1203
+ output_hidden_states=output_hidden_states,
1204
+ return_dict=return_dict,
1205
+ )
1206
+
1207
+ hidden_states = outputs[0]
1208
+ if self.config.pretraining_tp > 1:
1209
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
1210
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
1211
+ logits = torch.cat(logits, dim=-1)
1212
+ else:
1213
+ logits = self.lm_head(hidden_states)
1214
+ logits = logits.float()
1215
+
1216
+ loss = None
1217
+ if labels is not None:
1218
+ # Shift so that tokens < n predict n
1219
+ shift_logits = logits[..., :-1, :].contiguous()
1220
+ shift_labels = labels[..., 1:].contiguous()
1221
+ # Flatten the tokens
1222
+ loss_fct = CrossEntropyLoss()
1223
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1224
+ shift_labels = shift_labels.view(-1)
1225
+ # Enable model parallelism
1226
+ shift_labels = shift_labels.to(shift_logits.device)
1227
+ loss = loss_fct(shift_logits, shift_labels)
1228
+
1229
+ if not return_dict:
1230
+ output = (logits,) + outputs[1:]
1231
+ return (loss,) + output if loss is not None else output
1232
+
1233
+ return CausalLMOutputWithPast(
1234
+ loss=loss,
1235
+ logits=logits,
1236
+ past_key_values=outputs.past_key_values,
1237
+ hidden_states=outputs.hidden_states,
1238
+ attentions=outputs.attentions,
1239
+ )
1240
+
1241
+ def prepare_inputs_for_generation(
1242
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1243
+ ):
1244
+ if past_key_values is not None:
1245
+ if isinstance(past_key_values, Cache):
1246
+ cache_length = past_key_values.get_seq_length()
1247
+ past_length = past_key_values.seen_tokens
1248
+ max_cache_length = past_key_values.get_max_length()
1249
+ else:
1250
+ cache_length = past_length = past_key_values[0][0].shape[2]
1251
+ max_cache_length = None
1252
+
1253
+ # Keep only the unprocessed tokens:
1254
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
1255
+ # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
1256
+ # input)
1257
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
1258
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
1259
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
1260
+ # input_ids based on the past_length.
1261
+ elif past_length < input_ids.shape[1]:
1262
+ input_ids = input_ids[:, past_length:]
1263
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
1264
+
1265
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
1266
+ if (
1267
+ max_cache_length is not None
1268
+ and attention_mask is not None
1269
+ and cache_length + input_ids.shape[1] > max_cache_length
1270
+ ):
1271
+ attention_mask = attention_mask[:, -max_cache_length:]
1272
+
1273
+ position_ids = kwargs.get("position_ids", None)
1274
+ if attention_mask is not None and position_ids is None:
1275
+ # create position_ids on the fly for batch generation
1276
+ position_ids = attention_mask.long().cumsum(-1) - 1
1277
+ position_ids.masked_fill_(attention_mask == 0, 1)
1278
+ if past_key_values:
1279
+ position_ids = position_ids[:, -input_ids.shape[1] :]
1280
+
1281
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1282
+ if inputs_embeds is not None and past_key_values is None:
1283
+ model_inputs = {"inputs_embeds": inputs_embeds}
1284
+ else:
1285
+ model_inputs = {"input_ids": input_ids}
1286
+
1287
+ model_inputs.update(
1288
+ {
1289
+ "position_ids": position_ids,
1290
+ "past_key_values": past_key_values,
1291
+ "use_cache": kwargs.get("use_cache"),
1292
+ "attention_mask": attention_mask,
1293
+ }
1294
+ )
1295
+ return model_inputs
1296
+
1297
+ @staticmethod
1298
+ def _reorder_cache(past_key_values, beam_idx):
1299
+ reordered_past = ()
1300
+ for layer_past in past_key_values:
1301
+ reordered_past += (
1302
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1303
+ )
1304
+ return reordered_past
1305
+
1306
+
1307
+ @add_start_docstrings(
1308
+ """
1309
+ The LLaMa Model transformer with a sequence classification head on top (linear layer).
1310
+
1311
+ [`LlamaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1312
+ (e.g. GPT-2) do.
1313
+
1314
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1315
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1316
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1317
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1318
+ each row of the batch).
1319
+ """,
1320
+ LLAMA_START_DOCSTRING,
1321
+ )
1322
+ class LlamaForSequenceClassification(LlamaPreTrainedModel):
1323
+ def __init__(self, config):
1324
+ super().__init__(config)
1325
+ self.num_labels = config.num_labels
1326
+ self.model = LlamaModel(config)
1327
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1328
+
1329
+ # Initialize weights and apply final processing
1330
+ self.post_init()
1331
+
1332
+ def get_input_embeddings(self):
1333
+ return self.model.embed_tokens
1334
+
1335
+ def set_input_embeddings(self, value):
1336
+ self.model.embed_tokens = value
1337
+
1338
+ @add_start_docstrings_to_model_forward(LLAMA_INPUTS_DOCSTRING)
1339
+ def forward(
1340
+ self,
1341
+ input_ids: torch.LongTensor = None,
1342
+ attention_mask: Optional[torch.Tensor] = None,
1343
+ position_ids: Optional[torch.LongTensor] = None,
1344
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1345
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1346
+ labels: Optional[torch.LongTensor] = None,
1347
+ use_cache: Optional[bool] = None,
1348
+ output_attentions: Optional[bool] = None,
1349
+ output_hidden_states: Optional[bool] = None,
1350
+ return_dict: Optional[bool] = None,
1351
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1352
+ r"""
1353
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1354
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1355
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1356
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1357
+ """
1358
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1359
+
1360
+ transformer_outputs = self.model(
1361
+ input_ids,
1362
+ attention_mask=attention_mask,
1363
+ position_ids=position_ids,
1364
+ past_key_values=past_key_values,
1365
+ inputs_embeds=inputs_embeds,
1366
+ use_cache=use_cache,
1367
+ output_attentions=output_attentions,
1368
+ output_hidden_states=output_hidden_states,
1369
+ return_dict=return_dict,
1370
+ )
1371
+ hidden_states = transformer_outputs[0]
1372
+ logits = self.score(hidden_states)
1373
+
1374
+ if input_ids is not None:
1375
+ batch_size = input_ids.shape[0]
1376
+ else:
1377
+ batch_size = inputs_embeds.shape[0]
1378
+
1379
+ if self.config.pad_token_id is None and batch_size != 1:
1380
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1381
+ if self.config.pad_token_id is None:
1382
+ sequence_lengths = -1
1383
+ else:
1384
+ if input_ids is not None:
1385
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
1386
+ logits.device
1387
+ )
1388
+ else:
1389
+ sequence_lengths = -1
1390
+
1391
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1392
+
1393
+ loss = None
1394
+ if labels is not None:
1395
+ labels = labels.to(logits.device)
1396
+ if self.config.problem_type is None:
1397
+ if self.num_labels == 1:
1398
+ self.config.problem_type = "regression"
1399
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1400
+ self.config.problem_type = "single_label_classification"
1401
+ else:
1402
+ self.config.problem_type = "multi_label_classification"
1403
+
1404
+ if self.config.problem_type == "regression":
1405
+ loss_fct = MSELoss()
1406
+ if self.num_labels == 1:
1407
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1408
+ else:
1409
+ loss = loss_fct(pooled_logits, labels)
1410
+ elif self.config.problem_type == "single_label_classification":
1411
+ loss_fct = CrossEntropyLoss()
1412
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1413
+ elif self.config.problem_type == "multi_label_classification":
1414
+ loss_fct = BCEWithLogitsLoss()
1415
+ loss = loss_fct(pooled_logits, labels)
1416
+ if not return_dict:
1417
+ output = (pooled_logits,) + transformer_outputs[1:]
1418
+ return ((loss,) + output) if loss is not None else output
1419
+
1420
+ return SequenceClassifierOutputWithPast(
1421
+ loss=loss,
1422
+ logits=pooled_logits,
1423
+ past_key_values=transformer_outputs.past_key_values,
1424
+ hidden_states=transformer_outputs.hidden_states,
1425
+ attentions=transformer_outputs.attentions,
1426
+ )