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Initial commit for AquilaChat2-34B-16K

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added_tokens.json ADDED
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1
+ {
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+ "</s>": 100007,
3
+ "<|LDWANG|>": 100002,
4
+ "<|endofpiece|>": 100001,
5
+ "<|startofpiece|>": 100000,
6
+ "[CLS]": 100006,
7
+ "[MASK]": 100003,
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+ "[gMASK]": 100004,
9
+ "[sMASK]": 100005
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+ }
config.json ADDED
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1
+ {
2
+ "architectures": [
3
+ "AquilaForCausalLM"
4
+ ],
5
+ "auto_map": {
6
+ "AutoConfig": "configuration_aquila.AquilaConfig",
7
+ "AutoModelForCausalLM": "modeling_aquila.AquilaForCausalLM"
8
+ },
9
+ "rope_scaling": {
10
+ "factor": 4.0,
11
+ "type": "linear"
12
+ },
13
+ "bos_token_id": 100006,
14
+ "eos_token_id": 100007,
15
+ "hidden_act": "silu",
16
+ "hidden_size": 6144,
17
+ "initializer_range": 0.02,
18
+ "intermediate_size": 24576,
19
+ "max_position_embeddings": 4096,
20
+ "model_type": "aquila",
21
+ "num_attention_heads": 48,
22
+ "num_hidden_layers": 60,
23
+ "num_key_value_heads": 8,
24
+ "pad_token_id": 0,
25
+ "pretraining_tp": 1,
26
+ "rms_norm_eps": 1e-05,
27
+ "tie_word_embeddings": false,
28
+ "torch_dtype": "bfloat16",
29
+ "transformers_version": "4.31.0",
30
+ "use_cache": true,
31
+ "vocab_size": 100008
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+ }
configuration_aquila.py ADDED
@@ -0,0 +1,128 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2023 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
+ """ Aquila model configuration"""
21
+
22
+ from transformers import PretrainedConfig
23
+
24
+
25
+
26
+ class AquilaConfig(PretrainedConfig):
27
+ r"""
28
+ This is the configuration class to store the configuration of a [`AquilaModel`]. It is used to instantiate an Aquila
29
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
30
+ defaults will yield a similar configuration to that of the Aquila-7B.
31
+
32
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
33
+ documentation from [`PretrainedConfig`] for more information.
34
+
35
+
36
+ Args:
37
+ vocab_size (`int`, *optional*, defaults to 32000):
38
+ Vocabulary size of the Aquila model. Defines the number of different tokens that can be represented by the
39
+ `inputs_ids` passed when calling [`AquilaModel`]
40
+ hidden_size (`int`, *optional*, defaults to 4096):
41
+ Dimension of the hidden representations.
42
+ intermediate_size (`int`, *optional*, defaults to 11008):
43
+ Dimension of the MLP representations.
44
+ num_hidden_layers (`int`, *optional*, defaults to 32):
45
+ Number of hidden layers in the Transformer encoder.
46
+ num_attention_heads (`int`, *optional*, defaults to 32):
47
+ Number of attention heads for each attention layer in the Transformer encoder.
48
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
49
+ The non-linear activation function (function or string) in the decoder.
50
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
51
+ The maximum sequence length that this model might ever be used with. Typically set this to something large
52
+ just in case (e.g., 512 or 1024 or 2048).
53
+ initializer_range (`float`, *optional*, defaults to 0.02):
54
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
55
+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
56
+ The epsilon used by the rms normalization layers.
57
+ use_cache (`bool`, *optional*, defaults to `True`):
58
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
59
+ relevant if `config.is_decoder=True`.
60
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
61
+ Whether to tie weight embeddings
62
+ Example:
63
+
64
+ ```python
65
+ >>> from transformers import AquilaModel, AquilaConfig
66
+
67
+ >>> # Initializing a Aquila aquila-7b style configuration
68
+ >>> configuration = AquilaConfig()
69
+
70
+ >>> # Initializing a model from the aquila-7b style configuration
71
+ >>> model = AquilaModel(configuration)
72
+
73
+ >>> # Accessing the model configuration
74
+ >>> configuration = model.config
75
+ ```"""
76
+ model_type = "aquila"
77
+ keys_to_ignore_at_inference = ["past_key_values"]
78
+
79
+ def __init__(
80
+ self,
81
+ vocab_size=100008,
82
+ hidden_size=4096,
83
+ intermediate_size=11008,
84
+ num_hidden_layers=32,
85
+ num_attention_heads=32,
86
+ num_key_value_heads=None,
87
+ hidden_act="silu",
88
+ max_position_embeddings=2048,
89
+ initializer_range=0.02,
90
+ rms_norm_eps=1e-6,
91
+ use_cache=True,
92
+ pad_token_id=0,
93
+ bos_token_id=1,
94
+ eos_token_id=2,
95
+ pretraining_tp=1,
96
+ tie_word_embeddings=False,
97
+ rope_theta=10000.0,
98
+ rope_scaling=None,
99
+ **kwargs,
100
+ ):
101
+ self.vocab_size = vocab_size
102
+ self.max_position_embeddings = max_position_embeddings
103
+ self.hidden_size = hidden_size
104
+ self.intermediate_size = intermediate_size
105
+ self.num_hidden_layers = num_hidden_layers
106
+
107
+ # for backward compatibility
108
+ if num_key_value_heads is None:
109
+ num_key_value_heads = num_attention_heads
110
+
111
+ self.num_key_value_heads = num_key_value_heads
112
+
113
+ self.num_attention_heads = num_attention_heads
114
+ self.hidden_act = hidden_act
115
+ self.initializer_range = initializer_range
116
+ self.rms_norm_eps = rms_norm_eps
117
+ self.pretraining_tp = pretraining_tp
118
+ self.use_cache = use_cache
119
+ self.rope_theta = rope_theta
120
+ self.rope_scaling = rope_scaling
121
+
122
+ super().__init__(
123
+ pad_token_id=pad_token_id,
124
+ bos_token_id=bos_token_id,
125
+ eos_token_id=eos_token_id,
126
+ tie_word_embeddings=tie_word_embeddings,
127
+ **kwargs,
128
+ )
generation_config.json ADDED
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1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 100006,
4
+ "eos_token_id": 100007,
5
+ "pad_token_id": 0,
6
+ "transformers_version": "4.31.0"
7
+ }
merges.txt ADDED
The diff for this file is too large to render. See raw diff
 
modeling_aquila.py ADDED
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1
+ # coding=utf-8
2
+ # Copyright 2023 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 Aquila model."""
21
+ import math
22
+ from typing import List, Optional, Tuple, Union
23
+
24
+ import torch
25
+ import torch.utils.checkpoint
26
+ from torch import nn
27
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
28
+
29
+ from transformers.activations import ACT2FN
30
+ from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
31
+ from transformers.modeling_utils import PreTrainedModel
32
+ from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
33
+ from .configuration_aquila import AquilaConfig
34
+
35
+
36
+ logger = logging.get_logger(__name__)
37
+
38
+ _CONFIG_FOR_DOC = "AquilaConfig"
39
+
40
+
41
+ # Copied from transformers.models.bart.modeling_bart._make_causal_mask
42
+ def _make_causal_mask(
43
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
44
+ ):
45
+ """
46
+ Make causal mask used for bi-directional self-attention.
47
+ """
48
+ bsz, tgt_len = input_ids_shape
49
+ mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
50
+ mask_cond = torch.arange(mask.size(-1), device=device)
51
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
52
+ mask = mask.to(dtype)
53
+
54
+ if past_key_values_length > 0:
55
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
56
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
57
+
58
+
59
+ # Copied from transformers.models.bart.modeling_bart._expand_mask
60
+ def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
61
+ """
62
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
63
+ """
64
+ bsz, src_len = mask.size()
65
+ tgt_len = tgt_len if tgt_len is not None else src_len
66
+
67
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
68
+
69
+ inverted_mask = 1.0 - expanded_mask
70
+
71
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
72
+
73
+
74
+ # Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Aquila
75
+ class AquilaRMSNorm(nn.Module):
76
+ def __init__(self, hidden_size, eps=1e-6):
77
+ """
78
+ AquilaRMSNorm is equivalent to T5LayerNorm
79
+ """
80
+ super().__init__()
81
+ self.weight = nn.Parameter(torch.ones(hidden_size))
82
+ self.variance_epsilon = eps
83
+
84
+ def forward(self, hidden_states):
85
+ input_dtype = hidden_states.dtype
86
+ variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
87
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
88
+
89
+ return (self.weight * hidden_states).to(input_dtype)
90
+
91
+
92
+ # Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Aquila
93
+ class AquilaRotaryEmbedding(torch.nn.Module):
94
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
95
+ super().__init__()
96
+
97
+ self.dim = dim
98
+ self.max_position_embeddings = max_position_embeddings
99
+ self.base = base
100
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
101
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
102
+
103
+ # Build here to make `torch.jit.trace` work.
104
+ self._set_cos_sin_cache(
105
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
106
+ )
107
+
108
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
109
+ self.max_seq_len_cached = seq_len
110
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
111
+
112
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
113
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
114
+ emb = torch.cat((freqs, freqs), dim=-1)
115
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
116
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
117
+
118
+ def forward(self, x, seq_len=None):
119
+ # x: [bs, num_attention_heads, seq_len, head_size]
120
+ if seq_len > self.max_seq_len_cached:
121
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
122
+
123
+ return (
124
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
125
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
126
+ )
127
+
128
+ # Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Aquila
129
+ class AquilaLinearScalingRotaryEmbedding(AquilaRotaryEmbedding):
130
+ """AquilaRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
131
+
132
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
133
+ self.scaling_factor = scaling_factor
134
+ super().__init__(dim, max_position_embeddings, base, device)
135
+
136
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
137
+ self.max_seq_len_cached = seq_len
138
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
139
+ t = t / self.scaling_factor
140
+
141
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
142
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
143
+ emb = torch.cat((freqs, freqs), dim=-1)
144
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
145
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
146
+
147
+ # Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Aquila
148
+ class AquilaDynamicNTKScalingRotaryEmbedding(AquilaRotaryEmbedding):
149
+ """AquilaRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
150
+
151
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
152
+ self.scaling_factor = scaling_factor
153
+ super().__init__(dim, max_position_embeddings, base, device)
154
+
155
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
156
+ self.max_seq_len_cached = seq_len
157
+
158
+ if seq_len > self.max_position_embeddings:
159
+ base = self.base * (
160
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
161
+ ) ** (self.dim / (self.dim - 2))
162
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
163
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
164
+
165
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
166
+
167
+ freqs = torch.einsum("i,j->ij", t, self.inv_freq)
168
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
169
+ emb = torch.cat((freqs, freqs), dim=-1)
170
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :].to(dtype), persistent=False)
171
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :].to(dtype), persistent=False)
172
+
173
+
174
+ def rotate_half(x):
175
+ """Rotates half the hidden dims of the input."""
176
+ x1 = x[..., : x.shape[-1] // 2]
177
+ x2 = x[..., x.shape[-1] // 2 :]
178
+ return torch.cat((-x2, x1), dim=-1)
179
+
180
+
181
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids):
182
+ # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
183
+ cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
184
+ sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
185
+ cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
186
+ sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
187
+ q_embed = (q * cos) + (rotate_half(q) * sin)
188
+ k_embed = (k * cos) + (rotate_half(k) * sin)
189
+ return q_embed, k_embed
190
+
191
+
192
+ # Copied from transformers.models.llama.modeling_llama.LlamaMLP with Llama->Aquila
193
+ class AquilaMLP(nn.Module):
194
+ def __init__(self, config):
195
+ super().__init__()
196
+ self.config = config
197
+ self.hidden_size = config.hidden_size
198
+ self.intermediate_size = config.intermediate_size
199
+ self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
200
+ self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
201
+ self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
202
+ self.act_fn = ACT2FN[config.hidden_act]
203
+
204
+ def forward(self, x):
205
+ if self.config.pretraining_tp > 1:
206
+ slice = self.intermediate_size // self.config.pretraining_tp
207
+ gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
208
+ up_proj_slices = self.up_proj.weight.split(slice, dim=0)
209
+ down_proj_slices = self.down_proj.weight.split(slice, dim=1)
210
+
211
+ gate_proj = torch.cat(
212
+ [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
213
+ )
214
+ up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
215
+
216
+ intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
217
+ down_proj = [
218
+ F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
219
+ ]
220
+ down_proj = sum(down_proj)
221
+ else:
222
+ down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
223
+
224
+ return down_proj
225
+
226
+
227
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
228
+ """
229
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
230
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
231
+ """
232
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
233
+ if n_rep == 1:
234
+ return hidden_states
235
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
236
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
237
+
238
+
239
+ # Copied from transformers.models.llama.modeling_llama.LlamaAttention with Llama->Aquila
240
+ class AquilaAttention(nn.Module):
241
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
242
+ def __init__(self, config: AquilaConfig):
243
+ super().__init__()
244
+ self.config = config
245
+ self.hidden_size = config.hidden_size
246
+ self.num_heads = config.num_attention_heads
247
+ self.head_dim = self.hidden_size // self.num_heads
248
+ self.num_key_value_heads = config.num_key_value_heads
249
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
250
+ self.max_position_embeddings = config.max_position_embeddings
251
+ self.rope_theta = config.rope_theta
252
+
253
+ if (self.head_dim * self.num_heads) != self.hidden_size:
254
+ raise ValueError(
255
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
256
+ f" and `num_heads`: {self.num_heads})."
257
+ )
258
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
259
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
260
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
261
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
262
+ self._init_rope()
263
+
264
+ def _init_rope(self):
265
+ if self.config.rope_scaling is None:
266
+ self.rotary_emb = AquilaRotaryEmbedding(
267
+ self.head_dim,
268
+ max_position_embeddings=self.max_position_embeddings,
269
+ base=self.rope_theta,
270
+ )
271
+ else:
272
+ scaling_type = self.config.rope_scaling["type"]
273
+ scaling_factor = self.config.rope_scaling["factor"]
274
+ if scaling_type == "linear":
275
+ self.rotary_emb = AquilaLinearScalingRotaryEmbedding(
276
+ self.head_dim,
277
+ max_position_embeddings=self.max_position_embeddings,
278
+ scaling_factor=scaling_factor,
279
+ base=self.rope_theta,
280
+ )
281
+ elif scaling_type == "dynamic":
282
+ self.rotary_emb = AquilaDynamicNTKScalingRotaryEmbedding(
283
+ self.head_dim,
284
+ max_position_embeddings=self.max_position_embeddings,
285
+ scaling_factor=scaling_factor,
286
+ base=self.rope_theta,
287
+ )
288
+ else:
289
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
290
+
291
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
292
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
293
+
294
+ def forward(
295
+ self,
296
+ hidden_states: torch.Tensor,
297
+ attention_mask: Optional[torch.Tensor] = None,
298
+ position_ids: Optional[torch.LongTensor] = None,
299
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
300
+ output_attentions: bool = False,
301
+ use_cache: bool = False,
302
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
303
+ bsz, q_len, _ = hidden_states.size()
304
+
305
+ if self.config.pretraining_tp > 1:
306
+ key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
307
+ query_slices = self.q_proj.weight.split(
308
+ (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
309
+ )
310
+ key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
311
+ value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
312
+
313
+ query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
314
+ query_states = torch.cat(query_states, dim=-1)
315
+
316
+ key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
317
+ key_states = torch.cat(key_states, dim=-1)
318
+
319
+ value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
320
+ value_states = torch.cat(value_states, dim=-1)
321
+
322
+ else:
323
+ query_states = self.q_proj(hidden_states)
324
+ key_states = self.k_proj(hidden_states)
325
+ value_states = self.v_proj(hidden_states)
326
+
327
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
328
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
329
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
330
+
331
+ kv_seq_len = key_states.shape[-2]
332
+ if past_key_value is not None:
333
+ kv_seq_len += past_key_value[0].shape[-2]
334
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
335
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
336
+
337
+ if past_key_value is not None:
338
+ # reuse k, v, self_attention
339
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
340
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
341
+
342
+ past_key_value = (key_states, value_states) if use_cache else None
343
+
344
+ # repeat k/v heads if n_kv_heads < n_heads
345
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
346
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
347
+
348
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
349
+ attn_weights = torch.clamp(attn_weights, min=-1024., max=1024.)
350
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
351
+ raise ValueError(
352
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
353
+ f" {attn_weights.size()}"
354
+ )
355
+
356
+ if attention_mask is not None:
357
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
358
+ raise ValueError(
359
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
360
+ )
361
+ attn_weights = attn_weights + attention_mask
362
+
363
+ # upcast attention to fp32
364
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
365
+ attn_output = torch.matmul(attn_weights, value_states)
366
+
367
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
368
+ raise ValueError(
369
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
370
+ f" {attn_output.size()}"
371
+ )
372
+
373
+ attn_output = attn_output.transpose(1, 2).contiguous()
374
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
375
+
376
+ if self.config.pretraining_tp > 1:
377
+ attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
378
+ o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
379
+ attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
380
+ else:
381
+ attn_output = self.o_proj(attn_output)
382
+
383
+ if not output_attentions:
384
+ attn_weights = None
385
+
386
+ return attn_output, attn_weights, past_key_value
387
+
388
+
389
+ # Copied from transformers.models.llama.modeling_llama.LlamaDecoderLayer with Llama->Aquila
390
+ class AquilaDecoderLayer(nn.Module):
391
+ def __init__(self, config: AquilaConfig):
392
+ super().__init__()
393
+ self.hidden_size = config.hidden_size
394
+ self.self_attn = AquilaAttention(config=config)
395
+ self.mlp = AquilaMLP(config)
396
+ self.input_layernorm = AquilaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
397
+ self.post_attention_layernorm = AquilaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
398
+
399
+ def forward(
400
+ self,
401
+ hidden_states: torch.Tensor,
402
+ attention_mask: Optional[torch.Tensor] = None,
403
+ position_ids: Optional[torch.LongTensor] = None,
404
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
405
+ output_attentions: Optional[bool] = False,
406
+ use_cache: Optional[bool] = False,
407
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
408
+ """
409
+ Args:
410
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
411
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
412
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
413
+ output_attentions (`bool`, *optional*):
414
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
415
+ returned tensors for more detail.
416
+ use_cache (`bool`, *optional*):
417
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
418
+ (see `past_key_values`).
419
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
420
+ """
421
+
422
+ residual = hidden_states
423
+
424
+ hidden_states = self.input_layernorm(hidden_states)
425
+
426
+ # Self Attention
427
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
428
+ hidden_states=hidden_states,
429
+ attention_mask=attention_mask,
430
+ position_ids=position_ids,
431
+ past_key_value=past_key_value,
432
+ output_attentions=output_attentions,
433
+ use_cache=use_cache,
434
+ )
435
+ hidden_states = residual + hidden_states
436
+
437
+ # Fully Connected
438
+ residual = hidden_states
439
+ hidden_states = self.post_attention_layernorm(hidden_states)
440
+ hidden_states = self.mlp(hidden_states)
441
+ hidden_states = residual + hidden_states
442
+
443
+ outputs = (hidden_states,)
444
+
445
+ if output_attentions:
446
+ outputs += (self_attn_weights,)
447
+
448
+ if use_cache:
449
+ outputs += (present_key_value,)
450
+
451
+ return outputs
452
+
453
+ AQUILA_START_DOCSTRING = r"""
454
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
455
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
456
+ etc.)
457
+
458
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
459
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
460
+ and behavior.
461
+
462
+ Parameters:
463
+ config ([`AquilaConfig`]):
464
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
465
+ load the weights associated with the model, only the configuration. Check out the
466
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
467
+ """
468
+
469
+
470
+ @add_start_docstrings(
471
+ "The bare Aquila Model outputting raw hidden-states without any specific head on top.",
472
+ AQUILA_START_DOCSTRING,
473
+ )
474
+ # Copied from transformers.models.llama.modeling_llama.LlamaPreTrainedModel with Llama->Aquila
475
+ class AquilaPreTrainedModel(PreTrainedModel):
476
+ config_class = AquilaConfig
477
+ base_model_prefix = "model"
478
+ supports_gradient_checkpointing = True
479
+ _no_split_modules = ["AquilaDecoderLayer"]
480
+ _skip_keys_device_placement = "past_key_values"
481
+
482
+ def _init_weights(self, module):
483
+ std = self.config.initializer_range
484
+ if isinstance(module, nn.Linear):
485
+ module.weight.data.normal_(mean=0.0, std=std)
486
+ if module.bias is not None:
487
+ module.bias.data.zero_()
488
+ elif isinstance(module, nn.Embedding):
489
+ module.weight.data.normal_(mean=0.0, std=std)
490
+ if module.padding_idx is not None:
491
+ module.weight.data[module.padding_idx].zero_()
492
+
493
+ def _set_gradient_checkpointing(self, module, value=False):
494
+ if isinstance(module, AquilaModel):
495
+ module.gradient_checkpointing = value
496
+
497
+
498
+ AQUILA_INPUTS_DOCSTRING = r"""
499
+ Args:
500
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
501
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
502
+ it.
503
+
504
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
505
+ [`PreTrainedTokenizer.__call__`] for details.
506
+
507
+ [What are input IDs?](../glossary#input-ids)
508
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
509
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
510
+
511
+ - 1 for tokens that are **not masked**,
512
+ - 0 for tokens that are **masked**.
513
+
514
+ [What are attention masks?](../glossary#attention-mask)
515
+
516
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
517
+ [`PreTrainedTokenizer.__call__`] for details.
518
+
519
+ If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
520
+ `past_key_values`).
521
+
522
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
523
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
524
+ information on the default strategy.
525
+
526
+ - 1 indicates the head is **not masked**,
527
+ - 0 indicates the head is **masked**.
528
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
529
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
530
+ config.n_positions - 1]`.
531
+
532
+ [What are position IDs?](../glossary#position-ids)
533
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
534
+ Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
535
+ `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
536
+ `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
537
+
538
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
539
+ blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
540
+
541
+ If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
542
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
543
+ `decoder_input_ids` of shape `(batch_size, sequence_length)`.
544
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
545
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
546
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
547
+ model's internal embedding lookup matrix.
548
+ use_cache (`bool`, *optional*):
549
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
550
+ `past_key_values`).
551
+ output_attentions (`bool`, *optional*):
552
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
553
+ tensors for more detail.
554
+ output_hidden_states (`bool`, *optional*):
555
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
556
+ more detail.
557
+ return_dict (`bool`, *optional*):
558
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
559
+ """
560
+
561
+
562
+ @add_start_docstrings(
563
+ "The bare Aquila Model outputting raw hidden-states without any specific head on top.",
564
+ AQUILA_START_DOCSTRING,
565
+ )
566
+ # Copied from transformers.models.llama.modeling_llama.LlamaModel with LLAMA->AQUILA,Llama->Aquila
567
+ class AquilaModel(AquilaPreTrainedModel):
568
+ """
569
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`AquilaDecoderLayer`]
570
+
571
+ Args:
572
+ config: AquilaConfig
573
+ """
574
+
575
+ def __init__(self, config: AquilaConfig):
576
+ super().__init__(config)
577
+ self.padding_idx = config.pad_token_id
578
+ self.vocab_size = config.vocab_size
579
+
580
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
581
+ self.layers = nn.ModuleList([AquilaDecoderLayer(config) for _ in range(config.num_hidden_layers)])
582
+ self.norm = AquilaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
583
+
584
+ self.gradient_checkpointing = False
585
+ # Initialize weights and apply final processing
586
+ self.post_init()
587
+
588
+ def get_input_embeddings(self):
589
+ return self.embed_tokens
590
+
591
+ def set_input_embeddings(self, value):
592
+ self.embed_tokens = value
593
+
594
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
595
+ # create causal mask
596
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
597
+ combined_attention_mask = None
598
+ if input_shape[-1] > 1:
599
+ combined_attention_mask = _make_causal_mask(
600
+ input_shape,
601
+ inputs_embeds.dtype,
602
+ device=inputs_embeds.device,
603
+ past_key_values_length=past_key_values_length,
604
+ )
605
+
606
+ if attention_mask is not None:
607
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
608
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
609
+ inputs_embeds.device
610
+ )
611
+ combined_attention_mask = (
612
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
613
+ )
614
+
615
+ return combined_attention_mask
616
+
617
+ @add_start_docstrings_to_model_forward(AQUILA_INPUTS_DOCSTRING)
618
+ def forward(
619
+ self,
620
+ input_ids: torch.LongTensor = None,
621
+ attention_mask: Optional[torch.Tensor] = None,
622
+ position_ids: Optional[torch.LongTensor] = None,
623
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
624
+ inputs_embeds: Optional[torch.FloatTensor] = None,
625
+ use_cache: Optional[bool] = None,
626
+ output_attentions: Optional[bool] = None,
627
+ output_hidden_states: Optional[bool] = None,
628
+ return_dict: Optional[bool] = None,
629
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
630
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
631
+ output_hidden_states = (
632
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
633
+ )
634
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
635
+
636
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
637
+
638
+ # retrieve input_ids and inputs_embeds
639
+ if input_ids is not None and inputs_embeds is not None:
640
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
641
+ elif input_ids is not None:
642
+ batch_size, seq_length = input_ids.shape
643
+ elif inputs_embeds is not None:
644
+ batch_size, seq_length, _ = inputs_embeds.shape
645
+ else:
646
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
647
+
648
+ seq_length_with_past = seq_length
649
+ past_key_values_length = 0
650
+
651
+ if past_key_values is not None:
652
+ past_key_values_length = past_key_values[0][0].shape[2]
653
+ seq_length_with_past = seq_length_with_past + past_key_values_length
654
+
655
+ if position_ids is None:
656
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
657
+ position_ids = torch.arange(
658
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
659
+ )
660
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
661
+ else:
662
+ position_ids = position_ids.view(-1, seq_length).long()
663
+
664
+ if inputs_embeds is None:
665
+ inputs_embeds = self.embed_tokens(input_ids)
666
+ # embed positions
667
+ if attention_mask is None:
668
+ attention_mask = torch.ones(
669
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
670
+ )
671
+ attention_mask = self._prepare_decoder_attention_mask(
672
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
673
+ )
674
+
675
+ hidden_states = inputs_embeds
676
+
677
+ if self.gradient_checkpointing and self.training:
678
+ if use_cache:
679
+ logger.warning_once(
680
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
681
+ )
682
+ use_cache = False
683
+
684
+ # decoder layers
685
+ all_hidden_states = () if output_hidden_states else None
686
+ all_self_attns = () if output_attentions else None
687
+ next_decoder_cache = () if use_cache else None
688
+
689
+ for idx, decoder_layer in enumerate(self.layers):
690
+ if output_hidden_states:
691
+ all_hidden_states += (hidden_states,)
692
+
693
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
694
+
695
+ if self.gradient_checkpointing and self.training:
696
+
697
+ def create_custom_forward(module):
698
+ def custom_forward(*inputs):
699
+ # None for past_key_value
700
+ return module(*inputs, past_key_value, output_attentions)
701
+
702
+ return custom_forward
703
+
704
+ layer_outputs = torch.utils.checkpoint.checkpoint(
705
+ create_custom_forward(decoder_layer),
706
+ hidden_states,
707
+ attention_mask,
708
+ position_ids,
709
+ )
710
+ else:
711
+ layer_outputs = decoder_layer(
712
+ hidden_states,
713
+ attention_mask=attention_mask,
714
+ position_ids=position_ids,
715
+ past_key_value=past_key_value,
716
+ output_attentions=output_attentions,
717
+ use_cache=use_cache,
718
+ )
719
+
720
+ hidden_states = layer_outputs[0]
721
+
722
+ if use_cache:
723
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
724
+
725
+ if output_attentions:
726
+ all_self_attns += (layer_outputs[1],)
727
+
728
+ hidden_states = self.norm(hidden_states)
729
+
730
+ # add hidden states from the last decoder layer
731
+ if output_hidden_states:
732
+ all_hidden_states += (hidden_states,)
733
+
734
+ next_cache = next_decoder_cache if use_cache else None
735
+ if not return_dict:
736
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
737
+ return BaseModelOutputWithPast(
738
+ last_hidden_state=hidden_states,
739
+ past_key_values=next_cache,
740
+ hidden_states=all_hidden_states,
741
+ attentions=all_self_attns,
742
+ )
743
+
744
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM with LLAMA->AQUILA,Llama->Aquila
745
+ class AquilaForCausalLM(AquilaPreTrainedModel):
746
+ _tied_weights_keys = ["lm_head.weight"]
747
+
748
+ def __init__(self, config):
749
+ super().__init__(config)
750
+ self.model = AquilaModel(config)
751
+ self.vocab_size = config.vocab_size
752
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
753
+
754
+ # Initialize weights and apply final processing
755
+ self.post_init()
756
+
757
+ def get_input_embeddings(self):
758
+ return self.model.embed_tokens
759
+
760
+ def set_input_embeddings(self, value):
761
+ self.model.embed_tokens = value
762
+
763
+ def get_output_embeddings(self):
764
+ return self.lm_head
765
+
766
+ def set_output_embeddings(self, new_embeddings):
767
+ self.lm_head = new_embeddings
768
+
769
+ def set_decoder(self, decoder):
770
+ self.model = decoder
771
+
772
+ def get_decoder(self):
773
+ return self.model
774
+
775
+ @add_start_docstrings_to_model_forward(AQUILA_INPUTS_DOCSTRING)
776
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
777
+ def forward(
778
+ self,
779
+ input_ids: torch.LongTensor = None,
780
+ attention_mask: Optional[torch.Tensor] = None,
781
+ position_ids: Optional[torch.LongTensor] = None,
782
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
783
+ inputs_embeds: Optional[torch.FloatTensor] = None,
784
+ labels: Optional[torch.LongTensor] = None,
785
+ use_cache: Optional[bool] = None,
786
+ output_attentions: Optional[bool] = None,
787
+ output_hidden_states: Optional[bool] = None,
788
+ return_dict: Optional[bool] = None,
789
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
790
+ r"""
791
+ Args:
792
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
793
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
794
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
795
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
796
+
797
+ Returns:
798
+
799
+ Example:
800
+
801
+ ```python
802
+ >>> from transformers import AutoTokenizer, AquilaForCausalLM
803
+
804
+ >>> model = AquilaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
805
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
806
+
807
+ >>> prompt = "Hey, are you consciours? Can you talk to me?"
808
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
809
+
810
+ >>> # Generate
811
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
812
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
813
+ "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
814
+ ```"""
815
+
816
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
817
+ output_hidden_states = (
818
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
819
+ )
820
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
821
+
822
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
823
+ outputs = self.model(
824
+ input_ids=input_ids,
825
+ attention_mask=attention_mask,
826
+ position_ids=position_ids,
827
+ past_key_values=past_key_values,
828
+ inputs_embeds=inputs_embeds,
829
+ use_cache=use_cache,
830
+ output_attentions=output_attentions,
831
+ output_hidden_states=output_hidden_states,
832
+ return_dict=return_dict,
833
+ )
834
+
835
+ hidden_states = outputs[0]
836
+ if self.config.pretraining_tp > 1:
837
+ lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
838
+ logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
839
+ logits = torch.cat(logits, dim=-1)
840
+ else:
841
+ logits = self.lm_head(hidden_states)
842
+ logits = logits.float()
843
+
844
+ loss = None
845
+ if labels is not None:
846
+ # Shift so that tokens < n predict n
847
+ shift_logits = logits[..., :-1, :].contiguous()
848
+ shift_labels = labels[..., 1:].contiguous()
849
+ # Flatten the tokens
850
+ loss_fct = CrossEntropyLoss()
851
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
852
+ shift_labels = shift_labels.view(-1)
853
+ # Enable model parallelism
854
+ shift_labels = shift_labels.to(shift_logits.device)
855
+ loss = loss_fct(shift_logits, shift_labels)
856
+
857
+ if not return_dict:
858
+ output = (logits,) + outputs[1:]
859
+ return (loss,) + output if loss is not None else output
860
+
861
+ return CausalLMOutputWithPast(
862
+ loss=loss,
863
+ logits=logits,
864
+ past_key_values=outputs.past_key_values,
865
+ hidden_states=outputs.hidden_states,
866
+ attentions=outputs.attentions,
867
+ )
868
+
869
+ def prepare_inputs_for_generation(
870
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
871
+ ):
872
+ if past_key_values:
873
+ input_ids = input_ids[:, -1:]
874
+
875
+ position_ids = kwargs.get("position_ids", None)
876
+ if attention_mask is not None and position_ids is None:
877
+ # create position_ids on the fly for batch generation
878
+ position_ids = attention_mask.long().cumsum(-1) - 1
879
+ position_ids.masked_fill_(attention_mask == 0, 1)
880
+ if past_key_values:
881
+ position_ids = position_ids[:, -1].unsqueeze(-1)
882
+
883
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
884
+ if inputs_embeds is not None and past_key_values is None:
885
+ model_inputs = {"inputs_embeds": inputs_embeds}
886
+ else:
887
+ model_inputs = {"input_ids": input_ids}
888
+
889
+ model_inputs.update(
890
+ {
891
+ "position_ids": position_ids,
892
+ "past_key_values": past_key_values,
893
+ "use_cache": kwargs.get("use_cache"),
894
+ "attention_mask": attention_mask,
895
+ }
896
+ )
897
+ return model_inputs
898
+
899
+ @staticmethod
900
+ def _reorder_cache(past_key_values, beam_idx):
901
+ reordered_past = ()
902
+ for layer_past in past_key_values:
903
+ reordered_past += (
904
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
905
+ )
906
+ return reordered_past
907
+
908
+ @add_start_docstrings(
909
+ """
910
+ The LLaMa Model transformer with a sequence classification head on top (linear layer).
911
+
912
+ [`AquilaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
913
+ (e.g. GPT-2) do.
914
+
915
+ Since it does classification on the last token, it requires to know the position of the last token. If a
916
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
917
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
918
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
919
+ each row of the batch).
920
+ """,
921
+ AQUILA_START_DOCSTRING,
922
+ )
923
+ # Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->AQUILA,Llama->Aquila
924
+ class AquilaForSequenceClassification(AquilaPreTrainedModel):
925
+ _keys_to_ignore_on_load_missing = [r"lm_head.weight"]
926
+
927
+ def __init__(self, config):
928
+ super().__init__(config)
929
+ self.num_labels = config.num_labels
930
+ self.model = AquilaModel(config)
931
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
932
+
933
+ # Initialize weights and apply final processing
934
+ self.post_init()
935
+
936
+ def get_input_embeddings(self):
937
+ return self.model.embed_tokens
938
+
939
+ def set_input_embeddings(self, value):
940
+ self.model.embed_tokens = value
941
+
942
+ @add_start_docstrings_to_model_forward(AQUILA_INPUTS_DOCSTRING)
943
+ def forward(
944
+ self,
945
+ input_ids: torch.LongTensor = None,
946
+ attention_mask: Optional[torch.Tensor] = None,
947
+ position_ids: Optional[torch.LongTensor] = None,
948
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
949
+ inputs_embeds: Optional[torch.FloatTensor] = None,
950
+ labels: Optional[torch.LongTensor] = None,
951
+ use_cache: Optional[bool] = None,
952
+ output_attentions: Optional[bool] = None,
953
+ output_hidden_states: Optional[bool] = None,
954
+ return_dict: Optional[bool] = None,
955
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
956
+ r"""
957
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
958
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
959
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
960
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
961
+ """
962
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
963
+
964
+ transformer_outputs = self.model(
965
+ input_ids,
966
+ attention_mask=attention_mask,
967
+ position_ids=position_ids,
968
+ past_key_values=past_key_values,
969
+ inputs_embeds=inputs_embeds,
970
+ use_cache=use_cache,
971
+ output_attentions=output_attentions,
972
+ output_hidden_states=output_hidden_states,
973
+ return_dict=return_dict,
974
+ )
975
+ hidden_states = transformer_outputs[0]
976
+ logits = self.score(hidden_states)
977
+
978
+ if input_ids is not None:
979
+ batch_size = input_ids.shape[0]
980
+ else:
981
+ batch_size = inputs_embeds.shape[0]
982
+
983
+ if self.config.pad_token_id is None and batch_size != 1:
984
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
985
+ if self.config.pad_token_id is None:
986
+ sequence_lengths = -1
987
+ else:
988
+ if input_ids is not None:
989
+ sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).long().argmax(-1) - 1).to(
990
+ logits.device
991
+ )
992
+ else:
993
+ sequence_lengths = -1
994
+
995
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
996
+
997
+ loss = None
998
+ if labels is not None:
999
+ labels = labels.to(logits.device)
1000
+ if self.config.problem_type is None:
1001
+ if self.num_labels == 1:
1002
+ self.config.problem_type = "regression"
1003
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1004
+ self.config.problem_type = "single_label_classification"
1005
+ else:
1006
+ self.config.problem_type = "multi_label_classification"
1007
+
1008
+ if self.config.problem_type == "regression":
1009
+ loss_fct = MSELoss()
1010
+ if self.num_labels == 1:
1011
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1012
+ else:
1013
+ loss = loss_fct(pooled_logits, labels)
1014
+ elif self.config.problem_type == "single_label_classification":
1015
+ loss_fct = CrossEntropyLoss()
1016
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1017
+ elif self.config.problem_type == "multi_label_classification":
1018
+ loss_fct = BCEWithLogitsLoss()
1019
+ loss = loss_fct(pooled_logits, labels)
1020
+ if not return_dict:
1021
+ output = (pooled_logits,) + transformer_outputs[1:]
1022
+ return ((loss,) + output) if loss is not None else output
1023
+
1024
+ return SequenceClassifierOutputWithPast(
1025
+ loss=loss,
1026
+ logits=pooled_logits,
1027
+ past_key_values=transformer_outputs.past_key_values,
1028
+ hidden_states=transformer_outputs.hidden_states,
1029
+ attentions=transformer_outputs.attentions,
1030
+ )
1031
+
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+ }
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+ }
special_tokens_map.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": "[CLS]",
3
+ "eos_token": "</s>",
4
+ "pad_token": "<|endoftext|>",
5
+ "unk_token": "<|endoftext|>"
6
+ }
tokenizer.json ADDED
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vocab.json ADDED
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