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README.md ADDED
@@ -0,0 +1,57 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ license:
3
+ - other
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+ language:
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+ - en
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+ pipeline_tag: text-generation
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+ tags:
8
+ - pretrained
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+ inference:
10
+ parameters:
11
+ temperature: 0.7
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+ ---
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+
14
+ ## Model sheet for AstraQuasar-4.5B-v.0.1
15
+
16
+ **AstraQuasar-4.5B-v.0.1** is our first pre-trained Large Language Model (LLM) for text generation. It is a model with **4.5B parameters**.
17
+ AstraQuasar-4.5B-v.0.1 is built upon the foundation of the Phi-2 architecture, with **significant enhancements including an increased number of layers and the innovative introduction of a novel technique known as the duplicate trick.**
18
+
19
+ AstraQuasar-4.5B-v.0.1 at the moment is an under trained model. Serving as a demonstration of the potential of the duplication trick and its implications for future advancements in language modeling. Despite its nascent status, our model has already demonstrated superior performance compared to both the base Phi-2 model and earlier iterations of AstraQuasar-4.5B that do not utilize the duplication trick.
20
+
21
+ One of the key milestones achieved by AstraQuasar-4.5B-v.0.1 is its successful application of backpropagation on the duplication trick, setting a precedent for future research and development in this area.
22
+
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+ Our model's architecture is fully compatible with leading training frameworks such as Axolotl and LLaMA Factory, ensuring seamless integration into existing workflows leveraging the standard Hugging Face Transformers library.
24
+
25
+ ## Example:
26
+ AstraQuasar-4.5B-v.0.1 can be easily instantiated using the Hugging Face Transformers library:
27
+
28
+ from transformers import AutoTokenizer, AutoModelForCausalLM
29
+
30
+ model = AutoModelForCausalLM.from_pretrained("AstraMindAI/AstraQuasar-4.5B", trust_remote_code=True)
31
+ tokenizer = AutoTokenizer.from_pretrained("AstraMindAI/AstraQuasar-4.5B")
32
+
33
+ # you can optionally disable the duplicate trick
34
+ # model.model.duplicate_trick = False
35
+
36
+ # You can also disable the duplicate gradient calculation
37
+ # model.model.duplicate_grad = False
38
+
39
+ # You can specify the layer ranges for the duplicate trick
40
+ # model.model.layer_ranges = [(0, 16),(8, 24),(17, 32),(25, 40),(33, 49),(40, 56)]
41
+
42
+ prompt = "This is an example script ."
43
+ inputs = tokenizer(prompt, return_tensors="pt")
44
+
45
+ # Generate
46
+ generate_ids = model.generate(inputs.input_ids, max_length=30)
47
+ tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
48
+
49
+ Pre-training and fine-tuning can be performed using **accelerate**.
50
+
51
+ ## Notice
52
+
53
+ It's important to note that AstraQuasar-4.5B-v.0.1 is a pre-trained base model and does not incorporate any moderation mechanisms.
54
+
55
+ ## NEWS
56
+
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+ Stay tuned for exciting developments! A new architecture, **AstraPulsar**, is on the horizon, promising further advancements in language modeling.
added_tokens.json ADDED
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+ "\t\t\t\t": 50292,
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+ "\t\t\t\t\t\t": 50290,
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+ "\t\t\t\t\t\t\t": 50289,
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+ "\t\t\t\t\t\t\t\t": 50288,
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+ "\t\t\t\t\t\t\t\t\t": 50287,
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+ " ": 50286,
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+ " ": 50285,
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+ " ": 50284,
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+ " ": 50283,
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+ " ": 50282,
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+ " ": 50281,
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+ " ": 50280,
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+ " ": 50258,
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+ " ": 50257
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+ }
config.json ADDED
@@ -0,0 +1,41 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "_name_or_path": "AstraMindAI/AstraQuasar-4.5B",
3
+ "architectures": [
4
+ "QuasarForCausalLM"
5
+ ],
6
+ "attention_dropout": 0.0,
7
+ "auto_map": {
8
+ "AutoConfig": "AstraMindAI/AstraQuasar-4.5B--configuration_quasar.QuasarConfig",
9
+ "AutoModelForCausalLM": "AstraMindAI/AstraQuasar-4.5B--modeling_quasar.QuasarForCausalLM"
10
+ },
11
+ "bos_token_id": 50256,
12
+ "embd_pdrop": 0.0,
13
+ "eos_token_id": 50256,
14
+ "gated_activation": false,
15
+ "remove_ff_bias": true,
16
+ "simple_norm": false,
17
+ "hidden_act": "gelu_new",
18
+ "hidden_size": 2560,
19
+ "initializer_range": 0.02,
20
+ "intermediate_size": 10240,
21
+ "layer_norm_eps": 1e-05,
22
+ "max_position_embeddings": 32768,
23
+ "model_type": "phi",
24
+ "num_attention_heads": 32,
25
+ "num_hidden_layers": 57,
26
+ "num_key_value_heads": 32,
27
+ "partial_rotary_factor": 0.4,
28
+ "qk_layernorm": false,
29
+ "resid_pdrop": 0.1,
30
+ "rope_scaling": null,
31
+ "rope_theta": 10000.0,
32
+ "tie_word_embeddings": false,
33
+ "torch_dtype": "float16",
34
+ "transformers_version": "4.38.0.dev0",
35
+ "use_cache": true,
36
+ "vocab_size": 51200,
37
+ "duplicate_trick": true,
38
+ "duplicate_grad": true,
39
+ "layer_ranges": [[0, 16],[8, 24],[17, 32],[25, 40],[33, 49],[40, 56]],
40
+ "sliding_window": 2048
41
+ }
configuration_quasar.py ADDED
@@ -0,0 +1,253 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2024 AstraMind and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+
16
+ """ Quasar model configuration"""
17
+
18
+
19
+ from transformers.configuration_utils import PretrainedConfig
20
+ from transformers.utils import logging
21
+
22
+
23
+ logger = logging.get_logger(__name__)
24
+
25
+ QUASAR_PRETRAINED_CONFIG_ARCHIVE_MAP = {
26
+ "AstraMindAI/AstraQuasar-4.5B": "https://huggingface.co/AstraMindAI/AstraQuasar-4.5B/resolve/main/config.json",
27
+ }
28
+
29
+ #From phi-2 Phi -> Quasar
30
+ class QuasarConfig(PretrainedConfig):
31
+ r"""
32
+ This is the configuration class to store the configuration of a [`QuasarModel`]. It is used to instantiate an Quasar
33
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
34
+ defaults will yield a similar configuration to that of the Quasar
35
+ [microsoft/quasar-1](https://huggingface.co/microsoft/quasar-1).
36
+
37
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
38
+ documentation from [`PretrainedConfig`] for more information.
39
+
40
+ Args:
41
+ vocab_size (`int`, *optional*, defaults to 51200):
42
+ Vocabulary size of the Quasar model. Defines the number of different tokens that can be represented by the
43
+ `inputs_ids` passed when calling [`QuasarModel`].
44
+ hidden_size (`int`, *optional*, defaults to 2048):
45
+ Dimension of the hidden representations.
46
+ intermediate_size (`int`, *optional*, defaults to 8192):
47
+ Dimension of the MLP representations.
48
+ num_hidden_layers (`int`, *optional*, defaults to 24):
49
+ Number of hidden layers in the Transformer decoder.
50
+ num_attention_heads (`int`, *optional*, defaults to 32):
51
+ Number of attention heads for each attention layer in the Transformer decoder.
52
+ num_key_value_heads (`int`, *optional*):
53
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
54
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
55
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
56
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
57
+ by meanpooling all the original heads within that group. For more details checkout [this
58
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
59
+ `num_attention_heads`.
60
+ resid_pdrop (`float`, *optional*, defaults to 0.0):
61
+ Dropout probability for mlp outputs.
62
+ embd_pdrop (`int`, *optional*, defaults to 0.0):
63
+ The dropout ratio for the embeddings.
64
+ attention_dropout (`float`, *optional*, defaults to 0.0):
65
+ The dropout ratio after computing the attention scores.
66
+ hidden_act (`str` or `function`, *optional*, defaults to `"gelu_new"`):
67
+ The non-linear activation function (function or string) in the decoder.
68
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
69
+ The maximum sequence length that this model might ever be used with. Quasar-1 and Quasar-1.5 supports up to 2048
70
+ tokens.
71
+ initializer_range (`float`, *optional*, defaults to 0.02):
72
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
73
+ layer_norm_eps (`float`, *optional*, defaults to 1e-05):
74
+ The epsilon used by the rms normalization layers.
75
+ use_cache (`bool`, *optional*, defaults to `True`):
76
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
77
+ relevant if `config.is_decoder=True`. Whether to tie weight embeddings or not.
78
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
79
+ Whether to tie weight embeddings
80
+ rope_theta (`float`, *optional*, defaults to 10000.0):
81
+ The base period of the RoPE embeddings.
82
+ rope_scaling (`Dict`, *optional*):
83
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
84
+ strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
85
+ is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
86
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
87
+ these scaling strategies behave:
88
+ https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
89
+ is an experimental feature, subject to breaking API changes in future versions.
90
+ partial_rotary_factor (`float`, *optional*, defaults to 0.5):
91
+ Percentage of the query and keys which will have rotary embedding.
92
+ qk_layernorm (`bool`, *optional*, defaults to `False`):
93
+ Whether or not to normalize the Queries and Keys after projecting the hidden states.
94
+ bos_token_id (`int`, *optional*, defaults to 1):
95
+ Denotes beginning of sequences token id.
96
+ eos_token_id (`int`, *optional*, defaults to 2):
97
+ Denotes end of sequences token id.
98
+
99
+ Example:
100
+
101
+ ```python
102
+ >>> from transformers import AutoModel, QuasarConfig
103
+
104
+
105
+ >>> # Initializing a Quasar-1 style configuration
106
+ >>> configuration = QuasarConfig.from_pretrained("AstraMindAI/AstraQuasar-4.5B")
107
+
108
+ >>> # Initializing a model from the configuration
109
+ >>> model = QuasarModel(configuration, trust_remote_code=True)
110
+
111
+ >>> # Accessing the model configuration
112
+ >>> configuration = model.config
113
+ ```"""
114
+
115
+ model_type = "quasar"
116
+ keys_to_ignore_at_inference = ["past_key_values"]
117
+
118
+ def __init__(
119
+ self,
120
+ vocab_size=51200,
121
+ hidden_size=2560,
122
+ intermediate_size=8192,
123
+ num_hidden_layers=24,
124
+ num_attention_heads=32,
125
+ num_key_value_heads=None,
126
+ resid_pdrop=0.0,
127
+ embd_pdrop=0.0,
128
+ attention_dropout=0.0,
129
+ hidden_act="gelu_new",
130
+ max_position_embeddings=32768,
131
+ initializer_range=0.02,
132
+ layer_norm_eps=1e-5,
133
+ use_cache=True,
134
+ tie_word_embeddings=False,
135
+ rope_theta=10000.0,
136
+ rope_scaling=None,
137
+ partial_rotary_factor=0.5,
138
+ qk_layernorm=False,
139
+ bos_token_id=1,
140
+ eos_token_id=2,
141
+ ## Aggiunto
142
+ #duplicate_trick_v2=True,
143
+ #duplicate_rank=8,
144
+ #duplicate_dropout=0.0,
145
+ sliding_window=4096,
146
+ simple_norm=False,
147
+ remove_ff_bias=True,
148
+ gated_activation=False,
149
+ duplicate_trick=True,
150
+ duplicate_grad=True,
151
+ layer_ranges=[[0, 16],[8, 21],[12, 25],[16, 29],[25, 32]],
152
+ **kwargs,
153
+ ):
154
+ ##Aggiunti
155
+ self.sliding_window = sliding_window
156
+ self.simple_norm = simple_norm
157
+ self.remove_ff_bias = remove_ff_bias
158
+ self.gated_activation = gated_activation
159
+ self.duplicate_trick = duplicate_trick
160
+ self.duplicate_grad = duplicate_grad
161
+ self.layer_ranges = layer_ranges if layer_ranges is not None else []
162
+
163
+ ##V2###
164
+ #self.duplicate_trick_v2 = duplicate_trick_v2
165
+ #self.layer_ranges_duplicate_v2 = []
166
+ #self._assing_layer_ranges_duplicate_v2()
167
+ #self.duplicate_rank = duplicate_rank
168
+ #self.duplicate_dropout = duplicate_dropout
169
+ #self._duplicate_trick_v2_validation()
170
+ ####
171
+
172
+ self.vocab_size = vocab_size
173
+ self.hidden_size = hidden_size
174
+ self.intermediate_size = intermediate_size
175
+ self.num_hidden_layers = num_hidden_layers
176
+ self.num_attention_heads = num_attention_heads
177
+
178
+ if num_key_value_heads is None:
179
+ num_key_value_heads = num_attention_heads
180
+
181
+ self.num_key_value_heads = num_key_value_heads
182
+ self.resid_pdrop = resid_pdrop
183
+ self.embd_pdrop = embd_pdrop
184
+ self.attention_dropout = attention_dropout
185
+ self.hidden_act = hidden_act
186
+ self.max_position_embeddings = max_position_embeddings
187
+ self.initializer_range = initializer_range
188
+ self.layer_norm_eps = layer_norm_eps
189
+ self.use_cache = use_cache
190
+ self.rope_theta = rope_theta
191
+ self.rope_scaling = rope_scaling
192
+ self.partial_rotary_factor = partial_rotary_factor
193
+ self.qk_layernorm = qk_layernorm
194
+ self._rope_scaling_validation()
195
+
196
+ super().__init__(
197
+ bos_token_id=bos_token_id,
198
+ eos_token_id=eos_token_id,
199
+ tie_word_embeddings=tie_word_embeddings,
200
+ **kwargs,
201
+ )
202
+
203
+ def _assing_layer_ranges_duplicate_v2(self):
204
+ # Calcolo gli offset iniziali per ciascun intervallo nella lista unica
205
+ offsets = [0]
206
+ for i in range(1, len(self.layer_ranges)):
207
+ offset = offsets[-1] + self.layer_ranges[i - 1][1] - self.layer_ranges[i - 1][0]
208
+ offsets.append(offset)
209
+
210
+ # Seleziono solo gli intervalli dispari e calcolo le loro posizioni assolute
211
+ odd_intervals_positions = []
212
+ for i in range(1, len(self.layer_ranges), 2):
213
+ start, end = self.layer_ranges[i]
214
+ for n in range(start, end):
215
+ position = offsets[i] + (n - start)
216
+ odd_intervals_positions.append(position)
217
+
218
+ self.layer_ranges_duplicate_v2 = list(set(odd_intervals_positions))
219
+
220
+
221
+ def _duplicate_trick_v2_validation(self):
222
+ if self.duplicate_trick_v2 and self.duplicate_trick:
223
+ # warn just one time that only one of the two flags will be used
224
+ logger.warning(
225
+ "Both `duplicate_trick` and `duplicate_trick_v2` are set to True. Only `duplicate_trick_v2` will be used."
226
+ )
227
+ if self.duplicate_trick_v2 and self.duplicate_rank < 1:
228
+ raise ValueError("`duplicate_rank` must be a positive integer")
229
+ if self.duplicate_trick_v2 and not self.layer_ranges:
230
+ raise ValueError("`layer_ranges` must be set when `duplicate_trick_v2` is True")
231
+
232
+
233
+ # Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
234
+ def _rope_scaling_validation(self):
235
+ """
236
+ Validate the `rope_scaling` configuration.
237
+ """
238
+ if self.rope_scaling is None:
239
+ return
240
+
241
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
242
+ raise ValueError(
243
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
244
+ f"got {self.rope_scaling}"
245
+ )
246
+ rope_scaling_type = self.rope_scaling.get("type", None)
247
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
248
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
249
+ raise ValueError(
250
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
251
+ )
252
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
253
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
generation_config.json ADDED
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+ "bos_token_id": 50256,
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+ "eos_token_id": 50256,
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+ "transformers_version": "4.38.0.dev0"
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+ }
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+ }
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+ }
modeling_quasar.py ADDED
@@ -0,0 +1,1494 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # coding=utf-8
2
+ # Copyright 2023 Microsoft and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+
16
+ """ PyTorch Quasar model."""
17
+ import inspect
18
+ import math
19
+ from typing import List, Optional, Tuple, Union, Callable
20
+
21
+ import torch
22
+ import torch.nn.functional as F
23
+ import torch.utils.checkpoint
24
+ from torch import nn
25
+ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
26
+
27
+ from transformers.activations import ACT2FN
28
+ from transformers.cache_utils import Cache, DynamicCache
29
+ from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
30
+ from transformers.modeling_outputs import (
31
+ BaseModelOutputWithPast,
32
+ CausalLMOutputWithPast,
33
+ SequenceClassifierOutputWithPast,
34
+ TokenClassifierOutput,
35
+ )
36
+ from transformers.modeling_utils import PreTrainedModel
37
+ from transformers.utils import (
38
+ add_code_sample_docstrings,
39
+ add_start_docstrings,
40
+ add_start_docstrings_to_model_forward,
41
+ is_flash_attn_2_available,
42
+ is_flash_attn_greater_or_equal_2_10,
43
+ logging,
44
+ replace_return_docstrings,
45
+ )
46
+ from .configuration_quasar import QuasarConfig
47
+
48
+ try:
49
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
50
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
51
+
52
+ _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
53
+ except:
54
+ pass
55
+
56
+ logger = logging.get_logger(__name__)
57
+
58
+ _CHECKPOINT_FOR_DOC = "AstraMindAI/AstraQuasar-4.5B"
59
+ _CONFIG_FOR_DOC = "QuasarConfig"
60
+
61
+ QUASAR_PRETRAINED_MODEL_ARCHIVE_LIST = [
62
+ "AstraMindAI/AstraQuasar-4.5B",
63
+ ]
64
+
65
+
66
+
67
+
68
+
69
+ # Copied from transformers.models.llama.modeling_llama._get_unpad_data
70
+ def _get_unpad_data(attention_mask):
71
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
72
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
73
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
74
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
75
+ return (
76
+ indices,
77
+ cu_seqlens,
78
+ max_seqlen_in_batch,
79
+ )
80
+
81
+
82
+ # Copied from transformers.models.llama.modeling_llama.LlamaRotaryEmbedding with Llama->Quasar
83
+ class QuasarRotaryEmbedding(nn.Module):
84
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
85
+ super().__init__()
86
+
87
+ self.dim = dim
88
+ self.max_position_embeddings = max_position_embeddings
89
+ self.base = base
90
+ inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
91
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
92
+
93
+ # Build here to make `torch.jit.trace` work.
94
+ self._set_cos_sin_cache(
95
+ seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
96
+ )
97
+
98
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
99
+ self.max_seq_len_cached = seq_len
100
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
101
+
102
+ freqs = torch.outer(t, self.inv_freq)
103
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
104
+ emb = torch.cat((freqs, freqs), dim=-1)
105
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
106
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
107
+
108
+ def forward(self, x, seq_len=None):
109
+ # x: [bs, num_attention_heads, seq_len, head_size]
110
+ if seq_len > self.max_seq_len_cached:
111
+ self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
112
+
113
+ return (
114
+ self.cos_cached[:seq_len].to(dtype=x.dtype),
115
+ self.sin_cached[:seq_len].to(dtype=x.dtype),
116
+ )
117
+
118
+
119
+ # Copied from transformers.models.llama.modeling_llama.LlamaLinearScalingRotaryEmbedding with Llama->Quasar
120
+ class QuasarLinearScalingRotaryEmbedding(QuasarRotaryEmbedding):
121
+ """QuasarRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
122
+
123
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
124
+ self.scaling_factor = scaling_factor
125
+ super().__init__(dim, max_position_embeddings, base, device)
126
+
127
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
128
+ self.max_seq_len_cached = seq_len
129
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
130
+ t = t / self.scaling_factor
131
+
132
+ freqs = torch.outer(t, self.inv_freq)
133
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
134
+ emb = torch.cat((freqs, freqs), dim=-1)
135
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
136
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
137
+
138
+
139
+ # Copied from transformers.models.llama.modeling_llama.LlamaDynamicNTKScalingRotaryEmbedding with Llama->Quasar
140
+ class QuasarDynamicNTKScalingRotaryEmbedding(QuasarRotaryEmbedding):
141
+ """QuasarRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
142
+
143
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
144
+ self.scaling_factor = scaling_factor
145
+ super().__init__(dim, max_position_embeddings, base, device)
146
+
147
+ def _set_cos_sin_cache(self, seq_len, device, dtype):
148
+ self.max_seq_len_cached = seq_len
149
+
150
+ if seq_len > self.max_position_embeddings:
151
+ base = self.base * (
152
+ (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
153
+ ) ** (self.dim / (self.dim - 2))
154
+ inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
155
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
156
+
157
+ t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
158
+
159
+ freqs = torch.outer(t, self.inv_freq)
160
+ # Different from paper, but it uses a different permutation in order to obtain the same calculation
161
+ emb = torch.cat((freqs, freqs), dim=-1)
162
+ self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
163
+ self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
164
+
165
+
166
+ ##################################################################à
167
+
168
+ class QuasarMLP(nn.Module):
169
+ def __init__(self, config):
170
+ super().__init__()
171
+ self.config = config
172
+
173
+
174
+ self.activation_fn = ACT2FN[config.hidden_act]
175
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
176
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
177
+
178
+ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
179
+ hidden_states = self.fc1(hidden_states)
180
+ hidden_states = self.activation_fn(hidden_states)
181
+ hidden_states = self.fc2(hidden_states)
182
+ return hidden_states
183
+
184
+
185
+ ###################################################################
186
+ # Copied from transformers.models.llama.modeling_llama.rotate_half
187
+ def rotate_half(x):
188
+ """Rotates half the hidden dims of the input."""
189
+ x1 = x[..., : x.shape[-1] // 2]
190
+ x2 = x[..., x.shape[-1] // 2:]
191
+ return torch.cat((-x2, x1), dim=-1)
192
+
193
+
194
+ # Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
195
+ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
196
+ """Applies Rotary Position Embedding to the query and key tensors.
197
+ Args:
198
+ q (`torch.Tensor`): The query tensor.
199
+ k (`torch.Tensor`): The key tensor.
200
+ cos (`torch.Tensor`): The cosine part of the rotary embedding.
201
+ sin (`torch.Tensor`): The sine part of the rotary embedding.
202
+ position_ids (`torch.Tensor`):
203
+ The position indices of the tokens corresponding to the query and key tensors. For example, this can be
204
+ used to pass offsetted position ids when working with a KV-cache.
205
+ unsqueeze_dim (`int`, *optional*, defaults to 1):
206
+ The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and
207
+ sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note
208
+ that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and
209
+ k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
210
+ cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have
211
+ the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2.
212
+ Returns:
213
+ `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
214
+ """
215
+ cos = cos[position_ids].unsqueeze(unsqueeze_dim)
216
+ sin = sin[position_ids].unsqueeze(unsqueeze_dim)
217
+ q_embed = (q * cos) + (rotate_half(q) * sin)
218
+ k_embed = (k * cos) + (rotate_half(k) * sin)
219
+ return q_embed, k_embed
220
+
221
+
222
+ # Copied from transformers.models.llama.modeling_llama.repeat_kv with llama-> pulsar
223
+ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
224
+ """
225
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
226
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
227
+ """
228
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
229
+ if n_rep == 1:
230
+ return hidden_states
231
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
232
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
233
+
234
+
235
+ class QuasarAttention(nn.Module):
236
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
237
+
238
+ def __init__(self, config: QuasarConfig, layer_idx: Optional[int] = None):
239
+ super().__init__()
240
+ self.config = config
241
+ self.layer_idx = layer_idx
242
+ if layer_idx is None:
243
+ logger.warning_once(
244
+ f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
245
+ "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
246
+ "when creating this class."
247
+ )
248
+
249
+ self.attention_dropout = config.attention_dropout
250
+ self.hidden_size = config.hidden_size
251
+ self.num_heads = config.num_attention_heads
252
+ self.head_dim = self.hidden_size // self.num_heads
253
+ self.num_key_value_heads = config.num_key_value_heads
254
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
255
+ self.max_position_embeddings = config.max_position_embeddings
256
+ self.rope_theta = config.rope_theta
257
+ self.partial_rotary_factor = config.partial_rotary_factor
258
+ self.is_causal = True
259
+
260
+ if (self.head_dim * self.num_heads) != self.hidden_size:
261
+ raise ValueError(
262
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
263
+ f" and `num_heads`: {self.num_heads})."
264
+ )
265
+
266
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=True)
267
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
268
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=True)
269
+ self.dense = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=True)
270
+
271
+ self.qk_layernorm = config.qk_layernorm
272
+ if self.qk_layernorm:
273
+
274
+ self.q_layernorm = (
275
+ nn.LayerNorm(
276
+ config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
277
+ ))
278
+ self.k_layernorm = nn.LayerNorm(
279
+ config.hidden_size // self.num_heads, eps=config.layer_norm_eps, elementwise_affine=True
280
+ )
281
+
282
+ self._init_rope()
283
+
284
+ def _init_rope(self):
285
+ if self.config.rope_scaling is None:
286
+ self.rotary_emb = QuasarRotaryEmbedding(
287
+ int(self.partial_rotary_factor * self.head_dim),
288
+ max_position_embeddings=self.max_position_embeddings,
289
+ base=self.rope_theta,
290
+ )
291
+ else:
292
+ scaling_type = self.config.rope_scaling["type"]
293
+ scaling_factor = self.config.rope_scaling["factor"]
294
+ if scaling_type == "linear":
295
+ self.rotary_emb = QuasarLinearScalingRotaryEmbedding(
296
+ int(self.partial_rotary_factor * self.head_dim),
297
+ max_position_embeddings=self.max_position_embeddings,
298
+ scaling_factor=scaling_factor,
299
+ base=self.rope_theta,
300
+ )
301
+ elif scaling_type == "dynamic":
302
+ self.rotary_emb = QuasarDynamicNTKScalingRotaryEmbedding(
303
+ int(self.partial_rotary_factor * self.head_dim),
304
+ max_position_embeddings=self.max_position_embeddings,
305
+ scaling_factor=scaling_factor,
306
+ base=self.rope_theta,
307
+ )
308
+ else:
309
+ raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
310
+
311
+ # Quasar-2 has an attention overflow issue (with FP16) and requires autocast to be disabled
312
+ @torch.autocast("cpu", enabled=False)
313
+ @torch.autocast("cuda", enabled=False)
314
+ def forward(
315
+ self,
316
+ hidden_states: torch.Tensor,
317
+ attention_mask: Optional[torch.Tensor] = None,
318
+ position_ids: Optional[torch.LongTensor] = None,
319
+ past_key_value: Optional[Cache] = None,
320
+ output_attentions: bool = False,
321
+ use_cache: bool = False,
322
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
323
+ bsz, q_len, _ = hidden_states.size()
324
+
325
+ query_states = self.q_proj(hidden_states)
326
+ key_states = self.k_proj(hidden_states)
327
+ value_states = self.v_proj(hidden_states)
328
+
329
+ if self.qk_layernorm:
330
+ query_states = self.q_layernorm(query_states)
331
+ key_states = self.k_layernorm(key_states)
332
+
333
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
334
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
335
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
336
+
337
+ kv_seq_len = key_states.shape[-2]
338
+ if past_key_value is not None:
339
+ if self.layer_idx is None:
340
+ raise ValueError(
341
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
342
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
343
+ "with a layer index."
344
+ )
345
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
346
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
347
+
348
+ # Partial rotary embedding
349
+ query_rot, query_pass = (
350
+ query_states[..., : self.rotary_emb.dim],
351
+ query_states[..., self.rotary_emb.dim:],
352
+ )
353
+ key_rot, key_pass = (
354
+ key_states[..., : self.rotary_emb.dim],
355
+ key_states[..., self.rotary_emb.dim:],
356
+ )
357
+ # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
358
+ query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
359
+
360
+ # [batch_size, seq_length, num_heads, head_dim]
361
+ query_states = torch.cat((query_rot, query_pass), dim=-1)
362
+ key_states = torch.cat((key_rot, key_pass), dim=-1)
363
+
364
+ if past_key_value is not None:
365
+ cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
366
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
367
+
368
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
369
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
370
+
371
+ # Queries and keys upcast to fp32 is required by Quasar-2 to avoid overflow
372
+ attn_weights = torch.matmul(
373
+ query_states.to(torch.float32), key_states.to(torch.float32).transpose(2, 3)
374
+ ) / math.sqrt(self.head_dim)
375
+
376
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
377
+ raise ValueError(
378
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
379
+ f" {attn_weights.size()}"
380
+ )
381
+
382
+ if attention_mask is not None:
383
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
384
+ raise ValueError(
385
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
386
+ )
387
+ attn_weights = attn_weights + attention_mask
388
+
389
+ # upcast attention to fp32
390
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(value_states.dtype)
391
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
392
+
393
+ attn_output = torch.matmul(attn_weights, value_states)
394
+
395
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
396
+ raise ValueError(
397
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
398
+ f" {attn_output.size()}"
399
+ )
400
+
401
+ attn_output = attn_output.transpose(1, 2).contiguous()
402
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
403
+
404
+ attn_output = self.dense(attn_output)
405
+
406
+ if not output_attentions:
407
+ attn_weights = None
408
+
409
+ return attn_output, attn_weights, past_key_value
410
+
411
+
412
+ class QuasarFlashAttention2(QuasarAttention):
413
+ """
414
+ Quasar flash attention module. This module inherits from `QuasarAttention` as the weights of the module stays
415
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
416
+ flash attention and deal with padding tokens in case the input contains any of them.
417
+ """
418
+
419
+ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
420
+ def __init__(self, *args, **kwargs):
421
+ super().__init__(*args, **kwargs)
422
+
423
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
424
+ # 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.
425
+ # 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).
426
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
427
+
428
+ def forward(
429
+ self,
430
+ hidden_states: torch.Tensor,
431
+ attention_mask: Optional[torch.LongTensor] = None,
432
+ position_ids: Optional[torch.LongTensor] = None,
433
+ past_key_value: Optional[Cache] = None,
434
+ output_attentions: bool = False,
435
+ use_cache: bool = False,
436
+ **kwargs,
437
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
438
+ # QuasarFlashAttention2 attention does not support output_attentions
439
+
440
+ output_attentions = False
441
+
442
+ bsz, q_len, _ = hidden_states.size()
443
+
444
+ query_states = self.q_proj(hidden_states)
445
+ key_states = self.k_proj(hidden_states)
446
+ value_states = self.v_proj(hidden_states)
447
+
448
+ if self.qk_layernorm:
449
+ query_states = self.q_layernorm(query_states)
450
+ key_states = self.k_layernorm(key_states)
451
+
452
+ # Flash attention requires the input to have the shape
453
+ # batch_size x seq_length x head_dim x hidden_dim
454
+ # therefore we just need to keep the original shape
455
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
456
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
457
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
458
+
459
+ kv_seq_len = key_states.shape[-2]
460
+ if past_key_value is not None:
461
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
462
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
463
+
464
+ # Partial rotary embedding
465
+ query_rot, query_pass = (
466
+ query_states[..., : self.rotary_emb.dim],
467
+ query_states[..., self.rotary_emb.dim:],
468
+ )
469
+ key_rot, key_pass = (
470
+ key_states[..., : self.rotary_emb.dim],
471
+ key_states[..., self.rotary_emb.dim:],
472
+ )
473
+ # [batch_size, seq_length, num_heads, head_dim // config.partial_rotary_factor]
474
+ query_rot, key_rot = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, position_ids)
475
+
476
+ use_sliding_windows = (
477
+ _flash_supports_window_size
478
+ and getattr(self.config, "sliding_window", None) is not None
479
+ and kv_seq_len > self.config.sliding_window
480
+ )
481
+
482
+ if not _flash_supports_window_size:
483
+ logger.warning_once(
484
+ "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
485
+ " make sure to upgrade flash-attn library."
486
+ )
487
+ # [batch_size, seq_length, num_heads, head_dim]
488
+ query_states = torch.cat((query_rot, query_pass), dim=-1)
489
+ key_states = torch.cat((key_rot, key_pass), dim=-1)
490
+
491
+ if past_key_value is not None:
492
+ # Activate slicing cache only if the config has a value `sliding_windows` attribute
493
+ cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
494
+ if (
495
+ getattr(self.config, "sliding_window", None) is not None
496
+ and kv_seq_len > self.config.sliding_window
497
+ and cache_has_contents
498
+ ):
499
+ slicing_tokens = 1 - self.config.sliding_window
500
+
501
+ past_key = past_key_value[self.layer_idx][0]
502
+ past_value = past_key_value[self.layer_idx][1]
503
+
504
+ past_key = past_key[:, :, slicing_tokens:, :].contiguous()
505
+ past_value = past_value[:, :, slicing_tokens:, :].contiguous()
506
+
507
+ if past_key.shape[-2] != self.config.sliding_window - 1:
508
+ raise ValueError(
509
+ f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
510
+ f" {past_key.shape}"
511
+ )
512
+
513
+ if attention_mask is not None:
514
+ attention_mask = attention_mask[:, slicing_tokens:]
515
+ attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
516
+ cache_kwargs = {"sin": sin, "cos": cos, "partial_rotation_size": self.rotary_emb.dim}
517
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
518
+
519
+ # 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
520
+ # to be able to avoid many of these transpose/reshape/view.
521
+ query_states = query_states.transpose(1, 2)
522
+ key_states = key_states.transpose(1, 2)
523
+ value_states = value_states.transpose(1, 2)
524
+
525
+ attn_dropout = self.attention_dropout if self.training else 0.0
526
+
527
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
528
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
529
+ # cast them back in the correct dtype just to be sure everything works as expected.
530
+ # This might slowdown training & inference so it is recommended to not cast the LayerNorms
531
+ # in fp32.
532
+
533
+ if query_states.dtype == torch.float32:
534
+ if torch.is_autocast_enabled():
535
+ target_dtype = torch.get_autocast_gpu_dtype()
536
+ # Handle the case where the model is quantized
537
+ elif hasattr(self.config, "_pre_quantization_dtype"):
538
+ target_dtype = self.config._pre_quantization_dtype
539
+ else:
540
+ target_dtype = self.q_proj.weight.dtype
541
+
542
+ logger.warning_once(
543
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
544
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
545
+ f" {target_dtype}."
546
+ )
547
+
548
+ query_states = query_states.to(target_dtype)
549
+ key_states = key_states.to(target_dtype)
550
+ value_states = value_states.to(target_dtype)
551
+
552
+ attn_output = self._flash_attention_forward(
553
+ query_states, key_states, value_states, attention_mask, q_len, dropout=attn_dropout, softmax_scale=None,
554
+ use_sliding_windows=use_sliding_windows
555
+ )
556
+
557
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
558
+ attn_output = self.dense(attn_output)
559
+
560
+ if not output_attentions:
561
+ attn_weights = None
562
+
563
+ return attn_output, attn_weights, past_key_value
564
+
565
+ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._flash_attention_forward
566
+ def _flash_attention_forward(
567
+ self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None,
568
+ use_sliding_windows=False
569
+ ):
570
+ """
571
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
572
+ first unpad the input, then computes the attention scores and pad the final attention scores.
573
+ Args:
574
+ query_states (`torch.Tensor`):
575
+ Input query states to be passed to Flash Attention API
576
+ key_states (`torch.Tensor`):
577
+ Input key states to be passed to Flash Attention API
578
+ value_states (`torch.Tensor`):
579
+ Input value states to be passed to Flash Attention API
580
+ attention_mask (`torch.Tensor`):
581
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
582
+ position of padding tokens and 1 for the position of non-padding tokens.
583
+ dropout (`int`, *optional*):
584
+ Attention dropout
585
+ softmax_scale (`float`, *optional*):
586
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
587
+ use_sliding_windows (`bool`, *optional*):
588
+ Whether to use sliding windows for the attention computation. Default to False.
589
+ """
590
+ if not self._flash_attn_uses_top_left_mask:
591
+ causal = self.is_causal
592
+ else:
593
+ # 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__.
594
+ causal = self.is_causal and query_length != 1
595
+
596
+ # Contains at least one padding token in the sequence
597
+ if attention_mask is not None:
598
+ batch_size = query_states.shape[0]
599
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
600
+ query_states, key_states, value_states, attention_mask, query_length
601
+ )
602
+
603
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
604
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
605
+ if not use_sliding_windows:
606
+ attn_output_unpad = flash_attn_varlen_func(
607
+ query_states,
608
+ key_states,
609
+ value_states,
610
+ cu_seqlens_q=cu_seqlens_q,
611
+ cu_seqlens_k=cu_seqlens_k,
612
+ max_seqlen_q=max_seqlen_in_batch_q,
613
+ max_seqlen_k=max_seqlen_in_batch_k,
614
+ dropout_p=dropout,
615
+ softmax_scale=softmax_scale,
616
+ causal=causal,
617
+ )
618
+ else:
619
+ attn_output_unpad = flash_attn_varlen_func(
620
+ query_states,
621
+ key_states,
622
+ value_states,
623
+ cu_seqlens_q=cu_seqlens_q,
624
+ cu_seqlens_k=cu_seqlens_k,
625
+ max_seqlen_q=max_seqlen_in_batch_q,
626
+ max_seqlen_k=max_seqlen_in_batch_k,
627
+ dropout_p=dropout,
628
+ softmax_scale=softmax_scale,
629
+ causal=causal,
630
+ window_size=(self.config.sliding_window, self.config.sliding_window),
631
+ )
632
+
633
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
634
+ else:
635
+ if not use_sliding_windows:
636
+ attn_output = flash_attn_func(
637
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
638
+ )
639
+ else:
640
+ attn_output = flash_attn_func(
641
+ query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal,
642
+ window_size=(self.config.sliding_window, self.config.sliding_window)
643
+ )
644
+
645
+ return attn_output
646
+
647
+ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2._upad_input
648
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
649
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
650
+ batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
651
+
652
+ key_layer = index_first_axis(
653
+ key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
654
+ )
655
+ value_layer = index_first_axis(
656
+ value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
657
+ )
658
+ if query_length == kv_seq_len:
659
+ query_layer = index_first_axis(
660
+ query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
661
+ )
662
+ cu_seqlens_q = cu_seqlens_k
663
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
664
+ indices_q = indices_k
665
+ elif query_length == 1:
666
+ max_seqlen_in_batch_q = 1
667
+ cu_seqlens_q = torch.arange(
668
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
669
+ ) # There is a memcpy here, that is very bad.
670
+ indices_q = cu_seqlens_q[:-1]
671
+ query_layer = query_layer.squeeze(1)
672
+ else:
673
+ # The -q_len: slice assumes left padding.
674
+ attention_mask = attention_mask[:, -query_length:]
675
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
676
+
677
+ return (
678
+ query_layer,
679
+ key_layer,
680
+ value_layer,
681
+ indices_q,
682
+ (cu_seqlens_q, cu_seqlens_k),
683
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
684
+ )
685
+
686
+
687
+ QUASAR_ATTENTION_CLASSES = {
688
+ "eager": QuasarAttention,
689
+ "flash_attention_2": QuasarFlashAttention2,
690
+ }
691
+
692
+
693
+ class QuasarDecoderLayer(nn.Module):
694
+ def __init__(self, config: QuasarConfig, layer_idx: int):
695
+ super().__init__()
696
+ self.self_attn = QUASAR_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx=layer_idx)
697
+ self.mlp = QuasarMLP(config)
698
+
699
+ self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
700
+
701
+ self.resid_dropout = nn.Dropout(config.resid_pdrop)
702
+
703
+ def forward(
704
+ self,
705
+ hidden_states: torch.Tensor,
706
+ attention_mask: Optional[torch.Tensor] = None,
707
+ position_ids: Optional[torch.LongTensor] = None,
708
+ output_attentions: Optional[bool] = False,
709
+ use_cache: Optional[bool] = False,
710
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
711
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
712
+ """
713
+ Args:
714
+ hidden_states (`torch.FloatTensor`):
715
+ input to the layer of shape `(batch, seq_len, embed_dim)`
716
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
717
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
718
+ position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
719
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
720
+ `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids)
721
+ output_attentions (`bool`, *optional*):
722
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
723
+ returned tensors for more detail.
724
+ use_cache (`bool`, *optional*):
725
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
726
+ (see `past_key_values`).
727
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
728
+ """
729
+
730
+ residual = hidden_states
731
+
732
+ hidden_states = self.input_layernorm(hidden_states)
733
+
734
+ # Self Attention
735
+ attn_outputs, self_attn_weights, present_key_value = self.self_attn(
736
+ hidden_states=hidden_states,
737
+ attention_mask=attention_mask,
738
+ position_ids=position_ids,
739
+ past_key_value=past_key_value,
740
+ output_attentions=output_attentions,
741
+ use_cache=use_cache,
742
+ )
743
+ attn_outputs = self.resid_dropout(attn_outputs)
744
+
745
+ feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
746
+ hidden_states = attn_outputs + feed_forward_hidden_states + residual
747
+ outputs = (hidden_states,)
748
+
749
+ if output_attentions:
750
+ outputs += (self_attn_weights,)
751
+
752
+ if use_cache:
753
+ outputs += (present_key_value,)
754
+
755
+ return outputs
756
+
757
+
758
+ QUASAR_START_DOCSTRING = r"""
759
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
760
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
761
+ etc.)
762
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
763
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
764
+ and behavior.
765
+ Parameters:
766
+ config ([`QuasarConfig`]):
767
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
768
+ load the weights associated with the model, only the configuration. Check out the
769
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
770
+ """
771
+
772
+
773
+ @add_start_docstrings(
774
+ "The bare Quasar Model outputting raw hidden-states without any specific head on top.",
775
+ QUASAR_START_DOCSTRING,
776
+ )
777
+ class QuasarPreTrainedModel(PreTrainedModel):
778
+ config_class = QuasarConfig
779
+ base_model_prefix = "model"
780
+ supports_gradient_checkpointing = True
781
+ _no_split_modules = ["QuasarDecoderLayer"]
782
+ _skip_keys_device_placement = "past_key_values"
783
+ _supports_flash_attn_2 = True
784
+ _supports_cache_class = True
785
+
786
+ def _init_weights(self, module):
787
+ std = self.config.initializer_range
788
+ if isinstance(module, nn.Linear):
789
+ module.weight.data.normal_(mean=0.0, std=std)
790
+ if module.bias is not None:
791
+ module.bias.data.zero_()
792
+ elif isinstance(module, nn.Embedding):
793
+ module.weight.data.normal_(mean=0.0, std=std)
794
+ if module.padding_idx is not None:
795
+ module.weight.data[module.padding_idx].zero_()
796
+
797
+
798
+ QUASAR_INPUTS_DOCSTRING = r"""
799
+ Args:
800
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
801
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
802
+ it.
803
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
804
+ [`PreTrainedTokenizer.__call__`] for details.
805
+ [What are input IDs?](../glossary#input-ids)
806
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
807
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
808
+ - 1 for tokens that are **not masked**,
809
+ - 0 for tokens that are **masked**.
810
+ [What are attention masks?](../glossary#attention-mask)
811
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
812
+ [`PreTrainedTokenizer.__call__`] for details.
813
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
814
+ `past_key_values`).
815
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
816
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
817
+ information on the default strategy.
818
+ - 1 indicates the head is **not masked**,
819
+ - 0 indicates the head is **masked**.
820
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
821
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
822
+ config.n_positions - 1]`.
823
+ [What are position IDs?](../glossary#position-ids)
824
+ past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
825
+ Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
826
+ blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values`
827
+ returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`.
828
+ Two formats are allowed:
829
+ - a [`~cache_utils.Cache`] instance;
830
+ - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
831
+ shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
832
+ cache format.
833
+ The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
834
+ legacy cache format will be returned.
835
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
836
+ have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
837
+ of shape `(batch_size, sequence_length)`.
838
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
839
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
840
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
841
+ model's internal embedding lookup matrix.
842
+ use_cache (`bool`, *optional*):
843
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
844
+ `past_key_values`).
845
+ output_attentions (`bool`, *optional*):
846
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
847
+ tensors for more detail.
848
+ output_hidden_states (`bool`, *optional*):
849
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
850
+ more detail.
851
+ return_dict (`bool`, *optional*):
852
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
853
+ """
854
+
855
+
856
+ @add_start_docstrings(
857
+ "The bare Quasar Model outputting raw hidden-states without any specific head on top.",
858
+ QUASAR_START_DOCSTRING,
859
+ )
860
+ class QuasarModel(QuasarPreTrainedModel):
861
+ """
862
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`QuasarDecoderLayer`]
863
+ Args:
864
+ config: QuasarConfig
865
+ """
866
+
867
+ def __init__(self, config: QuasarConfig):
868
+ super().__init__(config)
869
+ self.padding_idx = config.pad_token_id
870
+ self.vocab_size = config.vocab_size
871
+
872
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
873
+ self.embed_dropout = nn.Dropout(config.embd_pdrop)
874
+ self.layers = nn.ModuleList(
875
+ [QuasarDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
876
+ )
877
+
878
+ self.final_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
879
+ self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
880
+
881
+ self.gradient_checkpointing = False
882
+ # Initialize weights and apply final processing
883
+ self.duplicate_grad = config.duplicate_grad
884
+ self.duplicate_trick = config.duplicate_trick
885
+ self.layer_ranges = [tuple(inner_list) for inner_list in config.layer_ranges]
886
+ if self.duplicate_trick and not self.layer_ranges:
887
+ raise ValueError("When using the duplicate trick, `layer_ranges` must be specified.")
888
+
889
+ self.post_init()
890
+
891
+ def get_input_embeddings(self):
892
+ return self.embed_tokens
893
+
894
+ def set_input_embeddings(self, value):
895
+ self.embed_tokens = value
896
+
897
+ def set_layer_idx(self, layer_idx, layer):
898
+ def set_layer_idx_fn(module):
899
+ if hasattr(module, 'self_attn'):
900
+ if hasattr(module.self_attn, 'layer_idx'):
901
+ module.self_attn.layer_idx = layer_idx
902
+
903
+ layer.apply(set_layer_idx_fn)
904
+
905
+ @add_start_docstrings_to_model_forward(QUASAR_INPUTS_DOCSTRING)
906
+ def forward(
907
+ self,
908
+ input_ids: torch.LongTensor = None,
909
+ attention_mask: Optional[torch.Tensor] = None,
910
+ position_ids: Optional[torch.LongTensor] = None,
911
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
912
+ inputs_embeds: Optional[torch.FloatTensor] = None,
913
+ use_cache: Optional[bool] = None,
914
+ output_attentions: Optional[bool] = None,
915
+ output_hidden_states: Optional[bool] = None,
916
+ return_dict: Optional[bool] = None,
917
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
918
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
919
+ output_hidden_states = (
920
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
921
+ )
922
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
923
+
924
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
925
+
926
+ # retrieve input_ids and inputs_embeds
927
+ if input_ids is not None and inputs_embeds is not None:
928
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
929
+ elif input_ids is not None:
930
+ batch_size, seq_length = input_ids.shape[:2]
931
+ elif inputs_embeds is not None:
932
+ batch_size, seq_length = inputs_embeds.shape[:2]
933
+ else:
934
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
935
+
936
+ past_key_values_length = 0
937
+
938
+ if self.gradient_checkpointing and self.training:
939
+ if use_cache:
940
+ logger.warning_once(
941
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
942
+ )
943
+ use_cache = False
944
+
945
+ if use_cache:
946
+ use_legacy_cache = not isinstance(past_key_values, Cache)
947
+ if use_legacy_cache:
948
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
949
+ past_key_values_length = past_key_values.get_usable_length(seq_length)
950
+
951
+ if position_ids is None:
952
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
953
+ position_ids = torch.arange(
954
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
955
+ )
956
+ position_ids = position_ids.unsqueeze(0)
957
+
958
+ if inputs_embeds is None:
959
+ inputs_embeds = self.embed_tokens(input_ids)
960
+
961
+ inputs_embeds = self.embed_dropout(inputs_embeds)
962
+
963
+ # Attention mask.
964
+ if self._use_flash_attention_2:
965
+ # 2d mask is passed through the layers
966
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
967
+ else:
968
+ # 4d mask is passed through the layers
969
+ attention_mask = _prepare_4d_causal_attention_mask(
970
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length,
971
+ sliding_window=self.config.sliding_window
972
+ )
973
+
974
+ hidden_states = inputs_embeds
975
+
976
+ # decoder layers
977
+ all_hidden_states = () if output_hidden_states else None
978
+ all_self_attns = () if output_attentions else None
979
+ next_decoder_cache = None
980
+
981
+ # Modify the forward method to use the duplicate trick
982
+ if self.duplicate_trick:
983
+ sequential_layer_idx = 0
984
+ # Iterate over each specified range of layers
985
+ for idx, (start_layer, end_layer) in enumerate(self.layer_ranges):
986
+ # if the layers are the original layers or self.duplicate_grad is False
987
+ duplicate_backprop = True
988
+ if idx % 2 != 0 and not self.duplicate_grad:
989
+ # if the layers are the duplicate layers and self.duplicate_grad is False
990
+ duplicate_backprop = False
991
+
992
+ for layer_idx in range(start_layer, end_layer + 1):
993
+ decoder_layer = self.layers[layer_idx] # Access the specific layer
994
+ if self.training and not duplicate_backprop:
995
+ for param in decoder_layer.parameters():
996
+ param.requires_grad = duplicate_backprop
997
+ self.set_layer_idx(sequential_layer_idx, decoder_layer)
998
+ # The rest remains the same as the original loop
999
+ if output_hidden_states:
1000
+ all_hidden_states += (hidden_states,)
1001
+
1002
+ if self.gradient_checkpointing and self.training:
1003
+ layer_outputs = self._gradient_checkpointing_func(
1004
+ decoder_layer.__call__,
1005
+ hidden_states,
1006
+ attention_mask,
1007
+ position_ids,
1008
+ past_key_values,
1009
+ output_attentions,
1010
+ )
1011
+ else:
1012
+ layer_outputs = decoder_layer(
1013
+ hidden_states,
1014
+ attention_mask=attention_mask,
1015
+ position_ids=position_ids,
1016
+ past_key_value=past_key_values,
1017
+ output_attentions=output_attentions,
1018
+ use_cache=use_cache,
1019
+ )
1020
+
1021
+ hidden_states = layer_outputs[0]
1022
+
1023
+ if use_cache:
1024
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1025
+
1026
+ if output_attentions:
1027
+ all_self_attns += (layer_outputs[1],)
1028
+ sequential_layer_idx += 1
1029
+ else:
1030
+ for decoder_layer in self.layers:
1031
+ if output_hidden_states:
1032
+ all_hidden_states += (hidden_states,)
1033
+
1034
+ if self.gradient_checkpointing and self.training:
1035
+ layer_outputs = self._gradient_checkpointing_func(
1036
+ decoder_layer.__call__,
1037
+ hidden_states,
1038
+ attention_mask,
1039
+ position_ids,
1040
+ past_key_values,
1041
+ output_attentions,
1042
+ )
1043
+ else:
1044
+ layer_outputs = decoder_layer(
1045
+ hidden_states,
1046
+ attention_mask=attention_mask,
1047
+ position_ids=position_ids,
1048
+ past_key_value=past_key_values,
1049
+ output_attentions=output_attentions,
1050
+ use_cache=use_cache,
1051
+ )
1052
+
1053
+ hidden_states = layer_outputs[0]
1054
+
1055
+ if use_cache:
1056
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
1057
+
1058
+ if output_attentions:
1059
+ all_self_attns += (layer_outputs[1],)
1060
+
1061
+ hidden_states = self.final_layernorm(hidden_states)
1062
+
1063
+ # add hidden states from the last decoder layer
1064
+ if output_hidden_states:
1065
+ all_hidden_states += (hidden_states,)
1066
+
1067
+ next_cache = None
1068
+ if use_cache:
1069
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
1070
+ if not return_dict:
1071
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
1072
+ return BaseModelOutputWithPast(
1073
+ last_hidden_state=hidden_states,
1074
+ past_key_values=next_cache,
1075
+ hidden_states=all_hidden_states,
1076
+ attentions=all_self_attns,
1077
+ )
1078
+
1079
+
1080
+ class QuasarForCausalLM(QuasarPreTrainedModel):
1081
+ _tied_weights_keys = ["lm_head.weight"]
1082
+
1083
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.__init__ with Llama->Quasar,bias=False->bias=True
1084
+ def __init__(self, config):
1085
+ super().__init__(config)
1086
+ self.model = QuasarModel(config)
1087
+ self.vocab_size = config.vocab_size
1088
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=True)
1089
+
1090
+ # Initialize weights and apply final processing
1091
+ self.post_init()
1092
+ if config.remove_ff_bias:
1093
+ self.model = self.remove_ff_linear_bias()
1094
+
1095
+ def remove_ff_linear_bias(self):
1096
+ for layer in self.model.layers:
1097
+ if isinstance(layer, QuasarDecoderLayer):
1098
+ ff_layer = layer.mlp
1099
+ for module in ff_layer.modules():
1100
+ if isinstance(module, nn.Linear):
1101
+ module.bias = None
1102
+ return self.model
1103
+
1104
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_input_embeddings
1105
+ def get_input_embeddings(self):
1106
+ return self.model.embed_tokens
1107
+
1108
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_input_embeddings
1109
+ def set_input_embeddings(self, value):
1110
+ self.model.embed_tokens = value
1111
+
1112
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_output_embeddings
1113
+ def get_output_embeddings(self):
1114
+ return self.lm_head
1115
+
1116
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_output_embeddings
1117
+ def set_output_embeddings(self, new_embeddings):
1118
+ self.lm_head = new_embeddings
1119
+
1120
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.set_decoder
1121
+ def set_decoder(self, decoder):
1122
+ self.model = decoder
1123
+
1124
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.get_decoder
1125
+ def get_decoder(self):
1126
+ return self.model
1127
+
1128
+ @add_start_docstrings_to_model_forward(QUASAR_INPUTS_DOCSTRING)
1129
+ @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
1130
+ def forward(
1131
+ self,
1132
+ input_ids: torch.LongTensor = None,
1133
+ attention_mask: Optional[torch.Tensor] = None,
1134
+ position_ids: Optional[torch.LongTensor] = None,
1135
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1136
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1137
+ labels: Optional[torch.LongTensor] = None,
1138
+ use_cache: Optional[bool] = None,
1139
+ output_attentions: Optional[bool] = None,
1140
+ output_hidden_states: Optional[bool] = None,
1141
+ return_dict: Optional[bool] = None,
1142
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1143
+ r"""
1144
+ Args:
1145
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
1146
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
1147
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
1148
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
1149
+ Returns:
1150
+ Example:
1151
+ ```python
1152
+ >>> from transformers import AutoTokenizer, AutoModelForCausalLM
1153
+ >>> model = AutoModelForCausalLM.from_pretrained("AstraMindAI/AstraQuasar-4.5B", trust_remote_code=True)
1154
+ >>> tokenizer = AutoTokenizer.from_pretrained("AstraMindAI/AstraQuasar-4.5B")
1155
+ >>> # you can optionally disable the duplicate trick
1156
+ >>> # model.model.duplicate_trick = False
1157
+ >>> # You can also disable the duplicate gradient calculation
1158
+ >>> # model.model.duplicate_grad = False
1159
+ >>> # You can specify the layer ranges for the duplicate trick
1160
+ >>> # model.model.layer_ranges = [(0, 16),(8, 24),(17, 32),(25, 40),(33, 49),(40, 56)]
1161
+ >>> prompt = "This is an example script ."
1162
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
1163
+ >>> # Generate
1164
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
1165
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
1166
+ 'This is an example script .\n\n\n\nfrom typing import List\n\ndef find_most_common_letter(words: List[str'
1167
+ ```"""
1168
+
1169
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
1170
+ output_hidden_states = (
1171
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
1172
+ )
1173
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1174
+
1175
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
1176
+ outputs = self.model(
1177
+ input_ids=input_ids,
1178
+ attention_mask=attention_mask,
1179
+ position_ids=position_ids,
1180
+ past_key_values=past_key_values,
1181
+ inputs_embeds=inputs_embeds,
1182
+ use_cache=use_cache,
1183
+ output_attentions=output_attentions,
1184
+ output_hidden_states=output_hidden_states,
1185
+ return_dict=return_dict,
1186
+ )
1187
+
1188
+ hidden_states = outputs[0]
1189
+ logits = self.lm_head(hidden_states)
1190
+ logits = logits.float()
1191
+
1192
+ loss = None
1193
+ if labels is not None:
1194
+ # Shift so that tokens < n predict n
1195
+ shift_logits = logits[..., :-1, :].contiguous()
1196
+ shift_labels = labels[..., 1:].contiguous()
1197
+ # Flatten the tokens
1198
+ loss_fct = CrossEntropyLoss()
1199
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
1200
+ shift_labels = shift_labels.view(-1)
1201
+ # Enable model parallelism
1202
+ shift_labels = shift_labels.to(shift_logits.device)
1203
+ loss = loss_fct(shift_logits, shift_labels)
1204
+
1205
+ if not return_dict:
1206
+ output = (logits,) + outputs[1:]
1207
+ return (loss,) + output if loss is not None else output
1208
+
1209
+ return CausalLMOutputWithPast(
1210
+ loss=loss,
1211
+ logits=logits,
1212
+ past_key_values=outputs.past_key_values,
1213
+ hidden_states=outputs.hidden_states,
1214
+ attentions=outputs.attentions,
1215
+ )
1216
+
1217
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM.prepare_inputs_for_generation
1218
+ def prepare_inputs_for_generation(
1219
+ self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
1220
+ ):
1221
+ if past_key_values is not None:
1222
+ if isinstance(past_key_values, Cache):
1223
+ cache_length = past_key_values.get_seq_length()
1224
+ past_length = past_key_values.seen_tokens
1225
+ max_cache_length = past_key_values.get_max_length()
1226
+ else:
1227
+ cache_length = past_length = past_key_values[0][0].shape[2]
1228
+ max_cache_length = None
1229
+
1230
+ # Keep only the unprocessed tokens:
1231
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
1232
+ # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
1233
+ # input)
1234
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
1235
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):]
1236
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
1237
+ # input_ids based on the past_length.
1238
+ elif past_length < input_ids.shape[1]:
1239
+ input_ids = input_ids[:, past_length:]
1240
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
1241
+
1242
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
1243
+ if (
1244
+ max_cache_length is not None
1245
+ and attention_mask is not None
1246
+ and cache_length + input_ids.shape[1] > max_cache_length
1247
+ ):
1248
+ attention_mask = attention_mask[:, -max_cache_length:]
1249
+
1250
+ position_ids = kwargs.get("position_ids", None)
1251
+ if attention_mask is not None and position_ids is None:
1252
+ # create position_ids on the fly for batch generation
1253
+ position_ids = attention_mask.long().cumsum(-1) - 1
1254
+ position_ids.masked_fill_(attention_mask == 0, 1)
1255
+ if past_key_values:
1256
+ position_ids = position_ids[:, -input_ids.shape[1]:]
1257
+
1258
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
1259
+ if inputs_embeds is not None and past_key_values is None:
1260
+ model_inputs = {"inputs_embeds": inputs_embeds}
1261
+ else:
1262
+ model_inputs = {"input_ids": input_ids}
1263
+
1264
+ model_inputs.update(
1265
+ {
1266
+ "position_ids": position_ids,
1267
+ "past_key_values": past_key_values,
1268
+ "use_cache": kwargs.get("use_cache"),
1269
+ "attention_mask": attention_mask,
1270
+ }
1271
+ )
1272
+ return model_inputs
1273
+
1274
+ @staticmethod
1275
+ # Copied from transformers.models.llama.modeling_llama.LlamaForCausalLM._reorder_cache
1276
+ def _reorder_cache(past_key_values, beam_idx):
1277
+ reordered_past = ()
1278
+ for layer_past in past_key_values:
1279
+ reordered_past += (
1280
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
1281
+ )
1282
+ return reordered_past
1283
+
1284
+
1285
+ @add_start_docstrings(
1286
+ """
1287
+ The QuasarModel with a sequence classification head on top (linear layer).
1288
+ [`QuasarForSequenceClassification`] uses the last token in order to do the classification, as other causal models
1289
+ (e.g. GPT-2) do.
1290
+ Since it does classification on the last token, it requires to know the position of the last token. If a
1291
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
1292
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
1293
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
1294
+ each row of the batch).
1295
+ """,
1296
+ QUASAR_START_DOCSTRING,
1297
+ )
1298
+ # Copied from transformers.models.llama.modeling_llama.LlamaForSequenceClassification with LLAMA->QUASAR,Llama->Quasar with self.transformer->self.model, transformer_outputs->model_outputs
1299
+ class QuasarForSequenceClassification(QuasarPreTrainedModel):
1300
+ def __init__(self, config):
1301
+ super().__init__(config)
1302
+ self.num_labels = config.num_labels
1303
+ self.model = QuasarModel(config)
1304
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
1305
+
1306
+ # Initialize weights and apply final processing
1307
+ self.post_init()
1308
+
1309
+ def get_input_embeddings(self):
1310
+ return self.model.embed_tokens
1311
+
1312
+ def set_input_embeddings(self, value):
1313
+ self.model.embed_tokens = value
1314
+
1315
+ @add_start_docstrings_to_model_forward(QUASAR_INPUTS_DOCSTRING)
1316
+ def forward(
1317
+ self,
1318
+ input_ids: torch.LongTensor = None,
1319
+ attention_mask: Optional[torch.Tensor] = None,
1320
+ position_ids: Optional[torch.LongTensor] = None,
1321
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
1322
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1323
+ labels: Optional[torch.LongTensor] = None,
1324
+ use_cache: Optional[bool] = None,
1325
+ output_attentions: Optional[bool] = None,
1326
+ output_hidden_states: Optional[bool] = None,
1327
+ return_dict: Optional[bool] = None,
1328
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
1329
+ r"""
1330
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1331
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1332
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1333
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1334
+ """
1335
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1336
+
1337
+ model_outputs = self.model(
1338
+ input_ids,
1339
+ attention_mask=attention_mask,
1340
+ position_ids=position_ids,
1341
+ past_key_values=past_key_values,
1342
+ inputs_embeds=inputs_embeds,
1343
+ use_cache=use_cache,
1344
+ output_attentions=output_attentions,
1345
+ output_hidden_states=output_hidden_states,
1346
+ return_dict=return_dict,
1347
+ )
1348
+ hidden_states = model_outputs[0]
1349
+ logits = self.score(hidden_states)
1350
+
1351
+ if input_ids is not None:
1352
+ batch_size = input_ids.shape[0]
1353
+ else:
1354
+ batch_size = inputs_embeds.shape[0]
1355
+
1356
+ if self.config.pad_token_id is None and batch_size != 1:
1357
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
1358
+ if self.config.pad_token_id is None:
1359
+ sequence_lengths = -1
1360
+ else:
1361
+ if input_ids is not None:
1362
+ # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
1363
+ sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
1364
+ sequence_lengths = sequence_lengths % input_ids.shape[-1]
1365
+ sequence_lengths = sequence_lengths.to(logits.device)
1366
+ else:
1367
+ sequence_lengths = -1
1368
+
1369
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
1370
+
1371
+ loss = None
1372
+ if labels is not None:
1373
+ labels = labels.to(logits.device)
1374
+ if self.config.problem_type is None:
1375
+ if self.num_labels == 1:
1376
+ self.config.problem_type = "regression"
1377
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1378
+ self.config.problem_type = "single_label_classification"
1379
+ else:
1380
+ self.config.problem_type = "multi_label_classification"
1381
+
1382
+ if self.config.problem_type == "regression":
1383
+ loss_fct = MSELoss()
1384
+ if self.num_labels == 1:
1385
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
1386
+ else:
1387
+ loss = loss_fct(pooled_logits, labels)
1388
+ elif self.config.problem_type == "single_label_classification":
1389
+ loss_fct = CrossEntropyLoss()
1390
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
1391
+ elif self.config.problem_type == "multi_label_classification":
1392
+ loss_fct = BCEWithLogitsLoss()
1393
+ loss = loss_fct(pooled_logits, labels)
1394
+ if not return_dict:
1395
+ output = (pooled_logits,) + model_outputs[1:]
1396
+ return ((loss,) + output) if loss is not None else output
1397
+
1398
+ return SequenceClassifierOutputWithPast(
1399
+ loss=loss,
1400
+ logits=pooled_logits,
1401
+ past_key_values=model_outputs.past_key_values,
1402
+ hidden_states=model_outputs.hidden_states,
1403
+ attentions=model_outputs.attentions,
1404
+ )
1405
+
1406
+
1407
+ @add_start_docstrings(
1408
+ """
1409
+ QuasarModel with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for
1410
+ Named-Entity-Recognition (NER) tasks.
1411
+ """,
1412
+ QUASAR_START_DOCSTRING,
1413
+ )
1414
+ # Copied from transformers.models.mpt.modeling_mpt.MptForTokenClassification with MPT->QUASAR,Mpt->Quasar,self.transformer->self.model,transformer_outputs->model_outputs
1415
+ class QuasarForTokenClassification(QuasarPreTrainedModel):
1416
+ def __init__(self, config: QuasarConfig):
1417
+ super().__init__(config)
1418
+ self.num_labels = config.num_labels
1419
+
1420
+ self.model = QuasarModel(config)
1421
+ if hasattr(config, "classifier_dropout") and config.classifier_dropout is not None:
1422
+ classifier_dropout = config.classifier_dropout
1423
+ elif hasattr(config, "hidden_dropout") and config.hidden_dropout is not None:
1424
+ classifier_dropout = config.hidden_dropout
1425
+ else:
1426
+ classifier_dropout = 0.1
1427
+ self.dropout = nn.Dropout(classifier_dropout)
1428
+ self.classifier = nn.Linear(config.hidden_size, config.num_labels)
1429
+
1430
+ # Initialize weights and apply final processing
1431
+ self.post_init()
1432
+
1433
+ @add_start_docstrings_to_model_forward(QUASAR_INPUTS_DOCSTRING)
1434
+ @add_code_sample_docstrings(
1435
+ checkpoint=_CHECKPOINT_FOR_DOC,
1436
+ output_type=TokenClassifierOutput,
1437
+ config_class=_CONFIG_FOR_DOC,
1438
+ )
1439
+ def forward(
1440
+ self,
1441
+ input_ids: Optional[torch.LongTensor] = None,
1442
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
1443
+ attention_mask: Optional[torch.Tensor] = None,
1444
+ inputs_embeds: Optional[torch.Tensor] = None,
1445
+ labels: Optional[torch.Tensor] = None,
1446
+ use_cache: Optional[bool] = None,
1447
+ output_attentions: Optional[bool] = None,
1448
+ output_hidden_states: Optional[bool] = None,
1449
+ return_dict: Optional[bool] = None,
1450
+ **deprecated_arguments,
1451
+ ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
1452
+ r"""
1453
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
1454
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
1455
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
1456
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
1457
+ """
1458
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1459
+
1460
+ model_outputs = self.model(
1461
+ input_ids,
1462
+ past_key_values=past_key_values,
1463
+ attention_mask=attention_mask,
1464
+ inputs_embeds=inputs_embeds,
1465
+ use_cache=use_cache,
1466
+ output_attentions=output_attentions,
1467
+ output_hidden_states=output_hidden_states,
1468
+ return_dict=return_dict,
1469
+ )
1470
+
1471
+ hidden_states = model_outputs[0]
1472
+ hidden_states = self.dropout(hidden_states)
1473
+ logits = self.classifier(hidden_states)
1474
+
1475
+ loss = None
1476
+ if labels is not None:
1477
+ # move labels to correct device to enable model parallelism
1478
+ labels = labels.to(logits.device)
1479
+ batch_size, seq_length = labels.shape
1480
+ loss_fct = CrossEntropyLoss()
1481
+ loss = loss_fct(
1482
+ logits.view(batch_size * seq_length, self.num_labels), labels.view(batch_size * seq_length)
1483
+ )
1484
+
1485
+ if not return_dict:
1486
+ output = (logits,) + model_outputs[2:]
1487
+ return ((loss,) + output) if loss is not None else output
1488
+
1489
+ return TokenClassifierOutput(
1490
+ loss=loss,
1491
+ logits=logits,
1492
+ hidden_states=model_outputs.hidden_states,
1493
+ attentions=model_outputs.attentions,
1494
+ )
special_tokens_map.json ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "bos_token": {
3
+ "content": "<|endoftext|>",
4
+ "lstrip": false,
5
+ "normalized": false,
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+ "rstrip": false,
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+ "single_word": false
8
+ },
9
+ "eos_token": {
10
+ "content": "<|endoftext|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
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+ "single_word": false
15
+ },
16
+ "unk_token": {
17
+ "content": "<|endoftext|>",
18
+ "lstrip": false,
19
+ "normalized": false,
20
+ "rstrip": false,
21
+ "single_word": false
22
+ }
23
+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer_config.json ADDED
@@ -0,0 +1,323 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ "bos_token": "<|endoftext|>",
318
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319
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320
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321
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322
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323
+ }
vocab.json ADDED
The diff for this file is too large to render. See raw diff