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README.md ADDED
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+ ---
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+ thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
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+ base_model: apple/OpenELM-270M-Instruct
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+ metrics:
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+ - memory_disk
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+ - memory_inference
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+ - inference_latency
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+ - inference_throughput
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+ - inference_CO2_emissions
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+ - inference_energy_consumption
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+ tags:
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+ - pruna-ai
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+ ---
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+ <!-- header start -->
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+ <!-- 200823 -->
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+ <div style="width: auto; margin-left: auto; margin-right: auto">
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+ <a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
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+ <img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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+ </a>
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+ </div>
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+ <!-- header end -->
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+
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+ [![Twitter](https://img.shields.io/twitter/follow/PrunaAI?style=social)](https://twitter.com/PrunaAI)
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+ [![GitHub](https://img.shields.io/github/followers/PrunaAI?label=Follow%20%40PrunaAI&style=social)](https://github.com/PrunaAI)
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+ [![LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue)](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
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+ [![Discord](https://img.shields.io/badge/Discord-Join%20Us-blue?style=social&logo=discord)](https://discord.gg/rskEr4BZJx)
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+
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+ # Simply make AI models cheaper, smaller, faster, and greener!
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+
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+ - Give a thumbs up if you like this model!
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+ - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
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+ - Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
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+ - Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
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+ - Join Pruna AI community on Discord [here](https://discord.gg/CP4VSgck) to share feedback/suggestions or get help.
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+
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+ ## Results
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+
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+ ![image info](./plots.png)
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+
40
+ **Frequently Asked Questions**
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+ - ***How does the compression work?*** The model is compressed with llm-int8.
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+ - ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
43
+ - ***How is the model efficiency evaluated?*** These results were obtained on HARDWARE_NAME with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
44
+ - ***What is the model format?*** We use safetensors.
45
+ - ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
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+ - ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
47
+ - ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
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+ - ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
49
+ - ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
50
+
51
+ ## Setup
52
+
53
+ You can run the smashed model with these steps:
54
+
55
+ 0. Check requirements from the original repo apple/OpenELM-270M-Instruct installed. In particular, check python, cuda, and transformers versions.
56
+ 1. Make sure that you have installed quantization related packages.
57
+ ```bash
58
+ pip install transformers accelerate bitsandbytes>0.37.0
59
+ ```
60
+ 2. Load & run the model.
61
+ ```python
62
+ from transformers import AutoModelForCausalLM, AutoTokenizer
63
+
64
+
65
+ model = AutoModelForCausalLM.from_pretrained("PrunaAI/apple-OpenELM-270M-Instruct-bnb-4bit-smashed", trust_remote_code=True, device_map='auto')
66
+ tokenizer = AutoTokenizer.from_pretrained("apple/OpenELM-270M-Instruct")
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+
68
+ input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
69
+
70
+ outputs = model.generate(input_ids, max_new_tokens=216)
71
+ tokenizer.decode(outputs[0])
72
+ ```
73
+
74
+ ## Configurations
75
+
76
+ The configuration info are in `smash_config.json`.
77
+
78
+ ## Credits & License
79
+
80
+ The license of the smashed model follows the license of the original model. Please check the license of the original model apple/OpenELM-270M-Instruct before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
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+
82
+ ## Want to compress other models?
83
+
84
+ - Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
85
+ - Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
config.json ADDED
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+ {
2
+ "_name_or_path": "/ceph/hdd/staff/charpent/.cache/modelserdl44_47u4rvkl1",
3
+ "activation_fn_name": "swish",
4
+ "architectures": [
5
+ "OpenELMForCausalLM"
6
+ ],
7
+ "auto_map": {
8
+ "AutoConfig": "configuration_openelm.OpenELMConfig",
9
+ "AutoModelForCausalLM": "modeling_openelm.OpenELMForCausalLM"
10
+ },
11
+ "bos_token_id": 1,
12
+ "eos_token_id": 2,
13
+ "ffn_dim_divisor": 256,
14
+ "ffn_multipliers": [
15
+ 0.5,
16
+ 0.73,
17
+ 0.97,
18
+ 1.2,
19
+ 1.43,
20
+ 1.67,
21
+ 1.9,
22
+ 2.13,
23
+ 2.37,
24
+ 2.6,
25
+ 2.83,
26
+ 3.07,
27
+ 3.3,
28
+ 3.53,
29
+ 3.77,
30
+ 4.0
31
+ ],
32
+ "ffn_with_glu": true,
33
+ "head_dim": 64,
34
+ "initializer_range": 0.02,
35
+ "max_context_length": 2048,
36
+ "model_dim": 1280,
37
+ "model_type": "openelm",
38
+ "normalization_layer_name": "rms_norm",
39
+ "normalize_qk_projections": true,
40
+ "num_gqa_groups": 4,
41
+ "num_kv_heads": [
42
+ 3,
43
+ 3,
44
+ 3,
45
+ 3,
46
+ 3,
47
+ 4,
48
+ 4,
49
+ 4,
50
+ 4,
51
+ 4,
52
+ 4,
53
+ 4,
54
+ 5,
55
+ 5,
56
+ 5,
57
+ 5
58
+ ],
59
+ "num_query_heads": [
60
+ 12,
61
+ 12,
62
+ 12,
63
+ 12,
64
+ 12,
65
+ 16,
66
+ 16,
67
+ 16,
68
+ 16,
69
+ 16,
70
+ 16,
71
+ 16,
72
+ 20,
73
+ 20,
74
+ 20,
75
+ 20
76
+ ],
77
+ "num_transformer_layers": 16,
78
+ "qkv_multipliers": [
79
+ 0.5,
80
+ 1.0
81
+ ],
82
+ "quantization_config": {
83
+ "_load_in_4bit": true,
84
+ "_load_in_8bit": false,
85
+ "bnb_4bit_compute_dtype": "bfloat16",
86
+ "bnb_4bit_quant_storage": "uint8",
87
+ "bnb_4bit_quant_type": "fp4",
88
+ "bnb_4bit_use_double_quant": false,
89
+ "llm_int8_enable_fp32_cpu_offload": false,
90
+ "llm_int8_has_fp16_weight": false,
91
+ "llm_int8_skip_modules": [
92
+ "lm_head"
93
+ ],
94
+ "llm_int8_threshold": 6.0,
95
+ "load_in_4bit": true,
96
+ "load_in_8bit": false,
97
+ "quant_method": "bitsandbytes"
98
+ },
99
+ "rope_freq_constant": 10000,
100
+ "rope_max_length": 4096,
101
+ "share_input_output_layers": true,
102
+ "torch_dtype": "float16",
103
+ "transformers_version": "4.41.2",
104
+ "use_cache": true,
105
+ "vocab_size": 32000
106
+ }
configuration_openelm.py ADDED
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+ #
2
+ # For licensing see accompanying LICENSE file.
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+ # Copyright (C) 2024 Apple Inc. All Rights Reserved.
4
+ #
5
+
6
+ """Implements HF OpenELMConfig based on PretrainedConfig"""
7
+ from numbers import Number
8
+ from typing import List, Optional, Union
9
+
10
+ import numpy as np
11
+ from transformers import PretrainedConfig
12
+
13
+
14
+ def make_divisible(
15
+ v: Union[float, int],
16
+ divisor: Optional[int] = 8,
17
+ min_value: Optional[Union[float, int]] = None,
18
+ ) -> Union[float, int]:
19
+ """
20
+ This function is taken from the original tf repo.
21
+ It ensures that all layers have a channel number that is divisible by the divisor
22
+ It can be seen at:
23
+ https://github.com/tensorflow/models/blob/2cfc99eff5e5eb729c6793d2f3d03aa1c9be2b15/research/slim/nets/mobilenet/mobilenet.py#L62
24
+
25
+ Args:
26
+ v: input value
27
+ divisor: default to 8
28
+ min_value: minimum divisor value
29
+ Returns:
30
+ new_v: new divisible value
31
+ """
32
+ if min_value is None:
33
+ min_value = divisor
34
+ new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
35
+ # Make sure that round down does not go down by more than 10%.
36
+ if new_v < 0.9 * v:
37
+ new_v += divisor
38
+ return new_v
39
+
40
+
41
+ def compute_heads(model_dim: int, head_dim: int) -> int:
42
+ """Compute the number of heads.
43
+
44
+ Args:
45
+ model_dim: Model dimension.
46
+ head_dim: Head dimension.
47
+
48
+ Returns:
49
+ An integer denoting number of heads in multi-head attention is returned.
50
+
51
+ Raises:
52
+ ValueError: if model dimension is not divisible by head dimension.
53
+ """
54
+ if model_dim % head_dim == 0:
55
+ return model_dim // head_dim
56
+ else:
57
+ raise ValueError(
58
+ f"Model dimension should be divisible by head dimension. Got: {model_dim} and {head_dim}."
59
+ )
60
+
61
+
62
+ OpenELM_CONFIGS = {
63
+ "OpenELM-270M": dict(
64
+ num_transformer_layers=16,
65
+ model_dim=1280,
66
+ head_dim=64,
67
+ num_gqa_groups=4,
68
+ normalize_qk_projections=True,
69
+ share_input_output_layers=True,
70
+ # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
71
+ ffn_multipliers=(0.5, 4.0),
72
+ qkv_multipliers=(0.5, 1.0),
73
+ ),
74
+ "OpenELM-450M": dict(
75
+ num_transformer_layers=20,
76
+ model_dim=1536,
77
+ head_dim=64,
78
+ num_gqa_groups=4,
79
+ normalize_qk_projections=True,
80
+ share_input_output_layers=True,
81
+ # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
82
+ ffn_multipliers=(0.5, 4.0),
83
+ qkv_multipliers=(0.5, 1.0),
84
+ ),
85
+ "OpenELM-1_1B": dict(
86
+ num_transformer_layers=28,
87
+ model_dim=2048,
88
+ head_dim=64,
89
+ num_gqa_groups=4,
90
+ normalize_qk_projections=True,
91
+ share_input_output_layers=True,
92
+ # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
93
+ ffn_multipliers=(0.5, 4.0),
94
+ qkv_multipliers=(0.5, 1.0),
95
+ ),
96
+ "OpenELM-3B": dict(
97
+ num_transformer_layers=36,
98
+ model_dim=3072,
99
+ head_dim=128,
100
+ num_gqa_groups=4,
101
+ normalize_qk_projections=True,
102
+ share_input_output_layers=True,
103
+ # Vary the FFN and QKV multipliers to create variable FFN and attention layers respectively.
104
+ ffn_multipliers=(0.5, 4.0),
105
+ qkv_multipliers=(0.5, 1.0),
106
+ ),
107
+ }
108
+
109
+
110
+ class OpenELMConfig(PretrainedConfig):
111
+ r"""
112
+ This is the configuration class to store the configuration of a [`OpenELMModel`]. It is used to instantiate an OpenELM model according to the specified arguments, defining the model architecture.
113
+
114
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
115
+ documentation from [`PretrainedConfig`] for more information.
116
+
117
+ Args:
118
+ vocab_size (`int`, *optional*, defaults to 32000):
119
+ Vocabulary size of the OpenELM model.
120
+ max_context_length (`int`, *optional*, defaults to 2048):
121
+ Maximum number of input tokens.
122
+ num_transformer_layers (`int`, *optional*, defaults to 12):
123
+ Number of hidden layers in the Transformer decoder.
124
+ model_dim (`int`, *optional*, defaults to 2048):
125
+ Dimension of the hidden representations.
126
+ head_dim (`int`, *optional*, defaults to 128):
127
+ The attention head dimension.
128
+ qkv_multipliers (`Union[Number, List[Number]]`, *optional*, defaults to 1.0):
129
+ If the qkv_multipliers is a Number, then all attention layers have the same latent dimensions,
130
+ resulting in uniform allocation of parameters.
131
+ If the qkv_multipliers is a List of Number, then each attention layer have different latent dimensions
132
+ assuming qkv_multipliers[0] != qkv_multipliers[1]. This results in variable allocation of parameters in attention layer.
133
+ This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
134
+ num_query_heads (`Union[int, None]`, *optional*, defaults to None):
135
+ The number of query heads, computed from `compute_heads(model_dim=model_dim, head_dim=head_dim)`.
136
+ num_gqa_groups (`int`, *optional*, defaults to 1):
137
+ This variable allows to switch between multi-head attention, group query attention, and multi-query attention.
138
+ When num_gqa_groups == 1, then it is multi-head attention.
139
+ When 1 < num_gqa_groups < num_heads and num_heads is divisible by num_gqa_groups, then it is group query attention
140
+ When num_gqa_groups == num_heads, then it is multi-query attention
141
+ ffn_multipliers (`Union[Number, List[Number]]`, *optional*, defaults to 4.0):
142
+ Feed-forward network (FFN) multipliers.
143
+ If the ffn_multipliers is a Number, then all FFN layers have the same latent dimensions,
144
+ resulting in uniform allocation of parameters.
145
+ If the ffn_multipliers is a List of Number, then each FFN layer have different latent dimensions
146
+ assuming ffn_multipliers[0] != ffn_multipliers[1]. This results in variable allocation of parameters in FFN layer.
147
+ This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
148
+ ffn_with_glu (`bool`, *optional*, defaults to True):
149
+ Whether to use FFN with Gated Linear Unit (GLU)
150
+ ffn_dim_divisor (`int`, *optional*, defaults to 256):
151
+ The ffn layer dimension divisor.
152
+ activation_fn_name (`str` or `function`, *optional*, defaults to `"swish"`):
153
+ The non-linear activation function (function or string) in the decoder.
154
+ normalization_layer_name (`str` or `function`, *optional*, defaults to `"rms_norm"`):
155
+ Type of normalization layer.
156
+ normalize_qk_projections (`bool`, *optional*, defaults to False):
157
+ Whether to normalize queries and keys after projections
158
+ share_input_output_layers (`bool`, *optional*, defaults to False):
159
+ Whether to share the embedding between input and output linear layer
160
+ rope_freq_constant (`int`, *optional*, defaults to 10000):
161
+ The base period of the RoPE embeddings.
162
+ rope_max_length (`int`, *optional*, defaults to 4096):
163
+ That rope_max_length is set to twice of max_context_length.
164
+ This allows flexibility in token lengths during training or fine-tuning.
165
+ initializer_range (`float`, *optional*, defaults to 0.02):
166
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
167
+ use_cache (`bool`, *optional*, defaults to `True`):
168
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
169
+ relevant if `config.is_decoder=True`.
170
+ bos_token_id (`int`, *optional*, defaults to 2):
171
+ Beginning of stream token id.
172
+ eos_token_id (`int`, *optional*, defaults to 1):
173
+ End of stream token id.
174
+ """
175
+
176
+ model_type = "openelm"
177
+
178
+ def __init__(
179
+ self,
180
+ vocab_size: int = 32000,
181
+ max_context_length: int = 2048,
182
+ num_transformer_layers: int = 12,
183
+ model_dim: int = 2048,
184
+ head_dim: int = 128,
185
+ qkv_multipliers: Union[Number, List[Number]] = 1.0,
186
+ num_query_heads: Union[int, None] = None,
187
+ num_gqa_groups: int = 1,
188
+ ffn_multipliers: Union[Number, List[Number]] = 4.0,
189
+ ffn_with_glu: bool = True,
190
+ ffn_dim_divisor: int = 256,
191
+ activation_fn_name: str = "swish",
192
+ normalization_layer_name: str = "rms_norm",
193
+ normalize_qk_projections: bool = False,
194
+ share_input_output_layers: bool = False,
195
+ rope_freq_constant: int = 10000,
196
+ rope_max_length: int = 4096,
197
+ initializer_range: float = 0.02,
198
+ use_cache: bool = True,
199
+ bos_token_id: int = 1,
200
+ eos_token_id: int = 2,
201
+ **kwargs,
202
+ ) -> None:
203
+ self.vocab_size = vocab_size
204
+ self.max_context_length = max_context_length
205
+ self.num_transformer_layers = num_transformer_layers
206
+ self.model_dim = model_dim
207
+ self.head_dim = head_dim
208
+ self.qkv_multipliers = qkv_multipliers
209
+ self.num_query_heads = num_query_heads
210
+ self.num_gqa_groups = num_gqa_groups
211
+ self.ffn_multipliers = ffn_multipliers
212
+ self.ffn_with_glu = ffn_with_glu
213
+ self.ffn_dim_divisor = ffn_dim_divisor
214
+ self.activation_fn_name = activation_fn_name
215
+ self.normalization_layer_name = normalization_layer_name
216
+ self.normalize_qk_projections = normalize_qk_projections
217
+ self.share_input_output_layers = share_input_output_layers
218
+ self.rope_freq_constant = rope_freq_constant
219
+ self.rope_max_length = rope_max_length
220
+ self.num_query_heads = (
221
+ compute_heads(model_dim=model_dim, head_dim=head_dim)
222
+ if num_query_heads is None
223
+ else num_query_heads
224
+ )
225
+ self.initializer_range = initializer_range
226
+
227
+ self.__post_init__()
228
+ super().__init__(
229
+ use_cache=use_cache,
230
+ bos_token_id=bos_token_id,
231
+ eos_token_id=eos_token_id,
232
+ **kwargs,
233
+ )
234
+
235
+ def __post_init__(self) -> None:
236
+ if self.num_gqa_groups is not None:
237
+ head_multiple_of = self.num_gqa_groups
238
+ else:
239
+ head_multiple_of = 2
240
+
241
+ if isinstance(self.qkv_multipliers, Number):
242
+ # All attention layers have the same latent dimensions, resulting in uniform allocation of parameters.
243
+ qkv_dim = make_divisible(
244
+ self.model_dim * self.qkv_multipliers,
245
+ divisor=self.head_dim * head_multiple_of,
246
+ )
247
+ query_dims = [int(qkv_dim)] * self.num_transformer_layers
248
+
249
+ elif (
250
+ isinstance(self.qkv_multipliers, (tuple, list))
251
+ and len(self.qkv_multipliers) == 2
252
+ ):
253
+ # Each attention layer have different latent dimensions assuming qkv_multipliers[0] != qkv_multipliers[1].
254
+ # This results in variable allocation of parameters in attention layer.
255
+ # This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
256
+ qkv_multipliers = [
257
+ round(v, 2)
258
+ for v in np.linspace(
259
+ self.qkv_multipliers[0],
260
+ self.qkv_multipliers[1],
261
+ num=self.num_transformer_layers,
262
+ dtype=float,
263
+ )
264
+ ]
265
+ # Make sure that scaled model dimension is divisible by scaled head dimension.
266
+ query_dims = [
267
+ int(
268
+ make_divisible(
269
+ self.model_dim * m, divisor=self.head_dim * head_multiple_of
270
+ )
271
+ )
272
+ for m in qkv_multipliers
273
+ ]
274
+ else:
275
+ raise NotImplementedError(
276
+ f"QKV multipliers should be a single number or a list containing exactly two numbers. Got: {qkv_multipliers}."
277
+ )
278
+
279
+ # compute the number of query, key, and value heads
280
+ # For multi-head and multi-query attention, the number of heads for query, key, and value are the same.
281
+ # For group query attention, the number of key and value heads are the same.
282
+ self.num_query_heads = [
283
+ int(compute_heads(q_dim, self.head_dim)) for q_dim in query_dims
284
+ ]
285
+ self.num_kv_heads = [
286
+ q_heads // self.num_gqa_groups for q_heads in self.num_query_heads
287
+ ]
288
+
289
+ # Feed-forward network (FFN) multipliers
290
+ if isinstance(self.ffn_multipliers, Number):
291
+ # All FFN layers have the same latent dimensions, resulting in uniform allocation of parameters.
292
+ self.ffn_multipliers = [self.ffn_multipliers] * self.num_transformer_layers
293
+ elif isinstance(self.ffn_multipliers, (tuple, list)):
294
+ # Each FFN layer have different latent dimensions assuming ffn_multipliers[0] != ffn_multipliers[1].
295
+ # This results in variable allocation of parameters in FFN layer.
296
+ # This scaling is known as layer-wise or block-wise scaling: https://arxiv.org/abs/2008.00623
297
+ if len(self.ffn_multipliers) == 2:
298
+ self.ffn_multipliers = [
299
+ round(v, 2)
300
+ for v in np.linspace(
301
+ self.ffn_multipliers[0],
302
+ self.ffn_multipliers[1],
303
+ num=self.num_transformer_layers,
304
+ dtype=float,
305
+ )
306
+ ]
307
+ else:
308
+ assert (
309
+ len(self.ffn_multipliers) == self.num_transformer_layers
310
+ ), f"{len(self.ffn_multipliers)=}!={self.num_transformer_layers=}"
311
+ else:
312
+ raise NotImplementedError(
313
+ f"FFN multipliers should be a single number or a list containing exactly two numbers. Got: {qkv_multipliers}."
314
+ )
315
+
316
+ # check num_query_heads divisible by num_kv_heads for every layer
317
+ for layer_idx in range(len(query_dims)):
318
+ assert self.num_query_heads[layer_idx] % self.num_kv_heads[layer_idx] == 0
generation_config.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "bos_token_id": 1,
4
+ "eos_token_id": 2,
5
+ "transformers_version": "4.41.2"
6
+ }
model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a8ae343d178c20b144587c51c6bd2a595cec680a1ffdbc05724edc4db838c791
3
+ size 211724875
modeling_openelm.py ADDED
@@ -0,0 +1,1008 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #
2
+ # For licensing see accompanying LICENSE file.
3
+ # Copyright (C) 2024 Apple Inc. All Rights Reserved.
4
+ #
5
+
6
+ from typing import List, Optional, Tuple, Union
7
+
8
+ import torch
9
+ import torch.utils.checkpoint
10
+ from torch import Tensor, nn
11
+ from torch.nn import CrossEntropyLoss
12
+ from torch.nn import functional as F
13
+ from transformers import PreTrainedModel
14
+ from transformers.activations import ACT2FN
15
+ from transformers.cache_utils import Cache, DynamicCache, StaticCache
16
+ from transformers.modeling_outputs import (
17
+ BaseModelOutputWithPast,
18
+ CausalLMOutputWithPast,
19
+ )
20
+ from transformers.utils import logging
21
+
22
+ logger = logging.get_logger(__name__)
23
+
24
+ # this import has to be relative, otherwise, when setting trust_remote_code=True
25
+ # huggingface transformers won't be able to load the module correctly
26
+ from .configuration_openelm import OpenELMConfig, make_divisible
27
+
28
+
29
+ class OpenELMRMSNorm(nn.Module):
30
+ def __init__(self, num_features: int, eps: float = 1e-6):
31
+ """
32
+ Initialize the OpenELMRMSNorm normalization layer.
33
+
34
+ Args:
35
+ dim (int): The dimension of the input tensor.
36
+ eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
37
+
38
+ Attributes:
39
+ eps (float): A small value added to the denominator for numerical stability.
40
+ weight (nn.Parameter): Learnable scaling parameter.
41
+
42
+ """
43
+ super().__init__()
44
+ self.eps = eps
45
+ self.weight = nn.Parameter(torch.ones(num_features))
46
+ self.num_features = num_features
47
+
48
+ def _norm(self, x: Tensor) -> Tensor:
49
+ """
50
+ Apply the OpenELMRMSNorm normalization to the input tensor.
51
+
52
+ Args:
53
+ x (torch.Tensor): The input tensor.
54
+
55
+ Returns:
56
+ torch.Tensor: The normalized tensor.
57
+
58
+ """
59
+ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
60
+
61
+ def forward(self, x: Tensor) -> Tensor:
62
+ """
63
+ Forward pass through the OpenELMRMSNorm layer.
64
+
65
+ Args:
66
+ x (torch.Tensor): The input tensor.
67
+
68
+ Returns:
69
+ torch.Tensor: The output tensor after applying OpenELMRMSNorm.
70
+
71
+ """
72
+ output = self._norm(x.float()).type_as(x)
73
+ return output * self.weight
74
+
75
+ def extra_repr(self) -> str:
76
+ return (
77
+ super().extra_repr() + f"num_features={self.num_features}, eps={self.eps}"
78
+ )
79
+
80
+
81
+ class OpenELMPreTrainedModel(PreTrainedModel):
82
+ config_class = OpenELMConfig
83
+ base_model_prefix = "transformer"
84
+ supports_gradient_checkpointing = True
85
+ _no_split_modules = ["OpenELMDecoderLayer"]
86
+ _skip_keys_device_placement = "past_key_values"
87
+
88
+ def __init__(self, *inputs, **kwargs) -> None:
89
+ super().__init__(*inputs, **kwargs)
90
+
91
+ def _init_weights(self, module: nn.Module) -> None:
92
+ """Initialize the weights."""
93
+ if isinstance(module, nn.Linear):
94
+ # Slightly different from the TF version which uses truncated_normal for initialization
95
+ # cf https://github.com/pytorch/pytorch/pull/5617
96
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
97
+ if module.bias is not None:
98
+ module.bias.data.zero_()
99
+ elif isinstance(module, nn.Embedding):
100
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
101
+ if module.padding_idx is not None:
102
+ module.weight.data[module.padding_idx].zero_()
103
+ elif isinstance(module, OpenELMRMSNorm):
104
+ module.weight.data.fill_(1.0)
105
+
106
+
107
+ def _rotate_half(x: Tensor) -> Tensor:
108
+ x1, x2 = x.chunk(2, dim=-1)
109
+ return torch.cat((-x2, x1), dim=-1)
110
+
111
+
112
+ def _apply_rotary_pos_emb(x: Tensor, pos_sin: Tensor, pos_cos: Tensor) -> Tensor:
113
+ return (x * pos_cos) + (_rotate_half(x) * pos_sin)
114
+
115
+
116
+ class OpenELMRotaryEmbedding(torch.nn.Module):
117
+ """
118
+ The rotary position embeddings (aka RoPE) from `RoFormer <https://arxiv.org/abs/2104.09864>`_.
119
+
120
+ RoPE encodes the position information of tokens using a rotation matrix, and is able to capture
121
+ explicit relative positional dependencies.
122
+
123
+ Args:
124
+ model_dim: The dimensionality of the model's hidden state.
125
+ max_seq_length: Maximum sequence length.
126
+ freq_constant: A constant used for computing frequencies.
127
+ """
128
+
129
+ def __init__(
130
+ self, model_dim: int, max_seq_length: int, freq_constant: int = 10000
131
+ ) -> None:
132
+ inv_freq = 1.0 / (
133
+ freq_constant
134
+ ** (torch.arange(0, model_dim, 2, dtype=torch.float32) / model_dim)
135
+ )
136
+ super().__init__()
137
+
138
+ self.model_dim = model_dim
139
+ self.freq_constant = freq_constant
140
+ self.max_seq_length = max_seq_length
141
+
142
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
143
+ self._cached_cos = None
144
+ self._cached_sin = None
145
+ self._cached_seq_length = max_seq_length
146
+ self._compute_sin_cos_embeddings(max_seq_length)
147
+
148
+ def extra_repr(self) -> str:
149
+ return f"\tmodel_dim={self.model_dim}, max_seq_length={self.max_seq_length}, freq_constant={self.freq_constant}"
150
+
151
+ def _compute_sin_cos_embeddings(
152
+ self,
153
+ key_len: int,
154
+ key_device: torch.device = torch.device("cpu"),
155
+ key_dtype: torch.dtype = torch.float32,
156
+ ) -> None:
157
+ """
158
+ Compute sine and cos embeddings.
159
+
160
+ Args:
161
+ key_len: Number of tokens in the key embeddings in the transformer model.
162
+ device: Device where the key embeddings are stored.
163
+ key_dtype: Data type of the key embeddings.
164
+
165
+ Returns:
166
+ None
167
+
168
+ ...note:
169
+ We recalculate the sine and cosine embeddings if any of the following conditions are met:
170
+ 1. The number of tokens in key embeddings are greater than the cached sequence length.
171
+ 2. Sine and cosine caches are empty.
172
+ 3. The device and data type of sine and cosine embeddings does not match with the key embeddings.
173
+ """
174
+ if (
175
+ key_len > self._cached_seq_length
176
+ or self._cached_cos is None
177
+ or (self._cached_cos is not None and self._cached_cos.device != key_device)
178
+ or (self._cached_cos is not None and self._cached_cos.dtype != key_dtype)
179
+ or self._cached_sin is None
180
+ or (self._cached_sin is not None and self._cached_sin.device != key_device)
181
+ or (self._cached_sin is not None and self._cached_sin.dtype != key_dtype)
182
+ ):
183
+ self._cached_seq_length = max(key_len, self._cached_seq_length)
184
+
185
+ # The shape of 'pos_index' is [number of key tokens]
186
+ pos_index = torch.arange(
187
+ self._cached_seq_length,
188
+ dtype=torch.float32,
189
+ device=self.inv_freq.device,
190
+ )
191
+ # The shape of 'pos_index_theta' is [number of key tokens, model dimension]
192
+ pos_index_theta = torch.einsum("i,j->ij", pos_index, self.inv_freq)
193
+ # The shape of 'emb' is [number of key tokens, model dimension]
194
+ emb = torch.cat((pos_index_theta, pos_index_theta), dim=-1)
195
+
196
+ # the shape of cos and sin embeddings is [number of key tokens, model_dim]
197
+ cos_emb = emb.cos().to(dtype=key_dtype, device=key_device)
198
+ sin_emb = emb.sin().to(dtype=key_dtype, device=key_device)
199
+
200
+ # the shape of cached cos and sin embeddings is [1, 1, number of key tokens, model_dim]
201
+ self._cached_cos = cos_emb[None, None, :, :]
202
+ self._cached_sin = sin_emb[None, None, :, :]
203
+
204
+ def forward(
205
+ self,
206
+ query: torch.Tensor,
207
+ key: torch.Tensor,
208
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
209
+ """
210
+ The forward function of RoPE embeddings.
211
+
212
+ Args:
213
+ query: Query embeddings in the transformer model. The shape of query embeddings is
214
+ [Batch, number of query heads, number of query tokens, model dimension].
215
+ key: Key embeddings in the transformer model. The shape of key embeddings is
216
+ [Batch, number of key heads, number of key tokens, model dimension].
217
+
218
+ Returns:
219
+ A tuple containing the query and key embeddings with positional information. The shape of the returned query
220
+ and key embeddings is the same as the input query and key embeddings respectively.
221
+
222
+ ...note:
223
+ The RoPE embedding computation is done in full-precision. After the computation, input query and key tensors
224
+ are casted to original input datatype.
225
+ """
226
+ dim = key.shape[-1]
227
+ key_len = key.shape[2]
228
+ query_len = query.shape[2]
229
+
230
+ assert dim == self.model_dim
231
+ assert key.device == query.device
232
+ assert key.dtype == query.dtype
233
+
234
+ # In the context of self-attention, the lengths of keys and queries are equal.
235
+ # However, in generation tasks, such as predicting the next token in a sequence, the lengths of keys and queries
236
+ # can differ. For instance, when employing key-value (KV) caching for sequence prediction, the keys
237
+ # represent embeddings of previous tokens and the current token, while the query corresponds
238
+ # to the embedding of the current token only.
239
+ assert (
240
+ key_len >= query_len
241
+ ), "Number of keys has to be greater than or equal to number of queries."
242
+
243
+ query_float = query.float()
244
+ key_float = key.float()
245
+
246
+ self._compute_sin_cos_embeddings(
247
+ key_len, key_device=key_float.device, key_dtype=key_float.dtype
248
+ )
249
+ query_float = _apply_rotary_pos_emb(
250
+ x=query_float,
251
+ pos_sin=self._cached_sin[..., key_len - query_len : key_len, :],
252
+ pos_cos=self._cached_cos[..., key_len - query_len : key_len, :],
253
+ )
254
+ key_float = _apply_rotary_pos_emb(
255
+ x=key_float,
256
+ pos_sin=self._cached_sin[..., :key_len, :],
257
+ pos_cos=self._cached_cos[..., :key_len, :],
258
+ )
259
+
260
+ return query_float.type_as(query), key_float.type_as(key)
261
+
262
+
263
+ class OpenELMMultiHeadCausalAttention(nn.Module):
264
+ def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
265
+ super().__init__()
266
+ self.layer_idx = layer_idx
267
+ head_dim = config.head_dim
268
+ q_heads = config.num_query_heads[layer_idx]
269
+ k_heads = config.num_kv_heads[layer_idx]
270
+ v_heads = config.num_kv_heads[layer_idx]
271
+
272
+ self.qkv_proj = nn.Linear(
273
+ in_features=config.model_dim,
274
+ out_features=(q_heads + k_heads + v_heads) * head_dim,
275
+ bias=False,
276
+ )
277
+
278
+ self.pos_embedding = OpenELMRotaryEmbedding(
279
+ model_dim=config.head_dim,
280
+ max_seq_length=config.rope_max_length,
281
+ freq_constant=config.rope_freq_constant,
282
+ )
283
+
284
+ if config.normalize_qk_projections:
285
+ self.q_norm = OpenELMRMSNorm(
286
+ num_features=config.head_dim,
287
+ )
288
+ self.k_norm = OpenELMRMSNorm(
289
+ num_features=config.head_dim,
290
+ )
291
+ else:
292
+ self.q_norm = None
293
+ self.k_norm = None
294
+
295
+ self.out_proj = nn.Linear(
296
+ in_features=q_heads * head_dim,
297
+ out_features=config.model_dim,
298
+ bias=False,
299
+ )
300
+
301
+ self.head_dim = config.head_dim
302
+ self.num_q_heads = q_heads
303
+ self.num_k_heads = k_heads
304
+ self.num_v_heads = v_heads
305
+ self.transformer_dim = config.model_dim
306
+ self.num_groups = self.num_q_heads // self.num_k_heads
307
+
308
+ def extra_repr(self) -> str:
309
+ return (
310
+ super().extra_repr()
311
+ + f"query_heads={self.num_q_heads}, key_heads={self.num_k_heads}, value_heads={self.num_v_heads}"
312
+ )
313
+
314
+ def forward(
315
+ self,
316
+ hidden_states: torch.Tensor,
317
+ attention_mask: Optional[torch.Tensor] = None,
318
+ past_key_value: Optional[Cache] = None,
319
+ output_attentions: bool = False,
320
+ use_cache: bool = False,
321
+ cache_position: Optional[torch.LongTensor] = None,
322
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
323
+ """
324
+ Forward pass of multi-head self-attention.
325
+
326
+ Args:
327
+ hidden_states: Input tensor of the shape [batch size, sequence length, model dimension].
328
+ past_key_value: Tensor storing the cached keys and values.
329
+ output_attentions: output attention weights.
330
+ use_cache: Specifies whether to use kv-cache for generation.
331
+ cache_position: used for updating the kv-cache.
332
+
333
+ Returns:
334
+ The output of the same shape as the input, optionally with a tensor containing cached keys and values.
335
+ """
336
+
337
+ # scaled_dot_product_attention does not return attention weights, set output_attentions to False
338
+ output_attentions = False
339
+ batch_size, seq_length, d_model = hidden_states.size()
340
+
341
+ # [B, S, d] --> [B, S, (q_h + k_h + v_h) * h]
342
+ qkv = self.qkv_proj(hidden_states)
343
+ # [B, S, (q_h + k_h + v_h) * h] --> [B, S, (q_h + k_h + v_h), h]
344
+ qkv = qkv.reshape(
345
+ batch_size,
346
+ seq_length,
347
+ self.num_q_heads + self.num_k_heads + self.num_v_heads,
348
+ self.head_dim,
349
+ )
350
+ # [B, S, (q_h + k_h + v_h), h] --> [B, (q_h + k_h + v_h), S, h]
351
+ qkv = qkv.transpose(1, 2)
352
+ # [B, (q_h + k_h + v_h), S, h] --> [B, q_h, S h], [B, k_h, S, h], [B, v_h, S, h]
353
+ queries, keys, values = qkv.split(
354
+ [self.num_q_heads, self.num_k_heads, self.num_v_heads], dim=1
355
+ )
356
+
357
+ if self.q_norm is not None:
358
+ queries = self.q_norm(queries)
359
+
360
+ if self.k_norm is not None:
361
+ keys = self.k_norm(keys)
362
+
363
+ past_key_value = getattr(self, "past_key_value", past_key_value)
364
+
365
+ if past_key_value is not None:
366
+ # sin and cos are specific to RoPE models; position_ids needed for the static cache
367
+ # cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
368
+ cache_kwargs = {"cache_position": cache_position}
369
+ keys, values = past_key_value.update(
370
+ keys, values, self.layer_idx, cache_kwargs
371
+ )
372
+
373
+ # Add positional embedding
374
+ queries, keys = self.pos_embedding(queries, keys)
375
+
376
+ if self.num_groups != 1:
377
+ # GQA
378
+ # [B, k_h, S, h] --> [B, q_h, S, h]
379
+ keys = keys.repeat_interleave(self.num_groups, dim=1)
380
+ # [B, v_h, S, h] --> [B, q_h, S, h]
381
+ values = values.repeat_interleave(self.num_groups, dim=1)
382
+
383
+ causal_mask = attention_mask
384
+ if attention_mask is not None and cache_position is not None:
385
+ causal_mask = causal_mask[:, :, cache_position, : keys.shape[-2]]
386
+
387
+ attn_output = F.scaled_dot_product_attention(
388
+ queries,
389
+ keys,
390
+ values,
391
+ attn_mask=causal_mask,
392
+ dropout_p=0,
393
+ )
394
+
395
+ attn_output = attn_output.transpose(1, 2).contiguous()
396
+ attn_output = attn_output.reshape(
397
+ batch_size, seq_length, self.num_q_heads * self.head_dim
398
+ )
399
+ attn_output = self.out_proj(attn_output)
400
+ if not output_attentions:
401
+ attn_weights = None
402
+ return attn_output, attn_weights, past_key_value
403
+
404
+
405
+ class OpenELMFeedForwardNetwork(nn.Module):
406
+ def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
407
+ super().__init__()
408
+ ffn_multiplier = config.ffn_multipliers[layer_idx]
409
+ intermediate_dim = int(
410
+ make_divisible(
411
+ ffn_multiplier * config.model_dim,
412
+ divisor=config.ffn_dim_divisor,
413
+ )
414
+ )
415
+ if config.ffn_with_glu:
416
+ # FFN with Gated linear unit, as described in https://arxiv.org/abs/2002.05202v1.
417
+ self.proj_1 = nn.Linear(
418
+ in_features=config.model_dim,
419
+ out_features=2 * intermediate_dim,
420
+ bias=False,
421
+ )
422
+ self.proj_2 = nn.Linear(
423
+ in_features=intermediate_dim,
424
+ out_features=config.model_dim,
425
+ bias=False,
426
+ )
427
+ self.ffn_with_glu = True
428
+ else:
429
+ # Standard FFN, as described in https://arxiv.org/abs/1706.03762
430
+ self.proj_1 = nn.Linear(
431
+ in_features=config.model_dim,
432
+ out_features=intermediate_dim,
433
+ bias=False,
434
+ )
435
+ self.proj_2 = nn.Linear(
436
+ in_features=intermediate_dim,
437
+ out_features=config.model_dim,
438
+ bias=False,
439
+ )
440
+ self.ffn_with_glu = False
441
+
442
+ self.act = ACT2FN[config.activation_fn_name]
443
+
444
+ def extra_repr(self) -> str:
445
+ return super().extra_repr() + f"(ffn_with_glu) : {self.ffn_with_glu}"
446
+
447
+ def forward(self, x: Tensor) -> Tensor:
448
+ """Forward function of FFN layer.
449
+
450
+ Args:
451
+ x: Input tensor of the shape [batch size, sequence length, model dimension].
452
+
453
+ Returns:
454
+ A tensor of the same shape as the input.
455
+ """
456
+ if self.ffn_with_glu:
457
+ y_12 = self.proj_1(x)
458
+ y_1, y_2 = y_12.chunk(2, dim=-1)
459
+ y = self.act(y_1) * y_2
460
+ return self.proj_2(y)
461
+ else:
462
+ return self.proj_2(self.act(self.proj_1(x)))
463
+
464
+
465
+ class OpenELMDecoderLayer(nn.Module):
466
+ def __init__(self, config: OpenELMConfig, layer_idx: int) -> None:
467
+ super().__init__()
468
+ self.attn = OpenELMMultiHeadCausalAttention(config=config, layer_idx=layer_idx)
469
+ self.ffn = OpenELMFeedForwardNetwork(config=config, layer_idx=layer_idx)
470
+ self.ffn_norm = OpenELMRMSNorm(
471
+ num_features=config.model_dim,
472
+ )
473
+ self.attn_norm = OpenELMRMSNorm(
474
+ num_features=config.model_dim,
475
+ )
476
+
477
+ def forward(
478
+ self,
479
+ hidden_states: torch.Tensor,
480
+ attention_mask: Optional[torch.Tensor] = None,
481
+ position_ids: Optional[torch.LongTensor] = None,
482
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
483
+ output_attentions: Optional[bool] = False,
484
+ use_cache: Optional[bool] = False,
485
+ cache_position: Optional[torch.LongTensor] = None,
486
+ **kwargs,
487
+ ) -> Tuple[
488
+ torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]
489
+ ]:
490
+ """
491
+ Args:
492
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
493
+ attention_mask (`torch.FloatTensor`, *optional*):
494
+ attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
495
+ query_sequence_length, key_sequence_length)` if default attention is used.
496
+ output_attentions (`bool`, *optional*):
497
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
498
+ returned tensors for more detail.
499
+ use_cache (`bool`, *optional*):
500
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
501
+ (see `past_key_values`).
502
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
503
+ """
504
+ residual = hidden_states
505
+ hidden_states = self.attn_norm(hidden_states)
506
+
507
+ # Self Attention
508
+ hidden_states, self_attn_weights, present_key_value = self.attn(
509
+ hidden_states=hidden_states,
510
+ attention_mask=attention_mask,
511
+ past_key_value=past_key_value,
512
+ output_attentions=output_attentions,
513
+ use_cache=use_cache,
514
+ cache_position=cache_position,
515
+ **kwargs,
516
+ )
517
+ hidden_states = residual + hidden_states
518
+
519
+ # Fully Connected
520
+ residual = hidden_states
521
+ hidden_states = self.ffn_norm(hidden_states)
522
+ hidden_states = self.ffn(hidden_states)
523
+ hidden_states = residual + hidden_states
524
+
525
+ outputs = (hidden_states,)
526
+
527
+ if output_attentions:
528
+ outputs += (self_attn_weights,)
529
+
530
+ if use_cache:
531
+ outputs += (present_key_value,)
532
+
533
+ return outputs
534
+
535
+
536
+ class OpenELMModel(OpenELMPreTrainedModel):
537
+ config_class = OpenELMConfig
538
+
539
+ def __init__(self, config: OpenELMConfig):
540
+ super().__init__(config)
541
+ self.config = config
542
+
543
+ self.token_embeddings = nn.Embedding(
544
+ embedding_dim=config.model_dim,
545
+ num_embeddings=config.vocab_size,
546
+ )
547
+
548
+ self.layers = nn.ModuleList(
549
+ OpenELMDecoderLayer(config=config, layer_idx=layer_idx)
550
+ for layer_idx in range(config.num_transformer_layers)
551
+ )
552
+ self.norm = OpenELMRMSNorm(num_features=config.model_dim)
553
+ if config.share_input_output_layers:
554
+ self.classifier = None
555
+ else:
556
+ self.classifier = nn.Linear(
557
+ in_features=config.model_dim,
558
+ out_features=config.vocab_size,
559
+ bias=False,
560
+ )
561
+ self.num_transformer_layers = config.num_transformer_layers
562
+ self.gradient_checkpointing = False
563
+
564
+ # Register a causal mask to separate causal and padding mask creation. Merging happens in the attention class.
565
+ # NOTE: This is not friendly with TorchScript, ONNX, ExportedProgram serialization for very large `max_context_length`.
566
+ causal_mask = torch.full(
567
+ (config.max_context_length, config.max_context_length),
568
+ fill_value=True,
569
+ dtype=torch.bool,
570
+ )
571
+ self.register_buffer(
572
+ "causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False
573
+ )
574
+
575
+ # Initialize weights and apply final processing
576
+ self.post_init()
577
+ self.reset_parameters(config=config)
578
+
579
+ def get_input_embeddings(self):
580
+ return self.token_embeddings
581
+
582
+ def set_input_embeddings(self, new_embeddings: torch.Tensor):
583
+ self.token_embeddings = new_embeddings
584
+
585
+ def reset_parameters(self, config: OpenELMConfig) -> None:
586
+ """Initialize the layers in Language Model
587
+
588
+ The initialization scheme is followed, following `OPT <https://arxiv.org/pdf/2205.01068.pdf>`_.
589
+
590
+ Args:
591
+ use_megatron_std: Use standard deviation as described in Megatron-LM.
592
+
593
+ Returns:
594
+ None
595
+ """
596
+ for module in self.modules():
597
+ if isinstance(module, nn.Linear):
598
+ std = module.in_features**-0.5
599
+ torch.nn.init.normal_(module.weight, mean=0.0, std=std)
600
+ if module.bias is not None:
601
+ torch.nn.init.zeros_(module.bias)
602
+ elif isinstance(module, nn.Embedding):
603
+ std = module.embedding_dim**-0.5
604
+ torch.nn.init.normal_(module.weight, mean=0.0, std=std)
605
+ elif isinstance(module, OpenELMRMSNorm):
606
+ if module.weight is not None:
607
+ torch.nn.init.ones_(module.weight)
608
+ if hasattr(module, "bias") and module.bias is not None:
609
+ torch.nn.init.zeros_(module.bias)
610
+
611
+ model_dim = config.model_dim
612
+ n_layers = config.num_transformer_layers
613
+ std = (model_dim**-0.5) * ((2 * n_layers) ** -0.5)
614
+ for param_name, param in self.named_parameters():
615
+ if param_name.endswith("out_proj.weight") or param_name.endswith(
616
+ "ffn.proj_2.weight"
617
+ ):
618
+ torch.nn.init.normal_(param, mean=0.0, std=std)
619
+
620
+ def forward(
621
+ self,
622
+ input_ids: torch.LongTensor = None,
623
+ attention_mask: Optional[torch.Tensor] = None,
624
+ position_ids: Optional[torch.LongTensor] = None,
625
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
626
+ inputs_embeds: Optional[torch.FloatTensor] = None,
627
+ use_cache: Optional[bool] = None,
628
+ output_attentions: Optional[bool] = None,
629
+ output_hidden_states: Optional[bool] = None,
630
+ return_dict: Optional[bool] = None,
631
+ cache_position: Optional[torch.LongTensor] = None,
632
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
633
+ output_attentions = (
634
+ output_attentions
635
+ if output_attentions is not None
636
+ else self.config.output_attentions
637
+ )
638
+ output_hidden_states = (
639
+ output_hidden_states
640
+ if output_hidden_states is not None
641
+ else self.config.output_hidden_states
642
+ )
643
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
644
+ return_dict = (
645
+ return_dict if return_dict is not None else self.config.use_return_dict
646
+ )
647
+
648
+ if (input_ids is None) ^ (inputs_embeds is not None):
649
+ raise ValueError(
650
+ "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one"
651
+ )
652
+
653
+ if self.gradient_checkpointing and self.training and use_cache:
654
+ logger.warning_once(
655
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
656
+ )
657
+ use_cache = False
658
+
659
+ if inputs_embeds is None:
660
+ inputs_embeds = self.token_embeddings(input_ids)
661
+
662
+ past_seen_tokens = 0
663
+ if use_cache: # kept for BC (cache positions)
664
+ if not isinstance(past_key_values, StaticCache):
665
+ past_key_values = DynamicCache.from_legacy_cache(past_key_values)
666
+ past_seen_tokens = past_key_values.get_seq_length()
667
+
668
+ if cache_position is None:
669
+ cache_position = torch.arange(
670
+ past_seen_tokens,
671
+ past_seen_tokens + inputs_embeds.shape[1],
672
+ device=inputs_embeds.device,
673
+ )
674
+
675
+ if position_ids is None:
676
+ position_ids = cache_position.unsqueeze(0)
677
+
678
+ causal_mask = self._update_causal_mask(attention_mask, inputs_embeds)
679
+
680
+ # embed positions
681
+ hidden_states = inputs_embeds
682
+
683
+ # decoder layers
684
+ all_hidden_states = () if output_hidden_states else None
685
+ all_self_attns = () if output_attentions else None
686
+ next_decoder_cache = None
687
+
688
+ for decoder_layer in self.layers:
689
+ if output_hidden_states:
690
+ all_hidden_states += (hidden_states,)
691
+
692
+ if self.gradient_checkpointing and self.training:
693
+ layer_outputs = self._gradient_checkpointing_func(
694
+ decoder_layer.__call__,
695
+ hidden_states,
696
+ causal_mask,
697
+ position_ids,
698
+ past_key_values,
699
+ output_attentions,
700
+ use_cache,
701
+ cache_position,
702
+ )
703
+ else:
704
+ layer_outputs = decoder_layer(
705
+ hidden_states,
706
+ attention_mask=causal_mask,
707
+ position_ids=position_ids,
708
+ past_key_value=past_key_values,
709
+ output_attentions=output_attentions,
710
+ use_cache=use_cache,
711
+ cache_position=cache_position,
712
+ )
713
+
714
+ hidden_states = layer_outputs[0]
715
+
716
+ if use_cache:
717
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
718
+
719
+ if output_attentions:
720
+ all_self_attns += (layer_outputs[1],)
721
+
722
+ hidden_states = self.norm(hidden_states)
723
+
724
+ # add hidden states from the last decoder layer
725
+ if output_hidden_states:
726
+ all_hidden_states += (hidden_states,)
727
+
728
+ next_cache = None
729
+ if use_cache:
730
+ next_cache = (
731
+ next_decoder_cache.to_legacy_cache()
732
+ if isinstance(next_decoder_cache, Cache)
733
+ else next_decoder_cache
734
+ )
735
+ if not return_dict:
736
+ return tuple(
737
+ v
738
+ for v in [hidden_states, next_cache, all_hidden_states, all_self_attns]
739
+ if v is not None
740
+ )
741
+ return BaseModelOutputWithPast(
742
+ last_hidden_state=hidden_states,
743
+ past_key_values=next_cache,
744
+ hidden_states=all_hidden_states,
745
+ attentions=all_self_attns,
746
+ )
747
+
748
+ def _update_causal_mask(self, attention_mask, input_tensor):
749
+ if self.config._attn_implementation == "flash_attention_2":
750
+ if attention_mask is not None and 0.0 in attention_mask:
751
+ return attention_mask
752
+ return None
753
+
754
+ batch_size, seq_length = input_tensor.shape[:2]
755
+ dtype = input_tensor.dtype
756
+ device = input_tensor.device
757
+
758
+ # support going beyond cached `max_position_embedding`
759
+ if seq_length > self.causal_mask.shape[-1]:
760
+ causal_mask = torch.full(
761
+ (2 * self.causal_mask.shape[-1], 2 * self.causal_mask.shape[-1]),
762
+ fill_value=1,
763
+ )
764
+ self.register_buffer(
765
+ "causal_mask", torch.triu(causal_mask, diagonal=1), persistent=False
766
+ )
767
+
768
+ # We use the current dtype to avoid any overflows
769
+ min_dtype = torch.finfo(dtype).min
770
+ causal_mask = (
771
+ self.causal_mask[None, None, :, :].repeat(batch_size, 1, 1, 1).to(dtype)
772
+ * min_dtype
773
+ )
774
+
775
+ causal_mask = causal_mask.to(dtype=dtype, device=device)
776
+ if attention_mask is not None and attention_mask.dim() == 2:
777
+ mask_length = attention_mask.shape[-1]
778
+ padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[
779
+ :, None, None, :
780
+ ].eq(0.0)
781
+ causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(
782
+ padding_mask, min_dtype
783
+ )
784
+
785
+ if self.config._attn_implementation == "sdpa" and attention_mask is not None:
786
+ # For dynamo, rather use a check on fullgraph=True once this is possible (https://github.com/pytorch/pytorch/pull/120400).
787
+ is_tracing = (
788
+ torch.jit.is_tracing()
789
+ or isinstance(input_tensor, torch.fx.Proxy)
790
+ or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
791
+ )
792
+ if not is_tracing and torch.any(attention_mask != 1):
793
+ # Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
794
+ # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
795
+ # Details: https://github.com/pytorch/pytorch/issues/110213
796
+ causal_mask = causal_mask.mul(
797
+ ~torch.all(causal_mask == min_dtype, dim=-1, keepdim=True)
798
+ ).to(dtype)
799
+
800
+ return causal_mask
801
+
802
+
803
+ class OpenELMForCausalLM(OpenELMPreTrainedModel):
804
+ _tied_weights_keys = ["lm_head.weight"]
805
+
806
+ def __init__(self, config: OpenELMConfig):
807
+ super().__init__(config)
808
+ self.transformer = OpenELMModel(config)
809
+ self.vocab_size = config.vocab_size
810
+ if config.share_input_output_layers:
811
+ self.lm_head = None
812
+ else:
813
+ self.lm_head = nn.Linear(config.model_dim, config.vocab_size, bias=False)
814
+
815
+ # Initialize weights and apply final processing
816
+ self.post_init()
817
+
818
+ def get_input_embeddings(self):
819
+ return self.transformer.token_embeddings
820
+
821
+ def set_input_embeddings(self, value):
822
+ self.transformer.token_embeddings = value
823
+
824
+ def get_output_embeddings(self):
825
+ return self.lm_head
826
+
827
+ def set_output_embeddings(self, new_embeddings):
828
+ self.lm_head = new_embeddings
829
+
830
+ def set_decoder(self, decoder):
831
+ self.transformer = decoder
832
+
833
+ def get_decoder(self):
834
+ return self.transformer
835
+
836
+ def forward(
837
+ self,
838
+ input_ids: torch.LongTensor = None,
839
+ attention_mask: Optional[torch.Tensor] = None,
840
+ position_ids: Optional[torch.LongTensor] = None,
841
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
842
+ inputs_embeds: Optional[torch.FloatTensor] = None,
843
+ labels: Optional[torch.LongTensor] = None,
844
+ use_cache: Optional[bool] = None,
845
+ output_attentions: Optional[bool] = None,
846
+ output_hidden_states: Optional[bool] = None,
847
+ return_dict: Optional[bool] = None,
848
+ cache_position: Optional[torch.LongTensor] = None,
849
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
850
+ output_attentions = (
851
+ output_attentions
852
+ if output_attentions is not None
853
+ else self.config.output_attentions
854
+ )
855
+ output_hidden_states = (
856
+ output_hidden_states
857
+ if output_hidden_states is not None
858
+ else self.config.output_hidden_states
859
+ )
860
+ return_dict = (
861
+ return_dict if return_dict is not None else self.config.use_return_dict
862
+ )
863
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
864
+ outputs = self.transformer(
865
+ input_ids=input_ids,
866
+ attention_mask=attention_mask,
867
+ position_ids=position_ids,
868
+ past_key_values=past_key_values,
869
+ inputs_embeds=inputs_embeds,
870
+ use_cache=use_cache,
871
+ output_attentions=output_attentions,
872
+ output_hidden_states=output_hidden_states,
873
+ return_dict=return_dict,
874
+ cache_position=cache_position,
875
+ )
876
+
877
+ hidden_states = outputs[0]
878
+ if self.lm_head is None:
879
+ # shared
880
+ logits = F.linear(
881
+ hidden_states, weight=self.transformer.token_embeddings.weight
882
+ )
883
+ else:
884
+ logits = self.lm_head(hidden_states)
885
+ logits = logits[:, : self.config.vocab_size]
886
+ loss = None
887
+ if labels is not None:
888
+ # Shift so that tokens < n predict n
889
+ shift_logits = logits[..., :-1, :].contiguous()
890
+ shift_labels = labels[..., 1:].contiguous()
891
+ # Flatten the tokens
892
+ loss_fct = CrossEntropyLoss()
893
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
894
+ shift_labels = shift_labels.view(-1)
895
+ # Enable model parallelism
896
+ shift_labels = shift_labels.to(shift_logits.device)
897
+ loss = loss_fct(shift_logits, shift_labels)
898
+
899
+ if not return_dict:
900
+ output = (logits,) + outputs[1:]
901
+ return (loss,) + output if loss is not None else output
902
+
903
+ return CausalLMOutputWithPast(
904
+ loss=loss,
905
+ logits=logits,
906
+ past_key_values=outputs.past_key_values,
907
+ hidden_states=outputs.hidden_states,
908
+ attentions=outputs.attentions,
909
+ )
910
+
911
+ def prepare_inputs_for_generation(
912
+ self,
913
+ input_ids,
914
+ past_key_values=None,
915
+ attention_mask=None,
916
+ inputs_embeds=None,
917
+ **kwargs,
918
+ ):
919
+ past_length = 0
920
+ if past_key_values is not None:
921
+ if isinstance(past_key_values, Cache):
922
+ cache_length = past_key_values.get_seq_length()
923
+ past_length = past_key_values.seen_tokens
924
+ max_cache_length = past_key_values.get_max_length()
925
+ else:
926
+ cache_length = past_length = past_key_values[0][0].shape[2]
927
+ max_cache_length = None
928
+
929
+ # Keep only the unprocessed tokens:
930
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
931
+ # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
932
+ # input)
933
+ if (
934
+ attention_mask is not None
935
+ and attention_mask.shape[1] > input_ids.shape[1]
936
+ ):
937
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
938
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
939
+ # input_ids based on the past_length.
940
+ elif past_length < input_ids.shape[1]:
941
+ input_ids = input_ids[:, past_length:]
942
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
943
+
944
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
945
+ if (
946
+ max_cache_length is not None
947
+ and attention_mask is not None
948
+ and cache_length + input_ids.shape[1] > max_cache_length
949
+ ):
950
+ attention_mask = attention_mask[:, -max_cache_length:]
951
+
952
+ position_ids = kwargs.get("position_ids", None)
953
+ if attention_mask is not None and position_ids is None:
954
+ # create position_ids on the fly for batch generation
955
+ position_ids = attention_mask.long().cumsum(-1) - 1
956
+ position_ids.masked_fill_(attention_mask == 0, 1)
957
+ if past_key_values:
958
+ position_ids = position_ids[:, -input_ids.shape[1] :]
959
+
960
+ if self.generation_config.cache_implementation == "static":
961
+ # generation with static cache
962
+ cache_position = kwargs.get("cache_position", None)
963
+ if cache_position is None:
964
+ past_length = 0
965
+ else:
966
+ past_length = cache_position[-1] + 1
967
+ input_ids = input_ids[:, past_length:]
968
+ position_ids = position_ids[:, past_length:]
969
+
970
+ # we should only keep a `cache_position` in generate, and do +=1.
971
+ # same goes for position ids. Could also help with continued generation.
972
+ cache_position = torch.arange(
973
+ past_length,
974
+ past_length + position_ids.shape[-1],
975
+ device=position_ids.device,
976
+ )
977
+
978
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
979
+ if inputs_embeds is not None and past_key_values is None:
980
+ model_inputs = {"inputs_embeds": inputs_embeds}
981
+ else:
982
+ # The `contiguous()` here is necessary to have a static stride during decoding. torchdynamo otherwise
983
+ # recompiles graphs as the stride of the inputs is a guard. Ref: https://github.com/huggingface/transformers/pull/29114
984
+ # We could use `next_tokens` directly instead.
985
+ model_inputs = {"input_ids": input_ids.contiguous()}
986
+
987
+ model_inputs.update(
988
+ {
989
+ "position_ids": position_ids.contiguous(),
990
+ "cache_position": cache_position,
991
+ "past_key_values": past_key_values,
992
+ "use_cache": kwargs.get("use_cache"),
993
+ "attention_mask": attention_mask,
994
+ }
995
+ )
996
+ return model_inputs
997
+
998
+ @staticmethod
999
+ def _reorder_cache(past_key_values, beam_idx):
1000
+ reordered_past = ()
1001
+ for layer_past in past_key_values:
1002
+ reordered_past += (
1003
+ tuple(
1004
+ past_state.index_select(0, beam_idx.to(past_state.device))
1005
+ for past_state in layer_past
1006
+ ),
1007
+ )
1008
+ return reordered_past
smash_config.json ADDED
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+ "cache_dir": "/ceph/hdd/staff/charpent/.cache/modelserdl44_4",
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+ "batch_size": 1,
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+ "model_name": "apple/OpenELM-270M-Instruct",
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+ "task": "text_text_generation",
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+ "max_batch_size": 1,
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+ "qtype_weight": "torch.qint8",
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+ "qtype_activation": "torch.quint8",
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+ "qobserver": "<class 'torch.ao.quantization.observer.MinMaxObserver'>",
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+ "qscheme": "torch.per_tensor_symmetric",
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+ "qconfig": "x86",
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+ "group_size": 128,
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+ "damp_percent": 0.1,
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+ "save_load_fn": "bitsandbytes"
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+ }
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+ }
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+ }
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+ }
tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
tokenizer.model ADDED
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+ version https://git-lfs.github.com/spec/v1
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+ size 499723
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+ {
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+ "add_bos_token": true,
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+ },
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+ "special": true
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+ }
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+ "clean_up_tokenization_spaces": false,
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+ "legacy": false,
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+ "model_max_length": 1000000000000000019884624838656,
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+ "pad_token": null,
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+ "padding_side": "right",
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+ "sp_model_kwargs": {},
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+ "tokenizer_class": "LlamaTokenizer",
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+ "unk_token": "<unk>",
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+ "use_default_system_prompt": false
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+ }