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README.md ADDED
@@ -0,0 +1,133 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: other
3
+ language:
4
+ - en
5
+ pipeline_tag: text-generation
6
+ ---
7
+ ## Model Summary
8
+
9
+ The language model phi-1.5 is a Transformer with 1.3 billion parameters. It was trained using the same data sources as [phi-1](https://huggingface.co/microsoft/phi-1), augmented with a new data source that consists of various NLP synthetic texts. When assessed against benchmarks testing common sense, language understanding, and logical reasoning, phi-1.5 demonstrates a nearly state-of-the-art performance among models with less than 10 billion parameters.
10
+
11
+ We **did not** fine-tune phi-1.5 either for **instruction following or through reinforcement learning from human feedback**. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more.
12
+
13
+ For a safer model release, we exclude generic web-crawl data sources such as common-crawl from the training. This strategy prevents direct exposure to potentially harmful online content, enhancing the model's safety without RLHF. However, the model is still vulnerable to generating harmful content. We hope the model can help the research community to further study the safety of language models.
14
+
15
+ ## Intended Uses
16
+ Given the nature of the training data, phi-1.5 is best suited for prompts using the QA format, the chat format, and the code format. Note that phi-1.5, being a base model, often produces irrelevant text following the main answer. In the following example, we've truncated the answer for illustrative purposes only.
17
+
18
+ #### QA format:
19
+
20
+ ```markdown
21
+ Write a detailed analogy between mathematics and a lighthouse.
22
+
23
+ Answer: Mathematics is like a lighthouse, guiding us through the vast ocean of numbers and calculations. Just as a lighthouse illuminates the darkness, mathematics provides us with a clear path to navigate through complex problems. It helps us make sense of the world around us, just like a lighthouse helps ships find their way home.
24
+ ```
25
+ where the model generates the text after "Answer:".
26
+
27
+ #### Chat format:
28
+
29
+ ```markdown
30
+ Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?
31
+
32
+ Bob: Have you tried using a timer? It can help you stay on track and avoid distractions.
33
+
34
+ Alice: That's a good idea. I'll give it a try.
35
+
36
+ Charlie: Another thing that can help is to break up your study sessions into smaller chunks. It's easier to concentrate on one thing at a time.
37
+
38
+ Alice: That makes sense. I'll try that too.
39
+
40
+ Bob: And don't forget to take breaks! It's important to give your brain a rest so you can come back to your studies with a fresh perspective.
41
+
42
+ Alice: Thanks for the advice, guys. I feel more motivated now.
43
+
44
+ Charlie: No problem, Alice. We're all in this together.
45
+
46
+ Bob: Yeah, and remember that it's okay to ask for help if you need it. We're here to support each other.
47
+ ```
48
+ where the model generates the text after the first "Bob:".
49
+
50
+ #### Code format:
51
+ ```python
52
+ def print_prime(n):
53
+ """
54
+ Print all primes between 1 and n
55
+ """
56
+ primes = []
57
+ for num in range(2, n+1):
58
+ is_prime = True
59
+ for i in range(2, int(math.sqrt(num))+1):
60
+ if num % i == 0:
61
+ is_prime = False
62
+ break
63
+ if is_prime:
64
+ primes.append(num)
65
+ print(primes)
66
+ ```
67
+ where the model generates the text after the comments.
68
+
69
+ **Notes**
70
+ * phi-1.5 is intended for research purposes. The model-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications.
71
+ * Direct adoption for production tasks is out of the scope of this research project. As a result, phi-1.5 has not been tested to ensure that it performs adequately for any production-level application. Please refer to the limitation sections of this document for more details.
72
+
73
+ ## Limitations of phi-1.5
74
+
75
+ * Generate Inaccurate Code and Facts: The model often produces incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
76
+ * Limited Scope for code: If the model generates Python scripts that utilize uncommon packages or scripts in other languages, we strongly recommend users manually verify all API uses.
77
+ * Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users.
78
+ * Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other language outside of English might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.
79
+ * Potential Societal Biases: Regardless of the safe data used for its training, the model is not entirely free from societal biases. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.
80
+ * Toxicity: Despite that the model is trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model for research purposes only -- We hope to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining.
81
+
82
+ ## Training
83
+
84
+ ### Model
85
+ * Architecture: a Transformer-based model with next-word prediction objective
86
+ * Dataset size: 30B tokens
87
+ * Training tokens: 150B tokens
88
+ * Precision: fp16
89
+ * GPUs: 32xA100-40G
90
+ * Training time: 8 days
91
+
92
+ ### Software
93
+ * [PyTorch](https://github.com/pytorch/pytorch)
94
+ * [DeepSpeed](https://github.com/microsoft/DeepSpeed)
95
+ * [flash-attention](https://github.com/HazyResearch/flash-attention)
96
+
97
+ ### License
98
+ The model is licensed under the [Research License](https://huggingface.co/microsoft/phi-1_5/resolve/main/Research%20License.docx).
99
+
100
+ ### Sample Code
101
+ ```python
102
+ import torch
103
+ from transformers import AutoModelForCausalLM, AutoTokenizer
104
+
105
+ torch.set_default_device('cuda')
106
+ model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
107
+ tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1_5", trust_remote_code=True, torch_dtype="auto")
108
+ inputs = tokenizer('''```python
109
+ def print_prime(n):
110
+ """
111
+ Print all primes between 1 and n
112
+ """''', return_tensors="pt", return_attention_mask=False)
113
+
114
+ outputs = model.generate(**inputs, max_length=200)
115
+ text = tokenizer.batch_decode(outputs)[0]
116
+ print(text)
117
+ ```
118
+
119
+ **Remark.** In the generation function, our model currently does not support beam search (`num_beams` >1) and `attention_mask' parameters.
120
+ Furthermore, in the forward pass of the model, we currently do not support outputting hidden states or attention values, or using custom input embeddings (instead of the model's).
121
+
122
+ ### Citation
123
+
124
+ You can find the paper at https://arxiv.org/abs/2309.05463
125
+
126
+ ```bib
127
+ @article{textbooks2,
128
+ title={Textbooks Are All You Need II: \textbf{phi-1.5} technical report},
129
+ author={Li, Yuanzhi and Bubeck, S{\'e}bastien and Eldan, Ronen and Del Giorno, Allie and Gunasekar, Suriya and Lee, Yin Tat},
130
+ journal={arXiv preprint arXiv:2309.05463},
131
+ year={2023}
132
+ }
133
+ ```
Research License.docx ADDED
Binary file (38.9 kB). View file
 
added_tokens.json ADDED
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1
+ {
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+ "\t\t": 50294,
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+ "\t\t\t": 50293,
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+ "\t\t\t\t": 50292,
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+ "\t\t\t\t\t": 50291,
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+ "\t\t\t\t\t\t": 50290,
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+ "\t\t\t\t\t\t\t": 50289,
8
+ "\t\t\t\t\t\t\t\t": 50288,
9
+ "\t\t\t\t\t\t\t\t\t": 50287,
10
+ " ": 50286,
11
+ " ": 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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+ " ": 50279,
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+ " ": 50278,
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+ " ": 50277,
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+ " ": 50276,
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+ " ": 50275,
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+ " ": 50274,
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+ " ": 50273,
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+ " ": 50272,
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+ " ": 50270,
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+ " ": 50269,
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+ " ": 50268,
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+ " ": 50265,
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+ " ": 50264,
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+ " ": 50263,
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+ " ": 50262,
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+ " ": 50261,
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+ " ": 50260,
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+ " ": 50259,
38
+ " ": 50258,
39
+ " ": 50257
40
+ }
config.json ADDED
@@ -0,0 +1,35 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_name_or_path": "phi-1.5-half",
3
+ "activation_function": "gelu_new",
4
+ "architecture": {
5
+ "block_cls": "parallel",
6
+ "mixer": {},
7
+ "mlp": {
8
+ "mlp_cls": "mlp"
9
+ }
10
+ },
11
+ "architectures": [
12
+ "MixFormerSequentialForCausalLM"
13
+ ],
14
+ "auto_map": {
15
+ "AutoConfig": "configuration_mixformer_sequential.MixFormerSequentialConfig",
16
+ "AutoModelForCausalLM": "modeling_mixformer_sequential.MixFormerSequentialForCausalLM"
17
+ },
18
+ "embd_layer": "default",
19
+ "embd_pdrop": 0.0,
20
+ "initializer_range": 0.02,
21
+ "layer_norm_epsilon": 1e-05,
22
+ "model_type": "mixformer-sequential",
23
+ "n_embd": 2048,
24
+ "n_head": 32,
25
+ "n_inner": null,
26
+ "n_layer": 24,
27
+ "n_positions": 2048,
28
+ "phyagi_version": "0.0.4.dev",
29
+ "resid_pdrop": 0.0,
30
+ "rotary_dim": 32,
31
+ "tie_word_embeddings": false,
32
+ "torch_dtype": "float16",
33
+ "transformers_version": "4.32.1",
34
+ "vocab_size": 51200
35
+ }
configuration_mixformer_sequential.py ADDED
@@ -0,0 +1,59 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Microsoft Corporation.
2
+ # Licensed under the MIT license.
3
+
4
+ import math
5
+ from typing import Any, Dict, List, Optional, Union
6
+
7
+ from transformers import PretrainedConfig
8
+
9
+
10
+ class MixFormerSequentialConfig(PretrainedConfig):
11
+ """MixFormer (sequential for DeepSpeed) configuration."""
12
+
13
+ model_type = "mixformer-sequential"
14
+
15
+ attribute_map = {
16
+ "max_position_embeddings": "n_positions",
17
+ "hidden_size": "n_embd",
18
+ "num_attention_heads": "n_head",
19
+ "num_hidden_layers": "n_layer",
20
+ "input_emb_layer": "embd_layer", # `input_emb_layer` key is for backward compatibility
21
+ "blocks": "architecture", # `blocks` key is for backward compatibility
22
+ }
23
+
24
+ def __init__(
25
+ self,
26
+ vocab_size: Optional[int] = 50304,
27
+ n_positions: Optional[int] = 2048,
28
+ n_embd: Optional[int] = 1024,
29
+ n_layer: Optional[int] = 20,
30
+ n_inner: Optional[int] = None,
31
+ n_head: Optional[int] = 16,
32
+ rotary_dim: Optional[int] = 32,
33
+ activation_function: Optional[str] = "gelu_new",
34
+ embd_layer: Optional[str] = "default",
35
+ architecture: Union[Dict[str, Any], List[Dict[str, Any]]] = None,
36
+ embd_pdrop: Optional[float] = 0.0,
37
+ resid_pdrop: Optional[float] = 0.0,
38
+ layer_norm_epsilon: Optional[float] = 1e-5,
39
+ initializer_range: Optional[float] = 0.02,
40
+ tie_word_embeddings: Optional[bool] = False,
41
+ pad_vocab_size_multiple: Optional[int] = 64,
42
+ **kwargs
43
+ ) -> None:
44
+ self.vocab_size = int(math.ceil(vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple)
45
+ self.n_positions = n_positions
46
+ self.n_embd = n_embd
47
+ self.n_layer = n_layer
48
+ self.n_inner = n_inner
49
+ self.n_head = n_head
50
+ self.rotary_dim = min(rotary_dim, n_embd // n_head)
51
+ self.activation_function = activation_function
52
+ self.embd_layer = embd_layer
53
+ self.architecture = architecture
54
+ self.embd_pdrop = embd_pdrop
55
+ self.resid_pdrop = resid_pdrop
56
+ self.layer_norm_epsilon = layer_norm_epsilon
57
+ self.initializer_range = initializer_range
58
+
59
+ super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
generation_config.json ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "transformers_version": "4.32.1"
4
+ }
merges.txt ADDED
The diff for this file is too large to render. See raw diff
 
modeling_mixformer_sequential.py ADDED
@@ -0,0 +1,778 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Microsoft Corporation.
2
+ # Licensed under the MIT license.
3
+
4
+ # BSD 3-Clause License
5
+ #
6
+ # Copyright (c) 2022, Tri Dao, trid@cs.stanford.edu.
7
+ # All rights reserved.
8
+ #
9
+ # Redistribution and use in source and binary forms, with or without
10
+ # modification, are permitted provided that the following conditions are met:
11
+ #
12
+ # * Redistributions of source code must retain the above copyright notice, this
13
+ # list of conditions and the following disclaimer.
14
+ #
15
+ # * Redistributions in binary form must reproduce the above copyright notice,
16
+ # this list of conditions and the following disclaimer in the documentation
17
+ # and/or other materials provided with the distribution.
18
+ #
19
+ # * Neither the name of the copyright holder nor the names of its
20
+ # contributors may be used to endorse or promote products derived from
21
+ # this software without specific prior written permission.
22
+ #
23
+ # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
24
+ # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
25
+ # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
26
+ # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
27
+ # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
28
+ # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
29
+ # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
30
+ # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
31
+ # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
32
+ # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
33
+
34
+ from __future__ import annotations
35
+
36
+ import math
37
+ import copy
38
+ from typing import Any, Dict, Optional, Tuple
39
+ from dataclasses import dataclass, field
40
+
41
+ import torch
42
+ import torch.nn as nn
43
+
44
+ from einops import rearrange
45
+ from transformers.activations import ACT2FN
46
+ from transformers import PretrainedConfig, PreTrainedModel
47
+ from transformers.modeling_outputs import CausalLMOutputWithPast
48
+
49
+ from .configuration_mixformer_sequential import MixFormerSequentialConfig
50
+
51
+ @dataclass
52
+ class InferenceParams:
53
+ """Inference parameters that are passed to the main model in order
54
+ to efficienly calculate and store the context during inference.
55
+ Adapted from https://github.com/Dao-AILab/flash-attention."""
56
+ max_sequence_len: int
57
+ max_batch_size: int
58
+ sequence_len_offset: int = 0
59
+ batch_size_offset: int = 0
60
+ key_value_memory_dict: dict = field(default_factory=dict)
61
+ fused_ft_kernel: bool = False
62
+ lengths_per_sample: Optional[torch.Tensor] = None
63
+
64
+
65
+ class Embedding(nn.Module):
66
+ """Token embedding with dropout."""
67
+
68
+ def __init__(self, config: PretrainedConfig) -> None:
69
+ super().__init__()
70
+
71
+ self.wte = nn.Embedding(config.vocab_size, config.n_embd)
72
+ self.drop = nn.Dropout(config.embd_pdrop)
73
+
74
+ def forward(self, input_ids: torch.LongTensor) -> torch.FloatTensor:
75
+ input_shape = input_ids.size()
76
+ input_ids = input_ids.view(-1, input_shape[-1])
77
+
78
+ hidden_states = self.wte(input_ids)
79
+ hidden_states = self.drop(hidden_states)
80
+
81
+ return hidden_states
82
+
83
+ class RotaryEmbedding(nn.Module):
84
+ """PyTorch implementation of `flash-attn` RotaryEmbedding layer.
85
+ Adapted from https://github.com/Dao-AILab/flash-attention."""
86
+
87
+ def __init__(
88
+ self,
89
+ dim: int,
90
+ base: Optional[int] = 10000,
91
+ scale_base: Optional[float] = None,
92
+ device: Optional[str] = None,
93
+ **kwargs,
94
+ ) -> None:
95
+ super().__init__()
96
+
97
+ if scale_base is not None:
98
+ raise NotImplementedError
99
+
100
+ # Generate and save the inverse frequency buffer (non-trainable)
101
+ self.dim = dim
102
+ self.base = base
103
+ self.scale_base = scale_base
104
+ self.device = device
105
+
106
+ inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
107
+ self.register_buffer("inv_freq", inv_freq)
108
+
109
+ scale = (
110
+ (torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
111
+ if scale_base is not None
112
+ else None
113
+ )
114
+ self.register_buffer("scale", scale)
115
+
116
+ self._seq_len_cached = 0
117
+ self._cos_cached = None
118
+ self._sin_cached = None
119
+ self._cos_k_cached = None
120
+ self._sin_k_cached = None
121
+
122
+ def _update_cos_sin_cache(self, x: torch.FloatTensor, seqlen_offset: Optional[int] = 0) -> None:
123
+ # Reset the tables if the sequence length has changed,
124
+ # or if we're on a new device (possibly due to tracing for instance)
125
+ seqlen = x.shape[1] + seqlen_offset
126
+
127
+ # Re-generate the inverse frequency buffer if it's not fp32
128
+ # (for instance if model.half() was called)
129
+ if self.inv_freq.dtype != "torch.float32":
130
+ self.inv_freq = 1.0 / (
131
+ self.base ** (torch.arange(0, self.dim, 2, device=self.device, dtype=torch.float32) / self.dim)
132
+ )
133
+
134
+ if seqlen > self._seq_len_cached or self._cos_cached.device != x.device or self._cos_cached.dtype != x.dtype:
135
+ self._seq_len_cached = seqlen
136
+ t = torch.arange(seqlen, device=x.device, dtype=torch.float32)
137
+
138
+ # Don't do einsum, it converts fp32 to fp16
139
+ # freqs = torch.einsum("i,j->ij", t, self.inv_freq)
140
+ freqs = torch.outer(t, self.inv_freq.to(device=t.device, dtype=torch.float32))
141
+ if self.scale is None:
142
+ self._cos_cached = torch.cos(freqs).to(x.dtype)
143
+ self._sin_cached = torch.sin(freqs).to(x.dtype)
144
+ else:
145
+ power = (
146
+ torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2
147
+ ) / self.scale_base
148
+ scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1")
149
+
150
+ # We want the multiplication by scale to happen in fp32
151
+ self._cos_cached = (torch.cos(freqs) * scale).to(x.dtype)
152
+ self._sin_cached = (torch.sin(freqs) * scale).to(x.dtype)
153
+ self._cos_k_cached = (torch.cos(freqs) / scale).to(x.dtype)
154
+ self._sin_k_cached = (torch.sin(freqs) / scale).to(x.dtype)
155
+
156
+ def apply_rotary_emb_qkv(
157
+ self,
158
+ qkv: torch.FloatTensor,
159
+ sin: torch.FloatTensor,
160
+ cos: torch.FloatTensor,
161
+ sin_k: Optional[torch.FloatTensor] = None,
162
+ cos_k: Optional[torch.FloatTensor] = None,
163
+ ) -> torch.FloatTensor:
164
+ _, seqlen, three, _, headdim = qkv.shape
165
+ assert three == 3
166
+
167
+ rotary_seqlen, rotary_dim = cos.shape
168
+ rotary_dim *= 2
169
+ assert rotary_dim <= headdim
170
+ assert seqlen <= rotary_seqlen
171
+
172
+ cos_k = cos if cos_k is None else cos_k
173
+ sin_k = sin if sin_k is None else sin_k
174
+ assert sin.shape == cos_k.shape == sin_k.shape == (rotary_seqlen, rotary_dim // 2)
175
+
176
+ q_rot = qkv[:, :, 0, :, :rotary_dim]
177
+ q_pass = qkv[:, :, 0, :, rotary_dim:]
178
+
179
+ k_rot = qkv[:, :, 1, :, :rotary_dim]
180
+ k_pass = qkv[:, :, 1, :, rotary_dim:]
181
+
182
+ # Splits the queries and keys in half
183
+ q1, q2 = q_rot.chunk(2, dim=-1)
184
+ k1, k2 = k_rot.chunk(2, dim=-1)
185
+ c, s = rearrange(cos[:seqlen], "s d -> s 1 d"), rearrange(sin[:seqlen], "s d -> s 1 d")
186
+
187
+ # Casts to fp32 are necessary to prevent fp16 overflow issues
188
+ q1, q2, k1, k2, c, s = [t.to(dtype=torch.float32) for t in [q1, q2, k1, k2, c, s]]
189
+
190
+ # Computes the new keys and queries, recasting to original dtype
191
+ q_rot = torch.cat([q1 * c - q2 * s, q1 * s + q2 * c], axis=-1).to(qkv.dtype)
192
+
193
+ k_rot = torch.cat([k1 * c - k2 * s, k1 * s + k2 * c], axis=-1).to(qkv.dtype)
194
+
195
+ return torch.cat(
196
+ [
197
+ torch.cat([q_rot, q_pass], axis=-1).unsqueeze(2),
198
+ torch.cat([k_rot, k_pass], axis=-1).unsqueeze(2),
199
+ qkv[:, :, 2:3, :, :],
200
+ ],
201
+ axis=2,
202
+ )
203
+
204
+ def forward(self, qkv: torch.Tensor, seqlen_offset: int = 0) -> Tuple[torch.Tensor, torch.Tensor]:
205
+ """Perform the forward pass.
206
+
207
+ Args:
208
+ qkv: Query, key and value tensors of shape (batch, seqlen, nheads, headdim) or (batch, seqlen, 3, nheads, headdim).
209
+ seqlen_offset: Used in generation where the passed `qkv` is only the last token in the batch.
210
+
211
+ Returns:
212
+ New `qkv` and the cached sinusoids.
213
+
214
+ """
215
+
216
+ self._update_cos_sin_cache(qkv, seqlen_offset)
217
+
218
+ return self.apply_rotary_emb_qkv(qkv, self._sin_cached[seqlen_offset:], self._cos_cached[seqlen_offset:])
219
+
220
+ def _update_kv_cache(kv, inference_params, layer_idx):
221
+ """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
222
+ Adapted from https://github.com/Dao-AILab/flash-attention."""
223
+ # Pre-allocate memory for key-values for inference.
224
+ num_heads, head_dim = kv.shape[-2:]
225
+ if layer_idx not in inference_params.key_value_memory_dict:
226
+ kv_cache = torch.empty(
227
+ inference_params.max_batch_size, inference_params.max_sequence_len, 2,
228
+ num_heads, head_dim, dtype=kv.dtype, device=kv.device
229
+ )
230
+ inference_params.key_value_memory_dict[layer_idx] = kv_cache
231
+ else:
232
+ kv_cache = inference_params.key_value_memory_dict[layer_idx]
233
+
234
+ # Adjust key and value for inference
235
+ batch_start = inference_params.batch_size_offset
236
+ batch_end = batch_start + kv.shape[0]
237
+ sequence_start = inference_params.sequence_len_offset
238
+ sequence_end = sequence_start + kv.shape[1]
239
+ assert batch_end <= (kv_cache.shape[0] if kv_cache is not None else v_cache.shape[0])
240
+ assert sequence_end <= (kv_cache.shape[1] if kv_cache is not None else v_cache.shape[2])
241
+
242
+ assert kv_cache is not None
243
+ kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
244
+ kv = kv_cache[batch_start:batch_end, :sequence_end, ...]
245
+ return kv
246
+
247
+
248
+ class MLP(nn.Module):
249
+ """Multi-Layer Perceptron.
250
+
251
+ Reference:
252
+ Attention Is All You Need.
253
+ https://arxiv.org/pdf/1706.03762.pdf.
254
+
255
+ """
256
+
257
+ def __init__(self, config: PretrainedConfig, n_inner: Optional[int] = None, act_fn: Optional[str] = None) -> None:
258
+ super().__init__()
259
+
260
+ act_fn = config.activation_function if act_fn is None else act_fn
261
+ assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
262
+
263
+ n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
264
+ n_inner = n_inner if n_inner is not None else 4 * config.n_embd
265
+
266
+ self.fc1 = nn.Linear(config.n_embd, n_inner)
267
+ self.fc2 = nn.Linear(n_inner, config.n_embd)
268
+ self.act = ACT2FN[act_fn]
269
+
270
+ def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
271
+ old_keys = [prefix + "fc_in.weight", prefix + "fc_out.weight", prefix + "fc_in.bias", prefix + "fc_out.bias"]
272
+ new_keys = [prefix + "fc1.weight", prefix + "fc2.weight", prefix + "fc1.bias", prefix + "fc2.bias"]
273
+
274
+ if all(k in state_dict for k in old_keys) and not all(k in state_dict for k in new_keys):
275
+ # Older version of `MLP` saved with different key names.
276
+ for old_key, new_key in zip(old_keys, new_keys):
277
+ state_dict[new_key] = state_dict.pop(old_key)
278
+
279
+ return super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
280
+
281
+ def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
282
+ hidden_states = self.fc1(hidden_states)
283
+ hidden_states = self.act(hidden_states)
284
+ hidden_states = self.fc2(hidden_states)
285
+
286
+ return hidden_states
287
+
288
+
289
+ class FusedMLP(nn.Module):
290
+ """Fused Multi-Layer Perceptron from `flash-attn`.
291
+
292
+ Reference:
293
+ https://github.com/HazyResearch/flash-attention/blob/main/flash_attn/ops/fused_dense.py.
294
+
295
+ """
296
+ def __init__(self, config: PretrainedConfig, n_inner: Optional[int] = None, act_fn: Optional[str] = None,
297
+ raise_on_missing: bool = False) -> None:
298
+ super().__init__()
299
+
300
+ act_fn = config.activation_function if act_fn is None else act_fn
301
+ assert act_fn in ACT2FN.keys(), f"`act_fn` must be one of: {ACT2FN.keys()}."
302
+
303
+ n_inner = getattr(config, "n_inner", None) if n_inner is None else n_inner
304
+ n_inner = n_inner if n_inner is not None else 4 * config.n_embd
305
+
306
+ gelu_activations = ["gelu_new", "gelu_fast", "gelu_approx"]
307
+ activation = "gelu_approx" if act_fn in gelu_activations else "relu"
308
+
309
+ self.mlp = MLP(config, n_inner=n_inner, act_fn=act_fn)
310
+
311
+ def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
312
+ return self.mlp(hidden_states)
313
+
314
+ class SelfAttention(nn.Module):
315
+ """Implement the scaled dot product attention with softmax.
316
+ Adapted from https://github.com/Dao-AILab/flash-attention.
317
+ Arguments
318
+ ---------
319
+ softmax_scale: The temperature to use for the softmax attention.
320
+ (default: 1/sqrt(d_keys) where d_keys is computed at
321
+ runtime)
322
+ attention_dropout: The dropout rate to apply to the attention
323
+ (default: 0.0)
324
+ """
325
+ def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
326
+ super().__init__()
327
+ self.causal = causal
328
+ self.softmax_scale = softmax_scale
329
+ self.drop = nn.Dropout(attention_dropout)
330
+
331
+ def forward(self, qkv, causal=None, key_padding_mask=None):
332
+ """Implements the multihead softmax attention.
333
+ Arguments
334
+ ---------
335
+ qkv: The tensor containing the query, key, and value. (B, S, 3, H, D)
336
+ causal: if passed, will override self.causal
337
+ key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
338
+ False means to mask out. (B, S)
339
+ """
340
+ batch_size, seqlen = qkv.shape[0], qkv.shape[1]
341
+ causal = self.causal if causal is None else causal
342
+ q, k, v = qkv.unbind(dim=2)
343
+ softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
344
+ scores = torch.einsum('bthd,bshd->bhts', q, k * softmax_scale)
345
+ if key_padding_mask is not None:
346
+ padding_mask = torch.full((batch_size, seqlen), -10000.0, dtype=scores.dtype,
347
+ device=scores.device)
348
+ padding_mask.masked_fill_(key_padding_mask, 0.0)
349
+ # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
350
+ scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
351
+ if causal:
352
+ # "triu_tril_cuda_template" not implemented for 'BFloat16'
353
+ # So we have to construct the mask in float
354
+ causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
355
+ # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
356
+ scores = scores + causal_mask.to(dtype=scores.dtype)
357
+ attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
358
+ attention_drop = self.drop(attention)
359
+ output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
360
+ return output
361
+
362
+
363
+ class CrossAttention(nn.Module):
364
+ """Implement the scaled dot product attention with softmax.
365
+ Adapted from https://github.com/Dao-AILab/flash-attention.
366
+ Arguments
367
+ ---------
368
+ softmax_scale: The temperature to use for the softmax attention.
369
+ (default: 1/sqrt(d_keys) where d_keys is computed at
370
+ runtime)
371
+ attention_dropout: The dropout rate to apply to the attention
372
+ (default: 0.0)
373
+ """
374
+ def __init__(self, causal=False, softmax_scale=None, attention_dropout=0.0):
375
+ super().__init__()
376
+ self.causal = causal
377
+ self.softmax_scale = softmax_scale
378
+ self.drop = nn.Dropout(attention_dropout)
379
+
380
+ def forward(self, q, kv, causal=None, key_padding_mask=None):
381
+ """Implements the multihead softmax attention.
382
+ Arguments
383
+ ---------
384
+ q: The tensor containing the query. (B, Sq, H, D)
385
+ kv: The tensor containing the key and value. (B, Sk, 2, H, D)
386
+ causal: if passed, will override self.causal
387
+ key_padding_mask: boolean mask to apply to the attention weights. True means to keep,
388
+ False means to mask out. (B, Sk)
389
+ """
390
+ batch_size, seqlen_q = q.shape[0], q.shape[1]
391
+ causal = self.causal if causal is None else causal
392
+ seqlen_k = kv.shape[1]
393
+ assert kv.shape[0] == batch_size and kv.shape[3] == q.shape[2] and kv.shape[4] == q.shape[3]
394
+ k, v = kv.unbind(dim=2)
395
+ softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
396
+ scores = torch.einsum('bthd,bshd->bhts', q, k * softmax_scale)
397
+ if key_padding_mask is not None:
398
+ padding_mask = torch.full((batch_size, seqlen_k), -10000.0, dtype=scores.dtype,
399
+ device=scores.device)
400
+ padding_mask.masked_fill_(key_padding_mask, 0.0)
401
+ # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
402
+ scores = scores + rearrange(padding_mask, 'b s -> b 1 1 s')
403
+ if causal:
404
+ # "triu_tril_cuda_template" not implemented for 'BFloat16'
405
+ # So we have to construct the mask in float
406
+ causal_mask = torch.triu(torch.full((seqlen_q, seqlen_k), -10000.0,
407
+ device=scores.device), 1)
408
+ # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess)
409
+ scores = scores + causal_mask.to(dtype=scores.dtype)
410
+ attention = torch.softmax(scores, dim=-1, dtype=v.dtype)
411
+ attention_drop = self.drop(attention)
412
+ output = torch.einsum('bhts,bshd->bthd', attention_drop, v)
413
+ return output
414
+
415
+ def find_mha_dims(
416
+ config: PretrainedConfig, n_head: Optional[int] = None, head_dim: Optional[int] = None
417
+ ) -> Tuple[int, int]:
418
+ """Validate and return the number of heads and head dimension for multi-head attention.
419
+
420
+ Args:
421
+ config: Model configuration.
422
+ n_head: Number of heads.
423
+ head_dim: Head dimension.
424
+
425
+ Returns:
426
+ Number of heads and head dimension.
427
+
428
+ """
429
+
430
+ assert all(
431
+ hasattr(config, attr) for attr in ["n_embd", "n_head"]
432
+ ), "`config` must have `n_embd` and `n_head` attributes."
433
+
434
+ if head_dim is None:
435
+ assert (
436
+ config.n_embd % config.n_head == 0
437
+ ), f"Hidden size ({config.n_embd}) must be divisible by the number of heads ({config.n_head})."
438
+
439
+ if n_head is None and head_dim is None:
440
+ head_dim = config.n_embd // config.n_head
441
+ n_head = config.n_head
442
+ elif n_head is None or head_dim is None:
443
+ raise ValueError("`n_head` and `head_dim` must be both specified or `None`.")
444
+
445
+ return n_head, head_dim
446
+
447
+
448
+ class MHA(nn.Module):
449
+ """Multi-head attention layer.
450
+ Adapted from https://github.com/Dao-AILab/flash-attention."""
451
+
452
+ def __init__(
453
+ self,
454
+ config: PretrainedConfig,
455
+ rotary_dim: Optional[int] = None,
456
+ n_head: Optional[int] = None,
457
+ head_dim: Optional[int] = None,
458
+ bias: Optional[bool] = True,
459
+ dropout: Optional[float] = 0.0,
460
+ softmax_scale: Optional[float] = None,
461
+ causal: Optional[bool] = True,
462
+ layer_idx: Optional[int] = None,
463
+ rotary_emb_scale_base: Optional[float] = None,
464
+ return_residual: Optional[bool] = False,
465
+ checkpointing: Optional[bool] = False,
466
+ device: Optional[str] = None,
467
+ dtype: Optional[torch.dtype] = None,
468
+ fused_dense: Optional[bool] = True,
469
+ flash_attn: Optional[bool] = True,
470
+ cutlass_attn: Optional[bool] = False,
471
+ flash_rotary: Optional[bool] = True,
472
+ raise_on_missing: Optional[bool] = False
473
+ ) -> None:
474
+ super().__init__()
475
+
476
+ factory_kwargs = {"device": device, "dtype": dtype}
477
+ n_head, head_dim = find_mha_dims(config, n_head, head_dim)
478
+
479
+ self.hidden_size = config.n_embd
480
+ self.n_head = n_head
481
+ self.head_dim = head_dim
482
+ self.op_size = n_head * head_dim
483
+
484
+ self.causal = causal
485
+ self.layer_idx = layer_idx
486
+ self.rotary_emb_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
487
+ self.fused_dense = fused_dense
488
+ self.flash_attn = flash_attn
489
+ self.cutlass_attn = cutlass_attn
490
+ self.flash_rotary = flash_rotary
491
+ self.return_residual = return_residual
492
+ self.checkpointing = checkpointing
493
+
494
+ if self.rotary_emb_dim > 0:
495
+ rotary_kwargs = {"device": device}
496
+ if rotary_emb_scale_base is not None and rotary_emb_scale_base > 0.0:
497
+ rotary_kwargs["scale_base"] = rotary_emb_scale_base
498
+
499
+ self.rotary_emb = RotaryEmbedding(self.rotary_emb_dim, **rotary_kwargs)
500
+ else:
501
+ pass
502
+
503
+ self.Wqkv = nn.Linear(self.hidden_size, 3 * self.op_size, bias=bias, **factory_kwargs)
504
+ self.out_proj = nn.Linear(self.op_size, self.hidden_size, bias=bias, **factory_kwargs)
505
+
506
+ self.inner_attn = SelfAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
507
+ self.inner_cross_attn = CrossAttention(causal=causal, softmax_scale=softmax_scale, attention_dropout=dropout)
508
+
509
+ def _update_kv_cache(self, kv: torch.FloatTensor, inference_params: InferenceParams) -> None:
510
+ """kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)
511
+ Adapted from https://github.com/Dao-AILab/flash-attention."""
512
+
513
+ assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"
514
+
515
+ return _update_kv_cache(kv, inference_params, self.layer_idx)
516
+
517
+ def forward(
518
+ self,
519
+ x: torch.FloatTensor,
520
+ x_kv: Optional[torch.FloatTensor] = None,
521
+ key_padding_mask: Optional[torch.BoolTensor] = None,
522
+ cu_seqlens: Optional[torch.LongTensor] = None,
523
+ max_seqlen: Optional[int] = None,
524
+ mixer_subset: Optional[torch.LongTensor] = None,
525
+ past_cache: Optional[InferenceParams] = None,
526
+ **kwargs
527
+ ) -> Tuple[torch.FloatTensor, torch.FloatTensor]:
528
+ """Perform the forward pass.
529
+
530
+ Args:
531
+ x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
532
+ cu_seqlens is None and max_seqlen is None, else (total, hidden_dim) where total
533
+ is the is the sum of the sequence lengths in the batch.
534
+ x_kv: (batch, seqlen, hidden_dim), only applicable for cross-attention. If None, use x.
535
+ key_padding_mask: boolean mask, True means to keep, False means to mask out.
536
+ (batch, seqlen). Only applicable when not using FlashAttention.
537
+ cu_seqlens: (batch_size + 1,), dtype torch.int32. The cumulative sequence lengths
538
+ of the sequences in the batch, used to index into x. Only applicable when using
539
+ FlashAttention.
540
+ max_seqlen: int. Maximum sequence length in the batch.
541
+ mixer_subset: for cross-attention only. If not None, will take a subset of x
542
+ before applying the query projection. Useful for e.g., ViT where we only care
543
+ about the CLS token in the last layer.
544
+ past_cache: For generation only.
545
+
546
+ Returns:
547
+ (batch, seqlen, hidden_dim) if cu_seqlens is None and max_seqlen is None,
548
+ else (total, hidden_dim) where total is the is the sum of the sequence lengths
549
+ in the batch.
550
+
551
+ """
552
+
553
+ if cu_seqlens is not None:
554
+ assert max_seqlen is not None
555
+ assert key_padding_mask is None
556
+ assert self.flash_attn
557
+ assert self.rotary_emb_dim == 0
558
+
559
+ if key_padding_mask is not None:
560
+ assert cu_seqlens is None
561
+ assert max_seqlen is None
562
+ assert not self.flash_attn
563
+
564
+ if past_cache is not None:
565
+ assert key_padding_mask is None
566
+ assert cu_seqlens is None and max_seqlen is None
567
+
568
+ attn_kwargs = {"key_padding_mask": key_padding_mask}
569
+
570
+ assert x_kv is None and mixer_subset is None
571
+
572
+ qkv = self.Wqkv(x)
573
+ qkv = rearrange(qkv, "... (three h d) -> ... three h d", three=3, d=self.head_dim)
574
+
575
+ if past_cache is None:
576
+ if self.rotary_emb_dim > 0:
577
+ qkv = self.rotary_emb(qkv)
578
+ context = self.inner_attn(qkv, **attn_kwargs)
579
+
580
+ else:
581
+ if self.rotary_emb_dim > 0:
582
+ qkv = self.rotary_emb(qkv, seqlen_offset=past_cache.sequence_len_offset)
583
+ q = qkv[:, :, 0]
584
+ kv = self._update_kv_cache(qkv[:, :, 1:], past_cache)
585
+ # If we're processing the prompt, causal=None (use self.causal).
586
+ # If we're decoding, then causal=False.
587
+ causal = None if past_cache.sequence_len_offset == 0 else False
588
+ context = self.inner_cross_attn(q, kv, causal=causal)
589
+
590
+ out = rearrange(context, "... h d -> ... (h d)")
591
+ out = self.out_proj(out)
592
+
593
+ return out if not self.return_residual else (out, x)
594
+
595
+ class ParallelBlock(nn.Module):
596
+ """Parallel block.
597
+
598
+ This block applies parallel mixer and MLP layers to the input (used in GPT-J and CodeGen).
599
+
600
+ """
601
+
602
+ def __init__(
603
+ self,
604
+ config: PretrainedConfig,
605
+ mixer: Optional[Dict[str, Any]] = None,
606
+ mlp: Optional[Dict[str, Any]] = None,
607
+ block_idx: Optional[int] = None,
608
+ ) -> None:
609
+ super().__init__()
610
+
611
+ self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
612
+ self.resid_dropout = nn.Dropout(config.resid_pdrop)
613
+ self.block_idx = block_idx
614
+
615
+ self.mixer = MHA(config=config, **mixer, layer_idx=block_idx)
616
+ mlp_cls = mlp.pop('mlp_cls')
617
+ if mlp_cls == 'fused_mlp':
618
+ self.mlp = FusedMLP(config=config, **mlp)
619
+ else:
620
+ self.mlp = MLP(config=config, **mlp)
621
+
622
+ def forward(self, hidden_states: torch.FloatTensor,
623
+ past_cache: Optional[torch.FloatTensor] = None) -> torch.FloatTensor:
624
+ residual = hidden_states
625
+ hidden_states = self.ln(hidden_states)
626
+
627
+ attn_outputs = self.mixer(hidden_states, past_cache=past_cache)
628
+ if isinstance(attn_outputs, tuple):
629
+ attn_outputs = attn_outputs[0]
630
+
631
+ attn_outputs = self.resid_dropout(attn_outputs)
632
+ feed_forward_hidden_states = self.resid_dropout(self.mlp(hidden_states))
633
+
634
+ hidden_states = attn_outputs + feed_forward_hidden_states + residual
635
+
636
+ return hidden_states
637
+
638
+ class CausalLMHead(nn.Module):
639
+ """Causal Language Modeling head.
640
+
641
+ Reference:
642
+ Improving Language Understanding by Generative Pre-Training.
643
+ https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
644
+
645
+ """
646
+
647
+ def __init__(self, config: PretrainedConfig) -> None:
648
+ super().__init__()
649
+
650
+ self.ln = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
651
+ self.linear = nn.Linear(config.n_embd, config.vocab_size)
652
+
653
+ def forward(self, hidden_states: torch.FloatTensor) -> torch.FloatTensor:
654
+ hidden_states = self.ln(hidden_states)
655
+ logits = self.linear(hidden_states).to(torch.float32)
656
+
657
+ return logits
658
+
659
+
660
+ class CausalLMLoss(nn.Module):
661
+ """Causal Language Modeling loss.
662
+
663
+ Reference:
664
+ Improving Language Understanding by Generative Pre-Training.
665
+ https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.
666
+
667
+ """
668
+
669
+ def __init__(self, shift_labels: Optional[bool] = True) -> None:
670
+ super().__init__()
671
+
672
+ self.shift_labels = shift_labels
673
+ self.loss_fct = nn.CrossEntropyLoss()
674
+
675
+ def forward(self, logits: torch.FloatTensor, labels: torch.LongTensor) -> torch.FloatTensor:
676
+ if self.shift_labels:
677
+ logits = logits[..., :-1, :].contiguous()
678
+ labels = labels[..., 1:].contiguous()
679
+
680
+ loss = self.loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))
681
+
682
+ return loss
683
+
684
+ class MixFormerSequentialPreTrainedModel(PreTrainedModel):
685
+ """MixFormer (sequential for DeepSpeed) pre-trained model."""
686
+
687
+ config_class = MixFormerSequentialConfig
688
+ base_model_prefix = "transformer"
689
+ supports_gradient_checkpointing = True
690
+
691
+ def __init__(self, *inputs, **kwargs) -> None:
692
+ super().__init__(*inputs, **kwargs)
693
+
694
+ def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs) -> Dict[str, Any]:
695
+ if "use_cache" in kwargs and not kwargs["use_cache"]:
696
+ return {"input_ids": input_ids}
697
+
698
+ if past_key_values is None or not (isinstance(past_key_values, InferenceParams)):
699
+ past_key_values = InferenceParams(
700
+ max_batch_size=input_ids.shape[0],
701
+ max_sequence_len=self.config.n_positions,
702
+ sequence_len_offset=0,
703
+ batch_size_offset=0,
704
+ fused_ft_kernel=False,
705
+ key_value_memory_dict={},
706
+ )
707
+ else:
708
+ # assume past_key_values has cached all but last token in input_ids
709
+ past_key_values.sequence_len_offset = len(input_ids[0]) - 1
710
+ input_ids = input_ids[:, -1].unsqueeze(-1)
711
+
712
+ return {"input_ids": input_ids, "past_key_values": past_key_values, **kwargs}
713
+
714
+
715
+ class MixFormerSequentialForCausalLM(MixFormerSequentialPreTrainedModel):
716
+ """MixFormer (sequential for DeepSpeed) for Causal Language Modeling."""
717
+
718
+ _keys_to_ignore_on_load_missing = [""]
719
+ _keys_to_ignore_on_load_unexpected = [r"layers\.\d+\.mlp.(fc_in|fc_out)\.(weight|bias)"]
720
+
721
+ def __init__(self, config: MixFormerSequentialConfig) -> None:
722
+ super().__init__(config)
723
+
724
+ modules = [Embedding(config)]
725
+ block_config = config.architecture
726
+
727
+ if not isinstance(block_config, list):
728
+ block_config = [block_config for _ in range(config.n_layer)]
729
+
730
+ if config.n_layer != len(block_config):
731
+ config.n_layer = len(block_config)
732
+
733
+ for block_idx, block in enumerate(block_config):
734
+ # `block_cls` with `legacy` value is for backward compatibility
735
+ # `path` key is for backward compatibility
736
+ block = copy.deepcopy(block) or {"block_cls": "parallel"}
737
+ block_cls = block.pop("path", None) or block.pop("block_cls", None)
738
+
739
+ block["block_idx"] = block_idx
740
+ modules.append(ParallelBlock(config, **block))
741
+
742
+ modules.append(CausalLMHead(config))
743
+
744
+ self.layers = nn.Sequential(*modules)
745
+ self.loss = CausalLMLoss()
746
+
747
+ self.post_init()
748
+
749
+ def get_input_embeddings(self) -> nn.Embedding:
750
+ return self.layers[0].wte
751
+
752
+ def set_input_embeddings(self, new_embeddings: nn.Embedding) -> None:
753
+ self.layers[0].wte = new_embeddings
754
+
755
+ def get_output_embeddings(self) -> nn.Linear:
756
+ return self.layers[-1].linear
757
+
758
+ def set_output_embeddings(self, new_embeddings: nn.Linear) -> None:
759
+ self.layers[-1].linear = new_embeddings
760
+
761
+ def forward(
762
+ self, input_ids: torch.LongTensor, labels: Optional[torch.LongTensor] = None,
763
+ past_key_values: Optional[torch.FloatTensor] = None, **kwargs
764
+ ) -> CausalLMOutputWithPast:
765
+
766
+ if not past_key_values:
767
+ lm_logits = self.layers(input_ids)
768
+ else:
769
+ hidden_layer = self.layers[0](input_ids)
770
+ for module in self.layers[1:-1]:
771
+ hidden_layer = module(hidden_layer, past_cache=past_key_values)
772
+ lm_logits = self.layers[-1](hidden_layer)
773
+
774
+ loss = None
775
+ if labels is not None:
776
+ loss = self.loss(lm_logits, labels)
777
+
778
+ return CausalLMOutputWithPast(loss=loss, logits=lm_logits, past_key_values=past_key_values)
pytorch_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ oid sha256:eab6a12a9a2b78cac8f8975aea9f3a5e89ddadcb9e0dad27e40965e57e235a4a
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+ size 2836623617
special_tokens_map.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
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+ "bos_token": "<|endoftext|>",
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+ "eos_token": "<|endoftext|>",
4
+ "unk_token": "<|endoftext|>"
5
+ }
tokenizer.json ADDED
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tokenizer_config.json ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "bos_token": "<|endoftext|>",
4
+ "clean_up_tokenization_spaces": true,
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+ "eos_token": "<|endoftext|>",
6
+ "model_max_length": 2048,
7
+ "tokenizer_class": "CodeGenTokenizer",
8
+ "unk_token": "<|endoftext|>"
9
+ }
vocab.json ADDED
The diff for this file is too large to render. See raw diff