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README.md ADDED
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1
+ ---
2
+ language:
3
+ - en
4
+ - zh
5
+ library_name: transformers
6
+ tags:
7
+ - Long Context
8
+ - chatglm
9
+ - llama
10
+ datasets:
11
+ - THUDM/LongAlign-10k
12
+ - THUDM/LongBench
13
+ ---
14
+ # LongAlign-6B-64k-base
15
+
16
+ <p align="center">
17
+ 🤗 <a href="https://huggingface.co/datasets/THUDM/LongAlign-10k" target="_blank">[LongAlign Dataset] </a> • 💻 <a href="https://github.com/THUDM/LongAlign" target="_blank">[Github Repo]</a> • 📃 <a href="https://arxiv.org/" target="_blank">[LongAlign Paper]</a>
18
+ </p>
19
+
20
+ **LongAlign** is the first full recipe for LLM alignment on long context. We propose the **LongAlign-10k** dataset, containing 10,000 long instruction data of 8k-64k in length. We investigate on trianing strategies, namely **packing (with loss weighting) and sorted batching**, which are all implemented in our code. For real-world long context evaluation, we introduce **Chat-LongBench** that evaluate the instruction-following capability on queries of 10k-100k length.
21
+
22
+ ## All Models
23
+
24
+ We open-sourced the following list of models:
25
+
26
+ |Model|Huggingface Repo|Description|
27
+ |---|---|---|
28
+ |**LongAlign-6B-64k-base**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-6B-64k-base) | **ChatGLM3-6B** with an extended 64k context window |
29
+ |**LongAlign-6B-64k**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-6B-64k) | Chat model by LongAlign training on LongAlign-6B-64k-base|
30
+ |**LongAlign-7B-64k-base**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-7B-64k-base) | **Llama-2-7B** with an extended 64k context window |
31
+ |**LongAlign-7B-64k**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-7B-64k) | Chat model by LongAlign training on LongAlign-7B-64k-base|
32
+ |**LongAlign-13B-64k-base**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-13B-64k-base) | **Llama-2-13B** with an extended 64k context window |
33
+ |**LongAlign-13B-64k**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/LongAlign-13B-64k) | Chat model by LongAlign training on LongAlign-13B-64k-base|
34
+ |**ChatGLM3-6B-128k**| [🤗 Huggingface Repo](https://huggingface.co/THUDM/chatglm3-6b-128k) | **ChatGLM3-6B** with a 128k context window|
35
+
36
+ ![](assets/leaderboard.png)
37
+
38
+ ## Model usage
39
+ Chat prompt template for LongAlign-6B-64k:
40
+ ```text
41
+ [Round 1]
42
+
43
+ 问:Hi!
44
+
45
+ 答:Hello! What can I assist you today?
46
+
47
+ [Round 2]
48
+
49
+ 问:What should I do if I can't sleep at night?
50
+
51
+ 答:
52
+ ```
53
+ Chat prompt template for LongAlign-7B-64k and LongAlign-13B-64k:
54
+ ```text
55
+ [INST]Hi![/INST]Hello! What can I assist you today?
56
+
57
+ [INST]What should I do if I can't sleep at night?[/INST]
58
+ ```
59
+ ChatGLM3-6B-128k uses the same prompt template as [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b).
60
+
61
+ A simple demo for deployment of the model:
62
+ ```python
63
+ from transformers import AutoTokenizer, AutoModelForCausalLM
64
+ import torch
65
+ tokenizer = AutoTokenizer.from_pretrained("THUDM/LongAlign-6B-64k", trust_remote_code=True)
66
+ model = AutoModelForCausalLM.from_pretrained("THUDM/LongAlign-6B-64k", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
67
+ model = model.eval()
68
+ query = open("assets/paper.txt").read() + "\n\nPlease summarize the paper."
69
+ response, history = model.chat(tokenizer, query, history=[], max_new_tokens=512, temperature=1)
70
+ print(response)
71
+ ```
72
+
73
+ ## Citation
74
+
75
+ If you find our work useful, please consider citing LongAlign:
76
+
77
+ ```
78
+
79
+ ```
assets/leaderboard.png ADDED
config.json ADDED
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1
+ {
2
+ "_name_or_path": "THUDM/LongAlign-6B-64k-base",
3
+ "add_bias_linear": false,
4
+ "add_qkv_bias": true,
5
+ "apply_query_key_layer_scaling": true,
6
+ "apply_residual_connection_post_layernorm": false,
7
+ "architectures": [
8
+ "ChatGLMForConditionalGeneration"
9
+ ],
10
+ "attention_dropout": 0.0,
11
+ "attention_softmax_in_fp32": true,
12
+ "auto_map": {
13
+ "AutoConfig": "configuration_chatglm.ChatGLMConfig",
14
+ "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
15
+ "AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
16
+ "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
17
+ "AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"
18
+ },
19
+ "bias_dropout_fusion": true,
20
+ "classifier_dropout": null,
21
+ "eos_token_id": 2,
22
+ "ffn_hidden_size": 13696,
23
+ "fp32_residual_connection": false,
24
+ "hidden_dropout": 0.0,
25
+ "hidden_size": 4096,
26
+ "kv_channels": 128,
27
+ "layernorm_epsilon": 1e-05,
28
+ "model_type": "chatglm",
29
+ "multi_query_attention": true,
30
+ "multi_query_group_num": 2,
31
+ "num_attention_heads": 32,
32
+ "num_layers": 28,
33
+ "original_rope": true,
34
+ "pad_token_id": 0,
35
+ "padded_vocab_size": 65024,
36
+ "post_layer_norm": true,
37
+ "pre_seq_len": null,
38
+ "prefix_projection": false,
39
+ "quantization_bit": 0,
40
+ "rmsnorm": true,
41
+ "rope_ratio": 200,
42
+ "seq_length": 65536,
43
+ "tie_word_embeddings": false,
44
+ "torch_dtype": "bfloat16",
45
+ "transformers_version": "4.33.0",
46
+ "use_cache": true,
47
+ "vocab_size": 65024
48
+ }
configuration_chatglm.py ADDED
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1
+ from transformers import PretrainedConfig
2
+
3
+
4
+ class ChatGLMConfig(PretrainedConfig):
5
+ model_type = "chatglm"
6
+ def __init__(
7
+ self,
8
+ num_layers=28,
9
+ padded_vocab_size=65024,
10
+ hidden_size=4096,
11
+ ffn_hidden_size=13696,
12
+ kv_channels=128,
13
+ num_attention_heads=32,
14
+ seq_length=2048,
15
+ hidden_dropout=0.0,
16
+ classifier_dropout=None,
17
+ attention_dropout=0.0,
18
+ layernorm_epsilon=1e-5,
19
+ rope_ratio=1,
20
+ rmsnorm=True,
21
+ apply_residual_connection_post_layernorm=False,
22
+ post_layer_norm=True,
23
+ add_bias_linear=False,
24
+ add_qkv_bias=False,
25
+ bias_dropout_fusion=True,
26
+ multi_query_attention=False,
27
+ multi_query_group_num=1,
28
+ apply_query_key_layer_scaling=True,
29
+ attention_softmax_in_fp32=True,
30
+ fp32_residual_connection=False,
31
+ quantization_bit=0,
32
+ pre_seq_len=None,
33
+ prefix_projection=False,
34
+ **kwargs
35
+ ):
36
+ self.num_layers = num_layers
37
+ self.vocab_size = padded_vocab_size
38
+ self.padded_vocab_size = padded_vocab_size
39
+ self.hidden_size = hidden_size
40
+ self.ffn_hidden_size = ffn_hidden_size
41
+ self.kv_channels = kv_channels
42
+ self.num_attention_heads = num_attention_heads
43
+ self.seq_length = seq_length
44
+ self.hidden_dropout = hidden_dropout
45
+ self.classifier_dropout = classifier_dropout
46
+ self.attention_dropout = attention_dropout
47
+ self.layernorm_epsilon = layernorm_epsilon
48
+ self.rope_ratio = rope_ratio
49
+ self.rmsnorm = rmsnorm
50
+ self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
51
+ self.post_layer_norm = post_layer_norm
52
+ self.add_bias_linear = add_bias_linear
53
+ self.add_qkv_bias = add_qkv_bias
54
+ self.bias_dropout_fusion = bias_dropout_fusion
55
+ self.multi_query_attention = multi_query_attention
56
+ self.multi_query_group_num = multi_query_group_num
57
+ self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
58
+ self.attention_softmax_in_fp32 = attention_softmax_in_fp32
59
+ self.fp32_residual_connection = fp32_residual_connection
60
+ self.quantization_bit = quantization_bit
61
+ self.pre_seq_len = pre_seq_len
62
+ self.prefix_projection = prefix_projection
63
+ super().__init__(**kwargs)
generation_config.json ADDED
@@ -0,0 +1,6 @@
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "eos_token_id": 2,
4
+ "pad_token_id": 0,
5
+ "transformers_version": "4.33.0"
6
+ }
modeling_chatglm.py ADDED
@@ -0,0 +1,1138 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """ PyTorch ChatGLM model. """
2
+
3
+ import math
4
+ import copy
5
+ import warnings
6
+ import re
7
+ import sys
8
+
9
+ import torch
10
+ import torch.utils.checkpoint
11
+ import torch.nn.functional as F
12
+ from torch import nn
13
+ from torch.nn import CrossEntropyLoss, LayerNorm
14
+ from torch.nn.utils import skip_init
15
+ from typing import Optional, Tuple, Union, List, Callable, Dict, Any
16
+
17
+ from transformers.modeling_outputs import (
18
+ BaseModelOutputWithPast,
19
+ CausalLMOutputWithPast,
20
+ )
21
+ from transformers.modeling_utils import PreTrainedModel
22
+ from transformers.utils import logging
23
+ from transformers.generation.logits_process import LogitsProcessor
24
+ from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput
25
+
26
+ from .configuration_chatglm import ChatGLMConfig
27
+ from einops import rearrange
28
+ try:
29
+ from flash_attn.flash_attn_interface import flash_attn_unpadded_func
30
+ except ImportError:
31
+ try:
32
+ # FlashAttention-2
33
+ from flash_attn.flash_attn_interface import flash_attn_varlen_func as flash_attn_unpadded_func
34
+ except ImportError:
35
+ flash_attn_unpadded_func = None
36
+
37
+ # flags required to enable jit fusion kernels
38
+
39
+ if sys.platform != 'darwin':
40
+ torch._C._jit_set_profiling_mode(False)
41
+ torch._C._jit_set_profiling_executor(False)
42
+ torch._C._jit_override_can_fuse_on_cpu(True)
43
+ torch._C._jit_override_can_fuse_on_gpu(True)
44
+
45
+ logger = logging.get_logger(__name__)
46
+
47
+ _CHECKPOINT_FOR_DOC = "THUDM/ChatGLM2-6B"
48
+ _CONFIG_FOR_DOC = "ChatGLM6BConfig"
49
+
50
+ CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
51
+ "THUDM/chatglm2-6b",
52
+ # See all ChatGLM models at https://huggingface.co/models?filter=chatglm
53
+ ]
54
+
55
+ def default_init(cls, *args, **kwargs):
56
+ return cls(*args, **kwargs)
57
+
58
+
59
+ class InvalidScoreLogitsProcessor(LogitsProcessor):
60
+ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
61
+ if torch.isnan(scores).any() or torch.isinf(scores).any():
62
+ scores.zero_()
63
+ scores[..., 5] = 5e4
64
+ return scores
65
+
66
+
67
+ class PrefixEncoder(torch.nn.Module):
68
+ """
69
+ The torch.nn model to encode the prefix
70
+ Input shape: (batch-size, prefix-length)
71
+ Output shape: (batch-size, prefix-length, 2*layers*hidden)
72
+ """
73
+
74
+ def __init__(self, config):
75
+ super().__init__()
76
+ self.prefix_projection = config.prefix_projection
77
+ if self.prefix_projection:
78
+ # Use a two-layer MLP to encode the prefix
79
+ self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size)
80
+ self.trans = torch.nn.Sequential(
81
+ torch.nn.Linear(config.hidden_size, config.hidden_size),
82
+ torch.nn.Tanh(),
83
+ torch.nn.Linear(config.hidden_size, config.num_layers * config.hidden_size * 2)
84
+ )
85
+ else:
86
+ self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_layers * config.hidden_size * 2)
87
+
88
+ def forward(self, prefix: torch.Tensor):
89
+ if self.prefix_projection:
90
+ prefix_tokens = self.embedding(prefix)
91
+ past_key_values = self.trans(prefix_tokens)
92
+ else:
93
+ past_key_values = self.embedding(prefix)
94
+ return past_key_values
95
+
96
+
97
+ def split_tensor_along_last_dim(
98
+ tensor: torch.Tensor,
99
+ num_partitions: int,
100
+ contiguous_split_chunks: bool = False,
101
+ ) -> List[torch.Tensor]:
102
+ """Split a tensor along its last dimension.
103
+
104
+ Arguments:
105
+ tensor: input tensor.
106
+ num_partitions: number of partitions to split the tensor
107
+ contiguous_split_chunks: If True, make each chunk contiguous
108
+ in memory.
109
+
110
+ Returns:
111
+ A list of Tensors
112
+ """
113
+ # Get the size and dimension.
114
+ last_dim = tensor.dim() - 1
115
+ last_dim_size = tensor.size()[last_dim] // num_partitions
116
+ # Split.
117
+ tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
118
+ # Note: torch.split does not create contiguous tensors by default.
119
+ if contiguous_split_chunks:
120
+ return tuple(chunk.contiguous() for chunk in tensor_list)
121
+
122
+ return tensor_list
123
+
124
+
125
+ class RotaryEmbedding(nn.Module):
126
+ def __init__(self, dim, original_impl=False, device=None, dtype=None):
127
+ super().__init__()
128
+ inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim))
129
+ self.register_buffer("inv_freq", inv_freq)
130
+ self.dim = dim
131
+ self.original_impl = original_impl
132
+ self.ratio = 200
133
+
134
+ def forward_impl(
135
+ self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000
136
+ ):
137
+ """Enhanced Transformer with Rotary Position Embedding.
138
+
139
+ Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
140
+ transformers/rope/__init__.py. MIT License:
141
+ https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
142
+ """
143
+ # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
144
+
145
+ base = base * self.ratio
146
+ theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem))
147
+
148
+ # Create position indexes `[0, 1, ..., seq_len - 1]`
149
+ seq_idx = torch.arange(seq_len, dtype=torch.float, device=device)
150
+
151
+ # Calculate the product of position index and $\theta_i$
152
+ idx_theta = torch.outer(seq_idx, theta).float()
153
+
154
+ cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
155
+
156
+ # this is to mimic the behaviour of complex32, else we will get different results
157
+ if dtype in (torch.float16, torch.bfloat16, torch.int8):
158
+ cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half()
159
+ return cache
160
+
161
+ def forward(self, max_seq_len, offset=0):
162
+ return self.forward_impl(
163
+ max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device
164
+ )
165
+
166
+
167
+ @torch.jit.script
168
+ def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
169
+ # x: [sq, b, np, hn]
170
+ sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3)
171
+ rot_dim = rope_cache.shape[-2] * 2
172
+ x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
173
+ # truncate to support variable sizes
174
+ rope_cache = rope_cache[:sq]
175
+ xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2)
176
+ rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2)
177
+ x_out2 = torch.stack(
178
+ [
179
+ xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
180
+ xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
181
+ ],
182
+ -1,
183
+ )
184
+ x_out2 = x_out2.flatten(3)
185
+ return torch.cat((x_out2, x_pass), dim=-1)
186
+
187
+
188
+ class RMSNorm(torch.nn.Module):
189
+ def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
190
+ super().__init__()
191
+ self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype))
192
+ self.eps = eps
193
+
194
+ def forward(self, hidden_states: torch.Tensor):
195
+ input_dtype = hidden_states.dtype
196
+ variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
197
+ hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
198
+
199
+ return (self.weight * hidden_states).to(input_dtype)
200
+
201
+
202
+ class CoreAttention(torch.nn.Module):
203
+ def __init__(self, config: ChatGLMConfig, layer_number):
204
+ super(CoreAttention, self).__init__()
205
+
206
+ self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
207
+ self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
208
+ if self.apply_query_key_layer_scaling:
209
+ self.attention_softmax_in_fp32 = True
210
+ self.layer_number = max(1, layer_number)
211
+
212
+ projection_size = config.kv_channels * config.num_attention_heads
213
+
214
+ # Per attention head and per partition values.
215
+ self.hidden_size_per_partition = projection_size
216
+ self.hidden_size_per_attention_head = projection_size // config.num_attention_heads
217
+ self.num_attention_heads_per_partition = config.num_attention_heads
218
+
219
+ self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
220
+ self.attention_dropout = config.attention_dropout
221
+
222
+ def forward(self, query_layer, key_layer, value_layer, attention_mask):
223
+ seqlen_q, batch_size = query_layer.shape[0], query_layer.shape[1]
224
+ seqlen_k = key_layer.shape[0]
225
+ query_layer, key_layer, value_layer = [rearrange(x, 's b ... -> (b s) ...') for x in [query_layer, key_layer, value_layer]]
226
+ # DO flash_attn_varlen_func
227
+ if attention_mask is None or attention_mask.ndim != 1:
228
+ cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32,
229
+ device=query_layer.device)
230
+ else:
231
+ assert seqlen_q == seqlen_k
232
+ cu_seqlens_q = attention_mask
233
+ if self.training:
234
+ assert seqlen_k == seqlen_q
235
+ is_causal = True
236
+ cu_seqlens_k = cu_seqlens_q
237
+ else:
238
+ is_causal = seqlen_q == seqlen_k
239
+ cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
240
+ device=query_layer.device) if not is_causal else cu_seqlens_q
241
+ self.attention_dropout = 0
242
+ context_layer = flash_attn_unpadded_func(
243
+ query_layer, key_layer, value_layer, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
244
+ self.attention_dropout,
245
+ softmax_scale=1.0 / self.norm_factor, causal=is_causal
246
+ )
247
+ context_layer = rearrange(context_layer, '(b s) ... -> s b ...', b=batch_size)
248
+ new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
249
+ context_layer = context_layer.reshape(*new_context_layer_shape)
250
+ return context_layer
251
+
252
+
253
+ class SelfAttention(torch.nn.Module):
254
+ """Parallel self-attention layer abstract class.
255
+
256
+ Self-attention layer takes input with size [s, b, h]
257
+ and returns output of the same size.
258
+ """
259
+
260
+ def __init__(self, config: ChatGLMConfig, layer_number, device=None):
261
+ super(SelfAttention, self).__init__()
262
+ self.layer_number = max(1, layer_number)
263
+
264
+ self.projection_size = config.kv_channels * config.num_attention_heads
265
+
266
+ # Per attention head and per partition values.
267
+ self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads
268
+ self.num_attention_heads_per_partition = config.num_attention_heads
269
+
270
+ self.multi_query_attention = config.multi_query_attention
271
+ self.qkv_hidden_size = 3 * self.projection_size
272
+ if self.multi_query_attention:
273
+ self.num_multi_query_groups_per_partition = config.multi_query_group_num
274
+ self.qkv_hidden_size = (
275
+ self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
276
+ )
277
+ self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size,
278
+ bias=config.add_bias_linear or config.add_qkv_bias,
279
+ device=device, **_config_to_kwargs(config)
280
+ )
281
+
282
+ self.core_attention = CoreAttention(config, self.layer_number)
283
+
284
+ # Output.
285
+ self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear,
286
+ device=device, **_config_to_kwargs(config)
287
+ )
288
+
289
+ def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
290
+ if self.multi_query_attention:
291
+ num_attention_heads = self.num_multi_query_groups_per_partition
292
+ else:
293
+ num_attention_heads = self.num_attention_heads_per_partition
294
+ return torch.empty(
295
+ inference_max_sequence_len,
296
+ batch_size,
297
+ num_attention_heads,
298
+ self.hidden_size_per_attention_head,
299
+ dtype=dtype,
300
+ device=device,
301
+ )
302
+
303
+ def forward(
304
+ self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
305
+ ):
306
+ # hidden_states: [sq, b, h]
307
+
308
+ # =================================================
309
+ # Pre-allocate memory for key-values for inference.
310
+ # =================================================
311
+ # =====================
312
+ # Query, Key, and Value
313
+ # =====================
314
+
315
+ # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
316
+ mixed_x_layer = self.query_key_value(hidden_states)
317
+
318
+ if self.multi_query_attention:
319
+ (query_layer, key_layer, value_layer) = mixed_x_layer.split(
320
+ [
321
+ self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
322
+ self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
323
+ self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
324
+ ],
325
+ dim=-1,
326
+ )
327
+ query_layer = query_layer.view(
328
+ query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
329
+ )
330
+ key_layer = key_layer.view(
331
+ key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
332
+ )
333
+ value_layer = value_layer.view(
334
+ value_layer.size()[:-1]
335
+ + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
336
+ )
337
+ else:
338
+ new_tensor_shape = mixed_x_layer.size()[:-1] + \
339
+ (self.num_attention_heads_per_partition,
340
+ 3 * self.hidden_size_per_attention_head)
341
+ mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
342
+
343
+ # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
344
+ (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
345
+
346
+ # apply relative positional encoding (rotary embedding)
347
+ if rotary_pos_emb is not None:
348
+ query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb)
349
+ key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)
350
+
351
+ # adjust key and value for inference
352
+ if use_cache:
353
+ if kv_cache is not None:
354
+ cache_k, cache_v = kv_cache
355
+ key_layer = torch.cat((cache_k, key_layer), dim=0)
356
+ value_layer = torch.cat((cache_v, value_layer), dim=0)
357
+ kv_cache = (key_layer, value_layer)
358
+ else:
359
+ kv_cache = None
360
+
361
+
362
+ if self.multi_query_attention:
363
+ key_layer = key_layer.unsqueeze(-2)
364
+ key_layer = key_layer.expand(
365
+ -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
366
+ )
367
+ key_layer = key_layer.contiguous().view(
368
+ key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
369
+ )
370
+ value_layer = value_layer.unsqueeze(-2)
371
+ value_layer = value_layer.expand(
372
+ -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
373
+ )
374
+ value_layer = value_layer.contiguous().view(
375
+ value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
376
+ )
377
+
378
+ # ==================================
379
+ # core attention computation
380
+ # ==================================
381
+
382
+ context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask)
383
+
384
+ # =================
385
+ # Output. [sq, b, h]
386
+ # =================
387
+
388
+ output = self.dense(context_layer)
389
+
390
+ return output, kv_cache
391
+
392
+
393
+ def _config_to_kwargs(args):
394
+ common_kwargs = {
395
+ "dtype": args.torch_dtype,
396
+ }
397
+ return common_kwargs
398
+
399
+
400
+ class MLP(torch.nn.Module):
401
+ """MLP.
402
+
403
+ MLP will take the input with h hidden state, project it to 4*h
404
+ hidden dimension, perform nonlinear transformation, and project the
405
+ state back into h hidden dimension.
406
+ """
407
+
408
+ def __init__(self, config: ChatGLMConfig, device=None):
409
+ super(MLP, self).__init__()
410
+
411
+ self.add_bias = config.add_bias_linear
412
+
413
+ # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
414
+ self.dense_h_to_4h = nn.Linear(
415
+ config.hidden_size,
416
+ config.ffn_hidden_size * 2,
417
+ bias=self.add_bias,
418
+ device=device,
419
+ **_config_to_kwargs(config)
420
+ )
421
+
422
+ def swiglu(x):
423
+ x = torch.chunk(x, 2, dim=-1)
424
+ return F.silu(x[0]) * x[1]
425
+
426
+ self.activation_func = swiglu
427
+
428
+ # Project back to h.
429
+ self.dense_4h_to_h = nn.Linear(
430
+ config.ffn_hidden_size,
431
+ config.hidden_size,
432
+ bias=self.add_bias,
433
+ device=device,
434
+ **_config_to_kwargs(config)
435
+ )
436
+
437
+ def forward(self, hidden_states):
438
+ # [s, b, 4hp]
439
+ intermediate_parallel = self.dense_h_to_4h(hidden_states)
440
+ intermediate_parallel = self.activation_func(intermediate_parallel)
441
+ # [s, b, h]
442
+ output = self.dense_4h_to_h(intermediate_parallel)
443
+ return output
444
+
445
+
446
+ class GLMBlock(torch.nn.Module):
447
+ """A single transformer layer.
448
+
449
+ Transformer layer takes input with size [s, b, h] and returns an
450
+ output of the same size.
451
+ """
452
+
453
+ def __init__(self, config: ChatGLMConfig, layer_number, device=None):
454
+ super(GLMBlock, self).__init__()
455
+ self.layer_number = layer_number
456
+
457
+ self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
458
+
459
+ self.fp32_residual_connection = config.fp32_residual_connection
460
+
461
+ LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
462
+ # Layernorm on the input data.
463
+ self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
464
+ dtype=config.torch_dtype)
465
+
466
+ # Self attention.
467
+ self.self_attention = SelfAttention(config, layer_number, device=device)
468
+ self.hidden_dropout = config.hidden_dropout
469
+
470
+ # Layernorm on the attention output
471
+ self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
472
+ dtype=config.torch_dtype)
473
+
474
+ # MLP
475
+ self.mlp = MLP(config, device=device)
476
+
477
+ def forward(
478
+ self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True,
479
+ ):
480
+ # hidden_states: [s, b, h]
481
+
482
+ # Layer norm at the beginning of the transformer layer.
483
+ layernorm_output = self.input_layernorm(hidden_states)
484
+ # Self attention.
485
+ attention_output, kv_cache = self.self_attention(
486
+ layernorm_output,
487
+ attention_mask,
488
+ rotary_pos_emb,
489
+ kv_cache=kv_cache,
490
+ use_cache=use_cache
491
+ )
492
+
493
+ # Residual connection.
494
+ if self.apply_residual_connection_post_layernorm:
495
+ residual = layernorm_output
496
+ else:
497
+ residual = hidden_states
498
+
499
+ layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training)
500
+ layernorm_input = residual + layernorm_input
501
+
502
+ # Layer norm post the self attention.
503
+ layernorm_output = self.post_attention_layernorm(layernorm_input)
504
+
505
+ # MLP.
506
+ mlp_output = self.mlp(layernorm_output)
507
+
508
+ # Second residual connection.
509
+ if self.apply_residual_connection_post_layernorm:
510
+ residual = layernorm_output
511
+ else:
512
+ residual = layernorm_input
513
+
514
+ output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training)
515
+ output = residual + output
516
+
517
+ return output, kv_cache
518
+
519
+
520
+ class GLMTransformer(torch.nn.Module):
521
+ """Transformer class."""
522
+
523
+ def __init__(self, config: ChatGLMConfig, device=None):
524
+ super(GLMTransformer, self).__init__()
525
+
526
+ self.fp32_residual_connection = config.fp32_residual_connection
527
+ self.post_layer_norm = config.post_layer_norm
528
+
529
+ # Number of layers.
530
+ self.num_layers = config.num_layers
531
+
532
+ # Transformer layers.
533
+ def build_layer(layer_number):
534
+ return GLMBlock(config, layer_number, device=device)
535
+
536
+ self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)])
537
+
538
+ if self.post_layer_norm:
539
+ LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
540
+ # Final layer norm before output.
541
+ self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
542
+ dtype=config.torch_dtype)
543
+
544
+ self.gradient_checkpointing = False
545
+
546
+ def _get_layer(self, layer_number):
547
+ return self.layers[layer_number]
548
+
549
+ def forward(
550
+ self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None,
551
+ use_cache: Optional[bool] = True,
552
+ output_hidden_states: Optional[bool] = False,
553
+ ):
554
+ if not kv_caches:
555
+ kv_caches = [None for _ in range(self.num_layers)]
556
+ presents = () if use_cache else None
557
+ if self.gradient_checkpointing and self.training:
558
+ if use_cache:
559
+ logger.warning_once(
560
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
561
+ )
562
+ use_cache = False
563
+
564
+ all_self_attentions = None
565
+ all_hidden_states = () if output_hidden_states else None
566
+ for index in range(self.num_layers):
567
+ if output_hidden_states:
568
+ all_hidden_states = all_hidden_states + (hidden_states,)
569
+
570
+ layer = self._get_layer(index)
571
+ if self.gradient_checkpointing and self.training:
572
+ layer_ret = torch.utils.checkpoint.checkpoint(
573
+ layer,
574
+ hidden_states,
575
+ attention_mask,
576
+ rotary_pos_emb,
577
+ kv_caches[index],
578
+ use_cache
579
+ )
580
+ else:
581
+ layer_ret = layer(
582
+ hidden_states,
583
+ attention_mask,
584
+ rotary_pos_emb,
585
+ kv_cache=kv_caches[index],
586
+ use_cache=use_cache
587
+ )
588
+ hidden_states, kv_cache = layer_ret
589
+ if use_cache:
590
+ presents = presents + (kv_cache,)
591
+
592
+ if output_hidden_states:
593
+ all_hidden_states = all_hidden_states + (hidden_states,)
594
+
595
+ # Final layer norm.
596
+ if self.post_layer_norm:
597
+ hidden_states = self.final_layernorm(hidden_states)
598
+
599
+ return hidden_states, presents, all_hidden_states, all_self_attentions
600
+
601
+
602
+ class ChatGLMPreTrainedModel(PreTrainedModel):
603
+ """
604
+ An abstract class to handle weights initialization and
605
+ a simple interface for downloading and loading pretrained models.
606
+ """
607
+
608
+ is_parallelizable = False
609
+ supports_gradient_checkpointing = True
610
+ config_class = ChatGLMConfig
611
+ base_model_prefix = "transformer"
612
+ _no_split_modules = ["GLMBlock"]
613
+
614
+ def _init_weights(self, module: nn.Module):
615
+ """Initialize the weights."""
616
+ return
617
+
618
+ def get_masks(self, input_ids, past_key_values, padding_mask=None):
619
+ batch_size, seq_length = input_ids.shape
620
+ full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device)
621
+ full_attention_mask.tril_()
622
+ past_length = 0
623
+ if past_key_values:
624
+ past_length = past_key_values[0][0].shape[0]
625
+ if past_length:
626
+ full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length,
627
+ device=input_ids.device), full_attention_mask), dim=-1)
628
+ if padding_mask is not None:
629
+ full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1)
630
+ if not past_length and padding_mask is not None:
631
+ full_attention_mask -= padding_mask.unsqueeze(-1) - 1
632
+ full_attention_mask = (full_attention_mask < 0.5).bool()
633
+ full_attention_mask.unsqueeze_(1)
634
+ return full_attention_mask
635
+
636
+ def get_position_ids(self, input_ids, device):
637
+ batch_size, seq_length = input_ids.shape
638
+ position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
639
+ return position_ids
640
+
641
+ def _set_gradient_checkpointing(self, module, value=False):
642
+ if isinstance(module, GLMTransformer):
643
+ module.gradient_checkpointing = value
644
+
645
+
646
+ class Embedding(torch.nn.Module):
647
+ """Language model embeddings."""
648
+
649
+ def __init__(self, config: ChatGLMConfig, device=None):
650
+ super(Embedding, self).__init__()
651
+
652
+ self.hidden_size = config.hidden_size
653
+ # Word embeddings (parallel).
654
+ self.word_embeddings = nn.Embedding(
655
+ config.padded_vocab_size,
656
+ self.hidden_size,
657
+ dtype=config.torch_dtype,
658
+ device=device
659
+ )
660
+ self.fp32_residual_connection = config.fp32_residual_connection
661
+
662
+ def forward(self, input_ids):
663
+ # Embeddings.
664
+ words_embeddings = self.word_embeddings(input_ids)
665
+ embeddings = words_embeddings
666
+ # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
667
+ embeddings = embeddings.transpose(0, 1).contiguous()
668
+ # If the input flag for fp32 residual connection is set, convert for float.
669
+ if self.fp32_residual_connection:
670
+ embeddings = embeddings.float()
671
+ return embeddings
672
+
673
+
674
+ class ChatGLMModel(ChatGLMPreTrainedModel):
675
+ def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
676
+ super().__init__(config)
677
+ if empty_init:
678
+ init_method = skip_init
679
+ else:
680
+ init_method = default_init
681
+ init_kwargs = {}
682
+ if device is not None:
683
+ init_kwargs["device"] = device
684
+ self.embedding = init_method(Embedding, config, **init_kwargs)
685
+
686
+ # Rotary positional embeddings
687
+ self.seq_length = config.seq_length
688
+ rotary_dim = (
689
+ config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels
690
+ )
691
+
692
+ self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device,
693
+ dtype=config.torch_dtype)
694
+ self.encoder = init_method(GLMTransformer, config, **init_kwargs)
695
+ self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False,
696
+ dtype=config.torch_dtype, **init_kwargs)
697
+ self.pre_seq_len = config.pre_seq_len
698
+ self.prefix_projection = config.prefix_projection
699
+ if self.pre_seq_len is not None:
700
+ for param in self.parameters():
701
+ param.requires_grad = False
702
+ self.prefix_tokens = torch.arange(self.pre_seq_len).long()
703
+ self.prefix_encoder = PrefixEncoder(config)
704
+ self.dropout = torch.nn.Dropout(0.1)
705
+
706
+ def get_input_embeddings(self):
707
+ return self.embedding.word_embeddings
708
+
709
+ def get_prompt(self, batch_size, device, dtype=torch.half):
710
+ prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
711
+ past_key_values = self.prefix_encoder(prefix_tokens).type(dtype)
712
+ past_key_values = past_key_values.view(
713
+ batch_size,
714
+ self.pre_seq_len,
715
+ self.num_layers * 2,
716
+ self.num_attention_heads,
717
+ self.hidden_size // self.num_attention_heads
718
+ )
719
+ # seq_len, b, nh, hidden_size
720
+ past_key_values = self.dropout(past_key_values)
721
+ past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2)
722
+ return past_key_values
723
+
724
+ def forward(
725
+ self,
726
+ input_ids,
727
+ position_ids: Optional[torch.Tensor] = None,
728
+ attention_mask: Optional[torch.BoolTensor] = None,
729
+ full_attention_mask: Optional[torch.BoolTensor] = None,
730
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
731
+ inputs_embeds: Optional[torch.Tensor] = None,
732
+ use_cache: Optional[bool] = None,
733
+ output_hidden_states: Optional[bool] = None,
734
+ return_dict: Optional[bool] = None,
735
+ ):
736
+ output_hidden_states = (
737
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
738
+ )
739
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
740
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
741
+
742
+ batch_size, seq_length = input_ids.shape
743
+
744
+ if inputs_embeds is None:
745
+ inputs_embeds = self.embedding(input_ids)
746
+
747
+ # if full_attention_mask is None:
748
+ # if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1):
749
+ # full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask)
750
+
751
+ # Rotary positional embeddings
752
+ rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
753
+ if position_ids is not None:
754
+ rotary_pos_emb = rotary_pos_emb[position_ids]
755
+ else:
756
+ rotary_pos_emb = rotary_pos_emb[None, :seq_length]
757
+ rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
758
+
759
+ if past_key_values is None:
760
+ if self.pre_seq_len is not None:
761
+ past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device,
762
+ dtype=inputs_embeds.dtype)
763
+
764
+ # Run encoder.
765
+ hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
766
+ inputs_embeds, attention_mask, rotary_pos_emb=rotary_pos_emb,
767
+ kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
768
+ )
769
+
770
+ if not return_dict:
771
+ return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
772
+
773
+ return BaseModelOutputWithPast(
774
+ last_hidden_state=hidden_states,
775
+ past_key_values=presents,
776
+ hidden_states=all_hidden_states,
777
+ attentions=all_self_attentions,
778
+ )
779
+
780
+ def quantize(self, weight_bit_width: int):
781
+ from .quantization import quantize
782
+ quantize(self.encoder, weight_bit_width)
783
+ return self
784
+
785
+
786
+ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
787
+ def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
788
+ super().__init__(config)
789
+
790
+ self.max_sequence_length = config.max_length
791
+ self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
792
+ self.config = config
793
+ self.quantized = False
794
+ self.pack_loss = False
795
+
796
+ if self.config.quantization_bit:
797
+ self.quantize(self.config.quantization_bit, empty_init=True)
798
+
799
+ def _update_model_kwargs_for_generation(
800
+ self,
801
+ outputs: ModelOutput,
802
+ model_kwargs: Dict[str, Any],
803
+ is_encoder_decoder: bool = False,
804
+ standardize_cache_format: bool = False,
805
+ ) -> Dict[str, Any]:
806
+ # update past_key_values
807
+ model_kwargs["past_key_values"] = self._extract_past_from_model_output(
808
+ outputs, standardize_cache_format=standardize_cache_format
809
+ )
810
+
811
+ # update attention mask
812
+ if "attention_mask" in model_kwargs:
813
+ attention_mask = model_kwargs["attention_mask"]
814
+ model_kwargs["attention_mask"] = torch.cat(
815
+ [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
816
+ )
817
+
818
+ # update position ids
819
+ if "position_ids" in model_kwargs:
820
+ position_ids = model_kwargs["position_ids"]
821
+ new_position_id = position_ids[..., -1:].clone()
822
+ new_position_id += 1
823
+ model_kwargs["position_ids"] = torch.cat(
824
+ [position_ids, new_position_id], dim=-1
825
+ )
826
+
827
+ model_kwargs["is_first_forward"] = False
828
+ return model_kwargs
829
+
830
+ def prepare_inputs_for_generation(
831
+ self,
832
+ input_ids: torch.LongTensor,
833
+ past_key_values: Optional[torch.Tensor] = None,
834
+ attention_mask: Optional[torch.Tensor] = None,
835
+ position_ids: Optional[torch.Tensor] = None,
836
+ is_first_forward: bool = True,
837
+ **kwargs
838
+ ) -> dict:
839
+ # only last token for input_ids if past is not None
840
+ if position_ids is None:
841
+ position_ids = self.get_position_ids(input_ids, device=input_ids.device)
842
+ if not is_first_forward:
843
+ position_ids = position_ids[..., -1:]
844
+ input_ids = input_ids[:, -1:]
845
+ return {
846
+ "input_ids": input_ids,
847
+ "past_key_values": past_key_values,
848
+ "position_ids": position_ids,
849
+ "attention_mask": attention_mask,
850
+ "return_last_logit": True
851
+ }
852
+
853
+ def forward(
854
+ self,
855
+ input_ids: Optional[torch.Tensor] = None,
856
+ position_ids: Optional[torch.Tensor] = None,
857
+ attention_mask: Optional[torch.Tensor] = None,
858
+ past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
859
+ inputs_embeds: Optional[torch.Tensor] = None,
860
+ labels: Optional[Tuple[torch.Tensor]] = None,
861
+ use_cache: Optional[bool] = None,
862
+ output_attentions: Optional[bool] = None,
863
+ output_hidden_states: Optional[bool] = None,
864
+ return_dict: Optional[bool] = None,
865
+ return_last_logit: Optional[bool] = False,
866
+ ):
867
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
868
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
869
+
870
+ transformer_outputs = self.transformer(
871
+ input_ids=input_ids,
872
+ position_ids=position_ids,
873
+ attention_mask=attention_mask,
874
+ past_key_values=past_key_values,
875
+ inputs_embeds=inputs_embeds,
876
+ use_cache=use_cache,
877
+ output_hidden_states=output_hidden_states,
878
+ return_dict=return_dict,
879
+ )
880
+
881
+ hidden_states = transformer_outputs[0]
882
+ if return_last_logit:
883
+ hidden_states = hidden_states[-1:]
884
+ lm_logits = self.transformer.output_layer(hidden_states)
885
+ lm_logits = lm_logits.transpose(0, 1).contiguous()
886
+
887
+ loss = None
888
+ if labels is not None:
889
+ lm_logits = lm_logits.to(torch.float32)
890
+ # Shift so that tokens < n predict n
891
+ shift_logits = lm_logits[..., :-1, :].contiguous()
892
+ if isinstance(labels, tuple) or isinstance(labels, list):
893
+ labels, weights = labels
894
+ shift_labels = labels[..., 1:].contiguous()
895
+ if self.pack_loss:
896
+ shift_weights = weights[..., 1:].contiguous()
897
+ loss_fct = CrossEntropyLoss(ignore_index=-100, reduction='none')
898
+ loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
899
+ loss = (loss * shift_weights).sum()
900
+ # loss *= weights
901
+ else:
902
+ loss_fct = CrossEntropyLoss(ignore_index=-100)
903
+ loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
904
+
905
+ lm_logits = lm_logits.to(hidden_states.dtype)
906
+ loss = loss.to(hidden_states.dtype)
907
+
908
+ if not return_dict:
909
+ output = (lm_logits,) + transformer_outputs[1:]
910
+ return ((loss,) + output) if loss is not None else output
911
+
912
+ return CausalLMOutputWithPast(
913
+ loss=loss,
914
+ logits=lm_logits,
915
+ past_key_values=transformer_outputs.past_key_values,
916
+ hidden_states=transformer_outputs.hidden_states,
917
+ attentions=transformer_outputs.attentions,
918
+ )
919
+
920
+ @staticmethod
921
+ def _reorder_cache(
922
+ past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
923
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
924
+ """
925
+ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
926
+ [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
927
+ beam_idx at every generation step.
928
+
929
+ Output shares the same memory storage as `past`.
930
+ """
931
+ return tuple(
932
+ (
933
+ layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)),
934
+ layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)),
935
+ )
936
+ for layer_past in past
937
+ )
938
+
939
+ def process_response(self, response):
940
+ response = response.strip()
941
+ response = response.replace("[[训练时间]]", "2023年")
942
+ return response
943
+
944
+ def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None):
945
+ prompt = tokenizer.build_prompt(query, history=history)
946
+ inputs = tokenizer([prompt], return_tensors="pt")
947
+ inputs = inputs.to(self.device)
948
+ return inputs
949
+
950
+ def build_stream_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None):
951
+ if history:
952
+ prompt = "\n\n[Round {}]\n\n问:{}\n\n答:".format(len(history) + 1, query)
953
+ input_ids = tokenizer.encode(prompt, add_special_tokens=False)
954
+ input_ids = input_ids[1:]
955
+ inputs = tokenizer.batch_encode_plus([(input_ids, None)], return_tensors="pt", add_special_tokens=False)
956
+ else:
957
+ prompt = "[Round {}]\n\n问:{}\n\n答:".format(len(history) + 1, query)
958
+ inputs = tokenizer([prompt], return_tensors="pt")
959
+ inputs = inputs.to(self.device)
960
+ return inputs
961
+
962
+ @torch.no_grad()
963
+ def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 8192, num_beams=1,
964
+ do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None, **kwargs):
965
+ if history is None:
966
+ history = []
967
+ if logits_processor is None:
968
+ logits_processor = LogitsProcessorList()
969
+ logits_processor.append(InvalidScoreLogitsProcessor())
970
+ gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
971
+ "temperature": temperature, "logits_processor": logits_processor, **kwargs}
972
+ inputs = self.build_inputs(tokenizer, query, history=history)
973
+ outputs = self.generate(**inputs, **gen_kwargs)
974
+ outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):]
975
+ response = tokenizer.decode(outputs)
976
+ response = self.process_response(response)
977
+ history = history + [(query, response)]
978
+ return response, history
979
+
980
+ @torch.no_grad()
981
+ def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, past_key_values=None,
982
+ max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None,
983
+ return_past_key_values=False, **kwargs):
984
+ if history is None:
985
+ history = []
986
+ if logits_processor is None:
987
+ logits_processor = LogitsProcessorList()
988
+ logits_processor.append(InvalidScoreLogitsProcessor())
989
+ gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p,
990
+ "temperature": temperature, "logits_processor": logits_processor, **kwargs}
991
+ if past_key_values is None and not return_past_key_values:
992
+ inputs = self.build_inputs(tokenizer, query, history=history)
993
+ else:
994
+ inputs = self.build_stream_inputs(tokenizer, query, history=history)
995
+ if past_key_values is not None:
996
+ past_length = past_key_values[0][0].shape[0]
997
+ if self.transformer.pre_seq_len is not None:
998
+ past_length -= self.transformer.pre_seq_len
999
+ inputs.position_ids += past_length
1000
+ attention_mask = inputs.attention_mask
1001
+ attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1)
1002
+ inputs['attention_mask'] = attention_mask
1003
+ for outputs in self.stream_generate(**inputs, past_key_values=past_key_values,
1004
+ return_past_key_values=return_past_key_values, **gen_kwargs):
1005
+ if return_past_key_values:
1006
+ outputs, past_key_values = outputs
1007
+ outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):]
1008
+ response = tokenizer.decode(outputs)
1009
+ if response and response[-1] != "�":
1010
+ response = self.process_response(response)
1011
+ new_history = history + [(query, response)]
1012
+ if return_past_key_values:
1013
+ yield response, new_history, past_key_values
1014
+ else:
1015
+ yield response, new_history
1016
+
1017
+ @torch.no_grad()
1018
+ def stream_generate(
1019
+ self,
1020
+ input_ids,
1021
+ generation_config: Optional[GenerationConfig] = None,
1022
+ logits_processor: Optional[LogitsProcessorList] = None,
1023
+ stopping_criteria: Optional[StoppingCriteriaList] = None,
1024
+ prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
1025
+ return_past_key_values=False,
1026
+ **kwargs,
1027
+ ):
1028
+ batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
1029
+
1030
+ if generation_config is None:
1031
+ generation_config = self.generation_config
1032
+ generation_config = copy.deepcopy(generation_config)
1033
+ model_kwargs = generation_config.update(**kwargs)
1034
+ bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
1035
+
1036
+ if isinstance(eos_token_id, int):
1037
+ eos_token_id = [eos_token_id]
1038
+
1039
+ has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
1040
+ if has_default_max_length and generation_config.max_new_tokens is None:
1041
+ warnings.warn(
1042
+ f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
1043
+ "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
1044
+ " recommend using `max_new_tokens` to control the maximum length of the generation.",
1045
+ UserWarning,
1046
+ )
1047
+ elif generation_config.max_new_tokens is not None:
1048
+ generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
1049
+ if not has_default_max_length:
1050
+ logger.warn(
1051
+ f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
1052
+ f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
1053
+ "Please refer to the documentation for more information. "
1054
+ "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
1055
+ UserWarning,
1056
+ )
1057
+
1058
+ if input_ids_seq_length >= generation_config.max_length:
1059
+ input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
1060
+ logger.warning(
1061
+ f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
1062
+ f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
1063
+ " increasing `max_new_tokens`."
1064
+ )
1065
+
1066
+ # 2. Set generation parameters if not already defined
1067
+ logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
1068
+ stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
1069
+
1070
+ logits_processor = self._get_logits_processor(
1071
+ generation_config=generation_config,
1072
+ input_ids_seq_length=input_ids_seq_length,
1073
+ encoder_input_ids=input_ids,
1074
+ prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
1075
+ logits_processor=logits_processor,
1076
+ )
1077
+
1078
+ stopping_criteria = self._get_stopping_criteria(
1079
+ generation_config=generation_config, stopping_criteria=stopping_criteria
1080
+ )
1081
+ logits_warper = self._get_logits_warper(generation_config)
1082
+
1083
+ unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
1084
+ scores = None
1085
+ while True:
1086
+ model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
1087
+ # forward pass to get next token
1088
+ outputs = self(
1089
+ **model_inputs,
1090
+ return_dict=True,
1091
+ output_attentions=False,
1092
+ output_hidden_states=False,
1093
+ )
1094
+
1095
+ next_token_logits = outputs.logits[:, -1, :]
1096
+
1097
+ # pre-process distribution
1098
+ next_token_scores = logits_processor(input_ids, next_token_logits)
1099
+ next_token_scores = logits_warper(input_ids, next_token_scores)
1100
+
1101
+ # sample
1102
+ probs = nn.functional.softmax(next_token_scores, dim=-1)
1103
+ if generation_config.do_sample:
1104
+ next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
1105
+ else:
1106
+ next_tokens = torch.argmax(probs, dim=-1)
1107
+
1108
+ # update generated ids, model inputs, and length for next step
1109
+ input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
1110
+ model_kwargs = self._update_model_kwargs_for_generation(
1111
+ outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
1112
+ )
1113
+ unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long())
1114
+ if return_past_key_values:
1115
+ yield input_ids, outputs.past_key_values
1116
+ else:
1117
+ yield input_ids
1118
+ # stop when each sentence is finished, or if we exceed the maximum length
1119
+ if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
1120
+ break
1121
+
1122
+ def quantize(self, bits: int, empty_init=False, device=None, **kwargs):
1123
+ if bits == 0:
1124
+ return
1125
+
1126
+ from .quantization import quantize
1127
+
1128
+ if self.quantized:
1129
+ logger.info("Already quantized.")
1130
+ return self
1131
+
1132
+ self.quantized = True
1133
+
1134
+ self.config.quantization_bit = bits
1135
+
1136
+ self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device,
1137
+ **kwargs)
1138
+ return self
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quantization.py ADDED
@@ -0,0 +1,188 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from torch.nn import Linear
2
+ from torch.nn.parameter import Parameter
3
+
4
+ import bz2
5
+ import torch
6
+ import base64
7
+ import ctypes
8
+ from transformers.utils import logging
9
+
10
+ from typing import List
11
+ from functools import partial
12
+
13
+ logger = logging.get_logger(__name__)
14
+
15
+ try:
16
+ from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up
17
+
18
+ class Kernel:
19
+ def __init__(self, code: bytes, function_names: List[str]):
20
+ self.code = code
21
+ self._function_names = function_names
22
+ self._cmodule = LazyKernelCModule(self.code)
23
+
24
+ for name in self._function_names:
25
+ setattr(self, name, KernelFunction(self._cmodule, name))
26
+
27
+ quantization_code = "$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"
28
+
29
+ kernels = Kernel(
30
+ bz2.decompress(base64.b64decode(quantization_code)),
31
+ [
32
+ "int4WeightCompression",
33
+ "int4WeightExtractionFloat",
34
+ "int4WeightExtractionHalf",
35
+ "int8WeightExtractionFloat",
36
+ "int8WeightExtractionHalf",
37
+ ],
38
+ )
39
+ except Exception as exception:
40
+ kernels = None
41
+ logger.warning("Failed to load cpm_kernels:" + str(exception))
42
+
43
+
44
+ class W8A16Linear(torch.autograd.Function):
45
+ @staticmethod
46
+ def forward(ctx, inp: torch.Tensor, quant_w: torch.Tensor, scale_w: torch.Tensor, weight_bit_width):
47
+ ctx.inp_shape = inp.size()
48
+ ctx.weight_bit_width = weight_bit_width
49
+ out_features = quant_w.size(0)
50
+ inp = inp.contiguous().view(-1, inp.size(-1))
51
+ weight = extract_weight_to_half(quant_w, scale_w, weight_bit_width)
52
+ ctx.weight_shape = weight.size()
53
+ output = inp.mm(weight.t())
54
+ ctx.save_for_backward(inp, quant_w, scale_w)
55
+ return output.view(*(ctx.inp_shape[:-1] + (out_features,)))
56
+
57
+ @staticmethod
58
+ def backward(ctx, grad_output: torch.Tensor):
59
+ inp, quant_w, scale_w = ctx.saved_tensors
60
+ weight = extract_weight_to_half(quant_w, scale_w, ctx.weight_bit_width)
61
+ grad_output = grad_output.contiguous().view(-1, weight.size(0))
62
+ grad_input = grad_output.mm(weight)
63
+ grad_weight = grad_output.t().mm(inp)
64
+ return grad_input.view(ctx.inp_shape), grad_weight.view(ctx.weight_shape), None, None
65
+
66
+
67
+ def compress_int4_weight(weight: torch.Tensor): # (n, m)
68
+ with torch.cuda.device(weight.device):
69
+ n, m = weight.size(0), weight.size(1)
70
+ assert m % 2 == 0
71
+ m = m // 2
72
+ out = torch.empty(n, m, dtype=torch.int8, device="cuda")
73
+ stream = torch.cuda.current_stream()
74
+
75
+ gridDim = (n, 1, 1)
76
+ blockDim = (min(round_up(m, 32), 1024), 1, 1)
77
+
78
+ kernels.int4WeightCompression(
79
+ gridDim,
80
+ blockDim,
81
+ 0,
82
+ stream,
83
+ [ctypes.c_void_p(weight.data_ptr()), ctypes.c_void_p(out.data_ptr()), ctypes.c_int32(n), ctypes.c_int32(m)],
84
+ )
85
+ return out
86
+
87
+
88
+ def extract_weight_to_half(weight: torch.Tensor, scale_list: torch.Tensor, source_bit_width: int):
89
+ assert scale_list.dtype in [torch.half, torch.bfloat16]
90
+ assert weight.dtype in [torch.int8]
91
+ if source_bit_width == 8:
92
+ return weight.to(scale_list.dtype) * scale_list[:, None]
93
+ elif source_bit_width == 4:
94
+ func = (
95
+ kernels.int4WeightExtractionHalf if scale_list.dtype == torch.half else kernels.int4WeightExtractionBFloat16
96
+ )
97
+ else:
98
+ assert False, "Unsupported bit-width"
99
+
100
+ with torch.cuda.device(weight.device):
101
+ n, m = weight.size(0), weight.size(1)
102
+ out = torch.empty(n, m * (8 // source_bit_width), dtype=scale_list.dtype, device="cuda")
103
+ stream = torch.cuda.current_stream()
104
+
105
+ gridDim = (n, 1, 1)
106
+ blockDim = (min(round_up(m, 32), 1024), 1, 1)
107
+
108
+ func(
109
+ gridDim,
110
+ blockDim,
111
+ 0,
112
+ stream,
113
+ [
114
+ ctypes.c_void_p(weight.data_ptr()),
115
+ ctypes.c_void_p(scale_list.data_ptr()),
116
+ ctypes.c_void_p(out.data_ptr()),
117
+ ctypes.c_int32(n),
118
+ ctypes.c_int32(m),
119
+ ],
120
+ )
121
+ return out
122
+
123
+
124
+ class QuantizedLinear(torch.nn.Module):
125
+ def __init__(self, weight_bit_width: int, weight, bias=None, device="cpu", dtype=None, empty_init=False, *args,
126
+ **kwargs):
127
+ super().__init__()
128
+ self.weight_bit_width = weight_bit_width
129
+
130
+ shape = weight.shape
131
+
132
+ if weight is None or empty_init:
133
+ self.weight = torch.empty(shape[0], shape[1] * weight_bit_width // 8, dtype=torch.int8, device=device)
134
+ self.weight_scale = torch.empty(shape[0], dtype=dtype, device=device)
135
+ else:
136
+ self.weight_scale = weight.abs().max(dim=-1).values / ((2 ** (weight_bit_width - 1)) - 1)
137
+ self.weight = torch.round(weight / self.weight_scale[:, None]).to(torch.int8)
138
+ if weight_bit_width == 4:
139
+ self.weight = compress_int4_weight(self.weight)
140
+
141
+ self.weight = Parameter(self.weight.to(device), requires_grad=False)
142
+ self.weight_scale = Parameter(self.weight_scale.to(device), requires_grad=False)
143
+ self.bias = Parameter(bias.to(device), requires_grad=False) if bias is not None else None
144
+
145
+ def forward(self, input):
146
+ output = W8A16Linear.apply(input, self.weight, self.weight_scale, self.weight_bit_width)
147
+ if self.bias is not None:
148
+ output = output + self.bias
149
+ return output
150
+
151
+
152
+ def quantize(model, weight_bit_width, empty_init=False, device=None):
153
+ """Replace fp16 linear with quantized linear"""
154
+ for layer in model.layers:
155
+ layer.self_attention.query_key_value = QuantizedLinear(
156
+ weight_bit_width=weight_bit_width,
157
+ weight=layer.self_attention.query_key_value.weight.to(torch.cuda.current_device()),
158
+ bias=layer.self_attention.query_key_value.bias,
159
+ dtype=layer.self_attention.query_key_value.weight.dtype,
160
+ device=layer.self_attention.query_key_value.weight.device if device is None else device,
161
+ empty_init=empty_init
162
+ )
163
+ layer.self_attention.dense = QuantizedLinear(
164
+ weight_bit_width=weight_bit_width,
165
+ weight=layer.self_attention.dense.weight.to(torch.cuda.current_device()),
166
+ bias=layer.self_attention.dense.bias,
167
+ dtype=layer.self_attention.dense.weight.dtype,
168
+ device=layer.self_attention.dense.weight.device if device is None else device,
169
+ empty_init=empty_init
170
+ )
171
+ layer.mlp.dense_h_to_4h = QuantizedLinear(
172
+ weight_bit_width=weight_bit_width,
173
+ weight=layer.mlp.dense_h_to_4h.weight.to(torch.cuda.current_device()),
174
+ bias=layer.mlp.dense_h_to_4h.bias,
175
+ dtype=layer.mlp.dense_h_to_4h.weight.dtype,
176
+ device=layer.mlp.dense_h_to_4h.weight.device if device is None else device,
177
+ empty_init=empty_init
178
+ )
179
+ layer.mlp.dense_4h_to_h = QuantizedLinear(
180
+ weight_bit_width=weight_bit_width,
181
+ weight=layer.mlp.dense_4h_to_h.weight.to(torch.cuda.current_device()),
182
+ bias=layer.mlp.dense_4h_to_h.bias,
183
+ dtype=layer.mlp.dense_4h_to_h.weight.dtype,
184
+ device=layer.mlp.dense_4h_to_h.weight.device if device is None else device,
185
+ empty_init=empty_init
186
+ )
187
+
188
+ return model
special_tokens_map.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {}
tokenization_chatglm.py ADDED
@@ -0,0 +1,252 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import torch
3
+ from typing import List, Optional, Union, Dict
4
+ from sentencepiece import SentencePieceProcessor
5
+ from transformers import PreTrainedTokenizer
6
+ from transformers.utils import logging, PaddingStrategy
7
+ from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
8
+
9
+ class SPTokenizer:
10
+ def __init__(self, model_path: str):
11
+ # reload tokenizer
12
+ assert os.path.isfile(model_path), model_path
13
+ self.sp_model = SentencePieceProcessor(model_file=model_path)
14
+
15
+ # BOS / EOS token IDs
16
+ self.n_words: int = self.sp_model.vocab_size()
17
+ self.bos_id: int = self.sp_model.bos_id()
18
+ self.eos_id: int = self.sp_model.eos_id()
19
+ self.pad_id: int = self.sp_model.unk_id()
20
+ assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
21
+
22
+ special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"]
23
+ self.special_tokens = {}
24
+ self.index_special_tokens = {}
25
+ for token in special_tokens:
26
+ self.special_tokens[token] = self.n_words
27
+ self.index_special_tokens[self.n_words] = token
28
+ self.n_words += 1
29
+
30
+ def tokenize(self, s: str):
31
+ return self.sp_model.EncodeAsPieces(s)
32
+
33
+ def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]:
34
+ assert type(s) is str
35
+ t = self.sp_model.encode(s)
36
+ if bos:
37
+ t = [self.bos_id] + t
38
+ if eos:
39
+ t = t + [self.eos_id]
40
+ return t
41
+
42
+ def decode(self, t: List[int]) -> str:
43
+ return self.sp_model.decode(t)
44
+
45
+ def decode_tokens(self, tokens: List[str]) -> str:
46
+ text = self.sp_model.DecodePieces(tokens)
47
+ return text
48
+
49
+ def convert_token_to_id(self, token):
50
+ """ Converts a token (str) in an id using the vocab. """
51
+ if token in self.special_tokens:
52
+ return self.special_tokens[token]
53
+ return self.sp_model.PieceToId(token)
54
+
55
+ def convert_id_to_token(self, index):
56
+ """Converts an index (integer) in a token (str) using the vocab."""
57
+ if index in self.index_special_tokens or index in [self.eos_id, self.bos_id, self.pad_id] or index < 0:
58
+ return ""
59
+ return self.sp_model.IdToPiece(index)
60
+
61
+
62
+ class ChatGLMTokenizer(PreTrainedTokenizer):
63
+ vocab_files_names = {"vocab_file": "tokenizer.model"}
64
+
65
+ model_input_names = ["input_ids", "attention_mask", "position_ids"]
66
+
67
+ def __init__(self, vocab_file, padding_side="left", **kwargs):
68
+ super().__init__(padding_side=padding_side, **kwargs)
69
+ self.name = "GLMTokenizer"
70
+
71
+ self.vocab_file = vocab_file
72
+ self.tokenizer = SPTokenizer(vocab_file)
73
+ self.special_tokens = {
74
+ "<bos>": self.tokenizer.bos_id,
75
+ "<eos>": self.tokenizer.eos_id,
76
+ "<pad>": self.tokenizer.pad_id
77
+ }
78
+
79
+ def get_command(self, token):
80
+ if token in self.special_tokens:
81
+ return self.special_tokens[token]
82
+ assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}"
83
+ return self.tokenizer.special_tokens[token]
84
+
85
+ @property
86
+ def pad_token(self) -> str:
87
+ return "<unk>"
88
+
89
+ @property
90
+ def pad_token_id(self):
91
+ return self.get_command("<pad>")
92
+
93
+ @property
94
+ def eos_token(self) -> str:
95
+ return "</s>"
96
+
97
+ @property
98
+ def eos_token_id(self):
99
+ return self.get_command("<eos>")
100
+
101
+ @property
102
+ def vocab_size(self):
103
+ return self.tokenizer.n_words
104
+
105
+ def get_vocab(self):
106
+ """ Returns vocab as a dict """
107
+ vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
108
+ vocab.update(self.added_tokens_encoder)
109
+ return vocab
110
+
111
+ def _tokenize(self, text, **kwargs):
112
+ return self.tokenizer.tokenize(text)
113
+
114
+ def _convert_token_to_id(self, token):
115
+ """ Converts a token (str) in an id using the vocab. """
116
+ return self.tokenizer.convert_token_to_id(token)
117
+
118
+ def _convert_id_to_token(self, index):
119
+ """Converts an index (integer) in a token (str) using the vocab."""
120
+ return self.tokenizer.convert_id_to_token(index)
121
+
122
+ def convert_tokens_to_string(self, tokens: List[str]) -> str:
123
+ return self.tokenizer.decode_tokens(tokens)
124
+
125
+ def save_vocabulary(self, save_directory, filename_prefix=None):
126
+ """
127
+ Save the vocabulary and special tokens file to a directory.
128
+
129
+ Args:
130
+ save_directory (`str`):
131
+ The directory in which to save the vocabulary.
132
+ filename_prefix (`str`, *optional*):
133
+ An optional prefix to add to the named of the saved files.
134
+
135
+ Returns:
136
+ `Tuple(str)`: Paths to the files saved.
137
+ """
138
+ if os.path.isdir(save_directory):
139
+ vocab_file = os.path.join(
140
+ save_directory, self.vocab_files_names["vocab_file"]
141
+ )
142
+ else:
143
+ vocab_file = save_directory
144
+
145
+ with open(self.vocab_file, 'rb') as fin:
146
+ proto_str = fin.read()
147
+
148
+ with open(vocab_file, "wb") as writer:
149
+ writer.write(proto_str)
150
+
151
+ return (vocab_file,)
152
+
153
+ def get_prefix_tokens(self):
154
+ prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")]
155
+ return prefix_tokens
156
+
157
+ def build_prompt(self, query, history=None):
158
+ if history is None:
159
+ history = []
160
+ prompt = ""
161
+ for i, (old_query, response) in enumerate(history):
162
+ prompt += "[Round {}]\n\n问:{}\n\n答:{}\n\n".format(i + 1, old_query, response)
163
+ prompt += "[Round {}]\n\n问:{}\n\n答:".format(len(history) + 1, query)
164
+ return prompt
165
+
166
+ def build_inputs_with_special_tokens(
167
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
168
+ ) -> List[int]:
169
+ """
170
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
171
+ adding special tokens. A BERT sequence has the following format:
172
+
173
+ - single sequence: `[CLS] X [SEP]`
174
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
175
+
176
+ Args:
177
+ token_ids_0 (`List[int]`):
178
+ List of IDs to which the special tokens will be added.
179
+ token_ids_1 (`List[int]`, *optional*):
180
+ Optional second list of IDs for sequence pairs.
181
+
182
+ Returns:
183
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
184
+ """
185
+ prefix_tokens = self.get_prefix_tokens()
186
+ token_ids_0 = prefix_tokens + token_ids_0
187
+ if token_ids_1 is not None:
188
+ token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("<eos>")]
189
+ return token_ids_0
190
+
191
+ def _pad(
192
+ self,
193
+ encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
194
+ max_length: Optional[int] = None,
195
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
196
+ pad_to_multiple_of: Optional[int] = None,
197
+ return_attention_mask: Optional[bool] = None,
198
+ ) -> dict:
199
+ """
200
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
201
+
202
+ Args:
203
+ encoded_inputs:
204
+ Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
205
+ max_length: maximum length of the returned list and optionally padding length (see below).
206
+ Will truncate by taking into account the special tokens.
207
+ padding_strategy: PaddingStrategy to use for padding.
208
+
209
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
210
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
211
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
212
+ The tokenizer padding sides are defined in self.padding_side:
213
+
214
+ - 'left': pads on the left of the sequences
215
+ - 'right': pads on the right of the sequences
216
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
217
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
218
+ `>= 7.5` (Volta).
219
+ return_attention_mask:
220
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
221
+ """
222
+ # Load from model defaults
223
+ assert self.padding_side == "left"
224
+
225
+ required_input = encoded_inputs[self.model_input_names[0]]
226
+ seq_length = len(required_input)
227
+
228
+ if padding_strategy == PaddingStrategy.LONGEST:
229
+ max_length = len(required_input)
230
+
231
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
232
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
233
+
234
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
235
+
236
+ # Initialize attention mask if not present.
237
+ if "attention_mask" not in encoded_inputs:
238
+ encoded_inputs["attention_mask"] = [1] * seq_length
239
+
240
+ if "position_ids" not in encoded_inputs:
241
+ encoded_inputs["position_ids"] = list(range(seq_length))
242
+
243
+ if needs_to_be_padded:
244
+ difference = max_length - len(required_input)
245
+
246
+ if "attention_mask" in encoded_inputs:
247
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
248
+ if "position_ids" in encoded_inputs:
249
+ encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
250
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
251
+
252
+ return encoded_inputs
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:e7dc4c393423b76e4373e5157ddc34803a0189ba96b21ddbb40269d31468a6f2
3
+ size 1018370
tokenizer_config.json ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoTokenizer": [
4
+ "tokenization_chatglm.ChatGLMTokenizer",
5
+ null
6
+ ]
7
+ },
8
+ "clean_up_tokenization_spaces": true,
9
+ "do_lower_case": false,
10
+ "model_max_length": 1000000000000000019884624838656,
11
+ "padding_side": "left",
12
+ "remove_space": false,
13
+ "tokenizer_class": "ChatGLMTokenizer"
14
+ }