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config.json ADDED
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+ {
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+ "_name_or_path": "/data/zhangzhexin/huggingface_pretrained_models/chatglm3-6b",
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+ "add_bias_linear": false,
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+ "add_qkv_bias": true,
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+ "apply_query_key_layer_scaling": true,
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+ "apply_residual_connection_post_layernorm": false,
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+ "architectures": [
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+ "ChatGLMForConditionalGeneration"
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+ ],
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+ "attention_dropout": 0.0,
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+ "attention_softmax_in_fp32": true,
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+ "auto_map": {
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+ "AutoConfig": "configuration_chatglm.ChatGLMConfig",
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+ "AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
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+ "AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
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+ "AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
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+ "AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"
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+ },
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+ "fp32_residual_connection": false,
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+ "hidden_dropout": 0.0,
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+ "hidden_size": 4096,
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+ "kv_channels": 128,
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+ "layernorm_epsilon": 1e-05,
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+ "model_type": "chatglm",
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+ "multi_query_attention": true,
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+ "multi_query_group_num": 2,
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+ "num_attention_heads": 32,
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+ "num_layers": 28,
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+ "original_rope": true,
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+ "pad_token_id": 0,
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+ "padded_vocab_size": 65024,
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+ "post_layer_norm": true,
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+ "pre_seq_len": null,
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+ "prefix_projection": false,
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+ "quantization_bit": 0,
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+ "rmsnorm": true,
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+ "seq_length": 8192,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float16",
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+ "transformers_version": "4.36.2",
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+ "use_cache": true,
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+ "vocab_size": 65024
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+ }
configuration_chatglm.py ADDED
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+ from transformers import PretrainedConfig
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+
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+
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+ class ChatGLMConfig(PretrainedConfig):
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+ model_type = "chatglm"
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+ def __init__(
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+ self,
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+ num_layers=28,
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+ padded_vocab_size=65024,
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+ hidden_size=4096,
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+ ffn_hidden_size=13696,
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+ kv_channels=128,
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+ num_attention_heads=32,
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+ seq_length=2048,
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+ hidden_dropout=0.0,
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+ classifier_dropout=None,
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+ attention_dropout=0.0,
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+ layernorm_epsilon=1e-5,
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+ rmsnorm=True,
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+ apply_residual_connection_post_layernorm=False,
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+ post_layer_norm=True,
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+ add_bias_linear=False,
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+ add_qkv_bias=False,
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+ bias_dropout_fusion=True,
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+ multi_query_attention=False,
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+ multi_query_group_num=1,
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+ apply_query_key_layer_scaling=True,
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+ attention_softmax_in_fp32=True,
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+ fp32_residual_connection=False,
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+ quantization_bit=0,
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+ pre_seq_len=None,
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+ prefix_projection=False,
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+ **kwargs
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+ ):
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+ self.num_layers = num_layers
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+ self.vocab_size = padded_vocab_size
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+ self.padded_vocab_size = padded_vocab_size
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+ self.hidden_size = hidden_size
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+ self.ffn_hidden_size = ffn_hidden_size
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+ self.kv_channels = kv_channels
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+ self.num_attention_heads = num_attention_heads
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+ self.seq_length = seq_length
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+ self.hidden_dropout = hidden_dropout
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+ self.classifier_dropout = classifier_dropout
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+ self.attention_dropout = attention_dropout
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+ self.layernorm_epsilon = layernorm_epsilon
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+ self.rmsnorm = rmsnorm
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+ self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
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+ self.post_layer_norm = post_layer_norm
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+ self.add_bias_linear = add_bias_linear
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+ self.add_qkv_bias = add_qkv_bias
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+ self.bias_dropout_fusion = bias_dropout_fusion
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+ self.multi_query_attention = multi_query_attention
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+ self.multi_query_group_num = multi_query_group_num
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+ self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
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+ self.attention_softmax_in_fp32 = attention_softmax_in_fp32
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+ self.fp32_residual_connection = fp32_residual_connection
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+ self.quantization_bit = quantization_bit
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+ self.pre_seq_len = pre_seq_len
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+ self.prefix_projection = prefix_projection
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+ super().__init__(**kwargs)
generation_config.json ADDED
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+ "eos_token_id": 2,
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+ "transformers_version": "4.36.2"
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+ }
207
+ }
modeling_chatglm.py ADDED
@@ -0,0 +1,1293 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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, MSELoss, BCEWithLogitsLoss
14
+ from torch.nn.utils import skip_init
15
+ from typing import Optional, Tuple, Union, List, Callable, Dict, Any
16
+ from copy import deepcopy
17
+
18
+ from transformers.modeling_outputs import (
19
+ BaseModelOutputWithPast,
20
+ CausalLMOutputWithPast,
21
+ SequenceClassifierOutputWithPast,
22
+ )
23
+ from transformers.modeling_utils import PreTrainedModel
24
+ from transformers.utils import logging
25
+ from transformers.generation.logits_process import LogitsProcessor
26
+ from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput
27
+
28
+ from .configuration_chatglm import ChatGLMConfig
29
+
30
+ # flags required to enable jit fusion kernels
31
+
32
+ if sys.platform != 'darwin':
33
+ torch._C._jit_set_profiling_mode(False)
34
+ torch._C._jit_set_profiling_executor(False)
35
+ torch._C._jit_override_can_fuse_on_cpu(True)
36
+ torch._C._jit_override_can_fuse_on_gpu(True)
37
+
38
+ logger = logging.get_logger(__name__)
39
+
40
+ _CHECKPOINT_FOR_DOC = "THUDM/ChatGLM"
41
+ _CONFIG_FOR_DOC = "ChatGLMConfig"
42
+
43
+ CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
44
+ "THUDM/chatglm3-6b",
45
+ # See all ChatGLM models at https://huggingface.co/models?filter=chatglm
46
+ ]
47
+
48
+
49
+ def default_init(cls, *args, **kwargs):
50
+ return cls(*args, **kwargs)
51
+
52
+
53
+ class InvalidScoreLogitsProcessor(LogitsProcessor):
54
+ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
55
+ if torch.isnan(scores).any() or torch.isinf(scores).any():
56
+ scores.zero_()
57
+ scores[..., 5] = 5e4
58
+ return scores
59
+
60
+
61
+ class PrefixEncoder(torch.nn.Module):
62
+ """
63
+ The torch.nn model to encode the prefix
64
+ Input shape: (batch-size, prefix-length)
65
+ Output shape: (batch-size, prefix-length, 2*layers*hidden)
66
+ """
67
+
68
+ def __init__(self, config: ChatGLMConfig):
69
+ super().__init__()
70
+ self.prefix_projection = config.prefix_projection
71
+ if self.prefix_projection:
72
+ # Use a two-layer MLP to encode the prefix
73
+ kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2
74
+ self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size)
75
+ self.trans = torch.nn.Sequential(
76
+ torch.nn.Linear(kv_size, config.hidden_size),
77
+ torch.nn.Tanh(),
78
+ torch.nn.Linear(config.hidden_size, kv_size)
79
+ )
80
+ else:
81
+ self.embedding = torch.nn.Embedding(config.pre_seq_len,
82
+ config.num_layers * config.kv_channels * config.multi_query_group_num * 2)
83
+
84
+ def forward(self, prefix: torch.Tensor):
85
+ if self.prefix_projection:
86
+ prefix_tokens = self.embedding(prefix)
87
+ past_key_values = self.trans(prefix_tokens)
88
+ else:
89
+ past_key_values = self.embedding(prefix)
90
+ return past_key_values
91
+
92
+
93
+ def split_tensor_along_last_dim(
94
+ tensor: torch.Tensor,
95
+ num_partitions: int,
96
+ contiguous_split_chunks: bool = False,
97
+ ) -> List[torch.Tensor]:
98
+ """Split a tensor along its last dimension.
99
+
100
+ Arguments:
101
+ tensor: input tensor.
102
+ num_partitions: number of partitions to split the tensor
103
+ contiguous_split_chunks: If True, make each chunk contiguous
104
+ in memory.
105
+
106
+ Returns:
107
+ A list of Tensors
108
+ """
109
+ # Get the size and dimension.
110
+ last_dim = tensor.dim() - 1
111
+ last_dim_size = tensor.size()[last_dim] // num_partitions
112
+ # Split.
113
+ tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
114
+ # Note: torch.split does not create contiguous tensors by default.
115
+ if contiguous_split_chunks:
116
+ return tuple(chunk.contiguous() for chunk in tensor_list)
117
+
118
+ return tensor_list
119
+
120
+
121
+ class RotaryEmbedding(nn.Module):
122
+ def __init__(self, dim, original_impl=False, device=None, dtype=None):
123
+ super().__init__()
124
+ inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim))
125
+ self.register_buffer("inv_freq", inv_freq)
126
+ self.dim = dim
127
+ self.original_impl = original_impl
128
+
129
+ def forward_impl(
130
+ self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000
131
+ ):
132
+ """Enhanced Transformer with Rotary Position Embedding.
133
+
134
+ Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
135
+ transformers/rope/__init__.py. MIT License:
136
+ https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
137
+ """
138
+ # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
139
+ theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem))
140
+
141
+ # Create position indexes `[0, 1, ..., seq_len - 1]`
142
+ seq_idx = torch.arange(seq_len, dtype=torch.float, device=device)
143
+
144
+ # Calculate the product of position index and $\theta_i$
145
+ idx_theta = torch.outer(seq_idx, theta).float()
146
+
147
+ cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
148
+
149
+ # this is to mimic the behaviour of complex32, else we will get different results
150
+ if dtype in (torch.float16, torch.bfloat16, torch.int8):
151
+ cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half()
152
+ return cache
153
+
154
+ def forward(self, max_seq_len, offset=0):
155
+ return self.forward_impl(
156
+ max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device
157
+ )
158
+
159
+
160
+ @torch.jit.script
161
+ def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
162
+ # x: [sq, b, np, hn]
163
+ sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3)
164
+ rot_dim = rope_cache.shape[-2] * 2
165
+ x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
166
+ # truncate to support variable sizes
167
+ rope_cache = rope_cache[:sq]
168
+ xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2)
169
+ rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2)
170
+ x_out2 = torch.stack(
171
+ [
172
+ xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
173
+ xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
174
+ ],
175
+ -1,
176
+ )
177
+ x_out2 = x_out2.flatten(3)
178
+ return torch.cat((x_out2, x_pass), dim=-1)
179
+
180
+
181
+ class RMSNorm(torch.nn.Module):
182
+ def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
183
+ super().__init__()
184
+ self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype))
185
+ self.eps = eps
186
+
187
+ def forward(self, hidden_states: torch.Tensor):
188
+ input_dtype = hidden_states.dtype
189
+ variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
190
+ hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
191
+
192
+ return (self.weight * hidden_states).to(input_dtype)
193
+
194
+
195
+ class CoreAttention(torch.nn.Module):
196
+ def __init__(self, config: ChatGLMConfig, layer_number):
197
+ super(CoreAttention, self).__init__()
198
+
199
+ self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
200
+ self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
201
+ if self.apply_query_key_layer_scaling:
202
+ self.attention_softmax_in_fp32 = True
203
+ self.layer_number = max(1, layer_number)
204
+
205
+ projection_size = config.kv_channels * config.num_attention_heads
206
+
207
+ # Per attention head and per partition values.
208
+ self.hidden_size_per_partition = projection_size
209
+ self.hidden_size_per_attention_head = projection_size // config.num_attention_heads
210
+ self.num_attention_heads_per_partition = config.num_attention_heads
211
+
212
+ coeff = None
213
+ self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
214
+ if self.apply_query_key_layer_scaling:
215
+ coeff = self.layer_number
216
+ self.norm_factor *= coeff
217
+ self.coeff = coeff
218
+
219
+ self.attention_dropout = torch.nn.Dropout(config.attention_dropout)
220
+
221
+ def forward(self, query_layer, key_layer, value_layer, attention_mask):
222
+ pytorch_major_version = int(torch.__version__.split('.')[0])
223
+ if pytorch_major_version >= 2:
224
+ query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]]
225
+ if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
226
+ context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
227
+ is_causal=True)
228
+ else:
229
+ if attention_mask is not None:
230
+ attention_mask = ~attention_mask
231
+ context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
232
+ attention_mask)
233
+ context_layer = context_layer.permute(2, 0, 1, 3)
234
+ new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
235
+ context_layer = context_layer.reshape(*new_context_layer_shape)
236
+ else:
237
+ # Raw attention scores
238
+
239
+ # [b, np, sq, sk]
240
+ output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))
241
+
242
+ # [sq, b, np, hn] -> [sq, b * np, hn]
243
+ query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
244
+ # [sk, b, np, hn] -> [sk, b * np, hn]
245
+ key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)
246
+
247
+ # preallocting input tensor: [b * np, sq, sk]
248
+ matmul_input_buffer = torch.empty(
249
+ output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype,
250
+ device=query_layer.device
251
+ )
252
+
253
+ # Raw attention scores. [b * np, sq, sk]
254
+ matmul_result = torch.baddbmm(
255
+ matmul_input_buffer,
256
+ query_layer.transpose(0, 1), # [b * np, sq, hn]
257
+ key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk]
258
+ beta=0.0,
259
+ alpha=(1.0 / self.norm_factor),
260
+ )
261
+
262
+ # change view to [b, np, sq, sk]
263
+ attention_scores = matmul_result.view(*output_size)
264
+
265
+ # ===========================
266
+ # Attention probs and dropout
267
+ # ===========================
268
+
269
+ # attention scores and attention mask [b, np, sq, sk]
270
+ if self.attention_softmax_in_fp32:
271
+ attention_scores = attention_scores.float()
272
+ if self.coeff is not None:
273
+ attention_scores = attention_scores * self.coeff
274
+ if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]:
275
+ attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3],
276
+ device=attention_scores.device, dtype=torch.bool)
277
+ attention_mask.tril_()
278
+ attention_mask = ~attention_mask
279
+ if attention_mask is not None:
280
+ attention_scores = attention_scores.masked_fill(attention_mask, float("-inf"))
281
+ attention_probs = F.softmax(attention_scores, dim=-1)
282
+ attention_probs = attention_probs.type_as(value_layer)
283
+
284
+ # This is actually dropping out entire tokens to attend to, which might
285
+ # seem a bit unusual, but is taken from the original Transformer paper.
286
+ attention_probs = self.attention_dropout(attention_probs)
287
+ # =========================
288
+ # Context layer. [sq, b, hp]
289
+ # =========================
290
+
291
+ # value_layer -> context layer.
292
+ # [sk, b, np, hn] --> [b, np, sq, hn]
293
+
294
+ # context layer shape: [b, np, sq, hn]
295
+ output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))
296
+ # change view [sk, b * np, hn]
297
+ value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)
298
+ # change view [b * np, sq, sk]
299
+ attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
300
+ # matmul: [b * np, sq, hn]
301
+ context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))
302
+ # change view [b, np, sq, hn]
303
+ context_layer = context_layer.view(*output_size)
304
+ # [b, np, sq, hn] --> [sq, b, np, hn]
305
+ context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
306
+ # [sq, b, np, hn] --> [sq, b, hp]
307
+ new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
308
+ context_layer = context_layer.view(*new_context_layer_shape)
309
+
310
+ return context_layer
311
+
312
+
313
+ class SelfAttention(torch.nn.Module):
314
+ """Parallel self-attention layer abstract class.
315
+
316
+ Self-attention layer takes input with size [s, b, h]
317
+ and returns output of the same size.
318
+ """
319
+
320
+ def __init__(self, config: ChatGLMConfig, layer_number, device=None):
321
+ super(SelfAttention, self).__init__()
322
+ self.layer_number = max(1, layer_number)
323
+
324
+ self.projection_size = config.kv_channels * config.num_attention_heads
325
+
326
+ # Per attention head and per partition values.
327
+ self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads
328
+ self.num_attention_heads_per_partition = config.num_attention_heads
329
+
330
+ self.multi_query_attention = config.multi_query_attention
331
+ self.qkv_hidden_size = 3 * self.projection_size
332
+ if self.multi_query_attention:
333
+ self.num_multi_query_groups_per_partition = config.multi_query_group_num
334
+ self.qkv_hidden_size = (
335
+ self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
336
+ )
337
+ self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size,
338
+ bias=config.add_bias_linear or config.add_qkv_bias,
339
+ device=device, **_config_to_kwargs(config)
340
+ )
341
+
342
+ self.core_attention = CoreAttention(config, self.layer_number)
343
+
344
+ # Output.
345
+ self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear,
346
+ device=device, **_config_to_kwargs(config)
347
+ )
348
+
349
+ def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
350
+ if self.multi_query_attention:
351
+ num_attention_heads = self.num_multi_query_groups_per_partition
352
+ else:
353
+ num_attention_heads = self.num_attention_heads_per_partition
354
+ return torch.empty(
355
+ inference_max_sequence_len,
356
+ batch_size,
357
+ num_attention_heads,
358
+ self.hidden_size_per_attention_head,
359
+ dtype=dtype,
360
+ device=device,
361
+ )
362
+
363
+ def forward(
364
+ self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
365
+ ):
366
+ # hidden_states: [sq, b, h]
367
+
368
+ # =================================================
369
+ # Pre-allocate memory for key-values for inference.
370
+ # =================================================
371
+ # =====================
372
+ # Query, Key, and Value
373
+ # =====================
374
+
375
+ # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
376
+ mixed_x_layer = self.query_key_value(hidden_states)
377
+
378
+ if self.multi_query_attention:
379
+ (query_layer, key_layer, value_layer) = mixed_x_layer.split(
380
+ [
381
+ self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
382
+ self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
383
+ self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
384
+ ],
385
+ dim=-1,
386
+ )
387
+ query_layer = query_layer.view(
388
+ query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
389
+ )
390
+ key_layer = key_layer.view(
391
+ key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
392
+ )
393
+ value_layer = value_layer.view(
394
+ value_layer.size()[:-1]
395
+ + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
396
+ )
397
+ else:
398
+ new_tensor_shape = mixed_x_layer.size()[:-1] + \
399
+ (self.num_attention_heads_per_partition,
400
+ 3 * self.hidden_size_per_attention_head)
401
+ mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
402
+
403
+ # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
404
+ (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
405
+
406
+ # apply relative positional encoding (rotary embedding)
407
+ if rotary_pos_emb is not None:
408
+ query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb)
409
+ key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)
410
+
411
+ # adjust key and value for inference
412
+ if kv_cache is not None:
413
+ cache_k, cache_v = kv_cache
414
+ key_layer = torch.cat((cache_k, key_layer), dim=0)
415
+ value_layer = torch.cat((cache_v, value_layer), dim=0)
416
+ if use_cache:
417
+ kv_cache = (key_layer, value_layer)
418
+ else:
419
+ kv_cache = None
420
+
421
+ if self.multi_query_attention:
422
+ key_layer = key_layer.unsqueeze(-2)
423
+ key_layer = key_layer.expand(
424
+ -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
425
+ )
426
+ key_layer = key_layer.contiguous().view(
427
+ key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
428
+ )
429
+ value_layer = value_layer.unsqueeze(-2)
430
+ value_layer = value_layer.expand(
431
+ -1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1
432
+ )
433
+ value_layer = value_layer.contiguous().view(
434
+ value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
435
+ )
436
+
437
+ # ==================================
438
+ # core attention computation
439
+ # ==================================
440
+
441
+ context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask)
442
+
443
+ # =================
444
+ # Output. [sq, b, h]
445
+ # =================
446
+
447
+ output = self.dense(context_layer)
448
+
449
+ return output, kv_cache
450
+
451
+
452
+ def _config_to_kwargs(args):
453
+ common_kwargs = {
454
+ "dtype": args.torch_dtype,
455
+ }
456
+ return common_kwargs
457
+
458
+
459
+ class MLP(torch.nn.Module):
460
+ """MLP.
461
+
462
+ MLP will take the input with h hidden state, project it to 4*h
463
+ hidden dimension, perform nonlinear transformation, and project the
464
+ state back into h hidden dimension.
465
+ """
466
+
467
+ def __init__(self, config: ChatGLMConfig, device=None):
468
+ super(MLP, self).__init__()
469
+
470
+ self.add_bias = config.add_bias_linear
471
+
472
+ # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
473
+ self.dense_h_to_4h = nn.Linear(
474
+ config.hidden_size,
475
+ config.ffn_hidden_size * 2,
476
+ bias=self.add_bias,
477
+ device=device,
478
+ **_config_to_kwargs(config)
479
+ )
480
+
481
+ def swiglu(x):
482
+ x = torch.chunk(x, 2, dim=-1)
483
+ return F.silu(x[0]) * x[1]
484
+
485
+ self.activation_func = swiglu
486
+
487
+ # Project back to h.
488
+ self.dense_4h_to_h = nn.Linear(
489
+ config.ffn_hidden_size,
490
+ config.hidden_size,
491
+ bias=self.add_bias,
492
+ device=device,
493
+ **_config_to_kwargs(config)
494
+ )
495
+
496
+ def forward(self, hidden_states):
497
+ # [s, b, 4hp]
498
+ intermediate_parallel = self.dense_h_to_4h(hidden_states)
499
+ intermediate_parallel = self.activation_func(intermediate_parallel)
500
+ # [s, b, h]
501
+ output = self.dense_4h_to_h(intermediate_parallel)
502
+ return output
503
+
504
+
505
+ class GLMBlock(torch.nn.Module):
506
+ """A single transformer layer.
507
+
508
+ Transformer layer takes input with size [s, b, h] and returns an
509
+ output of the same size.
510
+ """
511
+
512
+ def __init__(self, config: ChatGLMConfig, layer_number, device=None):
513
+ super(GLMBlock, self).__init__()
514
+ self.layer_number = layer_number
515
+
516
+ self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
517
+
518
+ self.fp32_residual_connection = config.fp32_residual_connection
519
+
520
+ LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
521
+ # Layernorm on the input data.
522
+ self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
523
+ dtype=config.torch_dtype)
524
+
525
+ # Self attention.
526
+ self.self_attention = SelfAttention(config, layer_number, device=device)
527
+ self.hidden_dropout = config.hidden_dropout
528
+
529
+ # Layernorm on the attention output
530
+ self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
531
+ dtype=config.torch_dtype)
532
+
533
+ # MLP
534
+ self.mlp = MLP(config, device=device)
535
+
536
+ def forward(
537
+ self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True,
538
+ ):
539
+ # hidden_states: [s, b, h]
540
+
541
+ # Layer norm at the beginning of the transformer layer.
542
+ layernorm_output = self.input_layernorm(hidden_states)
543
+ # Self attention.
544
+ attention_output, kv_cache = self.self_attention(
545
+ layernorm_output,
546
+ attention_mask,
547
+ rotary_pos_emb,
548
+ kv_cache=kv_cache,
549
+ use_cache=use_cache
550
+ )
551
+
552
+ # Residual connection.
553
+ if self.apply_residual_connection_post_layernorm:
554
+ residual = layernorm_output
555
+ else:
556
+ residual = hidden_states
557
+
558
+ layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training)
559
+ layernorm_input = residual + layernorm_input
560
+
561
+ # Layer norm post the self attention.
562
+ layernorm_output = self.post_attention_layernorm(layernorm_input)
563
+
564
+ # MLP.
565
+ mlp_output = self.mlp(layernorm_output)
566
+
567
+ # Second residual connection.
568
+ if self.apply_residual_connection_post_layernorm:
569
+ residual = layernorm_output
570
+ else:
571
+ residual = layernorm_input
572
+
573
+ output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training)
574
+ output = residual + output
575
+
576
+ return output, kv_cache
577
+
578
+
579
+ class GLMTransformer(torch.nn.Module):
580
+ """Transformer class."""
581
+
582
+ def __init__(self, config: ChatGLMConfig, device=None):
583
+ super(GLMTransformer, self).__init__()
584
+
585
+ self.fp32_residual_connection = config.fp32_residual_connection
586
+ self.post_layer_norm = config.post_layer_norm
587
+
588
+ # Number of layers.
589
+ self.num_layers = config.num_layers
590
+
591
+ # Transformer layers.
592
+ def build_layer(layer_number):
593
+ return GLMBlock(config, layer_number, device=device)
594
+
595
+ self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)])
596
+
597
+ if self.post_layer_norm:
598
+ LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
599
+ # Final layer norm before output.
600
+ self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
601
+ dtype=config.torch_dtype)
602
+
603
+ self.gradient_checkpointing = False
604
+
605
+ def _get_layer(self, layer_number):
606
+ return self.layers[layer_number]
607
+
608
+ def forward(
609
+ self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None,
610
+ use_cache: Optional[bool] = True,
611
+ output_hidden_states: Optional[bool] = False,
612
+ ):
613
+ if not kv_caches:
614
+ kv_caches = [None for _ in range(self.num_layers)]
615
+ presents = () if use_cache else None
616
+ if self.gradient_checkpointing and self.training:
617
+ if use_cache:
618
+ logger.warning_once(
619
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
620
+ )
621
+ use_cache = False
622
+
623
+ all_self_attentions = None
624
+ all_hidden_states = () if output_hidden_states else None
625
+ for index in range(self.num_layers):
626
+ if output_hidden_states:
627
+ all_hidden_states = all_hidden_states + (hidden_states,)
628
+
629
+ layer = self._get_layer(index)
630
+ if self.gradient_checkpointing and self.training:
631
+ layer_ret = torch.utils.checkpoint.checkpoint(
632
+ layer,
633
+ hidden_states,
634
+ attention_mask,
635
+ rotary_pos_emb,
636
+ kv_caches[index],
637
+ use_cache
638
+ )
639
+ else:
640
+ layer_ret = layer(
641
+ hidden_states,
642
+ attention_mask,
643
+ rotary_pos_emb,
644
+ kv_cache=kv_caches[index],
645
+ use_cache=use_cache
646
+ )
647
+ hidden_states, kv_cache = layer_ret
648
+ if use_cache:
649
+ presents = presents + (kv_cache,)
650
+
651
+ if output_hidden_states:
652
+ all_hidden_states = all_hidden_states + (hidden_states,)
653
+
654
+ # Final layer norm.
655
+ if self.post_layer_norm:
656
+ hidden_states = self.final_layernorm(hidden_states)
657
+
658
+ return hidden_states, presents, all_hidden_states, all_self_attentions
659
+
660
+
661
+ class ChatGLMPreTrainedModel(PreTrainedModel):
662
+ """
663
+ An abstract class to handle weights initialization and
664
+ a simple interface for downloading and loading pretrained models.
665
+ """
666
+
667
+ is_parallelizable = False
668
+ supports_gradient_checkpointing = True
669
+ config_class = ChatGLMConfig
670
+ base_model_prefix = "transformer"
671
+ _no_split_modules = ["GLMBlock"]
672
+
673
+ def _init_weights(self, module: nn.Module):
674
+ """Initialize the weights."""
675
+ return
676
+
677
+ def get_masks(self, input_ids, past_key_values, padding_mask=None):
678
+ batch_size, seq_length = input_ids.shape
679
+ full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device)
680
+ full_attention_mask.tril_()
681
+ past_length = 0
682
+ if past_key_values:
683
+ past_length = past_key_values[0][0].shape[0]
684
+ if past_length:
685
+ full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length,
686
+ device=input_ids.device), full_attention_mask), dim=-1)
687
+ if padding_mask is not None:
688
+ full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1)
689
+ if not past_length and padding_mask is not None:
690
+ full_attention_mask -= padding_mask.unsqueeze(-1) - 1
691
+ full_attention_mask = (full_attention_mask < 0.5).bool()
692
+ full_attention_mask.unsqueeze_(1)
693
+ return full_attention_mask
694
+
695
+ def get_position_ids(self, input_ids, device):
696
+ batch_size, seq_length = input_ids.shape
697
+ position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
698
+ return position_ids
699
+
700
+ def _set_gradient_checkpointing(self, module, value=False):
701
+ if isinstance(module, GLMTransformer):
702
+ module.gradient_checkpointing = value
703
+
704
+
705
+ class Embedding(torch.nn.Module):
706
+ """Language model embeddings."""
707
+
708
+ def __init__(self, config: ChatGLMConfig, device=None):
709
+ super(Embedding, self).__init__()
710
+
711
+ self.hidden_size = config.hidden_size
712
+ # Word embeddings (parallel).
713
+ self.word_embeddings = nn.Embedding(
714
+ config.padded_vocab_size,
715
+ self.hidden_size,
716
+ dtype=config.torch_dtype,
717
+ device=device
718
+ )
719
+ self.fp32_residual_connection = config.fp32_residual_connection
720
+
721
+ def forward(self, input_ids):
722
+ # Embeddings.
723
+ words_embeddings = self.word_embeddings(input_ids)
724
+ embeddings = words_embeddings
725
+ # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
726
+ embeddings = embeddings.transpose(0, 1).contiguous()
727
+ # If the input flag for fp32 residual connection is set, convert for float.
728
+ if self.fp32_residual_connection:
729
+ embeddings = embeddings.float()
730
+ return embeddings
731
+
732
+
733
+ class ChatGLMModel(ChatGLMPreTrainedModel):
734
+ def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
735
+ super().__init__(config)
736
+ if empty_init:
737
+ init_method = skip_init
738
+ else:
739
+ init_method = default_init
740
+ init_kwargs = {}
741
+ if device is not None:
742
+ init_kwargs["device"] = device
743
+ self.embedding = init_method(Embedding, config, **init_kwargs)
744
+ self.num_layers = config.num_layers
745
+ self.multi_query_group_num = config.multi_query_group_num
746
+ self.kv_channels = config.kv_channels
747
+
748
+ # Rotary positional embeddings
749
+ self.seq_length = config.seq_length
750
+ rotary_dim = (
751
+ config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels
752
+ )
753
+
754
+ self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, original_impl=config.original_rope, device=device,
755
+ dtype=config.torch_dtype)
756
+ self.encoder = init_method(GLMTransformer, config, **init_kwargs)
757
+ self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False,
758
+ dtype=config.torch_dtype, **init_kwargs)
759
+ self.pre_seq_len = config.pre_seq_len
760
+ self.prefix_projection = config.prefix_projection
761
+ if self.pre_seq_len is not None:
762
+ for param in self.parameters():
763
+ param.requires_grad = False
764
+ self.prefix_tokens = torch.arange(self.pre_seq_len).long()
765
+ self.prefix_encoder = PrefixEncoder(config)
766
+ self.dropout = torch.nn.Dropout(0.1)
767
+
768
+ def get_input_embeddings(self):
769
+ return self.embedding.word_embeddings
770
+
771
+ def get_prompt(self, batch_size, device, dtype=torch.half):
772
+ prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
773
+ past_key_values = self.prefix_encoder(prefix_tokens).type(dtype)
774
+ past_key_values = past_key_values.view(
775
+ batch_size,
776
+ self.pre_seq_len,
777
+ self.num_layers * 2,
778
+ self.multi_query_group_num,
779
+ self.kv_channels
780
+ )
781
+ # seq_len, b, nh, hidden_size
782
+ past_key_values = self.dropout(past_key_values)
783
+ past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2)
784
+ return past_key_values
785
+
786
+ def forward(
787
+ self,
788
+ input_ids,
789
+ position_ids: Optional[torch.Tensor] = None,
790
+ attention_mask: Optional[torch.BoolTensor] = None,
791
+ full_attention_mask: Optional[torch.BoolTensor] = None,
792
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
793
+ inputs_embeds: Optional[torch.Tensor] = None,
794
+ use_cache: Optional[bool] = None,
795
+ output_hidden_states: Optional[bool] = None,
796
+ return_dict: Optional[bool] = None,
797
+ ):
798
+ output_hidden_states = (
799
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
800
+ )
801
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
802
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
803
+
804
+ batch_size, seq_length = input_ids.shape
805
+
806
+ if inputs_embeds is None:
807
+ inputs_embeds = self.embedding(input_ids)
808
+
809
+ if self.pre_seq_len is not None:
810
+ if past_key_values is None:
811
+ past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device,
812
+ dtype=inputs_embeds.dtype)
813
+ if attention_mask is not None:
814
+ attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)),
815
+ attention_mask], dim=-1)
816
+
817
+ if full_attention_mask is None:
818
+ if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1):
819
+ full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask)
820
+
821
+ # Rotary positional embeddings
822
+ rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
823
+ if position_ids is not None:
824
+ rotary_pos_emb = rotary_pos_emb[position_ids]
825
+ else:
826
+ rotary_pos_emb = rotary_pos_emb[None, :seq_length]
827
+ rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
828
+
829
+ # Run encoder.
830
+ hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
831
+ inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb,
832
+ kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
833
+ )
834
+
835
+ if not return_dict:
836
+ return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
837
+
838
+ return BaseModelOutputWithPast(
839
+ last_hidden_state=hidden_states,
840
+ past_key_values=presents,
841
+ hidden_states=all_hidden_states,
842
+ attentions=all_self_attentions,
843
+ )
844
+
845
+ def quantize(self, weight_bit_width: int):
846
+ from .quantization import quantize
847
+ quantize(self.encoder, weight_bit_width)
848
+ return self
849
+
850
+
851
+ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
852
+ def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
853
+ super().__init__(config)
854
+
855
+ self.max_sequence_length = config.max_length
856
+ self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
857
+ self.config = config
858
+ self.quantized = False
859
+
860
+ if self.config.quantization_bit:
861
+ self.quantize(self.config.quantization_bit, empty_init=True)
862
+
863
+ def _update_model_kwargs_for_generation(
864
+ self,
865
+ outputs: ModelOutput,
866
+ model_kwargs: Dict[str, Any],
867
+ is_encoder_decoder: bool = False,
868
+ standardize_cache_format: bool = False,
869
+ ) -> Dict[str, Any]:
870
+ # update past_key_values
871
+ model_kwargs["past_key_values"] = self._extract_past_from_model_output(
872
+ outputs, standardize_cache_format=standardize_cache_format
873
+ )
874
+
875
+ # update attention mask
876
+ if "attention_mask" in model_kwargs:
877
+ attention_mask = model_kwargs["attention_mask"]
878
+ model_kwargs["attention_mask"] = torch.cat(
879
+ [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
880
+ )
881
+
882
+ # update position ids
883
+ if "position_ids" in model_kwargs:
884
+ position_ids = model_kwargs["position_ids"]
885
+ new_position_id = position_ids[..., -1:].clone()
886
+ new_position_id += 1
887
+ model_kwargs["position_ids"] = torch.cat(
888
+ [position_ids, new_position_id], dim=-1
889
+ )
890
+
891
+ model_kwargs["is_first_forward"] = False
892
+ return model_kwargs
893
+
894
+ def prepare_inputs_for_generation(
895
+ self,
896
+ input_ids: torch.LongTensor,
897
+ past_key_values: Optional[torch.Tensor] = None,
898
+ attention_mask: Optional[torch.Tensor] = None,
899
+ position_ids: Optional[torch.Tensor] = None,
900
+ use_cache: Optional[bool] = None,
901
+ is_first_forward: bool = True,
902
+ **kwargs
903
+ ) -> dict:
904
+ # only last token for input_ids if past is not None
905
+ if position_ids is None:
906
+ position_ids = self.get_position_ids(input_ids, device=input_ids.device)
907
+ if not is_first_forward:
908
+ if past_key_values is not None:
909
+ position_ids = position_ids[..., -1:]
910
+ input_ids = input_ids[:, -1:]
911
+ return {
912
+ "input_ids": input_ids,
913
+ "past_key_values": past_key_values,
914
+ "position_ids": position_ids,
915
+ "attention_mask": attention_mask,
916
+ "return_last_logit": True,
917
+ "use_cache": use_cache
918
+ }
919
+
920
+ def forward(
921
+ self,
922
+ input_ids: Optional[torch.Tensor] = None,
923
+ position_ids: Optional[torch.Tensor] = None,
924
+ attention_mask: Optional[torch.Tensor] = None,
925
+ past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
926
+ inputs_embeds: Optional[torch.Tensor] = None,
927
+ labels: Optional[torch.Tensor] = None,
928
+ use_cache: Optional[bool] = None,
929
+ output_attentions: Optional[bool] = None,
930
+ output_hidden_states: Optional[bool] = None,
931
+ return_dict: Optional[bool] = None,
932
+ return_last_logit: Optional[bool] = False,
933
+ ):
934
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
935
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
936
+
937
+ transformer_outputs = self.transformer(
938
+ input_ids=input_ids,
939
+ position_ids=position_ids,
940
+ attention_mask=attention_mask,
941
+ past_key_values=past_key_values,
942
+ inputs_embeds=inputs_embeds,
943
+ use_cache=use_cache,
944
+ output_hidden_states=output_hidden_states,
945
+ return_dict=return_dict,
946
+ )
947
+
948
+ hidden_states = transformer_outputs[0]
949
+ if return_last_logit:
950
+ hidden_states = hidden_states[-1:]
951
+ lm_logits = self.transformer.output_layer(hidden_states)
952
+ lm_logits = lm_logits.transpose(0, 1).contiguous()
953
+
954
+ loss = None
955
+ if labels is not None:
956
+ lm_logits = lm_logits.to(torch.float32)
957
+
958
+ # Shift so that tokens < n predict n
959
+ shift_logits = lm_logits[..., :-1, :].contiguous()
960
+ shift_labels = labels[..., 1:].contiguous()
961
+ # Flatten the tokens
962
+ loss_fct = CrossEntropyLoss(ignore_index=-100)
963
+ loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
964
+
965
+ lm_logits = lm_logits.to(hidden_states.dtype)
966
+ loss = loss.to(hidden_states.dtype)
967
+
968
+ if not return_dict:
969
+ output = (lm_logits,) + transformer_outputs[1:]
970
+ return ((loss,) + output) if loss is not None else output
971
+
972
+ return CausalLMOutputWithPast(
973
+ loss=loss,
974
+ logits=lm_logits,
975
+ past_key_values=transformer_outputs.past_key_values,
976
+ hidden_states=transformer_outputs.hidden_states,
977
+ attentions=transformer_outputs.attentions,
978
+ )
979
+
980
+ @staticmethod
981
+ def _reorder_cache(
982
+ past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
983
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
984
+ """
985
+ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
986
+ [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
987
+ beam_idx at every generation step.
988
+
989
+ Output shares the same memory storage as `past`.
990
+ """
991
+ return tuple(
992
+ (
993
+ layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)),
994
+ layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)),
995
+ )
996
+ for layer_past in past
997
+ )
998
+
999
+ def process_response(self, output, history):
1000
+ content = ""
1001
+ history = deepcopy(history)
1002
+ for response in output.split("<|assistant|>"):
1003
+ metadata, content = response.split("\n", maxsplit=1)
1004
+ if not metadata.strip():
1005
+ content = content.strip()
1006
+ history.append({"role": "assistant", "metadata": metadata, "content": content})
1007
+ content = content.replace("[[训练时间]]", "2023年")
1008
+ else:
1009
+ history.append({"role": "assistant", "metadata": metadata, "content": content})
1010
+ if history[0]["role"] == "system" and "tools" in history[0]:
1011
+ content = "\n".join(content.split("\n")[1:-1])
1012
+ def tool_call(**kwargs):
1013
+ return kwargs
1014
+ parameters = eval(content)
1015
+ content = {"name": metadata.strip(), "parameters": parameters}
1016
+ else:
1017
+ content = {"name": metadata.strip(), "content": content}
1018
+ return content, history
1019
+
1020
+ @torch.inference_mode()
1021
+ def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user",
1022
+ max_length: int = 8192, num_beams=1, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None,
1023
+ **kwargs):
1024
+ if history is None:
1025
+ history = []
1026
+ if logits_processor is None:
1027
+ logits_processor = LogitsProcessorList()
1028
+ logits_processor.append(InvalidScoreLogitsProcessor())
1029
+ gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
1030
+ "temperature": temperature, "logits_processor": logits_processor, **kwargs}
1031
+ inputs = tokenizer.build_chat_input(query, history=history, role=role)
1032
+ inputs = inputs.to(self.device)
1033
+ eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"),
1034
+ tokenizer.get_command("<|observation|>")]
1035
+ outputs = self.generate(**inputs, **gen_kwargs, eos_token_id=eos_token_id)
1036
+ outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
1037
+ response = tokenizer.decode(outputs)
1038
+ history.append({"role": role, "content": query})
1039
+ response, history = self.process_response(response, history)
1040
+ return response, history
1041
+
1042
+ @torch.inference_mode()
1043
+ def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, role: str = "user",
1044
+ past_key_values=None,max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8,
1045
+ logits_processor=None, return_past_key_values=False, **kwargs):
1046
+ if history is None:
1047
+ history = []
1048
+ if logits_processor is None:
1049
+ logits_processor = LogitsProcessorList()
1050
+ logits_processor.append(InvalidScoreLogitsProcessor())
1051
+ eos_token_id = [tokenizer.eos_token_id, tokenizer.get_command("<|user|>"),
1052
+ tokenizer.get_command("<|observation|>")]
1053
+ gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p,
1054
+ "temperature": temperature, "logits_processor": logits_processor, **kwargs}
1055
+ if past_key_values is None:
1056
+ inputs = tokenizer.build_chat_input(query, history=history, role=role)
1057
+ else:
1058
+ inputs = tokenizer.build_chat_input(query, role=role)
1059
+ inputs = inputs.to(self.device)
1060
+ if past_key_values is not None:
1061
+ past_length = past_key_values[0][0].shape[0]
1062
+ if self.transformer.pre_seq_len is not None:
1063
+ past_length -= self.transformer.pre_seq_len
1064
+ inputs.position_ids += past_length
1065
+ attention_mask = inputs.attention_mask
1066
+ attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1)
1067
+ inputs['attention_mask'] = attention_mask
1068
+ history.append({"role": role, "content": query})
1069
+ for outputs in self.stream_generate(**inputs, past_key_values=past_key_values,
1070
+ eos_token_id=eos_token_id, return_past_key_values=return_past_key_values,
1071
+ **gen_kwargs):
1072
+ if return_past_key_values:
1073
+ outputs, past_key_values = outputs
1074
+ outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
1075
+ response = tokenizer.decode(outputs)
1076
+ if response and response[-1] != "�":
1077
+ response, new_history = self.process_response(response, history)
1078
+ if return_past_key_values:
1079
+ yield response, new_history, past_key_values
1080
+ else:
1081
+ yield response, new_history
1082
+
1083
+ @torch.inference_mode()
1084
+ def stream_generate(
1085
+ self,
1086
+ input_ids,
1087
+ generation_config: Optional[GenerationConfig] = None,
1088
+ logits_processor: Optional[LogitsProcessorList] = None,
1089
+ stopping_criteria: Optional[StoppingCriteriaList] = None,
1090
+ prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
1091
+ return_past_key_values=False,
1092
+ **kwargs,
1093
+ ):
1094
+ batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
1095
+
1096
+ if generation_config is None:
1097
+ generation_config = self.generation_config
1098
+ generation_config = copy.deepcopy(generation_config)
1099
+ model_kwargs = generation_config.update(**kwargs)
1100
+ model_kwargs["use_cache"] = generation_config.use_cache
1101
+ bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
1102
+
1103
+ if isinstance(eos_token_id, int):
1104
+ eos_token_id = [eos_token_id]
1105
+ eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
1106
+
1107
+ has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
1108
+ if has_default_max_length and generation_config.max_new_tokens is None:
1109
+ warnings.warn(
1110
+ f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
1111
+ "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
1112
+ " recommend using `max_new_tokens` to control the maximum length of the generation.",
1113
+ UserWarning,
1114
+ )
1115
+ elif generation_config.max_new_tokens is not None:
1116
+ generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
1117
+ if not has_default_max_length:
1118
+ logger.warn(
1119
+ f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
1120
+ f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
1121
+ "Please refer to the documentation for more information. "
1122
+ "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
1123
+ UserWarning,
1124
+ )
1125
+
1126
+ if input_ids_seq_length >= generation_config.max_length:
1127
+ input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
1128
+ logger.warning(
1129
+ f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
1130
+ f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
1131
+ " increasing `max_new_tokens`."
1132
+ )
1133
+
1134
+ # 2. Set generation parameters if not already defined
1135
+ logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
1136
+ stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
1137
+
1138
+ logits_processor = self._get_logits_processor(
1139
+ generation_config=generation_config,
1140
+ input_ids_seq_length=input_ids_seq_length,
1141
+ encoder_input_ids=input_ids,
1142
+ prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
1143
+ logits_processor=logits_processor,
1144
+ )
1145
+
1146
+ stopping_criteria = self._get_stopping_criteria(
1147
+ generation_config=generation_config, stopping_criteria=stopping_criteria
1148
+ )
1149
+ logits_warper = self._get_logits_warper(generation_config)
1150
+
1151
+ unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
1152
+ scores = None
1153
+ while True:
1154
+ model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
1155
+ # forward pass to get next token
1156
+ outputs = self(
1157
+ **model_inputs,
1158
+ return_dict=True,
1159
+ output_attentions=False,
1160
+ output_hidden_states=False,
1161
+ )
1162
+
1163
+ next_token_logits = outputs.logits[:, -1, :]
1164
+
1165
+ # pre-process distribution
1166
+ next_token_scores = logits_processor(input_ids, next_token_logits)
1167
+ next_token_scores = logits_warper(input_ids, next_token_scores)
1168
+
1169
+ # sample
1170
+ probs = nn.functional.softmax(next_token_scores, dim=-1)
1171
+ if generation_config.do_sample:
1172
+ next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
1173
+ else:
1174
+ next_tokens = torch.argmax(probs, dim=-1)
1175
+ # update generated ids, model inputs, and length for next step
1176
+ input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
1177
+ model_kwargs = self._update_model_kwargs_for_generation(
1178
+ outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
1179
+ )
1180
+ unfinished_sequences = unfinished_sequences.mul(
1181
+ next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
1182
+ )
1183
+ if return_past_key_values:
1184
+ yield input_ids, outputs.past_key_values
1185
+ else:
1186
+ yield input_ids
1187
+ # stop when each sentence is finished, or if we exceed the maximum length
1188
+ if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
1189
+ break
1190
+
1191
+ def quantize(self, bits: int, empty_init=False, device=None, **kwargs):
1192
+ if bits == 0:
1193
+ return
1194
+
1195
+ from .quantization import quantize
1196
+
1197
+ if self.quantized:
1198
+ logger.info("Already quantized.")
1199
+ return self
1200
+
1201
+ self.quantized = True
1202
+
1203
+ self.config.quantization_bit = bits
1204
+
1205
+ self.transformer.encoder = quantize(self.transformer.encoder, bits, empty_init=empty_init, device=device,
1206
+ **kwargs)
1207
+ return self
1208
+
1209
+
1210
+ class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel):
1211
+ def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
1212
+ super().__init__(config)
1213
+
1214
+ self.num_labels = config.num_labels
1215
+ self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
1216
+
1217
+ self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half)
1218
+ if config.classifier_dropout is not None:
1219
+ self.dropout = nn.Dropout(config.classifier_dropout)
1220
+ else:
1221
+ self.dropout = None
1222
+ self.config = config
1223
+
1224
+ if self.config.quantization_bit:
1225
+ self.quantize(self.config.quantization_bit, empty_init=True)
1226
+
1227
+ def forward(
1228
+ self,
1229
+ input_ids: Optional[torch.LongTensor] = None,
1230
+ position_ids: Optional[torch.LongTensor] = None,
1231
+ attention_mask: Optional[torch.Tensor] = None,
1232
+ full_attention_mask: Optional[torch.Tensor] = None,
1233
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
1234
+ inputs_embeds: Optional[torch.LongTensor] = None,
1235
+ labels: Optional[torch.LongTensor] = None,
1236
+ use_cache: Optional[bool] = None,
1237
+ output_hidden_states: Optional[bool] = None,
1238
+ return_dict: Optional[bool] = None,
1239
+ ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]:
1240
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1241
+
1242
+ transformer_outputs = self.transformer(
1243
+ input_ids=input_ids,
1244
+ position_ids=position_ids,
1245
+ attention_mask=attention_mask,
1246
+ full_attention_mask=full_attention_mask,
1247
+ past_key_values=past_key_values,
1248
+ inputs_embeds=inputs_embeds,
1249
+ use_cache=use_cache,
1250
+ output_hidden_states=output_hidden_states,
1251
+ return_dict=return_dict,
1252
+ )
1253
+
1254
+ hidden_states = transformer_outputs[0]
1255
+ pooled_hidden_states = hidden_states[-1]
1256
+ if self.dropout is not None:
1257
+ pooled_hidden_states = self.dropout(pooled_hidden_states)
1258
+ logits = self.classifier_head(pooled_hidden_states)
1259
+
1260
+ loss = None
1261
+ if labels is not None:
1262
+ if self.config.problem_type is None:
1263
+ if self.num_labels == 1:
1264
+ self.config.problem_type = "regression"
1265
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1266
+ self.config.problem_type = "single_label_classification"
1267
+ else:
1268
+ self.config.problem_type = "multi_label_classification"
1269
+
1270
+ if self.config.problem_type == "regression":
1271
+ loss_fct = MSELoss()
1272
+ if self.num_labels == 1:
1273
+ loss = loss_fct(logits.squeeze().float(), labels.squeeze())
1274
+ else:
1275
+ loss = loss_fct(logits.float(), labels)
1276
+ elif self.config.problem_type == "single_label_classification":
1277
+ loss_fct = CrossEntropyLoss()
1278
+ loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1))
1279
+ elif self.config.problem_type == "multi_label_classification":
1280
+ loss_fct = BCEWithLogitsLoss()
1281
+ loss = loss_fct(logits.float(), labels.view(-1, self.num_labels))
1282
+
1283
+ if not return_dict:
1284
+ output = (logits,) + transformer_outputs[1:]
1285
+ return ((loss,) + output) if loss is not None else output
1286
+
1287
+ return SequenceClassifierOutputWithPast(
1288
+ loss=loss,
1289
+ logits=logits,
1290
+ past_key_values=transformer_outputs.past_key_values,
1291
+ hidden_states=transformer_outputs.hidden_states,
1292
+ attentions=transformer_outputs.attentions,
1293
+ )
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
tokenization_chatglm.py ADDED
@@ -0,0 +1,283 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import json
2
+ import os
3
+ import torch
4
+ from typing import List, Optional, Union, Dict
5
+ from sentencepiece import SentencePieceProcessor
6
+ from transformers import PreTrainedTokenizer
7
+ from transformers.utils import logging, PaddingStrategy
8
+ from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
9
+
10
+
11
+ class SPTokenizer:
12
+ def __init__(self, model_path: str):
13
+ # reload tokenizer
14
+ assert os.path.isfile(model_path), model_path
15
+ self.sp_model = SentencePieceProcessor(model_file=model_path)
16
+
17
+ # BOS / EOS token IDs
18
+ self.n_words: int = self.sp_model.vocab_size()
19
+ self.bos_id: int = self.sp_model.bos_id()
20
+ self.eos_id: int = self.sp_model.eos_id()
21
+ self.pad_id: int = self.sp_model.unk_id()
22
+ assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
23
+
24
+ special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop", "<|system|>", "<|user|>", "<|assistant|>",
25
+ "<|observation|>"]
26
+ self.special_tokens = {}
27
+ self.index_special_tokens = {}
28
+ for token in special_tokens:
29
+ self.special_tokens[token] = self.n_words
30
+ self.index_special_tokens[self.n_words] = token
31
+ self.n_words += 1
32
+
33
+ def tokenize(self, s: str):
34
+ return self.sp_model.EncodeAsPieces(s)
35
+
36
+ def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]:
37
+ assert type(s) is str
38
+ t = self.sp_model.encode(s)
39
+ if bos:
40
+ t = [self.bos_id] + t
41
+ if eos:
42
+ t = t + [self.eos_id]
43
+ return t
44
+
45
+ def decode(self, t: List[int]) -> str:
46
+ text, buffer = "", []
47
+ for token in t:
48
+ if token in self.index_special_tokens:
49
+ if buffer:
50
+ text += self.sp_model.decode(buffer)
51
+ buffer = []
52
+ text += self.index_special_tokens[token]
53
+ else:
54
+ buffer.append(token)
55
+ if buffer:
56
+ text += self.sp_model.decode(buffer)
57
+ return text
58
+
59
+ def decode_tokens(self, tokens: List[str]) -> str:
60
+ text = self.sp_model.DecodePieces(tokens)
61
+ return text
62
+
63
+ def convert_token_to_id(self, token):
64
+ """ Converts a token (str) in an id using the vocab. """
65
+ if token in self.special_tokens:
66
+ return self.special_tokens[token]
67
+ return self.sp_model.PieceToId(token)
68
+
69
+ def convert_id_to_token(self, index):
70
+ """Converts an index (integer) in a token (str) using the vocab."""
71
+ if index in self.index_special_tokens:
72
+ return self.index_special_tokens[index]
73
+ if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0:
74
+ return ""
75
+ return self.sp_model.IdToPiece(index)
76
+
77
+
78
+ class ChatGLMTokenizer(PreTrainedTokenizer):
79
+ vocab_files_names = {"vocab_file": "tokenizer.model"}
80
+
81
+ model_input_names = ["input_ids", "attention_mask", "position_ids"]
82
+
83
+ def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, **kwargs):
84
+ self.name = "GLMTokenizer"
85
+
86
+ self.vocab_file = vocab_file
87
+ self.tokenizer = SPTokenizer(vocab_file)
88
+ self.special_tokens = {
89
+ "<bos>": self.tokenizer.bos_id,
90
+ "<eos>": self.tokenizer.eos_id,
91
+ "<pad>": self.tokenizer.pad_id
92
+ }
93
+ super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs)
94
+
95
+ def get_command(self, token):
96
+ if token in self.special_tokens:
97
+ return self.special_tokens[token]
98
+ assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}"
99
+ return self.tokenizer.special_tokens[token]
100
+
101
+ @property
102
+ def unk_token(self) -> str:
103
+ return "<unk>"
104
+
105
+ @property
106
+ def pad_token(self) -> str:
107
+ return "<unk>"
108
+
109
+ @property
110
+ def pad_token_id(self):
111
+ return self.get_command("<pad>")
112
+
113
+ @property
114
+ def eos_token(self) -> str:
115
+ return "</s>"
116
+
117
+ @property
118
+ def eos_token_id(self):
119
+ return self.get_command("<eos>")
120
+
121
+ @property
122
+ def vocab_size(self):
123
+ return self.tokenizer.n_words
124
+
125
+ def get_vocab(self):
126
+ """ Returns vocab as a dict """
127
+ vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
128
+ vocab.update(self.added_tokens_encoder)
129
+ return vocab
130
+
131
+ def _tokenize(self, text, **kwargs):
132
+ return self.tokenizer.tokenize(text)
133
+
134
+ def _convert_token_to_id(self, token):
135
+ """ Converts a token (str) in an id using the vocab. """
136
+ return self.tokenizer.convert_token_to_id(token)
137
+
138
+ def _convert_id_to_token(self, index):
139
+ """Converts an index (integer) in a token (str) using the vocab."""
140
+ return self.tokenizer.convert_id_to_token(index)
141
+
142
+ def convert_tokens_to_string(self, tokens: List[str]) -> str:
143
+ return self.tokenizer.decode_tokens(tokens)
144
+
145
+ def save_vocabulary(self, save_directory, filename_prefix=None):
146
+ """
147
+ Save the vocabulary and special tokens file to a directory.
148
+
149
+ Args:
150
+ save_directory (`str`):
151
+ The directory in which to save the vocabulary.
152
+ filename_prefix (`str`, *optional*):
153
+ An optional prefix to add to the named of the saved files.
154
+
155
+ Returns:
156
+ `Tuple(str)`: Paths to the files saved.
157
+ """
158
+ if os.path.isdir(save_directory):
159
+ vocab_file = os.path.join(
160
+ save_directory, self.vocab_files_names["vocab_file"]
161
+ )
162
+ else:
163
+ vocab_file = save_directory
164
+
165
+ with open(self.vocab_file, 'rb') as fin:
166
+ proto_str = fin.read()
167
+
168
+ with open(vocab_file, "wb") as writer:
169
+ writer.write(proto_str)
170
+
171
+ return (vocab_file,)
172
+
173
+ def get_prefix_tokens(self):
174
+ prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")]
175
+ return prefix_tokens
176
+
177
+ def build_single_message(self, role, metadata, message):
178
+ assert role in ["system", "user", "assistant", "observation"], role
179
+ role_tokens = [self.get_command(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n")
180
+ message_tokens = self.tokenizer.encode(message)
181
+ tokens = role_tokens + message_tokens
182
+ return tokens
183
+
184
+ def build_chat_input(self, query, history=None, role="user"):
185
+ if history is None:
186
+ history = []
187
+ input_ids = []
188
+ for item in history:
189
+ content = item["content"]
190
+ if item["role"] == "system" and "tools" in item:
191
+ content = content + "\n" + json.dumps(item["tools"], indent=4, ensure_ascii=False)
192
+ input_ids.extend(self.build_single_message(item["role"], item.get("metadata", ""), content))
193
+ input_ids.extend(self.build_single_message(role, "", query))
194
+ input_ids.extend([self.get_command("<|assistant|>")])
195
+ return self.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True)
196
+
197
+ def build_inputs_with_special_tokens(
198
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
199
+ ) -> List[int]:
200
+ """
201
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
202
+ adding special tokens. A BERT sequence has the following format:
203
+
204
+ - single sequence: `[CLS] X [SEP]`
205
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
206
+
207
+ Args:
208
+ token_ids_0 (`List[int]`):
209
+ List of IDs to which the special tokens will be added.
210
+ token_ids_1 (`List[int]`, *optional*):
211
+ Optional second list of IDs for sequence pairs.
212
+
213
+ Returns:
214
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
215
+ """
216
+ prefix_tokens = self.get_prefix_tokens()
217
+ token_ids_0 = prefix_tokens + token_ids_0
218
+ if token_ids_1 is not None:
219
+ token_ids_0 = token_ids_0 + token_ids_1 + [self.get_command("<eos>")]
220
+ return token_ids_0
221
+
222
+ def _pad(
223
+ self,
224
+ encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
225
+ max_length: Optional[int] = None,
226
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
227
+ pad_to_multiple_of: Optional[int] = None,
228
+ return_attention_mask: Optional[bool] = None,
229
+ ) -> dict:
230
+ """
231
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
232
+
233
+ Args:
234
+ encoded_inputs:
235
+ Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
236
+ max_length: maximum length of the returned list and optionally padding length (see below).
237
+ Will truncate by taking into account the special tokens.
238
+ padding_strategy: PaddingStrategy to use for padding.
239
+
240
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
241
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
242
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
243
+ The tokenizer padding sides are defined in self.padding_side:
244
+
245
+ - 'left': pads on the left of the sequences
246
+ - 'right': pads on the right of the sequences
247
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
248
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
249
+ `>= 7.5` (Volta).
250
+ return_attention_mask:
251
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
252
+ """
253
+ # Load from model defaults
254
+ assert self.padding_side == "left"
255
+
256
+ required_input = encoded_inputs[self.model_input_names[0]]
257
+ seq_length = len(required_input)
258
+
259
+ if padding_strategy == PaddingStrategy.LONGEST:
260
+ max_length = len(required_input)
261
+
262
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
263
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
264
+
265
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
266
+
267
+ # Initialize attention mask if not present.
268
+ if "attention_mask" not in encoded_inputs:
269
+ encoded_inputs["attention_mask"] = [1] * seq_length
270
+
271
+ if "position_ids" not in encoded_inputs:
272
+ encoded_inputs["position_ids"] = list(range(seq_length))
273
+
274
+ if needs_to_be_padded:
275
+ difference = max_length - len(required_input)
276
+
277
+ if "attention_mask" in encoded_inputs:
278
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
279
+ if "position_ids" in encoded_inputs:
280
+ encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
281
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
282
+
283
+ 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 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "name_or_path": "THUDM/chatglm3-6b",
3
+ "remove_space": false,
4
+ "do_lower_case": false,
5
+ "tokenizer_class": "ChatGLMTokenizer",
6
+ "auto_map": {
7
+ "AutoTokenizer": [
8
+ "tokenization_chatglm.ChatGLMTokenizer",
9
+ null
10
+ ]
11
+ }
12
+ }