nikravan commited on
Commit
4da2485
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Initialize

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config.json ADDED
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+ {
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+ "_name_or_path": "../Model",
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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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+ "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": 40,
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+ "original_rope": true,
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+ "padded_vocab_size": 151552,
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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_config": {
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+ "_load_in_4bit": true,
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+ "_load_in_8bit": false,
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+ "bnb_4bit_compute_dtype": "bfloat16",
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+ "bnb_4bit_quant_storage": "uint8",
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+ "bnb_4bit_quant_type": "nf4",
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+ "bnb_4bit_use_double_quant": false,
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+ "llm_int8_threshold": 6.0,
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+ "load_in_4bit": true,
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+ "load_in_8bit": false,
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+ "quant_method": "bitsandbytes"
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+ },
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+ "rmsnorm": true,
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+ "rope_ratio": 1,
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+ "seq_length": 8192,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.41.2",
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+ "use_cache": true,
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+ "vision_config": {
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+ "dropout_prob": 0.0,
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+ "hidden_act": "gelu",
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+ "hidden_size": 1792,
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+ "image_size": 1120,
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+ "in_channels": 3,
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+ "intermediate_size": 15360,
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+ "layer_norm_eps": 1e-06,
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+ "num_heads": 16,
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+ "num_hidden_layers": 63,
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+ "num_positions": 6401,
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+ "patch_size": 14,
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+ "scaling_factor": 8
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+ },
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+ "vocab_size": 151552
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+ }
configuration_chatglm.py ADDED
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1
+ from transformers import PretrainedConfig
2
+
3
+
4
+ class ChatGLMConfig(PretrainedConfig):
5
+ model_type = "chatglm"
6
+
7
+ def __init__(
8
+ self,
9
+ num_layers=28,
10
+ padded_vocab_size=65024,
11
+ hidden_size=4096,
12
+ ffn_hidden_size=13696,
13
+ kv_channels=128,
14
+ num_attention_heads=32,
15
+ seq_length=2048,
16
+ hidden_dropout=0.0,
17
+ classifier_dropout=None,
18
+ attention_dropout=0.0,
19
+ layernorm_epsilon=1e-5,
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
+ rope_ratio=1,
29
+ apply_query_key_layer_scaling=True,
30
+ attention_softmax_in_fp32=True,
31
+ fp32_residual_connection=False,
32
+ pre_seq_len=None,
33
+ prefix_projection=False,
34
+ boi_token_id=None,
35
+ eoi_token_id=None,
36
+ **kwargs
37
+ ):
38
+ self.num_layers = num_layers
39
+ self.vocab_size = padded_vocab_size
40
+ self.padded_vocab_size = padded_vocab_size
41
+ self.hidden_size = hidden_size
42
+ self.ffn_hidden_size = ffn_hidden_size
43
+ self.kv_channels = kv_channels
44
+ self.num_attention_heads = num_attention_heads
45
+ self.seq_length = seq_length
46
+ self.hidden_dropout = hidden_dropout
47
+ self.classifier_dropout = classifier_dropout
48
+ self.attention_dropout = attention_dropout
49
+ self.layernorm_epsilon = layernorm_epsilon
50
+ self.rmsnorm = rmsnorm
51
+ self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
52
+ self.post_layer_norm = post_layer_norm
53
+ self.add_bias_linear = add_bias_linear
54
+ self.add_qkv_bias = add_qkv_bias
55
+ self.bias_dropout_fusion = bias_dropout_fusion
56
+ self.multi_query_attention = multi_query_attention
57
+ self.multi_query_group_num = multi_query_group_num
58
+ self.rope_ratio = rope_ratio
59
+ self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
60
+ self.attention_softmax_in_fp32 = attention_softmax_in_fp32
61
+ self.fp32_residual_connection = fp32_residual_connection
62
+ self.pre_seq_len = pre_seq_len
63
+ self.prefix_projection = prefix_projection
64
+ self.boi_token_id = boi_token_id
65
+ self.eoi_token_id = eoi_token_id
66
+ super().__init__(**kwargs)
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+ ],
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+ "pad_token_id": 151329,
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+ "transformers_version": "4.41.2"
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+ }
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model.safetensors.index.json ADDED
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modeling_chatglm.py ADDED
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1
+ """ PyTorch ChatGLM model. """
2
+ import json
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
+ from .visual import EVA2CLIPModel
30
+
31
+ # flags required to enable jit fusion kernels
32
+
33
+ if sys.platform != 'darwin':
34
+ torch._C._jit_set_profiling_mode(False)
35
+ torch._C._jit_set_profiling_executor(False)
36
+ torch._C._jit_override_can_fuse_on_cpu(True)
37
+ torch._C._jit_override_can_fuse_on_gpu(True)
38
+
39
+ logger = logging.get_logger(__name__)
40
+
41
+ LANGUAGE_TOKEN_TYPE = 0
42
+ VISION_TOKEN_TYPE = 1
43
+
44
+ _CHECKPOINT_FOR_DOC = "THUDM/ChatGLM"
45
+ _CONFIG_FOR_DOC = "ChatGLMConfig"
46
+
47
+ CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
48
+ "THUDM/chatglm3-6b",
49
+ # See all ChatGLM models at https://huggingface.co/models?filter=chatglm
50
+ ]
51
+
52
+
53
+ def default_init(cls, *args, **kwargs):
54
+ return cls(*args, **kwargs)
55
+
56
+
57
+ class InvalidScoreLogitsProcessor(LogitsProcessor):
58
+ def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
59
+ if torch.isnan(scores).any() or torch.isinf(scores).any():
60
+ scores.zero_()
61
+ scores[..., 198] = 5e4
62
+ return scores
63
+
64
+
65
+ class PrefixEncoder(torch.nn.Module):
66
+ """
67
+ The torch.nn model to encode the prefix
68
+ Input shape: (batch-size, prefix-length)
69
+ Output shape: (batch-size, prefix-length, 2*layers*hidden)
70
+ """
71
+
72
+ def __init__(self, config: ChatGLMConfig):
73
+ super().__init__()
74
+ self.prefix_projection = config.prefix_projection
75
+ if self.prefix_projection:
76
+ # Use a two-layer MLP to encode the prefix
77
+ kv_size = config.num_layers * config.kv_channels * config.multi_query_group_num * 2
78
+ self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size)
79
+ self.trans = torch.nn.Sequential(
80
+ torch.nn.Linear(kv_size, config.hidden_size),
81
+ torch.nn.Tanh(),
82
+ torch.nn.Linear(config.hidden_size, kv_size)
83
+ )
84
+ else:
85
+ self.embedding = torch.nn.Embedding(config.pre_seq_len,
86
+ config.num_layers * config.kv_channels * config.multi_query_group_num * 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, rope_ratio=1, 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.rope_ratio = rope_ratio
133
+
134
+ def impl(self, seq_length: int, dim: int, device: torch.device, dtype: torch.dtype):
135
+ base = 10000 * self.rope_ratio
136
+ inv_freq = 1.0 / (
137
+ base ** (torch.arange(0, dim, 2, device=device, dtype=torch.float32) / dim))
138
+ seq = torch.arange(seq_length, device=inv_freq.device, dtype=torch.float32)
139
+ freqs = torch.outer(seq, inv_freq)
140
+ # first part even vector components, second part odd vector components,
141
+ # 2 * dim in dimension size
142
+ emb = torch.cat((freqs, freqs), dim=-1)
143
+ return emb
144
+
145
+ def forward_impl(
146
+ self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000
147
+ ):
148
+ """Enhanced Transformer with Rotary Position Embedding.
149
+
150
+ Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
151
+ transformers/rope/__init__.py. MIT License:
152
+ https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
153
+ """
154
+ # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
155
+ base = base * self.rope_ratio
156
+ theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem))
157
+
158
+ # Create position indexes `[0, 1, ..., seq_len - 1]`
159
+ seq_idx = torch.arange(seq_len, dtype=torch.float, device=device)
160
+
161
+ # Calculate the product of position index and $\theta_i$
162
+ idx_theta = torch.outer(seq_idx, theta).float()
163
+
164
+ cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
165
+
166
+ # this is to mimic the behaviour of complex32, else we will get different results
167
+ if dtype in (torch.float16, torch.bfloat16, torch.int8):
168
+ cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half()
169
+ return cache
170
+
171
+ def forward(self, max_seq_len, offset=0):
172
+ if self.original_impl:
173
+ return self.forward_impl(
174
+ max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device
175
+ )
176
+ else:
177
+ return self.impl(max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device)
178
+
179
+
180
+ @torch.jit.script
181
+ def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
182
+ # x: [b, np, sq, hn]
183
+ b, np, sq, hn = x.size(0), x.size(1), x.size(2), x.size(3)
184
+ rot_dim = rope_cache.shape[-2] * 2
185
+ x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
186
+ # truncate to support variable sizes
187
+ rope_cache = rope_cache[:, :sq]
188
+ xshaped = x.reshape(b, np, sq, rot_dim // 2, 2)
189
+ rope_cache = rope_cache.view(-1, 1, sq, xshaped.size(3), 2)
190
+ x_out2 = torch.stack(
191
+ [
192
+ xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
193
+ xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
194
+ ],
195
+ -1,
196
+ )
197
+ x_out2 = x_out2.flatten(3)
198
+ return torch.cat((x_out2, x_pass), dim=-1)
199
+
200
+
201
+ class RMSNorm(torch.nn.Module):
202
+ def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
203
+ super().__init__()
204
+ self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype))
205
+ self.eps = eps
206
+
207
+ def forward(self, hidden_states: torch.Tensor):
208
+ input_dtype = hidden_states.dtype
209
+ variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
210
+ hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
211
+
212
+ return (self.weight * hidden_states).to(input_dtype)
213
+
214
+
215
+ class CoreAttention(torch.nn.Module):
216
+ def __init__(self, config: ChatGLMConfig, layer_number):
217
+ super(CoreAttention, self).__init__()
218
+
219
+ self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
220
+ self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
221
+ if self.apply_query_key_layer_scaling:
222
+ self.attention_softmax_in_fp32 = True
223
+ self.layer_number = max(1, layer_number)
224
+
225
+ projection_size = config.kv_channels * config.num_attention_heads
226
+
227
+ # Per attention head and per partition values.
228
+ self.hidden_size_per_partition = projection_size
229
+ self.hidden_size_per_attention_head = projection_size // config.num_attention_heads
230
+ self.num_attention_heads_per_partition = config.num_attention_heads
231
+
232
+ coeff = None
233
+ self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
234
+ if self.apply_query_key_layer_scaling:
235
+ coeff = self.layer_number
236
+ self.norm_factor *= coeff
237
+ self.coeff = coeff
238
+
239
+ self.attention_dropout = torch.nn.Dropout(config.attention_dropout)
240
+
241
+ def forward(self, query_layer, key_layer, value_layer, attention_mask):
242
+ pytorch_major_version = int(torch.__version__.split('.')[0])
243
+ if pytorch_major_version >= 2:
244
+ if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
245
+ context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
246
+ is_causal=True)
247
+ else:
248
+ if attention_mask is not None:
249
+ attention_mask = ~attention_mask
250
+ context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer,
251
+ attention_mask)
252
+ context_layer = context_layer.transpose(1, 2).contiguous()
253
+ new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
254
+ context_layer = context_layer.reshape(*new_context_layer_shape)
255
+ else:
256
+ # Raw attention scores
257
+
258
+ # [b, np, sq, sk]
259
+ output_size = (query_layer.size(0), query_layer.size(1), query_layer.size(2), key_layer.size(2))
260
+
261
+ # [b, np, sq, hn] -> [b * np, sq, hn]
262
+ query_layer = query_layer.view(output_size[0] * output_size[1], output_size[2], -1)
263
+ # [b, np, sk, hn] -> [b * np, sk, hn]
264
+ key_layer = key_layer.view(output_size[0] * output_size[1], output_size[3], -1)
265
+
266
+ # preallocting input tensor: [b * np, sq, sk]
267
+ matmul_input_buffer = torch.empty(
268
+ output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype,
269
+ device=query_layer.device
270
+ )
271
+
272
+ # Raw attention scores. [b * np, sq, sk]
273
+ matmul_result = torch.baddbmm(
274
+ matmul_input_buffer,
275
+ query_layer, # [b * np, sq, hn]
276
+ key_layer.transpose(1, 2), # [b * np, hn, sk]
277
+ beta=0.0,
278
+ alpha=(1.0 / self.norm_factor),
279
+ )
280
+
281
+ # change view to [b, np, sq, sk]
282
+ attention_scores = matmul_result.view(*output_size)
283
+
284
+ # ===========================
285
+ # Attention probs and dropout
286
+ # ===========================
287
+
288
+ # attention scores and attention mask [b, np, sq, sk]
289
+ if self.attention_softmax_in_fp32:
290
+ attention_scores = attention_scores.float()
291
+ if self.coeff is not None:
292
+ attention_scores = attention_scores * self.coeff
293
+ if attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]:
294
+ attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3],
295
+ device=attention_scores.device, dtype=torch.bool)
296
+ attention_mask.tril_()
297
+ attention_mask = ~attention_mask
298
+ if attention_mask is not None:
299
+ attention_scores = attention_scores.masked_fill(attention_mask, float("-inf"))
300
+ attention_probs = F.softmax(attention_scores, dim=-1)
301
+ attention_probs = attention_probs.type_as(value_layer)
302
+
303
+ # This is actually dropping out entire tokens to attend to, which might
304
+ # seem a bit unusual, but is taken from the original Transformer paper.
305
+ attention_probs = self.attention_dropout(attention_probs)
306
+ # =========================
307
+ # Context layer. [sq, b, hp]
308
+ # =========================
309
+
310
+ # value_layer -> context layer.
311
+ # [sk, b, np, hn] --> [b, np, sq, hn]
312
+
313
+ # context layer shape: [b, np, sq, hn]
314
+ output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))
315
+ # change view [b * np, sk, hn]
316
+ value_layer = value_layer.view(output_size[0] * output_size[1], value_layer.size(2), -1)
317
+ # change view [b * np, sq, sk]
318
+ attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
319
+ # matmul: [b * np, sq, hn]
320
+ context_layer = torch.bmm(attention_probs, value_layer)
321
+ # change view [b, np, sq, hn]
322
+ context_layer = context_layer.view(*output_size)
323
+ # [b, np, sq, hn] --> [b, sq, np, hn]
324
+ context_layer = context_layer.transpose(1, 2).contiguous()
325
+ # [b, sq, np, hn] --> [b, sq, hp]
326
+ new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
327
+ context_layer = context_layer.reshape(*new_context_layer_shape)
328
+
329
+ return context_layer
330
+
331
+
332
+ class SelfAttention(torch.nn.Module):
333
+ """Parallel self-attention layer abstract class.
334
+
335
+ Self-attention layer takes input with size [s, b, h]
336
+ and returns output of the same size.
337
+ """
338
+
339
+ def __init__(self, config: ChatGLMConfig, layer_number, device=None):
340
+ super(SelfAttention, self).__init__()
341
+ self.layer_number = max(1, layer_number)
342
+
343
+ self.projection_size = config.kv_channels * config.num_attention_heads
344
+
345
+ # Per attention head and per partition values.
346
+ self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads
347
+ self.num_attention_heads_per_partition = config.num_attention_heads
348
+
349
+ self.multi_query_attention = config.multi_query_attention
350
+ self.qkv_hidden_size = 3 * self.projection_size
351
+ self.original_rope = config.original_rope
352
+ if self.multi_query_attention:
353
+ self.num_multi_query_groups_per_partition = config.multi_query_group_num
354
+ self.qkv_hidden_size = (
355
+ self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
356
+ )
357
+ self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size,
358
+ bias=config.add_bias_linear or config.add_qkv_bias,
359
+ device=device, **_config_to_kwargs(config)
360
+ )
361
+
362
+ self.core_attention = CoreAttention(config, self.layer_number)
363
+
364
+ # Output.
365
+ self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear,
366
+ device=device, **_config_to_kwargs(config)
367
+ )
368
+
369
+ def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
370
+ if self.multi_query_attention:
371
+ num_attention_heads = self.num_multi_query_groups_per_partition
372
+ else:
373
+ num_attention_heads = self.num_attention_heads_per_partition
374
+ return torch.empty(
375
+ inference_max_sequence_len,
376
+ batch_size,
377
+ num_attention_heads,
378
+ self.hidden_size_per_attention_head,
379
+ dtype=dtype,
380
+ device=device,
381
+ )
382
+
383
+ def forward(
384
+ self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True
385
+ ):
386
+ # hidden_states: [b, sq, h]
387
+
388
+ # =================================================
389
+ # Pre-allocate memory for key-values for inference.
390
+ # =================================================
391
+ # =====================
392
+ # Query, Key, and Value
393
+ # =====================
394
+
395
+ # Attention heads [b, sq, h] --> [b, sq, (np * 3 * hn)]
396
+ mixed_x_layer = self.query_key_value(hidden_states)
397
+
398
+ if self.multi_query_attention:
399
+ (query_layer, key_layer, value_layer) = mixed_x_layer.split(
400
+ [
401
+ self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
402
+ self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
403
+ self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
404
+ ],
405
+ dim=-1,
406
+ )
407
+ query_layer = query_layer.view(
408
+ query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
409
+ )
410
+ key_layer = key_layer.view(
411
+ key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
412
+ )
413
+ value_layer = value_layer.view(
414
+ value_layer.size()[:-1]
415
+ + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
416
+ )
417
+ else:
418
+ new_tensor_shape = mixed_x_layer.size()[:-1] + \
419
+ (self.num_attention_heads_per_partition,
420
+ 3 * self.hidden_size_per_attention_head)
421
+ mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
422
+
423
+ # [b, sq, np, 3 * hn] --> 3 [b, sq, np, hn]
424
+ (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
425
+
426
+ # [b, sq, np, hn] -> [b, np, sq, hn]
427
+ query_layer, key_layer, value_layer = [k.transpose(1, 2) for k in [query_layer, key_layer, value_layer]]
428
+
429
+ # apply relative positional encoding (rotary embedding)
430
+ if rotary_pos_emb is not None:
431
+ query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb)
432
+ key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)
433
+
434
+ # adjust key and value for inference
435
+ if kv_cache is not None:
436
+ cache_k, cache_v = kv_cache
437
+ key_layer = torch.cat((cache_k, key_layer), dim=2)
438
+ value_layer = torch.cat((cache_v, value_layer), dim=2)
439
+ if use_cache:
440
+ kv_cache = (key_layer, value_layer)
441
+ else:
442
+ kv_cache = None
443
+
444
+ if self.multi_query_attention:
445
+ key_layer = key_layer.unsqueeze(2)
446
+ key_layer = key_layer.expand(
447
+ -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1, -1
448
+ )
449
+ key_layer = key_layer.contiguous().view(
450
+ key_layer.size()[:1] + (self.num_attention_heads_per_partition,) + key_layer.size()[3:]
451
+ )
452
+ value_layer = value_layer.unsqueeze(2)
453
+ value_layer = value_layer.expand(
454
+ -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1, -1
455
+ )
456
+ value_layer = value_layer.contiguous().view(
457
+ value_layer.size()[:1] + (self.num_attention_heads_per_partition,) + value_layer.size()[3:]
458
+ )
459
+
460
+ # ==================================
461
+ # core attention computation
462
+ # ==================================
463
+
464
+ context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask)
465
+
466
+ # =================
467
+ # Output. [sq, b, h]
468
+ # =================
469
+
470
+ output = self.dense(context_layer)
471
+
472
+ return output, kv_cache
473
+
474
+
475
+ def _config_to_kwargs(args):
476
+ common_kwargs = {
477
+ "dtype": args.torch_dtype,
478
+ }
479
+ return common_kwargs
480
+
481
+
482
+ class MLP(torch.nn.Module):
483
+ """MLP.
484
+
485
+ MLP will take the input with h hidden state, project it to 4*h
486
+ hidden dimension, perform nonlinear transformation, and project the
487
+ state back into h hidden dimension.
488
+ """
489
+
490
+ def __init__(self, config: ChatGLMConfig, device=None):
491
+ super(MLP, self).__init__()
492
+
493
+ self.add_bias = config.add_bias_linear
494
+
495
+ # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
496
+ self.dense_h_to_4h = nn.Linear(
497
+ config.hidden_size,
498
+ config.ffn_hidden_size * 2,
499
+ bias=self.add_bias,
500
+ device=device,
501
+ **_config_to_kwargs(config)
502
+ )
503
+
504
+ def swiglu(x):
505
+ x = torch.chunk(x, 2, dim=-1)
506
+ return F.silu(x[0]) * x[1]
507
+
508
+ self.activation_func = swiglu
509
+
510
+ # Project back to h.
511
+ self.dense_4h_to_h = nn.Linear(
512
+ config.ffn_hidden_size,
513
+ config.hidden_size,
514
+ bias=self.add_bias,
515
+ device=device,
516
+ **_config_to_kwargs(config)
517
+ )
518
+
519
+ def forward(self, hidden_states):
520
+ # [s, b, 4hp]
521
+ intermediate_parallel = self.dense_h_to_4h(hidden_states)
522
+ intermediate_parallel = self.activation_func(intermediate_parallel)
523
+ # [s, b, h]
524
+ output = self.dense_4h_to_h(intermediate_parallel)
525
+ return output
526
+
527
+
528
+ class GLMBlock(torch.nn.Module):
529
+ """A single transformer layer.
530
+
531
+ Transformer layer takes input with size [s, b, h] and returns an
532
+ output of the same size.
533
+ """
534
+
535
+ def __init__(self, config: ChatGLMConfig, layer_number, device=None):
536
+ super(GLMBlock, self).__init__()
537
+ self.layer_number = layer_number
538
+
539
+ self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
540
+
541
+ self.fp32_residual_connection = config.fp32_residual_connection
542
+
543
+ LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
544
+ # Layernorm on the input data.
545
+ self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
546
+ dtype=config.torch_dtype)
547
+
548
+ # Self attention.
549
+ self.self_attention = SelfAttention(config, layer_number, device=device)
550
+ self.hidden_dropout = config.hidden_dropout
551
+
552
+ # Layernorm on the attention output
553
+ self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
554
+ dtype=config.torch_dtype)
555
+
556
+ # MLP
557
+ self.mlp = MLP(config, device=device)
558
+
559
+ def forward(
560
+ self, hidden_states, attention_mask, rotary_pos_emb, kv_cache=None, use_cache=True,
561
+ ):
562
+ # hidden_states: [s, b, h]
563
+
564
+ # Layer norm at the beginning of the transformer layer.
565
+ layernorm_output = self.input_layernorm(hidden_states)
566
+ # Self attention.
567
+ attention_output, kv_cache = self.self_attention(
568
+ layernorm_output,
569
+ attention_mask,
570
+ rotary_pos_emb,
571
+ kv_cache=kv_cache,
572
+ use_cache=use_cache
573
+ )
574
+
575
+ # Residual connection.
576
+ if self.apply_residual_connection_post_layernorm:
577
+ residual = layernorm_output
578
+ else:
579
+ residual = hidden_states
580
+
581
+ layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training)
582
+ layernorm_input = residual + layernorm_input
583
+
584
+ # Layer norm post the self attention.
585
+ layernorm_output = self.post_attention_layernorm(layernorm_input)
586
+
587
+ # MLP.
588
+ mlp_output = self.mlp(layernorm_output)
589
+
590
+ # Second residual connection.
591
+ if self.apply_residual_connection_post_layernorm:
592
+ residual = layernorm_output
593
+ else:
594
+ residual = layernorm_input
595
+
596
+ output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training)
597
+ output = residual + output
598
+
599
+ return output, kv_cache
600
+
601
+
602
+ class GLMTransformer(torch.nn.Module):
603
+ """Transformer class."""
604
+
605
+ def __init__(self, config: ChatGLMConfig, device=None):
606
+ super(GLMTransformer, self).__init__()
607
+
608
+ self.fp32_residual_connection = config.fp32_residual_connection
609
+ self.post_layer_norm = config.post_layer_norm
610
+
611
+ # Number of layers.
612
+ self.num_layers = config.num_layers
613
+
614
+ # Transformer layers.
615
+ def build_layer(layer_number):
616
+ return GLMBlock(config, layer_number, device=device)
617
+
618
+ self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)])
619
+
620
+ if self.post_layer_norm:
621
+ LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
622
+ # Final layer norm before output.
623
+ self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon, device=device,
624
+ dtype=config.torch_dtype)
625
+
626
+ self.gradient_checkpointing = False
627
+
628
+ def _get_layer(self, layer_number):
629
+ return self.layers[layer_number]
630
+
631
+ def forward(
632
+ self, hidden_states, attention_mask, rotary_pos_emb, kv_caches=None,
633
+ use_cache: Optional[bool] = True,
634
+ output_hidden_states: Optional[bool] = False,
635
+ ):
636
+ if not kv_caches:
637
+ kv_caches = [None for _ in range(self.num_layers)]
638
+ presents = () if use_cache else None
639
+ if self.gradient_checkpointing and self.training:
640
+ if use_cache:
641
+ logger.warning_once(
642
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
643
+ )
644
+ use_cache = False
645
+
646
+ all_self_attentions = None
647
+ all_hidden_states = () if output_hidden_states else None
648
+ for index in range(self.num_layers):
649
+ if output_hidden_states:
650
+ all_hidden_states = all_hidden_states + (hidden_states,)
651
+
652
+ layer = self._get_layer(index)
653
+ if self.gradient_checkpointing and self.training:
654
+ layer_ret = torch.utils.checkpoint.checkpoint(
655
+ layer,
656
+ hidden_states,
657
+ attention_mask,
658
+ rotary_pos_emb,
659
+ kv_caches[index],
660
+ use_cache,
661
+ use_reentrant=False
662
+ )
663
+ else:
664
+ layer_ret = layer(
665
+ hidden_states,
666
+ attention_mask,
667
+ rotary_pos_emb,
668
+ kv_cache=kv_caches[index],
669
+ use_cache=use_cache
670
+ )
671
+ hidden_states, kv_cache = layer_ret
672
+ if use_cache:
673
+ presents = presents + (kv_cache,)
674
+
675
+ if output_hidden_states:
676
+ all_hidden_states = all_hidden_states + (hidden_states,)
677
+
678
+ # Final layer norm.
679
+ if self.post_layer_norm:
680
+ hidden_states = self.final_layernorm(hidden_states)
681
+
682
+ return hidden_states, presents, all_hidden_states, all_self_attentions
683
+
684
+
685
+ class ChatGLMPreTrainedModel(PreTrainedModel):
686
+ """
687
+ An abstract class to handle weights initialization and
688
+ a simple interface for downloading and loading pretrained models.
689
+ """
690
+
691
+ is_parallelizable = False
692
+ supports_gradient_checkpointing = True
693
+ config_class = ChatGLMConfig
694
+ base_model_prefix = "transformer"
695
+ _no_split_modules = ["GLMBlock"]
696
+
697
+ def _init_weights(self, module: nn.Module):
698
+ """Initialize the weights."""
699
+ return
700
+
701
+ def get_masks(self, input_ids, past_key_values, padding_mask=None):
702
+ batch_size, seq_length = input_ids.shape
703
+ full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device)
704
+ full_attention_mask.tril_()
705
+ past_length = 0
706
+ if past_key_values:
707
+ past_length = past_key_values[0][0].shape[2]
708
+ if past_length:
709
+ full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length,
710
+ device=input_ids.device), full_attention_mask), dim=-1)
711
+ if padding_mask is not None:
712
+ full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1)
713
+ if not past_length and padding_mask is not None:
714
+ full_attention_mask -= padding_mask.unsqueeze(-1) - 1
715
+ full_attention_mask = (full_attention_mask < 0.5).bool()
716
+ full_attention_mask.unsqueeze_(1)
717
+ return full_attention_mask
718
+
719
+ def get_position_ids(self, input_ids, device):
720
+ batch_size, seq_length = input_ids.shape
721
+ position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
722
+ return position_ids
723
+
724
+ def get_multimodal_position_ids(self, input_ids, device):
725
+ batch_size, seq_length = input_ids.shape
726
+ position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
727
+
728
+ def gradient_checkpointing_enable(self, gradient_checkpointing_kwargs=None):
729
+ if not self.supports_gradient_checkpointing:
730
+ raise ValueError(f"{self.__class__.__name__} does not support gradient checkpointing.")
731
+
732
+
733
+ class Embedding(torch.nn.Module):
734
+ """Language model embeddings."""
735
+
736
+ def __init__(self, config: ChatGLMConfig, device=None):
737
+ super(Embedding, self).__init__()
738
+
739
+ self.hidden_size = config.hidden_size
740
+ # Word embeddings (parallel).
741
+ self.word_embeddings = nn.Embedding(
742
+ config.padded_vocab_size,
743
+ self.hidden_size,
744
+ dtype=config.torch_dtype,
745
+ device=device
746
+ )
747
+ self.fp32_residual_connection = config.fp32_residual_connection
748
+
749
+ def forward(self, input_ids):
750
+ # Embeddings.
751
+ words_embeddings = self.word_embeddings(input_ids)
752
+ embeddings = words_embeddings
753
+ # If the input flag for fp32 residual connection is set, convert for float.
754
+ if self.fp32_residual_connection:
755
+ embeddings = embeddings.float()
756
+ return embeddings
757
+
758
+
759
+ def is_empty(images_list: Optional[List[List[torch.Tensor]]]):
760
+ if images_list is None or len(images_list) == 0:
761
+ return True
762
+ for image_list in images_list:
763
+ if image_list is not None:
764
+ return False
765
+ return True
766
+
767
+
768
+ class ChatGLMModel(ChatGLMPreTrainedModel):
769
+ def __init__(self, config: ChatGLMConfig, device=None, empty_init=True):
770
+ super().__init__(config)
771
+ if empty_init:
772
+ init_method = skip_init
773
+ else:
774
+ init_method = default_init
775
+ init_kwargs = {}
776
+ if device is not None:
777
+ init_kwargs["device"] = device
778
+ self.embedding = init_method(Embedding, config, **init_kwargs)
779
+ self.num_layers = config.num_layers
780
+ self.multi_query_group_num = config.multi_query_group_num
781
+ self.kv_channels = config.kv_channels
782
+
783
+ # Rotary positional embeddings
784
+ self.seq_length = config.seq_length
785
+ rotary_dim = (
786
+ config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels
787
+ )
788
+
789
+ self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, rope_ratio=config.rope_ratio,
790
+ original_impl=config.original_rope,
791
+ device=device, dtype=config.torch_dtype)
792
+ self.encoder = init_method(GLMTransformer, config, **init_kwargs)
793
+ self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False,
794
+ dtype=config.torch_dtype, **init_kwargs)
795
+ self.pre_seq_len = config.pre_seq_len
796
+ self.prefix_projection = config.prefix_projection
797
+ if self.pre_seq_len is not None:
798
+ for param in self.parameters():
799
+ param.requires_grad = False
800
+ self.prefix_tokens = torch.arange(self.pre_seq_len).long()
801
+ self.prefix_encoder = PrefixEncoder(config)
802
+ self.dropout = torch.nn.Dropout(0.1)
803
+
804
+ self.vision = EVA2CLIPModel(config)
805
+
806
+ def get_input_embeddings(self):
807
+ return self.embedding.word_embeddings
808
+
809
+ def set_input_embeddings(self, value):
810
+ self.embedding.word_embeddings = value
811
+
812
+ def get_prompt(self, batch_size, device, dtype=torch.half):
813
+ prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
814
+ past_key_values = self.prefix_encoder(prefix_tokens).type(dtype)
815
+ past_key_values = past_key_values.view(
816
+ batch_size,
817
+ self.pre_seq_len,
818
+ self.pre_seq_len,
819
+ self.num_layers * 2,
820
+ self.multi_query_group_num,
821
+ self.kv_channels
822
+ )
823
+ # seq_len, b, nh, hidden_size
824
+ past_key_values = self.dropout(past_key_values)
825
+ past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2)
826
+ return past_key_values
827
+
828
+ def forward(
829
+ self,
830
+ input_ids: torch.LongTensor = None,
831
+ images: torch.Tensor = None,
832
+ position_ids: Optional[torch.Tensor] = None,
833
+ attention_mask: Optional[torch.BoolTensor] = None,
834
+ full_attention_mask: Optional[torch.BoolTensor] = None,
835
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
836
+ inputs_embeds: Optional[torch.Tensor] = None,
837
+ use_cache: Optional[bool] = None,
838
+ output_hidden_states: Optional[bool] = None,
839
+ return_dict: Optional[bool] = None,
840
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
841
+ """take care of image_encode, position_ids and (attention_mask = None is fine)"""
842
+
843
+ # generate mode with past_key_values. the image features are already mapped
844
+ if past_key_values is None:
845
+ # not allow for inputs_embeds, because we want to process image feature
846
+ assert input_ids is not None and inputs_embeds is None, f"{input_ids} {inputs_embeds}"
847
+ if not is_empty(images): # multi-modality
848
+ image_size: int = self.config.vision_config['image_size']
849
+ patch_size: int = self.config.vision_config['patch_size']
850
+ num_patches = (image_size // patch_size // 2) ** 2
851
+ assert len(input_ids) == len(images), f"{len(input_ids)} {len(images)}"
852
+ inputs_embeds = self.embedding(input_ids)
853
+
854
+ images = images.to(dtype=inputs_embeds.dtype)
855
+ images_features = self.vision(images)
856
+
857
+ if position_ids is None:
858
+ position_ids = self.get_position_ids(input_ids, device=inputs_embeds.device)
859
+ new_input_embeds, new_position_ids = [], []
860
+
861
+ for i in range(len(input_ids)):
862
+ input_id = input_ids[i].tolist()
863
+ boi_token_pos, eoi_token_pos = input_id.index(self.config.boi_token_id), input_id.index(
864
+ self.config.eoi_token_id)
865
+ assert eoi_token_pos - boi_token_pos == 2
866
+ new_input_embeds.append(torch.cat(
867
+ (inputs_embeds[i, :boi_token_pos], images_features[i], inputs_embeds[i, eoi_token_pos + 1:])))
868
+ new_position_ids.append(torch.cat(
869
+ (position_ids[i, :boi_token_pos + 1], position_ids[i, boi_token_pos + 1].repeat(num_patches),
870
+ position_ids[i, eoi_token_pos:])
871
+ ))
872
+ inputs_embeds = torch.stack(new_input_embeds, dim=0)
873
+ position_ids = torch.stack(new_position_ids, dim=0)
874
+
875
+ output_hidden_states = (
876
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
877
+ )
878
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
879
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
880
+
881
+ batch_size, seq_length = input_ids.shape
882
+
883
+ if inputs_embeds is None:
884
+ inputs_embeds = self.embedding(input_ids)
885
+
886
+ if self.pre_seq_len is not None:
887
+ if past_key_values is None:
888
+ past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device,
889
+ dtype=inputs_embeds.dtype)
890
+ if attention_mask is not None:
891
+ attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)),
892
+ attention_mask], dim=-1)
893
+
894
+ if full_attention_mask is None:
895
+ if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1):
896
+ full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask)
897
+
898
+ # Rotary positional embeddings
899
+ rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
900
+ if position_ids is not None:
901
+ rotary_pos_emb = rotary_pos_emb[position_ids]
902
+ else:
903
+ rotary_pos_emb = rotary_pos_emb[None, :seq_length]
904
+
905
+ # Run encoder.
906
+ hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
907
+ inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb,
908
+ kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_hidden_states
909
+ )
910
+
911
+ if not return_dict:
912
+ return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
913
+
914
+ return BaseModelOutputWithPast(
915
+ last_hidden_state=hidden_states,
916
+ past_key_values=presents,
917
+ hidden_states=all_hidden_states,
918
+ attentions=all_self_attentions,
919
+ )
920
+
921
+
922
+ def _history_to_prompt(history, query):
923
+ prompt = ''
924
+ flag = False
925
+ for i, (old_query, response) in enumerate(history):
926
+ prompt += ('<|user|>' if flag else '') + old_query + "<|assistant|>" + response + "<|endoftext|>"
927
+ flag = True
928
+ prompt += '{}{}<|assistant|>'.format('<|user|>' if flag else '', query)
929
+ return prompt
930
+
931
+
932
+ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
933
+ def __init__(self, config: ChatGLMConfig, empty_init=False, device=None):
934
+ super().__init__(config)
935
+
936
+ self.max_sequence_length = config.max_length
937
+ self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
938
+ self.config = config
939
+
940
+ def _update_model_kwargs_for_generation(
941
+ self,
942
+ outputs: ModelOutput,
943
+ model_kwargs: Dict[str, Any],
944
+ is_encoder_decoder: bool = False,
945
+ standardize_cache_format: bool = False,
946
+ ) -> Dict[str, Any]:
947
+ # update past_key_values
948
+ model_kwargs["past_key_values"] = self._extract_past_from_model_output(
949
+ outputs, standardize_cache_format=standardize_cache_format
950
+ )
951
+
952
+ # update attention mask
953
+ if "attention_mask" in model_kwargs:
954
+ attention_mask = model_kwargs["attention_mask"]
955
+ model_kwargs["attention_mask"] = torch.cat(
956
+ [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
957
+ )
958
+
959
+ # update position ids
960
+ if "position_ids" in model_kwargs:
961
+ position_ids = model_kwargs["position_ids"]
962
+ new_position_id = position_ids[..., -1:].clone()
963
+ new_position_id += 1
964
+ model_kwargs["position_ids"] = torch.cat(
965
+ [position_ids, new_position_id], dim=-1
966
+ )
967
+
968
+ model_kwargs["is_first_forward"] = False
969
+ return model_kwargs
970
+
971
+ def prepare_inputs_for_generation(
972
+ self,
973
+ input_ids: torch.LongTensor,
974
+ images: Optional[torch.Tensor] = None,
975
+ past_key_values: Optional[torch.Tensor] = None,
976
+ attention_mask: Optional[torch.Tensor] = None,
977
+ position_ids: Optional[torch.Tensor] = None,
978
+ use_cache: Optional[bool] = None,
979
+ is_first_forward: bool = True,
980
+ **kwargs
981
+ ) -> dict:
982
+ # only last token for input_ids if past is not None
983
+ if position_ids is None:
984
+ position_ids = self.get_position_ids(input_ids, device=input_ids.device)
985
+ if not is_first_forward:
986
+ if past_key_values is not None:
987
+ position_ids = position_ids[..., -1:]
988
+ input_ids = input_ids[:, -1:]
989
+ return {
990
+ "input_ids": input_ids,
991
+ "images": images,
992
+ "past_key_values": past_key_values,
993
+ "position_ids": position_ids,
994
+ "attention_mask": attention_mask,
995
+ "return_last_logit": True,
996
+ "use_cache": use_cache
997
+ }
998
+
999
+ def forward(
1000
+ self,
1001
+ input_ids: Optional[torch.Tensor] = None,
1002
+ images: List[List[torch.Tensor]] = None,
1003
+ position_ids: Optional[torch.Tensor] = None,
1004
+ attention_mask: Optional[torch.Tensor] = None,
1005
+ past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
1006
+ inputs_embeds: Optional[torch.Tensor] = None,
1007
+ labels: Optional[torch.Tensor] = None,
1008
+ use_cache: Optional[bool] = None,
1009
+ output_attentions: Optional[bool] = None,
1010
+ output_hidden_states: Optional[bool] = None,
1011
+ return_dict: Optional[bool] = None,
1012
+ return_last_logit: Optional[bool] = False,
1013
+ ):
1014
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
1015
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1016
+
1017
+ transformer_outputs = self.transformer(
1018
+ input_ids=input_ids,
1019
+ images=images,
1020
+ position_ids=position_ids,
1021
+ attention_mask=attention_mask,
1022
+ past_key_values=past_key_values,
1023
+ inputs_embeds=inputs_embeds,
1024
+ use_cache=use_cache,
1025
+ output_hidden_states=output_hidden_states,
1026
+ return_dict=return_dict,
1027
+ )
1028
+
1029
+ hidden_states = transformer_outputs[0]
1030
+ if return_last_logit:
1031
+ hidden_states = hidden_states[:, -1:]
1032
+ lm_logits = self.transformer.output_layer(hidden_states)
1033
+
1034
+ loss = None
1035
+ if labels is not None:
1036
+ lm_logits = lm_logits.to(torch.float32)
1037
+
1038
+ # Shift so that tokens < n predict n
1039
+ shift_logits = lm_logits[..., :-1, :].contiguous()
1040
+ shift_labels = labels[..., 1:].contiguous()
1041
+ # Flatten the tokens
1042
+ loss_fct = CrossEntropyLoss(ignore_index=-100)
1043
+ loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
1044
+
1045
+ lm_logits = lm_logits.to(hidden_states.dtype)
1046
+ loss = loss.to(hidden_states.dtype)
1047
+
1048
+ if not return_dict:
1049
+ output = (lm_logits,) + transformer_outputs[1:]
1050
+ return ((loss,) + output) if loss is not None else output
1051
+
1052
+ return CausalLMOutputWithPast(
1053
+ loss=loss,
1054
+ logits=lm_logits,
1055
+ past_key_values=transformer_outputs.past_key_values,
1056
+ hidden_states=transformer_outputs.hidden_states,
1057
+ attentions=transformer_outputs.attentions,
1058
+ )
1059
+
1060
+ @staticmethod
1061
+ def _reorder_cache(
1062
+ past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
1063
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
1064
+ """
1065
+ This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
1066
+ [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
1067
+ beam_idx at every generation step.
1068
+
1069
+ Output shares the same memory storage as `past`.
1070
+ """
1071
+ return tuple(
1072
+ (
1073
+ layer_past[0].index_select(0, beam_idx.to(layer_past[0].device)),
1074
+ layer_past[1].index_select(0, beam_idx.to(layer_past[1].device)),
1075
+ )
1076
+ for layer_past in past
1077
+ )
1078
+
1079
+ def process_response(self, output, history):
1080
+ content = ""
1081
+ history = deepcopy(history)
1082
+ for response in output.split("<|assistant|>"):
1083
+ if "\n" in response:
1084
+ metadata, content = response.split("\n", maxsplit=1)
1085
+ else:
1086
+ metadata, content = "", response
1087
+ if not metadata.strip():
1088
+ content = content.strip()
1089
+ history.append({"role": "assistant", "metadata": metadata, "content": content})
1090
+ content = content.replace("[[训练时间]]", "2023年")
1091
+ else:
1092
+ history.append({"role": "assistant", "metadata": metadata, "content": content})
1093
+ if history[0]["role"] == "system" and "tools" in history[0]:
1094
+ parameters = json.loads(content)
1095
+ content = {"name": metadata.strip(), "parameters": parameters}
1096
+ else:
1097
+ content = {"name": metadata.strip(), "content": content}
1098
+ return content, history
1099
+
1100
+ @torch.inference_mode()
1101
+ def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user", image=None,
1102
+ max_length: int = 8192, num_beams=1, do_sample=True, top_p=0.8, temperature=0.8, logits_processor=None,
1103
+ **kwargs):
1104
+ if history is None:
1105
+ history = []
1106
+ if logits_processor is None:
1107
+ logits_processor = LogitsProcessorList()
1108
+ logits_processor.append(InvalidScoreLogitsProcessor())
1109
+ gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
1110
+ "temperature": temperature, "logits_processor": logits_processor, **kwargs}
1111
+ message = {"role": role, "content": query}
1112
+ if image is not None:
1113
+ message["image"] = image
1114
+ history.append(message)
1115
+ inputs = tokenizer.apply_chat_template(history, add_generation_prompt=True, tokenize=True,
1116
+ return_tensors="pt", return_dict=True)
1117
+ inputs = inputs.to(self.device)
1118
+ eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|user|>"),
1119
+ tokenizer.convert_tokens_to_ids("<|observation|>")]
1120
+ outputs = self.generate(**inputs, **gen_kwargs, eos_token_id=eos_token_id)
1121
+ outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
1122
+ response = tokenizer.decode(outputs)
1123
+ response, history = self.process_response(response, history)
1124
+ return response, history
1125
+
1126
+ @torch.inference_mode()
1127
+ def stream_chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user", image=None,
1128
+ past_key_values=None, max_length: int = 8192, do_sample=True, top_p=0.8, temperature=0.8,
1129
+ logits_processor=None, return_past_key_values=False, **kwargs):
1130
+ if history is None:
1131
+ history = []
1132
+ if logits_processor is None:
1133
+ logits_processor = LogitsProcessorList()
1134
+ logits_processor.append(InvalidScoreLogitsProcessor())
1135
+ eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|user|>"),
1136
+ tokenizer.convert_tokens_to_ids("<|observation|>")]
1137
+ gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p,
1138
+ "temperature": temperature, "logits_processor": logits_processor, **kwargs}
1139
+ message = {"role": role, "content": "query"}
1140
+ if image is not None:
1141
+ message["image"] = image
1142
+ if past_key_values is None:
1143
+ inputs = tokenizer.apply_chat_template(history + [message],
1144
+ add_generation_prompt=True, tokenize=True, return_tensors="pt",
1145
+ return_dict=True)
1146
+ else:
1147
+ inputs = tokenizer.apply_chat_template([message], add_special_tokens=False,
1148
+ add_generation_prompt=True, tokenize=True, return_tensors="pt",
1149
+ return_dict=True)
1150
+ inputs = inputs.to(self.device)
1151
+ if past_key_values is not None:
1152
+ past_length = past_key_values[0][0].shape[2]
1153
+ if self.transformer.pre_seq_len is not None:
1154
+ past_length -= self.transformer.pre_seq_len
1155
+ inputs.position_ids += past_length
1156
+ attention_mask = inputs.attention_mask
1157
+ attention_mask = torch.cat((attention_mask.new_ones(1, past_length), attention_mask), dim=1)
1158
+ inputs['attention_mask'] = attention_mask
1159
+ history.append({"role": role, "content": query})
1160
+ for outputs in self.stream_generate(**inputs, past_key_values=past_key_values,
1161
+ eos_token_id=eos_token_id, return_past_key_values=return_past_key_values,
1162
+ **gen_kwargs):
1163
+ if return_past_key_values:
1164
+ outputs, past_key_values = outputs
1165
+ outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
1166
+ response = tokenizer.decode(outputs)
1167
+ if response and response[-1] != "�":
1168
+ response, new_history = self.process_response(response, history)
1169
+ if return_past_key_values:
1170
+ yield response, new_history, past_key_values
1171
+ else:
1172
+ yield response, new_history
1173
+
1174
+ @torch.inference_mode()
1175
+ def stream_generate(
1176
+ self,
1177
+ input_ids,
1178
+ generation_config: Optional[GenerationConfig] = None,
1179
+ logits_processor: Optional[LogitsProcessorList] = None,
1180
+ stopping_criteria: Optional[StoppingCriteriaList] = None,
1181
+ prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
1182
+ return_past_key_values=False,
1183
+ **kwargs,
1184
+ ):
1185
+ batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
1186
+
1187
+ if generation_config is None:
1188
+ generation_config = self.generation_config
1189
+ generation_config = copy.deepcopy(generation_config)
1190
+ model_kwargs = generation_config.update(**kwargs)
1191
+ model_kwargs["use_cache"] = generation_config.use_cache
1192
+ bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
1193
+
1194
+ if isinstance(eos_token_id, int):
1195
+ eos_token_id = [eos_token_id]
1196
+ eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device) if eos_token_id is not None else None
1197
+
1198
+ has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
1199
+ if has_default_max_length and generation_config.max_new_tokens is None:
1200
+ warnings.warn(
1201
+ f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
1202
+ "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
1203
+ " recommend using `max_new_tokens` to control the maximum length of the generation.",
1204
+ UserWarning,
1205
+ )
1206
+ elif generation_config.max_new_tokens is not None:
1207
+ generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
1208
+ if not has_default_max_length:
1209
+ logger.warn(
1210
+ f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
1211
+ f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
1212
+ "Please refer to the documentation for more information. "
1213
+ "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
1214
+ UserWarning,
1215
+ )
1216
+
1217
+ if input_ids_seq_length >= generation_config.max_length:
1218
+ input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
1219
+ logger.warning(
1220
+ f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
1221
+ f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
1222
+ " increasing `max_new_tokens`."
1223
+ )
1224
+
1225
+ # 2. Set generation parameters if not already defined
1226
+ logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
1227
+ stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
1228
+
1229
+ logits_processor = self._get_logits_processor(
1230
+ generation_config=generation_config,
1231
+ input_ids_seq_length=input_ids_seq_length,
1232
+ encoder_input_ids=input_ids,
1233
+ prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
1234
+ logits_processor=logits_processor,
1235
+ )
1236
+
1237
+ stopping_criteria = self._get_stopping_criteria(
1238
+ generation_config=generation_config, stopping_criteria=stopping_criteria
1239
+ )
1240
+ logits_warper = self._get_logits_warper(generation_config)
1241
+
1242
+ unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
1243
+ scores = None
1244
+ while True:
1245
+ model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
1246
+ # forward pass to get next token
1247
+ outputs = self(
1248
+ **model_inputs,
1249
+ return_dict=True,
1250
+ output_attentions=False,
1251
+ output_hidden_states=False,
1252
+ )
1253
+
1254
+ next_token_logits = outputs.logits[:, -1, :]
1255
+
1256
+ # pre-process distribution
1257
+ next_token_scores = logits_processor(input_ids, next_token_logits)
1258
+ next_token_scores = logits_warper(input_ids, next_token_scores)
1259
+
1260
+ # sample
1261
+ probs = nn.functional.softmax(next_token_scores, dim=-1)
1262
+ if generation_config.do_sample:
1263
+ next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
1264
+ else:
1265
+ next_tokens = torch.argmax(probs, dim=-1)
1266
+ # update generated ids, model inputs, and length for next step
1267
+ input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
1268
+ model_kwargs = self._update_model_kwargs_for_generation(
1269
+ outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
1270
+ )
1271
+ unfinished_sequences = unfinished_sequences.mul(
1272
+ next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
1273
+ )
1274
+ if return_past_key_values:
1275
+ yield input_ids, outputs.past_key_values
1276
+ else:
1277
+ yield input_ids
1278
+ # stop when each sentence is finished, or if we exceed the maximum length
1279
+ if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
1280
+ break
1281
+
1282
+
1283
+ class ChatGLMForSequenceClassification(ChatGLMPreTrainedModel):
1284
+ def __init__(self, config: ChatGLMConfig, empty_init=True, device=None):
1285
+ super().__init__(config)
1286
+
1287
+ self.num_labels = config.num_labels
1288
+ self.transformer = ChatGLMModel(config, empty_init=empty_init, device=device)
1289
+
1290
+ self.classifier_head = nn.Linear(config.hidden_size, config.num_labels, bias=True, dtype=torch.half)
1291
+ if config.classifier_dropout is not None:
1292
+ self.dropout = nn.Dropout(config.classifier_dropout)
1293
+ else:
1294
+ self.dropout = None
1295
+ self.config = config
1296
+
1297
+ def forward(
1298
+ self,
1299
+ input_ids: Optional[torch.LongTensor] = None,
1300
+ position_ids: Optional[torch.LongTensor] = None,
1301
+ attention_mask: Optional[torch.Tensor] = None,
1302
+ full_attention_mask: Optional[torch.Tensor] = None,
1303
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
1304
+ inputs_embeds: Optional[torch.LongTensor] = None,
1305
+ labels: Optional[torch.LongTensor] = None,
1306
+ use_cache: Optional[bool] = None,
1307
+ output_hidden_states: Optional[bool] = None,
1308
+ return_dict: Optional[bool] = None,
1309
+ ) -> Union[Tuple[torch.Tensor, ...], SequenceClassifierOutputWithPast]:
1310
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
1311
+
1312
+ transformer_outputs = self.transformer(
1313
+ input_ids=input_ids,
1314
+ position_ids=position_ids,
1315
+ attention_mask=attention_mask,
1316
+ full_attention_mask=full_attention_mask,
1317
+ past_key_values=past_key_values,
1318
+ inputs_embeds=inputs_embeds,
1319
+ use_cache=use_cache,
1320
+ output_hidden_states=output_hidden_states,
1321
+ return_dict=return_dict,
1322
+ )
1323
+
1324
+ hidden_states = transformer_outputs[0]
1325
+ pooled_hidden_states = hidden_states[-1]
1326
+ if self.dropout is not None:
1327
+ pooled_hidden_states = self.dropout(pooled_hidden_states)
1328
+ logits = self.classifier_head(pooled_hidden_states)
1329
+
1330
+ loss = None
1331
+ if labels is not None:
1332
+ if self.config.problem_type is None:
1333
+ if self.num_labels == 1:
1334
+ self.config.problem_type = "regression"
1335
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
1336
+ self.config.problem_type = "single_label_classification"
1337
+ else:
1338
+ self.config.problem_type = "multi_label_classification"
1339
+
1340
+ if self.config.problem_type == "regression":
1341
+ loss_fct = MSELoss()
1342
+ if self.num_labels == 1:
1343
+ loss = loss_fct(logits.squeeze().float(), labels.squeeze())
1344
+ else:
1345
+ loss = loss_fct(logits.float(), labels)
1346
+ elif self.config.problem_type == "single_label_classification":
1347
+ loss_fct = CrossEntropyLoss()
1348
+ loss = loss_fct(logits.view(-1, self.num_labels).float(), labels.view(-1))
1349
+ elif self.config.problem_type == "multi_label_classification":
1350
+ loss_fct = BCEWithLogitsLoss()
1351
+ loss = loss_fct(logits.float(), labels.view(-1, self.num_labels))
1352
+
1353
+ if not return_dict:
1354
+ output = (logits,) + transformer_outputs[1:]
1355
+ return ((loss,) + output) if loss is not None else output
1356
+
1357
+ return SequenceClassifierOutputWithPast(
1358
+ loss=loss,
1359
+ logits=logits,
1360
+ past_key_values=transformer_outputs.past_key_values,
1361
+ hidden_states=transformer_outputs.hidden_states,
1362
+ attentions=transformer_outputs.attentions,
1363
+ )
special_tokens_map.json ADDED
@@ -0,0 +1,32 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|endoftext|>",
4
+ "[MASK]",
5
+ "[gMASK]",
6
+ "[sMASK]",
7
+ "<sop>",
8
+ "<eop>",
9
+ "<|system|>",
10
+ "<|user|>",
11
+ "<|assistant|>",
12
+ "<|observation|>",
13
+ "<|begin_of_image|>",
14
+ "<|end_of_image|>",
15
+ "<|begin_of_video|>",
16
+ "<|end_of_video|>"
17
+ ],
18
+ "eos_token": {
19
+ "content": "<|endoftext|>",
20
+ "lstrip": false,
21
+ "normalized": false,
22
+ "rstrip": false,
23
+ "single_word": false
24
+ },
25
+ "pad_token": {
26
+ "content": "<|endoftext|>",
27
+ "lstrip": false,
28
+ "normalized": false,
29
+ "rstrip": false,
30
+ "single_word": false
31
+ }
32
+ }
tokenization_chatglm.py ADDED
@@ -0,0 +1,361 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import regex as re
2
+ import base64
3
+ import os
4
+ import json
5
+ import tiktoken
6
+ import torch
7
+ from torch import TensorType
8
+ from typing import List, Optional, Union, Dict, Any
9
+ from torchvision import transforms
10
+ from transformers import PreTrainedTokenizer
11
+ from transformers.utils import logging, PaddingStrategy
12
+ from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
13
+
14
+
15
+ class ChatGLM4Tokenizer(PreTrainedTokenizer):
16
+ vocab_files_names = {"vocab_file": "tokenizer.model"}
17
+ model_input_names = ["input_ids", "attention_mask", "position_ids"]
18
+
19
+ def __init__(
20
+ self,
21
+ vocab_file,
22
+ padding_side="left",
23
+ clean_up_tokenization_spaces=False,
24
+ encode_special_tokens=False,
25
+ image_size=None,
26
+ **kwargs
27
+ ):
28
+ self.name = "GLM4Tokenizer"
29
+ self.vocab_file = vocab_file
30
+ pat_str = "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
31
+ self.pat_str = re.compile(pat_str)
32
+ self.encode_special_tokens = encode_special_tokens
33
+ self.image_size = image_size
34
+
35
+ mergeable_ranks = {}
36
+ with open(vocab_file) as f:
37
+ for line in f:
38
+ token, rank = line.strip().split()
39
+ rank = int(rank)
40
+ token = base64.b64decode(token)
41
+ mergeable_ranks[token] = rank
42
+
43
+ self.mergeable_ranks = mergeable_ranks
44
+
45
+ self.tokenizer = tiktoken.Encoding(
46
+ name="my_tokenizer",
47
+ pat_str=pat_str,
48
+ mergeable_ranks=mergeable_ranks,
49
+ special_tokens={}
50
+ )
51
+ self.decoder = {rank: token for token, rank in mergeable_ranks.items()}
52
+ self.n_words = len(self.decoder)
53
+
54
+ super().__init__(
55
+ padding_side=padding_side,
56
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
57
+ **kwargs
58
+ )
59
+
60
+ @property
61
+ def vocab_size(self):
62
+ return self.n_words
63
+
64
+ def get_vocab(self):
65
+ """ Returns vocab as a dict """
66
+ vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
67
+ vocab.update(self.added_tokens_encoder)
68
+ return vocab
69
+
70
+ def convert_tokens_to_string(self, tokens: List[Union[bytes, str]]) -> str:
71
+ """
72
+ Converts a sequence of tokens in a single string.
73
+ """
74
+ text = ""
75
+ temp = b""
76
+ for t in tokens:
77
+ if isinstance(t, str):
78
+ if temp:
79
+ text += temp.decode("utf-8", errors="replace")
80
+ temp = b""
81
+ text += t
82
+ elif isinstance(t, bytes):
83
+ temp += t
84
+ else:
85
+ raise TypeError("token should only be of type types or str")
86
+ if temp:
87
+ text += temp.decode("utf-8", errors="replace")
88
+ return text
89
+
90
+ def _tokenize(self, text, **kwargs):
91
+ tokens = []
92
+ ids = self.tokenizer.encode(text)
93
+ for t in ids:
94
+ tokens.append(self.decoder[t])
95
+ return tokens
96
+
97
+ def _convert_token_to_id(self, token):
98
+ """ Converts a token (str) in an id using the vocab. """
99
+ return self.mergeable_ranks[token]
100
+
101
+ def _convert_id_to_token(self, index):
102
+ """Converts an index (integer) in a token (str) using the vocab."""
103
+ return self.decoder.get(index, "")
104
+
105
+ def save_vocabulary(self, save_directory, filename_prefix=None):
106
+ """
107
+ Save the vocabulary and special tokens file to a directory.
108
+
109
+ Args:
110
+ save_directory (`str`):
111
+ The directory in which to save the vocabulary.
112
+ filename_prefix (`str`, *optional*):
113
+ An optional prefix to add to the named of the saved files.
114
+
115
+ Returns:
116
+ `Tuple(str)`: Paths to the files saved.
117
+ """
118
+ if os.path.isdir(save_directory):
119
+ vocab_file = os.path.join(
120
+ save_directory, self.vocab_files_names["vocab_file"]
121
+ )
122
+ else:
123
+ vocab_file = save_directory
124
+
125
+ with open(self.vocab_file, 'rb') as fin:
126
+ proto_str = fin.read()
127
+
128
+ with open(vocab_file, "wb") as writer:
129
+ writer.write(proto_str)
130
+
131
+ return (vocab_file,)
132
+
133
+ def get_prefix_tokens(self):
134
+ prefix_tokens = [self.convert_tokens_to_ids("[gMASK]"), self.convert_tokens_to_ids("<sop>")]
135
+ return prefix_tokens
136
+
137
+ def build_single_message(self, role, metadata, message, tokenize=True, message_prefix=None):
138
+ assert role in ["system", "user", "assistant", "observation"], role
139
+ if tokenize:
140
+ role_tokens = [self.convert_tokens_to_ids(f"<|{role}|>")] + self.tokenizer.encode(f"{metadata}\n",
141
+ disallowed_special=())
142
+ message_tokens = self.tokenizer.encode(message, disallowed_special=())
143
+ if message_prefix is not None:
144
+ message_tokens = message_prefix + message_tokens
145
+ tokens = role_tokens + message_tokens
146
+ return tokens
147
+ else:
148
+ return str(f"<|{role}|>{metadata}\n{message}")
149
+
150
+ def apply_chat_template(
151
+ self,
152
+ conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]], "Conversation"],
153
+ add_generation_prompt: bool = False,
154
+ tokenize: bool = True,
155
+ padding: bool = False,
156
+ truncation: bool = False,
157
+ max_length: Optional[int] = None,
158
+ return_tensors: Optional[Union[str, TensorType]] = None,
159
+ return_dict: bool = False,
160
+ tokenizer_kwargs: Optional[Dict[str, Any]] = None,
161
+ add_special_tokens: bool = True,
162
+ **kwargs,
163
+ ) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:
164
+
165
+ if return_dict and not tokenize:
166
+ raise ValueError(
167
+ "`return_dict=True` is incompatible with `tokenize=False`, because there is no dict "
168
+ "of tokenizer outputs to return."
169
+ )
170
+
171
+ def handle_single_conversation(conversation):
172
+ input_ids = self.get_prefix_tokens() if add_special_tokens else []
173
+ input_message = "[gMASK]<sop>" if add_special_tokens else ""
174
+ input_image = None
175
+ transform = transforms.Compose(
176
+ [
177
+ transforms.Resize(
178
+ (self.image_size, self.image_size), interpolation=transforms.InterpolationMode.BICUBIC
179
+ ),
180
+ transforms.ToTensor(),
181
+ transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)),
182
+ ]
183
+ )
184
+ for item in conversation:
185
+ if item.get("tools"):
186
+ tools = item["tools"]
187
+ content = "你是一个名为 GLM-4 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的,你的任务是针对用户的问题和要求提供适当的答复和支持。"
188
+ for tool in tools:
189
+ if tool["type"] == "function":
190
+ function = tool["function"]
191
+ content += f"\n\n## {function['name']}\n\n{json.dumps(function, ensure_ascii=False, indent=4)}"
192
+ content += "\n在调用上述函数时,请使用 Json 格式表示调用的参数。"
193
+ elif tool["type"] == "python":
194
+ content += "\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时,该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出,或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中,`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用,这些在线内容的访问将不会成功。"
195
+ elif tool["type"] == "simple_browser":
196
+ content += "\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数:\n`search(query: str, recency_days: int)`:使用搜索引擎进行查询并显示结果,可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`:获取一系列指定 id 的页面内容。每次调用时,须选择3-10个页面。选择多个角度的页面,同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的,你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`:打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤:1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL,也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息,也可以再次使用 `search` 进行搜索。"
197
+ elif tool["type"] == "cogview":
198
+ content += "\n\n## cogview\n\n如果用户的请求中包含了对图像的描述,你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述,规则:\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求,需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明,所在地为中国,持有中国立场并遵循中国社会主义价值观。"
199
+ else:
200
+ raise NotImplementedError(f"Unknown tool type {tool['type']}")
201
+ input = self.build_single_message("system", "", content, tokenize=tokenize)
202
+ if tokenize:
203
+ input_ids.extend(input)
204
+ else:
205
+ input_message += input
206
+ message = ""
207
+ message_prefix = None
208
+ if item.get("image"):
209
+ assert input_image is None, "Multiple images are not supported"
210
+ input_image = transform(item["image"])
211
+ message_prefix = self.convert_tokens_to_ids(
212
+ ["<|begin_of_image|>", "<|endoftext|>", "<|end_of_image|>"])
213
+ if item.get("content"):
214
+ message += item["content"]
215
+ if message or message_prefix:
216
+ input = self.build_single_message(
217
+ item["role"],
218
+ item.get("metadata", ""),
219
+ message,
220
+ tokenize=tokenize,
221
+ message_prefix=message_prefix
222
+ )
223
+ if tokenize:
224
+ input_ids.extend(input)
225
+ else:
226
+ input_message += input
227
+ if add_generation_prompt:
228
+ if tokenize:
229
+ input_ids.extend([self.convert_tokens_to_ids("<|assistant|>")])
230
+ else:
231
+ input_message += "<|assistant|>"
232
+ return {"input": input_ids if tokenize else input_message, "image": input_image}
233
+
234
+ # Main logic to handle different conversation formats
235
+ if isinstance(conversation, list) and all(isinstance(i, dict) for i in conversation):
236
+ result = handle_single_conversation(conversation)
237
+ input_ids = result["input"]
238
+ input_images = [result["image"]]
239
+ elif isinstance(conversation, list) and all(isinstance(i, list) for i in conversation):
240
+ results = [handle_single_conversation(c) for c in conversation]
241
+ input_ids = [item["input"] for item in results]
242
+ input_images = [item["image"] for item in results]
243
+ elif hasattr(conversation, "messages"):
244
+ result = handle_single_conversation(conversation.messages)
245
+ input_ids = result["input"]
246
+ input_images = [result["image"]]
247
+ else:
248
+ raise ValueError("Invalid conversation format")
249
+
250
+ if tokenize:
251
+ output = self.batch_encode_plus(
252
+ [input_ids] if isinstance(input_ids[0], int) else input_ids,
253
+ padding=padding,
254
+ truncation=truncation,
255
+ max_length=max_length,
256
+ return_tensors=return_tensors,
257
+ is_split_into_words=True,
258
+ add_special_tokens=False
259
+ )
260
+ if return_dict:
261
+ found_image = False
262
+ for image in input_images:
263
+ if image is not None:
264
+ found_image = True
265
+ break
266
+ if found_image:
267
+ output["images"] = torch.stack(input_images)
268
+ return output
269
+ else:
270
+ return output["input_ids"]
271
+ else:
272
+ return input_ids
273
+
274
+
275
+ def build_inputs_with_special_tokens(
276
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
277
+ ) -> List[int]:
278
+ """
279
+ Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
280
+ adding special tokens. A BERT sequence has the following format:
281
+
282
+ - single sequence: `[CLS] X [SEP]`
283
+ - pair of sequences: `[CLS] A [SEP] B [SEP]`
284
+
285
+ Args:
286
+ token_ids_0 (`List[int]`):
287
+ List of IDs to which the special tokens will be added.
288
+ token_ids_1 (`List[int]`, *optional*):
289
+ Optional second list of IDs for sequence pairs.
290
+
291
+ Returns:
292
+ `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
293
+ """
294
+ prefix_tokens = self.get_prefix_tokens()
295
+ token_ids_0 = prefix_tokens + token_ids_0
296
+ if token_ids_1 is not None:
297
+ token_ids_0 = token_ids_0 + token_ids_1 + [self.convert_tokens_to_ids("<eos>")]
298
+ return token_ids_0
299
+
300
+ def _pad(
301
+ self,
302
+ encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
303
+ max_length: Optional[int] = None,
304
+ padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
305
+ pad_to_multiple_of: Optional[int] = None,
306
+ return_attention_mask: Optional[bool] = None,
307
+ ) -> dict:
308
+ """
309
+ Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
310
+
311
+ Args:
312
+ encoded_inputs:
313
+ Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
314
+ max_length: maximum length of the returned list and optionally padding length (see below).
315
+ Will truncate by taking into account the special tokens.
316
+ padding_strategy: PaddingStrategy to use for padding.
317
+
318
+ - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
319
+ - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
320
+ - PaddingStrategy.DO_NOT_PAD: Do not pad
321
+ The tokenizer padding sides are defined in self.padding_side:
322
+
323
+ - 'left': pads on the left of the sequences
324
+ - 'right': pads on the right of the sequences
325
+ pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
326
+ This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
327
+ `>= 7.5` (Volta).
328
+ return_attention_mask:
329
+ (optional) Set to False to avoid returning attention mask (default: set to model specifics)
330
+ """
331
+ # Load from model defaults
332
+ assert self.padding_side == "left"
333
+
334
+ required_input = encoded_inputs[self.model_input_names[0]]
335
+ seq_length = len(required_input)
336
+
337
+ if padding_strategy == PaddingStrategy.LONGEST:
338
+ max_length = len(required_input)
339
+
340
+ if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
341
+ max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
342
+
343
+ needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
344
+
345
+ # Initialize attention mask if not present.
346
+ if "attention_mask" not in encoded_inputs:
347
+ encoded_inputs["attention_mask"] = [1] * seq_length
348
+
349
+ if "position_ids" not in encoded_inputs:
350
+ encoded_inputs["position_ids"] = list(range(seq_length))
351
+
352
+ if needs_to_be_padded:
353
+ difference = max_length - len(required_input)
354
+
355
+ if "attention_mask" in encoded_inputs:
356
+ encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
357
+ if "position_ids" in encoded_inputs:
358
+ encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
359
+ encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
360
+
361
+ return encoded_inputs
tokenizer.model ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5a493598071550244b2ee7f26118f3edec2150b9dfa967929a99052ac83fe716
3
+ size 2623634
tokenizer_config.json ADDED
@@ -0,0 +1,134 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "auto_map": {
3
+ "AutoTokenizer": [
4
+ "tokenization_chatglm.ChatGLM4Tokenizer",
5
+ null
6
+ ]
7
+ },
8
+ "added_tokens_decoder": {
9
+ "151329": {
10
+ "content": "<|endoftext|>",
11
+ "lstrip": false,
12
+ "normalized": false,
13
+ "rstrip": false,
14
+ "single_word": false,
15
+ "special": true
16
+ },
17
+ "151330": {
18
+ "content": "[MASK]",
19
+ "lstrip": false,
20
+ "normalized": false,
21
+ "rstrip": false,
22
+ "single_word": false,
23
+ "special": true
24
+ },
25
+ "151331": {
26
+ "content": "[gMASK]",
27
+ "lstrip": false,
28
+ "normalized": false,
29
+ "rstrip": false,
30
+ "single_word": false,
31
+ "special": true
32
+ },
33
+ "151332": {
34
+ "content": "[sMASK]",
35
+ "lstrip": false,
36
+ "normalized": false,
37
+ "rstrip": false,
38
+ "single_word": false,
39
+ "special": true
40
+ },
41
+ "151333": {
42
+ "content": "<sop>",
43
+ "lstrip": false,
44
+ "normalized": false,
45
+ "rstrip": false,
46
+ "single_word": false,
47
+ "special": true
48
+ },
49
+ "151334": {
50
+ "content": "<eop>",
51
+ "lstrip": false,
52
+ "normalized": false,
53
+ "rstrip": false,
54
+ "single_word": false,
55
+ "special": true
56
+ },
57
+ "151335": {
58
+ "content": "<|system|>",
59
+ "lstrip": false,
60
+ "normalized": false,
61
+ "rstrip": false,
62
+ "single_word": false,
63
+ "special": true
64
+ },
65
+ "151336": {
66
+ "content": "<|user|>",
67
+ "lstrip": false,
68
+ "normalized": false,
69
+ "rstrip": false,
70
+ "single_word": false,
71
+ "special": true
72
+ },
73
+ "151337": {
74
+ "content": "<|assistant|>",
75
+ "lstrip": false,
76
+ "normalized": false,
77
+ "rstrip": false,
78
+ "single_word": false,
79
+ "special": true
80
+ },
81
+ "151338": {
82
+ "content": "<|observation|>",
83
+ "lstrip": false,
84
+ "normalized": false,
85
+ "rstrip": false,
86
+ "single_word": false,
87
+ "special": true
88
+ },
89
+ "151339": {
90
+ "content": "<|begin_of_image|>",
91
+ "lstrip": false,
92
+ "normalized": false,
93
+ "rstrip": false,
94
+ "single_word": false,
95
+ "special": true
96
+ },
97
+ "151340": {
98
+ "content": "<|end_of_image|>",
99
+ "lstrip": false,
100
+ "normalized": false,
101
+ "rstrip": false,
102
+ "single_word": false,
103
+ "special": true
104
+ },
105
+ "151341": {
106
+ "content": "<|begin_of_video|>",
107
+ "lstrip": false,
108
+ "normalized": false,
109
+ "rstrip": false,
110
+ "single_word": false,
111
+ "special": true
112
+ },
113
+ "151342": {
114
+ "content": "<|end_of_video|>",
115
+ "lstrip": false,
116
+ "normalized": false,
117
+ "rstrip": false,
118
+ "single_word": false,
119
+ "special": true
120
+ }
121
+ },
122
+ "additional_special_tokens": ["<|endoftext|>", "[MASK]", "[gMASK]", "[sMASK]", "<sop>", "<eop>", "<|system|>",
123
+ "<|user|>", "<|assistant|>", "<|observation|>", "<|begin_of_image|>", "<|end_of_image|>",
124
+ "<|begin_of_video|>", "<|end_of_video|>"],
125
+ "clean_up_tokenization_spaces": false,
126
+ "do_lower_case": false,
127
+ "eos_token": "<|endoftext|>",
128
+ "pad_token": "<|endoftext|>",
129
+ "model_max_length": 1000000000000000019884624838656,
130
+ "padding_side": "left",
131
+ "remove_space": false,
132
+ "tokenizer_class": "ChatGLM4Tokenizer",
133
+ "image_size": 1120
134
+ }
visual.py ADDED
@@ -0,0 +1,171 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import torch
2
+ from torch import nn
3
+ from argparse import Namespace
4
+ import torch.nn.functional as F
5
+ from transformers.activations import ACT2FN
6
+ import math
7
+ from torch.nn import LayerNorm
8
+
9
+ def standard_attention(query_layer, key_layer, value_layer, scaling_attention_score=True):
10
+ if scaling_attention_score:
11
+ query_layer = query_layer / math.sqrt(query_layer.shape[-1])
12
+ attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
13
+
14
+ attention_probs = F.softmax(attention_scores, dim=-1)
15
+
16
+ context_layer = torch.matmul(attention_probs, value_layer)
17
+ return context_layer
18
+
19
+ def attention_fn_default(query_layer, key_layer, value_layer, scaling_attention_score=True):
20
+ if int(torch.__version__.split('.')[0]) >= 2 and scaling_attention_score:
21
+ # Pytorch 2.0 attention uses very much memory if attention_mask is float, and has NaN bug if attention_mask is None.
22
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
23
+ query_layer, key_layer, value_layer,
24
+ attn_mask=None,
25
+ dropout_p=0.,
26
+ is_causal=False
27
+ )
28
+ return attn_output
29
+ else:
30
+ return standard_attention(
31
+ query_layer, key_layer, value_layer, scaling_attention_score=scaling_attention_score
32
+ )
33
+
34
+ class PatchEmbedding(nn.Module):
35
+ def __init__(self, config):
36
+ super().__init__()
37
+ self.proj = nn.Conv2d(config.in_channels, config.hidden_size, kernel_size=config.patch_size, stride=config.patch_size)
38
+ self.cls_embedding = nn.Parameter(torch.zeros(1, config.hidden_size))
39
+ self.position_embedding = nn.Embedding(config.num_positions, config.hidden_size)
40
+
41
+ def forward(self, images: "tensor(B, C, H, W)") -> "tensor(B, L, D)":
42
+ x = self.proj(images)
43
+ x = x.flatten(2).transpose(1, 2)
44
+ cls_token = self.cls_embedding.expand(x.shape[0], -1, -1)
45
+ x = torch.cat((cls_token, x), dim=1)
46
+ x += self.position_embedding.weight.unsqueeze(0)
47
+ return x
48
+
49
+
50
+ class Attention(nn.Module):
51
+ def __init__(self, config):
52
+ super().__init__()
53
+ self.num_heads = config.num_heads
54
+ head_dim = config.hidden_size // config.num_heads
55
+ self.scale = head_dim ** -0.5
56
+ self.query_key_value = nn.Linear(config.hidden_size, config.hidden_size * 3)
57
+ self.dense = nn.Linear(config.hidden_size, config.hidden_size)
58
+ self.output_dropout = torch.nn.Dropout(config.dropout_prob)
59
+
60
+ def forward(self, x: "tensor(B, L, D)") -> "tensor(B, L, D)":
61
+ B, L, _ = x.shape
62
+ qkv = self.query_key_value(x)
63
+ qkv = qkv.reshape(B, L, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) # 3, B, H, L, D
64
+ q, k, v = qkv[0], qkv[1], qkv[2]
65
+
66
+ out = attention_fn_default(
67
+ q, k, v
68
+ )
69
+ output = self.dense(out.transpose(1, 2).view(B, L, -1))
70
+ output = self.output_dropout(output)
71
+ return output
72
+
73
+ def attention(self, q, k, v):
74
+ attn_weights = torch.matmul(q * self.scale, k.transpose(-2, -1))
75
+ attn_weights = attn_weights.softmax(dim=-1)
76
+ output = torch.matmul(attn_weights, v)
77
+ return output
78
+
79
+
80
+ class MLP(nn.Module):
81
+ def __init__(self, config):
82
+ super().__init__()
83
+ self.config = config
84
+ self.activation_fn = ACT2FN[config.hidden_act]
85
+ self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size)
86
+ self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size)
87
+
88
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
89
+ x = self.fc1(x)
90
+ x = self.activation_fn(x)
91
+ x = self.fc2(x)
92
+ return x
93
+
94
+
95
+ class TransformerLayer(nn.Module):
96
+ def __init__(self, config):
97
+ super().__init__()
98
+ self.input_layernorm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
99
+ self.attention = Attention(config)
100
+ self.mlp = MLP(config)
101
+ self.post_attention_layernorm = LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
102
+
103
+ def forward(self, hidden_states):
104
+ attention_input = hidden_states
105
+ attention_output = self.input_layernorm(self.attention(attention_input))
106
+ hidden_states = attention_input + attention_output
107
+ mlp_input = hidden_states
108
+ mlp_output = self.post_attention_layernorm(self.mlp(mlp_input))
109
+ output = mlp_input + mlp_output
110
+ return output
111
+
112
+
113
+ class Transformer(nn.Module):
114
+ def __init__(self, config):
115
+ super().__init__()
116
+ self.layers = nn.ModuleList([TransformerLayer(config) for _ in range(config.num_hidden_layers)])
117
+
118
+ def forward(self, hidden_states):
119
+ for layer_module in self.layers:
120
+ hidden_states = layer_module(hidden_states)
121
+ return hidden_states
122
+
123
+
124
+ class GLU(nn.Module):
125
+ def __init__(self, config, in_features):
126
+ super().__init__()
127
+ self.linear_proj = nn.Linear(in_features, config.hidden_size, bias=False)
128
+ self.norm1 = nn.LayerNorm(config.hidden_size)
129
+ self.act1 = nn.GELU()
130
+ self.act2 = nn.functional.silu
131
+ self.dense_h_to_4h = nn.Linear(config.hidden_size, config.ffn_hidden_size, bias=False)
132
+ self.gate_proj = nn.Linear(config.hidden_size, config.ffn_hidden_size, bias=False)
133
+ self.dense_4h_to_h = nn.Linear(config.ffn_hidden_size, config.hidden_size, bias=False)
134
+
135
+ def forward(self, x):
136
+ x = self.linear_proj(x)
137
+ x = self.act1(self.norm1(x))
138
+ x = self.act2(self.gate_proj(x)) * self.dense_h_to_4h(x)
139
+ x = self.dense_4h_to_h(x)
140
+ return x
141
+
142
+
143
+ class EVA2CLIPModel(nn.Module):
144
+ def __init__(self, config):
145
+ super().__init__()
146
+ vision_config = Namespace(**config.vision_config)
147
+ self.patch_embedding = PatchEmbedding(vision_config)
148
+ self.transformer = Transformer(vision_config)
149
+ self.linear_proj = GLU(config, in_features=config.hidden_size)
150
+ self.conv = nn.Conv2d(in_channels=vision_config.hidden_size, out_channels=config.hidden_size, kernel_size=2, stride=2)
151
+ self.boi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
152
+ self.eoi = nn.Parameter(torch.zeros(1, 1, config.hidden_size))
153
+ self.scaling_factor = vision_config.scaling_factor
154
+
155
+ def forward(self, images: "tensor(B, C, H, W)") -> "tensor(B, L, D)":
156
+ x = self.patch_embedding(images)
157
+ x = self.transformer(x)
158
+ x = x[:, 1:]
159
+
160
+ b, s, h = x.shape
161
+ grid_size = int(s**0.5)
162
+ x = x.view(b, grid_size, grid_size, h).permute(0, 3, 1, 2)
163
+ x = self.conv(x)
164
+
165
+ x = x.flatten(2).transpose(1, 2)
166
+ x = self.linear_proj(x)
167
+ boi = self.boi.expand(x.shape[0], -1, -1)
168
+ eoi = self.eoi.expand(x.shape[0], -1, -1)
169
+ x = torch.cat((boi, x, eoi), dim=1)
170
+ x = x / self.scaling_factor
171
+ return x