File size: 56,053 Bytes
8b6bc47 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 |
""" PyTorch ChatGLM model. """
import math
import copy
import os
import warnings
import re
import sys
import torch
import torch.utils.checkpoint
import torch.nn.functional as F
from torch import nn
from torch.nn import CrossEntropyLoss, LayerNorm
from torch.nn.utils import skip_init
from typing import Optional, Tuple, Union, List, Callable
from transformers.utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
)
from transformers.modeling_outputs import (
BaseModelOutputWithPast,
CausalLMOutputWithPast,
BaseModelOutputWithPastAndCrossAttentions,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging
from transformers.generation.logits_process import LogitsProcessor
from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
from .configuration_chatglm import ChatGLMConfig
# flags required to enable jit fusion kernels
if sys.platform != 'darwin':
torch._C._jit_set_profiling_mode(False)
torch._C._jit_set_profiling_executor(False)
torch._C._jit_override_can_fuse_on_cpu(True)
torch._C._jit_override_can_fuse_on_gpu(True)
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM-6B"
_CONFIG_FOR_DOC = "ChatGLM6BConfig"
CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
"THUDM/chatglm-6b",
# See all ChatGLM-6B models at https://huggingface.co/models?filter=chatglm
]
class InvalidScoreLogitsProcessor(LogitsProcessor):
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
if torch.isnan(scores).any() or torch.isinf(scores).any():
scores.zero_()
scores[..., 20005] = 5e4
return scores
def load_tf_weights_in_chatglm_6b(model, config, tf_checkpoint_path):
"""Load tf checkpoints in a pytorch model."""
try:
import re
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
"Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
"https://www.tensorflow.org/install/ for installation instructions."
)
raise
tf_path = os.path.abspath(tf_checkpoint_path)
logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
# Load weights from TF model
init_vars = tf.train.list_variables(tf_path)
names = []
arrays = []
for name, shape in init_vars:
logger.info(f"Loading TF weight {name} with shape {shape}")
array = tf.train.load_variable(tf_path, name)
names.append(name)
arrays.append(array)
for name, array in zip(names, arrays):
name = name.split("/")
# adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
# which are not required for using pretrained model
if any(
n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
for n in name
):
logger.info(f"Skipping {'/'.join(name)}")
continue
pointer = model
for m_name in name:
if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
scope_names = re.split(r"_(\d+)", m_name)
else:
scope_names = [m_name]
if scope_names[0] == "kernel" or scope_names[0] == "gamma":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
pointer = getattr(pointer, "bias")
elif scope_names[0] == "output_weights":
pointer = getattr(pointer, "weight")
elif scope_names[0] == "squad":
pointer = getattr(pointer, "classifier")
else:
try:
pointer = getattr(pointer, scope_names[0])
except AttributeError:
logger.info(f"Skipping {'/'.join(name)}")
continue
if len(scope_names) >= 2:
num = int(scope_names[1])
pointer = pointer[num]
if m_name[-11:] == "_embeddings":
pointer = getattr(pointer, "weight")
elif m_name == "kernel":
array = np.transpose(array)
try:
assert (
pointer.shape == array.shape
), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
except AssertionError as e:
e.args += (pointer.shape, array.shape)
raise
logger.info(f"Initialize PyTorch weight {name}")
pointer.data = torch.from_numpy(array)
return model
class PrefixEncoder(torch.nn.Module):
"""
The torch.nn model to encode the prefix
Input shape: (batch-size, prefix-length)
Output shape: (batch-size, prefix-length, 2*layers*hidden)
"""
def __init__(self, config):
super().__init__()
self.prefix_projection = config.prefix_projection
if self.prefix_projection:
# Use a two-layer MLP to encode the prefix
self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size)
self.trans = torch.nn.Sequential(
torch.nn.Linear(config.hidden_size, config.hidden_size),
torch.nn.Tanh(),
torch.nn.Linear(config.hidden_size, config.num_layers * config.hidden_size * 2)
)
else:
self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_layers * config.hidden_size * 2)
def forward(self, prefix: torch.Tensor):
if self.prefix_projection:
prefix_tokens = self.embedding(prefix)
past_key_values = self.trans(prefix_tokens)
else:
past_key_values = self.embedding(prefix)
return past_key_values
@torch.jit.script
def gelu_impl(x):
"""OpenAI's gelu implementation."""
return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x *
(1.0 + 0.044715 * x * x)))
def gelu(x):
return gelu_impl(x)
class RotaryEmbedding(torch.nn.Module):
def __init__(self, dim, base=10000, precision=torch.half, learnable=False):
super().__init__()
inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
inv_freq = inv_freq.half()
self.learnable = learnable
if learnable:
self.inv_freq = torch.nn.Parameter(inv_freq)
self.max_seq_len_cached = None
else:
self.register_buffer('inv_freq', inv_freq)
self.max_seq_len_cached = None
self.cos_cached = None
self.sin_cached = None
self.precision = precision
def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
error_msgs):
pass
def forward(self, x, seq_dim=1, seq_len=None):
if seq_len is None:
seq_len = x.shape[seq_dim]
if self.max_seq_len_cached is None or (seq_len > self.max_seq_len_cached):
self.max_seq_len_cached = None if self.learnable else seq_len
t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
freqs = torch.einsum('i,j->ij', t, self.inv_freq)
# Different from paper, but it uses a different permutation in order to obtain the same calculation
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
if self.precision == torch.bfloat16:
emb = emb.float()
# [sx, 1 (b * np), hn]
cos_cached = emb.cos()[:, None, :]
sin_cached = emb.sin()[:, None, :]
if self.precision == torch.bfloat16:
cos_cached = cos_cached.bfloat16()
sin_cached = sin_cached.bfloat16()
if self.learnable:
return cos_cached, sin_cached
self.cos_cached, self.sin_cached = cos_cached, sin_cached
return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...]
def _apply(self, fn):
if self.cos_cached is not None:
self.cos_cached = fn(self.cos_cached)
if self.sin_cached is not None:
self.sin_cached = fn(self.sin_cached)
return super()._apply(fn)
def rotate_half(x):
x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
return torch.cat((-x2, x1), dim=x1.ndim - 1) # dim=-1 triggers a bug in earlier torch versions
@torch.jit.script
def apply_rotary_pos_emb_index(q, k, cos, sin, position_id):
# position_id: [sq, b], q, k: [sq, b, np, hn], cos: [sq, 1, hn] -> [sq, b, 1, hn]
cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(2), \
F.embedding(position_id, sin.squeeze(1)).unsqueeze(2)
q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
return q, k
def attention_fn(
self,
query_layer,
key_layer,
value_layer,
attention_mask,
hidden_size_per_partition,
layer_id,
layer_past=None,
scaling_attention_score=True,
use_cache=False,
):
if layer_past is not None:
past_key, past_value = layer_past[0], layer_past[1]
key_layer = torch.cat((past_key, key_layer), dim=0)
value_layer = torch.cat((past_value, value_layer), dim=0)
# seqlen, batch, num_attention_heads, hidden_size_per_attention_head
seq_len, b, nh, hidden_size = key_layer.shape
if use_cache:
present = (key_layer, value_layer)
else:
present = None
query_key_layer_scaling_coeff = float(layer_id + 1)
if scaling_attention_score:
query_layer = query_layer / (math.sqrt(hidden_size) * query_key_layer_scaling_coeff)
# ===================================
# Raw attention scores. [b, np, s, s]
# ===================================
# [b, np, sq, sk]
output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))
# [sq, b, np, hn] -> [sq, b * np, hn]
query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
# [sk, b, np, hn] -> [sk, b * np, hn]
key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)
matmul_result = torch.empty(
output_size[0] * output_size[1],
output_size[2],
output_size[3],
dtype=query_layer.dtype,
device=query_layer.device,
)
matmul_result = torch.baddbmm(
matmul_result,
query_layer.transpose(0, 1), # [b * np, sq, hn]
key_layer.transpose(0, 1).transpose(1, 2), # [b * np, hn, sk]
beta=0.0,
alpha=1.0,
)
# change view to [b, np, sq, sk]
attention_scores = matmul_result.view(*output_size)
if self.scale_mask_softmax:
self.scale_mask_softmax.scale = query_key_layer_scaling_coeff
attention_probs = self.scale_mask_softmax(attention_scores, attention_mask.contiguous())
else:
if not (attention_mask == 0).all():
# if auto-regressive, skip
attention_scores.masked_fill_(attention_mask, -10000.0)
dtype = attention_scores.dtype
attention_scores = attention_scores.float()
attention_scores = attention_scores * query_key_layer_scaling_coeff
attention_probs = F.softmax(attention_scores, dim=-1)
attention_probs = attention_probs.type(dtype)
# =========================
# Context layer. [sq, b, hp]
# =========================
# value_layer -> context layer.
# [sk, b, np, hn] --> [b, np, sq, hn]
# context layer shape: [b, np, sq, hn]
output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))
# change view [sk, b * np, hn]
value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)
# change view [b * np, sq, sk]
attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)
# matmul: [b * np, sq, hn]
context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))
# change view [b, np, sq, hn]
context_layer = context_layer.view(*output_size)
# [b, np, sq, hn] --> [sq, b, np, hn]
context_layer = context_layer.permute(2, 0, 1, 3).contiguous()
# [sq, b, np, hn] --> [sq, b, hp]
new_context_layer_shape = context_layer.size()[:-2] + (hidden_size_per_partition,)
context_layer = context_layer.view(*new_context_layer_shape)
outputs = (context_layer, present, attention_probs)
return outputs
class SelfAttention(torch.nn.Module):
def __init__(self, hidden_size, num_attention_heads,
layer_id, hidden_size_per_attention_head=None, bias=True,
params_dtype=torch.float, position_encoding_2d=True):
super(SelfAttention, self).__init__()
self.layer_id = layer_id
self.hidden_size = hidden_size
self.hidden_size_per_partition = hidden_size
self.num_attention_heads = num_attention_heads
self.num_attention_heads_per_partition = num_attention_heads
self.position_encoding_2d = position_encoding_2d
self.rotary_emb = RotaryEmbedding(
self.hidden_size // (self.num_attention_heads * 2)
if position_encoding_2d
else self.hidden_size // self.num_attention_heads,
base=10000,
precision=torch.half,
learnable=False,
)
self.scale_mask_softmax = None
if hidden_size_per_attention_head is None:
self.hidden_size_per_attention_head = hidden_size // num_attention_heads
else:
self.hidden_size_per_attention_head = hidden_size_per_attention_head
self.inner_hidden_size = num_attention_heads * self.hidden_size_per_attention_head
# Strided linear layer.
self.query_key_value = skip_init(
torch.nn.Linear,
hidden_size,
3 * self.inner_hidden_size,
bias=bias,
dtype=params_dtype,
)
self.dense = skip_init(
torch.nn.Linear,
self.inner_hidden_size,
hidden_size,
bias=bias,
dtype=params_dtype,
)
@staticmethod
def attention_mask_func(attention_scores, attention_mask):
attention_scores.masked_fill_(attention_mask, -10000.0)
return attention_scores
def split_tensor_along_last_dim(self, tensor, num_partitions,
contiguous_split_chunks=False):
"""Split a tensor along its last dimension.
Arguments:
tensor: input tensor.
num_partitions: number of partitions to split the tensor
contiguous_split_chunks: If True, make each chunk contiguous
in memory.
"""
# Get the size and dimension.
last_dim = tensor.dim() - 1
last_dim_size = tensor.size()[last_dim] // num_partitions
# Split.
tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
# Note: torch.split does not create contiguous tensors by default.
if contiguous_split_chunks:
return tuple(chunk.contiguous() for chunk in tensor_list)
return tensor_list
def forward(
self,
hidden_states: torch.Tensor,
position_ids,
attention_mask: torch.Tensor,
layer_id,
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
use_cache: bool = False,
output_attentions: bool = False,
):
"""
hidden_states: [seq_len, batch, hidden_size]
attention_mask: [(1, 1), seq_len, seq_len]
"""
# [seq_len, batch, 3 * hidden_size]
mixed_raw_layer = self.query_key_value(hidden_states)
# [seq_len, batch, 3 * hidden_size] --> [seq_len, batch, num_attention_heads, 3 * hidden_size_per_attention_head]
new_tensor_shape = mixed_raw_layer.size()[:-1] + (
self.num_attention_heads_per_partition,
3 * self.hidden_size_per_attention_head,
)
mixed_raw_layer = mixed_raw_layer.view(*new_tensor_shape)
# [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
(query_layer, key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_raw_layer, 3)
if self.position_encoding_2d:
q1, q2 = query_layer.chunk(2, dim=(query_layer.ndim - 1))
k1, k2 = key_layer.chunk(2, dim=(key_layer.ndim - 1))
cos, sin = self.rotary_emb(q1, seq_len=position_ids.max() + 1)
position_ids, block_position_ids = position_ids[:, 0, :].transpose(0, 1).contiguous(), \
position_ids[:, 1, :].transpose(0, 1).contiguous()
q1, k1 = apply_rotary_pos_emb_index(q1, k1, cos, sin, position_ids)
q2, k2 = apply_rotary_pos_emb_index(q2, k2, cos, sin, block_position_ids)
query_layer = torch.concat([q1, q2], dim=(q1.ndim - 1))
key_layer = torch.concat([k1, k2], dim=(k1.ndim - 1))
else:
position_ids = position_ids.transpose(0, 1)
cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1)
# [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
query_layer, key_layer = apply_rotary_pos_emb_index(query_layer, key_layer, cos, sin, position_ids)
# [seq_len, batch, hidden_size]
context_layer, present, attention_probs = attention_fn(
self=self,
query_layer=query_layer,
key_layer=key_layer,
value_layer=value_layer,
attention_mask=attention_mask,
hidden_size_per_partition=self.hidden_size_per_partition,
layer_id=layer_id,
layer_past=layer_past,
use_cache=use_cache
)
output = self.dense(context_layer)
outputs = (output, present)
if output_attentions:
outputs += (attention_probs,)
return outputs # output, present, attention_probs
class GEGLU(torch.nn.Module):
def __init__(self):
super().__init__()
self.activation_fn = F.gelu
def forward(self, x):
# dim=-1 breaks in jit for pt<1.10
x1, x2 = x.chunk(2, dim=(x.ndim - 1))
return x1 * self.activation_fn(x2)
class GLU(torch.nn.Module):
def __init__(self, hidden_size, inner_hidden_size=None,
layer_id=None, bias=True, activation_func=gelu, params_dtype=torch.float):
super(GLU, self).__init__()
self.layer_id = layer_id
self.activation_func = activation_func
# Project to 4h.
self.hidden_size = hidden_size
if inner_hidden_size is None:
inner_hidden_size = 4 * hidden_size
self.inner_hidden_size = inner_hidden_size
self.dense_h_to_4h = skip_init(
torch.nn.Linear,
self.hidden_size,
self.inner_hidden_size,
bias=bias,
dtype=params_dtype,
)
# Project back to h.
self.dense_4h_to_h = skip_init(
torch.nn.Linear,
self.inner_hidden_size,
self.hidden_size,
bias=bias,
dtype=params_dtype,
)
def forward(self, hidden_states):
"""
hidden_states: [seq_len, batch, hidden_size]
"""
# [seq_len, batch, inner_hidden_size]
intermediate_parallel = self.dense_h_to_4h(hidden_states)
intermediate_parallel = self.activation_func(intermediate_parallel)
output = self.dense_4h_to_h(intermediate_parallel)
return output
class GLMBlock(torch.nn.Module):
def __init__(
self,
hidden_size,
num_attention_heads,
layernorm_epsilon,
layer_id,
inner_hidden_size=None,
hidden_size_per_attention_head=None,
layernorm=LayerNorm,
use_bias=True,
params_dtype=torch.float,
num_layers=28,
position_encoding_2d=True
):
super(GLMBlock, self).__init__()
# Set output layer initialization if not provided.
self.layer_id = layer_id
# Layernorm on the input data.
self.input_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)
self.position_encoding_2d = position_encoding_2d
# Self attention.
self.attention = SelfAttention(
hidden_size,
num_attention_heads,
layer_id,
hidden_size_per_attention_head=hidden_size_per_attention_head,
bias=use_bias,
params_dtype=params_dtype,
position_encoding_2d=self.position_encoding_2d
)
# Layernorm on the input data.
self.post_attention_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)
self.num_layers = num_layers
# GLU
self.mlp = GLU(
hidden_size,
inner_hidden_size=inner_hidden_size,
bias=use_bias,
layer_id=layer_id,
params_dtype=params_dtype,
)
def forward(
self,
hidden_states: torch.Tensor,
position_ids,
attention_mask: torch.Tensor,
layer_id,
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
use_cache: bool = False,
output_attentions: bool = False,
):
"""
hidden_states: [seq_len, batch, hidden_size]
attention_mask: [(1, 1), seq_len, seq_len]
"""
# Layer norm at the begining of the transformer layer.
# [seq_len, batch, hidden_size]
attention_input = self.input_layernorm(hidden_states)
# Self attention.
attention_outputs = self.attention(
attention_input,
position_ids,
attention_mask=attention_mask,
layer_id=layer_id,
layer_past=layer_past,
use_cache=use_cache,
output_attentions=output_attentions
)
attention_output = attention_outputs[0]
outputs = attention_outputs[1:]
# Residual connection.
alpha = (2 * self.num_layers) ** 0.5
hidden_states = attention_input * alpha + attention_output
mlp_input = self.post_attention_layernorm(hidden_states)
# MLP.
mlp_output = self.mlp(mlp_input)
# Second residual connection.
output = mlp_input * alpha + mlp_output
if use_cache:
outputs = (output,) + outputs
else:
outputs = (output,) + outputs[1:]
return outputs # hidden_states, present, attentions
class ChatGLMPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and
a simple interface for downloading and loading pretrained models.
"""
is_parallelizable = False
supports_gradient_checkpointing = True
config_class = ChatGLMConfig
base_model_prefix = "transformer"
_no_split_modules = ["GLM6BBlock"]
def __init__(self, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
def _init_weights(self, module: nn.Module):
"""Initialize the weights."""
return
def _set_gradient_checkpointing(self, module, value=False):
if isinstance(module, ChatGLMModel):
module.gradient_checkpointing = value
CHATGLM_6B_START_DOCSTRING = r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
usage and behavior.
Parameters:
config ([`~ChatGLM6BConfig`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the configuration.
Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
CHATGLM_6B_INPUTS_DOCSTRING = r"""
Args:
input_ids (`torch.LongTensor` of shape `({0})`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`ChatGLM6BTokenizer`].
See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
- 1 for tokens that are **not masked**,
- 0 for tokens that are **masked**.
[What are attention masks?](../glossary#attention-mask)
token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:
- 0 corresponds to a *sentence A* token,
- 1 corresponds to a *sentence B* token.
[What are token type IDs?](../glossary#token-type-ids)
position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
Indices of positions of each input sequence tokens in the position embeddings.
Selected in the range `[0, config.max_position_embeddings - 1]`.
[What are position IDs?](../glossary#position-ids)
head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
- 1 indicates the head is **not masked**,
- 0 indicates the head is **masked**.
inputs_embeds (`torch.FloatTensor` of shape `({0}, hidden_size)`, *optional*):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert *input_ids* indices into associated vectors
than the model's internal embedding lookup matrix.
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
tensors for more detail.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
"The bare ChatGLM-6B Model transformer outputting raw hidden-states without any specific head on top.",
CHATGLM_6B_START_DOCSTRING,
)
class ChatGLMModel(ChatGLMPreTrainedModel):
"""
The model can behave as an encoder (with only self-attention) as well
as a decoder, in which case a layer of cross-attention is added between
the self-attention layers, following the architecture described in [Attention is
all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani,
Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.
To behave as an decoder the model needs to be initialized with the
`is_decoder` argument of the configuration set to `True`.
To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder`
argument and `add_cross_attention` set to `True`; an
`encoder_hidden_states` is then expected as an input to the forward pass.
"""
def __init__(self, config: ChatGLMConfig):
super().__init__(config)
# recording parameters
self.max_sequence_length = config.max_sequence_length
self.hidden_size = config.hidden_size
self.params_dtype = torch.half
self.num_attention_heads = config.num_attention_heads
self.vocab_size = config.vocab_size
self.num_layers = config.num_layers
self.layernorm_epsilon = config.layernorm_epsilon
self.inner_hidden_size = config.inner_hidden_size
self.hidden_size_per_attention_head = self.hidden_size // self.num_attention_heads
self.position_encoding_2d = config.position_encoding_2d
self.pre_seq_len = config.pre_seq_len
self.prefix_projection = config.prefix_projection
self.word_embeddings = skip_init(
torch.nn.Embedding,
num_embeddings=self.vocab_size, embedding_dim=self.hidden_size,
dtype=self.params_dtype
)
self.gradient_checkpointing = False
def get_layer(layer_id):
return GLMBlock(
self.hidden_size,
self.num_attention_heads,
self.layernorm_epsilon,
layer_id,
inner_hidden_size=self.inner_hidden_size,
hidden_size_per_attention_head=self.hidden_size_per_attention_head,
layernorm=LayerNorm,
use_bias=True,
params_dtype=self.params_dtype,
position_encoding_2d=self.position_encoding_2d,
)
self.layers = torch.nn.ModuleList(
[get_layer(layer_id) for layer_id in range(self.num_layers)]
)
# Final layer norm before output.
self.final_layernorm = LayerNorm(self.hidden_size, eps=self.layernorm_epsilon)
if self.pre_seq_len is not None:
for param in self.parameters():
param.requires_grad = False
self.prefix_tokens = torch.arange(self.pre_seq_len).long()
self.prefix_encoder = PrefixEncoder(config)
self.dropout = torch.nn.Dropout(0.1)
# total_params = sum(p.numel() for p in self.parameters())
# trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
# print("Using p-tuning v2: # trainable_params = {} / {}".format(trainable_params, total_params))
def get_input_embeddings(self):
return self.word_embeddings
def set_input_embeddings(self, new_embeddings: torch.Tensor):
self.word_embeddings = new_embeddings
def get_prompt(self, batch_size, device, dtype=torch.half):
prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
past_key_values = self.prefix_encoder(prefix_tokens).type(dtype)
past_key_values = past_key_values.view(
batch_size,
self.pre_seq_len,
self.num_layers * 2,
self.num_attention_heads,
self.hidden_size // self.num_attention_heads
)
# seq_len, b, nh, hidden_size
past_key_values = self.dropout(past_key_values)
past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2)
# past_key_values = [(v[0], v[1]) for v in past_key_values]
return past_key_values
def get_masks(self, input_ids, device):
batch_size, seq_length = input_ids.shape
context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
attention_mask = torch.ones((batch_size, seq_length, seq_length), device=device)
attention_mask.tril_()
for i, context_length in enumerate(context_lengths):
attention_mask[i, :, :context_length] = 1
attention_mask.unsqueeze_(1)
attention_mask = (attention_mask < 0.5).bool()
return attention_mask
def get_position_ids(self, input_ids, mask_positions, device, gmask=False):
batch_size, seq_length = input_ids.shape
context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
if self.position_encoding_2d:
position_ids = torch.arange(seq_length, dtype=torch.long, device=device).expand(batch_size, seq_length)
if not gmask:
for i, context_length in enumerate(context_lengths):
position_ids[i, context_length:] = mask_positions[i]
block_position_ids = [torch.cat((
torch.zeros(context_length, dtype=torch.long, device=device),
torch.arange(seq_length - context_length, dtype=torch.long, device=device) + 1
)) for context_length in context_lengths]
block_position_ids = torch.stack(block_position_ids, dim=0)
position_ids = torch.stack((position_ids, block_position_ids), dim=1)
else:
position_ids = torch.arange(seq_length, dtype=torch.long, device=device).expand(batch_size, seq_length)
if not gmask:
for i, context_length in enumerate(context_lengths):
position_ids[context_length:] = mask_positions[i]
return position_ids
@add_start_docstrings_to_model_forward(CHATGLM_6B_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC,
output_type=BaseModelOutputWithPastAndCrossAttentions,
config_class=_CONFIG_FOR_DOC,
)
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
inputs_embeds: Optional[torch.LongTensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPast]:
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
if self.gradient_checkpointing and self.training:
if use_cache:
# logger.warning_once(
# "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
# )
use_cache = False
if input_ids is not None and inputs_embeds is not None:
raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
elif input_ids is not None:
batch_size, seq_length = input_ids.shape[:2]
elif inputs_embeds is not None:
batch_size, seq_length, _ = inputs_embeds.shape[:2]
else:
raise ValueError("You have to specify either input_ids or inputs_embeds")
if inputs_embeds is None:
inputs_embeds = self.word_embeddings(input_ids)
if past_key_values is None:
if self.pre_seq_len is not None:
past_key_values = self.get_prompt(batch_size=input_ids.shape[0], device=input_ids.device,
dtype=inputs_embeds.dtype)
else:
past_key_values = tuple([None] * len(self.layers))
if attention_mask is None:
attention_mask = self.get_masks(
input_ids,
device=input_ids.device
)
if self.pre_seq_len is not None:
prefix_attention_mask = torch.ones(batch_size, 1, input_ids.size(-1), self.pre_seq_len).to(
attention_mask.device)
prefix_attention_mask = (prefix_attention_mask < 0.5).bool()
attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=3)
if position_ids is None:
MASK, gMASK = 150000, 150001
mask_token = MASK if MASK in input_ids else gMASK
use_gmask = False if MASK in input_ids else gMASK
mask_positions = [seq.tolist().index(mask_token) for seq in input_ids]
position_ids = self.get_position_ids(
input_ids,
mask_positions=mask_positions,
device=input_ids.device,
gmask=use_gmask
)
# [seq_len, batch, hidden_size]
hidden_states = inputs_embeds.transpose(0, 1)
presents = () if use_cache else None
all_self_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
if attention_mask is None:
attention_mask = torch.zeros(1, 1, device=input_ids.device).bool()
else:
attention_mask = attention_mask.to(input_ids.device)
for i, layer in enumerate(self.layers):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
layer_past = past_key_values[i]
if self.gradient_checkpointing and self.training:
layer_ret = torch.utils.checkpoint.checkpoint(
layer,
hidden_states,
position_ids,
attention_mask,
torch.tensor(i),
layer_past,
use_cache,
output_attentions
)
else:
layer_ret = layer(
hidden_states,
position_ids=position_ids,
attention_mask=attention_mask,
layer_id=torch.tensor(i),
layer_past=layer_past,
use_cache=use_cache,
output_attentions=output_attentions
)
hidden_states = layer_ret[0]
if use_cache:
presents = presents + (layer_ret[1],)
if output_attentions:
all_self_attentions = all_self_attentions + (layer_ret[2 if use_cache else 1],)
# Final layer norm.
hidden_states = self.final_layernorm(hidden_states)
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
if not return_dict:
return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)
return BaseModelOutputWithPast(
last_hidden_state=hidden_states,
past_key_values=presents,
hidden_states=all_hidden_states,
attentions=all_self_attentions,
)
class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
def __init__(self, config: ChatGLMConfig):
super().__init__(config)
# self.hidden_size = config.hidden_size
# self.params_dtype = torch.half
# self.vocab_size = config.vocab_size
self.max_sequence_length = config.max_sequence_length
self.position_encoding_2d = config.position_encoding_2d
self.transformer = ChatGLMModel(config)
self.lm_head = skip_init(
nn.Linear,
config.hidden_size,
config.vocab_size,
bias=False,
dtype=torch.half
)
self.config = config
self.quantized = False
if self.config.quantization_bit:
self.quantize(self.config.quantization_bit, empty_init=True)
def get_output_embeddings(self):
return self.lm_head
def set_output_embeddings(self, new_embeddings):
self.lm_head = new_embeddings
def get_masks_and_position_ids(self, input_ids, mask_positions, device, gmask=False):
batch_size, seq_length = input_ids.shape
context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
attention_mask = torch.ones((batch_size, seq_length, seq_length), device=device)
attention_mask.tril_()
for i, context_length in enumerate(context_lengths):
attention_mask[i, :, :context_length] = 1
attention_mask.unsqueeze_(1)
attention_mask = (attention_mask < 0.5).bool()
batch_size, seq_length = input_ids.shape
context_lengths = [seq.tolist().index(self.config.bos_token_id) for seq in input_ids]
if self.position_encoding_2d:
position_ids = torch.arange(seq_length, dtype=torch.long, device=device).expand(batch_size, seq_length)
if not gmask:
for i, context_length in enumerate(context_lengths):
position_ids[i, context_length:] = mask_positions[i]
block_position_ids = [torch.cat((
torch.zeros(context_length, dtype=torch.long, device=device),
torch.arange(seq_length - context_length, dtype=torch.long, device=device) + 1
)) for context_length in context_lengths]
block_position_ids = torch.stack(block_position_ids, dim=0)
position_ids = torch.stack((position_ids, block_position_ids), dim=1)
else:
position_ids = torch.arange(seq_length, dtype=torch.long, device=device).expand(batch_size, seq_length)
if not gmask:
for i, context_length in enumerate(context_lengths):
position_ids[context_length:] = mask_positions[i]
return attention_mask, position_ids
def prepare_inputs_for_generation(
self,
input_ids: torch.LongTensor,
past: Optional[torch.Tensor] = None,
past_key_values: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
**kwargs
) -> dict:
batch_size, seq_length = input_ids.shape
MASK, gMASK = 150000, 150001
mask_token = MASK if MASK in input_ids else gMASK
use_gmask = False if MASK in input_ids else gMASK
seqs = input_ids.tolist()
mask_positions = [seq.index(mask_token) for seq in seqs]
# only last token for input_ids if past is not None
if past is not None or past_key_values is not None:
context_lengths = [seq.index(self.config.bos_token_id) for seq in seqs]
last_token = input_ids[:, -1].unsqueeze(-1)
if self.position_encoding_2d:
position_ids = torch.tensor(
[[mask_position, seq_length - context_length] for mask_position, context_length in
zip(mask_positions, context_lengths)], dtype=torch.long, device=input_ids.device).unsqueeze(-1)
else:
position_ids = torch.tensor([mask_position for mask_position in mask_positions], dtype=torch.long,
device=input_ids.device).unsqueeze(-1)
if past is None:
past = past_key_values
return {
"input_ids": last_token,
"past_key_values": past,
"position_ids": position_ids,
}
else:
attention_mask, position_ids = self.get_masks_and_position_ids(
input_ids,
mask_positions=mask_positions,
device=input_ids.device,
gmask=use_gmask
)
return {
"input_ids": input_ids,
"past_key_values": past,
"position_ids": position_ids,
"attention_mask": attention_mask
}
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
use_cache: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
):
use_cache = use_cache if use_cache is not None else self.config.use_cache
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
transformer_outputs = self.transformer(
input_ids=input_ids,
position_ids=position_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
hidden_states = transformer_outputs[0]
lm_logits = self.lm_head(hidden_states).permute(1, 0, 2).contiguous()
loss = None
if labels is not None:
lm_logits = lm_logits.to(torch.float32)
# Shift so that tokens < n predict n
shift_logits = lm_logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
# Flatten the tokens
loss_fct = CrossEntropyLoss(ignore_index=self.config.pad_token_id)
loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
lm_logits = lm_logits.to(hidden_states.dtype)
loss = loss.to(hidden_states.dtype)
if not return_dict:
output = (lm_logits,) + transformer_outputs[1:]
return ((loss,) + output) if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=lm_logits,
past_key_values=transformer_outputs.past_key_values,
hidden_states=transformer_outputs.hidden_states,
attentions=transformer_outputs.attentions,
)
@staticmethod
def _reorder_cache(
past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
"""
This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
[`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
Output shares the same memory storage as `past`.
"""
return tuple(
(
layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)),
layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)),
)
for layer_past in past
)
def process_response(self, response):
response = response.strip()
response = response.replace("[[训练时间]]", "2023年")
punkts = [
[",", ","],
["!", "!"],
[":", ":"],
[";", ";"],
["\?", "?"],
]
for item in punkts:
response = re.sub(r"([\u4e00-\u9fff])%s" % item[0], r"\1%s" % item[1], response)
response = re.sub(r"%s([\u4e00-\u9fff])" % item[0], r"%s\1" % item[1], response)
return response
@torch.no_grad()
def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048, num_beams=1,
do_sample=True, top_p=0.7, temperature=0.95, logits_processor=None, **kwargs):
if history is None:
history = []
if logits_processor is None:
logits_processor = LogitsProcessorList()
logits_processor.append(InvalidScoreLogitsProcessor())
gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
"temperature": temperature, "logits_processor": logits_processor, **kwargs}
if not history:
prompt = query
else:
prompt = ""
for i, (old_query, response) in enumerate(history):
prompt += "[Round {}]\n问:{}\n答:{}\n".format(i, old_query, response)
prompt += "[Round {}]\n问:{}\n答:".format(len(history), query)
input_ids = tokenizer([prompt], return_tensors="pt", padding=True)
input_ids = input_ids.to(self.device)
outputs = self.generate(**input_ids, **gen_kwargs)
outputs = outputs.tolist()[0][len(input_ids["input_ids"][0]):]
response = tokenizer.decode(outputs)
response = self.process_response(response)
history = history + [(query, response)]
return response, history
@torch.no_grad()
def stream_chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048,
do_sample=True, top_p=0.7, temperature=0.95, logits_processor=None, **kwargs):
if history is None:
history = []
if logits_processor is None:
logits_processor = LogitsProcessorList()
logits_processor.append(InvalidScoreLogitsProcessor())
gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p,
"temperature": temperature, "logits_processor": logits_processor, **kwargs}
if not history:
prompt = query
else:
prompt = ""
for i, (old_query, response) in enumerate(history):
prompt += "[Round {}]\n问:{}\n答:{}\n".format(i, old_query, response)
prompt += "[Round {}]\n问:{}\n答:".format(len(history), query)
input_ids = tokenizer([prompt], return_tensors="pt", padding=True)
input_ids = input_ids.to(self.device)
for outputs in self.stream_generate(**input_ids, **gen_kwargs):
outputs = outputs.tolist()[0][len(input_ids["input_ids"][0]):]
response = tokenizer.decode(outputs)
response = self.process_response(response)
new_history = history + [(query, response)]
yield response, new_history
@torch.no_grad()
def stream_generate(
self,
input_ids,
generation_config: Optional[GenerationConfig] = None,
logits_processor: Optional[LogitsProcessorList] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
**kwargs,
):
batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
if generation_config is None:
generation_config = self.generation_config
generation_config = copy.deepcopy(generation_config)
model_kwargs = generation_config.update(**kwargs)
bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
if isinstance(eos_token_id, int):
eos_token_id = [eos_token_id]
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
if has_default_max_length and generation_config.max_new_tokens is None:
warnings.warn(
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
" recommend using `max_new_tokens` to control the maximum length of the generation.",
UserWarning,
)
elif generation_config.max_new_tokens is not None:
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
if not has_default_max_length:
logger.warn(
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
"Please refer to the documentation for more information. "
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
UserWarning,
)
if input_ids_seq_length >= generation_config.max_length:
input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
logger.warning(
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
" increasing `max_new_tokens`."
)
# 2. Set generation parameters if not already defined
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
logits_processor = self._get_logits_processor(
generation_config=generation_config,
input_ids_seq_length=input_ids_seq_length,
encoder_input_ids=input_ids,
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
logits_processor=logits_processor,
)
stopping_criteria = self._get_stopping_criteria(
generation_config=generation_config, stopping_criteria=stopping_criteria
)
logits_warper = self._get_logits_warper(generation_config)
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
scores = None
while True:
model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
# forward pass to get next token
outputs = self(
**model_inputs,
return_dict=True,
output_attentions=False,
output_hidden_states=False,
)
next_token_logits = outputs.logits[:, -1, :]
# pre-process distribution
next_token_scores = logits_processor(input_ids, next_token_logits)
next_token_scores = logits_warper(input_ids, next_token_scores)
# sample
probs = nn.functional.softmax(next_token_scores, dim=-1)
if generation_config.do_sample:
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
else:
next_tokens = torch.argmax(probs, dim=-1)
# update generated ids, model inputs, and length for next step
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
model_kwargs = self._update_model_kwargs_for_generation(
outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
)
unfinished_sequences = unfinished_sequences.mul((sum(next_tokens != i for i in eos_token_id)).long())
# stop when each sentence is finished, or if we exceed the maximum length
if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
break
yield input_ids
def quantize(self, bits: int, empty_init=False, **kwargs):
if bits == 0:
return
from .quantization import quantize
if self.quantized:
logger.info("Already quantized.")
return self
self.quantized = True
self.config.quantization_bit = bits
self.transformer = quantize(self.transformer, bits, empty_init=empty_init, **kwargs)
return self |