File size: 92,827 Bytes
5268fef |
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 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 |
# coding=utf-8
# Copyright 2023 The OpenAI Authors and The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Flax whisper model."""
import random
from functools import partial
from typing import Dict, Optional, Tuple, Union
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
from flax.linen import combine_masks, make_causal_mask
from flax.linen.attention import dot_product_attention_weights
from flax.linen.partitioning import remat, scan_with_axes
from flax.traverse_util import flatten_dict, unflatten_dict
from jax import lax
from jax.random import PRNGKey
from transformers import WhisperConfig
from transformers.generation.flax_logits_process import (
FlaxLogitsProcessor,
FlaxLogitsProcessorList,
FlaxWhisperTimeStampLogitsProcessor,
)
from transformers.modeling_flax_outputs import (
FlaxBaseModelOutput,
FlaxBaseModelOutputWithPastAndCrossAttentions,
FlaxCausalLMOutputWithCrossAttentions,
FlaxSeq2SeqLMOutput,
FlaxSeq2SeqModelOutput,
)
from transformers.modeling_flax_utils import (
ACT2FN,
FlaxPreTrainedModel,
append_call_sample_docstring,
append_replace_return_docstrings,
overwrite_call_docstring,
)
from transformers.utils import (
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from .layers import Conv, DenseGeneral, Embed, LayerNorm, with_sharding_constraint
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "openai/whisper-tiny"
_CONFIG_FOR_DOC = "WhisperConfig"
WHISPER_START_DOCSTRING = r"""
This model inherits from [`FlaxPreTrainedModel`]. Check the superclass documentation for the generic methods the
library implements for all its models (such as downloading or saving, resizing the input embeddings, pruning heads
etc.) This model is also a Flax Linen
[flax.nn.Module](https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html) subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.
Finally, this model supports inherent JAX features such as:
- [Just-In-Time (JIT) compilation](https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit)
- [Automatic Differentiation](https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation)
- [Vectorization](https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap)
- [Parallelization](https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap)
Parameters:
config ([`WhisperConfig`]): 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 [`~FlaxPreTrainedModel.from_pretrained`] method to load the model weights.
dtype (`jax.numpy.dtype`, *optional*, defaults to `jax.numpy.float32`):
The data type of the computation. Can be one of `jax.numpy.float32`, `jax.numpy.float16` (on GPUs) and
`jax.numpy.bfloat16` (on TPUs). This can be used to enable mixed-precision training or half-precision
inference on GPUs or TPUs. If specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.** If you wish to change the dtype of the model parameters, see [`~FlaxPreTrainedModel.to_fp16`]
and [`~FlaxPreTrainedModel.to_bf16`].
"""
WHISPER_INPUTS_DOCSTRING = r"""
Args:
input_features (`numpy.ndarray` of shape `(batch_size, feature_size, sequence_length)`):
Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
[`WhisperFeatureExtractor`] should be used for extracting the features, padding and conversion into a
tensor of type `numpy.ndarray`. See [`~WhisperFeatureExtractor.__call__`]
attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, but
is not used. By default the silence in the input log mel spectrogram are ignored.
decoder_input_ids (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using
[`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids) Whisper uses the `decoder_start_token_id` as
the starting token for `decoder_input_ids` generation.
decoder_attention_mask (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1
in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Whisper does not use `position_ids` in the encoder as `input_features` is always the same size and doesn't
use masking, but this argument is preserved for compatibility. By default the silence in the input log mel
spectrogram are ignored.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
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.
"""
WHISPER_ENCODE_INPUTS_DOCSTRING = r"""
Args:
input_features (`numpy.ndarray` of shape `(batch_size, feature_size, sequence_length)`):
Float values mel features extracted from the raw speech waveform. Raw speech waveform can be obtained by
loading a `.flac` or `.wav` audio file into an array of type `List[float]` or a `numpy.ndarray`, *e.g.* via
the soundfile library (`pip install soundfile`). To prepare the array into `input_features`, the
[`WhisperFeatureExtractor`] should be used for extracting the mel features, padding and conversion into a
tensor of type `numpy.ndarray`. See [`~WhisperFeatureExtractor.__call__`].
attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Whisper does not support masking of the `input_features`, this argument is preserved for compatibility, but
is not used. By default the silence in the input log mel spectrogram are ignored.
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.
"""
WHISPER_DECODE_INPUTS_DOCSTRING = r"""
Args:
decoder_input_ids (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`):
Indices of decoder input sequence tokens in the vocabulary. Indices can be obtained using
[`WhisperTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
[What are decoder input IDs?](../glossary#decoder-input-ids)
encoder_outputs (`tuple(tuple(numpy.ndarray)`):
Tuple consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*: `attentions`)
`last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*) is a sequence of
hidden-states at the output of the last layer of the encoder. Used in the cross-attention of the decoder.
encoder_attention_mask (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Whisper does not support masking of the `input_features`, this argument is preserved for compatibility,
but it is not used. By default the silence in the input log mel spectrogram are ignored.
decoder_attention_mask (`numpy.ndarray` of shape `(batch_size, target_sequence_length)`, *optional*):
Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
be used by default. If you want to change padding behavior, you should modify to your needs. See diagram 1
in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy.
decoder_position_ids (`numpy.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Indices of positions of each decoder input sequence tokens in the position embeddings. Selected in the
range `[0, config.max_position_embeddings - 1]`.
past_key_values (`Dict[str, numpy.ndarray]`, *optional*, returned by `init_cache` or when passing previous `past_key_values`):
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape *[batch_size, max_length]*.
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.
"""
class FlaxStaticForceTokensLogitsProcessor(FlaxLogitsProcessor):
r"""
[`FlaxLogitsProcessor`] that takes a list of pairs of integers which indicates a mapping from generation indices to
token indices that will be forced before sampling. The processor will set their log probs to 0 and all other tokens
to `-inf` so that they are sampled at their corresponding index. This is a static version of the `transformers` logit
processor [`FlaxForceTokensLogitsProcessor`] that is compatible with sharded forced tokens.
Args:
force_token_map (`list`):
Map giving token ids and indices where they will be forced to be sampled.
"""
def __init__(self, force_token_map):
# The generic `transformers` logit processor builds `force_token_array` as a dictionary - this is not a valid
# JAX type, and so we switch to using a JAX array instead
force_token_map = jnp.array(force_token_map)
# Converts the array of format [[index, token]] containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced. For XLA compatibility,
# indexes without forced tokens will have a negative value. Note that the last token we ever need to force in
# Whisper is at position 3, so we only construct an array up to this index. The native version constructs a tensor
# dynamically according to the length of the `force_token_map`. Array shapes need to be concrete for XLA compatibility,
# so this is not permitted here.
force_token_array = jnp.ones(3, dtype=jnp.int32) * -1
for index, token in force_token_map:
force_token_array = force_token_array.at[index].set(token)
self.force_token_array = jnp.int32(force_token_array)
def __call__(self, input_ids: jnp.ndarray, scores: jnp.ndarray, cur_len: int) -> jnp.ndarray:
def _force_token(generation_idx):
batch_size = scores.shape[0]
current_token = self.force_token_array[generation_idx]
new_scores = jnp.ones_like(scores, dtype=scores.dtype) * -float("inf")
updates = jnp.zeros((batch_size, 1), dtype=scores.dtype)
new_scores = lax.dynamic_update_slice(new_scores, updates, (0, current_token))
return new_scores
scores = lax.cond(
cur_len >= self.force_token_array.shape[0],
# If the current length is geq than the length of force_token_array, the processor does nothing.
lambda: scores,
# Otherwise, it may force a certain token.
lambda: lax.cond(
self.force_token_array[cur_len] >= 0,
# Only valid (positive) tokens are forced
lambda: _force_token(cur_len),
# Otherwise, the processor does nothing.
lambda: scores,
),
)
return scores
class FlaxWhisperAttention(nn.Module):
config: WhisperConfig
embed_dim: int
num_heads: int
dropout: float = 0.0
causal: bool = False
bias: bool = True
dtype: jnp.dtype = jnp.float32
params_dtype: jnp.dtype = jnp.float32
def setup(self) -> None:
self.head_dim = self.embed_dim // self.num_heads
if self.head_dim * self.num_heads != self.embed_dim:
raise ValueError(
"embed_dim must be divisible by num_heads (got `embed_dim`:"
f" {self.embed_dim} and `num_heads`: {self.num_heads})."
)
dense = partial(
DenseGeneral,
self.embed_dim,
axis=-1,
dtype=self.dtype,
params_dtype=self.params_dtype,
kernel_axes=("embed", "joined_kv"),
)
self.q_proj = dense(use_bias=self.bias)
self.k_proj = dense(use_bias=False)
self.v_proj = dense(use_bias=self.bias)
self.out_proj = DenseGeneral(
self.embed_dim,
axis=-1,
dtype=self.dtype,
params_dtype=self.params_dtype,
kernel_axes=("joined_kv", "embed"),
use_bias=self.bias,
)
if self.causal:
self.causal_mask = make_causal_mask(
jnp.ones((1, self.config.max_target_positions), dtype="bool"),
dtype="bool",
)
def __call__(
self,
hidden_states: jnp.ndarray,
key_value_states: Optional[jnp.ndarray] = None,
attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
is_cross_attention = key_value_states is not None
batch_size = hidden_states.shape[0]
query_states = self.q_proj(hidden_states)
if is_cross_attention:
key_states = self.k_proj(key_value_states)
value_states = self.v_proj(key_value_states)
else:
key_states = self.k_proj(hidden_states)
value_states = self.v_proj(hidden_states)
query_states = self._split_heads(query_states)
key_states = self._split_heads(key_states)
value_states = self._split_heads(value_states)
query_states = with_sharding_constraint(query_states, ("batch", "length", "heads", "kv"))
key_states = with_sharding_constraint(key_states, ("batch", "length", "heads", "kv"))
value_states = with_sharding_constraint(value_states, ("batch", "length", "heads", "kv"))
if self.causal:
query_length, key_length = query_states.shape[1], key_states.shape[1]
if self.has_variable("cache", "cached_key"):
mask_shift = self.variables["cache"]["cache_index"]
# max_length of cached_key is last dim
max_decoder_length = self.variables["cache"]["cached_key"].shape[-1]
causal_mask = lax.dynamic_slice(
self.causal_mask,
(0, 0, mask_shift, 0),
(1, 1, query_length, max_decoder_length),
)
else:
causal_mask = self.causal_mask[:, :, :query_length, :key_length]
causal_mask = jnp.broadcast_to(causal_mask, (batch_size,) + causal_mask.shape[1:])
# combine masks if needed
if attention_mask is not None and self.causal:
attention_mask = jnp.broadcast_to(jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape)
attention_mask = combine_masks(attention_mask, causal_mask)
elif self.causal:
attention_mask = causal_mask
elif attention_mask is not None:
attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
key_states, value_states, attention_mask = self._concatenate_to_cache(
key_states, value_states, query_states, attention_mask
)
# Convert the boolean attention mask to an attention bias.
if attention_mask is not None:
# attention mask in the form of attention bias
attention_bias = lax.select(
attention_mask > 0,
jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
jnp.full(attention_mask.shape, jnp.finfo(self.dtype).min).astype(self.dtype),
)
else:
attention_bias = None
dropout_rng = None
if not deterministic and self.dropout > 0.0:
dropout_rng = self.make_rng("dropout")
attn_weights = dot_product_attention_weights(
query_states,
key_states,
bias=attention_bias,
dropout_rng=dropout_rng,
dropout_rate=self.dropout,
broadcast_dropout=True,
deterministic=deterministic,
dtype=self.dtype,
precision=None,
)
attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
attn_output = self._merge_heads(attn_output)
attn_output = self.out_proj(attn_output)
return attn_output, attn_weights
def _split_heads(self, hidden_state) -> jnp.ndarray:
return hidden_state.reshape(hidden_state.shape[:2] + (self.num_heads, self.head_dim))
def _merge_heads(self, hidden_state) -> jnp.ndarray:
return hidden_state.reshape(hidden_state.shape[:2] + (self.embed_dim,))
@nn.compact
def _concatenate_to_cache(self, key, value, query, attention_mask):
# The following code is largely copied from: https://github.com/google-research/t5x/blob/63d9addf628c6d8c547a407a32095fcb527bb20b/t5x/examples/scalable_t5/layers.py#L280-L284
is_initialized = self.has_variable("cache", "cached_key")
# The key and value have dimension [batch_size, seq_length, num_heads, head_dim],
# but we cache them as [batch_size, num_heads, head_dim, seq_length] as a TPU
# fusion optimization. This also enables the "scatter via one-hot
# broadcast" trick, which means we do a one-hot broadcast instead of a
# scatter/gather operations, resulting in a 3-4x speedup in practice.
def swap_dims(x):
return x[:-3] + tuple(x[i] for i in [-2, -1, -3])
cached_key = self.variable("cache", "cached_key", jnp.zeros, swap_dims(key.shape), key.dtype)
cached_value = self.variable("cache", "cached_value", jnp.zeros, swap_dims(value.shape), value.dtype)
cache_index = self.variable("cache", "cache_index", lambda: jnp.array(0, dtype=jnp.int32))
if is_initialized:
batch_size, num_heads, head_dim, seq_length = cached_key.value.shape
# During fast autoregressive decoding, we feed one position at a time,
# and cache the keys and values step by step.
# Sanity shape check of cached key against input query.
num_updated_cache_vectors = query.shape[1]
expected_shape = (batch_size, 1, num_heads, head_dim)
if num_updated_cache_vectors == 1 and expected_shape != query.shape:
raise ValueError(
"Autoregressive cache shape error, expected query shape"
f" {expected_shape} instead got {query.shape}"
)
# Create a OHE of the current index. NOTE: the index is increased below.
cur_index = cache_index.value
# In order to update the key, value caches with the current key and
# value, we move the seq_length axis to the back, similar to what we did for
# the cached ones above.
# Note these are currently the key and value of a single position, since
# we feed one position at a time.
one_token_key = jnp.moveaxis(key, -3, -1)
one_token_value = jnp.moveaxis(value, -3, -1)
# Update key, value caches with our new 1d spatial slices.
# We implement an efficient scatter into the cache via one-hot
# broadcast and addition.
if num_updated_cache_vectors > 1:
indices = jnp.eye(num_updated_cache_vectors, seq_length)[None, None]
key = cached_key.value + jnp.matmul(one_token_key, indices)
value = cached_value.value + jnp.matmul(one_token_value, indices)
else:
one_hot_indices = jax.nn.one_hot(cur_index, seq_length, dtype=key.dtype)
key = cached_key.value + one_token_key * one_hot_indices
value = cached_value.value + one_token_value * one_hot_indices
cached_key.value = key
cached_value.value = value
cache_index.value = cache_index.value + num_updated_cache_vectors
# Move the keys and values back to their original shapes.
key = jnp.moveaxis(key, -1, -3)
value = jnp.moveaxis(value, -1, -3)
# causal mask for cached decoder self-attention: our single query position should only
# attend to those key positions that have already been generated and cached, not the
# remaining zero elements.
pad_mask = jnp.broadcast_to(
jnp.arange(seq_length) < cur_index + num_updated_cache_vectors,
(batch_size,) + (1, num_updated_cache_vectors, seq_length),
)
attention_mask = combine_masks(pad_mask, attention_mask)
return key, value, attention_mask
class FlaxWhisperEncoderLayer(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
params_dtype: jnp.dtype = jnp.float32
use_scan: bool = False
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxWhisperAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.encoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype,
params_dtype=self.params_dtype,
)
self.self_attn_layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.activation_fn = ACT2FN[self.config.activation_function]
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
self.fc1 = DenseGeneral(
self.config.encoder_ffn_dim,
dtype=self.dtype,
params_dtype=self.params_dtype,
kernel_axes=("embed", "mlp"),
)
self.fc2 = DenseGeneral(
self.embed_dim,
dtype=self.dtype,
params_dtype=self.params_dtype,
kernel_axes=("mlp", "embed"),
)
self.final_layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
output_attentions: bool = True,
deterministic: bool = True,
all_hidden_states=None, # only used when `use_scan=True` -> we have to fetch the hidden states from within the layer
) -> Tuple[jnp.ndarray]:
if self.use_scan:
hidden_states = hidden_states[0]
hidden_states = with_sharding_constraint(hidden_states, ("batch", "length", "embed"))
residual = hidden_states
layernorm_output = self.self_attn_layer_norm(hidden_states)
layernorm_output = with_sharding_constraint(layernorm_output, ("batch", "length", "embed"))
attn_output, attn_weights = self.self_attn(hidden_states=layernorm_output, attention_mask=attention_mask)
attn_output = self.dropout_layer(attn_output, deterministic=deterministic)
attn_output = residual + attn_output
attn_output = with_sharding_constraint(attn_output, ("batch", "length", "embed"))
residual = attn_output
post_layer_norm = self.final_layer_norm(attn_output)
post_layer_norm = with_sharding_constraint(post_layer_norm, ("batch", "length", "embed"))
fc1_output = self.activation_fn(self.fc1(post_layer_norm))
fc1_output = self.activation_dropout_layer(fc1_output, deterministic=deterministic)
fc1_output = with_sharding_constraint(fc1_output, ("batch", "length", "mlp"))
hidden_states = self.fc2(fc1_output)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = with_sharding_constraint(hidden_states, ("batch", "length", "embed"))
outputs = (hidden_states,)
if output_attentions:
outputs += (attn_weights,)
if self.use_scan:
if all_hidden_states is not None:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (
outputs,
all_hidden_states,
)
return outputs
class FlaxWhisperEncoderLayerCollection(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
params_dtype: jnp.dtype = jnp.float32
use_scan: bool = False
gradient_checkpointing: bool = False
@nn.compact
def __call__(
self,
hidden_states,
attention_mask,
deterministic: bool = True,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
all_attentions = () if output_attentions else None
all_hidden_states = () if output_hidden_states else None
FlaxWhisperEncoderCheckpointLayer = (
remat(
FlaxWhisperEncoderLayer,
static_argnums=(2, 3),
prevent_cse=not self.use_scan,
)
if self.gradient_checkpointing
else FlaxWhisperEncoderLayer
)
if self.use_scan:
if output_attentions:
raise ValueError("Cannot use `scan` with `output_attentions` set to True")
# nicest behaviour for scan is to let the compiler figure out the correct shapes for the hidden states
# so we'll just pass an empty tuple as the carry initializer and hold on to the first hidden states for later
input_hidden_states = hidden_states
hidden_states = (hidden_states,)
hidden_states, all_hidden_states = scan_with_axes(
FlaxWhisperEncoderCheckpointLayer,
variable_axes={"params": 0, "cache": 0},
split_rngs={"params": True, "dropout": True},
in_axes=(
nn.broadcast,
nn.broadcast,
nn.broadcast,
nn.broadcast,
),
variable_carry="all_hidden_states",
length=self.config.encoder_layers,
)(
self.config,
dtype=self.dtype,
params_dtype=self.params_dtype,
use_scan=True,
name="FlaxEncoderScanLayers",
)(
hidden_states,
attention_mask,
output_attentions,
deterministic,
all_hidden_states, # tuple intializer (or None if not using output_hidden_states)
)
# remove the scan dimension
hidden_states = hidden_states[0]
if output_hidden_states:
# if we're using scan we'll surely be training -> return hidden states as a tensor rather than tuple
all_hidden_states = jnp.vstack([input_hidden_states[None, ...], all_hidden_states[0]])
else:
for layer_idx in range(self.config.encoder_layers):
if output_hidden_states:
all_hidden_states = all_hidden_states + (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (dropout_probability < self.config.encoder_layerdrop): # skip the layer
layer_outputs = (None, None)
else:
layer_outputs = FlaxWhisperEncoderCheckpointLayer(
self.config,
dtype=self.dtype,
params_dtype=self.params_dtype,
name=str(layer_idx),
)(
hidden_states,
attention_mask,
output_attentions,
deterministic,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_attentions = all_attentions + (layer_outputs[1],)
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = (hidden_states, all_hidden_states, all_attentions)
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_attentions,
)
class FlaxWhisperDecoderLayer(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
params_dtype: jnp.dtype = jnp.float32
use_scan: bool = False
def setup(self) -> None:
self.embed_dim = self.config.d_model
self.self_attn = FlaxWhisperAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
causal=True,
dtype=self.dtype,
params_dtype=self.params_dtype,
)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.activation_fn = ACT2FN[self.config.activation_function]
self.activation_dropout_layer = nn.Dropout(rate=self.config.activation_dropout)
self.self_attn_layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
self.encoder_attn = FlaxWhisperAttention(
config=self.config,
embed_dim=self.embed_dim,
num_heads=self.config.decoder_attention_heads,
dropout=self.config.attention_dropout,
dtype=self.dtype,
params_dtype=self.params_dtype,
)
self.encoder_attn_layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
self.fc1 = DenseGeneral(
self.config.decoder_ffn_dim,
dtype=self.dtype,
params_dtype=self.params_dtype,
kernel_axes=("embed", "mlp"),
)
self.fc2 = DenseGeneral(
self.embed_dim,
dtype=self.dtype,
params_dtype=self.params_dtype,
kernel_axes=("mlp", "embed"),
)
self.final_layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
def __call__(
self,
hidden_states: jnp.ndarray,
attention_mask: jnp.ndarray,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
init_cache: bool = False,
output_attentions: bool = True,
deterministic: bool = True,
all_hidden_states=None, # only used when `use_scan=True` -> we have to fetch the hidden states from within the layer
) -> Tuple[jnp.ndarray]:
if self.use_scan:
hidden_states = hidden_states[0]
hidden_states = with_sharding_constraint(hidden_states, ("batch", "length", "embed"))
residual = hidden_states
layer_norm_output = self.self_attn_layer_norm(hidden_states)
layer_norm_output = with_sharding_constraint(layer_norm_output, ("batch", "length", "embed"))
# Self Attention
self_attn_output, self_attn_weights = self.self_attn(
hidden_states=layer_norm_output,
attention_mask=attention_mask,
init_cache=init_cache,
)
self_attn_output = self.dropout_layer(self_attn_output, deterministic=deterministic)
self_attn_output = residual + self_attn_output
self_attn_output = with_sharding_constraint(self_attn_output, ("batch", "length", "embed"))
# Cross-Attention Block
cross_attn_weights = None
if encoder_hidden_states is not None:
residual = self_attn_output
encoder_layer_norm_output = self.encoder_attn_layer_norm(self_attn_output)
encoder_layer_norm_output = with_sharding_constraint(
encoder_layer_norm_output, ("batch", "length", "embed")
)
cross_attn_output, cross_attn_weights = self.encoder_attn(
hidden_states=encoder_layer_norm_output,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
)
cross_attn_output = self.dropout_layer(cross_attn_output, deterministic=deterministic)
cross_attn_output = residual + cross_attn_output
cross_attn_output = with_sharding_constraint(cross_attn_output, ("batch", "length", "embed"))
# Fully Connected
residual = cross_attn_output
post_layer_norm = self.final_layer_norm(cross_attn_output)
post_layer_norm = with_sharding_constraint(post_layer_norm, ("batch", "length", "embed"))
fc1_output = self.activation_fn(self.fc1(post_layer_norm))
fc1_output = self.activation_dropout_layer(fc1_output, deterministic=deterministic)
fc1_output = with_sharding_constraint(fc1_output, ("batch", "length", "mlp"))
hidden_states = self.fc2(fc1_output)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
hidden_states = residual + hidden_states
hidden_states = with_sharding_constraint(hidden_states, ("batch", "length", "embed"))
outputs = (hidden_states,)
if output_attentions:
outputs += (self_attn_weights, cross_attn_weights)
if self.use_scan:
if all_hidden_states is not None:
all_hidden_states = all_hidden_states + (hidden_states,)
outputs = (
outputs,
all_hidden_states,
)
return outputs
class FlaxWhisperDecoderLayerCollection(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
params_dtype: jnp.dtype = jnp.float32
use_scan: bool = False
gradient_checkpointing: bool = False
@nn.compact
def __call__(
self,
hidden_states,
attention_mask,
encoder_hidden_states: Optional[jnp.ndarray] = None,
encoder_attention_mask: Optional[jnp.ndarray] = None,
deterministic: bool = True,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
):
# decoder layers
all_hidden_states = () if output_hidden_states else None
all_self_attns = () if output_attentions else None
all_cross_attentions = () if (output_attentions and encoder_hidden_states is not None) else None
FlaxWhisperDecoderCheckpointLayer = (
remat(
FlaxWhisperDecoderLayer,
static_argnums=(4, 5, 6),
prevent_cse=not self.use_scan,
)
if self.gradient_checkpointing
else FlaxWhisperDecoderLayer
)
if self.use_scan:
if output_attentions:
raise ValueError("Cannot use `scan` with `output_attentions` set to True")
input_hidden_states = hidden_states
hidden_states = (hidden_states,)
hidden_states, all_hidden_states = scan_with_axes(
FlaxWhisperDecoderCheckpointLayer,
variable_axes={"params": 0, "cache": 0},
split_rngs={"params": True, "dropout": True},
in_axes=(
nn.broadcast,
nn.broadcast,
nn.broadcast,
nn.broadcast,
nn.broadcast,
nn.broadcast,
nn.broadcast,
),
variable_carry="all_hidden_states",
length=self.config.decoder_layers,
)(
self.config,
dtype=self.dtype,
params_dtype=self.params_dtype,
use_scan=True,
name="FlaxDecoderScanLayers",
)(
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
init_cache,
output_attentions,
deterministic,
all_hidden_states,
)
hidden_states = hidden_states[0]
if output_hidden_states:
# if we're using scan we'll surely be training -> return hidden states as a tensor rather than tuple
all_hidden_states = jnp.vstack([input_hidden_states[None, ...], all_hidden_states[0]])
else:
for layer_idx in range(self.config.decoder_layers):
if output_hidden_states:
all_hidden_states += (hidden_states,)
# add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
dropout_probability = random.uniform(0, 1)
if not deterministic and (dropout_probability < self.config.decoder_layerdrop):
layer_outputs = (None, None, None)
else:
layer_outputs = FlaxWhisperDecoderCheckpointLayer(
self.config,
dtype=self.dtype,
params_dtype=self.params_dtype,
name=str(layer_idx),
)(
hidden_states,
attention_mask,
encoder_hidden_states,
encoder_attention_mask,
init_cache,
output_attentions,
deterministic,
)
hidden_states = layer_outputs[0]
if output_attentions:
all_self_attns += (layer_outputs[1],)
if encoder_hidden_states is not None:
all_cross_attentions += (layer_outputs[2],)
# add hidden states from the last decoder layer
if output_hidden_states:
all_hidden_states += (hidden_states,)
outputs = [
hidden_states,
all_hidden_states,
all_self_attns,
all_cross_attentions,
]
if not return_dict:
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=hidden_states,
hidden_states=all_hidden_states,
attentions=all_self_attns,
cross_attentions=all_cross_attentions,
)
class FlaxWhisperEncoder(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
params_dtype: jnp.dtype = jnp.float32
use_scan: bool = False
gradient_checkpointing: bool = False
def setup(self) -> None:
self.conv1 = Conv(
self.config.d_model,
kernel_size=(3,),
padding=1,
dtype=self.dtype,
params_dtype=self.params_dtype,
kernel_axes=("channels", "num_mel", "embed"),
)
self.conv2 = Conv(
self.config.d_model,
kernel_size=(3,),
strides=2,
padding=1,
dtype=self.dtype,
params_dtype=self.params_dtype,
kernel_axes=("channels", "embed", "num_mel"),
)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.layers = FlaxWhisperEncoderLayerCollection(
self.config,
dtype=self.dtype,
params_dtype=self.params_dtype,
use_scan=self.use_scan,
gradient_checkpointing=self.gradient_checkpointing,
)
self.embed_positions = Embed(
self.config.max_source_positions,
self.config.d_model,
dtype=self.dtype,
params_dtype=self.params_dtype,
)
self.layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-05, params_dtype=self.params_dtype)
def __call__(
self,
input_features: jnp.ndarray,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
if input_features.shape[1:] != (
self.config.num_mel_bins,
self.config.max_source_positions * 2,
):
raise ValueError(
"input_features.shape[1:], must be equal to (self.config.num_mel_bins,"
" self.config.max_source_positions * 2) (got"
f" {input_features.shape[1:]}, but should be"
f" ({self.config.num_mel_bins},"
f" {self.config.max_source_positions * 2}))"
)
input_features = input_features.transpose(0, 2, 1)
hidden_states = jax.nn.gelu(self.conv1(input_features), approximate=False)
hidden_states = with_sharding_constraint(hidden_states, ("batch", "embed", "num_mel"))
hidden_states = jax.nn.gelu(self.conv2(hidden_states), approximate=False)
hidden_states = with_sharding_constraint(hidden_states, ("batch", "length", "embed"))
embed_positions = self.embed_positions(jnp.arange(self.config.max_source_positions))
# sinusoidal positional embeddings should not be trained
embed_positions = jax.lax.stop_gradient(embed_positions)
hidden_states = hidden_states + embed_positions
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask=None,
deterministic=deterministic,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_states = outputs[0]
last_hidden_states = self.layer_norm(last_hidden_states)
# update the last element in `hidden_states` after applying `layernorm` above
hidden_states = None
if output_hidden_states:
hidden_states = outputs[1]
if self.use_scan:
hidden_states = jnp.vstack([hidden_states[:-1], last_hidden_states[None, ...]])
else:
hidden_states = hidden_states[:-1] + (last_hidden_states,)
if not return_dict:
outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutput(
last_hidden_state=last_hidden_states,
hidden_states=hidden_states,
attentions=outputs.attentions,
)
class FlaxWhisperDecoder(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
params_dtype: jnp.dtype = jnp.float32
use_scan: bool = False
gradient_checkpointing: bool = False
def setup(self) -> None:
self.embed_tokens = Embed(
self.config.vocab_size,
self.config.d_model,
dtype=self.dtype,
params_dtype=self.params_dtype,
)
self.embed_positions = Embed(
self.config.max_target_positions,
self.config.d_model,
dtype=self.dtype,
params_dtype=self.params_dtype,
)
self.layers = FlaxWhisperDecoderLayerCollection(
self.config,
dtype=self.dtype,
params_dtype=self.params_dtype,
use_scan=self.use_scan,
gradient_checkpointing=self.gradient_checkpointing,
)
self.dropout_layer = nn.Dropout(rate=self.config.dropout)
self.layer_norm = LayerNorm(dtype=self.dtype, epsilon=1e-5, params_dtype=self.params_dtype)
def __call__(
self,
input_ids: jnp.ndarray,
attention_mask: jnp.ndarray,
position_ids: jnp.ndarray,
encoder_hidden_states: Optional[jnp.ndarray] = None,
freeze_embeddings: Optional[bool] = False,
init_cache: bool = False,
output_attentions: bool = False,
output_hidden_states: bool = False,
return_dict: bool = True,
deterministic: bool = True,
) -> Tuple[jnp.ndarray]:
input_embeds = self.embed_tokens(input_ids)
position_embeds = self.embed_positions(position_ids)
hidden_states = input_embeds + position_embeds
if freeze_embeddings:
hidden_states = jax.lax.stop_gradient(hidden_states)
hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
outputs = self.layers(
hidden_states,
attention_mask=attention_mask,
encoder_hidden_states=encoder_hidden_states,
deterministic=deterministic,
init_cache=init_cache,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
last_hidden_states = outputs[0]
last_hidden_states = self.layer_norm(last_hidden_states)
# update the last element in `hidden_states` after applying `layernorm` above
hidden_states = None
if output_hidden_states:
hidden_states = outputs[1]
if self.use_scan:
hidden_states = jnp.vstack([hidden_states[:-1], last_hidden_states[None, ...]])
else:
hidden_states = hidden_states[:-1] + (last_hidden_states,)
if not return_dict:
outputs = (last_hidden_states, hidden_states) + (outputs[2:] if output_hidden_states else outputs[1:])
return tuple(v for v in outputs if v is not None)
return FlaxBaseModelOutputWithPastAndCrossAttentions(
last_hidden_state=last_hidden_states,
hidden_states=hidden_states,
attentions=outputs.attentions,
cross_attentions=outputs.cross_attentions,
)
class FlaxWhisperModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
params_dtype: jnp.dtype = jnp.float32
use_scan: bool = False
gradient_checkpointing: bool = False
def setup(self) -> None:
self.encoder = FlaxWhisperEncoder(
self.config,
dtype=self.dtype,
params_dtype=self.params_dtype,
use_scan=self.use_scan,
gradient_checkpointing=self.gradient_checkpointing,
)
self.decoder = FlaxWhisperDecoder(
self.config,
dtype=self.dtype,
params_dtype=self.params_dtype,
use_scan=self.use_scan,
gradient_checkpointing=self.gradient_checkpointing,
)
def __call__(
self,
input_features: jnp.ndarray,
decoder_input_ids: jnp.ndarray,
decoder_attention_mask: jnp.ndarray,
decoder_position_ids: jnp.ndarray,
output_attentions: bool = False,
output_hidden_states: bool = False,
freeze_encoder: bool = False,
freeze_embeddings: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
encoder_outputs = self.encoder(
input_features,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
encoder_hidden_states = encoder_outputs[0]
if freeze_encoder:
encoder_hidden_states = jax.lax.stop_gradient(encoder_hidden_states)
decoder_outputs = self.decoder(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
encoder_hidden_states=encoder_hidden_states,
freeze_embeddings=freeze_embeddings,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=deterministic,
)
if not return_dict:
return decoder_outputs + encoder_outputs
return FlaxSeq2SeqModelOutput(
last_hidden_state=decoder_outputs.last_hidden_state,
decoder_hidden_states=decoder_outputs.hidden_states,
decoder_attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
encoder_last_hidden_state=encoder_outputs.last_hidden_state,
encoder_hidden_states=encoder_outputs.hidden_states,
encoder_attentions=encoder_outputs.attentions,
)
def _get_encoder_module(self):
return self.encoder
def _get_decoder_module(self):
return self.decoder
class FlaxWhisperPreTrainedModel(FlaxPreTrainedModel):
config_class = WhisperConfig
base_model_prefix: str = "model"
main_input_name = "input_features"
module_class: nn.Module = None
def __init__(
self,
config: WhisperConfig,
input_shape: Tuple[int, int, int] = None,
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
params_dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
# Can only use_scan=True in init if loading scanned weights -> need to handle use_scan=True and unrolled weights
use_scan: bool = False,
gradient_checkpointing: bool = False,
**kwargs,
):
self.use_scan = use_scan
self.gradient_checkpointing = gradient_checkpointing
module = self.module_class(
config=config,
dtype=dtype,
params_dtype=params_dtype,
use_scan=use_scan,
gradient_checkpointing=gradient_checkpointing,
**kwargs,
)
if input_shape is None:
input_shape = (1, config.num_mel_bins, 2 * config.max_source_positions)
super().__init__(
config,
module,
input_shape=input_shape,
seed=seed,
dtype=dtype,
_do_init=_do_init,
)
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> FrozenDict:
# init input tensors
input_features = jnp.zeros(input_shape, dtype="f4")
input_features = input_features.at[(..., -1)].set(self.config.eos_token_id)
decoder_input_ids = jnp.zeros((input_shape[0], 1), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
batch_size, sequence_length = decoder_input_ids.shape
decoder_position_ids = jnp.broadcast_to(jnp.arange(sequence_length)[None, :], (batch_size, sequence_length))
params_rng, dropout_rng = jax.random.split(rng)
rngs = {"params": params_rng, "dropout": dropout_rng}
random_params = self.module.init(
rngs,
input_features=input_features,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
)["params"]
if params is not None:
random_params = flatten_dict(unfreeze(random_params))
params = flatten_dict(unfreeze(params))
for missing_key in self._missing_keys:
params[missing_key] = random_params[missing_key]
self._missing_keys = set()
return freeze(unflatten_dict(params))
else:
return random_params
def enable_gradient_checkpointing(self):
self.gradient_checkpointing = True
self._module = self.module_class(
config=self.config,
dtype=self.dtype,
use_scan=self.use_scan,
gradient_checkpointing=self.gradient_checkpointing,
)
def enable_scan(self):
self.use_scan = True
self._module = self.module_class(
config=self.config,
dtype=self.dtype,
use_scan=self.use_scan,
gradient_checkpointing=self.gradient_checkpointing,
)
init_fn = partial(self.init_weights, input_shape=self.input_shape)
params_shape_tree = jax.eval_shape(init_fn, self.key)
# get the shape of the parameters
self._params_shape_tree = params_shape_tree
# save required_params as set
self._required_params = set(flatten_dict(unfreeze(params_shape_tree)).keys())
# initialize the parameters
if self._is_initialized:
self.params = self.convert_unroll_to_scan(self.params)
def disable_scan(self):
self.use_scan = False
self._module = self.module_class(
config=self.config,
dtype=self.dtype,
use_scan=self.use_scan,
gradient_checkpointing=self.gradient_checkpointing,
)
init_fn = partial(self.init_weights, input_shape=self.input_shape)
params_shape_tree = jax.eval_shape(init_fn, self.key)
# get the shape of the parameters
self._params_shape_tree = params_shape_tree
# save required_params as set
self._required_params = set(flatten_dict(unfreeze(params_shape_tree)).keys())
# initialize the parameters
if self._is_initialized:
self.params = self.convert_scan_to_unroll(self.params)
def convert_unroll_to_scan(self, params: Union[Dict, FrozenDict]):
r"""
Convert a `PyTree` of unrolled model parameters to a scanned block of model parameters. This method can be used
to explicitly convert the model parameters to scanned format. This returns a new `params` tree and does not
convert the `params` in place.
To illustrate the workings of this method, take the Flax BERT model. The unrolled structure for the query
projection params is as follows:
('bert', 'encoder', 'layer', '0', 'self_attn', 'q_proj') ('bert', 'encoder', 'layer', '1', 'self_attn',
'q_proj') ... ('bert', 'encoder', 'layer', '23', 'self_attn', 'q_proj')
This method takes each of the `q_proj` matrices for layers (0, ..., 23) and stacks them into a single 'super'
matrix, giving a *single* block of weights for all 24 layers compatible with the scanned model:
('bert', 'encoder', 'layer', 'ScanLayers', 'self_attn', 'q_proj')
When enabling scan with _do_init=True (default), this method will be called automatically under the hood. With
_do_init=False, it will have to be called explicitly (see example below).
Arguments:
params (`Union[Dict, FrozenDict]`):
A `PyTree` of model parameters.
Examples:
```python
>>> from distil_whisper import FlaxWhisperForConditionalGeneration
>>> # Download model and configuration from huggingface.co
>>> model, params = FlaxWhisperModel.from_pretrained("openai/whisper-tiny.en", _do_init=False)
>>> # By default, the model params will be in unrolled format. To illustrate the use of this method,
>>> # we'll first convert to scan format and then back to unrolled
>>> model.enable_scan()
>>> params = model.convert_unroll_to_scan(params)
>>> # now convert back to unrolled
>>> model.disable_scan()
>>> params = model.convert_scan_to_unroll(params)
```"""
if isinstance(params, FrozenDict):
params = unfreeze(params)
params = flatten_dict(params, sep="/")
keys = list(params.keys())
for k in keys:
# Identify all "unrolled" layers formed as part of the FlaxBertLayerCollection
# These params contain the identifier `layer` in their key
if "layers/0" in k:
if "decoder" in k:
block_prefix = "Decoder"
num_hidden_layers = self.config.decoder_layers
else:
block_prefix = "Encoder"
num_hidden_layers = self.config.encoder_layers
# Squash the keys for the N unrolled layers into one single key:
# (layer/0, ..., layer/N) -> layer/FlaxScanLayers
scan_key = k.replace("0", f"Flax{block_prefix}ScanLayers")
stacked_params = []
# Iterate over the unrolled layers (1,...,N)
for i in range(num_hidden_layers):
# Stack the params for the N layers into one super block
# and remove the unrolled layer params on the fly
# -> no memory overhead for conversion!
unrolled_layer = params.pop(k.replace("0", str(i)))
stacked_params.append(unrolled_layer)
params[scan_key] = jnp.stack(stacked_params)
# Finally, unflatten the dict to restore the nested pytree structure
params = unflatten_dict(params, sep="/")
return params
def convert_scan_to_unroll(self, params: Union[Dict, FrozenDict]):
r"""
Convert a `PyTree` of scanned model parameters to an unrolled stack of model parameters. This method can be
used to explicitly convert the model parameters to unrolled format. This returns a new `params` tree and does
not convert the `params` in place.
To illustrate the workings of this method, take the Flax BERT model. The scanned structure for the query
projection (`q_proj`) params is a single, stacked matrix of parameters over all N layers:
('bert', 'encoder', 'layer', 'FlaxScanLayers', 'self_attn', 'q_proj')
This method slices each layer of the `q_proj` scanned matrix into single, standalone layers, and replaces the
scanned matrix of parameteres on the fly:
('bert', 'encoder', 'layer', '0', 'self_attn', 'q_proj') ('bert', 'encoder', 'layer', '1', 'self_attn',
'q_proj') ... ('bert', 'encoder', 'layer', 'N', 'self_attn', 'q_proj')
When enabling scan with _do_init=True (default), this method will be called automatically under the hood. With
_do_init=False, it will have to be called explicitly (see example below).
Arguments:
params (`Union[Dict, FrozenDict]`):
A `PyTree` of model parameters.
Examples:
```python
>>> from distil_whisper import FlaxWhisperForConditionalGeneration
>>> # Download model and configuration from huggingface.co
>>> model, params = FlaxWhisperModel.from_pretrained("openai/whisper-tiny.en", _do_init=False)
>>> # By default, the model params will be in unrolled format. To illustrate the use of this method,
>>> # we'll first convert to scan format and then back to unrolled
>>> model.enable_scan()
>>> params = model.convert_unroll_to_scan(params)
>>> # now convert back to unrolled
>>> model.disable_scan()
>>> params = model.convert_scan_to_unroll(params)
```"""
if isinstance(params, FrozenDict):
params = unfreeze(params)
params = flatten_dict(params, sep="/")
keys = list(params.keys())
for k in keys:
# Identify all "scan" layers formed as part of the FlaxBertLayerCollection
# These params contain the identifier `FlaxScanLayers` in their key
if "FlaxEncoderScanLayers" in k:
# Remove the scan layer from the PyTree of params
scan_layer = params.pop(k)
# Unroll the key for the stacked scan matrix into N separate keys, indexed by layer number
# layer/FlaxScanLayers -> (layer/0, ..., layer/N)
for i in range(self.config.encoder_layers):
# Unstack the params for the i-th scan layer to unrolled
# and remove corresponding scan params on the fly
# -> no memory overhead for conversion!
unrolled_key = k.replace("FlaxEncoderScanLayers", str(i))
params[unrolled_key], scan_layer = scan_layer[0], scan_layer[1:]
elif "FlaxDecoderScanLayers" in k:
# Remove the scan layer from the PyTree of params
scan_layer = params.pop(k)
# Unroll the key for the stacked scan matrix into N separate keys, indexed by layer number
# layer/FlaxScanLayers -> (layer/0, ..., layer/N)
for i in range(self.config.decoder_layers):
# Unstack the params for the i-th scan layer to unrolled
# and remove corresponding scan params on the fly
# -> no memory overhead for conversion!
unrolled_key = k.replace("FlaxDecoderScanLayers", str(i))
params[unrolled_key], scan_layer = scan_layer[0], scan_layer[1:]
params = unflatten_dict(params, sep="/")
return params
# Copied from transformers.models.whisper.modeling_flax_whisper.FlaxWhisperPreTrainedModel.init_cache
def init_cache(self, batch_size, max_length, encoder_outputs):
r"""
Args:
batch_size (`int`):
batch_size used for fast auto-regressive decoding. Defines the batch size of the initialized cache.
max_length (`int`):
maximum possible length for auto-regressive decoding. Defines the sequence length of the initialized
cache.
encoder_outputs (`Union[FlaxBaseModelOutput, tuple(tuple(jnp.ndarray)]`):
`encoder_outputs` consists of (`last_hidden_state`, *optional*: `hidden_states`, *optional*:
`attentions`). `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)`, *optional*)
is a sequence of hidden-states at the output of the last layer of the encoder. Used in the
cross-attention of the decoder.
"""
# init input variables to retrieve cache
decoder_input_ids = jnp.ones((batch_size, max_length), dtype="i4")
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
decoder_position_ids = jnp.broadcast_to(
jnp.arange(jnp.atleast_2d(decoder_input_ids).shape[-1]),
decoder_input_ids.shape,
)
def _decoder_forward(
module,
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
):
decoder_module = module._get_decoder_module()
return decoder_module(
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
)
init_variables = self.module.init(
jax.random.PRNGKey(0),
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
encoder_hidden_states=encoder_outputs[0],
init_cache=True,
method=_decoder_forward, # we only need to call the decoder to init the cache
)
return unfreeze(init_variables["cache"])
@add_start_docstrings(WHISPER_ENCODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxBaseModelOutput, config_class=WhisperConfig)
def encode(
self,
input_features: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
**kwargs,
):
r"""
Returns:
Example:
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
>>> input_features = inputs.input_features
>>> encoder_outputs = model.encode(input_features=input_features)
```"""
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
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
def _encoder_forward(module, input_features, **kwargs):
encode_module = module._get_encoder_module()
return encode_module(input_features, **kwargs)
return self.module.apply(
{"params": params or self.params},
input_features=jnp.array(input_features, dtype="f4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
method=_encoder_forward,
)
@add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(
output_type=FlaxBaseModelOutputWithPastAndCrossAttentions,
config_class=WhisperConfig,
)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
freeze_embeddings: Optional[bool] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
>>> input_features = inputs.input_features
>>> encoder_outputs = model.encode(input_features=input_features)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
```"""
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
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
encoder_hidden_states = encoder_outputs[0]
batch_size, sequence_length = decoder_input_ids.shape
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
if decoder_attention_mask is not None:
decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
else:
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length))
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by FlaxWhisperAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(
module,
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
):
decoder_module = module._get_decoder_module()
return decoder_module(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
**kwargs,
)
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
freeze_embeddings=freeze_embeddings,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs, past = outputs
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs, past = outputs
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
@add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
def __call__(
self,
input_features: jnp.ndarray,
decoder_input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
freeze_encoder: Optional[bool] = None,
freeze_embeddings: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
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
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
# prepare decoder inputs
if decoder_position_ids is None:
if decoder_attention_mask is not None:
decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
else:
batch_size, sequence_length = decoder_input_ids.shape
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones_like(decoder_input_ids)
# Handle any PRNG if needed
rngs = {"dropout": dropout_rng} if dropout_rng is not None else {}
return self.module.apply(
{"params": params or self.params},
input_features=jnp.array(input_features, dtype="f4"),
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
freeze_encoder=freeze_encoder,
freeze_embeddings=freeze_embeddings,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
)
@add_start_docstrings(
("The bare Whisper Model transformer outputting raw hidden-states without any specific head on top."),
WHISPER_START_DOCSTRING,
)
class FlaxWhisperModel(FlaxWhisperPreTrainedModel):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32 # the dtype of the computation
params_dtype: jnp.dtype = jnp.float32
module_class = FlaxWhisperModule
append_call_sample_docstring(FlaxWhisperModel, _CHECKPOINT_FOR_DOC, FlaxSeq2SeqModelOutput, _CONFIG_FOR_DOC)
class FlaxWhisperForConditionalGenerationModule(nn.Module):
config: WhisperConfig
dtype: jnp.dtype = jnp.float32
params_dtype: jnp.dtype = jnp.float32
use_scan: bool = False
gradient_checkpointing: bool = False
def setup(self) -> None:
self.model = FlaxWhisperModule(
config=self.config,
dtype=self.dtype,
params_dtype=self.params_dtype,
use_scan=self.use_scan,
gradient_checkpointing=self.gradient_checkpointing,
)
self.lm_head = DenseGeneral(
self.config.vocab_size,
use_bias=False,
dtype=self.dtype,
params_dtype=self.params_dtype,
kernel_axes=("embed", "vocab"),
)
def _get_encoder_module(self):
return self.model.encoder
def _get_decoder_module(self):
return self.model.decoder
def __call__(
self,
input_features,
decoder_input_ids,
decoder_attention_mask: jnp.ndarray = None,
decoder_position_ids: jnp.ndarray = None,
position_ids: jnp.ndarray = None,
attention_mask: jnp.ndarray = None,
output_attentions: bool = False,
output_hidden_states: bool = False,
freeze_encoder: bool = False,
freeze_embeddings: bool = False,
return_dict: bool = True,
deterministic: bool = True,
):
outputs = self.model(
input_features=input_features,
decoder_input_ids=decoder_input_ids,
decoder_attention_mask=decoder_attention_mask,
decoder_position_ids=decoder_position_ids,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
freeze_encoder=freeze_encoder,
freeze_embeddings=freeze_embeddings,
return_dict=return_dict,
deterministic=deterministic,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = self.model.decoder.embed_tokens.variables["params"]["embedding"]
lm_logits = self.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = self.lm_head(hidden_states)
if not return_dict:
output = (lm_logits,) + outputs[1:]
return output
return FlaxSeq2SeqLMOutput(
logits=lm_logits,
decoder_hidden_states=outputs.decoder_hidden_states,
decoder_attentions=outputs.decoder_attentions,
cross_attentions=outputs.cross_attentions,
encoder_last_hidden_state=outputs.encoder_last_hidden_state,
encoder_hidden_states=outputs.encoder_hidden_states,
encoder_attentions=outputs.encoder_attentions,
)
@add_start_docstrings("The Whisper Model with a language modeling head.", WHISPER_START_DOCSTRING)
class FlaxWhisperForConditionalGeneration(FlaxWhisperPreTrainedModel):
module_class = FlaxWhisperForConditionalGenerationModule
@add_start_docstrings(WHISPER_DECODE_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=FlaxCausalLMOutputWithCrossAttentions, config_class=WhisperConfig)
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
freeze_embeddings: Optional[bool] = None,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
dropout_rng: PRNGKey = None,
):
r"""
Returns:
Example:
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
>>> input_features = inputs.input_features
>>> encoder_outputs = model.encode(input_features=input_features)
>>> decoder_start_token_id = model.config.decoder_start_token_id
>>> decoder_input_ids = jnp.ones((inputs.input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
>>> outputs = model.decode(decoder_input_ids, encoder_outputs)
>>> last_decoder_hidden_states = outputs.last_hidden_state
```"""
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
)
return_dict = return_dict if return_dict is not None else self.config.return_dict
encoder_hidden_states = encoder_outputs[0]
batch_size, sequence_length = decoder_input_ids.shape
if decoder_position_ids is None:
if past_key_values is not None:
raise ValueError("Make sure to provide `decoder_position_ids` when passing `past_key_values`.")
if decoder_attention_mask is not None:
decoder_position_ids = (decoder_attention_mask.cumsum(-1) * decoder_attention_mask) - 1
else:
decoder_position_ids = jnp.broadcast_to(
jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
)
if decoder_attention_mask is None:
decoder_attention_mask = jnp.ones((batch_size, sequence_length), dtype="i4")
# Handle any PRNG if needed
rngs = {}
if dropout_rng is not None:
rngs["dropout"] = dropout_rng
inputs = {"params": params or self.params}
# if past_key_values are passed then cache is already initialized a private flag init_cache has to be
# passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
# it can be changed by FlaxWhisperAttention module
if past_key_values:
inputs["cache"] = past_key_values
mutable = ["cache"]
else:
mutable = False
def _decoder_forward(
module,
decoder_input_ids,
decoder_attention_mask,
decoder_position_ids,
**kwargs,
):
decoder_module = module._get_decoder_module()
outputs = decoder_module(
input_ids=decoder_input_ids,
attention_mask=decoder_attention_mask,
position_ids=decoder_position_ids,
**kwargs,
)
hidden_states = outputs[0]
if self.config.tie_word_embeddings:
shared_embedding = module.model.decoder.embed_tokens.variables["params"]["embedding"]
lm_logits = module.lm_head.apply({"params": {"kernel": shared_embedding.T}}, hidden_states)
else:
lm_logits = module.lm_head(hidden_states)
return lm_logits, outputs
outputs = self.module.apply(
inputs,
decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
encoder_hidden_states=encoder_hidden_states,
freeze_embeddings=freeze_embeddings,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
deterministic=not train,
rngs=rngs,
mutable=mutable,
method=_decoder_forward,
)
if past_key_values is None:
lm_logits, decoder_outputs = outputs
else:
(lm_logits, decoder_outputs), past = outputs
if return_dict:
outputs = FlaxCausalLMOutputWithCrossAttentions(
logits=lm_logits,
hidden_states=decoder_outputs.hidden_states,
attentions=decoder_outputs.attentions,
cross_attentions=decoder_outputs.cross_attentions,
)
else:
outputs = (lm_logits,) + decoder_outputs[1:]
# add updated cache to model output
if past_key_values is not None and return_dict:
outputs["past_key_values"] = unfreeze(past["cache"])
return outputs
elif past_key_values is not None and not return_dict:
outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
return outputs
def generate(
self,
input_features,
generation_config=None,
logits_processor=None,
return_timestamps=None,
task=None,
language=None,
is_multilingual=None,
**kwargs,
):
if generation_config is None:
generation_config = self.generation_config
if return_timestamps is not None:
generation_config.return_timestamps = return_timestamps
if task is not None:
generation_config.task = task
if is_multilingual is not None:
generation_config.is_multilingual = is_multilingual
if language is not None:
generation_config.language = language
if kwargs is not None and "decoder_input_ids" in kwargs:
decoder_input_length = len(kwargs["decoder_input_ids"])
else:
decoder_input_length = 1
forced_decoder_ids = []
if hasattr(generation_config, "is_multilingual") and generation_config.is_multilingual:
if hasattr(generation_config, "language"):
forced_decoder_ids.append((1, generation_config.lang_to_id[generation_config.language]))
else:
forced_decoder_ids.append((1, None))
if hasattr(generation_config, "task"):
forced_decoder_ids.append((2, generation_config.task_to_id[generation_config.task]))
else:
forced_decoder_ids.append((2, generation_config.task_to_id["transcribe"]))
if (
hasattr(generation_config, "return_timestamps") and generation_config.return_timestamps
) or return_timestamps:
logits_processor = [
FlaxWhisperTimeStampLogitsProcessor(generation_config, self.config, decoder_input_length)
]
else:
if forced_decoder_ids and forced_decoder_ids[-1][0] != generation_config.no_timestamps_token_id:
idx = forced_decoder_ids[-1][0] + 1 if forced_decoder_ids else 1
forced_decoder_ids.append((idx, generation_config.no_timestamps_token_id))
if len(forced_decoder_ids) > 0:
generation_config.forced_decoder_ids = forced_decoder_ids
return super().generate(
input_features,
generation_config,
logits_processor=logits_processor,
**kwargs,
)
def pipeline_generate(
self,
input_features,
forced_decoder_ids,
return_timestamps=False,
generation_config=None,
**kwargs,
):
if generation_config is None:
generation_config = self.generation_config
# override the generation config forced decoder ids in preference of the ones we have set
generation_config.forced_decoder_ids = None
logits_processor = FlaxLogitsProcessorList()
logits_processor.append(FlaxStaticForceTokensLogitsProcessor(forced_decoder_ids))
if hasattr(generation_config, "return_timestamps") and return_timestamps:
logits_processor.append(FlaxWhisperTimeStampLogitsProcessor(generation_config, self.config, 1))
return super().generate(
input_features,
generation_config,
logits_processor=logits_processor,
**kwargs,
)
def prepare_inputs_for_generation(
self,
decoder_input_ids,
max_length,
attention_mask: Optional[jax.Array] = None,
decoder_attention_mask: Optional[jax.Array] = None,
encoder_outputs=None,
**kwargs,
):
# initializing the cache
batch_size, seq_length = decoder_input_ids.shape
past_key_values = self.init_cache(batch_size, max_length, encoder_outputs)
# Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
# But since the decoder uses a causal mask, those positions are masked anyways.
# Thus we can create a single static attention_mask here, which is more efficient for compilation
extended_attention_mask = jnp.ones((batch_size, max_length), dtype="i4")
if decoder_attention_mask is not None:
position_ids = decoder_attention_mask.cumsum(-1) - 1
extended_attention_mask = lax.dynamic_update_slice(extended_attention_mask, decoder_attention_mask, (0, 0))
else:
position_ids = jnp.broadcast_to(jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length))
return {
"past_key_values": past_key_values,
"encoder_outputs": encoder_outputs,
"encoder_attention_mask": attention_mask,
"decoder_attention_mask": extended_attention_mask,
"decoder_position_ids": position_ids,
}
def update_inputs_for_generation(self, model_outputs, model_kwargs):
model_kwargs["past_key_values"] = model_outputs.past_key_values
model_kwargs["decoder_position_ids"] = model_kwargs["decoder_position_ids"][:, -1:] + 1
return model_kwargs
FLAX_WHISPER_CONDITIONAL_GENERATION_DOCSTRING = r"""
Returns:
Transcription example:
```python
>>> from transformers import WhisperProcessor, FlaxWhisperForConditionalGeneration
>>> from datasets import load_dataset
>>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
>>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
>>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
>>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
>>> input_features = inputs.input_features
>>> generated_ids = model.generate(input_ids=input_features)
>>> transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
>>> transcription
' Mr. Quilter is the apostle of the middle classes, and we are glad to welcome his gospel.'
```
"""
overwrite_call_docstring(
FlaxWhisperForConditionalGeneration,
WHISPER_INPUTS_DOCSTRING + FLAX_WHISPER_CONDITIONAL_GENERATION_DOCSTRING,
)
append_replace_return_docstrings(
FlaxWhisperForConditionalGeneration,
output_type=FlaxSeq2SeqLMOutput,
config_class=_CONFIG_FOR_DOC,
)
|