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Upload UltravoxPipeline

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README.md ADDED
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+ ## How to Get Started with the Model
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1567
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1568
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1569
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1570
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1571
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1572
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1573
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1574
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1575
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1577
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1578
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1579
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1580
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1581
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1582
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1583
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1584
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1585
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1586
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1587
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1588
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1589
+ "lstrip": false,
1590
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1591
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1592
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1593
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1594
+ },
1595
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1596
+ "content": "<|reserved_special_token_191|>",
1597
+ "lstrip": false,
1598
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1599
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1600
+ "single_word": false,
1601
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1602
+ },
1603
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1604
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+ "lstrip": false,
1606
+ "normalized": false,
1607
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1608
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1609
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1610
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1611
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1612
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1614
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1615
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1616
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1618
+ },
1619
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1620
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1622
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1623
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1624
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1625
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1626
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1627
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1628
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1629
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1630
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1631
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1632
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1633
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1634
+ },
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1636
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1638
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1639
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1640
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1641
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1642
+ },
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1644
+ "content": "<|reserved_special_token_197|>",
1645
+ "lstrip": false,
1646
+ "normalized": false,
1647
+ "rstrip": false,
1648
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1649
+ "special": true
1650
+ },
1651
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1652
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1653
+ "lstrip": false,
1654
+ "normalized": false,
1655
+ "rstrip": false,
1656
+ "single_word": false,
1657
+ "special": true
1658
+ },
1659
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1660
+ "content": "<|reserved_special_token_199|>",
1661
+ "lstrip": false,
1662
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1663
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1664
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1665
+ "special": true
1666
+ },
1667
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1668
+ "content": "<|reserved_special_token_200|>",
1669
+ "lstrip": false,
1670
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1671
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1672
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1673
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1674
+ },
1675
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1676
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+ "lstrip": false,
1678
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1679
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1680
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1681
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1682
+ },
1683
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1684
+ "content": "<|reserved_special_token_202|>",
1685
+ "lstrip": false,
1686
+ "normalized": false,
1687
+ "rstrip": false,
1688
+ "single_word": false,
1689
+ "special": true
1690
+ },
1691
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1692
+ "content": "<|reserved_special_token_203|>",
1693
+ "lstrip": false,
1694
+ "normalized": false,
1695
+ "rstrip": false,
1696
+ "single_word": false,
1697
+ "special": true
1698
+ },
1699
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1700
+ "content": "<|reserved_special_token_204|>",
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+ "lstrip": false,
1702
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1703
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1704
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1705
+ "special": true
1706
+ },
1707
+ "128213": {
1708
+ "content": "<|reserved_special_token_205|>",
1709
+ "lstrip": false,
1710
+ "normalized": false,
1711
+ "rstrip": false,
1712
+ "single_word": false,
1713
+ "special": true
1714
+ },
1715
+ "128214": {
1716
+ "content": "<|reserved_special_token_206|>",
1717
+ "lstrip": false,
1718
+ "normalized": false,
1719
+ "rstrip": false,
1720
+ "single_word": false,
1721
+ "special": true
1722
+ },
1723
+ "128215": {
1724
+ "content": "<|reserved_special_token_207|>",
1725
+ "lstrip": false,
1726
+ "normalized": false,
1727
+ "rstrip": false,
1728
+ "single_word": false,
1729
+ "special": true
1730
+ },
1731
+ "128216": {
1732
+ "content": "<|reserved_special_token_208|>",
1733
+ "lstrip": false,
1734
+ "normalized": false,
1735
+ "rstrip": false,
1736
+ "single_word": false,
1737
+ "special": true
1738
+ },
1739
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1740
+ "content": "<|reserved_special_token_209|>",
1741
+ "lstrip": false,
1742
+ "normalized": false,
1743
+ "rstrip": false,
1744
+ "single_word": false,
1745
+ "special": true
1746
+ },
1747
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1748
+ "content": "<|reserved_special_token_210|>",
1749
+ "lstrip": false,
1750
+ "normalized": false,
1751
+ "rstrip": false,
1752
+ "single_word": false,
1753
+ "special": true
1754
+ },
1755
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1756
+ "content": "<|reserved_special_token_211|>",
1757
+ "lstrip": false,
1758
+ "normalized": false,
1759
+ "rstrip": false,
1760
+ "single_word": false,
1761
+ "special": true
1762
+ },
1763
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1764
+ "content": "<|reserved_special_token_212|>",
1765
+ "lstrip": false,
1766
+ "normalized": false,
1767
+ "rstrip": false,
1768
+ "single_word": false,
1769
+ "special": true
1770
+ },
1771
+ "128221": {
1772
+ "content": "<|reserved_special_token_213|>",
1773
+ "lstrip": false,
1774
+ "normalized": false,
1775
+ "rstrip": false,
1776
+ "single_word": false,
1777
+ "special": true
1778
+ },
1779
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1780
+ "content": "<|reserved_special_token_214|>",
1781
+ "lstrip": false,
1782
+ "normalized": false,
1783
+ "rstrip": false,
1784
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1785
+ "special": true
1786
+ },
1787
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1788
+ "content": "<|reserved_special_token_215|>",
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+ "lstrip": false,
1790
+ "normalized": false,
1791
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1794
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1795
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1796
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1798
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1799
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1800
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1809
+ "special": true
1810
+ },
1811
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1812
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+ "lstrip": false,
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1817
+ "special": true
1818
+ },
1819
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1820
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+ "lstrip": false,
1822
+ "normalized": false,
1823
+ "rstrip": false,
1824
+ "single_word": false,
1825
+ "special": true
1826
+ },
1827
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1828
+ "content": "<|reserved_special_token_220|>",
1829
+ "lstrip": false,
1830
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1831
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1832
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+ "special": true
1834
+ },
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1836
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1838
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1839
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1840
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1841
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1842
+ },
1843
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1844
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+ "lstrip": false,
1846
+ "normalized": false,
1847
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1848
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1849
+ "special": true
1850
+ },
1851
+ "128231": {
1852
+ "content": "<|reserved_special_token_223|>",
1853
+ "lstrip": false,
1854
+ "normalized": false,
1855
+ "rstrip": false,
1856
+ "single_word": false,
1857
+ "special": true
1858
+ },
1859
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1860
+ "content": "<|reserved_special_token_224|>",
1861
+ "lstrip": false,
1862
+ "normalized": false,
1863
+ "rstrip": false,
1864
+ "single_word": false,
1865
+ "special": true
1866
+ },
1867
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1868
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1869
+ "lstrip": false,
1870
+ "normalized": false,
1871
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1872
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1873
+ "special": true
1874
+ },
1875
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1876
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+ "lstrip": false,
1878
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1879
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1880
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1881
+ "special": true
1882
+ },
1883
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1884
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1885
+ "lstrip": false,
1886
+ "normalized": false,
1887
+ "rstrip": false,
1888
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1889
+ "special": true
1890
+ },
1891
+ "128236": {
1892
+ "content": "<|reserved_special_token_228|>",
1893
+ "lstrip": false,
1894
+ "normalized": false,
1895
+ "rstrip": false,
1896
+ "single_word": false,
1897
+ "special": true
1898
+ },
1899
+ "128237": {
1900
+ "content": "<|reserved_special_token_229|>",
1901
+ "lstrip": false,
1902
+ "normalized": false,
1903
+ "rstrip": false,
1904
+ "single_word": false,
1905
+ "special": true
1906
+ },
1907
+ "128238": {
1908
+ "content": "<|reserved_special_token_230|>",
1909
+ "lstrip": false,
1910
+ "normalized": false,
1911
+ "rstrip": false,
1912
+ "single_word": false,
1913
+ "special": true
1914
+ },
1915
+ "128239": {
1916
+ "content": "<|reserved_special_token_231|>",
1917
+ "lstrip": false,
1918
+ "normalized": false,
1919
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1920
+ "single_word": false,
1921
+ "special": true
1922
+ },
1923
+ "128240": {
1924
+ "content": "<|reserved_special_token_232|>",
1925
+ "lstrip": false,
1926
+ "normalized": false,
1927
+ "rstrip": false,
1928
+ "single_word": false,
1929
+ "special": true
1930
+ },
1931
+ "128241": {
1932
+ "content": "<|reserved_special_token_233|>",
1933
+ "lstrip": false,
1934
+ "normalized": false,
1935
+ "rstrip": false,
1936
+ "single_word": false,
1937
+ "special": true
1938
+ },
1939
+ "128242": {
1940
+ "content": "<|reserved_special_token_234|>",
1941
+ "lstrip": false,
1942
+ "normalized": false,
1943
+ "rstrip": false,
1944
+ "single_word": false,
1945
+ "special": true
1946
+ },
1947
+ "128243": {
1948
+ "content": "<|reserved_special_token_235|>",
1949
+ "lstrip": false,
1950
+ "normalized": false,
1951
+ "rstrip": false,
1952
+ "single_word": false,
1953
+ "special": true
1954
+ },
1955
+ "128244": {
1956
+ "content": "<|reserved_special_token_236|>",
1957
+ "lstrip": false,
1958
+ "normalized": false,
1959
+ "rstrip": false,
1960
+ "single_word": false,
1961
+ "special": true
1962
+ },
1963
+ "128245": {
1964
+ "content": "<|reserved_special_token_237|>",
1965
+ "lstrip": false,
1966
+ "normalized": false,
1967
+ "rstrip": false,
1968
+ "single_word": false,
1969
+ "special": true
1970
+ },
1971
+ "128246": {
1972
+ "content": "<|reserved_special_token_238|>",
1973
+ "lstrip": false,
1974
+ "normalized": false,
1975
+ "rstrip": false,
1976
+ "single_word": false,
1977
+ "special": true
1978
+ },
1979
+ "128247": {
1980
+ "content": "<|reserved_special_token_239|>",
1981
+ "lstrip": false,
1982
+ "normalized": false,
1983
+ "rstrip": false,
1984
+ "single_word": false,
1985
+ "special": true
1986
+ },
1987
+ "128248": {
1988
+ "content": "<|reserved_special_token_240|>",
1989
+ "lstrip": false,
1990
+ "normalized": false,
1991
+ "rstrip": false,
1992
+ "single_word": false,
1993
+ "special": true
1994
+ },
1995
+ "128249": {
1996
+ "content": "<|reserved_special_token_241|>",
1997
+ "lstrip": false,
1998
+ "normalized": false,
1999
+ "rstrip": false,
2000
+ "single_word": false,
2001
+ "special": true
2002
+ },
2003
+ "128250": {
2004
+ "content": "<|reserved_special_token_242|>",
2005
+ "lstrip": false,
2006
+ "normalized": false,
2007
+ "rstrip": false,
2008
+ "single_word": false,
2009
+ "special": true
2010
+ },
2011
+ "128251": {
2012
+ "content": "<|reserved_special_token_243|>",
2013
+ "lstrip": false,
2014
+ "normalized": false,
2015
+ "rstrip": false,
2016
+ "single_word": false,
2017
+ "special": true
2018
+ },
2019
+ "128252": {
2020
+ "content": "<|reserved_special_token_244|>",
2021
+ "lstrip": false,
2022
+ "normalized": false,
2023
+ "rstrip": false,
2024
+ "single_word": false,
2025
+ "special": true
2026
+ },
2027
+ "128253": {
2028
+ "content": "<|reserved_special_token_245|>",
2029
+ "lstrip": false,
2030
+ "normalized": false,
2031
+ "rstrip": false,
2032
+ "single_word": false,
2033
+ "special": true
2034
+ },
2035
+ "128254": {
2036
+ "content": "<|reserved_special_token_246|>",
2037
+ "lstrip": false,
2038
+ "normalized": false,
2039
+ "rstrip": false,
2040
+ "single_word": false,
2041
+ "special": true
2042
+ },
2043
+ "128255": {
2044
+ "content": "<|reserved_special_token_247|>",
2045
+ "lstrip": false,
2046
+ "normalized": false,
2047
+ "rstrip": false,
2048
+ "single_word": false,
2049
+ "special": true
2050
+ }
2051
+ },
2052
+ "bos_token": "<|begin_of_text|>",
2053
+ "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
+ "clean_up_tokenization_spaces": true,
2055
+ "eos_token": "<|eot_id|>",
2056
+ "model_input_names": [
2057
+ "input_ids",
2058
+ "attention_mask"
2059
+ ],
2060
+ "model_max_length": 131072,
2061
+ "pad_token": "<|eot_id|>",
2062
+ "tokenizer_class": "PreTrainedTokenizerFast"
2063
+ }
ultravox_config.py ADDED
@@ -0,0 +1,157 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import dataclasses
2
+ from enum import Enum
3
+ from typing import Any, Dict, List, Optional
4
+
5
+ import transformers
6
+
7
+
8
+ @dataclasses.dataclass
9
+ class LoraConfigSimplified:
10
+ """
11
+ Low Rank Approximation (LoRA) configuration.
12
+
13
+ Used for language and audio models separately.
14
+ """
15
+
16
+ # The rank of the approximation
17
+ r: int = 0
18
+ lora_alpha: float = 8
19
+ target_modules: Optional[List[str]] = dataclasses.field(
20
+ default_factory=lambda: ["k_proj", "q_proj", "linear_k", "linear_q"]
21
+ )
22
+
23
+
24
+ class LossFunction(str, Enum):
25
+ CrossEntropy = "ce"
26
+ KL_Divergence = "kl"
27
+
28
+
29
+ @dataclasses.dataclass
30
+ class LossConfig:
31
+ loss_function: LossFunction = LossFunction.KL_Divergence
32
+ kl_temperature: float = 2.0
33
+
34
+ @property
35
+ def requires_alt_fields(self):
36
+ return self.loss_function == LossFunction.KL_Divergence
37
+
38
+
39
+ class UltravoxConfig(transformers.PretrainedConfig):
40
+ r"""
41
+ This is the configuration class to store the configuration of a [`UltravoxForConditionalGeneration`]. It is used to instantiate an
42
+ Ultravox model according to the specified arguments, defining the model architecture.
43
+
44
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
45
+ documentation from [`PretrainedConfig`] for more information.
46
+
47
+ Args:
48
+ audio_config (`Wav2Vec2Config`, *optional*):
49
+ Custom audio config or dict
50
+ text_config (`Union[AutoConfig, dict]`, *optional*):
51
+ The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
52
+ ignore_index (`int`, *optional*, defaults to -100):
53
+ The ignore index for the loss function.
54
+ audio_token_index (`int`, *optional*, defaults to 32000):
55
+ The audio token index to encode the audio prompt.
56
+ stack_factor (`int`, *optional*, defaults to 8):
57
+ Audio downsampling factor for the multimodal projector.
58
+ norm_init (`float`, *optional*, defaults to 0.4):
59
+ The initialization value for the layer normalization.
60
+ projector_act (`str`, *optional*, defaults to `"swiglu"`):
61
+ The activation function used by the multimodal projector.
62
+ text_model_lora_config (`LoraConfigSimplified`, *optional*):
63
+ The LoRA configuration for finetuning the text model.
64
+ audio_model_lora_config (`LoraConfigSimplified`, *optional*):
65
+ The LoRA configuration for finetuning the audio model.
66
+
67
+
68
+ Example:
69
+
70
+ ```python
71
+ >>> from transformers import UltravoxForConditionalGeneration, Wav2Vec2Config, UltravoxConfig, LlamaConfig
72
+
73
+ >>> # Initializing an audio encoder config
74
+ >>> audio_config = Wav2Vec2Config()
75
+
76
+ >>> # Initializing a Llama config
77
+ >>> text_config = LlamaConfig()
78
+
79
+ >>> # Initializing a default configuration
80
+ >>> configuration = UltravoxConfig(audio_config, text_config)
81
+
82
+ >>> # Initializing a completely untrained model from the configuration
83
+ >>> model = UltravoxForConditionalGeneration(configuration)
84
+
85
+ >>> # Accessing the model configuration
86
+ >>> configuration = model.config
87
+
88
+ >>> # Initialize a model from pretrained checkpoints and random projector weights
89
+ >>> config = UltravoxConfig(audio_model_id="facebook/wav2vec2-base-960h", text_model_id="meta-llama/Llama-2-7b-chat-hf")
90
+ ```"""
91
+
92
+ model_type = "ultravox"
93
+ is_composition = False
94
+
95
+ def __init__(
96
+ self,
97
+ audio_config: Optional[Dict[str, Any]] = None,
98
+ text_config: Optional[Dict[str, Any]] = None,
99
+ audio_model_id: Optional[str] = None,
100
+ text_model_id: Optional[str] = None,
101
+ ignore_index: int = -100,
102
+ audio_token_index: int = 32000,
103
+ hidden_size: int = 4096,
104
+ stack_factor: int = 8,
105
+ norm_init: float = 0.4,
106
+ projector_act: str = "swiglu",
107
+ text_model_lora_config: Optional[LoraConfigSimplified] = None,
108
+ audio_model_lora_config: Optional[LoraConfigSimplified] = None,
109
+ **kwargs,
110
+ ):
111
+ self.ignore_index = ignore_index
112
+
113
+ self.audio_model_id = audio_model_id
114
+ self.text_model_id = text_model_id
115
+ self.audio_token_index = audio_token_index
116
+
117
+ self.hidden_size = hidden_size
118
+ self.stack_factor = stack_factor
119
+ self.norm_init = norm_init
120
+ self.projector_act = projector_act
121
+
122
+ if text_model_id is not None:
123
+ self.text_config: transformers.LlamaConfig = (
124
+ transformers.AutoConfig.from_pretrained(text_model_id)
125
+ )
126
+ else:
127
+ text_config = text_config or {}
128
+ self.text_config = transformers.CONFIG_MAPPING[
129
+ text_config.get("model_type", "llama")
130
+ ](**text_config)
131
+
132
+ if audio_model_id is not None:
133
+ self.audio_config: transformers.PretrainedConfig = (
134
+ transformers.AutoConfig.from_pretrained(audio_model_id)
135
+ )
136
+ else:
137
+ audio_config = audio_config or {}
138
+ self.audio_config = transformers.CONFIG_MAPPING[
139
+ audio_config.get("model_type", "wav2vec2")
140
+ ](**audio_config)
141
+
142
+ self.text_model_lora_config = (
143
+ text_model_lora_config
144
+ if isinstance(text_model_lora_config, dict)
145
+ else dataclasses.asdict(text_model_lora_config or LoraConfigSimplified())
146
+ )
147
+ self.audio_model_lora_config = (
148
+ audio_model_lora_config
149
+ if isinstance(audio_model_lora_config, dict)
150
+ else dataclasses.asdict(audio_model_lora_config or LoraConfigSimplified())
151
+ )
152
+
153
+ self.vocab_size = self.text_config.vocab_size
154
+
155
+ self.initializer_range = self.text_config.initializer_range
156
+
157
+ super().__init__(**kwargs)
ultravox_model.py ADDED
@@ -0,0 +1,634 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import logging
2
+ from typing import Any, Dict, Optional, Set, Tuple, Union
3
+
4
+ import peft
5
+ import torch
6
+ import torch.nn as nn
7
+ import torch.nn.functional as F
8
+ import transformers
9
+ import transformers.activations
10
+ import transformers.modeling_outputs
11
+ import transformers.models
12
+ from transformers.models.whisper import modeling_whisper as whisper
13
+
14
+ # We must use relative import in this directory to allow uploading to HF Hub
15
+ # Even "from . import X" pattern doesn't work (undocumented and unclear why)
16
+ from .ultravox_config import LossConfig
17
+ from .ultravox_config import LossFunction
18
+ from .ultravox_config import UltravoxConfig
19
+
20
+
21
+ class UltravoxModel(transformers.LlamaPreTrainedModel):
22
+ """
23
+ The Ultravox model which consists of an audio encoder and a language model.
24
+
25
+ Audio input is processed by the audio encoder, then every `stack_factor` frames are stacked together and
26
+ projected to the language model's embedding space using a few linear layers.
27
+ The text is embedded by the language model as usual and then the audio and text embeddings are merged together.
28
+
29
+ A special token `<|audio|>` is used to indicate the start of the audio embeddings in the merged embeddings.
30
+
31
+ Parameters:
32
+ config: Model configuration class with all the parameters of the model.
33
+ """
34
+
35
+ config_class = UltravoxConfig
36
+ config: UltravoxConfig # for type hinting
37
+ _no_split_modules = ["Wav2Vec2Model", "WhisperEncoder", "LlamaDecoderLayer"]
38
+ # We minimize the weights in state_dict in order to reduce the size of the checkpoint
39
+ # The issue is that load_pretrained() uses state_dict() keys to know what keys are expected
40
+ # As such we have to tell is to ignore some keys that are not always in the model
41
+ _keys_to_ignore_on_load_unexpected = ["audio_tower.*", "language_model.*"]
42
+ # Usually we load encoder weights from a pretrained model, so we don't want to load the decoder weights
43
+ # Technically we never hit this issue because these keys are already removed from state_dict() however,
44
+ # but there's no harm in keeping it here for when we change that behavior.
45
+ _keys_to_ignore_on_load_missing = ["audio_tower.*"]
46
+
47
+ def __init__(self, config: UltravoxConfig):
48
+ super().__init__(config)
49
+
50
+ self.keep_params: Set[str] = set()
51
+ self.vocab_size = config.vocab_size
52
+
53
+ self.audio_tower = self._create_audio_tower(config)
54
+ self.multi_modal_projector = UltravoxProjector(config)
55
+ self.language_model = self._create_language_model(config)
56
+
57
+ self.loss_config = LossConfig()
58
+ self.post_init()
59
+
60
+ def get_input_embeddings(self):
61
+ return self.language_model.get_input_embeddings()
62
+
63
+ def set_input_embeddings(self, value):
64
+ self.language_model.set_input_embeddings(value)
65
+
66
+ def get_output_embeddings(self):
67
+ return self.language_model.get_output_embeddings()
68
+
69
+ def set_output_embeddings(self, new_embeddings):
70
+ self.language_model.set_output_embeddings(new_embeddings)
71
+
72
+ def set_decoder(self, decoder):
73
+ self.language_model.set_decoder(decoder)
74
+
75
+ def get_decoder(self):
76
+ return self.language_model.get_decoder()
77
+
78
+ def tie_weights(self):
79
+ return self.language_model.tie_weights()
80
+
81
+ def set_loss_config(self, loss_config: LossConfig):
82
+ self.loss_config = loss_config
83
+
84
+ def _setup_cache(
85
+ self, cache_cls, max_batch_size: int, max_cache_len: Optional[int] = None
86
+ ):
87
+ self.language_model._setup_cache(cache_cls, max_batch_size, max_cache_len)
88
+
89
+ def _reorder_cache(self, past_key_values, beam_idx):
90
+ return self.language_model._reorder_cache(past_key_values, beam_idx)
91
+
92
+ def resize_token_embeddings(
93
+ self,
94
+ new_num_tokens: Optional[int] = None,
95
+ pad_to_multiple_of: Optional[int] = None,
96
+ ) -> nn.Embedding:
97
+ model_embeds = self.language_model.resize_token_embeddings(
98
+ new_num_tokens, pad_to_multiple_of
99
+ )
100
+ # update vocab size
101
+ self.config.text_config.vocab_size = model_embeds.num_embeddings
102
+ self.config.vocab_size = model_embeds.num_embeddings
103
+ self.vocab_size = model_embeds.num_embeddings
104
+ return model_embeds
105
+
106
+ def _compute_kl_loss(
107
+ self,
108
+ lm_output: transformers.modeling_outputs.CausalLMOutputWithPast,
109
+ labels: Optional[torch.Tensor] = None,
110
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
111
+ alt_input_ids: Optional[torch.Tensor] = None,
112
+ alt_attention_mask: Optional[torch.Tensor] = None,
113
+ alt_labels: Optional[torch.Tensor] = None,
114
+ **kwargs,
115
+ ):
116
+ # disable gradient computation for the teacher model
117
+ with torch.no_grad():
118
+ # compute the teacher (text-only) model's distribution
119
+ alt_inputs_embeds = self.get_input_embeddings().forward(alt_input_ids)
120
+ alt_lm_output = self.language_model.forward(
121
+ inputs_embeds=alt_inputs_embeds,
122
+ labels=alt_labels,
123
+ attention_mask=alt_attention_mask,
124
+ past_key_values=past_key_values,
125
+ **kwargs,
126
+ )
127
+ # compute the KL divergence loss between the two models
128
+ kl_loss = F.kl_div(
129
+ F.log_softmax(
130
+ lm_output.logits[labels != -100] / self.loss_config.kl_temperature,
131
+ dim=-1,
132
+ ),
133
+ F.softmax(
134
+ alt_lm_output.logits[alt_labels != -100]
135
+ / self.loss_config.kl_temperature,
136
+ dim=-1,
137
+ ),
138
+ reduction="batchmean",
139
+ )
140
+ return {"loss": kl_loss}
141
+
142
+ def forward(
143
+ self,
144
+ input_ids: torch.Tensor,
145
+ audio_values: Optional[torch.FloatTensor] = None,
146
+ inputs_embeds: Optional[torch.FloatTensor] = None,
147
+ labels: Optional[torch.Tensor] = None,
148
+ attention_mask: Optional[torch.Tensor] = None,
149
+ audio_token_start_idx: Optional[torch.Tensor] = None,
150
+ audio_token_len: Optional[torch.Tensor] = None,
151
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
152
+ # the alt_* fields are needed for KL divergence loss
153
+ alt_input_ids: Optional[torch.Tensor] = None,
154
+ alt_attention_mask: Optional[torch.Tensor] = None,
155
+ alt_labels: Optional[torch.Tensor] = None,
156
+ **kwargs,
157
+ ) -> Union[Tuple, transformers.modeling_outputs.CausalLMOutputWithPast]:
158
+ """
159
+ Forward pass for the Ultravox model.
160
+
161
+ `input_ids` are the tokenized text input. They are embedded by the language model as usual.
162
+ `audio_values` are processed by the audio encoder and then every `stack_factor` frames are stacked together and
163
+ projected to the language model's embedding space using a few linear layers.
164
+ The audio and text embeddings are merged together. A special token `<|audio|>` is used to indicate the start
165
+ of the audio embeddings in the merged embeddings.
166
+
167
+ Args:
168
+ input_ids: The tokenized text input.
169
+ audio_values: The processed audio values.
170
+ inputs_embeds: The embeddings for the input tokens.
171
+ labels: The tokenized text labels.
172
+ attention_mask: The attention mask for the input.
173
+ position_ids: The position ids for the input.
174
+ past_key_values: The past key value cache for the language model attention layers.
175
+ **kwargs: Additional keyword arguments. Passed directly to the language model.
176
+ """
177
+ if inputs_embeds is None:
178
+ # B x T -> B x T x D
179
+ inputs_embeds = self.get_input_embeddings().forward(input_ids)
180
+
181
+ if audio_values is not None:
182
+ assert (
183
+ audio_token_start_idx is not None and audio_token_len is not None
184
+ ), "audio_token_start_idx and audio_token_len must be provided if audio_values are provided."
185
+ assert (
186
+ len(audio_token_start_idx) == len(audio_token_len) == len(audio_values)
187
+ ), "audio_token_start_idx, audio_token_len, and audio_values must have the same batch size."
188
+
189
+ # B x A/3200 x D
190
+ audio_tower_output = self.audio_tower.forward(
191
+ audio_values
192
+ ).last_hidden_state
193
+ audio_tower_output = audio_tower_output.to(inputs_embeds.dtype)
194
+
195
+ audio_embeds = self.multi_modal_projector.forward(audio_tower_output)
196
+
197
+ # combine audio and text embeddings
198
+ for i, (audio, start, length) in enumerate(
199
+ zip(audio_embeds, audio_token_start_idx, audio_token_len)
200
+ ):
201
+ length = min(length, audio.shape[0])
202
+ inputs_embeds[i, start : start + length] = audio[:length]
203
+
204
+ lm_output = self.language_model.forward(
205
+ inputs_embeds=inputs_embeds,
206
+ labels=labels,
207
+ attention_mask=attention_mask,
208
+ past_key_values=past_key_values,
209
+ **kwargs,
210
+ )
211
+ if self.training:
212
+ if self.loss_config.loss_function == LossFunction.CrossEntropy:
213
+ return lm_output
214
+ elif self.loss_config.loss_function == LossFunction.KL_Divergence:
215
+ return self._compute_kl_loss(
216
+ lm_output=lm_output,
217
+ labels=labels,
218
+ past_key_values=past_key_values,
219
+ alt_input_ids=alt_input_ids,
220
+ alt_attention_mask=alt_attention_mask,
221
+ alt_labels=alt_labels,
222
+ **kwargs,
223
+ )
224
+ else:
225
+ raise ValueError(
226
+ f"Unsupported loss function: {self.loss_config.loss_function}"
227
+ )
228
+ else:
229
+ return lm_output
230
+
231
+ def prepare_inputs_for_generation(
232
+ self,
233
+ input_ids: torch.Tensor,
234
+ audio_values: Optional[torch.FloatTensor] = None,
235
+ audio_token_start_idx: Optional[torch.Tensor] = None,
236
+ audio_token_len: Optional[torch.Tensor] = None,
237
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]] = None,
238
+ attention_mask: Optional[torch.Tensor] = None,
239
+ inputs_embeds: Optional[torch.Tensor] = None,
240
+ cache_position: Optional[torch.Tensor] = None,
241
+ **kwargs,
242
+ ) -> Dict[str, Any]:
243
+ model_input = self.language_model.prepare_inputs_for_generation(
244
+ input_ids=input_ids,
245
+ past_key_values=past_key_values,
246
+ attention_mask=attention_mask,
247
+ inputs_embeds=inputs_embeds,
248
+ cache_position=cache_position,
249
+ **kwargs,
250
+ )
251
+
252
+ # include audio information in model_input only when it is needed during prefilling
253
+ # audio_token_start_idx should always be relative to the current cache position
254
+ prefill_start_idx = 0 if cache_position is None else cache_position[0]
255
+ if (
256
+ audio_values is not None
257
+ and audio_token_start_idx is not None
258
+ and prefill_start_idx <= torch.max(audio_token_start_idx)
259
+ ):
260
+ model_input["audio_values"] = audio_values
261
+ model_input["audio_token_start_idx"] = (
262
+ audio_token_start_idx - prefill_start_idx
263
+ )
264
+ model_input["audio_token_len"] = audio_token_len
265
+
266
+ return model_input
267
+
268
+ @classmethod
269
+ def _create_audio_tower(
270
+ cls, config: UltravoxConfig
271
+ ) -> Union[transformers.Wav2Vec2Model, "ModifiedWhisperEncoder"]:
272
+ if config.audio_model_id is not None:
273
+ if "whisper" in config.audio_model_id is not None:
274
+ audio_tower = ModifiedWhisperEncoder.from_pretrained(
275
+ config.audio_model_id
276
+ )
277
+ else:
278
+ audio_tower = transformers.AutoModel.from_pretrained(
279
+ config.audio_model_id
280
+ )
281
+ else:
282
+ if "whisper" in config.audio_config._name_or_path:
283
+ audio_tower = ModifiedWhisperEncoder(config.audio_config)
284
+ else:
285
+ with transformers.modeling_utils.no_init_weights():
286
+ # we only ever use from_config if the weights are retrained, hence initializing is not
287
+ # required. This makes the model quite creation faster since init on CPU is quite slow.
288
+ audio_tower = transformers.AutoModel.from_config(
289
+ config.audio_config
290
+ )
291
+
292
+ if isinstance(
293
+ audio_tower,
294
+ (transformers.Wav2Vec2BertModel, transformers.WhisperModel),
295
+ ):
296
+ # For these models we only need the encoder part
297
+ # Wav2Vec2BertModel -> Wav2Vec2BertEncoder
298
+ # WhisperModel -> WhisperEncoder
299
+ audio_tower = audio_tower.encoder
300
+
301
+ audio_tower = apply_lora(audio_tower, config.audio_model_lora_config)
302
+ return audio_tower
303
+
304
+ @classmethod
305
+ def _create_language_model(
306
+ cls, config: UltravoxConfig
307
+ ) -> transformers.LlamaForCausalLM:
308
+ if config.text_model_id is not None:
309
+ language_model = transformers.AutoModelForCausalLM.from_pretrained(
310
+ config.text_model_id, attn_implementation=config._attn_implementation
311
+ )
312
+ else:
313
+ with transformers.modeling_utils.no_init_weights():
314
+ # we only ever use from_config if the weights are retrained, hence initializing is not
315
+ # required. This makes the model quite creation faster since init on CPU is quite slow.
316
+ language_model = transformers.AutoModelForCausalLM.from_config(
317
+ config.text_config, attn_implementation=config._attn_implementation
318
+ )
319
+
320
+ language_model = apply_lora(language_model, config.text_model_lora_config)
321
+ return language_model
322
+
323
+ def _add_language_model_weights_to_keep(self):
324
+ if self.config.text_model_id is not None:
325
+ self.config.text_model_id = None
326
+ self.keep_params.update(
327
+ set(
328
+ [
329
+ f"language_model.{name}"
330
+ for name, _ in self.language_model.named_parameters()
331
+ ]
332
+ )
333
+ )
334
+
335
+ def _add_audio_tower_weights_to_keep(self):
336
+ if self.config.audio_model_id is not None:
337
+ self.config.audio_model_id = None
338
+ self.keep_params.update(
339
+ set(
340
+ [
341
+ f"audio_tower.{name}"
342
+ for name, _ in self.audio_tower.named_parameters()
343
+ ]
344
+ )
345
+ )
346
+
347
+ def merge_and_unload(self):
348
+ if isinstance(self.language_model, peft.PeftModel):
349
+ self.language_model = self.language_model.merge_and_unload()
350
+ # no need to download base language model weights anymore, so we can remove the id
351
+ self._add_language_model_weights_to_keep()
352
+
353
+ if isinstance(self.audio_tower, peft.PeftModel):
354
+ self.audio_tower = self.audio_tower.merge_and_unload()
355
+ # no need to download base audio model weights anymore, so we can remove the id
356
+ self._add_audio_tower_weights_to_keep()
357
+
358
+ for param in ["text_model_lora_config", "audio_model_lora_config"]:
359
+ if hasattr(self.config, param):
360
+ delattr(self.config, param)
361
+
362
+ def push_to_hub(self, *args, **kwargs):
363
+ self.merge_and_unload()
364
+ self.to(self.language_model.dtype)
365
+ return super().push_to_hub(*args, **kwargs)
366
+
367
+ def state_dict(self, *args, **kwargs):
368
+ named_params = dict(self.named_parameters())
369
+ state_dict = super().state_dict(*args, **kwargs)
370
+
371
+ state_dict = {
372
+ k: v
373
+ for k, v in state_dict.items()
374
+ if k in self.keep_params
375
+ or (k in named_params and named_params[k].requires_grad)
376
+ }
377
+ return state_dict
378
+
379
+ def load_state_dict(
380
+ self,
381
+ state_dict: Dict[str, Any],
382
+ *args,
383
+ **kwargs,
384
+ ):
385
+ self.keep_params.update(set(state_dict.keys()))
386
+ return super().load_state_dict(state_dict, *args, **kwargs)
387
+
388
+ def print_trainable_parameters(self):
389
+ """
390
+ Prints the number of trainable parameters in the model (reuses Peft model's method)
391
+ """
392
+ count_params = peft.peft_model.PeftModel.get_nb_trainable_parameters
393
+
394
+ trainable_params, all_param = count_params(self)
395
+
396
+ logging.info(
397
+ f"trainable params: {trainable_params:,d} || all params: {all_param:,d}"
398
+ f" || trainable%: {100 * trainable_params / all_param:.1f}%"
399
+ )
400
+
401
+ lm_trainable_params, lm_all_params = count_params(self.language_model)
402
+ audio_trainable_params, audio_all_params = count_params(self.audio_tower)
403
+
404
+ projector_trainable_params = (
405
+ trainable_params - lm_trainable_params - audio_trainable_params
406
+ )
407
+ projector_all_params = all_param - lm_all_params - audio_all_params
408
+
409
+ logging.info(
410
+ f"Trainable%: "
411
+ f" LLM: {100 * lm_trainable_params / lm_all_params:.1f}%"
412
+ f" || Audio Encoder: {100 * audio_trainable_params / audio_all_params:.1f}%"
413
+ f" || Projector: {100 * projector_trainable_params / projector_all_params:.1f}%"
414
+ )
415
+
416
+
417
+ def is_cache_empty(
418
+ past_key_values: Optional[Union[Tuple, transformers.cache_utils.Cache]]
419
+ ) -> bool:
420
+ """
421
+ Check if the cache is empty.
422
+ """
423
+ if past_key_values is None:
424
+ return True
425
+ if isinstance(past_key_values, tuple):
426
+ return all(len(c) == 0 for c in past_key_values)
427
+ return past_key_values.get_seq_length() == 0
428
+
429
+
430
+ def apply_lora(model: torch.nn.Module, lora_config: dict) -> torch.nn.Module:
431
+ """
432
+ Applies LoRA finetuning to the model. If the `r` parameter is set to 0, the model is frozen instead.
433
+ """
434
+ lora_config = peft.LoraConfig(**lora_config or {})
435
+
436
+ if lora_config.r == 0:
437
+ # freeze the model entirely
438
+ for param in model.parameters():
439
+ param.requires_grad = False
440
+ else:
441
+ model = peft.get_peft_model(model, lora_config)
442
+
443
+ return model
444
+
445
+
446
+ class StackAudioFrames(nn.Module):
447
+ """
448
+ Stack the audio embedding frames to reduce the sequence length by a factor of `stack_factor`.
449
+
450
+ The number of output frames will be `ceil(T / stack_factor) + 1` where `T` is the number of input frames.
451
+ NOTE: the extra +1 is intentional: in case the number of audio tokens are over-estimated by the processor,
452
+ we want to make sure `processor.audio_token_replacement` (i.e. EOS) doesn't get leaked into the middle of embeddings.
453
+ In most cases this extra padding will get removed in the model's forward function so it has no effect.
454
+ """
455
+
456
+ def __init__(self, stack_factor: int = 8):
457
+ super().__init__()
458
+ self.stack_factor = stack_factor
459
+
460
+ def forward(self, audio_embeds: torch.Tensor) -> torch.Tensor:
461
+ B, T, C = audio_embeds.shape
462
+ T_pad = (T + self.stack_factor - 1) // self.stack_factor * self.stack_factor
463
+ audio_embeds = F.pad(audio_embeds, (0, 0, 0, T_pad - T + self.stack_factor))
464
+ B, T, C = audio_embeds.shape
465
+ audio_embeds = audio_embeds.view(
466
+ B, T // self.stack_factor, C * self.stack_factor
467
+ )
468
+ return audio_embeds
469
+
470
+
471
+ class RMSNorm(transformers.models.llama.modeling_llama.LlamaRMSNorm):
472
+ def __init__(self, hidden_size: int, init: float = 1, eps: float = 1e-6):
473
+ super().__init__(hidden_size=hidden_size, eps=eps)
474
+ self.weight.data.fill_(init)
475
+
476
+
477
+ class SwiGLU(nn.Module):
478
+ def forward(self, x):
479
+ x, gate = x.chunk(2, dim=-1)
480
+ return F.silu(gate) * x
481
+
482
+
483
+ class UltravoxProjector(nn.Sequential):
484
+ def __init__(self, config: UltravoxConfig):
485
+ super().__init__()
486
+ self.hidden_dim = config.hidden_size
487
+ self._pad_and_stack = StackAudioFrames(config.stack_factor)
488
+ dim = config.audio_config.hidden_size * config.stack_factor
489
+ self.ln_pre = RMSNorm(dim, init=config.norm_init)
490
+ self.linear_1 = nn.Linear(dim, self.hidden_dim, bias=False)
491
+ dim = self.hidden_dim
492
+ self.act = transformers.activations.get_activation(config.projector_act)
493
+ dim = dim // 2 if config.projector_act == "swiglu" else dim
494
+ self.linear_2 = nn.Linear(dim, config.text_config.hidden_size, bias=False)
495
+ self.ln_post = RMSNorm(config.text_config.hidden_size, init=config.norm_init)
496
+
497
+ def forward(self, audio_features: torch.Tensor) -> torch.Tensor:
498
+ audio_features = self._pad_and_stack(audio_features)
499
+ audio_features = self.ln_pre(audio_features)
500
+ hidden_states = self.linear_1(audio_features)
501
+ hidden_states = self.act(hidden_states)
502
+ hidden_states = self.linear_2(hidden_states)
503
+ hidden_states = self.ln_post(hidden_states)
504
+ return hidden_states
505
+
506
+
507
+ class ModifiedWhisperEncoder(whisper.WhisperEncoder):
508
+ """
509
+ Encoder portion of OpenAI's Whisper model.
510
+
511
+ This implementation is a slightly modified version of HF Transformers' Whisper Encoder, with only a few fixes:
512
+ 1. base_model_prefix updated to allow for doing `.from_pretrained` directly on the encoder
513
+ 2. allow less than 30 second of audio padding to be passed in:
514
+ - relaxed ValueError check for `input_features` length to be less than or equal to `expected_seq_length` instead of strictly equal
515
+ - embed_pos is now sliced to match the length of `inputs_embeds`
516
+
517
+ Original: https://github.com/huggingface/transformers/blob/main/src/transformers/models/whisper/modeling_whisper.py
518
+ """
519
+
520
+ base_model_prefix = "model.encoder"
521
+
522
+ def forward(
523
+ self,
524
+ input_features,
525
+ attention_mask=None,
526
+ head_mask=None,
527
+ output_attentions=None,
528
+ output_hidden_states=None,
529
+ return_dict=None,
530
+ ):
531
+ expected_seq_length = (
532
+ self.config.max_source_positions
533
+ * self.conv1.stride[0]
534
+ * self.conv2.stride[0]
535
+ )
536
+ if input_features.shape[-1] > expected_seq_length:
537
+ raise ValueError(
538
+ f"Whisper expects the mel input features to be of length {expected_seq_length} or less, but found {input_features.shape[-1]}. Make sure to pad the input mel features to {expected_seq_length}."
539
+ )
540
+
541
+ output_attentions = (
542
+ output_attentions
543
+ if output_attentions is not None
544
+ else self.config.output_attentions
545
+ )
546
+ output_hidden_states = (
547
+ output_hidden_states
548
+ if output_hidden_states is not None
549
+ else self.config.output_hidden_states
550
+ )
551
+ return_dict = (
552
+ return_dict if return_dict is not None else self.config.use_return_dict
553
+ )
554
+ inputs_embeds = nn.functional.gelu(self.conv1(input_features))
555
+ inputs_embeds = nn.functional.gelu(self.conv2(inputs_embeds))
556
+
557
+ inputs_embeds = inputs_embeds.permute(0, 2, 1)
558
+ embed_pos = self.embed_positions.weight[: inputs_embeds.size(-2)]
559
+
560
+ hidden_states = inputs_embeds + embed_pos
561
+ hidden_states = nn.functional.dropout(
562
+ hidden_states, p=self.dropout, training=self.training
563
+ )
564
+
565
+ encoder_states = () if output_hidden_states else None
566
+ all_attentions = () if output_attentions else None
567
+
568
+ # check if head_mask has a correct number of layers specified if desired
569
+ if head_mask is not None:
570
+ assert head_mask.size()[0] == (
571
+ len(self.layers)
572
+ ), f"The head_mask should be specified for {len(self.layers)} layers, but it is for {head_mask.size()[0]}."
573
+
574
+ for idx, encoder_layer in enumerate(self.layers):
575
+ if output_hidden_states:
576
+ encoder_states = encoder_states + (hidden_states,)
577
+ # add LayerDrop (see https://arxiv.org/abs/1909.11556 for description)
578
+ to_drop = False
579
+ if self.training:
580
+ dropout_probability = torch.rand([])
581
+ if dropout_probability < self.layerdrop: # skip the layer
582
+ to_drop = True
583
+
584
+ if to_drop:
585
+ layer_outputs = (None, None)
586
+ else:
587
+ if self.gradient_checkpointing and self.training:
588
+ layer_outputs = self._gradient_checkpointing_func(
589
+ encoder_layer.__call__,
590
+ hidden_states,
591
+ None,
592
+ (head_mask[idx] if head_mask is not None else None),
593
+ output_attentions,
594
+ )
595
+ else:
596
+ layer_outputs = encoder_layer(
597
+ hidden_states,
598
+ None,
599
+ layer_head_mask=(
600
+ head_mask[idx] if head_mask is not None else None
601
+ ),
602
+ output_attentions=output_attentions,
603
+ )
604
+
605
+ hidden_states = layer_outputs[0]
606
+
607
+ if output_attentions:
608
+ all_attentions = all_attentions + (layer_outputs[1],)
609
+
610
+ hidden_states = self.layer_norm(hidden_states)
611
+ if output_hidden_states:
612
+ encoder_states = encoder_states + (hidden_states,)
613
+
614
+ if not return_dict:
615
+ return tuple(
616
+ v
617
+ for v in [hidden_states, encoder_states, all_attentions]
618
+ if v is not None
619
+ )
620
+ return transformers.modeling_outputs.BaseModelOutput(
621
+ last_hidden_state=hidden_states,
622
+ hidden_states=encoder_states,
623
+ attentions=all_attentions,
624
+ )
625
+
626
+
627
+ UltravoxConfig.register_for_auto_class()
628
+ UltravoxModel.register_for_auto_class()
629
+
630
+ transformers.AutoConfig.register("ultravox", UltravoxConfig)
631
+ transformers.AutoModel.register(UltravoxConfig, UltravoxModel)
632
+ # transformers.AutoProcessor.register(UltravoxConfig, UltravoxProcessor) # TODO: make processor work standalone
633
+
634
+ transformers.activations.ACT2FN["swiglu"] = SwiGLU
ultravox_pipeline.py ADDED
@@ -0,0 +1,127 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import logging
2
+ from typing import Any, Dict, List, Optional
3
+
4
+ import numpy as np
5
+ import transformers
6
+
7
+ # We must use relative import in this directory to allow uploading to HF Hub
8
+ # Even "from . import X" pattern doesn't work (undocumented and unclear why)
9
+ from .ultravox_model import UltravoxModel
10
+ from .ultravox_processing import UltravoxProcessor
11
+
12
+
13
+ class UltravoxPipeline(transformers.Pipeline):
14
+ def __init__(
15
+ self,
16
+ model: UltravoxModel,
17
+ tokenizer: Optional[transformers.PreTrainedTokenizerBase] = None,
18
+ audio_processor: Optional[transformers.ProcessorMixin] = None,
19
+ **kwargs
20
+ ):
21
+ if tokenizer is None:
22
+ try:
23
+ tokenizer = transformers.AutoTokenizer.from_pretrained(
24
+ model.config._name_or_path
25
+ )
26
+ except:
27
+ tokenizer = transformers.AutoTokenizer.from_pretrained(
28
+ model.config.text_model_id or model.config.text_config._name_or_path
29
+ )
30
+
31
+ if audio_processor is None:
32
+ audio_processor = transformers.AutoProcessor.from_pretrained(
33
+ model.config.audio_model_id or model.config.audio_config._name_or_path
34
+ )
35
+
36
+ self.processor = UltravoxProcessor(
37
+ audio_processor=audio_processor,
38
+ tokenizer=tokenizer,
39
+ stack_factor=model.config.stack_factor,
40
+ )
41
+
42
+ super().__init__(model=model, tokenizer=tokenizer, **kwargs)
43
+
44
+ def _sanitize_parameters(self, **kwargs):
45
+ generation_keys = ["temperature", "max_new_tokens", "repetition_penalty"]
46
+ generation_kwargs = {k: kwargs[k] for k in kwargs if k in generation_keys}
47
+ return {}, generation_kwargs, {}
48
+
49
+ def preprocess(self, inputs: Dict[str, Any]):
50
+ turns: list = inputs.get("turns", [])
51
+
52
+ audio = inputs.get("audio", None)
53
+ # Convert to float32 if needed.
54
+ if isinstance(audio, np.ndarray):
55
+ if audio.dtype == np.float64:
56
+ audio = audio.astype(np.float32)
57
+ elif audio.dtype == np.int16:
58
+ audio = audio.astype(np.float32) / np.float32(32768.0)
59
+ elif audio.dtype == np.int32:
60
+ audio = audio.astype(np.float32) / np.float32(2147483648.0)
61
+
62
+ if audio is not None and (len(turns) == 0 or turns[-1]["role"] != "user"):
63
+ prompt = inputs.get("prompt", "<|audio|>")
64
+ if "<|audio|>" not in prompt:
65
+ logging.warning(
66
+ "Prompt does not contain '<|audio|>', appending '<|audio|>' to the end of the prompt."
67
+ )
68
+
69
+ prompt += " <|audio|>"
70
+ turns.append({"role": "user", "content": prompt})
71
+
72
+ text = self.processor.tokenizer.apply_chat_template(
73
+ turns, add_generation_prompt=True, tokenize=False
74
+ )
75
+
76
+ if "sampling_rate" not in inputs and audio is not None:
77
+ logging.warning(
78
+ "No sampling rate provided, using default of 16kHz. We highly recommend providing the correct sampling rate."
79
+ )
80
+
81
+ output = self.processor(
82
+ text=text,
83
+ audio=audio,
84
+ sampling_rate=inputs.get("sampling_rate", 16000),
85
+ )
86
+ if "audio_values" in output:
87
+ output["audio_values"] = output["audio_values"].to(self.model.dtype)
88
+
89
+ return output
90
+
91
+ def _forward(
92
+ self,
93
+ model_inputs: Dict[str, Any],
94
+ temperature: Optional[float] = None,
95
+ max_new_tokens: Optional[int] = None,
96
+ repetition_penalty: float = 1.1,
97
+ ) -> List[int]:
98
+ temperature = temperature or None
99
+ do_sample = temperature is not None
100
+
101
+ terminators = [self.tokenizer.eos_token_id]
102
+ if "<|eot_id|>" in self.tokenizer.added_tokens_encoder:
103
+ terminators.append(self.tokenizer.convert_tokens_to_ids("<|eot_id|>"))
104
+
105
+ input_len = model_inputs["input_ids"].shape[1]
106
+
107
+ outputs = self.model.generate(
108
+ **model_inputs,
109
+ do_sample=do_sample,
110
+ temperature=temperature,
111
+ max_new_tokens=max_new_tokens,
112
+ repetition_penalty=repetition_penalty,
113
+ eos_token_id=terminators
114
+ )
115
+ return outputs[0][input_len:]
116
+
117
+ def postprocess(self, model_outputs) -> str:
118
+ output_text = self.tokenizer.decode(model_outputs, skip_special_tokens=True)
119
+ return output_text
120
+
121
+
122
+ transformers.pipelines.PIPELINE_REGISTRY.register_pipeline(
123
+ "ultravox-pipeline",
124
+ pipeline_class=UltravoxPipeline,
125
+ pt_model=transformers.AutoModel,
126
+ type="multimodal",
127
+ )
ultravox_processing.py ADDED
@@ -0,0 +1,180 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from typing import Optional, Union
2
+
3
+ import numpy as np
4
+ import torch
5
+ import transformers
6
+
7
+
8
+ class UltravoxProcessor(transformers.ProcessorMixin):
9
+ """
10
+ Constructs an Ultravox processor which wraps an audio processor and a tokenizer into a single processor.
11
+
12
+ Args:
13
+ audio_processor: The audio processor for the audio encoder.
14
+ tokenizer: The tokenizer for the language model.
15
+ """
16
+
17
+ attributes = ["audio_processor", "tokenizer"]
18
+ audio_processor_class = (
19
+ "Wav2Vec2Processor",
20
+ "SeamlessM4TFeatureExtractor",
21
+ "WhisperProcessor",
22
+ )
23
+ tokenizer_class = (
24
+ "PreTrainedTokenizer",
25
+ "PreTrainedTokenizerFast",
26
+ )
27
+
28
+ tokenizer: transformers.PreTrainedTokenizerBase
29
+ audio_processor: transformers.ProcessorMixin
30
+
31
+ def __init__(
32
+ self,
33
+ audio_processor=None,
34
+ tokenizer=None,
35
+ audio_padding: str = "longest",
36
+ encoder_ds_factor: int = 320,
37
+ stack_factor: int = 8,
38
+ audio_placeholder: str = "<|audio|>",
39
+ ):
40
+ """
41
+ Args:
42
+ audio_processor: The audio processor for the audio encoder.
43
+ tokenizer: The tokenizer for the language model.
44
+ audio_padding: The padding strategy for the audio encoder.
45
+ encoder_ds_factor: The downsample factor of the audio encoder.
46
+ stack_factor: The factor by which the audio encoder output is stacked in the multimodal projector.
47
+ audio_placeholder: The placeholder for the audio in the text.
48
+ """
49
+ self.audio_padding = audio_padding
50
+ self.encoder_ds_factor = encoder_ds_factor
51
+ self.stack_factor = stack_factor
52
+ self.audio_placeholder = audio_placeholder
53
+ self.audio_token_replacement = tokenizer.eos_token
54
+ assert (
55
+ self.audio_token_replacement is not None
56
+ ), "The tokenizer has no EOS token. Cannot recover."
57
+ if tokenizer.pad_token_id is None:
58
+ tokenizer.pad_token_id = tokenizer.eos_token_id
59
+
60
+ super().__init__(audio_processor=audio_processor, tokenizer=tokenizer)
61
+
62
+ def __call__(
63
+ self,
64
+ text: Optional[str] = None,
65
+ audio: Optional[Union[np.ndarray, torch.Tensor]] = None,
66
+ sampling_rate: Optional[int] = None,
67
+ return_tensors: Optional[
68
+ Union[str, transformers.TensorType]
69
+ ] = transformers.TensorType.PYTORCH,
70
+ **kwargs,
71
+ ) -> transformers.BatchFeature:
72
+ """
73
+ Main method to prepare for the model one text sequence and audio. This method forwards the `text`
74
+ and `kwargs` arguments to PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] if `text` is not `None` to encode
75
+ the text. To prepare the audio(s), this method forwards the `audio`, `sampling_rate` and `kwargs` arguments to
76
+ audio processor's [`~Wav2Vec2Processor.__call__`] if `audio` is not `None`. Please refer to the docstring
77
+ of the above two methods for more information.
78
+
79
+ Args:
80
+ text (`str`, `List[str]`):
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+ The sequence to be encoded. Sequence can be a string or (pretokenized string).
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+ audio (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
83
+ The audio to be prepared. Audio can be NumPy array or PyTorch tensor. In case of a
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+ NumPy array/PyTorch tensor, each audio should be of shape (C, T), where C is a number of channels, and T the
85
+ sample length of the audio.
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+ sampling_rate (`int`, *optional*, defaults to 16000):
87
+ Sampling rate of the input audio. We expect 16kHz audio. Don't change this value unless you know what
88
+ you are doing.
89
+ return_tensors (`str` or [`~utils.TensorType`], *optional*):
90
+ If set, will return tensors of a particular framework. Acceptable values are:
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+
92
+ - `'tf'`: Return TensorFlow `tf.constant` objects.
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+ - `'pt'`: Return PyTorch `torch.Tensor` objects.
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+ - `'np'`: Return NumPy `np.ndarray` objects.
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+ - `'jax'`: Return JAX `jnp.ndarray` objects.
96
+
97
+ Returns:
98
+ [`BatchFeature`]: A [`BatchFeature`] with the following fields:
99
+
100
+ - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
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+ - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
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+ `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
103
+ `None`).
104
+ - **audio_values** -- Processed audio values to be fed to a model. Returned when `audio` is not `None`.
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+ - **audio_token_len** -- Predicted number of audio frames: this value is guaranteed to be a close upper bound.
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+ Returned when `audio` is not `None`.
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+ - **audio_token_start_idx** -- The index in the tokenized text where the audio starts. Returned when `audio` is not `None`.
108
+ """
109
+ # TODO: Add support for multiple audio and text inputs.
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+ data = {}
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+ audio_embed_frames = 0
112
+ if audio is not None and len(audio) > 0:
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+ if self.audio_padding == "max_length":
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+ # 30 seconds is the expected length for Whisper
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+ assert sampling_rate is not None, "Sampling rate must be provided."
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+ audio_len = 30 * sampling_rate
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+ else:
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+ audio_len = audio.shape[-1]
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+ # It's guaranteed that the number of frames is less than or equal to this amount.
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+ # For Whisper this is exact AFAICT, but for Wav2Vec2 it's an upper bound.
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+ # Currently, StackAudioFrames makes sure an over-estimation won't cause issues by padding the audio embeddings.
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+ nb_encoder_frames = int(round(audio_len / self.encoder_ds_factor + 1e-4))
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+ audio_embed_frames = int(np.ceil(nb_encoder_frames / self.stack_factor))
124
+ data["audio_token_len"] = [audio_embed_frames]
125
+
126
+ # Main audio processing. The processor is model-specific.
127
+ x = self.audio_processor(
128
+ audio,
129
+ sampling_rate=sampling_rate,
130
+ padding="longest",
131
+ max_length=audio_len,
132
+ **kwargs,
133
+ )
134
+ if "input_features" in x:
135
+ data["audio_values"] = x.input_features
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+ else:
137
+ data["audio_values"] = x.input_values
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+
139
+ if text is not None:
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+ assert isinstance(
141
+ text, str
142
+ ), "Text must be a string. Batch mode not supported yet."
143
+ if self.audio_placeholder in text:
144
+ if "audio_token_len" not in data:
145
+ raise ValueError(
146
+ f"audio must be provided when using audio placeholder ({self.audio_placeholder}) in text."
147
+ )
148
+
149
+ start_idx = len(
150
+ self.tokenizer.encode(
151
+ text[: text.index(self.audio_placeholder)],
152
+ add_special_tokens=False,
153
+ )
154
+ )
155
+ data["audio_token_start_idx"] = [start_idx]
156
+
157
+ # Replace the audio placeholder with the audio token.
158
+ # e.g. "Transcribe\n<|audio|>" -> "Transcribe </s></s></s></s></s></s></s></s>"
159
+ # where the number of </s> is the number of audio frames.
160
+ text = text.replace(
161
+ self.audio_placeholder,
162
+ self.audio_token_replacement * audio_embed_frames,
163
+ )
164
+
165
+ # Special tokens like BOS should already have been added by the caller.
166
+ data.update(self.tokenizer([text], add_special_tokens=False, **kwargs))
167
+
168
+ return transformers.BatchFeature(data=data, tensor_type=return_tensors)
169
+
170
+ def batch_decode(self, *args, **kwargs):
171
+ return self.tokenizer.batch_decode(*args, **kwargs)
172
+
173
+ def decode(self, *args, **kwargs):
174
+ return self.tokenizer.decode(*args, **kwargs)
175
+
176
+ @property
177
+ def model_input_names(self):
178
+ tokenizer_input_names = self.tokenizer.model_input_names
179
+ audio_processor_input_names = self.audio_processor.model_input_names
180
+ return list(set(tokenizer_input_names + audio_processor_input_names))