Move RNNT Loss out of prototype (#1711)

CMakeLists.txt

@@ -56,7 +56,7 @@ endif()
 # Options
 option(BUILD_SOX "Build libsox statically" OFF)
 option(BUILD_KALDI "Build kaldi statically" ON)
-option(BUILD_RNNT "Enable RNN transducer" OFF)
+option(BUILD_RNNT "Enable RNN transducer" ON)
 option(BUILD_LIBTORCHAUDIO "Build C++ Library" ON)
 option(BUILD_TORCHAUDIO_PYTHON_EXTENSION "Build Python extension" OFF)
 option(USE_CUDA "Enable CUDA support" OFF)

README.md

@@ -82,6 +82,7 @@ conda install -y -c pytorch-nightly torchaudio
 
 The build process builds libsox and some codecs that torchaudio need to link to. This is achieved by setting the environment variable `BUILD_SOX=1`.
 The build process will fetch and build libmad, lame, flac, vorbis, opus, and libsox before building extension. This process requires `cmake` and `pkg-config`.
+The build process also builds the RNN transducer loss. This functionality can be disabled by setting the environment variable `BUILD_RNNT=0`.
 
 ```bash
 # Linux

build_tools/setup_helpers/extension.py

@@ -36,7 +36,7 @@ def _get_build(var, default=False):
 
 _BUILD_SOX = False if platform.system() == 'Windows' else _get_build("BUILD_SOX")
 _BUILD_KALDI = False if platform.system() == 'Windows' else _get_build("BUILD_KALDI", True)
-_BUILD_RNNT = _get_build("BUILD_RNNT")
+_BUILD_RNNT = _get_build("BUILD_RNNT", True)
 _USE_ROCM = _get_build("USE_ROCM")
 _USE_CUDA = _get_build("USE_CUDA", torch.cuda.is_available())
 

docs/source/functional.rst

@@ -256,6 +256,14 @@ vad
 
 .. autofunction:: spectral_centroid
 
+:hidden:`Loss`
+~~~~~~~~~~~~~~
+
+rnnt_loss
+---------
+
+.. autofunction:: rnnt_loss
+
 References
 ~~~~~~~~~~
 

docs/source/index.rst

@@ -39,7 +39,6 @@ The :mod:`torchaudio` package consists of I/O, popular datasets and common audio
    compliance.kaldi
    kaldi_io
    utils
-   rnnt_loss
 
 
 .. toctree::

docs/source/rnnt_loss.rst

-.. role:: hidden
-    :class: hidden-section
-
-torchaudio.prototype.rnnt_loss
-==============================
-
-.. currentmodule:: torchaudio.prototype.rnnt_loss
-
-.. note::
-
-    The RNN transducer loss is a prototype feature, see `here <https://pytorch.org/audio>`_ to learn more about the nomenclature. It is only available within the nightlies, and also needs to be imported explicitly using: :code:`from torchaudio.prototype.rnnt_loss import rnnt_loss, RNNTLoss`.
-
-rnnt_loss
-~~~~~~~~~
-
-.. autofunction:: rnnt_loss
-
-RNNTLoss
-~~~~~~~~
-
-.. autoclass:: RNNTLoss
-
-  .. automethod:: forward
-
-References
-~~~~~~~~~~
-
-.. footbibliography::

docs/source/transforms.rst

@@ -171,6 +171,12 @@ Transforms are common audio transforms. They can be chained together using :clas
 
   .. automethod:: forward
 
+:hidden:`RNNTLoss`
+~~~~~~~~~~~~~~~~~~
+
+.. autoclass:: RNNTLoss
+
+  .. automethod:: forward
 
 References
 ~~~~~~~~~~

examples/libtorchaudio/CMakeLists.txt

@@ -6,7 +6,7 @@ SET(BUILD_LIBTORCHAUDIO ON CACHE BOOL "Build libtorchaudio")
 SET(BUILD_SOX ON CACHE BOOL "Build libsox into libtorchaudio")
 
 SET(BUILD_KALDI OFF CACHE BOOL "Build Kaldi into libtorchaudio")
-SET(BUILD_RNNT OFF CACHE BOOL "Build RNN transducer into libtorchaudio")
+SET(BUILD_RNNT ON CACHE BOOL "Build RNN transducer into libtorchaudio")
 SET(BUILD_TORCHAUDIO_PYTHON_EXTENSION OFF CACHE BOOL "Build Python binding")
 
 find_package(Torch REQUIRED)

examples/libtorchaudio/README.md

@@ -22,6 +22,7 @@ cmake -GNinja \
       -DCMAKE_PREFIX_PATH="$(python -c 'import torch;print(torch.utils.cmake_prefix_path)')" \
       -DBUILD_SOX=ON \
       -DBUILD_KALDI=OFF \
+      -DBUILD_RNNT=ON \
       ..
 cmake --build .
 ```

packaging/build_wheel.sh

@@ -15,5 +15,5 @@ if [[ "$OSTYPE" == "msys" ]]; then
     python_tag="$(echo "cp$PYTHON_VERSION" | tr -d '.')"
     "$script_dir/vc_env_helper.bat" python setup.py bdist_wheel --plat-name win_amd64 --python-tag $python_tag
 else
-    BUILD_RNNT=1 BUILD_SOX=1 python setup.py bdist_wheel
+    BUILD_SOX=1 python setup.py bdist_wheel
 fi

packaging/torchaudio/build.sh

 #!/usr/bin/env bash
 set -ex
 
-BUILD_RNNT=1 BUILD_SOX=1 python setup.py install --single-version-externally-managed --record=record.txt
+BUILD_SOX=1 python setup.py install --single-version-externally-managed --record=record.txt

test/torchaudio_unittest/rnnt/utils.py → test/torchaudio_unittest/common_utils/rnnt_utils.py

 import unittest
 import random
 import torch
-from torchaudio.prototype.rnnt_loss import RNNTLoss
-
-from .numpy_transducer import NumpyTransducerLoss
+import numpy as np
+from torchaudio.functional import rnnt_loss
+
+
+class _NumpyTransducer(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        log_probs,
+        logit_lengths,
+        target_lengths,
+        targets,
+        blank=-1,
+    ):
+        device = log_probs.device
+        log_probs = log_probs.cpu().data.numpy()
+        logit_lengths = logit_lengths.cpu().data.numpy()
+        target_lengths = target_lengths.cpu().data.numpy()
+        targets = targets.cpu().data.numpy()
+
+        gradients, costs, _, _ = __class__.compute(
+            log_probs=log_probs,
+            logit_lengths=logit_lengths,
+            target_lengths=target_lengths,
+            targets=targets,
+            blank=blank,
+        )
+
+        costs = torch.FloatTensor(costs).to(device=device)
+        gradients = torch.FloatTensor(gradients).to(device=device)
+        ctx.grads = torch.autograd.Variable(gradients)
+
+        return costs
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        grad_output = grad_output.view(-1, 1, 1, 1).to(ctx.grads)
+        return ctx.grads.mul(grad_output), None, None, None, None, None, None, None, None
+
+    @staticmethod
+    def compute_alpha_one_sequence(log_probs, targets, blank=-1):
+        max_T, max_U, D = log_probs.shape
+        alpha = np.zeros((max_T, max_U), dtype=np.float32)
+        for t in range(1, max_T):
+            alpha[t, 0] = alpha[t - 1, 0] + log_probs[t - 1, 0, blank]
+
+        for u in range(1, max_U):
+            alpha[0, u] = alpha[0, u - 1] + log_probs[0, u - 1, targets[u - 1]]
+
+        for t in range(1, max_T):
+            for u in range(1, max_U):
+                skip = alpha[t - 1, u] + log_probs[t - 1, u, blank]
+                emit = alpha[t, u - 1] + log_probs[t, u - 1, targets[u - 1]]
+                alpha[t, u] = np.logaddexp(skip, emit)
+
+        cost = -(alpha[-1, -1] + log_probs[-1, -1, blank])
+        return alpha, cost
+
+    @staticmethod
+    def compute_beta_one_sequence(log_probs, targets, blank=-1):
+        max_T, max_U, D = log_probs.shape
+        beta = np.zeros((max_T, max_U), dtype=np.float32)
+        beta[-1, -1] = log_probs[-1, -1, blank]
+
+        for t in reversed(range(max_T - 1)):
+            beta[t, -1] = beta[t + 1, -1] + log_probs[t, -1, blank]
+
+        for u in reversed(range(max_U - 1)):
+            beta[-1, u] = beta[-1, u + 1] + log_probs[-1, u, targets[u]]
+
+        for t in reversed(range(max_T - 1)):
+            for u in reversed(range(max_U - 1)):
+                skip = beta[t + 1, u] + log_probs[t, u, blank]
+                emit = beta[t, u + 1] + log_probs[t, u, targets[u]]
+                beta[t, u] = np.logaddexp(skip, emit)
+
+        cost = -beta[0, 0]
+        return beta, cost
+
+    @staticmethod
+    def compute_gradients_one_sequence(
+        log_probs, alpha, beta, targets, blank=-1
+    ):
+        max_T, max_U, D = log_probs.shape
+        gradients = np.full(log_probs.shape, float("-inf"))
+        cost = -beta[0, 0]
+
+        gradients[-1, -1, blank] = alpha[-1, -1]
+
+        gradients[:-1, :, blank] = alpha[:-1, :] + beta[1:, :]
+
+        for u, l in enumerate(targets):
+            gradients[:, u, l] = alpha[:, u] + beta[:, u + 1]
+
+        gradients = -(np.exp(gradients + log_probs + cost))
+        return gradients
+
+    @staticmethod
+    def compute(
+        log_probs,
+        logit_lengths,
+        target_lengths,
+        targets,
+        blank=-1,
+    ):
+        gradients = np.zeros_like(log_probs)
+        B_tgt, max_T, max_U, D = log_probs.shape
+        B_src = logit_lengths.shape[0]
+
+        H = int(B_tgt / B_src)
+
+        alphas = np.zeros((B_tgt, max_T, max_U))
+        betas = np.zeros((B_tgt, max_T, max_U))
+        betas.fill(float("-inf"))
+        alphas.fill(float("-inf"))
+        costs = np.zeros(B_tgt)
+        for b_tgt in range(B_tgt):
+            b_src = int(b_tgt / H)
+            T = int(logit_lengths[b_src])
+            # NOTE: see https://arxiv.org/pdf/1211.3711.pdf Section 2.1
+            U = int(target_lengths[b_tgt]) + 1
+
+            seq_log_probs = log_probs[b_tgt, :T, :U, :]
+            seq_targets = targets[b_tgt, : int(target_lengths[b_tgt])]
+            alpha, alpha_cost = __class__.compute_alpha_one_sequence(
+                log_probs=seq_log_probs, targets=seq_targets, blank=blank
+            )
+
+            beta, beta_cost = __class__.compute_beta_one_sequence(
+                log_probs=seq_log_probs, targets=seq_targets, blank=blank
+            )
+
+            seq_gradients = __class__.compute_gradients_one_sequence(
+                log_probs=seq_log_probs,
+                alpha=alpha,
+                beta=beta,
+                targets=seq_targets,
+                blank=blank,
+            )
+            np.testing.assert_almost_equal(alpha_cost, beta_cost, decimal=2)
+            gradients[b_tgt, :T, :U, :] = seq_gradients
+            costs[b_tgt] = beta_cost
+            alphas[b_tgt, :T, :U] = alpha
+            betas[b_tgt, :T, :U] = beta
+
+        return gradients, costs, alphas, betas
+
+
+class NumpyTransducerLoss(torch.nn.Module):
+    def __init__(self, blank=-1):
+        super().__init__()
+        self.blank = blank
+
+    def forward(
+        self,
+        logits,
+        logit_lengths,
+        target_lengths,
+        targets,
+    ):
+        log_probs = torch.nn.functional.log_softmax(logits, dim=-1)
+        return _NumpyTransducer.apply(
+            log_probs,
+            logit_lengths,
+            target_lengths,
+            targets,
+            self.blank,
+        )
 
 
 def compute_with_numpy_transducer(data):
@@ -24,14 +189,13 @@ def compute_with_numpy_transducer(data):
 
 
 def compute_with_pytorch_transducer(data):
-    costs = RNNTLoss(
-        blank=data["blank"],
-        reduction="none",
-    )(
+    costs = rnnt_loss(
         logits=data["logits"],
         logit_lengths=data["logit_lengths"],
         target_lengths=data["target_lengths"],
         targets=data["targets"],
+        blank=data["blank"],
+        reduction="none",
     )
 
     loss = torch.sum(costs)

test/torchaudio_unittest/functional/autograd_cpu_test.py

 import torch
-from .autograd_impl import Autograd
+from .autograd_impl import Autograd, AutogradFloat32
 from torchaudio_unittest import common_utils
 
 
 class TestAutogradLfilterCPU(Autograd, common_utils.PytorchTestCase):
     dtype = torch.float64
     device = torch.device('cpu')
+
+
+class TestAutogradRNNTCPU(AutogradFloat32, common_utils.PytorchTestCase):
+    dtype = torch.float32
+    device = torch.device('cpu')

test/torchaudio_unittest/functional/autograd_cuda_test.py

 import torch
-from .autograd_impl import Autograd
+from .autograd_impl import Autograd, AutogradFloat32
 from torchaudio_unittest import common_utils
 
 
@@ -7,3 +7,9 @@ from torchaudio_unittest import common_utils
 class TestAutogradLfilterCUDA(Autograd, common_utils.PytorchTestCase):
     dtype = torch.float64
     device = torch.device('cuda')
+
+
+@common_utils.skipIfNoCuda
+class TestAutogradRNNTCUDA(AutogradFloat32, common_utils.PytorchTestCase):
+    dtype = torch.float32
+    device = torch.device('cuda')

test/torchaudio_unittest/functional/autograd_impl.py

@@ -8,6 +8,7 @@ from torch.autograd import gradcheck, gradgradcheck
 from torchaudio_unittest.common_utils import (
     TestBaseMixin,
     get_whitenoise,
+    rnnt_utils,
 )
 
 
@@ -192,3 +193,45 @@ class Autograd(TestBaseMixin):
         central_freq = torch.tensor(central_freq)
         Q = torch.tensor(Q)
         self.assert_grad(F.bandreject_biquad, (x, sr, central_freq, Q))
+
+
+class AutogradFloat32(TestBaseMixin):
+    def assert_grad(
+            self,
+            transform: Callable[..., Tensor],
+            inputs: Tuple[torch.Tensor],
+            enable_all_grad: bool = True,
+    ):
+        inputs_ = []
+        for i in inputs:
+            if torch.is_tensor(i):
+                i = i.to(dtype=self.dtype, device=self.device)
+                if enable_all_grad:
+                    i.requires_grad = True
+            inputs_.append(i)
+        # gradcheck with float32 requires higher atol and epsilon
+        assert gradcheck(transform, inputs, eps=1e-3, atol=1e-3, nondet_tol=0.)
+
+    @parameterized.expand([
+        (rnnt_utils.get_B1_T10_U3_D4_data, ),
+        (rnnt_utils.get_B2_T4_U3_D3_data, ),
+        (rnnt_utils.get_B1_T2_U3_D5_data, ),
+    ])
+    def test_rnnt_loss(self, data_func):
+        def get_data(data_func, device):
+            data = data_func()
+            if type(data) == tuple:
+                data = data[0]
+            return data
+
+        data = get_data(data_func, self.device)
+        inputs = (
+            data["logits"].to(torch.float32),  # logits
+            data["targets"],                # targets
+            data["logit_lengths"],          # logit_lengths
+            data["target_lengths"],         # target_lengths
+            data["blank"],                  # blank
+            -1,                             # clamp
+        )
+
+        self.assert_grad(F.rnnt_loss, inputs, enable_all_grad=False)

test/torchaudio_unittest/functional/functional_impl.py

@@ -9,7 +9,13 @@ import torchaudio.functional as F
 from parameterized import parameterized
 from scipy import signal
 
-from torchaudio_unittest.common_utils import TestBaseMixin, get_sinusoid, nested_params, get_whitenoise
+from torchaudio_unittest.common_utils import (
+    TestBaseMixin,
+    get_sinusoid,
+    nested_params,
+    get_whitenoise,
+    rnnt_utils,
+)
 
 
 class Functional(TestBaseMixin):
@@ -42,6 +48,15 @@ class Functional(TestBaseMixin):
 
         self.assertEqual(estimate, ground_truth, atol=atol, rtol=rtol)
 
+    def _test_costs_and_gradients(
+        self, data, ref_costs, ref_gradients, atol=1e-6, rtol=1e-2
+    ):
+        logits_shape = data["logits"].shape
+        costs, gradients = rnnt_utils.compute_with_pytorch_transducer(data=data)
+        self.assertEqual(costs, ref_costs, atol=atol, rtol=rtol)
+        self.assertEqual(logits_shape, gradients.shape)
+        self.assertEqual(gradients, ref_gradients, atol=atol, rtol=rtol)
+
     def test_lfilter_simple(self):
         """
         Create a very basic signal,
@@ -436,6 +451,50 @@ class Functional(TestBaseMixin):
         waveform_shift = F.pitch_shift(waveform, sample_rate, n_steps)
         assert waveform.size() == waveform_shift.size()
 
+    def test_rnnt_loss_basic_backward(self):
+        logits, targets, logit_lengths, target_lengths = rnnt_utils.get_basic_data(self.device)
+        loss = F.rnnt_loss(logits, targets, logit_lengths, target_lengths)
+        loss.backward()
+
+    def test_rnnt_loss_basic_forward_no_grad(self):
+        """In early stage, calls to `rnnt_loss` resulted in segmentation fault when
+        `logits` have `requires_grad = False`. This test makes sure that this no longer
+        occurs and the functional call runs without error.
+
+        See https://github.com/pytorch/audio/pull/1707
+        """
+        logits, targets, logit_lengths, target_lengths = rnnt_utils.get_basic_data(self.device)
+        logits.requires_grad_(False)
+        F.rnnt_loss(logits, targets, logit_lengths, target_lengths)
+
+    @parameterized.expand([
+        (rnnt_utils.get_B1_T2_U3_D5_data, torch.float32, 1e-6, 1e-2),
+        (rnnt_utils.get_B2_T4_U3_D3_data, torch.float32, 1e-6, 1e-2),
+        (rnnt_utils.get_B1_T2_U3_D5_data, torch.float16, 1e-3, 1e-2),
+        (rnnt_utils.get_B2_T4_U3_D3_data, torch.float16, 1e-3, 1e-2),
+    ])
+    def test_rnnt_loss_costs_and_gradients(self, data_func, dtype, atol, rtol):
+        data, ref_costs, ref_gradients = data_func(
+            dtype=dtype,
+            device=self.device,
+        )
+        self._test_costs_and_gradients(
+            data=data,
+            ref_costs=ref_costs,
+            ref_gradients=ref_gradients,
+            atol=atol,
+            rtol=rtol,
+        )
+
+    def test_rnnt_loss_costs_and_gradients_random_data_with_numpy_fp32(self):
+        seed = 777
+        for i in range(5):
+            data = rnnt_utils.get_random_data(dtype=torch.float32, device=self.device, seed=(seed + i))
+            ref_costs, ref_gradients = rnnt_utils.compute_with_numpy_transducer(data=data)
+            self._test_costs_and_gradients(
+                data=data, ref_costs=ref_costs, ref_gradients=ref_gradients
+            )
+
 
 class FunctionalCPUOnly(TestBaseMixin):
     def test_melscale_fbanks_no_warning_high_n_freq(self):

test/torchaudio_unittest/functional/torchscript_consistency_cpu_test.py

 import torch
 
 from torchaudio_unittest.common_utils import PytorchTestCase
-from .torchscript_consistency_impl import Functional
+from .torchscript_consistency_impl import Functional, FunctionalFloat32Only
 
 
-class TestFunctionalFloat32(Functional, PytorchTestCase):
+class TestFunctionalFloat32(Functional, FunctionalFloat32Only, PytorchTestCase):
     dtype = torch.float32
     device = torch.device('cpu')
 

test/torchaudio_unittest/functional/torchscript_consistency_cuda_test.py

 import torch
 
 from torchaudio_unittest.common_utils import skipIfNoCuda, PytorchTestCase
-from .torchscript_consistency_impl import Functional
+from .torchscript_consistency_impl import Functional, FunctionalFloat32Only
 
 
 @skipIfNoCuda
-class TestFunctionalFloat32(Functional, PytorchTestCase):
+class TestFunctionalFloat32(Functional, FunctionalFloat32Only, PytorchTestCase):
     dtype = torch.float32
     device = torch.device('cuda')
 

test/torchaudio_unittest/functional/torchscript_consistency_impl.py

@@ -692,3 +692,21 @@ class Functional(TempDirMixin, TestBaseMixin):
 
         tensor = torch.view_as_complex(torch.randn(2, 1025, 400, 2))
         self._assert_consistency_complex(func, tensor, test_paseudo_complex)
+
+
+class FunctionalFloat32Only(TestBaseMixin):
+    def test_rnnt_loss(self):
+        def func(tensor):
+            targets = torch.tensor([[1, 2]], device=tensor.device, dtype=torch.int32)
+            logit_lengths = torch.tensor([2], device=tensor.device, dtype=torch.int32)
+            target_lengths = torch.tensor([2], device=tensor.device, dtype=torch.int32)
+            return F.rnnt_loss(tensor, targets, logit_lengths, target_lengths)
+
+        logits = torch.tensor([[[[0.1, 0.6, 0.1, 0.1, 0.1],
+                                 [0.1, 0.1, 0.6, 0.1, 0.1],
+                                 [0.1, 0.1, 0.2, 0.8, 0.1]],
+                                [[0.1, 0.6, 0.1, 0.1, 0.1],
+                                 [0.1, 0.1, 0.2, 0.1, 0.1],
+                                 [0.7, 0.1, 0.2, 0.1, 0.1]]]])
+        tensor = logits.to(device=self.device, dtype=torch.float32)
+        self._assert_consistency(func, tensor)