Datasets:

echodict
/

NeMo

# Copyright (c) 2022, NVIDIA CORPORATION.  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.
import numpy as np
import pytest
import torch

from nemo.collections.asr.modules.conformer_encoder import ConformerEncoder


class TestStochasticDepth:
    """Testing stochastic depth functionality."""

    def test_stochastic_depth_model_creation(self):
        """Testing basic model creation and the drop probs are correctly assigned."""
        n_layers = 4
        model = ConformerEncoder(feat_in=10, n_layers=n_layers, d_model=4, feat_out=8)

        # checking that by default SD is disabled
        assert model.layer_drop_probs == [0.0] * n_layers

        # linear mode
        for drop_prob in [0.3, 0.5, 0.9]:
            for start_layer in [1, 3]:
                model = ConformerEncoder(
                    feat_in=10,
                    n_layers=n_layers,
                    d_model=4,
                    feat_out=8,
                    stochastic_depth_drop_prob=drop_prob,
                    stochastic_depth_start_layer=start_layer,
                )
                L = n_layers - start_layer
                assert model.layer_drop_probs == [0.0] * start_layer + [drop_prob * l / L for l in range(1, L + 1)]

        # uniform mode
        for drop_prob in [0.3, 0.5, 0.9]:
            model = ConformerEncoder(
                feat_in=10,
                n_layers=n_layers,
                d_model=4,
                feat_out=8,
                stochastic_depth_drop_prob=drop_prob,
                stochastic_depth_mode="uniform",
                stochastic_depth_start_layer=start_layer,
            )
            L = n_layers - start_layer
            assert model.layer_drop_probs == [0.0] * start_layer + [drop_prob] * L

        # checking for errors
        for drop_prob in [-1.0, 1.0]:
            with pytest.raises(ValueError, match="stochastic_depth_drop_prob has to be in"):
                ConformerEncoder(
                    feat_in=10,
                    n_layers=n_layers,
                    d_model=4,
                    feat_out=8,
                    stochastic_depth_drop_prob=drop_prob,
                    stochastic_depth_mode="uniform",
                )

        with pytest.raises(ValueError, match="stochastic_depth_mode has to be one of"):
            ConformerEncoder(feat_in=10, n_layers=n_layers, d_model=4, feat_out=8, stochastic_depth_mode="weird")

        for start_layer in [-1, 0, 5]:
            with pytest.raises(ValueError, match="stochastic_depth_start_layer has to be in"):
                ConformerEncoder(
                    feat_in=10,
                    n_layers=n_layers,
                    d_model=4,
                    feat_out=8,
                    stochastic_depth_start_layer=start_layer,
                )

    @pytest.mark.pleasefixme
    def test_stochastic_depth_forward(self):
        """Testing that forward works and we get randomness during training, but not during eval."""
        random_input = torch.rand((1, 2, 2))
        random_length = torch.tensor([2], dtype=torch.int64)

        model = ConformerEncoder(
            feat_in=2,
            n_layers=3,
            d_model=4,
            feat_out=4,
            stochastic_depth_drop_prob=0.8,
            dropout=0.0,
            dropout_pre_encoder=0.0,
            dropout_emb=0.0,
            conv_norm_type="layer_norm",
            conv_kernel_size=3,
        )
        model.train()
        outputs = [None] * 5
        for i in range(5):
            outputs[i] = model(audio_signal=random_input, length=random_length)[0]
        # checking that not all outputs are the same
        num_diff = 0
        for i in range(1, 5):
            if not torch.allclose(outputs[i], outputs[0]):
                num_diff += 1
        assert num_diff > 0

        model.eval()
        outputs = [None] * 5
        for i in range(5):
            outputs[i] = model(audio_signal=random_input, length=random_length)[0]
        # checking that not all outputs are the same
        num_diff = 0
        for i in range(1, 5):
            if not torch.allclose(outputs[i], outputs[0]):
                num_diff += 1
        assert num_diff == 0


class TestBypassPreEncode:
    """Testing bypass pre-encode functionality."""

    def test_bypass_pre_encode_forward(self):
        """Testing that forward works with "bypass pre-encode" mode."""
        # For pre-encoded embeddings, the shape is (batch_size, n_frames, emb_dim)
        batch_size = 2
        n_frames, emb_dim, feat_out = 17, 16, 8
        random_input = torch.rand((batch_size, n_frames, emb_dim))
        random_length = torch.tensor([n_frames], dtype=torch.int64)

        model = ConformerEncoder(
            feat_in=10,
            n_layers=3,
            d_model=emb_dim,
            feat_out=feat_out,
            stochastic_depth_drop_prob=0.0,
            dropout=0.0,
            dropout_pre_encoder=0.0,
            dropout_emb=0.0,
            conv_norm_type="layer_norm",
            conv_kernel_size=3,
        )
        model.train()
        fwd_outputs = model(audio_signal=random_input, length=random_length, bypass_pre_encode=True)[0]
        assert fwd_outputs.shape == (batch_size, feat_out, n_frames)

        model.eval()
        fwd_outputs = model(audio_signal=random_input, length=random_length, bypass_pre_encode=True)[0]
        assert fwd_outputs.shape == (batch_size, feat_out, n_frames)

    def test_error_shape_invalid_bypass_pre_encode_forward(self):
        """
        Testing that error messages are correctly triggered regarding "bypass pre-encode" mode.
        Both correct samples and wrongs samples are tested.
        (1) bypass_pre_encode = False (default):
            `audio_signal` must be a tensor containing audio features.
            Shape: (batch, self._feat_in, n_frames)
        (2) bypass_pre_encode = True:
            `audio_signal` must be a tensor containing pre-encoded embeddings.
            Shape: (batch, n_frame, self.d_model)
        """
        batch_size = 2
        n_frames, emb_dim, feat_in, feat_out = 17, 16, 10, 8

        pre_encode_input = torch.rand((batch_size, n_frames, emb_dim))
        feat_input = torch.rand((batch_size, feat_in, n_frames))
        input_length = torch.tensor([n_frames], dtype=torch.int64)

        model = ConformerEncoder(
            feat_in=feat_in,
            n_layers=3,
            d_model=emb_dim,
            feat_out=feat_out,
            stochastic_depth_drop_prob=0.0,
            dropout=0.0,
            dropout_pre_encoder=0.0,
            dropout_emb=0.0,
            conv_norm_type="layer_norm",
            conv_kernel_size=3,
        )
        sub_sampled_n_frames = np.ceil(n_frames / model.subsampling_factor)

        # Test with bypass_pre_encode = True, should be pre_encode_input but given feat_input.
        model.train()
        with pytest.raises(ValueError):
            model(audio_signal=feat_input, length=input_length, bypass_pre_encode=True)

        model.eval()
        with pytest.raises(ValueError):
            model(audio_signal=feat_input, length=input_length, bypass_pre_encode=True)

        # Test with bypass_pre_encode = True, given the correct input pre_encode_input.
        model.train()
        fwd_outputs = model(audio_signal=pre_encode_input, length=input_length, bypass_pre_encode=True)[0]
        assert fwd_outputs.shape == (batch_size, feat_out, n_frames)

        model.eval()
        fwd_outputs = model(audio_signal=pre_encode_input, length=input_length, bypass_pre_encode=True)[0]
        assert fwd_outputs.shape == (batch_size, feat_out, n_frames)

        # Test with bypass_pre_encode = False, should be feat_input but given pre_encode_input.
        model.train()
        with pytest.raises(ValueError):
            model(audio_signal=pre_encode_input, length=input_length, bypass_pre_encode=False)

        model.eval()
        with pytest.raises(ValueError):
            model(audio_signal=pre_encode_input, length=input_length, bypass_pre_encode=False)

        # Test with bypass_pre_encode = False, given the correct input feat_input.
        model.train()
        fwd_outputs = model(audio_signal=feat_input, length=input_length, bypass_pre_encode=False)[0]
        assert fwd_outputs.shape == (batch_size, feat_out, sub_sampled_n_frames)

        model.eval()
        fwd_outputs = model(audio_signal=feat_input, length=input_length, bypass_pre_encode=False)[0]
        assert fwd_outputs.shape == (batch_size, feat_out, sub_sampled_n_frames)

	# Copyright (c) 2022, NVIDIA CORPORATION. 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.
	import numpy as np
	import pytest
	import torch

	from nemo.collections.asr.modules.conformer_encoder import ConformerEncoder


	class TestStochasticDepth:
	"""Testing stochastic depth functionality."""

	def test_stochastic_depth_model_creation(self):
	"""Testing basic model creation and the drop probs are correctly assigned."""
	n_layers = 4
	model = ConformerEncoder(feat_in=10, n_layers=n_layers, d_model=4, feat_out=8)

	# checking that by default SD is disabled
	assert model.layer_drop_probs == [0.0] * n_layers

	# linear mode
	for drop_prob in [0.3, 0.5, 0.9]:
	for start_layer in [1, 3]:
	model = ConformerEncoder(
	feat_in=10,
	n_layers=n_layers,
	d_model=4,
	feat_out=8,
	stochastic_depth_drop_prob=drop_prob,
	stochastic_depth_start_layer=start_layer,
	)
	L = n_layers - start_layer
	assert model.layer_drop_probs == [0.0] * start_layer + [drop_prob * l / L for l in range(1, L + 1)]

	# uniform mode
	for drop_prob in [0.3, 0.5, 0.9]:
	model = ConformerEncoder(
	feat_in=10,
	n_layers=n_layers,
	d_model=4,
	feat_out=8,
	stochastic_depth_drop_prob=drop_prob,
	stochastic_depth_mode="uniform",
	stochastic_depth_start_layer=start_layer,
	)
	L = n_layers - start_layer
	assert model.layer_drop_probs == [0.0] * start_layer + [drop_prob] * L

	# checking for errors
	for drop_prob in [-1.0, 1.0]:
	with pytest.raises(ValueError, match="stochastic_depth_drop_prob has to be in"):
	ConformerEncoder(
	feat_in=10,
	n_layers=n_layers,
	d_model=4,
	feat_out=8,
	stochastic_depth_drop_prob=drop_prob,
	stochastic_depth_mode="uniform",
	)

	with pytest.raises(ValueError, match="stochastic_depth_mode has to be one of"):
	ConformerEncoder(feat_in=10, n_layers=n_layers, d_model=4, feat_out=8, stochastic_depth_mode="weird")

	for start_layer in [-1, 0, 5]:
	with pytest.raises(ValueError, match="stochastic_depth_start_layer has to be in"):
	ConformerEncoder(
	feat_in=10,
	n_layers=n_layers,
	d_model=4,
	feat_out=8,
	stochastic_depth_start_layer=start_layer,
	)

	@pytest.mark.pleasefixme
	def test_stochastic_depth_forward(self):
	"""Testing that forward works and we get randomness during training, but not during eval."""
	random_input = torch.rand((1, 2, 2))
	random_length = torch.tensor([2], dtype=torch.int64)

	model = ConformerEncoder(
	feat_in=2,
	n_layers=3,
	d_model=4,
	feat_out=4,
	stochastic_depth_drop_prob=0.8,
	dropout=0.0,
	dropout_pre_encoder=0.0,
	dropout_emb=0.0,
	conv_norm_type="layer_norm",
	conv_kernel_size=3,
	)
	model.train()
	outputs = [None] * 5
	for i in range(5):
	outputs[i] = model(audio_signal=random_input, length=random_length)[0]
	# checking that not all outputs are the same
	num_diff = 0
	for i in range(1, 5):
	if not torch.allclose(outputs[i], outputs[0]):
	num_diff += 1
	assert num_diff > 0

	model.eval()
	outputs = [None] * 5
	for i in range(5):
	outputs[i] = model(audio_signal=random_input, length=random_length)[0]
	# checking that not all outputs are the same
	num_diff = 0
	for i in range(1, 5):
	if not torch.allclose(outputs[i], outputs[0]):
	num_diff += 1
	assert num_diff == 0


	class TestBypassPreEncode:
	"""Testing bypass pre-encode functionality."""

	def test_bypass_pre_encode_forward(self):
	"""Testing that forward works with "bypass pre-encode" mode."""
	# For pre-encoded embeddings, the shape is (batch_size, n_frames, emb_dim)
	batch_size = 2
	n_frames, emb_dim, feat_out = 17, 16, 8
	random_input = torch.rand((batch_size, n_frames, emb_dim))
	random_length = torch.tensor([n_frames], dtype=torch.int64)

	model = ConformerEncoder(
	feat_in=10,
	n_layers=3,
	d_model=emb_dim,
	feat_out=feat_out,
	stochastic_depth_drop_prob=0.0,
	dropout=0.0,
	dropout_pre_encoder=0.0,
	dropout_emb=0.0,
	conv_norm_type="layer_norm",
	conv_kernel_size=3,
	)
	model.train()
	fwd_outputs = model(audio_signal=random_input, length=random_length, bypass_pre_encode=True)[0]
	assert fwd_outputs.shape == (batch_size, feat_out, n_frames)

	model.eval()
	fwd_outputs = model(audio_signal=random_input, length=random_length, bypass_pre_encode=True)[0]
	assert fwd_outputs.shape == (batch_size, feat_out, n_frames)

	def test_error_shape_invalid_bypass_pre_encode_forward(self):
	"""
	Testing that error messages are correctly triggered regarding "bypass pre-encode" mode.
	Both correct samples and wrongs samples are tested.

	(1) bypass_pre_encode = False (default):
	`audio_signal` must be a tensor containing audio features.
	Shape: (batch, self._feat_in, n_frames)
	(2) bypass_pre_encode = True:
	`audio_signal` must be a tensor containing pre-encoded embeddings.
	Shape: (batch, n_frame, self.d_model)
	"""
	batch_size = 2
	n_frames, emb_dim, feat_in, feat_out = 17, 16, 10, 8

	pre_encode_input = torch.rand((batch_size, n_frames, emb_dim))
	feat_input = torch.rand((batch_size, feat_in, n_frames))
	input_length = torch.tensor([n_frames], dtype=torch.int64)

	model = ConformerEncoder(
	feat_in=feat_in,
	n_layers=3,
	d_model=emb_dim,
	feat_out=feat_out,
	stochastic_depth_drop_prob=0.0,
	dropout=0.0,
	dropout_pre_encoder=0.0,
	dropout_emb=0.0,
	conv_norm_type="layer_norm",
	conv_kernel_size=3,
	)
	sub_sampled_n_frames = np.ceil(n_frames / model.subsampling_factor)

	# Test with bypass_pre_encode = True, should be pre_encode_input but given feat_input.
	model.train()
	with pytest.raises(ValueError):
	model(audio_signal=feat_input, length=input_length, bypass_pre_encode=True)

	model.eval()
	with pytest.raises(ValueError):
	model(audio_signal=feat_input, length=input_length, bypass_pre_encode=True)

	# Test with bypass_pre_encode = True, given the correct input pre_encode_input.
	model.train()
	fwd_outputs = model(audio_signal=pre_encode_input, length=input_length, bypass_pre_encode=True)[0]
	assert fwd_outputs.shape == (batch_size, feat_out, n_frames)

	model.eval()
	fwd_outputs = model(audio_signal=pre_encode_input, length=input_length, bypass_pre_encode=True)[0]
	assert fwd_outputs.shape == (batch_size, feat_out, n_frames)

	# Test with bypass_pre_encode = False, should be feat_input but given pre_encode_input.
	model.train()
	with pytest.raises(ValueError):
	model(audio_signal=pre_encode_input, length=input_length, bypass_pre_encode=False)

	model.eval()
	with pytest.raises(ValueError):
	model(audio_signal=pre_encode_input, length=input_length, bypass_pre_encode=False)

	# Test with bypass_pre_encode = False, given the correct input feat_input.
	model.train()
	fwd_outputs = model(audio_signal=feat_input, length=input_length, bypass_pre_encode=False)[0]
	assert fwd_outputs.shape == (batch_size, feat_out, sub_sampled_n_frames)

	model.eval()
	fwd_outputs = model(audio_signal=feat_input, length=input_length, bypass_pre_encode=False)[0]
	assert fwd_outputs.shape == (batch_size, feat_out, sub_sampled_n_frames)