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#
# 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.
from unittest.mock import Mock
import pytest
import torch
from nemo.collections.asr.modules.transformer.transformer import TransformerDecoderNM
from nemo.collections.asr.modules.transformer.transformer_generators import (
BeamSearchSequenceGenerator,
BeamSearchSequenceGeneratorWithFusionModels,
GreedySequenceGenerator,
)
from nemo.collections.asr.parts.context_biasing import GPUBoostingTreeModel
from nemo.collections.asr.parts.submodules.multitask_beam_decoding import TransformerAEDBeamInfer
from nemo.collections.asr.parts.submodules.multitask_greedy_decoding import TransformerAEDGreedyInfer
from nemo.collections.asr.parts.submodules.ngram_lm import NGramGPULanguageModel
from nemo.collections.asr.parts.submodules.token_classifier import TokenClassifier
@pytest.fixture()
def deterministic_rng():
state = torch.get_rng_state()
torch.manual_seed(0)
yield
torch.set_rng_state(state)
@pytest.fixture()
def decoder_nm(deterministic_rng):
return TransformerDecoderNM(
vocab_size=8,
hidden_size=2,
num_layers=1,
inner_size=4,
num_attention_heads=1,
max_sequence_length=32,
).eval()
@pytest.fixture()
def nnet(decoder_nm):
ans = (
decoder_nm.embedding,
decoder_nm.decoder,
TokenClassifier(hidden_size=2, num_classes=8),
)
ans = tuple(m.eval() for m in ans)
return ans
@pytest.fixture()
def inputs():
B, T, C = 1, 5, 2
return (
torch.tensor([[1]], dtype=torch.long), # decoder_input_ids
torch.ones(B, T, C, dtype=torch.float), # encoder_hidden_states
torch.ones(B, T, dtype=torch.float), # encoder_input_mask
)
@pytest.fixture()
def tokenizer():
tok = Mock()
tok.pad = 0
tok.bos = 1
tok.eos = 2
return tok
@pytest.mark.parametrize('with_confidence', [False, True])
@pytest.mark.parametrize('return_xattn_scores', [False, True])
def test_greedy_decoding(inputs, nnet, deterministic_rng, with_confidence, return_xattn_scores):
gen = GreedySequenceGenerator(
*nnet, return_xattn_scores=return_xattn_scores, preserve_step_confidence=with_confidence
)
output = gen(*inputs)
assert len(output) == 4
best_path, hypotheses, confidence, xattn_list = output
assert best_path is not None
assert torch.is_tensor(best_path)
assert best_path.shape == (1, 25)
if return_xattn_scores:
assert len(xattn_list) == len(nnet[1].layers)
assert xattn_list[0].shape == (1, 1, 24, 5)
else:
assert xattn_list is None
assert hypotheses is None
if with_confidence:
assert confidence is not None
assert torch.is_tensor(confidence)
assert confidence.shape == best_path.shape
else:
assert confidence is None
@pytest.mark.parametrize('return_xattn_scores', [False, True])
def test_temperature_sampling_decoding(inputs, nnet, return_xattn_scores):
gen = GreedySequenceGenerator(*nnet, return_xattn_scores=return_xattn_scores, temperature=10.0, n_samples=2)
output = gen(*inputs)
assert len(output) == 4
best_path, hypotheses, _, xatt_list = output
assert best_path is not None
assert torch.is_tensor(best_path)
assert best_path.shape[0] == 1
assert isinstance(hypotheses, list)
assert len(hypotheses) == 1
(seq0,) = hypotheses
assert seq0.shape[0] == 2
assert (seq0[0] != seq0[1]).any()
if return_xattn_scores:
assert len(xatt_list) == len(nnet[1].layers)
assert xatt_list[0].shape == (2, 1, 24, 5)
else:
assert xatt_list is None
def test_beam_decoding_beam_scores_false(inputs, nnet):
gen = BeamSearchSequenceGenerator(*nnet, beam_size=2)
output = gen(*inputs, return_beam_scores=False)
assert len(output) == 1
(best_path,) = output
assert best_path is not None
assert torch.is_tensor(best_path)
assert best_path.shape == (26,)
@pytest.mark.parametrize('return_xattn_scores', [False, True])
def test_beam_decoding_beam_scores_true(inputs, nnet, return_xattn_scores):
gen = BeamSearchSequenceGenerator(*nnet, return_xattn_scores=return_xattn_scores, beam_size=2)
output = gen(*inputs, return_beam_scores=True)
assert len(output) == 4
beam_paths, scores, best_path, xatt_scores_list = output
assert beam_paths is not None
assert isinstance(beam_paths, list)
assert len(beam_paths) == 1
(beam_paths_seq0,) = beam_paths
assert torch.is_tensor(beam_paths_seq0)
assert beam_paths_seq0.shape == (2, 26)
assert scores is not None
assert isinstance(scores, list)
assert len(scores) == 1
(scores_seq0,) = scores
assert torch.is_tensor(scores_seq0)
assert scores_seq0.shape == (2,)
assert best_path is not None
assert torch.is_tensor(best_path)
assert best_path.shape == (1, 26)
if return_xattn_scores:
assert xatt_scores_list is not None
assert isinstance(xatt_scores_list, list)
assert torch.is_tensor(xatt_scores_list[0])
assert xatt_scores_list[0].shape == (1, 1, 25, 5)
else:
assert xatt_scores_list is None
def test_beam_decoding_beam_scores_true_with_fusion_models(inputs, nnet):
"""Test decoding with dummy unigram LM and boosting tree"""
# load dummy ngpu-lm
lm = NGramGPULanguageModel.dummy_unigram_lm(vocab_size=8)
# load dummy boosting tree
boosting_tree = GPUBoostingTreeModel.dummy_boosting_tree(vocab_size=8)
fusion_models = [lm, boosting_tree]
fusion_models_alpha = [0.2, 0.2]
gen = BeamSearchSequenceGeneratorWithFusionModels(
*nnet,
return_xattn_scores=True,
fusion_models=fusion_models,
fusion_models_alpha=fusion_models_alpha,
beam_size=2,
)
output = gen(*inputs, return_beam_scores=True)
assert len(output) == 4
beam_paths, scores, best_path, xatt_scores_list = output
assert beam_paths is not None
assert isinstance(beam_paths, list)
assert len(beam_paths) == 1
(beam_paths_seq0,) = beam_paths
assert torch.is_tensor(beam_paths_seq0)
assert beam_paths_seq0.shape == (2, 26)
assert scores is not None
assert isinstance(scores, list)
assert len(scores) == 1
(scores_seq0,) = scores
assert torch.is_tensor(scores_seq0)
assert scores_seq0.shape == (2,)
assert best_path is not None
assert torch.is_tensor(best_path)
assert best_path.shape == (1, 26)
assert xatt_scores_list is not None
assert isinstance(xatt_scores_list, list)
assert torch.is_tensor(xatt_scores_list[0])
assert xatt_scores_list[0].shape == (1, 1, 25, 5)
@pytest.fixture()
def prompted_inputs():
B, T, C = 1, 5, 2
return (
torch.tensor([[1, 0, 2, 3, 4]], dtype=torch.long), # prompt
torch.ones(B, T, C, dtype=torch.float), # encoder_hidden_states
torch.ones(B, T, dtype=torch.float), # encoder_input_mask
)
def test_transformer_aed_beam_infer_strips_prompt(prompted_inputs, decoder_nm, nnet, tokenizer):
decoder_input_ids, encoder_hidden_states, encoder_input_mask = prompted_inputs
*_, classifier = nnet
# Run the actual top-level module used by MultiTask AED model for decoding.
# This module is expected to trim the prompt from the beginning, and eos and pad from the end.
gen = TransformerAEDBeamInfer(decoder_nm, classifier, tokenizer)
ans = gen(
encoder_hidden_states=encoder_hidden_states,
encoder_input_mask=encoder_input_mask,
decoder_input_ids=decoder_input_ids,
)
best_path = ans[0][0].y_sequence
assert best_path is not None
assert torch.is_tensor(best_path)
# Now run the underlying beam search generator that doesn't trim anything.
*_, (untrimmed,), _ = gen.beam_search(*prompted_inputs, return_beam_scores=True)
assert untrimmed is not None
assert torch.is_tensor(untrimmed)
# Check that the expected trimming has indeed been done.
torch.testing.assert_close(
untrimmed[decoder_input_ids.shape[1] :], best_path
) # stripped the prompt from the beggining
def test_transformer_aed_greedy_infer_strips_prompt(prompted_inputs, decoder_nm, nnet, tokenizer):
decoder_input_ids, encoder_hidden_states, encoder_input_mask = prompted_inputs
decoder_input_ids = torch.tensor([[1, 0, 2, 3, 4]], dtype=torch.long) # prompt
*_, classifier = nnet
# Run the actual top-level module used by MultiTask AED model for decoding.
# This module is expected to trim the prompt from the beginning, and eos and pad from the end.
gen = TransformerAEDGreedyInfer(decoder_nm, classifier, tokenizer)
ans = gen(
encoder_hidden_states=encoder_hidden_states,
encoder_input_mask=encoder_input_mask,
decoder_input_ids=decoder_input_ids,
)
best_path = ans[0][0].y_sequence
assert best_path is not None
assert torch.is_tensor(best_path)
# Now run the underlying beam search generator that doesn't trim anything.
(untrimmed,), _, _, _ = gen.greedy_search(*prompted_inputs)
assert untrimmed is not None
assert torch.is_tensor(untrimmed)
# Check that the expected trimming has indeed been done.
torch.testing.assert_close(
untrimmed[decoder_input_ids.shape[1] :], best_path
) # stripped the prompt from the beggining
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