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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.
import copy
import glob
import os
from pathlib import Path
import jiwer
import pytest
import torch
from omegaconf import open_dict
from tqdm import tqdm
from nemo.collections.asr.models import ASRModel
from nemo.collections.asr.models.ctc_models import EncDecCTCModel
from nemo.collections.asr.parts.submodules.ctc_beam_decoding import BeamBatchedCTCInfer
from nemo.collections.asr.parts.submodules.ngram_lm import NGramGPULanguageModel
from nemo.collections.asr.parts.submodules.rnnt_beam_decoding import BeamBatchedRNNTInfer
from nemo.collections.asr.parts.submodules.tdt_beam_decoding import BeamBatchedTDTInfer
from nemo.collections.asr.parts.utils import rnnt_utils
from nemo.core.utils import numba_utils
from nemo.core.utils.cuda_python_utils import skip_cuda_python_test_if_cuda_graphs_conditional_nodes_not_supported
from nemo.core.utils.numba_utils import __NUMBA_MINIMUM_VERSION__
from tests.collections.asr.decoding.utils import load_audio
RNNT_MODEL = "stt_en_conformer_transducer_small"
CTC_MODEL = "nvidia/stt_en_conformer_ctc_small"
TDT_MODEL = "nvidia/stt_en_fastconformer_tdt_large"
MAX_SAMPLES = 10
DEVICES = [torch.device("cpu")]
if torch.cuda.is_available():
DEVICES.append(torch.device('cuda'))
NUMBA_RNNT_LOSS_AVAILABLE = numba_utils.numba_cpu_is_supported(
__NUMBA_MINIMUM_VERSION__
) or numba_utils.numba_cuda_is_supported(__NUMBA_MINIMUM_VERSION__)
# available audio filename fixtures
@pytest.fixture(scope="module")
def test_audio_filenames(test_data_dir):
return tuple(glob.glob(os.path.join(test_data_dir, "asr", "test", "an4", "wav", "*.wav")))
# model fixtures
@pytest.fixture(scope="module")
def rnnt_model():
model = ASRModel.from_pretrained(model_name=RNNT_MODEL, map_location="cpu")
model.eval()
return model
@pytest.fixture(scope="module")
def tdt_model():
model = ASRModel.from_pretrained(model_name=TDT_MODEL, map_location="cpu")
model.eval()
return model
@pytest.fixture(scope="module")
def ctc_model():
model = ASRModel.from_pretrained(model_name=CTC_MODEL, map_location="cpu")
model.eval()
return model
# encoder output fixtures
@pytest.fixture(scope="module")
def get_rnnt_encoder_output(rnnt_model, test_audio_filenames):
encoder_output, encoded_lengths = get_transducer_model_encoder_output(
test_audio_filenames, MAX_SAMPLES, rnnt_model
)
return encoder_output, encoded_lengths
@pytest.fixture(scope="module")
def get_tdt_encoder_output(tdt_model, test_audio_filenames):
encoder_output, encoded_lengths = get_transducer_model_encoder_output(test_audio_filenames, MAX_SAMPLES, tdt_model)
return encoder_output, encoded_lengths
@pytest.fixture(scope="module")
def get_ctc_output(ctc_model, test_audio_filenames):
encoder_output, encoded_lengths = get_ctc_model_output(test_audio_filenames, MAX_SAMPLES, ctc_model)
return encoder_output, encoded_lengths
@pytest.fixture(scope="module")
def kenlm_model_path(tmp_path_factory, test_data_dir):
lm_path = Path(test_data_dir) / "asr/kenlm_ngram_lm/parakeet-tdt_ctc-110m-libri-1024.kenlm.tmp.arpa"
assert os.path.exists(lm_path), f"LM file not found: {lm_path}"
lm_nemo_path = tmp_path_factory.mktemp("lm") / f"{lm_path.name}.nemo"
NGramGPULanguageModel.from_file(lm_path, vocab_size=1024).save_to(f"{lm_nemo_path}")
return f"{lm_nemo_path}"
def get_transducer_model_encoder_output(
test_audio_filenames,
num_samples: int,
model: ASRModel,
device: torch.device = torch.device("cpu"),
dtype: torch.dtype = torch.float32,
):
audio_filepaths = test_audio_filenames[:num_samples]
with torch.no_grad():
model.preprocessor.featurizer.dither = 0.0
model.preprocessor.featurizer.pad_to = 0
model.eval()
all_inputs, all_lengths = [], []
for audio_file in tqdm(audio_filepaths, desc="Loading audio files"):
audio_tensor, _ = load_audio(audio_file)
all_inputs.append(audio_tensor)
all_lengths.append(torch.tensor(audio_tensor.shape[0], dtype=torch.int64))
input_batch = torch.nn.utils.rnn.pad_sequence(all_inputs, batch_first=True).to(device=device, dtype=dtype)
length_batch = torch.tensor(all_lengths, dtype=torch.int64).to(device)
encoded_outputs, encoded_length = model(input_signal=input_batch, input_signal_length=length_batch)
return encoded_outputs, encoded_length
def get_ctc_model_output(
test_audio_filenames,
num_samples: int,
model: ASRModel,
device: torch.device = torch.device("cpu"),
dtype: torch.dtype = torch.float32,
):
audio_filepaths = test_audio_filenames[:num_samples]
with torch.no_grad():
model.preprocessor.featurizer.dither = 0.0
model.preprocessor.featurizer.pad_to = 0
model.eval()
all_inputs, all_lengths = [], []
for audio_file in tqdm(audio_filepaths, desc="Loading audio files"):
audio_tensor, _ = load_audio(audio_file)
all_inputs.append(audio_tensor)
all_lengths.append(torch.tensor(audio_tensor.shape[0], dtype=torch.int64))
input_batch = torch.nn.utils.rnn.pad_sequence(all_inputs, batch_first=True).to(device=device, dtype=dtype)
length_batch = torch.tensor(all_lengths, dtype=torch.int64).to(device)
log_probs, encoded_length, _ = model(input_signal=input_batch, input_signal_length=length_batch)
return log_probs, encoded_length
def print_unit_test_info(strategy, batch_size, beam_size, allow_cuda_graphs, device):
print(
f"""Beam search algorithm: {strategy},
Batch size: {batch_size},
Beam size: {beam_size},
Cuda Graphs: {allow_cuda_graphs},
Decoding device: {device}
"""
)
def check_res_best_hyps(num_samples, hyps):
assert type(hyps) == list
assert type(hyps[0]) == rnnt_utils.Hypothesis
assert len(hyps) == num_samples
assert all(
[
hasattr(hyps[hyp_idx], "y_sequence")
and hasattr(hyps[hyp_idx], "score")
and hasattr(hyps[hyp_idx], "timestamp")
for hyp_idx in range(num_samples)
]
)
def print_res_best_hyps(hyps):
for hyp_idx, hyp in enumerate(hyps):
print("Sample: ", hyp_idx)
print("Decoded text: ", hyp.text)
print("Score: ", hyp.score)
print("Transcript", hyp.y_sequence)
print("Timesteps", hyp.timestamp)
print()
def check_res_nbest_hyps(num_samples, batch_nbest_hyps):
assert type(batch_nbest_hyps) == list
assert type(batch_nbest_hyps[0]) == rnnt_utils.NBestHypotheses
assert len(batch_nbest_hyps) == num_samples
for idx in range(num_samples):
assert all(
[
hasattr(batch_nbest_hyps[idx].n_best_hypotheses[hyp_idx], "y_sequence")
and hasattr(batch_nbest_hyps[idx].n_best_hypotheses[hyp_idx], "score")
and hasattr(batch_nbest_hyps[idx].n_best_hypotheses[hyp_idx], "timestamp")
for hyp_idx in range(len(batch_nbest_hyps[idx].n_best_hypotheses))
]
)
assert all(
[
len(batch_nbest_hyps[idx].n_best_hypotheses[hyp_idx].y_sequence) > 0
and len(batch_nbest_hyps[idx].n_best_hypotheses[hyp_idx].timestamp) > 0
for hyp_idx in range(len(batch_nbest_hyps[idx].n_best_hypotheses))
]
)
def print_res_nbest_hyps(batch_nbest_hyps):
for batch_idx, nbest_hyps in enumerate(batch_nbest_hyps):
print(f"Batch idx: {batch_idx}")
for idx, hyp in enumerate(nbest_hyps):
print(f"Hyp index: {idx + 1}")
print("Text: ", hyp.text)
print("Score: ", hyp.score)
print("Transcripts: ", hyp.y_sequence)
print("Timesteps: ", hyp.timestamp)
print()
def decode_text_from_hypotheses(hyps, model):
if isinstance(model, EncDecCTCModel):
return model.decoding.decode_hypothesis(hyps, fold_consecutive=False)
else:
return model.decoding.decode_hypothesis(hyps)
def decode_text_from_nbest_hypotheses(hyps, model):
if isinstance(model, EncDecCTCModel):
return [
model.decoding.decode_hypothesis(nbest_hyp.n_best_hypotheses, fold_consecutive=False) for nbest_hyp in hyps
]
else:
return [model.decoding.decode_hypothesis(nbest_hyp.n_best_hypotheses) for nbest_hyp in hyps]
class TestRNNTDecoding:
@pytest.mark.skipif(
not NUMBA_RNNT_LOSS_AVAILABLE,
reason='RNNTLoss has not been compiled with appropriate numba version.',
)
@pytest.mark.with_downloads
@pytest.mark.unit
@pytest.mark.parametrize(
"beam_config",
[
{"search_type": "malsd_batch", "allow_cuda_graphs": False},
{"search_type": "malsd_batch", "allow_cuda_graphs": True},
{"search_type": "maes_batch", "allow_cuda_graphs": False},
],
)
@pytest.mark.parametrize("beam_size", [4])
@pytest.mark.parametrize("batch_size", [4, 16])
@pytest.mark.parametrize("device", DEVICES)
def test_rnnt_beam_decoding_return_best_hypothesis(
self, test_audio_filenames, rnnt_model, get_rnnt_encoder_output, beam_config, device, batch_size, beam_size
):
num_samples = min(batch_size, len(test_audio_filenames))
model = rnnt_model.to(device)
encoder_output, encoded_lengths = get_rnnt_encoder_output
encoder_output, encoded_lengths = encoder_output[:num_samples].to(device), encoded_lengths[:num_samples].to(
device
)
vocab_size = model.tokenizer.vocab_size
decoding = BeamBatchedRNNTInfer(
model.decoder,
model.joint,
blank_index=vocab_size,
beam_size=beam_size,
score_norm=True,
return_best_hypothesis=True,
**beam_config,
)
print_unit_test_info(
strategy=beam_config['search_type'],
batch_size=batch_size,
beam_size=beam_size,
allow_cuda_graphs=beam_config.get('allow_cuda_graphs', True),
device=device,
)
with torch.no_grad():
hyps = decoding(encoder_output=encoder_output, encoded_lengths=encoded_lengths)[0]
check_res_best_hyps(num_samples, hyps)
hyps = decode_text_from_hypotheses(hyps, model)
print_res_best_hyps(hyps)
@pytest.mark.skipif(
not NUMBA_RNNT_LOSS_AVAILABLE,
reason='RNNTLoss has not been compiled with appropriate numba version.',
)
@pytest.mark.with_downloads
@pytest.mark.unit
@pytest.mark.skipif(not torch.cuda.is_available(), reason="Test is only GPU-based decoding")
@pytest.mark.parametrize(
"beam_config",
[
{"search_type": "malsd_batch", "allow_cuda_graphs": False},
{"search_type": "malsd_batch", "allow_cuda_graphs": True},
{"search_type": "maes_batch", "allow_cuda_graphs": False},
],
)
@pytest.mark.parametrize("beam_size", [4])
@pytest.mark.parametrize("batch_size", [4])
def test_rnnt_beam_decoding_return_nbest(
self, test_audio_filenames, rnnt_model, get_rnnt_encoder_output, beam_config, device, beam_size, batch_size
):
device = torch.device("cuda")
num_samples = min(batch_size, len(test_audio_filenames))
model = rnnt_model.to(device)
encoder_output, encoded_lengths = get_rnnt_encoder_output
encoder_output, encoded_lengths = encoder_output[:num_samples].to(device), encoded_lengths[:num_samples].to(
device
)
vocab_size = model.tokenizer.vocab_size
decoding = BeamBatchedRNNTInfer(
model.decoder,
model.joint,
blank_index=vocab_size,
beam_size=beam_size,
score_norm=True,
return_best_hypothesis=False,
**beam_config,
)
print_unit_test_info(
strategy=beam_config['search_type'],
batch_size=batch_size,
beam_size=beam_size,
allow_cuda_graphs=beam_config.get('allow_cuda_graphs', True),
device=device,
)
with torch.no_grad():
batch_nbest_hyps = decoding(encoder_output=encoder_output, encoded_lengths=encoded_lengths)[0]
check_res_nbest_hyps(num_samples, batch_nbest_hyps)
batch_nbest_hyps = decode_text_from_nbest_hypotheses(batch_nbest_hyps, model)
print_res_nbest_hyps(batch_nbest_hyps)
@pytest.mark.skipif(
not NUMBA_RNNT_LOSS_AVAILABLE,
reason='RNNTLoss has not been compiled with appropriate numba version.',
)
@pytest.mark.with_downloads
@pytest.mark.unit
@pytest.mark.skipif(not torch.cuda.is_available(), reason="Test is only GPU-based decoding")
@pytest.mark.parametrize(
"beam_config",
[
{"search_type": "malsd_batch", "allow_cuda_graphs": False},
{"search_type": "maes_batch", "allow_cuda_graphs": False},
{"search_type": "malsd_batch", "allow_cuda_graphs": True},
],
)
@pytest.mark.parametrize("batch_size", [4])
@pytest.mark.parametrize("beam_size", [4])
@pytest.mark.parametrize("pruning_mode", ["late", "early"])
@pytest.mark.parametrize("blank_lm_score_mode", ["no_score", "lm_weighted_full"])
def test_rnnt_beam_decoding_kenlm(
self,
kenlm_model_path,
test_audio_filenames,
rnnt_model,
get_rnnt_encoder_output,
beam_config,
device,
batch_size,
beam_size,
pruning_mode,
blank_lm_score_mode,
):
device = torch.device("cuda")
num_samples = min(batch_size, len(test_audio_filenames))
model = rnnt_model.to(device)
encoder_output, encoded_lengths = get_rnnt_encoder_output
encoder_output, encoded_lengths = encoder_output[:num_samples].to(device), encoded_lengths[:num_samples].to(
device
)
vocab_size = model.tokenizer.vocab_size
fusion_models = [NGramGPULanguageModel.from_file(lm_path=kenlm_model_path, vocab_size=vocab_size)]
fusion_models_alpha = [0.3]
decoding = BeamBatchedRNNTInfer(
model.decoder,
model.joint,
blank_index=vocab_size,
beam_size=beam_size,
score_norm=True,
return_best_hypothesis=True,
pruning_mode=pruning_mode,
blank_lm_score_mode=blank_lm_score_mode,
fusion_models=fusion_models,
fusion_models_alpha=fusion_models_alpha,
**beam_config,
)
print_unit_test_info(
strategy=beam_config['search_type'],
batch_size=batch_size,
beam_size=beam_size,
allow_cuda_graphs=beam_config.get('allow_cuda_graphs', True),
device=device,
)
with torch.no_grad():
hyps = decoding(encoder_output=encoder_output, encoded_lengths=encoded_lengths)[0]
check_res_best_hyps(num_samples, hyps)
hyps = decode_text_from_hypotheses(hyps, model)
print_res_best_hyps(hyps)
class TestTDTDecoding:
@pytest.mark.skipif(
not NUMBA_RNNT_LOSS_AVAILABLE,
reason='RNNTLoss has not been compiled with appropriate numba version.',
)
@pytest.mark.with_downloads
@pytest.mark.unit
@pytest.mark.parametrize(
"beam_config",
[
{"search_type": "malsd_batch", "allow_cuda_graphs": False},
{"search_type": "malsd_batch", "allow_cuda_graphs": True},
],
)
@pytest.mark.parametrize("beam_size", [4])
@pytest.mark.parametrize("batch_size", [4, 16])
@pytest.mark.parametrize("device", DEVICES)
def test_tdt_beam_decoding_return_best_hypothesis(
self, test_audio_filenames, tdt_model, get_tdt_encoder_output, beam_config, device, batch_size, beam_size
):
num_samples = min(batch_size, len(test_audio_filenames))
model = tdt_model.to(device)
encoder_output, encoded_lengths = get_tdt_encoder_output
encoder_output, encoded_lengths = encoder_output[:num_samples].to(device), encoded_lengths[:num_samples].to(
device
)
model_config = model.to_config_dict()
durations = list(model_config["model_defaults"]["tdt_durations"])
vocab_size = model.tokenizer.vocab_size
decoding = BeamBatchedTDTInfer(
model.decoder,
model.joint,
blank_index=vocab_size,
durations=durations,
beam_size=beam_size,
score_norm=True,
return_best_hypothesis=True,
**beam_config,
)
print_unit_test_info(
strategy=beam_config['search_type'],
batch_size=batch_size,
beam_size=beam_size,
allow_cuda_graphs=beam_config.get('allow_cuda_graphs', True),
device=device,
)
with torch.no_grad():
hyps = decoding(encoder_output=encoder_output, encoded_lengths=encoded_lengths)[0]
check_res_best_hyps(num_samples, hyps)
hyps = decode_text_from_hypotheses(hyps, model)
print_res_best_hyps(hyps)
@pytest.mark.skipif(
not NUMBA_RNNT_LOSS_AVAILABLE,
reason='RNNTLoss has not been compiled with appropriate numba version.',
)
@pytest.mark.with_downloads
@pytest.mark.unit
@pytest.mark.skipif(not torch.cuda.is_available(), reason="Test is only GPU-based decoding")
@pytest.mark.parametrize(
"beam_config",
[
{"search_type": "malsd_batch", "allow_cuda_graphs": False},
{"search_type": "malsd_batch", "allow_cuda_graphs": True},
],
)
@pytest.mark.parametrize("beam_size", [4])
@pytest.mark.parametrize("batch_size", [4])
def test_tdt_beam_decoding_return_nbest(
self, test_audio_filenames, tdt_model, get_tdt_encoder_output, beam_config, device, beam_size, batch_size
):
device = torch.device("cuda")
num_samples = min(batch_size, len(test_audio_filenames))
model = tdt_model.to(device)
encoder_output, encoded_lengths = get_tdt_encoder_output
encoder_output, encoded_lengths = encoder_output[:num_samples].to(device), encoded_lengths[:num_samples].to(
device
)
model_config = model.to_config_dict()
durations = list(model_config["model_defaults"]["tdt_durations"])
vocab_size = model.tokenizer.vocab_size
decoding = BeamBatchedTDTInfer(
model.decoder,
model.joint,
blank_index=vocab_size,
durations=durations,
beam_size=beam_size,
score_norm=True,
return_best_hypothesis=False,
**beam_config,
)
print_unit_test_info(
strategy=beam_config['search_type'],
batch_size=batch_size,
beam_size=beam_size,
allow_cuda_graphs=beam_config.get('allow_cuda_graphs', True),
device=device,
)
with torch.no_grad():
batch_nbest_hyps = decoding(encoder_output=encoder_output, encoded_lengths=encoded_lengths)[0]
check_res_nbest_hyps(num_samples, batch_nbest_hyps)
batch_nbest_hyps = decode_text_from_nbest_hypotheses(batch_nbest_hyps, model)
print_res_nbest_hyps(batch_nbest_hyps)
@pytest.mark.skipif(
not NUMBA_RNNT_LOSS_AVAILABLE,
reason='RNNTLoss has not been compiled with appropriate numba version.',
)
@pytest.mark.with_downloads
@pytest.mark.unit
@pytest.mark.skipif(not torch.cuda.is_available(), reason="Test is only GPU-based decoding")
@pytest.mark.parametrize(
"beam_config",
[
{"search_type": "malsd_batch", "allow_cuda_graphs": False},
{"search_type": "malsd_batch", "allow_cuda_graphs": True},
],
)
@pytest.mark.parametrize("batch_size", [4])
@pytest.mark.parametrize("beam_size", [4])
@pytest.mark.parametrize("pruning_mode", ["late", "early"])
@pytest.mark.parametrize("blank_lm_score_mode", ["lm_weighted_full", "no_score"])
def test_tdt_beam_decoding_kenlm(
self,
kenlm_model_path,
test_audio_filenames,
tdt_model,
get_tdt_encoder_output,
beam_config,
device,
batch_size,
beam_size,
pruning_mode,
blank_lm_score_mode,
):
device = torch.device("cuda")
num_samples = min(batch_size, len(test_audio_filenames))
model = tdt_model.to(device)
encoder_output, encoded_lengths = get_tdt_encoder_output
encoder_output, encoded_lengths = encoder_output[:num_samples].to(device), encoded_lengths[:num_samples].to(
device
)
model_config = model.to_config_dict()
durations = list(model_config["model_defaults"]["tdt_durations"])
vocab_size = model.tokenizer.vocab_size
fusion_models = [NGramGPULanguageModel.from_file(lm_path=kenlm_model_path, vocab_size=vocab_size)]
fusion_models_alpha = [0.3]
decoding = BeamBatchedTDTInfer(
model.decoder,
model.joint,
blank_index=vocab_size,
durations=durations,
beam_size=beam_size,
score_norm=True,
return_best_hypothesis=True,
pruning_mode=pruning_mode,
blank_lm_score_mode=blank_lm_score_mode,
fusion_models=fusion_models,
fusion_models_alpha=fusion_models_alpha,
**beam_config,
)
print_unit_test_info(
strategy=beam_config['search_type'],
batch_size=batch_size,
beam_size=beam_size,
allow_cuda_graphs=beam_config.get('allow_cuda_graphs', True),
device=device,
)
with torch.no_grad():
hyps = decoding(encoder_output=encoder_output, encoded_lengths=encoded_lengths)[0]
check_res_best_hyps(num_samples, hyps)
hyps = decode_text_from_hypotheses(hyps, model)
print_res_best_hyps(hyps)
class TestTransducerCudaGraphBeamDecoding:
"""
Tests CudaGraphs implementations from Transducer models (RNN-T and TDT)
"""
@pytest.mark.with_downloads
@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA decoder can run only on CUDA")
@pytest.mark.parametrize("force_mode", ["no_graphs", "no_while_loops", "full_graph"])
@pytest.mark.parametrize("model_type", ["rnnt", "tdt"])
def test_stated_stateless(self, test_audio_filenames, rnnt_model, tdt_model, model_type, force_mode: str):
"""
Compares pure Pytorch and with three modes of statefull implementations for double floating point precision.
1. Pure pytorch, but statefull implementation: no_graphs
2. With CudaGrpahs: no_while_loops and full_graph.
"""
if force_mode == "full_graph":
skip_cuda_python_test_if_cuda_graphs_conditional_nodes_not_supported()
batch_size = 16
device = torch.device("cuda")
model = rnnt_model.to(device) if model_type == "rnnt" else tdt_model.to(device)
decoding_config = copy.deepcopy(model.cfg.decoding)
with open_dict(decoding_config):
decoding_config["strategy"] = "malsd_batch"
decoding_config["beam"]["beam_size"] = 4
decoding_config["beam"]["return_best_hypothesis"] = False
decoding_config["beam"]["allow_cuda_graphs"] = False
model.change_decoding_strategy(decoding_config)
actual_hypotheses = model.transcribe(test_audio_filenames, batch_size=batch_size, num_workers=None)
actual_transcripts = [[hyp.text for hyp in actual_beam] for actual_beam in actual_hypotheses]
actual_scores = [[hyp.score for hyp in actual_beam] for actual_beam in actual_hypotheses]
actual_timestamps = [[hyp.timestamp for hyp in actual_beam] for actual_beam in actual_hypotheses]
# transcribe with use implementation with cuda graphs
decoding_config["beam"]["allow_cuda_graphs"] = True
model.change_decoding_strategy(decoding_config)
model.decoding.decoding._decoding_computer.force_cuda_graphs_mode(mode=force_mode)
cudagraph_hypotheses = model.transcribe(test_audio_filenames, batch_size=batch_size, num_workers=None)
cudagraph_transcripts = [[hyp.text for hyp in cudagraphs_beam] for cudagraphs_beam in cudagraph_hypotheses]
cudagraph_scores = [[hyp.score for hyp in cudagraph_beam] for cudagraph_beam in cudagraph_hypotheses]
cudagraph_timestamps = [[hyp.timestamp for hyp in cudagraph_beam] for cudagraph_beam in cudagraph_hypotheses]
for batch_idx in range(min(batch_size, len(test_audio_filenames))):
assert len(actual_transcripts[batch_idx]) == len(cudagraph_transcripts[batch_idx])
assert cudagraph_scores[batch_idx] == pytest.approx(
actual_scores[batch_idx], abs=1e-2
), f"Scores mismatch for batch_idx {batch_idx}"
assert (
cudagraph_timestamps[batch_idx] == actual_timestamps[batch_idx]
), f"Timestamps mismatch for batch_idx {batch_idx}"
wer = jiwer.wer(actual_transcripts[batch_idx], cudagraph_transcripts[batch_idx])
assert wer <= 1e-3, "Cuda graph greedy decoder should match original decoder implementation."
for actual, fast in zip(actual_transcripts[batch_idx], cudagraph_transcripts[batch_idx]):
if actual != fast:
print("Erroneous samples in batch:", batch_idx)
print("Original transcript:", actual)
print("New transcript:", fast)
@pytest.mark.with_downloads
@pytest.mark.skipif(
not (torch.cuda.is_available() and torch.cuda.is_bf16_supported()), reason="CUDA decoder can run only on CUDA"
)
@pytest.mark.parametrize("model_type", ["rnnt", "tdt"])
def test_stated_stateless_bf16(self, test_audio_filenames, rnnt_model, tdt_model, model_type):
"""
Checks that we are able to run without errors all decodings in bfloat16.
Computational errors accumulate, so just checking if algorithms run without errors
"""
batch_size = 16
device = torch.device("cuda")
model = rnnt_model.to(device) if model_type == "rnnt" else tdt_model.to(device)
decoding_config = copy.deepcopy(model.cfg.decoding)
# checking pytorch implementation
with open_dict(decoding_config):
decoding_config["strategy"] = "malsd_batch"
decoding_config["beam"]["beam_size"] = 4
decoding_config["beam"]["return_best_hypothesis"] = False
decoding_config["beam"]["allow_cuda_graphs"] = False
model.change_decoding_strategy(decoding_config)
with torch.cuda.amp.autocast(dtype=torch.bfloat16, enabled=True):
model.transcribe(test_audio_filenames, batch_size=batch_size, num_workers=None)
modes = ["no_graphs", "no_while_loops", "full_graph"]
for force_mode in modes:
if force_mode == "full_graph":
skip_cuda_python_test_if_cuda_graphs_conditional_nodes_not_supported()
# transcribe with use implementation with cuda graphs
decoding_config["beam"]["allow_cuda_graphs"] = True
model.change_decoding_strategy(decoding_config)
model.decoding.decoding._decoding_computer.force_cuda_graphs_mode(mode=force_mode)
with torch.cuda.amp.autocast(dtype=torch.bfloat16, enabled=True):
model.transcribe(test_audio_filenames, batch_size=batch_size, num_workers=None)
class TestCTCDecoding:
@pytest.mark.with_downloads
@pytest.mark.unit
@pytest.mark.parametrize(
"beam_config",
[
{"allow_cuda_graphs": False},
{"allow_cuda_graphs": True},
],
)
@pytest.mark.parametrize("beam_size", [4])
@pytest.mark.parametrize("batch_size", [4, 16])
@pytest.mark.parametrize("device", DEVICES)
def test_ctc_beam_decoding_return_best_hypothesis(
self, test_audio_filenames, ctc_model, get_ctc_output, beam_config, device, batch_size, beam_size
):
num_samples = min(batch_size, len(test_audio_filenames))
model = ctc_model.to(device)
log_probs, encoded_lengths = get_ctc_output
log_probs, encoded_lengths = log_probs[:num_samples].to(device), encoded_lengths[:num_samples].to(device)
vocab_size = model.tokenizer.vocab_size
decoding = BeamBatchedCTCInfer(
blank_index=vocab_size,
beam_size=beam_size,
return_best_hypothesis=True,
**beam_config,
)
print_unit_test_info(
strategy="beam_batch",
batch_size=batch_size,
beam_size=beam_size,
allow_cuda_graphs=beam_config.get('allow_cuda_graphs', True),
device=device,
)
with torch.no_grad():
hyps = decoding(decoder_output=log_probs, decoder_lengths=encoded_lengths)[0]
check_res_best_hyps(num_samples, hyps)
hyps = decode_text_from_hypotheses(hyps, model)
print_res_best_hyps(hyps)
@pytest.mark.with_downloads
@pytest.mark.unit
@pytest.mark.skipif(not torch.cuda.is_available(), reason="Test is only GPU-based decoding")
@pytest.mark.parametrize(
"beam_config",
[
{"allow_cuda_graphs": False},
{"allow_cuda_graphs": True},
],
)
@pytest.mark.parametrize("beam_size", [4])
@pytest.mark.parametrize("batch_size", [4])
def test_ctc_beam_decoding_return_nbest(
self, test_audio_filenames, ctc_model, get_ctc_output, beam_config, device, beam_size, batch_size
):
device = torch.device("cuda")
num_samples = min(batch_size, len(test_audio_filenames))
model = ctc_model.to(device)
log_probs, encoded_lengths = get_ctc_output
log_probs, encoded_lengths = log_probs[:num_samples].to(device), encoded_lengths[:num_samples].to(device)
vocab_size = model.tokenizer.vocab_size
decoding = BeamBatchedCTCInfer(
blank_index=vocab_size,
beam_size=beam_size,
return_best_hypothesis=False,
**beam_config,
)
print_unit_test_info(
strategy="beam_batch",
batch_size=batch_size,
beam_size=beam_size,
allow_cuda_graphs=beam_config.get('allow_cuda_graphs', True),
device=device,
)
with torch.no_grad():
batch_nbest_hyps = decoding(decoder_output=log_probs, decoder_lengths=encoded_lengths)[0]
check_res_nbest_hyps(num_samples, batch_nbest_hyps)
batch_nbest_hyps = decode_text_from_nbest_hypotheses(batch_nbest_hyps, model)
print_res_nbest_hyps(batch_nbest_hyps)
@pytest.mark.with_downloads
@pytest.mark.unit
@pytest.mark.skipif(not torch.cuda.is_available(), reason="Test is only GPU-based decoding")
@pytest.mark.parametrize(
"beam_config",
[
{"allow_cuda_graphs": False, "ngram_lm_alpha": 0.3, "beam_beta": 1.0},
{"allow_cuda_graphs": False, "ngram_lm_alpha": 0.3, "beam_beta": 1.0},
],
)
@pytest.mark.parametrize("batch_size", [4])
@pytest.mark.parametrize("beam_size", [4])
def test_ctc_beam_decoding_kenlm(
self,
kenlm_model_path,
test_audio_filenames,
ctc_model,
get_ctc_output,
beam_config,
device,
batch_size,
beam_size,
):
device = torch.device("cuda")
beam_config["ngram_lm_model"] = kenlm_model_path
num_samples = min(batch_size, len(test_audio_filenames))
model = ctc_model.to(device)
decoder_output, decoder_lengths = get_ctc_output
decoder_output, decoder_lengths = decoder_output[:num_samples].to(device), decoder_lengths[:num_samples].to(
device
)
vocab_size = model.tokenizer.vocab_size
decoding = BeamBatchedCTCInfer(
blank_index=vocab_size,
beam_size=beam_size,
return_best_hypothesis=True,
**beam_config,
)
print_unit_test_info(
strategy="beam_batch",
batch_size=batch_size,
beam_size=beam_size,
allow_cuda_graphs=beam_config.get('allow_cuda_graphs', True),
device=device,
)
with torch.no_grad():
hyps = decoding(decoder_output=decoder_output, decoder_lengths=decoder_lengths)[0]
check_res_best_hyps(num_samples, hyps)
hyps = decode_text_from_hypotheses(hyps, model)
print_res_best_hyps(hyps)
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