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# Copyright (c) 2020, 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.
"""
Script for calibrating a pretrained ASR model for quantization
"""
from argparse import ArgumentParser
import torch
from omegaconf import open_dict
from nemo.collections.asr.models import EncDecCTCModel
from nemo.utils import logging
try:
from pytorch_quantization import calib
from pytorch_quantization import nn as quant_nn
from pytorch_quantization import quant_modules
from pytorch_quantization.tensor_quant import QuantDescriptor
except ImportError:
raise ImportError(
"pytorch-quantization is not installed. Install from "
"https://github.com/NVIDIA/TensorRT/tree/master/tools/pytorch-quantization."
)
try:
from torch.cuda.amp import autocast
except ImportError:
from contextlib import contextmanager
@contextmanager
def autocast(enabled=None):
yield
can_gpu = torch.cuda.is_available()
def main():
parser = ArgumentParser()
parser.add_argument(
"--asr_model", type=str, default="QuartzNet15x5Base-En", required=True, help="Pass: 'QuartzNet15x5Base-En'",
)
parser.add_argument("--dataset", type=str, required=True, help="path to evaluation data")
parser.add_argument("--batch_size", type=int, default=256)
parser.add_argument(
"--dont_normalize_text",
default=False,
action='store_false',
help="Turn off trasnscript normalization. Recommended for non-English.",
)
parser.add_argument('--num_calib_batch', default=1, type=int, help="Number of batches for calibration.")
parser.add_argument('--calibrator', type=str, choices=["max", "histogram"], default="max")
parser.add_argument('--percentile', nargs='+', type=float, default=[99.9, 99.99, 99.999, 99.9999])
parser.add_argument("--amp", action="store_true", help="Use AMP in calibration.")
parser.set_defaults(amp=False)
args = parser.parse_args()
torch.set_grad_enabled(False)
# Initialize quantization
quant_desc_input = QuantDescriptor(calib_method=args.calibrator)
quant_nn.QuantConv2d.set_default_quant_desc_input(quant_desc_input)
quant_nn.QuantConvTranspose2d.set_default_quant_desc_input(quant_desc_input)
quant_nn.QuantLinear.set_default_quant_desc_input(quant_desc_input)
if args.asr_model.endswith('.nemo'):
logging.info(f"Using local ASR model from {args.asr_model}")
asr_model_cfg = EncDecCTCModel.restore_from(restore_path=args.asr_model, return_config=True)
with open_dict(asr_model_cfg):
asr_model_cfg.encoder.quantize = True
asr_model = EncDecCTCModel.restore_from(restore_path=args.asr_model, override_config_path=asr_model_cfg)
else:
logging.info(f"Using NGC cloud ASR model {args.asr_model}")
asr_model_cfg = EncDecCTCModel.from_pretrained(model_name=args.asr_model, return_config=True)
with open_dict(asr_model_cfg):
asr_model_cfg.encoder.quantize = True
asr_model = EncDecCTCModel.from_pretrained(model_name=args.asr_model, override_config_path=asr_model_cfg)
asr_model.setup_test_data(
test_data_config={
'sample_rate': 16000,
'manifest_filepath': args.dataset,
'labels': asr_model.decoder.vocabulary,
'batch_size': args.batch_size,
'normalize_transcripts': args.dont_normalize_text,
'shuffle': True,
}
)
asr_model.preprocessor.featurizer.dither = 0.0
asr_model.preprocessor.featurizer.pad_to = 0
if can_gpu:
asr_model = asr_model.cuda()
asr_model.eval()
# Enable calibrators
for name, module in asr_model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
module.disable_quant()
module.enable_calib()
else:
module.disable()
for i, test_batch in enumerate(asr_model.test_dataloader()):
if can_gpu:
test_batch = [x.cuda() for x in test_batch]
if args.amp:
with autocast():
_ = asr_model(input_signal=test_batch[0], input_signal_length=test_batch[1])
else:
_ = asr_model(input_signal=test_batch[0], input_signal_length=test_batch[1])
if i >= args.num_calib_batch:
break
# Save calibrated model(s)
model_name = args.asr_model.replace(".nemo", "") if args.asr_model.endswith(".nemo") else args.asr_model
if not args.calibrator == "histogram":
compute_amax(asr_model, method="max")
asr_model.save_to(F"{model_name}-max-{args.num_calib_batch*args.batch_size}.nemo")
else:
for percentile in args.percentile:
print(F"{percentile} percentile calibration")
compute_amax(asr_model, method="percentile")
asr_model.save_to(F"{model_name}-percentile-{percentile}-{args.num_calib_batch*args.batch_size}.nemo")
for method in ["mse", "entropy"]:
print(F"{method} calibration")
compute_amax(asr_model, method=method)
asr_model.save_to(F"{model_name}-{method}-{args.num_calib_batch*args.batch_size}.nemo")
def compute_amax(model, **kwargs):
for name, module in model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if module._calibrator is not None:
if isinstance(module._calibrator, calib.MaxCalibrator):
module.load_calib_amax()
else:
module.load_calib_amax(**kwargs)
print(F"{name:40}: {module}")
if can_gpu:
model.cuda()
if __name__ == '__main__':
main() # noqa pylint: disable=no-value-for-parameter
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