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test_model.py
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test_model.py
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#!/usr/bin/env python3
# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# 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.
"""
To run this file, do:
cd icefall/egs/csj/ASR
python ./pruned_transducer_stateless7_streaming/test_model.py
"""
import torch
from scaling_converter import convert_scaled_to_non_scaled
from train import get_params, get_transducer_model
def test_model():
params = get_params()
params.vocab_size = 500
params.blank_id = 0
params.context_size = 2
params.num_encoder_layers = "2,4,3,2,4"
params.feedforward_dims = "1024,1024,2048,2048,1024"
params.nhead = "8,8,8,8,8"
params.encoder_dims = "384,384,384,384,384"
params.attention_dims = "192,192,192,192,192"
params.encoder_unmasked_dims = "256,256,256,256,256"
params.zipformer_downsampling_factors = "1,2,4,8,2"
params.cnn_module_kernels = "31,31,31,31,31"
params.decoder_dim = 512
params.joiner_dim = 512
params.num_left_chunks = 4
params.short_chunk_size = 50
params.decode_chunk_len = 32
model = get_transducer_model(params)
num_param = sum([p.numel() for p in model.parameters()])
print(f"Number of model parameters: {num_param}")
# Test jit script
convert_scaled_to_non_scaled(model, inplace=True)
# We won't use the forward() method of the model in C++, so just ignore
# it here.
# Otherwise, one of its arguments is a ragged tensor and is not
# torch scriptabe.
model.__class__.forward = torch.jit.ignore(model.__class__.forward)
print("Using torch.jit.script")
model = torch.jit.script(model)
def test_model_jit_trace():
params = get_params()
params.vocab_size = 500
params.blank_id = 0
params.context_size = 2
params.num_encoder_layers = "2,4,3,2,4"
params.feedforward_dims = "1024,1024,2048,2048,1024"
params.nhead = "8,8,8,8,8"
params.encoder_dims = "384,384,384,384,384"
params.attention_dims = "192,192,192,192,192"
params.encoder_unmasked_dims = "256,256,256,256,256"
params.zipformer_downsampling_factors = "1,2,4,8,2"
params.cnn_module_kernels = "31,31,31,31,31"
params.decoder_dim = 512
params.joiner_dim = 512
params.num_left_chunks = 4
params.short_chunk_size = 50
params.decode_chunk_len = 32
model = get_transducer_model(params)
model.eval()
num_param = sum([p.numel() for p in model.parameters()])
print(f"Number of model parameters: {num_param}")
convert_scaled_to_non_scaled(model, inplace=True)
# Test encoder
def _test_encoder():
encoder = model.encoder
assert encoder.decode_chunk_size == params.decode_chunk_len // 2, (
encoder.decode_chunk_size,
params.decode_chunk_len,
)
T = params.decode_chunk_len + 7
x = torch.zeros(1, T, 80, dtype=torch.float32)
x_lens = torch.full((1,), T, dtype=torch.int32)
states = encoder.get_init_state(device=x.device)
encoder.__class__.forward = encoder.__class__.streaming_forward
traced_encoder = torch.jit.trace(encoder, (x, x_lens, states))
states1 = encoder.get_init_state(device=x.device)
states2 = traced_encoder.get_init_state(device=x.device)
for i in range(5):
x = torch.randn(1, T, 80, dtype=torch.float32)
x_lens = torch.full((1,), T, dtype=torch.int32)
y1, _, states1 = encoder.streaming_forward(x, x_lens, states1)
y2, _, states2 = traced_encoder(x, x_lens, states2)
assert torch.allclose(y1, y2, atol=1e-6), (i, (y1 - y2).abs().mean())
# Test decoder
def _test_decoder():
decoder = model.decoder
y = torch.zeros(10, decoder.context_size, dtype=torch.int64)
need_pad = torch.tensor([False])
traced_decoder = torch.jit.trace(decoder, (y, need_pad))
d1 = decoder(y, need_pad)
d2 = traced_decoder(y, need_pad)
assert torch.equal(d1, d2), (d1 - d2).abs().mean()
# Test joiner
def _test_joiner():
joiner = model.joiner
encoder_out_dim = joiner.encoder_proj.weight.shape[1]
decoder_out_dim = joiner.decoder_proj.weight.shape[1]
encoder_out = torch.rand(1, encoder_out_dim, dtype=torch.float32)
decoder_out = torch.rand(1, decoder_out_dim, dtype=torch.float32)
traced_joiner = torch.jit.trace(joiner, (encoder_out, decoder_out))
j1 = joiner(encoder_out, decoder_out)
j2 = traced_joiner(encoder_out, decoder_out)
assert torch.equal(j1, j2), (j1 - j2).abs().mean()
_test_encoder()
_test_decoder()
_test_joiner()
def main():
test_model()
test_model_jit_trace()
if __name__ == "__main__":
main()