mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
381 lines
9.6 KiB
Python
381 lines
9.6 KiB
Python
#!/usr/bin/env python3
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# Copyright 2022 Xiaomi Corp. (authors: Fangjun Kuang)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This file is to test that models can be exported to onnx.
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"""
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import os
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from icefall import is_module_available
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if not is_module_available("onnxruntime"):
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raise ValueError("Please 'pip install onnxruntime' first.")
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import onnxruntime as ort
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import torch
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from scaling_converter import convert_scaled_to_non_scaled
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from zipformer import (
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Conv2dSubsampling,
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RelPositionalEncoding,
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Zipformer,
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ZipformerEncoder,
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ZipformerEncoderLayer,
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)
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ort.set_default_logger_severity(3)
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def test_conv2d_subsampling():
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filename = "conv2d_subsampling.onnx"
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opset_version = 13
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N = 30
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T = 50
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num_features = 80
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d_model = 512
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x = torch.rand(N, T, num_features)
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encoder_embed = Conv2dSubsampling(num_features, d_model)
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encoder_embed.eval()
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encoder_embed = convert_scaled_to_non_scaled(encoder_embed, inplace=True)
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torch.onnx.export(
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encoder_embed,
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x,
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filename,
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verbose=False,
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opset_version=opset_version,
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input_names=["x"],
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output_names=["y"],
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dynamic_axes={
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"x": {0: "N", 1: "T"},
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"y": {0: "N", 1: "T"},
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},
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)
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options = ort.SessionOptions()
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options.inter_op_num_threads = 1
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options.intra_op_num_threads = 1
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session = ort.InferenceSession(
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filename,
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sess_options=options,
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providers=["CPUExecutionProvider"],
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)
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input_nodes = session.get_inputs()
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assert input_nodes[0].name == "x"
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assert input_nodes[0].shape == ["N", "T", num_features]
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inputs = {input_nodes[0].name: x.numpy()}
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onnx_y = session.run(["y"], inputs)[0]
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onnx_y = torch.from_numpy(onnx_y)
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torch_y = encoder_embed(x)
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assert torch.allclose(onnx_y, torch_y, atol=1e-05), (onnx_y - torch_y).abs().max()
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os.remove(filename)
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def test_rel_pos():
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filename = "rel_pos.onnx"
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opset_version = 13
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N = 30
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T = 50
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num_features = 80
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d_model = 512
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x = torch.rand(N, T, num_features)
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encoder_pos = RelPositionalEncoding(d_model, dropout_rate=0.1)
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encoder_pos.eval()
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encoder_pos = convert_scaled_to_non_scaled(encoder_pos, inplace=True)
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x = x.permute(1, 0, 2)
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torch.onnx.export(
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encoder_pos,
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x,
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filename,
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verbose=False,
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opset_version=opset_version,
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input_names=["x"],
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output_names=["pos_emb"],
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dynamic_axes={
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"x": {0: "N", 1: "T"},
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"pos_emb": {0: "N", 1: "T"},
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},
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)
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options = ort.SessionOptions()
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options.inter_op_num_threads = 1
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options.intra_op_num_threads = 1
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session = ort.InferenceSession(
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filename,
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sess_options=options,
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providers=["CPUExecutionProvider"],
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)
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input_nodes = session.get_inputs()
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assert input_nodes[0].name == "x"
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assert input_nodes[0].shape == ["N", "T", num_features]
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inputs = {input_nodes[0].name: x.numpy()}
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onnx_pos_emb = session.run(["pos_emb"], inputs)
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onnx_pos_emb = torch.from_numpy(onnx_pos_emb[0])
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torch_pos_emb = encoder_pos(x)
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assert torch.allclose(onnx_pos_emb, torch_pos_emb, atol=1e-05), (
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(onnx_pos_emb - torch_pos_emb).abs().max()
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)
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print(onnx_pos_emb.abs().sum(), torch_pos_emb.abs().sum())
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os.remove(filename)
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def test_zipformer_encoder_layer():
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filename = "zipformer_encoder_layer.onnx"
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opset_version = 13
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N = 30
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T = 50
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d_model = 384
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attention_dim = 192
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nhead = 8
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feedforward_dim = 1024
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dropout = 0.1
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cnn_module_kernel = 31
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pos_dim = 4
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x = torch.rand(N, T, d_model)
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encoder_pos = RelPositionalEncoding(d_model, dropout)
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encoder_pos.eval()
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encoder_pos = convert_scaled_to_non_scaled(encoder_pos, inplace=True)
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x = x.permute(1, 0, 2)
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pos_emb = encoder_pos(x)
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encoder_layer = ZipformerEncoderLayer(
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d_model,
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attention_dim,
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nhead,
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feedforward_dim,
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dropout,
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cnn_module_kernel,
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pos_dim,
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)
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encoder_layer.eval()
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encoder_layer = convert_scaled_to_non_scaled(encoder_layer, inplace=True)
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torch.onnx.export(
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encoder_layer,
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(x, pos_emb),
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filename,
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verbose=False,
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opset_version=opset_version,
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input_names=["x", "pos_emb"],
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output_names=["y"],
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dynamic_axes={
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"x": {0: "T", 1: "N"},
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"pos_emb": {0: "N", 1: "T"},
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"y": {0: "T", 1: "N"},
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},
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)
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options = ort.SessionOptions()
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options.inter_op_num_threads = 1
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options.intra_op_num_threads = 1
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session = ort.InferenceSession(
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filename,
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sess_options=options,
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providers=["CPUExecutionProvider"],
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)
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input_nodes = session.get_inputs()
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inputs = {
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input_nodes[0].name: x.numpy(),
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input_nodes[1].name: pos_emb.numpy(),
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}
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onnx_y = session.run(["y"], inputs)[0]
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onnx_y = torch.from_numpy(onnx_y)
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torch_y = encoder_layer(x, pos_emb)
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assert torch.allclose(onnx_y, torch_y, atol=1e-05), (onnx_y - torch_y).abs().max()
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print(onnx_y.abs().sum(), torch_y.abs().sum(), onnx_y.shape, torch_y.shape)
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os.remove(filename)
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def test_zipformer_encoder():
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filename = "zipformer_encoder.onnx"
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opset_version = 13
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N = 3
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T = 15
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d_model = 512
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attention_dim = 192
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nhead = 8
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feedforward_dim = 1024
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dropout = 0.1
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cnn_module_kernel = 31
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pos_dim = 4
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num_encoder_layers = 12
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warmup_batches = 4000.0
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warmup_begin = warmup_batches / (num_encoder_layers + 1)
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warmup_end = warmup_batches / (num_encoder_layers + 1)
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x = torch.rand(N, T, d_model)
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encoder_layer = ZipformerEncoderLayer(
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d_model,
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attention_dim,
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nhead,
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feedforward_dim,
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dropout,
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cnn_module_kernel,
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pos_dim,
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)
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encoder = ZipformerEncoder(
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encoder_layer, num_encoder_layers, dropout, warmup_begin, warmup_end
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)
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encoder.eval()
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encoder = convert_scaled_to_non_scaled(encoder, inplace=True)
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# jit_model = torch.jit.trace(encoder, (pos_emb))
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torch_y = encoder(x)
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torch.onnx.export(
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encoder,
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(x, torch.ones(1, dtype=torch.float32)),
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filename,
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verbose=False,
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opset_version=opset_version,
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input_names=["x"],
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output_names=["y"],
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dynamic_axes={
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"x": {0: "T", 1: "N"},
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"y": {0: "T", 1: "N"},
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},
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)
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options = ort.SessionOptions()
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options.inter_op_num_threads = 1
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options.intra_op_num_threads = 1
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session = ort.InferenceSession(
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filename,
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sess_options=options,
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providers=["CPUExecutionProvider"],
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)
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input_nodes = session.get_inputs()
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inputs = {
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input_nodes[0].name: x.numpy(),
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input_nodes[1].name: torch.ones(1, dtype=torch.float32).numpy(),
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}
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onnx_y = session.run(["y"], inputs)[0]
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onnx_y = torch.from_numpy(onnx_y)
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torch_y = encoder(x)
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assert torch.allclose(onnx_y, torch_y, atol=1e-05), (onnx_y - torch_y).abs().max()
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print(onnx_y.abs().sum(), torch_y.abs().sum(), onnx_y.shape, torch_y.shape)
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os.remove(filename)
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def test_zipformer():
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filename = "zipformer.onnx"
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opset_version = 11
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N = 3
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T = 15
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num_features = 80
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x = torch.rand(N, T, num_features)
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x_lens = torch.full((N,), fill_value=T, dtype=torch.int64)
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zipformer = Zipformer(num_features=num_features)
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zipformer.eval()
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zipformer = convert_scaled_to_non_scaled(zipformer, inplace=True)
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# jit_model = torch.jit.trace(zipformer, (x, x_lens))
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torch.onnx.export(
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zipformer,
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(x, x_lens),
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filename,
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verbose=False,
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opset_version=opset_version,
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input_names=["x", "x_lens"],
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output_names=["y", "y_lens"],
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dynamic_axes={
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"x": {0: "N", 1: "T"},
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"x_lens": {0: "N"},
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"y": {0: "N", 1: "T"},
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"y_lens": {0: "N"},
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},
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)
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options = ort.SessionOptions()
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options.inter_op_num_threads = 1
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options.intra_op_num_threads = 1
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session = ort.InferenceSession(
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filename,
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sess_options=options,
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providers=["CPUExecutionProvider"],
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)
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input_nodes = session.get_inputs()
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inputs = {
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input_nodes[0].name: x.numpy(),
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input_nodes[1].name: x_lens.numpy(),
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}
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onnx_y, onnx_y_lens = session.run(["y", "y_lens"], inputs)
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onnx_y = torch.from_numpy(onnx_y)
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onnx_y_lens = torch.from_numpy(onnx_y_lens)
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torch_y, torch_y_lens = zipformer(x, x_lens)
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assert torch.allclose(onnx_y, torch_y, atol=1e-05), (onnx_y - torch_y).abs().max()
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assert torch.allclose(onnx_y_lens, torch_y_lens, atol=1e-05), (
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(onnx_y_lens - torch_y_lens).abs().max()
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)
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print(onnx_y.abs().sum(), torch_y.abs().sum(), onnx_y.shape, torch_y.shape)
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print(onnx_y_lens, torch_y_lens)
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os.remove(filename)
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@torch.no_grad()
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def main():
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test_conv2d_subsampling()
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test_rel_pos()
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test_zipformer_encoder_layer()
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test_zipformer_encoder()
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test_zipformer()
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if __name__ == "__main__":
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torch.manual_seed(20221011)
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main()
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