mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
4e6e11c139
Summary: Added a number of opset10 tests from Caffe2 to ORT Pull Request resolved: https://github.com/pytorch/pytorch/pull/22993 Differential Revision: D16467954 Pulled By: bddppq fbshipit-source-id: 0b92694c7c0213bdf8e77e6f8e07e6bc8a85170a
920 lines
34 KiB
Python
920 lines
34 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import unittest
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import onnxruntime # noqa
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import torch
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import numpy as np
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import io
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import itertools
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from torch.nn.utils import rnn as rnn_utils
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from model_defs.lstm_flattening_result import LstmFlatteningResult
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from model_defs.rnn_model_with_packed_sequence import RnnModelWithPackedSequence
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from test_pytorch_common import skipIfUnsupportedMinOpsetVersion, skipIfUnsupportedOpsetVersion
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from test_pytorch_common import RNN_BATCH_SIZE, RNN_SEQUENCE_LENGTH, RNN_INPUT_SIZE, RNN_HIDDEN_SIZE
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import model_defs.word_language_model as word_language_model
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def run_model_test(self, model, batch_size=2, state_dict=None,
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input=None, use_gpu=True, rtol=0.001, atol=1e-7,
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example_outputs=None, do_constant_folding=True):
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model.eval()
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if input is None:
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input = torch.randn(batch_size, 3, 224, 224, requires_grad=True)
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with torch.no_grad():
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if isinstance(input, torch.Tensor):
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input = (input,)
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output = model(*input)
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if isinstance(output, torch.Tensor):
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output = (output,)
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# export the model to ONNX
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f = io.BytesIO()
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torch.onnx.export(model, input, f,
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opset_version=self.opset_version,
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example_outputs=output,
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do_constant_folding=do_constant_folding)
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input, _ = torch.jit._flatten(input)
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output, _ = torch.jit._flatten(output)
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def to_numpy(tensor):
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if tensor.requires_grad:
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return tensor.detach().cpu().numpy()
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else:
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return tensor.cpu().numpy()
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inputs = list(map(to_numpy, input))
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outputs = list(map(to_numpy, output))
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# compute onnxruntime output prediction
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ort_sess = onnxruntime.InferenceSession(f.getvalue())
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ort_inputs = dict((ort_sess.get_inputs()[i].name, input) for i, input in enumerate(inputs))
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ort_outs = ort_sess.run(None, ort_inputs)
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# compare onnxruntime and PyTorch results
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assert len(outputs) == len(ort_outs), "number of outputs differ"
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# compare onnxruntime and PyTorch results
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[np.testing.assert_allclose(out, ort_out, rtol=rtol, atol=atol) for out, ort_out in zip(outputs, ort_outs)]
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class TestONNXRuntime(unittest.TestCase):
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from torch.onnx.symbolic_helper import _export_onnx_opset_version
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opset_version = _export_onnx_opset_version
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def run_test(self, model, input, rtol=1e-3, atol=1e-7, do_constant_folding=True, batch_size=2, use_gpu=True):
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run_model_test(self, model, batch_size=batch_size,
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input=input, use_gpu=use_gpu, rtol=rtol, atol=atol,
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do_constant_folding=do_constant_folding)
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def run_word_language_model(self, model_name):
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ntokens = 50
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emsize = 5
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nhid = 5
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nlayers = 5
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dropout = 0.2
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tied = False
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batchsize = 5
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model = word_language_model.RNNModel(model_name, ntokens, emsize,
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nhid, nlayers, dropout, tied,
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batchsize)
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x = torch.arange(0, ntokens).long().view(-1, batchsize)
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# Only support CPU version, since tracer is not working in GPU RNN.
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self.run_test(model, (x, model.hidden))
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def test_word_language_model_RNN_TANH(self):
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self.run_word_language_model("RNN_TANH")
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def test_word_language_model_RNN_RELU(self):
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self.run_word_language_model("RNN_RELU")
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def test_word_language_model_LSTM(self):
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self.run_word_language_model("LSTM")
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def test_word_language_model_GRU(self):
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self.run_word_language_model("GRU")
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def test_index_1d(self):
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self._test_index_generic(lambda input: input[0])
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def test_index_2d_1dimslice(self):
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self._test_index_generic(lambda input: input[0:1, :])
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def test_index_2d_sliceint(self):
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self._test_index_generic(lambda input: input[1, :])
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def test_index_2d_neg_slice(self):
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self._test_index_generic(lambda input: input[0:-1, :])
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@skipIfUnsupportedMinOpsetVersion(9)
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def test_full_trace(self):
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class FullModel(torch.nn.Module):
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def forward(self, x):
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return torch.full((3, 4), x, dtype=torch.long)
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x = torch.tensor(12)
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self.run_test(FullModel(), x)
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@skipIfUnsupportedMinOpsetVersion(9)
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def test_full_script(self):
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class FullModelScripting(torch.jit.ScriptModule):
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@torch.jit.script_method
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def forward(self, x):
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return torch.full((3, 4), x, dtype=torch.long)
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x = torch.tensor(12)
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self.run_test(FullModelScripting(), x)
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def test_maxpool(self):
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model = torch.nn.MaxPool1d(2, stride=1)
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x = torch.randn(20, 16, 50)
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self.run_test(model, x)
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@skipIfUnsupportedMinOpsetVersion(8)
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def test_maxpool_adaptive(self):
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model = torch.nn.AdaptiveMaxPool1d((5), return_indices=False)
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x = torch.randn(20, 16, 50, requires_grad=True)
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self.run_test(model, x)
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def test_maxpool_2d(self):
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model = torch.nn.MaxPool2d(5, padding=(1, 2))
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x = torch.randn(1, 20, 16, 50, requires_grad=True)
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self.run_test(model, x)
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def test_maxpool_1d_ceil(self):
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model = torch.nn.MaxPool1d(3, 2, ceil_mode=True)
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x = torch.randn(20, 16, 50)
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self.run_test(model, x)
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def test_maxpool_2d_ceil(self):
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model = torch.nn.MaxPool2d(3, 2, ceil_mode=True)
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x = torch.randn(20, 16, 50, 32)
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self.run_test(model, x)
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def test_maxpool_3d_ceil(self):
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model = torch.nn.MaxPool3d(3, 2, ceil_mode=True)
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x = torch.randn(20, 16, 50, 44, 31)
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self.run_test(model, x)
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@skipIfUnsupportedMinOpsetVersion(8)
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def test_maxpool_with_indices(self):
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model = torch.nn.MaxPool1d(2, stride=1, return_indices=True)
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x = torch.randn(20, 16, 50)
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self.run_test(model, x)
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@skipIfUnsupportedMinOpsetVersion(10)
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def test_maxpool_dilation(self):
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model = torch.nn.MaxPool1d(2, stride=1, dilation=2)
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x = torch.randn(20, 16, 50)
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self.run_test(model, x)
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def test_avgpool(self):
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model = torch.nn.AvgPool1d(2, stride=1)
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x = torch.randn(20, 16, 50)
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self.run_test(model, x)
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def test_avgpool_1d_ceil(self):
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model = torch.nn.AvgPool1d(3, 2, ceil_mode=True)
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x = torch.randn(1, 1, 7)
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self.run_test(model, x)
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def test_avgpool_2d_ceil(self):
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model = torch.nn.AvgPool2d(3, 2, ceil_mode=True)
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x = torch.randn(20, 16, 50, 32)
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self.run_test(model, x)
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def test_avgpool_3d_ceil(self):
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model = torch.nn.AvgPool3d(3, 2, ceil_mode=True)
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x = torch.randn(20, 16, 50, 44, 31)
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self.run_test(model, x)
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def test_slice_trace(self):
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class MyModule(torch.nn.Module):
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def forward(self, x):
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return x[0:1]
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x = torch.randn(3)
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self.run_test(MyModule(), x)
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def test_slice_neg(self):
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class NegSlice(torch.nn.Module):
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def forward(self, x):
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return x[-1:]
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x = torch.randn(3, 4, 5)
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self.run_test(NegSlice(), x)
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def test_slice_neg_large(self):
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class NegSlice(torch.nn.Module):
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def forward(self, x):
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return x[:, :, :, :, -3]
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x = torch.randn(3, 4, 5, 6, 7)
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self.run_test(NegSlice(), x)
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@unittest.skip('https://github.com/pytorch/pytorch/issues/10984')
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def test_slice_neg_large_negone(self):
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class NegSlice(torch.nn.Module):
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def forward(self, x):
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return x[:, :, :, :, -1]
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x = torch.randn(3, 4, 5, 6, 7)
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self.run_test(NegSlice(), x)
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def test_slice_dynamic(self):
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class DynamicSliceExportMod(torch.nn.Module):
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def forward(self, x):
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results = []
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for i in range(4):
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results.append(x[:x.size(0) - i, i:x.size(2), i:3])
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return tuple(results)
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x = torch.rand(5, 5, 5)
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self.run_test(DynamicSliceExportMod(), x)
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def test_slice_dynamic_script(self):
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class DynamicSliceModel(torch.jit.ScriptModule):
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@torch.jit.script_method
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def forward(self, x):
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return x[1:x.size(0)]
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x = torch.rand(1, 2)
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self.run_test(DynamicSliceModel(), x)
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def test_slice_dynamic_to_end(self):
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class DynamicSliceExportMod(torch.nn.Module):
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def forward(self, x):
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results = []
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for i in range(4):
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results.append(x[:, i:, x.size(2) - 5])
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return tuple(results)
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x = torch.rand(5, 5, 5)
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self.run_test(DynamicSliceExportMod(), x)
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def _test_index_generic(self, fn):
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class MyModel(torch.nn.Module):
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def __init__(self):
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super(MyModel, self).__init__()
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def forward(self, input):
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return fn(input)
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m1 = torch.randn(3, 4, 5, 6, 7)
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self.run_test(MyModel(), m1)
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def test_tensor_index_advanced_indexing(self):
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self._test_index_generic(
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lambda input: input[:, torch.tensor([[0, 2], [1, 1]]), :, torch.tensor([2, 1]), torch.tensor([0, 3])])
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self._test_index_generic(lambda input: input[..., torch.tensor([2, 1]), torch.tensor([0, 3])])
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self._test_index_generic(lambda input: input[:, torch.tensor([0, 2]), None, 2:4, torch.tensor([[1, 3], [4, 0]])])
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self._test_index_generic(lambda input: input[:, torch.tensor([0, 2]), torch.tensor([1]), 2:4, torch.tensor([[1], [4]])])
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def test_tensor_index_advanced_indexing_consecutive(self):
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self._test_index_generic(lambda input: input[:, torch.tensor([0, 2]), torch.tensor([[1, 3], [4, 0]]), None])
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@skipIfUnsupportedMinOpsetVersion(10)
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def test_flip(self):
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class MyModule(torch.nn.Module):
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def forward(self, x):
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return torch.flip(x, dims=[0])
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x = torch.tensor(np.arange(6.0).reshape(2, 3))
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self.run_test(MyModule(), x)
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@skipIfUnsupportedMinOpsetVersion(9)
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def test_interpolate_scale(self):
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class MyModel(torch.nn.Module):
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def forward(self, x):
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return torch.nn.functional.interpolate(x, mode="nearest", scale_factor=2)
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x = torch.randn(1, 2, 3, 4, requires_grad=True)
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self.run_test(MyModel(), x)
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# NOTE: Supported in onnxruntime master, enable this after 0.5 release.
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@skipIfUnsupportedOpsetVersion([10])
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def test_interpolate_output_size(self):
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class MyModel(torch.nn.Module):
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def forward(self, x):
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return torch.nn.functional.interpolate(x, mode="nearest", size=(6, 8))
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x = torch.randn(1, 2, 3, 4, requires_grad=True)
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self.run_test(MyModel(), x)
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# NOTE: Supported in onnxruntime master, enable this after 0.5 release.
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@skipIfUnsupportedOpsetVersion([10])
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def test_interpolate_upsample(self):
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class MyModel(torch.nn.Module):
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def forward(self, x):
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size = [v * 2 for v in x.size()[2:]]
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# work around for now: turn the dynamic sizes into constant
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size = [int(i) for i in size]
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return torch.nn.functional.interpolate(x, mode="nearest", size=size)
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x = torch.randn(1, 2, 3, 4, requires_grad=True)
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self.run_test(MyModel(), x)
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@skipIfUnsupportedMinOpsetVersion(9)
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def test_interpolate_downsample(self):
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class MyModel(torch.nn.Module):
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def forward(self, x):
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return torch.nn.functional.interpolate(x, mode="nearest", scale_factor=[1, 1, 0.5, 0.5])
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x = torch.randn(1, 2, 3, 4, requires_grad=True)
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self.run_test(MyModel(), x)
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@skipIfUnsupportedOpsetVersion([7, 8])
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def test_interpolate_upsample_dynamic_sizes(self):
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class MyModel(torch.nn.Module):
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def forward(self, x):
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size = [v * 2 for v in x.size()[2:]]
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return torch.nn.functional.interpolate(x, mode="nearest", size=size)
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x = torch.randn(1, 2, 3, 4, requires_grad=True)
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self.run_test(MyModel(), x)
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def test_narrow(self):
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class NarrowModel(torch.nn.Module):
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def forward(self, input):
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return torch.narrow(input, 0, 0, 2)
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x = torch.randn(3, 3, requires_grad=True)
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self.run_test(NarrowModel(), x)
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# TODO: enable for opset 10 when ONNXRuntime version will be updated
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def test_index_select_constant_scaler_index(self):
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class IndexSelectScalerIndexModel(torch.nn.Module):
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def forward(self, x):
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index = 2
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return torch.index_select(x, 1, torch.tensor(index))
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x = torch.randn(3, 4)
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self.run_test(IndexSelectScalerIndexModel(), x)
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def test_index_select_scaler_index(self):
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class IndexSelectScalerIndexModel(torch.nn.Module):
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def __init__(self, index_base):
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super(IndexSelectScalerIndexModel, self).__init__()
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self.index_base = torch.tensor(index_base)
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def forward(self, x, index_offset):
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index = self.index_base + index_offset
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return torch.index_select(x, 1, index)
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x = torch.randn(3, 4)
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offset = 2
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index_offset = torch.tensor(offset)
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base = 1
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self.run_test(IndexSelectScalerIndexModel(base), (x, index_offset))
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# TODO: enable for opset 10 when ONNXRuntime version will be updated
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@skipIfUnsupportedOpsetVersion([10])
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def test_topk(self):
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class MyModule(torch.nn.Module):
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def forward(self, x):
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return torch.topk(x, 3)
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x = torch.arange(1., 6., requires_grad=True)
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self.run_test(MyModule(), x)
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@skipIfUnsupportedMinOpsetVersion(10)
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def test_topk_script(self):
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class MyModuleDynamic(torch.jit.ScriptModule):
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@torch.jit.script_method
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def forward(self, x, k):
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return torch.topk(x, k)
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x = torch.arange(1., 6., requires_grad=True)
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k = torch.tensor(3)
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self.run_test(MyModuleDynamic(), [x, k])
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def test_layer_norm(self):
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model = torch.nn.LayerNorm([10, 10])
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x = torch.randn(20, 5, 10, 10)
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self.run_test(model, x)
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def test_multinomial(self):
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class Multinomial(torch.nn.Module):
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def forward(self, weight):
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return torch.multinomial(weight, 3, replacement=True)
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class MultinomialNoReplacement(torch.nn.Module):
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def forward(self, weight):
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return torch.multinomial(weight, 1)
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weight = torch.tensor([[0, 10, 0, 0], [0, 0, 100, 0]], dtype=torch.float)
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self.run_test(Multinomial(), (weight,))
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self.run_test(MultinomialNoReplacement(), (weight,))
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def test_reduce_log_sum_exp(self):
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class ReduceLogSumExpModel(torch.nn.Module):
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def forward(self, input):
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a = torch.logsumexp(input, dim=0)
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b = torch.logsumexp(input, dim=(0, 1))
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return a + b
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x = torch.randn(4, 4, requires_grad=True)
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self.run_test(ReduceLogSumExpModel(), x)
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def test_lstm_constant_folding(self):
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class LstmNet(torch.nn.Module):
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def __init__(self, input_size, hidden_size, num_layers, bidirectional):
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super(LstmNet, self).__init__()
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self.lstm = torch.nn.LSTM(input_size, hidden_size, num_layers, bidirectional=bidirectional)
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def forward(self, input, initial_state):
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return self.lstm(input, initial_state)
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def get_LstmNet_model_and_inputs(input_size, hidden_size, num_layers, batch_size,
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seq_len, bidirectional):
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num_directions = 2 if bidirectional else 1
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model = LstmNet(input_size, hidden_size, num_layers, bidirectional)
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input = torch.randn(seq_len, batch_size, input_size)
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h0 = torch.randn(num_layers * num_directions, batch_size, hidden_size)
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c0 = torch.randn(num_layers * num_directions, batch_size, hidden_size)
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return model, (input, (h0, c0))
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batch_size1 = 3
|
|
model1, input1 = get_LstmNet_model_and_inputs(7, 3, 2, batch_size1, 5, True)
|
|
self.run_test(model1, input1, do_constant_folding=True)
|
|
|
|
batch_size2 = 4
|
|
model2, input2 = get_LstmNet_model_and_inputs(5, 4, 3, batch_size2, 7, False)
|
|
self.run_test(model2, input2, do_constant_folding=True)
|
|
|
|
def test_gru_constant_folding(self):
|
|
class GruNet(torch.nn.Module):
|
|
def __init__(self, input_size, hidden_size, num_layers, bidirectional):
|
|
super(GruNet, self).__init__()
|
|
self.mygru = torch.nn.GRU(input_size, hidden_size, num_layers, bidirectional=bidirectional)
|
|
|
|
def forward(self, input, initial_state):
|
|
out = self.mygru(input, initial_state)
|
|
return out
|
|
|
|
def get_GruNet_model_and_inputs(input_size, hidden_size, num_layers, batch_size,
|
|
seq_len, bidirectional):
|
|
num_directions = 2 if bidirectional else 1
|
|
model = GruNet(input_size, hidden_size, num_layers, bidirectional)
|
|
input = torch.randn(seq_len, batch_size, input_size)
|
|
h0 = torch.randn(num_layers * num_directions, batch_size, hidden_size)
|
|
return model, (input, h0)
|
|
|
|
batch_size1 = 3
|
|
model1, input1 = get_GruNet_model_and_inputs(7, 3, 2, batch_size1, 5, True)
|
|
self.run_test(model1, input1, do_constant_folding=True)
|
|
|
|
batch_size2 = 4
|
|
model2, input2 = get_GruNet_model_and_inputs(5, 4, 3, batch_size2, 7, False)
|
|
self.run_test(model2, input2, do_constant_folding=True)
|
|
|
|
@skipIfUnsupportedMinOpsetVersion(8)
|
|
def test_max_tensors(self):
|
|
class MaxModel(torch.nn.Module):
|
|
def forward(self, input, other):
|
|
return torch.max(input, other)
|
|
|
|
model = MaxModel()
|
|
x = torch.randn(4, 4, requires_grad=True)
|
|
y = torch.randn(4, 1, requires_grad=True)
|
|
self.run_test(model, (x, y))
|
|
|
|
@skipIfUnsupportedMinOpsetVersion(9)
|
|
def test_arange_end(self):
|
|
class ArangeScript(torch.jit.ScriptModule):
|
|
@torch.jit.script_method
|
|
def forward(self, a):
|
|
return torch.arange(a.size(0), dtype=torch.float).view(-1, 1) + a
|
|
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
outputs = ArangeScript()(x)
|
|
self.run_test(ArangeScript(), x)
|
|
|
|
class ArangeModel(torch.nn.Module):
|
|
def forward(self, a):
|
|
return torch.arange(a.size(0), dtype=torch.float).view(-1, 1) + a
|
|
|
|
self.run_test(ArangeModel(), x)
|
|
|
|
@skipIfUnsupportedMinOpsetVersion(9)
|
|
def test_arange_start_end(self):
|
|
class ArangeScript(torch.jit.ScriptModule):
|
|
@torch.jit.script_method
|
|
def forward(self, a):
|
|
return torch.arange(2, a.size(0) + 2, dtype=torch.float).view(-1, 1) + a
|
|
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
outputs = ArangeScript()(x)
|
|
self.run_test(ArangeScript(), x)
|
|
|
|
class ArangeModel(torch.nn.Module):
|
|
def forward(self, a):
|
|
return torch.arange(2, a.size(0) + 2, dtype=torch.float).view(-1, 1) + a
|
|
|
|
self.run_test(ArangeModel(), x)
|
|
|
|
@skipIfUnsupportedMinOpsetVersion(9)
|
|
def test_arange_start_end_step(self):
|
|
class ArangeScript(torch.jit.ScriptModule):
|
|
@torch.jit.script_method
|
|
def forward(self, a):
|
|
return torch.arange(2, a.size(0) * a.size(1) + 2, a.size(1), dtype=torch.float).view(-1, 1) + a
|
|
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
outputs = ArangeScript()(x)
|
|
self.run_test(ArangeScript(), x)
|
|
|
|
class ArangeModel(torch.nn.Module):
|
|
def forward(self, a):
|
|
return torch.arange(2, a.size(0) * a.size(1) + 2, a.size(1), dtype=torch.float).view(-1, 1) + a
|
|
|
|
self.run_test(ArangeModel(), x)
|
|
|
|
@skipIfUnsupportedMinOpsetVersion(9)
|
|
def test__dim_arange(self):
|
|
class DimArange(torch.nn.Module):
|
|
def forward(self, input):
|
|
return torch._dim_arange(input, 1)
|
|
|
|
x = torch.ones(5, 6)
|
|
self.run_test(DimArange(), x)
|
|
|
|
def test_gt(self):
|
|
class GreaterModel(torch.nn.Module):
|
|
def forward(self, input, other):
|
|
return input > other
|
|
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
y = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.run_test(GreaterModel(), (x, y))
|
|
|
|
x = torch.randint(10, (3, 4), dtype=torch.int32)
|
|
y = torch.randint(10, (3, 4), dtype=torch.int32)
|
|
self.run_test(GreaterModel(), (x, y))
|
|
|
|
def test_gt_scalar(self):
|
|
class GreaterModel(torch.nn.Module):
|
|
def forward(self, input):
|
|
return input > 1
|
|
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.run_test(GreaterModel(), x)
|
|
|
|
x = torch.randint(10, (3, 4), dtype=torch.int32)
|
|
self.run_test(GreaterModel(), x)
|
|
|
|
def test_lt(self):
|
|
class LessModel(torch.nn.Module):
|
|
def forward(self, input, other):
|
|
return input > other
|
|
|
|
x = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
y = torch.randn(1, 2, 3, 4, requires_grad=True)
|
|
self.run_test(LessModel(), (x, y))
|
|
|
|
x = torch.randint(10, (3, 4), dtype=torch.int32)
|
|
y = torch.randint(10, (3, 4), dtype=torch.int32)
|
|
self.run_test(LessModel(), (x, y))
|
|
|
|
def test_matmul(self):
|
|
class MatmulModel(torch.nn.Module):
|
|
def forward(self, input, other):
|
|
return torch.matmul(input, other)
|
|
|
|
x = torch.randn(3, 4, requires_grad=True)
|
|
y = torch.randn(4, 5, requires_grad=True)
|
|
self.run_test(MatmulModel(), (x, y))
|
|
|
|
x = torch.randint(10, (3, 4))
|
|
y = torch.randint(10, (4, 5))
|
|
self.run_test(MatmulModel(), (x, y))
|
|
|
|
def test_matmul_batch(self):
|
|
class MatmulModel(torch.nn.Module):
|
|
def forward(self, input, other):
|
|
return torch.matmul(input, other)
|
|
|
|
x = torch.randn(2, 3, 4, requires_grad=True)
|
|
y = torch.randn(2, 4, 5, requires_grad=True)
|
|
self.run_test(MatmulModel(), (x, y))
|
|
|
|
x = torch.randint(10, (2, 3, 4))
|
|
y = torch.randint(10, (2, 4, 5))
|
|
self.run_test(MatmulModel(), (x, y))
|
|
|
|
def test_view(self):
|
|
class ViewModel(torch.nn.Module):
|
|
def forward(self, input):
|
|
return input.view(4, 24)
|
|
|
|
x = torch.randint(10, (4, 2, 3, 4), dtype=torch.int32)
|
|
self.run_test(ViewModel(), x)
|
|
|
|
def test_flatten(self):
|
|
class FlattenModel(torch.nn.Module):
|
|
def forward(self, input):
|
|
return torch.flatten(input)
|
|
|
|
x = torch.randint(10, (1, 2, 3, 4))
|
|
self.run_test(FlattenModel(), x)
|
|
|
|
def test_flatten2d(self):
|
|
class FlattenModel(torch.nn.Module):
|
|
def forward(self, input):
|
|
return torch.flatten(input, 1)
|
|
|
|
x = torch.randint(10, (1, 2, 3, 4))
|
|
self.run_test(FlattenModel(), x)
|
|
|
|
@skipIfUnsupportedMinOpsetVersion(9)
|
|
def test_tensor_factories(self):
|
|
class TensorFactory(torch.nn.Module):
|
|
def forward(self, x):
|
|
return torch.zeros(x.size()) + torch.ones(x.size())
|
|
|
|
x = torch.randn(2, 3, 4)
|
|
self.run_test(TensorFactory(), x)
|
|
|
|
@skipIfUnsupportedMinOpsetVersion(9)
|
|
def test_tensor_factories_script(self):
|
|
class TensorFactory(torch.jit.ScriptModule):
|
|
@torch.jit.script_method
|
|
def forward(self, x):
|
|
return torch.zeros(x.shape, dtype=torch.float) + torch.ones(x.shape, dtype=torch.float)
|
|
|
|
x = torch.randn(2, 3, 4)
|
|
self.run_test(TensorFactory(), x)
|
|
|
|
@skipIfUnsupportedMinOpsetVersion(9)
|
|
def test_tensor_like_factories_script(self):
|
|
class TensorFactory(torch.jit.ScriptModule):
|
|
@torch.jit.script_method
|
|
def forward(self, x):
|
|
zeros = torch.zeros_like(x, dtype=torch.float, layout=torch.strided, device=torch.device('cpu'))
|
|
ones = torch.ones_like(x, dtype=torch.float, layout=torch.strided, device=torch.device('cpu'))
|
|
return zeros + ones
|
|
|
|
x = torch.randn(2, 3, 4)
|
|
self.run_test(TensorFactory(), x)
|
|
|
|
def test_sort(self):
|
|
class SortModel(torch.nn.Module):
|
|
def __init__(self, dim):
|
|
super(SortModel, self).__init__()
|
|
self.dim = dim
|
|
|
|
def forward(self, x):
|
|
return torch.sort(x, dim=self.dim, descending=True)
|
|
|
|
dim = 1
|
|
x = torch.randn(3, 4)
|
|
self.run_test(SortModel(dim), x)
|
|
|
|
@skipIfUnsupportedMinOpsetVersion(9)
|
|
def test_masked_fill(self):
|
|
class MaskedFillModel(torch.nn.Module):
|
|
def forward(self, x):
|
|
mask = torch.tensor([[0, 0, 1], [1, 1, 0]], dtype=torch.uint8)
|
|
return x.masked_fill(mask, 2)
|
|
|
|
x = torch.zeros(4, 2, 3, requires_grad=True)
|
|
self.run_test(MaskedFillModel(), x)
|
|
|
|
class MaskedFillModel2(torch.nn.Module):
|
|
def forward(self, x):
|
|
return x.masked_fill(x > 3, -1)
|
|
|
|
x = torch.arange(16).view(2, 2, 4).to(torch.float32)
|
|
self.run_test(MaskedFillModel2(), x)
|
|
|
|
def _dispatch_rnn_test(self, name, *args, **kwargs):
|
|
if name == 'elman':
|
|
self._elman_rnn_test(*args, **kwargs)
|
|
if name == 'lstm':
|
|
self._lstm_test(*args, **kwargs)
|
|
if name == 'gru':
|
|
self._gru_test(*args, **kwargs)
|
|
|
|
def _elman_rnn_test(self, layers, nonlinearity, bidirectional,
|
|
initial_state, packed_sequence, dropout):
|
|
batch_first = True if packed_sequence == 2 else False
|
|
model = torch.nn.RNN(RNN_INPUT_SIZE, RNN_HIDDEN_SIZE, layers, nonlinearity=nonlinearity,
|
|
bidirectional=bidirectional, dropout=dropout, batch_first=batch_first)
|
|
|
|
if packed_sequence == 1:
|
|
model = RnnModelWithPackedSequence(model, False)
|
|
if packed_sequence == 2:
|
|
model = RnnModelWithPackedSequence(model, True)
|
|
|
|
def make_input(batch_size):
|
|
seq_lengths = np.random.randint(1, RNN_SEQUENCE_LENGTH + 1, size=batch_size)
|
|
seq_lengths = list(reversed(sorted(map(int, seq_lengths))))
|
|
inputs = [torch.randn(l, RNN_INPUT_SIZE) for l in seq_lengths]
|
|
inputs = rnn_utils.pad_sequence(inputs, batch_first=batch_first)
|
|
inputs = [inputs]
|
|
|
|
directions = 2 if bidirectional else 1
|
|
|
|
if initial_state:
|
|
h0 = torch.randn(directions * layers, batch_size, RNN_HIDDEN_SIZE)
|
|
inputs.append(h0)
|
|
if packed_sequence != 0:
|
|
inputs.append(torch.IntTensor(seq_lengths))
|
|
if len(inputs) == 1:
|
|
input = inputs[0]
|
|
else:
|
|
input = tuple(inputs)
|
|
return input
|
|
|
|
input = make_input(RNN_BATCH_SIZE)
|
|
self.run_test(model, input, batch_size=RNN_BATCH_SIZE, atol=1e-7)
|
|
|
|
# test that the model still runs with a different batch size
|
|
other_input = make_input(RNN_BATCH_SIZE + 1)
|
|
self.run_test(model, other_input, batch_size=RNN_BATCH_SIZE + 1)
|
|
|
|
def _lstm_test(self, layers, bidirectional, initial_state,
|
|
packed_sequence, dropout):
|
|
batch_first = True if packed_sequence == 2 else False
|
|
model = LstmFlatteningResult(
|
|
RNN_INPUT_SIZE, RNN_HIDDEN_SIZE, layers,
|
|
bidirectional=bidirectional, dropout=dropout, batch_first=batch_first)
|
|
if packed_sequence == 1:
|
|
model = RnnModelWithPackedSequence(model, False)
|
|
if packed_sequence == 2:
|
|
model = RnnModelWithPackedSequence(model, True)
|
|
|
|
def make_input(batch_size):
|
|
seq_lengths = np.random.randint(1, RNN_SEQUENCE_LENGTH + 1, size=batch_size)
|
|
seq_lengths = list(reversed(sorted(map(int, seq_lengths))))
|
|
inputs = [torch.randn(l, RNN_INPUT_SIZE) for l in seq_lengths]
|
|
inputs = rnn_utils.pad_sequence(inputs, batch_first=batch_first)
|
|
inputs = [inputs]
|
|
|
|
directions = 2 if bidirectional else 1
|
|
|
|
if initial_state:
|
|
h0 = torch.randn(directions * layers, batch_size, RNN_HIDDEN_SIZE)
|
|
c0 = torch.randn(directions * layers, batch_size, RNN_HIDDEN_SIZE)
|
|
inputs.append((h0, c0))
|
|
if packed_sequence != 0:
|
|
inputs.append(torch.IntTensor(seq_lengths))
|
|
if len(inputs) == 1:
|
|
input = inputs[0]
|
|
else:
|
|
input = tuple(inputs)
|
|
return input
|
|
|
|
input = make_input(RNN_BATCH_SIZE)
|
|
self.run_test(model, input, batch_size=RNN_BATCH_SIZE)
|
|
|
|
# test that the model still runs with a different batch size
|
|
other_input = make_input(RNN_BATCH_SIZE + 1)
|
|
self.run_test(model, other_input, batch_size=RNN_BATCH_SIZE + 1)
|
|
|
|
def _gru_test(self, layers, bidirectional, initial_state,
|
|
packed_sequence, dropout):
|
|
batch_first = True if packed_sequence == 2 else False
|
|
model = torch.nn.GRU(RNN_INPUT_SIZE, RNN_HIDDEN_SIZE, layers, bidirectional=bidirectional, dropout=dropout,
|
|
batch_first=batch_first)
|
|
if packed_sequence == 1:
|
|
model = RnnModelWithPackedSequence(model, False)
|
|
if packed_sequence == 2:
|
|
model = RnnModelWithPackedSequence(model, True)
|
|
|
|
def make_input(batch_size):
|
|
seq_lengths = np.random.randint(1, RNN_SEQUENCE_LENGTH + 1, size=batch_size)
|
|
seq_lengths = list(reversed(sorted(map(int, seq_lengths))))
|
|
inputs = [torch.randn(l, RNN_INPUT_SIZE) for l in seq_lengths]
|
|
inputs = rnn_utils.pad_sequence(inputs, batch_first=batch_first)
|
|
inputs = [inputs]
|
|
|
|
directions = 2 if bidirectional else 1
|
|
|
|
if initial_state:
|
|
h0 = torch.randn(directions * layers, batch_size, RNN_HIDDEN_SIZE)
|
|
inputs.append(h0)
|
|
if packed_sequence != 0:
|
|
inputs.append(torch.IntTensor(seq_lengths))
|
|
if len(inputs) == 1:
|
|
input = inputs[0]
|
|
else:
|
|
input = tuple(inputs)
|
|
return input
|
|
|
|
input = make_input(RNN_BATCH_SIZE)
|
|
self.run_test(model, input, batch_size=RNN_BATCH_SIZE,)
|
|
|
|
# test that the model still runs with a different batch size
|
|
other_input = make_input(RNN_BATCH_SIZE + 1)
|
|
self.run_test(model, other_input, batch_size=RNN_BATCH_SIZE + 1)
|
|
|
|
|
|
def make_test(name, base, layer, bidirectional, initial_state,
|
|
variable_length, dropout,
|
|
**extra_kwargs):
|
|
test_name = str('_'.join([
|
|
'test', name, layer[1],
|
|
bidirectional[1], initial_state[1],
|
|
variable_length[1], dropout[1]
|
|
]))
|
|
|
|
# Cannot export with older opsets because of 'ConstantFill' op
|
|
# ConstantFill was a temp op removed at opset 8. This is no longer supported by onnxruntime
|
|
@skipIfUnsupportedMinOpsetVersion(9)
|
|
def f(self):
|
|
self._dispatch_rnn_test(
|
|
base,
|
|
layers=layer[0],
|
|
bidirectional=bidirectional[0],
|
|
initial_state=initial_state[0],
|
|
packed_sequence=variable_length[0],
|
|
dropout=dropout[0],
|
|
**extra_kwargs)
|
|
|
|
f.__name__ = test_name
|
|
setattr(TestONNXRuntime, f.__name__, f)
|
|
|
|
|
|
def setup_rnn_tests():
|
|
layers_opts = [
|
|
(1, 'unilayer'),
|
|
(3, 'trilayer')
|
|
]
|
|
bidirectional_opts = [
|
|
(False, 'forward'),
|
|
(True, 'bidirectional')
|
|
]
|
|
initial_state_opts = [
|
|
(True, 'with_initial_state'),
|
|
(False, 'no_initial_state')
|
|
]
|
|
variable_length_opts = [
|
|
(0, 'without_sequence_lengths'),
|
|
(1, 'with_variable_length_sequences'),
|
|
(2, 'with_batch_first_sequence_lengths')
|
|
]
|
|
dropout_opts = [
|
|
(0.2, 'with_dropout'),
|
|
(0.0, 'without_dropout')
|
|
]
|
|
test_count = 0
|
|
for (layer, bidirectional, initial_state, variable_length, dropout) in \
|
|
itertools.product(
|
|
layers_opts,
|
|
bidirectional_opts,
|
|
initial_state_opts,
|
|
variable_length_opts,
|
|
dropout_opts,
|
|
):
|
|
|
|
for base, name, extra_kwargs in (
|
|
('elman', 'elman_relu', {'nonlinearity': u'relu'}),
|
|
('elman', 'elman_tanh', {'nonlinearity': u'tanh'}),
|
|
('lstm', 'lstm', {}),
|
|
('gru', 'gru', {})
|
|
):
|
|
# This is a hack to skip elman_rnn bidirectional tests for now
|
|
# TODO: Revert this once elman_rnn bidirectional issue is fixed
|
|
if base == 'elman' and bidirectional[1] == 'bidirectional':
|
|
continue
|
|
make_test(name, base, layer, bidirectional, initial_state,
|
|
variable_length, dropout,
|
|
**extra_kwargs)
|
|
test_count += 1
|
|
|
|
# sanity check that a representative example does exist
|
|
TestONNXRuntime.test_gru_trilayer_forward_with_initial_state_without_sequence_lengths_with_dropout
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# make sure no one accidentally disables all the tests without
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# noticing
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# assert test_count == 192, test_count
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# TODO: Revert this once elman_rnn bidirectional issue is fixed
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if test_count != 144:
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raise ValueError('Expected 144 tests but found {}'.format(test_count))
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setup_rnn_tests()
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# opset 7 tests
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TestONNXRuntime_opset7 = type(str("TestONNXRuntime_opset7"),
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|
(unittest.TestCase,),
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dict(TestONNXRuntime.__dict__, opset_version=7))
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|
|
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# opset 8 tests
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|
TestONNXRuntime_opset8 = type(str("TestONNXRuntime_opset8"),
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|
(unittest.TestCase,),
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|
dict(TestONNXRuntime.__dict__, opset_version=8))
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|
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|
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# opset 10 tests
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|
TestONNXRuntime_opset10 = type(str("TestONNXRuntime_opset10"),
|
|
(unittest.TestCase,),
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|
dict(TestONNXRuntime.__dict__, opset_version=10))
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|
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|
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if __name__ == '__main__':
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|
unittest.main()
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