92649105ed
* Add eltwise types resolving. Support big int constants. * Update src/bindings/python/src/openvino/frontend/pytorch/decoder.py * Small fix * Fix some cases * Add tests for add in different types * Add tests for mul * Add tests for sub and div * Small fixes * Return list handling (needed for empty lists) * Add test for empty list * Update src/frontends/pytorch/src/op/mul.cpp Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> * Use refs instead of ptrs * Apply suggestions from code review Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com> * Apply code review suggestions * Fix code style * Add more eltwise ops --------- Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
102 lines
4.3 KiB
Python
102 lines
4.3 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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import torch
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from pytorch_layer_test_class import PytorchLayerTest
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class TestSub(PytorchLayerTest):
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def _prepare_input(self):
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return self.input_data
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def create_model(self):
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class aten_sub(torch.nn.Module):
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def forward(self, x, y, alpha: float):
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return torch.sub(x, y, alpha=alpha)
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ref_net = None
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return aten_sub(), ref_net, "aten::sub"
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@pytest.mark.parametrize('input_data', [(np.random.randn(2, 3, 4).astype(np.float32),
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np.random.randn(
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2, 3, 4).astype(np.float32),
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np.random.randn(1)),
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(np.random.randn(4, 2, 3).astype(np.float32),
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np.random.randn(
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1, 2, 3).astype(np.float32),
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np.random.randn(1)), ])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_sub(self, ie_device, precision, ir_version, input_data):
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self.input_data = input_data
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self._test(*self.create_model(), ie_device, precision, ir_version)
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class TestSubTypes(PytorchLayerTest):
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def _prepare_input(self):
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if len(self.lhs_shape) == 0:
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return (torch.randn(self.rhs_shape).to(self.rhs_type).numpy(),)
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elif len(self.rhs_shape) == 0:
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return (torch.randn(self.lhs_shape).to(self.lhs_type).numpy(),)
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return (torch.randn(self.lhs_shape).to(self.lhs_type).numpy(),
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torch.randn(self.rhs_shape).to(self.rhs_type).numpy())
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def create_model(self, lhs_type, lhs_shape, rhs_type, rhs_shape):
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class aten_sub(torch.nn.Module):
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def __init__(self, lhs_type, lhs_shape, rhs_type, rhs_shape):
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super().__init__()
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self.lhs_type = lhs_type
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self.rhs_type = rhs_type
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if len(lhs_shape) == 0:
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self.forward = self.forward1
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elif len(rhs_shape) == 0:
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self.forward = self.forward2
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else:
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self.forward = self.forward3
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def forward1(self, rhs):
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return torch.sub(torch.tensor(3).to(self.lhs_type), rhs.to(self.rhs_type), alpha=2)
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def forward2(self, lhs):
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return torch.sub(lhs.to(self.lhs_type), torch.tensor(3).to(self.rhs_type), alpha=2)
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def forward3(self, lhs, rhs):
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return torch.sub(lhs.to(self.lhs_type), rhs.to(self.rhs_type), alpha=2)
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ref_net = None
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return aten_sub(lhs_type, lhs_shape, rhs_type, rhs_shape), ref_net, "aten::sub"
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@pytest.mark.parametrize(("lhs_type", "rhs_type"),
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[[torch.int32, torch.int64],
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[torch.int32, torch.float32],
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# [torch.int32, torch.float64], fp64 produce ov error of eltwise constant fold
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[torch.int64, torch.int32],
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[torch.int64, torch.float32],
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# [torch.int64, torch.float64], fp64 produce ov error of eltwise constant fold
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[torch.float32, torch.int32],
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[torch.float32, torch.int64],
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# [torch.float32, torch.float64], fp64 produce ov error of eltwise constant fold
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])
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@pytest.mark.parametrize(("lhs_shape", "rhs_shape"), [([2, 3], [2, 3]),
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([2, 3], []),
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([], [2, 3]),
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_sub_types(self, ie_device, precision, ir_version, lhs_type, lhs_shape, rhs_type, rhs_shape):
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self.lhs_type = lhs_type
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self.lhs_shape = lhs_shape
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self.rhs_type = rhs_type
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self.rhs_shape = rhs_shape
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self._test(*self.create_model(lhs_type, lhs_shape, rhs_type, rhs_shape),
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ie_device, precision, ir_version)
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