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>
104 lines
4.0 KiB
Python
104 lines
4.0 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 TestMul(PytorchLayerTest):
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def _prepare_input(self):
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return (self.input_array.astype(self.input_type), self.other_array.astype(self.other_type))
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def create_model(self):
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class aten_mul(torch.nn.Module):
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def __init__(self):
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super(aten_mul, self).__init__()
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def forward(self, input_tensor, other_tensor):
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return torch.mul(input_tensor, other_tensor)
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ref_net = None
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return aten_mul(), ref_net, "aten::mul"
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@pytest.mark.parametrize(("input_array", "other_array"), [
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[np.array([0.2015, -0.4255, 2.6087]), np.array(100)],
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[np.array([[1.1207], [-0.3137], [0.0700], [0.8378]]),
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np.array([[0.5146, 0.1216, -0.5244, 2.2382]])],
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])
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_mul_pt_spec(self, input_array, other_array, ie_device, precision, ir_version):
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self.input_array = input_array
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self.input_type = np.float32
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self.other_array = other_array
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self.other_type = np.float32
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self._test(*self.create_model(), ie_device, precision, ir_version)
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class TestMulTypes(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_mul(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.mul(torch.tensor(3).to(self.lhs_type), rhs.to(self.rhs_type))
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def forward2(self, lhs):
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return torch.mul(lhs.to(self.lhs_type), torch.tensor(3).to(self.rhs_type))
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def forward3(self, lhs, rhs):
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return torch.mul(lhs.to(self.lhs_type), rhs.to(self.rhs_type))
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ref_net = None
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return aten_mul(lhs_type, lhs_shape, rhs_type, rhs_shape), ref_net, "aten::mul"
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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],
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[torch.int64, torch.int32],
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[torch.int64, torch.float32],
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[torch.int64, torch.float64],
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[torch.float32, torch.int32],
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[torch.float32, torch.int64],
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[torch.float32, torch.float64],
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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_mul_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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