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>
122 lines
5.0 KiB
Python
122 lines
5.0 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 TestDiv(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, rounding_mode):
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class aten_div(torch.nn.Module):
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def __init__(self, rounding_mode):
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super(aten_div, self).__init__()
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self.rounding_mode = rounding_mode
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def forward(self, input_tensor, other_tensor):
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return torch.div(input_tensor, other_tensor, rounding_mode=self.rounding_mode)
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ref_net = None
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return aten_div(rounding_mode), ref_net, "aten::div"
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@pytest.mark.parametrize(("input_array", "other_array"), [
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[np.array([0.7620, 2.5548, -0.5944, -0.7438, 0.9274]), np.array(0.5)],
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[np.array([[-0.3711, -1.9353, -0.4605, -0.2917],
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[0.1815, -1.0111, 0.9805, -1.5923],
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[0.1062, 1.4581, 0.7759, -1.2344],
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[-0.1830, -0.0313, 1.1908, -1.4757]]),
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np.array([0.8032, 0.2930, -0.8113, -0.2308])]
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])
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@pytest.mark.parametrize('rounding_mode', ([
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None,
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"floor",
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"trunc"
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]))
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_div_pt_spec(self, input_array, other_array, rounding_mode, 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(rounding_mode),
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ie_device, precision, ir_version)
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class TestDivTypes(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.randint(2, 5, self.rhs_shape).to(self.rhs_type).numpy(),)
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elif len(self.rhs_shape) == 0:
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return (10 * torch.randn(self.lhs_shape).to(self.lhs_type).numpy(),)
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return (10 * torch.randn(self.lhs_shape).to(self.lhs_type).numpy(),
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torch.randint(2, 5, self.rhs_shape).to(self.rhs_type).numpy())
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def create_model(self, lhs_type, lhs_shape, rhs_type, rhs_shape, rounding_mode):
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class aten_div(torch.nn.Module):
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def __init__(self, lhs_type, lhs_shape, rhs_type, rhs_shape, rounding_mode):
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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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self.rm = rounding_mode
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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.div(torch.tensor(3).to(self.lhs_type), rhs.to(self.rhs_type), rounding_mode=self.rm)
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def forward2(self, lhs):
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return torch.div(lhs.to(self.lhs_type), torch.tensor(3).to(self.rhs_type), rounding_mode=self.rm)
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def forward3(self, lhs, rhs):
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return torch.div(lhs.to(self.lhs_type), rhs.to(self.rhs_type), rounding_mode=self.rm)
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ref_net = None
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return aten_div(lhs_type, lhs_shape, rhs_type, rhs_shape, rounding_mode), ref_net, "aten::div"
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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.parametrize('rounding_mode', ([
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None,
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"floor",
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"trunc"
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]))
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@pytest.mark.nightly
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@pytest.mark.precommit
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def test_div_types(self, ie_device, precision, ir_version, lhs_type, lhs_shape, rhs_type, rhs_shape, rounding_mode):
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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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if rounding_mode == "floor" and not lhs_type.is_floating_point and not rhs_type.is_floating_point:
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pytest.skip("Floor rounding mode and int inputs produce wrong results")
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self._test(*self.create_model(lhs_type, lhs_shape, rhs_type, rhs_shape, rounding_mode),
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ie_device, precision, ir_version)
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