8d0381b0fe
* [PT FE] Implement custom op for types alignment * Fix code style * Fix inplace ops * Fix layer tests * Remove no longer needed change * Fix ovc tests * Fix fe tests
110 lines
4.5 KiB
Python
110 lines
4.5 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
|
|
# SPDX-License-Identifier: Apache-2.0
|
|
|
|
import numpy as np
|
|
import pytest
|
|
import torch
|
|
|
|
from pytorch_layer_test_class import PytorchLayerTest
|
|
|
|
|
|
class TestMul(PytorchLayerTest):
|
|
def _prepare_input(self):
|
|
return (self.input_array.astype(self.input_type), self.other_array.astype(self.other_type))
|
|
|
|
def create_model(self):
|
|
|
|
class aten_mul(torch.nn.Module):
|
|
def __init__(self):
|
|
super(aten_mul, self).__init__()
|
|
|
|
def forward(self, input_tensor, other_tensor):
|
|
return torch.mul(input_tensor, other_tensor)
|
|
|
|
ref_net = None
|
|
|
|
return aten_mul(), ref_net, "aten::mul"
|
|
|
|
@pytest.mark.parametrize(("input_array", "other_array"), [
|
|
[np.array([0.2015, -0.4255, 2.6087]), np.array(100)],
|
|
[np.array([[1.1207], [-0.3137], [0.0700], [0.8378]]),
|
|
np.array([[0.5146, 0.1216, -0.5244, 2.2382]])],
|
|
])
|
|
@pytest.mark.nightly
|
|
@pytest.mark.precommit
|
|
def test_mul_pt_spec(self, input_array, other_array, ie_device, precision, ir_version):
|
|
self.input_array = input_array
|
|
self.input_type = np.float32
|
|
self.other_array = other_array
|
|
self.other_type = np.float32
|
|
self._test(*self.create_model(), ie_device, precision, ir_version, use_convert_model=True)
|
|
|
|
|
|
class TestMulTypes(PytorchLayerTest):
|
|
|
|
def _prepare_input(self):
|
|
if len(self.lhs_shape) == 0:
|
|
return (torch.randn(self.rhs_shape).to(self.rhs_type).numpy(),)
|
|
elif len(self.rhs_shape) == 0:
|
|
return (torch.randn(self.lhs_shape).to(self.lhs_type).numpy(),)
|
|
return (torch.randn(self.lhs_shape).to(self.lhs_type).numpy(),
|
|
torch.randn(self.rhs_shape).to(self.rhs_type).numpy())
|
|
|
|
def create_model(self, lhs_type, lhs_shape, rhs_type, rhs_shape):
|
|
|
|
class aten_mul(torch.nn.Module):
|
|
def __init__(self, lhs_type, lhs_shape, rhs_type, rhs_shape):
|
|
super().__init__()
|
|
self.lhs_type = lhs_type
|
|
self.rhs_type = rhs_type
|
|
if len(lhs_shape) == 0:
|
|
self.forward = self.forward1
|
|
elif len(rhs_shape) == 0:
|
|
self.forward = self.forward2
|
|
else:
|
|
self.forward = self.forward3
|
|
|
|
def forward1(self, rhs):
|
|
return torch.mul(torch.tensor(3).to(self.lhs_type), rhs.to(self.rhs_type))
|
|
|
|
def forward2(self, lhs):
|
|
return torch.mul(lhs.to(self.lhs_type), torch.tensor(3).to(self.rhs_type))
|
|
|
|
def forward3(self, lhs, rhs):
|
|
return torch.mul(lhs.to(self.lhs_type), rhs.to(self.rhs_type))
|
|
|
|
ref_net = None
|
|
|
|
return aten_mul(lhs_type, lhs_shape, rhs_type, rhs_shape), ref_net, "aten::mul"
|
|
|
|
@pytest.mark.parametrize(("lhs_type", "rhs_type"),
|
|
[[torch.int32, torch.int64],
|
|
[torch.int32, torch.float32],
|
|
[torch.int32, torch.float64],
|
|
[torch.int64, torch.int32],
|
|
[torch.int64, torch.float32],
|
|
[torch.int64, torch.float64],
|
|
[torch.float32, torch.int32],
|
|
[torch.float32, torch.int64],
|
|
[torch.float32, torch.float64],
|
|
[torch.float16, torch.uint8],
|
|
[torch.uint8, torch.float16],
|
|
[torch.float16, torch.int32],
|
|
[torch.int32, torch.float16],
|
|
[torch.float16, torch.int64],
|
|
[torch.int64, torch.float16]
|
|
])
|
|
@pytest.mark.parametrize(("lhs_shape", "rhs_shape"), [([2, 3], [2, 3]),
|
|
([2, 3], []),
|
|
([], [2, 3]),
|
|
])
|
|
@pytest.mark.nightly
|
|
@pytest.mark.precommit
|
|
def test_mul_types(self, ie_device, precision, ir_version, lhs_type, lhs_shape, rhs_type, rhs_shape):
|
|
self.lhs_type = lhs_type
|
|
self.lhs_shape = lhs_shape
|
|
self.rhs_type = rhs_type
|
|
self.rhs_shape = rhs_shape
|
|
self._test(*self.create_model(lhs_type, lhs_shape, rhs_type, rhs_shape),
|
|
ie_device, precision, ir_version, freeze_model=False, trace_model=True)
|