wehub-resource-sync
400 lines
11 KiB
Python
400 lines
11 KiB
Python
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import unittest
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import numpy as np
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sys.path.append("../../legacy_test")
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from op_test import get_cuda_version
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from paddle.base import core
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sys.path.append("..")
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from op_test import (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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class TestMulOp(OpTest):
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def setUp(self):
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self.op_type = "mul"
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self.dtype = np.float64
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self.init_dtype_type()
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self.inputs = {
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'X': np.random.random((20, 5)).astype(self.dtype),
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'Y': np.random.random((5, 21)).astype(self.dtype),
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}
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self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
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def init_dtype_type(self):
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pass
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def test_check_output(self):
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# NODE(yjjiang11): This op will be deprecated.
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self.check_output(check_dygraph=False)
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', check_dygraph=False)
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Y'],
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'Out',
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max_relative_error=0.5,
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no_grad_set=set("X"),
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check_dygraph=False,
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)
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def test_check_grad_ignore_y(self):
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self.check_grad(
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['X'],
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'Out',
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max_relative_error=0.5,
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no_grad_set=set('Y'),
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check_dygraph=False,
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)
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class TestMulOp2(OpTest):
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def setUp(self):
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self.op_type = "mul"
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self.dtype = np.float64
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self.init_dtype_type()
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self.inputs = {
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'X': np.random.random((3, 4, 2, 9)).astype(self.dtype),
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'Y': np.random.random((3, 6, 1, 2, 3)).astype(self.dtype),
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}
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self.attrs = {
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'x_num_col_dims': 2,
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'y_num_col_dims': 2,
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}
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result = np.dot(
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self.inputs['X'].reshape(3 * 4, 2 * 9),
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self.inputs['Y'].reshape(3 * 6, 1 * 2 * 3),
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)
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result = result.reshape(3, 4, 1, 2, 3)
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self.outputs = {'Out': result}
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def init_dtype_type(self):
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pass
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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def test_check_grad_normal(self):
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self.check_grad(['X', 'Y'], 'Out', check_dygraph=False)
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Y'],
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'Out',
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max_relative_error=0.5,
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no_grad_set=set('X'),
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check_dygraph=False,
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)
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def test_check_grad_ignore_y(self):
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self.check_grad(
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['X'],
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'Out',
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max_relative_error=0.5,
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no_grad_set=set('Y'),
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check_dygraph=False,
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)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestMulFP16Op1(TestMulOp):
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def init_dtype_type(self):
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self.dtype = np.float16
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def test_check_output(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_output_with_place(place, check_dygraph=False)
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def test_check_grad_normal(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place,
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['X', 'Y'],
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'Out',
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check_dygraph=False,
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)
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def test_check_grad_ignore_x(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place,
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['Y'],
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'Out',
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no_grad_set=set("X"),
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check_dygraph=False,
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)
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def test_check_grad_ignore_y(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place,
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['X'],
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'Out',
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no_grad_set=set('Y'),
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check_dygraph=False,
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)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device()),
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"core is not compiled with CUDA",
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)
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class TestMulFP16Op2(TestMulOp2):
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def init_dtype_type(self):
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self.dtype = np.float16
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def test_check_output(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_output_with_place(place, check_dygraph=False)
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def test_check_grad_normal(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place,
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['X', 'Y'],
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'Out',
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check_dygraph=False,
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)
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def test_check_grad_ignore_x(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place,
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['Y'],
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'Out',
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no_grad_set=set("X"),
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check_dygraph=False,
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)
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def test_check_grad_ignore_y(self):
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place = get_device_place()
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if core.is_float16_supported(place):
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self.check_grad_with_place(
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place,
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['X'],
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'Out',
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no_grad_set=set('Y'),
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check_dygraph=False,
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)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestMulBF16Op1(OpTest):
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def setUp(self):
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self.op_type = "mul"
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self.init_dtype_type()
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self.inputs = {
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'X': np.random.random((20, 5)).astype(self.np_dtype),
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'Y': np.random.random((5, 21)).astype(self.np_dtype),
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}
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self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
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self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
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self.inputs['Y'] = convert_float_to_uint16(self.inputs['Y'])
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self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
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self.place = get_device_place()
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def init_dtype_type(self):
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self.dtype = np.uint16
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self.np_dtype = np.float32
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def test_check_output(self):
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self.check_output_with_place(self.place, check_dygraph=False)
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def test_check_grad_normal(self):
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self.check_grad_with_place(
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self.place, ['X', 'Y'], 'Out', check_dygraph=False
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)
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def test_check_grad_ignore_x(self):
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self.check_grad_with_place(
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self.place,
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['Y'],
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'Out',
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no_grad_set=set("X"),
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check_dygraph=False,
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)
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def test_check_grad_ignore_y(self):
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self.check_grad_with_place(
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self.place,
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['X'],
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'Out',
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no_grad_set=set('Y'),
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check_dygraph=False,
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)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA or not support bfloat16",
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)
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class TestMulBF16Op2(TestMulBF16Op1):
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def setUp(self):
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self.op_type = "mul"
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self.init_dtype_type()
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self.inputs = {
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'X': np.random.random((3, 4, 2, 9)).astype(self.np_dtype),
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'Y': np.random.random((3, 6, 1, 2, 3)).astype(self.np_dtype),
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}
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self.attrs = {
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'x_num_col_dims': 2,
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'y_num_col_dims': 2,
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}
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result = np.dot(
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self.inputs['X'].reshape(3 * 4, 2 * 9),
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self.inputs['Y'].reshape(3 * 6, 1 * 2 * 3),
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)
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result = result.reshape(3, 4, 1, 2, 3)
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self.outputs = {'Out': result}
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self.inputs['X'] = convert_float_to_uint16(self.inputs['X'])
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self.inputs['Y'] = convert_float_to_uint16(self.inputs['Y'])
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self.outputs['Out'] = convert_float_to_uint16(self.outputs['Out'])
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self.place = get_device_place()
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def test_check_grad_normal(self):
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self.check_grad_with_place(
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self.place,
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['X', 'Y'],
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'Out',
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numeric_grad_delta=0.02,
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check_dygraph=False,
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)
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def test_check_grad_ignore_x(self):
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self.check_grad_with_place(
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self.place,
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['Y'],
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'Out',
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numeric_grad_delta=0.02,
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no_grad_set=set("X"),
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check_dygraph=False,
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)
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def test_check_grad_ignore_y(self):
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self.check_grad_with_place(
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self.place,
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['X'],
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'Out',
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numeric_grad_delta=0.02,
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no_grad_set=set('Y'),
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check_dygraph=False,
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)
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# TODO: verify the requirements of CUDA ARCH
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or get_cuda_version() < 11060,
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"MatmulInt8 requires CUDA >= 11.6",
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)
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class TestMulInt8Op(OpTest):
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def setUp(self):
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self.op_type = "mul"
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self.dtype = np.int8
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self.init_dtype_type()
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self.inputs = {
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'X': np.random.randint(-127, 127, (8, 64)).astype(np.int32),
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'Y': np.random.randint(-127, 127, (64, 64)).astype(np.int32),
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}
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self.outputs = {'Out': np.dot(self.inputs['X'], self.inputs['Y'])}
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self.inputs['X'] = self.inputs['X'].astype(self.dtype)
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self.inputs['Y'] = self.inputs['Y'].astype(self.dtype)
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def init_dtype_type(self):
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pass
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(place, check_dygraph=False)
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def test_check_grad_normal(self):
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pass
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def test_check_grad_ignore_x(self):
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pass
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def test_check_grad_ignore_y(self):
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pass
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class TestMulInt8Op2(TestMulInt8Op):
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def setUp(self):
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self.op_type = "mul"
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self.dtype = np.int8
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self.init_dtype_type()
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self.inputs = {
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'X': np.random.randint(-127, 127, (3, 4, 2, 8)).astype(np.int32),
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'Y': np.random.randint(-127, 127, (4, 4, 1, 2, 4)).astype(np.int32),
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}
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self.attrs = {
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'x_num_col_dims': 2,
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'y_num_col_dims': 2,
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}
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result = np.dot(
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self.inputs['X'].reshape(3 * 4, 2 * 8),
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self.inputs['Y'].reshape(4 * 4, 1 * 2 * 4),
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)
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result = result.reshape(3, 4, 1, 2, 4)
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self.outputs = {'Out': result}
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self.inputs['X'] = self.inputs['X'].astype(self.dtype)
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self.inputs['Y'] = self.inputs['Y'].astype(self.dtype)
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(place, check_dygraph=False)
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def test_check_grad_normal(self):
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pass
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def test_check_grad_ignore_x(self):
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pass
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def test_check_grad_ignore_y(self):
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pass
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if __name__ == "__main__":
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unittest.main()
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