wehub-resource-sync
330 lines
10 KiB
Python
330 lines
10 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 unittest
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import gradient_checker
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import numpy as np
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from decorator_helper import prog_scope
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from op import Operator
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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,
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get_device_place,
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get_places,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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class TestScaleOp(OpTest):
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def setUp(self):
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self.op_type = "scale"
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self.python_api = paddle.scale
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self.dtype = np.float32
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self.init_dtype_type()
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self.public_python_api = paddle.scale
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self.prim_op_type = "prim"
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self.inputs = {'X': np.random.random((10, 10)).astype(self.dtype)}
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self.attrs = {'scale': -2.3}
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self.outputs = {
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'Out': self.inputs['X'] * self.dtype(self.attrs['scale'])
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}
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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_cinn=True, check_pir=True)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True, check_prim_pir=True)
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class TestScaleOpFP64(TestScaleOp):
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def init_dtype_type(self):
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self.dtype = np.float64
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# NOTE(dev): Scalar.to<float> has diff with double.
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self.rev_comp_atol = 1e-7
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class TestScaleOpScaleVariable(OpTest):
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def setUp(self):
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self.op_type = "scale"
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self.python_api = paddle.scale
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self.dtype = np.float64
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self.init_dtype_type()
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self.scale = -2.3
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self.inputs = {
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'X': np.random.random((10, 10)).astype(self.dtype),
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'ScaleTensor': np.array([self.scale]).astype('float64'),
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}
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self.attrs = {}
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self.outputs = {'Out': self.inputs['X'] * self.dtype(self.scale)}
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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_cinn=True, check_pir=True)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True)
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class TestScaleOpSelectedRows(unittest.TestCase):
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def init_dtype_type(self):
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pass
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def check_with_place(self, place, in_name, out_name):
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scope = core.Scope()
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self.dtype = np.float64
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self.init_dtype_type()
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# create and initialize Grad Variable
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in_height = 10
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in_rows = [0, 4, 7]
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in_row_numel = 12
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scale = 2.0
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in_selected_rows = scope.var(in_name).get_selected_rows()
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in_selected_rows.set_height(in_height)
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in_selected_rows.set_rows(in_rows)
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in_array = np.random.random((len(in_rows), in_row_numel)).astype(
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self.dtype
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)
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in_tensor = in_selected_rows.get_tensor()
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in_tensor.set(in_array, place)
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# create and initialize Param Variable
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out_selected_rows = scope.var(out_name).get_selected_rows()
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out_tensor = out_selected_rows.get_tensor()
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out_tensor._set_dims(in_tensor._get_dims())
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# create and run sgd operator
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scale_op = Operator("scale", X=in_name, Out=out_name, scale=scale)
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scale_op.run(scope, place)
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# get and compare result
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out_height = out_selected_rows.height()
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out_rows = out_selected_rows.rows()
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result_array = np.array(out_tensor)
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assert (in_array * scale == result_array).all()
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assert in_height == out_height
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assert in_rows == out_rows
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def test_scale_selected_rows(self):
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for place in get_places():
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self.check_with_place(place, 'in', 'out')
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def test_scale_selected_rows_inplace(self):
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for place in get_places():
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self.check_with_place(place, 'in', 'in')
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class TestScaleRaiseError(unittest.TestCase):
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def test_errors(self):
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paddle.enable_static()
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def test_type():
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paddle.scale([10])
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self.assertRaises(TypeError, test_type)
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# Add FP16 test
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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 TestScaleFp16Op(TestScaleOp):
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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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self.check_output(check_cinn=True, check_pir=True)
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def test_check_grad(self):
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self.check_grad(["X"], "Out", check_pir=True, check_prim_pir=True)
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@unittest.skipIf(
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not (paddle.is_compiled_with_cuda() or is_custom_device())
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or paddle.is_compiled_with_rocm(),
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"BFP16 test runs only on CUDA",
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)
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class TestScaleBF16Op(OpTest):
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def setUp(self):
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self.op_type = "scale"
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self.python_api = paddle.scale
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self.public_python_api = paddle.scale
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self.prim_op_type = "prim"
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self.dtype = np.uint16
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self.attrs = {'scale': -2.3}
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x = np.random.random((10, 10)).astype(np.float32)
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out = x * np.float32(self.attrs['scale'])
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self.inputs = {'X': convert_float_to_uint16(x)}
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self.outputs = {'Out': convert_float_to_uint16(out)}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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numeric_grad_delta=0.8,
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check_pir=True,
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check_prim_pir=True,
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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 TestScaleFp16OpSelectedRows(TestScaleOpSelectedRows):
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def init_dtype_type(self):
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self.dtype = np.float16
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def test_scale_selected_rows(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_with_place(place, 'in', 'out')
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def test_scale_selected_rows_inplace(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_with_place(place, 'in', 'in')
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class TestScaleApiStatic(unittest.TestCase):
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def _executed_api(self, x, scale=1.0, bias=0.0):
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return paddle.scale(x, scale, bias)
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def test_api(self):
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paddle.enable_static()
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input = np.random.random([2, 25]).astype("float32")
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main_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, paddle.static.Program()):
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x = paddle.static.data(name="x", shape=[2, 25], dtype="float32")
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out = self._executed_api(x, scale=2.0, bias=3.0)
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exe = paddle.static.Executor(place=paddle.CPUPlace())
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out = exe.run(main_prog, feed={"x": input}, fetch_list=[out])
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np.testing.assert_array_equal(out[0], input * 2.0 + 3.0)
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class TestScaleInplaceApiStatic(TestScaleApiStatic):
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def _executed_api(self, x, scale=1.0, bias=0.0):
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return x.scale_(scale, bias)
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class TestScaleApiDygraph(unittest.TestCase):
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def _executed_api(self, x, scale=1.0, bias=0.0):
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return paddle.scale(x, scale, bias)
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def test_api(self):
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with paddle.base.dygraph.guard():
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input = np.random.random([2, 25]).astype("float32")
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x = paddle.to_tensor(input)
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out = self._executed_api(x, scale=2.0, bias=3.0)
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np.testing.assert_array_equal(out.numpy(), input * 2.0 + 3.0)
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class TestScaleInplaceApiDygraph(TestScaleApiDygraph):
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def _executed_api(self, x, scale=1.0, bias=0.0):
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return x.scale_(scale, bias)
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class TestScaleDoubleGradCheck(unittest.TestCase):
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def scale_wrapper(self, x):
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return paddle.scale(x[0], scale=2.0)
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@prog_scope()
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def func(self, place):
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# the shape of input variable should be clearly specified, not include -1.
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eps = 0.005
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dtype = np.float32
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data = paddle.static.data('data', [2, 3], dtype)
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data.persistable = True
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out = paddle.scale(data, 2.0)
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data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
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gradient_checker.double_grad_check(
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[data], out, x_init=[data_arr], place=place, eps=eps
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)
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gradient_checker.double_grad_check_for_dygraph(
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self.scale_wrapper, [data], out, x_init=[data_arr], place=place
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)
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def test_grad(self):
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paddle.enable_static()
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for p in get_places():
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self.func(p)
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class TestScaleTripleGradCheck(unittest.TestCase):
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def scale_wrapper(self, x):
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return paddle.scale(x[0], scale=2.0)
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@prog_scope()
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def func(self, place):
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# the shape of input variable should be clearly specified, not include -1.
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eps = 0.005
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dtype = np.float32
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data = paddle.static.data('data', [2, 3], dtype)
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data.persistable = True
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out = paddle.scale(data, 2.0)
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data_arr = np.random.uniform(-1, 1, data.shape).astype(dtype)
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gradient_checker.triple_grad_check(
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[data], out, x_init=[data_arr], place=place, eps=eps
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)
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gradient_checker.triple_grad_check_for_dygraph(
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self.scale_wrapper, [data], out, x_init=[data_arr], place=place
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)
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def test_grad(self):
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paddle.enable_static()
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for p in get_places():
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self.func(p)
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class TestScaleOpZeroNumelVariable(unittest.TestCase):
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def test_check_zero_numel_cpu(self):
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with paddle.pir_utils.OldIrGuard():
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paddle.set_device('cpu')
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data = paddle.ones([0, 1])
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out = paddle.scale(data, 2)
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self.assertEqual(out, data)
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if paddle.is_compiled_with_cuda() or is_custom_device():
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paddle.set_device(get_device())
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data = paddle.ones([0, 1])
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out = paddle.scale(data, 2)
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self.assertEqual(out, data)
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if __name__ == "__main__":
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unittest.main()
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