wehub-resource-sync
320 lines
9.2 KiB
Python
320 lines
9.2 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_test import (
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OpTest,
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convert_float_to_uint16,
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convert_uint16_to_float,
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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 import base
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from paddle.base import Program, program_guard
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def cast_wrapper(x, out_dtype=None):
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return paddle.cast(x, out_dtype)
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class TestCastOpFp32ToFp64(OpTest):
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def setUp(self):
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self.init_shapes()
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ipt = np.random.random(size=self.input_shape)
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self.inputs = {'X': ipt.astype('float32')}
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self.outputs = {'Out': ipt.astype('float64')}
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self.attrs = {
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'in_dtype': paddle.float32,
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'out_dtype': paddle.float64,
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}
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self.op_type = 'cast'
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self.prim_op_type = "prim"
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self.python_api = cast_wrapper
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self.public_python_api = cast_wrapper
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def init_shapes(self):
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self.input_shape = [10, 10]
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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_grad(self):
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self.check_grad(
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['X'],
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['Out'],
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check_prim=True,
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check_prim_pir=True,
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check_pir=True,
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)
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class TestCastOpFp32ToFp64_ZeroDim(TestCastOpFp32ToFp64):
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def init_shapes(self):
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self.input_shape = ()
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class TestCastOpFp16ToFp32(OpTest):
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def setUp(self):
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ipt = np.random.random(size=[10, 10])
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self.inputs = {'X': ipt.astype('float16')}
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self.outputs = {'Out': ipt.astype('float32')}
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self.attrs = {
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'in_dtype': paddle.float16,
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'out_dtype': paddle.float32,
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}
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self.op_type = 'cast'
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self.prim_op_type = "prim"
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self.python_api = cast_wrapper
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self.public_python_api = cast_wrapper
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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_grad(self):
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self.check_grad(
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['X'],
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['Out'],
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check_prim=True,
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only_check_prim=True,
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check_pir=True,
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)
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class TestCastOpFp32ToFp16(OpTest):
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def setUp(self):
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ipt = np.random.random(size=[10, 10])
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self.inputs = {'X': ipt.astype('float32')}
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self.outputs = {'Out': ipt.astype('float16')}
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self.attrs = {
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'in_dtype': paddle.float32,
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'out_dtype': paddle.float16,
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}
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self.op_type = 'cast'
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self.prim_op_type = "prim"
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self.python_api = cast_wrapper
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self.public_python_api = cast_wrapper
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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_grad(self):
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self.check_grad(
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['X'],
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['Out'],
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check_prim=True,
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only_check_prim=True,
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check_pir=True,
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)
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@unittest.skipIf(
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not (
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(paddle.is_compiled_with_cuda() or is_custom_device())
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or is_custom_device()
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or paddle.is_compiled_with_rocm()
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),
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"BFP16 test runs only on CUDA",
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)
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class TestCastOpBf16ToFp32(OpTest):
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def setUp(self):
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ipt = np.array(np.random.randint(10, size=[10, 10])).astype('uint16')
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self.inputs = {'X': ipt}
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self.outputs = {'Out': convert_uint16_to_float(ipt)}
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self.attrs = {
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'in_dtype': paddle.bfloat16,
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'out_dtype': paddle.float32,
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}
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self.op_type = 'cast'
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self.prim_op_type = "prim"
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self.python_api = cast_wrapper
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self.public_python_api = cast_wrapper
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self.if_enable_cinn()
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def if_enable_cinn(self):
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self.enable_cinn = False
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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_grad(self):
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self.check_grad(
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['X'],
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['Out'],
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check_prim=True,
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only_check_prim=True,
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check_pir=True,
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)
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@unittest.skipIf(
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not (
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(paddle.is_compiled_with_cuda() or is_custom_device())
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or is_custom_device()
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or paddle.is_compiled_with_rocm()
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),
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"BFP16 test runs only on CUDA",
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)
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class TestCastOpFp32ToBf16(OpTest):
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def setUp(self):
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ipt = np.random.random(size=[10, 10]).astype('float32')
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self.inputs = {'X': ipt}
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self.outputs = {'Out': convert_float_to_uint16(ipt)}
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self.attrs = {
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'in_dtype': paddle.float32,
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'out_dtype': paddle.bfloat16,
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}
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self.op_type = 'cast'
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self.prim_op_type = "prim"
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self.python_api = cast_wrapper
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self.public_python_api = cast_wrapper
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self.if_enable_cinn()
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def if_enable_cinn(self):
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self.enable_cinn = False
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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_grad(self):
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self.check_grad(
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['X'],
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['Out'],
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check_prim=True,
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only_check_prim=True,
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check_pir=True,
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)
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class TestCastOpError(unittest.TestCase):
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def test_errors(self):
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paddle.enable_static()
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with program_guard(Program(), Program()):
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# The input type of cast_op must be Variable.
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x1 = base.create_lod_tensor(
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np.array([[-1]]), [[1]], base.CPUPlace()
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)
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self.assertRaises(TypeError, paddle.cast, x1, 'int32')
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paddle.disable_static()
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class TestCastOpEager(unittest.TestCase):
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def test_eager(self):
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with paddle.base.dygraph.base.guard():
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x = paddle.ones([2, 2], dtype="float16")
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x.stop_gradient = False
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out = paddle.cast(x, "float32")
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np.testing.assert_array_equal(
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out, np.ones([2, 2]).astype('float32')
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)
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out.backward()
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np.testing.assert_array_equal(x.gradient(), x.numpy())
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self.assertTrue(x.gradient().dtype == np.float16)
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class TestCastDoubleGradCheck(unittest.TestCase):
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def cast_wrapper(self, x):
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return paddle.cast(x[0], 'float64')
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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, 4], dtype)
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data.persistable = True
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out = paddle.cast(data, 'float64')
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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.cast_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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paddle.disable_static()
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class TestCastTripleGradCheck(unittest.TestCase):
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def cast_wrapper(self, x):
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return paddle.cast(x[0], 'float64')
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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, 4], dtype)
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data.persistable = True
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out = paddle.cast(data, 'float64')
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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.cast_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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paddle.disable_static()
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class TestCastInplaceContinuous(unittest.TestCase):
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def test_api_dygraph(self):
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def run(place):
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paddle.disable_static(place)
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x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]])
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target = x.cast("uint8")
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x.cast_("uint8")
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np.testing.assert_array_equal(target.numpy(), x.numpy())
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target = x.cast("float32")
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x.cast_("float32")
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np.testing.assert_array_equal(target.numpy(), x.numpy())
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run(paddle.CPUPlace())
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def test_api_pir(self):
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def run(place):
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paddle.disable_static(place)
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x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]])
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target = x.cast("int64")
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x.cast_(paddle.int64)
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np.testing.assert_array_equal(target.numpy(), x.numpy())
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target = x.cast("float32")
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x.cast_(paddle.float32)
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np.testing.assert_array_equal(target.numpy(), x.numpy())
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paddle.set_flags({"FLAGS_enable_pir_api": True})
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run(paddle.CPUPlace())
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if __name__ == '__main__':
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unittest.main()
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