wehub-resource-sync
276 lines
8.8 KiB
Python
276 lines
8.8 KiB
Python
# Copyright (c) 2024 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 numpy as np
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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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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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paddle.enable_static()
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np.random.seed(0)
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def atan2_grad(x1, x2, dout):
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dx1 = dout * x2 / (x1 * x1 + x2 * x2)
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dx2 = -dout * x1 / (x1 * x1 + x2 * x2)
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return dx1, dx2
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class TestAtan2(OpTest):
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def setUp(self):
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self.op_type = "atan2"
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self.prim_op_type = "prim"
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self.python_api = paddle.atan2
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self.public_python_api = paddle.atan2
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self.check_cinn = True
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self.init_dtype()
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x1 = np.random.uniform(-1, -0.1, [15, 17]).astype(self.dtype)
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x2 = np.random.uniform(0.1, 1, [15, 17]).astype(self.dtype)
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out = np.arctan2(x1, x2)
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self.inputs = {'X1': x1, 'X2': x2}
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self.outputs = {'Out': out}
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def test_check_grad(self):
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self.check_grad(
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['X1', 'X2'],
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'Out',
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check_cinn=self.check_cinn,
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check_pir=True,
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check_prim_pir=True,
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)
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def test_check_output(self):
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self.check_output(check_cinn=self.check_cinn, check_pir=True)
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def init_dtype(self):
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self.dtype = np.float64
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class TestAtan2_float(TestAtan2):
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def init_dtype(self):
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self.dtype = np.float32
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def test_check_grad(self):
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if self.dtype not in [np.int32, np.int64]:
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self.check_grad(
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['X1', 'X2'],
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'Out',
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user_defined_grads=atan2_grad(
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self.inputs['X1'],
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self.inputs['X2'],
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1 / self.inputs['X1'].size,
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),
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check_cinn=self.check_cinn,
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check_pir=True,
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check_prim_pir=True,
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)
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class TestAtan2_float16(TestAtan2_float):
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def init_dtype(self):
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self.dtype = np.float16
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class TestAtan2_int32(TestAtan2_float):
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def init_dtype(self):
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self.dtype = np.int32
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class TestAtan2_int64(TestAtan2_float):
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def init_dtype(self):
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self.dtype = np.int64
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class TestAtan2API(unittest.TestCase):
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def init_dtype(self):
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self.dtype = 'float64'
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self.shape = [11, 17]
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def setUp(self):
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self.init_dtype()
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self.x1 = np.random.uniform(0.1, 1, self.shape).astype(self.dtype)
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self.x2 = np.random.uniform(-1, -0.1, self.shape).astype(self.dtype)
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self.place = get_places()
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def test_static_api(self):
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paddle.enable_static()
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def run(place):
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with paddle.static.program_guard(paddle.static.Program()):
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X1 = paddle.static.data('X1', self.shape, dtype=self.dtype)
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X2 = paddle.static.data('X2', self.shape, dtype=self.dtype)
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out = paddle.atan2(X1, X2)
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exe = paddle.static.Executor(place)
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res = exe.run(feed={'X1': self.x1, 'X2': self.x2})
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out_ref = np.arctan2(self.x1, self.x2)
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for r in res:
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np.testing.assert_allclose(out_ref, r, rtol=1e-05)
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for place in self.place:
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run(place)
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def test_dygraph_api(self):
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def run(place):
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paddle.disable_static(place)
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X1 = paddle.to_tensor(self.x1)
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X2 = paddle.to_tensor(self.x2)
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out = paddle.atan2(X1, X2)
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out_ref = np.arctan2(self.x1, self.x2)
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np.testing.assert_allclose(out_ref, out.numpy(), rtol=1e-05)
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paddle.enable_static()
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for place in self.place:
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run(place)
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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 and not support the bfloat16",
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)
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class TestAtan2BF16OP(OpTest):
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def setUp(self):
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self.op_type = 'atan2'
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self.prim_op_type = 'prim'
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self.python_api = paddle.atan2
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self.public_python_api = paddle.atan2
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self.dtype = np.uint16
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self.check_cinn = True
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x1 = np.random.uniform(-1, -0.1, [15, 17]).astype('float64')
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x2 = np.random.uniform(0.1, 1, [15, 17]).astype('float64')
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out = np.arctan2(x1, x2)
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self.inputs = {
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'X1': convert_float_to_uint16(x1),
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'X2': convert_float_to_uint16(x2),
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}
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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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place = get_device_place()
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self.check_output_with_place(
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place, check_cinn=self.check_cinn, check_pir=True
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)
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def test_check_grad(self):
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place = get_device_place()
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self.check_grad_with_place(
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place,
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['X1', 'X2'],
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'Out',
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check_cinn=self.check_cinn,
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check_pir=True,
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check_prim_pir=True,
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)
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class TestAtan2Broadcasting(unittest.TestCase):
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def _get_places(self):
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places = [paddle.base.CPUPlace()]
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if paddle.is_compiled_with_cuda() or is_custom_device():
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places.append(get_device_place())
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return places
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def _generate_inputs_outputs(self, shapes):
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inputs = [
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np.random.random(shape).astype('float64') for shape in shapes[:2]
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]
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out_ref = np.arctan2(inputs[0], inputs[1])
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return inputs, out_ref
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def _test_with_shapes(self, shapes, place=None):
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inputs, out_ref = self._generate_inputs_outputs(shapes)
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if place is None: # Dygraph mode
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with paddle.base.dygraph.guard():
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tensors = [
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paddle.to_tensor(inp, stop_gradient=False) for inp in inputs
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]
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result = paddle.atan2(tensors[0], tensors[1])
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loss = paddle.sum(result)
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loss.backward()
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np.testing.assert_allclose(
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tensors[0].shape, tensors[0].grad.shape, rtol=1e-05
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)
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np.testing.assert_allclose(
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tensors[1].shape, tensors[1].grad.shape, rtol=1e-05
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)
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else: # Static mode
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with paddle.static.program_guard(paddle.static.Program()):
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data_tensors = [
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paddle.static.data(
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shape=shape, dtype='float64', name=f'x{i}'
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)
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for i, shape in enumerate(shapes)
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]
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result = paddle.atan2(data_tensors[0], data_tensors[1])
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exe = paddle.base.Executor(place=place)
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feed_dict = {f'x{i}': inp for i, inp in enumerate(inputs)}
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result = exe.run(
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paddle.static.default_main_program(),
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feed=feed_dict,
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fetch_list=[result],
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)[0]
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np.testing.assert_allclose(out_ref, result, rtol=1e-05)
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def test_api_with_dygraph_empty_tensor_input(self):
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self._test_with_shapes([(100,), (100, 100)])
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self._test_with_shapes([(), (5, 17, 6)])
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self._test_with_shapes([(111, 222, 333), (222, 333)])
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def _test_api_with_static_empty_tensor_input(self, place):
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self._test_with_shapes([(100,), (100, 100)], place)
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self._test_with_shapes([(), (5, 17, 6)], place)
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self._test_with_shapes([(111, 222, 333), (222, 333)], place)
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def test_api_with_static_empty_tensor_input(self):
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for place in self._get_places():
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self._test_api_with_static_empty_tensor_input(place)
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class TestAtan2EmptyTensorInput(TestAtan2Broadcasting):
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def test_api_with_dygraph_empty_tensor_input(self):
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self._test_with_shapes([(), (0,)])
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self._test_with_shapes([(0,), (0, 0)])
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self._test_with_shapes([(0, 0, 0), (0,)])
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self._test_with_shapes([(5, 17, 1, 6), (5, 17, 0, 6)])
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self._test_with_shapes([(5, 17, 6), (0, 5, 17, 6)])
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def _test_api_with_static_empty_tensor_input(self, place):
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self._test_with_shapes([(), (0,)], place)
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self._test_with_shapes([(0,), (0, 0)], place)
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self._test_with_shapes([(0, 0, 0), (0,)], place)
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self._test_with_shapes([(5, 17, 1, 6), (5, 17, 0, 6)], place)
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self._test_with_shapes([(5, 17, 6), (0, 5, 17, 6)])
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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