wehub-resource-sync
356 lines
11 KiB
Python
356 lines
11 KiB
Python
# Copyright (c) 2021 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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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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class TestLerp(OpTest):
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def setUp(self):
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self.op_type = "lerp"
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self.python_api = paddle.lerp
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self.prim_op_type = "comp"
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self.public_python_api = paddle.lerp
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self.init_dtype()
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self.init_shape()
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self.init_xyshape()
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self.init_wshape()
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if 0 in self.shape:
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x = np.random.rand(*self.xshape).astype(self.dtype)
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y = np.random.rand(*self.yshape).astype(self.dtype)
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else:
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x = np.arange(1.0, 101.0).astype(self.dtype).reshape(self.xshape)
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y = np.full(100, 10.0).astype(self.dtype).reshape(self.yshape)
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w = np.random.random(self.wshape).astype(self.dtype)
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self.inputs = {'X': x, 'Y': y, 'Weight': w}
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self.outputs = {'Out': x + w * (y - x)}
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def init_dtype(self):
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self.dtype = np.float64
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def init_shape(self):
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self.shape = [100]
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def init_xyshape(self):
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self.xshape = self.shape
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self.yshape = self.shape
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def init_wshape(self):
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self.wshape = [1]
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def test_check_output(self):
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self.check_output(check_pir=True, check_prim_pir=True)
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def test_check_grad(self):
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self.check_grad(['X', 'Y'], 'Out', check_pir=True, check_prim_pir=True)
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class TestLerpWithDim2(TestLerp):
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def init_shape(self):
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self.shape = [2, 50]
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class TestLerpWithDim3(TestLerp):
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def init_shape(self):
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self.shape = [2, 2, 25]
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class TestLerpWithDim4(TestLerp):
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def init_shape(self):
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self.shape = [2, 2, 5, 5]
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class TestLerpWithDim5(TestLerp):
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def init_shape(self):
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self.shape = [2, 1, 2, 5, 5]
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class TestLerpWithDim6(TestLerp):
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def init_shape(self):
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self.shape = [2, 1, 2, 5, 1, 5]
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class TestLerpWithDim6Fp16(TestLerp):
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def init_shape(self):
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self.shape = [2, 1, 2, 5, 1, 5]
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def init_dtype(self):
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self.dtype = np.float16
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class TestLerp_ZeroSize(TestLerp):
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def init_shape(self):
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self.shape = [2, 0]
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class TestLerpWihFp16BroadXY(TestLerp):
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def init_xyshape(self):
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self.xshape = [2, 1, 2, 5, 5]
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self.yshape = [2, 2, 1, 5, 5]
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def init_dtype(self):
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self.dtype = np.float16
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class TestLerpWithFp16BroadWToXY(TestLerp):
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def init_shape(self):
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self.shape = [2, 2, 5, 5]
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def init_wshape(self):
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self.wshape = [5]
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def init_dtype(self):
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self.dtype = np.float16
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class TestLerpBroadXY(TestLerp):
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def init_xyshape(self):
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self.xshape = [2, 1, 2, 5, 5]
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self.yshape = [2, 2, 1, 5, 5]
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class TestLerpBroadWToXY(TestLerp):
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def init_shape(self):
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self.shape = [2, 2, 5, 5]
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def init_wshape(self):
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self.wshape = [5]
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class TestLerpAPI(unittest.TestCase):
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def init_dtype(self):
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self.dtype = 'float32'
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def setUp(self):
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self.init_dtype()
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self.x = np.arange(1.0, 5.0).astype(self.dtype)
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self.y = np.full(4, 10.0).astype(self.dtype)
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self.w = np.asarray([0.75]).astype(self.dtype)
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self.res_ref = self.x + self.w * (self.y - self.x)
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def test_static_api(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('x', [1, 4], dtype=self.dtype)
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y = paddle.static.data('y', [1, 4], dtype=self.dtype)
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out = paddle.lerp(x, y, 0.5)
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exe = paddle.static.Executor(paddle.CPUPlace())
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res = exe.run(
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feed={
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'x': self.x.reshape([1, 4]),
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'y': self.y.reshape([1, 4]),
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}
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)
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for r in res:
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np.testing.assert_allclose(self.res_ref, r, rtol=1e-05)
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def test_dygraph_api(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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w = paddle.to_tensor(np.full(4, 0.75).astype(self.dtype))
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out = paddle.lerp(x, y, w)
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np.testing.assert_allclose(self.res_ref, out.numpy(), rtol=1e-05)
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paddle.enable_static()
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def test_inplace_api(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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x.lerp_(y, 0.75)
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np.testing.assert_allclose(self.res_ref, x.numpy(), rtol=1e-05)
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paddle.enable_static()
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def test_inplace_api_exception(self):
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paddle.disable_static()
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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w = paddle.to_tensor([0.75, 0.75], dtype=self.dtype)
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with self.assertRaises(ValueError):
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x.lerp_(y, w)
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paddle.enable_static()
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def test_x_broadcast_y(self):
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paddle.disable_static()
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x = np.arange(1.0, 21.0).astype(self.dtype).reshape([2, 2, 5])
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y = np.full(30, 10.0).astype(self.dtype).reshape([3, 2, 1, 5])
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out = paddle.lerp(paddle.to_tensor(x), paddle.to_tensor(y), 0.5)
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res_ref = x + 0.5 * (y - x)
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np.testing.assert_allclose(res_ref, out.numpy(), rtol=1e-05)
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paddle.enable_static()
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def test_x_y_broadcast_w(self):
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paddle.disable_static()
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x = np.arange(11.0, 21.0).astype(self.dtype).reshape([2, 5])
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y = np.full(20, 7.5).astype(self.dtype).reshape([2, 2, 5])
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w = np.full(40, 0.225).astype(self.dtype).reshape([2, 2, 2, 5])
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out = paddle.lerp(
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paddle.to_tensor(x), paddle.to_tensor(y), paddle.to_tensor(w)
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)
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res_ref = x + w * (y - x)
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np.testing.assert_allclose(res_ref, out.numpy(), rtol=1e-05)
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paddle.enable_static()
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def test_dygraph_compatibility(self):
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"""Test parameter aliases, out parameter, and various calling patterns."""
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paddle.disable_static()
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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w = paddle.to_tensor(np.full(4, 0.75).astype(self.dtype))
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paddle_dygraph_out = []
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# Position args
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out1 = paddle.lerp(x, y, 0.75)
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paddle_dygraph_out.append(out1)
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# Paddle keyword args
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out2 = paddle.lerp(x=x, y=y, weight=0.75)
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paddle_dygraph_out.append(out2)
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# Parameter aliases (input for x, end for y)
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out3 = paddle.lerp(input=x, end=y, weight=0.75)
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paddle_dygraph_out.append(out3)
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# Partial alias: input for x, y uses original name
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out4 = paddle.lerp(input=x, y=y, weight=0.75)
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paddle_dygraph_out.append(out4)
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# Partial alias: x uses original name, end for y
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out5 = paddle.lerp(x=x, end=y, weight=0.75)
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paddle_dygraph_out.append(out5)
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# Test out parameter
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out6 = paddle.empty([4], dtype=self.dtype)
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result6 = paddle.lerp(x, y, 0.75, out=out6)
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paddle_dygraph_out.append(out6)
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paddle_dygraph_out.append(result6)
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# Test out parameter with tensor weight
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out7 = paddle.empty([4], dtype=self.dtype)
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paddle.lerp(x, y, w, out=out7)
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paddle_dygraph_out.append(out7)
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# Test parameter aliases with out parameter
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out8 = paddle.empty([4], dtype=self.dtype)
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result8 = paddle.lerp(input=x, end=y, weight=0.75, out=out8)
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paddle_dygraph_out.append(out8)
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paddle_dygraph_out.append(result8)
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# Test out=None (default)
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out9 = paddle.lerp(x, y, 0.75, out=None)
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paddle_dygraph_out.append(out9)
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# Verify all outputs
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for out in paddle_dygraph_out:
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np.testing.assert_allclose(self.res_ref, out.numpy(), rtol=1e-05)
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paddle.enable_static()
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def test_out_parameter_broadcast(self):
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"""Test out parameter with broadcasting."""
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paddle.disable_static()
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x = np.arange(1.0, 21.0).astype(self.dtype).reshape([2, 2, 5])
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y = np.full(30, 10.0).astype(self.dtype).reshape([3, 2, 1, 5])
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res_ref = x + 0.5 * (y - x)
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# Create output tensor with broadcast shape
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out_tensor = paddle.empty(res_ref.shape, dtype=self.dtype)
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result = paddle.lerp(
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paddle.to_tensor(x), paddle.to_tensor(y), 0.5, out=out_tensor
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)
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np.testing.assert_allclose(res_ref, out_tensor.numpy(), rtol=1e-05)
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np.testing.assert_allclose(
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result.numpy(), out_tensor.numpy(), rtol=1e-05
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)
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paddle.enable_static()
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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 TestLerpBF16(TestLerp):
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def setUp(self):
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self.op_type = "lerp"
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self.python_api = paddle.lerp
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self.prim_op_type = "comp"
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self.public_python_api = paddle.lerp
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self.dtype = np.uint16
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self.init_shape()
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self.init_xyshape()
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self.init_wshape()
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x = np.arange(1.0, 101.0).astype("float32").reshape(self.xshape)
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y = np.full(100, 10.0).astype("float32").reshape(self.yshape)
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w = np.random.random(self.wshape).astype("float32")
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self.init_grad(w)
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self.inputs = {
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'X': convert_float_to_uint16(x),
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'Y': convert_float_to_uint16(y),
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'Weight': convert_float_to_uint16(w),
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}
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self.outputs = {'Out': convert_float_to_uint16(x + w * (y - x))}
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def init_shape(self):
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self.shape = [100]
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def init_xyshape(self):
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self.xshape = self.shape
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self.yshape = self.shape
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def init_wshape(self):
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self.wshape = [1]
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def init_grad(self, w):
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self.x_grad = (
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np.ones(self.xshape)
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* (1 - w)
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/ (np.prod(self.xshape) / np.prod(self.wshape))
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)
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self.y_grad = (
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np.ones(self.yshape)
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* w
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/ (np.prod(self.yshape) / np.prod(self.wshape))
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)
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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_pir=True, check_prim_pir=True)
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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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['X', 'Y'],
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'Out',
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user_defined_grads=[self.x_grad, self.y_grad],
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check_pir=True,
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check_prim_pir=True,
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)
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if __name__ == "__main__":
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unittest.main()
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