wehub-resource-sync
231 lines
8.0 KiB
Python
231 lines
8.0 KiB
Python
# Copyright (c) 2020 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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paddle_static_guard,
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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 core
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class TestBmmOp(OpTest):
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def setUp(self):
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self.op_type = "bmm"
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self.prim_op_type = "comp"
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self.python_api = paddle.Tensor.bmm
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self.public_python_api = paddle.Tensor.bmm
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X = np.random.random((10, 3, 4)).astype("float64")
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Y = np.random.random((10, 4, 5)).astype("float64")
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self.inputs = {'X': X, 'Y': Y}
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Out = np.matmul(X, Y)
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self.outputs = {'Out': Out}
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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_checkout_grad(self):
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self.check_grad(['X', 'Y'], 'Out', check_pir=True)
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class TestBmmFP16Op(OpTest):
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def setUp(self):
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self.op_type = "bmm"
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self.prim_op_type = "comp"
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self.dtype = np.float16
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self.python_api = paddle.Tensor.bmm
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self.public_python_api = paddle.Tensor.bmm
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X = np.random.random((10, 3, 4)).astype("float16")
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Y = np.random.random((10, 4, 5)).astype("float16")
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self.inputs = {'X': X, 'Y': Y}
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Out = np.matmul(X, Y)
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self.outputs = {'Out': Out}
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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_checkout_grad(self):
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self.check_grad(['X', 'Y'], 'Out', check_pir=True)
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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 TestBmmBF16Op(OpTest):
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def setUp(self):
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self.op_type = "bmm"
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self.prim_op_type = "comp"
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self.dtype = np.uint16
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self.python_api = paddle.Tensor.bmm
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self.public_python_api = paddle.Tensor.bmm
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X = np.random.random((10, 3, 4)).astype("float32")
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Y = np.random.random((10, 4, 5)).astype("float32")
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self.inputs = {'X': X, 'Y': Y}
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Out = np.matmul(X, Y)
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self.outputs = {'Out': Out}
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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_output(self):
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self.check_output_with_place(
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self.place, check_pir=True, check_prim_pir=True
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)
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def test_checkout_grad(self):
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self.check_grad_with_place(
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self.place, ['X', 'Y'], 'Out', check_pir=True
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)
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class API_TestBmm(unittest.TestCase):
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def test_out(self):
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with paddle_static_guard():
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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data1 = paddle.static.data(
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'data1', shape=[-1, 3, 4], dtype='float64'
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)
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data2 = paddle.static.data(
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'data2', shape=[-1, 4, 5], dtype='float64'
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)
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result_bmm = paddle.bmm(data1, data2)
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place = base.CPUPlace()
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exe = base.Executor(place)
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input1 = np.random.random([10, 3, 4]).astype('float64')
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input2 = np.random.random([10, 4, 5]).astype('float64')
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(result,) = exe.run(
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feed={"data1": input1, "data2": input2},
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fetch_list=[result_bmm],
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)
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expected_result = np.matmul(input1, input2)
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np.testing.assert_allclose(expected_result, result, rtol=1e-05)
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class API_TestDygraphBmm(unittest.TestCase):
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def test_out(self):
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input1 = np.array(
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[
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[[1.0, 1.0, 1.0], [2.0, 2.0, 2.0]],
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[[3.0, 3.0, 3.0], [4.0, 4.0, 4.0]],
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]
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)
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input2 = np.array(
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[
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[[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]],
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[[4.0, 4.0], [5.0, 5.0], [6.0, 6.0]],
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]
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)
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with base.dygraph.guard():
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x = paddle.to_tensor(input1)
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y = paddle.to_tensor(input2)
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out = paddle.bmm(x, y)
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out_np = out.numpy()
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expected_result = np.matmul(input1, input2)
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np.testing.assert_allclose(expected_result, out_np, rtol=1e-05)
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class TestBmmAPIError(unittest.TestCase):
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def test_api_error(self):
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x_data = np.arange(24, dtype='float32').reshape((2, 3, 4))
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y_data = np.arange(16, dtype='float32').reshape((2, 4, 2))
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y_data_wrong1 = np.arange(16, dtype='float32').reshape((2, 2, 4))
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y_data_wrong2 = np.arange(16, dtype='float32').reshape((2, 2, 2, 2))
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y_data_wrong3 = np.arange(24, dtype='float32').reshape((3, 4, 2))
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self.assertRaises(ValueError, paddle.bmm, x_data, y_data_wrong1)
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self.assertRaises(ValueError, paddle.bmm, x_data, y_data_wrong2)
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self.assertRaises(ValueError, paddle.bmm, x_data, y_data_wrong3)
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class TestBmmOp_ZeroSize(OpTest):
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def setUp(self):
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self.op_type = "bmm"
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self.python_api = paddle.bmm
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self.public_python_api = paddle.bmm
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X = np.random.random((10, 0, 4)).astype("float64")
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Y = np.random.random((10, 4, 5)).astype("float64")
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self.inputs = {'X': X, 'Y': Y}
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Out = np.matmul(X, Y)
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self.outputs = {'Out': 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_checkout_grad(self):
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self.check_grad(['X', 'Y'], 'Out', check_pir=True)
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class TestBmmOutAndParamDecorator(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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self.x_np = np.random.random((10, 3, 4)).astype("float64")
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self.y_np = np.random.random((10, 4, 5)).astype("float64")
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self.test_types = ["decorator", "out", "out_decorator"]
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def do_test(self, test_type):
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x = paddle.to_tensor(self.x_np, stop_gradient=False)
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y = paddle.to_tensor(self.y_np, stop_gradient=False)
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if test_type == 'raw':
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result = paddle.bmm(x, y)
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result.mean().backward()
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return result, x.grad, y.grad
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elif test_type == 'decorator':
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result = paddle.bmm(input=x, mat2=y)
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result.mean().backward()
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return result, x.grad, y.grad
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elif test_type == 'out':
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out = paddle.empty([10, 3, 5], dtype='float64')
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out.stop_gradient = False
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paddle.bmm(x, y, out=out)
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out.mean().backward()
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return out, x.grad, y.grad
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elif test_type == 'out_decorator':
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out = paddle.empty([10, 3, 5], dtype='float64')
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out.stop_gradient = False
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paddle.bmm(input=x, mat2=y, out=out)
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out.mean().backward()
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return out, x.grad, y.grad
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else:
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raise ValueError(f"Unknown test type: {test_type}")
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def test_all(self):
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out_std, grad_x_std, grad_y_std = self.do_test('raw')
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for test_type in self.test_types:
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out, grad_x, grad_y = self.do_test(test_type)
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np.testing.assert_allclose(out.numpy(), out_std.numpy(), rtol=1e-7)
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np.testing.assert_allclose(
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grad_x.numpy(), grad_x_std.numpy(), rtol=1e-7
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)
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np.testing.assert_allclose(
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grad_y.numpy(), grad_y_std.numpy(), rtol=1e-7
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)
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if __name__ == "__main__":
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unittest.main()
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