wehub-resource-sync
328 lines
10 KiB
Python
328 lines
10 KiB
Python
# Copyright (c) 2023 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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import paddle
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class TestPir(unittest.TestCase):
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def test_with_pir(self):
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paddle.enable_static()
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place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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exe = paddle.static.Executor(place)
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main_program = paddle.static.Program()
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new_scope = paddle.static.Scope()
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with paddle.static.scope_guard(new_scope):
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with paddle.static.program_guard(main_program):
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x = paddle.ones([2, 2], dtype="float32")
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y = paddle.ones([2, 2], dtype="float32")
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z = x + y
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out = exe.run(main_program, {}, fetch_list=[z])
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gold_res = np.ones([2, 2], dtype="float32") * 2
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np.testing.assert_array_equal(out[0], gold_res)
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class TestCombineOp(unittest.TestCase):
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def test_with_pir(self):
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paddle.enable_static()
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place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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exe = paddle.static.Executor(place)
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main_program = paddle.static.Program()
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new_scope = paddle.static.Scope()
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with paddle.static.scope_guard(new_scope):
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with paddle.static.program_guard(main_program):
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x = paddle.ones([2, 2], dtype="float32")
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y = paddle.ones([2, 2], dtype="float32")
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z = paddle.linalg.multi_dot([x, y])
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out = exe.run(main_program, {}, fetch_list=[z])
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gold_res = np.ones([2, 2], dtype="float32") * 2
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np.testing.assert_array_equal(out[0], gold_res)
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class TestFeedOp(unittest.TestCase):
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def test_with_pir(self):
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paddle.enable_static()
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place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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exe = paddle.static.Executor(place)
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main_program = paddle.static.Program()
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new_scope = paddle.static.Scope()
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with paddle.static.scope_guard(new_scope):
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with paddle.static.program_guard(main_program):
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x = paddle.static.data("x", [2, 2], dtype="float32")
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y = paddle.static.data("y", [2, 2], dtype="float32")
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z = x + y
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np_a = np.random.rand(2, 2).astype("float32")
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np_b = np.random.rand(2, 2).astype("float32")
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out = exe.run(
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main_program,
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feed={"x": np_a, "y": np_b},
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fetch_list=[z],
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)
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gold_res = np_a + np_b
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np.testing.assert_array_equal(out[0], gold_res)
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class TestSelectedRows(unittest.TestCase):
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def test_with_pir(self):
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# TODO(phlrain): support selected rows in GPU
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paddle.enable_static()
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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main_program = paddle.static.Program()
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new_scope = paddle.static.Scope()
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with paddle.static.scope_guard(new_scope):
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with paddle.static.program_guard(main_program):
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w = paddle.uniform([10, 10], dtype="float32")
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w.stop_gradient = False
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id = paddle.ones([2], dtype="int32")
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t = paddle.nn.functional.embedding(id, w, sparse=True)
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loss = paddle.mean(t)
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paddle.static.gradients(loss, w)
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out = exe.run(
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main_program,
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fetch_list=[loss],
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)
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class TestAddGradOp(unittest.TestCase):
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def test_with_pir(self):
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paddle.enable_static()
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place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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exe = paddle.static.Executor(place)
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main_program = paddle.static.Program()
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new_scope = paddle.static.Scope()
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with paddle.static.scope_guard(new_scope):
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with paddle.static.program_guard(main_program):
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x = paddle.static.data("x", [2, 2], dtype="float32")
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y = paddle.static.data("y", [2, 2], dtype="float32")
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x.stop_gradient = False
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z = x * y
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paddle.static.gradients(z, x)
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np_a = np.random.rand(2, 2).astype("float32")
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np_b = np.random.rand(2, 2).astype("float32")
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out = exe.run(
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main_program,
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feed={"x": np_a, "y": np_b},
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fetch_list=[z],
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)
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gold_res = np_a * np_b
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np.testing.assert_array_equal(out[0], gold_res)
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class TestPirDygraph(unittest.TestCase):
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def test_with_pir(self):
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paddle.disable_static()
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@paddle.jit.to_static
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def func(x, y):
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return x + y
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x = paddle.ones([2, 2], dtype='float32')
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y = paddle.ones([2, 2], dtype='float32')
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z = func(x, y)
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gold_res = np.ones([2, 2], dtype="float32") * 2
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np.testing.assert_array_equal(z.numpy(), gold_res)
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class TestPirBackwardDygraph(unittest.TestCase):
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def test_with_pir(self):
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paddle.disable_static()
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build_strategy = paddle.static.BuildStrategy()
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build_strategy.enable_inplace = False
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@paddle.jit.to_static(build_strategy=build_strategy, full_graph=True)
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def func(x, y):
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return x * y
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x = paddle.ones([2, 2], dtype='float32')
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y = paddle.ones([2, 2], dtype='float32')
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x.stop_gradient = False
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y.stop_gradient = False
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z = func(x, y)
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loss = z.mean()
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loss.backward()
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gold_res = np.ones([2, 2], dtype="float32")
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np.testing.assert_array_equal(z.numpy(), gold_res)
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gold_res = np.ones([2, 2], dtype="float32") * 0.25
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np.testing.assert_array_equal(x.gradient(), gold_res)
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np.testing.assert_array_equal(y.gradient(), gold_res)
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class TestPirReshapeBackwardDygraph(unittest.TestCase):
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def test_with_pir(self):
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paddle.disable_static()
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build_strategy = paddle.static.BuildStrategy()
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build_strategy.enable_inplace = False
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@paddle.jit.to_static(build_strategy=build_strategy, full_graph=True)
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def func(x, y):
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x = x.reshape([-1, 2, 2])
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y = y.reshape([-1, 2, 2])
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return x * y
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x = paddle.ones([2, 2], dtype='float32')
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y = paddle.ones([2, 2], dtype='float32')
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x.stop_gradient = False
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y.stop_gradient = False
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z = func(x, y)
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loss = z.mean()
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loss.backward()
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gold_res = np.ones([1, 2, 2], dtype="float32")
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np.testing.assert_array_equal(z.numpy(), gold_res)
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gold_res = np.ones([2, 2], dtype="float32") * 0.25
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np.testing.assert_array_equal(x.gradient(), gold_res)
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np.testing.assert_array_equal(y.gradient(), gold_res)
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class TestSplitOp(unittest.TestCase):
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def test_with_pir(self):
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paddle.enable_static()
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place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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exe = paddle.static.Executor(place)
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main_program = paddle.static.Program()
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new_scope = paddle.static.Scope()
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with paddle.static.scope_guard(new_scope):
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with paddle.static.program_guard(main_program):
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x = paddle.static.data("x", [6, 2], dtype="float32")
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out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=0)
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np_a = np.random.rand(6, 2).astype("float32")
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out = exe.run(
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main_program,
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feed={"x": np_a},
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fetch_list=[out0],
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)
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np.testing.assert_array_equal(out[0], np_a[0:2])
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class TestPirPrint(unittest.TestCase):
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def test_with_pir(self):
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paddle.enable_static()
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place = (
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paddle.CUDAPlace(0)
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if paddle.is_compiled_with_cuda()
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else paddle.CPUPlace()
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)
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exe = paddle.static.Executor(place)
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main_program = paddle.static.Program()
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new_scope = paddle.static.Scope()
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with paddle.static.scope_guard(new_scope):
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with paddle.static.program_guard(main_program):
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x = paddle.ones([2, 2], dtype="float32")
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y = paddle.ones([2, 2], dtype="float32")
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z = x + y
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z = paddle.static.Print(z)
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out = exe.run(main_program, {}, fetch_list=[z])
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gold_res = np.ones([2, 2], dtype="float32") * 2
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np.testing.assert_array_equal(out[0], gold_res)
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class TestPirConcatDygraph(unittest.TestCase):
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def test_with_pir(self):
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paddle.disable_static()
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@paddle.jit.to_static
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def func(x, y):
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return paddle.concat([paddle.shape(x), y], -1)
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x = paddle.ones([2, 2], dtype='float32')
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y = paddle.ones([2], dtype='int64') * 2
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z = func(x, y)
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gold_res = np.ones([4], dtype="float32") * 2
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np.testing.assert_array_equal(z.numpy(), gold_res)
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# TODO(phlrain): open this after fix pr(55509) conflict
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# class TestPirLogicalDygraph(unittest.TestCase):
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# def test_with_pir(self):
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# paddle.disable_static()
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# @paddle.jit.to_static
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# def func(x, y, z):
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# a = paddle.logical_and(x, y)
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# return z + a.cast("float32")
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# x = paddle.ones([2, 2], dtype='float32')
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# y = paddle.ones([2, 2], dtype='float32')
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# z = paddle.ones([2, 2], dtype='float32')
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# z = func(x, y, z)
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# gold_res = np.ones([2, 2], dtype="float32") * 2
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# np.testing.assert_array_equal(z.numpy(), gold_res)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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