wehub-resource-sync
269 lines
9.4 KiB
Python
269 lines
9.4 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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import paddle.nn.functional as F
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from paddle.autograd.ir_backward import grad
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from paddle.base import core
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from paddle.decomposition import decompose
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paddle.enable_static()
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class TestPrimMode(unittest.TestCase):
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def setUp(self):
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np.random.seed(2023)
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self.shape_x = [8, 16, 32, 64]
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self.shape_y = [8, 16, 32, 64]
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self.x = np.random.random(self.shape_x).astype("float32")
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self.y = np.random.random(self.shape_y).astype("float32")
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self.prog = None
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def base_net(self, flag=None):
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if flag == "forward":
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core._set_prim_forward_enabled(True)
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elif flag == "backward":
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core._set_prim_backward_enabled(True)
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elif flag == "all":
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core._set_prim_all_enabled(True)
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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x = paddle.static.data('x', self.shape_x, dtype='float32')
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y = paddle.static.data('y', self.shape_y, dtype='float32')
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x.stop_gradient = False
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y.stop_gradient = False
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divide_out = paddle.divide(x, y)
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sum_out = paddle.mean(divide_out, axis=0)
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[new_out] = decompose(main_program, [sum_out])
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gradients = grad(new_out, (x, y))
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exe = paddle.static.Executor()
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[fwd, dx, dy] = exe.run(
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feed={'x': self.x, 'y': self.y}, fetch_list=[new_out, gradients]
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)
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whole_ops = [op.name() for op in main_program.global_block().ops]
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self.prog = main_program
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if flag == "forward":
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core._set_prim_forward_enabled(False)
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assert (
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'pd_op.mean' not in whole_ops
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and 'pd_op.divide_grad' in whole_ops
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)
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elif flag == "backward":
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core._set_prim_backward_enabled(False)
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assert (
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'pd_op.mean' in whole_ops
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and 'pd_op.divide_grad' not in whole_ops
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)
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elif flag == "all":
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core._set_prim_all_enabled(False)
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assert (
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'pd_op.mean' not in whole_ops
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and 'pd_op.divide_grad' not in whole_ops
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)
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else:
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assert (
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'pd_op.mean' in whole_ops and 'pd_op.divide_grad' in whole_ops
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)
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return fwd, dx, dy
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def test_prim_forward(self):
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res_ref = self.base_net()
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res = self.base_net("forward")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_equal(ref, actual)
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def test_prim_backward(self):
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res_ref = self.base_net()
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res = self.base_net("backward")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_allclose(ref, actual, rtol=1e-6)
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def test_prim_all(self):
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res_ref = self.base_net()
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res = self.base_net("all")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_allclose(ref, actual, rtol=1e-6)
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def test_has_decomp(self):
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_ = self.base_net()
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for op in self.prog.global_block().ops:
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if op.name() == "pd_op.divide":
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self.assertEqual(core.has_decomp_rule(op), False)
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if op.name() == "pd_op.mean":
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self.assertEqual(core.has_decomp_rule(op), True)
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class TestReluSink(unittest.TestCase):
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def setUp(self):
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np.random.seed(2023)
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self.shape_x = [8, 16, 32, 64]
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self.x = np.random.random(self.shape_x).astype("float32")
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self.prog = None
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def base_net(self, flag=None):
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if flag == "forward":
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core._set_prim_forward_enabled(True)
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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x = paddle.static.data('x', self.shape_x, dtype='float32')
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x.stop_gradient = False
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sum_out = F.relu(x)
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[new_out] = decompose(main_program, [sum_out])
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gradients = grad(new_out, x)
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exe = paddle.static.Executor()
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[fwd, dx] = exe.run(
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feed={'x': self.x}, fetch_list=[new_out, gradients]
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)
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whole_ops = [op.name() for op in main_program.global_block().ops]
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self.prog = main_program
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if flag == "forward":
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core._set_prim_forward_enabled(False)
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assert 'pd_op.relu' not in whole_ops
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else:
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assert 'pd_op.relu' in whole_ops
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return fwd, dx
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def test_relu_forward(self):
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res_ref = self.base_net()
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res = self.base_net("forward")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_equal(ref, actual)
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class TestGeluSink(unittest.TestCase):
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def setUp(self):
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np.random.seed(2023)
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self.shape_x = [8, 16, 32, 64]
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self.x = np.random.random(self.shape_x).astype("float32")
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self.prog = None
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def base_net(self, approximate=True, flag=None):
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if flag == "forward":
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core._set_prim_forward_enabled(True)
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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x = paddle.static.data('x', self.shape_x, dtype='float32')
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x.stop_gradient = False
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sum_out = F.gelu(x, approximate=approximate)
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[new_out] = decompose(main_program, [sum_out])
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gradients = grad(new_out, x)
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exe = paddle.static.Executor()
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[fwd, dx] = exe.run(
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feed={'x': self.x}, fetch_list=[new_out, gradients]
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)
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whole_ops = [op.name() for op in main_program.global_block().ops]
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self.prog = main_program
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if flag == "forward":
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core._set_prim_forward_enabled(False)
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assert 'pd_op.gelu' not in whole_ops
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else:
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assert 'pd_op.gelu' in whole_ops
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return fwd, dx
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def test_gelu_forward_true(self):
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res_ref = self.base_net(approximate=True)
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res = self.base_net(approximate=True, flag="forward")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_allclose(ref, actual, rtol=1e-6)
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def test_gelu_approximate_false(self):
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res_ref = self.base_net(approximate=False)
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res = self.base_net(approximate=False, flag="forward")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_allclose(ref, actual, rtol=1e-6)
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class TestHardSwishSink(unittest.TestCase):
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def setUp(self):
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np.random.seed(2023)
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self.shape_x = [8, 16, 32, 64]
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self.x = np.random.random(self.shape_x).astype("float32")
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self.prog = None
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def base_net(self, flag=None):
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if flag == "forward":
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core._set_prim_forward_enabled(True)
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elif flag == "backward":
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core._set_prim_backward_enabled(True)
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elif flag == "all":
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core._set_prim_all_enabled(True)
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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x = paddle.static.data('x', self.shape_x, dtype='float32')
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x.stop_gradient = False
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sum_out = F.hardswish(x)
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[new_out] = decompose(main_program, [sum_out])
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gradients = grad(new_out, x)
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exe = paddle.static.Executor()
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[fwd, dx] = exe.run(
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feed={'x': self.x}, fetch_list=[new_out, gradients]
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)
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whole_ops = [op.name() for op in main_program.global_block().ops]
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self.prog = main_program
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if flag == "forward":
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core._set_prim_forward_enabled(False)
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assert 'pd_op.hardswish' not in whole_ops
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elif flag == "backward":
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core._set_prim_backward_enabled(False)
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assert (
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'pd_op.hardswish' in whole_ops
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and 'pd_op.hardswish_grad' not in whole_ops
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)
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elif flag == "all":
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core._set_prim_all_enabled(False)
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assert (
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'pd_op.hardswish' not in whole_ops
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and 'pd_op.hardswish_grad' not in whole_ops
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)
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else:
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assert (
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'pd_op.hardswish' in whole_ops
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and 'pd_op.hardswish_grad' in whole_ops
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)
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return fwd, dx
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def test_prim_forward(self):
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res_ref = self.base_net()
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res = self.base_net("forward")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_allclose(ref, actual, rtol=1e-3, atol=1e-3)
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def test_prim_backward(self):
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res_ref = self.base_net()
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res = self.base_net("backward")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_allclose(ref, actual, rtol=1e-3, atol=1e-3)
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def test_prim_all(self):
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res_ref = self.base_net()
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res = self.base_net("all")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_allclose(ref, actual, rtol=1e-3, atol=1e-3)
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if __name__ == "__main__":
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unittest.main()
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