wehub-resource-sync
334 lines
11 KiB
Python
334 lines
11 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 os
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import pathlib
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import sys
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import unittest
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import numpy as np
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import paddle
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from paddle import base
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from paddle.autograd.ir_backward import grad as ir_grad
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from paddle.framework import core
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sys.path.append(
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str(pathlib.Path(__file__).resolve().parents[2] / 'legacy_test')
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)
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from utils import static_guard
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class IfNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, x, y, cond):
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if cond:
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x = x + 1
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out1 = paddle.mean(x)
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out2 = paddle.mean(y)
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else:
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y = y + 1
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out1 = paddle.mean(x)
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out2 = paddle.mean(y)
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return out1, out2
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class WhileNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, x, y):
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while paddle.all(x < y):
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x = x + 1
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out = paddle.mean(y**2)
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return out
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class WhileAndIfNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, x, y):
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while paddle.all(x < y):
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if paddle.all(x + 1 < y):
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x = x + 0.5
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out = paddle.mean(y**2)
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else:
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x = x + 0.6
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out = paddle.mean(paddle.nn.functional.softmax(y))
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return out
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class TestPrimControlFlowIf(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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core._set_prim_forward_enabled(False)
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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]
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self.shape_y = [8, 16, 32]
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self.x_np = np.random.random(self.shape_x).astype("float32")
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self.y_np = np.random.random(self.shape_y).astype("float32")
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self.places = []
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if (
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os.environ.get('FLAGS_CI_both_cpu_and_gpu', 'False').lower()
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in ['1', 'true', 'on']
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or not paddle.is_compiled_with_cuda()
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):
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self.places.append(paddle.CPUPlace())
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if paddle.is_compiled_with_cuda():
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self.places.append(paddle.CUDAPlace(0))
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def get_control_if_res(self, x, y, cond, prim_forward=False):
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if prim_forward:
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core._set_prim_forward_enabled(True)
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net = IfNet()
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net = paddle.jit.to_static(net, full_graph=True)
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out1, out2 = net(x, y, cond)
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core._set_prim_forward_enabled(False)
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return out1, out2
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def test_decompose_if_true(self):
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for place in self.places:
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if isinstance(place, paddle.base.CPUPlace):
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paddle.set_device("cpu")
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elif isinstance(place, paddle.base.CUDAPlace):
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paddle.set_device("gpu")
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x = paddle.to_tensor(self.x_np, dtype="float32")
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y = paddle.to_tensor(self.y_np, dtype="float32")
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cond = paddle.full(shape=[1], fill_value=1)
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out1_baseline, out2_baseline = self.get_control_if_res(
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x, y, cond, prim_forward=False
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)
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out1, out2 = self.get_control_if_res(x, y, cond, prim_forward=True)
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np.testing.assert_allclose(out1_baseline, out1, rtol=1e-6, atol=0)
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np.testing.assert_allclose(out2_baseline, out2, rtol=1e-6, atol=0)
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def test_decompose_if_false(self):
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for place in self.places:
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if isinstance(place, paddle.base.CPUPlace):
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paddle.set_device("cpu")
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elif isinstance(place, paddle.base.CUDAPlace):
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paddle.set_device("gpu")
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x = paddle.to_tensor(self.x_np, dtype="float32")
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y = paddle.to_tensor(self.y_np, dtype="float32")
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cond = paddle.full(shape=[1], fill_value=0)
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out1_baseline, out2_baseline = self.get_control_if_res(
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x, y, cond, prim_forward=False
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)
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out1, out2 = self.get_control_if_res(x, y, cond, prim_forward=True)
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np.testing.assert_allclose(out1_baseline, out1, rtol=1e-6, atol=0)
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np.testing.assert_allclose(out2_baseline, out2, rtol=1e-6, atol=0)
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@classmethod
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def tearDownClass(cls):
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core._set_prim_forward_enabled(False)
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class TestPrimControlFlowWhile(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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core._set_prim_forward_enabled(False)
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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]
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self.shape_y = [8, 16, 32]
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self.x_np = np.random.random(self.shape_x).astype("float32")
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self.y_np = np.random.random(self.shape_y).astype("float32") + 3
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self.places = []
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if (
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os.environ.get('FLAGS_CI_both_cpu_and_gpu', 'False').lower()
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in ['1', 'true', 'on']
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or not paddle.is_compiled_with_cuda()
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):
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self.places.append(paddle.CPUPlace())
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if paddle.is_compiled_with_cuda():
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self.places.append(paddle.CUDAPlace(0))
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def get_control_while_res(self, x, y, prim_forward=False):
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if prim_forward:
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core._set_prim_forward_enabled(True)
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net = WhileNet()
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net = paddle.jit.to_static(net, full_graph=True)
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out = net(x, y)
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core._set_prim_forward_enabled(False)
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return out
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def test_decompose_while(self):
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for place in self.places:
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if isinstance(place, paddle.base.CPUPlace):
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paddle.set_device("cpu")
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elif isinstance(place, paddle.base.CUDAPlace):
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paddle.set_device("gpu")
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x = paddle.to_tensor(self.x_np, dtype="float32")
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y = paddle.to_tensor(self.y_np, dtype="float32")
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out_baseline = self.get_control_while_res(x, y, prim_forward=False)
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out = self.get_control_while_res(x, y, prim_forward=True)
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np.testing.assert_allclose(out_baseline, out, rtol=1e-6, atol=0)
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@classmethod
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def tearDownClass(cls):
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core._set_prim_forward_enabled(False)
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class TestPrimControlFlowWhileAndIf(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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core._set_prim_forward_enabled(False)
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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]
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self.shape_y = [8, 16, 32]
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self.x_np = np.random.random(self.shape_x).astype("float32")
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self.y_np = np.random.random(self.shape_y).astype("float32") + 3
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self.places = []
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if (
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os.environ.get('FLAGS_CI_both_cpu_and_gpu', 'False').lower()
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in ['1', 'true', 'on']
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or not paddle.is_compiled_with_cuda()
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):
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self.places.append(paddle.CPUPlace())
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if paddle.is_compiled_with_cuda():
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self.places.append(paddle.CUDAPlace(0))
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def get_control_flow_res(self, x, y, prim_forward=False):
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if prim_forward:
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core._set_prim_forward_enabled(True)
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net = WhileAndIfNet()
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net = paddle.jit.to_static(net, full_graph=True)
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out = net(x, y)
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core._set_prim_forward_enabled(False)
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return out
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def test_decompose_while_and_if(self):
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for place in self.places:
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if isinstance(place, paddle.base.CPUPlace):
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paddle.set_device("cpu")
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elif isinstance(place, paddle.base.CUDAPlace):
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paddle.set_device("gpu")
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x = paddle.to_tensor(self.x_np, dtype="float32")
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y = paddle.to_tensor(self.y_np, dtype="float32")
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out_baseline = self.get_control_flow_res(x, y, prim_forward=False)
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out = self.get_control_flow_res(x, y, prim_forward=True)
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np.testing.assert_allclose(out_baseline, out, rtol=1e-6, atol=0)
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@classmethod
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def tearDownClass(cls):
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core._set_prim_forward_enabled(False)
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class TestPrimControlFlowWhileBackward(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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core._set_prim_all_enabled(False)
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def setUp(self):
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np.random.seed(2023)
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self.shape_i = [1]
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self.shape_x = [1]
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self.i_np = np.random.random(self.shape_i).astype("float32")
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self.x_np = np.random.random(self.shape_x).astype("float32")
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def cond(self, i, x):
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return i < 3
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def body(self, i, x):
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x = paddle.pow(x, i)
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i = i + 1
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return [i, x]
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def get_while_prim_grad_res(self):
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core._set_prim_all_enabled(True)
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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i = paddle.static.data(name='i', shape=[1], dtype='float32')
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i.stop_gradient = False
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i.persistable = True
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x = paddle.static.data(name='x', shape=[1], dtype='float32')
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x.stop_gradient = False
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x.persistable = True
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out = paddle.static.nn.while_loop(self.cond, self.body, [i, x])
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[new_out] = paddle.decomposition.decomp.decompose(
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main_program, [out[1]]
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)
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out_grad = ir_grad(new_out, [x])
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place = (
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base.CUDAPlace(0)
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if core.is_compiled_with_cuda()
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else base.CPUPlace()
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)
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exe = base.Executor(place)
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out_grad = exe.run(
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main_program,
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feed={'i': self.i_np, 'x': self.x_np},
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fetch_list=[out_grad],
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)
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core._set_prim_all_enabled(False)
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return main_program, out_grad[0]
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def get_while_grad_res(self):
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core._set_prim_all_enabled(False)
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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i = paddle.static.data(name='i', shape=[1], dtype='float32')
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i.stop_gradient = False
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i.persistable = True
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x = paddle.static.data(name='x', shape=[1], dtype='float32')
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x.stop_gradient = False
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x.persistable = True
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out = paddle.static.nn.while_loop(self.cond, self.body, [i, x])
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out_grad = ir_grad(out, [x])
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place = (
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base.CUDAPlace(0)
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if core.is_compiled_with_cuda()
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else base.CPUPlace()
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)
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exe = base.Executor(place)
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out_grad = exe.run(
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main_program,
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feed={'i': self.i_np, 'x': self.x_np},
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fetch_list=[out_grad],
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)
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return main_program, out_grad[0]
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def test_while_loop_backward2(self):
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with static_guard():
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program_origin, out_grad_baseline = self.get_while_grad_res()
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program_prim, out_grad = self.get_while_prim_grad_res()
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np.testing.assert_allclose(
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out_grad_baseline, out_grad, rtol=1e-6, atol=0
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)
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assert len(
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program_origin.global_block().ops[-1].as_while_op().body().ops
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) != len(program_prim.global_block().ops[-1].as_while_op().body().ops)
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@classmethod
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def tearDownClass(cls):
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core._set_prim_all_enabled(False)
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if __name__ == "__main__":
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unittest.main()
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