wehub-resource-sync
120 lines
3.7 KiB
Python
120 lines
3.7 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 numpy as np
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import paddle
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import paddle.distributed as dist
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class TestSoftmaxApiForSemiAutoParallel:
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def __init__(self):
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self._dtype = os.getenv("dtype")
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self._backend = os.getenv("backend")
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self._seed = eval(os.getenv("seed"))
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self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"])
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self._rtol = 1e-6
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self._atol = 0
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# The gradient of softmax is tiny, ref test_softmax_op.py, use atol
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# to check the backward precision.
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self._bwd_rtol = 0
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self._bwd_atol = 1e-6
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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def check_tensor_eq(self, a, b, rtol=1e-6, atol=0):
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np1 = a.numpy()
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np2 = b.numpy()
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np.testing.assert_allclose(np1, np2, rtol=rtol, atol=atol, verbose=True)
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def test_body(self, x_shape, out_shape, x_placements, func):
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x = paddle.rand(x_shape, dtype=self._dtype)
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x.stop_gradient = False
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dist_x = dist.shard_tensor(x, self._mesh, x_placements)
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dist_x.stop_gradient = False
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dist_out = func(dist_x)
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out = func(x)
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self.check_tensor_eq(out, dist_out, self._rtol, self._atol)
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dist_out.sum().backward()
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out.sum().backward()
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self.check_tensor_eq(
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x.grad, dist_x.grad, self._bwd_rtol, self._bwd_atol
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)
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def test_softmax_shard(self):
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self.test_body(
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x_shape=[20, 30],
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out_shape=[4, 4],
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x_placements=[dist.Shard(0)],
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func=lambda x: paddle.nn.functional.softmax(x, axis=1),
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)
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def test_softmax_shard_along_axis(self):
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self.test_body(
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x_shape=[20, 30],
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out_shape=[20, 30],
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x_placements=[dist.Shard(1)],
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func=lambda x: paddle.nn.functional.softmax(x, axis=1),
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)
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def test_multi_axes(self):
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self.test_body(
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x_shape=[2, 4, 6, 10],
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out_shape=[2, 4, 6, 10],
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x_placements=[dist.Shard(0)],
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func=lambda x: paddle.nn.functional.softmax(x, axis=1),
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)
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def test_multi_axes_along_axis(self):
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self.test_body(
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x_shape=[2, 4, 6, 10],
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out_shape=[2, 4, 6, 10],
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x_placements=[dist.Shard(0)],
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func=lambda x: paddle.nn.functional.softmax(x, axis=0),
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)
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def test_negative_axis(self):
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self.test_body(
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x_shape=[2, 4, 6, 10],
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out_shape=[2, 4, 6, 10],
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x_placements=[dist.Shard(0)],
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func=lambda x: paddle.nn.functional.softmax(x, axis=-4),
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)
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def run_test_case(self):
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if self._backend == "cpu":
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paddle.set_device("cpu")
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elif self._backend == "gpu":
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paddle.set_device("gpu:" + str(dist.get_rank()))
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else:
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raise ValueError("Only support cpu or gpu backend.")
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self.test_softmax_shard()
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self.test_softmax_shard_along_axis()
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self.test_multi_axes()
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self.test_multi_axes_along_axis()
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self.test_negative_axis()
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if __name__ == '__main__':
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TestSoftmaxApiForSemiAutoParallel().run_test_case()
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