wehub-resource-sync
163 lines
5.0 KiB
Python
163 lines
5.0 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 TestBitwiseApiForSemiAutoParallel:
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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._check_grad = False
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self._rtol = 1e-6
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self._atol = 0.0
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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):
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np1 = a.numpy()
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np2 = b.numpy()
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np.testing.assert_allclose(
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np1, np2, rtol=self._rtol, atol=self._atol, verbose=True
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)
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def test_unary_body(self, x_shape, out_shape, x_placements, unary_func):
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x = paddle.randint(0, 100, x_shape, 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 = unary_func(dist_x)
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out = unary_func(x)
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self.check_tensor_eq(out, dist_out)
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if self._check_grad:
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dist_out.backward()
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out.backward()
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self.check_tensor_eq(x.grad, dist_x.grad)
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def test_binary_body(
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self,
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x_shape,
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y_shape,
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out_shape,
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x_placements,
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y_placements,
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binary_func,
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):
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x = paddle.randint(0, 100, x_shape, self._dtype)
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y = paddle.randint(0, 100, y_shape, self._dtype)
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x.stop_gradient = False
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y.stop_gradient = False
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dist_x = dist.shard_tensor(x, self._mesh, x_placements)
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dist_y = dist.shard_tensor(y, self._mesh, y_placements)
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dist_x.stop_gradient = False
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dist_y.stop_gradient = False
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dist_out = binary_func(dist_x, dist_y)
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out = binary_func(x, y)
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self.check_tensor_eq(out, dist_out)
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if self._check_grad:
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dist_out.backward()
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out.backward()
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self.check_tensor_eq(x.grad, dist_x.grad)
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self.check_tensor_eq(y.grad, dist_y.grad)
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def test_bitwise_and_x_shard(self):
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self.test_binary_body(
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x_shape=[16, 32],
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y_shape=[16, 32],
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out_shape=[16, 32],
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x_placements=[dist.Shard(0)],
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y_placements=[dist.Replicate()],
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binary_func=paddle.bitwise_and,
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)
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def test_bitwise_and_x_shard_broadcast(self):
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self.test_binary_body(
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x_shape=[16, 32],
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y_shape=[2, 16, 32],
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out_shape=[2, 16, 32],
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x_placements=[dist.Shard(0)],
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y_placements=[dist.Replicate()],
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binary_func=paddle.bitwise_and,
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)
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def test_bitwise_and_x_y_shard(self):
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if self._backend == "cpu":
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return
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self.test_binary_body(
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x_shape=[16, 32],
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y_shape=[16, 32],
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out_shape=[16, 32],
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x_placements=[dist.Shard(0)],
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y_placements=[dist.Shard(1)],
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binary_func=paddle.bitwise_and,
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)
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def test_bitwise_and_x_y_shard_broadcast(self):
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self.test_binary_body(
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x_shape=[4, 16, 32],
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y_shape=[16, 32],
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out_shape=[4, 16, 32],
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x_placements=[dist.Shard(0)],
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y_placements=[dist.Replicate()],
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binary_func=paddle.bitwise_and,
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)
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def test_bitwise_not_x_shard(self):
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self.test_unary_body(
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x_shape=[16, 32],
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out_shape=[16, 32],
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x_placements=[dist.Shard(0)],
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unary_func=paddle.bitwise_not,
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)
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def test_bitwise_not_x_shard_broadcast(self):
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self.test_binary_body(
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x_shape=[16, 32],
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y_shape=[2, 16, 32],
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out_shape=[2, 16, 32],
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x_placements=[dist.Shard(0)],
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y_placements=[dist.Replicate()],
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binary_func=paddle.bitwise_not,
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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_bitwise_and_x_shard()
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self.test_bitwise_and_x_shard_broadcast()
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self.test_bitwise_and_x_y_shard()
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self.test_bitwise_and_x_y_shard_broadcast()
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self.test_bitwise_not_x_shard()
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if __name__ == '__main__':
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TestBitwiseApiForSemiAutoParallel().run_test_case()
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