wehub-resource-sync
164 lines
4.9 KiB
Python
164 lines
4.9 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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from paddle.distributed import Replicate, Shard
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class TestEmbeddingApiForSemiAutoParallel:
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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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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(np1, np2, rtol=1e-05, verbose=True)
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def test_body(self, x_shape, w_shape, x_placements, w_placements):
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paddle.seed(self._seed)
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np.random.seed(self._seed)
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x_np = np.random.randint(0, 10, size=x_shape)
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w_np = np.random.random(size=w_shape).astype(self._dtype)
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x = paddle.to_tensor(x_np)
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w = paddle.to_tensor(w_np)
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x.stop_gradient = False
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w.stop_gradient = False
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dist_x = dist.shard_tensor(x_np, self._mesh, x_placements)
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dist_w = dist.shard_tensor(w_np, self._mesh, w_placements)
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dist_x.stop_gradient = False
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dist_w.stop_gradient = False
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out = paddle.nn.functional.embedding(x, weight=w)
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dist_out = paddle.nn.functional.embedding(dist_x, weight=dist_w)
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self.check_tensor_eq(out, dist_out)
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out.backward()
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dist_out.backward()
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self.check_tensor_eq(w.grad, dist_w.grad)
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out = paddle.nn.functional.embedding(input=x, weight=w)
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dist_out = paddle.nn.functional.embedding(input=dist_x, weight=dist_w)
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self.check_tensor_eq(out, dist_out)
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out.backward()
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dist_out.backward()
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self.check_tensor_eq(w.grad, dist_w.grad)
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return dist_out, dist_w.grad
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def test_non_shard(self):
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self.test_body(
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x_shape=[12, 16],
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w_shape=[10, 4],
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x_placements=[Replicate()],
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w_placements=[Replicate()],
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)
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def test_x_row_shard(self):
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self.test_body(
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x_shape=[12, 16],
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w_shape=[10, 4],
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x_placements=[Shard(0)],
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w_placements=[Replicate()],
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)
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def test_x_col_shard(self):
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self.test_body(
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x_shape=[12, 16],
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w_shape=[10, 4],
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x_placements=[Shard(1)],
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w_placements=[Replicate()],
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)
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def test_w_row_shard(self):
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self.test_body(
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x_shape=[12, 16],
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w_shape=[10, 4],
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x_placements=[Replicate()],
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w_placements=[Shard(0)],
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)
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def test_w_col_shard(self):
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self.test_body(
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x_shape=[12, 16],
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w_shape=[10, 4],
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x_placements=[Replicate()],
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w_placements=[Shard(1)],
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)
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def test_x_row_w_col_shard(self):
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try:
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self.test_body(
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x_shape=[12, 16],
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w_shape=[10, 4],
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x_placements=[Shard(0)],
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w_placements=[Shard(1)],
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)
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except RuntimeError as e:
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assert 'sharded by same mesh dimension ' in str(e)
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def test_x_col_w_row_shard(self):
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# Unimplemented cpu kernel for CReduceScatterOp
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if self._backend == "cpu":
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return
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self.test_body(
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x_shape=[12, 16],
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w_shape=[10, 4],
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x_placements=[Shard(1)],
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w_placements=[Shard(0)],
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)
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def test_both_col_shard(self):
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try:
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self.test_body(
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x_shape=[12, 16],
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w_shape=[10, 4],
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x_placements=[Shard(1)],
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w_placements=[Shard(1)],
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)
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except RuntimeError as e:
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assert 'sharded by same mesh dimension', str(e)
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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_non_shard()
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self.test_x_row_shard()
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self.test_x_col_shard()
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self.test_w_row_shard()
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self.test_w_col_shard()
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self.test_x_row_w_col_shard()
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self.test_x_col_w_row_shard()
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self.test_both_col_shard()
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if __name__ == '__main__':
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TestEmbeddingApiForSemiAutoParallel().run_test_case()
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