wehub-resource-sync
240 lines
7.8 KiB
Python
240 lines
7.8 KiB
Python
# Copyright (c) 2025 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 paddle
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from paddle.distributed.auto_parallel.pipelining.microbatch import (
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TensorChunkSpec,
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merge_chunks,
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split_args_kwargs_into_chunks,
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)
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class TestMicrobatch:
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def __init__(self):
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paddle.seed(2024)
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paddle.distributed.init_parallel_env()
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self.batch_size = 8
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self.feature_size = 4
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self.tensor = paddle.randn([self.batch_size, self.feature_size])
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self.rank = paddle.distributed.get_rank()
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def test_tensor_chunk_spec(self):
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# Test creation and string representation of TensorChunkSpec
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spec = TensorChunkSpec(0)
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assert spec.split_axis == 0
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assert str(spec) == "TensorChunkSpec(0)"
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assert "TensorChunkSpec(0)" in repr(spec)
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def test_split_args_kwargs(self):
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# Test basic parameter splitting
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args = (self.tensor,)
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kwargs = {"input": self.tensor}
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num_chunks = 2
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args_split, kwargs_split = split_args_kwargs_into_chunks(
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args, kwargs, num_chunks
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)
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assert len(args_split) == num_chunks
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assert len(kwargs_split) == num_chunks
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assert args_split[0][0].shape[0] == self.batch_size // num_chunks
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# Test splitting with non-tensor parameters
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args = (self.tensor, 42, "string")
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kwargs = {"tensor": self.tensor, "number": 42}
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num_chunks = 2
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args_split, kwargs_split = split_args_kwargs_into_chunks(
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args, kwargs, num_chunks
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)
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# Verify non-tensor parameters remain unchanged in each chunk
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assert args_split[0][1] == 42
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assert args_split[0][2] == "string"
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assert kwargs_split[0]["number"] == 42
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# Test splitting with custom specification
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tensor_2d = paddle.randn([4, 6])
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args = (tensor_2d,)
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args_chunk_spec = (TensorChunkSpec(1),) # Split on second dimension
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args_split, _ = split_args_kwargs_into_chunks(
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args, None, 2, args_chunk_spec
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)
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assert args_split[0][0].shape[1] == 3
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def test_merge_chunks(self):
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# Test merging chunks
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chunk1 = paddle.randn([4, 4])
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chunk2 = paddle.randn([4, 4])
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chunks = [chunk1, chunk2]
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chunk_spec = [TensorChunkSpec(0)]
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merged = merge_chunks(chunks, chunk_spec)
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assert merged.shape[0] == 8
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# Test merging chunks containing non-tensor values
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chunks = [(paddle.randn([4, 4]), 42)] * 2
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chunk_spec = [TensorChunkSpec(0), None]
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merged = merge_chunks(chunks, chunk_spec)
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assert merged[1] == 42
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# Test error cases
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try:
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# Test error when tensor size is smaller than number of chunks
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small_tensor = paddle.randn([1, 4])
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split_args_kwargs_into_chunks((small_tensor,), None, 2)
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raise AssertionError("Expected ValueError")
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except ValueError:
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pass
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try:
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# Test error when parameter count doesn't match chunk_spec length
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split_args_kwargs_into_chunks(
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(self.tensor,),
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None,
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2,
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(TensorChunkSpec(0), TensorChunkSpec(1)),
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)
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raise AssertionError("Expected ValueError")
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except AssertionError:
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pass
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# test merge empty chunks
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empty_chunks = []
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result = merge_chunks(empty_chunks, None)
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assert result == []
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# test tensor size smaller than chunks number
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small_tensor = paddle.randn([1, 4])
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try:
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split_args_kwargs_into_chunks((small_tensor,), None, 2)
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raise AssertionError("Expected ValueError")
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except ValueError:
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pass
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# test merge non-tensor with tensor spec
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chunks = [(42,), (42,)]
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chunk_spec = (TensorChunkSpec(0),)
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result = merge_chunks(chunks, chunk_spec)
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assert result[0] == 42
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def test_nested_structure(self):
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# test nested tensor
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nested_tensor = [
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[paddle.randn([4, 2]), paddle.randn([4, 2])],
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[paddle.randn([4, 2]), paddle.randn([4, 2])],
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]
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args = (nested_tensor,)
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kwargs = {"nested": nested_tensor}
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args_split, kwargs_split = split_args_kwargs_into_chunks(
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args, kwargs, 2
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)
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assert len(args_split) == 2
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assert len(args_split[0][0]) == 2
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assert len(args_split[0][0][0]) == 2
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assert args_split[0][0][0][0].shape == [2, 2]
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assert len(kwargs_split) == 2
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assert len(kwargs_split[0]["nested"]) == 2
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assert len(kwargs_split[0]["nested"][0]) == 2
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assert kwargs_split[0]["nested"][0][0].shape == [2, 2]
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merged_args = merge_chunks(
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args_split,
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[
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[TensorChunkSpec(0), TensorChunkSpec(0)],
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[TensorChunkSpec(0), TensorChunkSpec(0)],
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],
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)
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assert merged_args[0][0][0].shape == [4, 2]
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assert merged_args[0][1][1].shape == [4, 2]
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assert len(merged_args[0]) == 2
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assert len(merged_args[0][0]) == 2
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def test_dist_tensor_split_and_merge(self):
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# test dist tensor split and merge
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base_tensor = self.tensor
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dense_tensor, _ = split_args_kwargs_into_chunks(
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(base_tensor,),
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None,
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2,
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)
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mesh = paddle.distributed.ProcessMesh([0, 1], dim_names=["dp"])
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dist_tensor = paddle.distributed.shard_tensor(
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self.tensor,
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mesh,
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[paddle.distributed.Shard(0)],
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)
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dist_tensor_split, _ = split_args_kwargs_into_chunks(
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(dist_tensor,),
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None,
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2,
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)
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if self.rank == 0:
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is_equal = (
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dist_tensor_split[0][0]
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._local_value()
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.equal_all(dense_tensor[0][0][:2])
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)
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assert is_equal.item()
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is_equal = (
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dist_tensor_split[1][0]
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._local_value()
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.equal_all(dense_tensor[0][0][2:])
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)
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assert is_equal.item()
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else:
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is_equal = (
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dist_tensor_split[0][0]
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._local_value()
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.equal_all(dense_tensor[1][0][:2])
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)
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assert is_equal.item()
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is_equal = (
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dist_tensor_split[1][0]
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._local_value()
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.equal_all(dense_tensor[1][0][2:])
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)
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assert is_equal.item()
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chunk1 = dist_tensor_split[0][0]
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chunk2 = dist_tensor_split[1][0]
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chunk_spec = [TensorChunkSpec(0)]
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merged_chunk = merge_chunks([chunk1, chunk2], chunk_spec)
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if self.rank == 0:
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is_equal = merged_chunk._local_value().equal_all(base_tensor[:4])
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assert is_equal.item()
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else:
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is_equal = merged_chunk._local_value().equal_all(base_tensor[4:])
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assert is_equal.item()
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def run_all_tests(self):
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"""Run all test methods"""
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self.test_tensor_chunk_spec()
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self.test_split_args_kwargs()
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self.test_merge_chunks()
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self.test_nested_structure()
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self.test_dist_tensor_split_and_merge()
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if __name__ == "__main__":
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TestMicrobatch().run_all_tests()
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