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Ignore fetch operands when assign initial nodes #2929
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,73 @@ | ||
| # Copyright 1999-2021 Alibaba Group Holding Ltd. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| import numpy as np | ||
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||
| from .....config import Config | ||
| from .....core import ChunkGraph | ||
| from .....tensor.random import TensorRand | ||
| from .....tensor.arithmetic import TensorAdd | ||
| from .....tensor.fetch import TensorFetch | ||
| from .....resource import Resource | ||
| from ...core import Task | ||
| from ..analyzer import GraphAnalyzer | ||
| from ..assigner import GraphAssigner | ||
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| def test_assigner_with_fetch_inputs(): | ||
| band_num = 8 | ||
| all_bands = [(f"address_{i}", "numa-0") for i in range(band_num)] | ||
| inputs = [ | ||
| TensorFetch(key=str(i), source_key=str(i), dtype=np.dtype(int)).new_chunk([]) | ||
| for i in range(band_num) | ||
| ] | ||
| no_fetch_inputs = [TensorRand(i).new_chunk([]) for i in range(4)] | ||
| results = [TensorAdd(lhs=inp, rhs=1).new_chunk([inp]) for inp in inputs] | ||
| cur_assigns = dict( | ||
| (fetch_chunk.op.key, band[0][0]) | ||
| for fetch_chunk, band in zip(reversed(inputs), all_bands) | ||
| ) | ||
|
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||
| chunk_graph = ChunkGraph() | ||
| for fetch_chunk, add_chunk in zip(inputs, results): | ||
| chunk_graph.add_node(fetch_chunk) | ||
| chunk_graph.add_node(add_chunk) | ||
| chunk_graph.add_edge(fetch_chunk, add_chunk) | ||
| for inp in no_fetch_inputs: | ||
| results.append(inp) | ||
| chunk_graph.add_node(inp) | ||
| chunk_graph.results = results | ||
|
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| band_resource = dict((band, Resource(num_cpus=1)) for band in all_bands) | ||
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| task = Task("mock_task", "mock_session") | ||
| analyzer = GraphAnalyzer(chunk_graph, band_resource, task, Config()) | ||
| subtask_graph = analyzer.gen_subtask_graph(cur_assigns) | ||
|
|
||
| assigner = GraphAssigner( | ||
| chunk_graph, list(GraphAnalyzer._iter_start_ops(chunk_graph)), band_resource | ||
| ) | ||
| assigns = assigner.assign(cur_assigns) | ||
| key_to_assign = dict((c.key, band) for c, band in assigns.items()) | ||
| for subtask in subtask_graph: | ||
| input_chunks = list(subtask.chunk_graph.iter_indep()) | ||
| if all(isinstance(inp.op, TensorFetch) for inp in input_chunks): | ||
| # all inputs are fetch, expect band should be None | ||
| assert subtask.expect_band is None | ||
| else: | ||
| # if subtask has truly initial chunks, expect band should be | ||
| # same as assign results | ||
| for inp in input_chunks: | ||
| if not isinstance(inp.op, TensorFetch): | ||
| assert subtask.expect_band == key_to_assign[inp.key] |
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About if it is reasonable to add this API to execution, what's your opinion? @fyrestone
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The API is a bit wired for execution. From the title of the PR, I think may be the changes are related to graph construction not the execution?
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Can you use the chunk to bands info for the graph assigner? https://github.com/mars-project/mars/blob/master/mars/services/task/supervisor/processor.py#L241