mirror of https://github.com/hpcaitech/ColossalAI
[NFC] polish colossalai/builder/pipeline.py code style (#638)
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10591ecdf9
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c7c224ee17
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@ -1,7 +1,6 @@
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import copy
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import copy
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import heapq
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import heapq
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from colossalai.builder import build_model, build_layer
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from colossalai.builder import build_model, build_layer
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.core import global_context as gpc
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from colossalai.core import global_context as gpc
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@ -40,6 +39,7 @@ def _binary_partition(weights, st, ed):
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def _heap_addition(weights, intervals, add_cnt):
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def _heap_addition(weights, intervals, add_cnt):
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"""
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"""
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"""
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"""
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def _heap_push(heap, st, ed):
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def _heap_push(heap, st, ed):
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value = weights[ed - 1]
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value = weights[ed - 1]
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if st > 0:
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if st > 0:
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@ -162,7 +162,10 @@ def count_layer_params(layers):
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return param_counts
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return param_counts
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def build_pipeline_model_from_cfg(config, num_chunks: int = 1, partition_method: str = 'parameter', verbose: bool = False):
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def build_pipeline_model_from_cfg(config,
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num_chunks: int = 1,
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partition_method: str = 'parameter',
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verbose: bool = False):
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"""An initializer to split the model into different stages for pipeline parallelism.
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"""An initializer to split the model into different stages for pipeline parallelism.
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An example for the model config is shown below. The class VisionTransformerFromConfig should
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An example for the model config is shown below. The class VisionTransformerFromConfig should
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@ -218,7 +221,7 @@ def build_pipeline_model_from_cfg(config, num_chunks: int = 1, partition_method:
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log_str += f'\n===== stage={stage}, layers={num_layers} =====\n'
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log_str += f'\n===== stage={stage}, layers={num_layers} =====\n'
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for st, ed in parts[stage]:
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for st, ed in parts[stage]:
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for idx, layer in enumerate(layers[st: ed]):
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for idx, layer in enumerate(layers[st:ed]):
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log_str += f'\t{idx + st:2d}: {layer}\n'
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log_str += f'\t{idx + st:2d}: {layer}\n'
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logger.info(log_str, ranks=[0])
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logger.info(log_str, ranks=[0])
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