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[autoparallel] fix parameters sharding bug (#2716)

pull/2687/head^2
YuliangLiu0306 2023-02-15 12:25:50 +08:00 committed by GitHub
parent 2045d45ab7
commit 5b24987fa7
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1 changed files with 5 additions and 4 deletions
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9
colossalai/auto_parallel/passes/runtime_preparation_pass.py
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@ -426,8 +426,9 @@ def module_params_sharding_pass(gm: torch.fx.GraphModule, device_mesh: DeviceMes
# we could use .data here, because all the operations just happen before the real training
# loop, so we don't need to track these operations in the autograd graph.
param = torch.nn.Parameter(
shape_consistency_manager.apply_for_autoparallel_runtime(param.data, param.sharding_spec,
target_sharding_spec).detach().clone())
shape_consistency_manager.apply_for_autoparallel_runtime(param.data, param.sharding_spec,
target_sharding_spec).detach().clone())
return param
for node in nodes:
if node.op == 'call_module':
@ -438,7 +439,7 @@ def module_params_sharding_pass(gm: torch.fx.GraphModule, device_mesh: DeviceMes
setattr(target_module, 'processed', True)
for name, param in target_module.named_parameters():
target_sharding_spec = node.best_strategy.get_sharding_spec_by_name(name)
_shard_param(param, target_sharding_spec)
param = _shard_param(param, target_sharding_spec)
setattr(target_module, name, param)
_add_hook_for_grad_communication(node, param)
@ -469,7 +470,7 @@ def module_params_sharding_pass(gm: torch.fx.GraphModule, device_mesh: DeviceMes
target = getattr(target_module, atoms[-1])
target_sharding_spec = node.sharding_spec
_shard_param(target, target_sharding_spec)
target = _shard_param(target, target_sharding_spec)
assert hasattr(target_module, atoms[-1])
setattr(target_module, atoms[-1], target)