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Add plan paste for easily accessing the sharding plan #3389

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Add plan paste for easily accessing the sharding plan #3389

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25 changes: 16 additions & 9 deletions torchrec/distributed/planner/types.py
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Original file line number Diff line number Diff line change
Expand Up @@ -667,15 +667,22 @@ def __deepcopy__(
return result

def __str__(self) -> str:
str_obj: str = ""
str_obj += f"name: {self.name}"
str_obj += f"\nsharding type: {self.sharding_type}"
str_obj += f"\ncompute kernel: {self.compute_kernel}"
str_obj += f"\nnum shards: {len(self.shards)}"
for shard in self.shards:
str_obj += f"\n\t{str(shard)}"

return str_obj
tensor_metadata = f"{{shape: {tuple(self.tensor.shape)}, dtype: {self.tensor.dtype}, device: {self.tensor.device}}}"
shards_str = f"[{', '.join(str(shard) for shard in self.shards)}]"
return f"""{{
"name": "{self.name}",
"module_fqn": "{self.module[0]}",
"tensor": {tensor_metadata},
"input_lengths": {self.input_lengths},
"batch_size": {self.batch_size},
"sharding_type": "{self.sharding_type}",
"compute_kernel": "{self.compute_kernel}",
"shards": {shards_str},
"is_pooled": {self.is_pooled if self.module[1] else None},
"feature_names": {self.feature_names},
"cache_params": {self.cache_params},
"is_weighted": {self.is_weighted}
}}"""


class PartitionByType(Enum):
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