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create fx argument for sparsefeature types #708

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create fx argument for sparsefeature types #708

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29 changes: 28 additions & 1 deletion torchrec/distributed/embedding_types.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@

import torch
from fbgemm_gpu.split_table_batched_embeddings_ops import EmbeddingLocation
from torch import nn
from torch import fx, nn
from torchrec.distributed.types import (
ModuleSharder,
ParameterStorage,
Expand Down Expand Up @@ -88,6 +88,17 @@ def record_stream(self, stream: torch.cuda.streams.Stream) -> None:
if self.id_score_list_features is not None:
self.id_score_list_features.record_stream(stream)

def __fx_create_arg__(self, tracer: torch.fx.Tracer) -> fx.node.Argument:
return tracer.create_node(
"call_function",
SparseFeatures,
args=(
tracer.create_arg(self.id_list_features),
tracer.create_arg(self.id_score_list_features),
),
kwargs={},
)


class SparseFeaturesList(Multistreamable):
def __init__(self, features: List[SparseFeatures]) -> None:
Expand All @@ -109,6 +120,14 @@ def record_stream(self, stream: torch.cuda.streams.Stream) -> None:
for feature in self.features:
feature.record_stream(stream)

def __fx_create_arg__(self, tracer: torch.fx.Tracer) -> fx.node.Argument:
return tracer.create_node(
"call_function",
SparseFeaturesList,
args=(tracer.create_arg(self.features),),
kwargs={},
)


class ListOfSparseFeaturesList(Multistreamable):
def __init__(self, features: List[SparseFeaturesList]) -> None:
Expand All @@ -130,6 +149,14 @@ def record_stream(self, stream: torch.cuda.streams.Stream) -> None:
for feature in self.features_list:
feature.record_stream(stream)

def __fx_create_arg__(self, tracer: torch.fx.Tracer) -> fx.node.Argument:
return tracer.create_node(
"call_function",
ListOfSparseFeaturesList,
args=(tracer.create_arg(self.features_list),),
kwargs={},
)


@dataclass
class ShardedConfig:
Expand Down