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static config for sharded_modules #1766

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20 changes: 15 additions & 5 deletions torchrec/distributed/quant_embedding_kernel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -117,15 +117,21 @@ def _quantize_weight(
@torch.fx.wrap
def _unwrap_kjt(
features: KeyedJaggedTensor,
use_gpu: bool,
weighted: bool,
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
if features.device().type == "cuda":
if use_gpu:
return (
features.values().int(),
features.offsets().int(),
features.weights_or_none(),
features.weights_or_none() if weighted else None,
)
else:
return features.values(), features.offsets(), features.weights_or_none()
return (
features.values(),
features.offsets(),
features.weights_or_none() if weighted else None,
)


class QuantBatchedEmbeddingBag(
Expand Down Expand Up @@ -210,7 +216,9 @@ def get_tbes_to_register(
return {self._emb_module: self._config}

def forward(self, features: KeyedJaggedTensor) -> torch.Tensor:
indices, offsets, per_sample_weights = _unwrap_kjt(features)
indices, offsets, per_sample_weights = _unwrap_kjt(
features, self.emb_module.row_alignment == 16, self._config.is_weighted
)
# Conditional call of .forward function for FX:
# emb_module() can go through FX only if emb_module is registered in named_modules (FX node call_module)
# emb_module.forward() does not require registering emb_module in named_modules (FX node call_function)
Expand Down Expand Up @@ -372,7 +380,9 @@ def split_embedding_weights(
]

def forward(self, features: KeyedJaggedTensor) -> torch.Tensor:
values, offsets, _ = _unwrap_kjt(features)
values, offsets, _ = _unwrap_kjt(
features, self.emb_module.row_alignment == 16, False
)
if self._emb_module_registered:
return self.emb_module(
indices=values,
Expand Down