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Remove guard_size_oblivious from embedding_backward_split_meta_template. #4973

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Remove guard_size_oblivious from embedding_backward_split_meta_template. #4973

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534ce1b
use guard_or_true instead of guard_size_oblivious in sparse_ops.py
laithsakka Oct 4, 2025
bbda1ee
Remove guard_size_oblivious from embedding_backward_split_meta_template.
laithsakka Oct 4, 2025
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4 changes: 2 additions & 2 deletions fbgemm_gpu/codegen/training/backward/embedding_backward_split_meta_template.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -159,7 +159,7 @@ Tensor {{ mdesc }}_embedding{{ ndesc }}_backward_codegen_{{ optimizer }}_{{ desc
{%- endif %}

// short-circuit if there are zero indices.
if (TORCH_GUARD_SIZE_OBLIVIOUS(indices.sym_numel().sym_eq(0))) {
if (TORCH_GUARD_OR_FALSE(indices.sym_numel().sym_eq(0))) {
{%- if dense %}
return grad_dev_weights;
{%- elif optimizer == "none" %}
Expand Down Expand Up @@ -213,7 +213,7 @@ Tensor {{ mdesc }}_embedding{{ ndesc }}_backward_codegen_{{ optimizer }}_{{ desc

// Took allocation from https://www.internalfb.com/code/fbsource/fbcode/deeplearning/fbgemm/fbgemm_gpu/src/split_embeddings_utils.cu?lines=339-347
Tensor sorted_linear_indices_run;
if (TORCH_GUARD_SIZE_OBLIVIOUS(total_unique_indices.sym_gt(0))) {
if (TORCH_GUARD_OR_TRUE(total_unique_indices.sym_gt(0))) {
sorted_linear_indices_run = at::empty_symint({total_unique_indices}, indices.options());
} else {
sorted_linear_indices_run = at::empty_like(indices);
Expand Down
8 changes: 4 additions & 4 deletions fbgemm_gpu/fbgemm_gpu/sparse_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -49,7 +49,7 @@

import torch.utils._pytree as pytree
from torch import SymInt, Tensor
from torch.fx.experimental.symbolic_shapes import guard_size_oblivious
from torch.fx.experimental.symbolic_shapes import guard_or_true


if hasattr(torch.library, "register_fake"):
Expand Down Expand Up @@ -251,7 +251,7 @@ def tbe_input_combine_abstract(
torch._check(index.is_contiguous())
torch._check(offset.is_contiguous())
total_indices = total_indices + index.numel()
if guard_size_oblivious(weight.numel() > 0):
if guard_or_true(weight.numel() > 0):
torch._check(weight.dim() == 1)
torch._check(weight.numel() == index.numel())
torch._check(weight.is_contiguous())
Expand Down Expand Up @@ -288,7 +288,7 @@ def tbe_input_combine_with_length_abstract(
torch._check(offset.is_contiguous())
total_indices = total_indices + index.numel()
total_offsets = total_offsets + offset.numel()
if guard_size_oblivious(weight.numel() > 0):
if guard_or_true(weight.numel() > 0):
torch._check(weight.dim() == 1)
torch._check(weight.numel() == index.numel())
torch._check(weight.is_contiguous())
Expand Down Expand Up @@ -807,7 +807,7 @@ def batch_index_select_dim0_forward_cpu_impl_abstract(
torch._check(num_inputs == len(input_rows))
torch._check(num_inputs == len(input_columns))

if permute_output_dim_0_1 and guard_size_oblivious(len(input_num_indices) > 0):
if permute_output_dim_0_1 and guard_or_true(len(input_num_indices) > 0):
# All num_indices must be the same if permute_output_dim_0_1 is True
for x in input_num_indices:
torch._check(x == input_num_indices[0])
Expand Down
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