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[ExecuTorch] Allow using custom SDPA for non-float32 dtypes in llama demo #5548

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[ExecuTorch] Allow using custom SDPA for non-float32 dtypes in llama demo #5548

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12 changes: 10 additions & 2 deletions examples/models/llama2/source_transformation/sdpa.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,9 @@ def __init__(
dim: int,
):
super().__init__()
self.kv_cache = kv_cache
# Custom op only supports float32 currently. Converting to/from float32 is
# faster than not having the op.
self.kv_cache = kv_cache.to(torch.float)
self.dim = dim

def forward(
Expand All @@ -36,6 +38,12 @@ def forward(
seqlen,
mask,
):
# Custom op only supports float32 currently. Converting to/from float32 is
# faster than not having the op.
input_dtype = q.dtype
q = q.to(dtype=torch.float)
k = k.to(dtype=torch.float)
v = v.to(dtype=torch.float)
output = torch.ops.llama.sdpa_with_kv_cache(
q,
k,
Expand All @@ -48,7 +56,7 @@ def forward(
0, # dropout probability. Ignored by the code
True, # is_causal
)
return output.view(bsz, seqlen, self.dim)
return output.view(bsz, seqlen, self.dim).to(dtype=input_dtype)


def _replace_sdpa_with_custom_op(module: torch.nn.Module):
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