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Fix bytes_to_scalar for float/complex on RISC-V #193334

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Fix bytes_to_scalar for float/complex on RISC-V #193334

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32 changes: 12 additions & 20 deletions torch/testing/_internal/common_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5977,27 +5977,19 @@ def check_bytes(byte_list):
if not (0 <= byte <= 255):
raise AssertionError(f"byte value out of range: expected 0 <= byte <= 255, got {byte}")

if dtype.is_complex:
if len(byte_list) != (num_bytes * 2):
raise AssertionError(
f"expected len(byte_list) == {num_bytes * 2} for complex dtype, got {len(byte_list)}"
)
check_bytes(byte_list)
real = ctype.from_buffer((ctypes.c_byte * num_bytes)(
*byte_list[:num_bytes])).value
imag = ctype.from_buffer((ctypes.c_byte * num_bytes)(
*byte_list[num_bytes:])).value
res = real + 1j * imag
else:
if len(byte_list) != num_bytes:
raise AssertionError(
f"expected len(byte_list) == {num_bytes}, got {len(byte_list)}"
)
check_bytes(byte_list)
res = ctype.from_buffer((ctypes.c_byte * num_bytes)(
*byte_list)).value
expected_len = num_bytes * 2 if dtype.is_complex else num_bytes
if len(byte_list) != expected_len:
raise AssertionError(
f"expected len(byte_list) == {expected_len}"
f"{' for complex dtype' if dtype.is_complex else ''}, got {len(byte_list)}"
)
check_bytes(byte_list)

return torch.tensor(res, device=device, dtype=dtype)
# Reinterpret the raw bytes as the target dtype to preserve exact bit
# patterns (e.g. NaN payloads, which are not preserved when round-tripping
# through Python float/complex, especially on architectures like RISC-V
# that canonicalize NaNs).
return torch.tensor(byte_list, dtype=torch.uint8, device=device).view(dtype=dtype).squeeze(0)


def copy_func(f):
Expand Down