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Enable specifying output dtype for fp8 quantized communication #5154

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Enable specifying output dtype for fp8 quantized communication #5154

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17 changes: 15 additions & 2 deletions fbgemm_gpu/fbgemm_gpu/quantize_comm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -123,6 +123,7 @@ def _dequantize_tensor(
comm_precision: SparseType,
ctx: Optional[QuantizationContext] = None,
is_fwd: bool = True,
fp8_output_dtype: Optional[SparseType] = None,
) -> torch.Tensor:
if comm_precision == SparseType.FP32:
assert quantized_tensor.dtype == torch.float
Expand All @@ -137,8 +138,14 @@ def _dequantize_tensor(
if ctx is not None and ctx.row_dim > 0:
row_dim_quant = ctx.row_dim_quant
quantized_tensor_2d = quantized_tensor.view((-1, row_dim_quant))
# use provided fp8_output_dtype or default to FP32 (0)
output_dtype_int = (
fp8_output_dtype.as_int() if fp8_output_dtype is not None else 0
)
dequant_tensor = torch.ops.fbgemm.FP8RowwiseQuantizedToFloat(
quantized_tensor_2d, is_fwd
quantized_tensor_2d,
is_fwd,
output_dtype_int,
)
return dequant_tensor.view(-1)
else:
Expand Down Expand Up @@ -168,6 +175,7 @@ def __init__(
row_dim: Optional[int] = None,
is_fwd: bool = True,
rounding_mode: Optional[RoundingMode] = None,
fp8_output_dtype: Optional[SparseType] = None,
) -> None:
if loss_scale is not None:
if comm_precision not in [SparseType.FP16, SparseType.BF16]:
Expand All @@ -185,6 +193,7 @@ def __init__(
self._is_fwd = is_fwd
self._row_dim: int = -1 if row_dim is None else row_dim
self._rounding_mode: Optional[RoundingMode] = rounding_mode
self._fp8_output_dtype: Optional[SparseType] = fp8_output_dtype
if self._comm_precision == SparseType.MX4:
self._row_dim = MX_GROUP_SIZE_DEFAULT if row_dim is None else row_dim
self._rounding_mode = (
Expand Down Expand Up @@ -216,7 +225,11 @@ def decode(
f"## decoder {self._comm_precision} {self._loss_scale} ##"
):
dequantized_tensor = _dequantize_tensor(
input_tensor, self._comm_precision, ctx, self._is_fwd
input_tensor,
self._comm_precision,
ctx,
self._is_fwd,
fp8_output_dtype=self._fp8_output_dtype,
)
return dequantized_tensor

Expand Down
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