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Sep 11, 2025
Merged

All variants of quantized relu #14080

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84 changes: 78 additions & 6 deletions backends/cadence/aot/ref_implementations.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -748,13 +748,12 @@ def quantized_conv_nhwc_depthwise_asym8sxsym8s_asym8s_per_tensor() -> torch.Tens
def quantized_conv_nhwc_depthwise_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_relu")
def quantized_relu(
def quantized_relu_common(
X: torch.Tensor,
X_zero_point: torch.Tensor,
X_zero_point: torch.Tensor | int,
out_zero_point: int,
out_multiplier: torch.Tensor,
out_shift: torch.Tensor,
out_multiplier: int,
out_shift: int,
) -> torch.Tensor:
"""
Quantized ReLU operation followed by requantization.
Expand All @@ -770,7 +769,7 @@ def quantized_relu(
if X.dtype not in supported_dtypes:
raise ValueError(f"X dtype must be one of {supported_dtypes}. Got {X.dtype}")

out_scale = -out_multiplier * (1 / (1 << 31)) * (2 ** out_shift[0])
out_scale = -out_multiplier * (1 / (1 << 31)) * (2**out_shift)
dequantized_X = torch.where(X > X_zero_point, X - X_zero_point, torch.zeros_like(X))
return quantize_per_tensor(
dequantized_X,
Expand All @@ -782,6 +781,79 @@ def quantized_relu(
)


def quantized_relu_variant(
per_tensor: bool,
dtype: torch.dtype | None = None,
) -> Callable[[Callable[..., torch.Tensor]], Callable[..., torch.Tensor]]:
"""Create a quantized relu variant with type checking."""

def decorator(_: Callable[..., torch.Tensor]) -> Callable[..., torch.Tensor]:
def variant(
X: torch.Tensor,
X_zero_point: torch.Tensor | int,
out_zero_point: int,
out_multiplier: torch.Tensor | int,
out_shift: torch.Tensor | int,
) -> torch.Tensor:
if per_tensor:
if dtype and X.dtype != dtype:
raise ValueError(f"X dtype must be {dtype}. Got {X.dtype}")

assert isinstance(out_shift, int)
assert isinstance(out_multiplier, int)
_out_shift = out_shift
_out_multiplier = out_multiplier
else:
assert isinstance(out_multiplier, torch.Tensor)
if out_multiplier.numel() > 1:
raise ValueError("Only scalar out_multiplier is supported")

assert isinstance(out_shift, torch.Tensor)
if out_shift.numel() > 1:
raise ValueError("Only scalar out_shift is supported")

assert isinstance(X_zero_point, torch.Tensor)
if X_zero_point.shape != X.shape:
raise ValueError(
f"X_zero_point shape must be {X.shape}. Got {X_zero_point.shape}"
)

_out_multiplier = int(out_multiplier.item())
_out_shift = int(out_shift.item())

return quantized_relu_common(
X,
X_zero_point,
out_zero_point,
_out_multiplier,
_out_shift,
)

return variant

return decorator


@impl(m, "quantized_relu")
@quantized_relu_variant(False)
def quantized_relu() -> torch.Tensor: ...


@impl(m, "quantized_relu.per_tensor")
@quantized_relu_variant(True)
def quantized_relu_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_relu_asym8s_asym8s.per_tensor")
@quantized_relu_variant(True, torch.int8)
def quantized_relu_asym8s_asym8s_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_relu_asym8u_asym8u.per_tensor")
@quantized_relu_variant(True, torch.uint8)
def quantized_relu_asym8u_asym8u_per_tensor() -> torch.Tensor: ...


@impl(m, "requantize")
def requantize(
input: torch.Tensor,
Expand Down
159 changes: 105 additions & 54 deletions backends/cadence/aot/tests/test_ref_implementations.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -884,73 +884,124 @@ def test_quantized_conv_per_tensor(
@expand(
[
# Test case 1: Basic int8 case with negative scale
(
"basic_int8",
torch.tensor([-1, 0, 1, 3], dtype=torch.int8), # input
torch.tensor([0], dtype=torch.int8), # X_zero_point (scalar broadcast)
0, # out_zero_point
torch.tensor([1073741824]), # out_multiplier (0.5 * 2^31)
torch.tensor([0]), # out_shift
torch.int8, # dtype
torch.tensor(
[0, 0, 0, -2], dtype=torch.int8
), # expected: relu(-1,0,1,3) = (0,0,1,3) * (-0.5) + 0 = (0,0,-0.5,-1.5) -> (0,0,0,-2)
),
*[
(
"basic_int8",
torch.tensor([-1, 0, 1, 3], dtype=dtype), # input
0, # X_zero_point (scalar broadcast)
0, # out_zero_point
1073741824, # out_multiplier (0.5 * 2^31)
0, # out_shift
dtype, # dtype
torch.tensor(
[0, 0, 0, -2], dtype=dtype
), # expected: relu(-1,0,1,3) = (0,0,1,3) * (-0.5) + 0 = (0,0,-0.5,-1.5) -> (0,0,0,-2)
)
for dtype in [torch.int8]
],
# Test case 2: uint8 with non-zero zero point
(
"uint8_with_zp",
torch.tensor([126, 128, 130, 132], dtype=torch.uint8), # input
torch.tensor([128], dtype=torch.uint8), # X_zero_point
64, # out_zero_point
torch.tensor([536870912]), # out_multiplier (0.25 * 2^31)
torch.tensor([0]), # out_shift
torch.uint8, # dtype
torch.tensor(
[64, 64, 64, 63], dtype=torch.uint8
), # expected: relu(-2,0,2,4) = (0,0,2,4) * (-0.25) + 64 = (64,64,63.5,63) -> (64,64,64,63)
),
*[
(
"uint8_with_zp",
torch.tensor([126, 128, 130, 132], dtype=dtype), # input
128, # X_zero_point
64, # out_zero_point
536870912, # out_multiplier (0.25 * 2^31)
0, # out_shift
dtype, # dtype
torch.tensor(
[64, 64, 64, 63], dtype=dtype
), # expected: relu(-2,0,2,4) = (0,0,2,4) * (-0.25) + 64 = (64,64,63.5,63) -> (64,64,64,63)
)
for dtype in [torch.uint8]
],
# Test case 3: All negative values (should all become zero after ReLU)
(
"all_negative_int8",
torch.tensor([-5, -3, -1], dtype=torch.int8), # input
torch.tensor([0], dtype=torch.int8), # X_zero_point
10, # out_zero_point
torch.tensor([1073741824]), # out_multiplier (0.5 * 2^31)
torch.tensor([0]), # out_shift
torch.int8, # dtype
torch.tensor(
[10, 10, 10], dtype=torch.int8
), # expected: relu(-5,-3,-1) = (0,0,0) * (-0.5) + 10 = (10,10,10)
),
*[
(
"all_negative_int8",
torch.tensor([-5, -3, -1], dtype=dtype), # input
0, # X_zero_point
10, # out_zero_point
1073741824, # out_multiplier (0.5 * 2^31)
0, # out_shift
dtype, # dtype
torch.tensor(
[10, 10, 10], dtype=dtype
), # expected: relu(-5,-3,-1) = (0,0,0) * (-0.5) + 10 = (10,10,10)
)
for dtype in [torch.int8]
],
# Test case 4: All positive values with shift (scale becomes -0.25)
(
"positive_with_shift",
torch.tensor([2, 4, 6, 8], dtype=torch.int8), # input
torch.tensor([1], dtype=torch.int8), # X_zero_point
5, # out_zero_point
torch.tensor([1073741824]), # out_multiplier (0.5 * 2^31)
torch.tensor([1]), # out_shift (multiply by 2^1 = 2)
torch.int8, # dtype
torch.tensor(
[4, 2, 0, -2], dtype=torch.int8
), # expected: relu(1,3,5,7) = (1,3,5,7) * (-1.0) + 5 = (4,2,0,-2)
),
*[
(
"positive_with_shift",
torch.tensor([2, 4, 6, 8], dtype=dtype), # input
1, # X_zero_point
5, # out_zero_point
1073741824, # out_multiplier (0.5 * 2^31)
1, # out_shift (multiply by 2^1 = 2)
dtype, # dtype
torch.tensor(
[4, 2, 0, -2], dtype=dtype
), # expected: relu(1,3,5,7) = (1,3,5,7) * (-1.0) + 5 = (4,2,0,-2)
)
for dtype in [torch.int8, torch.uint8]
],
# Test case 4: Non-per-tensor
*[
(
"non_per_tensor",
torch.tensor([-1, -2, -3, 1, 2, 3], dtype=dtype), # input
torch.tensor([0, 0, 0, 1, 1, 1]), # X_zero_point
5, # out_zero_point
torch.tensor([1073741824]), # out_multiplier (0.5 * 2^31)
torch.tensor([1]), # out_shift (multiply by 2^1 = 2)
dtype, # dtype
torch.tensor([5, 5, 5, 5, 4, 3], dtype=dtype),
)
for dtype in [torch.int8]
],
]
)
def test_quantized_relu(
self,
name: str,
X: torch.Tensor,
X_zero_point: torch.Tensor,
X_zero_point: torch.Tensor | int,
out_zero_point: int,
out_multiplier: torch.Tensor,
out_shift: torch.Tensor,
out_multiplier: torch.Tensor | int,
out_shift: torch.Tensor | int,
dtype: torch.dtype,
expected_output: torch.Tensor,
) -> None:
output = torch.ops.cadence.quantized_relu(
X, X_zero_point, out_zero_point, out_multiplier, out_shift
)

if isinstance(X_zero_point, int):
assert isinstance(out_multiplier, int)
assert isinstance(out_shift, int)

match dtype:
case torch.int8:
quantized_relu = (
torch.ops.cadence.quantized_relu_asym8s_asym8s.per_tensor
)
case torch.uint8:
quantized_relu = (
torch.ops.cadence.quantized_relu_asym8u_asym8u.per_tensor
)
case _:
quantized_relu = torch.ops.cadence.quantized_relu_per_tensor

output = quantized_relu(
X,
X_zero_point,
out_zero_point,
out_multiplier,
out_shift,
)
else:
output = torch.ops.cadence.quantized_relu(
X, X_zero_point, out_zero_point, out_multiplier, out_shift
)

# Verify output properties
self.assertEqual(output.dtype, dtype, f"Output dtype should be {dtype}")
Expand Down
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