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Merged
facebook-github-bot merged 1 commit into from
Sep 9, 2025
Merged

Remove quantized_conv and leave just per tensor variants #13958

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68 changes: 31 additions & 37 deletions backends/cadence/aot/ref_implementations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -296,7 +296,7 @@ def quantized_layer_norm_per_tensor(
)


def quantized_conv(
def quantized_conv_per_tensor(
input_tensor: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
Expand All @@ -305,12 +305,12 @@ def quantized_conv(
dilation: tuple[int, int],
groups: int,
in_zero_point: int,
weight_zero_point: torch.Tensor,
bias_scale: torch.Tensor,
weight_zero_point: int,
bias_scale: float,
output_scale: float,
output_zero_point: int,
out_multiplier: torch.Tensor,
out_shift: torch.Tensor,
out_multiplier: int,
out_shift: int,
) -> torch.Tensor:
"""
Quantized convolution operation.
Expand All @@ -324,19 +324,13 @@ def quantized_conv(
- dilation (Tuple[int]): The dilation of the convolution
- groups (int): The number of groups
- in_zero_point (int): The quantized mapping of zero for the input
- weight_zero_point (Tensor): The quantized mapping of zero for the weight
- bias_scale (Tensor): The quantized bias scale
- weight_zero_point (int): The quantized mapping of zero for the weight
- bias_scale (float): The quantized bias scale
- output_scale (float): The scale of the output
- output_zero_point (int): The zero point of the output
- out_multiplier (Tensor): Unused
- out_shift (Tensor): Unused
- out_multiplier (int): Unused
- out_shift (int): Unused
"""
if weight_zero_point.view(-1).shape != (1,):
raise ValueError("Weight zero point must be a scalar")

if bias_scale.view(-1).shape != (1,):
raise ValueError("Bias scale must be a scalar")

if len(input_tensor.shape) == 3:
float_out = torch.nn.functional.conv1d(
(input_tensor - in_zero_point).float(),
Expand Down Expand Up @@ -371,8 +365,8 @@ def quantized_conv(
)


@impl(m, "quantized_conv_nchw")
def quantized_conv_nchw(
@impl(m, "quantized_conv_nchw_per_tensor")
def quantized_conv_nchw_per_tensor(
input_tensor: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
Expand All @@ -381,12 +375,12 @@ def quantized_conv_nchw(
dilation: tuple[int, int],
groups: int,
in_zero_point: int,
weight_zero_point: torch.Tensor,
bias_scale: torch.Tensor,
weight_zero_point: int,
bias_scale: float,
output_scale: float,
output_zero_point: int,
out_multiplier: torch.Tensor,
out_shift: torch.Tensor,
out_multiplier: int,
out_shift: int,
) -> torch.Tensor:
"""
Quantized convolution operation.
Expand All @@ -400,16 +394,16 @@ def quantized_conv_nchw(
- dilation (Tuple[int]): The dilation of the convolution
- groups (int): The number of groups
- in_zero_point (int): The quantized mapping of zero for the input
- weight_zero_point (Tensor): The quantized mapping of zero for the weight
- bias_scale (Tensor): The quantized bias scale
- weight_zero_point (int): The quantized mapping of zero for the weight
- bias_scale (float): The quantized bias scale
- output_scale (float): The scale of the output
- output_zero_point (int): The zero point of the output
- out_multiplier (Tensor): Unused
- out_shift (Tensor): Unused
- out_multiplier (int): Unused
- out_shift (int): Unused
"""
if not input_tensor.is_contiguous(memory_format=torch.contiguous_format):
raise ValueError("Input tensor must be in NCHW format")
return quantized_conv(
return quantized_conv_per_tensor(
input_tensor,
weight,
bias,
Expand All @@ -427,8 +421,8 @@ def quantized_conv_nchw(
)


@impl(m, "quantized_conv_nhwc")
def quantized_conv_nhwc(
@impl(m, "quantized_conv_nhwc_per_tensor")
def quantized_conv_nhwc_per_tensor(
input_tensor: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
Expand All @@ -437,12 +431,12 @@ def quantized_conv_nhwc(
dilation: tuple[int, int],
groups: int,
in_zero_point: int,
weight_zero_point: torch.Tensor,
bias_scale: torch.Tensor,
weight_zero_point: int,
bias_scale: float,
output_scale: float,
output_zero_point: int,
out_multiplier: torch.Tensor,
out_shift: torch.Tensor,
out_multiplier: int,
out_shift: int,
) -> torch.Tensor:
"""
Quantized convolution operation.
Expand All @@ -456,18 +450,18 @@ def quantized_conv_nhwc(
- dilation (Tuple[int]): The dilation of the convolution
- groups (int): The number of groups
- in_zero_point (int): The quantized mapping of zero for the input
- weight_zero_point (Tensor): The quantized mapping of zero for the weight
- bias_scale (Tensor): The quantized bias scale
- weight_zero_point (int): The quantized mapping of zero for the weight
- bias_scale (float): The quantized bias scale
- output_scale (float): The scale of the output
- output_zero_point (int): The zero point of the output
- out_multiplier (Tensor): Unused
- out_shift (Tensor): Unused
- out_multiplier (int): Unused
- out_shift (int): Unused
"""

if not input_tensor.is_contiguous(memory_format=torch.channels_last):
raise ValueError("Input tensor must be in NHWC format")

return quantized_conv(
return quantized_conv_per_tensor(
input_tensor,
weight,
bias,
Expand Down
62 changes: 27 additions & 35 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 @@ -15,8 +15,8 @@
dequantize_per_tensor,
quantize_per_tensor,
quantized_add,
quantized_conv_nchw,
quantized_conv_nhwc,
quantized_conv_nchw_per_tensor,
quantized_conv_nhwc_per_tensor,
quantized_layer_norm_per_tensor,
quantized_linear,
quantized_relu,
Expand Down Expand Up @@ -356,8 +356,8 @@ def test_quantized_layer_norm_per_tensor(
(1, 1), # dilation
1, # groups
0, # in_zero_point
torch.tensor([0], dtype=torch.int8), # weight_zero_point
torch.tensor([1.0], dtype=torch.float32), # bias_scale
0, # weight_zero_point
1.0, # bias_scale
0.1, # output_scale
0, # output_zero_point
torch.tensor(
Expand Down Expand Up @@ -387,8 +387,8 @@ def test_quantized_layer_norm_per_tensor(
(1, 1), # dilation
1, # groups
0, # in_zero_point
torch.tensor([0], dtype=torch.int8), # weight_zero_point
torch.tensor([1.0], dtype=torch.float32), # bias_scale
0, # weight_zero_point
1.0, # bias_scale
0.25, # output_scale
0, # output_zero_point
typing.cast(None, torch.Tensor),
Expand Down Expand Up @@ -416,8 +416,8 @@ def test_quantized_layer_norm_per_tensor(
(1, 1), # dilation
1, # groups
128, # in_zero_point
torch.tensor([128], dtype=torch.uint8), # weight_zero_point
torch.tensor([0.1], dtype=torch.float32), # bias_scale
128, # weight_zero_point
0.1, # bias_scale
0.1, # output_scale
128, # output_zero_point
typing.cast(None, torch.Tensor),
Expand Down Expand Up @@ -447,8 +447,8 @@ def test_quantized_layer_norm_per_tensor(
(1, 1), # dilation (padding for 2D, actual dilation is dilation[1])
1, # groups
0, # in_zero_point
torch.tensor([0], dtype=torch.int8), # weight_zero_point
torch.tensor([1.0], dtype=torch.float32), # bias_scale
0, # weight_zero_point
1.0, # bias_scale
0.5, # output_scale
0, # output_zero_point
typing.cast(None, torch.Tensor),
Expand Down Expand Up @@ -482,8 +482,8 @@ def test_quantized_layer_norm_per_tensor(
(1, 1), # dilation
1, # groups
0, # in_zero_point
torch.tensor([0], dtype=torch.int8), # weight_zero_point
torch.tensor([1.0], dtype=torch.float32), # bias_scale
0, # weight_zero_point
1.0, # bias_scale
0.2, # output_scale
0, # output_zero_point
typing.cast(None, torch.Tensor),
Expand Down Expand Up @@ -523,8 +523,8 @@ def test_quantized_layer_norm_per_tensor(
(1, 1), # dilation
1, # groups
0, # in_zero_point
torch.tensor([0], dtype=torch.int16), # weight_zero_point
torch.tensor([1.0], dtype=torch.float32), # bias_scale
0, # weight_zero_point
1.0, # bias_scale
0.1, # output_scale
0, # output_zero_point
typing.cast(None, torch.Tensor),
Expand Down Expand Up @@ -576,12 +576,8 @@ def test_quantized_layer_norm_per_tensor(
(1, 1), # dilation
1, # groups
0, # in_zero_point
torch.tensor(
[0], dtype=torch.int16
), # weight_zero_point for each output channel
torch.tensor(
[1.0], dtype=torch.float32
), # bias_scale for each channel
0, # weight_zero_point
1.0, # bias_scale
0.05, # output_scale
0, # output_zero_point
typing.cast(None, torch.Tensor),
Expand Down Expand Up @@ -623,12 +619,8 @@ def test_quantized_layer_norm_per_tensor(
(1, 1), # dilation
2, # groups (grouped convolution)
0, # in_zero_point
torch.tensor(
[0], dtype=torch.int8
), # weight_zero_point for each output channel
torch.tensor(
[1.0], dtype=torch.float32
), # bias_scale for each channel
0, # weight_zero_point
1.0, # bias_scale
0.2, # output_scale
0, # output_zero_point
typing.cast(None, torch.Tensor),
Expand Down Expand Up @@ -666,8 +658,8 @@ def test_quantized_layer_norm_per_tensor(
(1, 1), # dilation
1, # groups
0, # in_zero_point
torch.tensor([0], dtype=torch.int8), # weight_zero_point
torch.tensor([1.0], dtype=torch.float32), # bias_scale
0, # weight_zero_point
1.0, # bias_scale
0.5, # output_scale
0, # output_zero_point
typing.cast(None, torch.Tensor),
Expand All @@ -682,7 +674,7 @@ def test_quantized_layer_norm_per_tensor(
],
]
)
def test_quantized_conv(
def test_quantized_conv_per_tensor(
self,
input_tensor: torch.Tensor,
weight: torch.Tensor,
Expand All @@ -692,12 +684,12 @@ def test_quantized_conv(
dilation: tuple[int, int],
groups: int,
in_zero_point: int,
weight_zero_point: torch.Tensor,
bias_scale: torch.Tensor,
weight_zero_point: int,
bias_scale: float,
output_scale: float,
output_zero_point: int,
out_multiplier: torch.Tensor,
out_shift: torch.Tensor,
out_multiplier: int,
out_shift: int,
dtype: torch.dtype,
expected_output: torch.Tensor,
memory_format: torch.memory_format,
Expand All @@ -710,9 +702,9 @@ def test_quantized_conv(
input_tensor = input_tensor.to(memory_format=memory_format)

conv = (
quantized_conv_nchw
quantized_conv_nchw_per_tensor
if memory_format == torch.contiguous_format
else quantized_conv_nhwc
else quantized_conv_nhwc_per_tensor
)

output = conv(
Expand Down
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