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Rename conv -> conv2d, conv1d_nchw -> conv1d_ncl, conv1d_nhwc -> conv1d_nlc #14310

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Merged
merged 1 commit into from
Sep 17, 2025
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Rename conv -> conv2d, conv1d_nchw -> conv1d_ncl, conv1d_nhwc -> conv1d_nlc #14310

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4 changes: 2 additions & 2 deletions backends/cadence/aot/TARGETS
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Original file line number Diff line number Diff line change
Expand Up @@ -153,8 +153,8 @@ executorch_generated_lib(
"//executorch/backends/cadence/generic/operators:dequantize_per_tensor",
"//executorch/backends/cadence/generic/operators:quantize_per_tensor",
"//executorch/backends/cadence/generic/operators:quantized_add_out",
"//executorch/backends/cadence/generic/operators:quantized_conv_nchw_out",
"//executorch/backends/cadence/generic/operators:quantized_conv_nhwc_out",
"//executorch/backends/cadence/generic/operators:quantized_conv2d_nchw_out",
"//executorch/backends/cadence/generic/operators:quantized_conv2d_nhwc_out",
"//executorch/backends/cadence/generic/operators:quantized_fully_connected_out",
"//executorch/backends/cadence/generic/operators:quantized_layer_norm",
"//executorch/backends/cadence/generic/operators:quantized_linear_out",
Expand Down
80 changes: 40 additions & 40 deletions backends/cadence/aot/functions.yaml
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80 changes: 40 additions & 40 deletions backends/cadence/aot/functions_hifi.yaml
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168 changes: 88 additions & 80 deletions backends/cadence/aot/ops_registrations.py
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6 changes: 3 additions & 3 deletions backends/cadence/aot/quantizer/patterns.py
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Original file line number Diff line number Diff line change
Expand Up @@ -247,7 +247,7 @@ def get_anchors(
)

def replacement_op(self) -> OpOverload:
return torch.ops.cadence.quantized_conv_nchw.default
return torch.ops.cadence.quantized_conv2d_nchw.default


class Conv2dPattern(QuantizationPattern):
Expand Down Expand Up @@ -286,7 +286,7 @@ def get_anchors(
)

def replacement_op(self) -> OpOverload:
return torch.ops.cadence.quantized_conv_nchw.default
return torch.ops.cadence.quantized_conv2d_nchw.default


class LayerNormPattern(QuantizationPattern):
Expand Down Expand Up @@ -460,7 +460,7 @@ def get_anchors(
)

def replacement_op(self) -> OpOverload:
return torch.ops.cadence.quantized_conv_nchw.default
return torch.ops.cadence.quantized_conv2d_nchw.default


# Conv1d + regular relu op fusion
Expand Down
84 changes: 46 additions & 38 deletions backends/cadence/aot/ref_implementations.py
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Expand Up @@ -623,8 +623,8 @@ def quantized_conv_per_tensor(
)


@impl(m, "quantized_conv_nchw.per_tensor")
def quantized_conv_nchw_per_tensor(
@impl(m, "quantized_conv2d_nchw.per_tensor")
def quantized_conv2d_nchw_per_tensor(
input_tensor: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
Expand Down Expand Up @@ -679,8 +679,8 @@ def quantized_conv_nchw_per_tensor(
)


@impl(m, "quantized_conv_nhwc.per_tensor")
def quantized_conv_nhwc_per_tensor(
@impl(m, "quantized_conv2d_nhwc.per_tensor")
def quantized_conv2d_nhwc_per_tensor(
input_tensor: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor,
Expand Down Expand Up @@ -800,7 +800,7 @@ def variant(
# Call the appropriate base function
match layout:
case "nchw":
return quantized_conv_nchw_per_tensor(
return quantized_conv2d_nchw_per_tensor(
input_tensor,
weight,
bias,
Expand All @@ -817,7 +817,7 @@ def variant(
out_shift,
)
case "nhwc":
return quantized_conv_nhwc_per_tensor(
return quantized_conv2d_nhwc_per_tensor(
input_tensor,
weight,
bias,
Expand All @@ -841,84 +841,92 @@ def variant(
return decorator


@impl(m, "quantized_conv_nchw_asym8sxsym8s_asym8s.per_tensor")
@impl(m, "quantized_conv2d_nchw_asym8sxsym8s_asym8s.per_tensor")
@quantized_conv_variant("nchw", torch.int8, torch.int8)
def quantized_conv_nchw_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nchw_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_conv_nchw_asym8uxsym8u_asym8u.per_tensor")
@impl(m, "quantized_conv2d_nchw_asym8uxsym8u_asym8u.per_tensor")
@quantized_conv_variant("nchw", torch.uint8, torch.uint8)
def quantized_conv_nchw_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nchw_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_conv_nhwc_asym8sxsym8s_asym8s.per_tensor")
@impl(m, "quantized_conv2d_nhwc_asym8sxsym8s_asym8s.per_tensor")
@quantized_conv_variant("nhwc", torch.int8, torch.int8)
def quantized_conv_nhwc_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nhwc_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_conv_nhwc_asym8uxsym8u_asym8u.per_tensor")
@impl(m, "quantized_conv2d_nhwc_asym8uxsym8u_asym8u.per_tensor")
@quantized_conv_variant("nhwc", torch.uint8, torch.uint8)
def quantized_conv_nhwc_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nhwc_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_conv_nchw_dilated_asym8sxsym8s_asym8s.per_tensor")
@impl(m, "quantized_conv2d_nchw_dilated_asym8sxsym8s_asym8s.per_tensor")
@quantized_conv_variant("nchw", torch.int8, torch.int8)
def quantized_conv_nchw_dilated_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nchw_dilated_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_conv_nchw_dilated_asym8uxsym8u_asym8u.per_tensor")
@impl(m, "quantized_conv2d_nchw_dilated_asym8uxsym8u_asym8u.per_tensor")
@quantized_conv_variant("nchw", torch.uint8, torch.uint8)
def quantized_conv_nchw_dilated_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nchw_dilated_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_conv_nhwc_dilated_asym8sxsym8s_asym8s.per_tensor")
@impl(m, "quantized_conv2d_nhwc_dilated_asym8sxsym8s_asym8s.per_tensor")
@quantized_conv_variant("nhwc", torch.int8, torch.int8)
def quantized_conv_nhwc_dilated_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nhwc_dilated_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_conv_nhwc_dilated_asym8uxsym8u_asym8u.per_tensor")
@impl(m, "quantized_conv2d_nhwc_dilated_asym8uxsym8u_asym8u.per_tensor")
@quantized_conv_variant("nhwc", torch.uint8, torch.uint8)
def quantized_conv_nhwc_dilated_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nhwc_dilated_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_conv_nchw_depthwise_asym8sxsym8s_asym8s.per_tensor")
@impl(m, "quantized_conv2d_nchw_depthwise_asym8sxsym8s_asym8s.per_tensor")
@quantized_conv_variant("nchw", torch.int8, torch.int8)
def quantized_conv_nchw_depthwise_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nchw_depthwise_asym8sxsym8s_asym8s_per_tensor() -> (
torch.Tensor
): ...


@impl(m, "quantized_conv_nchw_depthwise_asym8uxsym8u_asym8u.per_tensor")
@impl(m, "quantized_conv2d_nchw_depthwise_asym8uxsym8u_asym8u.per_tensor")
@quantized_conv_variant("nchw", torch.uint8, torch.uint8)
def quantized_conv_nchw_depthwise_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nchw_depthwise_asym8uxsym8u_asym8u_per_tensor() -> (
torch.Tensor
): ...


@impl(m, "quantized_conv_nhwc_depthwise_asym8sxsym8s_asym8s.per_tensor")
@impl(m, "quantized_conv2d_nhwc_depthwise_asym8sxsym8s_asym8s.per_tensor")
@quantized_conv_variant("nhwc", torch.int8, torch.int8)
def quantized_conv_nhwc_depthwise_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nhwc_depthwise_asym8sxsym8s_asym8s_per_tensor() -> (
torch.Tensor
): ...


@impl(m, "quantized_conv_nhwc_depthwise_asym8uxsym8u_asym8u.per_tensor")
@impl(m, "quantized_conv2d_nhwc_depthwise_asym8uxsym8u_asym8u.per_tensor")
@quantized_conv_variant("nhwc", torch.uint8, torch.uint8)
def quantized_conv_nhwc_depthwise_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...
def quantized_conv2d_nhwc_depthwise_asym8uxsym8u_asym8u_per_tensor() -> (
torch.Tensor
): ...


@impl(m, "quantized_conv1d_nchw_asym8sxsym8s_asym8s.per_tensor")
@impl(m, "quantized_conv1d_ncl_asym8sxsym8s_asym8s.per_tensor")
@quantized_conv_variant("nchw", torch.int8, torch.int8, is_1d=True)
def quantized_conv1d_nchw_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...
def quantized_conv1d_ncl_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_conv1d_nchw_asym8uxsym8u_asym8u.per_tensor")
@impl(m, "quantized_conv1d_ncl_asym8uxsym8u_asym8u.per_tensor")
@quantized_conv_variant("nchw", torch.uint8, torch.uint8, is_1d=True)
def quantized_conv1d_nchw_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...
def quantized_conv1d_ncl_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_conv1d_nhwc_asym8sxsym8s_asym8s.per_tensor")
@impl(m, "quantized_conv1d_nlc_asym8sxsym8s_asym8s.per_tensor")
@quantized_conv_variant("nhwc", torch.int8, torch.int8, is_1d=True)
def quantized_conv1d_nhwc_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...
def quantized_conv1d_nlc_asym8sxsym8s_asym8s_per_tensor() -> torch.Tensor: ...


@impl(m, "quantized_conv1d_nhwc_asym8uxsym8u_asym8u.per_tensor")
@impl(m, "quantized_conv1d_nlc_asym8uxsym8u_asym8u.per_tensor")
@quantized_conv_variant("nhwc", torch.uint8, torch.uint8, is_1d=True)
def quantized_conv1d_nhwc_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...
def quantized_conv1d_nlc_asym8uxsym8u_asym8u_per_tensor() -> torch.Tensor: ...


def quantized_relu_common(
Expand Down
32 changes: 16 additions & 16 deletions backends/cadence/aot/replace_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -787,8 +787,8 @@ class ReplaceTrivialConvWithLinear(ExportPass):

trivial_conv_op_to_linear_op: Dict[EdgeOpOverload, EdgeOpOverload] = {
exir_ops.edge.cadence.convolution.default: exir_ops.edge.aten.linear.default,
exir_ops.edge.cadence.quantized_conv_nchw.default: exir_ops.edge.cadence.quantized_linear.default,
exir_ops.edge.cadence.quantized_conv_nhwc.default: exir_ops.edge.cadence.quantized_linear.default,
exir_ops.edge.cadence.quantized_conv2d_nchw.default: exir_ops.edge.cadence.quantized_linear.default,
exir_ops.edge.cadence.quantized_conv2d_nhwc.default: exir_ops.edge.cadence.quantized_linear.default,
}

def call_operator(self, op, args, kwargs, meta):
Expand All @@ -800,8 +800,8 @@ def call_operator(self, op, args, kwargs, meta):
# extra args holding at least the zero point and scale of input, weight, bias,
# and output tensor.
quantized_op = (
op == exir_ops.edge.cadence.quantized_conv_nchw.default
or op == exir_ops.edge.cadence.quantized_conv_nhwc.default
op == exir_ops.edge.cadence.quantized_conv2d_nchw.default
or op == exir_ops.edge.cadence.quantized_conv2d_nhwc.default
)
assert (len(args) == 8 and not quantized_op) or (
len(args) >= 12 and quantized_op
Expand Down Expand Up @@ -979,18 +979,18 @@ def call_operator(
) -> ProxyValue:
if op not in {
exir_ops.edge.cadence.convolution.default,
exir_ops.edge.cadence.quantized_conv_nchw.default,
exir_ops.edge.cadence.quantized_conv2d_nchw.default,
}:
return super().call_operator(op, args, kwargs, meta)

quantized_op = op == exir_ops.edge.cadence.quantized_conv_nchw.default
quantized_op = op == exir_ops.edge.cadence.quantized_conv2d_nchw.default

if not quantized_op and len(args) == 8 and args[-1] is True:
# Already in NHWC layout.
return super().call_operator(op, args, kwargs, meta)

new_op = (
exir_ops.edge.cadence.quantized_conv_nhwc.default
exir_ops.edge.cadence.quantized_conv2d_nhwc.default
if quantized_op
else exir_ops.edge.cadence.convolution.default
)
Expand Down Expand Up @@ -1067,8 +1067,8 @@ class ReplaceConvWithIm2RowAndLinear(ExportPass):
# decompose to.
conv_op_to_linear_op: Dict[EdgeOpOverload, EdgeOpOverload] = {
exir_ops.edge.cadence.convolution.default: exir_ops.edge.aten.linear.default,
exir_ops.edge.cadence.quantized_conv_nchw.default: exir_ops.edge.cadence.quantized_linear.default,
exir_ops.edge.cadence.quantized_conv_nhwc.default: exir_ops.edge.cadence.quantized_linear.default,
exir_ops.edge.cadence.quantized_conv2d_nchw.default: exir_ops.edge.cadence.quantized_linear.default,
exir_ops.edge.cadence.quantized_conv2d_nhwc.default: exir_ops.edge.cadence.quantized_linear.default,
}

def call_operator(self, op, args, kwargs, meta):
Expand All @@ -1077,8 +1077,8 @@ def call_operator(self, op, args, kwargs, meta):

# Get the relevant args from convolution node.
quantized_op = (
op == exir_ops.edge.cadence.quantized_conv_nchw.default
or op == exir_ops.edge.cadence.quantized_conv_nhwc.default
op == exir_ops.edge.cadence.quantized_conv2d_nchw.default
or op == exir_ops.edge.cadence.quantized_conv2d_nhwc.default
)
assert (len(args) == 8 and not quantized_op) or (
len(args) >= 12 and quantized_op
Expand Down Expand Up @@ -1110,7 +1110,7 @@ def call_operator(self, op, args, kwargs, meta):
# channel_last layout is specified by the channel_last arg of conv
# op, which is either the last argument (15th) or implicitely False
# if the op is quantized, or the last argument if not.
channel_last = op == exir_ops.edge.cadence.quantized_conv_nhwc.default
channel_last = op == exir_ops.edge.cadence.quantized_conv2d_nhwc.default
# The weight tensor is [out_channels, in_channels, X] for NCHW layout,
# and [out_channels, X, in_channels] for NHWC layout. Here, X is the
# kernel_width for conv1d, and X = kernel_height * kernel_width for
Expand Down Expand Up @@ -1622,12 +1622,12 @@ class ReplaceSingleElementTensorArgumentsFromFullOpWithScalarPass(ExportPass):
exir_ops.edge.cadence.quantized_add.per_tensor,
[1, 2, 4, 5],
),
exir_ops.edge.cadence.quantized_conv_nchw: (
exir_ops.edge.cadence.quantized_conv_nchw.per_tensor,
exir_ops.edge.cadence.quantized_conv2d_nchw: (
exir_ops.edge.cadence.quantized_conv2d_nchw.per_tensor,
[8, 9, 12, 13],
),
exir_ops.edge.cadence.quantized_conv_nhwc: (
exir_ops.edge.cadence.quantized_conv_nhwc.per_tensor,
exir_ops.edge.cadence.quantized_conv2d_nhwc: (
exir_ops.edge.cadence.quantized_conv2d_nhwc.per_tensor,
[8, 9, 12, 13],
),
exir_ops.edge.cadence.quantized_fully_connected: (
Expand Down
28 changes: 14 additions & 14 deletions backends/cadence/aot/tests/test_ref_implementations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -906,40 +906,40 @@ def test_quantized_conv_per_tensor(

convs = [
(
torch.ops.cadence.quantized_conv_nchw.per_tensor
torch.ops.cadence.quantized_conv2d_nchw.per_tensor
if memory_format == torch.contiguous_format
else torch.ops.cadence.quantized_conv_nhwc.per_tensor
else torch.ops.cadence.quantized_conv2d_nhwc.per_tensor
)
]

optimized_convs = []
if input_tensor.dtype == torch.int8 and weight.dtype == torch.int8:
if memory_format == torch.contiguous_format:
optimized_convs = [
torch.ops.cadence.quantized_conv_nchw_asym8sxsym8s_asym8s.per_tensor,
torch.ops.cadence.quantized_conv_nchw_dilated_asym8sxsym8s_asym8s.per_tensor,
torch.ops.cadence.quantized_conv_nchw_depthwise_asym8sxsym8s_asym8s.per_tensor,
torch.ops.cadence.quantized_conv2d_nchw_asym8sxsym8s_asym8s.per_tensor,
torch.ops.cadence.quantized_conv2d_nchw_dilated_asym8sxsym8s_asym8s.per_tensor,
torch.ops.cadence.quantized_conv2d_nchw_depthwise_asym8sxsym8s_asym8s.per_tensor,
]

else:
optimized_convs = [
torch.ops.cadence.quantized_conv_nhwc_asym8sxsym8s_asym8s.per_tensor,
torch.ops.cadence.quantized_conv_nhwc_dilated_asym8sxsym8s_asym8s.per_tensor,
torch.ops.cadence.quantized_conv_nhwc_depthwise_asym8sxsym8s_asym8s.per_tensor,
torch.ops.cadence.quantized_conv2d_nhwc_asym8sxsym8s_asym8s.per_tensor,
torch.ops.cadence.quantized_conv2d_nhwc_dilated_asym8sxsym8s_asym8s.per_tensor,
torch.ops.cadence.quantized_conv2d_nhwc_depthwise_asym8sxsym8s_asym8s.per_tensor,
]
elif input_tensor.dtype == torch.uint8 and weight.dtype == torch.uint8:
if memory_format == torch.contiguous_format:
optimized_convs = [
torch.ops.cadence.quantized_conv_nchw_asym8uxsym8u_asym8u.per_tensor,
torch.ops.cadence.quantized_conv_nchw_dilated_asym8uxsym8u_asym8u.per_tensor,
torch.ops.cadence.quantized_conv_nchw_depthwise_asym8uxsym8u_asym8u.per_tensor,
torch.ops.cadence.quantized_conv2d_nchw_asym8uxsym8u_asym8u.per_tensor,
torch.ops.cadence.quantized_conv2d_nchw_dilated_asym8uxsym8u_asym8u.per_tensor,
torch.ops.cadence.quantized_conv2d_nchw_depthwise_asym8uxsym8u_asym8u.per_tensor,
]

else:
optimized_convs = [
torch.ops.cadence.quantized_conv_nhwc_asym8uxsym8u_asym8u.per_tensor,
torch.ops.cadence.quantized_conv_nhwc_dilated_asym8uxsym8u_asym8u.per_tensor,
torch.ops.cadence.quantized_conv_nhwc_depthwise_asym8uxsym8u_asym8u.per_tensor,
torch.ops.cadence.quantized_conv2d_nhwc_asym8uxsym8u_asym8u.per_tensor,
torch.ops.cadence.quantized_conv2d_nhwc_dilated_asym8uxsym8u_asym8u.per_tensor,
torch.ops.cadence.quantized_conv2d_nhwc_depthwise_asym8uxsym8u_asym8u.per_tensor,
]

convs.extend(optimized_convs)
Expand Down
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