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Merged
meta-codesync merged 1 commit into from
Feb 14, 2026
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Migrate quantized_conv2d tests to graph builder #17451

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310 changes: 266 additions & 44 deletions backends/cadence/hifi/operators/op_quantized_conv2d_nchw_out.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -175,6 +175,45 @@ void xa_opt_quantized_conv2d_nchw(
bool conv1d = input.dim() == 3;
constexpr int kNnlibMaxDim = 4;

WORD32 input_height = conv1d ? 1 : input.size(2);
WORD32 input_width = conv1d ? input.size(2) : input.size(3);
WORD32 input_channels = input.size(1);
WORD32 kernel_height = conv1d ? 1 : weight.size(2);
WORD32 kernel_width = conv1d ? weight.size(2) : weight.size(3);
WORD32 kernel_channels = weight.size(1);
WORD32 out_channels = weight.size(0);
WORD32 out_height = conv1d ? 1 : out.size(2);
WORD32 out_width = conv1d ? out.size(2) : out.size(3);
WORD32 batches = input.size(0);

WORD32 x_stride = stride[1];
WORD32 y_stride = stride[0];
WORD32 x_padding = padding[1];
WORD32 y_padding = padding[0];
WORD32 dilation_width = dilation[1];
WORD32 dilation_height = dilation[0];

WORD32 input_zero_bias = -in_zero_point;
WORD32 kernel_zero_bias = -weight_zero_point;

WORD32 out_multiplier32[out_channels];
WORD32 out_shift32[out_channels];

float out_scale = 1. / output_scale;

for (int i = 0; i < out_channels; i++) {
out_multiplier32[i] = bias_scale * out_scale * 2147483648;
out_shift32[i] = 0;
}

WORD32 out_zero_bias = output_zero_point;
WORD32 inp_precision = 8;
WORD32 kernel_precision = 8;
pVOID p_scratch = nullptr;
WORD32* ptr_scratch;

WORD32 scratch_size = 0;

if (input.scalar_type() == ScalarType::Char) {
WORD8* __restrict__ p_out =
(WORD8* __restrict__)out.mutable_data_ptr<int8_t>();
Expand All @@ -185,48 +224,6 @@ void xa_opt_quantized_conv2d_nchw(
WORD32* __restrict__ p_bias =
(WORD32* __restrict__)bias.const_data_ptr<int32_t>();

WORD32 input_height = conv1d ? 1 : input.size(2);
WORD32 input_width = conv1d ? input.size(2) : input.size(3);
WORD32 input_channels = input.size(1);
WORD32 kernel_height = conv1d ? 1 : weight.size(2);
WORD32 kernel_width = conv1d ? weight.size(2) : weight.size(3);
WORD32 kernel_channels = weight.size(1);
WORD32 out_channels = weight.size(0);
WORD32 out_height = conv1d ? 1 : out.size(2);
WORD32 out_width = conv1d ? out.size(2) : out.size(3);
WORD32 batches = input.size(0);

WORD32 x_stride = stride[1];
WORD32 y_stride = stride[0];
WORD32 x_padding = padding[1];
WORD32 y_padding = padding[0];
WORD32 dilation_width = dilation[1];
WORD32 dilation_height = dilation[0];

// WORD32* kernel_bias_ptr =
// (WORD32*)weight_zero_point.const_data_ptr<int32_t>();

WORD32 input_zero_bias = -in_zero_point;
WORD32 kernel_zero_bias = -weight_zero_point;

WORD32 out_multiplier32[out_channels];
WORD32 out_shift32[out_channels];

float out_scale = 1. / output_scale;

for (int i = 0; i < out_channels; i++) {
out_multiplier32[i] = bias_scale * out_scale * 2147483648;
out_shift32[i] = 0;
}

WORD32 out_zero_bias = output_zero_point;
WORD32 inp_precision = 8;
WORD32 kernel_precision = 8;
pVOID p_scratch = nullptr;
WORD32* ptr_scratch;

WORD32 scratch_size = 0;

if (groups == 1) {
WORD32 out_data_format = 1;

Expand All @@ -245,13 +242,13 @@ void xa_opt_quantized_conv2d_nchw(
WORD8* pkernel = (WORD8*)ALIGN_PTR(ptr2, 8);

WORD32 p_inp_shape[kNnlibMaxDim];
p_inp_shape[0] = input.size(0);
p_inp_shape[0] = batches;
p_inp_shape[1] = input_channels;
p_inp_shape[2] = input_height;
p_inp_shape[3] = input_width;

WORD32 p_out_shape[kNnlibMaxDim];
p_out_shape[0] = input.size(0);
p_out_shape[0] = batches;
p_out_shape[1] = input_height;
p_out_shape[2] = input_width;
p_out_shape[3] = input_channels;
Expand Down Expand Up @@ -439,6 +436,231 @@ void xa_opt_quantized_conv2d_nchw(
return;
}
}

if (input.scalar_type() == ScalarType::Byte) {
UWORD8* __restrict__ p_out =
(UWORD8* __restrict__)out.mutable_data_ptr<uint8_t>();
UWORD8* __restrict__ p_inp =
(UWORD8* __restrict__)input.const_data_ptr<uint8_t>();
UWORD8* __restrict__ p_kernel =
(UWORD8* __restrict__)weight.const_data_ptr<uint8_t>();
WORD32* __restrict__ p_bias =
(WORD32* __restrict__)bias.const_data_ptr<int32_t>();

WORD32 out_multiplier = out_multiplier32[0];
WORD32 out_shift = out_shift32[0];

if (groups == 1) {
WORD32 out_data_format = 1;

UWORD8* ptr1 = (UWORD8*)kernels::allocate_temp_memory(
ctx,
((batches * input_channels * input_height * input_width) + 8) *
sizeof(UWORD8));

UWORD8* ptr2 = (UWORD8*)kernels::allocate_temp_memory(
ctx,
((out_channels * kernel_channels * kernel_height * kernel_width) +
8) *
sizeof(UWORD8));

UWORD8* pin = (UWORD8*)ALIGN_PTR(ptr1, 8);
UWORD8* pkernel = (UWORD8*)ALIGN_PTR(ptr2, 8);

WORD32 p_inp_shape[kNnlibMaxDim];
p_inp_shape[0] = batches;
p_inp_shape[1] = input_channels;
p_inp_shape[2] = input_height;
p_inp_shape[3] = input_width;

WORD32 p_out_shape[kNnlibMaxDim];
p_out_shape[0] = batches;
p_out_shape[1] = input_height;
p_out_shape[2] = input_width;
p_out_shape[3] = input_channels;

WORD32 p_permute_vec[kNnlibMaxDim] = {0, 2, 3, 1};

xa_nn_transpose_8_8(
(WORD8*)pin,
p_out_shape,
(WORD8*)p_inp,
p_inp_shape,
p_permute_vec,
kNnlibMaxDim,
kNnlibMaxDim);

WORD32 p_inp_shape1[kNnlibMaxDim];
p_inp_shape1[0] = out_channels;
p_inp_shape1[1] = kernel_channels;
p_inp_shape1[2] = kernel_height;
p_inp_shape1[3] = kernel_width;

WORD32 p_out_shape1[kNnlibMaxDim];
p_out_shape1[0] = out_channels;
p_out_shape1[1] = kernel_height;
p_out_shape1[2] = kernel_width;
p_out_shape1[3] = kernel_channels;

xa_nn_transpose_8_8(
(WORD8*)pkernel,
p_out_shape1,
(WORD8*)p_kernel,
p_inp_shape1,
p_permute_vec,
kNnlibMaxDim,
kNnlibMaxDim);

scratch_size = xa_nn_conv2d_getsize(
input_height,
input_width,
input_channels,
kernel_height,
kernel_width,
kernel_channels,
dilation_height,
dilation_width,
y_stride,
y_padding,
x_stride,
x_padding,
out_height,
out_width,
out_channels,
inp_precision,
kernel_precision,
out_data_format);

scratch_size = scratch_size < 0 ? 0 : scratch_size;

ptr_scratch = (WORD32*)kernels::allocate_temp_memory(ctx, scratch_size);

p_scratch = (pVOID)ALIGN_PTR(ptr_scratch, 8);

for (int _n = 0; _n < batches; _n++) {
UWORD8* in_batch =
pin + _n * input_channels * input_height * input_width;
UWORD8* out_batch = p_out + _n * out_channels * out_height * out_width;

xa_nn_conv2d_std_asym8uxasym8u(
out_batch,
in_batch,
pkernel,
p_bias,
input_height,
input_width,
input_channels,
kernel_height,
kernel_width,
out_channels,
x_stride,
y_stride,
x_padding,
y_padding,
out_height,
out_width,
input_zero_bias,
kernel_zero_bias,
out_multiplier,
out_shift,
out_zero_bias,
out_data_format,
p_scratch);
}
return;
}

if (groups == input_channels) {
WORD32 channels_multiplier = out_channels / input_channels;

scratch_size = xa_nn_conv2d_depthwise_getsize(
input_height,
input_width,
input_channels,
kernel_height,
kernel_width,
channels_multiplier,
x_stride,
y_stride,
x_padding,
y_padding,
out_height,
out_width,
inp_precision,
1); // NCHW

scratch_size = scratch_size < 0 ? 0 : scratch_size;

ptr_scratch = (WORD32*)kernels::allocate_temp_memory(ctx, scratch_size);

p_scratch = (pVOID)ALIGN_PTR(ptr_scratch, 8);

UWORD8* ptr1 = (UWORD8*)kernels::allocate_temp_memory(
ctx,
((batches * out_channels * out_height * out_width) + 8) *
sizeof(UWORD8));

UWORD8* p_out_temp = (UWORD8*)ALIGN_PTR(ptr1, 8);

for (int _n = 0; _n < batches; _n++) {
UWORD8* in_batch =
p_inp + _n * input_channels * input_height * input_width;
UWORD8* out_batch =
p_out_temp + _n * out_channels * out_height * out_width;

xa_nn_conv2d_depthwise_asym8uxasym8u(
out_batch,
p_kernel,
in_batch,
p_bias,
input_height,
input_width,
input_channels,
kernel_height,
kernel_width,
channels_multiplier,
x_stride,
y_stride,
x_padding,
y_padding,
out_height,
out_width,
input_zero_bias,
kernel_zero_bias,
out_multiplier,
out_shift,
out_zero_bias,
1, // NCHW
0, // NHWC
p_scratch);
}

WORD32 p_inp_shape[kNnlibMaxDim];
p_inp_shape[0] = batches;
p_inp_shape[1] = out_height;
p_inp_shape[2] = out_width;
p_inp_shape[3] = out_channels;

WORD32 p_out_shape[kNnlibMaxDim];
p_out_shape[0] = batches;
p_out_shape[1] = out_channels;
p_out_shape[2] = out_height;
p_out_shape[3] = out_width;

WORD32 p_permute_vec[kNnlibMaxDim] = {0, 3, 1, 2};

xa_nn_transpose_8_8(
(WORD8*)p_out,
p_out_shape,
(WORD8*)p_out_temp,
p_inp_shape,
p_permute_vec,
kNnlibMaxDim,
kNnlibMaxDim);

return;
}
}
}

// The quantized convolution kernel. in_scale and weight_scale are implicit in
Expand Down
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