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[Inductor][Quant] Change the QConv output scale name
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ghstack-source-id: c1224c830802161e59f32f136cf2749a7b6ff2cd
Pull Request resolved: #124246
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@leslie-fang-intel
leslie-fang-intel committed Apr 28, 2024
1 parent 1429e47 commit 6e0da69
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Showing 5 changed files with 23 additions and 23 deletions.
2 changes: 1 addition & 1 deletion aten/src/ATen/native/quantized/cpu/OnednnUtils.h
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Expand Up @@ -485,7 +485,7 @@ static at::Tensor _quantized_convolution_onednn(
torch::List<int64_t> dilation,
bool transposed,
int64_t groups,
double inv_output_scale,
double output_scale,
int64_t output_zero_point,
c10::optional<at::Tensor> accum=c10::nullopt, // accum to fused with conv add
double accum_scale=1.0,
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26 changes: 13 additions & 13 deletions aten/src/ATen/native/quantized/cpu/qconv.cpp
View file
Expand Up @@ -1397,7 +1397,7 @@ static at::Tensor _quantized_convolution_onednn(
torch::List<int64_t> dilation,
bool transposed,
int64_t groups,
double inv_output_scale, // inv_output_scale is the reciprocal of scale in fake quant
double output_scale,
int64_t output_zero_point,
c10::optional<at::Tensor> accum, // accum to fused with conv add
double accum_scale,
Expand All @@ -1420,10 +1420,10 @@ static at::Tensor _quantized_convolution_onednn(
bool bfloat16_output = output_dtype.has_value() && (output_dtype.value() == c10::kBFloat16);
if (fp32_output || bfloat16_output) {
// When fp32 or bf16 output, oneDNN expects op_attr doesn't set_scales and set_zero_points.
// So, we will use default inv_output_scale as 1.0 and output_zero_point as 0, since
// when inv_output_scale is 1.0, we will skip invoking of op_attr.set_scales in ideep;
// So, we will use default output_scale as 1.0 and output_zero_point as 0, since
// when output_scale is 1.0, we will skip invoking of op_attr.set_scales in ideep;
// when output_zero_point is 0, we will skip invoking of op_attr.set_zero_points in ideep.
TORCH_CHECK(inv_output_scale == 1.0, " (ONEDNN): fp32 or bf16 output, inv_output_scale must be 1.0.");
TORCH_CHECK(output_scale == 1.0, " (ONEDNN): fp32 or bf16 output, output_scale must be 1.0.");
TORCH_CHECK(output_zero_point == 0, " (ONEDNN): fp32 or bf16 output, output_zero_point must be 0");
}

Expand Down Expand Up @@ -1634,7 +1634,7 @@ static at::Tensor _quantized_convolution_onednn(
int oc_per_group = packed_weight.get_dim(0) / groups;
int wei_scale_mask = ideep::utils::conv_weight_scale_mask(weight_scales.numel(), oc_per_group, groups, false);
op_attr.set_scales_mask(DNNL_ARG_WEIGHTS, wei_scale_mask);
if (inv_output_scale != 1.0f) {
if (output_scale != 1.0f) {
op_attr.set_scales_mask(DNNL_ARG_DST, 0);
}
if (output_zero_point != 0) {
Expand Down Expand Up @@ -1671,13 +1671,13 @@ static at::Tensor _quantized_convolution_onednn(
}
tensor src_scales_t = tensor(ideep::scale_t(1, act_scale));
tensor wei_scales_t = tensor(weights_scales);
tensor dst_scales_t = tensor(ideep::scale_t(1, inv_output_scale));
tensor dst_scales_t = tensor(ideep::scale_t(1, output_scale));
tensor src_zp_t = tensor(ideep::zero_point_t(1, act_zero_point));
tensor dst_zp_t = tensor(ideep::zero_point_t(1, output_zero_point));
if (act_scale != 1.0f) {
args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_SRC, src_scales_t});
}
if (inv_output_scale != 1.0f) {
if (output_scale != 1.0f) {
args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_DST, dst_scales_t});
}
args.insert({DNNL_ARG_ATTR_SCALES | DNNL_ARG_WEIGHTS, wei_scales_t});
Expand All @@ -1697,7 +1697,7 @@ static at::Tensor _quantized_convolution_onednn(
const ideep::scale_t accum_ideep_scale = ideep::scale_t(1, 1.0/accum_scale);
const ideep::zero_point_t accum_ideep_zero_points = ideep::zero_point_t(1, accum_zero_point);
// Set the dst scale and zero point with the value of accum.
// The true scale and zero point is stored in ideep::scale_t(scale_size, inv_output_scale) and dst_zero_points.
// The true scale and zero point is stored in ideep::scale_t(scale_size, output_scale) and dst_zero_points.
dst.set_scale(accum_ideep_scale);
dst.set_zero_point(accum_ideep_zero_points);
}
Expand All @@ -1707,7 +1707,7 @@ static at::Tensor _quantized_convolution_onednn(
ideep::convolution_forward::prepare(
params, src, packed_weight, expected_bias, dst_dims, dst,
stride.vec(), dilation.vec(), padding.vec(), padding.vec(), groups,
src_scales, weights_scales, ideep::scale_t(1, 1.0f / inv_output_scale),
src_scales, weights_scales, ideep::scale_t(1, 1.0f / output_scale),
src_zero_points, dst_zero_points,
op_attr, dnnl::algorithm::convolution_direct,
dnnl::prop_kind::forward_inference,
Expand Down Expand Up @@ -1872,7 +1872,7 @@ class QConvoneDNN final {
torch::List<int64_t> padding,
torch::List<int64_t> dilation,
int64_t groups,
double inv_output_scale, // inv_output_scale is the reciprocal of scale in fake quant
double output_scale,
int64_t output_zero_point,
c10::optional<c10::ScalarType> output_dtype,
c10::string_view attr,
Expand Down Expand Up @@ -1900,7 +1900,7 @@ class QConvoneDNN final {
act, act_scale, act_zero_point,
weight, weight_scales, weight_zero_points,
bias, stride, padding, dilation, /*transposed*/false,
groups, inv_output_scale, output_zero_point,
groups, output_scale, output_zero_point,
/*accum*/c10::nullopt, /*accum_scale*/0.0, /*accum_zero_point*/0,
/*output_dtype*/output_dtype, /*binary_attr*/c10::nullopt, /*binary_alpha*/c10::nullopt,
/*unary_attr*/attr, /*unary_scalars*/scalars, /*unary_algorithm*/algorithm
Expand All @@ -1924,7 +1924,7 @@ class QConvoneDNN final {
torch::List<int64_t> padding,
torch::List<int64_t> dilation,
int64_t groups,
double inv_output_scale, // inv_output_scale is the reciprocal of scale in fake quant
double output_scale,
int64_t output_zero_point,
c10::optional<c10::ScalarType> output_dtype,
c10::string_view binary_attr,
Expand Down Expand Up @@ -1952,7 +1952,7 @@ class QConvoneDNN final {
act, act_scale, act_zero_point,
weight, weight_scales, weight_zero_points,
bias, stride, padding, dilation, /*transposed*/false,
groups, inv_output_scale, output_zero_point,
groups, output_scale, output_zero_point,
accum, accum_scale, accum_zero_point,
/*output_dtype*/output_dtype, binary_attr, alpha,
unary_attr, unary_scalars, unary_algorithm
Expand Down
8 changes: 4 additions & 4 deletions aten/src/ATen/native/quantized/library.cpp
View file
Expand Up @@ -257,12 +257,12 @@ TORCH_LIBRARY(onednn, m) {
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv_prepack(Tensor weight, Tensor w_scales, float x_scale, int x_zp, int[] stride, int[] padding, int[] dilation, int groups, int[]? x_shape=None) -> Tensor"));

// Conv1D/2D/3D with unary postop
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv1d_pointwise(Tensor qx, float x_scale, int x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float inv_output_scale, int output_zero_point, ScalarType? output_dtype, str attr, Scalar?[] scalars, str? algorithm) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv2d_pointwise(Tensor qx, float x_scale, int x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float inv_output_scale, int output_zero_point, ScalarType? output_dtype, str attr, Scalar?[] scalars, str? algorithm) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv3d_pointwise(Tensor qx, float x_scale, int x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float inv_output_scale, int output_zero_point, ScalarType? output_dtype, str attr, Scalar?[] scalars, str? algorithm) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv1d_pointwise(Tensor qx, float x_scale, int x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point, ScalarType? output_dtype, str attr, Scalar?[] scalars, str? algorithm) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv2d_pointwise(Tensor qx, float x_scale, int x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point, ScalarType? output_dtype, str attr, Scalar?[] scalars, str? algorithm) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv3d_pointwise(Tensor qx, float x_scale, int x_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point, ScalarType? output_dtype, str attr, Scalar?[] scalars, str? algorithm) -> Tensor"));

// Conv2D with binary postop
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv2d_pointwise.binary(Tensor qx, float x_scale, int x_zero_point, Tensor qaccum, float accum_scale, int accum_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float inv_output_scale, int output_zero_point, ScalarType? output_dtype, str binary_attr, Scalar? alpha, str? unary_attr, Scalar?[] unary_scalars, str? unary_algorithm) -> Tensor"));
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qconv2d_pointwise.binary(Tensor qx, float x_scale, int x_zero_point, Tensor qaccum, float accum_scale, int accum_zero_point, Tensor qw, Tensor w_scale, Tensor w_zero_point, Tensor? bias, int[] stride, int[] padding, int[] dilation, int groups, float output_scale, int output_zero_point, ScalarType? output_dtype, str binary_attr, Scalar? alpha, str? unary_attr, Scalar?[] unary_scalars, str? unary_algorithm) -> Tensor"));

// Linear prepack
m.def(TORCH_SELECTIVE_SCHEMA("onednn::qlinear_prepack(Tensor weight, int[]? x_shape) -> Tensor"));
Expand Down
4 changes: 2 additions & 2 deletions torch/_inductor/fx_passes/quantization.py
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Expand Up @@ -174,7 +174,7 @@ def get_dequantize_qconv_pt2e_pattern(users=1):
KeywordArg("padding"),
KeywordArg("dilation"),
KeywordArg("groups"),
KeywordArg("inv_output_scale"), # inv_output_scale = 1.0
KeywordArg("output_scale"), # output_scale = 1.0
KeywordArg("output_zero_point"), # output_zero_point = 0
KeywordArg("output_dtype"), # output_dtype = None
KeywordArg("attr"), # attr = "none"
Expand Down Expand Up @@ -1509,7 +1509,7 @@ def qconv_weight_prepack(match: Match, *args, **kwargs):
padding,
dilation,
groups,
1.0, # inv_output_scale
1.0, # output_scale
0, # output_zero_point
dtype, # output_dtype
"none", # attr
Expand Down
6 changes: 3 additions & 3 deletions torch/_inductor/ir.py
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Expand Up @@ -6634,7 +6634,7 @@ def __init__(
torch::List<int64_t> padding,
torch::List<int64_t> dilation,
int64_t groups,
double inv_output_scale,
double output_scale,
int64_t output_zero_point,
c10::optional<c10::ScalarType> output_dtype,
c10::string_view attr,
Expand Down Expand Up @@ -6810,7 +6810,7 @@ def __init__(
torch::List<int64_t> padding,
torch::List<int64_t> dilation,
int64_t groups,
double inv_output_scale,
double output_scale,
int64_t output_zero_point,
c10::optional<c10::ScalarType> output_dtype,
c10::string_view binary_attr,
Expand Down Expand Up @@ -7026,7 +7026,7 @@ def __init__(
at::Tensor weight_scales,
at::Tensor weight_zero_points,
c10::optional<at::Tensor> bias,
double inv_output_scale,
double output_scale,
int64_t output_zero_point,
c10::optional<c10::ScalarType> output_dtype,
c10::string_view post_op_name,
Expand Down

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