Why Gemfury? Push, build, and install  RubyGems npm packages Python packages Maven artifacts PHP packages Go Modules Bower components Debian packages RPM packages NuGet packages

edgify / torch   python

Repository URL to install this package:

Version: 2.0.1+cpu 

/ include / ATen / native / quantized / cpu / QuantizedOps.h

#pragma once
#include <ATen/core/Tensor.h>
#include <ATen/core/IListRef.h>
#include <ATen/Dispatch.h>
#include <ATen/TensorIterator.h>
#include <ATen/native/Activation.h>
#include <ATen/native/DispatchStub.h>

namespace at {
namespace native {

using qrelu_fn = void (*)(const at::Tensor& /*qx*/, at::Tensor& /*qy*/);
using qrelu_leaky_fn = void (*)(Tensor& /*out*/, const Tensor& /*qx*/,
                                const Scalar& /*negval_*/);
using qgelu_fn = void (*)(const at::Tensor& /*qx*/, at::Tensor& /*qy*/, GeluType /* approximate */);
using qsigmoid_fn = void (*)(const at::Tensor& /*qx*/, at::Tensor& /*qy*/, double output_scale, int64_t output_zero_point);
using qhardsigmoid_fn = void (*)(const at::Tensor& /*qx*/, at::Tensor& /*qy*/);
using qclamp_fn = void (*)(
    const at::Tensor& /*qx*/,
    const Scalar& min,
    const Scalar& max,
    at::Tensor& /*qy*/);
using qclamp_minmax_fn = void (*)(
    const at::Tensor& /*qx*/,
    const Scalar& /*min or max*/,
    at::Tensor& /*qy*/);
using qthreshold_fn = void (*)(
    const at::Tensor& /*qx*/,
    const Scalar& threshold,
    const Scalar& value,
    at::Tensor& /*qy*/);
using qtanh_fn = void (*)(const at::Tensor& /*qx*/, at::Tensor& /*qy*/);
using qelu_fn = void(*)(
    const at::Tensor& /*qx*/,
    const Scalar& /*alpha*/,
    const Scalar& /*scale*/,
    const Scalar& /*input_scale*/,
    at::Tensor& /*qy*/);
using qbinary_fn =
    void (*)(Tensor& /*out*/, const Tensor& /*self*/, const Tensor& /*other*/);
using qadd_scalar_fn =
    void (*)(Tensor& /*out*/, const Tensor& /*self*/, const Scalar& other /*other*/);
using qhardswish_fn = void (*)(const at::Tensor& /*qx*/, at::Tensor& /*qy*/);
using qdropout_fn = void(*)(
    const at::Tensor& /*qx*/,
    const Scalar& /*p*/,
    bool training /*training*/,
    at::Tensor& /*qy*/);
using qmaxpool_2d_fn = void (*)(
    const Tensor& qx,
    int64_t iC, // input/output channels
    int64_t iH,
    int64_t iW, // input sizes
    int64_t oH,
    int64_t oW, // output sizes
    int64_t kH,
    int64_t kW, // kernel size
    int64_t sH,
    int64_t sW, // strides
    int64_t pH,
    int64_t pW, // padding
    int64_t dH,
    int64_t dW, // dilation
    Tensor& qy);
using qadaptive_avg_pool2d_fn = void (*)(
    const Tensor& qx,
    Tensor& qy,
    int64_t sizeB,
    int64_t sizeC,
    int64_t isizeH,
    int64_t isizeW,
    int64_t osizeH,
    int64_t osizeW,
    int64_t istrideB,
    int64_t istrideC,
    int64_t istrideH,
    int64_t istrideW);
using qadaptive_avg_pool3d_fn = void (*)(
    const Tensor& qx,
    Tensor& qy,
    int64_t sizeB,
    int64_t sizeC,
    int64_t isizeD,
    int64_t isizeH,
    int64_t isizeW,
    int64_t osizeD,
    int64_t osizeH,
    int64_t osizeW,
    int64_t istrideB,
    int64_t istrideC,
    int64_t istrideD,
    int64_t istrideH,
    int64_t istrideW);
using qavg_pool2d_fn = void (*)(
    const Tensor& qx,
    Tensor& qy,
    int64_t nBatch,
    int64_t nInputPlane,
    int64_t inputWidth,
    int64_t inputHeight,
    int64_t outputWidth,
    int64_t outputHeight,
    int kW,
    int kH,
    int dW,
    int dH,
    int padW,
    int padH,
    bool count_include_pad,
    c10::optional<int64_t> divisor_override);

using qavg_pool3d_fn = void (*)(
    const Tensor& qx,
    Tensor& qy,
    int64_t nBatch,
    int64_t nInputPlane,
    int64_t inputWidth,
    int64_t inputHeight,
    int64_t inputDepth,
    int64_t outputWidth,
    int64_t outputHeight,
    int64_t outputDepth,
    int kW,
    int kH,
    int kD,
    int dW,
    int dH,
    int dD,
    int padW,
    int padH,
    int padD,
    bool count_include_pad,
    c10::optional<int64_t> divisor_override);

using qupsample_bilinear2d_fn = void (*)(
    Tensor& output,
    const Tensor& input,
    int64_t input_height,
    int64_t input_width,
    int64_t output_height,
    int64_t output_width,
    int64_t nbatch,
    int64_t channels,
    bool align_corners,
    c10::optional<double> scales_h,
    c10::optional<double> scales_w);

using qcat_nhwc_fn = Tensor (*)(
    const MaterializedITensorListRef& qxs,
    int64_t dim,
    double scale,
    int64_t zero_point);
using qtopk_fn = void(*)(Tensor&, Tensor&, const Tensor&, int64_t, int64_t, bool, bool);

using qbatch_norm_fn = void(*)(int64_t, int64_t, int64_t, int64_t, int64_t, const Tensor&, const Tensor&, const Tensor&, Tensor&);

using qnormalize_fn = void (*)(
    const Tensor& /* X */,
    const Tensor& /* gamma */,
    const Tensor& /* beta */,
    bool /* affine_per_channel */,
    int /* num_channels */,
    int /* num_groups */,
    int64_t /* M */,
    int64_t /* N */,
    double /* eps */,
    Tensor* /* Y */);

using qmean_inner_dim_fn = void (*)(
    const Tensor& /* X */,
    OptionalIntArrayRef /* opt_dim */,
    bool /* keepdim */,
    c10::optional<ScalarType> /* opt_dtype */,
    Tensor& /* Y */);

using qstd_inner_dim_fn = void (*)(
    const Tensor& /* X */,
    OptionalIntArrayRef /* dim */,
    optional<int64_t> /* unbiased */,
    bool /* keepdim */,
    Tensor& /* Y */);

using qnormalize_nhwc_fn = void (*)(
    const Tensor& /* X */,
    const Tensor& /* gamma */,
    const Tensor& /* beta */,
    bool /* affine_per_channel */,
    int /* num_channels */,
    int /* num_groups */,
    int64_t /* M */,
    int64_t /* N */,
    double /* eps */,
    Tensor* /* Y */);

using qprelu_fn = void (*)(Tensor& /*out*/, const Tensor& /*qx*/,
                           const Tensor& /*qw*/);

DECLARE_DISPATCH(qadaptive_avg_pool2d_fn, qadaptive_avg_pool2d_nhwc_stub);
DECLARE_DISPATCH(qadaptive_avg_pool3d_fn, qadaptive_avg_pool3d_ndhwc_stub);
DECLARE_DISPATCH(qadd_scalar_fn, qadd_scalar_relu_stub);
DECLARE_DISPATCH(qadd_scalar_fn, qadd_scalar_stub);
DECLARE_DISPATCH(qavg_pool2d_fn, qavg_pool2d_nhwc_stub);
DECLARE_DISPATCH(qavg_pool3d_fn, qavg_pool3d_nhwc_stub);
DECLARE_DISPATCH(qbatch_norm_fn, qbatch_norm_relu_stub);
DECLARE_DISPATCH(qbatch_norm_fn, qbatch_norm_stub);
DECLARE_DISPATCH(qbinary_fn, qadd_relu_stub);
DECLARE_DISPATCH(qbinary_fn, qadd_stub);
DECLARE_DISPATCH(qbinary_fn, qmul_relu_stub);
DECLARE_DISPATCH(qbinary_fn, qmul_stub);
DECLARE_DISPATCH(qcat_nhwc_fn, qcat_nhwc_stub);
DECLARE_DISPATCH(qcat_nhwc_fn, qcat_relu_nhwc_stub);
DECLARE_DISPATCH(qclamp_fn, qclamp_stub);
DECLARE_DISPATCH(qclamp_minmax_fn, qclamp_min_stub);
DECLARE_DISPATCH(qclamp_minmax_fn, qclamp_max_stub);
DECLARE_DISPATCH(qelu_fn, qelu_stub);
DECLARE_DISPATCH(qhardsigmoid_fn, qhardsigmoid_stub);
DECLARE_DISPATCH(qhardswish_fn, qhardswish_stub);
DECLARE_DISPATCH(qdropout_fn, qdropout_stub);
DECLARE_DISPATCH(qmaxpool_2d_fn, qmaxpool_2d_nhwc_stub);
DECLARE_DISPATCH(qnormalize_fn, quantized_normalize_stub);
DECLARE_DISPATCH(qnormalize_nhwc_fn, quantized_groupnorm_nhwc_stub);
DECLARE_DISPATCH(qrelu_fn, qrelu_stub);
DECLARE_DISPATCH(qrelu_leaky_fn, qrelu_leaky_stub);
DECLARE_DISPATCH(qgelu_fn, qgelu_stub);
DECLARE_DISPATCH(qsigmoid_fn, qsigmoid_stub);
DECLARE_DISPATCH(qtanh_fn, qtanh_stub);
DECLARE_DISPATCH(qthreshold_fn, qthreshold_stub);
DECLARE_DISPATCH(qtopk_fn, qtopk_stub);
DECLARE_DISPATCH(qupsample_bilinear2d_fn, qupsample_bilinear2d_nhwc_stub);
DECLARE_DISPATCH(qmean_inner_dim_fn, qmean_inner_dim_stub);
DECLARE_DISPATCH(qstd_inner_dim_fn, qstd_inner_dim_stub);
DECLARE_DISPATCH(qprelu_fn, qprelu_stub);

} // namespace native
} // namespace at