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// baracuda_quantize.cuh
//
// Templated kernels and INSTANTIATE macros for the quantization op family
// (Phase 8 Milestone 8.1 — Category P from the comprehensive plan).
//
// Ops shipped here (trailblazer):
// quantize_per_tensor — q = clamp(round(x/scale) + zp, qmin, qmax)
// quantize_per_tensor_bw — dx = (dy / scale) * in_range_mask (STE)
// dequantize_per_tensor — x = scale * (q - zp)
// dequantize_per_tensor_bw — dq = dy * scale
// quantize_per_channel — same as per_tensor but scale[c]/zp[c] per axis slice
// quantize_per_channel_bw — dx = (dy / scale[c]) * in_range_mask[c] (STE)
// dequantize_per_channel — x = scale[c] * (q - zp[c])
// dequantize_per_channel_bw— dq = dy * scale[c]
// fake_quantize — y = scale * (clamp(round(x/scale)+zp, qmin, qmax) - zp)
// fake_quantize_bw — dx = dy * in_range_mask (STE, NO 1/scale)
//
// Trailblazer dtype scope:
// Input FP : float, double, __half, __nv_bfloat16
// Output Q : int8_t, uint8_t (sub-byte s4/u4 deferred to 8.2+)
// scale : same FP dtype as input
// zero_pt : i32 (wide enough for any byte-range qmin/qmax)
//
// STE convention (READ TWICE — easy to get wrong):
// The "in-range mask" for BW is NOT saved during FW. BW re-computes it
// from the original input `x` (which the caller retains for autograd)
// plus `scale`/`zero_point`. Mask = (qmin <= round(x/scale)+zp <= qmax).
// - quantize_bw : dx = dy / scale (where in-range; else 0).
// - fake_quantize_bw : dx = dy (where in-range; else 0).
// The 1/scale factor is omitted because the FW's dequant-side
// multiplication by scale exactly cancels the STE 1/scale.
//
// Per-channel kernels assume the caller has padded rank to MAX_RANK (=4)
// with 1's so the kernel sees a fixed-rank tensor; the `axis` field
// selects which of the 4 dims indexes scale[]/zp[].
//
// Status codes returned by the launchers mirror the rest of the kernel
// surface:
// 0 success
// 1 misaligned operand
// 2 invalid problem
// 3 unsupported
// 4 workspace too small
// 5 internal kernel error (launch failure)
#ifndef BARACUDA_QUANTIZE_CUH
#define BARACUDA_QUANTIZE_CUH
#include <cstdint>
#include <cuda_bf16.h>
#include <cuda_fp16.h>
#include <cuda_runtime.h>
namespace baracuda { namespace quantize {
inline constexpr int MAX_RANK = 4;
// =============================================================================
// FP → float promotion helpers — every kernel does math at float and casts
// back. Matches the f32-detour convention used elsewhere in the kernel
// family for f16 / bf16.
// =============================================================================
template <typename T> __device__ __forceinline__ float to_float(T v);
template <typename T> __device__ __forceinline__ T from_float(float v);
template <> __device__ __forceinline__ float to_float<float>(float v) { return v; }
template <> __device__ __forceinline__ float to_float<double>(double v) { return (float)v; }
template <> __device__ __forceinline__ float to_float<__half>(__half v) { return __half2float(v); }
template <> __device__ __forceinline__ float to_float<__nv_bfloat16>(__nv_bfloat16 v) {
return __bfloat162float(v);
}
template <> __device__ __forceinline__ float from_float<float>(float v) { return v; }
template <> __device__ __forceinline__ double from_float<double>(float v) { return (double)v; }
template <> __device__ __forceinline__ __half from_float<__half>(float v) { return __float2half(v); }
template <> __device__ __forceinline__ __nv_bfloat16 from_float<__nv_bfloat16>(float v) {
return __float2bfloat16(v);
}
// Specialized "do math at the native FP precision" variants for f64 so we
// don't lose precision through the float detour. Used inside the per-cell
// loop only for the f64 case.
__device__ __forceinline__ double to_double(double v) { return v; }
__device__ __forceinline__ double to_double(float v) { return (double)v; }
__device__ __forceinline__ double to_double(__half v) { return (double)__half2float(v); }
__device__ __forceinline__ double to_double(__nv_bfloat16 v) {
return (double)__bfloat162float(v);
}
// =============================================================================
// Round-half-to-even helpers. Match `__float2int_rn` / `__double2int_rn`
// semantics — same convention used by the integer-GEMM saturating-cast
// helpers and by PyTorch's `torch.round`.
// =============================================================================
__device__ __forceinline__ int32_t round_to_int_f(float v) { return __float2int_rn(v); }
__device__ __forceinline__ int32_t round_to_int_d(double v) { return __double2int_rn(v); }
// =============================================================================
// quantize_per_tensor FW kernel — q = clamp(round(x/scale) + zp, qmin, qmax)
// =============================================================================
template <typename TIn, typename TOut>
__global__ void quantize_per_tensor_kernel(
const TIn* __restrict__ x,
TOut* __restrict__ q,
int64_t numel,
float scale, // f32 scale (input dtype already promoted on host side)
int32_t zero_point,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
float inv_scale = 1.0f / scale;
for (int64_t i = tid; i < numel; i += step) {
float xf = to_float<TIn>(x[i]);
int32_t r = round_to_int_f(xf * inv_scale) + zero_point;
if (r < q_min) r = q_min;
if (r > q_max) r = q_max;
q[i] = static_cast<TOut>(r);
}
}
// f64 specialization — do the divide / round at f64 precision then narrow.
template <typename TOut>
__global__ void quantize_per_tensor_kernel_f64(
const double* __restrict__ x,
TOut* __restrict__ q,
int64_t numel,
double scale,
int32_t zero_point,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
double inv_scale = 1.0 / scale;
for (int64_t i = tid; i < numel; i += step) {
int32_t r = round_to_int_d(x[i] * inv_scale) + zero_point;
if (r < q_min) r = q_min;
if (r > q_max) r = q_max;
q[i] = static_cast<TOut>(r);
}
}
template <typename TIn, typename TOut>
__host__ inline int32_t launch_quantize_per_tensor(
const TIn* x, TOut* q,
int64_t numel, float scale, int32_t zero_point,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
quantize_per_tensor_kernel<TIn, TOut><<<blocks, kBlock, 0, stream>>>(
x, q, numel, scale, zero_point, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
template <typename TOut>
__host__ inline int32_t launch_quantize_per_tensor_f64(
const double* x, TOut* q,
int64_t numel, double scale, int32_t zero_point,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
quantize_per_tensor_kernel_f64<TOut><<<blocks, kBlock, 0, stream>>>(
x, q, numel, scale, zero_point, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
// =============================================================================
// quantize_per_tensor BW kernel — STE.
// dx[i] = (dy[i] / scale) * in_range_mask(x[i])
// where in_range = (qmin <= round(x/scale) + zp <= qmax).
// Mask is recomputed from the saved input `x` — no separate mask tensor.
// =============================================================================
template <typename TIn>
__global__ void quantize_per_tensor_backward_kernel(
const TIn* __restrict__ x,
const TIn* __restrict__ dy,
TIn* __restrict__ dx,
int64_t numel,
float scale,
int32_t zero_point,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
float inv_scale = 1.0f / scale;
for (int64_t i = tid; i < numel; i += step) {
float xf = to_float<TIn>(x[i]);
float dyf = to_float<TIn>(dy[i]);
int32_t r = round_to_int_f(xf * inv_scale) + zero_point;
bool in_range = (r >= q_min) && (r <= q_max);
float gx = in_range ? (dyf * inv_scale) : 0.0f;
dx[i] = from_float<TIn>(gx);
}
}
template <>
__global__ void quantize_per_tensor_backward_kernel<double>(
const double* __restrict__ x,
const double* __restrict__ dy,
double* __restrict__ dx,
int64_t numel,
float scale_f32_unused,
int32_t zero_point,
int32_t q_min,
int32_t q_max)
{
// f64 path: caller invokes the dedicated `_f64` launcher with a
// double scale; this template specialization should not be reached.
// Fall back to single-precision math if it is.
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
double inv_scale = 1.0 / (double)scale_f32_unused;
for (int64_t i = tid; i < numel; i += step) {
int32_t r = round_to_int_d(x[i] * inv_scale) + zero_point;
bool in_range = (r >= q_min) && (r <= q_max);
dx[i] = in_range ? (dy[i] * inv_scale) : 0.0;
}
}
// Dedicated f64 BW kernel (preserves f64 precision in scale).
__global__ inline void quantize_per_tensor_backward_kernel_f64(
const double* __restrict__ x,
const double* __restrict__ dy,
double* __restrict__ dx,
int64_t numel,
double scale,
int32_t zero_point,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
double inv_scale = 1.0 / scale;
for (int64_t i = tid; i < numel; i += step) {
int32_t r = round_to_int_d(x[i] * inv_scale) + zero_point;
bool in_range = (r >= q_min) && (r <= q_max);
dx[i] = in_range ? (dy[i] * inv_scale) : 0.0;
}
}
template <typename TIn>
__host__ inline int32_t launch_quantize_per_tensor_backward(
const TIn* x, const TIn* dy, TIn* dx,
int64_t numel, float scale, int32_t zero_point,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
quantize_per_tensor_backward_kernel<TIn><<<blocks, kBlock, 0, stream>>>(
x, dy, dx, numel, scale, zero_point, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
__host__ inline int32_t launch_quantize_per_tensor_backward_f64(
const double* x, const double* dy, double* dx,
int64_t numel, double scale, int32_t zero_point,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
quantize_per_tensor_backward_kernel_f64<<<blocks, kBlock, 0, stream>>>(
x, dy, dx, numel, scale, zero_point, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
// =============================================================================
// dequantize_per_tensor FW kernel — x = scale * (q - zp).
// =============================================================================
template <typename TIn, typename TOut>
__global__ void dequantize_per_tensor_kernel(
const TOut* __restrict__ q, // int input
TIn* __restrict__ x, // FP output
int64_t numel,
float scale,
int32_t zero_point)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
int32_t qi = (int32_t)q[i];
float xf = scale * (float)(qi - zero_point);
x[i] = from_float<TIn>(xf);
}
}
template <typename TOut>
__global__ void dequantize_per_tensor_kernel_f64(
const TOut* __restrict__ q,
double* __restrict__ x,
int64_t numel,
double scale,
int32_t zero_point)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
int32_t qi = (int32_t)q[i];
x[i] = scale * (double)(qi - zero_point);
}
}
template <typename TIn, typename TOut>
__host__ inline int32_t launch_dequantize_per_tensor(
const TOut* q, TIn* x,
int64_t numel, float scale, int32_t zero_point,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
dequantize_per_tensor_kernel<TIn, TOut><<<blocks, kBlock, 0, stream>>>(
q, x, numel, scale, zero_point);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
template <typename TOut>
__host__ inline int32_t launch_dequantize_per_tensor_f64(
const TOut* q, double* x,
int64_t numel, double scale, int32_t zero_point,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
dequantize_per_tensor_kernel_f64<TOut><<<blocks, kBlock, 0, stream>>>(
q, x, numel, scale, zero_point);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
// =============================================================================
// dequantize_per_tensor BW kernel — dq = dy * scale. Output is the input
// FP gradient `dq` (cast back to FP since the quant-graph's gradient
// continues to flow in FP space; the integer `q` is non-differentiable).
// Note: PyTorch reports `dq` as same-FP-dtype as `dy` here, NOT as int.
// =============================================================================
template <typename TIn>
__global__ void dequantize_per_tensor_backward_kernel(
const TIn* __restrict__ dy,
TIn* __restrict__ dq,
int64_t numel,
float scale)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
float dyf = to_float<TIn>(dy[i]);
dq[i] = from_float<TIn>(dyf * scale);
}
}
__global__ inline void dequantize_per_tensor_backward_kernel_f64(
const double* __restrict__ dy,
double* __restrict__ dq,
int64_t numel,
double scale)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
dq[i] = dy[i] * scale;
}
}
template <typename TIn>
__host__ inline int32_t launch_dequantize_per_tensor_backward(
const TIn* dy, TIn* dq,
int64_t numel, float scale,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
dequantize_per_tensor_backward_kernel<TIn><<<blocks, kBlock, 0, stream>>>(
dy, dq, numel, scale);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
__host__ inline int32_t launch_dequantize_per_tensor_backward_f64(
const double* dy, double* dq,
int64_t numel, double scale,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
dequantize_per_tensor_backward_kernel_f64<<<blocks, kBlock, 0, stream>>>(
dy, dq, numel, scale);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
// =============================================================================
// fake_quantize FW kernel — y = scale * (clamp(round(x/scale)+zp, qmin, qmax) - zp).
// Stays in FP space; output dtype == input dtype.
// =============================================================================
template <typename TIn>
__global__ void fake_quantize_kernel(
const TIn* __restrict__ x,
TIn* __restrict__ y,
int64_t numel,
float scale,
int32_t zero_point,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
float inv_scale = 1.0f / scale;
for (int64_t i = tid; i < numel; i += step) {
float xf = to_float<TIn>(x[i]);
int32_t r = round_to_int_f(xf * inv_scale) + zero_point;
if (r < q_min) r = q_min;
if (r > q_max) r = q_max;
float yf = scale * (float)(r - zero_point);
y[i] = from_float<TIn>(yf);
}
}
__global__ inline void fake_quantize_kernel_f64(
const double* __restrict__ x,
double* __restrict__ y,
int64_t numel,
double scale,
int32_t zero_point,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
double inv_scale = 1.0 / scale;
for (int64_t i = tid; i < numel; i += step) {
int32_t r = round_to_int_d(x[i] * inv_scale) + zero_point;
if (r < q_min) r = q_min;
if (r > q_max) r = q_max;
y[i] = scale * (double)(r - zero_point);
}
}
template <typename TIn>
__host__ inline int32_t launch_fake_quantize(
const TIn* x, TIn* y,
int64_t numel, float scale, int32_t zero_point,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
fake_quantize_kernel<TIn><<<blocks, kBlock, 0, stream>>>(
x, y, numel, scale, zero_point, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
__host__ inline int32_t launch_fake_quantize_f64(
const double* x, double* y,
int64_t numel, double scale, int32_t zero_point,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
fake_quantize_kernel_f64<<<blocks, kBlock, 0, stream>>>(
x, y, numel, scale, zero_point, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
// =============================================================================
// fake_quantize BW kernel — dx = dy * in_range_mask. NO 1/scale factor.
// =============================================================================
template <typename TIn>
__global__ void fake_quantize_backward_kernel(
const TIn* __restrict__ x,
const TIn* __restrict__ dy,
TIn* __restrict__ dx,
int64_t numel,
float scale,
int32_t zero_point,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
float inv_scale = 1.0f / scale;
for (int64_t i = tid; i < numel; i += step) {
float xf = to_float<TIn>(x[i]);
float dyf = to_float<TIn>(dy[i]);
int32_t r = round_to_int_f(xf * inv_scale) + zero_point;
bool in_range = (r >= q_min) && (r <= q_max);
dx[i] = from_float<TIn>(in_range ? dyf : 0.0f);
}
}
__global__ inline void fake_quantize_backward_kernel_f64(
const double* __restrict__ x,
const double* __restrict__ dy,
double* __restrict__ dx,
int64_t numel,
double scale,
int32_t zero_point,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
double inv_scale = 1.0 / scale;
for (int64_t i = tid; i < numel; i += step) {
int32_t r = round_to_int_d(x[i] * inv_scale) + zero_point;
bool in_range = (r >= q_min) && (r <= q_max);
dx[i] = in_range ? dy[i] : 0.0;
}
}
template <typename TIn>
__host__ inline int32_t launch_fake_quantize_backward(
const TIn* x, const TIn* dy, TIn* dx,
int64_t numel, float scale, int32_t zero_point,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
fake_quantize_backward_kernel<TIn><<<blocks, kBlock, 0, stream>>>(
x, dy, dx, numel, scale, zero_point, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
__host__ inline int32_t launch_fake_quantize_backward_f64(
const double* x, const double* dy, double* dx,
int64_t numel, double scale, int32_t zero_point,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
fake_quantize_backward_kernel_f64<<<blocks, kBlock, 0, stream>>>(
x, dy, dx, numel, scale, zero_point, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
// =============================================================================
// Per-channel helpers: caller pads the tensor rank to MAX_RANK (=4) with
// extents of 1. `axis` selects which dim indexes the per-channel scale[]
// and zero_point[] vectors. Channel index is computed from the flat
// element index by decomposing it into a 4-D coord using the contiguous
// shape, then picking coord[axis].
//
// All inputs are assumed contiguous (row-major) for the trailblazer —
// strided per-channel quantize is deferred.
// =============================================================================
struct PcShape4 { int32_t d[MAX_RANK]; };
__device__ __forceinline__ int32_t pc_axis_coord(
int64_t linear, const PcShape4 shape, int32_t axis)
{
// Decompose linear → coord assuming row-major contiguous shape.
int64_t rem = linear;
int64_t coord[MAX_RANK] = {0};
for (int d = MAX_RANK - 1; d >= 0; --d) {
int32_t s = shape.d[d];
if (s <= 0) { coord[d] = 0; continue; }
coord[d] = rem % (int64_t)s;
rem /= (int64_t)s;
}
return (int32_t)coord[axis];
}
// ----- per-channel quantize FW -----
template <typename TIn, typename TOut>
__global__ void quantize_per_channel_kernel(
const TIn* __restrict__ x,
const TIn* __restrict__ scale, // [C]
const int32_t* __restrict__ zp, // [C]
TOut* __restrict__ q,
int64_t numel,
PcShape4 shape,
int32_t axis,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
int32_t c = pc_axis_coord(i, shape, axis);
float s_f = to_float<TIn>(scale[c]);
float xf = to_float<TIn>(x[i]);
int32_t r = round_to_int_f(xf / s_f) + zp[c];
if (r < q_min) r = q_min;
if (r > q_max) r = q_max;
q[i] = static_cast<TOut>(r);
}
}
template <typename TOut>
__global__ void quantize_per_channel_kernel_f64(
const double* __restrict__ x,
const double* __restrict__ scale,
const int32_t* __restrict__ zp,
TOut* __restrict__ q,
int64_t numel,
PcShape4 shape,
int32_t axis,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
int32_t c = pc_axis_coord(i, shape, axis);
int32_t r = round_to_int_d(x[i] / scale[c]) + zp[c];
if (r < q_min) r = q_min;
if (r > q_max) r = q_max;
q[i] = static_cast<TOut>(r);
}
}
template <typename TIn, typename TOut>
__host__ inline int32_t launch_quantize_per_channel(
const TIn* x, const TIn* scale, const int32_t* zp, TOut* q,
int64_t numel,
const int32_t* shape_host, int32_t axis,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
if (axis < 0 || axis >= MAX_RANK) return 2;
PcShape4 sh = {};
for (int i = 0; i < MAX_RANK; ++i) sh.d[i] = shape_host[i];
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
quantize_per_channel_kernel<TIn, TOut><<<blocks, kBlock, 0, stream>>>(
x, scale, zp, q, numel, sh, axis, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
template <typename TOut>
__host__ inline int32_t launch_quantize_per_channel_f64(
const double* x, const double* scale, const int32_t* zp, TOut* q,
int64_t numel,
const int32_t* shape_host, int32_t axis,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
if (axis < 0 || axis >= MAX_RANK) return 2;
PcShape4 sh = {};
for (int i = 0; i < MAX_RANK; ++i) sh.d[i] = shape_host[i];
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
quantize_per_channel_kernel_f64<TOut><<<blocks, kBlock, 0, stream>>>(
x, scale, zp, q, numel, sh, axis, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
// ----- per-channel quantize BW (STE) -----
template <typename TIn>
__global__ void quantize_per_channel_backward_kernel(
const TIn* __restrict__ x,
const TIn* __restrict__ scale,
const int32_t* __restrict__ zp,
const TIn* __restrict__ dy,
TIn* __restrict__ dx,
int64_t numel,
PcShape4 shape,
int32_t axis,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
int32_t c = pc_axis_coord(i, shape, axis);
float s_f = to_float<TIn>(scale[c]);
float inv = 1.0f / s_f;
float xf = to_float<TIn>(x[i]);
float dyf = to_float<TIn>(dy[i]);
int32_t r = round_to_int_f(xf * inv) + zp[c];
bool in_range = (r >= q_min) && (r <= q_max);
dx[i] = from_float<TIn>(in_range ? (dyf * inv) : 0.0f);
}
}
__global__ inline void quantize_per_channel_backward_kernel_f64(
const double* __restrict__ x,
const double* __restrict__ scale,
const int32_t* __restrict__ zp,
const double* __restrict__ dy,
double* __restrict__ dx,
int64_t numel,
PcShape4 shape,
int32_t axis,
int32_t q_min,
int32_t q_max)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
int32_t c = pc_axis_coord(i, shape, axis);
double inv = 1.0 / scale[c];
int32_t r = round_to_int_d(x[i] * inv) + zp[c];
bool in_range = (r >= q_min) && (r <= q_max);
dx[i] = in_range ? (dy[i] * inv) : 0.0;
}
}
template <typename TIn>
__host__ inline int32_t launch_quantize_per_channel_backward(
const TIn* x, const TIn* scale, const int32_t* zp,
const TIn* dy, TIn* dx,
int64_t numel,
const int32_t* shape_host, int32_t axis,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
if (axis < 0 || axis >= MAX_RANK) return 2;
PcShape4 sh = {};
for (int i = 0; i < MAX_RANK; ++i) sh.d[i] = shape_host[i];
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
quantize_per_channel_backward_kernel<TIn><<<blocks, kBlock, 0, stream>>>(
x, scale, zp, dy, dx, numel, sh, axis, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
__host__ inline int32_t launch_quantize_per_channel_backward_f64(
const double* x, const double* scale, const int32_t* zp,
const double* dy, double* dx,
int64_t numel,
const int32_t* shape_host, int32_t axis,
int32_t q_min, int32_t q_max,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
if (axis < 0 || axis >= MAX_RANK) return 2;
PcShape4 sh = {};
for (int i = 0; i < MAX_RANK; ++i) sh.d[i] = shape_host[i];
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
quantize_per_channel_backward_kernel_f64<<<blocks, kBlock, 0, stream>>>(
x, scale, zp, dy, dx, numel, sh, axis, q_min, q_max);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
// ----- per-channel dequantize FW -----
template <typename TIn, typename TOut>
__global__ void dequantize_per_channel_kernel(
const TOut* __restrict__ q,
const TIn* __restrict__ scale,
const int32_t* __restrict__ zp,
TIn* __restrict__ x,
int64_t numel,
PcShape4 shape,
int32_t axis)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
int32_t c = pc_axis_coord(i, shape, axis);
float s_f = to_float<TIn>(scale[c]);
int32_t qi = (int32_t)q[i];
float xf = s_f * (float)(qi - zp[c]);
x[i] = from_float<TIn>(xf);
}
}
template <typename TOut>
__global__ void dequantize_per_channel_kernel_f64(
const TOut* __restrict__ q,
const double* __restrict__ scale,
const int32_t* __restrict__ zp,
double* __restrict__ x,
int64_t numel,
PcShape4 shape,
int32_t axis)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
int32_t c = pc_axis_coord(i, shape, axis);
int32_t qi = (int32_t)q[i];
x[i] = scale[c] * (double)(qi - zp[c]);
}
}
template <typename TIn, typename TOut>
__host__ inline int32_t launch_dequantize_per_channel(
const TOut* q, const TIn* scale, const int32_t* zp, TIn* x,
int64_t numel,
const int32_t* shape_host, int32_t axis,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
if (axis < 0 || axis >= MAX_RANK) return 2;
PcShape4 sh = {};
for (int i = 0; i < MAX_RANK; ++i) sh.d[i] = shape_host[i];
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
dequantize_per_channel_kernel<TIn, TOut><<<blocks, kBlock, 0, stream>>>(
q, scale, zp, x, numel, sh, axis);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
template <typename TOut>
__host__ inline int32_t launch_dequantize_per_channel_f64(
const TOut* q, const double* scale, const int32_t* zp, double* x,
int64_t numel,
const int32_t* shape_host, int32_t axis,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
if (axis < 0 || axis >= MAX_RANK) return 2;
PcShape4 sh = {};
for (int i = 0; i < MAX_RANK; ++i) sh.d[i] = shape_host[i];
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
dequantize_per_channel_kernel_f64<TOut><<<blocks, kBlock, 0, stream>>>(
q, scale, zp, x, numel, sh, axis);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
// ----- per-channel dequantize BW -----
template <typename TIn>
__global__ void dequantize_per_channel_backward_kernel(
const TIn* __restrict__ scale,
const TIn* __restrict__ dy,
TIn* __restrict__ dq,
int64_t numel,
PcShape4 shape,
int32_t axis)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
int32_t c = pc_axis_coord(i, shape, axis);
float s_f = to_float<TIn>(scale[c]);
float dyf = to_float<TIn>(dy[i]);
dq[i] = from_float<TIn>(dyf * s_f);
}
}
__global__ inline void dequantize_per_channel_backward_kernel_f64(
const double* __restrict__ scale,
const double* __restrict__ dy,
double* __restrict__ dq,
int64_t numel,
PcShape4 shape,
int32_t axis)
{
int64_t tid = (int64_t)blockIdx.x * (int64_t)blockDim.x + (int64_t)threadIdx.x;
int64_t step = (int64_t)gridDim.x * (int64_t)blockDim.x;
for (int64_t i = tid; i < numel; i += step) {
int32_t c = pc_axis_coord(i, shape, axis);
dq[i] = dy[i] * scale[c];
}
}
template <typename TIn>
__host__ inline int32_t launch_dequantize_per_channel_backward(
const TIn* scale, const TIn* dy, TIn* dq,
int64_t numel,
const int32_t* shape_host, int32_t axis,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
if (axis < 0 || axis >= MAX_RANK) return 2;
PcShape4 sh = {};
for (int i = 0; i < MAX_RANK; ++i) sh.d[i] = shape_host[i];
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
dequantize_per_channel_backward_kernel<TIn><<<blocks, kBlock, 0, stream>>>(
scale, dy, dq, numel, sh, axis);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
__host__ inline int32_t launch_dequantize_per_channel_backward_f64(
const double* scale, const double* dy, double* dq,
int64_t numel,
const int32_t* shape_host, int32_t axis,
cudaStream_t stream)
{
if (numel < 0) return 2;
if (numel == 0) return 0;
if (axis < 0 || axis >= MAX_RANK) return 2;
PcShape4 sh = {};
for (int i = 0; i < MAX_RANK; ++i) sh.d[i] = shape_host[i];
constexpr int kBlock = 256;
constexpr int64_t kMaxBlocks = 65535;
int64_t blocks_i64 = (numel + kBlock - 1) / kBlock;
int blocks = static_cast<int>(blocks_i64 > kMaxBlocks ? kMaxBlocks : blocks_i64);
if (blocks <= 0) blocks = 1;
dequantize_per_channel_backward_kernel_f64<<<blocks, kBlock, 0, stream>>>(
scale, dy, dq, numel, sh, axis);
cudaError_t err = cudaGetLastError();
return (err == cudaSuccess) ? 0 : 5;
}
}} // namespace baracuda::quantize
// =============================================================================
// INSTANTIATE macros — emit `extern "C"` launcher per (op, dtype) pair.
// =============================================================================
// quantize_per_tensor — f32-scale variant. TIn ∈ {f32, f16, bf16}, TOut ∈ {s8, u8}.
#define BARACUDA_KERNELS_QUANTIZE_PER_TENSOR_INSTANTIATE(NAME, TIN, TOUT) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
float scale, \
int32_t zero_point, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
void* q, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || q == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_quantize_per_tensor<TIN, TOUT>( \
static_cast<const TIN*>(x), \
static_cast<TOUT*>(q), \
numel, scale, zero_point, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
float /*scale*/, \
int32_t /*zero_point*/, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*q*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
return 0; \
}
// quantize_per_tensor — f64 variant (carries f64 scale).
#define BARACUDA_KERNELS_QUANTIZE_PER_TENSOR_F64_INSTANTIATE(NAME, TOUT) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
double scale, \
int32_t zero_point, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
void* q, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || q == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_quantize_per_tensor_f64<TOUT>( \
static_cast<const double*>(x), \
static_cast<TOUT*>(q), \
numel, scale, zero_point, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
double /*scale*/, \
int32_t /*zero_point*/, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*q*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
return 0; \
}
// quantize_per_tensor BW — f32-scale variant.
#define BARACUDA_KERNELS_QUANTIZE_PER_TENSOR_BW_INSTANTIATE(NAME, TIN) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
float scale, \
int32_t zero_point, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
const void* dy, \
void* dx, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || dy == nullptr || dx == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_quantize_per_tensor_backward<TIN>( \
static_cast<const TIN*>(x), \
static_cast<const TIN*>(dy), \
static_cast<TIN*>(dx), \
numel, scale, zero_point, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
float /*scale*/, \
int32_t /*zero_point*/, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*dy*/, \
const void* /*dx*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
return 0; \
}
// quantize_per_tensor BW — f64 variant.
#define BARACUDA_KERNELS_QUANTIZE_PER_TENSOR_BW_F64_INSTANTIATE(NAME) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
double scale, \
int32_t zero_point, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
const void* dy, \
void* dx, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || dy == nullptr || dx == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_quantize_per_tensor_backward_f64( \
static_cast<const double*>(x), \
static_cast<const double*>(dy), \
static_cast<double*>(dx), \
numel, scale, zero_point, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
double /*scale*/, \
int32_t /*zero_point*/, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*dy*/, \
const void* /*dx*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
return 0; \
}
// dequantize_per_tensor — f32-scale variant.
#define BARACUDA_KERNELS_DEQUANTIZE_PER_TENSOR_INSTANTIATE(NAME, TIN, TOUT) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
float scale, \
int32_t zero_point, \
const void* q, \
void* x, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (q == nullptr || x == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_dequantize_per_tensor<TIN, TOUT>( \
static_cast<const TOUT*>(q), \
static_cast<TIN*>(x), \
numel, scale, zero_point, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
float /*scale*/, \
int32_t /*zero_point*/, \
const void* /*q*/, \
const void* /*x*/) \
{ \
if (numel < 0) return 2; \
return 0; \
}
#define BARACUDA_KERNELS_DEQUANTIZE_PER_TENSOR_F64_INSTANTIATE(NAME, TOUT) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
double scale, \
int32_t zero_point, \
const void* q, \
void* x, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (q == nullptr || x == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_dequantize_per_tensor_f64<TOUT>( \
static_cast<const TOUT*>(q), \
static_cast<double*>(x), \
numel, scale, zero_point, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
double /*scale*/, \
int32_t /*zero_point*/, \
const void* /*q*/, \
const void* /*x*/) \
{ \
if (numel < 0) return 2; \
return 0; \
}
// dequantize_per_tensor BW — dq = dy * scale. f32-scale variant.
#define BARACUDA_KERNELS_DEQUANTIZE_PER_TENSOR_BW_INSTANTIATE(NAME, TIN) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
float scale, \
const void* dy, \
void* dq, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (dy == nullptr || dq == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_dequantize_per_tensor_backward<TIN>( \
static_cast<const TIN*>(dy), \
static_cast<TIN*>(dq), \
numel, scale, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
float /*scale*/, \
const void* /*dy*/, \
const void* /*dq*/) \
{ \
if (numel < 0) return 2; \
return 0; \
}
#define BARACUDA_KERNELS_DEQUANTIZE_PER_TENSOR_BW_F64_INSTANTIATE(NAME) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
double scale, \
const void* dy, \
void* dq, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (dy == nullptr || dq == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_dequantize_per_tensor_backward_f64( \
static_cast<const double*>(dy), \
static_cast<double*>(dq), \
numel, scale, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
double /*scale*/, \
const void* /*dy*/, \
const void* /*dq*/) \
{ \
if (numel < 0) return 2; \
return 0; \
}
// fake_quantize FW — f32-scale.
#define BARACUDA_KERNELS_FAKE_QUANTIZE_INSTANTIATE(NAME, TIN) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
float scale, \
int32_t zero_point, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
void* y, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || y == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_fake_quantize<TIN>( \
static_cast<const TIN*>(x), \
static_cast<TIN*>(y), \
numel, scale, zero_point, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
float /*scale*/, \
int32_t /*zero_point*/, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*y*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
return 0; \
}
// fake_quantize FW — f64.
#define BARACUDA_KERNELS_FAKE_QUANTIZE_F64_INSTANTIATE(NAME) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
double scale, \
int32_t zero_point, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
void* y, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || y == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_fake_quantize_f64( \
static_cast<const double*>(x), \
static_cast<double*>(y), \
numel, scale, zero_point, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
double /*scale*/, \
int32_t /*zero_point*/, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*y*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
return 0; \
}
// fake_quantize BW.
#define BARACUDA_KERNELS_FAKE_QUANTIZE_BW_INSTANTIATE(NAME, TIN) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
float scale, \
int32_t zero_point, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
const void* dy, \
void* dx, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || dy == nullptr || dx == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_fake_quantize_backward<TIN>( \
static_cast<const TIN*>(x), \
static_cast<const TIN*>(dy), \
static_cast<TIN*>(dx), \
numel, scale, zero_point, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
float /*scale*/, \
int32_t /*zero_point*/, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*dy*/, \
const void* /*dx*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
return 0; \
}
#define BARACUDA_KERNELS_FAKE_QUANTIZE_BW_F64_INSTANTIATE(NAME) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
double scale, \
int32_t zero_point, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
const void* dy, \
void* dx, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || dy == nullptr || dx == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_fake_quantize_backward_f64( \
static_cast<const double*>(x), \
static_cast<const double*>(dy), \
static_cast<double*>(dx), \
numel, scale, zero_point, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
double /*scale*/, \
int32_t /*zero_point*/, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*dy*/, \
const void* /*dx*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
return 0; \
}
// Per-channel quantize FW.
#define BARACUDA_KERNELS_QUANTIZE_PER_CHANNEL_INSTANTIATE(NAME, TIN, TOUT) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
const void* scale, \
const void* zero_point, \
void* q, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || scale == nullptr || zero_point == nullptr || \
q == nullptr || shape4 == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_quantize_per_channel<TIN, TOUT>( \
static_cast<const TIN*>(x), \
static_cast<const TIN*>(scale), \
static_cast<const int32_t*>(zero_point), \
static_cast<TOUT*>(q), \
numel, shape4, axis, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*scale*/, \
const void* /*zero_point*/, \
const void* /*q*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
if (axis < 0 || axis >= baracuda::quantize::MAX_RANK) return 2; \
if (numel > 0 && shape4 == nullptr) return 2; \
return 0; \
}
#define BARACUDA_KERNELS_QUANTIZE_PER_CHANNEL_F64_INSTANTIATE(NAME, TOUT) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
const void* scale, \
const void* zero_point, \
void* q, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || scale == nullptr || zero_point == nullptr || \
q == nullptr || shape4 == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_quantize_per_channel_f64<TOUT>( \
static_cast<const double*>(x), \
static_cast<const double*>(scale), \
static_cast<const int32_t*>(zero_point), \
static_cast<TOUT*>(q), \
numel, shape4, axis, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*scale*/, \
const void* /*zero_point*/, \
const void* /*q*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
if (axis < 0 || axis >= baracuda::quantize::MAX_RANK) return 2; \
if (numel > 0 && shape4 == nullptr) return 2; \
return 0; \
}
// Per-channel quantize BW (STE).
#define BARACUDA_KERNELS_QUANTIZE_PER_CHANNEL_BW_INSTANTIATE(NAME, TIN) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
const void* scale, \
const void* zero_point, \
const void* dy, \
void* dx, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || scale == nullptr || zero_point == nullptr || \
dy == nullptr || dx == nullptr || shape4 == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_quantize_per_channel_backward<TIN>( \
static_cast<const TIN*>(x), \
static_cast<const TIN*>(scale), \
static_cast<const int32_t*>(zero_point), \
static_cast<const TIN*>(dy), \
static_cast<TIN*>(dx), \
numel, shape4, axis, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*scale*/, \
const void* /*zero_point*/, \
const void* /*dy*/, \
const void* /*dx*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
if (axis < 0 || axis >= baracuda::quantize::MAX_RANK) return 2; \
if (numel > 0 && shape4 == nullptr) return 2; \
return 0; \
}
#define BARACUDA_KERNELS_QUANTIZE_PER_CHANNEL_BW_F64_INSTANTIATE(NAME) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
int32_t q_min, \
int32_t q_max, \
const void* x, \
const void* scale, \
const void* zero_point, \
const void* dy, \
void* dx, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (x == nullptr || scale == nullptr || zero_point == nullptr || \
dy == nullptr || dx == nullptr || shape4 == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_quantize_per_channel_backward_f64( \
static_cast<const double*>(x), \
static_cast<const double*>(scale), \
static_cast<const int32_t*>(zero_point), \
static_cast<const double*>(dy), \
static_cast<double*>(dx), \
numel, shape4, axis, q_min, q_max, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
int32_t q_min, \
int32_t q_max, \
const void* /*x*/, \
const void* /*scale*/, \
const void* /*zero_point*/, \
const void* /*dy*/, \
const void* /*dx*/) \
{ \
if (numel < 0) return 2; \
if (q_min > q_max) return 2; \
if (axis < 0 || axis >= baracuda::quantize::MAX_RANK) return 2; \
if (numel > 0 && shape4 == nullptr) return 2; \
return 0; \
}
// Per-channel dequantize FW.
#define BARACUDA_KERNELS_DEQUANTIZE_PER_CHANNEL_INSTANTIATE(NAME, TIN, TOUT) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
const void* q, \
const void* scale, \
const void* zero_point, \
void* x, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (q == nullptr || scale == nullptr || zero_point == nullptr || \
x == nullptr || shape4 == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_dequantize_per_channel<TIN, TOUT>( \
static_cast<const TOUT*>(q), \
static_cast<const TIN*>(scale), \
static_cast<const int32_t*>(zero_point), \
static_cast<TIN*>(x), \
numel, shape4, axis, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
const void* /*q*/, \
const void* /*scale*/, \
const void* /*zero_point*/, \
const void* /*x*/) \
{ \
if (numel < 0) return 2; \
if (axis < 0 || axis >= baracuda::quantize::MAX_RANK) return 2; \
if (numel > 0 && shape4 == nullptr) return 2; \
return 0; \
}
#define BARACUDA_KERNELS_DEQUANTIZE_PER_CHANNEL_F64_INSTANTIATE(NAME, TOUT) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
const void* q, \
const void* scale, \
const void* zero_point, \
void* x, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (q == nullptr || scale == nullptr || zero_point == nullptr || \
x == nullptr || shape4 == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_dequantize_per_channel_f64<TOUT>( \
static_cast<const TOUT*>(q), \
static_cast<const double*>(scale), \
static_cast<const int32_t*>(zero_point), \
static_cast<double*>(x), \
numel, shape4, axis, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
const void* /*q*/, \
const void* /*scale*/, \
const void* /*zero_point*/, \
const void* /*x*/) \
{ \
if (numel < 0) return 2; \
if (axis < 0 || axis >= baracuda::quantize::MAX_RANK) return 2; \
if (numel > 0 && shape4 == nullptr) return 2; \
return 0; \
}
// Per-channel dequantize BW — dq[i] = dy[i] * scale[c].
#define BARACUDA_KERNELS_DEQUANTIZE_PER_CHANNEL_BW_INSTANTIATE(NAME, TIN) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
const void* scale, \
const void* dy, \
void* dq, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (scale == nullptr || dy == nullptr || dq == nullptr || shape4 == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_dequantize_per_channel_backward<TIN>( \
static_cast<const TIN*>(scale), \
static_cast<const TIN*>(dy), \
static_cast<TIN*>(dq), \
numel, shape4, axis, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
const void* /*scale*/, \
const void* /*dy*/, \
const void* /*dq*/) \
{ \
if (numel < 0) return 2; \
if (axis < 0 || axis >= baracuda::quantize::MAX_RANK) return 2; \
if (numel > 0 && shape4 == nullptr) return 2; \
return 0; \
}
#define BARACUDA_KERNELS_DEQUANTIZE_PER_CHANNEL_BW_F64_INSTANTIATE(NAME) \
extern "C" int32_t baracuda_kernels_##NAME##_run( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
const void* scale, \
const void* dy, \
void* dq, \
void* /*workspace*/, size_t /*workspace_bytes*/, \
void* stream_ptr) \
{ \
if (scale == nullptr || dy == nullptr || dq == nullptr || shape4 == nullptr) return 2; \
cudaStream_t stream = static_cast<cudaStream_t>(stream_ptr); \
return baracuda::quantize::launch_dequantize_per_channel_backward_f64( \
static_cast<const double*>(scale), \
static_cast<const double*>(dy), \
static_cast<double*>(dq), \
numel, shape4, axis, stream); \
} \
extern "C" int32_t baracuda_kernels_##NAME##_can_implement( \
int64_t numel, \
const int32_t* shape4, \
int32_t axis, \
const void* /*scale*/, \
const void* /*dy*/, \
const void* /*dq*/) \
{ \
if (numel < 0) return 2; \
if (axis < 0 || axis >= baracuda::quantize::MAX_RANK) return 2; \
if (numel > 0 && shape4 == nullptr) return 2; \
return 0; \
}
#endif // BARACUDA_QUANTIZE_CUH