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2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 | #include <ATen/Dispatch.h>
#include <ATen/cuda/CUDAContext.h>
#include <c10/cuda/CUDAException.h>
#include <c10/cuda/CUDAGuard.h>
#include "../torch-ext/torch_binding.h"
#include <cuda_bf16.h>
#include <cuda_fp16.h>
#include <mma.h>
#include <algorithm>
#include <cstdint>
#include <cstdlib>
using namespace nvcuda;
namespace {
// Escape hatch (and A/B benchmarking toggle) for the cp.async-pipelined mma64
// path: set ORBITQUANT_MMA64_DISABLE_PIPELINE=1 to force the legacy kernel.
inline bool orbitquant_mma64_pipeline_disabled() {
static const bool disabled = []() {
char const *value = std::getenv("ORBITQUANT_MMA64_DISABLE_PIPELINE");
return value != nullptr && value[0] != '\0' && value[0] != '0';
}();
return disabled;
}
// ORBITQUANT_MMA64_FORCE_PIPELINE=1 extends the pipeline to every eligible
// width for re-benchmarking on other GPUs.
inline bool orbitquant_mma64_pipeline_forced() {
static const bool forced = []() {
char const *value = std::getenv("ORBITQUANT_MMA64_FORCE_PIPELINE");
return value != nullptr && value[0] != '\0' && value[0] != '0';
}();
return forced;
}
} // namespace
__device__ __forceinline__ uint32_t unpack_lowbit_index(
uint8_t const *__restrict__ packed_weight_indices,
int64_t value_offset,
int64_t bits,
uint32_t mask) {
const int64_t bit_start = value_offset * bits;
const int64_t byte_index = bit_start >> 3;
const int64_t bit_offset = bit_start & 7;
uint32_t raw = packed_weight_indices[byte_index];
if (bit_offset + bits > 8) {
raw |= static_cast<uint32_t>(packed_weight_indices[byte_index + 1]) << 8;
}
return (raw >> bit_offset) & mask;
}
template <int Bits>
__device__ __forceinline__ uint32_t unpack_lowbit_index_const(
uint8_t const *__restrict__ packed_weight_indices,
int64_t value_offset) {
constexpr uint32_t mask = (1u << Bits) - 1u;
const int64_t bit_start = value_offset * Bits;
const int64_t byte_index = bit_start >> 3;
const int64_t bit_offset = bit_start & 7;
uint32_t raw = packed_weight_indices[byte_index];
if (bit_offset + Bits > 8) {
raw |= static_cast<uint32_t>(packed_weight_indices[byte_index + 1]) << 8;
}
return (raw >> bit_offset) & mask;
}
template <typename T>
__device__ __forceinline__ T orbitquant_mma_from_float(float value);
template <>
__device__ __forceinline__ half orbitquant_mma_from_float<half>(float value) {
return __float2half(value);
}
template <>
__device__ __forceinline__ __nv_bfloat16 orbitquant_mma_from_float<__nv_bfloat16>(
float value) {
return __float2bfloat16(value);
}
template <int Bits, int Values, typename mma_t>
__device__ __forceinline__ void decode_mma64_weight_segment(
mma_t *__restrict__ destination,
uint8_t const *__restrict__ packed_weight_indices,
c10::BFloat16 const *__restrict__ row_norms,
float const *__restrict__ centroids,
int64_t global_col,
int64_t global_k,
int64_t in_features,
bool valid_segment) {
constexpr uint32_t mask = (1u << Bits) - 1u;
constexpr int word_count = (Values * Bits + 31) / 32;
uint32_t packed_words[word_count] = {};
float norm = 0.0f;
if (valid_segment) {
const int64_t value_offset = global_col * in_features + global_k;
const int64_t byte_index = (value_offset * Bits) >> 3;
auto const *words = reinterpret_cast<uint32_t const *>(
packed_weight_indices + byte_index);
#pragma unroll
for (int word = 0; word < word_count; ++word) {
packed_words[word] = words[word];
}
norm = static_cast<float>(row_norms[global_col]);
}
#pragma unroll
for (int index_offset = 0; index_offset < Values; ++index_offset) {
const int bit_start = index_offset * Bits;
const int word_index = bit_start >> 5;
const int shift = bit_start & 31;
uint32_t raw = packed_words[word_index] >> shift;
if (shift + Bits > 32) {
raw |= packed_words[word_index + 1] << (32 - shift);
}
const uint32_t codebook_index = raw & mask;
const float value = valid_segment ? norm * centroids[codebook_index] : 0.0f;
destination[index_offset] = orbitquant_mma_from_float<mma_t>(value);
}
}
template <int Bits, int Values, typename mma_t>
__device__ __forceinline__ void decode_mma64_weight_segment_from_stage(
mma_t *__restrict__ destination,
uint8_t const *__restrict__ staged_bytes,
float const *__restrict__ centroids,
float norm,
bool valid_segment) {
constexpr uint32_t mask = (1u << Bits) - 1u;
constexpr int word_count = (Values * Bits + 31) / 32;
uint32_t packed_words[word_count] = {};
if (valid_segment) {
auto const *words = reinterpret_cast<uint32_t const *>(staged_bytes);
#pragma unroll
for (int word = 0; word < word_count; ++word) {
packed_words[word] = words[word];
}
}
#pragma unroll
for (int index_offset = 0; index_offset < Values; ++index_offset) {
const int bit_start = index_offset * Bits;
const int word_index = bit_start >> 5;
const int shift = bit_start & 31;
uint32_t raw = packed_words[word_index] >> shift;
if (shift + Bits > 32) {
raw |= packed_words[word_index + 1] << (32 - shift);
}
const uint32_t codebook_index = raw & mask;
const float value = valid_segment ? norm * centroids[codebook_index] : 0.0f;
destination[index_offset] = orbitquant_mma_from_float<mma_t>(value);
}
}
// WMMA fragments (and the bf16 variants in particular) only exist on sm80+.
// Multi-architecture builds still instantiate these templates for older
// targets, so the bodies compile to empty stubs there; the host dispatch
// requires compute capability >= 8 before launching either kernel.
template <typename storage_t, typename mma_t, int Bits>
__global__ void orbitquant_packed_matmul_mma64_kernel(
storage_t *__restrict__ out,
storage_t const *__restrict__ x,
uint8_t const *__restrict__ packed_weight_indices,
c10::BFloat16 const *__restrict__ row_norms,
float const *__restrict__ centroids,
storage_t const *__restrict__ bias,
bool has_bias,
int64_t rows,
int64_t out_features,
int64_t in_features) {
#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 800
constexpr int tile_m = 128;
constexpr int tile_n = 128;
constexpr int tile_k = 64;
constexpr int padded_k = 72;
constexpr int warps_per_block = 8;
constexpr int warp_tile = 16;
constexpr int col_tiles = tile_n / warp_tile;
constexpr int x_vector_values = 8;
constexpr int x_vectors_per_row = tile_k / x_vector_values;
constexpr int weight_segment_values = Bits == 3 ? 32 : 16;
constexpr int weight_segments_per_row = tile_k / weight_segment_values;
static_assert(sizeof(storage_t) == sizeof(mma_t));
__shared__ mma_t x_tile[tile_m * padded_k];
__shared__ mma_t weight_tile[tile_n * padded_k];
__shared__ float accumulator_tile[warps_per_block * warp_tile * warp_tile];
const int warp_id = threadIdx.x / warpSize;
const int lane = threadIdx.x & (warpSize - 1);
const int64_t block_row = int64_t(blockIdx.y) * tile_m;
const int64_t block_col = int64_t(blockIdx.x) * tile_n;
wmma::fragment<wmma::accumulator, warp_tile, warp_tile, warp_tile, float>
accumulators[col_tiles];
#pragma unroll
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
wmma::fill_fragment(accumulators[col_tile], 0.0f);
}
for (int64_t k_start = 0; k_start < in_features; k_start += tile_k) {
constexpr int x_vector_tasks = tile_m * x_vectors_per_row;
for (int task = threadIdx.x; task < x_vector_tasks; task += blockDim.x) {
const int local_row = task / x_vectors_per_row;
const int local_vector = task - local_row * x_vectors_per_row;
const int local_k = local_vector * x_vector_values;
const int64_t global_row = block_row + local_row;
auto *destination = reinterpret_cast<uint4 *>(
x_tile + local_row * padded_k + local_k);
if (global_row < rows) {
auto const *source = reinterpret_cast<uint4 const *>(
x + global_row * in_features + k_start + local_k);
*destination = *source;
} else {
*destination = make_uint4(0, 0, 0, 0);
}
}
constexpr int weight_tasks = tile_n * weight_segments_per_row;
for (int weight_task = threadIdx.x; weight_task < weight_tasks;
weight_task += blockDim.x) {
const int local_col = weight_task / weight_segments_per_row;
const int local_segment =
weight_task - local_col * weight_segments_per_row;
const int local_k = local_segment * weight_segment_values;
const int64_t global_col = block_col + local_col;
decode_mma64_weight_segment<Bits, weight_segment_values>(
weight_tile + local_col * padded_k + local_k,
packed_weight_indices,
row_norms,
centroids,
global_col,
k_start + local_k,
in_features,
global_col < out_features);
}
__syncthreads();
#pragma unroll
for (int local_k = 0; local_k < tile_k; local_k += warp_tile) {
wmma::fragment<wmma::matrix_a, warp_tile, warp_tile, warp_tile, mma_t,
wmma::row_major>
lhs;
wmma::load_matrix_sync(
lhs,
x_tile + warp_id * warp_tile * padded_k + local_k,
padded_k);
#pragma unroll
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
wmma::fragment<wmma::matrix_b, warp_tile, warp_tile, warp_tile, mma_t,
wmma::col_major>
rhs;
wmma::load_matrix_sync(
rhs,
weight_tile + col_tile * warp_tile * padded_k + local_k,
padded_k);
wmma::mma_sync(
accumulators[col_tile], lhs, rhs, accumulators[col_tile]);
}
}
__syncthreads();
}
float *warp_accumulator = accumulator_tile + warp_id * warp_tile * warp_tile;
#pragma unroll
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
wmma::store_matrix_sync(
warp_accumulator,
accumulators[col_tile],
warp_tile,
wmma::mem_row_major);
__syncwarp();
for (int offset = lane; offset < warp_tile * warp_tile; offset += warpSize) {
const int local_row = offset / warp_tile;
const int local_col = offset - local_row * warp_tile;
const int64_t global_row = block_row + warp_id * warp_tile + local_row;
const int64_t global_col = block_col + col_tile * warp_tile + local_col;
if (global_row < rows && global_col < out_features) {
float value = warp_accumulator[offset];
if (has_bias) {
value += static_cast<float>(bias[global_col]);
}
out[global_row * out_features + global_col] =
static_cast<storage_t>(value);
}
}
__syncwarp();
}
#endif // __CUDA_ARCH__ >= 800
}
template <int Bytes>
__device__ __forceinline__ void copy_async(
void *__restrict__ destination,
void const *__restrict__ source) {
#if __CUDA_ARCH__ >= 800
const uint32_t shared_address =
static_cast<uint32_t>(__cvta_generic_to_shared(destination));
if constexpr (Bytes == 16) {
asm volatile(
"cp.async.ca.shared.global [%0], [%1], 16;\n" : : "r"(shared_address),
"l"(source));
} else {
static_assert(Bytes == 8);
asm volatile(
"cp.async.ca.shared.global [%0], [%1], 8;\n" : : "r"(shared_address),
"l"(source));
}
#else
if constexpr (Bytes == 16) {
*reinterpret_cast<uint4 *>(destination) =
*reinterpret_cast<uint4 const *>(source);
} else {
static_assert(Bytes == 8);
*reinterpret_cast<uint2 *>(destination) =
*reinterpret_cast<uint2 const *>(source);
}
#endif
}
__device__ __forceinline__ void commit_async_copies() {
#if __CUDA_ARCH__ >= 800
asm volatile("cp.async.commit_group;\n" : :);
#endif
}
__device__ __forceinline__ void wait_for_async_copies() {
#if __CUDA_ARCH__ >= 800
asm volatile("cp.async.wait_group 0;\n" : :);
#endif
}
// cp.async-pipelined variant of the mma64 kernel (sm80+). Interior blocks
// double-buffer the X tile and the packed weight bytes so global loads overlap
// the MMA work; edge blocks keep the guarded synchronous path.
template <typename storage_t, typename mma_t, int Bits>
__global__ void orbitquant_packed_matmul_mma64_pipelined_kernel(
storage_t *__restrict__ out,
storage_t const *__restrict__ x,
uint8_t const *__restrict__ packed_weight_indices,
c10::BFloat16 const *__restrict__ row_norms,
float const *__restrict__ centroids,
storage_t const *__restrict__ bias,
bool has_bias,
int64_t rows,
int64_t out_features,
int64_t in_features) {
#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 800
constexpr int tile_m = 128;
constexpr int tile_n = 128;
constexpr int tile_k = 64;
constexpr int padded_k = 72;
constexpr int warps_per_block = 8;
constexpr int warp_tile = 16;
constexpr int col_tiles = tile_n / warp_tile;
constexpr int x_vector_values = 8;
constexpr int x_vectors_per_row = tile_k / x_vector_values;
constexpr int seg_stride = tile_k * Bits / 8;
constexpr int weight_segment_values = Bits == 3 ? 32 : 16;
constexpr int weight_segments_per_row = tile_k / weight_segment_values;
constexpr int segment_bytes = weight_segment_values * Bits / 8;
constexpr int weight_copy_bytes = Bits == 3 ? 8 : 16;
static_assert(sizeof(storage_t) == sizeof(mma_t));
static_assert(Bits == 2 || Bits == 3 || Bits == 4 || Bits == 6);
static_assert(seg_stride % weight_copy_bytes == 0);
extern __shared__ __align__(16) uint8_t dynamic_shared[];
mma_t *x_tiles = reinterpret_cast<mma_t *>(dynamic_shared);
mma_t *weight_tile = x_tiles + 2 * tile_m * padded_k;
float *accumulator_tile =
reinterpret_cast<float *>(weight_tile + tile_n * padded_k);
uint8_t *packed_stage = reinterpret_cast<uint8_t *>(
accumulator_tile + warps_per_block * warp_tile * warp_tile);
const int warp_id = threadIdx.x / warpSize;
const int lane = threadIdx.x & (warpSize - 1);
const int64_t block_row = int64_t(blockIdx.y) * tile_m;
const int64_t block_col = int64_t(blockIdx.x) * tile_n;
const int64_t packed_row_bytes = in_features * Bits / 8;
wmma::fragment<wmma::accumulator, warp_tile, warp_tile, warp_tile, float>
accumulators[col_tiles];
#pragma unroll
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
wmma::fill_fragment(accumulators[col_tile], 0.0f);
}
const bool interior =
block_row + tile_m <= rows && block_col + tile_n <= out_features;
if (interior) {
constexpr int x_copies = tile_m * x_vectors_per_row;
constexpr int w_copies = tile_n * seg_stride / weight_copy_bytes;
auto stage_tile = [&](int buffer, int64_t k_start) {
mma_t *x_destination = x_tiles + buffer * tile_m * padded_k;
for (int task = threadIdx.x; task < x_copies; task += blockDim.x) {
const int local_row = task / x_vectors_per_row;
const int local_vector = task - local_row * x_vectors_per_row;
copy_async<16>(
x_destination + local_row * padded_k + local_vector * x_vector_values,
x + (block_row + local_row) * in_features + k_start +
local_vector * x_vector_values);
}
uint8_t *stage_destination = packed_stage + buffer * tile_n * seg_stride;
for (int task = threadIdx.x; task < w_copies; task += blockDim.x) {
const int byte_offset = task * weight_copy_bytes;
const int local_col = byte_offset / seg_stride;
const int seg_byte = byte_offset - local_col * seg_stride;
copy_async<weight_copy_bytes>(
stage_destination + byte_offset,
packed_weight_indices + (block_col + local_col) * packed_row_bytes +
(k_start * Bits) / 8 + seg_byte);
}
commit_async_copies();
};
stage_tile(0, 0);
wait_for_async_copies();
__syncthreads();
for (int64_t k_start = 0; k_start < in_features; k_start += tile_k) {
const int buffer = static_cast<int>((k_start / tile_k) & 1);
const bool has_next = k_start + tile_k < in_features;
if (has_next) {
stage_tile(buffer ^ 1, k_start + tile_k);
}
uint8_t const *stage_source = packed_stage + buffer * tile_n * seg_stride;
constexpr int weight_tasks = tile_n * weight_segments_per_row;
for (int task = threadIdx.x; task < weight_tasks; task += blockDim.x) {
const int local_col = task / weight_segments_per_row;
const int local_segment = task - local_col * weight_segments_per_row;
decode_mma64_weight_segment_from_stage<Bits, weight_segment_values>(
weight_tile + local_col * padded_k +
local_segment * weight_segment_values,
stage_source + local_col * seg_stride + local_segment * segment_bytes,
centroids,
static_cast<float>(row_norms[block_col + local_col]),
true);
}
__syncthreads();
mma_t const *x_source = x_tiles + buffer * tile_m * padded_k;
#pragma unroll
for (int local_k = 0; local_k < tile_k; local_k += warp_tile) {
wmma::fragment<wmma::matrix_a, warp_tile, warp_tile, warp_tile, mma_t,
wmma::row_major>
lhs;
wmma::load_matrix_sync(
lhs,
x_source + warp_id * warp_tile * padded_k + local_k,
padded_k);
#pragma unroll
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
wmma::fragment<wmma::matrix_b, warp_tile, warp_tile, warp_tile, mma_t,
wmma::col_major>
rhs;
wmma::load_matrix_sync(
rhs,
weight_tile + col_tile * warp_tile * padded_k + local_k,
padded_k);
wmma::mma_sync(
accumulators[col_tile], lhs, rhs, accumulators[col_tile]);
}
}
__syncthreads();
if (has_next) {
wait_for_async_copies();
__syncthreads();
}
}
} else {
mma_t *x_tile = x_tiles;
for (int64_t k_start = 0; k_start < in_features; k_start += tile_k) {
constexpr int x_vector_tasks = tile_m * x_vectors_per_row;
for (int task = threadIdx.x; task < x_vector_tasks; task += blockDim.x) {
const int local_row = task / x_vectors_per_row;
const int local_vector = task - local_row * x_vectors_per_row;
const int local_k = local_vector * x_vector_values;
const int64_t global_row = block_row + local_row;
auto *destination = reinterpret_cast<uint4 *>(
x_tile + local_row * padded_k + local_k);
if (global_row < rows) {
auto const *source = reinterpret_cast<uint4 const *>(
x + global_row * in_features + k_start + local_k);
*destination = *source;
} else {
*destination = make_uint4(0, 0, 0, 0);
}
}
constexpr int weight_tasks = tile_n * weight_segments_per_row;
for (int weight_task = threadIdx.x; weight_task < weight_tasks;
weight_task += blockDim.x) {
const int local_col = weight_task / weight_segments_per_row;
const int local_segment =
weight_task - local_col * weight_segments_per_row;
const int local_k = local_segment * weight_segment_values;
const int64_t global_col = block_col + local_col;
decode_mma64_weight_segment<Bits, weight_segment_values>(
weight_tile + local_col * padded_k + local_k,
packed_weight_indices,
row_norms,
centroids,
global_col,
k_start + local_k,
in_features,
global_col < out_features);
}
__syncthreads();
#pragma unroll
for (int local_k = 0; local_k < tile_k; local_k += warp_tile) {
wmma::fragment<wmma::matrix_a, warp_tile, warp_tile, warp_tile, mma_t,
wmma::row_major>
lhs;
wmma::load_matrix_sync(
lhs,
x_tile + warp_id * warp_tile * padded_k + local_k,
padded_k);
#pragma unroll
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
wmma::fragment<wmma::matrix_b, warp_tile, warp_tile, warp_tile, mma_t,
wmma::col_major>
rhs;
wmma::load_matrix_sync(
rhs,
weight_tile + col_tile * warp_tile * padded_k + local_k,
padded_k);
wmma::mma_sync(
accumulators[col_tile], lhs, rhs, accumulators[col_tile]);
}
}
__syncthreads();
}
}
float *warp_accumulator = accumulator_tile + warp_id * warp_tile * warp_tile;
#pragma unroll
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
wmma::store_matrix_sync(
warp_accumulator,
accumulators[col_tile],
warp_tile,
wmma::mem_row_major);
__syncwarp();
for (int offset = lane; offset < warp_tile * warp_tile; offset += warpSize) {
const int local_row = offset / warp_tile;
const int local_col = offset - local_row * warp_tile;
const int64_t global_row = block_row + warp_id * warp_tile + local_row;
const int64_t global_col = block_col + col_tile * warp_tile + local_col;
if (global_row < rows && global_col < out_features) {
float value = warp_accumulator[offset];
if (has_bias) {
value += static_cast<float>(bias[global_col]);
}
out[global_row * out_features + global_col] =
static_cast<storage_t>(value);
}
}
__syncwarp();
}
#endif // __CUDA_ARCH__ >= 800
}
__device__ __forceinline__ uint8_t orbitquant_bucketize_w4(
float value,
float const *__restrict__ boundaries) {
int index = value > boundaries[7] ? 8 : 0;
index += value > boundaries[index + 3] ? 4 : 0;
index += value > boundaries[index + 1] ? 2 : 0;
index += value > boundaries[index] ? 1 : 0;
return static_cast<uint8_t>(index);
}
template <typename storage_t, typename index_t, int Dim>
__global__ void orbitquant_rpbh_quantize_pack_w4_kernel(
uint8_t *__restrict__ packed_out,
float *__restrict__ norms_out,
storage_t const *__restrict__ x,
index_t const *__restrict__ permutation,
int8_t const *__restrict__ signs,
float const *__restrict__ boundaries,
float eps,
float inv_sqrt_block,
int64_t rows) {
extern __shared__ __align__(16) float shared[];
float *values = shared;
float *reduction = values + Dim;
float *boundary_table = reduction + blockDim.x;
const int tid = threadIdx.x;
const int64_t row = blockIdx.x;
float squared_sum = 0.0f;
for (int col = tid; col < Dim; col += blockDim.x) {
const int64_t source_col = permutation[col];
const float value =
static_cast<float>(x[row * Dim + source_col]) *
static_cast<float>(signs[col]);
values[col] = value;
squared_sum = fmaf(value, value, squared_sum);
}
reduction[tid] = squared_sum;
if (tid < 15) {
boundary_table[tid] = boundaries[tid];
}
__syncthreads();
for (int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
if (tid < stride) {
reduction[tid] += reduction[tid + stride];
}
__syncthreads();
}
const float norm = sqrtf(reduction[0]);
if (tid == 0) {
norms_out[row] = norm;
}
const float inv_norm = 1.0f / (norm + eps);
for (int col = tid; col < Dim; col += blockDim.x) {
values[col] *= inv_norm;
}
__syncthreads();
#pragma unroll
for (int butterfly_width = 1; butterfly_width < Dim;
butterfly_width <<= 1) {
constexpr int butterflies = Dim / 2;
for (int butterfly = tid; butterfly < butterflies;
butterfly += blockDim.x) {
const int group = butterfly / butterfly_width;
const int offset = butterfly - group * butterfly_width;
const int left = group * (butterfly_width * 2) + offset;
const int right = left + butterfly_width;
const float lhs = values[left];
const float rhs = values[right];
values[left] = lhs + rhs;
values[right] = lhs - rhs;
}
__syncthreads();
}
constexpr int packed_dim = Dim / 2;
for (int byte_col = tid; byte_col < packed_dim; byte_col += blockDim.x) {
const float low_value = values[byte_col * 2] * inv_sqrt_block;
const float high_value = values[byte_col * 2 + 1] * inv_sqrt_block;
const uint8_t low = orbitquant_bucketize_w4(low_value, boundary_table);
const uint8_t high = orbitquant_bucketize_w4(high_value, boundary_table);
packed_out[row * packed_dim + byte_col] =
static_cast<uint8_t>(low | (high << 4));
}
}
template <typename storage_t, typename index_t, int Dim, int OrbitBlock>
__global__ void orbitquant_rpbh_quantize_int8_kernel(
int8_t *__restrict__ int8_out,
float *__restrict__ norms_out,
storage_t const *__restrict__ x,
index_t const *__restrict__ permutation,
int8_t const *__restrict__ signs,
float const *__restrict__ boundaries,
int8_t const *__restrict__ codes,
float eps,
float inv_sqrt_block,
int64_t rows) {
static_assert(Dim % OrbitBlock == 0);
extern __shared__ __align__(16) float shared[];
float *values = shared;
float *reduction = values + Dim;
float *boundary_table = reduction + blockDim.x;
int8_t *code_table = reinterpret_cast<int8_t *>(boundary_table + 15);
const int tid = threadIdx.x;
const int64_t row = blockIdx.x;
float squared_sum = 0.0f;
for (int col = tid; col < Dim; col += blockDim.x) {
const int64_t source_col = permutation[col];
const float value =
static_cast<float>(x[row * Dim + source_col]) *
static_cast<float>(signs[col]);
values[col] = value;
squared_sum = fmaf(value, value, squared_sum);
}
reduction[tid] = squared_sum;
if (tid < 15) {
boundary_table[tid] = boundaries[tid];
}
if (tid < 16) {
code_table[tid] = codes[tid];
}
__syncthreads();
for (int stride = blockDim.x / 2; stride > 0; stride >>= 1) {
if (tid < stride) {
reduction[tid] += reduction[tid + stride];
}
__syncthreads();
}
const float norm = sqrtf(reduction[0]);
if (tid == 0) {
norms_out[row] = norm;
}
const float inv_norm = 1.0f / (norm + eps);
for (int col = tid; col < Dim; col += blockDim.x) {
values[col] *= inv_norm;
}
__syncthreads();
#pragma unroll
for (int butterfly_width = 1; butterfly_width < OrbitBlock;
butterfly_width <<= 1) {
constexpr int butterflies = Dim / 2;
constexpr int butterflies_per_block = OrbitBlock / 2;
for (int butterfly = tid; butterfly < butterflies;
butterfly += blockDim.x) {
const int orbit_block = butterfly / butterflies_per_block;
const int local_butterfly =
butterfly - orbit_block * butterflies_per_block;
const int group = local_butterfly / butterfly_width;
const int offset = local_butterfly - group * butterfly_width;
const int left = orbit_block * OrbitBlock +
group * (butterfly_width * 2) + offset;
const int right = left + butterfly_width;
const float lhs = values[left];
const float rhs = values[right];
values[left] = lhs + rhs;
values[right] = lhs - rhs;
}
__syncthreads();
}
for (int col = tid; col < Dim; col += blockDim.x) {
const float value = values[col] * inv_sqrt_block;
const uint8_t index = orbitquant_bucketize_w4(value, boundary_table);
int8_out[row * Dim + col] = code_table[index];
}
}
template <
typename storage_t,
int TileM,
int TileN,
bool AsyncPacked,
bool KMajorWeight>
__global__ void orbitquant_packed_w4a4_int8_mma_kernel(
storage_t *__restrict__ out,
uint8_t const *__restrict__ packed_activations,
uint8_t const *__restrict__ packed_weight_indices,
float const *__restrict__ token_norms,
c10::BFloat16 const *__restrict__ row_norms,
int8_t const *__restrict__ activation_codes,
int8_t const *__restrict__ weight_codes,
storage_t const *__restrict__ bias,
bool has_bias,
float activation_scale,
float weight_scale,
int64_t rows,
int64_t out_features,
int64_t in_features) {
#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 800
constexpr int tile_k = 64;
constexpr int packed_tile_k = tile_k / 2;
constexpr int padded_k = 80;
constexpr int warp_tile = 16;
constexpr int warp_rows = TileM / warp_tile;
constexpr int col_tiles_per_warp = 8;
constexpr int warp_col_groups = TileN / (col_tiles_per_warp * warp_tile);
constexpr int warps_per_block = warp_rows * warp_col_groups;
static_assert(TileM == 128 || TileM == 256);
static_assert(TileN == 128 || TileN == 256);
static_assert(warps_per_block == 8 || warps_per_block == 16);
extern __shared__ __align__(16) uint8_t shared_memory[];
int8_t *activation_tile = reinterpret_cast<int8_t *>(shared_memory);
int8_t *weight_tile = activation_tile + TileM * padded_k;
int32_t *accumulator_tile = reinterpret_cast<int32_t *>(
weight_tile + TileN * padded_k);
int8_t *code_tables = reinterpret_cast<int8_t *>(
accumulator_tile + warps_per_block * warp_tile * warp_tile);
uint8_t *packed_activation_stage =
reinterpret_cast<uint8_t *>(code_tables + 32);
uint8_t *packed_weight_stage =
packed_activation_stage + (AsyncPacked ? TileM * packed_tile_k : 0);
const int warp_id = threadIdx.x / warpSize;
const int lane = threadIdx.x & (warpSize - 1);
const int warp_row = warp_id % warp_rows;
const int warp_col_group = warp_id / warp_rows;
const int64_t block_row = int64_t(blockIdx.y) * TileM;
const int64_t block_col = int64_t(blockIdx.x) * TileN;
const int64_t packed_row_stride = in_features / 2;
if (threadIdx.x < 16) {
code_tables[threadIdx.x] = activation_codes[threadIdx.x];
code_tables[16 + threadIdx.x] = weight_codes[threadIdx.x];
}
wmma::fragment<wmma::accumulator, warp_tile, warp_tile, warp_tile, int>
accumulators[col_tiles_per_warp];
#pragma unroll
for (int col_tile = 0; col_tile < col_tiles_per_warp; ++col_tile) {
wmma::fill_fragment(accumulators[col_tile], 0);
}
__syncthreads();
const bool use_async =
AsyncPacked && block_row + TileM <= rows &&
block_col + TileN <= out_features && out_features % 16 == 0;
if constexpr (AsyncPacked) {
if (use_async) {
constexpr int activation_vectors = TileM * packed_tile_k / 16;
for (int vector = threadIdx.x; vector < activation_vectors;
vector += blockDim.x) {
const int byte_offset = vector * 16;
const int local_row = byte_offset / packed_tile_k;
const int local_k_byte = byte_offset - local_row * packed_tile_k;
copy_async<16>(
packed_activation_stage + byte_offset,
packed_activations +
(block_row + local_row) * packed_row_stride + local_k_byte);
}
constexpr int weight_vectors = packed_tile_k * TileN / 16;
for (int vector = threadIdx.x; vector < weight_vectors;
vector += blockDim.x) {
const int byte_offset = vector * 16;
if constexpr (KMajorWeight) {
const int local_k_byte = byte_offset / TileN;
const int local_col = byte_offset - local_k_byte * TileN;
copy_async<16>(
packed_weight_stage + byte_offset,
packed_weight_indices + local_k_byte * out_features + block_col +
local_col);
} else {
const int local_col = byte_offset / packed_tile_k;
const int local_k_byte = byte_offset - local_col * packed_tile_k;
copy_async<16>(
packed_weight_stage + byte_offset,
packed_weight_indices +
(block_col + local_col) * packed_row_stride + local_k_byte);
}
}
commit_async_copies();
wait_for_async_copies();
__syncthreads();
}
}
for (int64_t k_start = 0; k_start < in_features; k_start += tile_k) {
constexpr int activation_tasks = TileM * packed_tile_k;
for (int task = threadIdx.x; task < activation_tasks; task += blockDim.x) {
const int local_row = task / packed_tile_k;
const int local_k_byte = task - local_row * packed_tile_k;
const int64_t global_row = block_row + local_row;
uint8_t packed = 0;
if (use_async) {
packed = packed_activation_stage[task];
} else if (global_row < rows) {
packed = packed_activations[
global_row * packed_row_stride + k_start / 2 + local_k_byte];
}
const int destination = local_row * padded_k + local_k_byte * 2;
activation_tile[destination] = code_tables[packed & 15u];
activation_tile[destination + 1] = code_tables[packed >> 4];
}
constexpr int weight_tasks = packed_tile_k * TileN;
for (int task = threadIdx.x; task < weight_tasks; task += blockDim.x) {
const int local_k_byte = task / TileN;
const int local_col = task - local_k_byte * TileN;
const int64_t global_col = block_col + local_col;
uint8_t packed = 0;
if (use_async) {
if constexpr (KMajorWeight) {
packed = packed_weight_stage[task];
} else {
packed = packed_weight_stage[local_col * packed_tile_k + local_k_byte];
}
} else if (global_col < out_features) {
if constexpr (KMajorWeight) {
packed = packed_weight_indices[
(k_start / 2 + local_k_byte) * out_features + global_col];
} else {
packed = packed_weight_indices[
global_col * packed_row_stride + k_start / 2 + local_k_byte];
}
}
const int destination = local_col * padded_k + local_k_byte * 2;
weight_tile[destination] = code_tables[16 + (packed & 15u)];
weight_tile[destination + 1] = code_tables[16 + (packed >> 4)];
}
__syncthreads();
const bool has_next_tile = k_start + tile_k < in_features;
if constexpr (AsyncPacked) {
if (use_async && has_next_tile) {
const int64_t next_k_byte = (k_start + tile_k) / 2;
constexpr int activation_vectors = TileM * packed_tile_k / 16;
for (int vector = threadIdx.x; vector < activation_vectors;
vector += blockDim.x) {
const int byte_offset = vector * 16;
const int local_row = byte_offset / packed_tile_k;
const int local_k_byte = byte_offset - local_row * packed_tile_k;
copy_async<16>(
packed_activation_stage + byte_offset,
packed_activations +
(block_row + local_row) * packed_row_stride + next_k_byte +
local_k_byte);
}
constexpr int weight_vectors = packed_tile_k * TileN / 16;
for (int vector = threadIdx.x; vector < weight_vectors;
vector += blockDim.x) {
const int byte_offset = vector * 16;
if constexpr (KMajorWeight) {
const int local_k_byte = byte_offset / TileN;
const int local_col = byte_offset - local_k_byte * TileN;
copy_async<16>(
packed_weight_stage + byte_offset,
packed_weight_indices +
(next_k_byte + local_k_byte) * out_features + block_col +
local_col);
} else {
const int local_col = byte_offset / packed_tile_k;
const int local_k_byte = byte_offset - local_col * packed_tile_k;
copy_async<16>(
packed_weight_stage + byte_offset,
packed_weight_indices +
(block_col + local_col) * packed_row_stride + next_k_byte +
local_k_byte);
}
}
commit_async_copies();
}
}
#pragma unroll
for (int local_k = 0; local_k < tile_k; local_k += warp_tile) {
wmma::fragment<wmma::matrix_a, warp_tile, warp_tile, warp_tile, signed char,
wmma::row_major>
lhs;
wmma::load_matrix_sync(
lhs,
reinterpret_cast<signed char const *>(
activation_tile + warp_row * warp_tile * padded_k + local_k),
padded_k);
#pragma unroll
for (int col_tile = 0; col_tile < col_tiles_per_warp; ++col_tile) {
wmma::fragment<wmma::matrix_b, warp_tile, warp_tile, warp_tile, signed char,
wmma::col_major>
rhs;
wmma::load_matrix_sync(
rhs,
reinterpret_cast<signed char const *>(
weight_tile +
(warp_col_group * col_tiles_per_warp + col_tile) * warp_tile *
padded_k +
local_k),
padded_k);
wmma::mma_sync(
accumulators[col_tile], lhs, rhs, accumulators[col_tile]);
}
}
__syncthreads();
if constexpr (AsyncPacked) {
if (use_async && has_next_tile) {
wait_for_async_copies();
__syncthreads();
}
}
}
int32_t *warp_accumulator =
accumulator_tile + warp_id * warp_tile * warp_tile;
const float surrogate_scale = activation_scale * weight_scale;
#pragma unroll
for (int col_tile = 0; col_tile < col_tiles_per_warp; ++col_tile) {
wmma::store_matrix_sync(
warp_accumulator,
accumulators[col_tile],
warp_tile,
wmma::mem_row_major);
__syncwarp();
for (int offset = lane; offset < warp_tile * warp_tile; offset += warpSize) {
const int local_row = offset / warp_tile;
const int local_col = offset - local_row * warp_tile;
const int64_t global_row = block_row + warp_row * warp_tile + local_row;
const int64_t global_col =
block_col +
(warp_col_group * col_tiles_per_warp + col_tile) * warp_tile +
local_col;
if (global_row < rows && global_col < out_features) {
float value = static_cast<float>(warp_accumulator[offset]);
value *= token_norms[global_row] *
static_cast<float>(row_norms[global_col]) * surrogate_scale;
if (has_bias) {
value += static_cast<float>(bias[global_col]);
}
out[global_row * out_features + global_col] =
static_cast<storage_t>(value);
}
}
__syncwarp();
}
#endif // __CUDA_ARCH__ >= 800
}
template <int Bits>
__global__ void orbitquant_packed_matmul_wmma_bf16_kernel(
c10::BFloat16 *__restrict__ out,
c10::BFloat16 const *__restrict__ x,
uint8_t const *__restrict__ packed_weight_indices,
c10::BFloat16 const *__restrict__ row_norms,
float const *__restrict__ centroids,
c10::BFloat16 const *__restrict__ bias,
bool has_bias,
int64_t rows,
int64_t out_features,
int64_t in_features) {
#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 800
constexpr int tile = 16;
constexpr int col_tiles = 4;
constexpr int warps_per_block = 8;
constexpr int rows_per_block = tile * warps_per_block;
__shared__ __nv_bfloat16 x_tile[warps_per_block * tile * tile];
__shared__ __nv_bfloat16 w_tile[col_tiles * tile * tile];
__shared__ float acc_tile[warps_per_block * col_tiles * tile * tile];
const int warp_id = threadIdx.x / warpSize;
const int lane = threadIdx.x & (warpSize - 1);
const int64_t row_start = blockIdx.y * rows_per_block + warp_id * tile;
const int64_t col_start = blockIdx.x * tile * col_tiles;
__nv_bfloat16 *warp_x_tile = x_tile + warp_id * tile * tile;
wmma::fragment<wmma::matrix_a, tile, tile, tile, __nv_bfloat16, wmma::row_major> a_frag;
wmma::fragment<wmma::accumulator, tile, tile, tile, float> c_frag[col_tiles];
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
wmma::fill_fragment(c_frag[col_tile], 0.0f);
}
for (int64_t k_start = 0; k_start < in_features; k_start += tile) {
for (int offset = lane; offset < tile * tile; offset += warpSize) {
const int local_row = offset / tile;
const int local_k = offset - local_row * tile;
const int64_t global_row = row_start + local_row;
const int64_t global_k = k_start + local_k;
float value = 0.0f;
if (global_row < rows && global_k < in_features) {
value = static_cast<float>(x[global_row * in_features + global_k]);
}
warp_x_tile[offset] = __float2bfloat16(value);
}
for (int load_col_tile = warp_id; load_col_tile < col_tiles;
load_col_tile += warps_per_block) {
__nv_bfloat16 *warp_w_tile = w_tile + load_col_tile * tile * tile;
const int64_t tile_col_start = col_start + load_col_tile * tile;
for (int offset = lane; offset < tile * tile; offset += warpSize) {
const int local_k = offset / tile;
const int local_col = offset - local_k * tile;
const int64_t global_k = k_start + local_k;
const int64_t global_col = tile_col_start + local_col;
float value = 0.0f;
if (global_col < out_features && global_k < in_features) {
const int64_t value_offset = global_col * in_features + global_k;
const uint32_t index = unpack_lowbit_index_const<Bits>(
packed_weight_indices, value_offset);
value = static_cast<float>(row_norms[global_col]) * centroids[index];
}
warp_w_tile[offset] = __float2bfloat16(value);
}
}
__syncthreads();
wmma::load_matrix_sync(a_frag, warp_x_tile, tile);
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
wmma::fragment<wmma::matrix_b, tile, tile, tile, __nv_bfloat16, wmma::row_major>
b_frag;
wmma::load_matrix_sync(b_frag, w_tile + col_tile * tile * tile, tile);
wmma::mma_sync(c_frag[col_tile], a_frag, b_frag, c_frag[col_tile]);
}
__syncthreads();
}
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
float *warp_acc_tile = acc_tile + (warp_id * col_tiles + col_tile) * tile * tile;
wmma::store_matrix_sync(warp_acc_tile, c_frag[col_tile], tile, wmma::mem_row_major);
__syncwarp();
for (int offset = lane; offset < tile * tile; offset += warpSize) {
const int local_row = offset / tile;
const int local_col = offset - local_row * tile;
const int64_t global_row = row_start + local_row;
const int64_t global_col = col_start + col_tile * tile + local_col;
if (global_row < rows && global_col < out_features) {
float value = warp_acc_tile[offset];
if (has_bias) {
value += static_cast<float>(bias[global_col]);
}
out[global_row * out_features + global_col] = static_cast<c10::BFloat16>(value);
}
}
}
#endif // __CUDA_ARCH__ >= 800
}
template <int Bits>
__global__ void orbitquant_packed_matmul_wmma_half_kernel(
c10::Half *__restrict__ out,
c10::Half const *__restrict__ x,
uint8_t const *__restrict__ packed_weight_indices,
c10::BFloat16 const *__restrict__ row_norms,
float const *__restrict__ centroids,
c10::Half const *__restrict__ bias,
bool has_bias,
int64_t rows,
int64_t out_features,
int64_t in_features) {
constexpr int tile = 16;
constexpr int col_tiles = 4;
constexpr int warps_per_block = 8;
constexpr int rows_per_block = tile * warps_per_block;
__shared__ half x_tile[warps_per_block * tile * tile];
__shared__ half w_tile[col_tiles * tile * tile];
__shared__ float acc_tile[warps_per_block * col_tiles * tile * tile];
const int warp_id = threadIdx.x / warpSize;
const int lane = threadIdx.x & (warpSize - 1);
const int64_t row_start = blockIdx.y * rows_per_block + warp_id * tile;
const int64_t col_start = blockIdx.x * tile * col_tiles;
half *warp_x_tile = x_tile + warp_id * tile * tile;
wmma::fragment<wmma::matrix_a, tile, tile, tile, half, wmma::row_major> a_frag;
wmma::fragment<wmma::accumulator, tile, tile, tile, float> c_frag[col_tiles];
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
wmma::fill_fragment(c_frag[col_tile], 0.0f);
}
for (int64_t k_start = 0; k_start < in_features; k_start += tile) {
for (int offset = lane; offset < tile * tile; offset += warpSize) {
const int local_row = offset / tile;
const int local_k = offset - local_row * tile;
const int64_t global_row = row_start + local_row;
const int64_t global_k = k_start + local_k;
float value = 0.0f;
if (global_row < rows && global_k < in_features) {
value = static_cast<float>(x[global_row * in_features + global_k]);
}
warp_x_tile[offset] = __float2half(value);
}
for (int load_col_tile = warp_id; load_col_tile < col_tiles;
load_col_tile += warps_per_block) {
half *warp_w_tile = w_tile + load_col_tile * tile * tile;
const int64_t tile_col_start = col_start + load_col_tile * tile;
for (int offset = lane; offset < tile * tile; offset += warpSize) {
const int local_k = offset / tile;
const int local_col = offset - local_k * tile;
const int64_t global_k = k_start + local_k;
const int64_t global_col = tile_col_start + local_col;
float value = 0.0f;
if (global_col < out_features && global_k < in_features) {
const int64_t value_offset = global_col * in_features + global_k;
const uint32_t index = unpack_lowbit_index_const<Bits>(
packed_weight_indices, value_offset);
value = static_cast<float>(row_norms[global_col]) * centroids[index];
}
warp_w_tile[offset] = __float2half(value);
}
}
__syncthreads();
wmma::load_matrix_sync(a_frag, warp_x_tile, tile);
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
wmma::fragment<wmma::matrix_b, tile, tile, tile, half, wmma::row_major> b_frag;
wmma::load_matrix_sync(b_frag, w_tile + col_tile * tile * tile, tile);
wmma::mma_sync(c_frag[col_tile], a_frag, b_frag, c_frag[col_tile]);
}
__syncthreads();
}
for (int col_tile = 0; col_tile < col_tiles; ++col_tile) {
float *warp_acc_tile = acc_tile + (warp_id * col_tiles + col_tile) * tile * tile;
wmma::store_matrix_sync(warp_acc_tile, c_frag[col_tile], tile, wmma::mem_row_major);
__syncwarp();
for (int offset = lane; offset < tile * tile; offset += warpSize) {
const int local_row = offset / tile;
const int local_col = offset - local_row * tile;
const int64_t global_row = row_start + local_row;
const int64_t global_col = col_start + col_tile * tile + local_col;
if (global_row < rows && global_col < out_features) {
float value = warp_acc_tile[offset];
if (has_bias) {
value += static_cast<float>(bias[global_col]);
}
out[global_row * out_features + global_col] = static_cast<c10::Half>(value);
}
}
}
}
template <typename scalar_t>
__global__ void orbitquant_packed_matmul_small_rows_kernel(
scalar_t *__restrict__ out,
scalar_t const *__restrict__ x,
uint8_t const *__restrict__ packed_weight_indices,
c10::BFloat16 const *__restrict__ row_norms,
float const *__restrict__ centroids,
scalar_t const *__restrict__ bias,
bool has_bias,
int64_t rows,
int64_t out_features,
int64_t in_features,
int64_t bits) {
constexpr int channels_per_warp = 4;
const int lane = threadIdx.x;
const int64_t row = blockIdx.y;
const int64_t col_start = int64_t(blockIdx.x) * channels_per_warp;
const uint32_t mask = (1u << bits) - 1u;
float accumulators[channels_per_warp] = {};
float norms[channels_per_warp];
#pragma unroll
for (int col_offset = 0; col_offset < channels_per_warp; ++col_offset) {
const int64_t col = col_start + col_offset;
norms[col_offset] =
col < out_features ? static_cast<float>(row_norms[col]) : 0.0f;
}
for (int64_t k = lane; k < in_features; k += warpSize) {
const float x_value = static_cast<float>(x[row * in_features + k]);
#pragma unroll
for (int col_offset = 0; col_offset < channels_per_warp; ++col_offset) {
const int64_t col = col_start + col_offset;
if (col < out_features) {
const int64_t value_offset = col * in_features + k;
const uint32_t index =
unpack_lowbit_index(packed_weight_indices, value_offset, bits, mask);
accumulators[col_offset] += x_value * norms[col_offset] * centroids[index];
}
}
}
#pragma unroll
for (int col_offset = 0; col_offset < channels_per_warp; ++col_offset) {
#pragma unroll
for (int offset = warpSize / 2; offset > 0; offset >>= 1) {
accumulators[col_offset] +=
__shfl_down_sync(0xffffffffu, accumulators[col_offset], offset);
}
const int64_t col = col_start + col_offset;
if (lane == 0 && col < out_features) {
const float value =
accumulators[col_offset] +
(has_bias ? static_cast<float>(bias[col]) : 0.0f);
out[row * out_features + col] = static_cast<scalar_t>(value);
}
}
}
template <typename scalar_t>
__global__ void orbitquant_packed_matmul_tiled_kernel(
scalar_t *__restrict__ out,
scalar_t const *__restrict__ x,
uint8_t const *__restrict__ packed_weight_indices,
c10::BFloat16 const *__restrict__ row_norms,
float const *__restrict__ centroids,
scalar_t const *__restrict__ bias,
bool has_bias,
int64_t rows,
int64_t out_features,
int64_t in_features,
int64_t bits,
int64_t block_k) {
extern __shared__ float shared[];
float *x_tile = shared;
float *w_tile = shared + blockDim.y * block_k;
const int64_t row = blockIdx.y * blockDim.y + threadIdx.y;
const int64_t col = blockIdx.x * blockDim.x + threadIdx.x;
const int64_t local_row = threadIdx.y;
const int64_t local_col = threadIdx.x;
const int64_t thread_linear = threadIdx.y * blockDim.x + threadIdx.x;
const int64_t thread_count = blockDim.x * blockDim.y;
const uint32_t mask = (1u << bits) - 1u;
const bool output_valid = row < rows && col < out_features;
float acc =
output_valid && has_bias ? static_cast<float>(bias[col]) : 0.0f;
for (int64_t k_start = 0; k_start < in_features; k_start += block_k) {
const int64_t x_tile_values = blockDim.y * block_k;
for (int64_t offset = thread_linear; offset < x_tile_values; offset += thread_count) {
const int64_t tile_row = offset / block_k;
const int64_t tile_k = offset - tile_row * block_k;
const int64_t global_row = blockIdx.y * blockDim.y + tile_row;
const int64_t global_k = k_start + tile_k;
float value = 0.0f;
if (global_row < rows && global_k < in_features) {
value = static_cast<float>(x[global_row * in_features + global_k]);
}
x_tile[offset] = value;
}
const int64_t w_tile_values = block_k * blockDim.x;
for (int64_t offset = thread_linear; offset < w_tile_values; offset += thread_count) {
const int64_t tile_k = offset / blockDim.x;
const int64_t tile_col = offset - tile_k * blockDim.x;
const int64_t global_k = k_start + tile_k;
const int64_t global_col = blockIdx.x * blockDim.x + tile_col;
float value = 0.0f;
if (global_col < out_features && global_k < in_features) {
const int64_t value_offset = global_col * in_features + global_k;
const uint32_t index =
unpack_lowbit_index(packed_weight_indices, value_offset, bits, mask);
value = static_cast<float>(row_norms[global_col]) * centroids[index];
}
w_tile[offset] = value;
}
__syncthreads();
if (output_valid) {
for (int64_t tile_k = 0; tile_k < block_k; ++tile_k) {
acc += x_tile[local_row * block_k + tile_k] * w_tile[tile_k * blockDim.x + local_col];
}
}
__syncthreads();
}
if (output_valid) {
out[row * out_features + col] = static_cast<scalar_t>(acc);
}
}
void matmul_packed_weight(
torch::Tensor &out,
torch::Tensor const &x,
torch::Tensor const &packed_weight_indices,
torch::Tensor const &row_norms,
torch::Tensor const ¢roids,
torch::Tensor const &bias,
bool has_bias,
int64_t bits,
int64_t out_features,
int64_t in_features,
int64_t block_m,
int64_t block_n,
int64_t block_k) {
TORCH_CHECK(x.device().is_cuda(), "x must be a CUDA tensor");
TORCH_CHECK(out.device().is_cuda(), "out must be a CUDA tensor");
TORCH_CHECK(packed_weight_indices.device().is_cuda(), "packed weights must be CUDA tensors");
TORCH_CHECK(row_norms.device().is_cuda(), "row norms must be CUDA tensors");
TORCH_CHECK(centroids.device().is_cuda(), "centroids must be CUDA tensors");
TORCH_CHECK(x.is_contiguous(), "x must be contiguous");
TORCH_CHECK(out.is_contiguous(), "out must be contiguous");
TORCH_CHECK(packed_weight_indices.is_contiguous(), "packed weights must be contiguous");
TORCH_CHECK(row_norms.is_contiguous(), "row norms must be contiguous");
TORCH_CHECK(centroids.is_contiguous(), "centroids must be contiguous");
TORCH_CHECK(packed_weight_indices.scalar_type() == torch::kUInt8, "packed weights must be uint8");
TORCH_CHECK(
row_norms.scalar_type() == torch::kBFloat16,
"CUDA row_norms must be bfloat16");
TORCH_CHECK(centroids.scalar_type() == torch::kFloat, "centroids must be float32");
TORCH_CHECK(x.dim() == 2, "x must be rank 2");
TORCH_CHECK(out.dim() == 2, "out must be rank 2");
TORCH_CHECK(out.scalar_type() == x.scalar_type(), "out dtype must match x dtype");
TORCH_CHECK(bits > 0 && bits <= 8, "bits must be in [1, 8]");
TORCH_CHECK(block_m > 0 && block_n > 0 && block_k > 0, "tile sizes must be positive");
TORCH_CHECK(x.size(1) == in_features, "x has an unexpected input dimension");
TORCH_CHECK(out.size(0) == x.size(0), "out has an unexpected row count");
TORCH_CHECK(out.size(1) == out_features, "out has an unexpected output dimension");
TORCH_CHECK(row_norms.numel() == out_features, "row_norms must match out_features");
TORCH_CHECK(centroids.numel() >= (1LL << bits), "centroids must contain 2**bits values");
const int64_t packed_bytes = (out_features * in_features * bits + 7) / 8;
TORCH_CHECK(packed_weight_indices.numel() >= packed_bytes, "packed weights are too short");
if (has_bias) {
TORCH_CHECK(bias.device().is_cuda(), "bias must be a CUDA tensor");
TORCH_CHECK(bias.is_contiguous(), "bias must be contiguous");
TORCH_CHECK(bias.scalar_type() == x.scalar_type(), "CUDA bias dtype must match x");
TORCH_CHECK(bias.numel() == out_features, "bias must match out_features");
}
if (x.numel() == 0 || out_features == 0) {
return;
}
const int threads_n = static_cast<int>(std::min<int64_t>(std::max<int64_t>(block_n, 1), 64));
const int threads_m = static_cast<int>(std::min<int64_t>(
x.size(0),
std::min<int64_t>(std::max<int64_t>(block_m, 1), 1024 / threads_n)));
const int tile_k = static_cast<int>(std::min<int64_t>(std::max<int64_t>(block_k, 1), 128));
const dim3 block(threads_n, threads_m);
const dim3 grid(
(out_features + threads_n - 1) / threads_n,
(x.size(0) + threads_m - 1) / threads_m);
const size_t shared_bytes = static_cast<size_t>(threads_m * tile_k + tile_k * threads_n) *
sizeof(float);
const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
const cudaDeviceProp *mma_properties = at::cuda::getCurrentDeviceProperties();
if (x.size(0) <= 8) {
constexpr int channels_per_warp = 4;
const dim3 small_rows_block(32);
const dim3 small_rows_grid(
(out_features + channels_per_warp - 1) / channels_per_warp,
x.size(0));
AT_DISPATCH_FLOATING_TYPES_AND2(
at::kHalf, at::kBFloat16, x.scalar_type(),
"orbitquant_packed_matmul_cuda_small_rows", [&] {
orbitquant_packed_matmul_small_rows_kernel<scalar_t>
<<<small_rows_grid, small_rows_block, 0, stream>>>(
out.data_ptr<scalar_t>(),
x.data_ptr<scalar_t>(),
packed_weight_indices.data_ptr<uint8_t>(),
row_norms.data_ptr<c10::BFloat16>(),
centroids.data_ptr<float>(),
has_bias ? bias.data_ptr<scalar_t>() : nullptr,
has_bias,
x.size(0),
out_features,
in_features,
bits);
});
C10_CUDA_KERNEL_LAUNCH_CHECK();
return;
}
if (x.scalar_type() == at::kBFloat16 && x.size(0) >= 9 &&
mma_properties->major >= 8) {
if (in_features % 64 == 0 &&
(bits == 2 || bits == 3 || bits == 4 || bits == 6)) {
constexpr int mma_tile_m = 128;
constexpr int mma_tile_n = 128;
const dim3 mma_block(256);
const dim3 mma_grid(
(out_features + mma_tile_n - 1) / mma_tile_n,
(x.size(0) + mma_tile_m - 1) / mma_tile_m);
#define ORBITQUANT_LAUNCH_MMA64_PIPELINED(STORAGE_TYPE, MMA_TYPE, BITS_VALUE) \
do { \
constexpr int kSegStride = 64 * (BITS_VALUE) / 8; \
const int pipelined_shared_bytes = static_cast<int>( \
2 * 128 * 72 * sizeof(MMA_TYPE) + 128 * 72 * sizeof(MMA_TYPE) + \
8 * 16 * 16 * sizeof(float) + 2 * 128 * kSegStride); \
if (pipelined_shared_bytes > mma_properties->sharedMemPerBlock) { \
C10_CUDA_CHECK(cudaFuncSetAttribute( \
orbitquant_packed_matmul_mma64_pipelined_kernel<STORAGE_TYPE, \
MMA_TYPE, \
BITS_VALUE>, \
cudaFuncAttributeMaxDynamicSharedMemorySize, \
pipelined_shared_bytes)); \
} \
orbitquant_packed_matmul_mma64_pipelined_kernel<STORAGE_TYPE, MMA_TYPE, \
BITS_VALUE> \
<<<mma_grid, mma_block, pipelined_shared_bytes, stream>>>( \
reinterpret_cast<STORAGE_TYPE *>(out.data_ptr()), \
reinterpret_cast<STORAGE_TYPE const *>(x.data_ptr()), \
packed_weight_indices.data_ptr<uint8_t>(), \
row_norms.data_ptr<c10::BFloat16>(), \
centroids.data_ptr<float>(), \
has_bias ? bias.data_ptr<STORAGE_TYPE>() : nullptr, \
has_bias, \
x.size(0), \
out_features, \
in_features); \
} while (0)
// The cp.async pipeline wins when the launch is latency-bound: with cold L2
// (each layer's weights are evicted between calls in a real model) it is
// 1.35-1.46x faster for W2/W4/W6 on RTX 4060 Ti, A40, and RTX 4090 whenever
// the grid fits in one wave (blocks <= SM count), and it regresses up to 15%
// once the grid oversubscribes the device. W3 uses denser 8-byte copies and
// wins from 128 rows even for oversubscribed grids on RTX 6000 Ada; shorter
// W3 launches stay on the legacy kernel. Measured 2026-07.
// ORBITQUANT_MMA64_FORCE_PIPELINE=1 bypasses the grid gate,
// ORBITQUANT_MMA64_DISABLE_PIPELINE=1 forces the legacy kernel everywhere.
#define ORBITQUANT_MMA64_USE_PIPELINE(BITS_VALUE) \
((orbitquant_mma64_pipeline_forced() || \
((BITS_VALUE) == 3 ? x.size(0) >= 128 \
: static_cast<int64_t>(mma_grid.x) * mma_grid.y <= \
mma_properties->multiProcessorCount)) && \
!orbitquant_mma64_pipeline_disabled() && \
mma_properties->major >= 8 && \
(2 * 128 * 72 * 2 + 128 * 72 * 2 + 8 * 16 * 16 * 4 + \
2 * 128 * (64 * (BITS_VALUE) / 8)) <= \
static_cast<int>(mma_properties->sharedMemPerBlockOptin))
#define ORBITQUANT_LAUNCH_MMA64_BF16(BITS_VALUE) \
orbitquant_packed_matmul_mma64_kernel<c10::BFloat16, __nv_bfloat16, \
BITS_VALUE><<<mma_grid, mma_block, 0, \
stream>>>( \
reinterpret_cast<c10::BFloat16 *>(out.data_ptr()), \
reinterpret_cast<c10::BFloat16 const *>(x.data_ptr()), \
packed_weight_indices.data_ptr<uint8_t>(), \
row_norms.data_ptr<c10::BFloat16>(), \
centroids.data_ptr<float>(), \
has_bias ? bias.data_ptr<c10::BFloat16>() : nullptr, \
has_bias, \
x.size(0), \
out_features, \
in_features)
switch (bits) {
case 2:
if (ORBITQUANT_MMA64_USE_PIPELINE(2)) {
ORBITQUANT_LAUNCH_MMA64_PIPELINED(c10::BFloat16, __nv_bfloat16, 2);
} else {
ORBITQUANT_LAUNCH_MMA64_BF16(2);
}
break;
case 3:
if (ORBITQUANT_MMA64_USE_PIPELINE(3)) {
ORBITQUANT_LAUNCH_MMA64_PIPELINED(c10::BFloat16, __nv_bfloat16, 3);
} else {
ORBITQUANT_LAUNCH_MMA64_BF16(3);
}
break;
case 4:
if (ORBITQUANT_MMA64_USE_PIPELINE(4)) {
ORBITQUANT_LAUNCH_MMA64_PIPELINED(c10::BFloat16, __nv_bfloat16, 4);
} else {
ORBITQUANT_LAUNCH_MMA64_BF16(4);
}
break;
case 6:
if (ORBITQUANT_MMA64_USE_PIPELINE(6)) {
ORBITQUANT_LAUNCH_MMA64_PIPELINED(c10::BFloat16, __nv_bfloat16, 6);
} else {
ORBITQUANT_LAUNCH_MMA64_BF16(6);
}
break;
}
#undef ORBITQUANT_LAUNCH_MMA64_BF16
C10_CUDA_KERNEL_LAUNCH_CHECK();
return;
}
constexpr int tile = 16;
constexpr int col_tiles = 4;
constexpr int rows_per_block = tile * 8;
const dim3 wmma_block(256);
const dim3 wmma_grid(
(out_features + tile * col_tiles - 1) / (tile * col_tiles),
(x.size(0) + rows_per_block - 1) / rows_per_block);
#define ORBITQUANT_LAUNCH_BF16(BITS_VALUE) \
orbitquant_packed_matmul_wmma_bf16_kernel<BITS_VALUE><<<wmma_grid, wmma_block, 0, \
stream>>>( \
reinterpret_cast<c10::BFloat16 *>(out.data_ptr()), \
reinterpret_cast<c10::BFloat16 const *>(x.data_ptr()), \
packed_weight_indices.data_ptr<uint8_t>(), \
row_norms.data_ptr<c10::BFloat16>(), \
centroids.data_ptr<float>(), \
has_bias ? bias.data_ptr<c10::BFloat16>() : nullptr, \
has_bias, \
x.size(0), \
out_features, \
in_features)
switch (bits) {
case 1:
ORBITQUANT_LAUNCH_BF16(1);
break;
case 2:
ORBITQUANT_LAUNCH_BF16(2);
break;
case 3:
ORBITQUANT_LAUNCH_BF16(3);
break;
case 4:
ORBITQUANT_LAUNCH_BF16(4);
break;
case 5:
ORBITQUANT_LAUNCH_BF16(5);
break;
case 6:
ORBITQUANT_LAUNCH_BF16(6);
break;
case 7:
ORBITQUANT_LAUNCH_BF16(7);
break;
case 8:
ORBITQUANT_LAUNCH_BF16(8);
break;
}
#undef ORBITQUANT_LAUNCH_BF16
C10_CUDA_KERNEL_LAUNCH_CHECK();
return;
}
if (x.scalar_type() == at::kHalf && x.size(0) >= 9 &&
mma_properties->major >= 8) {
if (in_features % 64 == 0 &&
(bits == 2 || bits == 3 || bits == 4 || bits == 6)) {
constexpr int mma_tile_m = 128;
constexpr int mma_tile_n = 128;
const dim3 mma_block(256);
const dim3 mma_grid(
(out_features + mma_tile_n - 1) / mma_tile_n,
(x.size(0) + mma_tile_m - 1) / mma_tile_m);
#define ORBITQUANT_LAUNCH_MMA64_HALF(BITS_VALUE) \
orbitquant_packed_matmul_mma64_kernel<c10::Half, half, BITS_VALUE> \
<<<mma_grid, mma_block, 0, stream>>>( \
reinterpret_cast<c10::Half *>(out.data_ptr()), \
reinterpret_cast<c10::Half const *>(x.data_ptr()), \
packed_weight_indices.data_ptr<uint8_t>(), \
row_norms.data_ptr<c10::BFloat16>(), \
centroids.data_ptr<float>(), \
has_bias ? bias.data_ptr<c10::Half>() : nullptr, \
has_bias, \
x.size(0), \
out_features, \
in_features)
switch (bits) {
case 2:
if (ORBITQUANT_MMA64_USE_PIPELINE(2)) {
ORBITQUANT_LAUNCH_MMA64_PIPELINED(c10::Half, half, 2);
} else {
ORBITQUANT_LAUNCH_MMA64_HALF(2);
}
break;
case 3:
if (ORBITQUANT_MMA64_USE_PIPELINE(3)) {
ORBITQUANT_LAUNCH_MMA64_PIPELINED(c10::Half, half, 3);
} else {
ORBITQUANT_LAUNCH_MMA64_HALF(3);
}
break;
case 4:
if (ORBITQUANT_MMA64_USE_PIPELINE(4)) {
ORBITQUANT_LAUNCH_MMA64_PIPELINED(c10::Half, half, 4);
} else {
ORBITQUANT_LAUNCH_MMA64_HALF(4);
}
break;
case 6:
if (ORBITQUANT_MMA64_USE_PIPELINE(6)) {
ORBITQUANT_LAUNCH_MMA64_PIPELINED(c10::Half, half, 6);
} else {
ORBITQUANT_LAUNCH_MMA64_HALF(6);
}
break;
}
#undef ORBITQUANT_LAUNCH_MMA64_HALF
#undef ORBITQUANT_MMA64_USE_PIPELINE
#undef ORBITQUANT_LAUNCH_MMA64_PIPELINED
C10_CUDA_KERNEL_LAUNCH_CHECK();
return;
}
constexpr int tile = 16;
constexpr int col_tiles = 4;
constexpr int rows_per_block = tile * 8;
const dim3 wmma_block(256);
const dim3 wmma_grid(
(out_features + tile * col_tiles - 1) / (tile * col_tiles),
(x.size(0) + rows_per_block - 1) / rows_per_block);
#define ORBITQUANT_LAUNCH_HALF(BITS_VALUE) \
orbitquant_packed_matmul_wmma_half_kernel<BITS_VALUE><<<wmma_grid, wmma_block, 0, \
stream>>>( \
reinterpret_cast<c10::Half *>(out.data_ptr()), \
reinterpret_cast<c10::Half const *>(x.data_ptr()), \
packed_weight_indices.data_ptr<uint8_t>(), \
row_norms.data_ptr<c10::BFloat16>(), \
centroids.data_ptr<float>(), \
has_bias ? bias.data_ptr<c10::Half>() : nullptr, \
has_bias, \
x.size(0), \
out_features, \
in_features)
switch (bits) {
case 1:
ORBITQUANT_LAUNCH_HALF(1);
break;
case 2:
ORBITQUANT_LAUNCH_HALF(2);
break;
case 3:
ORBITQUANT_LAUNCH_HALF(3);
break;
case 4:
ORBITQUANT_LAUNCH_HALF(4);
break;
case 5:
ORBITQUANT_LAUNCH_HALF(5);
break;
case 6:
ORBITQUANT_LAUNCH_HALF(6);
break;
case 7:
ORBITQUANT_LAUNCH_HALF(7);
break;
case 8:
ORBITQUANT_LAUNCH_HALF(8);
break;
}
#undef ORBITQUANT_LAUNCH_HALF
C10_CUDA_KERNEL_LAUNCH_CHECK();
return;
}
AT_DISPATCH_FLOATING_TYPES_AND2(
at::kHalf, at::kBFloat16, x.scalar_type(), "orbitquant_packed_matmul_cuda", [&] {
orbitquant_packed_matmul_tiled_kernel<scalar_t><<<grid, block, shared_bytes, stream>>>(
out.data_ptr<scalar_t>(),
x.data_ptr<scalar_t>(),
packed_weight_indices.data_ptr<uint8_t>(),
row_norms.data_ptr<c10::BFloat16>(),
centroids.data_ptr<float>(),
has_bias ? bias.data_ptr<scalar_t>() : nullptr,
has_bias,
x.size(0),
out_features,
in_features,
bits,
tile_k);
});
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
void matmul_packed_w4a4_int8(
torch::Tensor &out,
torch::Tensor const &packed_activations,
torch::Tensor const &packed_weight_indices,
torch::Tensor const &token_norms,
torch::Tensor const &row_norms,
torch::Tensor const &activation_codes,
torch::Tensor const &weight_codes,
torch::Tensor const &bias,
bool has_bias,
double activation_scale,
double weight_scale,
int64_t out_features,
int64_t in_features,
int64_t tile_m,
int64_t tile_n,
bool async_packed,
bool weight_k_major) {
TORCH_CHECK(out.device().is_cuda(), "out must be a CUDA tensor");
TORCH_CHECK(
packed_activations.device().is_cuda(),
"packed activations must be a CUDA tensor");
TORCH_CHECK(
packed_weight_indices.device().is_cuda(),
"packed weights must be a CUDA tensor");
TORCH_CHECK(token_norms.device().is_cuda(), "token norms must be a CUDA tensor");
TORCH_CHECK(row_norms.device().is_cuda(), "row norms must be a CUDA tensor");
TORCH_CHECK(
activation_codes.device().is_cuda(),
"activation surrogate codes must be a CUDA tensor");
TORCH_CHECK(
weight_codes.device().is_cuda(),
"weight surrogate codes must be a CUDA tensor");
TORCH_CHECK(out.is_contiguous(), "out must be contiguous");
TORCH_CHECK(
packed_activations.is_contiguous(), "packed activations must be contiguous");
TORCH_CHECK(
packed_weight_indices.is_contiguous(), "packed weights must be contiguous");
TORCH_CHECK(token_norms.is_contiguous(), "token norms must be contiguous");
TORCH_CHECK(row_norms.is_contiguous(), "row norms must be contiguous");
TORCH_CHECK(
activation_codes.is_contiguous(), "activation surrogate codes must be contiguous");
TORCH_CHECK(
weight_codes.is_contiguous(), "weight surrogate codes must be contiguous");
TORCH_CHECK(
packed_activations.scalar_type() == torch::kUInt8,
"packed activations must be uint8");
TORCH_CHECK(
packed_weight_indices.scalar_type() == torch::kUInt8,
"packed weights must be uint8");
TORCH_CHECK(token_norms.scalar_type() == torch::kFloat, "token norms must be float32");
TORCH_CHECK(row_norms.scalar_type() == torch::kBFloat16, "row norms must be bfloat16");
TORCH_CHECK(
activation_codes.scalar_type() == torch::kChar,
"activation surrogate codes must be int8");
TORCH_CHECK(
weight_codes.scalar_type() == torch::kChar,
"weight surrogate codes must be int8");
TORCH_CHECK(
out.scalar_type() == torch::kBFloat16 || out.scalar_type() == torch::kHalf,
"packed W4A4 INT8 output must be bfloat16 or float16");
TORCH_CHECK(packed_activations.dim() == 2, "packed activations must be rank 2");
TORCH_CHECK(out.dim() == 2, "out must be rank 2");
TORCH_CHECK(
in_features > 0 && in_features % 64 == 0,
"in_features must be positive and divisible by 64");
TORCH_CHECK(out_features >= 0, "out_features must be non-negative");
TORCH_CHECK(
(tile_m == 128 && tile_n == 128) ||
(tile_m == 256 && tile_n == 128) ||
(tile_m == 128 && tile_n == 256),
"packed W4A4 INT8 tile must be 128x128, 256x128, or 128x256");
TORCH_CHECK(
packed_activations.size(1) == in_features / 2,
"packed activations have an unexpected input dimension");
const int64_t rows = packed_activations.size(0);
TORCH_CHECK(out.size(0) == rows, "out has an unexpected row count");
TORCH_CHECK(out.size(1) == out_features, "out has an unexpected output dimension");
TORCH_CHECK(token_norms.numel() == rows, "token norms must match rows");
TORCH_CHECK(row_norms.numel() == out_features, "row norms must match out_features");
TORCH_CHECK(activation_codes.numel() == 16, "activation codes must contain 16 values");
TORCH_CHECK(weight_codes.numel() == 16, "weight codes must contain 16 values");
TORCH_CHECK(
packed_weight_indices.numel() == out_features * (in_features / 2),
"K-major packed weights have an unexpected size");
if (has_bias) {
TORCH_CHECK(bias.device().is_cuda(), "bias must be a CUDA tensor");
TORCH_CHECK(bias.is_contiguous(), "bias must be contiguous");
TORCH_CHECK(bias.scalar_type() == out.scalar_type(), "bias dtype must match out");
TORCH_CHECK(bias.numel() == out_features, "bias must match out_features");
}
if (rows == 0 || out_features == 0) {
return;
}
const at::cuda::OptionalCUDAGuard device_guard(device_of(packed_activations));
const cudaDeviceProp *properties = at::cuda::getCurrentDeviceProperties();
TORCH_CHECK(
properties->major > 7 || (properties->major == 7 && properties->minor >= 5),
"packed W4A4 INT8 Tensor Core matmul requires compute capability 7.5+");
const int warp_count = static_cast<int>((tile_m / 16) * (tile_n / 128));
const dim3 block(warp_count * 32);
const dim3 grid(
(out_features + tile_n - 1) / tile_n,
(rows + tile_m - 1) / tile_m);
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
const int shared_bytes = static_cast<int>(
tile_m * 80 + tile_n * 80 + warp_count * 16 * 16 * sizeof(int32_t) + 32 +
(async_packed ? (tile_m + tile_n) * 32 : 0));
TORCH_CHECK(
shared_bytes <= properties->sharedMemPerBlockOptin,
"packed W4A4 INT8 tile requires ",
shared_bytes,
" bytes of shared memory, but the device supports ",
properties->sharedMemPerBlockOptin);
#define ORBITQUANT_LAUNCH_PACKED_W4A4_INT8( \
STORAGE_TYPE, TILE_M, TILE_N, ASYNC_PACKED, K_MAJOR_WEIGHT) \
if (shared_bytes > properties->sharedMemPerBlock) { \
C10_CUDA_CHECK(cudaFuncSetAttribute( \
orbitquant_packed_w4a4_int8_mma_kernel< \
STORAGE_TYPE, TILE_M, TILE_N, ASYNC_PACKED, K_MAJOR_WEIGHT>, \
cudaFuncAttributeMaxDynamicSharedMemorySize, \
shared_bytes)); \
} \
orbitquant_packed_w4a4_int8_mma_kernel< \
STORAGE_TYPE, TILE_M, TILE_N, ASYNC_PACKED, K_MAJOR_WEIGHT> \
<<<grid, block, shared_bytes, stream>>>( \
reinterpret_cast<STORAGE_TYPE *>(out.data_ptr()), \
packed_activations.data_ptr<uint8_t>(), \
packed_weight_indices.data_ptr<uint8_t>(), \
token_norms.data_ptr<float>(), \
row_norms.data_ptr<c10::BFloat16>(), \
activation_codes.data_ptr<int8_t>(), \
weight_codes.data_ptr<int8_t>(), \
has_bias ? bias.data_ptr<STORAGE_TYPE>() : nullptr, \
has_bias, \
static_cast<float>(activation_scale), \
static_cast<float>(weight_scale), \
rows, \
out_features, \
in_features)
#define ORBITQUANT_DISPATCH_PACKED_W4A4_TILE( \
STORAGE_TYPE, ASYNC_PACKED, K_MAJOR_WEIGHT) \
if (tile_m == 256) { \
ORBITQUANT_LAUNCH_PACKED_W4A4_INT8( \
STORAGE_TYPE, 256, 128, ASYNC_PACKED, K_MAJOR_WEIGHT); \
} else if (tile_n == 256) { \
ORBITQUANT_LAUNCH_PACKED_W4A4_INT8( \
STORAGE_TYPE, 128, 256, ASYNC_PACKED, K_MAJOR_WEIGHT); \
} else { \
ORBITQUANT_LAUNCH_PACKED_W4A4_INT8( \
STORAGE_TYPE, 128, 128, ASYNC_PACKED, K_MAJOR_WEIGHT); \
}
#define ORBITQUANT_DISPATCH_PACKED_W4A4_LAYOUT(STORAGE_TYPE, ASYNC_PACKED) \
if (weight_k_major) { \
ORBITQUANT_DISPATCH_PACKED_W4A4_TILE(STORAGE_TYPE, ASYNC_PACKED, true); \
} else { \
ORBITQUANT_DISPATCH_PACKED_W4A4_TILE(STORAGE_TYPE, ASYNC_PACKED, false); \
}
if (out.scalar_type() == torch::kBFloat16) {
if (async_packed) {
ORBITQUANT_DISPATCH_PACKED_W4A4_LAYOUT(c10::BFloat16, true);
} else {
ORBITQUANT_DISPATCH_PACKED_W4A4_LAYOUT(c10::BFloat16, false);
}
} else {
if (async_packed) {
ORBITQUANT_DISPATCH_PACKED_W4A4_LAYOUT(c10::Half, true);
} else {
ORBITQUANT_DISPATCH_PACKED_W4A4_LAYOUT(c10::Half, false);
}
}
#undef ORBITQUANT_DISPATCH_PACKED_W4A4_LAYOUT
#undef ORBITQUANT_DISPATCH_PACKED_W4A4_TILE
#undef ORBITQUANT_LAUNCH_PACKED_W4A4_INT8
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
void quantize_activations_packed_w4(
torch::Tensor &packed_out,
torch::Tensor &norms_out,
torch::Tensor const &x,
torch::Tensor const &permutation,
torch::Tensor const &signs,
torch::Tensor const &boundaries,
double eps,
double inv_sqrt_block,
int64_t threads) {
TORCH_CHECK(packed_out.device().is_cuda(), "packed_out must be a CUDA tensor");
TORCH_CHECK(norms_out.device().is_cuda(), "norms_out must be a CUDA tensor");
TORCH_CHECK(x.device().is_cuda(), "x must be a CUDA tensor");
TORCH_CHECK(permutation.device().is_cuda(), "permutation must be a CUDA tensor");
TORCH_CHECK(signs.device().is_cuda(), "signs must be a CUDA tensor");
TORCH_CHECK(boundaries.device().is_cuda(), "boundaries must be a CUDA tensor");
TORCH_CHECK(packed_out.is_contiguous(), "packed_out must be contiguous");
TORCH_CHECK(norms_out.is_contiguous(), "norms_out must be contiguous");
TORCH_CHECK(x.is_contiguous(), "x must be contiguous");
TORCH_CHECK(permutation.is_contiguous(), "permutation must be contiguous");
TORCH_CHECK(signs.is_contiguous(), "signs must be contiguous");
TORCH_CHECK(boundaries.is_contiguous(), "boundaries must be contiguous");
TORCH_CHECK(
packed_out.scalar_type() == torch::kUInt8,
"packed_out must be uint8");
TORCH_CHECK(norms_out.scalar_type() == torch::kFloat, "norms_out must be float32");
TORCH_CHECK(
x.scalar_type() == torch::kBFloat16 || x.scalar_type() == torch::kHalf,
"x must be bfloat16 or float16");
TORCH_CHECK(
permutation.scalar_type() == torch::kLong ||
permutation.scalar_type() == torch::kInt,
"permutation must be int32 or int64");
TORCH_CHECK(signs.scalar_type() == torch::kChar, "signs must be int8");
TORCH_CHECK(boundaries.scalar_type() == torch::kFloat, "boundaries must be float32");
TORCH_CHECK(x.dim() == 2, "x must be rank 2");
TORCH_CHECK(packed_out.dim() == 2, "packed_out must be rank 2");
TORCH_CHECK(norms_out.dim() == 1, "norms_out must be rank 1");
const int64_t rows = x.size(0);
const int64_t dim = x.size(1);
TORCH_CHECK(
dim == 512 || dim == 1024 || dim == 2048 || dim == 4096 ||
dim == 8192 || dim == 16384,
"native packed W4 activation quantization supports dimensions "
"512, 1024, 2048, 4096, 8192, and 16384");
TORCH_CHECK(
threads == 128 || threads == 256 || threads == 512,
"native packed W4 activation quantization threads must be 128, 256, or 512");
TORCH_CHECK(
packed_out.size(0) == rows && packed_out.size(1) == dim / 2,
"packed_out has an unexpected shape");
TORCH_CHECK(norms_out.numel() == rows, "norms_out must match rows");
TORCH_CHECK(permutation.numel() == dim, "permutation must match the input dimension");
TORCH_CHECK(signs.numel() == dim, "signs must match the input dimension");
TORCH_CHECK(boundaries.numel() == 15, "boundaries must contain 15 values");
if (rows == 0) {
return;
}
const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
const cudaDeviceProp *properties = at::cuda::getCurrentDeviceProperties();
const int shared_bytes = static_cast<int>((dim + threads + 15) * sizeof(float));
TORCH_CHECK(
shared_bytes <= properties->sharedMemPerBlockOptin,
"native packed W4 activation quantization requires ",
shared_bytes,
" bytes of shared memory, but the device supports ",
properties->sharedMemPerBlockOptin);
const dim3 block(static_cast<unsigned int>(threads));
const dim3 grid(static_cast<unsigned int>(rows));
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
#define ORBITQUANT_LAUNCH_RPBH_PACK_W4(STORAGE_TYPE, INDEX_TYPE, DIM_VALUE) \
if (shared_bytes > properties->sharedMemPerBlock) { \
C10_CUDA_CHECK(cudaFuncSetAttribute( \
orbitquant_rpbh_quantize_pack_w4_kernel<STORAGE_TYPE, INDEX_TYPE, \
DIM_VALUE>, \
cudaFuncAttributeMaxDynamicSharedMemorySize, \
shared_bytes)); \
} \
orbitquant_rpbh_quantize_pack_w4_kernel<STORAGE_TYPE, INDEX_TYPE, DIM_VALUE> \
<<<grid, block, shared_bytes, stream>>>( \
packed_out.data_ptr<uint8_t>(), \
norms_out.data_ptr<float>(), \
reinterpret_cast<STORAGE_TYPE const *>(x.data_ptr()), \
permutation.data_ptr<INDEX_TYPE>(), \
signs.data_ptr<int8_t>(), \
boundaries.data_ptr<float>(), \
static_cast<float>(eps), \
static_cast<float>(inv_sqrt_block), \
rows)
#define ORBITQUANT_DISPATCH_RPBH_PACK_W4(STORAGE_TYPE, INDEX_TYPE) \
switch (dim) { \
case 512: \
ORBITQUANT_LAUNCH_RPBH_PACK_W4(STORAGE_TYPE, INDEX_TYPE, 512); \
break; \
case 1024: \
ORBITQUANT_LAUNCH_RPBH_PACK_W4(STORAGE_TYPE, INDEX_TYPE, 1024); \
break; \
case 2048: \
ORBITQUANT_LAUNCH_RPBH_PACK_W4(STORAGE_TYPE, INDEX_TYPE, 2048); \
break; \
case 4096: \
ORBITQUANT_LAUNCH_RPBH_PACK_W4(STORAGE_TYPE, INDEX_TYPE, 4096); \
break; \
case 8192: \
ORBITQUANT_LAUNCH_RPBH_PACK_W4(STORAGE_TYPE, INDEX_TYPE, 8192); \
break; \
case 16384: \
ORBITQUANT_LAUNCH_RPBH_PACK_W4(STORAGE_TYPE, INDEX_TYPE, 16384); \
break; \
}
const bool int32_permutation = permutation.scalar_type() == torch::kInt;
if (x.scalar_type() == torch::kBFloat16) {
if (int32_permutation) {
ORBITQUANT_DISPATCH_RPBH_PACK_W4(c10::BFloat16, int32_t);
} else {
ORBITQUANT_DISPATCH_RPBH_PACK_W4(c10::BFloat16, int64_t);
}
} else {
if (int32_permutation) {
ORBITQUANT_DISPATCH_RPBH_PACK_W4(c10::Half, int32_t);
} else {
ORBITQUANT_DISPATCH_RPBH_PACK_W4(c10::Half, int64_t);
}
}
#undef ORBITQUANT_DISPATCH_RPBH_PACK_W4
#undef ORBITQUANT_LAUNCH_RPBH_PACK_W4
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
void quantize_activations_int8(
torch::Tensor &int8_out,
torch::Tensor &norms_out,
torch::Tensor const &x,
torch::Tensor const &permutation,
torch::Tensor const &signs,
torch::Tensor const &boundaries,
torch::Tensor const &codes,
double eps,
double inv_sqrt_block,
int64_t threads) {
TORCH_CHECK(int8_out.device().is_cuda(), "int8_out must be a CUDA tensor");
TORCH_CHECK(norms_out.device().is_cuda(), "norms_out must be a CUDA tensor");
TORCH_CHECK(x.device().is_cuda(), "x must be a CUDA tensor");
TORCH_CHECK(permutation.device().is_cuda(), "permutation must be a CUDA tensor");
TORCH_CHECK(signs.device().is_cuda(), "signs must be a CUDA tensor");
TORCH_CHECK(boundaries.device().is_cuda(), "boundaries must be a CUDA tensor");
TORCH_CHECK(codes.device().is_cuda(), "codes must be a CUDA tensor");
TORCH_CHECK(int8_out.is_contiguous(), "int8_out must be contiguous");
TORCH_CHECK(norms_out.is_contiguous(), "norms_out must be contiguous");
TORCH_CHECK(x.is_contiguous(), "x must be contiguous");
TORCH_CHECK(permutation.is_contiguous(), "permutation must be contiguous");
TORCH_CHECK(signs.is_contiguous(), "signs must be contiguous");
TORCH_CHECK(boundaries.is_contiguous(), "boundaries must be contiguous");
TORCH_CHECK(codes.is_contiguous(), "codes must be contiguous");
TORCH_CHECK(int8_out.scalar_type() == torch::kChar, "int8_out must be int8");
TORCH_CHECK(norms_out.scalar_type() == torch::kFloat, "norms_out must be float32");
TORCH_CHECK(
x.scalar_type() == torch::kBFloat16 || x.scalar_type() == torch::kHalf,
"x must be bfloat16 or float16");
TORCH_CHECK(
permutation.scalar_type() == torch::kLong ||
permutation.scalar_type() == torch::kInt,
"permutation must be int32 or int64");
TORCH_CHECK(signs.scalar_type() == torch::kChar, "signs must be int8");
TORCH_CHECK(boundaries.scalar_type() == torch::kFloat, "boundaries must be float32");
TORCH_CHECK(codes.scalar_type() == torch::kChar, "codes must be int8");
TORCH_CHECK(x.dim() == 2, "x must be rank 2");
TORCH_CHECK(int8_out.dim() == 2, "int8_out must be rank 2");
TORCH_CHECK(norms_out.dim() == 1, "norms_out must be rank 1");
const int64_t rows = x.size(0);
const int64_t dim = x.size(1);
TORCH_CHECK(
dim == 512 || dim == 1024 || dim == 2048 || dim == 4096 ||
dim == 8192 || dim == 12288 || dim == 16384,
"native INT8 activation quantization supports dimensions "
"512, 1024, 2048, 4096, 8192, 12288, and 16384");
TORCH_CHECK(
threads == 128 || threads == 256 || threads == 512,
"native INT8 activation quantization threads must be 128, 256, or 512");
TORCH_CHECK(
int8_out.size(0) == rows && int8_out.size(1) == dim,
"int8_out has an unexpected shape");
TORCH_CHECK(norms_out.numel() == rows, "norms_out must match rows");
TORCH_CHECK(permutation.numel() == dim, "permutation must match the input dimension");
TORCH_CHECK(signs.numel() == dim, "signs must match the input dimension");
TORCH_CHECK(boundaries.numel() == 15, "boundaries must contain 15 values");
TORCH_CHECK(codes.numel() == 16, "codes must contain 16 values");
if (rows == 0) {
return;
}
const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
const cudaDeviceProp *properties = at::cuda::getCurrentDeviceProperties();
const int shared_bytes =
static_cast<int>((dim + threads + 15) * sizeof(float) + 16);
TORCH_CHECK(
shared_bytes <= properties->sharedMemPerBlockOptin,
"native INT8 activation quantization requires ",
shared_bytes,
" bytes of shared memory, but the device supports ",
properties->sharedMemPerBlockOptin);
const dim3 block(static_cast<unsigned int>(threads));
const dim3 grid(static_cast<unsigned int>(rows));
const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
#define ORBITQUANT_LAUNCH_RPBH_INT8( \
STORAGE_TYPE, INDEX_TYPE, DIM_VALUE, ORBIT_BLOCK_VALUE) \
if (shared_bytes > properties->sharedMemPerBlock) { \
C10_CUDA_CHECK(cudaFuncSetAttribute( \
orbitquant_rpbh_quantize_int8_kernel< \
STORAGE_TYPE, INDEX_TYPE, DIM_VALUE, ORBIT_BLOCK_VALUE>, \
cudaFuncAttributeMaxDynamicSharedMemorySize, \
shared_bytes)); \
} \
orbitquant_rpbh_quantize_int8_kernel< \
STORAGE_TYPE, INDEX_TYPE, DIM_VALUE, ORBIT_BLOCK_VALUE> \
<<<grid, block, shared_bytes, stream>>>( \
int8_out.data_ptr<int8_t>(), \
norms_out.data_ptr<float>(), \
reinterpret_cast<STORAGE_TYPE const *>(x.data_ptr()), \
permutation.data_ptr<INDEX_TYPE>(), \
signs.data_ptr<int8_t>(), \
boundaries.data_ptr<float>(), \
codes.data_ptr<int8_t>(), \
static_cast<float>(eps), \
static_cast<float>(inv_sqrt_block), \
rows)
#define ORBITQUANT_DISPATCH_RPBH_INT8(STORAGE_TYPE, INDEX_TYPE) \
switch (dim) { \
case 512: \
ORBITQUANT_LAUNCH_RPBH_INT8(STORAGE_TYPE, INDEX_TYPE, 512, 512); \
break; \
case 1024: \
ORBITQUANT_LAUNCH_RPBH_INT8(STORAGE_TYPE, INDEX_TYPE, 1024, 1024); \
break; \
case 2048: \
ORBITQUANT_LAUNCH_RPBH_INT8(STORAGE_TYPE, INDEX_TYPE, 2048, 2048); \
break; \
case 4096: \
ORBITQUANT_LAUNCH_RPBH_INT8(STORAGE_TYPE, INDEX_TYPE, 4096, 4096); \
break; \
case 8192: \
ORBITQUANT_LAUNCH_RPBH_INT8(STORAGE_TYPE, INDEX_TYPE, 8192, 8192); \
break; \
case 12288: \
ORBITQUANT_LAUNCH_RPBH_INT8(STORAGE_TYPE, INDEX_TYPE, 12288, 4096); \
break; \
case 16384: \
ORBITQUANT_LAUNCH_RPBH_INT8(STORAGE_TYPE, INDEX_TYPE, 16384, 16384); \
break; \
}
const bool int32_permutation = permutation.scalar_type() == torch::kInt;
if (x.scalar_type() == torch::kBFloat16) {
if (int32_permutation) {
ORBITQUANT_DISPATCH_RPBH_INT8(c10::BFloat16, int32_t);
} else {
ORBITQUANT_DISPATCH_RPBH_INT8(c10::BFloat16, int64_t);
}
} else {
if (int32_permutation) {
ORBITQUANT_DISPATCH_RPBH_INT8(c10::Half, int32_t);
} else {
ORBITQUANT_DISPATCH_RPBH_INT8(c10::Half, int64_t);
}
}
#undef ORBITQUANT_DISPATCH_RPBH_INT8
#undef ORBITQUANT_LAUNCH_RPBH_INT8
C10_CUDA_KERNEL_LAUNCH_CHECK();
}
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