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#pragma once
#include "src/turbomind/kernels/attention/quantization.h"
#include "src/turbomind/kernels/core/array_ops.h"
#include "src/turbomind/kernels/core/common.h"
#include "src/turbomind/kernels/core/math.h"
#include "src/turbomind/kernels/gemm/test/test_utils.h"
#include "src/turbomind/kernels/gemm/types.h"
#include <thrust/execution_policy.h>
#include <thrust/universal_vector.h>
namespace turbomind::gemm {
// quantize using `scale` and `zeros`,
template<class T>
__global__ void find_stats(Array<T, 2>* minmax, const T* src, int N, int K, int G)
{
int n_idx = blockIdx.x * blockDim.x + threadIdx.x;
int k_idx = blockIdx.y;
if (n_idx >= N || k_idx * G >= K) {
return;
}
float minval = std::numeric_limits<float>::infinity();
float maxval = -minval;
const int L = min(K, G);
for (int k = 0; k < L; k += 8) {
Array<T, 8> vec;
Load(vec, &src[n_idx * K + k_idx * G + k]);
PRAGMA_UNROLL
for (int i = 0; i < vec.size(); ++i) {
minval = __hmin(minval, vec[i]);
maxval = __hmax(maxval, vec[i]);
}
}
// store in n-major
Store(minmax[k_idx * N + n_idx].data(), Array<T, 2>{minval, maxval});
}
template<class Q, bool asym, class T>
__global__ void find_params(T* param, const Array<T, 2>* minmax, int count)
{
int global_idx = threadIdx.x + blockIdx.x * blockDim.x;
if (global_idx >= count) {
return;
}
auto stats = minmax[global_idx];
const float inv_q_max = fdividef(1.f, (1 << bitsof<Q>)-1);
static_assert(asym);
float scale = (T)(((float)stats[1] - (float)stats[0]) * inv_q_max);
// force trivial scale / zero for debugging
if constexpr (0) {
stats[0] = 0;
scale = 1.f;
}
Store(param + global_idx * 2, Array<T, 2>{scale, stats[0]});
}
template<class Q, class T>
__global__ void quantize(uint16_t* dst, T* pseudo, const T* src, const T* stats, int N, int K, int G)
{
static_assert(bitsof<Q> <= 16);
static_assert(bitsof<T> == 16); // fp16 & bf16
int n_idx = blockIdx.x * blockDim.x + threadIdx.x;
int k_idx = blockIdx.y;
if (n_idx >= N || k_idx * G >= K) {
return;
}
Array<T, 2> param;
Load(param, stats + (k_idx * N + n_idx) * 2);
float inv_scale = fdividef(1.f, param[0]);
const int L = min(K, G);
for (int k = 0; k < L; k += 8) {
Array<T, 8> vi;
Array<uint16_t, 8> vo;
Load(vi, &src[n_idx * K + k_idx * G + k]);
PRAGMA_UNROLL
for (int i = 0; i < 8; ++i) {
float u = (static_cast<float>(vi[i] - param[1])) * inv_scale;
vo[i] = quant<uint16_t>(u, bitsof<Q>);
}
Store(&dst[n_idx * K + k_idx * G + k], vo);
if (pseudo) {
Array<T, 8> vf;
PRAGMA_UNROLL
for (int i = 0; i < 8; ++i) {
vf[i] = __hfma(static_cast<T>(vo[i]), param[0], param[1]);
}
Store(&pseudo[n_idx * K + k_idx * G + k], vf);
}
}
}
template<class T>
__global__ void transpose(const T* src, T* dst, int s, int c)
{
const int cid = threadIdx.x + blockIdx.x * blockDim.x;
const int sid = threadIdx.y + blockIdx.y * blockDim.y;
if (sid < s && cid < c) {
dst[cid * s + sid] = src[sid * c + cid];
}
}
template<class T>
void invokeTranspose(const T* src, T* dst, int s, int c, cudaStream_t stream)
{
const dim3 block{32, 16};
const dim3 grid(ceil_div<int>(c, block.x), ceil_div<int>(s, block.y));
transpose<<<grid, block, 0, stream>>>(src, dst, s, c);
}
template<class D, class S>
void Quantize(const thrust::universal_vector<S>& x,
int m,
int k,
Order order,
int group_size,
thrust::universal_vector<S>& x_p, // pseudo-quantized
thrust::universal_vector<uint16_t>& x_q, // quantized ushort
thrust::universal_vector<S>& x_u, // scales & zeros (always m-major)
cudaStream_t stream)
{
auto policy = thrust::device.on(stream);
thrust::universal_vector<S> _x(x.size());
thrust::universal_vector<S> _x_p(x.size());
thrust::universal_vector<uint16_t> _x_q(x.size());
thrust::universal_vector<Array<S, 2>> stats(ceil_div(k, group_size) * m);
x_p.resize(x.size());
x_q.resize(x.size());
/// FIXME: correct the size
x_u.resize(stats.size() * 2);
if (order == Order::kRowMajor) {
thrust::copy(policy, x.begin(), x.end(), _x.begin());
}
else {
invokeTranspose(x.data().get(), _x.data().get(), k, m, stream);
}
const int block = std::min(256, m);
const dim3 grid(ceil_div(m, block), ceil_div(k, group_size));
find_stats<<<grid, block, 0, stream>>>(stats.data().get(), //
_x.data().get(),
m,
k,
group_size);
find_params<D, true><<<ceil_div<int>(stats.size(), 256), 256, 0, stream>>>( //
x_u.data().get(),
stats.data().get(),
stats.size());
quantize<D><<<grid, block, 0, stream>>>(_x_q.data().get(), //
_x_p.data().get(),
_x.data().get(),
x_u.data().get(),
m,
k,
group_size);
if (order == Order::kRowMajor) {
thrust::copy(policy, _x_p.begin(), _x_p.end(), x_p.begin());
thrust::copy(policy, _x_q.begin(), _x_q.end(), x_q.begin());
}
else {
invokeTranspose(_x_p.data().get(), x_p.data().get(), m, k, stream);
invokeTranspose(_x_q.data().get(), x_q.data().get(), m, k, stream);
}
cudaStreamSynchronize(stream);
// Compare(_x_p.data().get(), _x.data().get(), k, k, m);
const int kg = ceil_div(k, group_size);
for (int i = 0; i < m * kg; ++i) {
// int mi = i % m;
// int ki = i / m;
// x_u[i * 2] = i;
// x_u[i * 2 + 1] = i;
// x_u[i * 2] = i * 2;
// x_u[i * 2 + 1] = i * 2 + 1;
}
}
} // namespace turbomind::gemm
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