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import numpy as np
import torch
import torch.nn as nn
import math
def quantize(x, scale, zero, maxq):
q = torch.clamp(torch.round(x / scale) + zero, 0, maxq)
return scale * (q - zero)
class Quantizer(nn.Module):
def __init__(self, shape=1):
super(Quantizer, self).__init__()
self.register_buffer('maxq', torch.tensor(0))
self.register_buffer('scale', torch.zeros(shape))
self.register_buffer('zero', torch.zeros(shape))
def configure(
self,
bits, perchannel=False, sym=True,
mse=False, norm=2.4, grid=100, maxshrink=.8
):
self.maxq = torch.tensor(2 ** bits - 1)
self.perchannel = perchannel
self.sym = sym
self.mse = mse
self.norm = norm
self.grid = grid
self.maxshrink = maxshrink
def find_params(self, x, weight=False):
dev = x.device
self.maxq = self.maxq.to(dev)
shape = x.shape
if self.perchannel:
if weight:
x = x.flatten(1)
else:
if len(shape) == 4:
x = x.permute([1, 0, 2, 3])
x = x.flatten(1)
if len(shape) == 3:
x = x.reshape((-1, shape[-1])).t()
if len(shape) == 2:
x = x.t()
else:
x = x.flatten().unsqueeze(0)
tmp = torch.zeros(x.shape[0], device=dev)
xmin = torch.minimum(x.min(1)[0], tmp)
xmax = torch.maximum(x.max(1)[0], tmp)
if self.sym:
xmax = torch.maximum(torch.abs(xmin), xmax)
tmp = xmin < 0
if torch.any(tmp):
xmin[tmp] = -xmax[tmp]
tmp = (xmin == 0) & (xmax == 0)
xmin[tmp] = -1
xmax[tmp] = +1
self.scale = (xmax - xmin) / self.maxq
if self.sym:
self.zero = torch.full_like(self.scale, (self.maxq + 1) / 2)
else:
self.zero = torch.round(-xmin / self.scale)
if self.mse:
best = torch.full([x.shape[0]], float('inf'), device=dev)
for i in range(int(self.maxshrink * self.grid)):
p = 1 - i / self.grid
xmin1 = p * xmin
xmax1 = p * xmax
scale1 = (xmax1 - xmin1) / self.maxq
zero1 = torch.round(-xmin1 / scale1) if not self.sym else self.zero
q = quantize(x, scale1.unsqueeze(1), zero1.unsqueeze(1), self.maxq)
q -= x
q.abs_()
q.pow_(self.norm)
err = torch.sum(q, 1)
tmp = err < best
if torch.any(tmp):
best[tmp] = err[tmp]
self.scale[tmp] = scale1[tmp]
self.zero[tmp] = zero1[tmp]
if not self.perchannel:
if weight:
tmp = shape[0]
else:
tmp = shape[1] if len(shape) != 3 else shape[2]
self.scale = self.scale.repeat(tmp)
self.zero = self.zero.repeat(tmp)
if weight:
shape = [-1] + [1] * (len(shape) - 1)
self.scale = self.scale.reshape(shape)
self.zero = self.zero.reshape(shape)
return
if len(shape) == 4:
self.scale = self.scale.reshape((1, -1, 1, 1))
self.zero = self.zero.reshape((1, -1, 1, 1))
if len(shape) == 3:
self.scale = self.scale.reshape((1, 1, -1))
self.zero = self.zero.reshape((1, 1, -1))
if len(shape) == 2:
self.scale = self.scale.unsqueeze(0)
self.zero = self.zero.unsqueeze(0)
def quantize(self, x):
if self.ready():
return quantize(x, self.scale, self.zero, self.maxq)
return x
def enabled(self):
return self.maxq > 0
def ready(self):
return torch.all(self.scale != 0)
try:
import importlib
quant_cuda = importlib.import_module("quant_cuda")
except:
import os
import sys
argv = sys.argv
sys.argv = ['quant.py','install']
dir_path = os.path.dirname(os.path.realpath(__file__))
from setuptools import setup, Extension
from torch.utils import cpp_extension
os.chdir(dir_path)
cucode = '''
#include <torch/all.h>
#include <torch/python.h>
#include <cuda.h>
#include <cuda_runtime.h>
// atomicAdd for double-precision floating-point numbers on hardware with
// compute capability < 6.0 from:
// https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#atomic-functions
#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 600
__device__ double atomicAdd(
double* address,
double val
) {
unsigned long long int* address_as_ull = (unsigned long long int*)address;
unsigned long long int old = *address_as_ull, assumed;
do {
assumed = old;
old = atomicCAS(
address_as_ull,
assumed,
__double_as_longlong(val + __longlong_as_double(assumed))
);
// Note: uses integer comparison to avoid hang in case of NaN (since NaN != NaN)
} while (assumed != old);
return __longlong_as_double(old);
}
#endif
template <typename scalar_t>
__global__ void VecQuant2MatMulKernel(
const scalar_t* __restrict__ vec,
const int* __restrict__ mat,
scalar_t* __restrict__ mul,
const scalar_t* __restrict__ scales,
const int* __restrict__ zeros,
int batch,
int vec_height,
int height,
int width,
int zero_width,
int groupsize
);
template <typename scalar_t>
__global__ void VecQuant3MatMulKernel(
const scalar_t* __restrict__ vec,
const int* __restrict__ mat,
scalar_t* __restrict__ mul,
const scalar_t* __restrict__ scales,
const int* __restrict__ zeros,
int batch,
int vec_height,
int height,
int width,
int zero_width,
int groupsize
);
template <typename scalar_t>
__global__ void VecQuant4MatMulKernel(
const scalar_t* __restrict__ vec,
const int* __restrict__ mat,
scalar_t* __restrict__ mul,
const scalar_t* __restrict__ scales,
const int* __restrict__ zeros,
int batch,
int vec_height,
int height,
int width,
int zero_width,
int groupsize
);
template <typename scalar_t>
__global__ void VecQuant8MatMulKernel(
const scalar_t* __restrict__ vec,
const int* __restrict__ mat,
scalar_t* __restrict__ mul,
const scalar_t* __restrict__ scales,
const int* __restrict__ zeros,
int batch,
int vec_height,
int height,
int width,
int zero_width,
int groupsize
);
const int BLOCKWIDTH = 256;
const int BLOCKHEIGHT2 = 16;
const int BLOCKHEIGHT3 = 24;
const int BLOCKHEIGHT4 = 32;
const int BLOCKHEIGHT8 = 64;
__device__ inline unsigned int as_unsigned(int i) {
return *reinterpret_cast<unsigned int*>(&i);
}
void vecquant2matmul_cuda(
torch::Tensor vec,
torch::Tensor mat,
torch::Tensor mul,
torch::Tensor scales,
torch::Tensor zeros,
int groupsize
) {
int batch = vec.size(0);
int vec_height = vec.size(1);
int height = mat.size(0);
int width = mat.size(1);
int zero_width = zeros.size(1);
dim3 blocks(
(height + BLOCKHEIGHT2 - 1) / BLOCKHEIGHT2,
(width + BLOCKWIDTH - 1) / BLOCKWIDTH,
batch
);
dim3 threads(BLOCKWIDTH);
AT_DISPATCH_FLOATING_TYPES(
vec.type(), "vecquant2matmul_cuda", ([&] {
VecQuant2MatMulKernel<<<blocks, threads>>>(
vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
scales.data<scalar_t>(), zeros.data<int>(),
batch, vec_height, height, width, zero_width, groupsize
);
})
);
}
template <typename scalar_t>
__global__ void VecQuant2MatMulKernel(
const scalar_t* __restrict__ vec,
const int* __restrict__ mat,
scalar_t* __restrict__ mul,
const scalar_t* __restrict__ scales,
const int* __restrict__ zeros,
int batch,
int vec_height,
int height,
int width,
int zero_width,
int groupsize
) {
int b = blockIdx.z;
int h = BLOCKHEIGHT2 * blockIdx.x;
int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;
__shared__ scalar_t blockvec[BLOCKWIDTH];
blockvec[threadIdx.x] = vec[b * vec_height + blockIdx.x * BLOCKWIDTH + threadIdx.x];
__syncthreads();
scalar_t res = 0;
int i = width * h + w;
int g_h = h * 16;
int k = 0;
int z_w = w / 16;
int z_mod = (w % 16) * 2;
unsigned int tmp;
while (k < BLOCKWIDTH) {
tmp = as_unsigned(mat[i]);
int g = (g_h + k) / groupsize;
scalar_t scale = scales[g * width + w];
scalar_t zero = scale * scalar_t((as_unsigned(zeros[g * zero_width + z_w]) >> z_mod & 0x3) + 1);
res += (scale * scalar_t((tmp >> 0) & 0x3) - zero) * blockvec[k + 0];
res += (scale * scalar_t((tmp >> 2) & 0x3) - zero) * blockvec[k + 1];
res += (scale * scalar_t((tmp >> 4) & 0x3) - zero) * blockvec[k + 2];
res += (scale * scalar_t((tmp >> 6) & 0x3) - zero) * blockvec[k + 3];
res += (scale * scalar_t((tmp >> 8) & 0x3) - zero) * blockvec[k + 4];
res += (scale * scalar_t((tmp >> 10) & 0x3) - zero) * blockvec[k + 5];
res += (scale * scalar_t((tmp >> 12) & 0x3) - zero) * blockvec[k + 6];
res += (scale * scalar_t((tmp >> 14) & 0x3) - zero) * blockvec[k + 7];
res += (scale * scalar_t((tmp >> 16) & 0x3) - zero) * blockvec[k + 8];
res += (scale * scalar_t((tmp >> 18) & 0x3) - zero) * blockvec[k + 9];
res += (scale * scalar_t((tmp >> 20) & 0x3) - zero) * blockvec[k + 10];
res += (scale * scalar_t((tmp >> 22) & 0x3) - zero) * blockvec[k + 11];
res += (scale * scalar_t((tmp >> 24) & 0x3) - zero) * blockvec[k + 12];
res += (scale * scalar_t((tmp >> 26) & 0x3) - zero) * blockvec[k + 13];
res += (scale * scalar_t((tmp >> 28) & 0x3) - zero) * blockvec[k + 14];
res += (scale * scalar_t((tmp >> 30) & 0x3) - zero) * blockvec[k + 15];
i += width;
k += 16;
}
atomicAdd(&mul[b * width + w], res);
}
void vecquant3matmul_cuda(
torch::Tensor vec,
torch::Tensor mat,
torch::Tensor mul,
torch::Tensor scales,
torch::Tensor zeros,
int groupsize
) {
int batch = vec.size(0);
int vec_height = vec.size(1);
int height = mat.size(0);
int width = mat.size(1);
int zero_width = zeros.size(1);
dim3 blocks(
(height + BLOCKHEIGHT3 - 1) / BLOCKHEIGHT3,
(width + BLOCKWIDTH - 1) / BLOCKWIDTH,
batch
);
dim3 threads(BLOCKWIDTH);
AT_DISPATCH_FLOATING_TYPES(
vec.type(), "vecquant3matmul_cuda", ([&] {
VecQuant3MatMulKernel<<<blocks, threads>>>(
vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
scales.data<scalar_t>(), zeros.data<int>(),
batch, vec_height, height, width, zero_width, groupsize
);
})
);
}
template <typename scalar_t>
__global__ void VecQuant3MatMulKernel(
const scalar_t* __restrict__ vec,
const int* __restrict__ mat,
scalar_t* __restrict__ mul,
const scalar_t* __restrict__ scales,
const int* __restrict__ zeros,
int batch,
int vec_height,
int height,
int width,
int zero_width,
int groupsize
) {
int b = blockIdx.z;
int h = BLOCKHEIGHT3 * blockIdx.x;
int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;
__shared__ scalar_t blockvec[BLOCKWIDTH];
blockvec[threadIdx.x] = vec[b * vec_height + blockIdx.x * BLOCKWIDTH + threadIdx.x];
__syncthreads();
scalar_t res = 0;
int i = width * h + w;
int g_h = (h / 3) * 32;
int k = 0;
int z_w = (w / 32) * 3; // ((w / 256) * 24) / 3
int z_mod = w % 32;
int z_bit;
if (z_mod != 10){
if (z_mod != 21){
z_bit = z_mod;
if (z_bit > 21){
z_bit -= 22;
z_bit *= 3;
z_bit += 2;
z_w += 2;
} else if (z_bit > 10){
z_bit -= 11;
z_bit *= 3;
z_bit += 1;
z_w += 1;
} else {
z_bit *= 3;
}
} else {
z_w += 1;
}
}
unsigned int tmp1;
unsigned int tmp2;
unsigned int tmp;
unsigned int z_tmp;
while (k < BLOCKWIDTH) {
tmp1 = as_unsigned(mat[i]);
int g = (g_h + k) / groupsize;
scalar_t scale = scales[g * width + w];
scalar_t zero;
if (z_mod == 10) {
z_tmp = (as_unsigned(zeros[g * zero_width + z_w]) >> 30) | ((as_unsigned(zeros[g * zero_width + (z_w + 1)]) << 2) & 0x4);
zero = scale * scalar_t((z_tmp) + 1);
} else if (z_mod == 21){
z_tmp = (as_unsigned(zeros[g * zero_width + z_w]) >> 31) | ((as_unsigned(zeros[g * zero_width + (z_w + 1)]) << 1) & 0x6);
zero = scale * scalar_t((z_tmp) + 1);
} else {
zero = scale * scalar_t(((as_unsigned(zeros[g * zero_width + z_w]) >> z_bit) & 0x7) + 1);
}
res += (scale * scalar_t((tmp1 >> 0) & 0x7) - zero) * blockvec[k + 0];
res += (scale * scalar_t((tmp1 >> 3) & 0x7) - zero) * blockvec[k + 1];
res += (scale * scalar_t((tmp1 >> 6) & 0x7) - zero) * blockvec[k + 2];
res += (scale * scalar_t((tmp1 >> 9) & 0x7) - zero) * blockvec[k + 3];
res += (scale * scalar_t((tmp1 >> 12) & 0x7) - zero) * blockvec[k + 4];
res += (scale * scalar_t((tmp1 >> 15) & 0x7) - zero) * blockvec[k + 5];
res += (scale * scalar_t((tmp1 >> 18) & 0x7) - zero) * blockvec[k + 6];
res += (scale * scalar_t((tmp1 >> 21) & 0x7) - zero) * blockvec[k + 7];
res += (scale * scalar_t((tmp1 >> 24) & 0x7) - zero) * blockvec[k + 8];
res += (scale * scalar_t((tmp1 >> 27) & 0x7) - zero) * blockvec[k + 9];
i += width;
tmp2 = as_unsigned(mat[i]);
tmp = (tmp1 >> 30) | ((tmp2 << 2) & 0x4);
tmp2 >>= 1;
res += (scale * scalar_t(tmp) - zero) * blockvec[k + 10];
k += 11;
res += (scale * scalar_t((tmp2 >> 0) & 0x7) - zero) * blockvec[k + 0];
res += (scale * scalar_t((tmp2 >> 3) & 0x7) - zero) * blockvec[k + 1];
res += (scale * scalar_t((tmp2 >> 6) & 0x7) - zero) * blockvec[k + 2];
res += (scale * scalar_t((tmp2 >> 9) & 0x7) - zero) * blockvec[k + 3];
res += (scale * scalar_t((tmp2 >> 12) & 0x7) - zero) * blockvec[k + 4];
res += (scale * scalar_t((tmp2 >> 15) & 0x7) - zero) * blockvec[k + 5];
res += (scale * scalar_t((tmp2 >> 18) & 0x7) - zero) * blockvec[k + 6];
res += (scale * scalar_t((tmp2 >> 21) & 0x7) - zero) * blockvec[k + 7];
res += (scale * scalar_t((tmp2 >> 24) & 0x7) - zero) * blockvec[k + 8];
res += (scale * scalar_t((tmp2 >> 27) & 0x7) - zero) * blockvec[k + 9];
i += width;
tmp1 = as_unsigned(mat[i]);
tmp = (tmp2 >> 30) | ((tmp1 << 1) & 0x6);
tmp1 >>= 2;
res += (scale * scalar_t(tmp) - zero) * blockvec[k + 10];
k += 11;
res += (scale * scalar_t((tmp1 >> 0) & 0x7) - zero) * blockvec[k + 0];
res += (scale * scalar_t((tmp1 >> 3) & 0x7) - zero) * blockvec[k + 1];
res += (scale * scalar_t((tmp1 >> 6) & 0x7) - zero) * blockvec[k + 2];
res += (scale * scalar_t((tmp1 >> 9) & 0x7) - zero) * blockvec[k + 3];
res += (scale * scalar_t((tmp1 >> 12) & 0x7) - zero) * blockvec[k + 4];
res += (scale * scalar_t((tmp1 >> 15) & 0x7) - zero) * blockvec[k + 5];
res += (scale * scalar_t((tmp1 >> 18) & 0x7) - zero) * blockvec[k + 6];
res += (scale * scalar_t((tmp1 >> 21) & 0x7) - zero) * blockvec[k + 7];
res += (scale * scalar_t((tmp1 >> 24) & 0x7) - zero) * blockvec[k + 8];
res += (scale * scalar_t((tmp1 >> 27) & 0x7) - zero) * blockvec[k + 9];
i += width;
k += 10;
}
atomicAdd(&mul[b * width + w], res);
}
void vecquant4matmul_cuda(
torch::Tensor vec,
torch::Tensor mat,
torch::Tensor mul,
torch::Tensor scales,
torch::Tensor zeros,
int groupsize
) {
int batch = vec.size(0);
int vec_height = vec.size(1);
int height = mat.size(0);
int width = mat.size(1);
int zero_width = zeros.size(1);
dim3 blocks(
(height + BLOCKHEIGHT4 - 1) / BLOCKHEIGHT4,
(width + BLOCKWIDTH - 1) / BLOCKWIDTH,
batch
);
dim3 threads(BLOCKWIDTH);
AT_DISPATCH_FLOATING_TYPES(
vec.type(), "vecquant4matmul_cuda", ([&] {
VecQuant4MatMulKernel<<<blocks, threads>>>(
vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
scales.data<scalar_t>(), zeros.data<int>(),
batch, vec_height, height, width, zero_width, groupsize
);
})
);
}
template <typename scalar_t>
__global__ void VecQuant4MatMulKernel(
const scalar_t* __restrict__ vec,
const int* __restrict__ mat,
scalar_t* __restrict__ mul,
const scalar_t* __restrict__ scales,
const int* __restrict__ zeros,
int batch,
int vec_height,
int height,
int width,
int zero_width,
int groupsize
) {
int b = blockIdx.z;
int h = BLOCKHEIGHT4 * blockIdx.x;
int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;
__shared__ scalar_t blockvec[BLOCKWIDTH];
blockvec[threadIdx.x] = vec[b * vec_height + blockIdx.x * BLOCKWIDTH + threadIdx.x];
__syncthreads();
scalar_t res = 0;
int i = width * h + w;
int g_h = h * 8;
int k = 0;
int z_w = w / 8;
int z_mod = (w % 8) * 4;
unsigned int tmp;
while (k < BLOCKWIDTH) {
tmp = as_unsigned(mat[i]);
int g = (g_h + k) / groupsize;
scalar_t scale = scales[g * width + w];
scalar_t zero = scale * scalar_t(((as_unsigned(zeros[g * zero_width + z_w]) >> z_mod) & 0xF) + 1);
res += (scale * scalar_t((tmp >> 0) & 0xF) - zero) * blockvec[k + 0];
res += (scale * scalar_t((tmp >> 4) & 0xF) - zero) * blockvec[k + 1];
res += (scale * scalar_t((tmp >> 8) & 0xF) - zero) * blockvec[k + 2];
res += (scale * scalar_t((tmp >> 12) & 0xF) - zero) * blockvec[k + 3];
res += (scale * scalar_t((tmp >> 16) & 0xF) - zero) * blockvec[k + 4];
res += (scale * scalar_t((tmp >> 20) & 0xF) - zero) * blockvec[k + 5];
res += (scale * scalar_t((tmp >> 24) & 0xF) - zero) * blockvec[k + 6];
res += (scale * scalar_t((tmp >> 28) & 0xF) - zero) * blockvec[k + 7];
i += width;
k += 8;
}
atomicAdd(&mul[b * width + w], res);
}
void vecquant8matmul_cuda(
torch::Tensor vec,
torch::Tensor mat,
torch::Tensor mul,
torch::Tensor scales,
torch::Tensor zeros,
int groupsize
) {
int batch = vec.size(0);
int vec_height = vec.size(1);
int height = mat.size(0);
int width = mat.size(1);
int zero_width = zeros.size(1);
dim3 blocks(
(height + BLOCKHEIGHT8 - 1) / BLOCKHEIGHT8,
(width + BLOCKWIDTH - 1) / BLOCKWIDTH,
batch
);
dim3 threads(BLOCKWIDTH);
AT_DISPATCH_FLOATING_TYPES(
vec.type(), "vecquant8matmul_cuda", ([&] {
VecQuant8MatMulKernel<<<blocks, threads>>>(
vec.data<scalar_t>(), mat.data<int>(), mul.data<scalar_t>(),
scales.data<scalar_t>(), zeros.data<int>(),
batch, vec_height, height, width, zero_width, groupsize
);
})
);
}
template <typename scalar_t>
__global__ void VecQuant8MatMulKernel(
const scalar_t* __restrict__ vec,
const int* __restrict__ mat,
scalar_t* __restrict__ mul,
const scalar_t* __restrict__ scales,
const int* __restrict__ zeros,
int batch,
int vec_height,
int height,
int width,
int zero_width,
int groupsize
) {
int b = blockIdx.z;
int h = BLOCKHEIGHT8 * blockIdx.x;
int w = BLOCKWIDTH * blockIdx.y + threadIdx.x;
__shared__ scalar_t blockvec[BLOCKWIDTH];
blockvec[threadIdx.x] = vec[b * vec_height + blockIdx.x * BLOCKWIDTH + threadIdx.x];
__syncthreads();
scalar_t res = 0;
int i = width * h + w;
int g_h = h * 4;
int k = 0;
int z_w = w / 4;
int z_mod = (w % 4) * 8;
unsigned int tmp;
while (k < BLOCKWIDTH) {
tmp = as_unsigned(mat[i]);
int g = (g_h + k) / groupsize;
scalar_t scale = scales[g * width + w];
scalar_t zero = scale * scalar_t(((as_unsigned(zeros[g * zero_width + z_w]) >> z_mod) & 0xFF) + 1);
res += (scale * scalar_t((tmp >> 0) & 0xFF) - zero) * blockvec[k + 0];
res += (scale * scalar_t((tmp >> 8) & 0xFF) - zero) * blockvec[k + 1];
res += (scale * scalar_t((tmp >> 16) & 0xFF) - zero) * blockvec[k + 2];
res += (scale * scalar_t((tmp >> 24) & 0xFF) - zero) * blockvec[k + 3];
i += width;
k += 4;
}
atomicAdd(&mul[b * width + w], res);
}
'''
with open("quant_cuda_kernel.cu","w") as f:
f.write(cucode)
cppcode = '''
#include <torch/all.h>
#include <torch/python.h>
#include <c10/cuda/CUDAGuard.h>
void vecquant2matmul_cuda(
torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
torch::Tensor scales, torch::Tensor zeros,
int groupsize
);
void vecquant2matmul(
torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
torch::Tensor scales, torch::Tensor zeros,
int groupsize
) {
const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
vecquant2matmul_cuda(vec, mat, mul, scales, zeros,groupsize);
}
void vecquant3matmul_cuda(
torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
torch::Tensor scales, torch::Tensor zeros,
int groupsize
);
void vecquant3matmul(
torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
torch::Tensor scales, torch::Tensor zeros,
int groupsize
) {
const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
vecquant3matmul_cuda(vec, mat, mul, scales, zeros, groupsize);
}
void vecquant4matmul_cuda(
torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
torch::Tensor scales, torch::Tensor zeros,
int groupsize
);
void vecquant4matmul(
torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
torch::Tensor scales, torch::Tensor zeros,
int groupsize
) {
const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
vecquant4matmul_cuda(vec, mat, mul, scales, zeros, groupsize);
}
void vecquant8matmul_cuda(
torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
torch::Tensor scales, torch::Tensor zeros,
int groupsize
);
void vecquant8matmul(
torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
torch::Tensor scales, torch::Tensor zeros,
int groupsize
) {
const at::cuda::OptionalCUDAGuard device_guard(device_of(vec));
vecquant8matmul_cuda(vec, mat, mul, scales, zeros, groupsize);
}
PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
m.def("vecquant2matmul", &vecquant2matmul, "Vector 2-bit Quantized Matrix Multiplication (CUDA)");
m.def("vecquant3matmul", &vecquant3matmul, "Vector 3-bit Quantized Matrix Multiplication (CUDA)");
m.def("vecquant4matmul", &vecquant4matmul, "Vector 4-bit Quantized Matrix Multiplication (CUDA)");
m.def("vecquant8matmul", &vecquant8matmul, "Vector 8-bit Quantized Matrix Multiplication (CUDA)");
}
'''
with open("quant_cuda.cpp","w") as f:
f.write(cppcode)
setup(
name='quant_cuda',
ext_modules=[cpp_extension.CUDAExtension(
'quant_cuda', ['quant_cuda.cpp', 'quant_cuda_kernel.cu']
)],
cmdclass={'build_ext': cpp_extension.BuildExtension}
)
os.chdir(os.getcwd())
sys.argv = argv
for i in sys.path:
if i.endswith("site-packages"):
for j in os.listdir(i):
if j.find("quant_cuda") != -1:
sys.path.append(os.path.join(i,j))
break
break
import importlib
quant_cuda = importlib.import_module("quant_cuda")
# Assumes layer is perfectly divisible into 256 * 256 blocks
class QuantLinear(nn.Module):
def __init__(self, bits, groupsize, infeatures, outfeatures):
super().__init__()
if bits not in [2,3,4,8]:
raise NotImplementedError("Only 2,3,4,8 bits are supported.")
self.infeatures = infeatures
self.outfeatures = outfeatures
self.bits = bits
if groupsize != -1 and groupsize < 32 and groupsize != int(math.pow(2,int(math.log2(groupsize)))):
raise NotImplementedError("groupsize supports powers of 2 greater than 32. (e.g. : 32,64,128,etc)")
groupsize = groupsize if groupsize != -1 else infeatures
self.groupsize = groupsize
self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures/groupsize),outfeatures // 256 * (bits * 8)), dtype=torch.int))
self.register_buffer('scales', torch.zeros((math.ceil(infeatures/groupsize),outfeatures)))
self.register_buffer('bias', torch.zeros(outfeatures))
self.register_buffer(
'qweight', torch.zeros((infeatures // 256 * (bits * 8), outfeatures), dtype=torch.int)
)
self._initialized_quant_state = False
def pack(self, linear, scales, zeros):
scales = scales.t().contiguous()
zeros = zeros.t().contiguous()
scale_zeros = zeros * scales
self.scales = scales.clone()
if linear.bias is not None:
self.bias = linear.bias.clone()
intweight = []
for idx in range(self.infeatures):
g_idx = idx // self.groupsize
intweight.append(torch.round((linear.weight.data[:,idx] + scale_zeros[g_idx]) / self.scales[g_idx]).to(torch.int)[:,None])
intweight = torch.cat(intweight,dim=1)
intweight = intweight.t().contiguous()
intweight = intweight.numpy().astype(np.uint32)
qweight = np.zeros(
(intweight.shape[0] // 256 * (self.bits * 8), intweight.shape[1]), dtype=np.uint32
)
i = 0
row = 0
while row < qweight.shape[0]:
if self.bits in [2,4,8]:
for j in range(i, i + (32//self.bits)):
qweight[row] |= intweight[j] << (self.bits * (j - i))
i += 32//self.bits
row += 1
elif self.bits == 3:
for j in range(i, i + 10):
qweight[row] |= intweight[j] << (3 * (j - i))
i += 10
qweight[row] |= intweight[i] << 30
row += 1
qweight[row] |= (intweight[i] >> 2) & 1
i += 1
for j in range(i, i + 10):
qweight[row] |= intweight[j] << (3 * (j - i) + 1)
i += 10
qweight[row] |= intweight[i] << 31
row += 1
qweight[row] |= (intweight[i] >> 1) & 0x3
i += 1
for j in range(i, i + 10):
qweight[row] |= intweight[j] << (3 * (j - i) + 2)
i += 10
row += 1
else:
raise NotImplementedError("Only 2,3,4,8 bits are supported.")
qweight = qweight.astype(np.int32)
self.qweight = torch.from_numpy(qweight)
zeros -= 1;
zeros = zeros.numpy().astype(np.uint32)
qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 256 * (self.bits * 8)), dtype=np.uint32)
i = 0
col = 0
while col < qzeros.shape[1]:
if self.bits in [2,4,8]:
for j in range(i, i + (32//self.bits)):
qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
i += 32//self.bits
col += 1
elif self.bits == 3:
for j in range(i, i + 10):
qzeros[:, col] |= zeros[:, j] << (3 * (j - i))
i += 10
qzeros[:, col] |= zeros[:, i] << 30
col += 1
qzeros[:, col] |= (zeros[:, i] >> 2) & 1
i += 1
for j in range(i, i + 10):
qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 1)
i += 10
qzeros[:, col] |= zeros[:, i] << 31
col += 1
qzeros[:, col] |= (zeros[:, i] >> 1) & 0x3
i += 1
for j in range(i, i + 10):
qzeros[:, col] |= zeros[:, j] << (3 * (j - i) + 2)
i += 10
col += 1
else:
raise NotImplementedError("Only 2,3,4,8 bits are supported.")
qzeros = qzeros.astype(np.int32)
self.qzeros = torch.from_numpy(qzeros)
def forward(self, x):
intermediate_dtype = torch.float32
if not self._initialized_quant_state:
# Do we even have a bias? Check for at least one non-zero element.
if self.bias is not None and bool(torch.any(self.bias != 0)):
# Then make sure it's the right type.
self.bias.data = self.bias.data.to(intermediate_dtype)
else:
self.bias = None
outshape = list(x.shape)
outshape[-1] = self.outfeatures
x = x.reshape(-1, x.shape[-1])
if self.bias is None:
y = torch.zeros(x.shape[0], outshape[-1], dtype=intermediate_dtype, device=x.device)
else:
y = self.bias.clone().repeat(x.shape[0], 1)
output_dtype = x.dtype
x = x.to(intermediate_dtype)
if self.bits == 2:
quant_cuda.vecquant2matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
elif self.bits == 3:
quant_cuda.vecquant3matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
elif self.bits == 4:
quant_cuda.vecquant4matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
elif self.bits == 8:
quant_cuda.vecquant8matmul(x, self.qweight, y, self.scales, self.qzeros, self.groupsize)
else:
raise NotImplementedError("Only 2,3,4,8 bits are supported.")
y = y.to(output_dtype)
return y.reshape(outshape)
def make_quant(module, names, bits, groupsize, name=''):
if isinstance(module, QuantLinear):
return
for attr in dir(module):
tmp = getattr(module, attr)
name1 = name + '.' + attr if name != '' else attr
if name1 in names:
setattr(
module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features)
)
for name1, child in module.named_children():
make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1) |