import math
import numpy as np
import torch
import torch.nn as nn
from torch.cuda.amp import custom_bwd, custom_fwd
try:
import triton
import triton.language as tl
from . import custom_autotune
# code based https://github.com/fpgaminer/GPTQ-triton
@custom_autotune.autotune(
configs=[
triton.Config({
'BLOCK_SIZE_M': 64,
'BLOCK_SIZE_N': 256,
'BLOCK_SIZE_K': 32,
'GROUP_SIZE_M': 8
}, num_stages=4, num_warps=4),
triton.Config({
'BLOCK_SIZE_M': 128,
'BLOCK_SIZE_N': 128,
'BLOCK_SIZE_K': 32,
'GROUP_SIZE_M': 8
}, num_stages=4, num_warps=4),
triton.Config({
'BLOCK_SIZE_M': 64,
'BLOCK_SIZE_N': 128,
'BLOCK_SIZE_K': 32,
'GROUP_SIZE_M': 8
}, num_stages=4, num_warps=4),
triton.Config({
'BLOCK_SIZE_M': 128,
'BLOCK_SIZE_N': 32,
'BLOCK_SIZE_K': 32,
'GROUP_SIZE_M': 8
}, num_stages=4, num_warps=4),
triton.Config({
'BLOCK_SIZE_M': 64,
'BLOCK_SIZE_N': 64,
'BLOCK_SIZE_K': 32,
'GROUP_SIZE_M': 8
}, num_stages=4, num_warps=4),
triton.Config({
'BLOCK_SIZE_M': 64,
'BLOCK_SIZE_N': 128,
'BLOCK_SIZE_K': 32,
'GROUP_SIZE_M': 8
}, num_stages=2, num_warps=8),
triton.Config({
'BLOCK_SIZE_M': 64,
'BLOCK_SIZE_N': 64,
'BLOCK_SIZE_K': 64,
'GROUP_SIZE_M': 8
}, num_stages=3, num_warps=8),
triton.Config({
'BLOCK_SIZE_M': 32,
'BLOCK_SIZE_N': 32,
'BLOCK_SIZE_K': 128,
'GROUP_SIZE_M': 8
}, num_stages=2, num_warps=4),
],
key=['M', 'N', 'K'],
nearest_power_of_two=True,
prune_configs_by={
'early_config_prune': custom_autotune.matmul248_kernel_config_pruner,
'perf_model': None,
'top_k': None,
},
)
@triton.jit
def matmul_248_kernel(a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, g_ptr, M, N, K, bits, maxq, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales, stride_zeros,
NO_GROUP: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr):
"""
Compute the matrix multiplication C = A x B.
A is of shape (M, K) float16
B is of shape (K//8, N) int32
C is of shape (M, N) float16
scales is of shape (G, N) float16
zeros is of shape (G, N) float16
g_ptr is of shape (K) int32
"""
infearure_per_bits = 32 // bits
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
num_pid_in_group = GROUP_SIZE_M * num_pid_n
group_id = pid // num_pid_in_group
first_pid_m = group_id * GROUP_SIZE_M
group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
pid_m = first_pid_m + (pid % group_size_m)
pid_n = (pid % num_pid_in_group) // group_size_m
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
a_mask = (offs_am[:, None] < M)
# b_ptrs is set up such that it repeats elements along the K axis 8 times
b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
g_ptrs = g_ptr + offs_k
# shifter is used to extract the N bits of each element in the 32-bit word from B
scales_ptrs = scales_ptr + offs_bn[None, :]
zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)
if NO_GROUP:
scales = tl.load(scales_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = (zeros + 1)
shifter = (offs_k % infearure_per_bits) * bits
zeros_shifter = (offs_bn % infearure_per_bits) * bits
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, num_pid_k):
g_idx = tl.load(g_ptrs)
# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
if not NO_GROUP:
scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = (zeros + 1)
a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
# Now we need to unpack b (which is N-bit values) into 32-bit values
b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
b = (b - zeros) * scales # Scale and shift
accumulator += tl.dot(a, b)
a_ptrs += BLOCK_SIZE_K
b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
g_ptrs += BLOCK_SIZE_K
c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
tl.store(c_ptrs, accumulator, mask=c_mask)
@custom_autotune.autotune(configs=[
triton.Config({
'BLOCK_SIZE_M': 64,
'BLOCK_SIZE_N': 32,
'BLOCK_SIZE_K': 256,
'GROUP_SIZE_M': 8
}, num_stages=4, num_warps=4),
triton.Config({
'BLOCK_SIZE_M': 128,
'BLOCK_SIZE_N': 32,
'BLOCK_SIZE_K': 128,
'GROUP_SIZE_M': 8
}, num_stages=4, num_warps=4),
triton.Config({
'BLOCK_SIZE_M': 64,
'BLOCK_SIZE_N': 32,
'BLOCK_SIZE_K': 128,
'GROUP_SIZE_M': 8
}, num_stages=4, num_warps=4),
triton.Config({
'BLOCK_SIZE_M': 128,
'BLOCK_SIZE_N': 32,
'BLOCK_SIZE_K': 32,
'GROUP_SIZE_M': 8
}, num_stages=4, num_warps=4),
triton.Config({
'BLOCK_SIZE_M': 64,
'BLOCK_SIZE_N': 32,
'BLOCK_SIZE_K': 64,
'GROUP_SIZE_M': 8
}, num_stages=4, num_warps=4),
triton.Config({
'BLOCK_SIZE_M': 64,
'BLOCK_SIZE_N': 32,
'BLOCK_SIZE_K': 128,
'GROUP_SIZE_M': 8
}, num_stages=2, num_warps=8),
triton.Config({
'BLOCK_SIZE_M': 64,
'BLOCK_SIZE_N': 64,
'BLOCK_SIZE_K': 64,
'GROUP_SIZE_M': 8
}, num_stages=3, num_warps=8),
triton.Config({
'BLOCK_SIZE_M': 32,
'BLOCK_SIZE_N': 128,
'BLOCK_SIZE_K': 32,
'GROUP_SIZE_M': 8
}, num_stages=2, num_warps=4),
],
key=['M', 'N', 'K'],
nearest_power_of_two=True)
@triton.jit
def transpose_matmul_248_kernel(a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, g_ptr, M, N, K, bits, maxq, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales,
stride_zeros, NO_GROUP: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr):
"""
Compute the matrix multiplication C = A x B.
A is of shape (M, N) float16
B is of shape (K//8, N) int32
C is of shape (M, K) float16
scales is of shape (G, N) float16
zeros is of shape (G, N) float16
g_ptr is of shape (K) int32
"""
infearure_per_bits = 32 // bits
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
num_pid_in_group = GROUP_SIZE_M * num_pid_k
group_id = pid // num_pid_in_group
first_pid_m = group_id * GROUP_SIZE_M
group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
pid_m = first_pid_m + (pid % group_size_m)
pid_k = (pid % num_pid_in_group) // group_size_m
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
offs_n = tl.arange(0, BLOCK_SIZE_N)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
a_mask = (offs_am[:, None] < M)
# b_ptrs is set up such that it repeats elements along the K axis 8 times
b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
g_ptrs = g_ptr + offs_bk
g_idx = tl.load(g_ptrs)
# shifter is used to extract the N bits of each element in the 32-bit word from B
scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales
zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros
if NO_GROUP:
scales = tl.load(scales_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = (zeros + 1)
shifter = (offs_bk % infearure_per_bits) * bits
zeros_shifter = (offs_n % infearure_per_bits) * bits
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
for k in range(0, num_pid_n):
# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
if not NO_GROUP:
scales = tl.load(scales_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = (zeros + 1)
a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
# Now we need to unpack b (which is N-bit values) into 32-bit values
b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
b = (b - zeros) * scales # Scale and shift
b = tl.trans(b)
accumulator += tl.dot(a, b)
a_ptrs += BLOCK_SIZE_N
b_ptrs += BLOCK_SIZE_N
scales_ptrs += BLOCK_SIZE_N
zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
tl.store(c_ptrs, accumulator, mask=c_mask)
except:
print('trioton not installed.')
def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq, no_group):
with torch.cuda.device(input.device):
output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']), )
matmul_248_kernel[grid](input, qweight, output, scales, qzeros, g_idx, input.shape[0], qweight.shape[1], input.shape[1], bits, maxq, input.stride(0), input.stride(1), qweight.stride(0),
qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0), no_group)
return output
def transpose_matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq, no_group):
with torch.cuda.device(input.device):
output_dim = (qweight.shape[0] * 32) // bits
output = torch.empty((input.shape[0], output_dim), device='cuda', dtype=torch.float16)
grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_dim, META['BLOCK_SIZE_K']), )
transpose_matmul_248_kernel[grid](input, qweight, output, scales, qzeros, g_idx, input.shape[0], qweight.shape[1], output_dim, bits, maxq, input.stride(0), input.stride(1), qweight.stride(0),
qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0), no_group)
return output
class QuantLinearFunction(torch.autograd.Function):
@staticmethod
@custom_fwd(cast_inputs=torch.float16)
def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq, no_group):
output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq, no_group)
ctx.save_for_backward(qweight, scales, qzeros, g_idx)
ctx.bits, ctx.maxq, ctx.no_group = bits, maxq, no_group
return output
@staticmethod
@custom_bwd
def backward(ctx, grad_output):
qweight, scales, qzeros, g_idx = ctx.saved_tensors
bits, maxq, no_group = ctx.bits, ctx.maxq, ctx.no_group
grad_input = None
if ctx.needs_input_grad[0]:
grad_input = transpose_matmul248(grad_output, qweight, scales, qzeros, g_idx, bits, maxq, no_group)
return grad_input, None, None, None, None, None, None
class QuantLinear(nn.Module):
def __init__(self, bits, groupsize, infeatures, outfeatures, bias):
super().__init__()
if bits not in [2, 4, 8]:
raise NotImplementedError("Only 2,4,8 bits are supported.")
self.infeatures = infeatures
self.outfeatures = outfeatures
self.bits = bits
self.maxq = 2**self.bits - 1
self.groupsize = groupsize if groupsize != -1 else infeatures
self.no_group = math.ceil(infeatures / self.groupsize) == 1
self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32))
self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32))
self.register_buffer('scales', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16))
self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32))
if bias:
self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16))
else:
self.bias = None
def pack(self, linear, scales, zeros, g_idx=None):
self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
scales = scales.t().contiguous()
zeros = zeros.t().contiguous()
scale_zeros = zeros * scales
self.scales = scales.clone().half()
if linear.bias is not None:
self.bias = linear.bias.clone().half()
intweight = []
for idx in range(self.infeatures):
intweight.append(torch.round((linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]]) / self.scales[self.g_idx[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] // 32 * self.bits, 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
else:
raise NotImplementedError("Only 2,4,8 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] // 32 * self.bits), 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
else:
raise NotImplementedError("Only 2,4,8 bits are supported.")
qzeros = qzeros.astype(np.int32)
self.qzeros = torch.from_numpy(qzeros)
def forward(self, x):
out_shape = x.shape[:-1] + (self.outfeatures, )
out = QuantLinearFunction.apply(x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, self.g_idx, self.bits, self.maxq, self.no_group)
out = out + self.bias if self.bias is not None else out
return out.reshape(out_shape)
def make_quant_linear(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:
delattr(module, attr)
setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None))
for name1, child in module.named_children():
make_quant_linear(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)
def autotune_warmup_linear(model, transpose=False):
"""
Pre-tunes the quantized kernel
"""
from tqdm import tqdm
kn_values = {}
for _, m in model.named_modules():
if not isinstance(m, QuantLinear):
continue
k = m.infeatures
n = m.outfeatures
if (k, n) not in kn_values:
kn_values[(k, n)] = (m.qweight.cuda(), m.scales.cuda(), m.qzeros.cuda(), m.g_idx.cuda(), m.bits, m.maxq, m.no_group)
print(f'Found {len(kn_values)} unique KN Linear values.')
print('Warming up autotune cache ...')
with torch.no_grad():
for m in tqdm(range(0, 12)):
m = 2**m # [1, 2048]
for (k, n), (qweight, scales, qzeros, g_idx, bits, maxq, no_group) in kn_values.items():
a = torch.randn(m, k, dtype=torch.float16, device='cuda')
matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq, no_group)
if transpose:
a = torch.randn(m, n, dtype=torch.float16, device='cuda')
transpose_matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq, no_group)
del kn_values