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HDCharles/microbenchmarks.py

Created February 24, 2024 16:46
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microbenchmarks
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microbenchmarks.py
import torch
import torch.nn.functional as F
import triton
import triton.language as tl
from triton.ops.matmul import matmul as triton_matmul
from triton.ops.matmul import _kernel
from triton import Config
from torch._inductor import config
from torch import _dynamo
torch._inductor.config.coordinate_descent_tuning = True
import torchao
from torchao.quantization.quant_primitives import groupwise_affine_quantize_tensor
aten = torch.ops.aten
def get_configs_io_bound():
configs = []
for num_stages in [2, 3, 4, 5, 6]:
for block_m in [16, 32]:
for block_k in [32, 64]:
for block_n in [32, 64, 128, 256]:
num_warps = 2 if block_n <= 64 else 4
configs.append(
Config({'BLOCK_SIZE_M': block_m, 'BLOCK_SIZE_N': block_n, 'BLOCK_SIZE_K': block_k, 'GROUP_SIZE_M': 8},
num_stages=num_stages, num_warps=num_warps))
return configs
config_list = [
Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
# good for int8
Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
Config({'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
]+get_configs_io_bound()
@triton.autotune(
configs = [
Config({'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=1),
Config({'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=1),
]+config_list,
key=['M', 'N', 'K'],
)
@triton.jit
def int8_weight_only_linear_kernel(
# Pointers to matrices
x_ptr, w_ptr, b_ptr, s_ptr, y_ptr,
# Matrix dimensions
M, N, K,
# The stride variables represent how much to increase the ptr by when moving by 1
# element in a particular dimension. E.g. `stride_am` is how much to increase `x_ptr`
# by to get the element one row down (A has M rows).
stride_xm, stride_xk,
stride_wk, stride_wn,
stride_b,
stride_ym, stride_yn,
# Meta-parameters
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
GROUP_SIZE_M: tl.constexpr,
):
"""Kernel for computing the matmul C = A x B.
A has shape (M, K), B has shape (K, N) and C has shape (M, N)
"""
# -----------------------------------------------------------
# Map program ids `pid` to the block of Y it should compute.
# This is done in a grouped ordering to promote L2 data reuse.
# See above `L2 Cache Optimizations` section for details.
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_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
# ----------------------------------------------------------
# Create pointers for the first blocks of X and W.
# We will advance this pointer as we move in the K direction
# and accumulate
# `x_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
# `w_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
# See above `Pointer Arithmetics` section for details
offs_xm = tl.max_contiguous((pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M,BLOCK_SIZE_M)
offs_wn = tl.max_contiguous((pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N,BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
x_ptrs = x_ptr + (offs_xm[:, None] * stride_xm + offs_k[None, :] * stride_xk)
w_ptrs = w_ptr + (offs_k[:, None] * stride_wk + offs_wn[None, :] * stride_wn)
b_ptrs = b_ptr + (offs_wn * stride_b)
step_w = BLOCK_SIZE_K * stride_wk
step_x = BLOCK_SIZE_K * stride_xk
# -----------------------------------------------------------
# Iterate to compute a block of the Y matrix.
# We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
# of fp32 values for higher accuracy.
# `accumulator` will be converted back to fp16 after the loop.
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
# Load the next block of A and B, generate a mask by checking the K dimension.
# If it is out of bounds, set it to 0.
x = tl.load(x_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
w = tl.load(w_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
# We accumulate along the K dimension.
accumulator += tl.dot(x, w.to(tl.bfloat16))
# Advance the ptrs to the next K block.
x_ptrs += step_x
w_ptrs += step_w
s = tl.load(s_ptr)
b = tl.load(b_ptrs)
y = (accumulator.to(tl.bfloat16) * s + b)
# y = accumulator
# -----------------------------------------------------------
# Write back the block of the output matrix Y with masks.
offs_ym = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_yn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
y_ptrs = y_ptr + stride_ym * offs_ym[:, None] + stride_yn * offs_yn[None, :]
y_mask = (offs_ym[:, None] < M) & (offs_yn[None, :] < N)
tl.store(y_ptrs, y, mask=y_mask)
def int8_weight_only_linear(x, w, b, s):
# Check constraints.
assert x.shape[1] == w.shape[0], "Incompatible dimensions"
# assert x.is_contiguous(), "Matrix x must be contiguous"
# assert w.is_contiguous(), "Matrix w must be contiguous"
M, K = x.shape
K, N = w.shape
assert b.shape[0] == N
# Allocates output.
y = torch.empty((M, N), device=x.device, dtype=x.dtype)
# 1D launch kernel where each block gets its own program.
grid = lambda META: (
triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),
)
int8_weight_only_linear_kernel[grid](
x, w, b, s, y,
M, N, K,
x.stride(0), x.stride(1),
w.stride(0), w.stride(1),
b.stride(0),
y.stride(0), y.stride(1),
)
return y
@triton.autotune(
configs = [
Config({'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 256, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
Config({'BLOCK_SIZE_M': 16, 'BLOCK_SIZE_N': 16, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
]+config_list,
key=['M', 'N', 'K'],
)
@triton.jit
def uint4x2_weight_only_linear_kernel(
# Pointers to matrices
x_ptr, w_ptr, b_ptr, s_ptr, y_ptr,
# Matrix dimensions
M, N, K, # x is Mx(K*2) and w is KxN
# The stride variables represent how much to increase the ptr by when moving by 1
# element in a particular dimension. E.g. `stride_am` is how much to increase `x_ptr`
# by to get the element one row down (A has M rows).
stride_xm, stride_xk,
stride_wk, stride_wn,
stride_b,
stride_ym, stride_yn,
# Meta-parameters
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
GROUP_SIZE_M: tl.constexpr,
):
"""Kernel for computing the matmul C = A x B.
A has shape (M, K), B has shape (K, N) and C has shape (M, N)
"""
# -----------------------------------------------------------
# Map program ids `pid` to the block of Y it should compute.
# This is done in a grouped ordering to promote L2 data reuse.
# See above `L2 Cache Optimizations` section for details.
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_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
# ----------------------------------------------------------
# Create pointers for the first blocks of X and W.
# We will advance this pointer as we move in the K direction
# and accumulate
# `x_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
# `w_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
# See above `Pointer Arithmetics` section for details
offs_xm = tl.max_contiguous((pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M,BLOCK_SIZE_M)
offs_wn = tl.max_contiguous((pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N,BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
x_ptrs = x_ptr + (offs_xm[:, None] * stride_xm + offs_k[None, :] * stride_xk)
w_ptrs = w_ptr + (offs_k[:, None]//2 * stride_wk + offs_wn[None, :] * stride_wn)
w_shifts = (offs_k % 2) * 4
b_ptrs = b_ptr + (offs_wn * stride_b)
step_w = BLOCK_SIZE_K//2 * stride_wk
step_x = BLOCK_SIZE_K * stride_xk
# -----------------------------------------------------------
# Iterate to compute a block of the Y matrix.
# We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
# of fp32 values for higher accuracy.
# `accumulator` will be converted back to fp16 after the loop.
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
# Load the next block of A and B, generate a mask by checking the K dimension.
# If it is out of bounds, set it to 0.
x = tl.load(x_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
w = tl.load(w_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
w = ((w >> w_shifts[:, None]) & 0xF) - 8
# We accumulate along the K dimension.
accumulator += tl.dot(x, w.to(tl.bfloat16))
# Advance the ptrs to the next K block.
x_ptrs += step_x
w_ptrs += step_w
s = tl.load(s_ptr)
b = tl.load(b_ptrs)
y = (accumulator.to(tl.bfloat16) * s)+b
# -----------------------------------------------------------
# Write back the block of the output matrix Y with masks.
offs_ym = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_yn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
y_ptrs = y_ptr + stride_ym * offs_ym[:, None] + stride_yn * offs_yn[None, :]
y_mask = (offs_ym[:, None] < M) & (offs_yn[None, :] < N)
tl.store(y_ptrs, y, mask=y_mask)
def uint4x2_weight_only_linear(x, w, b, s):
# Check constraints.
assert x.shape[1] == w.shape[0]*2, "Incompatible dimensions"
# assert x.is_contiguous(), "Matrix x must be contiguous"
# assert w.is_contiguous(), "Matrix w must be contiguous"
M, K = x.shape
_, N = w.shape
assert b.shape[0] == N
# Allocates output.
y = torch.empty((M, N), device=x.device, dtype=x.dtype)
# 1D launch kernel where each block gets its own program.
grid = lambda META: (
triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),
)
uint4x2_weight_only_linear_kernel[grid](
x, w, b, s, y,
M, N, K,
x.stride(0), x.stride(1),
w.stride(0), w.stride(1),
b.stride(0),
y.stride(0), y.stride(1),
)
return y
quantiles = [0.5, 0.2, 0.8]
result = {}
for D in [2**6, 2**7, 2**8, 2**9, 2**10, 2**11]: #, 2**10, 2**12, 2**14]:
result[D]={}
result[D]["cublas linear"]={}
result[D]["triton matmul"]={}
result[D]["int8 linear"]={}
result[D]["alt int8 linear"]={}
result[D]["uint4x2 linear"]={}
result[D]["int4 tinygemm"]={}
N = D
for t_x in [0,1]:
for t_w in [1,0]:
print("D tx tw",D, t_x, t_w)
x = torch.randn(1,D).to('cuda').to(torch.bfloat16)
w_bf16 = torch.randn(D, N, dtype=torch.bfloat16).cuda()
bias = torch.randn(N, dtype=torch.bfloat16).cuda()
if t_x:
x = x.t().contiguous().t()
if t_w:
w_bf16 = w_bf16.t().contiguous().t()
print("cublas linear")
try:
torch.nn.functional.linear(x, w_bf16, bias)
torch.cuda.synchronize()
result[D]["cublas linear"][(t_x, t_w)] = triton.testing.do_bench(lambda: torch.nn.functional.linear(x, w_bf16, bias), quantiles=quantiles)[0]
except:
print("err")
pass
torch.cuda.synchronize()
print("int4 tinygemm")
try:
I=min(D//32,8)
G=min(128, D//I)
w_int4, scales_and_zeros = groupwise_affine_quantize_tensor(w_bf16, 4, 32)
w_int4pack = aten._convert_weight_to_int4pack(w_int4.contiguous(), I)
del w_int4
result[D]["int4 tinygemm"][(t_x, t_w)] = triton.testing.do_bench(lambda: aten._weight_int4pack_mm(x.contiguous(), w_int4pack, 32, scales_and_zeros), quantiles=quantiles)[0]
del w_bf16, scales_and_zeros, w_int4pack
except:
print("err")
pass
w_int8 = torch.randint(-128, 127, (D, N), dtype=torch.int8).cuda()
if t_w:
w_int8 = w_int8.t().contiguous().t()
scale = torch.randn(N, dtype=torch.bfloat16).cuda()
print("triton matmul")
try:
triton_matmul(x, w_int8)
torch.cuda.synchronize()
result[D]["triton matmul"][(t_x, t_w)] = triton.testing.do_bench(lambda: triton_matmul(x, w_int8), quantiles=quantiles)[0]
except:
print("err")
pass
torch.cuda.synchronize()
torch._dynamo.reset()
print("alt wo mm")
def alt_wo_mm(x, w_int8, bias, scale):
return (x.unsqueeze(-1) * w_int8.unsqueeze(0)).sum(dim=1)*scale+bias
# return torch.mm(x, w_int8)*scale+bias
comp_fn=torch.compile(alt_wo_mm, mode='max-autotune')
comp_fn(x, w_int8, bias, scale)
comp_fn(x, w_int8, bias, scale)
comp_fn(x, w_int8, bias, scale)
result[D]["alt int8 linear"][(t_x, t_w)] = triton.testing.do_bench(lambda: comp_fn(x, w_int8, bias, scale), quantiles=quantiles)[0]
print("int8 linear")
try:
int8_weight_only_linear(x, w_int8, bias, scale)
torch.cuda.synchronize()
result[D]["int8 linear"][(t_x, t_w)] = triton.testing.do_bench(lambda: int8_weight_only_linear(x, w_int8, bias, scale), quantiles=quantiles)[0]
except:
print("err")
pass
torch.cuda.synchronize()
del w_int8
w_uint4x2 = torch.randint(0, 255, (D//2, N), dtype=torch.uint8).cuda()
if t_w:
w_uint4x2 = w_uint4x2.t().contiguous().t()
print("uint4x2 linear")
try:
assert t_w==1
# uint4x2_weight_only_linear(x, w_uint4x2, bias, scale)
torch.cuda.synchronize()
result[D]["uint4x2 linear"][(t_x, t_w)] = triton.testing.do_bench(lambda: uint4x2_weight_only_linear(x, w_uint4x2, bias, scale), quantiles=quantiles)[0]
except:
print("err")
pass
torch.cuda.synchronize()
del w_uint4x2, scale, bias
caches = {"triton matmul": _kernel.cache, "int8 linear": int8_weight_only_linear_kernel.cache, "uint4x2 linear": uint4x2_weight_only_linear_kernel.cache}
print("| X . W | X . Wt | Xt . W | Xt . Wt | model | config")
for D in result.keys():
print(f"{1}, {D}, {D}")
for name in result[D].keys():
r = result[D][name]
used_config = None
if name in caches:
cache = caches[name]
for key,config in cache.items():
if key[0]==1 and key[1]==D and key[2]==D:
used_config=config
break
print(f"| {(r[(0,0)] if (0,0) in r else 0):2.4f} | {(r[(0,1)] if (0,1) in r else 0):2.4f} | {(r[(1,0)] if (1,0) in r else 0):2.4f} | {(r[(1,1)] if (1,1) in r else 0):2.4f} | {name:<15} | {used_config}")
# install: pip install -U --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/Triton-Nightly/pypi/simple/ triton-nightly
# instlal: pip install torchao
# using torch compiled from 2.1.0a0+git9c2122d or nightly
# using cuda 12.0 on A100 GPU
"""
| X . W | X . Wt | Xt . W | Xt . Wt | model | config
1, 64, 64
| 0.0103 | 0.0089 | 0.0095 | 0.0083 | cublas linear | None
| 0.1582 | 0.1514 | 0.1542 | 0.1551 | triton matmul | BLOCK_M: 32, BLOCK_N: 32, BLOCK_K: 32, SPLIT_K: 1, num_warps: 2, num_ctas: 1, num_stages: 6, enable_warp_specialization: False, enable_persistent: False
| 0.0927 | 0.1114 | 0.0900 | 0.1010 | int8 linear | BLOCK_SIZE_M: 32, BLOCK_SIZE_N: 256, BLOCK_SIZE_K: 32, GROUP_SIZE_M: 8, num_warps: 4, num_ctas: 1, num_stages: 4, enable_warp_specialization: False, enable_persistent: False
| 0.0250 | 0.0248 | 0.0252 | 0.0248 | alt int8 linear | None
| 0.0000 | 0.1145 | 0.0000 | 0.0969 | uint4x2 linear | BLOCK_SIZE_M: 32, BLOCK_SIZE_N: 128, BLOCK_SIZE_K: 64, GROUP_SIZE_M: 8, num_warps: 4, num_ctas: 1, num_stages: 6, enable_warp_specialization: False, enable_persistent: False
| 0.0072 | 0.0077 | 0.0072 | 0.0077 | int4 tinygemm | None
1, 128, 128
| 0.0091 | 0.0086 | 0.0092 | 0.0086 | cublas linear | None
| 0.1548 | 0.1761 | 0.1569 | 0.1776 | triton matmul | BLOCK_M: 16, BLOCK_N: 32, BLOCK_K: 64, SPLIT_K: 1, num_warps: 2, num_ctas: 1, num_stages: 5, enable_warp_specialization: False, enable_persistent: False
| 0.1129 | 0.1096 | 0.1029 | 0.0940 | int8 linear | BLOCK_SIZE_M: 32, BLOCK_SIZE_N: 64, BLOCK_SIZE_K: 64, GROUP_SIZE_M: 8, num_warps: 2, num_ctas: 1, num_stages: 3, enable_warp_specialization: False, enable_persistent: False
| 0.0257 | 0.0253 | 0.0256 | 0.0251 | alt int8 linear | None
| 0.0000 | 0.1070 | 0.0000 | 0.0928 | uint4x2 linear | BLOCK_SIZE_M: 16, BLOCK_SIZE_N: 64, BLOCK_SIZE_K: 64, GROUP_SIZE_M: 8, num_warps: 2, num_ctas: 1, num_stages: 6, enable_warp_specialization: False, enable_persistent: False
| 0.0080 | 0.0081 | 0.0080 | 0.0080 | int4 tinygemm | None
1, 256, 256
| 0.0089 | 0.0097 | 0.0089 | 0.0097 | cublas linear | None
| 0.1685 | 0.1805 | 0.1648 | 0.1735 | triton matmul | BLOCK_M: 16, BLOCK_N: 64, BLOCK_K: 32, SPLIT_K: 1, num_warps: 2, num_ctas: 1, num_stages: 5, enable_warp_specialization: False, enable_persistent: False
| 0.0938 | 0.0991 | 0.1099 | 0.1094 | int8 linear | BLOCK_SIZE_M: 32, BLOCK_SIZE_N: 64, BLOCK_SIZE_K: 32, GROUP_SIZE_M: 8, num_warps: 2, num_ctas: 1, num_stages: 4, enable_warp_specialization: False, enable_persistent: False
| 0.0284 | 0.0260 | 0.0286 | 0.0250 | alt int8 linear | None
| 0.0000 | 0.1109 | 0.0000 | 0.1018 | uint4x2 linear | BLOCK_SIZE_M: 16, BLOCK_SIZE_N: 128, BLOCK_SIZE_K: 64, GROUP_SIZE_M: 8, num_warps: 4, num_ctas: 1, num_stages: 6, enable_warp_specialization: False, enable_persistent: False
| 0.0083 | 0.0083 | 0.0077 | 0.0083 | int4 tinygemm | None
1, 512, 512
| 0.0111 | 0.0110 | 0.0118 | 0.0102 | cublas linear | None
| 0.1663 | 0.1720 | 0.1597 | 0.1666 | triton matmul | BLOCK_M: 16, BLOCK_N: 32, BLOCK_K: 32, SPLIT_K: 1, num_warps: 2, num_ctas: 1, num_stages: 5, enable_warp_specialization: False, enable_persistent: False
| 0.1089 | 0.1111 | 0.1189 | 0.0973 | int8 linear | BLOCK_SIZE_M: 16, BLOCK_SIZE_N: 32, BLOCK_SIZE_K: 32, GROUP_SIZE_M: 8, num_warps: 2, num_ctas: 1, num_stages: 6, enable_warp_specialization: False, enable_persistent: False
| 0.0300 | 0.0264 | 0.0300 | 0.0265 | alt int8 linear | None
| 0.0000 | 0.0961 | 0.0000 | 0.0975 | uint4x2 linear | BLOCK_SIZE_M: 16, BLOCK_SIZE_N: 32, BLOCK_SIZE_K: 64, GROUP_SIZE_M: 8, num_warps: 2, num_ctas: 1, num_stages: 4, enable_warp_specialization: False, enable_persistent: False
| 0.0086 | 0.0087 | 0.0086 | 0.0083 | int4 tinygemm | None
1, 1024, 1024
| 0.0114 | 0.0172 | 0.0119 | 0.0167 | cublas linear | None
| 0.1698 | 0.1659 | 0.1594 | 0.1691 | triton matmul | BLOCK_M: 16, BLOCK_N: 32, BLOCK_K: 32, SPLIT_K: 1, num_warps: 2, num_ctas: 1, num_stages: 5, enable_warp_specialization: False, enable_persistent: False
| 0.1048 | 0.1034 | 0.0933 | 0.1035 | int8 linear | BLOCK_SIZE_M: 32, BLOCK_SIZE_N: 64, BLOCK_SIZE_K: 64, GROUP_SIZE_M: 8, num_warps: 2, num_ctas: 1, num_stages: 4, enable_warp_specialization: False, enable_persistent: False
| 0.0359 | 0.0312 | 0.0357 | 0.0313 | alt int8 linear | None
| 0.0000 | 0.1000 | 0.0000 | 0.1064 | uint4x2 linear | BLOCK_SIZE_M: 16, BLOCK_SIZE_N: 16, BLOCK_SIZE_K: 256, GROUP_SIZE_M: 8, num_warps: 2, num_ctas: 1, num_stages: 5, enable_warp_specialization: False, enable_persistent: False
| 0.0100 | 0.0100 | 0.0094 | 0.0103 | int4 tinygemm | None
1, 2048, 2048
| 0.0174 | 0.0235 | 0.0174 | 0.0229 | cublas linear | None
| 0.1655 | 0.1730 | 0.1585 | 0.1605 | triton matmul | BLOCK_M: 16, BLOCK_N: 32, BLOCK_K: 64, SPLIT_K: 1, num_warps: 2, num_ctas: 1, num_stages: 6, enable_warp_specialization: False, enable_persistent: False
| 0.1054 | 0.1046 | 0.0956 | 0.0980 | int8 linear | BLOCK_SIZE_M: 16, BLOCK_SIZE_N: 32, BLOCK_SIZE_K: 64, GROUP_SIZE_M: 8, num_warps: 2, num_ctas: 1, num_stages: 6, enable_warp_specialization: False, enable_persistent: False
| 0.0458 | 0.0380 | 0.0457 | 0.0379 | alt int8 linear | None
| 0.0000 | 0.1135 | 0.0000 | 0.1096 | uint4x2 linear | BLOCK_SIZE_M: 16, BLOCK_SIZE_N: 16, BLOCK_SIZE_K: 128, GROUP_SIZE_M: 8, num_warps: 2, num_ctas: 1, num_stages: 5, enable_warp_specialization: False, enable_persistent: False
| 0.0125 | 0.0124 | 0.0125 | 0.0125 | int4 tinygemm | None
"""
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