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bench_2_4.py
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bench_2_4.py
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import torch
import marlin
import time
def benchmark(f, warmup=1, iter=10):
for i in range(warmup + iter):
f()
# We do not synchronize here in order to hide the kernel launch overhead during benchmarkining as this will also
# happen during realistic model inference as many launches are submitted to the kernel queue.
if i == warmup - 1:
torch.cuda.synchronize()
tick = time.time()
torch.cuda.synchronize()
res = (time.time() - tick) / iter
# Make sure there is enough to "cool down" the GPU in between benchmarks to avoid throttling for later runs when
# we execute many benchmarks consecutively
time.sleep(1.0)
return res
def get_problem(m, n, k, groupsize=-1):
if groupsize == -1:
groupsize = k
dev = torch.device("cuda:0")
A = torch.randn((m, k), dtype=torch.half, device=dev)
B = torch.randint(low=-(2**31), high=2**31, size=(k * n // 8,), device=dev)
B_ref = torch.randn((k, n), dtype=torch.half, device=dev)
C = torch.zeros((m, n), dtype=torch.half, device=dev)
s = torch.zeros((k // groupsize, n), dtype=torch.half, device=dev)
torch.cuda.synchronize()
return A, B, C, B_ref, s
def gen_2_4(m, n, k, dev):
B = torch.randint(low=-(2**31), high=2**31, size=(k * n // 8 // 2,), device=dev)
meta = torch.ones((n * k // 16,), dtype=torch.int16, device=dev) * (-4370)
return B, meta
def get_problem_24(m, n, k, groupsize=-1):
dev = torch.device("cuda:0")
B, meta = gen_2_4(m, n, k, dev)
if groupsize == -1:
s = torch.zeros((1, n), dtype=torch.half, device=dev)
else:
s = torch.zeros(
(((k // 2) // (groupsize // 2)), n), dtype=torch.half, device=dev
)
torch.cuda.synchronize()
return B, s, meta
def benchmark_dense(A, B, C):
res = benchmark(lambda: torch.matmul(A, B, out=C))
return {
"s": res,
"TFLOP/s": 2 * A.numel() * C.shape[1] / res / 10**12,
"GB/s": (2 * A.numel() + 2 * B.numel() + 2 * C.numel()) / res / 10**9,
}
def benchmark_quant_24(A, B, meta, C, s, thread_k, thread_m, sms):
workspace = torch.zeros(
C.shape[1] // 128 * 16, device=torch.device("cuda:0"), dtype=torch.int32
)
# print("A:", A.shape, "B:", B.shape, "meta:", meta.shape, "C:", C.shape, "s:", s.shape)
res = benchmark(
lambda: marlin.mul_2_4(A, B, meta, C, s, workspace, thread_k, thread_m, sms)
)
return {
"s": res,
"TFLOP/s": 2 * A.numel() * C.shape[1] / res / 10**12,
"GB/s": (2 * A.numel() + 4 * B.numel() * 2 + 2 * C.numel() + 2 * s.numel())
/ res
/ 10**9,
}
def benchmark_quant(A, B, C, s, thread_k, thread_n, sms):
workspace = torch.zeros(
C.shape[1] // 128 * 16, device=torch.device("cuda:0"), dtype=torch.int32
)
# print("A:", A.shape, "B:", B.shape, "C:", C.shape, "s:", s.shape)
res = benchmark(lambda: marlin.mul(A, B, C, s, workspace, thread_k, thread_n, sms))
return {
"s": res,
"TFLOP/s": 2 * A.numel() * C.shape[1] / res / 10**12,
"GB/s": (2 * A.numel() + 4 * B.numel() + 2 * C.numel() + 2 * s.numel())
/ res
/ 10**9,
}
# Pass the SM count for known GPUs to avoid the kernel having to query this information (this is very minor)
gpu = torch.cuda.get_device_name(0)
if "A100" in gpu:
SMS = 108
elif "A10" in gpu:
SMS = 72
elif "3090" in gpu:
SMS = 82
elif "A6000" in gpu:
SMS = 84
elif "4090" in gpu:
SMS = 128
else:
SMS = -1
MODELS = {
#'ideal': [
# (4 * 256 * SMS, 256 * SMS)
# ],
"Llama7B": [(4096, 3 * 4096), (4096, 4096), (4096, 2 * 10752), (10752, 4096)],
"Llama13B": [(5120, 3 * 5120), (5120, 5120), (5120, 2 * 13568), (13568, 5120)],
"Llama33B": [(6656, 3 * 6656), (6656, 6656), (6656, 2 * 17664), (17664, 6656)],
"Llama65B": [(8192, 3 * 8192), (8192, 8192), (8192, 2 * 21760), (21760, 8192)],
"Falcon180B": [
# Note that parallel attention and FC allows layer fusions
(14848, 14848 * 5 + 1024),
(14848 * 5, 14848),
],
}
# Set to true in order to run a more complete benchmark sweep; the default is reproduce README experiments
ALL = True
# print("groupsize,model,batch,tot_q_s,tot_q_2_4_s,tot_q_sp,tot_q_2_4_sp")
for groupsize in [-1, 128] if ALL else [128]:
print("groupsize=%d" % groupsize)
print()
for model, layers in MODELS.items():
print(model)
if ALL:
batchsizes = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048]
else:
batchsizes = [1, 2, 4, 8, 16, 32, 64, 128, 256]
for batch in batchsizes:
if not ALL and model != "ideal" and batch != 16:
continue
tot_q = {"s": 0, "TFLOP/s": 0, "GB/s": 0, "speedup": 0}
tot_q_24 = {"s": 0, "TFLOP/s": 0, "GB/s": 0, "speedup": 0}
for layer in layers:
A, B, C, B_ref, s = get_problem(batch, layer[1], layer[0], groupsize)
B_24, s_24, meta = get_problem_24(batch, layer[1], layer[0], groupsize)
res_d = benchmark_dense(A, B_ref, C)
if model == "ideal" and batch == 16:
# This is a special case constructed to be optimal for a thread-shape different than the default one
res_q_24 = benchmark_quant_24(A, B_24, meta, C, s_24, 64, 256, SMS)
res_q = benchmark_quant(A, B, C, s, 64, 256, SMS)
else:
res_q_24 = benchmark_quant_24(A, B_24, meta, C, s_24, -1, -1, SMS)
res_q = benchmark_quant(A, B, C, s, -1, -1, SMS)
#
res_q["speedup"] = res_d["s"] / res_q["s"]
tot_q["s"] += res_q["s"]
#
res_q_24["speedup"] = res_d["s"] / res_q_24["s"]
tot_q_24["s"] += res_q_24["s"]
for k in tot_q:
if k != "s":
tot_q[k] += res_q[k] * res_q["s"]
for k in tot_q_24:
if k != "s":
tot_q_24[k] += res_q_24[k] * res_q_24["s"]
del A
del B
del C
del B_ref
del s
del B_24
del s_24
del meta
for k in tot_q:
if k != "s":
tot_q[k] /= tot_q["s"]
for k in tot_q_24:
if k != "s":
tot_q_24[k] /= tot_q_24["s"]
print(
"[NN] batch=%04d: s=%.5f, TFLOP/s=%07.3f, GB/s=%08.3f, speedup=%.2f"
% (batch, tot_q["s"], tot_q["TFLOP/s"], tot_q["GB/s"], tot_q["speedup"])
)
print(
"[2:4] batch=%04d: s=%.5f, TFLOP/s=%07.3f, GB/s=%08.3f, speedup=%.2f"
% (
batch,
tot_q_24["s"],
tot_q_24["TFLOP/s"],
tot_q_24["GB/s"],
tot_q_24["speedup"],
)
)
# print(str(groupsize)+',%s,%04d,%.5f,%.5f,%.2f,%.2f' % (
# model, batch, tot_q['s'], tot_q_24['s'], tot_q['speedup'], tot_q_24['speedup']))
print()