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adding fused moe kernel config for A100 TP2 #3240

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adding fused moe kernel config for A100 TP2 #3240

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44 changes: 32 additions & 12 deletions benchmarks/kernels/benchmark_mixtral_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,22 +12,39 @@

def main():
method = fused_moe
for bs in [
1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
2048, 3072, 4096
]:
run_grid(bs, method=method)


def run_grid(bs, method):
d_model = 4096
num_total_experts = 8
top_k = 2
tp_size = 2
model_intermediate_size = 14336
num_layers = 32
num_calls = 100
best_configs = {}

for bs in [
1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
2048, 3072, 4096
]:
best_configs.update(
run_grid(bs=bs,
method=method,
d_model=d_model,
num_total_experts=num_total_experts,
top_k=top_k,
tp_size=tp_size,
model_intermediate_size=model_intermediate_size,
num_layers=num_layers))

device_name = torch.cuda.get_device_name().replace(" ", "_")
filename = f"E={num_total_experts},N={model_intermediate_size//tp_size},device_name={device_name}.json"
print(f"writing combined configs to file {filename}")
with open(filename, 'w') as fd:
json.dump(best_configs, fd, indent=4)


def run_grid(bs: int, method, d_model: int, num_total_experts: int, top_k: int,
tp_size: int, model_intermediate_size: int,
num_layers: int) -> float:
num_calls = 100
num_warmup_trials = 1
num_trials = 1

Expand Down Expand Up @@ -64,7 +81,7 @@ def run_grid(bs, method):
print(f'{tp_size=} {bs=}')
print(f'{config}')
# warmup
print(f'warming up')
print('warming up')
try:
for _ in range(num_warmup_trials):
run_timing(
Expand All @@ -82,7 +99,7 @@ def run_grid(bs, method):
continue

# trial
print(f'benchmarking')
print('benchmarking')
for _ in range(num_trials):
kernel_dur_ms = run_timing(
num_calls=num_calls,
Expand All @@ -109,11 +126,14 @@ def run_grid(bs, method):

print("best_time_us", best_time_us)
print("best_config", best_config)
bs_best_config = {str(bs): best_config}

filename = "/tmp/config.jsonl"
print(f"writing config to file {filename}")
with open(filename, "a") as f:
f.write(json.dumps({str(bs): best_config}) + "\n")
f.write(json.dumps(bs_best_config) + "\n")

return bs_best_config


def run_timing(num_calls: int, bs: int, d_model: int, num_total_experts: int,
Expand Down
146 changes: 146 additions & 0 deletions ...model_executor/layers/fused_moe/configs/E=8,N=7168,device_name=NVIDIA_A100-SXM4-80GB.json
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Original file line number Diff line number Diff line change
@@ -0,0 +1,146 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 4
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 4
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"48": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"64": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"96": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"128": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 4
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 4
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 4
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
},
"1536": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
},
"2048": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 4
},
"3072": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 4
},
"4096": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 4
}
}