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[Kernel] change benchmark script so that result can be directly used; tune moe kernel in A100/H100 with tp=2,4,8 #3389

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Merged
merged 11 commits into from
Mar 14, 2024
Merged

[Kernel] change benchmark script so that result can be directly used; tune moe kernel in A100/H100 with tp=2,4,8 #3389

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2f356ed
change benchmark script so that result can be directly used
youkaichao Mar 13, 2024
005eaf2
update function name
youkaichao Mar 13, 2024
02ba322
update moe kernel tuning config
youkaichao Mar 14, 2024
8f98e08
add more moe kernel tuning config
youkaichao Mar 14, 2024
b936d3f
add more moe kernel tuning config
youkaichao Mar 14, 2024
e82d641
add more moe kernel tuning config
youkaichao Mar 14, 2024
be38ca7
add newline in the end of file
youkaichao Mar 14, 2024
b4a60f5
expose vllm.model_executor.layers.fused_moe.get_config_file_name
youkaichao Mar 14, 2024
db79917
Merge remote-tracking branch 'vllm/main' into moe-kernel-tuning
youkaichao Mar 14, 2024
8c44d59
fix yapf
youkaichao Mar 14, 2024
9b8f7c5
fix ruff
youkaichao Mar 14, 2024
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30 changes: 19 additions & 11 deletions benchmarks/kernels/benchmark_mixtral_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,13 +2,13 @@
import os
import sys

os.environ['CUDA_VISIBLE_DEVICES'] = '0'

from vllm.model_executor.layers.fused_moe import fused_moe
from vllm.model_executor.layers.fused_moe import fused_moe, get_config_file_name
import torch
import torch.nn.functional as F
import triton

os.environ['CUDA_VISIBLE_DEVICES'] = '0'


def main():
method = fused_moe
Expand Down Expand Up @@ -64,7 +64,7 @@ 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 +82,7 @@ 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 @@ -103,17 +103,25 @@ def run_grid(bs, method):
best_config = config
best_time_us = kernel_dur_us

print(
f'{kernel_dur_us=:.1f} {model_dur_ms=:.1f} {bs=} {tp_size=} {top_k=} {num_total_experts=} {d_model=} {model_intermediate_size=} {num_layers=}'
)
print(f'{kernel_dur_us=:.1f} {model_dur_ms=:.1f}'
f' {bs=} {tp_size=} {top_k=} {num_total_experts=} '
f'{d_model=} {model_intermediate_size=} {num_layers=}')

print("best_time_us", best_time_us)
print("best_config", best_config)

filename = "/tmp/config.jsonl"
# holds Dict[str, Dict[str, int]]
filename = get_config_file_name(num_total_experts,
model_intermediate_size // tp_size)
print(f"writing config to file {filename}")
with open(filename, "a") as f:
f.write(json.dumps({str(bs): best_config}) + "\n")
existing_content = {}
if os.path.exists(filename):
with open(filename, "r") as f:
existing_content = json.load(f)
existing_content[str(bs)] = best_config
with open(filename, "w") as f:
json.dump(existing_content, f, indent=4)
f.write("\n")


def run_timing(num_calls: int, bs: int, d_model: int, num_total_experts: int,
Expand Down
6 changes: 5 additions & 1 deletion vllm/model_executor/layers/fused_moe/__init__.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,9 @@
from vllm.model_executor.layers.fused_moe.fused_moe import fused_moe
from vllm.model_executor.layers.fused_moe.fused_moe import (
fused_moe,
get_config_file_name,
)

__all__ = [
"fused_moe",
"get_config_file_name",
]
146 changes: 146 additions & 0 deletions ...model_executor/layers/fused_moe/configs/E=8,N=1792,device_name=NVIDIA_A100-SXM4-80GB.json
View file
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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": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 4
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 4
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 4
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 4
},
"24": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 4
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 4
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 4
},
"64": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 4
},
"96": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 4
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 4
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
},
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"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
}
}
146 changes: 146 additions & 0 deletions ...model_executor/layers/fused_moe/configs/E=8,N=1792,device_name=NVIDIA_H100_80GB_HBM3.json
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -0,0 +1,146 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 4
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 4
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
},
"24": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"32": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"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": 64,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 4
},
"96": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"128": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"256": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 4
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 4
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"1536": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 4
},
"2048": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
},
"3072": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
},
"4096": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 4
}
}
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