[Kernel] Enhance MoE benchmarking & tuning script (#4921)
This commit is contained in:
Woosuk Kwon
319
benchmarks/kernels/benchmark_moe.py
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319
benchmarks/kernels/benchmark_moe.py
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import argparse
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import time
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from datetime import datetime
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from typing import Any, Dict, List, Tuple
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import ray
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import torch
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import triton
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from ray.experimental.tqdm_ray import tqdm
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from transformers import AutoConfig
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from vllm.model_executor.layers.fused_moe.fused_moe import *
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def benchmark_config(
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config: Dict[str, int],
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num_tokens: int,
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num_experts: int,
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shard_intermediate_size: int,
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hidden_size: int,
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topk: int,
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dtype: torch.dtype,
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use_fp8: bool,
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num_iters: int = 100,
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) -> float:
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init_dtype = torch.float16 if use_fp8 else dtype
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x = torch.randn(num_tokens, hidden_size, dtype=dtype)
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w1 = torch.randn(num_experts,
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shard_intermediate_size,
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hidden_size,
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dtype=init_dtype)
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w2 = torch.randn(num_experts,
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hidden_size,
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shard_intermediate_size // 2,
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dtype=init_dtype)
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gating_output = torch.randn(num_iters,
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num_tokens,
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num_experts,
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dtype=torch.float32)
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w1_scale = None
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w2_scale = None
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a1_scale = None
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a2_scale = None
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if use_fp8:
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w1_scale = torch.randn(num_experts, dtype=torch.float32)
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w2_scale = torch.randn(num_experts, dtype=torch.float32)
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a1_scale = torch.randn(1, dtype=torch.float32)
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a2_scale = torch.randn(1, dtype=torch.float32)
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w1 = w1.to(torch.float8_e4m3fn)
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w2 = w2.to(torch.float8_e4m3fn)
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input_gating = torch.empty(num_tokens, num_experts, dtype=torch.float32)
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def prepare(i: int):
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input_gating.copy_(gating_output[i])
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def run():
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fused_moe(
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x,
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w1,
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w2,
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input_gating,
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topk,
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renormalize=True,
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inplace=True,
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override_config=config,
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use_fp8=use_fp8,
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w1_scale=w1_scale,
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w2_scale=w2_scale,
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a1_scale=a1_scale,
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a2_scale=a2_scale,
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)
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# JIT compilation & warmup
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run()
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torch.cuda.synchronize()
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# Capture 10 invocations with CUDA graph
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graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(graph):
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for _ in range(10):
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run()
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torch.cuda.synchronize()
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# Warmup
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for _ in range(5):
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graph.replay()
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torch.cuda.synchronize()
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start_event = torch.cuda.Event(enable_timing=True)
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end_event = torch.cuda.Event(enable_timing=True)
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latencies = []
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for i in range(num_iters):
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prepare(i)
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torch.cuda.synchronize()
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start_event.record()
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graph.replay()
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end_event.record()
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end_event.synchronize()
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latencies.append(start_event.elapsed_time(end_event))
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avg = sum(latencies) / (num_iters * 10) * 1000 # us
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graph.reset()
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return avg
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def get_configs_compute_bound() -> List[Dict[str, int]]:
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# Reduced search space for faster tuning.
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# TODO(woosuk): Increase the search space and use a performance model to
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# prune the search space.
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configs = []
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for num_stages in [2, 3, 4, 5]:
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for block_m in [16, 32, 64, 128, 256]:
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for block_k in [64, 128, 256]:
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for block_n in [32, 64, 128, 256]:
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for num_warps in [4, 8]:
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for group_size in [1, 16, 32, 64]:
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configs.append({
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"BLOCK_SIZE_M": block_m,
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"BLOCK_SIZE_N": block_n,
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"BLOCK_SIZE_K": block_k,
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"GROUP_SIZE_M": group_size,
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"num_warps": num_warps,
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"num_stages": num_stages,
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})
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return configs
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@ray.remote(num_gpus=1)
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class BenchmarkWorker:
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def __init__(self, seed: int) -> None:
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torch.set_default_device("cuda")
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torch.cuda.manual_seed_all(seed)
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self.seed = seed
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def benchmark(
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self,
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num_tokens: int,
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num_experts: int,
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shard_intermediate_size: int,
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hidden_size: int,
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topk: int,
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dtype: torch.dtype,
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use_fp8: bool,
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) -> Tuple[Dict[str, int], float]:
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torch.cuda.manual_seed_all(self.seed)
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dtype_str = "float8" if use_fp8 else None
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# NOTE(woosuk): The current naming convention uses w2.shape[2], which
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# is the intermediate size after silu_and_mul.
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op_config = get_moe_configs(num_experts, shard_intermediate_size // 2,
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dtype_str)
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if op_config is None:
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config = get_default_config(num_tokens, num_experts,
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shard_intermediate_size, hidden_size,
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topk, dtype_str)
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else:
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config = op_config[min(op_config.keys(),
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key=lambda x: abs(x - num_tokens))]
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kernel_time = benchmark_config(config, num_tokens, num_experts,
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shard_intermediate_size, hidden_size,
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topk, dtype, use_fp8)
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return config, kernel_time
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def tune(
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self,
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num_tokens: int,
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num_experts: int,
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shard_intermediate_size: int,
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hidden_size: int,
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topk: int,
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dtype: torch.dtype,
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use_fp8: bool,
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search_space: List[Dict[str, int]],
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) -> Dict[str, int]:
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best_config = None
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best_time = float("inf")
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for config in tqdm(search_space):
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try:
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kernel_time = benchmark_config(config,
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num_tokens,
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num_experts,
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shard_intermediate_size,
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hidden_size,
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topk,
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dtype,
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use_fp8,
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num_iters=10)
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except triton.runtime.autotuner.OutOfResources:
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# Some configurations may be invalid and fail to compile.
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continue
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if kernel_time < best_time:
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best_time = kernel_time
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best_config = config
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now = datetime.now()
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print(f"{now.ctime()}] Completed tuning for batch_size={num_tokens}")
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return best_config
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def sort_config(config: Dict[str, int]) -> Dict[str, int]:
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return {
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"BLOCK_SIZE_M": config["BLOCK_SIZE_M"],
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"BLOCK_SIZE_N": config["BLOCK_SIZE_N"],
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"BLOCK_SIZE_K": config["BLOCK_SIZE_K"],
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"GROUP_SIZE_M": config["GROUP_SIZE_M"],
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"num_warps": config["num_warps"],
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"num_stages": config["num_stages"],
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}
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def save_configs(
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configs: Dict[int, Dict[str, int]],
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num_experts: int,
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shard_intermediate_size: int,
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hidden_size: int,
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topk: int,
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dtype: torch.dtype,
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use_fp8: bool,
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) -> None:
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dtype_str = "float8" if use_fp8 else None
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# NOTE(woosuk): The current naming convention uses w2.shape[2], which
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# is the intermediate size after silu_and_mul.
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filename = get_config_file_name(num_experts, shard_intermediate_size // 2,
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dtype_str)
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print(f"Writing best config to {filename}...")
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with open(filename, "w") as f:
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json.dump(configs, f, indent=4)
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f.write("\n")
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def main(args: argparse.Namespace):
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print(args)
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config = AutoConfig.from_pretrained(args.model)
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if config.architectures[0] == "DbrxForCausalLM":
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E = config.ffn_config.moe_num_experts
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topk = config.ffn_config.moe_top_k
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intermediate_size = config.ffn_config.ffn_hidden_size
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shard_intermediate_size = 2 * intermediate_size // args.tp_size
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else:
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# Default: Mixtral.
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E = config.num_local_experts
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topk = config.num_experts_per_tok
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intermediate_size = config.intermediate_size
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shard_intermediate_size = 2 * intermediate_size // args.tp_size
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hidden_size = config.hidden_size
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dtype = config.torch_dtype
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use_fp8 = args.dtype == "fp8"
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if args.batch_size is None:
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batch_sizes = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096]
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else:
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batch_sizes = [args.batch_size]
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ray.init()
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num_gpus = int(ray.available_resources()["GPU"])
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workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
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def _distribute(method: str, inputs: List[Any]) -> List[Any]:
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outputs = []
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worker_idx = 0
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for input_args in inputs:
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worker = workers[worker_idx]
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worker_method = getattr(worker, method)
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output = worker_method.remote(*input_args)
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outputs.append(output)
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worker_idx = (worker_idx + 1) % num_gpus
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return ray.get(outputs)
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if args.tune:
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search_space = get_configs_compute_bound()
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print(f"Start tuning over {len(search_space)} configurations...")
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start = time.time()
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configs = _distribute(
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"tune", [(batch_size, E, shard_intermediate_size, hidden_size,
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topk, dtype, use_fp8, search_space)
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for batch_size in batch_sizes])
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best_configs = {
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M: sort_config(config)
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for M, config in zip(batch_sizes, configs)
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}
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save_configs(best_configs, E, shard_intermediate_size, hidden_size,
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topk, dtype, use_fp8)
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end = time.time()
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print(f"Tuning took {end - start:.2f} seconds")
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else:
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outputs = _distribute("benchmark",
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[(batch_size, E, shard_intermediate_size,
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hidden_size, topk, dtype, use_fp8)
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for batch_size in batch_sizes])
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for batch_size, (config, kernel_time) in zip(batch_sizes, outputs):
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print(f"Batch size: {batch_size}, config: {config}")
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print(f"Kernel time: {kernel_time:.2f} us")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--model",
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type=str,
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default="mistralai/Mixtral-8x7B-Instruct-v0.1")
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parser.add_argument("--tp-size", "-tp", type=int, default=2)
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parser.add_argument("--dtype",
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type=str,
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choices=["auto", "fp8"],
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default="auto")
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parser.add_argument("--seed", type=int, default=0)
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parser.add_argument("--batch-size", type=int, required=False)
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parser.add_argument("--tune", action="store_true")
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args = parser.parse_args()
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main(args)
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