93 lines
2.7 KiB
Python
93 lines
2.7 KiB
Python
import itertools
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import torch
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import triton
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from sgl_kernel import ep_moe_silu_and_mul
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from sglang.srt.layers.moe.ep_moe.kernels import silu_and_mul_triton_kernel
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batch_size_range = [64, 128, 256, 512, 640, 768, 1024, 2048, 4096]
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hidden_size_range = [1024, 2048, 4096, 8192]
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block_size_range = [128, 256, 512]
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configs = list(itertools.product(batch_size_range, hidden_size_range, block_size_range))
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@triton.testing.perf_report(
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triton.testing.Benchmark(
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x_names=["batch_size", "hidden_size", "block_size"],
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x_vals=[list(cfg) for cfg in configs],
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line_arg="provider",
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line_vals=["cuda", "triton"],
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line_names=["CUDA Kernel", "Triton Kernel"],
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styles=[("green", "-"), ("orange", "-")],
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ylabel="us",
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plot_name="ep-moe-silu-and-mul-performance",
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args={},
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)
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)
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def benchmark(batch_size, hidden_size, block_size, provider):
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dtype = torch.bfloat16
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device = torch.device("cuda")
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half_hidden_size = hidden_size // 2
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start_expert_id, end_expert_id = 0, 255
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block_size = 512
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quantiles = [0.5, 0.2, 0.8]
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def alloc_tensors():
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gateup_output = torch.randn(batch_size, hidden_size, dtype=dtype, device=device)
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down_input = torch.empty(
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batch_size, half_hidden_size, dtype=dtype, device=device
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)
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reorder_topk_ids = torch.randint(
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start_expert_id,
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end_expert_id + 1,
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(batch_size,),
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dtype=torch.int32,
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device=device,
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)
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scales = torch.rand(
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end_expert_id - start_expert_id + 1, dtype=torch.float32, device=device
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)
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return gateup_output, down_input, reorder_topk_ids, scales
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if provider == "cuda":
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gateup, down, ids, scales = alloc_tensors()
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def run_cuda():
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ep_moe_silu_and_mul(
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gateup,
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down,
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ids,
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scales,
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start_expert_id,
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end_expert_id,
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)
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ms, min_ms, max_ms = triton.testing.do_bench(run_cuda, quantiles=quantiles)
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elif provider == "triton":
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gateup, down, ids, scales = alloc_tensors()
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def run_triton():
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silu_and_mul_triton_kernel[(batch_size,)](
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gateup.view(-1),
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down.view(-1),
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hidden_size,
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ids,
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scales,
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start_expert_id,
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end_expert_id,
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block_size,
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)
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ms, min_ms, max_ms = triton.testing.do_bench(run_triton, quantiles=quantiles)
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else:
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raise ValueError(f"Unknown provider: {provider}")
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return 1000 * ms, 1000 * max_ms, 1000 * min_ms
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if __name__ == "__main__":
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benchmark.run(print_data=True)
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