94 lines
2.7 KiB
Python
94 lines
2.7 KiB
Python
import torch
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import triton
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from sgl_kernel import ep_moe_post_reorder
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from sglang.srt.layers.moe.ep_moe.kernels import post_reorder_triton_kernel
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batch_sizes = [64, 128, 256, 512, 640, 768, 1024, 2048, 4096]
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configs = [(bs,) for bs in batch_sizes]
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@triton.testing.perf_report(
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triton.testing.Benchmark(
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x_names=["batch_size"],
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x_vals=[list(_) for _ 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-post-reorder-performance",
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args={},
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)
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)
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def benchmark(batch_size, provider):
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dtype = torch.bfloat16
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device = torch.device("cuda")
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hidden_size, topk, start_expert_id, end_expert_id, block_size = 4096, 8, 0, 255, 512
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def alloc_tensors():
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down_output = torch.randn(
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batch_size * topk, hidden_size, dtype=dtype, device=device
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)
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output = torch.zeros(batch_size, hidden_size, dtype=dtype, device=device)
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src2dst = torch.randint(
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0, batch_size * topk, (batch_size, topk), dtype=torch.int32, device=device
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)
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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, topk),
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dtype=torch.int32,
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device=device,
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)
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topk_weights = torch.rand(batch_size, topk, dtype=dtype, device=device)
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return down_output, output, src2dst, topk_ids, topk_weights
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quantiles = [0.5, 0.2, 0.8]
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if provider == "cuda":
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d_out, out, s2d, tk_ids, tk_weights = alloc_tensors()
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def run_cuda():
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ep_moe_post_reorder(
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d_out,
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out,
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s2d,
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tk_ids,
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tk_weights,
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start_expert_id,
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end_expert_id,
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topk,
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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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d_out, out, s2d, tk_ids, tk_weights = alloc_tensors()
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def run_triton():
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post_reorder_triton_kernel[(batch_size,)](
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d_out.view(-1),
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out.view(-1),
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s2d.view(-1),
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tk_ids.view(-1),
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tk_weights.view(-1),
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start_expert_id,
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end_expert_id,
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topk,
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hidden_size,
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0,
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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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