130 lines
3.4 KiB
Python
130 lines
3.4 KiB
Python
import itertools
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from typing import Tuple
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import torch
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import triton
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import triton.testing
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from sgl_kernel import rmsnorm
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from sglang.jit_kernel.benchmark.utils import is_in_ci
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from sglang.jit_kernel.norm import fused_inplace_qknorm
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from sglang.srt.utils import get_current_device_stream_fast
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IS_CI = is_in_ci()
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alt_stream = torch.cuda.Stream()
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def sglang_aot_qknorm(
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q: torch.Tensor,
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k: torch.Tensor,
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q_weight: torch.Tensor,
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k_weight: torch.Tensor,
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) -> None:
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head_dim = q.shape[-1]
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q = q.view(-1, head_dim)
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k = k.view(-1, head_dim)
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current_stream = get_current_device_stream_fast()
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alt_stream.wait_stream(current_stream)
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rmsnorm(q, q_weight, out=q)
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with torch.cuda.stream(alt_stream):
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rmsnorm(k, k_weight, out=k)
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current_stream.wait_stream(alt_stream)
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def sglang_jit_qknorm(
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q: torch.Tensor,
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k: torch.Tensor,
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q_weight: torch.Tensor,
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k_weight: torch.Tensor,
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) -> None:
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fused_inplace_qknorm(q, k, q_weight, k_weight)
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def flashinfer_qknorm(
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q: torch.Tensor,
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k: torch.Tensor,
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q_weight: torch.Tensor,
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k_weight: torch.Tensor,
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) -> None:
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from flashinfer import rmsnorm
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rmsnorm(q, q_weight, out=q)
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rmsnorm(k, k_weight, out=k)
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@torch.compile()
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def torch_impl_qknorm(
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q: torch.Tensor,
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k: torch.Tensor,
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q_weight: torch.Tensor,
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k_weight: torch.Tensor,
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eps: float = 1e-6,
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) -> None:
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q_mean = q.float().pow(2).mean(dim=-1, keepdim=True)
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k_mean = k.float().pow(2).mean(dim=-1, keepdim=True)
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q_norm = (q_mean + eps).rsqrt()
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k_norm = (k_mean + eps).rsqrt()
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q.copy_(q.float() * q_norm * q_weight.float())
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k.copy_(k.float() * k_norm * k_weight.float())
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HEAD_DIM = 128
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DTYPE = torch.bfloat16
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DEVICE = "cuda"
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if IS_CI:
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BS_RANGE = [16]
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GQA_RANGE = [4]
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KV_HEAD_RANGE = [1]
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else:
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BS_RANGE = [2**n for n in range(0, 14)]
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GQA_RANGE = [4, 8]
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KV_HEAD_RANGE = [1, 2, 4, 8]
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LINE_VALS = ["aot", "jit", "fi", "torch"]
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LINE_NAMES = ["SGL AOT Kernel", "SGL JIT Kernel", "FlashInfer", "PyTorch"]
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STYLES = [("orange", "-"), ("blue", "--"), ("green", "-."), ("red", ":")]
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configs = list(itertools.product(GQA_RANGE, KV_HEAD_RANGE, BS_RANGE))
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@triton.testing.perf_report(
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triton.testing.Benchmark(
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x_names=["GQA", "num_kv_heads", "batch_size"],
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x_vals=configs,
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line_arg="provider",
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line_vals=LINE_VALS,
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line_names=LINE_NAMES,
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styles=STYLES,
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ylabel="us",
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plot_name="qknorm-performance",
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args={},
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)
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)
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def benchmark(
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batch_size: int, GQA: int, num_kv_heads: int, provider: str
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) -> Tuple[float, float, float]:
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num_qo_heads = GQA * num_kv_heads
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q = torch.randn((batch_size, num_qo_heads, HEAD_DIM), dtype=DTYPE, device=DEVICE)
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k = torch.randn((batch_size, num_kv_heads, HEAD_DIM), dtype=DTYPE, device=DEVICE)
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q_weight = torch.randn(HEAD_DIM, dtype=DTYPE, device=DEVICE)
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k_weight = torch.randn(HEAD_DIM, dtype=DTYPE, device=DEVICE)
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FN_MAP = {
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"aot": sglang_aot_qknorm,
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"jit": sglang_jit_qknorm,
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"fi": flashinfer_qknorm,
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"torch": torch_impl_qknorm,
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}
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fn = lambda: FN_MAP[provider](q, k, q_weight, k_weight)
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quantiles = [0.5, 0.2, 0.8]
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ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(fn, quantiles=quantiles) # type: ignore
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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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