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https://github.com/firestar5683/StarPilot.git
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firestar5683
22a8575199
Keep official openpilot ancestry and dependency licenses. Store resources directly in Git, remove LFS setup requirements, and leave the downloadable big model outside the source tree.
51 lines
2.2 KiB
Python
51 lines
2.2 KiB
Python
# kernel8_batched_gmem.s from https://seb-v.github.io/optimization/update/2025/01/20/Fast-GPU-Matrix-multiplication.html
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# sudo PATH=/opt/homebrew/Cellar/llvm/20.1.6/bin:$PATH AMD_LLVM=0 AMD=1 DEBUG=2 python3 extra/gemm/amd_matmul.py
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import pathlib
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from tinygrad import Tensor, Device, Context, GlobalCounters
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from tinygrad.helpers import getenv
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from tinygrad.uop.ops import UOp, Ops, KernelInfo
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from tinygrad.renderer import Estimates
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from tinygrad.engine.realize import run_linear
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N = 4096
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run_count = 5
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def make_matmul_kernel(name:str, src:str, local_size:int):
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def fxn(a:UOp, b:UOp, c:UOp) -> UOp:
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threads = UOp.special(local_size, "lidx0")
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wg_x = UOp.special(N//128, "gidx0")
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wg_y = UOp.special(N//128, "gidx1")
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sink = UOp.sink(a.base, b.base, c.base, threads, wg_x, wg_y, arg=KernelInfo(name, estimates=Estimates(ops=2*N**3, mem=3*N*N*4)))
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lib = Device[Device.DEFAULT].compiler.compile_cached(src)
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return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.LINEAR, src=(*sink.src, sink)),
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UOp(Ops.SOURCE, arg=src), UOp(Ops.BINARY, arg=lib)))
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return fxn
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if __name__ == "__main__":
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if getenv("ASM") == 1:
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src = (pathlib.Path(__file__).parent / "amd_seb" / "kernel8_batched_gmem.s").read_text()
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name, local_size = "kernel", 128
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elif getenv("ASM") == -1:
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src = (pathlib.Path(__file__).parent / "amd_seb" / "kernel3_registers.cpp").read_text()
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name, local_size = "kernel3_registers", 256
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elif getenv("ASM") == -2:
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src = (pathlib.Path(__file__).parent / "amd_seb" / "kernel4_gmem_df.cpp").read_text()
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name, local_size = "kernel4_gmem_db", 256
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else:
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src = (pathlib.Path(__file__).parent / "amd_seb" / "kernel5_lds_optim.cpp").read_text()
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name, local_size = "kernel5_lds_optim", 128
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a = Tensor.randn(N, N).realize()
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b = Tensor.randn(N, N).realize()
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c = Tensor.zeros(N, N).contiguous().realize()
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GlobalCounters.reset()
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with Context(DEBUG=2):
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for _ in range(run_count): tc = (a@b).realize()
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linear = Tensor.custom_kernel(a, b, c, fxn=make_matmul_kernel(name, src, local_size))[2].schedule_linear()
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GlobalCounters.reset()
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with Context(DEBUG=2):
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for _ in range(run_count): run_linear(linear)
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print(f"custom {(c-tc).square().mean().item()}")
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