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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.
44 lines
1.6 KiB
Python
44 lines
1.6 KiB
Python
import pathlib
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from tinygrad import Device, Tensor
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from tinygrad.helpers import Context
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from tinygrad.runtime.support.compiler_cuda import pretty_ptx, NVCCCompiler
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if __name__ == "__main__":
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code = (pathlib.Path(__file__).parent / "fa.cu").read_text()
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device = Device["CUDA"]
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kitten_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "--expt-relaxed-constexpr", "-DKITTENS_4090"]
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lib = NVCCCompiler(device.compiler.arch, kitten_args).compile(code)
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kernel_name = lib.decode().split(".globl\t")[1].split("\n")[0]
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print("kernel name", kernel_name)
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print(pretty_ptx(lib.decode()))
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prg = device.runtime(kernel_name, lib)
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prg.smem = 16384 * 3
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B, N, H, D = 16, 1024, 16, 64
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q = Tensor.randn(B, N, H, D, device='CUDA', dtype="bfloat16")
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k = Tensor.randn(B, N, H, D, device='CUDA', dtype="bfloat16")
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v = Tensor.randn(B, N, H, D, device='CUDA', dtype="bfloat16")
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out = Tensor.empty(B, N, H, D, device='CUDA', dtype="bfloat16")
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Tensor.realize(q, k, v, out)
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NUM_WORKERS = 4
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ROWS = 16 * (64 // D)
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gsz = (N // (ROWS*NUM_WORKERS), H, B)
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for _ in range(5):
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et = prg(out.uop.buffer.ensure_allocated()._buf, q.uop.buffer._buf, k.uop.buffer._buf, v.uop.buffer._buf,
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global_size=gsz, local_size=(ROWS*NUM_WORKERS,1,1), wait=True)
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attn_flops = 2 * B * H * N * N * D + \
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4 * B * H * N * N + \
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2 * B * H * N * N * D
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print(f"{attn_flops/(et*1e9):2f} GFLOPS")
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for _ in range(5):
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with Context(DEBUG=2):
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ref = q.scaled_dot_product_attention(k, v)
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ref, out = ref.float(), out.float()
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print((ref-out).mean().item(), (ref-out).max().item())
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