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[HGEMM] Add PyTorch HGEMM profile (#59)
* Create prof.py * Update .gitignore * Update .gitignore * Update README.md
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@@ -13,5 +13,6 @@ __pycache__ | |
| *.pt | ||
| *.pth | ||
| *.nsys* | ||
| *.sqlite | ||
| *.ncu* | ||
| *.sqlite* | ||
| *.engine | ||
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| *.so | ||
| *.a | ||
| *.dylib | ||
| *.dll | ||
| *.lib | ||
| .DS_Store | ||
| build | ||
| *.whl | ||
| tmp | ||
| __pycache__ | ||
| *.onnx | ||
| *.engine | ||
| *.pt | ||
| *.pth | ||
| *.nsys* | ||
| *.ncu* | ||
| *.sqlite* | ||
| *.engine |
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| import torch | ||
| import time | ||
| from torch.utils.cpp_extension import load | ||
| from functools import partial | ||
| from typing import Optional | ||
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| torch.set_grad_enabled(False) | ||
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| # # Load the CUDA kernel as a python module | ||
| # lib = load(name='hgemm_lib', | ||
| # sources=['hgemm.cu'], | ||
| # extra_cuda_cflags=[ | ||
| # "-O3", | ||
| # "-U__CUDA_NO_HALF_OPERATORS__", | ||
| # "-U__CUDA_NO_HALF_CONVERSIONS__", | ||
| # "-U__CUDA_NO_HALF2_OPERATORS__", | ||
| # "-U__CUDA_NO_BFLOAT16_CONVERSIONS__", | ||
| # "--expt-relaxed-constexpr", | ||
| # "--expt-extended-lambda", | ||
| # "--use_fast_math" | ||
| # ], | ||
| # extra_cflags=['-std=c++17']) | ||
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| def run_benchmark(perf_func: callable, | ||
| a: torch.Tensor, b: torch.Tensor, | ||
| tag: str, out: Optional[torch.Tensor] = None, | ||
| warmup: int = 1, iters: int = 10, | ||
| show_all: bool = False): | ||
| if out is not None: | ||
| out.fill_(0) | ||
| if out is not None: | ||
| for i in range(warmup): | ||
| perf_func(a, b, out) | ||
| else: | ||
| for i in range(warmup): | ||
| _ = perf_func(a, b) | ||
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| torch.cuda.synchronize() | ||
| start = time.time() | ||
| # iters | ||
| if out is not None: | ||
| for i in range(iters): | ||
| perf_func(a, b, out) | ||
| else: | ||
| for i in range(iters): | ||
| out = perf_func(a, b) | ||
| torch.cuda.synchronize() | ||
| end = time.time() | ||
| total_time = (end - start) * 1000 # ms | ||
| mean_time = total_time / iters | ||
| out_info = f"out_{tag}" | ||
| out_val = out.flatten().detach().cpu().numpy().tolist()[:3] | ||
| out_val = [round(v, 8) for v in out_val] | ||
| out_val = [f"{v:<12}" for v in out_val] | ||
| print(f"{out_info:>32}: {out_val}, time:{mean_time:.6f}ms") | ||
| if show_all: print(out) | ||
| return out.clone(), mean_time | ||
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| # Ms = [1024, 2048, 4096] | ||
| # Ns = [1024, 2048, 4096] | ||
| # Ks = [256, 512, 1024] | ||
| Ms = [1024] | ||
| Ns = [1024] | ||
| Ks = [256] | ||
| MNKs = [(M, N, K) for M in Ms for N in Ns for K in Ks] | ||
| for (M, N, K) in MNKs: | ||
| print("-" * 110) | ||
| print(" " * 45 + f"M={M}, N={N}, K={K}") | ||
| a = torch.randn((M, K)).cuda().half().contiguous() | ||
| b = torch.randn((K, N)).cuda().half().contiguous() | ||
| c = torch.randn((M, N)).cuda().half().contiguous() | ||
| # run_benchmark(lib.hgemm_naive_f16, a, b, "f16", c) | ||
| # run_benchmark(lib.hgemm_sliced_k_f16, a, b, "f16(sk)", c) | ||
| # run_benchmark(lib.hgemm_t_4x4_sliced_k_f16x4_pack_bcf, a, b, "f16x4pack(t4x4bcf)", c) | ||
| # run_benchmark(lib.hgemm_t_4x4_sliced_k_f16x4_pack_bcf_offset, a, b, "f16x4pack(t4x4offset)", c) | ||
| # run_benchmark(lib.hgemm_t_8x8_sliced_k_f16x4, a, b, "f16x4(t8x8sk)", c) | ||
| # run_benchmark(lib.hgemm_t_8x8_sliced_k_f16x4_bcf, a, b, "f16x4(t8x8bcf)", c) | ||
| # run_benchmark(lib.hgemm_t_8x8_sliced_k_f16x4_pack, a, b, "f16x4pack(t8x8sk)", c) | ||
| # run_benchmark(lib.hgemm_t_8x8_sliced_k_f16x4_pack_bcf, a, b, "f16x4pack(bcf)", c) | ||
| # run_benchmark(lib.hgemm_t_8x8_sliced_k_f16x4_pack_bcf_offset, a, b, "f16x4pack(bcf+offset)", c) | ||
| # run_benchmark(lib.hgemm_t_8x8_sliced_k_f16x8_pack_bcf, a, b, "f16x8pack(bcf)", c) | ||
| # run_benchmark(lib.hgemm_t_8x8_sliced_k_f16x8_pack_bcf_offset, a, b, "f16x8pack(bcf+offset)", c) | ||
| # run_benchmark(lib.hgemm_t_8x8_sliced_k_f16x8_pack_bcf_dbuf, a, b, "f16x8pack(dbuf)", c) | ||
| run_benchmark(partial(torch.matmul, out=c), a, b, "f16_th") | ||
| print("-" * 110) | ||
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