# Source: https://github.com/triton-lang/kernels/blob/main/kernels/matmul_perf_model.py # License: MIT from triton-lang/kernels """ MIT License Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ # @lint-ignore-every LICENSELINT # flake8: noqa # pyre-ignore-all-errors # fmt: off import functools import heapq import torch from triton import cdiv from triton.runtime import driver from triton.testing import ( get_dram_gbps, get_max_simd_tflops, get_max_tensorcore_tflops, nvsmi, ) @functools.lru_cache() def get_clock_rate_in_khz(): try: return nvsmi(["clocks.max.sm"])[0] * 1e3 except FileNotFoundError: import pynvml pynvml.nvmlInit() handle = pynvml.nvmlDeviceGetHandleByIndex(0) return pynvml.nvmlDeviceGetMaxClockInfo(handle, pynvml.NVML_CLOCK_SM) * 1e3 def get_tensorcore_tflops(device, num_ctas, num_warps, dtype): """return compute throughput in TOPS""" total_warps = num_ctas * min(num_warps, 4) num_subcores = ( driver.active.utils.get_device_properties(device)["multiprocessor_count"] * 4 ) # on recent GPUs tflops = ( min(num_subcores, total_warps) / num_subcores * get_max_tensorcore_tflops(dtype, get_clock_rate_in_khz(), device) ) return tflops def get_simd_tflops(device, num_ctas, num_warps, dtype): """return compute throughput in TOPS""" total_warps = num_ctas * min(num_warps, 4) num_subcores = ( driver.active.utils.get_device_properties(device)["multiprocessor_count"] * 4 ) # on recent GPUs tflops = ( min(num_subcores, total_warps) / num_subcores * get_max_simd_tflops(dtype, get_clock_rate_in_khz(), device) ) return tflops def get_tflops(device, num_ctas, num_warps, dtype): capability = torch.cuda.get_device_capability(device) if capability[0] < 8 and dtype == torch.float32: return get_simd_tflops(device, num_ctas, num_warps, dtype) return get_tensorcore_tflops(device, num_ctas, num_warps, dtype) def estimate_matmul_time( # backend, device, num_warps, num_stages, # A, B, C, # M, N, K, # BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, # debug=False, **kwargs, # ): """return estimated running time in ms = max(compute, loading) + store""" device = torch.cuda.current_device() dtype = A.dtype dtsize = A.element_size() num_cta_m = cdiv(M, BLOCK_M) num_cta_n = cdiv(N, BLOCK_N) num_cta_k = SPLIT_K num_ctas = num_cta_m * num_cta_n * num_cta_k # If the input is smaller than the block size M, N = max(M, BLOCK_M), max(N, BLOCK_N) # time to compute total_ops = 2 * M * N * K / (1024 * 1024 * 1024) # GOPS tput = get_tflops(device, num_ctas, num_warps, dtype) compute_ms = total_ops / tput # time to load data num_sm = driver.active.utils.get_device_properties(device)["multiprocessor_count"] active_cta_ratio = min(1, num_ctas / num_sm) active_cta_ratio_bw1 = min( 1, num_ctas / 32 ) # 32 active ctas are enough to saturate active_cta_ratio_bw2 = max( min(1, (num_ctas - 32) / (108 - 32)), 0 ) # 32-108, remaining 5% dram_bw = get_dram_gbps(device) * ( active_cta_ratio_bw1 * 0.95 + active_cta_ratio_bw2 * 0.05 ) # in GB/s l2_bw = dram_bw * 4 # rough estimation (should be 4.7 for A100?) # assume 80% of (following) loads are in L2 cache load_a_dram = M * K * dtsize * (1 + 0.2 * (num_cta_n - 1)) load_a_l2 = M * K * dtsize * 0.8 * (num_cta_n - 1) load_b_dram = N * K * dtsize * (1 + 0.2 * (num_cta_m - 1)) load_b_l2 = N * K * dtsize * 0.8 * (num_cta_m - 1) # total total_dram = (load_a_dram + load_b_dram) / (1024 * 1024) # MB total_l2 = (load_a_l2 + load_b_l2) / (1024 * 1024) # loading time in ms load_ms = total_dram / dram_bw + total_l2 / l2_bw # estimate storing time store_bw = dram_bw * 0.6 # :o store_c_dram = M * N * dtsize * SPLIT_K / (1024 * 1024) # MB if SPLIT_K == 1: store_ms = store_c_dram / store_bw else: reduce_bw = store_bw store_ms = store_c_dram / reduce_bw # c.zero_() zero_ms = M * N * 2 / (1024 * 1024) / store_bw store_ms += zero_ms total_time_ms = max(compute_ms, load_ms) + store_ms if debug: print( f"Total time: {total_time_ms}ms, compute time: {compute_ms}ms, " f"loading time: {load_ms}ms, store time: {store_ms}ms, " f"Activate CTAs: {active_cta_ratio*100}%" ) return total_time_ms def early_config_prune(configs, named_args, **kwargs): device = torch.cuda.current_device() capability = torch.cuda.get_device_capability() # BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages dtsize = named_args["A"].element_size() dtype = named_args["A"].dtype # 1. make sure we have enough smem pruned_configs = [] for config in configs: kw = config.kwargs BLOCK_M, BLOCK_N, BLOCK_K, num_stages = ( kw["BLOCK_M"], kw["BLOCK_N"], kw["BLOCK_K"], config.num_stages, ) max_shared_memory = driver.active.utils.get_device_properties(device)[ "max_shared_mem" ] required_shared_memory = (BLOCK_M + BLOCK_N) * BLOCK_K * num_stages * dtsize if required_shared_memory <= max_shared_memory: pruned_configs.append(config) configs = pruned_configs # Some dtypes do not allow atomic_add if dtype not in [torch.float16, torch.float32]: configs = [config for config in configs if config.kwargs["SPLIT_K"] == 1] # group configs by (BLOCK_M,_N,_K, SPLIT_K, num_warps) configs_map = {} for config in configs: kw = config.kwargs BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages = ( kw["BLOCK_M"], kw["BLOCK_N"], kw["BLOCK_K"], kw["SPLIT_K"], config.num_warps, config.num_stages, ) key = (BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps) if key in configs_map: configs_map[key].append((config, num_stages)) else: configs_map[key] = [(config, num_stages)] pruned_configs = [] for k, v in configs_map.items(): BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps = k if capability[0] >= 8: # compute cycles (only works for ampere GPUs) mmas = BLOCK_M * BLOCK_N * BLOCK_K / (16 * 8 * 16) mma_cycles = mmas / min(4, num_warps) * 8 ldgsts_latency = 300 # Does this matter? optimal_num_stages = ldgsts_latency / mma_cycles # nearest stages, prefer large #stages nearest = heapq.nsmallest( 2, v, key=lambda x: ( 10 + abs(x[1] - optimal_num_stages) if (x[1] - optimal_num_stages) < 0 else x[1] - optimal_num_stages ), ) for n in nearest: pruned_configs.append(n[0]) else: # Volta & Turing only supports num_stages <= 2 random_config = v[0][0] random_config.num_stages = 2 pruned_configs.append(random_config) return pruned_configs