# mypy: allow-untyped-defs """Provides optimal triton kernel parameters. Aim --- The usage of optimal triton kernel parameters may increase the performance of operations several times. For example, for large tensor shapes, the usage of a bsr tensor as mat1 argument in addmm-based operations typically outperforms the corresponding operation with strided-only inputs when the blocked representation of a tensor provides a better alignement with memory access than what the strided representation would provide. Pre-computed kernel parameters ------------------------------ This script finds and stores the optimal triton kernel parameters for a specific set of shape configurations. For instance, the set of shape configurations of the bsr_dense_addmm kernel is defined as input, out: M x N strided tensor mat1: M x K bsr tensor with blocksize (BM, BK) and given sparsity mat2: M x N strided tensor dtype = float16, bfloat16, float32 sparsity = 0.5 M = 256, 512, ..., 16384 K = M N = 256, 512, ..., 131072 BM = 16, 32, ..., 128 BK = BM alpha = 1 beta = 0, 1 GPUs: NVIDIA A100-SXM4-80GB Approximations -------------- It is practically infeasible to pre-compute optimal kernel parameter for all possible shape configurations as well as for all existing GPUs. Therefore, we'll assume that the pre-computed optimal parameters are good enough approximations when 1) the used GPU is any of NVIDIA A100 Tensor Core GPUs, 2) the actual sparsity of mat1 is different from sparsity value 0.5. If a particular shape configuration does not fall in the set of pre-computed kernel parameters, or it does not match with the listed approximations above, or the used GPU device is not a NVIDIA A100 GPU, then a reference set of triton kernel parameters will be used when executing operations. The reference kernel parameters are defined in torch/sparse/_triton_ops.py, see bsr_dense_addmm_meta function, for instance. Computing optimal kernel parameters ----------------------------------- If the approximations listed above are unacceptable, e.g. when one seeks a maximal performance possible, the optimal kernel parameters for a particular GPU can be computed by simply running this script in the pytorch developement tree:: cd /path/to/pytorch python setup.py develop python torch/sparse/_triton_ops_meta.py This will compute the optimal kernel parameters for the GPU device available in the host system for all shape configurations listed in "Pre-computed kernel parameters" above. The results will be stored in the database of kernel parameters. Currently, this database is defined as this module (see "BEGIN GENERATED DATA" comment below) that will be modified when the script is run. Create a pytorch PR with the corresponding modifications in this file to make the computed optimal kernel parameters available for other users as pre-computed kernel parameters. Moreover, one can compute the optimal kernel parameters for a specific set of shape configurations and specific sparsity patterns. For that, use tuning functions provided by this module: tune_bsr_dense_addmm(input, mat1, mat2, beta=1, alpha=1, out=None, verbose=False, store=False) -> meta The tuning functions return a dictionary of optimal kernel parameters that can be passed to the corresponding operation, e.g. bsr_dense_addmm(..., meta=meta) Or, when store==True, the optimal kernel parameters will be stored in the database of pre-computed kernel parameters in runtime so that all addmm-based operations such as torch.addmm, torch.mm, torch.nn.functional.linear will benefit from using the computed optimal set of kernel parameters. Note that running tune_bsr_dense_addmm can take several minutes. So, use it wisely, e.g. by implementing persisten storage of optimized kernel parameters. See the source code of get_meta and tune_bsr_dense_addmm to learn how to register a custom set of optimal kernel parameters for addmm-based operations. """ __all__ = ["get_meta", "tune_bsr_dense_addmm", "tune__int_bsr_dense_addmm"] import inspect import itertools import re import warnings from typing import Any, Dict import torch from torch.hub import tqdm from torch.testing import make_tensor def get_meta(op, key, device_name=None, version=(0, torch.float16, 0.5), exact=False): """Return triton kernel meta parameters of the specified op and its inputs key. Parameters ---------- op (str): The name of an operation that implementation uses meta parameters. key (tuple): A tuple of op input parameters, e.g. shapes, etc. device_name (optional, str): The name of a device for which op parameters are provided. version (optional, hashable): Specifies the version of parameters. exact (optional, bool): When True, the returned data (if available) corresponds exactly to the specified device_name and version information. Otherwise, if the corresponding data is not available but there exists a data set that is computed for a similar GPU device, then this data set will be returned. Returns ------- result (dict): The requested mapping of parameter names and values, or None when no data is available. If the input `key` contains `"*"`, the result will be a dictionary of keys and mappings that match with the given `key`. """ if device_name is None: device_name = torch.cuda.get_device_name() op_data = _operation_device_version_data.get((op, device_name, version)) if op_data is None and not exact: # A lack of op data could be due to using a (slightly) # different GPU model compared to a model for which optimal # meta parameters have been computed. In the following we'll # assume that there is a set of GPU models that all have # a similar set of optimal meta parameters. if re.match(r"NVIDIA A100[^\d]", device_name) is not None: device_name = "NVIDIA A100-SXM4-80GB" else: return op_data = _operation_device_version_data.get((op, device_name, version)) if op_data is None: return matching_data = {} if "*" in key: for op_key in op_data: if [None for k1, k2 in zip(op_key, key) if k2 != "*" and k1 != k2]: continue matching_data[op_key] = op_data[op_key] else: values = op_data.get(key) if values is not None: matching_data[key] = values matching_meta = {} for op_key, values in matching_data.items(): if op == "scatter_mm": names = ( "GROUP_SIZE", "SPLIT_N", "TILE_M", "TILE_N", "num_stages", "num_warps", ) meta = dict(zip(names, values)) elif op in {"bsr_dense_addmm", "_int_bsr_dense_addmm"}: meta = dict( zip(("GROUP_SIZE_ROW", "SPLIT_N", "num_stages", "num_warps"), values) ) else: raise NotImplementedError(f"names for {op=}") if "*" not in key: return meta matching_meta[op_key] = meta if "*" in key: return matching_meta def update(op, device_name, version, key, value): """Update the db of op parameters.""" # avoid storing possible optimization failures: assert value, (op, device_name, version, key, value) if (op, device_name, version) in _operation_device_version_data: if _operation_device_version_data[op, device_name, version].get(key) == value: return _operation_device_version_data[op, device_name, version][key] = value else: _operation_device_version_data[op, device_name, version] = {key: value} def dump(): """Store the current runtime db state to the module file.""" current_file = inspect.getfile(dump) f = open(current_file) current_content = f.read() f.close() begin_data_str = "# BEGIN GENERATED DATA\n" begin_data_index = current_content.find(begin_data_str) end_data_index = current_content.find(" # END GENERATED DATA\n") if begin_data_index == -1 or end_data_index == -1: warnings.warn( f"{current_file} cannot be updated:" " BEGIN/END GENERATED DATA comment blocks appear to be corrupted" ) return def sort_key(key): op, device_name, version = key version = tuple( (str(item) if isinstance(item, torch.dtype) else item) for item in version ) return (op, device_name, version) part1 = current_content[: begin_data_index + len(begin_data_str)] part2 = current_content[end_data_index:] data_part = [] for op_key in sorted(_operation_device_version_data, key=sort_key): data_part.append(" " + repr(op_key).replace("'", '"') + ": {") op_data = _operation_device_version_data[op_key] for key in sorted(op_data): data_part.append(f" {key}: {op_data[key]},") data_part.append(" },") new_content = part1 + "\n".join(data_part) + "\n" + part2 if current_content != new_content: f = open(current_file, "w") f.write(new_content) f.close() def minimize( target_func, initial_parameters, reference_parameters, step_func, max_step=2, verbose=False, all_values=None, ): """Find a dict of parameters that minimizes the target function using the initial dict of parameters and a step function that progresses a specified parameter in a dict of parameters. Parameters ---------- target_func (callable): a functional with the signature ``target_func(parameters: dict) -> float`` initial_parameters (dict): a set of parameters used as an initial value to the minimization process. reference_parameters (dict): a set of parameters used as an reference value with respect to which the speed up is computed. step_func (callable): a functional with the signature ``step_func(parameter_name:str, parameter_value:int, direction:int, parameters:dict) -> int`` that increments or decrements (when ``direction`` is positive or negative, respectively) the parameter with given name and value. When return value is equal to ``parameter_value``, it means that no step along the given direction can be made. Returns ------- parameters (dict): a set of parameters that minimizes the target function. speedup_incr (float): a speedup change given in percentage. timing (float): the value of the target function at the parameters. sensitivity_message (str): a message containing sensitivity. information of parameters around the target function minimizer. """ def to_key(parameters): return tuple(parameters[k] for k in sorted(parameters)) def from_key(key, parameters): return dict(zip(sorted(parameters), key)) if all_values is None: all_values = {} directions = list(range(-max_step, max_step + 1)) names = sorted(initial_parameters) all_directions = [] for d_tuple in itertools.product(*((directions,) * len(names))): dist = sum(map(abs, d_tuple)) if dist > 0 and dist <= max_step: all_directions.append((dist, d_tuple)) all_directions.sort() try: reference_target = target_func(reference_parameters) except Exception as msg: if verbose and "out of resource" not in str(msg): print(f"{reference_parameters=} lead to failure: {msg}.") reference_target = None if reference_target is not None: all_values[to_key(reference_parameters)] = reference_target parameters = initial_parameters try: initial_target = target_func(parameters) except Exception as msg: if reference_target is None: if verbose: print( f"{initial_parameters=} lead to failure: {msg}. Optimization failed!" ) return {}, -1, -1, f"{msg}" if verbose and "out of resource" not in str(msg): print( f"{initial_parameters=} lead to failure: {msg}. Using reference parameters instead of initial parameters." ) parameters = reference_parameters initial_target = reference_target if reference_target is None: if verbose: print("Using initial parameters instead of reference parameters.") reference_target = initial_target initial_key = to_key(parameters) minimal_target = all_values[initial_key] = initial_target pbar = tqdm( total=len(all_directions), desc="Tuning...", disable=not verbose, ncols=75, ) while True: for i, (_, d_tuple) in enumerate(all_directions): pbar.update(1) next_parameters = parameters.copy() for name, direction in zip(names, d_tuple): value = next_parameters[name] if direction == 0: continue next_value = step_func(name, value, direction, parameters) if next_value == value: break next_parameters[name] = next_value else: next_key = to_key(next_parameters) if next_key in all_values: continue try: next_target = target_func(next_parameters) except Exception as msg: all_values[next_key] = str(msg) if verbose and "out of resource" not in str(msg): print(f"{next_parameters=} lead to failure: {msg}. Skipping.") continue all_values[next_key] = next_target if next_target < minimal_target: minimal_target = next_target parameters = next_parameters pbar.total += i + 1 break else: # ensure stable minimizer: minimizer_keys = { k for k, v in all_values.items() if isinstance(v, float) and abs(1 - v / minimal_target) < 0.001 } minimizer_key = ( initial_key if initial_key in minimizer_keys else min(minimizer_keys) ) minimizer_target = all_values[minimizer_key] parameters = from_key(minimizer_key, parameters) speedup_incr = (1 - minimal_target / reference_target) * 100 if speedup_incr < 0: if verbose: print( f"{speedup_incr=} is negative. Rerunning minimize with reference parameters as initial parameters." ) return minimize( target_func, reference_parameters, reference_parameters, step_func, max_step=max_step, verbose=verbose, all_values=all_values, ) sensitivity = [] for name in parameters: value = parameters[name] rel_diffs = [] for direction in range(-max_step, max_step + 1): if direction == 0: continue next_value = step_func(name, value, direction, parameters) if next_value == value: rel_diffs.append(0) continue next_parameters = parameters.copy() next_parameters[name] = next_value next_key = to_key(next_parameters) next_target = all_values.get(next_key) if next_target is None or isinstance(next_target, str): rel_diffs.append(0) continue rel_diff = (next_target / minimal_target - 1) * 100 rel_diffs.append(rel_diff) sensitivity.append((max(rel_diffs), rel_diffs, name)) sensitivity_message = [f"timing0={initial_target:.3f}"] for _, rel_diffs, name in sorted(sensitivity, reverse=True): left_diffs = "|".join( [f"{rel_diff:.1f}" for rel_diff in rel_diffs[:max_step]] ) right_diffs = "|".join( [f"{rel_diff:.1f}" for rel_diff in rel_diffs[max_step:]] ) sensitivity_message.append( f"{name}={parameters[name]} ({left_diffs}...{right_diffs} %)" ) sensitivity_message = ", ".join(sensitivity_message) return parameters, speedup_incr, minimal_target, sensitivity_message def create_blocked_tensor(B, M, N, blocksize, sparsity, dtype, device): assert ( sparsity <= 1.0 and sparsity >= 0.0 ), "sparsity should be a value between 0 and 1" assert M % blocksize[0] == 0 assert N % blocksize[1] == 0 shape = (B, M // blocksize[0], N // blocksize[1])[int(B == 0) :] A = torch.bernoulli( torch.full(shape, 1 - sparsity, dtype=torch.float32, device=device) ).to(dtype) expected_nnz = int((1 - sparsity) * M * N / (blocksize[0] * blocksize[1])) nonzero_indices = A.flatten().nonzero() actual_nnz = nonzero_indices.shape[0] if actual_nnz > expected_nnz: selected_nonzeros = torch.randperm(actual_nnz)[: actual_nnz - expected_nnz] A.flatten()[nonzero_indices[selected_nonzeros]] = 0 elif actual_nnz < expected_nnz: zero_indices = (A == 0).flatten().nonzero() selected_zeros = torch.randperm(zero_indices.shape[0])[ : expected_nnz - actual_nnz ] A.flatten()[zero_indices[selected_zeros]] = 1 A = torch.repeat_interleave(A, blocksize[0], dim=-2) A = torch.repeat_interleave(A, blocksize[1], dim=-1) return A def optimize_scatter_mm( m, k, n, bm, bk, dtype=torch.float16, device="cuda", sparsity=0.5, force=False ): import triton from torch.sparse._triton_ops import bsr_scatter_mm, bsr_scatter_mm_indices_data key = (m, k, n, bm, bk) version = (0, dtype, sparsity) device_name = torch.cuda.get_device_name() reference_meta = dict( GROUP_SIZE=1, TILE_M=16, TILE_N=16, SPLIT_N=n // 16, num_stages=1, num_warps=1, ) initial_meta = get_meta( "scatter_mm", key, device_name=device_name, version=version, exact=True ) if initial_meta is None: initial_meta = get_meta( "bsr_dense_addmm", key, device_name=device_name, version=(0, dtype, 0.5), exact=True, ) if initial_meta is None: initial_meta = reference_meta elif not force: return torch.manual_seed(0) bsr = create_blocked_tensor( 0, m, k, (bm, bk), sparsity, dtype, device ).to_sparse_bsr((bm, bk)) dense = make_tensor(k, n, dtype=dtype, device=device) def bench(meta, bsr=bsr, dense=dense): indices_data = bsr_scatter_mm_indices_data( bsr, dense, indices_format="bsr_strided_mm_compressed", **meta ) def test_func(): return bsr_scatter_mm(bsr, dense, indices_data=indices_data) ms_min = triton.testing.do_bench( test_func, warmup=500, rep=100, fast_flush=False ) return ms_min def step_meta_parameter(name, value, direction, meta, m=m, n=n, k=k, bm=bm, bk=bk): # return next value in positive or negative direction, or # input value if the step will result an invalid # value. The input value is assumed to be valid. is_log = name in {"SPLIT_N", "TILE_M", "TILE_N", "num_warps"} min_value = dict( SPLIT_N=1, TILE_M=16, TILE_N=16, num_warps=1, num_stages=1, GROUP_SIZE=1 )[name] max_value = dict( SPLIT_N=n // meta["TILE_N"], TILE_M=bm, TILE_N=n // meta["SPLIT_N"] ).get(name) value_step = dict( SPLIT_N=2, TILE_M=2, TILE_N=2, num_warps=2, num_stages=1, GROUP_SIZE=1 )[name] if is_log: next_value = ( value * value_step**direction if direction > 0 else value // (value_step ** abs(direction)) ) else: next_value = value + value_step * direction if min_value is not None: next_value = max(next_value, min_value) if max_value is not None: next_value = min(next_value, max_value) if name == "SPLIT_N" and n % next_value != 0: return value # Hard-skip parameter combinations that break CUDA state for pytorch: if (dtype, name, next_value, m, n, k, bm, bk) in { (torch.float32, "num_warps", 32, 256, 256, 256, 16, 16), (torch.float32, "num_warps", 16, 256, 256, 256, 32, 32), (torch.float32, "num_warps", 16, 256, 256, 256, 64, 64), (torch.float32, "num_warps", 16, 256, 256, 256, 128, 128), (torch.float32, "num_warps", 16, 512, 512, 256, 128, 128), } and re.match(r"NVIDIA A100[^\d]", device_name) is not None: return value return next_value meta, speedup, timing, sensitivity_message = minimize( bench, initial_meta, reference_meta, step_meta_parameter ) if initial_meta is not reference_meta and initial_meta == meta and not force: return print(f"{meta=} {speedup=:.1f} % {timing=:.3f} ms") if speedup < 0: return device_name = torch.cuda.get_device_name() update( "scatter_mm", device_name, version, key, tuple(meta[k] for k in sorted(meta)) ) def tune__int_bsr_dense_addmm( input, bsr, dense, *, beta=1, alpha=1, out=None, store=False, verbose=False, force=False, ): return tune_bsr_dense_addmm( input, bsr, dense, beta=beta, alpha=alpha, out=out, store=store, verbose=verbose, force=force, opname="_int_bsr_dense_addmm", ) def tune_bsr_dense_addmm( input, bsr, dense, *, beta=1, alpha=1, out=None, store=False, verbose=False, force=False, opname=None, ): """Tune bsr_dense_addmm kernel parameters against the given inputs. When store is True, the tuning results will be stored in the database of kernel parameters. """ import triton if opname is None: opname = "bsr_dense_addmm" if opname == "_int_bsr_dense_addmm": from torch.sparse._triton_ops import _int_bsr_dense_addmm as bsr_dense_addmm else: from torch.sparse._triton_ops import bsr_dense_addmm N = dense.shape[-1] values = bsr.values() crow_indices = bsr.crow_indices() batch_ndim = crow_indices.dim() - 1 M, K = bsr.shape[batch_ndim : batch_ndim + 2] BM, BK = values.shape[batch_ndim + 1 : batch_ndim + 3] # Reference parameters is a set of parameters that leads to a # successful kernel call and the corresponding timing is used as a # reference for computing speedups. Avoid changing the reference # parameters when possible. reference_meta = dict( GROUP_SIZE_ROW=1, num_stages=1, num_warps=4, SPLIT_N=max(N // BM, 1) ) # Compute the key of parameters: sparsity = round(1 - bsr._nnz() * BM * BK / (M * K), 2) dtype = bsr.dtype version = (0, dtype, sparsity) key = (M, K, N, BM, BK, beta == 0, beta == 1, alpha == 1) # For tuning, for an initial state, use parameters from the # database if available, otherwise, use the reference parameters. initial_meta = get_meta(opname, key, version=version, exact=True) if initial_meta is None: may_skip_update = False initial_meta = get_meta(opname, key, version=(0, dtype, 0.5), exact=True) if initial_meta is None: initial_meta = reference_meta elif not force: return initial_meta else: may_skip_update = True # The target function that is minimized in the tuning process: def bench(meta, input=input, bsr=bsr, dense=dense, alpha=alpha, out=out): def test_func(): return bsr_dense_addmm( input, bsr, dense, beta=beta, alpha=alpha, meta=meta, out=out ) return triton.testing.do_bench(test_func, warmup=500, rep=100, fast_flush=False) # The step function that increments a specified meta parameter: def step_meta_parameter(name, value, direction, meta, M=M, N=N, K=K, BM=BM, BK=BK): # return next value in positive or negative direction, or # input value if the step will result an invalid # value. The input value is assumed to be valid. is_log = name in {"SPLIT_N", "num_warps"} min_value = dict(SPLIT_N=1, num_warps=1, num_stages=1, GROUP_SIZE_ROW=1)[name] max_value = dict(SPLIT_N=max(N // BM, 1)).get(name) value_step = dict(SPLIT_N=2, num_warps=2, num_stages=1, GROUP_SIZE_ROW=1)[name] if is_log: next_value = ( value * value_step**direction if direction > 0 else value // (value_step ** abs(direction)) ) else: next_value = value + value_step * direction if min_value is not None: next_value = max(next_value, min_value) if max_value is not None: next_value = min(next_value, max_value) if name == "SPLIT_N" and N % next_value != 0: return value return next_value # Tune: meta, speedup, timing, sensitivity_message = minimize( bench, initial_meta, reference_meta, step_meta_parameter, max_step=2, verbose=verbose, ) if verbose: print(f"-> {sensitivity_message}, {speedup=:.1f} %, {timing=:.3f} ms") if store and not ( may_skip_update and meta == initial_meta and initial_meta is not reference_meta ): device_name = torch.cuda.get_device_name() update( opname, device_name, version, key, tuple(meta[k] for k in sorted(meta)), ) return meta def optimize_bsr_dense_addmm( m, k, n, bm, bk, beta=1, alpha=1, dtype=torch.float16, device="cuda", sparsity=0.5, force=False, verbose=False, opname=None, ): torch.manual_seed(0) bsr = create_blocked_tensor( 0, m, k, (bm, bk), sparsity, dtype, device ).to_sparse_bsr((bm, bk)) dense = make_tensor(k, n, dtype=dtype, device=device) input = make_tensor(m, n, dtype=dtype, device=device) tune_bsr_dense_addmm( input, bsr, dense, beta=beta, alpha=alpha, store=True, force=force, verbose=verbose, opname=opname, ) def main(op="scatter_mm", force=False, dtype=torch.float16, verbose=True): import itertools sizes_lst = [ 256, 512, 1024, 2048, 4096, 8192, 16384, 32768, 65536, 131072, 50432, ] sizes3_lst = [3 * sz for sz in [64, 128] + sizes_lst if sz <= 2048] shapes_lst = [(sz, sz) for sz in sizes_lst[:-4] + sizes3_lst] shapes_lst.extend([(3072, 768), (768, 3072)]) if dtype is torch.int8: # triton does not support smaller blocks than 32 blocksize_lst = [(32, 32), (64, 64), (128, 128), (256, 256)] else: blocksize_lst = [(16, 16), (32, 32), (64, 64), (128, 128)] sparsity_lst = [0.5, 0.7, 0.3][:1] for sparsity in sparsity_lst: print(f"{op, dtype, sparsity=}") try: for (M, K), N, (BM, BK) in itertools.product( shapes_lst, sizes_lst, blocksize_lst ): if not (BM <= M and BK <= K and M % BM == 0 and K % BK == 0): continue if op == "scatter_mm": optimize_scatter_mm( M, K, N, BM, BK, force=force, sparsity=sparsity, dtype=dtype ) elif op in {"bsr_dense_addmm", "_int_bsr_dense_addmm"}: if M == K and N == 50432: continue print(f"{M, K, N, (BM, BK)=}") for alpha, beta in [(1, 1), (1, 0)]: optimize_bsr_dense_addmm( M, K, N, BM, BK, beta=beta, alpha=alpha, force=force, sparsity=sparsity, dtype=dtype, verbose=verbose, opname=op, ) else: raise NotImplementedError(op) except KeyboardInterrupt: break except Exception as msg: dump() raise dump() if 0: # Check performance dependence on sparsity and apply # adjustments when differences are noticable (more than 10%). # # When using NVIDIA A100 GPU, the performance dependence on # sparsity is insignificant (0 % ... 10 %) for majority of # shapes/blocksizes combinations. However, for a very few # specific size combinations, the effect of sparsity on # performance can be up to 20 %. for (M, K), N, (BM, BK) in itertools.product( shapes_lst, sizes_lst, blocksize_lst ): meta_lst: list = [] key = (M, K, N, BM, BK) for sparsity1 in sparsity_lst: torch.manual_seed(0) bsr = create_blocked_tensor( 0, M, K, (BM, BK), sparsity1, dtype, device="cuda" ).to_sparse_bsr((BM, BK)) dense = make_tensor(K, N, dtype=dtype, device="cuda") meta_lst = [] for sparsity in sparsity_lst: meta = get_meta(op, key, version=(0, dtype, sparsity), exact=True) if meta is None: continue def bench(meta, bsr=bsr, dense=dense): import triton if op == "scatter_mm": from torch.sparse._triton_ops import ( bsr_scatter_mm, bsr_scatter_mm_indices_data, ) indices_data = bsr_scatter_mm_indices_data( bsr, dense, indices_format="bsr_strided_mm_compressed", **meta, ) def test_func(): return bsr_scatter_mm( bsr, dense, indices_data=indices_data ) else: raise NotImplementedError(op) ms_min = triton.testing.do_bench( test_func, warmup=500, rep=100, fast_flush=False ) return ms_min meta_lst.append( (bench(meta), sparsity, tuple(meta[k] for k in sorted(meta))) ) if not meta_lst: continue meta_lst = sorted(meta_lst) index = next( i for i, item in enumerate(meta_lst) if item[1] == sparsity1 ) if meta_lst[0][2] == meta_lst[index][2]: continue speeddiff = (1 - meta_lst[index][0] / meta_lst[0][0]) * 100 if abs(speeddiff) < 10: continue print(sparsity1, index, key, meta_lst, speeddiff) if index > 0: device_name = torch.cuda.get_device_name() meta = get_meta( op, key, version=(0, dtype, meta_lst[0][1]), exact=True ) update( op, device_name, (0, dtype, sparsity1), key, tuple(meta[k] for k in sorted(meta)), ) print("update") dump() _operation_device_version_data: Dict[Any, Dict] = { # Warning: the data in between the BEGIN/END DATA comment lines # below is generated. It can be updated either manually or via # calling dump function defined above. # # Legend [op: key -> data]: # scatter_mm : M, K, N, Ms, Ks -> GROUP_SIZE, SPLIT_N, TILE_M, TILE_N, num_stages, num_warps # bsr_dense_addmm : M, K, N, Ms, Ks, beta==0, beta==1, alpha==1 -> GROUP_SIZE_ROW, SPLIT_N, num_stages, num_warps # # BEGIN GENERATED DATA ("_int_bsr_dense_addmm", "NVIDIA A100-SXM4-80GB", (0, torch.int8, 0.5)): { (192, 192, 256, 32, 32, False, True, True): (2, 8, 1, 4), (192, 192, 256, 32, 32, True, False, True): (2, 8, 5, 4), (192, 192, 512, 32, 32, False, True, True): (1, 16, 1, 4), (192, 192, 512, 32, 32, True, False, True): (1, 16, 5, 4), (192, 192, 1024, 32, 32, False, True, True): (1, 32, 1, 4), (192, 192, 1024, 32, 32, True, False, True): (4, 32, 4, 4), (192, 192, 2048, 32, 32, False, True, True): (2, 64, 1, 4), (192, 192, 2048, 32, 32, True, False, True): (3, 16, 5, 4), (192, 192, 4096, 32, 32, False, True, True): (1, 128, 1, 4), (192, 192, 4096, 32, 32, True, False, True): (1, 128, 1, 4), (192, 192, 8192, 32, 32, False, True, True): (1, 256, 1, 4), (192, 192, 8192, 32, 32, True, False, True): (1, 64, 3, 4), (192, 192, 16384, 32, 32, False, True, True): (2, 512, 1, 4), (192, 192, 16384, 32, 32, True, False, True): (5, 128, 1, 4), (192, 192, 32768, 32, 32, False, True, True): (1, 1024, 1, 4), (192, 192, 32768, 32, 32, True, False, True): (1, 256, 1, 4), (192, 192, 65536, 32, 32, False, True, True): (1, 1024, 1, 8), (192, 192, 65536, 32, 32, True, False, True): (1, 512, 1, 4), (192, 192, 131072, 32, 32, False, True, True): (1, 2048, 1, 8), (192, 192, 131072, 32, 32, True, False, True): (2, 512, 1, 4), (256, 256, 256, 32, 32, False, True, True): (4, 8, 1, 4), (256, 256, 256, 32, 32, True, False, True): (1, 8, 6, 4), (256, 256, 256, 64, 64, False, True, True): (1, 4, 1, 16), (256, 256, 256, 64, 64, True, False, True): (1, 4, 4, 4), (256, 256, 256, 128, 128, False, True, True): (3, 2, 1, 16), (256, 256, 256, 128, 128, True, False, True): (1, 2, 1, 4), (256, 256, 512, 32, 32, False, True, True): (2, 16, 1, 4), (256, 256, 512, 32, 32, True, False, True): (2, 16, 4, 4), (256, 256, 512, 64, 64, False, True, True): (7, 8, 1, 16), (256, 256, 512, 64, 64, True, False, True): (3, 8, 3, 4), (256, 256, 512, 128, 128, False, True, True): (1, 4, 1, 32), (256, 256, 512, 128, 128, True, False, True): (1, 4, 1, 4), (256, 256, 1024, 32, 32, False, True, True): (1, 32, 1, 4), (256, 256, 1024, 32, 32, True, False, True): (1, 8, 6, 4), (256, 256, 1024, 64, 64, False, True, True): (2, 16, 1, 16), (256, 256, 1024, 64, 64, True, False, True): (1, 16, 5, 4), (256, 256, 1024, 128, 128, False, True, True): (4, 8, 1, 32), (256, 256, 1024, 128, 128, True, False, True): (1, 8, 2, 4), (256, 256, 2048, 32, 32, False, True, True): (1, 64, 1, 4), (256, 256, 2048, 32, 32, True, False, True): (2, 32, 3, 2), (256, 256, 2048, 64, 64, False, True, True): (2, 32, 1, 16), (256, 256, 2048, 64, 64, True, False, True): (1, 16, 3, 4), (256, 256, 2048, 128, 128, False, True, True): (1, 16, 1, 32), (256, 256, 2048, 128, 128, True, False, True): (1, 16, 2, 4), (256, 256, 4096, 32, 32, False, True, True): (2, 128, 1, 4), (256, 256, 4096, 32, 32, True, False, True): (1, 32, 3, 2), (256, 256, 4096, 64, 64, False, True, True): (2, 64, 1, 8), (256, 256, 4096, 64, 64, True, False, True): (1, 64, 3, 2), (256, 256, 4096, 128, 128, False, True, True): (2, 32, 1, 32), (256, 256, 4096, 128, 128, True, False, True): (3, 32, 2, 8), (256, 256, 8192, 32, 32, False, True, True): (1, 256, 1, 4), (256, 256, 8192, 32, 32, True, False, True): (1, 64, 1, 4), (256, 256, 8192, 64, 64, False, True, True): (1, 128, 1, 8), (256, 256, 8192, 64, 64, True, False, True): (2, 128, 1, 4), (256, 256, 8192, 128, 128, False, True, True): (4, 64, 1, 32), (256, 256, 8192, 128, 128, True, False, True): (3, 64, 1, 4), (256, 256, 16384, 32, 32, False, True, True): (1, 256, 1, 8), (256, 256, 16384, 32, 32, True, False, True): (3, 128, 1, 4), (256, 256, 16384, 64, 64, False, True, True): (2, 256, 1, 8), (256, 256, 16384, 64, 64, True, False, True): (2, 256, 1, 4), (256, 256, 16384, 128, 128, False, True, True): (2, 128, 1, 32), (256, 256, 16384, 128, 128, True, False, True): (4, 128, 2, 4), (256, 256, 32768, 32, 32, False, True, True): (2, 512, 1, 8), (256, 256, 32768, 32, 32, True, False, True): (1, 256, 1, 4), (256, 256, 32768, 64, 64, False, True, True): (1, 512, 1, 8), (256, 256, 32768, 64, 64, True, False, True): (1, 512, 1, 4), (256, 256, 32768, 128, 128, False, True, True): (2, 256, 1, 32), (256, 256, 32768, 128, 128, True, False, True): (1, 256, 2, 4), (256, 256, 65536, 32, 32, False, True, True): (1, 1024, 1, 8), (256, 256, 65536, 32, 32, True, False, True): (1, 512, 1, 4), (256, 256, 65536, 64, 64, False, True, True): (1, 1024, 1, 8), (256, 256, 65536, 64, 64, True, False, True): (1, 512, 1, 4), (256, 256, 65536, 128, 128, False, True, True): (2, 512, 1, 16), (256, 256, 65536, 128, 128, True, False, True): (1, 512, 1, 4), (256, 256, 131072, 32, 32, False, True, True): (1, 2048, 1, 8), (256, 256, 131072, 32, 32, True, False, True): (2, 1024, 1, 4), (256, 256, 131072, 64, 64, False, True, True): (1, 2048, 1, 8), (256, 256, 131072, 64, 64, True, False, True): (2, 512, 1, 4), (256, 256, 131072, 128, 128, False, True, True): (2, 1024, 1, 16), (256, 256, 131072, 128, 128, True, False, True): (4, 1024, 1, 4), (384, 384, 256, 32, 32, False, True, True): (1, 8, 1, 4), (384, 384, 256, 32, 32, True, False, True): (5, 8, 5, 4), (384, 384, 256, 64, 64, False, True, True): (2, 4, 1, 16), (384, 384, 256, 64, 64, True, False, True): (1, 4, 5, 4), (384, 384, 512, 32, 32, False, True, True): (2, 16, 1, 4), (384, 384, 512, 32, 32, True, False, True): (1, 16, 4, 4), (384, 384, 512, 64, 64, False, True, True): (3, 8, 1, 16), (384, 384, 512, 64, 64, True, False, True): (3, 8, 3, 4), (384, 384, 1024, 32, 32, False, True, True): (2, 32, 1, 4), (384, 384, 1024, 32, 32, True, False, True): (1, 8, 6, 4), (384, 384, 1024, 64, 64, False, True, True): (2, 16, 1, 16), (384, 384, 1024, 64, 64, True, False, True): (1, 16, 5, 4), (384, 384, 2048, 32, 32, False, True, True): (1, 64, 1, 4), (384, 384, 2048, 32, 32, True, False, True): (3, 16, 4, 4), (384, 384, 2048, 64, 64, False, True, True): (2, 32, 1, 16), (384, 384, 2048, 64, 64, True, False, True): (1, 16, 4, 4), (384, 384, 4096, 32, 32, False, True, True): (4, 64, 1, 8), (384, 384, 4096, 32, 32, True, False, True): (4, 32, 1, 4), (384, 384, 4096, 64, 64, False, True, True): (1, 64, 1, 8), (384, 384, 4096, 64, 64, True, False, True): (1, 64, 1, 4), (384, 384, 8192, 32, 32, False, True, True): (1, 128, 1, 8), (384, 384, 8192, 32, 32, True, False, True): (3, 64, 1, 1), (384, 384, 8192, 64, 64, False, True, True): (2, 128, 1, 8), (384, 384, 8192, 64, 64, True, False, True): (1, 64, 2, 2), (384, 384, 16384, 32, 32, False, True, True): (1, 256, 1, 8), (384, 384, 16384, 32, 32, True, False, True): (1, 128, 1, 4), (384, 384, 16384, 64, 64, False, True, True): (2, 256, 1, 8), (384, 384, 16384, 64, 64, True, False, True): (2, 128, 1, 4), (384, 384, 32768, 32, 32, False, True, True): (1, 512, 1, 8), (384, 384, 32768, 32, 32, True, False, True): (1, 256, 1, 4), (384, 384, 32768, 64, 64, False, True, True): (1, 512, 1, 8), (384, 384, 32768, 64, 64, True, False, True): (1, 256, 3, 2), (384, 384, 65536, 32, 32, False, True, True): (1, 1024, 1, 8), (384, 384, 65536, 32, 32, True, False, True): (1, 512, 3, 2), (384, 384, 65536, 64, 64, False, True, True): (2, 1024, 1, 8), (384, 384, 65536, 64, 64, True, False, True): (3, 256, 3, 4), (384, 384, 131072, 32, 32, False, True, True): (1, 2048, 1, 8), (384, 384, 131072, 32, 32, True, False, True): (1, 1024, 3, 2), (384, 384, 131072, 64, 64, False, True, True): (1, 2048, 1, 8), (384, 384, 131072, 64, 64, True, False, True): (2, 512, 3, 4), (512, 512, 256, 32, 32, False, True, True): (1, 8, 1, 4), (512, 512, 256, 32, 32, True, False, True): (4, 8, 4, 4), (512, 512, 256, 64, 64, False, True, True): (3, 4, 1, 16), (512, 512, 256, 64, 64, True, False, True): (2, 4, 5, 4), (512, 512, 256, 128, 128, False, True, True): (4, 2, 1, 16), (512, 512, 256, 128, 128, True, False, True): (1, 2, 3, 4), (512, 512, 256, 256, 256, False, True, True): (1, 1, 1, 32), (512, 512, 256, 256, 256, True, False, True): (2, 1, 1, 32), (512, 512, 512, 32, 32, False, True, True): (3, 16, 1, 4), (512, 512, 512, 32, 32, True, False, True): (1, 8, 4, 2), (512, 512, 512, 64, 64, False, True, True): (2, 8, 1, 16), (512, 512, 512, 64, 64, True, False, True): (2, 8, 5, 4), (512, 512, 512, 128, 128, False, True, True): (3, 4, 1, 16), (512, 512, 512, 128, 128, True, False, True): (1, 4, 3, 4), (512, 512, 512, 256, 256, False, True, True): (1, 2, 1, 32), (512, 512, 512, 256, 256, True, False, True): (2, 2, 1, 32), (512, 512, 1024, 32, 32, False, True, True): (2, 32, 1, 4), (512, 512, 1024, 32, 32, True, False, True): (4, 16, 3, 2), (512, 512, 1024, 64, 64, False, True, True): (4, 16, 1, 16), (512, 512, 1024, 64, 64, True, False, True): (1, 8, 4, 4), (512, 512, 1024, 128, 128, False, True, True): (1, 8, 1, 32), (512, 512, 1024, 128, 128, True, False, True): (1, 8, 3, 4), (512, 512, 1024, 256, 256, False, True, True): (4, 4, 1, 32), (512, 512, 1024, 256, 256, True, False, True): (2, 4, 1, 32), (512, 512, 2048, 32, 32, False, True, True): (3, 32, 1, 8), (512, 512, 2048, 32, 32, True, False, True): (1, 16, 3, 4), (512, 512, 2048, 64, 64, False, True, True): (1, 32, 1, 8), (512, 512, 2048, 64, 64, True, False, True): (1, 32, 3, 2), (512, 512, 2048, 128, 128, False, True, True): (4, 16, 1, 32), (512, 512, 2048, 128, 128, True, False, True): (1, 16, 3, 4), (512, 512, 2048, 256, 256, False, True, True): (1, 8, 1, 32), (512, 512, 2048, 256, 256, True, False, True): (3, 8, 1, 32), (512, 512, 4096, 32, 32, False, True, True): (1, 64, 1, 8), (512, 512, 4096, 32, 32, True, False, True): (5, 32, 1, 4), (512, 512, 4096, 64, 64, False, True, True): (1, 64, 1, 8), (512, 512, 4096, 64, 64, True, False, True): (1, 64, 1, 4), (512, 512, 4096, 128, 128, False, True, True): (5, 32, 1, 32), (512, 512, 4096, 128, 128, True, False, True): (2, 32, 3, 4), (512, 512, 4096, 256, 256, False, True, True): (1, 16, 1, 32), (512, 512, 4096, 256, 256, True, False, True): (3, 16, 1, 32), (512, 512, 8192, 32, 32, False, True, True): (3, 128, 1, 8), (512, 512, 8192, 32, 32, True, False, True): (3, 64, 1, 4), (512, 512, 8192, 64, 64, False, True, True): (4, 128, 1, 8), (512, 512, 8192, 64, 64, True, False, True): (1, 64, 3, 2), (512, 512, 8192, 128, 128, False, True, True): (5, 64, 1, 32), (512, 512, 8192, 128, 128, True, False, True): (1, 64, 2, 4), (512, 512, 8192, 256, 256, False, True, True): (1, 32, 1, 32), (512, 512, 8192, 256, 256, True, False, True): (1, 32, 1, 32), (512, 512, 16384, 32, 32, False, True, True): (1, 256, 1, 8), (512, 512, 16384, 32, 32, True, False, True): (2, 128, 1, 4), (512, 512, 16384, 64, 64, False, True, True): (2, 256, 1, 8), (512, 512, 16384, 64, 64, True, False, True): (1, 128, 3, 2), (512, 512, 16384, 128, 128, False, True, True): (4, 128, 1, 16), (512, 512, 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512, 128, 128): (2, 1, 32, 64, 1, 4), (8192, 8192, 1024, 16, 16): (3, 8, 16, 64, 3, 1), (8192, 8192, 1024, 32, 32): (1, 16, 32, 32, 1, 1), (8192, 8192, 1024, 64, 64): (1, 8, 64, 32, 1, 2), (8192, 8192, 1024, 128, 128): (2, 4, 32, 64, 1, 4), (8192, 8192, 2048, 16, 16): (1, 8, 16, 64, 3, 1), (8192, 8192, 2048, 32, 32): (1, 16, 32, 32, 1, 1), (8192, 8192, 2048, 64, 64): (2, 8, 64, 32, 1, 2), (8192, 8192, 2048, 128, 128): (4, 1, 32, 64, 1, 4), (8192, 8192, 4096, 16, 16): (1, 8, 16, 64, 3, 1), (8192, 8192, 4096, 32, 32): (1, 16, 32, 32, 1, 1), (8192, 8192, 4096, 64, 64): (1, 4, 64, 32, 1, 2), (8192, 8192, 4096, 128, 128): (3, 1, 32, 64, 1, 4), (8192, 8192, 8192, 16, 16): (1, 8, 16, 64, 3, 1), (8192, 8192, 8192, 32, 32): (1, 8, 32, 32, 1, 1), (8192, 8192, 8192, 64, 64): (1, 8, 64, 32, 1, 2), (8192, 8192, 8192, 128, 128): (4, 1, 32, 64, 1, 4), (8192, 8192, 16384, 16, 16): (3, 4, 16, 64, 3, 1), (8192, 8192, 16384, 32, 32): (1, 8, 32, 32, 1, 1), (8192, 8192, 16384, 64, 64): (2, 2, 64, 32, 1, 2), (8192, 8192, 16384, 128, 128): (7, 1, 32, 64, 1, 4), (8192, 8192, 32768, 16, 16): (1, 4, 16, 64, 3, 1), (8192, 8192, 32768, 32, 32): (1, 8, 32, 32, 1, 1), (8192, 8192, 32768, 64, 64): (3, 2, 64, 32, 1, 2), (8192, 8192, 32768, 128, 128): (6, 1, 32, 64, 1, 4), (8192, 8192, 65536, 16, 16): (1, 4, 16, 64, 3, 1), (8192, 8192, 65536, 32, 32): (4, 8, 32, 32, 1, 1), (8192, 8192, 65536, 64, 64): (1, 2, 64, 32, 1, 2), (8192, 8192, 65536, 128, 128): (4, 1, 32, 64, 1, 4), (8192, 8192, 131072, 16, 16): (1, 4, 16, 64, 3, 1), (8192, 8192, 131072, 32, 32): (1, 8, 32, 32, 1, 1), (8192, 8192, 131072, 64, 64): (5, 4, 64, 32, 1, 2), (8192, 8192, 131072, 128, 128): (1, 4096, 128, 16, 1, 8), (16384, 16384, 256, 16, 16): (1, 4, 16, 64, 3, 1), (16384, 16384, 256, 32, 32): (1, 8, 32, 32, 1, 1), (16384, 16384, 256, 64, 64): (1, 4, 64, 32, 1, 2), (16384, 16384, 256, 128, 128): (1, 4, 32, 64, 1, 4), (16384, 16384, 512, 16, 16): (1, 8, 16, 64, 3, 1), (16384, 16384, 512, 32, 32): (1, 16, 32, 32, 1, 1), (16384, 16384, 512, 64, 64): (1, 4, 64, 32, 1, 2), (16384, 16384, 512, 128, 128): (3, 1, 32, 64, 1, 4), (16384, 16384, 1024, 16, 16): (1, 8, 16, 64, 3, 1), (16384, 16384, 1024, 32, 32): (1, 16, 32, 32, 1, 1), (16384, 16384, 1024, 64, 64): (2, 4, 64, 32, 1, 2), (16384, 16384, 1024, 128, 128): (1, 2, 32, 64, 1, 4), (16384, 16384, 2048, 16, 16): (1, 4, 16, 64, 3, 1), (16384, 16384, 2048, 32, 32): (1, 16, 32, 32, 1, 1), (16384, 16384, 2048, 64, 64): (3, 4, 64, 32, 1, 2), (16384, 16384, 2048, 128, 128): (2, 1, 32, 64, 1, 4), (16384, 16384, 4096, 16, 16): (4, 8, 16, 64, 3, 1), (16384, 16384, 4096, 32, 32): (5, 16, 32, 32, 1, 1), (16384, 16384, 4096, 64, 64): (3, 2, 64, 32, 1, 2), (16384, 16384, 4096, 128, 128): (2, 1, 32, 64, 1, 4), (16384, 16384, 8192, 16, 16): (1, 4, 16, 64, 3, 1), (16384, 16384, 8192, 32, 32): (1, 4, 32, 32, 1, 1), (16384, 16384, 8192, 64, 64): (1, 2, 64, 32, 1, 2), (16384, 16384, 8192, 128, 128): (2, 1, 32, 64, 1, 4), (16384, 16384, 16384, 16, 16): (1, 8, 16, 64, 3, 1), (16384, 16384, 16384, 32, 32): (1, 4, 32, 32, 1, 1), (16384, 16384, 16384, 64, 64): (1, 2, 64, 32, 1, 2), (16384, 16384, 16384, 128, 128): (3, 1, 32, 64, 1, 4), (16384, 16384, 32768, 16, 16): (1, 4, 16, 64, 3, 1), (16384, 16384, 32768, 32, 32): (1, 2, 32, 32, 1, 1), (16384, 16384, 32768, 64, 64): (3, 2, 64, 32, 1, 2), (16384, 16384, 32768, 128, 128): (3, 1, 32, 64, 1, 4), (16384, 16384, 65536, 16, 16): (1, 8, 16, 64, 3, 1), (16384, 16384, 65536, 32, 32): (1, 4, 32, 32, 1, 1), (16384, 16384, 65536, 64, 64): (4, 4, 64, 32, 1, 2), (16384, 16384, 65536, 128, 128): (5, 1, 32, 64, 1, 4), (16384, 16384, 131072, 16, 16): (1, 2, 16, 64, 3, 1), (16384, 16384, 131072, 32, 32): (1, 4, 32, 32, 1, 1), (16384, 16384, 131072, 64, 64): (1, 2, 64, 32, 1, 2), (16384, 16384, 131072, 128, 128): (1, 4096, 128, 16, 1, 8), }, # END GENERATED DATA } if __name__ == "__main__": for dtype in [torch.int8]: for op in ["_int_bsr_dense_addmm"]: main(op=op, force=False, dtype=dtype) for dtype in [torch.float16, torch.bfloat16, torch.float32]: for op in ["bsr_dense_addmm"]: main(op=op, force=False, dtype=dtype)