1"""Common utilities that are useful for all the benchmarks.""" 2import numpy as np 3 4from mlir import ir 5from mlir.dialects import arith 6from mlir.dialects import func 7from mlir.dialects import memref 8from mlir.dialects import scf 9from mlir.passmanager import PassManager 10 11 12def setup_passes(mlir_module): 13 """Setup pass pipeline parameters for benchmark functions.""" 14 opt = ( 15 "parallelization-strategy=none" 16 ) 17 pipeline = f"builtin.module(sparsifier{{{opt}}})" 18 PassManager.parse(pipeline).run(mlir_module.operation) 19 20 21def create_sparse_np_tensor(dimensions, number_of_elements): 22 """Constructs a numpy tensor of dimensions `dimensions` that has only a 23 specific number of nonzero elements, specified by the `number_of_elements` 24 argument. 25 """ 26 tensor = np.zeros(dimensions, np.float64) 27 tensor_indices_list = [ 28 [np.random.randint(0, dimension) for dimension in dimensions] 29 for _ in range(number_of_elements) 30 ] 31 for tensor_indices in tensor_indices_list: 32 current_tensor = tensor 33 for tensor_index in tensor_indices[:-1]: 34 current_tensor = current_tensor[tensor_index] 35 current_tensor[tensor_indices[-1]] = np.random.uniform(1, 100) 36 return tensor 37 38 39def get_kernel_func_from_module(module: ir.Module) -> func.FuncOp: 40 """Takes an mlir module object and extracts the function object out of it. 41 This function only works for a module with one region, one block, and one 42 operation. 43 """ 44 assert ( 45 len(module.operation.regions) == 1 46 ), "Expected kernel module to have only one region" 47 assert ( 48 len(module.operation.regions[0].blocks) == 1 49 ), "Expected kernel module to have only one block" 50 assert ( 51 len(module.operation.regions[0].blocks[0].operations) == 1 52 ), "Expected kernel module to have only one operation" 53 return module.operation.regions[0].blocks[0].operations[0] 54 55 56def emit_timer_func() -> func.FuncOp: 57 """Returns the declaration of nanoTime function. If nanoTime function is 58 used, the `MLIR_RUNNER_UTILS` and `MLIR_C_RUNNER_UTILS` must be included. 59 """ 60 i64_type = ir.IntegerType.get_signless(64) 61 nanoTime = func.FuncOp("nanoTime", ([], [i64_type]), visibility="private") 62 nanoTime.attributes["llvm.emit_c_interface"] = ir.UnitAttr.get() 63 return nanoTime 64 65 66def emit_benchmark_wrapped_main_func(kernel_func, timer_func): 67 """Takes a function and a timer function, both represented as FuncOp 68 objects, and returns a new function. This new function wraps the call to 69 the original function between calls to the timer_func and this wrapping 70 in turn is executed inside a loop. The loop is executed 71 len(kernel_func.type.results) times. This function can be used to 72 create a "time measuring" variant of a function. 73 """ 74 i64_type = ir.IntegerType.get_signless(64) 75 memref_of_i64_type = ir.MemRefType.get([ir.ShapedType.get_dynamic_size()], i64_type) 76 wrapped_func = func.FuncOp( 77 # Same signature and an extra buffer of indices to save timings. 78 "main", 79 (kernel_func.arguments.types + [memref_of_i64_type], kernel_func.type.results), 80 visibility="public", 81 ) 82 wrapped_func.attributes["llvm.emit_c_interface"] = ir.UnitAttr.get() 83 84 num_results = len(kernel_func.type.results) 85 with ir.InsertionPoint(wrapped_func.add_entry_block()): 86 timer_buffer = wrapped_func.arguments[-1] 87 zero = arith.ConstantOp.create_index(0) 88 n_iterations = memref.DimOp(timer_buffer, zero) 89 one = arith.ConstantOp.create_index(1) 90 iter_args = list(wrapped_func.arguments[-num_results - 1 : -1]) 91 loop = scf.ForOp(zero, n_iterations, one, iter_args) 92 with ir.InsertionPoint(loop.body): 93 start = func.CallOp(timer_func, []) 94 call = func.CallOp( 95 kernel_func, 96 wrapped_func.arguments[: -num_results - 1] + loop.inner_iter_args, 97 ) 98 end = func.CallOp(timer_func, []) 99 time_taken = arith.SubIOp(end, start) 100 memref.StoreOp(time_taken, timer_buffer, [loop.induction_variable]) 101 scf.YieldOp(list(call.results)) 102 func.ReturnOp(loop) 103 104 return wrapped_func 105