3655069234
This commit moves FuncOp out of the builtin dialect, and into the Func dialect. This move has been planned in some capacity from the moment we made FuncOp an operation (years ago). This commit handles the functional aspects of the move, but various aspects are left untouched to ease migration: func::FuncOp is re-exported into mlir to reduce the actual API churn, the assembly format still accepts the unqualified `func`. These temporary measures will remain for a little while to simplify migration before being removed. Differential Revision: https://reviews.llvm.org/D121266
108 lines
4.2 KiB
Python
108 lines
4.2 KiB
Python
"""Common utilities that are useful for all the benchmarks."""
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import numpy as np
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import mlir.all_passes_registration
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from mlir import ir
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from mlir.dialects import arith
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from mlir.dialects import builtin
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from mlir.dialects import func
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from mlir.dialects import memref
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from mlir.dialects import scf
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from mlir.passmanager import PassManager
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def setup_passes(mlir_module):
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"""Setup pass pipeline parameters for benchmark functions.
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"""
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opt = (
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"parallelization-strategy=0"
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" vectorization-strategy=0 vl=1 enable-simd-index32=False"
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)
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pipeline = f"sparse-compiler{{{opt}}}"
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PassManager.parse(pipeline).run(mlir_module)
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def create_sparse_np_tensor(dimensions, number_of_elements):
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"""Constructs a numpy tensor of dimensions `dimensions` that has only a
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specific number of nonzero elements, specified by the `number_of_elements`
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argument.
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"""
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tensor = np.zeros(dimensions, np.float64)
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tensor_indices_list = [
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[np.random.randint(0, dimension) for dimension in dimensions]
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for _ in range(number_of_elements)
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]
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for tensor_indices in tensor_indices_list:
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current_tensor = tensor
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for tensor_index in tensor_indices[:-1]:
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current_tensor = current_tensor[tensor_index]
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current_tensor[tensor_indices[-1]] = np.random.uniform(1, 100)
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return tensor
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def get_kernel_func_from_module(module: ir.Module) -> func.FuncOp:
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"""Takes an mlir module object and extracts the function object out of it.
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This function only works for a module with one region, one block, and one
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operation.
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"""
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assert len(module.operation.regions) == 1, \
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"Expected kernel module to have only one region"
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assert len(module.operation.regions[0].blocks) == 1, \
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"Expected kernel module to have only one block"
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assert len(module.operation.regions[0].blocks[0].operations) == 1, \
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"Expected kernel module to have only one operation"
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return module.operation.regions[0].blocks[0].operations[0]
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def emit_timer_func() -> func.FuncOp:
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"""Returns the declaration of nano_time function. If nano_time function is
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used, the `MLIR_RUNNER_UTILS` and `MLIR_C_RUNNER_UTILS` must be included.
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"""
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i64_type = ir.IntegerType.get_signless(64)
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nano_time = func.FuncOp(
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"nano_time", ([], [i64_type]), visibility="private")
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nano_time.attributes["llvm.emit_c_interface"] = ir.UnitAttr.get()
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return nano_time
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def emit_benchmark_wrapped_main_func(func, timer_func):
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"""Takes a function and a timer function, both represented as FuncOp
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objects, and returns a new function. This new function wraps the call to
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the original function between calls to the timer_func and this wrapping
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in turn is executed inside a loop. The loop is executed
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len(func.type.results) times. This function can be used to create a
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"time measuring" variant of a function.
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"""
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i64_type = ir.IntegerType.get_signless(64)
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memref_of_i64_type = ir.MemRefType.get([-1], i64_type)
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wrapped_func = func.FuncOp(
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# Same signature and an extra buffer of indices to save timings.
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"main",
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(func.arguments.types + [memref_of_i64_type], func.type.results),
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visibility="public"
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)
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wrapped_func.attributes["llvm.emit_c_interface"] = ir.UnitAttr.get()
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num_results = len(func.type.results)
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with ir.InsertionPoint(wrapped_func.add_entry_block()):
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timer_buffer = wrapped_func.arguments[-1]
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zero = arith.ConstantOp.create_index(0)
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n_iterations = memref.DimOp(ir.IndexType.get(), timer_buffer, zero)
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one = arith.ConstantOp.create_index(1)
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iter_args = list(wrapped_func.arguments[-num_results - 1:-1])
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loop = scf.ForOp(zero, n_iterations, one, iter_args)
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with ir.InsertionPoint(loop.body):
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start = func.CallOp(timer_func, [])
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call = func.CallOp(
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func,
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wrapped_func.arguments[:-num_results - 1] + loop.inner_iter_args
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)
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end = func.CallOp(timer_func, [])
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time_taken = arith.SubIOp(end, start)
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memref.StoreOp(time_taken, timer_buffer, [loop.induction_variable])
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scf.YieldOp(list(call.results))
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func.ReturnOp(loop)
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return wrapped_func
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