30ceb783e2
The SparseTensor passes currently use opaque numbers for the CLI, despite using an enum internally. This patch exposes the enums instead of numbered items that are matched back to the enum. Fixes https://github.com/llvm/llvm-project/issues/53389 Differential Revision: https://reviews.llvm.org/D123876 Please also see: https://reviews.llvm.org/D118379 https://reviews.llvm.org/D117919
106 lines
4.2 KiB
Python
106 lines
4.2 KiB
Python
"""Common utilities that are useful for all the benchmarks."""
|
|
import numpy as np
|
|
|
|
from mlir import ir
|
|
from mlir.dialects import arith
|
|
from mlir.dialects import func
|
|
from mlir.dialects import memref
|
|
from mlir.dialects import scf
|
|
from mlir.passmanager import PassManager
|
|
|
|
|
|
def setup_passes(mlir_module):
|
|
"""Setup pass pipeline parameters for benchmark functions.
|
|
"""
|
|
opt = (
|
|
"parallelization-strategy=none"
|
|
" vectorization-strategy=none vl=1 enable-simd-index32=False"
|
|
)
|
|
pipeline = f"sparse-compiler{{{opt}}}"
|
|
PassManager.parse(pipeline).run(mlir_module)
|
|
|
|
|
|
def create_sparse_np_tensor(dimensions, number_of_elements):
|
|
"""Constructs a numpy tensor of dimensions `dimensions` that has only a
|
|
specific number of nonzero elements, specified by the `number_of_elements`
|
|
argument.
|
|
"""
|
|
tensor = np.zeros(dimensions, np.float64)
|
|
tensor_indices_list = [
|
|
[np.random.randint(0, dimension) for dimension in dimensions]
|
|
for _ in range(number_of_elements)
|
|
]
|
|
for tensor_indices in tensor_indices_list:
|
|
current_tensor = tensor
|
|
for tensor_index in tensor_indices[:-1]:
|
|
current_tensor = current_tensor[tensor_index]
|
|
current_tensor[tensor_indices[-1]] = np.random.uniform(1, 100)
|
|
return tensor
|
|
|
|
|
|
def get_kernel_func_from_module(module: ir.Module) -> func.FuncOp:
|
|
"""Takes an mlir module object and extracts the function object out of it.
|
|
This function only works for a module with one region, one block, and one
|
|
operation.
|
|
"""
|
|
assert len(module.operation.regions) == 1, \
|
|
"Expected kernel module to have only one region"
|
|
assert len(module.operation.regions[0].blocks) == 1, \
|
|
"Expected kernel module to have only one block"
|
|
assert len(module.operation.regions[0].blocks[0].operations) == 1, \
|
|
"Expected kernel module to have only one operation"
|
|
return module.operation.regions[0].blocks[0].operations[0]
|
|
|
|
|
|
def emit_timer_func() -> func.FuncOp:
|
|
"""Returns the declaration of nanoTime function. If nanoTime function is
|
|
used, the `MLIR_RUNNER_UTILS` and `MLIR_C_RUNNER_UTILS` must be included.
|
|
"""
|
|
i64_type = ir.IntegerType.get_signless(64)
|
|
nanoTime = func.FuncOp(
|
|
"nanoTime", ([], [i64_type]), visibility="private")
|
|
nanoTime.attributes["llvm.emit_c_interface"] = ir.UnitAttr.get()
|
|
return nanoTime
|
|
|
|
|
|
def emit_benchmark_wrapped_main_func(kernel_func, timer_func):
|
|
"""Takes a function and a timer function, both represented as FuncOp
|
|
objects, and returns a new function. This new function wraps the call to
|
|
the original function between calls to the timer_func and this wrapping
|
|
in turn is executed inside a loop. The loop is executed
|
|
len(kernel_func.type.results) times. This function can be used to
|
|
create a "time measuring" variant of a function.
|
|
"""
|
|
i64_type = ir.IntegerType.get_signless(64)
|
|
memref_of_i64_type = ir.MemRefType.get([-1], i64_type)
|
|
wrapped_func = func.FuncOp(
|
|
# Same signature and an extra buffer of indices to save timings.
|
|
"main",
|
|
(kernel_func.arguments.types + [memref_of_i64_type],
|
|
kernel_func.type.results),
|
|
visibility="public"
|
|
)
|
|
wrapped_func.attributes["llvm.emit_c_interface"] = ir.UnitAttr.get()
|
|
|
|
num_results = len(kernel_func.type.results)
|
|
with ir.InsertionPoint(wrapped_func.add_entry_block()):
|
|
timer_buffer = wrapped_func.arguments[-1]
|
|
zero = arith.ConstantOp.create_index(0)
|
|
n_iterations = memref.DimOp(ir.IndexType.get(), timer_buffer, zero)
|
|
one = arith.ConstantOp.create_index(1)
|
|
iter_args = list(wrapped_func.arguments[-num_results - 1:-1])
|
|
loop = scf.ForOp(zero, n_iterations, one, iter_args)
|
|
with ir.InsertionPoint(loop.body):
|
|
start = func.CallOp(timer_func, [])
|
|
call = func.CallOp(
|
|
kernel_func,
|
|
wrapped_func.arguments[:-num_results - 1] + loop.inner_iter_args
|
|
)
|
|
end = func.CallOp(timer_func, [])
|
|
time_taken = arith.SubIOp(end, start)
|
|
memref.StoreOp(time_taken, timer_buffer, [loop.induction_variable])
|
|
scf.YieldOp(list(call.results))
|
|
func.ReturnOp(loop)
|
|
|
|
return wrapped_func
|