vasilito
cb424d7448
verify-patch-sanity.py validates every active recipe .patch has internally- consistent hunk line counts — catching the 'malformed patch at line N' failure at commit/CI/preflight time instead of hours into a cook. This cycle hit that class three times (qtwaylandscanner, sddm, xwayland), each only discovered when cookbook tried to apply the patch. Running it across the repo found 29 latent malformed patches (validated against GNU patch: e.g. relibc/P3-sysv-ipc reproduces 'malformed patch at line 22'). They were harmless only because they sit in vendored recipes (baked, not re- applied) — but would fail on any version-bump re-derivation. --fix recounts the hunk headers (body untouched) and repaired all 29. Wired into build-preflight.sh (Phase 1.0D) and redbear-ci.yml, with a unit test (test-patch-sanity.sh). Skips archived/legacy trees and unvalidatable formats (empty placeholders, bare-@@ git hunks).
105 lines
4.2 KiB
Python
105 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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from mlir import ir
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from mlir.dialects import arith
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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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opt = (
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"parallelization-strategy=none"
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)
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pipeline = f"builtin.module(sparsifier{{{opt}}})"
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PassManager.parse(pipeline).run(mlir_module.operation)
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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 (
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len(module.operation.regions) == 1
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), "Expected kernel module to have only one region"
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assert (
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len(module.operation.regions[0].blocks) == 1
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), "Expected kernel module to have only one block"
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assert (
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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 nanoTime function. If nanoTime 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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nanoTime = func.FuncOp("nanoTime", ([], [i64_type]), visibility="private")
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nanoTime.attributes["llvm.emit_c_interface"] = ir.UnitAttr.get()
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return nanoTime
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def emit_benchmark_wrapped_main_func(kernel_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(kernel_func.type.results) times. This function can be used to
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create a "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([ir.ShapedType.get_dynamic_size()], 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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(kernel_func.arguments.types + [memref_of_i64_type], kernel_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(kernel_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(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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kernel_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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