    def _should_build_profiler(
        self, workloads: List[str], new_op_instance: OrderedDict
    ):
        """
        Check if we should build profilers. If we have a cached
        entry for this gemm instance, we update this gemm op's
        relevant attributes with the cached result and return False.
        """
        # We are forced to use the cache, so we skip building profilers.
        if environ.force_profiler_cache():
            return False
        target = backend.target.Target.current()

        build_profiler = True
        # Now, let's query if all of our workloads have cache entries. If that
        # is the case, it is safely to skip generating and building profilers.
        if not target.use_dummy_profiling_results():
            tmp_key = next(iter(new_op_instance.keys()))
            tmp_op = new_op_instance[tmp_key]
            build_profiler = False
            for wkl in workloads:
                exec_entry_sha1 = sha1(wkl.encode("utf-8")).hexdigest()
                query = GemmQueryEntry(
                    # 1 is subtracted from the type enum values for consistency with the existing
                    # cache databases; due to the "void" type being added to the DataType enum as
                    # the very first enum member (and shifting the values of other enum members) in
                    # https://github.com/NVIDIA/cutlass/commit/7c04f954151f606e60608061e891785fba229ae2
                    dtype_a=tmp_op.A.element.value - 1,
                    dtype_b=tmp_op.B.element.value - 1,
                    dtype_c=tmp_op.C.element.value - 1,
                    dtype_acc=tmp_op.accumulator_type().value - 1,
                    major_a=tmp_op.A.layout.value,
                    major_b=tmp_op.B.layout.value,
                    major_c=tmp_op.C.layout.value,
                    op_type=self._attrs["op"],
                    device=target._arch,
                    epilogue=tmp_op.epilogue_functor.value,
                    exec_entry_sha1=exec_entry_sha1,
                    pshape=self._attrs["permute_shape"],
                )
                cache_value = target.query_profile_cache("gemm", query.__dict__)
                if cache_value is not None and not target.force_profile():
                    _LOGGER.info(
                        f'Load profiling result for {self._attrs["name"]} '
                        f"from cache: {cache_value}",
                    )
                    best_algo, workspace, split_k = cache_value
                    self._attrs["exec_path"][wkl].algo = best_algo
                    self._attrs["workspace"] = max(self._attrs["workspace"], workspace)
                    self._attrs["split_k"] = split_k
                else:
                    # cache miss - we will have to generate and build profilers
                    build_profiler = True
        return build_profiler
Check if we should build profilers. If we have a cached
entry for this gemm instance, we update this gemm op's
relevant attributes with the cached result and return False.
python/aitemplate/compiler/ops/gemm_universal/gemm_common.py