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""+#+##F#t#+I+(6*:+!hy)!rrforce_disable_cacheszcpp.cxx TYPE_CHECKINGmax_autotune_pointwisemax_autotune_gemmmax_autotune_gemm_backendsmax_autotune_conv_backendsmax_autotune_gemm_search_space+max_autotune_subproc_result_timeout_seconds-max_autotune_subproc_graceful_timeout_seconds.max_autotune_subproc_terminate_timeout_secondszaot_inductor.presetsz cuda.archz cuda.versionzcuda.cutlass_dirz cuda.cuda_cxxz rocm.archzrocm.ck_supported_archz rocm.ck_dirzrocm.rocm_home"check_stack_no_cycles_TESTING_ONLYsleep_sec_TESTING_ONLYz#triton.inject_relu_bug_TESTING_ONLY reorder_for_compute_comm_overlapenabled_metric_tablesztriton.debug_sync_graphztriton.debug_sync_kernelprofile_bandwidth_regexdisable_cpp_codegenztrace.save_real_tensorspre_grad_fusion_optionsexternal_matmulz'test_configs.autotune_choice_name_regexz'test_configs.autotune_choice_desc_regex)z'cpp.enable_floating_point_contract_flagpost_grad_custom_pre_passpost_grad_custom_post_pass'reorder_for_compute_comm_overlap_passesjoint_custom_post_passjoint_custom_pre_passpre_grad_custom_pass)rq!compiled_autograd_kwargs_overridefail_on_recompile_limit_hitsuppress_errors)ztorch._inductor.configztorch._dynamo.configMODULE_DEFAULTSc eZdZUdZeed<eed<dejdfde de de de ed ed e f d Z d efd Zdeded dfdZd,dZd efdZdeed efdZdeded dfdZded efdZdeded efdZd-de de d efdZd.ded dfd Zd.ded dfd!Zd"e d#e ed dfd$Z deded efd%Z!d/d&e d'e"d e#efd(Z$ded)ed e efd*Z%ded)e#e&eefd e efd+Z'y)0 ConfigFuzzera This tool makes it easy to search through config state-space with a minimal reproduction or test, either for debugging or just bug hunting. It has two entry points: - bisect, which randomly flips configs and tries to find the minimal reproduction upon failure. - fuzz_n_tuple, which tries every combination of n configs. This grows quickly as a function of n, so beware. bisect is recommended, but fuzz_n_tuple can give you peace of mind that a new config will compose with every other config. The main interface is a function factory that will return Callables to be torch.compiled. This function factory should return a test function when it's called. Said test function returns a boolean, which determines whether the ConfigFuzzer considers it a successful run or not. Throwing an exception from within the function will be considered a failure as well. # Example usage: ```python import torch._inductor.config as cfg def create_simple_test_model_gpu() -> FactoryOutputType: batch_size = 32 seq_length = 50 hidden_size = 768 def test_fn() -> bool: inp = torch.randn(batch_size, seq_length, hidden_size, device="cuda") weight = torch.randn(hidden_size, hidden_size, device="cuda") matmul_output = inp @ weight final_output = torch.nn.LayerNorm(hidden_size, device="cuda")(matmul_output) return True return test_fn fuzzer = ConfigFuzzer(cfg, create_simple_test_model_gpu, seed=2) # Test every pair of configs: results = fuzzer.fuzz_n_tuple(n, max_combinations=10000000) visualize_results(n, results) # Test random configs with bisection: ret = fuzzer.bisect(num_attempts=10) # reproduce a failing config fuzzer.reproduce( [{"triton.autotune_pointwise": ..., "coordinate_descent_tuning": ...}] ) ``` The list of known failures on inductor config are: cpp_wrapper, triton_debug_sync_graph cpp_wrapper, triton_debug_sync_kernel cpp_wrapper, disable_cpp_codegen combo_kernels, benchmark_combo_kernel, profile_bandwidth, profile_bandwidth_regex trace.enabled, trace.save_real_tensors sampleryNi config_moduletest_model_fn_factoryseedr test_timeoutctjdkrtjdy||_||_i|_||_||_|jj|_ tj||_ |J|jjtvr#t|jj|_yt#d||_y)a Args: config_module: The module containing the configs to fuzz test_model_fn_factory: Function that returns a test model, which runs and returns True if successful, or the outputs if they should be compared with eager seed: Randomness seed. default: Default values for the config. 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