From a0017bca98688f96511ee49f67264958ccfb8b53 Mon Sep 17 00:00:00 2001 From: Simon Guo Date: Tue, 23 Dec 2025 01:08:49 +0000 Subject: [PATCH 01/11] start code checker to avoid reward hacking --- src/code_check.py | 57 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 src/code_check.py diff --git a/src/code_check.py b/src/code_check.py new file mode 100644 index 00000000..1ba543b4 --- /dev/null +++ b/src/code_check.py @@ -0,0 +1,57 @@ + +""" +TODO: ongoing effort + +Code validity checker +- Make sure it is actually CUDA / DSL code (valid) + + +Call site: +- generation +- eval -> throw error to reject +""" + +def check_valid_kernel_code(code: str, backend: str) -> bool: + """ + Check if the provided code is valid CUDA/DSL code for the given backend. + """ + # TODO: Implement actual code validation logic + match backend: + case "cuda": + # Validate CUDA-specific syntax + pass + case "triton": + # Validate Triton-specific syntax + pass + case _: + # TO ADD MORE + # Unknown backend + return False + return True + +def check_valid_cuda(code: str) -> bool: + """ + Check if the provided code is valid CUDA/DSL code. + This is a placeholder for the actual implementation. + """ + + # TODO: Implement actual code validation logic + + # migrate from Kevin implementations + + return True + +# list torch ops that are allowed +# we probably won't allow torch operations + +# decide degree on the torch code allowed +# do we all in cuda? do we want part of it to be in cuda? + +def check_triton_code(code: str) -> bool: + """ + Check if the provided code is valid Triton code. + """ + # TODO: Implement actual code validation logic + # detect it is triton jit + # + return True \ No newline at end of file From 6d579c373cd645e6a2d55e3591aeee5afb9b3c25 Mon Sep 17 00:00:00 2001 From: Willy-Chan Date: Tue, 23 Dec 2025 00:07:55 -0500 Subject: [PATCH 02/11] prelim thunderkittens code check --- src/code_check.py | 40 ++++++++++++++++++++++++++++++++++++++++ 1 file changed, 40 insertions(+) diff --git a/src/code_check.py b/src/code_check.py index 1ba543b4..d65da483 100644 --- a/src/code_check.py +++ b/src/code_check.py @@ -11,6 +11,8 @@ - eval -> throw error to reject """ +import re + def check_valid_kernel_code(code: str, backend: str) -> bool: """ Check if the provided code is valid CUDA/DSL code for the given backend. @@ -23,6 +25,8 @@ def check_valid_kernel_code(code: str, backend: str) -> bool: case "triton": # Validate Triton-specific syntax pass + case "thunderkittens": + return check_thunderkittens_code(code) case _: # TO ADD MORE # Unknown backend @@ -54,4 +58,40 @@ def check_triton_code(code: str) -> bool: # TODO: Implement actual code validation logic # detect it is triton jit # + return True + +def check_thunderkittens_code(code: str) -> bool: + """ + Check if the provided code is valid ThunderKittens code. + + Uses the following heuristics that the code: + 1. Contains ThunderKittens-specific namespace patterns: + - "kittens::warp" or "kittens::warpgroup" or "::warpgroup::" or "::warp::" or "tma::" TODO: Get a big namespace list! + 2. Contains tile declarations: + - st_{bf/fl}<...> (shared memory tiles) + - rt_{bf/fl}<...> (register tiles) + """ + # (1) Namespace patterns to search for: if it contains a single one of these, then it's valid! + # TODO: For more complicated programs, you really want to make sure it's following the producer-consumer pattern + # - could search specifically for "producer" and "consumer" structs, or the presence of "tma::load_async" + warp_patterns = [ + r"kittens::warp\b", + r"kittens::warpgroup\b", + r"::warpgroup::", + r"::warp::" + ] + has_warp_pattern = any(re.search(pattern, code) for pattern in warp_patterns) + if not has_warp_pattern: + return False + + # (1) Check that the file actually uses tiles: st_<...> or rt_<...> + # Pattern matches: st_bf<...>, st_fl<...>, rt_bf<...>, rt_fl<...>, etc. (any type) + # TODO: we don't look for global gl here... + # Also handles namespaced versions like kittens::st_bf<...> + tile_pattern = r"(?:kittens::)?(?:st|rt)_\w+\s*<[^>]+>" + has_tiles = bool(re.search(tile_pattern, code)) + if not has_tiles: + return False + + # ALL of the above conditions must be met for this code string to pass return True \ No newline at end of file From 0ca10f37e88fffe1af9bd28d851c5e850eafe40c Mon Sep 17 00:00:00 2001 From: Willy-Chan Date: Tue, 23 Dec 2025 00:22:18 -0500 Subject: [PATCH 03/11] optional producer-consumer semantics, setup for compilation check --- src/code_check.py | 19 +++++++++++++++++-- 1 file changed, 17 insertions(+), 2 deletions(-) diff --git a/src/code_check.py b/src/code_check.py index d65da483..420b1d03 100644 --- a/src/code_check.py +++ b/src/code_check.py @@ -78,13 +78,15 @@ def check_thunderkittens_code(code: str) -> bool: r"kittens::warp\b", r"kittens::warpgroup\b", r"::warpgroup::", - r"::warp::" + r"::warp::", + r"warpgroup::", + r"warp::" ] has_warp_pattern = any(re.search(pattern, code) for pattern in warp_patterns) if not has_warp_pattern: return False - # (1) Check that the file actually uses tiles: st_<...> or rt_<...> + # (2) Check that the file actually uses tiles: st_<...> or rt_<...> # Pattern matches: st_bf<...>, st_fl<...>, rt_bf<...>, rt_fl<...>, etc. (any type) # TODO: we don't look for global gl here... # Also handles namespaced versions like kittens::st_bf<...> @@ -92,6 +94,19 @@ def check_thunderkittens_code(code: str) -> bool: has_tiles = bool(re.search(tile_pattern, code)) if not has_tiles: return False + + # # (3) Producer-Consumer semantics + # pc_patterns = [ + # r"tma::\b", + # r"load_async\b" + # ] + # has_pc_pattern = any(re.search(pattern, code) for pattern in pc_patterns) + # if not has_pc_pattern: + # return False + + passes_compilation = False + # TODO: Try compilation here and return false if it fails + # ALL of the above conditions must be met for this code string to pass return True \ No newline at end of file From 80cb0d1730517e690c5c917594b9ad79ea93ff04 Mon Sep 17 00:00:00 2001 From: Willy-Chan Date: Sat, 27 Dec 2025 13:32:54 -0500 Subject: [PATCH 04/11] proposed rejection behavior --- scripts/generate_samples.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/scripts/generate_samples.py b/scripts/generate_samples.py index e47c6e87..fa1e9310 100644 --- a/scripts/generate_samples.py +++ b/scripts/generate_samples.py @@ -18,6 +18,7 @@ read_file, set_gpu_arch, ) +from src.code_check import check_thunderkittens_code """ Batch Generate Samples for Particular Level @@ -162,6 +163,14 @@ def generate_sample_single( # check LLM is able to generate custom CUDA code assert custom_kernel is not None, "Custom CUDA code generation failed" + # TODO: What should be the behavior here? Add more backends + # Validate ThunderKittens code if backend is thunderkittens + if config.backend == "thunderkittens": + if not check_thunderkittens_code(custom_kernel): + raise ValueError( + f"Generated code for problem {work.problem_id} sample {work.sample_id} doesn't seem to use valid ThunderKittens constructs" + ) + if config.verbose: print( f"Generated sample {work.sample_id} for problem {problem_number}: {problem_name}" @@ -239,7 +248,7 @@ def main(config: GenerationConfig): include_hardware = include_hardware.lower() in ["true", "1", "yes"] config.include_hardware_info = include_hardware - supported_backends = {"cuda", "triton", "cute", "tilelang"} + supported_backends = {"cuda", "triton", "cute", "tilelang", "thunderkittens"} backend = config.backend.lower() if backend not in supported_backends: raise ValueError( From 76cef066e8e81673c1734eeb186901ceee711b3f Mon Sep 17 00:00:00 2001 From: Simon Guo Date: Sat, 3 Jan 2026 23:22:18 +0000 Subject: [PATCH 05/11] an initial modularized version, needs to simplify and cleaning --- src/code_check.py | 318 ++++++++++++++++++++++++++++++++++- src/kernel_static_checker.py | 264 +++++++++++++++++++++++++++++ 2 files changed, 579 insertions(+), 3 deletions(-) create mode 100644 src/kernel_static_checker.py diff --git a/src/code_check.py b/src/code_check.py index 420b1d03..a10b5782 100644 --- a/src/code_check.py +++ b/src/code_check.py @@ -1,10 +1,20 @@ """ -TODO: ongoing effort +TODO: ongoing effort to catch reward hacking +by looking at the generated code format. + + +All checks are done during runtime Code validity checker - Make sure it is actually CUDA / DSL code (valid) +We support checking against +- torch +- cuda +- triton +- thunderkittens + Call site: - generation @@ -13,7 +23,17 @@ import re -def check_valid_kernel_code(code: str, backend: str) -> bool: + +# you can configure this! we have suggestions +DEFAULT_FORBIDDEN_GROUPS = [] # def not allowed +DEFAULT_WARNING_GROUPS = [] # might be valid but we want to warn + +def check_valid_kernel_code( + code: str, + backend: str, + precision: str, # fp16, fp32, bf16 + forbidden_groups: list[str] = DEFAULT_FORBIDDEN_GROUPS, + warning_groups: list[str] = DEFAULT_WARNING_GROUPS) -> bool: """ Check if the provided code is valid CUDA/DSL code for the given backend. """ @@ -60,6 +80,17 @@ def check_triton_code(code: str) -> bool: # return True + + + +# some notes here +# # +# What prevents cheating? + +# Enforcement happens mainly through Triton's compiler: @triton.jit functions cannot contain PyTorch operations – they'll fail to compile. This prevents high-level API usage in kernels. We add wrapper-side checks too. For testing, we rely on random inputs and subprocess execution. However, we trust the LLM-generated test harness itself—we don't statically analyze it for cheating. + +# The compositional validation matters: individual kernel correctness doesn't guarantee the full pipeline works. Integration exposes shape mismatches, accumulating errors, and semantic misunderstandings that individual tests miss. + def check_thunderkittens_code(code: str) -> bool: """ Check if the provided code is valid ThunderKittens code. @@ -109,4 +140,285 @@ def check_thunderkittens_code(code: str) -> bool: # ALL of the above conditions must be met for this code string to pass - return True \ No newline at end of file + return True + + +### from ohter projects + + + +# from kevin this is my reward function hacks +# we will strip part of the code to use generic checker functions +def reward_func(queries, prompts, labels, metadata, external_model=False): + worker_id = metadata.get("rank", 0) + global_step = metadata.get("step", 10000) + refinement_step = metadata.get("refinement_step", 0) + train = metadata.get("train", True) # Get the train flag from metadata + + sample_indices = metadata.get("sample_indices", [0] * len(queries)) # Extract refinement indices with default + print(f"[Reward {worker_id}] Refinement Step {refinement_step} Sample Indices: {sample_indices}") + print(f"[Reward {worker_id}] Train mode: {train}") + labels = [json.loads(label) for label in labels] + ref_arch_src = [label["ref_arch_src"] for label in labels] + baseline_runtime = [label["baseline_runtime"] for label in labels] + level = [label["level"] for label in labels] + task_id = [label["task_id"] for label in labels] + output_dir = labels[0]["output_dir"] + base_process_index = worker_id + if not external_model: + parse_pattern = r".*?.*?```(.*?)```.*?$" + else: + parse_pattern = r"```(.*?)```" + verbose = False + + eval_cache_dir = f"{output_dir}/eval_cache" + os.makedirs(eval_cache_dir, exist_ok=True) + + # Initialize result storage + rewards = [0.0] * len(queries) + tool_answers = [""] * len(queries) + + def save_output_entry(entry): + arch_output_dir = f"{output_dir}/step_{global_step}" + arch_output_path = f"{arch_output_dir}/device_{base_process_index}.json" + os.makedirs(arch_output_dir, exist_ok=True) + + # Read existing data or create new array + data = [] + if os.path.exists(arch_output_path): + try: + with open(arch_output_path, 'r') as f: + data = json.loads(f.read()) + except json.JSONDecodeError: + pass + + # Append and write back + data.append(entry) + with open(arch_output_path, 'w') as f: + json.dump(data, f, indent=2) + + def handle_parsing_error(idx, content, error_type, error_msg, reward=0.0): + min_length, max_length = 0, 0 # Set appropriate values or make these parameters + len_reward = calculate_length_reward(False, len(content), min_length, max_length) + rewards[idx] = reward + + # Create an entry for the error + eval_result = KernelExecResult() + eval_result.metadata = register_and_format_exception(error_type, error_msg, eval_result.metadata) + entry = _create_entry( + level[idx], task_id[idx], global_step, base_process_index, + eval_result, baseline_runtime[idx], reward, prompts[idx], + content, min_length, max_length, error_type, error_msg, + len_reward, 0, refinement_step=refinement_step, + sample_index=sample_indices[idx] # Add refinement index + ) + eval_result_dict = response_for_kernel_eval(eval_result, external_model) + + # Save the entry + save_output_entry(entry) + return eval_result_dict # Parsing failed + + # Parse completions and prepare valid items for processing + valid_items = [] + for idx, (prompt, completion, ref_arch, base_runtime, ind_level, id) in enumerate(zip(prompts, queries, ref_arch_src, baseline_runtime, level, task_id)): + # Create a unique process index for each task + unique_process_index = f"{base_process_index}_{idx}" + eval_cache_path = f"{eval_cache_dir}/eval_results_{unique_process_index}.json" + + # Parse the completion to extract CUDA kernel + matches = re.match(parse_pattern, completion, re.DOTALL) + if not matches: # prompt comes with 2 codeblocks + print(f"[Reward {unique_process_index} EXIT] had no match") + eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", "Parsing failed. No valid code block or thinking too long.", 0.0) + tool_answers[idx] = eval_result_dict + continue + + # Extract and clean up the CUDA kernel code + custom_cuda = matches.group(1).strip() + for code_type in ["python", "cpp", "cuda"]: + if custom_cuda.startswith(code_type): + custom_cuda = custom_cuda[len(code_type):].strip() + + # Verify the CUDA kernel looks valid + if ("__global__" not in custom_cuda) or ("load_inline(" not in custom_cuda): + print(f"[Reward {unique_process_index} EXIT] output has no cuda kernel") + eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", "Parsing failed. Has no valid CUDA kernel.", 0.0) + tool_answers[idx] = eval_result_dict + continue + + # Verify the CUDA kernel does not use torch.nn (except for allowed utility functions) + # Split code into lines and filter out comments + code_lines = [line for line in custom_cuda.split('\n') if not line.strip().startswith('#')] + non_comment_code = '\n'.join(code_lines) + + # Check for disallowed nn patterns + if re.search( + r'nn\.(?!(Module|parameter|Parameter|ParameterList|ParameterDict|ModuleList|ModuleDict|init)\b)', + non_comment_code + ): + print(f"[Reward {unique_process_index} EXIT] output has disallowed nn. in custom cuda") + eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", + "Parsing failed. You're not allowed to use torch.nn in the custom CUDA kernel (except for containers, nn.Parameter, and nn.init).", 0.0) + tool_answers[idx] = eval_result_dict + continue + + # List of disallowed torch functions + disallowed_torch_functions = [ + "torch.conv1d", "torch.conv2d", "torch.conv3d", + "torch.conv_transpose1d", "torch.conv_transpose2d", "torch.conv_transpose3d", + "torch.avg_pool1d", "torch.avg_pool2d", "torch.avg_pool3d", + "torch.max_pool1d", "torch.max_pool2d", "torch.max_pool3d", + "torch.adaptive_avg_pool1d", "torch.adaptive_avg_pool2d", "torch.adaptive_avg_pool3d", + "torch.adaptive_max_pool1d", "torch.adaptive_max_pool2d", "torch.adaptive_max_pool3d", + "torch.relu", "torch.hardtanh", "torch.elu", "torch.selu", + "torch.leaky_relu", "torch.gelu", "torch.softsign", "torch.softplus", + "torch.softmax", "torch.log_softmax", "torch.tanh", "torch.sigmoid", + "torch.hardsigmoid", "torch.silu", "torch.mish", + "torch.batch_norm", "torch.group_norm", "torch.layer_norm", + "torch.instance_norm", "torch.rms_norm", "torch.normalize", "torch.linear", + "torch.cross_entropy", "torch.kl_div", "torch.mse_loss", + "torch.huber_loss", "torch.triplet_margin_loss", "torch.cosine_similarity", + "torch.logsumexp", "torch.log_softmax", "torch.clamp", "torch.dropout" + ] + + # Create efficient regex pattern for all disallowed functions + # The pattern uses a lookahead (?=...) to ensure we only match complete function references: + # - \s*\( : followed by parentheses (function call) + # - \s : followed by whitespace (end of identifier) + # - $ : at the end of a line + disallowed_pattern = r'\b(' + '|'.join(re.escape(func) for func in disallowed_torch_functions) + r')(?=\s*\(|\s|$)' + + # Single regex search instead of multiple string checks + match = re.search(disallowed_pattern, non_comment_code) + if match: + print(f"[Reward {unique_process_index} EXIT] output uses disallowed function by calling nn.Functional from torch: {match.group(0)}") + eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", + f"Parsing failed. You're not allowed to use torch.nn.functional in the custom CUDA kernel by calling {match.group(0)}.", 0.0) + tool_answers[idx] = eval_result_dict + continue + + # If we reach here, parsing was successful - prepare item data + item_data = { + "prompt": prompt, + "completion": completion, + "ref_arch": ref_arch, + "baseline_runtime": base_runtime, + "ind_level": ind_level, + "id": id, + "process_index": unique_process_index, + "current_step": global_step, + "refinement_step": refinement_step, + "sample_index": sample_indices[idx], # Add refinement index + "device": base_process_index, + "output_dir": output_dir, + "eval_cache_path": eval_cache_path, + "custom_cuda": custom_cuda + } + valid_items.append((idx, item_data)) + + # Process valid items if any exist + if valid_items: + + # Determine number of workers based on available items + max_workers = min(len(valid_items), MAX_CPU) + print(f"[Reward {worker_id}] Running with {max_workers} workers for {len(valid_items)} items") + + with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor: + # Submit tasks + futures = [] + for original_idx, item in valid_items: + future = executor.submit(_process_single_item, item, verbose, train, external_model) + futures.append((future, original_idx)) + + # Process results + for future, original_idx in futures: + try: + reward, eval_result_dict, entry = future.result() + rewards[original_idx] = reward + tool_answers[original_idx] = eval_result_dict + + # Save the entry using the helper function + save_output_entry(entry) + + except Exception as exc: + print(f"[Reward {worker_id}_{original_idx}] Processing item generated an exception: {exc}") + else: + print(f"[Reward {worker_id}] No valid items to process") + + # Clean up temp files + for idx, item_data in valid_items: + try: + if os.path.exists(item_data["eval_cache_path"]): + os.unlink(item_data["eval_cache_path"]) + except: + pass + + print(f"[Reward {worker_id} Refinement Step {refinement_step}] Finished processing {len(valid_items)} items") + print(f"[Reward {worker_id} Refinement Step {refinement_step}] Rewards: {rewards}") + + return rewards, tool_answers + + +# from another project +def check_for_cheating(kernel_code: str) -> bool: + """ + Check if the kernel code is cheating using Kevin-32B paper rules. + + Kevin paper identifies three cheating patterns that receive zero reward: + 1. PyTorch wrapping: Using torch.nn or torch.nn.functional + 2. Try-except fallback: Wrapping incomplete CUDA in exception handlers + 3. Inheritance bypass: Using 'pass' to inherit from reference + + Reference: arXiv:2507.11948 "Kevin: Multi-Turn RL for Generating CUDA Kernels" + """ + # Kevin Rule 1: PyTorch wrapping detection + # Zero reward for kernels containing torch.nn or torch.nn.functional + pytorch_patterns = [ + "torch.nn.functional", + "torch.nn.", + "F.conv", "F.linear", "F.relu", "F.gelu", "F.softmax", + "F.batch_norm", "F.layer_norm", "F.dropout", + "nn.functional.", + ] + for pattern in pytorch_patterns: + if pattern in kernel_code: + return True + + # Kevin Rule 2: Try-except fallback pattern detection + # Models wrap incomplete CUDA in exception handlers that fall back to PyTorch + # Zero reward for kernels containing try/except + if "try:" in kernel_code or "except:" in kernel_code or "except " in kernel_code: + return True + + # Kevin Rule 3: Inheritance bypass detection + # Model inherits from reference using 'pass' + # Zero reward for kernels containing pass statements + # Be careful to not match 'passed' or similar words + import re + if re.search(r'\bpass\b', kernel_code): + return True + + # Additional check: no custom kernel implementation at all + has_triton_kernel = "@triton.jit" in kernel_code or "@triton.autotune" in kernel_code + has_cuda_kernel = "load_inline" in kernel_code or "cpp_extension" in kernel_code + has_cute_kernel = "cute::" in kernel_code or "from cutlass" in kernel_code + has_tilelang = "@T.prim_func" in kernel_code or "tvm.build" in kernel_code + + has_custom_implementation = any([ + has_triton_kernel, + has_cuda_kernel, + has_cute_kernel, + has_tilelang + ]) + + # If no custom implementation, check for direct torch ops + if not has_custom_implementation: + torch_ops = [ + "torch.mm", "torch.bmm", "torch.matmul", + "torch.conv", "torch.einsum", + ] + for op in torch_ops: + if op in kernel_code: + return True + + return False \ No newline at end of file diff --git a/src/kernel_static_checker.py b/src/kernel_static_checker.py new file mode 100644 index 00000000..df5939c1 --- /dev/null +++ b/src/kernel_static_checker.py @@ -0,0 +1,264 @@ +""" +Kernel Static Checker - Pattern-based validation for GPU kernel code. + +Detects reward hacking patterns in generated kernel code: +- Bypass hacks (PyTorch wrapping, try-except fallback, inheritance bypass) +- Forbidden high-level torch operations +- Backend implementation requirements (CUDA, Triton, ThunderKittens) + +Usage: + result = validate_kernel(code, backend="cuda", forbidden=["pytorch_wrap", "forbidden_ops"]) + if not result["valid"]: + for error in result["errors"]: + print(f"ERROR: {error}") +""" + +import re +from typing import List, Tuple, Dict, Any, Optional + +# ============================================================================= +# GENERIC PATTERNS (apply to all backends) +# ============================================================================= + +# PyTorch wrapping patterns (Kevin paper: zero reward for torch.nn/torch.nn.functional) +PYTORCH_WRAP_PATTERNS = [ + r"torch\.nn\.functional", + r"\bnn\.functional\.", + r"\bF\.(conv|linear|relu|gelu|softmax|batch_norm|layer_norm|dropout)", +] + +# Disallowed nn patterns (allow only containers, Parameter, init) +DISALLOWED_NN_PATTERN = r'nn\.(?!(Module|parameter|Parameter|ParameterList|ParameterDict|ModuleList|ModuleDict|init)\b)' + +# Try-except patterns (Kevin paper: zero reward for try/except fallback) +TRY_EXCEPT_PATTERNS = [r"\btry\s*:", r"\bexcept\s*:", r"\bexcept\s+\w+"] + +# Pass statement (Kevin paper: zero reward for inheritance bypass) +PASS_PATTERN = r"\bpass\b" + +# Forbidden high-level torch operations +FORBIDDEN_TORCH_OPS = [ + # Convolutions + "torch.conv1d", "torch.conv2d", "torch.conv3d", + "torch.conv_transpose1d", "torch.conv_transpose2d", "torch.conv_transpose3d", + # Pooling + "torch.avg_pool1d", "torch.avg_pool2d", "torch.avg_pool3d", + "torch.max_pool1d", "torch.max_pool2d", "torch.max_pool3d", + "torch.adaptive_avg_pool1d", "torch.adaptive_avg_pool2d", "torch.adaptive_avg_pool3d", + "torch.adaptive_max_pool1d", "torch.adaptive_max_pool2d", "torch.adaptive_max_pool3d", + # Activations + "torch.relu", "torch.hardtanh", "torch.elu", "torch.selu", + "torch.leaky_relu", "torch.gelu", "torch.softsign", "torch.softplus", + "torch.softmax", "torch.log_softmax", "torch.tanh", "torch.sigmoid", + "torch.hardsigmoid", "torch.silu", "torch.mish", + # Normalization + "torch.batch_norm", "torch.group_norm", "torch.layer_norm", + "torch.instance_norm", "torch.rms_norm", "torch.normalize", + # Linear & Loss + "torch.linear", "torch.cross_entropy", "torch.kl_div", "torch.mse_loss", + "torch.huber_loss", "torch.triplet_margin_loss", "torch.cosine_similarity", + # Others + "torch.logsumexp", "torch.clamp", "torch.dropout", +] + +# ============================================================================= +# BACKEND-SPECIFIC PATTERNS +# ============================================================================= + +# CUDA patterns +CUDA_KERNEL_REQUIRED = ["__global__", "load_inline"] + +# Triton patterns +TRITON_JIT_PATTERN = r"@triton\.(jit|autotune)" +TRITON_OPS_PATTERN = r"\btl\.\w+" + +# ThunderKittens patterns +TK_WARP_PATTERNS = [ + r"kittens::warp\b", r"kittens::warpgroup\b", + r"::warpgroup::", r"::warp::", r"warpgroup::", r"warp::" +] +TK_TILE_PATTERN = r"(?:kittens::)?(?:st|rt)_\w+\s*<[^>]+>" + +# ============================================================================= +# TODO: Future checks from DeepReinforce blog +# ============================================================================= +# STREAM_PATTERNS = [r"torch\.cuda\.Stream\s*\(", r"with\s+torch\.cuda\.stream\s*\("] +# THREAD_PATTERNS = [r"threading\.Thread\s*\(", r"multiprocessing\.Process\s*\("] +# LAZY_EVAL_PATTERNS = [r"torch\.Tensor\._make_subclass", r"def\s+__new__\s*\("] +# MONKEY_PATCH_PATTERNS = [r"torch\.cuda\.Event\.elapsed_time\s*="] +# DTYPE_CONVERSION_PATTERNS = [r"\.half\s*\(\)", r"\.bfloat16\s*\(\)", r"\.float16\s*\(\)"] +# AUTOCAST_PATTERNS = [r"torch\.autocast", r"torch\.cuda\.amp\.autocast"] + +# ============================================================================= +# CHECK FUNCTIONS +# ============================================================================= + +def _strip_comments(code: str) -> str: + """Remove # and // comments from code.""" + lines = [] + for line in code.split('\n'): + if '#' in line: + line = line[:line.index('#')] + if '//' in line: + line = line[:line.index('//')] + lines.append(line) + return '\n'.join(lines) + + +def check_pytorch_wrap(code: str) -> Tuple[bool, str]: + """Check for PyTorch API wrapping (torch.nn.functional, F.*, etc.).""" + code = _strip_comments(code) + for pattern in PYTORCH_WRAP_PATTERNS: + if re.search(pattern, code): + return (True, "Uses torch.nn.functional or F.* - not a custom kernel") + if re.search(DISALLOWED_NN_PATTERN, code): + return (True, "Uses disallowed torch.nn (only containers, Parameter, init allowed)") + return (False, "") + + +def check_try_except(code: str) -> Tuple[bool, str]: + """Check for try-except blocks (potential PyTorch fallback).""" + code = _strip_comments(code) + for pattern in TRY_EXCEPT_PATTERNS: + if re.search(pattern, code): + return (True, "Contains try-except block (potential fallback)") + return (False, "") + + +def check_pass_stmt(code: str) -> Tuple[bool, str]: + """Check for pass statements (inheritance bypass).""" + code = _strip_comments(code) + if re.search(PASS_PATTERN, code): + return (True, "Contains 'pass' statement (inheritance bypass)") + return (False, "") + + +def check_forbidden_ops(code: str) -> Tuple[bool, str]: + """Check for forbidden high-level torch operations.""" + code = _strip_comments(code) + pattern = r'\b(' + '|'.join(re.escape(f) for f in FORBIDDEN_TORCH_OPS) + r')(?=\s*\(|\s|$)' + match = re.search(pattern, code) + if match: + return (True, f"Uses forbidden operation: {match.group(0)}") + return (False, "") + + +def check_cuda_impl(code: str) -> Tuple[bool, str]: + """Check for valid CUDA kernel implementation.""" + code = _strip_comments(code) + missing = [p for p in CUDA_KERNEL_REQUIRED if p not in code] + if missing: + return (True, f"Missing CUDA requirements: {', '.join(missing)}") + return (False, "") + + +def check_triton_impl(code: str) -> Tuple[bool, str]: + """Check for valid Triton kernel implementation.""" + code = _strip_comments(code) + if not re.search(TRITON_JIT_PATTERN, code): + return (True, "Missing @triton.jit or @triton.autotune") + if not re.search(TRITON_OPS_PATTERN, code): + return (True, "No tl.* operations found in Triton kernel") + return (False, "") + + +def check_tk_impl(code: str) -> Tuple[bool, str]: + """Check for valid ThunderKittens kernel implementation.""" + code = _strip_comments(code) + if not any(re.search(p, code) for p in TK_WARP_PATTERNS): + return (True, "Missing ThunderKittens warp/warpgroup patterns") + if not re.search(TK_TILE_PATTERN, code): + return (True, "Missing ThunderKittens tile declarations (st_*/rt_*)") + return (False, "") + + +# ============================================================================= +# CHECK REGISTRY +# ============================================================================= + +# All available checks +CHECK_FUNCTIONS: Dict[str, callable] = { + # Bypass hacks + "pytorch_wrap": check_pytorch_wrap, + "try_except": check_try_except, + "pass_stmt": check_pass_stmt, + # Forbidden operations + "forbidden_ops": check_forbidden_ops, + # Implementation checks (backend-specific) + "cuda_impl": check_cuda_impl, + "triton_impl": check_triton_impl, + "tk_impl": check_tk_impl, +} + +# Suggested presets +STRICT_CHECKS = ["pytorch_wrap", "try_except", "pass_stmt", "forbidden_ops"] +WARNING_CHECKS = [] # Add checks here that should warn but not fail + +# Backend implementation check mapping +BACKEND_IMPL_CHECK = { + "cuda": "cuda_impl", + "triton": "triton_impl", + "thunderkittens": "tk_impl", +} + +# ============================================================================= +# MAIN ENTRY POINT +# ============================================================================= + +def validate_kernel_static( + code: str, + backend: Optional[str] = None, + forbidden: Optional[List[str]] = None, + warnings: Optional[List[str]] = None, +) -> Dict[str, Any]: + """ + Validate kernel code against configurable check groups. + + Args: + code: Kernel source code + backend: "cuda", "triton", or "thunderkittens" (adds impl check) + forbidden: List of check names that cause errors (default: STRICT_CHECKS) + warnings: List of check names that cause warnings (default: WARNING_CHECKS) + + Returns: + { + "valid": bool, # True if no forbidden checks failed + "errors": List[str], # Error messages + "warnings": List[str], # Warning messages + "details": Dict[str, bool] # Per-check results + } + """ + if forbidden is None: + forbidden = STRICT_CHECKS.copy() + if warnings is None: + warnings = WARNING_CHECKS.copy() + + # Add backend implementation check if specified + if backend and backend in BACKEND_IMPL_CHECK: + impl_check = BACKEND_IMPL_CHECK[backend] + if impl_check not in forbidden: + forbidden.append(impl_check) + + errors = [] + warnings_list = [] + details = {} + + # Run all requested checks + all_checks = set(forbidden + warnings) + for check_name in all_checks: + if check_name not in CHECK_FUNCTIONS: + continue + has_issue, msg = CHECK_FUNCTIONS[check_name](code) + details[check_name] = has_issue + if has_issue: + if check_name in forbidden: + errors.append(msg) + elif check_name in warnings: + warnings_list.append(msg) + + return { + "valid": len(errors) == 0, + "errors": errors, + "warnings": warnings_list, + "details": details, + } From a933c49e34fc80e0b2eb7dbff97ae8c3ae018ee3 Mon Sep 17 00:00:00 2001 From: Simon Guo Date: Sun, 4 Jan 2026 16:32:50 +0000 Subject: [PATCH 06/11] clearer organization for static reward hack checking --- src/code_check.py | 1 + src/code_check_old.py | 423 +++++ src/kernel_static_checker.py | 408 ++-- src/unit_tests/test_static_checker.py | 270 +++ uv.lock | 2533 +++++++++++++++++++++++++ 5 files changed, 3485 insertions(+), 150 deletions(-) create mode 100644 src/code_check_old.py create mode 100644 src/unit_tests/test_static_checker.py create mode 100644 uv.lock diff --git a/src/code_check.py b/src/code_check.py index a10b5782..2c298902 100644 --- a/src/code_check.py +++ b/src/code_check.py @@ -4,6 +4,7 @@ by looking at the generated code format. +WIP this is all the code i wrote All checks are done during runtime Code validity checker diff --git a/src/code_check_old.py b/src/code_check_old.py new file mode 100644 index 00000000..39c359ed --- /dev/null +++ b/src/code_check_old.py @@ -0,0 +1,423 @@ + +""" +TODO: ongoing effort to catch reward hacking +by looking at the generated code format. + + +All checks are done during runtime + +Code validity checker +- Make sure it is actually CUDA / DSL code (valid) + +We support checking against +- torch +- cuda +- triton +- thunderkittens + + +Call site: +- generation +- eval -> throw error to reject +""" + +import re + + +# you can configure this! we have suggestions +DEFAULT_FORBIDDEN_GROUPS = [] # def not allowed +DEFAULT_WARNING_GROUPS = [] # might be valid but we want to warn + +def check_valid_kernel_code( + code: str, + backend: str, + forbidden_groups: list[str] = DEFAULT_FORBIDDEN_GROUPS, + warning_groups: list[str] = DEFAULT_WARNING_GROUPS) -> bool: + """ + Check if the provided code is valid CUDA/DSL code for the given backend. + """ + # TODO: Implement actual code validation logic + match backend: + case "cuda": + # Validate CUDA-specific syntax + pass + case "triton": + # Validate Triton-specific syntax + pass + case "thunderkittens": + return check_thunderkittens_code(code) + case _: + # TO ADD MORE + # Unknown backend + return False + return True + +def check_valid_cuda(code: str) -> bool: + """ + Check if the provided code is valid CUDA/DSL code. + This is a placeholder for the actual implementation. + """ + + # TODO: Implement actual code validation logic + + # migrate from Kevin implementations + + return True + +# list torch ops that are allowed +# we probably won't allow torch operations + +# decide degree on the torch code allowed +# do we all in cuda? do we want part of it to be in cuda? + +def check_triton_code(code: str) -> bool: + """ + Check if the provided code is valid Triton code. + """ + # TODO: Implement actual code validation logic + # detect it is triton jit + # + return True + + + + +# some notes here +# # +# What prevents cheating? + +# Enforcement happens mainly through Triton's compiler: @triton.jit functions cannot contain PyTorch operations – they'll fail to compile. This prevents high-level API usage in kernels. We add wrapper-side checks too. For testing, we rely on random inputs and subprocess execution. However, we trust the LLM-generated test harness itself—we don't statically analyze it for cheating. + +# The compositional validation matters: individual kernel correctness doesn't guarantee the full pipeline works. Integration exposes shape mismatches, accumulating errors, and semantic misunderstandings that individual tests miss. + +def check_thunderkittens_code(code: str) -> bool: + """ + Check if the provided code is valid ThunderKittens code. + + Uses the following heuristics that the code: + 1. Contains ThunderKittens-specific namespace patterns: + - "kittens::warp" or "kittens::warpgroup" or "::warpgroup::" or "::warp::" or "tma::" TODO: Get a big namespace list! + 2. Contains tile declarations: + - st_{bf/fl}<...> (shared memory tiles) + - rt_{bf/fl}<...> (register tiles) + """ + # (1) Namespace patterns to search for: if it contains a single one of these, then it's valid! + # TODO: For more complicated programs, you really want to make sure it's following the producer-consumer pattern + # - could search specifically for "producer" and "consumer" structs, or the presence of "tma::load_async" + warp_patterns = [ + r"kittens::warp\b", + r"kittens::warpgroup\b", + r"::warpgroup::", + r"::warp::", + r"warpgroup::", + r"warp::" + ] + has_warp_pattern = any(re.search(pattern, code) for pattern in warp_patterns) + if not has_warp_pattern: + return False + + # (2) Check that the file actually uses tiles: st_<...> or rt_<...> + # Pattern matches: st_bf<...>, st_fl<...>, rt_bf<...>, rt_fl<...>, etc. (any type) + # TODO: we don't look for global gl here... + # Also handles namespaced versions like kittens::st_bf<...> + tile_pattern = r"(?:kittens::)?(?:st|rt)_\w+\s*<[^>]+>" + has_tiles = bool(re.search(tile_pattern, code)) + if not has_tiles: + return False + + # # (3) Producer-Consumer semantics + # pc_patterns = [ + # r"tma::\b", + # r"load_async\b" + # ] + # has_pc_pattern = any(re.search(pattern, code) for pattern in pc_patterns) + # if not has_pc_pattern: + # return False + + passes_compilation = False + # TODO: Try compilation here and return false if it fails + + + # ALL of the above conditions must be met for this code string to pass + return True + + +### from ohter projects + + + +# from kevin this is my reward function hacks +# we will strip part of the code to use generic checker functions +def reward_func(queries, prompts, labels, metadata, external_model=False): + worker_id = metadata.get("rank", 0) + global_step = metadata.get("step", 10000) + refinement_step = metadata.get("refinement_step", 0) + train = metadata.get("train", True) # Get the train flag from metadata + + sample_indices = metadata.get("sample_indices", [0] * len(queries)) # Extract refinement indices with default + print(f"[Reward {worker_id}] Refinement Step {refinement_step} Sample Indices: {sample_indices}") + print(f"[Reward {worker_id}] Train mode: {train}") + labels = [json.loads(label) for label in labels] + ref_arch_src = [label["ref_arch_src"] for label in labels] + baseline_runtime = [label["baseline_runtime"] for label in labels] + level = [label["level"] for label in labels] + task_id = [label["task_id"] for label in labels] + output_dir = labels[0]["output_dir"] + base_process_index = worker_id + if not external_model: + parse_pattern = r".*?.*?```(.*?)```.*?$" + else: + parse_pattern = r"```(.*?)```" + verbose = False + + eval_cache_dir = f"{output_dir}/eval_cache" + os.makedirs(eval_cache_dir, exist_ok=True) + + # Initialize result storage + rewards = [0.0] * len(queries) + tool_answers = [""] * len(queries) + + def save_output_entry(entry): + arch_output_dir = f"{output_dir}/step_{global_step}" + arch_output_path = f"{arch_output_dir}/device_{base_process_index}.json" + os.makedirs(arch_output_dir, exist_ok=True) + + # Read existing data or create new array + data = [] + if os.path.exists(arch_output_path): + try: + with open(arch_output_path, 'r') as f: + data = json.loads(f.read()) + except json.JSONDecodeError: + pass + + # Append and write back + data.append(entry) + with open(arch_output_path, 'w') as f: + json.dump(data, f, indent=2) + + def handle_parsing_error(idx, content, error_type, error_msg, reward=0.0): + min_length, max_length = 0, 0 # Set appropriate values or make these parameters + len_reward = calculate_length_reward(False, len(content), min_length, max_length) + rewards[idx] = reward + + # Create an entry for the error + eval_result = KernelExecResult() + eval_result.metadata = register_and_format_exception(error_type, error_msg, eval_result.metadata) + entry = _create_entry( + level[idx], task_id[idx], global_step, base_process_index, + eval_result, baseline_runtime[idx], reward, prompts[idx], + content, min_length, max_length, error_type, error_msg, + len_reward, 0, refinement_step=refinement_step, + sample_index=sample_indices[idx] # Add refinement index + ) + eval_result_dict = response_for_kernel_eval(eval_result, external_model) + + # Save the entry + save_output_entry(entry) + return eval_result_dict # Parsing failed + + # Parse completions and prepare valid items for processing + valid_items = [] + for idx, (prompt, completion, ref_arch, base_runtime, ind_level, id) in enumerate(zip(prompts, queries, ref_arch_src, baseline_runtime, level, task_id)): + # Create a unique process index for each task + unique_process_index = f"{base_process_index}_{idx}" + eval_cache_path = f"{eval_cache_dir}/eval_results_{unique_process_index}.json" + + # Parse the completion to extract CUDA kernel + matches = re.match(parse_pattern, completion, re.DOTALL) + if not matches: # prompt comes with 2 codeblocks + print(f"[Reward {unique_process_index} EXIT] had no match") + eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", "Parsing failed. No valid code block or thinking too long.", 0.0) + tool_answers[idx] = eval_result_dict + continue + + # Extract and clean up the CUDA kernel code + custom_cuda = matches.group(1).strip() + for code_type in ["python", "cpp", "cuda"]: + if custom_cuda.startswith(code_type): + custom_cuda = custom_cuda[len(code_type):].strip() + + # Verify the CUDA kernel looks valid + if ("__global__" not in custom_cuda) or ("load_inline(" not in custom_cuda): + print(f"[Reward {unique_process_index} EXIT] output has no cuda kernel") + eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", "Parsing failed. Has no valid CUDA kernel.", 0.0) + tool_answers[idx] = eval_result_dict + continue + + # Verify the CUDA kernel does not use torch.nn (except for allowed utility functions) + # Split code into lines and filter out comments + code_lines = [line for line in custom_cuda.split('\n') if not line.strip().startswith('#')] + non_comment_code = '\n'.join(code_lines) + + # Check for disallowed nn patterns + if re.search( + r'nn\.(?!(Module|parameter|Parameter|ParameterList|ParameterDict|ModuleList|ModuleDict|init)\b)', + non_comment_code + ): + print(f"[Reward {unique_process_index} EXIT] output has disallowed nn. in custom cuda") + eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", + "Parsing failed. You're not allowed to use torch.nn in the custom CUDA kernel (except for containers, nn.Parameter, and nn.init).", 0.0) + tool_answers[idx] = eval_result_dict + continue + + # List of disallowed torch functions + disallowed_torch_functions = [ + "torch.conv1d", "torch.conv2d", "torch.conv3d", + "torch.conv_transpose1d", "torch.conv_transpose2d", "torch.conv_transpose3d", + "torch.avg_pool1d", "torch.avg_pool2d", "torch.avg_pool3d", + "torch.max_pool1d", "torch.max_pool2d", "torch.max_pool3d", + "torch.adaptive_avg_pool1d", "torch.adaptive_avg_pool2d", "torch.adaptive_avg_pool3d", + "torch.adaptive_max_pool1d", "torch.adaptive_max_pool2d", "torch.adaptive_max_pool3d", + "torch.relu", "torch.hardtanh", "torch.elu", "torch.selu", + "torch.leaky_relu", "torch.gelu", "torch.softsign", "torch.softplus", + "torch.softmax", "torch.log_softmax", "torch.tanh", "torch.sigmoid", + "torch.hardsigmoid", "torch.silu", "torch.mish", + "torch.batch_norm", "torch.group_norm", "torch.layer_norm", + "torch.instance_norm", "torch.rms_norm", "torch.normalize", "torch.linear", + "torch.cross_entropy", "torch.kl_div", "torch.mse_loss", + "torch.huber_loss", "torch.triplet_margin_loss", "torch.cosine_similarity", + "torch.logsumexp", "torch.log_softmax", "torch.clamp", "torch.dropout" + ] + + # Create efficient regex pattern for all disallowed functions + # The pattern uses a lookahead (?=...) to ensure we only match complete function references: + # - \s*\( : followed by parentheses (function call) + # - \s : followed by whitespace (end of identifier) + # - $ : at the end of a line + disallowed_pattern = r'\b(' + '|'.join(re.escape(func) for func in disallowed_torch_functions) + r')(?=\s*\(|\s|$)' + + # Single regex search instead of multiple string checks + match = re.search(disallowed_pattern, non_comment_code) + if match: + print(f"[Reward {unique_process_index} EXIT] output uses disallowed function by calling nn.Functional from torch: {match.group(0)}") + eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", + f"Parsing failed. You're not allowed to use torch.nn.functional in the custom CUDA kernel by calling {match.group(0)}.", 0.0) + tool_answers[idx] = eval_result_dict + continue + + # If we reach here, parsing was successful - prepare item data + item_data = { + "prompt": prompt, + "completion": completion, + "ref_arch": ref_arch, + "baseline_runtime": base_runtime, + "ind_level": ind_level, + "id": id, + "process_index": unique_process_index, + "current_step": global_step, + "refinement_step": refinement_step, + "sample_index": sample_indices[idx], # Add refinement index + "device": base_process_index, + "output_dir": output_dir, + "eval_cache_path": eval_cache_path, + "custom_cuda": custom_cuda + } + valid_items.append((idx, item_data)) + + # Process valid items if any exist + if valid_items: + + # Determine number of workers based on available items + max_workers = min(len(valid_items), MAX_CPU) + print(f"[Reward {worker_id}] Running with {max_workers} workers for {len(valid_items)} items") + + with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor: + # Submit tasks + futures = [] + for original_idx, item in valid_items: + future = executor.submit(_process_single_item, item, verbose, train, external_model) + futures.append((future, original_idx)) + + # Process results + for future, original_idx in futures: + try: + reward, eval_result_dict, entry = future.result() + rewards[original_idx] = reward + tool_answers[original_idx] = eval_result_dict + + # Save the entry using the helper function + save_output_entry(entry) + + except Exception as exc: + print(f"[Reward {worker_id}_{original_idx}] Processing item generated an exception: {exc}") + else: + print(f"[Reward {worker_id}] No valid items to process") + + # Clean up temp files + for idx, item_data in valid_items: + try: + if os.path.exists(item_data["eval_cache_path"]): + os.unlink(item_data["eval_cache_path"]) + except: + pass + + print(f"[Reward {worker_id} Refinement Step {refinement_step}] Finished processing {len(valid_items)} items") + print(f"[Reward {worker_id} Refinement Step {refinement_step}] Rewards: {rewards}") + + return rewards, tool_answers + + +# from another project +def check_for_cheating(kernel_code: str) -> bool: + """ + Check if the kernel code is cheating using Kevin-32B paper rules. + + Kevin paper identifies three cheating patterns that receive zero reward: + 1. PyTorch wrapping: Using torch.nn or torch.nn.functional + 2. Try-except fallback: Wrapping incomplete CUDA in exception handlers + 3. Inheritance bypass: Using 'pass' to inherit from reference + + Reference: arXiv:2507.11948 "Kevin: Multi-Turn RL for Generating CUDA Kernels" + """ + # Kevin Rule 1: PyTorch wrapping detection + # Zero reward for kernels containing torch.nn or torch.nn.functional + pytorch_patterns = [ + "torch.nn.functional", + "torch.nn.", + "F.conv", "F.linear", "F.relu", "F.gelu", "F.softmax", + "F.batch_norm", "F.layer_norm", "F.dropout", + "nn.functional.", + ] + for pattern in pytorch_patterns: + if pattern in kernel_code: + return True + + # Kevin Rule 2: Try-except fallback pattern detection + # Models wrap incomplete CUDA in exception handlers that fall back to PyTorch + # Zero reward for kernels containing try/except + if "try:" in kernel_code or "except:" in kernel_code or "except " in kernel_code: + return True + + # Kevin Rule 3: Inheritance bypass detection + # Model inherits from reference using 'pass' + # Zero reward for kernels containing pass statements + # Be careful to not match 'passed' or similar words + import re + if re.search(r'\bpass\b', kernel_code): + return True + + # Additional check: no custom kernel implementation at all + has_triton_kernel = "@triton.jit" in kernel_code or "@triton.autotune" in kernel_code + has_cuda_kernel = "load_inline" in kernel_code or "cpp_extension" in kernel_code + has_cute_kernel = "cute::" in kernel_code or "from cutlass" in kernel_code + has_tilelang = "@T.prim_func" in kernel_code or "tvm.build" in kernel_code + + has_custom_implementation = any([ + has_triton_kernel, + has_cuda_kernel, + has_cute_kernel, + has_tilelang + ]) + + # If no custom implementation, check for direct torch ops + if not has_custom_implementation: + torch_ops = [ + "torch.mm", "torch.bmm", "torch.matmul", + "torch.conv", "torch.einsum", + ] + for op in torch_ops: + if op in kernel_code: + return True + + return False \ No newline at end of file diff --git a/src/kernel_static_checker.py b/src/kernel_static_checker.py index df5939c1..75f4da98 100644 --- a/src/kernel_static_checker.py +++ b/src/kernel_static_checker.py @@ -1,45 +1,120 @@ """ Kernel Static Checker - Pattern-based validation for GPU kernel code. -Detects reward hacking patterns in generated kernel code: +The goal is flag reward hacking patterns (both strictly prohibited and possible ones). +through statically examining the code. + +Warning: This list is by no means complete and nor this is not a replacement for runtime checks. +We welcome feedback and contributions as community find new ways of hacks. + - Bypass hacks (PyTorch wrapping, try-except fallback, inheritance bypass) -- Forbidden high-level torch operations -- Backend implementation requirements (CUDA, Triton, ThunderKittens) +- Disallow some high-level torch operations (depends on the settings) +- Backend implementation requirements, that CUDA or DSL features must be used Usage: - result = validate_kernel(code, backend="cuda", forbidden=["pytorch_wrap", "forbidden_ops"]) - if not result["valid"]: - for error in result["errors"]: - print(f"ERROR: {error}") + result = validate_kernel_static(code, backend="cuda") + will return a tuple (valid, errors, warnings) """ import re -from typing import List, Tuple, Dict, Any, Optional +from typing import List, Tuple, Dict, Any, Optional, Callable + +def _strip_comments(code: str) -> str: + """Remove # and // comments from code.""" + lines = [] + for line in code.split('\n'): + if '#' in line: + line = line[:line.index('#')] + if '//' in line: + line = line[:line.index('//')] + lines.append(line) + return '\n'.join(lines) + # ============================================================================= -# GENERIC PATTERNS (apply to all backends) +# BYPASS CHECKS - Strictly Prohibited +# some of this is from Kevin RL Paper (arxiv:2507.11948) # ============================================================================= -# PyTorch wrapping patterns (Kevin paper: zero reward for torch.nn/torch.nn.functional) +# --- Try-Except Fallback --- +# Rationale: Models wrap incomplete CUDA in exception handlers that fall back to PyTorch. +# This allows them to pass tests without actually implementing the kernel. +TRY_EXCEPT_PATTERNS = [r"\btry\s*:", r"\bexcept\s*:", r"\bexcept\s+\w+"] + +# --- Pass Statement / Inheritance Bypass --- +# Rationale: Model inherits from reference class and uses 'pass' to do nothing, +# effectively just calling the parent implementation. +PASS_PATTERN = r"\bpass\b" + +def check_code_bypass(code: str) -> Tuple[bool, str]: + """ + Check for code bypass patterns (strictly prohibited). + + Detects two Kevin paper bypass techniques: + 1. Try-Except Fallback: Models wrap incomplete CUDA in exception handlers + that fall back to PyTorch when custom code fails. + 2. Pass Statement: Models inherit from reference and use 'pass' to do nothing, + effectively calling parent implementation. + + Both receive zero reward per Kevin paper rules. + Uses word boundary for 'pass' to avoid matching 'passed', 'bypass', etc. + """ + code = _strip_comments(code) + + # Check for try-except fallback + for pattern in TRY_EXCEPT_PATTERNS: + if re.search(pattern, code): + return (True, "Contains try-except block (potential fallback bypass)") + + # Check for pass statement + if re.search(PASS_PATTERN, code): + return (True, "Contains 'pass' statement (inheritance bypass)") + + return (False, "") + +# Since KernelBench problems uses PyTorch as a reference +# sometimes model chose to wrap PyTorch high-level APIs instead of implementing a custom kernel. +# Depends on the setting that is used, (configure this in the STRICT and WARNING checks) +# some left model replaces some ops with custom kernels in required backend, +# others require the model to write the whole program in custom kernels. + +# --- PyTorch Wrapping --- +# Rationale: Using torch.nn.functional or F.* means the model is just wrapping +# PyTorch high-level APIs instead of implementing a custom kernel. +# This defeats the purpose of kernel generation. PYTORCH_WRAP_PATTERNS = [ r"torch\.nn\.functional", r"\bnn\.functional\.", r"\bF\.(conv|linear|relu|gelu|softmax|batch_norm|layer_norm|dropout)", ] +# Allow only containers (Module, ModuleList, etc.), Parameter, and init +PYTORCH_DISALLOWED_NN_PATTERN = r'torch\.nn\.(?!(Module|parameter|Parameter|ParameterList|ParameterDict|ModuleList|ModuleDict|init)\b)' -# Disallowed nn patterns (allow only containers, Parameter, init) -DISALLOWED_NN_PATTERN = r'nn\.(?!(Module|parameter|Parameter|ParameterList|ParameterDict|ModuleList|ModuleDict|init)\b)' - -# Try-except patterns (Kevin paper: zero reward for try/except fallback) -TRY_EXCEPT_PATTERNS = [r"\btry\s*:", r"\bexcept\s*:", r"\bexcept\s+\w+"] +def check_pytorch_wrap(code: str) -> Tuple[bool, str]: + """ + Check for PyTorch API wrapping (torch.nn.functional, F.*, etc.). + + Kevin paper rule: Zero reward for kernels containing torch.nn or torch.nn.functional. + These indicate the model is wrapping PyTorch instead of writing custom kernels. + """ + code = _strip_comments(code) + for pattern in PYTORCH_WRAP_PATTERNS: + if re.search(pattern, code): + return (True, "Uses torch.nn.functional or F.* - not a custom kernel") + # NOTE: should we make this strict or optional? + if re.search(PYTORCH_DISALLOWED_NN_PATTERN, code): + return (True, "Uses disallowed torch.nn (only containers, Parameter, init allowed)") + return (False, "") -# Pass statement (Kevin paper: zero reward for inheritance bypass) -PASS_PATTERN = r"\bpass\b" -# Forbidden high-level torch operations -FORBIDDEN_TORCH_OPS = [ +# --- Forbidden Torch Computational Operations --- +# Rationale: These are high-level PyTorch ops that conduct computation +# Using them directly defeat the purpose writing custom CUDA kernels. +TORCH_COMPUTATION_OPS = [ + # Matrix operations + "torch.mm", "torch.bmm", "torch.matmul", "torch.einsum", # Convolutions - "torch.conv1d", "torch.conv2d", "torch.conv3d", + "torch.conv1d", "torch.conv2d", "torch.conv3d", "torch.conv", "torch.conv_transpose1d", "torch.conv_transpose2d", "torch.conv_transpose3d", # Pooling "torch.avg_pool1d", "torch.avg_pool2d", "torch.avg_pool3d", @@ -59,206 +134,239 @@ "torch.huber_loss", "torch.triplet_margin_loss", "torch.cosine_similarity", # Others "torch.logsumexp", "torch.clamp", "torch.dropout", -] +] # hopefully didn't miss any other ones + +def check_torch_computation_ops(code: str) -> Tuple[bool, str]: + """ + Check for high-level torch computation operations. + + Note: This check is optional/taste-based. Some projects allow partial + torch usage, others require pure custom kernels. Configure as needed. + """ + code = _strip_comments(code) + pattern = r'\b(' + '|'.join(re.escape(f) for f in TORCH_COMPUTATION_OPS) + r')(?=\s*\(|\s|$)' + match = re.search(pattern, code) + if match: + return (True, f"Uses torch computation op: {match.group(0)}") + return (False, "") # ============================================================================= -# BACKEND-SPECIFIC PATTERNS +# Backend Specific Checks # ============================================================================= -# CUDA patterns -CUDA_KERNEL_REQUIRED = ["__global__", "load_inline"] +# <========= CUDA CHECKS =========> +# Rationale: Valid CUDA kernels must have __global__ (kernel definition) and +# use load_inline or cpp_extension (PyTorch's inline compilation). +CUDA_COMPILE_PATTERNS = ["load_inline", "cpp_extension"] -# Triton patterns +def check_cuda_impl(code: str) -> Tuple[bool, str]: + """ + Check for valid CUDA kernel implementation. + + Requirements: + - Must have __global__ void kernel_name (kernel definition) + - Must have load_inline or cpp_extension (PyTorch inline compilation) + """ + code = _strip_comments(code) + if "__global__" not in code: + return (True, "Missing __global__ kernel definition") + if not any(p in code for p in CUDA_COMPILE_PATTERNS): + return (True, "Missing load_inline or cpp_extension for compilation") + return (False, "") + +# <========= TRITON CHECKS =========> +# Rationale: Triton kernels are compiled from @triton.jit decorated functions. +# They must use tl.* operations (tl.load, tl.store, etc.) for actual kernel work. TRITON_JIT_PATTERN = r"@triton\.(jit|autotune)" TRITON_OPS_PATTERN = r"\btl\.\w+" -# ThunderKittens patterns +def check_triton_impl(code: str) -> Tuple[bool, str]: + """ + Check for valid Triton kernel implementation. + + Requirements: + - Must have @triton.jit or @triton.autotune decorator + - Must have tl.* operations (enforces actual Triton code, not wrapper) + + Note: Triton's compiler itself prevents PyTorch ops inside @triton.jit. + """ + code = _strip_comments(code) + if not re.search(TRITON_JIT_PATTERN, code): + return (True, "Missing @triton.jit or @triton.autotune") + if not re.search(TRITON_OPS_PATTERN, code): + return (True, "No tl.* operations found in Triton kernel") + return (False, "") + + +# <========= THUNDERKITTENS CHECKS =========> +# Rationale: ThunderKittens uses warp/warpgroup primitives and tile abstractions. +# Valid TK code must have namespace patterns and tile declarations. TK_WARP_PATTERNS = [ r"kittens::warp\b", r"kittens::warpgroup\b", r"::warpgroup::", r"::warp::", r"warpgroup::", r"warp::" ] TK_TILE_PATTERN = r"(?:kittens::)?(?:st|rt)_\w+\s*<[^>]+>" -# ============================================================================= -# TODO: Future checks from DeepReinforce blog -# ============================================================================= -# STREAM_PATTERNS = [r"torch\.cuda\.Stream\s*\(", r"with\s+torch\.cuda\.stream\s*\("] -# THREAD_PATTERNS = [r"threading\.Thread\s*\(", r"multiprocessing\.Process\s*\("] -# LAZY_EVAL_PATTERNS = [r"torch\.Tensor\._make_subclass", r"def\s+__new__\s*\("] -# MONKEY_PATCH_PATTERNS = [r"torch\.cuda\.Event\.elapsed_time\s*="] -# DTYPE_CONVERSION_PATTERNS = [r"\.half\s*\(\)", r"\.bfloat16\s*\(\)", r"\.float16\s*\(\)"] -# AUTOCAST_PATTERNS = [r"torch\.autocast", r"torch\.cuda\.amp\.autocast"] - -# ============================================================================= -# CHECK FUNCTIONS -# ============================================================================= - -def _strip_comments(code: str) -> str: - """Remove # and // comments from code.""" - lines = [] - for line in code.split('\n'): - if '#' in line: - line = line[:line.index('#')] - if '//' in line: - line = line[:line.index('//')] - lines.append(line) - return '\n'.join(lines) - - -def check_pytorch_wrap(code: str) -> Tuple[bool, str]: - """Check for PyTorch API wrapping (torch.nn.functional, F.*, etc.).""" +def check_tk_impl(code: str) -> Tuple[bool, str]: + """ + Check for valid ThunderKittens kernel implementation. + + Requirements: + - Must have warp/warpgroup namespace patterns (kittens::warp, etc.) + - Must have tile declarations (st_bf<...>, rt_fl<...>, etc.) + + TODO: Add producer-consumer pattern check for complex kernels. + """ code = _strip_comments(code) - for pattern in PYTORCH_WRAP_PATTERNS: - if re.search(pattern, code): - return (True, "Uses torch.nn.functional or F.* - not a custom kernel") - if re.search(DISALLOWED_NN_PATTERN, code): - return (True, "Uses disallowed torch.nn (only containers, Parameter, init allowed)") + if not any(re.search(p, code) for p in TK_WARP_PATTERNS): + return (True, "Missing ThunderKittens warp/warpgroup patterns") + if not re.search(TK_TILE_PATTERN, code): + return (True, "Missing ThunderKittens tile declarations (st_*/rt_*)") return (False, "") -def check_try_except(code: str) -> Tuple[bool, str]: - """Check for try-except blocks (potential PyTorch fallback).""" - code = _strip_comments(code) - for pattern in TRY_EXCEPT_PATTERNS: - if re.search(pattern, code): - return (True, "Contains try-except block (potential fallback)") - return (False, "") - +# <========= CUTE/CUTLASS CHECKS =========> +# CUTLASS uses cute:: namespace for tensor operations +# Check: https://github.com/NVIDIA/cutlass +CUTE_PATTERNS = [ + r"cute::", # cute:: namespace (CuTe library) + r"cutlass::", # cutlass:: namespace + r"from cutlass", # Python CUTLASS bindings +] -def check_pass_stmt(code: str) -> Tuple[bool, str]: - """Check for pass statements (inheritance bypass).""" +def check_cute_impl(code: str) -> Tuple[bool, str]: + """Check for valid CUTLASS/CuTe kernel implementation.""" code = _strip_comments(code) - if re.search(PASS_PATTERN, code): - return (True, "Contains 'pass' statement (inheritance bypass)") + if not any(p in code for p in ["cute::", "cutlass::", "from cutlass"]): + return (True, "Missing cute:: or cutlass:: namespace") return (False, "") -def check_forbidden_ops(code: str) -> Tuple[bool, str]: - """Check for forbidden high-level torch operations.""" - code = _strip_comments(code) - pattern = r'\b(' + '|'.join(re.escape(f) for f in FORBIDDEN_TORCH_OPS) + r')(?=\s*\(|\s|$)' - match = re.search(pattern, code) - if match: - return (True, f"Uses forbidden operation: {match.group(0)}") - return (False, "") - +# <========= TILELANG CHECKS =========> +# TileLang uses TVM's T.prim_func decorator +# https://github.com/tile-ai/tilelang +TILELANG_PATTERNS = [ + r"@T\.prim_func", # TVM primitive function decorator + r"tvm\.build", # TVM build call + r"T\.grid", # TileLang grid +] -def check_cuda_impl(code: str) -> Tuple[bool, str]: - """Check for valid CUDA kernel implementation.""" +def check_tilelang_impl(code: str) -> Tuple[bool, str]: + """Check for valid TileLang kernel implementation.""" code = _strip_comments(code) - missing = [p for p in CUDA_KERNEL_REQUIRED if p not in code] - if missing: - return (True, f"Missing CUDA requirements: {', '.join(missing)}") + if not re.search(r"@T\.prim_func", code): + return (True, "Missing @T.prim_func decorator") return (False, "") -def check_triton_impl(code: str) -> Tuple[bool, str]: - """Check for valid Triton kernel implementation.""" - code = _strip_comments(code) - if not re.search(TRITON_JIT_PATTERN, code): - return (True, "Missing @triton.jit or @triton.autotune") - if not re.search(TRITON_OPS_PATTERN, code): - return (True, "No tl.* operations found in Triton kernel") - return (False, "") - +# ============================================================================= +# TODO: Future checks from our adversarial hack PR and DeepReinforce blog +# ============================================================================= +# maybe we can group some of that later +# def check_stream_injection(code): ... # torch.cuda.Stream() +# def check_thread_injection(code): ... # threading.Thread() +# def check_lazy_eval(code): ... # torch.Tensor._make_subclass +# def check_monkey_patch(code): ... # torch.cuda.Event.elapsed_time = +# def check_precision_downgrade(code): ... # .half(), .bfloat16() # <-- this could be a soft check -def check_tk_impl(code: str) -> Tuple[bool, str]: - """Check for valid ThunderKittens kernel implementation.""" - code = _strip_comments(code) - if not any(re.search(p, code) for p in TK_WARP_PATTERNS): - return (True, "Missing ThunderKittens warp/warpgroup patterns") - if not re.search(TK_TILE_PATTERN, code): - return (True, "Missing ThunderKittens tile declarations (st_*/rt_*)") - return (False, "") +# ============================================================================= +# in the future, we can add a LM as a judge checker +# ============================================================================= # ============================================================================= -# CHECK REGISTRY +# REGISTRY & PRESETS # ============================================================================= -# All available checks -CHECK_FUNCTIONS: Dict[str, callable] = { - # Bypass hacks +CHECK_FUNCTIONS: Dict[str, Callable[[str], Tuple[bool, str]]] = { + "code_bypass": check_code_bypass, "pytorch_wrap": check_pytorch_wrap, - "try_except": check_try_except, - "pass_stmt": check_pass_stmt, - # Forbidden operations - "forbidden_ops": check_forbidden_ops, - # Implementation checks (backend-specific) + "torch_computation_ops": check_torch_computation_ops, "cuda_impl": check_cuda_impl, + + # backend-specific checks "triton_impl": check_triton_impl, "tk_impl": check_tk_impl, + "cute_impl": check_cute_impl, + "tilelang_impl": check_tilelang_impl, } -# Suggested presets -STRICT_CHECKS = ["pytorch_wrap", "try_except", "pass_stmt", "forbidden_ops"] -WARNING_CHECKS = [] # Add checks here that should warn but not fail +# These checks are NECESSARY for all kernels (strict = error) +STRICT_CHECKS = [ + "code_bypass", + "pytorch_wrap", +] + +# Backend-specific checks are added later at entry point + +# These are optional checks (by taste) - flagged as warnings +# Move to STRICT_CHECKS if you want to enforce them +WARNING_CHECKS: List[str] = [ + "torch_computation_ops", # up to user to allow torch computation ops +] -# Backend implementation check mapping BACKEND_IMPL_CHECK = { "cuda": "cuda_impl", "triton": "triton_impl", "thunderkittens": "tk_impl", + "cute": "cute_impl", + "cutlass": "cute_impl", # alias + "tilelang": "tilelang_impl", } + # ============================================================================= # MAIN ENTRY POINT # ============================================================================= def validate_kernel_static( code: str, - backend: Optional[str] = None, + backend: str = "cuda", + precision: str = "fp16", forbidden: Optional[List[str]] = None, warnings: Optional[List[str]] = None, -) -> Dict[str, Any]: +) -> Tuple[bool, List[str], List[str]]: """ - Validate kernel code against configurable check groups. + Validate kernel code through statically inspecting the code + We configure the checks against check groups that we have provided for common hacks. + Note we do not guarantee that all checks are exhaustive. This is also only on the static level. Args: code: Kernel source code - backend: "cuda", "triton", or "thunderkittens" (adds impl check) - forbidden: List of check names that cause errors (default: STRICT_CHECKS) - warnings: List of check names that cause warnings (default: WARNING_CHECKS) - + backend: "cuda", "triton", or "thunderkittens" + precision: "fp16", "fp32", or "bf16" (for future precision checks) + forbidden: Check categories that cause errors (default: STRICT_CHECKS) + warnings: Check categories that cause warnings (default: WARNING_CHECKS) + Returns: - { - "valid": bool, # True if no forbidden checks failed - "errors": List[str], # Error messages - "warnings": List[str], # Warning messages - "details": Dict[str, bool] # Per-check results - } + (valid, errors, warnings) + valid: bool + errors: List[str] + warnings: List[str] """ - if forbidden is None: - forbidden = STRICT_CHECKS.copy() - if warnings is None: - warnings = WARNING_CHECKS.copy() + # Copy defaults to avoid mutating global lists + forbidden_checks = list(forbidden) if forbidden is not None else list(STRICT_CHECKS) + warning_checks = list(warnings) if warnings is not None else list(WARNING_CHECKS) # Add backend implementation check if specified - if backend and backend in BACKEND_IMPL_CHECK: + if backend in BACKEND_IMPL_CHECK: impl_check = BACKEND_IMPL_CHECK[backend] - if impl_check not in forbidden: - forbidden.append(impl_check) + if impl_check not in forbidden_checks: + forbidden_checks.append(impl_check) - errors = [] - warnings_list = [] - details = {} + # Aggregate results + errors: List[str] = [] + warnings_list: List[str] = [] - # Run all requested checks - all_checks = set(forbidden + warnings) - for check_name in all_checks: + for check_name in set(forbidden_checks + warning_checks): if check_name not in CHECK_FUNCTIONS: continue has_issue, msg = CHECK_FUNCTIONS[check_name](code) - details[check_name] = has_issue if has_issue: - if check_name in forbidden: + if check_name in forbidden_checks: errors.append(msg) - elif check_name in warnings: + else: warnings_list.append(msg) - return { - "valid": len(errors) == 0, - "errors": errors, - "warnings": warnings_list, - "details": details, - } + valid = len(errors) == 0 # valid if no errors + return valid, errors, warnings_list diff --git a/src/unit_tests/test_static_checker.py b/src/unit_tests/test_static_checker.py new file mode 100644 index 00000000..76d74c8d --- /dev/null +++ b/src/unit_tests/test_static_checker.py @@ -0,0 +1,270 @@ +""" +Tests for kernel_static_checker.py +[WIP] will use more realisitic adverserial kernels +Run with: pytest src/unit_tests/test_static_checker.py -v +""" + +import pytest +from src.kernel_static_checker import ( + validate_kernel_static, + check_code_bypass, + check_pytorch_wrap, + check_torch_computation_ops, + check_cuda_impl, + check_triton_impl, + check_tk_impl, + check_cute_impl, + check_tilelang_impl, +) + + +# ============================================================================= +# Test Code Bypass Detection (try-except + pass) +# ============================================================================= + +def test_bypass_try_except(): + code = "try:\n result = kernel(x)\nexcept:\n result = torch.matmul(x, w)" + has_issue, msg = check_code_bypass(code) + assert has_issue, "Should detect try-except" + assert "try-except" in msg + + +def test_bypass_pass_statement(): + code = "class MyKernel:\n def forward(self, x):\n pass" + has_issue, msg = check_code_bypass(code) + assert has_issue, "Should detect pass statement" + assert "pass" in msg + + +def test_bypass_pass_in_word(): + code = "# This check has passed all tests" + has_issue, msg = check_code_bypass(code) + assert not has_issue, "Should not match 'passed'" + + +def test_no_bypass(): + code = "result = kernel(x)" + has_issue, msg = check_code_bypass(code) + assert not has_issue, "Clean code should pass" + + +# ============================================================================= +# Test PyTorch Wrapping Detection +# ============================================================================= + +def test_pytorch_wrap_functional(): + code = "import torch.nn.functional as F\nresult = F.relu(x)" + has_issue, msg = check_pytorch_wrap(code) + assert has_issue, "Should detect F.relu" + + +def test_pytorch_wrap_nn_functional(): + code = "result = torch.nn.functional.conv2d(x, w)" + has_issue, msg = check_pytorch_wrap(code) + assert has_issue, "Should detect torch.nn.functional" + + +def test_pytorch_wrap_allows_module(): + code = "class Model(torch.nn.Module):\n def forward(self, x): return x" + has_issue, msg = check_pytorch_wrap(code) + assert not has_issue, "Should allow torch.nn.Module" + + +def test_pytorch_wrap_allows_parameter(): + code = "self.weight = torch.nn.Parameter(torch.randn(10))" + has_issue, msg = check_pytorch_wrap(code) + assert not has_issue, "Should allow torch.nn.Parameter" + + +# ============================================================================= +# Test Torch Computation Ops Detection +# ============================================================================= + +def test_torch_ops_conv2d(): + code = "result = torch.conv2d(x, w)" + has_issue, msg = check_torch_computation_ops(code) + assert has_issue, "Should detect torch.conv2d" + + +def test_torch_ops_matmul(): + code = "result = torch.matmul(x, w)" + has_issue, msg = check_torch_computation_ops(code) + assert has_issue, "Should detect torch.matmul" + + +def test_torch_ops_allowed(): + code = "result = torch.zeros(10)" # tensor creation is fine + has_issue, msg = check_torch_computation_ops(code) + assert not has_issue, "torch.zeros should be allowed" + + +# ============================================================================= +# Test CUDA Implementation Check +# ============================================================================= + +def test_cuda_valid(): + code = ''' +cuda_src = """ +__global__ void my_kernel(float* out) { + out[0] = 1.0f; +} +""" +module = load_inline(name="my_kernel", cuda_sources=[cuda_src]) +''' + has_issue, msg = check_cuda_impl(code) + assert not has_issue, "Valid CUDA should pass" + + +def test_cuda_missing_global(): + code = "module = load_inline(name='test', cuda_sources=[src])" + has_issue, msg = check_cuda_impl(code) + assert has_issue, "Should detect missing __global__" + + +def test_cuda_missing_load_inline(): + code = "__global__ void kernel() {}" + has_issue, msg = check_cuda_impl(code) + assert has_issue, "Should detect missing load_inline" + + +def test_cuda_cpp_extension(): + code = "__global__ void kernel() {}\nfrom torch.utils.cpp_extension import load" + has_issue, msg = check_cuda_impl(code) + assert not has_issue, "cpp_extension should also be valid" + + +# ============================================================================= +# Test Triton Implementation Check +# ============================================================================= + +def test_triton_valid(): + code = ''' +@triton.jit +def my_kernel(x_ptr, BLOCK: tl.constexpr): + pid = tl.program_id(0) + x = tl.load(x_ptr + pid) +''' + has_issue, msg = check_triton_impl(code) + assert not has_issue, "Valid Triton should pass" + + +def test_triton_missing_jit(): + code = "def my_kernel(x_ptr):\n x = tl.load(x_ptr)" + has_issue, msg = check_triton_impl(code) + assert has_issue, "Should detect missing @triton.jit" + + +def test_triton_missing_tl_ops(): + code = "@triton.jit\ndef my_kernel():\n return 1" + has_issue, msg = check_triton_impl(code) + assert has_issue, "Should detect missing tl.* ops" + + +# ============================================================================= +# Test ThunderKittens Implementation Check +# ============================================================================= + +def test_tk_valid(): + code = ''' +using namespace kittens; +__global__ void my_kernel() { + warpgroup::sync(); + rt_bf<16, 16> reg_tile; + st_bf<16, 16> shared_tile; +} +''' + has_issue, msg = check_tk_impl(code) + assert not has_issue, "Valid TK should pass" + + +def test_tk_missing_warp(): + code = "rt_bf<16, 16> tile;" + has_issue, msg = check_tk_impl(code) + assert has_issue, "Should detect missing warp patterns" + + +def test_tk_missing_tiles(): + code = "using namespace kittens;\nwarpgroup::sync();" + has_issue, msg = check_tk_impl(code) + assert has_issue, "Should detect missing tile declarations" + + +# ============================================================================= +# Test CuTe/CUTLASS Implementation Check +# ============================================================================= + +def test_cute_valid(): + code = "cute::copy(src, dst);" + has_issue, msg = check_cute_impl(code) + assert not has_issue, "Valid CuTe should pass" + + +def test_cute_cutlass_namespace(): + code = "cutlass::gemm::GemmCoord coord;" + has_issue, msg = check_cute_impl(code) + assert not has_issue, "CUTLASS namespace should pass" + + +def test_cute_missing(): + code = "__global__ void kernel() {}" + has_issue, msg = check_cute_impl(code) + assert has_issue, "Should detect missing cute::" + + +# ============================================================================= +# Test TileLang Implementation Check +# ============================================================================= + +def test_tilelang_valid(): + code = "@T.prim_func\ndef kernel(): pass" + has_issue, msg = check_tilelang_impl(code) + assert not has_issue, "Valid TileLang should pass" + + +def test_tilelang_missing(): + code = "def kernel(): pass" + has_issue, msg = check_tilelang_impl(code) + assert has_issue, "Should detect missing @T.prim_func" + + +# ============================================================================= +# Test validate_kernel_static Integration +# ============================================================================= + +def test_validate_cuda_valid(): + code = ''' +cuda_src = """ +__global__ void kernel(float* out) { out[0] = 1.0f; } +""" +module = load_inline(name="test", cuda_sources=[cuda_src]) + +class ModelNew(torch.nn.Module): + def forward(self, x): + return module.kernel(x) +''' + valid, errors, warnings = validate_kernel_static(code, backend="cuda") + assert valid, f"Valid CUDA should pass: {errors}" + + +def test_validate_with_bypass(): + code = "try:\n x = kernel()\nexcept:\n x = torch.matmul(a, b)" + valid, errors, warnings = validate_kernel_static(code, backend="cuda") + assert not valid, "Should detect bypass" + assert len(errors) > 0 + + +def test_validate_custom_checks(): + code = "torch.matmul(x, w)" # torch op, no bypass + + # Default: torch_computation_ops is in warnings + valid, errors, warnings = validate_kernel_static( + code, backend="cuda", + forbidden=["code_bypass"], # only check bypass + warnings=["torch_computation_ops"] + ) + # This will fail on cuda_impl (no __global__), but let's check warnings work + assert len(warnings) > 0, "Should have torch op warning" + + +if __name__ == "__main__": 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to catch reward hacking -by looking at the generated code format. - - -WIP this is all the code i wrote -All checks are done during runtime - -Code validity checker -- Make sure it is actually CUDA / DSL code (valid) - -We support checking against -- torch -- cuda -- triton -- thunderkittens - - -Call site: -- generation -- eval -> throw error to reject -""" - -import re - - -# you can configure this! we have suggestions -DEFAULT_FORBIDDEN_GROUPS = [] # def not allowed -DEFAULT_WARNING_GROUPS = [] # might be valid but we want to warn - -def check_valid_kernel_code( - code: str, - backend: str, - precision: str, # fp16, fp32, bf16 - forbidden_groups: list[str] = DEFAULT_FORBIDDEN_GROUPS, - warning_groups: list[str] = DEFAULT_WARNING_GROUPS) -> bool: - """ - Check if the provided code is valid CUDA/DSL code for the given backend. - """ - # TODO: Implement actual code validation logic - match backend: - case "cuda": - # Validate CUDA-specific syntax - pass - case "triton": - # Validate Triton-specific syntax - pass - case "thunderkittens": - return check_thunderkittens_code(code) - case _: - # TO ADD MORE - # Unknown backend - return False - return True - -def check_valid_cuda(code: str) -> bool: - """ - Check if the provided code is valid CUDA/DSL code. - This is a placeholder for the actual implementation. - """ - - # TODO: Implement actual code validation logic - - # migrate from Kevin implementations - - return True - -# list torch ops that are allowed -# we probably won't allow torch operations - -# decide degree on the torch code allowed -# do we all in cuda? do we want part of it to be in cuda? - -def check_triton_code(code: str) -> bool: - """ - Check if the provided code is valid Triton code. - """ - # TODO: Implement actual code validation logic - # detect it is triton jit - # - return True - - - - -# some notes here -# # -# What prevents cheating? - -# Enforcement happens mainly through Triton's compiler: @triton.jit functions cannot contain PyTorch operations – they'll fail to compile. This prevents high-level API usage in kernels. We add wrapper-side checks too. For testing, we rely on random inputs and subprocess execution. However, we trust the LLM-generated test harness itself—we don't statically analyze it for cheating. - -# The compositional validation matters: individual kernel correctness doesn't guarantee the full pipeline works. Integration exposes shape mismatches, accumulating errors, and semantic misunderstandings that individual tests miss. - -def check_thunderkittens_code(code: str) -> bool: - """ - Check if the provided code is valid ThunderKittens code. - - Uses the following heuristics that the code: - 1. Contains ThunderKittens-specific namespace patterns: - - "kittens::warp" or "kittens::warpgroup" or "::warpgroup::" or "::warp::" or "tma::" TODO: Get a big namespace list! - 2. Contains tile declarations: - - st_{bf/fl}<...> (shared memory tiles) - - rt_{bf/fl}<...> (register tiles) - """ - # (1) Namespace patterns to search for: if it contains a single one of these, then it's valid! - # TODO: For more complicated programs, you really want to make sure it's following the producer-consumer pattern - # - could search specifically for "producer" and "consumer" structs, or the presence of "tma::load_async" - warp_patterns = [ - r"kittens::warp\b", - r"kittens::warpgroup\b", - r"::warpgroup::", - r"::warp::", - r"warpgroup::", - r"warp::" - ] - has_warp_pattern = any(re.search(pattern, code) for pattern in warp_patterns) - if not has_warp_pattern: - return False - - # (2) Check that the file actually uses tiles: st_<...> or rt_<...> - # Pattern matches: st_bf<...>, st_fl<...>, rt_bf<...>, rt_fl<...>, etc. (any type) - # TODO: we don't look for global gl here... - # Also handles namespaced versions like kittens::st_bf<...> - tile_pattern = r"(?:kittens::)?(?:st|rt)_\w+\s*<[^>]+>" - has_tiles = bool(re.search(tile_pattern, code)) - if not has_tiles: - return False - - # # (3) Producer-Consumer semantics - # pc_patterns = [ - # r"tma::\b", - # r"load_async\b" - # ] - # has_pc_pattern = any(re.search(pattern, code) for pattern in pc_patterns) - # if not has_pc_pattern: - # return False - - passes_compilation = False - # TODO: Try compilation here and return false if it fails - - - # ALL of the above conditions must be met for this code string to pass - return True - - -### from ohter projects - - - -# from kevin this is my reward function hacks -# we will strip part of the code to use generic checker functions -def reward_func(queries, prompts, labels, metadata, external_model=False): - worker_id = metadata.get("rank", 0) - global_step = metadata.get("step", 10000) - refinement_step = metadata.get("refinement_step", 0) - train = metadata.get("train", True) # Get the train flag from metadata - - sample_indices = metadata.get("sample_indices", [0] * len(queries)) # Extract refinement indices with default - print(f"[Reward {worker_id}] Refinement Step {refinement_step} Sample Indices: {sample_indices}") - print(f"[Reward {worker_id}] Train mode: {train}") - labels = [json.loads(label) for label in labels] - ref_arch_src = [label["ref_arch_src"] for label in labels] - baseline_runtime = [label["baseline_runtime"] for label in labels] - level = [label["level"] for label in labels] - task_id = [label["task_id"] for label in labels] - output_dir = labels[0]["output_dir"] - base_process_index = worker_id - if not external_model: - parse_pattern = r".*?.*?```(.*?)```.*?$" - else: - parse_pattern = r"```(.*?)```" - verbose = False - - eval_cache_dir = f"{output_dir}/eval_cache" - os.makedirs(eval_cache_dir, exist_ok=True) - - # Initialize result storage - rewards = [0.0] * len(queries) - tool_answers = [""] * len(queries) - - def save_output_entry(entry): - arch_output_dir = f"{output_dir}/step_{global_step}" - arch_output_path = f"{arch_output_dir}/device_{base_process_index}.json" - os.makedirs(arch_output_dir, exist_ok=True) - - # Read existing data or create new array - data = [] - if os.path.exists(arch_output_path): - try: - with open(arch_output_path, 'r') as f: - data = json.loads(f.read()) - except json.JSONDecodeError: - pass - - # Append and write back - data.append(entry) - with open(arch_output_path, 'w') as f: - json.dump(data, f, indent=2) - - def handle_parsing_error(idx, content, error_type, error_msg, reward=0.0): - min_length, max_length = 0, 0 # Set appropriate values or make these parameters - len_reward = calculate_length_reward(False, len(content), min_length, max_length) - rewards[idx] = reward - - # Create an entry for the error - eval_result = KernelExecResult() - eval_result.metadata = register_and_format_exception(error_type, error_msg, eval_result.metadata) - entry = _create_entry( - level[idx], task_id[idx], global_step, base_process_index, - eval_result, baseline_runtime[idx], reward, prompts[idx], - content, min_length, max_length, error_type, error_msg, - len_reward, 0, refinement_step=refinement_step, - sample_index=sample_indices[idx] # Add refinement index - ) - eval_result_dict = response_for_kernel_eval(eval_result, external_model) - - # Save the entry - save_output_entry(entry) - return eval_result_dict # Parsing failed - - # Parse completions and prepare valid items for processing - valid_items = [] - for idx, (prompt, completion, ref_arch, base_runtime, ind_level, id) in enumerate(zip(prompts, queries, ref_arch_src, baseline_runtime, level, task_id)): - # Create a unique process index for each task - unique_process_index = f"{base_process_index}_{idx}" - eval_cache_path = f"{eval_cache_dir}/eval_results_{unique_process_index}.json" - - # Parse the completion to extract CUDA kernel - matches = re.match(parse_pattern, completion, re.DOTALL) - if not matches: # prompt comes with 2 codeblocks - print(f"[Reward {unique_process_index} EXIT] had no match") - eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", "Parsing failed. No valid code block or thinking too long.", 0.0) - tool_answers[idx] = eval_result_dict - continue - - # Extract and clean up the CUDA kernel code - custom_cuda = matches.group(1).strip() - for code_type in ["python", "cpp", "cuda"]: - if custom_cuda.startswith(code_type): - custom_cuda = custom_cuda[len(code_type):].strip() - - # Verify the CUDA kernel looks valid - if ("__global__" not in custom_cuda) or ("load_inline(" not in custom_cuda): - print(f"[Reward {unique_process_index} EXIT] output has no cuda kernel") - eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", "Parsing failed. Has no valid CUDA kernel.", 0.0) - tool_answers[idx] = eval_result_dict - continue - - # Verify the CUDA kernel does not use torch.nn (except for allowed utility functions) - # Split code into lines and filter out comments - code_lines = [line for line in custom_cuda.split('\n') if not line.strip().startswith('#')] - non_comment_code = '\n'.join(code_lines) - - # Check for disallowed nn patterns - if re.search( - r'nn\.(?!(Module|parameter|Parameter|ParameterList|ParameterDict|ModuleList|ModuleDict|init)\b)', - non_comment_code - ): - print(f"[Reward {unique_process_index} EXIT] output has disallowed nn. in custom cuda") - eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", - "Parsing failed. You're not allowed to use torch.nn in the custom CUDA kernel (except for containers, nn.Parameter, and nn.init).", 0.0) - tool_answers[idx] = eval_result_dict - continue - - # List of disallowed torch functions - disallowed_torch_functions = [ - "torch.conv1d", "torch.conv2d", "torch.conv3d", - "torch.conv_transpose1d", "torch.conv_transpose2d", "torch.conv_transpose3d", - "torch.avg_pool1d", "torch.avg_pool2d", "torch.avg_pool3d", - "torch.max_pool1d", "torch.max_pool2d", "torch.max_pool3d", - "torch.adaptive_avg_pool1d", "torch.adaptive_avg_pool2d", "torch.adaptive_avg_pool3d", - "torch.adaptive_max_pool1d", "torch.adaptive_max_pool2d", "torch.adaptive_max_pool3d", - "torch.relu", "torch.hardtanh", "torch.elu", "torch.selu", - "torch.leaky_relu", "torch.gelu", "torch.softsign", "torch.softplus", - "torch.softmax", "torch.log_softmax", "torch.tanh", "torch.sigmoid", - "torch.hardsigmoid", "torch.silu", "torch.mish", - "torch.batch_norm", "torch.group_norm", "torch.layer_norm", - "torch.instance_norm", "torch.rms_norm", "torch.normalize", "torch.linear", - "torch.cross_entropy", "torch.kl_div", "torch.mse_loss", - "torch.huber_loss", "torch.triplet_margin_loss", "torch.cosine_similarity", - "torch.logsumexp", "torch.log_softmax", "torch.clamp", "torch.dropout" - ] - - # Create efficient regex pattern for all disallowed functions - # The pattern uses a lookahead (?=...) to ensure we only match complete function references: - # - \s*\( : followed by parentheses (function call) - # - \s : followed by whitespace (end of identifier) - # - $ : at the end of a line - disallowed_pattern = r'\b(' + '|'.join(re.escape(func) for func in disallowed_torch_functions) + r')(?=\s*\(|\s|$)' - - # Single regex search instead of multiple string checks - match = re.search(disallowed_pattern, non_comment_code) - if match: - print(f"[Reward {unique_process_index} EXIT] output uses disallowed function by calling nn.Functional from torch: {match.group(0)}") - eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", - f"Parsing failed. You're not allowed to use torch.nn.functional in the custom CUDA kernel by calling {match.group(0)}.", 0.0) - tool_answers[idx] = eval_result_dict - continue - - # If we reach here, parsing was successful - prepare item data - item_data = { - "prompt": prompt, - "completion": completion, - "ref_arch": ref_arch, - "baseline_runtime": base_runtime, - "ind_level": ind_level, - "id": id, - "process_index": unique_process_index, - "current_step": global_step, - "refinement_step": refinement_step, - "sample_index": sample_indices[idx], # Add refinement index - "device": base_process_index, - "output_dir": output_dir, - "eval_cache_path": eval_cache_path, - "custom_cuda": custom_cuda - } - valid_items.append((idx, item_data)) - - # Process valid items if any exist - if valid_items: - - # Determine number of workers based on available items - max_workers = min(len(valid_items), MAX_CPU) - print(f"[Reward {worker_id}] Running with {max_workers} workers for {len(valid_items)} items") - - with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor: - # Submit tasks - futures = [] - for original_idx, item in valid_items: - future = executor.submit(_process_single_item, item, verbose, train, external_model) - futures.append((future, original_idx)) - - # Process results - for future, original_idx in futures: - try: - reward, eval_result_dict, entry = future.result() - rewards[original_idx] = reward - tool_answers[original_idx] = eval_result_dict - - # Save the entry using the helper function - save_output_entry(entry) - - except Exception as exc: - print(f"[Reward {worker_id}_{original_idx}] Processing item generated an exception: {exc}") - else: - print(f"[Reward {worker_id}] No valid items to process") - - # Clean up temp files - for idx, item_data in valid_items: - try: - if os.path.exists(item_data["eval_cache_path"]): - os.unlink(item_data["eval_cache_path"]) - except: - pass - - print(f"[Reward {worker_id} Refinement Step {refinement_step}] Finished processing {len(valid_items)} items") - print(f"[Reward {worker_id} Refinement Step {refinement_step}] Rewards: {rewards}") - - return rewards, tool_answers - - -# from another project -def check_for_cheating(kernel_code: str) -> bool: - """ - Check if the kernel code is cheating using Kevin-32B paper rules. - - Kevin paper identifies three cheating patterns that receive zero reward: - 1. PyTorch wrapping: Using torch.nn or torch.nn.functional - 2. Try-except fallback: Wrapping incomplete CUDA in exception handlers - 3. Inheritance bypass: Using 'pass' to inherit from reference - - Reference: arXiv:2507.11948 "Kevin: Multi-Turn RL for Generating CUDA Kernels" - """ - # Kevin Rule 1: PyTorch wrapping detection - # Zero reward for kernels containing torch.nn or torch.nn.functional - pytorch_patterns = [ - "torch.nn.functional", - "torch.nn.", - "F.conv", "F.linear", "F.relu", "F.gelu", "F.softmax", - "F.batch_norm", "F.layer_norm", "F.dropout", - "nn.functional.", - ] - for pattern in pytorch_patterns: - if pattern in kernel_code: - return True - - # Kevin Rule 2: Try-except fallback pattern detection - # Models wrap incomplete CUDA in exception handlers that fall back to PyTorch - # Zero reward for kernels containing try/except - if "try:" in kernel_code or "except:" in kernel_code or "except " in kernel_code: - return True - - # Kevin Rule 3: Inheritance bypass detection - # Model inherits from reference using 'pass' - # Zero reward for kernels containing pass statements - # Be careful to not match 'passed' or similar words - import re - if re.search(r'\bpass\b', kernel_code): - return True - - # Additional check: no custom kernel implementation at all - has_triton_kernel = "@triton.jit" in kernel_code or "@triton.autotune" in kernel_code - has_cuda_kernel = "load_inline" in kernel_code or "cpp_extension" in kernel_code - has_cute_kernel = "cute::" in kernel_code or "from cutlass" in kernel_code - has_tilelang = "@T.prim_func" in kernel_code or "tvm.build" in kernel_code - - has_custom_implementation = any([ - has_triton_kernel, - has_cuda_kernel, - has_cute_kernel, - has_tilelang - ]) - - # If no custom implementation, check for direct torch ops - if not has_custom_implementation: - torch_ops = [ - "torch.mm", "torch.bmm", "torch.matmul", - "torch.conv", "torch.einsum", - ] - for op in torch_ops: - if op in kernel_code: - return True - - return False \ No newline at end of file diff --git a/src/code_check_old.py b/src/code_check_old.py deleted file mode 100644 index 39c359ed..00000000 --- a/src/code_check_old.py +++ /dev/null @@ -1,423 +0,0 @@ - -""" -TODO: ongoing effort to catch reward hacking -by looking at the generated code format. - - -All checks are done during runtime - -Code validity checker -- Make sure it is actually CUDA / DSL code (valid) - -We support checking against -- torch -- cuda -- triton -- thunderkittens - - -Call site: -- generation -- eval -> throw error to reject -""" - -import re - - -# you can configure this! we have suggestions -DEFAULT_FORBIDDEN_GROUPS = [] # def not allowed -DEFAULT_WARNING_GROUPS = [] # might be valid but we want to warn - -def check_valid_kernel_code( - code: str, - backend: str, - forbidden_groups: list[str] = DEFAULT_FORBIDDEN_GROUPS, - warning_groups: list[str] = DEFAULT_WARNING_GROUPS) -> bool: - """ - Check if the provided code is valid CUDA/DSL code for the given backend. - """ - # TODO: Implement actual code validation logic - match backend: - case "cuda": - # Validate CUDA-specific syntax - pass - case "triton": - # Validate Triton-specific syntax - pass - case "thunderkittens": - return check_thunderkittens_code(code) - case _: - # TO ADD MORE - # Unknown backend - return False - return True - -def check_valid_cuda(code: str) -> bool: - """ - Check if the provided code is valid CUDA/DSL code. - This is a placeholder for the actual implementation. - """ - - # TODO: Implement actual code validation logic - - # migrate from Kevin implementations - - return True - -# list torch ops that are allowed -# we probably won't allow torch operations - -# decide degree on the torch code allowed -# do we all in cuda? do we want part of it to be in cuda? - -def check_triton_code(code: str) -> bool: - """ - Check if the provided code is valid Triton code. - """ - # TODO: Implement actual code validation logic - # detect it is triton jit - # - return True - - - - -# some notes here -# # -# What prevents cheating? - -# Enforcement happens mainly through Triton's compiler: @triton.jit functions cannot contain PyTorch operations – they'll fail to compile. This prevents high-level API usage in kernels. We add wrapper-side checks too. For testing, we rely on random inputs and subprocess execution. However, we trust the LLM-generated test harness itself—we don't statically analyze it for cheating. - -# The compositional validation matters: individual kernel correctness doesn't guarantee the full pipeline works. Integration exposes shape mismatches, accumulating errors, and semantic misunderstandings that individual tests miss. - -def check_thunderkittens_code(code: str) -> bool: - """ - Check if the provided code is valid ThunderKittens code. - - Uses the following heuristics that the code: - 1. Contains ThunderKittens-specific namespace patterns: - - "kittens::warp" or "kittens::warpgroup" or "::warpgroup::" or "::warp::" or "tma::" TODO: Get a big namespace list! - 2. Contains tile declarations: - - st_{bf/fl}<...> (shared memory tiles) - - rt_{bf/fl}<...> (register tiles) - """ - # (1) Namespace patterns to search for: if it contains a single one of these, then it's valid! - # TODO: For more complicated programs, you really want to make sure it's following the producer-consumer pattern - # - could search specifically for "producer" and "consumer" structs, or the presence of "tma::load_async" - warp_patterns = [ - r"kittens::warp\b", - r"kittens::warpgroup\b", - r"::warpgroup::", - r"::warp::", - r"warpgroup::", - r"warp::" - ] - has_warp_pattern = any(re.search(pattern, code) for pattern in warp_patterns) - if not has_warp_pattern: - return False - - # (2) Check that the file actually uses tiles: st_<...> or rt_<...> - # Pattern matches: st_bf<...>, st_fl<...>, rt_bf<...>, rt_fl<...>, etc. (any type) - # TODO: we don't look for global gl here... - # Also handles namespaced versions like kittens::st_bf<...> - tile_pattern = r"(?:kittens::)?(?:st|rt)_\w+\s*<[^>]+>" - has_tiles = bool(re.search(tile_pattern, code)) - if not has_tiles: - return False - - # # (3) Producer-Consumer semantics - # pc_patterns = [ - # r"tma::\b", - # r"load_async\b" - # ] - # has_pc_pattern = any(re.search(pattern, code) for pattern in pc_patterns) - # if not has_pc_pattern: - # return False - - passes_compilation = False - # TODO: Try compilation here and return false if it fails - - - # ALL of the above conditions must be met for this code string to pass - return True - - -### from ohter projects - - - -# from kevin this is my reward function hacks -# we will strip part of the code to use generic checker functions -def reward_func(queries, prompts, labels, metadata, external_model=False): - worker_id = metadata.get("rank", 0) - global_step = metadata.get("step", 10000) - refinement_step = metadata.get("refinement_step", 0) - train = metadata.get("train", True) # Get the train flag from metadata - - sample_indices = metadata.get("sample_indices", [0] * len(queries)) # Extract refinement indices with default - print(f"[Reward {worker_id}] Refinement Step {refinement_step} Sample Indices: {sample_indices}") - print(f"[Reward {worker_id}] Train mode: {train}") - labels = [json.loads(label) for label in labels] - ref_arch_src = [label["ref_arch_src"] for label in labels] - baseline_runtime = [label["baseline_runtime"] for label in labels] - level = [label["level"] for label in labels] - task_id = [label["task_id"] for label in labels] - output_dir = labels[0]["output_dir"] - base_process_index = worker_id - if not external_model: - parse_pattern = r".*?.*?```(.*?)```.*?$" - else: - parse_pattern = r"```(.*?)```" - verbose = False - - eval_cache_dir = f"{output_dir}/eval_cache" - os.makedirs(eval_cache_dir, exist_ok=True) - - # Initialize result storage - rewards = [0.0] * len(queries) - tool_answers = [""] * len(queries) - - def save_output_entry(entry): - arch_output_dir = f"{output_dir}/step_{global_step}" - arch_output_path = f"{arch_output_dir}/device_{base_process_index}.json" - os.makedirs(arch_output_dir, exist_ok=True) - - # Read existing data or create new array - data = [] - if os.path.exists(arch_output_path): - try: - with open(arch_output_path, 'r') as f: - data = json.loads(f.read()) - except json.JSONDecodeError: - pass - - # Append and write back - data.append(entry) - with open(arch_output_path, 'w') as f: - json.dump(data, f, indent=2) - - def handle_parsing_error(idx, content, error_type, error_msg, reward=0.0): - min_length, max_length = 0, 0 # Set appropriate values or make these parameters - len_reward = calculate_length_reward(False, len(content), min_length, max_length) - rewards[idx] = reward - - # Create an entry for the error - eval_result = KernelExecResult() - eval_result.metadata = register_and_format_exception(error_type, error_msg, eval_result.metadata) - entry = _create_entry( - level[idx], task_id[idx], global_step, base_process_index, - eval_result, baseline_runtime[idx], reward, prompts[idx], - content, min_length, max_length, error_type, error_msg, - len_reward, 0, refinement_step=refinement_step, - sample_index=sample_indices[idx] # Add refinement index - ) - eval_result_dict = response_for_kernel_eval(eval_result, external_model) - - # Save the entry - save_output_entry(entry) - return eval_result_dict # Parsing failed - - # Parse completions and prepare valid items for processing - valid_items = [] - for idx, (prompt, completion, ref_arch, base_runtime, ind_level, id) in enumerate(zip(prompts, queries, ref_arch_src, baseline_runtime, level, task_id)): - # Create a unique process index for each task - unique_process_index = f"{base_process_index}_{idx}" - eval_cache_path = f"{eval_cache_dir}/eval_results_{unique_process_index}.json" - - # Parse the completion to extract CUDA kernel - matches = re.match(parse_pattern, completion, re.DOTALL) - if not matches: # prompt comes with 2 codeblocks - print(f"[Reward {unique_process_index} EXIT] had no match") - eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", "Parsing failed. No valid code block or thinking too long.", 0.0) - tool_answers[idx] = eval_result_dict - continue - - # Extract and clean up the CUDA kernel code - custom_cuda = matches.group(1).strip() - for code_type in ["python", "cpp", "cuda"]: - if custom_cuda.startswith(code_type): - custom_cuda = custom_cuda[len(code_type):].strip() - - # Verify the CUDA kernel looks valid - if ("__global__" not in custom_cuda) or ("load_inline(" not in custom_cuda): - print(f"[Reward {unique_process_index} EXIT] output has no cuda kernel") - eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", "Parsing failed. Has no valid CUDA kernel.", 0.0) - tool_answers[idx] = eval_result_dict - continue - - # Verify the CUDA kernel does not use torch.nn (except for allowed utility functions) - # Split code into lines and filter out comments - code_lines = [line for line in custom_cuda.split('\n') if not line.strip().startswith('#')] - non_comment_code = '\n'.join(code_lines) - - # Check for disallowed nn patterns - if re.search( - r'nn\.(?!(Module|parameter|Parameter|ParameterList|ParameterDict|ModuleList|ModuleDict|init)\b)', - non_comment_code - ): - print(f"[Reward {unique_process_index} EXIT] output has disallowed nn. in custom cuda") - eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", - "Parsing failed. You're not allowed to use torch.nn in the custom CUDA kernel (except for containers, nn.Parameter, and nn.init).", 0.0) - tool_answers[idx] = eval_result_dict - continue - - # List of disallowed torch functions - disallowed_torch_functions = [ - "torch.conv1d", "torch.conv2d", "torch.conv3d", - "torch.conv_transpose1d", "torch.conv_transpose2d", "torch.conv_transpose3d", - "torch.avg_pool1d", "torch.avg_pool2d", "torch.avg_pool3d", - "torch.max_pool1d", "torch.max_pool2d", "torch.max_pool3d", - "torch.adaptive_avg_pool1d", "torch.adaptive_avg_pool2d", "torch.adaptive_avg_pool3d", - "torch.adaptive_max_pool1d", "torch.adaptive_max_pool2d", "torch.adaptive_max_pool3d", - "torch.relu", "torch.hardtanh", "torch.elu", "torch.selu", - "torch.leaky_relu", "torch.gelu", "torch.softsign", "torch.softplus", - "torch.softmax", "torch.log_softmax", "torch.tanh", "torch.sigmoid", - "torch.hardsigmoid", "torch.silu", "torch.mish", - "torch.batch_norm", "torch.group_norm", "torch.layer_norm", - "torch.instance_norm", "torch.rms_norm", "torch.normalize", "torch.linear", - "torch.cross_entropy", "torch.kl_div", "torch.mse_loss", - "torch.huber_loss", "torch.triplet_margin_loss", "torch.cosine_similarity", - "torch.logsumexp", "torch.log_softmax", "torch.clamp", "torch.dropout" - ] - - # Create efficient regex pattern for all disallowed functions - # The pattern uses a lookahead (?=...) to ensure we only match complete function references: - # - \s*\( : followed by parentheses (function call) - # - \s : followed by whitespace (end of identifier) - # - $ : at the end of a line - disallowed_pattern = r'\b(' + '|'.join(re.escape(func) for func in disallowed_torch_functions) + r')(?=\s*\(|\s|$)' - - # Single regex search instead of multiple string checks - match = re.search(disallowed_pattern, non_comment_code) - if match: - print(f"[Reward {unique_process_index} EXIT] output uses disallowed function by calling nn.Functional from torch: {match.group(0)}") - eval_result_dict = handle_parsing_error(idx, completion, "parsing_error", - f"Parsing failed. You're not allowed to use torch.nn.functional in the custom CUDA kernel by calling {match.group(0)}.", 0.0) - tool_answers[idx] = eval_result_dict - continue - - # If we reach here, parsing was successful - prepare item data - item_data = { - "prompt": prompt, - "completion": completion, - "ref_arch": ref_arch, - "baseline_runtime": base_runtime, - "ind_level": ind_level, - "id": id, - "process_index": unique_process_index, - "current_step": global_step, - "refinement_step": refinement_step, - "sample_index": sample_indices[idx], # Add refinement index - "device": base_process_index, - "output_dir": output_dir, - "eval_cache_path": eval_cache_path, - "custom_cuda": custom_cuda - } - valid_items.append((idx, item_data)) - - # Process valid items if any exist - if valid_items: - - # Determine number of workers based on available items - max_workers = min(len(valid_items), MAX_CPU) - print(f"[Reward {worker_id}] Running with {max_workers} workers for {len(valid_items)} items") - - with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor: - # Submit tasks - futures = [] - for original_idx, item in valid_items: - future = executor.submit(_process_single_item, item, verbose, train, external_model) - futures.append((future, original_idx)) - - # Process results - for future, original_idx in futures: - try: - reward, eval_result_dict, entry = future.result() - rewards[original_idx] = reward - tool_answers[original_idx] = eval_result_dict - - # Save the entry using the helper function - save_output_entry(entry) - - except Exception as exc: - print(f"[Reward {worker_id}_{original_idx}] Processing item generated an exception: {exc}") - else: - print(f"[Reward {worker_id}] No valid items to process") - - # Clean up temp files - for idx, item_data in valid_items: - try: - if os.path.exists(item_data["eval_cache_path"]): - os.unlink(item_data["eval_cache_path"]) - except: - pass - - print(f"[Reward {worker_id} Refinement Step {refinement_step}] Finished processing {len(valid_items)} items") - print(f"[Reward {worker_id} Refinement Step {refinement_step}] Rewards: {rewards}") - - return rewards, tool_answers - - -# from another project -def check_for_cheating(kernel_code: str) -> bool: - """ - Check if the kernel code is cheating using Kevin-32B paper rules. - - Kevin paper identifies three cheating patterns that receive zero reward: - 1. PyTorch wrapping: Using torch.nn or torch.nn.functional - 2. Try-except fallback: Wrapping incomplete CUDA in exception handlers - 3. Inheritance bypass: Using 'pass' to inherit from reference - - Reference: arXiv:2507.11948 "Kevin: Multi-Turn RL for Generating CUDA Kernels" - """ - # Kevin Rule 1: PyTorch wrapping detection - # Zero reward for kernels containing torch.nn or torch.nn.functional - pytorch_patterns = [ - "torch.nn.functional", - "torch.nn.", - "F.conv", "F.linear", "F.relu", "F.gelu", "F.softmax", - "F.batch_norm", "F.layer_norm", "F.dropout", - "nn.functional.", - ] - for pattern in pytorch_patterns: - if pattern in kernel_code: - return True - - # Kevin Rule 2: Try-except fallback pattern detection - # Models wrap incomplete CUDA in exception handlers that fall back to PyTorch - # Zero reward for kernels containing try/except - if "try:" in kernel_code or "except:" in kernel_code or "except " in kernel_code: - return True - - # Kevin Rule 3: Inheritance bypass detection - # Model inherits from reference using 'pass' - # Zero reward for kernels containing pass statements - # Be careful to not match 'passed' or similar words - import re - if re.search(r'\bpass\b', kernel_code): - return True - - # Additional check: no custom kernel implementation at all - has_triton_kernel = "@triton.jit" in kernel_code or "@triton.autotune" in kernel_code - has_cuda_kernel = "load_inline" in kernel_code or "cpp_extension" in kernel_code - has_cute_kernel = "cute::" in kernel_code or "from cutlass" in kernel_code - has_tilelang = "@T.prim_func" in kernel_code or "tvm.build" in kernel_code - - has_custom_implementation = any([ - has_triton_kernel, - has_cuda_kernel, - has_cute_kernel, - has_tilelang - ]) - - # If no custom implementation, check for direct torch ops - if not has_custom_implementation: - torch_ops = [ - "torch.mm", "torch.bmm", "torch.matmul", - "torch.conv", "torch.einsum", - ] - for op in torch_ops: - if op in kernel_code: - return True - - return False \ No newline at end of file diff --git a/src/kernel_static_checker.py b/src/kernel_static_checker.py index 75f4da98..b63d5596 100644 --- a/src/kernel_static_checker.py +++ b/src/kernel_static_checker.py @@ -4,6 +4,10 @@ The goal is flag reward hacking patterns (both strictly prohibited and possible ones). through statically examining the code. +In the future we can add +- AST-based detections +- LM as a judge checker + Warning: This list is by no means complete and nor this is not a replacement for runtime checks. We welcome feedback and contributions as community find new ways of hacks. @@ -49,15 +53,11 @@ def _strip_comments(code: str) -> str: def check_code_bypass(code: str) -> Tuple[bool, str]: """ Check for code bypass patterns (strictly prohibited). - - Detects two Kevin paper bypass techniques: 1. Try-Except Fallback: Models wrap incomplete CUDA in exception handlers that fall back to PyTorch when custom code fails. 2. Pass Statement: Models inherit from reference and use 'pass' to do nothing, effectively calling parent implementation. - - Both receive zero reward per Kevin paper rules. - Uses word boundary for 'pass' to avoid matching 'passed', 'bypass', etc. + Uses word boundary for 'pass' to avoid matching 'passed', 'bypass', etc. """ code = _strip_comments(code) @@ -72,44 +72,36 @@ def check_code_bypass(code: str) -> Tuple[bool, str]: return (False, "") -# Since KernelBench problems uses PyTorch as a reference -# sometimes model chose to wrap PyTorch high-level APIs instead of implementing a custom kernel. -# Depends on the setting that is used, (configure this in the STRICT and WARNING checks) -# some left model replaces some ops with custom kernels in required backend, -# others require the model to write the whole program in custom kernels. - -# --- PyTorch Wrapping --- -# Rationale: Using torch.nn.functional or F.* means the model is just wrapping -# PyTorch high-level APIs instead of implementing a custom kernel. -# This defeats the purpose of kernel generation. -PYTORCH_WRAP_PATTERNS = [ - r"torch\.nn\.functional", - r"\bnn\.functional\.", - r"\bF\.(conv|linear|relu|gelu|softmax|batch_norm|layer_norm|dropout)", -] -# Allow only containers (Module, ModuleList, etc.), Parameter, and init +# Since KernelBench problems uses PyTorch as a reference, there could be settigs where +# Model generated code +# 1. Replaces some (not all) ops with custom kernels, others are kept in Torch +# 2. All compuational ops must be replaced with custom kernels +# Depends the setting you use, you can move the checks below (pytorch_wrap, torch_computation_ops) +# from WARNING to STRICT + +# --- PyTorch NN Module Wrapping --- +# Allows: nn.Module, nn.Parameter, nn.ParameterList, nn.ParameterDict, +# nn.ModuleList, nn.ModuleDict, nn.init (needed for model structure) +# Blocks: nn.Linear, nn.Conv2d, nn.ReLU, etc. (compute layers) PYTORCH_DISALLOWED_NN_PATTERN = r'torch\.nn\.(?!(Module|parameter|Parameter|ParameterList|ParameterDict|ModuleList|ModuleDict|init)\b)' def check_pytorch_wrap(code: str) -> Tuple[bool, str]: """ - Check for PyTorch API wrapping (torch.nn.functional, F.*, etc.). + Check for PyTorch nn module usage (nn.Linear, nn.Conv2d, etc.). - Kevin paper rule: Zero reward for kernels containing torch.nn or torch.nn.functional. - These indicate the model is wrapping PyTorch instead of writing custom kernels. + Allows containers (nn.Module, nn.Parameter, nn.init) needed for model structure. + Blocks compute layers (nn.Linear, nn.Conv2d, nn.ReLU, etc.). """ code = _strip_comments(code) - for pattern in PYTORCH_WRAP_PATTERNS: - if re.search(pattern, code): - return (True, "Uses torch.nn.functional or F.* - not a custom kernel") - # NOTE: should we make this strict or optional? if re.search(PYTORCH_DISALLOWED_NN_PATTERN, code): - return (True, "Uses disallowed torch.nn (only containers, Parameter, init allowed)") + return (True, "Uses torch.nn compute layer (only containers, Parameter, init allowed)") return (False, "") -# --- Forbidden Torch Computational Operations --- -# Rationale: These are high-level PyTorch ops that conduct computation -# Using them directly defeat the purpose writing custom CUDA kernels. +# --- Torch Computation Operations --- +# Rationale: These are high-level PyTorch ops that conduct computation. +# Using them directly defeats the purpose of writing custom kernels. +# Includes both torch.* and F.* (torch.nn.functional) patterns. TORCH_COMPUTATION_OPS = [ # Matrix operations "torch.mm", "torch.bmm", "torch.matmul", "torch.einsum", @@ -134,20 +126,36 @@ def check_pytorch_wrap(code: str) -> Tuple[bool, str]: "torch.huber_loss", "torch.triplet_margin_loss", "torch.cosine_similarity", # Others "torch.logsumexp", "torch.clamp", "torch.dropout", -] # hopefully didn't miss any other ones +] + +# F.* patterns (torch.nn.functional equivalents) +TORCH_FUNCTIONAL_PATTERNS = [ + r"torch\.nn\.functional\.\w+", # torch.nn.functional.* + r"\bnn\.functional\.\w+", # nn.functional.* + r"\bF\.(conv|linear|relu|gelu|softmax|batch_norm|layer_norm|dropout|max_pool|avg_pool)", +] def check_torch_computation_ops(code: str) -> Tuple[bool, str]: """ Check for high-level torch computation operations. - Note: This check is optional/taste-based. Some projects allow partial - torch usage, others require pure custom kernels. Configure as needed. + Matches both torch.* ops (torch.matmul) and F.* ops (F.relu). + This check is optional/taste-based. Configure as needed. """ code = _strip_comments(code) - pattern = r'\b(' + '|'.join(re.escape(f) for f in TORCH_COMPUTATION_OPS) + r')(?=\s*\(|\s|$)' - match = re.search(pattern, code) + + # Check torch.* ops + torch_pattern = r'\b(' + '|'.join(re.escape(f) for f in TORCH_COMPUTATION_OPS) + r')(?=\s*\(|\s|$)' + match = re.search(torch_pattern, code) if match: return (True, f"Uses torch computation op: {match.group(0)}") + + # Check F.* / nn.functional ops + for pattern in TORCH_FUNCTIONAL_PATTERNS: + match = re.search(pattern, code) + if match: + return (True, f"Uses torch.nn.functional op: {match.group(0)}") + return (False, "") # ============================================================================= @@ -260,17 +268,170 @@ def check_tilelang_impl(code: str) -> Tuple[bool, str]: # ============================================================================= -# TODO: Future checks from our adversarial hack PR and DeepReinforce blog +# TIMING MANIPULATION CHECKS - Reward Hacking Patterns +# From adversarial hack PR and DeepReinforce blog # ============================================================================= -# maybe we can group some of that later -# def check_stream_injection(code): ... # torch.cuda.Stream() -# def check_thread_injection(code): ... # threading.Thread() -# def check_lazy_eval(code): ... # torch.Tensor._make_subclass -# def check_monkey_patch(code): ... # torch.cuda.Event.elapsed_time = -# def check_precision_downgrade(code): ... # .half(), .bfloat16() # <-- this could be a soft check +# <========= STREAM INJECTION CHECKS =========> +# Rationale: Models may use CUDA streams to defer computation, manipulate timing, +# or execute operations asynchronously to cheat benchmark measurements. +STREAM_PATTERNS = [ + r"torch\.cuda\.Stream\s*\(", # torch.cuda.Stream() + r"cuda\.Stream\s*\(", # cuda.Stream() (if imported) + r"with\s+torch\.cuda\.stream", # with torch.cuda.stream(...) + r"\.wait_stream\s*\(", # .wait_stream() method + r"\.record_stream\s*\(", # .record_stream() method +] + +def check_stream_injection(code: str) -> Tuple[bool, str]: + """ + Check for CUDA stream injection patterns. + + Detects code that uses CUDA streams to potentially manipulate timing: + 1. Stream creation: torch.cuda.Stream(), cuda.Stream() + 2. Stream context managers: with torch.cuda.stream(...) + 3. Stream synchronization: .wait_stream(), .record_stream() + + Rationale: Streams can defer computation or change execution order, + potentially affecting benchmark timing measurements. + """ + code = _strip_comments(code) + + for pattern in STREAM_PATTERNS: + if re.search(pattern, code): + if "wait_stream" in pattern or "record_stream" in pattern: + return (True, "Uses stream synchronization (potential timing manipulation)") + elif "with" in pattern: + return (True, "Uses stream context manager (potential timing manipulation)") + else: + return (True, "Uses CUDA streams (potential timing manipulation)") + + return (False, "") + + +# <========= THREAD INJECTION CHECKS =========> +# Rationale: Models may use threading to parallelize work or manipulate execution +# order in ways that could affect benchmark timing. +THREAD_PATTERNS = [ + r"threading\.Thread\s*\(", # threading.Thread() + r"import\s+threading", # import threading + r"from\s+threading\s+import", # from threading import ... + r"multiprocessing\.(Process|Pool|Manager|Queue|Pipe)", + r"import\s+multiprocessing", # import multiprocessing + r"concurrent\.futures", # concurrent.futures (thread pools) + r"ThreadPoolExecutor", # ThreadPoolExecutor + r"ProcessPoolExecutor", # ProcessPoolExecutor +] + +def check_thread_injection(code: str) -> Tuple[bool, str]: + """ + Check for thread/multiprocessing injection patterns. + + Detects code that uses threading or multiprocessing: + 1. Thread creation: threading.Thread() + 2. Threading imports: import threading + 3. Multiprocessing: Process, Pool, Manager, Queue, Pipe + 4. Concurrent futures: ThreadPoolExecutor, ProcessPoolExecutor + + Rationale: Threading can defer computation or change execution order, + affecting timing measurements. + + Note: ProcessPoolExecutor might be legitimate in eval code but should + not appear in kernel implementations. + """ + code = _strip_comments(code) + + for pattern in THREAD_PATTERNS: + if re.search(pattern, code): + if "multiprocessing" in pattern: + return (True, "Uses multiprocessing (potential timing manipulation)") + elif "concurrent" in pattern or "Executor" in pattern: + return (True, "Uses concurrent futures (potential timing manipulation)") + else: + return (True, "Uses threading (potential timing manipulation)") + + return (False, "") + + +# <========= LAZY EVALUATION CHECKS =========> +# Rationale: Models may create fake/lazy tensors that don't actually compute +# anything, passing correctness checks without real implementation. +LAZY_TENSOR_PATTERNS = [ + r"_make_subclass", # torch.Tensor._make_subclass (common lazy hack) + r"class\s+\w+.*\(torch\.Tensor\)", # Custom tensor subclasses + r"class\s+\w+.*\(Tensor\)", # Custom tensor subclasses (imported Tensor) + r"torch\.Tensor\.__new__", # Direct tensor construction (potential lazy) +] + +def check_lazy_eval(code: str) -> Tuple[bool, str]: + """ + Check for lazy tensor creation patterns. + + Detects patterns commonly used to create lazy/fake tensors: + 1. _make_subclass: Common way to create custom tensor subclasses + 2. Custom tensor subclasses: Classes inheriting from torch.Tensor + 3. Direct tensor construction: torch.Tensor.__new__ manipulation + + Rationale: Lazy tensors can pass correctness checks without actually + computing anything, which is a form of reward hacking. + """ + code = _strip_comments(code) + + for pattern in LAZY_TENSOR_PATTERNS: + if re.search(pattern, code): + if "_make_subclass" in pattern: + return (True, "Uses _make_subclass (potential lazy tensor hack)") + elif "class" in pattern: + return (True, "Defines custom tensor subclass (potential lazy tensor hack)") + else: + return (True, "Uses direct tensor construction (potential lazy tensor hack)") + + return (False, "") + + +# <========= Timing Monkey Patch CHECKS =========> +# Rationale: Models may monkey-patch torch timing functions to fake benchmark results. +# This detects static patterns where timing functions are reassigned. +# especially when relying on timing markers like cuda.Event or time.perf_counter. +TIMING_EVENT_PATCH_PATTERNS = [ + r"torch\.cuda\.Event\.record\s*=", # torch.cuda.Event.record = ... + r"torch\.cuda\.Event\.elapsed_time\s*=", # torch.cuda.Event.elapsed_time = ... + r"torch\.cuda\.synchronize\s*=", # torch.cuda.synchronize = ... + r"torch\.cuda\.Event\s*=", # torch.cuda.Event = ... (replacing class) + r"time\.perf_counter\s*=", # time.perf_counter = ... + r"time\.time\s*=", # time.time = ... +] + +def check_timing_event_patch(code: str) -> Tuple[bool, str]: + """ + Check for monkey patching of timing functions (static analysis). + + Detects code that reassigns torch or time functions used for benchmarking: + 1. torch.cuda.Event.record = ... + 2. torch.cuda.Event.elapsed_time = ... + 3. torch.cuda.synchronize = ... + 4. time.perf_counter = ... + + Rationale: Monkey patching timing functions can fake benchmark results, + making slow code appear fast. + """ + code = _strip_comments(code) + + for pattern in TIMING_EVENT_PATCH_PATTERNS: + if re.search(pattern, code): + return (True, "Reassigns timing function (monkey patch detected)") + + return (False, "") + + +# ============================================================================= +# TODO: Future checks # ============================================================================= -# in the future, we can add a LM as a judge checker +# def check_precision_downgrade(code): ... # .half(), .bfloat16() - could be soft check +# this a bit tricky and up to the user to decide + +# ============================================================================= +# In the future, we can add a AST-based checker and a LM-as-a-judge checker # ============================================================================= @@ -279,32 +440,40 @@ def check_tilelang_impl(code: str) -> Tuple[bool, str]: # ============================================================================= CHECK_FUNCTIONS: Dict[str, Callable[[str], Tuple[bool, str]]] = { + # Bypass checks (strict) "code_bypass": check_code_bypass, "pytorch_wrap": check_pytorch_wrap, + "timing_event_patch": check_timing_event_patch, # clearly malicious + + # Torch ops (depends on your setups) "torch_computation_ops": check_torch_computation_ops, + + # Timing manipulation checks (usually warnings) + "stream_injection": check_stream_injection, + "thread_injection": check_thread_injection, + "lazy_eval": check_lazy_eval, + + # Backend-specific implementation checks + # should be strict "cuda_impl": check_cuda_impl, - - # backend-specific checks "triton_impl": check_triton_impl, "tk_impl": check_tk_impl, "cute_impl": check_cute_impl, "tilelang_impl": check_tilelang_impl, } +# Here are some presets for you to use +# You are welcome to adapt them to your settings # These checks are NECESSARY for all kernels (strict = error) STRICT_CHECKS = [ "code_bypass", - "pytorch_wrap", + "timing_event_patch", + "thread_injection", + "lazy_eval", ] # Backend-specific checks are added later at entry point - -# These are optional checks (by taste) - flagged as warnings -# Move to STRICT_CHECKS if you want to enforce them -WARNING_CHECKS: List[str] = [ - "torch_computation_ops", # up to user to allow torch computation ops -] - +# per backend implementation check, usually strict BACKEND_IMPL_CHECK = { "cuda": "cuda_impl", "triton": "triton_impl", @@ -314,6 +483,15 @@ def check_tilelang_impl(code: str) -> Tuple[bool, str]: "tilelang": "tilelang_impl", } +# These are optional checks (by user's decision) - flagged as warnings +# Move to STRICT_CHECKS if you want to enforce them +WARNING_CHECKS: List[str] = [ + # up to user to allow program to still have some torch computation ops + "pytorch_wrap", + "torch_computation_ops", + "stream_injection", # could have legitimate uses (async ops), but should be careful! +] + # ============================================================================= # MAIN ENTRY POINT diff --git a/src/unit_tests/test_static_checker.py b/src/unit_tests/test_static_checker.py index 76d74c8d..e44c4716 100644 --- a/src/unit_tests/test_static_checker.py +++ b/src/unit_tests/test_static_checker.py @@ -266,5 +266,81 @@ def test_validate_custom_checks(): assert len(warnings) > 0, "Should have torch op warning" +# ============================================================================= +# Test Real DSL Examples (No False Positives) +# These tests verify that valid DSL kernels from src/prompts pass the checker +# ============================================================================= + +import os +from pathlib import Path + +# Path to DSL examples +PROMPTS_DIR = Path(__file__).parent.parent / "prompts" + + +def test_cuda_example_passes(): + """Real CUDA example should pass all strict checks.""" + example_path = PROMPTS_DIR / "model_new_ex_add.py" + if not example_path.exists(): + pytest.skip("CUDA example not found") + + code = example_path.read_text() + valid, errors, warnings = validate_kernel_static( + code, + backend="cuda", + forbidden=["code_bypass"], # Don't check pytorch_wrap since example imports F unused + warnings=[] + ) + assert valid, f"CUDA example should pass: {errors}" + + +def test_triton_example_passes(): + """Real Triton example should pass all strict checks.""" + example_path = PROMPTS_DIR / "model_new_ex_add_triton.py" + if not example_path.exists(): + pytest.skip("Triton example not found") + + code = example_path.read_text() + valid, errors, warnings = validate_kernel_static( + code, + backend="triton", + forbidden=["code_bypass", "triton_impl"], + warnings=[] + ) + assert valid, f"Triton example should pass: {errors}" + + +def test_cute_example_passes(): + """Real CuTe/CUTLASS example should pass all strict checks.""" + example_path = PROMPTS_DIR / "model_new_ex_add_cute.py" + if not example_path.exists(): + pytest.skip("CuTe example not found") + + code = example_path.read_text() + valid, errors, warnings = validate_kernel_static( + code, + backend="cute", + forbidden=["code_bypass", "cute_impl"], + warnings=[] + ) + assert valid, f"CuTe example should pass: {errors}" + + +def test_tilelang_example_passes(): + """Real TileLang example should pass all strict checks.""" + example_path = PROMPTS_DIR / "model_new_ex_add_tilelang.py" + if not example_path.exists(): + pytest.skip("TileLang example not found") + + code = example_path.read_text() + valid, errors, warnings = validate_kernel_static( + code, + backend="tilelang", + forbidden=["code_bypass", "tilelang_impl"], + warnings=[] + ) + assert valid, f"TileLang example should pass: {errors}" + + if __name__ == "__main__": pytest.main([__file__, "-v"]) diff --git a/uv.lock b/uv.lock deleted file mode 100644 index 1b741e1e..00000000 --- a/uv.lock +++ /dev/null @@ -1,2533 +0,0 @@ -version = 1 -revision = 3 -requires-python = "==3.10.*" -resolution-markers = [ - 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Errors: {errors}. Warnings: {warnings}" + if warnings: + print(f"Static check warnings for level {config.level} problem {config.problem_id}: {warnings}") + # this should be optional if config.log: with open(os.path.join(config.logdir, f"generated_kernel_level_{config.level}_problem_{config.problem_id}.py"), "w") as f: diff --git a/scripts/generate_and_eval_single_sample_modal.py b/scripts/generate_and_eval_single_sample_modal.py index d8dae68f..7d964cf6 100644 --- a/scripts/generate_and_eval_single_sample_modal.py +++ b/scripts/generate_and_eval_single_sample_modal.py @@ -283,6 +283,18 @@ def main(config: EvalConfig): # check LLM is able to generate custom kernel code assert custom_kernel is not None, f"Custom {config.backend} kernel code generation failed" + # Optional: static code checker for kernel code using regex matching + # NOTE: by no means is this checker complete, but it could help catch some potential hacks + from kernelbench.kernel_static_checker import validate_kernel_static + static_check_status, errors, warnings = validate_kernel_static( + custom_kernel, + backend=config.backend, + precision=config.precision, + ) + assert static_check_status, f"Static check failed for level {config.level} problem {config.problem_id}. Errors: {errors}. Warnings: {warnings}" + if warnings: + print(f"Static check warnings for level {config.level} problem {config.problem_id}: {warnings}") + # this should be optional if config.log: with open(os.path.join(config.logdir, f"generated_kernel_level_{config.level}_problem_{config.problem_id}.py"), "w") as f: diff --git a/scripts/generate_samples.py b/scripts/generate_samples.py index 9eee2a82..1b5bf09f 100644 --- a/scripts/generate_samples.py +++ b/scripts/generate_samples.py @@ -18,7 +18,7 @@ read_file, set_gpu_arch, ) -from src.code_check import check_thunderkittens_code +from kernelbench.kernel_static_checker import validate_kernel_static """ Batch Generate Samples for Particular Level @@ -163,13 +163,17 @@ def generate_sample_single( # check LLM is able to generate custom CUDA code assert custom_kernel is not None, "Custom CUDA code generation failed" - # TODO: What should be the behavior here? Add more backends - # Validate ThunderKittens code if backend is thunderkittens - if config.backend == "thunderkittens": - if not check_thunderkittens_code(custom_kernel): - raise ValueError( - f"Generated code for problem {work.problem_id} sample {work.sample_id} doesn't seem to use valid ThunderKittens constructs" - ) + # Optional: we provide a static code checker for kernel code using regex matching + # NOTE: by no means, is this checker complete, but it might could help catch some potential hacks and issues + static_check_status, error, warnings = validate_kernel_static(custom_kernel, + backend=config.backend, + precision=config.precision, + # uses the default set of forbidden and warning patterns, + # you could adapt the patterns to your own setting (degree of banning cuda stream, allowing some torch ops) + ) + assert static_check_status, f"Static check failed for sample {work.sample_id} for problem {problem_number}: {problem_name}. Error: {error}. Warnings: {warnings}" + if warnings: + print(f"Static check warnings for sample {work.sample_id} for problem {problem_number}: {problem_name}. Warnings: {warnings}") if config.verbose: print( diff --git a/scripts/run_and_check.py b/scripts/run_and_check.py index 37ab9732..b8c1d89c 100644 --- a/scripts/run_and_check.py +++ b/scripts/run_and_check.py @@ -10,6 +10,7 @@ from kernelbench import utils as kernel_utils from scripts.generate_baseline_time import measure_program_time from kernelbench.utils import read_file +from kernelbench.kernel_static_checker import validate_kernel_static # Modal setup app = modal.App("run_and_check") @@ -279,6 +280,17 @@ def main(config: ScriptConfig): kernel_src = read_file(config.kernel_src_path) + # Optional: static code checker for kernel code using regex matching + # NOTE: by no means is this checker complete, but it could help catch some potential hacks + static_check_status, errors, warnings = validate_kernel_static( + kernel_src, + backend=config.backend, + precision=config.precision, + ) + assert static_check_status, f"Static check failed. Errors: {errors}. Warnings: {warnings}" + if warnings: + print(f"[WARN] Static check warnings: {warnings}") + # Start Evaluation assert config.eval_mode in ["local", "modal"], "eval_mode must be either 'local' or 'modal'" diff --git a/src/kernelbench/unit_tests/test_static_checker.py b/src/kernelbench/unit_tests/test_static_checker.py index e44c4716..d4411fff 100644 --- a/src/kernelbench/unit_tests/test_static_checker.py +++ b/src/kernelbench/unit_tests/test_static_checker.py @@ -1,345 +1,165 @@ """ Tests for kernel_static_checker.py -[WIP] will use more realisitic adverserial kernels -Run with: pytest src/unit_tests/test_static_checker.py -v -""" - -import pytest -from src.kernel_static_checker import ( - validate_kernel_static, - check_code_bypass, - check_pytorch_wrap, - check_torch_computation_ops, - check_cuda_impl, - check_triton_impl, - check_tk_impl, - check_cute_impl, - check_tilelang_impl, -) - - -# ============================================================================= -# Test Code Bypass Detection (try-except + pass) -# ============================================================================= - -def test_bypass_try_except(): - code = "try:\n result = kernel(x)\nexcept:\n result = torch.matmul(x, w)" - has_issue, msg = check_code_bypass(code) - assert has_issue, "Should detect try-except" - assert "try-except" in msg - - -def test_bypass_pass_statement(): - code = "class MyKernel:\n def forward(self, x):\n pass" - has_issue, msg = check_code_bypass(code) - assert has_issue, "Should detect pass statement" - assert "pass" in msg - - -def test_bypass_pass_in_word(): - code = "# This check has passed all tests" - has_issue, msg = check_code_bypass(code) - assert not has_issue, "Should not match 'passed'" - - -def test_no_bypass(): - code = "result = kernel(x)" - has_issue, msg = check_code_bypass(code) - assert not has_issue, "Clean code should pass" - - -# ============================================================================= -# Test PyTorch Wrapping Detection -# ============================================================================= - -def test_pytorch_wrap_functional(): - code = "import torch.nn.functional as F\nresult = F.relu(x)" - has_issue, msg = check_pytorch_wrap(code) - assert has_issue, "Should detect F.relu" +Validates that the static checker correctly identifies: +- Valid DSL kernels (no false positives) +- Known adversarial/hack patterns (no false negatives) -def test_pytorch_wrap_nn_functional(): - code = "result = torch.nn.functional.conv2d(x, w)" - has_issue, msg = check_pytorch_wrap(code) - assert has_issue, "Should detect torch.nn.functional" +We welcome contributions to improve the static checker and providing adversarial kernels. -def test_pytorch_wrap_allows_module(): - code = "class Model(torch.nn.Module):\n def forward(self, x): return x" - has_issue, msg = check_pytorch_wrap(code) - assert not has_issue, "Should allow torch.nn.Module" - +Run with: uv run pytest src/kernelbench/unit_tests/test_static_checker.py -v +""" -def test_pytorch_wrap_allows_parameter(): - code = "self.weight = torch.nn.Parameter(torch.randn(10))" - has_issue, msg = check_pytorch_wrap(code) - assert not has_issue, "Should allow torch.nn.Parameter" +import pytest +from pathlib import Path +from kernelbench.kernel_static_checker import validate_kernel_static # ============================================================================= -# Test Torch Computation Ops Detection +# Fixtures - Common paths and helpers # ============================================================================= -def test_torch_ops_conv2d(): - code = "result = torch.conv2d(x, w)" - has_issue, msg = check_torch_computation_ops(code) - assert has_issue, "Should detect torch.conv2d" +@pytest.fixture +def prompts_dir(): + """Path to DSL example kernels.""" + return Path(__file__).parent.parent / "prompts" +@pytest.fixture +def test_kernels_dir(): + """Path to adversarial test kernels.""" + return Path(__file__).parent / "test_kernels" -def test_torch_ops_matmul(): - code = "result = torch.matmul(x, w)" - has_issue, msg = check_torch_computation_ops(code) - assert has_issue, "Should detect torch.matmul" - -def test_torch_ops_allowed(): - code = "result = torch.zeros(10)" # tensor creation is fine - has_issue, msg = check_torch_computation_ops(code) - assert not has_issue, "torch.zeros should be allowed" +def read_kernel(path: Path) -> str: + """Read kernel code from file, skip if not found.""" + if not path.exists(): + pytest.skip(f"Kernel file not found: {path}") + return path.read_text() # ============================================================================= -# Test CUDA Implementation Check +# Valid DSL Kernels - Should Pass (No False Positives) +# These are real, correct kernels from src/kernelbench/prompts/ # ============================================================================= -def test_cuda_valid(): - code = ''' -cuda_src = """ -__global__ void my_kernel(float* out) { - out[0] = 1.0f; -} -""" -module = load_inline(name="my_kernel", cuda_sources=[cuda_src]) -''' - has_issue, msg = check_cuda_impl(code) - assert not has_issue, "Valid CUDA should pass" - - -def test_cuda_missing_global(): - code = "module = load_inline(name='test', cuda_sources=[src])" - has_issue, msg = check_cuda_impl(code) - assert has_issue, "Should detect missing __global__" - - -def test_cuda_missing_load_inline(): - code = "__global__ void kernel() {}" - has_issue, msg = check_cuda_impl(code) - assert has_issue, "Should detect missing load_inline" - - -def test_cuda_cpp_extension(): - code = "__global__ void kernel() {}\nfrom torch.utils.cpp_extension import load" - has_issue, msg = check_cuda_impl(code) - assert not has_issue, "cpp_extension should also be valid" - - -# ============================================================================= -# Test Triton Implementation Check -# ============================================================================= - -def test_triton_valid(): - code = ''' -@triton.jit -def my_kernel(x_ptr, BLOCK: tl.constexpr): - pid = tl.program_id(0) - x = tl.load(x_ptr + pid) -''' - has_issue, msg = check_triton_impl(code) - assert not has_issue, "Valid Triton should pass" - - -def test_triton_missing_jit(): - code = "def my_kernel(x_ptr):\n x = tl.load(x_ptr)" - has_issue, msg = check_triton_impl(code) - assert has_issue, "Should detect missing @triton.jit" - - -def test_triton_missing_tl_ops(): - code = "@triton.jit\ndef my_kernel():\n return 1" - has_issue, msg = check_triton_impl(code) - assert has_issue, "Should detect missing tl.* ops" - +def test_cuda_example_valid(prompts_dir): + """Real CUDA kernel example should pass with default settings.""" + code = read_kernel(prompts_dir / "model_new_ex_add.py") + valid, errors, warnings = validate_kernel_static(code, backend="cuda") + # May have warnings (F import), but should be valid + assert valid or "import F" in str(warnings), f"CUDA example should pass: {errors}" -# ============================================================================= -# Test ThunderKittens Implementation Check -# ============================================================================= -def test_tk_valid(): - code = ''' -using namespace kittens; -__global__ void my_kernel() { - warpgroup::sync(); - rt_bf<16, 16> reg_tile; - st_bf<16, 16> shared_tile; -} -''' - has_issue, msg = check_tk_impl(code) - assert not has_issue, "Valid TK should pass" +def test_triton_example_valid(prompts_dir): + """Real Triton kernel example should pass.""" + code = read_kernel(prompts_dir / "model_new_ex_add_triton.py") + valid, errors, warnings = validate_kernel_static(code, backend="triton") + assert valid or len(warnings) > 0, f"Triton example should pass: {errors}" -def test_tk_missing_warp(): - code = "rt_bf<16, 16> tile;" - has_issue, msg = check_tk_impl(code) - assert has_issue, "Should detect missing warp patterns" +def test_cute_example_valid(prompts_dir): + """Real CuTe/CUTLASS kernel example should pass.""" + code = read_kernel(prompts_dir / "model_new_ex_add_cute.py") + valid, errors, warnings = validate_kernel_static(code, backend="cute") + assert valid or len(warnings) > 0, f"CuTe example should pass: {errors}" -def test_tk_missing_tiles(): - code = "using namespace kittens;\nwarpgroup::sync();" - has_issue, msg = check_tk_impl(code) - assert has_issue, "Should detect missing tile declarations" +def test_tilelang_example_valid(prompts_dir): + """Real TileLang kernel example should pass.""" + code = read_kernel(prompts_dir / "model_new_ex_add_tilelang.py") + valid, errors, warnings = validate_kernel_static(code, backend="tilelang") + assert valid or len(warnings) > 0, f"TileLang example should pass: {errors}" # ============================================================================= -# Test CuTe/CUTLASS Implementation Check +# Adversarial Kernels - Should Detect Issues +# These are known hack patterns from test_kernels/ # ============================================================================= -def test_cute_valid(): - code = "cute::copy(src, dst);" - has_issue, msg = check_cute_impl(code) - assert not has_issue, "Valid CuTe should pass" - - -def test_cute_cutlass_namespace(): - code = "cutlass::gemm::GemmCoord coord;" - has_issue, msg = check_cute_impl(code) - assert not has_issue, "CUTLASS namespace should pass" - - -def test_cute_missing(): - code = "__global__ void kernel() {}" - has_issue, msg = check_cute_impl(code) - assert has_issue, "Should detect missing cute::" - +def test_stream_kernel_flagged(test_kernels_dir): + """Non-default stream kernel should trigger stream_injection warning.""" + code = read_kernel(test_kernels_dir / "non_default_stream_kernel.py") + valid, errors, warnings = validate_kernel_static(code, backend="cuda") + # Stream injection is in warnings by default + all_messages = errors + warnings + has_stream_warning = any("stream" in msg.lower() for msg in all_messages) + # Note: The CUDA code is in a string literal, so static checker may not catch it + # This test documents the limitation -# ============================================================================= -# Test TileLang Implementation Check -# ============================================================================= -def test_tilelang_valid(): - code = "@T.prim_func\ndef kernel(): pass" - has_issue, msg = check_tilelang_impl(code) - assert not has_issue, "Valid TileLang should pass" +def test_result_reuse_kernel_flagged(test_kernels_dir): + """Result reuse (empty tensor) kernel - static checker can't catch this.""" + code = read_kernel(test_kernels_dir / "result_reuse_kernel.py") + valid, errors, warnings = validate_kernel_static(code, backend="cuda") + # This is a runtime check, static checker won't catch it + # Just verify it doesn't crash -def test_tilelang_missing(): - code = "def kernel(): pass" - has_issue, msg = check_tilelang_impl(code) - assert has_issue, "Should detect missing @T.prim_func" +def test_zero_out_kernel_flagged(test_kernels_dir): + """Zero-out kernel - static checker can't catch this.""" + code = read_kernel(test_kernels_dir / "zero_out_kernel.py") + valid, errors, warnings = validate_kernel_static(code, backend="cuda") + # This is a correctness issue, not detectable statically # ============================================================================= -# Test validate_kernel_static Integration +# Hack Patterns - Synthetic Examples # ============================================================================= -def test_validate_cuda_valid(): - code = ''' -cuda_src = """ -__global__ void kernel(float* out) { out[0] = 1.0f; } +def test_bypass_try_except(): + """Try-except fallback should be flagged as error.""" + code = """ +try: + result = custom_kernel(x) +except: + result = torch.matmul(x, w) # Fallback to torch """ -module = load_inline(name="test", cuda_sources=[cuda_src]) - -class ModelNew(torch.nn.Module): - def forward(self, x): - return module.kernel(x) -''' valid, errors, warnings = validate_kernel_static(code, backend="cuda") - assert valid, f"Valid CUDA should pass: {errors}" + assert not valid, "Try-except should be flagged" + assert any("try-except" in e.lower() for e in errors) -def test_validate_with_bypass(): - code = "try:\n x = kernel()\nexcept:\n x = torch.matmul(a, b)" +def test_bypass_pass_statement(): + """Pass statement (inheritance bypass) should be flagged.""" + code = """ +class ModelNew(Model): + def forward(self, x): + pass # Does nothing, inherits parent +""" valid, errors, warnings = validate_kernel_static(code, backend="cuda") - assert not valid, "Should detect bypass" - assert len(errors) > 0 + assert not valid, "Pass statement should be flagged" + assert any("pass" in e.lower() for e in errors) -def test_validate_custom_checks(): - code = "torch.matmul(x, w)" # torch op, no bypass - - # Default: torch_computation_ops is in warnings - valid, errors, warnings = validate_kernel_static( - code, backend="cuda", - forbidden=["code_bypass"], # only check bypass - warnings=["torch_computation_ops"] - ) - # This will fail on cuda_impl (no __global__), but let's check warnings work - assert len(warnings) > 0, "Should have torch op warning" +def test_lazy_eval_make_subclass(): + """_make_subclass (lazy tensor hack) should be flagged.""" + code = """ +fake_tensor = torch.Tensor._make_subclass(FakeTensor, real_tensor) +""" + valid, errors, warnings = validate_kernel_static(code, backend="cuda") + assert not valid, "_make_subclass should be flagged" -# ============================================================================= -# Test Real DSL Examples (No False Positives) -# These tests verify that valid DSL kernels from src/prompts pass the checker -# ============================================================================= +def test_timing_monkey_patch(): + """Monkey patching timing functions should be flagged.""" + code = """ +# Override timing to fake benchmarks +torch.cuda.Event.elapsed_time = lambda self, end: 0.001 +""" + valid, errors, warnings = validate_kernel_static(code, backend="cuda") + assert not valid, "Timing monkey patch should be flagged" -import os -from pathlib import Path -# Path to DSL examples -PROMPTS_DIR = Path(__file__).parent.parent / "prompts" - - -def test_cuda_example_passes(): - """Real CUDA example should pass all strict checks.""" - example_path = PROMPTS_DIR / "model_new_ex_add.py" - if not example_path.exists(): - pytest.skip("CUDA example not found") - - code = example_path.read_text() - valid, errors, warnings = validate_kernel_static( - code, - backend="cuda", - forbidden=["code_bypass"], # Don't check pytorch_wrap since example imports F unused - warnings=[] - ) - assert valid, f"CUDA example should pass: {errors}" - - -def test_triton_example_passes(): - """Real Triton example should pass all strict checks.""" - example_path = PROMPTS_DIR / "model_new_ex_add_triton.py" - if not example_path.exists(): - pytest.skip("Triton example not found") - - code = example_path.read_text() - valid, errors, warnings = validate_kernel_static( - code, - backend="triton", - forbidden=["code_bypass", "triton_impl"], - warnings=[] - ) - assert valid, f"Triton example should pass: {errors}" - - -def test_cute_example_passes(): - """Real CuTe/CUTLASS example should pass all strict checks.""" - example_path = PROMPTS_DIR / "model_new_ex_add_cute.py" - if not example_path.exists(): - pytest.skip("CuTe example not found") - - code = example_path.read_text() - valid, errors, warnings = validate_kernel_static( - code, - backend="cute", - forbidden=["code_bypass", "cute_impl"], - warnings=[] - ) - assert valid, f"CuTe example should pass: {errors}" - - -def test_tilelang_example_passes(): - """Real TileLang example should pass all strict checks.""" - example_path = PROMPTS_DIR / "model_new_ex_add_tilelang.py" - if not example_path.exists(): - pytest.skip("TileLang example not found") - - code = example_path.read_text() - valid, errors, warnings = validate_kernel_static( - code, - backend="tilelang", - forbidden=["code_bypass", "tilelang_impl"], - warnings=[] - ) - assert valid, f"TileLang example should pass: {errors}" +def test_thread_injection(): + """Threading in kernel code should be flagged.""" + code = """ +import threading +t = threading.Thread(target=background_work) +t.start() +""" + valid, errors, warnings = validate_kernel_static(code, backend="cuda") + assert not valid, "Threading should be flagged" if __name__ == "__main__": From 92395ecaff916a08eed1b0c9f463f90bdcaf5224 Mon Sep 17 00:00:00 2001 From: Simon Guo Date: Mon, 5 Jan 2026 20:49:54 +0000 Subject: [PATCH 09/11] gate behind a flag for now as a start for the checker system --- EVAL.md | 3 +++ README.md | 18 ++-------------- scripts/generate_and_eval_single_sample.py | 21 +++++++++++-------- .../generate_and_eval_single_sample_modal.py | 21 +++++++++++-------- scripts/generate_samples.py | 21 +++++++++++-------- scripts/run_and_check.py | 19 ++++++++++------- src/kernelbench/kernel_static_checker.py | 2 ++ 7 files changed, 54 insertions(+), 51 deletions(-) diff --git a/EVAL.md b/EVAL.md index c6f70fb0..b9880560 100644 --- a/EVAL.md +++ b/EVAL.md @@ -36,6 +36,9 @@ We have (and continue to) implement various approaches to conduct kernel timing Check out `timing.py` to see available timing methods and `src/unit_tests/test_eval_timing.py` to test out various timing methods (including leveraging `cuda_event` marker, Triton `do_bench`, `host_time` E2E time). @palic and team is working on a blogpost explaining the different tradeoffs soon. +### Checkers +There are potentially many ways model might reward hack and we would like to catch the known ways through checkers [experimental and WIP]. We start with `kernel_static_checker.py`, which is a regex-based checker on the genenrated code against set of rules. We plan to add AST-based, LM-as-a-judge, and more runtime checks in the future. We welcome suggestions and contributions here. + ### Unit Tests with Adversarial Examples We've included some unit tests for the eval script in `src/unit_tests/test_eval_adversarial.py`. These tests run adversarial kernels (see `src/unit_tests/test_kernels/`) that contain examples of reward hacking that we've seen from LLMs and ensures that the eval script catches them, either by failing their correctness checks or flagging them for excessive speedups. Examples include: - Reusing computations cached during the PyTorch reference diff --git a/README.md b/README.md index da701a16..3686574a 100644 --- a/README.md +++ b/README.md @@ -117,24 +117,10 @@ uv run python scripts/generate_and_eval_single_sample.py dataset_src=huggingface * **`precision`** - You can specify the precision of tensor by `precision=fp32`. Currently all of our reported results are `fp32` but we added support for `fp16` & `bf16`. * **`backend`** - We are also supporting other GPU programming languages beyond `cuda`. Simply specify `backend=triton`. For now we support DSLs: `cuda`, `triton`, `cute`, `tilelang`, `thunderkittens`. -Check the config fields for comprehensive set of options. Note we provide the model with a one-shot example by default along with the minimum set of info; you can check out other prompt settings or construct your own in `src/prompt_constructor_toml.py`. - -### Running Thunderkittens Locally -If you plan on using `scripts/generate_and_eval_single_sample.py` using `backend=thunderkittens`, make sure to git clone the ThunderKittens repo and you set the following environment variable to point to your local ThunderKittens directory: - -```bash -export THUNDERKITTENS_ROOT=/Users/willychan/Desktop/projects/KernelBench/ThunderKittens -``` -As seen in `src/kernelbench/prompts/model_new_ex_add_thunderkittens.py`, the generated kernels should have the following line: +Note on setting up ThunderKittens (TK) locally: to use `backend=thunderkittens`, you need to git clone the ThunderKittens repo and set the following environment variable to point to your local ThunderKittens directory, `export THUNDERKITTENS_ROOT=`, and all ThunderKitten programs as shown in the [example](src/kernelbench/prompts/model_new_ex_add_thunderkittens.py), should contain `tk_root = os.environ.get("THUNDERKITTENS_ROOT", "/root/ThunderKittens")`, which enable the kernel to include the right TK primitives. In addition, we only support BF16 for TK right now. -```bash -tk_root = os.environ.get("THUNDERKITTENS_ROOT", "/root/ThunderKittens") -``` - -This allows the kernel to include the right TK primitives. - -*NOTE*: Right now, all generated ThunderKittens kernels are required to be in datatype format BF16. FP16 support is TBD. +Check the config fields for comprehensive set of options. Note we provide the model with a one-shot example by default along with the minimum set of info; you can check out other prompt settings or construct your own in `src/prompt_constructor_toml.py`. ### Run on all problems diff --git a/scripts/generate_and_eval_single_sample.py b/scripts/generate_and_eval_single_sample.py index dbfe3785..0b571792 100644 --- a/scripts/generate_and_eval_single_sample.py +++ b/scripts/generate_and_eval_single_sample.py @@ -83,6 +83,8 @@ def __init__(self): self.hardware_gpu_name = None self.custom_prompt_key = None + self.check_kernel = True # [experimental] optional static checker catching potential hacking patterns + def verbose_logging(self): self.log = True self.log_prompt = True @@ -262,15 +264,16 @@ def main(config: EvalConfig): # Optional: static code checker for kernel code using regex matching # NOTE: by no means is this checker complete, but it could help catch some potential hacks - from kernelbench.kernel_static_checker import validate_kernel_static - static_check_status, errors, warnings = validate_kernel_static( - custom_kernel, - backend=config.backend, - precision=config.precision, - ) - assert static_check_status, f"Static check failed for level {config.level} problem {config.problem_id}. Errors: {errors}. Warnings: {warnings}" - if warnings: - print(f"Static check warnings for level {config.level} problem {config.problem_id}: {warnings}") + if config.check_kernel: + from kernelbench.kernel_static_checker import validate_kernel_static + static_check_status, errors, warnings = validate_kernel_static( + custom_kernel, + backend=config.backend, + precision=config.precision, + ) + assert static_check_status, f"Static check failed for level {config.level} problem {config.problem_id}. Errors: {errors}. Warnings: {warnings}" + if warnings: + print(f"Static check warnings for level {config.level} problem {config.problem_id}: {warnings}") # this should be optional if config.log: diff --git a/scripts/generate_and_eval_single_sample_modal.py b/scripts/generate_and_eval_single_sample_modal.py index 7d964cf6..2a8e6aba 100644 --- a/scripts/generate_and_eval_single_sample_modal.py +++ b/scripts/generate_and_eval_single_sample_modal.py @@ -80,6 +80,8 @@ def __init__(self): self.hardware_gpu_name = None self.custom_prompt_key = None + self.check_kernel = True # [experimental] optional static checker catching potential hacking patterns + def verbose_logging(self): self.log = True self.log_prompt = True @@ -285,15 +287,16 @@ def main(config: EvalConfig): # Optional: static code checker for kernel code using regex matching # NOTE: by no means is this checker complete, but it could help catch some potential hacks - from kernelbench.kernel_static_checker import validate_kernel_static - static_check_status, errors, warnings = validate_kernel_static( - custom_kernel, - backend=config.backend, - precision=config.precision, - ) - assert static_check_status, f"Static check failed for level {config.level} problem {config.problem_id}. Errors: {errors}. Warnings: {warnings}" - if warnings: - print(f"Static check warnings for level {config.level} problem {config.problem_id}: {warnings}") + if config.check_kernel: + from kernelbench.kernel_static_checker import validate_kernel_static + static_check_status, errors, warnings = validate_kernel_static( + custom_kernel, + backend=config.backend, + precision=config.precision, + ) + assert static_check_status, f"Static check failed for level {config.level} problem {config.problem_id}. Errors: {errors}. Warnings: {warnings}" + if warnings: + print(f"Static check warnings for level {config.level} problem {config.problem_id}: {warnings}") # this should be optional if config.log: diff --git a/scripts/generate_samples.py b/scripts/generate_samples.py index 1b5bf09f..40d718f8 100644 --- a/scripts/generate_samples.py +++ b/scripts/generate_samples.py @@ -85,6 +85,8 @@ def __init__(self): self.hardware_gpu_name = None self.custom_prompt_key = None + self.check_kernel = True # [experimental] optional static checker catching potential hacking patterns + def greedy(self): # For greedy decoding, epsecially baseline eval self.greedy_sample = True @@ -165,15 +167,16 @@ def generate_sample_single( # Optional: we provide a static code checker for kernel code using regex matching # NOTE: by no means, is this checker complete, but it might could help catch some potential hacks and issues - static_check_status, error, warnings = validate_kernel_static(custom_kernel, - backend=config.backend, - precision=config.precision, - # uses the default set of forbidden and warning patterns, - # you could adapt the patterns to your own setting (degree of banning cuda stream, allowing some torch ops) - ) - assert static_check_status, f"Static check failed for sample {work.sample_id} for problem {problem_number}: {problem_name}. Error: {error}. Warnings: {warnings}" - if warnings: - print(f"Static check warnings for sample {work.sample_id} for problem {problem_number}: {problem_name}. Warnings: {warnings}") + if config.check_kernel: + static_check_status, error, warnings = validate_kernel_static(custom_kernel, + backend=config.backend, + precision=config.precision, + # uses the default set of forbidden and warning patterns, + # you could adapt the patterns to your own setting (degree of banning cuda stream, allowing some torch ops) + ) + assert static_check_status, f"Static check failed for sample {work.sample_id} for problem {problem_number}: {problem_name}. Error: {error}. Warnings: {warnings}" + if warnings: + print(f"Static check warnings for sample {work.sample_id} for problem {problem_number}: {problem_name}. Warnings: {warnings}") if config.verbose: print( diff --git a/scripts/run_and_check.py b/scripts/run_and_check.py index b8c1d89c..8d150b7c 100644 --- a/scripts/run_and_check.py +++ b/scripts/run_and_check.py @@ -121,6 +121,8 @@ def __init__(self): self.precision = "fp32" self.backend = "cuda" + self.check_kernel = True # [experimental] optional static checker catching potential hacking patterns + def __repr__(self): return f"ScriptConfig({self.to_dict()})" @@ -282,14 +284,15 @@ def main(config: ScriptConfig): # Optional: static code checker for kernel code using regex matching # NOTE: by no means is this checker complete, but it could help catch some potential hacks - static_check_status, errors, warnings = validate_kernel_static( - kernel_src, - backend=config.backend, - precision=config.precision, - ) - assert static_check_status, f"Static check failed. Errors: {errors}. Warnings: {warnings}" - if warnings: - print(f"[WARN] Static check warnings: {warnings}") + if config.check_kernel: + static_check_status, errors, warnings = validate_kernel_static( + kernel_src, + backend=config.backend, + precision=config.precision, + ) + assert static_check_status, f"Static check failed. Errors: {errors}. Warnings: {warnings}" + if warnings: + print(f"[WARN] Static check warnings: {warnings}") # Start Evaluation assert config.eval_mode in ["local", "modal"], "eval_mode must be either 'local' or 'modal'" diff --git a/src/kernelbench/kernel_static_checker.py b/src/kernelbench/kernel_static_checker.py index b63d5596..8c731d71 100644 --- a/src/kernelbench/kernel_static_checker.py +++ b/src/kernelbench/kernel_static_checker.py @@ -75,7 +75,9 @@ def check_code_bypass(code: str) -> Tuple[bool, str]: # Since KernelBench problems uses PyTorch as a reference, there could be settigs where # Model generated code # 1. Replaces some (not all) ops with custom kernels, others are kept in Torch +# --> More practical from a performance perspective (ie. make better systems) as you want to use whatever makes the best system for your use case. # 2. All compuational ops must be replaced with custom kernels +# --> Could be helpful from an eval (model ability on transpile + optimization) / RL training perspective # Depends the setting you use, you can move the checks below (pytorch_wrap, torch_computation_ops) # from WARNING to STRICT From 53004e9488a984db3cd04c5b3df3bcb3f964cead Mon Sep 17 00:00:00 2001 From: ethanboneh Date: Mon, 5 Jan 2026 19:59:12 -0800 Subject: [PATCH 10/11] precision downgrades --- src/kernelbench/kernel_static_checker.py | 117 +++++- src/kernelbench/unit_tests/test_precision.py | 395 +++++++++++++++++++ 2 files changed, 506 insertions(+), 6 deletions(-) create mode 100644 src/kernelbench/unit_tests/test_precision.py diff --git a/src/kernelbench/kernel_static_checker.py b/src/kernelbench/kernel_static_checker.py index 8c731d71..86c8abb0 100644 --- a/src/kernelbench/kernel_static_checker.py +++ b/src/kernelbench/kernel_static_checker.py @@ -426,11 +426,105 @@ def check_timing_event_patch(code: str) -> Tuple[bool, str]: return (False, "") -# ============================================================================= -# TODO: Future checks -# ============================================================================= -# def check_precision_downgrade(code): ... # .half(), .bfloat16() - could be soft check -# this a bit tricky and up to the user to decide +# <========= PRECISION DOWNGRADE CHECKS =========> +# Rationale: Models may downgrade precision internally (FP32 -> FP16) +# to make kernels faster while still returning FP32 output. This is a reward +# hacking pattern where models cheat on timing while appearing correct. +# This might be a valid optimization, but is still worth flagging as a warning. +# +# We use explicit, high-confidence patterns that indicate intentional precision +# downgrading. These patterns have minimal false positives and clear semantic intent. + +# Specific patterns that indicate FP32 -> FP16 precision downgrading +FP32_TO_FP16_PATTERNS = [ + # ========== CUDA / CUDA C++ ========== + # 1.1 Explicit float -> half intrinsics (⭐ gold standard) + # __float2half(f), __float2half_rn(f) + r"__float2half(_rn)?\s*\(", + + # 1.2 Explicit C-style cast to __half + # (__half)f + r"\(\s*__half\s*\)\s*[\w\->\.]+", + + # 1.3 static_cast / static_cast<__half> + # static_cast(f), static_cast<__half>(f) + r"static_cast\s*<\s*(__half|half)\s*>\s*\(", + + # ========== Triton (Python) ========== + # 2.1 Explicit tl.astype(..., tl.float16) (⭐ best signal) + # tl.astype(x, tl.float16) + r"tl\.astype\s*\(\s*[^,]+,\s*tl\.float16\s*\)", + + # ========== CUTLASS ========== + # 3.1 NumericConverter float -> half (⭐ extremely reliable) + # NumericConverter + r"NumericConverter\s*<\s*half_t\s*,\s*float\s*>", + + # 3.2 Epilogue output type = FP16 + # LinearCombination + r"LinearCombination\s*<\s*half_t", + + # ========== CUTE (when used directly) ========== + # 4.1 Explicit type conversion utility + # type_convert(x) + r"type_convert\s*<\s*half_t\s*>\s*\(", + + # ========== ThunderKittens ========== + # 5.1 Explicit half types in cast contexts + # tk::half(x) + r"tk::half\s*\(", + + # ========== Additional gaping patterns ========== + # PyTorch explicit casts (common in inline CUDA contexts) + r"\.half\s*\(", # .half() -> FP16 + r"\.float16\s*\(", # .float16() -> FP16 + r"\.to\s*\(\s*torch\.(float16|half)\b", # .to(torch.float16) + r"\.to\s*\(\s*dtype\s*=\s*torch\.(float16|half)\b", # .to(dtype=torch.float16) +] + +def check_precision_downgrade(code: str, precision: str = "fp32") -> Tuple[bool, str]: + """ + Check for FP32 -> FP16 precision downgrading patterns in kernel code. + + Detects code that downgrades precision from FP32 to FP16 internally, + which can make kernels faster but with reduced precision. This is a reward + hacking pattern where models cheat on timing while appearing correct. + + Currently only checks for FP32 -> FP16 downgrades. Other precision combinations + (e.g., FP16 -> BF16) are not flagged. + + Args: + code: Kernel source code + precision: Required precision - only checks when "fp32" or "float32" + + Returns: + (True, error_message) if FP32 -> FP16 downgrade detected + (False, "") if no downgrade detected + + Examples of detected patterns: + - .half(), .float16() + - .to(torch.float16), .to(torch.half) + - dtype=torch.float16 + - __half, half2 (CUDA) + - tl.float16 (Triton) + """ + code = _strip_comments(code) + precision = precision.lower() + + # Normalize precision to standard form + precision_map = {"fp32": "fp32", "float32": "fp32", "fp16": "fp16", "bf16": "bf16", "bfloat16": "bf16"} + precision = precision_map.get(precision, precision) + + # Only check for FP32 -> FP16 downgrades + if precision != "fp32": + return (False, "") + + # Check for FP16 patterns + for pattern in FP32_TO_FP16_PATTERNS: + if re.search(pattern, code): + return (True, "Precision downgrade detected: required FP32 but code uses FP16") + + return (False, "") # ============================================================================= # In the future, we can add a AST-based checker and a LM-as-a-judge checker @@ -454,6 +548,7 @@ def check_timing_event_patch(code: str) -> Tuple[bool, str]: "stream_injection": check_stream_injection, "thread_injection": check_thread_injection, "lazy_eval": check_lazy_eval, + "precision_downgrade": check_precision_downgrade, # precision-dependent # Backend-specific implementation checks # should be strict @@ -464,6 +559,9 @@ def check_timing_event_patch(code: str) -> Tuple[bool, str]: "tilelang_impl": check_tilelang_impl, } +# Checks that require additional parameters beyond just code +PRECISION_DEPENDENT_CHECKS = {"precision_downgrade"} + # Here are some presets for you to use # You are welcome to adapt them to your settings # These checks are NECESSARY for all kernels (strict = error) @@ -492,6 +590,7 @@ def check_timing_event_patch(code: str) -> Tuple[bool, str]: "pytorch_wrap", "torch_computation_ops", "stream_injection", # could have legitimate uses (async ops), but should be careful! + "precision_downgrade", # precision downgrading - can be intentional but often a hack ] @@ -541,7 +640,13 @@ def validate_kernel_static( for check_name in set(forbidden_checks + warning_checks): if check_name not in CHECK_FUNCTIONS: continue - has_issue, msg = CHECK_FUNCTIONS[check_name](code) + + # Handle precision-dependent checks + if check_name in PRECISION_DEPENDENT_CHECKS: + has_issue, msg = CHECK_FUNCTIONS[check_name](code, precision) + else: + has_issue, msg = CHECK_FUNCTIONS[check_name](code) + if has_issue: if check_name in forbidden_checks: errors.append(msg) diff --git a/src/kernelbench/unit_tests/test_precision.py b/src/kernelbench/unit_tests/test_precision.py new file mode 100644 index 00000000..1e8a2299 --- /dev/null +++ b/src/kernelbench/unit_tests/test_precision.py @@ -0,0 +1,395 @@ +""" +Unit tests for precision downgrade detection in FP32 kernels. + +Tests the check_precision_downgrade function to ensure it correctly detects +various patterns that indicate precision downgrading (FP32 -> FP16), +which is a reward hacking pattern. + +Note: The current implementation only checks for FP32 -> FP16 downgrades, +not BF16 downgrades. + +Run with pytest: + pytest src/kernelbench/unit_tests/test_precision.py -v + or + uv run pytest src/kernelbench/unit_tests/test_precision.py -v +""" + +import os +import sys +import pytest + +# Add src directory to path for imports (consistent with other test files) +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) + +from kernelbench.kernel_static_checker import check_precision_downgrade + + +# ============================================================================ +# Test Cases for FP32 -> FP16 Precision Downgrades (PyTorch patterns) +# ============================================================================ + +def test_fp32_half_method(): + """Test detection of .half() method call downgrading FP32 to FP16.""" + code = """ + def forward(self, x): + x = x.half() + return x * 2 + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect .half() method call" + assert "FP16" in message + + +def test_fp32_float16_method(): + """Test detection of .float16() method call.""" + code = """ + x = input_tensor.float16() + return x + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect .float16() method call" + assert "FP16" in message + + +def test_fp32_to_torch_half(): + """Test detection of .to(torch.half) pattern.""" + code = """ + def forward(self, x): + x = x.to(torch.half) + return x + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect .to(torch.half)" + assert "FP16" in message + + +def test_fp32_to_torch_float16(): + """Test detection of .to(torch.float16) pattern.""" + code = """ + x = x.to(torch.float16) + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect .to(torch.float16)" + assert "FP16" in message + + +def test_fp32_to_dtype_float16(): + """Test detection of .to(dtype=torch.float16) pattern.""" + code = """ + def forward(self, x): + return x.to(dtype=torch.float16) + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect .to(dtype=torch.float16)" + assert "FP16" in message + + +def test_fp32_to_dtype_half(): + """Test detection of .to(dtype=torch.half) pattern.""" + code = """ + x.to(dtype=torch.half) + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect .to(dtype=torch.half)" + assert "FP16" in message + + +# ============================================================================ +# Test Cases for FP32 -> FP16 Precision Downgrades (CUDA patterns) +# ============================================================================ + +def test_fp32_cuda_float2half(): + """Test detection of CUDA __float2half intrinsic.""" + code = """ + __global__ void kernel(float* input, __half* output) { + output[0] = __float2half(input[0]); + } + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect CUDA __float2half" + assert "FP16" in message + + +def test_fp32_cuda_float2half_rn(): + """Test detection of CUDA __float2half_rn intrinsic.""" + code = """ + __half result = __float2half_rn(value); + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect CUDA __float2half_rn" + assert "FP16" in message + + +def test_fp32_cuda_cast_to_half(): + """Test detection of CUDA C-style cast to __half.""" + code = """ + __half h = (__half)float_value; + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect CUDA cast to __half" + assert "FP16" in message + + +def test_fp32_cuda_static_cast_half(): + """Test detection of CUDA static_cast.""" + code = """ + __half h = static_cast<__half>(float_value); + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect CUDA static_cast<__half>" + assert "FP16" in message + + +# ============================================================================ +# Test Cases for FP32 -> FP16 Precision Downgrades (Triton patterns) +# ============================================================================ + +def test_fp32_triton_astype_float16(): + """Test detection of Triton tl.astype(..., tl.float16).""" + code = """ + @triton.jit + def kernel(X, Y): + x = tl.load(X) + x_fp16 = tl.astype(x, tl.float16) + tl.store(Y, x_fp16) + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect Triton tl.astype(..., tl.float16)" + assert "FP16" in message + + +# ============================================================================ +# Test Cases for FP32 -> FP16 Precision Downgrades (CUTLASS patterns) +# ============================================================================ + +def test_fp32_cutlass_numeric_converter(): + """Test detection of CUTLASS NumericConverter.""" + code = """ + NumericConverter converter; + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect CUTLASS NumericConverter" + assert "FP16" in message + + +def test_fp32_cutlass_linear_combination(): + """Test detection of CUTLASS LinearCombination.""" + code = """ + LinearCombination epilogue; + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect CUTLASS LinearCombination" + assert "FP16" in message + + +def test_fp32_cute_type_convert(): + """Test detection of CuTe type_convert.""" + code = """ + auto result = type_convert(input); + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect CuTe type_convert" + assert "FP16" in message + + +# ============================================================================ +# Test Cases for FP32 -> FP16 Precision Downgrades (ThunderKittens patterns) +# ============================================================================ + +def test_fp32_thunderkittens_half(): + """Test detection of ThunderKittens tk::half().""" + code = """ + using namespace tk; + auto h = tk::half(value); + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect ThunderKittens tk::half()" + assert "FP16" in message + + +# ============================================================================ +# Test Cases for Valid FP32 Code (Should NOT be detected) +# ============================================================================ + +def test_fp32_legitimate_code_no_downgrade(): + """Test that legitimate FP32 code is not flagged.""" + code = """ + def forward(self, x): + # Legitimate FP32 operations + x = x * 2.0 + y = torch.matmul(x, x.t()) + return y.float() # This is fine, float() returns float32 + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is False, "Should not detect downgrade in legitimate FP32 code" + assert message == "" + + +def test_fp32_with_comments_mentioning_half(): + """Test that comments mentioning half precision don't trigger false positives.""" + code = """ + def forward(self, x): + # Note: This should use FP32, not FP16 + # Don't use .half() here! + x = x * 2.0 + return x + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is False, "Comments should not trigger detection" + + +def test_fp32_string_literals(): + """Test that string literals mentioning precision don't trigger false positives.""" + code = """ + def forward(self, x): + error_msg = "dtype=torch.float16 is not allowed" + print(error_msg) + return x + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is False, "String literals should not trigger detection" + + +def test_fp32_function_names_containing_half(): + """Test that function names containing 'half' don't trigger false positives.""" + code = """ + def compute_half_tensor(self, x): + # Function name contains 'half' but doesn't downgrade precision + return x * 0.5 + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is False, "Function names should not trigger detection" + + +# ============================================================================ +# Test Cases for Edge Cases +# ============================================================================ + +def test_fp32_case_insensitive_precision(): + """Test that precision parameter is case-insensitive.""" + code = """ + x = x.half() + """ + detected1, _ = check_precision_downgrade(code, precision="FP32") + detected2, _ = check_precision_downgrade(code, precision="fp32") + detected3, _ = check_precision_downgrade(code, precision="Fp32") + assert detected1 == detected2 == detected3 == True, "Should handle case-insensitive precision" + + +def test_fp32_alternative_precision_names(): + """Test that alternative precision names are normalized correctly.""" + code = """ + x = x.half() + """ + detected1, _ = check_precision_downgrade(code, precision="float32") + detected2, _ = check_precision_downgrade(code, precision="fp32") + assert detected1 == detected2 == True, "Should normalize float32 to fp32" + + +def test_fp32_non_fp32_precision_skips_check(): + """Test that non-FP32 precision skips the check (implementation only checks FP32).""" + code = """ + x = x.half() + """ + detected, message = check_precision_downgrade(code, precision="fp16") + assert detected is False, "Should skip check for non-FP32 precision" + assert message == "" + + +def test_fp32_unknown_precision_skips_check(): + """Test that unknown precision skips the check.""" + code = """ + x = x.half() + """ + detected, message = check_precision_downgrade(code, precision="int8") + assert detected is False, "Should skip check for unknown precision" + assert message == "" + + +def test_fp32_empty_code(): + """Test that empty code doesn't trigger detection.""" + code = "" + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is False, "Empty code should not trigger detection" + + +def test_fp32_whitespace_only(): + """Test that whitespace-only code doesn't trigger detection.""" + code = " \n\n\t \n " + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is False, "Whitespace-only code should not trigger detection" + + +# ============================================================================ +# Test Cases for Real-World Scenarios +# ============================================================================ + +def test_fp32_matmul_with_hidden_downgrade(): + """Test detection in a realistic matmul kernel that downgrades internally.""" + code = """ + @triton.jit + def matmul_kernel(A, B, C, M, N, K): + # Compute matrix multiplication + a = tl.load(A) + b = tl.load(B) + + # Sneaky precision downgrade + a = tl.astype(a, tl.float16) + b = tl.astype(b, tl.float16) + + c = tl.dot(a, b) + tl.store(C, c) + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect hidden precision downgrade in matmul" + assert "FP16" in message + + +def test_fp32_cuda_kernel_with_half(): + """Test detection in a realistic CUDA kernel.""" + code = """ + __global__ void add_kernel(float* a, float* b, float* c, int n) { + int idx = blockIdx.x * blockDim.x + threadIdx.x; + if (idx < n) { + __half a_half = __float2half(a[idx]); + __half b_half = __float2half(b[idx]); + c[idx] = __half2float(a_half + b_half); + } + } + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect CUDA kernel using __float2half" + assert "FP16" in message + + +def test_fp32_complex_code_with_downgrade(): + """Test detection in complex code with multiple operations.""" + code = """ + def forward(self, x, y): + # Some preprocessing + x = x * 2.0 + y = y + 1.0 + + # Main computation with downgrade + x = x.to(dtype=torch.float16) + result = torch.matmul(x, y) + + # Post-processing + result = result * 3.0 + return result + """ + detected, message = check_precision_downgrade(code, precision="fp32") + assert detected is True, "Should detect downgrade in complex code" + assert "FP16" in message + + +# ============================================================================ +# Note on BF16 Tests +# ============================================================================ +# The current implementation only checks for FP32 -> FP16 downgrades. +# BF16 downgrade detection is not yet implemented. These tests document +# expected behavior when BF16 support is added in the future. + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) From 2aab8cd11a5e8ade4fdde0867e54bb04d8d5225b Mon Sep 17 00:00:00 2001 From: ethanboneh Date: Mon, 5 Jan 2026 20:04:37 -0800 Subject: [PATCH 11/11] precision downgrade check and test --- src/kernelbench/kernel_static_checker.py | 6 +- .../unit_tests/test_validate_kernel_static.py | 423 ++++++++++++++++++ 2 files changed, 427 insertions(+), 2 deletions(-) create mode 100644 src/kernelbench/unit_tests/test_validate_kernel_static.py diff --git a/src/kernelbench/kernel_static_checker.py b/src/kernelbench/kernel_static_checker.py index 86c8abb0..c8832a1a 100644 --- a/src/kernelbench/kernel_static_checker.py +++ b/src/kernelbench/kernel_static_checker.py @@ -21,7 +21,7 @@ """ import re -from typing import List, Tuple, Dict, Any, Optional, Callable +from typing import List, Tuple, Dict, Any, Optional, Callable, Union def _strip_comments(code: str) -> str: """Remove # and // comments from code.""" @@ -535,7 +535,9 @@ def check_precision_downgrade(code: str, precision: str = "fp32") -> Tuple[bool, # REGISTRY & PRESETS # ============================================================================= -CHECK_FUNCTIONS: Dict[str, Callable[[str], Tuple[bool, str]]] = { +# Check functions can take either (code) or (code, precision) arguments +# Most checks take only code, but precision-dependent checks take both +CHECK_FUNCTIONS: Dict[str, Union[Callable[[str], Tuple[bool, str]], Callable[[str, str], Tuple[bool, str]]]] = { # Bypass checks (strict) "code_bypass": check_code_bypass, "pytorch_wrap": check_pytorch_wrap, diff --git a/src/kernelbench/unit_tests/test_validate_kernel_static.py b/src/kernelbench/unit_tests/test_validate_kernel_static.py new file mode 100644 index 00000000..55d97a2c --- /dev/null +++ b/src/kernelbench/unit_tests/test_validate_kernel_static.py @@ -0,0 +1,423 @@ +""" +Unit tests for validate_kernel_static function. + +Tests the main entry point function to ensure it correctly: +- Passes precision to precision-dependent checks +- Categorizes errors vs warnings correctly +- Handles backend-specific checks +- Respects forbidden/warnings parameters +- Returns correct output format + +Run with pytest: + pytest src/kernelbench/unit_tests/test_validate_kernel_static.py -v + or + uv run pytest src/kernelbench/unit_tests/test_validate_kernel_static.py -v +""" + +import os +import sys +import pytest + +# Add src directory to path for imports (consistent with other test files) +sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) + +from kernelbench.kernel_static_checker import validate_kernel_static + + +# ============================================================================ +# Test Basic Function Signature and Return Values +# ============================================================================ + +def test_validate_kernel_static_returns_tuple(): + """Test that validate_kernel_static returns a tuple of (valid, errors, warnings).""" + code = "x = 1 + 1" + result = validate_kernel_static(code) + + assert isinstance(result, tuple), "Should return a tuple" + assert len(result) == 3, "Should return (valid, errors, warnings)" + valid, errors, warnings = result + assert isinstance(valid, bool), "First element should be bool" + assert isinstance(errors, list), "Second element should be list" + assert isinstance(warnings, list), "Third element should be list" + + +def test_validate_kernel_static_defaults(): + """Test that validate_kernel_static works with default parameters.""" + code = "x = 1 + 1" + valid, errors, warnings = validate_kernel_static(code) + + # Should work without errors for simple valid code + assert isinstance(valid, bool) + assert isinstance(errors, list) + assert isinstance(warnings, list) + + +# ============================================================================ +# Test Precision Parameter Passing +# ============================================================================ + +def test_precision_passed_to_precision_checker_fp32(): + """Test that precision parameter is correctly passed to precision-dependent checks.""" + # Code with FP32 -> FP16 downgrade + code = """ + def forward(self, x): + x = x.half() # Precision downgrade + return x + """ + + # With fp32 precision, should detect downgrade (as warning by default) + valid, errors, warnings = validate_kernel_static(code, precision="fp32") + + # Check that precision downgrade was detected (should be in warnings by default) + all_messages = errors + warnings + has_precision_warning = any("precision" in msg.lower() or "fp16" in msg.lower() + for msg in all_messages) + assert has_precision_warning, "Should detect precision downgrade with fp32 precision" + + +def test_precision_passed_to_precision_checker_fp16(): + """Test that fp16 precision skips FP32 -> FP16 downgrade check.""" + # Code with FP32 -> FP16 downgrade + code = """ + def forward(self, x): + x = x.half() # Precision downgrade + return x + """ + + # With fp16 precision, precision downgrade check should be skipped + valid, errors, warnings = validate_kernel_static(code, precision="fp16") + + # Should not detect precision downgrade (check is skipped for non-FP32) + all_messages = errors + warnings + has_precision_warning = any("precision" in msg.lower() or "fp16" in msg.lower() + for msg in all_messages) + # This is expected - the check only runs for fp32 + # So for fp16, it won't flag this + + +def test_precision_case_insensitive(): + """Test that precision parameter is case-insensitive.""" + code = """ + def forward(self, x): + x = x.half() + return x + """ + + # Test different case variations + result1 = validate_kernel_static(code, precision="FP32") + result2 = validate_kernel_static(code, precision="fp32") + result3 = validate_kernel_static(code, precision="Fp32") + + # All should produce the same result + assert result1 == result2 == result3, "Precision should be case-insensitive" + + +def test_precision_alternative_names(): + """Test that alternative precision names are normalized.""" + code = """ + def forward(self, x): + x = x.half() + return x + """ + + # float32 should be normalized to fp32 + result1 = validate_kernel_static(code, precision="float32") + result2 = validate_kernel_static(code, precision="fp32") + + assert result1 == result2, "float32 should be normalized to fp32" + + +# ============================================================================ +# Test Error vs Warning Categorization +# ============================================================================ + +def test_strict_checks_are_errors(): + """Test that strict checks (like code_bypass) produce errors.""" + code = """ + try: + result = custom_kernel(x) + except: + result = torch.matmul(x, w) # Fallback to torch + """ + + valid, errors, warnings = validate_kernel_static(code) + + assert not valid, "Code with strict violations should be invalid" + assert len(errors) > 0, "Strict checks should produce errors, not warnings" + assert any("try-except" in e.lower() or "bypass" in e.lower() + for e in errors), "Should flag bypass in errors" + + +def test_warning_checks_are_warnings(): + """Test that warning checks produce warnings, not errors.""" + code = """ + def forward(self, x): + x = x.half() # Precision downgrade - in warnings by default + return x + """ + + # Test with default settings - precision_downgrade should be in warnings + valid, errors, warnings = validate_kernel_static( + code, + precision="fp32" + # Using defaults - precision_downgrade is in WARNING_CHECKS + ) + + # Check that precision downgrade message is in warnings (if detected) + # Note: backend impl checks might add errors, but precision should be in warnings + precision_warnings = [w for w in warnings if "precision" in w.lower() or "fp16" in w.lower()] + precision_errors = [e for e in errors if "precision" in e.lower() or "fp16" in e.lower()] + + if precision_warnings or precision_errors: + # If precision downgrade is detected, it should be in warnings, not errors + assert len(precision_warnings) > 0, "Precision downgrade should be in warnings (default)" + assert len(precision_errors) == 0, "Precision downgrade should not be in errors (default)" + + +def test_custom_forbidden_checks(): + """Test that custom forbidden checks produce errors.""" + code = """ + def forward(self, x): + x = x.half() # Precision downgrade + return x + """ + + # Make precision_downgrade a forbidden check (error) instead of warning + valid, errors, warnings = validate_kernel_static( + code, + precision="fp32", + forbidden=["precision_downgrade"] + ) + + assert not valid, "Should be invalid when precision_downgrade is forbidden" + assert len(errors) > 0, "Forbidden checks should produce errors" + assert any("precision" in e.lower() or "fp16" in e.lower() + for e in errors), "Should flag precision downgrade in errors" + + +def test_custom_warnings_list(): + """Test that custom warnings list works.""" + code = """ + try: + result = custom_kernel(x) + except: + result = torch.matmul(x, w) + """ + + # Move code_bypass to warnings instead of errors + # Use a backend that won't add strict impl checks + valid, errors, warnings = validate_kernel_static( + code, + backend="cuda", # Explicit backend + forbidden=[], # No forbidden checks + warnings=["code_bypass"] # Make bypass a warning + ) + + # Note: Backend might add impl checks, so we check that code_bypass + # appears in warnings (not errors) if it's detected + all_messages = errors + warnings + bypass_messages = [msg for msg in all_messages if "bypass" in msg.lower() or "try-except" in msg.lower()] + + if bypass_messages: + # If bypass is detected, it should be in warnings, not errors + bypass_in_warnings = any(msg in warnings for msg in bypass_messages) + assert bypass_in_warnings, "Bypass should be in warnings when specified as warning" + + +# ============================================================================ +# Test Backend Parameter Handling +# ============================================================================ + +def test_backend_adds_impl_check(): + """Test that backend parameter adds appropriate implementation check.""" + code = """ + # This code doesn't have CUDA implementation + def forward(self, x): + return x * 2 + """ + + valid, errors, warnings = validate_kernel_static(code, backend="cuda") + + # Should check for CUDA implementation (cuda_impl check) + # The exact behavior depends on what cuda_impl check does, + # but we can verify the backend parameter is processed + assert isinstance(valid, bool) + assert isinstance(errors, list) + assert isinstance(warnings, list) + + +def test_different_backends(): + """Test that different backends are handled correctly.""" + code = """ + def forward(self, x): + return x * 2 + """ + + # Test multiple backends + backends = ["cuda", "triton", "thunderkittens", "cute", "tilelang"] + + for backend in backends: + valid, errors, warnings = validate_kernel_static(code, backend=backend) + assert isinstance(valid, bool) + assert isinstance(errors, list) + assert isinstance(warnings, list) + + +# ============================================================================ +# Test Edge Cases +# ============================================================================ + +def test_empty_code(): + """Test handling of empty code.""" + code = "" + + valid, errors, warnings = validate_kernel_static(code) + + assert isinstance(valid, bool) + assert isinstance(errors, list) + assert isinstance(warnings, list) + + +def test_whitespace_only_code(): + """Test handling of whitespace-only code.""" + code = " \n\n\t \n " + + valid, errors, warnings = validate_kernel_static(code) + + assert isinstance(valid, bool) + assert isinstance(errors, list) + assert isinstance(warnings, list) + + +def test_unknown_check_name(): + """Test that unknown check names are ignored.""" + code = "x = 1" + + # Should not crash with unknown check names + valid, errors, warnings = validate_kernel_static( + code, + forbidden=["unknown_check_that_doesnt_exist"], + warnings=["another_unknown_check"] + ) + + assert isinstance(valid, bool) + assert isinstance(errors, list) + assert isinstance(warnings, list) + + +def test_multiple_precision_dependent_checks(): + """Test that multiple precision-dependent checks work (if any exist in future).""" + code = """ + def forward(self, x): + x = x.half() + return x + """ + + # Currently only precision_downgrade is precision-dependent + valid, errors, warnings = validate_kernel_static(code, precision="fp32") + + assert isinstance(valid, bool) + assert isinstance(errors, list) + assert isinstance(warnings, list) + + +# ============================================================================ +# Test Integration: Precision + Backend + Custom Checks +# ============================================================================ + +def test_integration_precision_backend_forbidden(): + """Test integration of precision, backend, and custom forbidden checks.""" + code = """ + def forward(self, x): + x = x.half() # Precision downgrade + return x + """ + + valid, errors, warnings = validate_kernel_static( + code, + backend="cuda", + precision="fp32", + forbidden=["precision_downgrade"] + ) + + assert not valid, "Should be invalid with precision downgrade as forbidden" + assert len(errors) > 0, "Should have errors" + assert any("precision" in e.lower() or "fp16" in e.lower() + for e in errors), "Should flag precision downgrade" + + +def test_integration_all_parameters(): + """Test with all parameters specified.""" + code = """ + def forward(self, x): + return x * 2.0 + """ + + valid, errors, warnings = validate_kernel_static( + code, + backend="triton", + precision="fp16", + forbidden=["code_bypass"], + warnings=["precision_downgrade"] + ) + + assert isinstance(valid, bool) + assert isinstance(errors, list) + assert isinstance(warnings, list) + + +# ============================================================================ +# Test Precision Check Integration +# ============================================================================ + +def test_precision_check_in_warnings_by_default(): + """Test that precision_downgrade is in warnings by default.""" + code = """ + def forward(self, x): + x = x.half() + return x + """ + + valid, errors, warnings = validate_kernel_static(code, precision="fp32") + + # precision_downgrade should be in WARNING_CHECKS by default + # So it should produce warnings, not errors + all_messages = errors + warnings + has_precision_msg = any("precision" in msg.lower() or "fp16" in msg.lower() + for msg in all_messages) + + if has_precision_msg: + # If detected, should be in warnings, not errors (by default) + precision_in_warnings = any("precision" in msg.lower() or "fp16" in msg.lower() + for msg in warnings) + assert precision_in_warnings, "Precision downgrade should be in warnings by default" + + +def test_precision_check_respects_forbidden(): + """Test that precision_downgrade respects forbidden parameter.""" + code = """ + def forward(self, x): + x = x.half() + return x + """ + + # Make precision_downgrade forbidden + valid, errors, warnings = validate_kernel_static( + code, + precision="fp32", + forbidden=["precision_downgrade"], + warnings=[] # Remove from warnings + ) + + # Should produce errors, not warnings + has_precision_in_errors = any("precision" in msg.lower() or "fp16" in msg.lower() + for msg in errors) + + if has_precision_in_errors: + assert not valid, "Should be invalid when precision downgrade is forbidden" + assert len(errors) > 0, "Should have errors" + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) +