From 2a2baf6a62729b71d9e8b00f8ea6fa7046eea05f Mon Sep 17 00:00:00 2001 From: PaliC Date: Wed, 9 Jul 2025 16:49:40 -0700 Subject: [PATCH 1/4] [huggingface tracer] Add suite based off of results from tracer --- BackendBench/huggingface_tracer/__init__.py | 20 + .../manual_ops_mapping.json | 74 + BackendBench/huggingface_tracer/suite.py | 341 + .../tracer_ops_and_shapes/README.md | 87 + .../tracer_ops_and_shapes/sample_inputs.json | 31334 ++++++++++++++++ .../huggingface_tracer/tracer_parser.py | 233 + scripts/main.py | 40 +- 7 files changed, 32126 insertions(+), 3 deletions(-) create mode 100644 BackendBench/huggingface_tracer/__init__.py create mode 100644 BackendBench/huggingface_tracer/manual_ops_mapping.json create mode 100644 BackendBench/huggingface_tracer/suite.py create mode 100644 BackendBench/huggingface_tracer/tracer_ops_and_shapes/README.md create mode 100644 BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json create mode 100644 BackendBench/huggingface_tracer/tracer_parser.py diff --git a/BackendBench/huggingface_tracer/__init__.py b/BackendBench/huggingface_tracer/__init__.py new file mode 100644 index 0000000..8054c98 --- /dev/null +++ b/BackendBench/huggingface_tracer/__init__.py @@ -0,0 +1,20 @@ +""" +HuggingFace Tracer Test Suite Package. + +This package provides functionality for creating and running test suites +based on HuggingFace tracer data. +""" + +from .suite import ( + build_huggingface_tracer_tests, + HuggingFaceTracerOpTest, + HuggingFaceTracerTest, + HuggingFaceTracerTestSuite, +) + +__all__ = [ + "HuggingFaceTracerTest", + "HuggingFaceTracerOpTest", + "HuggingFaceTracerTestSuite", + "build_huggingface_tracer_tests", +] diff --git a/BackendBench/huggingface_tracer/manual_ops_mapping.json b/BackendBench/huggingface_tracer/manual_ops_mapping.json new file mode 100644 index 0000000..f9d8843 --- /dev/null +++ b/BackendBench/huggingface_tracer/manual_ops_mapping.json @@ -0,0 +1,74 @@ +{ + "lift_fresh": { + "cpu": ["torch.float32", "torch.int64", "torch.uint8"], + "cuda": ["torch.float32", "torch.int64", "torch.uint8"] + }, + "_to_copy": { + "cpu": ["torch.bool", "torch.float32", "torch.int64", "torch.uint8"], + "cuda": ["torch.bool", "torch.float32", "torch.int64", "torch.uint8"] + }, + "convolution": { + "cpu": ["torch.float32"], + "cuda": ["torch.float32"] + }, + "_scaled_dot_product_efficient_attention": { + "cpu": ["torch.float32"], + "cuda": ["torch.float32"] + }, + "detach": { + "cpu": ["torch.float32"], + "cuda": ["torch.float32"] + }, + "_has_compatible_shallow_copy_type": { + "cpu": ["torch.float32"], + "cuda": ["torch.float32"] + }, + "detach_": { + "cpu": ["torch.int64"], + "cuda": ["torch.int64"] + }, + "is_nonzero": { + "cpu": ["torch.bool"], + "cuda": ["torch.bool"] + }, + "linalg_vector_norm": { + "cpu": ["torch.float32"], + "cuda": ["torch.float32"] + }, + "_local_scalar_dense": { + "cpu": ["torch.bool", "torch.float32"], + "cuda": ["torch.bool", "torch.float32"] + }, + "cudnn_batch_norm": { + "cpu": ["torch.float32"], + "cuda": ["torch.float32"] + }, + "max_pool2d_with_indices": { + "cpu": ["torch.float32"], + "cuda": ["torch.float32"] + }, + "copy_": { + "cpu": ["torch.float32"], + "cuda": ["torch.float32"] + }, + "upsample_nearest2d": { + "cpu": ["torch.float32"], + "cuda": ["torch.float32"] + }, + "alias": { + "cpu": ["torch.float32"], + "cuda": ["torch.float32"] + }, + "type_as": { + "cpu": ["torch.int32", "torch.int64"], + "cuda": ["torch.int32", "torch.int64"] + }, + "_scaled_dot_product_flash_attention_for_cpu": { + "cpu": ["torch.float32"], + "cuda": [] + }, + "_softmax": { + "cpu": ["torch.float32"], + "cuda": ["torch.float32"] + } +} diff --git a/BackendBench/huggingface_tracer/suite.py b/BackendBench/huggingface_tracer/suite.py new file mode 100644 index 0000000..9a7c95e --- /dev/null +++ b/BackendBench/huggingface_tracer/suite.py @@ -0,0 +1,341 @@ +""" +HuggingFace Tracer Test Suite. + +This module provides test suite functionality for HuggingFace tracer data, +including test classes and the main test suite implementation. +""" + +import logging +import os +from typing import Any, Dict, List, Optional + +import torch + +from BackendBench.suite import OpTest, TestSuite +from torch.testing._internal.common_methods_invocations import op_db + +from .tracer_parser import ( + create_single_tensor, + create_tensor_list, + load_json_data, + select_unique_inputs, + SPECIAL_CASES, +) + +logger = logging.getLogger(__name__) + +# todo: This is a manual mapping of the ops that are not supported by opinfo but are still present +# in the huggingface models. This is a temporary solution until we have a better way of +# handling these ops. + +MANUAL_OPS_FILE = "manual_ops_mapping.json" + + +class HuggingFaceTracerTest: + """Test class for individual HuggingFace tracer test cases.""" + + def __init__(self, *args, **kwargs): + """ + Initialize a tracer test case. + + Args: + *args: Positional arguments for the test + **kwargs: Keyword arguments for the test + """ + self.args = args + self.kwargs = kwargs + + +class HuggingFaceTracerOpTest(OpTest): + """OpTest implementation for HuggingFace tracer data.""" + + def __init__( + self, + op_name: str, + selected_unique_inputs: List[Dict[str, Any]], + device: str = "cpu", + dtype: torch.dtype = torch.float32, + ): + """ + Initialize an operation test. + + Args: + op_name: Name of the PyTorch operation + selected_unique_inputs: List of selected input combinations + device: Device to run tests on + dtype: Default data type for tensors + """ + self.op_name = op_name + self.op = self._get_torch_op(op_name) + self._selected_unique_inputs = selected_unique_inputs + self.performance_tests = [] + self.device = device + self.dtype = dtype + + def _get_torch_op(self, op_name: str): + """ + Convert operator name to torch operation. + + Args: + op_name: String name of the operation + + Returns: + PyTorch operation object or None if not found + """ + try: + # Handle common torch operation patterns + if "." in op_name: + parts = op_name.split(".") + if len(parts) == 2: + op_base, overload = parts + op_packet = getattr(torch.ops.aten, op_base) + return getattr(op_packet, overload) + return getattr(torch.ops.aten, op_name) + except AttributeError: + logger.warning(f"Could not find torch operation for {op_name}") + return None + + @property + def correctness_tests(self): + """Generate tests from selected unique_inputs.""" + for combination in self._selected_unique_inputs: + args = self._convert_args_to_tensors(combination) + yield HuggingFaceTracerTest(*args) + + def _convert_args_to_tensors(self, combination: Dict[str, Any]) -> List[Any]: + """ + Convert JSON combination to actual tensor objects using new schema. + + Args: + combination: Dictionary containing input metadata + + Returns: + List of converted arguments (tensors and non-tensors) + """ + input_shapes = combination["input_shapes"] + input_dtypes = combination["input_dtypes"] + non_tensor_inputs = combination["non_tensor_inputs"] + tensor_lists = combination.get("tensor_lists", {}) + + converted_args = [] + logger.debug(f"Converting args for {self.op_name}: {combination}") + + for i, (shape, dtype_str, non_tensor_input) in enumerate( + zip(input_shapes, input_dtypes, non_tensor_inputs) + ): + converted_arg = self._convert_single_arg( + shape, dtype_str, non_tensor_input, tensor_lists, i + ) + converted_args.append(converted_arg) + + return converted_args + + def _convert_single_arg( + self, + shape: Any, + dtype_str: str, + non_tensor_input: Any, + tensor_lists: Dict[str, Any], + arg_index: int, + ) -> Any: + """ + Convert a single argument from JSON representation to actual object. + + Args: + shape: Shape information (list or None) + dtype_str: String representation of dtype + non_tensor_input: Non-tensor input value + tensor_lists: Dictionary of tensor list metadata + arg_index: Index of the argument for error reporting + + Returns: + Converted argument (tensor, list of tensors, or other value) + """ + if non_tensor_input is not None: + return self._handle_non_tensor_input(non_tensor_input, dtype_str, tensor_lists) + elif dtype_str == "": + return None + elif dtype_str == "" and shape is None: + logger.warning( + f"Found dtype but no tensor_list_ref for argument {arg_index}" + ) + return [] + else: + return self._handle_tensor_input(shape, dtype_str, arg_index) + + def _handle_non_tensor_input( + self, non_tensor_input: Any, dtype_str: str, tensor_lists: Dict[str, Any] + ) -> Any: + """Handle non-tensor inputs including tensor list references.""" + # Check if this is a tensor list reference + if isinstance(non_tensor_input, dict) and "tensor_list_ref" in non_tensor_input: + tensor_list_ref = str(non_tensor_input["tensor_list_ref"]) + if tensor_list_ref in tensor_lists: + tensor_list_metadata = tensor_lists[tensor_list_ref] + return create_tensor_list(tensor_list_metadata, self.device, self.dtype) + else: + logger.warning(f"Tensor list reference {tensor_list_ref} not found in tensor_lists") + return [] # Empty list as fallback + + # Handle torch.dtype conversion + elif dtype_str == "" and isinstance(non_tensor_input, str): + try: + return getattr(torch, non_tensor_input.replace("torch.", "")) + except AttributeError: + logger.warning(f"Could not convert {non_tensor_input} to torch dtype") + return non_tensor_input + + # Regular non-tensor input + else: + return non_tensor_input + + def _handle_tensor_input(self, shape: Any, dtype_str: str, arg_index: int) -> torch.Tensor: + """Handle tensor inputs.""" + if isinstance(shape, list): + return create_single_tensor(shape, dtype_str, self.device, self.dtype) + else: + raise ValueError( + f"Invalid shape for tensor input {arg_index}: {shape}. Expected a list." + ) + + +def build_huggingface_tracer_tests( + json_file_path: str, + op_filter: Optional[List[str]] = None, + device: str = "cpu", + dtype: torch.dtype = torch.float32, +) -> List[HuggingFaceTracerOpTest]: + """ + Build HuggingFace tracer tests from JSON data. + + Args: + json_file_path: Path to JSON file containing operator data + op_filter: Optional list of operator names to include (None = include all) + device: Device to run tests on (e.g., "cuda", "cpu") + dtype: Default data type for tensors + + Returns: + List of HuggingFaceTracerOpTest objects + """ + data = load_json_data(json_file_path) + + op_tests = [] + + # create op_info mapping to test dtypes + op_dtype_filter = {op.name.split(".")[-1]: op.supported_dtypes(device) for op in op_db} + manual_ops = load_json_data(os.path.join(os.path.dirname(__file__), MANUAL_OPS_FILE)) + for op in manual_ops: + dtype_list = manual_ops[op].get(device, []) + # convert to set to match with op_info datatype + ops_set = set() + for dtype_str in dtype_list: + # Convert string representation to actual torch dtype + if dtype_str.startswith("torch."): + dtype_obj = getattr(torch, dtype_str.replace("torch.", "")) + ops_set.add(dtype_obj) + + # this might not be true, but inplace ops and normal ops should support the same dtypes + # todo: confirm the above + + if op[-1] == "_": + op = op[:-1] + op_dtype_filter[op] = ops_set + logging.info(f"op_dtype_filter: {op_dtype_filter}") + + skipped_no_op_info = [] + skipped_no_dtype_tests = [] + + for op in op_dtype_filter: + logger.debug(f"op: {op}, dtypes: {op_dtype_filter[op]}") + + for op_name, op_data in data.items(): + # Apply filter if provided + if op_filter and op_name not in op_filter: + continue + if op_name in SPECIAL_CASES: + logger.warning(f"Skipping special case op {op_name}") + continue + + # this might not be true, but inplace ops and normal ops should support the same dtypes + # todo: confirm the above + op_name_no_overload = op_name.split(".")[0] + if op_name_no_overload[-1] == "_": + op_name_no_overload = op_name_no_overload[:-1] + # Skip if no op_info + if op_name_no_overload not in op_dtype_filter: + logger.warning( + f"Skipping {op_name}: op not found in op_info we should add these manually later" + ) + skipped_no_op_info.append(op_name) + continue + # Skip if no unique_inputs + if "unique_inputs" not in op_data or not op_data["unique_inputs"]: + logger.debug(f"Skipping {op_name}: no unique_inputs found") + continue + # Skip if no supported dtypes + if dtype not in op_dtype_filter[op_name_no_overload]: + logger.debug(f"Skipping {op_name}: dtype {dtype} not supported according to op_info") + skipped_no_dtype_tests.append(op_name) + continue + + # Select best unique_inputs + selected_unique_inputs = select_unique_inputs(op_data["unique_inputs"], dtype) + + if selected_unique_inputs or len(selected_unique_inputs) > 0: + op_test = HuggingFaceTracerOpTest( + op_name, selected_unique_inputs, device=device, dtype=dtype + ) + op_tests.append(op_test) + logger.debug( + f"Created test for {op_name} with {len(selected_unique_inputs)} unique_inputs on {device}" + ) + else: + logger.debug(f"Skipping {op_name}: no unique_inputs found for dtype {dtype}") + skipped_no_dtype_tests.append(op_name) + + logger.info(f"While building tests, skipped {len(skipped_no_op_info)} ops with no op_info") + logger.info( + f"While building tests, skipped {len(skipped_no_dtype_tests)} ops with no dtype tests" + ) + logger.info( + "Skipped ops with no op_info or were manually added: \n" + "\n".join(skipped_no_op_info) + ) + logger.info( + f"Skipped ops as they don't support testing {dtype} on {device}: \n" + + "\n".join(skipped_no_dtype_tests) + ) + + return op_tests + + +class HuggingFaceTracerTestSuite(TestSuite): + """Test suite for HuggingFace tracer data.""" + + def __init__( + self, + name: str, + device: str, + dtype: torch.dtype, + json_file_path: str = "sample_inputs.json", + filter: Optional[List[str]] = None, + ): + """ + Initialize HuggingFace tracer test suite. + + Args: + name: Name of the test suite + device: Device to run tests on (e.g., "cuda", "cpu") + dtype: Default data type for tensors + json_file_path: Path to JSON file with operator data + filter: Optional list of operator names to include + """ + self.device = device + self.dtype = dtype + + op_tests = build_huggingface_tracer_tests(json_file_path, filter, device, dtype) + super().__init__(name, op_tests) + + logger.info( + f"Created HuggingFace tracer suite '{name}' with {len(op_tests)} " + f"operator tests on {device} with dtype {dtype}" + ) diff --git a/BackendBench/huggingface_tracer/tracer_ops_and_shapes/README.md b/BackendBench/huggingface_tracer/tracer_ops_and_shapes/README.md new file mode 100644 index 0000000..20f6229 --- /dev/null +++ b/BackendBench/huggingface_tracer/tracer_ops_and_shapes/README.md @@ -0,0 +1,87 @@ +# Sample Inputs Schema + +This directory contains outputs of the huggingface tracer which store traced PyTorch operation inputs from HuggingFace models. + +'sample_inputs.json' contains an example of what these look like with the outputs from 20 models. + +## Schema Structure + +```json +{ + "operation_name": { + "total_calls": , + "unique_input_count": , + "unique_inputs": [ + { + "op_name": "", + "input_shapes": [, ...], + "input_dtypes": ["", ...], + "non_tensor_inputs": [, ...], + "tensor_lists": {}, + "count": + } + ] + } +} +``` + +## Field Descriptions + +- **`input_shapes`**: List of tensor shapes (e.g., `[1, 3, 224, 224]`) or `null` for non-tensor inputs +- **`input_dtypes`**: List of type strings (e.g., `"torch.float32"`, `""`, `""` +- **`non_tensor_inputs`**: Actual non-tensor values, `null` for tensors, or `{"tensor_list_ref": }` for tensor lists +- **`tensor_lists`**: Metadata for tensor lists, keyed by string IDs: + ```json + { + "0": { + "length": , + "shapes": [[], ...], + "dtypes": ["", ...] + } + } + ``` +- **`count`**: Frequency of this input combination in the traced data + +**Note**: All dtypes (in input_dtypes and tensor_lists) are strings, not Python types (e.g., `torch.float32` instead of `float32`) as they are serialized in the JSON file. They should be converted to Python types before use. + +## Examples + +**Simple tensor input:** +```json +"input_shapes": [[2, 13]], +"input_dtypes": ["torch.int64"], +"non_tensor_inputs": [null] +``` + +**Tensor list input:** +```json +"input_shapes": [null, null], +"input_dtypes": ["", ""], +"non_tensor_inputs": [{"tensor_list_ref": 0}, 1], +"tensor_lists": { + "0": { + "length": 3, + "shapes": [[1, 128, 20, 20], [1, 128, 20, 20], [1, 128, 20, 20]], + "dtypes": ["torch.float32", "torch.float32", "torch.float32"] + } +} +``` + +**Example entry with non-tensor inputs** +```json +"convolution.default": { + "total_calls": 108, + "unique_input_count": 67, + "unique_inputs": [ + { + "op_name": "convolution.default", + "input_shapes": [[1, 256, 14, 14], [1024, 256, 1, 1], null, null, null, null, null, null, null], + "input_dtypes": ["torch.float32", "torch.float32", "", "", "", "", "", "", ""], + "non_tensor_inputs": [null, null, null, [1, 1], [0, 0], [1, 1], false, [0, 0], 1], + "tensor_lists": {}, + "count": 6 + }, + ... + ] + } +``` diff --git a/BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json b/BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json new file mode 100644 index 0000000..3ae827c --- /dev/null +++ b/BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json @@ -0,0 +1,31334 @@ +{ + "lift_fresh.default": { + "total_calls": 38, + "unique_input_count": 5, + "unique_inputs": [ + { + "op_name": "lift_fresh.default", + "input_shapes": [ + [ + 1, + 512 + ] + ], + "input_dtypes": [ + "torch.int64" + ], + "non_tensor_inputs": [ + null + ], + "tensor_lists": {}, + "count": 26 + }, + { + "op_name": "lift_fresh.default", + "input_shapes": [ + [ + 2, + 13 + ] + ], + "input_dtypes": [ + "torch.int64" + ], + "non_tensor_inputs": [ + null + ], + "tensor_lists": {}, + "count": 8 + }, + { + "op_name": "lift_fresh.default", + "input_shapes": [ + [ + 224, + 224, + 3 + ] + ], + "input_dtypes": [ + "torch.uint8" 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}, + "exp.default": { + "total_calls": 3, + "unique_input_count": 1, + "unique_inputs": [ + { + "op_name": "exp.default", + "input_shapes": [ + [] + ], + "input_dtypes": [ + "torch.float32" + ], + "non_tensor_inputs": [ + null + ], + "tensor_lists": {}, + "count": 3 + } + ] + }, + "_softmax.default": { + "total_calls": 3, + "unique_input_count": 1, + "unique_inputs": [ + { + "op_name": "_softmax.default", + "input_shapes": [ + [ + 1, + 2 + ], + null, + null + ], + "input_dtypes": [ + "torch.float32", + "", + "" + ], + "non_tensor_inputs": [ + null, + 1, + false + ], + "tensor_lists": {}, + "count": 3 + } + ] + } +} diff --git a/BackendBench/huggingface_tracer/tracer_parser.py b/BackendBench/huggingface_tracer/tracer_parser.py new file mode 100644 index 0000000..ff5ecbc --- /dev/null +++ b/BackendBench/huggingface_tracer/tracer_parser.py @@ -0,0 +1,233 @@ +""" +Helper module for parsing HuggingFace tracer data. + +This module contains utilities for loading, processing, and selecting +unique inputs from HuggingFace tracer JSON data. +""" + +import json +import logging +from typing import Any, Dict, List + +import torch + +logger = logging.getLogger(__name__) + +# Operations that require special handling due to input constraints +# These ops have requirements on inputs that make randomized tensors unsuitable +SPECIAL_CASES = { + "embedding.default", # requires second arg tensor to describe dims of first arg + "index.Tensor", # requires list of tensors with indices within bounds of first arg + "meshgrid.indexing", # requires last argument to be indexing method string + "empty_like.default", # correctness testing doesn't make sense without special handling +} + + +def load_json_data(json_file_path: str) -> Dict[str, Any]: + """ + Load operator data from JSON file. + + Args: + json_file_path: Path to JSON file containing operator data + + Returns: + Dictionary containing the loaded JSON data + + Raises: + FileNotFoundError: If the JSON file doesn't exist + json.JSONDecodeError: If the JSON format is invalid + """ + try: + with open(json_file_path, "r") as f: + return json.load(f) + except FileNotFoundError: + logger.error(f"JSON file not found: {json_file_path}") + raise + except json.JSONDecodeError as e: + logger.error(f"Invalid JSON format in {json_file_path}: {e}") + raise + + +def calculate_tensor_magnitude(combination: Dict[str, Any]) -> float: + """ + Calculate a magnitude metric for tensor arguments to determine 'largest'. + + Args: + combination: Dictionary containing input_shapes and other metadata + + Returns: + Float representing the total magnitude (product of all tensor dimensions) + """ + total_magnitude = 0.0 + input_shapes = combination["input_shapes"] + + for shape in input_shapes: + if ( + isinstance(shape, list) + and len(shape) > 0 + and all(isinstance(x, int) for x in shape) + ): + # Calculate product of dimensions (total tensor size) + magnitude = 1 + for dim in shape: + magnitude *= dim + total_magnitude += magnitude + + return total_magnitude + + +def select_unique_inputs( + unique_inputs: List[Dict[str, Any]], + dtype, + max_popular: int = 5, + max_largest: int = 5, +) -> List[Dict[str, Any]]: + """ + Select the most relevant unique inputs based on popularity and size. + + Selects up to max_popular most popular unique_inputs and max_largest + largest unique_inputs, ensuring uniqueness by avoiding duplicates. + + Args: + unique_inputs: List of unique input combinations + dtype: Data type to use for tensors, we will filter to only those with this dtype + max_popular: Maximum number of popular inputs to select + max_largest: Maximum number of largest inputs to select + + Returns: + List of selected unique input combinations + """ + + # Filter to only those with the specified dtype in the cases of tensors + for input in unique_inputs: + for dtype in input["input_dtypes"]: + if dtype.startswith("torch.") and dtype != str(dtype): + continue + for _, entry in input["tensor_lists"].items(): + for dtype in entry["dtypes"]: + if dtype.startswith("torch.") and dtype != str(dtype): + continue + + # Sort by count (popularity) descending + popular_unique_inputs = sorted( + unique_inputs, key=lambda x: x["count"], reverse=True + )[:max_popular] + + # Sort by magnitude descending + largest_unique_inputs = sorted( + unique_inputs, + key=lambda x: calculate_tensor_magnitude(x), + reverse=True, + ) + + # Create set of selected unique_inputs (using input_shapes as key for uniqueness) + selected = {} + + # Add popular unique_inputs first + for combo in popular_unique_inputs: + key = str(combo["input_shapes"]) # Use string representation as key + selected[key] = combo + + # Add largest unique_inputs, skipping duplicates + for combo in largest_unique_inputs: + key = str(combo["input_shapes"]) + if key not in selected: + selected[key] = combo + if len(selected) >= max_popular + max_largest: + break + + return list(selected.values()) + + +def create_single_tensor( + shape: List[int], + dtype_str: str, + device: str = "cpu", + default_dtype: torch.dtype = torch.float32, +) -> torch.Tensor: + """ + Create a single tensor with the given shape and dtype. + + Args: + shape: List of integers representing tensor dimensions + dtype_str: String representation of the desired dtype + device: Device to create tensor on + default_dtype: Fallback dtype if conversion fails + + Returns: + PyTorch tensor with specified properties + """ + # Convert dtype string to actual torch dtype + torch_dtype = default_dtype + if dtype_str and isinstance(dtype_str, str): + try: + if dtype_str.startswith("torch."): + dtype_name = dtype_str.replace("torch.", "") + torch_dtype = getattr(torch, dtype_name) + except AttributeError: + logger.warning( + f"Could not convert {dtype_str} to torch dtype, using {torch_dtype}" + ) + + # Create tensor with appropriate method based on dtype + if torch_dtype in [torch.float16, torch.float32, torch.float64, torch.bfloat16]: + # Floating point types - use randn + tensor = torch.randn(shape, dtype=torch_dtype, device=device) + elif torch_dtype in [ + torch.int8, + torch.int16, + torch.int32, + torch.int64, + torch.uint8, + ]: + # Integer types - use randint with reasonable range + tensor = torch.randint(0, 10, shape, dtype=torch_dtype, device=device) + elif torch_dtype == torch.bool: + # Boolean type - use randint and cast to bool + tensor = torch.randint(0, 2, shape, dtype=torch.uint8, device=device).bool() + elif torch_dtype in [torch.complex64, torch.complex128]: + # Complex types - create from real and imaginary parts + real_dtype = torch.float32 if torch_dtype == torch.complex64 else torch.float64 + real_part = torch.randn(shape, dtype=real_dtype, device=device) + imag_part = torch.randn(shape, dtype=real_dtype, device=device) + tensor = torch.complex(real_part, imag_part) + else: + # Fallback - try to create zeros and cast + try: + tensor = torch.zeros(shape, dtype=torch_dtype, device=device) + except Exception as e: + logger.warning(f"Could not create tensor with dtype {torch_dtype}: {e}") + tensor = torch.randn(shape, device=device) + + return tensor + + +def create_tensor_list( + tensor_list_metadata: Dict[str, Any], + device: str = "cpu", + default_dtype: torch.dtype = torch.float32, +) -> List[torch.Tensor]: + """ + Create a list of tensors from tensor list metadata. + + Args: + tensor_list_metadata: Dictionary containing length, shapes, and dtypes + device: Device to create tensors on + default_dtype: Fallback dtype if conversion fails + + Returns: + List of PyTorch tensors + """ + length = tensor_list_metadata["length"] + shapes = tensor_list_metadata["shapes"] + dtypes = tensor_list_metadata["dtypes"] + + tensor_list = [] + for j in range(length): + # Use last shape/dtype if not enough provided + shape = shapes[j] if j < len(shapes) else shapes[-1] + dtype_str = dtypes[j] if j < len(dtypes) else dtypes[-1] + tensor = create_single_tensor(shape, dtype_str, device, default_dtype) + tensor_list.append(tensor) + + return tensor_list diff --git a/scripts/main.py b/scripts/main.py index 3aa1d62..a218496 100644 --- a/scripts/main.py +++ b/scripts/main.py @@ -7,18 +7,33 @@ import BackendBench.eval as eval import click import torch +from BackendBench.huggingface_tracer import HuggingFaceTracerTestSuite +from BackendBench.llm_client import ClaudeKernelGenerator from BackendBench.opinfo_suite import OpInfoTestSuite from BackendBench.suite import SmokeTestSuite -from BackendBench.llm_client import ClaudeKernelGenerator logger = logging.getLogger(__name__) +def setup_logging(): + """Setup logging configuration.""" + logging_level = os.environ.get("LOG_LEVEL", "WARNING") + numeric_level = getattr(logging, logging_level.upper(), None) + if not isinstance(numeric_level, int): + raise ValueError(f"Invalid log level: {logging_level}") + + logging.basicConfig( + level=numeric_level, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", + ) + + @click.command() @click.option( "--suite", default="smoke", - type=click.Choice(["smoke", "opinfo"]), + type=click.Choice(["smoke", "opinfo", "huggingface"]), help="Which suite to run", ) @click.option( @@ -40,6 +55,8 @@ help="Maximum attempts for LLM kernel generation with feedback", ) def cli(suite, backend, ops, llm_max_attempts): + # Setup logging first + setup_logging() if ops: ops = ops.split(",") @@ -62,11 +79,17 @@ def cli(suite, backend, ops, llm_max_attempts): torch.bfloat16, filter=ops, ), + "huggingface": lambda: HuggingFaceTracerTestSuite( + name="huggingface_tracer_cuda_bfloat16", + device="cuda", + dtype=torch.float32, + json_file_path="/home/sahanp/repos/BackendBench/BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json", + filter=ops, + ), }[suite]() overall_correctness = [] overall_performance = [] - for test in suite: if test.op not in backend: continue @@ -102,6 +125,17 @@ def setup_llm_backend(llm_backend, llm_client, suite_name, ops_filter, max_attem torch.bfloat16, filter=ops_filter, ) + elif suite_name == "huggingface": + suite = HuggingFaceTracerTestSuite( + name="huggingface_tracer_cuda_float32", + device="cuda", + dtype=torch.float32, + json_file_path=os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json", + ), + filter=ops_filter, + ) else: raise ValueError(f"Unknown suite: {suite_name}") From 29b07d8b37498b669a1824629d0fcbfe43478ca9 Mon Sep 17 00:00:00 2001 From: PaliC Date: Wed, 9 Jul 2025 22:33:05 -0700 Subject: [PATCH 2/4] fixed issues --- .../huggingface_tracer/tracer_parser.py | 22 +++++------ scripts/main.py | 37 ++++++++++++++----- 2 files changed, 37 insertions(+), 22 deletions(-) diff --git a/BackendBench/huggingface_tracer/tracer_parser.py b/BackendBench/huggingface_tracer/tracer_parser.py index ff5ecbc..59a1fc6 100644 --- a/BackendBench/huggingface_tracer/tracer_parser.py +++ b/BackendBench/huggingface_tracer/tracer_parser.py @@ -48,7 +48,7 @@ def load_json_data(json_file_path: str) -> Dict[str, Any]: raise -def calculate_tensor_magnitude(combination: Dict[str, Any]) -> float: +def calculate_tensor_shape_magnitude(combination: Dict[str, Any]) -> float: """ Calculate a magnitude metric for tensor arguments to determine 'largest'. @@ -56,7 +56,7 @@ def calculate_tensor_magnitude(combination: Dict[str, Any]) -> float: combination: Dictionary containing input_shapes and other metadata Returns: - Float representing the total magnitude (product of all tensor dimensions) + Float representing the total "magnitude" (product of all tensor dimensions) from the shape """ total_magnitude = 0.0 input_shapes = combination["input_shapes"] @@ -100,12 +100,13 @@ def select_unique_inputs( # Filter to only those with the specified dtype in the cases of tensors for input in unique_inputs: - for dtype in input["input_dtypes"]: - if dtype.startswith("torch.") and dtype != str(dtype): + for tensor_dtype in input["input_dtypes"]: + if tensor_dtype.startswith("torch.") and tensor_dtype != str(dtype): continue for _, entry in input["tensor_lists"].items(): - for dtype in entry["dtypes"]: - if dtype.startswith("torch.") and dtype != str(dtype): + for tensor_dtype in entry["dtypes"]: + # all types should be tensors already + tensor_dtype != str(dtype): continue # Sort by count (popularity) descending @@ -116,7 +117,7 @@ def select_unique_inputs( # Sort by magnitude descending largest_unique_inputs = sorted( unique_inputs, - key=lambda x: calculate_tensor_magnitude(x), + key=lambda x: calculate_tensor_shape_magnitude(x), reverse=True, ) @@ -192,12 +193,7 @@ def create_single_tensor( imag_part = torch.randn(shape, dtype=real_dtype, device=device) tensor = torch.complex(real_part, imag_part) else: - # Fallback - try to create zeros and cast - try: - tensor = torch.zeros(shape, dtype=torch_dtype, device=device) - except Exception as e: - logger.warning(f"Could not create tensor with dtype {torch_dtype}: {e}") - tensor = torch.randn(shape, device=device) + raise ValueError(f"Unsupported dtype: {dtype_str}") return tensor diff --git a/scripts/main.py b/scripts/main.py index a218496..d4d1ff3 100644 --- a/scripts/main.py +++ b/scripts/main.py @@ -83,7 +83,10 @@ def cli(suite, backend, ops, llm_max_attempts): name="huggingface_tracer_cuda_bfloat16", device="cuda", dtype=torch.float32, - json_file_path="/home/sahanp/repos/BackendBench/BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json", + json_file_path=os.path.join( + os.path.dirname(os.path.dirname(__file__)), + "BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json", + ), filter=ops, ), }[suite]() @@ -109,7 +112,9 @@ def cli(suite, backend, ops, llm_max_attempts): mean_correctness = torch.tensor(overall_correctness).mean().item() geomean_perf = torch.tensor(overall_performance).log().mean().exp().item() - print(f"correctness score (mean pass rate over all operators): {mean_correctness:.2f}") + print( + f"correctness score (mean pass rate over all operators): {mean_correctness:.2f}" + ) print(f"performance score (geomean speedup over all operators): {geomean_perf:.2f}") @@ -187,29 +192,41 @@ def feedback_callback(kernel_code: str, attempt: int) -> tuple[bool, Dict]: successful_ops += 1 # Save summary of this operation - summary_file = os.path.join(llm_backend.kernels_dir, f"{op_name}_summary.txt") + summary_file = os.path.join( + llm_backend.kernels_dir, f"{op_name}_summary.txt" + ) with open(summary_file, "w") as f: f.write(f"Operation: {op_name}\n") f.write(f"Full op: {op_str}\n") f.write(f"Attempts used: {attempts_used}/{max_attempts}\n") f.write("Final status: Success\n") - f.write(f"Final kernel file: {op_name}_kernel_attempt_{attempts_used}.py\n") + f.write( + f"Final kernel file: {op_name}_kernel_attempt_{attempts_used}.py\n" + ) except Exception as e: - print(f"✗ Kernel passed tests but failed final compilation for {op_name}: {e}") + print( + f"✗ Kernel passed tests but failed final compilation for {op_name}: {e}" + ) success = False if not success: print(f"✗ Skipping {op_name} - failed all {attempts_used} attempts") # Save summary of this operation - summary_file = os.path.join(llm_backend.kernels_dir, f"{op_name}_summary.txt") + summary_file = os.path.join( + llm_backend.kernels_dir, f"{op_name}_summary.txt" + ) with open(summary_file, "w") as f: f.write(f"Operation: {op_name}\n") f.write(f"Full op: {op_str}\n") f.write(f"Attempts used: {attempts_used}/{max_attempts}\n") - f.write("Final status: Failed - All attempts failed correctness tests\n") - f.write(f"Last kernel file: {op_name}_kernel_attempt_{attempts_used}.py\n") + f.write( + "Final status: Failed - All attempts failed correctness tests\n" + ) + f.write( + f"Last kernel file: {op_name}_kernel_attempt_{attempts_used}.py\n" + ) # Continue with other operations # Print summary @@ -228,7 +245,9 @@ def feedback_callback(kernel_code: str, attempt: int) -> tuple[bool, Dict]: print(f"{'=' * 60}\n") # Save overall summary - overall_summary_file = os.path.join(llm_backend.kernels_dir, "OVERALL_SUMMARY.txt") + overall_summary_file = os.path.join( + llm_backend.kernels_dir, "OVERALL_SUMMARY.txt" + ) with open(overall_summary_file, "w") as f: f.write("LLM Backend Generation Summary\n") f.write(f"{'=' * 40}\n") From 10b32dc0590967467803a36e4dcaf8a0d92d4943 Mon Sep 17 00:00:00 2001 From: PaliC Date: Thu, 10 Jul 2025 12:08:40 -0700 Subject: [PATCH 3/4] fixed issues --- .../{tracer_ops_and_shapes => }/README.md | 2 +- BackendBench/huggingface_tracer/suite.py | 48 +- .../tracer_ops_and_shapes/sample_inputs.json | 31334 ---------------- .../huggingface_tracer/tracer_parser.py | 43 +- scripts/main.py | 8 - 5 files changed, 64 insertions(+), 31371 deletions(-) rename BackendBench/huggingface_tracer/{tracer_ops_and_shapes => }/README.md (93%) delete mode 100644 BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json diff --git a/BackendBench/huggingface_tracer/tracer_ops_and_shapes/README.md b/BackendBench/huggingface_tracer/README.md similarity index 93% rename from BackendBench/huggingface_tracer/tracer_ops_and_shapes/README.md rename to BackendBench/huggingface_tracer/README.md index 20f6229..6461932 100644 --- a/BackendBench/huggingface_tracer/tracer_ops_and_shapes/README.md +++ b/BackendBench/huggingface_tracer/README.md @@ -2,7 +2,7 @@ This directory contains outputs of the huggingface tracer which store traced PyTorch operation inputs from HuggingFace models. -'sample_inputs.json' contains an example of what these look like with the outputs from 20 models. +'[hf_op_trace.json](https://huggingface.co/datasets/GPUMODE/huggingface_op_trace/resolve/main/hf_op_trace.json)' contains an example of what these look like with the outputs from 20 models. ## Schema Structure diff --git a/BackendBench/huggingface_tracer/suite.py b/BackendBench/huggingface_tracer/suite.py index 9a7c95e..256a87d 100644 --- a/BackendBench/huggingface_tracer/suite.py +++ b/BackendBench/huggingface_tracer/suite.py @@ -22,6 +22,7 @@ SPECIAL_CASES, ) +DEFAULT_JSON_SOURCE = "https://huggingface.co/datasets/GPUMODE/huggingface_op_trace/resolve/main/hf_op_trace.json" logger = logging.getLogger(__name__) # todo: This is a manual mapping of the ops that are not supported by opinfo but are still present @@ -152,7 +153,9 @@ def _convert_single_arg( Converted argument (tensor, list of tensors, or other value) """ if non_tensor_input is not None: - return self._handle_non_tensor_input(non_tensor_input, dtype_str, tensor_lists) + return self._handle_non_tensor_input( + non_tensor_input, dtype_str, tensor_lists + ) elif dtype_str == "": return None elif dtype_str == "" and shape is None: @@ -174,7 +177,9 @@ def _handle_non_tensor_input( tensor_list_metadata = tensor_lists[tensor_list_ref] return create_tensor_list(tensor_list_metadata, self.device, self.dtype) else: - logger.warning(f"Tensor list reference {tensor_list_ref} not found in tensor_lists") + logger.warning( + f"Tensor list reference {tensor_list_ref} not found in tensor_lists" + ) return [] # Empty list as fallback # Handle torch.dtype conversion @@ -189,7 +194,9 @@ def _handle_non_tensor_input( else: return non_tensor_input - def _handle_tensor_input(self, shape: Any, dtype_str: str, arg_index: int) -> torch.Tensor: + def _handle_tensor_input( + self, shape: Any, dtype_str: str, arg_index: int + ) -> torch.Tensor: """Handle tensor inputs.""" if isinstance(shape, list): return create_single_tensor(shape, dtype_str, self.device, self.dtype) @@ -200,7 +207,7 @@ def _handle_tensor_input(self, shape: Any, dtype_str: str, arg_index: int) -> to def build_huggingface_tracer_tests( - json_file_path: str, + json_source: str, op_filter: Optional[List[str]] = None, device: str = "cpu", dtype: torch.dtype = torch.float32, @@ -209,7 +216,7 @@ def build_huggingface_tracer_tests( Build HuggingFace tracer tests from JSON data. Args: - json_file_path: Path to JSON file containing operator data + json_source: Path to JSON file or URL containing operator data op_filter: Optional list of operator names to include (None = include all) device: Device to run tests on (e.g., "cuda", "cpu") dtype: Default data type for tensors @@ -217,13 +224,17 @@ def build_huggingface_tracer_tests( Returns: List of HuggingFaceTracerOpTest objects """ - data = load_json_data(json_file_path) + data = load_json_data(json_source) op_tests = [] # create op_info mapping to test dtypes - op_dtype_filter = {op.name.split(".")[-1]: op.supported_dtypes(device) for op in op_db} - manual_ops = load_json_data(os.path.join(os.path.dirname(__file__), MANUAL_OPS_FILE)) + op_dtype_filter = { + op.name.split(".")[-1]: op.supported_dtypes(device) for op in op_db + } + manual_ops = load_json_data( + os.path.join(os.path.dirname(__file__), MANUAL_OPS_FILE) + ) for op in manual_ops: dtype_list = manual_ops[op].get(device, []) # convert to set to match with op_info datatype @@ -274,7 +285,9 @@ def build_huggingface_tracer_tests( continue # Skip if no supported dtypes if dtype not in op_dtype_filter[op_name_no_overload]: - logger.debug(f"Skipping {op_name}: dtype {dtype} not supported according to op_info") + logger.debug( + f"Skipping {op_name}: dtype {dtype} not supported according to op_info" + ) skipped_no_dtype_tests.append(op_name) continue @@ -290,15 +303,20 @@ def build_huggingface_tracer_tests( f"Created test for {op_name} with {len(selected_unique_inputs)} unique_inputs on {device}" ) else: - logger.debug(f"Skipping {op_name}: no unique_inputs found for dtype {dtype}") + logger.debug( + f"Skipping {op_name}: no unique_inputs found for dtype {dtype}" + ) skipped_no_dtype_tests.append(op_name) - logger.info(f"While building tests, skipped {len(skipped_no_op_info)} ops with no op_info") + logger.info( + f"While building tests, skipped {len(skipped_no_op_info)} ops with no op_info" + ) logger.info( f"While building tests, skipped {len(skipped_no_dtype_tests)} ops with no dtype tests" ) logger.info( - "Skipped ops with no op_info or were manually added: \n" + "\n".join(skipped_no_op_info) + "Skipped ops with no op_info or were manually added: \n" + + "\n".join(skipped_no_op_info) ) logger.info( f"Skipped ops as they don't support testing {dtype} on {device}: \n" @@ -316,7 +334,7 @@ def __init__( name: str, device: str, dtype: torch.dtype, - json_file_path: str = "sample_inputs.json", + json_source: str = DEFAULT_JSON_SOURCE, filter: Optional[List[str]] = None, ): """ @@ -326,13 +344,13 @@ def __init__( name: Name of the test suite device: Device to run tests on (e.g., "cuda", "cpu") dtype: Default data type for tensors - json_file_path: Path to JSON file with operator data + json_source: Path to JSON file or URL containing operator data (defaults to HuggingFace dataset) filter: Optional list of operator names to include """ self.device = device self.dtype = dtype - op_tests = build_huggingface_tracer_tests(json_file_path, filter, device, dtype) + op_tests = build_huggingface_tracer_tests(json_source, filter, device, dtype) super().__init__(name, op_tests) logger.info( diff --git a/BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json b/BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json deleted file mode 100644 index 3ae827c..0000000 --- a/BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json +++ /dev/null @@ -1,31334 +0,0 @@ -{ - "lift_fresh.default": { - "total_calls": 38, - "unique_input_count": 5, - "unique_inputs": [ - { - "op_name": "lift_fresh.default", - "input_shapes": [ - [ - 1, - 512 - ] - ], - "input_dtypes": [ - "torch.int64" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 26 - }, - { - "op_name": "lift_fresh.default", - "input_shapes": [ - [ - 2, - 13 - ] - ], - "input_dtypes": [ - "torch.int64" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 8 - }, - { - "op_name": "lift_fresh.default", - "input_shapes": [ - [ - 224, - 224, - 3 - ] - ], - "input_dtypes": [ - "torch.uint8" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 2 - }, - { - "op_name": "lift_fresh.default", - "input_shapes": [ - [ - 1, - 3, - 224, - 224 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 1 - }, - { - "op_name": "lift_fresh.default", - "input_shapes": [ - [ - 1, - 3, - 640, - 640 - ] - ], - "input_dtypes": [ - "torch.uint8" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 1 - } - ] - }, - "_to_copy.default": { - "total_calls": 491, - "unique_input_count": 44, - "unique_inputs": [ - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 64 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 133 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 128 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 65 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [] - ], - "input_dtypes": [ - "torch.int64" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 57 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 32 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 45 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 64, - 64, - 3, - 3 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 34 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 256 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 30 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 16 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 15 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 32, - 32, - 3, - 3 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 12 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 128, - 128, - 3, - 3 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 10 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 256, - 384, - 1, - 1 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 6 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 128, - 192, - 1, - 1 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 6 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 64, - 64, - 1, - 1 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 5 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 16, - 16, - 3, - 3 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 4 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 128, - 256, - 1, - 1 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 4 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 64, - 128, - 3, - 3 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 4 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 64, - 256, - 3, - 3 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 4 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 1, - 3, - 224, - 224 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 3 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 1, - 64, - 1, - 1 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 3 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 1 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 3 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 2, - 1, - 7, - 7 - ] - ], - "input_dtypes": [ - "torch.int64" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 3 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 2, - 1, - 7, - 7 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 3 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 2, - 7 - ] - ], - "input_dtypes": [ - "torch.int64" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 3 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 3, - 224, - 224 - ] - ], - "input_dtypes": [ - "torch.uint8" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 2 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 1, - 1000 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 2 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 16, - 3, - 3, - 3 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 2 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 32, - 16, - 3, - 3 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 2 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 32, - 32, - 1, - 1 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 2 - }, - { - "op_name": "_to_copy.default", - "input_shapes": [ - [ - 32, - 48, - 1, - 1 - ] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 2 - }, - { - "op_name": "_to_copy.default", - 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], - "non_tensor_inputs": [ - null, - null - ], - "tensor_lists": {}, - "count": 12 - }, - { - "op_name": "matmul.default", - "input_shapes": [ - [ - 1, - 12, - 512, - 64 - ], - [ - 1, - 12, - 64, - 512 - ] - ], - "input_dtypes": [ - "torch.float32", - "torch.float32" - ], - "non_tensor_inputs": [ - null, - null - ], - "tensor_lists": {}, - "count": 12 - }, - { - "op_name": "matmul.default", - "input_shapes": [ - [ - 1, - 12, - 512, - 512 - ], - [ - 1, - 12, - 512, - 64 - ] - ], - "input_dtypes": [ - "torch.float32", - "torch.float32" - ], - "non_tensor_inputs": [ - null, - null - ], - "tensor_lists": {}, - "count": 12 - } - ] - }, - "_scaled_dot_product_flash_attention_for_cpu.default": { - "total_calls": 96, - "unique_input_count": 5, - "unique_inputs": [ - { - "op_name": "_scaled_dot_product_flash_attention_for_cpu.default", - "input_shapes": [ - [ - 1, - 16, - 257, - 64 - ], - [ - 1, - 16, - 257, - 64 - ], - [ - 1, - 16, - 257, - 64 - ] - ], - "input_dtypes": [ - "torch.float32", - "torch.float32", - "torch.float32" - ], - "non_tensor_inputs": [ - null, - null, - null - ], - "tensor_lists": {}, - "count": 24 - }, - { - "op_name": "_scaled_dot_product_flash_attention_for_cpu.default", - "input_shapes": [ - [ - 2, - 12, - 7, - 64 - ], - [ - 2, - 12, - 7, - 64 - ], - [ - 2, - 12, - 7, - 64 - ] - ], - "input_dtypes": [ - "torch.float32", - "torch.float32", - "torch.float32" - ], - "non_tensor_inputs": [ - null, - null, - null - ], - "tensor_lists": {}, - "count": 24 - }, - { - "op_name": "_scaled_dot_product_flash_attention_for_cpu.default", - "input_shapes": [ - [ - 1, - 16, - 577, - 64 - ], - [ - 1, - 16, - 577, - 64 - ], - [ - 1, - 16, - 577, - 64 - ] - ], - "input_dtypes": [ - "torch.float32", - "torch.float32", - "torch.float32" - ], - "non_tensor_inputs": [ - null, - null, - null - ], - "tensor_lists": {}, - "count": 24 - }, - { - "op_name": "_scaled_dot_product_flash_attention_for_cpu.default", - "input_shapes": [ - [ - 1, - 12, - 50, - 64 - ], - [ - 1, - 12, - 50, - 64 - ], - [ - 1, - 12, - 50, - 64 - ] - ], - "input_dtypes": [ - "torch.float32", - "torch.float32", - "torch.float32" - ], - "non_tensor_inputs": [ - null, - null, - null - ], - "tensor_lists": {}, - "count": 12 - }, - { - "op_name": "_scaled_dot_product_flash_attention_for_cpu.default", - "input_shapes": [ - [ - 2, - 8, - 7, - 64 - ], - [ - 2, - 8, - 7, - 64 - ], - [ - 2, - 8, - 7, - 64 - ] - ], - "input_dtypes": [ - "torch.float32", - "torch.float32", - "torch.float32" - ], - "non_tensor_inputs": [ - null, - null, - null - ], - "tensor_lists": {}, - "count": 12 - } - ] - }, - "lt.Tensor": { - "total_calls": 3, - "unique_input_count": 1, - "unique_inputs": [ - { - "op_name": "lt.Tensor", - "input_shapes": [ - [ - 7 - ], - [ - 7, - 1 - ] - ], - "input_dtypes": [ - "torch.int64", - "torch.int64" - ], - "non_tensor_inputs": [ - null, - null - ], - "tensor_lists": {}, - "count": 3 - } - ] - }, - "masked_fill_.Scalar": { - "total_calls": 3, - "unique_input_count": 1, - "unique_inputs": [ - { - "op_name": "masked_fill_.Scalar", - "input_shapes": [ - [ - 7, - 7 - ], - [ - 7, - 7 - ], - null - ], - "input_dtypes": [ - "torch.float32", - "torch.bool", - "" - ], - "non_tensor_inputs": [ - null, - null, - 0 - ], - "tensor_lists": {}, - "count": 3 - } - ] - }, - "argmax.default": { - "total_calls": 3, - "unique_input_count": 1, - "unique_inputs": [ - { - "op_name": "argmax.default", - "input_shapes": [ - [ - 2, - 7 - ], - null - ], - "input_dtypes": [ - "torch.int32", - "" - ], - "non_tensor_inputs": [ - null, - -1 - ], - "tensor_lists": {}, - "count": 3 - } - ] - }, - "pow.Tensor_Scalar": { - "total_calls": 12, - "unique_input_count": 6, - "unique_inputs": [ - { - "op_name": "pow.Tensor_Scalar", - "input_shapes": [ - [ - 1, - 1 - ], - null - ], - "input_dtypes": [ - "torch.float32", - "" - ], - "non_tensor_inputs": [ - null, - 0.5 - ], - "tensor_lists": {}, - "count": 3 - }, - { - "op_name": "pow.Tensor_Scalar", - "input_shapes": [ - [ - 2, - 1 - ], - null - ], - "input_dtypes": [ - "torch.float32", - "" - ], - "non_tensor_inputs": [ - null, - 0.5 - ], - "tensor_lists": {}, - "count": 3 - }, - { - "op_name": "pow.Tensor_Scalar", - "input_shapes": [ - [ - 1, - 768 - ], - null - ], - "input_dtypes": [ - "torch.float32", - "" - ], - "non_tensor_inputs": [ - null, - 2 - ], - "tensor_lists": {}, - "count": 2 - }, - { - "op_name": "pow.Tensor_Scalar", - "input_shapes": [ - [ - 2, - 768 - ], - null - ], - "input_dtypes": [ - "torch.float32", - "" - ], - "non_tensor_inputs": [ - null, - 2 - ], - "tensor_lists": {}, - "count": 2 - }, - { - "op_name": "pow.Tensor_Scalar", - "input_shapes": [ - [ - 1, - 512 - ], - null - ], - "input_dtypes": [ - "torch.float32", - "" - ], - "non_tensor_inputs": [ - null, - 2 - ], - "tensor_lists": {}, - "count": 1 - }, - { - "op_name": "pow.Tensor_Scalar", - "input_shapes": [ - [ - 2, - 512 - ], - null - ], - "input_dtypes": [ - "torch.float32", - "" - ], - "non_tensor_inputs": [ - null, - 2 - ], - "tensor_lists": {}, - "count": 1 - } - ] - }, - "exp.default": { - "total_calls": 3, - "unique_input_count": 1, - "unique_inputs": [ - { - "op_name": "exp.default", - "input_shapes": [ - [] - ], - "input_dtypes": [ - "torch.float32" - ], - "non_tensor_inputs": [ - null - ], - "tensor_lists": {}, - "count": 3 - } - ] - }, - "_softmax.default": { - "total_calls": 3, - "unique_input_count": 1, - "unique_inputs": [ - { - "op_name": "_softmax.default", - "input_shapes": [ - [ - 1, - 2 - ], - null, - null - ], - "input_dtypes": [ - "torch.float32", - "", - "" - ], - "non_tensor_inputs": [ - null, - 1, - false - ], - "tensor_lists": {}, - "count": 3 - } - ] - } -} diff --git a/BackendBench/huggingface_tracer/tracer_parser.py b/BackendBench/huggingface_tracer/tracer_parser.py index 59a1fc6..c16d177 100644 --- a/BackendBench/huggingface_tracer/tracer_parser.py +++ b/BackendBench/huggingface_tracer/tracer_parser.py @@ -8,6 +8,8 @@ import json import logging from typing import Any, Dict, List +from urllib.parse import urlparse +from urllib.request import urlopen import torch @@ -23,12 +25,12 @@ } -def load_json_data(json_file_path: str) -> Dict[str, Any]: +def load_json_data(json_source: str) -> Dict[str, Any]: """ - Load operator data from JSON file. + Load operator data from JSON file or URL. Args: - json_file_path: Path to JSON file containing operator data + json_source: Path to JSON file or URL containing operator data Returns: Dictionary containing the loaded JSON data @@ -36,16 +38,31 @@ def load_json_data(json_file_path: str) -> Dict[str, Any]: Raises: FileNotFoundError: If the JSON file doesn't exist json.JSONDecodeError: If the JSON format is invalid + Exception: If URL cannot be accessed """ - try: - with open(json_file_path, "r") as f: - return json.load(f) - except FileNotFoundError: - logger.error(f"JSON file not found: {json_file_path}") - raise - except json.JSONDecodeError as e: - logger.error(f"Invalid JSON format in {json_file_path}: {e}") - raise + # Check if the source is a URL + parsed_url = urlparse(json_source) + if parsed_url.scheme in ("http", "https"): + try: + logger.info(f"Loading JSON data from URL: {json_source}") + with urlopen(json_source) as response: + data = response.read().decode("utf-8") + return json.loads(data) + except Exception as e: + logger.error(f"Failed to load JSON from URL {json_source}: {e}") + raise + else: + # Handle as local file path + try: + logger.info(f"Loading JSON data from local file: {json_source}") + with open(json_source, "r") as f: + return json.load(f) + except FileNotFoundError: + logger.error(f"JSON file not found: {json_source}") + raise + except json.JSONDecodeError as e: + logger.error(f"Invalid JSON format in {json_source}: {e}") + raise def calculate_tensor_shape_magnitude(combination: Dict[str, Any]) -> float: @@ -106,7 +123,7 @@ def select_unique_inputs( for _, entry in input["tensor_lists"].items(): for tensor_dtype in entry["dtypes"]: # all types should be tensors already - tensor_dtype != str(dtype): + if tensor_dtype != str(dtype): continue # Sort by count (popularity) descending diff --git a/scripts/main.py b/scripts/main.py index d4d1ff3..242b10e 100644 --- a/scripts/main.py +++ b/scripts/main.py @@ -83,10 +83,6 @@ def cli(suite, backend, ops, llm_max_attempts): name="huggingface_tracer_cuda_bfloat16", device="cuda", dtype=torch.float32, - json_file_path=os.path.join( - os.path.dirname(os.path.dirname(__file__)), - "BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json", - ), filter=ops, ), }[suite]() @@ -135,10 +131,6 @@ def setup_llm_backend(llm_backend, llm_client, suite_name, ops_filter, max_attem name="huggingface_tracer_cuda_float32", device="cuda", dtype=torch.float32, - json_file_path=os.path.join( - os.path.dirname(os.path.dirname(__file__)), - "BackendBench/huggingface_tracer/tracer_ops_and_shapes/sample_inputs.json", - ), filter=ops_filter, ) else: From 5bccf25e39e81dd562735b4cf2f3b13a3d3afa38 Mon Sep 17 00:00:00 2001 From: PaliC Date: Thu, 10 Jul 2025 12:10:19 -0700 Subject: [PATCH 4/4] fixed issues --- BackendBench/huggingface_tracer/suite.py | 39 ++++++------------- .../huggingface_tracer/tracer_parser.py | 16 +++----- scripts/main.py | 32 ++++----------- 3 files changed, 25 insertions(+), 62 deletions(-) diff --git a/BackendBench/huggingface_tracer/suite.py b/BackendBench/huggingface_tracer/suite.py index 256a87d..08d1663 100644 --- a/BackendBench/huggingface_tracer/suite.py +++ b/BackendBench/huggingface_tracer/suite.py @@ -22,7 +22,9 @@ SPECIAL_CASES, ) -DEFAULT_JSON_SOURCE = "https://huggingface.co/datasets/GPUMODE/huggingface_op_trace/resolve/main/hf_op_trace.json" +DEFAULT_JSON_SOURCE = ( + "https://huggingface.co/datasets/GPUMODE/huggingface_op_trace/resolve/main/hf_op_trace.json" +) logger = logging.getLogger(__name__) # todo: This is a manual mapping of the ops that are not supported by opinfo but are still present @@ -153,9 +155,7 @@ def _convert_single_arg( Converted argument (tensor, list of tensors, or other value) """ if non_tensor_input is not None: - return self._handle_non_tensor_input( - non_tensor_input, dtype_str, tensor_lists - ) + return self._handle_non_tensor_input(non_tensor_input, dtype_str, tensor_lists) elif dtype_str == "": return None elif dtype_str == "" and shape is None: @@ -177,9 +177,7 @@ def _handle_non_tensor_input( tensor_list_metadata = tensor_lists[tensor_list_ref] return create_tensor_list(tensor_list_metadata, self.device, self.dtype) else: - logger.warning( - f"Tensor list reference {tensor_list_ref} not found in tensor_lists" - ) + logger.warning(f"Tensor list reference {tensor_list_ref} not found in tensor_lists") return [] # Empty list as fallback # Handle torch.dtype conversion @@ -194,9 +192,7 @@ def _handle_non_tensor_input( else: return non_tensor_input - def _handle_tensor_input( - self, shape: Any, dtype_str: str, arg_index: int - ) -> torch.Tensor: + def _handle_tensor_input(self, shape: Any, dtype_str: str, arg_index: int) -> torch.Tensor: """Handle tensor inputs.""" if isinstance(shape, list): return create_single_tensor(shape, dtype_str, self.device, self.dtype) @@ -229,12 +225,8 @@ def build_huggingface_tracer_tests( op_tests = [] # create op_info mapping to test dtypes - op_dtype_filter = { - op.name.split(".")[-1]: op.supported_dtypes(device) for op in op_db - } - manual_ops = load_json_data( - os.path.join(os.path.dirname(__file__), MANUAL_OPS_FILE) - ) + op_dtype_filter = {op.name.split(".")[-1]: op.supported_dtypes(device) for op in op_db} + manual_ops = load_json_data(os.path.join(os.path.dirname(__file__), MANUAL_OPS_FILE)) for op in manual_ops: dtype_list = manual_ops[op].get(device, []) # convert to set to match with op_info datatype @@ -285,9 +277,7 @@ def build_huggingface_tracer_tests( continue # Skip if no supported dtypes if dtype not in op_dtype_filter[op_name_no_overload]: - logger.debug( - f"Skipping {op_name}: dtype {dtype} not supported according to op_info" - ) + logger.debug(f"Skipping {op_name}: dtype {dtype} not supported according to op_info") skipped_no_dtype_tests.append(op_name) continue @@ -303,20 +293,15 @@ def build_huggingface_tracer_tests( f"Created test for {op_name} with {len(selected_unique_inputs)} unique_inputs on {device}" ) else: - logger.debug( - f"Skipping {op_name}: no unique_inputs found for dtype {dtype}" - ) + logger.debug(f"Skipping {op_name}: no unique_inputs found for dtype {dtype}") skipped_no_dtype_tests.append(op_name) - logger.info( - f"While building tests, skipped {len(skipped_no_op_info)} ops with no op_info" - ) + logger.info(f"While building tests, skipped {len(skipped_no_op_info)} ops with no op_info") logger.info( f"While building tests, skipped {len(skipped_no_dtype_tests)} ops with no dtype tests" ) logger.info( - "Skipped ops with no op_info or were manually added: \n" - + "\n".join(skipped_no_op_info) + "Skipped ops with no op_info or were manually added: \n" + "\n".join(skipped_no_op_info) ) logger.info( f"Skipped ops as they don't support testing {dtype} on {device}: \n" diff --git a/BackendBench/huggingface_tracer/tracer_parser.py b/BackendBench/huggingface_tracer/tracer_parser.py index c16d177..a2b3612 100644 --- a/BackendBench/huggingface_tracer/tracer_parser.py +++ b/BackendBench/huggingface_tracer/tracer_parser.py @@ -79,11 +79,7 @@ def calculate_tensor_shape_magnitude(combination: Dict[str, Any]) -> float: input_shapes = combination["input_shapes"] for shape in input_shapes: - if ( - isinstance(shape, list) - and len(shape) > 0 - and all(isinstance(x, int) for x in shape) - ): + if isinstance(shape, list) and len(shape) > 0 and all(isinstance(x, int) for x in shape): # Calculate product of dimensions (total tensor size) magnitude = 1 for dim in shape: @@ -127,9 +123,9 @@ def select_unique_inputs( continue # Sort by count (popularity) descending - popular_unique_inputs = sorted( - unique_inputs, key=lambda x: x["count"], reverse=True - )[:max_popular] + popular_unique_inputs = sorted(unique_inputs, key=lambda x: x["count"], reverse=True)[ + :max_popular + ] # Sort by magnitude descending largest_unique_inputs = sorted( @@ -183,9 +179,7 @@ def create_single_tensor( dtype_name = dtype_str.replace("torch.", "") torch_dtype = getattr(torch, dtype_name) except AttributeError: - logger.warning( - f"Could not convert {dtype_str} to torch dtype, using {torch_dtype}" - ) + logger.warning(f"Could not convert {dtype_str} to torch dtype, using {torch_dtype}") # Create tensor with appropriate method based on dtype if torch_dtype in [torch.float16, torch.float32, torch.float64, torch.bfloat16]: diff --git a/scripts/main.py b/scripts/main.py index 242b10e..b77dc19 100644 --- a/scripts/main.py +++ b/scripts/main.py @@ -108,9 +108,7 @@ def cli(suite, backend, ops, llm_max_attempts): mean_correctness = torch.tensor(overall_correctness).mean().item() geomean_perf = torch.tensor(overall_performance).log().mean().exp().item() - print( - f"correctness score (mean pass rate over all operators): {mean_correctness:.2f}" - ) + print(f"correctness score (mean pass rate over all operators): {mean_correctness:.2f}") print(f"performance score (geomean speedup over all operators): {geomean_perf:.2f}") @@ -184,41 +182,29 @@ def feedback_callback(kernel_code: str, attempt: int) -> tuple[bool, Dict]: successful_ops += 1 # Save summary of this operation - summary_file = os.path.join( - llm_backend.kernels_dir, f"{op_name}_summary.txt" - ) + summary_file = os.path.join(llm_backend.kernels_dir, f"{op_name}_summary.txt") with open(summary_file, "w") as f: f.write(f"Operation: {op_name}\n") f.write(f"Full op: {op_str}\n") f.write(f"Attempts used: {attempts_used}/{max_attempts}\n") f.write("Final status: Success\n") - f.write( - f"Final kernel file: {op_name}_kernel_attempt_{attempts_used}.py\n" - ) + f.write(f"Final kernel file: {op_name}_kernel_attempt_{attempts_used}.py\n") except Exception as e: - print( - f"✗ Kernel passed tests but failed final compilation for {op_name}: {e}" - ) + print(f"✗ Kernel passed tests but failed final compilation for {op_name}: {e}") success = False if not success: print(f"✗ Skipping {op_name} - failed all {attempts_used} attempts") # Save summary of this operation - summary_file = os.path.join( - llm_backend.kernels_dir, f"{op_name}_summary.txt" - ) + summary_file = os.path.join(llm_backend.kernels_dir, f"{op_name}_summary.txt") with open(summary_file, "w") as f: f.write(f"Operation: {op_name}\n") f.write(f"Full op: {op_str}\n") f.write(f"Attempts used: {attempts_used}/{max_attempts}\n") - f.write( - "Final status: Failed - All attempts failed correctness tests\n" - ) - f.write( - f"Last kernel file: {op_name}_kernel_attempt_{attempts_used}.py\n" - ) + f.write("Final status: Failed - All attempts failed correctness tests\n") + f.write(f"Last kernel file: {op_name}_kernel_attempt_{attempts_used}.py\n") # Continue with other operations # Print summary @@ -237,9 +223,7 @@ def feedback_callback(kernel_code: str, attempt: int) -> tuple[bool, Dict]: print(f"{'=' * 60}\n") # Save overall summary - overall_summary_file = os.path.join( - llm_backend.kernels_dir, "OVERALL_SUMMARY.txt" - ) + overall_summary_file = os.path.join(llm_backend.kernels_dir, "OVERALL_SUMMARY.txt") with open(overall_summary_file, "w") as f: f.write("LLM Backend Generation Summary\n") f.write(f"{'=' * 40}\n")