diff --git "a/hf_op_trace.json" "b/hf_op_trace.json" --- "a/hf_op_trace.json" +++ "b/hf_op_trace.json" @@ -1,229 +1,31334 @@ -""" -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_shape_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) from the shape - """ - 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 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 tensor_dtype in entry["dtypes"]: - # all types should be tensors already - tensor_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_shape_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: - raise ValueError(f"Unsupported dtype: {dtype_str}") - - 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 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