Dataset Object

.env.example

@@ -9,7 +9,7 @@ OPENAI_API_KEY=sk-...
 ANTHROPIC_API_KEY=sk-ant-api03-...
 
 # Google Gemini
-GEMINI_API_KEY=...
+GEMINI_API_KEY=
 
 # DeepSeek
 DEEPSEEK_API_KEY=sk-...

scripts/benchmark_eval_analysis.py

@@ -53,7 +53,7 @@ def patch(eval_results, dataset):
     """
     Patch the eval results with the dataset
     """
-    for pid in range(1, len(dataset) + 1):
+    for pid in dataset.get_problem_ids():
         if str(pid) not in eval_results:
             eval_results[str(pid)] = {
                 "sample_id": 0,
@@ -161,19 +161,40 @@ def analyze_greedy_eval(run_name, hardware, baseline, level,
     )
 
     # Extract the speedup values
-    is_correct = np.array([entry["correctness"] for entry in eval_results.values()])
-    baseline_speed = np.array(
-        [entry["mean"] for entry in baseline_results[f"level{level}"].values()]
-    )
-    actual_speed = np.array([entry["runtime"] for entry in eval_results.values()])
+    is_correct_list = []
+    baseline_speed_list = []
+    actual_speed_list = []
+
+    # Sort problem IDs to ensure consistent order
+    sorted_pids = sorted(dataset.get_problem_ids())
+
+    for pid in sorted_pids:
+        # Get eval result
+        if str(pid) not in eval_results:
+            print(f"Warning: Problem {pid} not found in eval results")
+            continue
+        eval_entry = eval_results[str(pid)]
+
+        # Get baseline result
+        problem = dataset.get_problem_by_id(pid)
+        problem_name = problem.name
+
+        if problem_name not in baseline_results[f"level{level}"]:
+            print(f"Warning: Problem {problem_name} not found in baseline results")
+            continue
+
+        baseline_entry = baseline_results[f"level{level}"][problem_name]
+
+        is_correct_list.append(eval_entry["correctness"])
+        actual_speed_list.append(eval_entry["runtime"])
+        baseline_speed_list.append(baseline_entry["mean"])
+
+    is_correct = np.array(is_correct_list)
+    baseline_speed = np.array(baseline_speed_list)
+    actual_speed = np.array(actual_speed_list)
     n = len(is_correct)
 
-    assert (
-        len(baseline_speed) == n
-    ), "Baseline speedup values do not match the number of eval results"
-    assert (
-        len(actual_speed) == n
-    ), "Actual speedup values do not match the number of eval results"
+    print(f"Aligned {n} problems for analysis")
 
     # Calculate the metrics
     gmsr_correct = geometric_mean_speed_ratio_correct_only(

scripts/eval_from_generations.py

@@ -12,7 +12,6 @@
 import pydra
 import torch
 
-from datasets import load_dataset
 from pydra import Config, REQUIRED
 
 # Import only what we need
@@ -255,36 +254,17 @@ def evaluate_single_sample_modal(
 
 
 def fetch_ref_arch_from_problem_id(
-    dataset, problem_id: int, dataset_src: str
+    dataset, problem_id: int, dataset_src: str = None
 ) -> str | None:
     """
-    Fetch reference architecture from problem directory
-    Either from Hugging Face or Local Dataset
+    Fetch reference architecture from problem directory.
+    Uses the unified dataset interface.
+
+    Note: dataset_src parameter is kept for backward compatibility but ignored
+    since the dataset object already handles both sources.
     """
-    if dataset_src == "huggingface":
-        curr_problem_row = dataset.filter(
-            lambda x: x["problem_id"] == problem_id, num_proc=None, desc=None
-        )
-        ref_arch_src = curr_problem_row["code"][0]
-        problem_name = curr_problem_row["name"][0]
-
-    elif dataset_src == "local":
-        problem_idx_in_dataset = (
-            problem_id - 1
-        )  # due to dataset list being 0-indexed locally
-        ref_arch_path = dataset[problem_idx_in_dataset]
-
-        problem_name = os.path.basename(ref_arch_path)
-        ref_arch_src = read_file(ref_arch_path)
-
-    # verify
-    # Extract problem number from problem name (e.g. "1" from "1_Square_matrix_multiplication_.py")
-    problem_number = int(problem_name.split("_")[0])
-    assert (
-        problem_number == problem_id
-    ), f"Problem number in filename ({problem_number}) does not match config problem_id ({problem_id})"
-
-    return ref_arch_src
+    problem = dataset.get_problem_by_id(problem_id)
+    return problem.code
 
 
 def fetch_kernel_from_disk(
@@ -822,57 +802,48 @@ def main(config: EvalConfig):
     if mp.get_start_method(allow_none=True) is None:
         mp.set_start_method("spawn")
 
-    # Dataset Configurations
-    if config.dataset_src == "huggingface":
-        dataset = load_dataset(config.dataset_name)
-        curr_level_dataset = dataset[f"level_{config.level}"]
-    elif config.dataset_src == "local":
-        curr_level_dataset = construct_kernelbench_dataset(config.level)
-
-    num_problems_in_level = len(curr_level_dataset)
-
-    # Determine which problem IDs to evaluate
-    # you can either specify a list of problem IDs (prioritize) or a subset range
-    # NOTE: later once the dataset PR is in we will link the representative subset as a built-in preset too
-    if config.problem_ids is not None:
-        # Use specific problem IDs if provided
-        problem_id_list = config.problem_ids
-        for pid in problem_id_list:
-            assert 1 <= pid <= num_problems_in_level, f"Problem ID {pid} out of range for Level {config.level}"
-    elif config.subset == (None, None):
-        problem_id_list = list(range(1, num_problems_in_level + 1))
+    # Dataset Configurations - Unified loading
+    dataset = construct_kernelbench_dataset(
+        level=config.level,
+        source=config.dataset_src,
+        dataset_name=config.dataset_name,
+    )
+
+    all_problem_ids = dataset.get_problem_ids()
+
+    if config.subset == (None, None):
+        problem_ids_to_run = all_problem_ids
     else:
-        assert (
-            config.subset[0] >= 1 and config.subset[1] <= num_problems_in_level
-        ), f"Subset range {config.subset} out of range for Level {config.level}"
-        problem_id_list = list(range(config.subset[0], config.subset[1] + 1))
+        start, end = config.subset
+        problem_ids_to_run = [pid for pid in all_problem_ids if start <= pid <= end]
+        if not problem_ids_to_run:
+            print(f"Warning: No problems found in subset range {config.subset}")
 
     print(
-        f"Evaluating {config.num_samples_per_problem} sample(s) each for level {config.level} problems: {problem_id_list}"
+        f"Evaluating {config.num_samples_per_problem} sample(s) each for level {config.level} problems: {problem_ids_to_run}"
     )
 
     run_dir = os.path.join(config.runs_dir, config.run_name)
     eval_file_path = os.path.join(run_dir, f"eval_results.json")
 
     # To Debug
-    # single_eval_example(config, curr_level_dataset, run_dir, eval_file_path)
+    # single_eval_example(config, dataset, run_dir, eval_file_path)
 
     total_work = []
-    for problem_id in problem_id_list:
+    for problem_id in problem_ids_to_run:
         for sample_id in range(config.num_samples_per_problem):
             if not check_if_eval_exists_local(problem_id, sample_id, eval_file_path):
                 total_work.append((problem_id, sample_id))
 
     print(
         f"Start evaluation on {len(total_work)} unevaluated samples"
-        f" for problems: {problem_id_list}"
+        f" in range: {problem_ids_to_run}"
     )
     # Build Cache on CPU as that is faster (only for local mode)
     if config.build_cache and config.eval_mode == "local":
         compile.batch_compile(total_work, config.to_dict())
 
-    # Batch Eval on multiple GPUs in parallel
-    batch_eval(total_work, config, curr_level_dataset, run_dir, eval_file_path)
+    batch_eval(total_work, config, dataset, run_dir, eval_file_path)
 
     # Calculate pass@k metrics if multiple samples per problem were evaluated
     if config.num_samples_per_problem > 1:

scripts/generate_and_eval_single_sample.py

@@ -5,16 +5,12 @@
 import json
 import modal
 
-from datasets import load_dataset
-
-from kernelbench.dataset import construct_kernelbench_dataset
 from kernelbench.eval import eval_kernel_against_ref
 from kernelbench.prompt_constructor_toml import get_prompt_for_backend, get_custom_prompt
 from kernelbench.utils import (
     create_inference_server_from_presets,
     extract_first_code,
     query_server,
-    read_file,
     set_gpu_arch,
 )
 from kernelbench.eval import get_torch_dtype_from_string
@@ -116,13 +112,14 @@ def main(config: EvalConfig):
 
     print(f"Starting Eval with config: {config}")
 
-    # Configurations
-
-    if config.dataset_src == "huggingface":
-        dataset = load_dataset(config.dataset_name)
-        curr_level_dataset = dataset[f"level_{config.level}"]
-    elif config.dataset_src == "local":
-        curr_level_dataset = construct_kernelbench_dataset(config.level)
+    # Configurations - Unified dataset loading (works for both HF and local)
+    from kernelbench.dataset import construct_kernelbench_dataset
+
+    dataset = construct_kernelbench_dataset(
+        level=config.level,
+        source=config.dataset_src,
+        dataset_name=config.dataset_name,
+    )
 
     if config.gpu_arch:
         set_gpu_arch(config.gpu_arch)  # otherwise build for all architectures
@@ -131,41 +128,16 @@ def main(config: EvalConfig):
         os.makedirs(config.logdir, exist_ok=True)
 
     # Problem Checks
-    num_problems = len(curr_level_dataset)
+    num_problems = len(dataset)
     print(f"Number of problems in Level {config.level}: {num_problems}")
     print(
         f"Start Generation + Evaluation for Level {config.level} Problem {config.problem_id}"
     )
 
-    assert (
-        config.problem_id <= num_problems
-    ), f"Problem ID {config.problem_id} out of range for Level {config.level}"
-
-    # TODO: refactor dataset fetching logic to be as clean as posisble.
-    # 1. Fetch Problem
-    if config.dataset_src == "huggingface":
-
-        curr_problem_row = curr_level_dataset.filter(
-            lambda x: x["problem_id"] == config.problem_id
-        )
-        ref_arch_src = curr_problem_row["code"][0]
-        problem_name = curr_problem_row["name"][0]
-
-    elif config.dataset_src == "local":
-        problem_idx_in_dataset = (
-            config.problem_id - 1
-        )  # due to dataset list being 0-indexed locally
-        ref_arch_path = curr_level_dataset[problem_idx_in_dataset]
-
-        problem_name = os.path.basename(ref_arch_path)
-        ref_arch_src = read_file(ref_arch_path)
-    # import pdb; pdb.set_trace()
-
-    # Extract problem number from problem name (e.g. "1" from "1_Square_matrix_multiplication_.py")
-    problem_number = int(problem_name.split("_")[0])
-    assert (
-        problem_number == config.problem_id
-    ), f"Problem number in filename ({problem_number}) does not match config problem_id ({config.problem_id})"
+    # Fetch problem - unified interface, no branching needed
+    problem = dataset.get_problem_by_id(config.problem_id)
+    ref_arch_src = problem.code
+    problem_name = problem.name
 
     # 2. Generate Sample
     # Create inference function with config parameters

scripts/generate_and_eval_single_sample_modal.py

@@ -11,10 +11,8 @@
 import json
 import modal
 
-from datasets import load_dataset
-
-#from src.dataset import construct_kernelbench_dataset
-from kernelbench.utils import extract_first_code, query_server, set_gpu_arch, read_file, create_inference_server_from_presets
+from kernelbench.dataset import construct_kernelbench_dataset
+from kernelbench.utils import extract_first_code, query_server, set_gpu_arch, create_inference_server_from_presets
 
 app = modal.App("eval_single_sample")
 
@@ -155,41 +153,25 @@ def main(config: EvalConfig):
 
     print(f"Starting Eval with config: {config}")
 
-    # Configurations
-
-    if config.dataset_src == "huggingface":
-        dataset = load_dataset(config.dataset_name)
-        curr_level_dataset = dataset[f"level_{config.level}"]
+    # Configurations - Unified dataset loading (works for both HF and local)
+    dataset = construct_kernelbench_dataset(
+        level=config.level,
+        source=config.dataset_src,
+        dataset_name=config.dataset_name,
+    )
 
     if config.log:
         os.makedirs(config.logdir, exist_ok=True)
 
     # Problem Checks
-    num_problems = len(curr_level_dataset)
+    num_problems = len(dataset)
     print(f"Number of problems in Level {config.level}: {num_problems}")
     print(f"Start Generation + Evaluation for Level {config.level} Problem {config.problem_id}")
 
-    assert config.problem_id <= num_problems, f"Problem ID {config.problem_id} out of range for Level {config.level}"
-
-
-    # 1. Fetch Problem
-    if config.dataset_src == "huggingface":
-
-        curr_problem_row = curr_level_dataset.filter(lambda x: x["problem_id"] == config.problem_id)
-        ref_arch_src = curr_problem_row["code"][0]
-        problem_name = curr_problem_row["name"][0]
-
-    elif config.dataset_src == "local":
-        problem_idx_in_dataset = config.problem_id - 1 # due to dataset list being 0-indexed locally
-        ref_arch_path = curr_level_dataset[problem_idx_in_dataset]
-
-        problem_name = os.path.basename(ref_arch_path)
-        ref_arch_src = read_file(ref_arch_path)
-    # import pdb; pdb.set_trace()
-
-    # Extract problem number from problem name (e.g. "1" from "1_Square_matrix_multiplication_.py")
-    problem_number = int(problem_name.split("_")[0])
-    assert problem_number == config.problem_id, f"Problem number in filename ({problem_number}) does not match config problem_id ({config.problem_id})"
+    # Fetch problem - unified interface, no branching needed
+    problem = dataset.get_problem_by_id(config.problem_id)
+    ref_arch_src = problem.code
+    problem_name = problem.name
 
 
     # 2. Generate Sample