-
Notifications
You must be signed in to change notification settings - Fork 269
[TILE ENGINE] Restructure Tile Engine's benchmarking and profiling #3546
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Open
arai713
wants to merge
5
commits into
develop
Choose a base branch
from
arai/ck_tile/tile_engine_restructure
base: develop
Could not load branches
Branch not found: {{ refName }}
Loading
Could not load tags
Nothing to show
Loading
Are you sure you want to change the base?
Some commits from the old base branch may be removed from the timeline,
and old review comments may become outdated.
Open
Changes from all commits
Commits
Show all changes
5 commits
Select commit
Hold shift + click to select a range
89ce8a6
Restructure Tile Engine's benchmarking process
arai713 50dffb9
Restructure Tile Engine's profiling process
arai713 96aac9a
Adding README back into the gemm directory and integrate new preshuff…
arai713 7d88298
disabling the gemm tile engine tests
arai713 0465957
Merge branch 'develop' into arai/ck_tile/tile_engine_restructure
ThomasNing File filter
Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,2 @@ | ||
| # Copyright (c) Advanced Micro Devices, Inc., or its affiliates. | ||
| # SPDX-License-Identifier: MIT |
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,283 @@ | ||
| #!/usr/bin/env python3 | ||
| # Copyright (c) Advanced Micro Devices, Inc., or its affiliates. | ||
| # SPDX-License-Identifier: MIT | ||
|
|
||
| import json | ||
| import subprocess | ||
| import csv | ||
| from pathlib import Path | ||
| from typing import List, Dict, Optional | ||
|
|
||
|
|
||
| def run_kernel( | ||
| build_dir: Path, kernel_path: Path, params: Dict[str, str], verbose: bool = False | ||
| ) -> Optional[Dict]: | ||
| """Run a single kernel with given parameters and save output to individual JSON file""" | ||
| # Create results directory | ||
| results_dir = build_dir / "results" | ||
| results_dir.mkdir(exist_ok=True) | ||
|
|
||
| # Generate unique JSON filename for this kernel | ||
| json_file = results_dir / f"{kernel_path.stem}.json" | ||
|
|
||
| cmd = [str(kernel_path)] | ||
|
|
||
| # Add parameters | ||
| for key, value in params.items(): | ||
| cmd.append(f"-{key}={value}") | ||
|
|
||
| # Add JSON output flag for clean JSON output | ||
| cmd.append("-json_output=true") | ||
|
|
||
| if verbose: | ||
| print(f"Running: {' '.join(cmd)}") | ||
|
|
||
| try: | ||
| result = subprocess.run(cmd, capture_output=True, text=True, timeout=60) | ||
|
|
||
| if result.returncode != 0: | ||
| print(f"Error running {kernel_path.name}: {result.stderr}") | ||
| return None | ||
|
|
||
| # Save raw output to individual JSON file | ||
| output = result.stdout.strip() | ||
| if output: | ||
| with open(json_file, "w") as f: | ||
| f.write(output) | ||
|
|
||
| # Parse the JSON file | ||
| return parse_json_file(json_file, verbose=verbose) | ||
| else: | ||
| print(f"No output from {kernel_path.name}") | ||
| return None | ||
|
|
||
| except subprocess.TimeoutExpired: | ||
| print(f"Timeout running {kernel_path.name}") | ||
| return None | ||
| except Exception as e: | ||
| print(f"Error running {kernel_path.name}: {e}") | ||
| return None | ||
|
|
||
|
|
||
| def parse_json_file(json_file: Path, verbose: bool = False) -> Optional[Dict]: | ||
| """Parse JSON data from individual kernel output file""" | ||
| try: | ||
| with open(json_file, "r") as f: | ||
| content = f.read().strip() | ||
|
|
||
| # Parse the JSON directly since executables produce clean JSON | ||
| data = json.loads(content) | ||
|
|
||
| # Return the complete JSON data as-is, just add some convenience fields | ||
| result = data.copy() | ||
| if "perf_result" in data: | ||
| perf = data["perf_result"] | ||
| # Add convenience fields for backward compatibility | ||
| result["time_ms"] = perf.get("latency(ms)", 0) | ||
| result["tflops"] = perf.get("tflops(TFlops)", 0) | ||
| result["bandwidth_gb_s"] = perf.get("bandwidth(GB/s)", 0) | ||
|
|
||
| return result | ||
|
|
||
| except json.JSONDecodeError as e: | ||
| if verbose: | ||
| print(f"Failed to parse JSON from {json_file}: {e}") | ||
| return None | ||
| except Exception as e: | ||
| if verbose: | ||
| print(f"Error reading JSON file {json_file}: {e}") | ||
| return None | ||
|
|
||
|
|
||
| def find_best_kernel(results: List[Dict], metric: str = "tflops") -> Optional[Dict]: | ||
| """Find the best performing kernel based on metric""" | ||
| if not results: | ||
| return None | ||
|
|
||
| if metric == "tflops": | ||
| return max(results, key=lambda x: x.get("tflops", 0)) | ||
| elif metric == "time_ms": | ||
| return min(results, key=lambda x: x.get("time_ms", float("inf"))) | ||
| elif metric == "bandwidth_gb_s": | ||
| return max(results, key=lambda x: x.get("bandwidth_gb_s", 0)) | ||
| else: | ||
| raise ValueError(f"Unknown metric: {metric}") | ||
|
|
||
|
|
||
| def export_csv(results: List[Dict], filename: str, verbose: bool = False): | ||
| """Export all results to CSV""" | ||
| if not results: | ||
| print("No results to export") | ||
| return | ||
|
|
||
| # Get all unique keys from results | ||
| all_keys = set() | ||
| for result in results: | ||
| all_keys.update(result.keys()) | ||
|
|
||
| # Sort keys for consistent output | ||
| fieldnames = sorted(all_keys) | ||
|
|
||
| with open(filename, "w", newline="") as csvfile: | ||
| writer = csv.DictWriter(csvfile, fieldnames=fieldnames) | ||
| writer.writeheader() | ||
| writer.writerows(results) | ||
|
|
||
| print(f"Results exported to {filename}") | ||
|
|
||
|
|
||
| def export_best_kernels(best_kernels: Dict, filename: str, verbose: bool = False): | ||
| """Export best kernel selections to file""" | ||
| with open(filename, "w") as f: | ||
| f.write("# Best kernel selections\n") | ||
| f.write( | ||
| "# Format: problem_size -> kernel_name (TFLOPS, bandwidth, latency)\n\n" | ||
| ) | ||
|
|
||
| for key, kernel in sorted(best_kernels.items()): | ||
| f.write( | ||
| f"{key}: {kernel['name']} ({kernel['tflops']:.2f} TFLOPS, {kernel['bandwidth_gb_s']:.2f} GB/s, {kernel['time_ms']:.2f}ms)\n" | ||
| ) | ||
|
|
||
| print(f"Best kernels exported to {filename}") | ||
|
|
||
|
|
||
| def export_json( | ||
| results: List[Dict], filename: str, best_kernels: Dict = None, verbose: bool = False | ||
| ): | ||
| """Export all results and best kernels to JSON with comprehensive metadata""" | ||
| from datetime import datetime | ||
|
|
||
| # Calculate comprehensive summary statistics for all metrics | ||
| successful_results = [r for r in results if r.get("tflops", 0) > 0] | ||
|
|
||
| tflops_values = [r.get("tflops", 0) for r in successful_results] | ||
| bandwidth_values = [r.get("bandwidth_gb_s", 0) for r in successful_results] | ||
| latency_values = [ | ||
| r.get("time_ms", 0) for r in successful_results if r.get("time_ms", 0) > 0 | ||
| ] | ||
|
|
||
| # Performance breakdown by kernel type | ||
| pipeline_stats = {} | ||
| scheduler_stats = {} | ||
| data_type_stats = {} | ||
|
|
||
| for result in successful_results: | ||
| # Get config info from the new structure | ||
| config = result.get("config", {}) | ||
|
|
||
| # Pipeline statistics | ||
| pipeline = config.get("pipeline", "unknown") | ||
| if pipeline not in pipeline_stats: | ||
| pipeline_stats[pipeline] = { | ||
| "count": 0, | ||
| "avg_tflops": 0, | ||
| "best_tflops": 0, | ||
| } | ||
| pipeline_stats[pipeline]["count"] += 1 | ||
| pipeline_stats[pipeline]["best_tflops"] = max( | ||
| pipeline_stats[pipeline]["best_tflops"], result.get("tflops", 0) | ||
| ) | ||
|
|
||
| # Scheduler statistics | ||
| scheduler = config.get("scheduler", "unknown") | ||
| if scheduler not in scheduler_stats: | ||
| scheduler_stats[scheduler] = { | ||
| "count": 0, | ||
| "avg_tflops": 0, | ||
| "best_tflops": 0, | ||
| } | ||
| scheduler_stats[scheduler]["count"] += 1 | ||
| scheduler_stats[scheduler]["best_tflops"] = max( | ||
| scheduler_stats[scheduler]["best_tflops"], result.get("tflops", 0) | ||
| ) | ||
|
|
||
| # Data type statistics | ||
| data_type = config.get("data_type", "unknown") | ||
| if data_type not in data_type_stats: | ||
| data_type_stats[data_type] = { | ||
| "count": 0, | ||
| "avg_tflops": 0, | ||
| "best_tflops": 0, | ||
| } | ||
| data_type_stats[data_type]["count"] += 1 | ||
| data_type_stats[data_type]["best_tflops"] = max( | ||
| data_type_stats[data_type]["best_tflops"], result.get("tflops", 0) | ||
| ) | ||
|
|
||
| # Calculate averages for breakdown stats | ||
| for stats_dict, field_name in [ | ||
| (pipeline_stats, "pipeline"), | ||
| (scheduler_stats, "scheduler"), | ||
| (data_type_stats, "data_type"), | ||
| ]: | ||
| for key in stats_dict: | ||
| relevant_results = [ | ||
| r | ||
| for r in successful_results | ||
| if r.get("config", {}).get(field_name, "unknown") == key | ||
| ] | ||
| if relevant_results: | ||
| stats_dict[key]["avg_tflops"] = sum( | ||
| r.get("tflops", 0) for r in relevant_results | ||
| ) / len(relevant_results) | ||
|
|
||
| output_data = { | ||
| "benchmark_metadata": { | ||
| "timestamp": datetime.now().isoformat(), | ||
| "total_kernels_tested": len(results), | ||
| "unique_kernels": len(set(r.get("name", "unknown") for r in results)), | ||
| "successful_runs": len(successful_results), | ||
| "failed_runs": len(results) - len(successful_results), | ||
| }, | ||
| "performance_summary": { | ||
| "tflops_stats": { | ||
| "best": max(tflops_values, default=0), | ||
| "average": sum(tflops_values) / len(tflops_values) | ||
| if tflops_values | ||
| else 0, | ||
| "min": min(tflops_values, default=0), | ||
| "median": sorted(tflops_values)[len(tflops_values) // 2] | ||
| if tflops_values | ||
| else 0, | ||
| }, | ||
| "bandwidth_stats": { | ||
| "best_gb_s": max(bandwidth_values, default=0), | ||
| "average_gb_s": sum(bandwidth_values) / len(bandwidth_values) | ||
| if bandwidth_values | ||
| else 0, | ||
| "min_gb_s": min(bandwidth_values, default=0), | ||
| "median_gb_s": sorted(bandwidth_values)[len(bandwidth_values) // 2] | ||
| if bandwidth_values | ||
| else 0, | ||
| }, | ||
| "latency_stats": { | ||
| "best_ms": min(latency_values, default=0), | ||
| "average_ms": sum(latency_values) / len(latency_values) | ||
| if latency_values | ||
| else 0, | ||
| "max_ms": max(latency_values, default=0), | ||
| "median_ms": sorted(latency_values)[len(latency_values) // 2] | ||
| if latency_values | ||
| else 0, | ||
| }, | ||
| "kernel_type_breakdown": { | ||
| "by_pipeline": pipeline_stats, | ||
| "by_scheduler": scheduler_stats, | ||
| "by_data_type": data_type_stats, | ||
| }, | ||
| "total_problem_configurations": len(best_kernels) if best_kernels else 0, | ||
| }, | ||
| "kernel_results": results, | ||
| "best_kernels_by_problem": best_kernels or {}, | ||
| } | ||
|
|
||
| with open(filename, "w") as f: | ||
| json.dump(output_data, f, indent=2) | ||
|
|
||
| print(f"JSON results exported to {filename}") | ||
| print(f" - Total kernels: {len(results)}") | ||
| print(f" - Successful runs: {len(successful_results)}") | ||
| print(f" - Best TFLOPS: {max(tflops_values, default=0):.2f}") | ||
| print(f" - Best bandwidth: {max(bandwidth_values, default=0):.2f} GB/s") | ||
| print(f" - Best latency: {min(latency_values, default=0):.2f}ms") |
Oops, something went wrong.
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Uh oh!
There was an error while loading. Please reload this page.