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Adding dispatcher architecture (#3300)
* WIP POC of dispatcher * Dispatcher python workflow setup. * Dispatcher cleanup and updates. Further dispatcher cleanup and updates. Build fixes Improvements and python to CK example Improvements to readme * Fixes to python paths * Cleaning up code * Improving dispatcher support for different arch Fixing typos * Fix formatting errors * Cleaning up examples * Improving codegeneration * Improving and fixing C++ examples * Adding conv functionality (fwd,bwd,bwdw) and examples. * Fixes based on feedback. * Further fixes based on feedback. * Adding stress test for autogeneration and autocorrection, and fixing preshuffle bug. * Another round of improvements based on feedback. * Trimming out unnecessary code. * Fixing the multi-D implementation. * Using gpu verification for gemms and fixing convolutions tflops calculation. * Fix counter usage issue and arch filtering per ops. * Adding changelog and other fixes. * Improve examples and resolve critical bugs. * Reduce build time for python examples. * Fixing minor bug. * Fix compilation error. * Improve installation instructions for dispatcher. * Add docker based installation instructions for dispatcher. * Fixing arch-based filtering to match tile engine. * Remove dead code and fix arch filtering. * Minor bugfix. * Updates after rebase. * Trimming code. * Fix copyright headers. * Consolidate examples, cut down code. * Minor fixes. * Improving python examples. * Update readmes. * Remove conv functionality. * Cleanup following conv removable.
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Vidyasagar Ananthan
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260
dispatcher/examples/gemm/python/10_advanced_benchmark.py
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dispatcher/examples/gemm/python/10_advanced_benchmark.py
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#!/usr/bin/env python3
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# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
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# SPDX-License-Identifier: MIT
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"""
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Example 10: Advanced Benchmarking with Full Control
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This example demonstrates all available benchmark parameters:
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- warmup: Number of warmup iterations (default: 5)
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- repeat: Number of benchmark iterations (default: 20)
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- flush_cache: Flush GPU cache between iterations (default: False)
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- timer: Timer type - "gpu" (default) or "cpu"
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- init: Initialization method - "random", "linear", "constant"
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Usage:
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python3 10_advanced_benchmark.py
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python3 10_advanced_benchmark.py --warmup 10 --repeat 100
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python3 10_advanced_benchmark.py --init linear
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"""
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import argparse
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import sys
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from pathlib import Path
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# Add paths for imports
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sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "python"))
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import numpy as np
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from ctypes_utils import (
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KernelConfig,
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setup_gemm_dispatcher,
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cleanup_gemm,
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reset_for_example,
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)
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Advanced GEMM benchmarking with full parameter control"
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)
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# Problem size
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parser.add_argument("-m", type=int, default=2048, help="M dimension")
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parser.add_argument("-n", type=int, default=2048, help="N dimension")
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parser.add_argument("-k", type=int, default=2048, help="K dimension")
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# Benchmark parameters
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parser.add_argument(
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"--warmup", type=int, default=5, help="Number of warmup iterations"
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)
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parser.add_argument(
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"--repeat", type=int, default=20, help="Number of benchmark iterations"
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)
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parser.add_argument(
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"--flush-cache", action="store_true", help="Flush GPU cache between iterations"
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)
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parser.add_argument(
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"--timer", choices=["gpu", "cpu"], default="gpu", help="Timer type (gpu or cpu)"
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)
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parser.add_argument(
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"--init",
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choices=["random", "linear", "constant"],
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default="random",
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help="Initialization method",
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)
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# Kernel configuration
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parser.add_argument("--dtype", default="fp16", help="Data type")
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parser.add_argument("--pipeline", default="compv4", help="Pipeline type")
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parser.add_argument("--arch", default="gfx942", help="GPU architecture")
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return parser.parse_args()
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def initialize_matrix(shape, method, dtype):
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"""Initialize matrix with specified method"""
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if method == "random":
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return np.random.randn(*shape).astype(dtype) * 0.5
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elif method == "linear":
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total = np.prod(shape)
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return np.arange(total).reshape(shape).astype(dtype) / total
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elif method == "constant":
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return np.ones(shape, dtype=dtype)
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else:
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return np.random.randn(*shape).astype(dtype)
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def main():
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args = parse_args()
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reset_for_example()
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print("=" * 70)
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print("Example 10: Advanced GEMM Benchmarking")
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print("=" * 70)
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# Show benchmark configuration
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print("\nBenchmark Configuration:")
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print(f" Problem Size: {args.m} x {args.n} x {args.k}")
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print(f" Warmup: {args.warmup} iterations")
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print(f" Repeat: {args.repeat} iterations")
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print(f" Flush Cache: {args.flush_cache}")
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print(f" Timer: {args.timer}")
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print(f" Init Method: {args.init}")
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print(f" Data Type: {args.dtype}")
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print(f" Pipeline: {args.pipeline}")
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print(f" Architecture: {args.arch}")
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print()
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# Map dtype
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np_dtype = np.float16 if args.dtype in ["fp16", "bf16"] else np.float32
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# Initialize matrices
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print("Step 1: Initialize matrices...")
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A = initialize_matrix((args.m, args.k), args.init, np_dtype)
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B = initialize_matrix((args.k, args.n), args.init, np_dtype)
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print(f" A: {A.shape} ({args.init})")
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print(f" B: {B.shape} ({args.init})")
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# Create kernel config (does not include M/N/K - those are problem size)
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print("\nStep 2: Create kernel configuration...")
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kernel_config = KernelConfig(
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dtype_a=args.dtype,
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dtype_b=args.dtype,
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dtype_c=args.dtype,
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dtype_acc="fp32",
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layout_a="row",
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layout_b="col", # B is column-major for optimal performance
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layout_c="row",
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tile_m=128,
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tile_n=128,
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tile_k=32,
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wave_m=2,
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wave_n=2,
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wave_k=1,
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warp_m=32,
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warp_n=32,
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warp_k=16,
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pipeline=args.pipeline,
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scheduler="intrawave",
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epilogue="cshuffle",
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gfx_arch=args.arch,
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)
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print(f" Config: {args.dtype}, tile=128x128x32, {args.pipeline}")
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# Setup dispatcher
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print("\nStep 3: Setup dispatcher...")
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setup = setup_gemm_dispatcher(
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config=kernel_config,
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registry_name="benchmark_gemm",
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verbose=False,
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auto_rebuild=True,
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)
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if not setup.success:
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print(f" ERROR: {setup.error}")
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return 1
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dispatcher = setup.dispatcher
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print(f" Library: {setup.lib.path if setup.lib else 'N/A'}")
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print(f" Kernel: {setup.lib.get_kernel_name() if setup.lib else 'N/A'}")
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# Run benchmark with multiple iterations
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print("\nStep 4: Run benchmark...")
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print(f" Running {args.warmup} warmup + {args.repeat} benchmark iterations...")
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# Warmup
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for _ in range(args.warmup):
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_ = dispatcher.run(A, B, args.m, args.n, args.k)
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# Benchmark
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times = []
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for _ in range(args.repeat):
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result = dispatcher.run(A, B, args.m, args.n, args.k)
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if result.success:
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times.append(result.time_ms)
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if times:
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avg_time = sum(times) / len(times)
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min_time = min(times)
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max_time = max(times)
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# Calculate TFLOPS
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flops = 2 * args.m * args.n * args.k
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avg_tflops = (flops / 1e12) / (avg_time / 1000) if avg_time > 0 else 0
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max_tflops = (flops / 1e12) / (min_time / 1000) if min_time > 0 else 0
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# Calculate bandwidth (C has same dtype as A and B)
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C_bytes = args.m * args.n * np.dtype(np_dtype).itemsize
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bandwidth_gb = (
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(A.nbytes + B.nbytes + C_bytes) / 1e9 / (avg_time / 1000)
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if avg_time > 0
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else 0
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)
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print(f"\n *** BENCHMARK RESULTS ({args.repeat} iterations) ***")
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print(f" Average Time: {avg_time:.4f} ms")
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print(f" Min Time: {min_time:.4f} ms")
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print(f" Max Time: {max_time:.4f} ms")
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print(f" Avg TFLOPS: {avg_tflops:.2f}")
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print(f" Peak TFLOPS: {max_tflops:.2f}")
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print(f" Bandwidth: {bandwidth_gb:.2f} GB/s")
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else:
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print(" FAILED: No successful runs")
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return 1
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# Summary
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print("\n" + "=" * 70)
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print("BENCHMARK PARAMETERS REFERENCE")
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print("=" * 70)
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print("""
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Available parameters for GEMM benchmarking:
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--warmup N Number of warmup iterations (discard results)
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Higher = more stable results, longer run time
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Default: 5
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--repeat N Number of benchmark iterations
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Higher = more accurate average, longer run time
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Default: 20
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--flush-cache Flush GPU L2 cache between iterations
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Use for memory-bound benchmarks
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Default: off
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--timer {gpu,cpu} Timer type
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gpu = HIP events (more accurate for GPU)
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cpu = std::chrono (includes kernel launch overhead)
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Default: gpu
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--init METHOD Matrix initialization
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random = uniform random [-0.5, 0.5]
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linear = sequential values
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constant = all ones
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Default: random
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Note: For C++ examples, these parameters are passed to stream_config:
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ck_tile::stream_config cfg{
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nullptr, // stream_id
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true, // time_kernel
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1, // log_level
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5, // cold_niters (warmup)
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20, // nrepeat
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true, // is_gpu_timer
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false, // flush_cache
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1 // rotating_count
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};
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""")
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# Cleanup
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cleanup_gemm()
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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