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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.
This commit is contained in:
Vidyasagar Ananthan
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GitHub
GitHub
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44f481a45c
commit
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171
dispatcher/examples/gemm/python/03_benchmark.py
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171
dispatcher/examples/gemm/python/03_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 03: Benchmark
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Performance benchmarking with compute-optimized kernel configuration.
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Complexity: ★★★☆☆
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Usage:
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python3 03_benchmark.py
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python3 03_benchmark.py --help
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python3 03_benchmark.py --size 4096
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python3 03_benchmark.py --dtype bf16 --iterations 20
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"""
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import sys
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import argparse
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from pathlib import Path
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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 main():
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parser = argparse.ArgumentParser(
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description="GEMM Benchmark Example - performance testing",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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python3 03_benchmark.py # Default benchmark suite
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python3 03_benchmark.py --size 4096 # Single size benchmark
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python3 03_benchmark.py --dtype bf16 # BF16 benchmark
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python3 03_benchmark.py --iterations 20 # More iterations
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""",
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)
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parser.add_argument(
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"--dtype",
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default="bf16",
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choices=["fp16", "bf16", "fp32"],
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help="Data type (default: bf16)",
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)
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parser.add_argument(
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"--size",
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type=int,
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default=0,
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help="Single problem size MxNxK (default: run all sizes)",
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)
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parser.add_argument(
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"--warmup", type=int, default=3, help="Warmup iterations (default: 3)"
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)
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parser.add_argument(
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"--iterations", type=int, default=10, help="Benchmark iterations (default: 10)"
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)
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parser.add_argument(
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"--arch", default="gfx942", help="Target architecture (default: gfx942)"
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)
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args = parser.parse_args()
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reset_for_example()
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print("=" * 60)
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print("Example 03: Benchmark")
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print("=" * 60)
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# =========================================================================
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# Step 1: Setup dispatcher with compute-optimized config
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# =========================================================================
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print("\nStep 1: Setup Dispatcher")
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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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tile_m=128,
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tile_n=128,
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tile_k=32,
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pipeline="compv4",
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scheduler="intrawave",
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gfx_arch=args.arch,
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)
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setup = setup_gemm_dispatcher(config, registry_name="benchmark", verbose=True)
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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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# =========================================================================
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# Step 2: Benchmark
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# =========================================================================
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print("\nStep 2: Benchmark")
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if args.size > 0:
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sizes = [(args.size, args.size, args.size)]
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else:
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sizes = [
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(512, 512, 512),
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(1024, 1024, 1024),
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(2048, 2048, 2048),
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(4096, 4096, 4096),
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(1024, 2048, 512),
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(2048, 1024, 2048),
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]
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np_dtype = np.float16 if args.dtype in ["fp16", "bf16"] else np.float32
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print(f" Warmup: {args.warmup}, Iterations: {args.iterations}\n")
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print(f" {'Size':<20} | {'Min (ms)':>10} | {'Avg (ms)':>10} | {'TFLOPS':>10}")
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print(" " + "-" * 60)
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all_tflops = []
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for M, N, K in sizes:
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if not dispatcher.is_supported(M, N, K):
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continue
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A = np.random.randn(M, K).astype(np_dtype) * 0.1
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B = np.random.randn(K, N).astype(np_dtype) * 0.1
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# Warmup
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for _ in range(args.warmup):
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dispatcher.run(A, B, M, N, K)
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# Benchmark
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times = []
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for _ in range(args.iterations):
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result = dispatcher.run(A, B, M, N, 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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min_time = min(times)
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avg_time = sum(times) / len(times)
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tflops = (2.0 * M * N * K / (avg_time * 1e-3)) / 1e12
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all_tflops.append(tflops)
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print(
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f" {M:>4}x{N:>4}x{K:<4} | {min_time:>10.4f} | {avg_time:>10.4f} | {tflops:>10.2f}"
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)
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# Cleanup
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cleanup_gemm()
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# Summary
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print("\n" + "=" * 60)
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print("Summary")
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print("=" * 60)
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if all_tflops:
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print(f" Average: {sum(all_tflops) / len(all_tflops):.2f} TFLOPS")
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print(f" Peak: {max(all_tflops):.2f} TFLOPS")
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print("=" * 60)
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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