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.

dispatcher/examples/gemm/python/02_batch_gemm.py

@@ -0,0 +1,149 @@
+#!/usr/bin/env python3
+
+# Copyright (c) Advanced Micro Devices, Inc., or its affiliates.
+# SPDX-License-Identifier: MIT
+
+"""
+Example 02: Batch GEMM
+
+Runs multiple GEMM operations with different sizes.
+
+Complexity: ★★☆☆☆
+
+Usage:
+    python3 02_batch_gemm.py
+    python3 02_batch_gemm.py --help
+    python3 02_batch_gemm.py --dtype bf16
+"""
+
+import sys
+import argparse
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "python"))
+import numpy as np
+
+from ctypes_utils import (
+    KernelConfig,
+    setup_gemm_dispatcher,
+    cleanup_gemm,
+    reset_for_example,
+)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Batch GEMM Example - runs multiple sizes",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  python3 02_batch_gemm.py                    # Default FP16
+  python3 02_batch_gemm.py --dtype bf16       # BF16 GEMM
+  python3 02_batch_gemm.py --max-size 2048    # Limit max size
+        """,
+    )
+    parser.add_argument(
+        "--dtype",
+        default="fp16",
+        choices=["fp16", "bf16", "fp32"],
+        help="Data type (default: fp16)",
+    )
+    parser.add_argument(
+        "--max-size",
+        type=int,
+        default=4096,
+        help="Maximum problem size (default: 4096)",
+    )
+    parser.add_argument(
+        "--arch", default="gfx942", help="Target architecture (default: gfx942)"
+    )
+    args = parser.parse_args()
+
+    reset_for_example()
+
+    print("=" * 60)
+    print("Example 02: Batch GEMM")
+    print("=" * 60)
+
+    # =========================================================================
+    # Step 1: Setup dispatcher
+    # =========================================================================
+    print("\nStep 1: Setup Dispatcher")
+
+    config = KernelConfig(
+        dtype_a=args.dtype,
+        dtype_b=args.dtype,
+        dtype_c=args.dtype,
+        tile_m=128,
+        tile_n=128,
+        tile_k=32,
+        gfx_arch=args.arch,
+    )
+
+    setup = setup_gemm_dispatcher(config, registry_name="batch_gemm", verbose=True)
+    if not setup.success:
+        print(f"  ERROR: {setup.error}")
+        return 1
+
+    dispatcher = setup.dispatcher
+
+    # =========================================================================
+    # Step 2: Run batch of different sizes
+    # =========================================================================
+    print("\nStep 2: Run Batch")
+
+    # Generate sizes up to max_size
+    all_sizes = [
+        (256, 256, 256),
+        (512, 512, 512),
+        (1024, 1024, 1024),
+        (2048, 2048, 2048),
+        (4096, 4096, 4096),
+    ]
+    sizes = [(m, n, k) for m, n, k in all_sizes if max(m, n, k) <= args.max_size]
+
+    np_dtype = np.float16 if args.dtype in ["fp16", "bf16"] else np.float32
+
+    print(f"\n  {'Size':<20} | {'Time (ms)':>12} | {'TFLOPS':>10} | {'Status':>8}")
+    print("  " + "-" * 60)
+
+    total_ops = 0
+    total_time = 0
+
+    for M, N, K in sizes:
+        if not dispatcher.is_supported(M, N, K):
+            print(f"  {M:>4}x{N:>4}x{K:<4} | {'N/A':>12} | {'N/A':>10} | Skipped")
+            continue
+
+        A = np.random.randn(M, K).astype(np_dtype) * 0.1
+        B = np.random.randn(K, N).astype(np_dtype) * 0.1
+
+        result = dispatcher.run(A, B, M, N, K)
+
+        if result.success:
+            total_ops += 2 * M * N * K
+            total_time += result.time_ms
+            print(
+                f"  {M:>4}x{N:>4}x{K:<4} | {result.time_ms:>12.4f} | {result.tflops:>10.2f} | OK"
+            )
+        else:
+            print(f"  {M:>4}x{N:>4}x{K:<4} | {'N/A':>12} | {'N/A':>10} | Error")
+
+    print("  " + "-" * 60)
+
+    if total_time > 0:
+        avg_tflops = (total_ops / 1e12) / (total_time / 1000)
+        print(f"\n  Total: {total_time:.2f} ms, Average: {avg_tflops:.2f} TFLOPS")
+
+    # Cleanup
+    cleanup_gemm()
+
+    print("\n" + "=" * 60)
+    print("Batch GEMM complete!")
+    print("=" * 60)
+
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())