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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
9e049a32a1
166
dispatcher/examples/gemm/python/05_numpy_integration.py
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166
dispatcher/examples/gemm/python/05_numpy_integration.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 05: NumPy Integration
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Shows how to create a GPU-accelerated matmul wrapper.
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Complexity: ★★☆☆☆
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Usage:
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python3 05_numpy_integration.py
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python3 05_numpy_integration.py --help
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python3 05_numpy_integration.py --dtype bf16
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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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Dispatcher,
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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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class GPUMatmul:
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"""GPU-accelerated matrix multiplication wrapper."""
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def __init__(self, dispatcher: Dispatcher):
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self.dispatcher = dispatcher
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def __call__(self, A: np.ndarray, B: np.ndarray) -> np.ndarray:
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"""Compute C = A @ B on GPU with CPU fallback."""
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M, K = A.shape
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K2, N = B.shape
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if K != K2:
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raise ValueError(f"Dimension mismatch: {A.shape} @ {B.shape}")
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if not self.dispatcher.is_supported(M, N, K):
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return np.matmul(A, B)
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result = self.dispatcher.run(A, B, M, N, K)
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return result.output if result.success else np.matmul(A, B)
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def main():
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parser = argparse.ArgumentParser(
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description="NumPy Integration Example - GPU-accelerated matmul wrapper",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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python3 05_numpy_integration.py # Default FP16
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python3 05_numpy_integration.py --dtype bf16 # BF16 mode
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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="fp16",
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choices=["fp16", "bf16", "fp32"],
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help="Data type (default: fp16)",
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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 05: NumPy Integration")
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print("=" * 60)
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# =========================================================================
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# Step 1: Setup dispatcher
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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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gfx_arch=args.arch,
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)
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setup = setup_gemm_dispatcher(config, registry_name="numpy", 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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np_dtype = np.float16 if args.dtype in ["fp16", "bf16"] else np.float32
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# =========================================================================
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# Step 2: Create GPU matmul wrapper
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# =========================================================================
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print("\nStep 2: Create GPUMatmul")
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gpu_matmul = GPUMatmul(dispatcher=dispatcher)
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print(" gpu_matmul ready")
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# =========================================================================
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# Step 3: Demo - Simple multiplication using gpu_matmul
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# =========================================================================
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print("\nStep 3: Demo - Simple Multiplication")
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A = np.random.randn(1024, 512).astype(np_dtype) * 0.1
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B = np.random.randn(512, 256).astype(np_dtype) * 0.1
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# Use the gpu_matmul wrapper
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C = gpu_matmul(A, B)
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print(f" gpu_matmul result: {C.shape}, sum={C.sum():.4f}")
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M, K = A.shape
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_, N = B.shape
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result = dispatcher.run(A, B, M, N, K)
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print(f" A: {A.shape}, B: {B.shape} -> C: {result.output.shape}")
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print(f" GPU: {result.time_ms:.4f} ms, {result.tflops:.2f} TFLOPS")
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# =========================================================================
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# Step 4: Demo - FFN block
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# =========================================================================
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print("\nStep 4: Demo - FFN Block")
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batch, hidden, ffn = 128, 768, 3072
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X = np.random.randn(batch, hidden).astype(np_dtype) * 0.02
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W1 = np.random.randn(hidden, ffn).astype(np_dtype) * 0.02
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W2 = np.random.randn(ffn, hidden).astype(np_dtype) * 0.02
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result1 = dispatcher.run(X, W1, batch, ffn, hidden)
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H = result1.output
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result2 = dispatcher.run(H, W2, batch, hidden, ffn)
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print(f" X: {X.shape} -> H: {H.shape} -> Y: {result2.output.shape}")
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print(f" Total: {result1.time_ms + result2.time_ms:.4f} ms")
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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("NumPy Integration Pattern:")
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print("=" * 60)
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print(" 1. setup_gemm_dispatcher(config)")
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print(" 2. GPUMatmul(dispatcher)")
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print(" 3. C = gpu_matmul(A, B)")
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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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