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Split regression tests into separate file
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,152 @@ | ||
| import tune_gemm | ||
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| import json | ||
| import pytest | ||
| import warnings | ||
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| @pytest.mark.parametrize('config', [ | ||
| # M // BLOCK_M * N // BLOCK_N % 304 == 0 | ||
| # 1 workgroup / CU | ||
| { | ||
| 'M': 4864, 'N': 4096, 'K': 4096, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| { | ||
| 'M': 4864, 'N': 4096, 'K': 4160, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| { | ||
| 'M': 4864, 'N': 4096, 'K': 4224, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| { | ||
| 'M': 4864, 'N': 4096, 'K': 4288, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| # 1 workgroup / CU masked loadK | ||
| { | ||
| 'M': 4864, 'N': 4096, 'K': 4097, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| { | ||
| 'M': 4864, 'N': 4096, 'K': 4098, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| { | ||
| 'M': 4864, 'N': 4096, 'K': 4100, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| { | ||
| 'M': 4864, 'N': 4096, 'K': 4104, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| { | ||
| 'M': 4864, 'N': 4096, 'K': 4112, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| # 2 workgroups / CU | ||
| { | ||
| 'M': 4864, 'N': 8192, 'K': 4096, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| { | ||
| 'M': 4864, 'N': 8192, 'K': 4160, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| { | ||
| 'M': 4864, 'N': 8192, 'K': 8192, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| { | ||
| 'M': 4864, 'N': 8192, 'K': 8256, 'rowMajorA': 'T', 'rowMajorB': 'N', 'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, | ||
| 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 4, 'SPLIT_K': 1, 'num_warps': 8, 'num_stages': 0, 'waves_per_eu': 0, | ||
| 'matrix_instr_nonkdim': 16, 'kpack': 2 | ||
| }, | ||
| ], ids=lambda val: f"Config: {val}") | ||
| class TestRegression: | ||
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| @classmethod | ||
| def setup_class(self): | ||
| self.test_results = [] | ||
| try: | ||
| with open('gemm-performance-report-reference.json', 'r') as ref_file: | ||
| self.reference_data = json.load(ref_file) | ||
| except FileNotFoundError: | ||
| warnings.warn("No reference file found. There will be no regression detected!") | ||
| self.reference_data = [] | ||
|
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| @classmethod | ||
| def teardown_class(self): | ||
| with open('gemm-performance-report.json', 'w') as out_file: | ||
| json.dump(self.test_results, out_file) | ||
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| def test_matmul_performance_regression(self, config): | ||
| # Get GPU ids | ||
| gpus = [0] | ||
| jobs = 1 | ||
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| # Get element type | ||
| dtype_a = 'fp16' | ||
| dtype_b = 'fp16' | ||
| dtype_c = 'fp16' | ||
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| rotating_buffer_size = 0 | ||
| bias_vector = False | ||
| icache_flush = False | ||
| iters = 200 | ||
| benchmark = True | ||
| init_type = 'randn' | ||
| num_threads = 32 | ||
| skipWarmup = False | ||
| verbose_level = 0 | ||
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| M, N, K, col_a, col_b, runConfig = tune_gemm.process_item(config) | ||
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| # Before tuning starts, clear cache and previously generated kernel files | ||
| tune_gemm.run_bash_command("rm -rf ~/.triton/cache") | ||
| tune_gemm.run_bash_command(f"rm -rf {tune_gemm.get_filename_myKernels()}") | ||
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| # Append new configs into the tuning space | ||
| tune_gemm.generate_matmul_kernels([runConfig]) | ||
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| bias_size = M if bias_vector else 0 | ||
| minTime, bestConfig, compile_time, profile_time, post_time = tune_gemm.tune_gemm_config( | ||
| M, N, K, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, [runConfig], benchmark, jobs, iters, | ||
| skipWarmup=skipWarmup, num_threads=num_threads, gpus=gpus, verbose=verbose_level, | ||
| rotating_buffer_size=rotating_buffer_size, bias_size=bias_size, icache_flush=icache_flush) | ||
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| # post processing the numbers | ||
| perf_tflops = lambda us: 2 * M * N * K * 1e-12 / (us * 1e-6) | ||
| tri_tflops = perf_tflops(minTime) | ||
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| # Add to global results | ||
| self.test_results.append({'config': config, 'tflops': float(tri_tflops)}) | ||
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| # Look for reference run | ||
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| reference_run = None | ||
| for run in self.reference_data: | ||
| if run['config'] == config: | ||
| reference_run = run | ||
| break | ||
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| if reference_run is not None: | ||
| performance_ratio = tri_tflops / reference_run['tflops'] | ||
| slowdown_threshold = 0.97 | ||
| assert performance_ratio > slowdown_threshold, f'Performance regressed by {(100.0 * (1.0 - performance_ratio)):.2f}% (threshold={((1.0 - slowdown_threshold) * 100.0 ):.2f}%)' | ||
| else: | ||
| warnings.warn(f"No reference file found. There will be no regression detected for config = {config}") |
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