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import os |
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import torch |
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class Names: |
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GEN_FOLDER = "gen" |
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GEN_SUFFIX = "_gen_triton_code" |
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REF_SUFFIX = "_ref_triton_code" |
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RET_SEPERATOR = "*#*#" |
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PYTEST_SEPARATOR = "&"*100 |
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GPU = torch.cuda.get_device_name(0).replace(" ", "_") if torch.cuda.is_available() else None |
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PASS_NUM = 'pass_num' |
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FILE_NAME = 'file_name' |
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CALL_STATUS = 'call_status' |
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EXEC_STATUS = 'exec_status' |
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STDOUT = 'stdout' |
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STDERR = 'stderr' |
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DIFFICULTY = 'difficulty' |
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PREDICT = 'predict' |
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FILE = 'file' |
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DIFFICULTY = 'difficulty' |
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LABEL = 'label' |
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SPEEDUP = 'speedup' |
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REPO_ROOT = os.path.abspath(os.path.dirname(__file__)) |
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TMP_ROOT = "tmp2" |
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TBG_ROOT = os.path.join(REPO_ROOT, "data", "TritonBench") |
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TBG_DATA_ROOT= os.path.join(TBG_ROOT, "data", "TritonBench_G_v1") |
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TBG_PERF_GOLD_ROOT = os.path.join(TBG_ROOT, "performance_metrics", "perf_G", f"{Names.GPU}_golden_metrics") |
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NATIVE_PERF_GOLD_ROOT = os.path.join(TBG_ROOT, "performance_metrics", "perf_G", "golden_metrics") |
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TBG_PERF_GOLD_DATA_ROOT = os.path.join(TBG_ROOT, "performance_metrics", "perf_G", f"{Names.GPU}_golden_results") |
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ROCm_ROOT = os.path.join(REPO_ROOT, "data", "ROCm") |
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ROCm_DATA_ROOT= os.path.join(ROCm_ROOT, "data", "ROCm_v1") |
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ROCm_DATA_AUTOTUNE_ROOT= os.path.join(ROCm_ROOT, "data", "ROCm_v1_autotune") |
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ROCM_PERF_GOLD_DATA_ROOT = os.path.join(ROCm_ROOT, "data", "performance", "golden_results") |
