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https://github.com/ROCm/composable_kernel.git
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fix doc
This commit is contained in:
Yanxing-Shi
@@ -20,25 +20,24 @@ make tile_engine_gemm -j
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## tile_engine_gemm inputs
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```
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-m m dimension (default:3840)
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-n n dimension (default:4096)
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-k k dimension (default:2048)
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-stride_a Tensor A stride (default:0)
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-stride_b Tensor B stride (default:0)
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-stride_c Tensor C stride (default:0)
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-split_k SplitK value (default:1)
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-v No validation: 0, Validation on CPU: 1, Validation on GPU: 2 (default:2)
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-metric The metric value of kernel performance - latency: 0, tflops: 1, bandwidth: 2 (default:0)
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-warmup Number of iterations before benchmark the kernel (default:50)
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-repeat Number of iterations to benchmark the kernel (default:100)
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-timer gpu:gpu timer, cpu:cpu timer (default:gpu)
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-init Value for initializing tensor - random: 0, linear: 1, constant(1): 2 (default:0)
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-pipeline possible values are: compv3, compv4, mem (default:compv3)
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-scheduler possible values are: intrawave, interwave (default:intrawave)
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-epilogue possible values are: cshuffle, default (default:cshuffle)
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-pad_m Pad in m direction - true/false (default:false)
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-pad_n Pad in n direction - true/false (default:false)
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-pad_k Pad in k direction - true/false (default:false)
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-m The value for m dimension. Default is 3840.
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-n The value for n dimension. Default is 4096.
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-k The value for k dimension. Default is 2048.
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-stride_a The stride value for tensor A. Default is 0.
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-stride_b The stride value for tensor B. Default is 0.
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-stride_c The stride value for tensor C Default is 0.
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-split_k The split value for k dimension. Default is 1.
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-v The type of validation. Set to 0 for no validation, 1 for validation on CPU, or 2 for validation on GPU. Default is 2, validation on GPU.
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-warmup The number of iterations before benchmark the kernel. Default is 50.
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-repeat The number of iterations to benchmark the kernel. Default is 100.
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-timer The type of timer. Possible values are gpu timer or cpu timer. Default is gpu timer.
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-init The method of tensor initialization. Set to 0 for random, to 1 for linear, or 2 for constant(1). Default is 0, random.
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-metric Metric with which to measure kernel performance. Set to 0 for latency, 1 for tflops, or 2 for bandwidth. Default is 0, latency.
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-pipeline The type of pipeline. Possible values are compv3, compv4 or mem. Default is compv3.
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-epilogue The type of epilogue. Possible values are cshuffle or default. Default is csshuffle.
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-pad_m Whether pad or not in m direction. Possible values are true or false. Default is false.
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-pad_n Whether pad or not in n direction. Possible values are true or false. Default is false.
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-pad_k Whether pad or not in k direction. Possible values are true or false. Default is false.
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Note: pipeline, scheduler, epilogue, pad_m, pad_n, pad_k should be one of the options specified in instance_combination.json
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```
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@@ -225,25 +225,56 @@ auto calculate_rtol_atol(const ck_tile::index_t K,
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inline auto create_args(int argc, char* argv[])
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{
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ck_tile::ArgParser arg_parser;
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arg_parser.insert("m", "3840", "m dimension")
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.insert("n", "4096", "n dimension")
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.insert("k", "2048", "k dimension")
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.insert("stride_a", "0", "Tensor A stride")
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.insert("stride_b", "0", "Tensor B stride")
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.insert("stride_c", "0", "Tensor C stride")
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.insert("split_k", "1", "splitK value")
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.insert("v", "2", "0. No validation, 1. Validation on CPU, 2. Validation on GPU")
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.insert("warmup", "50", "number of iterations before benchmark the kernel")
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.insert("repeat", "100", "number of iterations to benchmark the kernel")
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.insert("timer", "gpu", "gpu:gpu timer, cpu:cpu timer")
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.insert("init", "0", "0:random, 1:linear, 2:constant(1)")
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.insert("metric", "0", "0:latency, 1:tflops, 2:bandwidth")
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.insert("pipeline", "compv3", "compv3, compv4, mem")
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.insert("scheduler", "intrawave", "intrawave, interwave")
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.insert("epilogue", "cshuffle", "cshuffle, default")
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.insert("pad_m", "false", "true, false")
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.insert("pad_n", "false", "true, false")
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.insert("pad_k", "false", "true, false");
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arg_parser.insert("m", "3840", "The value for m dimension. Default is 3840.")
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.insert("n", "4096", "The value for n dimension. Default is 4096.")
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.insert("k", "2048", "The value for k dimension. Default is 2048.")
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.insert("stride_a", "0", "The stride value for tensor A. Default is 0.")
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.insert("stride_b", "0", "The stride value for tensor B. Default is 0.")
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.insert("stride_c", "0", "The stride value for tensor C Default is 0.")
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.insert("split_k", "1", "The split value for k dimension. Default is 1.")
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.insert("v",
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"2",
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"The type of validation. Set to 0 for no validation, 1 for validation on CPU, or 2 "
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"for validation on GPU. Default is 2, validation on GPU.")
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.insert(
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"warmup", "50", "The number of iterations before benchmark the kernel. Default is 50.")
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.insert(
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"repeat", "100", "The number of iterations to benchmark the kernel. Default is 100.")
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.insert(
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"timer",
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"gpu",
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"The type of timer. Possible values are gpu timer or cpu timer. Default is gpu timer.")
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.insert("init",
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"0",
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"The method of tensor initialization. Set to 0 for random, to 1 for linear, or 2 "
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"for constant(1). Default is 0, random.")
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.insert("metric",
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"0",
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"Metric with which to measure kernel performance. Set to 0 for latency, 1 for "
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"tflops, or 2 for bandwidth. Default is 0, latency.")
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.insert(
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"pipeline",
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"compv3",
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"The type of pipeline. Possible values are compv3, compv4 or mem. Default is compv3.")
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.insert("scheduler",
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"The type of pipeline. Possible values are compv3, compv4 or mem. Default is "
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"compv3.")
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.insert(
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"epilogue",
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"cshuffle",
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"The type of epilogue. Possible values are cshuffle or default. Default is csshuffle.")
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.insert("pad_m",
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"false",
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"Whether pad or not in m direction. Possible values are true or false. Default is "
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"false.")
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.insert("pad_n",
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"false",
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"Whether pad or not in n direction. Possible values are true or false. Default is "
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"false.")
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.insert("pad_k",
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"false",
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"Whether pad or not in k direction. Possible values are true or false. Default is "
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"false.");
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bool result = arg_parser.parse(argc, argv);
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return std::make_tuple(result, arg_parser);
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