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a2edd7d802
* UniforFill with integer values.
* Log tested instance type string.
* Add UT for all convolution specializations.
* debugging conv
* Fix dangling reference bug.
* Small refinements.
* Fix call to error checking function.
* Small refinements to tests.
* Configure error tolerance
* Change problem size.
* Remove OddC case from types that do not support it.
* Add helper traits for AccumulatorDataType.
* Print first 5 errs in check_err for integral types.
* Rename FillUniform to FillUniformDistribution
* Refactor
* Do not use typed tests.
* Instead use plain fixture class with templatized member functions.
* Initialize tensors with integer values.
* Refine test instances.
* Properly set accumulator data type.
* Add another "big" instance.
* Refactor convolution tests.
* Revert "debugging conv"
This reverts commit b109516455.
* Add pragma once + format + small refinement.
* Fix some unwanted changes.
* Clang-format
* Fix profile_convnd to use renamed tensor initializer.
* Add instances for ConvFWDND kernel case 2D
* Helpers to get ConvNDFwd 2D instances.
* Refactoring.
* Remove "small block" instance as it was generating compiler errors.
* Remove default template parameters values.
* Refine and fix test.
* Fix problem with default template parameter types.
* Adjust error thresholds for floating point values test.
* Use integer values initialization for instances test.
* Add tests for ConvNDFwd 2D case.
* Remove AccumulatorDataType type trait.
* Update unit-tests.
* Remove operator<< overload.
* Unlock conv1d/3d nd fwd instances.
* Enable skipping calculating reference using flag.
* Fix number of channels for first ResNet50 layer.
* Clang-format.
Co-authored-by: Adam Osewski <aosewski@amd.com>
Co-authored-by: Chao Liu <chao.liu2@amd.com>
Instructions for example_convnd_fwd_xdl
Run example_convnd_fwd_xdl
#arg1: verification (0=no, 1=yes)
#arg2: initialization (0=no init, 1=integer value, 2=decimal value)
#arg3: run kernel # of times (>1)
#arg4: N spatial dimensions (default 2)
#Following arguments (depending on number of spatial dims):
# N, K, C,
# <filter spatial dimensions>, (ie Y, X for 2D)
# <input image spatial dimensions>, (ie Hi, Wi for 2D)
# <strides>, (ie Sy, Sx for 2D)
# <dilations>, (ie Dy, Dx for 2D)
# <left padding>, (ie LeftPy, LeftPx for 2D)
# <right padding>, (ie RightPy, RightPx for 2D)
./bin/example_convnd_fwd_xdl 0 1 100
Result (MI100 @ 1087Mhz, 33.4TFlops peak FP32)
input: dim 4, lengths {128, 192, 71, 71}, strides {967872, 1, 13632, 192}
weights: dim 4, lengths {256, 192, 3, 3}, strides {1728, 1, 576, 192}
output: dim 4, lengths {128, 256, 36, 36}, strides {331776, 1, 9216, 256}
arg.a_grid_desc_k0_m_k1_{432, 165888, 4}
arg.b_grid_desc_k0_n_k1_{432, 256, 4}
arg.c_grid_desc_m_n_{ 165888, 256}
launch_and_time_kernel: grid_dim {1296, 1, 1}, block_dim {256, 1, 1}
Warm up
Start running 100 times...
Perf: 4.43736 ms, 33.0753 TFlops, 150.357 GB/s