mirror of
https://github.com/ROCm/composable_kernel.git
synced 2026-05-11 00:40:09 +00:00
Pool3d fwd (#697)
* Expand the base class of pool2d, prepare to share base class with pool3d * Add pool3d device op * Add pool3d f16 example * Refactor the base class. implement generic pooling in the future * clang format * get original index in max pooling * Add outputindex to base class * Fix dimension * Add pooling instance * Use indexType instead * Remove useless header * Extract IndexDataType to template * Extract pooling reference code * clang format * clang format * Fix typo * Add tensor stride * Add missing header * Add index stride and output stride * Refine naming * Add type to base class * Rename file * Use proper size * Fix typo * Refine naming * Modify the argument into vector. * Add max pool profiler * Refine naming * Support f32 pool * Fix typo * Add avg pool2d fwd in profiler * clang format * Rename AccDatatype to ComputeDatatype * Fix init * test pool * Extract variable * Add client example * Check the pooling dim * clang format * Connect argv and arg_parser * Add found check * Remove useless header * Refine naming * Adjust the order of device_pool_fwd
This commit is contained in:
rocking
@@ -9,7 +9,7 @@
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
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#include "ck/tensor_operation/gpu/device/device_pool2d_fwd.hpp"
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#include "ck/tensor_operation/gpu/device/device_pool_fwd.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_2d_reduction_threadwise.hpp"
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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@@ -20,16 +20,18 @@ namespace device {
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template <typename InDataType,
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typename OutDataType,
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typename AccDataType,
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typename IndexDataType, // enable if OutputIndex == true
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typename ComputeDataType,
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ck::ReduceTensorOp ReduceOpId,
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bool OuputIndex,
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bool OutputIndex,
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ck::index_t BlockSize,
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ck::index_t ReduceMThreadClusterSize,
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ck::index_t ReduceKThreadClusterSize,
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ck::index_t ReduceMThreadSliceSize,
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ck::index_t ReduceKThreadSliceSize,
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ck::index_t InSrcOutDstVectorSize>
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struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd<ReduceOpId>
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struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C
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: public DevicePoolFwd<4, 2, InDataType, OutDataType, IndexDataType, ReduceOpId, OutputIndex>
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{
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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@@ -38,7 +40,8 @@ struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd
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static constexpr auto I4 = Number<4>{};
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static constexpr auto I5 = Number<5>{};
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using IndexDataType = int32_t;
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static constexpr index_t InOutRank = 4;
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static constexpr index_t WindowRank = 2;
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using ReduceOperation = typename reduce_binary_operator<ReduceOpId>::opType;
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@@ -59,12 +62,12 @@ struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd
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static auto MakeABGridDescriptor_A_M_K_B_M(ck::index_t N,
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ck::index_t C,
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std::array<ck::index_t, 2> input_spatial_lengths,
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std::array<ck::index_t, 2> window_spatial_lengths,
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std::array<ck::index_t, 2> output_spatial_lengths,
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std::array<ck::index_t, 2> window_strides,
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std::array<ck::index_t, 2> input_left_pads,
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std::array<ck::index_t, 2> input_right_pads)
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std::vector<ck::index_t> input_spatial_lengths,
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std::vector<ck::index_t> window_spatial_lengths,
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std::vector<ck::index_t> output_spatial_lengths,
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std::vector<ck::index_t> window_strides,
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std::vector<ck::index_t> input_left_pads,
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std::vector<ck::index_t> input_right_pads)
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{
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const index_t Hi = input_spatial_lengths[0];
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const index_t Wi = input_spatial_lengths[1];
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@@ -141,9 +144,7 @@ struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd
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return make_tuple(in_grid_desc_reducem_reducek, out_grid_desc_reducem);
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}
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using ABGridDescs = decltype(
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MakeABGridDescriptor_A_M_K_B_M(1, 1, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}, {1, 1}));
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using ABGridDescs = decltype(MakeABGridDescriptor_A_M_K_B_M(1, 1, {}, {}, {}, {}, {}, {}));
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using AGridDesc_M_K = remove_cvref_t<decltype(ABGridDescs{}[I0])>;
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using BGridDesc_M = remove_cvref_t<decltype(ABGridDescs{}[I1])>;
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@@ -152,15 +153,15 @@ struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd
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{
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Argument(const InDataType* p_in_dev,
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OutDataType* p_out_dev,
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int* p_out_indices_dev,
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IndexDataType* p_out_indices_dev,
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ck::index_t N,
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ck::index_t C,
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std::array<ck::index_t, 2>& input_spatial_lengths,
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std::array<ck::index_t, 2>& window_spatial_lengths,
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std::array<ck::index_t, 2>& output_spatial_lengths,
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std::array<ck::index_t, 2>& window_strides,
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std::array<ck::index_t, 2>& input_left_pads,
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std::array<ck::index_t, 2>& input_right_pads)
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std::vector<ck::index_t>& input_spatial_lengths,
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std::vector<ck::index_t>& window_spatial_lengths,
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std::vector<ck::index_t>& output_spatial_lengths,
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std::vector<ck::index_t>& window_strides,
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std::vector<ck::index_t>& input_left_pads,
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std::vector<ck::index_t>& input_right_pads)
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: p_in_dev_{p_in_dev},
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p_out_dev_{p_out_dev},
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p_out_indices_dev_{p_out_indices_dev},
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@@ -190,7 +191,7 @@ struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd
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const InDataType* p_in_dev_;
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OutDataType* p_out_dev_;
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int* p_out_indices_dev_;
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IndexDataType* p_out_indices_dev_;
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AGridDesc_M_K a_grid_desc_m_k_;
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BGridDesc_M b_grid_desc_m_;
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InElementwiseOperation in_element_op_;
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@@ -208,7 +209,7 @@ struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd
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using gridwise_reduce =
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GridwiseReduction_mk_to_m_threadwise<InDataType,
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OutDataType,
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AccDataType,
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ComputeDataType,
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IndexDataType,
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AGridDesc_M_K,
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BGridDesc_M,
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@@ -224,17 +225,19 @@ struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd
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InSrcOutDstVectorSize,
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InSrcOutDstVectorSize>;
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const auto kernel = kernel_reduce_threadwise<gridwise_reduce,
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OuputIndex,
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false, // don't have index input
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InDataType,
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OutDataType,
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AccDataType,
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IndexDataType,
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AGridDesc_M_K,
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BGridDesc_M,
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InElementwiseOperation,
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AccElementwiseOperation>;
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const auto kernel =
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kernel_reduce_threadwise<gridwise_reduce,
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OutputIndex,
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true, // pooling need to return global index
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false, // don't have index input
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InDataType,
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OutDataType,
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ComputeDataType,
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IndexDataType,
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AGridDesc_M_K,
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BGridDesc_M,
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InElementwiseOperation,
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AccElementwiseOperation>;
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ck::index_t ReduceM = arg.a_grid_desc_m_k_.GetLength(I0);
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@@ -280,22 +283,42 @@ struct DevicePool2dFwd_Input_N_Hi_Wi_C_Output_N_Ho_Wo_C : public DevicePool2dFwd
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MakeArgumentPointer(const void* p_in_dev,
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void* p_out_dev,
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void* p_out_indices_dev,
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ck::index_t N,
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ck::index_t C,
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std::array<ck::index_t, 2> input_spatial_lengths,
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std::array<ck::index_t, 2> window_spatial_lengths,
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std::array<ck::index_t, 2> output_spatial_lengths,
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std::array<ck::index_t, 2> window_strides,
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std::array<ck::index_t, 2> input_left_pads,
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std::array<ck::index_t, 2> input_right_pads) override
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std::vector<ck::index_t> input_lengths,
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std::vector<ck::index_t> window_lengths,
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std::vector<ck::index_t> output_lengths,
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std::vector<ck::index_t>, // Suppose tensor layout = NHWC
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std::vector<ck::index_t>, // Suppose tensor layout = NHWC
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std::vector<ck::index_t>, // Suppose tensor layout = NHWC
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std::vector<ck::index_t> window_strides,
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std::vector<ck::index_t> input_left_pads,
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std::vector<ck::index_t> input_right_pads,
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std::vector<ck::index_t> pooling_dims) override
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{
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if(input_lengths.size() != InOutRank || window_lengths.size() != WindowRank ||
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input_lengths.size() != InOutRank || window_strides.size() != WindowRank ||
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input_left_pads.size() != WindowRank || input_right_pads.size() != WindowRank)
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throw std::runtime_error("dimension is incorrect");
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if(pooling_dims != std::vector<ck::index_t>{2, 3})
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throw std::runtime_error("pooling_dims only support {2, 3} in pool2d so far");
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index_t N = input_lengths[0];
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index_t C = input_lengths[1];
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index_t Hi = input_lengths[2];
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index_t Wi = input_lengths[3];
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index_t Ho = output_lengths[2];
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index_t Wo = output_lengths[3];
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std::vector<ck::index_t> input_spatial_lengths = {Hi, Wi};
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std::vector<ck::index_t> output_spatial_lengths = {Ho, Wo};
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return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_dev),
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static_cast<OutDataType*>(p_out_dev),
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static_cast<int*>(p_out_indices_dev),
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static_cast<IndexDataType*>(p_out_indices_dev),
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N,
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C,
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input_spatial_lengths,
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window_spatial_lengths,
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window_lengths,
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output_spatial_lengths,
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window_strides,
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input_left_pads,
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@@ -0,0 +1,357 @@
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// SPDX-License-Identifier: MIT
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// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
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#pragma once
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#include <iostream>
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#include <sstream>
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#include "ck/tensor_description/tensor_descriptor.hpp"
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#include "ck/tensor_description/tensor_descriptor_helper.hpp"
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#include "ck/tensor_operation/gpu/device/reduction_operator_mapping.hpp"
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#include "ck/tensor_operation/gpu/device/device_pool_fwd.hpp"
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#include "ck/tensor_operation/gpu/grid/gridwise_2d_reduction_threadwise.hpp"
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#include "ck/host_utility/device_prop.hpp"
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#include "ck/host_utility/kernel_launch.hpp"
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namespace ck {
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namespace tensor_operation {
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namespace device {
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template <typename InDataType,
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typename OutDataType,
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typename IndexDataType, // enable if OutputIndex == true
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typename ComputeDataType,
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ck::ReduceTensorOp ReduceOpId,
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bool OutputIndex,
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ck::index_t BlockSize,
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ck::index_t MThreadClusterSize,
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ck::index_t KThreadClusterSize,
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ck::index_t MThreadSliceSize,
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ck::index_t KThreadSliceSize,
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ck::index_t InSrcOutDstVectorSize>
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struct DevicePool3dFwd_Input_N_Di_Hi_Wi_C_Output_N_Do_Ho_Wo_C
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: public DevicePoolFwd<5, 3, InDataType, OutDataType, IndexDataType, ReduceOpId, OutputIndex>
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{
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static constexpr auto I0 = Number<0>{};
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static constexpr auto I1 = Number<1>{};
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static constexpr auto I2 = Number<2>{};
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static constexpr auto I3 = Number<3>{};
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static constexpr auto I4 = Number<4>{};
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static constexpr auto I5 = Number<5>{};
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static constexpr index_t InOutRank = 5;
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static constexpr index_t WindowRank = 3;
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using ReduceOperation = typename reduce_binary_operator<ReduceOpId>::opType;
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using InElementwiseOperation =
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typename reduce_unary_operator<ReduceOpId, true, true>::InElementwiseOperation;
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using AccElementwiseOperation =
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typename reduce_unary_operator<ReduceOpId, true, true>::AccElementwiseOperation;
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// for NDHWC, the dim C is the vector Dim for both input and output in memory, which is not
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// reduced.
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static constexpr index_t InSrcOutDstVectorDim = 0;
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static constexpr ck::index_t M_BlockTileSize = MThreadClusterSize * MThreadSliceSize;
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static constexpr ck::index_t K_BlockTileSize = KThreadClusterSize * KThreadSliceSize;
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static auto MakeABGridDescriptor_A_M_K_B_M(ck::index_t N,
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ck::index_t C,
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std::vector<ck::index_t> input_spatial_lengths,
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std::vector<ck::index_t> window_spatial_lengths,
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std::vector<ck::index_t> output_spatial_lengths,
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std::vector<ck::index_t> window_strides,
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std::vector<ck::index_t> input_left_pads,
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std::vector<ck::index_t> input_right_pads)
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{
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const index_t Di = input_spatial_lengths[0];
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const index_t Hi = input_spatial_lengths[1];
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const index_t Wi = input_spatial_lengths[2];
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const index_t Do = output_spatial_lengths[0];
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const index_t Ho = output_spatial_lengths[1];
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const index_t Wo = output_spatial_lengths[2];
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const index_t Z = window_spatial_lengths[0];
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const index_t Y = window_spatial_lengths[1];
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const index_t X = window_spatial_lengths[2];
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const index_t ConvStrideD = window_strides[0];
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const index_t ConvStrideH = window_strides[1];
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const index_t ConvStrideW = window_strides[2];
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const index_t InLeftPadD = input_left_pads[0];
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const index_t InLeftPadH = input_left_pads[1];
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const index_t InLeftPadW = input_left_pads[2];
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const index_t InRightPadD = input_right_pads[0];
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const index_t InRightPadH = input_right_pads[1];
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const index_t InRightPadW = input_right_pads[2];
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const index_t MRaw = N * Do * Ho * Wo * C;
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const index_t MPad = math::integer_least_multiple(MRaw, M_BlockTileSize) - MRaw;
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const index_t KRaw = Z * Y * X;
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const index_t KPad = math::integer_least_multiple(KRaw, K_BlockTileSize) - KRaw;
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// A[ReduceM, ReduceK]
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const auto in_grid_desc_n_di_hi_wi_c =
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make_naive_tensor_descriptor_packed(make_tuple(N, Di, Hi, Wi, C));
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const auto in_grid_desc_n_dip_hip_wip_c = transform_tensor_descriptor(
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in_grid_desc_n_di_hi_wi_c,
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make_tuple(make_pass_through_transform(N),
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make_pad_transform(Di, InLeftPadD, InRightPadD),
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make_pad_transform(Hi, InLeftPadH, InRightPadH),
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make_pad_transform(Wi, InLeftPadW, InRightPadW),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}));
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const auto in_grid_desc_n_z_do_y_ho_x_wo_c = transform_tensor_descriptor(
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in_grid_desc_n_dip_hip_wip_c,
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make_tuple(make_pass_through_transform(N),
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make_embed_transform(make_tuple(Z, Do), make_tuple(I1, ConvStrideD)),
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make_embed_transform(make_tuple(Y, Ho), make_tuple(I1, ConvStrideH)),
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make_embed_transform(make_tuple(X, Wo), make_tuple(I1, ConvStrideW)),
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make_pass_through_transform(C)),
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make_tuple(Sequence<0>{}, Sequence<1>{}, Sequence<2>{}, Sequence<3>{}, Sequence<4>{}),
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make_tuple(Sequence<0>{},
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Sequence<1, 2>{},
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Sequence<3, 4>{},
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Sequence<5, 6>{},
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Sequence<7>{}));
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const auto in_grid_desc_reducemraw_reducekraw = transform_tensor_descriptor(
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in_grid_desc_n_z_do_y_ho_x_wo_c,
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make_tuple(make_merge_transform(make_tuple(N, Do, Ho, Wo, C)),
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make_merge_transform(make_tuple(Z, Y, X))),
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make_tuple(Sequence<0, 2, 4, 6, 7>{}, Sequence<1, 3, 5>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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const auto in_grid_desc_reducem_reducek = transform_tensor_descriptor(
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in_grid_desc_reducemraw_reducekraw,
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make_tuple(make_right_pad_transform(MRaw, MPad), make_right_pad_transform(KRaw, KPad)),
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make_tuple(Sequence<0>{}, Sequence<1>{}),
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make_tuple(Sequence<0>{}, Sequence<1>{}));
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// B[ReduceM]
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const auto out_grid_desc_reducemraw =
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make_naive_tensor_descriptor_packed(make_tuple(N * Do * Ho * Wo * C));
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const auto out_grid_desc_reducem =
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transform_tensor_descriptor(out_grid_desc_reducemraw,
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make_tuple(make_right_pad_transform(MRaw, MPad)),
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make_tuple(Sequence<0>{}),
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make_tuple(Sequence<0>{}));
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return make_tuple(in_grid_desc_reducem_reducek, out_grid_desc_reducem);
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}
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using ABGridDescs = decltype(MakeABGridDescriptor_A_M_K_B_M(1, 1, {}, {}, {}, {}, {}, {}));
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using AGridDesc_M_K = remove_cvref_t<decltype(ABGridDescs{}[I0])>;
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using BGridDesc_M = remove_cvref_t<decltype(ABGridDescs{}[I1])>;
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struct Argument : public BaseArgument
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{
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Argument(const InDataType* p_in_dev,
|
||||
OutDataType* p_out_dev,
|
||||
IndexDataType* p_out_indices_dev,
|
||||
ck::index_t N,
|
||||
ck::index_t C,
|
||||
std::vector<ck::index_t>& input_spatial_lengths,
|
||||
std::vector<ck::index_t>& window_spatial_lengths,
|
||||
std::vector<ck::index_t>& output_spatial_lengths,
|
||||
std::vector<ck::index_t>& window_strides,
|
||||
std::vector<ck::index_t>& input_left_pads,
|
||||
std::vector<ck::index_t>& input_right_pads)
|
||||
: p_in_dev_{p_in_dev},
|
||||
p_out_dev_{p_out_dev},
|
||||
p_out_indices_dev_{p_out_indices_dev},
|
||||
a_grid_desc_m_k_{},
|
||||
b_grid_desc_m_{}
|
||||
{
|
||||
const auto descs = MakeABGridDescriptor_A_M_K_B_M(N,
|
||||
C,
|
||||
input_spatial_lengths,
|
||||
window_spatial_lengths,
|
||||
output_spatial_lengths,
|
||||
window_strides,
|
||||
input_left_pads,
|
||||
input_right_pads);
|
||||
|
||||
a_grid_desc_m_k_ = descs[I0];
|
||||
b_grid_desc_m_ = descs[I1];
|
||||
|
||||
invariant_lowest_length_ = C;
|
||||
|
||||
int32_t reduceLength =
|
||||
window_spatial_lengths[0] * window_spatial_lengths[1] * window_spatial_lengths[2];
|
||||
|
||||
std::tie(in_element_op_, acc_element_op_) =
|
||||
reduce_unary_operator<ReduceOpId, true, true>::GetElementwiseOperator(reduceLength);
|
||||
}
|
||||
|
||||
const InDataType* p_in_dev_;
|
||||
OutDataType* p_out_dev_;
|
||||
IndexDataType* p_out_indices_dev_;
|
||||
AGridDesc_M_K a_grid_desc_m_k_;
|
||||
BGridDesc_M b_grid_desc_m_;
|
||||
InElementwiseOperation in_element_op_;
|
||||
AccElementwiseOperation acc_element_op_;
|
||||
|
||||
// for checking vector load/store
|
||||
ck::index_t invariant_lowest_length_;
|
||||
};
|
||||
|
||||
struct Invoker : public BaseInvoker
|
||||
{
|
||||
float Run(const Argument& arg, const StreamConfig& stream_config = StreamConfig{})
|
||||
{
|
||||
using gridwise_reduce =
|
||||
GridwiseReduction_mk_to_m_threadwise<InDataType,
|
||||
OutDataType,
|
||||
ComputeDataType,
|
||||
IndexDataType,
|
||||
AGridDesc_M_K,
|
||||
BGridDesc_M,
|
||||
ReduceOperation,
|
||||
InElementwiseOperation,
|
||||
AccElementwiseOperation,
|
||||
InMemoryDataOperationEnum::Set,
|
||||
false, // propagate_nan
|
||||
BlockSize,
|
||||
MThreadSliceSize,
|
||||
KThreadSliceSize,
|
||||
InSrcOutDstVectorDim,
|
||||
InSrcOutDstVectorSize,
|
||||
InSrcOutDstVectorSize>;
|
||||
|
||||
const auto kernel =
|
||||
kernel_reduce_threadwise<gridwise_reduce,
|
||||
OutputIndex,
|
||||
true, // pooling need to return global index
|
||||
false, // don't have index input
|
||||
InDataType,
|
||||
OutDataType,
|
||||
ComputeDataType,
|
||||
IndexDataType,
|
||||
AGridDesc_M_K,
|
||||
BGridDesc_M,
|
||||
InElementwiseOperation,
|
||||
AccElementwiseOperation>;
|
||||
|
||||
ck::index_t M = arg.a_grid_desc_m_k_.GetLength(I0);
|
||||
|
||||
const index_t grid_size = (M / M_BlockTileSize);
|
||||
|
||||
return launch_and_time_kernel(stream_config,
|
||||
kernel,
|
||||
dim3(grid_size),
|
||||
dim3(BlockSize),
|
||||
0,
|
||||
arg.a_grid_desc_m_k_,
|
||||
arg.b_grid_desc_m_,
|
||||
arg.in_element_op_,
|
||||
arg.acc_element_op_,
|
||||
float(1),
|
||||
arg.p_in_dev_,
|
||||
nullptr,
|
||||
float(0),
|
||||
arg.p_out_dev_,
|
||||
arg.p_out_indices_dev_);
|
||||
}
|
||||
|
||||
float Run(const BaseArgument* p_arg,
|
||||
const StreamConfig& stream_config = StreamConfig{}) override
|
||||
{
|
||||
return Run(*dynamic_cast<const Argument*>(p_arg), stream_config);
|
||||
}
|
||||
};
|
||||
|
||||
bool IsSupportedArgument(const BaseArgument* p_arg) override
|
||||
{
|
||||
const Argument* pArg = dynamic_cast<const Argument*>(p_arg);
|
||||
|
||||
if(pArg->invariant_lowest_length_ % InSrcOutDstVectorSize != 0)
|
||||
{
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
std::unique_ptr<BaseArgument>
|
||||
MakeArgumentPointer(const void* p_in_dev,
|
||||
void* p_out_dev,
|
||||
void* p_out_indices_dev,
|
||||
std::vector<ck::index_t> input_lengths,
|
||||
std::vector<ck::index_t> window_lengths,
|
||||
std::vector<ck::index_t> output_lengths,
|
||||
std::vector<ck::index_t>, // Suppose tensor layout = NDHWC
|
||||
std::vector<ck::index_t>, // Suppose tensor layout = NDHWC
|
||||
std::vector<ck::index_t>, // Suppose tensor layout = NDHWC
|
||||
std::vector<ck::index_t> window_strides,
|
||||
std::vector<ck::index_t> input_left_pads,
|
||||
std::vector<ck::index_t> input_right_pads,
|
||||
std::vector<ck::index_t> pooling_dims) override
|
||||
{
|
||||
if(input_lengths.size() != InOutRank || window_lengths.size() != WindowRank ||
|
||||
input_lengths.size() != InOutRank || window_strides.size() != WindowRank ||
|
||||
input_left_pads.size() != WindowRank || input_right_pads.size() != WindowRank)
|
||||
throw std::runtime_error("dimension is incorrect");
|
||||
|
||||
if(pooling_dims != std::vector<ck::index_t>{2, 3, 4})
|
||||
throw std::runtime_error("pooling_dims only support {2, 3, 4} in pool3d so far");
|
||||
|
||||
index_t N = input_lengths[0];
|
||||
index_t C = input_lengths[1];
|
||||
index_t Di = input_lengths[2];
|
||||
index_t Hi = input_lengths[3];
|
||||
index_t Wi = input_lengths[4];
|
||||
index_t Do = output_lengths[2];
|
||||
index_t Ho = output_lengths[3];
|
||||
index_t Wo = output_lengths[4];
|
||||
|
||||
std::vector<ck::index_t> input_spatial_lengths = {Di, Hi, Wi};
|
||||
std::vector<ck::index_t> output_spatial_lengths = {Do, Ho, Wo};
|
||||
|
||||
return std::make_unique<Argument>(static_cast<const InDataType*>(p_in_dev),
|
||||
static_cast<OutDataType*>(p_out_dev),
|
||||
static_cast<IndexDataType*>(p_out_indices_dev),
|
||||
N,
|
||||
C,
|
||||
input_spatial_lengths,
|
||||
window_lengths,
|
||||
output_spatial_lengths,
|
||||
window_strides,
|
||||
input_left_pads,
|
||||
input_right_pads);
|
||||
}
|
||||
|
||||
std::unique_ptr<BaseInvoker> MakeInvokerPointer() override
|
||||
{
|
||||
return std::make_unique<Invoker>(Invoker{});
|
||||
}
|
||||
|
||||
std::string GetTypeString() const override
|
||||
{
|
||||
auto str = std::stringstream();
|
||||
|
||||
// clang-format off
|
||||
str << "DevicePool3dFwd_Input_N_Di_Hi_Wi_C_Output_N_Do_Ho_Wo_C<" << BlockSize << ",";
|
||||
str << "M_C" << MThreadClusterSize << "_S" << MThreadSliceSize << ",";
|
||||
str << "K_C" << KThreadClusterSize << "_S" << KThreadSliceSize << ",";
|
||||
str <<"InSrcOutDstVectorSize_" << InSrcOutDstVectorSize << ">";
|
||||
// clang-format on
|
||||
|
||||
return str.str();
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace device
|
||||
} // namespace tensor_operation
|
||||
} // namespace ck
|
||||
@@ -28,6 +28,7 @@ template <typename InDataType,
|
||||
typename AccElementwiseOperation,
|
||||
bool PropagateNan,
|
||||
bool OutputIndex,
|
||||
bool TransformIndexKtoGlobal,
|
||||
bool HaveIndexInputIfOutputIndex,
|
||||
index_t BlockSize,
|
||||
index_t MThreadSliceSize,
|
||||
@@ -260,6 +261,7 @@ struct DeviceReduceThreadWise : public DeviceReduce<InDataType,
|
||||
|
||||
const auto kernel = kernel_reduce_threadwise<GridwiseReduce,
|
||||
OutputIndex,
|
||||
TransformIndexKtoGlobal,
|
||||
HaveIndexInput,
|
||||
InDataType,
|
||||
OutDataType,
|
||||
|
||||
Reference in New Issue
Block a user