From 6bb87950290aaead4f90780785818e3e2e24fd6e Mon Sep 17 00:00:00 2001 From: wangyan Date: Sun, 9 Jun 2024 12:37:11 +0800 Subject: [PATCH 01/85] yan devel --- source/op/pt/CMakeLists.txt | 2 +- source/op/pt/tabulate_multi_device.cc | 452 ++++++++++++ .../pt/test/op/test_tabulate_fusion_se_a.py | 666 ++++++++++++++++++ 3 files changed, 1119 insertions(+), 1 deletion(-) create mode 100644 source/op/pt/tabulate_multi_device.cc create mode 100644 source/op/pt/test/op/test_tabulate_fusion_se_a.py diff --git a/source/op/pt/CMakeLists.txt b/source/op/pt/CMakeLists.txt index 3254e5e852..78a0e8aa1f 100644 --- a/source/op/pt/CMakeLists.txt +++ b/source/op/pt/CMakeLists.txt @@ -1,4 +1,4 @@ -file(GLOB OP_SRC print_summary.cc comm.cc) +file(GLOB OP_SRC print_summary.cc comm.cc tabulate_multi_device.cc) add_library(deepmd_op_pt MODULE ${OP_SRC}) # link: libdeepmd libtorch diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc new file mode 100644 index 0000000000..ab5862215d --- /dev/null +++ b/source/op/pt/tabulate_multi_device.cc @@ -0,0 +1,452 @@ +#include +#include "tabulate.h" +#include +#include + +#if defined(GOOGLE_CUDA) || defined(TENSORFLOW_USE_ROCM) +#include "device.h" +#endif + +void GetTensorDevice(const torch::Tensor& t, std::string& str){ + if(t.device().is_cuda()) + str = "GPU"; + else + str = "CPU"; +} + +void print_first_five_elements(const torch::Tensor& tensor, const std::string& name) { + std::cout << "First five elements of " << name << ":" << std::endl; + std::cout << tensor.slice(0, 0, 5) << std::endl; +} + +template +void TabulateFusionSeAForward( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size, + torch::Tensor& descriptor_tensor +){ + // check input shape + if(table_tensor.dim() != 2) + throw std::invalid_argument("Dim of table should be 2"); + if(em_x_tensor.dim() != 2) + throw std::invalid_argument("Dim of input should be 2"); + if(em_tensor.dim() != 3) + throw std::invalid_argument("Dim of input should be 3"); + // get the device + std::string device; + GetTensorDevice(table_tensor, device); + // flat the tensors + FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); + + const FPTYPE* table = table_tensor.view({-1}).data_ptr(); + const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); + const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); + const FPTYPE* em = em_tensor.view({-1}).data_ptr(); + const FPTYPE* two_embed = nullptr; + const int64_t nloc = em_tensor.size(0); + const int64_t nnei = em_tensor.size(1); + // compute + if (device == "GPU") { +#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM + deepmd::tabulate_fusion_se_a_gpu(descriptor, table, table_info, em_x, em, + two_embed, nloc, nnei, last_layer_size); +#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_a_cpu(descriptor, table, table_info, em_x, em, + two_embed, nloc, nnei, last_layer_size); + } +} + +template +void TabulateFusionSeAGradForward( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dy_tensor, + const torch::Tensor& descriptor_tensor, + torch::Tensor& dy_dem_x_tensor, + torch::Tensor& dy_dem_tensor, + torch::Tensor& dy_dtwo_tensor +){ + // check input shape + if (dy_tensor.dim() != 3) { + throw std::invalid_argument("Dim of dy_tensor should be 3"); + } + // check input value + // print_first_five_elements(table_tensor, "table_tensor"); + // print_first_five_elements(table_info_tensor, "table_info_tensor"); + // print_first_five_elements(em_x_tensor, "em_x_tensor"); + // print_first_five_elements(em_tensor, "em_tensor"); + print_first_five_elements(dy_tensor, "dy_tensor"); + print_first_five_elements(descriptor_tensor, "descriptor_tensor"); + print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); + print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); + print_first_five_elements(dy_dtwo_tensor, "dy_dtwo_tensor"); + + // get the device + std::string device; + GetTensorDevice(table_tensor, device); + // flat the tensors + FPTYPE* dy_dem_x = dy_dem_x_tensor.view({-1}).data_ptr(); + FPTYPE* dy_dem = dy_dem_tensor.view({-1}).data_ptr(); + FPTYPE* dy_dtwo = dy_dtwo_tensor.view({-1}).data_ptr(); + + const FPTYPE* table = table_tensor.view({-1}).data_ptr(); + const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); + const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); + const FPTYPE* em = em_tensor.view({-1}).data_ptr(); + const FPTYPE* two_embed = nullptr; + const FPTYPE* dy = dy_tensor.view({-1}).data_ptr(); + const FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); + const int64_t nloc = em_tensor.size(0); + const int64_t nnei = em_tensor.size(1); + const int64_t last_layer_size = descriptor_tensor.size(2); + // compute + if (device == "GPU") { +#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM + deepmd::tabulate_fusion_se_a_grad_gpu(dy_dem_x, dy_dem, dy_dtwo, table, + table_info, em_x, em, two_embed, dy, + nloc, nnei, last_layer_size); +#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_a_grad_cpu(dy_dem_x, dy_dem, dy_dtwo, table, + table_info, em_x, em, two_embed, dy, + nloc, nnei, last_layer_size); + } + print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); + print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); + print_first_five_elements(dy_dtwo_tensor, "dy_dtwo_tensor"); + +} + +template +void TabulateFusionSeAGradGradForward( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dz_dy_dem_x_tensor, + const torch::Tensor& dz_dy_dem_tensor, + const torch::Tensor& descriptor_tensor, + bool is_sorted, + torch::Tensor& dz_dy_tensor +){ + // Check input shape + if (dz_dy_dem_x_tensor.dim() != 2) + throw std::invalid_argument("Dim of dz_dy_dem_x should be 2"); + if (dz_dy_dem_tensor.dim() != 3) + throw std::invalid_argument("Dim of dz_dy_dem should be 3"); + // get the device + std::string device; + GetTensorDevice(table_tensor, device); + // flat the tensors + FPTYPE* dz_dy = dz_dy_tensor.view({-1}).data_ptr(); + const FPTYPE* table = table_tensor.view({-1}).data_ptr(); + const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); + const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); + const FPTYPE* em = em_tensor.view({-1}).data_ptr(); + const FPTYPE* two_embed = nullptr; + const FPTYPE* dz_dy_dem_x = dz_dy_dem_x_tensor.view({-1}).data_ptr(); + const FPTYPE* dz_dy_dem = dz_dy_dem_tensor.view({-1}).data_ptr(); + const FPTYPE* dz_dy_dtwo = nullptr; + const int64_t nloc = em_tensor.size(0); + const int64_t nnei = em_tensor.size(1); + const int64_t last_layer_size = descriptor_tensor.size(2); + // compute + if (device == "GPU") { +#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM + deepmd::tabulate_fusion_se_a_grad_grad_gpu( + dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, + dz_dy_dtwo, nloc, nnei, last_layer_size, is_sorted); +#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM + TORCH_CHECK(last_layer_size <= 1024, + "In the process of model compression, the size of the " + "last layer of embedding net must be less than 1024!"); + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_a_grad_grad_cpu( + dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, + dz_dy_dtwo, nloc, nnei, last_layer_size, is_sorted); + } +} + + +class TabulateFusionSeAOp : public torch::autograd::Function { + public: + static torch::autograd::variable_list forward( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size + ){ + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if(type_flag) { + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, last_layer_size); + } else { + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, last_layer_size); + } + } + + // std::vector + template + static torch::autograd::variable_list forward_t( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size + ){ + // allocate output tensors + auto options = + torch::TensorOptions() + .dtype(table_tensor.dtype()) + .device(table_tensor.device()); + torch::Tensor descriptor_tensor = torch::empty( + {em_tensor.size(0), 4, last_layer_size}, options); + // compute + TabulateFusionSeAForward(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, last_layer_size, descriptor_tensor); + // save data + ctx->save_for_backward({ + table_tensor, table_info_tensor, em_x_tensor, + em_tensor, descriptor_tensor + }); + return { descriptor_tensor }; + } + + static torch::autograd::variable_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output + ){ + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if(type_flag) { + return backward_t(ctx, grad_output); + } else{ + return backward_t(ctx, grad_output); + } + } + + template + static torch::autograd::variable_list backward_t( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output + ){ + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + torch::Tensor table_info_tensor = saved_variables[1]; + torch::Tensor em_x_tensor = saved_variables[2]; + torch::Tensor em_tensor = saved_variables[3]; + torch::Tensor descriptor_tensor = saved_variables[4]; + + torch::Tensor dy_tensor = grad_output[0]; + // allocate output tensors + auto options = torch::TensorOptions() + .dtype(em_x_tensor.dtype()) + .device(em_x_tensor.device()); + torch::Tensor dy_dem_x_tensor = torch::zeros( + em_x_tensor.sizes(), options + ); + torch::Tensor dy_dem_tensor = torch::zeros( + em_tensor.sizes(), options + ); + torch::Tensor dy_dtwo_tensor = torch::zeros( + dy_tensor.sizes(), options + ); + // compute + TabulateFusionSeAGradForward(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, dy_tensor, descriptor_tensor, + dy_dem_x_tensor, dy_dem_tensor, dy_dtwo_tensor); + + return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, at::Tensor()}; + } +}; + +class TabulateFusionSeAGradOp : public torch::autograd::Function { + private: + std::string device; + + public: + static torch::autograd::variable_list forward( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dy_tensor, + const torch::Tensor& descriptor_tensor + ) { + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if(type_flag) { + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, dy_tensor, + descriptor_tensor); + } else{ + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, dy_tensor, + descriptor_tensor); + } + } + + template + static torch::autograd::variable_list forward_t( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dy_tensor, + const torch::Tensor& descriptor_tensor + ){ + // Allocate output tensors + torch::Tensor dy_dem_x_tensor = torch::empty_like(em_x_tensor); + torch::Tensor dy_dem_tensor = torch::empty_like(em_tensor); + // compute + // TabulateFusionSeAGradForward(table_tensor, table_info_tensor, em_x_tensor, + // em_tensor, dy_tensor, descriptor_tensor, + // dy_dem_x_tensor, dy_dem_tensor); + // save data + ctx->save_for_backward({ + table_tensor, table_info_tensor, em_x_tensor, + em_tensor, descriptor_tensor + }); + + return torch::autograd::variable_list{ dy_dem_x_tensor, dy_dem_tensor }; + } + + static torch::autograd::variable_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output + ){ + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if(type_flag) { + return backward_t(ctx, grad_output); + } else{ + return backward_t(ctx, grad_output); + } + } + + template + static torch::autograd::variable_list backward_t( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output + ){ + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + torch::Tensor table_info_tensor = saved_variables[1]; + torch::Tensor em_x_tensor = saved_variables[2]; + torch::Tensor em_tensor = saved_variables[3]; + torch::Tensor descriptor_tensor = saved_variables[4]; + + bool is_sorted = true; + + torch::Tensor dz_dy_dem_x_tensor = grad_output[0]; + torch::Tensor dz_dy_dem_tensor = grad_output[1]; + // allocate output tensors + torch::Tensor dz_dy_tensor = torch::empty_like(descriptor_tensor); + // compute + TabulateFusionSeAGradGradForward(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, dz_dy_dem_x_tensor, dz_dy_dem_tensor, + descriptor_tensor, is_sorted, dz_dy_tensor); + + return torch::autograd::variable_list{at::Tensor(), at::Tensor(), at::Tensor(), at::Tensor(), dz_dy_tensor, at::Tensor()}; + } + +}; + +class TabulateFusionSeAGradGradOp : public torch::autograd::Function { + public: + static torch::autograd::variable_list forward( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dz_dy_dem_x_tensor, + const torch::Tensor& dz_dy_dem_tensor, + const torch::Tensor& descriptor_tensor, + bool is_sorted + ){ + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if(type_flag) { + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, dz_dy_dem_x_tensor, + dz_dy_dem_tensor, descriptor_tensor, is_sorted); + } else{ + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, dz_dy_dem_x_tensor, + dz_dy_dem_tensor, descriptor_tensor, is_sorted); + } + } + + template + static torch::autograd::variable_list forward_t( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dz_dy_dem_x_tensor, + const torch::Tensor& dz_dy_dem_tensor, + const torch::Tensor& descriptor_tensor, + bool is_sorted + ) { + // Allocate output tensor + torch::Tensor dz_dy_tensor = torch::empty_like(descriptor_tensor); + // compute + TabulateFusionSeAGradGradForward(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, dz_dy_dem_x_tensor, dz_dy_dem_tensor, + descriptor_tensor, is_sorted, dz_dy_tensor); + + return torch::autograd::variable_list{ dz_dy_tensor }; + } +}; + +// class TabulateFusionSeAttenOp : public torch::autograd::Function{ +// private: +// std::string device; + +// public: +// explicit TabulateFusionSeAttenOp(int last_layer_size, bool is_sorted) +// : last_layer_size(last_layer_size), is_sorted(is_sorted) {} + +// static std::vector forward( +// torch::autograd::AutogradContext* ctx, +// const torch::Tensor& table_tensor, +// const torch::Tensor& table_info_tensor, +// const torch::Tensor& em_x_tensor, +// const torch::Tensor& em_tensor, +// int last_layer_size, +// bool is_sorted +// ){ + +// } +// }; + +std::vector tabluate_fusion_se_a( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size +){ + return TabulateFusionSeAOp::apply(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, last_layer_size); +} + +TORCH_LIBRARY_FRAGMENT(deepmd, m) { m.def("tabulate_fusion_se_a", tabluate_fusion_se_a); } diff --git a/source/op/pt/test/op/test_tabulate_fusion_se_a.py b/source/op/pt/test/op/test_tabulate_fusion_se_a.py new file mode 100644 index 0000000000..3b1efdd15e --- /dev/null +++ b/source/op/pt/test/op/test_tabulate_fusion_se_a.py @@ -0,0 +1,666 @@ +import torch +import unittest +import deepmd.pt.cxx_op + +class TestTabulateFusionSeAOp(unittest.TestCase): + + def setUp(self): + self.table_tensor = torch.tensor([ + 6.348551343037398542e-01, 4.209465843706336474e-04, + 6.390862740714405368e-03, -1.544448595628262176e-04, + -1.891095227974180087e-04, 2.695025951562175852e-05, + -1.317549846042939343e+00, -5.624478206903206490e-02, + 1.274284553146523905e-02, -6.836227424141475689e-04, + -1.438066096020836407e-04, -1.854932873974712940e-06, + -9.996964112615246423e-01, 6.928234423723647617e-02, + -4.974719973810486084e-03, -2.019584729176823030e-04, + 1.077254539742680247e-04, -8.024209768588029797e-06, + 3.552689563657350780e-01, -3.578299775339799371e-02, + -1.319946251007718743e-03, 1.016701374495701440e-03, + -1.057336720791906388e-04, 5.182678943855506567e-06, + 1.227750369557627286e+00, 4.100352079064395472e-02, + 3.586869164810712295e-03, -4.304540913340443135e-04, + -1.269943482892440004e-04, 1.459465404430219674e-05, + -1.472642501673147031e+00, -1.611354921283318364e-01, + 1.645427874390196360e-02, 2.107392978135091402e-04, + -2.193541011180757461e-04, 1.915392497459551146e-05, + -2.855174490181606739e-01, 9.774337856626263976e-02, + -2.140891880666230714e-03, -7.148328890055103638e-04, + 1.965696332267534503e-05, -4.593489654121371453e-06, + -1.468441009949382314e+00, -6.360828127262234399e-02, + 4.751283295356955282e-03, 8.711899561753186068e-05, + -9.937008678852959884e-06, 4.273569346584811685e-07, + 6.348599826995243722e-01, 5.487167506364742930e-04, + 6.386116198716365253e-03, -1.619832375568118791e-04, + -1.877328309473502049e-04, 2.134130914519164856e-05, + -1.318111020264137512e+00, -5.599013082054477008e-02, + 1.272225054666903735e-02, -6.893710047488201898e-04, + -1.434367581078517366e-04, 3.329508890614227371e-05, + -9.990040854920316793e-01, 6.918278968071900348e-02, + -4.980714172967731085e-03, 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-6.954454205710857090e-04, + 2.907017700829073683e-05, 9.120785771591908463e-06, + -1.480971935090678926e+00, -6.169766439371183325e-02, + 4.800832758035045861e-03, 7.743502257440657043e-05, + -1.440171540732098418e-05, -4.489324897938611976e-06, + 6.355509554770921721e-01, 4.194364255265300989e-03, + 6.156587518227093006e-03, -3.584539136959086518e-04, + -1.505562336471176987e-04, 2.631189526673375584e-05, + -1.333295991901433553e+00, -4.879824528740911438e-02, + 1.205629889598585497e-02, -8.346035033896359156e-04, + -1.072962342948566929e-04, 2.412331753624817981e-05, + -9.793640468817854661e-01, 6.625405011186732973e-02, + -5.102126473064734317e-03, -8.551069374443776396e-05, + 8.618032279329005427e-05, -1.422030758858379208e-05, + 3.444418516979214084e-01, -3.631195473807800889e-02, + -4.625381215785304145e-04, 8.881537622047225473e-04, + -1.080757789189670570e-04, 5.820590714360855199e-08, + 1.240361649325028681e+00, 4.302664794411619614e-02, + 3.137220402938139478e-03, -5.615677039256951981e-04, + -9.125763978623760322e-05, 2.367398552885374808e-05, + -1.519498310980496925e+00, -1.512290469691385253e-01, + 1.652996628226939199e-02, -3.745688059096337011e-05, + -1.938906911473592626e-04, 1.811217640451412989e-05, + -2.564062357251438717e-01, 9.626832379335603651e-02, + -2.771163091665611831e-03, -6.829069315554202020e-04, + 3.363238372709415958e-05, 8.623099725596635004e-06, + -1.487093617252511990e+00, -6.073523464295225993e-02, + 4.823154268625621383e-03, 7.122599345182346051e-05, + -1.664931178025436733e-05, -4.312450972708557703e-06 + ]).reshape(8,132) + self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1]) + self.em_x_tensor = torch.tensor([0.0343909, 0.11357423, 0.0858676, 0.19337772, + 0.1935728, 0.0477744, 0.05845198, 0.19080509, + 0.16111261, 0.07179262, 0.10078013, 0.04640909, + 0.10433399, 0.15650861, 0.17527857, 0.04249097]).reshape(4,4) + self.em_tensor = torch.tensor([0.0343909, 0.08394249, 0.06791791, 0.00903334, 0.11357423, 0.10597251, + 0.05738069, 0.10071109, 0.0858676, 0.17410445, 0.05390256, 0.09495758, + 0.19337772, 0.02045487, 0.04095526, 0.18431305, 0.1935728, 0.03930614, + 0.0304133, 0.15261676, 0.0477744, 0.06838737, 0.12824902, 0.14125861, + 0.05845198, 0.12731053, 0.0315968, 0.14927774, 0.19080509, 0.19206871, + 0.14361383, 0.04083437, 0.16111261, 0.19944826, 0.16563484, 0.00797179, + 0.07179262, 0.16993159, 0.01834742, 0.08405, 0.10078013, 0.0773945, + 0.09541813, 0.0042979, 0.04640909, 0.07968697, 0.18046262, 0.11724063, + 0.10433399, 0.16910201, 0.10653732, 0.07434702, 0.15650861, 0.0350976, + 0.04088021, 0.15753491, 0.17527857, 0.03178642, 0.01599623, 0.08095053, + 0.04249097, 0.17082205, 0.18275348, 0.02921504]).reshape(4,4,4) + self.table_info_tensor.requires_grad = True + self.table_tensor.requires_grad = True + self.em_x_tensor.requires_grad = True + self.em_tensor.requires_grad = True + self.two_embed = torch.empty(4, 4, 8) + self.last_layer_size = 8 + self.nloc = 4 + self.nnei = 4 + # forward test + self.expected_descriptor_tensor = torch.tensor([0.2713011, -0.56606281, -0.42305039, 0.14965803, 0.52695372, + -0.63845663, -0.11624505, -0.63103203, 0.24412213, -0.50842224, + -0.38203148, 0.1353771, 0.47343798, -0.57158622, -0.10647548, + -0.56671287, 0.13979394, -0.29123603, -0.21862063, 0.07744574, + 0.27118433, -0.32761487, -0.06077287, -0.32463492, 0.24704819, + -0.51555848, -0.38509326, 0.1362072, 0.47992214, -0.58168358, + -0.10566162, -0.57473633, 0.31158834, -0.65068838, -0.48501479, + 0.17143258, 0.60565326, -0.73506803, -0.13233106, -0.72541595, + 0.27121003, -0.5656669, -0.42318034, 0.14974857, 0.52662422, + -0.637633, -0.11658482, -0.6305842, 0.21202135, -0.44212972, + -0.33094666, 0.11713047, 0.41162829, -0.4982129, -0.0913087, + -0.49286515, 0.30733526, -0.64054639, -0.48022212, 0.17004692, + 0.59640929, -0.72111726, -0.13304347, -0.71402776, 0.24135931, + -0.50316388, -0.37699907, 0.13347531, 0.46846154, -0.56664651, + -0.10429212, -0.56088123, 0.33429479, -0.69669061, -0.52246841, + 0.18502927, 0.64867706, -0.78417021, -0.14487244, -0.77659533, + 0.29200237, -0.60840668, -0.45656557, 0.16172246, 0.56650319, + -0.68453038, -0.12681616, -0.67817995, 0.13559139, -0.28210652, + -0.21258614, 0.07539812, 0.26274303, -0.3166084, -0.05968776, + -0.31443544, 0.30394432, -0.63428311, -0.47381417, 0.16759396, + 0.59043739, -0.71559513, -0.13003802, -0.70708354, 0.25830471, + -0.53796239, -0.40421268, 0.14323456, 0.50094757, -0.6048126, + -0.11264426, -0.59964242, 0.21979687, -0.45763438, -0.34413143, + 0.12197404, 0.42616899, -0.51425659, -0.09609854, -0.51009828, + 0.2172166, -0.45326447, -0.33866506, 0.11979851, 0.421936, + -0.51130404, -0.09300045, -0.50528542]).reshape(4,4,8) + # backward test + self.expected_dy_dem_x = torch.tensor([-0.02067741, -0.03787612, -0.04180199, -0.04158797, + -0.03938578, -0.04047081, -0.03819692, -0.05383372, + -0.05179508, -0.03552708, -0.02812173, -0.04451295, + -0.04586229, -0.03794369, -0.02917727, -0.04478649]).reshape(4,4) + self.expected_dy_dem = torch.tensor([-3.32965609, -3.32965609, -3.32965609, -3.32965609, -3.33781886, + -3.33781886, -3.33781886, -3.33781886, -3.33501296, -3.33501296, + -3.33501296, -3.33501296, -3.34559974, -3.34559974, -3.34559974, + -3.34559974, -3.34561821, -3.34561821, -3.34561821, -3.34561821, + -3.33106684, -3.33106684, -3.33106684, -3.33106684, -3.33218328, + -3.33218328, -3.33218328, -3.33218328, -3.34535585, -3.34535585, + -3.34535585, -3.34535585, -3.34250754, -3.34250754, -3.34250754, + -3.34250754, -3.33356685, -3.33356685, -3.33356685, -3.33356685, + -3.33652989, -3.33652989, -3.33652989, -3.33652989, -3.3309235, + -3.3309235, -3.3309235, -3.3309235, -3.33688909, -3.33688909, + -3.33688909, -3.33688909, -3.34206038, -3.34206038, -3.34206038, + -3.34206038, -3.34387412, -3.34387412, -3.34387412, -3.34387412, + -3.33051143, -3.33051143, -3.33051143, -3.33051143]).reshape(4,4,4) + + def test_forward(self): + # Call the forward function + forward_result = torch.ops.deepmd.tabulate_fusion_se_a( + self.table_tensor, self.table_info_tensor, self.em_x_tensor, + self.em_tensor, self.last_layer_size + ) + + descriptor_tensor = forward_result[0] + + # Check the shape + self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) + + # Check the values + self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) + + def test_backward(self): + # Call the forward function + forward_result = torch.ops.deepmd.tabulate_fusion_se_a( + self.table_tensor, self.table_info_tensor, self.em_x_tensor, + self.em_tensor, self.last_layer_size + ) + + print('table_tensor', self.table_tensor.requires_grad) + print('table_info_tensor', self.table_info_tensor.requires_grad) + print('em_x_tensor', self.em_x_tensor.requires_grad) + print('em_tensor', self.em_tensor.requires_grad) + + descriptor_tensor = forward_result[0] + + print('descriptor_tensor', descriptor_tensor.requires_grad) + # Check the forward + self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) + + + # Create a loss and perform backward + loss = descriptor_tensor.sum() + loss.backward() + + # Check gradients + self.assertIsNotNone(self.em_x_tensor.grad) + self.assertIsNotNone(self.em_tensor.grad) + + # Check the shapes of the gradients + self.assertEqual(self.em_x_tensor.grad.shape, self.expected_dy_dem_x.shape) + self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) + + # Check the values of the gradients + print('-----------------------------------------------') + print(self.em_x_tensor.grad[:5]) + print(self.expected_dy_dem_x[:5]) + print('-----------------------------------------------') + print(self.em_tensor.grad[:5]) + print(self.expected_dy_dem[:5]) + self.assertTrue(torch.allclose(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5)) + self.assertTrue(torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5)) + +if __name__ == '__main__': + deepmd.pt.cxx_op.ENABLE_CUSTOMIZED_OP + unittest.main() \ No newline at end of file From 7ecd1229a754fc94986a376656200866eaf365e5 Mon Sep 17 00:00:00 2001 From: wangyan Date: Mon, 10 Jun 2024 12:48:52 +0800 Subject: [PATCH 02/85] tabulate_fusion_se_t --- source/op/pt/tabulate_multi_device.cc | 197 ++++++++++++++++++++++---- 1 file changed, 170 insertions(+), 27 deletions(-) diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index ab5862215d..5f4b5bee38 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -25,6 +25,7 @@ void TabulateFusionSeAForward( const torch::Tensor& table_info_tensor, const torch::Tensor& em_x_tensor, const torch::Tensor& em_tensor, + const torch::Tensor& two_embed_tensor, int64_t last_layer_size, torch::Tensor& descriptor_tensor ){ @@ -35,6 +36,8 @@ void TabulateFusionSeAForward( throw std::invalid_argument("Dim of input should be 2"); if(em_tensor.dim() != 3) throw std::invalid_argument("Dim of input should be 3"); + if(two_embed_tensor == nullptr || two_embed_tensor.dim() != 2) + throw std::invalid_argument("Dim of input should be 2"); // get the device std::string device; GetTensorDevice(table_tensor, device); @@ -45,7 +48,8 @@ void TabulateFusionSeAForward( const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); - const FPTYPE* two_embed = nullptr; + const FPTYPE* two_embed = (two_embed_tensor == nullptr) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); + const int64_t nloc = em_tensor.size(0); const int64_t nnei = em_tensor.size(1); // compute @@ -66,6 +70,7 @@ void TabulateFusionSeAGradForward( const torch::Tensor& table_info_tensor, const torch::Tensor& em_x_tensor, const torch::Tensor& em_tensor, + const torch::Tensor& two_embed_tensor, const torch::Tensor& dy_tensor, const torch::Tensor& descriptor_tensor, torch::Tensor& dy_dem_x_tensor, @@ -77,29 +82,23 @@ void TabulateFusionSeAGradForward( throw std::invalid_argument("Dim of dy_tensor should be 3"); } // check input value - // print_first_five_elements(table_tensor, "table_tensor"); - // print_first_five_elements(table_info_tensor, "table_info_tensor"); - // print_first_five_elements(em_x_tensor, "em_x_tensor"); - // print_first_five_elements(em_tensor, "em_tensor"); print_first_five_elements(dy_tensor, "dy_tensor"); print_first_five_elements(descriptor_tensor, "descriptor_tensor"); print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); - print_first_five_elements(dy_dtwo_tensor, "dy_dtwo_tensor"); - // get the device std::string device; GetTensorDevice(table_tensor, device); // flat the tensors FPTYPE* dy_dem_x = dy_dem_x_tensor.view({-1}).data_ptr(); FPTYPE* dy_dem = dy_dem_tensor.view({-1}).data_ptr(); - FPTYPE* dy_dtwo = dy_dtwo_tensor.view({-1}).data_ptr(); + FPTYPE* dy_dtwo = (dy_dtwo_tensor == nullptr) ? nullptr : dy_dtwo_tensor.view({-1}).data_ptr(); const FPTYPE* table = table_tensor.view({-1}).data_ptr(); const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); - const FPTYPE* two_embed = nullptr; + const FPTYPE* two_embed = (two_embed_tensor == nullptr) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); const FPTYPE* dy = dy_tensor.view({-1}).data_ptr(); const FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); const int64_t nloc = em_tensor.size(0); @@ -129,8 +128,10 @@ void TabulateFusionSeAGradGradForward( const torch::Tensor& table_info_tensor, const torch::Tensor& em_x_tensor, const torch::Tensor& em_tensor, + const torch::Tensor& two_embed_tensor, const torch::Tensor& dz_dy_dem_x_tensor, const torch::Tensor& dz_dy_dem_tensor, + const torch::Tensor& dz_dy_dtwo_tensor, const torch::Tensor& descriptor_tensor, bool is_sorted, torch::Tensor& dz_dy_tensor @@ -145,14 +146,15 @@ void TabulateFusionSeAGradGradForward( GetTensorDevice(table_tensor, device); // flat the tensors FPTYPE* dz_dy = dz_dy_tensor.view({-1}).data_ptr(); + const FPTYPE* table = table_tensor.view({-1}).data_ptr(); const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); - const FPTYPE* two_embed = nullptr; + const FPTYPE* two_embed = (two_embed_tensor == nullptr) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); const FPTYPE* dz_dy_dem_x = dz_dy_dem_x_tensor.view({-1}).data_ptr(); const FPTYPE* dz_dy_dem = dz_dy_dem_tensor.view({-1}).data_ptr(); - const FPTYPE* dz_dy_dtwo = nullptr; + const FPTYPE* dz_dy_dtwo = (dz_dy_dtwo_tensor == nullptr) ? nullptr : dz_dy_dtwo_tensor.view({-1}).data_ptr(); const int64_t nloc = em_tensor.size(0); const int64_t nnei = em_tensor.size(1); const int64_t last_layer_size = descriptor_tensor.size(2); @@ -173,6 +175,141 @@ void TabulateFusionSeAGradGradForward( } } +template +void TabulateFusionSeTForward( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size, + torch::Tensor& descriptor_tensor +){ + // check input shape + if(table_tensor.dim() != 2) + throw std::invalid_argument("Dim of table should be 2"); + if(em_x_tensor.dim() != 2) + throw std::invalid_argument("Dim of input should be 2"); + if(em_tensor.dim() != 3) + throw std::invalid_argument("Dim of input should be 3"); + // get the device + std::string device; + GetTensorDevice(table_tensor, device); + // flat the tensors + FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); + + const FPTYPE* table = table_tensor.view({-1}).data_ptr(); + const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); + const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); + const FPTYPE* em = em_tensor.view({-1}).data_ptr(); + const int64_t nloc = em_tensor.size(0); + const int64_t nnei_i = em_tensor.size(1); + const int64_t nnei_j = em_tensor.size(2); + // compute + if (device == "GPU") { +#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM + deepmd::tabulate_fusion_se_t_gpu(descriptor, table, table_info, em_x, em, + nloc, nnei_i, nnei_j, last_layer_size); +#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_t_cpu(descriptor, table, table_info, em_x, em, + nloc, nnei_i, nnei_j, last_layer_size); + } +} + +template +void TabulateFusionSeTGradForward( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dy_tensor, + const torch::Tensor& descriptor_tensor, + torch::Tensor& dy_dem_x_tensor, + torch::Tensor& dy_dem_tensor, +){ + // check input shape + if (dy_tensor.dim() != 2) { + throw std::invalid_argument("Dim of dy_tensor should be 2"); + } + // get the device + std::string device; + GetTensorDevice(table_tensor, device); + // flat the tensors + FPTYPE* dy_dem_x = dy_dem_x_tensor.view({-1}).data_ptr(); + FPTYPE* dy_dem = dy_dem_tensor.view({-1}).data_ptr(); + + const FPTYPE* table = table_tensor.view({-1}).data_ptr(); + const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); + const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); + const FPTYPE* em = em_tensor.view({-1}).data_ptr(); + const FPTYPE* dy = dy_tensor.view({-1}).data_ptr(); + const FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); + const int64_t nloc = em_tensor.size(0); + const int64_t nnei_i = em_tensor.size(1); + const int64_t nnei_j = em_tensor.size(2); + const int64_t last_layer_size = descriptor_tensor.size(1); + // compute + if (device == "GPU") { +#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM + deepmd::tabulate_fusion_se_t_grad_gpu(dy_dem_x, dy_dem, table, table_info, + em_x, em, dy, nloc, nnei_i, nnei_j, + last_layer_size); +#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_t_grad_cpu(dy_dem_x, dy_dem, table, table_info, + em_x, em, dy, nloc, nnei_i, nnei_j, + last_layer_size); + } +} + +template +void TabulateFusionSeTGradGradForward( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dz_dy_dem_x_tensor, + const torch::Tensor& dz_dy_dem_tensor, + const torch::Tensor& descriptor_tensor, + torch::Tensor& dz_dy_tensor +){ + // Check input shape + if (dz_dy_dem_x_tensor.dim() != 2) + throw std::invalid_argument("Dim of dz_dy_dem_x should be 2"); + if (dz_dy_dem_tensor.dim() != 3) + throw std::invalid_argument("Dim of dz_dy_dem should be 3"); + // get the device + std::string device; + GetTensorDevice(table_tensor, device); + // flat the tensors + FPTYPE* dz_dy = dz_dy_tensor.view({-1}).data_ptr(); + + const FPTYPE* table = table_tensor.view({-1}).data_ptr(); + const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); + const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); + const FPTYPE* em = em_tensor.view({-1}).data_ptr(); + const FPTYPE* dz_dy_dem_x = dz_dy_dem_x_tensor.view({-1}).data_ptr(); + const FPTYPE* dz_dy_dem = dz_dy_dem_tensor.view({-1}).data_ptr(); + const int64_t nloc = em_tensor.size(0); + const int64_t nnei_i = em_tensor.size(1); + const int64_t nnei_j = em_tensor.size(2); + const int64_t last_layer_size = descriptor_tensor.size(1); + // compute + if (device == "GPU") { +#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM + deepmd::tabulate_fusion_se_t_grad_grad_gpu( + dz_dy, table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, nloc, + nnei_i, nnei_j, last_layer_size); +#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM + TORCH_CHECK(last_layer_size <= 1024, + "In the process of model compression, the size of the " + "last layer of embedding net must be less than 1024!"); + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_t_grad_grad_cpu( + dz_dy, table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, nloc, + nnei_i, nnei_j, last_layer_size); + } +} class TabulateFusionSeAOp : public torch::autograd::Function { public: @@ -213,7 +350,8 @@ class TabulateFusionSeAOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, last_layer_size, descriptor_tensor); + em_tensor, nullptr, last_layer_size, + descriptor_tensor); // save data ctx->save_for_backward({ table_tensor, table_info_tensor, em_x_tensor, @@ -261,13 +399,14 @@ class TabulateFusionSeAOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, dy_tensor, descriptor_tensor, - dy_dem_x_tensor, dy_dem_tensor, dy_dtwo_tensor); + em_tensor, nullptr, dy_tensor, + descriptor_tensor, dy_dem_x_tensor, dy_dem_tensor, + nullptr); return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, at::Tensor()}; } @@ -310,12 +449,13 @@ class TabulateFusionSeAGradOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - // em_tensor, dy_tensor, descriptor_tensor, - // dy_dem_x_tensor, dy_dem_tensor); + TabulateFusionSeAGradForward(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, nullptr, dy_tensor, + descriptor_tensor, dy_dem_x_tensor, dy_dem_tensor, + nullptr); // save data ctx->save_for_backward({ table_tensor, table_info_tensor, em_x_tensor, @@ -361,10 +501,12 @@ class TabulateFusionSeAGradOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, dz_dy_dem_x_tensor, dz_dy_dem_tensor, - descriptor_tensor, is_sorted, dz_dy_tensor); + em_tensor, nullptr, dz_dy_dem_x_tensor, + dz_dy_dem_tensor, nullptr, descriptor_tensor, + is_sorted, dz_dy_tensor); - return torch::autograd::variable_list{at::Tensor(), at::Tensor(), at::Tensor(), at::Tensor(), dz_dy_tensor, at::Tensor()}; + return torch::autograd::variable_list{at::Tensor(), at::Tensor(), at::Tensor(), + at::Tensor(), dz_dy_tensor, at::Tensor()}; } }; @@ -410,8 +552,9 @@ class TabulateFusionSeAGradGradOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, dz_dy_dem_x_tensor, dz_dy_dem_tensor, - descriptor_tensor, is_sorted, dz_dy_tensor); + em_tensor, nullptr, dz_dy_dem_x_tensor, + dz_dy_dem_tensor, nullptr, descriptor_tensor, + is_sorted, dz_dy_tensor); return torch::autograd::variable_list{ dz_dy_tensor }; } From ab670ed8acd16282c82903ad5bbfa1cb1921ff9d Mon Sep 17 00:00:00 2001 From: wangyan Date: Mon, 10 Jun 2024 16:55:42 +0800 Subject: [PATCH 03/85] tabulate_fusion_all_op_basic_verion --- source/op/pt/tabulate_multi_device.cc | 455 ++++++++++++++++++++++++-- 1 file changed, 421 insertions(+), 34 deletions(-) diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index 5f4b5bee38..cf60037d17 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -225,7 +225,7 @@ void TabulateFusionSeTGradForward( const torch::Tensor& dy_tensor, const torch::Tensor& descriptor_tensor, torch::Tensor& dy_dem_x_tensor, - torch::Tensor& dy_dem_tensor, + torch::Tensor& dy_dem_tensor ){ // check input shape if (dy_tensor.dim() != 2) { @@ -311,6 +311,122 @@ void TabulateFusionSeTGradGradForward( } } +template +void TabulateFusionSeRForward( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size, + torch::Tensor& descriptor_tensor +){ + // check input shape + if(table_tensor.dim() != 2) + throw std::invalid_argument("Dim of table should be 2"); + if(em_tensor.dim() != 2) + throw std::invalid_argument("Dim of input should be 2"); + // get the device + std::string device; + GetTensorDevice(table_tensor, device); + // flat the tensors + FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); + + const FPTYPE* table = table_tensor.view({-1}).data_ptr(); + const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); + const FPTYPE* em = em_tensor.view({-1}).data_ptr(); + const int64_t nloc = em_tensor.size(0); + const int64_t nnei = em_tensor.size(1); + // compute + if (device == "GPU") { +#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM + deepmd::tabulate_fusion_se_r_gpu(descriptor, table, table_info, em, nloc, + nnei, last_layer_size); +#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_r_cpu(descriptor, table, table_info, em, nloc, + nnei, last_layer_size); + } +} + +template +void TabulateFusionSeRGradForward( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dy_tensor, + const torch::Tensor& descriptor_tensor, + torch::Tensor& dy_dem_tensor +){ + // check input shape + if (dy_tensor.dim() != 3) { + throw std::invalid_argument("Dim of dy_tensor should be 3"); + } + // get the device + std::string device; + GetTensorDevice(table_tensor, device); + // flat the tensors + FPTYPE* dy_dem = dy_dem_tensor.view({-1}).data_ptr(); + + const FPTYPE* table = table_tensor.view({-1}).data_ptr(); + const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); + const FPTYPE* em = em_tensor.view({-1}).data_ptr(); + const FPTYPE* dy = dy_tensor.view({-1}).data_ptr(); + const FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); + const int64_t nloc = em_tensor.size(0); + const int64_t nnei = em_tensor.size(1); + const int64_t last_layer_size = descriptor_tensor.size(2); + // compute + if (device == "GPU") { +#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM + deepmd::tabulate_fusion_se_r_grad_gpu(dy_dem, table, table_info, em, dy, + nloc, nnei, last_layer_size); +#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_r_grad_cpu(dy_dem, table, table_info, em, dy, + nloc, nnei, last_layer_size); + } +} + +template +void TabulateFusionSeRGradGradForward( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dz_dy_dem_tensor, + const torch::Tensor& descriptor_tensor, + torch::Tensor& dz_dy_tensor +){ + // Check input shape + if (dz_dy_dem_tensor.dim() != 2) + throw std::invalid_argument("Dim of dz_dy_dem should be 2"); + // get the device + std::string device; + GetTensorDevice(table_tensor, device); + // flat the tensors + FPTYPE* dz_dy = dz_dy_tensor.view({-1}).data_ptr(); + + const FPTYPE* table = table_tensor.view({-1}).data_ptr(); + const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); + const FPTYPE* em = em_tensor.view({-1}).data_ptr(); + const FPTYPE* dz_dy_dem = dz_dy_dem_tensor.view({-1}).data_ptr(); + const int64_t nloc = em_tensor.size(0); + const int64_t nnei = em_tensor.size(1); + const int64_t last_layer_size = descriptor_tensor.size(2); + // compute + if (device == "GPU") { +#if GOOGLE_CUDA || TENSORFLOW_USE_ROCM + deepmd::tabulate_fusion_se_r_grad_grad_gpu( + dz_dy, table, table_info, em, dz_dy_dem, nloc, nnei, last_layer_size); +#endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM + OP_REQUIRES(context, (last_layer_size <= 1024), + errors::InvalidArgument( + "In the process of model compression, the size of the " + "last layer of embedding net must be less than 1024!")); + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_r_grad_grad_cpu( + dz_dy, table, table_info, em, dz_dy_dem, nloc, nnei, last_layer_size); + } +} + class TabulateFusionSeAOp : public torch::autograd::Function { public: static torch::autograd::variable_list forward( @@ -331,7 +447,6 @@ class TabulateFusionSeAOp : public torch::autograd::Function template static torch::autograd::variable_list forward_t( torch::autograd::AutogradContext* ctx, @@ -364,7 +479,6 @@ class TabulateFusionSeAOp : public torch::autograd::Functionget_saved_variables(); torch::Tensor table_tensor = saved_variables[0]; bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; @@ -390,18 +504,8 @@ class TabulateFusionSeAOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, em_tensor, nullptr, dy_tensor, @@ -560,26 +664,271 @@ class TabulateFusionSeAGradGradOp : public torch::autograd::Function{ -// private: -// std::string device; - -// public: -// explicit TabulateFusionSeAttenOp(int last_layer_size, bool is_sorted) -// : last_layer_size(last_layer_size), is_sorted(is_sorted) {} - -// static std::vector forward( -// torch::autograd::AutogradContext* ctx, -// const torch::Tensor& table_tensor, -// const torch::Tensor& table_info_tensor, -// const torch::Tensor& em_x_tensor, -// const torch::Tensor& em_tensor, -// int last_layer_size, -// bool is_sorted -// ){ +class TabulateFusionSeAttenOp : public torch::autograd::Function{ + public: + static std::vector forward( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& two_embed_tensor, + int64_t last_layer_size, + bool is_sorted + ){ + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if(type_flag){ + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, two_embed_tensor, + last_layer_size, is_sorted); + } else{ + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, two_embed_tensor, + last_layer_size, is_sorted); + } + } + + template + static torch::autograd::variable_list forward_t( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& two_embed_tensor, + int64_t last_layer_size, + bool is_sorted + ){ + // allocate output tensors + auto options = + torch::TensorOptions() + .dtype(table_tensor.dtype()) + .device(table_tensor.device()); + torch::Tensor descriptor_tensor = torch::empty( + {em_tensor.size(0), 4, last_layer_size}, options); + // compute + TabulateFusionSeAForward(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, two_embed_tensor, last_layer_size, + descriptor_tensor); + // save data + ctx->save_for_backward({ + table_tensor, table_info_tensor, em_x_tensor, + em_tensor, two_embed_tensor, descriptor_tensor + }); + return { descriptor_tensor }; + } + + static torch::autograd::variable_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output + ){ + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if(type_flag) { + return backward_t(ctx, grad_output); + } else{ + return backward_t(ctx, grad_output); + } + } + + template + static torch::autograd::variable_list backward_t( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output + ){ + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + torch::Tensor table_info_tensor = saved_variables[1]; + torch::Tensor em_x_tensor = saved_variables[2]; + torch::Tensor em_tensor = saved_variables[3]; + torch::Tensor two_embed_tensor = saved_variables[4]; + torch::Tensor descriptor_tensor = saved_variables[5]; + + torch::Tensor dy_tensor = grad_output[0]; + // allocate output tensors + torch::Tensor dy_dem_x_tensor = torch::zeros_like(em_x_tensor); + torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); + torch::Tensor dy_dtwo_tensor = torch::zeros_like(two_embed_tensor); + // compute + TabulateFusionSeAGradForward(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, two_embed_tensor, dy_tensor, + descriptor_tensor, dy_dem_x_tensor, dy_dem_tensor, + dy_dtwo_tensor); -// } -// }; + return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, + dy_dtwo_tensor, at::Tensor(), at::Tensor()}; + } +}; + +class TabulateFusionSeTOp : public torch::autograd::Function{ + public: + static std::vector forward( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size + ){ + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if(type_flag){ + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, last_layer_size); + } else{ + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, last_layer_size); + } + } + + template + static torch::autograd::variable_list forward_t( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size + ){ + // allocate output tensors + auto options = + torch::TensorOptions() + .dtype(table_tensor.dtype()) + .device(table_tensor.device()); + torch::Tensor descriptor_tensor = torch::empty( + {em_tensor.size(0), last_layer_size}, options); + // compute + TabulateFusionSeTForward(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, last_layer_size, descriptor_tensor); + // save data + ctx->save_for_backward({ + table_tensor, table_info_tensor, em_x_tensor, + em_tensor, descriptor_tensor + }); + return { descriptor_tensor }; + } + + static torch::autograd::variable_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output + ){ + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if(type_flag) { + return backward_t(ctx, grad_output); + } else{ + return backward_t(ctx, grad_output); + } + } + + template + static torch::autograd::variable_list backward_t( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output + ){ + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + torch::Tensor table_info_tensor = saved_variables[1]; + torch::Tensor em_x_tensor = saved_variables[2]; + torch::Tensor em_tensor = saved_variables[3]; + torch::Tensor descriptor_tensor = saved_variables[4]; + + torch::Tensor dy_tensor = grad_output[0]; + // allocate output tensors + torch::Tensor dy_dem_x_tensor = torch::zeros_like(em_x_tensor); + torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); + // compute + TabulateFusionSeTGradForward(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, dy_tensor, descriptor_tensor, + dy_dem_x_tensor, dy_dem_tensor); + + return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, at::Tensor()}; + } +}; + +class TabulateFusionSeROp : public torch::autograd::Function{ + public: + static std::vector forward( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size + ){ + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if(type_flag){ + return forward_t(ctx, table_tensor, table_info_tensor, + em_tensor, last_layer_size); + } else{ + return forward_t(ctx, table_tensor, table_info_tensor, + em_tensor, last_layer_size); + } + } + + template + static torch::autograd::variable_list forward_t( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size + ){ + // allocate output tensors + auto options = + torch::TensorOptions() + .dtype(table_tensor.dtype()) + .device(table_tensor.device()); + torch::Tensor descriptor_tensor = torch::empty( + {em_tensor.size(0), em_tensor.size(1), last_layer_size}, options); + // compute + TabulateFusionSeRForward(table_tensor, table_info_tensor, em_tensor, + last_layer_size, descriptor_tensor); + // save data + ctx->save_for_backward({ + table_tensor, table_info_tensor, em_tensor, descriptor_tensor + }); + return { descriptor_tensor }; + } + + static torch::autograd::variable_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output + ){ + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if(type_flag) { + return backward_t(ctx, grad_output); + } else{ + return backward_t(ctx, grad_output); + } + } + + template + static torch::autograd::variable_list backward_t( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output + ){ + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + torch::Tensor table_info_tensor = saved_variables[1]; + torch::Tensor em_tensor = saved_variables[2]; + torch::Tensor descriptor_tensor = saved_variables[3]; + + torch::Tensor dy_tensor = grad_output[0]; + // allocate output tensors + torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); + // compute + TabulateFusionSeRGradForward(table_tensor, table_info_tensor, em_tensor, + dy_tensor, descriptor_tensor, dy_dem_tensor); + + return {at::Tensor(), at::Tensor(), dy_dem_tensor, at::Tensor()}; + } +}; std::vector tabluate_fusion_se_a( const torch::Tensor& table_tensor, @@ -592,4 +941,42 @@ std::vector tabluate_fusion_se_a( em_tensor, last_layer_size); } +std::vector tabluate_fusion_se_atten( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& two_embed_tensor, + int64_t last_layer_size, + bool is_sorted +){ + return TabulateFusionSeAttenOp::apply(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, two_embed_tensor, last_layer_size, + is_sorted); +} + +std::vector tabluate_fusion_se_t( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size +){ + return TabulateFusionSeTOp::apply(table_tensor, table_info_tensor, em_x_tensor, + em_tensor, last_layer_size); +} + +std::vector tabluate_fusion_se_r( + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size +){ + return TabulateFusionSeROp::apply(table_tensor, table_info_tensor, em_tensor, + last_layer_size); +} + TORCH_LIBRARY_FRAGMENT(deepmd, m) { m.def("tabulate_fusion_se_a", tabluate_fusion_se_a); } +TORCH_LIBRARY_FRAGMENT(deepmd, m) { m.def("tabulate_fusion_se_atten", tabluate_fusion_se_atten); } +TORCH_LIBRARY_FRAGMENT(deepmd, m) { m.def("tabulate_fusion_se_t", tabluate_fusion_se_t); } +TORCH_LIBRARY_FRAGMENT(deepmd, m) { m.def("tabulate_fusion_se_r", tabluate_fusion_se_r); } \ No newline at end of file From 9fc3fb0d6d89a41403c4918f5015069f7d6dbbc9 Mon Sep 17 00:00:00 2001 From: wangyan Date: Mon, 10 Jun 2024 20:06:05 +0800 Subject: [PATCH 04/85] compile safe version --- source/op/pt/tabulate_multi_device.cc | 40 ++++++++++++++------------- 1 file changed, 21 insertions(+), 19 deletions(-) diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index cf60037d17..50b70d3b22 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -36,7 +36,7 @@ void TabulateFusionSeAForward( throw std::invalid_argument("Dim of input should be 2"); if(em_tensor.dim() != 3) throw std::invalid_argument("Dim of input should be 3"); - if(two_embed_tensor == nullptr || two_embed_tensor.dim() != 2) + if(two_embed_tensor.defined() && two_embed_tensor.dim() != 2) throw std::invalid_argument("Dim of input should be 2"); // get the device std::string device; @@ -48,7 +48,7 @@ void TabulateFusionSeAForward( const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); - const FPTYPE* two_embed = (two_embed_tensor == nullptr) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); + const FPTYPE* two_embed = (!two_embed_tensor.defined()) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); const int64_t nloc = em_tensor.size(0); const int64_t nnei = em_tensor.size(1); @@ -92,13 +92,13 @@ void TabulateFusionSeAGradForward( // flat the tensors FPTYPE* dy_dem_x = dy_dem_x_tensor.view({-1}).data_ptr(); FPTYPE* dy_dem = dy_dem_tensor.view({-1}).data_ptr(); - FPTYPE* dy_dtwo = (dy_dtwo_tensor == nullptr) ? nullptr : dy_dtwo_tensor.view({-1}).data_ptr(); + FPTYPE* dy_dtwo = (!dy_dtwo_tensor.defined()) ? nullptr : dy_dtwo_tensor.view({-1}).data_ptr(); const FPTYPE* table = table_tensor.view({-1}).data_ptr(); const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); - const FPTYPE* two_embed = (two_embed_tensor == nullptr) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); + const FPTYPE* two_embed = (!two_embed_tensor.defined()) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); const FPTYPE* dy = dy_tensor.view({-1}).data_ptr(); const FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); const int64_t nloc = em_tensor.size(0); @@ -151,10 +151,10 @@ void TabulateFusionSeAGradGradForward( const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); - const FPTYPE* two_embed = (two_embed_tensor == nullptr) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); + const FPTYPE* two_embed = (!two_embed_tensor.defined()) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); const FPTYPE* dz_dy_dem_x = dz_dy_dem_x_tensor.view({-1}).data_ptr(); const FPTYPE* dz_dy_dem = dz_dy_dem_tensor.view({-1}).data_ptr(); - const FPTYPE* dz_dy_dtwo = (dz_dy_dtwo_tensor == nullptr) ? nullptr : dz_dy_dtwo_tensor.view({-1}).data_ptr(); + const FPTYPE* dz_dy_dtwo = (!dz_dy_dtwo_tensor.defined()) ? nullptr : dz_dy_dtwo_tensor.view({-1}).data_ptr(); const int64_t nloc = em_tensor.size(0); const int64_t nnei = em_tensor.size(1); const int64_t last_layer_size = descriptor_tensor.size(2); @@ -417,10 +417,9 @@ void TabulateFusionSeRGradGradForward( deepmd::tabulate_fusion_se_r_grad_grad_gpu( dz_dy, table, table_info, em, dz_dy_dem, nloc, nnei, last_layer_size); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM - OP_REQUIRES(context, (last_layer_size <= 1024), - errors::InvalidArgument( - "In the process of model compression, the size of the " - "last layer of embedding net must be less than 1024!")); + TORCH_CHECK(last_layer_size <= 1024, + "In the process of model compression, the size of the " + "last layer of embedding net must be less than 1024!"); } else if (device == "CPU") { deepmd::tabulate_fusion_se_r_grad_grad_cpu( dz_dy, table, table_info, em, dz_dy_dem, nloc, nnei, last_layer_size); @@ -465,7 +464,7 @@ class TabulateFusionSeAOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, nullptr, last_layer_size, + em_tensor, at::Tensor(), last_layer_size, descriptor_tensor); // save data ctx->save_for_backward({ @@ -500,17 +499,19 @@ class TabulateFusionSeAOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, nullptr, dy_tensor, + em_tensor, two_embed_tensor, dy_tensor, descriptor_tensor, dy_dem_x_tensor, dy_dem_tensor, - nullptr); + dy_dtwo_tensor); return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, at::Tensor()}; } @@ -555,11 +556,12 @@ class TabulateFusionSeAGradOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, nullptr, dy_tensor, + em_tensor, at::Tensor(), dy_tensor, descriptor_tensor, dy_dem_x_tensor, dy_dem_tensor, - nullptr); + dy_dtwo_tensor); // save data ctx->save_for_backward({ table_tensor, table_info_tensor, em_x_tensor, @@ -605,8 +607,8 @@ class TabulateFusionSeAGradOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, nullptr, dz_dy_dem_x_tensor, - dz_dy_dem_tensor, nullptr, descriptor_tensor, + em_tensor, at::Tensor(), dz_dy_dem_x_tensor, + dz_dy_dem_tensor, at::Tensor(), descriptor_tensor, is_sorted, dz_dy_tensor); return torch::autograd::variable_list{at::Tensor(), at::Tensor(), at::Tensor(), @@ -656,8 +658,8 @@ class TabulateFusionSeAGradGradOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, nullptr, dz_dy_dem_x_tensor, - dz_dy_dem_tensor, nullptr, descriptor_tensor, + em_tensor, at::Tensor(), dz_dy_dem_x_tensor, + dz_dy_dem_tensor, at::Tensor(), descriptor_tensor, is_sorted, dz_dy_tensor); return torch::autograd::variable_list{ dz_dy_tensor }; From cab50c9f3382911fef6b8352d4cde1e9a4991837 Mon Sep 17 00:00:00 2001 From: wangyan Date: Mon, 10 Jun 2024 21:00:18 +0800 Subject: [PATCH 05/85] compile safe version --- source/op/pt/tabulate_multi_device.cc | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index 50b70d3b22..053d517fb3 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -118,7 +118,6 @@ void TabulateFusionSeAGradForward( } print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); - print_first_five_elements(dy_dtwo_tensor, "dy_dtwo_tensor"); } @@ -513,6 +512,9 @@ class TabulateFusionSeAOp : public torch::autograd::Function Date: Thu, 13 Jun 2024 11:29:35 +0800 Subject: [PATCH 06/85] se_a & se_atten --- source/op/pt/tabulate_multi_device.cc | 27 +- .../test/op/test_tabulate_fusion_se_atten.py | 706 ++++++++++++++++++ 2 files changed, 720 insertions(+), 13 deletions(-) create mode 100644 source/op/pt/test/op/test_tabulate_fusion_se_atten.py diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index 053d517fb3..c5f744619e 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -82,10 +82,10 @@ void TabulateFusionSeAGradForward( throw std::invalid_argument("Dim of dy_tensor should be 3"); } // check input value - print_first_five_elements(dy_tensor, "dy_tensor"); - print_first_five_elements(descriptor_tensor, "descriptor_tensor"); - print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); - print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); + // print_first_five_elements(dy_tensor, "dy_tensor"); + // print_first_five_elements(descriptor_tensor, "descriptor_tensor"); + // print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); + // print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); // get the device std::string device; GetTensorDevice(table_tensor, device); @@ -116,8 +116,8 @@ void TabulateFusionSeAGradForward( table_info, em_x, em, two_embed, dy, nloc, nnei, last_layer_size); } - print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); - print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); + // print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); + // print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); } @@ -501,7 +501,8 @@ class TabulateFusionSeAOp : public torch::autograd::Function Date: Thu, 13 Jun 2024 12:04:46 +0800 Subject: [PATCH 07/85] se_r --- .../pt/test/op/test_tabulate_fusion_se_r.py | 618 ++++++++++++++++++ 1 file changed, 618 insertions(+) create mode 100644 source/op/pt/test/op/test_tabulate_fusion_se_r.py diff --git a/source/op/pt/test/op/test_tabulate_fusion_se_r.py b/source/op/pt/test/op/test_tabulate_fusion_se_r.py new file mode 100644 index 0000000000..74a5697fa2 --- /dev/null +++ b/source/op/pt/test/op/test_tabulate_fusion_se_r.py @@ -0,0 +1,618 @@ +import torch +import unittest +import deepmd.pt.cxx_op + +class TestTabulateFusionSeAOp(unittest.TestCase): + + def setUp(self): + self.table_tensor = torch.tensor([ + 6.348551343037398542e-01, 4.209465843706336474e-04, + 6.390862740714405368e-03, -1.544448595628262176e-04, + -1.891095227974180087e-04, 2.695025951562175852e-05, + -1.317549846042939343e+00, -5.624478206903206490e-02, + 1.274284553146523905e-02, -6.836227424141475689e-04, + -1.438066096020836407e-04, -1.854932873974712940e-06, + -9.996964112615246423e-01, 6.928234423723647617e-02, 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-5.102126473064734317e-03, -8.551069374443776396e-05, + 8.618032279329005427e-05, -1.422030758858379208e-05, + 3.444418516979214084e-01, -3.631195473807800889e-02, + -4.625381215785304145e-04, 8.881537622047225473e-04, + -1.080757789189670570e-04, 5.820590714360855199e-08, + 1.240361649325028681e+00, 4.302664794411619614e-02, + 3.137220402938139478e-03, -5.615677039256951981e-04, + -9.125763978623760322e-05, 2.367398552885374808e-05, + -1.519498310980496925e+00, -1.512290469691385253e-01, + 1.652996628226939199e-02, -3.745688059096337011e-05, + -1.938906911473592626e-04, 1.811217640451412989e-05, + -2.564062357251438717e-01, 9.626832379335603651e-02, + -2.771163091665611831e-03, -6.829069315554202020e-04, + 3.363238372709415958e-05, 8.623099725596635004e-06, + -1.487093617252511990e+00, -6.073523464295225993e-02, + 4.823154268625621383e-03, 7.122599345182346051e-05, + -1.664931178025436733e-05, -4.312450972708557703e-06 + ]).reshape(8,132) + self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1]) + self.em_tensor = torch.tensor([0.0343909, 0.11357423, 0.0858676, 0.19337772, + 0.1935728, 0.0477744, 0.05845198, 0.19080509, + 0.16111261, 0.07179262, 0.10078013, 0.04640909, + 0.10433399, 0.15650861, 0.17527857, 0.04249097]).reshape(4,4) + self.table_info_tensor.requires_grad = True + self.table_tensor.requires_grad = True + self.em_tensor.requires_grad = True + self.last_layer_size = 8 + self.nloc = 4 + self.nnei = 4 + # forward test + self.expected_descriptor_tensor = torch.tensor([0.634877, -1.319469, -0.997320, 0.354037, 1.229165, -1.478165, + -0.282159, -1.470623, 0.634985, -1.323774, -0.991892, 0.351189, + 1.232453, -1.490731, -0.274445, -1.475604, 0.634938, -1.322286, + -0.993784, 0.352187, 1.231297, -1.486357, -0.277141, -1.473868, + 0.635174, -1.327955, -0.986486, 0.348307, 1.235810, -1.503186, + -0.266701, -1.480563, 0.635175, -1.327965, -0.986473, 0.348300, + 1.235819, -1.503216, -0.266682, -1.480575, 0.634890, -1.320208, + -0.996398, 0.353557, 1.229717, -1.480303, -0.280853, -1.471469, + 0.634902, -1.320794, -0.995664, 0.353173, 1.230159, -1.482005, + -0.279812, -1.472143, 0.635167, -1.327823, -0.986659, 0.348400, + 1.235701, -1.502788, -0.266950, -1.480404, 0.635088, -1.326284, + -0.988664, 0.349474, 1.234448, -1.498176, -0.269828, -1.478565, + 0.634918, -1.321522, -0.994748, 0.352694, 1.230712, -1.484126, + -0.278511, -1.472983, 0.634962, -1.323089, -0.992765, 0.351650, + 1.231919, -1.488714, -0.275689, -1.474803, 0.634888, -1.320133, + -0.996492, 0.353606, 1.229661, -1.480085, -0.280986, -1.471383, + 0.634968, -1.323280, -0.992522, 0.351522, 1.232067, -1.489275, + -0.275344, -1.475026, 0.635077, -1.326043, -0.988976, 0.349640, + 1.234254, -1.497458, -0.270275, -1.478280, 0.635124, -1.327021, + -0.987707, 0.348962, 1.235045, -1.500380, -0.268455, -1.479444, + 0.634885, -1.319917, -0.996762, 0.353746, 1.229499, -1.479460, + -0.281368, -1.471135]).reshape(4,4,8) + # backward test + self.expected_dy_dem = torch.tensor([-0.105883, -0.100297, -0.102247, -0.094712, -0.094698, -0.104937, + -0.104182, -0.094891, -0.096964, -0.103240, -0.101197, -0.105033, + -0.100947, -0.097286, -0.095974, -0.105310]).reshape(4,4) + + def test_forward(self): + # Call the forward function + forward_result = torch.ops.deepmd.tabulate_fusion_se_r( + self.table_tensor, self.table_info_tensor, self.em_tensor, self.last_layer_size + ) + + descriptor_tensor = forward_result[0] + + # Check the shape + self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) + + # Check the values + self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) + + def test_backward(self): + # Call the forward function + forward_result = torch.ops.deepmd.tabulate_fusion_se_r( + self.table_tensor, self.table_info_tensor, self.em_tensor, self.last_layer_size + ) + + descriptor_tensor = forward_result[0] + + # Check the forward + self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) + + + # Create a loss and perform backward + loss = descriptor_tensor.sum() + loss.backward() + + # Check gradients + self.assertIsNotNone(self.em_tensor.grad) + + # Check the shapes of the gradients + self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) + + # Check the values of the gradients + self.assertTrue(torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5)) + +if __name__ == '__main__': + deepmd.pt.cxx_op.ENABLE_CUSTOMIZED_OP + unittest.main() \ No newline at end of file From c09a7a77e6d3f6524116415e75efb08391a10114 Mon Sep 17 00:00:00 2001 From: wangyan Date: Thu, 13 Jun 2024 12:06:24 +0800 Subject: [PATCH 08/85] remove print --- source/op/pt/tabulate_multi_device.cc | 4 +- .../pt/test/op/test_tabulate_fusion_se_a.py | 12 - .../pt/test/op/test_tabulate_fusion_se_t.py | 5237 +++++++++++++++++ 3 files changed, 5239 insertions(+), 14 deletions(-) create mode 100644 source/op/pt/test/op/test_tabulate_fusion_se_t.py diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index c5f744619e..0dd734c2b2 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -841,7 +841,7 @@ class TabulateFusionSeTOp : public torch::autograd::Function Date: Thu, 13 Jun 2024 19:24:48 +0800 Subject: [PATCH 09/85] move pt op test --- source/{op/pt/test/op => tests/pt}/test_tabulate_fusion_se_a.py | 0 .../{op/pt/test/op => tests/pt}/test_tabulate_fusion_se_atten.py | 0 source/{op/pt/test/op => tests/pt}/test_tabulate_fusion_se_r.py | 0 source/{op/pt/test/op => tests/pt}/test_tabulate_fusion_se_t.py | 0 4 files changed, 0 insertions(+), 0 deletions(-) rename source/{op/pt/test/op => tests/pt}/test_tabulate_fusion_se_a.py (100%) rename source/{op/pt/test/op => tests/pt}/test_tabulate_fusion_se_atten.py (100%) rename source/{op/pt/test/op => tests/pt}/test_tabulate_fusion_se_r.py (100%) rename source/{op/pt/test/op => tests/pt}/test_tabulate_fusion_se_t.py (100%) diff --git a/source/op/pt/test/op/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py similarity index 100% rename from source/op/pt/test/op/test_tabulate_fusion_se_a.py rename to source/tests/pt/test_tabulate_fusion_se_a.py diff --git a/source/op/pt/test/op/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py similarity index 100% rename from source/op/pt/test/op/test_tabulate_fusion_se_atten.py rename to source/tests/pt/test_tabulate_fusion_se_atten.py diff --git a/source/op/pt/test/op/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py similarity index 100% rename from source/op/pt/test/op/test_tabulate_fusion_se_r.py rename to source/tests/pt/test_tabulate_fusion_se_r.py diff --git a/source/op/pt/test/op/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py similarity index 100% rename from source/op/pt/test/op/test_tabulate_fusion_se_t.py rename to source/tests/pt/test_tabulate_fusion_se_t.py From 763c7b43ae2a6bb7a65be04ba5d84af90826fb11 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 13 Jun 2024 11:41:30 +0000 Subject: [PATCH 10/85] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- source/op/pt/tabulate_multi_device.cc | 1194 +- source/tests/pt/test_tabulate_fusion_se_a.py | 2007 +- .../tests/pt/test_tabulate_fusion_se_atten.py | 2198 +- source/tests/pt/test_tabulate_fusion_se_r.py | 1834 +- source/tests/pt/test_tabulate_fusion_se_t.py | 17404 +++++++++++----- 5 files changed, 16993 insertions(+), 7644 deletions(-) diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index 0dd734c2b2..9a6a595e78 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -1,43 +1,50 @@ +// SPDX-License-Identifier: LGPL-3.0-or-later #include -#include "tabulate.h" -#include + #include +#include + +#include "tabulate.h" #if defined(GOOGLE_CUDA) || defined(TENSORFLOW_USE_ROCM) #include "device.h" #endif -void GetTensorDevice(const torch::Tensor& t, std::string& str){ - if(t.device().is_cuda()) +void GetTensorDevice(const torch::Tensor& t, std::string& str) { + if (t.device().is_cuda()) { str = "GPU"; - else + } else { str = "CPU"; + } } -void print_first_five_elements(const torch::Tensor& tensor, const std::string& name) { - std::cout << "First five elements of " << name << ":" << std::endl; - std::cout << tensor.slice(0, 0, 5) << std::endl; +void print_first_five_elements(const torch::Tensor& tensor, + const std::string& name) { + std::cout << "First five elements of " << name << ":" << std::endl; + std::cout << tensor.slice(0, 0, 5) << std::endl; } template -void TabulateFusionSeAForward( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_x_tensor, - const torch::Tensor& em_tensor, - const torch::Tensor& two_embed_tensor, - int64_t last_layer_size, - torch::Tensor& descriptor_tensor -){ +void TabulateFusionSeAForward(const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& two_embed_tensor, + int64_t last_layer_size, + torch::Tensor& descriptor_tensor) { // check input shape - if(table_tensor.dim() != 2) + if (table_tensor.dim() != 2) { throw std::invalid_argument("Dim of table should be 2"); - if(em_x_tensor.dim() != 2) + } + if (em_x_tensor.dim() != 2) { throw std::invalid_argument("Dim of input should be 2"); - if(em_tensor.dim() != 3) + } + if (em_tensor.dim() != 3) { throw std::invalid_argument("Dim of input should be 3"); - if(two_embed_tensor.defined() && two_embed_tensor.dim() != 2) + } + if (two_embed_tensor.defined() && two_embed_tensor.dim() != 2) { throw std::invalid_argument("Dim of input should be 2"); + } // get the device std::string device; GetTensorDevice(table_tensor, device); @@ -48,35 +55,36 @@ void TabulateFusionSeAForward( const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); - const FPTYPE* two_embed = (!two_embed_tensor.defined()) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); + const FPTYPE* two_embed = + (!two_embed_tensor.defined()) + ? nullptr + : two_embed_tensor.view({-1}).data_ptr(); const int64_t nloc = em_tensor.size(0); const int64_t nnei = em_tensor.size(1); // compute if (device == "GPU") { #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_a_gpu(descriptor, table, table_info, em_x, em, - two_embed, nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_a_gpu(descriptor, table, table_info, em_x, em, + two_embed, nloc, nnei, last_layer_size); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM } else if (device == "CPU") { deepmd::tabulate_fusion_se_a_cpu(descriptor, table, table_info, em_x, em, - two_embed, nloc, nnei, last_layer_size); + two_embed, nloc, nnei, last_layer_size); } } template -void TabulateFusionSeAGradForward( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_x_tensor, - const torch::Tensor& em_tensor, - const torch::Tensor& two_embed_tensor, - const torch::Tensor& dy_tensor, - const torch::Tensor& descriptor_tensor, - torch::Tensor& dy_dem_x_tensor, - torch::Tensor& dy_dem_tensor, - torch::Tensor& dy_dtwo_tensor -){ +void TabulateFusionSeAGradForward(const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& two_embed_tensor, + const torch::Tensor& dy_tensor, + const torch::Tensor& descriptor_tensor, + torch::Tensor& dy_dem_x_tensor, + torch::Tensor& dy_dem_tensor, + torch::Tensor& dy_dtwo_tensor) { // check input shape if (dy_tensor.dim() != 3) { throw std::invalid_argument("Dim of dy_tensor should be 3"); @@ -92,13 +100,18 @@ void TabulateFusionSeAGradForward( // flat the tensors FPTYPE* dy_dem_x = dy_dem_x_tensor.view({-1}).data_ptr(); FPTYPE* dy_dem = dy_dem_tensor.view({-1}).data_ptr(); - FPTYPE* dy_dtwo = (!dy_dtwo_tensor.defined()) ? nullptr : dy_dtwo_tensor.view({-1}).data_ptr(); + FPTYPE* dy_dtwo = (!dy_dtwo_tensor.defined()) + ? nullptr + : dy_dtwo_tensor.view({-1}).data_ptr(); const FPTYPE* table = table_tensor.view({-1}).data_ptr(); const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); - const FPTYPE* two_embed = (!two_embed_tensor.defined()) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); + const FPTYPE* two_embed = + (!two_embed_tensor.defined()) + ? nullptr + : two_embed_tensor.view({-1}).data_ptr(); const FPTYPE* dy = dy_tensor.view({-1}).data_ptr(); const FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); const int64_t nloc = em_tensor.size(0); @@ -107,9 +120,9 @@ void TabulateFusionSeAGradForward( // compute if (device == "GPU") { #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_a_grad_gpu(dy_dem_x, dy_dem, dy_dtwo, table, - table_info, em_x, em, two_embed, dy, - nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_a_grad_gpu(dy_dem_x, dy_dem, dy_dtwo, table, + table_info, em_x, em, two_embed, dy, + nloc, nnei, last_layer_size); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM } else if (device == "CPU") { deepmd::tabulate_fusion_se_a_grad_cpu(dy_dem_x, dy_dem, dy_dtwo, table, @@ -118,28 +131,27 @@ void TabulateFusionSeAGradForward( } // print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); // print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); - } template -void TabulateFusionSeAGradGradForward( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_x_tensor, - const torch::Tensor& em_tensor, - const torch::Tensor& two_embed_tensor, - const torch::Tensor& dz_dy_dem_x_tensor, - const torch::Tensor& dz_dy_dem_tensor, - const torch::Tensor& dz_dy_dtwo_tensor, - const torch::Tensor& descriptor_tensor, - bool is_sorted, - torch::Tensor& dz_dy_tensor -){ +void TabulateFusionSeAGradGradForward(const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& two_embed_tensor, + const torch::Tensor& dz_dy_dem_x_tensor, + const torch::Tensor& dz_dy_dem_tensor, + const torch::Tensor& dz_dy_dtwo_tensor, + const torch::Tensor& descriptor_tensor, + bool is_sorted, + torch::Tensor& dz_dy_tensor) { // Check input shape - if (dz_dy_dem_x_tensor.dim() != 2) + if (dz_dy_dem_x_tensor.dim() != 2) { throw std::invalid_argument("Dim of dz_dy_dem_x should be 2"); - if (dz_dy_dem_tensor.dim() != 3) + } + if (dz_dy_dem_tensor.dim() != 3) { throw std::invalid_argument("Dim of dz_dy_dem should be 3"); + } // get the device std::string device; GetTensorDevice(table_tensor, device); @@ -150,21 +162,27 @@ void TabulateFusionSeAGradGradForward( const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); - const FPTYPE* two_embed = (!two_embed_tensor.defined()) ? nullptr : two_embed_tensor.view({-1}).data_ptr(); + const FPTYPE* two_embed = + (!two_embed_tensor.defined()) + ? nullptr + : two_embed_tensor.view({-1}).data_ptr(); const FPTYPE* dz_dy_dem_x = dz_dy_dem_x_tensor.view({-1}).data_ptr(); const FPTYPE* dz_dy_dem = dz_dy_dem_tensor.view({-1}).data_ptr(); - const FPTYPE* dz_dy_dtwo = (!dz_dy_dtwo_tensor.defined()) ? nullptr : dz_dy_dtwo_tensor.view({-1}).data_ptr(); + const FPTYPE* dz_dy_dtwo = + (!dz_dy_dtwo_tensor.defined()) + ? nullptr + : dz_dy_dtwo_tensor.view({-1}).data_ptr(); const int64_t nloc = em_tensor.size(0); const int64_t nnei = em_tensor.size(1); const int64_t last_layer_size = descriptor_tensor.size(2); // compute if (device == "GPU") { #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_a_grad_grad_gpu( - dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, - dz_dy_dtwo, nloc, nnei, last_layer_size, is_sorted); + deepmd::tabulate_fusion_se_a_grad_grad_gpu( + dz_dy, table, table_info, em_x, em, two_embed, dz_dy_dem_x, dz_dy_dem, + dz_dy_dtwo, nloc, nnei, last_layer_size, is_sorted); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM - TORCH_CHECK(last_layer_size <= 1024, + TORCH_CHECK(last_layer_size <= 1024, "In the process of model compression, the size of the " "last layer of embedding net must be less than 1024!"); } else if (device == "CPU") { @@ -175,21 +193,22 @@ void TabulateFusionSeAGradGradForward( } template -void TabulateFusionSeTForward( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_x_tensor, - const torch::Tensor& em_tensor, - int64_t last_layer_size, - torch::Tensor& descriptor_tensor -){ +void TabulateFusionSeTForward(const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size, + torch::Tensor& descriptor_tensor) { // check input shape - if(table_tensor.dim() != 2) + if (table_tensor.dim() != 2) { throw std::invalid_argument("Dim of table should be 2"); - if(em_x_tensor.dim() != 2) + } + if (em_x_tensor.dim() != 2) { throw std::invalid_argument("Dim of input should be 2"); - if(em_tensor.dim() != 3) + } + if (em_tensor.dim() != 3) { throw std::invalid_argument("Dim of input should be 3"); + } // get the device std::string device; GetTensorDevice(table_tensor, device); @@ -206,26 +225,24 @@ void TabulateFusionSeTForward( // compute if (device == "GPU") { #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_t_gpu(descriptor, table, table_info, em_x, em, - nloc, nnei_i, nnei_j, last_layer_size); + deepmd::tabulate_fusion_se_t_gpu(descriptor, table, table_info, em_x, em, + nloc, nnei_i, nnei_j, last_layer_size); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM - } else if (device == "CPU") { - deepmd::tabulate_fusion_se_t_cpu(descriptor, table, table_info, em_x, em, - nloc, nnei_i, nnei_j, last_layer_size); - } + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_t_cpu(descriptor, table, table_info, em_x, em, + nloc, nnei_i, nnei_j, last_layer_size); + } } template -void TabulateFusionSeTGradForward( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_x_tensor, - const torch::Tensor& em_tensor, - const torch::Tensor& dy_tensor, - const torch::Tensor& descriptor_tensor, - torch::Tensor& dy_dem_x_tensor, - torch::Tensor& dy_dem_tensor -){ +void TabulateFusionSeTGradForward(const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dy_tensor, + const torch::Tensor& descriptor_tensor, + torch::Tensor& dy_dem_x_tensor, + torch::Tensor& dy_dem_tensor) { // check input shape if (dy_tensor.dim() != 2) { throw std::invalid_argument("Dim of dy_tensor should be 2"); @@ -250,33 +267,33 @@ void TabulateFusionSeTGradForward( // compute if (device == "GPU") { #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_t_grad_gpu(dy_dem_x, dy_dem, table, table_info, - em_x, em, dy, nloc, nnei_i, nnei_j, - last_layer_size); + deepmd::tabulate_fusion_se_t_grad_gpu(dy_dem_x, dy_dem, table, table_info, + em_x, em, dy, nloc, nnei_i, nnei_j, + last_layer_size); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM - } else if (device == "CPU") { - deepmd::tabulate_fusion_se_t_grad_cpu(dy_dem_x, dy_dem, table, table_info, - em_x, em, dy, nloc, nnei_i, nnei_j, - last_layer_size); - } + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_t_grad_cpu(dy_dem_x, dy_dem, table, table_info, + em_x, em, dy, nloc, nnei_i, nnei_j, + last_layer_size); + } } template -void TabulateFusionSeTGradGradForward( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_x_tensor, - const torch::Tensor& em_tensor, - const torch::Tensor& dz_dy_dem_x_tensor, - const torch::Tensor& dz_dy_dem_tensor, - const torch::Tensor& descriptor_tensor, - torch::Tensor& dz_dy_tensor -){ +void TabulateFusionSeTGradGradForward(const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dz_dy_dem_x_tensor, + const torch::Tensor& dz_dy_dem_tensor, + const torch::Tensor& descriptor_tensor, + torch::Tensor& dz_dy_tensor) { // Check input shape - if (dz_dy_dem_x_tensor.dim() != 2) + if (dz_dy_dem_x_tensor.dim() != 2) { throw std::invalid_argument("Dim of dz_dy_dem_x should be 2"); - if (dz_dy_dem_tensor.dim() != 3) + } + if (dz_dy_dem_tensor.dim() != 3) { throw std::invalid_argument("Dim of dz_dy_dem should be 3"); + } // get the device std::string device; GetTensorDevice(table_tensor, device); @@ -296,33 +313,33 @@ void TabulateFusionSeTGradGradForward( // compute if (device == "GPU") { #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_t_grad_grad_gpu( - dz_dy, table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, nloc, - nnei_i, nnei_j, last_layer_size); + deepmd::tabulate_fusion_se_t_grad_grad_gpu(dz_dy, table, table_info, em_x, + em, dz_dy_dem_x, dz_dy_dem, nloc, + nnei_i, nnei_j, last_layer_size); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM - TORCH_CHECK(last_layer_size <= 1024, + TORCH_CHECK(last_layer_size <= 1024, "In the process of model compression, the size of the " "last layer of embedding net must be less than 1024!"); - } else if (device == "CPU") { - deepmd::tabulate_fusion_se_t_grad_grad_cpu( - dz_dy, table, table_info, em_x, em, dz_dy_dem_x, dz_dy_dem, nloc, - nnei_i, nnei_j, last_layer_size); - } + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_t_grad_grad_cpu(dz_dy, table, table_info, em_x, + em, dz_dy_dem_x, dz_dy_dem, nloc, + nnei_i, nnei_j, last_layer_size); + } } template -void TabulateFusionSeRForward( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_tensor, - int64_t last_layer_size, - torch::Tensor& descriptor_tensor -){ +void TabulateFusionSeRForward(const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size, + torch::Tensor& descriptor_tensor) { // check input shape - if(table_tensor.dim() != 2) + if (table_tensor.dim() != 2) { throw std::invalid_argument("Dim of table should be 2"); - if(em_tensor.dim() != 2) + } + if (em_tensor.dim() != 2) { throw std::invalid_argument("Dim of input should be 2"); + } // get the device std::string device; GetTensorDevice(table_tensor, device); @@ -337,24 +354,22 @@ void TabulateFusionSeRForward( // compute if (device == "GPU") { #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_r_gpu(descriptor, table, table_info, em, nloc, - nnei, last_layer_size); + deepmd::tabulate_fusion_se_r_gpu(descriptor, table, table_info, em, nloc, + nnei, last_layer_size); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM - } else if (device == "CPU") { - deepmd::tabulate_fusion_se_r_cpu(descriptor, table, table_info, em, nloc, - nnei, last_layer_size); - } + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_r_cpu(descriptor, table, table_info, em, nloc, + nnei, last_layer_size); + } } template -void TabulateFusionSeRGradForward( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_tensor, - const torch::Tensor& dy_tensor, - const torch::Tensor& descriptor_tensor, - torch::Tensor& dy_dem_tensor -){ +void TabulateFusionSeRGradForward(const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dy_tensor, + const torch::Tensor& descriptor_tensor, + torch::Tensor& dy_dem_tensor) { // check input shape if (dy_tensor.dim() != 3) { throw std::invalid_argument("Dim of dy_tensor should be 3"); @@ -376,27 +391,26 @@ void TabulateFusionSeRGradForward( // compute if (device == "GPU") { #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_r_grad_gpu(dy_dem, table, table_info, em, dy, - nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_r_grad_gpu(dy_dem, table, table_info, em, dy, + nloc, nnei, last_layer_size); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM - } else if (device == "CPU") { - deepmd::tabulate_fusion_se_r_grad_cpu(dy_dem, table, table_info, em, dy, - nloc, nnei, last_layer_size); - } + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_r_grad_cpu(dy_dem, table, table_info, em, dy, + nloc, nnei, last_layer_size); + } } template -void TabulateFusionSeRGradGradForward( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_tensor, - const torch::Tensor& dz_dy_dem_tensor, - const torch::Tensor& descriptor_tensor, - torch::Tensor& dz_dy_tensor -){ +void TabulateFusionSeRGradGradForward(const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& dz_dy_dem_tensor, + const torch::Tensor& descriptor_tensor, + torch::Tensor& dz_dy_tensor) { // Check input shape - if (dz_dy_dem_tensor.dim() != 2) + if (dz_dy_dem_tensor.dim() != 2) { throw std::invalid_argument("Dim of dz_dy_dem should be 2"); + } // get the device std::string device; GetTensorDevice(table_tensor, device); @@ -413,216 +427,206 @@ void TabulateFusionSeRGradGradForward( // compute if (device == "GPU") { #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM - deepmd::tabulate_fusion_se_r_grad_grad_gpu( - dz_dy, table, table_info, em, dz_dy_dem, nloc, nnei, last_layer_size); + deepmd::tabulate_fusion_se_r_grad_grad_gpu( + dz_dy, table, table_info, em, dz_dy_dem, nloc, nnei, last_layer_size); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM - TORCH_CHECK(last_layer_size <= 1024, + TORCH_CHECK(last_layer_size <= 1024, "In the process of model compression, the size of the " "last layer of embedding net must be less than 1024!"); - } else if (device == "CPU") { - deepmd::tabulate_fusion_se_r_grad_grad_cpu( - dz_dy, table, table_info, em, dz_dy_dem, nloc, nnei, last_layer_size); - } + } else if (device == "CPU") { + deepmd::tabulate_fusion_se_r_grad_grad_cpu( + dz_dy, table, table_info, em, dz_dy_dem, nloc, nnei, last_layer_size); + } } -class TabulateFusionSeAOp : public torch::autograd::Function { - public: - static torch::autograd::variable_list forward( - torch::autograd::AutogradContext* ctx, - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_x_tensor, - const torch::Tensor& em_tensor, - int64_t last_layer_size - ){ - bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; - if(type_flag) { - return forward_t(ctx, table_tensor, table_info_tensor, - em_x_tensor, em_tensor, last_layer_size); - } else { - return forward_t(ctx, table_tensor, table_info_tensor, - em_x_tensor, em_tensor, last_layer_size); - } +class TabulateFusionSeAOp + : public torch::autograd::Function { + public: + static torch::autograd::variable_list forward( + torch::autograd::AutogradContext* ctx, + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size) { + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if (type_flag) { + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, last_layer_size); + } else { + return forward_t(ctx, table_tensor, table_info_tensor, em_x_tensor, + em_tensor, last_layer_size); } + } - template - static torch::autograd::variable_list forward_t( + template + static torch::autograd::variable_list forward_t( torch::autograd::AutogradContext* ctx, const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, const torch::Tensor& em_x_tensor, const torch::Tensor& em_tensor, - int64_t last_layer_size - ){ - // allocate output tensors - auto options = - torch::TensorOptions() - .dtype(table_tensor.dtype()) - .device(table_tensor.device()); - torch::Tensor descriptor_tensor = torch::empty( - {em_tensor.size(0), 4, last_layer_size}, options); - // compute - TabulateFusionSeAForward(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, at::Tensor(), last_layer_size, - descriptor_tensor); - // save data - ctx->save_for_backward({ - table_tensor, table_info_tensor, em_x_tensor, - em_tensor, descriptor_tensor - }); - return { descriptor_tensor }; - } + int64_t last_layer_size) { + // allocate output tensors + auto options = torch::TensorOptions() + .dtype(table_tensor.dtype()) + .device(table_tensor.device()); + torch::Tensor descriptor_tensor = + torch::empty({em_tensor.size(0), 4, last_layer_size}, options); + // compute + TabulateFusionSeAForward(table_tensor, table_info_tensor, + em_x_tensor, em_tensor, at::Tensor(), + last_layer_size, descriptor_tensor); + // save data + ctx->save_for_backward({table_tensor, table_info_tensor, em_x_tensor, + em_tensor, descriptor_tensor}); + return {descriptor_tensor}; + } - static torch::autograd::variable_list backward( - torch::autograd::AutogradContext* ctx, - torch::autograd::variable_list grad_output - ){ - torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); - torch::Tensor table_tensor = saved_variables[0]; - bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; - if(type_flag) { - return backward_t(ctx, grad_output); - } else{ - return backward_t(ctx, grad_output); - } + static torch::autograd::variable_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output) { + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if (type_flag) { + return backward_t(ctx, grad_output); + } else { + return backward_t(ctx, grad_output); } + } - template - static torch::autograd::variable_list backward_t( + template + static torch::autograd::variable_list backward_t( torch::autograd::AutogradContext* ctx, - torch::autograd::variable_list grad_output - ){ - // load data - torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); - torch::Tensor table_tensor = saved_variables[0]; - torch::Tensor table_info_tensor = saved_variables[1]; - torch::Tensor em_x_tensor = saved_variables[2]; - torch::Tensor em_tensor = saved_variables[3]; - torch::Tensor two_embed_tensor = at::Tensor(); - torch::Tensor descriptor_tensor = saved_variables[4]; - - // ensure the gradient output is contiguous - torch::Tensor dy_tensor = grad_output[0].contiguous(); - // allocate output tensors - torch::Tensor dy_dem_x_tensor = torch::zeros_like(em_x_tensor); - torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); - torch::Tensor dy_dtwo_tensor = at::Tensor(); - // compute - TabulateFusionSeAGradForward(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, two_embed_tensor, dy_tensor, - descriptor_tensor, dy_dem_x_tensor, dy_dem_tensor, - dy_dtwo_tensor); - - // std::cout << "----------------------------------------------" << std::endl; - // print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); - // print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); - return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, at::Tensor()}; - } + torch::autograd::variable_list grad_output) { + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + torch::Tensor table_info_tensor = saved_variables[1]; + torch::Tensor em_x_tensor = saved_variables[2]; + torch::Tensor em_tensor = saved_variables[3]; + torch::Tensor two_embed_tensor = at::Tensor(); + torch::Tensor descriptor_tensor = saved_variables[4]; + + // ensure the gradient output is contiguous + torch::Tensor dy_tensor = grad_output[0].contiguous(); + // allocate output tensors + torch::Tensor dy_dem_x_tensor = torch::zeros_like(em_x_tensor); + torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); + torch::Tensor dy_dtwo_tensor = at::Tensor(); + // compute + TabulateFusionSeAGradForward( + table_tensor, table_info_tensor, em_x_tensor, em_tensor, + two_embed_tensor, dy_tensor, descriptor_tensor, dy_dem_x_tensor, + dy_dem_tensor, dy_dtwo_tensor); + + // std::cout << "----------------------------------------------" << + // std::endl; print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); + // print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); + return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, + at::Tensor()}; + } }; -class TabulateFusionSeAGradOp : public torch::autograd::Function { - private: - std::string device; +class TabulateFusionSeAGradOp + : public torch::autograd::Function { + private: + std::string device; - public: - static torch::autograd::variable_list forward( + public: + static torch::autograd::variable_list forward( torch::autograd::AutogradContext* ctx, const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, const torch::Tensor& em_x_tensor, const torch::Tensor& em_tensor, const torch::Tensor& dy_tensor, - const torch::Tensor& descriptor_tensor - ) { - bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; - if(type_flag) { - return forward_t(ctx, table_tensor, table_info_tensor, - em_x_tensor, em_tensor, dy_tensor, - descriptor_tensor); - } else{ - return forward_t(ctx, table_tensor, table_info_tensor, - em_x_tensor, em_tensor, dy_tensor, - descriptor_tensor); - } + const torch::Tensor& descriptor_tensor) { + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if (type_flag) { + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, dy_tensor, + descriptor_tensor); + } else { + return forward_t(ctx, table_tensor, table_info_tensor, em_x_tensor, + em_tensor, dy_tensor, descriptor_tensor); } + } - template - static torch::autograd::variable_list forward_t( + template + static torch::autograd::variable_list forward_t( torch::autograd::AutogradContext* ctx, const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, const torch::Tensor& em_x_tensor, const torch::Tensor& em_tensor, const torch::Tensor& dy_tensor, - const torch::Tensor& descriptor_tensor - ){ - // Allocate output tensors - torch::Tensor dy_dem_x_tensor = torch::zeros_like(em_x_tensor); - torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); - torch::Tensor dy_dtwo_tensor = at::Tensor(); - // compute - TabulateFusionSeAGradForward(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, at::Tensor(), dy_tensor, - descriptor_tensor, dy_dem_x_tensor, dy_dem_tensor, - dy_dtwo_tensor); - // save data - ctx->save_for_backward({ - table_tensor, table_info_tensor, em_x_tensor, - em_tensor, descriptor_tensor - }); - - return torch::autograd::variable_list{ dy_dem_x_tensor, dy_dem_tensor }; - } - - static torch::autograd::variable_list backward( - torch::autograd::AutogradContext* ctx, - torch::autograd::variable_list grad_output - ){ - // load data - torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); - torch::Tensor table_tensor = saved_variables[0]; - bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; - if(type_flag) { - return backward_t(ctx, grad_output); - } else{ - return backward_t(ctx, grad_output); - } - } + const torch::Tensor& descriptor_tensor) { + // Allocate output tensors + torch::Tensor dy_dem_x_tensor = torch::zeros_like(em_x_tensor); + torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); + torch::Tensor dy_dtwo_tensor = at::Tensor(); + // compute + TabulateFusionSeAGradForward( + table_tensor, table_info_tensor, em_x_tensor, em_tensor, at::Tensor(), + dy_tensor, descriptor_tensor, dy_dem_x_tensor, dy_dem_tensor, + dy_dtwo_tensor); + // save data + ctx->save_for_backward({table_tensor, table_info_tensor, em_x_tensor, + em_tensor, descriptor_tensor}); + + return torch::autograd::variable_list{dy_dem_x_tensor, dy_dem_tensor}; + } - template - static torch::autograd::variable_list backward_t( - torch::autograd::AutogradContext* ctx, - torch::autograd::variable_list grad_output - ){ - // load data - torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); - torch::Tensor table_tensor = saved_variables[0]; - torch::Tensor table_info_tensor = saved_variables[1]; - torch::Tensor em_x_tensor = saved_variables[2]; - torch::Tensor em_tensor = saved_variables[3]; - torch::Tensor descriptor_tensor = saved_variables[4]; - - bool is_sorted = true; - - torch::Tensor dz_dy_dem_x_tensor = grad_output[0].contiguous(); - torch::Tensor dz_dy_dem_tensor = grad_output[1].contiguous(); - // allocate output tensors - torch::Tensor dz_dy_tensor = torch::empty_like(descriptor_tensor); - // compute - TabulateFusionSeAGradGradForward(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, at::Tensor(), dz_dy_dem_x_tensor, - dz_dy_dem_tensor, at::Tensor(), descriptor_tensor, - is_sorted, dz_dy_tensor); - - return torch::autograd::variable_list{at::Tensor(), at::Tensor(), at::Tensor(), - at::Tensor(), dz_dy_tensor, at::Tensor()}; + static torch::autograd::variable_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output) { + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if (type_flag) { + return backward_t(ctx, grad_output); + } else { + return backward_t(ctx, grad_output); } + } + template + static torch::autograd::variable_list backward_t( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output) { + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + torch::Tensor table_info_tensor = saved_variables[1]; + torch::Tensor em_x_tensor = saved_variables[2]; + torch::Tensor em_tensor = saved_variables[3]; + torch::Tensor descriptor_tensor = saved_variables[4]; + + bool is_sorted = true; + + torch::Tensor dz_dy_dem_x_tensor = grad_output[0].contiguous(); + torch::Tensor dz_dy_dem_tensor = grad_output[1].contiguous(); + // allocate output tensors + torch::Tensor dz_dy_tensor = torch::empty_like(descriptor_tensor); + // compute + TabulateFusionSeAGradGradForward( + table_tensor, table_info_tensor, em_x_tensor, em_tensor, at::Tensor(), + dz_dy_dem_x_tensor, dz_dy_dem_tensor, at::Tensor(), descriptor_tensor, + is_sorted, dz_dy_tensor); + + return torch::autograd::variable_list{at::Tensor(), at::Tensor(), + at::Tensor(), at::Tensor(), + dz_dy_tensor, at::Tensor()}; + } }; -class TabulateFusionSeAGradGradOp : public torch::autograd::Function { - public: - static torch::autograd::variable_list forward( +class TabulateFusionSeAGradGradOp + : public torch::autograd::Function { + public: + static torch::autograd::variable_list forward( torch::autograd::AutogradContext* ctx, const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, @@ -631,22 +635,21 @@ class TabulateFusionSeAGradGradOp : public torch::autograd::Function(ctx, table_tensor, table_info_tensor, - em_x_tensor, em_tensor, dz_dy_dem_x_tensor, - dz_dy_dem_tensor, descriptor_tensor, is_sorted); - } else{ - return forward_t(ctx, table_tensor, table_info_tensor, - em_x_tensor, em_tensor, dz_dy_dem_x_tensor, - dz_dy_dem_tensor, descriptor_tensor, is_sorted); - } + bool is_sorted) { + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if (type_flag) { + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, dz_dy_dem_x_tensor, + dz_dy_dem_tensor, descriptor_tensor, is_sorted); + } else { + return forward_t(ctx, table_tensor, table_info_tensor, em_x_tensor, + em_tensor, dz_dy_dem_x_tensor, dz_dy_dem_tensor, + descriptor_tensor, is_sorted); } + } - template - static torch::autograd::variable_list forward_t( + template + static torch::autograd::variable_list forward_t( torch::autograd::AutogradContext* ctx, const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, @@ -655,23 +658,23 @@ class TabulateFusionSeAGradGradOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, at::Tensor(), dz_dy_dem_x_tensor, - dz_dy_dem_tensor, at::Tensor(), descriptor_tensor, - is_sorted, dz_dy_tensor); - - return torch::autograd::variable_list{ dz_dy_tensor }; - } + bool is_sorted) { + // Allocate output tensor + torch::Tensor dz_dy_tensor = torch::empty_like(descriptor_tensor); + // compute + TabulateFusionSeAGradGradForward( + table_tensor, table_info_tensor, em_x_tensor, em_tensor, at::Tensor(), + dz_dy_dem_x_tensor, dz_dy_dem_tensor, at::Tensor(), descriptor_tensor, + is_sorted, dz_dy_tensor); + + return torch::autograd::variable_list{dz_dy_tensor}; + } }; -class TabulateFusionSeAttenOp : public torch::autograd::Function{ - public: - static std::vector forward( +class TabulateFusionSeAttenOp + : public torch::autograd::Function { + public: + static std::vector forward( torch::autograd::AutogradContext* ctx, const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, @@ -679,22 +682,21 @@ class TabulateFusionSeAttenOp : public torch::autograd::Function(ctx, table_tensor, table_info_tensor, - em_x_tensor, em_tensor, two_embed_tensor, - last_layer_size, is_sorted); - } else{ - return forward_t(ctx, table_tensor, table_info_tensor, - em_x_tensor, em_tensor, two_embed_tensor, - last_layer_size, is_sorted); - } + bool is_sorted) { + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if (type_flag) { + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, two_embed_tensor, + last_layer_size, is_sorted); + } else { + return forward_t(ctx, table_tensor, table_info_tensor, em_x_tensor, + em_tensor, two_embed_tensor, last_layer_size, + is_sorted); } + } - template - static torch::autograd::variable_list forward_t( + template + static torch::autograd::variable_list forward_t( torch::autograd::AutogradContext* ctx, const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, @@ -702,286 +704,276 @@ class TabulateFusionSeAttenOp : public torch::autograd::Function(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, two_embed_tensor, last_layer_size, - descriptor_tensor); - // save data - ctx->save_for_backward({ - table_tensor, table_info_tensor, em_x_tensor, - em_tensor, two_embed_tensor, descriptor_tensor - }); - return { descriptor_tensor }; - } + bool is_sorted) { + // allocate output tensors + auto options = torch::TensorOptions() + .dtype(table_tensor.dtype()) + .device(table_tensor.device()); + torch::Tensor descriptor_tensor = + torch::empty({em_tensor.size(0), 4, last_layer_size}, options); + // compute + TabulateFusionSeAForward(table_tensor, table_info_tensor, + em_x_tensor, em_tensor, two_embed_tensor, + last_layer_size, descriptor_tensor); + // save data + ctx->save_for_backward({table_tensor, table_info_tensor, em_x_tensor, + em_tensor, two_embed_tensor, descriptor_tensor}); + return {descriptor_tensor}; + } - static torch::autograd::variable_list backward( - torch::autograd::AutogradContext* ctx, - torch::autograd::variable_list grad_output - ){ - torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); - torch::Tensor table_tensor = saved_variables[0]; - bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; - if(type_flag) { - return backward_t(ctx, grad_output); - } else{ - return backward_t(ctx, grad_output); - } + static torch::autograd::variable_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output) { + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if (type_flag) { + return backward_t(ctx, grad_output); + } else { + return backward_t(ctx, grad_output); } + } - template - static torch::autograd::variable_list backward_t( + template + static torch::autograd::variable_list backward_t( torch::autograd::AutogradContext* ctx, - torch::autograd::variable_list grad_output - ){ - // load data - torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); - torch::Tensor table_tensor = saved_variables[0]; - torch::Tensor table_info_tensor = saved_variables[1]; - torch::Tensor em_x_tensor = saved_variables[2]; - torch::Tensor em_tensor = saved_variables[3]; - torch::Tensor two_embed_tensor = saved_variables[4]; - torch::Tensor descriptor_tensor = saved_variables[5]; - - torch::Tensor dy_tensor = grad_output[0].contiguous(); - // allocate output tensors - torch::Tensor dy_dem_x_tensor = torch::zeros_like(em_x_tensor); - torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); - torch::Tensor dy_dtwo_tensor = torch::zeros_like(two_embed_tensor); - // compute - TabulateFusionSeAGradForward(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, two_embed_tensor, dy_tensor, - descriptor_tensor, dy_dem_x_tensor, dy_dem_tensor, - dy_dtwo_tensor); - - return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, - dy_dtwo_tensor, at::Tensor(), at::Tensor()}; - } + torch::autograd::variable_list grad_output) { + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + torch::Tensor table_info_tensor = saved_variables[1]; + torch::Tensor em_x_tensor = saved_variables[2]; + torch::Tensor em_tensor = saved_variables[3]; + torch::Tensor two_embed_tensor = saved_variables[4]; + torch::Tensor descriptor_tensor = saved_variables[5]; + + torch::Tensor dy_tensor = grad_output[0].contiguous(); + // allocate output tensors + torch::Tensor dy_dem_x_tensor = torch::zeros_like(em_x_tensor); + torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); + torch::Tensor dy_dtwo_tensor = torch::zeros_like(two_embed_tensor); + // compute + TabulateFusionSeAGradForward( + table_tensor, table_info_tensor, em_x_tensor, em_tensor, + two_embed_tensor, dy_tensor, descriptor_tensor, dy_dem_x_tensor, + dy_dem_tensor, dy_dtwo_tensor); + + return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, + dy_dtwo_tensor, at::Tensor(), at::Tensor()}; + } }; -class TabulateFusionSeTOp : public torch::autograd::Function{ - public: - static std::vector forward( +class TabulateFusionSeTOp + : public torch::autograd::Function { + public: + static std::vector forward( torch::autograd::AutogradContext* ctx, const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, const torch::Tensor& em_x_tensor, const torch::Tensor& em_tensor, - int64_t last_layer_size - ){ - bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; - if(type_flag){ - return forward_t(ctx, table_tensor, table_info_tensor, - em_x_tensor, em_tensor, last_layer_size); - } else{ - return forward_t(ctx, table_tensor, table_info_tensor, - em_x_tensor, em_tensor, last_layer_size); - } + int64_t last_layer_size) { + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if (type_flag) { + return forward_t(ctx, table_tensor, table_info_tensor, + em_x_tensor, em_tensor, last_layer_size); + } else { + return forward_t(ctx, table_tensor, table_info_tensor, em_x_tensor, + em_tensor, last_layer_size); } + } - template - static torch::autograd::variable_list forward_t( + template + static torch::autograd::variable_list forward_t( torch::autograd::AutogradContext* ctx, const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, const torch::Tensor& em_x_tensor, const torch::Tensor& em_tensor, - int64_t last_layer_size - ){ - // allocate output tensors - auto options = - torch::TensorOptions() - .dtype(table_tensor.dtype()) - .device(table_tensor.device()); - torch::Tensor descriptor_tensor = torch::empty( - {em_tensor.size(0), last_layer_size}, options); - // compute - TabulateFusionSeTForward(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, last_layer_size, descriptor_tensor); - // save data - ctx->save_for_backward({ - table_tensor, table_info_tensor, em_x_tensor, - em_tensor, descriptor_tensor - }); - return { descriptor_tensor }; - } + int64_t last_layer_size) { + // allocate output tensors + auto options = torch::TensorOptions() + .dtype(table_tensor.dtype()) + .device(table_tensor.device()); + torch::Tensor descriptor_tensor = + torch::empty({em_tensor.size(0), last_layer_size}, options); + // compute + TabulateFusionSeTForward(table_tensor, table_info_tensor, + em_x_tensor, em_tensor, last_layer_size, + descriptor_tensor); + // save data + ctx->save_for_backward({table_tensor, table_info_tensor, em_x_tensor, + em_tensor, descriptor_tensor}); + return {descriptor_tensor}; + } - static torch::autograd::variable_list backward( - torch::autograd::AutogradContext* ctx, - torch::autograd::variable_list grad_output - ){ - torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); - torch::Tensor table_tensor = saved_variables[0]; - bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; - if(type_flag) { - return backward_t(ctx, grad_output); - } else{ - return backward_t(ctx, grad_output); - } + static torch::autograd::variable_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output) { + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if (type_flag) { + return backward_t(ctx, grad_output); + } else { + return backward_t(ctx, grad_output); } + } - template - static torch::autograd::variable_list backward_t( + template + static torch::autograd::variable_list backward_t( torch::autograd::AutogradContext* ctx, - torch::autograd::variable_list grad_output - ){ - // load data - torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); - torch::Tensor table_tensor = saved_variables[0]; - torch::Tensor table_info_tensor = saved_variables[1]; - torch::Tensor em_x_tensor = saved_variables[2]; - torch::Tensor em_tensor = saved_variables[3]; - torch::Tensor descriptor_tensor = saved_variables[4]; - - torch::Tensor dy_tensor = grad_output[0].contiguous(); - // allocate output tensors - torch::Tensor dy_dem_x_tensor = torch::zeros_like(em_x_tensor); - torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); - // compute - TabulateFusionSeTGradForward(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, dy_tensor, descriptor_tensor, - dy_dem_x_tensor, dy_dem_tensor); - - return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, at::Tensor()}; - } + torch::autograd::variable_list grad_output) { + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + torch::Tensor table_info_tensor = saved_variables[1]; + torch::Tensor em_x_tensor = saved_variables[2]; + torch::Tensor em_tensor = saved_variables[3]; + torch::Tensor descriptor_tensor = saved_variables[4]; + + torch::Tensor dy_tensor = grad_output[0].contiguous(); + // allocate output tensors + torch::Tensor dy_dem_x_tensor = torch::zeros_like(em_x_tensor); + torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); + // compute + TabulateFusionSeTGradForward( + table_tensor, table_info_tensor, em_x_tensor, em_tensor, dy_tensor, + descriptor_tensor, dy_dem_x_tensor, dy_dem_tensor); + + return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, + at::Tensor()}; + } }; -class TabulateFusionSeROp : public torch::autograd::Function{ - public: - static std::vector forward( +class TabulateFusionSeROp + : public torch::autograd::Function { + public: + static std::vector forward( torch::autograd::AutogradContext* ctx, const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, const torch::Tensor& em_tensor, - int64_t last_layer_size - ){ - bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; - if(type_flag){ - return forward_t(ctx, table_tensor, table_info_tensor, - em_tensor, last_layer_size); - } else{ - return forward_t(ctx, table_tensor, table_info_tensor, - em_tensor, last_layer_size); - } + int64_t last_layer_size) { + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if (type_flag) { + return forward_t(ctx, table_tensor, table_info_tensor, em_tensor, + last_layer_size); + } else { + return forward_t(ctx, table_tensor, table_info_tensor, em_tensor, + last_layer_size); } + } - template - static torch::autograd::variable_list forward_t( + template + static torch::autograd::variable_list forward_t( torch::autograd::AutogradContext* ctx, const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, const torch::Tensor& em_tensor, - int64_t last_layer_size - ){ - // allocate output tensors - auto options = - torch::TensorOptions() - .dtype(table_tensor.dtype()) - .device(table_tensor.device()); - torch::Tensor descriptor_tensor = torch::empty( + int64_t last_layer_size) { + // allocate output tensors + auto options = torch::TensorOptions() + .dtype(table_tensor.dtype()) + .device(table_tensor.device()); + torch::Tensor descriptor_tensor = torch::empty( {em_tensor.size(0), em_tensor.size(1), last_layer_size}, options); - // compute - TabulateFusionSeRForward(table_tensor, table_info_tensor, em_tensor, - last_layer_size, descriptor_tensor); - // save data - ctx->save_for_backward({ - table_tensor, table_info_tensor, em_tensor, descriptor_tensor - }); - return { descriptor_tensor }; - } + // compute + TabulateFusionSeRForward(table_tensor, table_info_tensor, em_tensor, + last_layer_size, descriptor_tensor); + // save data + ctx->save_for_backward( + {table_tensor, table_info_tensor, em_tensor, descriptor_tensor}); + return {descriptor_tensor}; + } - static torch::autograd::variable_list backward( - torch::autograd::AutogradContext* ctx, - torch::autograd::variable_list grad_output - ){ - torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); - torch::Tensor table_tensor = saved_variables[0]; - bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; - if(type_flag) { - return backward_t(ctx, grad_output); - } else{ - return backward_t(ctx, grad_output); - } + static torch::autograd::variable_list backward( + torch::autograd::AutogradContext* ctx, + torch::autograd::variable_list grad_output) { + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + bool type_flag = (table_tensor.dtype() == torch::kDouble) ? true : false; + if (type_flag) { + return backward_t(ctx, grad_output); + } else { + return backward_t(ctx, grad_output); } + } - template - static torch::autograd::variable_list backward_t( + template + static torch::autograd::variable_list backward_t( torch::autograd::AutogradContext* ctx, - torch::autograd::variable_list grad_output - ){ - // load data - torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); - torch::Tensor table_tensor = saved_variables[0]; - torch::Tensor table_info_tensor = saved_variables[1]; - torch::Tensor em_tensor = saved_variables[2]; - torch::Tensor descriptor_tensor = saved_variables[3]; - - torch::Tensor dy_tensor = grad_output[0].contiguous(); - // allocate output tensors - torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); - // compute - TabulateFusionSeRGradForward(table_tensor, table_info_tensor, em_tensor, - dy_tensor, descriptor_tensor, dy_dem_tensor); - - return {at::Tensor(), at::Tensor(), dy_dem_tensor, at::Tensor()}; - } + torch::autograd::variable_list grad_output) { + // load data + torch::autograd::variable_list saved_variables = ctx->get_saved_variables(); + torch::Tensor table_tensor = saved_variables[0]; + torch::Tensor table_info_tensor = saved_variables[1]; + torch::Tensor em_tensor = saved_variables[2]; + torch::Tensor descriptor_tensor = saved_variables[3]; + + torch::Tensor dy_tensor = grad_output[0].contiguous(); + // allocate output tensors + torch::Tensor dy_dem_tensor = torch::zeros_like(em_tensor); + // compute + TabulateFusionSeRGradForward(table_tensor, table_info_tensor, + em_tensor, dy_tensor, + descriptor_tensor, dy_dem_tensor); + + return {at::Tensor(), at::Tensor(), dy_dem_tensor, at::Tensor()}; + } }; std::vector tabluate_fusion_se_a( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_x_tensor, - const torch::Tensor& em_tensor, - int64_t last_layer_size -){ - return TabulateFusionSeAOp::apply(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, last_layer_size); + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size) { + return TabulateFusionSeAOp::apply(table_tensor, table_info_tensor, + em_x_tensor, em_tensor, last_layer_size); } std::vector tabluate_fusion_se_atten( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_x_tensor, - const torch::Tensor& em_tensor, - const torch::Tensor& two_embed_tensor, - int64_t last_layer_size, - bool is_sorted -){ - return TabulateFusionSeAttenOp::apply(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, two_embed_tensor, last_layer_size, - is_sorted); + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + const torch::Tensor& two_embed_tensor, + int64_t last_layer_size, + bool is_sorted) { + return TabulateFusionSeAttenOp::apply( + table_tensor, table_info_tensor, em_x_tensor, em_tensor, two_embed_tensor, + last_layer_size, is_sorted); } std::vector tabluate_fusion_se_t( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_x_tensor, - const torch::Tensor& em_tensor, - int64_t last_layer_size -){ - return TabulateFusionSeTOp::apply(table_tensor, table_info_tensor, em_x_tensor, - em_tensor, last_layer_size); + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_x_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size) { + return TabulateFusionSeTOp::apply(table_tensor, table_info_tensor, + em_x_tensor, em_tensor, last_layer_size); } std::vector tabluate_fusion_se_r( - const torch::Tensor& table_tensor, - const torch::Tensor& table_info_tensor, - const torch::Tensor& em_tensor, - int64_t last_layer_size -){ + const torch::Tensor& table_tensor, + const torch::Tensor& table_info_tensor, + const torch::Tensor& em_tensor, + int64_t last_layer_size) { return TabulateFusionSeROp::apply(table_tensor, table_info_tensor, em_tensor, last_layer_size); } -TORCH_LIBRARY_FRAGMENT(deepmd, m) { m.def("tabulate_fusion_se_a", tabluate_fusion_se_a); } -TORCH_LIBRARY_FRAGMENT(deepmd, m) { m.def("tabulate_fusion_se_atten", tabluate_fusion_se_atten); } -TORCH_LIBRARY_FRAGMENT(deepmd, m) { m.def("tabulate_fusion_se_t", tabluate_fusion_se_t); } -TORCH_LIBRARY_FRAGMENT(deepmd, m) { m.def("tabulate_fusion_se_r", tabluate_fusion_se_r); } \ No newline at end of file +TORCH_LIBRARY_FRAGMENT(deepmd, m) { + m.def("tabulate_fusion_se_a", tabluate_fusion_se_a); +} +TORCH_LIBRARY_FRAGMENT(deepmd, m) { + m.def("tabulate_fusion_se_atten", tabluate_fusion_se_atten); +} +TORCH_LIBRARY_FRAGMENT(deepmd, m) { + m.def("tabulate_fusion_se_t", tabluate_fusion_se_t); +} +TORCH_LIBRARY_FRAGMENT(deepmd, m) { + m.def("tabulate_fusion_se_r", tabluate_fusion_se_r); +} diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index 9cb3a84898..b6d7a62456 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -1,556 +1,1162 @@ -import torch +# SPDX-License-Identifier: LGPL-3.0-or-later import unittest + +import torch + import deepmd.pt.cxx_op + class TestTabulateFusionSeAOp(unittest.TestCase): - def setUp(self): - self.table_tensor = torch.tensor([ - 6.348551343037398542e-01, 4.209465843706336474e-04, - 6.390862740714405368e-03, -1.544448595628262176e-04, - -1.891095227974180087e-04, 2.695025951562175852e-05, - -1.317549846042939343e+00, -5.624478206903206490e-02, - 1.274284553146523905e-02, -6.836227424141475689e-04, - -1.438066096020836407e-04, -1.854932873974712940e-06, - -9.996964112615246423e-01, 6.928234423723647617e-02, - -4.974719973810486084e-03, -2.019584729176823030e-04, - 1.077254539742680247e-04, -8.024209768588029797e-06, - 3.552689563657350780e-01, -3.578299775339799371e-02, - -1.319946251007718743e-03, 1.016701374495701440e-03, - -1.057336720791906388e-04, 5.182678943855506567e-06, - 1.227750369557627286e+00, 4.100352079064395472e-02, - 3.586869164810712295e-03, -4.304540913340443135e-04, - -1.269943482892440004e-04, 1.459465404430219674e-05, - -1.472642501673147031e+00, -1.611354921283318364e-01, - 1.645427874390196360e-02, 2.107392978135091402e-04, - -2.193541011180757461e-04, 1.915392497459551146e-05, - -2.855174490181606739e-01, 9.774337856626263976e-02, - -2.140891880666230714e-03, -7.148328890055103638e-04, - 1.965696332267534503e-05, -4.593489654121371453e-06, - -1.468441009949382314e+00, -6.360828127262234399e-02, - 4.751283295356955282e-03, 8.711899561753186068e-05, - -9.937008678852959884e-06, 4.273569346584811685e-07, - 6.348599826995243722e-01, 5.487167506364742930e-04, - 6.386116198716365253e-03, -1.619832375568118791e-04, - -1.877328309473502049e-04, 2.134130914519164856e-05, - -1.318111020264137512e+00, -5.599013082054477008e-02, - 1.272225054666903735e-02, -6.893710047488201898e-04, - -1.434367581078517366e-04, 3.329508890614227371e-05, - -9.990040854920316793e-01, 6.918278968071900348e-02, - -4.980714172967731085e-03, -1.976574487947816198e-04, - 1.070037204086153902e-04, -7.859875077388093586e-06, - 3.549109954092205532e-01, -3.580909209068139365e-02, - -1.289508598157979719e-03, 1.012474257117017967e-03, - -1.054418924402112718e-04, -1.245498322204730900e-05, - 1.228160763020727630e+00, 4.107512853046493134e-02, - 3.573879491390910459e-03, -4.355190226638688713e-04, - -1.258433981470396103e-04, 1.610862268100766631e-05, - -1.474252210958008291e+00, -1.608063442081248406e-01, - 1.646046950167207382e-02, 2.019843636566674109e-04, - -2.185756589083626730e-04, 1.978479879983412190e-05, - -2.845402300363228942e-01, 9.770034635718018168e-02, - -2.162325119197382531e-03, -7.140472215558940627e-04, - 1.956302663031799223e-05, 1.932584474244053378e-05, - -1.469076617546759334e+00, -6.351322951074317436e-02, - 4.753890907276497185e-03, 8.672114560243554321e-05, - -1.004574434175897967e-05, -4.345700882560937596e-06, - 6.348661083147921769e-01, 6.763897297752743953e-04, - 6.381144275303845745e-03, -1.694690463885140694e-04, - -1.868179426353836598e-04, 3.439291082765030046e-05, - -1.318669650038090335e+00, -5.573589319299507294e-02, - 1.270148368741391351e-02, -6.950749719342792137e-04, - -1.422194703304518733e-04, 3.454751241752252323e-05, - -9.983127558632299836e-01, 6.908311652764687061e-02, - -4.986579772806746212e-03, -1.933888092529071571e-04, - 1.068327546750306073e-04, -2.976978385983384886e-05, - 3.545527765488725169e-01, -3.583457894275744043e-02, - -1.259197760082061621e-03, 1.008246479193084487e-03, - -1.059401869200098984e-04, 1.721968053146218465e-06, - 1.228571871257205572e+00, 4.114647496201748883e-02, - 3.560738575723638825e-03, -4.405332425718102457e-04, - -1.251648759618972115e-04, 3.659080417076460655e-05, - -1.475858628153338792e+00, -1.604770750960976822e-01, - 1.646639808472218428e-02, 1.932598402043995316e-04, - -2.175904819601363058e-04, 1.230256868634094333e-05, - -2.835634435191126679e-01, 9.765688571984927624e-02, - -2.183734604613508240e-03, -7.132463811570244078e-04, - 2.021887442373574272e-05, 1.321401495096886281e-05, - -1.469711274366155784e+00, -6.341812571665436660e-02, - 4.756486470714936521e-03, 8.631384191910702040e-05, - -1.010516500002806932e-05, -1.110874413279218719e-05, - 6.348735101551836735e-01, 8.039610290153098582e-04, - 6.375948457075718626e-03, -1.769074132993461279e-04, - -1.855677150383903214e-04, 3.421271436711027645e-05, - -1.319225739518145257e+00, -5.548207260888919634e-02, - 1.268054645200545304e-02, -7.007297564176242621e-04, - -1.408885818822980523e-04, 3.124701885930576017e-05, - -9.976224235482542557e-01, 6.898332734138989952e-02, - -4.992317635216104131e-03, -1.891404922064061889e-04, - 1.053957535708985289e-04, -1.089286646983666076e-06, - 3.541943058468561834e-01, -3.585946084769019160e-02, - -1.229013912637771933e-03, 1.004009466262262241e-03, - -1.059129033455631863e-04, -4.941663399086282537e-06, - 1.228983691638902087e+00, 4.121755707472917613e-02, - 3.547447845420277635e-03, -4.455036207721562607e-04, - -1.239172256532283074e-04, 3.437341080261359686e-05, - -1.477461752073406132e+00, -1.601476900261984693e-01, - 1.647206544856073471e-02, 1.845724864086241608e-04, - -2.173853638475303177e-04, 3.620505631412716563e-05, - -2.825870937484175061e-01, 9.761299713537928413e-02, - -2.205119732548723246e-03, -7.124245958910824846e-04, - 2.074820558303217398e-05, 1.209381466404663338e-05, - -1.470344979888463577e+00, -6.332297013406351649e-02, - 4.759069711794740656e-03, 8.589935708505183382e-05, - -1.045842324058424788e-05, -6.134254562752213537e-06, - 6.348821871815598650e-01, 9.314261853726121809e-04, - 6.370530236175125580e-03, -1.842978984547447257e-04, - -1.840210089691990327e-04, 2.234897510077387526e-05, - -1.319779292891724465e+00, -5.522867246076747227e-02, - 1.265944033870337014e-02, -7.063360380236871801e-04, - -1.393416734992873119e-04, 1.931167378610719847e-05, - -9.969330896946905218e-01, 6.888342466806646192e-02, - -4.997928623431705138e-03, -1.849303524006284602e-04, - 1.053651633995249134e-04, -2.870133904891753420e-05, - 3.538355893399378616e-01, -3.588374034700148041e-02, - -1.198957225773849763e-03, 9.997681359810027708e-04, - -1.060678155548662341e-04, -4.107776618240329050e-06, - 1.229396221507694564e+00, 4.128837188660083868e-02, - 3.534008730169808672e-03, -4.504275777948374090e-04, - -1.224778886969254976e-04, 2.455513266683544498e-05, - -1.479061581584721008e+00, -1.598181942132129441e-01, - 1.647747255391585064e-02, 1.759082956613747337e-04, - -2.158335508261176197e-04, 6.406725844410341030e-06, - -2.816111850012528728e-01, 9.756868109694678826e-02, - -2.226479900633348240e-03, -7.115823288942964460e-04, - 2.121038517729223415e-05, 1.358027318850170435e-05, - -1.470977733597038872e+00, -6.322776301216057049e-02, - 4.761640356162846754e-03, 8.547576468445008296e-05, - -1.081874527005240631e-05, -8.845528475774308509e-07, - 6.348921383103013349e-01, 1.058780765759985421e-03, - 6.364891110105044131e-03, -1.916363332792569681e-04, - -1.827768871456785058e-04, 2.275707291847725182e-05, - -1.320330314380025793e+00, -5.497569611120622923e-02, - 1.263816684562326688e-02, -7.118908987616576157e-04, - -1.380182662155302303e-04, 1.630252530406085050e-05, - -9.962447554247517711e-01, 6.878341103651769428e-02, - -5.003413601927745452e-03, -1.807403991329658622e-04, - 1.040363362483998831e-04, -4.422604643727719699e-06, - 3.534766330394523148e-01, -3.590741998555346121e-02, - -1.169027863565602274e-03, 9.955202772264954043e-04, - -1.060447700647724903e-04, -1.021743279826507342e-05, - 1.229809458175783687e+00, 4.135891644424664892e-02, - 3.520422661584679015e-03, -4.553035794622276055e-04, - -1.210679214963379874e-04, 1.595827246550979495e-05, - -1.480658115605847147e+00, -1.594885928526604546e-01, - 1.648262036665308974e-02, 1.672799673730459213e-04, - -2.148155690753495697e-04, -1.867405535452657550e-06, - -2.806357215496423363e-01, 9.752393810975558408e-02, - -2.247814508535729908e-03, -7.107227883497464890e-04, - 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-4.625381215785304145e-04, + 8.881537622047225473e-04, + -1.080757789189670570e-04, + 5.820590714360855199e-08, + 1.240361649325028681e00, + 4.302664794411619614e-02, + 3.137220402938139478e-03, + -5.615677039256951981e-04, + -9.125763978623760322e-05, + 2.367398552885374808e-05, + -1.519498310980496925e00, + -1.512290469691385253e-01, + 1.652996628226939199e-02, + -3.745688059096337011e-05, + -1.938906911473592626e-04, + 1.811217640451412989e-05, + -2.564062357251438717e-01, + 9.626832379335603651e-02, + -2.771163091665611831e-03, + -6.829069315554202020e-04, + 3.363238372709415958e-05, + 8.623099725596635004e-06, + -1.487093617252511990e00, + -6.073523464295225993e-02, + 4.823154268625621383e-03, + 7.122599345182346051e-05, + -1.664931178025436733e-05, + -4.312450972708557703e-06, + ] + ).reshape(8, 132) self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1]) - self.em_x_tensor = torch.tensor([0.0343909, 0.11357423, 0.0858676, 0.19337772, - 0.1935728, 0.0477744, 0.05845198, 0.19080509, - 0.16111261, 0.07179262, 0.10078013, 0.04640909, - 0.10433399, 0.15650861, 0.17527857, 0.04249097]).reshape(4,4) - self.em_tensor = torch.tensor([0.0343909, 0.08394249, 0.06791791, 0.00903334, 0.11357423, 0.10597251, - 0.05738069, 0.10071109, 0.0858676, 0.17410445, 0.05390256, 0.09495758, - 0.19337772, 0.02045487, 0.04095526, 0.18431305, 0.1935728, 0.03930614, - 0.0304133, 0.15261676, 0.0477744, 0.06838737, 0.12824902, 0.14125861, - 0.05845198, 0.12731053, 0.0315968, 0.14927774, 0.19080509, 0.19206871, - 0.14361383, 0.04083437, 0.16111261, 0.19944826, 0.16563484, 0.00797179, - 0.07179262, 0.16993159, 0.01834742, 0.08405, 0.10078013, 0.0773945, - 0.09541813, 0.0042979, 0.04640909, 0.07968697, 0.18046262, 0.11724063, - 0.10433399, 0.16910201, 0.10653732, 0.07434702, 0.15650861, 0.0350976, - 0.04088021, 0.15753491, 0.17527857, 0.03178642, 0.01599623, 0.08095053, - 0.04249097, 0.17082205, 0.18275348, 0.02921504]).reshape(4,4,4) + self.em_x_tensor = torch.tensor( + [ + 0.0343909, + 0.11357423, + 0.0858676, + 0.19337772, + 0.1935728, + 0.0477744, + 0.05845198, + 0.19080509, + 0.16111261, + 0.07179262, + 0.10078013, + 0.04640909, + 0.10433399, + 0.15650861, + 0.17527857, + 0.04249097, + ] + ).reshape(4, 4) + self.em_tensor = torch.tensor( + [ + 0.0343909, + 0.08394249, + 0.06791791, + 0.00903334, + 0.11357423, + 0.10597251, + 0.05738069, + 0.10071109, + 0.0858676, + 0.17410445, + 0.05390256, + 0.09495758, + 0.19337772, + 0.02045487, + 0.04095526, + 0.18431305, + 0.1935728, + 0.03930614, + 0.0304133, + 0.15261676, + 0.0477744, + 0.06838737, + 0.12824902, + 0.14125861, + 0.05845198, + 0.12731053, + 0.0315968, + 0.14927774, + 0.19080509, + 0.19206871, + 0.14361383, + 0.04083437, + 0.16111261, + 0.19944826, + 0.16563484, + 0.00797179, + 0.07179262, + 0.16993159, + 0.01834742, + 0.08405, + 0.10078013, + 0.0773945, + 0.09541813, + 0.0042979, + 0.04640909, + 0.07968697, + 0.18046262, + 0.11724063, + 0.10433399, + 0.16910201, + 0.10653732, + 0.07434702, + 0.15650861, + 0.0350976, + 0.04088021, + 0.15753491, + 0.17527857, + 0.03178642, + 0.01599623, + 0.08095053, + 0.04249097, + 0.17082205, + 0.18275348, + 0.02921504, + ] + ).reshape(4, 4, 4) self.table_info_tensor.requires_grad = True self.table_tensor.requires_grad = True self.em_x_tensor.requires_grad = True @@ -560,56 +1166,236 @@ def setUp(self): self.nloc = 4 self.nnei = 4 # forward test - self.expected_descriptor_tensor = torch.tensor([0.2713011, -0.56606281, -0.42305039, 0.14965803, 0.52695372, - -0.63845663, -0.11624505, -0.63103203, 0.24412213, -0.50842224, - -0.38203148, 0.1353771, 0.47343798, -0.57158622, -0.10647548, - -0.56671287, 0.13979394, -0.29123603, -0.21862063, 0.07744574, - 0.27118433, -0.32761487, -0.06077287, -0.32463492, 0.24704819, - -0.51555848, -0.38509326, 0.1362072, 0.47992214, -0.58168358, - -0.10566162, -0.57473633, 0.31158834, -0.65068838, -0.48501479, - 0.17143258, 0.60565326, -0.73506803, -0.13233106, -0.72541595, - 0.27121003, -0.5656669, -0.42318034, 0.14974857, 0.52662422, - -0.637633, -0.11658482, -0.6305842, 0.21202135, -0.44212972, - -0.33094666, 0.11713047, 0.41162829, -0.4982129, -0.0913087, - -0.49286515, 0.30733526, -0.64054639, -0.48022212, 0.17004692, - 0.59640929, -0.72111726, -0.13304347, -0.71402776, 0.24135931, - -0.50316388, -0.37699907, 0.13347531, 0.46846154, -0.56664651, - -0.10429212, -0.56088123, 0.33429479, -0.69669061, -0.52246841, - 0.18502927, 0.64867706, -0.78417021, -0.14487244, -0.77659533, - 0.29200237, -0.60840668, -0.45656557, 0.16172246, 0.56650319, - -0.68453038, -0.12681616, -0.67817995, 0.13559139, -0.28210652, - -0.21258614, 0.07539812, 0.26274303, -0.3166084, -0.05968776, - -0.31443544, 0.30394432, -0.63428311, -0.47381417, 0.16759396, - 0.59043739, -0.71559513, -0.13003802, -0.70708354, 0.25830471, - -0.53796239, -0.40421268, 0.14323456, 0.50094757, -0.6048126, - -0.11264426, -0.59964242, 0.21979687, -0.45763438, -0.34413143, - 0.12197404, 0.42616899, -0.51425659, -0.09609854, -0.51009828, - 0.2172166, -0.45326447, -0.33866506, 0.11979851, 0.421936, - -0.51130404, -0.09300045, -0.50528542]).reshape(4,4,8) + self.expected_descriptor_tensor = torch.tensor( + [ + 0.2713011, + -0.56606281, + -0.42305039, + 0.14965803, + 0.52695372, + -0.63845663, + -0.11624505, + -0.63103203, + 0.24412213, + -0.50842224, + -0.38203148, + 0.1353771, + 0.47343798, + -0.57158622, + -0.10647548, + -0.56671287, + 0.13979394, + -0.29123603, + -0.21862063, + 0.07744574, + 0.27118433, + -0.32761487, + -0.06077287, + -0.32463492, + 0.24704819, + -0.51555848, + -0.38509326, + 0.1362072, + 0.47992214, + -0.58168358, + -0.10566162, + -0.57473633, + 0.31158834, + -0.65068838, + -0.48501479, + 0.17143258, + 0.60565326, + -0.73506803, + -0.13233106, + -0.72541595, + 0.27121003, + -0.5656669, + -0.42318034, + 0.14974857, + 0.52662422, + -0.637633, + -0.11658482, + -0.6305842, + 0.21202135, + -0.44212972, + -0.33094666, + 0.11713047, + 0.41162829, + -0.4982129, + -0.0913087, + -0.49286515, + 0.30733526, + -0.64054639, + -0.48022212, + 0.17004692, + 0.59640929, + -0.72111726, + -0.13304347, + -0.71402776, + 0.24135931, + -0.50316388, + -0.37699907, + 0.13347531, + 0.46846154, + -0.56664651, + -0.10429212, + -0.56088123, + 0.33429479, + -0.69669061, + -0.52246841, + 0.18502927, + 0.64867706, + -0.78417021, + -0.14487244, + -0.77659533, + 0.29200237, + -0.60840668, + -0.45656557, + 0.16172246, + 0.56650319, + -0.68453038, + -0.12681616, + -0.67817995, + 0.13559139, + -0.28210652, + -0.21258614, + 0.07539812, + 0.26274303, + -0.3166084, + -0.05968776, + -0.31443544, + 0.30394432, + -0.63428311, + -0.47381417, + 0.16759396, + 0.59043739, + -0.71559513, + -0.13003802, + -0.70708354, + 0.25830471, + -0.53796239, + -0.40421268, + 0.14323456, + 0.50094757, + -0.6048126, + -0.11264426, + -0.59964242, + 0.21979687, + -0.45763438, + -0.34413143, + 0.12197404, + 0.42616899, + -0.51425659, + -0.09609854, + -0.51009828, + 0.2172166, + -0.45326447, + -0.33866506, + 0.11979851, + 0.421936, + -0.51130404, + -0.09300045, + -0.50528542, + ] + ).reshape(4, 4, 8) # backward test - self.expected_dy_dem_x = torch.tensor([-0.02067741, -0.03787612, -0.04180199, -0.04158797, - -0.03938578, -0.04047081, -0.03819692, -0.05383372, - -0.05179508, -0.03552708, -0.02812173, -0.04451295, - -0.04586229, -0.03794369, -0.02917727, -0.04478649]).reshape(4,4) - self.expected_dy_dem = torch.tensor([-3.32965609, -3.32965609, -3.32965609, -3.32965609, -3.33781886, - -3.33781886, -3.33781886, -3.33781886, -3.33501296, -3.33501296, - -3.33501296, -3.33501296, -3.34559974, -3.34559974, -3.34559974, - -3.34559974, -3.34561821, -3.34561821, -3.34561821, -3.34561821, - -3.33106684, -3.33106684, -3.33106684, -3.33106684, -3.33218328, - -3.33218328, -3.33218328, -3.33218328, -3.34535585, -3.34535585, - -3.34535585, -3.34535585, -3.34250754, -3.34250754, -3.34250754, - -3.34250754, -3.33356685, -3.33356685, -3.33356685, -3.33356685, - -3.33652989, -3.33652989, -3.33652989, -3.33652989, -3.3309235, - -3.3309235, -3.3309235, -3.3309235, -3.33688909, -3.33688909, - -3.33688909, -3.33688909, -3.34206038, -3.34206038, -3.34206038, - -3.34206038, -3.34387412, -3.34387412, -3.34387412, -3.34387412, - -3.33051143, -3.33051143, -3.33051143, -3.33051143]).reshape(4,4,4) - + self.expected_dy_dem_x = torch.tensor( + [ + -0.02067741, + -0.03787612, + -0.04180199, + -0.04158797, + -0.03938578, + -0.04047081, + -0.03819692, + -0.05383372, + -0.05179508, + -0.03552708, + -0.02812173, + -0.04451295, + -0.04586229, + -0.03794369, + -0.02917727, + -0.04478649, + ] + ).reshape(4, 4) + self.expected_dy_dem = torch.tensor( + [ + -3.32965609, + -3.32965609, + -3.32965609, + -3.32965609, + -3.33781886, + -3.33781886, + -3.33781886, + -3.33781886, + -3.33501296, + -3.33501296, + -3.33501296, + -3.33501296, + -3.34559974, + -3.34559974, + -3.34559974, + -3.34559974, + -3.34561821, + -3.34561821, + -3.34561821, + -3.34561821, + -3.33106684, + -3.33106684, + -3.33106684, + -3.33106684, + -3.33218328, + -3.33218328, + -3.33218328, + -3.33218328, + -3.34535585, + -3.34535585, + -3.34535585, + -3.34535585, + -3.34250754, + -3.34250754, + -3.34250754, + -3.34250754, + -3.33356685, + -3.33356685, + -3.33356685, + -3.33356685, + -3.33652989, + -3.33652989, + -3.33652989, + -3.33652989, + -3.3309235, + -3.3309235, + -3.3309235, + -3.3309235, + -3.33688909, + -3.33688909, + -3.33688909, + -3.33688909, + -3.34206038, + -3.34206038, + -3.34206038, + -3.34206038, + -3.34387412, + -3.34387412, + -3.34387412, + -3.34387412, + -3.33051143, + -3.33051143, + -3.33051143, + -3.33051143, + ] + ).reshape(4, 4, 4) + def test_forward(self): # Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_a( - self.table_tensor, self.table_info_tensor, self.em_x_tensor, - self.em_tensor, self.last_layer_size + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.last_layer_size, ) descriptor_tensor = forward_result[0] @@ -618,20 +1404,30 @@ def test_forward(self): self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) # Check the values - self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) - + self.assertTrue( + torch.allclose( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 + ) + ) + def test_backward(self): # Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_a( - self.table_tensor, self.table_info_tensor, self.em_x_tensor, - self.em_tensor, self.last_layer_size + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.last_layer_size, ) descriptor_tensor = forward_result[0] # Check the forward - self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) - + self.assertTrue( + torch.allclose( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 + ) + ) # Create a loss and perform backward loss = descriptor_tensor.sum() @@ -646,9 +1442,14 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - self.assertTrue(torch.allclose(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5)) - self.assertTrue(torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5)) + self.assertTrue( + torch.allclose(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5) + ) + self.assertTrue( + torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5) + ) + -if __name__ == '__main__': +if __name__ == "__main__": deepmd.pt.cxx_op.ENABLE_CUSTOMIZED_OP - unittest.main() \ No newline at end of file + unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index fc4b7967d8..8f235969ac 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -1,601 +1,1294 @@ -import torch +# SPDX-License-Identifier: LGPL-3.0-or-later import unittest + +import torch + import deepmd.pt.cxx_op + class TestTabulateFusionSeAttenOp(unittest.TestCase): - def setUp(self): - self.table_tensor = torch.tensor([ - 6.348551343037398542e-01, 4.209465843706336474e-04, - 6.390862740714405368e-03, -1.544448595628262176e-04, - -1.891095227974180087e-04, 2.695025951562175852e-05, - -1.317549846042939343e+00, -5.624478206903206490e-02, - 1.274284553146523905e-02, -6.836227424141475689e-04, - -1.438066096020836407e-04, -1.854932873974712940e-06, - -9.996964112615246423e-01, 6.928234423723647617e-02, - -4.974719973810486084e-03, -2.019584729176823030e-04, - 1.077254539742680247e-04, -8.024209768588029797e-06, - 3.552689563657350780e-01, -3.578299775339799371e-02, - -1.319946251007718743e-03, 1.016701374495701440e-03, - -1.057336720791906388e-04, 5.182678943855506567e-06, - 1.227750369557627286e+00, 4.100352079064395472e-02, - 3.586869164810712295e-03, -4.304540913340443135e-04, - -1.269943482892440004e-04, 1.459465404430219674e-05, - -1.472642501673147031e+00, -1.611354921283318364e-01, - 1.645427874390196360e-02, 2.107392978135091402e-04, - -2.193541011180757461e-04, 1.915392497459551146e-05, - -2.855174490181606739e-01, 9.774337856626263976e-02, - -2.140891880666230714e-03, -7.148328890055103638e-04, - 1.965696332267534503e-05, -4.593489654121371453e-06, - -1.468441009949382314e+00, -6.360828127262234399e-02, - 4.751283295356955282e-03, 8.711899561753186068e-05, - -9.937008678852959884e-06, 4.273569346584811685e-07, - 6.348599826995243722e-01, 5.487167506364742930e-04, - 6.386116198716365253e-03, -1.619832375568118791e-04, - -1.877328309473502049e-04, 2.134130914519164856e-05, - -1.318111020264137512e+00, -5.599013082054477008e-02, - 1.272225054666903735e-02, -6.893710047488201898e-04, - -1.434367581078517366e-04, 3.329508890614227371e-05, - -9.990040854920316793e-01, 6.918278968071900348e-02, - -4.980714172967731085e-03, -1.976574487947816198e-04, - 1.070037204086153902e-04, -7.859875077388093586e-06, - 3.549109954092205532e-01, -3.580909209068139365e-02, - -1.289508598157979719e-03, 1.012474257117017967e-03, - -1.054418924402112718e-04, -1.245498322204730900e-05, - 1.228160763020727630e+00, 4.107512853046493134e-02, - 3.573879491390910459e-03, -4.355190226638688713e-04, - -1.258433981470396103e-04, 1.610862268100766631e-05, - -1.474252210958008291e+00, -1.608063442081248406e-01, - 1.646046950167207382e-02, 2.019843636566674109e-04, - -2.185756589083626730e-04, 1.978479879983412190e-05, - -2.845402300363228942e-01, 9.770034635718018168e-02, - -2.162325119197382531e-03, -7.140472215558940627e-04, - 1.956302663031799223e-05, 1.932584474244053378e-05, - -1.469076617546759334e+00, -6.351322951074317436e-02, - 4.753890907276497185e-03, 8.672114560243554321e-05, - -1.004574434175897967e-05, -4.345700882560937596e-06, - 6.348661083147921769e-01, 6.763897297752743953e-04, - 6.381144275303845745e-03, -1.694690463885140694e-04, - -1.868179426353836598e-04, 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-5.727587674967719880e-06, + 6.351934280628791507e-01, + 2.952908467203564646e-03, + 6.254828202758994093e-03, + -2.956111985445306826e-04, + -1.636502852942454153e-04, + 2.616921494951480123e-05, + -1.328294780757159899e00, + -5.123410165425365537e-02, + 1.229999603970671068e-02, + -7.892274520450543677e-04, + -1.195721301312790567e-04, + 2.454197033093738297e-05, + -9.860403788833298488e-01, + 6.727155810173718331e-02, + -5.071157514069617352e-03, + -1.214296539729165295e-04, + 9.340570341953608358e-05, + -1.444050153586573228e-05, + 3.480675228394242149e-01, + -3.619237065717702262e-02, + -7.354603960058733389e-04, + 9.313051737393654526e-04, + -1.076930273455606579e-04, + -7.696053039474192446e-07, + 1.236090908935226107e00, + 4.238273390417521269e-02, + 3.299974870987111650e-03, + -5.226642260988254756e-04, + -1.032474625011560351e-04, + 2.396475265799989632e-05, + -1.504210088727871764e00, + -1.545353795944727493e-01, + 1.652939400402650763e-02, + 4.186078937618800693e-05, + -2.027012231708198600e-04, + 1.761148452766873776e-05, + -2.660600935582757565e-01, + 9.680193929166537592e-02, + -2.564364883962782712e-03, + -6.954454205710857090e-04, + 2.907017700829073683e-05, + 9.120785771591908463e-06, + -1.480971935090678926e00, + -6.169766439371183325e-02, + 4.800832758035045861e-03, + 7.743502257440657043e-05, + -1.440171540732098418e-05, + -4.489324897938611976e-06, + 6.355509554770921721e-01, + 4.194364255265300989e-03, + 6.156587518227093006e-03, + -3.584539136959086518e-04, + -1.505562336471176987e-04, + 2.631189526673375584e-05, + -1.333295991901433553e00, + -4.879824528740911438e-02, + 1.205629889598585497e-02, + -8.346035033896359156e-04, + -1.072962342948566929e-04, + 2.412331753624817981e-05, + -9.793640468817854661e-01, + 6.625405011186732973e-02, + -5.102126473064734317e-03, + -8.551069374443776396e-05, + 8.618032279329005427e-05, + -1.422030758858379208e-05, + 3.444418516979214084e-01, + -3.631195473807800889e-02, + -4.625381215785304145e-04, + 8.881537622047225473e-04, + -1.080757789189670570e-04, + 5.820590714360855199e-08, + 1.240361649325028681e00, + 4.302664794411619614e-02, + 3.137220402938139478e-03, + -5.615677039256951981e-04, + -9.125763978623760322e-05, + 2.367398552885374808e-05, + -1.519498310980496925e00, + -1.512290469691385253e-01, + 1.652996628226939199e-02, + -3.745688059096337011e-05, + -1.938906911473592626e-04, + 1.811217640451412989e-05, + -2.564062357251438717e-01, + 9.626832379335603651e-02, + -2.771163091665611831e-03, + -6.829069315554202020e-04, + 3.363238372709415958e-05, + 8.623099725596635004e-06, + -1.487093617252511990e00, + -6.073523464295225993e-02, + 4.823154268625621383e-03, + 7.122599345182346051e-05, + -1.664931178025436733e-05, + -4.312450972708557703e-06, + ] + ).reshape(8, 132) self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1]) - self.em_x_tensor = torch.tensor([0.0343909, 0.11357423, 0.0858676, 0.19337772, - 0.1935728, 0.0477744, 0.05845198, 0.19080509, - 0.16111261, 0.07179262, 0.10078013, 0.04640909, - 0.10433399, 0.15650861, 0.17527857, 0.04249097]).reshape(4,4) - self.em_tensor = torch.tensor([0.0343909, 0.08394249, 0.06791791, 0.00903334, 0.11357423, 0.10597251, - 0.05738069, 0.10071109, 0.0858676, 0.17410445, 0.05390256, 0.09495758, - 0.19337772, 0.02045487, 0.04095526, 0.18431305, 0.1935728, 0.03930614, - 0.0304133, 0.15261676, 0.0477744, 0.06838737, 0.12824902, 0.14125861, - 0.05845198, 0.12731053, 0.0315968, 0.14927774, 0.19080509, 0.19206871, - 0.14361383, 0.04083437, 0.16111261, 0.19944826, 0.16563484, 0.00797179, - 0.07179262, 0.16993159, 0.01834742, 0.08405, 0.10078013, 0.0773945, - 0.09541813, 0.0042979, 0.04640909, 0.07968697, 0.18046262, 0.11724063, - 0.10433399, 0.16910201, 0.10653732, 0.07434702, 0.15650861, 0.0350976, - 0.04088021, 0.15753491, 0.17527857, 0.03178642, 0.01599623, 0.08095053, - 0.04249097, 0.17082205, 0.18275348, 0.02921504]).reshape(4,4,4) - self.two_embed_tensor = torch.tensor([ - 0.41783850884461693, 0.06917892522383784, 0.07309949640440838, - 0.57828038123179, 0.30460107001129133, 0.0641857998132136, - 0.016519028000859692, 0.46818914782665344, 0.7524658161955905, - 0.7366050152276675, 0.5442923017739666, 0.6984532784508917, - 0.8794579292532613, 0.933333068809702, 0.052557248156142045, - 0.3158695444821408, 0.6104896498153188, 0.3190616402773879, - 0.39327308944220873, 0.9555810861515368, 0.45845946239660273, - 0.2836952640436372, 0.7129235830370116, 0.21678811087765415, - 0.8589385334305147, 0.8664288996198418, 0.2392088190073245, - 0.44554156483185636, 0.4554902141228184, 0.6929437508125064, - 0.680397459717037, 0.47499455998030615, 0.19872841218252735, - 0.0593083660501722, 0.20593103822290515, 0.8377626566866462, - 0.9006561442856688, 0.9451358048366522, 0.03886827486931199, - 0.9395629463676399, 0.0018941296317954714, 0.08140115779980839, - 0.9309153205767321, 0.4697357303240055, 0.9164471895052549, - 0.5957401092143415, 0.40338864067603986, 0.9096349710860572, - 0.027870073369474335, 0.9614765307496669, 0.3142035164603587, - 0.4027282032956391, 0.05129061735552376, 0.18018240347684844, - 0.8391548601089657, 0.25198333808352436, 0.07903335895654717, - 0.9831396742713334, 0.21328884297544115, 0.8119626188647525, - 0.1734453905861253, 0.014174310472666818, 0.06890574596678134, - 0.3490769935686088, 0.34055562797730554, 0.9834924063503578, - 0.1689164263315952, 0.9611024936313157, 0.6796725725159389, - 0.7902946379060674, 0.3045916985592084, 0.6923776720247495, - 0.5626994287153583, 0.12132066580981216, 0.13356198804830732, - 0.5332034125146011, 0.6155216974624633, 0.3080851791499254, - 0.8391387652641518, 0.8588772315368923, 0.9414859699900482, - 0.9852118289755771, 0.7514252073835589, 0.6780090883007501, - 0.1472721338720271, 0.4785493098407567, 0.8825343095166535, - 0.1562449821247882, 0.5809096109347806, 0.653605647812403, - 0.26158060329219845, 0.28359029181509054, 0.23111396285536823, - 0.6711415141607222, 0.5955230293073148, 0.14336394912405104, - 0.48478135042139503, 0.34621668898158153, 0.7962234329935334, - 0.40204538487553787, 0.09600971949708359, 0.985025266359638, - 0.4949655728846287, 0.23503981206241742, 0.607828476455725, - 0.21634419784756398, 0.04166567958728129, 0.132198384508056, - 0.4112021863641492, 0.9441979803962212, 0.993462871462463, - 0.4524002115880147, 0.6322719605196645, 0.5121196654684579, - 0.7844974619880201, 0.6783684708633317, 0.6402712236722511, - 0.43899788665378925, 0.6060330628471464, 0.7082475921988166, - 0.1614968711069913, 0.6289247345866867, 0.4034261331727077, - 0.7906075239905527, 0.9325509002602962, 0.44489583733770977, - 0.5194672674960213, 0.04635102497306032 - ]).reshape(8, 16) + self.em_x_tensor = torch.tensor( + [ + 0.0343909, + 0.11357423, + 0.0858676, + 0.19337772, + 0.1935728, + 0.0477744, + 0.05845198, + 0.19080509, + 0.16111261, + 0.07179262, + 0.10078013, + 0.04640909, + 0.10433399, + 0.15650861, + 0.17527857, + 0.04249097, + ] + ).reshape(4, 4) + self.em_tensor = torch.tensor( + [ + 0.0343909, + 0.08394249, + 0.06791791, + 0.00903334, + 0.11357423, + 0.10597251, + 0.05738069, + 0.10071109, + 0.0858676, + 0.17410445, + 0.05390256, + 0.09495758, + 0.19337772, + 0.02045487, + 0.04095526, + 0.18431305, + 0.1935728, + 0.03930614, + 0.0304133, + 0.15261676, + 0.0477744, + 0.06838737, + 0.12824902, + 0.14125861, + 0.05845198, + 0.12731053, + 0.0315968, + 0.14927774, + 0.19080509, + 0.19206871, + 0.14361383, + 0.04083437, + 0.16111261, + 0.19944826, + 0.16563484, + 0.00797179, + 0.07179262, + 0.16993159, + 0.01834742, + 0.08405, + 0.10078013, + 0.0773945, + 0.09541813, + 0.0042979, + 0.04640909, + 0.07968697, + 0.18046262, + 0.11724063, + 0.10433399, + 0.16910201, + 0.10653732, + 0.07434702, + 0.15650861, + 0.0350976, + 0.04088021, + 0.15753491, + 0.17527857, + 0.03178642, + 0.01599623, + 0.08095053, + 0.04249097, + 0.17082205, + 0.18275348, + 0.02921504, + ] + ).reshape(4, 4, 4) + self.two_embed_tensor = torch.tensor( + [ + 0.41783850884461693, + 0.06917892522383784, + 0.07309949640440838, + 0.57828038123179, + 0.30460107001129133, + 0.0641857998132136, + 0.016519028000859692, + 0.46818914782665344, + 0.7524658161955905, + 0.7366050152276675, + 0.5442923017739666, + 0.6984532784508917, + 0.8794579292532613, + 0.933333068809702, + 0.052557248156142045, + 0.3158695444821408, + 0.6104896498153188, + 0.3190616402773879, + 0.39327308944220873, + 0.9555810861515368, + 0.45845946239660273, + 0.2836952640436372, + 0.7129235830370116, + 0.21678811087765415, + 0.8589385334305147, + 0.8664288996198418, + 0.2392088190073245, + 0.44554156483185636, + 0.4554902141228184, + 0.6929437508125064, + 0.680397459717037, + 0.47499455998030615, + 0.19872841218252735, + 0.0593083660501722, + 0.20593103822290515, + 0.8377626566866462, + 0.9006561442856688, + 0.9451358048366522, + 0.03886827486931199, + 0.9395629463676399, + 0.0018941296317954714, + 0.08140115779980839, + 0.9309153205767321, + 0.4697357303240055, + 0.9164471895052549, + 0.5957401092143415, + 0.40338864067603986, + 0.9096349710860572, + 0.027870073369474335, + 0.9614765307496669, + 0.3142035164603587, + 0.4027282032956391, + 0.05129061735552376, + 0.18018240347684844, + 0.8391548601089657, + 0.25198333808352436, + 0.07903335895654717, + 0.9831396742713334, + 0.21328884297544115, + 0.8119626188647525, + 0.1734453905861253, + 0.014174310472666818, + 0.06890574596678134, + 0.3490769935686088, + 0.34055562797730554, + 0.9834924063503578, + 0.1689164263315952, + 0.9611024936313157, + 0.6796725725159389, + 0.7902946379060674, + 0.3045916985592084, + 0.6923776720247495, + 0.5626994287153583, + 0.12132066580981216, + 0.13356198804830732, + 0.5332034125146011, + 0.6155216974624633, + 0.3080851791499254, + 0.8391387652641518, + 0.8588772315368923, + 0.9414859699900482, + 0.9852118289755771, + 0.7514252073835589, + 0.6780090883007501, + 0.1472721338720271, + 0.4785493098407567, + 0.8825343095166535, + 0.1562449821247882, + 0.5809096109347806, + 0.653605647812403, + 0.26158060329219845, + 0.28359029181509054, + 0.23111396285536823, + 0.6711415141607222, + 0.5955230293073148, + 0.14336394912405104, + 0.48478135042139503, + 0.34621668898158153, + 0.7962234329935334, + 0.40204538487553787, + 0.09600971949708359, + 0.985025266359638, + 0.4949655728846287, + 0.23503981206241742, + 0.607828476455725, + 0.21634419784756398, + 0.04166567958728129, + 0.132198384508056, + 0.4112021863641492, + 0.9441979803962212, + 0.993462871462463, + 0.4524002115880147, + 0.6322719605196645, + 0.5121196654684579, + 0.7844974619880201, + 0.6783684708633317, + 0.6402712236722511, + 0.43899788665378925, + 0.6060330628471464, + 0.7082475921988166, + 0.1614968711069913, + 0.6289247345866867, + 0.4034261331727077, + 0.7906075239905527, + 0.9325509002602962, + 0.44489583733770977, + 0.5194672674960213, + 0.04635102497306032, + ] + ).reshape(8, 16) self.table_info_tensor.requires_grad = False self.table_tensor.requires_grad = False self.em_x_tensor.requires_grad = True @@ -606,49 +1299,238 @@ def setUp(self): self.nnei = 4 self.is_sorted = True # forward test - self.expected_descriptor_tensor = torch.tensor([0.473477, -0.938671, -0.566066, 0.243465, 0.820256, -1.03738, - -0.1701, -0.871079, 0.395671, -0.716406, -0.518235, 0.240325, - 0.729525, -0.821716, -0.146507, -0.743918, 0.228466, -0.423251, - -0.285282, 0.129922, 0.412289, -0.479284, -0.079999, -0.444168, - 0.434929, -0.866715, -0.520729, 0.22332, 0.749817, -0.954691, - -0.159364, -0.787858, 0.346693, -0.994381, -0.627092, 0.29814, - 0.919532, -1.07189, -0.156985, -1.17893, 0.288148, -0.988518, - -0.574846, 0.245017, 0.696643, -0.791873, -0.158166, -0.923282, - 0.223783, -0.685911, -0.496191, 0.192425, 0.622787, -0.666019, - -0.116214, -0.792771, 0.331461, -0.910623, -0.697545, 0.270823, - 0.943644, -1.10325, -0.186431, -1.19188, 0.379218, -0.896241, - -0.500721, 0.229773, 0.689497, -0.908129, -0.166598, -0.849655, - 0.5138, -1.15374, -0.656861, 0.31042, 0.981434, -1.23231, - -0.233144, -1.2304, 0.45798, -1.10749, -0.604885, 0.261653, - 0.78797, -1.13627, -0.19813, -0.931011, 0.213153, -0.412743, - -0.258851, 0.106663, 0.367201, -0.484, -0.10065, -0.45465, - 0.471219, -0.881373, -0.715617, 0.24295, 0.869534, -1.23335, - -0.221008, -1.03448, 0.354185, -0.78891, -0.632616, 0.224055, - 0.759759, -1.03588, -0.175253, -0.726695, 0.293533, -0.680751, - -0.515893, 0.193818, 0.681708, -0.85917, -0.150909, -0.603596, - 0.336416, -0.611786, -0.478383, 0.164921, 0.608194, -0.915656, - -0.162872, -0.723229]).reshape(4,4,8) + self.expected_descriptor_tensor = torch.tensor( + [ + 0.473477, + -0.938671, + -0.566066, + 0.243465, + 0.820256, + -1.03738, + -0.1701, + -0.871079, + 0.395671, + -0.716406, + -0.518235, + 0.240325, + 0.729525, + -0.821716, + -0.146507, + -0.743918, + 0.228466, + -0.423251, + -0.285282, + 0.129922, + 0.412289, + -0.479284, + -0.079999, + -0.444168, + 0.434929, + -0.866715, + -0.520729, + 0.22332, + 0.749817, + -0.954691, + -0.159364, + -0.787858, + 0.346693, + -0.994381, + -0.627092, + 0.29814, + 0.919532, + -1.07189, + -0.156985, + -1.17893, + 0.288148, + -0.988518, + -0.574846, + 0.245017, + 0.696643, + -0.791873, + -0.158166, + -0.923282, + 0.223783, + -0.685911, + -0.496191, + 0.192425, + 0.622787, + -0.666019, + -0.116214, + -0.792771, + 0.331461, + -0.910623, + -0.697545, + 0.270823, + 0.943644, + -1.10325, + -0.186431, + -1.19188, + 0.379218, + -0.896241, + -0.500721, + 0.229773, + 0.689497, + -0.908129, + -0.166598, + -0.849655, + 0.5138, + -1.15374, + -0.656861, + 0.31042, + 0.981434, + -1.23231, + -0.233144, + -1.2304, + 0.45798, + -1.10749, + -0.604885, + 0.261653, + 0.78797, + -1.13627, + -0.19813, + -0.931011, + 0.213153, + -0.412743, + -0.258851, + 0.106663, + 0.367201, + -0.484, + -0.10065, + -0.45465, + 0.471219, + -0.881373, + -0.715617, + 0.24295, + 0.869534, + -1.23335, + -0.221008, + -1.03448, + 0.354185, + -0.78891, + -0.632616, + 0.224055, + 0.759759, + -1.03588, + -0.175253, + -0.726695, + 0.293533, + -0.680751, + -0.515893, + 0.193818, + 0.681708, + -0.85917, + -0.150909, + -0.603596, + 0.336416, + -0.611786, + -0.478383, + 0.164921, + 0.608194, + -0.915656, + -0.162872, + -0.723229, + ] + ).reshape(4, 4, 8) # backward test - self.expected_dy_dem_x = torch.tensor([-0.0294449, -0.0948144, -0.039285, -0.0826351, -0.114794, -0.0530287, - -0.0404511, -0.0964537, -0.148872, -0.0394916, -0.0330467, -0.0767711, - -0.0893684, -0.0666033, -0.0360863, -0.0532634]).reshape(4,4) - self.expected_dy_dem = torch.tensor([-3.43749, -3.43749, -3.43749, -3.43749, -4.91768, -4.91768, -4.91768, - -4.91768, -3.79784, -3.79784, -3.79784, -3.79784, -5.39484, -5.39484, - -5.39484, -5.39484, -4.91866, -4.91866, -4.91866, -4.91866, -5.40551, - -5.40551, -5.40551, -5.40551, -5.56471, -5.56471, -5.56471, -5.56471, - -4.87029, -4.87029, -4.87029, -4.87029, -5.71263, -5.71263, -5.71263, - -5.71263, -4.27996, -4.27996, -4.27996, -4.27996, -5.55454, -5.55454, - -5.55454, -5.55454, -5.07277, -5.07277, -5.07277, -5.07277, -5.96781, - -5.96781, -5.96781, -5.96781, -5.08156, -5.08156, -5.08156, -5.08156, - -5.23843, -5.23843, -5.23843, -5.23843, -3.90654, -3.90654, -3.90654, - -3.90654]).reshape(4,4,4) - + self.expected_dy_dem_x = torch.tensor( + [ + -0.0294449, + -0.0948144, + -0.039285, + -0.0826351, + -0.114794, + -0.0530287, + -0.0404511, + -0.0964537, + -0.148872, + -0.0394916, + -0.0330467, + -0.0767711, + -0.0893684, + -0.0666033, + -0.0360863, + -0.0532634, + ] + ).reshape(4, 4) + self.expected_dy_dem = torch.tensor( + [ + -3.43749, + -3.43749, + -3.43749, + -3.43749, + -4.91768, + -4.91768, + -4.91768, + -4.91768, + -3.79784, + -3.79784, + -3.79784, + -3.79784, + -5.39484, + -5.39484, + -5.39484, + -5.39484, + -4.91866, + -4.91866, + -4.91866, + -4.91866, + -5.40551, + -5.40551, + -5.40551, + -5.40551, + -5.56471, + -5.56471, + -5.56471, + -5.56471, + -4.87029, + -4.87029, + -4.87029, + -4.87029, + -5.71263, + -5.71263, + -5.71263, + -5.71263, + -4.27996, + -4.27996, + -4.27996, + -4.27996, + -5.55454, + -5.55454, + -5.55454, + -5.55454, + -5.07277, + -5.07277, + -5.07277, + -5.07277, + -5.96781, + -5.96781, + -5.96781, + -5.96781, + -5.08156, + -5.08156, + -5.08156, + -5.08156, + -5.23843, + -5.23843, + -5.23843, + -5.23843, + -3.90654, + -3.90654, + -3.90654, + -3.90654, + ] + ).reshape(4, 4, 4) + def test_forward(self): # Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_atten( - self.table_tensor, self.table_info_tensor, self.em_x_tensor, - self.em_tensor, self.two_embed_tensor, self.last_layer_size, - self.is_sorted + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.two_embed_tensor, + self.last_layer_size, + self.is_sorted, ) descriptor_tensor = forward_result[0] @@ -657,27 +1539,38 @@ def test_forward(self): self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) # Check the values - self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) - + self.assertTrue( + torch.allclose( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 + ) + ) + def test_backward(self): # Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_atten( - self.table_tensor, self.table_info_tensor, self.em_x_tensor, - self.em_tensor, self.two_embed_tensor, self.last_layer_size, - self.is_sorted + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.two_embed_tensor, + self.last_layer_size, + self.is_sorted, ) - - print('table_tensor', self.table_tensor.requires_grad) - print('table_info_tensor', self.table_info_tensor.requires_grad) - print('em_x_tensor', self.em_x_tensor.requires_grad) - print('em_tensor', self.em_tensor.requires_grad) + + print("table_tensor", self.table_tensor.requires_grad) + print("table_info_tensor", self.table_info_tensor.requires_grad) + print("em_x_tensor", self.em_x_tensor.requires_grad) + print("em_tensor", self.em_tensor.requires_grad) descriptor_tensor = forward_result[0] - print('descriptor_tensor', descriptor_tensor.requires_grad) + print("descriptor_tensor", descriptor_tensor.requires_grad) # Check the forward - self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) - + self.assertTrue( + torch.allclose( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 + ) + ) # Create a loss and perform backward loss = descriptor_tensor.sum() @@ -692,15 +1585,20 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - print('-----------------------------------------------') + print("-----------------------------------------------") print(self.em_x_tensor.grad[:5]) print(self.expected_dy_dem_x[:5]) - print('-----------------------------------------------') + print("-----------------------------------------------") print(self.em_tensor.grad[:5]) print(self.expected_dy_dem[:5]) - self.assertTrue(torch.allclose(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5)) - self.assertTrue(torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5)) + self.assertTrue( + torch.allclose(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5) + ) + self.assertTrue( + torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5) + ) + -if __name__ == '__main__': +if __name__ == "__main__": deepmd.pt.cxx_op.ENABLE_CUSTOMIZED_OP - unittest.main() \ No newline at end of file + unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index 74a5697fa2..0d2095a363 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -1,545 +1,1094 @@ -import torch +# SPDX-License-Identifier: LGPL-3.0-or-later import unittest + +import torch + import deepmd.pt.cxx_op + class TestTabulateFusionSeAOp(unittest.TestCase): - def setUp(self): - self.table_tensor = torch.tensor([ - 6.348551343037398542e-01, 4.209465843706336474e-04, - 6.390862740714405368e-03, -1.544448595628262176e-04, - -1.891095227974180087e-04, 2.695025951562175852e-05, - -1.317549846042939343e+00, -5.624478206903206490e-02, - 1.274284553146523905e-02, -6.836227424141475689e-04, - -1.438066096020836407e-04, -1.854932873974712940e-06, - -9.996964112615246423e-01, 6.928234423723647617e-02, - -4.974719973810486084e-03, -2.019584729176823030e-04, - 1.077254539742680247e-04, -8.024209768588029797e-06, - 3.552689563657350780e-01, -3.578299775339799371e-02, - -1.319946251007718743e-03, 1.016701374495701440e-03, - -1.057336720791906388e-04, 5.182678943855506567e-06, - 1.227750369557627286e+00, 4.100352079064395472e-02, - 3.586869164810712295e-03, -4.304540913340443135e-04, - -1.269943482892440004e-04, 1.459465404430219674e-05, - -1.472642501673147031e+00, -1.611354921283318364e-01, - 1.645427874390196360e-02, 2.107392978135091402e-04, - -2.193541011180757461e-04, 1.915392497459551146e-05, - -2.855174490181606739e-01, 9.774337856626263976e-02, - -2.140891880666230714e-03, -7.148328890055103638e-04, - 1.965696332267534503e-05, -4.593489654121371453e-06, - -1.468441009949382314e+00, -6.360828127262234399e-02, - 4.751283295356955282e-03, 8.711899561753186068e-05, - -9.937008678852959884e-06, 4.273569346584811685e-07, - 6.348599826995243722e-01, 5.487167506364742930e-04, - 6.386116198716365253e-03, -1.619832375568118791e-04, - -1.877328309473502049e-04, 2.134130914519164856e-05, - -1.318111020264137512e+00, -5.599013082054477008e-02, - 1.272225054666903735e-02, -6.893710047488201898e-04, - -1.434367581078517366e-04, 3.329508890614227371e-05, - -9.990040854920316793e-01, 6.918278968071900348e-02, - -4.980714172967731085e-03, -1.976574487947816198e-04, - 1.070037204086153902e-04, -7.859875077388093586e-06, - 3.549109954092205532e-01, -3.580909209068139365e-02, - -1.289508598157979719e-03, 1.012474257117017967e-03, - -1.054418924402112718e-04, -1.245498322204730900e-05, - 1.228160763020727630e+00, 4.107512853046493134e-02, - 3.573879491390910459e-03, -4.355190226638688713e-04, - -1.258433981470396103e-04, 1.610862268100766631e-05, - -1.474252210958008291e+00, -1.608063442081248406e-01, - 1.646046950167207382e-02, 2.019843636566674109e-04, - -2.185756589083626730e-04, 1.978479879983412190e-05, - -2.845402300363228942e-01, 9.770034635718018168e-02, - -2.162325119197382531e-03, -7.140472215558940627e-04, - 1.956302663031799223e-05, 1.932584474244053378e-05, - -1.469076617546759334e+00, -6.351322951074317436e-02, - 4.753890907276497185e-03, 8.672114560243554321e-05, - -1.004574434175897967e-05, -4.345700882560937596e-06, - 6.348661083147921769e-01, 6.763897297752743953e-04, - 6.381144275303845745e-03, -1.694690463885140694e-04, - -1.868179426353836598e-04, 3.439291082765030046e-05, - -1.318669650038090335e+00, -5.573589319299507294e-02, - 1.270148368741391351e-02, -6.950749719342792137e-04, - -1.422194703304518733e-04, 3.454751241752252323e-05, - -9.983127558632299836e-01, 6.908311652764687061e-02, - -4.986579772806746212e-03, -1.933888092529071571e-04, - 1.068327546750306073e-04, -2.976978385983384886e-05, - 3.545527765488725169e-01, -3.583457894275744043e-02, - -1.259197760082061621e-03, 1.008246479193084487e-03, - -1.059401869200098984e-04, 1.721968053146218465e-06, - 1.228571871257205572e+00, 4.114647496201748883e-02, - 3.560738575723638825e-03, -4.405332425718102457e-04, - -1.251648759618972115e-04, 3.659080417076460655e-05, - -1.475858628153338792e+00, -1.604770750960976822e-01, - 1.646639808472218428e-02, 1.932598402043995316e-04, - -2.175904819601363058e-04, 1.230256868634094333e-05, - -2.835634435191126679e-01, 9.765688571984927624e-02, - -2.183734604613508240e-03, -7.132463811570244078e-04, - 2.021887442373574272e-05, 1.321401495096886281e-05, - -1.469711274366155784e+00, -6.341812571665436660e-02, - 4.756486470714936521e-03, 8.631384191910702040e-05, - -1.010516500002806932e-05, -1.110874413279218719e-05, - 6.348735101551836735e-01, 8.039610290153098582e-04, - 6.375948457075718626e-03, -1.769074132993461279e-04, - -1.855677150383903214e-04, 3.421271436711027645e-05, - -1.319225739518145257e+00, -5.548207260888919634e-02, - 1.268054645200545304e-02, -7.007297564176242621e-04, - -1.408885818822980523e-04, 3.124701885930576017e-05, - -9.976224235482542557e-01, 6.898332734138989952e-02, - -4.992317635216104131e-03, -1.891404922064061889e-04, - 1.053957535708985289e-04, -1.089286646983666076e-06, - 3.541943058468561834e-01, -3.585946084769019160e-02, - -1.229013912637771933e-03, 1.004009466262262241e-03, - -1.059129033455631863e-04, -4.941663399086282537e-06, - 1.228983691638902087e+00, 4.121755707472917613e-02, - 3.547447845420277635e-03, -4.455036207721562607e-04, - -1.239172256532283074e-04, 3.437341080261359686e-05, - -1.477461752073406132e+00, -1.601476900261984693e-01, - 1.647206544856073471e-02, 1.845724864086241608e-04, - 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3.137220402938139478e-03, + -5.615677039256951981e-04, + -9.125763978623760322e-05, + 2.367398552885374808e-05, + -1.519498310980496925e00, + -1.512290469691385253e-01, + 1.652996628226939199e-02, + -3.745688059096337011e-05, + -1.938906911473592626e-04, + 1.811217640451412989e-05, + -2.564062357251438717e-01, + 9.626832379335603651e-02, + -2.771163091665611831e-03, + -6.829069315554202020e-04, + 3.363238372709415958e-05, + 8.623099725596635004e-06, + -1.487093617252511990e00, + -6.073523464295225993e-02, + 4.823154268625621383e-03, + 7.122599345182346051e-05, + -1.664931178025436733e-05, + -4.312450972708557703e-06, + ] + ).reshape(8, 132) self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1]) - self.em_tensor = torch.tensor([0.0343909, 0.11357423, 0.0858676, 0.19337772, - 0.1935728, 0.0477744, 0.05845198, 0.19080509, - 0.16111261, 0.07179262, 0.10078013, 0.04640909, - 0.10433399, 0.15650861, 0.17527857, 0.04249097]).reshape(4,4) + self.em_tensor = torch.tensor( + [ + 0.0343909, + 0.11357423, + 0.0858676, + 0.19337772, + 0.1935728, + 0.0477744, + 0.05845198, + 0.19080509, + 0.16111261, + 0.07179262, + 0.10078013, + 0.04640909, + 0.10433399, + 0.15650861, + 0.17527857, + 0.04249097, + ] + ).reshape(4, 4) self.table_info_tensor.requires_grad = True self.table_tensor.requires_grad = True self.em_tensor.requires_grad = True @@ -547,37 +1096,167 @@ def setUp(self): self.nloc = 4 self.nnei = 4 # forward test - self.expected_descriptor_tensor = torch.tensor([0.634877, -1.319469, -0.997320, 0.354037, 1.229165, -1.478165, - -0.282159, -1.470623, 0.634985, -1.323774, -0.991892, 0.351189, - 1.232453, -1.490731, -0.274445, -1.475604, 0.634938, -1.322286, - -0.993784, 0.352187, 1.231297, -1.486357, -0.277141, -1.473868, - 0.635174, -1.327955, -0.986486, 0.348307, 1.235810, -1.503186, - -0.266701, -1.480563, 0.635175, -1.327965, -0.986473, 0.348300, - 1.235819, -1.503216, -0.266682, -1.480575, 0.634890, -1.320208, - -0.996398, 0.353557, 1.229717, -1.480303, -0.280853, -1.471469, - 0.634902, -1.320794, -0.995664, 0.353173, 1.230159, -1.482005, - -0.279812, -1.472143, 0.635167, -1.327823, -0.986659, 0.348400, - 1.235701, -1.502788, -0.266950, -1.480404, 0.635088, -1.326284, - -0.988664, 0.349474, 1.234448, -1.498176, -0.269828, -1.478565, - 0.634918, -1.321522, -0.994748, 0.352694, 1.230712, -1.484126, - -0.278511, -1.472983, 0.634962, -1.323089, -0.992765, 0.351650, - 1.231919, -1.488714, -0.275689, -1.474803, 0.634888, -1.320133, - -0.996492, 0.353606, 1.229661, -1.480085, -0.280986, -1.471383, - 0.634968, -1.323280, -0.992522, 0.351522, 1.232067, -1.489275, - -0.275344, -1.475026, 0.635077, -1.326043, -0.988976, 0.349640, - 1.234254, -1.497458, -0.270275, -1.478280, 0.635124, -1.327021, - -0.987707, 0.348962, 1.235045, -1.500380, -0.268455, -1.479444, - 0.634885, -1.319917, -0.996762, 0.353746, 1.229499, -1.479460, - -0.281368, -1.471135]).reshape(4,4,8) + self.expected_descriptor_tensor = torch.tensor( + [ + 0.634877, + -1.319469, + -0.997320, + 0.354037, + 1.229165, + -1.478165, + -0.282159, + -1.470623, + 0.634985, + -1.323774, + -0.991892, + 0.351189, + 1.232453, + -1.490731, + -0.274445, + -1.475604, + 0.634938, + -1.322286, + -0.993784, + 0.352187, + 1.231297, + -1.486357, + -0.277141, + -1.473868, + 0.635174, + -1.327955, + -0.986486, + 0.348307, + 1.235810, + -1.503186, + -0.266701, + -1.480563, + 0.635175, + -1.327965, + -0.986473, + 0.348300, + 1.235819, + -1.503216, + -0.266682, + -1.480575, + 0.634890, + -1.320208, + -0.996398, + 0.353557, + 1.229717, + -1.480303, + -0.280853, + -1.471469, + 0.634902, + -1.320794, + -0.995664, + 0.353173, + 1.230159, + -1.482005, + -0.279812, + -1.472143, + 0.635167, + -1.327823, + -0.986659, + 0.348400, + 1.235701, + -1.502788, + -0.266950, + -1.480404, + 0.635088, + -1.326284, + -0.988664, + 0.349474, + 1.234448, + -1.498176, + -0.269828, + -1.478565, + 0.634918, + -1.321522, + -0.994748, + 0.352694, + 1.230712, + -1.484126, + -0.278511, + -1.472983, + 0.634962, + -1.323089, + -0.992765, + 0.351650, + 1.231919, + -1.488714, + -0.275689, + -1.474803, + 0.634888, + -1.320133, + -0.996492, + 0.353606, + 1.229661, + -1.480085, + -0.280986, + -1.471383, + 0.634968, + -1.323280, + -0.992522, + 0.351522, + 1.232067, + -1.489275, + -0.275344, + -1.475026, + 0.635077, + -1.326043, + -0.988976, + 0.349640, + 1.234254, + -1.497458, + -0.270275, + -1.478280, + 0.635124, + -1.327021, + -0.987707, + 0.348962, + 1.235045, + -1.500380, + -0.268455, + -1.479444, + 0.634885, + -1.319917, + -0.996762, + 0.353746, + 1.229499, + -1.479460, + -0.281368, + -1.471135, + ] + ).reshape(4, 4, 8) # backward test - self.expected_dy_dem = torch.tensor([-0.105883, -0.100297, -0.102247, -0.094712, -0.094698, -0.104937, - -0.104182, -0.094891, -0.096964, -0.103240, -0.101197, -0.105033, - -0.100947, -0.097286, -0.095974, -0.105310]).reshape(4,4) - + self.expected_dy_dem = torch.tensor( + [ + -0.105883, + -0.100297, + -0.102247, + -0.094712, + -0.094698, + -0.104937, + -0.104182, + -0.094891, + -0.096964, + -0.103240, + -0.101197, + -0.105033, + -0.100947, + -0.097286, + -0.095974, + -0.105310, + ] + ).reshape(4, 4) + def test_forward(self): # Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_r( - self.table_tensor, self.table_info_tensor, self.em_tensor, self.last_layer_size + self.table_tensor, + self.table_info_tensor, + self.em_tensor, + self.last_layer_size, ) descriptor_tensor = forward_result[0] @@ -586,19 +1265,29 @@ def test_forward(self): self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) # Check the values - self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) - + self.assertTrue( + torch.allclose( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 + ) + ) + def test_backward(self): # Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_r( - self.table_tensor, self.table_info_tensor, self.em_tensor, self.last_layer_size + self.table_tensor, + self.table_info_tensor, + self.em_tensor, + self.last_layer_size, ) descriptor_tensor = forward_result[0] # Check the forward - self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) - + self.assertTrue( + torch.allclose( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 + ) + ) # Create a loss and perform backward loss = descriptor_tensor.sum() @@ -611,8 +1300,11 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - self.assertTrue(torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5)) + self.assertTrue( + torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5) + ) + -if __name__ == '__main__': +if __name__ == "__main__": deepmd.pt.cxx_op.ENABLE_CUSTOMIZED_OP - unittest.main() \ No newline at end of file + unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py index 2c9263df1f..438d664433 100644 --- a/source/tests/pt/test_tabulate_fusion_se_t.py +++ b/source/tests/pt/test_tabulate_fusion_se_t.py @@ -1,1891 +1,5267 @@ -import torch +# SPDX-License-Identifier: LGPL-3.0-or-later import unittest + +import torch + import deepmd.pt.cxx_op + class TestTabulateFusionSeTOp(unittest.TestCase): - def setUp(self): - self.table_tensor = torch.tensor([ - -1.0600000163027882e+02, 7.7059358807135015e+02, -5.6954714749735385e+03, - 1.2167808756610991e+03, -7.6199102434332218e+01, 1.0706136029373441e+00, - -1.0600000164528124e+02, 7.7059358630452323e+02, -5.6954715659539552e+03, - 1.2167808757436076e+03, -7.6199099707724926e+01, 1.0706134206080884e+00, - -1.0600000163027882e+02, 7.7059358807135015e+02, -5.6954714749735385e+03, - 1.2167808756610991e+03, -7.6199102434332218e+01, 1.0706136029373441e+00, - -1.0600000164528124e+02, 7.7059358630452323e+02, -5.6954715659539552e+03, - 1.2167808757436076e+03, -7.6199099707724926e+01, 1.0706134206080884e+00, - -9.6000006759336443e+01, 6.2969719646863621e+02, -4.2053706363664551e+03, - 9.0372155784831205e+02, -5.7600014239472898e+01, 8.6528676197113796e-01, - -9.6000006828502180e+01, 6.2969718981238339e+02, -4.2053709121998018e+03, - 9.0372156236848912e+02, -5.7600006817493266e+01, 8.6528625106787871e-01, - -9.6000006759336443e+01, 6.2969719646863621e+02, -4.2053706363664551e+03, - 9.0372155784831205e+02, -5.7600014239472898e+01, 8.6528676197113796e-01, - -9.6000006828502180e+01, 6.2969718981238339e+02, -4.2053709121998018e+03, - 9.0372156236848912e+02, -5.7600006817493266e+01, 8.6528625106787871e-01, - -8.6000028021606425e+01, 5.0303296429845562e+02, -3.0008648248894533e+03, - 6.4939597734382562e+02, -4.2250984019314707e+01, 6.8180015607155764e-01, - -8.6000028340480625e+01, 5.0303293978396903e+02, -3.0008656209622986e+03, - 6.4939600529391078e+02, -4.2250965541906716e+01, 6.8179882734268982e-01, - -8.6000028021606425e+01, 5.0303296429845562e+02, -3.0008648248894533e+03, - 6.4939597734382562e+02, -4.2250984019314707e+01, 6.8180015607155764e-01, - -8.6000028340480625e+01, 5.0303293978396903e+02, -3.0008656209622986e+03, - 6.4939600529353049e+02, -4.2250965541830588e+01, 6.8179882733888086e-01, - -7.6000116148038558e+01, 3.9060139597613619e+02, -2.0515743554479322e+03, - 4.4772754091167945e+02, -2.9848087537832814e+01, 5.2014755686537917e-01, - -7.6000117618125429e+01, 3.9060130821883052e+02, -2.0515765138621105e+03, - 4.4772766653712006e+02, -2.9848047259266409e+01, 5.2014443989116910e-01, - -7.6000116148038558e+01, 3.9060139597613619e+02, -2.0515743554479322e+03, - 4.4772754091167945e+02, -2.9848087537832814e+01, 5.2014755686537917e-01, - -7.6000117618125742e+01, 3.9060130821877993e+02, -2.0515765138659344e+03, - 4.4772766652483722e+02, -2.9848047256692499e+01, 5.2014443976043645e-01, - -6.6000481290731443e+01, 2.9240425245900917e+02, -1.3271250821434478e+03, - 2.9263955624337893e+02, -2.0087224005740719e+01, 3.8031147992206349e-01, - -6.6000488067863742e+01, 2.9240394960550276e+02, -1.3271304743966571e+03, - 2.9264002765325057e+02, -2.0087154325946980e+01, 3.8030522013794582e-01, - -6.6000481290731443e+01, 2.9240425245900917e+02, -1.3271250821434478e+03, - 2.9263955624337893e+02, -2.0087224005740719e+01, 3.8031147992206349e-01, - -6.6000488067883694e+01, 2.9240394960308691e+02, -1.3271304745319526e+03, - 2.9264002727267626e+02, -2.0087154245656002e+01, 3.8030521605011575e-01, - -5.6001992867343972e+01, 2.0844745574402617e+02, -7.9715799906587699e+02, - 1.7805563184427194e+02, -1.2663929104029080e+01, 2.6224978307822894e-01, - -5.6002024103130161e+01, 2.0844646075692629e+02, -7.9717003898786652e+02, - 1.7805715054974732e+02, -1.2663864677938077e+01, 2.6224029170957303e-01, - -5.6001992867343972e+01, 2.0844745574402617e+02, -7.9715799906587699e+02, - 1.7805563184427194e+02, -1.2663929104029080e+01, 2.6224978307822894e-01, - -5.6002024104383771e+01, 2.0844646064871867e+02, -7.9717004324410516e+02, - 1.7805714044473001e+02, -1.2663862524337585e+01, 2.6224018166598279e-01, - -4.6008230210744550e+01, 1.3874976550319553e+02, -4.3134867537287749e+02, - 9.7902623595157010e+01, -7.2734403121911884e+00, 1.6589123996688057e-01, - -4.6008373996710617e+01, 1.3874671965012058e+02, -4.3137141216256458e+02, - 9.7906861443792735e+01, -7.2735856084076280e+00, 1.6588642735924275e-01, - -4.6008230210744550e+01, 1.3874976550319553e+02, -4.3134867537287749e+02, - 9.7902623595157010e+01, -7.2734403121911884e+00, 1.6589123996688057e-01, - -4.6008374075307870e+01, 1.3874671513440606e+02, -4.3137152784492957e+02, - 9.7906652364871050e+01, -7.2735401377994249e+00, 1.6588408717348646e-01, - -3.6033642533368131e+01, 8.3364086172019398e+01, -1.9942175516407502e+02, - 4.6124022747838069e+01, -3.6130563858549958e+00, 9.1249773312287188e-02, - -3.6034298111245583e+01, 8.3355843868269616e+01, -1.9945266030093268e+02, - 4.6135000705962462e+01, 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3fe4b64fe78f14f2f1b02a57df6edd4adc5a08c8 Mon Sep 17 00:00:00 2001 From: wangyan Date: Thu, 13 Jun 2024 19:56:52 +0800 Subject: [PATCH 11/85] remove print --- source/tests/pt/test_tabulate_fusion_se_atten.py | 12 ------------ source/tests/pt/test_tabulate_fusion_se_r.py | 1 - 2 files changed, 13 deletions(-) diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index fc4b7967d8..d29e1a76bc 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -667,14 +667,8 @@ def test_backward(self): self.is_sorted ) - print('table_tensor', self.table_tensor.requires_grad) - print('table_info_tensor', self.table_info_tensor.requires_grad) - print('em_x_tensor', self.em_x_tensor.requires_grad) - print('em_tensor', self.em_tensor.requires_grad) - descriptor_tensor = forward_result[0] - print('descriptor_tensor', descriptor_tensor.requires_grad) # Check the forward self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) @@ -692,12 +686,6 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - print('-----------------------------------------------') - print(self.em_x_tensor.grad[:5]) - print(self.expected_dy_dem_x[:5]) - print('-----------------------------------------------') - print(self.em_tensor.grad[:5]) - print(self.expected_dy_dem[:5]) self.assertTrue(torch.allclose(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5)) self.assertTrue(torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5)) diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index 74a5697fa2..6fda45e476 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -599,7 +599,6 @@ def test_backward(self): # Check the forward self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) - # Create a loss and perform backward loss = descriptor_tensor.sum() loss.backward() From b89ceed6a783be6fc1c177264eeeb0df430fdbab Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 13 Jun 2024 12:08:00 +0000 Subject: [PATCH 12/85] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- source/tests/pt/test_tabulate_fusion_se_atten.py | 11 ++++++++--- source/tests/pt/test_tabulate_fusion_se_r.py | 7 +++++-- 2 files changed, 13 insertions(+), 5 deletions(-) diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index 3a5253ad93..565c5f68a6 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -1556,7 +1556,7 @@ def test_backward(self): self.last_layer_size, self.is_sorted, ) - + descriptor_tensor = forward_result[0] # Check the forward @@ -1579,8 +1579,13 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - self.assertTrue(torch.allclose(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5)) - self.assertTrue(torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5)) + self.assertTrue( + torch.allclose(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5) + ) + self.assertTrue( + torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5) + ) + if __name__ == "__main__": deepmd.pt.cxx_op.ENABLE_CUSTOMIZED_OP diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index 78bc04873a..0d2095a363 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -1283,8 +1283,11 @@ def test_backward(self): descriptor_tensor = forward_result[0] # Check the forward - self.assertTrue(torch.allclose(descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5)) - + self.assertTrue( + torch.allclose( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 + ) + ) # Create a loss and perform backward loss = descriptor_tensor.sum() From 5a8b77eafd18e9e5cbda092abceeb3999df6c182 Mon Sep 17 00:00:00 2001 From: wangyan Date: Thu, 13 Jun 2024 22:24:31 +0800 Subject: [PATCH 13/85] fix pull request warning --- source/op/pt/tabulate_multi_device.cc | 19 ++++++++----------- 1 file changed, 8 insertions(+), 11 deletions(-) diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index 9a6a595e78..8cc87edef5 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -113,7 +113,6 @@ void TabulateFusionSeAGradForward(const torch::Tensor& table_tensor, ? nullptr : two_embed_tensor.view({-1}).data_ptr(); const FPTYPE* dy = dy_tensor.view({-1}).data_ptr(); - const FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); const int64_t nloc = em_tensor.size(0); const int64_t nnei = em_tensor.size(1); const int64_t last_layer_size = descriptor_tensor.size(2); @@ -259,7 +258,6 @@ void TabulateFusionSeTGradForward(const torch::Tensor& table_tensor, const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); const FPTYPE* dy = dy_tensor.view({-1}).data_ptr(); - const FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); const int64_t nloc = em_tensor.size(0); const int64_t nnei_i = em_tensor.size(1); const int64_t nnei_j = em_tensor.size(2); @@ -384,7 +382,6 @@ void TabulateFusionSeRGradForward(const torch::Tensor& table_tensor, const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); const FPTYPE* dy = dy_tensor.view({-1}).data_ptr(); - const FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); const int64_t nloc = em_tensor.size(0); const int64_t nnei = em_tensor.size(1); const int64_t last_layer_size = descriptor_tensor.size(2); @@ -923,7 +920,7 @@ class TabulateFusionSeROp } }; -std::vector tabluate_fusion_se_a( +std::vector tabulate_fusion_se_a( const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, const torch::Tensor& em_x_tensor, @@ -933,7 +930,7 @@ std::vector tabluate_fusion_se_a( em_x_tensor, em_tensor, last_layer_size); } -std::vector tabluate_fusion_se_atten( +std::vector tabulate_fusion_se_atten( const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, const torch::Tensor& em_x_tensor, @@ -946,7 +943,7 @@ std::vector tabluate_fusion_se_atten( last_layer_size, is_sorted); } -std::vector tabluate_fusion_se_t( +std::vector tabulate_fusion_se_t( const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, const torch::Tensor& em_x_tensor, @@ -956,7 +953,7 @@ std::vector tabluate_fusion_se_t( em_x_tensor, em_tensor, last_layer_size); } -std::vector tabluate_fusion_se_r( +std::vector tabulate_fusion_se_r( const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, const torch::Tensor& em_tensor, @@ -966,14 +963,14 @@ std::vector tabluate_fusion_se_r( } TORCH_LIBRARY_FRAGMENT(deepmd, m) { - m.def("tabulate_fusion_se_a", tabluate_fusion_se_a); + m.def("tabulate_fusion_se_a", tabulate_fusion_se_a); } TORCH_LIBRARY_FRAGMENT(deepmd, m) { - m.def("tabulate_fusion_se_atten", tabluate_fusion_se_atten); + m.def("tabulate_fusion_se_atten", tabulate_fusion_se_atten); } TORCH_LIBRARY_FRAGMENT(deepmd, m) { - m.def("tabulate_fusion_se_t", tabluate_fusion_se_t); + m.def("tabulate_fusion_se_t", tabulate_fusion_se_t); } TORCH_LIBRARY_FRAGMENT(deepmd, m) { - m.def("tabulate_fusion_se_r", tabluate_fusion_se_r); + m.def("tabulate_fusion_se_r", tabulate_fusion_se_r); } From 25ca8c19d1b2865d33ba13777a75aff8d8319de8 Mon Sep 17 00:00:00 2001 From: wangyan Date: Thu, 13 Jun 2024 22:35:25 +0800 Subject: [PATCH 14/85] fix pr warning --- source/tests/pt/test_tabulate_fusion_se_a.py | 1 - source/tests/pt/test_tabulate_fusion_se_atten.py | 1 - source/tests/pt/test_tabulate_fusion_se_r.py | 1 - source/tests/pt/test_tabulate_fusion_se_t.py | 1 - 4 files changed, 4 deletions(-) diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index b6d7a62456..863b3c9b45 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -1451,5 +1451,4 @@ def test_backward(self): if __name__ == "__main__": - deepmd.pt.cxx_op.ENABLE_CUSTOMIZED_OP unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index 565c5f68a6..7557db771d 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -1588,5 +1588,4 @@ def test_backward(self): if __name__ == "__main__": - deepmd.pt.cxx_op.ENABLE_CUSTOMIZED_OP unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index 0d2095a363..2d7aa03034 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -1306,5 +1306,4 @@ def test_backward(self): if __name__ == "__main__": - deepmd.pt.cxx_op.ENABLE_CUSTOMIZED_OP unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py index 438d664433..a0376f8675 100644 --- a/source/tests/pt/test_tabulate_fusion_se_t.py +++ b/source/tests/pt/test_tabulate_fusion_se_t.py @@ -12199,5 +12199,4 @@ def setUp(self): if __name__ == "__main__": - deepmd.pt.cxx_op.ENABLE_CUSTOMIZED_OP unittest.main() From f1c43f588ea2347eddeffc3e09036bc48294364b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Thu, 13 Jun 2024 14:36:30 +0000 Subject: [PATCH 15/85] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- source/tests/pt/test_tabulate_fusion_se_a.py | 2 -- source/tests/pt/test_tabulate_fusion_se_atten.py | 2 -- source/tests/pt/test_tabulate_fusion_se_r.py | 2 -- source/tests/pt/test_tabulate_fusion_se_t.py | 2 -- 4 files changed, 8 deletions(-) diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index 863b3c9b45..a70833a783 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -3,8 +3,6 @@ import torch -import deepmd.pt.cxx_op - class TestTabulateFusionSeAOp(unittest.TestCase): def setUp(self): diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index 7557db771d..748d15b4e6 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -3,8 +3,6 @@ import torch -import deepmd.pt.cxx_op - class TestTabulateFusionSeAttenOp(unittest.TestCase): def setUp(self): diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index 2d7aa03034..4c6f64880c 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -3,8 +3,6 @@ import torch -import deepmd.pt.cxx_op - class TestTabulateFusionSeAOp(unittest.TestCase): def setUp(self): diff --git a/source/tests/pt/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py index a0376f8675..ba184b951a 100644 --- a/source/tests/pt/test_tabulate_fusion_se_t.py +++ b/source/tests/pt/test_tabulate_fusion_se_t.py @@ -3,8 +3,6 @@ import torch -import deepmd.pt.cxx_op - class TestTabulateFusionSeTOp(unittest.TestCase): def setUp(self): From 179e1759699531640515fc730b17b4d405ecffe1 Mon Sep 17 00:00:00 2001 From: wangyan Date: Sun, 16 Jun 2024 22:46:18 +0800 Subject: [PATCH 16/85] gpu test debug --- source/op/pt/tabulate_multi_device.cc | 65 ++++++++++++++----- source/tests/pt/test_tabulate_fusion_se_a.py | 55 +++++++++------- .../tests/pt/test_tabulate_fusion_se_atten.py | 46 +++++++------ source/tests/pt/test_tabulate_fusion_se_r.py | 35 +++++----- source/tests/pt/test_tabulate_fusion_se_t.py | 25 +++++-- 5 files changed, 145 insertions(+), 81 deletions(-) diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index 8cc87edef5..ae5c3127ec 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -18,10 +18,32 @@ void GetTensorDevice(const torch::Tensor& t, std::string& str) { } } -void print_first_five_elements(const torch::Tensor& tensor, - const std::string& name) { - std::cout << "First five elements of " << name << ":" << std::endl; - std::cout << tensor.slice(0, 0, 5) << std::endl; +#include + +void checkPointerLocation(const void* ptr, const std::string& name) { + cudaPointerAttributes attributes; + cudaError_t err = cudaPointerGetAttributes(&attributes, ptr); + + if (err != cudaSuccess) { + std::cerr << "Error checking pointer " << name << ": " << cudaGetErrorString(err) << std::endl; + return; + } + + if (attributes.type == cudaMemoryTypeDevice) { + std::cout << "Pointer " << name << " is located in device memory." << std::endl; + } else if (attributes.type == cudaMemoryTypeHost) { + std::cout << "Pointer " << name << " is located in host memory." << std::endl; + } else { + std::cout << "Pointer " << name << " is of unknown memory type." << std::endl; + } +} + +void check_contiguity(const torch::Tensor& tensor, const std::string& name) { + if (tensor.is_contiguous()) { + std::cout << name << " is contiguous" << std::endl; + } else { + std::cout << name << " is not contiguous" << std::endl; + } } template @@ -48,6 +70,18 @@ void TabulateFusionSeAForward(const torch::Tensor& table_tensor, // get the device std::string device; GetTensorDevice(table_tensor, device); + // debug + std::cout << "table_tensor device: " << table_tensor.device().type() << std::endl; + std::cout << "table_info_tensor device: " << table_info_tensor.device().type() << std::endl; + std::cout << "em_x_tensor device: " << em_x_tensor.device().type() << std::endl; + std::cout << "em_tensor device: " << em_tensor.device().type() << std::endl; + std::cout << "descriptor_tensor device before computation: " << descriptor_tensor.device().type() << std::endl; + + check_contiguity(table_tensor, "table_tensor"); + check_contiguity(table_info_tensor, "table_info_tensor"); + check_contiguity(em_x_tensor, "em_x_tensor"); + check_contiguity(em_tensor, "em_tensor"); + check_contiguity(descriptor_tensor, "descriptor_tensor"); // flat the tensors FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); @@ -55,6 +89,11 @@ void TabulateFusionSeAForward(const torch::Tensor& table_tensor, const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); + checkPointerLocation(descriptor, "descriptor"); + checkPointerLocation(table, "table"); + checkPointerLocation(table_info, "table_info"); + checkPointerLocation(em_x, "em_x"); + checkPointerLocation(em, "em"); const FPTYPE* two_embed = (!two_embed_tensor.defined()) ? nullptr @@ -65,6 +104,7 @@ void TabulateFusionSeAForward(const torch::Tensor& table_tensor, // compute if (device == "GPU") { #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM + std::cout << "checkpoint enter gpu function" << std::endl; deepmd::tabulate_fusion_se_a_gpu(descriptor, table, table_info, em_x, em, two_embed, nloc, nnei, last_layer_size); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM @@ -89,12 +129,6 @@ void TabulateFusionSeAGradForward(const torch::Tensor& table_tensor, if (dy_tensor.dim() != 3) { throw std::invalid_argument("Dim of dy_tensor should be 3"); } - // check input value - // print_first_five_elements(dy_tensor, "dy_tensor"); - // print_first_five_elements(descriptor_tensor, "descriptor_tensor"); - // print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); - // print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); - // get the device std::string device; GetTensorDevice(table_tensor, device); // flat the tensors @@ -128,8 +162,6 @@ void TabulateFusionSeAGradForward(const torch::Tensor& table_tensor, table_info, em_x, em, two_embed, dy, nloc, nnei, last_layer_size); } - // print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); - // print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); } template @@ -470,6 +502,12 @@ class TabulateFusionSeAOp .device(table_tensor.device()); torch::Tensor descriptor_tensor = torch::empty({em_tensor.size(0), 4, last_layer_size}, options); + // test device + // std::cout << "table_tensor device: " << table_tensor.device().type() << std::endl; + // std::cout << "table_info_tensor device: " << table_info_tensor.device().type() << std::endl; + // std::cout << "em_x_tensor device: " << em_x_tensor.device().type() << std::endl; + // std::cout << "em_tensor device: " << em_tensor.device().type() << std::endl; + // std::cout << "descriptor_tensor device: " << descriptor_tensor.device().type() << std::endl; // compute TabulateFusionSeAForward(table_tensor, table_info_tensor, em_x_tensor, em_tensor, at::Tensor(), @@ -518,9 +556,6 @@ class TabulateFusionSeAOp two_embed_tensor, dy_tensor, descriptor_tensor, dy_dem_x_tensor, dy_dem_tensor, dy_dtwo_tensor); - // std::cout << "----------------------------------------------" << - // std::endl; print_first_five_elements(dy_dem_x_tensor, "dy_dem_x_tensor"); - // print_first_five_elements(dy_dem_tensor, "dy_dem_tensor"); return {at::Tensor(), at::Tensor(), dy_dem_x_tensor, dy_dem_tensor, at::Tensor()}; } diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index 863b3c9b45..89272cbbbf 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -5,6 +5,10 @@ import deepmd.pt.cxx_op +from deepmd.pt.utils import ( + env, +) + class TestTabulateFusionSeAOp(unittest.TestCase): def setUp(self): @@ -1066,9 +1070,10 @@ def setUp(self): 7.122599345182346051e-05, -1.664931178025436733e-05, -4.312450972708557703e-06, - ] + ], + device=env.DEVICE ).reshape(8, 132) - self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1]) + self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE) self.em_x_tensor = torch.tensor( [ 0.0343909, @@ -1087,7 +1092,8 @@ def setUp(self): 0.15650861, 0.17527857, 0.04249097, - ] + ], + device=env.DEVICE ).reshape(4, 4) self.em_tensor = torch.tensor( [ @@ -1155,13 +1161,13 @@ def setUp(self): 0.17082205, 0.18275348, 0.02921504, - ] + ], + device=env.DEVICE ).reshape(4, 4, 4) self.table_info_tensor.requires_grad = True self.table_tensor.requires_grad = True self.em_x_tensor.requires_grad = True self.em_tensor.requires_grad = True - self.two_embed = torch.empty(4, 4, 8) self.last_layer_size = 8 self.nloc = 4 self.nnei = 4 @@ -1296,7 +1302,8 @@ def setUp(self): -0.51130404, -0.09300045, -0.50528542, - ] + ], + device=env.DEVICE ).reshape(4, 4, 8) # backward test self.expected_dy_dem_x = torch.tensor( @@ -1317,7 +1324,8 @@ def setUp(self): -0.03794369, -0.02917727, -0.04478649, - ] + ], + device=env.DEVICE ).reshape(4, 4) self.expected_dy_dem = torch.tensor( [ @@ -1385,10 +1393,15 @@ def setUp(self): -3.33051143, -3.33051143, -3.33051143, - ] + ], + device=env.DEVICE ).reshape(4, 4, 4) def test_forward(self): + print("table_tensor device:", self.table_tensor.device) + print("table_info_tensor device:", self.table_info_tensor.device) + print("em_x_tensor device:", self.em_x_tensor.device) + print("em_tensor device:", self.em_tensor.device) # Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_a( self.table_tensor, @@ -1404,13 +1417,15 @@ def test_forward(self): self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) # Check the values - self.assertTrue( - torch.allclose( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 - ) + torch.testing.assert_close( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 ) def test_backward(self): + print("table_tensor device:", self.table_tensor.device) + print("table_info_tensor device:", self.table_info_tensor.device) + print("em_x_tensor device:", self.em_x_tensor.device) + print("em_tensor device:", self.em_tensor.device) # Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_a( self.table_tensor, @@ -1423,10 +1438,8 @@ def test_backward(self): descriptor_tensor = forward_result[0] # Check the forward - self.assertTrue( - torch.allclose( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 - ) + torch.testing.assert_close( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 ) # Create a loss and perform backward @@ -1442,13 +1455,11 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - self.assertTrue( - torch.allclose(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5) - ) - self.assertTrue( - torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5) - ) + torch.testing.assert_close(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5, rtol=1e-5) + + torch.testing.assert_close(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5) if __name__ == "__main__": + env.DEVICE = 'cpu' unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index 7557db771d..279904c984 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -5,6 +5,10 @@ import deepmd.pt.cxx_op +from deepmd.pt.utils import ( + env, +) + class TestTabulateFusionSeAttenOp(unittest.TestCase): def setUp(self): @@ -1066,9 +1070,10 @@ def setUp(self): 7.122599345182346051e-05, -1.664931178025436733e-05, -4.312450972708557703e-06, - ] + ], + device=env.DEVICE ).reshape(8, 132) - self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1]) + self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE) self.em_x_tensor = torch.tensor( [ 0.0343909, @@ -1087,7 +1092,8 @@ def setUp(self): 0.15650861, 0.17527857, 0.04249097, - ] + ], + device=env.DEVICE ).reshape(4, 4) self.em_tensor = torch.tensor( [ @@ -1155,7 +1161,8 @@ def setUp(self): 0.17082205, 0.18275348, 0.02921504, - ] + ], + device=env.DEVICE ).reshape(4, 4, 4) self.two_embed_tensor = torch.tensor( [ @@ -1287,7 +1294,8 @@ def setUp(self): 0.44489583733770977, 0.5194672674960213, 0.04635102497306032, - ] + ], + device=env.DEVICE ).reshape(8, 16) self.table_info_tensor.requires_grad = False self.table_tensor.requires_grad = False @@ -1429,7 +1437,8 @@ def setUp(self): -0.915656, -0.162872, -0.723229, - ] + ], + device=env.DEVICE ).reshape(4, 4, 8) # backward test self.expected_dy_dem_x = torch.tensor( @@ -1518,7 +1527,8 @@ def setUp(self): -3.90654, -3.90654, -3.90654, - ] + ], + device=env.DEVICE ).reshape(4, 4, 4) def test_forward(self): @@ -1539,10 +1549,8 @@ def test_forward(self): self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) # Check the values - self.assertTrue( - torch.allclose( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 - ) + torch.testing.assert_close( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 ) def test_backward(self): @@ -1560,10 +1568,8 @@ def test_backward(self): descriptor_tensor = forward_result[0] # Check the forward - self.assertTrue( - torch.allclose( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 - ) + torch.testing.assert_close( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 ) # Create a loss and perform backward @@ -1579,13 +1585,11 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - self.assertTrue( - torch.allclose(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5) - ) - self.assertTrue( - torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5) - ) + torch.testing.assert_close(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5, rtol=1e-5) + + torch.testing.assert_close(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5) if __name__ == "__main__": + env.DEVICE = 'cpu' unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index 2d7aa03034..d21e0dc314 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -5,6 +5,10 @@ import deepmd.pt.cxx_op +from deepmd.pt.utils import ( + env, +) + class TestTabulateFusionSeAOp(unittest.TestCase): def setUp(self): @@ -1066,9 +1070,10 @@ def setUp(self): 7.122599345182346051e-05, -1.664931178025436733e-05, -4.312450972708557703e-06, - ] + ], + device=env.DEVICE ).reshape(8, 132) - self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1]) + self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE) self.em_tensor = torch.tensor( [ 0.0343909, @@ -1087,7 +1092,8 @@ def setUp(self): 0.15650861, 0.17527857, 0.04249097, - ] + ], + device=env.DEVICE ).reshape(4, 4) self.table_info_tensor.requires_grad = True self.table_tensor.requires_grad = True @@ -1226,7 +1232,8 @@ def setUp(self): -1.479460, -0.281368, -1.471135, - ] + ], + device=env.DEVICE ).reshape(4, 4, 8) # backward test self.expected_dy_dem = torch.tensor( @@ -1247,7 +1254,8 @@ def setUp(self): -0.097286, -0.095974, -0.105310, - ] + ], + device=env.DEVICE ).reshape(4, 4) def test_forward(self): @@ -1265,10 +1273,8 @@ def test_forward(self): self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) # Check the values - self.assertTrue( - torch.allclose( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 - ) + torch.testing.assert_close( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 ) def test_backward(self): @@ -1283,10 +1289,8 @@ def test_backward(self): descriptor_tensor = forward_result[0] # Check the forward - self.assertTrue( - torch.allclose( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5 - ) + torch.testing.assert_close( + descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 ) # Create a loss and perform backward @@ -1300,10 +1304,9 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - self.assertTrue( - torch.allclose(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5) - ) + torch.testing.assert_close(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5) if __name__ == "__main__": + env.DEVICE = 'cuda:0' unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py index a0376f8675..238d7c423e 100644 --- a/source/tests/pt/test_tabulate_fusion_se_t.py +++ b/source/tests/pt/test_tabulate_fusion_se_t.py @@ -5,6 +5,10 @@ import deepmd.pt.cxx_op +from deepmd.pt.utils import ( + env, +) + class TestTabulateFusionSeTOp(unittest.TestCase): def setUp(self): @@ -1402,7 +1406,8 @@ def setUp(self): -4.5894061525528980e02, 1.1623761628208563e01, 7.8927378661620728e-01, - ] + ], + device=env.DEVICE ).reshape(8, 174) self.table_info_tensor = torch.tensor( [ @@ -1412,7 +1417,8 @@ def setUp(self): 1.0000000000000000e00, 1.0000000000000000e01, -1.0000000000000000e00, - ] + ], + device=env.DEVICE ) self.em_x_tensor = torch.tensor( [ @@ -4488,7 +4494,8 @@ def setUp(self): 1.9232787391811296e-01, -9.1370205009458638e-03, 5.2699958100439070e-01, - ] + ], + device=env.DEVICE ).reshape(192, 4, 4) # 3072 self.dy = torch.tensor( [ @@ -5260,7 +5267,8 @@ def setUp(self): -8.8057490788653024e-04, -5.8240597405691328e-04, -3.9217722061436343e-04, - ] + ], + device=env.DEVICE ) self.em_tensor = self.em_x_tensor self.table_info_tensor.requires_grad = False @@ -6042,7 +6050,8 @@ def setUp(self): 5.2969306085468259e02, 5.0356864383992928e02, 5.2869887670804815e02, - ] + ], + device=env.DEVICE ).reshape(8, 174) self.expected_dy_dem_x = torch.tensor( [ @@ -9118,7 +9127,8 @@ def setUp(self): -2.2439596920688072e-04, 1.0549891235828525e-05, -5.8598966626073352e-04, - ] + ], + device=env.DEVICE ) self.expected_dy_dem = torch.tensor( [ @@ -12194,7 +12204,8 @@ def setUp(self): 2.3496098746511552e-04, 4.7088258363160609e-04, -1.4457178691538466e-04, - ] + ], + device=env.DEVICE ) From 4cc1478a7cd0309513798ea0d87107bac374788f Mon Sep 17 00:00:00 2001 From: wangyan Date: Sun, 16 Jun 2024 22:53:51 +0800 Subject: [PATCH 17/85] merge --- source/tests/pt/test_tabulate_fusion_se_a.py | 2 +- source/tests/pt/test_tabulate_fusion_se_atten.py | 2 +- source/tests/pt/test_tabulate_fusion_se_r.py | 2 +- source/tests/pt/test_tabulate_fusion_se_t.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index c1247ec08e..1ba3b0e53c 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -4,7 +4,7 @@ import torch from deepmd.pt.utils import ( - env, + env ) diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index c2355d8178..0211e76788 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -4,7 +4,7 @@ import torch from deepmd.pt.utils import ( - env, + env ) diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index c0d9b4d303..11b05f95c6 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -4,7 +4,7 @@ import torch from deepmd.pt.utils import ( - env, + env ) diff --git a/source/tests/pt/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py index dfc4ef09e8..c18a1e70bf 100644 --- a/source/tests/pt/test_tabulate_fusion_se_t.py +++ b/source/tests/pt/test_tabulate_fusion_se_t.py @@ -4,7 +4,7 @@ import torch from deepmd.pt.utils import ( - env, + env ) From 5921a60a4fde54b28ebdfc74b0723b7aa4ffa96f Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sun, 16 Jun 2024 14:54:35 +0000 Subject: [PATCH 18/85] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- source/op/pt/tabulate_multi_device.cc | 65 +++++++++++-------- source/tests/pt/test_tabulate_fusion_se_a.py | 28 ++++---- .../tests/pt/test_tabulate_fusion_se_atten.py | 28 ++++---- source/tests/pt/test_tabulate_fusion_se_r.py | 20 +++--- source/tests/pt/test_tabulate_fusion_se_t.py | 16 ++--- 5 files changed, 91 insertions(+), 66 deletions(-) diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index ae5c3127ec..628d5ecc2f 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -21,29 +21,33 @@ void GetTensorDevice(const torch::Tensor& t, std::string& str) { #include void checkPointerLocation(const void* ptr, const std::string& name) { - cudaPointerAttributes attributes; - cudaError_t err = cudaPointerGetAttributes(&attributes, ptr); + cudaPointerAttributes attributes; + cudaError_t err = cudaPointerGetAttributes(&attributes, ptr); - if (err != cudaSuccess) { - std::cerr << "Error checking pointer " << name << ": " << cudaGetErrorString(err) << std::endl; - return; - } + if (err != cudaSuccess) { + std::cerr << "Error checking pointer " << name << ": " + << cudaGetErrorString(err) << std::endl; + return; + } - if (attributes.type == cudaMemoryTypeDevice) { - std::cout << "Pointer " << name << " is located in device memory." << std::endl; - } else if (attributes.type == cudaMemoryTypeHost) { - std::cout << "Pointer " << name << " is located in host memory." << std::endl; - } else { - std::cout << "Pointer " << name << " is of unknown memory type." << std::endl; - } + if (attributes.type == cudaMemoryTypeDevice) { + std::cout << "Pointer " << name << " is located in device memory." + << std::endl; + } else if (attributes.type == cudaMemoryTypeHost) { + std::cout << "Pointer " << name << " is located in host memory." + << std::endl; + } else { + std::cout << "Pointer " << name << " is of unknown memory type." + << std::endl; + } } void check_contiguity(const torch::Tensor& tensor, const std::string& name) { - if (tensor.is_contiguous()) { - std::cout << name << " is contiguous" << std::endl; - } else { - std::cout << name << " is not contiguous" << std::endl; - } + if (tensor.is_contiguous()) { + std::cout << name << " is contiguous" << std::endl; + } else { + std::cout << name << " is not contiguous" << std::endl; + } } template @@ -71,11 +75,15 @@ void TabulateFusionSeAForward(const torch::Tensor& table_tensor, std::string device; GetTensorDevice(table_tensor, device); // debug - std::cout << "table_tensor device: " << table_tensor.device().type() << std::endl; - std::cout << "table_info_tensor device: " << table_info_tensor.device().type() << std::endl; - std::cout << "em_x_tensor device: " << em_x_tensor.device().type() << std::endl; + std::cout << "table_tensor device: " << table_tensor.device().type() + << std::endl; + std::cout << "table_info_tensor device: " << table_info_tensor.device().type() + << std::endl; + std::cout << "em_x_tensor device: " << em_x_tensor.device().type() + << std::endl; std::cout << "em_tensor device: " << em_tensor.device().type() << std::endl; - std::cout << "descriptor_tensor device before computation: " << descriptor_tensor.device().type() << std::endl; + std::cout << "descriptor_tensor device before computation: " + << descriptor_tensor.device().type() << std::endl; check_contiguity(table_tensor, "table_tensor"); check_contiguity(table_info_tensor, "table_info_tensor"); @@ -503,12 +511,13 @@ class TabulateFusionSeAOp torch::Tensor descriptor_tensor = torch::empty({em_tensor.size(0), 4, last_layer_size}, options); // test device - // std::cout << "table_tensor device: " << table_tensor.device().type() << std::endl; - // std::cout << "table_info_tensor device: " << table_info_tensor.device().type() << std::endl; - // std::cout << "em_x_tensor device: " << em_x_tensor.device().type() << std::endl; - // std::cout << "em_tensor device: " << em_tensor.device().type() << std::endl; - // std::cout << "descriptor_tensor device: " << descriptor_tensor.device().type() << std::endl; - // compute + // std::cout << "table_tensor device: " << table_tensor.device().type() << + // std::endl; std::cout << "table_info_tensor device: " << + // table_info_tensor.device().type() << std::endl; std::cout << "em_x_tensor + // device: " << em_x_tensor.device().type() << std::endl; std::cout << + // "em_tensor device: " << em_tensor.device().type() << std::endl; std::cout + // << "descriptor_tensor device: " << descriptor_tensor.device().type() << + // std::endl; compute TabulateFusionSeAForward(table_tensor, table_info_tensor, em_x_tensor, em_tensor, at::Tensor(), last_layer_size, descriptor_tensor); diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index 1ba3b0e53c..221cf7c028 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -4,7 +4,7 @@ import torch from deepmd.pt.utils import ( - env + env, ) @@ -1069,9 +1069,11 @@ def setUp(self): -1.664931178025436733e-05, -4.312450972708557703e-06, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(8, 132) - self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE) + self.table_info_tensor = torch.tensor( + [0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE + ) self.em_x_tensor = torch.tensor( [ 0.0343909, @@ -1091,7 +1093,7 @@ def setUp(self): 0.17527857, 0.04249097, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4) self.em_tensor = torch.tensor( [ @@ -1160,7 +1162,7 @@ def setUp(self): 0.18275348, 0.02921504, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4, 4) self.table_info_tensor.requires_grad = True self.table_tensor.requires_grad = True @@ -1301,7 +1303,7 @@ def setUp(self): -0.09300045, -0.50528542, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4, 8) # backward test self.expected_dy_dem_x = torch.tensor( @@ -1323,7 +1325,7 @@ def setUp(self): -0.02917727, -0.04478649, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4) self.expected_dy_dem = torch.tensor( [ @@ -1392,7 +1394,7 @@ def setUp(self): -3.33051143, -3.33051143, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4, 4) def test_forward(self): @@ -1453,11 +1455,15 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - torch.testing.assert_close(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5, rtol=1e-5) + torch.testing.assert_close( + self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5, rtol=1e-5 + ) - torch.testing.assert_close(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5) + torch.testing.assert_close( + self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5 + ) if __name__ == "__main__": - env.DEVICE = 'cpu' + env.DEVICE = "cpu" unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index 0211e76788..fa05d55bf4 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -4,7 +4,7 @@ import torch from deepmd.pt.utils import ( - env + env, ) @@ -1069,9 +1069,11 @@ def setUp(self): -1.664931178025436733e-05, -4.312450972708557703e-06, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(8, 132) - self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE) + self.table_info_tensor = torch.tensor( + [0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE + ) self.em_x_tensor = torch.tensor( [ 0.0343909, @@ -1091,7 +1093,7 @@ def setUp(self): 0.17527857, 0.04249097, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4) self.em_tensor = torch.tensor( [ @@ -1160,7 +1162,7 @@ def setUp(self): 0.18275348, 0.02921504, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4, 4) self.two_embed_tensor = torch.tensor( [ @@ -1293,7 +1295,7 @@ def setUp(self): 0.5194672674960213, 0.04635102497306032, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(8, 16) self.table_info_tensor.requires_grad = False self.table_tensor.requires_grad = False @@ -1436,7 +1438,7 @@ def setUp(self): -0.162872, -0.723229, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4, 8) # backward test self.expected_dy_dem_x = torch.tensor( @@ -1526,7 +1528,7 @@ def setUp(self): -3.90654, -3.90654, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4, 4) def test_forward(self): @@ -1583,11 +1585,15 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - torch.testing.assert_close(self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5, rtol=1e-5) + torch.testing.assert_close( + self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5, rtol=1e-5 + ) - torch.testing.assert_close(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5) + torch.testing.assert_close( + self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5 + ) if __name__ == "__main__": - env.DEVICE = 'cpu' + env.DEVICE = "cpu" unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index 11b05f95c6..b89215458a 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -4,7 +4,7 @@ import torch from deepmd.pt.utils import ( - env + env, ) @@ -1069,9 +1069,11 @@ def setUp(self): -1.664931178025436733e-05, -4.312450972708557703e-06, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(8, 132) - self.table_info_tensor = torch.tensor([0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE) + self.table_info_tensor = torch.tensor( + [0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE + ) self.em_tensor = torch.tensor( [ 0.0343909, @@ -1091,7 +1093,7 @@ def setUp(self): 0.17527857, 0.04249097, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4) self.table_info_tensor.requires_grad = True self.table_tensor.requires_grad = True @@ -1231,7 +1233,7 @@ def setUp(self): -0.281368, -1.471135, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4, 8) # backward test self.expected_dy_dem = torch.tensor( @@ -1253,7 +1255,7 @@ def setUp(self): -0.095974, -0.105310, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4) def test_forward(self): @@ -1302,9 +1304,11 @@ def test_backward(self): self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) # Check the values of the gradients - torch.testing.assert_close(self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5) + torch.testing.assert_close( + self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5 + ) if __name__ == "__main__": - env.DEVICE = 'cuda:0' + env.DEVICE = "cuda:0" unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py index c18a1e70bf..33c5abc262 100644 --- a/source/tests/pt/test_tabulate_fusion_se_t.py +++ b/source/tests/pt/test_tabulate_fusion_se_t.py @@ -4,7 +4,7 @@ import torch from deepmd.pt.utils import ( - env + env, ) @@ -1405,7 +1405,7 @@ def setUp(self): 1.1623761628208563e01, 7.8927378661620728e-01, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(8, 174) self.table_info_tensor = torch.tensor( [ @@ -1416,7 +1416,7 @@ def setUp(self): 1.0000000000000000e01, -1.0000000000000000e00, ], - device=env.DEVICE + device=env.DEVICE, ) self.em_x_tensor = torch.tensor( [ @@ -4493,7 +4493,7 @@ def setUp(self): -9.1370205009458638e-03, 5.2699958100439070e-01, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(192, 4, 4) # 3072 self.dy = torch.tensor( [ @@ -5266,7 +5266,7 @@ def setUp(self): -5.8240597405691328e-04, -3.9217722061436343e-04, ], - device=env.DEVICE + device=env.DEVICE, ) self.em_tensor = self.em_x_tensor self.table_info_tensor.requires_grad = False @@ -6049,7 +6049,7 @@ def setUp(self): 5.0356864383992928e02, 5.2869887670804815e02, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(8, 174) self.expected_dy_dem_x = torch.tensor( [ @@ -9126,7 +9126,7 @@ def setUp(self): 1.0549891235828525e-05, -5.8598966626073352e-04, ], - device=env.DEVICE + device=env.DEVICE, ) self.expected_dy_dem = torch.tensor( [ @@ -12203,7 +12203,7 @@ def setUp(self): 4.7088258363160609e-04, -1.4457178691538466e-04, ], - device=env.DEVICE + device=env.DEVICE, ) From b63209c4c0fc5aa2f74558e09f0be7dc403d8228 Mon Sep 17 00:00:00 2001 From: wangyan Date: Mon, 17 Jun 2024 11:45:43 +0800 Subject: [PATCH 19/85] table_info set cpu --- source/op/pt/tabulate_multi_device.cc | 63 +------------------ source/tests/pt/test_tabulate_fusion_se_a.py | 4 +- .../tests/pt/test_tabulate_fusion_se_atten.py | 6 +- source/tests/pt/test_tabulate_fusion_se_r.py | 3 +- 4 files changed, 7 insertions(+), 69 deletions(-) diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index 628d5ecc2f..bdc6f63f94 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -18,38 +18,6 @@ void GetTensorDevice(const torch::Tensor& t, std::string& str) { } } -#include - -void checkPointerLocation(const void* ptr, const std::string& name) { - cudaPointerAttributes attributes; - cudaError_t err = cudaPointerGetAttributes(&attributes, ptr); - - if (err != cudaSuccess) { - std::cerr << "Error checking pointer " << name << ": " - << cudaGetErrorString(err) << std::endl; - return; - } - - if (attributes.type == cudaMemoryTypeDevice) { - std::cout << "Pointer " << name << " is located in device memory." - << std::endl; - } else if (attributes.type == cudaMemoryTypeHost) { - std::cout << "Pointer " << name << " is located in host memory." - << std::endl; - } else { - std::cout << "Pointer " << name << " is of unknown memory type." - << std::endl; - } -} - -void check_contiguity(const torch::Tensor& tensor, const std::string& name) { - if (tensor.is_contiguous()) { - std::cout << name << " is contiguous" << std::endl; - } else { - std::cout << name << " is not contiguous" << std::endl; - } -} - template void TabulateFusionSeAForward(const torch::Tensor& table_tensor, const torch::Tensor& table_info_tensor, @@ -74,22 +42,6 @@ void TabulateFusionSeAForward(const torch::Tensor& table_tensor, // get the device std::string device; GetTensorDevice(table_tensor, device); - // debug - std::cout << "table_tensor device: " << table_tensor.device().type() - << std::endl; - std::cout << "table_info_tensor device: " << table_info_tensor.device().type() - << std::endl; - std::cout << "em_x_tensor device: " << em_x_tensor.device().type() - << std::endl; - std::cout << "em_tensor device: " << em_tensor.device().type() << std::endl; - std::cout << "descriptor_tensor device before computation: " - << descriptor_tensor.device().type() << std::endl; - - check_contiguity(table_tensor, "table_tensor"); - check_contiguity(table_info_tensor, "table_info_tensor"); - check_contiguity(em_x_tensor, "em_x_tensor"); - check_contiguity(em_tensor, "em_tensor"); - check_contiguity(descriptor_tensor, "descriptor_tensor"); // flat the tensors FPTYPE* descriptor = descriptor_tensor.view({-1}).data_ptr(); @@ -97,11 +49,6 @@ void TabulateFusionSeAForward(const torch::Tensor& table_tensor, const FPTYPE* table_info = table_info_tensor.view({-1}).data_ptr(); const FPTYPE* em_x = em_x_tensor.view({-1}).data_ptr(); const FPTYPE* em = em_tensor.view({-1}).data_ptr(); - checkPointerLocation(descriptor, "descriptor"); - checkPointerLocation(table, "table"); - checkPointerLocation(table_info, "table_info"); - checkPointerLocation(em_x, "em_x"); - checkPointerLocation(em, "em"); const FPTYPE* two_embed = (!two_embed_tensor.defined()) ? nullptr @@ -112,7 +59,6 @@ void TabulateFusionSeAForward(const torch::Tensor& table_tensor, // compute if (device == "GPU") { #if GOOGLE_CUDA || TENSORFLOW_USE_ROCM - std::cout << "checkpoint enter gpu function" << std::endl; deepmd::tabulate_fusion_se_a_gpu(descriptor, table, table_info, em_x, em, two_embed, nloc, nnei, last_layer_size); #endif // GOOGLE_CUDA || TENSORFLOW_USE_ROCM @@ -510,14 +456,7 @@ class TabulateFusionSeAOp .device(table_tensor.device()); torch::Tensor descriptor_tensor = torch::empty({em_tensor.size(0), 4, last_layer_size}, options); - // test device - // std::cout << "table_tensor device: " << table_tensor.device().type() << - // std::endl; std::cout << "table_info_tensor device: " << - // table_info_tensor.device().type() << std::endl; std::cout << "em_x_tensor - // device: " << em_x_tensor.device().type() << std::endl; std::cout << - // "em_tensor device: " << em_tensor.device().type() << std::endl; std::cout - // << "descriptor_tensor device: " << descriptor_tensor.device().type() << - // std::endl; compute + // compute TabulateFusionSeAForward(table_tensor, table_info_tensor, em_x_tensor, em_tensor, at::Tensor(), last_layer_size, descriptor_tensor); diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index 221cf7c028..728efb300d 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -1071,8 +1071,9 @@ def setUp(self): ], device=env.DEVICE, ).reshape(8, 132) + # always on cpu self.table_info_tensor = torch.tensor( - [0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE + [0, 0.2, 0.4, 0.01, 0.1, -1], device='cpu' ) self.em_x_tensor = torch.tensor( [ @@ -1465,5 +1466,4 @@ def test_backward(self): if __name__ == "__main__": - env.DEVICE = "cpu" unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index fa05d55bf4..96994aeaf6 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -1072,7 +1072,7 @@ def setUp(self): device=env.DEVICE, ).reshape(8, 132) self.table_info_tensor = torch.tensor( - [0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE + [0, 0.2, 0.4, 0.01, 0.1, -1], device='cpu' ) self.em_x_tensor = torch.tensor( [ @@ -1459,7 +1459,8 @@ def setUp(self): -0.0666033, -0.0360863, -0.0532634, - ] + ], + device=env.DEVICE ).reshape(4, 4) self.expected_dy_dem = torch.tensor( [ @@ -1595,5 +1596,4 @@ def test_backward(self): if __name__ == "__main__": - env.DEVICE = "cpu" unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index b89215458a..a450861b03 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -1072,7 +1072,7 @@ def setUp(self): device=env.DEVICE, ).reshape(8, 132) self.table_info_tensor = torch.tensor( - [0, 0.2, 0.4, 0.01, 0.1, -1], device=env.DEVICE + [0, 0.2, 0.4, 0.01, 0.1, -1], device='cpu' ) self.em_tensor = torch.tensor( [ @@ -1310,5 +1310,4 @@ def test_backward(self): if __name__ == "__main__": - env.DEVICE = "cuda:0" unittest.main() From a824a8036538c791956a22ab52e9d2632c6b12ee Mon Sep 17 00:00:00 2001 From: wangyan Date: Mon, 17 Jun 2024 11:50:34 +0800 Subject: [PATCH 20/85] remove print --- source/tests/pt/test_tabulate_fusion_se_a.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index 728efb300d..709febdb54 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -1399,10 +1399,6 @@ def setUp(self): ).reshape(4, 4, 4) def test_forward(self): - print("table_tensor device:", self.table_tensor.device) - print("table_info_tensor device:", self.table_info_tensor.device) - print("em_x_tensor device:", self.em_x_tensor.device) - print("em_tensor device:", self.em_tensor.device) # Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_a( self.table_tensor, @@ -1423,10 +1419,6 @@ def test_forward(self): ) def test_backward(self): - print("table_tensor device:", self.table_tensor.device) - print("table_info_tensor device:", self.table_info_tensor.device) - print("em_x_tensor device:", self.em_x_tensor.device) - print("em_tensor device:", self.em_tensor.device) # Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_a( self.table_tensor, From 8527819508e86d96d50bea3fc392a5bad11cde01 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 17 Jun 2024 03:51:42 +0000 Subject: [PATCH 21/85] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- source/tests/pt/test_tabulate_fusion_se_a.py | 2 +- source/tests/pt/test_tabulate_fusion_se_atten.py | 4 ++-- source/tests/pt/test_tabulate_fusion_se_r.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index 709febdb54..9eeaf78949 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -1073,7 +1073,7 @@ def setUp(self): ).reshape(8, 132) # always on cpu self.table_info_tensor = torch.tensor( - [0, 0.2, 0.4, 0.01, 0.1, -1], device='cpu' + [0, 0.2, 0.4, 0.01, 0.1, -1], device="cpu" ) self.em_x_tensor = torch.tensor( [ diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index 96994aeaf6..b97517724a 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -1072,7 +1072,7 @@ def setUp(self): device=env.DEVICE, ).reshape(8, 132) self.table_info_tensor = torch.tensor( - [0, 0.2, 0.4, 0.01, 0.1, -1], device='cpu' + [0, 0.2, 0.4, 0.01, 0.1, -1], device="cpu" ) self.em_x_tensor = torch.tensor( [ @@ -1460,7 +1460,7 @@ def setUp(self): -0.0360863, -0.0532634, ], - device=env.DEVICE + device=env.DEVICE, ).reshape(4, 4) self.expected_dy_dem = torch.tensor( [ diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index a450861b03..d3cf64f8a1 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -1072,7 +1072,7 @@ def setUp(self): device=env.DEVICE, ).reshape(8, 132) self.table_info_tensor = torch.tensor( - [0, 0.2, 0.4, 0.01, 0.1, -1], device='cpu' + [0, 0.2, 0.4, 0.01, 0.1, -1], device="cpu" ) self.em_tensor = torch.tensor( [ From e47dcba409af1b70f2946155a1f7f76260d86d2e Mon Sep 17 00:00:00 2001 From: wangyan Date: Mon, 17 Jun 2024 19:33:29 +0800 Subject: [PATCH 22/85] add dtype=float64 --- source/op/pt/tabulate_multi_device.cc | 2 ++ source/tests/pt/test_tabulate_fusion_se_a.py | 23 +++++++++++++++---- .../tests/pt/test_tabulate_fusion_se_atten.py | 23 +++++++++++++++---- source/tests/pt/test_tabulate_fusion_se_r.py | 18 +++++++++++---- 4 files changed, 52 insertions(+), 14 deletions(-) diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index bdc6f63f94..76b1758c6b 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -26,6 +26,8 @@ void TabulateFusionSeAForward(const torch::Tensor& table_tensor, const torch::Tensor& two_embed_tensor, int64_t last_layer_size, torch::Tensor& descriptor_tensor) { + std::cout << "FPTYPE is: " << typeid(FPTYPE).name() << std::endl; + std::cout << "table_tensor type is: " << table_tensor.dtype() << std::endl; // check input shape if (table_tensor.dim() != 2) { throw std::invalid_argument("Dim of table should be 2"); diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index 9eeaf78949..1677149309 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -7,9 +7,15 @@ env, ) +dtype = torch.float64 + class TestTabulateFusionSeAOp(unittest.TestCase): def setUp(self): + if dtype == torch.float64: + self.prec = 1e-10 + elif dtype == torch.float32: + self.prec = 1e-5 self.table_tensor = torch.tensor( [ 6.348551343037398542e-01, @@ -1069,11 +1075,12 @@ def setUp(self): -1.664931178025436733e-05, -4.312450972708557703e-06, ], + dtype=dtype, device=env.DEVICE, ).reshape(8, 132) # always on cpu self.table_info_tensor = torch.tensor( - [0, 0.2, 0.4, 0.01, 0.1, -1], device="cpu" + [0, 0.2, 0.4, 0.01, 0.1, -1], dtype=dtype, device="cpu" ) self.em_x_tensor = torch.tensor( [ @@ -1094,6 +1101,7 @@ def setUp(self): 0.17527857, 0.04249097, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4) self.em_tensor = torch.tensor( @@ -1163,6 +1171,7 @@ def setUp(self): 0.18275348, 0.02921504, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4, 4) self.table_info_tensor.requires_grad = True @@ -1304,6 +1313,7 @@ def setUp(self): -0.09300045, -0.50528542, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4, 8) # backward test @@ -1326,6 +1336,7 @@ def setUp(self): -0.02917727, -0.04478649, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4) self.expected_dy_dem = torch.tensor( @@ -1395,6 +1406,7 @@ def setUp(self): -3.33051143, -3.33051143, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4, 4) @@ -1415,7 +1427,7 @@ def test_forward(self): # Check the values torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 + descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec ) def test_backward(self): @@ -1432,7 +1444,7 @@ def test_backward(self): # Check the forward torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 + descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec ) # Create a loss and perform backward @@ -1449,13 +1461,14 @@ def test_backward(self): # Check the values of the gradients torch.testing.assert_close( - self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5, rtol=1e-5 + self.em_x_tensor.grad, self.expected_dy_dem_x, atol=self.prec, rtol=self.prec ) torch.testing.assert_close( - self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5 + self.em_tensor.grad, self.expected_dy_dem, atol=self.prec, rtol=self.prec ) if __name__ == "__main__": + env.DEVICE = 'cpu' unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index b97517724a..9a4f47e9eb 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -7,9 +7,15 @@ env, ) +dtype = torch.float64 + class TestTabulateFusionSeAttenOp(unittest.TestCase): def setUp(self): + if dtype == torch.float64: + self.prec = 1e-10 + elif dtype == torch.float32: + self.prec = 1e-5 self.table_tensor = torch.tensor( [ 6.348551343037398542e-01, @@ -1069,10 +1075,11 @@ def setUp(self): -1.664931178025436733e-05, -4.312450972708557703e-06, ], + dtype=dtype, device=env.DEVICE, ).reshape(8, 132) self.table_info_tensor = torch.tensor( - [0, 0.2, 0.4, 0.01, 0.1, -1], device="cpu" + [0, 0.2, 0.4, 0.01, 0.1, -1], dtype=dtype, device="cpu" ) self.em_x_tensor = torch.tensor( [ @@ -1093,6 +1100,7 @@ def setUp(self): 0.17527857, 0.04249097, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4) self.em_tensor = torch.tensor( @@ -1162,6 +1170,7 @@ def setUp(self): 0.18275348, 0.02921504, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4, 4) self.two_embed_tensor = torch.tensor( @@ -1295,6 +1304,7 @@ def setUp(self): 0.5194672674960213, 0.04635102497306032, ], + dtype=dtype, device=env.DEVICE, ).reshape(8, 16) self.table_info_tensor.requires_grad = False @@ -1438,6 +1448,7 @@ def setUp(self): -0.162872, -0.723229, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4, 8) # backward test @@ -1460,6 +1471,7 @@ def setUp(self): -0.0360863, -0.0532634, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4) self.expected_dy_dem = torch.tensor( @@ -1529,6 +1541,7 @@ def setUp(self): -3.90654, -3.90654, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4, 4) @@ -1551,7 +1564,7 @@ def test_forward(self): # Check the values torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 + descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec ) def test_backward(self): @@ -1570,7 +1583,7 @@ def test_backward(self): # Check the forward torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 + descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec ) # Create a loss and perform backward @@ -1587,11 +1600,11 @@ def test_backward(self): # Check the values of the gradients torch.testing.assert_close( - self.em_x_tensor.grad, self.expected_dy_dem_x, atol=1e-5, rtol=1e-5 + self.em_x_tensor.grad, self.expected_dy_dem_x, atol=self.prec, rtol=self.prec ) torch.testing.assert_close( - self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5 + self.em_tensor.grad, self.expected_dy_dem, atol=self.prec, rtol=self.prec ) diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index d3cf64f8a1..fc713ba043 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -7,9 +7,15 @@ env, ) +dtype = torch.float64 + class TestTabulateFusionSeAOp(unittest.TestCase): def setUp(self): + if dtype == torch.float64: + self.prec = 1e-10 + elif dtype == torch.float32: + self.prec = 1e-5 self.table_tensor = torch.tensor( [ 6.348551343037398542e-01, @@ -1069,10 +1075,11 @@ def setUp(self): -1.664931178025436733e-05, -4.312450972708557703e-06, ], + dtype=dtype, device=env.DEVICE, ).reshape(8, 132) self.table_info_tensor = torch.tensor( - [0, 0.2, 0.4, 0.01, 0.1, -1], device="cpu" + [0, 0.2, 0.4, 0.01, 0.1, -1], dtype=dtype, device="cpu" ) self.em_tensor = torch.tensor( [ @@ -1093,6 +1100,7 @@ def setUp(self): 0.17527857, 0.04249097, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4) self.table_info_tensor.requires_grad = True @@ -1233,6 +1241,7 @@ def setUp(self): -0.281368, -1.471135, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4, 8) # backward test @@ -1255,6 +1264,7 @@ def setUp(self): -0.095974, -0.105310, ], + dtype=dtype, device=env.DEVICE, ).reshape(4, 4) @@ -1274,7 +1284,7 @@ def test_forward(self): # Check the values torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 + descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec ) def test_backward(self): @@ -1290,7 +1300,7 @@ def test_backward(self): # Check the forward torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=1e-5, rtol=1e-5 + descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec ) # Create a loss and perform backward @@ -1305,7 +1315,7 @@ def test_backward(self): # Check the values of the gradients torch.testing.assert_close( - self.em_tensor.grad, self.expected_dy_dem, atol=1e-5, rtol=1e-5 + self.em_tensor.grad, self.expected_dy_dem, atol=self.prec, rtol=self.prec ) From 95a956657b0b8c3ecd59c46b5f95113cdab3de9d Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 17 Jun 2024 11:34:51 +0000 Subject: [PATCH 23/85] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- source/tests/pt/test_tabulate_fusion_se_a.py | 17 +++++++++++++---- .../tests/pt/test_tabulate_fusion_se_atten.py | 15 ++++++++++++--- source/tests/pt/test_tabulate_fusion_se_r.py | 10 ++++++++-- 3 files changed, 33 insertions(+), 9 deletions(-) diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index 1677149309..f29415f125 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -1427,7 +1427,10 @@ def test_forward(self): # Check the values torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, ) def test_backward(self): @@ -1444,7 +1447,10 @@ def test_backward(self): # Check the forward torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, ) # Create a loss and perform backward @@ -1461,7 +1467,10 @@ def test_backward(self): # Check the values of the gradients torch.testing.assert_close( - self.em_x_tensor.grad, self.expected_dy_dem_x, atol=self.prec, rtol=self.prec + self.em_x_tensor.grad, + self.expected_dy_dem_x, + atol=self.prec, + rtol=self.prec, ) torch.testing.assert_close( @@ -1470,5 +1479,5 @@ def test_backward(self): if __name__ == "__main__": - env.DEVICE = 'cpu' + env.DEVICE = "cpu" unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index 9a4f47e9eb..eb532fce86 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -1564,7 +1564,10 @@ def test_forward(self): # Check the values torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, ) def test_backward(self): @@ -1583,7 +1586,10 @@ def test_backward(self): # Check the forward torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, ) # Create a loss and perform backward @@ -1600,7 +1606,10 @@ def test_backward(self): # Check the values of the gradients torch.testing.assert_close( - self.em_x_tensor.grad, self.expected_dy_dem_x, atol=self.prec, rtol=self.prec + self.em_x_tensor.grad, + self.expected_dy_dem_x, + atol=self.prec, + rtol=self.prec, ) torch.testing.assert_close( diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index fc713ba043..6c4f384364 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -1284,7 +1284,10 @@ def test_forward(self): # Check the values torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, ) def test_backward(self): @@ -1300,7 +1303,10 @@ def test_backward(self): # Check the forward torch.testing.assert_close( - descriptor_tensor, self.expected_descriptor_tensor, atol=self.prec, rtol=self.prec + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, ) # Create a loss and perform backward From 114f7a6c01afb295b1eab0fe39f65ddcbd9b4652 Mon Sep 17 00:00:00 2001 From: wangyan Date: Mon, 17 Jun 2024 21:31:41 +0800 Subject: [PATCH 24/85] add dtype=float64 --- source/op/pt/tabulate_multi_device.cc | 2 - source/tests/pt/test_tabulate_fusion_se_a.py | 422 +- .../tests/pt/test_tabulate_fusion_se_atten.py | 416 +- source/tests/pt/test_tabulate_fusion_se_r.py | 288 +- source/tests/pt/test_tabulate_fusion_se_t.py | 13155 ++++++++-------- 5 files changed, 6796 insertions(+), 7487 deletions(-) diff --git a/source/op/pt/tabulate_multi_device.cc b/source/op/pt/tabulate_multi_device.cc index 76b1758c6b..bdc6f63f94 100644 --- a/source/op/pt/tabulate_multi_device.cc +++ b/source/op/pt/tabulate_multi_device.cc @@ -26,8 +26,6 @@ void TabulateFusionSeAForward(const torch::Tensor& table_tensor, const torch::Tensor& two_embed_tensor, int64_t last_layer_size, torch::Tensor& descriptor_tensor) { - std::cout << "FPTYPE is: " << typeid(FPTYPE).name() << std::endl; - std::cout << "table_tensor type is: " << table_tensor.dtype() << std::endl; // check input shape if (table_tensor.dim() != 2) { throw std::invalid_argument("Dim of table should be 2"); diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index f29415f125..9b5c05fa02 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -1184,134 +1184,134 @@ def setUp(self): # forward test self.expected_descriptor_tensor = torch.tensor( [ - 0.2713011, - -0.56606281, - -0.42305039, - 0.14965803, - 0.52695372, - -0.63845663, - -0.11624505, - -0.63103203, - 0.24412213, - -0.50842224, - -0.38203148, - 0.1353771, - 0.47343798, - -0.57158622, - -0.10647548, - -0.56671287, - 0.13979394, - -0.29123603, - -0.21862063, - 0.07744574, - 0.27118433, - -0.32761487, - -0.06077287, - -0.32463492, - 0.24704819, - -0.51555848, - -0.38509326, - 0.1362072, - 0.47992214, - -0.58168358, - -0.10566162, - -0.57473633, - 0.31158834, - -0.65068838, - -0.48501479, - 0.17143258, - 0.60565326, - -0.73506803, - -0.13233106, - -0.72541595, - 0.27121003, - -0.5656669, - -0.42318034, - 0.14974857, - 0.52662422, - -0.637633, - -0.11658482, - -0.6305842, - 0.21202135, - -0.44212972, - -0.33094666, - 0.11713047, - 0.41162829, - -0.4982129, - -0.0913087, - -0.49286515, - 0.30733526, - -0.64054639, - -0.48022212, - 0.17004692, - 0.59640929, - -0.72111726, - -0.13304347, - -0.71402776, - 0.24135931, - -0.50316388, - -0.37699907, - 0.13347531, - 0.46846154, - -0.56664651, - -0.10429212, - -0.56088123, - 0.33429479, - -0.69669061, - -0.52246841, - 0.18502927, - 0.64867706, - -0.78417021, - -0.14487244, - -0.77659533, - 0.29200237, - -0.60840668, - -0.45656557, - 0.16172246, - 0.56650319, - -0.68453038, - -0.12681616, - -0.67817995, - 0.13559139, - -0.28210652, - -0.21258614, - 0.07539812, - 0.26274303, - -0.3166084, - -0.05968776, - -0.31443544, - 0.30394432, - -0.63428311, - -0.47381417, - 0.16759396, - 0.59043739, - -0.71559513, - -0.13003802, - -0.70708354, - 0.25830471, - -0.53796239, - -0.40421268, - 0.14323456, - 0.50094757, - -0.6048126, - -0.11264426, - -0.59964242, - 0.21979687, - -0.45763438, - -0.34413143, - 0.12197404, - 0.42616899, - -0.51425659, - -0.09609854, - -0.51009828, - 0.2172166, - -0.45326447, - -0.33866506, - 0.11979851, - 0.421936, - -0.51130404, - -0.09300045, - -0.50528542, + 0.2713010991854039, + -0.5660628160978955, + -0.4230503961233804, + 0.14965802865129818, + 0.5269537220240132, + -0.6384566368739288, + -0.11624505007495309, + -0.6310320354395895, + 0.24412212410338252, + -0.5084222360348541, + -0.3820314749241062, + 0.1353770997654753, + 0.4734379786819688, + -0.5715862139964242, + -0.10647548073978085, + -0.5667128671678037, + 0.13979393629121145, + -0.2912360135099118, + -0.21862062309471242, + 0.0774457356172342, + 0.2711843179288344, + -0.3276148559472541, + -0.06077287203673235, + -0.3246349003705672, + 0.24704819325244173, + -0.51555848202734, + -0.3850932628970095, + 0.1362072061097241, + 0.4799221442877444, + -0.5816835832792213, + -0.10566161861294662, + -0.5747363397856997, + 0.3115883382215896, + -0.6506883878140057, + -0.4850147890277097, + 0.1714325837291713, + 0.6056532652377606, + -0.7350680305117758, + -0.13233106208913875, + -0.7254159568199746, + 0.2712100312735679, + -0.5656668988983458, + -0.423180345296733, + 0.14974857024944524, + 0.5266242202317545, + -0.6376329980619129, + -0.11658482011618507, + -0.6305841995062695, + 0.21202134995532176, + -0.44212972830581004, + -0.33094666630320135, + 0.1171304680138136, + 0.4116282973058779, + -0.49821290159157117, + -0.0913087049986295, + -0.49286515716995555, + 0.3073352620583199, + -0.6405463996728692, + -0.48022213268980996, + 0.17004692071229927, + 0.5964092995598747, + -0.7211172753937779, + -0.13304346833797515, + -0.7140277774981623, + 0.24135931626467969, + -0.5031638848589246, + -0.3769990721972972, + 0.13347531041756877, + 0.4684615467688112, + -0.5666465173184999, + -0.10429212140716522, + -0.5608812343251992, + 0.33429479916822996, + -0.6966906185519567, + -0.5224684150875668, + 0.1850292768610088, + 0.6486770724987723, + -0.7841702188445613, + -0.14487244407008348, + -0.7765953436864135, + 0.2920023645166421, + -0.6084066648243805, + -0.45656556423057065, + 0.16172245683335545, + 0.5665031788673434, + -0.68453036136064, + -0.12681615685082662, + -0.6781799312012713, + 0.1355913887851541, + -0.28210651239855183, + -0.2125861375354654, + 0.07539812279917796, + 0.2627430315734105, + -0.3166083947691034, + -0.05968776068495512, + -0.314435441368215, + 0.3039443167162163, + -0.6342831095043088, + -0.4738141723760223, + 0.16759395880105882, + 0.5904373874978512, + -0.7155951337421371, + -0.1300380195196118, + -0.7070835391843331, + 0.25830471641609376, + -0.5379623982551216, + -0.40421268847222747, + 0.14323456063074608, + 0.5009475712655791, + -0.6048126036159627, + -0.11264425943960855, + -0.5996424258133577, + 0.21979686345452815, + -0.45763436166403704, + -0.34413141738362185, + 0.12197403142496444, + 0.426168976528326, + -0.5142565732600977, + -0.0960985398529971, + -0.5100982631949256, + 0.21721660807904586, + -0.45326448307918804, + -0.3386650653893315, + 0.1197985132761583, + 0.4219360127187033, + -0.5113040538633717, + -0.09300044948770746, + -0.5052854291543631, ], dtype=dtype, device=env.DEVICE, @@ -1319,92 +1319,92 @@ def setUp(self): # backward test self.expected_dy_dem_x = torch.tensor( [ - -0.02067741, - -0.03787612, - -0.04180199, - -0.04158797, - -0.03938578, - -0.04047081, - -0.03819692, - -0.05383372, - -0.05179508, - -0.03552708, - -0.02812173, - -0.04451295, - -0.04586229, - -0.03794369, - -0.02917727, - -0.04478649, + -0.02067741234134639, + -0.037876115867122244, + -0.041801992795897414, + -0.04158797219225682, + -0.03938577535008901, + -0.04047080940333281, + -0.03819691803756371, + -0.05383372190821694, + -0.051795083742471035, + -0.03552707650567376, + -0.02812172878706858, + -0.044512948316127884, + -0.04586229371985228, + -0.037943692770837076, + -0.02917727398183635, + -0.04478649455427308, ], dtype=dtype, device=env.DEVICE, ).reshape(4, 4) self.expected_dy_dem = torch.tensor( [ - -3.32965609, - -3.32965609, - -3.32965609, - -3.32965609, - -3.33781886, - -3.33781886, - -3.33781886, - -3.33781886, - -3.33501296, - -3.33501296, - -3.33501296, - -3.33501296, - -3.34559974, - -3.34559974, - -3.34559974, - -3.34559974, - -3.34561821, - -3.34561821, - -3.34561821, - -3.34561821, - -3.33106684, - -3.33106684, - -3.33106684, - -3.33106684, - -3.33218328, - -3.33218328, - -3.33218328, - -3.33218328, - -3.34535585, - -3.34535585, - -3.34535585, - -3.34535585, - -3.34250754, - -3.34250754, - -3.34250754, - -3.34250754, - -3.33356685, - -3.33356685, - -3.33356685, - -3.33356685, - -3.33652989, - -3.33652989, - -3.33652989, - -3.33652989, - -3.3309235, - -3.3309235, - -3.3309235, - -3.3309235, - -3.33688909, - -3.33688909, - -3.33688909, - -3.33688909, - -3.34206038, - -3.34206038, - -3.34206038, - -3.34206038, - -3.34387412, - -3.34387412, - -3.34387412, - -3.34387412, - -3.33051143, - -3.33051143, - -3.33051143, - -3.33051143, + -3.3296560873139764, + -3.3296560873139764, + -3.3296560873139764, + -3.3296560873139764, + -3.337818861718732, + -3.337818861718732, + -3.337818861718732, + -3.337818861718732, + -3.33501295970411, + -3.33501295970411, + -3.33501295970411, + -3.33501295970411, + -3.345599737642763, + -3.345599737642763, + -3.345599737642763, + -3.345599737642763, + -3.3456182126661695, + -3.3456182126661695, + -3.3456182126661695, + -3.3456182126661695, + -3.33106684078773, + -3.33106684078773, + -3.33106684078773, + -3.33106684078773, + -3.33218327918215, + -3.33218327918215, + -3.33218327918215, + -3.33218327918215, + -3.3453558489853616, + -3.3453558489853616, + -3.3453558489853616, + -3.3453558489853616, + -3.3425075397870057, + -3.3425075397870057, + -3.3425075397870057, + -3.3425075397870057, + -3.333566847126196, + -3.333566847126196, + -3.333566847126196, + -3.333566847126196, + -3.336529893308974, + -3.336529893308974, + -3.336529893308974, + -3.336529893308974, + -3.330923503981002, + -3.330923503981002, + -3.330923503981002, + -3.330923503981002, + -3.3368890892700986, + -3.3368890892700986, + -3.3368890892700986, + -3.3368890892700986, + -3.3420603756052665, + -3.3420603756052665, + -3.3420603756052665, + -3.3420603756052665, + -3.343874115987605, + -3.343874115987605, + -3.343874115987605, + -3.343874115987605, + -3.330511428849272, + -3.330511428849272, + -3.330511428849272, + -3.330511428849272, ], dtype=dtype, device=env.DEVICE, @@ -1474,10 +1474,12 @@ def test_backward(self): ) torch.testing.assert_close( - self.em_tensor.grad, self.expected_dy_dem, atol=self.prec, rtol=self.prec + self.em_tensor.grad, + self.expected_dy_dem, + atol=self.prec, + rtol=self.prec, ) if __name__ == "__main__": - env.DEVICE = "cpu" unittest.main() diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index eb532fce86..18043b9b23 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -1319,134 +1319,134 @@ def setUp(self): # forward test self.expected_descriptor_tensor = torch.tensor( [ - 0.473477, - -0.938671, - -0.566066, - 0.243465, - 0.820256, - -1.03738, - -0.1701, - -0.871079, - 0.395671, - -0.716406, - -0.518235, - 0.240325, - 0.729525, - -0.821716, - -0.146507, - -0.743918, - 0.228466, - -0.423251, - -0.285282, - 0.129922, - 0.412289, - -0.479284, - -0.079999, - -0.444168, - 0.434929, - -0.866715, - -0.520729, - 0.22332, - 0.749817, - -0.954691, - -0.159364, - -0.787858, - 0.346693, - -0.994381, - -0.627092, - 0.29814, - 0.919532, - -1.07189, - -0.156985, - -1.17893, - 0.288148, - -0.988518, - -0.574846, - 0.245017, - 0.696643, - -0.791873, - -0.158166, - -0.923282, - 0.223783, - -0.685911, - -0.496191, - 0.192425, - 0.622787, - -0.666019, - -0.116214, - -0.792771, - 0.331461, - -0.910623, - -0.697545, - 0.270823, - 0.943644, - -1.10325, - -0.186431, - -1.19188, - 0.379218, - -0.896241, - -0.500721, - 0.229773, - 0.689497, - -0.908129, - -0.166598, - -0.849655, - 0.5138, - -1.15374, - -0.656861, - 0.31042, - 0.981434, - -1.23231, - -0.233144, - -1.2304, - 0.45798, - -1.10749, - -0.604885, - 0.261653, - 0.78797, - -1.13627, - -0.19813, - -0.931011, - 0.213153, - -0.412743, - -0.258851, - 0.106663, - 0.367201, - -0.484, - -0.10065, - -0.45465, - 0.471219, - -0.881373, - -0.715617, - 0.24295, - 0.869534, - -1.23335, - -0.221008, - -1.03448, - 0.354185, - -0.78891, - -0.632616, - 0.224055, - 0.759759, - -1.03588, - -0.175253, - -0.726695, - 0.293533, - -0.680751, - -0.515893, - 0.193818, - 0.681708, - -0.85917, - -0.150909, - -0.603596, - 0.336416, - -0.611786, - -0.478383, - 0.164921, - 0.608194, - -0.915656, - -0.162872, - -0.723229, + 0.47347690809281584, + -0.938671106172836, + -0.566066031386074, + 0.24346508156830923, + 0.8202563571070155, + -1.0373756242429473, + -0.17010015427406364, + -0.8710788156620061, + 0.395670826145944, + -0.7164061254932106, + -0.5182353938571188, + 0.24032480431966494, + 0.7295250448255718, + -0.8217164571528093, + -0.14650667106275897, + -0.743917788428106, + 0.2284657683159583, + -0.42325060475528936, + -0.28528185044441623, + 0.1299218265387629, + 0.4122891899913208, + -0.4792844297628545, + -0.07999903336182355, + -0.44416840002965857, + 0.4349292047304616, + -0.866714677458846, + -0.5207292765686371, + 0.22332001940248375, + 0.7498173442166285, + -0.9546906311960559, + -0.15936409133917512, + -0.7878577875263373, + 0.3466925422373803, + -0.994380783370134, + -0.6270917901063118, + 0.2981402728321051, + 0.9195320780786727, + -1.0718892456307918, + -0.15698461575270795, + -1.1789262485734189, + 0.28814823365263476, + -0.9885184495221915, + -0.5748462943377031, + 0.24501712277073154, + 0.6966426111509829, + -0.7918732495676203, + -0.15816622107875547, + -0.9232820446171233, + 0.22378298591000056, + -0.6859112389106587, + -0.49619127436326704, + 0.19242493499693383, + 0.622786588111436, + -0.666018566891193, + -0.11621443478059659, + -0.7927712244868067, + 0.33146082229500645, + -0.910623259021886, + -0.6975451800757693, + 0.2708230994848638, + 0.9436440642240583, + -1.103250728415007, + -0.18643132646601496, + -1.1918841520358467, + 0.37921786279033454, + -0.8962410980736447, + -0.500721492855562, + 0.22977304492608347, + 0.6894967253035347, + -0.9081285527067445, + -0.16659833566436824, + -0.8496545388057982, + 0.5137998029000233, + -1.1537368698160295, + -0.656860645256254, + 0.31042037927337496, + 0.9814343841081181, + -1.232310461500326, + -0.23314406631295234, + -1.2304015706558842, + 0.45797993385377606, + -1.1074919572397988, + -0.6048852416894798, + 0.2616527005842335, + 0.7879703504421955, + -1.1362711906177663, + -0.1981304325148623, + -0.9310107317132751, + 0.21315309858654777, + -0.41274344906220745, + -0.2588508380504396, + 0.1066634142045425, + 0.3672005243972004, + -0.4840002903711901, + -0.10065024885011888, + -0.4546504875519408, + 0.47121911760467616, + -0.8813734369794723, + -0.7156169154744415, + 0.242949784111888, + 0.8695344392466614, + -1.2333547296658691, + -0.22100811739419962, + -1.0344804237112, + 0.3541853556808732, + -0.7889099992546985, + -0.632615806745115, + 0.2240548602116392, + 0.7597588014275503, + -1.035877717989762, + -0.17525344544740995, + -0.7266950510645241, + 0.293533442380667, + -0.6807511051238859, + -0.5158926321437481, + 0.19381846340306683, + 0.6817081536687413, + -0.8591698632437857, + -0.15090870856670646, + -0.6035960397897837, + 0.3364163288609897, + -0.6117855193715979, + -0.4783834090534011, + 0.1649210671780133, + 0.6081937728291197, + -0.9156559135117243, + -0.16287243502858786, + -0.7232291367106685, ], dtype=dtype, device=env.DEVICE, @@ -1454,92 +1454,92 @@ def setUp(self): # backward test self.expected_dy_dem_x = torch.tensor( [ - -0.0294449, - -0.0948144, - -0.039285, - -0.0826351, - -0.114794, - -0.0530287, - -0.0404511, - -0.0964537, - -0.148872, - -0.0394916, - -0.0330467, - -0.0767711, - -0.0893684, - -0.0666033, - -0.0360863, - -0.0532634, + -0.02944485238565673, + -0.09481442615634611, + -0.039285023803917796, + -0.08263513336597483, + -0.1147941391226924, + -0.053028707974760975, + -0.04045111384033326, + -0.09645372744447589, + -0.148871652361389, + -0.03949164509537857, + -0.03304671059396837, + -0.07677112538315375, + -0.08936844295120971, + -0.0666033025810816, + -0.036086280282677796, + -0.053263385364202, ], dtype=dtype, device=env.DEVICE, ).reshape(4, 4) self.expected_dy_dem = torch.tensor( [ - -3.43749, - -3.43749, - -3.43749, - -3.43749, - -4.91768, - -4.91768, - -4.91768, - -4.91768, - -3.79784, - -3.79784, - -3.79784, - -3.79784, - -5.39484, - -5.39484, - -5.39484, - -5.39484, - -4.91866, - -4.91866, - -4.91866, - -4.91866, - -5.40551, - -5.40551, - -5.40551, - -5.40551, - -5.56471, - -5.56471, - -5.56471, - -5.56471, - -4.87029, - -4.87029, - -4.87029, - -4.87029, - -5.71263, - -5.71263, - -5.71263, - -5.71263, - -4.27996, - -4.27996, - -4.27996, - -4.27996, - -5.55454, - -5.55454, - -5.55454, - -5.55454, - -5.07277, - -5.07277, - -5.07277, - -5.07277, - -5.96781, - -5.96781, - -5.96781, - -5.96781, - -5.08156, - -5.08156, - -5.08156, - -5.08156, - -5.23843, - -5.23843, - -5.23843, - -5.23843, - -3.90654, - -3.90654, - -3.90654, - -3.90654, + -3.437493391458747, + -3.437493391458747, + -3.437493391458747, + -3.437493391458747, + -4.917683334085319, + -4.917683334085319, + -4.917683334085319, + -4.917683334085319, + -3.7978352380265443, + -3.7978352380265443, + -3.7978352380265443, + -3.7978352380265443, + -5.39483968657882, + -5.39483968657882, + -5.39483968657882, + -5.39483968657882, + -4.918657812120523, + -4.918657812120523, + -4.918657812120523, + -4.918657812120523, + -5.405511948034504, + -5.405511948034504, + -5.405511948034504, + -5.405511948034504, + -5.5647056342671615, + -5.5647056342671615, + -5.5647056342671615, + -5.5647056342671615, + -4.870290792037633, + -4.870290792037633, + -4.870290792037633, + -4.870290792037633, + -5.712629223988493, + -5.712629223988493, + -5.712629223988493, + -5.712629223988493, + -4.279958255143791, + -4.279958255143791, + -4.279958255143791, + -4.279958255143791, + -5.554543471933205, + -5.554543471933205, + -5.554543471933205, + -5.554543471933205, + -5.072772403587814, + -5.072772403587814, + -5.072772403587814, + -5.072772403587814, + -5.967810024526445, + -5.967810024526445, + -5.967810024526445, + -5.967810024526445, + -5.08155970167425, + -5.08155970167425, + -5.08155970167425, + -5.08155970167425, + -5.238429358303623, + -5.238429358303623, + -5.238429358303623, + -5.238429358303623, + -3.906538220487487, + -3.906538220487487, + -3.906538220487487, + -3.906538220487487, ], dtype=dtype, device=env.DEVICE, diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index 6c4f384364..2907e6257e 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -1112,134 +1112,134 @@ def setUp(self): # forward test self.expected_descriptor_tensor = torch.tensor( [ - 0.634877, - -1.319469, - -0.997320, - 0.354037, - 1.229165, - -1.478165, - -0.282159, - -1.470623, - 0.634985, - -1.323774, - -0.991892, - 0.351189, - 1.232453, - -1.490731, - -0.274445, - -1.475604, - 0.634938, - -1.322286, - -0.993784, - 0.352187, - 1.231297, - -1.486357, - -0.277141, - -1.473868, - 0.635174, - -1.327955, - -0.986486, - 0.348307, - 1.235810, - -1.503186, - -0.266701, - -1.480563, - 0.635175, - -1.327965, - -0.986473, - 0.348300, - 1.235819, - -1.503216, - -0.266682, - -1.480575, - 0.634890, - -1.320208, - -0.996398, - 0.353557, - 1.229717, - -1.480303, - -0.280853, - -1.471469, - 0.634902, - -1.320794, - -0.995664, - 0.353173, - 1.230159, - -1.482005, - -0.279812, - -1.472143, - 0.635167, - -1.327823, - -0.986659, - 0.348400, - 1.235701, - -1.502788, - -0.266950, - -1.480404, - 0.635088, - -1.326284, - -0.988664, - 0.349474, - 1.234448, - -1.498176, - -0.269828, - -1.478565, - 0.634918, - -1.321522, - -0.994748, - 0.352694, - 1.230712, - -1.484126, - -0.278511, - -1.472983, - 0.634962, - -1.323089, - -0.992765, - 0.351650, - 1.231919, - -1.488714, - -0.275689, - -1.474803, - 0.634888, - -1.320133, - -0.996492, - 0.353606, - 1.229661, - -1.480085, - -0.280986, - -1.471383, - 0.634968, - -1.323280, - -0.992522, - 0.351522, - 1.232067, - -1.489275, - -0.275344, - -1.475026, - 0.635077, - -1.326043, - -0.988976, - 0.349640, - 1.234254, - -1.497458, - -0.270275, - -1.478280, - 0.635124, - -1.327021, - -0.987707, - 0.348962, - 1.235045, - -1.500380, - -0.268455, - -1.479444, - 0.634885, - -1.319917, - -0.996762, - 0.353746, - 1.229499, - -1.479460, - -0.281368, - -1.471135, + 0.6348771631809248, + -1.3194691113291661, + -0.9973196209241018, + 0.354036826929785, + 1.229164742167305, + -1.4781646269685296, + -0.2821585274143141, + -1.4706229329558798, + 0.634985122151867, + -1.3237744576184003, + -0.9918921699941985, + 0.3511893756810418, + 1.2324529289292472, + -1.4907308230849954, + -0.27444497948193763, + -1.475603858301356, + 0.6349382934267259, + -1.3222859349253087, + -0.9937841045345152, + 0.35218726188637794, + 1.2312974110088875, + -1.4863573766557947, + -0.27714069648435136, + -1.4738678134261312, + 0.635174147464672, + -1.3279549545501694, + -0.9864860923352163, + 0.34830716632779013, + 1.235810383474792, + -1.5031859872970013, + -0.26670125536196, + -1.48056314536567, + 0.6351747075878561, + -1.3279649806303675, + -0.9864729560930998, + 0.3483001078156069, + 1.2358186430848, + -1.5032161761365208, + -0.26668236472889295, + -1.4805751935655516, + 0.6348898134171228, + -1.320207897862343, + -0.996397864565745, + 0.35355654279185456, + 1.229717427214055, + -1.4803030761521567, + -0.28085278195255303, + -1.4714690036779643, + 0.6349015416696653, + -1.3207940654749135, + -0.9956637569426188, + 0.3531730613337772, + 1.2301592741196672, + -1.482004932451056, + -0.2798116121790224, + -1.472142789257649, + 0.63516680538694, + -1.3278226472708177, + -0.9866593634585263, + 0.34840024552503546, + 1.235701482917897, + -1.5027877523544166, + -0.2669503949447871, + -1.4804042247866864, + 0.6350880729003239, + -1.3262837737167732, + -0.9886640555953151, + 0.34947378247466837, + 1.2344477758485055, + -1.4981756178487995, + -0.26982830526911433, + -1.4785654185805013, + 0.6349182326958803, + -1.3215223841369783, + -0.9947481627455212, + 0.3526935713672062, + 1.230712444499081, + -1.4841259603235413, + -0.2785114943045205, + -1.4729830941778024, + 0.6349622896803752, + -1.3230894924528385, + -0.9927648497591829, + 0.35165036475319683, + 1.2319186867506238, + -1.4887144441648619, + -0.27568933247281807, + -1.4748031156434684, + 0.63488841840565, + -1.3201327386056192, + -0.9964918149720808, + 0.3536055582326517, + 1.2296609874447608, + -1.4800851938933008, + -0.28098595018607436, + -1.4713827704069893, + 0.6349684240930827, + -1.3232801685109328, + -0.9925222773369173, + 0.3515223472158862, + 1.232066971980551, + -1.4892750825477157, + -0.27534360663790663, + -1.4750256975261462, + 0.6350768567116333, + -1.326043208512698, + -0.9889756942263948, + 0.3496401112248936, + 1.234253909454779, + -1.4974578690711353, + -0.2702749376389924, + -1.478279543547352, + 0.6351242549804302, + -1.327020671488853, + -0.9877065253616449, + 0.34896178641041964, + 1.2350451717803512, + -1.5003796519427108, + -0.2684547326157591, + -1.4794437477498377, + 0.6348845468620932, + -1.319916787317926, + -0.9967615341650043, + 0.3537461965952468, + 1.2294990915247286, + -1.4794595816676606, + -0.2813681581698538, + -1.4711352025108961, ], dtype=dtype, device=env.DEVICE, @@ -1247,22 +1247,22 @@ def setUp(self): # backward test self.expected_dy_dem = torch.tensor( [ - -0.105883, - -0.100297, - -0.102247, - -0.094712, - -0.094698, - -0.104937, - -0.104182, - -0.094891, - -0.096964, - -0.103240, - -0.101197, - -0.105033, - -0.100947, - -0.097286, - -0.095974, - -0.105310, + -0.10588345474250505, + -0.1002972786439324, + -0.10224731275660418, + -0.0947116532720767, + -0.09469805979213963, + -0.10493653217842225, + -0.10418182788008934, + -0.09489094713093613, + -0.09696412406683491, + -0.10323988208957913, + -0.10119709956091572, + -0.10503308350390633, + -0.10094704040733082, + -0.09728619911848688, + -0.09597416541247616, + -0.10531022473788323, ], dtype=dtype, device=env.DEVICE, diff --git a/source/tests/pt/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py index 33c5abc262..45ca8e141f 100644 --- a/source/tests/pt/test_tabulate_fusion_se_t.py +++ b/source/tests/pt/test_tabulate_fusion_se_t.py @@ -7,9 +7,15 @@ env, ) +dtype = torch.float64 + class TestTabulateFusionSeTOp(unittest.TestCase): def setUp(self): + if dtype == torch.float64: + self.prec = 1e-10 + elif dtype == torch.float32: + self.prec = 1e-5 self.table_tensor = torch.tensor( [ -1.0600000163027882e02, @@ -1405,8 +1411,9 @@ def setUp(self): 1.1623761628208563e01, 7.8927378661620728e-01, ], + dtype=dtype, device=env.DEVICE, - ).reshape(8, 174) + ).reshape(8, 174) # 1392 self.table_info_tensor = torch.tensor( [ -2.1000000000000000e01, @@ -1416,7 +1423,8 @@ def setUp(self): 1.0000000000000000e01, -1.0000000000000000e00, ], - device=env.DEVICE, + dtype=dtype, + device='cpu', ) self.em_x_tensor = torch.tensor( [ @@ -4493,782 +4501,10 @@ def setUp(self): -9.1370205009458638e-03, 5.2699958100439070e-01, ], + dtype=dtype, device=env.DEVICE, - ).reshape(192, 4, 4) # 3072 - self.dy = torch.tensor( - [ - -3.7309172874861328e-03, - 1.3333653131861634e-03, - 8.8577244948839816e-04, - 4.4771776498148510e-04, - -3.2036744215949474e-03, - 1.5721737770764861e-03, - 7.5774810984830811e-04, - -7.6103439612831651e-06, - -1.5642091060224157e-03, - 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-47.23946396000424, + -10.016315823570197, + -3.067158177158884, + -47.23946396000424, + 12.241074446763497, + -12.901093764851336, + -12.541742817907949, + -10.016315823570197, + -12.901093764851336, + -8.756593049534866, + -12.356064221142336, + -3.067158177158884, + -12.541742817907949, + -12.356064221142336, + -8.937492585169423, + 61.15604505860266, + -49.837188273884905, + -3.494465767077082, + -11.675323667561182, + -49.837188273884905, + 13.652972821902853, + -13.79366901499275, + -9.851373493983237, + -3.494465767077082, + -13.79366901499275, + -4.573768775025706, + -10.993426112011017, + -11.675323667561182, + -9.851373493983237, + -10.993426112011017, + -8.03628973211977, ], + dtype=dtype, device=env.DEVICE, + ).reshape(192, 4, 4) + + def test_forward(self): + # Call the forward function + forward_result = torch.ops.deepmd.tabulate_fusion_se_t( + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.last_layer_size, + ) + + descriptor_tensor = forward_result[0] + + # Check the shape + self.assertEqual(descriptor_tensor.shape, self.expected_descriptor_tensor.shape) + + # Check the values + torch.testing.assert_close( + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, + ) + + def test_backward(self): + # Call the forward function + forward_result = torch.ops.deepmd.tabulate_fusion_se_t( + self.table_tensor, + self.table_info_tensor, + self.em_x_tensor, + self.em_tensor, + self.last_layer_size, + ) + + descriptor_tensor = forward_result[0] + + # Check the forward + torch.testing.assert_close( + descriptor_tensor, + self.expected_descriptor_tensor, + atol=self.prec, + rtol=self.prec, + ) + + # Create a loss and perform backward + loss = descriptor_tensor.sum() + loss.backward() + + # Check gradients + self.assertIsNotNone(self.em_x_tensor.grad) + self.assertIsNotNone(self.em_tensor.grad) + + # Check the shapes of the gradients + self.assertEqual(self.em_x_tensor.grad.shape, self.expected_dy_dem_x.shape) + self.assertEqual(self.em_tensor.grad.shape, self.expected_dy_dem.shape) + + # Check the values of the gradients + torch.testing.assert_close( + self.em_x_tensor.grad, + self.expected_dy_dem_x, + atol=self.prec, + rtol=self.prec, + ) + + torch.testing.assert_close( + self.em_tensor.grad, + self.expected_dy_dem, + atol=self.prec, + rtol=self.prec, ) From 9e677f4ee8ab053802b93510eec64201599dc359 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Mon, 17 Jun 2024 13:32:24 +0000 Subject: [PATCH 25/85] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- source/tests/pt/test_tabulate_fusion_se_a.py | 6 +- source/tests/pt/test_tabulate_fusion_se_t.py | 12298 ++++++++--------- 2 files changed, 6152 insertions(+), 6152 deletions(-) diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index 9b5c05fa02..f15762e42b 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -1474,9 +1474,9 @@ def test_backward(self): ) torch.testing.assert_close( - self.em_tensor.grad, - self.expected_dy_dem, - atol=self.prec, + self.em_tensor.grad, + self.expected_dy_dem, + atol=self.prec, rtol=self.prec, ) diff --git a/source/tests/pt/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py index 45ca8e141f..46fab1d445 100644 --- a/source/tests/pt/test_tabulate_fusion_se_t.py +++ b/source/tests/pt/test_tabulate_fusion_se_t.py @@ -1413,7 +1413,7 @@ def setUp(self): ], dtype=dtype, device=env.DEVICE, - ).reshape(8, 174) # 1392 + ).reshape(8, 174) # 1392 self.table_info_tensor = torch.tensor( [ -2.1000000000000000e01, @@ -1424,7 +1424,7 @@ def setUp(self): -1.0000000000000000e00, ], dtype=dtype, - device='cpu', + device="cpu", ) self.em_x_tensor = torch.tensor( [ @@ -5291,3077 +5291,3077 @@ def setUp(self): self.expected_dy_dem_x = torch.tensor( [ 5.648489055364202, - -0.8109841888364551, - -0.24536867097411239, - -1.1747441933374314, - -0.8109841888364551, - 5.551778760144183, - -1.177917429853053, - -1.679018415609313, - -0.24536867097411239, - -1.177917429853053, - 3.066855971667982, - 1.0527786223200397, - -1.1747441933374314, - -1.679018415609313, - 1.0527786223200397, - 2.154128070312613, - 4.941344648306369, - -0.2103366776681705, - -1.367487115395829, - -1.4564034402591373, - -0.2103366776681705, - 3.608285604419272, - -1.5040667548923485, - 0.7193959503618953, - -1.367487115395829, - -1.5040667548923485, - 3.254731998053089, - 0.5591533452715003, - -1.4564034402591373, - 0.7193959503618953, - 0.5591533452715003, - 1.1890751500286143, - 5.930646543070966, - -0.5504936332829675, - -0.5526209441647617, - -0.9416633243620358, - -0.5504936332829675, - 4.459184963787069, - -0.7803876483286257, - 0.5396747709549512, - -0.5526209441647617, - 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Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_t( @@ -11467,7 +11467,7 @@ def test_forward(self): atol=self.prec, rtol=self.prec, ) - + def test_backward(self): # Call the forward function forward_result = torch.ops.deepmd.tabulate_fusion_se_t( @@ -11509,9 +11509,9 @@ def test_backward(self): ) torch.testing.assert_close( - self.em_tensor.grad, - self.expected_dy_dem, - atol=self.prec, + self.em_tensor.grad, + self.expected_dy_dem, + atol=self.prec, rtol=self.prec, ) From 22ae3b70113d4265d30f1ecd393cd04c0b1732ce Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Mon, 17 Jun 2024 17:37:46 -0400 Subject: [PATCH 26/85] test both float64 and float32 Signed-off-by: Jinzhe Zeng --- source/tests/pt/test_tabulate_fusion_se_a.py | 6 +++++- source/tests/pt/test_tabulate_fusion_se_atten.py | 6 +++++- source/tests/pt/test_tabulate_fusion_se_r.py | 6 +++++- source/tests/pt/test_tabulate_fusion_se_t.py | 9 +++++++-- 4 files changed, 22 insertions(+), 5 deletions(-) diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index f15762e42b..3067d17624 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -7,11 +7,15 @@ env, ) -dtype = torch.float64 +from ..consistent.common import ( + parameterized, +) +@parameterized((torch.float64, torch.float32)) class TestTabulateFusionSeAOp(unittest.TestCase): def setUp(self): + (dtype,) = self.param if dtype == torch.float64: self.prec = 1e-10 elif dtype == torch.float32: diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index 18043b9b23..cc8a642333 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -7,11 +7,15 @@ env, ) -dtype = torch.float64 +from ..consistent.common import ( + parameterized, +) +@parameterized((torch.float64, torch.float32)) class TestTabulateFusionSeAttenOp(unittest.TestCase): def setUp(self): + (dtype,) = self.param if dtype == torch.float64: self.prec = 1e-10 elif dtype == torch.float32: diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index 2907e6257e..4b9c841a46 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -7,11 +7,15 @@ env, ) -dtype = torch.float64 +from ..consistent.common import ( + parameterized, +) +@parameterized((torch.float64, torch.float32)) class TestTabulateFusionSeAOp(unittest.TestCase): def setUp(self): + (dtype,) = self.param if dtype == torch.float64: self.prec = 1e-10 elif dtype == torch.float32: diff --git a/source/tests/pt/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py index 46fab1d445..e3f213971c 100644 --- a/source/tests/pt/test_tabulate_fusion_se_t.py +++ b/source/tests/pt/test_tabulate_fusion_se_t.py @@ -7,15 +7,20 @@ env, ) -dtype = torch.float64 +from ..consistent.common import ( + parameterized, +) +@parameterized((torch.float64, torch.float32)) class TestTabulateFusionSeTOp(unittest.TestCase): def setUp(self): + (dtype,) = self.param if dtype == torch.float64: self.prec = 1e-10 elif dtype == torch.float32: - self.prec = 1e-5 + # JZ: not sure the reason, but 1e-5 cannot pass the grad test + self.prec = 1e-3 self.table_tensor = torch.tensor( [ -1.0600000163027882e02, From 9920e57bf62410d6305e0b05a4bad7abfafce7a2 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Mon, 17 Jun 2024 17:40:23 -0400 Subject: [PATCH 27/85] skip tests if customized ops are not enables Signed-off-by: Jinzhe Zeng --- source/tests/pt/test_tabulate_fusion_se_a.py | 4 ++++ source/tests/pt/test_tabulate_fusion_se_atten.py | 4 ++++ source/tests/pt/test_tabulate_fusion_se_r.py | 4 ++++ source/tests/pt/test_tabulate_fusion_se_t.py | 4 ++++ 4 files changed, 16 insertions(+) diff --git a/source/tests/pt/test_tabulate_fusion_se_a.py b/source/tests/pt/test_tabulate_fusion_se_a.py index 3067d17624..f811463ff0 100644 --- a/source/tests/pt/test_tabulate_fusion_se_a.py +++ b/source/tests/pt/test_tabulate_fusion_se_a.py @@ -3,6 +3,9 @@ import torch +from deepmd.pt.cxx_op import ( + ENABLE_CUSTOMIZED_OP, +) from deepmd.pt.utils import ( env, ) @@ -13,6 +16,7 @@ @parameterized((torch.float64, torch.float32)) +@unittest.skipIf(not ENABLE_CUSTOMIZED_OP, "PyTorch customized OPs are not built") class TestTabulateFusionSeAOp(unittest.TestCase): def setUp(self): (dtype,) = self.param diff --git a/source/tests/pt/test_tabulate_fusion_se_atten.py b/source/tests/pt/test_tabulate_fusion_se_atten.py index cc8a642333..99c6e5c7d0 100644 --- a/source/tests/pt/test_tabulate_fusion_se_atten.py +++ b/source/tests/pt/test_tabulate_fusion_se_atten.py @@ -3,6 +3,9 @@ import torch +from deepmd.pt.cxx_op import ( + ENABLE_CUSTOMIZED_OP, +) from deepmd.pt.utils import ( env, ) @@ -13,6 +16,7 @@ @parameterized((torch.float64, torch.float32)) +@unittest.skipIf(not ENABLE_CUSTOMIZED_OP, "PyTorch customized OPs are not built") class TestTabulateFusionSeAttenOp(unittest.TestCase): def setUp(self): (dtype,) = self.param diff --git a/source/tests/pt/test_tabulate_fusion_se_r.py b/source/tests/pt/test_tabulate_fusion_se_r.py index 4b9c841a46..9abfc72dc3 100644 --- a/source/tests/pt/test_tabulate_fusion_se_r.py +++ b/source/tests/pt/test_tabulate_fusion_se_r.py @@ -3,6 +3,9 @@ import torch +from deepmd.pt.cxx_op import ( + ENABLE_CUSTOMIZED_OP, +) from deepmd.pt.utils import ( env, ) @@ -13,6 +16,7 @@ @parameterized((torch.float64, torch.float32)) +@unittest.skipIf(not ENABLE_CUSTOMIZED_OP, "PyTorch customized OPs are not built") class TestTabulateFusionSeAOp(unittest.TestCase): def setUp(self): (dtype,) = self.param diff --git a/source/tests/pt/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py index e3f213971c..2cb1fa5bb1 100644 --- a/source/tests/pt/test_tabulate_fusion_se_t.py +++ b/source/tests/pt/test_tabulate_fusion_se_t.py @@ -3,6 +3,9 @@ import torch +from deepmd.pt.cxx_op import ( + ENABLE_CUSTOMIZED_OP, +) from deepmd.pt.utils import ( env, ) @@ -13,6 +16,7 @@ @parameterized((torch.float64, torch.float32)) +@unittest.skipIf(not ENABLE_CUSTOMIZED_OP, "PyTorch customized OPs are not built") class TestTabulateFusionSeTOp(unittest.TestCase): def setUp(self): (dtype,) = self.param From 1523196543db982a4842981ec8b719ee2fd95b61 Mon Sep 17 00:00:00 2001 From: Jinzhe Zeng Date: Fri, 21 Jun 2024 02:33:59 -0400 Subject: [PATCH 28/85] reduce test size from 192 atoms to 4 atoms Signed-off-by: Jinzhe Zeng --- source/tests/pt/test_tabulate_fusion_se_t.py | 9786 +----------------- 1 file changed, 5 insertions(+), 9781 deletions(-) diff --git a/source/tests/pt/test_tabulate_fusion_se_t.py b/source/tests/pt/test_tabulate_fusion_se_t.py index 2cb1fa5bb1..3fe34c702e 100644 --- a/source/tests/pt/test_tabulate_fusion_se_t.py +++ b/source/tests/pt/test_tabulate_fusion_se_t.py 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- self.em_tensor = self.em_x_tensor.reshape(192, 4, 4) + ).reshape(4, 16) # 3072 + self.em_tensor = self.em_x_tensor.reshape(4, 4, 4) self.table_info_tensor.requires_grad = False self.table_tensor.requires_grad = False self.em_x_tensor.requires_grad = True @@ -4541,762 +1533,10 @@ def setUp(self): 2.6256636737020518e00, 3.3955783231847523e00, 2.7091329174140033e00, - -1.9231004620365049e00, - -4.6499941633630704e-01, - -1.1594526098009617e00, - -1.2686640472208488e00, - 2.0867847214069872e00, - 3.0003750888529219e00, - 3.6325449823191440e00, - 2.8788902557067368e00, - 1.2684738158575621e00, - 1.8537695728403008e00, - 2.1955525109720693e00, - 1.7836450721166277e00, - 1.8550735634159015e00, - 2.4434013845454778e00, - 3.0971074319021614e00, - 2.4481507963338514e00, - 1.6439641588553517e00, - 1.9173245315063490e00, - 2.4213050183154365e00, - 2.0154649449162125e00, - 8.6044027444396542e-01, - 1.6761956340909820e00, - 1.9714372427825169e00, - 1.4694269993819085e00, - 1.1578881590922248e00, - 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2001 From: wangyan Date: Sun, 7 Jul 2024 10:36:00 +0800 Subject: [PATCH 29/85] basic descriptor se_a --- deepmd/pt/common.py | 102 ++++ deepmd/pt/descriptor/__init__.py | 0 deepmd/pt/descriptor/se.py | 23 + deepmd/pt/descriptor/se_a.py | 714 +++++++++++++++++++++++++++ deepmd/pt/env.py | 23 + deepmd/pt/utils/graph.py | 18 + deepmd/pt/utils/spin.py | 51 ++ deepmd/pt/utils/tabulate.py | 806 +++++++++++++++++++++++++++++++ 8 files changed, 1737 insertions(+) create mode 100644 deepmd/pt/common.py create mode 100644 deepmd/pt/descriptor/__init__.py create mode 100644 deepmd/pt/descriptor/se.py create mode 100644 deepmd/pt/descriptor/se_a.py create mode 100644 deepmd/pt/env.py create mode 100644 deepmd/pt/utils/graph.py create mode 100644 deepmd/pt/utils/spin.py create mode 100644 deepmd/pt/utils/tabulate.py diff --git a/deepmd/pt/common.py b/deepmd/pt/common.py new file mode 100644 index 0000000000..6319452e4f --- /dev/null +++ b/deepmd/pt/common.py @@ -0,0 +1,102 @@ +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Union, +) + +from deepmd.common import ( + VALID_ACTIVATION, + VALID_PRECISION, + expand_sys_str, + get_np_precision, + j_loader, + make_default_mesh, + select_idx_map, +) + +from deepmd.pt.env import ( + GLOBAL_PT_FLOAT_PRECISION, + torch, +) + +import torch.nn.functional as F + +if TYPE_CHECKING: + from deepmd.common import ( + _ACTIVATION, + _PRECISION, + ) + +ACTIVATION_FN_DICT = { + "relu": F.relu, + "relu6": F.relu6, + "softplus": F.softplus, + "sigmoid": torch.sigmoid, + "tanh": torch.tanh, + "gelu": F.gelu, + # PyTorch has no gelu_tf + "gelu_tf": lambda x: x * 0.5 * (1.0 + torch.erf(x / 1.41421)), + "linear": lambda x: x, + "none": lambda x: x, +} +assert VALID_ACTIVATION.issubset(ACTIVATION_FN_DICT.keys()) + +PRECISION_DICT = { + "default": GLOBAL_PT_FLOAT_PRECISION, + "float16": torch.float16, + "float32": torch.float32, + "float64": torch.float64, + "bfloat16": torch.bfloat16, +} +assert VALID_PRECISION.issubset(PRECISION_DICT.keys()) + +def get_activation_func( + activation_fn: Union[_ACTIVATION, None] +) -> Callable[[torch.Tensor], torch.Tensor]: + """Get activation function callable based on string name. + + Parameters + ---------- + activation_fn : _ACTIVATION + One of the defined activation functions + + Returns + ------- + Callable[[torch.Tensor], torch.Tensor] + Corresponding PyTorch callable + + Raises + ------ + RuntimeError + If unknown activation function is specified + """ + if activation_fn is None: + activation_fn = "none" + assert activation_fn is not None + if activation_fn.lower() not in ACTIVATION_FN_DICT: + raise RuntimeError(f"{activation_fn} is not a valid activation function") + return ACTIVATION_FN_DICT[activation_fn.lower()] + + +def get_precision(precision: _PRECISION) -> Any: + """Convert str to PyTorch dtype constant. + + Parameters + ---------- + precision : _PRECISION + One of the allowed precisions + + Returns + ------- + torch.dtype + Appropriate PyTorch dtype constant + + Raises + ------ + RuntimeError + If supplied precision string does not have a corresponding PyTorch dtype constant + """ + if precision not in PRECISION_DICT: + raise RuntimeError(f"{precision} is not a valid precision") + return PRECISION_DICT[precision] \ No newline at end of file diff --git a/deepmd/pt/descriptor/__init__.py b/deepmd/pt/descriptor/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/deepmd/pt/descriptor/se.py b/deepmd/pt/descriptor/se.py new file mode 100644 index 0000000000..b04f95d45e --- /dev/null +++ b/deepmd/pt/descriptor/se.py @@ -0,0 +1,23 @@ +import re +from typing import ( + List, + Optional, + Set, + Tuple, +) + +from deepmd.dpmodel.utils.network import ( + EmbeddingNet, + NetworkCollection, +) +from deepmd.pt.env import ( + EMBEDDING_NET_PATTERN, +) +from deepmd.tf.utils.update_sel import ( + UpdateSel, +) +from deepmd.utils.data_system import ( + DeepmdDataSystem, +) + +import torch \ No newline at end of file diff --git a/deepmd/pt/descriptor/se_a.py b/deepmd/pt/descriptor/se_a.py new file mode 100644 index 0000000000..ad2c7fd699 --- /dev/null +++ b/deepmd/pt/descriptor/se_a.py @@ -0,0 +1,714 @@ +# SPDX-License-Identifier: LGPL-3.0-or-later +from typing import ( + List, + Optional, + Tuple, +) + +import numpy as np + +from deepmd.dpmodel.utils.env_mat import ( + EnvMat, +) + +from deepmd.pt.cxx_op import ( + ENABLE_CUSTOMIZED_OP, +) + +from deepmd.pt.utils.graph import ( + get_extra_embedding_net_suffix, +) + +from deepmd.pt.utils.tabulate import ( + DPTabulate, +) + +from deepmd.pt.utils.spin import ( + Spin, +) + +from deepmd.pt.env import ( + GLOBAL_NP_FLOAT_PRECISION, + GLOBAL_PT_FLOAT_PRECISION, +) + +from deepmd.pt.common import ( + get_activation_func, + get_np_precision, + get_precision, +) + +import torch +import torch.nn.functional as F + + +class DescrptSeA(): + r"""DeepPot-SE constructed from all information (both angular and radial) of + atomic configurations. The embedding takes the distance between atoms as input. + + The descriptor :math:`\mathcal{D}^i \in \mathcal{R}^{M_1 \times M_2}` is given by [1]_ + + .. math:: + \mathcal{D}^i = (\mathcal{G}^i)^T \mathcal{R}^i (\mathcal{R}^i)^T \mathcal{G}^i_< + + where :math:`\mathcal{R}^i \in \mathbb{R}^{N \times 4}` is the coordinate + matrix, and each row of :math:`\mathcal{R}^i` can be constructed as follows + + .. math:: + (\mathcal{R}^i)_j = [ + \begin{array}{c} + s(r_{ji}) & \frac{s(r_{ji})x_{ji}}{r_{ji}} & \frac{s(r_{ji})y_{ji}}{r_{ji}} & \frac{s(r_{ji})z_{ji}}{r_{ji}} + \end{array} + ] + + where :math:`\mathbf{R}_{ji}=\mathbf{R}_j-\mathbf{R}_i = (x_{ji}, y_{ji}, z_{ji})` is + the relative coordinate and :math:`r_{ji}=\lVert \mathbf{R}_{ji} \lVert` is its norm. + The switching function :math:`s(r)` is defined as: + + .. math:: + s(r)= + \begin{cases} + \frac{1}{r}, & r