add spconv

diopi_test/python/conformance/diopi_functions.py

@@ -6206,3 +6206,16 @@ def spmm(input, mat2) -> Tensor:
     ret = func(input.context(), out, input, mat2)
     check_returncode(ret)
     return out
+
+def spconv(in_feat, kernel,neighbor_map,sum_nnz,neighbor_address,q_neighbor_address,output_size,
+           qsum_nnz,transpose,allow_tf32,allow_fp16) -> Tensor:
+
+    out_channel = kernel.size().data[2]
+    func = check_function("diopiSpConv")
+    # at::Tensor out_feat = torch::zeros({output_size, out_channel}, 
+    #         at::device(in_feat.device()).dtype(in_feat.scalar_type()));
+    out_feat = Tensor(list([output_size, out_channel]), dtype=in_feat.get_dtype())
+    ret = func(in_feat.context(),out_feat,in_feat,kernel,neighbor_map,sum_nnz,neighbor_address,
+               q_neighbor_address,output_size,qsum_nnz,transpose,allow_tf32,allow_fp16 )
+    check_returncode(ret)
+    return out_feat

diopi_test/python/conformance/mytest.ipynb

@@ -0,0 +1 @@
+{"cells":[{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"ename":"ModuleNotFoundError","evalue":"No module named 'diopilib'","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mdiopilib\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mconformance\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdiopi_functions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m spmm,spconv\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mconformance\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdiopi_runtime\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Tensor\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'diopilib'"]}],"source":["import torch\n","import diopilib\n","from conformance.diopi_functions import spmm,spconv\n","from conformance.diopi_runtime import Tensor\n","import numpy as np\n","# from torch.nn.functional import dropout\n","# sparse matrix:\n","# 0.5,  0,  1\n","# 0,    0,  2\n","# 1,    3,  0.6\n","\n","# M, K, N = 4096, 4096, 128\n","\n","# a = torch.randn((M,K),dtype=torch.float32)\n","# a = dropout(a, p=0.9)\n","# sparse_a = a.to_sparse_csr()\n","# print(a)\n","# print(sparse_a)\n","# b = np.random.randn(K,N).astype(np.float32)\n","# print(b)\n","\n","# input = Tensor.from_numpy(b)\n","# row_ptr = Tensor.from_numpy(sparse_a.crow_indices().numpy().astype(np.int32))\n","# col_ind = Tensor.from_numpy(sparse_a.col_indices().numpy().astype(np.int32))\n","# values = Tensor.from_numpy(sparse_a.values().numpy().astype(np.float32))\n","# print(list(input.size().data))\n","# c = spmm(row_ptr, col_ind, values, input)\n","# c_ref = a @ torch.from_numpy(b)\n","# # c = rbrmsr_spmm(sparse_a.crow_indices(), sparse_a.col_indices(), sparse_a.values(), b)\n","\n","\n","spconv_data = torch.load('spconv_data.pth')\n","in_feat = spconv_data['in_feats']\n","kernel = spconv_data['kernel']\n","neighbor_map = spconv_data['neighbor_map']\n","sum_nnz = spconv_data['sum_nnz']\n","neighbor_address = spconv_data['neighbor_address']\n","q_neighbor_address = spconv_data['q_neighbor_address']\n","output_size = spconv_data['output_size']\n","qsum_nnz = spconv_data['qsum_nnz'].item()\n","\n","in_feat = Tensor.from_numpy(in_feat.numpy().astype(np.float16))\n","kernel = Tensor.from_numpy(kernel.numpy().astype(np.float16))\n","neighbor_map = Tensor.from_numpy(neighbor_map.numpy().astype(np.int32))\n","neighbor_address = Tensor.from_numpy(neighbor_address.numpy().astype(np.int32))\n","q_neighbor_address = Tensor.from_numpy(q_neighbor_address.numpy().astype(np.int32))\n","\n","out = spconv(in_feat, kernel,neighbor_map,sum_nnz,neighbor_address,q_neighbor_address,output_size,\n","           qsum_nnz,False,True,True) \n","\n","print(out)"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["ours: tensor([[ 1.9180e+02, -3.3436e+02, -2.5628e+02,  ..., -1.2209e+02,\n","          1.2449e+02,  8.9544e+00],\n","        [ 5.4242e+00, -9.1456e+01, -8.8789e+01,  ...,  2.9239e+02,\n","          5.9588e+02, -6.7169e+01],\n","        [ 9.6391e+01, -2.8461e+02, -8.9830e+01,  ...,  2.4198e+02,\n","          1.9330e+02, -2.7285e+02],\n","        ...,\n","        [-9.6388e+01, -1.8851e+02, -3.9614e+02,  ...,  3.3607e+02,\n","          9.8614e+01, -2.6860e+02],\n","        [-8.1585e+01, -1.1838e+02, -1.9467e+02,  ...,  3.6153e+02,\n","         -1.0657e+02, -3.8279e+02],\n","        [ 1.8823e+02, -3.1072e+02, -2.3838e+02,  ..., -3.6757e-01,\n","         -5.7795e+01, -1.2785e+02]])\n","ref:  tensor([[ 1.9180e+02, -3.3436e+02, -2.5628e+02,  ..., -1.2209e+02,\n","          1.2449e+02,  8.9544e+00],\n","        [ 5.4243e+00, -9.1456e+01, -8.8789e+01,  ...,  2.9239e+02,\n","          5.9588e+02, -6.7170e+01],\n","        [ 9.6391e+01, -2.8461e+02, -8.9830e+01,  ...,  2.4198e+02,\n","          1.9330e+02, -2.7285e+02],\n","        ...,\n","        [-9.6388e+01, -1.8851e+02, -3.9614e+02,  ...,  3.3607e+02,\n","          9.8614e+01, -2.6860e+02],\n","        [-8.1585e+01, -1.1838e+02, -1.9467e+02,  ...,  3.6153e+02,\n","         -1.0657e+02, -3.8279e+02],\n","        [ 1.8823e+02, -3.1072e+02, -2.3838e+02,  ..., -3.6760e-01,\n","         -5.7795e+01, -1.2785e+02]])\n"]}],"source":["c_ours = torch.from_numpy(c.numpy())\n","print(\"ours:\", c_ours)\n","print(\"ref: \", c_ref)\n","assert torch.allclose(c_ours, c_ref, rtol=1e-03, atol=1e-03)"]}],"metadata":{"kernelspec":{"display_name":"eda","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.18"}},"nbformat":4,"nbformat_minor":2}

impl/torch/functions/functions_sparse.cpp

@@ -51,5 +51,29 @@ diopiError_t diopiSpMM(diopiContextHandle_t ctx, diopiTensorHandle_t out, diopiC
     return diopiErrorOccurred;
 }
 
+
+extern "C" diopiError_t diopiSpConv(diopiContextHandle_t ctx, diopiTensorHandle_t out_feat, diopiTensorHandle_t in_feat,
+        diopicTensorHandle_t kernel, diopiTensorHandle_t neighbor_map,const int sum_nnz, 
+        diopiTensorHandle_t neighbor_address, diopiTensorHandle_t q_neighbor_address, const int output_size, 
+        const int qsum_nnz, const bool transpose, const bool allow_tf32, const bool allow_fp16 ) {
+
+    impl::aten::setCurStream(ctx);
+
+    auto atIn_feat = impl::aten::buildATen(in_feat);
+    auto atOut_feat = impl::aten::buildATen(out_feat);
+    auto atKernel = impl::aten::buildATen(kernel);
+    auto atNeighbor_map = impl::aten::buildATen(neighbor_map);
+    auto atNeighbor_address = impl::aten::buildATen(neighbor_address);
+    auto atQ_neighbor_address = impl::aten::buildATen(q_neighbor_address);
+    atOut_feat.zero_();
+
+    sparse::ops::conv_forward_fetch_on_demand_cuda(atIn_feat, atOut_feat, atKernel, atNeighbor_map, sum_nnz, 
+            atNeighbor_address,atQ_neighbor_address,output_size,
+            qsum_nnz,transpose,allow_tf32,allow_fp16);
+
+    return diopiSuccess;
+
+}
+
 }  // namespace cuda
 }  // namespace impl