upgrade dlpack to 0.5

conda/environments/cudf_dev_cuda11.0.yml

@@ -44,7 +44,7 @@ dependencies:
   - dask==2021.4.0
   - distributed>=2.22.0,<=2021.4.0
   - streamz
-  - dlpack==0.3
+  - dlpack>=0.5,<0.6.0a0
   - arrow-cpp=1.0.1
   - arrow-cpp-proc * cuda
   - double-conversion

conda/environments/cudf_dev_cuda11.2.yml

@@ -44,7 +44,7 @@ dependencies:
   - dask==2021.4.0
   - distributed>=2.22.0,<=2021.4.0
   - streamz
-  - dlpack==0.3
+  - dlpack>=0.5,<0.6.0a0
   - arrow-cpp=1.0.1
   - arrow-cpp-proc * cuda
   - double-conversion

conda/recipes/cudf/meta.yaml

@@ -29,7 +29,7 @@ requirements:
     - cython >=0.29,<0.30
     - setuptools
     - numba >=0.53.1
-    - dlpack 0.3
+    - dlpack>=0.5,<0.6.0a0
     - pyarrow 1.0.1
     - libcudf {{ version }}
     - rmm {{ minor_version }}

conda/recipes/libcudf/meta.yaml

@@ -39,7 +39,7 @@ requirements:
     - cudatoolkit {{ cuda_version }}.*
     - arrow-cpp 1.0.1
     - arrow-cpp-proc * cuda
-    - dlpack 0.3
+    - dlpack>=0.5,<0.6.0a0
   run:
     - {{ pin_compatible('cudatoolkit', max_pin='x.x') }}
     - arrow-cpp-proc * cuda

cpp/cmake/thirdparty/CUDF_GetDLPack.cmake

@@ -36,6 +36,6 @@ function(find_and_configure_dlpack VERSION)
     set(DLPACK_INCLUDE_DIR "${dlpack_SOURCE_DIR}/include" PARENT_SCOPE)
 endfunction()
 
-set(CUDF_MIN_VERSION_dlpack 0.3)
+set(CUDF_MIN_VERSION_dlpack 0.5)
 
 find_and_configure_dlpack(${CUDF_MIN_VERSION_dlpack})

cpp/include/cudf/interop.hpp

@@ -35,8 +35,8 @@ namespace cudf {
 /**
  * @brief Convert a DLPack DLTensor into a cudf table
  *
- * The `device_type` of the DLTensor must be `kDLGPU`, `kDLCPU`, or
- * `kDLCPUPinned`, and `device_id` must match the current device. The `ndim`
+ * The `device_type` of the DLTensor must be `kDLCPU`, `kDLCuda`, or
+ * `kDLCUDAHost`, and `device_id` must match the current device. The `ndim`
  * must be set to 1 or 2. The `dtype` must have 1 lane and the bitsize must
  * match a supported `cudf::data_type`.
  *

cpp/libcudf_kafka/cmake/thirdparty/CUDF_KAFKA_GetCUDF.cmake

@@ -28,7 +28,7 @@ function(find_and_configure_cudf VERSION)
     endif()
 endfunction()
 
-set(CUDA_KAFKA_MIN_VERSION_cudf "${CUDA_KAFKA_VERSION_MAJOR}.${CUDA_KAFKA_VERSION_MINOR}")
+set(CUDA_KAFKA_MIN_VERSION_cudf "${CUDA_KAFKA_VERSION_MAJOR}.${CUDA_KAFKA_VERSION_MINOR}.00")
 find_and_configure_cudf(${CUDA_KAFKA_MIN_VERSION_cudf})
 
 if(cudf_ADDED)

cpp/src/interop/dlpack.cpp

@@ -137,15 +137,15 @@ std::unique_ptr<table> from_dlpack(DLManagedTensor const* managed_tensor,
   auto const& tensor = managed_tensor->dl_tensor;
 
   // We can copy from host or device pointers
-  CUDF_EXPECTS(kDLGPU == tensor.ctx.device_type || kDLCPU == tensor.ctx.device_type ||
-                 kDLCPUPinned == tensor.ctx.device_type,
-               "DLTensor must be GPU, CPU, or pinned type");
+  CUDF_EXPECTS(tensor.device.device_type == kDLCPU || tensor.device.device_type == kDLCUDA ||
+                 tensor.device.device_type == kDLCUDAHost,
+               "DLTensor device type must be CPU, CUDA or CUDAHost");
 
   // Make sure the current device ID matches the Tensor's device ID
-  if (tensor.ctx.device_type != kDLCPU) {
+  if (tensor.device.device_type != kDLCPU) {
     int device_id = 0;
     CUDA_TRY(cudaGetDevice(&device_id));
-    CUDF_EXPECTS(tensor.ctx.device_id == device_id, "DLTensor device ID must be current device");
+    CUDF_EXPECTS(tensor.device.device_id == device_id, "DLTensor device ID must be current device");
   }
 
   // Currently only 1D and 2D tensors are supported
@@ -234,8 +234,8 @@ DLManagedTensor* to_dlpack(table_view const& input,
     tensor.strides[1] = num_rows;
   }
 
-  CUDA_TRY(cudaGetDevice(&tensor.ctx.device_id));
-  tensor.ctx.device_type = kDLGPU;
+  CUDA_TRY(cudaGetDevice(&tensor.device.device_id));
+  tensor.device.device_type = kDLCUDA;
 
   // If there is only one column, then a 1D tensor can just copy the pointer
   // to the data in the column, and the deleter should not delete the original

cpp/tests/interop/dlpack_test.cpp

@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2019, NVIDIA CORPORATION.
+ * Copyright (c) 2019-2021, NVIDIA CORPORATION.
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -111,7 +111,7 @@ TEST_F(DLPackUntypedTests, UnsupportedDeviceTypeFromDlpack)
   unique_managed_tensor tensor(cudf::to_dlpack(input));
 
   // Spoof an unsupported device type
-  tensor->dl_tensor.ctx.device_type = kDLOpenCL;
+  tensor->dl_tensor.device.device_type = kDLOpenCL;
   EXPECT_THROW(cudf::from_dlpack(tensor.get()), cudf::logic_error);
 }
 
@@ -122,7 +122,7 @@ TEST_F(DLPackUntypedTests, InvalidDeviceIdFromDlpack)
   unique_managed_tensor tensor(cudf::to_dlpack(input));
 
   // Spoof the wrong device ID
-  tensor->dl_tensor.ctx.device_id += 1;
+  tensor->dl_tensor.device.device_id += 1;
   EXPECT_THROW(cudf::from_dlpack(tensor.get()), cudf::logic_error);
 }
 
@@ -242,7 +242,7 @@ TYPED_TEST(DLPackNumericTests, ToDlpack1D)
 
   auto const& tensor = result->dl_tensor;
   validate_dtype<TypeParam>(tensor.dtype);
-  EXPECT_EQ(kDLGPU, tensor.ctx.device_type);
+  EXPECT_EQ(kDLCUDA, tensor.device.device_type);
   EXPECT_EQ(1, tensor.ndim);
   EXPECT_EQ(uint64_t{0}, tensor.byte_offset);
   EXPECT_EQ(nullptr, tensor.strides);
@@ -275,7 +275,7 @@ TYPED_TEST(DLPackNumericTests, ToDlpack2D)
 
   auto const& tensor = result->dl_tensor;
   validate_dtype<TypeParam>(tensor.dtype);
-  EXPECT_EQ(kDLGPU, tensor.ctx.device_type);
+  EXPECT_EQ(kDLCUDA, tensor.device.device_type);
   EXPECT_EQ(2, tensor.ndim);
   EXPECT_EQ(uint64_t{0}, tensor.byte_offset);
 
@@ -341,12 +341,12 @@ TYPED_TEST(DLPackNumericTests, FromDlpackCpu)
   int64_t strides[2] = {1, 5};
 
   DLManagedTensor tensor{};
-  tensor.dl_tensor.ctx.device_type = kDLCPU;
-  tensor.dl_tensor.dtype           = get_dtype<T>();
-  tensor.dl_tensor.ndim            = 2;
-  tensor.dl_tensor.byte_offset     = offset;
-  tensor.dl_tensor.shape           = shape;
-  tensor.dl_tensor.strides         = strides;
+  tensor.dl_tensor.device.device_type = kDLCPU;
+  tensor.dl_tensor.dtype              = get_dtype<T>();
+  tensor.dl_tensor.ndim               = 2;
+  tensor.dl_tensor.byte_offset        = offset;
+  tensor.dl_tensor.shape              = shape;
+  tensor.dl_tensor.strides            = strides;
 
   thrust::host_vector<T> host_vector(data.begin(), data.end());
   tensor.dl_tensor.data = host_vector.data();