DLPack Conversion API

include/tvm/runtime/c_runtime_api.h

@@ -445,6 +445,32 @@ TVM_DLL int TVMArrayCopyFromTo(TVMArrayHandle from,
                                TVMArrayHandle to,
                                TVMStreamHandle stream);
 
+/*!
+ * \brief Produce an array from the DLManagedTensor that shares data memory
+ * with the DLManagedTensor.
+ * \param from The source DLManagedTensor.
+ * \param out The output array handle.
+ * \return 0 when success, -1 when failure happens
+ */
+TVM_DLL int TVMArrayFromDLPack(DLManagedTensor* from,
+                               TVMArrayHandle* out);
+
+/*!
+ * \brief Produce a DLMangedTensor from the array that shares data memory with
+ * the array.
+ * \param from The source array.
+ * \param out The DLManagedTensor handle.
+ * \return 0 when success, -1 when failure happens
+ */
+TVM_DLL int TVMArrayToDLPack(TVMArrayHandle from,
+                             DLManagedTensor** out);
+
+/*!
+ * \brief Delete (free) a DLManagedTensor's data.
+ * \param dltensor Pointer to the DLManagedTensor. 
+ */
+TVM_DLL void TVMDLManagedTensorCallDeleter(DLManagedTensor* dltensor);
+
 /*!
  * \brief Create a new runtime stream.
  *

include/tvm/runtime/ndarray.h

@@ -155,7 +155,7 @@ class NDArray {
    * that is DLPack compatible.
    *
    * The memory is retained until the NDArray went out of scope.
-   *
+   * \param tensor The DLPack tensor to copy from.
    * \return The created NDArray view.
    */
   TVM_DLL static NDArray FromDLPack(DLManagedTensor* tensor);

python/tvm/_ffi/ndarray.py

@@ -5,7 +5,7 @@
 import sys
 import ctypes
 import numpy as np
-from .base import _LIB, check_call, c_array, string_types, _FFI_MODE
+from .base import _LIB, check_call, c_array, string_types, _FFI_MODE, c_str
 from .runtime_ctypes import TVMType, TVMContext, TVMArray, TVMArrayHandle
 from .runtime_ctypes import TypeCode, tvm_shape_index_t
 
@@ -28,6 +28,17 @@
     from ._ctypes.ndarray import NDArrayBase as _NDArrayBase
 
 
+TVMPyCapsuleDestructor = ctypes.CFUNCTYPE(None, ctypes.c_void_p)
+_c_str_dltensor = c_str('dltensor')
+
+
+# used for PyCapsule manipulation
+if hasattr(ctypes, 'pythonapi'):
+    ctypes.pythonapi.PyCapsule_GetName.restype = ctypes.c_char_p
+    ctypes.pythonapi.PyCapsule_GetPointer.restype = ctypes.c_void_p
+    ctypes.pythonapi.PyCapsule_New.restype = ctypes.py_object
+
+
 def context(dev_type, dev_id=0):
     """Construct a TVM context with given device type and id.
 
@@ -62,6 +73,7 @@ def context(dev_type, dev_id=0):
         dev_type = TVMContext.STR2MASK[dev_type]
     return TVMContext(dev_type, dev_id)
 
+
 def numpyasarray(np_data):
     """Return a TVMArray representation of a numpy array.
     """
@@ -112,6 +124,42 @@ def empty(shape, dtype="float32", ctx=context(1, 0)):
         ctypes.byref(handle)))
     return _make_array(handle, False)
 
+
+def from_dlpack(dltensor):
+    """Produce an array from a DLPack tensor without memory copy.
+    Retreives the underlying DLPack tensor's pointer to create an array from the
+    data. Removes the original DLPack tensor's destructor as now the array is
+    responsible for destruction.
+
+    Parameters
+    ----------
+    dltensor : DLPack tensor
+
+    Returns
+    -------
+    arr: tvm.nd.NDArray
+        The array view of the tensor data.
+    """
+    dltensor = ctypes.py_object(dltensor)
+    name = ctypes.pythonapi.PyCapsule_GetName(dltensor)
+    ptr = ctypes.pythonapi.PyCapsule_GetPointer(dltensor, name)
+    handle = TVMArrayHandle()
+    check_call(_LIB.TVMArrayFromDLPack(ptr, ctypes.byref(handle)))
+    ctypes.pythonapi.PyCapsule_SetDestructor(dltensor, None)
+    return _make_array(handle, False)
+
+
+def _dlpack_deleter(pycapsule):
+    pycapsule = ctypes.py_object(pycapsule)
+    if ctypes.pythonapi.PyCapsule_IsValid(pycapsule, _c_str_dltensor):
+        ptr = ctypes.pythonapi.PyCapsule_GetPointer(pycapsule, _c_str_dltensor)
+        _LIB.TVMDLManagedTensorCallDeleter(ptr)
+        ctypes.pythonapi.PyCapsule_SetDestructor(dltensor, TVMPyCapsuleDestructor(0))
+
+
+_c_dlpack_deleter = TVMPyCapsuleDestructor(_dlpack_deleter)
+
+
 class NDArrayBase(_NDArrayBase):
     """A simple Device/CPU Array object in runtime."""
     @property
@@ -260,6 +308,18 @@ def copyto(self, target):
             raise ValueError("Unsupported target type %s" % str(type(target)))
         return target
 
+    def to_dlpack(self):
+        """Produce an array from a DLPack Tensor without copying memory
+
+        Returns
+        -------
+        dlpack : DLPack tensor view of the array data
+        """
+        handle = ctypes.c_void_p()
+        check_call(_LIB.TVMArrayToDLPack(self.handle, ctypes.byref(handle)))
+        return ctypes.pythonapi.PyCapsule_New(handle, _c_str_dltensor, _c_dlpack_deleter)
+
+
 def free_extension_handle(handle, type_code):
     """Free c++ extension type handle
 

python/tvm/contrib/dlpack.py

@@ -0,0 +1,43 @@
+"""Wrapping functions to bridge frameworks with DLPack support to TVM"""
+from .. import ndarray
+
+def convert_func(tvm_func, tensor_type, to_dlpack_func):
+    """Convert a tvm function into one that accepts a tensor from another
+       framework, provided the other framework supports DLPACK
+
+    Parameters
+    ----------
+    tvm_func: Function
+        Built tvm function operating on arrays
+
+    tensor_type: Type
+        Type of the tensors of the target framework
+
+    to_dlpack_func: Function
+        Function to convert the source tensors to DLPACK
+    """
+    assert(callable(tvm_func))
+
+    def _wrapper(*args):
+        args = tuple(ndarray.from_dlpack(to_dlpack_func(arg))\
+            if isinstance(arg, tensor_type) else arg for arg in args)
+        return tvm_func(*args)
+
+    return _wrapper
+
+def to_pytorch_func(tvm_func):
+    """Convert a tvm function into one that accepts PyTorch tensors
+
+    Parameters
+    ----------
+    tvm_func: Function
+        Built tvm function operating on arrays
+
+    Returns
+    -------
+    wrapped_func: Function
+        Wrapped tvm function that operates on PyTorch tensors
+    """
+    import torch
+    import torch.utils.dlpack
+    return convert_func(tvm_func, torch.Tensor, torch.utils.dlpack.to_dlpack)

python/tvm/ndarray.py

@@ -8,7 +8,7 @@
 import numpy as _np
 
 from ._ffi.ndarray import TVMContext, TVMType, NDArrayBase
-from ._ffi.ndarray import context, empty
+from ._ffi.ndarray import context, empty, from_dlpack
 from ._ffi.ndarray import _set_class_ndarray
 from ._ffi.ndarray import register_extension, free_extension_handle
 

src/runtime/ndarray.cc

@@ -93,6 +93,16 @@ struct NDArray::Internal {
     arr.data_ = nullptr;
     return tensor;
   }
+  // Container to DLManagedTensor
+  static DLManagedTensor* ToDLPack(NDArray::Container* from) {
+    CHECK(from != nullptr);
+    DLManagedTensor* ret = new DLManagedTensor();
+    ret->dl_tensor = from->dl_tensor;
+    ret->manager_ctx = from;
+    from->IncRef();
+    ret->deleter = NDArrayDLPackDeleter;
+    return ret;
+  }
 };
 
 NDArray NDArray::CreateView(std::vector<int64_t> shape,
@@ -115,13 +125,7 @@ NDArray NDArray::CreateView(std::vector<int64_t> shape,
 }
 
 DLManagedTensor* NDArray::ToDLPack() const {
-  CHECK(data_ != nullptr);
-  DLManagedTensor* ret = new DLManagedTensor();
-  ret->dl_tensor = data_->dl_tensor;
-  ret->manager_ctx = const_cast<NDArray*>(this);
-  data_->IncRef();
-  ret->deleter = NDArrayDLPackDeleter;
-  return ret;
+  return Internal::ToDLPack(data_);
 }
 
 NDArray NDArray::Empty(std::vector<int64_t> shape,
@@ -213,6 +217,24 @@ int TVMArrayCopyFromTo(TVMArrayHandle from,
   API_END();
 }
 
+int TVMArrayFromDLPack(DLManagedTensor* from,
+                       TVMArrayHandle* out) {
+  API_BEGIN();
+  *out = NDArray::Internal::MoveAsDLTensor(NDArray::FromDLPack(from));
+  API_END();
+}
+
+int TVMArrayToDLPack(TVMArrayHandle from,
+                     DLManagedTensor** out) {
+  API_BEGIN();
+  *out = NDArray::Internal::ToDLPack(reinterpret_cast<NDArray::Container*>(from));
+  API_END();
+}
+
+void TVMDLManagedTensorCallDeleter(DLManagedTensor* dltensor) {
+  (*(dltensor->deleter))(dltensor);
+}
+
 int TVMArrayCopyFromBytes(TVMArrayHandle handle,
                           void* data,
                           size_t nbytes) {

tests/python/contrib/test_dlpack.py

@@ -0,0 +1,44 @@
+import tvm
+import numpy as np
+from tvm.contrib.dlpack import to_pytorch_func
+
+def test():
+    a = np.random.randn(1337)
+    tvm_a = tvm.nd.array(a)
+    np.testing.assert_equal(tvm.nd.from_dlpack(tvm_a.to_dlpack()).asnumpy(), a)
+
+    try:
+        import torch
+        import torch.utils.dlpack
+
+        x = torch.rand(56, 56)
+        tvm_x = tvm.nd.from_dlpack(torch.utils.dlpack.to_dlpack(x))
+        np.testing.assert_equal(x.numpy(), tvm_x.asnumpy())
+        y = tvm.nd.from_dlpack(tvm_x.to_dlpack())
+        np.testing.assert_equal(y.asnumpy(), tvm_x.asnumpy())
+        np.testing.assert_equal(torch.utils.dlpack.from_dlpack(y.to_dlpack()).numpy(), tvm_x.asnumpy())
+
+        n = tvm.convert(137)
+        xx = torch.rand(137,137)
+        yy = torch.rand(137,137)
+        zz2 = torch.empty(137,137)
+        zz = xx.mm(yy)
+        XX = tvm.placeholder((n,n), name='X')
+        YY = tvm.placeholder((n,n), name='Y')
+
+        k = tvm.reduce_axis((0, n), name='k')
+        ZZ = tvm.compute((n,n), lambda i,j : tvm.sum(XX[i,k]*YY[k,j], axis=k))
+        s = tvm.create_schedule(ZZ.op)
+        f = tvm.build(s, [XX, YY, ZZ], target_host='llvm', name='f')
+
+        f_pytorch = to_pytorch_func(f)
+        zz2 = torch.empty(137,137)
+        f_pytorch(xx, yy, zz2)
+        np.testing.assert_allclose(zz.numpy(), zz2.numpy(), rtol=1e-6)
+
+    except ImportError:
+        pass
+
+
+if __name__ ==  '__main__':
+    test()