[TVM] Automatic differentiation for tensor expressions

docs/api/python/dev.rst

@@ -70,3 +70,8 @@ tvm.make
 ~~~~~~~~
 .. automodule:: tvm.make
    :members:
+
+tvm.testing
+~~~~~~~~~~~
+.. automodule:: tvm.testing
+   :members:

docs/api/python/index.rst

@@ -15,6 +15,7 @@ Python API
    container
    function
    autotvm
+   autodiff
    graph_runtime
    rpc
    bridge

docs/api/python/topi.rst

@@ -67,6 +67,7 @@ List of operators
    topi.not_equal
    topi.greater_equal
    topi.less_equal
+   topi.tensordot
    topi.image.resize
 
 
@@ -123,6 +124,7 @@ topi
 .. autofunction:: topi.power
 .. autofunction:: topi.greater
 .. autofunction:: topi.less
+.. autofunction:: topi.tensordot
 
 topi.nn
 ~~~~~~~

include/tvm/autodiff.h

@@ -0,0 +1,150 @@
+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file autodiff.h
+ * \brief Automatic differentiation of tensor expressions.
+ */
+#ifndef TVM_AUTODIFF_H_
+#define TVM_AUTODIFF_H_
+
+#include <tvm/ir.h>
+#include <tvm/tensor.h>
+
+namespace tvm {
+namespace ir {
+
+class DifferentiationResult;
+
+/*! \brief Node to represent a differentiation result */
+class DifferentiationResultNode : public Node {
+ public:
+  /*! \brief The requested adjoints, i.e. Jacobians or gradients wrt to the given inputs */
+  Array<Tensor> result;
+  /*! \brief A map from tensors to the corresponding adjoints (including internal nodes) */
+  Map<Tensor, Tensor> adjoints;
+  /*! \brief Single summands of the adjoints*/
+  Map<Tensor, Map<Tensor, Tensor>> adjoint_summands;
+  /*! \brief constructor */
+  DifferentiationResultNode() {}
+
+  void VisitAttrs(AttrVisitor* v) final {
+    v->Visit("result", &result);
+    v->Visit("adjoints", &adjoints);
+    v->Visit("adjoint_summands", &adjoint_summands);
+  }
+  TVM_DLL static DifferentiationResult make(Array<Tensor> result,
+                                            Map<Tensor, Tensor> adjoints,
+                                            Map<Tensor, Map<Tensor, Tensor>> adjoint_summands);
+
+  static constexpr const char* _type_key = "DifferentiationResult";
+  TVM_DECLARE_NODE_TYPE_INFO(DifferentiationResultNode, Node);
+};
+
+/*!
+ * \brief A result of differentiation.
+ */
+class DifferentiationResult : public NodeRef {
+ public:
+  /*! \brief default constructor, used internally */
+  DifferentiationResult() {}
+  explicit DifferentiationResult(NodePtr<Node> n) : NodeRef(n) {}
+  /*!
+   * \brief access the internal node container
+   * \return the pointer to the internal node container
+   */
+  inline const DifferentiationResultNode* operator->() const {
+    return static_cast<const DifferentiationResultNode*>(node_.get());
+  }
+  /*! \brief specify container node */
+  using ContainerType = DifferentiationResultNode;
+};
+
+
+/*! \brief A type of a "local" differentiation function for reverse mode AD
+ *
+ *  A function of this type is a building block for reverse-mode automatic differentiation. It
+ *  should take three tensors: `output`, `input` and `head`, `head` being the adjoint corresponding
+ *  to the `output`, and return (a summand of) the adjoint corresponding to the input. In other
+ *  words, it should differentiate `output` wrt `input` and multiply the result by `head` with
+ *  tensor dot product (`head` should be on the left of the multiplication). `input` should be an
+ *  immediate dependency of `output` (should be called from within the body of `output`).
+ *
+ *  See also ::DiffBuildingBlock, which might be considered the reference implementation.
+ */
+using FDiffBuildingBlock = std::function<Tensor(const Tensor& output,
+                                                const Tensor& input,
+                                                const Tensor& head)>;
+
+/*!
+ * \brief Take the derivative of the expression with respect to the given variable.
+ * \param expr The expression to differentiate.
+ * \param var The variable to differentiate with respect to.
+ * \return The expression for the derivative.
+ */
+EXPORT Expr Derivative(const Expr& expr, const VarExpr& var);
+
+/*!
+ * \brief Get the tensor representing the Jacobian of the output with respect to the input.
+ *
+ *  Note that if \p output depends on \p input indirectly (by using some other tensor
+ *  depending on \p input), this dependency won't contribute to the resulting Jacobian.
+ *  For such cases use the function ::Differentiate.
+ *
+ * \param output The tensor to differentiate.
+ * \param input The input tensor, which \p output should directly use.
+ * \param optimize Whether to perform optimizations like lifting of nonzeroness conditions.
+ * \return The tensor representing the Jacobian of shape `output.shape + input.shape`.
+ */
+EXPORT Tensor Jacobian(const Tensor& output, const Tensor& input, bool optimize = true);
+
+/*!
+ * \brief The building block for reverse-mode AD.
+ *
+ *  Differentiate \p output wrt \p input and multiply the result by \p head on the left using tensor
+ *  dot product. \p input must be an immediate dependency of \p output (must be called from within
+ *  the body of \p output). That is, the function will compute a summand of the adjoint for \p input
+ *  given the adjoint for \p output (which is called \p head here).
+ *
+ * \param output The tensor to differentiate.
+ * \param input The input tensor, which \p output should directly use.
+ * \param head The adjoint of \p output. Must be of shape `prefix + output.shape`
+ * \return The tensor representing the adjoint of \p input of shape `prefix + input.shape`.
+ */
+EXPORT Tensor DiffBuildingBlock(const Tensor& output, const Tensor& input, const Tensor& head);
+
+/*!
+ * \brief Perform reverse mode automatic differentiation.
+ *
+ *  Each item of the `result` field of the result is an adjoint for the corresponding item of
+ *  \p inputs, i.e. \p head multiplied by the Jacobian of \p output with respect to the
+ *  corresponding item of \p inputs.
+ *
+ * \param output The tensor to differentiate.
+ * \param inputs The array of input tensors. When the array is empty, will perform differentiation
+ *               wrt all tensors the output depends on.
+ * \param head The adjoint of the output, in other words, some tensor, by which the Jacobians
+ *             will be multiplied. Its shape must be of the form `prefix + output.shape`. If the
+ *             null pointer is provided, the identity tensor of shape
+ *             `output.shape + output.shape` will be used.
+ * \param fdiff The function performing differentiation and multiplication, see
+ *              ::FDiffBuildingBlock.
+ * \param override_deps A map from tensors to their dependencies (`InputTensors()` are used by
+ *                      default). Overriding dependencies may be useful to treat a group of tensors
+ *                      as a single supertensor. In this case the fdiff functions should also be
+ *                      modified accordingly.
+ * \return An object of type DifferentiationResult which contains three fields:
+ *         - `result` An array of adjoints corresponding to \p inputs.
+ *         - `adjoints` A map from tensors to the corresponding adjoints (includes intermediate
+ *            tensors).
+ *         - `adjoint_summands` A map from tensors to maps from parent tensors to individual
+ *            summands of the adjoint.
+ */
+EXPORT DifferentiationResult Differentiate(
+    const Tensor& output,
+    const Array<Tensor>& inputs = Array<Tensor>(),
+    const Tensor& head = Tensor(),
+    const FDiffBuildingBlock& fdiff = DiffBuildingBlock,
+    const Map<Tensor, Array<Tensor>>& override_deps = Map<Tensor, Array<Tensor>>());
+
+}  // namespace ir
+}  // namespace tvm
+#endif  // TVM_AUTODIFF_H_

include/tvm/ir_operator.h

@@ -85,6 +85,16 @@ inline const uint64_t* as_const_uint(const Expr& x) {
  */
 inline bool is_const_int(const Expr& x, int64_t value);
 
+/*!
+ * \brief Check if the given expr is a const of any type equal to the given integer value.
+ * \param e The expression.
+ * \param value The value to compare to.
+ * \return Whether the expression is a const equal to the value.
+ * \tparam ValueType The value type
+ */
+template <typename ValueType>
+inline bool is_const_value(const Expr& e, ValueType value);
+
 /*!
  * \brief Check whether stmt is nop.
  * \param stmt The input statement
@@ -515,6 +525,26 @@ inline bool is_const_int(const Expr& x, int64_t value) {
   return false;
 }
 
+template <typename ValueType>
+inline bool is_const_value(const Expr& e, ValueType value) {
+  static_assert(std::is_integral<ValueType>::value,
+                "Comparison to non-integer values is forbidden.");
+  // This implementation was copy-pasted from HalideIR
+  if (const ir::IntImm* i = e.as<ir::IntImm>()) {
+    return i->value == value;
+  } else if (const ir::UIntImm* i = e.as<ir::UIntImm>()) {
+    return (value >= 0) && (i->value == (uint64_t)value);
+  } else if (const ir::FloatImm* i = e.as<ir::FloatImm>()) {
+    return i->value == value;
+  } else if (const ir::Cast* c = e.as<ir::Cast>()) {
+    return is_const_value(c->value, value);
+  } else if (const ir::Broadcast* b = e.as<ir::Broadcast>()) {
+    return is_const_value(b->value, value);
+  } else {
+    return false;
+  }
+}
+
 inline bool is_no_op(const Stmt& stmt) {
   if (!stmt.defined()) return true;
   if (const auto* op = stmt.as<ir::Evaluate>()) {

python/tvm/__init__.py

@@ -19,6 +19,7 @@
 from . import generic
 from . import hybrid
 from . import testing
+from . import autodiff
 
 from . import ndarray as nd
 from .ndarray import context, cpu, gpu, opencl, cl, vulkan, metal, mtl
@@ -36,6 +37,7 @@
 from .schedule import create_schedule
 from .build_module import build, lower, build_config
 from .tag import tag_scope
+from .autodiff import differentiate
 
 # Contrib initializers
 from .contrib import rocm as _rocm, nvcc as _nvcc, sdaccel as _sdaccel