tvm
autodiff.h
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19 
25 #ifndef TVM_TE_AUTODIFF_H_
26 #define TVM_TE_AUTODIFF_H_
27 
28 #include <tvm/runtime/object.h>
29 #include <tvm/tir/expr.h>
30 
31 #include "tensor.h"
32 
33 namespace tvm {
35 namespace te {
36 
43 PrimExpr Derivative(const PrimExpr& expr, const Var& var);
44 
56 Tensor Jacobian(const Tensor& output, const Tensor& input);
57 
72 Tensor VectorJacobianProduct(const Tensor& output, const Tensor& input, const Tensor& head);
73 
90 TVM_DLL Array<Tensor> Gradient(const Tensor& output, const Array<Tensor>& inputs,
91  const Tensor& head = Tensor());
92 
93 } // namespace te
94 } // namespace tvm
95 
96 #endif // TVM_TE_AUTODIFF_H_
Reference to PrimExprNode.
Definition: expr.h:115
Array, container representing a contiguous sequence of ObjectRefs.
Definition: array.h:289
Tensor structure representing a possible input, or intermediate computation result.
Definition: tensor.h:102
a named variable in TIR
Definition: var.h:89
Array< Tensor > Gradient(const Tensor &output, const Array< Tensor > &inputs, const Tensor &head=Tensor())
Perform reverse mode automatic differentiation.
Tensor VectorJacobianProduct(const Tensor &output, const Tensor &input, const Tensor &head)
The building block for reverse-mode AD.
PrimExpr Derivative(const PrimExpr &expr, const Var &var)
Take the derivative of the expression with respect to the given variable.
Tensor Jacobian(const Tensor &output, const Tensor &input)
Get the tensor representing the Jacobian of the output with respect to the input.
Var var(std::string name_hint, DataType t=DataType::Int(32))
Construct a new Var expression.
runtime implementation for LibTorch/TorchScript.
Definition: analyzer.h:36
A managed object in the TVM runtime.
Dataflow tensor object.
TIR expressions.