add document

lint

lint

save

save

add more case

save

error

lint

lint

commit

do

lint

save

fix lint

wrap it back as func

lint

save

remove dead comment

fix style

fix lint

Update src/relay/pass/partial_eval.cc

Co-Authored-By: MarisaKirisame <[email protected]>

Update src/relay/pass/partial_eval.cc

Co-Authored-By: MarisaKirisame <[email protected]>

Update src/relay/pass/partial_eval.cc

Co-Authored-By: MarisaKirisame <[email protected]>

Update src/relay/pass/partial_eval.cc

Co-Authored-By: MarisaKirisame <[email protected]>

Update src/relay/pass/partial_eval.cc

Co-Authored-By: MarisaKirisame <[email protected]>

Update src/relay/pass/partial_eval.cc

Co-Authored-By: MarisaKirisame <[email protected]>

address review feedback

pe now handle freevar. as a result preserving function is now trivial.

test

add basic test, implement pretty printing for generic function

test

lint

fix segfault

save

save

do

test

fix another error

address comment

commit

save

address review feedback

add test for invalidate, fix error in lookup

rename cont to boduy

fix error and add regression test

Update src/relay/pass/partial_eval.cc

Co-Authored-By: MarisaKirisame <[email protected]>

fix error, add test case

fix lint

remove extra line

fix some error

pe

commit

save

save

save

save

save (pe/dce broken)

[DOCKER] Pin flatbuffers checkout to the last release tag (apache#2823). (apache#2879)

[Relay][Text Format] Reverse CallNode Print Order (apache#2882)

[NNPACK] Modernize test (apache#2868)

[Relay] Add list update to prelude (apache#2866)

Add missing sgx includes (apache#2878)

Fix setting up hints for getaddrinfo (apache#2872)

[ARITH] RewriteSimplifier: improved cmp simplification (apache#2851)

do (apache#2883)

[RELAY][Frontend][TF] decompile tf control flow (apache#2830)

* decompile tf control flow

* Add docs

* remove import relay

* move tests under tensorflow frontend

* minor fix

Enhance upsample operator to adapt onnx opset version 9 (apache#2840)

Use version invariant rustfmt (apache#2886)

[Relay][Op] Add group conv2d dispatch to topi function (apache#2870)

* [Relay][Op] Add group conv2d dispatch to topi function

* Rerun tests

[Apps] [howto_deploy] fix cxx-flags order and build directory (apache#2888)

fix prelu, now can use on 2d input and add one test (apache#2875)

Add dense schedules to __init__ for cpu (apache#2855)

* Add dense schedules to __init__ for cpu

* Add documentation for topi::shape

* Add additional imports to topi CPU __init__.

[TESTS] Improve script robustness (apache#2893)

A number of test scripts use the '|| exit 1' idiom.  This has two
issues, first process exit codes are defined to be in the range 0-255.
Second, more importantly, the idiom is fragile because it requires
that every possible failure point be explicitly coded.  This patch
removes the idiom in favour of "set -e" as used in the docker scripts
as a more robust mechanism to ensure that script failures are always
caught and propagated by default.

[Relay] Fix name of bias in testing.mlp (apache#2892)

winograd_nnpack (apache#2721)

[Relay] Fix Relay ARM CPU depthwise spatial pack schedule alter op layout issue. (apache#2861)

* Fix Relay ARM CPU spatial pack depthwise alter op layout issue.

* Update tune_relay_arm.py

[TESTS] Import script robustness (set -u) (apache#2896)

Adopt the "set -u" idiom from the docker scripts as a mechanism to
improve future robustness.

[DOCKER] Upgrade ci-cpu to latest v0.50 (apache#2901)

Allow linking against MKLML (apache#2902)

[COMMUNITY] ASF mentors (apache#2906)

[Relay] Allow converting keras.layers.Sequential (apache#2842)

* Allow converting keras.layers.Sequential

* Use existing new_var function

* Only update expr when missing

* Add test

[Relay] clean up hd, change tl (apache#2917)

Turn on USE_SORT by default (apache#2916)

[TEST] Cache test data (apache#2921)

Unified error handling in NNVM and Relay frontends (apache#2828)

add support for mxnet smooth_l1 (apache#2905)

[Relay] Add support for TupleGetItem in op fusion (apache#2914)

[Relay, TOPI]  Deformable conv2d (apache#2908)

* [Relay, TOPI] Add deformable conv2d

* Moved to op level2

* Fix lint

* Moved to level2 & bug fix

* Update comments

* Disabled flaky test of conv2d

TVM debugresult dump to Chrome Tracing (apache#2922)

[Relay] add test for second order ad (apache#2754)

* do second order

* add comment

* better name

* use tvm assert all close

* refire ci

Revert "[Relay] add test for second order ad (apache#2754)" (apache#2926)

This reverts commit f5ca991.

[Tutorial] Cache the test data in tutorial (apache#2923)

[AUTOTVM] Refactor measure build func (apache#2927)

Fix intersect of modular set (apache#2904)

Fix comment bugs and code style

[Relay, OpFusion] Fix handling TupleGetItem for nested tuples (apache#2929)

Consistent result of DetectLinearEquation() when an empy vars is passed (apache#2860)

[FRONTEND][ONNX] Some bug fixes and Shape operator fixed for relay. (apache#2850)

* [FRONTEND][ONNX] Some bug fixes and Shape operator fixed for relay.

* 	* test cases

* 	* ci error

Outdated renaming for flatten in ONNX converter (apache#2843)

[FRONTEND][TENSORFLOW] bug fix for tensorflow official slim models. (apache#2864)

* [FRONTEND][TENSORFLOW] bug fix for tensorflow official slim models.

* 	* review comments

Fix vcvtph2ps codegen (apache#2925)

Port changes

More fixes

save

save

Changes to schedules and mxnet importer

Author: MarisaKirisame

CONTRIBUTORS.md

@@ -1,10 +1,21 @@
 TVM Contributors
 ================
-TVM adopts the Apache style model and governs by merit. We believe that it is important to create an inclusive community where everyone can use,
+TVM adopts the Apache way and governs by merit. We believe that it is important to create an inclusive community where everyone can use,
 contribute to, and influence the direction of the project. We actively invite contributors who have earned the merit to be part of the development community.
 
 See the [community structure document](http://docs.tvm.ai/contribute/community.html) for the explanation of community structure and contribution guidelines.
 
+## Mentors
+
+TVM is now part of the Apache Incubator.
+We are fortunate to have the following mentors.
+
+- Markus Weimer @markusweimer
+- Sebastian Schelter @sscdotopen
+- Byung-Gon Chun @bgchun
+- Henry Saputra @hsaputra
+- Timothy Chen @tnachen
+- Furkan KAMACI @kamaci
 
 ## Committers
 

Jenkinsfile

@@ -22,7 +22,7 @@
 //
 ci_lint = "tvmai/ci-lint:v0.50"
 ci_gpu = "tvmai/ci-gpu:v0.51"
-ci_cpu = "tvmai/ci-cpu:v0.41"
+ci_cpu = "tvmai/ci-cpu:v0.50"
 ci_i386 = "tvmai/ci-i386:v0.50"
 
 // tvm libraries

apps/howto_deploy/Makefile

@@ -31,4 +31,4 @@ lib/cpp_deploy_pack: cpp_deploy.cc lib/test_addone_sys.o lib/libtvm_runtime_pack
 # Deploy using pre-built libtvm_runtime.so
 lib/cpp_deploy_normal: cpp_deploy.cc lib/test_addone_sys.o
 	@mkdir -p $(@D)
-	$(CXX) $(PKG_CFLAGS) -o $@  $^ $(PKG_LDFLAGS) -ltvm_runtime
+	$(CXX) $(PKG_CFLAGS) -o $@  $^ -ltvm_runtime $(PKG_LDFLAGS)

apps/howto_deploy/run_example.sh

@@ -3,8 +3,8 @@ echo "Build the libraries.."
 mkdir -p lib
 make
 echo "Run the example"
-export LD_LIBRARY_PATH=../../lib:${LD_LIBRARY_PATH}
-export DYLD_LIBRARY_PATH=../../lib:${DYLD_LIBRARY_PATH}
+export LD_LIBRARY_PATH=../../build:${LD_LIBRARY_PATH}
+export DYLD_LIBRARY_PATH=../../build:${DYLD_LIBRARY_PATH}
 
 echo "Run the deployment with all in one packed library..."
 lib/cpp_deploy_pack

cmake/config.cmake

@@ -127,7 +127,7 @@ set(USE_MPS OFF)
 set(USE_ROCBLAS OFF)
 
 # Whether use contrib sort
-set(USE_SORT OFF)
+set(USE_SORT ON)
 
 # Build ANTLR parser for Relay text format
 set(USE_ANTLR OFF)

cmake/modules/SGX.cmake

@@ -48,4 +48,6 @@ if(NOT USE_SGX STREQUAL "OFF")
     -L${USE_SGX}/lib64 -l${_urts_lib}
     -L${RUST_SGX_SDK}/sgx_ustdc -lsgx_ustdc)
   list(APPEND RUNTIME_SRCS ${RUNTIME_SGX_SRCS})
+
+  include_directories(${RUST_SGX_SDK}/edl ${RUST_SGX_SDK}/common)
 endif()

cmake/modules/contrib/BLAS.cmake

@@ -10,7 +10,7 @@ elseif(USE_BLAS STREQUAL "mkl")
   if(NOT IS_DIRECTORY ${USE_MKL_PATH})
     set(USE_MKL_PATH /opt/intel/mkl)
   endif()
-  find_library(BLAS_LIBRARY mkl_rt ${USE_MKL_PATH}/lib/ ${USE_MKL_PATH}/lib/intel64)
+  find_library(BLAS_LIBRARY NAMES mkl_rt mklml_gnu HINTS ${USE_MKL_PATH}/lib/ ${USE_MKL_PATH}/lib/intel64)
   include_directories(${USE_MKL_PATH}/include)
   list(APPEND TVM_RUNTIME_LINKER_LIBS ${BLAS_LIBRARY})
   list(APPEND RUNTIME_SRCS ${CBLAS_CONTRIB_SRC})

docker/install/ubuntu_install_rust.sh

@@ -9,12 +9,10 @@ apt-get update && apt-get install -y --no-install-recommends curl
 export RUSTUP_HOME=/opt/rust
 export CARGO_HOME=/opt/rust
 # this rustc is one supported by the installed version of rust-sgx-sdk
-curl -s -S -L https://sh.rustup.rs -sSf | sh -s -- -y --no-modify-path --default-toolchain nightly-2019-01-28
+curl -s -S -L https://sh.rustup.rs -sSf | sh -s -- -y --no-modify-path --default-toolchain nightly-2019-03-24
 . $CARGO_HOME/env
-rustup component add rust-src
-cargo install sccache
-cargo install rustfmt-nightly --version 1.0.1 --force
-cargo install xargo
+rustup component add rustfmt
+cargo install sccache --no-default-features
 
 # make rust usable by all users
 chmod -R a+w /opt/rust

docker/install/ubuntu_install_tflite.sh

@@ -5,7 +5,7 @@ set -u
 set -o pipefail
 
 # Download, build and install flatbuffers
-git clone --depth=1 --recursive https://github.com/google/flatbuffers.git
+git clone --branch=v1.10.0 --depth=1 --recursive https://github.com/google/flatbuffers.git
 cd flatbuffers
 cmake -G "Unix Makefiles" -DCMAKE_BUILD_TYPE=Release
 make install -j8

include/tvm/relay/attrs/nn.h

@@ -155,6 +155,24 @@ struct Conv2DWinogradAttrs : public tvm::AttrsNode<Conv2DWinogradAttrs> {
   }
 };
 
+/*! \brief Attributes used in winograd weight transformation operators */
+struct Conv2DWinogradNNPACKWeightTransformAttrs
+    : public tvm::AttrsNode<Conv2DWinogradNNPACKWeightTransformAttrs> {
+  int convolution_algorithm;
+  DataType out_dtype;
+
+  TVM_DECLARE_ATTRS(Conv2DWinogradNNPACKWeightTransformAttrs,
+                    "relay.attrs.Conv2DWinogradNNPACKWeightTransformAttrs") {
+    TVM_ATTR_FIELD(convolution_algorithm)
+        .describe(
+            "The convolution algorithm for Winograd NNPACK. "
+            "E.g. tvm.contrib.nnpack.ConvolutionAlgorithm.WT_8x8 for WT_8x8, "
+            "tvm.contrib.nnpack.ConvolutionAlgorithm.WT_8x8_FP16 for WT_8x8_FP16");
+    TVM_ATTR_FIELD(out_dtype)
+        .set_default(NullValue<DataType>())
+        .describe("Output data type, set to explicit type under mixed precision setting");
+  }
+};
 
 /*! \brief Attributes used in softmax operators */
 struct SoftmaxAttrs : public tvm::AttrsNode<SoftmaxAttrs> {
@@ -438,6 +456,67 @@ struct L2NormalizeAttrs : public tvm::AttrsNode<L2NormalizeAttrs> {
   }
 };
 
+
+/*! \brief Attributes for DeformableConv2D operator */
+struct DeformableConv2DAttrs : public tvm::AttrsNode<DeformableConv2DAttrs> {
+  Array<IndexExpr> strides;
+  Array<IndexExpr> padding;
+  Array<IndexExpr> dilation;
+  int deformable_groups;
+  int groups;
+  IndexExpr channels;
+  Array<IndexExpr> kernel_size;
+  std::string data_layout;
+  std::string kernel_layout;
+  std::string out_layout;
+  DataType out_dtype;
+
+  TVM_DECLARE_ATTRS(DeformableConv2DAttrs, "relay.attrs.DeformableConv2DAttrs") {
+    TVM_ATTR_FIELD(strides).set_default(Array<IndexExpr>({1, 1}))
+        .describe("Specifies the strides of the convolution.");
+    TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 0}))
+        .describe("If padding is non-zero, then the input is implicitly zero-padded"
+                  "on both sides for padding number of points");
+    TVM_ATTR_FIELD(dilation).set_default(Array<IndexExpr>({1, 1}))
+        .describe("Specifies the dilation rate to use for dilated convolution.");
+    TVM_ATTR_FIELD(deformable_groups).set_default(1)
+        .describe("Controls the connections between inputs and offsets."
+                  "Input channels are partitioned into multiple deformable groups. Offsets"
+                  "are shared across input channels in the same deformable group.");
+    TVM_ATTR_FIELD(groups).set_default(1)
+        .describe("Controls the connections between inputs and outputs."
+                  "At groups=1, all inputs are convolved to all outputs."
+                  "At groups=2, the operation becomes equivalent to having two convolution"
+                  "layers side by side, each seeing half the input channels, and producing"
+                  "half the output channels, and both subsequently concatenated.");
+    TVM_ATTR_FIELD(channels)
+        .describe("The number of output channels in the convolution."
+                  " If it is not set, inferred by shape of the weight.")
+        .set_default(NullValue<IndexExpr>());
+    TVM_ATTR_FIELD(kernel_size)
+        .describe("Specifies the dimensions of the convolution window.")
+        .set_default(NullValue<Array<IndexExpr> >());
+    TVM_ATTR_FIELD(data_layout).set_default("NCHW")
+        .describe("Dimension ordering of input data. Can be 'NCHW', 'NHWC', etc."
+                  "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                  "dimensions respectively. Convolution is applied on the 'H' and"
+                  "'W' dimensions.");
+    TVM_ATTR_FIELD(kernel_layout).set_default("OIHW")
+        .describe("Dimension ordering of weight. Can be 'OIHW', 'OIHW16o16i', etc."
+                  "'O', 'I', 'H', 'W' stands for num_filter, input_channel, height, and width"
+                  "dimensions respectively.");
+    TVM_ATTR_FIELD(out_layout).set_default("")
+        .describe("Dimension ordering of output. Can be 'NCHW', 'NHWC', etc."
+                  "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                  "dimensions respectively. Default to be same as input layout.");
+
+    // use 0 bits to indicate none.
+    TVM_ATTR_FIELD(out_dtype)
+        .set_default(NullValue<DataType>())
+        .describe("Output data type, set to explicit type under mixed precision setting");
+  }
+};
+
 }  // namespace relay
 }  // namespace tvm
 #endif  // TVM_RELAY_ATTRS_NN_H_

include/tvm/relay/expr.h

@@ -166,6 +166,26 @@ class VarNode : public ExprNode {
 
 RELAY_DEFINE_NODE_REF(Var, VarNode, Expr);
 
+/*! \brief Hash Var by it's id.
+ * Different VarNode might has same vid, and they are considered to be the same var in such case.
+ * Use VarHash to hash Var by id.
+ */
+struct VarHash {
+  size_t operator()(const Var& v) const {
+    return v->vid.hash();
+  }
+};
+
+/*! \brief Compare Var by it's id.
+ * Different VarNode might has same vid, and they are considered to be the same var in such case.
+ * Use VarEqual to compare Var by id.
+ */
+struct VarEqual {
+  bool operator()(const Var& l, const Var& r) const {
+    return l->vid.get() == r->vid.get();
+  }
+};
+
 /*!
  * \brief Global variable that leaves in the top-level module.
  * This is used to enable recursive calls between function.
@@ -503,7 +523,7 @@ RELAY_DEFINE_NODE_REF(RefWrite, RefWriteNode, Expr);
  * rewriting pass such as layout or type transformation.
  *
  * Subclass TempExprNode allows us to pattern match on
- * specific kind TempExpr and use them for expression rewriting.
+ * specific kind of TempExpr and use them for expression rewriting.
  *
  * TempExpr should only be used within a pass,
  */
@@ -521,6 +541,25 @@ class TempExprNode : public ExprNode {
 
 RELAY_DEFINE_NODE_REF(TempExpr, TempExprNode, Expr);
 
+class Annotate;
+class AnnotateNode : public ExprNode {
+ public:
+  Expr expr;
+  NodeRef annotation;
+  void VisitAttrs(tvm::AttrVisitor* v) final {
+    v->Visit("expr", &expr);
+    v->Visit("annotation", &annotation);
+    v->Visit("_checked_type_", &checked_type_);
+  }
+
+  TVM_DLL static Annotate make(Expr expr, NodeRef annotation);
+
+  static constexpr const char* _type_key = "relay.AnnotateNode";
+  TVM_DECLARE_NODE_TYPE_INFO(AnnotateNode, ExprNode);
+};
+
+RELAY_DEFINE_NODE_REF(Annotate, AnnotateNode, Expr);
+
 // implementataions
 inline const Type& ExprNode::checked_type() const {
   CHECK(checked_type_.defined()) << "internal error: the type checker has "

include/tvm/relay/expr_functor.h

@@ -71,6 +71,7 @@ class ExprFunctor<R(const Expr& n, Args...)> {
    * \return The result of the call
    */
   virtual R VisitExpr(const Expr& n, Args... args) {
+    CHECK(n.defined());
     static FType vtable = InitVTable();
     return vtable(n, this, std::forward<Args>(args)...);
   }
@@ -97,6 +98,7 @@ class ExprFunctor<R(const Expr& n, Args...)> {
   virtual R VisitExpr_(const RefWriteNode* op, Args... args) EXPR_FUNCTOR_DEFAULT;
   virtual R VisitExpr_(const ConstructorNode* op, Args... args) EXPR_FUNCTOR_DEFAULT;
   virtual R VisitExpr_(const MatchNode* op, Args... args) EXPR_FUNCTOR_DEFAULT;
+  virtual R VisitExpr_(const AnnotateNode* op, Args... args) EXPR_FUNCTOR_DEFAULT;
   virtual R VisitExprDefault_(const Node* op, Args...) {
     throw Error(std::string("Do not have a default for ") + op->type_key());
   }
@@ -121,6 +123,7 @@ class ExprFunctor<R(const Expr& n, Args...)> {
     RELAY_EXPR_FUNCTOR_DISPATCH(RefWriteNode);
     RELAY_EXPR_FUNCTOR_DISPATCH(ConstructorNode);
     RELAY_EXPR_FUNCTOR_DISPATCH(MatchNode);
+    RELAY_EXPR_FUNCTOR_DISPATCH(AnnotateNode);
     return vtable;
   }
 };
@@ -151,6 +154,7 @@ class ExprVisitor
   void VisitExpr_(const RefWriteNode* op) override;
   void VisitExpr_(const ConstructorNode* op) override;
   void VisitExpr_(const MatchNode* op) override;
+  void VisitExpr_(const AnnotateNode* op) override;
   virtual void VisitType(const Type& t);
   virtual void VisitClause(const Clause& c);
   virtual void VisitPattern(const Pattern& c);
@@ -193,6 +197,7 @@ class ExprMutator
   Expr VisitExpr_(const RefWriteNode* op) override;
   Expr VisitExpr_(const ConstructorNode* op) override;
   Expr VisitExpr_(const MatchNode* op) override;
+  Expr VisitExpr_(const AnnotateNode* op) override;
 
   /*!
    * \brief Used to visit the types inside of expressions.

include/tvm/relay/pass.h

@@ -46,7 +46,7 @@
 #include <tvm/relay/module.h>
 #include <tvm/relay/op_attr_types.h>
 #include <tvm/relay/type.h>
-
+#include <tvm/relay/adt.h>
 #include <string>
 #include <vector>
 
@@ -326,6 +326,17 @@ TVM_DLL bool WellFormed(const Expr& expr);
  */
 TVM_DLL tvm::Array<Var> BoundVars(const Expr& expr);
 
+/*! \brief Get all bound variables from pattern pat.
+ *
+ * Bound variables are all variables that got bound by the pat.
+ * They only have meaning inside that expr, and can only be used in it.
+ *
+ * \param pat the Pattern.
+ *
+ * \return List of bound vars, in the PostDFS order in the expression.
+ */
+TVM_DLL tvm::Array<Var> BoundVars(const Pattern& pat);
+
 /*! \brief Get free type parameters from expression expr.
  *
  * Free variables are variables that are not bound by a
@@ -413,12 +424,13 @@ TVM_DLL tvm::Array<TypeVar> AllTypeVars(const Type& t, const Module& mod);
 
 /*! \brief Remove expressions which does not effect the program result.
  *
- * It will remove let bindings which are not referenced, and branches that will
- * not be entered.
+ * It will remove let bindings which are not referenced,
+ * and inline let bindings that are only used once.
  *
- * For example, this pass should turn `let a = 1 in 2` into `2`, as the value of
- * the expression does not depend on a. Another example is `if (true) then 1
- * else 2` will be optimized into 1.
+ * For example, this pass should turn `let a = 1 in 2` into `2`,
+ * as the value of the expression does not depend on a.
+ *
+ * As another example, `let a = 1 in a` will be optimized into 1.
  *
  * \param e the expression to optimize.
  *
@@ -527,7 +539,7 @@ struct StructuralHash {
  *
  * \return expression in A-Normal Form
  */
-Expr ToANormalForm(const Expr& e, const Module& mod);
+TVM_DLL Expr ToANormalForm(const Expr& e, const Module& mod);
 
 /*! \brief Remove let binding and directly share via pointer instead.
  *
@@ -538,8 +550,14 @@ Expr ToANormalForm(const Expr& e, const Module& mod);
  *
  * \return the expression in graph normal form.
  */
-Expr ToGraphNormalForm(const Expr& e);
+TVM_DLL Expr ToGraphNormalForm(const Expr& e);
 
+/*! \brief Aggressive constant propagation/constant folding/inlining.
+ * It will do as much computation in compile time as possible.
+ * It has two benefit: remove runtime overhead, and allow more optimization (typically fusion).
+ * As a side effect, code size will explode.
+ */
+Expr PartialEval(const Expr& e, const Module& mod);
 }  // namespace relay
 }  // namespace tvm
 

include/tvm/relay/pattern_functor.h

@@ -71,6 +71,7 @@ class PatternFunctor<R(const Pattern& n, Args...)> {
    * \return The result of the call
    */
   virtual R VisitPattern(const Pattern& n, Args... args) {
+    CHECK(n.defined());
     static FType vtable = InitVTable();
     return vtable(n, this, std::forward<Args>(args)...);
   }

nnvm/include/nnvm/top/nn.h

@@ -183,6 +183,26 @@ struct WinogradWeightTransformParam : public dmlc::Parameter<WinogradWeightTrans
     static const constexpr int kWeight = 0;
 };
 
+struct WinogradNNPACKWeightTransformParam
+    : public dmlc::Parameter<WinogradNNPACKWeightTransformParam> {
+  int convolution_algorithm;
+  int out_dtype;
+
+  DMLC_DECLARE_PARAMETER(WinogradNNPACKWeightTransformParam) {
+    DMLC_DECLARE_FIELD(convolution_algorithm)
+        .describe(
+            "The convolution algorithm for Winograd NNPACK. "
+            "E.g. tvm.contrib.nnpack.ConvolutionAlgorithm.WT_8x8 for WT_8x8, "
+            "tvm.contrib.nnpack.ConvolutionAlgorithm.WT_8x8_FP16 for WT_8x8_FP16");
+    DMLC_DECLARE_DTYPE_FIELD(out_dtype)
+        .add_enum("same", -1)
+        .set_default(-1)
+        .describe("Output data type, set to explicit type under mixed precision setting");
+  }
+
+  static const constexpr int kWeight = 0;
+};
+
 struct WinogradConv2DParam : public dmlc::Parameter<WinogradConv2DParam> {
   int channels;
   TShape kernel_size;