Fix sanity

apache · Oct 19, 2021 · f0b5603 · f0b5603
1 parent 77df20e
commit f0b5603
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Showing 5 changed files with 27 additions and 28 deletions.
diff --git a/src/operator/nn/dnnl/dnnl_batch_dot-inl.h b/src/operator/nn/dnnl/dnnl_batch_dot-inl.h
@@ -41,23 +41,22 @@ namespace op {
 enum DotIn { lhs = 0, rhs, lhs_min, lhs_max, rhs_min, rhs_max };
 enum DotOut { out = 0, out_min, out_max };
 
-
 struct DNNLDotParam : public dmlc::Parameter<DNNLDotParam> {
   bool transpose_a;
   bool transpose_b;
   bool quantized;
 
   dmlc::optional<float> min_calib_range;  // min float value calculated from calibration dataset
   dmlc::optional<float> max_calib_range;  // max float value calculated from calibration dataset
-  bool enable_float_output;  // min float value calculated from calibration dataset
+  bool enable_float_output;               // min float value calculated from calibration dataset
   DMLC_DECLARE_PARAMETER(DNNLDotParam) {
     DMLC_DECLARE_FIELD(transpose_a)
         .describe("If true then transpose the first input before dot.")
         .set_default(false);
     DMLC_DECLARE_FIELD(transpose_b)
         .describe("If true then transpose the second input before dot.")
         .set_default(false);
-      DMLC_DECLARE_FIELD(quantized).set_default(false).describe("enable quantization");
+    DMLC_DECLARE_FIELD(quantized).set_default(false).describe("enable quantization");
     DMLC_DECLARE_FIELD(min_calib_range)
         .set_default(dmlc::optional<float>())
         .describe(
@@ -110,13 +109,13 @@ class DNNLBatchDotFwd {
 
 template <bool subgraph = true>
 void DNNLBatchDotForward(const nnvm::NodeAttrs& attrs,
-                           const OpContext& ctx,
-                           const std::vector<NDArray>& inputs,
-                           const std::vector<OpReqType>& req,
-                           const std::vector<NDArray>& outputs) {
+                         const OpContext& ctx,
+                         const std::vector<NDArray>& inputs,
+                         const std::vector<OpReqType>& req,
+                         const std::vector<NDArray>& outputs) {
   DNNLDotParam dnnl_param;
   if (!subgraph) {
-    const DotParam& param    = nnvm::get<DotParam>(attrs.parsed);
+    const DotParam& param  = nnvm::get<DotParam>(attrs.parsed);
     dnnl_param.transpose_a = param.transpose_a;
     dnnl_param.transpose_b = param.transpose_b;
     dnnl_param.quantized   = false;

diff --git a/src/operator/nn/dnnl/dnnl_batch_dot.cc b/src/operator/nn/dnnl/dnnl_batch_dot.cc
@@ -63,8 +63,8 @@ DNNLBatchDotFwd& DNNLBatchDotFwd::GetCached(const DNNLDotParam& param,
 }
 
 dnnl::primitive_attr GetQuantizationAttributes(const DNNLDotParam& param,
-                                                 const std::vector<NDArray>& inputs,
-                                                 const std::vector<NDArray>& outputs) {
+                                               const std::vector<NDArray>& inputs,
+                                               const std::vector<NDArray>& outputs) {
   dnnl::primitive_attr attr;
   float out_scale_ = 1.f;
   float lhs_scale_ = GetQuantizeScale(inputs[DotIn::lhs].dtype(),

diff --git a/src/operator/subgraph/dnnl/dnnl_batch_dot.cc b/src/operator/subgraph/dnnl/dnnl_batch_dot.cc
@@ -39,8 +39,8 @@ namespace mxnet {
 namespace op {
 
 bool DNNLBatchDotShape(const nnvm::NodeAttrs& attrs,
-                         mxnet::ShapeVector* in_shapes,
-                         mxnet::ShapeVector* out_shapes) {
+                       mxnet::ShapeVector* in_shapes,
+                       mxnet::ShapeVector* out_shapes) {
   const DNNLDotParam& param = nnvm::get<DNNLDotParam>(attrs.parsed);
   mxnet::ShapeVector base_in_shapes;
   mxnet::ShapeVector base_out_shapes;
@@ -70,9 +70,9 @@ bool DNNLBatchDotShape(const nnvm::NodeAttrs& attrs,
 }
 
 bool DNNLBatchDotType(const nnvm::NodeAttrs& attrs,
-                        std::vector<int>* in_types,
-                        std::vector<int>* out_types) {
-  const DNNLDotParam& param  = nnvm::get<DNNLDotParam>(attrs.parsed);
+                      std::vector<int>* in_types,
+                      std::vector<int>* out_types) {
+  const DNNLDotParam& param    = nnvm::get<DNNLDotParam>(attrs.parsed);
   const size_t base_num_inputs = 2;
   if (param.quantized) {
     CHECK(in_types->at(DotIn::lhs) == mshadow::kInt8 || in_types->at(DotIn::lhs) == mshadow::kUint8)
@@ -107,10 +107,10 @@ bool DNNLBatchDotType(const nnvm::NodeAttrs& attrs,
 }
 
 inline static bool DNNLBatchDotStorageType(const nnvm::NodeAttrs& attrs,
-                                             const int dev_mask,
-                                             DispatchMode* dispatch_mode,
-                                             std::vector<int>* in_attrs,
-                                             std::vector<int>* out_attrs) {
+                                           const int dev_mask,
+                                           DispatchMode* dispatch_mode,
+                                           std::vector<int>* in_attrs,
+                                           std::vector<int>* out_attrs) {
   return DNNLStorageType(attrs, dev_mask, true, dispatch_mode, in_attrs, out_attrs);
 }
 
@@ -157,10 +157,10 @@ NNVM_REGISTER_OP(_sg_dnnl_batch_dot)
                             [](const NodeAttrs& attrs) { return QuantizeType::kMust; })
     .set_attr<FQuantizedOp>("FQuantizedOp",
                             [](const NodeAttrs& attrs) {
-                              nnvm::ObjectPtr node        = nnvm::Node::Create();
-                              node->attrs.op              = Op::Get("_sg_dnnl_batch_dot");
-                              node->attrs.name            = "quantized_" + attrs.name;
-                              node->attrs.dict            = attrs.dict;
+                              nnvm::ObjectPtr node          = nnvm::Node::Create();
+                              node->attrs.op                = Op::Get("_sg_dnnl_batch_dot");
+                              node->attrs.name              = "quantized_" + attrs.name;
+                              node->attrs.dict              = attrs.dict;
                               node->attrs.dict["quantized"] = "True";
 
                               if (node->op()->attr_parser != nullptr) {

diff --git a/src/operator/subgraph/dnnl/dnnl_matmul_post_quantize_property.h b/src/operator/subgraph/dnnl/dnnl_matmul_post_quantize_property.h
@@ -183,8 +183,8 @@ class SgDNNLMatmulPostQuantizeProperty : public SubgraphProperty {
   }
 
   SubgraphSelectorPtr CreateSubgraphSelector() const override {
-    auto selector = std::make_shared<SgDNNLMatmulPostQuantizeSelector>(disable_fuse_all,
-                                                                            disable_float_output);
+    auto selector =
+        std::make_shared<SgDNNLMatmulPostQuantizeSelector>(disable_fuse_all, disable_float_output);
     return selector;
   }
 

diff --git a/src/operator/tensor/dot-inl.h b/src/operator/tensor/dot-inl.h
@@ -1516,15 +1516,15 @@ void BatchDotForward_(const nnvm::NodeAttrs& attrs,
   });
 }
 
-template<typename ParamType>
+template <typename ParamType>
 inline bool BatchDotShape(const nnvm::NodeAttrs& attrs,
                           mxnet::ShapeVector* in_attrs,
                           mxnet::ShapeVector* out_attrs) {
   CHECK_EQ(in_attrs->size(), 2U);
   CHECK_EQ(out_attrs->size(), 1U);
   const ParamType& param = nnvm::get<ParamType>(attrs.parsed);
-  mxnet::TShape& lshape = (*in_attrs)[0];
-  mxnet::TShape& rshape = (*in_attrs)[1];
+  mxnet::TShape& lshape  = (*in_attrs)[0];
+  mxnet::TShape& rshape  = (*in_attrs)[1];
   // return false if lhs and rhs both have fully unknown shape
   if (!ndim_is_known(lshape) || !ndim_is_known(rshape))
     return false;