[quant][graphmode][refactor] Move the whitelists to a centeralized pl…

…ace (pytorch#35721)

Summary: Pull Request resolved: pytorch#35721

Test Plan:
.

Imported from OSS

Differential Revision: D20771829

fbshipit-source-id: f6ec3afe2d8034acbdbd81e5a6fbd4a2a76aa7ac

torch/csrc/jit/passes/quantization.cpp

@@ -65,6 +65,22 @@ struct PatternsAndModules {
   Module packed_params_module;
 };
 
+std::vector<std::string> _quantizable_call_funcs = {
+    "conv2d",
+    "linear",
+};
+
+std::vector<std::string> _quantizable_aten_funcs = {
+    "conv2d",
+    "conv3d",
+    "linear",
+    "addmm",
+    "matmul",
+    "add_",
+    "add",
+    "cat",
+};
+
 // These are the prim::CallFunctions that doesn't require observation and
 // have a single input Tensor
 // example: `prim::CallFunction(%dropout, %input_tensor, ...)
@@ -81,9 +97,35 @@ std::vector<std::string> _single_input_general_call_funcs = {
     "relu",
 };
 
-std::vector<std::string> _quantizable_call_funcs = {
-    "conv2d",
-    "linear",
+// Similar to prim::CallFunctions, there are aten ops that doesn't
+// require observation and have a single input Tensor
+// e.g. `aten::max_pool2d(%input_tensor, ...)`
+std::vector<std::string> _single_input_general_aten_funcs = {
+    "max_pool2d",
+    "avg_pool2d",
+    "flatten",
+    "max",
+    "min",
+    "mean",
+    "upsample_nearest1d",
+    "upsample_nearest2d",
+    "upsample_nearest3d",
+    "adaptive_avg_pool1d",
+    "adaptive_avg_pool2d",
+    "adaptive_avg_pool3d",
+    "upsample_linear1d",
+    "upsample_bilinear2d",
+    "upsample_trilinear3d",
+    "upsample_bicubic2d",
+    "dropout",
+    "reshape",
+    "chunk",
+    "view",
+    "transpose",
+    "contiguous",
+    "permute",
+    "repeat_interleave",
+    "relu",
 };
 
 void fillQConfigMap(
@@ -167,33 +209,6 @@ bool isAddScalar(Node* n) {
 // the quantization parameters for `v` given the list of values
 std::vector<Value*> getPassThroughInputs(Value* v) {
   Node* n = v->node();
-  std::vector<std::string> single_input_aten_funcs = {
-      "max_pool2d",
-      "avg_pool2d",
-      "flatten",
-      "max",
-      "min",
-      "mean",
-      "upsample_nearest1d",
-      "upsample_nearest2d",
-      "upsample_nearest3d",
-      "adaptive_avg_pool1d",
-      "adaptive_avg_pool2d",
-      "adaptive_avg_pool3d",
-      "upsample_linear1d",
-      "upsample_bilinear2d",
-      "upsample_trilinear3d",
-      "upsample_bicubic2d",
-      "dropout",
-      "reshape",
-      "chunk",
-      "view",
-      "transpose",
-      "contiguous",
-      "permute",
-      "repeat_interleave",
-      "relu",
-  };
   if (isFunctionNode(
           n,
           // We don't have call functions
@@ -207,7 +222,7 @@ std::vector<Value*> getPassThroughInputs(Value* v) {
           // We don't have call functions
           // after inline
           /* call_funcs = */ {},
-          /* aten_funcs = */ single_input_aten_funcs) ||
+          /* aten_funcs = */ _single_input_general_aten_funcs) ||
       (n->kind() == Symbol::aten("sort") && v->offset() == 0)) {
     return {n->input(0)};
   } else if (n->kind() == prim::If && n->outputs().size() == 1) {
@@ -242,16 +257,7 @@ bool nodeQuantizable(Node* n) {
       /* call_funcs = */
       _quantizable_call_funcs,
       /* aten_funcs = */
-      {
-          "conv2d",
-          "conv3d",
-          "linear",
-          "addmm",
-          "matmul",
-          "add_",
-          "add",
-          "cat",
-      });
+      _quantizable_aten_funcs);
 }
 
 // We don't want to analyze the graph for some `builtin` CallFunctions