Implement Tanh Gelu Approximation (#61439)

Summary:
1. Implements pytorch/pytorch#39853
2. Adds approximate boolean flag to Gelu
3. Enables Tanh Gelu approximation
4. Adds double backward support for Gelu
5. Enable Tanh Gelu in NvFuser

```
def gelu(x, approximate : str = 'none'):
    if approximate == 'tanh':
        # sqrt(2/pi) = 0.7978845608028654
        return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * (x + 0.044715 * torch.pow(x, 3.0))))
    else:
        return x * normcdf(x)
```

Linking XLA PR - pytorch/xla#3039

Pull Request resolved: pytorch/pytorch#61439

Reviewed By: cpuhrsch

Differential Revision: D33850228

Pulled By: jbschlosser

fbshipit-source-id: 3cc33fb298e480d7ecc5c67716da019d60c6ab33
(cherry picked from commit 3a53b3e94fd58190d1261efd3cf41b53506fb96e)

torch/csrc/jit/codegen/cuda/parser.cpp

@@ -12,6 +12,8 @@
 #include <torch/csrc/jit/frontend/function_schema_parser.h>
 #include <torch/csrc/jit/ir/constants.h>
 
+#include <ATen/native/Activation.h>
+
 #include <unordered_map>
 #include <utility>
 
@@ -2273,7 +2275,8 @@ class IrParser {
     }
 
     {
-      auto ptr_op = getOperatorForLiteral("aten::gelu(Tensor self) -> Tensor");
+      auto ptr_op = getOperatorForLiteral(
+          "aten::gelu(Tensor self, int approximate=0) -> Tensor");
       REGISTER_PARSE_RULE(
           ptr_op,
           {
@@ -2283,7 +2286,20 @@ class IrParser {
                 c10::nullopt, value_map[node->inputs()[0]->unique()]);
             auto self = list_val.front();
             list_val.pop_front();
-            auto out = gelu(self);
+
+            auto approximate = constant_as<int64_t>(node->input(1));
+            TORCH_INTERNAL_ASSERT(
+                approximate.has_value(),
+                "The approximate parameter is required.");
+            const bool kApproximate = approximate.value();
+
+            Val* out = nullptr;
+            if (kApproximate == at::Gelu::Tanh) {
+              out = fast_gelu(self);
+            } else {
+              out = unaryOp(UnaryOpType::Gelu, self);
+            }
+
             value_map.emplace(
                 node->output()->unique(), ValueHolder(out, format));
           },
@@ -2293,7 +2309,7 @@ class IrParser {
 
     {
       auto ptr_op = getOperatorForLiteral(
-          "aten::gelu_backward(Tensor grad, Tensor self) -> Tensor");
+          "aten::gelu_backward(Tensor grad_output, Tensor self, int approximate=0) -> Tensor");
       REGISTER_PARSE_RULE(
           ptr_op,
           {
@@ -2308,7 +2324,19 @@ class IrParser {
             auto self = list_val.front();
             list_val.pop_front();
 
-            auto grad_in = gelu_backward(grad_out, self);
+            auto approximate = constant_as<int64_t>(node->input(2));
+            TORCH_INTERNAL_ASSERT(
+                approximate.has_value(),
+                "The approximate parameter is required.");
+            const bool kApproximate = approximate.value();
+
+            Val* grad_in = nullptr;
+            if (kApproximate == at::Gelu::Tanh) {
+              grad_in = fast_gelu_backward(grad_out, self);
+            } else {
+              grad_in = gelu_backward(grad_out, self);
+            }
+
             value_map.emplace(
                 node->output()->unique(), ValueHolder(grad_in, format));
           },
@@ -3015,6 +3043,38 @@ bool insertProfileIValue(ProfilingRecord* pr, Node* node, size_t offset) {
     }
   }
 
+  static auto gelu_schema =
+      getOperatorForLiteral(
+          "aten::gelu(Tensor self, int approximate=0) -> Tensor")
+          ->schema();
+  if (node->matches(gelu_schema)) {
+    switch (offset) {
+      // argument 1: approximate;
+      case 1:
+        profileInt(pr, node, offset);
+        break;
+      default:
+        return false;
+    }
+    return true;
+  }
+
+  static auto gelu_backward_schema =
+      getOperatorForLiteral(
+          "aten::gelu_backward(Tensor grad_output, Tensor self, int approximate=0) -> Tensor")
+          ->schema();
+  if (node->matches(gelu_backward_schema)) {
+    switch (offset) {
+      // argument 2: approximate;
+      case 2:
+        profileInt(pr, node, offset);
+        break;
+      default:
+        return false;
+    }
+    return true;
+  }
+
   static auto softmax_backward_data_schema =
       getOperatorForLiteral(
           "aten::_softmax_backward_data(Tensor grad_output, Tensor output, int dim, ScalarType input_dtype) -> Tensor")