[On Device Quantization][pytorch]Make insert_quant_dequant support ondevice ptq (#83570)

Summary:
This diff adds a way to:
- clone previously observed method
- Add calls to observer's calculate_qparams methods
- Extract the scale and zero point
- Use them to insert quant dequant nodes

Now for forward method we have
- observe_forward
- quantize_forward

observe_forward is used post training to observer statistics. In the
case of dynamic PTQ this requires just running that method once to
update weight observer statistics.

quantize_forward method will be used to use the observer
statistics to calculate quantization parameters and apply that to quant
dequant op.

Subsequent diffs will replace dequant + op with their quantized op
counter parts and replace quantize ops with relevant packed params class
where possible

Test Plan:
To be written

Reviewers:

Subscribers:

Tasks:

Tags:

Differential Revision: [D38771419](https://our.internmc.facebook.com/intern/diff/D38771419)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/83570
Approved by: https://github.com/jerryzh168

torch/csrc/jit/python/init.cpp

@@ -443,6 +443,22 @@ void initJITBindings(PyObject* module) {
           py::arg("inplace"),
           py::arg("debug"),
           py::arg("quant_type_int") = 1)
+      .def(
+          "_jit_pass_insert_quant_dequant_for_ondevice_ptq",
+          [](Module& module,
+             const std::string& method_name,
+             bool inplace,
+             bool debug,
+             int quant_type_int) {
+            auto quant_type = static_cast<QuantType>(quant_type_int);
+            return InsertQuantDeQuantOnDevicePTQ(
+                module, method_name, inplace, debug, quant_type);
+          },
+          py::arg("module"),
+          py::arg("method_name"),
+          py::arg("inplace"),
+          py::arg("debug"),
+          py::arg("quant_type_int") = 1)
       .def(
           "_jit_pass_insert_prepack_unpack",
           [](std::shared_ptr<Graph>& g) { return InsertPrepackUnpack(g); })