[Eager] make fast through to linear (#41945)

* make fast through to linear

* make fast through to linear

* add to do for later upgrades

* support build once for now

paddle/fluid/pybind/custom_handwrite_op_funcs.h → paddle/fluid/pybind/eager_custom_python_api.h

@@ -54,9 +54,53 @@ static PyObject *eager_api_run_program(PyObject *self, PyObject *args,
   }
 }
 
+static PyObject *eager_api_final_state_linear(PyObject *self, PyObject *args,
+                                              PyObject *kwargs) {
+  PyThreadState *tstate = nullptr;
+  try {
+    auto x = GetTensorFromArgs("linear", "X", args, 0, false);
+    auto weight = GetTensorFromArgs("linear", "weight", args, 1, false);
+    auto bias = GetTensorFromArgs("linear", "Bias", args, 2, true);
+    tstate = PyEval_SaveThread();
+    if (bias.initialized()) {
+      auto mm_out =
+          matmul_final_state_dygraph_function(x, weight, false, false);
+      auto out = add_final_state_dygraph_function(bias, mm_out);
+      PyEval_RestoreThread(tstate);
+      tstate = nullptr;
+      return ToPyObject(out);
+    } else {
+      auto mm_out =
+          matmul_final_state_dygraph_function(x, weight, false, false);
+      PyEval_RestoreThread(tstate);
+      tstate = nullptr;
+      return ToPyObject(mm_out);
+    }
+  } catch (paddle::platform::EnforceNotMet &exception) {
+    if (tstate) {
+      PyEval_RestoreThread(tstate);
+    }
+    std::ostringstream sout;
+    sout << exception.what();
+    sout << "  [operator < linear > error]";
+    exception.set_error_str(sout.str());
+    ThrowExceptionToPython(std::current_exception());
+    return nullptr;
+  } catch (...) {
+    if (tstate) {
+      PyEval_RestoreThread(tstate);
+    }
+    ThrowExceptionToPython(std::current_exception());
+    return nullptr;
+  }
+}
+
 static PyMethodDef CustomEagerFinalStateMethods[] = {
     {"run_program", (PyCFunction)(void (*)(void))eager_api_run_program,
      METH_VARARGS | METH_KEYWORDS,
      "C++ interface function for run_program in dygraph."},
-
+    {"final_state_linear",
+     (PyCFunction)(void (*)(void))eager_api_final_state_linear,
+     METH_VARARGS | METH_KEYWORDS,
+     "C++ interface function for run_program in dygraph."},
     {nullptr, nullptr, 0, nullptr}};

paddle/fluid/pybind/eager_op_function_generator.cc

@@ -134,7 +134,7 @@ const char* PYBIND_ITEM_TEMPLATE = R"(  {"%s", (PyCFunction)(void(*)(void))%s, M
 // need to be handwritten in CUSTOM_HANDWRITE_OP_FUNC_FILE
 std::unordered_set<std::string> CUSTOM_HANDWRITE_OPS_SET = {"run_program"};
 const char* CUSTOM_HANDWRITE_OP_FUNC_FILE =
-  "#include \"paddle/fluid/pybind/custom_handwrite_op_funcs.h\"\n";
+  "#include \"paddle/fluid/pybind/eager_custom_python_api.h\"\n";
 
 // clang-format on
 static inline bool FindInsMap(const std::string& op_type,

python/paddle/fluid/dygraph/layers.py

@@ -36,7 +36,7 @@ from .base import program_desc_tracing_guard, param_guard, in_declarative_mode,
 from paddle.fluid import framework
 from ..param_attr import ParamAttr
 from paddle.fluid.executor import Executor, global_scope
-from paddle.fluid.framework import _non_static_mode, convert_np_dtype_to_dtype_
+from paddle.fluid.framework import _non_static_mode, convert_np_dtype_to_dtype_, in_dygraph_mode
 from paddle.fluid.framework import _current_expected_place as _get_device
 from paddle.fluid.core import VarDesc
 from paddle.fluid.dygraph import no_grad
@@ -918,7 +918,12 @@ class Layer(object):
         return outputs
 
     def __call__(self, *inputs, **kwargs):
-        return self._dygraph_call_func(*inputs, **kwargs)
+        if (not in_declarative_mode()) and (not self._forward_pre_hooks) \
+            and (not self._forward_post_hooks) and (not self._built) and in_dygraph_mode():
+            self._build_once(*inputs, **kwargs)
+            return self.forward(*inputs, **kwargs)
+        else:
+            return self._dygraph_call_func(*inputs, **kwargs)
 
     def forward(self, *inputs, **kwargs):
         """

python/paddle/nn/functional/common.py

@@ -1534,12 +1534,8 @@ def linear(x, weight, bias=None, name=None):
           #     [2.1077576  2.1077576  2.1077576  2.1077576 ]]
     """
     if in_dygraph_mode():
-        pre_bias = _C_ops.final_state_matmul(x, weight, False, False)
-
-        if bias is None:
-            return pre_bias
-
-        return _C_ops.final_state_add(pre_bias, bias)
+        #TODO(jiabin): using addmm for fast forward route 
+        return _C_ops.final_state_linear(x, weight, bias)
     else:
         if _in_legacy_dygraph():
             pre_bias = _C_ops.matmul_v2(x, weight, 'trans_x', False, 'trans_y',