Implement model (and some factor) coder with evolving (#52)

* store code into FBImplementation

* fix path related bugs

* fix a bug

* fix factor related small bugs

* re-submit all model related code

* new code to model coder

* finish the model evolving code

---------

Co-authored-by: xuyang1 <xuyang1@microsoft.com>

rdagent/components/coder/model_coder/benchmark/eval.py

@@ -22,7 +22,7 @@ class ModelImpValEval:
     - If the model structure is similar, the output will change in similar way when we change the input.
 
     Challenge:
-    - The key difference between it and implementing factors is that we have parameters in the layers (Factor operators often have no parameters or are given parameters).
+    - The key difference between it and implementing models is that we have parameters in the layers (Model operators often have no parameters or are given parameters).
     - we try to initialize the model param in similar value. So only the model structure is different.
 
     Comparing the correlation of following sequences
@@ -41,9 +41,8 @@ class ModelImpValEval:
         for _ in range(round_n):
             # run different model initial parameters.
             for init_val in [-0.2, -0.1, 0.1, 0.2]:
-                data, exec_config = get_data_conf(init_val)
-                gt_res = gt.execute(data=data, config=exec_config)
-                res = gen.execute(data=data, config=exec_config)
+                _, gt_res = gt.execute(input_value=init_val, param_init_value=init_val)
+                _, res = gen.execute(input_value=init_val, param_init_value=init_val)
                 eval_pairs.append((res, gt_res))
 
         # flat and concat the output