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/one_shot/__init__.py

@@ -30,7 +30,6 @@ class ModelCodeWriter(TaskGenerator[ModelExperiment]):
                 description=t.description,
                 formulation=t.formulation,
                 variables=t.variables,
-                execute_desc=mti.execute_desc(),
             )
             system_prompt = sys_prompt_tpl.render()
 

rdagent/components/coder/model_coder/one_shot/prompt.yaml

@@ -1,8 +1,8 @@
 
 
-code_implement_sys: -|
-  You are an assistant whose job is to answer user's question."
-code_implement_user: -|
+code_implement_sys: |-
+  You are an assistant whose job is to answer user's question.
+code_implement_user: |-
   With the following given information, write a python code using pytorch and torch_geometric to implement the model.
   This model is in the graph learning field, only have one layer.
   The input will be node_feature [num_nodes, dim_feature] and edge_index [2, num_edges]  (It would be the input of the forward model)
@@ -13,6 +13,15 @@ code_implement_user: -|
   3. model formulation:{{formulation}}
   4. model variables:{{variables}}.
   You must complete the forward function as far as you can do.
-  \# Execution
-  Your implemented code will be exectued in the follow way
-  {{execute_desc}}
+  Execution Your implemented code will be executed in the follow way:
+  The the implemented code will be placed in a file like [uuid]/model.py
+  We'll import the model in the implementation in file `model.py` after setting the cwd into the directory
+  - from model import model_cls (So you must have a variable named `model_cls` in the file)
+    - So your implemented code could follow the following pattern
+      ```Python
+      class XXXLayer(torch.nn.Module):
+          ...
+      model_cls = XXXLayer
+      ```
+  - initialize the model by initializing it `model_cls(input_dim=INPUT_DIM)`
+  - And then verify the model by comparing the output tensors by feeding specific input tensor.