Add available pattern handlers for graph optimization in documentation (#553)

Author: Only-bottle

docs/description/netspresso/enums/graph_optimize.md

@@ -11,3 +11,21 @@ Graph optimization enums for configuring pattern handlers in NetsPresso's graph
       show_root_heading: true
       show_source: false
       show_symbol_type_toc: true
+
+## Available Pattern Handlers
+
+| Category | Pattern Name | Status | Description |
+|----------|--------------|--------|-------------|
+| **Fuse** | `PatternHandlerFuseBNToConv` | ✓ | Fuse batch normalization to convolution layers |
+| **Fuse** | `PatternHandlerFuseMathIntoConv` | ✓ | Fuse math operators (add, sub, mul, div) to convolution recursively |
+| **Fuse** | `PatternHandlerFuseMultiReshapeTranspose` | ✓ | Fuse multiple reshapes and transposes into one |
+| **Fuse** | `PatternHandlerFuseMultiBranchReshapeTranspose` | To do | Fuse multiple branched reshapes and transposes into one |
+| **Fuse** | `PatternHandlerFuseContinuousConcat` | ✓ | Fuse continuous concatenation operations |
+| **Fuse** | `PatternHandlerFoldingGeLU` | ✓ | Optimize GeLU activation functions by folding |
+| **Fuse** | `PatternHandlerFoldingSilu` | ✓ | Optimize SiLU activation functions by folding |
+| **Fuse** | `PatternHandlerFoldingRMSNorm` | ✓ | Optimize RMS normalization layers by folding |
+| **Fuse** | `PatternHandlerFoldingLayerNorm` | ✓ | Optimize layer normalization operations by folding |
+| **Remove** | `PatternHandlerRemoveUselessSlice` | ✓ | Remove meaningless tensor slice operators |
+| **Replace** | `PatternHandlerReplaceNegToConv` | ✓ | Replace negation by depthwise convolution |
+| **Replace** | `PatternHandlerReplaceMatmulToConv` | ✓ | Replace matrix multiplication with convolution operations |
+| **Optimize** | `PatternHandlerChangeAxisOfSoftmax` | ✓ | Optimize softmax operations by changing axis configurations |

netspresso/simulator/simulator.py

@@ -48,20 +48,20 @@ def create_metadata_with_status(status, error_message=None):
 
     def _upload_model_file(self, model_path: str) -> ResponseModelItem:
         # Get presigned_model_upload_url
-        presigned_url_response = launcher_client_v2.graph_optimizer.presigned_model_upload_url(
+        presigned_url_response = launcher_client_v2.simulator.presigned_model_upload_url(
             access_token=self.token_handler.tokens.access_token,
             input_model_path=model_path,
         )
 
         # Upload model_file
-        launcher_client_v2.graph_optimizer.upload_model_file(
+        launcher_client_v2.simulator.upload_model_file(
             access_token=self.token_handler.tokens.access_token,
             input_model_path=model_path,
             presigned_upload_url=presigned_url_response.data.presigned_upload_url,
         )
 
         # Validate model_file
-        validate_model_response = launcher_client_v2.graph_optimizer.validate_model_file(
+        validate_model_response = launcher_client_v2.simulator.validate_model_file(
             access_token=self.token_handler.tokens.access_token,
             input_model_path=model_path,
             ai_model_id=presigned_url_response.data.ai_model_id,
@@ -76,20 +76,19 @@ def simulate_model(
         output_dir: str,
         dataset_path: Optional[str] = None,
     ) -> SimulatorMetadata:
-        """Optimize a model to the specified framework.
+        """Simulate a model to the specified framework.
 
         Args:
-            input_model_path (str): The file path where the model is located.
-            output_dir (str): The local folder path to save the optimized model.
-            pattern_handlers (List[GraphOptimizePatternHandler]): The pattern handlers to optimize the model.
-            wait_until_done (bool): If True, wait for the graph optimize result before returning the function.
-                                If False, request the graph optimize and return  the function immediately.
+            base_model_path (str): The file path where the base model is located.
+            target_model_path (str): The file path where the target model is located.
+            output_dir (str): The local folder path to save the simulation result.
+            dataset_path (str): The file path where the dataset is located.
 
         Raises:
-            e: If an error occurs during the graph optimize.
+            e: If an error occurs during the simulation.
 
         Returns:
-            GraphOptimizerMetadata: Graph optimize metadata.
+            SimulatorMetadata: Simulator metadata.
         """
 
         netspresso_analytics.send_event(event_name="simulate_model_using_np")