bug fix, update readme, delete launch_parameters (#25)

README.md

@@ -26,10 +26,18 @@ We have a [live demo](https://vidur.westus2.cloudapp.azure.com/) that captures t
 * __Instructions on adding a new model to existing or new SKUs can be found [here](docs/profiling.md)__.
 * All models support a maximum context length of 4k except `Llama3-8B` and `Llama3-70B` which support 16k context length by passing additional CLI params:
 
+For random forrest:
 ```text
---sklearn_execution_time_predictor_prediction_max_prefill_chunk_size 16384 \
---sklearn_execution_time_predictor_prediction_max_batch_size 512 \
---sklearn_execution_time_predictor_prediction_max_tokens_per_request 16384 \
+--random_forrest_execution_time_predictor_config_prediction_max_prefill_chunk_size 16384 \
+--random_forrest_execution_time_predictor_config_prediction_max_batch_size 512 \
+--random_forrest_execution_time_predictor_config_prediction_max_tokens_per_request 16384 \
+```
+
+For linear regression:
+```text
+--linear_regression_execution_time_predictor_config_prediction_max_prefill_chunk_size 16384 \
+--linear_regression_execution_time_predictor_config_prediction_max_batch_size 512 \
+--linear_regression_execution_time_predictor_config_prediction_max_tokens_per_request 16384 \
 ```
 
 * Pipeline parallelism is supported for all models. The PP dimension should divide the number of layers in the model.
@@ -97,26 +105,20 @@ or a big example with all the parameters,
 
 ```sh
 python -m vidur.main  \
---replica_device a100 \
---replica_model_name meta-llama/Llama-2-7b-hf  \
---cluster_num_replicas 1 \
---replica_num_tensor_parallel_workers 1 \
---replica_num_pipeline_stages 1 \
---request_generator_provider synthetic \
---synthetic_request_generator_length_provider trace \
---synthetic_request_generator_interval_provider static \
---request_generator_max_tokens 4096 \
---trace_request_length_generator_trace_file ./data/processed_traces/arxiv_summarization_stats_llama2_tokenizer_filtered_v2.csv \
---synthetic_request_generator_num_requests 128  \
---request_generator_provider synthetic \
---synthetic_request_generator_length_provider trace \
---synthetic_request_generator_interval_provider static \
---request_generator_max_tokens 4096 \
---trace_request_length_generator_trace_file ./data/processed_traces/arxiv_summarization_stats_llama2_tokenizer_filtered_v2.csv \
---synthetic_request_generator_num_requests 128  \
---replica_scheduler_provider vllm  \
---replica_scheduler_batch_size_cap 256  \
---vllm_scheduler_max_tokens_in_batch 4096
+--replica_config_device a100 \
+--replica_config_model_name meta-llama/Llama-2-7b-hf  \
+--cluster_config_num_replicas 1 \
+--replica_config_tensor_parallel_size 1 \
+--replica_config_num_pipeline_stages 1 \
+--request_generator_config_type synthetic \
+--length_generator_config_type trace \
+--interval_generator_config_type static \
+--[trace|zipf|uniform|fixed]_request_length_generator_config_max_tokens 4096 \
+--trace_request_length_generator_config_trace_file ./data/processed_traces/arxiv_summarization_stats_llama2_tokenizer_filtered_v2.csv \
+--synthetic_request_generator_config_num_requests 128  \
+--replica_scheduler_config_type vllm  \
+--[vllm|lightllm|orca|faster_transformer|sarathi]_scheduler_config_batch_size_cap 256  \
+--[vllm|lightllm]_scheduler_config_max_tokens_in_batch 4096
 ```
 
 The simulator supports a plethora of parameters for the simulation description which can be found [here](docs/launch_parameters.md).

vidur/config/flat_dataclass.py

@@ -90,7 +90,7 @@ def create_from_cli_args(cls) -> Any:
         nargs = None
         action = None
         field_type = field.type
-        help_text = field.metadata.get("help", None)
+        help_text = cls.metadata_mapping[field.name].get("help", None)
 
         if is_list(field.type):
             assert is_composed_of_primitives(field.type)
@@ -113,15 +113,17 @@ def create_from_cli_args(cls) -> Any:
 
         # handle cases with default and default factory args
         if field.default is not MISSING:
-            arg_params["default"] = field.default
+            value = field.default
+            if callable(value):
+                value = value()
+            arg_params["default"] = value
         elif field.default_factory is not MISSING:
             arg_params["default"] = field.default_factory()
         else:
             arg_params["required"] = True
 
         if nargs:
             arg_params["nargs"] = nargs
-
         parser.add_argument(f"--{field.name}", **arg_params)
 
     args = parser.parse_args()
@@ -139,6 +141,7 @@ def create_flat_dataclass(input_dataclass: Any) -> Any:
     processed_classes = set()
     dataclass_args = defaultdict(list)
     dataclass_dependencies = defaultdict(set)
+    metadata_mapping = {}
 
     def process_dataclass(_input_dataclass, prefix=""):
         if _input_dataclass in processed_classes:
@@ -165,6 +168,7 @@ def process_dataclass(_input_dataclass, prefix=""):
                 meta_fields_with_defaults.append(
                     (type_field_name, type(default_value), default_value)
                 )
+                metadata_mapping[type_field_name] = field.metadata
 
                 assert hasattr(field_type, "__dataclass_fields__")
                 for subclass in get_all_subclasses(field_type):
@@ -202,6 +206,7 @@ def process_dataclass(_input_dataclass, prefix=""):
             dataclass_args[_input_dataclass].append(
                 (prefixed_name, field.name, field_type)
             )
+            metadata_mapping[prefixed_name] = field.metadata
 
     process_dataclass(input_dataclass)
 
@@ -211,6 +216,7 @@ def process_dataclass(_input_dataclass, prefix=""):
     # Metadata fields
     FlatClass.dataclass_args = dataclass_args
     FlatClass.dataclass_dependencies = dataclass_dependencies
+    FlatClass.metadata_mapping = metadata_mapping
 
     # Helper methods
     FlatClass.reconstruct_original_dataclass = reconstruct_original_dataclass