[Cherry-Pick][Feature Branch][DeepSparse Evaluation API] Update lm-eval, perplexity, additional datasets

setup.py

@@ -150,6 +150,7 @@ def _parse_requirements_file(file_path):
     "accelerate<0.26",
     "scikit-learn",
     "seqeval",
+    "evaluate",
 ]
 _sentence_transformers_integration_deps = ["optimum-deepsparse"] + _torch_deps
 
@@ -310,7 +311,7 @@ def _setup_entry_points() -> Dict:
             f"deepsparse.image_classification.eval={ic_eval}",
             "deepsparse.license=deepsparse.license:main",
             "deepsparse.validate_license=deepsparse.license:validate_license_cli",
-            "deepsparse.eval=deepsparse.evaluation.cli:main",
+            "deepsparse.evaluate=deepsparse.evaluation.cli:main",
         ]
     }
 

src/deepsparse/evaluation/cli.py

@@ -20,7 +20,7 @@
   Module for evaluating models on the various evaluation integrations
 
 OPTIONS:
-    --model_path MODEL_PATH
+    MODEL_PATH
                         A path to an ONNX model, local directory containing ONNX model
                         (including all the auxiliary files) or a SparseZoo stub
     -d DATASET, --dataset DATASET
@@ -72,7 +72,7 @@
 
 from deepsparse.evaluation.evaluator import evaluate
 from deepsparse.evaluation.results import Result, save_result
-from deepsparse.evaluation.utils import args_to_dict, get_save_path
+from deepsparse.evaluation.utils import get_save_path, parse_kwarg_tuples
 from deepsparse.operators.engine_operator import (
     DEEPSPARSE_ENGINE,
     ORT_ENGINE,
@@ -88,12 +88,10 @@
         ignore_unknown_options=True,
     )
 )
-@click.option(
-    "--model_path",
+@click.argument(
+    "model_path",
     type=click.Path(dir_okay=True, file_okay=True),
     required=True,
-    help="A path to an ONNX model, local directory containing ONNX model"
-    "(including all the auxiliary files) or a SparseZoo stub",
 )
 @click.option(
     "-d",
@@ -178,7 +176,7 @@ def main(
     # join datasets to a list if multiple datasets are passed
     datasets = list(dataset) if not isinstance(dataset, str) else dataset
     # format kwargs to a  dict
-    integration_args = args_to_dict(integration_args)
+    integration_args = parse_kwarg_tuples(integration_args)
 
     _LOGGER.info(
         f"Creating {engine_type} pipeline to evaluate from model path: {model_path}"
@@ -203,7 +201,7 @@ def main(
         **integration_args,
     )
 
-    _LOGGER.info(f"Evaluation done. Results:\n{result}")
+    _LOGGER.info(f"Evaluation done. Results:\n{result.formatted}")
 
     save_path = get_save_path(
         save_path=save_path,

src/deepsparse/evaluation/evaluator.py

@@ -65,7 +65,6 @@ def evaluate(
     return eval_integration(
         pipeline=pipeline,
         datasets=datasets,
-        engine_type=engine_type,
         batch_size=batch_size,
         splits=splits,
         metrics=metrics,

src/deepsparse/evaluation/integrations/__init__.py

@@ -15,7 +15,7 @@
 # flake8: noqa: F401
 
 
-def try_import_lm_evaluation_harness(raise_error=False):
+def try_import_lm_evaluation_harness(raise_error=True):
     try:
         import lm_eval
 
@@ -24,11 +24,11 @@ def try_import_lm_evaluation_harness(raise_error=False):
         if raise_error:
             raise ImportError(
                 "Unable to import lm_eval. "
-                "To install run 'pip install "
-                "git+https://github.com/EleutherAI/lm-evaluation-harness@b018a7d51'"
+                "To install run 'pip install lm-eval==0.4.0'"
             )
         return False
 
 
 if try_import_lm_evaluation_harness(raise_error=False):
     from .lm_evaluation_harness import *
+from .perplexity import *