From 05e55e55bc6c3d0b68ef9423ac558285e2c1b993 Mon Sep 17 00:00:00 2001 From: George Date: Thu, 7 Sep 2023 16:36:46 -0400 Subject: [PATCH] Request V2 files from graphql (#361) * Request V2 files from graphql * Change underscores to dashes in archive names * download to appropriate destination * better error handling * test edits * comments * undo comment --------- Co-authored-by: Danny Guinther --- src/sparsezoo/api/query_parser.py | 6 +- src/sparsezoo/model/model.py | 23 +++---- src/sparsezoo/model/utils.py | 28 +++----- src/sparsezoo/objects/directory.py | 2 +- .../bert_pruned80_quant-none-vnni.json | 1 + .../analyze/bert_pruned_quantized.json | 1 - tests/sparsezoo/analyze/helpers.py | 4 +- tests/sparsezoo/model/test_model.py | 53 +++++++-------- tests/sparsezoo/model/test_utils.py | 68 +++++++++---------- 9 files changed, 84 insertions(+), 102 deletions(-) create mode 100644 tests/sparsezoo/analyze/bert_pruned80_quant-none-vnni.json delete mode 100644 tests/sparsezoo/analyze/bert_pruned_quantized.json diff --git a/src/sparsezoo/api/query_parser.py b/src/sparsezoo/api/query_parser.py index 3e51c0ea..a8f8b64a 100644 --- a/src/sparsezoo/api/query_parser.py +++ b/src/sparsezoo/api/query_parser.py @@ -121,8 +121,10 @@ def _parse_fields(self) -> None: def parse_list_fields_to_string(self, fields: List[str]) -> str: parsed_fields = "" for field in fields: - camel_case_field = to_camel_case(field) - parsed_fields += f"{camel_case_field} " + field_without_arguments, sep, args = field.partition("(") + camel_case_field = to_camel_case(field_without_arguments) + args_str = f"{sep}{args}" if args else "" + parsed_fields += f"{camel_case_field}{args_str} " if camel_case_field in DEFAULT_FIELDS: stringified_fields = self.parse_list_fields_to_string( DEFAULT_FIELDS.get(camel_case_field) diff --git a/src/sparsezoo/model/model.py b/src/sparsezoo/model/model.py index 8a6206e1..f6fde91c 100644 --- a/src/sparsezoo/model/model.py +++ b/src/sparsezoo/model/model.py @@ -110,12 +110,12 @@ def __init__(self, source: str, download_path: Optional[str] = None): self.sample_originals: Directory = self._directory_from_files( files, directory_class=Directory, - display_name="sample_originals", + display_name="sample-originals", ) self.sample_inputs: NumpyDirectory = self._directory_from_files( files, directory_class=NumpyDirectory, - display_name="sample_inputs", + display_name="sample-inputs", ) self.model_card: File = self._file_from_files(files, display_name="model.md") @@ -123,7 +123,7 @@ def __init__(self, source: str, download_path: Optional[str] = None): self.sample_outputs = self._directory_from_files( files, directory_class=NumpyDirectory, - display_name="sample_outputs", + display_name="sample-outputs", allow_multiple_outputs=True, regex=True, ) @@ -133,7 +133,7 @@ def __init__(self, source: str, download_path: Optional[str] = None): ] = self._sample_outputs_list_to_dict(self.sample_outputs) self.sample_labels: Directory = self._directory_from_files( - files, directory_class=Directory, display_name="sample_labels" + files, directory_class=Directory, display_name="sample-labels" ) self.deployment: SelectDirectory = self._directory_from_files( @@ -150,12 +150,9 @@ def __init__(self, source: str, download_path: Optional[str] = None): self.logs: Directory = self._directory_from_files(files, display_name="logs") - self.recipes: SelectDirectory = self._directory_from_files( - files, - directory_class=SelectDirectory, - display_name="recipe", - stub_params=self.stub_params, - ) + self.recipes = self._file_from_files(files, display_name="^recipe", regex=True) + if isinstance(self.recipes, File): + self.recipes = [self.recipes] self._onnx_gz: OnnxGz = self._directory_from_files( files, directory_class=OnnxGz, display_name="model.onnx.tar.gz" @@ -691,7 +688,7 @@ def _sample_outputs_list_to_dict( if not isinstance(directories, list): # if found a single 'sample_outputs' directory, # assume it should be mapped to its the native framework - expected_name = "sample_outputs" + expected_name = "sample-outputs" if directories.name not in [expected_name, expected_name + ".tar.gz"]: raise ValueError( "Found single folder (or tar.gz archive)" @@ -701,7 +698,7 @@ def _sample_outputs_list_to_dict( engine_to_numpydir_map["framework"] = directories else: - # if found multiple 'sample_outputs' directories, + # if found multiple 'sample-outputs' directories, # use directory name to relate it with the appropriate # inference engine for directory in directories: @@ -710,7 +707,7 @@ def _sample_outputs_list_to_dict( engine_name = engine_name.replace(".tar.gz", "") if engine_name not in ENGINES: raise ValueError( - f"The name of the 'sample_outputs' directory should " + f"The name of the 'sample-outputs' directory should " f"end with an engine name (one of the {ENGINES}). " f"However, the name is {directory.name}." ) diff --git a/src/sparsezoo/model/utils.py b/src/sparsezoo/model/utils.py index 9c47402b..d1684b33 100644 --- a/src/sparsezoo/model/utils.py +++ b/src/sparsezoo/model/utils.py @@ -142,7 +142,7 @@ def load_files_from_stub( fields=[ "model_id", "model_onnx_size_compressed_bytes", - "files", + "files(version: 2)", "benchmark_results", "training_results", "repo_name", @@ -168,9 +168,11 @@ def load_files_from_stub( model_id = model["model_id"] files = model.get("files") + if len(files) == 0: + raise ValueError(f"No files found for stub {stub}") + include_file_download_url(files) files = restructure_request_json(request_json=files) - if params is not None: files = filter_files(files=files, params=params) @@ -308,7 +310,7 @@ def save_outputs_to_tar( path = os.path.join( os.path.dirname(sample_inputs.path), - f"sample_outputs_{engine_type}", + f"sample-outputs_{engine_type}", ) if not os.path.exists(path): os.mkdir(path) @@ -382,26 +384,14 @@ def restructure_request_json( file_dict_deployment["file_type"] = "deployment" request_json.append(file_dict_deployment) - # create recipes - recipe_dicts_list = fetch_from_request_json(request_json, "file_type", "recipe") - for (idx, file_dict) in recipe_dicts_list: - display_name = file_dict["display_name"] - # make sure that recipe name has a - # format `recipe_{...}`. - prefix = "recipe_" - if not display_name.startswith(prefix): - display_name = prefix + display_name - file_dict["display_name"] = display_name - request_json[idx] = file_dict - # restructure inputs/labels/originals/outputs directories # use `sample-inputs.tar.gz` to simulate non-existent directories files_to_create = [ - "sample_inputs.tar.gz", - "sample_labels.tar.gz", - "sample_originals.tar.gz", - "sample_outputs.tar.gz", + "sample-inputs.tar.gz", + "sample-labels.tar.gz", + "sample-originals.tar.gz", + "sample-outputs.tar.gz", ] types = ["inputs", "labels", "originals", "outputs"] for file_name, type in zip(files_to_create, types): diff --git a/src/sparsezoo/objects/directory.py b/src/sparsezoo/objects/directory.py index f83e06f2..fe98778c 100644 --- a/src/sparsezoo/objects/directory.py +++ b/src/sparsezoo/objects/directory.py @@ -194,7 +194,7 @@ def download( else: for file in self.files: file.download( - destination_path=os.path.join(destination_path, self.name) + destination_path=destination_path, ) file._path = os.path.join(destination_path, self.name, file.name) diff --git a/tests/sparsezoo/analyze/bert_pruned80_quant-none-vnni.json b/tests/sparsezoo/analyze/bert_pruned80_quant-none-vnni.json new file mode 100644 index 00000000..5680bd3d --- /dev/null +++ b/tests/sparsezoo/analyze/bert_pruned80_quant-none-vnni.json @@ -0,0 +1 @@ +{"node_counts": {"Gather": 97, "MatMulInteger": 73, "Unsqueeze": 99, "Shape": 97, "DequantizeLinear": 27, "Cast": 74, "Add": 174, "Sub": 26, "Mul": 123, "Slice": 1, "ReduceMean": 50, "Pow": 25, "Sqrt": 25, "Div": 49, "QuantizeLinear": 97, "Concat": 48, 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"parameterized_prunable": false, "sparse_node": false, "quantized_node": false, "zero_point": 0}, {"name": "MatMul_2576_bias_add_quant_rescale_mul", "node_id": "4520", "op_type": "Mul", "inputs": [{"name": "MatMul_2576_bias_add_quant_output_cast", "shape": [1, 384, 2], "dtype": "float32"}], "outputs": [{"name": "4520", "shape": [1, 384, 2], "dtype": "float32"}], "parameter_summary": {"total": 0, "pruned": 0, "block_structure": {"single": {"zero": 0, "non_zero": 0, "sparsity": 0}, "block4": {"zero": 0, "non_zero": 0, "sparsity": 0}}, "precision": {}}, "operation_summary": {"ops": {"total": 768, "pruned": 0, "block_structure": {"single": {"dense": 768, "sparse": 0, "sparsity": 0.0}, "block4": {"dense": 768, "sparse": 0, "sparsity": 0.0}}, "precision": {"float32": {"dense": 768, "sparse": 0, "sparsity": 0.0}}}, "macs": {"total": 0, "pruned": 0, "block_structure": {"single": {"dense": 0, "sparse": 0, "sparsity": 0}, "block4": {"dense": 0, "sparse": 0, "sparsity": 0}}, "precision": {}}}, "parameters": [], "parameterized_prunable": false, "sparse_node": false, "quantized_node": false, "zero_point": 0}, {"name": "Split_2578", "node_id": "4521", "op_type": "Split", "inputs": [{"name": "4520", "shape": [1, 384, 2], "dtype": "float32"}], "outputs": [{"name": "4521", "shape": [1, 384, 1], "dtype": "float32"}], "parameter_summary": {"total": 0, "pruned": 0, "block_structure": {"single": {"zero": 0, "non_zero": 0, "sparsity": 0}, "block4": {"zero": 0, "non_zero": 0, "sparsity": 0}}, "precision": {}}, "operation_summary": {"ops": {"total": 0, "pruned": 0, "block_structure": {"single": {"dense": 0, "sparse": 0, "sparsity": 0}, "block4": {"dense": 0, "sparse": 0, "sparsity": 0}}, "precision": {}}, "macs": {"total": 0, "pruned": 0, "block_structure": {"single": {"dense": 0, "sparse": 0, "sparsity": 0}, "block4": {"dense": 0, "sparse": 0, "sparsity": 0}}, "precision": {}}}, "parameters": [], "parameterized_prunable": false, "sparse_node": false, "quantized_node": false, "zero_point": 0}, {"name": "Squeeze_2580", "node_id": "end_logits", "op_type": "Squeeze", "inputs": [], "outputs": [{"name": "end_logits", "shape": [1, 384], "dtype": "float32"}], "parameter_summary": {"total": 0, "pruned": 0, "block_structure": {"single": {"zero": 0, "non_zero": 0, "sparsity": 0}, "block4": {"zero": 0, "non_zero": 0, "sparsity": 0}}, "precision": {}}, "operation_summary": {"ops": {"total": 0, "pruned": 0, "block_structure": {"single": {"dense": 0, "sparse": 0, "sparsity": 0}, "block4": {"dense": 0, "sparse": 0, "sparsity": 0}}, "precision": {}}, "macs": {"total": 0, "pruned": 0, "block_structure": {"single": {"dense": 0, "sparse": 0, "sparsity": 0}, "block4": {"dense": 0, "sparse": 0, "sparsity": 0}}, "precision": {}}}, "parameters": [], "parameterized_prunable": false, "sparse_node": false, "quantized_node": false, "zero_point": 0}, {"name": "Squeeze_2579", "node_id": "start_logits", "op_type": "Squeeze", "inputs": [{"name": "4521", "shape": [1, 384, 1], "dtype": "float32"}], "outputs": [{"name": "start_logits", "shape": [1, 384], "dtype": "float32"}], "parameter_summary": {"total": 0, "pruned": 0, "block_structure": {"single": {"zero": 0, "non_zero": 0, "sparsity": 0}, "block4": {"zero": 0, "non_zero": 0, "sparsity": 0}}, "precision": {}}, "operation_summary": {"ops": {"total": 0, "pruned": 0, "block_structure": {"single": {"dense": 0, "sparse": 0, "sparsity": 0}, "block4": {"dense": 0, "sparse": 0, "sparsity": 0}}, "precision": {}}, "macs": {"total": 0, "pruned": 0, "block_structure": {"single": {"dense": 0, "sparse": 0, "sparsity": 0}, "block4": {"dense": 0, "sparse": 0, "sparsity": 0}}, "precision": {}}}, "parameters": [], "parameterized_prunable": false, "sparse_node": false, "quantized_node": false, "zero_point": 0}]} \ No newline at end of file diff --git a/tests/sparsezoo/analyze/helpers.py b/tests/sparsezoo/analyze/helpers.py index 04675161..2d197fbe 100644 --- a/tests/sparsezoo/analyze/helpers.py +++ b/tests/sparsezoo/analyze/helpers.py @@ -50,9 +50,9 @@ "bert_pruned_quantized": { "stub": ( "zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/" - "12layer_pruned80_quant-none-vnni" + "pruned80_quant-none-vnni" ), - "truth": f"{os.path.dirname(__file__)}/bert_pruned_quantized.json", + "truth": f"{os.path.dirname(__file__)}/bert_pruned80_quant-none-vnni.json", }, "resnet50_pruned_quantized": { "stub": ( diff --git a/tests/sparsezoo/model/test_model.py b/tests/sparsezoo/model/test_model.py index 1f574ce3..89b44ff7 100644 --- a/tests/sparsezoo/model/test_model.py +++ b/tests/sparsezoo/model/test_model.py @@ -32,15 +32,14 @@ "onnx", "model.onnx", "model.onnx.tar.gz", - "recipe", - "sample_inputs.tar.gz", - "sample_originals.tar.gz", - "sample_labels.tar.gz", - "sample_outputs.tar.gz", - "sample_inputs", - "sample_originals", - "sample_labels", - "sample_outputs", + "sample-inputs.tar.gz", + "sample-originals.tar.gz", + "sample-labels.tar.gz", + "sample-outputs.tar.gz", + "sample-inputs", + "sample-originals", + "sample-labels", + "sample-outputs", "benchmarks.yaml", "eval.yaml", "analysis.yaml", @@ -116,9 +115,7 @@ def test_model_from_stub(self, setup): @staticmethod def _assert_correct_files_downloaded(model, args): - if args[0] == "recipe": - assert len(model.recipes.available) == 1 - elif args[0] == "checkpoint": + if args[0] == "checkpoint": assert len(model.training.available) == 1 elif args[0] == "deployment": assert len(model.training.available) == 1 @@ -141,7 +138,7 @@ def _assert_validation_results_exist(model): "pytorch/sparseml/imagenet/pruned-moderate" ), True, - files_ic, + files_ic.union({"recipe.md", "recipe_transfer_learn.md"}), ), ( ( @@ -150,7 +147,7 @@ def _assert_validation_results_exist(model): "pytorch/huggingface/squad/pruned80_quant-none-vnni" ), False, - files_nlp, + files_nlp.union({"recipe.md"}), ), ( ( @@ -159,22 +156,22 @@ def _assert_validation_results_exist(model): "pytorch/ultralytics/coco/pruned_quant-aggressive_94" ), True, - files_yolo, + files_yolo.union({"recipe.md", "recipe_transfer_learn.md"}), ), ( "yolov5-x-coco-pruned70.4block_quantized", False, - files_yolo, + files_yolo.union({"recipe.md", "recipe_transfer_learn.md"}), ), ( "yolov5-n6-voc_coco-pruned55", False, - files_yolo, + files_yolo.union({"recipe.md"}), ), ( "resnet_v1-50-imagenet-channel30_pruned90_quantized", False, - files_yolo, + files_yolo.union({"recipe.md", "recipe_transfer_classification.md"}), ), ], scope="function", @@ -196,11 +193,10 @@ def test_folder_structure(self, setup): _, clone_sample_outputs, expected_files, temp_dir = setup if clone_sample_outputs: for file_name in [ - "sample_outputs_onnxruntime", - "sample_outputs_deepsparse", + "sample-outputs_onnxruntime", + "sample-outputs_deepsparse", ]: expected_files.update({file_name, file_name + ".tar.gz"}) - assert not set(os.listdir(temp_dir.name)).difference(expected_files) def test_validate(self, setup): @@ -246,19 +242,19 @@ def _add_mock_files(directory_path: str, clone_sample_outputs: bool): ) Path(optional_recipe_yaml).touch() - # add remaining `sample_{...}` files, that may be potentially + # add remaining `sample-{...}` files, that may be potentially # missing - mock_sample_file = os.path.join(directory_path, "sample_inputs.tar.gz") - for file_name in ["sample_originals.tar.gz", "sample_labels.tar.gz"]: + mock_sample_file = os.path.join(directory_path, "sample-inputs.tar.gz") + for file_name in ["sample-originals.tar.gz", "sample-labels.tar.gz"]: expected_file_dir = os.path.join(directory_path, file_name) if not os.path.isfile(expected_file_dir): shutil.copyfile(mock_sample_file, expected_file_dir) if clone_sample_outputs: - sample_outputs_file = os.path.join(directory_path, "sample_outputs.tar.gz") + sample_outputs_file = os.path.join(directory_path, "sample-outputs.tar.gz") for file_name in [ - "sample_outputs_onnxruntime.tar.gz", - "sample_outputs_deepsparse.tar.gz", + "sample-outputs_onnxruntime.tar.gz", + "sample-outputs_deepsparse.tar.gz", ]: shutil.copyfile( sample_outputs_file, os.path.join(directory_path, file_name) @@ -271,12 +267,11 @@ def _test_generate_outputs_single_engine(self, engine, model_directory): if engine == "onnxruntime": # test whether the functionality saves the numpy files to tar properly tar_file_expected_path = os.path.join( - directory_path, f"sample_outputs_{engine}.tar.gz" + directory_path, f"sample-outputs_{engine}.tar.gz" ) if os.path.isfile(tar_file_expected_path): os.remove(tar_file_expected_path) save_to_tar = True - output_expected = next(iter(model_directory.sample_outputs[engine])) output_expected = list(output_expected.values()) output = next( diff --git a/tests/sparsezoo/model/test_utils.py b/tests/sparsezoo/model/test_utils.py index 6f052a69..2dc23d82 100644 --- a/tests/sparsezoo/model/test_utils.py +++ b/tests/sparsezoo/model/test_utils.py @@ -18,50 +18,50 @@ import pytest from sparsezoo.model import Model, load_files_from_stub, setup_model +from sparsezoo.objects import Directory EXPECTED_IC_FILES = { - "originals": {"sample_originals.tar.gz"}, - "outputs": {"sample_outputs.tar.gz"}, - "recipe": {"recipe_original.md", "recipe_transfer_learn.md"}, - "labels": {"sample_labels.tar.gz"}, + "originals": {"sample-originals.tar.gz"}, + "outputs": {"sample-outputs.tar.gz"}, + "recipe": {"recipe.md", "recipe_transfer_learn.md"}, + "labels": {"sample-labels.tar.gz"}, "onnx": {"model.onnx"}, - "training": {"model.pth"}, + "training": {"training/model.pth"}, "card": {"model.md"}, - "inputs": {"sample_inputs.tar.gz"}, + "inputs": {"sample-inputs.tar.gz"}, "deployment": {"model.onnx"}, } EXPECTED_NLP_FILES = { - "outputs": {"sample_outputs.tar.gz"}, - "recipe": {"recipe_original.md"}, + "outputs": {"sample-outputs.tar.gz"}, + "recipe": {"recipe.md"}, "onnx": {"model.onnx"}, "training": { - "pytorch_model.bin", - "training_args.bin", - "tokenizer_config.json", - "tokenizer.json", - "vocab.txt", - "special_tokens_map.json", - "config.json", + "training/pytorch_model.bin", + "training/training_args.bin", + "training/tokenizer_config.json", + "training/tokenizer.json", + "training/vocab.txt", + "training/special_tokens_map.json", + "training/config.json", }, "card": {"model.md"}, - "inputs": {"sample_inputs.tar.gz"}, + "inputs": {"sample-inputs.tar.gz"}, "deployment": { - "model.onnx", - "tokenizer.json", - "config.json", - "tokenizer_config.json", + "deployment/model.onnx", + "deployment/tokenizer.json", + "deployment/config.json", }, } EXPECTED_YOLO_FILES = { - "originals": {"sample_originals.tar.gz"}, - "outputs": {"sample_outputs.tar.gz"}, - "recipe": {"recipe_original.md", "recipe_transfer_learn.md"}, + "originals": {"sample-originals.tar.gz"}, + "outputs": {"sample-outputs.tar.gz"}, + "recipe": {"recipe.md", "recipe_transfer_learn.md"}, "onnx": {"model.onnx"}, - "training": {"model.ckpt.pt", "model.pt"}, + "training": {"training/model.pt"}, "card": {"model.md"}, - "inputs": {"sample_inputs.tar.gz"}, - "deployment": {"model.onnx"}, + "inputs": {"sample-inputs.tar.gz"}, + "deployment": {"deployment/model.onnx"}, } @@ -112,7 +112,6 @@ def test_load_files_from_stub(stub, expected_files): def check_extraneous_files(expected_files, temp_dir, ignore_external_data): files_in_directory = set(os.listdir(temp_dir.name)) extra_files = files_in_directory - expected_files - for file in extra_files: # ignore model.onnx.tar.gz and model.data files valid_extra_file = ignore_external_data and ( @@ -154,7 +153,6 @@ def test_setup_model_from_paths(self, setup): deployment_path = model.deployment.path onnx_model_path = model.onnx_model.path sample_inputs_path = model.sample_inputs.path - recipes_path = model.recipes.path setup_model( output_dir=temp_dir.name, @@ -163,19 +161,19 @@ def test_setup_model_from_paths(self, setup): onnx_model=onnx_model_path, sample_inputs=sample_inputs_path, # TODO: .path() needs to be supported for dict-like obj - # sample_outputs=model.sample_outputs.path, - recipes=recipes_path, + sample_outputs=model.sample_output.path + if isinstance(model.sample_outputs, Directory) + else None, ) expected_files = { "training", "deployment", - "recipe", + "recipe.md", "model.onnx", "model.onnx.tar.gz", - "sample_inputs.tar.gz", + "sample-inputs.tar.gz", } - check_extraneous_files(expected_files, temp_dir, ignore_external_data) def test_setup_model_from_objects(self, setup): @@ -203,10 +201,10 @@ def test_setup_model_from_objects(self, setup): expected_files = { "training", "deployment", - "recipe", + "recipe.md", "model.onnx", "model.onnx.tar.gz", - "sample_inputs", + "sample-inputs", } check_extraneous_files(expected_files, temp_dir, ignore_external_data) download_dir.cleanup()