Remove datasets as required dependency (#2087)

* remove datasets required dependency

* install datasets when needed

* add datasets installed when needed

* style

* add require dataset

* divide datasets tests

* import datasets only when needed

Author: echarlaix

.github/workflows/dev_test_benckmark.yml

@@ -23,7 +23,7 @@ jobs:
     - name: Install dependencies
       run: |
         pip install wheel
-        pip install .[tests,onnxruntime,benchmark]
+        pip install .[tests,onnxruntime,benchmark] datasets
         pip install -U git+https://github.com/huggingface/evaluate
         pip install -U git+https://github.com/huggingface/diffusers
         pip install -U git+https://github.com/huggingface/transformers

.github/workflows/test_benckmark.yml

@@ -30,7 +30,7 @@ jobs:
       - name: Install dependencies
         run: |
           pip install wheel
-          pip install .[tests,onnxruntime,benchmark]
+          pip install .[tests,onnxruntime,benchmark] datasets
       - name: Test with unittest
         run: |
           python -m unittest discover --start-directory tests/benchmark --pattern 'test_*.py'

.github/workflows/test_utils.yml

@@ -37,4 +37,13 @@ jobs:
       - name: Test with pytest
         working-directory: tests
         run: |
-          python -m pytest -s -vvvv utils
+          pytest utils -s -n auto -m "not datasets_test" --durations=0
+
+      - name: Install datasets
+        run: |
+          pip install datasets
+
+      - name: Tests needing datasets
+        working-directory: tests
+        run: |
+          pytest utils -s -n auto -m "datasets_test" --durations=0

optimum/gptq/data.py

@@ -18,7 +18,12 @@
 
 import numpy as np
 import torch
-from datasets import load_dataset
+
+from optimum.utils.import_utils import DATASETS_IMPORT_ERROR, is_datasets_available
+
+
+if is_datasets_available():
+    from datasets import load_dataset
 
 
 """
@@ -113,6 +118,9 @@ def pad_block(block, pads):
 
 
 def get_wikitext2(tokenizer: Any, seqlen: int, nsamples: int, split: str = "train"):
+    if not is_datasets_available():
+        raise ImportError(DATASETS_IMPORT_ERROR.format("get_wikitext2"))
+
     if split == "train":
         data = load_dataset("wikitext", "wikitext-2-raw-v1", split="train")
     elif split == "validation":
@@ -132,6 +140,9 @@ def get_wikitext2(tokenizer: Any, seqlen: int, nsamples: int, split: str = "trai
 
 
 def get_c4(tokenizer: Any, seqlen: int, nsamples: int, split: str = "train"):
+    if not is_datasets_available():
+        raise ImportError(DATASETS_IMPORT_ERROR.format("get_c4"))
+
     if split == "train":
         data = load_dataset("allenai/c4", split="train", data_files={"train": "en/c4-train.00000-of-01024.json.gz"})
     elif split == "validation":
@@ -157,6 +168,9 @@ def get_c4(tokenizer: Any, seqlen: int, nsamples: int, split: str = "train"):
 
 
 def get_c4_new(tokenizer: Any, seqlen: int, nsamples: int, split: str = "train"):
+    if not is_datasets_available():
+        raise ImportError(DATASETS_IMPORT_ERROR.format("get_c4_new"))
+
     if split == "train":
         data = load_dataset("allenai/c4", split="train", data_files={"train": "en/c4-train.00000-of-01024.json.gz"})
     elif split == "validation":

optimum/gptq/quantizer.py

@@ -88,7 +88,7 @@ def __init__(
             dataset (`Union[List[str], str, Any]`, defaults to `None`):
                 The dataset used for quantization. You can provide your own dataset in a list of string or in a list of tokenized data
                 (e.g. [{ "input_ids": [ 1, 100, 15, ... ],"attention_mask": [ 1, 1, 1, ... ]},...])
-                or just use the original datasets used in GPTQ paper ['wikitext2','c4','c4-new','ptb','ptb-new'].
+                or just use the original datasets used in GPTQ paper ['wikitext2','c4','c4-new'].
             group_size (int, defaults to 128):
                 The group size to use for quantization. Recommended value is 128 and -1 uses per-column quantization.
             damp_percent (`float`, defaults to `0.1`):

optimum/onnxruntime/configuration.py

@@ -18,9 +18,8 @@
 from dataclasses import asdict, dataclass, field
 from enum import Enum
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
 
-from datasets import Dataset
 from packaging.version import Version, parse
 
 from onnxruntime import __version__ as ort_version
@@ -33,6 +32,10 @@
 from ..utils import logging
 
 
+if TYPE_CHECKING:
+    from datasets import Dataset
+
+
 logger = logging.get_logger(__name__)
 
 # This value is used to indicate ORT which axis it should use to quantize an operator "per-channel"
@@ -117,7 +120,9 @@ def create_calibrator(
 
 class AutoCalibrationConfig:
     @staticmethod
-    def minmax(dataset: Dataset, moving_average: bool = False, averaging_constant: float = 0.01) -> CalibrationConfig:
+    def minmax(
+        dataset: "Dataset", moving_average: bool = False, averaging_constant: float = 0.01
+    ) -> CalibrationConfig:
         """
         Args:
             dataset (`Dataset`):
@@ -151,7 +156,7 @@ def minmax(dataset: Dataset, moving_average: bool = False, averaging_constant: f
 
     @staticmethod
     def entropy(
-        dataset: Dataset,
+        dataset: "Dataset",
         num_bins: int = 128,
         num_quantized_bins: int = 128,
     ) -> CalibrationConfig:
@@ -188,7 +193,7 @@ def entropy(
         )
 
     @staticmethod
-    def percentiles(dataset: Dataset, num_bins: int = 2048, percentile: float = 99.999) -> CalibrationConfig:
+    def percentiles(dataset: "Dataset", num_bins: int = 2048, percentile: float = 99.999) -> CalibrationConfig:
         """
         Args:
             dataset (`Dataset`):

optimum/onnxruntime/model.py

@@ -14,17 +14,20 @@
 
 import logging
 import os
-from typing import Callable, Dict, List, Optional, Union
+from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Union
 
 import numpy as np
-from datasets import Dataset
 from transformers import EvalPrediction
 from transformers.trainer_pt_utils import nested_concat
 from transformers.trainer_utils import EvalLoopOutput
 
 from onnxruntime import InferenceSession
 
 
+if TYPE_CHECKING:
+    from datasets import Dataset
+
+
 logger = logging.getLogger(__name__)
 
 
@@ -59,7 +62,7 @@ def __init__(
         self.session = InferenceSession(str(model_path), providers=[execution_provider])
         self.onnx_input_names = {input_key.name: idx for idx, input_key in enumerate(self.session.get_inputs())}
 
-    def evaluation_loop(self, dataset: Dataset):
+    def evaluation_loop(self, dataset: "Dataset"):
         """
         Run evaluation and returns metrics and predictions.
 

optimum/onnxruntime/quantization.py

@@ -21,14 +21,14 @@
 from typing import TYPE_CHECKING, Callable, Dict, List, Optional, Tuple, Union
 
 import onnx
-from datasets import Dataset, load_dataset
 from packaging.version import Version, parse
 from transformers import AutoConfig
 
 from onnxruntime import __version__ as ort_version
 from onnxruntime.quantization import CalibrationDataReader, QuantFormat, QuantizationMode, QuantType
 from onnxruntime.quantization.onnx_quantizer import ONNXQuantizer
 from onnxruntime.quantization.qdq_quantizer import QDQQuantizer
+from optimum.utils.import_utils import requires_backends
 
 from ..quantization_base import OptimumQuantizer
 from ..utils.save_utils import maybe_save_preprocessors
@@ -40,6 +40,7 @@
 
 
 if TYPE_CHECKING:
+    from datasets import Dataset
     from transformers import PretrainedConfig
 
 LOGGER = logging.getLogger(__name__)
@@ -48,7 +49,7 @@
 class ORTCalibrationDataReader(CalibrationDataReader):
     __slots__ = ["batch_size", "dataset", "_dataset_iter"]
 
-    def __init__(self, dataset: Dataset, batch_size: int = 1):
+    def __init__(self, dataset: "Dataset", batch_size: int = 1):
         if dataset is None:
             raise ValueError("Provided dataset is None.")
 
@@ -158,7 +159,7 @@ def from_pretrained(
 
     def fit(
         self,
-        dataset: Dataset,
+        dataset: "Dataset",
         calibration_config: CalibrationConfig,
         onnx_augmented_model_name: Union[str, Path] = "augmented_model.onnx",
         operators_to_quantize: Optional[List[str]] = None,
@@ -212,7 +213,7 @@ def fit(
 
     def partial_fit(
         self,
-        dataset: Dataset,
+        dataset: "Dataset",
         calibration_config: CalibrationConfig,
         onnx_augmented_model_name: Union[str, Path] = "augmented_model.onnx",
         operators_to_quantize: Optional[List[str]] = None,
@@ -428,7 +429,7 @@ def get_calibration_dataset(
         seed: int = 2016,
         use_auth_token: Optional[Union[bool, str]] = None,
         token: Optional[Union[bool, str]] = None,
-    ) -> Dataset:
+    ) -> "Dataset":
         """
         Creates the calibration `datasets.Dataset` to use for the post-training static quantization calibration step.
 
@@ -474,6 +475,10 @@ def get_calibration_dataset(
                 "provided."
             )
 
+        requires_backends(self, ["datasets"])
+
+        from datasets import load_dataset
+
         calib_dataset = load_dataset(
             dataset_name,
             name=dataset_config_name,
@@ -492,7 +497,7 @@ def get_calibration_dataset(
 
         return self.clean_calibration_dataset(processed_calib_dataset)
 
-    def clean_calibration_dataset(self, dataset: Dataset) -> Dataset:
+    def clean_calibration_dataset(self, dataset: "Dataset") -> "Dataset":
         model = onnx.load(self.onnx_model_path)
         model_inputs = {input.name for input in model.graph.input}
         ignored_columns = list(set(dataset.column_names) - model_inputs)

optimum/onnxruntime/runs/calibrator.py

@@ -1,6 +1,4 @@
-from typing import Dict, List
-
-from datasets import Dataset
+from typing import TYPE_CHECKING, Dict, List
 
 from ...runs_base import Calibrator
 from .. import ORTQuantizer
@@ -9,10 +7,14 @@
 from ..preprocessors.passes import ExcludeGeLUNodes, ExcludeLayerNormNodes, ExcludeNodeAfter, ExcludeNodeFollowedBy
 
 
+if TYPE_CHECKING:
+    from datasets import Dataset
+
+
 class OnnxRuntimeCalibrator(Calibrator):
     def __init__(
         self,
-        calibration_dataset: Dataset,
+        calibration_dataset: "Dataset",
         quantizer: ORTQuantizer,
         model_path: str,
         qconfig: QuantizationConfig,

optimum/runs_base.py

@@ -2,13 +2,12 @@
 import subprocess
 from contextlib import contextmanager
 from time import perf_counter_ns
-from typing import Set
+from typing import TYPE_CHECKING, Set
 
 import numpy as np
 import optuna
 import torch
 import transformers
-from datasets import Dataset
 from tqdm import trange
 
 from . import version as optimum_version
@@ -21,6 +20,9 @@
 from .utils.runs import RunConfig, cpu_info_command
 
 
+if TYPE_CHECKING:
+    from datasets import Dataset
+
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 
@@ -34,7 +36,7 @@ def get_autoclass_name(task):
 
 class Calibrator:
     def __init__(
-        self, calibration_dataset: Dataset, quantizer, model_path, qconfig, calibration_params, node_exclusion
+        self, calibration_dataset: "Dataset", quantizer, model_path, qconfig, calibration_params, node_exclusion
     ):
         self.calibration_dataset = calibration_dataset
         self.quantizer = quantizer