add initial implementation of dataloader v2

Signed-off-by: Dushyant Behl <dushyantbehl@users.noreply.github.com>

examples/predefined_configs/apply_custom_template.yaml

@@ -0,0 +1,14 @@
+dataloader:
+    type: default
+datasets:
+  - name: apply_custom_data_template
+    data_paths:
+      - "FILE_PATH"
+    data_handlers:
+      - name: tokenize_and_apply_instruction_masking
+        arguments:
+          remove_columns: all
+          batched: false
+          fn_kwargs:
+            dataset_text_field: "dataset_text_field"
+            dataset_template: "dataset_template"

examples/predefined_configs/pretokenized_json_data.yaml

@@ -0,0 +1,6 @@
+dataloader:
+    type: default
+datasets:
+  - name: pretokenized_dataset
+    data_paths:
+      - "FILE_PATH"

examples/predefined_configs/tokenize_and_instruction_masking.yaml

@@ -0,0 +1,14 @@
+dataloader:
+    type: default
+datasets:
+  - name: text_dataset_input_output_masking
+    data_paths:
+      - "FILE_PATH"
+    data_handlers:
+      - name: tokenize_and_apply_instruction_masking
+        arguments:
+          remove_columns: all
+          batched: false
+          fn_kwargs:
+            input_field: "INPUT"
+            output_field: "OUTPUT"

tuning/data/data_config.py

@@ -0,0 +1,149 @@
+# Copyright The FMS HF Tuning Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Standard
+from dataclasses import dataclass
+from typing import Dict, List, Optional
+import logging
+import os
+
+# Local
+from tuning.utils.utils import load_yaml_or_json
+
+
+@dataclass
+class DataHandlerConfig:
+    name: str
+    arguments: Optional[Dict]
+
+
+@dataclass
+class DataSetConfig:
+    name: str
+    data_paths: List[str]
+    sampling: Optional[Dict] = None
+    splitter_arguments: Optional[Dict] = None
+    data_handlers: Optional[List[DataHandlerConfig]] = None
+
+
+@dataclass
+class DataLoaderConfig:
+    type: Optional[str] = "default"
+    streaming: Optional[bool] = None
+
+
+@dataclass
+class DataConfig:
+    dataloader: DataLoaderConfig
+    datasets: List[DataSetConfig]
+
+
+def _validate_data_handler_config(data_handler) -> DataHandlerConfig:
+    kwargs = data_handler
+    assert isinstance(kwargs, dict), "data_handlers in data_config needs to be a dict"
+    assert "name" in kwargs and isinstance(
+        kwargs["name"], str
+    ), "data_handlers need to have a name with type str"
+    assert "arguments" in kwargs, "data handlers need to have arguments"
+    assert isinstance(
+        kwargs["arguments"], dict
+    ), "data handler arguments should be of the type dict"
+    return DataHandlerConfig(**kwargs)
+
+
+def _validate_dataset_config(dataset_config) -> DataSetConfig:
+    c = DataSetConfig()
+    kwargs = dataset_config
+    assert isinstance(kwargs, dict), "dataset_config in data_config needs to be a dict"
+    if "name" in kwargs:
+        assert isinstance(kwargs["name"], str), "dataset name should be string"
+        c.name = kwargs["name"]
+    if "data_paths" not in kwargs:
+        raise ValueError("data_paths should be specified for each dataset")
+    else:
+        data_paths = kwargs["data_paths"]
+        # TODO: Support that data_paths can be a directory or directories
+        assert (isinstance(data_paths, List), "data_paths should be an array of files")
+        c.data_paths = []
+        for p in data_paths:
+            assert isinstance(p, str), f"path {p} should be of the type string"
+            assert os.path.exists(p), f"data_paths {p} does not exist"
+            if not os.isabs(p):
+                _p = os.path.abspath(p)
+                logging.warning(
+                    f" Provided path {p} is not absolute changing it to {_p}"
+                )
+                p = _p
+            c.data_paths.append(p)
+    if "sampling" in kwargs:
+        sampling_kwargs = kwargs["sampling"]
+        assert isinstance(
+            Dict, sampling_kwargs
+        ), "sampling arguments should be of the type dict"
+        if "ratio" in sampling_kwargs:
+            ratio = sampling_kwargs["ratio"]
+            assert (
+                (isinstance(ratio, float) and (0 <= ratio <= 1.0)),
+                f"sampling ratio: {ratio} should be float and in range [0.0,1.0]",
+            )
+        c.sampling = sampling_kwargs
+    if "splitter_arguments" in kwargs:
+        splitter_kwargs = kwargs["splitter_arguments"]
+        assert isinstance(
+            Dict, splitter_kwargs
+        ), "splitter_arguments should be of the type dict"
+        c.splitter_arguments = splitter_kwargs
+    if "data_handlers" in kwargs:
+        c.data_handlers = []
+        for handler in kwargs["data_handlers"]:
+            c.data_handlers.append(_validate_data_handler_config(handler))
+    return c
+
+
+def _validate_dataloader_config(dataloader_config) -> DataLoaderConfig:
+    kwargs = dataloader_config
+    c = DataLoaderConfig()
+    assert isinstance(kwargs, dict), "dataloader in data_config needs to be a dict"
+    if "streaming" in kwargs:
+        assert (
+            isinstance(kwargs["streaming"], bool),
+            "streaming should be a boolean true or false",
+        )
+        c.streaming = kwargs["streaming"]
+    return c
+
+
+def validate_data_config(dataconfig: DataConfig):
+    _validate_dataloader_config(dataconfig.dataloader)
+    for d in dataconfig.datasets:
+        _validate_dataset_config(d)
+
+
+def load_and_validate_data_config(data_config_file: str) -> DataConfig:
+    raw_data = load_yaml_or_json(data_config_file)
+    assert isinstance(
+        raw_data, Dict
+    ), f"The provided data_config file is invalid: {data_config_file}"
+    data_config = DataConfig()
+    assert "datasets" in raw_data, "datasets should be provided in data config"
+    assert isinstance(
+        raw_data["datasets"], List
+    ), "datasets should be provided as a list"
+    data_config.datasets = []
+    for d in raw_data["datasets"]:
+        data_config.datasets.append(_validate_dataset_config(d))
+    if "dataloader" in data_config:
+        dataloader = _validate_dataloader_config(raw_data["dataloader"])
+        data_config.dataloader = dataloader
+    return data_config

tuning/data/data_handlers.py

@@ -0,0 +1,83 @@
+# Copyright The FMS HF Tuning Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Definition of some predefined data preprocessing functions that we need.
+
+# Standard
+from typing import Dict, List
+
+# Third Party
+from transformers import AutoTokenizer
+
+# Local
+from tuning.utils.data_utils import custom_data_formatter
+from tuning.utils.preprocessing_utils import combine_sequence
+
+
+def tokenize_and_apply_instruction_masking(
+    element: Dict[str, str],
+    tokenizer: AutoTokenizer,
+    input_field_name: str,
+    output_field_name: str,
+    **tokenizer_kwargs,
+):
+    input = element[input_field_name]
+    output = element[output_field_name]
+
+    # TODO: Eventually move the code here
+    combined = combine_sequence(input, output, eos_token=tokenizer.eos_token)
+
+    tokenized_comb_seqs = tokenizer(combined, **tokenizer_kwargs)
+    tokenized_input = tokenizer(input, **tokenizer_kwargs)
+
+    masked_labels = [-100] * len(
+        tokenized_input.input_ids
+    ) + tokenized_comb_seqs.input_ids[len(tokenized_input.input_ids) :]
+
+    # Any benefit of retaining the old columns?
+    return {
+        "input_ids": tokenized_comb_seqs.input_ids,
+        "labels": masked_labels,
+        "attention_mask": tokenized_comb_seqs.attention_mask,
+    }
+
+
+def apply_dataset_formatting(
+    element: Dict[str, str], tokenizer: AutoTokenizer, dataset_text_field: str, **kwargs
+):
+    return {
+        f"{dataset_text_field}": element[f"{dataset_text_field}"] + tokenizer.eos_token
+    }
+
+
+def apply_custom_data_formatting_template(
+    element: Dict[str, str],
+    tokenizer: AutoTokenizer,
+    dataset_text_field: str,
+    template: str,
+    **kwargs,
+):
+    template += tokenizer.eos_token
+
+    # TODO: Eventually move the code here.
+    custom_data_formatter(
+        element=element, formatted_dataset_field=dataset_text_field, template=template
+    )
+
+
+AVAILABLE_DATA_HANDLERS = {
+    "tokenize_and_apply_instruction_masking": tokenize_and_apply_instruction_masking,
+    "apply_dataset_formatting": apply_dataset_formatting,
+    "apply_custom_data_formatting_template": apply_dataset_formatting,
+}