Setting default values in training job config

build/launch_training.py

@@ -28,12 +28,11 @@
 
 # First Party
 import logging
+
+# Local
 from tuning import sft_trainer
-from tuning.config import configs, peft_config
 from tuning.utils.merge_model_utils import create_merged_model
-
-# Third Party
-import transformers
+from build.utils import process_launch_training_args
 
 
 def txt_to_obj(txt):
@@ -67,69 +66,31 @@ def main():
     logging.basicConfig(level=LOGLEVEL)
 
     logging.info("Attempting to launch training script")
-    parser = transformers.HfArgumentParser(
-        dataclass_types=(
-            configs.ModelArguments,
-            configs.DataArguments,
-            configs.TrainingArguments,
-            peft_config.LoraConfig,
-            peft_config.PromptTuningConfig,
-        )
-    )
-    peft_method_parsed = "pt"
+
     json_path = os.getenv("SFT_TRAINER_CONFIG_JSON_PATH")
     json_env_var = os.getenv("SFT_TRAINER_CONFIG_JSON_ENV_VAR")
 
     # accepts either path to JSON file or encoded string config
     if json_path:
-        (
-            model_args,
-            data_args,
-            training_args,
-            lora_config,
-            prompt_tuning_config,
-        ) = parser.parse_json_file(json_path, allow_extra_keys=True)
-
-        contents = ""
         with open(json_path, "r", encoding="utf-8") as f:
-            contents = json.load(f)
-        peft_method_parsed = contents.get("peft_method")
-        logging.debug("Input params parsed: %s", contents)
+            job_config_dict = json.load(f)
     elif json_env_var:
         job_config_dict = txt_to_obj(json_env_var)
-        logging.debug("Input params parsed: %s", job_config_dict)
-
-        (
-            model_args,
-            data_args,
-            training_args,
-            lora_config,
-            prompt_tuning_config,
-        ) = parser.parse_dict(job_config_dict, allow_extra_keys=True)
-
-        peft_method_parsed = job_config_dict.get("peft_method")
     else:
         raise ValueError(
             "Must set environment variable 'SFT_TRAINER_CONFIG_JSON_PATH' \
         or 'SFT_TRAINER_CONFIG_JSON_ENV_VAR'."
         )
 
-    tune_config = None
-    merge_model = False
-    if peft_method_parsed == "lora":
-        tune_config = lora_config
-        merge_model = True
-    elif peft_method_parsed == "pt":
-        tune_config = prompt_tuning_config
-
-    logging.debug(
-        "Parameters used to launch training: \
-    model_args %s, data_args %s, training_args %s, tune_config %s",
+    logging.debug("Input params parsed: %s", job_config_dict)
+
+    (
         model_args,
         data_args,
         training_args,
         tune_config,
-    )
+        merge_model,
+    ) = process_launch_training_args(job_config_dict)
 
     original_output_dir = training_args.output_dir
     with tempfile.TemporaryDirectory() as tempdir:

build/utils.py

@@ -0,0 +1,73 @@
+# 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
+import logging
+
+# Third Party
+import transformers
+
+# Local
+from tuning.config import configs, peft_config
+
+
+def process_launch_training_args(job_config_dict):
+    """Return parsed config for tuning to pass to SFT Trainer
+    Args:
+        job_config_dict: dict
+    Return:
+        model_args: configs.ModelArguments
+        data_args: configs.DataArguments
+        training_args: configs.TrainingArguments
+        tune_config: peft_config.LoraConfig | peft_config.PromptTuningConfig
+        merge_model: bool
+    """
+    parser = transformers.HfArgumentParser(
+        dataclass_types=(
+            configs.ModelArguments,
+            configs.DataArguments,
+            configs.TrainingArguments,
+            peft_config.LoraConfig,
+            peft_config.PromptTuningConfig,
+        )
+    )
+
+    (
+        model_args,
+        data_args,
+        training_args,
+        lora_config,
+        prompt_tuning_config,
+    ) = parser.parse_dict(job_config_dict, allow_extra_keys=True)
+
+    peft_method_parsed = job_config_dict.get("peft_method")
+
+    tune_config = None
+    merge_model = False
+    if peft_method_parsed == "lora":
+        tune_config = lora_config
+        merge_model = True
+    elif peft_method_parsed == "pt":
+        tune_config = prompt_tuning_config
+
+    logging.debug(
+        "Parameters used to launch training: \
+    model_args %s, data_args %s, training_args %s, tune_config %s",
+        model_args,
+        data_args,
+        training_args,
+        tune_config,
+    )
+
+    return model_args, data_args, training_args, tune_config, merge_model

pytest.ini

@@ -0,0 +1,4 @@
+[pytest]
+# Register tests from `build` dir, removing `build` from norecursedirs default list, 
+# see https://doc.pytest.org/en/latest/reference/reference.html#confval-norecursedirs
+norecursedirs = *.egg .* _darcs CVS dist node_modules venv {arch} 

tests/build/__init__.py

@@ -0,0 +1,13 @@
+# 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.

tests/build/dummy_job_config.json

@@ -0,0 +1,34 @@
+{
+    "accelerate_launch_args": {
+      "use_fsdp": true,
+      "env": ["env1", "env2"],
+      "dynamo_use_dynamic": true,
+      "num_machines": 1,
+      "num_processes":2,
+      "main_process_port": 1234,
+      "fsdp_backward_prefetch_policy": "TRANSFORMER_BASED_WRAP",
+      "fsdp_sharding_strategy": 1,
+      "fsdp_state_dict_type": "FULL_STATE_DICT",
+      "fsdp_cpu_ram_efficient_loading": true,
+      "fsdp_sync_module_states": true,
+      "config_file": "fixtures/accelerate_fsdp_defaults.yaml"
+    },
+    "multi_gpu": true,
+    "model_name_or_path": "bigscience/bloom-560m",
+    "training_data_path": "data/twitter_complaints_small.json",
+    "output_dir": "bloom-twitter",
+    "num_train_epochs": 5.0,
+    "per_device_train_batch_size": 4,
+    "per_device_eval_batch_size": 4,
+    "gradient_accumulation_steps": 4,
+    "learning_rate": 0.03,
+    "weight_decay": 0.000001,
+    "lr_scheduler_type": "cosine",
+    "logging_steps": 1.0,
+    "packing": false,
+    "include_tokens_per_second": true,
+    "response_template": "### Label:",
+    "dataset_text_field": "output",
+    "use_flash_attn": false,
+    "tokenizer_name_or_path": "bigscience/bloom-560m"
+  }

tests/build/test_utils.py

@@ -0,0 +1,80 @@
+# 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
+import copy
+import json
+import os
+
+# Third Party
+import pytest
+
+# Local
+from tuning.config.peft_config import LoraConfig, PromptTuningConfig
+from build.utils import process_launch_training_args
+
+HAPPY_PATH_DUMMY_CONFIG_PATH = os.path.join(
+    os.path.dirname(__file__), "dummy_job_config.json"
+)
+
+
+# Note: job_config dict gets modified during process_launch_training_args
+@pytest.fixture(scope="session")
+def job_config():
+    with open(HAPPY_PATH_DUMMY_CONFIG_PATH, "r", encoding="utf-8") as f:
+        dummy_job_config_dict = json.load(f)
+    return dummy_job_config_dict
+
+
+def test_process_launch_training_args(job_config):
+    job_config_copy = copy.deepcopy(job_config)
+    (
+        model_args,
+        data_args,
+        training_args,
+        tune_config,
+        merge_model,
+    ) = process_launch_training_args(job_config_copy)
+    assert str(model_args.torch_dtype) == "torch.bfloat16"
+    assert data_args.dataset_text_field == "output"
+    assert training_args.output_dir == "bloom-twitter"
+    assert tune_config == None
+    assert merge_model == False
+
+
+def test_process_launch_training_args_defaults(job_config):
+    job_config_defaults = copy.deepcopy(job_config)
+    assert "torch_dtype" not in job_config_defaults
+    assert job_config_defaults["use_flash_attn"] == False
+    assert "save_strategy" not in job_config_defaults
+    model_args, _, training_args, _, _ = process_launch_training_args(
+        job_config_defaults
+    )
+    assert str(model_args.torch_dtype) == "torch.bfloat16"
+    assert model_args.use_flash_attn == False
+    assert training_args.save_strategy.value == "epoch"
+
+
+def test_process_launch_training_args_peft_method(job_config):
+    job_config_pt = copy.deepcopy(job_config)
+    job_config_pt["peft_method"] = "pt"
+    _, _, _, tune_config, merge_model = process_launch_training_args(job_config_pt)
+    assert type(tune_config) == PromptTuningConfig
+    assert merge_model == False
+
+    job_config_lora = copy.deepcopy(job_config)
+    job_config_lora["peft_method"] = "lora"
+    _, _, _, tune_config, merge_model = process_launch_training_args(job_config_lora)
+    assert type(tune_config) == LoraConfig
+    assert merge_model == True

tests/utils/__init__.py

@@ -0,0 +1,13 @@
+# 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.

tuning/config/configs.py

@@ -72,3 +72,21 @@ class TrainingArguments(transformers.TrainingArguments):
         default=False,
         metadata={"help": "Packing to be enabled in SFT Trainer, default is False"},
     )
+    save_strategy: str = field(
+        default="epoch",
+        metadata={
+            "help": "The checkpoint save strategy to adopt during training. \
+            Possible values are 'no'(no save is done during training), \
+            'epoch' (save is done at the end of each epoch), \
+            'steps' (save is done every `save_steps`)"
+        },
+    )
+    logging_strategy: str = field(
+        default="epoch",
+        metadata={
+            "help": "The logging strategy to adopt during training. \
+            Possible values are 'no'(no logging is done during training), \
+            'epoch' (logging is done at the end of each epoch), \
+            'steps' (logging is done every `logging_steps`)"
+        },
+    )