feat: add scanner tracker

build/Dockerfile

@@ -23,6 +23,7 @@ ARG WHEEL_VERSION=""
 ARG ENABLE_AIM=false
 ARG ENABLE_MLFLOW=false
 ARG ENABLE_FMS_ACCELERATION=true
+ARG ENABLE_SCANNER=false
 
 ## Base Layer ##################################################################
 FROM registry.access.redhat.com/ubi9/ubi:${BASE_UBI_IMAGE_TAG} AS base
@@ -111,6 +112,7 @@ ARG USER
 ARG USER_UID
 ARG ENABLE_FMS_ACCELERATION
 ARG ENABLE_AIM
+ARG ENABLE_SCANNER
 
 RUN dnf install -y git && \
     # perl-Net-SSLeay.x86_64 and server_key.pem are installed with git as dependencies
@@ -154,7 +156,11 @@ RUN if [[ "${ENABLE_AIM}" == "true" ]]; then \
 
 RUN if [[ "${ENABLE_MLFLOW}" == "true" ]]; then \
     python -m pip install --user "$(head bdist_name)[mlflow]"; \
-fi
+    fi
+
+RUN if [[ "${ENABLE_SCANNER}" == "true" ]]; then \
+        python -m pip install --user "$(head bdist_name)[scanner-dev]"; \
+    fi
 
     # Clean up the wheel module. It's only needed by flash-attn install
 RUN python -m pip uninstall wheel build -y && \

pyproject.toml

@@ -47,6 +47,7 @@ aim = ["aim>=3.19.0,<4.0"]
 mlflow = ["mlflow"]
 fms-accel = ["fms-acceleration>=0.6"]
 gptq-dev = ["auto_gptq>0.4.2", "optimum>=1.15.0"]
+scanner-dev = ["HFResourceScanner>=0.1.0"]
 
 
 [tool.setuptools.packages.find]

tests/build/test_launch_script.py

@@ -16,12 +16,14 @@
 """
 
 # Standard
+import json
 import os
 import tempfile
 import glob
 
 # Third Party
 import pytest
+from transformers.utils.import_utils import _is_package_available
 
 # First Party
 from build.accelerate_launch import main
@@ -31,7 +33,10 @@
     USER_ERROR_EXIT_CODE,
     INTERNAL_ERROR_EXIT_CODE,
 )
-from tuning.config.tracker_configs import FileLoggingTrackerConfig
+from tuning.config.tracker_configs import (
+    FileLoggingTrackerConfig,
+    HFResourceScannerConfig,
+)
 
 SCRIPT = "tuning/sft_trainer.py"
 MODEL_NAME = "Maykeye/TinyLLama-v0"
@@ -246,6 +251,38 @@ def test_lora_with_lora_post_process_for_vllm_set_to_true():
         assert os.path.exists(new_embeddings_file_path)
 
 
+@pytest.mark.skipif(
+    not _is_package_available("HFResourceScanner"),
+    reason="Only runs if HFResourceScanner is installed",
+)
+def test_launch_with_HFResourceScanner_enabled():
+    with tempfile.TemporaryDirectory() as tempdir:
+        setup_env(tempdir)
+        scanner_outfile = os.path.join(
+            tempdir, HFResourceScannerConfig.scanner_output_filename
+        )
+        TRAIN_KWARGS = {
+            **BASE_LORA_KWARGS,
+            **{
+                "output_dir": tempdir,
+                "save_model_dir": tempdir,
+                "lora_post_process_for_vllm": True,
+                "gradient_accumulation_steps": 1,
+                "trackers": ["hf_resource_scanner"],
+                "scanner_output_filename": scanner_outfile,
+            },
+        }
+        serialized_args = serialize_args(TRAIN_KWARGS)
+        os.environ["SFT_TRAINER_CONFIG_JSON_ENV_VAR"] = serialized_args
+
+        assert main() == 0
+        assert os.path.exists(scanner_outfile) is True
+        with open(scanner_outfile, "r", encoding="utf-8") as f:
+            scanner_res = json.load(f)
+        assert scanner_res["time_data"] is not None
+        assert scanner_res["mem_data"] is not None
+
+
 def test_bad_script_path():
     """Check for appropriate error for an invalid training script location"""
     with tempfile.TemporaryDirectory() as tempdir:

tests/test_sft_trainer.py

@@ -363,6 +363,7 @@ def test_parse_arguments(job_config):
         _,
         _,
         _,
+        _,
     ) = sft_trainer.parse_arguments(parser, job_config_copy)
     assert str(model_args.torch_dtype) == "torch.bfloat16"
     assert data_args.dataset_text_field == "output"
@@ -390,6 +391,7 @@ def test_parse_arguments_defaults(job_config):
         _,
         _,
         _,
+        _,
     ) = sft_trainer.parse_arguments(parser, job_config_defaults)
     assert str(model_args.torch_dtype) == "torch.bfloat16"
     assert model_args.use_flash_attn is False
@@ -400,14 +402,14 @@ def test_parse_arguments_peft_method(job_config):
     parser = sft_trainer.get_parser()
     job_config_pt = copy.deepcopy(job_config)
     job_config_pt["peft_method"] = "pt"
-    _, _, _, _, tune_config, _, _, _, _, _, _, _, _ = sft_trainer.parse_arguments(
+    _, _, _, _, tune_config, _, _, _, _, _, _, _, _, _ = sft_trainer.parse_arguments(
         parser, job_config_pt
     )
     assert isinstance(tune_config, peft_config.PromptTuningConfig)
 
     job_config_lora = copy.deepcopy(job_config)
     job_config_lora["peft_method"] = "lora"
-    _, _, _, _, tune_config, _, _, _, _, _, _, _, _ = sft_trainer.parse_arguments(
+    _, _, _, _, tune_config, _, _, _, _, _, _, _, _, _ = sft_trainer.parse_arguments(
         parser, job_config_lora
     )
     assert isinstance(tune_config, peft_config.LoraConfig)
@@ -1053,12 +1055,18 @@ def _test_run_inference(checkpoint_path):
 
 
 def _validate_training(
-    tempdir, check_eval=False, train_logs_file="training_logs.jsonl"
+    tempdir,
+    check_eval=False,
+    train_logs_file="training_logs.jsonl",
+    check_scanner_file=False,
 ):
     assert any(x.startswith("checkpoint-") for x in os.listdir(tempdir))
     train_logs_file_path = "{}/{}".format(tempdir, train_logs_file)
     _validate_logfile(train_logs_file_path, check_eval)
 
+    if check_scanner_file:
+        _validate_hf_resource_scanner_file(tempdir)
+
 
 def _validate_logfile(log_file_path, check_eval=False):
     train_log_contents = ""
@@ -1073,6 +1081,18 @@ def _validate_logfile(log_file_path, check_eval=False):
         assert "validation_loss" in train_log_contents
 
 
+def _validate_hf_resource_scanner_file(tempdir):
+    scanner_file_path = os.path.join(tempdir, "scanner_output.json")
+    assert os.path.exists(scanner_file_path) is True
+    assert os.path.getsize(scanner_file_path) > 0
+
+    with open(scanner_file_path, "r", encoding="utf-8") as f:
+        scanner_contents = json.load(f)
+
+    assert scanner_contents["time_data"] is not None
+    assert scanner_contents["mem_data"] is not None
+
+
 def _get_checkpoint_path(dir_path):
     return os.path.join(dir_path, "checkpoint-5")
 

tests/trackers/test_hf_resource_scanner_tracker.py

@@ -0,0 +1,88 @@
+# 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.
+
+# SPDX-License-Identifier: Apache-2.0
+# https://spdx.dev/learn/handling-license-info/
+
+
+# Standard
+import copy
+import os
+import tempfile
+
+# Third Party
+from transformers.utils.import_utils import _is_package_available
+import pytest
+
+# First Party
+from tests.test_sft_trainer import (
+    DATA_ARGS,
+    MODEL_ARGS,
+    TRAIN_ARGS,
+    _get_checkpoint_path,
+    _test_run_causallm_ft,
+    _test_run_inference,
+    _validate_training,
+)
+
+# Local
+from tuning import sft_trainer
+from tuning.config.tracker_configs import HFResourceScannerConfig, TrackerConfigFactory
+
+## HF Resource Scanner Tracker Tests
+
+
+@pytest.mark.skipif(
+    not _is_package_available("HFResourceScanner"),
+    reason="Only runs if HFResourceScanner is installed",
+)
+def test_run_with_hf_resource_scanner_tracker():
+    """Ensure that training succeeds with a good tracker name"""
+    with tempfile.TemporaryDirectory() as tempdir:
+        train_args = copy.deepcopy(TRAIN_ARGS)
+        train_args.trackers = ["hf_resource_scanner"]
+
+        _test_run_causallm_ft(TRAIN_ARGS, MODEL_ARGS, DATA_ARGS, tempdir)
+        _test_run_inference(_get_checkpoint_path(tempdir))
+
+
+@pytest.mark.skipif(
+    not _is_package_available("HFResourceScanner"),
+    reason="Only runs if HFResourceScanner is installed",
+)
+def test_sample_run_with_hf_resource_scanner_updated_filename():
+    """Ensure that hf_resource_scanner output filename can be updated"""
+
+    with tempfile.TemporaryDirectory() as tempdir:
+        train_args = copy.deepcopy(TRAIN_ARGS)
+        train_args.gradient_accumulation_steps = 1
+        train_args.output_dir = tempdir
+
+        # add hf_resource_scanner to the list of requested tracker
+        train_args.trackers = ["hf_resource_scanner"]
+
+        scanner_output_file = "scanner_output.json"
+
+        tracker_configs = TrackerConfigFactory(
+            hf_resource_scanner_config=HFResourceScannerConfig(
+                scanner_output_filename=os.path.join(tempdir, scanner_output_file)
+            )
+        )
+
+        sft_trainer.train(
+            MODEL_ARGS, DATA_ARGS, train_args, tracker_configs=tracker_configs
+        )
+
+        # validate ft tuning configs
+        _validate_training(tempdir, check_scanner_file=True)

tuning/config/tracker_configs.py

@@ -16,6 +16,11 @@
 from dataclasses import dataclass
 
 
+@dataclass
+class HFResourceScannerConfig:
+    scanner_output_filename: str = "scanner_output.json"
+
+
 @dataclass
 class FileLoggingTrackerConfig:
     training_logs_filename: str = "training_logs.jsonl"
@@ -80,3 +85,4 @@ class TrackerConfigFactory:
     file_logger_config: FileLoggingTrackerConfig = None
     aim_config: AimConfig = None
     mlflow_config: MLflowConfig = None
+    hf_resource_scanner_config: HFResourceScannerConfig = None

tuning/sft_trainer.py

@@ -52,6 +52,7 @@
 from tuning.config.tracker_configs import (
     AimConfig,
     FileLoggingTrackerConfig,
+    HFResourceScannerConfig,
     MLflowConfig,
     TrackerConfigFactory,
 )
@@ -458,6 +459,7 @@ def get_parser():
             peft_config.PromptTuningConfig,
             FileLoggingTrackerConfig,
             AimConfig,
+            HFResourceScannerConfig,
             QuantizedLoraConfig,
             FusedOpsAndKernelsConfig,
             AttentionAndDistributedPackingConfig,
@@ -506,6 +508,8 @@ def parse_arguments(parser, json_config=None):
             Configuration for training log file.
         AimConfig
             Configuration for AIM stack.
+        HFResourceScannerConfig
+            Configuration for HFResourceScanner.
         QuantizedLoraConfig
             Configuration for quantized LoRA (a form of PEFT).
         FusedOpsAndKernelsConfig
@@ -529,6 +533,7 @@ def parse_arguments(parser, json_config=None):
             prompt_tuning_config,
             file_logger_config,
             aim_config,
+            hf_resource_scanner_config,
             quantized_lora_config,
             fusedops_kernels_config,
             attention_and_distributed_packing_config,
@@ -547,6 +552,7 @@ def parse_arguments(parser, json_config=None):
             prompt_tuning_config,
             file_logger_config,
             aim_config,
+            hf_resource_scanner_config,
             quantized_lora_config,
             fusedops_kernels_config,
             attention_and_distributed_packing_config,
@@ -574,6 +580,7 @@ def parse_arguments(parser, json_config=None):
         tune_config,
         file_logger_config,
         aim_config,
+        hf_resource_scanner_config,
         quantized_lora_config,
         fusedops_kernels_config,
         attention_and_distributed_packing_config,
@@ -597,6 +604,7 @@ def main():
             tune_config,
             file_logger_config,
             aim_config,
+            hf_resource_scanner_config,
             quantized_lora_config,
             fusedops_kernels_config,
             attention_and_distributed_packing_config,
@@ -611,7 +619,7 @@ def main():
         logger.debug(
             "Input args parsed: \
             model_args %s, data_args %s, training_args %s, trainer_controller_args %s, \
-            tune_config %s, file_logger_config, %s aim_config %s, \
+            tune_config %s, file_logger_config %s, aim_config %s, hf_resource_scanner_config %s, \
             quantized_lora_config %s, fusedops_kernels_config %s, \
             attention_and_distributed_packing_config, %s,\
             mlflow_config %s, fast_moe_config %s, \
@@ -623,6 +631,7 @@ def main():
             tune_config,
             file_logger_config,
             aim_config,
+            hf_resource_scanner_config,
             quantized_lora_config,
             fusedops_kernels_config,
             attention_and_distributed_packing_config,
@@ -656,6 +665,7 @@ def main():
         file_logger_config=file_logger_config,
         aim_config=aim_config,
         mlflow_config=mlflow_config,
+        hf_resource_scanner_config=hf_resource_scanner_config,
     )
 
     if training_args.output_dir: