Add EnvironmentLogger Callback

llmfoundry/callbacks/__init__.py

@@ -16,6 +16,7 @@
 
 from llmfoundry.callbacks.async_eval_callback import AsyncEval
 from llmfoundry.callbacks.curriculum_learning_callback import CurriculumLearning
+from llmfoundry.callbacks.envlogger import EnvironmentLoggerCallback
 from llmfoundry.callbacks.eval_gauntlet_callback import EvalGauntlet
 from llmfoundry.callbacks.eval_output_logging_callback import EvalOutputLogging
 from llmfoundry.callbacks.fdiff_callback import FDiffMetrics
@@ -55,8 +56,8 @@
 callbacks.register('eval_output_logging', func=EvalOutputLogging)
 callbacks.register('mbmoe_tok_per_expert', func=MegaBlocksMoE_TokPerExpert)
 callbacks.register('run_timeout', func=RunTimeoutCallback)
-
 callbacks.register('loss_perp_v_len', func=LossPerpVsContextLengthLogger)
+callbacks.register('env_logger', func=EnvironmentLoggerCallback)
 
 callbacks_with_config.register('async_eval', func=AsyncEval)
 callbacks_with_config.register('curriculum_learning', func=CurriculumLearning)

llmfoundry/callbacks/envlogger.py

@@ -0,0 +1,177 @@
+# Copyright 2024 MosaicML LLM Foundry authors
+# SPDX-License-Identifier: Apache-2.0
+
+import os
+import platform
+import socket
+from typing import Any, Optional
+
+import git
+import pkg_resources
+import psutil
+import torch
+from composer.core import Callback, State
+from composer.loggers import Logger
+
+
+class EnvironmentLoggerCallback(Callback):
+    """A callback for logging environment information during model training.
+
+    This callback collects various pieces of information about the training environment,
+    including git repository details, package versions, system information, GPU details,
+    distributed training setup, NVIDIA driver information, and Docker container details.
+
+    Args:
+        workspace_dir (str): The directory containing the workspace. Defaults to '/workspace'.
+        log_git (bool): Whether to log git repository information. Defaults to True.
+        log_packages (bool): Whether to log package versions. Defaults to True.
+        log_nvidia (bool): Whether to log NVIDIA driver information. Defaults to True.
+        log_docker (bool): Whether to log Docker container information. Defaults to True.
+        log_system (bool): Whether to log system information. Defaults to False.
+        log_gpu (bool): Whether to log GPU information. Defaults to False.
+        log_distributed (bool): Whether to log distributed training information. Defaults to False.
+        packages_to_log (list[str]): A list of package names to log versions for. Defaults to None.
+
+    The collected information is logged as hyperparameters at the start of model fitting.
+    """
+
+    PACKAGES_TO_LOG = [
+        'llm-foundry',
+        'mosaicml',
+        'megablocks',
+        'grouped-gemm',
+        'torch',
+        'flash_attn',
+        'transformers',
+        'datasets',
+        'peft',
+    ]
+
+    def __init__(
+        self,
+        workspace_dir: str = '/workspace',
+        log_git: bool = True,
+        log_nvidia: bool = True,
+        log_docker: bool = True,
+        log_packages: bool = True,
+        log_system: bool = False,
+        log_gpu: bool = False,
+        log_distributed: bool = False,
+        packages_to_log: Optional[list[str]] = None,
+    ):
+        self.workspace_dir = workspace_dir
+        self.log_git = log_git
+        self.log_packages = log_packages
+        self.log_nvidia = log_nvidia
+        self.log_docker = log_docker
+        self.log_system = log_system
+        self.log_gpu = log_gpu
+        self.log_distributed = log_distributed
+        self.env_data: dict[str, Any] = {}
+        self.packages_to_log = packages_to_log or self.PACKAGES_TO_LOG
+
+    def _get_git_info(self, repo_path: str) -> dict[str, str]:
+        repo = git.Repo(repo_path)
+        return {
+            'commit_hash': repo.head.commit.hexsha,
+            'branch': repo.active_branch.name,
+        }
+
+    def _get_package_version(self, package_name: str) -> Optional[str]:
+        try:
+            return pkg_resources.get_distribution(package_name).version
+        except pkg_resources.DistributionNotFound:
+            return None
+
+    def _get_system_info(self) -> dict[str, Any]:
+        return {
+            'python_version': platform.python_version(),
+            'os': f'{platform.system()} {platform.release()}',
+            'hostname': socket.gethostname(),
+            'cpu_info': {
+                'model': platform.processor(),
+                'cores': psutil.cpu_count(logical=False),
+                'threads': psutil.cpu_count(logical=True),
+            },
+            'memory': {
+                'total': psutil.virtual_memory().total,
+                'available': psutil.virtual_memory().available,
+            },
+        }
+
+    def _get_gpu_info(self) -> dict[str, Any]:
+        if torch.cuda.is_available():
+            return {
+                'model': torch.cuda.get_device_name(0),
+                'count': torch.cuda.device_count(),
+                'memory': {
+                    'total': torch.cuda.get_device_properties(0).total_memory,
+                    'allocated': torch.cuda.memory_allocated(0),
+                },
+            }
+        return {'available': False}
+
+    def _get_nvidia_info(self) -> dict[str, Any]:
+        if torch.cuda.is_available():
+            nccl_version = torch.cuda.nccl.version()  # type: ignore
+            return {
+                'cuda_version':
+                    torch.version.cuda,  # type: ignore[attr-defined]
+                'cudnn_version': str(torch.backends.cudnn.version(
+                )),  # type: ignore[attr-defined]
+                'nccl_version': '.'.join(
+                    map(str, nccl_version),
+                ),
+            }
+
+        return {'available': False}
+
+    def _get_distributed_info(self) -> dict[str, Any]:
+        return {
+            'world_size': int(os.environ.get('WORLD_SIZE', 1)),
+            'local_world_size': int(os.environ.get('LOCAL_WORLD_SIZE', 1)),
+            'rank': int(os.environ.get('RANK', 0)),
+            'local_rank': int(os.environ.get('LOCAL_RANK', 0)),
+        }
+
+    def _get_docker_info(self) -> dict[str, Any]:
+        from mcli import sdk
+
+        run = sdk.get_run(os.environ['RUN_NAME'])
+        image, tag = run.image.split(':')
+        return {
+            'image': image,
+            'tag': tag,
+        }
+
+    def fit_start(self, state: State, logger: Logger) -> None:
+        # Collect environment data
+        if self.log_git:
+            self.env_data['git_info'] = {
+                folder:
+                self._get_git_info(os.path.join(self.workspace_dir, folder))
+                for folder in os.listdir(self.workspace_dir)
+                if os.path.isdir(os.path.join(self.workspace_dir, folder))
+            }
+
+        if self.log_packages:
+            self.env_data['package_versions'] = {
+                pkg: self._get_package_version(pkg)
+                for pkg in self.packages_to_log
+            }
+        if self.log_nvidia:
+            self.env_data['nvidia'] = self._get_nvidia_info()
+
+        if self.log_docker:
+            self.env_data['docker'] = self._get_docker_info()
+        if self.log_system:
+            self.env_data['system_info'] = self._get_system_info()
+
+        if self.log_gpu:
+            self.env_data['gpu_info'] = self._get_gpu_info()
+
+        if self.log_distributed:
+            self.env_data['distributed_info'] = self._get_distributed_info()
+
+        # Log the collected data
+        logger.log_hyperparameters({'environment_data': self.env_data})

setup.py

@@ -75,6 +75,7 @@
     'tenacity>=8.2.3,<9',
     'catalogue>=2,<3',
     'typer<1',
+    'GitPython==3.1.43',
 ]
 
 extra_deps = {}