feat: Added cfg.cudnn_deterministic_mode flag

recipes/dev/early_exit_finetune_distributed.py

@@ -233,7 +233,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These are public properties which are updated by the checkpoint loader
         # when ``resume_from_checkpoint`` is `True` or validated in tests
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/dev/generate_v2.py

@@ -78,7 +78,7 @@ def __init__(self, cfg: DictConfig) -> None:
         self._device = utils.get_device(device=cfg.device)
         self._dtype = training.get_dtype(dtype=cfg.dtype, device=self._device)
         self._logger = utils.get_logger(cfg.log_level)
-        training.set_seed(seed=cfg.seed)
+        training.set_seed(seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None))
 
     def setup(self, cfg: DictConfig) -> None:
         """Setup the model and transforms."""

recipes/dev/generate_v2_distributed.py

@@ -87,7 +87,7 @@ def __init__(self, cfg: DictConfig) -> None:
         dist.init_process_group(backend="nccl")
         _, rank = utils.get_world_size_and_rank()
         self._is_rank_zero = rank == 0
-        training.set_seed(seed=cfg.seed)
+        training.set_seed(seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None))
 
     def setup(self, cfg: DictConfig) -> None:
         """Setup the model and transforms."""

recipes/eleuther_eval.py

@@ -451,7 +451,7 @@ def __init__(self, cfg: DictConfig) -> None:
         self.device = utils.get_device(device=cfg.device)
         self.dtype = training.get_dtype(dtype=cfg.dtype, device=self.device)
         self.logger = utils.get_logger(cfg.get("log_level", "info"))
-        training.set_seed(seed=cfg.seed)
+        training.set_seed(seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None))
 
         # Eval specific variables
         self.limit = cfg.limit

recipes/full_dpo_distributed.py

@@ -180,7 +180,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These attributes constitute the recipe state and are updated by ``load_checkpoint``
         # when ``resume_from_checkpoint`` is ``True``
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/full_finetune_distributed.py

@@ -198,7 +198,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These are public properties which are updated by the checkpoint loader
         # when ``resume_from_checkpoint`` is `True` or validated in tests
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/full_finetune_single_device.py

@@ -184,7 +184,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These are public properties which are updated by the checkpoint loader
         # when ``resume_from_checkpoint`` is `True` or validated in tests
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/generate.py

@@ -40,7 +40,7 @@ def __init__(self, cfg: DictConfig) -> None:
         self._quantizer = config.instantiate(cfg.quantizer)
         self._quantization_mode = training.get_quantizer_mode(self._quantizer)
 
-        training.set_seed(seed=cfg.seed)
+        training.set_seed(seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None))
 
     def setup(self, cfg: DictConfig) -> None:
         checkpointer = config.instantiate(cfg.checkpointer)

recipes/knowledge_distillation_distributed.py

@@ -130,7 +130,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These are public properties which are updated by the checkpoint loader
         # when ``resume_from_checkpoint`` is `True` or validated in tests
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/knowledge_distillation_single_device.py

@@ -128,7 +128,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These are public properties which are updated by the checkpoint loader
         # when ``resume_from_checkpoint`` is `True` or validated in tests
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/lora_dpo_distributed.py

@@ -172,7 +172,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These attributes constitute the recipe state and are updated by ``load_checkpoint``
         # when ``resume_from_checkpoint`` is ``True``
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/lora_dpo_single_device.py

@@ -129,7 +129,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These are public properties which are updated by the checkpoint loader
         # when ``resume_from_checkpoint`` is `True` or validated in tests
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/lora_finetune_distributed.py

@@ -152,7 +152,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These attributes constitute the recipe state and are updated by ``load_checkpoint``
         # when ``resume_from_checkpoint`` is ``True``
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/lora_finetune_distributed_multi_dataset.py

@@ -155,7 +155,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These attributes constitute the recipe state and are updated by ``load_checkpoint``
         # when ``resume_from_checkpoint`` is ``True``
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/lora_finetune_single_device.py

@@ -144,7 +144,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These are public properties which are updated by the checkpoint loader
         # when ``resume_from_checkpoint`` is `True` or validated in tests
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/ppo_full_finetune_single_device.py

@@ -133,7 +133,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These are public properties which are updated by the checkpoint loader
         # when ``resume_from_checkpoint`` is `True` or validated in tests
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         # manually setting up a generator for the recipe
         self._rng = torch.Generator(self._device).manual_seed(self.seed)
         self._total_steps = 0

recipes/qat_distributed.py

@@ -202,7 +202,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These are public properties which are updated by the checkpoint loader
         # when ``resume_from_checkpoint`` is `True` or validated in tests
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/qat_lora_finetune_distributed.py

@@ -174,7 +174,9 @@ def __init__(self, cfg: DictConfig) -> None:
 
         # These attributes constitute the recipe state and are updated by ``load_checkpoint``
         # when ``resume_from_checkpoint`` is ``True``
-        self.seed = training.set_seed(seed=cfg.seed)
+        self.seed = training.set_seed(
+            seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None)
+        )
         self.epochs_run = 0
         self.total_epochs = cfg.epochs
         self.max_steps_per_epoch = cfg.max_steps_per_epoch

recipes/quantize.py

@@ -50,7 +50,7 @@ def __init__(self, cfg: DictConfig) -> None:
         self._dtype = training.get_dtype(dtype=cfg.dtype, device=self._device)
         self._quantizer = config.instantiate(cfg.quantizer)
         self._quantization_mode = training.get_quantizer_mode(self._quantizer)
-        training.set_seed(seed=cfg.seed)
+        training.set_seed(seed=cfg.seed, debug_mode=cfg.get("deterministic_mode", None))
 
     def load_checkpoint(self, checkpointer_cfg: DictConfig) -> Dict[str, Any]:
         self._checkpointer = config.instantiate(checkpointer_cfg)