Better guards for DeepSpeed imports

trl/models/utils.py

@@ -18,7 +18,6 @@
 from dataclasses import dataclass
 from typing import TYPE_CHECKING, Literal, Optional, Union
 
-from accelerate.utils import is_deepspeed_available
 from packaging import version
 from transformers import PreTrainedModel, PreTrainedTokenizer
 
@@ -30,12 +29,10 @@
     AutoModelForSeq2SeqLMWithValueHead,
 )
 
-if is_deepspeed_available():
-    import deepspeed
-
 if TYPE_CHECKING:
     from accelerate import Accelerator
     from deepspeed.runtime.engine import DeepSpeedEngine
+    from torch.nn import Module
     from torch.nn.parallel.distributed import DistributedDataParallel
 
 
@@ -167,6 +164,8 @@ def iter_params(module, recurse=False):
 
 def add_hooks(model: "DeepSpeedEngine") -> None:
     """Adds the optimizer hooks from a DeepSpeed ZeRO-3 model."""
+    import deepspeed
+
     if not hasattr(model, "optimizer"):  # before the first training step, the model has no optimizer
         return
     if model.optimizer is not None and hasattr(model.optimizer, "parameter_offload"):
@@ -214,6 +213,8 @@ def unwrap_model_for_generation(
         if not gather_deepspeed3_params:
             yield accelerator.unwrap_model(model)
         else:
+            import deepspeed
+
             with deepspeed.zero.GatheredParameters(model.parameters()):
                 remove_hooks(model)
                 yield accelerator.unwrap_model(model)
@@ -222,8 +223,13 @@ def unwrap_model_for_generation(
         yield unwrapped_model
 
 
-def prepare_deepspeed(model, accelerator):
-    # Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
+def prepare_deepspeed(model: "Module", accelerator: "Accelerator"):
+    """Prepares the model for DeepSpeed inference or evaluation by initializing it with the appropriate configuration.
+
+    Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
+    """
+    import deepspeed  # local import (instead of top-level) to avoid DS init interfering with other backends (like vllm): https://github.com/deepspeedai/DeepSpeed/issues/7252
+
     deepspeed_plugin = accelerator.state.deepspeed_plugin
     config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config)
     stage = config_kwargs["zero_optimization"]["stage"]

trl/trainer/bco_trainer.py

@@ -19,7 +19,6 @@
 import warnings
 from collections import defaultdict
 from contextlib import contextmanager, nullcontext
-from copy import deepcopy
 from operator import itemgetter
 from typing import TYPE_CHECKING, Any, Callable, Literal, Optional, Union
 
@@ -32,7 +31,7 @@
 import transformers
 from accelerate import PartialState
 from accelerate.logging import get_logger
-from accelerate.utils import is_deepspeed_available, tqdm
+from accelerate.utils import tqdm
 from datasets import Dataset
 from packaging import version
 from torch.utils.data import DataLoader, SequentialSampler
@@ -56,7 +55,7 @@
 
 from ..data_utils import maybe_apply_chat_template
 from ..import_utils import is_joblib_available
-from ..models import PreTrainedModelWrapper, create_reference_model
+from ..models import create_reference_model, prepare_deepspeed
 from .bco_config import BCOConfig
 from .utils import (
     DPODataCollatorWithPadding,
@@ -83,9 +82,6 @@
 if is_joblib_available():
     import joblib
 
-if is_deepspeed_available():
-    import deepspeed
-
 if TYPE_CHECKING:
     from transformers import PreTrainedModel, PreTrainedTokenizer
 
@@ -712,7 +708,7 @@ def make_inputs_require_grad(module, input, output):
                 )
         else:
             if self.is_deepspeed_enabled:
-                self.ref_model = self._prepare_deepspeed(self.ref_model)
+                self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator)
             else:
                 self.ref_model = self.accelerator.prepare_model(self.ref_model, evaluation_mode=True)
 
@@ -846,37 +842,6 @@ def _get_sample_prompt_embeddings(self, dataset: Dataset, sample_size: int = 512
 
         return all_embeddings
 
-    def _prepare_deepspeed(self, model: PreTrainedModelWrapper):
-        # Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
-        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
-        config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config)
-
-        if model is not None:
-            if hasattr(model, "config"):
-                hidden_size = (
-                    max(model.config.hidden_sizes)
-                    if getattr(model.config, "hidden_sizes", None)
-                    else getattr(model.config, "hidden_size", None)
-                )
-                if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3:
-                    # Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0`
-                    # This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081
-                    config_kwargs.update(
-                        {
-                            "zero_optimization.reduce_bucket_size": hidden_size * hidden_size,
-                            "zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size,
-                            "zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size,
-                        }
-                    )
-
-        # If ZeRO-3 is used, we shard both the active and reference model.
-        # Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0)
-        if config_kwargs["zero_optimization"]["stage"] != 3:
-            config_kwargs["zero_optimization"]["stage"] = 0
-        model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
-        model.eval()
-        return model
-
     def _save_optimizer_and_scheduler(self, output_dir):
         output_dir = output_dir if output_dir is not None else self.args.output_dir
         super()._save_optimizer_and_scheduler(output_dir)

trl/trainer/callbacks.py

@@ -19,7 +19,7 @@
 import torch
 from accelerate import Accelerator
 from accelerate.state import AcceleratorState
-from accelerate.utils import gather_object, is_comet_ml_available, is_deepspeed_available, is_wandb_available
+from accelerate.utils import gather_object, is_comet_ml_available, is_wandb_available
 from rich.console import Console, Group
 from rich.live import Live
 from rich.panel import Panel
@@ -44,9 +44,6 @@
 from .utils import log_table_to_comet_experiment
 
 
-if is_deepspeed_available():
-    import deepspeed
-
 if is_comet_ml_available():
     pass
 
@@ -115,6 +112,8 @@ def _sync_target_model(model, target_model, alpha):
     def sync_target_model(model, target_model, alpha):
         deepspeed_plugin = AcceleratorState().deepspeed_plugin
         if deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3:
+            import deepspeed
+
             with deepspeed.zero.GatheredParameters(
                 list(model.parameters()) + list(target_model.parameters()), modifier_rank=0
             ):

trl/trainer/dpo_trainer.py

@@ -19,7 +19,6 @@
 import warnings
 from collections import defaultdict
 from contextlib import contextmanager, nullcontext
-from copy import deepcopy
 from dataclasses import dataclass
 from typing import Any, Callable, Literal, Optional, Union
 
@@ -30,7 +29,7 @@
 import torch.nn.functional as F
 import transformers
 from accelerate import PartialState
-from accelerate.utils import is_deepspeed_available, tqdm
+from accelerate.utils import tqdm
 from datasets import Dataset, IterableDataset
 from packaging import version
 from torch.utils.data import DataLoader
@@ -53,7 +52,7 @@
 from transformers.utils import is_peft_available, is_torch_xpu_available
 
 from ..data_utils import maybe_apply_chat_template, maybe_extract_prompt
-from ..models import PreTrainedModelWrapper, create_reference_model
+from ..models import create_reference_model, prepare_deepspeed
 from ..models.utils import prepare_fsdp
 from .callbacks import SyncRefModelCallback
 from .dpo_config import DPOConfig, FDivergenceConstants, FDivergenceType
@@ -80,9 +79,6 @@
 if is_wandb_available():
     import wandb
 
-if is_deepspeed_available():
-    import deepspeed
-
 
 @dataclass
 class DataCollatorForPreference(DataCollatorMixin):
@@ -510,7 +506,7 @@ def make_inputs_require_grad(module, input, output):
                 )
         else:
             if self.is_deepspeed_enabled:
-                self.ref_model = self._prepare_deepspeed(self.ref_model)
+                self.ref_model = prepare_deepspeed(self.ref_model, self.accelerator)
             elif self.is_fsdp_enabled:
                 self.ref_model = prepare_fsdp(self.ref_model, self.accelerator)
             else:
@@ -676,37 +672,6 @@ def process_row(features, processing_class, max_prompt_length, max_completion_le
 
         return output
 
-    def _prepare_deepspeed(self, model: PreTrainedModelWrapper):
-        # Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
-        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
-        config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config)
-
-        if model is not None:
-            if hasattr(model, "config"):
-                hidden_size = (
-                    max(model.config.hidden_sizes)
-                    if getattr(model.config, "hidden_sizes", None)
-                    else getattr(model.config, "hidden_size", None)
-                )
-                if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3:
-                    # Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0`
-                    # This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081
-                    config_kwargs.update(
-                        {
-                            "zero_optimization.reduce_bucket_size": hidden_size * hidden_size,
-                            "zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size,
-                            "zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size,
-                        }
-                    )
-
-        # If ZeRO-3 is used, we shard both the active and reference model.
-        # Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0)
-        if config_kwargs["zero_optimization"]["stage"] != 3:
-            config_kwargs["zero_optimization"]["stage"] = 0
-        model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
-        model.eval()
-        return model
-
     def _set_signature_columns_if_needed(self):
         # If `self.args.remove_unused_columns` is True, non-signature columns are removed.
         # By default, this method sets `self._signature_columns` to the model's expected inputs.

trl/trainer/gkd_trainer.py

@@ -15,13 +15,11 @@
 import os
 import random
 import textwrap
-from copy import deepcopy
 from typing import Any, Callable, Optional, Union
 
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from accelerate.utils import is_deepspeed_available
 from datasets import Dataset
 from transformers import (
     AutoModelForCausalLM,
@@ -38,7 +36,7 @@
 from transformers.trainer_utils import EvalPrediction
 from transformers.utils import is_peft_available
 
-from ..models import PreTrainedModelWrapper
+from ..models import prepare_deepspeed
 from ..models.utils import unwrap_model_for_generation
 from .gkd_config import GKDConfig
 from .sft_trainer import SFTTrainer
@@ -51,10 +49,6 @@
 )
 
 
-if is_deepspeed_available():
-    import deepspeed
-
-
 if is_peft_available():
     from peft import PeftConfig
 
@@ -124,7 +118,7 @@ def __init__(
             disable_dropout_in_model(self.model)
 
         if self.is_deepspeed_enabled:
-            self.teacher_model = self._prepare_deepspeed(teacher_model)
+            self.teacher_model = prepare_deepspeed(teacher_model, self.accelerator)
         else:
             self.teacher_model = self.accelerator.prepare_model(teacher_model, evaluation_mode=True)
 
@@ -311,37 +305,6 @@ def training_step(
         loss = super().training_step(model, inputs, num_items_in_batch)
         return loss
 
-    def _prepare_deepspeed(self, model: PreTrainedModelWrapper):
-        # Adapted from accelerate: https://github.com/huggingface/accelerate/blob/739b135f8367becb67ffaada12fe76e3aa60fefd/src/accelerate/accelerator.py#L1473
-        deepspeed_plugin = self.accelerator.state.deepspeed_plugin
-        config_kwargs = deepcopy(deepspeed_plugin.deepspeed_config)
-
-        if model is not None:
-            if hasattr(model, "config"):
-                hidden_size = (
-                    max(model.config.hidden_sizes)
-                    if getattr(model.config, "hidden_sizes", None)
-                    else getattr(model.config, "hidden_size", None)
-                )
-                if hidden_size is not None and config_kwargs["zero_optimization"]["stage"] == 3:
-                    # Note that `stage3_prefetch_bucket_size` can produce DeepSpeed messages like: `Invalidate trace cache @ step 0: expected module 1, but got module 0`
-                    # This is expected and is not an error, see: https://github.com/microsoft/DeepSpeed/discussions/4081
-                    config_kwargs.update(
-                        {
-                            "zero_optimization.reduce_bucket_size": hidden_size * hidden_size,
-                            "zero_optimization.stage3_param_persistence_threshold": 10 * hidden_size,
-                            "zero_optimization.stage3_prefetch_bucket_size": 0.9 * hidden_size * hidden_size,
-                        }
-                    )
-
-        # If ZeRO-3 is used, we shard both the active and reference model.
-        # Otherwise, we assume the reference model fits in memory and is initialized on each device with ZeRO disabled (stage 0)
-        if config_kwargs["zero_optimization"]["stage"] != 3:
-            config_kwargs["zero_optimization"]["stage"] = 0
-        model, *_ = deepspeed.initialize(model=model, config=config_kwargs)
-        model.eval()
-        return model
-
     def create_model_card(
         self,
         model_name: Optional[str] = None,

trl/trainer/grpo_trainer.py

@@ -47,7 +47,7 @@
 from ..data_utils import apply_chat_template, is_conversational, maybe_apply_chat_template
 from ..extras.profiling import profiling_context, profiling_decorator
 from ..extras.vllm_client import VLLMClient
-from ..import_utils import is_deepspeed_available, is_liger_kernel_available, is_rich_available, is_vllm_available
+from ..import_utils import is_liger_kernel_available, is_rich_available, is_vllm_available
 from ..models import create_reference_model, prepare_deepspeed, unwrap_model_for_generation
 from .callbacks import SyncRefModelCallback
 from .grpo_config import GRPOConfig
@@ -61,9 +61,6 @@
 )
 
 
-if is_deepspeed_available():
-    import deepspeed
-
 if is_peft_available():
     from peft import PeftConfig, get_peft_model
 
@@ -839,7 +836,12 @@ def _move_model_to_vllm(self):
         # For DeepSpeed ZeRO-3, we need to gather all parameters before operations
         deepspeed_plugin = self.accelerator.state.deepspeed_plugin
         zero_stage_3 = deepspeed_plugin is not None and deepspeed_plugin.zero_stage == 3
-        gather_if_zero3 = deepspeed.zero.GatheredParameters if zero_stage_3 else nullcontext
+        if zero_stage_3:
+            import deepspeed
+
+            gather_if_zero3 = deepspeed.zero.GatheredParameters
+        else:
+            gather_if_zero3 = nullcontext
 
         if is_peft_model(self.model):
             # With PEFT and DeepSpeed ZeRO Stage 3, we must gather the full model at once before merging, as merging