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Fix unique memory address when doing group-offloading with disk (#11767)
* fix memory address problem * add more tests * updates * updates * update * _group_id = group_id * update * Apply suggestions from code review Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com> * update * update * update * fix --------- Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
This commit is contained in:
Sayak Paul
GitHub
@@ -12,6 +12,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import hashlib
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import os
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from contextlib import contextmanager, nullcontext
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from dataclasses import dataclass
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@@ -37,7 +38,7 @@ logger = get_logger(__name__) # pylint: disable=invalid-name
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_GROUP_OFFLOADING = "group_offloading"
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_LAYER_EXECUTION_TRACKER = "layer_execution_tracker"
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_LAZY_PREFETCH_GROUP_OFFLOADING = "lazy_prefetch_group_offloading"
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_GROUP_ID_LAZY_LEAF = "lazy_leafs"
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_SUPPORTED_PYTORCH_LAYERS = (
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torch.nn.Conv1d, torch.nn.Conv2d, torch.nn.Conv3d,
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torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d, torch.nn.ConvTranspose3d,
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@@ -82,6 +83,7 @@ class ModuleGroup:
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low_cpu_mem_usage: bool = False,
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onload_self: bool = True,
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offload_to_disk_path: Optional[str] = None,
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group_id: Optional[int] = None,
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) -> None:
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self.modules = modules
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self.offload_device = offload_device
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@@ -100,7 +102,10 @@ class ModuleGroup:
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self._is_offloaded_to_disk = False
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if self.offload_to_disk_path:
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self.safetensors_file_path = os.path.join(self.offload_to_disk_path, f"group_{id(self)}.safetensors")
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# Instead of `group_id or str(id(self))` we do this because `group_id` can be "" as well.
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self.group_id = group_id if group_id is not None else str(id(self))
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short_hash = _compute_group_hash(self.group_id)
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self.safetensors_file_path = os.path.join(self.offload_to_disk_path, f"group_{short_hash}.safetensors")
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all_tensors = []
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for module in self.modules:
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@@ -609,6 +614,7 @@ def _apply_group_offloading_block_level(module: torch.nn.Module, config: GroupOf
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for i in range(0, len(submodule), config.num_blocks_per_group):
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current_modules = submodule[i : i + config.num_blocks_per_group]
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group_id = f"{name}_{i}_{i + len(current_modules) - 1}"
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group = ModuleGroup(
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modules=current_modules,
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offload_device=config.offload_device,
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@@ -621,6 +627,7 @@ def _apply_group_offloading_block_level(module: torch.nn.Module, config: GroupOf
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record_stream=config.record_stream,
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low_cpu_mem_usage=config.low_cpu_mem_usage,
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onload_self=True,
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group_id=group_id,
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)
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matched_module_groups.append(group)
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for j in range(i, i + len(current_modules)):
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@@ -655,6 +662,7 @@ def _apply_group_offloading_block_level(module: torch.nn.Module, config: GroupOf
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stream=None,
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record_stream=False,
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onload_self=True,
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group_id=f"{module.__class__.__name__}_unmatched_group",
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)
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if config.stream is None:
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_apply_group_offloading_hook(module, unmatched_group, None, config=config)
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@@ -686,6 +694,7 @@ def _apply_group_offloading_leaf_level(module: torch.nn.Module, config: GroupOff
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record_stream=config.record_stream,
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low_cpu_mem_usage=config.low_cpu_mem_usage,
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onload_self=True,
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group_id=name,
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)
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_apply_group_offloading_hook(submodule, group, None, config=config)
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modules_with_group_offloading.add(name)
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@@ -732,6 +741,7 @@ def _apply_group_offloading_leaf_level(module: torch.nn.Module, config: GroupOff
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record_stream=config.record_stream,
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low_cpu_mem_usage=config.low_cpu_mem_usage,
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onload_self=True,
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group_id=name,
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)
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_apply_group_offloading_hook(parent_module, group, None, config=config)
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@@ -753,6 +763,7 @@ def _apply_group_offloading_leaf_level(module: torch.nn.Module, config: GroupOff
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record_stream=False,
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low_cpu_mem_usage=config.low_cpu_mem_usage,
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onload_self=True,
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group_id=_GROUP_ID_LAZY_LEAF,
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)
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_apply_lazy_group_offloading_hook(module, unmatched_group, None, config=config)
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@@ -873,6 +884,12 @@ def _get_group_onload_device(module: torch.nn.Module) -> torch.device:
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raise ValueError("Group offloading is not enabled for the provided module.")
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def _compute_group_hash(group_id):
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hashed_id = hashlib.sha256(group_id.encode("utf-8")).hexdigest()
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# first 16 characters for a reasonably short but unique name
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return hashed_id[:16]
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def _maybe_remove_and_reapply_group_offloading(module: torch.nn.Module) -> None:
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r"""
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Removes the group offloading hook from the module and re-applies it. This is useful when the module has been
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@@ -1,4 +1,5 @@
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import functools
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import glob
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import importlib
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import importlib.metadata
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import inspect
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@@ -18,7 +19,7 @@ from collections import UserDict
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from contextlib import contextmanager
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from io import BytesIO, StringIO
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from pathlib import Path
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from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
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from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Set, Tuple, Union
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import numpy as np
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import PIL.Image
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@@ -1392,6 +1393,103 @@ if TYPE_CHECKING:
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else:
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DevicePropertiesUserDict = UserDict
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if is_torch_available():
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from diffusers.hooks.group_offloading import (
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_GROUP_ID_LAZY_LEAF,
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_SUPPORTED_PYTORCH_LAYERS,
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_compute_group_hash,
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_find_parent_module_in_module_dict,
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_gather_buffers_with_no_group_offloading_parent,
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_gather_parameters_with_no_group_offloading_parent,
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)
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def _get_expected_safetensors_files(
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module: torch.nn.Module,
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offload_to_disk_path: str,
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offload_type: str,
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num_blocks_per_group: Optional[int] = None,
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) -> Set[str]:
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expected_files = set()
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def get_hashed_filename(group_id: str) -> str:
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short_hash = _compute_group_hash(group_id)
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return os.path.join(offload_to_disk_path, f"group_{short_hash}.safetensors")
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if offload_type == "block_level":
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if num_blocks_per_group is None:
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raise ValueError("num_blocks_per_group must be provided for 'block_level' offloading.")
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# Handle groups of ModuleList and Sequential blocks
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unmatched_modules = []
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for name, submodule in module.named_children():
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if not isinstance(submodule, (torch.nn.ModuleList, torch.nn.Sequential)):
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unmatched_modules.append(module)
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continue
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for i in range(0, len(submodule), num_blocks_per_group):
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current_modules = submodule[i : i + num_blocks_per_group]
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if not current_modules:
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continue
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group_id = f"{name}_{i}_{i + len(current_modules) - 1}"
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expected_files.add(get_hashed_filename(group_id))
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# Handle the group for unmatched top-level modules and parameters
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for module in unmatched_modules:
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expected_files.add(get_hashed_filename(f"{module.__class__.__name__}_unmatched_group"))
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elif offload_type == "leaf_level":
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# Handle leaf-level module groups
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for name, submodule in module.named_modules():
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if isinstance(submodule, _SUPPORTED_PYTORCH_LAYERS):
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# These groups will always have parameters, so a file is expected
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expected_files.add(get_hashed_filename(name))
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# Handle groups for non-leaf parameters/buffers
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modules_with_group_offloading = {
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name for name, sm in module.named_modules() if isinstance(sm, _SUPPORTED_PYTORCH_LAYERS)
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}
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parameters = _gather_parameters_with_no_group_offloading_parent(module, modules_with_group_offloading)
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buffers = _gather_buffers_with_no_group_offloading_parent(module, modules_with_group_offloading)
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all_orphans = parameters + buffers
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if all_orphans:
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parent_to_tensors = {}
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module_dict = dict(module.named_modules())
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for tensor_name, _ in all_orphans:
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parent_name = _find_parent_module_in_module_dict(tensor_name, module_dict)
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if parent_name not in parent_to_tensors:
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parent_to_tensors[parent_name] = []
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parent_to_tensors[parent_name].append(tensor_name)
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for parent_name in parent_to_tensors:
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# A file is expected for each parent that gathers orphaned tensors
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expected_files.add(get_hashed_filename(parent_name))
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expected_files.add(get_hashed_filename(_GROUP_ID_LAZY_LEAF))
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else:
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raise ValueError(f"Unsupported offload_type: {offload_type}")
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return expected_files
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def _check_safetensors_serialization(
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module: torch.nn.Module,
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offload_to_disk_path: str,
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offload_type: str,
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num_blocks_per_group: Optional[int] = None,
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) -> bool:
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if not os.path.isdir(offload_to_disk_path):
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return False, None, None
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expected_files = _get_expected_safetensors_files(
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module, offload_to_disk_path, offload_type, num_blocks_per_group
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)
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actual_files = set(glob.glob(os.path.join(offload_to_disk_path, "*.safetensors")))
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missing_files = expected_files - actual_files
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extra_files = actual_files - expected_files
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is_correct = not missing_files and not extra_files
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return is_correct, extra_files, missing_files
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class Expectations(DevicePropertiesUserDict):
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def get_expectation(self) -> Any:
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@@ -61,6 +61,7 @@ from diffusers.utils import (
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from diffusers.utils.hub_utils import _add_variant
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from diffusers.utils.testing_utils import (
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CaptureLogger,
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_check_safetensors_serialization,
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backend_empty_cache,
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backend_max_memory_allocated,
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backend_reset_peak_memory_stats,
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@@ -1702,18 +1703,43 @@ class ModelTesterMixin:
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model.enable_layerwise_casting(storage_dtype=storage_dtype, compute_dtype=compute_dtype)
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_ = model(**inputs_dict)[0]
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@parameterized.expand([(False, "block_level"), (True, "leaf_level")])
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@parameterized.expand([("block_level", False), ("leaf_level", True)])
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@require_torch_accelerator
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@torch.no_grad()
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def test_group_offloading_with_disk(self, record_stream, offload_type):
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@torch.inference_mode()
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def test_group_offloading_with_disk(self, offload_type, record_stream, atol=1e-5):
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if not self.model_class._supports_group_offloading:
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pytest.skip("Model does not support group offloading.")
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torch.manual_seed(0)
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def _has_generator_arg(model):
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sig = inspect.signature(model.forward)
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params = sig.parameters
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return "generator" in params
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def _run_forward(model, inputs_dict):
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accepts_generator = _has_generator_arg(model)
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if accepts_generator:
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inputs_dict["generator"] = torch.manual_seed(0)
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torch.manual_seed(0)
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return model(**inputs_dict)[0]
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if self.__class__.__name__ == "AutoencoderKLCosmosTests" and offload_type == "leaf_level":
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pytest.skip("With `leaf_type` as the offloading type, it fails. Needs investigation.")
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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torch.manual_seed(0)
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model = self.model_class(**init_dict)
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model.eval()
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model.to(torch_device)
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output_without_group_offloading = _run_forward(model, inputs_dict)
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torch.manual_seed(0)
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model = self.model_class(**init_dict)
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model.eval()
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additional_kwargs = {} if offload_type == "leaf_level" else {"num_blocks_per_group": 1}
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num_blocks_per_group = None if offload_type == "leaf_level" else 1
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additional_kwargs = {} if offload_type == "leaf_level" else {"num_blocks_per_group": num_blocks_per_group}
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with tempfile.TemporaryDirectory() as tmpdir:
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model.enable_group_offload(
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torch_device,
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@@ -1724,8 +1750,25 @@ class ModelTesterMixin:
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**additional_kwargs,
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)
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has_safetensors = glob.glob(f"{tmpdir}/*.safetensors")
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self.assertTrue(len(has_safetensors) > 0, "No safetensors found in the offload directory.")
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_ = model(**inputs_dict)[0]
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self.assertTrue(has_safetensors, "No safetensors found in the directory.")
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# For "leaf-level", there is a prefetching hook which makes this check a bit non-deterministic
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# in nature. So, skip it.
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if offload_type != "leaf_level":
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is_correct, extra_files, missing_files = _check_safetensors_serialization(
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module=model,
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offload_to_disk_path=tmpdir,
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offload_type=offload_type,
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num_blocks_per_group=num_blocks_per_group,
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)
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if not is_correct:
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if extra_files:
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raise ValueError(f"Found extra files: {', '.join(extra_files)}")
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elif missing_files:
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raise ValueError(f"Following files are missing: {', '.join(missing_files)}")
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output_with_group_offloading = _run_forward(model, inputs_dict)
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self.assertTrue(torch.allclose(output_without_group_offloading, output_with_group_offloading, atol=atol))
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def test_auto_model(self, expected_max_diff=5e-5):
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if self.forward_requires_fresh_args:
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