update

Author: a-r-r-o-w

src/diffusers/hooks/_cfg_parallel.py

@@ -0,0 +1,65 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# 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.
+
+import torch
+import torch.distributed as dist
+
+from ..utils import get_logger
+from ._common import _BATCHED_INPUT_IDENTIFIERS
+from .hooks import HookRegistry, ModelHook
+
+
+logger = get_logger(__name__)  # pylint: disable=invalid-name
+
+_CFG_PARALLEL = "cfg_parallel"
+
+
+class CFGParallelHook(ModelHook):
+    def initialize_hook(self, module):
+        if not dist.is_initialized():
+            raise RuntimeError("Distributed environment not initialized.")
+        return module
+
+    def new_forward(self, module: torch.nn.Module, *args, **kwargs):
+        if len(args) > 0:
+            logger.warning(
+                "CFGParallelHook is an example hook that does not work with batched positional arguments. Please use with caution."
+            )
+
+        world_size = dist.get_world_size()
+        rank = dist.get_rank()
+
+        assert world_size == 2, "This is an example hook designed to only work with 2 processes."
+
+        for key in list(kwargs.keys()):
+            if key not in _BATCHED_INPUT_IDENTIFIERS or kwargs[key] is None:
+                continue
+            kwargs[key] = torch.chunk(kwargs[key], world_size, dim=0)[rank].contiguous()
+
+        output = self.fn_ref.original_forward(*args, **kwargs)
+        sample = output[0]
+        sample_list = [torch.empty_like(sample) for _ in range(world_size)]
+        dist.all_gather(sample_list, sample)
+        sample = torch.cat(sample_list, dim=0).contiguous()
+
+        return_dict = kwargs.get("return_dict", False)
+        if not return_dict:
+            return (sample, *output[1:])
+        return output.__class__(sample, *output[1:])
+
+
+def apply_cfg_parallel(module: torch.nn.Module) -> None:
+    registry = HookRegistry.check_if_exists_or_initialize(module)
+    hook = CFGParallelHook()
+    registry.register_hook(hook, _CFG_PARALLEL)

src/diffusers/hooks/_common.py

@@ -0,0 +1,26 @@
+from ..models.attention_processor import Attention, MochiAttention
+
+
+_ATTENTION_CLASSES = (Attention, MochiAttention)
+
+_SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS = ("blocks", "transformer_blocks", "single_transformer_blocks", "layers")
+_TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS = ("temporal_transformer_blocks",)
+_CROSS_TRANSFORMER_BLOCK_IDENTIFIERS = ("blocks", "transformer_blocks", "layers")
+
+_ALL_TRANSFORMER_BLOCK_IDENTIFIERS = tuple(
+    {
+        *_SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS,
+        *_TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS,
+        *_CROSS_TRANSFORMER_BLOCK_IDENTIFIERS,
+    }
+)
+
+_BATCHED_INPUT_IDENTIFIERS = (
+    "hidden_states",
+    "encoder_hidden_states",
+    "pooled_projections",
+    "timestep",
+    "attention_mask",
+    "encoder_attention_mask",
+    "guidance",
+)

src/diffusers/hooks/pyramid_attention_broadcast.py

@@ -20,19 +20,18 @@
 
 from ..models.attention_processor import Attention, MochiAttention
 from ..utils import logging
+from ._common import (
+    _ATTENTION_CLASSES,
+    _CROSS_TRANSFORMER_BLOCK_IDENTIFIERS,
+    _SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS,
+    _TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS,
+)
 from .hooks import HookRegistry, ModelHook
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
-_ATTENTION_CLASSES = (Attention, MochiAttention)
-
-_SPATIAL_ATTENTION_BLOCK_IDENTIFIERS = ("blocks", "transformer_blocks", "single_transformer_blocks")
-_TEMPORAL_ATTENTION_BLOCK_IDENTIFIERS = ("temporal_transformer_blocks",)
-_CROSS_ATTENTION_BLOCK_IDENTIFIERS = ("blocks", "transformer_blocks")
-
-
 @dataclass
 class PyramidAttentionBroadcastConfig:
     r"""
@@ -76,9 +75,9 @@ class PyramidAttentionBroadcastConfig:
     temporal_attention_timestep_skip_range: Tuple[int, int] = (100, 800)
     cross_attention_timestep_skip_range: Tuple[int, int] = (100, 800)
 
-    spatial_attention_block_identifiers: Tuple[str, ...] = _SPATIAL_ATTENTION_BLOCK_IDENTIFIERS
-    temporal_attention_block_identifiers: Tuple[str, ...] = _TEMPORAL_ATTENTION_BLOCK_IDENTIFIERS
-    cross_attention_block_identifiers: Tuple[str, ...] = _CROSS_ATTENTION_BLOCK_IDENTIFIERS
+    spatial_attention_block_identifiers: Tuple[str, ...] = _SPATIAL_TRANSFORMER_BLOCK_IDENTIFIERS
+    temporal_attention_block_identifiers: Tuple[str, ...] = _TEMPORAL_TRANSFORMER_BLOCK_IDENTIFIERS
+    cross_attention_block_identifiers: Tuple[str, ...] = _CROSS_TRANSFORMER_BLOCK_IDENTIFIERS
 
     current_timestep_callback: Callable[[], int] = None