kill off old vanilla CLIP dependency (not used anymore)

pyproject.toml

@@ -24,7 +24,6 @@ dynamic = ["version"]
 dependencies = [
   "accelerate >= 0.26.1",
   "chardet == 5.1.0",
-  "clip @ git+https://github.com/openai/CLIP.git@a1d071733d7111c9c014f024669f959182114e33#egg=clip",
   "colorama >= 0.4.3, < 0.5.0",
   "colorcet >= 3.0.1, < 4.0.0",
   "crc32c >= 2.3",

src/neurosis/fsdp/diffusionpolicy.py

@@ -4,7 +4,6 @@
 
 import lightning.pytorch as pl
 import torch
-from clip.model import ResidualAttentionBlock
 from lightning.fabric.plugins import CheckpointIO, ClusterEnvironment
 from lightning.fabric.plugins.collectives.torch_collective import default_pg_timeout
 from lightning.pytorch.plugins.precision import Precision
@@ -13,9 +12,9 @@
 from torch.distributed.fsdp.wrap import ModuleWrapPolicy
 from torch.nn import Module
 from torch.nn.modules.batchnorm import _BatchNorm
-from transformers.models.clip.modeling_clip import CLIPEncoderLayer
+from transformers.models.clip.modeling_clip import CLIPAttention, CLIPEncoderLayer
 
-from neurosis.models.autoencoder import AutoencoderKL
+from neurosis.models.autoencoder import AutoencoderKL, FSDPAutoencoderKL
 from neurosis.models.autoencoder_hf import AutoencoderKL as HFAutoencoderKL
 from neurosis.models.text_encoder import (
     FrozenCLIPEmbedder,
@@ -36,21 +35,22 @@
 
     _POLICY = Union[set[Type[Module]], Callable[[Module, bool, int], bool], ModuleWrapPolicy]
     _SHARDING_STRATEGY = Union[
-        ShardingStrategy, Literal["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD"]
+        ShardingStrategy,
+        Literal["FULL_SHARD", "SHARD_GRAD_OP", "NO_SHARD", "HYBRID_SHARD", "_HYBRID_SHARD_ZERO2"],
     ]
 
 
 class DiffusionFsdpPolicy(ModuleWrapPolicy):
     def __init__(self):
         module_classes = {
+            CLIPAttention,
             CLIPEncoderLayer,
             Decoder,
             Encoder,
             FrozenCLIPEmbedder,
             FrozenOpenCLIPEmbedder2,
             FrozenT5Embedder,
             GeneralConditioner,
-            ResidualAttentionBlock,
             SpatialTransformer,
             TimestepEmbedSequential,
             UNetModel,
@@ -87,7 +87,7 @@ def __post_init__(self):
 
         if self.vae_fp32:
             fp32_classes.extend(
-                [AutoencoderKL, Decoder, Encoder, HFAutoencoderKL],
+                [AutoencoderKL, Decoder, Encoder, FSDPAutoencoderKL, HFAutoencoderKL],
             )
 
         fp32_classes = sorted(set(fp32_classes), key=lambda x: x.__name__)

src/neurosis/modules/attention.py

@@ -14,7 +14,7 @@
 logger = logging.getLogger(__name__)
 
 try:
-    from xformers import (
+    from xformers import (  # type: ignore
         __version__ as xformers_version,
         ops as xops,
     )
@@ -512,19 +512,34 @@ def _forward(
         additional_tokens: Optional[Tensor] = None,
         n_times_crossframe_attn_in_self: int = 0,
     ) -> Tensor:
-        x = (
+        y = self.norm1(x)
+        if self.disable_self_attn:
+            x = x + self.attn1(
+                y,
+                context=context,
+                additional_tokens=additional_tokens,
+                n_times_crossframe_attn_in_self=0,
+            )
+        else:
+            x = x + self.attn1(
+                y,
+                context=None,
+                additional_tokens=additional_tokens,
+                n_times_crossframe_attn_in_self=n_times_crossframe_attn_in_self,
+            )
+
+        x = x + (
             self.attn1(
                 self.norm1(x),
                 context=context if self.disable_self_attn else None,
                 additional_tokens=additional_tokens,
-                n_times_crossframe_attn_in_self=n_times_crossframe_attn_in_self
-                if not self.disable_self_attn
-                else 0,
+                n_times_crossframe_attn_in_self=0
+                if self.disable_self_attn
+                else n_times_crossframe_attn_in_self,
             )
-            + x
         )
-        x = self.attn2(self.norm2(x), context=context, additional_tokens=additional_tokens) + x
-        x = self.ff(self.norm3(x)) + x
+        x = x + self.attn2(self.norm2(x), context=context, additional_tokens=additional_tokens)
+        x = x + self.ff(self.norm3(x))
         return x