Create caching.py

src/caching.py

@@ -0,0 +1,294 @@
+# caching.py
+
+import functools
+import unittest
+import contextlib
+import dataclasses
+from collections import defaultdict
+from typing import DefaultDict, Dict
+import torch
+from diffusers import DiffusionPipeline, FluxTransformer2DModel
+
+
+@dataclasses.dataclass
+class CacheContext:
+    buffers: Dict[str, torch.Tensor] = dataclasses.field(default_factory=dict)
+    incremental_name_counters: DefaultDict[str, int] = dataclasses.field(default_factory=lambda: defaultdict(int))
+
+    def get_incremental_name(self, name=None):
+        if name is None:
+            name = "default"
+        idx = self.incremental_name_counters[name]
+        self.incremental_name_counters[name] += 1
+        return f"{name}_{idx}"
+
+    def reset_incremental_names(self):
+        self.incremental_name_counters.clear()
+
+    @torch.compiler.disable
+    def get_buffer(self, name):
+        return self.buffers.get(name)
+
+    @torch.compiler.disable
+    def set_buffer(self, name, buffer):
+        self.buffers[name] = buffer
+
+    def clear_buffers(self):
+        self.buffers.clear()
+
+
+@torch.compiler.disable
+def get_buffer(name):
+    cache_context = get_current_cache_context()
+    assert cache_context is not None, "cache_context must be set before"
+    return cache_context.get_buffer(name)
+
+
+@torch.compiler.disable
+def set_buffer(name, buffer):
+    cache_context = get_current_cache_context()
+    assert cache_context is not None, "cache_context must be set before"
+    cache_context.set_buffer(name, buffer)
+
+
+_current_cache_context = None
+
+
+def create_cache_context():
+    return CacheContext()
+
+
+def get_current_cache_context():
+    return _current_cache_context
+
+
+def set_current_cache_context(cache_context=None):
+    global _current_cache_context
+    _current_cache_context = cache_context
+
+
+@contextlib.contextmanager
+def cache_context(cache_context):
+    global _current_cache_context
+    old_cache_context = _current_cache_context
+    _current_cache_context = cache_context
+    try:
+        yield
+    finally:
+        _current_cache_context = old_cache_context
+
+
+
+@torch.compiler.disable
+def apply_prev_hidden_states_residual(hidden_states, encoder_hidden_states):
+    hidden_states_residual = get_buffer("hidden_states_residual")
+    assert hidden_states_residual is not None, "hidden_states_residual must be set before"
+    hidden_states = hidden_states_residual + hidden_states
+
+    encoder_hidden_states_residual = get_buffer("encoder_hidden_states_residual")
+    assert encoder_hidden_states_residual is not None, "encoder_hidden_states_residual must be set before"
+    encoder_hidden_states = encoder_hidden_states_residual + encoder_hidden_states
+
+    hidden_states = hidden_states.contiguous()
+    encoder_hidden_states = encoder_hidden_states.contiguous()
+
+    return hidden_states, encoder_hidden_states
+
+
+def are_two_tensors_similar(t1, t2, *, threshold=0.85):
+    mean_diff = (t1 - t2).abs().mean()
+    mean_t1 = t1.abs().mean()
+    diff = mean_diff / mean_t1
+    return diff.item() < threshold
+
+@torch.compiler.disable
+def get_can_use_cache(first_hidden_states_residual, threshold, parallelized=False):
+    prev_first_hidden_states_residual = get_buffer("first_hidden_states_residual")
+    can_use_cache = prev_first_hidden_states_residual is not None and are_two_tensors_similar(
+        prev_first_hidden_states_residual,
+        first_hidden_states_residual,
+    )
+    return can_use_cache
+
+
+class CachedTransformerBlocks(torch.nn.Module):
+    def __init__(
+        self,
+        transformer_blocks,
+        single_transformer_blocks=None,
+        *,
+        transformer=None,
+        residual_diff_threshold,
+        return_hidden_states_first=True,
+    ):
+        super().__init__()
+        self.transformer = transformer
+        self.transformer_blocks = transformer_blocks
+        self.single_transformer_blocks = single_transformer_blocks
+        self.residual_diff_threshold = residual_diff_threshold
+        self.return_hidden_states_first = return_hidden_states_first
+
+    def forward(self, hidden_states, encoder_hidden_states, *args, **kwargs):
+        if self.residual_diff_threshold <= 0.0:
+            for block in self.transformer_blocks:
+                hidden_states, encoder_hidden_states = block(hidden_states, encoder_hidden_states, *args, **kwargs)
+                if not self.return_hidden_states_first:
+                    hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states
+            if self.single_transformer_blocks is not None:
+                hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+                for block in self.single_transformer_blocks:
+                    hidden_states = block(hidden_states, *args, **kwargs)
+                hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :]
+            return (
+                (hidden_states, encoder_hidden_states)
+                if self.return_hidden_states_first
+                else (encoder_hidden_states, hidden_states)
+            )
+
+        original_hidden_states = hidden_states
+        first_transformer_block = self.transformer_blocks[0]
+        hidden_states, encoder_hidden_states = first_transformer_block(
+            hidden_states, encoder_hidden_states, *args, **kwargs
+        )
+        if not self.return_hidden_states_first:
+            hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states
+        first_hidden_states_residual = hidden_states - original_hidden_states
+        del original_hidden_states
+
+        can_use_cache = get_can_use_cache(
+            first_hidden_states_residual,
+            threshold=self.residual_diff_threshold,
+            parallelized=self.transformer is not None and getattr(self.transformer, "_is_parallelized", False),
+        )
+
+        torch._dynamo.graph_break()
+        if can_use_cache:
+            del first_hidden_states_residual
+            hidden_states, encoder_hidden_states = apply_prev_hidden_states_residual(
+                hidden_states, encoder_hidden_states
+            )
+        else:
+            set_buffer("first_hidden_states_residual", first_hidden_states_residual)
+            del first_hidden_states_residual
+            (
+                hidden_states,
+                encoder_hidden_states,
+                hidden_states_residual,
+                encoder_hidden_states_residual,
+            ) = self.call_remaining_transformer_blocks(hidden_states, encoder_hidden_states, *args, **kwargs)
+            set_buffer("hidden_states_residual", hidden_states_residual)
+            set_buffer("encoder_hidden_states_residual", encoder_hidden_states_residual)
+        torch._dynamo.graph_break()
+
+        return (
+            (hidden_states, encoder_hidden_states)
+            if self.return_hidden_states_first
+            else (encoder_hidden_states, hidden_states)
+        )
+
+    def call_remaining_transformer_blocks(self, hidden_states, encoder_hidden_states, *args, **kwargs):
+        original_hidden_states = hidden_states
+        original_encoder_hidden_states = encoder_hidden_states
+        for block in self.transformer_blocks[1:]:
+            hidden_states, encoder_hidden_states = block(hidden_states, encoder_hidden_states, *args, **kwargs)
+            if not self.return_hidden_states_first:
+                hidden_states, encoder_hidden_states = encoder_hidden_states, hidden_states
+        if self.single_transformer_blocks is not None:
+            hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+            for block in self.single_transformer_blocks:
+                hidden_states = block(hidden_states, *args, **kwargs)
+            encoder_hidden_states, hidden_states = hidden_states.split(
+                [encoder_hidden_states.shape[1], hidden_states.shape[1] - encoder_hidden_states.shape[1]], dim=1
+            )
+
+        # hidden_states_shape = hidden_states.shape
+        # encoder_hidden_states_shape = encoder_hidden_states.shape
+        hidden_states = hidden_states.reshape(-1).contiguous().reshape(original_hidden_states.shape)
+        encoder_hidden_states = (
+            encoder_hidden_states.reshape(-1).contiguous().reshape(original_encoder_hidden_states.shape)
+        )
+
+        # hidden_states = hidden_states.contiguous()
+        # encoder_hidden_states = encoder_hidden_states.contiguous()
+
+        hidden_states_residual = hidden_states - original_hidden_states
+        encoder_hidden_states_residual = encoder_hidden_states - original_encoder_hidden_states
+
+        hidden_states_residual = hidden_states_residual.reshape(-1).contiguous().reshape(original_hidden_states.shape)
+        encoder_hidden_states_residual = (
+            encoder_hidden_states_residual.reshape(-1).contiguous().reshape(original_encoder_hidden_states.shape)
+        )
+
+        return hidden_states, encoder_hidden_states, hidden_states_residual, encoder_hidden_states_residual
+
+
+def apply_cache_on_transformer(
+    transformer: FluxTransformer2DModel,
+    *,
+    residual_diff_threshold=0.15,
+):
+    cached_transformer_blocks = torch.nn.ModuleList(
+        [
+            CachedTransformerBlocks(
+                transformer.transformer_blocks,
+                transformer.single_transformer_blocks,
+                transformer=transformer,
+                residual_diff_threshold=residual_diff_threshold,
+                return_hidden_states_first=False,
+            )
+        ]
+    )
+    dummy_single_transformer_blocks = torch.nn.ModuleList()
+
+    original_forward = transformer.forward
+
+    @functools.wraps(original_forward)
+    def new_forward(
+        self,
+        *args,
+        **kwargs,
+    ):
+        with unittest.mock.patch.object(
+            self,
+            "transformer_blocks",
+            cached_transformer_blocks,
+        ), unittest.mock.patch.object(
+            self,
+            "single_transformer_blocks",
+            dummy_single_transformer_blocks,
+        ):
+            return original_forward(
+                *args,
+                **kwargs,
+            )
+
+    transformer.forward = new_forward.__get__(transformer)
+
+    return transformer
+
+
+def apply_cache_on_pipe(
+    pipe: DiffusionPipeline,
+    *,
+    shallow_patch: bool = False,
+    **kwargs,
+):
+    original_call = pipe.__class__.__call__
+
+    if not getattr(original_call, "_is_cached", False):
+
+        @functools.wraps(original_call)
+        def new_call(self, *args, **kwargs):
+            with cache_context(create_cache_context()):
+                return original_call(self, *args, **kwargs)
+
+        pipe.__class__.__call__ = new_call
+
+        new_call._is_cached = True
+
+    if not shallow_patch:
+        apply_cache_on_transformer(pipe.transformer, **kwargs)
+
+    pipe._is_cached = True
+
+    return pipe