wip tracking down MPS slicing support

ldm/invoke/generator/diffusers_pipeline.py

@@ -307,8 +307,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         if is_xformers_available() and not Globals.disable_xformers:
             self.enable_xformers_memory_efficient_attention()
         else:
-            slice_size = 4 # or 2, or 8. i chose this arbitrarily.
-            self.enable_attention_slicing(slice_size=slice_size)
+            if torch.backends.mps.is_available():
+                # until pytorch #91617 is fixed, slicing is borked on MPS
+                # https://github.com/pytorch/pytorch/issues/91617
+                # fix is in https://github.com/kulinseth/pytorch/pull/222 but no idea when it will get merged to pytorch mainline.
+                pass
+            else:
+                slice_size = 4 # or 2, or 8. i chose this arbitrarily.
+                self.enable_attention_slicing(slice_size=slice_size)
 
 
     def image_from_embeddings(self, latents: torch.Tensor, num_inference_steps: int,

ldm/models/diffusion/cross_attention_control.py

@@ -344,10 +344,14 @@ def setup_cross_attention_control(model, context: Context, is_running_diffusers
     if is_running_diffusers:
         unet = model
         old_attn_processors = unet.attn_processors
-        # try to re-use an existing slice size
-        default_slice_size = 4
-        slice_size = next((p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size)
-        unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
+        if torch.backends.mps.is_available():
+            # see note in StableDiffusionGeneratorPipeline.__init__ about borked slicing on MPS
+            unet.set_attn_processor(SwapCrossAttnProcessor())
+        else:
+            # try to re-use an existing slice size
+            default_slice_size = 4
+            slice_size = next((p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size)
+            unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
         return old_attn_processors
     else:
         context.register_cross_attention_modules(model)
@@ -605,7 +609,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
         )
 
         # do slices
-        for i in range(hidden_states.shape[0] // self.slice_size):
+        for i in range(max(1,hidden_states.shape[0] // self.slice_size)):
             start_idx = i * self.slice_size
             end_idx = (i + 1) * self.slice_size
 
@@ -630,6 +634,8 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
                 remapped_original_attn_slice * mask + \
                 modified_attn_slice * inverse_mask
 
+            del remapped_original_attn_slice, modified_attn_slice
+
             attn_slice = torch.bmm(attn_slice, modified_value[start_idx:end_idx])
             hidden_states[start_idx:end_idx] = attn_slice
 
@@ -648,4 +654,76 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
 class SwapCrossAttnProcessor(SlicedSwapCrossAttnProcesser):
 
     def __init__(self):
-        super(SwapCrossAttnProcessor, self).__init__(slice_size=1e6) # big number so we never slice
+        super(SwapCrossAttnProcessor, self).__init__(slice_size=int(1e9))
+
+    # theoretically this class could simply inherit from SlicedSwapCrossAttnProcesser
+    # and consist wholly of an __init__ method that just calls super().__init__(slice_size=1000000000)
+    # - such a giant slice size would resolve to 'no slicing' at runtime.
+    # however, pytorch MPS is borked until https://github.com/kulinseth/pytorch/pull/222 is merged into
+    # mainline pytorch. so for now this has to be a full implementation.
+
+    def no__call__(self, attn: CrossAttention, hidden_states, encoder_hidden_states=None, attention_mask=None,
+                 # kwargs
+                 swap_cross_attn_context: SwapCrossAttnContext=None):
+
+        attention_type = CrossAttentionType.SELF if encoder_hidden_states is None else CrossAttentionType.TOKENS
+
+        # if cross-attention control is not in play, just call through to the base implementation.
+        if attention_type == CrossAttentionType.SELF or \
+            swap_cross_attn_context is None or \
+            not swap_cross_attn_context.wants_cross_attention_control(attention_type):
+            #print(f"SwapCrossAttnContext for {attention_type} not active - passing request to superclass")
+            return super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask)
+        #else:
+        #    print(f"SwapCrossAttnContext for {attention_type} active")
+
+        batch_size, sequence_length, _ = hidden_states.shape
+        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length)
+
+        query = attn.to_q(hidden_states)
+        query = attn.head_to_batch_dim(query)
+
+        # helper function
+        def get_attention_probs(embeddings):
+            this_key = attn.to_k(embeddings)
+            this_key = attn.head_to_batch_dim(this_key)
+            return attn.get_attention_scores(query, this_key, attention_mask)
+
+        # tokens (cross) attention
+        # first, find attention probabilities for the "original" prompt
+        original_text_embeddings = encoder_hidden_states
+        original_attention_probs = get_attention_probs(original_text_embeddings)
+
+        # then, find attention probabilities for the "modified" prompt
+        modified_text_embeddings = swap_cross_attn_context.modified_text_embeddings
+        modified_attention_probs = get_attention_probs(modified_text_embeddings)
+
+        # because the prompt modifications may result in token sequences shifted forwards or backwards,
+        # the original attention probabilities must be remapped to account for token index changes in the
+        # modified prompt
+        remapped_original_attention_probs = torch.index_select(original_attention_probs, -1,
+                                                               swap_cross_attn_context.index_map).clone()
+
+        # only some tokens taken from the original attention probabilities. this is controlled by the mask.
+        mask = swap_cross_attn_context.mask
+        inverse_mask = 1 - mask.clone()
+        attention_probs = \
+            remapped_original_attention_probs * mask + \
+            modified_attention_probs * inverse_mask
+
+        # for the "value" just use the modified text embeddings.
+        value = attn.to_v(modified_text_embeddings)
+
+        value = attn.head_to_batch_dim(value)
+
+        hidden_states = torch.bmm(attention_probs, value)
+        hidden_states = attn.batch_to_head_dim(hidden_states)
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+
+        return hidden_states
+
+