NPU Adaption for FLUX

examples/dreambooth/train_dreambooth_flux.py

@@ -58,7 +58,7 @@
 )
 from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card
 from diffusers.utils.torch_utils import is_compiled_module
-
+from diffusers.utils.import_utils import is_torch_npu_available
 
 if is_wandb_available():
     import wandb
@@ -68,6 +68,10 @@
 
 logger = get_logger(__name__)
 
+if is_torch_npu_available():
+    import torch_npu
+    torch.npu.config.allow_internal_format = False
+    torch.npu.set_compile_mode(jit_compile=False)
 
 def save_model_card(
     repo_id: str,
@@ -1073,6 +1077,9 @@ def main(args):
             del pipeline
             if torch.cuda.is_available():
                 torch.cuda.empty_cache()
+            elif is_torch_npu_available():
+                torch_npu.npu.empty_cache()
+
 
     # Handle the repository creation
     if accelerator.is_main_process:
@@ -1359,6 +1366,8 @@ def compute_text_embeddings(prompt, text_encoders, tokenizers):
         gc.collect()
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
+        elif is_torch_npu_available():
+            torch_npu.npu.empty_cache()
 
     # If custom instance prompts are NOT provided (i.e. the instance prompt is used for all images),
     # pack the statically computed variables appropriately here. This is so that we don't
@@ -1722,7 +1731,10 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                 )
                 if not args.train_text_encoder:
                     del text_encoder_one, text_encoder_two
-                    torch.cuda.empty_cache()
+                    if torch.cuda.is_available():
+                        torch.cuda.empty_cache()
+                    elif is_torch_npu_available():
+                        torch_npu.npu.empty_cache()
                     gc.collect()
 
     # Save the lora layers

src/diffusers/models/attention_processor.py

@@ -1778,7 +1778,28 @@ def __call__(
             query = apply_rotary_emb(query, image_rotary_emb)
             key = apply_rotary_emb(key, image_rotary_emb)
 
-        hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
+        if is_torch_npu_available():
+            if query.dtype in (torch.float16, torch.bfloat16):
+                hidden_states = torch_npu.npu_fusion_attention(
+                    query,
+                    key,
+                    value,
+                    attn.heads,
+                    input_layout="BNSD",
+                    pse=None,
+                    scale=1.0 / math.sqrt(query.shape[-1]),
+                    pre_tockens=65536,
+                    next_tockens=65536,
+                    keep_prob=1.0,
+                    sync=False,
+                    inner_precise=0,
+                )[0]
+            else:
+                hidden_states = F.scaled_dot_product_attention(
+                    query, key, value, dropout_p=0.0, is_causal=False
+                )
+        else:
+            hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
         hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
         hidden_states = hidden_states.to(query.dtype)
 
@@ -1872,7 +1893,28 @@ def __call__(
             query = apply_rotary_emb(query, image_rotary_emb)
             key = apply_rotary_emb(key, image_rotary_emb)
 
-        hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
+        if is_torch_npu_available():
+            if query.dtype in (torch.float16, torch.bfloat16):
+                hidden_states = torch_npu.npu_fusion_attention(
+                    query,
+                    key,
+                    value,
+                    attn.heads,
+                    input_layout="BNSD",
+                    pse=None,
+                    scale=1.0 / math.sqrt(query.shape[-1]),
+                    pre_tockens=65536,
+                    next_tockens=65536,
+                    keep_prob=1.0,
+                    sync=False,
+                    inner_precise=0,
+                )[0]
+            else:
+                hidden_states = F.scaled_dot_product_attention(
+                    query, key, value, dropout_p=0.0, is_causal=False
+                )
+        else:
+            hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
         hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
         hidden_states = hidden_states.to(query.dtype)