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,
@@ -189,6 +193,8 @@ def log_validation(
     del pipeline
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
+    elif is_torch_npu_available():
+        torch_npu.npu.empty_cache()
 
     return images
 
@@ -1035,7 +1041,8 @@ def main(args):
         cur_class_images = len(list(class_images_dir.iterdir()))
 
         if cur_class_images < args.num_class_images:
-            has_supported_fp16_accelerator = torch.cuda.is_available() or torch.backends.mps.is_available()
+            has_supported_fp16_accelerator = (torch.cuda.is_available() or torch.backends.mps.is_available()
+                                              or is_torch_npu_available())
             torch_dtype = torch.float16 if has_supported_fp16_accelerator else torch.float32
             if args.prior_generation_precision == "fp32":
                 torch_dtype = torch.float32
@@ -1073,6 +1080,9 @@ 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 +1369,8 @@ 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 +1734,10 @@ 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

@@ -1798,6 +1798,110 @@ def __call__(
         else:
             return hidden_states
 
+class FluxAttnProcessor2_0_NPU:
+    """Attention processor used typically in processing the SD3-like self-attention projections."""
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError("FluxAttnProcessor2_0_NPU requires PyTorch 2.0 and torch NPU, to use it, please upgrade PyTorch to 2.0.")
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+
+        # `sample` projections.
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(hidden_states)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
+        if encoder_hidden_states is not None:
+            # `context` projections.
+            encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
+            encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
+            encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
+
+            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+
+            if attn.norm_added_q is not None:
+                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
+            if attn.norm_added_k is not None:
+                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
+
+            # attention
+            query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
+            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
+            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
+
+        if image_rotary_emb is not None:
+            from .embeddings import apply_rotary_emb
+
+            query = apply_rotary_emb(query, image_rotary_emb)
+            key = apply_rotary_emb(key, image_rotary_emb)
+
+        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
+            )
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        if encoder_hidden_states is not None:
+            encoder_hidden_states, hidden_states = (
+                hidden_states[:, : encoder_hidden_states.shape[1]],
+                hidden_states[:, encoder_hidden_states.shape[1] :],
+            )
+
+            # linear proj
+            hidden_states = attn.to_out[0](hidden_states)
+            # dropout
+            hidden_states = attn.to_out[1](hidden_states)
+            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+            return hidden_states, encoder_hidden_states
+        else:
+            return hidden_states
 
 class FusedFluxAttnProcessor2_0:
     """Attention processor used typically in processing the SD3-like self-attention projections."""
@@ -1892,6 +1996,117 @@ def __call__(
         else:
             return hidden_states
 
+class FusedFluxAttnProcessor2_0_NPU:
+    """Attention processor used typically in processing the SD3-like self-attention projections."""
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "FusedFluxAttnProcessor2_0_NPU requires PyTorch 2.0 and torch NPU, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+
+        # `sample` projections.
+        qkv = attn.to_qkv(hidden_states)
+        split_size = qkv.shape[-1] // 3
+        query, key, value = torch.split(qkv, split_size, dim=-1)
+
+        inner_dim = key.shape[-1]
+        head_dim = inner_dim // attn.heads
+
+        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
+        # `context` projections.
+        if encoder_hidden_states is not None:
+            encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
+            split_size = encoder_qkv.shape[-1] // 3
+            (
+                encoder_hidden_states_query_proj,
+                encoder_hidden_states_key_proj,
+                encoder_hidden_states_value_proj,
+            ) = torch.split(encoder_qkv, split_size, dim=-1)
+
+            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+
+            if attn.norm_added_q is not None:
+                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
+            if attn.norm_added_k is not None:
+                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)
+
+            # attention
+            query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
+            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
+            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
+
+        if image_rotary_emb is not None:
+            from .embeddings import apply_rotary_emb
+
+            query = apply_rotary_emb(query, image_rotary_emb)
+            key = apply_rotary_emb(key, image_rotary_emb)
+
+        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
+            )
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        if encoder_hidden_states is not None:
+            encoder_hidden_states, hidden_states = (
+                hidden_states[:, : encoder_hidden_states.shape[1]],
+                hidden_states[:, encoder_hidden_states.shape[1] :],
+            )
+
+            # linear proj
+            hidden_states = attn.to_out[0](hidden_states)
+            # dropout
+            hidden_states = attn.to_out[1](hidden_states)
+            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+            return hidden_states, encoder_hidden_states
+        else:
+            return hidden_states
 
 class CogVideoXAttnProcessor2_0:
     r"""

src/diffusers/models/transformers/transformer_flux.py

@@ -27,12 +27,15 @@
     Attention,
     AttentionProcessor,
     FluxAttnProcessor2_0,
+    FluxAttnProcessor2_0_NPU,
     FusedFluxAttnProcessor2_0,
+    FusedFluxAttnProcessor2_0_NPU,
 )
 from ...models.modeling_utils import ModelMixin
 from ...models.normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle
 from ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
 from ...utils.torch_utils import maybe_allow_in_graph
+from ...utils.import_utils import is_torch_npu_available
 from ..embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, FluxPosEmbed
 from ..modeling_outputs import Transformer2DModelOutput
 
@@ -64,7 +67,10 @@ def __init__(self, dim, num_attention_heads, attention_head_dim, mlp_ratio=4.0):
         self.act_mlp = nn.GELU(approximate="tanh")
         self.proj_out = nn.Linear(dim + self.mlp_hidden_dim, dim)
 
-        processor = FluxAttnProcessor2_0()
+        if is_torch_npu_available():
+            processor = FluxAttnProcessor2_0_NPU()
+        else:
+            processor = FluxAttnProcessor2_0()
         self.attn = Attention(
             query_dim=dim,
             cross_attention_dim=None,
@@ -369,7 +375,10 @@ def fuse_qkv_projections(self):
             if isinstance(module, Attention):
                 module.fuse_projections(fuse=True)
 
-        self.set_attn_processor(FusedFluxAttnProcessor2_0())
+        if is_torch_npu_available():
+            self.set_attn_processor(FusedFluxAttnProcessor2_0_NPU())
+        else:
+            self.set_attn_processor(FusedFluxAttnProcessor2_0())
 
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.unfuse_qkv_projections
     def unfuse_qkv_projections(self):