LTX-2 minor fixes

src/maxdiffusion/configs/ltx2_video.yml

@@ -2,6 +2,8 @@
 hardware: 'tpu'
 skip_jax_distributed_system: False
 attention: 'flash'
+a2v_attention_kernel: 'flash'
+v2a_attention_kernel: 'dot_product'
 attention_sharding_uniform: True 
 precision: 'bf16'
 scan_layers: True
@@ -68,6 +70,7 @@ flash_block_sizes: {
   block_kv_dkv_compute: 2048,
   use_fused_bwd_kernel: True,
 }
+flash_min_seq_length: 4096
 dcn_context_parallelism: 1
 dcn_tensor_parallelism: 1
 ici_data_parallelism: 1

src/maxdiffusion/models/ltx2/attention_ltx2.py

@@ -16,6 +16,7 @@
 
 from typing import Optional, Tuple
 from flax import nnx
+import jax
 import jax.numpy as jnp
 from ... import common_types
 from ..attention_flax import NNXAttentionOp
@@ -347,6 +348,7 @@ def __init__(
       attention_kernel: str = "flash",
       rope_type: str = "interleaved",
       flash_block_sizes: BlockSizes = None,
+      flash_min_seq_length: int = 4096,
   ):
     self.heads = heads
     self.rope_type = rope_type
@@ -434,6 +436,7 @@ def __init__(
         axis_names_q=(common_types.BATCH, common_types.SELF_ATTN_HEAD, common_types.SELF_ATTN_Q_LENGTH, common_types.D_KV),
         axis_names_kv=(common_types.BATCH, common_types.SELF_ATTN_HEAD, common_types.SELF_ATTN_KV_LENGTH, common_types.D_KV),
         flash_block_sizes=flash_block_sizes,
+        flash_min_seq_length=flash_min_seq_length,
     )
 
   def __call__(
@@ -447,46 +450,49 @@ def __call__(
     # Determine context (Self or Cross)
     context = encoder_hidden_states if encoder_hidden_states is not None else hidden_states
 
-    # 1. Project
-    query = self.to_q(hidden_states)
-    key = self.to_k(context)
-    value = self.to_v(context)
+    # 1. Project and Norm
+    with jax.named_scope("QKV Projection"):
+      query = self.to_q(hidden_states)
+      key = self.to_k(context)
+      value = self.to_v(context)
 
-    # 2. Norm (Full Inner Dimension)
-    query = self.norm_q(query)
-    key = self.norm_k(key)
+    with jax.named_scope("QKV Norm"):
+      query = self.norm_q(query)
+      key = self.norm_k(key)
 
     # 3. Apply RoPE to tensors of shape [B, S, InnerDim]
     # Frequencies are shape [B, S, InnerDim]
     # 3. Apply RoPE
-    if rotary_emb is not None:
-      if hasattr(self, "rope_type") and self.rope_type == "split":
-        # Split RoPE: passing full freqs [B, H, S, D//2]
-        # apply_split_rotary_emb handles reshaping query/key
-
-        query = apply_split_rotary_emb(query, rotary_emb)
-
-        if k_rotary_emb is not None:
-          key = apply_split_rotary_emb(key, k_rotary_emb)
-        elif encoder_hidden_states is None:
-          key = apply_split_rotary_emb(key, rotary_emb)
-
-      else:
-        # Interleaved (Default)
-        query = apply_rotary_emb(query, rotary_emb)
-        if k_rotary_emb is not None:
-          key = apply_rotary_emb(key, k_rotary_emb)
-        elif encoder_hidden_states is None:
-          key = apply_rotary_emb(key, rotary_emb)
-
-    # 4. Attention
-    # NNXAttentionOp expects flattened input [B, S, InnerDim] for flash kernel
-    attn_output = self.attention_op.apply_attention(query=query, key=key, value=value, attention_mask=attention_mask)
-
-    # 7. Output Projection
-    hidden_states = self.to_out(attn_output)
-
-    if self.dropout_layer is not None:
-      hidden_states = self.dropout_layer(hidden_states)
+    with jax.named_scope("Apply RoPE"):
+      if rotary_emb is not None:
+        if hasattr(self, "rope_type") and self.rope_type == "split":
+          # Split RoPE: passing full freqs [B, H, S, D//2]
+          # apply_split_rotary_emb handles reshaping query/key
+
+          query = apply_split_rotary_emb(query, rotary_emb)
+
+          if k_rotary_emb is not None:
+            key = apply_split_rotary_emb(key, k_rotary_emb)
+          elif encoder_hidden_states is None:
+            key = apply_split_rotary_emb(key, rotary_emb)
+
+        else:
+          # Interleaved (Default)
+          query = apply_rotary_emb(query, rotary_emb)
+          if k_rotary_emb is not None:
+            key = apply_rotary_emb(key, k_rotary_emb)
+          elif encoder_hidden_states is None:
+            key = apply_rotary_emb(key, rotary_emb)
+
+    with jax.named_scope("Attention and Output Project"):
+      # 4. Attention
+      # NNXAttentionOp expects flattened input [B, S, InnerDim] for flash kernel
+      attn_output = self.attention_op.apply_attention(query=query, key=key, value=value, attention_mask=attention_mask)
+
+      # 7. Output Projection
+      hidden_states = self.to_out(attn_output)
+
+      if self.dropout_layer is not None:
+        hidden_states = self.dropout_layer(hidden_states)
 
     return hidden_states

src/maxdiffusion/models/ltx2/text_encoders/text_encoders_ltx2.py

@@ -108,11 +108,12 @@ def __call__(
     Returns:
         (video_embeds, audio_embeds, new_attention_mask)
     """
-    # 1. Shared Feature Extraction
-    features = self.feature_extractor(hidden_states, attention_mask)
+    with jax.named_scope("Text Encoder Forward"):
+      # 1. Shared Feature Extraction
+      features = self.feature_extractor(hidden_states, attention_mask)
 
-    # 2. Parallel Connection
-    video_embeds, new_attention_mask = self.video_embeddings_connector(features, attention_mask)
-    audio_embeds, _ = self.audio_embeddings_connector(features, attention_mask)
+      # 2. Parallel Connection
+      video_embeds, new_attention_mask = self.video_embeddings_connector(features, attention_mask)
+      audio_embeds, _ = self.audio_embeddings_connector(features, attention_mask)
 
-    return video_embeds, audio_embeds, new_attention_mask
+      return video_embeds, audio_embeds, new_attention_mask