Fix VAE decoding error and use base 2 and experimental scheduler

README.md

@@ -628,16 +628,16 @@ To generate images, run the following command:
 We added ring attention support for Wan models. Below are the stats for one `720p` (81 frames) video generation (with CFG DP):
 | Accelerator |  Model | Attention Type | Inference Steps | Sharding | e2e Generation Time |
 | -- | -- | -- | -- | -- | -- | 
-| v7x-8 | WAN 2.1 | Tokamax Flash | 50 | dp2-fsdp1-context4-tp1 | 264.2 |
-| v7x-8 | WAN 2.1 | Tokamax Ring | 50 | dp2-fsdp1-context4-tp1 | **252.4** |
-| v7x-8 | WAN 2.2 | Tokamax Flash | 40 | dp2-fsdp1-context4-tp1 | 212.7 |
-| v7x-8 | WAN 2.2 | Tokamax Ring | 40 | dp2-fsdp1-context4-tp1 | **201.7** |
+| v7x-8 | WAN 2.1 | Tokamax Flash | 50 | dp2-fsdp1-context4-tp1 | **249.3** |
+| v7x-8 | WAN 2.1 | Tokamax Ring | 50 | dp2-fsdp1-context4-tp1 | 252.4 |
+| v7x-8 | WAN 2.2 | Tokamax Flash | 40 | dp2-fsdp1-context4-tp1 | **194.4** |
+| v7x-8 | WAN 2.2 | Tokamax Ring | 40 | dp2-fsdp1-context4-tp1 | 201.7 |
 
 | Accelerator |  Model | Attention Type | Inference Steps | Sharding | e2e Generation Time |
 | -- | -- | -- | -- | -- | -- | 
-| v7x-16 | WAN 2.1 | Tokamax Flash | 50 | dp2-fsdp1-context8-tp1 | 146.6 |
-| v7x-16 | WAN 2.1 | Tokamax Ring | 50 | dp2-fsdp1-context8-tp1 | **137.2** |
-| v7x-16 | WAN 2.2 | Tokamax Flash | 40 | dp2-fsdp1-context8-tp1 | **117.8** |
+| v7x-16 | WAN 2.1 | Tokamax Flash | 50 | dp2-fsdp1-context8-tp1 | **127.1** |
+| v7x-16 | WAN 2.1 | Tokamax Ring | 50 | dp2-fsdp1-context8-tp1 | 137.2 |
+| v7x-16 | WAN 2.2 | Tokamax Flash | 40 | dp2-fsdp1-context8-tp1 | **106.0** |
 | v7x-16 | WAN 2.2 | Tokamax Ring | 40 | dp2-fsdp1-context8-tp1 | 137.5 |
 
 (* There are some known stability issues for ring attention on 16 TPUs, please use `tokamax_flash` attention instead.)

src/maxdiffusion/configs/base_wan_14b.yml

@@ -62,6 +62,8 @@ jit_initializers: True
 from_pt: True
 split_head_dim: True
 attention: 'flash' # Supported attention: dot_product, flash, tokamax_flash, cudnn_flash_te, ring, tokamax_ring, ulysses
+use_base2_exp: True
+use_experimental_scheduler: True
 flash_min_seq_length: 0
 
 # If mask_padding_tokens is True, we pass in segment ids to splash attention to avoid attending to padding tokens.

src/maxdiffusion/configs/base_wan_1_3b.yml

@@ -61,6 +61,8 @@ jit_initializers: True
 from_pt: True
 split_head_dim: True
 attention: 'flash' # Supported attention: dot_product, flash, cudnn_flash_te, ring, ulysses
+use_base2_exp: True
+use_experimental_scheduler: True
 flash_min_seq_length: 0
 
 # If mask_padding_tokens is True, we pass in segment ids to splash attention to avoid attending to padding tokens.

src/maxdiffusion/configs/base_wan_27b.yml

@@ -62,6 +62,8 @@ jit_initializers: True
 from_pt: True
 split_head_dim: True
 attention: 'flash' # Supported attention: dot_product, flash, cudnn_flash_te, ring, ulysses
+use_base2_exp: True
+use_experimental_scheduler: True
 flash_min_seq_length: 4096
 # If mask_padding_tokens is True, we pass in segment ids to splash attention to avoid attending to padding tokens.
 # Else we do not pass in segment ids and on vpu bound hardware like trillium this is faster.

src/maxdiffusion/configs/base_wan_i2v_14b.yml

@@ -61,6 +61,8 @@ jit_initializers: True
 from_pt: True
 split_head_dim: True
 attention: 'flash' # Supported attention: dot_product, flash, cudnn_flash_te, ring, ulysses
+use_base2_exp: True
+use_experimental_scheduler: True
 flash_min_seq_length: 4096
 dropout: 0.0
 

src/maxdiffusion/configs/base_wan_i2v_27b.yml

@@ -61,6 +61,8 @@ jit_initializers: True
 from_pt: True
 split_head_dim: True
 attention: 'flash' # Supported attention: dot_product, flash, cudnn_flash_te, ring, ulysses
+use_base2_exp: True
+use_experimental_scheduler: True
 flash_min_seq_length: 4096
 dropout: 0.0
 

src/maxdiffusion/generate_wan.py

@@ -302,14 +302,7 @@ def run(config, pipeline=None, filename_prefix="", commit_hash=None):
       f"{'=' * 50}"
   )
 
-  s0 = time.perf_counter()
-  if max_utils.profiler_enabled(config):
-    with max_utils.Profiler(config):
-      videos = call_pipeline(config, pipeline, prompt, negative_prompt)
-    generation_time_with_profiler = time.perf_counter() - s0
-    max_logging.log(f"generation_time_with_profiler: {generation_time_with_profiler}")
-    if writer and jax.process_index() == 0:
-      writer.add_scalar("inference/generation_time_with_profiler", generation_time_with_profiler, global_step=0)
+  videos = call_pipeline(config, pipeline, prompt, negative_prompt)
 
   return saved_video_path
 

src/maxdiffusion/models/attention_flax.py

@@ -272,6 +272,7 @@ def convert_to_tokamax_splash_config(
     attn_logits_soft_cap: float | None = None,
     fuse_reciprocal: bool = True,
     use_base2_exp: bool = False,
+    use_experimental_scheduler: bool = False,
     max_logit_const: float | None = None,
     interpret: bool = False,
     dq_reduction_steps: int | None = None,
@@ -294,6 +295,7 @@ def convert_to_tokamax_splash_config(
       attn_logits_soft_cap=attn_logits_soft_cap,
       fuse_reciprocal=fuse_reciprocal,
       use_base2_exp=use_base2_exp,
+      use_experimental_scheduler=use_experimental_scheduler,
       max_logit_const=max_logit_const,
       interpret=interpret,
       dq_reduction_steps=dq_reduction_steps,
@@ -314,6 +316,8 @@ def _tpu_flash_attention(
     mask_padding_tokens: bool = True,
     residual_checkpoint_name: str | None = None,
     attention_mask: jax.Array = None,
+    use_base2_exp: bool = False,
+    use_experimental_scheduler: bool = False,
 ) -> jax.Array:
   """TPU Flash Attention"""
 
@@ -399,7 +403,12 @@ def wrap_flash_attention(query, key, value):
       splash_kernel = tokamax_splash_attention_kernel.make_splash_mha(
           mask=mask,
           q_seq_shards=1,  # the sizes of the axis is sharding over seq_len
-          config=convert_to_tokamax_splash_config(block_sizes, residual_checkpoint_name=residual_checkpoint_name),
+          config=convert_to_tokamax_splash_config(
+              block_sizes,
+              residual_checkpoint_name=residual_checkpoint_name,
+              use_base2_exp=use_base2_exp,
+              use_experimental_scheduler=use_experimental_scheduler,
+          ),
           save_residuals=False,
       )
     elif attention_kernel == "tokamax_ring":
@@ -409,7 +418,12 @@ def wrap_flash_attention(query, key, value):
       splash_kernel = tokamax_ring_attention_kernel.make_ring_attention(
           mask=mask,
           is_mqa=False,
-          config=convert_to_tokamax_splash_config(block_sizes, residual_checkpoint_name=residual_checkpoint_name),
+          config=convert_to_tokamax_splash_config(
+              block_sizes,
+              residual_checkpoint_name=residual_checkpoint_name,
+              use_base2_exp=use_base2_exp,
+              use_experimental_scheduler=use_experimental_scheduler,
+          ),
           save_residuals=False,
           ring_axis="context",
           rotate_segment_ids=False,  # We don't rotate segment ids in tokamax ring attention because our segment ids is for padding each kv shard has same segment ids
@@ -473,13 +487,13 @@ def ring_scan_body(carry, _):
         raise ValueError("ring attention requires context > 1")
     return attention_output[:, :, :query_seq_len, :kv_size].astype(query.dtype)
 
-  devices_in_data_context = mesh.shape["data"] * mesh.shape["context"]
+  devices_in_batch_sharding = mesh.shape["data"] * (mesh.shape["fsdp"] if "fsdp" in mesh.shape else 1)
   # This warning might show up when doing model eval for example, when calculating model flops
   # and that is expected.
-  if not (query.shape[0] / devices_in_data_context).is_integer():
+  if not (query.shape[0] / devices_in_batch_sharding).is_integer():
     max_logging.log(
-        "Warning, batch dimension should be shardable among the devices in data and context"
-        f" axis, batch dimension: {query.shape[0]}, devices_in_data_context: {devices_in_data_context}"
+        "Warning, batch dimension should be shardable among the devices in data and fsdp"
+        f" axis, batch dimension: {query.shape[0]}, devices_in_batch_sharding: {devices_in_batch_sharding}"
     )
   x = wrap_flash_attention(query, key, value)
   # Trim back to original sequence length after context-axis padding.
@@ -614,11 +628,11 @@ def wrap_ulysses_attention(query, key, value):
     attention_output = jax.lax.all_to_all(attention_output, axis_name=axis_name, split_axis=2, concat_axis=1, tiled=True)
     return attention_output
 
-  devices_in_data_context = mesh.shape["data"] * num_shards
-  if not (query.shape[0] / devices_in_data_context).is_integer():
+  devices_in_batch_sharding = mesh.shape["data"] * (mesh.shape["fsdp"] if "fsdp" in mesh.shape else 1)
+  if not (query.shape[0] / devices_in_batch_sharding).is_integer():
     max_logging.log(
-        "Warning, batch dimension should be shardable among the devices in data and context"
-        f" axis, batch dimension: {query.shape[0]}, devices_in_data_context: {devices_in_data_context}"
+        "Warning, batch dimension should be shardable among the devices in data and fsdp"
+        f" axis, batch dimension: {query.shape[0]}, devices_in_batch_sharding: {devices_in_batch_sharding}"
     )
   x = wrap_ulysses_attention(query, key, value)
   x = x[:, :, :orig_q_seq_len, :]
@@ -741,6 +755,8 @@ def _apply_attention(
     mask_padding_tokens: bool = True,
     residual_checkpoint_name: str | None = None,
     attention_mask: Array = None,
+    use_base2_exp: bool = False,
+    use_experimental_scheduler: bool = False,
 ):
   """Routes to different attention kernels."""
   _check_attention_inputs(query, key, value)
@@ -789,6 +805,8 @@ def _apply_attention(
         mask_padding_tokens=mask_padding_tokens,
         residual_checkpoint_name=residual_checkpoint_name,
         attention_mask=attention_mask,
+        use_base2_exp=use_base2_exp,
+        use_experimental_scheduler=use_experimental_scheduler,
     )
   elif "ring" in attention_kernel:
     return _tpu_flash_attention(
@@ -983,8 +1001,12 @@ def __init__(
       quant: Quant = None,
       mask_padding_tokens: bool = True,
       residual_checkpoint_name: str | None = None,
+      use_base2_exp: bool = False,
+      use_experimental_scheduler: bool = False,
   ):
     self.dpa_layer = None
+    self.use_base2_exp = use_base2_exp
+    self.use_experimental_scheduler = use_experimental_scheduler
     if attention_kernel == "cudnn_flash_te":
       from transformer_engine.jax.flax.transformer import DotProductAttention  # pytype: disable=import-error
 
@@ -1045,6 +1067,8 @@ def apply_attention(self, query: Array, key: Array, value: Array, attention_mask
         mask_padding_tokens=self.mask_padding_tokens,
         residual_checkpoint_name=self.residual_checkpoint_name,
         attention_mask=attention_mask,
+        use_base2_exp=self.use_base2_exp if hasattr(self, "use_base2_exp") else False,
+        use_experimental_scheduler=self.use_experimental_scheduler if hasattr(self, "use_experimental_scheduler") else False,
     )
 
 
@@ -1063,6 +1087,8 @@ class AttentionOp(nn.Module):
   flash_block_sizes: BlockSizes = None
   dtype: DType = jnp.float32
   quant: Quant = None
+  use_base2_exp: bool = False
+  use_experimental_scheduler: bool = False
 
   def setup(self):
     self.dpa_layer = None
@@ -1108,6 +1134,8 @@ def apply_attention(self, query: Array, key: Array, value: Array, attention_mask
         flash_block_sizes=self.flash_block_sizes,
         dpa_layer=self.dpa_layer,
         attention_mask=attention_mask,
+        use_base2_exp=self.use_base2_exp,
+        use_experimental_scheduler=self.use_experimental_scheduler,
     )
 
 
@@ -1144,6 +1172,8 @@ def __init__(
       enable_jax_named_scopes: bool = False,
       added_kv_proj_dim: Optional[int] = None,  # New for I2V
       image_seq_len: Optional[int] = None,  # New for I2V
+      use_base2_exp: bool = False,
+      use_experimental_scheduler: bool = False,
   ):
     if attention_kernel in {"flash", "cudnn_flash_te"} and mesh is None:
       raise ValueError(f"The flash attention kernel requires a value for mesh, but mesh is {self.mesh}")
@@ -1186,6 +1216,8 @@ def __init__(
         quant=quant,
         mask_padding_tokens=mask_padding_tokens,
         residual_checkpoint_name=residual_checkpoint_name,
+        use_base2_exp=use_base2_exp,
+        use_experimental_scheduler=use_experimental_scheduler,
     )
     # None axes corresponds to the stacked weights across all blocks
     # because of the use of nnx.vmap and nnx.scan.

src/maxdiffusion/models/wan/autoencoder_kl_wan.py

@@ -1206,7 +1206,7 @@ def _decode(
       fm1, fm2, fm3, fm4 = out_chunk_1[:, 0, ...], out_chunk_1[:, 1, ...], out_chunk_1[:, 2, ...], out_chunk_1[:, 3, ...]
       axis = 1 if fm1.shape[0] > 1 else 0
       fm1, fm2, fm3, fm4 = [jnp.expand_dims(f, axis=axis) for f in [fm1, fm2, fm3, fm4]]
-      out_1 = jnp.concatenate([fm1, fm3, fm2, fm4], axis=1)
+      out_1 = jnp.concatenate([fm1, fm2, fm3, fm4], axis=1)
 
       out_list = [out_0, out_1]
 
@@ -1226,7 +1226,7 @@ def scan_fn(carry, chunk_in):
           fm1, fm2, fm3, fm4 = out_chunk[:, 0, ...], out_chunk[:, 1, ...], out_chunk[:, 2, ...], out_chunk[:, 3, ...]
           axis = 1 if fm1.shape[0] > 1 else 0
           fm1, fm2, fm3, fm4 = [jnp.expand_dims(f, axis=axis) for f in [fm1, fm2, fm3, fm4]]
-          new_chunk = jnp.concatenate([fm1, fm3, fm2, fm4], axis=1)
+          new_chunk = jnp.concatenate([fm1, fm2, fm3, fm4], axis=1)
 
           return next_feat_map, new_chunk
 

src/maxdiffusion/models/wan/transformers/transformer_wan.py

@@ -291,6 +291,8 @@ def __init__(
       dropout: float = 0.0,
       mask_padding_tokens: bool = True,
       enable_jax_named_scopes: bool = False,
+      use_base2_exp: bool = False,
+      use_experimental_scheduler: bool = False,
   ):
     self.enable_jax_named_scopes = enable_jax_named_scopes
 
@@ -315,6 +317,8 @@ def __init__(
         mask_padding_tokens=mask_padding_tokens,
         residual_checkpoint_name="self_attn",
         enable_jax_named_scopes=enable_jax_named_scopes,
+        use_base2_exp=use_base2_exp,
+        use_experimental_scheduler=use_experimental_scheduler,
     )
 
     # 1. Cross-attention
@@ -339,6 +343,8 @@ def __init__(
         mask_padding_tokens=mask_padding_tokens,
         residual_checkpoint_name="cross_attn",
         enable_jax_named_scopes=enable_jax_named_scopes,
+        use_base2_exp=use_base2_exp,
+        use_experimental_scheduler=use_experimental_scheduler,
     )
     assert cross_attn_norm is True
     self.norm2 = FP32LayerNorm(rngs=rngs, dim=dim, eps=eps, elementwise_affine=True)
@@ -486,6 +492,8 @@ def __init__(
       mask_padding_tokens: bool = True,
       scan_layers: bool = True,
       enable_jax_named_scopes: bool = False,
+      use_base2_exp: bool = False,
+      use_experimental_scheduler: bool = False,
   ):
     inner_dim = num_attention_heads * attention_head_dim
     out_channels = out_channels or in_channels
@@ -547,6 +555,8 @@ def init_block(rngs):
           enable_jax_named_scopes=enable_jax_named_scopes,
           added_kv_proj_dim=added_kv_proj_dim,
           image_seq_len=image_seq_len,
+          use_base2_exp=use_base2_exp,
+          use_experimental_scheduler=use_experimental_scheduler,
       )
 
     self.gradient_checkpoint = GradientCheckpointType.from_str(remat_policy)

src/maxdiffusion/pipelines/wan/wan_pipeline.py

@@ -67,7 +67,6 @@ def cast_with_exclusion(path, x, dtype_to_cast):
   path_str = ".".join(str(k.key) if isinstance(k, jax.tree_util.DictKey) else str(k) for k in path)
 
   if any(keyword in path_str.lower() for keyword in exclusion_keywords):
-    print("is_norm_path: ", path)
     # Keep LayerNorm/GroupNorm weights and biases in full precision
     return x.astype(jnp.float32)
   else:
@@ -139,6 +138,8 @@ def create_model(rngs: nnx.Rngs, wan_config: dict):
   wan_config["mask_padding_tokens"] = config.mask_padding_tokens
   wan_config["scan_layers"] = config.scan_layers
   wan_config["enable_jax_named_scopes"] = config.enable_jax_named_scopes
+  wan_config["use_base2_exp"] = config.use_base2_exp
+  wan_config["use_experimental_scheduler"] = config.use_experimental_scheduler
 
   # 2. eval_shape - will not use flops or create weights on device
   # thus not using HBM memory.

src/maxdiffusion/pipelines/wan/wan_pipeline_2_1.py

@@ -22,6 +22,8 @@
 import jax
 import jax.numpy as jnp
 from ...schedulers.scheduling_unipc_multistep_flax import FlaxUniPCMultistepScheduler
+import numpy as np
+from ... import max_utils
 
 
 class WanPipeline2_1(WanPipeline):
@@ -142,6 +144,7 @@ def __call__(
         retention_ratio=retention_ratio,
         height=height,
         mag_ratios_base=getattr(config, "mag_ratios_base", None),
+        config=self.config,
     )
 
     with self.mesh, nn_partitioning.axis_rules(self.config.logical_axis_rules):
@@ -175,6 +178,7 @@ def run_inference_2_1(
     retention_ratio: float = 0.2,
     height: int = 480,
     mag_ratios_base: Optional[List[float]] = None,
+    config=None,
 ):
   """Denoising loop for WAN 2.1 T2V with FasterCache CFG-Cache.
 
@@ -253,7 +257,16 @@ def run_inference_2_1(
     skip_warmup = magcache_init[7]
     mag_ratios = magcache_init[8]
 
+  first_profiling_step = config.skip_first_n_steps_for_profiler if config else 0
+  profiler_steps = config.profiler_steps if config else 0
+  last_profiling_step = np.clip(first_profiling_step + profiler_steps - 1, first_profiling_step, num_inference_steps - 1)
+
+  profiler = None
   for step in range(num_inference_steps):
+    if config and max_utils.profiler_enabled(config) and step == first_profiling_step:
+      profiler = max_utils.Profiler(config)
+      profiler.start()
+
     t = jnp.array(scheduler_state.timesteps, dtype=jnp.int32)[step]
 
     if use_magcache and do_cfg:
@@ -328,4 +341,8 @@ def run_inference_2_1(
 
     latents, scheduler_state = scheduler.step(scheduler_state, noise_pred, t, latents).to_tuple()
 
+    if config and max_utils.profiler_enabled(config) and step == last_profiling_step:
+      if profiler:
+        profiler.stop()
+
   return latents