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[PR 1/2] SVG implementation for LTX 2 #497

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[PR 1/2] SVG implementation for LTX 2 #497

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126 changes: 124 additions & 2 deletions src/maxdiffusion/models/ltx2/attention_ltx2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,7 @@
import jax.numpy as jnp
from ... import common_types
from ..attention_flax import NNXAttentionOp
from ..wan.transformers import svg_attention
from .logical_sharding_ltx2 import get_sharding_specs, LTX2DiTShardingSpecs

Array = common_types.Array
Expand Down Expand Up @@ -352,7 +353,58 @@ def __init__(
use_base2_exp: bool = False,
use_experimental_scheduler: bool = False,
enable_jax_named_scopes: bool = False,
attention_config: Optional[dict] = None,
):
attention_config = {
"use_base2_exp": use_base2_exp,
"use_experimental_scheduler": use_experimental_scheduler,
"ulysses_shards": ulysses_shards,
"ulysses_attention_chunks": ulysses_attention_chunks,
"use_svg_attention": False,
"svg_implementation": "official_svg",
"svg_spatial_density": 0.25,
"svg_sample_max_row": 10000,
"svg_profile_query_count": 64,
"svg_profile_seed": 0,
"svg_dense_layer_fraction": 0.0,
"svg_dense_timestep_fraction": 0.0,
"svg_active_start_step": -1,
"svg_active_end_step": -1,
"svg_active_start_layer": -1,
"svg_active_end_layer": -1,
"svg_num_train_timesteps": 1000,
"svg_num_layers": 48,
"svg_include_first_frame": True,
"svg_global_stride": 0,
"svg_global_offset": 0,
"svg_high_noise_density": -1.0,
"svg_low_noise_density": -1.0,
"svg_flash_block_sizes": None,
**(attention_config or {}),
}

self.is_self_attention = context_dim is None
self.use_svg_attention = bool(attention_config["use_svg_attention"]) and self.is_self_attention
self.svg_implementation = attention_config["svg_implementation"]
self.svg_spatial_density = attention_config["svg_spatial_density"]
self.svg_sample_max_row = attention_config["svg_sample_max_row"]
self.svg_profile_query_count = attention_config["svg_profile_query_count"]
self.svg_profile_seed = attention_config["svg_profile_seed"]
self.svg_dense_layer_fraction = attention_config["svg_dense_layer_fraction"]
self.svg_dense_timestep_fraction = attention_config["svg_dense_timestep_fraction"]
self.svg_active_start_step = attention_config["svg_active_start_step"]
self.svg_active_end_step = attention_config["svg_active_end_step"]
self.svg_active_start_layer = attention_config["svg_active_start_layer"]
self.svg_active_end_layer = attention_config["svg_active_end_layer"]
self.svg_num_train_timesteps = attention_config["svg_num_train_timesteps"]
self.svg_num_layers = attention_config["svg_num_layers"]
self.svg_include_first_frame = attention_config["svg_include_first_frame"]
self.svg_global_stride = attention_config["svg_global_stride"]
self.svg_global_offset = attention_config["svg_global_offset"]
self.svg_high_noise_density = attention_config["svg_high_noise_density"]
self.svg_low_noise_density = attention_config["svg_low_noise_density"]
self.svg_flash_block_sizes = attention_config["svg_flash_block_sizes"]

self.heads = heads
self.rope_type = rope_type
self.dim_head = dim_head
Expand Down Expand Up @@ -542,10 +594,22 @@ def __call__(
k_rotary_emb: Optional[Tuple[Array, Array]] = None,
perturbation_mask: Optional[Array] = None,
cached_kv: Optional[Tuple[Array, Array]] = None,
deterministic: bool = True,
spatiotemporal_shape: Optional[Tuple[int, int, int]] = None,
svg_layer_index: Optional[int | jax.Array] = None,
svg_timestep: Optional[int | float | jax.Array] = None,
svg_step_index: Optional[int | jax.Array] = None,
) -> Array:
# Determine context (Self or Cross)
is_self_attention = encoder_hidden_states is None
context = encoder_hidden_states if encoder_hidden_states is not None else hidden_states

if self.use_svg_attention and is_self_attention:
if not deterministic:
raise ValueError("SVG attention supports deterministic inference only.")
if spatiotemporal_shape is None:
raise ValueError("SVG attention requires spatiotemporal_shape.")

# 1. Project and Norm
with self.named_scope("QKV Projection"):
query = self.to_q(hidden_states)
Expand Down Expand Up @@ -586,8 +650,66 @@ def __call__(

with self.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)
if self.use_svg_attention and is_self_attention and spatiotemporal_shape is not None:
is_active = svg_attention.is_svg_active(
step_index=svg_step_index,
layer_index=svg_layer_index,
timestep=svg_timestep,
start_step=self.svg_active_start_step,
end_step=self.svg_active_end_step,
start_layer=self.svg_active_start_layer,
end_layer=self.svg_active_end_layer,
dense_layer_fraction=self.svg_dense_layer_fraction,
dense_timestep_fraction=self.svg_dense_timestep_fraction,
num_train_timesteps=self.svg_num_train_timesteps,
num_layers=self.svg_num_layers,
)

def run_dense(_):
return self.attention_op.apply_attention(
query=query,
key=key,
value=value,
attention_mask=attention_mask,
)

def run_sparse_svg(_):
execution_band_width = svg_attention.svg_execution_band_width(
spatiotemporal_shape,
self.svg_spatial_density,
)
sparse_config = {
"use_svg_attention": True,
"mask_type": "svg_spatial",
"band_width": execution_band_width,
"include_first_frame": self.svg_include_first_frame,
"global_stride": self.svg_global_stride,
"global_offset": self.svg_global_offset,
"profile_query_count": self.svg_profile_query_count,
"profile_seed": self.svg_profile_seed,
"sample_max_row": self.svg_sample_max_row,
"custom_flash_block_sizes": self.svg_flash_block_sizes,
"svg_step_index": svg_step_index,
"svg_layer_index": svg_layer_index,
"svg_timestep": svg_timestep,
}
return self.attention_op.apply_attention(
query=query,
key=key,
value=value,
attention_mask=attention_mask,
spatiotemporal_shape=spatiotemporal_shape,
sparse_config_override=sparse_config,
)

with self.named_scope("apply_attention"):
if isinstance(is_active, bool):
attn_output = run_sparse_svg(None) if is_active else run_dense(None)
else:
attn_output = jax.lax.cond(is_active, run_sparse_svg, run_dense, operand=None)
else:
with self.named_scope("apply_attention"):
attn_output = self.attention_op.apply_attention(query=query, key=key, value=value, attention_mask=attention_mask)

if perturbation_mask is not None:
# value is [B, S, InnerDim]
Expand Down
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