[WAN 2.2] 2D Ring Attention with Custom Kernel

src/maxdiffusion/configs/base_wan_27b.yml

@@ -83,7 +83,13 @@ jit_initializers: True
 # Set true to load weights from pytorch
 from_pt: True
 split_head_dim: True
-attention: 'flash' # Supported attention: dot_product, flash, tokamax_flash, cudnn_flash_te, ring, tokamax_ring, ulysses, ulysses_custom, ulysses_ring
+attention: 'flash' # Supported attention: dot_product, flash, tokamax_flash, cudnn_flash_te, ring, tokamax_ring, tokamax_ring_custom, ulysses, ulysses_custom, ulysses_ring, ulysses_ring_custom, ulysses_ring_custom_bidir
+#
+# Best 2D-ring / USP (Ulysses x ring) configs for WAN2.2-T2V-A14B (720x1280, 81 frames)
+# Set attention=ulysses_ring_custom and ulysses_shards=U (ring degree R=CP/U):
+#   CP4  (v7x-8):  ulysses_shards=2 (R=2), BQ=9472
+#   CP8  (v7x-8):  ulysses_shards=4 (R=2), BQ=9472
+#   CP16 (v7x-16): ulysses_shards=8 (R=2), BQ=9472
 use_base2_exp: True
 use_experimental_scheduler: True
 # For attention=ulysses_ring, hidden Ulysses shard count; ring shards are context / this.

src/maxdiffusion/kernels/custom_splash_attention.py

@@ -59,6 +59,8 @@ def _flash_attention_kernel(
     l_scratch_ref,
     o_scratch_ref,
     o_ref,
+    l_ring_ref=None,
+    m_ring_ref=None,
     *,
     mask_value: float,
     grid_width: int,
@@ -70,6 +72,7 @@ def _flash_attention_kernel(
     q_seq_len: int,
     kv_seq_len: int,
     use_base2_exp: bool = True,
+    fuse_reciprocal: bool = True,
 ):
   float32 = jnp.float32
   head_dim_v_repeats, rem = divmod(head_dim_v, NUM_SUBLANES)
@@ -192,8 +195,18 @@ def last_body():
   @pl.when(j == grid_width - 1)
   def end():
     l = l_scratch_ref[...]
-    l_inv = jnp.tile(1.0 / l, (head_dim_v_repeats, 1))
-    o_ref[...] = (o_scratch_ref[...] * l_inv).astype(o_ref.dtype)
+    if fuse_reciprocal:
+      l_inv = jnp.tile(1.0 / l, (head_dim_v_repeats, 1))
+      o_ref[...] = (o_scratch_ref[...] * l_inv).astype(o_ref.dtype)
+    else:
+      # Ring path: emit the un-normalized numerator plus the running softmax
+      # stats (max logit `m` and linear denominator `l`) so the outer ring loop
+      # can merge shard contributions and normalize only once at the very end.
+      o_ref[...] = o_scratch_ref[...].astype(o_ref.dtype)
+    if l_ring_ref is not None:
+      l_ring_ref[...] = l.astype(l_ring_ref.dtype)
+    if m_ring_ref is not None:
+      m_ring_ref[...] = m_scratch_ref[...].astype(m_ring_ref.dtype)
 
 
 def _flash_attention_kernel_mhpt(
@@ -437,6 +450,116 @@ def v_index_map(h, i, j, *_):
   return all_out[-1]
 
 
+def _splash_attention_forward_ring(
+    q: jax.Array,
+    k: jax.Array,
+    v: jax.Array,
+    block_sizes: _BlockSizes,
+    bkv_compute_in: int,
+    q_seq_len: int | None = None,
+    kv_seq_len: int | None = None,
+    use_base2_exp: bool = True,
+    use_experimental_scheduler: bool = False,
+    vmem_limit_bytes: int | None = None,
+):
+  """Ring-specific forward path that returns pre-reciprocal fp32 accumulators.
+
+  Mirrors `_splash_attention_forward`, but instead of normalizing the output by
+  the softmax denominator inside the kernel, it returns the un-normalized
+  numerator (`out`) together with the per-row max logit (`m`) and linear softmax
+  denominator (`l`). The outer ring loop merges these shard contributions and
+  normalizes only once at the very end (see
+  `ring_attention_kernel._custom_ring_attention_forward`).
+
+  Returns:
+    A tuple `(out, m, l)` where
+      - `out` has shape `(num_q_heads, q_seq_len, head_dim_v)` (fp32, un-normalized),
+      - `m` and `l` have shape `(num_q_heads, q_seq_len)` (fp32).
+  """
+  num_q_heads, padded_q_seq_len, head_dim_qk = q.shape
+  head_dim_v = v.shape[-1]
+  bq, bkv = block_sizes.block_q, block_sizes.block_kv
+  bkv_compute = block_sizes.block_kv_compute
+  num_kv_heads = k.shape[0]
+  padded_kv_seq_len = k.shape[1]
+
+  actual_q_seq_len = q_seq_len if q_seq_len is not None else padded_q_seq_len
+  actual_kv_seq_len = kv_seq_len if kv_seq_len is not None else padded_kv_seq_len
+  q_heads_per_kv_head = num_q_heads // num_kv_heads
+
+  def q_index_map(h, i, j, *_):
+    return (h, i, 0)
+
+  def out_index_map(h, i, j, *_):
+    return h, 0, i
+
+  def k_index_map(h, i, j, *_):
+    return (h // q_heads_per_kv_head, j, 0)
+
+  def v_index_map(h, i, j, *_):
+    return (h // q_heads_per_kv_head, j, 0)
+
+  in_specs = [
+      pl.BlockSpec((None, bq, head_dim_qk), q_index_map),
+      pl.BlockSpec((None, bkv, head_dim_qk), k_index_map),
+      pl.BlockSpec((None, bkv, head_dim_v), v_index_map),
+  ]
+  out_shapes = [
+      jax.ShapeDtypeStruct((NUM_SUBLANES, bq), jnp.float32),
+      jax.ShapeDtypeStruct((NUM_SUBLANES, bq), jnp.float32),
+      jax.ShapeDtypeStruct((head_dim_v, bq), jnp.float32),
+      jax.ShapeDtypeStruct((num_q_heads, head_dim_v, actual_q_seq_len), jnp.float32),
+      jax.ShapeDtypeStruct((num_q_heads, NUM_SUBLANES, actual_q_seq_len), jnp.float32),
+      jax.ShapeDtypeStruct((num_q_heads, NUM_SUBLANES, actual_q_seq_len), jnp.float32),
+  ]
+  out_specs = [
+      pl.BlockSpec((NUM_SUBLANES, bq), lambda *_: (0, 0)),
+      pl.BlockSpec((NUM_SUBLANES, bq), lambda *_: (0, 0)),
+      pl.BlockSpec((head_dim_v, bq), lambda *_: (0, 0)),
+      pl.BlockSpec((None, head_dim_v, bq), out_index_map),
+      pl.BlockSpec((None, NUM_SUBLANES, bq), out_index_map),
+      pl.BlockSpec((None, NUM_SUBLANES, bq), out_index_map),
+  ]
+  grid_width = (actual_kv_seq_len + bkv - 1) // bkv
+  grid_height = (actual_q_seq_len + bq - 1) // bq
+  grid = (num_q_heads, grid_height, grid_width)
+
+  all_out = pl.pallas_call(
+      functools.partial(
+          _flash_attention_kernel,
+          mask_value=DEFAULT_MASK_VALUE,
+          grid_width=grid_width,
+          bq=bq,
+          bkv=bkv,
+          bkv_compute=bkv_compute,
+          bkv_compute_in=bkv_compute_in,
+          head_dim_v=head_dim_v,
+          q_seq_len=actual_q_seq_len,
+          kv_seq_len=actual_kv_seq_len,
+          use_base2_exp=use_base2_exp,
+          fuse_reciprocal=False,
+      ),
+      grid_spec=pltpu.PrefetchScalarGridSpec(
+          num_scalar_prefetch=0,
+          in_specs=in_specs,
+          out_specs=out_specs,
+          grid=grid,
+      ),
+      compiler_params=pltpu.CompilerParams(
+          dimension_semantics=("parallel", "arbitrary", "arbitrary"),
+          flags={"XLA_TPU_FORCE_LP_LLO_SCHEDULER": use_experimental_scheduler},
+          disable_bounds_checks=True,
+          skip_device_barrier=True,
+          vmem_limit_bytes=vmem_limit_bytes,
+      ),
+      out_shape=out_shapes,
+  )(q, k, v)
+  out = jnp.swapaxes(all_out[3], 1, 2)  # (h, head_dim_v, s) -> (h, s, head_dim_v)
+  l = all_out[4][:, 0, :]  # (h, s)
+  m = all_out[5][:, 0, :]  # (h, s)
+  return out, m, l
+
+
 def _splash_attention_forward_mhpt(
     q: jax.Array,
     k: jax.Array,