[Diffusion] Fuse LongCat Image normalization and modulation

python/sglang/multimodal_gen/runtime/models/dits/longcat_image.py

@@ -32,6 +32,7 @@
     AdaLayerNormZeroSingle,
 )
 
+from sglang.kernels.ops import diffusion as diffusion_ops
 from sglang.kernels.ops.diffusion import (
     BitExactFusionGate,
     can_use_fused_inplace_qknorm_rope,
@@ -62,6 +63,76 @@
 logger = init_logger(__name__)
 
 _LONGCAT_QKNORM_ROPE = BitExactFusionGate("LongCat fused QKNorm+RoPE")
+_LONGCAT_LN_MOD = BitExactFusionGate("LongCat fused LN+modulate", per_signature=True)
+
+
+def _longcat_norm_modulate(
+    norm: nn.Module,
+    x: torch.Tensor,
+    scale: torch.Tensor,
+    shift: torch.Tensor,
+) -> torch.Tensor:
+    if torch.is_grad_enabled() or torch.compiler.is_compiling():
+        return norm(x) * (1 + scale[:, None]) + shift[:, None]
+    # LayerNorm's reduction depends on the live aten dispatch. Verify each
+    # shape/stride before using the bit-exact fused kernel, outside capture.
+    if (
+        not _LONGCAT_LN_MOD.disabled
+        and x.is_cuda
+        and diffusion_ops.is_plain_layer_norm(norm, x.shape[-1])
+        and diffusion_ops.can_use_fused_layernorm_modulate(x, scale, shift)
+    ):
+        sig = (
+            x.shape,
+            x.stride(),
+            scale.shape,
+            scale.stride(),
+            shift.shape,
+            shift.stride(),
+            norm.eps,
+            x.dtype,
+            x.device,
+        )
+        verified = _LONGCAT_LN_MOD.is_verified(sig)
+        if verified or not torch.cuda.is_current_stream_capturing():
+            try:
+                out = diffusion_ops.fused_layernorm_modulate(x, scale, shift, norm.eps)
+            except Exception as exc:
+                _LONGCAT_LN_MOD.on_exception(exc, logger=logger)
+            else:
+                if verified:
+                    return out
+                reference = norm(x) * (1 + scale[:, None]) + shift[:, None]
+                return _LONGCAT_LN_MOD.accept_or_fallback(
+                    out, reference, sig=sig, logger=logger
+                )
+    return norm(x) * (1 + scale[:, None]) + shift[:, None]
+
+
+class _LongCatAdaLayerNormZero(AdaLayerNormZero):
+    def forward(
+        self,
+        x: torch.Tensor,
+        timestep: Optional[torch.Tensor] = None,
+        class_labels: Optional[torch.LongTensor] = None,
+        hidden_dtype: Optional[torch.dtype] = None,
+        emb: Optional[torch.Tensor] = None,
+    ):
+        if self.emb is not None:
+            emb = self.emb(timestep, class_labels, hidden_dtype=hidden_dtype)
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = emb.chunk(
+            6, dim=1
+        )
+        x = _longcat_norm_modulate(self.norm, x, scale_msa, shift_msa)
+        return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
+
+
+class _LongCatAdaLayerNormZeroSingle(AdaLayerNormZeroSingle):
+    def forward(self, x: torch.Tensor, emb: Optional[torch.Tensor] = None):
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1)
+        return _longcat_norm_modulate(self.norm, x, scale_msa, shift_msa), gate_msa
 
 
 def _longcat_qknorm_rope_reference(
@@ -500,7 +571,7 @@ def __init__(
     ):
         super().__init__()
         self.mlp_hidden_dim = int(dim * mlp_ratio)
-        self.norm = AdaLayerNormZeroSingle(dim)
+        self.norm = _LongCatAdaLayerNormZeroSingle(dim)
         # proj_mlp: ColumnParallelLinear with gather_output=False keeps output
         # head-sharded, consistent with attn_output from _LongCatSingleAttention.
         self.proj_mlp = ColumnParallelLinear(
@@ -619,8 +690,8 @@ def __init__(
         prefix: str = "",
     ):
         super().__init__()
-        self.norm1 = AdaLayerNormZero(dim)
-        self.norm1_context = AdaLayerNormZero(dim)
+        self.norm1 = _LongCatAdaLayerNormZero(dim)
+        self.norm1_context = _LongCatAdaLayerNormZero(dim)
         self.attn = _LongCatJointAttention(
             dim=dim,
             num_attention_heads=num_attention_heads,
@@ -666,9 +737,8 @@ def forward(
             hidden_states, attn_output, gate_msa.unsqueeze(1)
         )
 
-        norm_hidden_states = self.norm2(hidden_states)
-        norm_hidden_states = (
-            norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+        norm_hidden_states = _longcat_norm_modulate(
+            self.norm2, hidden_states, scale_mlp, shift_mlp
         )
         ff_output = self.ff(norm_hidden_states)
         hidden_states = residual_gate_add(
@@ -679,10 +749,8 @@ def forward(
             encoder_hidden_states, context_attn_output, c_gate_msa.unsqueeze(1)
         )
 
-        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
-        norm_encoder_hidden_states = (
-            norm_encoder_hidden_states * (1 + c_scale_mlp[:, None])
-            + c_shift_mlp[:, None]
+        norm_encoder_hidden_states = _longcat_norm_modulate(
+            self.norm2_context, encoder_hidden_states, c_scale_mlp, c_shift_mlp
         )
         context_ff_output = self.ff_context(norm_encoder_hidden_states)
         encoder_hidden_states = residual_gate_add(

test/registered/kernel/diffusion/test_longcat_image_norm_modulate.py

@@ -0,0 +1,131 @@
+"""LongCat normalization parity and graph-safe fusion dispatch."""
+
+import unittest
+from unittest.mock import patch
+
+import torch
+from diffusers.models.normalization import AdaLayerNormZero, AdaLayerNormZeroSingle
+
+import sglang.multimodal_gen.runtime.models.dits.longcat_image as longcat
+from sglang.kernels.ops.diffusion import BitExactFusionGate, modulate_scale_shift
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.test_utils import CustomTestCase
+
+register_cuda_ci(est_time=45, stage="base-b-kernel-unit", runner_config="1-gpu-large")
+
+
+class TestLongCatNormModulation(CustomTestCase):
+    def setUp(self):
+        super().setUp()
+        self.original_gate = longcat._LONGCAT_LN_MOD
+        longcat._LONGCAT_LN_MOD = BitExactFusionGate("test", per_signature=True)
+        torch.manual_seed(42)
+
+    def tearDown(self):
+        longcat._LONGCAT_LN_MOD = self.original_gate
+        super().tearDown()
+
+    def require_cuda(self):
+        if not torch.cuda.is_available():
+            self.skipTest("CUDA required")
+
+    @torch.inference_mode()
+    def test_adaln_checkpoint_and_output_parity(self):
+        for device, dtype, dim, seq in [
+            ("cpu", torch.float32, 64, 17),
+            ("cuda", torch.bfloat16, 3072, 512),
+            ("cuda", torch.bfloat16, 3072, 4608),
+        ]:
+            if device == "cuda" and not torch.cuda.is_available():
+                continue
+            for reference_cls, candidate_cls in [
+                (AdaLayerNormZero, longcat._LongCatAdaLayerNormZero),
+                (AdaLayerNormZeroSingle, longcat._LongCatAdaLayerNormZeroSingle),
+            ]:
+                with self.subTest(device=device, seq=seq, cls=reference_cls.__name__):
+                    reference = reference_cls(dim).to(device=device, dtype=dtype)
+                    candidate = candidate_cls(dim).to(device=device, dtype=dtype)
+                    candidate.load_state_dict(reference.state_dict(), strict=True)
+                    x = torch.randn(1, seq, dim, device=device, dtype=dtype)
+                    emb = torch.randn(1, dim, device=device, dtype=dtype)
+                    expected, actual = reference(x, emb=emb), candidate(x, emb=emb)
+                    for a, b in zip(expected, actual, strict=True):
+                        self.assertTrue(torch.equal(a, b))
+                    if device == "cuda":
+                        self.assertTrue(longcat._LONGCAT_LN_MOD.verified)
+                        self.assertFalse(longcat._LONGCAT_LN_MOD.disabled)
+
+    def inputs(self, seq=4096):
+        self.require_cuda()
+        x = torch.randn(1, seq, 3072, device="cuda", dtype=torch.bfloat16)
+        modulation = torch.randn(1, 6 * 3072, device="cuda", dtype=torch.bfloat16)
+        shift, scale, *_ = modulation.chunk(6, dim=-1)
+        norm = torch.nn.LayerNorm(3072, elementwise_affine=False, eps=1e-6).cuda()
+        return norm, x, scale, shift
+
+    def test_grad_enabled_uses_differentiable_reference(self):
+        norm, x, scale, shift = self.inputs(seq=17)
+        with torch.inference_mode():
+            longcat._longcat_norm_modulate(norm, x, scale, shift)
+        self.assertTrue(longcat._LONGCAT_LN_MOD.verified)
+        leaves = [t.detach().clone().requires_grad_() for t in (x, scale, shift)]
+        refs = [t.detach().clone().requires_grad_() for t in leaves]
+        with patch.object(longcat.diffusion_ops, "fused_layernorm_modulate") as fused:
+            actual = longcat._longcat_norm_modulate(norm, *leaves)
+            actual.float().sum().backward()
+            fused.assert_not_called()
+        expected = norm(refs[0]) * (1 + refs[1][:, None]) + refs[2][:, None]
+        expected.float().sum().backward()
+        self.assertTrue(torch.equal(actual, expected))
+        for a, b in zip(leaves, refs, strict=True):
+            self.assertIsNotNone(a.grad)
+            self.assertTrue(torch.equal(a.grad, b.grad))
+
+    @torch.inference_mode()
+    def test_changed_inputs_are_used_by_graph_replay(self):
+        norm, x, scale, shift = self.inputs()
+        longcat._longcat_norm_modulate(norm, x, scale, shift)
+        self.assertTrue(longcat._LONGCAT_LN_MOD.verified)
+        graph = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(graph):
+            actual = longcat._longcat_norm_modulate(norm, x, scale, shift)
+        x.add_(0.25)
+        scale.neg_()
+        shift.mul_(0.5)
+        graph.replay()
+        expected = norm(x) * (1 + scale[:, None]) + shift[:, None]
+        self.assertTrue(torch.equal(actual, expected))
+
+    @torch.inference_mode()
+    def test_unverified_capture_uses_eager_reference(self):
+        norm, x, scale, shift = self.inputs(seq=17)
+        expected = modulate_scale_shift(norm(x), scale, shift)
+        with patch.object(longcat.diffusion_ops, "fused_layernorm_modulate") as fused:
+            graph = torch.cuda.CUDAGraph()
+            with torch.cuda.graph(graph):
+                actual = longcat._longcat_norm_modulate(norm, x, scale, shift)
+            graph.replay()
+            fused.assert_not_called()
+        self.assertFalse(longcat._LONGCAT_LN_MOD.verified)
+        self.assertTrue(torch.equal(actual, expected))
+
+    @torch.inference_mode()
+    def test_mismatch_disables_fusion_and_returns_reference(self):
+        norm, x, scale, shift = self.inputs(seq=17)
+        expected = norm(x) * (1 + scale[:, None]) + shift[:, None]
+        with patch.object(
+            longcat.diffusion_ops,
+            "fused_layernorm_modulate",
+            return_value=torch.zeros_like(x),
+        ):
+            actual = longcat._longcat_norm_modulate(norm, x, scale, shift)
+        self.assertTrue(longcat._LONGCAT_LN_MOD.disabled)
+        self.assertTrue(torch.equal(actual, expected))
+        with patch.object(longcat.diffusion_ops, "fused_layernorm_modulate") as fused:
+            actual = longcat._longcat_norm_modulate(norm, x, scale, shift)
+            fused.assert_not_called()
+        self.assertTrue(torch.equal(actual, expected))
+
+
+if __name__ == "__main__":
+    unittest.main()