[diffusion] FLUX.2 bit-exact residual-gate fast path (H200 klein-4B 50-step denoise -1.2%) (#33823)

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

python/sglang/multimodal_gen/runtime/models/dits/flux_2.py

@@ -20,6 +20,10 @@ from diffusers.models.attention import AttentionModuleMixin
 from diffusers.models.embeddings import TimestepEmbedding, Timesteps
 from diffusers.models.normalization import AdaLayerNormContinuous
 
+from sglang.kernels.ops.diffusion.residual_gate_add import (
+    can_use_residual_gate_add_cuda,
+    residual_gate_add_cuda,
+)
 from sglang.multimodal_gen.configs.models.dits.flux import FluxConfig
 from sglang.multimodal_gen.runtime.distributed import (
     divide,
@@ -65,6 +69,40 @@ from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger
 
 logger = init_logger(__name__)  # pylint: disable=invalid-name
 
+_FLUX2_RESIDUAL_GATE_CUDA_DISABLED = False
+
+
+def _flux2_residual_gate_add(
+    residual: torch.Tensor,
+    update: torch.Tensor,
+    gate: torch.Tensor,
+) -> torch.Tensor:
+    """Single-kernel ``residual + gate * update``, bit-exact vs the eager pair.
+
+    Restricted to half dtypes: there the kernel reproduces the eager pair's
+    two-step rounding exactly (verified by ``torch.equal``), while for fp32 it
+    would contract to an fma (one rounding) and stop being bit-exact. The
+    kernel's row-broadcast gate only covers ``[1, ..., 1, D]``; batched
+    ``[B>1, 1, D]`` gates fail ``can_use_residual_gate_add_cuda`` and take the
+    eager fallback below.
+    """
+    global _FLUX2_RESIDUAL_GATE_CUDA_DISABLED
+
+    if (
+        not _FLUX2_RESIDUAL_GATE_CUDA_DISABLED
+        and residual.dtype in (torch.float16, torch.bfloat16)
+        and can_use_residual_gate_add_cuda(residual, update, gate)
+    ):
+        try:
+            return residual_gate_add_cuda(residual, update, gate)
+        except Exception as exc:
+            if torch.compiler.is_compiling():
+                raise
+            logger.warning_once(f"Disabling FLUX.2 residual-gate CUDA fast path: {exc}")
+            _FLUX2_RESIDUAL_GATE_CUDA_DISABLED = True
+
+    return residual + gate * update
+
 
 def _get_qkv_projections(
     attn: "Flux2Attention", hidden_states, encoder_hidden_states=None
@@ -656,7 +694,7 @@ class Flux2SingleTransformerBlock(nn.Module):
             **joint_attention_kwargs,
         )
 
-        hidden_states = hidden_states + mod_gate * attn_output
+        hidden_states = _flux2_residual_gate_add(hidden_states, attn_output, mod_gate)
         if hidden_states.dtype == torch.float16:
             hidden_states = hidden_states.clip(-65504, 65504)
 
@@ -774,18 +812,18 @@ class Flux2TransformerBlock(nn.Module):
         attn_output, context_attn_output = attention_outputs
 
         # Process attention outputs for the image stream (`hidden_states`).
-        attn_output = gate_msa * attn_output
-        hidden_states = hidden_states + attn_output
+        hidden_states = _flux2_residual_gate_add(hidden_states, attn_output, gate_msa)
 
         norm_hidden_states = self.norm2(hidden_states)
         norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp
 
         ff_output = self.ff(norm_hidden_states)
-        hidden_states = hidden_states + gate_mlp * ff_output
+        hidden_states = _flux2_residual_gate_add(hidden_states, ff_output, gate_mlp)
 
         # Process attention outputs for the text stream (`encoder_hidden_states`).
-        context_attn_output = c_gate_msa * context_attn_output
-        encoder_hidden_states = encoder_hidden_states + context_attn_output
+        encoder_hidden_states = _flux2_residual_gate_add(
+            encoder_hidden_states, context_attn_output, c_gate_msa
+        )
 
         norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
         norm_encoder_hidden_states = (
@@ -793,7 +831,9 @@ class Flux2TransformerBlock(nn.Module):
         )
 
         context_ff_output = self.ff_context(norm_encoder_hidden_states)
-        encoder_hidden_states = encoder_hidden_states + c_gate_mlp * context_ff_output
+        encoder_hidden_states = _flux2_residual_gate_add(
+            encoder_hidden_states, context_ff_output, c_gate_mlp
+        )
         if encoder_hidden_states.dtype == torch.float16:
             encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)