From 62e025a4f34d16eeedfc1c93e50a48a69098df7f Mon Sep 17 00:00:00 2001
From: liminfei-amd <91481003+liminfei-amd@users.noreply.github.com>
Date: Fri, 10 Jul 2026 10:30:26 +0800
Subject: [PATCH] Fix FP8 activation quantization for >2D activations in
 mixed_precision_ops (#14643)

mixed_precision_ops.Linear.forward only quantized activations that were 2D, or
3D (reshaped to 2D). Inputs with rank >= 4 (e.g. Anima's MLP activations, which
are not reshaped to 3D the way the attention path is) fell through the
`input_reshaped.ndim == 2` guard and reached scaled_mm as bf16, silently
dispatching a bf16 kernel instead of FP8. Since MLP is roughly half the compute,
the FP8 speedup was far below expectation.

Generalize the existing 3D->2D reshape to any rank >= 3 (flatten the leading
dims, keep the contraction dim) and reshape the output back to the original
leading dims. 2D and 3D inputs are handled exactly as before; only rank >= 4
inputs change (now quantized instead of skipped). This matches the rank-agnostic
handling already used by the training path (flatten(0, -2) / unflatten).

Fixes #14595.
---
 comfy/ops.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/comfy/ops.py b/comfy/ops.py
index 0c6fe4cb4..13c2604fb 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -1257,7 +1257,7 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 run_every_op()
 
                 input_shape = input.shape
-                reshaped_3d = False
+                reshaped_nd = False
                 #If cast needs to apply lora, it should be done in the compute dtype
                 compute_dtype = input.dtype
 
@@ -1294,12 +1294,12 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                 # Inference path (unchanged)
                 if _use_quantized and quantize_input:
 
-                    # Reshape 3D tensors to 2D for quantization (needed for NVFP4 and others)
-                    input_reshaped = input.reshape(-1, input_shape[2]) if input.ndim == 3 else input
+                    # Reshape >=3D tensors to 2D for quantization (needed for NVFP4 and others)
+                    input_reshaped = input.reshape(-1, input_shape[-1]) if input.ndim >= 3 else input
 
                     # Fall back to non-quantized for non-2D tensors
                     if input_reshaped.ndim == 2:
-                        reshaped_3d = input.ndim == 3
+                        reshaped_nd = input.ndim >= 3
                         # dtype is now implicit in the layout class
                         scale = getattr(self, 'input_scale', None)
                         if scale is not None:
@@ -1314,9 +1314,9 @@ def mixed_precision_ops(quant_config={}, compute_dtype=torch.bfloat16, full_prec
                     weight_only_quant=weight_only_quant,
                 )
 
-                # Reshape output back to 3D if input was 3D
-                if reshaped_3d:
-                    output = output.reshape((input_shape[0], input_shape[1], self.weight.shape[0]))
+                # Reshape output back to original rank if input was >2D
+                if reshaped_nd:
+                    output = output.reshape((*input_shape[:-1], self.weight.shape[0]))
 
                 return output