From ded8ada86a3962477433054debbcef1d45161850 Mon Sep 17 00:00:00 2001
From: Bram Wasti <bwasti@fb.com>
Date: Thu, 30 Oct 2025 01:28:45 -0400
Subject: [PATCH] Add more dims for batch invariant shims (#27489)

Signed-off-by: Bram Wasti <bwasti@meta.com>
Signed-off-by: Bram Wasti <bwasti@fb.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
---
 vllm/model_executor/layers/batch_invariant.py | 44 ++++++++++++++++++-
 1 file changed, 42 insertions(+), 2 deletions(-)

diff --git a/vllm/model_executor/layers/batch_invariant.py b/vllm/model_executor/layers/batch_invariant.py
index 208ffb30e5ed2..5706786bccb1d 100644
--- a/vllm/model_executor/layers/batch_invariant.py
+++ b/vllm/model_executor/layers/batch_invariant.py
@@ -478,9 +478,48 @@ def matmul_batch_invariant(a, b, *, out=None):
     elif a.ndim == 3 and b.ndim == 3:
         # Handle batched case like bmm
         return bmm_batch_invariant(a, b, out=out)
+    elif a.ndim == 3 and b.ndim == 2:
+        # Handle 3D x 2D: common for linear layers
+        # (batch, seq, hidden) @ (hidden, out) -> (batch, seq, out)
+        # Reshape to 2D, do mm, reshape back
+        batch, seq, hidden = a.shape
+        a_2d = a.reshape(-1, hidden)
+        result_2d = matmul_persistent(a_2d, b)
+        result = result_2d.reshape(batch, seq, -1)
+        if out is not None:
+            out.copy_(result)
+            return out
+        return result
+    elif a.ndim == 2 and b.ndim == 3:
+        # Handle 2D x 3D: (M, K) @ (B, K, N) -> (B, M, N)
+        # By broadcasting `a` to 3D, we can reuse the batched matrix
+        # multiplication logic.
+        a_expanded = a.unsqueeze(0).expand(b.shape[0], -1, -1)
+        return bmm_batch_invariant(a_expanded, b, out=out)
+    elif a.ndim == 4 and b.ndim == 4:
+        # Handle 4D attention tensors: [batch, heads, seq, dim]
+        # Reshape to 3D, process, reshape back
+        batch, heads, seq_a, dim_a = a.shape
+        _, _, dim_b, seq_b = b.shape
+
+        # Reshape to [batch*heads, seq_a, dim_a]
+        a_3d = a.reshape(batch * heads, seq_a, dim_a)
+        b_3d = b.reshape(batch * heads, dim_b, seq_b)
+
+        # Do batched matmul
+        result_3d = bmm_batch_invariant(a_3d, b_3d)
+
+        # Reshape back to [batch, heads, seq_a, seq_b]
+        result = result_3d.reshape(batch, heads, seq_a, seq_b)
+
+        if out is not None:
+            out.copy_(result)
+            return out
+        return result
     else:
         raise ValueError(
-            f"matmul_batch_invariant currently only supports 2D x 2D and 3D x 3D, "
+            f"matmul_batch_invariant currently only supports 2D x 2D, 3D x 3D, "
+            f"3D x 2D, 2D x 3D, and 4D x 4D, "
             f"got shapes {a.shape} and {b.shape}"
         )
 
@@ -667,7 +706,8 @@ def rms_norm_batch_invariant(
 
 
 def linear_batch_invariant(input, weight, bias=None):
-    output = mm_batch_invariant(input, weight.t())
+    output = matmul_batch_invariant(input, weight.t())
+
     if bias is not None:
         output = output + bias
     return output