diff --git a/docker/pytorch-aarch64/patches/blas_to_mkl_acl.patch b/docker/pytorch-aarch64/patches/blas_to_mkl_acl.patch
index 26a9e36d..a36698ea 100644
--- a/docker/pytorch-aarch64/patches/blas_to_mkl_acl.patch
+++ b/docker/pytorch-aarch64/patches/blas_to_mkl_acl.patch
@@ -16,10 +16,10 @@
 # *******************************************************************************
 
 diff --git a/aten/src/ATen/native/LinearAlgebra.cpp b/aten/src/ATen/native/LinearAlgebra.cpp
-index c658d4427c..5c792f0e73 100644
+index a0531c50c96..55102c9d2f5 100644
 --- a/aten/src/ATen/native/LinearAlgebra.cpp
 +++ b/aten/src/ATen/native/LinearAlgebra.cpp
-@@ -1308,6 +1308,16 @@ static void addmm_impl_cpu_(
+@@ -1420,6 +1420,20 @@ static void addmm_impl_cpu_(
    AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND(kBFloat16,
        result.scalar_type(), "addmm_impl_cpu_",
        [&]{
@@ -29,21 +29,25 @@ index c658d4427c..5c792f0e73 100644
 +        // that will call then into ACL GEMM kernel and also additionaly have support
 +        // for running kernel with BF16 instructions
 +        if(transpose_a && !transpose_b) {
++          if (transpose_c) {
 +            mkldnn_matmul(b, a, c, beta.to<float>(), alpha.to<float>());
-+            return;
++          } else {
++            mkldnn_matmul(a, b, c, beta.to<float>(), alpha.to<float>());
++          }
++          return;
 +        }
 +        #endif
          using opmath_t = at::opmath_type<scalar_t>;
          at::native::cpublas::gemm(
              transpose_a ? a.is_conj() ? TransposeType::ConjTranspose : TransposeType::Transpose : TransposeType::NoTranspose,
 diff --git a/aten/src/ATen/native/mkldnn/Matmul.cpp b/aten/src/ATen/native/mkldnn/Matmul.cpp
-index d41ebac635..e2cc13fe00 100644
+index 383d2965923..b15056d7161 100644
 --- a/aten/src/ATen/native/mkldnn/Matmul.cpp
 +++ b/aten/src/ATen/native/mkldnn/Matmul.cpp
-@@ -128,23 +128,25 @@ void mkldnn_matmul(
+@@ -130,23 +130,25 @@ void mkldnn_matmul(
                (mat1.dim() == 1 && mat2.dim() == 1),  // aten::dot
                "mkldnn_matmul:  unsupported dims for mat and mat2");
- 
+
 +#if defined(__aarch64__)
 +  // oneDNN fast-maths mode (enabled by setting the environment variable ONEDNN_DEFAULT_FPMATH_MODE=BF16) will dispatch
 +  // fp32 inputs to bf16 kernels where HW permits. So, both fp32 and bf16 inputs are permitted.
@@ -58,7 +62,7 @@ index d41ebac635..e2cc13fe00 100644
 +#else
    TORCH_CHECK(mkldnn_bf16_device_check(),
      "mkldnn_matmul: mkldnn_matmul bf16 path needs the cpu support avx512bw, avx512vl and avx512dq, or AWS Graviton3");
- 
+
 -#if defined(__aarch64__)
 -  if (mkldnn_bf16_device_check_arm()) {
 -     //onednn fastmath mode can leverage bf16 HW even for the fp32 input, e.g. Arm Neoverse V1
@@ -76,6 +80,6 @@ index d41ebac635..e2cc13fe00 100644
 -                 mat2.scalar_type() == at::kBFloat16 &&
 -                 result.scalar_type() == at::kBFloat16, "mkldnn_matmul:  only enabled for bf16 path");
 -  }
- 
+
    auto mat1_unsqueezed = mat1.dim() == 1 ? mat1.unsqueeze(0) : mat1;
    auto mat2_unsqueezed = mat2.dim() == 1 ? mat2.unsqueeze(1) : mat2;