diff --git a/benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_inductor_torchbench_inference.csv b/benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_inductor_torchbench_inference.csv
index 1ec72cc22b8..f6a0bff369a 100644
--- a/benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_inductor_torchbench_inference.csv
+++ b/benchmarks/dynamo/ci_expected_accuracy/dynamic_cpu_inductor_torchbench_inference.csv
@@ -318,7 +318,7 @@ timm_vovnet,pass,0
 
 
 
-torch_multimodal_clip,pass,3
+torch_multimodal_clip,pass,0
 
 
 
diff --git a/test/export/test_export.py b/test/export/test_export.py
index 72be959d9e7..35ca85b9161 100755
--- a/test/export/test_export.py
+++ b/test/export/test_export.py
@@ -1087,6 +1087,93 @@ graph():
         args = (torch.randn(15, 3, 256, 256), torch.ones(15, 32, 256, 256))
         self.assertEqual(gm(*args), m(*args))
 
+    # stride() is called for an undefined tensor
+    @testing.expectedFailureCppRuntimeNonStrict
+    def test_native_multi_attention_head(self):
+        embed_dim = 64
+        num_heads = 4
+        bs = 16
+        sl = 8
+        device = "cpu"
+
+        q = 6 * torch.rand(bs, sl, embed_dim, device=device, dtype=torch.float32) - 3
+        k = q
+        v = q
+
+        qkv = torch.nn.Linear(
+            embed_dim, 3 * embed_dim, device=device, dtype=torch.float32
+        )
+        proj = torch.nn.Linear(embed_dim, embed_dim, device=device, dtype=torch.float32)
+
+        class NativeMHA(torch.nn.Module):
+            def __init__(
+                self,
+                embed_dim,
+                num_heads,
+                qkv,
+                proj,
+                need_weights,
+                average_attn_weights,
+                mask_type,
+            ):
+                super().__init__()
+                self.qkv = qkv
+                self.proj = proj
+                self.embed_dim = embed_dim
+                self.num_heads = num_heads
+                self.need_weights = need_weights
+                self.average_attn_weights = average_attn_weights
+                self.mask_type = mask_type
+
+            def forward(self, q, k, v, key_padding_mask):
+                return torch._native_multi_head_attention(
+                    q,
+                    k,
+                    v,
+                    self.embed_dim,
+                    self.num_heads,
+                    self.qkv.weight,
+                    self.qkv.bias,
+                    self.proj.weight,
+                    self.proj.bias,
+                    key_padding_mask,
+                    need_weights=False,
+                    average_attn_weights=False,
+                    mask_type=1,  # mask_type = 1 => src_key_padding_mask, mask_type = 0 => src_mask
+                )
+
+        for mask_type in (0, 1):
+            for need_weights in (True, False):
+                for average_attn_weights in (True, False):
+                    npt = NativeMHA(
+                        embed_dim=embed_dim,
+                        num_heads=num_heads,
+                        qkv=qkv,
+                        proj=proj,
+                        need_weights=need_weights,
+                        average_attn_weights=average_attn_weights,
+                        mask_type=mask_type,
+                    )
+                    sample_input = (q, k, v, None)
+
+                    ep = export(
+                        npt,
+                        args=sample_input,
+                        dynamic_shapes={
+                            "q": {
+                                0: Dim("dim0_q", max=1024),
+                            },
+                            "k": {
+                                0: Dim("dim0_k", max=1024),
+                            },
+                            "v": {
+                                0: Dim("dim0_v", max=1024),
+                            },
+                            "key_padding_mask": None,
+                        },
+                    )
+                    self.assertEqual(ep.module()(*sample_input), npt(*sample_input))
+
     def test_unused_constant(self):
         class M(torch.nn.Module):
             def forward(self, x):
diff --git a/torch/_meta_registrations.py b/torch/_meta_registrations.py
index 8435b80f34c..1b067df9f4d 100644
--- a/torch/_meta_registrations.py
+++ b/torch/_meta_registrations.py
@@ -7790,6 +7790,56 @@ def _create_unary_float_meta_func(func):
     return _f
 
 
+# Implementation follows cuda implementation native_multi_head_attention_cuda
+@register_meta(aten._native_multi_head_attention.default)
+def native_multi_head_attention_fake(
+    query,
+    key,
+    value,
+    embed_dim,
+    num_head,
+    qkv_weight,
+    qkv_bias,
+    proj_weight,
+    proj_bias,
+    mask=None,
+    need_weights=True,
+    average_attn_weights=True,
+    mask_type=None,
+):
+    if query.is_nested or key.is_nested or value.is_nested:
+        raise NotImplementedError(
+            "_native_multi_head_attention fake implementation does not support nested tensors"
+        )
+
+    if query.numel() == 0:
+        return (query.new_empty(query.shape), query.new_empty(0))
+
+    B = query.size(0)  # B: batch size
+    T = query.size(1)  # T: target sequence length
+
+    # In native_multi_head_attention_cuda,
+    # we have proj = transform0213_gemm_nt_bias(attn_ctx, proj_weight, proj_bias, query)
+    # , which does attn_ctx @ proj_weight.T + proj_bias
+    # so the last dim of output shape is proj_weight.size(0)
+    output_dim = proj_weight.size(0)
+    output = query.new_empty(B, T, output_dim)
+
+    if need_weights:
+        if average_attn_weights:
+            # When averaging attention weights, shape is [B, T, T] (averaged over heads)
+            # T = query seq len, S = key/value seq len
+            attn_weights = query.new_empty(B, T, T)
+        else:
+            # When not averaging, shape is [B, num_head, T, T]
+            # T = query seq len, S = key/value seq len
+            attn_weights = query.new_empty(B, num_head, T, T)
+    else:
+        attn_weights = query.new_empty(0)
+
+    return (output, attn_weights)
+
+
 def _create_binary_float_meta_func(func):
     @register_meta(func)
     @out_wrapper()