is_linear fix for MHA (#1141)

* [wip] fix for mha

Summary: filter fn may need access to parent types of module as in the
case with mha

Test Plan: TODO

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* testing

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* fixing dtype and device in test

Summary:

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* fixing test

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test/integration/test_integration.py

@@ -1327,6 +1327,34 @@ def test_autoquant_compile(self, device, dtype, m1, m2, k, n):
         sqnr = SQNR(out, out2)
         self.assertTrue(sqnr >= 30)
 
+    @parameterized.expand(COMMON_DEVICE_DTYPE)
+    @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_5, "autoquant requires 2.5+.")
+    def test_autoquant_mha(self, device, dtype):
+        if device != "cuda" or not torch.cuda.is_available():
+            self.skipTest(f"autoquant currently does not support {device}")
+        class MHAModel(torch.nn.Module):
+            def __init__(self):
+                super().__init__()
+                self.mha = torch.nn.MultiheadAttention(4096, 32)
+                self.lin = torch.nn.Linear(4096, 4096)
+
+            def forward(self, x):
+                y = self.mha(x, x, x)[0]
+                return self.lin(y)
+
+        mod = MHAModel().to(device).to(dtype)
+        input = torch.randn(1,1,4096).to(device).to(dtype)
+        out=mod(*input)
+
+        torchao.autoquant(mod, set_inductor_config=False)
+        assert not isinstance(mod.mha.out_proj.weight, AutoQuantizableLinearWeight)
+        assert isinstance(mod.lin.weight, AutoQuantizableLinearWeight)
+        mod(*input)
+        from torchao.quantization.autoquant import AUTOQUANT_CACHE
+        assert len(AUTOQUANT_CACHE)>0
+
+
+
     @parameterized.expand(COMMON_DEVICE_DTYPE)
     @unittest.skipIf(not TORCH_VERSION_AT_LEAST_2_5, "autoquant requires 2.5+.")
     def test_autoquant_manual(self, device, dtype):

torchao/quantization/quant_api.py

@@ -235,6 +235,7 @@ def _is_linear(mod, *args):
         and not isinstance(mod.weight, AffineQuantizedTensor)
         and not isinstance(mod.weight, LinearActivationQuantizedTensor)
         and not isinstance(mod.weight, AffineFakeQuantizedTensor)
+        and not isinstance(mod, nn.modules.linear.NonDynamicallyQuantizableLinear)
     )
 
 import torch.nn.utils.parametrize as parametrize