Fix default instantation of dynamic quantized LSTM

Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/31433

Test Plan: Imported from OSS

Differential Revision: D19164539

Pulled By: jamesr66a

fbshipit-source-id: 7045817ab3dfb530c4480a10523c4c6bcdbfc7eb

test/test_quantization.py

@@ -695,6 +695,33 @@ class PostTrainingDynamicQuantTest(QuantizationTestCase):
             else:
                 self.assertEqual(packed_val, ref_val)
 
+        # Test default instantiation
+        seq_len = 128
+        batch = 16
+        input_size = 3
+        hidden_size = 7
+        num_layers = 2
+        bias = True
+        bidirectional = False
+
+        x = torch.rand(seq_len, batch, input_size)
+        h = torch.rand(num_layers * (bidirectional + 1), batch, hidden_size)
+        c = torch.rand(num_layers * (bidirectional + 1), batch, hidden_size)
+
+        dtype = torch.qint8
+
+        cell_dq = torch.nn.quantized.dynamic.LSTM(input_size=input_size,
+                                                  hidden_size=hidden_size,
+                                                  num_layers=num_layers,
+                                                  bias=bias,
+                                                  batch_first=False,
+                                                  dropout=0.0,
+                                                  bidirectional=bidirectional,
+                                                  dtype=dtype)
+
+        y, (h, c) = cell_dq(x, (h, c))
+
+
 @unittest.skipUnless('fbgemm' in torch.backends.quantized.supported_engines,
                      " Quantized operations require FBGEMM. FBGEMM is only optimized for CPUs"
                      " with instruction set support avx2 or newer.")