diff --git a/test/quantization/test_quantize_jit.py b/test/quantization/test_quantize_jit.py
index 88848d2267f..e6e02fb811c 100644
--- a/test/quantization/test_quantize_jit.py
+++ b/test/quantization/test_quantize_jit.py
@@ -2177,11 +2177,23 @@ class TestQuantizeJitOps(QuantizationTestCase):
                            .run(m.graph)
 
     def test_hardswish(self):
-        data = [(torch.rand((1, 2, 5, 5), dtype=torch.float), torch.randint(0, 1, (1,), dtype=torch.long)) for _ in range(2)]
-        hardswish = torch.nn.Hardswish()
-        for tracing in [True, False]:
-            m = self.checkGraphModeOp(hardswish, data, "quantized::hardswish", tracing)
+        class FunctionalHardswish(torch.nn.Module):
+            def __init__(self, inplace):
+                super(FunctionalHardswish, self).__init__()
+                self.inplace = inplace
+
+            def forward(self, input):
+                return torch.nn.functional.hardswish(input, inplace=self.inplace)
+
+        modules = [torch.nn.Hardswish(), FunctionalHardswish(True),
+                   FunctionalHardswish(False)]
+
+        for test_case in itertools.product([True, False], modules):
+            tracing, m = test_case
+            m = self.checkGraphModeOp(
+                m, self.img_data, "quantized::hardswish", tracing)
             FileCheck().check_not("aten::hardswish") \
+                       .check_not("aten::hardswish_") \
                        .run(m.graph)
 
     def test_elu(self):
diff --git a/torch/csrc/jit/passes/quantization/helper.cpp b/torch/csrc/jit/passes/quantization/helper.cpp
index feae91e5592..a9dfbc6e31f 100644
--- a/torch/csrc/jit/passes/quantization/helper.cpp
+++ b/torch/csrc/jit/passes/quantization/helper.cpp
@@ -34,6 +34,7 @@ std::vector<std::string> _static_quantizable_aten_funcs = {
     "addmm",
     "matmul",
     "hardswish",
+    "hardswish_",
     "elu",
     "elu_",
     "batch_norm",
diff --git a/torch/csrc/jit/passes/quantization/quantization_patterns.h b/torch/csrc/jit/passes/quantization/quantization_patterns.h
index 62bcf1a5d5e..654e9e8f1df 100644
--- a/torch/csrc/jit/passes/quantization/quantization_patterns.h
+++ b/torch/csrc/jit/passes/quantization/quantization_patterns.h
@@ -848,6 +848,9 @@ graph(%a_quant, %alpha, %scale, %input_scale, %r_scale, %r_zero_point, %r_dtype)
   auto hardswish = getObservedQParamOpFusionInfo(
       "aten::hardswish", "quantized::hardswish", {}, {});
 
+  auto hardswish_ = getObservedQParamOpFusionInfo(
+      "aten::hardswish_", "quantized::hardswish", {}, {});
+
   auto layer_norm = getObservedQParamOpFusionInfo(
       "aten::layer_norm",
       "quantized::layer_norm",
@@ -968,6 +971,7 @@ graph(%a_quant, %alpha, %scale, %input_scale, %r_scale, %r_zero_point, %r_dtype)
       {"quantized::mul", mul, quantized_mul},
       {"quantized::mul", inplace_mul, quantized_mul},
       hardswish,
+      hardswish_,
       layer_norm,
       group_norm,
       instance_norm,
diff --git a/torch/testing/_internal/common_quantization.py b/torch/testing/_internal/common_quantization.py
index d0576ec99e4..0c97f07c09a 100644
--- a/torch/testing/_internal/common_quantization.py
+++ b/torch/testing/_internal/common_quantization.py
@@ -7,6 +7,7 @@ r"""Importing this file includes common utility methods and base clases for
 checking quantization api and properties of resulting modules.
 """
 
+import copy
 import io
 import functools
 import torch
@@ -282,8 +283,12 @@ class QuantizationTestCase(TestCase):
                 # make sure it runs
                 outputs[d] = models[d](inputs)
             else:
+                # module under test can contain in-place ops, and we depend on
+                # input data staying constant for comparisons
+                data_copy = copy.deepcopy(data)
                 models[d] = quantize_jit(
-                    model, qconfig_dict, test_only_eval_fn, [data], inplace=False, debug=d)
+                    model, qconfig_dict, test_only_eval_fn, [data_copy], inplace=False,
+                    debug=d)
                 # make sure it runs
                 outputs[d] = models[d](*inputs)