fake_quantize: respect device affinity

test/quantization/test_workflow_module.py

@@ -41,6 +41,7 @@
 from torch.testing._internal.common_quantized import (
     override_quantized_engine,
     supported_qengines,
+    override_qengines,
 )
 
 # Reference method for fake quantize
@@ -782,3 +783,37 @@ def forward(self, x):
             self.assertTrue(
                 isinstance(fused_model.conv.bn, nn.SyncBatchNorm),
                 "Expected BN to be converted to SyncBN")
+
+    @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
+    @unittest.skipIf(not TEST_CUDA, "CUDA unavailable")
+    @override_qengines
+    def test_device_affinity(self):
+        """
+        Tests that converting a model to QAT respects device affinity
+        """
+        class Model(nn.Module):
+
+            def __init__(self):
+                super(Model, self).__init__()
+                self.linear = nn.Linear(2, 2)
+
+            def forward(self, x):
+                x = self.linear(x)
+                return x
+
+        model = Model()
+        model.qconfig = torch.quantization.get_default_qat_qconfig(torch.backends.quantized.engine)
+        device = torch.device('cuda:0')
+        model.to(device)
+        torch.quantization.prepare_qat(model, inplace=True)
+        model_devices = {p.device for p in model.parameters()} | \
+            {p.device for p in model.buffers()}
+        self.assertEqual(len(model_devices), 1,
+                         message="Model must only have one device after QAT")
+        model_device = next(iter(model_devices))
+        self.assertEqual(model_device, device,
+                         message="QAT must respect device affinity")
+
+        # ensure that running an input on CUDA works without any needed changes
+        input = torch.randn(2, device=device)
+        model(input)

torch/quantization/quantize.py

@@ -85,10 +85,20 @@ def add_observer_(module):
     Return:
         None, module is modified inplace with added observer modules and forward_hooks
     """
+    # respect device affinity when adding observers
+    # devices = {p.device for p in module.parameters()}
+    devices = get_unique_devices_(module)
+    assert len(devices) == 1, (
+        "add_observer_ only works with cpu or single-device CUDA modules, "
+        "but got devices {}".format(devices)
+    )
+    device = next(iter(devices))
+
     for child in module.children():
         if type(child) == nnq.FloatFunctional or type(child) == nnq.QFunctional:
             if hasattr(child, 'qconfig') and child.qconfig is not None:
-                child.activation_post_process = child.qconfig.activation()
+                child.activation_post_process = \
+                    child.qconfig.activation().to(device)
         else:
             add_observer_(child)
 
@@ -97,9 +107,13 @@ def add_observer_(module):
     if hasattr(module, 'qconfig') and module.qconfig is not None and \
        len(module._modules) == 0 and not isinstance(module, torch.nn.Sequential):
         # observer and hook will be gone after we swap the module
-        module.add_module('activation_post_process', module.qconfig.activation())
+        module.add_module('activation_post_process', module.qconfig.activation().to(device))
         module.register_forward_hook(_observer_forward_hook)
 
+def get_unique_devices_(module):
+    return {p.device for p in module.parameters()} | \
+        {p.device for p in module.buffers()}
+
 def add_quant_dequant(module):
     r"""Wrap the leaf child module in QuantWrapper if it has a valid qconfig
     Note that this function will modify the children of module inplace and it
@@ -352,7 +366,14 @@ def swap_module(mod, mapping):
     # Always replace dequantstub with dequantize
     if hasattr(mod, 'qconfig') and mod.qconfig is not None or type(mod) == DeQuantStub:
         if type(mod) in mapping:
-            new_mod = mapping[type(mod)].from_float(mod)
+            # respect device affinity when swapping modules
+            devices = get_unique_devices_(mod)
+            assert len(devices) == 1, (
+                "swap_module only works with cpu or single-device CUDA modules, "
+                "but got devices {}".format(devices)
+            )
+            device = next(iter(devices))
+            new_mod = mapping[type(mod)].from_float(mod).to(device)
     return new_mod
 
 def get_observer_dict(mod, target_dict, prefix=""):