def test_conv_linear(self):
for qengine in supported_qengines:
with override_quantized_engine(qengine):
model = ManualConvLinearQATModel()
model = prepare_qat(model)
self.checkObservers(model)
test_only_train_fn(model, self.img_data_2d_train)
model = convert(model)
def checkQuantized(model):
self.assertEqual(type(model.conv), nnq.Conv2d)
self.assertEqual(type(model.fc1), nnq.Linear)
self.assertEqual(type(model.fc2), nnq.Linear)
test_only_eval_fn(model, self.img_data_2d)
self.checkScriptable(model, self.img_data_2d)
self.checkNoQconfig(model)
checkQuantized(model)
model = ManualConvLinearQATModel()
model = quantize_qat(model, test_only_train_fn,
[self.img_data_2d_train])
checkQuantized(model)
def test_embedding_bag_linear(self):
for qengine in supported_qengines:
with override_quantized_engine(qengine):
model = ManualEmbeddingBagLinear().train()
model = prepare_qat(
model, mapping=get_embedding_qat_module_mappings())
self.checkObservers(model)
test_only_train_fn(model, self.embed_linear_data_train)
# make sure not activation_post_process is inserted for EmbeddingBag
self.assertFalse(hasattr(model, "activation_post_process"))
# make sure that FakeQuant zero_points are correct dtype
self.assertEqual(model.emb.weight_fake_quant.zero_point.dtype,
torch.float32)
self.assertEqual(
model.linear.weight_fake_quant.zero_point.dtype,
torch.int32)
model = convert(
model,
mapping=get_embedding_static_quant_module_mappings())
def checkQuantized(model):
# Make sure EmbeddingBag is now a quantized EmbeddingBag.
self.assertTrue(type(model.emb), nn.quantized.EmbeddingBag)
# Also test that Linear has been quantized.
self.assertTrue(type(model.linear), nnq.Linear)
test_only_eval_fn(model, self.embed_data)
self.checkScriptable(model, self.embed_data)
self.checkNoQconfig(model)
checkQuantized(model)
model = ManualEmbeddingBagLinear()
def test_defused_embedding_bag_linear(self):
for qengine in supported_qengines:
with override_quantized_engine(qengine):
model = DeFusedEmbeddingBagLinear().train()
model = prepare_qat(
model, mapping=get_embedding_qat_module_mappings())
self.checkObservers(model)
test_only_train_fn(model, self.embed_linear_data_train)
# make sure activation_post_process is inserted after Linear.
self.assertEqual(type(model.linear.activation_post_process),
FusedMovingAvgObsFakeQuantize)
# make sure that Embedding has a noop for activation.
self.assertEqual(type(model.emb.activation_post_process),
NoopObserver)
model = convert(
model,
mapping=get_embedding_static_quant_module_mappings())
def checkQuantized(model):
# make sure Embedding is now a QuantizedEmbedding
self.assertEqual(type(model.emb), nn.quantized.Embedding)
# make sure Linear is now a QuantizedLinear
self.assertEqual(type(model.linear), nn.quantized.Linear)
test_only_eval_fn(model, self.embed_data)
self.checkScriptable(model, self.embed_data)
self.checkNoQconfig(model)
checkQuantized(model)
def test_embedding_bag_linear(self):
for qengine in supported_qengines:
with override_quantized_engine(qengine):
model = ManualEmbeddingBagLinear().train()
model = prepare_qat(model)
self.checkObservers(model)
train_indices = [[
torch.randint(0, 10, (12, 12)),
torch.randn((12, 1))
] for _ in range(2)]
eval_output = [[torch.randint(0, 10, (12, 1))]]
test_only_train_fn(model, train_indices)
# make sure not activation_post_process is inserted for EmbeddingBag
self.assertFalse(hasattr(model, "activation_post_process"))
model = convert(model)
def checkQuantized(model):
# Make sure EmbeddingBag is now a quantized EmbeddingBag.
self.assertTrue(type(model.emb), nn.quantized.EmbeddingBag)
# Also test that Linear has been quantized.
self.assertTrue(type(model.linear), nnq.Linear)
test_only_eval_fn(model, eval_output)
self.checkScriptable(model, eval_output)
self.checkNoQconfig(model)
checkQuantized(model)
model = ManualEmbeddingBagLinear()
model = quantize_qat(model, test_only_train_fn,
[train_indices])
checkQuantized(model)
def test_dynamic_qat_linear(self):
for qengine in supported_qengines:
with override_quantized_engine(qengine):
# Dynamic QAT without memoryless observers should fail
with self.assertRaisesRegex(
ValueError,
"Dynamic QAT requires a memoryless observer." +
"This means a MovingAverage observer with averaging constant equal to 1"
):
model = ManualLinearDynamicQATModel(default_qat_qconfig)
model = prepare_qat(
model, mapping={torch.nn.Linear: nnqatd.Linear})
model = ManualLinearDynamicQATModel()
model = prepare_qat(model,
mapping={torch.nn.Linear: nnqatd.Linear})
self.assertEqual(type(model.fc1), nnqatd.Linear)
self.assertEqual(type(model.fc2), nnqatd.Linear)
self.checkObservers(model)
test_only_train_fn(model, self.train_data)
model = convert(model, mapping={nnqatd.Linear: nnqd.Linear})
self.assertEqual(type(model.fc1), nnqd.Linear)
self.assertEqual(type(model.fc2), nnqd.Linear)
test_only_eval_fn(model, self.calib_data)
self.checkScriptable(model, self.calib_data)
self.checkNoQconfig(model)
def test_fuse_module_train(self):
model = ModelForFusion(default_qat_qconfig).train()
# Test step by step fusion
model = fuse_modules_qat(model, ['conv1', 'bn1', 'relu1'])
model = fuse_modules_qat(model, ['sub1.conv', 'sub1.bn'])
self.assertEqual(type(model.conv1), nni.ConvBnReLU2d,
msg="Fused Conv + BN + Relu first layer")
self.assertEqual(type(model.bn1), torch.nn.Identity,
msg="Fused Conv + BN + Relu (skipped BN)")
self.assertEqual(type(model.relu1), torch.nn.Identity,
msg="Fused Conv + BN + Relu (skipped Relu)")
self.assertEqual(type(model.sub1.conv), nni.ConvBn2d,
msg="Fused submodule Conv + BN")
self.assertEqual(type(model.sub1.bn), torch.nn.Identity,
msg="Fused submodule Conv + BN (skipped BN)")
self.assertEqual(type(model.sub2.conv), torch.nn.Conv2d,
msg="Non-fused submodule Conv")
self.assertEqual(type(model.sub2.relu), torch.nn.ReLU,
msg="Non-fused submodule ReLU")
model = prepare_qat(model)
self.checkObservers(model)
def checkQAT(model):
self.assertEqual(type(model.conv1), nniqat.ConvBnReLU2d)
self.assertEqual(type(model.bn1), nn.Identity)
self.assertEqual(type(model.relu1), nn.Identity)
self.assertEqual(type(model.sub1.conv), nniqat.ConvBn2d)
self.assertEqual(type(model.sub1.bn), nn.Identity)
self.assertEqual(type(model.sub2.conv), nn.Conv2d)
self.assertEqual(type(model.sub2.relu), nn.ReLU)
checkQAT(model)
test_only_train_fn(model, self.img_data_1d_train)
model = convert(model)
def checkQuantized(model):
self.assertEqual(type(model.conv1), nniq.ConvReLU2d)
self.assertEqual(type(model.bn1), nn.Identity)
self.assertEqual(type(model.relu1), nn.Identity)
self.assertEqual(type(model.sub1.conv), nnq.Conv2d)
self.assertEqual(type(model.sub1.bn), nn.Identity)
self.assertEqual(type(model.sub2.conv), nn.Conv2d)
self.assertEqual(type(model.sub2.relu), nn.ReLU)
test_only_eval_fn(model, self.img_data_1d)
self.checkNoQconfig(model)
with self.assertRaisesRegex(RuntimeError, "Could not run 'aten::native_batch_norm' with arguments from the 'QuantizedCPU'"):
checkQuantized(model)
model = ModelForFusion(default_qat_qconfig).train()
model = fuse_modules_qat(
model,
[['conv1', 'bn1', 'relu1'],
['sub1.conv', 'sub1.bn']])
model = quantize_qat(model, test_only_train_fn, [self.img_data_1d_train])
with self.assertRaisesRegex(RuntimeError, "Could not run 'aten::native_batch_norm' with arguments from the 'QuantizedCPU'"):
checkQuantized(model)
def test_train_save_load_eval(self):
r"""Test QAT flow of creating a model, doing QAT and saving the quantized state_dict
During eval, we first call prepare_qat and conver on the model and then load the state_dict
and compare results against original model
"""
for qengine in supported_qengines:
with override_quantized_engine(qengine):
model = TwoLayerLinearModel()
model = torch.ao.quantization.QuantWrapper(model)
model.qconfig = torch.ao.quantization.get_default_qat_qconfig(
qengine)
model = prepare_qat(model)
fq_state_dict = model.state_dict()
test_only_train_fn(model, self.train_data)
model = convert(model)
quant_state_dict = model.state_dict()
x = torch.rand(2, 5, dtype=torch.float)
ref = model(x)
# Create model again for eval. Check result using quantized state_dict
model = TwoLayerLinearModel()
model = torch.ao.quantization.QuantWrapper(model)
model.qconfig = torch.ao.quantization.get_default_qat_qconfig(
qengine)
torch.ao.quantization.prepare_qat(model, inplace=True)
new_state_dict = model.state_dict()
# Check to make sure the model after prepare_qat has the same state_dict as original.
self.assertEqual(set(fq_state_dict.keys()),
set(new_state_dict.keys()))
torch.ao.quantization.convert(model, inplace=True)
model.eval()
model.load_state_dict(quant_state_dict)
out = model(x)
self.assertEqual(ref, out)
# Check model created using prepare has same state dict as quantized state_dict
model = TwoLayerLinearModel()
model.eval()
model = torch.ao.quantization.QuantWrapper(model)
model.qconfig = torch.ao.quantization.get_default_qconfig(
qengine)
torch.ao.quantization.prepare(model, inplace=True)
torch.ao.quantization.convert(model, inplace=True)
self.assertEqual(set(model.state_dict().keys()),
set(quant_state_dict.keys()))
model.eval()
model.load_state_dict(quant_state_dict)
out = model(x)
self.assertEqual(ref, out)
def test_dropout(self):
for qengine in supported_qengines:
with override_quantized_engine(qengine):
model = ManualDropoutQATModel(qengine)
model = prepare_qat(model)
self.checkObservers(model)
test_only_train_fn(model, self.train_data)
model = convert(model)
def checkQuantized(model):
self.assertEqual(type(model.fc1), nnq.Linear)
self.assertEqual(type(model.dropout), nnq.Dropout)
test_only_eval_fn(model, self.calib_data)
self.checkScriptable(model, self.calib_data)
self.checkNoQconfig(model)
checkQuantized(model)
model = quantize_qat(ManualDropoutQATModel(qengine),
test_only_train_fn, [self.train_data])
checkQuantized(model)
