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Dynamic ViT #2476

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f515179
Dynamic Shapes
mcr229 Apr 11, 2024
9f7ead7
dynamic qd8-fc test with 2 batch dims
mcr229 Apr 11, 2024
14b9da9
dynamic mobilenetv2
mcr229 Apr 11, 2024
cd20eb0
dynamic mv3
mcr229 Apr 11, 2024
fc8c937
Dynamic ResNet
mcr229 Apr 11, 2024
eed0c61
Dynamic ViT (#2476)
mcr229 Apr 11, 2024
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3 changes: 1 addition & 2 deletions backends/xnnpack/test/models/deeplab_v3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,5 @@ def test_fp32_dl3(self):
.partition()
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)
6 changes: 2 additions & 4 deletions backends/xnnpack/test/models/edsr.py
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Expand Up @@ -25,8 +25,7 @@ def test_fp32_edsr(self):
.partition()
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)

def test_qs8_edsr(self):
Expand All @@ -38,6 +37,5 @@ def test_qs8_edsr(self):
.partition()
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)
28 changes: 12 additions & 16 deletions backends/xnnpack/test/models/emformer_rnnt.py
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Expand Up @@ -21,8 +21,8 @@ def __init__(self):
self.rnnt = decoder.model

class Joiner(EmformerRnnt):
def forward(self, predict_inputs):
return self.rnnt.join(*predict_inputs)
def forward(self, a, b, c, d):
return self.rnnt.join(a, b, c, d)

def get_example_inputs(self):
join_inputs = (
Expand All @@ -31,7 +31,7 @@ def get_example_inputs(self):
torch.rand([1, 128, 1024]),
torch.tensor([128]),
)
return (join_inputs,)
return join_inputs

def test_fp32_emformer_joiner(self):
joiner = self.Joiner()
Expand All @@ -43,21 +43,19 @@ def test_fp32_emformer_joiner(self):
.check(["torch.ops.higher_order.executorch_call_delegate"])
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)

class Predictor(EmformerRnnt):
def forward(self, predict_inputs):
return self.rnnt.predict(*predict_inputs)
def forward(self, a, b):
return self.rnnt.predict(a, b, None)

def get_example_inputs(self):
predict_inputs = (
torch.zeros([1, 128], dtype=int),
torch.tensor([128], dtype=int),
None,
)
return (predict_inputs,)
return predict_inputs

@unittest.skip("T183426271")
def test_fp32_emformer_predictor(self):
Expand All @@ -70,20 +68,19 @@ def test_fp32_emformer_predictor(self):
.check(["torch.ops.higher_order.executorch_call_delegate"])
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)

class Transcriber(EmformerRnnt):
def forward(self, predict_inputs):
return self.rnnt.transcribe(*predict_inputs)
def forward(self, a, b):
return self.rnnt.transcribe(a, b)

def get_example_inputs(self):
transcribe_inputs = (
torch.randn(1, 128, 80),
torch.tensor([128]),
)
return (transcribe_inputs,)
return transcribe_inputs

def test_fp32_emformer_transcriber(self):
transcriber = self.Transcriber()
Expand All @@ -95,6 +92,5 @@ def test_fp32_emformer_transcriber(self):
.check(["torch.ops.higher_order.executorch_call_delegate"])
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)
6 changes: 2 additions & 4 deletions backends/xnnpack/test/models/inception_v3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -42,8 +42,7 @@ def test_fp32_ic3(self):
.check_not(list(self.all_operators))
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)

def test_qs8_ic3(self):
Expand All @@ -63,6 +62,5 @@ def test_qs8_ic3(self):
.check_not(list(ops_after_quantization))
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)
6 changes: 2 additions & 4 deletions backends/xnnpack/test/models/inception_v4.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,8 +39,7 @@ def test_fp32_ic4(self):
.check_not(list(self.all_operators))
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)

def test_qs8_ic4(self):
Expand All @@ -60,6 +59,5 @@ def test_qs8_ic4(self):
.check_not(list(ops_after_quantization))
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)
3 changes: 1 addition & 2 deletions backends/xnnpack/test/models/llama2_et_example.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,5 @@ def _test(self, dtype: torch.dtype = torch.float):
.dump_artifact()
.to_executorch()
.serialize()
.run_method()
.compare_outputs(atol=5e-2)
.run_method_and_compare_outputs(atol=5e-2)
)
3 changes: 1 addition & 2 deletions backends/xnnpack/test/models/mobilebert.py
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,5 @@ def test_fp32_mobilebert(self):
.check_not(list(self.supported_ops))
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)
23 changes: 17 additions & 6 deletions backends/xnnpack/test/models/mobilenet_v2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,9 +29,15 @@ class TestMobileNetV2(unittest.TestCase):
}

def test_fp32_mv2(self):
dynamic_shapes = (
{
2: torch.export.Dim("height", min=224, max=455),
3: torch.export.Dim("width", min=224, max=455),
},
)

(
Tester(self.mv2, self.model_inputs)
Tester(self.mv2, self.model_inputs, dynamic_shapes=dynamic_shapes)
.export()
.to_edge()
.check(list(self.all_operators))
Expand All @@ -40,8 +46,7 @@ def test_fp32_mv2(self):
.check_not(list(self.all_operators))
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs(num_runs=10)
)

def test_qs8_mv2(self):
Expand All @@ -50,8 +55,15 @@ def test_qs8_mv2(self):
"executorch_exir_dialects_edge__ops_aten__native_batch_norm_legit_no_training_default",
}

dynamic_shapes = (
{
2: torch.export.Dim("height", min=224, max=455),
3: torch.export.Dim("width", min=224, max=455),
},
)

(
Tester(self.mv2, self.model_inputs)
Tester(self.mv2, self.model_inputs, dynamic_shapes=dynamic_shapes)
.quantize(Quantize(calibrate=False))
.export()
.to_edge()
Expand All @@ -61,6 +73,5 @@ def test_qs8_mv2(self):
.check_not(list(ops_after_quantization))
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs(num_runs=10)
)
16 changes: 10 additions & 6 deletions backends/xnnpack/test/models/mobilenet_v3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,12 @@ class TestMobileNetV3(unittest.TestCase):
mv3 = models.mobilenetv3.mobilenet_v3_small(pretrained=True)
mv3 = mv3.eval()
model_inputs = (torch.ones(1, 3, 224, 224),)
dynamic_shapes = (
{
2: torch.export.Dim("height", min=224, max=455),
3: torch.export.Dim("width", min=224, max=455),
},
)

all_operators = {
"executorch_exir_dialects_edge__ops_aten__native_batch_norm_legit_no_training_default",
Expand All @@ -33,7 +39,7 @@ class TestMobileNetV3(unittest.TestCase):

def test_fp32_mv3(self):
(
Tester(self.mv3, self.model_inputs)
Tester(self.mv3, self.model_inputs, dynamic_shapes=self.dynamic_shapes)
.export()
.to_edge()
.check(list(self.all_operators))
Expand All @@ -42,8 +48,7 @@ def test_fp32_mv3(self):
.check_not(list(self.all_operators))
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs(num_runs=5)
)

def test_qs8_mv3(self):
Expand All @@ -53,7 +58,7 @@ def test_qs8_mv3(self):
ops_after_lowering = self.all_operators

(
Tester(self.mv3, self.model_inputs)
Tester(self.mv3, self.model_inputs, dynamic_shapes=self.dynamic_shapes)
.quantize(Quantize(calibrate=False))
.export()
.to_edge()
Expand All @@ -63,6 +68,5 @@ def test_qs8_mv3(self):
.check_not(list(ops_after_lowering))
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs(num_runs=5)
)
68 changes: 51 additions & 17 deletions backends/xnnpack/test/models/resnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,29 +14,63 @@


class TestResNet18(unittest.TestCase):
def test_fp32_resnet18(self):
inputs = (torch.ones(1, 3, 224, 224),)
inputs = (torch.ones(1, 3, 224, 224),)
dynamic_shapes = (
{
2: torch.export.Dim("height", min=224, max=455),
3: torch.export.Dim("width", min=224, max=455),
},
)

class DynamicResNet(torch.nn.Module):
def __init__(self):
super().__init__()
self.model = torchvision.models.resnet18()

def forward(self, x):
x = torch.nn.functional.interpolate(
x,
size=(224, 224),
mode="bilinear",
align_corners=True,
antialias=False,
)
return self.model(x)

def _test_exported_resnet(self, tester):
(
Tester(torchvision.models.resnet18(), inputs)
.export()
tester.export()
.to_edge()
.partition()
.check_not(
[
"executorch_exir_dialects_edge__ops_aten_convolution_default",
"executorch_exir_dialects_edge__ops_aten_mean_dim",
]
)
.check(["torch.ops.higher_order.executorch_call_delegate"])
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
.run_method_and_compare_outputs()
)

def test_fp32_resnet18(self):
self._test_exported_resnet(Tester(torchvision.models.resnet18(), self.inputs))

def test_qs8_resnet18(self):
inputs = (torch.ones(1, 3, 224, 224),)
(
Tester(torchvision.models.resnet18(), inputs)
.quantize(Quantize(calibrate=False))
.export()
.to_edge()
.partition()
.to_executorch()
.serialize()
.run_method()
.compare_outputs()
quantized_tester = Tester(torchvision.models.resnet18(), self.inputs).quantize(
Quantize(calibrate=False)
)
self._test_exported_resnet(quantized_tester)

def test_fp32_resnet18_dynamic(self):
self._test_exported_resnet(
Tester(self.DynamicResNet(), self.inputs, self.dynamic_shapes)
)

def test_qs8_resnet18_dynamic(self):
self._test_exported_resnet(
Tester(self.DynamicResNet(), self.inputs, self.dynamic_shapes).quantize(
Quantize(calibrate=False)
)
)
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