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Add TF ResNet50 to tank tests. (#261)

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* Add TensorFlow Resnet50 test to shark tank.
This commit is contained in:
Ean Garvey
2022-08-12 11:20:43 -05:00
committed by GitHub
parent 02b1e7ac36
commit 4b5d09fc6c
7 changed files with 152 additions and 32 deletions
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10
generate_sharktank.py
View File

@@ -93,8 +93,11 @@ def save_torch_model(torch_model_list):
def save_tf_model(tf_model_list):
from tank.model_utils_tf import get_causal_lm_model
from tank.model_utils_tf import get_causal_image_model
from tank.model_utils_tf import (
get_causal_image_model,
get_causal_lm_model,
get_keras_model,
)
with open(tf_model_list) as csvfile:
tf_reader = csv.reader(csvfile, delimiter=",")
@@ -110,7 +113,8 @@ def save_tf_model(tf_model_list):
model, input, _ = get_causal_lm_model(tf_model_name)
if model_type == "img":
model, input, _ = get_causal_image_model(tf_model_name)
if model_type == "keras":
model, input, _ = get_keras_model(tf_model_name)
tf_model_name = tf_model_name.replace("/", "_")
tf_model_dir = os.path.join(WORKDIR, str(tf_model_name) + "_tf")
os.makedirs(tf_model_dir, exist_ok=True)

9
shark/iree_utils/benchmark_utils.py
View File

@@ -34,9 +34,12 @@ def tensor_to_type_str(input_tensors: tuple, mlir_dialect: str):
dtype_string = str(input_tensor.dtype).replace("torch.", "")
elif mlir_dialect in ["mhlo", "tflite"]:
dtype = input_tensor.dtype
dtype_string = re.findall("'[^\"]*'", str(dtype))[0].replace(
"'", ""
)
try:
dtype_string = re.findall("'[^\"]*'", str(dtype))[0].replace(
"'", ""
)
except IndexError:
dtype_string = str(dtype)
regex_split = re.compile("([a-zA-Z]+)([0-9]+)")
match = regex_split.match(dtype_string)
mlir_type_string = str(match.group(1)[0]) + str(match.group(2))

6
shark/iree_utils/compile_utils.py
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@@ -124,15 +124,15 @@ def export_iree_module_to_vmfb(
module,
device: str,
directory: str,
frontend: str = "torch",
mlir_dialect: str = "linalg",
func_name: str = "forward",
model_config_path: str = None,
):
# Compiles the module given specs and saves it as .vmfb file.
flatbuffer_blob = compile_module_to_flatbuffer(
module, device, frontend, func_name, model_config_path
module, device, mlir_dialect, func_name, model_config_path
)
module_name = f"{frontend}_{func_name}_{device}"
module_name = f"{mlir_dialect}_{func_name}_{device}"
filename = os.path.join(directory, module_name + ".vmfb")
print(f"Saved vmfb in {filename}.")
with open(filename, "wb") as f:

47
shark/shark_benchmark_runner.py
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@@ -34,22 +34,21 @@ class SharkBenchmarkRunner(SharkRunner):
function_name: str = "forward",
device: str = "none",
mlir_dialect: str = "linalg",
frontend: str = "torch",
):
self.device = shark_args.device if device == "none" else device
self.frontend = frontend
self.frontend_model = None
self.vmfb_file = None
self.mlir_dialect = mlir_dialect
SharkRunner.__init__(
self,
mlir_module,
function_name,
device,
mlir_dialect,
self.mlir_dialect,
)
if self.vmfb_file == None:
self.vmfb_file = export_iree_module_to_vmfb(
mlir_module, device, shark_args.repro_dir, self.frontend
mlir_module, device, shark_args.repro_dir, self.mlir_dialect
)
def setup_cl(self, input_tensors):
@@ -59,11 +58,12 @@ class SharkBenchmarkRunner(SharkRunner):
input_tensors,
mlir_dialect=self.mlir_dialect,
)
print(self.benchmark_cl)
def benchmark_frontend(self, inputs, modelname):
if self.frontend in ["pytorch", "torch"]:
if self.mlir_dialect in ["linalg", "torch"]:
return self.benchmark_torch(modelname)
elif self.frontend in ["tensorflow", "tf"]:
elif self.mlir_dialect in ["mhlo", "tf"]:
return self.benchmark_tf(inputs, modelname)
def benchmark_torch(self, modelname):
@@ -99,26 +99,27 @@ class SharkBenchmarkRunner(SharkRunner):
]
def benchmark_tf(self, frontend_model, inputs):
for i in range(shark_args.num_warmup_iterations):
frontend_model.forward(*inputs)
# for i in range(shark_args.num_warmup_iterations):
# frontend_model.forward(*inputs)
begin = time.time()
for i in range(shark_args.num_iterations):
out = frontend_model.forward(*inputs)
if i == shark_args.num_iterations - 1:
end = time.time()
break
print(
f"TF benchmark:{shark_args.num_iterations/(end-begin)} iter/second, Total Iterations:{shark_args.num_iterations}"
)
return [
f"{shark_args.num_iterations/(end-begin)}",
f"{((end-begin)/shark_args.num_iterations)*1000}",
]
# begin = time.time()
# for i in range(shark_args.num_iterations):
# out = frontend_model.forward(*inputs)
# if i == shark_args.num_iterations - 1:
# end = time.time()
# break
# print(
# f"TF benchmark:{shark_args.num_iterations/(end-begin)} iter/second, Total Iterations:{shark_args.num_iterations}"
# )
# return [
# f"{shark_args.num_iterations/(end-begin)}",
# f"{((end-begin)/shark_args.num_iterations)*1000}",
# ]
return ["n/a", "n/a"]
def benchmark_c(self):
result = run_benchmark_module(self.benchmark_cl)
print(f"Shark-{self.frontend} C-benchmark:{result} iter/second")
print(f"Shark-IREE-C benchmark:{result} iter/second")
return [f"{result}", f"{1000/result}"]
def benchmark_python(self, inputs):
@@ -132,7 +133,7 @@ class SharkBenchmarkRunner(SharkRunner):
if i == shark_args.num_iterations - 1:
end = time.time()
print(
f"Shark-{self.frontend} Python-benchmark:{shark_args.num_iterations/(end-begin)} iter/second, Total Iterations:{shark_args.num_iterations}"
f"Shark-IREE Python benchmark:{shark_args.num_iterations/(end-begin)} iter/second, Total Iterations:{shark_args.num_iterations}"
)
return [
f"{shark_args.num_iterations/(end-begin)}",

44
tank/model_utils_tf.py
View File

@@ -147,6 +147,50 @@ def get_causal_lm_model(hf_name, text="Hello, this is the default text."):
return model, test_input, actual_out
##################### TensorFlow Keras Resnet Models #########################################################
# Static shape, including batch size (1).
# Can be dynamic once dynamic shape support is ready.
INPUT_SHAPE = [1, 224, 224, 3]
tf_model = tf.keras.applications.resnet50.ResNet50(
weights="imagenet", include_top=True, input_shape=tuple(INPUT_SHAPE[1:])
)
class ResNetModule(tf.Module):
def __init__(self):
super(ResNetModule, self).__init__()
self.m = tf_model
self.m.predict = lambda x: self.m.call(x, training=False)
@tf.function(input_signature=[tf.TensorSpec(INPUT_SHAPE, tf.float32)])
def forward(self, inputs):
return self.m.predict(inputs)
def load_image(path_to_image):
image = tf.io.read_file(path_to_image)
image = tf.image.decode_image(image, channels=3)
image = tf.image.resize(image, (224, 224))
image = image[tf.newaxis, :]
return image
def get_keras_model(modelname):
model = ResNetModule()
content_path = tf.keras.utils.get_file(
"YellowLabradorLooking_new.jpg",
"https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg",
)
content_image = load_image(content_path)
input_tensor = tf.keras.applications.resnet50.preprocess_input(
content_image
)
input_data = tf.expand_dims(input_tensor, 0)
actual_out = model.forward(*input_data)
return model, input_data, actual_out
##################### Tensorflow Hugging Face Image Classification Models ###################################
from transformers import TFAutoModelForImageClassification
from transformers import ConvNextFeatureExtractor, ViTFeatureExtractor

67
tank/resnet50_tf/resnet50_tf_test.py Normal file
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@@ -0,0 +1,67 @@
from shark.shark_inference import SharkInference
from shark.iree_utils._common import check_device_drivers, device_driver_info
from shark.shark_downloader import download_tf_model
import unittest
import numpy as np
import pytest
import numpy as np
class Resnet50ModuleTester:
def __init__(
self,
benchmark=False,
):
self.benchmark = benchmark
def create_and_check_module(self, dynamic, device):
model, func_name, inputs, golden_out = download_tf_model("resnet50")
shark_module = SharkInference(
model,
func_name,
device=device,
mlir_dialect="mhlo",
is_benchmark=self.benchmark,
)
shark_module.compile()
result = shark_module.forward(inputs)
np.testing.assert_allclose(golden_out, result, rtol=1e-02, atol=1e-03)
if self.benchmark == True:
shark_module.shark_runner.benchmark_all_csv(
(inputs), "resnet50", dynamic, device, "tensorflow"
)
class Resnet50ModuleTest(unittest.TestCase):
@pytest.fixture(autouse=True)
def configure(self, pytestconfig):
self.module_tester = Resnet50ModuleTester(self)
self.module_tester.benchmark = pytestconfig.getoption("benchmark")
def test_module_static_cpu(self):
dynamic = False
device = "cpu"
self.module_tester.create_and_check_module(dynamic, device)
@pytest.mark.skipif(
check_device_drivers("gpu"), reason=device_driver_info("gpu")
)
def test_module_static_gpu(self):
dynamic = False
device = "gpu"
self.module_tester.create_and_check_module(dynamic, device)
@pytest.mark.skipif(
check_device_drivers("vulkan"), reason=device_driver_info("vulkan")
)
def test_module_static_vulkan(self):
dynamic = False
device = "vulkan"
self.module_tester.create_and_check_module(dynamic, device)
if __name__ == "__main__":
unittest.main()

1
tank/tf/tf_model_list.csv
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@@ -11,6 +11,7 @@ microsoft/layoutlm-base-uncased,hf
google/mobilebert-uncased,hf
microsoft/mpnet-base,hf
roberta-base,hf
resnet50,keras
xlm-roberta-base,hf
microsoft/MiniLM-L12-H384-uncased,hf
funnel-transformer/small,hf
1 model_name model_type
11 google/mobilebert-uncased hf
12 microsoft/mpnet-base hf
13 roberta-base hf
14 resnet50 keras
15 xlm-roberta-base hf
16 microsoft/MiniLM-L12-H384-uncased hf
17 funnel-transformer/small hf