From 319369331dbc0426fe50c3d9143f502f67ce3fe8 Mon Sep 17 00:00:00 2001
From: Yifan Zhao <yifanz16@illinois.edu>
Date: Sat, 13 Mar 2021 22:33:08 -0600
Subject: [PATCH] Updated predtuner and test script
---
hpvm/projects/predtuner | 2 +-
.../dnn_benchmarks/pytorch/test_tuning.py | 75 ++++++++++++-------
2 files changed, 49 insertions(+), 28 deletions(-)
diff --git a/hpvm/projects/predtuner b/hpvm/projects/predtuner
index 6ff4237cf4..cef0789732 160000
--- a/hpvm/projects/predtuner
+++ b/hpvm/projects/predtuner
@@ -1 +1 @@
-Subproject commit 6ff4237cf4386ebb4fcaeb5e448ef6eac8a41c91
+Subproject commit cef07897325f0427246a1f2d71fbf9562656465f
diff --git a/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py b/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py
index 32d982187a..d0451b70b4 100644
--- a/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py
+++ b/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py
@@ -2,9 +2,9 @@ import os
import shutil
import site
from pathlib import Path
-from subprocess import Popen
import torch
+from predtuner import config_pylogger
from predtuner.pipedbin import PipedBinaryApp
from torch2hpvm import BinDataset, ModelExporter
from torch.nn import Module
@@ -12,32 +12,38 @@ from torch.nn import Module
site.addsitedir(os.path.dirname(__file__))
import dnn
+# Set up logger to put log file in /tmp
+msg_logger = config_pylogger(output_dir="/tmp", verbose=True)
+
+
benchmarks = [
- (dnn.LeNet, 1, 28, 5000, "lenet_mnist"),
- (dnn.AlexNet, 3, 32, 5000, "alexnet_cifar10"),
- (dnn.AlexNet2, 3, 32, 5000, "alexnet2_cifar10"),
- (dnn.AlexNetImageNet, 3, 224, 500, "alexnet_imagenet"),
- (dnn.MobileNet, 3, 32, 5000, "mobilenet_cifar10"),
- (dnn.ResNet18, 3, 32, 5000, "resnet18_cifar10"),
- (dnn.ResNet50, 3, 224, 100, "resnet50_imagenet"),
- (dnn.VGG16Cifar10, 3, 32, 5000, "vgg16_cifar10"),
- (dnn.VGG16Cifar100, 3, 32, 5000, "vgg16_cifar100"),
- (dnn.VGG16ImageNet, 3, 224, 100, "vgg16_imagenet"),
+ (dnn.LeNet, 1, 28, 500, "lenet_mnist"),
+ (dnn.AlexNet, 3, 32, 500, "alexnet_cifar10"),
+ (dnn.AlexNet2, 3, 32, 500, "alexnet2_cifar10"),
+ (dnn.AlexNetImageNet, 3, 224, 100, "alexnet_imagenet"),
+ (dnn.MobileNet, 3, 32, 500, "mobilenet_cifar10"),
+ (dnn.ResNet18, 3, 32, 500, "resnet18_cifar10"),
+ (dnn.ResNet50, 3, 224, 50, "resnet50_imagenet"),
+ (dnn.VGG16Cifar10, 3, 32, 500, "vgg16_cifar10"),
+ (dnn.VGG16Cifar100, 3, 32, 500, "vgg16_cifar100"),
+ (dnn.VGG16ImageNet, 3, 224, 50, "vgg16_imagenet"),
]
+model_param = Path(__file__).parent / "../model_params"
+
+
+def generate(model_cls, nch, img_size, batch_size, pathname):
+ codegen_dir = Path(f"/tmp/{pathname}_tune")
+ build_dir = codegen_dir / "build"
+ metadata_file = codegen_dir / "ops.json"
+ binary_file = build_dir / pathname
+ build_dir = codegen_dir / "build"
+ # if binary_file.is_file() and metadata_file.is_file():
+ # return binary_file, metadata_file
-self_folder = Path(__file__).parent
-model_cls, nch, img_size, batch_size, pathname = benchmarks[0]
-codegen_dir = Path(f"/tmp/{pathname}_tune")
-build_dir = codegen_dir / "build"
-metadata_file = codegen_dir / "ops.json"
-binary_file = build_dir / pathname
-conf_file = codegen_dir / ModelExporter.config_file_name
-if not binary_file.is_file() or not metadata_file.is_file():
print(f"Generating {pathname} to {codegen_dir}")
if codegen_dir.exists():
shutil.rmtree(codegen_dir)
-
- params = self_folder / "../model_params" / pathname
+ params = model_param / pathname
dataset_shape = 5000, nch, img_size, img_size
bin_tuneset = BinDataset(
params / "tune_input.bin", params / "tune_labels.bin", dataset_shape
@@ -46,14 +52,29 @@ if not binary_file.is_file() or not metadata_file.is_file():
params / "test_input.bin", params / "test_labels.bin", dataset_shape
)
model: Module = model_cls()
- checkpoint = self_folder / "../model_params" / f"{pathname}.pth.tar"
+ checkpoint = model_param / f"{pathname}.pth.tar"
model.load_state_dict(torch.load(checkpoint.as_posix()))
-
exporter = ModelExporter(
model, bin_tuneset, bin_testset, codegen_dir, target="hpvm_tensor_inspect"
)
exporter.generate(batch_size=batch_size).compile(binary_file, build_dir)
-app = PipedBinaryApp("test", binary_file, metadata_file)
-tuner = app.get_tuner()
-tuner.tune(100, 3.0, is_threshold_relative=True, perf_model="perf_linear", qos_model="qos_p1")
-tuner.dump_configs("configs.json")
+ return binary_file, metadata_file
+
+
+def main():
+ for model_cls, nch, img_size, batch_size, pathname in benchmarks:
+ print(f"Testing {pathname}")
+ binary_file, metadata_file = generate(
+ model_cls, nch, img_size, batch_size, pathname
+ )
+ app = PipedBinaryApp("test", binary_file, metadata_file)
+ tuner = app.get_tuner()
+ tuner.tune(100, 3.0, 3.0, True, 50, cost_model="cost_linear")
+ tuner.dump_configs("configs.json")
+ fig = tuner.plot_configs(show_qos_loss=True)
+ fig.savefig("configs.png", dpi=300)
+ app.dump_hpvm_configs(tuner.best_configs, "hpvm_confs.txt")
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file
--
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