From db54bdd812e0525590b347b444c1dd80669f6c18 Mon Sep 17 00:00:00 2001
From: Yifan Zhao <yifanz16@illinois.edu>
Date: Mon, 1 Feb 2021 04:50:59 -0600
Subject: [PATCH] Fixed corner cases and added test for generating all 10
benchmarks
---
.../torch2hpvm/torch2hpvm/graph_builder.py | 8 +++-
.../torch2hpvm/torch2hpvm/graph_ir.py | 14 +++++--
.../dnn_benchmarks/pytorch/dnn/__init__.py | 2 +-
hpvm/test/dnn_benchmarks/pytorch/dnn/vgg16.py | 5 +++
.../dnn_benchmarks/pytorch/test_frontend.py | 42 +++++++++++++++----
5 files changed, 57 insertions(+), 14 deletions(-)
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/graph_builder.py b/hpvm/projects/torch2hpvm/torch2hpvm/graph_builder.py
index 640c845ff2..7739a64f53 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/graph_builder.py
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/graph_builder.py
@@ -221,9 +221,13 @@ class DFG(object):
"Identity": g.IdentityNode,
"Flatten": g.FlattenNode,
}
- if node.op_type in one_to_one_nodes:
+ if node.op_type not in one_to_one_nodes:
+ return None
+ try:
return one_to_one_nodes[node.op_type](node.name, **attrs)
- return None
+ except (TypeError, KeyError, ValueError, RuntimeError):
+ node_class = one_to_one_nodes[node.op_type]
+ raise ValueError(f"Node ({node_class}) creation failed")
def def_use(nodes: Iterable) -> Tuple[dict, dict]:
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/graph_ir.py b/hpvm/projects/torch2hpvm/torch2hpvm/graph_ir.py
index 3309072723..a088e6eae5 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/graph_ir.py
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/graph_ir.py
@@ -134,18 +134,23 @@ class Conv2DNode(DFGNode):
raise ValueError("Convolution with different padding is unsupported")
if list(dilations) != [1, 1]:
raise ValueError("Dilation > 1 is unsupported")
- if group != 1:
- raise ValueError("Group > 1 is unsupported")
+ self.group = group
self.pads = pads[0]
self.sh, self.sw = strides
def codegen(self):
return (
"tensorConvolution",
- [self.pads, self.pads, self.sh, self.sw],
+ [self.pads, self.pads, self.sh, self.sw, 1, self.group],
)
def hpvm_codegen(self):
+ if self.group != 1:
+ return (
+ "__hpvm__tensor_group_convolution",
+ # 1 is conv_mode -- should always be 1
+ [self.pads, self.pads, self.sh, self.sw, 1, self.group],
+ )
return (
"__hpvm__tensor_convolution",
[self.pads, self.pads, self.sh, self.sw],
@@ -323,7 +328,8 @@ class BatchNormalizationNode(DFGNode):
input_shapes: Tuple[ShapeT, ShapeT],
output_shape: ShapeT,
epsilon: float,
- axis: int,
+ axis: int = None,
+ momentum: float = None,
):
super().__init__(name, input_shapes, output_shape)
self.epsilon = epsilon
diff --git a/hpvm/test/dnn_benchmarks/pytorch/dnn/__init__.py b/hpvm/test/dnn_benchmarks/pytorch/dnn/__init__.py
index 6d7deb9955..f4a16c6a2c 100644
--- a/hpvm/test/dnn_benchmarks/pytorch/dnn/__init__.py
+++ b/hpvm/test/dnn_benchmarks/pytorch/dnn/__init__.py
@@ -1,6 +1,6 @@
from .alexnet import AlexNet, AlexNet2, AlexNetImageNet
from .datasets import CIFAR, MNIST, ImageNet
from .lenet import LeNet
-from .vgg16 import VGG16Cifar10, VGG16Cifar100
+from .vgg16 import VGG16Cifar10, VGG16Cifar100, VGG16ImageNet
from .mobilenet import MobileNet
from .resnet import ResNet18, ResNet50
diff --git a/hpvm/test/dnn_benchmarks/pytorch/dnn/vgg16.py b/hpvm/test/dnn_benchmarks/pytorch/dnn/vgg16.py
index 3c518fd511..43ac8f9818 100644
--- a/hpvm/test/dnn_benchmarks/pytorch/dnn/vgg16.py
+++ b/hpvm/test/dnn_benchmarks/pytorch/dnn/vgg16.py
@@ -39,3 +39,8 @@ class VGG16Cifar10(_VGG16):
class VGG16Cifar100(_VGG16):
def __init__(self):
super().__init__([512, 512, 100])
+
+
+class VGG16ImageNet(_VGG16):
+ def __init__(self):
+ super().__init__([25088, 4096, 4096, 1000])
diff --git a/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py b/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
index 214d1a4237..1ca46cd702 100644
--- a/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
+++ b/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
@@ -1,9 +1,37 @@
+import os
+import site
from pathlib import Path
-from torch2hpvm import ModelExporter, BinDataset
-from dnn import AlexNet
+import shutil
-prefix = Path(__file__).parent / "../model_params/alexnet_cifar10"
-dataset_shape = 5000, 3, 32, 32
-bin_tuneset = BinDataset(prefix / "tune_input.bin", prefix / "tune_labels.bin", dataset_shape)
-bin_testset = BinDataset(prefix / "test_input.bin", prefix / "test_labels.bin", dataset_shape)
-ModelExporter(AlexNet(), bin_tuneset, bin_testset, "/tmp/alexnet", True).export_all()
+from torch2hpvm import BinDataset, ModelExporter
+
+site.addsitedir(os.path.dirname(__file__))
+import dnn
+
+benchmarks = [
+ (dnn.LeNet, 1, 28, "lenet_mnist"),
+ (dnn.AlexNet, 3, 32, "alexnet_cifar10"),
+ (dnn.AlexNet2, 3, 32, "alexnet2_cifar10"),
+ (dnn.AlexNetImageNet, 3, 224, "alexnet_imagenet"),
+ (dnn.MobileNet, 3, 32, "mobilenet_cifar10"),
+ (dnn.ResNet18, 3, 32, "resnet18_cifar10"),
+ (dnn.ResNet50, 3, 224, "resnet50_imagenet"),
+ (dnn.VGG16Cifar10, 3, 32, "vgg16_cifar10"),
+ (dnn.VGG16Cifar100, 3, 32, "vgg16_cifar100"),
+ (dnn.VGG16ImageNet, 3, 224, "vgg16_imagenet"),
+]
+self_folder = Path(__file__).parent
+for model_cls, nch, img_size, pathname in benchmarks:
+ target = Path(f"/tmp/{pathname}")
+ print(f"Generating {pathname} to {target}")
+ if target.exists():
+ shutil.rmtree(target)
+ prefix = self_folder / "../model_params" / pathname
+ dataset_shape = 5000, nch, img_size, img_size
+ bin_tuneset = BinDataset(
+ prefix / "tune_input.bin", prefix / "tune_labels.bin", dataset_shape
+ )
+ bin_testset = BinDataset(
+ prefix / "test_input.bin", prefix / "test_labels.bin", dataset_shape
+ )
+ ModelExporter(model_cls(), bin_tuneset, bin_testset, target, True).export_all()
--
GitLab