diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index bd5edbd1a467666f67c66be132b3a9d9bbd2d540..8bcc4738d02d7f07a497131d74f9a0ff6f119048 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -14,15 +14,20 @@ cache:
- hpvm/llvm/
when: always
-build:
+build-and-test:
stage: build
tags:
- hpvm
script:
- - pwd
- source activate hpvm && cd hpvm
+ - mv /root/cfe-9.0.0.src.tar.xz /root/llvm-9.0.0.src.tar.xz ./
+ - mv /root/model_params ./test/dnn_benchmarks
- ./install.sh -j32 -t "X86" DCMAKE_BUILD_TYPE=Release
- - cd ..
+ - cd build
+ - make -j32 check-hpvm-pass
+ - make -j32 check-hpvm-dnn
+ - make -j32 check-hpvm-profiler
+ - make -j32 check-hpvm-torch2hpvm
only:
- hpvm-release-exp
- merge_requests
diff --git a/hpvm/docs/components/hpvm-profiler.rst b/hpvm/docs/components/hpvm-profiler.rst
index 8a0e6603d3b7111d2735a86b5db26d7aa834ebb6..820456799ddf0570be6b92564e35077e31fcd3da 100644
--- a/hpvm/docs/components/hpvm-profiler.rst
+++ b/hpvm/docs/components/hpvm-profiler.rst
@@ -1,6 +1,6 @@
HPVM Profiler API
======================
-.. autofunction:: hpvm_profiler.profile_configs
+.. autofunction:: hpvm_profiler.profile_config_file
.. autofunction:: hpvm_profiler.plot_hpvm_configs
diff --git a/hpvm/docs/getting-started.rst b/hpvm/docs/getting-started.rst
index 82a582283e7f7071a77ef55c1e2d9eca5fa9668d..6976fa012112eace8bc842658d5ea28b31ff04b6 100644
--- a/hpvm/docs/getting-started.rst
+++ b/hpvm/docs/getting-started.rst
@@ -207,14 +207,14 @@ we obtained in the tuning step.
.. code-block:: python
- from hpvm_profiler import profile_configs, plot_hpvm_configs
+ from hpvm_profiler import profile_config_file, plot_hpvm_configs
# Set `target_binary` to the path of the plain binary.
target_binary = "./alexnet2_cifar10/build/alexnet2_cifar10"
# Set `config_file` to the config file produced in tuning, such as "hpvm_confs.txt".
config_file = "hpvm_confs.txt"
out_config_file = "hpvm_confs_profiled.txt"
- profile_configs(target_binary, config_file, out_config_file)
+ profile_config_file(target_binary, config_file, out_config_file)
plot_hpvm_configs(out_config_file, "configs_profiled.png")
``hpvm_confs_profiled.txt`` contains the profiled configurations in HPVM format,
diff --git a/hpvm/projects/hpvm-profiler/hpvm_profiler/__init__.py b/hpvm/projects/hpvm-profiler/hpvm_profiler/__init__.py
index 4e91fbbe4a4af2c16b7583443360a09d88b0ac61..baaf645cb9f5a1c0f7f71a9d9b01269206a9cf18 100644
--- a/hpvm/projects/hpvm-profiler/hpvm_profiler/__init__.py
+++ b/hpvm/projects/hpvm-profiler/hpvm_profiler/__init__.py
@@ -1,19 +1,18 @@
-from dataclasses import dataclass
from pathlib import Path
from subprocess import PIPE, CalledProcessError
from typing import Iterable, List, Tuple, Union
import matplotlib.pyplot as plt
-from tqdm import trange
PathLike = Union[Path, str]
conf_opening, conf_closing = "+++++", "-----"
-def profile_configs(
+def profile_config_file(
binary_path: PathLike,
config_path: PathLike,
output_config_path: PathLike,
+ progress_bar: bool = True,
profile_filename: str = "profile_info.txt",
qos_filename: str = "final_accuracy",
) -> None:
@@ -33,39 +32,69 @@ def profile_configs(
It contains a single float number as the QoS of this run.
This defaults to "final_accuracy" and should not be changed for HPVM binaries.
"""
-
- from subprocess import check_call
- from tempfile import NamedTemporaryFile
-
# Read first line ("the float") and configs in config file
header, configs = read_hpvm_configs(Path(config_path))
if not configs:
raise ValueError("Config file with no configs is unsupported.")
- temp_file = NamedTemporaryFile("w")
- baseline_time, baseline_acc = None, None
- for idx in trange(len(configs), desc="Configs profiled"):
- config = configs[idx]
- # Write config to temp config file
- write_hpvm_config(header, [config], Path(temp_file.name))
- # Run binary_path binary,
- # which generates `profile_filename` and `qos_filename` file in cwd.
- try:
- check_call([str(binary_path), "-c", str(temp_file.name)])
- except CalledProcessError as e:
- print("Output from the program:")
- print(e.output)
- raise e
- # Read these two files for time and QoS info.
- time = _read_profile_file(Path(profile_filename))
- acc = _read_qos_file(Path(qos_filename))
- if idx == 0:
- baseline_time, baseline_acc = time, acc
- continue
- assert baseline_time is not None and baseline_acc is not None
+ # Modifies configs in place.
+ profile_configs(
+ binary_path,
+ configs[1:],
+ configs[0],
+ progress_bar,
+ profile_filename,
+ qos_filename,
+ )
+ write_hpvm_configs(header, configs, Path(output_config_path))
+
+
+def profile_configs(
+ binary_path: PathLike,
+ configs: Iterable["Config"],
+ baseline_config: "Config",
+ progress_bar: bool = True,
+ profile_filename: str = "profile_info.txt",
+ qos_filename: str = "final_accuracy",
+) -> None:
+ """Profile a sequence of HPVM configs.
+ This function modifies argument `configs` in place."""
+
+ from tqdm import tqdm
+
+ baseline_time, baseline_acc = measure_config(binary_path, baseline_config)
+ iterable = tqdm(configs, desc="Configs profiled") if progress_bar else configs
+ for config in iterable:
+ time, acc = measure_config(binary_path, config, profile_filename, qos_filename)
speedup = baseline_time / time
config.update_profile_results(speedup, acc, baseline_acc)
- write_hpvm_config(header, configs, Path(output_config_path))
+ return configs
+
+
+def measure_config(
+ binary_path: PathLike,
+ config: "Config",
+ profile_filename: str = "profile_info.txt",
+ qos_filename: str = "final_accuracy",
+):
+ from subprocess import check_call
+ from tempfile import NamedTemporaryFile
+ import os
+
+ temp_file = NamedTemporaryFile("w")
+ write_hpvm_configs("0.0", [config], Path(temp_file.name))
+ # Run binary_path binary,
+ # which generates `profile_filename` and `qos_filename` file in cwd.
+ try:
+ with open(os.devnull, "w") as f:
+ check_call([str(binary_path), "-c", str(temp_file.name)], stdout=f)
+ except CalledProcessError as e:
+ print("Output from the program:")
+ print(e.output)
+ raise e
+ time = _read_profile_file(Path(profile_filename))
+ acc = _read_qos_file(Path(qos_filename))
temp_file.close()
+ return time, acc
def plot_hpvm_configs(
@@ -102,19 +131,27 @@ def plot_hpvm_configs(
return fig
-@dataclass
class Config:
- conf_name: str
- speedup: float
- energy: float
- qos: float
- qos_loss: float
- # We don't care about the information in this part, and we don't parse this.
- config_body: List[str]
+ def __init__(
+ self,
+ conf_name: str,
+ speedup: float,
+ energy: float,
+ qos: float,
+ qos_loss: float,
+ config_body: List[str],
+ ):
+ self.conf_name = conf_name
+ self.speedup = speedup
+ self.energy = energy
+ self.qos = qos
+ self.qos_loss = qos_loss
+ # We don't care about the information in this part, and we don't parse this.
+ self.config_body = config_body
def update_profile_results(self, speedup: float, qos: float, base_qos: float):
recorded_base_qos = self.qos + self.qos_loss
- if abs(recorded_base_qos - base_qos) > 0.02:
+ if abs(recorded_base_qos - base_qos) > 0.025:
raise ValueError(
f"Baseline QoS mismatch. Original: {recorded_base_qos}, measured: {base_qos}"
)
@@ -157,15 +194,13 @@ def read_hpvm_configs(config_file: PathLike) -> Tuple[str, List[Config]]:
return header, ret_configs
-def write_hpvm_config(header: str, configs: Iterable[Config], to_file: PathLike):
-
+def write_hpvm_configs(header: str, configs: Iterable[Config], to_file: PathLike):
text_segs = [header] + [str(config) for config in configs]
with open(to_file, "w") as f:
f.write("\n".join(text_segs))
f.flush()
-
def _read_profile_file(profile_file_path: Path):
with profile_file_path.open() as f:
target_lines = [line.strip() for line in f if "Total Time" in line]
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/compile.py b/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
index 172448a60d4f65fc4aafc09c9a76d9cb492ff7b0..d53776b363595dd10b8f46f792474b941f444f2b 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
@@ -173,6 +173,8 @@ class ModelExporter:
args = [
"hpvm-clang",
+ "-O3",
+ "-fno-exceptions",
str(self.codefile),
str(output_binary),
*self.compile_args,
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/graph_ir.py b/hpvm/projects/torch2hpvm/torch2hpvm/graph_ir.py
index a088e6eae5c7cd8fb3db62f5046aa5d9ac945726..5c248f829adef15093b853891927f353aca30c4b 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/graph_ir.py
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/graph_ir.py
@@ -198,12 +198,6 @@ class _Pool2DNode(DFGNode, abc.ABC):
[self.pool_type, *self.kernel_shape, *self.pads, *self.strides,],
)
- def hpvm_codegen(self):
- return (
- "__hpvm__tensor_pool_max",
- [*self.kernel_shape, *self.pads, *self.strides],
- )
-
def get_flops(self) -> int:
input0 = self.input_shapes[0]
return np.prod(input0) if input0 else 0
@@ -214,12 +208,24 @@ class MaxPool2DNode(_Pool2DNode):
op_type = "MaxPool2D"
hpvm_op_type = "maxpool"
+ def hpvm_codegen(self):
+ return (
+ "__hpvm__tensor_pool_max",
+ [*self.kernel_shape, *self.pads, *self.strides],
+ )
+
class AveragePool2DNode(_Pool2DNode):
pool_type = "1"
op_type = "AveragePool2D"
hpvm_op_type = "avgpool"
+ def hpvm_codegen(self):
+ return (
+ "__hpvm__tensor_pool_mean",
+ [*self.kernel_shape, *self.pads, *self.strides],
+ )
+
class BiasAddNode(DFGNode):
op_type = "BiasAdd"
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm.cpp.in b/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm.cpp.in
index 1f6dd875ffa6b39ab57609d7690c9a9ad3944b44..fa252a3e0ce063697d56e771afbfbde69d0c5641 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm.cpp.in
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm.cpp.in
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
{% for node in nodes %}
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in b/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in
index 94a8e0a534c04b323b4b66f369ab2d624a2a745f..8074704ece0988d7897c1e93b41f1ea3c43deb35 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in
@@ -2,7 +2,6 @@
#include <string>
#include <array>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
// For writing binary to file descriptors
diff --git a/hpvm/test/CMakeLists.txt b/hpvm/test/CMakeLists.txt
index 3c4f26472317f511edaab98c5e4a4f8ed7ba2dfb..4ff98a5386d91ce50b755d7e507a84e0fbe1c4dd 100644
--- a/hpvm/test/CMakeLists.txt
+++ b/hpvm/test/CMakeLists.txt
@@ -8,5 +8,6 @@ set(CLANG_CXX ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/clang++)
add_subdirectory(hpvm_pass) # Passes test suite
add_subdirectory(benchmarks)
add_subdirectory(dnn_benchmarks/hpvm-c) # HPVM-C DNN accuracy test suite
+add_subdirectory(dnn_benchmarks/pytorch) # Torch frontend test suite
add_subdirectory(dnn_benchmarks/tensor-rt-src) # tensor_runtime DNN (build only, no tests)
add_subdirectory(dnn_benchmarks/profiling) # hpvm-profiler test suite
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/CMakeLists.txt b/hpvm/test/dnn_benchmarks/hpvm-c/CMakeLists.txt
index 9f34317d34157d57468c60cb854828b5c54f1cde..f56312e9c3dabf22731bdc910672748c67ddf50d 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/CMakeLists.txt
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/CMakeLists.txt
@@ -12,7 +12,7 @@ function(compile_hpvm_c target_name src_filepath codegen_target)
DEPENDS ${generated_file_path} hpvm-clang
COMMAND hpvm-clang
${generated_file_path} ${output_bin_path} -O3 -fno-exceptions
- -t ${codegen_target} -I ${CMAKE_CURRENT_SOURCE_DIR}/include ${ARGN}
+ -t ${codegen_target} ${ARGN}
)
add_custom_target(${target_name} DEPENDS ${output_bin_path})
set(test_compile_targets ${test_compile_targets} ${target_name} PARENT_SCOPE)
@@ -49,16 +49,17 @@ foreach(dir ${entries})
endforeach(dir)
# Install an accuracy comparator under build/bin for test suite.
-set(BIN_DIR ${LLVM_BINARY_DIR}/${LLVM_TOOLS_INSTALL_DIR})
+set(BIN_DIR ${CMAKE_RUNTIME_OUTPUT_DIRECTORY})
add_custom_command(
OUTPUT ${BIN_DIR}/check_dnn_acc.py
COMMAND cp ${CMAKE_CURRENT_SOURCE_DIR}/check_dnn_acc.py ${BIN_DIR}
COMMAND chmod +x ${BIN_DIR}/check_dnn_acc.py
DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/check_dnn_acc.py
)
+add_custom_target(check_dnn_acc DEPENDS ${BIN_DIR}/check_dnn_acc.py)
message(STATUS "List of HPVM-C DNN benchmarks: ${test_compile_targets}")
-add_custom_target(dnn_benchmarks DEPENDS ${test_compile_targets} ${BIN_DIR}/check_dnn_acc.py)
+add_custom_target(dnn_benchmarks DEPENDS ${test_compile_targets})
message(STATUS "Target name for compiling all DNN benchmarks: dnn_benchmarks")
# --[ llvm-lit test setup
@@ -73,6 +74,6 @@ configure_lit_site_cfg(
)
add_lit_testsuite(check-hpvm-dnn "Running HPVM DNNs"
${CMAKE_CURRENT_BINARY_DIR}
- DEPENDS dnn_benchmarks # Compile all dnn benchmarks to run them
+ DEPENDS dnn_benchmarks check_dnn_acc # Compile all dnn benchmarks to run them
ARGS "-j1" # Run DNN benchmarks sequentially
)
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet2_cifar10/alexnet2_cifar10.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet2_cifar10/alexnet2_cifar10.cpp
index 39f49784d76470c4e0bab213127369806e1e2531..2faf1413bcdb7c87e280107d38913ae86740a414 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet2_cifar10/alexnet2_cifar10.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet2_cifar10/alexnet2_cifar10.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet2_cifar10/alexnet2_cifar10_cudnn.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet2_cifar10/alexnet2_cifar10_cudnn.cpp
index dafd1a6ae084c4e1bf819ce1ac94e667c696eb24..bca6ca47cd48015524b496b90219f24e1f27ddb9 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet2_cifar10/alexnet2_cifar10_cudnn.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet2_cifar10/alexnet2_cifar10_cudnn.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_cifar10/alexnet_cifar10.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_cifar10/alexnet_cifar10.cpp
index 64350c590bb181fa4eaab4b2bf5fb37f69e11c09..d274d52ec18af99393f47d9fdb69b0b593dcbefc 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_cifar10/alexnet_cifar10.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_cifar10/alexnet_cifar10.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_cifar10/alexnet_cifar10_cudnn.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_cifar10/alexnet_cifar10_cudnn.cpp
index 72af2ff4a1b33aabac427d203101c32c4a7403c7..e82985d04fea11c1d30079e4eacbbee81c95080a 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_cifar10/alexnet_cifar10_cudnn.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_cifar10/alexnet_cifar10_cudnn.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_imagenet/alexnet_imagenet.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_imagenet/alexnet_imagenet.cpp
index 37e7a34a51a14b6903d549f271d3c0c83822fec8..c058e913c9f7c5bca6eb304759a380d319495caf 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_imagenet/alexnet_imagenet.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_imagenet/alexnet_imagenet.cpp
@@ -1,7 +1,6 @@
#include <config.h>
#include <hpvm.h>
#include <string>
-#include <tensorTypes.h>
#include <tensorUtils.h>
void var_0_node(void *t1, size_t bytes_t1, void *t2, size_t bytes_t2) {
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_imagenet/alexnet_imagenet_cudnn.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_imagenet/alexnet_imagenet_cudnn.cpp
index 1206d7bac4b9dcff2b4cfd7183f4a3e5f65d73d9..26e717fd732567eb9e6b97f19c60428e564fc9e5 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_imagenet/alexnet_imagenet_cudnn.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/alexnet_imagenet/alexnet_imagenet_cudnn.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/lenet_mnist/lenet_mnist.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/lenet_mnist/lenet_mnist.cpp
index d7ab4238ebac5598b92c432aced85a602bb5ce89..8185d9dc69b6899cad46833d71d18be01653bfb3 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/lenet_mnist/lenet_mnist.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/lenet_mnist/lenet_mnist.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/lenet_mnist/lenet_mnist_cudnn.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/lenet_mnist/lenet_mnist_cudnn.cpp
index 26acc65a99287ea9f20e037dd996635315d76e48..a0cd32151e5743d51df34edbe041e0fe8485aced 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/lenet_mnist/lenet_mnist_cudnn.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/lenet_mnist/lenet_mnist_cudnn.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/mobilenet_cifar10/mobilenet_cifar10.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/mobilenet_cifar10/mobilenet_cifar10.cpp
index 5f8c63dbfbfb800dc6f60f9ed9a6108dee0a9a48..77b448d81d1b352f8ac4ee9e3fc943e69f466772 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/mobilenet_cifar10/mobilenet_cifar10.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/mobilenet_cifar10/mobilenet_cifar10.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/mobilenet_cifar10/mobilenet_cifar10_cudnn.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/mobilenet_cifar10/mobilenet_cifar10_cudnn.cpp
index 2070089053ef0b6e7e0ca33c2c6cc4cea17b8e29..adb140bd699e74be7199f54888ee4249e5515004 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/mobilenet_cifar10/mobilenet_cifar10_cudnn.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/mobilenet_cifar10/mobilenet_cifar10_cudnn.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet18_cifar10/resnet18_cifar10.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet18_cifar10/resnet18_cifar10.cpp
index 5b580f26821e67cc96c8347e485b792f40105176..ef94b055bd6a741405c4c9da55958143d3b8c4d1 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet18_cifar10/resnet18_cifar10.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet18_cifar10/resnet18_cifar10.cpp
@@ -1,7 +1,6 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet18_cifar10/resnet18_cifar10_cudnn.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet18_cifar10/resnet18_cifar10_cudnn.cpp
index 735e2c9abab91f00560faa5496e234321027b82c..ecfa22957352ca2c418c5beb9b041762da9b6de9 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet18_cifar10/resnet18_cifar10_cudnn.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet18_cifar10/resnet18_cifar10_cudnn.cpp
@@ -1,7 +1,6 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet50_imagenet/resnet50_imagenet.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet50_imagenet/resnet50_imagenet.cpp
index 160563064cc47effd463c4915b0c7f0d93bff56f..37a4111411229602ca18f806c2186af54728081e 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet50_imagenet/resnet50_imagenet.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet50_imagenet/resnet50_imagenet.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet50_imagenet/resnet50_imagenet_cudnn.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet50_imagenet/resnet50_imagenet_cudnn.cpp
index c5cf2cb3a0177a5cce9ad0cf460484e63ded0ecd..1ac5141bca54d7dc60bb63c09cde9dcb8f8c6d32 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet50_imagenet/resnet50_imagenet_cudnn.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/resnet50_imagenet/resnet50_imagenet_cudnn.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar10/vgg16_cifar10.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar10/vgg16_cifar10.cpp
index bec6139c2d089e90d09fa239e1b15c9a835fd4ea..c1de0703df94b3f27dfd55b0379377ecf5f0edbe 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar10/vgg16_cifar10.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar10/vgg16_cifar10.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar10/vgg16_cifar10_cudnn.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar10/vgg16_cifar10_cudnn.cpp
index 4fa7d5c121bacff122821fe983ed443e3c6db249..7bda1213358d0c37d16623425bf19bace4d3a715 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar10/vgg16_cifar10_cudnn.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar10/vgg16_cifar10_cudnn.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar100/vgg16_cifar100.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar100/vgg16_cifar100.cpp
index 8666030fba4390d29d9324f5a5c7d60324325f05..bee78428df49c52f06bfa618afd7920d113e1647 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar100/vgg16_cifar100.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar100/vgg16_cifar100.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar100/vgg16_cifar100_cudnn.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar100/vgg16_cifar100_cudnn.cpp
index 6d01caa3b7c0875cff4f3e16131ddd09195e92b7..c12855437b28686528ff4c916a987bfa7b2f280e 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar100/vgg16_cifar100_cudnn.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_cifar100/vgg16_cifar100_cudnn.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_imagenet/vgg16_imagenet.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_imagenet/vgg16_imagenet.cpp
index b1b2b4f2e312b6372e10a2fce3ef12eab2dddded..b046f4255185e47b44be1a78ca29c05189fc894b 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_imagenet/vgg16_imagenet.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_imagenet/vgg16_imagenet.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_imagenet/vgg16_imagenet_cudnn.cpp b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_imagenet/vgg16_imagenet_cudnn.cpp
index eb29e45805671072428318412f27b05d0da90199..b06c992f3c2108544676c6e7f27810e3ef7244fc 100644
--- a/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_imagenet/vgg16_imagenet_cudnn.cpp
+++ b/hpvm/test/dnn_benchmarks/hpvm-c/benchmarks/vgg16_imagenet/vgg16_imagenet_cudnn.cpp
@@ -1,6 +1,5 @@
#include <string>
#include <hpvm.h>
-#include <tensorTypes.h>
#include <tensorUtils.h>
#include <config.h>
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/include/tensorTypes.h b/hpvm/test/dnn_benchmarks/hpvm-c/include/tensorTypes.h
deleted file mode 100644
index 726080efe7e1a06363e7fca191f9708219d5baeb..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/include/tensorTypes.h
+++ /dev/null
@@ -1,39 +0,0 @@
-
-#ifndef TYPES_HEADER
-#define TYPES_HEADER
-
-
-/*struct Dimension_t{
- int num_dims;
- size_t* dim_sizes;
-};
-
-
-struct Tensor_t{
- int tensor_id; // used for indexing (in the tensor runtime)
- int data_type; // {float_type, double_type, half_type, int_type}
- int data_format; // {nchw, nhwc}
- void* host_data;
- size_t num_elems; // Total elements
- size_t size_in_bytes; // Total size in bytes
- struct Dimension_t dims;
-};
-
-
-enum Tensor_type_t{
- float_type,
- double_type,
- half_type,
- int_type
-};
-
-
-// NOTE: Currently only NCHW is supported due to limited cuDNN support
-enum Tensor_format_t{
- nchw,
- nhwc
-};
-
-*/
-
-#endif
diff --git a/hpvm/test/dnn_benchmarks/hpvm-c/include/tensorUtils.h b/hpvm/test/dnn_benchmarks/hpvm-c/include/tensorUtils.h
deleted file mode 100644
index 1d5ac7d908b0990f21de885c645786997640264c..0000000000000000000000000000000000000000
--- a/hpvm/test/dnn_benchmarks/hpvm-c/include/tensorUtils.h
+++ /dev/null
@@ -1,758 +0,0 @@
-
-// Header guards
-#ifndef UTILS_HEADER
-#define UTILS_HEADER
-
-#include <sstream>
-#include <vector>
-#include <bits/stdc++.h>
-#include <tensor_runtime.h>
-#include <tensor.h>
-#include <cmath>
-
-std::vector<float> run_accuracies;
-
-void printTensorInfo(void *tensor_ptr) {
-
- struct Tensor *tensor = (struct Tensor *)tensor_ptr;
-
- if (tensor->gpu_data != NULL) {
- printf("Successful cudaMalloc \n");
- }
-
- printf("tensor dims = %d \n", tensor->dims.num_dims);
- printf("dim1_size = %lu \n", tensor->dims.dim_sizes[0]);
- printf("dim2_size = %lu \n", tensor->dims.dim_sizes[1]);
- printf("num_elems = %lu \n", tensor->num_elems);
-}
-
-// FIXIT: Move this to debug.h and include in all files
-void dumpWeightsToFile(char *file_name, void *weights_ptr) {
-
- struct Tensor *weights = (Tensor *)weights_ptr;
- // Move data back to host
- hpvm_request_tensor(weights, 0);
-
- FILE *fp = fopen(file_name, "wb");
- if (fp == NULL) {
- printf("File %s could not be created. Check if directory exists \n",
- file_name);
- abort();
- }
-
- // printf("size_in_bytes = %lu \n", weights->size_in_bytes);
- size_t bytes_written =
- fwrite(weights->host_data, 1, weights->size_in_bytes, fp);
- // printf("bytes_written = %lu \n", bytes_written);
- fclose(fp);
-}
-
-void fillTensorWithOnes(void *tensor_ptr) {
-
- struct Tensor *tensor = (struct Tensor *)tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- // initialization is specific to the floating point type
- if (tensor->data_type == CUDNN_DATA_FLOAT) {
- float *data_arr = (float *)tensor->host_data;
- for (unsigned int i = 0; i < tensor->num_elems; i++) {
- data_arr[i] = 1.0;
- }
- }
-}
-
-void fillWithOnesAndTwos(void *tensor_ptr) {
-
- struct Tensor *tensor = (struct Tensor *)tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- // initialization is specific to the floating point type
- if (tensor->data_type == CUDNN_DATA_FLOAT) {
- float *data_arr = (float *)tensor->host_data;
- for (unsigned int i = 0; i < tensor->num_elems / 2; i++) {
- data_arr[i] = 1.0;
- }
-
- for (unsigned int i = tensor->num_elems / 2; i < tensor->num_elems; i++) {
- data_arr[i] = 2.0;
- }
- }
-}
-
-void fillTensorWithVal(void *tensor_ptr, float target_value) {
-
- struct Tensor *tensor = (struct Tensor *)tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- // initialization is specific to the floating point type
- if (tensor->data_type == CUDNN_DATA_FLOAT) {
- float *data_arr = (float *)tensor->host_data;
- for (unsigned int i = 0; i < tensor->num_elems; i++) {
- data_arr[i] = target_value;
- }
- }
-}
-
-void fillTensorWithNegOnes(void *tensor_ptr) {
-
- struct Tensor *tensor = (struct Tensor *)tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- // initialization is specific to the floating point type
- if (tensor->data_type == CUDNN_DATA_FLOAT) {
- float *data_arr = (float *)tensor->host_data;
- for (unsigned int i = 0; i < tensor->num_elems; i++) {
- data_arr[i] = -1.0;
- }
- }
-}
-
-void fillTensorVals(void *tensor_ptr) {
-
- struct Tensor *tensor = (struct Tensor *)tensor_ptr;
- // initialization is specific to the floating point type
- if (tensor->data_type == CUDNN_DATA_FLOAT) {
- float *data_arr = (float *)tensor->host_data;
- for (unsigned int i = 0; i < tensor->num_elems; i++) {
- data_arr[i] = i + 1;
- }
- }
-}
-
-void printTensorValues(void *tensor_ptr) {
-
- struct Tensor *tensor = (struct Tensor *)tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- // printing is specific to the floating point type
- if (tensor->data_type == CUDNN_DATA_FLOAT) {
- float *data_arr = (float *)tensor->host_data;
- for (unsigned int i = 0; i < tensor->num_elems; i++) {
- printf("%f,", data_arr[i]);
- }
- }
-
- printf("\n");
-}
-
-void printTensorDims(void *tensor_ptr) {
-
- struct Tensor *tensor = (struct Tensor *)tensor_ptr;
-
- printf("Num_elems = %lu \n", tensor->num_elems);
- for (int i = 0; i < tensor->dims.num_dims; i++) {
- printf("dim[%d] = %lu \n", i, tensor->dims.dim_sizes[i]);
- }
-}
-
-void compareTensors(void *tensor1_ptr, void *tensor2_ptr) {
-
- struct Tensor *tensor1 = (struct Tensor *)tensor1_ptr;
- struct Tensor *tensor2 = (struct Tensor *)tensor2_ptr;
-
- hpvm_request_tensor(tensor1, 0);
- hpvm_request_tensor(tensor2, 0);
-
- float *tensor_data1 = (float *)tensor1->host_data;
- float *tensor_data2 = (float *)tensor2->host_data;
-
- for (unsigned int i = 0; i < tensor1->num_elems; i++) {
- if (tensor_data1[i] != tensor_data2[i]) {
- printf("Tensor data mismatch at index %d \n", i);
- abort();
- }
- }
-}
-
-void compareValues(void *tensor_ptr, float *data, size_t num_elems) {
-
- struct Tensor *tensor = (struct Tensor *)tensor_ptr;
-
- hpvm_request_tensor(tensor, 0);
-
- float *tensor_data = (float *)tensor->host_data;
- for (unsigned int i = 0; i < num_elems; i++) {
- if (tensor_data[i] != data[i]) {
- printf("Tensor data mismatch");
- abort();
- }
- }
-}
-
-void *readInputTensor(const char *file_name, int data_type, int dim1_size,
- int dim2_size, int dim3_size, int dim4_size) {
-
- int type_size = 4; // NOTE: Assuming floating point tensors
- int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
- int size_in_bytes = type_size * dim1_size * dim2_size * dim3_size * dim4_size;
- uint8_t *file_data = (uint8_t *)malloc(sizeof(char) * num_elems);
- float *tensor_data = (float *)malloc(sizeof(float) * num_elems);
- int file_header_size = 16;
-
- FILE *file = fopen(file_name, "rb");
- if (file == NULL) {
- printf("Data file %s is not found. Aborting... \n", file_name);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_CUR); // Skipping the file header
- size_t bytes_read = fread(file_data, 1, sizeof(uint8_t) * num_elems, file);
-
- fclose(file);
-
- for (size_t i = 0; i < num_elems; ++i) {
- tensor_data[i] = (float)file_data[i] / 255.0f;
- }
-
- // NOTE: Using NCHW format
- struct Tensor *input = (struct Tensor *)create4DTensor(
- data_type, nchw, dim1_size, dim2_size, dim3_size, dim4_size);
-
- initTensorData(input, tensor_data, size_in_bytes);
- // compareValues(input, tensor_data, num_elems);
-
- return input;
-}
-
-//*** FIXIT: Move this to CPU-only
-struct Tensor *readTrainedWeightsCPU(const char *file_name, int data_type,
- int dim1_size, int dim2_size,
- int dim3_size, int dim4_size) {
-
- // FIXIT: Don't assume floating point types
- int type_size = 4; // NOTE: Assuming floating point tensors
- long int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
- long int size_in_bytes =
- type_size * dim1_size * dim2_size * dim3_size * dim4_size;
- float *tensor_data = (float *)malloc(sizeof(float) * num_elems);
- int file_header_size = 0;
-
- FILE *file = fopen(file_name, "rb");
- if (file == NULL) {
- printf("Data file %s is not found. Aborting... \n", file_name);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_CUR); // Skipping the file header
- size_t bytes_read = fread(tensor_data, 1, size_in_bytes, file);
-
- printf("size in bytes = %lu, bytes read = %lu \n", size_in_bytes, bytes_read);
-
- fclose(file);
-
- struct Tensor *weights = (struct Tensor *)create4DTensor(
- data_type, nchw, dim1_size, dim2_size, dim3_size, dim4_size);
-
- initTensorData(weights, tensor_data, size_in_bytes);
- // compareValues(weights, tensor_data, num_elems);
- free(tensor_data);
-
- return weights;
-}
-
-struct Tensor *readTrainedWeights(const char *file_name, int data_type,
- long int dim1_size, long int dim2_size,
- long int dim3_size, long int dim4_size) {
-
- // FIXIT: Don't assume floating point types
- int type_size = 4; // NOTE: Assuming floating point tensors
- long int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
- long int size_in_bytes =
- type_size * dim1_size * dim2_size * dim3_size * dim4_size;
- float *tensor_data = (float *)malloc(sizeof(float) * num_elems);
- printf("size_in_bytes = %lu \n", size_in_bytes);
-
- int file_header_size = 0;
-
- FILE *file = fopen(file_name, "rb");
- if (file == NULL) {
- printf("Data file %s is not found. Aborting... \n", file_name);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_CUR); // Skipping the file header
- size_t bytes_read = fread(tensor_data, 1, size_in_bytes, file);
-
- // printf("size in bytes = %lu, bytes read = %lu \n", size_in_bytes,
- // bytes_read);
-
- fclose(file);
-
- struct Tensor *weights = (struct Tensor *)create4DTensor(
- data_type, nchw, dim1_size, dim2_size, dim3_size, dim4_size);
-
- initTensorData(weights, tensor_data, size_in_bytes);
- // compareValues(weights, tensor_data, num_elems);
- free(tensor_data);
-
- return weights;
-}
-
-struct Tensor *readInputBatch(const char *file_name, long data_type, long start,
- long end, long dim2_size, long dim3_size,
- long dim4_size) {
-
- long int dim1_size = end - start;
- // FIXIT: Don't assume floating point types
- long int type_size = 4; // NOTE: Assuming floating point tensors
- long int num_elems = dim1_size * dim2_size * dim3_size * dim4_size;
- long int size_in_bytes =
- type_size * dim1_size * dim2_size * dim3_size * dim4_size;
- float *tensor_data = (float *)malloc(sizeof(float) * num_elems);
- long int file_header_size =
- type_size * start * dim2_size * dim3_size * dim4_size;
-
- FILE *file = fopen(file_name, "rb");
- if (file == NULL) {
- printf("Data file %s is not found. Aborting... \n", file_name);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_SET); // Skipping the file header
- size_t bytes_read = fread(tensor_data, 1, size_in_bytes, file);
-
- fclose(file);
-
- // printf ("FIXED input BATCH read \n");
-
- struct Tensor *weights = (struct Tensor *)create4DTensor(
- data_type, nchw, dim1_size, dim2_size, dim3_size, dim4_size);
-
- initTensorData(weights, tensor_data, size_in_bytes);
- free(tensor_data);
-
- return weights;
-}
-
-uint8_t *readLabels(const char *labels_file, int num_labels) {
-
- uint8_t *labels = (uint8_t *)malloc(sizeof(uint8_t) * num_labels);
- FILE *file = fopen(labels_file, "rb");
- if (file == NULL) {
- printf("Data file %s is not found. Aborting...\n", labels_file);
- abort();
- }
-
- size_t bytes_read = fread(labels, 1, sizeof(uint8_t) * num_labels, file);
-
- fclose(file);
-
- return labels;
-}
-
-uint32_t *readLabels3(const char *labels_file, int num_labels) {
-
- uint32_t *labels = (uint32_t *)malloc(sizeof(uint32_t) * num_labels);
- FILE *file = fopen(labels_file, "rb");
- if (file == NULL) {
- printf("Data file %s is not found. Aborting...\n", labels_file);
- abort();
- }
-
- size_t bytes_read = fread(labels, 1, sizeof(uint32_t) * num_labels, file);
-
- fclose(file);
-
- return labels;
-}
-
-uint8_t *readLabelsBatch(const char *labels_file, int start, int end) {
-
- int num_labels = end - start;
- int file_header_size = sizeof(uint8_t) * start;
-
- uint8_t *labels = (uint8_t *)malloc(sizeof(uint8_t) * num_labels);
- FILE *file = fopen(labels_file, "rb");
- if (file == NULL) {
- printf("Data file %s is not found. Aborting...\n", labels_file);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_SET); // Skipping the file header
-
- size_t bytes_read = fread(labels, 1, sizeof(uint8_t) * num_labels, file);
-
- fclose(file);
-
- // printf("--labels bytes_read = %lu \n", bytes_read);
- return labels;
-}
-
-uint32_t *readLabelsBatch3(const char *labels_file, int start, int end) {
-
- int num_labels = end - start;
- int file_header_size = sizeof(uint32_t) * start;
-
- uint32_t *labels = (uint32_t *)malloc(sizeof(uint32_t) * num_labels);
- FILE *file = fopen(labels_file, "rb");
- if (file == NULL) {
- printf("Data file %s is not found. Aborting...\n", labels_file);
- abort();
- }
-
- fseek(file, file_header_size, SEEK_SET); // Skipping the file header
-
- size_t bytes_read = fread(labels, 1, sizeof(uint32_t) * num_labels, file);
-
- fclose(file);
-
- return labels;
-}
-
-void computeAccuracy(const char *labels_file, int num_labels,
- void *result_ptr) {
-
- struct Tensor *result = (struct Tensor *)result_ptr;
-
- uint8_t *labels = readLabels(labels_file, num_labels);
- size_t batch_dim = result->dims.dim_sizes[0];
- size_t channels = result->dims.dim_sizes[1];
- float *data = (float *)result->host_data;
- int num_errors = 0;
-
- for (int i = 0; i < batch_dim; i++) {
- int chosen = 0;
- for (int id = 1; id < 10; ++id) {
- if (data[i * channels + chosen] < data[i * channels + id])
- chosen = id;
- }
-
- // printf("chosen = %d, label = %d \n", chosen, labels[i]);
- if (chosen != labels[i])
- num_errors++;
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
- FILE *fp = fopen("final_accuracy", "w+");
- if (fp != NULL) {
-
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
-
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- fclose(fp);
- }
-}
-
-// NOTE: batch_size and num_classes are Unused arguments
-float computeAccuracy2(uint8_t *labels, int batch_size, void *result_ptr,
- size_t num_classes = 10) {
-
- struct Tensor *result = (struct Tensor *)result_ptr;
-
- size_t batch_dim = result->dims.dim_sizes[0];
- num_classes = result->dims.dim_sizes[1];
- float *data = (float *)result->host_data;
- int num_errors = 0;
-
- printf("batch_dim = %lu, channels = %lu \n", batch_dim, num_classes);
-
- for (unsigned int i = 0; i < batch_dim; i++) {
-
- int chosen = 0;
- for (int id = 1; id < num_classes; ++id) {
- if (data[i * num_classes + chosen] < data[i * num_classes + id])
- chosen = id;
- }
-
- if (chosen != labels[i])
- num_errors++;
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
- FILE *fp = fopen("final_accuracy", "w+");
- if (fp != NULL) {
-
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
-
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-
- return accuracy;
-}
-
-float computeAccuracy3(uint32_t *labels, void *result_ptr) {
-
- struct Tensor *result = (struct Tensor *)result_ptr;
-
- size_t batch_dim = result->dims.dim_sizes[0];
- size_t num_classes = result->dims.dim_sizes[1];
- float *data = (float *)result->host_data;
- int num_errors = 0;
-
- printf("batch_dim = %lu, num_classes = %lu \n", batch_dim, num_classes);
-
- for (int i = 0; i < batch_dim; i++) {
-
- int chosen = 0;
- for (int id = 1; id < num_classes; ++id) {
- if (data[i * num_classes + chosen] < data[i * num_classes + id])
- chosen = id;
- }
-
- if (chosen != labels[i])
- num_errors++;
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
- FILE *fp = fopen("final_accuracy", "w+");
- if (fp != NULL) {
-
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
-
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-
- return accuracy;
-}
-
-struct ClassProb {
- float prob;
- int index;
-};
-
-bool descendFloatComp(ClassProb obj1, ClassProb obj2) {
- return obj1.prob > obj2.prob;
-}
-
-float computeTop5Accuracy(uint8_t *labels, int num_labels, void *result_ptr,
- unsigned num_classes = 10) {
-
- struct Tensor *result = (struct Tensor *)result_ptr;
-
- size_t batch_dim = result->dims.dim_sizes[0];
- size_t channels = result->dims.dim_sizes[1];
- float *data = (float *)result->host_data;
- int num_errors = 0;
-
- printf("batch_dim = %lu, channels = %lu \n", batch_dim, channels);
-
- for (int i = 0; i < num_labels; i++) {
-
- std::vector<ClassProb> elem_probs;
- for (int id = 0; id < num_classes; ++id) {
- ClassProb cProb;
- cProb.prob = data[i * channels + id];
- cProb.index = id;
- elem_probs.push_back(cProb);
- }
-
- std:
- sort(elem_probs.begin(), elem_probs.end(), descendFloatComp);
- // Check if any of top-5 predictions matches
- bool matched = false;
- for (int j = 0; j < 5; j++) {
- ClassProb cProb = elem_probs[j];
- if (cProb.index == labels[i])
- matched = true;
- }
-
- if (!matched)
- num_errors += 1;
- }
-
- float accuracy = ((batch_dim - num_errors) * 1.0 / batch_dim * 1.0) * 100.0;
- printf("****** Accuracy = %f \n\n", accuracy);
-
- FILE *fp = fopen("final_accuracy", "w+");
- if (fp != NULL) {
-
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
-
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-
- return accuracy;
-}
-
-void dumpFinalAccuracy(float accuracy) {
-
- printf("\n\n **** Final Accuracy = %f \n", accuracy);
-
- FILE *fp = fopen("final_accuracy", "w+");
- if (fp != NULL) {
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
-
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-
- run_accuracies.push_back(accuracy);
-}
-
-void dumpAvgPSNR(float avg_psnr) {
-
- FILE *fp = fopen("avg_psnr", "w+");
- if (fp != NULL) {
- std::ostringstream ss;
- ss << std::fixed << avg_psnr;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-}
-
-void dumpPSNRStd(float psnr_std) {
-
- FILE *fp = fopen("psnr_std.txt", "w+");
- if (fp != NULL) {
- std::ostringstream ss;
- ss << std::fixed << psnr_std;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- }
-
- fclose(fp);
-}
-
-void dumpExecutionAccuracies() {
-
- FILE *fp = fopen("run_accuracies.txt", "w+");
- if (fp != NULL) {
- for (int i = 0; i < run_accuracies.size(); i++) {
- float accuracy = run_accuracies[i];
- std::ostringstream ss;
- ss << std::fixed << accuracy;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- fwrite("\n", 1, 1, fp);
- }
- }
-
- fclose(fp);
-}
-
-float readPSNRFromFile(const char *file_name) {
-
- float psnr;
- FILE *pFile = fopen(file_name, "r");
- if (pFile == NULL) {
- printf("ERROR: psnr.txt not found! \n");
- abort();
- }
-
- fscanf(pFile, "%f", &psnr);
- printf("**** PSNR read = %f \n\n", psnr);
- return psnr;
-}
-
-float computePSNRViolation(void *gold_ptr, void *approx_ptr,
- float PSNR_threshold) {
-
- PSNR_threshold = readPSNRFromFile("psnr.txt");
- std::vector<float> psnr_list;
-
- struct Tensor *gold_tensor = (struct Tensor *)gold_ptr;
- struct Tensor *approx_tensor = (struct Tensor *)approx_ptr;
-
- size_t *dim_sizes = gold_tensor->dims.dim_sizes;
- size_t batch_dim = dim_sizes[0];
- size_t image_size = dim_sizes[1] * dim_sizes[2] * dim_sizes[3];
-
- printf("batch_dim = %lu, image_size = %lu \n", batch_dim, image_size);
-
- float *gold_data = (float *)gold_tensor->host_data;
- float *approx_data = (float *)approx_tensor->host_data;
-
- FILE *fp = fopen("img_psnr.txt", "w+");
-
- float sum_psnr = 0.0;
- int num_errors = 0;
- for (size_t i = 0; i < batch_dim; i++) {
- float mse_sum = 0.0;
- float max_val = -999999;
- size_t offset = i * image_size;
-
- for (size_t j = 0; j < image_size; j++) {
- float diff = gold_data[offset + j] - approx_data[offset + j];
- float diff_square = diff * diff;
- mse_sum += diff_square;
-
- if (max_val < gold_data[offset + j]) {
- max_val = gold_data[offset + j];
- }
- }
-
- mse_sum = mse_sum / image_size;
- float psnr = 20 * log10(255 / sqrt(mse_sum));
-
- sum_psnr += psnr;
- if (psnr < PSNR_threshold)
- num_errors += 1;
-
- printf("PSNR value = %f \n", psnr);
- psnr_list.push_back(psnr);
-
- std::ostringstream ss;
- ss << std::fixed << psnr;
- std::string print_str = ss.str();
- fwrite(print_str.c_str(), 1, print_str.length(), fp);
- fwrite("\n", 1, 1, fp);
- }
-
- float violation_rate = (num_errors * 1.0) / batch_dim * 100.0;
- printf("*** violation_rate= %f \n\n", violation_rate);
-
- float avg_psnr = sum_psnr / batch_dim;
- printf("*** avg_psnr = %f \n\n", avg_psnr);
- dumpAvgPSNR(avg_psnr);
-
- float success_rate = 100.0 - violation_rate;
- dumpFinalAccuracy(success_rate);
-
- fclose(fp);
-
- float var = 0.0;
- for (size_t i = 0; i < batch_dim; i++) {
- var = var + (psnr_list[i] - avg_psnr) * (psnr_list[i] - avg_psnr);
- }
-
- var /= batch_dim;
- float std = sqrt(var);
-
- dumpPSNRStd(std);
-
- return violation_rate;
-}
-
-void dumpOutput(void *output_ptr, const char *file_name) {
-
- struct Tensor *out_tensor = (struct Tensor *)output_ptr;
- size_t size_in_bytes = out_tensor->size_in_bytes;
- printf("** Output size = %lu \n", size_in_bytes);
-
- float *host_data = (float *)out_tensor->host_data;
- FILE *fd = fopen(file_name, "w+");
- fwrite(host_data, 1, size_in_bytes, fd);
- fclose(fd);
-}
-
-#endif
diff --git a/hpvm/test/dnn_benchmarks/profiling/test_hpvm_c_profiling.py b/hpvm/test/dnn_benchmarks/profiling/test_hpvm_c_profiling.py
index 230fdf8b73dfd7959cfaa98fe06eafe6a75087b1..853b0dc3e23a3ea847748ecaeda62650e99ee430 100755
--- a/hpvm/test/dnn_benchmarks/profiling/test_hpvm_c_profiling.py
+++ b/hpvm/test/dnn_benchmarks/profiling/test_hpvm_c_profiling.py
@@ -2,7 +2,7 @@
from pathlib import Path
from sys import argv
-from hpvm_profiler import profile_configs, read_hpvm_configs
+from hpvm_profiler import profile_configs, read_hpvm_configs, write_hpvm_configs
# relative to cwd()
benchmarks_bindir = Path("../hpvm-c")
@@ -17,4 +17,6 @@ dnn = argv[1]
bench_bin_file = benchmarks_bindir / f"hpvm_{dnn}"
config_file = benchmarks_srcdir / dnn / "data/tuner_confs.txt"
out_config_file = f"./{dnn}.txt"
-profile_configs(bench_bin_file, config_file, out_config_file)
+header, configs = read_hpvm_configs(config_file)
+profile_configs(bench_bin_file, configs[1:6], configs[0], progress_bar=False)
+write_hpvm_configs(header, configs[:6], out_config_file)
diff --git a/hpvm/test/dnn_benchmarks/pytorch/CMakeLists.txt b/hpvm/test/dnn_benchmarks/pytorch/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..778593a57ddfc3a6abcc4ed045f02614535739f8
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/CMakeLists.txt
@@ -0,0 +1,18 @@
+# --[ llvm-lit test setup
+# lit.cfg.py looks for tests in CMAKE_CURRENT_BINARY_DIR (see lit.cfg.py)
+# as most of the tests require some kind of compilation / generation
+# which is best done over there.
+configure_lit_site_cfg(
+ ../../lit.site.cfg.py.in
+ ${CMAKE_CURRENT_BINARY_DIR}/lit.site.cfg.py
+ MAIN_CONFIG
+ ${CMAKE_CURRENT_SOURCE_DIR}/lit.cfg.py
+)
+add_lit_testsuite(check-hpvm-torch2hpvm "Run tests for package torch2hpvm"
+ ${CMAKE_CURRENT_BINARY_DIR}
+ # We depend on check_dnn_acc.py defined in ../hpvm-c/
+ # to compare the inference accuracy of our frontend-generated binary
+ # to that of the baseline.
+ DEPENDS check_dnn_acc
+ ARGS "-j1" # Run frontend generation sequentially
+)
diff --git a/hpvm/test/dnn_benchmarks/pytorch/alexnet2_cifar10.test b/hpvm/test/dnn_benchmarks/pytorch/alexnet2_cifar10.test
new file mode 100644
index 0000000000000000000000000000000000000000..4adf30226b19179be066f150b36ef3bd4a010636
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/alexnet2_cifar10.test
@@ -0,0 +1,2 @@
+RUN: test_frontend.py alexnet2_cifar10
+RUN: check_dnn_acc.py final_accuracy alexnet2_cifar10
diff --git a/hpvm/test/dnn_benchmarks/pytorch/alexnet_cifar10.test b/hpvm/test/dnn_benchmarks/pytorch/alexnet_cifar10.test
new file mode 100644
index 0000000000000000000000000000000000000000..cffec91e415cda256a72de5a04abb956336519d7
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/alexnet_cifar10.test
@@ -0,0 +1,2 @@
+RUN: test_frontend.py alexnet_cifar10
+RUN: check_dnn_acc.py final_accuracy alexnet_cifar10
diff --git a/hpvm/test/dnn_benchmarks/pytorch/alexnet_imagenet.test b/hpvm/test/dnn_benchmarks/pytorch/alexnet_imagenet.test
new file mode 100644
index 0000000000000000000000000000000000000000..126de1bfe80106bbd803ace37534cd38ab54a67c
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/alexnet_imagenet.test
@@ -0,0 +1,2 @@
+RUN: test_frontend.py alexnet_imagenet
+RUN: check_dnn_acc.py final_accuracy alexnet_imagenet
diff --git a/hpvm/test/dnn_benchmarks/pytorch/lenet_mnist.test b/hpvm/test/dnn_benchmarks/pytorch/lenet_mnist.test
new file mode 100644
index 0000000000000000000000000000000000000000..b87a976bcd1bfa8d637f1298d5259bccb8781419
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/lenet_mnist.test
@@ -0,0 +1,2 @@
+RUN: test_frontend.py lenet_mnist
+RUN: check_dnn_acc.py final_accuracy lenet_mnist
diff --git a/hpvm/test/dnn_benchmarks/pytorch/lit.cfg.py b/hpvm/test/dnn_benchmarks/pytorch/lit.cfg.py
new file mode 100644
index 0000000000000000000000000000000000000000..34473d24bea3565d0e2865c7026b43538f927ce7
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/lit.cfg.py
@@ -0,0 +1,36 @@
+# -*- Python -*-
+
+# Configuration file for the 'lit' test runner.
+
+import os
+
+import lit.formats
+from lit.llvm import llvm_config
+
+# name: The name of this test suite.
+config.name = "HPVM-Torch2HPVM"
+
+# testFormat: The test format to use to interpret tests.
+config.test_format = lit.formats.ShTest(False)
+
+# suffixes: A list of file extensions to treat as test files. This is overriden
+# by individual lit.local.cfg files in the test subdirectories.
+config.suffixes = [".test"]
+
+# test_source_root: The root path where tests are located.
+config.test_source_root = os.path.dirname(__file__)
+
+# test_exec_root: The root path where tests should be run.
+current_source_dir = os.path.dirname(os.path.relpath(__file__, config.llvm_src_root))
+current_binary_dir = os.path.join(config.llvm_obj_root, current_source_dir)
+config.test_exec_root = current_binary_dir
+
+# Tweak the PATH to include the tools dir.
+llvm_config.with_environment("PATH", config.llvm_tools_dir, append_path=True)
+
+# Add substitution for check_dnn_acc.py which goes under build/bin.
+llvm_config.add_tool_substitutions(
+ ["check_dnn_acc.py"], os.path.join(config.llvm_obj_root, "bin")
+)
+# Add substitution for our main script in this directory.
+llvm_config.add_tool_substitutions(["test_frontend.py"], config.test_source_root)
diff --git a/hpvm/test/dnn_benchmarks/pytorch/mobilenet_cifar10.test b/hpvm/test/dnn_benchmarks/pytorch/mobilenet_cifar10.test
new file mode 100644
index 0000000000000000000000000000000000000000..9964887b420a3896c83eff0114a419ad10740dc1
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/mobilenet_cifar10.test
@@ -0,0 +1,2 @@
+RUN: test_frontend.py mobilenet_cifar10
+RUN: check_dnn_acc.py final_accuracy mobilenet_cifar10
diff --git a/hpvm/test/dnn_benchmarks/pytorch/resnet18_cifar10.test b/hpvm/test/dnn_benchmarks/pytorch/resnet18_cifar10.test
new file mode 100644
index 0000000000000000000000000000000000000000..71e0881a3f6d81a2982ac3fbd2dddd849f23a08b
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/resnet18_cifar10.test
@@ -0,0 +1,2 @@
+RUN: test_frontend.py resnet18_cifar10
+RUN: check_dnn_acc.py final_accuracy resnet18_cifar10
diff --git a/hpvm/test/dnn_benchmarks/pytorch/resnet50_imagenet.test b/hpvm/test/dnn_benchmarks/pytorch/resnet50_imagenet.test
new file mode 100644
index 0000000000000000000000000000000000000000..b1ff2e6a92f506da299c1f94ebec10ddd1958159
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/resnet50_imagenet.test
@@ -0,0 +1,2 @@
+RUN: test_frontend.py resnet50_imagenet
+RUN: check_dnn_acc.py final_accuracy resnet50_imagenet
diff --git a/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py b/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
old mode 100644
new mode 100755
index 2fb1de17ee226571e6cd6b808640bf35280932db..3c20c6ea5a472a693156b4881b58d4e0f1fc8575
--- a/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
+++ b/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
@@ -1,52 +1,56 @@
+#!/usr/bin/env python3
import os
import shutil
import site
from pathlib import Path
from subprocess import run
-import torch
+from sys import argv
+import torch
from torch2hpvm import BinDataset, ModelExporter
from torch.nn import Module
site.addsitedir(os.path.dirname(__file__))
import dnn
-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"),
-]
+benchmarks = {
+ "lenet_mnist": (dnn.LeNet, 1, 28, 1000),
+ "alexnet_cifar10": (dnn.AlexNet, 3, 32, 500),
+ "alexnet2_cifar10": (dnn.AlexNet2, 3, 32, 500),
+ "alexnet_imagenet": (dnn.AlexNetImageNet, 3, 224, 500),
+ "mobilenet_cifar10": (dnn.MobileNet, 3, 32, 500),
+ "resnet18_cifar10": (dnn.ResNet18, 3, 32, 500),
+ "resnet50_imagenet": (dnn.ResNet50, 3, 224, 25),
+ "vgg16_cifar10": (dnn.VGG16Cifar10, 3, 32, 500),
+ "vgg16_cifar100": (dnn.VGG16Cifar100, 3, 32, 500),
+ "vgg16_imagenet": (dnn.VGG16ImageNet, 3, 224, 10),
+}
self_folder = Path(__file__).parent
-for model_cls, nch, img_size, batch_size, pathname in benchmarks:
- codegen_dir = Path(f"/tmp/{pathname}")
- print(f"Generating {pathname} to {codegen_dir}")
- if codegen_dir.exists():
- shutil.rmtree(codegen_dir)
+netname = argv[1]
+model_cls, nch, img_size, batch_size = benchmarks[netname]
+codegen_dir = Path(f"./{netname}")
+print(f"Generating {netname} to {codegen_dir}")
+if codegen_dir.exists():
+ shutil.rmtree(codegen_dir)
- params = self_folder / "../model_params" / pathname
- dataset_shape = 5000, nch, img_size, img_size
- bin_tuneset = BinDataset(
- params / "tune_input.bin", params / "tune_labels.bin", dataset_shape
- )
- bin_testset = BinDataset(
- params / "test_input.bin", params / "test_labels.bin", dataset_shape
- )
- model: Module = model_cls()
- checkpoint = self_folder / "../model_params/pytorch" / f"{pathname}.pth.tar"
- model.load_state_dict(torch.load(checkpoint.as_posix()))
+params = self_folder / "../model_params" / netname
+dataset_shape = 5000, nch, img_size, img_size
+bin_tuneset = BinDataset(
+ params / "tune_input.bin", params / "tune_labels.bin", dataset_shape
+)
+bin_testset = BinDataset(
+ params / "test_input.bin", params / "test_labels.bin", dataset_shape
+)
+model: Module = model_cls()
+checkpoint = self_folder / "../model_params/pytorch" / f"{netname}.pth.tar"
+model.load_state_dict(torch.load(checkpoint.as_posix()))
+print(model)
- build_dir = codegen_dir / "build"
- target_binary = build_dir / pathname
- conf_file = self_folder / "../hpvm-c/benchmarks" / pathname / "data/tuner_confs.txt"
- exporter = ModelExporter(
- model, bin_tuneset, bin_testset, codegen_dir, config_file=conf_file
- )
- exporter.generate(batch_size=batch_size).compile(target_binary, build_dir)
- run([str(target_binary), "test"], check=True)
+build_dir = codegen_dir / "build"
+target_binary = build_dir / netname
+conf_file = self_folder / "../hpvm-c/benchmarks" / netname / "data/tuner_confs.txt"
+exporter = ModelExporter(
+ model, bin_tuneset, bin_testset, codegen_dir, config_file=conf_file
+)
+exporter.generate(batch_size=batch_size).compile(target_binary, build_dir)
+run([str(target_binary), "test"], check=True)
diff --git a/hpvm/test/dnn_benchmarks/pytorch/vgg16_cifar10.test b/hpvm/test/dnn_benchmarks/pytorch/vgg16_cifar10.test
new file mode 100644
index 0000000000000000000000000000000000000000..5544c75d2823fb31da6624e109c81567770d18ad
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/vgg16_cifar10.test
@@ -0,0 +1,2 @@
+RUN: test_frontend.py vgg16_cifar10
+RUN: check_dnn_acc.py final_accuracy vgg16_cifar10
diff --git a/hpvm/test/dnn_benchmarks/pytorch/vgg16_cifar100.test b/hpvm/test/dnn_benchmarks/pytorch/vgg16_cifar100.test
new file mode 100644
index 0000000000000000000000000000000000000000..66bd69ee377b4dd84071e3c63ec631f3c041512a
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/vgg16_cifar100.test
@@ -0,0 +1,2 @@
+RUN: test_frontend.py vgg16_cifar100
+RUN: check_dnn_acc.py final_accuracy vgg16_cifar100
diff --git a/hpvm/test/dnn_benchmarks/pytorch/vgg16_imagenet.test b/hpvm/test/dnn_benchmarks/pytorch/vgg16_imagenet.test
new file mode 100644
index 0000000000000000000000000000000000000000..6529998ec4e4d62d14fc6b99d42474f3161d2eb7
--- /dev/null
+++ b/hpvm/test/dnn_benchmarks/pytorch/vgg16_imagenet.test
@@ -0,0 +1,2 @@
+RUN: test_frontend.py vgg16_imagenet
+RUN: check_dnn_acc.py final_accuracy vgg16_imagenet