diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/compile.py b/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
index 52b6a5bc21dbbe8df16af6972df370321013aca5..ee65a539d3ac35f4e65c6414c4337a2066b2c692 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
@@ -154,8 +154,6 @@ class ModelExporter:
"knob_speedup": knob_speedup,
"op_knobs": op_knobs,
"baseline_knob": baseline_knob,
- "tune_args": "tune",
- "test_args": "test",
},
f,
indent=2,
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in b/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in
index 00a15dc9553fcfab7ecbcda1be61e31cfde9dfd9..f6024fe8641a40b4d61567ab74991a9f2a177be1 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in
@@ -10,14 +10,16 @@
#include <sys/stat.h>
/**** Routines for Handling Piped Execution ***/
-bool fifo_wait(const std::string &filename) {
+int fifo_wait(const std::string &filename) {
std::ifstream file{filename};
std::string line;
std::getline(file, line);
- if (line == "next")
- return true;
+ if (line == "test")
+ return 1;
+ if (line == "tune")
+ return 2;
if (line == "stop")
- return false;
+ return 0;
std::cout << "Invalid fifo file content \"" << line << "\"\n";
abort();
}
@@ -33,15 +35,6 @@ void make_fifo(const std::string &filename) {
file << "{{conf_path}}\n"; // Write path to config file in FIFO file
return;
}
-
- if (errno == EEXIST) {
- if (unlink(filename.c_str()) < 0) {
- std::cout << "Error removing existing file: " << strerror(errno) << '\n';
- abort();
- }
- make_fifo(filename);
- return;
- }
std::cout << "Error making FIFO file: " << strerror(errno) << '\n';
abort();
}
@@ -105,20 +98,13 @@ typedef struct __attribute__((__packed__)) {
const int batch_size = {{batch_size}}, input_size = {{input_size}}, batch_count = input_size / batch_size;
-int main(int argc, char *argv[]){
- if (argc != 2) {
- std::cout << "Usage: " << argv[0] << " {tune|test}\n";
- return 1;
- }
- std::string arg1 = argv[1];
- if (arg1 != "tune" && arg1 != "test") {
- std::cout << "Usage: " << argv[0] << " {tune|test}\n";
- return 1;
- }
-
+int main(){
std::string dir_prefix = "{{prefix}}/";
- std::string input_path = dir_prefix + arg1 + "_input.bin";
- std::string labels_path = dir_prefix + arg1 + "_labels.bin";
+ std::string test_input = dir_prefix + "test_input.bin";
+ std::string test_labels = dir_prefix + "test_labels.bin";
+ std::string tune_input = dir_prefix + "tune_input.bin";
+ std::string tune_labels = dir_prefix + "tune_labels.bin";
+
{% for w in weights %}
std::string {{w.name}}_path = dir_prefix + "{{w.filename}}";
void* {{w.name}} = readTrainedWeights({{w.name}}_path.c_str(), 0, {{w.shape|join(', ')}});
@@ -132,19 +118,23 @@ int main(int argc, char *argv[]){
{% endfor %}
make_fifo("{{fifo_path}}");
- while (fifo_wait("{{fifo_path}}")) {
+ int ret = 0;
+ while ((ret = fifo_wait("{{fifo_path}}"))) {
__hpvm__init();
startMemTracking();
+ const auto *input_pth = (ret == 1 ? test_input : tune_input).c_str();
+ const auto *labels_pth = (ret == 1 ? test_labels : tune_labels).c_str();
+
for (int i = 0; i < batch_count; i++){
int start = i * batch_size, end = start + batch_size;
- copyInputBatch(input_path.c_str(), start, end, {{input_shape|join(', ')}}, {{input_name}});
+ copyInputBatch(input_pth, start, end, {{input_shape|join(', ')}}, {{input_name}});
void* dfg = __hpvm__launch(0, root, (void*) args);
__hpvm__wait(dfg);
void *result = static_cast<RootIn*>(args)->r.tensor;
hpvm_request_tensor(result, 0);
- llvm_hpvm_invokeRtControl(result, labels_path.c_str(), start, end);
+ llvm_hpvm_invokeRtControl(result, labels_pth, start, end);
freeBatchMemory();
}
__hpvm__cleanup();
diff --git a/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py b/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py
index 4da55559a2c2b5abe45d432aaac0930bada5faf5..34a179b6864e57a10fb9a3516d35e9566c24aefb 100644
--- a/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py
+++ b/hpvm/test/dnn_benchmarks/pytorch/test_tuning.py
@@ -1,20 +1,11 @@
-import shutil
-from pathlib import Path
-from subprocess import run
-import torch
-
-from torch2hpvm import BinDataset, ModelExporter
-from torch.nn import Module
-from predtuner.pipedbin import PipedBinaryApp
-
-
import os
import shutil
import site
from pathlib import Path
-from subprocess import run
-import torch
+from subprocess import Popen
+import torch
+from predtuner.pipedbin import PipedBinaryApp
from torch2hpvm import BinDataset, ModelExporter
from torch.nn import Module
@@ -37,29 +28,30 @@ benchmarks = [
self_folder = Path(__file__).parent
model_cls, nch, img_size, batch_size, pathname = benchmarks[0]
codegen_dir = Path(f"/tmp/{pathname}_tune")
-print(f"Generating {pathname} 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" / f"{pathname}.pth.tar"
-model.load_state_dict(torch.load(checkpoint.as_posix()))
-
build_dir = codegen_dir / "build"
-target_binary = build_dir / pathname
-exporter = ModelExporter(
- model, bin_tuneset, bin_testset, codegen_dir, target="hpvm_tensor_inspect"
-)
-exporter.generate(batch_size=batch_size).compile(target_binary, build_dir)
-run([str(target_binary), "test"], check=True)
-
-# build_dir = codegen_dir / "build"
-# print(PipedBinaryApp("test", codegen_dir / "ops.json", build_dir / "lenet_mnist", build_dir))
+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
+ 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" / 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", codegen_dir, "ops.json", f"build/{pathname}", "hpvm_fifo")
+app.measure_qos_perf({}, False)