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Commit 6ef2c37e authored by Yifan Zhao's avatar Yifan Zhao
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Changed communication protocol

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hpvm/projects/torch2hpvm/torch2hpvm/compile.py
+ 0
− 2
View file @ 6ef2c37e
......@@ -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,
......
hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm_inspect.cpp.in
+ 19
− 29
View file @ 6ef2c37e
......@@ -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();
......
hpvm/test/dnn_benchmarks/pytorch/test_tuning.py
+ 29
− 37
View file @ 6ef2c37e
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)
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