Move SGX enclave signing key to gist (#1393)

apps/sgx/Makefile

@@ -76,8 +76,12 @@ lib/tvm_t.o: lib/tvm_t.c
 lib/test_addone.so: $(TVM_ROOT)/src/runtime/sgx/trusted/runtime.cc lib/tvm_t.o lib/test_addone_sys.o
 	$(CXX) $^ -o $@ $(pkg_cflags) $(pkg_ldflags) $(enclave_cxxflags) $(enclave_ldflags) -g
 
+# The demo enclave signing key
+lib/enclave.pem:
+	curl -Lso $@ https://gist.githubusercontent.com/nhynes/8a2d80068a92e672f8b0b7d710ceb404/raw/2d5ae5fbe83198ede49465fdc6535065e093543b/tvm_sgx_demo.pem
+
 # The signed enclave
-lib/test_addone.signed.so: lib/test_addone.so enclave_config.xml
+lib/test_addone.signed.so: lib/test_addone.so enclave_config.xml lib/enclave.pem
 	$(sgx_enclave_signer) sign -key lib/enclave.pem -enclave $< -out $@ -config enclave_config.xml
 
 clean:

apps/sgx/prepare_test_libs.py

+"""Script to prepare test_addone_sys.o"""
+
+from os import path as osp
+
+import tvm
+
+CWD = osp.dirname(osp.abspath(osp.expanduser(__file__)))
+
+
+def main():
+    out_dir = osp.join(CWD, 'lib')
+
+    n = tvm.var('n')
+    A = tvm.placeholder((n,), name='A')
+    B = tvm.compute(A.shape, lambda *i: A(*i) + 1, name='B')
+    s = tvm.create_schedule(B.op)
+    s[B].parallel(s[B].op.axis[0])
+    print(tvm.lower(s, [A, B], simple_mode=True))
+
+    # Compile library in system library mode
+    fadd_syslib = tvm.build(s, [A, B], 'llvm --system-lib')
+    fadd_syslib.save(osp.join(out_dir, 'test_addone_sys.o'))
+
+
+if __name__ == '__main__':
+    main()

nnvm/examples/sgx/.gitignore

-lib/
-bin/

nnvm/examples/sgx/Makefile

-# Makefile for example to deploy TVM modules in SGX.
-
-PYTHON ?= python
-
-NNVM_ROOT := $(shell cd ../../; pwd)
-TVM_ROOT := $(NNVM_ROOT)/tvm
-DMLC_CORE_ROOT := $(NNVM_ROOT)/dmlc-core
-
-SGX_SDK ?= /opt/sgxsdk
-SGX_MODE ?= SIM
-SGX_ARCH ?= x64
-SGX_DEBUG ?= 1
-
-sgx_edger8r := $(SGX_SDK)/bin/x64/sgx_edger8r
-sgx_enclave_signer := $(SGX_SDK)/bin/x64/sgx_sign
-
-ifneq ($(SGX_MODE), HW)
-	sgx_sim := _sim
-endif
-urts_library_name := sgx_urts$(sgx_sim)
-trts_library_name := sgx_trts$(sgx_sim)
-tservice_library_name := sgx_tservice$(sgx_sim)
-uservice_library_name := sgx_uae_service$(sgx_sim)
-
-pkg_cflags := -std=c++11 -O2 -fPIC\
-	-I$(NNVM_ROOT)/include\
-	-I$(NNVM_ROOT)\
-	-I$(TVM_ROOT)/include\
-	-I$(TVM_ROOT)/dlpack/include\
-	-I$(DMLC_CORE_ROOT)/include\
-	-DDMLC_LOG_STACK_TRACE=0\
-
-pkg_ldflags := -L$(TVM_ROOT)/lib
-
-enclave_include_paths := -I$(SGX_SDK)/include\
-	-I$(SGX_SDK)/include/tlibc\
-	-I$(SGX_SDK)/include/libcxx\
-	-I$(SGX_SDK)/include/stdc++\
-
-enclave_cflags := -static -nostdinc\
-	-fvisibility=hidden -fpie -fstack-protector-strong\
-	-ffunction-sections -fdata-sections\
-	-DDMLC_CXX11_THREAD_LOCAL=0\
-	$(enclave_include_paths)\
-
-enclave_cxxflags := -nostdinc++ $(enclave_cflags)
-
-enclave_ldflags :=\
-	-Wl,--no-undefined -nostdlib -nodefaultlibs -nostartfiles -L$(SGX_SDK)/lib64\
-	-Wl,--whole-archive -l$(trts_library_name) -Wl,--no-whole-archive\
-	-Wl,--start-group\
-	-lsgx_tstdc -lsgx_tstdcxx -lsgx_tcxx -lsgx_tcrypto -lsgx_tkey_exchange -l$(tservice_library_name)\
-	-Wl,--end-group\
-	-Wl,-Bstatic -Wl,-Bsymbolic -Wl,--no-undefined\
-	-Wl,-pie,-eenclave_entry -Wl,--export-dynamic\
-	-Wl,--defsym,__ImageBase=0 -Wl,--gc-sections
-
-app_cflags := -I$(SGX_SDK)/include -Ilib
-
-app_ldflags := -L$(SGX_SDK)/lib64\
-	-l$(urts_library_name) -l$(uservice_library_name) -lpthread\
-
-.PHONY: clean all
-
-all: lib/model.signed.so bin/run_model
-
-# The code library built by TVM
-lib/deploy_%.o: build_model.py
-	@mkdir -p $(@D)
-	$(PYTHON) build_model.py
-
-# EDL files
-lib/model_%.c: model.edl $(sgx_edger8r)
-	@mkdir -p $(@D)
-	$(sgx_edger8r) $< --trusted-dir $(@D) --untrusted-dir $(@D) --search-path $(SGX_SDK)/include
-
-lib/model_%.o: lib/model_%.c
-	$(CC) $(enclave_cflags) -c $< -o $@
-
-# The enclave library
-lib/model.so: enclave.cc $(TVM_ROOT)/sgx/sgx_runtime.cc lib/model_t.o lib/deploy_lib.o
-	$(CXX) $^ -o $@ $(pkg_cflags) $(pkg_ldflags) $(enclave_cxxflags) $(enclave_ldflags)\
-		-Wl,--format=binary -Wl,lib/deploy_graph.json -Wl,lib/deploy_params.bin -Wl,--format=default
-
-# The signed enclave
-lib/model.signed.so: lib/model.so enclave_config.xml
-	$(sgx_enclave_signer) sign -key enclave_private.pem -enclave $< -out $@ -config enclave_config.xml
-
-# An app that runs the enclave
-bin/run_model: app.cc lib/model_u.o
-	@mkdir -p $(@D)
-	$(CXX) $^ -o $@ $(app_cflags) $(app_ldflags)
-
-# Debugging binary that runs TVM without SGX
-bin/run_model_nosgx: enclave.cc $(TVM_ROOT)/sgx/sgx_runtime.cc lib/deploy_lib.o
-	@mkdir -p $(@D)
-	$(CXX) $^ -o $@ $(pkg_cflags) $(pkg_ldflags)\
-		-Wl,--format=binary -Wl,lib/deploy_graph.json -Wl,lib/deploy_params.bin -Wl,--format=default
-
-
-clean:
-	rm -rf lib bin

nnvm/examples/sgx/README.md

-# TVM in Intel SGX Example
-
-This application demonstrates running a ResNet18 using NNVM inside of an
-[Intel SGX](https://software.intel.com/en-us/blogs/2013/09/26/protecting-application-secrets-with-intel-sgx) trusted computing environment.
-
-## Prerequisites
-
-1. A GNU/Linux environment
-2. NNVM, TVM compiled with LLVM, and their corresponding Python modules
-3. The [Linux SGX SDK](https://github.com/intel/linux-sgx) [link to pre-built libraries](https://01.org/intel-software-guard-extensions/downloads)
-4. `pip install --user mxnet pillow`
-
-## Running the example
-
-`SGX_SDK=/path/to/sgxsdk bash run_example.sh`
-
-If everything goes well, you should see a lot of build messages and below them
-the text `It's a tabby!`.
-
-## High-level overview
-
-First of all, it helps to think of an SGX enclave as a library that can be called
-to perform trusted computation.
-In this library, one can use other libraries like TVM.
-
-Building this example performs the following steps:
-
-1. Downloads a pre-trained MXNet ResNet and a
-   [test image](https://github.com/BVLC/caffe/blob/master/examples/images/cat.jpg)
-2. Converts the ResNet to an NNVM graph + library
-3. Links the graph JSON definition, params, and runtime library into into an SGX
-   enclave along with some code that performs inference.
-4. Compiles and runs an executable that loads the enclave and requests that it perform
-   inference on the image.
-   which invokes the TVM module.
-
-For more information on building, please refer to the `Makefile`.  
-For more information on the TVM module, please refer to `../howto_deploy`.  
-For more in formation on SGX enclaves, please refer to the [SGX Enclave Demo](https://github.com/intel/linux-sgx/tree/master/SampleCode/SampleEnclave/)

nnvm/examples/sgx/app.cc

-#include <cstdio>
-#include <sstream>
-#include <fstream>
-#include <iostream>
-
-#include "sgx_urts.h"
-#include "sgx_eid.h"
-#include "model_u.h"
-
-#define TOKEN_FILENAME   "bin/enclave.token"
-#define ENCLAVE_FILENAME "lib/model.signed.so"
-
-sgx_enclave_id_t global_eid = 0;  // global EID shared by multiple threads
-
-typedef struct _sgx_errlist_t {
-  sgx_status_t err;
-  const char *msg;
-} sgx_errlist_t;
-
-/* Error code returned by sgx_create_enclave */
-static sgx_errlist_t sgx_errlist[] = {
-  { SGX_ERROR_DEVICE_BUSY, "SGX device was busy." },
-  { SGX_ERROR_ENCLAVE_FILE_ACCESS, "Can't open enclave file." },
-  { SGX_ERROR_ENCLAVE_LOST, "Power transition occurred." },
-  { SGX_ERROR_INVALID_ATTRIBUTE, "Enclave was not authorized." },
-  { SGX_ERROR_INVALID_ENCLAVE, "Invalid enclave image." },
-  { SGX_ERROR_INVALID_ENCLAVE_ID, "Invalid enclave identification." },
-  { SGX_ERROR_INVALID_METADATA, "Invalid enclave metadata." },
-  { SGX_ERROR_INVALID_PARAMETER, "Invalid parameter." },
-  { SGX_ERROR_INVALID_SIGNATURE, "Invalid enclave signature." },
-  { SGX_ERROR_INVALID_VERSION, "Enclave version was invalid." },
-  { SGX_ERROR_MEMORY_MAP_CONFLICT, "Memory map conflicted." },
-  { SGX_ERROR_NO_DEVICE, "Invalid SGX device." },
-  { SGX_ERROR_OUT_OF_EPC, "Out of EPC memory." },
-  { SGX_ERROR_OUT_OF_MEMORY, "Out of memory." },
-  { SGX_ERROR_UNEXPECTED, "Unexpected error occurred." },
-};
-
-/* Check error conditions for loading enclave */
-void print_error_message(sgx_status_t status)
-{
-  size_t idx = 0;
-  size_t ttl = sizeof sgx_errlist/sizeof sgx_errlist[0];
-
-  for (idx = 0; idx < ttl; idx++) {
-    if(status == sgx_errlist[idx].err) {
-      printf("Error: %s\n", sgx_errlist[idx].msg);
-      break;
-    }
-  }
-
-  if (idx == ttl)
-    printf("Error code is 0x%X. Please refer to the \"Intel SGX SDK Developer Reference\" for more details.\n", status);
-}
-
-/* Initialize the enclave:
- *   Step 1: try to retrieve the launch token saved by last transaction
- *   Step 2: call sgx_create_enclave to initialize an enclave instance
- *   Step 3: save the launch token if it is updated
- */
-int initialize_enclave(void)
-{
-  sgx_launch_token_t token = {0};
-  sgx_status_t sgx_status = SGX_ERROR_UNEXPECTED;
-  int updated = 0;
-
-  /* Step 1: try to retrieve the launch token saved by last transaction
-   *     if there is no token, then create a new one.
-   */
-  FILE *fp = fopen(TOKEN_FILENAME, "rb");
-  if (fp == NULL && (fp = fopen(TOKEN_FILENAME, "wb")) == NULL) {
-    printf("Warning: Failed to create/open the launch token file \"%s\".\n", TOKEN_FILENAME);
-    return -1;
-  }
-
-  /* read the token from saved file */
-  size_t read_num = fread(token, 1, sizeof(sgx_launch_token_t), fp);
-  if (read_num != 0 && read_num != sizeof(sgx_launch_token_t)) {
-    /* if token is invalid, clear the buffer */
-    memset(&token, 0x0, sizeof(sgx_launch_token_t));
-    printf("Warning: Invalid launch token read from \"%s\".\n", TOKEN_FILENAME);
-  }
-
-  /* Step 2: call sgx_create_enclave to initialize an enclave instance */
-  /* Debug Support: set 2nd parameter to 1 */
-  sgx_status = sgx_create_enclave(ENCLAVE_FILENAME, SGX_DEBUG_FLAG, &token, &updated, &global_eid, NULL);
-  if (sgx_status != SGX_SUCCESS) {
-    print_error_message(sgx_status);
-    if (fp != NULL) fclose(fp);
-    return -1;
-  }
-
-  /* Step 3: save the launch token if it is updated */
-  if (updated == 0 || fp == NULL) {
-    /* if the token is not updated, or file handler is invalid, do not perform saving */
-    if (fp != NULL) fclose(fp);
-    return 0;
-  }
-
-  /* reopen the file with write capablity */
-  fp = freopen(TOKEN_FILENAME, "wb", fp);
-  if (fp == NULL) return 0;
-  size_t write_num = fwrite(token, 1, sizeof(sgx_launch_token_t), fp);
-  if (write_num != sizeof(sgx_launch_token_t))
-    printf("Warning: Failed to save launch token to \"%s\".\n", TOKEN_FILENAME);
-  fclose(fp);
-  return 0;
-}
-
-int SGX_CDECL main(int argc, char *argv[]) {
-  if(initialize_enclave() < 0){
-    printf("Failed to initialize enclave.\n");
-    return -1;
-  }
-
-  std::ifstream f_img("bin/cat.bin", std::ios::binary);
-  std::string img(static_cast<std::stringstream const&>(
-                  std::stringstream() << f_img.rdbuf()).str());
-
-  unsigned predicted_class;
-  sgx_status_t sgx_status = SGX_ERROR_UNEXPECTED;
-  sgx_status = ecall_infer(global_eid, &predicted_class, img.c_str());
-  if (sgx_status != SGX_SUCCESS) {
-    print_error_message(sgx_status);
-  }
-
-  sgx_destroy_enclave(global_eid);
-  if (predicted_class == 281) {
-    std::cout << "It's a tabby!" << std::endl;
-    return 0;
-  }
-  std::cerr << "Inference failed! Predicted class: " <<
-    predicted_class << std::endl;
-  return 1;
-}

nnvm/examples/sgx/build_model.py

-"""Creates a neural network graph module, the system library, and params.
-Heavily inspired by tutorials/from_mxnet.py
-"""
-from __future__ import print_function
-import ast
-import os
-from os import path as osp
-import tempfile
-
-import mxnet as mx
-from mxnet.gluon.model_zoo.vision import get_model
-from mxnet.gluon.utils import download
-import nnvm
-import nnvm.compiler
-import numpy as np
-from PIL import Image
-import tvm
-
-
-EXAMPLE_ROOT = osp.abspath(osp.join(osp.dirname(__file__)))
-BIN_DIR = osp.join(EXAMPLE_ROOT, 'bin')
-LIB_DIR = osp.join(EXAMPLE_ROOT, 'lib')
-
-TVM_TARGET = 'llvm --system-lib'
-
-
-def _download_model_and_image(out_dir):
-    mx_model = get_model('resnet18_v1', pretrained=True)
-
-    img_path = osp.join(out_dir, 'cat.png')
-    bin_img_path = osp.join(out_dir, 'cat.bin')
-    download(
-        'https://github.com/dmlc/mxnet.js/blob/master/data/cat.png?raw=true',
-        img_path)
-    img = Image.open(img_path).resize((224, 224))
-    img = _transform_image(img)
-    img.astype('float32').tofile(bin_img_path)
-    shape_dict = {'data': img.shape}
-
-    return mx_model, shape_dict
-
-
-def _transform_image(image):
-    image = np.array(image) - np.array([123., 117., 104.])
-    image /= np.array([58.395, 57.12, 57.375])
-    image = image.transpose((2, 0, 1))
-    image = image[np.newaxis, :]
-    return image
-
-
-def main():
-    # load the model, input image, and imagenet classes
-    mx_model, shape_dict = _download_model_and_image(BIN_DIR)
-
-    # convert the model, add a softmax
-    sym, params = nnvm.frontend.from_mxnet(mx_model)
-    sym = nnvm.sym.softmax(sym)
-
-    # build the graph
-    graph, lib, params = nnvm.compiler.build(
-        sym, TVM_TARGET, shape_dict, params=params)
-
-    # save the built graph
-    if not osp.isdir(LIB_DIR):
-        os.mkdir(LIB_DIR)
-    lib.save(osp.join(LIB_DIR, 'deploy_lib.o'))
-    with open(osp.join(LIB_DIR, 'deploy_graph.json'), 'w') as f_graph_json:
-        f_graph_json.write(graph.json())
-    with open(osp.join(LIB_DIR, 'deploy_params.bin'), 'wb') as f_params:
-        f_params.write(nnvm.compiler.save_param_dict(params))
-
-
-if __name__ == '__main__':
-    main()

nnvm/examples/sgx/enclave.cc

-#include <string.h>
-#include <dlpack/dlpack.h>
-#include <tvm/runtime/module.h>
-#include <tvm/runtime/c_runtime_api.h>
-#include <tvm/runtime/registry.h>
-#include <tvm/runtime/packed_func.h>
-#include "tvm/src/runtime/graph/graph_runtime.cc"
-#ifndef _LIBCPP_SGX_CONFIG
-#include <iostream>
-#include <fstream>
-#include <sstream>
-#include "tvm/src/runtime/file_util.cc"
-#endif
-
-// the statically linked graph json and params
-extern char _binary_lib_deploy_params_bin_start[];
-extern char _binary_lib_deploy_params_bin_end[];
-extern char _binary_lib_deploy_graph_json_start[];
-extern char _binary_lib_deploy_graph_json_end[];
-
-int RunInference(const char* img) {
-  tvm::runtime::Module graph_lib =
-    (*tvm::runtime::Registry::Get("module._GetSystemLib"))();
-
-  size_t graph_json_size = ((size_t)_binary_lib_deploy_graph_json_end -
-                            (size_t)_binary_lib_deploy_graph_json_start);
-  size_t graph_params_size = ((size_t)_binary_lib_deploy_params_bin_end -
-                              (size_t)_binary_lib_deploy_params_bin_start);
-  std::string graph_json(_binary_lib_deploy_graph_json_start, graph_json_size);
-  std::string graph_params(_binary_lib_deploy_params_bin_start, graph_params_size);
-
-  int device_type = kDLCPU;
-  int device_id = 0;
-
-  TVMContext ctx;
-  ctx.device_type = static_cast<DLDeviceType>(device_type);
-  ctx.device_id = device_id;
-  std::shared_ptr<tvm::runtime::GraphRuntime> graph_rt =
-    std::make_shared<tvm::runtime::GraphRuntime>();
-
-  graph_rt->Init(graph_json, graph_lib, ctx);
-  graph_rt->LoadParams(graph_params);
-
-  DLTensor* input;
-  DLTensor* output;
-  int ndim = 2;
-  int dtype_code = kDLFloat;
-  int dtype_bits = 32;
-  int dtype_lanes = 1;
-
-  int batch_size = 1;
-  int64_t input_shape[4] = {batch_size, 3, 224, 224};
-  int64_t output_shape[1] = {1000 /* num_classes */};
-  TVMArrayAlloc(input_shape, 4 /* ndim */, dtype_code, dtype_bits, dtype_lanes,
-                device_type, device_id, &input);
-  TVMArrayAlloc(output_shape, 1, dtype_code, dtype_bits, dtype_lanes,
-                device_type, device_id, &output);
-  memcpy(input->data, img, sizeof(float)*batch_size*3*224*224);
-
-  graph_rt->SetInput(graph_rt->GetInputIndex("data"), input);
-  graph_rt->Run();
-  graph_rt->GetOutput(0, output);
-
-  float max_prob = 0;
-  unsigned max_class = -1;
-  for (int i = 0; i < 1000; ++i) {
-    float p = static_cast<float*>(output->data)[i];
-    if (p > max_prob) {
-      max_prob = p;
-      max_class = i;
-    }
-  }
-
-  return max_class;
-}
-
-
-extern "C" {
-int ecall_infer(const char* img) {
-  return RunInference(img);
-}
-}
-
-#ifndef _LIBCPP_SGX_CONFIG
-int main(void) {
-  std::ifstream f_img("bin/cat.bin", std::ios::binary);
-  std::string img(static_cast<std::stringstream const&>(
-                  std::stringstream() << f_img.rdbuf()).str());
-  unsigned predicted_class = RunInference(img.c_str());
-  if (predicted_class == 281) {
-    std::cout << "It's a tabby!" << std::endl;
-    return 0;
-  }
-  std::cerr << "Inference failed! Predicted class: " <<
-    predicted_class << std::endl;
-  return -1;
-}
-#endif

nnvm/examples/sgx/enclave_config.xml

-<EnclaveConfiguration>
-  <ProdID>0</ProdID>
-  <ISVSVN>0</ISVSVN>
-  <StackMaxSize>0x2000</StackMaxSize>
-  <HeapMaxSize>0x6794000</HeapMaxSize>
-  <TCSNum>1</TCSNum>
-  <TCSPolicy>1</TCSPolicy>
-  <DisableDebug>0</DisableDebug>
-  <MiscSelect>0</MiscSelect>
-  <MiscMask>0xFFFFFFFF</MiscMask>
-</EnclaveConfiguration>

nnvm/examples/sgx/model.edl

-enclave {
-    from "sgx_tstdc.edl" import sgx_thread_wait_untrusted_event_ocall, sgx_thread_set_untrusted_event_ocall, sgx_thread_setwait_untrusted_events_ocall, sgx_thread_set_multiple_untrusted_events_ocall;
-
-    trusted {
-        public unsigned ecall_infer([user_check] const char* img);
-    };
-};

nnvm/examples/sgx/run_example.sh

-#!/bin/bash
-
-sgx_sdk=${SGX_SDK:=/opt/sgxsdk}
-
-make
-echo "========================="
-LD_LIBRARY_PATH="$sgx_sdk/lib64":${LD_LIBRARY_PATH} bin/run_model