Add TensorFlow examples - ResNet50 and BERT models

tensorflow/BERT/.gitignore

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+/models/
+/data/
+/output/

tensorflow/BERT/Makefile

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+# BERT sample for TensorFlow
+
+ARCH_LIBDIR ?= /lib/$(shell $(CC) -dumpmachine)
+SGX_SIGNER_KEY ?= ../../../Pal/src/host/Linux-SGX/signer/enclave-key.pem
+
+ifeq ($(DEBUG),1)
+GRAMINE_LOG_LEVEL = debug
+else
+GRAMINE_LOG_LEVEL = error
+endif
+
+.PHONY: all
+all: python.manifest
+ifeq ($(SGX),1)
+all: python.manifest.sgx python.sig python.token
+endif
+
+BERT_DATASET = https://storage.googleapis.com/bert_models/2019_05_30/wwm_uncased_L-24_H-1024_A-16.zip
+SQUAAD_DATASET = https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json
+CHECKPOINTS = https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/bert_large_checkpoints.zip
+BERT_FP32_MODEL = https://storage.googleapis.com/intel-optimized-tensorflow/models/v2_4_0/fp32_bert_squad.pb
+
+.PHONY: collateral
+collateral:
+	test -d models || git clone https://github.com/IntelAI/models.git
+	mkdir -p data
+	test -f data/wwm_uncased_L-24_H-1024_A-16.zip || wget $(BERT_DATASET) -P data/
+	test -d data/wwm_uncased_L-24_H-1024_A-16 || unzip data/wwm_uncased_L-24_H-1024_A-16.zip -d data
+	test -f data/wwm_uncased_L-24_H-1024_A-16/dev-v1.1.json || wget $(SQUAAD_DATASET) -P data/wwm_uncased_L-24_H-1024_A-16
+	test -f data/bert_large_checkpoints.zip || wget $(CHECKPOINTS) -P data/
+	test -d data/bert_large_checkpoints || unzip data/bert_large_checkpoints.zip -d data
+	test -f data/fp32_bert_squad.pb || wget $(BERT_FP32_MODEL) -P data/
+
+python.manifest: python.manifest.template collateral
+	gramine-manifest \
+		-Dlog_level=$(GRAMINE_LOG_LEVEL) \
+		-Darch_libdir=$(ARCH_LIBDIR) \
+		-Dentrypoint=$(realpath $(shell sh -c "command -v python3")) \
+		-Dpythondistpath=$(PYTHONDISTPATH) \
+		$< >$@
+
+python.manifest.sgx: python.manifest
+	@test -s $(SGX_SIGNER_KEY) || \
+	    { echo "SGX signer private key was not found, please specify SGX_SIGNER_KEY!"; exit 1; }
+	gramine-sgx-sign \
+		--key $(SGX_SIGNER_KEY) \
+		--manifest $< \
+		--output $@
+
+python.sig: python.manifest.sgx
+
+python.token: python.sig
+	gramine-sgx-get-token --output $@ --sig $<
+
+.PHONY: clean
+clean:
+	$(RM) *.manifest *.manifest.sgx *.token *.sig
+
+.PHONY: distclean
+distclean: clean
+	$(RM) -r models/ data/

tensorflow/BERT/python.manifest.template

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+libos.entrypoint = "{{ entrypoint }}"
+loader.entrypoint = "file:{{ gramine.libos }}"
+
+loader.log_level = "{{ log_level }}"
+
+loader.insecure__use_cmdline_argv = true
+loader.insecure__use_host_env = true
+loader.insecure__disable_aslr = true
+
+loader.env.LD_LIBRARY_PATH = "{{ python.stdlib }}/lib:/lib:{{ arch_libdir }}:/usr/lib:/usr/{{ arch_libdir }}"
+loader.pal_internal_mem_size = "512M"
+
+fs.mount.lib.type = "chroot"
+fs.mount.lib.path = "/lib"
+fs.mount.lib.uri = "file:{{ gramine.runtimedir() }}"
+
+fs.mount.lib2.type = "chroot"
+fs.mount.lib2.path = "{{ arch_libdir }}"
+fs.mount.lib2.uri = "file:{{ arch_libdir }}"
+
+fs.mount.usr.type = "chroot"
+fs.mount.usr.path = "/usr"
+fs.mount.usr.uri = "file:/usr"
+
+fs.mount.pyhome.type = "chroot"
+fs.mount.pyhome.path = "{{ python.stdlib }}"
+fs.mount.pyhome.uri = "file:{{ python.stdlib }}"
+
+fs.mount.pydisthome.type = "chroot"
+fs.mount.pydisthome.path = "{{ python.distlib }}"
+fs.mount.pydisthome.uri = "file:{{ python.distlib }}"
+
+fs.mount.pydistpath.type = "chroot"
+fs.mount.pydistpath.path = "{{ pythondistpath }}"
+fs.mount.pydistpath.uri = "file:{{ pythondistpath }}"
+
+fs.mount.tmp.type = "tmpfs"
+fs.mount.tmp.path = "/tmp"
+fs.mount.tmp.uri = "file:/tmp"
+
+sgx.enclave_size = "32G"
+sgx.thread_num = 256
+sgx.preheat_enclave = true
+sgx.nonpie_binary = true
+
+sgx.trusted_files = [
+  "file:{{ gramine.runtimedir() }}/",
+  "file:{{ arch_libdir }}/",
+  "file:/usr/{{ arch_libdir }}/",
+  "file:{{ gramine.libos }}",
+  "file:{{ entrypoint }}",
+  "file:{{ python.stdlib }}/",
+  "file:{{ python.distlib }}/",
+  "file:{{ pythondistpath }}/",
+  "file:models/",
+  "file:data/",
+]
+
+sgx.allowed_files = [
+  "file:output/",
+]

tensorflow/README.md

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+## Inference on TensorFlow BERT and ResNet50 models
+
+This directory contains steps and artifacts to run inference with TensorFlow BERT and ResNet50
+sample workloads on Gramine. Specifically, both these examples use pre-trained models to run
+inference.
+
+### Bidirectional Encoder Representations from Transformers (BERT)
+
+BERT is a method of pre-training language representations and then using that trained model for
+downstream NLP tasks like 'question answering'. BERT is an unsupervised, deeply bidirectional system
+for pre-training NLP.
+In this BERT sample, we use **BERT-Large, Uncased (Whole Word Masking)** model and perform int8
+inference. More details about BERT can be found at https://github.com/google-research/bert.
+
+### Residual Network (ResNet)
+
+ResNet50 is a convolutional neural network that is 50 layers deep.
+In this ResNet50 (v1.5) sample, we use a pre-trained model and perform int8 inference.
+More details about ResNet50 can be found at https://github.com/IntelAI/models/tree/icx-launch-public/benchmarks/image_recognition/tensorflow/resnet50v1_5.
+
+## Pre-requisites
+
+- Install unzip.
+- Upgrade pip/pip3.
+- Install TensorFlow using `pip install intel-tensorflow-avx512==2.4.0`.
+
+## Build BERT or ResNet50 sample
+
+- To build the non-SGX version, do `make PYTHONDISTPATH=path_to_python_dist_packages/`.
+- To build the SGX version, do `make PYTHONDISTPATH=path_to_python_dist_packages/ SGX=1`.
+- Typically, `path_to_python_dist_packages` is `/usr/local/lib/python3.6/dist-packages`, but can
+change based on python's installation directory.
+- To clean the sample, do `make clean`.
+- To clean and remove downloaded models and datasets, do `make distclean`.
+
+**WARNING:** Building BERT sample downloads about 5GB of data.
+
+## Run inference on BERT model
+
+- To run int8 inference on `gramine-sgx` (SGX version):
+```
+OMP_NUM_THREADS=36 KMP_AFFINITY=granularity=fine,verbose,compact,1,0 taskset -c 0-35 gramine-sgx \
+./python models/models/language_modeling/tensorflow/bert_large/inference/run_squad.py \
+--init_checkpoint=data/bert_large_checkpoints/model.ckpt-3649 \
+--vocab_file=data/wwm_uncased_L-24_H-1024_A-16/vocab.txt \
+--bert_config_file=data/wwm_uncased_L-24_H-1024_A-16/bert_config.json \
+--predict_file=data/wwm_uncased_L-24_H-1024_A-16/dev-v1.1.json \
+--precision=int8 \
+--output_dir=output/bert-squad-output \
+--predict_batch_size=32 \
+--experimental_gelu=True \
+--optimized_softmax=True \
+--input_graph=data/fp32_bert_squad.pb \
+--do_predict=True --mode=benchmark \
+--inter_op_parallelism_threads=1 \
+--intra_op_parallelism_threads=36
+```
+- To run int8 inference on `gramine-direct` (non-SGX version), replace `gramine-sgx` with
+`gramine-direct` in the above command.
+- To run int8 inference natively (outside Gramine), replace `gramine-sgx ./python` with
+`python3` in the above command.
+
+## Run inference on ResNet50 model
+
+- To run inference on `gramine-sgx` (SGX version):
+```
+OMP_NUM_THREADS=36 KMP_AFFINITY=granularity=fine,verbose,compact,1,0 taskset -c 0-35 gramine-sgx \
+./python models/models/image_recognition/tensorflow/resnet50v1_5/inference/eval_image_classifier_inference.py \
+--input-graph=resnet50v1_5_int8_pretrained_model.pb \
+--num-inter-threads=1 \
+--num-intra-threads=36 \
+--batch-size=32 \
+--warmup-steps=50 \
+--steps=500
+```
+- To run inference on `gramine-direct` (non-SGX version), replace `gramine-sgx` with
+`gramine-direct` in the above command.
+- To run inference natively (outside Gramine), replace `gramine-sgx ./python` with
+`python3` in the above command.
+
+## Notes on optimal performance
+
+Above commands are for a 36-core system. Please set the following options accordingly for optimal
+performance:
+
+- Assuming that X is the number of cores per socket, set `OMP_NUM_THREADS=X`,
+  `intra_op_parallelism_threads=X` for BERT and `num_intra_threads=X` for ResNet50.
+- Specify the whole range of cores available on one of the sockets in `taskset`.
+- If hyperthreading is enabled: use `KMP_AFFINITY=granularity=fine,verbose,compact,1,0`.
+- If hyperthreading is disabled: use `KMP_AFFINITY=granularity=fine,verbose,compact`.
+- Note that `OMP_NUM_THREADS` sets the maximum number of threads to
+  use for OpenMP parallel regions, and `KMP_AFFINITY` binds OpenMP threads
+  to physical processing units.
+- The options `batch-size`, `warmup-steps` and `steps` can be varied for ResNet50 sample.
+- To get the number of cores per socket, do `lscpu | grep 'Core(s) per socket'`.
+
+## Performance considerations
+### CPU frequency scaling
+
+Linux systems have CPU frequency scaling governor that helps the system to scale the CPU frequency
+to achieve best performance or to save power based on the requirement. To achieve the best
+performance, please set the CPU frequency scaling governor to `performance` mode.
+
+```bash
+for ((i=0; i<$(nproc); i++)); do
+    echo 'performance' > /sys/devices/system/cpu/cpu$i/cpufreq/scaling_governor;
+done
+```
+
+### Manifest options for performance
+
+- Preheat manifest option pre-faults the enclave memory and moves the performance penalty to
+Gramine-SGX startup (before the workload starts executing). To use the preheat option, make sure
+that `sgx.preheat_enclave = true` is added to the manifest template.
+
+### Memory allocator libraries
+
+TCMalloc and mimalloc are memory allocator libraries from Google and Microsoft that can help
+improve performance significantly based on the workloads. Only one of these
+allocators can be used at the same time.
+
+#### TCMalloc
+
+(Please update the binary location and name if different from default.)
+- Install tcmalloc: `sudo apt-get install google-perftools`
+- Modify the manifest template file:
+    - Add `loader.env.LD_PRELOAD = "/usr/lib/x86_64-linux-gnu/libtcmalloc.so.4"`
+    - Append below entries to `sgx.trusted_files`:
+        - `"file:/usr/lib/x86_64-linux-gnu/libtcmalloc.so.4"`
+        - `"file:/usr/lib/x86_64-linux-gnu/libunwind.so.8"`
+- Save the manifest template and rebuild this example.
+
+#### mimalloc
+
+(Please update the binary location and name if different from default.)
+- Install mimalloc using the steps from https://github.com/microsoft/mimalloc
+- Modify the manifest template file:
+    - Add the `/usr/local` FS mount point:
+        - `fs.mount.usr_local.type = "chroot"`
+        - `fs.mount.usr_local.path = "/usr/local"`
+        - `fs.mount.usr_local.uri = "file:/usr/local"`
+    - Add `loader.env.LD_PRELOAD = "/usr/local/lib/mimalloc-1.7/libmimalloc.so.1.7"`
+    - Append below entry to `sgx.trusted_files`:
+        - `"file:/usr/local/lib/mimalloc-1.7/libmimalloc.so.1.7"`
+- Save the manifest template and rebuild this example.

tensorflow/ResNet50/.gitignore

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+/models/
+/resnet50v1_5_int8_pretrained_model.pb

tensorflow/ResNet50/Makefile

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+# ResNet50 sample for TensorFlow
+
+ARCH_LIBDIR ?= /lib/$(shell $(CC) -dumpmachine)
+SGX_SIGNER_KEY ?= ../../../Pal/src/host/Linux-SGX/signer/enclave-key.pem
+
+ifeq ($(DEBUG),1)
+GRAMINE_LOG_LEVEL = debug
+else
+GRAMINE_LOG_LEVEL = error
+endif
+
+.PHONY: all
+all: python.manifest
+ifeq ($(SGX),1)
+all: python.manifest.sgx python.sig python.token
+endif
+
+.PHONY: collateral
+collateral:
+	test -d models || git clone https://github.com/IntelAI/models.git
+	test -f resnet50v1_5_int8_pretrained_model.pb || wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_8/resnet50v1_5_int8_pretrained_model.pb
+
+python.manifest: python.manifest.template collateral
+	gramine-manifest \
+		-Dlog_level=$(GRAMINE_LOG_LEVEL) \
+		-Darch_libdir=$(ARCH_LIBDIR) \
+		-Dentrypoint=$(realpath $(shell sh -c "command -v python3")) \
+		-Dpythondistpath=$(PYTHONDISTPATH) \
+		$< >$@
+
+python.manifest.sgx: python.manifest
+	@test -s $(SGX_SIGNER_KEY) || \
+	    { echo "SGX signer private key was not found, please specify SGX_SIGNER_KEY!"; exit 1; }
+	gramine-sgx-sign \
+		--key $(SGX_SIGNER_KEY) \
+		--manifest $< \
+		--output $@
+
+python.sig: python.manifest.sgx
+
+python.token: python.sig
+	gramine-sgx-get-token --output $@ --sig $<
+
+.PHONY: clean
+clean:
+	$(RM) *.manifest *.manifest.sgx *.token *.sig
+
+.PHONY: distclean
+distclean: clean
+	$(RM) -r models/ resnet50v1_5_int8_pretrained_model.pb

tensorflow/ResNet50/python.manifest.template

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+loader.entrypoint = "file:{{ gramine.libos }}"
+libos.entrypoint = "{{ entrypoint }}"
+
+loader.log_level = "{{ log_level }}"
+
+loader.insecure__use_cmdline_argv = true
+loader.insecure__use_host_env = true
+loader.insecure__disable_aslr = true
+
+loader.env.LD_LIBRARY_PATH = "{{ python.stdlib }}/lib:/lib:{{ arch_libdir }}:/usr/lib:/usr/{{ arch_libdir }}"
+
+loader.pal_internal_mem_size = "512M"
+
+fs.mount.lib.type = "chroot"
+fs.mount.lib.path = "/lib"
+fs.mount.lib.uri = "file:{{ gramine.runtimedir() }}"
+
+fs.mount.lib2.type = "chroot"
+fs.mount.lib2.path = "{{ arch_libdir }}"
+fs.mount.lib2.uri = "file:{{ arch_libdir }}"
+
+fs.mount.usr.type = "chroot"
+fs.mount.usr.path = "/usr"
+fs.mount.usr.uri = "file:/usr"
+
+fs.mount.bin.type = "chroot"
+fs.mount.bin.path = "/bin"
+fs.mount.bin.uri = "file:/bin"
+
+fs.mount.pyhome.type = "chroot"
+fs.mount.pyhome.path = "{{ python.stdlib }}"
+fs.mount.pyhome.uri = "file:{{ python.stdlib }}"
+
+fs.mount.pydisthome.type = "chroot"
+fs.mount.pydisthome.path = "{{ python.distlib }}"
+fs.mount.pydisthome.uri = "file:{{ python.distlib }}"
+
+fs.mount.pydistpath.type = "chroot"
+fs.mount.pydistpath.path = "{{ pythondistpath }}"
+fs.mount.pydistpath.uri = "file:{{ pythondistpath }}"
+
+fs.mount.tmp.type = "tmpfs"
+fs.mount.tmp.path = "/tmp"
+fs.mount.tmp.uri = "file:/tmp"
+
+sgx.enclave_size = "32G"
+sgx.thread_num = 300
+sgx.preheat_enclave = true
+sgx.nonpie_binary = true
+
+sgx.trusted_files = [
+  "file:{{ gramine.runtimedir() }}/",
+  "file:{{ arch_libdir }}/",
+  "file:/usr/{{ arch_libdir }}/",
+  "file:{{ gramine.libos }}",
+  "file:{{ entrypoint }}",
+  "file:{{ python.stdlib }}/",
+  "file:{{ python.distlib }}/",
+  "file:{{ pythondistpath }}/",
+  "file:models/",
+  "file:resnet50v1_5_int8_pretrained_model.pb",
+]