Merge pull request #1564 from lissyx/remove-aot

Remove AOT

bin/benchmark_nc.py

@@ -125,12 +125,10 @@ def maybe_inspect_zip(models):
     that were inside.
     '''
 
-    if len(models) > 1:
+    if not(is_zip_file(models)):
         return models
 
-    # With AOT, we may have just one file that is not a ZIP file
+    if len(models) > 1:
-    # so verify that we don't have a .zip extension
-    if not(is_zip_file(models)):
         return models
 
     if len(models) < 1:
@@ -376,23 +374,18 @@ def establish_ssh(target=None, auto_trust=False, allow_agent=True, look_keys=Tru
 
     return ssh_conn
 
-def run_benchmarks(dir, models, wav, alphabet, lm_binary=None, trie=None, iters=-1, extra_aot_model=None):
+def run_benchmarks(dir, models, wav, alphabet, lm_binary=None, trie=None, iters=-1):
     r'''
     Core of the running of the benchmarks. We will run on all of models, against
     the WAV file provided as wav, and the provided alphabet.
-
-    If supplied extra_aot_model, add another pass with the .so built AOT model.
     '''
 
     assert_valid_dir(dir)
 
     inference_times = [ ]
 
-    if extra_aot_model:
-        models.append(extra_aot_model)
-
     for model in models:
-        model_filename = '' if model is extra_aot_model else model
+        model_filename = model
 
         current_model = {
           'name':   model,
@@ -457,8 +450,6 @@ def handle_args():
                                  help='Local directory where to copy stuff. This will be mirrored to the remote system if needed (make sure to use path that will work on both).')
     parser.add_argument('--models', nargs='+', required=False,
                                  help='List of files (protocolbuffer) to work on. Might be a zip file.')
-    parser.add_argument('--so-model', required=False,
-                                 help='Perform one step using AOT-based .so model')
     parser.add_argument('--wav', required=False,
                                  help='WAV file to pass to native_client. Supply again in plotting mode to draw realine line.')
     parser.add_argument('--alphabet', required=False,
@@ -483,19 +474,6 @@ def do_main():
     if not cli_args.models or not cli_args.wav or not cli_args.alphabet:
         raise AssertionError('Missing arguments (models, wav or alphabet)')
 
-    if cli_args.so_model:
-        '''
-        Verify we have a string that matches the format described in
-        reduce_filename above: NAME.aot.EPOCHS.XXX.YYY.so
-         - Where XXX is a variation on the model size for example
-         - And where YYY is a const related to the training dataset
-        '''
-
-        parts = cli_args.so_model.split('.')
-        assert len(parts) == 6
-        assert parts[1]   == 'aot'
-        assert parts[-1]  == 'so'
-
     if cli_args.dir is not None and not os.path.isdir(cli_args.dir):
         raise AssertionError('Inexistent temp directory')
 
@@ -514,9 +492,9 @@ def do_main():
     if cli_args.lm_binary and cli_args.trie:
         dest_lm_binary = os.path.join(tempdir, os.path.basename(cli_args.lm_binary))
         dest_trie = os.path.join(tempdir, os.path.basename(cli_args.trie))
-        inference_times = run_benchmarks(dir=tempdir, models=dest_sorted_models, extra_aot_model=cli_args.so_model, wav=dest_wav, alphabet=dest_alphabet, lm_binary=dest_lm_binary, trie=dest_trie, iters=cli_args.iters)
+        inference_times = run_benchmarks(dir=tempdir, models=dest_sorted_models, wav=dest_wav, alphabet=dest_alphabet, lm_binary=dest_lm_binary, trie=dest_trie, iters=cli_args.iters)
     else:
-        inference_times = run_benchmarks(dir=tempdir, models=dest_sorted_models, extra_aot_model=cli_args.so_model, wav=dest_wav, alphabet=dest_alphabet, iters=cli_args.iters)
+        inference_times = run_benchmarks(dir=tempdir, models=dest_sorted_models, wav=dest_wav, alphabet=dest_alphabet, iters=cli_args.iters)
 
     if cli_args.csv:
         produce_csv(input=inference_times, output=cli_args.csv)

bin/benchmark_plotter.py

@@ -35,9 +35,6 @@ def reduce_filename(f):
     '''
 
     f = os.path.basename(f).split('.')
-    if f[1] == 'aot':
-        return 'AOT:' + str(keep_only_digits(f[-3]))
-    else:
     return keep_only_digits(f[-3])
 
 def ingest_csv(datasets=None, range=None):

native_client/BUILD

@@ -1,49 +1,7 @@
 # Description: Deepspeech native client library.
 
 load("@org_tensorflow//tensorflow:tensorflow.bzl",
-     "if_linux_x86_64", "tf_cc_shared_object", "if_cuda")
+     "tf_cc_shared_object", "if_cuda")
-
-load("@org_tensorflow//tensorflow/compiler/aot:tfcompile.bzl",
-     "tf_library")
-
-load(":deepspeech.bzl", "if_native_model")
-
-config_setting(
-    name = "ds_native_model",
-    values = {
-        "define": "DS_NATIVE_MODEL=1"
-    }
-)
-
-tf_library(
-    name = "deepspeech_model_core",
-    cpp_class = "DeepSpeech::nativeModel",
-    # We don't need tests or benchmark binaries
-    gen_test=False, gen_benchmark=False,
-    # graph and config will be generated at build time thanks to the matching
-    # genrule.
-    graph = "tfcompile.model.pb",
-    config = "tfcompile.config.pbtxt",
-    # This depends on //tensorflow:rpi3 condition defined in mozilla/tensorflow
-    tfcompile_flags = select({
-        "//tensorflow:rpi3": str('--target_cpu="cortex-a53"'),
-        "//conditions:default": str('')
-    }),
-)
-
-genrule(
-    name = "tfcompile.config",
-    srcs = ["tfcompile.config.pbtxt.src"],
-    outs = ["tfcompile.config.pbtxt"],
-    cmd = "$(location :model_size.sh) $(SRCS) $(DS_MODEL_TIMESTEPS) $(DS_MODEL_FRAMESIZE) >$@",
-    tools = [":model_size.sh"]
-)
-
-genrule(
-    name = "tfcompile.model",
-    outs = ["tfcompile.model.pb"],
-    cmd = "cp $(DS_MODEL_FILE) $@"
-)
 
 genrule(
     name = "ds_git_version",
@@ -68,17 +26,13 @@ tf_cc_shared_object(
             "kiss_fft130/_kiss_fft_guts.h",
             "kiss_fft130/tools/kiss_fftr.h",
             "ds_version.h"] +
-           if_native_model(["deepspeech_model_core.h"]) +
            glob(["kenlm/lm/*.cc", "kenlm/util/*.cc", "kenlm/util/double-conversion/*.cc",
                  "kenlm/lm/*.hh", "kenlm/util/*.hh", "kenlm/util/double-conversion/*.h"],
                 exclude = ["kenlm/*/*test.cc", "kenlm/*/*main.cc"]) +
            glob(["boost_locale/**/*.hpp"]),
     # -Wno-sign-compare to silent a lot of warnings from tensorflow itself,
     # which makes it harder to see our own warnings
-    copts = ["-Wno-sign-compare", "-fvisibility=hidden"] + if_native_model([
+    copts = ["-Wno-sign-compare", "-fvisibility=hidden"],
-        "-DDS_MODEL_TIMESTEPS=$(DS_MODEL_TIMESTEPS)",
-        "-DDS_NATIVE_MODEL=1",
-    ]),
     deps = [
         "//tensorflow/core:core_cpu",
         "//tensorflow/core:direct_session",
@@ -108,20 +62,13 @@ tf_cc_shared_object(
         #### Needed by production model produced without "--use_seq_length False"
         #"//tensorflow/core/kernels:logging_ops",         # Assert
         #"//tensorflow/core/kernels:reverse_sequence_op", # ReverseSequence
-    ] + if_native_model([
+    ] + if_cuda([
-        "//tensorflow/compiler/tf2xla:xla_compiled_cpu_function",
-    ])+ if_cuda([
         "//tensorflow/core:core",
     ]),
     includes = ["kenlm", "boost_locale", "c_speech_features", "kiss_fft130"],
     defines = ["KENLM_MAX_ORDER=6"],
 )
 
-tf_cc_shared_object(
-    name = "libdeepspeech_model.so",
-    deps = [":deepspeech_model_core"]
-)
-
 tf_cc_shared_object(
     name = "libctc_decoder_with_kenlm.so",
     srcs = [

native_client/README.md

@@ -92,32 +92,6 @@ cd ../DeepSpeech/native_client
 make deepspeech
 ```
 
-## Building with AOT model
-
-First, please note that this is still experimental. AOT model relies on TensorFlow's [AOT tfcompile](https://www.tensorflow.org/performance/xla/tfcompile) tooling. It takes a protocol buffer file graph as input, and produces a .so library that one can call from C++ code.
-To experiment, you will need to build TensorFlow from [github.com/mozilla/tensorflow r1.6 branch](https://github.com/mozilla/tensorflow/tree/r1.6). Follow TensorFlow's documentation for the configuration of your system.
-When building, you will have to add some extra parameter and targets.
-
-Bazel defines:
-* `--define=DS_NATIVE_MODEL=1`: to toggle AOT support.
-* `--define=DS_MODEL_TIMESTEPS=x`: to define how many timesteps you want to handle. Relying on prebuilt model implies we need to use a fixed value for how much audio value we want to use. Timesteps defines that value, and an audio file bigger than this will just be dealt with over several samples. This means there's a compromise between quality and minimum audio segment you want to handle.
-* `--define=DS_MODEL_FRAMESIZE=y`: to define your model framesize, this is the second component of your model's input layer shape. Can be extracted using TensorFlow's `summarize_graph` tool.
-* `--define=DS_MODEL_FILE=/path/to/graph.pb`: the model you want to use
-
-Bazel targets:
-* `//native_client:libdeepspeech_model.so`: to produce `libdeepspeech_model.so`
-
-In the end, the previous example becomes (no change for `libctc_decoder_with_kenlm.so`):
-
-```
-bazel build --config=monolithic -c opt --copt=-O3 --copt=-fvisibility=hidden --define=DS_NATIVE_MODEL=1 --define=DS_MODEL_TIMESTEPS=64 --define=DS_MODEL_FRAMESIZE=494 --define=DS_MODEL_FILE=/tmp/model.ldc93s1.pb //native_client:libdeepspeech_model.so //native_client:libdeepspeech.so //native_client:generate_trie
-```
-
-Later, when building either `deepspeech` binaries or bindings, you will have to add some extra variables to your `make` command-line (assuming `TFDIR` points to your TensorFlow's git clone):
-```
-EXTRA_LIBS="-ldeepspeech_model"
-```
-
 ## Installing
 
 After building, the library files and binary can optionally be installed to a system path for ease of development. This is also a required step for bindings generation.

native_client/deepspeech.bzl

@@ -1,5 +0,0 @@
-def if_native_model(a):
-    return select({
-        ":ds_native_model": a,
-        "//conditions:default": []
-    })

native_client/deepspeech.cc

@@ -1,12 +1,3 @@
-#ifdef DS_NATIVE_MODEL
-#define EIGEN_USE_THREADS
-#define EIGEN_USE_CUSTOM_THREAD_POOL
-
-#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
-
-#include "native_client/deepspeech_model_core.h" // generated
-#endif
-
 #include <algorithm>
 #include <iostream>
 #include <memory>
@@ -109,7 +100,6 @@ struct ModelState {
   Alphabet* alphabet;
   KenLMBeamScorer* scorer;
   unsigned int beam_width;
-  bool run_aot;
   unsigned int n_steps;
   unsigned int mfcc_feats_per_timestep;
   unsigned int n_context;
@@ -149,7 +139,6 @@ ModelState::ModelState()
   , alphabet(nullptr)
   , scorer(nullptr)
   , beam_width(0)
-  , run_aot(false)
   , n_steps(-1)
   , mfcc_feats_per_timestep(-1)
   , n_context(-1)
@@ -297,32 +286,6 @@ ModelState::infer(const float* aMfcc, unsigned int n_frames, vector<float>& logi
 {
   const size_t num_classes = alphabet->GetSize() + 1; // +1 for blank
 
-  if (run_aot) {
-#ifdef DS_NATIVE_MODEL
-    Eigen::ThreadPool tp(2);  // Size the thread pool as appropriate.
-    Eigen::ThreadPoolDevice device(&tp, tp.NumThreads());
-
-    nativeModel nm(nativeModel::AllocMode::RESULTS_PROFILES_AND_TEMPS_ONLY);
-    nm.set_thread_pool(&device);
-
-    for (int ot = 0; ot < n_frames; ot += DS_MODEL_TIMESTEPS) {
-      nm.set_arg0_data(&(aMfcc[ot * mfcc_feats_per_timestep]));
-      nm.Run();
-
-      // The CTCDecoder works with log-probs.
-      for (int t = 0; t < DS_MODEL_TIMESTEPS, (ot + t) < n_frames; ++t) {
-        for (int b = 0; b < BATCH_SIZE; ++b) {
-          for (int c = 0; c < num_classes; ++c) {
-            logits_output.push_back(nm.result0(t, b, c));
-          }
-        }
-      }
-    }
-#else
-    std::cerr << "No support for native model built-in." << std::endl;
-    return;
-#endif // DS_NATIVE_MODEL
-  } else {
   Tensor input(DT_FLOAT, TensorShape({BATCH_SIZE, n_steps, 2*n_context+1, MFCC_FEATURES}));
 
   auto input_mapped = input.flat<float>();
@@ -353,7 +316,6 @@ ModelState::infer(const float* aMfcc, unsigned int n_frames, vector<float>& logi
     logits_output.push_back(logits_mapped(t));
   }
 }
-}
 
 char*
 ModelState::decode(vector<float>& logits)
@@ -423,16 +385,14 @@ DS_CreateModel(const char* aModelPath,
   model->ncontext   = aNContext;
   model->alphabet   = new Alphabet(aAlphabetConfigPath);
   model->beam_width = aBeamWidth;
-  model->run_aot    = false;
 
   *retval = nullptr;
 
   DS_PrintVersions();
 
   if (!aModelPath || strlen(aModelPath) < 1) {
-    std::cerr << "No model specified, will rely on built-in model." << std::endl;
+    std::cerr << "No model specified, cannot continue." << std::endl;
-    model->run_aot = true;
+    return error::INVALID_ARGUMENT;
-    return 0;
   }
 
   Status status;

native_client/model_size.sh

@@ -1,11 +0,0 @@
-#!/bin/sh
-
-# Fail if we don't have *three* arguments
-#  $1 => file to generate
-#  $2 => dimension to use for timesteps
-#  $3 => dimension to use for framesize
-if [ $# -ne 3 ]; then
-    exit 1
-fi;
-
-sed -e "s|\$DS_MODEL_TIMESTEPS|$2|g" -e "s|\$DS_MODEL_FRAMESIZE|$3|g" < $1

native_client/tfcompile.config.pbtxt.src

@@ -1,32 +0,0 @@
-# Each feed is a positional input argument for the generated function.  The order
-# of each entry matches the order of each input argument.  Here “x_hold” and “y_hold”
-# refer to the names of placeholder nodes defined in the graph.
-#
-## From: input_tensor = tf.placeholder(tf.float32, [None, None, n_input + 2*n_input*n_context], name='input_node')
-feed {
-  id { node_name: "input_node" }
-  shape {
-    dim { size: 1 } # Batch size of 1
-    dim { size: $DS_MODEL_TIMESTEPS } # aNFrames: 146 for data/ldc93s1/LDC93S1.wav
-    dim { size: $DS_MODEL_FRAMESIZE } # frameSize: n_input + 2*n_input*n_context] ; n_input=26, n_context=9
-  }
-}
-
-#feed {
-#  id { node_name: "input_lengths" }
-#  shape {
-#    dim { size: 1 }
-#  }
-#}
-
-# Each fetch is a positional output argument for the generated function.  The order
-# of each entry matches the order of each output argument.  Here “x_y_prod”
-# refers to the name of a matmul node defined in the graph.
-#fetch {
-#  id { node_name: "output_node" }
-#}
-
-fetch {
-  id { node_name: "logits" }
-}
-

taskcluster/aot-test-model-build.sh

@@ -1,29 +0,0 @@
-#!/bin/bash
-
-set -xe
-
-source $(dirname "$0")/../tc-tests-utils.sh
-
-source ${DS_ROOT_TASK}/DeepSpeech/tf/tc-vars.sh
-
-BAZEL_TARGETS="
-//native_client:libdeepspeech.so
-${BAZEL_AOT_TARGETS}"
-
-BAZEL_ENV_FLAGS="TF_NEED_CUDA=0"
-SYSTEM_TARGET=host
-
-EXTRA_LOCAL_CFLAGS="${EXTRA_AOT_CFLAGS}"
-EXTRA_LOCAL_LDFLAGS="${EXTRA_AOT_LDFLAGS}"
-EXTRA_LOCAL_LIBS="${EXTRA_AOT_LIBS}"
-
-do_get_model_parameters "${DEEPSPEECH_TEST_MODEL}" AOT_MODEL_PARAMS
-BAZEL_BUILD_FLAGS="${BAZEL_OPT_FLAGS} ${BAZEL_EXTRA_FLAGS} ${BAZEL_AOT_BUILD_FLAGS} ${AOT_MODEL_PARAMS}"
-
-do_bazel_build
-
-do_deepspeech_binary_build
-
-do_deepspeech_python_build
-
-do_deepspeech_nodejs_build

taskcluster/arm64-build.sh

@@ -16,17 +16,6 @@ BAZEL_ENV_FLAGS="TF_NEED_CUDA=0"
 SYSTEM_TARGET=rpi3-armv8
 SYSTEM_RASPBIAN=/tmp/multistrap-armbian64-stretch
 
-if [ $1 = "--aot" ]; then
-  EXTRA_LOCAL_CFLAGS="${EXTRA_AOT_CFLAGS}"
-  EXTRA_LOCAL_LDFLAGS="${EXTRA_AOT_LDFLAGS}"
-  EXTRA_LOCAL_LIBS="${EXTRA_AOT_LIBS}"
-
-  do_get_model_parameters "${DEEPSPEECH_PROD_MODEL}" AOT_MODEL_PARAMS
-
-  BAZEL_TARGETS="${BAZEL_AOT_TARGETS} ${BAZEL_TARGETS}"
-  BAZEL_BUILD_FLAGS="${BAZEL_BUILD_FLAGS} ${BAZEL_AOT_BUILD_FLAGS} ${AOT_MODEL_PARAMS}"
-fi;
-
 maybe_install_xldd
 
 do_bazel_build

taskcluster/host-build.sh

@@ -15,17 +15,6 @@ BAZEL_BUILD_FLAGS="${BAZEL_OPT_FLAGS} ${BAZEL_EXTRA_FLAGS}"
 BAZEL_ENV_FLAGS="TF_NEED_CUDA=0"
 SYSTEM_TARGET=host
 
-if [ $1 = "--aot" ]; then
-  EXTRA_LOCAL_CFLAGS="${EXTRA_AOT_CFLAGS}"
-  EXTRA_LOCAL_LDFLAGS="${EXTRA_AOT_LDFLAGS}"
-  EXTRA_LOCAL_LIBS="${EXTRA_AOT_LIBS}"
-
-  do_get_model_parameters "${DEEPSPEECH_PROD_MODEL}" AOT_MODEL_PARAMS
-
-  BAZEL_TARGETS="${BAZEL_AOT_TARGETS} ${BAZEL_TARGETS}"
-  BAZEL_BUILD_FLAGS="${BAZEL_BUILD_FLAGS} ${BAZEL_AOT_BUILD_FLAGS} ${AOT_MODEL_PARAMS}"
-fi;
-
 do_bazel_build
 
 do_deepspeech_binary_build

taskcluster/rpi3-build.sh

@@ -16,17 +16,6 @@ BAZEL_ENV_FLAGS="TF_NEED_CUDA=0"
 SYSTEM_TARGET=rpi3
 SYSTEM_RASPBIAN=/tmp/multistrap-raspbian-stretch
 
-if [ $1 = "--aot" ]; then
-  EXTRA_LOCAL_CFLAGS="${EXTRA_AOT_CFLAGS}"
-  EXTRA_LOCAL_LDFLAGS="${EXTRA_AOT_LDFLAGS}"
-  EXTRA_LOCAL_LIBS="${EXTRA_AOT_LIBS}"
-
-  do_get_model_parameters "${DEEPSPEECH_PROD_MODEL}" AOT_MODEL_PARAMS
-
-  BAZEL_TARGETS="${BAZEL_AOT_TARGETS} ${BAZEL_TARGETS}"
-  BAZEL_BUILD_FLAGS="${BAZEL_BUILD_FLAGS} ${BAZEL_AOT_BUILD_FLAGS} ${AOT_MODEL_PARAMS}"
-fi;
-
 maybe_install_xldd
 
 do_bazel_build

taskcluster/test-armbian-opt-base.tyml

@@ -32,14 +32,12 @@ then:
     env:
       $let:
         training: { $eval: as_slugid("test-training_upstream-linux-amd64-py27mu-opt") }
-        linux_arm64_aot_prod: { $eval: as_slugid("linux-arm64-cpu-aot_prod-opt") }
         linux_arm64_build: { $eval: as_slugid("linux-arm64-cpu-opt") }
         node_package: { $eval: as_slugid("node-package") }
       in:
         CONVERT_GRAPHDEF_MEMMAPPED: ${build.convert_graphdef}
         DEEPSPEECH_ARTIFACTS_ROOT: https://queue.taskcluster.net/v1/task/${linux_arm64_build}/artifacts/public
         DEEPSPEECH_NODEJS: https://queue.taskcluster.net/v1/task/${node_package}/artifacts/public
-        DEEPSPEECH_AOT_ARTIFACTS_ROOT: https://queue.taskcluster.net/v1/task/${linux_arm64_aot_prod}/artifacts/public
         DEEPSPEECH_TEST_MODEL: https://queue.taskcluster.net/v1/task/${training}/artifacts/public/output_graph.pb
         DEEPSPEECH_PROD_MODEL: https://github.com/reuben/DeepSpeech/releases/download/v0.2.0-prod/output_graph.pb
         DEEPSPEECH_PROD_MODEL_MMAP: https://github.com/reuben/DeepSpeech/releases/download/v0.2.0-prod/output_graph.pbmm

taskcluster/test-linux-opt-base.tyml

@@ -32,7 +32,6 @@ then:
     env:
       $let:
         training: { $eval: as_slugid("test-training_upstream-linux-amd64-py27mu-opt") }
-        linux_amd64_aot_test: { $eval: as_slugid("linux-amd64-cpu-aot_test-opt") }
         linux_amd64_build: { $eval: as_slugid("linux-amd64-cpu-opt") }
         linux_amd64_ctc: { $eval: as_slugid("linux-amd64-ctc-opt") }
         node_package: { $eval: as_slugid("node-package") }
@@ -40,7 +39,6 @@ then:
         CONVERT_GRAPHDEF_MEMMAPPED: ${build.convert_graphdef}
         DEEPSPEECH_ARTIFACTS_ROOT: https://queue.taskcluster.net/v1/task/${linux_amd64_build}/artifacts/public
         DEEPSPEECH_NODEJS: https://queue.taskcluster.net/v1/task/${node_package}/artifacts/public
-        DEEPSPEECH_AOT_ARTIFACTS_ROOT: https://queue.taskcluster.net/v1/task/${linux_amd64_aot_test}/artifacts/public
         DEEPSPEECH_LIBCTC: https://queue.taskcluster.net/v1/task/${linux_amd64_ctc}/artifacts/public/decoder.tar.xz
         DEEPSPEECH_TEST_MODEL: https://queue.taskcluster.net/v1/task/${training}/artifacts/public/output_graph.pb
         DEEPSPEECH_PROD_MODEL: https://github.com/reuben/DeepSpeech/releases/download/v0.2.0-prod/output_graph.pb

taskcluster/test-raspbian-opt-base.tyml

@@ -32,14 +32,12 @@ then:
     env:
       $let:
         training: { $eval: as_slugid("test-training_upstream-linux-amd64-py27mu-opt") }
-        linux_rpi3_aot_prod: { $eval: as_slugid("linux-rpi3-cpu-aot_prod-opt") }
         linux_rpi3_build: { $eval: as_slugid("linux-rpi3-cpu-opt") }
         node_package: { $eval: as_slugid("node-package") }
       in:
         CONVERT_GRAPHDEF_MEMMAPPED: ${build.convert_graphdef}
         DEEPSPEECH_ARTIFACTS_ROOT: https://queue.taskcluster.net/v1/task/${linux_rpi3_build}/artifacts/public
         DEEPSPEECH_NODEJS: https://queue.taskcluster.net/v1/task/${node_package}/artifacts/public
-        DEEPSPEECH_AOT_ARTIFACTS_ROOT: https://queue.taskcluster.net/v1/task/${linux_rpi3_aot_prod}/artifacts/public
         DEEPSPEECH_TEST_MODEL: https://queue.taskcluster.net/v1/task/${training}/artifacts/public/output_graph.pb
         DEEPSPEECH_PROD_MODEL: https://github.com/reuben/DeepSpeech/releases/download/v0.2.0-prod/output_graph.pb
         DEEPSPEECH_PROD_MODEL_MMAP: https://github.com/reuben/DeepSpeech/releases/download/v0.2.0-prod/output_graph.pbmm

tc-benchmark-tests.sh

@@ -8,7 +8,6 @@ exec_benchmark()
 {
     model_file="$1"
     run_postfix=$2
-    aot_model=$3
 
     mkdir -p /tmp/bench-ds/ || true
     mkdir -p /tmp/bench-ds-nolm/ || true
@@ -18,14 +17,9 @@ exec_benchmark()
     png=${TASKCLUSTER_ARTIFACTS}/benchmark-${run_postfix}.png
     svg=${TASKCLUSTER_ARTIFACTS}/benchmark-${run_postfix}.svg
 
-    AOT_MODEL_ARGS=""
-    if [ ! -z "${aot_model}" ]; then
-        AOT_MODEL_ARGS="--so-model ${aot_model}"
-    fi;
-
     python ${DS_ROOT_TASK}/DeepSpeech/ds/bin/benchmark_nc.py \
         --dir /tmp/bench-ds/ \
-        --models ${model_file} ${AOT_MODEL_ARGS} \
+        --models ${model_file} \
         --wav /tmp/LDC93S1.wav \
         --alphabet /tmp/alphabet.txt \
         --lm_binary /tmp/lm.binary \
@@ -34,7 +28,7 @@ exec_benchmark()
 
     python ${DS_ROOT_TASK}/DeepSpeech/ds/bin/benchmark_nc.py \
         --dir /tmp/bench-ds-nolm/ \
-        --models ${model_file} ${AOT_MODEL_ARGS} \
+        --models ${model_file} \
         --wav /tmp/LDC93S1.wav \
         --alphabet /tmp/alphabet.txt \
         --csv ${csv_nolm}
@@ -91,12 +85,7 @@ done;
 # Let's prepare another model for single-model codepath
 mv /tmp/${model_name} /tmp/test.frozen.e75.lstm494.ldc93s1.pb
 
-# We don't need download_material here, benchmark code should take care of it.
-if [ "$1" = "--aot" ]; then
-    export TASKCLUSTER_SCHEME=${DEEPSPEECH_AOT_ARTIFACTS_ROOT}/native_client.tar.xz
-else
 export TASKCLUSTER_SCHEME=${DEEPSPEECH_ARTIFACTS_ROOT}/native_client.tar.xz
-fi;
 
 install_pyenv "${PYENV_ROOT}"
 install_pyenv_virtualenv "$(pyenv root)/plugins/pyenv-virtualenv"
@@ -108,15 +97,9 @@ source ${PYENV_ROOT}/versions/${pyver}/envs/${PYENV_NAME}/bin/activate
 
 pip install -r ${DS_ROOT_TASK}/DeepSpeech/ds/requirements.txt | cat
 
-exec_benchmark "/tmp/test.frozen.e75.lstm494.ldc93s1.pb" "single-model_noAOT"
+exec_benchmark "/tmp/test.frozen.e75.lstm494.ldc93s1.pb" "single-model"
-exec_benchmark "/tmp/test.frozen.e75.lstm100-900.ldc93s1.zip" "zipfile-model_noAOT"
+exec_benchmark "/tmp/test.frozen.e75.lstm100-900.ldc93s1.zip" "zipfile-model"
-exec_benchmark "${model_list}" "multi-model_noAOT"
+exec_benchmark "${model_list}" "multi-model"
-
-if [ "$1" = "--aot" ]; then
-    exec_benchmark "/tmp/test.frozen.e75.lstm494.ldc93s1.pb" "single-model_AOT" "test.aot.e5.lstm494.ldc93s1.so"
-    exec_benchmark "/tmp/test.frozen.e75.lstm100-900.ldc93s1.zip" "zipfile-model_AOT" "test.aot.e5.lstm494.ldc93s1.so"
-    exec_benchmark "${model_list}" "multi-model_AOT" "test.aot.e5.lstm494.ldc93s1.so"
-fi;
 
 deactivate
 pyenv uninstall --force ${PYENV_NAME}

tc-cpp-ds-tests.sh

@@ -4,9 +4,7 @@ set -xe
 
 source $(dirname "$0")/tc-tests-utils.sh
 
-aot_model=$1
+download_material "${TASKCLUSTER_TMP_DIR}/ds"
-
-download_material "${TASKCLUSTER_TMP_DIR}/ds" "${aot_model}"
 
 export PATH=${TASKCLUSTER_TMP_DIR}/ds/:$PATH
 

tc-node-tests.sh

@@ -5,7 +5,6 @@ set -xe
 source $(dirname "$0")/tc-tests-utils.sh
 
 nodever=$1
-aot_model=$2
 
 if [ -z "${nodever}" ]; then
     echo "No node version given, aborting."
@@ -21,10 +20,6 @@ NODE_ROOT="${DS_ROOT_TASK}/ds-test/"
 export NODE_PATH="${NODE_ROOT}/node_modules/"
 export PATH="${NODE_PATH}/.bin/:$PATH"
 
-if [ "${aot_model}" = "--aot" ]; then
-    npm install --prefix ${NODE_ROOT} ${DEEPSPEECH_AOT_ARTIFACTS_ROOT}/deepspeech-${DS_VERSION}.tgz
-else
 npm install --prefix ${NODE_ROOT} ${DEEPSPEECH_NODEJS}/deepspeech-${DS_VERSION}.tgz
-fi
 
 run_all_inference_tests

tc-python-tests.sh

@@ -5,7 +5,6 @@ set -xe
 source $(dirname "$0")/tc-tests-utils.sh
 
 pyver_full=$1
-aot_model=$2
 
 if [ -z "${pyver_full}" ]; then
     echo "No python version given, aborting."
@@ -47,11 +46,7 @@ platform=$(python -c 'import sys; import platform; plat = platform.system().lowe
 whl_ds_version="$(python -c 'from pkg_resources import parse_version; print(parse_version("'${DS_VERSION}'"))')"
 deepspeech_pkg="deepspeech-${whl_ds_version}-cp${pyver_pkg}-cp${pyver_pkg}${py_unicode_type}-${platform}.whl"
 
-if [ "${aot_model}" = "--aot" ]; then
-    deepspeech_pkg_url=${DEEPSPEECH_AOT_ARTIFACTS_ROOT}/${deepspeech_pkg}
-else
 deepspeech_pkg_url=${DEEPSPEECH_ARTIFACTS_ROOT}/${deepspeech_pkg}
-fi
 LD_LIBRARY_PATH=${PY37_LDPATH}:$LD_LIBRARY_PATH pip install --verbose --only-binary :all: ${PY37_SOURCE_PACKAGE} --upgrade ${deepspeech_pkg_url} | cat
 
 run_all_inference_tests

tc-tests-utils.sh

@@ -28,15 +28,6 @@ export DS_DSDIR=${DS_ROOT_TASK}/DeepSpeech/ds
 
 export BAZEL_CTC_TARGETS="//native_client:libctc_decoder_with_kenlm.so"
 
-export EXTRA_AOT_CFLAGS=""
-export EXTRA_AOT_LDFLAGS=""
-export EXTRA_AOT_LIBS="-ldeepspeech_model"
-
-export BAZEL_AOT_BUILD_FLAGS="--define=DS_NATIVE_MODEL=1 --define=DS_MODEL_TIMESTEPS=64"
-export BAZEL_AOT_TARGETS="
-//native_client:libdeepspeech_model.so
-"
-
 export DS_VERSION="$(cat ${DS_DSDIR}/VERSION)"
 
 model_source="${DEEPSPEECH_TEST_MODEL}"
@@ -160,37 +151,6 @@ assert_correct_ldc93s1_prodmodel()
   assert_correct_inference "$1" "she had tired or so and greasy wash war or year"
 }
 
-assert_correct_ldc93s1_somodel()
-{
-    somodel_nolm=$(strip "$1")
-    somodel_withlm=$(strip "$2")
-
-    # We want to be able to return non zero value from the function, while not
-    # failing the whole execution
-    set +e
-
-    assert_correct_ldc93s1 "${somodel_nolm}"
-    so_nolm=$?
-
-    assert_correct_ldc93s1 "${somodel_withlm}"
-    so_lm=$?
-
-    set -e
-
-    # We accept that with no LM there may be errors, but we do not accept that
-    # for LM. For now.
-    if [ ${so_lm} -eq 1 ] && [ ${so_nolm} -eq 1 -o ${so_nolm} -eq 0 ];
-    then
-        exit 1
-    elif [ ${so_lm} -eq 0 ] && [ ${so_nolm} -eq 1 -o ${so_nolm} -eq 0 ];
-    then
-        exit 0
-    else
-        echo "Unexpected status"
-        exit 2
-    fi
-}
-
 assert_correct_warning_upsampling()
 {
   assert_shows_something "$1" "erratic speech recognition"
@@ -232,23 +192,6 @@ run_all_inference_tests()
 
   phrase_pbmodel_withlm_mono_8k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_8000.wav 2>&1 1>/dev/null)
   assert_correct_warning_upsampling "${phrase_pbmodel_withlm_mono_8k}"
-
-  if [ "${aot_model}" = "--aot" ]; then
-      phrase_somodel_nolm=$(deepspeech --model "" --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1.wav)
-      phrase_somodel_withlm=$(deepspeech --model "" --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1.wav)
-
-      assert_correct_ldc93s1_somodel "${phrase_somodel_nolm}" "${phrase_somodel_withlm}"
-
-      phrase_somodel_nolm_stereo_44k=$(deepspeech --model "" --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav)
-      phrase_somodel_withlm_stereo_44k=$(deepspeech --model "" --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav)
-
-      assert_correct_ldc93s1_somodel "${phrase_somodel_nolm_stereo_44k}" "${phrase_somodel_withlm_stereo_44k}"
-
-      phrase_somodel_nolm_mono_8k=$(deepspeech --model "" --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_8000.wav 2>&1 1>/dev/null)
-      phrase_somodel_withlm_stereo_44k=$(deepspeech --model "" --alphabet ${TASKCLUSTER_TMP_DIR}/alphabet.txt --lm ${TASKCLUSTER_TMP_DIR}/lm.binary --trie ${TASKCLUSTER_TMP_DIR}/trie --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_8000.wav 2>&1 1>/dev/null)
-
-      assert_correct_warning_upsampling "${phrase_somodel_nolm_mono_8k}" "${phrase_somodel_withlm_mono_8k}"
-  fi;
 }
 
 run_prod_inference_tests()
@@ -297,11 +240,6 @@ download_native_client_files()
   generic_download_tarxz "$1" "${DEEPSPEECH_ARTIFACTS_ROOT}/native_client.tar.xz"
 }
 
-download_aot_model_files()
-{
-  generic_download_tarxz "$1" "${DEEPSPEECH_AOT_ARTIFACTS_ROOT}/native_client.tar.xz"
-}
-
 download_ctc_kenlm()
 {
   generic_download_tarxz "$1" "${DEEPSPEECH_LIBCTC}"
@@ -325,14 +263,8 @@ download_for_frozen()
 download_material()
 {
   target_dir=$1
-  maybe_aot=$2
 
-  if [ "${maybe_aot}" = "--aot" ]; then
-    download_aot_model_files "${target_dir}"
-  else
   download_native_client_files "${target_dir}"
-  fi
-
   download_data
 
   ls -hal ${TASKCLUSTER_TMP_DIR}/${model_name} ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} ${TASKCLUSTER_TMP_DIR}/LDC93S1*.wav ${TASKCLUSTER_TMP_DIR}/alphabet.txt
@@ -377,30 +309,6 @@ maybe_install_xldd()
   fi
 }
 
-do_get_model_parameters()
-{
-  local __result=$2
-  model_url=$1
-  model_file=/tmp/$(basename "${model_url}")
-
-  if [ -z "${model_url}" ]; then
-    echo "Empty URL for model"
-    exit 1
-  fi;
-
-  wget "${model_url}" -O "${model_file}"
-  wget -P "/tmp/" "${SUMMARIZE_GRAPH_BINARY}" && chmod +x /tmp/summarize_graph
-
-  if [ ! -f "${model_file}" ]; then
-    echo "No such model: ${model_file}"
-    exit 1
-  fi;
-
-  model_width=$(/tmp/summarize_graph --in_graph="${model_file}" | grep "inputs" | grep -Eo "shape=\[\?,\?,[[:digit:]]+" | cut -d',' -f3)
-
-  eval $__result="'--define=DS_MODEL_FRAMESIZE=${model_width} --define=DS_MODEL_FILE=${model_file}'"
-}
-
 # Checks whether we run a patched version of bazel.
 # Patching is required to dump computeKey() parameters to .ckd files
 # See bazel.patch