Upgrade to TF v2.11

Author: jaeyoo

WORKSPACE

@@ -3,8 +3,8 @@
 
 load("@bazel_tools//tools/build_defs/repo:http.bzl", "http_archive")
 
-EIGEN_COMMIT = "12e8d57108c50d8a63605c6eb0144c838c128337"
-EIGEN_SHA256 = "f689246e342c3955af48d26ce74ac34d21b579a00675c341721a735937919b02"
+EIGEN_COMMIT = "3bb6a48d8c171cf20b5f8e48bfb4e424fbd4f79e"
+EIGEN_SHA256 = "eca9847b3fe6249e0234a342b78f73feec07d29f534e914ba5f920f3e09383a3"
 
 
 http_archive(
@@ -33,10 +33,10 @@ http_archive(
 
 http_archive(
     name = "org_tensorflow",
-    sha256 = "249b48ddee927801c7a4f8e5442cf1a3c860f6f46b85a2ff7a78b501507dd561",
-    strip_prefix = "tensorflow-2.7.0",
+    sha256 = "e52cda3bae45f0ae0fccd4055e9fa29892b414f70e2df94df9a3a10319c75fff",
+    strip_prefix = "tensorflow-2.11.0",
     urls = [
-        "https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.7.0.zip",
+        "https://github.com/tensorflow/tensorflow/archive/refs/tags/v2.11.0.zip",
     ],
 )
 

benchmarks/README.md

@@ -24,7 +24,7 @@ Some notes on benchmark configuration:
 
 For example, to benchmark a dense depth-10 Clifford circuit over 5 qubits call:
 ```
-bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --cxxopt="-msse2" \
+bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" --cxxopt="-msse2" \
   --cxxopt="-msse3" --cxxopt="-msse4" \
   benchmarks/scripts:benchmark_clifford_circuit -- \
   --n_moments 5 --n_qubits 4 \
@@ -39,7 +39,7 @@ benchmarks/scripts/reports/CliffordBenchmarks.benchmark_clifford_circuit_4_5_1
 To benchmark the parameter shift differentiation method on a random depth-10 4-qubit circuit with 10 parameters call, where the circuit will be differentiated
 over 50 trials, each time over a batch of 10 circuits.
 ```
-bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" --cxxopt="-msse2" \
+bazel run -c opt --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" --cxxopt="-msse2" \
   --cxxopt="-msse3" --cxxopt="-msse4" \
   benchmarks/scripts:benchmark_op_gradients -- \
   --n_moments 10 --n_qubits 4 --n_symbols 10 \

configure.sh

@@ -99,13 +99,12 @@ if [[ "$PIP_MANYLINUX2010" == "0" ]]; then
   write_to_bazelrc "build:cuda --crosstool_top=@local_config_cuda//crosstool:toolchain"
 fi
 
-
+write_to_bazelrc "build --experimental_repo_remote_exec"
 write_to_bazelrc "build --spawn_strategy=standalone"
 write_to_bazelrc "build --strategy=Genrule=standalone"
-write_to_bazelrc "build --experimental_repo_remote_exec"
 write_to_bazelrc "build -c opt"
-write_to_bazelrc "build --cxxopt=\"-D_GLIBCXX_USE_CXX11_ABI=0\""
-write_to_bazelrc "build --cxxopt=\"-std=c++14\""
+write_to_bazelrc "build --cxxopt=\"-D_GLIBCXX_USE_CXX11_ABI=1\""
+write_to_bazelrc "build --cxxopt=\"-std=c++17\""
 
 
 if is_windows; then

docs/install.md

@@ -84,21 +84,21 @@ As noted in the TensorFlow
 guide, the <a href="https://bazel.build/" class="external">Bazel</a>
 build system will be required.
 
-Our latest source builds use TensorFlow 2.7.0. To ensure compatibility we use `bazel` version 3.7.2. To remove any existing version of Bazel:
+Our latest source builds use TensorFlow 2.11.0. To ensure compatibility we use `bazel` version 5.1.0. To remove any existing version of Bazel:
 
 <!-- common_typos_disable -->
 <pre class="devsite-click-to-copy">
   <code class="devsite-terminal">sudo apt-get remove bazel</code>
 </pre>
 <!-- common_typos_enable -->
 
-Download and install `bazel` version 3.7.2:
+Download and install `bazel` version 5.1.0:
 
 <!-- common_typos_disable -->
 <pre class="devsite-click-to-copy">
-  <code class="devsite-terminal">wget https://github.com/bazelbuild/bazel/releases/download/3.7.2/bazel_3.7.2-linux-x86_64.deb
+  <code class="devsite-terminal">wget https://github.com/bazelbuild/bazel/releases/download/5.1.0/bazel_5.1.0-linux-x86_64.deb
 </code>
-  <code class="devsite-terminal">sudo dpkg -i bazel_3.7.2-linux-x86_64.deb</code>
+  <code class="devsite-terminal">sudo dpkg -i bazel_5.1.0-linux-x86_64.deb</code>
 </pre>
 <!-- common_typos_enable -->
 
@@ -122,7 +122,7 @@ Finally, confirm installation of the correct `bazel` version:
 ### 4. Build TensorFlow from source
 
 Here we adapt instructions from the TensorFlow [build from source](https://www.tensorflow.org/install/source)
-guide, see the link for further details. TensorFlow Quantum is compatible with TensorFlow version&nbsp;2.7.0.
+guide, see the link for further details. TensorFlow Quantum is compatible with TensorFlow version&nbsp;2.11.0.
 
 Download the
 <a href="https://github.com/tensorflow/tensorflow" class="external">TensorFlow source code</a>:
@@ -131,7 +131,7 @@ Download the
 <pre class="devsite-click-to-copy">
   <code class="devsite-terminal">git clone https://github.com/tensorflow/tensorflow.git</code>
   <code class="devsite-terminal">cd tensorflow</code>
-  <code class="devsite-terminal">git checkout v2.7.0</code>
+  <code class="devsite-terminal">git checkout v2.11.0</code>
 </pre>
 
 Be sure the virtual environment you created in step 2 is activated. Then, install the TensorFlow dependencies:
@@ -142,6 +142,7 @@ Be sure the virtual environment you created in step 2 is activated. Then, instal
   <code class="devsite-terminal">pip install -U keras_applications --no-deps</code>
   <code class="devsite-terminal">pip install -U keras_preprocessing --no-deps</code>
   <code class="devsite-terminal">pip install numpy==1.19.5</code>
+  <code class="devsite-terminal">pip install packaging requests</code>
 </pre>
 <!-- common_typos_enable -->
 
@@ -153,11 +154,11 @@ Configure the TensorFlow build. When asked for the Python interpreter and librar
 </pre>
 <!-- common_typos_enable -->
 
-Build the TensorFlow package:
+Build the TensorFlow package (Since TF v2.8, `_GLIBCXX_USE_CXX11_ABI` is set to 1, and the c++ codes are all compiled with `-std=c++17`):
 
 <!-- common_typos_disable -->
 <pre class="devsite-click-to-copy">
-  <code class="devsite-terminal">bazel build -c opt --cxxopt="-O3" --cxxopt="-march=native" --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" //tensorflow/tools/pip_package:build_pip_package</code>
+  <code class="devsite-terminal">bazel build -c opt --cxxopt="-O3" --cxxopt="-march=native" --cxxopt="-std=c++17" --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" //tensorflow/tools/pip_package:build_pip_package</code>
 </pre>
 <!-- common_typos_enable -->
 
@@ -193,7 +194,7 @@ Build the TensorFlow Quantum pip package and install:
 <!-- common_typos_disable -->
 <pre class="devsite-click-to-copy">
   <code class="devsite-terminal">./configure.sh</code>
-  <code class="devsite-terminal">bazel build -c opt --cxxopt="-O3" --cxxopt="-march=native" --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" release:build_pip_package</code>
+  <code class="devsite-terminal">bazel build -c opt --cxxopt="-O3" --cxxopt="-march=native" --cxxopt="-std=c++17" --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=1" release:build_pip_package</code>
   <code class="devsite-terminal">bazel-bin/release/build_pip_package /tmp/tfquantum/</code>
   <code class="devsite-terminal">python3 -m pip install /tmp/tfquantum/<var>name_of_generated_wheel</var>.whl</code>
 </pre>

docs/tutorials/barren_plateaus.ipynb

@@ -297,7 +297,7 @@
         "    # Prep the inputs as tensors\n",
         "    circuit_tensor = tfq.convert_to_tensor(circuits)\n",
         "    values_tensor = tf.convert_to_tensor(\n",
-        "        np.random.uniform(0, 2 * np.pi, (n_circuits, 1)).astype(np.float32))\n",
+        "        np.random.uniform(0, 2 * np.pi, (n_circuits, 1)).astype(float))\n",
         "\n",
         "    # Use TensorFlow GradientTape to track gradients.\n",
         "    with tf.GradientTape() as g:\n",

docs/tutorials/gradients.ipynb

@@ -370,7 +370,7 @@
       "outputs": [],
       "source": [
         "# Make input_points = [batch_size, 1] array.\n",
-        "input_points = np.linspace(0, 5, 200)[:, np.newaxis].astype(np.float32)\n",
+        "input_points = np.linspace(0, 5, 200)[:, np.newaxis].astype(float)\n",
         "exact_outputs = expectation_calculation(my_circuit,\n",
         "                                        operators=pauli_x,\n",
         "                                        symbol_names=['alpha'],\n",

docs/tutorials/hello_many_worlds.ipynb

@@ -389,7 +389,7 @@
       },
       "outputs": [],
       "source": [
-        "batch_vals = np.array(np.random.uniform(0, 2 * np.pi, (5, 2)), dtype=np.float32)"
+        "batch_vals = np.array(np.random.uniform(0, 2 * np.pi, (5, 2)), dtype=float)"
       ]
     },
     {
@@ -739,10 +739,10 @@
       "outputs": [],
       "source": [
         "# The command input values to the classical NN.\n",
-        "commands = np.array([[0], [1]], dtype=np.float32)\n",
+        "commands = np.array([[0], [1]], dtype=float)\n",
         "\n",
         "# The desired Z expectation value at output of quantum circuit.\n",
-        "expected_outputs = np.array([[1], [-1]], dtype=np.float32)"
+        "expected_outputs = np.array([[1], [-1]], dtype=float)"
       ]
     },
     {
@@ -1108,10 +1108,10 @@
         "operator_data = tfq.convert_to_tensor([[cirq.X(qubit)], [cirq.Z(qubit)]])\n",
         "\n",
         "# The command input values to the classical NN.\n",
-        "commands = np.array([[0], [1]], dtype=np.float32)\n",
+        "commands = np.array([[0], [1]], dtype=float)\n",
         "\n",
         "# The desired expectation value at output of quantum circuit.\n",
-        "expected_outputs = np.array([[1], [-1]], dtype=np.float32)"
+        "expected_outputs = np.array([[1], [-1]], dtype=float)"
       ]
     },
     {

docs/tutorials/mnist.ipynb

@@ -480,8 +480,8 @@
       "source": [
         "THRESHOLD = 0.5\n",
         "\n",
-        "x_train_bin = np.array(x_train_nocon > THRESHOLD, dtype=np.float32)\n",
-        "x_test_bin = np.array(x_test_small > THRESHOLD, dtype=np.float32)"
+        "x_train_bin = np.array(x_train_nocon > THRESHOLD, dtype=float)\n",
+        "x_test_bin = np.array(x_test_small > THRESHOLD, dtype=float)"
       ]
     },
     {

docs/tutorials/quantum_reinforcement_learning.ipynb

@@ -864,7 +864,7 @@
     "    actions = np.concatenate([ep['actions'] for ep in episodes])\n",
     "    rewards = [ep['rewards'] for ep in episodes]\n",
     "    returns = np.concatenate([compute_returns(ep_rwds, gamma) for ep_rwds in rewards])\n",
-    "    returns = np.array(returns, dtype=np.float32)\n",
+    "    returns = np.array(returns, dtype=float)\n",
     "\n",
     "    id_action_pairs = np.array([[i, a] for i, a in enumerate(actions)])\n",
     "    \n",
@@ -1456,9 +1456,9 @@
     "            training_batch = np.random.choice(replay_memory, size=batch_size)\n",
     "            Q_learning_update(np.asarray([x['state'] for x in training_batch]),\n",
     "                              np.asarray([x['action'] for x in training_batch]),\n",
-    "                              np.asarray([x['reward'] for x in training_batch], dtype=np.float32),\n",
+    "                              np.asarray([x['reward'] for x in training_batch], dtype=float),\n",
     "                              np.asarray([x['next_state'] for x in training_batch]),\n",
-    "                              np.asarray([x['done'] for x in training_batch], dtype=np.float32),\n",
+    "                              np.asarray([x['done'] for x in training_batch], dtype=float),\n",
     "                              model, gamma, n_actions)\n",
     "        \n",
     "        # Update target model\n",

release/setup.py

@@ -53,13 +53,13 @@ def finalize_options(self):
 REQUIRED_PACKAGES = [
     'cirq-core==0.13.1', 'cirq-google>=0.13.1', 'sympy == 1.8',
     'googleapis-common-protos==1.52.0', 'google-api-core==1.21.0',
-    'google-auth==1.18.0', 'protobuf==3.17.3'
+    'google-auth==1.18.0', 'protobuf==3.19.4'
 ]
 
 # placed as extra to not have required overwrite existing nightly installs if
 # they exist.
-EXTRA_PACKAGES = ['tensorflow == 2.7.0']
-CUR_VERSION = '0.7.2'
+EXTRA_PACKAGES = ['tensorflow == 2.11.0']
+CUR_VERSION = '0.7.3'
 
 
 class BinaryDistribution(Distribution):