Merge branch 'keras-team:master' into master

Author: arjunverma2004

api_gen.py

@@ -84,9 +84,13 @@ def create_legacy_directory(package_dir):
         for fname in fnames:
             if fname.endswith(".py"):
                 legacy_fpath = os.path.join(root, fname)
-                tf_keras_root = root.replace("/_legacy", "/_tf_keras/keras")
+                tf_keras_root = root.replace(
+                    os.path.join(os.path.sep, "_legacy"),
+                    os.path.join(os.path.sep, "_tf_keras", "keras"),
+                )
                 core_api_fpath = os.path.join(
-                    root.replace("/_legacy", ""), fname
+                    root.replace(os.path.join(os.path.sep, "_legacy"), ""),
+                    fname,
                 )
                 if not os.path.exists(tf_keras_root):
                     os.makedirs(tf_keras_root)
@@ -125,7 +129,7 @@ def create_legacy_directory(package_dir):
                             r"\n",
                             core_api_contents,
                         )
-                    legacy_contents = core_api_contents + "\n" + legacy_contents
+                    legacy_contents = f"{core_api_contents}\n{legacy_contents}"
                 with open(tf_keras_fpath, "w") as f:
                     f.write(legacy_contents)
 

conftest.py

@@ -1,9 +1,3 @@
-import os
-
-# When using jax.experimental.enable_x64 in unit test, we want to keep the
-# default dtype with 32 bits, aligning it with Keras's default.
-os.environ["JAX_DEFAULT_DTYPE_BITS"] = "32"
-
 try:
     # When using torch and tensorflow, torch needs to be imported first,
     # otherwise it will segfault upon import. This should force the torch

guides/distributed_training_with_tensorflow.py

@@ -194,7 +194,8 @@ def make_or_restore_model():
     # Either restore the latest model, or create a fresh one
     # if there is no checkpoint available.
     checkpoints = [
-        checkpoint_dir + "/" + name for name in os.listdir(checkpoint_dir)
+        os.path.join(checkpoint_dir, name)
+        for name in os.listdir(checkpoint_dir)
     ]
     if checkpoints:
         latest_checkpoint = max(checkpoints, key=os.path.getctime)
@@ -216,7 +217,7 @@ def run_training(epochs=1):
             # This callback saves a SavedModel every epoch
             # We include the current epoch in the folder name.
             keras.callbacks.ModelCheckpoint(
-                filepath=checkpoint_dir + "/ckpt-{epoch}.keras",
+                filepath=os.path.join(checkpoint_dir, "ckpt-{epoch}.keras"),
                 save_freq="epoch",
             )
         ]

guides/training_with_built_in_methods.py

@@ -1133,7 +1133,8 @@ def make_or_restore_model():
     # Either restore the latest model, or create a fresh one
     # if there is no checkpoint available.
     checkpoints = [
-        checkpoint_dir + "/" + name for name in os.listdir(checkpoint_dir)
+        os.path.join(checkpoint_dir, name)
+        for name in os.listdir(checkpoint_dir)
     ]
     if checkpoints:
         latest_checkpoint = max(checkpoints, key=os.path.getctime)
@@ -1148,7 +1149,8 @@ def make_or_restore_model():
     # This callback saves the model every 100 batches.
     # We include the training loss in the saved model name.
     keras.callbacks.ModelCheckpoint(
-        filepath=checkpoint_dir + "/model-loss={loss:.2f}.keras", save_freq=100
+        filepath=os.path.join(checkpoint_dir, "model-loss={loss:.2f}.keras"),
+        save_freq=100,
     )
 ]
 model.fit(x_train, y_train, epochs=1, callbacks=callbacks)

integration_tests/import_test.py

@@ -42,9 +42,19 @@ def create_virtualenv():
         # Create virtual environment
         "python3 -m venv test_env",
     ]
-    os.environ["PATH"] = (
-        "/test_env/bin/" + os.pathsep + os.environ.get("PATH", "")
+    os.environ["PATH"] = os.pathsep.join(
+        (
+            os.path.join(os.getcwd(), "test_env", "bin"),
+            os.environ.get("PATH", ""),
+        )
     )
+    if os.name == "nt":
+        os.environ["PATH"] = os.pathsep.join(
+            (
+                os.path.join(os.getcwd(), "test_env", "Scripts"),
+                os.environ["PATH"],
+            )
+        )
     run_commands_local(env_setup)
 
 
@@ -53,18 +63,17 @@ def manage_venv_installs(whl_path):
     backend_pkg, backend_extra_url = BACKEND_REQ[backend.backend()]
     install_setup = [
         # Installs the backend's package and common requirements
-        "pip install " + backend_extra_url + backend_pkg,
+        f"pip install {backend_extra_url}{backend_pkg}",
         "pip install -r requirements-common.txt",
         "pip install pytest",
         # Ensure other backends are uninstalled
-        "pip uninstall -y "
-        + BACKEND_REQ[other_backends[0]][0]
-        + " "
-        + BACKEND_REQ[other_backends[1]][0]
-        + " "
-        + BACKEND_REQ[other_backends[2]][0],
+        "pip uninstall -y {0} {1} {2}".format(
+            BACKEND_REQ[other_backends[0]][0],
+            BACKEND_REQ[other_backends[1]][0],
+            BACKEND_REQ[other_backends[2]][0],
+        ),
         # Install `.whl` package
-        "pip install " + whl_path,
+        f"pip install {whl_path}",
     ]
     # Install flax for JAX when NNX is enabled
     if backend.backend() == "jax" and config.is_nnx_enabled():
@@ -102,7 +111,11 @@ def run_commands_venv(commands):
     for command in commands:
         print(f"Running command: {command}")
         cmd_with_args = command.split(" ")
-        cmd_with_args[0] = "test_env/bin/" + cmd_with_args[0]
+        cmd_with_args[0] = os.path.join(
+            "test_env",
+            "Scripts" if os.name == "nt" else "bin",
+            cmd_with_args[0],
+        )
         p = subprocess.Popen(cmd_with_args)
         assert p.wait() == 0
 

integration_tests/model_visualization_test.py

@@ -44,7 +44,7 @@ def get_node_dict(graph, path=""):
 
         for subgraph in graph.get_subgraphs():
             sub_nodes = get_node_dict(
-                subgraph, path=path + subgraph.get_label() + " > "
+                subgraph, path=f"{path}{subgraph.get_label()} > "
             )
             nodes.update(sub_nodes)
 
@@ -85,7 +85,7 @@ def get_edges(graph):
 class ModelVisualizationTest(testing.TestCase):
     def multi_plot_model(self, model, name, expand_nested=False):
         if expand_nested:
-            name = name + "-expand_nested"
+            name = f"{name}-expand_nested"
 
         TEST_CASES = [
             {},
@@ -130,7 +130,7 @@ def multi_plot_model(self, model, name, expand_nested=False):
 
         for test_case in TEST_CASES:
             tags = [v if k == "rankdir" else k for k, v in test_case.items()]
-            file_name = "-".join([name] + tags) + ".png"
+            file_name = f"{'-'.join([name] + tags)}.png"
             plot_model(
                 model, file_name, expand_nested=expand_nested, **test_case
             )

keras/api/_tf_keras/keras/ops/__init__.py

@@ -195,6 +195,7 @@
 from keras.src.ops.numpy import heaviside as heaviside
 from keras.src.ops.numpy import histogram as histogram
 from keras.src.ops.numpy import hstack as hstack
+from keras.src.ops.numpy import hypot as hypot
 from keras.src.ops.numpy import identity as identity
 from keras.src.ops.numpy import imag as imag
 from keras.src.ops.numpy import inner as inner
@@ -204,6 +205,7 @@
 from keras.src.ops.numpy import isinf as isinf
 from keras.src.ops.numpy import isnan as isnan
 from keras.src.ops.numpy import isneginf as isneginf
+from keras.src.ops.numpy import isposinf as isposinf
 from keras.src.ops.numpy import kaiser as kaiser
 from keras.src.ops.numpy import left_shift as left_shift
 from keras.src.ops.numpy import less as less

keras/api/_tf_keras/keras/ops/numpy/__init__.py

@@ -83,6 +83,7 @@
 from keras.src.ops.numpy import heaviside as heaviside
 from keras.src.ops.numpy import histogram as histogram
 from keras.src.ops.numpy import hstack as hstack
+from keras.src.ops.numpy import hypot as hypot
 from keras.src.ops.numpy import identity as identity
 from keras.src.ops.numpy import imag as imag
 from keras.src.ops.numpy import inner as inner
@@ -92,6 +93,7 @@
 from keras.src.ops.numpy import isinf as isinf
 from keras.src.ops.numpy import isnan as isnan
 from keras.src.ops.numpy import isneginf as isneginf
+from keras.src.ops.numpy import isposinf as isposinf
 from keras.src.ops.numpy import kaiser as kaiser
 from keras.src.ops.numpy import left_shift as left_shift
 from keras.src.ops.numpy import less as less

keras/api/_tf_keras/keras/quantizers/__init__.py

@@ -7,6 +7,7 @@
 from keras.src.quantizers import deserialize as deserialize
 from keras.src.quantizers import get as get
 from keras.src.quantizers import serialize as serialize
+from keras.src.quantizers.gptq_config import GPTQConfig as GPTQConfig
 from keras.src.quantizers.quantizers import AbsMaxQuantizer as AbsMaxQuantizer
 from keras.src.quantizers.quantizers import Quantizer as Quantizer
 from keras.src.quantizers.quantizers import abs_max_quantize as abs_max_quantize

keras/api/ops/__init__.py

@@ -195,6 +195,7 @@
 from keras.src.ops.numpy import heaviside as heaviside
 from keras.src.ops.numpy import histogram as histogram
 from keras.src.ops.numpy import hstack as hstack
+from keras.src.ops.numpy import hypot as hypot
 from keras.src.ops.numpy import identity as identity
 from keras.src.ops.numpy import imag as imag
 from keras.src.ops.numpy import inner as inner
@@ -204,6 +205,7 @@
 from keras.src.ops.numpy import isinf as isinf
 from keras.src.ops.numpy import isnan as isnan
 from keras.src.ops.numpy import isneginf as isneginf
+from keras.src.ops.numpy import isposinf as isposinf
 from keras.src.ops.numpy import kaiser as kaiser
 from keras.src.ops.numpy import left_shift as left_shift
 from keras.src.ops.numpy import less as less