Add preliminary support of OpenVINO as Keras 3 backend

keras/src/backend/__init__.py

@@ -40,6 +40,10 @@
 elif backend() == "numpy":
     from keras.src.backend.numpy import *  # noqa: F403
 
+    distribution_lib = None
+elif backend() == "openvino":
+    from keras.src.backend.openvino import *  # noqa: F403
+
     distribution_lib = None
 else:
     raise ValueError(f"Unable to import backend : {backend()}")

keras/src/backend/exports.py

@@ -15,6 +15,11 @@
 
     BackendVariable = NumpyVariable
     backend_name_scope = backend.common.name_scope.name_scope
+elif backend.backend() == "openvino":
+    from keras.src.backend.openvino.core import Variable as OpenVINOVariable
+
+    BackendVariable = OpenVINOVariable
+    backend_name_scope = backend.common.name_scope.name_scope
 else:
     raise RuntimeError(f"Invalid backend: {backend.backend()}")
 

keras/src/backend/openvino/__init__.py

@@ -0,0 +1,21 @@
+from keras.src.backend.openvino import core
+from keras.src.backend.openvino import image
+from keras.src.backend.openvino import linalg
+from keras.src.backend.openvino import math
+from keras.src.backend.openvino import nn
+from keras.src.backend.openvino import numpy
+from keras.src.backend.openvino import random
+from keras.src.backend.openvino.core import SUPPORTS_SPARSE_TENSORS
+from keras.src.backend.openvino.core import Variable
+from keras.src.backend.openvino.core import cast
+from keras.src.backend.openvino.core import compute_output_spec
+from keras.src.backend.openvino.core import cond
+from keras.src.backend.openvino.core import convert_to_numpy
+from keras.src.backend.openvino.core import convert_to_tensor
+from keras.src.backend.openvino.core import is_tensor
+from keras.src.backend.openvino.core import shape
+from keras.src.backend.openvino.core import vectorized_map
+from keras.src.backend.openvino.rnn import cudnn_ok
+from keras.src.backend.openvino.rnn import gru
+from keras.src.backend.openvino.rnn import lstm
+from keras.src.backend.openvino.rnn import rnn

keras/src/backend/openvino/core.py

@@ -0,0 +1,258 @@
+import contextlib
+
+import numpy as np
+
+from keras.src.backend.common import global_state
+from keras.src import tree
+from keras.src.backend.common import KerasVariable
+from keras.src.backend.common import standardize_dtype
+from keras.src.backend.common.dtypes import result_type
+from keras.src.backend.common.keras_tensor import KerasTensor
+from keras.src.backend.common.stateless_scope import StatelessScope
+import openvino as ov
+
+SUPPORTS_SPARSE_TENSORS = False
+
+OPENVINO_DTYPES = {
+    "float16": ov.Type.f16,
+    "float32": ov.Type.f32,
+    "float64": ov.Type.f64,
+    "uint8": ov.Type.u8,
+    "uint16": ov.Type.u16,
+    "uint32": ov.Type.u32,
+    "int8": ov.Type.i8,
+    "int16": ov.Type.i16,
+    "int32": ov.Type.i32,
+    "int64": ov.Type.i64,
+    "bfloat16": ov.Type.bf16,
+    "bool": ov.Type.boolean,
+    "float8_e4m3fn": ov.Type.f8e4m3,
+    "float8_e5m2": ov.Type.f8e5m2,
+}
+
+
+def ov_to_keras_type(ov_type):
+    for _keras_type, _ov_type in OPENVINO_DTYPES.items():
+        if ov_type == _ov_type:
+            return _keras_type
+    raise ValueError(
+        f"Requested OpenVINO type has no keras analogue '{ov_type.to_string()}'"
+    )
+
+@contextlib.contextmanager
+def device_scope(device_name):
+    current_device = _parse_device_input(device_name)
+    global_state.set_global_attribute("openvino_device", current_device)
+
+
+def get_device():
+    device = global_state.get_global_attribute("openvino_device", None)
+    if device is None:
+        return "CPU"
+    return device
+
+
+def _parse_device_input(device_name):
+    if isinstance(device_name, str):
+        # We support string value like "cpu:0", "gpu:1", and need to convert
+        # "gpu" to "cuda"
+        device_name = device_name.upper()
+        device_type, _ = device_name.split(":")
+        return device_type
+    else:
+        raise ValueError(
+            "Invalid value for argument `device_name`. "
+            "Expected a string like 'gpu:0' or 'cpu'. "
+            f"Received: device_name='{device_name}'"
+        )
+    return device_name
+
+
+class Variable(KerasVariable):
+    def _initialize(self, value):
+        self._value = np.array(value, dtype=self._dtype)
+
+    def _direct_assign(self, value):
+        self._value = np.array(value, dtype=self._dtype)
+
+    def _convert_to_tensor(self, value, dtype=None):
+        return convert_to_tensor(value, dtype=dtype)
+
+    # Overload native accessor.
+    def __array__(self):
+        return self.value
+
+
+def convert_to_tensor(x, dtype=None, sparse=None):
+    if sparse:
+        raise ValueError("`sparse=True` is not supported with numpy backend")
+    if dtype is not None:
+        dtype = standardize_dtype(dtype)
+    if isinstance(x, Variable):
+        if dtype and dtype != x.dtype:
+            return x.value.astype(dtype)
+        return x.value
+    if not is_tensor(x) and standardize_dtype(dtype) == "bfloat16":
+        # Can't create bfloat16 arrays on the fly (e.g. from a h5 Dataset).
+        # Instead we convert "as is" (to stored dtype) and cast.
+        return np.asarray(x).astype(dtype)
+    if dtype is None:
+        dtype = result_type(
+            *[getattr(item, "dtype", type(item)) for item in tree.flatten(x)]
+        )
+    return np.array(x, dtype=dtype)
+
+
+def convert_to_numpy(x):
+    return np.array(x)
+
+
+def is_tensor(x):
+    if isinstance(x, (np.generic, np.ndarray)):
+        return True
+    return False
+
+
+def shape(x):
+    return x.shape
+
+
+def cast(x, dtype):
+    raise NotImplementedError(
+        "`cast` is not supported with openvino backend"
+    )
+
+
+def cond(pred, true_fn, false_fn):
+    raise NotImplementedError(
+        "`cond` is not supported with openvino backend"
+    )
+
+
+def vectorized_map(function, elements):
+    raise NotImplementedError(
+        "`vectorized_map` is not supported with openvino backend"
+    )
+
+
+# Shape / dtype inference util
+def compute_output_spec(fn, *args, **kwargs):
+    with StatelessScope():
+
+        def has_none_shape(x):
+            if isinstance(x, KerasTensor):
+                return None in x.shape
+            return False
+
+        none_in_shape = any(map(has_none_shape, tree.flatten((args, kwargs))))
+
+        def convert_keras_tensor_to_numpy(x, fill_value=None):
+            if isinstance(x, KerasTensor):
+                shape = list(x.shape)
+                if fill_value:
+                    for i, e in enumerate(shape):
+                        if e is None:
+                            shape[i] = fill_value
+                return np.empty(
+                    shape=shape,
+                    dtype=x.dtype,
+                )
+            return x
+
+        args_1, kwargs_1 = tree.map_structure(
+            lambda x: convert_keras_tensor_to_numpy(x, fill_value=83),
+            (args, kwargs),
+        )
+        outputs_1 = fn(*args_1, **kwargs_1)
+
+        outputs = outputs_1
+
+        if none_in_shape:
+            args_2, kwargs_2 = tree.map_structure(
+                lambda x: convert_keras_tensor_to_numpy(x, fill_value=89),
+                (args, kwargs),
+            )
+            outputs_2 = fn(*args_2, **kwargs_2)
+
+            flat_out_1 = tree.flatten(outputs_1)
+            flat_out_2 = tree.flatten(outputs_2)
+
+            flat_out = []
+            for x1, x2 in zip(flat_out_1, flat_out_2):
+                shape = list(x1.shape)
+                for i, e in enumerate(x2.shape):
+                    if e != shape[i]:
+                        shape[i] = None
+                flat_out.append(KerasTensor(shape, standardize_dtype(x1.dtype)))
+            outputs = tree.pack_sequence_as(outputs_1, flat_out)
+
+        def convert_numpy_to_keras_tensor(x):
+            if is_tensor(x):
+                return KerasTensor(x.shape, standardize_dtype(x.dtype))
+            return x
+
+        output_spec = tree.map_structure(convert_numpy_to_keras_tensor, outputs)
+    return output_spec
+
+
+def scan(f, init, xs=None, length=None, reverse=False, unroll=1):
+    raise NotImplementedError(
+        "`scan` is not supported with openvino backend"
+    )
+
+
+def scatter(indices, values, shape):
+    raise NotImplementedError(
+        "`scatter` is not supported with openvino backend"
+    )
+
+
+def scatter_update(inputs, indices, updates):
+    raise NotImplementedError(
+        "`scatter_update` is not supported with openvino backend"
+    )
+
+
+def slice(inputs, start_indices, lengths):
+    raise NotImplementedError(
+        "`slice` is not supported with openvino backend"
+    )
+
+
+def slice_update(inputs, start_indices, updates):
+    raise NotImplementedError(
+        "`slice_update` is not supported with openvino backend"
+    )
+
+
+def while_loop(
+    cond,
+    body,
+    loop_vars,
+    maximum_iterations=None,
+):
+    raise NotImplementedError(
+        "`while_loop` is not supported with openvino backend"
+    )
+
+
+def fori_loop(lower, upper, body_fun, init_val):
+    raise NotImplementedError(
+        "`fori_loop` is not supported with openvino backend"
+    )
+
+
+def stop_gradient(x):
+    return x
+
+
+def unstack(x, num=None, axis=0):
+    raise NotImplementedError(
+        "`unstack` is not supported with openvino backend"
+    )
+
+
+def custom_gradient(fun):
+    raise NotImplementedError(
+        "`custom_gradient` is not supported with numpy backend"
+    )