[PaddlePaddle Hackathon 2] 94. Add Paddle as a new backend of DeepXDE

.gitignore

@@ -9,4 +9,4 @@ docs/_build/
 .ipynb_checkpoints
 
 # VSCode
-.vscode/
+.vscode/

deepxde/backend/__init__.py

@@ -23,7 +23,7 @@ def _missing_api(*args, **kwargs):
 
 
 def load_backend(mod_name):
-    if mod_name not in ["tensorflow.compat.v1", "tensorflow", "pytorch", "jax"]:
+    if mod_name not in ["tensorflow.compat.v1", "tensorflow", "pytorch", "jax", "paddle"]:
         raise NotImplementedError("Unsupported backend: %s" % mod_name)
 
     print("Using backend: %s\n" % mod_name, file=sys.stderr, flush=True)
@@ -72,7 +72,7 @@ def get_preferred_backend():
             config_dict = json.load(config_file)
             backend_name = config_dict.get("backend", "").lower()
 
-    if backend_name in ["tensorflow.compat.v1", "tensorflow", "pytorch", "jax"]:
+    if backend_name in ["tensorflow.compat.v1", "tensorflow", "pytorch", "jax", "paddle"]:
         return backend_name
     print(
         "Deepxde backend not selected or invalid. Assuming tensorflow.compat.v1 for now.",

deepxde/backend/backend.py

@@ -20,6 +20,7 @@
 tf = None
 torch = None
 jax = None
+paddle = None
 
 ###############################################################################
 # Tensor, data type and context interfaces

deepxde/backend/paddle/__init__.py

@@ -0,0 +1 @@
+from .tensor import *  # pylint: disable=redefined-builtin

deepxde/backend/paddle/tensor.py

@@ -0,0 +1,110 @@
+"""paddle backend implementation"""
+import paddle
+
+
+if paddle.device.is_compiled_with_cuda():
+    paddle.device.set_device("gpu")
+
+lib = paddle
+
+
+def data_type_dict():
+    return {
+        "float16": paddle.float16,
+        "float32": paddle.float32,
+        "float64": paddle.float64,
+        "uint8": paddle.uint8,
+        "int8": paddle.int8,
+        "int16": paddle.int16,
+        "int32": paddle.int32,
+        "int64": paddle.int64,
+        "bool": paddle.bool,
+    }
+
+
+def is_tensor(obj):
+    return paddle.is_tensor(obj)
+
+
+def shape(input_tensor):
+    return input_tensor.shape
+
+
+def ndim(input_tensor):
+    return input_tensor.ndim
+
+
+def Variable(initial_value, dtype=None):
+    return paddle.to_tensor(initial_value, dtype=dtype, stop_gradient=False)
+
+
+def as_tensor(data, dtype=None):
+    if isinstance(data, paddle.Tensor) or paddle.is_tensor(data):
+        if dtype is None or data.dtype == dtype:
+            return data
+        return data.astype(dtype)
+    return paddle.to_tensor(data, dtype=dtype)
+
+
+def from_numpy(np_array):
+    return paddle.to_tensor(np_array)
+
+
+def to_numpy(input_tensor):
+    return input_tensor.detach().cpu().numpy()
+
+
+def elu(x):
+    return paddle.nn.functional.elu(x)
+
+
+def relu(x):
+    return paddle.nn.functional.relu(x)
+
+
+def selu(x):
+    return paddle.nn.functional.selu(x)
+
+
+def sigmoid(x):
+    return paddle.nn.functional.sigmoid(x)
+
+
+def silu(x):
+    return paddle.nn.functional.silu(x)
+
+
+def sin(x):
+    return paddle.sin(x)
+
+
+def square(x):
+    return paddle.square(x)
+
+
+def tanh(x):
+    return paddle.tanh(x)
+
+
+def mean(input_tensor, dim, keepdims=False):
+    return paddle.mean(input_tensor, axis=dim, keepdim=keepdims)
+
+
+def reduce_mean(input_tensor):
+    return paddle.mean(input_tensor)
+
+
+def sum(input_tensor, dim, keepdims=False):
+    return paddle.sum(input_tensor, axis=dim, keepdim=keepdims)
+
+
+def reduce_sum(input_tensor):
+    return paddle.sum(input_tensor)
+
+
+def zeros(shape, dtype):
+    return paddle.zeros(shape, dtype=dtype)
+
+
+def zeros_like(input_tensor):
+    return paddle.zeros_like(input_tensor)

deepxde/backend/set_default_backend.py

@@ -13,7 +13,7 @@ def set_default_backend(backend_name):
     print(
         'Setting the default backend to "{}". You can change it in the '
         "~/.deepxde/config.json file or export the DDEBACKEND environment variable. "
-        "Valid options are: tensorflow.compat.v1, tensorflow, pytorch, jax (all lowercase)".format(
+        "Valid options are: tensorflow.compat.v1, tensorflow, pytorch, jax, paddle (all lowercase)".format(
             backend_name
         )
     )
@@ -25,7 +25,7 @@ def set_default_backend(backend_name):
         "backend",
         nargs=1,
         type=str,
-        choices=["tensorflow.compat.v1", "tensorflow", "pytorch", "jax"],
+        choices=["tensorflow.compat.v1", "tensorflow", "pytorch", "jax", "paddle"],
         help="Set default backend",
     )
     args = parser.parse_args()

deepxde/callbacks.py

@@ -312,6 +312,8 @@ def on_train_begin(self):
             self.value = [var.numpy() for var in self.var_list]
         elif backend_name == "pytorch":
             self.value = [var.detach().item() for var in self.var_list]
+        elif backend_name == "paddle":
+            self.value = [var.detach().item() for var in self.var_list]
         print(
             self.model.train_state.epoch,
             list_to_str(self.value, precision=self.precision),

deepxde/config.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 
-from .backend import backend_name, tf, torch
+from .backend import backend_name, tf, torch, paddle
 from .real import Real
 
 random_seed = None
@@ -57,6 +57,8 @@ def set_random_seed(seed):
     elif backend_name == "tensorflow":
         tf.random.set_seed(seed)  # tf CPU seed
         os.environ["TF_DETERMINISTIC_OPS"] = "1"
+    elif backend_name == "paddle":
+        paddle.seed(seed)
     elif backend_name == "pytorch":
         torch.manual_seed(seed)
     elif backend_name == "jax":

deepxde/data/pde.py

@@ -125,7 +125,7 @@ def __init__(
         self.test()
 
     def losses(self, targets, outputs, loss_fn, inputs, model, aux=None):
-        if backend_name in ["tensorflow.compat.v1", "tensorflow", "pytorch"]:
+        if backend_name in ["tensorflow.compat.v1", "tensorflow", "pytorch", "paddle"]:
             outputs_pde = outputs
         elif backend_name == "jax":
             # JAX requires pure functions
@@ -152,6 +152,7 @@ def losses(self, targets, outputs, loss_fn, inputs, model, aux=None):
             )
 
         bcs_start = np.cumsum([0] + self.num_bcs)
+        bcs_start = list(map(int, bcs_start))
         error_f = [fi[bcs_start[-1] :] for fi in f]
         losses = [
             loss_fn[i](bkd.zeros_like(error), error) for i, error in enumerate(error_f)

deepxde/gradients.py

@@ -1,6 +1,6 @@
 __all__ = ["jacobian", "hessian"]
 
-from .backend import backend_name, tf, torch, jax
+from .backend import backend_name, tf, torch, jax, paddle
 
 
 class Jacobian:
@@ -18,7 +18,7 @@ def __init__(self, ys, xs):
         self.ys = ys
         self.xs = xs
 
-        if backend_name in ["tensorflow.compat.v1", "tensorflow", "pytorch"]:
+        if backend_name in ["tensorflow.compat.v1", "tensorflow", "pytorch", "paddle"]:
             self.dim_y = ys.shape[1]
         elif backend_name == "jax":
             # For backend jax, a tuple of a jax array and a callable is passed as one of
@@ -50,6 +50,9 @@ def __call__(self, i=0, j=None):
                 self.J[i] = torch.autograd.grad(
                     y, self.xs, grad_outputs=torch.ones_like(y), create_graph=True
                 )[0]
+            elif backend_name == "paddle":
+                y = self.ys[:, i : i + 1] if self.dim_y > 1 else self.ys
+                self.J[i] = paddle.autograd.grad(y, self.xs, create_graph=True)[0]
             elif backend_name == "jax":
                 # Here, we use jax.grad to compute the gradient of a function. This is
                 # different from TensorFlow and PyTorch that the input of a function is
@@ -67,7 +70,7 @@ def __call__(self, i=0, j=None):
                 grad_fn = jax.grad(lambda x: self.ys[1](x)[i])
                 self.J[i] = (jax.vmap(grad_fn)(self.xs), grad_fn)
 
-        if backend_name in ["tensorflow.compat.v1", "tensorflow", "pytorch"]:
+        if backend_name in ["tensorflow.compat.v1", "tensorflow", "pytorch", "paddle"]:
             return (
                 self.J[i] if j is None or self.dim_x == 1 else self.J[i][:, j : j + 1]
             )
@@ -141,7 +144,7 @@ def __call__(self, ys, xs, i=0, j=None):
         #     f(x)
         if backend_name in ["tensorflow.compat.v1", "tensorflow"]:
             key = (ys.ref(), xs.ref())
-        elif backend_name == "pytorch":
+        elif backend_name in ["pytorch", "paddle"]:
             key = (ys, xs)
         elif backend_name == "jax":
             key = (id(ys[0]), id(xs))
@@ -197,7 +200,7 @@ class Hessian:
     """
 
     def __init__(self, y, xs, component=None, grad_y=None):
-        if backend_name in ["tensorflow.compat.v1", "tensorflow", "pytorch"]:
+        if backend_name in ["tensorflow.compat.v1", "tensorflow", "pytorch", "paddle"]:
             dim_y = y.shape[1]
         elif backend_name == "jax":
             dim_y = y[0].shape[0]
@@ -239,7 +242,7 @@ def __init__(self):
     def __call__(self, y, xs, component=None, i=0, j=0, grad_y=None):
         if backend_name in ["tensorflow.compat.v1", "tensorflow"]:
             key = (y.ref(), xs.ref(), component)
-        elif backend_name == "pytorch":
+        elif backend_name in ["pytorch", "paddle"]:
             key = (y, xs, component)
         elif backend_name == "jax":
             key = (id(y[0]), id(xs), component)

deepxde/icbc/boundary_conditions.py

@@ -242,7 +242,7 @@ def wrapper_cache_auxiliary(X, beg, end, aux_var):
             return wrapper_nocache
         if utils.get_num_args(func) == 2:
             return wrapper_nocache_auxiliary
-    if backend_name == "pytorch":
+    if backend_name in ["paddle", "pytorch"]:
         if utils.get_num_args(func) == 1:
             return wrapper_cache
         if utils.get_num_args(func) == 2: