Backend paddle: Support deeponet and other examples

deepxde/icbc/boundary_conditions.py

@@ -193,9 +193,9 @@ def __init__(
         self.batch_size = batch_size
 
         if batch_size is not None:  # batch iterator and state
-            if backend_name != "pytorch":
+            if backend_name not in ["pytorch","paddle"] :
                 raise RuntimeError(
-                    "batch_size only implemented for pytorch backend"
+                    "batch_size only implemented for pytorch and paddle backend"
                 )
             self.batch_sampler = data.sampler.BatchSampler(
                 len(self), shuffle=shuffle

deepxde/nn/paddle/deeponet.py

@@ -75,8 +75,12 @@ def forward(self, inputs):
             raise AssertionError(
                 "Output sizes of branch net and trunk net do not match."
             )
-        x = paddle.einsum("bi,bi->b", x_func, x_loc)  # [batch_size, ]
-        x = paddle.reshape(x, [-1, 1])  # reshape [batch_size, ] to [batch_size, 1]
+        # Use the following formula to temporarily replace paddle.einsum()
+        # Because no higher(>=2) orderderivatives for the op now
+        x = paddle.sum(x_func*x_loc,axis = 1,keepdim=True)
+        # TODO:
+        # x = paddle.einsum("bi,bi->b", x_func, x_loc)  # [batch_size, ]
+        # x = paddle.reshape(x, [-1, 1])  # reshape [batch_size, ] to [batch_size, 1]
         # Add bias
         if self.use_bias:
             x += self.b
@@ -143,7 +147,11 @@ def forward(self, inputs):
             raise AssertionError(
                 "Output sizes of branch net and trunk net do not match."
             )
-        x = paddle.einsum("bi,ni->bn", x_func, x_loc)
+        # Use the following formula to temporarily replace paddle.einsum()
+        # Because no higher(>=2) orderderivatives for the op now
+        x = x_func@x_loc.T
+        # TODO:
+        # x = paddle.einsum("bi,ni->bn", x_func, x_loc)
         # Add bias
         x += self.b
 

examples/operator/advection_aligned_pideeponet.py

@@ -1,8 +1,19 @@
-"""Backend supported: tensorflow.compat.v1, tensorflow"""
+"""Backend supported: tensorflow.compat.v1, tensorflow, paddle"""
 import deepxde as dde
 import matplotlib.pyplot as plt
 import numpy as np
-from deepxde.backend import tf
+
+if dde.backend.backend_name == "paddle":
+    dim_x = 5
+    sin = dde.backend.paddle.sin
+    cos = dde.backend.paddle.cos
+    concat = dde.backend.paddle.concat
+else:
+    dim_x = 2
+    sin = dde.backend.tf.sin
+    cos = dde.backend.tf.cos
+    concat = dde.backend.tf.concat
+
 
 # PDE
 def pde(x, y, v):
@@ -36,7 +47,7 @@ def func_ic(x, v):
 # Net
 net = dde.nn.DeepONetCartesianProd(
     [50, 128, 128, 128],
-    [2, 128, 128, 128],
+    [dim_x, 128, 128, 128],
     "tanh",
     "Glorot normal",
 )
@@ -45,9 +56,7 @@ def func_ic(x, v):
 def periodic(x):
     x, t = x[:, :1], x[:, 1:]
     x *= 2 * np.pi
-    return tf.concat(
-        [tf.math.cos(x), tf.math.sin(x), tf.math.cos(2 * x), tf.math.sin(2 * x), t], 1
-    )
+    return concat([cos(x), sin(x), cos(2 * x), sin(2 * x), t], 1)
 
 
 net.apply_feature_transform(periodic)

examples/operator/advection_aligned_pideeponet_2d.py

@@ -1,8 +1,19 @@
-"""Backend supported: tensorflow.compat.v1, tensorflow"""
+"""Backend supported: tensorflow.compat.v1, tensorflow, paddle"""
 import deepxde as dde
 import matplotlib.pyplot as plt
 import numpy as np
-from deepxde.backend import tf
+
+if dde.backend.backend_name == "paddle":
+    dim_x = 5
+    sin = dde.backend.paddle.sin
+    cos = dde.backend.paddle.cos
+    concat = dde.backend.paddle.concat
+else:
+    dim_x = 2
+    sin = dde.backend.tf.sin
+    cos = dde.backend.tf.cos
+    concat = dde.backend.tf.concat
+
 
 # PDE
 def pde(x, y, v):
@@ -41,18 +52,15 @@ def boundary(x, on_boundary):
 # Net
 net = dde.nn.DeepONetCartesianProd(
     [50, 128, 128, 128],
-    [2, 128, 128, 128],
+    [dim_x, 128, 128, 128],
     "tanh",
     "Glorot normal",
 )
 
-
 def periodic(x):
     x, t = x[:, :1], x[:, 1:]
     x *= 2 * np.pi
-    return tf.concat(
-        [tf.math.cos(x), tf.math.sin(x), tf.math.cos(2 * x), tf.math.sin(2 * x), t], 1
-    )
+    return concat([cos(x), sin(x), cos(2 * x), sin(2 * x), t], 1)
 
 
 net.apply_feature_transform(periodic)

examples/operator/advection_unaligned_pideeponet.py

@@ -1,8 +1,19 @@
-"""Backend supported: tensorflow.compat.v1"""
+"""Backend supported: tensorflow.compat.v1, paddle"""
 import deepxde as dde
 import matplotlib.pyplot as plt
 import numpy as np
-from deepxde.backend import tf
+
+if dde.backend.backend_name == "paddle":
+    dim_x = 5
+    sin = dde.backend.paddle.sin
+    cos = dde.backend.paddle.cos
+    concat = dde.backend.paddle.concat
+else:
+    dim_x = 2
+    sin = dde.backend.tf.sin
+    cos = dde.backend.tf.cos
+    concat = dde.backend.tf.concat
+
 
 # PDE
 def pde(x, y, v):
@@ -36,7 +47,7 @@ def func_ic(x, v):
 # Net
 net = dde.nn.DeepONet(
     [50, 128, 128, 128],
-    [2, 128, 128, 128],
+    [dim_x, 128, 128, 128],
     "tanh",
     "Glorot normal",
 )
@@ -45,9 +56,7 @@ def func_ic(x, v):
 def periodic(x):
     x, t = x[:, :1], x[:, 1:]
     x *= 2 * np.pi
-    return tf.concat(
-        [tf.math.cos(x), tf.math.sin(x), tf.math.cos(2 * x), tf.math.sin(2 * x), t], 1
-    )
+    return concat([cos(x), sin(x), cos(2 * x), sin(2 * x), t], 1)
 
 
 net.apply_feature_transform(periodic)

examples/operator/advection_unaligned_pideeponet_2d.py

@@ -1,8 +1,18 @@
-"""Backend supported: tensorflow.compat.v1"""
+"""Backend supported: tensorflow.compat.v1, paddle"""
 import deepxde as dde
 import matplotlib.pyplot as plt
 import numpy as np
-from deepxde.backend import tf
+
+if dde.backend.backend_name == "paddle":
+    dim_x = 5
+    sin = dde.backend.paddle.sin
+    cos = dde.backend.paddle.cos
+    concat = dde.backend.paddle.concat
+else:
+    dim_x = 2
+    sin = dde.backend.tf.sin
+    cos = dde.backend.tf.cos
+    concat = dde.backend.tf.concat
 
 # PDE
 def pde(x, y, v):
@@ -39,7 +49,7 @@ def boundary(x, on_boundary):
 # Net
 net = dde.nn.DeepONet(
     [50, 128, 128, 128],
-    [2, 128, 128, 128],
+    [dim_x, 128, 128, 128],
     "tanh",
     "Glorot normal",
 )
@@ -48,9 +58,7 @@ def boundary(x, on_boundary):
 def periodic(x):
     x, t = x[:, :1], x[:, 1:]
     x *= 2 * np.pi
-    return tf.concat(
-        [tf.math.cos(x), tf.math.sin(x), tf.math.cos(2 * x), tf.math.sin(2 * x), t], 1
-    )
+    return concat([cos(x), sin(x), cos(2 * x), sin(2 * x), t], 1)
 
 
 net.apply_feature_transform(periodic)

examples/operator/antiderivative_aligned_pideeponet.py

@@ -1,8 +1,13 @@
-"""Backend supported: tensorflow.compat.v1, tensorflow"""
+"""Backend supported: tensorflow.compat.v1, tensorflow, paddle"""
 import deepxde as dde
 import matplotlib.pyplot as plt
 import numpy as np
-from deepxde.backend import tf
+
+if dde.backend.backend_name == "paddle":
+    transpose = dde.backend.paddle.transpose
+else:
+    transpose = dde.backend.tf.transpose
+
 
 
 dde.config.disable_xla_jit()
@@ -37,7 +42,7 @@ def pde(x, u, v):
 
 # Hard constraint zero IC
 def zero_ic(inputs, outputs):
-    return outputs * tf.transpose(inputs[1])
+    return outputs * transpose(inputs[1],[1,0])
 
 
 net.apply_output_transform(zero_ic)

examples/operator/antiderivative_unaligned_pideeponet.py

@@ -1,4 +1,4 @@
-"""Backend supported: tensorflow.compat.v1"""
+"""Backend supported: tensorflow.compat.v1, paddle"""
 import deepxde as dde
 import matplotlib.pyplot as plt
 import numpy as np

examples/operator/diff_rec_aligned_pideeponet.py

@@ -1,4 +1,4 @@
-"""Backend supported: tensorflow.compat.v1, tensorflow"""
+"""Backend supported: tensorflow.compat.v1, tensorflow, paddle"""
 import deepxde as dde
 import matplotlib.pyplot as plt
 import numpy as np

examples/operator/diff_rec_unaligned_pideeponet.py

@@ -1,4 +1,4 @@
-"""Backend supported: tensorflow.compat.v1"""
+"""Backend supported: tensorflow.compat.v1, paddle"""
 import deepxde as dde
 import matplotlib.pyplot as plt
 import numpy as np

examples/pinn_forward/Burgers.py

@@ -1,4 +1,4 @@
-"""Backend supported: tensorflow.compat.v1, tensorflow, pytorch"""
+"""Backend supported: tensorflow.compat.v1, tensorflow, pytorch, paddle"""
 import deepxde as dde
 import numpy as np