Merge branch 'develop' into describe-1

Author: JewelRoam

paddle_samples/PaddleScience/euler_beam/subgraph_0/graph_hash.txt

@@ -0,0 +1 @@
+6f3dcd3f048c2f76bf01f0b9d90372d402b7ed0431ad5102bd9f54d53cbada4c

paddle_samples/PaddleScience/euler_beam/subgraph_0/graph_net.json

@@ -0,0 +1,6 @@
+{
+    "framework": "paddle",
+    "model_name": "euler_beam",
+    "num_devices_required": 1,
+    "num_nodes_required": 1
+}

paddle_samples/PaddleScience/euler_beam/subgraph_0/input_meta.py

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+class Program_weight_tensor_data_0:
+    name = "data_0"
+    shape = [100, 1]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_data_1:
+    name = "data_1"
+    shape = [100, 1]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_data_2:
+    name = "data_2"
+    shape = [100, 1]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_data_3:
+    name = "data_3"
+    shape = [100, 1]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None

paddle_samples/PaddleScience/euler_beam/subgraph_0/model.py

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+import paddle
+
+
+class GraphModule(paddle.nn.Layer):
+    def __init__(self):
+        super().__init__()
+
+    def forward(
+        self,
+        parameter_0,
+        parameter_1,
+        parameter_2,
+        parameter_3,
+        parameter_4,
+        parameter_5,
+        parameter_6,
+        parameter_7,
+        data_0,
+        data_1,
+        data_2,
+        data_3,
+    ):
+        # pd_op.matmul: (100x20xf32) <- (100x1xf32, 1x20xf32)
+        matmul_0 = paddle._C_ops.matmul(data_1, parameter_7, False, False)
+        del data_1, parameter_7
+
+        # pd_op.add: (100x20xf32) <- (100x20xf32, 20xf32)
+        add_0 = paddle._C_ops.add(matmul_0, parameter_6)
+        del matmul_0, parameter_6
+
+        # pd_op.tanh: (100x20xf32) <- (100x20xf32)
+        tanh_0 = paddle._C_ops.tanh(add_0)
+        del add_0
+
+        # pd_op.matmul: (100x20xf32) <- (100x20xf32, 20x20xf32)
+        matmul_1 = paddle._C_ops.matmul(tanh_0, parameter_5, False, False)
+        del parameter_5, tanh_0
+
+        # pd_op.add: (100x20xf32) <- (100x20xf32, 20xf32)
+        add_1 = paddle._C_ops.add(matmul_1, parameter_4)
+        del matmul_1, parameter_4
+
+        # pd_op.tanh: (100x20xf32) <- (100x20xf32)
+        tanh_1 = paddle._C_ops.tanh(add_1)
+        del add_1
+
+        # pd_op.matmul: (100x20xf32) <- (100x20xf32, 20x20xf32)
+        matmul_2 = paddle._C_ops.matmul(tanh_1, parameter_3, False, False)
+        del parameter_3, tanh_1
+
+        # pd_op.add: (100x20xf32) <- (100x20xf32, 20xf32)
+        add_2 = paddle._C_ops.add(matmul_2, parameter_2)
+        del matmul_2, parameter_2
+
+        # pd_op.tanh: (100x20xf32) <- (100x20xf32)
+        tanh_2 = paddle._C_ops.tanh(add_2)
+        del add_2
+
+        # pd_op.matmul: (100x1xf32) <- (100x20xf32, 20x1xf32)
+        matmul_3 = paddle._C_ops.matmul(tanh_2, parameter_1, False, False)
+        del parameter_1, tanh_2
+
+        # pd_op.add: (100x1xf32) <- (100x1xf32, 1xf32)
+        add_3 = paddle._C_ops.add(matmul_3, parameter_0)
+        del matmul_3, parameter_0
+
+        # pd_op.subtract: (100x1xf32) <- (100x1xf32, 100x1xf32)
+        subtract_0 = paddle._C_ops.subtract(add_3, data_2)
+        del data_2
+
+        # pd_op.full: (xf32) <- ()
+        full_0 = paddle._C_ops.full(
+            [], float("2"), paddle.float32, paddle.framework._current_expected_place()
+        )
+
+        # pd_op.elementwise_pow: (100x1xf32) <- (100x1xf32, xf32)
+        elementwise_pow_0 = paddle._C_ops.elementwise_pow(subtract_0, full_0)
+        del full_0, subtract_0
+
+        # pd_op.multiply: (100x1xf32) <- (100x1xf32, 100x1xf32)
+        multiply_0 = paddle._C_ops.multiply(elementwise_pow_0, data_3)
+        del data_3, elementwise_pow_0
+
+        # pd_op.full_int_array: (0xi64) <- ()
+        full_int_array_0 = []
+
+        # pd_op.full_int_array: (2xi64) <- ()
+        full_int_array_1 = [0, 1]
+
+        # pd_op.sum: (xf32) <- (100x1xf32, 2xi64)
+        sum_0 = paddle._C_ops.sum(multiply_0, full_int_array_1, paddle.float32, False)
+        del full_int_array_1, multiply_0
+
+        # pd_op.full: (xf32) <- ()
+        full_1 = paddle._C_ops.full(
+            [], float("100"), paddle.float32, paddle.framework._current_expected_place()
+        )
+
+        # pd_op.divide: (xf32) <- (xf32, xf32)
+        divide_0 = paddle._C_ops.divide(sum_0, full_1)
+        del add_3, full_1, sum_0
+
+        return divide_0

paddle_samples/PaddleScience/euler_beam/subgraph_0/weight_meta.py

@@ -0,0 +1,70 @@
+class Program_weight_tensor_parameter_0:
+    name = "parameter_0"
+    shape = [1]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_parameter_1:
+    name = "parameter_1"
+    shape = [20, 1]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_parameter_2:
+    name = "parameter_2"
+    shape = [20]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_parameter_3:
+    name = "parameter_3"
+    shape = [20, 20]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_parameter_4:
+    name = "parameter_4"
+    shape = [20]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_parameter_5:
+    name = "parameter_5"
+    shape = [20, 20]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_parameter_6:
+    name = "parameter_6"
+    shape = [20]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_parameter_7:
+    name = "parameter_7"
+    shape = [1, 20]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None

paddle_samples/PaddleScience/euler_beam/subgraph_1/graph_hash.txt

@@ -0,0 +1 @@
+8b51d7c27aa8386c19c24188ab1b30250189db4262db16ec473fd8581b36358d

paddle_samples/PaddleScience/euler_beam/subgraph_1/graph_net.json

@@ -0,0 +1,6 @@
+{
+    "framework": "paddle",
+    "model_name": "euler_beam",
+    "num_devices_required": 1,
+    "num_nodes_required": 1
+}

paddle_samples/PaddleScience/euler_beam/subgraph_1/input_meta.py

@@ -0,0 +1,67 @@
+class Program_weight_tensor_data_0:
+    name = "data_0"
+    shape = [100, 1]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_data_1:
+    name = "data_1"
+    shape = [100, 1]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_data_2:
+    name = "data_2"
+    shape = [4, 1]
+    dtype = "float32"
+    data = [-1.0, -1.0, 1.0, 1.0]
+
+
+class Program_weight_tensor_data_3:
+    name = "data_3"
+    shape = [4, 1]
+    dtype = "float32"
+    data = [0.0, 0.0, 1.0, 1.0]
+
+
+class Program_weight_tensor_data_4:
+    name = "data_4"
+    shape = [100, 1]
+    dtype = "float32"
+    min_val = float("0")
+    max_val = float("0.5")
+    data = None
+
+
+class Program_weight_tensor_data_5:
+    name = "data_5"
+    shape = [4, 1]
+    dtype = "float32"
+    data = [0.0, 0.0, 0.0, 0.0]
+
+
+class Program_weight_tensor_data_6:
+    name = "data_6"
+    shape = [4, 1]
+    dtype = "float32"
+    data = [0.0, 0.0, 0.0, 0.0]
+
+
+class Program_weight_tensor_data_7:
+    name = "data_7"
+    shape = [4, 1]
+    dtype = "float32"
+    data = [0.0, 0.0, 0.0, 0.0]
+
+
+class Program_weight_tensor_data_8:
+    name = "data_8"
+    shape = [4, 1]
+    dtype = "float32"
+    data = [0.0, 0.0, 0.0, 0.0]