[New Sample] Add Some Tabular Model Computational Graph (#141)

Author: ooooo-create

samples/transformers-auto-model/sadhaklal_mlp-california-housing/graph_hash.txt

@@ -0,0 +1 @@
+02456f748bbfa8c39ad3f211091f075c4953e0a3de4611edfdb244a1bbfd06bd

samples/transformers-auto-model/sadhaklal_mlp-california-housing/graph_net.json

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+{
+    "framework": "torch",
+    "num_devices_required": 1,
+    "num_nodes_required": 1,
+    "dynamic": true
+}

samples/transformers-auto-model/sadhaklal_mlp-california-housing/input_meta.py

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+import torch
+
+
+class GraphModule(torch.nn.Module):
+    def forward(
+        self,
+        L_self_modules_fc1_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_fc1_parameters_bias_: torch.nn.parameter.Parameter,
+        s77: torch.SymInt,
+        L_x_: torch.Tensor,
+        L_self_modules_fc2_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_fc2_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_fc3_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_fc3_parameters_bias_: torch.nn.parameter.Parameter,
+        L_self_modules_fc4_parameters_weight_: torch.nn.parameter.Parameter,
+        L_self_modules_fc4_parameters_bias_: torch.nn.parameter.Parameter,
+    ):
+        l_self_modules_fc1_parameters_weight_ = L_self_modules_fc1_parameters_weight_
+        l_self_modules_fc1_parameters_bias_ = L_self_modules_fc1_parameters_bias_
+        l_x_ = L_x_
+        l_self_modules_fc2_parameters_weight_ = L_self_modules_fc2_parameters_weight_
+        l_self_modules_fc2_parameters_bias_ = L_self_modules_fc2_parameters_bias_
+        l_self_modules_fc3_parameters_weight_ = L_self_modules_fc3_parameters_weight_
+        l_self_modules_fc3_parameters_bias_ = L_self_modules_fc3_parameters_bias_
+        l_self_modules_fc4_parameters_weight_ = L_self_modules_fc4_parameters_weight_
+        l_self_modules_fc4_parameters_bias_ = L_self_modules_fc4_parameters_bias_
+        linear = torch._C._nn.linear(
+            l_x_,
+            l_self_modules_fc1_parameters_weight_,
+            l_self_modules_fc1_parameters_bias_,
+        )
+        l_x_ = (
+            l_self_modules_fc1_parameters_weight_
+        ) = l_self_modules_fc1_parameters_bias_ = None
+        act = torch.relu(linear)
+        linear = None
+        linear_1 = torch._C._nn.linear(
+            act,
+            l_self_modules_fc2_parameters_weight_,
+            l_self_modules_fc2_parameters_bias_,
+        )
+        act = (
+            l_self_modules_fc2_parameters_weight_
+        ) = l_self_modules_fc2_parameters_bias_ = None
+        act_1 = torch.relu(linear_1)
+        linear_1 = None
+        linear_2 = torch._C._nn.linear(
+            act_1,
+            l_self_modules_fc3_parameters_weight_,
+            l_self_modules_fc3_parameters_bias_,
+        )
+        act_1 = (
+            l_self_modules_fc3_parameters_weight_
+        ) = l_self_modules_fc3_parameters_bias_ = None
+        act_2 = torch.relu(linear_2)
+        linear_2 = None
+        linear_3 = torch._C._nn.linear(
+            act_2,
+            l_self_modules_fc4_parameters_weight_,
+            l_self_modules_fc4_parameters_bias_,
+        )
+        act_2 = (
+            l_self_modules_fc4_parameters_weight_
+        ) = l_self_modules_fc4_parameters_bias_ = None
+        return (linear_3,)

samples/transformers-auto-model/sadhaklal_mlp-california-housing/input_tensor_constraints.py

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+class Program_weight_tensor_meta_L_self_modules_fc1_parameters_weight_:
+    name = "L_self_modules_fc1_parameters_weight_"
+    shape = [50, 8]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.030
+    std = 0.233
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_fc1_parameters_bias_:
+    name = "L_self_modules_fc1_parameters_bias_"
+    shape = [50]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.027
+    std = 0.228
+    data = None
+
+
+class Program_weight_tensor_meta_s77:
+    name = "s77"
+    shape = []
+    dtype = "torch.int64"
+    device = "cpu"
+    mean = None
+    std = None
+    data = [4]
+
+
+class Program_weight_tensor_meta_L_x_:
+    name = "L_x_"
+    shape = [3, 8]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = -0.082
+    std = 0.654
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_fc2_parameters_weight_:
+    name = "L_self_modules_fc2_parameters_weight_"
+    shape = [50, 50]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.000
+    std = 0.105
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_fc2_parameters_bias_:
+    name = "L_self_modules_fc2_parameters_bias_"
+    shape = [50]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.010
+    std = 0.115
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_fc3_parameters_weight_:
+    name = "L_self_modules_fc3_parameters_weight_"
+    shape = [50, 50]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.011
+    std = 0.121
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_fc3_parameters_bias_:
+    name = "L_self_modules_fc3_parameters_bias_"
+    shape = [50]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.039
+    std = 0.099
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_fc4_parameters_weight_:
+    name = "L_self_modules_fc4_parameters_weight_"
+    shape = [1, 50]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.065
+    std = 0.139
+    data = None
+
+
+class Program_weight_tensor_meta_L_self_modules_fc4_parameters_bias_:
+    name = "L_self_modules_fc4_parameters_bias_"
+    shape = [1]
+    dtype = "torch.float32"
+    device = "cpu"
+    mean = 0.070
+    std = 0.000
+    data = None

samples/transformers-auto-model/sadhaklal_mlp-california-housing/model.py

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+17586f580ad833be5fce667982761fc09e68d4e7e42ec7dd9a852ecb5a0c58ab

samples/transformers-auto-model/sadhaklal_mlp-california-housing/weight_meta.py

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+{
+    "framework": "torch",
+    "num_devices_required": 1,
+    "num_nodes_required": 1,
+    "dynamic": true
+}