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add gnn model
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‎samples/torchgeometric/ARGA/graph_hash.txt‎

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fbb2e9edfb9a2cfaebb8cb70ff32543f9a5e26929ae3d838aee1b472e91f9625

‎samples/torchgeometric/ARGA/graph_net.json‎

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{
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"framework": "torch",
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"num_devices_required": 1,
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"num_nodes_required": 1,
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"dynamic": false
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}

‎samples/torchgeometric/ARGA/input_meta.py‎

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‎samples/torchgeometric/ARGA/input_tensor_constraints.py‎

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‎samples/torchgeometric/ARGA/model.py‎

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import torch
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from torch import device
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from torch import inf
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class GraphModule(torch.nn.Module):
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def forward(
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self,
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L_args_0_: torch.Tensor,
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L_args_1_: torch.Tensor,
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L_self_modules_encoder_modules_lin_parameters_weight_: torch.nn.parameter.Parameter,
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L_self_modules_encoder_parameters_bias_: torch.nn.parameter.Parameter,
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):
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l_args_0_ = L_args_0_
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l_args_1_ = L_args_1_
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l_self_modules_encoder_modules_lin_parameters_weight_ = (
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L_self_modules_encoder_modules_lin_parameters_weight_
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)
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l_self_modules_encoder_parameters_bias_ = (
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L_self_modules_encoder_parameters_bias_
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)
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getitem = l_args_1_[0]
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getitem_1 = l_args_1_[1]
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mask = getitem != getitem_1
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getitem = getitem_1 = None
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arange = torch.arange(0, 1000, device=device(type="cpu"))
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view = arange.view(1, -1)
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arange = None
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loop_index = view.repeat(2, 1)
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view = None
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edge_index = l_args_1_[(slice(None, None, None), mask)]
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l_args_1_ = mask = None
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sym_size_int = torch.ops.aten.sym_size.int(edge_index, 1)
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_check_is_size = torch._check_is_size(sym_size_int)
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_check_is_size = None
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ge = sym_size_int >= 0
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_assert_scalar_default = torch.ops.aten._assert_scalar.default(
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ge, "Runtime assertion failed for expression u0 >= 0 on node 'ge'"
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)
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ge = _assert_scalar_default = None
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le = sym_size_int <= 100
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_assert_scalar_default_1 = torch.ops.aten._assert_scalar.default(
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le, "Runtime assertion failed for expression u0 <= 100 on node 'le'"
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)
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le = _assert_scalar_default_1 = None
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edge_index_1 = torch.cat([edge_index, loop_index], dim=1)
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edge_index = loop_index = None
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sym_sum = torch.sym_sum([1000, sym_size_int])
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sym_size_int = None
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edge_weight = torch.ones(
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(sym_sum,), dtype=torch.float32, device=device(type="cpu")
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)
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sym_sum = None
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row = edge_index_1[0]
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col = edge_index_1[1]
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view_1 = col.view((-1,))
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index = view_1.expand_as(edge_weight)
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view_1 = None
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new_zeros = edge_weight.new_zeros((1000,))
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deg = new_zeros.scatter_add_(0, index, edge_weight)
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new_zeros = index = None
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deg_inv_sqrt = deg.pow_(-0.5)
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deg = None
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eq = deg_inv_sqrt.__eq__(inf)
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masked_fill_ = deg_inv_sqrt.masked_fill_(eq, 0)
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eq = masked_fill_ = None
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getitem_7 = deg_inv_sqrt[row]
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row = None
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mul = getitem_7 * edge_weight
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getitem_7 = edge_weight = None
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getitem_8 = deg_inv_sqrt[col]
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deg_inv_sqrt = col = None
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edge_weight_1 = mul * getitem_8
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mul = getitem_8 = None
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x = torch._C._nn.linear(
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l_args_0_, l_self_modules_encoder_modules_lin_parameters_weight_, None
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)
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l_args_0_ = l_self_modules_encoder_modules_lin_parameters_weight_ = None
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edge_index_i = edge_index_1[1]
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edge_index_j = edge_index_1[0]
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edge_index_1 = None
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x_j = x.index_select(-2, edge_index_j)
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x = edge_index_j = None
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view_2 = edge_weight_1.view(-1, 1)
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edge_weight_1 = None
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out = view_2 * x_j
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view_2 = x_j = None
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view_3 = edge_index_i.view((-1, 1))
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edge_index_i = None
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index_1 = view_3.expand_as(out)
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view_3 = None
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new_zeros_1 = out.new_zeros((1000, 16))
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out_1 = new_zeros_1.scatter_add_(0, index_1, out)
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new_zeros_1 = index_1 = out = None
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out_2 = out_1 + l_self_modules_encoder_parameters_bias_
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out_1 = l_self_modules_encoder_parameters_bias_ = None
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return (out_2,)

‎samples/torchgeometric/ARGA/weight_meta.py‎

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class Program_weight_tensor_meta_L_args_0_:
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name = "L_args_0_"
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shape = [1000, 32]
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dtype = "torch.float32"
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device = "cpu"
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mean = 0.005
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std = 1.003
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data = None
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class Program_weight_tensor_meta_L_args_1_:
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name = "L_args_1_"
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shape = [2, 100]
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dtype = "torch.int64"
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device = "cpu"
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mean = None
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std = None
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data = [
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335,
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828,
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690,
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591,
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666,
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694,
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510,
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679,
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195,
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702,
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947,
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806,
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623,
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207,
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457,
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678,
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243,
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398,
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130,
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12,
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514,
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364,
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940,
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232,
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373,
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901,
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532,
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665,
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608,
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280,
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176,
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20,
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113,
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941,
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900,
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741,
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628,
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718,
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172+
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575,
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177,
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347,
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892,
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216,
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476,
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953,
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]
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class Program_weight_tensor_meta_L_self_modules_encoder_modules_lin_parameters_weight_:
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name = "L_self_modules_encoder_modules_lin_parameters_weight_"
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shape = [16, 32]
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dtype = "torch.float32"
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device = "cpu"
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mean = 0.018
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std = 0.207
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data = None
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class Program_weight_tensor_meta_L_self_modules_encoder_parameters_bias_:
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name = "L_self_modules_encoder_parameters_bias_"
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shape = [16]
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dtype = "torch.float32"
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device = "cpu"
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mean = 0.000
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std = 0.000
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data = None

‎samples/torchgeometric/DeepGraphInfomax/graph_net.json‎

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{
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"framework": "torch",
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"num_devices_required": 1,
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"num_nodes_required": 1,
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"dynamic": false
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}

‎samples/torchgeometric/DeepGraphInfomax/input_meta.py‎

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‎samples/torchgeometric/DeepGraphInfomax/input_tensor_constraints.py‎

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