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delete unused imports #130

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Nov 24, 2024
Merged

delete unused imports #130

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4 changes: 2 additions & 2 deletions examples/ft_transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -64,7 +64,7 @@ def __init__(
col_stats_dict=col_stats_dict,
)

self.convs = FTTransformerConv(
self.conv = FTTransformerConv(
dim=hidden_dim,
layers=layers,
use_cls=True,
Expand All @@ -77,7 +77,7 @@ def __init__(

def forward(self, x) -> Tensor:
x = self.transform(x)
x_cls = self.convs(x)
x_cls = self.conv(x)
out = self.fc(x_cls)
return out

Expand Down
4 changes: 2 additions & 2 deletions examples/tab_transformer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,8 +77,8 @@ def __init__(

def forward(self, x):
x = self.transform(x)
for tab_transformer_conv in self.convs:
x = tab_transformer_conv(x)
for conv in self.convs:
x = conv(x)
out = self.fc(x.mean(dim=1))
return out

Expand Down
136 changes: 67 additions & 69 deletions examples/tape/core/GNNs/RevGAT/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,19 +51,19 @@ def forward(self, x):

class GATConv(nn.Module):
def __init__(
self,
in_feats,
out_feats,
num_heads=1,
feat_drop=0.0,
attn_drop=0.0,
edge_drop=0.0,
negative_slope=0.2,
use_attn_dst=True,
residual=False,
activation=None,
allow_zero_in_degree=False,
use_symmetric_norm=False,
self,
in_feats,
out_feats,
num_heads=1,
feat_drop=0.0,
attn_drop=0.0,
edge_drop=0.0,
negative_slope=0.2,
use_attn_dst=True,
residual=False,
activation=None,
allow_zero_in_degree=False,
use_symmetric_norm=False,
):
super(GATConv, self).__init__()
self._num_heads = num_heads
Expand All @@ -73,17 +73,18 @@ def __init__(
self._use_symmetric_norm = use_symmetric_norm
if isinstance(in_feats, tuple):
self.fc_src = nn.Linear(
self._in_src_feats, out_feats * num_heads, bias=False)
self._in_src_feats, out_feats * num_heads, bias=False
)
self.fc_dst = nn.Linear(
self._in_dst_feats, out_feats * num_heads, bias=False)
self._in_dst_feats, out_feats * num_heads, bias=False
)
else:
self.fc = nn.Linear(self._in_src_feats,
out_feats * num_heads, bias=False)
self.attn_l = nn.Parameter(
torch.FloatTensor(size=(1, num_heads, out_feats)))
self.fc = nn.Linear(self._in_src_feats, out_feats * num_heads, bias=False)
self.attn_l = nn.Parameter(torch.FloatTensor(size=(1, num_heads, out_feats)))
if use_attn_dst:
self.attn_r = nn.Parameter(
torch.FloatTensor(size=(1, num_heads, out_feats)))
torch.FloatTensor(size=(1, num_heads, out_feats))
)
else:
self.register_buffer("attn_r", None)
self.feat_drop = nn.Dropout(feat_drop)
Expand All @@ -94,7 +95,8 @@ def __init__(
self.leaky_relu = nn.LeakyReLU(negative_slope)
if residual:
self.res_fc = nn.Linear(
self._in_dst_feats, num_heads * out_feats, bias=False)
self._in_dst_feats, num_heads * out_feats, bias=False
)
else:
self.register_buffer("res_fc", None)
self.reset_parameters()
Expand Down Expand Up @@ -128,15 +130,12 @@ def forward(self, graph, feat, perm=None):
if not hasattr(self, "fc_src"):
self.fc_src, self.fc_dst = self.fc, self.fc
feat_src, feat_dst = h_src, h_dst
feat_src = self.fc_src(
h_src).view(-1, self._num_heads, self._out_feats)
feat_dst = self.fc_dst(
h_dst).view(-1, self._num_heads, self._out_feats)
feat_src = self.fc_src(h_src).view(-1, self._num_heads, self._out_feats)
feat_dst = self.fc_dst(h_dst).view(-1, self._num_heads, self._out_feats)
else:
h_src = self.feat_drop(feat)
feat_src = h_src
feat_src = self.fc(
h_src).view(-1, self._num_heads, self._out_feats)
feat_src = self.fc(h_src).view(-1, self._num_heads, self._out_feats)
if graph.is_block:
h_dst = h_src[: graph.number_of_dst_nodes()]
feat_dst = feat_src[: graph.number_of_dst_nodes()]
Expand Down Expand Up @@ -174,13 +173,13 @@ def forward(self, graph, feat, perm=None):

if self.training and self.edge_drop > 0:
if perm is None:
perm = torch.randperm(
graph.number_of_edges(), device=e.device)
perm = torch.randperm(graph.number_of_edges(), device=e.device)
bound = int(graph.number_of_edges() * self.edge_drop)
eids = perm[bound:]
graph.edata["a"] = torch.zeros_like(e)
graph.edata["a"][eids] = self.attn_drop(
edge_softmax(graph, e[eids], eids=eids))
edge_softmax(graph, e[eids], eids=eids)
)
else:
graph.edata["a"] = self.attn_drop(edge_softmax(graph, e))

Expand All @@ -197,8 +196,7 @@ def forward(self, graph, feat, perm=None):

# residual
if self.res_fc is not None:
resval = self.res_fc(h_dst).view(
h_dst.shape[0], -1, self._out_feats)
resval = self.res_fc(h_dst).view(h_dst.shape[0], -1, self._out_feats)
rst = rst + resval

# activation
Expand All @@ -209,20 +207,20 @@ def forward(self, graph, feat, perm=None):

class RevGATBlock(nn.Module):
def __init__(
self,
node_feats,
edge_feats,
edge_emb,
out_feats,
n_heads=1,
attn_drop=0.0,
edge_drop=0.0,
negative_slope=0.2,
residual=True,
activation=None,
use_attn_dst=True,
allow_zero_in_degree=True,
use_symmetric_norm=False,
self,
node_feats,
edge_feats,
edge_emb,
out_feats,
n_heads=1,
attn_drop=0.0,
edge_drop=0.0,
negative_slope=0.2,
residual=True,
activation=None,
use_attn_dst=True,
allow_zero_in_degree=True,
use_symmetric_norm=False,
):
super(RevGATBlock, self).__init__()

Expand Down Expand Up @@ -269,21 +267,22 @@ def forward(self, x, graph, dropout_mask=None, perm=None, efeat=None):

class RevGAT(nn.Module):
def __init__(
self,
in_feats,
n_classes,
n_hidden,
n_layers,
n_heads,
activation,
dropout=0.0,
input_drop=0.0,
attn_drop=0.0,
edge_drop=0.0,
use_attn_dst=True,
use_symmetric_norm=False,
group=2, input_norm=True,
use_pred=False,
self,
in_feats,
n_classes,
n_hidden,
n_layers,
n_heads,
activation,
dropout=0.0,
input_drop=0.0,
attn_drop=0.0,
edge_drop=0.0,
use_attn_dst=True,
use_symmetric_norm=False,
group=2,
input_norm=True,
use_pred=False,
):
super().__init__()
self.in_feats = in_feats
Expand All @@ -295,7 +294,7 @@ def __init__(
self.use_pred = use_pred

if self.use_pred:
self.encoder = torch.nn.Embedding(n_classes+1, n_hidden)
self.encoder = torch.nn.Embedding(n_classes + 1, n_hidden)
self.convs = nn.ModuleList()
self.norm = nn.BatchNorm1d(n_heads * n_hidden)
if input_norm:
Expand Down Expand Up @@ -339,11 +338,11 @@ def __init__(
else:
Fms.append(copy.deepcopy(fm))

invertible_module = memgcn.GroupAdditiveCoupling(Fms,
group=self.group)
invertible_module = memgcn.GroupAdditiveCoupling(Fms, group=self.group)

conv = memgcn.InvertibleModuleWrapper(fn=invertible_module,
keep_input=False)
conv = memgcn.InvertibleModuleWrapper(
fn=invertible_module, keep_input=False
)

self.convs.append(conv)

Expand All @@ -359,7 +358,7 @@ def forward(self, graph, feat, output_hidden_layer=None):
if self.use_pred:
h = self.encoder(h)
h = torch.flatten(h, start_dim=1)
if hasattr(self, 'input_norm'):
if hasattr(self, "input_norm"):
h = self.input_norm(h)
# h2 = self.input_norm(h2)
h = self.input_drop(h)
Expand All @@ -370,8 +369,7 @@ def forward(self, graph, feat, output_hidden_layer=None):

self.perms = []
for i in range(self.n_layers):
perm = torch.randperm(graph.number_of_edges(),
device=graph.device)
perm = torch.randperm(graph.number_of_edges(), device=graph.device)
self.perms.append(perm)

h = self.convs[0](graph, h, self.perms[0]).flatten(1, -1)
Expand Down
29 changes: 7 additions & 22 deletions rllm/data/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,40 +4,25 @@
from .storage import BaseStorage, NodeStorage, EdgeStorage, recursive_apply # noqa
from .view import MappingView, KeysView, ValuesView, ItemsView # noqa

dataset_classes = [
__all__ = [
# dataset_classes
"Dataset",
]

graph_data_classes = [
# graph_data_classes
"BaseGraph",
"GraphData",
"HeteroGraphData",
]

table_data_classes = [
# table_data_classes
"BaseTable",
"TableDataset",
"TableData",
]

storage_classes = [
"TableDataset",
# storage_classes
"BaseStorage",
"NodeStorage",
"EdgeStorage",
"recursive_apply",
]

view_classes = [
# view_classes
"MappingView",
"KeysView",
"ValuesView",
"ItemsView",
]

__all__ = (
dataset_classes
+ graph_data_classes
+ table_data_classes
+ storage_classes
+ view_classes
)
5 changes: 1 addition & 4 deletions rllm/llm/llm_module/base.py
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Original file line number Diff line number Diff line change
@@ -1,8 +1,5 @@
from abc import ABC, abstractmethod
from typing import (
Any,
Sequence,
)
from typing import Sequence

from rllm.llm.types import (
ChatMessage,
Expand Down
2 changes: 1 addition & 1 deletion rllm/llm/llm_module/langchain_llm.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
from typing import Any, Callable, Optional, Sequence
from typing import Callable, Optional, Sequence

from rllm.llm.types import (
LLMMetadata,
Expand Down
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