Drop support for PyTorch 1.12

CHANGELOG.md

@@ -70,6 +70,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 ### Removed
 
+- Dropped support for PyTorch 1.12 ([#10248](https://github.com/pyg-team/pytorch_geometric/pull/10248))
 - Dropped support for PyTorch 1.11 ([#10247](https://github.com/pyg-team/pytorch_geometric/pull/10247))
 
 ## [2.6.0] - 2024-09-13

test/nn/conv/test_gat_conv.py

@@ -58,12 +58,10 @@ def forward(
     assert result[1][1].size() == (7, 2)
     assert result[1][1].min() >= 0 and result[1][1].max() <= 1
 
-    if torch_geometric.typing.WITH_PT113:
-        # PyTorch < 1.13 does not support multi-dimensional CSR values :(
-        result = conv(x1, adj1.t(), return_attention_weights=True)
-        assert torch.allclose(result[0], out, atol=1e-6)
-        assert result[1][0].size() == torch.Size([4, 4, 2])
-        assert result[1][0]._nnz() == 7
+    result = conv(x1, adj1.t(), return_attention_weights=True)
+    assert torch.allclose(result[0], out, atol=1e-6)
+    assert result[1][0].size() == torch.Size([4, 4, 2])
+    assert result[1][0]._nnz() == 7
 
     if torch_geometric.typing.WITH_TORCH_SPARSE:
         result = conv(x1, adj2.t(), return_attention_weights=True)

test/nn/conv/test_gatv2_conv.py

@@ -56,12 +56,10 @@ def forward(
     assert result[1][1].size() == (7, 2)
     assert result[1][1].min() >= 0 and result[1][1].max() <= 1
 
-    if torch_geometric.typing.WITH_PT113:
-        # PyTorch < 1.13 does not support multi-dimensional CSR values :(
-        result = conv(x1, adj1.t(), return_attention_weights=True)
-        assert torch.allclose(result[0], out, atol=1e-6)
-        assert result[1][0].size() == torch.Size([4, 4, 2])
-        assert result[1][0]._nnz() == 7
+    result = conv(x1, adj1.t(), return_attention_weights=True)
+    assert torch.allclose(result[0], out, atol=1e-6)
+    assert result[1][0].size() == torch.Size([4, 4, 2])
+    assert result[1][0]._nnz() == 7
 
     if torch_geometric.typing.WITH_TORCH_SPARSE:
         result = conv(x1, adj2.t(), return_attention_weights=True)

test/nn/conv/test_message_passing.py

@@ -122,12 +122,11 @@ def test_my_conv_basic():
         assert torch.allclose(conv((x1, None), adj2.t()), out2, atol=1e-6)
 
     # Test gradient computation for `torch.sparse` tensors:
-    if torch_geometric.typing.WITH_PT112:
-        conv.fuse = True
-        torch_adj_t = adj1.t().requires_grad_()
-        out = conv((x1, x2), torch_adj_t)
-        out.sum().backward()
-        assert torch_adj_t.grad is not None
+    conv.fuse = True
+    torch_adj_t = adj1.t().requires_grad_()
+    out = conv((x1, x2), torch_adj_t)
+    out.sum().backward()
+    assert torch_adj_t.grad is not None
 
 
 def test_my_conv_save(tmp_path):

test/nn/conv/test_signed_conv.py

@@ -33,7 +33,7 @@ def test_signed_conv():
     if torch_geometric.typing.WITH_TORCH_SPARSE:
         assert torch.allclose(conv2(out1, adj2.t(), adj2.t()), out2)
 
-    if is_full_test() and torch_geometric.typing.WITH_PT112:
+    if is_full_test():
         jit1 = torch.jit.script(conv1)
         jit2 = torch.jit.script(conv2)
         assert torch.allclose(jit1(x, edge_index, edge_index), out1)
@@ -62,7 +62,7 @@ def test_signed_conv():
         assert torch.allclose(conv2((out1, out1[:2]), adj2.t(), adj2.t()),
                               out2[:2], atol=1e-6)
 
-    if is_full_test() and torch_geometric.typing.WITH_PT112:
+    if is_full_test():
         assert torch.allclose(jit1((x, x[:2]), edge_index, edge_index),
                               out1[:2], atol=1e-6)
         assert torch.allclose(jit2((out1, out1[:2]), edge_index, edge_index),

test/nn/models/test_basic_gnn.py

@@ -1,14 +1,12 @@
 import os
 import os.path as osp
 import random
-import sys
 import warnings
 
 import pytest
 import torch
 import torch.nn.functional as F
 
-import torch_geometric.typing
 from torch_geometric.data import Data
 from torch_geometric.loader import NeighborLoader
 from torch_geometric.nn import SAGEConv
@@ -220,10 +218,6 @@ def test_compile_basic(device):
 
 
 def test_packaging():
-    if (not torch_geometric.typing.WITH_PT113 and sys.version_info.major == 3
-            and sys.version_info.minor >= 10):
-        return  # Unsupported Python version
-
     warnings.filterwarnings('ignore', '.*TypedStorage is deprecated.*')
 
     os.makedirs(torch.hub._get_torch_home(), exist_ok=True)

test/nn/pool/connect/test_filter_edges.py

@@ -1,6 +1,5 @@
 import torch
 
-import torch_geometric.typing
 from torch_geometric.nn.pool.connect import FilterEdges
 from torch_geometric.nn.pool.select import SelectOutput
 from torch_geometric.testing import is_full_test
@@ -26,7 +25,7 @@ def test_filter_edges():
     assert out1.edge_attr.tolist() == [3, 5]
     assert out1.batch.tolist() == [0, 1]
 
-    if torch_geometric.typing.WITH_PT113 and is_full_test():
+    if is_full_test():
         jit = torch.jit.script(connect)
         out2 = jit(select_output, edge_index, edge_attr, batch)
         torch.equal(out1.edge_index, out2.edge_index)

test/nn/pool/test_asap.py

@@ -2,7 +2,6 @@
 
 import torch
 
-import torch_geometric.typing
 from torch_geometric.nn import ASAPooling, GCNConv, GraphConv
 from torch_geometric.testing import is_full_test, onlyFullTest, onlyLinux
 
@@ -23,7 +22,7 @@ def test_asap():
         assert out[0].size() == (num_nodes // 2, in_channels)
         assert out[1].size() == (2, 2)
 
-        if torch_geometric.typing.WITH_PT113 and is_full_test():
+        if is_full_test():
             torch.jit.script(pool)
 
         pool = ASAPooling(in_channels, ratio=0.5, GNN=GNN, add_self_loops=True)

test/nn/pool/test_pan_pool.py

@@ -1,6 +1,5 @@
 import torch
 
-import torch_geometric.typing
 from torch_geometric.nn import PANConv, PANPooling
 from torch_geometric.testing import is_full_test, withPackage
 
@@ -25,7 +24,7 @@ def test_pan_pooling():
     assert perm.size() == (2, )
     assert score.size() == (2, )
 
-    if torch_geometric.typing.WITH_PT113 and is_full_test():
+    if is_full_test():
         jit = torch.jit.script(pool)
         out = jit(x, M)
         assert torch.allclose(h, out[0])

test/nn/pool/test_sag_pool.py

@@ -1,6 +1,5 @@
 import torch
 
-import torch_geometric.typing
 from torch_geometric.nn import (
     GATConv,
     GCNConv,
@@ -40,7 +39,7 @@ def test_sag_pooling():
         assert out3[0].size() == (2, in_channels)
         assert out3[1].size() == (2, 2)
 
-        if torch_geometric.typing.WITH_PT113 and is_full_test():
+        if is_full_test():
             jit1 = torch.jit.script(pool1)
             assert torch.allclose(jit1(x, edge_index)[0], out1[0])
 

test/nn/pool/test_topk_pool.py

@@ -1,6 +1,5 @@
 import torch
 
-import torch_geometric.typing
 from torch_geometric.nn.pool import TopKPooling
 from torch_geometric.nn.pool.connect.filter_edges import filter_adj
 from torch_geometric.testing import is_full_test
@@ -49,7 +48,7 @@ def test_topk_pooling():
     assert out3[0].size() == (2, in_channels)
     assert out3[1].size() == (2, 2)
 
-    if torch_geometric.typing.WITH_PT113 and is_full_test():
+    if is_full_test():
         jit1 = torch.jit.script(pool1)
         assert torch.allclose(jit1(x, edge_index)[0], out1[0])
 

test/utils/test_sort_edge_index.py

@@ -14,11 +14,10 @@ def test_sort_edge_index():
     out = sort_edge_index(edge_index)
     assert out.tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
 
-    if torch_geometric.typing.WITH_PT113:
-        torch_geometric.typing.MAX_INT64 = 1
-        out = sort_edge_index(edge_index)
-        torch_geometric.typing.MAX_INT64 = torch.iinfo(torch.int64).max
-        assert out.tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
+    torch_geometric.typing.MAX_INT64 = 1
+    out = sort_edge_index(edge_index)
+    torch_geometric.typing.MAX_INT64 = torch.iinfo(torch.int64).max
+    assert out.tolist() == [[0, 1, 1, 2], [1, 0, 2, 1]]
 
     out = sort_edge_index((edge_index[0], edge_index[1]))
     assert isinstance(out, tuple)

torch_geometric/__init__.py

@@ -57,12 +57,12 @@
     '__version__',
 ]
 
-if not torch_geometric.typing.WITH_PT112:
+if not torch_geometric.typing.WITH_PT113:
     import warnings as std_warnings
 
-    std_warnings.warn("PyG 2.7.0 dropped support for PyTorch 1.11. Consider "
-                      "upgrading to PyTorch 1.12.0+ or downgrading to "
-                      "PyG 2.6.0. ")
+    std_warnings.warn("PyG 2.7 removed support for PyTorch < 1.13. Consider "
+                      "Consider upgrading to PyTorch >= 1.13 or downgrading "
+                      "to PyG <= 2.6. ")
 
 # Serialization ###############################################################
 

torch_geometric/data/collate.py

@@ -191,10 +191,8 @@ def _collate(
             if torch_geometric.typing.WITH_PT20:
                 storage = elem.untyped_storage()._new_shared(
                     numel * elem.element_size(), device=elem.device)
-            elif torch_geometric.typing.WITH_PT112:
-                storage = elem.storage()._new_shared(numel, device=elem.device)
             else:
-                storage = elem.storage()._new_shared(numel)
+                storage = elem.storage()._new_shared(numel, device=elem.device)
             shape = list(elem.size())
             if cat_dim is None or elem.dim() == 0:
                 shape = [len(values)] + shape

torch_geometric/edge_index.py

@@ -298,8 +298,7 @@ def __new__(
                 indptr = None
             data = torch.stack([row, col], dim=0)
 
-        if (torch_geometric.typing.WITH_PT112
-                and data.layout == torch.sparse_csc):
+        if data.layout == torch.sparse_csc:
             row = data.row_indices()
             indptr = data.ccol_indices()
 
@@ -882,10 +881,6 @@ def to_sparse_csc(  # type: ignore
                 If not specified, non-zero elements will be assigned a value of
                 :obj:`1.0`. (default: :obj:`None`)
         """
-        if not torch_geometric.typing.WITH_PT112:
-            raise NotImplementedError(
-                "'to_sparse_csc' not supported for PyTorch < 1.12")
-
         (colptr, row), perm = self.get_csc()
         if value is not None and perm is not None:
             value = value[perm]
@@ -922,7 +917,7 @@ def to_sparse(  # type: ignore
             return self.to_sparse_coo(value)
         if layout == torch.sparse_csr:
             return self.to_sparse_csr(value)
-        if torch_geometric.typing.WITH_PT112 and layout == torch.sparse_csc:
+        if layout == torch.sparse_csc:
             return self.to_sparse_csc(value)
 
         raise ValueError(f"Unexpected tensor layout (got '{layout}')")

torch_geometric/nn/dense/linear.py

@@ -1,4 +1,3 @@
-import copy
 import math
 import sys
 import time
@@ -114,25 +113,6 @@ def __init__(
 
         self.reset_parameters()
 
-    def __deepcopy__(self, memo):
-        # PyTorch<1.13 cannot handle deep copies of uninitialized parameters :(
-        # TODO Drop this code once PyTorch 1.12 is no longer supported.
-        out = Linear(
-            self.in_channels,
-            self.out_channels,
-            self.bias is not None,
-            self.weight_initializer,
-            self.bias_initializer,
-        ).to(self.weight.device)
-
-        if self.in_channels > 0:
-            out.weight = copy.deepcopy(self.weight, memo)
-
-        if self.bias is not None:
-            out.bias = copy.deepcopy(self.bias, memo)
-
-        return out
-
     def reset_parameters(self):
         r"""Resets all learnable parameters of the module."""
         reset_weight_(self.weight, self.in_channels, self.weight_initializer)

torch_geometric/nn/pool/connect/base.py

@@ -4,7 +4,6 @@
 import torch
 from torch import Tensor
 
-import torch_geometric.typing
 from torch_geometric.nn.pool.select import SelectOutput
 
 
@@ -49,8 +48,7 @@ def __init__(
         self.batch = batch
 
 
-if torch_geometric.typing.WITH_PT113:
-    ConnectOutput = torch.jit.script(ConnectOutput)
+ConnectOutput = torch.jit.script(ConnectOutput)
 
 
 class Connect(torch.nn.Module):

torch_geometric/nn/pool/select/base.py

@@ -4,8 +4,6 @@
 import torch
 from torch import Tensor
 
-import torch_geometric.typing
-
 
 @dataclass(init=False)
 class SelectOutput:
@@ -64,8 +62,7 @@ def __init__(
         self.weight = weight
 
 
-if torch_geometric.typing.WITH_PT113:
-    SelectOutput = torch.jit.script(SelectOutput)
+SelectOutput = torch.jit.script(SelectOutput)
 
 
 class Select(torch.nn.Module):

torch_geometric/typing.py

@@ -21,7 +21,6 @@
 WITH_PT24 = WITH_PT20 and int(torch.__version__.split('.')[1]) >= 4
 WITH_PT25 = WITH_PT20 and int(torch.__version__.split('.')[1]) >= 5
 WITH_PT26 = WITH_PT20 and int(torch.__version__.split('.')[1]) >= 6
-WITH_PT112 = WITH_PT20 or int(torch.__version__.split('.')[1]) >= 12
 WITH_PT113 = WITH_PT20 or int(torch.__version__.split('.')[1]) >= 13
 
 WITH_WINDOWS = os.name == 'nt'

torch_geometric/utils/_lexsort.py

@@ -1,11 +1,7 @@
 from typing import List
 
-import numpy as np
-import torch
 from torch import Tensor
 
-import torch_geometric.typing
-
 
 def lexsort(
     keys: List[Tensor],
@@ -28,11 +24,6 @@ def lexsort(
     """
     assert len(keys) >= 1
 
-    if not torch_geometric.typing.WITH_PT113:
-        keys = [k.neg() for k in keys] if descending else keys
-        out = np.lexsort([k.detach().cpu().numpy() for k in keys], axis=dim)
-        return torch.from_numpy(out).to(keys[0].device)
-
     out = keys[0].argsort(dim=dim, descending=descending, stable=True)
     for k in keys[1:]:
         index = k.gather(dim, out)