fix

Signed-off-by: 0oshowero0 <o0shower0o@outlook.com>

Author: 0oshowero0

tests/test_tensor_utils.py

@@ -0,0 +1,196 @@
+# Copyright 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2025 The TransferQueue Team
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Unit tests for transfer_queue.utils.tensor_utils."""
+
+import pytest
+import torch
+
+from transfer_queue.utils.tensor_utils import (
+    allocate_empty_tensors,
+    compute_stride,
+    get_nbytes,
+    merge_contiguous_memory,
+)
+
+
+class TestComputeStride:
+    """Tests for compute_stride."""
+
+    def test_3d(self):
+        assert compute_stride((2, 3, 4)) == (12, 4, 1)
+
+    def test_1d(self):
+        assert compute_stride((5,)) == (1,)
+
+    def test_scalar(self):
+        assert compute_stride(()) == ()
+
+    def test_2d(self):
+        assert compute_stride((3, 5)) == (5, 1)
+
+
+class TestGetNbytes:
+    """Tests for get_nbytes."""
+
+    def test_basic(self):
+        dtypes = [torch.float32, torch.int32]
+        shapes = [(2, 3), (4,)]
+        result = get_nbytes(dtypes, shapes)
+        assert result == [2 * 3 * 4, 4 * 4]  # float32=4, int32=4
+
+    def test_scalar(self):
+        dtypes = [torch.float64]
+        shapes = [()]
+        result = get_nbytes(dtypes, shapes)
+        assert result == [8]  # scalar = 1 element
+
+    def test_list_shape(self):
+        dtypes = [torch.float32]
+        shapes = [[]]  # list instead of tuple
+        result = get_nbytes(dtypes, shapes)
+        assert result == [4]
+
+    def test_mixed_dtypes(self):
+        dtypes = [torch.float16, torch.float32, torch.int64]
+        shapes = [(10,), (10,), (10,)]
+        result = get_nbytes(dtypes, shapes)
+        assert result == [10 * 2, 10 * 4, 10 * 8]
+
+
+class TestAllocateEmptyTensors:
+    """Tests for allocate_empty_tensors."""
+
+    def test_basic(self):
+        dtypes = [torch.float32, torch.float32, torch.int32]
+        shapes = [(2, 3), (4,), (5,)]
+        tensors, ptrs, region_ptrs, region_sizes = allocate_empty_tensors(dtypes, shapes)
+
+        assert len(tensors) == 3
+        assert len(ptrs) == 3
+        assert len(region_ptrs) == 2  # float32 group + int32 group
+        assert len(region_sizes) == 2
+
+        # Same dtype tensors share the same underlying storage
+        assert tensors[0].untyped_storage().data_ptr() == region_ptrs[0]
+        assert tensors[1].untyped_storage().data_ptr() == region_ptrs[0]
+        assert tensors[2].untyped_storage().data_ptr() == region_ptrs[1]
+
+        # Shapes are correct
+        assert list(tensors[0].shape) == [2, 3]
+        assert list(tensors[1].shape) == [4]
+        assert list(tensors[2].shape) == [5]
+
+    def test_scalar(self):
+        dtypes = [torch.float32, torch.int32]
+        shapes = [(), ()]
+        tensors, ptrs, region_ptrs, region_sizes = allocate_empty_tensors(dtypes, shapes)
+
+        assert len(tensors) == 2
+        assert tensors[0].numel() == 1
+        assert tensors[1].numel() == 1
+        assert len(region_ptrs) == 2
+
+    def test_empty(self):
+        result = allocate_empty_tensors([], [])
+        assert result == ([], [], [], [])
+
+    def test_regions_complex(self):
+        """Mixed dtypes and shapes: verify region counts, sizes, and per-tensor offsets."""
+        dtypes = [
+            torch.float32,  # group 0: (2, 3) -> 6 elements
+            torch.int32,  # group 1: (4,) -> 4 elements
+            torch.float32,  # group 0: scalar -> 1 element
+            torch.float64,  # group 2: (2, 2) -> 4 elements
+            torch.int32,  # group 1: (3, 2) -> 6 elements
+        ]
+        shapes = [(2, 3), (4,), (), (2, 2), (3, 2)]
+        tensors, ptrs, region_ptrs, region_sizes = allocate_empty_tensors(dtypes, shapes)
+
+        # 3 dtype groups in insertion order: float32, int32, float64
+        assert len(region_ptrs) == 3
+        assert len(region_sizes) == 3
+        assert len(set(region_ptrs)) == 3  # distinct allocations
+
+        # float32 region: 6 + 1 = 7 elements * 4 bytes = 28 bytes
+        assert region_sizes[0] == 7 * 4
+        # int32 region: 4 + 6 = 10 elements * 4 bytes = 40 bytes
+        assert region_sizes[1] == 10 * 4
+        # float64 region: 4 elements * 8 bytes = 32 bytes
+        assert region_sizes[2] == 4 * 8
+
+        # Per-tensor ptrs must lie inside their respective regions
+        # tensor 0 (float32, shape (2,3), offset 0)
+        assert ptrs[0] == region_ptrs[0]
+        # tensor 1 (int32, shape (4,), offset 0)
+        assert ptrs[1] == region_ptrs[1]
+        # tensor 2 (float32, scalar, offset 6)
+        assert ptrs[2] == region_ptrs[0] + 6 * 4
+        # tensor 3 (float64, shape (2,2), offset 0)
+        assert ptrs[3] == region_ptrs[2]
+        # tensor 4 (int32, shape (3,2), offset 4)
+        assert ptrs[4] == region_ptrs[1] + 4 * 4
+
+
+class TestMergeContiguousMemory:
+    """Tests for merge_contiguous_memory."""
+
+    def test_basic_merge(self):
+        ptrs = [0, 10, 30]
+        sizes = [10, 20, 10]
+        merged_ptrs, merged_sizes = merge_contiguous_memory(ptrs, sizes)
+        # 0+10=10 (contiguous with 10), 10+20=30 (contiguous with 30) -> all merge into [0]
+        assert merged_ptrs == [0]
+        assert merged_sizes == [40]
+
+    def test_no_contiguous(self):
+        ptrs = [0, 100, 200]
+        sizes = [50, 50, 50]
+        merged_ptrs, merged_sizes = merge_contiguous_memory(ptrs, sizes)
+        assert merged_ptrs == [0, 100, 200]
+        assert merged_sizes == [50, 50, 50]
+
+    def test_unsorted_input(self):
+        ptrs = [100, 0, 50]
+        sizes = [50, 50, 50]
+        merged_ptrs, merged_sizes = merge_contiguous_memory(ptrs, sizes)
+        # After sorting: 0, 50, 100; all contiguous -> merge into [0]
+        assert merged_ptrs == [0]
+        assert merged_sizes == [150]
+
+    def test_single_region(self):
+        ptrs = [10]
+        sizes = [100]
+        merged_ptrs, merged_sizes = merge_contiguous_memory(ptrs, sizes)
+        assert merged_ptrs == [10]
+        assert merged_sizes == [100]
+
+    def test_empty(self):
+        assert merge_contiguous_memory([], []) == ([], [])
+
+    def test_mismatched_lengths_both_empty_not_triggered(self):
+        # If one is empty and other is not, should raise ValueError
+        with pytest.raises(ValueError, match="ptrs and sizes must have the same length"):
+            merge_contiguous_memory([], [10])
+
+        with pytest.raises(ValueError, match="ptrs and sizes must have the same length"):
+            merge_contiguous_memory([0], [])
+
+    def test_three_continuous(self):
+        ptrs = [0, 10, 20]
+        sizes = [10, 10, 10]
+        merged_ptrs, merged_sizes = merge_contiguous_memory(ptrs, sizes)
+        assert merged_ptrs == [0]
+        assert merged_sizes == [30]

transfer_queue/utils/tensor_utils.py

@@ -16,7 +16,6 @@
 import logging
 import operator
 import os
-import warnings
 from functools import reduce
 
 import torch
@@ -181,13 +180,3 @@ def merge_contiguous_memory(ptrs: list[int], sizes: list[int]) -> tuple[list[int
     merged_sizes.append(current_size)
 
     return merged_ptrs, merged_sizes
-
-
-def merge_continues_memory(ptrs: list[int], sizes: list[int]) -> tuple[list[int], list[int]]:
-    """Deprecated alias for :func:`merge_contiguous_memory`."""
-    warnings.warn(
-        "merge_continues_memory is deprecated, use merge_contiguous_memory instead",
-        DeprecationWarning,
-        stacklevel=2,
-    )
-    return merge_contiguous_memory(ptrs, sizes)