# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. # # 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. # [AUTO-GENERATED] # Target file: python/paddle/optimizer/fusion_utils.py # Coverage target: get_current_device_type, get_align, FusionStorage class # 未覆盖行: FusionStorageHelper class (requires IPC metadata) import unittest import numpy as np import paddle from paddle.optimizer.fusion_utils import ( FusionStorage, get_align, get_current_device_type, ) class TestGetCurrentDeviceType(unittest.TestCase): """Test get_current_device_type function. 测试 get_current_device_type 函数。""" def test_returns_string(self): """get_current_device_type should return a string. get_current_device_type 应该返回字符串。""" result = get_current_device_type() self.assertIsInstance(result, str) def test_returns_valid_device(self): """get_current_device_type should return 'gpu', 'xpu', or 'unknown'. get_current_device_type 应该返回 'gpu'、'xpu' 或 'unknown'。""" result = get_current_device_type() self.assertIn(result, ["gpu", "xpu", "unknown"]) def test_consistent_result(self): """Multiple calls should return the same result (cached). 多次调用应返回相同结果（缓存）。""" result1 = get_current_device_type() result2 = get_current_device_type() self.assertEqual(result1, result2) class TestGetAlign(unittest.TestCase): """Test get_align function. 测试 get_align 函数。""" def setUp(self): paddle.disable_static() def test_get_align_float32(self): """Test alignment for float32 tensor. 测试 float32 张量的对齐。""" t = paddle.zeros([10], dtype=paddle.float32) # Should return an integer alignment value result = get_align(t) self.assertIsInstance(result, (int, np.integer)) def test_get_align_float16(self): """Test alignment for float16 tensor. 测试 float16 张量的对齐。""" t = paddle.zeros([10], dtype=paddle.float16) result = get_align(t) self.assertIsInstance(result, (int, np.integer)) def test_get_align_bfloat16(self): """Test alignment for bfloat16 tensor. 测试 bfloat16 张量的对齐。""" t = paddle.zeros([10], dtype=paddle.bfloat16) result = get_align(t) self.assertIsInstance(result, (int, np.integer)) def test_get_align_large_tensor(self): """Test alignment for a large tensor that may already be aligned. 测试已经对齐的大张量的对齐值。""" # 256 bytes / 4 bytes per float32 = 64 elements -> should be aligned t = paddle.zeros([64], dtype=paddle.float32) result = get_align(t) self.assertEqual(result, 0) def test_get_align_unaligned_tensor(self): """Test alignment for a small unaligned tensor. 测试小的未对齐张量的对齐值。""" t = paddle.zeros([3], dtype=paddle.float32) result = get_align(t) # 3 * 4 = 12 bytes, 256 - 12 = 244 remaining, 244 / 4 = 61 self.assertGreaterEqual(result, 0) def test_get_align_2d_tensor(self): """Test alignment for 2D tensor. 测试二维张量的对齐值。""" t = paddle.zeros([5, 10], dtype=paddle.float32) result = get_align(t) self.assertIsInstance(result, (int, np.integer)) def test_get_align_result_non_negative(self): """Alignment result should always be non-negative. 对齐结果应始终为非负。""" t = paddle.zeros([7], dtype=paddle.float32) result = get_align(t) self.assertGreaterEqual(result, 0) class TestFusionStorage(unittest.TestCase): """Test FusionStorage class. 测试 FusionStorage 类。""" def setUp(self): paddle.disable_static() def test_basic_creation(self): """Test basic FusionStorage creation with accumulators and master_weights. 测试使用 accumulators 和 master_weights 创建基本 FusionStorage。""" acc = paddle.randn([10], dtype=paddle.float32) mw = paddle.randn([10], dtype=paddle.float32) accumulators = {"momentum": {"weight": acc}} master_weights = {"weight": mw} storage = FusionStorage(accumulators, master_weights) self.assertIsNotNone(storage.buffer) self.assertEqual(storage.buffer.dtype, paddle.float32) def test_with_merged_model_params(self): """Test FusionStorage with merged_model_params. 测试带有 merged_model_params 的 FusionStorage。""" acc = paddle.randn([8], dtype=paddle.float32) mw = paddle.randn([8], dtype=paddle.float32) mp = paddle.randn([8], dtype=paddle.float32) accumulators = {"momentum": {"w": acc}} master_weights = {"w": mw} merged_model_params = {"w": mp} storage = FusionStorage( accumulators, master_weights, merged_model_params=merged_model_params, ) self.assertIsNotNone(storage.buffer) self.assertIsNotNone(storage.merged_model_params_meta) def test_buffer_shape(self): """Test that buffer size accounts for alignment padding. 测试 buffer 大小考虑了对齐填充。""" acc = paddle.randn([10], dtype=paddle.float32) mw = paddle.randn([10], dtype=paddle.float32) accumulators = {"momentum": {"w": acc}} master_weights = {"w": mw} storage = FusionStorage(accumulators, master_weights) # Buffer should be at least as large as raw data raw_size = 10 + 10 self.assertGreaterEqual(storage.buffer.shape[0], raw_size) def test_mapping_tensor_preserves_values(self): """Test that mapping_tensor copies values into the buffer correctly. 测试 mapping_tensor 正确地将值复制到 buffer 中。""" acc_val = paddle.ones([5], dtype=paddle.float32) * 3.14 mw_val = paddle.ones([5], dtype=paddle.float32) * 2.71 accumulators = {"momentum": {"w": acc_val.clone()}} master_weights = {"w": mw_val.clone()} storage = FusionStorage(accumulators, master_weights) # Check that accumulator values are in buffer acc_meta = storage.accumulators_meta["momentum"]["w"] buf_slice = storage.buffer._slice(acc_meta["start"], acc_meta["end"]) # The first 5 elements should be 3.14 np.testing.assert_array_almost_equal( buf_slice.numpy()[:5], np.full(5, 3.14), decimal=5 ) # Check that master weight values are in buffer mw_meta = storage.master_weights_meta["w"] mw_slice = storage.buffer._slice(mw_meta["start"], mw_meta["end"]) np.testing.assert_array_almost_equal( mw_slice.numpy()[:5], np.full(5, 2.71), decimal=5 ) def test_accumulators_meta_structure(self): """Test that accumulators_meta has correct keys. 测试 accumulators_meta 包含正确的键。""" acc = paddle.randn([4], dtype=paddle.float32) mw = paddle.randn([4], dtype=paddle.float32) accumulators = {"sgd": {"param1": acc}} master_weights = {"param1": mw} storage = FusionStorage(accumulators, master_weights) self.assertIn("sgd", storage.accumulators_meta) self.assertIn("param1", storage.accumulators_meta["sgd"]) meta = storage.accumulators_meta["sgd"]["param1"] self.assertIn("start", meta) self.assertIn("end", meta) self.assertIn("name", meta) self.assertIn("shape", meta) def test_master_weights_meta_structure(self): """Test that master_weights_meta has correct keys. 测试 master_weights_meta 包含正确的键。""" acc = paddle.randn([6], dtype=paddle.float32) mw = paddle.randn([6], dtype=paddle.float32) accumulators = {"momentum": {"p": acc}} master_weights = {"p": mw} storage = FusionStorage(accumulators, master_weights) self.assertIn("p", storage.master_weights_meta) meta = storage.master_weights_meta["p"] self.assertIn("start", meta) self.assertIn("end", meta) self.assertIn("name", meta) self.assertIn("shape", meta) def test_merged_model_params_meta_structure(self): """Test that merged_model_params_meta is populated correctly. 测试 merged_model_params_meta 正确填充。""" acc = paddle.randn([4], dtype=paddle.float32) mw = paddle.randn([4], dtype=paddle.float32) mp = paddle.randn([4], dtype=paddle.float32) accumulators = {"adam": {"p": acc}} master_weights = {"p": mw} merged = {"p": mp} storage = FusionStorage( accumulators, master_weights, merged_model_params=merged ) self.assertIn("p", storage.merged_model_params_meta) meta = storage.merged_model_params_meta["p"] self.assertIn("start", meta) self.assertIn("end", meta) self.assertIn("shape", meta) def test_multiple_accumulator_groups(self): """Test with multiple accumulator groups. 测试多个累加器组的情况。""" acc1 = paddle.randn([3], dtype=paddle.float32) acc2 = paddle.randn([3], dtype=paddle.float32) mw = paddle.randn([3], dtype=paddle.float32) accumulators = { "momentum": {"w": acc1}, "variance": {"w": acc2}, } master_weights = {"w": mw} storage = FusionStorage(accumulators, master_weights) self.assertIn("momentum", storage.accumulators_meta) self.assertIn("variance", storage.accumulators_meta) def test_none_merged_model_params(self): """Test FusionStorage with merged_model_params=None (default). 测试 merged_model_params=None（默认值）的 FusionStorage。""" acc = paddle.randn([4], dtype=paddle.float32) mw = paddle.randn([4], dtype=paddle.float32) accumulators = {"momentum": {"w": acc}} master_weights = {"w": mw} storage = FusionStorage( accumulators, master_weights, merged_model_params=None ) self.assertIsNone(storage.merged_model_params) self.assertEqual(storage.merged_model_params_meta, {}) def test_assert_accumulators_dict(self): """Test that accumulators must be a dict. 测试 accumulators 必须是字典。""" mw = paddle.randn([4], dtype=paddle.float32) with self.assertRaises(AssertionError): FusionStorage("not_a_dict", {"w": mw}) def test_assert_master_weights_dict(self): """Test that master_weights must be a dict. 测试 master_weights 必须是字典。""" acc = paddle.randn([4], dtype=paddle.float32) with self.assertRaises(AssertionError): FusionStorage({"w": acc}, "not_a_dict") def test_assert_merged_model_params_type(self): """Test that merged_model_params must be dict or None. 测试 merged_model_params 必须是字典或 None。""" acc = paddle.randn([4], dtype=paddle.float32) mw = paddle.randn([4], dtype=paddle.float32) with self.assertRaises(AssertionError): FusionStorage({"w": acc}, {"w": mw}, merged_model_params="invalid") @unittest.skipIf( not paddle.is_compiled_with_cuda(), "GPU required for IPC metadata" ) def test_buffer_ipc_meta_on_gpu(self): """Test buffer_ipc_meta property on GPU. 测试 GPU 上的 buffer_ipc_meta 属性。""" acc = paddle.randn([4], dtype=paddle.float32) mw = paddle.randn([4], dtype=paddle.float32) accumulators = {"momentum": {"w": acc.cuda()}} master_weights = {"w": mw.cuda()} storage = FusionStorage(accumulators, master_weights) # On CUDA (non-ROCm), buffer_ipc_meta should return IPC metadata meta = storage.buffer_ipc_meta # meta could be None on ROCm or could be a tuple on CUDA if not paddle.is_compiled_with_rocm(): self.assertIsNotNone(meta) def test_dtype_float16_storage(self): """Test FusionStorage with float16 dtype. 测试 float16 类型的 FusionStorage。""" acc = paddle.randn([8], dtype=paddle.float16) mw = paddle.randn([8], dtype=paddle.float16) # Note: FusionStorage requires matching dtype, but we test float16 # which uses align=2 vs float32 align=4 accumulators = {"momentum": {"w": acc}} master_weights = {"w": mw} storage = FusionStorage( accumulators, master_weights, dtype=paddle.float16 ) self.assertEqual(storage.dtype, paddle.float16) self.assertEqual(storage.buffer.dtype, paddle.float16) def test_meta_shape_matches(self): """Test that stored shape in metadata matches original tensor shape. 测试元数据中存储的形状与原始张量形状匹配。""" acc = paddle.randn([3, 4], dtype=paddle.float32) mw = paddle.randn([3, 4], dtype=paddle.float32) accumulators = {"momentum": {"w": acc}} master_weights = {"w": mw} storage = FusionStorage(accumulators, master_weights) self.assertEqual( tuple(storage.accumulators_meta["momentum"]["w"]["shape"]), (3, 4), ) self.assertEqual( tuple(storage.master_weights_meta["w"]["shape"]), (3, 4) ) if __name__ == "__main__": unittest.main()