# # Copyright (c) 2026 Huawei Technologies Co., Ltd. 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. from unittest.mock import MagicMock, patch import torch from tests.ut.base import TestBase from vllm_ascend._310p.attention.attention_v1 import ( AscendAttentionBackend310, AscendAttentionBackendImpl310, AscendAttentionMetadataBuilder310, AscendAttentionState, ) from vllm_ascend._310p.attention.metadata_builder import ( AscendAttentionMetadataBuilder310 as AscendMetadataBuilder310Direct, ) class TestAscendAttentionBackend310(TestBase): def setUp(self): self.mock_config = MagicMock() self.utils_patcher = patch("vllm_ascend.attention.utils.get_current_vllm_config", return_value=self.mock_config) self.utils_patcher.start() def test_get_impl_cls(self): self.assertEqual(AscendAttentionBackend310.get_impl_cls(), AscendAttentionBackendImpl310) def test_get_builder_cls(self): self.assertEqual(AscendAttentionBackend310.get_builder_cls(), AscendAttentionMetadataBuilder310) def test_get_kv_cache_shape_not(self): result = AscendAttentionBackend310.get_kv_cache_shape(10, 20, 30, 40) self.assertEqual(result, (2, 10, 75, 20, 16)) class TestAscendAttentionBackendImpl310(TestBase): def setUp(self): self.attention_type = MagicMock() self.attention_type.DECODER = "decoder" self.attention_type.ENCODER = "encoder" self.attn_metadata = MagicMock() self.attn_metadata.return_value = "1" self.mock_vllm_config = MagicMock() self.layer_no_quant = MagicMock(spec=["layer_name", "_k_scale_float", "_v_scale_float"]) self.layer_no_quant.layer_name = "test_layer" self.layer_no_quant._k_scale_float = 1.0 self.layer_no_quant._v_scale_float = 1.0 self.config_patcher = patch( "vllm_ascend.attention.attention_v1.get_current_vllm_config", return_value=self.mock_vllm_config ) self.config_patcher.start() self.impl = AscendAttentionBackendImpl310( num_heads=8, head_size=128, scale=1.0, num_kv_heads=8, alibi_slopes=None, sliding_window=None, kv_cache_dtype="float16", logits_soft_cap=None, attn_type=self.attention_type.DECODER, kv_sharing_target_layer_name=None, ) @patch("torch_npu._npu_reshape_and_cache") @patch("torch_npu._npu_flash_attention") @patch("vllm_ascend.ascend_forward_context.get_forward_context") def test_forward_prefill_310(self, mock_get_forward_context, mock_npu_flash_attention, mock_npu_reshape_and_cache): """Test forward pass in PrefillNoCache state.""" query = torch.randn(10, 8, 64) key = torch.randn(10, 8, 64) value = torch.randn(10, 8, 64) output = torch.empty_like(query) metadata = self.attn_metadata metadata.attn_state = AscendAttentionState.PrefillNoCache metadata.attn_mask = torch.randn(1, 1, 10, 10) metadata.query_lens = torch.tensor([10]) metadata.seq_lens = torch.tensor([10]) metadata.actual_seq_lengths_q = [10] metadata.block_tables = torch.zeros(1, 5, dtype=torch.long) metadata.num_actual_tokens = 10 metadata.num_decode_tokens = 0 metadata.num_decodes = 0 metadata.num_prefills = 10 metadata.slot_mapping = torch.zeros(10, dtype=torch.long) self.impl.support_compressed_mask = False mock_get_forward_context.return_value = MagicMock(capturing=False) mock_npu_flash_attention.return_value = torch.ones(10, 8, 64) result = self.impl.forward_impl(query, key, value, None, metadata, output) mock_npu_flash_attention.assert_called_once() _, kwargs = mock_npu_flash_attention.call_args self.assertIs(kwargs["query"], query) self.assertIs(kwargs["key"], key) self.assertIs(kwargs["value"], value) self.assertIs(kwargs["mask"], metadata.attn_mask) self.assertIs(kwargs["seq_len"], metadata.seq_lens) self.assertEqual(kwargs["scale_value"], self.impl.scale) self.assertEqual(kwargs["num_heads"], self.impl.num_heads) self.assertEqual(kwargs["num_kv_heads"], self.impl.num_kv_heads) self.assertIs(kwargs["out"], output) self.assertIs(result, output) @patch("torch_npu.npu_format_cast", return_value=torch.randn((1, 128, 16, 16), dtype=torch.float16)) @patch("torch_npu._npu_reshape_and_cache") @patch("torch_npu._npu_paged_attention_splitfuse") @patch("vllm_ascend.ascend_forward_context.get_forward_context") def test_forward_chunked_prefill_310( self, mock_get_forward_context, mock_npu_paged_attention_splitfuse, mock_npu_reshape_and_cache, mock_format_cast, ): """Test forward pass in ChunkedPrefill state""" query = torch.randn(5, 8, 64) key, value = None, None output = torch.empty_like(query) metadata = self.attn_metadata metadata.attn_state = AscendAttentionState.ChunkedPrefill metadata.attn_mask = torch.randn(1, 128, 16, 16) metadata.query_lens = torch.tensor([5]) metadata.seq_lens = torch.tensor([1, 4]) metadata.query_start_loc = torch.tensor([0, 1, 5]) metadata.actual_seq_lengths_q = [5] metadata.block_tables = torch.zeros(1, 5, dtype=torch.long) metadata.num_actual_tokens = 10 metadata.num_decode_tokens = 0 metadata.num_decodes = 0 metadata.num_prefills = 10 metadata.slot_mapping = torch.zeros(10, dtype=torch.long) self.impl.support_compressed_mask = False mock_get_forward_context.return_value = MagicMock(capturing=False) mock_npu_paged_attention_splitfuse.return_value = torch.ones(5, 8, 64) output = self.impl.forward_impl(query, key, value, None, metadata, output) mock_npu_paged_attention_splitfuse.assert_called_once() @patch("torch_npu.npu_format_cast", return_value=torch.randn((1, 128, 16, 16), dtype=torch.float16)) @patch("torch_npu._npu_reshape_and_cache") @patch("torch_npu._npu_paged_attention_splitfuse") @patch("vllm_ascend.ascend_forward_context.get_forward_context") def test_forward_prefill_cache_hit_310( self, mock_get_forward_context, mock_npu_paged_attention_splitfuse, mock_npu_reshape_and_cache, mock_format_cast, ): """Test forward pass in PrefillCacheHit state""" query = torch.randn(5, 8, 64) key, value = None, None output = torch.empty_like(query) metadata = self.attn_metadata metadata.attn_state = AscendAttentionState.PrefillCacheHit metadata.attn_mask = torch.randn(1, 128, 16, 16) metadata.query_lens = torch.tensor([5]) metadata.seq_lens = torch.tensor([1, 4]) metadata.query_start_loc = torch.tensor([0, 1, 5]) metadata.actual_seq_lengths_q = [5] metadata.block_tables = torch.zeros(1, 5, dtype=torch.long) metadata.num_actual_tokens = 10 metadata.num_decode_tokens = 0 metadata.num_decodes = 0 metadata.num_prefills = 10 metadata.slot_mapping = torch.zeros(10, dtype=torch.long) self.impl.support_compressed_mask = False mock_get_forward_context.return_value = MagicMock(capturing=False) mock_npu_paged_attention_splitfuse.return_value = torch.ones(5, 8, 64) output = self.impl.forward_impl(query, key, value, None, metadata, output) mock_npu_paged_attention_splitfuse.assert_called_once() @patch("vllm_ascend.attention.attention_v1.using_paged_attention") @patch("torch_npu._npu_paged_attention", create=True) @patch("torch_npu._npu_reshape_and_cache") @patch("vllm_ascend.ascend_forward_context.get_forward_context") def test_forward_paged_attention_310( self, mock_get_forward_context, mock_npu_reshape_and_cache, mock_paged_attention, mock_using_paged_attention ): """Test forward pass in DecodeOnly state""" query = torch.randn(4, 8 * 64) key, value = None, None output = torch.empty_like(query) metadata = self.attn_metadata metadata.attn_state = AscendAttentionState.DecodeOnly metadata.seq_lens = torch.tensor([4]) metadata.block_tables = torch.zeros(1, 5, dtype=torch.long) metadata.num_actual_tokens = 4 metadata.slot_mapping = torch.zeros(4, dtype=torch.long) metadata.num_decodes = 4 metadata.num_prefills = 0 mock_using_paged_attention.return_value = True mock_get_forward_context.return_value = MagicMock(capturing=False) output = self.impl.forward_impl(query, key, value, None, metadata, output) mock_paged_attention.assert_called_once() @patch("vllm_ascend._310p.attention.attention_v1.AscendAttentionBackendImpl310.forward_chunked_prefill_310") def test_forward_mtp_310(self, mock_chunked_prefill): query = torch.randn(4, 8 * 64) key, value = None, None output = torch.empty_like(query) metadata = self.attn_metadata metadata.attn_state = AscendAttentionState.SpecDecoding mock_chunked_prefill.return_value = output result = self.impl.forward_impl(query, key, value, None, metadata, output) mock_chunked_prefill.assert_called_once_with(query, metadata, output) self.assertIs(result, output) class TestAscendAttentionMetadataBuilder310(TestBase): def test_fill_query_lens_cpu_without_buffer(self): builder = AscendMetadataBuilder310Direct.__new__(AscendMetadataBuilder310Direct) builder._query_lens_cpu_buffer = None query_start_loc_cpu = torch.tensor([0, 1, 5, 11, 20], dtype=torch.int32) result = builder._fill_query_lens_cpu(num_reqs=3, query_start_loc_cpu=query_start_loc_cpu, is_drafting=False) expected = torch.tensor([1, 4, 6], dtype=torch.int32) torch.testing.assert_close(result, expected) def test_fill_query_lens_cpu_with_buffer_not_drafting(self): builder = AscendMetadataBuilder310Direct.__new__(AscendMetadataBuilder310Direct) builder._query_lens_cpu_buffer = torch.zeros(10, dtype=torch.int32, device="cpu") query_start_loc_cpu = torch.tensor([0, 1, 5, 11, 20], dtype=torch.int32) result = builder._fill_query_lens_cpu(num_reqs=3, query_start_loc_cpu=query_start_loc_cpu, is_drafting=False) expected = torch.tensor([1, 4, 6], dtype=torch.int32) torch.testing.assert_close(result, expected) assert result.data_ptr() == builder._query_lens_cpu_buffer[:3].data_ptr() def test_fill_query_lens_cpu_with_buffer_is_drafting(self): builder = AscendMetadataBuilder310Direct.__new__(AscendMetadataBuilder310Direct) builder._query_lens_cpu_buffer = torch.zeros(10, dtype=torch.int32, device="cpu") query_start_loc_cpu = torch.tensor([0, 1, 5, 11, 20], dtype=torch.int32) result1 = builder._fill_query_lens_cpu(num_reqs=3, query_start_loc_cpu=query_start_loc_cpu, is_drafting=True) result2 = builder._fill_query_lens_cpu(num_reqs=3, query_start_loc_cpu=query_start_loc_cpu, is_drafting=True) expected = torch.tensor([1, 4, 6], dtype=torch.int32) torch.testing.assert_close(result1, expected) torch.testing.assert_close(result2, expected) assert result1.data_ptr() != builder._query_lens_cpu_buffer[:3].data_ptr() assert result2.data_ptr() != builder._query_lens_cpu_buffer[:3].data_ptr() def test_build_for_drafting_calls_build_with_is_drafting_true(self): builder = object.__new__(AscendMetadataBuilder310Direct) builder._query_lens_cpu_buffer = torch.zeros(10, dtype=torch.int32, device="cpu") builder.device = torch.device("cpu") from vllm.v1.kv_cache_interface import AttentionSpec from vllm_ascend._310p.attention.attention_mask import AttentionMaskBuilder310 builder.attn_mask_builder = AttentionMaskBuilder310(torch.device("cpu"), 4096) builder.kv_cache_spec = AttentionSpec( block_size=128, num_kv_heads=2, head_size=64, dtype=torch.float16, ) builder.layer_names = [] builder.vllm_config = MagicMock() builder.vllm_config.model_config.max_model_len = 4096 builder.vllm_config.scheduler_config.max_num_seqs = 8 common_attn_metadata = MagicMock() common_attn_metadata.num_reqs = 2 common_attn_metadata.query_start_loc = torch.tensor([0, 1, 3]) common_attn_metadata.query_start_loc_cpu = torch.tensor([0, 1, 3]) common_attn_metadata.seq_lens = torch.tensor([1, 2]) with patch.object(AscendMetadataBuilder310Direct.__bases__[0], "build", return_value=MagicMock()) as mock_build: result = builder.build_for_drafting(common_attn_metadata=common_attn_metadata, draft_index=0) mock_build.assert_called_once_with(0, common_attn_metadata, True) assert result is not None