from unittest.mock import MagicMock, patch import torch from tests.ut.base import TestBase from vllm_ascend.attention.attention_v1 import (AscendAttentionBackend, AscendAttentionBackendImpl, AscendAttentionMetadataBuilder, AscendAttentionState) from vllm_ascend.attention.utils import AscendCommonAttentionMetadata from vllm_ascend.utils import AscendDeviceType class TestAscendAttentionBackend(TestBase): def setUp(self): self.mock_config = MagicMock() mock_parallel_config = MagicMock() mock_parallel_config.prefill_context_parallel_size = 1 mock_parallel_config.decode_context_parallel_size = 1 self.mock_config.parallel_config = mock_parallel_config self.utils_patcher = patch( 'vllm_ascend.attention.utils.get_current_vllm_config', return_value=self.mock_config) self.utils_patcher.start() from vllm_ascend.attention.utils import enable_cp enable_cp.cache_clear() def test_get_name(self): self.assertEqual(AscendAttentionBackend.get_name(), "CUSTOM") def test_get_impl_cls(self): self.assertEqual(AscendAttentionBackend.get_impl_cls(), AscendAttentionBackendImpl) def test_get_builder_cls(self): self.assertEqual(AscendAttentionBackend.get_builder_cls(), AscendAttentionMetadataBuilder) @patch('vllm_ascend.utils.get_ascend_device_type', return_value=AscendDeviceType.A3) def test_get_kv_cache_shape_not_310p(self, mock_soc_version): result = AscendAttentionBackend.get_kv_cache_shape(10, 20, 30, 40) self.assertEqual(result, (2, 10, 20, 30, 40)) def test_swap_blocks(self): src_kv_cache = [torch.zeros((10, 20)), torch.zeros((10, 20))] dst_kv_cache = [torch.zeros((10, 20)), torch.zeros((10, 20))] src_to_dst = torch.tensor([[0, 1], [2, 3]]) AscendAttentionBackend.swap_blocks(src_kv_cache, dst_kv_cache, src_to_dst) self.assertTrue(torch.all(dst_kv_cache[0][1] == src_kv_cache[0][0])) self.assertTrue(torch.all(dst_kv_cache[1][3] == src_kv_cache[1][2])) def test_copy_blocks(self): kv_caches = [torch.zeros((10, 20)), torch.zeros((10, 20))] src_to_dists = torch.tensor([[0, 1], [2, 3]]) AscendAttentionBackend.copy_blocks(kv_caches, src_to_dists) self.assertTrue(torch.all(kv_caches[0][1] == kv_caches[0][0])) self.assertTrue(torch.all(kv_caches[1][3] == kv_caches[1][2])) class TestAscendAttentionMetadataBuilder(TestBase): def setUp(self): self.mock_vllm_config = MagicMock() self.mock_vllm_config.speculative_config = None self.mock_vllm_config.model_config.max_model_len = 640 self.mock_vllm_config.model_config.hf_text_config.sliding_window = None self.mock_vllm_config.cache_config.block_size = 64 self.mock_vllm_config.compilation_config.cudagraph_mode = None self.mock_vllm_config.scheduler_config.max_num_seqs = 10 self.mock_vllm_config.scheduler_config.decode_max_num_seqs = 10 self.mock_vllm_config.scheduler_config.chunked_prefill_enabled = False self.mock_device = 'cpu:0' torch.Tensor.pin_memory = lambda x: x # noqa self.builder = AscendAttentionMetadataBuilder(None, None, self.mock_vllm_config, self.mock_device) def test_reorder_batch(self): mock_input_batch = MagicMock() mock_scheduler_output = MagicMock() result = self.builder.reorder_batch(mock_input_batch, mock_scheduler_output) self.assertFalse(result) @patch('vllm_ascend.attention.attention_v1.AscendMetadata') @patch('vllm_ascend.utils.get_ascend_device_type', return_value=AscendDeviceType.A3) def test_build_non_310p(self, mock_soc_version, mock_ascend_metadata): common_attn_metadata = AscendCommonAttentionMetadata( query_start_loc=torch.tensor([0, 2, 5, 9]), query_start_loc_cpu=torch.tensor([0, 2, 5, 9]), seq_lens_cpu=torch.tensor([4, 5, 6]), num_reqs=3, num_actual_tokens=15, max_query_len=6, decode_token_per_req=torch.tensor([1, 1, 1]), block_table_tensor=torch.zeros((10, 10)), slot_mapping=torch.tensor(range(20)), actual_seq_lengths_q=torch.tensor([0, 1, 2]), positions=torch.tensor([10, 10]), attn_state=AscendAttentionState.ChunkedPrefill, num_computed_tokens_cpu=None, seq_lens=None, max_seq_len=6) mock_model = MagicMock() self.builder.build(1, common_attn_metadata, mock_model) class TestAscendAttentionBackendImpl(TestBase): def setUp(self): self.mock_event = MagicMock() self.mock_event.record.return_value = None self.mock_event.wait.return_value = None self.mock_stream = MagicMock() self.event_patcher = patch('torch_npu.npu.Event', return_value=self.mock_event) self.stream_patcher = patch('torch_npu.npu.current_stream', return_value=self.mock_stream) self.event_patcher.start() self.stream_patcher.start() self.layer = MagicMock() self.layer.layer_name = "test_layer" self.layer._k_scale_float = 1.0 self.layer._v_scale_float = 1.0 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.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.mock_vllm_config = MagicMock() 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 = AscendAttentionBackendImpl( num_heads=8, head_size=64, 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) self.impl_192 = AscendAttentionBackendImpl( num_heads=8, head_size=192, 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) self.impl_error = AscendAttentionBackendImpl( num_heads=8, head_size=192, scale=1.0, num_kv_heads=8, alibi_slopes=None, sliding_window=None, kv_cache_dtype="float16", logits_soft_cap=None, attn_type=None, kv_sharing_target_layer_name=None) self.impl_swa = AscendAttentionBackendImpl( num_heads=8, head_size=64, scale=1.0, num_kv_heads=8, alibi_slopes=None, sliding_window=1024, kv_cache_dtype="float16", logits_soft_cap=None, attn_type=self.attention_type.DECODER, kv_sharing_target_layer_name=None) def test_forward_no_attn_metadata(self): """Test forward pass when attn_metadata is None""" query = torch.randn(10, 8 * 64) key = torch.randn(10, 8 * 64) value = torch.randn(10, 8 * 64) kv_cache = torch.empty(2, 0, 0, 8, 64) layer = self.layer_no_quant output = torch.empty_like(query) output = self.impl.forward(layer, query, key, value, kv_cache, None, output) assert output.shape == (10, 8 * 64) @patch('torch_npu._npu_reshape_and_cache') @patch('torch_npu.npu_fused_infer_attention_score') @patch('vllm_ascend.attention.attention_v1.get_forward_context') def test_forward_fused_infer_attention( self, mock_get_forward_context, mock_npu_fused_infer_attention_score, mock_npu_reshape_and_cache): """Test forward pass in PrefillCacheHit state""" query = torch.randn(10, 8, 64) key = torch.randn(10, 8, 64) value = torch.randn(10, 8, 64) kv_cache = torch.empty(2, 5, 128, 8, 64) output = torch.empty_like(query) metadata = self.attn_metadata metadata.attn_state = AscendAttentionState.PrefillCacheHit 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) layer = self.layer_no_quant mock_get_forward_context.return_value = MagicMock(capturing=False) mock_npu_fused_infer_attention_score.return_value = (torch.ones( 10, 8, 64), torch.ones(10, 8, 64)) output = self.impl.forward(layer, query, key, value, kv_cache, metadata, output) mock_npu_fused_infer_attention_score.assert_called_once() assert output.shape == (10, 8, 64) @patch('vllm_ascend.attention.attention_v1.using_paged_attention') @patch('torch_npu._npu_paged_attention') @patch('torch_npu._npu_reshape_and_cache') @patch('vllm_ascend.attention.attention_v1.get_forward_context') def test_forward_paged_attention(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 = torch.randn(4, 8 * 64) value = torch.randn(4, 8 * 64) kv_cache = torch.empty(2, 5, 128, 8, 64) 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 layer = self.layer_no_quant mock_using_paged_attention.return_value = True mock_get_forward_context.return_value = MagicMock(capturing=False) output = self.impl.forward(layer, query, key, value, kv_cache, metadata, output) mock_paged_attention.assert_called_once() assert output.shape == (4, 8 * 64) @patch('vllm_ascend.attention.attention_v1.get_forward_context') @patch('torch_npu.npu_fused_infer_attention_score') @patch('torch_npu._npu_reshape_and_cache') def test_forward_decode_only_swa(self, mock_npu_reshape_and_cache, mock_fused_infer_attention_score, mock_get_forward_context): """Test forward pass in DecodeOnly state""" query = torch.randn(10, 8 * 64) key = torch.randn(10, 8 * 64) value = torch.randn(10, 8 * 64) kv_cache = torch.empty(2, 5, 128, 8, 64) output = torch.empty(10, 8, 64) mock_get_forward_context.return_value = MagicMock(capturing=False) metadata = self.attn_metadata metadata.attn_state = AscendAttentionState.DecodeOnly metadata.seq_lens = torch.tensor([10] * 10) metadata.block_tables = torch.zeros(1, 5, dtype=torch.long) metadata.num_actual_tokens = 100 metadata.slot_mapping = torch.zeros(10, dtype=torch.long) metadata.num_decodes = 10 metadata.num_prefills = 0 layer = self.layer_no_quant mock_fused_infer_attention_score.return_value = (torch.ones(10, 8, 64), 1) output = self.impl_swa.forward(layer, query, key, value, kv_cache, metadata, output) print(output.shape) mock_fused_infer_attention_score.assert_called_once() assert output.shape == (10, 8, 64) @patch('vllm_ascend.attention.attention_v1.get_forward_context') @patch('torch_npu._npu_paged_attention') @patch('torch_npu.npu_fused_infer_attention_score') @patch('torch_npu._npu_reshape_and_cache') def test_forward_decode_only_swa_seq_len_mismatch( self, mock_npu_reshape_and_cache, mock_fused_infer_attention_score, mock_paged_attention, mock_get_forward_context): """Test forward pass in DecodeOnly state when seq)len_mismatch""" query = torch.randn(10, 8, 64) key = torch.randn(10, 8, 64) value = torch.randn(10, 8, 64) kv_cache = torch.empty(2, 5, 128, 8, 64) output = torch.empty_like(query) metadata = self.attn_metadata metadata.attn_state = AscendAttentionState.DecodeOnly metadata.seq_lens = torch.tensor([10]) # len == 1 != query.size(0)==10 metadata.block_tables = torch.zeros(1, 5, dtype=torch.long) metadata.num_actual_tokens = 10 metadata.slot_mapping = torch.zeros(10, dtype=torch.long) layer = self.layer_no_quant metadata.num_decodes = 10 metadata.num_prefills = 0 metadata.actual_seq_lengths_q = [10] mock_get_forward_context.return_value = MagicMock(capturing=False) mock_fused_infer_attention_score.return_value = (torch.ones(10, 8, 64), torch.ones(10, 8, 64)) output = self.impl_swa.forward(layer, query, key, value, kv_cache, metadata, output) mock_paged_attention.assert_not_called() mock_fused_infer_attention_score.assert_called_once() assert output.shape == (10, 8, 64)