from unittest.mock import MagicMock, patch import numpy as np import torch from vllm.distributed.parallel_state import GroupCoordinator from vllm.model_executor.layers.linear import LinearBase from tests.ut.base import TestBase from vllm_ascend.attention.attention_v1 import AscendAttentionState from vllm_ascend.attention.mla_v1 import (AscendMLABackend, AscendMLADecodeMetadata, AscendMLAImpl, AscendMLAMetadata, AscendMLAMetadataBuilder, AscendMLAPrefillMetadata) class TestAscendMLABackend(TestBase): def test_get_name(self): self.assertEqual(AscendMLABackend.get_name(), "ASCEND_MLA") def test_get_metadata_cls(self): self.assertEqual(AscendMLABackend.get_metadata_cls(), AscendMLAMetadata) def test_get_builder_cls(self): self.assertEqual(AscendMLABackend.get_builder_cls(), AscendMLAMetadataBuilder) def test_get_kv_cache_shape(self): result = AscendMLABackend.get_kv_cache_shape(2, 4, 8, 128) self.assertEqual(result, (2, 4, 8, 128)) def test_get_impl_cls(self): result = AscendMLABackend.get_impl_cls() self.assertEqual(result, AscendMLAImpl) class TestAscendMLAPrefillMetadata(TestBase): def test_ascend_mla_prefill_metadata_default(self): attn_mask = torch.tensor([[1, 0], [1, 1]], dtype=torch.bool) query_lens = [1, 2] seq_lens = [2, 2] context_lens = torch.tensor([1, 2]) input_positions = torch.tensor([0, 1, 0, 1]) query_start_loc = torch.tensor([0, 1, 3]) block_table = torch.tensor([[0, 1], [2, 3]]) max_query_len = 2 max_seq_lens = 2 metadata = AscendMLAPrefillMetadata(attn_mask=attn_mask, query_lens=query_lens, seq_lens=seq_lens, context_lens=context_lens, input_positions=input_positions, query_start_loc=query_start_loc, block_table=block_table, max_query_len=max_query_len, max_seq_lens=max_seq_lens) self.assertIs(metadata.attn_mask, attn_mask) self.assertEqual(metadata.query_lens, query_lens) self.assertEqual(metadata.seq_lens, seq_lens) self.assertIs(metadata.context_lens, context_lens) self.assertIs(metadata.input_positions, input_positions) self.assertIs(metadata.query_start_loc, query_start_loc) self.assertIs(metadata.block_table, block_table) self.assertEqual(metadata.max_query_len, max_query_len) self.assertEqual(metadata.max_seq_lens, max_seq_lens) self.assertIsNone(metadata.chunked_context) def test_ascend_mla_prefill_metadata_with_chunked_context(self): cu_seq_lens = torch.tensor([0, 2, 4]) starts = torch.tensor([0, 2]) seq_tot = [2, 2] max_seq_lens = [2, 2] workspace = torch.randn(2, 4) chunk_seq_lens = torch.tensor([2, 2]) chunked_context = AscendMLAPrefillMetadata.ChunkedContextMetadata( cu_seq_lens=cu_seq_lens, starts=starts, seq_tot=seq_tot, max_seq_lens=max_seq_lens, workspace=workspace, chunk_seq_lens=chunk_seq_lens) metadata = AscendMLAPrefillMetadata( attn_mask=torch.tensor([[1, 0], [1, 1]], dtype=torch.bool), query_lens=[1, 2], seq_lens=[2, 2], context_lens=torch.tensor([1, 2]), input_positions=torch.tensor([0, 1, 0, 1]), query_start_loc=torch.tensor([0, 1, 3]), block_table=torch.tensor([[0, 1], [2, 3]]), max_query_len=2, max_seq_lens=2, chunked_context=chunked_context) self.assertIsNotNone(metadata.chunked_context) self.assertIs(metadata.chunked_context.cu_seq_lens, cu_seq_lens) self.assertIs(metadata.chunked_context.starts, starts) self.assertEqual(metadata.chunked_context.seq_tot, seq_tot) self.assertEqual(metadata.chunked_context.max_seq_lens, max_seq_lens) self.assertIs(metadata.chunked_context.workspace, workspace) self.assertIs(metadata.chunked_context.chunk_seq_lens, chunk_seq_lens) class TestAscendMLADecodeMetadata(TestBase): def test_ascend_mla_decode_metadata_default(self): input_positions = torch.tensor([[1, 2, 3, 4], [1, 2, 3, 4]]) block_table = torch.tensor([[0, 3, 2, 1], [0, 2, 1, 3]]) seq_lens = torch.tensor([[2], [3]]) max_seq_lens = 4 seq_lens_list = [2, 3] attn_mask = None metadata = AscendMLADecodeMetadata(input_positions, block_table, seq_lens, max_seq_lens, seq_lens_list, attn_mask) self.assertIs(metadata.input_positions, input_positions) self.assertIs(metadata.block_table, block_table) self.assertIs(metadata.seq_lens, seq_lens) self.assertEqual(metadata.max_seq_lens, max_seq_lens) self.assertEqual(metadata.seq_lens_list, seq_lens_list) self.assertIsNone(attn_mask) class TestAscendMLAMetadata(TestBase): def test_ascend_mla_metadata_default(self): num_actual_tokens = 100 slot_mapping = torch.randn(100, 4, 1024) query_start_loc = torch.tensor([1, 2, 3, 4]) seq_lens = [30, 50] block_tables = torch.randint(0, 100, (100, 4)) num_decodes = 4 num_decode_tokens = 8 num_prefills = 8 num_input_tokens = 2 query_lens = None head_dim = None attn_mask = None attn_state = AscendAttentionState.ChunkedPrefill decode = None prefill = None metadata = AscendMLAMetadata(num_actual_tokens, slot_mapping, query_start_loc, seq_lens, block_tables, num_decodes, num_decode_tokens, num_prefills, num_input_tokens, query_lens, head_dim, attn_mask, attn_state, decode, prefill) self.assertEqual(metadata.num_actual_tokens, num_actual_tokens) self.assertIs(metadata.slot_mapping, slot_mapping) self.assertIs(metadata.query_start_loc, query_start_loc) self.assertEqual(metadata.seq_lens, seq_lens) self.assertIs(metadata.block_tables, block_tables) self.assertEqual(metadata.num_decodes, num_decodes) self.assertEqual(metadata.num_decode_tokens, num_decode_tokens) self.assertEqual(metadata.num_prefills, num_prefills) self.assertEqual(metadata.num_input_tokens, num_input_tokens) self.assertEqual(metadata.query_lens, query_lens) self.assertEqual(metadata.head_dim, head_dim) self.assertEqual(metadata.attn_mask, attn_mask) self.assertEqual(metadata.attn_state, attn_state) self.assertEqual(metadata.decode, decode) self.assertEqual(metadata.prefill, prefill) class TestAscendMLAMetadataBuilder(TestBase): def test_ascend_mla_metadata_builder_default(self): runner = MagicMock() runner.scheduler_config = MagicMock() runner.model_config = MagicMock() runner.scheduler_config.max_num_seqs = 4 runner.model_config.max_model_len = 1024 runner.model_config.get_head_size.return_value = 64 runner.model_config.dtype = torch.float16 runner.chunked_prefill_enabled = False runner.device = "cpu" runner.block_size = 16 runner.decode_token_per_req = 1 ascend_config = MagicMock() ascend_config.torchair_graph_config = MagicMock() ascend_config.torchair_graph_config.enabled = True with patch("vllm_ascend.attention.mla_v1.get_ascend_config", return_value=ascend_config): builder = AscendMLAMetadataBuilder(runner) self.assertEqual(builder.runner, runner) self.assertEqual(builder.block_size, runner.block_size) self.assertEqual(builder.chunked_prefill_enabled, runner.chunked_prefill_enabled) self.assertEqual(builder.torchair_graph_enabled, True) @patch("vllm_ascend.attention.mla_v1.get_ascend_config") def test_reorder_batch_with_torchair_graph(self, ascend_config): runner = MagicMock() runner.chunked_prefill_enabled = False runner.decode_token_per_req = 1 ascend_config.torchair_graph_config = MagicMock() ascend_config.torchair_graph_config.enabled = True builder = AscendMLAMetadataBuilder(runner) input_batch = MagicMock() input_batch.req_ids = [0, 1, 2, 3] scheduler_output = MagicMock() scheduler_output.num_scheduled_tokens = {0: 2, 1: 1, 2: 3, 3: 1} scheduler_output.scheduled_spec_decode_tokens = { 0: [1], 1: [], 2: [1, 1], 3: [] } input_batch.swap_states = MagicMock() modified = builder.reorder_batch(input_batch, scheduler_output) self.assertFalse(modified) self.assertEqual(builder._num_decodes, 4) self.assertEqual(builder._num_prefills, 0) self.assertEqual(builder._num_decode_tokens, 7) self.assertEqual(builder._num_prefill_tokens, 0) input_batch.swap_states.assert_not_called() def test_reorder_batch_without_torchair_graph(self): ascend_config = MagicMock() runner = MagicMock() runner.chunked_prefill_enabled = False runner.decode_token_per_req = 1 ascend_config.torchair_graph_config = MagicMock() ascend_config.torchair_graph_config.enabled = False with patch("vllm_ascend.attention.mla_v1.get_ascend_config", return_value=ascend_config): builder = AscendMLAMetadataBuilder(runner) input_batch = MagicMock() input_batch.req_ids = [0, 1, 2, 3] scheduler_output = MagicMock() scheduler_output.num_scheduled_tokens = {0: 1, 1: 3, 2: 1, 3: 2} scheduler_output.scheduled_spec_decode_tokens = { 0: [], 1: [1], 2: [], 3: [] } input_batch.swap_states = MagicMock() modified = builder.reorder_batch(input_batch, scheduler_output) self.assertTrue(modified) self.assertEqual(builder._num_decodes, 2) self.assertEqual(builder._num_prefills, 2) self.assertEqual(builder._num_decode_tokens, 2) self.assertEqual(builder._num_prefill_tokens, 5) input_batch.swap_states.assert_called_once_with(1, 2) @patch("vllm_ascend.attention.mla_v1.get_ascend_config") def test_get_graph_runner_block_tables_normal(self, mock_ascend_config): ascend_config = MagicMock() mock_ascend_config.return_value = ascend_config ascend_config.torchair_graph_config.enabled = False runner = MagicMock() runner.graph_block_tables = torch.zeros((8, 64), dtype=torch.int32) runner.chunked_prefill_enabled = False runner.decode_token_per_req = 1 builder = AscendMLAMetadataBuilder(runner=runner) block_tables = torch.randint(0, 100, (3, 10), dtype=torch.int32) result = builder._get_graph_runner_block_tables(3, block_tables) self.assertEqual(result.shape[0], 3) self.assertEqual(result.shape[1], 64) self.assertTrue(torch.equal(result[:, :10], block_tables)) @patch("vllm_ascend.attention.mla_v1.get_ascend_config") def test_get_graph_runner_block_tables_truncated(self, mock_ascend_config): ascend_config = MagicMock() mock_ascend_config.return_value = ascend_config ascend_config.torchair_graph_config.enabled = False runner = MagicMock() runner.graph_block_tables = torch.zeros((8, 4), dtype=torch.int32) runner.chunked_prefill_enabled = False runner.decode_token_per_req = 1 builder = AscendMLAMetadataBuilder(runner=runner) block_tables = torch.randint(0, 100, (3, 10), dtype=torch.int32) result = builder._get_graph_runner_block_tables(3, block_tables) self.assertEqual(result.shape[0], 3) self.assertEqual(result.shape[1], 4) self.assertTrue(torch.equal(result, block_tables[:, :4])) @patch("vllm_ascend.attention.mla_v1.get_ascend_config") def test_get_graph_runner_block_tables_from_numpy(self, mock_ascend_config): ascend_config = MagicMock() mock_ascend_config.return_value = ascend_config ascend_config.torchair_graph_config.enabled = False runner = MagicMock() runner.graph_block_tables = np.zeros((8, 64), dtype=np.int32) runner.chunked_prefill_enabled = False runner.decode_token_per_req = 1 builder = AscendMLAMetadataBuilder(runner=runner) block_tables = torch.randint(0, 100, (3, 10), dtype=torch.int32) result = builder._get_graph_runner_block_tables(3, block_tables) self.assertEqual(result.shape[0], 3) self.assertEqual(result.shape[1], 64) self.assertTrue(torch.equal(result[:, :10], block_tables)) @patch("vllm_ascend.attention.mla_v1.get_ascend_config") def test_build_dummy(self, mock_ascend_config): ascend_config = MagicMock() mock_ascend_config.return_value = ascend_config ascend_config.torchair_graph_config.enabled = False runner = MagicMock() runner.model_config = MagicMock() runner.device = "cpu" runner.graph_block_tables = torch.zeros((8, 64), dtype=torch.int32) runner.model_config.get_head_size.return_value = 64 runner.chunked_prefill_enabled = False runner.attn_mask = torch.zeros((1, 1), dtype=torch.bool) runner.spec_attn_mask = torch.zeros((1, 1), dtype=torch.bool) runner.dtype = torch.float16 runner.decode_token_per_req = 1 builder = AscendMLAMetadataBuilder(runner=runner, metadata_cls=AscendMLAMetadata) builder.rope_dim = 64 with patch.object(builder, "_get_graph_runner_block_tables", side_effect=lambda x, y: y): metadata = builder.build_torchair_graph_dummy(3, 3) sin_golden = torch.ones(3, 1, 1, 64, dtype=runner.dtype, device=runner.device) cos_golden = torch.ones(3, 1, 1, 64, dtype=runner.dtype, device=runner.device) self.assertIsInstance(metadata, AscendMLAMetadata) self.assertEqual(metadata.num_input_tokens, 3) self.assertEqual(metadata.num_actual_tokens, 3) self.assertEqual(metadata.num_decodes, 1) self.assertEqual(metadata.num_decode_tokens, 1) self.assertEqual(metadata.num_prefills, 0) self.assertEqual(metadata.attn_state, AscendAttentionState.DecodeOnly) self.assertIsNone(metadata.prefill) self.assertIsInstance(metadata.decode, AscendMLADecodeMetadata) self.assertEqual(metadata.block_tables.shape[0], 3) self.assertEqual(metadata.block_tables.shape[1], 64) self.assertEqual(metadata.seq_lens.shape[0], 3) self.assertEqual(metadata.slot_mapping.shape[0], 3) self.assertEqual(metadata.query_start_loc.shape[0], 3) assert torch.equal(sin_golden, metadata.decode.sin) assert torch.equal(cos_golden, metadata.decode.cos) class TestAscendMLAImpl(TestBase): @patch('vllm.distributed.parallel_state._TP', new_callable=lambda: MagicMock(spec=GroupCoordinator)) @patch("vllm.distributed.get_tensor_model_parallel_world_size", return_value=2) @patch("vllm.config.get_current_vllm_config") @patch("vllm_ascend.attention.mla_v1.get_ascend_config") def setUp(self, ascend_config, vllm_config, mock_get_tp_size, mock_tp): mock_tp.world_size = 2 ascend_config.torchair_graph_config.enabled = True ascend_config.torchair_graph_config.enable_kv_nz = False speculative_config = MagicMock() speculative_config.num_speculative_tokens = 4 vllm_config.speculative_config = speculative_config num_heads = 256 head_size = 1024 scale = 0.1 num_kv_heads = 8 kv_cache_dtype = "auto" kv_a_layernorm = MagicMock() kv_a_layernorm.weight = torch.randn(96) kv_a_layernorm.variance_epsilon = 1e-6 kwargs = { "q_lora_rank": 64, "kv_lora_rank": 32, "qk_nope_head_dim": 64, "qk_rope_head_dim": 32, "qk_head_dim": 96, "v_head_dim": 128, "rotary_emb": MagicMock(), "q_proj": MagicMock(), "kv_b_proj": MagicMock(), "o_proj": MagicMock(), "kv_a_proj_with_mqa": MagicMock(), "kv_a_layernorm": kv_a_layernorm, } self.impl = AscendMLAImpl(num_heads=num_heads, head_size=head_size, scale=scale, num_kv_heads=num_kv_heads, alibi_slopes=None, sliding_window=None, kv_cache_dtype=kv_cache_dtype, blocksparse_params=None, logits_soft_cap=None, attn_type=None, kv_sharing_target_layer_name=None, **kwargs) def test_init(self): self.assertEqual(self.impl.num_heads, 256) self.assertEqual(self.impl.head_size, 1024) self.assertEqual(self.impl.scale, 0.1) self.assertEqual(self.impl.num_kv_heads, 8) self.assertEqual(self.impl.kv_cache_dtype, "auto") self.assertEqual(self.impl.q_lora_rank, 64) self.assertEqual(self.impl.kv_lora_rank, 32) self.assertEqual(self.impl.qk_nope_head_dim, 64) self.assertEqual(self.impl.qk_rope_head_dim, 32) self.assertEqual(self.impl.qk_head_dim, 96) self.assertEqual(self.impl.v_head_dim, 128) self.assertIsNotNone(self.impl.rotary_emb) self.assertIsNotNone(self.impl.q_proj) self.assertIsNotNone(self.impl.kv_b_proj) self.assertIsNotNone(self.impl.o_proj) self.assertIsNotNone(self.impl.kv_a_proj_with_mqa) self.assertIsNotNone(self.impl.kv_a_layernorm) self.assertEqual(self.impl.num_queries_per_kv, 32) self.assertEqual(self.impl.tp_size, 2) self.assertTrue(self.impl.torchair_graph_enabled) def test_v_up_proj_and_o_proj(self): batch_size = 4 x = torch.randn(batch_size, self.impl.num_heads, self.impl.kv_lora_rank) self.impl.o_proj.return_value = (torch.randn( batch_size, self.impl.num_heads * self.impl.v_head_dim), ) if not hasattr(self.impl, 'W_UV') or self.impl.W_UV is None: self.impl.W_UV = torch.randn(self.impl.num_heads, self.impl.kv_lora_rank, self.impl.v_head_dim) result = self.impl._v_up_proj_and_o_proj(x) self.assertEqual(result.shape[0], batch_size) self.assertEqual(result.shape[1], self.impl.num_heads * self.impl.v_head_dim) def test_q_proj_and_k_up_proj(self): batch_size = 4 x = torch.randn(batch_size, self.impl.num_heads, self.impl.qk_head_dim) q_proj_output = torch.randn(batch_size, self.impl.num_heads, self.impl.qk_head_dim) self.impl.q_proj.return_value = (q_proj_output, ) if not hasattr(self.impl, 'W_UK_T') or self.impl.W_UK_T is None: self.impl.W_UK_T = torch.randn(self.impl.num_heads, self.impl.qk_nope_head_dim, self.impl.kv_lora_rank) result = self.impl._q_proj_and_k_up_proj(x) ql_nope, q_pe = result self.assertEqual(ql_nope.shape[0], batch_size) self.assertEqual(ql_nope.shape[1], self.impl.num_heads) self.assertEqual(ql_nope.shape[2], self.impl.kv_lora_rank) self.assertEqual(q_pe.shape[0], batch_size) self.assertEqual(q_pe.shape[1], self.impl.num_heads) self.assertEqual(q_pe.shape[2], self.impl.qk_rope_head_dim) def test_process_weights_after_loading(self): layer = MagicMock(spec=LinearBase) layer.input_size_per_partition = 10 quant_method = MagicMock() apply = MagicMock() quant_method.apply = apply layer.quant_method = quant_method shape_0 = self.impl.num_heads * (self.impl.qk_nope_head_dim + self.impl.v_head_dim) shape_1 = self.impl.kv_lora_rank layer.weight = torch.randn(shape_0, shape_1) self.impl.kv_b_proj = layer apply.return_value = layer.weight.T self.impl.process_weights_after_loading(torch.bfloat16) self.assertEqual(self.impl.W_UK_T.shape[0], self.impl.num_heads) self.assertEqual(self.impl.W_UK_T.shape[1], self.impl.qk_nope_head_dim) self.assertEqual(self.impl.W_UK_T.shape[2], self.impl.kv_lora_rank) self.assertEqual(self.impl.W_UV.shape[0], self.impl.num_heads) self.assertEqual(self.impl.W_UV.shape[1], self.impl.kv_lora_rank) self.assertEqual(self.impl.W_UV.shape[2], self.impl.v_head_dim) def test_compute_prefill_context_none(self): batch_size = 4 kv_cache = torch.randn(10, 1, 1, 192) query = torch.randn(batch_size, self.impl.num_heads, self.impl.qk_head_dim) metadata = MagicMock() metadata.prefill = None prefix_out = torch.randn(2, 16, 128) prefix_lse = torch.randn(2, 16, 8) out, lse = self.impl._compute_prefill_context(query, kv_cache, 32, metadata, prefix_out, prefix_lse) self.assertTrue(torch.equal(prefix_out, out)) self.assertTrue(torch.equal(prefix_lse, lse)) @patch("torch_npu.atb.npu_paged_cache_load") @patch("torch_npu.atb.npu_ring_mla") def test_compute_prefill_context(self, mock_ring, mock_load): S, N, D, VD = 2, self.impl.num_heads, self.impl.qk_head_dim, self.impl.v_head_dim _, AND = self.impl.qk_rope_head_dim, self.impl.qk_nope_head_dim latent_kv_dim = self.impl.kv_lora_rank num_blocks, block_size = 100, 20 query = torch.randn(S, N, D) kv_cache_0 = torch.randn(num_blocks, block_size, N, latent_kv_dim) kv_cache_1 = torch.randn(num_blocks, block_size, N, D) kv_cache = [kv_cache_0, kv_cache_1] prefix_out = torch.randn(S, N, 128) prefix_lse = torch.randn(S, N) self.impl.kv_b_proj.return_value = (torch.randn(8, N, VD + AND), ) chunk_ctx = MagicMock() chunk_ctx.seq_tot = [8] chunk_ctx.chunk_seq_lens = [torch.tensor([8])] chunk_ctx.starts = [torch.tensor([0])] prefill_meta = MagicMock() prefill_meta.chunked_context = chunk_ctx prefill_meta.query_lens = [8] prefill_meta.block_table = torch.randint(0, 100, (S, 4)) meta = MagicMock() meta.prefill = prefill_meta out, lse = self.impl._compute_prefill_context(query, kv_cache, 32, meta, prefix_out, prefix_lse) mock_load.assert_called_once() mock_ring.assert_called_once() self.assertEqual(out.shape, prefix_out.shape) self.assertEqual(lse.shape, prefix_lse.shape) @patch("torch_npu.npu_kv_rmsnorm_rope_cache") def test_exec_kv(self, mock_kv_cache): batch_size = 2 hidden = torch.randn(batch_size, 128) cos = torch.randn(batch_size, 32) sin = torch.randn(batch_size, 32) kv_cache = (torch.randn( 4, 8, self.impl.kv_lora_rank + self.impl.qk_rope_head_dim), torch.randn( 4, 8, self.impl.kv_lora_rank + self.impl.qk_rope_head_dim)) slots = torch.arange(batch_size, dtype=torch.long) proj_out = torch.randn( batch_size, self.impl.num_kv_heads, 1, self.impl.kv_lora_rank + self.impl.qk_rope_head_dim) self.impl.kv_a_proj_with_mqa.return_value = (proj_out, ) mock_kv_cache.return_value = (torch.randn(batch_size, self.impl.num_kv_heads, 1, self.impl.qk_rope_head_dim), torch.randn(batch_size, self.impl.num_kv_heads, 1, self.impl.kv_lora_rank), None, None) k_pe, k_nope, kv = self.impl.exec_kv(hidden, cos, sin, kv_cache, slots) self.impl.kv_a_proj_with_mqa.assert_called_once_with(hidden) mock_kv_cache.assert_called_once() self.assertEqual(k_pe.shape, (batch_size, self.impl.num_kv_heads, 1, self.impl.qk_rope_head_dim)) self.assertEqual( k_nope.shape, (batch_size, self.impl.num_kv_heads, 1, self.impl.kv_lora_rank)) self.assertEqual(kv.shape, (batch_size, self.impl.num_kv_heads, 1, self.impl.kv_lora_rank + self.impl.qk_rope_head_dim)) @patch("torch_npu.npu_kv_rmsnorm_rope_cache") def test_exec_kv_prefill(self, mock_kv): B, N, S, H = 2, self.impl.num_kv_heads, 1, 128 hidden_states = torch.randn(B, N, S, H) cos = torch.randn(B, S, 32) sin = torch.randn(B, S, 32) kv_cache = ( torch.randn(100, 8, self.impl.kv_lora_rank + self.impl.qk_rope_head_dim), torch.randn(100, 8, self.impl.kv_lora_rank + self.impl.qk_rope_head_dim), ) slots = torch.arange(B * S, dtype=torch.long) proj_out = torch.randn( B, N, S, self.impl.kv_lora_rank + self.impl.qk_rope_head_dim) self.impl.kv_a_proj_with_mqa.return_value = (proj_out, ) mock_kv.return_value = (None, None, torch.randn(B, self.impl.num_kv_heads, S, self.impl.qk_rope_head_dim), torch.randn(B, self.impl.num_kv_heads, S, self.impl.kv_lora_rank)) k_pe, k_nope = self.impl.exec_kv_prefill(hidden_states, cos, sin, kv_cache, slots) self.impl.kv_a_proj_with_mqa.assert_called_once_with(hidden_states) mock_kv.assert_called_once() self.assertEqual( k_pe.shape, (B, self.impl.num_kv_heads, S, self.impl.qk_rope_head_dim)) self.assertEqual( k_nope.shape, (B, self.impl.num_kv_heads, S, self.impl.kv_lora_rank)) @patch("torch_npu.npu_interleave_rope") def test_rope_single(self, mock_rope): B, N, D = 2, 16, 1024 x = torch.randn(B, N, D) cos = torch.randn(B, N, 1, D) sin = torch.randn(B, N, 1, D) mock_rope.return_value = x.view(B, N, 1, D) result = self.impl.rope_single(x, cos, sin) self.assertEqual(result.shape[0], B) self.assertEqual(result.shape[1], N) self.assertEqual(result.shape[2], D) mock_rope.assert_called_once() @patch("vllm_ascend.attention.mla_v1.AscendMLAImpl._v_up_proj_and_o_proj") @patch("torch_npu._npu_paged_attention_mla") def test_forward_decode_without_graph(self, mock_page_attention_mla, mock_up_proj): self.impl.running_in_graph = False num_tokens = 100 num_blocks = 256 block_size = 4 q_nope = torch.randn(num_tokens, self.impl.num_heads, self.impl.qk_nope_head_dim) q_pe = torch.randn(num_tokens, self.impl.num_heads, self.impl.qk_rope_head_dim) kv_c_and_k_pe_cache = torch.randn(num_blocks, block_size, self.impl.num_heads, self.impl.kv_lora_rank) metadata = MagicMock() metadata.decode = MagicMock() metadata.decode.block_table = MagicMock() metadata.decode.seq_lens = 10 mock_page_attention_mla.return_value = torch.randn( num_tokens, self.impl.num_heads, self.impl.kv_lora_rank) mock_up_proj.return_value = torch.randn(num_tokens, self.impl.num_heads, self.impl.v_head_dim) result = self.impl._forward_decode(q_nope, q_pe, None, None, kv_c_and_k_pe_cache, metadata) self.assertEqual(result.shape[0], num_tokens) self.assertEqual(result.shape[1], self.impl.num_heads) self.assertEqual(result.shape[2], self.impl.v_head_dim) mock_up_proj.assert_called_once() mock_page_attention_mla.assert_called_once()