# 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. # This file is a part of the vllm-ascend project. from unittest.mock import MagicMock, patch import numpy as np import pytest import torch from vllm_ascend.attention.utils import AscendCommonAttentionMetadata, split_decodes_and_prefills from vllm_ascend.worker.pcp_utils import PCPManager @pytest.mark.parametrize( "pcp_size, dcp_size, num_reqs, query_lens, num_decodes, use_mla, total_tokens, expect_not_none", [ (1, 1, 5, [10, 20, 30, 40, 50], 2, False, 100, False), (1, 2, 3, [20, 30, 40], 1, False, 50, True), (2, 1, 4, [5, 10, 40, 60], 2, False, 100, True), (2, 1, 4, [5, 10, 40, 60], 2, True, 100, True), (2, 1, 3, [5, 10, 15], 3, False, 50, True), (2, 1, 3, [40, 50, 60], 0, False, 150, True), ], ) def test_generate_pcp_metadata_basic( pcp_size, dcp_size, num_reqs, query_lens, num_decodes, use_mla, total_tokens, expect_not_none ): vllm_config = MagicMock() vllm_config.model_config = MagicMock() vllm_config.model_config.use_mla = use_mla vllm_config.parallel_config.cp_kv_cache_interleave_size = 64 vllm_config.speculative_config.num_speculative_tokens = 0 pcp_manager = PCPManager( pcp_world_size=pcp_size, pcp_rank=0, dcp_world_size=dcp_size, dcp_rank=0, max_buffer_num_tokens=10000, max_num_reqs=1000, device="cpu", vllm_config=vllm_config, use_async_scheduling=False, pin_memory=False, ) input_batch = MagicMock() input_batch.num_reqs = num_reqs num_computed_tokens = [] num_prompt_tokens = [] num_tokens = [] for i in range(num_reqs): if i < num_decodes: num_computed_tokens.append(query_lens[i]) num_prompt_tokens.append(query_lens[i] // 2) num_tokens.append(query_lens[i]) else: num_computed_tokens.append(0) num_prompt_tokens.append(query_lens[i]) num_tokens.append(query_lens[i]) input_batch.num_computed_tokens_cpu = np.array(num_computed_tokens) input_batch.num_prompt_tokens = torch.tensor(num_prompt_tokens) input_batch.num_tokens = torch.tensor(num_tokens) num_scheduled_tokens = np.array(query_lens) - input_batch.num_computed_tokens_cpu query_lens = torch.tensor(query_lens) result, _ = pcp_manager.generate_pcp_metadata( total_tokens, query_lens, input_batch, num_scheduled_tokens, torch.tensor([]), num_reqs_padded=num_reqs, num_reqs=num_reqs, ) if not expect_not_none: assert result is None, f"Expected to return None, but got {type(result)}" else: assert result is not None, "Expected to return a metadata object, but got None." assert hasattr(result, "num_actual_tokens_pcp_padded") assert hasattr(result, "num_computed_tokens_of_pcp_dcp") if pcp_size > 1: assert hasattr(result, "pcp_allgather_restore_idx") has_prefill_requests = (num_reqs - num_decodes) > 0 if has_prefill_requests: assert hasattr(result, "q_head_idx_tensor") assert hasattr(result, "q_tail_idx_tensor") assert hasattr(result, "q_full_idx") assert hasattr(result, "kv_with_q_head_nomask_idx_tensor") assert hasattr(result, "kv_with_q_head_mask_idx_tensor") assert hasattr(result, "kv_with_q_tail_nomask_idx_tensor") assert hasattr(result, "kv_with_q_tail_mask_idx_tensor") assert hasattr(result, "kv_tail_proj_idx_tensor") assert hasattr(result, "kv_with_q_head_attn_idx_in_tail_tensor") assert hasattr(result, "kv_with_q_tail_attn_idx_in_tail_tensor") assert hasattr(result, "attn_mask_seqlens") assert hasattr(result, "head_attn_nomask_seqlens") assert hasattr(result, "tail_attn_nomask_seqlens") assert hasattr(result, "head_actual_seq_lengths_kv") assert hasattr(result, "tail_actual_seq_lengths_kv") @pytest.mark.parametrize( "pcp_size, pcp_rank, query_lens", [ (2, 0, [8]), (2, 1, [8]), (4, 0, [8, 12]), (4, 3, [8, 12]), ], ) def test_generate_pcp_metadata_mla_tail_projection_indices(pcp_size, pcp_rank, query_lens): vllm_config = MagicMock() vllm_config.model_config = MagicMock() vllm_config.model_config.use_mla = True vllm_config.model_config.hf_config.model_type = "deepseek_v2" vllm_config.parallel_config.cp_kv_cache_interleave_size = 64 vllm_config.scheduler_config.max_num_batched_tokens = 10000 vllm_config.scheduler_config.max_num_seqs = 1000 vllm_config.speculative_config.num_speculative_tokens = 0 pcp_manager = PCPManager( pcp_world_size=pcp_size, pcp_rank=pcp_rank, dcp_world_size=1, dcp_rank=0, max_buffer_num_tokens=10000, max_num_reqs=1000, device="cpu", vllm_config=vllm_config, use_async_scheduling=False, pin_memory=False, ) num_reqs = len(query_lens) num_scheduled_tokens = np.array(query_lens, dtype=np.int32) num_computed_tokens = np.zeros(num_reqs, dtype=np.int32) num_prompt_tokens = np.array(query_lens, dtype=np.int32) pcp_manager.init_batch_info( num_scheduled_tokens, num_reqs, num_computed_tokens, num_prompt_tokens, ) input_batch = MagicMock() input_batch.num_reqs = num_reqs input_batch.num_computed_tokens_cpu = np.zeros(num_reqs, dtype=np.int32) input_batch.num_prompt_tokens = torch.tensor(query_lens) input_batch.num_tokens = torch.tensor(query_lens) result, _ = pcp_manager.generate_pcp_metadata( int(num_scheduled_tokens.sum()), torch.tensor(query_lens, dtype=torch.int32), input_batch, num_scheduled_tokens, torch.zeros((num_reqs, 1), dtype=torch.int32), num_reqs_padded=num_reqs, num_reqs=num_reqs, ) assert result is not None tail_idx = result.kv_tail_proj_idx_tensor full_kv_len = int(num_scheduled_tokens.sum()) * pcp_size assert tail_idx.numel() <= full_kv_len assert tail_idx.numel() > 0 assert tail_idx.min().item() >= 0 assert tail_idx.max().item() < full_kv_len expected_tail_idx: list[int] = [] expected_head_attn_idx_in_tail = [] expected_tail_attn_idx_in_tail = [] expected_head_actual_seq_lengths_kv = [] expected_tail_actual_seq_lengths_kv = [] kv_req_offset = 0 q_head_chunk_id = pcp_rank q_tail_chunk_id = pcp_size * 2 - 1 - pcp_rank for seq_len in query_lens: chunk_len = seq_len // 2 tail_proj_offset = len(expected_tail_idx) tail_proj_len = chunk_len * (q_tail_chunk_id + 1) expected_tail_idx.extend(list(range(kv_req_offset, kv_req_offset + tail_proj_len))) expected_head_attn_idx_in_tail.extend( list(range(tail_proj_offset, tail_proj_offset + chunk_len * (q_head_chunk_id + 1))) ) expected_tail_attn_idx_in_tail.extend(list(range(tail_proj_offset, tail_proj_offset + tail_proj_len))) expected_head_actual_seq_lengths_kv.append(len(expected_head_attn_idx_in_tail)) expected_tail_actual_seq_lengths_kv.append(len(expected_tail_attn_idx_in_tail)) kv_req_offset += seq_len * pcp_size assert torch.equal(tail_idx.cpu(), torch.tensor(expected_tail_idx, dtype=tail_idx.dtype)) head_attn_idx = result.kv_with_q_head_attn_idx_in_tail_tensor tail_attn_idx = result.kv_with_q_tail_attn_idx_in_tail_tensor assert torch.equal( head_attn_idx.cpu(), torch.tensor(expected_head_attn_idx_in_tail, dtype=head_attn_idx.dtype), ) assert torch.equal( tail_attn_idx.cpu(), torch.tensor(expected_tail_attn_idx_in_tail, dtype=tail_attn_idx.dtype), ) assert result.head_actual_seq_lengths_kv == expected_head_actual_seq_lengths_kv assert result.tail_actual_seq_lengths_kv == expected_tail_actual_seq_lengths_kv @pytest.mark.parametrize( "tokens, num_reqs, num_computed_tokens, num_prompt_tokens, pcp_size, pcp_rank, expected_pcp_tokens", [ # Case 1: prefill only ([8, 12, 16], 3, [0, 0, 0], [8, 12, 16], 4, 0, [2, 4, 4]), # # Case 2: mix prefill and decode ([8, 4, 12], 3, [8, 4, 0], [8, 0, 12], 4, 0, [2, 2, 4]), # # Case 3: request which need to be padded ([3, 7, 9], 3, [0, 0, 0], [3, 7, 9], 4, 0, [2, 2, 4]), # Case 4: single request ([10], 1, [0], [10], 4, 0, [4]), ], ) def test_update_tokens_for_pcp_basic( tokens, num_reqs, num_computed_tokens, num_prompt_tokens, pcp_size, pcp_rank, expected_pcp_tokens ): vllm_config = MagicMock() vllm_config.model_config = MagicMock() vllm_config.speculative_config.num_speculative_tokens = 0 vllm_config.scheduler_config.max_num_seqs = 1000 pcp_manager = PCPManager( pcp_world_size=pcp_size, pcp_rank=0, dcp_world_size=1, dcp_rank=0, max_buffer_num_tokens=10000, max_num_reqs=1000, device="cpu", vllm_config=vllm_config, use_async_scheduling=False, pin_memory=False, ) input_batch = MagicMock() input_batch.num_reqs = num_reqs input_batch.num_computed_tokens_cpu = np.array(num_computed_tokens, dtype=np.int32) input_batch.num_prompt_tokens = np.array(num_prompt_tokens, dtype=np.int32) arange_np = np.arange(10000) num_scheduled_tokens = np.array(tokens) pcp_manager.init_batch_info( num_scheduled_tokens, num_reqs, input_batch.num_computed_tokens_cpu, input_batch.num_prompt_tokens, ) pcp_tokens_result, positions_result = pcp_manager.update_tokens_for_pcp(num_scheduled_tokens, arange_np) assert np.array_equal(pcp_tokens_result, expected_pcp_tokens), ( f"Expected pcp_tokens: {expected_pcp_tokens}, got: {pcp_tokens_result}" ) total_pcp_tokens: int = np.sum(pcp_tokens_result) assert positions_result.shape == (total_pcp_tokens,), ( f"Positions shape mismatch. Expected length {total_pcp_tokens}, got {positions_result.shape}" ) def test_split_decodes_short_extend_with_default_false(): """Short extends should be treated as prefills by default.""" long_seq_metadata = MagicMock() long_seq_metadata.query_lens_pcp_full_cpu = torch.tensor([3], dtype=torch.int32) long_seq_metadata.max_query_len_pcp_full = 3 query_start_loc_cpu = torch.tensor([0, 2], dtype=torch.int32) common_attn_metadata = AscendCommonAttentionMetadata( query_start_loc=query_start_loc_cpu, query_start_loc_cpu=query_start_loc_cpu, seq_lens=torch.tensor([173], dtype=torch.int32), num_reqs=1, num_actual_tokens=2, max_query_len=2, max_seq_len=173, block_table_tensor=torch.zeros((1, 1), dtype=torch.int32), slot_mapping=torch.arange(2, dtype=torch.int32), is_prefilling=torch.tensor([True]), prefill_context_parallel_metadata=long_seq_metadata, ) num_decodes, num_prefills, num_decode_tokens, num_prefill_tokens = split_decodes_and_prefills( common_attn_metadata, decode_threshold=4, treat_short_extends_as_decodes=False, ) assert num_decodes == 0 assert num_prefills == 1 assert num_decode_tokens == 0 assert num_prefill_tokens == 2 # yapf: disable @pytest.mark.parametrize( "seq_lens, pcp_world_size, dcp_world_size, cp_kv_cache_interleave_size, target", [ # without pcp and dcp (torch.tensor([1, 2, 128, 129]), 1, 1, 1, torch.tensor([[[1]], [[2]], [[128]], [[129]]])), # pcp (torch.tensor([1, 2, 128, 129]), 2, 1, 1, torch.tensor([[[1], [0]], [[1], [1]], [[64], [64]], [[65], [64]]])), # dcp (torch.tensor([1, 2, 128, 129]), 1, 2, 1, torch.tensor([[[1, 0]], [[1, 1]], [[64, 64]], [[65, 64]]])), # pcp + dcp (torch.tensor([1, 2, 128, 129]), 2, 2, 1, torch.tensor([[[1, 0], [0, 0]], [[1, 1], [0, 0]], [[32, 32], [32, 32]], [[33, 32], [32, 32]]])), # specify interleave_size (torch.tensor([1, 2, 128, 129]), 2, 1, 2, torch.tensor([[[1], [0]], [[2], [0]], [[64], [64]], [[65], [64]]])), (torch.tensor([1, 2, 128, 129]), 2, 1, 128, torch.tensor([[[1], [0]], [[2], [0]], [[128], [0]], [[128], [1]]])), (torch.tensor([1, 2, 128, 129, 256, 257]), 2, 2, 128, torch.tensor([[[1, 0], [0, 0]], [[2, 0], [0, 0]], [[128, 0], [0, 0]], [[128, 1], [0, 0]], [[128, 128], [0, 0]], [[128, 128], [1, 0]]])), ] ) # yapf: enable def test_get_cp_local_seq_lens( seq_lens, pcp_world_size, dcp_world_size, cp_kv_cache_interleave_size, target, ): vllm_config = MagicMock() vllm_config.model_config = MagicMock() vllm_config.speculative_config.num_speculative_tokens = 0 pcp_manager = PCPManager(pcp_world_size=pcp_world_size, pcp_rank=0, dcp_world_size=dcp_world_size, dcp_rank=0, max_buffer_num_tokens=10000, max_num_reqs=1000, device="cpu", vllm_config=vllm_config, use_async_scheduling=False, pin_memory=False) ret = pcp_manager._get_cp_local_seq_lens(seq_lens, pcp_world_size, dcp_world_size, cp_kv_cache_interleave_size) assert torch.equal(ret, target) # yapf: disable @pytest.mark.parametrize( "req_ids, num_computed_tokens," \ "token_ids_tensor_list," \ "num_reqs, total_num_scheduled_tokens, num_scheduled_tokens," \ "target_input_ids_pcp_full, target_query_start_loc_pcp_full", [ # prefill ( ['0'], np.array([0]), [torch.tensor([0, 671, 6102, 294, 8760, 344])], 1, 6, {'0': 6}, torch.tensor([0, 671, 6102, 294, 8760, 344]), torch.tensor([0, 6]) ), # decode ( ['0'], np.array([6]), [torch.tensor([0, 671, 6102, 294, 8760, 344, 88907, 0])], 1, 2, {'0': 2}, torch.tensor([88907, 0]), torch.tensor([0, 2]) ), # decode + prefill ( ['0', '1'], np.array([6, 0]), [ torch.tensor([0, 671, 6102, 294, 8760, 344, 88907, 0]), torch.tensor([0, 19923, 14, 1026, 2329, 344, 9807, 14, 342, 1030]), ], 2, 12, {'0': 2, '1': 10}, torch.tensor([88907, 0, 0, 19923, 14, 1026, 2329, 344, 9807, 14, 342, 1030]), torch.tensor([0, 2, 12]) ), # decodes + prefills ( ['0', '1', '2', '3'], np.array([6, 8, 0, 0]), [ torch.tensor([0, 671, 6102, 294, 8760, 344, 88907, 0]), torch.tensor([0, 19923, 14, 1026, 2329, 344, 9807, 14, 342, 0]), torch.tensor([0, 671, 8749, 294, 3702, 4106, 344, 88907]), torch.tensor([0, 671, 5335, 1469, 7539, 305, 6397]), ], 4, 19, {'0': 2, '1': 2, '2': 8, '3': 7}, torch.tensor([88907, 0, 342, 0, 0, 671, 8749, 294, 3702, 4106, 344, 88907, 0, 671, 5335, 1469, 7539, 305, 6397]), torch.tensor([0, 2, 4, 12, 19]) ), ]) # yapf: enable def test_generate_pcp_mtp_input( req_ids, num_computed_tokens, token_ids_tensor_list, num_reqs, total_num_scheduled_tokens, num_scheduled_tokens, target_input_ids_pcp_full, target_query_start_loc_pcp_full, ): max_num_reqs = 4 max_model_len = 4096 max_num_tokens = 4096 vllm_config = MagicMock() vllm_config.model_config = MagicMock() vllm_config.speculative_config.num_speculative_tokens = 1 vllm_config.scheduler_config.max_num_seqs = max_num_reqs vllm_config.scheduler_config.max_num_batched_tokens = max_model_len pcp_manager = PCPManager(pcp_world_size=2, pcp_rank=0, dcp_world_size=1, dcp_rank=0, max_buffer_num_tokens=max_num_tokens, max_num_reqs=max_num_reqs, device="cpu", vllm_config=vllm_config, use_async_scheduling=False, pin_memory=False) arange_np = np.arange(max_model_len) input_batch = MagicMock() input_batch.num_computed_tokens_cpu = \ np.zeros(max_num_reqs, dtype=np.int32) token_ids_cpu_tensor = torch.zeros( (max_num_reqs, max_model_len), device="cpu", dtype=torch.int32, ) input_batch.token_ids_cpu_tensor = token_ids_cpu_tensor input_batch.token_ids_cpu = token_ids_cpu_tensor.numpy() token_ids_cpu_tensor = input_batch.token_ids_cpu_tensor # Set input_batch input_batch.req_ids = req_ids input_batch.num_computed_tokens_cpu[:num_computed_tokens. size] = num_computed_tokens for i, token_ids_tensor in enumerate(token_ids_tensor_list): token_ids_cpu_tensor[i][:token_ids_tensor.size(0)] = token_ids_tensor num_prompt_tokens = np.zeros(max_num_reqs, dtype=np.int32) for i, req_id in enumerate(req_ids): if num_computed_tokens[i] > 0: num_prompt_tokens[i] = num_computed_tokens[i] else: num_prompt_tokens[i] = num_scheduled_tokens[req_id] input_batch.num_prompt_tokens = num_prompt_tokens pcp_manager.init_batch_info( np.array(list(num_scheduled_tokens.values())), num_reqs, input_batch.num_computed_tokens_cpu, input_batch.num_prompt_tokens, ) with patch.object(torch.Tensor, "pin_memory", lambda tensor: tensor): pcp_manager.generate_pcp_mtp_input(total_num_scheduled_tokens, num_scheduled_tokens, False, input_batch, arange_np) assert torch.equal( pcp_manager.input_ids_pcp_full.cpu[:total_num_scheduled_tokens], target_input_ids_pcp_full) assert torch.equal(pcp_manager.query_start_loc_pcp_full.cpu[:num_reqs + 1], target_query_start_loc_pcp_full) # yapf: disable @pytest.mark.parametrize( "pcp_size, num_scheduled_tokens, num_decode_reqs," " expected_cu_num_scheduled_tokens", [ # Case 1: no prefill reqs -> returned unchanged. (2, [1, 1], 2, [1, 2]), # Case 2: prefill only (num_decode_reqs == 0). # pcp_tokens (local prefill len) = [ceil(3/4)*2, ceil(5/4)*2] = [2, 4] # cu=[3, 8]; pads cumsum=[1, 4]; base=0 # prefill_cu=[2, 6]; final = [2*2-1, 6*2-4] = [3, 8] (2, [3, 5], 0, [3, 8]), # Case 3: mix decode + prefill, pcp_size=2. # cu=[1, 2, 5, 10]; decode part [1, 2] stays unchanged # pcp_tokens[2:] = [2, 4]; pads[2:] cumsum=[1, 4]; base=cu[1]=2 # prefill_cu=[4, 8]; final[2:] = [4*2-1, 8*2-4] = [7, 12] (2, [1, 1, 3, 5], 2, [1, 2, 7, 12]), # Case 4: pcp_size=4, mix decode + prefill with uneven prefill tokens. # cu=[1, 2, 7, 16]; decode part [1, 2] stays unchanged # pcp_tokens[2:] = [ceil(5/8)*2, ceil(9/8)*2] = [2, 4] # pads[2:] cumsum=[3, 10]; base=cu[1]=2 # prefill_cu=[4, 8]; final[2:] = [4*4-3, 8*4-10] = [13, 22] (4, [1, 1, 5, 9], 2, [1, 2, 13, 22]), # Case 5: single prefill req, pcp_size=2. # pcp_tokens = [ceil(7/4)*2] = [4]; pads cumsum=[1]; base=0 # prefill_cu=[4]; final = [4*2-1] = [7] (2, [7], 0, [7]), # Case 6: prefill req already aligned to 2*pcp_size (no pad). # pcp_tokens = [4]; pads=[0]; base=0; final = [4*2-0] = [8] (2, [8], 0, [8]), ], ) # yapf: enable def test_adjust_cu_num_scheduled_tokens_for_pcp( pcp_size, num_scheduled_tokens, num_decode_reqs, expected_cu_num_scheduled_tokens, ): vllm_config = MagicMock() vllm_config.model_config = MagicMock() vllm_config.speculative_config.num_speculative_tokens = 0 vllm_config.scheduler_config.max_num_batched_tokens = 10000 vllm_config.scheduler_config.max_num_seqs = 1000 pcp_manager = PCPManager( pcp_world_size=pcp_size, pcp_rank=0, dcp_world_size=1, dcp_rank=0, max_buffer_num_tokens=10000, max_num_reqs=1000, device="cpu", vllm_config=vllm_config, use_async_scheduling=False, pin_memory=False, ) num_reqs = len(num_scheduled_tokens) num_scheduled_tokens_np = np.array(num_scheduled_tokens, dtype=np.int32) num_computed_tokens = np.array( [num_scheduled_tokens[i] if i < num_decode_reqs else 0 for i in range(num_reqs)], dtype=np.int32, ) num_prompt_tokens = num_scheduled_tokens_np.copy() pcp_manager.init_batch_info( num_scheduled_tokens_np, num_reqs, num_computed_tokens, num_prompt_tokens, ) cu_num_scheduled_tokens = np.cumsum(num_scheduled_tokens_np) pcp_manager.update_tokens_for_pcp(num_scheduled_tokens_np, np.arange(10000)) assert pcp_manager.num_decode_reqs == num_decode_reqs num_pcp_pads = pcp_manager.num_pcp_pads_cpu[:num_reqs].astype(np.int32) result = pcp_manager.adjust_cu_num_scheduled_tokens_for_pcp( cu_num_scheduled_tokens, num_pcp_pads ) assert np.array_equal( result, np.array(expected_cu_num_scheduled_tokens, dtype=np.int32) ), ( f"Expected {expected_cu_num_scheduled_tokens}, got {result.tolist()}" )