6029bea480
### What this PR does / why we need it?
Adding UT for DCP/PCP
-vLLM version: v0.12.0
-vLLM main:
ad32e3e19c
Signed-off-by: zengran <zengran2@huawei.com>
322 lines
13 KiB
Python
322 lines
13 KiB
Python
from unittest.mock import MagicMock, patch
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import torch
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from vllm.distributed.parallel_state import GroupCoordinator
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from tests.ut.base import TestBase
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from vllm_ascend.attention.attention_cp import AscendAttentionCPImpl
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class TestAscendAttentionCPImpl(TestBase):
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@patch('vllm_ascend.attention.attention_cp.get_pcp_group')
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@patch('vllm.distributed.parallel_state._PCP',
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new_callable=lambda: MagicMock(spec=GroupCoordinator))
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@patch('vllm_ascend.attention.attention_cp.get_dcp_group')
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@patch('vllm.distributed.parallel_state._DCP',
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new_callable=lambda: MagicMock(spec=GroupCoordinator))
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@patch("vllm.distributed.get_decode_context_model_parallel_world_size",
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return_value=1)
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def setUp(self, mock_get_dcp_size, mock_dcp, mock_get_dcp_group, mock_pcp,
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mock_get_pcp_group):
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mock_dcp.world_size = 2
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mock_dcp.rank_in_group = 0
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dcp_group = MagicMock(spec=GroupCoordinator)
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dcp_group.rank_in_group = 0
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dcp_group.world_size = 2
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dcp_group.device_group = MagicMock()
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mock_get_dcp_group.return_value = dcp_group
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mock_pcp.world_size = 2
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mock_pcp.rank_in_group = 0
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pcp_group = MagicMock(spec=GroupCoordinator)
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pcp_group.rank_in_group = 0
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pcp_group.world_size = 2
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pcp_group.device_group = MagicMock()
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mock_get_pcp_group.return_value = pcp_group
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self.layer = MagicMock()
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self.layer.layer_name = "test_layer"
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self.layer._k_scale_float = 1.0
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self.layer._v_scale_float = 1.0
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self.attention_type = MagicMock()
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self.attention_type.DECODER = "decoder"
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self.attention_type.ENCODER = "encoder"
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self.attn_metadata = MagicMock()
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self.attn_metadata.return_value = "1"
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self.layer_no_quant = MagicMock(
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spec=['layer_name', '_k_scale_float', '_v_scale_float'])
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self.layer_no_quant.layer_name = "test_layer"
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self.layer_no_quant._k_scale_float = 1.0
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self.layer_no_quant._v_scale_float = 1.0
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self.impl = AscendAttentionCPImpl(
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num_heads=8,
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head_size=64,
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scale=1.0,
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num_kv_heads=8,
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alibi_slopes=None,
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sliding_window=None,
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kv_cache_dtype="float16",
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logits_soft_cap=None,
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attn_type=self.attention_type.DECODER,
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kv_sharing_target_layer_name=None)
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def test_init(self):
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self.assertEqual(self.impl.pcp_size, 2)
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self.assertEqual(self.impl.pcp_rank, 0)
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self.assertEqual(self.impl.dcp_size, 2)
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self.assertEqual(self.impl.dcp_rank, 0)
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def test_forward_prefill_cp(self):
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query = torch.randn(2, 4, 128)
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key = torch.randn(4, 1, 128)
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value = torch.randn(4, 1, 128)
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def mock_attention_with_nomask_and_mask(q, k_mask, **kwargs):
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mock_output = torch.randn_like(q)
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mock_lse = torch.randn_like(k_mask)
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return mock_output, mock_lse
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self.impl._attention_with_nomask_and_mask = MagicMock()
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self.impl._attention_with_nomask_and_mask.side_effect = mock_attention_with_nomask_and_mask
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attn_metadata = MagicMock()
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attn_metadata.prefill = MagicMock()
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attn_metadata.prefill.pcp_metadata.q_head_idx = torch.tensor([0])
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attn_metadata.prefill.pcp_metadata.q_tail_idx = torch.tensor([1])
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attn_metadata.prefill.pcp_metadata.q_full_idx = torch.tensor([0, 1])
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attn_metadata.prefill.pcp_metadata.kv_with_q_head_mask_idx = torch.tensor(
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[0])
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attn_metadata.prefill.pcp_metadata.kv_with_q_tail_nomask_idx = torch.tensor(
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[0])
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attn_metadata.prefill.pcp_metadata.kv_with_q_tail_mask_idx = torch.tensor(
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[0])
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output, attn_lse = self.impl._forward_prefill_cp(
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query, key, value, attn_metadata)
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self.assertEqual(output.shape[0], 2)
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self.assertEqual(output.shape[1], 4)
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self.assertEqual(output.shape[2], 128)
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@patch('vllm_ascend.attention.attention_cp.get_dcp_group')
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@patch('vllm.distributed.parallel_state._DCP')
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@patch("torch_npu.npu_fused_infer_attention_score")
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@patch("torch.distributed.all_gather")
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@patch("torch.distributed.all_to_all_single")
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@patch('vllm_ascend.attention.attention_cp.get_forward_context')
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def test_forward_decode_pcp_dcp(self, mock_get_forward_context,
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mock_all_to_all_single, mock_all_gather,
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mock_npu_fused_infer_attention_score,
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mock_dcp, mock_get_dcp_group):
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def mock_dcp_all_gather_func(tensor, dim):
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return torch.cat([tensor, tensor], dim=dim)
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mock_dcp.world_size = 2
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mock_dcp.rank_in_group = 0
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dcp_group = MagicMock(spec=GroupCoordinator)
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dcp_group.rank_in_group = 0
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dcp_group.world_size = 2
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dcp_group.device_group = MagicMock()
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dcp_group.all_gather = mock_dcp_all_gather_func
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mock_get_dcp_group.return_value = dcp_group
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query = torch.randn(2, 4, 128)
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self.impl.key_cache = torch.randn(100, 128, 1, 128)
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self.impl.value_cache = torch.randn(100, 128, 1, 128)
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def mock_npu_attention_update(attn_out_lse_list):
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mock_output = torch.randn(attn_out_lse_list[0].shape[0],
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attn_out_lse_list[0].shape[1],
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attn_out_lse_list[0].shape[2] - 1)
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return mock_output
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self.impl._npu_attention_update = MagicMock()
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self.impl._npu_attention_update.side_effect = mock_npu_attention_update
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mock_get_forward_context.return_value = MagicMock(capturing=False)
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mock_all_to_all_single.side_effect = lambda output, input, *args, **kwargs: output.copy_(
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input)
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def mock_all_gather_func(tensor_list, tensor, group=None):
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tensor_list[0] = tensor
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tensor_list[1] = tensor.clone()
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mock_all_gather.side_effect = mock_all_gather_func
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def mock_npu_fused_infer_attention_score_func(query, k_nope, value,
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**common_kwargs):
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mock_output = torch.randn_like(query)
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mock_lse = torch.randn(query.shape[0], query.shape[1], 1)
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return mock_output, mock_lse
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mock_npu_fused_infer_attention_score.side_effect = mock_npu_fused_infer_attention_score_func
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attn_metadata = MagicMock()
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attn_metadata.decode_meta = MagicMock()
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attn_metadata.decode_meta.batch_seq_mask = torch.tensor(
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[1, 0], dtype=torch.bool)
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output = self.impl._forward_decode_pcp_dcp(query, attn_metadata)
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self.assertEqual(output.shape[0], 2)
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self.assertEqual(output.shape[1], 4)
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self.assertEqual(output.shape[2], 128)
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@patch('vllm_ascend.attention.attention_cp.get_pcp_group')
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@patch('vllm.distributed.parallel_state._PCP')
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@patch('vllm_ascend.attention.attention_cp.get_dcp_group')
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@patch('vllm.distributed.parallel_state._DCP')
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def test_prefill_query_all_gather(self, mock_dcp, mock_get_dcp_group,
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mock_pcp, mock_get_pcp_group):
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query = torch.randn(2, 4, 128)
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def mock_all_gather_func(tensor, dim):
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return torch.cat([tensor, tensor], dim=dim)
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dcp_group = MagicMock(spec=GroupCoordinator)
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dcp_group.all_gather = mock_all_gather_func
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mock_get_dcp_group.return_value = dcp_group
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pcp_group = MagicMock(spec=GroupCoordinator)
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pcp_group.all_gather = mock_all_gather_func
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mock_get_pcp_group.return_value = pcp_group
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attn_metadata = MagicMock()
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attn_metadata.prefill = MagicMock()
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attn_metadata.prefill.chunked_context = MagicMock()
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attn_metadata.prefill.chunked_context.cp_kv_recover_idx_for_chunk = torch.tensor(
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[1, 2, 3, 0])
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output = self.impl._prefill_query_all_gather(attn_metadata, query)
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self.assertEqual(output.shape[0], 4)
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self.assertEqual(output.shape[1], 8)
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self.assertEqual(output.shape[2], 128)
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@patch('torch.ops.npu.npu_fused_infer_attention_score')
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def test_compute_prefill_context(self, mock_npu_attention):
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block_num = 100
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block_size = 128
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kv_num_heads = 1
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head_size = 128
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kv_cache = (torch.randn(block_num, block_size, kv_num_heads,
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head_size),
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torch.randn(block_num, block_size, kv_num_heads,
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head_size))
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batch_size = 1024
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self.impl.head_size = head_size
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self.impl.num_heads = 4
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num_heads = self.impl.num_heads * self.impl.dcp_size
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query = torch.randn(batch_size, num_heads, head_size)
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attn_metadata = MagicMock()
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attn_metadata.prefill = MagicMock()
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attn_metadata.prefill.chunked_context = MagicMock()
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attn_metadata.prefill.chunked_context.local_context_lens_allranks = torch.tensor(
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[[[256, 256], [256, 256]]])
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attn_metadata.prefill.chunked_context.batch_chunk_seq_mask = torch.randint(
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0, 2, (1024, ), dtype=torch.bool)
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def mock_load_kv_for_chunk(attn_metadata, kv_cache,
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local_chunked_kv_lens_rank, query,
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total_toks):
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return torch.randn(total_toks, kv_num_heads,
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head_size), torch.randn(total_toks,
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kv_num_heads, head_size)
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self.impl._load_kv_for_chunk = MagicMock()
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self.impl._load_kv_for_chunk.side_effect = mock_load_kv_for_chunk
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mock_npu_attention.return_value = torch.randn(batch_size, num_heads,
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head_size), torch.randn(
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batch_size,
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num_heads, 1)
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result_output, result_lse = self.impl._compute_prefill_context(
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query, kv_cache, attn_metadata)
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self.assertEqual(result_output.shape[0], batch_size)
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self.assertEqual(result_output.shape[1], self.impl.num_heads)
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self.assertEqual(result_output.shape[2], head_size)
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self.assertEqual(result_lse.shape[0], batch_size)
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self.assertEqual(result_lse.shape[1], self.impl.num_heads)
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self.assertEqual(result_lse.shape[2], 1)
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@patch('torch_npu.atb.npu_paged_cache_load')
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def test_load_kv_for_chunk(self, mock_npu_paged_cache_load):
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block_num = 100
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block_size = 128
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num_heads = 1
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head_size = 128
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kv_cache = (torch.randn(block_num, block_size, num_heads, head_size),
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torch.randn(block_num, block_size, num_heads, head_size))
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query = torch.randn(4, 8, 128)
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total_toks = 256
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local_chunked_kv_lens_rank = torch.randn(total_toks)
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attn_metadata = MagicMock()
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key, value = self.impl._load_kv_for_chunk(attn_metadata, kv_cache,
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local_chunked_kv_lens_rank,
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query, total_toks)
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self.assertEqual(key.shape[0], total_toks)
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self.assertEqual(key.shape[1], num_heads)
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self.assertEqual(key.shape[2], head_size)
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self.assertEqual(value.shape[0], total_toks)
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self.assertEqual(value.shape[1], num_heads)
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self.assertEqual(value.shape[2], head_size)
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@patch('vllm_ascend.attention.attention_cp.get_pcp_group')
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@patch('vllm.distributed.parallel_state._PCP')
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@patch('torch_npu._npu_reshape_and_cache')
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def test_reshape_and_cache(self, mock_npu_reshape_and_cache, mock_pcp,
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mock_get_pcp_group):
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num_tokens = 4
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block_num = 100
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block_size = 128
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num_heads = 1
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head_size = 128
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self.impl.head_size = head_size
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kv_cache = (torch.randn(block_num, block_size, num_heads, head_size),
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torch.randn(block_num, block_size, num_heads, head_size))
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attn_metadata = MagicMock()
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attn_metadata.num_decode_tokens = 1
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attn_metadata.num_decodes = 1
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attn_metadata.num_prefills = 1
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attn_metadata.slot_mapping = torch.randn(2)
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attn_metadata.num_actual_tokens_pcp_padded = num_tokens * self.impl.pcp_size
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attn_metadata.prefill = MagicMock()
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attn_metadata.prefill.pcp_allgather_restore_idx = torch.tensor(
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[0, 3, 1, 2, 0, 0, 0, 0])
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key = torch.randn(num_tokens, num_heads, head_size)
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value = torch.randn(num_tokens, num_heads, head_size)
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def mock_all_gather_func(tensor, dim):
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return torch.cat([tensor, tensor], dim=dim)
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pcp_group = MagicMock(spec=GroupCoordinator)
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pcp_group.all_gather = mock_all_gather_func
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mock_get_pcp_group.return_value = pcp_group
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key, value = self.impl.reshape_and_cache(key, value, kv_cache,
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attn_metadata)
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self.assertEqual(key.shape[0], num_tokens * self.impl.pcp_size)
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self.assertEqual(key.shape[1], num_heads)
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self.assertEqual(key.shape[2], head_size)
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self.assertEqual(value.shape[0], num_tokens * self.impl.pcp_size)
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self.assertEqual(value.shape[1], num_heads)
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self.assertEqual(value.shape[2], head_size)
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