5c9f4a40c6
### What this PR does / why we need it?
Currently, the MTP model still runs in eager in full graph mode. This PR
adapts the MTP with the full graph capture and execution. When the graph
mode is set to "FULL_DECODE_ONLY", the MTP will run in full-graph to
improve the performance.
The change in both disable_padded_drafter_batch is True and False case
include:
1. Add _mtp_graph_params in acl_graph.py to isolate the data of main
model and the data of MTP.
2. Padding some metadata in mla_v1.py when in fullgraph mode.
3. Fixed the essential data address that will be used in model.forward.
4. Adapted according to the aclgraph capture framwork:
1). Rebuild MTP model with ACLGraphWrapper.
2). Add common attn metadata when start capture in MTP dummy_run.
3). Add common attn metadata update in MTP.
4). Addapted data update when num_speculative_tokens > 1.
5. Add a patch of MTP to adapt vllm v0.11.0.
Existing Issues:
1. When disable_padded_drafter_batch=True and running in FullGraph mode,
the data of the first-round requests in MTP is abnormal. We need to
identify the cause subsequently.
2. When disable_padded_drafter_batch=False and running in FullGraph
mode, the acceptance rate of the second and third tokens will decrease
(For example, if we set the num_speculative_tokens=3, the acceptance
rate of first token is 90%, the second is only 50% lower than 60%, the
third is only 20% lower than 30%). The reason is that the data processed
after the model runs does not match. This is a problem from another PR.
It works fine in eager and PIECEWISE mode, but has problem in FullGraph
mode. Once we have a solution, we will submit a bugfix.
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
- vLLM version: v0.11.0
- vLLM main:
2918c1b49c
---------
Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
1087 lines
47 KiB
Python
1087 lines
47 KiB
Python
from unittest.mock import MagicMock, patch
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import torch
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from vllm.config import CacheConfig, ModelConfig, SchedulerConfig, VllmConfig
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from vllm.distributed.parallel_state import GroupCoordinator
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from vllm.model_executor.layers.linear import LinearBase
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from tests.ut.base import TestBase
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from vllm_ascend.attention.attention_v1 import AscendAttentionState
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from vllm_ascend.attention.mla_v1 import (AscendMLABackend,
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AscendMLADecodeMetadata,
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AscendMLAImpl, AscendMLAMetadata,
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AscendMLAMetadataBuilder,
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AscendMLAPrefillMetadata)
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from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
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class TestAscendMLABackend(TestBase):
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def test_get_name(self):
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self.assertEqual(AscendMLABackend.get_name(), "ASCEND_MLA")
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def test_get_builder_cls(self):
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self.assertEqual(AscendMLABackend.get_builder_cls(),
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AscendMLAMetadataBuilder)
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def test_get_kv_cache_shape(self):
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result = AscendMLABackend.get_kv_cache_shape(2, 4, 8, 128)
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self.assertEqual(result, (2, 4, 8, 128))
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def test_get_impl_cls(self):
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result = AscendMLABackend.get_impl_cls()
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self.assertEqual(result, AscendMLAImpl)
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class TestAscendMLAPrefillMetadata(TestBase):
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def test_ascend_mla_prefill_metadata_default(self):
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attn_mask = torch.tensor([[1, 0], [1, 1]], dtype=torch.bool)
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query_lens = [1, 2]
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seq_lens = [2, 2]
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context_lens = torch.tensor([1, 2])
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input_positions = torch.tensor([0, 1, 0, 1])
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query_start_loc = torch.tensor([0, 1, 3])
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block_table = torch.tensor([[0, 1], [2, 3]])
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max_query_len = 2
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max_seq_lens = 2
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metadata = AscendMLAPrefillMetadata(attn_mask=attn_mask,
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query_lens=query_lens,
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seq_lens=seq_lens,
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context_lens=context_lens,
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input_positions=input_positions,
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query_start_loc=query_start_loc,
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block_table=block_table,
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max_query_len=max_query_len,
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max_seq_lens=max_seq_lens)
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self.assertIs(metadata.attn_mask, attn_mask)
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self.assertEqual(metadata.query_lens, query_lens)
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self.assertEqual(metadata.seq_lens, seq_lens)
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self.assertIs(metadata.context_lens, context_lens)
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self.assertIs(metadata.input_positions, input_positions)
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self.assertIs(metadata.query_start_loc, query_start_loc)
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self.assertIs(metadata.block_table, block_table)
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self.assertEqual(metadata.max_query_len, max_query_len)
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self.assertEqual(metadata.max_seq_lens, max_seq_lens)
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self.assertIsNone(metadata.chunked_context)
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def test_ascend_mla_prefill_metadata_with_chunked_context(self):
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cu_seq_lens = torch.tensor([0, 2, 4])
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starts = torch.tensor([0, 2])
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seq_tot = [2, 2]
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max_seq_lens = [2, 2]
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workspace = torch.randn(2, 4)
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chunk_seq_lens = torch.tensor([2, 2])
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chunked_context = AscendMLAPrefillMetadata.ChunkedContextMetadata(
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cu_seq_lens=cu_seq_lens,
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starts=starts,
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seq_tot=seq_tot,
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max_seq_lens=max_seq_lens,
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workspace=workspace,
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chunk_seq_lens=chunk_seq_lens,
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chunk_seq_lens_npu=chunk_seq_lens)
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metadata = AscendMLAPrefillMetadata(
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attn_mask=torch.tensor([[1, 0], [1, 1]], dtype=torch.bool),
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query_lens=[1, 2],
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seq_lens=[2, 2],
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context_lens=torch.tensor([1, 2]),
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input_positions=torch.tensor([0, 1, 0, 1]),
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query_start_loc=torch.tensor([0, 1, 3]),
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block_table=torch.tensor([[0, 1], [2, 3]]),
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max_query_len=2,
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max_seq_lens=2,
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chunked_context=chunked_context)
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self.assertIsNotNone(metadata.chunked_context)
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self.assertIs(metadata.chunked_context.cu_seq_lens, cu_seq_lens)
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self.assertIs(metadata.chunked_context.starts, starts)
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self.assertEqual(metadata.chunked_context.seq_tot, seq_tot)
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self.assertEqual(metadata.chunked_context.max_seq_lens, max_seq_lens)
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self.assertIs(metadata.chunked_context.workspace, workspace)
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self.assertIs(metadata.chunked_context.chunk_seq_lens, chunk_seq_lens)
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self.assertIs(metadata.chunked_context.chunk_seq_lens_npu,
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chunk_seq_lens)
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class TestAscendMLADecodeMetadata(TestBase):
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def test_ascend_mla_decode_metadata_default(self):
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input_positions = torch.tensor([[1, 2, 3, 4], [1, 2, 3, 4]])
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block_table = torch.tensor([[0, 3, 2, 1], [0, 2, 1, 3]])
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seq_lens = torch.tensor([[2], [3]])
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max_seq_lens = 4
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seq_lens_list = [2, 3]
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attn_mask = None
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metadata = AscendMLADecodeMetadata(input_positions, block_table,
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seq_lens, max_seq_lens,
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seq_lens_list, attn_mask)
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self.assertIs(metadata.input_positions, input_positions)
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self.assertIs(metadata.block_table, block_table)
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self.assertIs(metadata.seq_lens, seq_lens)
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self.assertEqual(metadata.max_seq_lens, max_seq_lens)
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self.assertEqual(metadata.seq_lens_list, seq_lens_list)
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self.assertIsNone(attn_mask)
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class TestAscendMLAMetadata(TestBase):
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def test_ascend_mla_metadata_default(self):
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num_actual_tokens_pcp_padded = 100
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num_actual_tokens = 100
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slot_mapping = torch.randn(100, 4, 1024)
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query_start_loc = torch.tensor([1, 2, 3, 4])
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seq_lens = [30, 50]
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block_tables = torch.randint(0, 100, (100, 4))
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num_decodes = 4
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num_decode_tokens = 8
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num_prefills = 8
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num_input_tokens = 2
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query_lens = None
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head_dim = None
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attn_mask = None
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attn_state = AscendAttentionState.ChunkedPrefill
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decode = None
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prefill = None
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metadata = AscendMLAMetadata(
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num_actual_tokens_pcp_padded, num_actual_tokens, slot_mapping,
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query_start_loc, seq_lens, block_tables, num_decodes,
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num_decode_tokens, num_prefills, num_input_tokens, query_lens,
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head_dim, attn_mask, attn_state, decode, prefill)
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self.assertEqual(metadata.num_actual_tokens, num_actual_tokens)
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self.assertIs(metadata.slot_mapping, slot_mapping)
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self.assertIs(metadata.query_start_loc, query_start_loc)
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self.assertEqual(metadata.seq_lens, seq_lens)
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self.assertIs(metadata.block_tables, block_tables)
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self.assertEqual(metadata.num_decodes, num_decodes)
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self.assertEqual(metadata.num_decode_tokens, num_decode_tokens)
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self.assertEqual(metadata.num_prefills, num_prefills)
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self.assertEqual(metadata.num_input_tokens, num_input_tokens)
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self.assertEqual(metadata.query_lens, query_lens)
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self.assertEqual(metadata.head_dim, head_dim)
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self.assertEqual(metadata.attn_mask, attn_mask)
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self.assertEqual(metadata.attn_state, attn_state)
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self.assertEqual(metadata.decode, decode)
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self.assertEqual(metadata.prefill, prefill)
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class TestAscendMLAMetadataBuilder(TestBase):
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@patch('vllm.distributed.parallel_state.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 test_ascend_mla_metadata_builder_default(self, mock_get_dcp_size,
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mock_dcp, mock_get_dcp_group):
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mock_vllm_config = MagicMock()
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mock_vllm_config.model_config.max_model_len = 1024
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mock_vllm_config.model_config.get_head_size.return_value = 64
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mock_vllm_config.model_config.dtype = torch.float16
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mock_vllm_config.cache_config.block_size = 16
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mock_vllm_config.scheduler_config.max_num_seqs = 4
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mock_vllm_config.scheduler_config.decode_max_num_seqs = 4
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mock_vllm_config.scheduler_config.chunked_prefill_enabled = False
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mock_device = 'cpu'
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mock_dcp.world_size = 1
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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 = 1
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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_vllm_config.speculative_config = None
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ascend_config = MagicMock()
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with patch("vllm_ascend.attention.mla_v1.get_ascend_config",
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return_value=ascend_config):
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builder = AscendMLAMetadataBuilder(None, None, mock_vllm_config,
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mock_device)
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self.assertEqual(builder.block_size,
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mock_vllm_config.cache_config.block_size)
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self.assertEqual(
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builder.chunked_prefill_enabled,
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mock_vllm_config.scheduler_config.chunked_prefill_enabled)
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@patch('vllm.distributed.parallel_state.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 test_ascend_mla_metadata_builder_spec_decode(self, mock_get_dcp_size,
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mock_dcp,
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mock_get_dcp_group):
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mock_vllm_config = MagicMock()
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mock_vllm_config.model_config.max_model_len = 1024
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mock_vllm_config.model_config.get_head_size.return_value = 64
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mock_vllm_config.model_config.dtype = torch.float16
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mock_vllm_config.cache_config.block_size = 16
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mock_vllm_config.scheduler_config.max_num_seqs = 4
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mock_vllm_config.scheduler_config.decode_max_num_seqs = 4
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mock_vllm_config.scheduler_config.chunked_prefill_enabled = False
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mock_device = 'cpu'
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mock_dcp.world_size = 1
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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 = 1
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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_spec_config = MagicMock()
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mock_spec_config.num_speculative_tokens = 3
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mock_vllm_config.speculative_config = mock_spec_config
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ascend_config = MagicMock()
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with patch("vllm_ascend.attention.mla_v1.get_ascend_config",
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return_value=ascend_config):
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builder = AscendMLAMetadataBuilder(None, None, mock_vllm_config,
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mock_device)
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self.assertEqual(builder.block_size,
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mock_vllm_config.cache_config.block_size)
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self.assertEqual(
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builder.chunked_prefill_enabled,
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mock_vllm_config.scheduler_config.chunked_prefill_enabled)
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@patch('vllm.distributed.parallel_state.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 test_ascend_mla_metadata_builder_build_full_graph(
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self, mock_get_dcp_size, mock_dcp, mock_get_dcp_group):
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mock_vllm_config = MagicMock()
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mock_vllm_config.model_config.max_model_len = 1024
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mock_vllm_config.model_config.get_head_size.return_value = 64
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mock_vllm_config.model_config.dtype = torch.float16
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mock_vllm_config.cache_config.block_size = 16
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mock_vllm_config.scheduler_config.max_num_seqs = 4
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mock_vllm_config.scheduler_config.decode_max_num_seqs = 4
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mock_vllm_config.scheduler_config.chunked_prefill_enabled = False
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mock_device = 'cpu'
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mock_dcp.world_size = 1
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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 = 1
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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_spec_config = MagicMock()
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mock_spec_config.num_speculative_tokens = 1
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mock_spec_config.disable_padded_drafter_batch = True
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mock_vllm_config.speculative_config = mock_spec_config
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builder = AscendMLAMetadataBuilder(None, None, mock_vllm_config,
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mock_device)
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common_metadata = MagicMock()
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model = MagicMock()
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common_metadata.graph_pad_size = 8
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common_metadata.num_reqs = 4
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common_metadata.num_actual_tokens = 5
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common_metadata.max_query_len = 5
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common_metadata.seq_lens_cpu = torch.Tensor([9, 10, 8, 8]).int()
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common_metadata.query_start_loc = torch.Tensor([0, 1, 2, 4, 5]).int()
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common_metadata.query_start_loc_cpu = torch.Tensor([0, 1, 2, 4,
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5]).int()
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common_metadata.positions = torch.Tensor([1, 2, 3, 4, 5, 6]).int()
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block_table = torch.Tensor([[1, 0], [2, 0], [3, 0], [4, 0]]).int()
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common_metadata.block_table_tensor = block_table
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common_metadata.prefill_context_parallel_metadata = None
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metadata = builder.build(0, common_metadata, model)
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self.assertEqual(metadata.decode.actual_seq_lengths_q,
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[1, 2, 4, 5, 6, 6, 7, 8])
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self.assertEqual(metadata.decode.block_table.shape[0], 8)
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@patch('vllm.distributed.parallel_state.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 test_reorder_batch(self, mock_get_dcp_size, mock_dcp,
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mock_get_dcp_group):
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ascend_config = MagicMock()
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mock_vllm_config = MagicMock()
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mock_vllm_config.model_config.max_model_len = 1024
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mock_vllm_config.cache_config.block_size = 16
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mock_vllm_config.scheduler_config.max_num_seqs = 4
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mock_vllm_config.scheduler_config.decode_max_num_seqs = 4
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mock_vllm_config.scheduler_config.chunked_prefill_enabled = False
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mock_device = 'cpu'
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mock_dcp.world_size = 1
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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 = 1
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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_vllm_config.speculative_config = None
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with patch("vllm_ascend.attention.mla_v1.get_ascend_config",
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return_value=ascend_config):
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builder = AscendMLAMetadataBuilder(None, None, mock_vllm_config,
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mock_device)
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builder.decode_threshold = 1
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input_batch = MagicMock()
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input_batch.req_ids = [0, 1, 2, 3]
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scheduler_output = MagicMock()
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scheduler_output.num_scheduled_tokens = {0: 1, 1: 3, 2: 1, 3: 2}
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scheduler_output.scheduled_spec_decode_tokens = {
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0: [],
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1: [1],
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2: [],
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3: []
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}
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input_batch.swap_states = MagicMock()
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modified = builder.reorder_batch(input_batch, scheduler_output)
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self.assertTrue(modified)
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input_batch.swap_states.assert_called_once_with(1, 2)
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@patch('vllm.distributed.parallel_state.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 test_pad_actual_seq_lens_q_mtp_disable_pad(self, mock_get_dcp_size,
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mock_dcp,
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mock_get_dcp_group):
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mock_vllm_config = MagicMock()
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mock_vllm_config.model_config.max_model_len = 1024
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mock_vllm_config.model_config.get_head_size.return_value = 64
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mock_vllm_config.model_config.dtype = torch.float16
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mock_vllm_config.cache_config.block_size = 16
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mock_vllm_config.scheduler_config.max_num_seqs = 4
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mock_vllm_config.scheduler_config.decode_max_num_seqs = 4
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mock_vllm_config.scheduler_config.chunked_prefill_enabled = False
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mock_device = 'cpu'
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mock_vllm_config.speculative_config = None
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mock_dcp.world_size = 1
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dcp_group = MagicMock(spec=GroupCoordinator)
|
|
dcp_group.rank_in_group = 0
|
|
dcp_group.world_size = 1
|
|
dcp_group.device_group = MagicMock()
|
|
mock_get_dcp_group.return_value = dcp_group
|
|
|
|
builder = AscendMLAMetadataBuilder(None, None, mock_vllm_config,
|
|
mock_device)
|
|
input_seq_lens = [1, 2, 4, 5]
|
|
expect_output = [1, 2, 4, 5, 6, 6, 7, 8]
|
|
num_reqs = 4
|
|
num_reqs_pad_size = 4
|
|
output_seq_lens = builder.pad_actual_seq_len_q_mtp_disable_pad(
|
|
num_reqs_pad_size, num_reqs, input_seq_lens)
|
|
self.assertEqual(output_seq_lens, expect_output)
|
|
|
|
@patch('vllm.distributed.parallel_state.get_dcp_group')
|
|
@patch('vllm.distributed.parallel_state._DCP',
|
|
new_callable=lambda: MagicMock(spec=GroupCoordinator))
|
|
@patch("vllm.distributed.get_decode_context_model_parallel_world_size",
|
|
return_value=1)
|
|
def test_pad_actual_seq_lens_q_mtp_enable_pad(self, mock_get_dcp_size,
|
|
mock_dcp,
|
|
mock_get_dcp_group):
|
|
mock_vllm_config = MagicMock()
|
|
mock_vllm_config.model_config.max_model_len = 1024
|
|
mock_vllm_config.model_config.get_head_size.return_value = 64
|
|
mock_vllm_config.model_config.dtype = torch.float16
|
|
mock_vllm_config.cache_config.block_size = 16
|
|
mock_vllm_config.scheduler_config.max_num_seqs = 4
|
|
mock_vllm_config.scheduler_config.decode_max_num_seqs = 4
|
|
mock_vllm_config.scheduler_config.chunked_prefill_enabled = False
|
|
mock_device = 'cpu'
|
|
mock_vllm_config.speculative_config = None
|
|
|
|
mock_dcp.world_size = 1
|
|
dcp_group = MagicMock(spec=GroupCoordinator)
|
|
dcp_group.rank_in_group = 0
|
|
dcp_group.world_size = 1
|
|
dcp_group.device_group = MagicMock()
|
|
mock_get_dcp_group.return_value = dcp_group
|
|
common_metadata = MagicMock()
|
|
common_metadata.actual_seq_lengths_q = [2, 4, 6, 8]
|
|
|
|
builder = AscendMLAMetadataBuilder(None, None, mock_vllm_config,
|
|
mock_device)
|
|
input_seq_lens = [2, 4, 6]
|
|
expect_output = [2, 4, 6, 8]
|
|
num_reqs = 3
|
|
num_reqs_pad_size = 1
|
|
output_seq_lens = builder.pad_actual_seq_len_q_mtp_enable_pad(
|
|
num_reqs_pad_size, num_reqs, input_seq_lens, common_metadata)
|
|
self.assertEqual(output_seq_lens, expect_output)
|
|
|
|
|
|
class TestAscendMLAMetadataBuilderBuild(TestBase):
|
|
|
|
def setUp(self):
|
|
self.mock_vllm_config = MagicMock(spec=VllmConfig)
|
|
self.mock_vllm_config.model_config = ModelConfig(max_model_len=2048)
|
|
self.mock_vllm_config.model_config.hf_text_config.qk_rope_head_dim = 32
|
|
self.mock_vllm_config.cache_config = CacheConfig(block_size=32)
|
|
self.mock_vllm_config.scheduler_config = SchedulerConfig(
|
|
max_num_seqs=8, chunked_prefill_enabled=True)
|
|
self.mock_vllm_config.speculative_config = None
|
|
self.mock_device = torch.device("cpu")
|
|
|
|
self.kv_cache_spec = MagicMock()
|
|
self.kv_cache_spec.num_layers = 32
|
|
self.kv_cache_spec.head_size = 128
|
|
self.kv_cache_spec.num_heads = 32
|
|
|
|
@patch(
|
|
"vllm_ascend.attention.mla_v1.get_decode_context_model_parallel_world_size"
|
|
)
|
|
@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
|
|
def test_build_prefix_no_cache_metadata(self, mock_get_ascend_config,
|
|
mock_dcp_world_size):
|
|
mock_dcp_world_size.return_value = 1
|
|
|
|
common_attn_metadata = AscendCommonAttentionMetadata(
|
|
query_start_loc=torch.tensor([0, 3, 7]),
|
|
query_start_loc_cpu=torch.tensor([0, 3, 7]),
|
|
seq_lens_cpu=torch.tensor([5, 6]),
|
|
num_reqs=2,
|
|
num_actual_tokens=10,
|
|
max_query_len=5,
|
|
decode_token_per_req=torch.tensor([1, 1]),
|
|
block_table_tensor=torch.zeros((10, 10)),
|
|
slot_mapping=torch.tensor(range(20)),
|
|
actual_seq_lengths_q=torch.tensor([0, 1]),
|
|
positions=torch.tensor([10, 10]),
|
|
attn_mask=torch.ones((10, 10)),
|
|
spec_attn_mask=None,
|
|
attn_state=AscendAttentionState.PrefillNoCache,
|
|
num_computed_tokens_cpu=None,
|
|
seq_lens=None)
|
|
|
|
base_inputs = {
|
|
"num_actual_tokens": 10,
|
|
"slot_mapping": torch.tensor(range(10)),
|
|
"query_start_loc": torch.tensor([0, 3, 7]),
|
|
"seq_lens": torch.tensor([5, 6]),
|
|
"block_tables": torch.zeros((10, 10)),
|
|
"num_prefills": 2,
|
|
}
|
|
|
|
builder = AscendMLAMetadataBuilder(kv_cache_spec=self.kv_cache_spec,
|
|
layer_names=["layer_0", "layer_1"],
|
|
vllm_config=self.mock_vllm_config,
|
|
device=self.mock_device)
|
|
|
|
mock_model = MagicMock()
|
|
metadata = builder.build(1, common_attn_metadata, mock_model)
|
|
|
|
self.assertIsInstance(metadata, AscendMLAMetadata)
|
|
self.assertEqual(metadata.num_actual_tokens,
|
|
base_inputs["num_actual_tokens"])
|
|
self.assertTrue(
|
|
torch.all(metadata.slot_mapping == base_inputs["slot_mapping"]))
|
|
self.assertEqual(metadata.head_dim, self.kv_cache_spec.head_size)
|
|
|
|
@patch(
|
|
"vllm_ascend.attention.mla_v1.get_decode_context_model_parallel_world_size"
|
|
)
|
|
@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
|
|
def test_build_chunked_prefix_metadata(self, mock_get_ascend_config,
|
|
mock_dcp_world_size):
|
|
mock_dcp_world_size.return_value = 1
|
|
|
|
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_mask=torch.ones((15, 15)),
|
|
spec_attn_mask=None,
|
|
attn_state=AscendAttentionState.ChunkedPrefill,
|
|
num_computed_tokens_cpu=None,
|
|
seq_lens=None)
|
|
|
|
base_inputs = {
|
|
"num_actual_tokens": 15,
|
|
"slot_mapping": torch.tensor(range(15)),
|
|
"query_start_loc": torch.tensor([0, 2, 5, 9]),
|
|
"seq_lens": torch.tensor([4, 5, 6]),
|
|
"block_tables": torch.zeros((10, 10)),
|
|
"num_prefills": 3,
|
|
}
|
|
|
|
builder = AscendMLAMetadataBuilder(kv_cache_spec=self.kv_cache_spec,
|
|
layer_names=["layer_0", "layer_1"],
|
|
vllm_config=self.mock_vllm_config,
|
|
device=self.mock_device)
|
|
|
|
mock_model = MagicMock()
|
|
metadata = builder.build(1, common_attn_metadata, mock_model)
|
|
|
|
self.assertIsInstance(metadata, AscendMLAMetadata)
|
|
self.assertEqual(metadata.num_actual_tokens,
|
|
base_inputs["num_actual_tokens"])
|
|
self.assertTrue(
|
|
torch.all(metadata.slot_mapping == base_inputs["slot_mapping"]))
|
|
self.assertEqual(metadata.head_dim, self.kv_cache_spec.head_size)
|
|
|
|
@patch(
|
|
"vllm_ascend.attention.mla_v1.get_decode_context_model_parallel_world_size"
|
|
)
|
|
@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
|
|
def test_build_decode_only_metadata(self, mock_get_ascend_config,
|
|
mock_dcp_world_size):
|
|
mock_dcp_world_size.return_value = 1
|
|
|
|
common_attn_metadata = AscendCommonAttentionMetadata(
|
|
query_start_loc=torch.tensor([0, 1, 2, 3]),
|
|
query_start_loc_cpu=torch.tensor([0, 1, 2, 3]),
|
|
seq_lens_cpu=torch.tensor([4, 5, 6]),
|
|
num_reqs=3,
|
|
num_actual_tokens=3,
|
|
max_query_len=1,
|
|
block_table_tensor=torch.zeros((10, 10)),
|
|
slot_mapping=torch.tensor(range(3)),
|
|
actual_seq_lengths_q=torch.tensor([0, 1, 2]),
|
|
decode_token_per_req=torch.tensor([1, 1, 1]),
|
|
positions=torch.tensor([10, 10]),
|
|
attn_mask=torch.ones((3, 3)),
|
|
spec_attn_mask=None,
|
|
attn_state=AscendAttentionState.DecodeOnly,
|
|
num_computed_tokens_cpu=None,
|
|
seq_lens=None)
|
|
|
|
base_inputs = {
|
|
"num_actual_tokens": 3,
|
|
"slot_mapping": torch.tensor(range(3)),
|
|
"query_start_loc": torch.tensor([0, 1, 2, 3]),
|
|
"seq_lens": torch.tensor([4, 5, 6]),
|
|
"num_decodes": 3,
|
|
}
|
|
|
|
builder = AscendMLAMetadataBuilder(kv_cache_spec=self.kv_cache_spec,
|
|
layer_names=["layer_0", "layer_1"],
|
|
vllm_config=self.mock_vllm_config,
|
|
device=self.mock_device)
|
|
|
|
mock_model = MagicMock()
|
|
metadata = builder.build(1, common_attn_metadata, mock_model)
|
|
|
|
self.assertIsInstance(metadata, AscendMLAMetadata)
|
|
self.assertEqual(metadata.num_actual_tokens,
|
|
base_inputs["num_actual_tokens"])
|
|
self.assertTrue(
|
|
torch.all(metadata.slot_mapping == base_inputs["slot_mapping"]))
|
|
self.assertEqual(metadata.head_dim, self.kv_cache_spec.head_size)
|
|
|
|
@patch(
|
|
"vllm_ascend.attention.mla_v1.get_decode_context_model_parallel_world_size"
|
|
)
|
|
@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
|
|
def test_build_for_graph_capture_decode_only(self, mock_get_ascend_config,
|
|
mock_dcp_world_size):
|
|
mock_dcp_world_size.return_value = 1
|
|
|
|
common_attn_metadata = AscendCommonAttentionMetadata(
|
|
query_start_loc=torch.tensor([0, 1, 2, 3]),
|
|
query_start_loc_cpu=torch.tensor([0, 1, 2, 3]),
|
|
seq_lens_cpu=torch.tensor([4, 5, 6]),
|
|
num_reqs=3,
|
|
num_actual_tokens=3,
|
|
max_query_len=1,
|
|
block_table_tensor=torch.zeros((10, 10)),
|
|
slot_mapping=torch.tensor(range(3)),
|
|
actual_seq_lengths_q=torch.tensor([0, 1, 2]),
|
|
decode_token_per_req=torch.tensor([1, 1, 1]),
|
|
positions=torch.tensor([10, 10]),
|
|
attn_mask=torch.ones((3, 3)),
|
|
spec_attn_mask=None,
|
|
attn_state=AscendAttentionState.DecodeOnly,
|
|
num_computed_tokens_cpu=None,
|
|
seq_lens=None)
|
|
|
|
base_inputs = {
|
|
"num_actual_tokens": 3,
|
|
"slot_mapping": torch.tensor(range(3)),
|
|
"query_start_loc": torch.tensor([0, 1, 2, 3]),
|
|
"seq_lens": torch.tensor([4, 5, 6]),
|
|
"num_decodes": 3,
|
|
}
|
|
|
|
builder = AscendMLAMetadataBuilder(kv_cache_spec=self.kv_cache_spec,
|
|
layer_names=["layer_0", "layer_1"],
|
|
vllm_config=self.mock_vllm_config,
|
|
device=self.mock_device)
|
|
|
|
mock_model = MagicMock()
|
|
metadata = builder.build_for_graph_capture(
|
|
common_attn_metadata, AscendAttentionState.DecodeOnly, mock_model)
|
|
|
|
self.assertIsInstance(metadata, AscendMLAMetadata)
|
|
self.assertEqual(metadata.num_actual_tokens,
|
|
base_inputs["num_actual_tokens"])
|
|
self.assertTrue(
|
|
torch.all(metadata.slot_mapping == base_inputs["slot_mapping"]))
|
|
self.assertEqual(metadata.head_dim, self.kv_cache_spec.head_size)
|
|
|
|
@patch(
|
|
"vllm_ascend.attention.mla_v1.get_decode_context_model_parallel_world_size"
|
|
)
|
|
@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
|
|
def test_build_for_graph_capture_prefill(self, mock_get_ascend_config,
|
|
mock_dcp_world_size):
|
|
mock_dcp_world_size.return_value = 1
|
|
|
|
common_attn_metadata = AscendCommonAttentionMetadata(
|
|
query_start_loc=torch.tensor([0, 3, 7]),
|
|
query_start_loc_cpu=torch.tensor([0, 3, 7]),
|
|
seq_lens_cpu=torch.tensor([5, 6]),
|
|
num_reqs=2,
|
|
num_actual_tokens=10,
|
|
max_query_len=5,
|
|
decode_token_per_req=torch.tensor([1, 1]),
|
|
block_table_tensor=torch.zeros((10, 10)),
|
|
slot_mapping=torch.tensor(range(20)),
|
|
actual_seq_lengths_q=torch.tensor([0, 1]),
|
|
positions=torch.tensor([10, 10]),
|
|
attn_mask=torch.ones((10, 10)),
|
|
spec_attn_mask=None,
|
|
attn_state=AscendAttentionState.PrefillNoCache,
|
|
num_computed_tokens_cpu=None,
|
|
seq_lens=None)
|
|
|
|
builder = AscendMLAMetadataBuilder(kv_cache_spec=self.kv_cache_spec,
|
|
layer_names=["layer_0", "layer_1"],
|
|
vllm_config=self.mock_vllm_config,
|
|
device=self.mock_device)
|
|
|
|
mock_model = MagicMock()
|
|
|
|
with self.assertRaises(NotImplementedError) as ctx:
|
|
builder.build_for_graph_capture(
|
|
common_attn_metadata, AscendAttentionState.PrefillNoCache,
|
|
mock_model)
|
|
self.assertIn(
|
|
"Currently we only support building dummy metadata for DecodeOnly and SpecDecoding state",
|
|
str(ctx.exception))
|
|
|
|
|
|
class TestAscendMLAImpl(TestBase):
|
|
|
|
@patch('vllm.distributed.parallel_state._DCP',
|
|
new_callable=lambda: MagicMock(spec=GroupCoordinator))
|
|
@patch("vllm.distributed.get_decode_context_model_parallel_world_size",
|
|
return_value=1)
|
|
@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_ascend.attention.mla_v1.get_current_vllm_config")
|
|
@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
|
|
def setUp(self, ascend_config, get_current_vllm_config, mock_get_tp_size,
|
|
mock_tp, mock_get_dcp_size, mock_dcp):
|
|
mock_tp.world_size = 2
|
|
mock_tp.rank_in_group = MagicMock()
|
|
mock_tp.device_group = MagicMock()
|
|
mock_dcp.world_size = 1
|
|
mock_dcp.rank_in_group = MagicMock()
|
|
mock_dcp.device_group = MagicMock()
|
|
vllm_config = MagicMock()
|
|
speculative_config = MagicMock()
|
|
model_config = MagicMock()
|
|
speculative_config.num_speculative_tokens = 4
|
|
vllm_config.speculative_config = speculative_config
|
|
model_config.dtype = torch.float16
|
|
vllm_config.model_config = model_config
|
|
get_current_vllm_config.return_value = vllm_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 = {
|
|
"kv_lora_rank": 32,
|
|
"qk_nope_head_dim": 64,
|
|
"qk_rope_head_dim": 32,
|
|
"qk_head_dim": 96,
|
|
"v_head_dim": 128,
|
|
"q_lora_rank": 64,
|
|
"q_proj": MagicMock(),
|
|
"q_b_proj": MagicMock(),
|
|
"kv_b_proj": MagicMock(),
|
|
"o_proj": MagicMock(),
|
|
"kv_a_proj_with_mqa": MagicMock(),
|
|
"fused_qkv_a_proj": MagicMock(),
|
|
"kv_a_layernorm": kv_a_layernorm,
|
|
"rotary_emb": MagicMock(),
|
|
}
|
|
|
|
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.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.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)
|
|
|
|
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)
|
|
|
|
@patch('vllm_ascend.utils._ENABLE_NZ', True)
|
|
@patch('torch_npu.npu_format_cast')
|
|
def test_process_weights_after_loading(self, mock_format_cast):
|
|
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
|
|
mock_format_cast.return_value = layer.weight
|
|
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)
|
|
q_pe = query[..., self.impl.qk_nope_head_dim:]
|
|
q_nope = query[..., :self.impl.qk_nope_head_dim]
|
|
|
|
out, lse = self.impl._compute_prefill_context(q_nope, q_pe, 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)
|
|
q_nope = query[..., :self.impl.qk_nope_head_dim]
|
|
q_pe = query[..., self.impl.qk_nope_head_dim:]
|
|
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.chunk_seq_lens_npu = [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
|
|
self.impl.prefill_mask = torch.triu(
|
|
torch.ones(512, 512, device=q_nope.device, dtype=q_nope.dtype), 1)
|
|
|
|
out, lse = self.impl._compute_prefill_context(q_nope, q_pe, 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('vllm_ascend.attention.mla_v1.get_forward_context')
|
|
@patch("vllm_ascend.attention.mla_v1.AscendMLAImpl._v_up_proj")
|
|
@patch("torch_npu.npu_fused_infer_attention_score")
|
|
def test_forward_decode_without_graph(self,
|
|
mock_npu_fused_infer_attention_score,
|
|
mock_up_proj,
|
|
mock_get_forward_context):
|
|
num_tokens = 100
|
|
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)
|
|
k_nope = torch.randn(num_tokens, self.impl.num_heads,
|
|
self.impl.qk_nope_head_dim)
|
|
k_pe = torch.randn(num_tokens, self.impl.num_heads,
|
|
self.impl.qk_rope_head_dim)
|
|
metadata = MagicMock()
|
|
metadata.decode = MagicMock()
|
|
metadata.decode.block_table = MagicMock()
|
|
metadata.decode.seq_lens = 10
|
|
mock_npu_fused_infer_attention_score.return_value = [
|
|
torch.randn(num_tokens, self.impl.num_heads,
|
|
self.impl.kv_lora_rank), None
|
|
]
|
|
mock_up_proj.return_value = torch.randn(num_tokens,
|
|
self.impl.num_heads,
|
|
self.impl.v_head_dim)
|
|
mock_get_forward_context.return_value = MagicMock(capturing=False)
|
|
result = self.impl._forward_decode(q_nope, q_pe, k_nope, k_pe,
|
|
block_size, 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_npu_fused_infer_attention_score.assert_called_once()
|
|
|
|
@patch("torch.ops.vllm.maybe_all_gather_and_maybe_unpad")
|
|
@patch("vllm_ascend.attention.mla_v1.maybe_npu_prefetch")
|
|
def test_mla_preprocess(self, magic_npu_fetch,
|
|
mock_maybe_all_gather_and_maybe_unpad):
|
|
magic_npu_fetch.return_value = MagicMock()
|
|
mock_maybe_all_gather_and_maybe_unpad.side_effect = lambda x, label: x
|
|
batch_size = 4
|
|
seq_len = 8
|
|
hidden_size = 1024
|
|
hidden_states = torch.randn(batch_size * seq_len, hidden_size)
|
|
|
|
kv_cache = MagicMock()
|
|
|
|
attn_metadata = MagicMock()
|
|
attn_metadata.num_decodes = 2
|
|
attn_metadata.num_prefills = 2
|
|
attn_metadata.num_decode_tokens = 2
|
|
attn_metadata.num_actual_tokens = 4
|
|
num_prefill_tokens = 2
|
|
attn_metadata.slot_mapping = torch.arange(4)
|
|
attn_metadata.decode.cos = torch.randn(2, 64)
|
|
attn_metadata.decode.sin = torch.randn(2, 64)
|
|
attn_metadata.prefill.cos = torch.randn(2, 64)
|
|
attn_metadata.prefill.sin = torch.randn(2, 64)
|
|
|
|
self.impl.q_a_layernorm = MagicMock()
|
|
self.impl.q_a_layernorm.return_value = torch.randn(
|
|
attn_metadata.num_actual_tokens, self.impl.num_heads,
|
|
self.impl.qk_rope_head_dim)
|
|
self.impl.kv_a_proj_with_mqa = MagicMock()
|
|
self.impl.kv_a_proj_with_mqa.return_value = [
|
|
torch.randn(num_prefill_tokens, self.impl.num_heads,
|
|
self.impl.qk_rope_head_dim + self.impl.kv_lora_rank)
|
|
]
|
|
self.impl.fused_qkv_a_proj = MagicMock()
|
|
self.impl.fused_qkv_a_proj.return_value = [
|
|
torch.randn(
|
|
num_prefill_tokens, self.impl.num_heads,
|
|
self.impl.qk_rope_head_dim + self.impl.kv_lora_rank +
|
|
self.impl.q_lora_rank)
|
|
]
|
|
self.impl.q_proj = MagicMock()
|
|
self.impl.q_proj.return_value = [
|
|
torch.randn(num_prefill_tokens, self.impl.num_heads,
|
|
self.impl.qk_head_dim)
|
|
]
|
|
self.impl.kv_b_proj = MagicMock()
|
|
self.impl.kv_b_proj.return_value = [
|
|
torch.randn(num_prefill_tokens, self.impl.num_heads,
|
|
self.impl.v_head_dim + self.impl.qk_nope_head_dim)
|
|
]
|
|
self.impl.rope_single = MagicMock(side_effect=lambda x, cos, sin: x)
|
|
self.impl.exec_kv_decode = MagicMock()
|
|
self.impl.exec_kv_decode.return_value = [MagicMock(), MagicMock()]
|
|
self.impl.exec_kv_prefill = MagicMock()
|
|
self.impl.exec_kv_prefill.return_value = [
|
|
torch.randn(num_prefill_tokens, self.impl.num_heads,
|
|
self.impl.qk_rope_head_dim),
|
|
torch.randn(num_prefill_tokens, self.impl.num_heads,
|
|
self.impl.kv_lora_rank)
|
|
]
|
|
self.impl._q_proj_and_k_up_proj = MagicMock()
|
|
self.impl._q_proj_and_k_up_proj.return_value = [
|
|
MagicMock(), MagicMock()
|
|
]
|
|
self.impl.num_kv_heads = self.impl.num_heads
|
|
|
|
decode_res, prefill_res = self.impl._mla_preprocess(
|
|
"mock_layer",
|
|
hidden_states,
|
|
kv_cache,
|
|
attn_metadata,
|
|
need_gather_q_kv=False)
|
|
|
|
self.assertIsNotNone(decode_res)
|
|
self.assertIsNotNone(prefill_res)
|
|
|
|
@patch("torch_npu.npu_kv_rmsnorm_rope_cache")
|
|
def test_exec_kv_prefill(self, mock_kv_rmsnorm_rope_cache):
|
|
B = 2
|
|
N = self.impl.num_kv_heads
|
|
D = self.impl.kv_lora_rank + self.impl.qk_rope_head_dim
|
|
kv_no_split = torch.randn(B, N, D)
|
|
self.impl.enable_kv_nz = None
|
|
self.impl.kv_a_layernorm.weight = MagicMock()
|
|
self.impl.kv_a_layernorm.variance_epsilon = MagicMock()
|
|
cos = MagicMock()
|
|
sin = MagicMock()
|
|
slots = MagicMock()
|
|
kv_cache = [MagicMock(), MagicMock()]
|
|
|
|
mock_kv_rmsnorm_rope_cache.return_value = [
|
|
None, None,
|
|
torch.randn(B, N, 1, self.impl.qk_rope_head_dim),
|
|
torch.randn(B, N, 1, self.impl.kv_lora_rank)
|
|
]
|
|
|
|
k_pe, k_nope = self.impl.exec_kv_prefill(kv_no_split, cos, sin,
|
|
kv_cache, slots)
|
|
|
|
self.assertEqual(k_pe.shape[-1], self.impl.qk_rope_head_dim)
|
|
self.assertEqual(k_nope.shape[-1], self.impl.kv_lora_rank)
|
|
|
|
@patch("torch_npu.npu_kv_rmsnorm_rope_cache")
|
|
def test_exec_kv_decode(self, mock_kv_rmsnorm_rope_cache):
|
|
B = 2
|
|
N = self.impl.num_kv_heads
|
|
D = self.impl.kv_lora_rank + self.impl.qk_rope_head_dim
|
|
kv_no_split = torch.randn(B, N, D)
|
|
self.impl.enable_kv_nz = None
|
|
self.impl.kv_a_layernorm.weight = MagicMock()
|
|
self.impl.kv_a_layernorm.variance_epsilon = MagicMock()
|
|
cos = MagicMock()
|
|
sin = MagicMock()
|
|
slots = MagicMock()
|
|
kv_cache = [MagicMock(), MagicMock()]
|
|
|
|
mock_kv_rmsnorm_rope_cache.return_value = [
|
|
torch.randn(B, N, 1, self.impl.qk_rope_head_dim),
|
|
torch.randn(B, N, 1, self.impl.kv_lora_rank), None, None
|
|
]
|
|
|
|
k_pe, k_nope = self.impl.exec_kv_decode(kv_no_split, cos, sin,
|
|
kv_cache, slots)
|
|
|
|
self.assertEqual(k_pe.shape[-1], self.impl.qk_rope_head_dim)
|
|
self.assertEqual(k_nope.shape[-1], self.impl.kv_lora_rank)
|
|
|
|
@patch('vllm_ascend.attention.mla_v1.get_forward_context')
|
|
@patch("torch_npu.npu_fused_infer_attention_score")
|
|
def test_forward_decode(self, mock_npu_fused_infer_attention_score,
|
|
mock_get_forward_context):
|
|
B = 2
|
|
N = self.impl.num_kv_heads
|
|
BS = 100
|
|
HD = self.impl.v_head_dim
|
|
self.impl.kv_lora_rank = 256
|
|
self.impl.spec_token_num = 1
|
|
self.impl._v_up_proj = MagicMock()
|
|
self.impl._v_up_proj.return_value = torch.randn(B, N, HD)
|
|
q_nope = torch.randn(B, N, self.impl.qk_nope_head_dim)
|
|
q_pe = torch.randn(B, N, self.impl.qk_rope_head_dim)
|
|
k_nope = torch.randn(BS, N, self.impl.kv_lora_rank)
|
|
k_pe = torch.randn(BS, N, self.impl.qk_rope_head_dim)
|
|
attn_metadata = MagicMock()
|
|
attn_metadata.attn_state = AscendAttentionState.SpecDecoding
|
|
attn_metadata.decode = MagicMock()
|
|
attn_metadata.decode.actual_seq_lengths_q = MagicMock()
|
|
attn_metadata.decode.seq_lens_list = MagicMock()
|
|
self.impl.enable_kv_nz = True
|
|
|
|
mock_npu_fused_infer_attention_score.return_value = [
|
|
torch.randn(B, N, self.impl.kv_lora_rank), None
|
|
]
|
|
mock_get_forward_context.return_value = MagicMock(capturing=False)
|
|
result = self.impl._forward_decode(q_nope, q_pe, k_nope, k_pe, BS,
|
|
attn_metadata)
|
|
|
|
self.assertEqual(result.shape[0], B)
|
|
self.assertEqual(result.shape[1], N)
|
|
self.assertEqual(result.shape[2], HD)
|