c8d1df3a3f
### What this PR does / why we need it?
In order to support fused kernels, multi-stream, communication
optimization etc, it's better to aggregate all opreations in Attention
layer togather. This PR tries to refactor mla_v1 by moving all MLA
preprocessing ops into mla_v1 attention impl.
Note that new mla_v1 doesn't take torchair into consideration. So this
PR can only be merged after torchair related mla_v1 is isolated into a
new file.
### Does this PR introduce _any_ user-facing change?
NO
### How was this patch tested?
### Features Test
<img width="506" height="141" alt="image"
src="https://github.com/user-attachments/assets/f1ab2906-a1ac-4450-8433-94811cd89466"
/>
### Performance After Refact
<img width="648" height="486" alt="image"
src="https://github.com/user-attachments/assets/e33e038c-c5d9-4ba7-a8e9-1ac22f9833eb"
/>
### Performance Before Refact
<img width="618" height="494" alt="image"
src="https://github.com/user-attachments/assets/83861dc2-dc51-4af3-9310-90ab10c43bb1"
/>
- vLLM version: v0.10.1.1
- vLLM main:
e03940762b
---------
Signed-off-by: lwq <liwenquan5@huawei.com>
Signed-off-by: whx-sjtu <2952154980@qq.com>
Signed-off-by: SunnyLee219 <3294305115@qq.com>
Co-authored-by: lwq <liwenquan5@huawei.com>
Co-authored-by: whx-sjtu <2952154980@qq.com>
459 lines
20 KiB
Python
459 lines
20 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 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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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_metadata_cls(self):
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self.assertEqual(AscendMLABackend.get_metadata_cls(),
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AscendMLAMetadata)
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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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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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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 = 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(num_actual_tokens, slot_mapping,
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query_start_loc, seq_lens, block_tables,
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num_decodes, num_decode_tokens,
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num_prefills, num_input_tokens,
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query_lens, head_dim, attn_mask,
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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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def test_ascend_mla_metadata_builder_default(self):
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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.chunked_prefill_enabled = False
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mock_device = 'cpu'
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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(mock_vllm_config, 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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def test_reorder_batch(self):
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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.chunked_prefill_enabled = False
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mock_device = 'cpu'
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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(mock_vllm_config, 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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class TestAscendMLAImpl(TestBase):
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@patch('vllm.distributed.parallel_state._TP',
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new_callable=lambda: MagicMock(spec=GroupCoordinator))
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@patch("vllm.distributed.get_tensor_model_parallel_world_size",
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return_value=2)
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@patch("vllm.config.get_current_vllm_config")
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@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
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def setUp(self, ascend_config, vllm_config, mock_get_tp_size, mock_tp):
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mock_tp.world_size = 2
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speculative_config = MagicMock()
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speculative_config.num_speculative_tokens = 4
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vllm_config.speculative_config = speculative_config
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num_heads = 256
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head_size = 1024
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scale = 0.1
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num_kv_heads = 8
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kv_cache_dtype = "auto"
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kv_a_layernorm = MagicMock()
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kv_a_layernorm.weight = torch.randn(96)
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kv_a_layernorm.variance_epsilon = 1e-6
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kwargs = {
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"q_lora_rank": 64,
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"kv_lora_rank": 32,
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"qk_nope_head_dim": 64,
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"qk_rope_head_dim": 32,
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"qk_head_dim": 96,
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"v_head_dim": 128,
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"rotary_emb": MagicMock(),
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"q_proj": MagicMock(),
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"kv_b_proj": MagicMock(),
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"o_proj": MagicMock(),
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"kv_a_proj_with_mqa": MagicMock(),
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"kv_a_layernorm": kv_a_layernorm,
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}
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self.impl = AscendMLAImpl(num_heads=num_heads,
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head_size=head_size,
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scale=scale,
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num_kv_heads=num_kv_heads,
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alibi_slopes=None,
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sliding_window=None,
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kv_cache_dtype=kv_cache_dtype,
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blocksparse_params=None,
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logits_soft_cap=None,
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attn_type=None,
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kv_sharing_target_layer_name=None,
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**kwargs)
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def test_init(self):
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self.assertEqual(self.impl.num_heads, 256)
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self.assertEqual(self.impl.head_size, 1024)
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self.assertEqual(self.impl.scale, 0.1)
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self.assertEqual(self.impl.num_kv_heads, 8)
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self.assertEqual(self.impl.kv_cache_dtype, "auto")
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self.assertEqual(self.impl.q_lora_rank, 64)
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self.assertEqual(self.impl.kv_lora_rank, 32)
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self.assertEqual(self.impl.qk_nope_head_dim, 64)
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self.assertEqual(self.impl.qk_rope_head_dim, 32)
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self.assertEqual(self.impl.qk_head_dim, 96)
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self.assertEqual(self.impl.v_head_dim, 128)
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self.assertIsNotNone(self.impl.rotary_emb)
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self.assertIsNotNone(self.impl.q_proj)
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self.assertIsNotNone(self.impl.kv_b_proj)
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self.assertIsNotNone(self.impl.o_proj)
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self.assertIsNotNone(self.impl.kv_a_proj_with_mqa)
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self.assertIsNotNone(self.impl.kv_a_layernorm)
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self.assertEqual(self.impl.num_queries_per_kv, 32)
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self.assertEqual(self.impl.tp_size, 2)
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def test_v_up_proj(self):
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batch_size = 4
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x = torch.randn(batch_size, self.impl.num_heads,
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self.impl.kv_lora_rank)
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if not hasattr(self.impl, 'W_UV') or self.impl.W_UV is None:
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self.impl.W_UV = torch.randn(self.impl.num_heads,
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self.impl.kv_lora_rank,
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self.impl.v_head_dim)
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result = self.impl._v_up_proj(x)
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self.assertEqual(result.shape[0], batch_size)
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self.assertEqual(result.shape[1],
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self.impl.num_heads * self.impl.v_head_dim)
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def test_q_proj_and_k_up_proj(self):
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batch_size = 4
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x = torch.randn(batch_size, self.impl.num_heads, self.impl.qk_head_dim)
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q_proj_output = torch.randn(batch_size, self.impl.num_heads,
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self.impl.qk_head_dim)
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self.impl.q_proj.return_value = (q_proj_output, )
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if not hasattr(self.impl, 'W_UK_T') or self.impl.W_UK_T is None:
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self.impl.W_UK_T = torch.randn(self.impl.num_heads,
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self.impl.qk_nope_head_dim,
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self.impl.kv_lora_rank)
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result = self.impl._q_proj_and_k_up_proj(x)
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ql_nope, q_pe = result
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self.assertEqual(ql_nope.shape[0], batch_size)
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self.assertEqual(ql_nope.shape[1], self.impl.num_heads)
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self.assertEqual(ql_nope.shape[2], self.impl.kv_lora_rank)
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self.assertEqual(q_pe.shape[0], batch_size)
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self.assertEqual(q_pe.shape[1], self.impl.num_heads)
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self.assertEqual(q_pe.shape[2], self.impl.qk_rope_head_dim)
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def test_process_weights_after_loading(self):
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layer = MagicMock(spec=LinearBase)
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layer.input_size_per_partition = 10
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quant_method = MagicMock()
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apply = MagicMock()
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quant_method.apply = apply
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layer.quant_method = quant_method
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shape_0 = self.impl.num_heads * (self.impl.qk_nope_head_dim +
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self.impl.v_head_dim)
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shape_1 = self.impl.kv_lora_rank
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layer.weight = torch.randn(shape_0, shape_1)
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self.impl.kv_b_proj = layer
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apply.return_value = layer.weight.T
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self.impl.process_weights_after_loading(torch.bfloat16)
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self.assertEqual(self.impl.W_UK_T.shape[0], self.impl.num_heads)
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self.assertEqual(self.impl.W_UK_T.shape[1], self.impl.qk_nope_head_dim)
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self.assertEqual(self.impl.W_UK_T.shape[2], self.impl.kv_lora_rank)
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self.assertEqual(self.impl.W_UV.shape[0], self.impl.num_heads)
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self.assertEqual(self.impl.W_UV.shape[1], self.impl.kv_lora_rank)
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self.assertEqual(self.impl.W_UV.shape[2], self.impl.v_head_dim)
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def test_compute_prefill_context_none(self):
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batch_size = 4
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kv_cache = torch.randn(10, 1, 1, 192)
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query = torch.randn(batch_size, self.impl.num_heads,
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self.impl.qk_head_dim)
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metadata = MagicMock()
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metadata.prefill = None
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prefix_out = torch.randn(2, 16, 128)
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prefix_lse = torch.randn(2, 16, 8)
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q_pe = query[..., self.impl.qk_nope_head_dim:]
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q_nope = query[..., :self.impl.qk_nope_head_dim]
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out, lse = self.impl._compute_prefill_context(q_nope, q_pe, kv_cache,
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32, metadata, prefix_out,
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prefix_lse)
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self.assertTrue(torch.equal(prefix_out, out))
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self.assertTrue(torch.equal(prefix_lse, lse))
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@patch("torch_npu.atb.npu_paged_cache_load")
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@patch("torch_npu.atb.npu_ring_mla")
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def test_compute_prefill_context(self, mock_ring, mock_load):
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S, N, D, VD = 2, self.impl.num_heads, self.impl.qk_head_dim, self.impl.v_head_dim
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_, AND = self.impl.qk_rope_head_dim, self.impl.qk_nope_head_dim
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latent_kv_dim = self.impl.kv_lora_rank
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num_blocks, block_size = 100, 20
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query = torch.randn(S, N, D)
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q_nope = query[..., :self.impl.qk_nope_head_dim]
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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.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.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):
|
|
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)
|
|
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()
|