15d73f248e
### What this PR does / why we need it?
Refactor the `capture_model` method in model_runner to directly reuse
the method from vLLM.
Currently, most of the logic in the capture_model method is similar to
that in the vllm code. Directly using the vllm method can reduce the
maintenance cost of the vllm-ascend code. Modify as follows:
1、refactor capture_model function, directly inheriting community methods
2、refactor initialize_aclgraph_capture function, move to
initialize_attn_backend
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: release/v0.13.0
- vLLM main:
ad32e3e19c
Signed-off-by: weiguihua2 <weiguihua2@huawei.com>
Co-authored-by: weijinqian0 <1184188277@qq.com>
190 lines
7.6 KiB
Python
190 lines
7.6 KiB
Python
import sys
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from unittest.mock import MagicMock, patch
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import torch
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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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if 'torch_npu._inductor' not in sys.modules:
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sys.modules['torch_npu._inductor'] = MagicMock()
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from vllm_ascend.attention.sfa_v1 import (AscendSFABackend, AscendSFAImpl,
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AscendSFAMetadata,
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AscendSFAMetadataBuilder)
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class TestAscendSFABackend(TestBase):
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def test_get_name(self):
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self.assertEqual(AscendSFABackend.get_name(), "ASCEND_SFA")
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def test_get_builder_cls(self):
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self.assertEqual(AscendSFABackend.get_builder_cls(),
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AscendSFAMetadataBuilder)
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def test_get_kv_cache_shape(self):
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result = AscendSFABackend.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 = AscendSFABackend.get_impl_cls()
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self.assertEqual(result, AscendSFAImpl)
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class TestAscendSFAMetadata(TestBase):
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def test_ascend_sfa_metadata_default(self):
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has_prefill = True
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num_actual_tokens = 100
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slot_mapping = torch.randn(100, 4, 1024)
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seq_lens = torch.tensor([30, 50])
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cum_query_lens = torch.tensor([0, 30, 80])
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block_tables = torch.randint(0, 100, (100, 4))
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rope_dim = 32
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max_seq_len = int(seq_lens.max().item())
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sin = torch.randn(max_seq_len, rope_dim)
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cos = torch.randn(max_seq_len, rope_dim)
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num_input_tokens = 2
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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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metadata = AscendSFAMetadata(
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has_prefill=has_prefill,
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num_actual_tokens=num_actual_tokens,
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slot_mapping=slot_mapping,
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seq_lens=seq_lens,
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cum_query_lens=cum_query_lens,
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block_tables=block_tables,
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sin=sin,
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cos=cos,
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num_input_tokens=num_input_tokens,
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head_dim=head_dim,
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attn_mask=attn_mask,
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attn_state=attn_state,
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)
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self.assertEqual(metadata.has_prefill, has_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.assertTrue(torch.equal(metadata.seq_lens, seq_lens))
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self.assertTrue(torch.equal(metadata.cum_query_lens, cum_query_lens))
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self.assertIs(metadata.block_tables, block_tables)
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self.assertIs(metadata.sin, sin)
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self.assertIs(metadata.cos, cos)
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self.assertEqual(metadata.num_input_tokens, num_input_tokens)
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self.assertIs(metadata.head_dim, head_dim)
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self.assertIs(metadata.attn_mask, attn_mask)
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self.assertEqual(metadata.attn_state, attn_state)
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class TestAscendSFAMetadataBuilder(TestBase):
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def test_ascend_sfa_metadata_builder_default(self):
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kv_cache_spec = MagicMock()
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layer_names = ["layer1", "layer2"]
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vllm_config = MagicMock()
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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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device = torch.device("cpu")
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builder = AscendSFAMetadataBuilder(kv_cache_spec=kv_cache_spec,
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layer_names=layer_names,
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vllm_config=vllm_config,
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device=device)
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assert builder.device == device
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assert builder.vllm_config == vllm_config
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@patch("vllm_ascend.attention.sfa_v1.get_cos_and_sin_mla")
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def test_ascend_sfa_metadata_builder_build(self, mock_get_cos_and_sin_mla):
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kv_cache_spec = MagicMock()
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layer_names = ["layer1", "layer2"]
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vllm_config = MagicMock()
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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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device = torch.device("cpu")
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builder = AscendSFAMetadataBuilder(kv_cache_spec=kv_cache_spec,
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layer_names=layer_names,
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vllm_config=vllm_config,
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device=device)
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common_attn_metadata = MagicMock()
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common_attn_metadata.num_reqs = 10
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common_attn_metadata.num_actual_tokens = 100
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common_attn_metadata.query_start_loc = torch.tensor(
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[0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
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common_attn_metadata.query_start_loc_cpu = torch.tensor(
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[0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
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common_attn_metadata.slot_mapping = torch.randn(100, 4, 1024)
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common_attn_metadata.seq_lens_cpu = torch.tensor([2] * 10)
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common_attn_metadata.positions = torch.randn(100)
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common_attn_metadata.attn_mask = None
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common_attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
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common_attn_metadata.block_table_tensor = torch.randn(100, 4)
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common_attn_metadata.cos = None
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common_attn_metadata.sin = None
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common_attn_metadata.num_input_tokens = 100
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mock_get_cos_and_sin_mla.return_value = (torch.randn(100),
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torch.randn(100))
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metadata = builder.build(
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common_prefix_len=10,
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common_attn_metadata=common_attn_metadata,
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)
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assert isinstance(metadata, AscendSFAMetadata)
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assert metadata.num_actual_tokens == common_attn_metadata.num_actual_tokens
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assert metadata.slot_mapping.shape == (100, 4, 1024)
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@patch("vllm_ascend.attention.sfa_v1.get_cos_and_sin_mla")
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def test_ascend_sfa_metadata_builder_build_for_graph_capture(
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self, mock_get_cos_and_sin_mla):
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kv_cache_spec = MagicMock()
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layer_names = ["layer1", "layer2"]
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vllm_config = MagicMock()
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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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device = torch.device("cpu")
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builder = AscendSFAMetadataBuilder(kv_cache_spec=kv_cache_spec,
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layer_names=layer_names,
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vllm_config=vllm_config,
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device=device)
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common_attn_metadata = MagicMock()
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common_attn_metadata.num_reqs = 10
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common_attn_metadata.num_actual_tokens = 100
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common_attn_metadata.query_start_loc = torch.tensor(
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[0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
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common_attn_metadata.query_start_loc_cpu = torch.tensor(
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[0, 10, 20, 30, 40, 50, 60, 70, 80, 90])
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common_attn_metadata.slot_mapping = torch.randn(100, 4, 1024)
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common_attn_metadata.seq_lens_cpu = torch.tensor([2] * 10)
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common_attn_metadata.positions = torch.randn(100)
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common_attn_metadata.attn_mask = None
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common_attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
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common_attn_metadata.block_table_tensor = torch.randn(100, 4)
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common_attn_metadata.cos = None
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common_attn_metadata.sin = None
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common_attn_metadata.num_input_tokens = 100
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mock_get_cos_and_sin_mla.return_value = (torch.randn(100),
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torch.randn(100))
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attn_metadata = builder.build_for_graph_capture(
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common_attn_metadata=common_attn_metadata,
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attn_state=AscendAttentionState.DecodeOnly,
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)
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assert isinstance(attn_metadata, AscendSFAMetadata)
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assert attn_metadata.attn_state == AscendAttentionState.DecodeOnly
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