0c1d239df4
### What this PR does / why we need it? Use Base test and cleanup all manaul patch code - Cleanup EPLB config to avoid tmp test file - Use BaseTest with global cache - Add license - Add a doc to setup unit test in local env ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? CI passed Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
498 lines
21 KiB
Python
498 lines
21 KiB
Python
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 (AscendAttentionBackend,
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AscendAttentionBackendImpl,
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AscendAttentionMetadataBuilder,
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AscendAttentionState,
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AscendMetadata,
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CommonAttentionState)
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class TestAscendAttentionBackend(TestBase):
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def test_get_name(self):
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self.assertEqual(AscendAttentionBackend.get_name(), "ASCEND")
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def test_get_impl_cls(self):
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self.assertEqual(AscendAttentionBackend.get_impl_cls(),
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AscendAttentionBackendImpl)
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def test_get_metadata_cls(self):
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self.assertEqual(AscendAttentionBackend.get_metadata_cls(),
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AscendMetadata)
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def test_get_state_cls(self):
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self.assertEqual(AscendAttentionBackend.get_state_cls(),
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CommonAttentionState)
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def test_get_builder_cls(self):
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self.assertEqual(AscendAttentionBackend.get_builder_cls(),
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AscendAttentionMetadataBuilder)
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@patch('vllm_ascend.attention.attention_v1.is_310p')
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def test_get_kv_cache_shape_310p(self, mock_is_310p):
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mock_is_310p.return_value = True
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result = AscendAttentionBackend.get_kv_cache_shape(10, 20, 30, 40)
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self.assertEqual(result, (2, 10, 30 * 40 // 16, 20, 16))
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@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=False)
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def test_get_kv_cache_shape_not_310p(self, mock_is_310p):
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result = AscendAttentionBackend.get_kv_cache_shape(10, 20, 30, 40)
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self.assertEqual(result, (2, 10, 20, 30, 40))
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def test_get_bsh_kv_cache_shape(self):
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result = AscendAttentionBackend.get_bsh_kv_cache_shape(10, 20, 30, 40)
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self.assertEqual(result, (2, 10, 20, 30 * 40))
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def test_swap_blocks(self):
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src_kv_cache = [torch.zeros((10, 20)), torch.zeros((10, 20))]
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dst_kv_cache = [torch.zeros((10, 20)), torch.zeros((10, 20))]
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src_to_dst = torch.tensor([[0, 1], [2, 3]])
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AscendAttentionBackend.swap_blocks(src_kv_cache, dst_kv_cache,
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src_to_dst)
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self.assertTrue(torch.all(dst_kv_cache[0][1] == src_kv_cache[0][0]))
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self.assertTrue(torch.all(dst_kv_cache[1][3] == src_kv_cache[1][2]))
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def test_copy_blocks(self):
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kv_caches = [torch.zeros((10, 20)), torch.zeros((10, 20))]
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src_to_dists = torch.tensor([[0, 1], [2, 3]])
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AscendAttentionBackend.copy_blocks(kv_caches, src_to_dists)
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self.assertTrue(torch.all(kv_caches[0][1] == kv_caches[0][0]))
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self.assertTrue(torch.all(kv_caches[1][3] == kv_caches[1][2]))
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class TestAscendAttentionMetadataBuilder(TestBase):
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def setUp(self):
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self.mock_runner = MagicMock()
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self.builder = AscendAttentionMetadataBuilder(self.mock_runner)
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def test_reorder_batch(self):
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mock_input_batch = MagicMock()
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mock_scheduler_output = MagicMock()
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result = self.builder.reorder_batch(mock_input_batch,
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mock_scheduler_output)
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self.assertFalse(result)
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@patch('vllm_ascend.attention.attention_v1.AscendMetadata')
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@patch('torch_npu.npu_format_cast')
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@patch('vllm_ascend.utils.nd_to_nz_2d')
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@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=True)
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def test_build_prefill_no_cache(self, mock_is_310p, mock_nd_to_nz_2d,
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mock_npu_format_cast,
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mock_ascend_metadata):
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num_reqs = 2
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num_actual_tokens = 10
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max_query_len = 5
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common_prefix_len = 1
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self.mock_runner.input_batch.block_table = [MagicMock()]
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self.mock_runner.input_batch.block_table[
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0].get_device_tensor.return_value = torch.zeros((10, 10))
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self.mock_runner.max_num_blocks_per_req = 10
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self.mock_runner.query_lens = torch.tensor([3, 4])
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self.mock_runner.seq_lens_cpu = torch.tensor([5, 6])
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self.mock_runner.slot_mapping_cpu = torch.tensor(range(20))
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self.mock_runner.device = 'cpu:0'
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self.mock_runner.attn_mask = torch.ones((10, 10))
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self.mock_runner.attn_state = AscendAttentionState.PrefillNoCache
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self.mock_runner.query_start_loc_cpu = torch.tensor([0, 3, 7])
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mock_nz_tensor = MagicMock()
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mock_nd_to_nz_2d.return_value = mock_nz_tensor
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mock_npu_format_cast.return_value = mock_nz_tensor
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self.builder.build(num_reqs, num_actual_tokens, max_query_len,
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common_prefix_len)
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@patch('vllm_ascend.attention.attention_v1.AscendMetadata')
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@patch('torch_npu.npu_format_cast')
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@patch('vllm_ascend.utils.nd_to_nz_spec')
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@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=True)
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@patch('vllm_ascend.attention.attention_v1.AscendAttentionState')
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def test_build_chunked_prefill(self, mock_ascend_attention_state,
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mock_is_310p, mock_nd_to_nz_spec,
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mock_npu_format_cast, mock_ascend_metadata):
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num_reqs = 3
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num_actual_tokens = 15
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max_query_len = 6
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self.mock_runner.input_batch.block_table = [MagicMock()]
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self.mock_runner.input_batch.block_table[
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0].get_device_tensor.return_value = torch.zeros((10, 10))
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self.mock_runner.max_num_blocks_per_req = 10
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self.mock_runner.query_lens = torch.tensor([2, 3, 4])
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self.mock_runner.seq_lens_cpu = torch.tensor([4, 5, 6])
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self.mock_runner.slot_mapping_cpu = torch.tensor(range(20))
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self.mock_runner.device = 'cpu:0'
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self.mock_runner.attn_mask = torch.ones((15, 15))
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self.mock_runner.attn_state = AscendAttentionState.ChunkedPrefill
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self.mock_runner.query_start_loc_cpu = torch.tensor([0, 2, 5, 9])
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mock_ascend_attention_state = MagicMock()
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mock_ascend_attention_state.PrefillNoCache = 0
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mock_nz_tensor = MagicMock()
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mock_nd_to_nz_spec.return_value = mock_nz_tensor
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mock_npu_format_cast.return_value = mock_nz_tensor
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self.builder.build(num_reqs, num_actual_tokens, max_query_len, 0)
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@patch('vllm_ascend.attention.attention_v1.AscendMetadata')
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@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=False)
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def test_build_non_310p(self, mock_is_310p, mock_ascend_metadata):
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num_reqs = 3
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num_actual_tokens = 15
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max_query_len = 6
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self.mock_runner.input_batch.block_table = [MagicMock()]
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self.mock_runner.input_batch.block_table[
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0].get_device_tensor.return_value = torch.zeros((10, 10))
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self.mock_runner.max_num_blocks_per_req = 10
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self.mock_runner.query_lens = torch.tensor([2, 3, 4])
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self.mock_runner.seq_lens_cpu = torch.tensor([4, 5, 6])
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self.mock_runner.slot_mapping_cpu = torch.tensor(range(20))
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self.mock_runner.device = 'cpu:0'
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self.mock_runner.attn_mask = torch.ones((15, 15))
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self.mock_runner.attn_state = AscendAttentionState.ChunkedPrefill
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self.mock_runner.query_start_loc_cpu = torch.tensor([0, 2, 5, 9])
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self.builder.build(num_reqs, num_actual_tokens, max_query_len, 0)
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class TestAscendAttentionBackendImpl(TestBase):
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def setUp(self):
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self.layer = MagicMock()
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self.layer.layer_name = "test_layer"
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self.layer._k_scale_float = 1.0
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self.layer._v_scale_float = 1.0
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self.attention_type = MagicMock()
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self.attention_type.DECODER = "decoder"
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self.attention_type.ENCODER = "encoder"
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self.attn_metadata = MagicMock()
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self.attn_metadata.return_value = "1"
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self.layer_no_quant = MagicMock(
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spec=['layer_name', '_k_scale_float', '_v_scale_float'])
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self.layer_no_quant.layer_name = "test_layer"
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self.layer_no_quant._k_scale_float = 1.0
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self.layer_no_quant._v_scale_float = 1.0
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self.impl = AscendAttentionBackendImpl(
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num_heads=8,
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head_size=64,
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scale=1.0,
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num_kv_heads=8,
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alibi_slopes=None,
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sliding_window=None,
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kv_cache_dtype="float16",
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attn_type=self.attention_type.DECODER)
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self.impl_192 = AscendAttentionBackendImpl(
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num_heads=8,
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head_size=192,
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scale=1.0,
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num_kv_heads=8,
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alibi_slopes=None,
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sliding_window=None,
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kv_cache_dtype="float16",
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attn_type=self.attention_type.DECODER)
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self.impl_error = AscendAttentionBackendImpl(num_heads=8,
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head_size=192,
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scale=1.0,
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num_kv_heads=8,
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alibi_slopes=None,
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sliding_window=None,
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kv_cache_dtype="float16",
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attn_type=None)
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@patch('torch.ops.vllm.unified_ascend_attention_with_output')
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def test_forward_trace_flag_true(self, mock_unified_attention):
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"""Test forward pass when trace_flag is True"""
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query = torch.randn(10, 8 * 64)
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key = torch.randn(10, 8 * 64)
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value = torch.randn(10, 8 * 64)
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kv_cache = torch.empty(2, 0, 0, 8, 64)
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metadata = self.attn_metadata
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layer = self.layer
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output = self.impl.forward(layer,
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query,
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key,
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value,
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kv_cache,
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metadata,
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trace_flag=True)
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mock_unified_attention.assert_called_once()
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assert output.shape == (10, 8 * 64)
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@patch('torch_npu._npu_paged_attention_splitfuse')
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def test_forward_with_quant_method(self, mock_paged_attention):
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"""Test forward pass when layer has quant_method"""
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query = torch.randn(10, 8 * 64)
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key = torch.randn(10, 8 * 64)
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value = torch.randn(10, 8 * 64)
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kv_cache = torch.ones(1, 1, 10, 8, 64, dtype=torch.int8)
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metadata = MagicMock()
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metadata.num_actual_tokens = torch.randn(10, 8 * 64)
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metadata.block_tables = torch.randn(10, 8 * 64)
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metadata.seq_lens = torch.randn(10, 8 * 64)
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metadata.attn_mask = torch.randn(10, 8 * 64)
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metadata.query_lens = torch.randn(10, 8 * 64)
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layer = self.layer
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layer.quant_method = MagicMock()
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layer.quant_method.apply.return_value = kv_cache
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output = self.impl.forward(layer,
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query,
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key,
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value,
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kv_cache,
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metadata,
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trace_flag=False)
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layer.quant_method.apply.assert_called_once()
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assert output.shape == (10, 8 * 64)
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def test_forward_no_attn_metadata(self):
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"""Test forward pass when attn_metadata is None"""
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query = torch.randn(10, 8 * 64)
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key = torch.randn(10, 8 * 64)
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value = torch.randn(10, 8 * 64)
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kv_cache = torch.empty(2, 0, 0, 8, 64)
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layer = self.layer_no_quant
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output = self.impl.forward(layer,
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query,
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key,
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value,
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kv_cache,
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None,
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trace_flag=False)
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assert output.shape == (10, 8 * 64)
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@patch('torch_npu._npu_reshape_and_cache')
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@patch('torch_npu._npu_flash_attention')
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def test_forward_prefill_no_cache(self, mock_flash_attention,
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mock_reshape_cache):
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"""Test forward pass in PrefillNoCache state"""
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query = torch.randn(10, 8 * 64)
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key = torch.randn(10, 8 * 64)
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value = torch.randn(10, 8 * 64)
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kv_cache = torch.empty(2, 5, 128, 8, 64)
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metadata = self.attn_metadata
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metadata.attn_state = AscendAttentionState.PrefillNoCache
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metadata.attn_mask = torch.randn(1, 1, 10, 10)
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metadata.seq_lens = torch.tensor([10])
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metadata.num_actual_tokens = 10
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metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
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layer = self.layer_no_quant
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# layer.quant_method.apply.return_value = metadata
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print(self.layer_no_quant._v_scale_float)
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output = self.impl.forward(layer,
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query,
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key,
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value,
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kv_cache,
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metadata,
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trace_flag=False)
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mock_reshape_cache.assert_called_once()
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mock_flash_attention.assert_called_once()
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assert output.shape == (10, 8 * 64)
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@patch('torch_npu._npu_reshape_and_cache')
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@patch('torch_npu._npu_flash_attention_qlens')
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def test_forward_prefill_cache_hit(self, mock_flash_attention_qlens,
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mock_npu_reshape_and_cache):
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"""Test forward pass in PrefillCacheHit state"""
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query = torch.randn(10, 8 * 64)
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key = torch.randn(10, 8 * 64)
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value = torch.randn(10, 8 * 64)
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kv_cache = torch.empty(2, 5, 128, 8, 64)
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metadata = self.attn_metadata
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metadata.attn_state = AscendAttentionState.PrefillCacheHit
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metadata.attn_mask = torch.randn(1, 1, 10, 10)
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metadata.query_lens = torch.tensor([10])
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metadata.seq_lens = torch.tensor([10])
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metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
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metadata.num_actual_tokens = 10
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metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
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layer = self.layer_no_quant
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output = self.impl.forward(layer,
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query,
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key,
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value,
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kv_cache,
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metadata,
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trace_flag=False)
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mock_flash_attention_qlens.assert_called_once()
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assert output.shape == (10, 8 * 64)
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@patch('torch_npu._npu_reshape_and_cache')
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@patch('torch_npu._npu_paged_attention')
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def test_forward_decode_only(self, mock_paged_attention,
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mock_npu_reshape_and_cache):
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"""Test forward pass in DecodeOnly state"""
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query = torch.randn(10, 8 * 64)
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key = torch.randn(10, 8 * 64)
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value = torch.randn(10, 8 * 64)
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kv_cache = torch.empty(2, 5, 128, 8, 64)
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metadata = self.attn_metadata
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metadata.attn_state = AscendAttentionState.DecodeOnly
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metadata.seq_lens = torch.tensor([10])
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metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
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metadata.num_actual_tokens = 10
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metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
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layer = self.layer_no_quant
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output = self.impl.forward(layer,
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query,
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key,
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value,
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kv_cache,
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metadata,
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trace_flag=False)
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mock_paged_attention.assert_called_once()
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assert output.shape == (10, 8 * 64)
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@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=False)
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@patch('torch_npu._npu_reshape_and_cache')
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@patch('vllm_ascend.attention.attention_v1.vanilla_chunked_prefill')
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def test_forward_head_size_192(self, mock_vanilla_prefill,
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mock_npu_reshape_and_cache, mock_is_310p):
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"""Test forward pass when head_size is 192"""
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self.impl.head_size = 192
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query = torch.randn(10, 8 * 192)
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key = torch.randn(10, 8 * 192)
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value = torch.randn(10, 8 * 192)
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kv_cache = torch.empty(2, 5, 128, 8, 192)
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metadata = self.attn_metadata
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metadata.attn_mask = torch.randn(1, 1, 10, 10)
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metadata.query_lens = torch.tensor([10])
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metadata.seq_lens = torch.tensor([10])
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metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
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metadata.num_actual_tokens = 10
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metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
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layer = self.layer_no_quant
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mock_vanilla_prefill.return_value = MagicMock()
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def mock_tensor(data, device=None, **kwargs):
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if device == "npu":
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return metadata.attn_mask
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return torch.tensor(data, **kwargs)
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with patch("torch.tensor", side_effect=mock_tensor):
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output = self.impl_192.forward(layer,
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query,
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key,
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value,
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kv_cache,
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metadata,
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trace_flag=False)
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mock_vanilla_prefill.assert_called_once()
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assert output.shape == (10, 8 * 192)
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@patch('torch_npu._npu_reshape_and_cache')
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@patch('torch_npu._npu_paged_attention_splitfuse')
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def test_forward_normal_v1_situation(self, mock_paged_attention,
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mock_npu_reshape_and_cache):
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"""Test forward pass in normal V1 situation"""
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query = torch.randn(10, 8 * 64)
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key = torch.randn(10, 8 * 64)
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value = torch.randn(10, 8 * 64)
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kv_cache = torch.empty(2, 5, 128, 8, 64)
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metadata = self.attn_metadata
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metadata.attn_mask = torch.randn(1, 1, 10, 10)
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metadata.query_lens = torch.tensor([10])
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metadata.seq_lens = torch.tensor([10])
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metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
|
|
metadata.num_actual_tokens = 10
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|
metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
|
|
layer = self.layer_no_quant
|
|
|
|
output = self.impl.forward(layer,
|
|
query,
|
|
key,
|
|
value,
|
|
kv_cache,
|
|
metadata,
|
|
trace_flag=False)
|
|
|
|
mock_paged_attention.assert_called_once()
|
|
assert output.shape == (10, 8 * 64)
|
|
|
|
@patch('torch_npu.npu_format_cast')
|
|
@patch('torch_npu._npu_reshape_and_cache')
|
|
@patch('torch_npu._npu_paged_attention_splitfuse')
|
|
@patch('vllm_ascend.attention.attention_v1.is_310p', return_value=True)
|
|
def test_forward_310p_device(self, mock_is_310p, mock_paged_attention,
|
|
mock_npu_reshape_and_cache,
|
|
mock_npu_format_cast):
|
|
"""Test forward pass on 310P device"""
|
|
query = torch.randn(10, 8 * 64)
|
|
key = torch.randn(10, 8 * 64)
|
|
value = torch.randn(10, 8 * 64)
|
|
kv_cache = torch.empty(2, 5, 128, 8, 64)
|
|
metadata = self.attn_metadata
|
|
metadata.attn_mask = torch.randn(1, 1, 10, 10)
|
|
metadata.query_lens = torch.tensor([10])
|
|
metadata.seq_lens = torch.tensor([10])
|
|
metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
|
|
metadata.num_actual_tokens = 10
|
|
metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
|
|
layer = self.layer_no_quant
|
|
|
|
mock_npu_format_cast.return_value = metadata.attn_mask
|
|
output = self.impl.forward(layer,
|
|
query,
|
|
key,
|
|
value,
|
|
kv_cache,
|
|
metadata,
|
|
trace_flag=False)
|
|
|
|
mock_paged_attention.assert_called_once()
|
|
assert output.shape == (10, 8 * 64)
|
|
|
|
@patch('torch_npu._npu_reshape_and_cache')
|
|
def test_forward_raise_error(self, mock_paged_attention):
|
|
query = torch.randn(10, 8 * 64)
|
|
key = torch.randn(10, 8 * 64)
|
|
value = torch.randn(10, 8 * 64)
|
|
kv_cache = torch.empty(2, 5, 128, 8, 64)
|
|
metadata = self.attn_metadata
|
|
metadata.attn_mask = torch.randn(1, 1, 10, 10)
|
|
metadata.query_lens = torch.tensor([10])
|
|
metadata.seq_lens = torch.tensor([10])
|
|
metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
|
|
metadata.num_actual_tokens = 10
|
|
metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
|
|
layer = self.layer_no_quant
|
|
|
|
with self.assertRaises(NotImplementedError):
|
|
self.impl_error.forward(layer,
|
|
query,
|
|
key,
|
|
value,
|
|
kv_cache,
|
|
metadata,
|
|
trace_flag=False)
|