eb43a475f4
chunkprefill mla only support eager mode now，we want to optimaze it by
support torchair graph, the idea is simple, when all the request is
running in decode, use torchair graph to deal with it, else when
chunkprefill or prefill only, use the eager mode
- vLLM version: v0.10.0
- vLLM main:
ebf7605b0d
Signed-off-by: haojiangzheng <justineric096@gmail.com>
Co-authored-by: haojiangzheng <justineric096@gmail.com>
694 lines
30 KiB
Python
694 lines
30 KiB
Python
from unittest.mock import MagicMock, patch
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import numpy as np
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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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runner = MagicMock()
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runner.scheduler_config = MagicMock()
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runner.model_config = MagicMock()
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runner.scheduler_config.max_num_seqs = 4
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runner.model_config.max_model_len = 1024
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runner.model_config.get_head_size.return_value = 64
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runner.model_config.dtype = torch.float16
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runner.chunked_prefill_enabled = False
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runner.device = "cpu"
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runner.block_size = 16
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runner.decode_token_per_req = 1
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ascend_config = MagicMock()
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ascend_config.torchair_graph_config = MagicMock()
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ascend_config.torchair_graph_config.enabled = True
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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(runner)
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self.assertEqual(builder.runner, runner)
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self.assertEqual(builder.block_size, runner.block_size)
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self.assertEqual(builder.chunked_prefill_enabled,
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runner.chunked_prefill_enabled)
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self.assertEqual(builder.torchair_graph_enabled, True)
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@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
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def test_reorder_batch_with_torchair_graph(self, ascend_config):
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runner = MagicMock()
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runner.chunked_prefill_enabled = False
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runner.decode_token_per_req = 1
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ascend_config.torchair_graph_config = MagicMock()
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ascend_config.torchair_graph_config.enabled = True
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builder = AscendMLAMetadataBuilder(runner)
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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: 2, 1: 1, 2: 3, 3: 1}
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scheduler_output.scheduled_spec_decode_tokens = {
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0: [1],
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1: [],
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2: [1, 1],
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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.assertFalse(modified)
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self.assertEqual(builder._num_decodes, 4)
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self.assertEqual(builder._num_prefills, 0)
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self.assertEqual(builder._num_decode_tokens, 7)
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self.assertEqual(builder._num_prefill_tokens, 0)
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input_batch.swap_states.assert_not_called()
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def test_reorder_batch_without_torchair_graph(self):
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ascend_config = MagicMock()
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runner = MagicMock()
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runner.chunked_prefill_enabled = False
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runner.decode_token_per_req = 1
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ascend_config.torchair_graph_config = MagicMock()
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ascend_config.torchair_graph_config.enabled = False
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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(runner)
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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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self.assertEqual(builder._num_decodes, 2)
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self.assertEqual(builder._num_prefills, 2)
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self.assertEqual(builder._num_decode_tokens, 2)
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self.assertEqual(builder._num_prefill_tokens, 5)
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input_batch.swap_states.assert_called_once_with(1, 2)
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@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
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def test_get_graph_runner_block_tables_normal(self, mock_ascend_config):
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ascend_config = MagicMock()
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mock_ascend_config.return_value = ascend_config
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ascend_config.torchair_graph_config.enabled = False
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runner = MagicMock()
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runner.graph_block_tables = torch.zeros((8, 64), dtype=torch.int32)
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runner.chunked_prefill_enabled = False
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runner.decode_token_per_req = 1
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builder = AscendMLAMetadataBuilder(runner=runner)
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block_tables = torch.randint(0, 100, (3, 10), dtype=torch.int32)
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result = builder._get_graph_runner_block_tables(3, block_tables)
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self.assertEqual(result.shape[0], 3)
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self.assertEqual(result.shape[1], 64)
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self.assertTrue(torch.equal(result[:, :10], block_tables))
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@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
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def test_get_graph_runner_block_tables_truncated(self, mock_ascend_config):
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ascend_config = MagicMock()
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mock_ascend_config.return_value = ascend_config
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ascend_config.torchair_graph_config.enabled = False
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runner = MagicMock()
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runner.graph_block_tables = torch.zeros((8, 4), dtype=torch.int32)
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runner.chunked_prefill_enabled = False
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runner.decode_token_per_req = 1
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builder = AscendMLAMetadataBuilder(runner=runner)
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block_tables = torch.randint(0, 100, (3, 10), dtype=torch.int32)
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result = builder._get_graph_runner_block_tables(3, block_tables)
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self.assertEqual(result.shape[0], 3)
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self.assertEqual(result.shape[1], 4)
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self.assertTrue(torch.equal(result, block_tables[:, :4]))
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@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
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def test_get_graph_runner_block_tables_from_numpy(self,
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mock_ascend_config):
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ascend_config = MagicMock()
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mock_ascend_config.return_value = ascend_config
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ascend_config.torchair_graph_config.enabled = False
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runner = MagicMock()
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runner.graph_block_tables = np.zeros((8, 64), dtype=np.int32)
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runner.chunked_prefill_enabled = False
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runner.decode_token_per_req = 1
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builder = AscendMLAMetadataBuilder(runner=runner)
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block_tables = torch.randint(0, 100, (3, 10), dtype=torch.int32)
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result = builder._get_graph_runner_block_tables(3, block_tables)
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self.assertEqual(result.shape[0], 3)
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self.assertEqual(result.shape[1], 64)
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self.assertTrue(torch.equal(result[:, :10], block_tables))
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@patch("vllm_ascend.attention.mla_v1.get_ascend_config")
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def test_build_dummy(self, mock_ascend_config):
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ascend_config = MagicMock()
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mock_ascend_config.return_value = ascend_config
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ascend_config.torchair_graph_config.enabled = False
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runner = MagicMock()
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runner.model_config = MagicMock()
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runner.device = "cpu"
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runner.graph_block_tables = torch.zeros((8, 64), dtype=torch.int32)
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runner.model_config.get_head_size.return_value = 64
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runner.chunked_prefill_enabled = False
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runner.attn_mask = torch.zeros((1, 1), dtype=torch.bool)
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runner.spec_attn_mask = torch.zeros((1, 1), dtype=torch.bool)
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runner.dtype = torch.float16
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runner.decode_token_per_req = 1
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builder = AscendMLAMetadataBuilder(runner=runner,
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metadata_cls=AscendMLAMetadata)
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builder.rope_dim = 64
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with patch.object(builder,
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"_get_graph_runner_block_tables",
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side_effect=lambda x, y: y):
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metadata = builder.build_torchair_graph_dummy(3, 3)
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sin_golden = torch.ones(3,
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1,
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1,
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64,
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dtype=runner.dtype,
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device=runner.device)
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cos_golden = torch.ones(3,
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1,
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1,
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64,
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dtype=runner.dtype,
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device=runner.device)
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self.assertIsInstance(metadata, AscendMLAMetadata)
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self.assertEqual(metadata.num_input_tokens, 3)
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self.assertEqual(metadata.num_actual_tokens, 3)
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self.assertEqual(metadata.num_decodes, 1)
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self.assertEqual(metadata.num_decode_tokens, 1)
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self.assertEqual(metadata.num_prefills, 0)
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self.assertEqual(metadata.attn_state, AscendAttentionState.DecodeOnly)
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self.assertIsNone(metadata.prefill)
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self.assertIsInstance(metadata.decode, AscendMLADecodeMetadata)
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self.assertEqual(metadata.block_tables.shape[0], 3)
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self.assertEqual(metadata.block_tables.shape[1], 64)
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self.assertEqual(metadata.seq_lens.shape[0], 3)
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self.assertEqual(metadata.slot_mapping.shape[0], 3)
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self.assertEqual(metadata.query_start_loc.shape[0], 3)
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assert torch.equal(sin_golden, metadata.decode.sin)
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assert torch.equal(cos_golden, metadata.decode.cos)
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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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ascend_config.torchair_graph_config.enabled = True
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ascend_config.torchair_graph_config.enable_kv_nz = False
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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,
|
|
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.q_lora_rank, 64)
|
|
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.rotary_emb)
|
|
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)
|
|
self.assertTrue(self.impl.torchair_graph_enabled)
|
|
|
|
def test_v_up_proj_and_o_proj(self):
|
|
batch_size = 4
|
|
x = torch.randn(batch_size, self.impl.num_heads,
|
|
self.impl.kv_lora_rank)
|
|
|
|
self.impl.o_proj.return_value = (torch.randn(
|
|
batch_size, self.impl.num_heads * self.impl.v_head_dim), )
|
|
if not hasattr(self.impl, 'W_UV') or self.impl.W_UV is None:
|
|
self.impl.W_UV = torch.randn(self.impl.num_heads,
|
|
self.impl.kv_lora_rank,
|
|
self.impl.v_head_dim)
|
|
result = self.impl._v_up_proj_and_o_proj(x)
|
|
|
|
self.assertEqual(result.shape[0], batch_size)
|
|
self.assertEqual(result.shape[1],
|
|
self.impl.num_heads * self.impl.v_head_dim)
|
|
|
|
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)
|
|
|
|
def test_process_weights_after_loading(self):
|
|
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
|
|
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)
|
|
out, lse = self.impl._compute_prefill_context(query, 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)
|
|
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
|
|
|
|
out, lse = self.impl._compute_prefill_context(query, 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("torch_npu.npu_kv_rmsnorm_rope_cache")
|
|
def test_exec_kv(self, mock_kv_cache):
|
|
batch_size = 2
|
|
hidden = torch.randn(batch_size, 128)
|
|
cos = torch.randn(batch_size, 32)
|
|
sin = torch.randn(batch_size, 32)
|
|
kv_cache = (torch.randn(
|
|
4, 8, self.impl.kv_lora_rank + self.impl.qk_rope_head_dim),
|
|
torch.randn(
|
|
4, 8,
|
|
self.impl.kv_lora_rank + self.impl.qk_rope_head_dim))
|
|
slots = torch.arange(batch_size, dtype=torch.long)
|
|
|
|
proj_out = torch.randn(
|
|
batch_size, self.impl.num_kv_heads, 1,
|
|
self.impl.kv_lora_rank + self.impl.qk_rope_head_dim)
|
|
self.impl.kv_a_proj_with_mqa.return_value = (proj_out, )
|
|
|
|
mock_kv_cache.return_value = (torch.randn(batch_size,
|
|
self.impl.num_kv_heads, 1,
|
|
self.impl.qk_rope_head_dim),
|
|
torch.randn(batch_size,
|
|
self.impl.num_kv_heads, 1,
|
|
self.impl.kv_lora_rank),
|
|
None, None)
|
|
|
|
k_pe, k_nope, kv = self.impl.exec_kv(hidden, cos, sin, kv_cache, slots)
|
|
|
|
self.impl.kv_a_proj_with_mqa.assert_called_once_with(hidden)
|
|
mock_kv_cache.assert_called_once()
|
|
self.assertEqual(k_pe.shape, (batch_size, self.impl.num_kv_heads, 1,
|
|
self.impl.qk_rope_head_dim))
|
|
self.assertEqual(
|
|
k_nope.shape,
|
|
(batch_size, self.impl.num_kv_heads, 1, self.impl.kv_lora_rank))
|
|
self.assertEqual(kv.shape,
|
|
(batch_size, self.impl.num_kv_heads, 1,
|
|
self.impl.kv_lora_rank + self.impl.qk_rope_head_dim))
|
|
|
|
@patch("torch_npu.npu_kv_rmsnorm_rope_cache")
|
|
def test_exec_kv_prefill(self, mock_kv):
|
|
B, N, S, H = 2, self.impl.num_kv_heads, 1, 128
|
|
hidden_states = torch.randn(B, N, S, H)
|
|
cos = torch.randn(B, S, 32)
|
|
sin = torch.randn(B, S, 32)
|
|
kv_cache = (
|
|
torch.randn(100, 8,
|
|
self.impl.kv_lora_rank + self.impl.qk_rope_head_dim),
|
|
torch.randn(100, 8,
|
|
self.impl.kv_lora_rank + self.impl.qk_rope_head_dim),
|
|
)
|
|
|
|
slots = torch.arange(B * S, dtype=torch.long)
|
|
|
|
proj_out = torch.randn(
|
|
B, N, S, self.impl.kv_lora_rank + self.impl.qk_rope_head_dim)
|
|
self.impl.kv_a_proj_with_mqa.return_value = (proj_out, )
|
|
|
|
mock_kv.return_value = (None, None,
|
|
torch.randn(B, self.impl.num_kv_heads, S,
|
|
self.impl.qk_rope_head_dim),
|
|
torch.randn(B, self.impl.num_kv_heads, S,
|
|
self.impl.kv_lora_rank))
|
|
|
|
k_pe, k_nope = self.impl.exec_kv_prefill(hidden_states, cos, sin,
|
|
kv_cache, slots)
|
|
|
|
self.impl.kv_a_proj_with_mqa.assert_called_once_with(hidden_states)
|
|
mock_kv.assert_called_once()
|
|
|
|
self.assertEqual(
|
|
k_pe.shape,
|
|
(B, self.impl.num_kv_heads, S, self.impl.qk_rope_head_dim))
|
|
self.assertEqual(
|
|
k_nope.shape,
|
|
(B, self.impl.num_kv_heads, S, self.impl.kv_lora_rank))
|
|
|
|
@patch("torch_npu.npu_interleave_rope")
|
|
def test_rope_single(self, mock_rope):
|
|
B, N, D = 2, 16, 1024
|
|
x = torch.randn(B, N, D)
|
|
cos = torch.randn(B, N, 1, D)
|
|
sin = torch.randn(B, N, 1, D)
|
|
mock_rope.return_value = x.view(B, N, 1, D)
|
|
result = self.impl.rope_single(x, cos, sin)
|
|
self.assertEqual(result.shape[0], B)
|
|
self.assertEqual(result.shape[1], N)
|
|
self.assertEqual(result.shape[2], D)
|
|
mock_rope.assert_called_once()
|
|
|
|
@patch("vllm_ascend.attention.mla_v1.AscendMLAImpl._v_up_proj_and_o_proj")
|
|
@patch("torch_npu._npu_paged_attention_mla")
|
|
def test_forward_decode_without_graph(self, mock_page_attention_mla,
|
|
mock_up_proj):
|
|
self.impl.running_in_graph = False
|
|
self.impl.running_chunkprefilll_with_torchair = False
|
|
num_tokens = 100
|
|
num_blocks = 256
|
|
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)
|
|
kv_c_and_k_pe_cache = torch.randn(num_blocks, block_size,
|
|
self.impl.num_heads,
|
|
self.impl.kv_lora_rank)
|
|
metadata = MagicMock()
|
|
metadata.decode = MagicMock()
|
|
metadata.decode.block_table = MagicMock()
|
|
metadata.decode.seq_lens = 10
|
|
mock_page_attention_mla.return_value = torch.randn(
|
|
num_tokens, self.impl.num_heads, self.impl.kv_lora_rank)
|
|
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, None, None,
|
|
kv_c_and_k_pe_cache, 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_page_attention_mla.assert_called_once()
|