b881fab416
### What this PR does / why we need it?
mooncake layerwise support pcp function
PCP (Prefill Context Parallelism) Support: Introduced explicit support
for Prefill Context Parallelism (PCP) and Decode Context Parallelism
(DCP) in the Mooncake layerwise KV cache transfer mechanism, allowing
for more granular control and awareness of parallel configurations
during data transfer.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
By ci
- vLLM version: v0.15.0
- vLLM main:
d7e17aaacd
---------
Signed-off-by: wangxiaoteng <wangxiaoteng@huawei.com>
Signed-off-by: liziyu <liziyu16@huawei.com>
Co-authored-by: liziyu <liziyu16@huawei.com>
682 lines
29 KiB
Python
682 lines
29 KiB
Python
from typing import List
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from unittest.mock import MagicMock, patch
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import torch
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from tests.ut.attention.utils import patch_distributed_groups
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from tests.ut.base import TestBase
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from vllm_ascend.attention.attention_v1 import AscendMetadata
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from vllm_ascend.attention.context_parallel.attention_cp import \
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AscendAttentionCPImpl
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from vllm_ascend.attention.context_parallel.common_cp import (
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AscendMetadataForPrefill, AscendPCPMetadata)
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class TestAscendAttentionCPImpl(TestBase):
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@patch_distributed_groups(dcp_size=2, pcp_size=2, needs_mocks=False)
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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.mock_vllm_config = MagicMock()
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self.config_patcher = patch(
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'vllm_ascend.attention.attention_v1.get_current_vllm_config',
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return_value=self.mock_vllm_config)
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self.config_patcher.start()
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self.impl = AscendAttentionCPImpl(
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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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logits_soft_cap=None,
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attn_type=self.attention_type.DECODER,
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kv_sharing_target_layer_name=None)
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def test_init(self):
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self.assertEqual(self.impl.pcp_size, 2)
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self.assertEqual(self.impl.pcp_rank, 0)
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self.assertEqual(self.impl.dcp_size, 2)
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self.assertEqual(self.impl.dcp_rank, 0)
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def test_forward_prefill_cp(self):
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query = torch.randn(2, 4, 128)
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key = torch.randn(4, 1, 128)
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value = torch.randn(4, 1, 128)
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def mock_attention_with_nomask_and_mask(q, k_mask, **kwargs):
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mock_output = torch.randn_like(q)
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mock_lse = torch.randn_like(k_mask)
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return mock_output, mock_lse
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self.impl._attention_with_nomask_and_mask = MagicMock()
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self.impl._attention_with_nomask_and_mask.side_effect = mock_attention_with_nomask_and_mask
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attn_metadata = MagicMock()
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attn_metadata.prefill = MagicMock()
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attn_metadata.prefill.pcp_metadata.q_head_idx = torch.tensor([0])
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attn_metadata.prefill.pcp_metadata.q_tail_idx = torch.tensor([1])
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attn_metadata.prefill.pcp_metadata.q_full_idx = torch.tensor([0, 1])
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attn_metadata.prefill.pcp_metadata.kv_with_q_head_mask_idx = torch.tensor(
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[0])
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attn_metadata.prefill.pcp_metadata.kv_with_q_tail_nomask_idx = torch.tensor(
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[0])
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attn_metadata.prefill.pcp_metadata.kv_with_q_tail_mask_idx = torch.tensor(
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[0])
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output, attn_lse = self.impl._forward_prefill_cp(
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query, key, value, attn_metadata)
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self.assertEqual(output.shape[0], 2)
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self.assertEqual(output.shape[1], 4)
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self.assertEqual(output.shape[2], 128)
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@patch('torch_npu.npu_attention_update')
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@patch("torch_npu.npu_fused_infer_attention_score")
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@patch(
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'vllm_ascend.attention.context_parallel.attention_cp.get_forward_context'
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)
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@patch_distributed_groups(dcp_size=2, pcp_size=2)
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def test_forward_decode_pcp_dcp(self, mock_all2all, mock_dcp, mock_pcp,
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mock_get_forward_context,
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mock_npu_fused_infer_attention_score,
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mock_npu_attention_update):
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query = torch.randn(2, 4, 64)
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self.impl.key_cache = torch.randn(100, 64, 1, 64)
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self.impl.value_cache = torch.randn(100, 64, 1, 64)
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# Mock output
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mock_npu_attention_update.return_value = (torch.randn(2 * 4, 64), None)
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mock_get_forward_context.return_value = MagicMock(capturing=False)
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def mock_npu_fused_infer_attention_score_func(query, k_nope, value,
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**common_kwargs):
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mock_output = torch.randn_like(query)
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mock_lse = torch.randn(query.shape[0], query.shape[1], 1)
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return mock_output, mock_lse
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mock_npu_fused_infer_attention_score.side_effect = mock_npu_fused_infer_attention_score_func
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attn_metadata = MagicMock()
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attn_metadata.decode_meta = MagicMock()
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attn_metadata.decode_meta.batch_seq_mask = torch.tensor(
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[1, 0], dtype=torch.bool)
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output = self.impl._forward_decode_pcp_dcp(query, attn_metadata)
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self.assertEqual(output.shape[0], 2)
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self.assertEqual(output.shape[1], 4)
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self.assertEqual(output.shape[2], 64)
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@patch_distributed_groups(dcp_size=2, pcp_size=2, needs_mocks=False)
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def test_prefill_query_all_gather(self):
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query = torch.randn(2, 4, 128)
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attn_metadata = MagicMock()
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attn_metadata.prefill = MagicMock()
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attn_metadata.prefill.chunked_context = MagicMock()
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attn_metadata.prefill.chunked_context.cp_kv_recover_idx_for_chunk = torch.tensor(
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[1, 2, 3, 0])
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output = self.impl._prefill_query_all_gather(attn_metadata, query)
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self.assertEqual(output.shape[0], 4)
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self.assertEqual(output.shape[1], 8)
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self.assertEqual(output.shape[2], 128)
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@patch('torch.ops.npu.npu_fused_infer_attention_score')
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@patch_distributed_groups(dcp_size=2, pcp_size=2, needs_mocks=False)
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def test_compute_prefill_context(self, mock_npu_attention):
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block_num = 100
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block_size = 128
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kv_num_heads = 1
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head_size = 128
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kv_cache = (torch.randn(block_num, block_size, kv_num_heads,
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head_size),
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torch.randn(block_num, block_size, kv_num_heads,
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head_size))
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batch_size = 1024
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self.impl.head_size = head_size
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self.impl.num_heads = 4
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num_heads = self.impl.num_heads * self.impl.dcp_size
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query = torch.randn(batch_size, num_heads, head_size)
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attn_metadata = MagicMock()
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attn_metadata.prefill = MagicMock()
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attn_metadata.prefill.chunked_context = MagicMock()
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local_context_lens_allranks = torch.tensor([[[256, 256], [256, 256]]])
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attn_metadata.prefill.chunked_context.local_context_lens_allranks = local_context_lens_allranks
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attn_metadata.prefill.chunked_context.batch_chunk_seq_mask = torch.randint(
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0, 2, (1024, ), dtype=torch.bool)
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attn_metadata.prefill.chunked_context.local_total_toks = local_context_lens_allranks[:,
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0,
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0].sum(
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)
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def mock_load_kv_for_chunk(attn_metadata, kv_cache,
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local_chunked_kv_lens_rank, query,
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total_toks):
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return torch.randn(total_toks, kv_num_heads,
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head_size), torch.randn(total_toks,
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kv_num_heads, head_size)
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self.impl._load_kv_for_chunk = MagicMock()
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self.impl._load_kv_for_chunk.side_effect = mock_load_kv_for_chunk
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mock_npu_attention.return_value = torch.randn(batch_size, num_heads,
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head_size), torch.randn(
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batch_size,
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num_heads, 1)
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context_output = self.impl._compute_prefill_context(
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query, kv_cache, attn_metadata)
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local_context_output = torch.cat(context_output,
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dim=-1).permute([1, 2,
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0]).contiguous()
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global_context_output = self.impl._gather_global_context_output(
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local_context_output)
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global_context_output = global_context_output.permute([2, 0, 1
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]).contiguous()
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result_output, result_lse = self.impl._update_global_context_output(
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global_context_output)
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self.assertEqual(result_output.shape[0], batch_size)
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self.assertEqual(result_output.shape[1], self.impl.num_heads)
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self.assertEqual(result_output.shape[2], head_size)
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self.assertEqual(result_lse.shape[0], batch_size)
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self.assertEqual(result_lse.shape[1], self.impl.num_heads)
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self.assertEqual(result_lse.shape[2], 1)
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@patch('torch_npu.atb.npu_paged_cache_load')
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def test_load_kv_for_chunk(self, mock_npu_paged_cache_load):
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block_num = 100
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block_size = 128
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num_heads = 1
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head_size = 128
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kv_cache = (torch.randn(block_num, block_size, num_heads, head_size),
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torch.randn(block_num, block_size, num_heads, head_size))
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query = torch.randn(4, 8, 128)
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total_toks = 256
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local_chunked_kv_lens_rank = torch.randn(total_toks)
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attn_metadata = MagicMock()
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key, value = self.impl._load_kv_for_chunk(attn_metadata, kv_cache,
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local_chunked_kv_lens_rank,
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query, total_toks)
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self.assertEqual(key.shape[0], total_toks)
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self.assertEqual(key.shape[1], num_heads)
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self.assertEqual(key.shape[2], head_size)
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self.assertEqual(value.shape[0], total_toks)
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self.assertEqual(value.shape[1], num_heads)
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self.assertEqual(value.shape[2], head_size)
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@patch('torch_npu.Event', create=True)
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@patch('torch_npu._npu_reshape_and_cache')
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@patch_distributed_groups(dcp_size=2, pcp_size=2, needs_mocks=False)
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def test_reshape_and_cache(self, mock_event_class, mock_npu_reshape_and_cache):
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num_tokens = 4
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block_num = 100
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block_size = 128
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num_heads = 1
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head_size = 128
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self.impl.head_size = head_size
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self.impl.is_kv_producer = False
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kv_cache = (torch.randn(block_num, block_size, num_heads, head_size),
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torch.randn(block_num, block_size, num_heads, head_size))
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attn_metadata = MagicMock()
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attn_metadata.num_decode_tokens = 1
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attn_metadata.num_decodes = 1
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attn_metadata.num_prefills = 1
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attn_metadata.slot_mapping = torch.randn(2)
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attn_metadata.num_actual_tokens_pcp_padded = num_tokens * self.impl.pcp_size
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attn_metadata.prefill = MagicMock()
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attn_metadata.prefill.pcp_metadata.pcp_allgather_restore_idx = torch.tensor(
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[0, 3, 1, 2, 0, 0, 0, 0])
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key = torch.randn(num_tokens, num_heads, head_size)
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value = torch.randn(num_tokens, num_heads, head_size)
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key, value = self.impl.reshape_and_cache(key, value, kv_cache,
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attn_metadata)
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self.assertEqual(key.shape[0], num_tokens * self.impl.pcp_size)
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self.assertEqual(key.shape[1], num_heads)
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self.assertEqual(key.shape[2], head_size)
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self.assertEqual(value.shape[0], num_tokens * self.impl.pcp_size)
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self.assertEqual(value.shape[1], num_heads)
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self.assertEqual(value.shape[2], head_size)
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class TestUpdateNpuAttnOutLse(TestBase):
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@patch_distributed_groups(needs_mocks=False)
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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 = AscendAttentionCPImpl(
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num_heads=8,
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head_size=64,
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scale=0.125,
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num_kv_heads=2,
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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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logits_soft_cap=None,
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attn_type=self.attention_type.DECODER,
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kv_sharing_target_layer_name=None)
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self.impl.pcp_size = 1
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self.impl.dcp_size = 1
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self.batch_size = 2
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# sequence length per batch
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self.q_lens_per_batch = [32, 64]
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self.kv_lens_nomask_per_batch = [32, 64]
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self.kv_lens_mask_per_batch = [32, 64]
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# TND layout requires cumulative sum computation.
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self.q_seqlens_cumsum = self._cumsum(self.q_lens_per_batch) # [32, 96]
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self.kv_seqlens_nomask_cumsum = self._cumsum(
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self.kv_lens_nomask_per_batch) # [32, 96]
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self.kv_seqlens_mask_cumsum = self._cumsum(
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self.kv_lens_mask_per_batch) # [32, 96]
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# Compute T value in TND layout
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self.q_total_tokens = self.q_seqlens_cumsum[-1]
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self.kv_total_nomask = self.kv_seqlens_nomask_cumsum[-1] #
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self.kv_total_mask = self.kv_seqlens_mask_cumsum[-1]
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def _cumsum(self, arr: List[int]) -> List[int]:
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result = []
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total = 0
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for val in arr:
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total += val
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result.append(total)
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return result
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def _build_attn_metadata(self, with_chunked_context=False):
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attn_metadata = AscendMetadata()
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attn_metadata.num_prefills = self.batch_size
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attn_metadata.num_decodes = 0
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attn_metadata.num_actual_tokens = self.q_total_tokens
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prefill_metadata = AscendMetadataForPrefill()
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pcp_metadata = AscendPCPMetadata()
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pcp_metadata.attn_mask_seqlens = self.kv_seqlens_mask_cumsum
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pcp_metadata.head_attn_nomask_seqlens = self.kv_seqlens_nomask_cumsum
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pcp_metadata.tail_attn_nomask_seqlens = self.kv_seqlens_nomask_cumsum
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prefill_metadata.pcp_metadata = pcp_metadata
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prefill_metadata.actual_seq_lengths_q = torch.tensor(
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self.q_seqlens_cumsum)
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if with_chunked_context:
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chunked_context = AscendMetadataForPrefill.ChunkedContextMetadata(
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actual_chunk_seq_lengths=self.kv_seqlens_mask_cumsum,
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actual_seq_lengths_kv=self.kv_seqlens_mask_cumsum,
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starts=None,
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chunk_seq_mask_filtered_indices=None)
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prefill_metadata.chunked_context = chunked_context
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else:
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prefill_metadata.chunked_context = None
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attn_metadata.prefill = prefill_metadata
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attn_metadata.decode_meta = None
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return attn_metadata
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@patch('torch.ops.npu.npu_fused_infer_attention_score')
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def test_attention_with_nomask_none(self, mock_npu_attention):
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# Mock input data
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q = torch.randn(self.q_total_tokens, self.impl.num_heads,
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self.impl.head_size)
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q_seqlens = self.q_seqlens_cumsum
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k_nomask = None
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v_nomask = None
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kv_seqlens_nomask = self.kv_seqlens_nomask_cumsum
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k_mask = torch.randn(self.kv_total_mask, self.impl.num_kv_heads,
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self.impl.head_size)
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v_mask = torch.randn(self.kv_total_mask, self.impl.num_kv_heads,
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self.impl.head_size)
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kv_seqlens_mask = self.kv_seqlens_mask_cumsum
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mask = torch.randn(self.q_total_tokens, self.kv_total_mask)
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attn_metadata = self._build_attn_metadata(with_chunked_context=False)
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# Mock output
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mock_npu_attention.return_value = torch.randn(96, 8, 64), torch.randn(
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96, 8, 1)
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# Call the method under test
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output, attn_lse = self.impl._attention_with_nomask_and_mask(
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q, q_seqlens, k_nomask, v_nomask, kv_seqlens_nomask, k_mask,
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v_mask, kv_seqlens_mask, mask, attn_metadata)
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# Verify only mask attention was invoked
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mock_npu_attention.assert_called_with(
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q,
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k_mask,
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v_mask,
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num_heads=self.impl.num_heads,
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num_key_value_heads=self.impl.num_kv_heads,
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input_layout="TND",
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atten_mask=mask,
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scale=self.impl.scale,
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sparse_mode=3,
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antiquant_mode=0,
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antiquant_scale=None,
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softmax_lse_flag=True,
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actual_seq_lengths_kv=kv_seqlens_mask,
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actual_seq_lengths=q_seqlens)
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# Assert the method call
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self.assertEqual(mock_npu_attention.call_count, 1)
|
|
self.assertIsInstance(output, torch.Tensor)
|
|
self.assertIsInstance(attn_lse, torch.Tensor)
|
|
self.assertEqual(output.shape, (96, 8, 64))
|
|
self.assertEqual(attn_lse.shape, (96, 8, 1))
|
|
|
|
@patch('torch.ops.npu.npu_fused_infer_attention_score')
|
|
@patch(
|
|
'vllm_ascend.attention.context_parallel.attention_cp.AscendAttentionCPImpl._update_out_and_lse'
|
|
)
|
|
def test_attention_with_nomask_and_mask_chunk(
|
|
self, mock_update_out_and_lse,
|
|
mock_npu_fused_infer_attention_score):
|
|
# Mock input data
|
|
q = torch.randn(self.q_total_tokens, self.impl.num_heads,
|
|
self.impl.head_size)
|
|
k_nomask = torch.randn(self.kv_total_nomask, self.impl.num_kv_heads,
|
|
self.impl.head_size)
|
|
v_nomask = torch.randn(self.kv_total_nomask, self.impl.num_kv_heads,
|
|
self.impl.head_size)
|
|
k_mask = torch.randn(self.kv_total_mask, self.impl.num_kv_heads,
|
|
self.impl.head_size)
|
|
v_mask = torch.randn(self.kv_total_mask, self.impl.num_kv_heads,
|
|
self.impl.head_size)
|
|
|
|
mask = torch.randn(self.q_total_tokens, self.kv_total_mask)
|
|
attn_metadata = self._build_attn_metadata(with_chunked_context=True)
|
|
|
|
# Mock output
|
|
mock_npu_fused_infer_attention_score.return_value = torch.randn(
|
|
self.q_total_tokens, self.impl.num_heads,
|
|
self.impl.head_size), torch.randn(self.q_total_tokens,
|
|
self.impl.num_heads, 1)
|
|
mock_update_out_and_lse.return_value = torch.randn(
|
|
self.q_total_tokens, self.impl.num_heads,
|
|
self.impl.head_size), torch.randn(self.q_total_tokens,
|
|
self.impl.num_heads, 1)
|
|
# Call the method under test
|
|
output, attn_lse = self.impl._attention_with_nomask_and_mask(
|
|
q=q,
|
|
q_seqlens=self.q_seqlens_cumsum,
|
|
k_nomask=k_nomask,
|
|
v_nomask=v_nomask,
|
|
kv_seqlens_nomask=self.kv_seqlens_nomask_cumsum,
|
|
k_mask=k_mask,
|
|
v_mask=v_mask,
|
|
kv_seqlens_mask=self.kv_seqlens_mask_cumsum,
|
|
mask=mask,
|
|
attn_metadata=attn_metadata)
|
|
# Assert the method call
|
|
self.assertEqual(mock_npu_fused_infer_attention_score.call_count, 2)
|
|
self.assertIsNotNone(output)
|
|
self.assertIsNotNone(attn_lse)
|
|
|
|
@patch('torch.ops.npu.npu_fused_infer_attention_score')
|
|
@patch(
|
|
'vllm_ascend.attention.context_parallel.attention_cp.AscendAttentionCPImpl._npu_attn_out_lse_update'
|
|
)
|
|
def test_attention_with_nomask_and_mask_nochunk(
|
|
self, mock_npu_attn_out_lse_update,
|
|
mock_npu_fused_infer_attention_score):
|
|
# Mock input data
|
|
q = torch.randn(self.q_total_tokens, self.impl.num_heads,
|
|
self.impl.head_size)
|
|
k_nomask = torch.randn(self.kv_total_nomask, self.impl.num_kv_heads,
|
|
self.impl.head_size)
|
|
v_nomask = torch.randn(self.kv_total_nomask, self.impl.num_kv_heads,
|
|
self.impl.head_size)
|
|
k_mask = torch.randn(self.kv_total_mask, self.impl.num_kv_heads,
|
|
self.impl.head_size)
|
|
v_mask = torch.randn(self.kv_total_mask, self.impl.num_kv_heads,
|
|
self.impl.head_size)
|
|
mask = torch.randn(self.q_total_tokens, self.kv_total_mask)
|
|
|
|
attn_metadata = self._build_attn_metadata(with_chunked_context=True)
|
|
attn_metadata.prefill.chunked_context = None
|
|
|
|
# Mock output
|
|
mock_npu_fused_infer_attention_score.return_value = torch.randn(
|
|
self.q_total_tokens, self.impl.num_heads,
|
|
self.impl.head_size), torch.randn(self.q_total_tokens,
|
|
self.impl.num_heads, 1)
|
|
mock_npu_attn_out_lse_update.return_value = torch.randn(
|
|
self.q_total_tokens, self.impl.num_heads, self.impl.head_size)
|
|
|
|
# Call the method under test
|
|
output, attn_lse = self.impl._attention_with_nomask_and_mask(
|
|
q=q,
|
|
q_seqlens=self.q_seqlens_cumsum,
|
|
k_nomask=k_nomask,
|
|
v_nomask=v_nomask,
|
|
kv_seqlens_nomask=self.kv_seqlens_nomask_cumsum,
|
|
k_mask=k_mask,
|
|
v_mask=v_mask,
|
|
kv_seqlens_mask=self.kv_seqlens_mask_cumsum,
|
|
mask=mask,
|
|
attn_metadata=attn_metadata)
|
|
# Assert the method call
|
|
mock_npu_attn_out_lse_update.assert_called_once()
|
|
self.assertEqual(mock_npu_fused_infer_attention_score.call_count, 2)
|
|
self.assertIsNotNone(output)
|
|
self.assertEqual(attn_lse, None)
|
|
|
|
@patch(
|
|
'vllm_ascend.attention.context_parallel.attention_cp.AscendAttentionCPImpl._npu_attn_out_lse_update'
|
|
)
|
|
def test_update_chunk_attn_out_lse_with_current_attn_out_lse(
|
|
self, mock_npu_attn_out_lse_update):
|
|
# Mock input data
|
|
current_attn_output_prefill = torch.randn(32764, 8, 128)
|
|
current_attn_lse_prefill = torch.randn(32764, 8, 1)
|
|
attn_output_full_chunk = torch.randn(65528, 8, 128)
|
|
attn_lse_full_chunk = torch.randn(65528, 8, 1)
|
|
prefill_query = torch.randn(32764, 8, 128)
|
|
# mock attn_metadata
|
|
attn_metadata = self._build_attn_metadata(with_chunked_context=True)
|
|
attn_metadata.prefill.chunked_context.chunk_seq_mask_filtered_indices = torch.arange(
|
|
32764, dtype=torch.int32)
|
|
attn_metadata.prefill.chunked_context.kv_inverse_idx_for_chunk = torch.arange(
|
|
32764, dtype=torch.int32)
|
|
# Mock output
|
|
mock_npu_attn_out_lse_update.return_value = torch.randn(32764, 8, 128)
|
|
# test pcp_size > 1
|
|
self.impl.pcp_size = 2
|
|
self.impl.pcp_rank = 0
|
|
self.impl.dcp_group = None
|
|
self.impl.pcp_group = None
|
|
# Call the method under test
|
|
self.impl._update_chunk_attn_out_lse_with_current_attn_out_lse(
|
|
current_attn_output_prefill, current_attn_lse_prefill,
|
|
attn_output_full_chunk, attn_lse_full_chunk, prefill_query,
|
|
attn_metadata)
|
|
# Assert the method call
|
|
self.impl._npu_attn_out_lse_update.assert_called_once()
|
|
# test pcp_size = 1
|
|
self.impl.pcp_size = 1
|
|
self.impl._update_chunk_attn_out_lse_with_current_attn_out_lse(
|
|
current_attn_output_prefill, current_attn_lse_prefill,
|
|
attn_output_full_chunk, attn_lse_full_chunk, prefill_query,
|
|
attn_metadata)
|
|
self.assertEqual(self.impl._npu_attn_out_lse_update.call_count, 2)
|
|
|
|
@patch('torch_npu.npu_attention_update')
|
|
def test_npu_attn_out_lse_update(self, mock_npu_attention_update):
|
|
# Mock input data
|
|
attn_lse_mask = torch.randn(8, 128, 1)
|
|
attn_lse_nomask = torch.randn(8, 128, 1)
|
|
attn_out_mask = torch.randn(8, 128, 128)
|
|
attn_out_nomask = torch.randn(8, 128, 128)
|
|
|
|
# Mock output
|
|
mock_npu_attention_update.return_value = (torch.randn(8 * 128,
|
|
128), None)
|
|
|
|
# Call the method under test
|
|
output = self.impl._npu_attn_out_lse_update(attn_lse_mask,
|
|
attn_lse_nomask,
|
|
attn_out_mask,
|
|
attn_out_nomask)
|
|
|
|
# Assert the method call
|
|
self.assertIsInstance(output, torch.Tensor)
|
|
self.assertEqual(output.shape, (8, 128, 128))
|
|
|
|
mock_npu_attention_update.assert_called_once()
|
|
|
|
def test_update_out_and_lse(self):
|
|
# Mock input data
|
|
out_list = torch.randn(3, 2, 4,
|
|
8) # [N, batch_size, num_heads, head_size]
|
|
lse_list = torch.randn(3, 2, 4, 1) # [N, batch_size, num_heads, 1]
|
|
|
|
# Call the method under test
|
|
out_final, lse_final = self.impl._update_out_and_lse(
|
|
out_list, lse_list)
|
|
|
|
# Assert the method call
|
|
self.assertEqual(out_final.shape,
|
|
(2, 4, 8)) # [batch_size, num_heads, head_size]
|
|
self.assertEqual(lse_final.shape,
|
|
(2, 4, 1)) # [batch_size, num_heads, 1]
|
|
|
|
self.assertIsInstance(out_final, torch.Tensor)
|
|
self.assertIsInstance(lse_final, torch.Tensor)
|
|
|
|
@patch_distributed_groups(dcp_size=2, pcp_size=3)
|
|
def test_update_chunk_attn_out_lse_dcp2_pcp3(self, mock_all_to_all_single,
|
|
mock_dcp, mock_pcp):
|
|
# Mock input data
|
|
prefix_chunk_output = torch.randn(2, 4, 8)
|
|
prefix_chunk_lse = torch.randn(2, 4, 1)
|
|
self.impl.dcp_size = 2
|
|
self.impl.pcp_size = 3
|
|
self.impl.head_size = 8
|
|
|
|
# Call the method under test
|
|
chunk_data = torch.cat([prefix_chunk_output, prefix_chunk_lse],
|
|
dim=-1).permute([1, 2, 0]).contiguous()
|
|
global_context_output = self.impl._gather_global_context_output(
|
|
chunk_data)
|
|
global_context_output = global_context_output.permute([2, 0, 1
|
|
]).contiguous()
|
|
output, lse = self.impl._update_global_context_output(
|
|
global_context_output)
|
|
|
|
# Assert the method call
|
|
self.assertIsInstance(output, torch.Tensor)
|
|
self.assertIsInstance(lse, torch.Tensor)
|
|
self.assertEqual(output.shape, (2, 2, 8))
|
|
self.assertEqual(lse.shape, (2, 2, 1))
|
|
|
|
mock_all_to_all_single.assert_called_once()
|
|
mock_pcp.all_gather.assert_called_once()
|
|
|
|
@patch_distributed_groups(dcp_size=2)
|
|
def test_update_chunk_attn_out_lse_dcp2_pcp1(self, mock_all_to_all_single,
|
|
mock_dcp, mock_pcp):
|
|
# Mock input data
|
|
prefix_chunk_output = torch.randn(2, 4, 8)
|
|
prefix_chunk_lse = torch.randn(2, 4, 1)
|
|
|
|
self.impl.dcp_size = 2
|
|
self.impl.pcp_size = 1
|
|
self.impl.head_size = 8
|
|
|
|
# Call the method under test
|
|
chunk_data = torch.cat([prefix_chunk_output, prefix_chunk_lse],
|
|
dim=-1).permute([1, 2, 0]).contiguous()
|
|
global_context_output = self.impl._gather_global_context_output(
|
|
chunk_data)
|
|
global_context_output = global_context_output.permute([2, 0, 1
|
|
]).contiguous()
|
|
output, lse = self.impl._update_global_context_output(
|
|
global_context_output)
|
|
|
|
# Assert the method call
|
|
self.assertIsInstance(output, torch.Tensor)
|
|
self.assertIsInstance(lse, torch.Tensor)
|
|
self.assertEqual(output.shape, (2, 2, 8))
|
|
self.assertEqual(lse.shape, (2, 2, 1))
|
|
|
|
mock_all_to_all_single.assert_called_once()
|
|
mock_pcp.all_gather.assert_not_called()
|
|
|
|
@patch_distributed_groups(pcp_size=2)
|
|
def test_update_chunk_attn_out_lse_dcp1_pcp2(self, mock_all_to_all_single,
|
|
mock_dcp, mock_pcp):
|
|
# Mock input data
|
|
prefix_chunk_output = torch.randn(2, 4, 8)
|
|
prefix_chunk_lse = torch.randn(2, 4, 1)
|
|
|
|
self.impl.dcp_size = 1
|
|
self.impl.pcp_size = 2
|
|
self.impl.head_size = 8
|
|
|
|
# Call the method under test
|
|
chunk_data = torch.cat([prefix_chunk_output, prefix_chunk_lse],
|
|
dim=-1).permute([1, 2, 0]).contiguous()
|
|
global_context_output = self.impl._gather_global_context_output(
|
|
chunk_data)
|
|
global_context_output = global_context_output.permute([2, 0, 1
|
|
]).contiguous()
|
|
output, lse = self.impl._update_global_context_output(
|
|
global_context_output)
|
|
|
|
# Assert the method call
|
|
self.assertIsInstance(output, torch.Tensor)
|
|
self.assertIsInstance(lse, torch.Tensor)
|
|
self.assertEqual(output.shape, (2, 4, 8))
|
|
self.assertEqual(lse.shape, (2, 4, 1))
|
|
|
|
mock_all_to_all_single.assert_not_called()
|
|
mock_pcp.all_gather.assert_called_once()
|