[Attention] Refactor attention metadata builder interface (#20466)

Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

tests/v1/spec_decode/test_eagle.py

@@ -6,6 +6,10 @@ from unittest import mock
 import pytest
 import torch
 
+from tests.v1.attention.utils import (BatchSpec, _Backend,
+                                      create_common_attn_metadata,
+                                      create_standard_kv_cache_spec,
+                                      get_attention_backend)
 from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, ModelConfig,
                          ParallelConfig, SchedulerConfig, SpeculativeConfig,
                          VllmConfig)
@@ -64,13 +68,19 @@ def test_prepare_inputs():
     """
     device = torch.device(current_platform.device_type)
 
-    # a = 4, b = 7, c = 5
+    # q1 = 4, q2 = 7, q3 = 5
     # n1 = 1, n2 = 3, n3 = 2
 
-    # Cumulative lengths: [0, 4, 11, 16]
-    cu_target_query_lens = torch.tensor([0, 4, 11, 16],
-                                        dtype=torch.int32,
-                                        device=device)
+    batch_spec = BatchSpec(
+        seq_lens=[4, 7, 5],
+        query_lens=[4, 7, 5],
+    )
+
+    common_attn_metadata = create_common_attn_metadata(
+        batch_spec,
+        block_size=16,
+        device=device,
+    )
 
     # Rejected tokens per request: [1, 3, 2]
     num_rejected_tokens = torch.tensor([1, 3, 2],
@@ -104,15 +114,13 @@ def test_prepare_inputs():
         ],
         dtype=torch.int32,
         device=device)
+    proposer = _create_proposer("eagle", 1)
 
-    # n1 + n2 + n3 - a - b -c
-    num_tokens = cu_target_query_lens[-1].item() - num_rejected_tokens.sum(
-    ).item()
+    updated_metadata, token_indices = proposer.prepare_inputs(
+        common_attn_metadata, num_rejected_tokens.cpu())
 
-    cu_num_tokens, token_indices = EagleProposer.prepare_inputs(
-        cu_target_query_lens, num_rejected_tokens, num_tokens)
-
-    assert torch.equal(cu_num_tokens, expected_cu_num_tokens)
+    assert torch.equal(updated_metadata.query_start_loc,
+                       expected_cu_num_tokens)
     assert token_indices.shape[0] == expected_cu_num_tokens[-1].item()
     assert torch.equal(token_indices, expected_token_indices)
 
@@ -209,6 +217,7 @@ def test_propose(num_speculative_tokens):
     seq_len_2 = 3
     total_tokens = seq_len_1 + seq_len_2
     vocab_size = 100
+    seq_lens = [seq_len_1, seq_len_2]
 
     # Create proposer first so we can use its actual hidden_size
     proposer = _create_proposer("eagle", num_speculative_tokens)
@@ -270,9 +279,16 @@ def test_propose(num_speculative_tokens):
     proposer.attn_layer_names = ["layer.0"]
 
     # Create input tensors
-    cu_num_tokens = torch.tensor([0, seq_len_1, total_tokens],
-                                 dtype=torch.int32,
-                                 device=device)
+    batch_spec = BatchSpec(
+        seq_lens=seq_lens,
+        query_lens=seq_lens,
+    )
+
+    common_attn_metadata = create_common_attn_metadata(
+        batch_spec,
+        block_size=16,
+        device=device,
+    )
 
     target_token_ids = torch.randint(0,
                                      vocab_size, (total_tokens, ),
@@ -284,25 +300,29 @@ def test_propose(num_speculative_tokens):
     target_hidden_states = torch.randn(total_tokens,
                                        hidden_size,
                                        device=device)
-    target_slot_mapping = torch.randint(0,
-                                        100, (total_tokens, ),
-                                        device=device)
     next_token_ids = torch.randint(0,
                                    vocab_size, (batch_size, ),
                                    dtype=torch.int32,
                                    device=device)
-    block_table = torch.randint(0, 10, (batch_size, 10), device=device)
-
     sampling_metadata = mock.MagicMock()
 
-    # Call the method under test
+    attn_metadata_builder_cls, _ = get_attention_backend(
+        _Backend.FLASH_ATTN_VLLM_V1)
+    attn_metadata_builder = attn_metadata_builder_cls(
+        kv_cache_spec=create_standard_kv_cache_spec(proposer.vllm_config),
+        vllm_config=proposer.vllm_config,
+        device=device,
+    )
+
+    # Mock runner for attention metadata building
+    proposer.runner = mock.MagicMock()
+    proposer.runner.attn_metadata_builders = [attn_metadata_builder]
+
     result = proposer.propose(target_token_ids=target_token_ids,
                               target_positions=target_positions,
                               target_hidden_states=target_hidden_states,
-                              target_slot_mapping=target_slot_mapping,
                               next_token_ids=next_token_ids,
-                              cu_num_tokens=cu_num_tokens,
-                              block_table=block_table,
+                              common_attn_metadata=common_attn_metadata,
                               sampling_metadata=sampling_metadata)
 
     assert result.shape == (batch_size, num_speculative_tokens)