[V1][Spec Decode] Eagle unit tests (#17350)

Signed-off-by: wwl2755 <wangwenlong2755@gmail.com>

Author: wwl2755

tests/v1/spec_decode/test_eagle.py

@@ -0,0 +1,340 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from unittest import mock
+
+import pytest
+import torch
+
+from vllm.config import (CacheConfig, DeviceConfig, LoadConfig, ModelConfig,
+                         ParallelConfig, SchedulerConfig, SpeculativeConfig,
+                         VllmConfig)
+from vllm.v1.spec_decode.eagle import EagleProposer
+
+model_dir = "meta-llama/Llama-3.1-8B-Instruct"
+eagle_dir = "yuhuili/EAGLE-LLaMA3.1-Instruct-8B"
+eagle3_dir = "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B"
+
+
+def _create_proposer(method: str, k: int) -> EagleProposer:
+    model_config = ModelConfig(model=model_dir,
+                               task="generate",
+                               max_model_len=100,
+                               tokenizer=model_dir,
+                               tokenizer_mode="auto",
+                               dtype="auto",
+                               seed=None,
+                               trust_remote_code=False)
+
+    # Choose model directory based on method
+    draft_model_dir = eagle_dir if method == "eagle" else eagle3_dir
+
+    speculative_config = SpeculativeConfig(
+        target_model_config=model_config,
+        target_parallel_config=ParallelConfig(),
+        model=draft_model_dir,
+        method=method,
+        num_speculative_tokens=k,
+    )
+
+    vllm_config = VllmConfig(model_config=model_config,
+                             cache_config=CacheConfig(),
+                             speculative_config=speculative_config,
+                             device_config=DeviceConfig(device="cuda"),
+                             parallel_config=ParallelConfig(),
+                             load_config=LoadConfig(),
+                             scheduler_config=SchedulerConfig())
+
+    return EagleProposer(vllm_config=vllm_config, device='cuda')
+
+
+def test_prepare_inputs():
+    """
+    cu_target_query_lens: [0, a, a + b, a + b + c]
+    num_rejected_tokens: [n1, n2, n3]
+    num_tokens_per_req: [a - n1, b - n2, c - n3]
+    cu_num_tokens: [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
+    token_indices: [0, 1, ..., a - n1 - 1,
+                    a, a + 1, ..., a + b - n2 - 1,
+                    a + b, a + b + 1, ..., a + b + c - n3 - 1]
+    """
+    device = torch.device('cuda')
+
+    # a = 4, b = 7, c = 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)
+
+    # Rejected tokens per request: [1, 3, 2]
+    num_rejected_tokens = torch.tensor([1, 3, 2],
+                                       dtype=torch.int32,
+                                       device=device)
+
+    # Expected calculations:
+    # query_len_per_req = [4, 7, 5]
+    # num_tokens_per_req = [3, 4, 3]  (after subtracting rejected tokens)
+    # Expected cumulative counts: [0, 3, 7, 10]
+    expected_cu_num_tokens = torch.tensor([0, 3, 7, 10],
+                                          dtype=torch.int32,
+                                          device=device)
+
+    # Expected token indices (mapped from original positions):
+    # First request: indices 0, 1, 2      (keeping first 3 from positions 0-3)
+    # Second request: indices 4, 5, 6, 7  (keeping first 4 from positions 4-10)
+    # Third request: indices 11, 12, 13   (keeping first 3 from positions 11-15)
+    expected_token_indices = torch.tensor(
+        [
+            0,
+            1,
+            2,  # First request: 3 tokens (4-1)
+            4,
+            5,
+            6,
+            7,  # Second request: 4 tokens (7-3)
+            11,
+            12,
+            13  # Third request: 3 tokens (5-2)
+        ],
+        dtype=torch.int32,
+        device=device)
+
+    cu_num_tokens, token_indices = EagleProposer.prepare_inputs(
+        cu_target_query_lens, num_rejected_tokens)
+
+    assert torch.equal(cu_num_tokens, expected_cu_num_tokens)
+    assert token_indices.shape[0] == expected_cu_num_tokens[-1].item()
+    assert torch.equal(token_indices, expected_token_indices)
+
+
+@pytest.mark.parametrize(
+    "method,proposer_helper,draft_model_dir,target_attribute_path", [
+        ("eagle", lambda k: _create_proposer("eagle", k), eagle_dir,
+         ('lm_head', )),
+        ("eagle3", lambda k: _create_proposer("eagle3", k), eagle3_dir,
+         ('model', 'embed_tokens')),
+    ])
+@mock.patch('vllm.v1.spec_decode.eagle.get_layers_from_vllm_config')
+@mock.patch('vllm.v1.spec_decode.eagle.ModelRegistry')
+@mock.patch('vllm.v1.spec_decode.eagle.get_model_loader')
+@mock.patch('vllm.v1.spec_decode.eagle.set_default_torch_dtype')
+@mock.patch('vllm.v1.spec_decode.eagle.set_current_vllm_config')
+def test_load_model(mock_set_config, mock_set_dtype, mock_get_loader,
+                    mock_registry, mock_get_layers, method, proposer_helper,
+                    draft_model_dir, target_attribute_path):
+
+    # Setup mock for model class
+    mock_model_cls = mock.MagicMock()
+    mock_registry.resolve_model_cls.return_value = (mock_model_cls,
+                                                    "test_arch")
+
+    # Create a real context manager for mocks
+    class MockContextManager:
+
+        def __init__(self):
+            pass
+
+        def __enter__(self):
+            return None
+
+        def __exit__(self, exc_type, exc_val, exc_tb):
+            return False
+
+    # Make the mocks return actual context manager objects
+    mock_set_dtype.return_value = MockContextManager()
+    mock_set_config.return_value = MockContextManager()
+
+    # Setup mocks for attention layers
+    target_attn_layers = {
+        "target_attn_1": mock.MagicMock(),
+        "target_attn_2": mock.MagicMock()
+    }
+    # Draft model has one extra attention layer compared to target model
+    all_attn_layers = {
+        **target_attn_layers, "draft_extra_attn": mock.MagicMock()
+    }
+
+    # Make mock_get_layers return different values for each call
+    mock_get_layers.side_effect = [target_attn_layers, all_attn_layers]
+
+    # Setup model loader mock
+    mock_loader = mock.MagicMock()
+    mock_get_loader.return_value = mock_loader
+
+    # Setup model mock
+    mock_model = mock.MagicMock()
+    mock_model_cls.return_value = mock_model
+    mock_model.to.return_value = mock_model
+
+    # Configure mock to test the attribute sharing path
+    if method == "eagle":
+        # For eagle, test the lm_head path
+        mock_model.load_weights.return_value = {
+            "model.embed_tokens.weight": torch.zeros(1)
+        }
+    else:
+        # For eagle3, test the embed_tokens path
+        mock_model.load_weights.return_value = {}
+
+    # Setup target model with the appropriate attributes
+    target_model = mock.MagicMock()
+
+    # Create the necessary attributes on the target model
+    current_obj = target_model
+    for i, attr in enumerate(target_attribute_path):
+        if i == len(target_attribute_path) - 1:
+            # Set the last attribute in the path to a MagicMock
+            setattr(current_obj, attr, mock.MagicMock())
+        else:
+            # Create intermediate objects if needed
+            setattr(current_obj, attr, mock.MagicMock())
+            current_obj = getattr(current_obj, attr)
+
+    # Create proposer using the helper function
+    proposer = proposer_helper(k=8)
+
+    # Call the method under test
+    proposer.load_model(target_model)
+
+    # Verify common interactions
+    mock_get_loader.assert_called_once()
+    mock_model_cls.assert_called_once()
+    mock_model.to.assert_called_once()
+    mock_model.load_weights.assert_called_once()
+
+    # Verify the loader was called with the right config
+    mock_get_loader.assert_called_once_with(proposer.vllm_config.load_config)
+
+    # Verify the specific attribute sharing based on the method
+    if method == "eagle":
+        assert proposer.model.lm_head == target_model.lm_head
+    else:
+        assert proposer.model.model.embed_tokens == \
+            target_model.model.embed_tokens
+
+
+@pytest.mark.parametrize("num_speculative_tokens", [1, 3, 8])
+def test_propose(num_speculative_tokens):
+    # Use GPU device
+    device = torch.device('cuda')
+
+    # Setup test parameters
+    batch_size = 2
+    seq_len_1 = 5
+    seq_len_2 = 3
+    total_tokens = seq_len_1 + seq_len_2
+    vocab_size = 100
+
+    # Create proposer first so we can use its actual hidden_size
+    proposer = _create_proposer("eagle", num_speculative_tokens)
+    # Get the hidden_size from the proposer to ensure consistency
+    hidden_size = proposer.hidden_size
+
+    # Helper to create deterministic logits that will produce specific tokens
+    def create_deterministic_logits(token_ids):
+        logits = torch.full((batch_size, vocab_size), -100.0, device=device)
+        for i, token_id in enumerate(token_ids):
+            logits[i, token_id] = 100.0
+        return logits
+
+    # We mock a model that returns deterministic logits
+    # Sequence 1: 42, 43, 44, ...
+    # Sequence 2: 60, 61, 62, ...
+    base_token_ids = [42, 60]
+
+    # Skip loading the model and replace it with a mock directly
+    # Create the mock model with deterministic outputs
+    model_mock = mock.MagicMock()
+
+    # Setup for model forward calls
+    forward_returns = []
+    for i in range(num_speculative_tokens):
+        if i == 0:
+            # First call uses all tokens
+            h_logits = torch.zeros(total_tokens, hidden_size, device=device)
+            h_states = torch.zeros(total_tokens, hidden_size, device=device)
+        else:
+            # Subsequent calls use batch_size tokens
+            h_logits = torch.zeros(batch_size, hidden_size, device=device)
+            h_states = torch.zeros(batch_size, hidden_size, device=device)
+        forward_returns.append((h_logits, h_states))
+
+    # For single token case, we only need the first item;
+    # for multi-token, we need the sequence
+    if num_speculative_tokens == 1:
+        model_mock.return_value = forward_returns[0]
+    else:
+        model_mock.side_effect = forward_returns
+
+    # Setup for compute_logits calls
+    logits_returns = []
+    for i in range(num_speculative_tokens):
+        # For each call, increment the base token IDs
+        current_tokens = [base_id + i for base_id in base_token_ids]
+        logits_returns.append(create_deterministic_logits(current_tokens))
+
+    if num_speculative_tokens == 1:
+        model_mock.compute_logits.return_value = logits_returns[0]
+    else:
+        model_mock.compute_logits.side_effect = logits_returns
+
+    # Assign the mock to the proposer
+    proposer.model = model_mock
+
+    # Create input tensors
+    cu_num_tokens = torch.tensor([0, seq_len_1, total_tokens],
+                                 dtype=torch.int32,
+                                 device=device)
+
+    target_token_ids = torch.randint(0,
+                                     vocab_size, (total_tokens, ),
+                                     device=device)
+    target_positions = torch.cat([
+        torch.arange(seq_len_1, device=device),
+        torch.arange(seq_len_2, device=device)
+    ])
+    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
+    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,
+                              sampling_metadata=sampling_metadata)
+
+    assert result.shape == (batch_size, num_speculative_tokens)
+
+    # Create expected tokens based on our token pattern
+    if num_speculative_tokens == 1:
+        # Example for num_speculative_tokens=1:
+        # [[42], [60]]
+        expected_tokens = torch.tensor(
+            [[base_token_ids[0]], [base_token_ids[1]]], device=device)
+    else:
+        # Example for num_speculative_tokens=3:
+        # [[42, 43, 44], [60, 61, 62]]
+        expected_tokens = torch.zeros((batch_size, num_speculative_tokens),
+                                      dtype=torch.int64,
+                                      device=device)
+        for i in range(batch_size):
+            for j in range(num_speculative_tokens):
+                expected_tokens[i, j] = base_token_ids[i] + j
+
+    # Verify all tokens match our expectations
+    assert torch.equal(result, expected_tokens)

vllm/v1/spec_decode/eagle.py

@@ -223,6 +223,8 @@ def propose(
             hidden_states = hidden_states[:batch_size]
             logits = self.model.compute_logits(last_hidden_states[:batch_size],
                                                None)
+
+            # TODO(wenlong): get more than one token for tree attention
             draft_token_ids = logits.argmax(dim=-1)
             draft_token_ids_list.append(draft_token_ids)
 
@@ -251,6 +253,8 @@ def prepare_inputs(
         # [a, b, c] -> [a - n1, b - n2, c - n3]
         num_tokens_per_req = query_len_per_req - num_rejected_tokens
 
+        # [a - n1, b - n2, c - n3] ->
+        # [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
         cu_num_tokens = torch.empty_like(cu_target_query_lens)
         torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
         cu_num_tokens[0] = 0