[Feat] Support async_scheduler and disable_padded_drafter_batch in eagle (#4893)

### What this PR does / why we need it?
We refactored the eagle_proposer.py to adapt the framework of eagle.py
in vllm-v0.12.0, to support the logit of padded drafter batch and
async-scheduler.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: anon189Ty <Stari_Falcon@outlook.com>
Co-authored-by: drslark <slarksblood@qq.com>

tests/e2e/singlecard/spec_decode_v1/test_v1_spec_decode.py

@@ -7,9 +7,10 @@ import random
 from typing import Any
 
 import pytest
+from transformers import AutoTokenizer
 from vllm import LLM, SamplingParams
 
-from tests.e2e.conftest import VllmRunner
+from tests.e2e.conftest import VllmRunner, cleanup_dist_env_and_memory
 
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
@@ -115,41 +116,67 @@ def test_eagle_correctness(
     Compare the outputs of a original LLM and a speculative LLM
     should be the same when using eagle speculative decoding.
     '''
-    pytest.skip("To be aligned with GPU")
-    ref_llm = LLM(model=model_name, max_model_len=2048, enforce_eager=False)
-    ref_outputs = ref_llm.chat(test_prompts, sampling_config)
-    del ref_llm
-
+    # NOTE: e2e of eagle has many problems before.
+    # We first check whether it is functioning properly.
+    # Should fix the e2e with VllmRunner in future.
     spec_model_name = eagle3_model_name() if use_eagle3 else eagle_model_name()
-    with VllmRunner(
-            model_name,
-            max_num_seqs=1,
-            max_num_batched_tokens=2048,
-            gpu_memory_utilization=0.6,
-            speculative_config={
-                "method": "eagle3" if use_eagle3 else "eagle",
-                "model": spec_model_name,
-                "num_speculative_tokens": 2,
-                "max_model_len": 128,
-            },
-            max_model_len=128,
-            enforce_eager=False,
-    ) as runner:
-        spec_outputs = runner.model.chat(test_prompts, sampling_config)
+    tokenizer = AutoTokenizer.from_pretrained(model_name,
+                                              trust_remote_code=True)
+    prompts = [{
+        "role": "user",
+        "content": "Hello, my name is"
+    }, {
+        "role": "user",
+        "content": "The president of the United States is"
+    }, {
+        "role": "user",
+        "content": "The capital of France is"
+    }, {
+        "role": "user",
+        "content": "The future of AI is"
+    }]
+    prompts = [
+        tokenizer.apply_chat_template(
+            [prompt],
+            tokenize=False,
+            add_generation_prompt=True,
+        ) for prompt in prompts
+    ]
 
-    matches = 0
-    misses = 0
-    for ref_output, spec_output in zip(ref_outputs, spec_outputs):
-        if ref_output.outputs[0].text == spec_output.outputs[0].text:
-            matches += 1
-        else:
-            misses += 1
-            print(f"ref_output: {ref_output.outputs[0].text}")
-            print(f"spec_output: {spec_output.outputs[0].text}")
+    sampling_params = SamplingParams(
+        max_tokens=300,
+        temperature=0.0,
+        ignore_eos=False,
+    )
 
-    # Heuristic: expect at least 66% of the prompts to match exactly
-    # Upon failure, inspect the outputs to check for inaccuracy.
-    assert matches > int(0.66 * len(ref_outputs))
+    # Create an LLM.
+    llm = LLM(
+        model=model_name,
+        tensor_parallel_size=1,
+        pipeline_parallel_size=1,
+        data_parallel_size=1,
+        disable_log_stats=False,
+        max_model_len=4096,
+        seed=1024,
+        async_scheduling=True,
+        compilation_config={
+            "level": 3,
+            "cudagraph_mode": "FULL_DECODE_ONLY",
+            "cudagraph_num_of_warmups": 1,
+            "cudagraph_capture_sizes": [12],
+        },
+        speculative_config={
+            "disable_padded_drafter_batch": False,
+            "method": "eagle3" if use_eagle3 else "eagle",
+            "model": spec_model_name,
+            "num_speculative_tokens": 2,
+            "max_model_len": 128,
+            "draft_vocab_size": 128256,
+        },
+    )
+    llm.generate(prompts, sampling_params)
+    cleanup_dist_env_and_memory()
+    del llm
 
 
 @pytest.mark.skip(

tests/ut/spec_decode/test_eagle_proposer.py

@@ -26,6 +26,13 @@ class TestEagleProposerInitialization(TestBase):
         self.vllm_config.model_config.dtype = torch.float16
         self.vllm_config.model_config.max_model_len = 2048
 
+        self.mock_cpugpubuffer = patch(
+            "vllm_ascend.spec_decode.eagle_proposer.CpuGpuBuffer")
+        self.mock_cpugpubuffer.start()
+
+    def tearDown(self):
+        self.mock_cpugpubuffer.stop()
+
     def test_initialization_eagle(self):
         self.vllm_config.speculative_config.method = "eagle"
         self.vllm_config.speculative_config.draft_model_config.get_hidden_size.return_value = 4096
@@ -44,7 +51,7 @@ class TestEagleProposerInitialization(TestBase):
         self.assertEqual(proposer.input_ids.shape, (1024, ))
         self.assertEqual(proposer.positions.shape, (1024, ))
         self.assertEqual(proposer.hidden_states.shape, (1024, 4096))
-        self.assertEqual(proposer.arange.shape, (33, ))
+        self.assertEqual(proposer.arange.shape, (1024, ))
 
     def test_initialization_eagle3(self):
         self.vllm_config.speculative_config.method = "eagle3"
@@ -77,10 +84,16 @@ class TestEagleProposerLoadModel(TestBase):
         self.vllm_config.model_config.dtype = torch.float16
         self.vllm_config.model_config.max_model_len = 2048
 
+        self.mock_cpugpubuffer = patch(
+            "vllm_ascend.spec_decode.eagle_proposer.CpuGpuBuffer")
+        self.mock_cpugpubuffer.start()
         self.proposer = EagleProposer(vllm_config=self.vllm_config,
                                       device=self.device,
                                       runner=self.runner)
 
+    def tearDown(self):
+        self.mock_cpugpubuffer.stop()
+
     @patch(
         "vllm_ascend.spec_decode.eagle_proposer.get_layers_from_vllm_config")
     @patch("vllm_ascend.spec_decode.eagle_proposer.get_model")
@@ -172,11 +185,17 @@ class TestEagleProposerDummyRun(TestBase):
         self.vllm_config.model_config.dtype = torch.float16
         self.vllm_config.model_config.max_model_len = 2048
 
+        self.mock_cpugpubuffer = patch(
+            "vllm_ascend.spec_decode.eagle_proposer.CpuGpuBuffer")
+        self.mock_cpugpubuffer.start()
         self.proposer = EagleProposer(vllm_config=self.vllm_config,
                                       device=self.device,
                                       runner=self.runner)
         self.proposer.model = MagicMock()
 
+    def tearDown(self):
+        self.mock_cpugpubuffer.stop()
+
     @patch("vllm_ascend.spec_decode.eagle_proposer.set_ascend_forward_context")
     def test_dummy_run_basic(self, mock_context):
         num_tokens = 32
@@ -216,6 +235,9 @@ class TestEagleProposerGenerateTokenIds(TestBase):
         self.vllm_config.model_config.dtype = torch.float16
         self.vllm_config.model_config.max_model_len = 2048
 
+        self.mock_cpugpubuffer = patch(
+            "vllm_ascend.spec_decode.eagle_proposer.CpuGpuBuffer")
+        self.mock_cpugpubuffer.start()
         self.proposer = EagleProposer(vllm_config=self.vllm_config,
                                       device=self.device,
                                       runner=self.runner)
@@ -223,7 +245,12 @@ class TestEagleProposerGenerateTokenIds(TestBase):
         self.proposer._propose = MagicMock(
             return_value=torch.tensor([[1, 2], [3, 4], [5, 6]]))
 
-    def test_generate_token_ids_without_metadata(self):
+    def tearDown(self):
+        self.mock_cpugpubuffer.stop()
+
+    # TODO: This is equivalent to disable_padded_drafter_batch=True.
+    # We need to add some cases about disable_padded_drafter_batch=False in future.
+    def test_generate_token_ids(self):
         valid_sampled = [[20, 30, 40]]
         scheduler_output = MagicMock()
         scheduler_output.num_scheduled_tokens = [2, 1, 3]
@@ -239,7 +266,7 @@ class TestEagleProposerGenerateTokenIds(TestBase):
             return_value={"layer_0": mock_attn_metadata})
 
         result = self.proposer.generate_token_ids(
-            valid_sampled_token_ids=valid_sampled,
+            sampled_token_ids=valid_sampled,
             scheduler_output=scheduler_output,
             positions=positions,
             num_scheduled_tokens=num_scheduled,
@@ -247,36 +274,13 @@ class TestEagleProposerGenerateTokenIds(TestBase):
         )
 
         self.proposer._propose.assert_called_once()
-        self.assertEqual(result, [[1, 2], [3, 4], [5, 6]])
-
-    def test_generate_token_ids_with_metadata(self):
-        valid_sampled = [[5], [6, 7], [8, 9, 10]]
-        spec_metadata = MagicMock()
-        spec_metadata.num_draft_tokens = [2, 3, 4]
-
-        mock_attn_metadata = MagicMock()
-        mock_attn_metadata.slot_mapping = torch.tensor([0, 1, 2, 3, 4, 5])
-        mock_attn_metadata.query_start_loc = torch.tensor([0, 1, 3, 6])
-        mock_attn_metadata.block_tables = MagicMock()
-        self.proposer._get_eagle_atten_dict = MagicMock(
-            return_value={"layer_0": mock_attn_metadata})
-        self.proposer._prepare_inputs = MagicMock(
-            return_value=(torch.tensor([0, 2, 5]), torch.tensor([1, 3, 5])))
-
-        result = self.proposer.generate_token_ids(
-            valid_sampled_token_ids=valid_sampled,
-            spec_decode_metadata=spec_metadata,
-            positions=torch.randn(6, 1),
-            hidden_states=torch.randn(6, 4096),
-        )
-
-        self.proposer._prepare_inputs.assert_called_once()
-        self.assertEqual(self.proposer._propose.call_count, 1)
-        self.assertEqual(len(result), 3)
+        self.assertEqual(result.numpy().tolist(), [[1, 2], [3, 4], [5, 6]])
 
 
 class TestEagleProposerHelperMethods(TestBase):
 
+    # TODO: Can add some tests about prepare_next_token_ids in future.
+
     def setUp(self):
         self.vllm_config = MagicMock(spec=VllmConfig)
         self.vllm_config.scheduler_config = MagicMock(max_num_seqs=3)
@@ -293,21 +297,29 @@ class TestEagleProposerHelperMethods(TestBase):
         self.vllm_config.model_config.dtype = torch.float16
         self.vllm_config.model_config.max_model_len = 2048
 
+        self.mock_cpugpubuffer = patch(
+            "vllm_ascend.spec_decode.eagle_proposer.CpuGpuBuffer")
+        self.mock_cpugpubuffer.start()
         self.proposer = EagleProposer(vllm_config=self.vllm_config,
                                       device=self.device,
                                       runner=self.runner)
 
+    def tearDown(self):
+        self.mock_cpugpubuffer.stop()
+
+    # TODO: This is equivalent to disable_padded_drafter_batch=True.
+    # We need to add a test_prepare_inputs_padded in future.
     def test_prepare_inputs(self):
         self.proposer.token_arange_np = np.arange(10)
         mock_attn = MagicMock()
         mock_attn.slot_mapping = torch.tensor([0, 1, 2, 3, 4, 5])
         num_rejected = torch.tensor([1, 0, 1], device=self.device)
+        mock_return_attn = MagicMock()
 
         with patch.object(self.proposer,
-                          '_prepare_inputs',
-                          return_value=(torch.tensor([0, 2, 5]),
+                          'prepare_inputs',
+                          return_value=(mock_return_attn,
                                         torch.tensor([1, 2, 4]))):
-            cu_num_tokens, indices = self.proposer._prepare_inputs(
+            return_attn, indices = self.proposer.prepare_inputs(
                 mock_attn, num_rejected)
-            self.assertEqual(cu_num_tokens.tolist(), [0, 2, 5])
             self.assertEqual(indices.tolist(), [1, 2, 4])

vllm_ascend/attention/attention_v1.py

@@ -730,6 +730,9 @@ class AscendAttentionBackendImpl(AttentionImpl):
                 self.key_cache, self.value_cache = kv_cache[0], kv_cache[1]
             slots = attn_metadata.slot_mapping
             if get_ascend_device_type() == AscendDeviceType._910_95:
+                # TODO: Once eagle running to here, it may has error because of the 0 dim of slot_mapping.
+                # Should check if the 0 dim of slot_mapping must equal to the 0 dim of key.
+                # If it's necessary, the slots should be sliced.
                 torch_npu.npu_scatter_pa_kv_cache(
                     key=key[:attn_metadata.num_actual_tokens],
                     value=value[:attn_metadata.num_actual_tokens].contiguous(),
@@ -742,7 +745,7 @@ class AscendAttentionBackendImpl(AttentionImpl):
                     value=value[:attn_metadata.num_actual_tokens],
                     key_cache=self.key_cache,
                     value_cache=self.value_cache,
-                    slot_indices=slots)
+                    slot_indices=slots[:attn_metadata.num_actual_tokens])
         return key, value
 
     def forward_impl(

vllm_ascend/attention/utils.py

@@ -119,6 +119,35 @@ class AscendCommonAttentionMetadata:
     prefill_context_parallel_metadata: Optional[
         AscendPrefillContextParallelMetadata] = None
 
+    # TODO: Remove it when vLLM no longer uses this function.
+    def unpadded(self, num_actual_tokens: int,
+                 num_actual_reqs: int) -> "AscendCommonAttentionMetadata":
+        # This only use to eagle now. It will be use to enforce_eager in future.
+        return AscendCommonAttentionMetadata(
+            query_start_loc=self.query_start_loc[:num_actual_reqs + 1],
+            query_start_loc_cpu=self.query_start_loc_cpu[:num_actual_reqs + 1],
+            seq_lens=self.seq_lens[:num_actual_reqs],
+            seq_lens_cpu=self.seq_lens_cpu[:num_actual_reqs],
+            num_computed_tokens_cpu=self.
+            num_computed_tokens_cpu[:num_actual_reqs],
+            num_reqs=num_actual_reqs,
+            num_actual_tokens=num_actual_tokens,
+            max_query_len=self.max_query_len,
+            decode_token_per_req=self.decode_token_per_req,
+            block_table_tensor=self.block_table_tensor[:num_actual_reqs],
+            slot_mapping=self.slot_mapping[:num_actual_tokens],
+            actual_seq_lengths_q=self.actual_seq_lengths_q[:num_actual_tokens],
+            positions=self.positions[:num_actual_tokens],
+            attn_mask=self.attn_mask,
+            spec_attn_mask=self.spec_attn_mask,
+            attn_state=self.attn_state,
+            is_only_prefill=self.is_only_prefill,
+            graph_pad_size=-1,  # It should be -1 when not run in fullgraph mode.
+            num_input_tokens=num_actual_tokens,
+            prefill_context_parallel_metadata=self.
+            prefill_context_parallel_metadata,
+        )
+
 
 def filter_chunked_req_indices(
     seq_len: torch.Tensor,

vllm_ascend/spec_decode/eagle_proposer.py

@@ -14,19 +14,25 @@ from vllm.model_executor.model_loader import get_model
 from vllm.model_executor.models import supports_multimodal
 from vllm.model_executor.models.llama_eagle3 import Eagle3LlamaForCausalLM
 from vllm.utils.platform_utils import is_pin_memory_available
+from vllm.v1.attention.backends.utils import CommonAttentionMetadata
 from vllm.v1.core.sched.output import SchedulerOutput
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
+from vllm.v1.utils import CpuGpuBuffer
+from vllm.v1.worker.gpu_input_batch import CachedRequestState, InputBatch
 
 from vllm_ascend.ascend_forward_context import set_ascend_forward_context
 from vllm_ascend.attention.attention_mask import AttentionMaskBuilder
-from vllm_ascend.attention.attention_v1 import (AscendAttentionState,
-                                                AscendMetadata)
+from vllm_ascend.attention.attention_v1 import AscendAttentionState
 from vllm_ascend.attention.utils import AscendCommonAttentionMetadata
 from vllm_ascend.spec_decode.interface import Proposer, SpecDcodeType
 
 PADDING_SLOT_ID = -1
 
+_DEFAULT_FIRST_LAYER = 'model.layers.0.self_attn.attn'
+
+_FIRST_LAYERS = {"Qwen3NextForCausalLM": 'model.layers.3.self_attn.attn'}
+
 
 class EagleProposer(Proposer):
 
@@ -54,6 +60,19 @@ class EagleProposer(Proposer):
             sorted(
                 self.vllm_config.compilation_config.cudagraph_capture_sizes))
 
+        max_batch_size = vllm_config.scheduler_config.max_num_seqs
+        # Currently we do not use pcp. This is used to adapt the pcp branch.
+        self.pcp_size = 0
+        self.backup_next_token_ids = CpuGpuBuffer(
+            max_batch_size,
+            dtype=torch.int32,
+            pin_memory=is_pin_memory_available(),
+            device=device,
+            with_numpy=True,
+        )
+        self.decode_threshold = 1 + \
+            self.vllm_config.speculative_config.num_speculative_tokens
+
         # persistent buffers for cuda graph
         self.input_ids = torch.zeros(
             self.vllm_config.scheduler_config.max_num_batched_tokens,
@@ -71,12 +90,13 @@ class EagleProposer(Proposer):
         self.max_num_tokens = (
             vllm_config.scheduler_config.max_num_batched_tokens)
         self.token_arange_np = np.arange(self.max_num_tokens)
-        # We need +1 here because the arange is used to set query_start_loc,
-        # which has one more element than batch_size.
-        self.arange = torch.arange(vllm_config.scheduler_config.max_num_seqs +
-                                   1,
+        max_num_slots_for_arange = max(self.max_num_tokens, max_batch_size + 1)
+        self.arange = torch.arange(max_num_slots_for_arange,
                                    device=device,
                                    dtype=torch.int32)
+        self.arange_cpu = torch.arange(max_num_slots_for_arange,
+                                       device="cpu",
+                                       dtype=torch.int32)
         self.attn_mask_builder = AttentionMaskBuilder(self.device)
 
     def load_model(self, model: nn.Module) -> None:
@@ -135,8 +155,7 @@ class EagleProposer(Proposer):
             dummy_compute_logits(self.hidden_states)
 
     def generate_token_ids(self,
-                           valid_sampled_token_ids: torch.Tensor
-                           | list[list[int]],
+                           sampled_token_ids: torch.Tensor | list[list[int]],
                            sampling_metadata: SamplingMetadata = None,
                            scheduler_output: SchedulerOutput = None,
                            spec_decode_metadata: SpecDecodeMetadata = None,
@@ -144,273 +163,155 @@ class EagleProposer(Proposer):
                            num_scheduled_tokens: int = 0,
                            hidden_states: torch.Tensor = None,
                            aux_hidden_states: torch.Tensor = None):
+        common_attn_metadata = self.runner.spec_decode_common_attn_metadata
 
-        attn_metadata = self._get_eagle_atten_dict(scheduler_output)
-        next_token_ids: list[int] = []
-        for i, token_ids in enumerate(valid_sampled_token_ids):
-            if token_ids:
-                # Common case.
-                next_token_id = token_ids[-1]
-            else:
-                # Partial prefill (rare case).
-                # Get the next token id from the request state.
-                req_id = self.runner.input_batch.req_ids[i]
-                req_state = self.runner.requests[req_id]
-                seq_len = (req_state.num_computed_tokens +
-                           scheduler_output.num_scheduled_tokens[req_id])
-
-                next_token_id = req_state.get_token_id(seq_len)
-            next_token_ids.append(next_token_id)
-        next_token_ids = torch.tensor(next_token_ids,
-                                      dtype=torch.int32,
-                                      device=self.device)
-        eagle_attn_metadata = attn_metadata[self.attn_layer_name]
-        if spec_decode_metadata is None:
-            # input_ids can be None for multimodal models.
-            target_token_ids = self.runner.input_ids.gpu[:num_scheduled_tokens]
-            target_positions = positions[:num_scheduled_tokens]
-            if self.name == SpecDcodeType.EAGLE3:
-                target_hidden_states = torch.cat(
-                    [h[:num_scheduled_tokens] for h in aux_hidden_states],
-                    dim=-1)
-            else:
-                target_hidden_states = hidden_states[:num_scheduled_tokens]
-            target_slot_mapping = eagle_attn_metadata.slot_mapping
-            cu_num_tokens = eagle_attn_metadata.query_start_loc
+        if self.vllm_config.speculative_config.disable_padded_drafter_batch:
+            # When padded-batch is disabled, the sampled_token_ids should be
+            # the cpu-side list[list[int]] of valid sampled tokens for each
+            # request, with invalid requests having empty lists.
+            assert isinstance(sampled_token_ids, list), \
+                "sampled_token_ids should be a python list when" \
+                "padded-batch is disabled."
+            next_token_ids = self.prepare_next_token_ids_cpu(
+                sampled_token_ids, self.runner.requests,
+                self.runner.input_batch, scheduler_output.num_scheduled_tokens)
         else:
-            num_draft_tokens = spec_decode_metadata.num_draft_tokens
-            num_rejected_tokens = [
-                n + 1 - len(valid_sampled_token_ids[i]) if n > 0 else 0
-                for i, n in enumerate(num_draft_tokens)
-            ]
-            num_rejected_tokens = torch.tensor(
-                num_rejected_tokens,
-                dtype=torch.int32,
-                device=self.device,
-            )
-            cu_num_tokens, token_indices =\
-                    self._prepare_inputs(eagle_attn_metadata, num_rejected_tokens)
-            target_token_ids = self.runner.input_ids.gpu[token_indices]
-            target_positions = positions[token_indices]
-            if self.name == SpecDcodeType.EAGLE3:
-                target_hidden_states = torch.cat(
-                    [h[token_indices] for h in aux_hidden_states], dim=-1)
+            # When using padded-batch, the sampled_token_ids should be
+            # the gpu tensor of sampled tokens for each request, of shape
+            # (num_reqs, num_spec_tokens + 1) with rejected tokens having
+            # value -1.
+            assert isinstance(sampled_token_ids, torch.Tensor), \
+                "sampled_token_ids should be a torch.Tensor when" \
+                "padded-batch is enabled."
+            next_token_ids, valid_sampled_tokens_count = \
+                self.prepare_next_token_ids_padded(
+                    common_attn_metadata,
+                    sampled_token_ids,
+                    self.runner.requests,
+                    self.runner.input_batch,
+                    self.runner.discard_request_indices.gpu,
+                    self.runner.num_discarded_requests
+                )
+            self._copy_valid_sampled_token_count(next_token_ids,
+                                                 valid_sampled_tokens_count)
+
+        req_scheduled_tokens = scheduler_output.num_scheduled_tokens
+        if self.pcp_size > 1:
+            long_seq_metadata = self.runner.long_seq_metadata
+            input_ids_pcp_full = self.runner.input_ids_pcp_full
+            query_start_loc_pcp_full = self.runner.query_start_loc_pcp_full
+            query_start_loc_pcp_full_cpu = self.runner.query_start_loc_pcp_full_cpu
+            num_reqs = self.runner.input_batch.num_reqs
+            ori_query_lens = query_start_loc_pcp_full_cpu[1:num_reqs+1] - \
+                query_start_loc_pcp_full_cpu[:num_reqs]
+            num_prefill_reqs = (ori_query_lens
+                                > self.decode_threshold).sum().item()
+            num_decode_reqs = num_reqs - num_prefill_reqs
+        else:
+            long_seq_metadata = None
+            num_prefill_reqs = 0
+            num_decode_reqs = 0
+        if spec_decode_metadata is None:
+            # update pcp related params
+            if self.pcp_size > 1:
+                token_indices_to_sample = \
+                    query_start_loc_pcp_full_cpu[1:num_reqs + 1] - 1
+                target_token_ids = input_ids_pcp_full[:num_scheduled_tokens]
+                target_positions = positions[:num_scheduled_tokens]
+                target_hidden_states = hidden_states
             else:
-                target_hidden_states = hidden_states[token_indices]
-            target_slot_mapping = eagle_attn_metadata.slot_mapping[
-                token_indices]
+                token_indices_to_sample = None
+                # input_ids can be None for multimodal models.
+                target_token_ids = self.runner.input_ids.gpu[:
+                                                             num_scheduled_tokens]
+                target_positions = positions[:num_scheduled_tokens]
+                if self.name == SpecDcodeType.EAGLE3:
+                    target_hidden_states = torch.cat(
+                        [h[:num_scheduled_tokens] for h in aux_hidden_states],
+                        dim=-1)
+                else:
+                    target_hidden_states = hidden_states[:num_scheduled_tokens]
+        else:
+            if self.pcp_size > 1:
+                common_attn_metadata.query_start_loc_cpu = \
+                    query_start_loc_pcp_full_cpu[:num_reqs + 1]
+                common_attn_metadata.query_start_loc = \
+                    query_start_loc_pcp_full[:num_reqs + 1]
+            if self.vllm_config.speculative_config.disable_padded_drafter_batch:
+                # NOTE: Currently, MTP-fullgraph is incompatibility with pcp
+                token_indices_to_sample = None
+                common_attn_metadata, token_indices =\
+                    self.prepare_inputs(
+                        common_attn_metadata,
+                        sampled_token_ids,
+                        spec_decode_metadata.num_draft_tokens)
+            else:
+                common_attn_metadata, token_indices, \
+                    token_indices_to_sample =\
+                        self.prepare_inputs_padded(
+                            common_attn_metadata,
+                            spec_decode_metadata,
+                            valid_sampled_tokens_count)
+            if self.pcp_size > 1:
+                target_token_ids = input_ids_pcp_full[token_indices]
+                target_positions = positions
+                target_hidden_states = hidden_states
+            else:
+                target_token_ids = self.runner.input_ids.gpu[token_indices]
+                target_positions = positions[token_indices]
+                if self.name == SpecDcodeType.EAGLE3:
+                    target_hidden_states = torch.cat(
+                        [h[token_indices] for h in aux_hidden_states], dim=-1)
+                else:
+                    target_hidden_states = hidden_states[token_indices]
 
         draft_token_ids = self._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=eagle_attn_metadata.block_tables,
+            last_token_indices=token_indices_to_sample,
+            common_attn_metadata=common_attn_metadata,
             sampling_metadata=sampling_metadata,
+            req_scheduled_tokens=req_scheduled_tokens,
+            long_seq_metadata=long_seq_metadata,
+            num_prefill_reqs=num_prefill_reqs,
+            num_decode_reqs=num_decode_reqs,
+            scheduler_output=scheduler_output,
+            num_scheduled_tokens=num_scheduled_tokens,
         )
-        spec_token_ids = draft_token_ids.tolist()
-        return spec_token_ids
 
-    def _get_eagle_atten_dict(
-        self,
-        scheduler_output: "SchedulerOutput",
-    ):
-        total_num_scheduled_tokens = scheduler_output.total_num_scheduled_tokens
-        assert total_num_scheduled_tokens > 0
-        num_reqs = self.runner.input_batch.num_reqs
-        assert num_reqs > 0
-
-        # OPTIMIZATION: Start copying the block table first.
-        # This way, we can overlap the copy with the following CPU operations.
-        self.runner.input_batch.block_table.commit_block_table(num_reqs)
-
-        # Get the number of scheduled tokens for each request.
-        req_ids = self.runner.input_batch.req_ids
-        tokens = [scheduler_output.num_scheduled_tokens[i] for i in req_ids]
-        num_scheduled_tokens = np.array(tokens, dtype=np.int32)
-        max_num_scheduled_tokens = max(tokens)
-        self.runner.query_lens = torch.from_numpy(num_scheduled_tokens)
-        # Get request indices.
-        # E.g., [2, 5, 3] -> [0, 0, 1, 1, 1, 1, 1, 2, 2, 2]
-        req_indices = np.repeat(self.runner.arange_np[:num_reqs],
-                                num_scheduled_tokens)
-
-        # cu_num_tokens: [2, 5, 3] -> [2, 7, 10]
-        # arange: [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
-        cu_num_tokens, arange = self._get_cumsum_and_arange(
-            num_scheduled_tokens)
-
-        # Get positions.
-        positions_np = self.runner.positions.np[:total_num_scheduled_tokens]
-        np.add(self.runner.input_batch.num_computed_tokens_cpu[req_indices],
-               arange,
-               out=positions_np)
-
-        # Calculate M-RoPE positions.
-        # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
-        if self.runner.uses_mrope:
-            self.runner._calc_mrope_positions(scheduler_output)
-
-        # Get token indices.
-        # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
-        # -> [0, 1, M, M + 1, M + 2, M + 3, M + 4, 2 * M, 2 * M + 1, 2 * M + 2]
-        # where M is the max_model_len.
-        token_indices = (
-            positions_np +
-            req_indices * self.runner.input_batch.token_ids_cpu.shape[1])
-
-        # NOTE(woosuk): We use torch.index_select instead of np.take here
-        # because torch.index_select is much faster than np.take for large
-        # tensors.
-        torch.index_select(
-            self.runner.input_batch.token_ids_cpu_tensor.flatten(),
-            0,
-            torch.from_numpy(token_indices),
-            out=self.runner.input_ids.cpu[:total_num_scheduled_tokens])
-
-        # Prepare the attention metadata for each KV cache group and make layers
-        # in the same group share the same metadata.
-        # NOTE(Chen): there is exactly one KV cache group that contains all
-        # attetnion layers in the model for now, so the current logic for
-        # getting attn_metadata is not related to kv_cache_group information.
-        # Will extend this part to support multiple KV cache groups later.
-        for kv_cache_group_id, kv_cache_group_spec in enumerate(
-                self.runner.kv_cache_config.kv_cache_groups):
-            block_size = kv_cache_group_spec.kv_cache_spec.block_size
-            block_table = self.runner.input_batch.block_table[
-                kv_cache_group_id]
-            # E.g., [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
-            # -> [0, 0, K, K, K + 1, K + 1, K + 2, 2 * K, 2 * K, 2 * K + 1]
-            # where K is the max_num_blocks_per_req and the block size is 2.
-            # NOTE(woosuk): We can't simply use `token_indices // block_size`
-            # here because M (max_model_len) is not necessarily divisible by
-            # block_size.
-            block_table_indices = (
-                req_indices * block_table.max_num_blocks_per_req +
-                positions_np // block_size)
-            block_table_cpu = block_table.get_cpu_tensor()
-            block_numbers = block_table_cpu.flatten(
-            )[block_table_indices].numpy()
-            block_offsets = positions_np % block_size
-            np.add(
-                block_numbers * block_size,
-                block_offsets,
-                out=block_table.slot_mapping.np[:total_num_scheduled_tokens])
-
-        # Prepare the attention metadata.
-        self.runner.query_start_loc.np[0] = 0
-        self.runner.query_start_loc.np[1:num_reqs + 1] = cu_num_tokens
-
-        self.runner.seq_lens.np[:num_reqs] = (
-            self.runner.input_batch.num_computed_tokens_cpu[:num_reqs] +
-            num_scheduled_tokens)
-
-        # Copy the tensors to the NPU.
-        self.runner.input_ids.gpu[:total_num_scheduled_tokens].copy_(
-            self.runner.input_ids.cpu[:total_num_scheduled_tokens],
-            non_blocking=True)
-        if self.runner.uses_mrope:
-            # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
-            self.runner.mrope_positions.gpu[:, :total_num_scheduled_tokens] \
-                .copy_(
-                    self.runner.
-                    mrope_positions.cpu[:, :total_num_scheduled_tokens],
-                    non_blocking=True)
-        else:
-            # Common case (1D positions)
-            self.runner.positions.gpu[:total_num_scheduled_tokens].copy_(
-                self.runner.positions.cpu[:total_num_scheduled_tokens],
-                non_blocking=True)
-
-        self.runner.query_start_loc.gpu[:num_reqs + 1].copy_(
-            self.runner.query_start_loc.cpu[:num_reqs + 1], non_blocking=True)
-        self.runner.seq_lens.gpu[:num_reqs].copy_(
-            self.runner.seq_lens.cpu[:num_reqs], non_blocking=True)
-
-        # Fill unused with -1. Needed for reshape_and_cache
-        self.runner.seq_lens.gpu[num_reqs:].fill_(0)
-        self.runner.query_start_loc.gpu[num_reqs + 1:].fill_(-1)
-
-        attn_metadata = {}
-        # Prepare the attention metadata for each KV cache group and make layers
-        # in the same group share the same metadata.
-        for kv_cache_group_id, kv_cache_group_spec in enumerate(
-                self.runner.kv_cache_config.kv_cache_groups):
-            common_attn_metadata = AscendCommonAttentionMetadata(
-                query_start_loc=self.runner.query_start_loc.gpu[:num_reqs + 1],
-                query_start_loc_cpu=self.runner.query_start_loc.cpu[:num_reqs +
-                                                                    1],
-                seq_lens_cpu=self.runner.seq_lens.cpu,
-                num_reqs=num_reqs,
-                max_query_len=max_num_scheduled_tokens,
-                num_actual_tokens=total_num_scheduled_tokens,
-                actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
-                block_table_tensor=self.runner.input_batch.block_table[0].
-                get_device_tensor(),
-                slot_mapping=self.runner.input_batch.block_table[0].
-                slot_mapping.gpu,
-                positions=self.runner.positions.gpu,
-                attn_mask=self.runner.attn_mask,
-                spec_attn_mask=self.runner.spec_attn_mask,
-                attn_state=self.runner.attn_state,
-                decode_token_per_req=self.runner.decode_token_per_req,
-                num_computed_tokens_cpu=None,
-                seq_lens=None)
-            builder = self.runner.attn_groups[0][0].get_metadata_builder()
-            attn_metadata_i = builder.build(0, common_attn_metadata,
-                                            self.runner.get_model())
-            for layer_name in kv_cache_group_spec.layer_names:
-                attn_metadata[layer_name] = attn_metadata_i
-
-        return attn_metadata
-
-    def _get_cumsum_and_arange(
-        self,
-        num_tokens: np.ndarray,
-        cumsum_dtype: Optional[np.dtype] = None,
-    ) -> tuple[np.ndarray, np.ndarray]:
-        """Get the cumulative sum and batched arange of the given array.
-        # E.g., [2, 5, 3] -> ([2, 7, 10], [0, 1, 0, 1, 2, 3, 4, 0, 1, 2])
-        # Equivalent to but faster than:
-        # np.concatenate([np.arange(n) for n in num_tokens])
-        """
-        # Step 1. [2, 5, 3] -> [2, 7, 10]
-        cu_num_tokens = np.cumsum(num_tokens, dtype=cumsum_dtype)
-        total_num_tokens = cu_num_tokens[-1]
-        # Step 2. [2, 7, 10] -> [0, 0, 2, 2, 2, 2, 2, 7, 7, 7]
-        cumsums_offsets = np.repeat(cu_num_tokens - num_tokens, num_tokens)
-        # Step 3. [0, 1, 0, 1, 2, 3, 4, 0, 1, 2]
-        arange = self.runner.arange_np[:total_num_tokens] - cumsums_offsets
-
-        return cu_num_tokens, arange
+        return draft_token_ids
 
     def _propose(
         self,
         # [num_tokens]
         target_token_ids: torch.Tensor,
-        # [num_tokens]
+        # [num_tokens] or [3, num_tokens] when M-RoPE is enabled
         target_positions: torch.Tensor,
         # [num_tokens, hidden_size]
         target_hidden_states: torch.Tensor,
-        # [num_tokens]
-        target_slot_mapping: torch.Tensor,
         # [batch_size]
         next_token_ids: torch.Tensor,
-        # [batch_size + 1] starting with 0
-        cu_num_tokens: torch.Tensor,
-        # [batch_size, max_num_blocks_per_req]
-        block_table: torch.Tensor,
+        last_token_indices: Optional[torch.Tensor],
+        common_attn_metadata: CommonAttentionMetadata,
         sampling_metadata: SamplingMetadata,
+        mm_embed_inputs: Optional[tuple[list[torch.Tensor],
+                                        torch.Tensor]] = None,
+        req_scheduled_tokens=None,
+        long_seq_metadata=None,
+        num_prefill_reqs=0,
+        num_decode_reqs=0,
+        scheduler_output: SchedulerOutput = None,
+        num_scheduled_tokens: int = 0,
     ) -> torch.Tensor:
-        device = cu_num_tokens.device
-        cu_num_tokens = cu_num_tokens.cpu()
-        block_table = block_table.cpu()
+
         num_tokens = target_token_ids.shape[0]
         batch_size = next_token_ids.shape[0]
-        last_token_indices = cu_num_tokens[1:] - 1
-        target_positions = target_positions.cpu()
+
+        if last_token_indices is None:
+            last_token_indices = common_attn_metadata.query_start_loc[1:] - 1
+
         if self.name == SpecDcodeType.EAGLE3:
             assert isinstance(self.model, Eagle3LlamaForCausalLM)
             target_hidden_states = self.model.combine_hidden_states(
@@ -423,34 +324,7 @@ class EagleProposer(Proposer):
         # Replace the last token with the next token.
         # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
         self.input_ids[last_token_indices] = next_token_ids
-        seq_lens = (target_positions[last_token_indices] + 1).int()
 
-        query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1]
-        max_query_len = query_lens.max().item()
-        attn_mask = self.runner.attn_mask
-
-        common_attn_metadata = AscendCommonAttentionMetadata(
-            query_start_loc=cu_num_tokens.to(device),
-            query_start_loc_cpu=cu_num_tokens,
-            seq_lens_cpu=seq_lens.cpu(),
-            max_query_len=max_query_len,
-            num_reqs=batch_size,
-            num_actual_tokens=num_tokens,
-            actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
-            block_table_tensor=self.runner.input_batch.block_table[0].
-            get_device_tensor(),
-            slot_mapping=target_slot_mapping,
-            positions=target_positions,
-            attn_mask=attn_mask,
-            spec_attn_mask=self.runner.spec_attn_mask,
-            attn_state=self.runner.attn_state,
-            decode_token_per_req=self.runner.decode_token_per_req,
-            num_computed_tokens_cpu=None,
-            seq_lens=None)
-        # FIXME(woosuk): The below two ops cause synchronization. Optimize.
-        builder = self.runner.attn_groups[0][0].get_metadata_builder()
-        attn_metadata = builder.build(0, common_attn_metadata,
-                                      self.runner.get_model())
         if self.use_cuda_graph and \
             num_tokens <= self.cudagraph_batch_sizes[-1]:
             num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
@@ -458,9 +332,14 @@ class EagleProposer(Proposer):
             num_input_tokens = num_tokens
 
         # copy inputs to buffer for cudagraph
-        self.positions[:num_tokens] = target_positions.to(device)
+        self.positions[:num_tokens] = target_positions
         self.hidden_states[:num_tokens] = target_hidden_states
-        attn_metadata.block_tables = block_table.to(device)
+
+        # FIXME(woosuk): The below two ops cause synchronization. Optimize.
+        builder = self.runner.attn_groups[0][0].get_metadata_builder()
+        attn_metadata = builder.build(0, common_attn_metadata,
+                                      self.runner.get_model())
+
         with set_ascend_forward_context(attn_metadata,
                                         self.vllm_config,
                                         num_tokens=num_input_tokens):
@@ -482,12 +361,14 @@ class EagleProposer(Proposer):
         draft_token_ids_tensor = torch.zeros(
             (self.vllm_config.speculative_config.num_speculative_tokens,
              *draft_token_ids.shape),
-            dtype=draft_token_ids.dtype)
+            dtype=draft_token_ids.dtype,
+            device=self.device)
         draft_token_ids_tensor[0] = draft_token_ids
 
-        positions_cpu = target_positions[last_token_indices].cpu().to(
-            torch.int64)
+        positions = target_positions[last_token_indices]
         hidden_states = hidden_states[last_token_indices]
+        last_token_indices = self.arange[:batch_size]
+
         if self.use_cuda_graph and \
             batch_size <= self.cudagraph_batch_sizes[-1]:
             input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
@@ -496,16 +377,14 @@ class EagleProposer(Proposer):
 
         attn_metadata.num_actual_tokens = batch_size
         attn_metadata.max_query_len = 1
-        attn_metadata.query_start_loc = self.arange[:batch_size + 1]
+        attn_metadata.query_start_loc = self.arange_cpu[:batch_size + 1]
         attn_metadata.query_start_loc_list = attn_metadata.query_start_loc[
             1:].tolist()
         attn_metadata.num_decodes, attn_metadata.num_prefills, attn_metadata.num_decode_tokens, attn_metadata.num_prefill_tokens = 0, batch_size, 0, batch_size
         attn_metadata.num_actual_tokens_pcp_padded = attn_metadata.num_decode_tokens + attn_metadata.num_prefill_tokens
-        query_lens.fill_(1)
-        attn_metadata.query_lens = query_lens
 
         attn_metadata.actual_seq_lengths_q = [1 + i for i in range(batch_size)]
-        attn_metadata.seq_lens_list = seq_lens.tolist()
+        attn_metadata.seq_lens_list = attn_metadata.seq_lens.tolist()
         attn_metadata.attn_state = AscendAttentionState.ChunkedPrefill
         for now_speculative in range(
                 self.vllm_config.speculative_config.num_speculative_tokens -
@@ -513,8 +392,8 @@ class EagleProposer(Proposer):
             # Update the inputs.
             # cast to int32 is crucial when eagle model is compiled.
             # tensor.argmax() returns int64 by default.
-            input_ids = draft_token_ids_tensor[now_speculative].to(device)
-            positions_cpu += 1
+            input_ids = draft_token_ids_tensor[now_speculative]
+            positions += 1
 
             # NOTE(woosuk): We should handle the case where the draft model
             # generates tokens beyond the max model length. Since it is complex
@@ -522,16 +401,15 @@ class EagleProposer(Proposer):
             # but adjust the position ids and slot mappings to avoid the
             # out-of-range access during the model execution. The draft tokens
             # generated with this adjustment should be ignored.
-            exceeds_max_model_len = positions_cpu >= self.vllm_config.model_config.max_model_len
+            exceeds_max_model_len = positions >= self.vllm_config.model_config.max_model_len
             # Mask out the position ids that exceed the max model length.
             # Otherwise, we may get out-of-range error in RoPE.
-            clamped_positions_cpu = torch.where(exceeds_max_model_len, 0,
-                                                positions_cpu)
-            clamped_positions = clamped_positions_cpu.to(device)
+            clamped_positions = torch.where(exceeds_max_model_len, 0,
+                                            positions)
 
             # TODO: Increment the sequence lengths.
 
-            attn_metadata.seq_lens += 1
+            attn_metadata.seq_lens = attn_metadata.seq_lens + 1
             attn_metadata.seq_lens_list = [
                 _ + 1 for _ in attn_metadata.seq_lens_list
             ]
@@ -542,22 +420,22 @@ class EagleProposer(Proposer):
             # TODO: sequence length to 1 to minimize their overheads in attention.
 
             # Compute the slot mapping.
-            block_numbers = (clamped_positions_cpu // self.block_size)
-            block_ids = block_table.gather(dim=1,
-                                           index=block_numbers.view(-1, 1))
+            block_numbers = (clamped_positions // self.block_size)
+            block_ids = attn_metadata.block_tables.gather(
+                dim=1, index=block_numbers.view(-1, 1))
             block_ids = block_ids.view(-1)
-            slot_mapping_cpu = (
+            slot_mapping_tmp = (
                 block_ids * self.vllm_config.cache_config.block_size +
-                clamped_positions_cpu % self.block_size)
+                clamped_positions % self.block_size)
 
             # Mask out the slot mappings that exceed the max model length.
             # Otherwise, the KV cache will be inadvertently updated with the
             # padding tokens.
-            slot_mapping_cpu.masked_fill_(exceeds_max_model_len,
+            slot_mapping_tmp.masked_fill_(exceeds_max_model_len,
                                           PADDING_SLOT_ID)
             # NOTE: ASCEND slot_mapping must on cpu
-            attn_metadata.slot_mapping = slot_mapping_cpu.to(
-                torch.int32).to(device)
+            attn_metadata.slot_mapping[:slot_mapping_tmp.shape[0]].copy_(
+                slot_mapping_tmp.to(torch.int32))
             # copy inputs to buffer for cudagraph
             self.input_ids[:batch_size] = input_ids
             self.positions[:batch_size] = clamped_positions
@@ -565,7 +443,6 @@ class EagleProposer(Proposer):
             attn_mask = self.attn_mask_builder.get_splitfuse_attn_mask()
 
             attn_metadata.attn_mask = attn_mask
-            attn_metadata.block_tables = block_table.to(device)
             # Run the model.
             with set_ascend_forward_context(attn_metadata,
                                             self.vllm_config,
@@ -581,49 +458,188 @@ class EagleProposer(Proposer):
 
             # TODO(wenlong): get more than one token for tree attention
             draft_token_ids = logits.argmax(dim=-1)
-            draft_token_ids_tensor[now_speculative + 1] = draft_token_ids.cpu()
+            draft_token_ids_tensor[now_speculative + 1] = draft_token_ids
 
         # [batch_size, num_speculative_tokens]
         draft_token_ids = draft_token_ids_tensor.swapaxes(0, 1)
         return draft_token_ids
 
-    def _prepare_inputs(
+    def _get_attn_metadata(self, attn_metadata):
+        if attn_metadata is not None and isinstance(attn_metadata, dict):
+            architecture = self.vllm_config.model_config.architecture
+            layer_name = _FIRST_LAYERS.get(architecture, _DEFAULT_FIRST_LAYER)
+            attn_metadata = attn_metadata[layer_name]
+
+        return attn_metadata
+
+    def prepare_next_token_ids_cpu(
         self,
-        eagle_attn_metadata: AscendMetadata,
-        # [batch_size]
-        num_rejected_tokens: torch.Tensor,
+        sampled_token_ids: list[list[int]],
+        requests: dict[str, CachedRequestState],
+        gpu_input_batch: InputBatch,
+        num_scheduled_tokens: dict[str, int],
+    ) -> torch.Tensor:
+        """
+        This function is used to prepare the inputs for speculative decoding.
+        It calculates the next token ids for each request based on the sampled
+        token ids from the CPU. If a request has no sampled token ids (e.g.,
+        during the initial decoding steps), it falls back to using the request
+        state to get the next token id.
+        """
+        req_ids = gpu_input_batch.req_ids
+        next_token_ids: list[int] = []
+        for i, token_ids in enumerate(sampled_token_ids):
+            if token_ids:
+                # Common case.
+                next_token_id = token_ids[-1]
+            else:
+                # Partial prefill (rare case).
+                # Get the next token id from the request state.
+                req_id = req_ids[i]
+                req_state = requests[req_id]
+                seq_len = req_state.num_computed_tokens + num_scheduled_tokens[
+                    req_id]
+                next_token_id = req_state.get_token_id(seq_len)
+            next_token_ids.append(next_token_id)
+        next_token_ids = torch.tensor(next_token_ids,
+                                      dtype=torch.int32,
+                                      device=self.input_ids.device)
+        return next_token_ids
+
+    def prepare_next_token_ids_padded(
+        self,
+        common_attn_metadata: CommonAttentionMetadata,
+        sampled_token_ids: torch.Tensor,
+        requests: dict[str, CachedRequestState],
+        gpu_input_batch: InputBatch,
+        discard_request_indices: torch.Tensor,
+        num_discarded_requests: int,
     ) -> tuple[torch.Tensor, torch.Tensor]:
         """
-        This function is used to prepare the inputs for the spec decode.
+        This function is used to prepare the inputs for speculative decoding.
+        It calculates the next token ids and the number of valid sampled tokens
+        for each request, considering the "discarded" requests whose next token
+        is not sampled and comes from `request.get_token_id()` instead.
+        It also accounts for the rejected tokens in `sampled_token_ids`.
+        This function must use device functions to operate on the inputs, and
+        should not introduce any blocking CPU-GPU synchronization.
+        """
+        # TODO(Ben): Combine this into a custom fused kernel
+
+        # Precompute get_token_id for when there is no valid next token
+        num_reqs = gpu_input_batch.num_reqs
+        self.backup_next_token_ids.np[:num_reqs] = np.array([
+            requests[gpu_input_batch.req_ids[i]].get_token_id(
+                common_attn_metadata.seq_lens_cpu[i].item())
+            for i in range(num_reqs)
+        ])
+        self.backup_next_token_ids.copy_to_gpu(num_reqs)
+
+        # Mask out the sampled tokens indices that should not be sampled.
+        discard_sampled_tokens_req_indices = discard_request_indices[:
+                                                                     num_discarded_requests]
+
+        valid_sampled_token_ids_gpu = sampled_token_ids.clone()
+        valid_sampled_token_ids_gpu.index_fill_(
+            0, discard_sampled_tokens_req_indices, -1)
+
+        # Generate a mask for all valid tokens within those requests
+        valid_mask = (valid_sampled_token_ids_gpu != -1) & (
+            valid_sampled_token_ids_gpu < gpu_input_batch.vocab_size)
+
+        # Count the number of valid tokens in each request
+        valid_sampled_tokens_count = valid_mask.sum(dim=1)
+
+        # Get the rightmost valid index per row
+        last_valid_indices = valid_sampled_tokens_count - 1
+        last_valid_indices_safe = torch.clamp(last_valid_indices, min=0)
+
+        # Get last valid token from each row
+        # (assume undefined state where there is no valid token)
+        selected_tokens = torch.gather(
+            valid_sampled_token_ids_gpu, 1,
+            last_valid_indices_safe.unsqueeze(1)).squeeze(1)
+
+        # Use last token if valid, pre-computed backup if not
+        batch_size = valid_sampled_token_ids_gpu.shape[0]
+        next_token_ids = torch.where(
+            last_valid_indices != -1,
+            selected_tokens,
+            self.backup_next_token_ids.gpu[:batch_size],
+        )
+
+        return next_token_ids, valid_sampled_tokens_count
+
+    def _copy_valid_sampled_token_count(
+            self, next_token_ids: torch.Tensor,
+            valid_sampled_tokens_count: torch.Tensor) -> None:
+        if self.runner.valid_sampled_token_count_event is not None:
+            default_stream = torch.npu.current_stream()
+            # initialize a new stream to overlap the copy operation with
+            # prepare_input of draft model.
+            with torch.npu.stream(
+                    self.runner.valid_sampled_token_count_copy_stream):
+                self.runner.valid_sampled_token_count_copy_stream.wait_stream(
+                    default_stream)  # type: ignore
+                self.runner.valid_sampled_token_count_cpu[:
+                                                          valid_sampled_tokens_count
+                                                          .shape[0]].copy_(
+                                                              valid_sampled_tokens_count,
+                                                              non_blocking=True
+                                                          )
+                self.runner.valid_sampled_token_count_event.record()
+
+            self.runner.input_batch.prev_sampled_token_ids = next_token_ids.unsqueeze(
+                1)
+
+    def prepare_inputs(
+        self,
+        common_attn_metadata: CommonAttentionMetadata,
+        sampled_token_ids: list[list[int]],
+        num_draft_tokens: list[int],
+    ) -> tuple[CommonAttentionMetadata, torch.Tensor]:
+        """
+        This function is used to prepare the inputs for speculative decoding.
         It updates to the common_attn_metadata to account for the rejected
         tokens (and newly sampled tokens). It also returns the token indices
         of the tokens that should be fed to the speculator.
         """
         # E.g.
         #  common_attn_metadata.query_start_loc{_cpu}:
-        #         [0, q1, q1 + q2, q1 + q2 + q3]
+        #       [0, q1, q1 + q2, q1 + q2 + q3]
         #  common_attn_metadata.seq_lens{_cpu}: [s1, s2, s3]
         #  num_rejected_tokens: [n1, n2, n3]
         # This function computes the intermediate values:
         #  num_tokens_per_req: [q1 - n1, q2 - n2, q3 - n3]
         # And returns:
         #  common_attn_metadata.query_start_loc{_cpu}:
-        #         [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
+        #       [0, q1 - n1, q1 + q2 - n1 - n2, q1 + q2 + q3 - n1 - n2 - n3]
         #  common_attn_metadata.seq_lens{_cpu}:
-        #         [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
+        #       [s1 - n1 + 1, s2 - n2 + 1, s3 - n3 + 1]
         #  token_indices: [0, 1, ..., q1 - n1 - 1,
-        #                  q1, q1 + 1, ..., q1 + q2 - n2 - 1,
-        #                  q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
-        num_rejected_tokens_cpu = num_rejected_tokens.to("cpu")
-        cu_target_query_lens = eagle_attn_metadata.query_start_loc
-        device = eagle_attn_metadata.query_start_loc.device
-        query_start_loc_cpu = cu_target_query_lens.to("cpu")
+        #                 q1, q1 + 1, ..., q1 + q2 - n2 - 1,
+        #                 q1 + q2, q1 + q2 + 1, ..., q1 + q2 + q3 - n3 - 1]
+
+        num_actual_reqs = len(num_draft_tokens)
+        num_rejected_tokens = [
+            n + 1 - len(sampled_token_ids[i]) if n > 0 else 0
+            for i, n in enumerate(num_draft_tokens)
+        ]
+        num_rejected_tokens = torch.tensor(num_rejected_tokens,
+                                           dtype=torch.int32)
+
+        device = common_attn_metadata.query_start_loc.device
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu[:
+                                                                       num_actual_reqs
+                                                                       + 1]
+        seq_lens_cpu = common_attn_metadata.seq_lens_cpu[:num_actual_reqs]
+        new_seq_lens_cpu = seq_lens_cpu - num_rejected_tokens
 
         # [0, q1, q1 + q2, q1 + q2 + q3] -> [q1, q2, q3]
-        new_query_len_per_req = (query_start_loc_cpu[1:] -
-                                 query_start_loc_cpu[:-1])
+        new_query_len_per_req = query_start_loc_cpu[
+            1:] - query_start_loc_cpu[:-1]
         # [q1, q2, q3] -> [q1 - n1, q2 - n2, q3 - n3]
-        new_num_tokens_per_req = new_query_len_per_req - num_rejected_tokens_cpu
+        new_num_tokens_per_req = new_query_len_per_req - num_rejected_tokens
         new_num_tokens_per_req_np = new_num_tokens_per_req.numpy()
 
         # [q1 - n1, q2 - n2, q3 - n3] ->
@@ -631,7 +647,8 @@ class EagleProposer(Proposer):
         new_query_start_loc_cpu = torch.zeros(
             query_start_loc_cpu.shape,
             dtype=torch.int32,
-            pin_memory=is_pin_memory_available())
+            pin_memory=is_pin_memory_available(),
+        )
         new_query_start_loc_np = new_query_start_loc_cpu.numpy()
         np.cumsum(new_num_tokens_per_req_np, out=new_query_start_loc_np[1:])
 
@@ -646,8 +663,8 @@ class EagleProposer(Proposer):
         # [0, 1, 2, 3, 4, 5, 6, 7, 8] ->
         # [0, 1, 0, 1, 2, 3, 0, 1, 2]
         #  _r1_  ____r2____  ___r3__
-        token_offests = self.token_arange_np[:total_num_tokens] \
-            - new_query_start_locs_expanded
+        token_offests = (self.token_arange_np[:total_num_tokens] -
+                         new_query_start_locs_expanded)
 
         # Expand starting positions to match token pattern
         # [0, q1, q1 + q2] ->
@@ -656,21 +673,101 @@ class EagleProposer(Proposer):
         old_query_start_locs_expanded = np.repeat(
             query_start_loc_cpu[:-1].numpy(), new_num_tokens_per_req_np)
         # Final token indices are:
-        # [0, 1,                                   // req 1
-        #  q1 + 0, q1 + 1, q1 + 2, q1 + 3,         // req 2
-        #  q1 + q2 + 0, q1 + q2 + 1, q1 + q2 + 2]  // req 3
+        # [0, 1,                                // req 1
+        #  q1 + 0, q1 + 1, q1 + 2, q1 + 3,       // req 2
+        #  q1 + q2 + 0, q1 + q2 + 1, q1 + q2 + 2] // req 3
         token_indices_np = token_offests + old_query_start_locs_expanded
         token_indices = torch.from_numpy(token_indices_np).to(
             device, non_blocking=True)
 
-        # need use npu
-        query_len_per_req = (cu_target_query_lens[1:] -
-                             cu_target_query_lens[:-1])
-        num_tokens_per_req = query_len_per_req - num_rejected_tokens
+        common_attn_metadata.slot_mapping[:token_indices.shape[0]].copy_(
+            common_attn_metadata.slot_mapping[token_indices])
+        common_attn_metadata.slot_mapping[token_indices.shape[0]:].fill_(-1)
 
-        # [a - n1, b - n2, c - n3] ->
-        # [0, a - n1, a + b - n1 - n2, a + b + c - n1 - n2 - n3]
-        cu_num_tokens = torch.zeros_like(cu_target_query_lens)
-        torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
+        # NOTE: Currently positions and seq_lens are not used in mla_v1 forward
+        # so we do not need to fixed them. But if they are used in the future,
+        # we should fixed them.
+        spec_common_attn_metadata = AscendCommonAttentionMetadata(
+            query_start_loc=new_query_start_loc_cpu.to(device,
+                                                       non_blocking=True),
+            query_start_loc_cpu=new_query_start_loc_cpu,
+            seq_lens=new_seq_lens_cpu.to(device, non_blocking=True),
+            seq_lens_cpu=new_seq_lens_cpu,
+            num_computed_tokens_cpu=common_attn_metadata.
+            num_computed_tokens_cpu,
+            num_reqs=common_attn_metadata.num_reqs,
+            num_actual_tokens=total_num_tokens,
+            num_input_tokens=common_attn_metadata.num_input_tokens,
+            max_query_len=new_query_len_per_req.max().item(),
+            block_table_tensor=common_attn_metadata.block_table_tensor,
+            slot_mapping=common_attn_metadata.slot_mapping,
+            actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
+            positions=common_attn_metadata.positions[token_indices],
+            attn_mask=self.runner.attn_mask,
+            spec_attn_mask=self.runner.spec_attn_mask,
+            attn_state=self.runner.attn_state,
+            decode_token_per_req=self.runner.decode_token_per_req,
+        )
+        return spec_common_attn_metadata, token_indices
 
-        return cu_num_tokens, token_indices
+    def prepare_inputs_padded(
+        self,
+        common_attn_metadata: CommonAttentionMetadata,
+        spec_decode_metadata: SpecDecodeMetadata,
+        valid_sampled_tokens_count: torch.Tensor,
+    ) -> tuple[CommonAttentionMetadata, torch.Tensor, torch.Tensor]:
+        """
+        This function is used to prepare the inputs for speculative decoding
+        It updates the common_attn_metadata for speculative decoding,
+        but does not consider the rejected tokens. Instead, all tokens
+        are included as inputs to the speculator, with the rejected tokens
+        used as padding and filtered out later by `token_indices_to_sample`.
+        No blocking CPU operations should be introduced in this function.
+        """
+        num_draft_tokens_gpu = torch.cat([
+            spec_decode_metadata.cu_num_draft_tokens[0:1],
+            spec_decode_metadata.cu_num_draft_tokens[1:] -
+            spec_decode_metadata.cu_num_draft_tokens[:-1],
+        ])
+
+        num_rejected_tokens_gpu = torch.where(
+            num_draft_tokens_gpu > 0,
+            num_draft_tokens_gpu + 1 - valid_sampled_tokens_count,
+            torch.zeros_like(num_draft_tokens_gpu),
+        )
+
+        query_start_loc_cpu = common_attn_metadata.query_start_loc_cpu
+
+        new_query_len_per_req = query_start_loc_cpu[
+            1:] - query_start_loc_cpu[:-1]
+
+        total_num_tokens = query_start_loc_cpu[-1].item()
+        token_indices = self.arange[:total_num_tokens]
+
+        # NOTE: Currently positions and seq_lens are not used in mla_v1 forward
+        # so we do not need to fixed them. But if they are used in the future,
+        # we should fixed them.
+        spec_common_attn_metadata = AscendCommonAttentionMetadata(
+            query_start_loc=common_attn_metadata.query_start_loc,
+            query_start_loc_cpu=query_start_loc_cpu,
+            seq_lens_cpu=common_attn_metadata.seq_lens_cpu,
+            num_reqs=common_attn_metadata.num_reqs,
+            num_actual_tokens=total_num_tokens,
+            num_input_tokens=common_attn_metadata.num_input_tokens,
+            max_query_len=new_query_len_per_req.max().item(),
+            actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
+            block_table_tensor=common_attn_metadata.block_table_tensor,
+            slot_mapping=common_attn_metadata.slot_mapping,
+            positions=common_attn_metadata.positions,
+            attn_mask=self.runner.attn_mask,
+            spec_attn_mask=self.runner.spec_attn_mask,
+            attn_state=self.runner.attn_state,
+            decode_token_per_req=self.runner.decode_token_per_req,
+            num_computed_tokens_cpu=common_attn_metadata.
+            num_computed_tokens_cpu,
+            seq_lens=common_attn_metadata.seq_lens)
+
+        token_indices_to_sample = (common_attn_metadata.query_start_loc[1:] -
+                                   1 - num_rejected_tokens_gpu)
+
+        return spec_common_attn_metadata, token_indices, token_indices_to_sample

vllm_ascend/worker/model_runner_v1.py

@@ -801,7 +801,8 @@ class NPUModelRunner(GPUModelRunner):
             self.requests[r].num_tokens for r in self.input_batch.req_ids
         ]
         num_tokens_np = np.array(num_tokens, dtype=np.int32)
-        num_reqs = self.input_batch.num_reqs
+        base_num_reqs = self.input_batch.num_reqs
+        num_reqs = base_num_reqs
         if self.pcp_size > 1:
             # while pcp > 1, we need the original num_scheduled_tokens before split
             # to calculate discard_requests_mask
@@ -1106,6 +1107,11 @@ class NPUModelRunner(GPUModelRunner):
             if self.speculative_config and \
                 self.spec_decode_common_attn_metadata is None:
                 self.spec_decode_common_attn_metadata = common_attn_metadata
+                if self.speculative_config.method in ("eagle", "eagle3") and \
+                        self.vllm_config.compilation_config.cudagraph_mode.has_full_cudagraphs():
+                    self.spec_decode_common_attn_metadata = \
+                        self.spec_decode_common_attn_metadata.unpadded(
+                            total_num_scheduled_tokens, base_num_reqs)
 
             for attn_group in self.attn_groups[kv_cache_group_id]:
                 common_prefix_len = 0
@@ -1591,7 +1597,7 @@ class NPUModelRunner(GPUModelRunner):
         with ProfileExecuteDuration().capture_async("Draft"):
             if self.speculative_config:
                 use_padded_batch_for_eagle = self.speculative_config and \
-                    self.speculative_config.method == "mtp" and \
+                    self.speculative_config.use_eagle() and \
                     not self.speculative_config.disable_padded_drafter_batch
                 if use_padded_batch_for_eagle:
                     # EAGLE speculative decoding can use the GPU sampled tokens