大改

vllm_npu/spec_decode/__init__.py

@@ -0,0 +1,33 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# Copyright 2023 The vLLM team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# This file is a part of the vllm-ascend project.
+# Adapted from vllm-project/vllm/vllm/worker/gpu_model_runner.py
+#
+from vllm_npu.spec_decode.eagle_proposer import EagleProposer
+from vllm_npu.spec_decode.mtp_proposer import MtpProposer
+from vllm_npu.spec_decode.ngram_proposer import NgramProposer
+
+
+def get_spec_decode_method(method, vllm_config, device, runner):
+    if method == "ngram":
+        return NgramProposer(vllm_config, device, runner)
+    elif method in ["eagle", "eagle3"]:
+        return EagleProposer(vllm_config, device, runner)
+    elif method == 'deepseek_mtp':
+        return MtpProposer(vllm_config, device, runner)
+    else:
+        raise ValueError("Unknown speculative decoding method: "
+                         f"{method}")

vllm_npu/spec_decode/eagle_proposer.py

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+# SPDX-License-Identifier: Apache-2.0
+from typing import Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+from vllm.attention.layer import Attention
+from vllm.config import (CompilationLevel, CUDAGraphMode, VllmConfig,
+                         get_layers_from_vllm_config)
+from vllm.distributed.parallel_state import get_pp_group
+from vllm.logger import logger
+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.v1.core.sched.output import SchedulerOutput
+from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
+
+from vllm_npu.ascend_forward_context import set_ascend_forward_context
+from vllm_npu.attention.attention_mask import AttentionMaskBuilder
+from vllm_npu.attention.attention_v1 import AscendAttentionState
+from vllm_npu.attention.utils import AscendCommonAttentionMetadata
+from vllm_npu.spec_decode.interface import Proposer, SpecDcodeType
+
+PADDING_SLOT_ID = -1
+
+
+class EagleProposer(Proposer):
+
+    def __init__(self,
+                 vllm_config: VllmConfig,
+                 device: torch.device,
+                 runner=None):
+        self.name = SpecDcodeType.EAGLE if vllm_config.speculative_config.method == "eagle" else SpecDcodeType.EAGLE3
+        self.vllm_config = vllm_config
+        self.device = device
+        self.runner = runner
+
+        self.block_size = vllm_config.cache_config.block_size
+        # We need to get the hidden size from the draft model config because
+        # the draft model's hidden size can be different from the target model's
+        # hidden size (e.g., Llama 3.3 70B).
+        self.hidden_size = vllm_config.speculative_config.draft_model_config.get_hidden_size(
+        )
+
+        self.use_cuda_graph = (self.vllm_config.compilation_config.level
+                               == CompilationLevel.PIECEWISE and
+                               not self.vllm_config.model_config.enforce_eager)
+        self.cudagraph_batch_sizes = list(
+            reversed(
+                self.vllm_config.compilation_config.cudagraph_capture_sizes))
+
+        # persistent buffers for cuda graph
+        self.input_ids = torch.zeros(
+            self.vllm_config.scheduler_config.max_num_batched_tokens,
+            dtype=torch.int32,
+            device=device)
+        self.positions = torch.zeros(
+            self.vllm_config.scheduler_config.max_num_batched_tokens,
+            dtype=torch.int64,
+            device=device)
+        self.hidden_states = torch.zeros(
+            (self.vllm_config.scheduler_config.max_num_batched_tokens,
+             self.hidden_size),
+            dtype=self.vllm_config.model_config.dtype,
+            device=device)
+        # 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,
+                                   device=device,
+                                   dtype=torch.int32)
+        attn_mask_len = self.vllm_config.model_config.max_model_len
+        self.attn_mask_builder = AttentionMaskBuilder(
+            attn_mask_len, self.vllm_config.model_config.dtype, device=device)
+
+    def load_model(self, model: nn.Module) -> None:
+        target_attn_layer_names = set(
+            get_layers_from_vllm_config(self.vllm_config, Attention).keys())
+        self.model = get_model(vllm_config=self.vllm_config,
+                               model_config=self.vllm_config.
+                               speculative_config.draft_model_config)
+        draft_attn_layer_names = (
+            get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
+            target_attn_layer_names)
+        self.attn_layer_name = next(iter(draft_attn_layer_names))
+
+        # share embed_tokens with the target model if needed
+        if get_pp_group().world_size == 1:
+            logger.info(
+                "The EAGLE head shares the same vocab embedding" \
+                " with the target model."
+            )
+            self.model.model.embed_tokens = model.model.embed_tokens
+        else:
+            logger.info(
+                "Since PP > 1, the EAGLE head loaded its own vocab embedding" \
+                " weights instead of sharing them with the target model."
+            )
+
+        # share lm_head with the target model if needed
+        # some model definition do not define lm_head explicitly
+        # and reuse embed_tokens for lm_head, e.g., CohereForCausalLM
+        if self.name == SpecDcodeType.EAGLE and hasattr(model, "lm_head"):
+            logger.info("Loading EAGLE LM head weights from the target model.")
+            if supports_multimodal(model):
+                self.model.lm_head = model.get_language_model().lm_head
+            else:
+                self.model.lm_head = model.lm_head
+
+    @torch.inference_mode()
+    def dummy_run(self,
+                  num_tokens: int,
+                  with_prefill: bool = False,
+                  skip_attn: bool = False,
+                  num_reqs: int = 0,
+                  num_tokens_across_dp: Optional[torch.Tensor] = None,
+                  aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
+                  batch_descriptor=None,
+                  dummy_compute_logits=lambda hidden_states: None):
+        moe_comm_type = self.runner._select_moe_comm_method(
+            num_tokens, with_prefill)
+        with set_ascend_forward_context(None,
+                                        self.vllm_config,
+                                        moe_comm_type=moe_comm_type,
+                                        num_tokens=num_tokens):
+            self.model(
+                input_ids=self.input_ids[:num_tokens],
+                positions=self.positions[:num_tokens],
+                hidden_states=self.hidden_states[:num_tokens],
+            )
+            dummy_compute_logits(self.hidden_states)
+
+    def generate_token_ids(self,
+                           valid_sampled_token_ids: list[list[int]],
+                           sampling_metadata: SamplingMetadata = None,
+                           scheduler_output: SchedulerOutput = None,
+                           spec_decode_metadata: SpecDecodeMetadata = None,
+                           positions: torch.Tensor = None,
+                           num_scheduled_tokens: int = 0,
+                           hidden_states: torch.Tensor = None,
+                           attn_metadata=None,
+                           aux_hidden_states: torch.Tensor = None):
+
+        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[: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
+        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,
+            )
+            num_tokens = num_scheduled_tokens - sum(num_rejected_tokens)
+            cu_num_tokens, token_indices = self._prepare_inputs(
+                eagle_attn_metadata.query_start_loc, num_rejected_tokens,
+                num_tokens)
+            target_token_ids = self.runner.input_ids[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]
+            target_slot_mapping = eagle_attn_metadata.slot_mapping[
+                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,
+            sampling_metadata=sampling_metadata,
+        )
+        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[: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[:, :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[:total_num_scheduled_tokens].copy_(
+                self.runner.positions_cpu[:total_num_scheduled_tokens],
+                non_blocking=True)
+
+        self.runner.query_start_loc[:num_reqs + 1].copy_(
+            self.runner.query_start_loc_cpu[:num_reqs + 1], non_blocking=True)
+        self.runner.seq_lens[: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[num_reqs:].fill_(0)
+        self.runner.query_start_loc[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[: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.slot_mapping,
+                positions=self.runner.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=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
+
+    def _propose(
+        self,
+        # [num_tokens]
+        target_token_ids: torch.Tensor,
+        # [num_tokens]
+        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,
+        sampling_metadata: SamplingMetadata,
+    ) -> 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 self.name == SpecDcodeType.EAGLE3:
+            assert isinstance(self.model, Eagle3LlamaForCausalLM)
+            target_hidden_states = self.model.combine_hidden_states(
+                target_hidden_states)
+            assert target_hidden_states.shape[-1] == self.hidden_size
+
+        # Shift the input ids by one token.
+        # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
+        self.input_ids[:num_tokens - 1] = target_token_ids[1:]
+        # 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)
+        else:
+            num_input_tokens = num_tokens
+
+        with_prefill = attn_metadata.attn_state not in [
+            AscendAttentionState.DecodeOnly, AscendAttentionState.SpecDecoding
+        ]
+        moe_comm_type = self.runner._select_moe_comm_method(
+            num_input_tokens, with_prefill)
+
+        # copy inputs to buffer for cudagraph
+        self.positions[:num_tokens] = target_positions.to(device)
+        self.hidden_states[:num_tokens] = target_hidden_states
+        attn_metadata.block_tables = block_table.to(device)
+        with set_ascend_forward_context(attn_metadata,
+                                        self.vllm_config,
+                                        moe_comm_type=moe_comm_type,
+                                        num_tokens=num_input_tokens):
+            last_hidden_states, hidden_states = self.model(
+                input_ids=self.input_ids[:num_input_tokens],
+                positions=self.positions[:num_input_tokens],
+                hidden_states=self.hidden_states[:num_input_tokens],
+            )
+        sample_hidden_states = last_hidden_states[last_token_indices]
+        logits = self.model.compute_logits(sample_hidden_states)
+        draft_token_ids = logits.argmax(dim=-1)
+
+        # Early exit if there is only one draft token to be generated.
+        if self.vllm_config.speculative_config.num_speculative_tokens == 1:
+            # [batch_size, 1]
+            return draft_token_ids.view(-1, 1)
+
+        # Generate the remaining draft tokens.
+        draft_token_ids_tensor = torch.zeros(
+            (self.vllm_config.speculative_config.num_speculative_tokens,
+             *draft_token_ids.shape),
+            dtype=draft_token_ids.dtype)
+        draft_token_ids_tensor[0] = draft_token_ids
+
+        positions_cpu = target_positions[last_token_indices].cpu().to(
+            torch.int64)
+        hidden_states = hidden_states[last_token_indices]
+        if self.use_cuda_graph and \
+            batch_size <= self.cudagraph_batch_sizes[-1]:
+            input_batch_size = self.vllm_config.pad_for_cudagraph(batch_size)
+        else:
+            input_batch_size = batch_size
+
+        moe_comm_type = self.runner._select_moe_comm_method(
+            input_batch_size, False)
+
+        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_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.attn_state = AscendAttentionState.ChunkedPrefill
+        for now_speculative in range(
+                self.vllm_config.speculative_config.num_speculative_tokens -
+                1):
+            # 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
+
+            # NOTE(woosuk): We should handle the case where the draft model
+            # generates tokens beyond the max model length. Since it is complex
+            # to remove such requests from the batch, we keep them in the batch
+            # 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
+            # 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)
+
+            # TODO: Increment the sequence lengths.
+
+            attn_metadata.seq_lens += 1
+            attn_metadata.seq_lens_list = [
+                _ + 1 for _ in attn_metadata.seq_lens_list
+            ]
+            # TODO: Consider max model length.
+            # attn_metadata.max_seq_len = min(attn_metadata.max_seq_len,
+            #                                 self.max_model_len)
+            # For the requests that exceed the max model length, we set the
+            # 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_ids = block_ids.view(-1)
+            slot_mapping_cpu = (
+                block_ids * self.vllm_config.cache_config.block_size +
+                clamped_positions_cpu % 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,
+                                          PADDING_SLOT_ID)
+            # NOTE: ASCEND slot_mapping must on cpu
+            attn_metadata.slot_mapping = slot_mapping_cpu.to(
+                torch.int32).to(device)
+            # copy inputs to buffer for cudagraph
+            self.input_ids[:batch_size] = input_ids
+            self.positions[:batch_size] = clamped_positions
+            self.hidden_states[:batch_size] = hidden_states
+            attn_mask = self.attn_mask_builder.get_splitfuse_attn_mask(
+                attn_metadata.seq_lens, positions_cpu,
+                self.vllm_config.model_config.dtype, self.device)
+
+            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,
+                                            moe_comm_type=moe_comm_type,
+                                            num_tokens=input_batch_size):
+
+                last_hidden_states, hidden_states = self.model(
+                    input_ids=self.input_ids[:input_batch_size],
+                    positions=self.positions[:input_batch_size],
+                    hidden_states=self.hidden_states[:input_batch_size],
+                )
+            hidden_states = hidden_states[:batch_size]
+            logits = self.model.compute_logits(last_hidden_states[:batch_size])
+
+            # 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()
+
+        # [batch_size, num_speculative_tokens]
+        draft_token_ids = draft_token_ids_tensor.swapaxes(0, 1)
+        return draft_token_ids
+
+    def _prepare_inputs(
+        self,
+        # [batch_size + 1]
+        cu_target_query_lens: torch.Tensor,
+        # [batch_size]
+        num_rejected_tokens: torch.Tensor,
+        num_tokens: int,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # 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]
+
+        # [0, a, a + b, a + b + c] -> [a, b, c]
+        query_len_per_req = (cu_target_query_lens[1:] -
+                             cu_target_query_lens[:-1])
+        # [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.zeros_like(cu_target_query_lens)
+        torch.cumsum(num_tokens_per_req, dim=0, out=cu_num_tokens[1:])
+        token_indices = torch.empty(
+            num_tokens,
+            dtype=torch.int32,
+            device=cu_target_query_lens.device,
+        )
+        BLOCK_SIZE = 1024
+        self._prepare_eagle_input_sequential(
+            token_indices,
+            cu_target_query_lens,
+            cu_num_tokens,
+            block_size=BLOCK_SIZE,
+        )
+        return cu_num_tokens, token_indices
+
+    def _prepare_eagle_input_sequential(self, out_tensor: torch.Tensor,
+                                        cu_query_lens: torch.Tensor,
+                                        cu_num_tokens: torch.Tensor,
+                                        block_size: int):
+        num_programs = len(cu_num_tokens) - 1
+        for pid in range(num_programs):
+            start_pos = cu_num_tokens[pid].item()
+            end_pos = cu_num_tokens[pid + 1].item()
+            num_tokens = end_pos - start_pos
+            index_start = cu_query_lens[pid].item()
+            num_blocks = int(
+                torch.ceil(torch.tensor(num_tokens / block_size)).item())
+
+            for i in range(num_blocks):
+                offset_tensor = torch.arange(0,
+                                             block_size,
+                                             dtype=torch.int32,
+                                             device=out_tensor.device)
+                global_start_offset = i * block_size
+                target_indices = torch.tensor(
+                    start_pos + global_start_offset,
+                    dtype=torch.int32,
+                    device=out_tensor.device) + offset_tensor
+                values_to_store = torch.tensor(
+                    index_start + global_start_offset,
+                    dtype=torch.int32,
+                    device=out_tensor.device) + offset_tensor
+                mask = (target_indices >= start_pos) & \
+                    (target_indices < end_pos) & \
+                    (offset_tensor < num_tokens)
+                out_tensor[target_indices[mask]] = values_to_store[mask]

vllm_npu/spec_decode/interface.py

@@ -0,0 +1,54 @@
+import enum
+from typing import Optional
+
+import torch
+from vllm.config import CUDAGraphMode, VllmConfig
+from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.sample.metadata import SamplingMetadata
+from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
+
+
+class SpecDcodeType(enum.Enum):
+    NGRAM = 0
+    EAGLE = 1
+    EAGLE3 = 2
+    MTP = 4
+
+
+class Proposer:
+
+    def __init__(self,
+                 vllm_config: VllmConfig,
+                 device: torch.device = None,
+                 runner=None):
+        pass
+
+    def load_model(self, model):
+        """Called by load_model in model_runner"""
+        raise NotImplementedError
+
+    @torch.inference_mode()
+    def dummy_run(self,
+                  num_tokens: int,
+                  with_prefill: bool = False,
+                  skip_attn: bool = False,
+                  num_reqs: int = 0,
+                  num_tokens_across_dp: Optional[torch.Tensor] = None,
+                  aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
+                  batch_descriptor=None,
+                  dummy_compute_logits=lambda hidden_states: None):
+        """Called by dummy_run in modle_runner"""
+        raise NotImplementedError
+
+    def generate_token_ids(self,
+                           valid_sampled_token_ids: list[list[int]],
+                           sampling_metadata: SamplingMetadata = None,
+                           scheduler_output: SchedulerOutput = None,
+                           spec_decode_metadata: SpecDecodeMetadata = None,
+                           positions: torch.Tensor = None,
+                           num_scheduled_tokens: int = 0,
+                           hidden_states: torch.Tensor = None,
+                           attn_metadata=None,
+                           aux_hidden_states: torch.Tensor = None):
+        """Called by execute_model in model_runner"""
+        raise NotImplementedError

vllm_npu/spec_decode/mtp_proposer.py

@@ -0,0 +1,675 @@
+import types
+
+import torch
+import torch.nn as nn
+import torchair
+from torchair import patch_for_hcom
+from vllm.attention.layer import Attention
+from vllm.config import (CUDAGraphMode, VllmConfig,
+                         get_layers_from_vllm_config, set_current_vllm_config)
+from vllm.forward_context import BatchDescriptor, get_forward_context
+from vllm.model_executor.model_loader import get_model_loader
+from vllm.model_executor.model_loader.utils import (
+    process_weights_after_loading, set_default_torch_dtype)
+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_npu.ascend_config import get_ascend_config
+from vllm_npu.ascend_forward_context import set_ascend_forward_context
+from vllm_npu.attention.utils import AscendCommonAttentionMetadata
+from vllm_npu.patch.worker.patch_deepseek_mtp import \
+    AscendDeepSeekMTP as DeepSeekMTP
+from vllm_npu.spec_decode.interface import Proposer, SpecDcodeType
+from vllm_npu.torchair.models.torchair_deepseek_mtp import \
+    TorchairDeepSeekMTP
+from vllm_npu.torchair.utils import (TORCHAIR_CACHE_DIR,
+                                        TorchairCommonAttentionMetadata)
+from vllm_npu.utils import ProfileExecuteDuration, lmhead_tp_enable
+
+PADDING_SLOT_ID = -1
+
+
+class MtpProposer(Proposer):
+
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        device,
+        runner,
+    ):
+        self.name = SpecDcodeType.MTP
+        self.vllm_config = vllm_config
+        self.device = device
+        self.runner = runner
+        self.num_speculative_tokens = vllm_config.speculative_config.num_speculative_tokens
+
+        # persistent buffers for graph
+        self.input_ids = torch.zeros(self.runner.max_num_tokens,
+                                     dtype=torch.int32,
+                                     device=self.device)
+        self.positions = torch.zeros(self.runner.max_num_tokens,
+                                     dtype=torch.int64,
+                                     device=self.device)
+        self.hidden_states = torch.zeros(
+            (self.runner.max_num_tokens,
+             vllm_config.model_config.get_hidden_size()),
+            dtype=self.runner.dtype,
+            device=self.device)
+        self.torchair_compiled_model = None  # type: ignore
+        self.torchair_compiled_models = {}  # type: ignore
+        self.torchair_graph_enabled = get_ascend_config(
+        ).torchair_graph_config.enabled
+        self.enable_shared_expert_dp = get_ascend_config(
+        ).enable_shared_expert_dp
+        # 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,
+                                   device=self.runner.device,
+                                   dtype=torch.int32)
+        self.use_sparse = hasattr(vllm_config.model_config.hf_config,
+                                  "index_topk")
+
+    def load_model(self, model) -> None:
+        loader = get_model_loader(self.vllm_config.load_config)
+
+        target_attn_layer_names = set(
+            get_layers_from_vllm_config(self.vllm_config, Attention).keys())
+        draft_model_config = \
+            self.vllm_config.speculative_config.draft_model_config
+        target_device = self.vllm_config.device_config.device
+
+        with set_default_torch_dtype(
+                draft_model_config.dtype), set_current_vllm_config(
+                    self.vllm_config):
+            if self.torchair_graph_enabled or (
+                    self.enable_shared_expert_dp
+                    and self.vllm_config.model_config.use_mla):
+                self.model = TorchairDeepSeekMTP(
+                    vllm_config=self.vllm_config).to(target_device)
+            else:
+                self.model = DeepSeekMTP(
+                    vllm_config=self.vllm_config).to(target_device)
+
+        draft_attn_layer_names = (
+            get_layers_from_vllm_config(self.vllm_config, Attention).keys() -
+            target_attn_layer_names)
+
+        assert len(draft_attn_layer_names) == 1
+        self.attn_layer_name = list(draft_attn_layer_names)
+
+        self.model.load_weights(
+            loader.get_all_weights(
+                self.vllm_config.speculative_config.draft_model_config,
+                self.model))
+        process_weights_after_loading(self.model, draft_model_config,
+                                      target_device)
+
+    @torch.inference_mode()
+    def dummy_run(self,
+                  num_tokens: int,
+                  with_prefill: bool = False,
+                  skip_attn: bool = False,
+                  num_reqs: int = 0,
+                  num_tokens_across_dp=None,
+                  aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
+                  batch_descriptor=None,
+                  dummy_compute_logits=lambda hidden_states: None) -> None:
+        if not self.torchair_graph_enabled:
+            # TODO: adapt enable_dbo later
+            (num_tokens, num_tokens_across_dp, with_prefill,
+             _) = self.runner._sync_metadata_across_dp(num_tokens,
+                                                       with_prefill, False)
+
+        moe_comm_type = self.runner._select_moe_comm_method(
+            num_tokens, with_prefill)
+
+        is_running_torchair = self.torchair_graph_enabled and \
+            not with_prefill
+
+        if is_running_torchair:
+            skip_attn = False
+        if skip_attn:
+            attn_metadata = None
+        else:
+            common_attn_metadata = TorchairCommonAttentionMetadata(
+                num_reqs=num_reqs,
+                num_actual_tokens=1,
+                actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
+                attn_mask=self.runner.attn_mask,
+                spec_attn_mask=self.runner.spec_attn_mask,
+                decode_token_per_req=self.runner.decode_token_per_req,
+            )
+            attn_metadata = self.runner.attn_metadata_builder.build_torchair_graph_dummy(
+                common_attn_metadata)
+
+        input_ids = self.input_ids[:num_tokens]
+        positions = self.positions[:num_tokens]
+        previous_hidden_states = self.hidden_states[:num_tokens]
+        for _ in range(self.num_speculative_tokens):
+            with set_ascend_forward_context(
+                    attn_metadata,
+                    self.vllm_config,
+                    num_tokens=num_tokens,
+                    with_prefill=with_prefill,
+                    num_tokens_across_dp=num_tokens_across_dp,
+                    reserved_mc2_mask=self.runner.reserved_mc2_mask,
+                    moe_comm_type=moe_comm_type,
+                    in_profile_run=self.runner.in_profile_run,
+                    num_actual_tokens=0,
+                    aclgraph_runtime_mode=aclgraph_runtime_mode,
+                    batch_descriptor=batch_descriptor):
+                if is_running_torchair:
+                    assert attn_metadata is not None
+                    torch._dynamo.mark_static(input_ids)
+                    torch._dynamo.mark_static(positions)
+                    torch._dynamo.mark_static(previous_hidden_states)
+                    torch._dynamo.mark_static(attn_metadata.decode.block_table)
+                    torch._dynamo.mark_static(
+                        attn_metadata.decode.input_positions)
+                    if hasattr(attn_metadata.decode, "sin"):
+                        torch._dynamo.mark_static(attn_metadata.decode.sin)
+                        torch._dynamo.mark_static(attn_metadata.decode.cos)
+                    torch._dynamo.mark_static(get_forward_context().mc2_mask)
+                    torch._dynamo.mark_static(attn_metadata.slot_mapping)
+                    torch._dynamo.mark_static(attn_metadata.decode.attn_mask)
+                    torchair_compiled_model = self._get_torchair_lazy_compiled_model(
+                        num_tokens)
+                    torchair_compiled_model(
+                        input_ids=input_ids,
+                        positions=positions,
+                        hidden_states=previous_hidden_states,
+                        inputs_embeds=None,
+                        intermediate_tensors=None,
+                        attn_metadata=attn_metadata,
+                        kv_caches=self.runner.kv_caches[-1:],
+                        spec_step_idx=0)
+                else:
+                    self.model(input_ids=input_ids,
+                               positions=positions,
+                               hidden_states=previous_hidden_states)
+                dummy_compute_logits(previous_hidden_states)
+            if with_prefill:
+                break
+
+    def generate_token_ids(self,
+                           valid_sampled_token_ids: list[list[int]],
+                           sampling_metadata: SamplingMetadata = None,
+                           scheduler_output: SchedulerOutput = None,
+                           spec_decode_metadata: SpecDecodeMetadata = None,
+                           positions: torch.Tensor = None,
+                           num_scheduled_tokens: int = 0,
+                           hidden_states: torch.Tensor = None,
+                           attn_metadata=None,
+                           aux_hidden_states: torch.Tensor = None):
+        if attn_metadata is not None and isinstance(attn_metadata, dict):
+            attn_metadata = attn_metadata['model.layers.0.self_attn.attn']
+        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)
+        accepted_token_indices = None
+        if spec_decode_metadata is None:
+            # input_ids can be None for multimodal models.
+            target_token_ids = self.runner.input_ids[:num_scheduled_tokens]
+            target_positions = positions[:num_scheduled_tokens]
+            target_hidden_states = hidden_states[:num_scheduled_tokens]
+            target_slot_mapping = attn_metadata.slot_mapping
+            cu_num_tokens = attn_metadata.query_start_loc
+        else:
+            # TODO(woosuk): Refactor this.
+            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, accepted_token_indices, target_token_ids, \
+                target_positions, target_hidden_states, target_slot_mapping = self._prepare_inputs(
+                attn_metadata.query_start_loc,
+                num_rejected_tokens,
+                self.runner.input_ids[:num_scheduled_tokens],
+                positions[:num_scheduled_tokens],
+                hidden_states[:num_scheduled_tokens],
+                attn_metadata.slot_mapping[:num_scheduled_tokens],
+                is_torchair_graph=self.runner._build_drafter_prepare_inputs_torchair_param(),
+            )
+
+        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=attn_metadata.block_tables,
+            sampling_metadata=sampling_metadata,
+            token_indices=accepted_token_indices)
+        spec_token_ids = draft_token_ids.tolist()
+        return spec_token_ids
+
+    def _prepare_inputs(
+        self,
+        # [batch_size + 1]
+        cu_target_query_lens: torch.Tensor,
+        # [batch_size]
+        num_rejected_tokens: torch.Tensor,
+        token_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        slot_mapping: torch.Tensor,
+        is_torchair_graph: bool = False
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor,
+               torch.Tensor, torch.Tensor]:
+        # 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]
+        # [0, a, a + b, a + b + c] -> [a, b, c]
+        query_len_per_req = (cu_target_query_lens[1:] -
+                             cu_target_query_lens[:-1])
+        # [a, b, c] -> [a - n1, b - n2, c - n3]
+        num_tokens_per_req = query_len_per_req - num_rejected_tokens
+        if is_torchair_graph:
+            cu_num_tokens = cu_target_query_lens
+            relative_index = query_len_per_req - num_rejected_tokens - 1
+            token_indices = cu_num_tokens[:-1] + relative_index
+            # the seq len of each bath is padded to 1+num_speculative_tokens, thus input is same as the main model
+            target_token_ids = token_ids
+            target_positions = positions
+            target_hidden_states = hidden_states
+            target_slot_mapping = slot_mapping
+        else:
+            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
+
+            # FIXME(woosuk): Avoid synchronization.
+            num_tokens = cu_num_tokens[-1].item()
+            token_indices = torch.zeros(
+                num_tokens,
+                dtype=torch.int32,
+                device=cu_num_tokens.device,
+            )
+
+            BLOCK_SIZE = 1024
+            self._prepare_input_kernel(
+                token_indices,
+                cu_target_query_lens,
+                cu_num_tokens,
+                block_size=BLOCK_SIZE,
+            )
+            target_token_ids = token_ids[token_indices]
+            target_positions = positions[token_indices]
+            target_hidden_states = hidden_states[token_indices]
+            target_slot_mapping = slot_mapping[token_indices]
+        return cu_num_tokens, token_indices, target_token_ids, target_positions, target_hidden_states, target_slot_mapping
+
+    def _propose(
+            self,
+            # [num_tokens]
+            target_token_ids: torch.Tensor,
+            # [num_tokens]
+            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,
+            sampling_metadata: SamplingMetadata,
+            token_indices=None) -> torch.Tensor:
+        num_tokens = target_token_ids.shape[0]
+        batch_size = next_token_ids.shape[0]
+        last_token_indices = cu_num_tokens[1:] - 1
+
+        # Shift the input ids by one token.
+        # E.g., [a1, b1, b2, c1, c2, c3] -> [b1, b2, c1, c2, c3, c3]
+        self.input_ids[:num_tokens - 1] = target_token_ids[1:]
+        # Replace the last token with the next token.
+        # E.g., [b1, b2, c1, c2, c3, c3] -> [a2, b2, b3, c2, c3, c4]
+        if token_indices is not None and self.torchair_graph_enabled:
+            last_token_indices = token_indices
+
+        self.input_ids[last_token_indices] = next_token_ids
+
+        query_lens = cu_num_tokens[1:] - cu_num_tokens[:-1]
+        max_query_len = query_lens.max().item()
+
+        # FIXME: reorder_batch() needs to be called before build()
+        # because fields of attn_metadata_builder needs to be updated.
+        # However, currently reorder_batch() takes input_batch and
+        # scheduler_output as arguments, we should probably refactor
+        # the method to use new data structures which are independent
+        # from input_batch and scheduler_output.
+        # self.runner.attn_metadata_builder.reorder_batch(
+        #     input_batch=self.runner.input_batch,
+        #     scheduler_output=self.runner.scheduler_output,
+        # )
+        is_running_torchair = self.torchair_graph_enabled and \
+            not self.runner.with_prefill
+
+        if is_running_torchair:
+            # Torchair graph mode, padding is same as the main model
+            num_input_tokens = self.runner.graph_pad_size
+        elif (self.runner.use_aclgraph
+              and num_tokens <= self.runner.aclgraph_batch_sizes[-1]):
+            # Acl graph mode, add padding to the batch size
+            num_input_tokens = self.vllm_config.pad_for_cudagraph(num_tokens)
+        else:
+            # Eager mode, no padding needed
+            num_input_tokens = num_tokens
+
+        seq_lens = target_positions[last_token_indices] + 1
+        seq_lens = seq_lens.int()
+        common_attn_metadata = AscendCommonAttentionMetadata(
+            query_start_loc=cu_num_tokens[:batch_size + 1],
+            query_start_loc_cpu=cu_num_tokens[:batch_size + 1].cpu(),
+            seq_lens_cpu=seq_lens.cpu(),
+            num_reqs=batch_size,
+            num_actual_tokens=num_tokens,
+            max_query_len=max_query_len,
+            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=self.runner.attn_mask,
+            spec_attn_mask=self.runner.spec_attn_mask,
+            attn_state=self.runner.attn_state,
+            graph_pad_size=self.runner.graph_pad_size,
+            decode_token_per_req=self.runner.decode_token_per_req,
+            num_computed_tokens_cpu=None,
+            seq_lens=None)
+
+        if not self.torchair_graph_enabled:
+            builder = self.runner.attn_groups[0][0].get_metadata_builder()
+            attn_metadata_mtp = builder.build(0, common_attn_metadata,
+                                              self.runner.get_model())
+
+            attn_metadata = {}
+            for layer_name in self.attn_layer_name:
+                attn_metadata[layer_name] = attn_metadata_mtp
+
+        else:
+            attn_metadata = self.runner.attn_metadata_builder.build(
+                0, common_attn_metadata, self.runner.get_model())
+
+        self.positions[:num_tokens] = target_positions
+        self.hidden_states[:num_tokens] = target_hidden_states
+
+        if not self.torchair_graph_enabled:
+            # torch mode need to update num_tokens_across_dp
+            # TODO: adapt enable_dbo later
+            (num_input_tokens, num_tokens_across_dp, with_prefill,
+             _) = self.runner._sync_metadata_across_dp(
+                 num_input_tokens, self.runner.with_prefill, False)
+        else:
+            # torchair mode can reuse self.runner.num_tokens_across_dp
+            num_tokens_across_dp = self.runner.num_tokens_across_dp
+            with_prefill = self.runner.with_prefill
+
+        moe_comm_type = self.runner._select_moe_comm_method(
+            num_input_tokens, with_prefill)
+        batch_descriptor = BatchDescriptor(num_tokens=num_input_tokens,
+                                           uniform_decode=False)
+        aclgraph_runtime_mode, batch_descriptor = \
+            self.runner.aclgraph_dispatcher.dispatch(batch_descriptor)
+
+        for step in range(self.num_speculative_tokens):
+            with set_ascend_forward_context(
+                    attn_metadata,
+                    self.vllm_config,
+                    num_tokens=num_input_tokens,
+                    with_prefill=with_prefill,
+                    num_tokens_across_dp=num_tokens_across_dp,
+                    reserved_mc2_mask=self.runner.reserved_mc2_mask,
+                    moe_comm_type=moe_comm_type,
+                    aclgraph_runtime_mode=aclgraph_runtime_mode,
+                    batch_descriptor=batch_descriptor,
+                    in_profile_run=self.runner.in_profile_run,
+                    num_actual_tokens=num_tokens):
+                with ProfileExecuteDuration().capture_async('mtp_forward'):
+                    model_kwargs = {}
+                    model_kwargs["attn_metadata"] = attn_metadata
+                    if self.torchair_graph_enabled:
+                        model_kwargs["kv_caches"] = self.runner.kv_caches[-1:]
+                    if is_running_torchair:
+                        torchair_compiled_model = self._get_torchair_lazy_compiled_model(
+                            num_input_tokens)
+                        hidden_states = torchair_compiled_model(
+                            input_ids=self.input_ids[:num_input_tokens],
+                            positions=self.positions[:num_input_tokens],
+                            hidden_states=self.
+                            hidden_states[:num_input_tokens],
+                            inputs_embeds=None,
+                            intermediate_tensors=None,
+                            spec_step_idx=0,
+                            **model_kwargs)
+                    else:
+                        hidden_states = self.model(
+                            input_ids=self.input_ids[:num_input_tokens],
+                            positions=self.positions[:num_input_tokens],
+                            hidden_states=self.hidden_states[:num_input_tokens]
+                        )
+
+            num_indices = last_token_indices.shape[0]
+            if lmhead_tp_enable():
+                if not self.runner.with_prefill:
+                    max_num_reqs_across_dp = num_input_tokens
+                else:
+                    max_num_reqs_across_dp = self.vllm_config.scheduler_config.max_num_seqs
+                last_token_indices = nn.functional.pad(
+                    last_token_indices,
+                    (0, max_num_reqs_across_dp - num_indices))
+
+            sample_hidden_states = hidden_states[last_token_indices]
+            logits = self.model.compute_logits(sample_hidden_states)
+            if lmhead_tp_enable() and num_indices < logits.shape[0]:
+                logits = logits[:num_indices]
+                last_token_indices = last_token_indices[:num_indices]
+            draft_token_ids = logits.argmax(dim=-1)
+
+            if self.num_speculative_tokens == 1:
+                # [batch_size, 1]
+                return draft_token_ids.view(-1, 1)
+
+            if step == 0:
+                draft_token_ids_list = [draft_token_ids]
+            else:
+                draft_token_ids_list.append(draft_token_ids)
+
+            # prepare next mtp inputs
+            # mtp>1: prefill skip or decode skip last loop
+            if with_prefill:
+                for _ in range(self.num_speculative_tokens - 1):
+                    draft_token_ids_list.append(draft_token_ids)
+            if step == self.num_speculative_tokens - 1 or with_prefill:
+                break
+
+            if not self.torchair_graph_enabled:
+                attn_metadata_i = attn_metadata[self.attn_layer_name[0]]
+            else:
+                attn_metadata_i = attn_metadata
+
+            if step == 0:
+                positions = target_positions[last_token_indices]
+                hidden_states = hidden_states[last_token_indices]
+                slot_mapping = attn_metadata_i.slot_mapping[last_token_indices]
+                attn_metadata_i.slot_mapping.fill_(-1)
+                attn_metadata_i.query_start_loc = self.arange[:batch_size + 1]
+                last_token_indices = self.arange[:batch_size]
+                if attn_metadata_i.num_decode_tokens != 0:
+                    attn_metadata_i.num_decode_tokens = batch_size
+                if is_running_torchair:
+                    attn_metadata_i.num_actual_tokens = batch_size
+                    attn_metadata_i.query_lens = [1] * batch_size
+
+            input_ids = draft_token_ids_list[-1].int()
+            positions += 1
+
+            if not self.torchair_graph_enabled:
+                attn_metadata_i.decode.actual_seq_lengths_q = attn_metadata_i.query_start_loc[
+                    1:batch_size + 1].tolist()
+                attn_metadata_i.decode.cos = builder.cos_cache[
+                    positions].unsqueeze(1).unsqueeze(2)
+                attn_metadata_i.decode.sin = builder.sin_cache[
+                    positions].unsqueeze(1).unsqueeze(2)
+
+            # NOTE(woosuk): We should handle the case where the draft model
+            # generates tokens beyond the max model length. Since it is complex
+            # to remove such requests from the batch, we keep them in the batch
+            # 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 >= self.runner.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 = torch.where(exceeds_max_model_len, 0,
+                                            positions)
+            # Increment the sequence lengths.
+            attn_metadata_i.seq_lens[:batch_size] += 1
+            # For the requests that exceed the max model length, we set the
+            # sequence length to 1 to minimize their overheads in attention.
+            exceeds_max_model_len_cpu = exceeds_max_model_len.to(
+                attn_metadata_i.seq_lens.device, non_blocking=False)
+            attn_metadata_i.seq_lens[:batch_size].masked_fill_(
+                exceeds_max_model_len_cpu, 1)
+            # 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 += 1
+            slot_mapping.masked_fill_(exceeds_max_model_len, PADDING_SLOT_ID)
+
+            # copy inputs to buffer for cudagraph
+            self.input_ids[:batch_size] = input_ids
+            self.positions[:batch_size] = clamped_positions
+            self.hidden_states[:hidden_states.shape[0]] = hidden_states
+            attn_metadata_i.slot_mapping[:batch_size] = slot_mapping
+
+            if attn_metadata_i.prefill is not None:
+                attn_metadata_i.prefill.seq_lens = attn_metadata_i.seq_lens
+                attn_metadata_i.prefill.seq_lens_list = attn_metadata_i.prefill.seq_lens.tolist(
+                )
+                attn_metadata_i.prefill.context_lens = attn_metadata_i.seq_lens
+                attn_metadata_i.prefill.input_positions = self.positions[:
+                                                                         num_input_tokens]
+                attn_metadata_i.prefill.max_seq_lens += 1
+                attn_metadata_i.prefill.max_seq_lens = min(
+                    attn_metadata_i.prefill.max_seq_lens,
+                    self.runner.model_config.max_model_len)
+            if attn_metadata_i.decode is not None:
+                attn_metadata_i.decode.seq_lens = attn_metadata_i.seq_lens
+                attn_metadata_i.decode.seq_lens_list = attn_metadata_i.decode.seq_lens.tolist(
+                )
+                attn_metadata_i.decode.input_positions = self.positions[:
+                                                                        num_input_tokens]
+                attn_metadata_i.decode.max_seq_lens += 1
+                attn_metadata_i.decode.max_seq_lens = min(
+                    attn_metadata_i.decode.max_seq_lens,
+                    self.runner.model_config.max_model_len)
+
+        # mtp>1: [batch_size, k]
+        draft_token_ids = torch.stack(draft_token_ids_list, dim=1)
+        return draft_token_ids
+
+    def _get_torchair_lazy_compiled_model(self, batch_size: int):
+        if batch_size < 0 or batch_size > self.runner.torchair_graph_batch_sizes[
+                -1]:
+            raise ValueError(
+                f"Bad graph batch size:{batch_size}! max_graph_batch_sizes:{self.runner.torchair_graph_batch_sizes[-1]}"
+            )
+
+        compiled_model = self.torchair_compiled_models.get(
+            batch_size
+        ) if self.runner.use_cached_npu_graph else self.torchair_compiled_model
+
+        if compiled_model:
+            return compiled_model
+
+        patch_for_hcom()
+        config = torchair.CompilerConfig()
+        config.experimental_config.frozen_parameter = True
+        config.experimental_config.tiling_schedule_optimize = True
+        config.experimental_config.enable_view_optimize = \
+        get_ascend_config().torchair_graph_config.enable_view_optimize
+        torch.npu.set_compile_mode(jit_compile=False)
+        if not self.runner.use_cached_npu_graph:
+            npu_backend = torchair.get_npu_backend(compiler_config=config)
+            self.torchair_compiled_model = torch.compile(
+                self.model,
+                dynamic=not self.use_sparse,
+                fullgraph=True,
+                backend=npu_backend)
+            return self.torchair_compiled_model
+        else:
+            # Generate a new forward proxy code object to prevent the invalidation of
+            # compilation cache caused by dynamo retracing
+            forward_proxy_name = f"{self.model.__class__.__name__}_forward_with_batch_size_{batch_size}"
+            forward_fn = self.model.forward
+            code = forward_fn.__code__
+            # Mark code object with a new proxy name
+            modified_code = code.replace(co_name=forward_proxy_name, )
+
+            modified_func = types.FunctionType(modified_code,
+                                               forward_fn.__globals__,
+                                               name=forward_proxy_name,
+                                               argdefs=forward_fn.__defaults__)
+
+            self.model.__dict__[forward_proxy_name] = modified_func.__get__(
+                self.model, nn.Module)
+            self.torchair_compiled_models[
+                batch_size] = torchair.inference.cache_compile(
+                    self.model.__dict__[forward_proxy_name],
+                    dynamic=not self.use_sparse,
+                    fullgraph=True,
+                    cache_dir=TORCHAIR_CACHE_DIR,
+                    config=config,
+                    ge_cache=False)
+            return self.torchair_compiled_models[batch_size]
+
+    # TODO Using torch instead of triton may result in poor performance
+    def _prepare_input_kernel(self, out_ptr: torch.Tensor,
+                              cu_query_lens: torch.Tensor,
+                              cu_num_tokens: torch.Tensor, block_size: int):
+        device = cu_query_lens.device
+        dtype = out_ptr.dtype
+
+        offsets = torch.arange(block_size, device=device, dtype=dtype)
+        start_pos = cu_num_tokens[:-1]
+        end_pos = cu_num_tokens[1:]
+        num_tokens = end_pos - start_pos
+
+        global_indices = (start_pos.view(-1, 1) + offsets.view(1, -1))
+        values = (cu_query_lens[:-1].view(-1, 1) + offsets.view(1, -1))
+
+        mask = (offsets.view(1, -1) < num_tokens.view(-1, 1))
+
+        global_indices_flat = global_indices[mask]
+        values_flat = values[mask]
+        out_ptr[global_indices_flat] = values_flat

vllm_npu/spec_decode/ngram_proposer.py

@@ -0,0 +1,72 @@
+import torch
+from vllm.config import CUDAGraphMode
+from vllm.v1.spec_decode.ngram_proposer import \
+    NgramProposer as VllmNgramProposer
+
+from vllm_npu.spec_decode.interface import Proposer, SpecDcodeType
+
+
+class NgramProposer(VllmNgramProposer, Proposer):
+
+    def __init__(self, vllm_config, device, runner):
+        super().__init__(vllm_config)
+        self.name = SpecDcodeType.NGRAM
+        self.device = device
+        self.runner = runner
+
+    def load_model(self, *args, **kwargs):
+        # No model to load.
+        pass
+
+    @torch.inference_mode()
+    def dummy_run(self,
+                  num_tokens,
+                  with_prefill=None,
+                  skip_attn=None,
+                  num_reqs=None,
+                  num_tokens_across_dp=None,
+                  aclgraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
+                  batch_descriptor=None,
+                  dummy_compute_logits=lambda hidden_states: None):
+        pass
+
+    def generate_token_ids(self,
+                           valid_sampled_token_ids,
+                           sampling_metadata=None,
+                           scheduler_output=None,
+                           spec_decode_metadata=None,
+                           positions=None,
+                           num_scheduled_tokens=None,
+                           hidden_states=None,
+                           attn_metadata=None,
+                           aux_hidden_states=None) -> list[list[int]]:
+        valid_ngram_requests = []
+        for i, sampled_ids in enumerate(valid_sampled_token_ids):
+            num_sampled_ids = len(sampled_ids)
+            if not num_sampled_ids:
+                continue
+
+            req_id = self.runner.input_batch.req_ids[i]
+            if req_id in self.runner.input_batch.spec_decode_unsupported_reqs:
+                continue
+
+            num_tokens = self.runner.input_batch.num_tokens_no_spec[i]
+            if num_tokens >= self.runner.input_batch.max_model_len:
+                # Skip requests that have already reached the max model length.
+                continue
+
+            start_idx = self.runner.input_batch.num_tokens_no_spec[i]
+            end_idx = start_idx + num_sampled_ids
+            self.runner.input_batch.token_ids_cpu[
+                i, start_idx:end_idx] = sampled_ids
+
+            valid_ngram_requests.append(i)
+
+        draft_token_ids = self.batch_propose(
+            len(valid_sampled_token_ids),
+            valid_ngram_requests,
+            self.runner.input_batch.num_tokens_no_spec,
+            self.runner.input_batch.token_ids_cpu,
+        )
+
+        return draft_token_ids