Add minimal vLLM 0.16.1 build repo for BI-V150

vllm/v1/worker/gpu/README.md

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+# [Experimental] Model Runner V2
+
+This directory contains the new model runner which is under active development.
+Ping [Woosuk Kwon](https://github.com/WoosukKwon) for any changes.

vllm/v1/worker/gpu/async_utils.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import contextlib
+
+import numpy as np
+import torch
+
+from vllm.v1.outputs import AsyncModelRunnerOutput, LogprobsTensors, ModelRunnerOutput
+from vllm.v1.worker.gpu.sample.output import SamplerOutput
+
+
+class AsyncOutput(AsyncModelRunnerOutput):
+    def __init__(
+        self,
+        model_runner_output: ModelRunnerOutput,
+        sampler_output: SamplerOutput,
+        num_sampled_tokens: torch.Tensor,
+        main_stream: torch.cuda.Stream,
+        copy_stream: torch.cuda.Stream,
+        copy_event: torch.cuda.Event,
+    ):
+        # NOTE(woosuk): We must retain references to the GPU tensors,
+        # as the copy operations are performed on a different CUDA stream than
+        # the one where the tensors were created.
+        self.model_runner_output = model_runner_output
+        self.sampler_output = sampler_output
+        self.num_sampled_tokens = num_sampled_tokens
+        self.copy_event = copy_event
+
+        with stream(copy_stream, main_stream):
+            copy_stream.wait_stream(main_stream)
+
+            self.sampled_token_ids = async_copy_to_np(sampler_output.sampled_token_ids)
+            self.logprobs_tensors: LogprobsTensors | None = None
+            if sampler_output.logprobs_tensors is not None:
+                self.logprobs_tensors = (
+                    sampler_output.logprobs_tensors.to_cpu_nonblocking()
+                )
+            self.num_nans: np.ndarray | None = None
+            if sampler_output.num_nans is not None:
+                self.num_nans = async_copy_to_np(sampler_output.num_nans)
+            self.num_sampled_tokens_np = async_copy_to_np(num_sampled_tokens)
+            self.prompt_logprobs_dict = {
+                k: v.to_cpu_nonblocking() if v is not None else None
+                for k, v in self.model_runner_output.prompt_logprobs_dict.items()
+            }
+            self.copy_event.record(copy_stream)
+
+    def get_output(self) -> ModelRunnerOutput:
+        self.copy_event.synchronize()
+
+        # NOTE(woosuk): The following code is to ensure compatibility with
+        # the existing model runner.
+        # Going forward, we should keep the data structures as NumPy arrays
+        # rather than Python lists.
+        sampled_token_ids: list[list[int]] = self.sampled_token_ids.tolist()
+        num_sampled_tokens: list[int] = self.num_sampled_tokens_np.tolist()
+        for token_ids, num_tokens in zip(sampled_token_ids, num_sampled_tokens):
+            del token_ids[num_tokens:]
+        self.model_runner_output.sampled_token_ids = sampled_token_ids
+
+        if self.num_nans is not None:
+            self.model_runner_output.num_nans_in_logits = dict(
+                zip(self.model_runner_output.req_ids, self.num_nans.tolist())
+            )
+
+        if self.logprobs_tensors is not None:
+            self.model_runner_output.logprobs = self.logprobs_tensors.tolists()
+        self.model_runner_output.prompt_logprobs_dict = self.prompt_logprobs_dict
+        return self.model_runner_output
+
+
+def async_copy_to_np(x: torch.Tensor) -> np.ndarray:
+    return x.to("cpu", non_blocking=True).numpy()
+
+
+@contextlib.contextmanager
+def stream(to_stream: torch.cuda.Stream, from_stream: torch.cuda.Stream):
+    """Lightweight version of torch.cuda.stream() context manager which
+    avoids current_stream and device lookups.
+    """
+    try:
+        torch.cuda.set_stream(to_stream)
+        yield
+    finally:
+        torch.cuda.set_stream(from_stream)

vllm/v1/worker/gpu/attn_utils.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Sequence
+from typing import Any, cast
+
+import torch
+
+from vllm.config import VllmConfig, get_layers_from_vllm_config
+from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
+from vllm.v1.attention.backend import AttentionBackend, CommonAttentionMetadata
+from vllm.v1.kv_cache_interface import (
+    AttentionSpec,
+    KVCacheConfig,
+    KVCacheSpec,
+    UniformTypeKVCacheSpecs,
+)
+from vllm.v1.worker.utils import AttentionGroup, bind_kv_cache
+
+
+def get_kv_cache_spec(vllm_config: VllmConfig) -> dict[str, KVCacheSpec]:
+    kv_cache_spec: dict[str, KVCacheSpec] = {}
+    layer_type = cast(type[Any], AttentionLayerBase)
+    attn_layers = get_layers_from_vllm_config(vllm_config, layer_type)
+    for layer_name, attn_module in attn_layers.items():
+        # Skip modules that don't need KV cache (eg encoder-only attention)
+        if spec := attn_module.get_kv_cache_spec(vllm_config):
+            kv_cache_spec[layer_name] = spec
+    return kv_cache_spec
+
+
+def init_attn_backend(
+    kv_cache_config: KVCacheConfig, vllm_config: VllmConfig, device: torch.device
+):
+    attn_backends: dict[str, type[AttentionBackend]] = {}
+    attn_groups: list[list[AttentionGroup]] = []
+    attn_backend_workspace: torch.Tensor | None = None
+    for kv_cache_group_id, kv_cache_group_spec in enumerate(
+        kv_cache_config.kv_cache_groups
+    ):
+        layer_names = kv_cache_group_spec.layer_names
+
+        layer_type = cast(type[Any], AttentionLayerBase)
+        attn_layers = get_layers_from_vllm_config(vllm_config, layer_type, layer_names)
+
+        group_map: dict[tuple[tuple[str, str], KVCacheSpec], AttentionGroup] = {}
+        group_order: list[tuple[tuple[str, str], KVCacheSpec]] = []
+
+        for layer_name in layer_names:
+            attn_backend = attn_layers[layer_name].get_attn_backend()
+            attn_backends[layer_name] = attn_backend
+
+            layer_kv_cache_spec: KVCacheSpec = kv_cache_group_spec.kv_cache_spec
+            if isinstance(layer_kv_cache_spec, UniformTypeKVCacheSpecs):
+                layer_kv_cache_spec = layer_kv_cache_spec.kv_cache_specs[layer_name]
+
+            key = (attn_backend.full_cls_name(), layer_kv_cache_spec)
+            if key not in group_map:
+                group_map[key] = AttentionGroup(
+                    attn_backend,
+                    [layer_name],
+                    layer_kv_cache_spec,
+                    kv_cache_group_id,
+                )
+                group_order.append(key)
+            else:
+                group_map[key].layer_names.append(layer_name)
+
+        groups = [group_map[key] for key in group_order]
+        for group in groups:
+            group.create_metadata_builders(
+                vllm_config=vllm_config,
+                device=device,
+                kernel_block_size=None,
+                num_metadata_builders=1,
+            )
+            builder = group.get_metadata_builder(0)
+            if attn_backend_workspace is None:
+                if hasattr(builder, "_get_workspace_buffer"):
+                    attn_backend_workspace = builder._get_workspace_buffer()
+            else:
+                if hasattr(builder, "set_workspace_buffer"):
+                    builder.set_workspace_buffer(attn_backend_workspace)
+        attn_groups.append(groups)
+    return attn_backends, attn_groups
+
+
+def _allocate_kv_cache(kv_cache_config: KVCacheConfig, device: torch.device):
+    kv_cache_raw_tensors: dict[str, torch.Tensor] = {}
+    for kv_cache_tensor in kv_cache_config.kv_cache_tensors:
+        tensor = torch.zeros(kv_cache_tensor.size, dtype=torch.int8, device=device)
+        for layer_name in kv_cache_tensor.shared_by:
+            kv_cache_raw_tensors[layer_name] = tensor
+
+    layer_names = set()
+    for group in kv_cache_config.kv_cache_groups:
+        for layer_name in group.layer_names:
+            layer_names.add(layer_name)
+    assert layer_names == set(kv_cache_raw_tensors.keys()), (
+        "Some layers are not correctly initialized"
+    )
+    return kv_cache_raw_tensors
+
+
+def _reshape_kv_cache(
+    kv_cache_config: KVCacheConfig,
+    kv_cache_raw_tensors: dict[str, torch.Tensor],
+    attn_backends: dict[str, AttentionBackend],
+) -> dict[str, torch.Tensor]:
+    kv_caches: dict[str, torch.Tensor] = {}
+    for kv_cache_group_spec in kv_cache_config.kv_cache_groups:
+        kv_cache_spec = kv_cache_group_spec.kv_cache_spec
+        assert isinstance(kv_cache_spec, AttentionSpec)
+        for layer_name in kv_cache_group_spec.layer_names:
+            raw_tensor = kv_cache_raw_tensors[layer_name]
+            assert raw_tensor.numel() % kv_cache_spec.page_size_bytes == 0
+            num_blocks = raw_tensor.numel() // kv_cache_spec.page_size_bytes
+
+            attn_backend = attn_backends[layer_name]
+            kv_cache_shape = attn_backend.get_kv_cache_shape(
+                num_blocks,
+                kv_cache_spec.block_size,
+                kv_cache_spec.num_kv_heads,
+                kv_cache_spec.head_size,
+            )
+
+            # FIXME(woosuk): Add kv_cache_stride_order to all attention backends.
+            try:
+                kv_cache_stride_order = attn_backend.get_kv_cache_stride_order()
+                assert len(kv_cache_stride_order) == len(kv_cache_shape)
+            except (AttributeError, NotImplementedError):
+                kv_cache_stride_order = tuple(range(len(kv_cache_shape)))
+
+            kv_cache_shape = tuple(kv_cache_shape[i] for i in kv_cache_stride_order)
+            inv_order = [
+                kv_cache_stride_order.index(i)
+                for i in range(len(kv_cache_stride_order))
+            ]
+
+            dtype = kv_cache_spec.dtype
+            raw_tensor = raw_tensor.view(dtype)
+            raw_tensor = raw_tensor.view(kv_cache_shape)
+            kv_caches[layer_name] = raw_tensor.permute(*inv_order)
+    return kv_caches
+
+
+def init_kv_cache(
+    runner_kv_caches: list[torch.Tensor],
+    forward_context: dict[str, Any],
+    kv_cache_config: KVCacheConfig,
+    attn_backends: dict[str, AttentionBackend],
+    device: torch.device,
+) -> dict[str, torch.Tensor]:
+    kv_cache_raw_tensors = _allocate_kv_cache(kv_cache_config, device)
+    kv_caches = _reshape_kv_cache(kv_cache_config, kv_cache_raw_tensors, attn_backends)
+    bind_kv_cache(kv_caches, forward_context, runner_kv_caches)
+    return kv_caches
+
+
+def build_slot_mappings_by_layer(
+    slot_mappings: torch.Tensor, kv_cache_config: KVCacheConfig
+) -> dict[str, torch.Tensor]:
+    slot_mappings_by_layer: dict[str, torch.Tensor] = {}
+    kv_cache_groups = kv_cache_config.kv_cache_groups
+    for slot_mapping, kv_cache_group in zip(slot_mappings, kv_cache_groups):
+        for layer_name in kv_cache_group.layer_names:
+            slot_mappings_by_layer[layer_name] = slot_mapping
+    return slot_mappings_by_layer
+
+
+def build_attn_metadata(
+    attn_groups: list[list[AttentionGroup]],
+    num_reqs: int,
+    num_tokens: int,
+    query_start_loc_gpu: torch.Tensor,
+    query_start_loc_cpu: torch.Tensor,
+    max_query_len: int,
+    seq_lens: torch.Tensor,
+    max_seq_len: int,
+    block_tables: Sequence[torch.Tensor],
+    slot_mappings: torch.Tensor,
+    kv_cache_config: KVCacheConfig,
+    dcp_local_seq_lens: torch.Tensor | None = None,
+) -> dict[str, Any]:
+    seq_lens = seq_lens[:num_reqs]
+    if dcp_local_seq_lens is not None:
+        dcp_local_seq_lens = dcp_local_seq_lens[:num_reqs]
+
+    attn_metadata: dict[str, Any] = {}
+    num_kv_cache_groups = len(kv_cache_config.kv_cache_groups)
+    for i in range(num_kv_cache_groups):
+        block_table = block_tables[i]
+        slot_mapping = slot_mappings[i]
+
+        common_attn_metadata = CommonAttentionMetadata(
+            query_start_loc=query_start_loc_gpu,
+            query_start_loc_cpu=query_start_loc_cpu,
+            seq_lens=seq_lens,
+            max_seq_len=max_seq_len,
+            num_reqs=num_reqs,
+            num_actual_tokens=num_tokens,
+            max_query_len=max_query_len,
+            block_table_tensor=block_table,
+            slot_mapping=slot_mapping,
+            causal=True,
+            dcp_local_seq_lens=dcp_local_seq_lens,
+        )
+
+        for attn_group in attn_groups[i]:
+            attn_metadata_builder = attn_group.get_metadata_builder(0)
+            metadata = attn_metadata_builder.build(
+                common_prefix_len=0, common_attn_metadata=common_attn_metadata
+            )
+            for layer_name in attn_group.layer_names:
+                attn_metadata[layer_name] = metadata
+    return attn_metadata

vllm/v1/worker/gpu/block_table.py

@@ -0,0 +1,253 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterable
+
+import torch
+
+from vllm.triton_utils import tl, triton
+from vllm.utils.math_utils import cdiv
+from vllm.v1.attention.backends.utils import PAD_SLOT_ID
+from vllm.v1.worker.gpu.buffer_utils import StagedWriteTensor, UvaBackedTensor
+
+
+class BlockTables:
+    def __init__(
+        self,
+        block_sizes: list[int],
+        max_num_reqs: int,
+        max_num_batched_tokens: int,
+        max_model_len: int,
+        device: torch.device,
+        cp_size: int = 1,
+        cp_rank: int = 0,
+        cp_interleave: int = 1,
+    ):
+        self.block_sizes = block_sizes
+        self.max_num_reqs = max_num_reqs
+        self.max_num_batched_tokens = max_num_batched_tokens
+        self.max_model_len = max_model_len
+        self.device = device
+
+        self.cp_size = cp_size
+        self.cp_rank = cp_rank
+        self.cp_interleave = cp_interleave
+
+        self.num_kv_cache_groups = len(self.block_sizes)
+        # num_kv_cache_groups x [max_num_reqs, max_num_blocks]
+        self.block_tables: list[StagedWriteTensor] = []
+        for i in range(self.num_kv_cache_groups):
+            block_size = self.block_sizes[i]
+            # When using DCP, each request's KV cache is sharded among different ranks.
+            # As a result, one block on the current rank covers `block_size * cp_size`
+            # tokens in the full, global (unsharded) sequence.
+            max_num_blocks = cdiv(self.max_model_len, block_size * self.cp_size)
+            block_table = StagedWriteTensor(
+                (self.max_num_reqs, max_num_blocks),
+                dtype=torch.int32,
+                device=device,
+            )
+            self.block_tables.append(block_table)
+        self.block_table_ptrs = self._make_ptr_tensor(
+            [b.gpu for b in self.block_tables]
+        )
+        self.block_table_strides = torch.tensor(
+            [b.gpu.stride(0) for b in self.block_tables],
+            dtype=torch.int64,
+            device=self.device,
+        )
+
+        self.block_sizes_tensor = torch.tensor(
+            self.block_sizes, dtype=torch.int32, device=self.device
+        )
+        self.num_blocks = UvaBackedTensor(
+            (self.num_kv_cache_groups, self.max_num_reqs),
+            dtype=torch.int32,
+        )
+
+        # Block tables used for model's forward pass.
+        # num_kv_cache_groups x [max_num_reqs, max_num_blocks]
+        self.input_block_tables: list[torch.Tensor] = [
+            torch.zeros_like(b.gpu) for b in self.block_tables
+        ]
+        self.input_block_table_ptrs = self._make_ptr_tensor(self.input_block_tables)
+
+        self.slot_mappings = torch.zeros(
+            self.num_kv_cache_groups,
+            self.max_num_batched_tokens,
+            dtype=torch.int64,
+            device=self.device,
+        )
+
+    def _make_ptr_tensor(self, x: Iterable[torch.Tensor]) -> torch.Tensor:
+        # NOTE(woosuk): Use uint64 instead of int64 to cover all possible addresses.
+        return torch.tensor(
+            [t.data_ptr() for t in x], dtype=torch.uint64, device=self.device
+        )
+
+    def append_block_ids(
+        self,
+        req_index: int,
+        new_block_ids: tuple[list[int], ...],
+        overwrite: bool,
+    ) -> None:
+        for i in range(self.num_kv_cache_groups):
+            start = self.num_blocks.np[i, req_index] if not overwrite else 0
+            block_ids = new_block_ids[i]
+            self.block_tables[i].stage_write(req_index, start, block_ids)
+            self.num_blocks.np[i, req_index] = start + len(block_ids)
+
+    def apply_staged_writes(self) -> None:
+        # TODO(woosuk): This can be inefficient since it launches one kernel per
+        # block table. Implement a kernel to handle all block tables at once.
+        for block_table in self.block_tables:
+            block_table.apply_write()
+        self.num_blocks.copy_to_uva()
+
+    def gather_block_tables(
+        self, idx_mapping: torch.Tensor
+    ) -> tuple[torch.Tensor, ...]:
+        num_reqs = idx_mapping.shape[0]
+        _gather_block_tables_kernel[(self.num_kv_cache_groups, num_reqs)](
+            idx_mapping,
+            self.block_table_ptrs,
+            self.input_block_table_ptrs,
+            self.block_table_strides,
+            self.num_blocks.gpu,
+            self.num_blocks.gpu.stride(0),
+            BLOCK_SIZE=1024,  # type: ignore
+        )
+        return tuple(block_table[:num_reqs] for block_table in self.input_block_tables)
+
+    def get_dummy_block_tables(self, num_reqs: int) -> tuple[torch.Tensor, ...]:
+        return tuple(block_table[:num_reqs] for block_table in self.input_block_tables)
+
+    def compute_slot_mappings(
+        self,
+        idx_mapping: torch.Tensor,
+        query_start_loc: torch.Tensor,
+        positions: torch.Tensor,
+    ) -> torch.Tensor:
+        num_reqs = idx_mapping.shape[0]
+        num_tokens = positions.shape[0]
+        num_groups = self.num_kv_cache_groups
+        _compute_slot_mappings_kernel[(num_groups, num_reqs + 1)](
+            num_tokens,
+            self.max_num_batched_tokens,
+            idx_mapping,
+            query_start_loc,
+            positions,
+            self.block_table_ptrs,
+            self.block_table_strides,
+            self.block_sizes_tensor,
+            self.slot_mappings,
+            self.slot_mappings.stride(0),
+            self.cp_rank,
+            CP_SIZE=self.cp_size,
+            CP_INTERLEAVE=self.cp_interleave,
+            PAD_ID=PAD_SLOT_ID,
+            TRITON_BLOCK_SIZE=1024,  # type: ignore
+        )
+        return self.slot_mappings[:, :num_tokens]
+
+    def get_dummy_slot_mappings(self, num_tokens: int) -> torch.Tensor:
+        self.slot_mappings.fill_(PAD_SLOT_ID)
+        return self.slot_mappings[:, :num_tokens]
+
+
+@triton.jit
+def _gather_block_tables_kernel(
+    batch_idx_to_req_idx,  # [batch_size]
+    src_block_table_ptrs,  # [num_kv_cache_groups]
+    dst_block_table_ptrs,  # [num_kv_cache_groups]
+    block_table_strides,  # [num_kv_cache_groups]
+    num_blocks_ptr,  # [num_kv_cache_groups, max_num_reqs]
+    num_blocks_stride,
+    BLOCK_SIZE: tl.constexpr,
+):
+    # kv cache group id
+    group_id = tl.program_id(0)
+    batch_idx = tl.program_id(1)
+    req_idx = tl.load(batch_idx_to_req_idx + batch_idx)
+
+    group_num_blocks_ptr = num_blocks_ptr + group_id * num_blocks_stride
+    num_blocks = tl.load(group_num_blocks_ptr + req_idx)
+
+    stride = tl.load(block_table_strides + group_id)
+    src_block_table_ptr = _load_ptr(src_block_table_ptrs + group_id, tl.int32)
+    src_row_ptr = src_block_table_ptr + req_idx * stride
+    dst_block_table_ptr = _load_ptr(dst_block_table_ptrs + group_id, tl.int32)
+    dst_row_ptr = dst_block_table_ptr + batch_idx * stride
+
+    for i in tl.range(0, num_blocks, BLOCK_SIZE):
+        offset = i + tl.arange(0, BLOCK_SIZE)
+        block_ids = tl.load(src_row_ptr + offset, mask=offset < num_blocks)
+        tl.store(dst_row_ptr + offset, block_ids, mask=offset < num_blocks)
+
+
+@triton.jit
+def _compute_slot_mappings_kernel(
+    num_tokens,
+    max_num_tokens,
+    idx_mapping,  # [num_reqs]
+    query_start_loc,  # [num_reqs + 1]
+    pos,  # [num_tokens]
+    block_table_ptrs,  # [num_kv_cache_groups]
+    block_table_strides,  # [num_kv_cache_groups]
+    block_sizes,  # [num_kv_cache_groups]
+    slot_mappings_ptr,  # [num_kv_cache_groups, max_num_tokens]
+    slot_mappings_stride,
+    cp_rank,
+    CP_SIZE: tl.constexpr,
+    CP_INTERLEAVE: tl.constexpr,
+    PAD_ID: tl.constexpr,
+    TRITON_BLOCK_SIZE: tl.constexpr,
+):
+    # kv cache group id
+    group_id = tl.program_id(0)
+    batch_idx = tl.program_id(1)
+    slot_mapping_ptr = slot_mappings_ptr + group_id * slot_mappings_stride
+
+    if batch_idx == tl.num_programs(1) - 1:
+        # Pad remaining slots to -1. This is needed for CUDA graphs.
+        for i in range(num_tokens, max_num_tokens, TRITON_BLOCK_SIZE):
+            offset = i + tl.arange(0, TRITON_BLOCK_SIZE)
+            tl.store(slot_mapping_ptr + offset, PAD_ID, mask=offset < max_num_tokens)
+        return
+
+    block_table_ptr = _load_ptr(block_table_ptrs + group_id, tl.int32)
+    block_table_stride = tl.load(block_table_strides + group_id)
+    block_size = tl.load(block_sizes + group_id)
+
+    req_state_idx = tl.load(idx_mapping + batch_idx)
+    start_idx = tl.load(query_start_loc + batch_idx)
+    end_idx = tl.load(query_start_loc + batch_idx + 1)
+    for i in range(start_idx, end_idx, TRITON_BLOCK_SIZE):
+        offset = i + tl.arange(0, TRITON_BLOCK_SIZE)
+        positions = tl.load(pos + offset, mask=offset < end_idx, other=0)
+
+        block_indices = positions // (block_size * CP_SIZE)
+        block_offsets = positions % (block_size * CP_SIZE)
+        block_numbers = tl.load(
+            block_table_ptr + req_state_idx * block_table_stride + block_indices
+        )
+
+        if CP_SIZE == 1:
+            # Common case: Context parallelism is not used.
+            slot_ids = block_numbers * block_size + block_offsets
+        else:
+            # Context parallelism is used.
+            is_local = block_offsets // CP_INTERLEAVE % CP_SIZE == cp_rank
+            rounds = block_offsets // (CP_INTERLEAVE * CP_SIZE)
+            remainder = block_offsets % CP_INTERLEAVE
+            local_offsets = rounds * CP_INTERLEAVE + remainder
+            slot_ids = block_numbers * block_size + local_offsets
+            slot_ids = tl.where(is_local, slot_ids, PAD_ID)
+
+        tl.store(slot_mapping_ptr + offset, slot_ids, mask=offset < end_idx)
+
+
+@triton.jit
+def _load_ptr(ptr_to_ptr, elem_dtype):
+    ptr = tl.load(ptr_to_ptr)
+    ptr = tl.cast(ptr, tl.pointer_type(elem_dtype))
+    return tl.multiple_of(ptr, 16)

vllm/v1/worker/gpu/buffer_utils.py

@@ -0,0 +1,220 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Iterable, Sequence
+from functools import partial
+
+import numpy as np
+import torch
+
+from vllm.triton_utils import tl, triton
+from vllm.utils.platform_utils import is_uva_available
+from vllm.utils.torch_utils import (
+    async_tensor_h2d,
+    get_accelerator_view_from_cpu_tensor,
+)
+
+
+def async_copy_to_gpu(
+    x: torch.Tensor | np.ndarray,
+    out: torch.Tensor | None = None,
+    device: torch.device | None = None,
+) -> torch.Tensor:
+    if isinstance(x, np.ndarray):
+        x = torch.from_numpy(x)
+    assert x.is_cpu
+
+    if out is None:
+        assert device is not None
+        out = torch.empty_like(x, device=device)
+
+    # CPU-to-CPU copy
+    tmp = x.pin_memory()
+    assert tmp is not x
+
+    # CPU-to-GPU copy
+    return out.copy_(tmp, non_blocking=True)
+
+
+class UvaBuffer:
+    def __init__(self, size: int | Sequence[int], dtype: torch.dtype):
+        if not is_uva_available():
+            raise RuntimeError("UVA is not available")
+        self.cpu = torch.zeros(size, dtype=dtype, device="cpu", pin_memory=True)
+        self.np = self.cpu.numpy()
+        self.uva = get_accelerator_view_from_cpu_tensor(self.cpu)
+
+
+class UvaBufferPool:
+    def __init__(
+        self,
+        size: int | Sequence[int],
+        dtype: torch.dtype,
+        max_concurrency: int = 2,
+    ):
+        self.size = size
+        self.dtype = dtype
+        self.max_concurrency = max_concurrency
+
+        # UVA buffers for concurrency
+        self._uva_bufs = [UvaBuffer(size, dtype) for _ in range(max_concurrency)]
+        # Current buffer index
+        self._curr = 0
+
+    def copy_to_uva(self, x: torch.Tensor | np.ndarray | list) -> torch.Tensor:
+        # Round robin to the next buffer.
+        self._curr = (self._curr + 1) % self.max_concurrency
+        buf = self._uva_bufs[self._curr]
+        # CPU-to-CPU copy
+        dst = buf.cpu if isinstance(x, torch.Tensor) else buf.np
+        n = len(x)
+        dst[:n] = x
+        return buf.uva[:n]
+
+    def copy_to_gpu(
+        self,
+        x: torch.Tensor | np.ndarray,
+        out: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        uva = self.copy_to_uva(x)
+        # CPU-to-GPU copy
+        return uva.clone() if out is None else out.copy_(uva, non_blocking=True)
+
+
+class UvaBackedTensor:
+    def __init__(
+        self, size: int | Sequence[int], dtype: torch.dtype, max_concurrency: int = 2
+    ):
+        self.dtype = dtype
+        self.max_concurrency = max_concurrency
+
+        # Source of truth
+        self.cpu = torch.zeros(size, dtype=dtype, device="cpu", pin_memory=False)
+        self.np = self.cpu.numpy()
+
+        # Buffers for concurrency
+        self.pool = UvaBufferPool(size, dtype, max_concurrency)
+        self.gpu = self.pool.copy_to_uva(self.np)
+
+    def copy_to_uva(self, n: int | None = None) -> torch.Tensor:
+        # CPU-to-CPU copy
+        self.gpu = self.pool.copy_to_uva(self.np[:n] if n is not None else self.np)
+        return self.gpu
+
+
+class StagedWriteTensor:
+    def __init__(
+        self,
+        size: int | Sequence[int],
+        dtype: torch.dtype,
+        device: torch.device,
+        max_concurrency: int = 2,
+        uva_instead_of_gpu: bool = False,
+    ):
+        supported_dtypes = [torch.int32, torch.int64, torch.float32]
+        if dtype not in supported_dtypes:
+            raise ValueError(
+                f"Unsupported dtype {dtype}: should be one of {supported_dtypes}"
+            )
+        self.num_rows = size if isinstance(size, int) else size[0]
+        self.dtype = dtype
+        self.device = device
+        self.max_concurrency = max_concurrency
+
+        if not uva_instead_of_gpu:
+            # Create a GPU tensor (default)
+            self.gpu = torch.zeros(size, dtype=dtype, device=device)
+        else:
+            # For a large but not-frequently-accessed tensor, we can use UVA instead of
+            # GPU to save GPU memory
+            self._uva_buf = UvaBuffer(size, dtype)
+            self.gpu = self._uva_buf.uva
+
+        self._staged_write_indices: list[int] = []
+        self._staged_write_starts: list[int] = []
+        self._staged_write_contents: list[int | float] = []
+        self._staged_write_cu_lens: list[int] = []
+
+        new_buffer = partial(UvaBufferPool, max_concurrency=max_concurrency)
+
+        self.write_indices = new_buffer(self.num_rows, dtype=torch.int32)
+        self.write_starts = new_buffer(self.num_rows, dtype=torch.int32)
+        self.write_cu_lens = new_buffer(self.num_rows, dtype=torch.int32)
+
+    def stage_write(
+        self, index: int, start: int, x: Iterable[int] | Iterable[float]
+    ) -> None:
+        assert index >= 0
+        assert start >= 0
+        if not x:
+            return
+        self._staged_write_indices.append(index)
+        self._staged_write_starts.append(start)
+        self._staged_write_contents.extend(x)
+        self._staged_write_cu_lens.append(len(self._staged_write_contents))
+
+    def stage_write_elem(self, index: int, x: int) -> None:
+        assert index >= 0
+        self._staged_write_indices.append(index)
+        self._staged_write_starts.append(0)
+        self._staged_write_contents.append(x)
+        self._staged_write_cu_lens.append(len(self._staged_write_contents))
+
+    def apply_write(self) -> None:
+        n = len(self._staged_write_indices)
+        if n == 0:
+            return
+
+        indices_uva = self.write_indices.copy_to_uva(self._staged_write_indices)
+        starts_uva = self.write_starts.copy_to_uva(self._staged_write_starts)
+        cu_lens_uva = self.write_cu_lens.copy_to_uva(self._staged_write_cu_lens)
+
+        # Special handling for write_contents
+        write_contents = async_tensor_h2d(
+            self._staged_write_contents, self.dtype, self.device, pin_memory=True
+        )
+
+        # Write diffs to the GPU buffer
+        _apply_write_kernel[(n,)](
+            self.gpu,
+            self.gpu.stride(0),
+            indices_uva,
+            starts_uva,
+            write_contents,
+            cu_lens_uva,
+            BLOCK_SIZE=1024,
+        )
+        # Clear the staged writes
+        self.clear_staged_writes()
+
+    def clear_staged_writes(self) -> None:
+        self._staged_write_indices.clear()
+        self._staged_write_starts.clear()
+        self._staged_write_contents.clear()
+        self._staged_write_cu_lens.clear()
+
+
+@triton.jit
+def _apply_write_kernel(
+    output_ptr,
+    output_stride,
+    write_indices_ptr,
+    write_starts_ptr,
+    write_contents_ptr,
+    write_cu_lens_ptr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    row_idx = tl.load(write_indices_ptr + pid)
+    start_idx = tl.load(write_starts_ptr + pid)
+
+    cu_start = tl.load(write_cu_lens_ptr + pid - 1) if pid > 0 else 0
+    cu_end = tl.load(write_cu_lens_ptr + pid)
+    content_len = cu_end - cu_start
+
+    for i in range(0, content_len, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < content_len
+        content = tl.load(write_contents_ptr + cu_start + block, mask=mask)
+        tl.store(
+            output_ptr + row_idx * output_stride + start_idx + block, content, mask=mask
+        )

vllm/v1/worker/gpu/cp_utils.py

@@ -0,0 +1,61 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+from vllm.triton_utils import tl, triton
+
+
+def prepare_dcp_local_seq_lens(
+    dcp_local_seq_lens: torch.Tensor,
+    seq_lens: torch.Tensor,
+    num_reqs: int,
+    dcp_size: int,
+    dcp_rank: int,
+    cp_interleave: int,
+) -> None:
+    """Populate the persistent DCP local seq_lens buffer (CUDA graph safe)."""
+    if dcp_size == 1:
+        return
+
+    max_num_reqs = dcp_local_seq_lens.shape[0]
+    BLOCK_SIZE = 128
+    num_blocks = triton.cdiv(max_num_reqs, BLOCK_SIZE)
+    _dcp_local_seq_lens_kernel[(num_blocks,)](
+        dcp_local_seq_lens,
+        seq_lens,
+        dcp_size,
+        dcp_rank,
+        cp_interleave,
+        num_reqs,
+        max_num_reqs,
+        BLOCK_SIZE,
+    )
+
+
+@triton.jit
+def _dcp_local_seq_lens_kernel(
+    out_ptr,
+    seq_lens_ptr,
+    dcp_size,
+    dcp_rank,
+    cp_interleave,
+    num_reqs,
+    max_num_reqs,
+    BLOCK_SIZE: tl.constexpr,
+):
+    pid = tl.program_id(0)
+    block = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+
+    seq_lens = tl.load(seq_lens_ptr + block, mask=block < num_reqs)
+
+    # Distribute KV cache among different ranks, in a round-robin manner.
+    rounds = seq_lens // (dcp_size * cp_interleave)
+    remainder = seq_lens % (dcp_size * cp_interleave)
+
+    remainder = tl.maximum(remainder - dcp_rank * cp_interleave, 0)
+    remainder = tl.minimum(remainder, cp_interleave)
+    local_seq_lens = rounds * cp_interleave + remainder
+
+    # For [num_reqs, max_num_reqs), pad with 0
+    local_seq_lens = tl.where(block < num_reqs, local_seq_lens, 0)
+    tl.store(out_ptr + block, local_seq_lens, mask=block < max_num_reqs)

vllm/v1/worker/gpu/cudagraph_utils.py

@@ -0,0 +1,462 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Callable
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn as nn
+from tqdm import tqdm
+
+from vllm.config import VllmConfig
+from vllm.config.compilation import CUDAGraphMode
+from vllm.distributed.parallel_state import graph_capture, is_global_first_rank
+from vllm.forward_context import BatchDescriptor, set_forward_context
+from vllm.model_executor.offloader.base import get_offloader
+from vllm.utils.math_utils import cdiv
+from vllm.v1.kv_cache_interface import KVCacheConfig
+from vllm.v1.worker.gpu.attn_utils import (
+    build_attn_metadata,
+    build_slot_mappings_by_layer,
+)
+from vllm.v1.worker.gpu.block_table import BlockTables
+from vllm.v1.worker.gpu.dp_utils import make_num_tokens_across_dp
+from vllm.v1.worker.gpu.input_batch import InputBuffers
+from vllm.v1.worker.utils import AttentionGroup
+
+
+class CudaGraphManager:
+    def __init__(
+        self,
+        vllm_config: VllmConfig,
+        uses_mrope: bool,
+        use_aux_hidden_state_outputs: bool,
+        device: torch.device,
+    ):
+        self.vllm_config = vllm_config
+        self.scheduler_config = vllm_config.scheduler_config
+        self.uses_mrope = uses_mrope
+        self.use_aux_hidden_state_outputs = use_aux_hidden_state_outputs
+        self.device = device
+
+        self.max_model_len = vllm_config.model_config.max_model_len
+        self.max_num_reqs = self.scheduler_config.max_num_seqs
+        self.max_num_tokens = self.scheduler_config.max_num_batched_tokens
+        self.dp_size = vllm_config.parallel_config.data_parallel_size
+
+        self.uniform_decode_query_len = 1
+        spec_config = vllm_config.speculative_config
+        if spec_config is not None:
+            self.uniform_decode_query_len += spec_config.num_speculative_tokens
+
+        self.compilation_config = vllm_config.compilation_config
+        assert self.compilation_config is not None
+        self.cudagraph_mode = self.compilation_config.cudagraph_mode
+
+        use_uniform_decode_cudagraph = (
+            self.cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
+            and self.cudagraph_mode.separate_routine()
+        )
+        self.cudagraph_sizes, self.uniform_decode_cudagraph_sizes = get_cudagraph_sizes(
+            self.compilation_config.cudagraph_capture_sizes,
+            self.max_num_reqs,
+            self.max_num_tokens,
+            self.cudagraph_mode,
+            self.uniform_decode_query_len,
+            use_uniform_decode_cudagraph,
+        )
+
+        self.graphs: dict[int, torch.cuda.CUDAGraph] = {}
+        self.pool = None
+        if self.cudagraph_mode != CUDAGraphMode.NONE:
+            self.pool = torch.cuda.graph_pool_handle()
+        self.hidden_states: torch.Tensor | None = None
+        self.aux_hidden_states: list[torch.Tensor] = []
+
+    def needs_capture(self) -> bool:
+        return len(self.cudagraph_sizes) > 0
+
+    def get_cudagraph_size(
+        self, num_tokens: int, uniform_decode: bool = False
+    ) -> int | None:
+        if uniform_decode and self.uniform_decode_cudagraph_sizes:
+            return self.uniform_decode_cudagraph_sizes.get(num_tokens)
+        return self.cudagraph_sizes.get(num_tokens)
+
+    def capture_graph(
+        self,
+        num_tokens: int,
+        capture_cg_mode: CUDAGraphMode,
+        model: nn.Module,
+        input_buffers: InputBuffers,
+        mrope_positions: torch.Tensor | None,
+        inputs_embeds: torch.Tensor | None,
+        block_tables: BlockTables,
+        attn_groups: list[list[AttentionGroup]],
+        kv_cache_config: KVCacheConfig,
+        has_lora: bool = False,
+        uniform_decode: bool = False,
+    ) -> None:
+        # select and check capture function
+        assert capture_cg_mode in [CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL], (
+            f"Invalid capture_cudagraph_mode for capture: {capture_cg_mode}"
+        )
+        if capture_cg_mode == CUDAGraphMode.PIECEWISE:
+            capture_fn = self._capture_piecewise_graph
+        else:
+            capture_fn = self._capture_full_graph
+        # prepare inputs
+        if uniform_decode:
+            num_reqs = min(
+                cdiv(num_tokens, self.uniform_decode_query_len),
+                self.max_num_reqs,
+            )
+        else:
+            num_reqs = min(num_tokens, self.max_num_reqs)
+        input_ids = input_buffers.input_ids[:num_tokens]
+        positions = input_buffers.positions[:num_tokens]
+        if self.uses_mrope:
+            assert mrope_positions is not None
+            positions = mrope_positions[:, :num_tokens]
+        if inputs_embeds is not None:
+            inputs_embeds = inputs_embeds[:num_tokens]
+        attn_metadata, slot_mappings = prepare_inputs_to_capture(
+            num_reqs,
+            num_tokens,
+            input_buffers,
+            block_tables,
+            attn_groups,
+            self.max_model_len,
+            kv_cache_config,
+            uniform_decode_query_len=(
+                self.uniform_decode_query_len if uniform_decode else 0
+            ),
+        )
+        num_tokens_across_dp = make_num_tokens_across_dp(self.dp_size, num_tokens)
+
+        # Warm up.
+        with set_forward_context(
+            attn_metadata,
+            self.vllm_config,
+            num_tokens=num_tokens,
+            cudagraph_runtime_mode=CUDAGraphMode.NONE,
+            num_tokens_across_dp=num_tokens_across_dp,
+            slot_mapping=slot_mappings,
+        ):
+            model_output = model(
+                input_ids=input_ids,
+                positions=positions,
+                inputs_embeds=inputs_embeds,
+            )
+            if self.use_aux_hidden_state_outputs:
+                hidden_states, aux_hidden_states = model_output
+            else:
+                hidden_states = model_output
+                aux_hidden_states = None
+
+        # Allocate output buffers if not already done.
+        if self.hidden_states is None:
+            self.hidden_states = torch.empty_like(hidden_states)
+        if self.use_aux_hidden_state_outputs and not self.aux_hidden_states:
+            self.aux_hidden_states = [torch.empty_like(x) for x in aux_hidden_states]
+
+        capture_fn(
+            num_tokens=num_tokens,
+            num_reqs=num_reqs,
+            model=model,
+            input_ids=input_ids,
+            positions=positions,
+            inputs_embeds=inputs_embeds,
+            num_tokens_across_dp=num_tokens_across_dp,
+            attn_metadata=attn_metadata,
+            slot_mappings=slot_mappings,
+            has_lora=has_lora,
+        )
+
+    def _capture_full_graph(
+        self,
+        num_tokens: int,
+        num_reqs: int,
+        model: nn.Module,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        inputs_embeds: torch.Tensor | None,
+        num_tokens_across_dp: torch.Tensor,
+        attn_metadata: dict[str, Any] | None,
+        slot_mappings: dict[str, torch.Tensor] | None,
+        has_lora: bool = False,
+    ) -> None:
+        assert attn_metadata is not None
+        # Capture the graph.
+        assert num_tokens not in self.graphs
+        graph = torch.cuda.CUDAGraph()
+
+        # Sync offloader's copy stream before capture.
+        # Ensure any pre-capture prefetches from offloader are complete.
+        get_offloader().sync_prev_onload()
+
+        with (
+            set_forward_context(
+                attn_metadata=attn_metadata,
+                vllm_config=self.vllm_config,
+                num_tokens=num_tokens,
+                cudagraph_runtime_mode=CUDAGraphMode.NONE,
+                num_tokens_across_dp=num_tokens_across_dp,
+                slot_mapping=slot_mappings,
+            ),
+            torch.cuda.graph(graph, self.pool),
+        ):
+            model_output = model(
+                input_ids=input_ids,
+                positions=positions,
+                inputs_embeds=inputs_embeds,
+            )
+            # Join offloader's copy stream after forward to avoid unjoined
+            # stream error. The last layer's start_prefetch forks copy_stream,
+            # but wait_prefetch only happens in the next forward pass.
+            get_offloader().join_after_forward()
+
+            if self.use_aux_hidden_state_outputs:
+                hidden_states, aux_hidden_states = model_output
+            else:
+                hidden_states = model_output
+                aux_hidden_states = None
+
+            # Copy outputs to the output buffers.
+            assert self.hidden_states is not None
+            self.hidden_states[:num_tokens] = hidden_states
+            if self.use_aux_hidden_state_outputs:
+                for i, aux_hidden in enumerate(aux_hidden_states):
+                    self.aux_hidden_states[i][:num_tokens] = aux_hidden
+        self.graphs[num_tokens] = graph
+
+    def _capture_piecewise_graph(
+        self,
+        num_tokens: int,
+        num_reqs: int,
+        model: nn.Module,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        inputs_embeds: torch.Tensor | None,
+        num_tokens_across_dp: torch.Tensor,
+        attn_metadata: dict[str, Any] | None,
+        slot_mappings: dict[str, torch.Tensor] | None,
+        has_lora: bool = False,
+    ) -> None:
+        # create batch descriptor for piecewise cudagraph dispatch key
+        batch_descriptor = BatchDescriptor(num_tokens=num_tokens, has_lora=has_lora)
+
+        # Capture run - CUDAGraphWrapper inside torch.compile will auto capture.
+        with set_forward_context(
+            attn_metadata=None,  # piecewise no need attn_metadata
+            vllm_config=self.vllm_config,
+            num_tokens=num_tokens,
+            cudagraph_runtime_mode=CUDAGraphMode.PIECEWISE,
+            num_tokens_across_dp=num_tokens_across_dp,
+            batch_descriptor=batch_descriptor,
+            slot_mapping=slot_mappings,
+        ):
+            model(
+                input_ids=input_ids,
+                positions=positions,
+                inputs_embeds=inputs_embeds,
+            )
+
+    @torch.inference_mode()
+    def capture(
+        self,
+        model: nn.Module,
+        input_buffers: InputBuffers,
+        mrope_positions: torch.Tensor | None,
+        inputs_embeds: torch.Tensor | None,
+        block_tables: BlockTables,
+        attn_groups: list[list[AttentionGroup]],
+        kv_cache_config: KVCacheConfig,
+        has_lora: bool = False,
+    ) -> None:
+        common_kwargs = dict(
+            device=self.device,
+            capture_fn=self.capture_graph,
+            model=model,
+            input_buffers=input_buffers,
+            mrope_positions=mrope_positions,
+            inputs_embeds=inputs_embeds,
+            block_tables=block_tables,
+            attn_groups=attn_groups,
+            kv_cache_config=kv_cache_config,
+            has_lora=has_lora,
+        )
+
+        # Phase 1: Capture for mixed prefill-decode batches if needed.
+        mixed_mode = self.cudagraph_mode.mixed_mode()
+        if mixed_mode != CUDAGraphMode.NONE:
+            capture_graphs(
+                cudagraph_sizes=self.cudagraph_sizes,
+                capture_cudagraph_mode=mixed_mode,
+                desc=f"Capturing CUDA graphs (mixed, {mixed_mode.name})",
+                uniform_decode=False,
+                **common_kwargs,
+            )
+
+        # Phase 2: Capture FULL graphs for uniform decode batches if needed.
+        # This is only needed if we use a separate routine for decode batches
+        # and the decode_mode is FULL.
+        if self.uniform_decode_cudagraph_sizes:
+            capture_graphs(
+                cudagraph_sizes=self.uniform_decode_cudagraph_sizes,
+                capture_cudagraph_mode=CUDAGraphMode.FULL,
+                desc="Capturing CUDA graphs (decode, FULL)",
+                uniform_decode=True,
+                **common_kwargs,
+            )
+
+    def get_cudagraph_runtime_mode(
+        self, num_reqs: int, num_tokens: int, max_query_len: int
+    ) -> tuple[CUDAGraphMode, int | None]:
+        is_uniform_decode = (max_query_len == self.uniform_decode_query_len) and (
+            num_tokens == max_query_len * num_reqs
+        )
+
+        cudagraph_size = self.get_cudagraph_size(num_tokens, is_uniform_decode)
+        if cudagraph_size is None:
+            cudagraph_mode = CUDAGraphMode.NONE
+        elif is_uniform_decode:
+            cudagraph_mode = self.cudagraph_mode.decode_mode()
+        else:
+            cudagraph_mode = self.cudagraph_mode.mixed_mode()
+
+        if (
+            cudagraph_mode == CUDAGraphMode.FULL
+            and cudagraph_size is not None
+            and cudagraph_size not in self.graphs
+        ):
+            # If graph wasn't captured yet, fall back to eager.
+            # This might happen when the dummy run is called before capture.
+            cudagraph_mode = CUDAGraphMode.NONE
+            cudagraph_size = None
+        return cudagraph_mode, cudagraph_size
+
+    def run_fullgraph(
+        self, num_tokens: int
+    ) -> torch.Tensor | tuple[torch.Tensor, list[torch.Tensor]]:
+        assert num_tokens in self.graphs, f"No cudagraph for {num_tokens} tokens"
+        # Sync offloader before replay - needed when transitioning from
+        # eager/piecewise to full cudagraph (e.g., prefill → decode).
+        # The previous eager iteration's start_prefetch may have queued
+        # H2D copies on copy_stream that the graph's captured events
+        # cannot see. Without this, replay could overwrite static buffers
+        # while those copies are still in flight.
+        get_offloader().sync_prev_onload()
+        self.graphs[num_tokens].replay()
+        assert self.hidden_states is not None
+        hidden_states = self.hidden_states[:num_tokens]
+        if not self.use_aux_hidden_state_outputs:
+            return hidden_states
+        return hidden_states, [x[:num_tokens] for x in self.aux_hidden_states]
+
+
+def get_cudagraph_sizes(
+    capture_sizes: list[int] | None,
+    max_num_reqs: int,
+    max_num_tokens: int,
+    cudagraph_mode: CUDAGraphMode,
+    uniform_decode_query_len: int = 1,
+    uniform_decode_cudagraph: bool = False,
+) -> tuple[dict[int, int], dict[int, int]]:
+    # Support both FULL and PIECEWISE cudagraph modes
+    if cudagraph_mode == CUDAGraphMode.NONE:
+        return {}, {}
+    if not capture_sizes:
+        return {}, {}
+
+    capture_sizes = sorted(capture_sizes)
+    if not capture_sizes:
+        return {}, {}
+
+    cudagraph_sizes: dict[int, int] = {}
+    for i in range(1, capture_sizes[-1] + 1):
+        for x in capture_sizes:
+            if i <= x:
+                cudagraph_sizes[i] = x
+                break
+
+    uniform_decode_cudagraph_sizes: dict[int, int] = {}
+    if uniform_decode_cudagraph:
+        max_num_tokens = max_num_reqs * uniform_decode_query_len
+        uniform_decode_cudagraph_sizes = {
+            k: v
+            for k, v in cudagraph_sizes.items()
+            if v <= max_num_tokens and v >= uniform_decode_query_len
+        }
+    return cudagraph_sizes, uniform_decode_cudagraph_sizes
+
+
+def capture_graphs(
+    cudagraph_sizes: dict[int, int],
+    device: torch.device,
+    capture_fn: Callable,
+    capture_cudagraph_mode: CUDAGraphMode,
+    desc: str = "Capturing CUDA graphs",
+    **capture_kwargs,
+) -> None:
+    # Capture larger graphs first.
+    sizes_to_capture = sorted(set(cudagraph_sizes.values()), reverse=True)
+    if is_global_first_rank():
+        sizes_to_capture = tqdm(sizes_to_capture, desc=desc)
+
+    with graph_capture(device=device):
+        for size in sizes_to_capture:
+            capture_fn(size, capture_cudagraph_mode, **capture_kwargs)
+
+
+def prepare_inputs_to_capture(
+    num_reqs: int,
+    num_tokens: int,
+    input_buffers: InputBuffers,
+    block_tables: BlockTables,
+    attn_groups: list[list[AttentionGroup]],
+    max_model_len: int,
+    kv_cache_config: KVCacheConfig,
+    uniform_decode_query_len: int = 0,
+) -> tuple[dict[str, Any], dict[str, torch.Tensor]]:
+    if uniform_decode_query_len > 0:
+        num_tokens_per_req = uniform_decode_query_len
+    else:
+        num_tokens_per_req = num_tokens // num_reqs
+
+    query_start_loc_np = np.arange(num_reqs + 1, dtype=np.int32) * num_tokens_per_req
+    query_start_loc_np[-1] = num_tokens
+    query_start_loc_cpu = torch.from_numpy(query_start_loc_np)
+    input_buffers.query_start_loc[: num_reqs + 1] = query_start_loc_cpu
+    input_buffers.query_start_loc[num_reqs + 1 :] = num_tokens
+    query_start_loc = input_buffers.query_start_loc[: num_reqs + 1]
+
+    # HACK(woosuk): For faster warmup, we set seq_lens (GPU) to num_tokens
+    # rather than max_model_len.
+    input_buffers.seq_lens[:num_reqs] = num_tokens
+    input_buffers.seq_lens[num_reqs:] = 0
+
+    input_buffers.dcp_local_seq_lens[:num_reqs] = num_tokens
+    input_buffers.dcp_local_seq_lens[num_reqs:] = 0
+
+    input_block_tables = [x[:num_reqs] for x in block_tables.input_block_tables]
+    slot_mappings = block_tables.slot_mappings[:, :num_tokens]
+    slot_mappings_by_layer = build_slot_mappings_by_layer(
+        slot_mappings, kv_cache_config
+    )
+
+    attn_metadata = build_attn_metadata(
+        attn_groups=attn_groups,
+        num_reqs=num_reqs,
+        num_tokens=num_tokens,
+        query_start_loc_gpu=query_start_loc,
+        query_start_loc_cpu=query_start_loc_cpu,
+        max_query_len=num_tokens_per_req,
+        seq_lens=input_buffers.seq_lens,
+        max_seq_len=max_model_len,
+        block_tables=input_block_tables,
+        slot_mappings=slot_mappings,
+        kv_cache_config=kv_cache_config,
+        dcp_local_seq_lens=input_buffers.dcp_local_seq_lens,
+    )
+    return attn_metadata, slot_mappings_by_layer

vllm/v1/worker/gpu/dp_utils.py

@@ -0,0 +1,77 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+import torch.distributed as dist
+
+from vllm.distributed.parallel_state import get_dp_group
+
+
+def make_num_tokens_across_dp(dp_size: int, num_tokens: int) -> torch.Tensor | None:
+    if dp_size == 1:
+        return None
+    return torch.full((dp_size,), num_tokens, dtype=torch.int32, device="cpu")
+
+
+def get_batch_metadata_across_dp(
+    num_tokens: int,
+    cudagraph_size: int,
+    cudagraph_runtime_mode: int,
+    dp_size: int,
+    dp_rank: int,
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    assert dp_size > 1
+    # Use CPU group to avoid CPU-GPU synchronization.
+    group = get_dp_group().cpu_group
+    tensor = torch.zeros(3, dp_size, dtype=torch.int32, device="cpu")
+    tensor[0][dp_rank] = num_tokens
+    tensor[1][dp_rank] = cudagraph_size
+    tensor[2][dp_rank] = cudagraph_runtime_mode
+    dist.all_reduce(tensor, group=group)
+    return tensor[0], tensor[1], tensor[2]
+
+
+def get_cudagraph_and_dp_padding(
+    num_tokens: int,
+    cudagraph_size: int | None,
+    cudagraph_runtime_mode: int,
+    dp_size: int,
+    dp_rank: int,
+) -> tuple[int, torch.Tensor | None, int]:
+    if dp_size == 1:
+        if cudagraph_size is not None:
+            return cudagraph_size, None, cudagraph_runtime_mode
+        else:
+            return num_tokens, None, cudagraph_runtime_mode
+
+    # Convert None to -1 for sync (indicates no cudagraph available)
+    if num_tokens == 0:
+        cudagraph_size = 0
+    elif cudagraph_size is None:
+        cudagraph_size = -1
+
+    num_tokens_across_dp, cudagraph_size_across_dp, cudagraph_mode_across_dp = (
+        get_batch_metadata_across_dp(
+            num_tokens, cudagraph_size, cudagraph_runtime_mode, dp_size, dp_rank
+        )
+    )
+    if torch.all(num_tokens_across_dp == 0).item():
+        # All ranks have zero tokens to run.
+        return 0, None, 0
+
+    # Synchronize cudagraph_runtime_mode across ranks by taking the minimum.
+    synced_cudagraph_mode = int(cudagraph_mode_across_dp.min().item())
+    # Check if all ranks have valid cudagraph_size.
+    all_have_cudagraph = torch.all(cudagraph_size_across_dp != -1).item()
+
+    if synced_cudagraph_mode != 0 and all_have_cudagraph:
+        # All ranks use cudagraph. Pad to max cudagraph_size.
+        max_cudagraph_size = int(cudagraph_size_across_dp.max().item())
+        num_tokens_across_dp[:] = max_cudagraph_size
+        return max_cudagraph_size, num_tokens_across_dp, synced_cudagraph_mode
+    else:
+        # Fall back to eager mode (no cudagraph).
+        # Either some rank doesn't have cudagraph size or mode is NONE.
+        synced_cudagraph_mode = 0
+        num_tokens_across_dp = torch.clamp(num_tokens_across_dp, min=1)
+        num_tokens_after_padding = int(num_tokens_across_dp[dp_rank].item())
+        return num_tokens_after_padding, num_tokens_across_dp, synced_cudagraph_mode

vllm/v1/worker/gpu/input_batch.py

@@ -0,0 +1,548 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+from typing import Any
+
+import numpy as np
+import torch
+
+from vllm.triton_utils import tl, triton
+from vllm.utils import random_uuid
+
+
+class InputBuffers:
+    def __init__(
+        self,
+        max_num_reqs: int,
+        max_num_tokens: int,
+        device: torch.device,
+    ):
+        self.max_num_reqs = max_num_reqs
+        self.max_num_tokens = max_num_tokens
+        self.device = device
+
+        self.input_ids = torch.zeros(max_num_tokens, dtype=torch.int32, device=device)
+        self.positions = torch.zeros(max_num_tokens, dtype=torch.int64, device=device)
+        self.query_start_loc = torch.zeros(
+            max_num_reqs + 1, dtype=torch.int32, device=device
+        )
+        self.seq_lens = torch.zeros(max_num_reqs, dtype=torch.int32, device=device)
+        # DCP: per-request local seq_lens buffer
+        self.dcp_local_seq_lens = torch.zeros(
+            max_num_reqs, dtype=torch.int32, device=device
+        )
+
+
+@dataclass
+class InputBatch:
+    # batch_idx -> req_id
+    req_ids: list[str]
+    num_reqs: int
+
+    # batch_idx -> req_state_idx
+    idx_mapping: torch.Tensor
+    idx_mapping_np: np.ndarray
+    # Identical to idx_mapping except for spec decoding.
+    expanded_idx_mapping: torch.Tensor
+    # [total_num_logits] position within request for each logit
+    expanded_local_pos: torch.Tensor
+
+    # [num_reqs]
+    # batch_idx -> num_scheduled_tokens
+    num_scheduled_tokens: np.ndarray
+    # sum(num_scheduled_tokens)
+    num_tokens: int
+    num_tokens_after_padding: int
+    num_draft_tokens: int
+
+    # [num_reqs + 1]
+    query_start_loc: torch.Tensor
+    query_start_loc_np: np.ndarray
+    # [num_reqs]
+    seq_lens: torch.Tensor
+
+    # [num_tokens_after_padding]
+    input_ids: torch.Tensor
+    # [num_tokens_after_padding]
+    positions: torch.Tensor
+    # [3, num_tokens_after_padding]
+    mrope_positions: torch.Tensor | None
+    # [num_tokens_after_padding, hidden_size]
+    inputs_embeds: torch.Tensor | None
+
+    # layer_name -> Metadata
+    attn_metadata: dict[str, Any]
+    # layer_name -> slot_mapping
+    slot_mappings: dict[str, torch.Tensor]
+
+    # [total_num_logits]
+    logits_indices: torch.Tensor
+    # [num_reqs + 1]
+    cu_num_logits: torch.Tensor
+    cu_num_logits_np: np.ndarray
+
+    # Whether any requests in batch use structured output.
+    has_structured_output_reqs: bool
+
+    @classmethod
+    def make_dummy(
+        cls,
+        num_reqs: int,
+        num_tokens: int,
+        input_buffers: InputBuffers,
+        device: torch.device,
+    ) -> "InputBatch":
+        assert 0 < num_reqs <= num_tokens
+        req_ids = [f"req_{i}_{random_uuid()}" for i in range(num_reqs)]
+        idx_mapping_np = np.arange(num_reqs, dtype=np.int32)
+        idx_mapping = torch.arange(num_reqs, dtype=torch.int32, device=device)
+        expanded_idx_mapping = idx_mapping
+        expanded_local_pos = torch.zeros(num_reqs, dtype=torch.int32, device=device)
+        num_scheduled_tokens = np.full(num_reqs, num_tokens // num_reqs, dtype=np.int32)
+        num_scheduled_tokens[-1] += num_tokens % num_reqs
+        assert int(num_scheduled_tokens.sum()) == num_tokens
+
+        # seq_len equals to query_len
+        input_buffers.seq_lens[:num_reqs] = num_tokens // num_reqs
+        input_buffers.seq_lens[num_reqs - 1] += num_tokens % num_reqs
+        # Pad for full CUDA graph mode.
+        input_buffers.seq_lens[num_reqs:] = 0
+        seq_lens = input_buffers.seq_lens[:num_reqs]
+
+        query_start_loc_np = np.empty(num_reqs + 1, dtype=np.int32)
+        query_start_loc_np[0] = 0
+        np.cumsum(num_scheduled_tokens, out=query_start_loc_np[1:])
+        input_buffers.query_start_loc[:1] = 0
+        torch.cumsum(
+            seq_lens, dim=0, out=input_buffers.query_start_loc[1 : num_reqs + 1]
+        )
+        # Pad for full CUDA graph mode.
+        input_buffers.query_start_loc[num_reqs + 1 :] = num_tokens
+        query_start_loc = input_buffers.query_start_loc[: num_reqs + 1]
+
+        input_ids = input_buffers.input_ids[:num_tokens].zero_()
+        positions = input_buffers.positions[:num_tokens].zero_()
+
+        # attn_metadata = defaultdict(lambda: None)
+        logits_indices = query_start_loc[1:] - 1
+        cu_num_logits = torch.arange(num_reqs + 1, device=device, dtype=torch.int32)
+        cu_num_logits_np = np.arange(num_reqs + 1, dtype=np.int32)
+        return cls(
+            req_ids=req_ids,
+            num_reqs=num_reqs,
+            idx_mapping=idx_mapping,
+            idx_mapping_np=idx_mapping_np,
+            expanded_idx_mapping=expanded_idx_mapping,
+            expanded_local_pos=expanded_local_pos,
+            num_scheduled_tokens=num_scheduled_tokens,
+            num_tokens=num_tokens,
+            num_tokens_after_padding=num_tokens,
+            num_draft_tokens=0,
+            query_start_loc=query_start_loc,
+            query_start_loc_np=query_start_loc_np,
+            seq_lens=seq_lens,
+            input_ids=input_ids,
+            positions=positions,
+            mrope_positions=None,
+            inputs_embeds=None,
+            attn_metadata=None,  # type: ignore
+            slot_mappings=None,  # type: ignore
+            logits_indices=logits_indices,
+            cu_num_logits=cu_num_logits,
+            cu_num_logits_np=cu_num_logits_np,
+            has_structured_output_reqs=False,
+        )
+
+
+@triton.jit
+def _prepare_prefill_inputs_kernel(
+    input_ids_ptr,
+    next_prefill_tokens_ptr,
+    idx_mapping_ptr,
+    query_start_loc_ptr,
+    all_token_ids_ptr,
+    all_token_ids_stride,
+    prefill_lens_ptr,
+    num_computed_tokens_ptr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
+    prefill_len = tl.load(prefill_lens_ptr + req_state_idx)
+    num_computed = tl.load(num_computed_tokens_ptr + req_state_idx)
+    if num_computed >= prefill_len:
+        # Not prefill.
+        return
+
+    query_start = tl.load(query_start_loc_ptr + batch_idx)
+    query_end = tl.load(query_start_loc_ptr + batch_idx + 1)
+    query_len = query_end - query_start
+
+    request_ptr = all_token_ids_ptr + req_state_idx * all_token_ids_stride
+    for i in range(0, query_len, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < query_len
+        tokens = tl.load(request_ptr + num_computed + block, mask=mask)
+        tl.store(input_ids_ptr + query_start + block, tokens, mask=mask)
+
+    next_pos = num_computed + query_len
+    if next_pos < prefill_len:
+        next_token = tl.load(request_ptr + next_pos)
+        tl.store(next_prefill_tokens_ptr + req_state_idx, next_token)
+
+
+def prepare_prefill_inputs(
+    input_ids: torch.Tensor,
+    next_prefill_tokens: torch.Tensor,
+    idx_mapping: torch.Tensor,
+    query_start_loc: torch.Tensor,
+    all_token_ids: torch.Tensor,
+    prefill_len: torch.Tensor,
+    num_computed_tokens: torch.Tensor,
+) -> None:
+    num_reqs = idx_mapping.shape[0]
+    _prepare_prefill_inputs_kernel[(num_reqs,)](
+        input_ids,
+        next_prefill_tokens,
+        idx_mapping,
+        query_start_loc,
+        all_token_ids,
+        all_token_ids.stride(0),
+        prefill_len,
+        num_computed_tokens,
+        BLOCK_SIZE=1024,
+    )
+
+
+@triton.jit
+def _prepare_pos_seq_lens_kernel(
+    pos_ptr,
+    seq_lens_ptr,
+    idx_mapping_ptr,
+    query_start_loc_ptr,
+    num_computed_tokens_ptr,
+    max_num_reqs,
+    BLOCK_SIZE: tl.constexpr,
+):
+    req_id = tl.program_id(0)
+    num_reqs = tl.num_programs(0) - 1
+    if req_id == num_reqs:
+        # Pad unused seq_lens as 0 for full CUDA graphs.
+        for i in tl.range(num_reqs, max_num_reqs, BLOCK_SIZE):
+            block = i + tl.arange(0, BLOCK_SIZE)
+            mask = block < max_num_reqs
+            tl.store(seq_lens_ptr + block, 0, mask=mask)
+        return
+
+    req_state_idx = tl.load(idx_mapping_ptr + req_id)
+    num_computed_tokens = tl.load(num_computed_tokens_ptr + req_state_idx)
+
+    start = tl.load(query_start_loc_ptr + req_id)
+    end = tl.load(query_start_loc_ptr + req_id + 1)
+    query_len = end - start
+
+    seq_len = num_computed_tokens + query_len
+    tl.store(seq_lens_ptr + req_id, seq_len)
+
+    for i in tl.range(0, query_len, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < query_len
+        pos = num_computed_tokens + block
+        tl.store(pos_ptr + start + block, pos, mask=mask)
+
+
+def prepare_pos_seq_lens(
+    idx_mapping: torch.Tensor,
+    query_start_loc: torch.Tensor,
+    num_computed_tokens: torch.Tensor,
+    pos: torch.Tensor,
+    seq_lens: torch.Tensor,
+) -> None:
+    num_reqs = idx_mapping.shape[0]
+    # NOTE(woosuk): We do +1 because the last thread block is used
+    # to pad unused seq_lens as 0 for full CUDA graphs.
+    _prepare_pos_seq_lens_kernel[(num_reqs + 1,)](
+        pos,
+        seq_lens,
+        idx_mapping,
+        query_start_loc,
+        num_computed_tokens,
+        seq_lens.shape[0],
+        BLOCK_SIZE=1024,
+    )
+
+
+@triton.jit
+def _combine_sampled_and_draft_tokens_kernel(
+    input_ids_ptr,
+    idx_mapping_ptr,
+    last_sampled_tokens_ptr,
+    query_start_loc_ptr,
+    seq_lens_ptr,
+    prefill_len_ptr,
+    draft_tokens_ptr,
+    draft_tokens_stride,
+    cu_num_logits_ptr,
+    logits_indices_ptr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
+
+    # Get the number of logits and draft tokens.
+    cu_num_logits_start = tl.load(cu_num_logits_ptr + batch_idx)
+    cu_num_logits_end = tl.load(cu_num_logits_ptr + batch_idx + 1)
+    num_logits = cu_num_logits_end - cu_num_logits_start
+    num_draft_tokens = num_logits - 1
+
+    # Compute the logits indices.
+    block = tl.arange(0, BLOCK_SIZE)
+    query_end = tl.load(query_start_loc_ptr + batch_idx + 1)
+    logits_start = query_end - num_logits
+    tl.store(
+        logits_indices_ptr + cu_num_logits_start + block,
+        logits_start + block,
+        mask=block < num_logits,
+    )
+
+    seq_len = tl.load(seq_lens_ptr + batch_idx)
+    prefill_len = tl.load(prefill_len_ptr + req_state_idx)
+    if seq_len <= prefill_len:
+        # Handling prefill tokens. No sampled or draft tokens.
+        return
+
+    # Write the last sampled token ID to input_ids.
+    last_token_id = tl.load(last_sampled_tokens_ptr + req_state_idx)
+    tl.store(input_ids_ptr + query_end - num_logits, last_token_id)
+
+    # Write the draft tokens (if any) to input_ids.
+    if num_draft_tokens > 0:
+        mask = block < num_draft_tokens
+        draft_tokens = tl.load(
+            draft_tokens_ptr + req_state_idx * draft_tokens_stride + block,
+            mask=mask,
+        )
+        tl.store(
+            input_ids_ptr + query_end - num_draft_tokens + block,
+            draft_tokens,
+            mask=mask,
+        )
+
+
+def combine_sampled_and_draft_tokens(
+    input_ids: torch.Tensor,
+    idx_mapping: torch.Tensor,
+    last_sampled_tokens: torch.Tensor,
+    query_start_loc: torch.Tensor,
+    seq_lens: torch.Tensor,
+    prefill_len: torch.Tensor,
+    draft_tokens: torch.Tensor,
+    cu_num_logits: torch.Tensor,
+    num_logits: int,
+) -> torch.Tensor:
+    num_reqs = seq_lens.shape[0]
+    num_speculative_steps = draft_tokens.shape[-1]
+
+    logits_indices = torch.empty(
+        num_logits,
+        dtype=torch.int64,
+        device=input_ids.device,
+    )
+    _combine_sampled_and_draft_tokens_kernel[(num_reqs,)](
+        input_ids,
+        idx_mapping,
+        last_sampled_tokens,
+        query_start_loc,
+        seq_lens,
+        prefill_len,
+        draft_tokens,
+        draft_tokens.stride(0),
+        cu_num_logits,
+        logits_indices,
+        # NOTE(woosuk): Add 1 to ensure the block can cover the last sampled token
+        # in addition to all draft tokens.
+        BLOCK_SIZE=triton.next_power_of_2(num_speculative_steps + 1),
+    )
+    return logits_indices
+
+
+@triton.jit
+def _get_num_sampled_and_rejected_kernel(
+    num_sampled_ptr,
+    num_rejected_ptr,
+    seq_lens_ptr,
+    cu_num_logits_ptr,
+    idx_mapping_ptr,
+    prefill_len_ptr,
+):
+    batch_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
+
+    seq_len = tl.load(seq_lens_ptr + batch_idx)
+    prefill_len = tl.load(prefill_len_ptr + req_state_idx)
+    is_chunked_prefilling = seq_len < prefill_len
+
+    num_sampled = tl.load(num_sampled_ptr + batch_idx)
+    num_sampled = tl.where(is_chunked_prefilling, 0, num_sampled)
+    tl.store(num_sampled_ptr + batch_idx, num_sampled)
+
+    logits_start = tl.load(cu_num_logits_ptr + batch_idx)
+    logits_end = tl.load(cu_num_logits_ptr + batch_idx + 1)
+    num_logits = logits_end - logits_start
+
+    num_rejected = num_logits - num_sampled
+    num_rejected = tl.where(is_chunked_prefilling, 0, num_rejected)
+    tl.store(num_rejected_ptr + batch_idx, num_rejected)
+
+
+def get_num_sampled_and_rejected(
+    num_sampled: torch.Tensor,
+    seq_lens: torch.Tensor,
+    cu_num_logits: torch.Tensor,
+    idx_mapping: torch.Tensor,
+    prefill_len: torch.Tensor,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    num_reqs = idx_mapping.shape[0]
+    num_rejected = torch.empty_like(num_sampled)
+    _get_num_sampled_and_rejected_kernel[(num_reqs,)](
+        num_sampled,
+        num_rejected,
+        seq_lens,
+        cu_num_logits,
+        idx_mapping,
+        prefill_len,
+    )
+    return num_sampled, num_rejected
+
+
+@triton.jit
+def _post_update_kernel(
+    idx_mapping_ptr,
+    num_computed_tokens_ptr,
+    last_sampled_tokens_ptr,
+    output_bin_counts_ptr,
+    output_bin_counts_stride,
+    sampled_tokens_ptr,
+    sampled_tokens_stride,
+    num_sampled_ptr,
+    num_rejected_ptr,
+    query_start_loc_ptr,
+    all_token_ids_ptr,
+    all_token_ids_stride,
+    total_len_ptr,
+):
+    req_id = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + req_id)
+
+    total_len = tl.load(total_len_ptr + req_state_idx)
+    num_sampled = tl.load(num_sampled_ptr + req_id)
+    if num_sampled > 0:
+        token_id = tl.load(
+            sampled_tokens_ptr + req_id * sampled_tokens_stride + num_sampled - 1
+        )
+        tl.store(last_sampled_tokens_ptr + req_state_idx, token_id)
+        tl.store(total_len_ptr + req_state_idx, total_len + num_sampled)
+
+    for i in range(num_sampled):
+        token_id = tl.load(sampled_tokens_ptr + req_id * sampled_tokens_stride + i)
+        token_ptr = (
+            output_bin_counts_ptr + req_state_idx * output_bin_counts_stride + token_id
+        )
+        count = tl.load(token_ptr)
+        count += 1
+        tl.store(token_ptr, count)
+        tl.store(
+            all_token_ids_ptr + req_state_idx * all_token_ids_stride + total_len + i,
+            token_id,
+        )
+
+    query_start = tl.load(query_start_loc_ptr + req_id)
+    query_end = tl.load(query_start_loc_ptr + req_id + 1)
+    query_len = query_end - query_start
+    num_rejected = tl.load(num_rejected_ptr + req_id)
+
+    num_computed = tl.load(num_computed_tokens_ptr + req_state_idx)
+    num_computed += query_len - num_rejected
+    tl.store(num_computed_tokens_ptr + req_state_idx, num_computed)
+
+
+def post_update(
+    # [num_reqs]
+    idx_mapping: torch.Tensor,
+    # [max_num_reqs]
+    num_computed_tokens: torch.Tensor,
+    # [max_num_reqs]
+    last_sampled_tokens: torch.Tensor,
+    # [max_num_reqs, vocab_size]
+    output_bin_counts: torch.Tensor,
+    # [num_reqs, num_speculative_steps + 1]
+    sampled_tokens: torch.Tensor,
+    # [num_reqs]
+    num_sampled: torch.Tensor,
+    # [num_reqs]
+    num_rejected: torch.Tensor,
+    # [num_reqs + 1]
+    query_start_loc: torch.Tensor,
+    # [max_num_reqs, max_model_len]
+    all_token_ids: torch.Tensor,
+    # [max_num_reqs]
+    total_len: torch.Tensor,
+) -> None:
+    num_reqs = idx_mapping.shape[0]
+    _post_update_kernel[(num_reqs,)](
+        idx_mapping,
+        num_computed_tokens,
+        last_sampled_tokens,
+        output_bin_counts,
+        output_bin_counts.stride(0),
+        sampled_tokens,
+        sampled_tokens.stride(0),
+        num_sampled,
+        num_rejected,
+        query_start_loc,
+        all_token_ids,
+        all_token_ids.stride(0),
+        total_len,
+        num_warps=1,
+    )
+
+
+@triton.jit
+def _expand_idx_mapping_kernel(
+    idx_mapping_ptr,
+    expanded_idx_mapping_ptr,
+    expanded_local_pos_ptr,
+    cu_num_logits_ptr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+    start_idx = tl.load(cu_num_logits_ptr + req_idx)
+    end_idx = tl.load(cu_num_logits_ptr + req_idx + 1)
+    num_tokens = end_idx - start_idx
+
+    block = tl.arange(0, BLOCK_SIZE)
+    mask = block < num_tokens
+    req_state_idx = tl.load(idx_mapping_ptr + req_idx)
+    tl.store(expanded_idx_mapping_ptr + start_idx + block, req_state_idx, mask=mask)
+    tl.store(expanded_local_pos_ptr + start_idx + block, block, mask=mask)
+
+
+def expand_idx_mapping(
+    idx_mapping: torch.Tensor,
+    total_num_logits: int,
+    cu_num_logits: torch.Tensor,
+    max_expand_len: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    num_reqs = idx_mapping.shape[0]
+    expanded_idx_mapping = idx_mapping.new_empty(total_num_logits)
+    expanded_local_pos = torch.empty(
+        total_num_logits, dtype=torch.int32, device=idx_mapping.device
+    )
+    _expand_idx_mapping_kernel[(num_reqs,)](
+        idx_mapping,
+        expanded_idx_mapping,
+        expanded_local_pos,
+        cu_num_logits,
+        BLOCK_SIZE=triton.next_power_of_2(max_expand_len),
+    )
+    return expanded_idx_mapping, expanded_local_pos

vllm/v1/worker/gpu/kv_connector.py

@@ -0,0 +1,134 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import copy
+from typing import TYPE_CHECKING
+
+import torch
+
+from vllm.config import VllmConfig
+from vllm.distributed.kv_transfer import (
+    get_kv_transfer_group,
+    has_kv_transfer_group,
+    kv_transfer_state,
+)
+from vllm.distributed.kv_transfer.kv_connector.utils import copy_kv_blocks
+from vllm.forward_context import (
+    get_forward_context,
+    is_forward_context_available,
+    set_forward_context,
+)
+from vllm.v1.outputs import (
+    EMPTY_MODEL_RUNNER_OUTPUT,
+    KVConnectorOutput,
+    ModelRunnerOutput,
+)
+
+if TYPE_CHECKING:
+    from vllm.v1.core.sched.output import SchedulerOutput
+
+
+class KVConnector:
+    """KVConnector interface used by GPUModelRunner."""
+
+    def pre_forward(self, scheduler_output: "SchedulerOutput") -> None:
+        pass
+
+    def post_forward(
+        self, scheduler_output: "SchedulerOutput", wait_for_save: bool = True
+    ) -> KVConnectorOutput | None:
+        return None
+
+    def no_forward(self, scheduler_output: "SchedulerOutput") -> ModelRunnerOutput:
+        return EMPTY_MODEL_RUNNER_OUTPUT
+
+    def set_disabled(self, disabled: bool) -> None:
+        pass
+
+
+class ActiveKVConnector(KVConnector):
+    def __init__(
+        self, vllm_config: VllmConfig, kv_caches_dict: dict[str, torch.Tensor]
+    ):
+        self.vllm_config = vllm_config
+        self.kv_connector = get_kv_transfer_group()
+        # Register kv caches with KV Connector if applicable.
+        # TODO: support cross_layers_kv_cache
+        # (see https://github.com/vllm-project/vllm/pull/27743)
+        self.kv_connector.register_kv_caches(kv_caches_dict)
+        self.kv_connector.set_host_xfer_buffer_ops(copy_kv_blocks)
+
+        self._disabled = False
+
+    def pre_forward(self, scheduler_output: "SchedulerOutput") -> None:
+        if self._disabled:
+            return
+
+        if scheduler_output.preempted_req_ids:
+            self.kv_connector.handle_preemptions(scheduler_output.preempted_req_ids)
+        kv_connector_metadata = scheduler_output.kv_connector_metadata
+        assert kv_connector_metadata is not None
+        self.kv_connector.bind_connector_metadata(kv_connector_metadata)
+
+        # TODO: sort out KV Connectors' use of forward_context
+        if is_forward_context_available():
+            self.kv_connector.start_load_kv(get_forward_context())
+        else:
+            with set_forward_context(None, self.vllm_config):
+                self.kv_connector.start_load_kv(get_forward_context())
+
+    def post_forward(
+        self,
+        scheduler_output: "SchedulerOutput",
+        wait_for_save: bool = True,
+        clear_metadata: bool = True,
+    ) -> KVConnectorOutput | None:
+        if self._disabled:
+            return None
+
+        output = KVConnectorOutput()
+        if wait_for_save:
+            self.kv_connector.wait_for_save()
+        output.finished_sending, output.finished_recving = (
+            self.kv_connector.get_finished(scheduler_output.finished_req_ids)
+        )
+        output.invalid_block_ids = self.kv_connector.get_block_ids_with_load_errors()
+        output.kv_connector_stats = self.kv_connector.get_kv_connector_stats()
+        output.kv_cache_events = self.kv_connector.get_kv_connector_kv_cache_events()
+        if clear_metadata:
+            self.kv_connector.clear_connector_metadata()
+        return output
+
+    def clear_metadata(self) -> None:
+        """Clear the connector metadata. Call this after draft model runs."""
+        if not self._disabled:
+            self.kv_connector.clear_connector_metadata()
+
+    def no_forward(self, scheduler_output: "SchedulerOutput") -> ModelRunnerOutput:
+        if self._disabled:
+            return EMPTY_MODEL_RUNNER_OUTPUT
+
+        self.pre_forward(scheduler_output)
+        kv_connector_output = self.post_forward(scheduler_output, wait_for_save=False)
+        if kv_connector_output is None or kv_connector_output.is_empty():
+            return EMPTY_MODEL_RUNNER_OUTPUT
+        output = copy.copy(EMPTY_MODEL_RUNNER_OUTPUT)
+        output.kv_connector_output = kv_connector_output
+        return output
+
+    def set_disabled(self, disabled: bool) -> None:
+        # Ensure that layer-wise connector hooks aren't called when disabled.
+        kv_transfer_state._KV_CONNECTOR_AGENT = None if disabled else self.kv_connector
+        self._disabled = disabled
+
+
+NO_OP_KV_CONNECTOR = KVConnector()
+
+
+def get_kv_connector(
+    vllm_config: VllmConfig, kv_caches_dict: dict[str, torch.Tensor]
+) -> KVConnector:
+    if not has_kv_transfer_group():
+        # No-op connector.
+        return NO_OP_KV_CONNECTOR
+
+    return ActiveKVConnector(vllm_config, kv_caches_dict)

vllm/v1/worker/gpu/lora_utils.py

@@ -0,0 +1,44 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import numpy as np
+
+from vllm.lora.request import LoRARequest
+
+NO_LORA_ID = 0
+
+
+class LoraState:
+    def __init__(self, max_num_reqs: int):
+        self.lora_ids = np.zeros(max_num_reqs, dtype=np.int32)
+        self.lora_ids.fill(NO_LORA_ID)
+        # req_id -> lora_request
+        self.lora_requests: dict[str, LoRARequest] = {}
+
+    def add_request(
+        self, req_id: str, req_index: int, lora_request: LoRARequest | None
+    ) -> None:
+        if lora_request is not None:
+            self.lora_requests[req_id] = lora_request
+            self.lora_ids[req_index] = lora_request.lora_int_id
+        else:
+            self.lora_ids[req_index] = NO_LORA_ID
+
+    def remove_request(self, req_id: str) -> None:
+        self.lora_requests.pop(req_id, None)
+
+    def make_lora_inputs(
+        self,
+        req_ids: list[str],
+        idx_mapping: np.ndarray,
+        num_scheduled_tokens: np.ndarray,
+    ) -> tuple[tuple[int, ...], tuple[int, ...], set[LoRARequest]]:
+        lora_ids = self.lora_ids[idx_mapping]
+        prompt_lora_mapping = tuple(lora_ids)
+        token_lora_mapping = tuple(lora_ids.repeat(num_scheduled_tokens))
+
+        active_lora_requests: set[LoRARequest] = set()
+        for req_id in req_ids:
+            lora_request = self.lora_requests.get(req_id)
+            if lora_request is not None:
+                active_lora_requests.add(lora_request)
+        return prompt_lora_mapping, token_lora_mapping, active_lora_requests

vllm/v1/worker/gpu/metrics/logits.py

@@ -0,0 +1,42 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+from torch._inductor.runtime.triton_helpers import libdevice
+
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def _num_nans_kernel(
+    logits_ptr,
+    logits_stride,
+    num_nans_ptr,
+    vocab_size,
+    BLOCK_SIZE: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+    num_nans = 0
+    for i in range(0, vocab_size, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < vocab_size
+        logits = tl.load(
+            logits_ptr + req_idx * logits_stride + block, mask=mask, other=0
+        )
+        logits = logits.to(tl.float32)
+        is_nan = libdevice.isnan(logits).to(tl.int1)
+        num_nans += tl.sum(is_nan).to(tl.int32)
+    tl.store(num_nans_ptr + req_idx, num_nans)
+
+
+def get_num_nans(logits: torch.Tensor) -> torch.Tensor:
+    num_reqs, vocab_size = logits.shape
+    BLOCK_SIZE = 8192
+    num_nans = torch.empty(num_reqs, dtype=torch.int32, device=logits.device)
+    _num_nans_kernel[(num_reqs,)](
+        logits,
+        logits.stride(0),
+        num_nans,
+        vocab_size,
+        BLOCK_SIZE=BLOCK_SIZE,
+    )
+    return num_nans

vllm/v1/worker/gpu/mm/encoder_runner.py

@@ -0,0 +1,183 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import numpy as np
+import torch
+
+from vllm.model_executor.models.interfaces import SupportsMultiModal
+from vllm.multimodal.inputs import MultiModalFeatureSpec, MultiModalKwargsItem
+from vllm.multimodal.utils import group_mm_kwargs_by_modality
+from vllm.v1.worker.utils import sanity_check_mm_encoder_outputs
+
+
+class EncoderRunner:
+    def __init__(
+        self,
+        max_num_tokens: int,
+        hidden_size: int,
+        dtype: torch.dtype,
+        device: torch.device,
+    ):
+        self.max_num_tokens = max_num_tokens
+        self.hidden_size = hidden_size
+        self.dtype = dtype
+        self.device = device
+
+        self.inputs_embeds = torch.zeros(
+            max_num_tokens, hidden_size, dtype=dtype, device=device
+        )
+        self.req_id_to_mm_features: dict[str, list[MultiModalFeatureSpec]] = {}
+        self.encoder_cache: dict[str, torch.Tensor] = {}
+
+    def reset_mm_cache(self) -> None:
+        """
+        Clear the multi-modal cache that was used during profiling,
+        but no longer needed during inference.
+        """
+        # TODO: Implement MM budget for encoder dummy run
+        pass
+
+    def reset_encoder_cache(self) -> None:
+        """Clear the GPU-side encoder cache storing vision embeddings.
+
+        This should be called when model weights are updated to ensure
+        stale embeddings computed with old weights are not reused.
+        """
+        self.encoder_cache.clear()
+
+    def add_request(self, req_id: str, mm_features: list[MultiModalFeatureSpec]):
+        self.req_id_to_mm_features[req_id] = mm_features
+
+    def free_encoder_cache(self, mm_hash: str) -> None:
+        self.encoder_cache.pop(mm_hash, None)
+
+    def remove_request(self, req_id: str) -> None:
+        self.req_id_to_mm_features.pop(req_id, None)
+
+    def prepare_mm_inputs(
+        self, scheduled_encoder_inputs: dict[str, list[int]]
+    ) -> tuple[list[str], list[tuple[str, MultiModalKwargsItem]]]:
+        mm_hashes: list[str] = []
+        mm_kwargs: list[tuple[str, MultiModalKwargsItem]] = []
+        for req_id, encoder_input_ids in scheduled_encoder_inputs.items():
+            mm_features = self.req_id_to_mm_features[req_id]
+            for mm_input_id in encoder_input_ids:
+                mm_feature = mm_features[mm_input_id]
+                if mm_feature.data is None:
+                    continue
+                mm_hashes.append(mm_feature.identifier)
+                mm_kwargs.append((mm_feature.modality, mm_feature.data))
+
+        return mm_hashes, mm_kwargs
+
+    @torch.inference_mode()
+    def execute_mm_encoder(
+        self,
+        model: SupportsMultiModal,
+        mm_hashes: list[str],
+        mm_kwargs: list[tuple[str, MultiModalKwargsItem]],
+    ) -> list[torch.Tensor]:
+        if not mm_hashes:
+            return []
+
+        encoder_outputs: list[torch.Tensor] = []
+        for modality, num_items, mm_kwargs_group in group_mm_kwargs_by_modality(
+            mm_kwargs, device=self.device, pin_memory=False
+        ):
+            curr_group_outputs = model.embed_multimodal(**mm_kwargs_group)
+            sanity_check_mm_encoder_outputs(
+                curr_group_outputs, expected_num_items=num_items
+            )
+            encoder_outputs.extend(curr_group_outputs)
+
+        # Cache the encoder outputs by mm_hash
+        self.encoder_cache.update(zip(mm_hashes, encoder_outputs))
+        return encoder_outputs
+
+    def gather_mm_embeddings(
+        self,
+        req_ids: list[str],
+        total_num_scheduled_tokens: int,
+        num_scheduled_tokens: np.ndarray,
+        query_start_loc: np.ndarray,
+        prefill_lens: np.ndarray,
+        computed_prefill_lens: np.ndarray,
+    ) -> tuple[list[torch.Tensor], torch.Tensor]:
+        is_prefilling = (computed_prefill_lens < prefill_lens).tolist()
+        all_decode = not any(is_prefilling)
+        if all_decode:
+            # All decode requests, so no need to gather any embeddings.
+            return [], torch.zeros(
+                total_num_scheduled_tokens, dtype=torch.bool, device=self.device
+            )
+
+        query_start = computed_prefill_lens.tolist()
+        query_end = (computed_prefill_lens + num_scheduled_tokens).tolist()
+
+        mm_embeds: list[torch.Tensor] = []
+        is_mm_embed = torch.zeros(
+            total_num_scheduled_tokens, dtype=torch.bool, device="cpu", pin_memory=True
+        )
+        for i, req_id in enumerate(req_ids):
+            if not is_prefilling[i]:
+                # OPTIMIZATION: Skip decode requests.
+                continue
+
+            mm_features = self.req_id_to_mm_features[req_id]
+            for mm_feature in mm_features:
+                pos_info = mm_feature.mm_position
+                start_pos = pos_info.offset
+                num_encoder_tokens = pos_info.length
+
+                if start_pos >= query_end[i]:
+                    # The encoder output is not needed in this step.
+                    break
+                if start_pos + num_encoder_tokens <= query_start[i]:
+                    # The encoder output is already processed and stored
+                    # in the decoder's KV cache.
+                    continue
+
+                start_idx = max(query_start[i] - start_pos, 0)
+                end_idx = min(query_end[i] - start_pos, num_encoder_tokens)
+                assert start_idx < end_idx
+                curr_embeds_start, curr_embeds_end = (
+                    pos_info.get_embeds_indices_in_range(start_idx, end_idx)
+                )
+                # If there are no embeddings in the current range, we skip
+                # gathering the embeddings.
+                if curr_embeds_start == curr_embeds_end:
+                    continue
+
+                mm_hash = mm_feature.identifier
+                encoder_output = self.encoder_cache.get(mm_hash, None)
+                assert encoder_output is not None, f"Encoder cache miss for {mm_hash}."
+
+                if (is_embed := pos_info.is_embed) is not None:
+                    is_embed = is_embed[start_idx:end_idx]
+                    mm_embeds_item = encoder_output[curr_embeds_start:curr_embeds_end]
+                else:
+                    mm_embeds_item = encoder_output[start_idx:end_idx]
+
+                req_start_pos = query_start_loc[i] + start_pos - query_start[i]
+                is_mm_embed[req_start_pos + start_idx : req_start_pos + end_idx] = (
+                    True if is_embed is None else is_embed
+                )
+                mm_embeds.append(mm_embeds_item)
+
+        # Copy the is_mm_embed tensor to the GPU.
+        is_mm_embed = is_mm_embed.to(device=self.device, non_blocking=True)
+        return mm_embeds, is_mm_embed
+
+    @torch.inference_mode()
+    def get_inputs_embeds(
+        self,
+        model: SupportsMultiModal,
+        input_ids: torch.Tensor,
+        mm_embeds: list[torch.Tensor],
+        is_mm_embed: torch.Tensor,
+    ) -> torch.Tensor:
+        x = model.embed_input_ids(
+            input_ids, multimodal_embeddings=mm_embeds, is_multimodal=is_mm_embed
+        )
+        # Copy to the pre-allocated buffer for CUDA graphs.
+        self.inputs_embeds[: x.shape[0]] = x
+        return self.inputs_embeds

vllm/v1/worker/gpu/mm/mrope_utils.py

@@ -0,0 +1,136 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+from vllm.model_executor.models.interfaces import SupportsMRoPE
+from vllm.triton_utils import tl, triton
+from vllm.v1.worker.gpu.buffer_utils import StagedWriteTensor, UvaBackedTensor
+
+
+class MRopeState:
+    def __init__(
+        self,
+        max_num_reqs: int,
+        max_num_tokens: int,
+        max_model_len: int,
+        device: torch.device,
+    ):
+        self.max_num_reqs = max_num_reqs
+        self.max_num_tokens = max_num_tokens
+        self.max_model_len = max_model_len
+        self.device = device
+
+        # NOTE(woosuk): This tensor can be extremely large (e.g., several GBs)
+        # wasting a lot of CPU memory.
+        self.prefill_mrope_positions = StagedWriteTensor(
+            (max_num_reqs * 3, max_model_len),
+            dtype=torch.int32,
+            device=device,
+            uva_instead_of_gpu=True,
+        )
+        self.prefill_mrope_delta = UvaBackedTensor(max_num_reqs, dtype=torch.int32)
+
+        # NOTE: `mrope_positions` is implemented with one additional dummy
+        # position on purpose to make it non-contiguous so that it can work
+        # with torch compile.
+        # See detailed explanation in https://github.com/vllm-project/vllm/pull/12128#discussion_r1926431923
+        # NOTE: When M-RoPE is enabled, position ids are 3D regardless of
+        # the modality of inputs. For text-only inputs, each dimension has
+        # identical position IDs, making M-RoPE functionally equivalent to
+        # 1D-RoPE.
+        # See page 5 of https://arxiv.org/abs/2409.12191
+        self.mrope_positions = torch.zeros(
+            (3, max_num_tokens + 1), dtype=torch.int64, device=device
+        )
+
+    def init_prefill_mrope_positions(
+        self,
+        req_idx: int,
+        mrope_model: SupportsMRoPE,
+        prefill_token_ids: list[int],
+        mm_features: list,
+    ) -> None:
+        prefill_mrope_positions, prefill_mrope_delta = (
+            mrope_model.get_mrope_input_positions(prefill_token_ids, mm_features)
+        )
+        for i in range(3):
+            pos = prefill_mrope_positions[i].tolist()
+            self.prefill_mrope_positions.stage_write(3 * req_idx + i, 0, pos)
+        self.prefill_mrope_delta.np[req_idx] = prefill_mrope_delta
+
+    def apply_staged_writes(self) -> None:
+        self.prefill_mrope_positions.apply_write()
+        self.prefill_mrope_delta.copy_to_uva()
+
+    def prepare_mrope_positions(
+        self,
+        idx_mapping: torch.Tensor,
+        query_start_loc: torch.Tensor,
+        prefill_lens: torch.Tensor,
+        num_computed_tokens: torch.Tensor,
+    ) -> None:
+        num_reqs = idx_mapping.shape[0]
+        _prepare_mrope_positions_kernel[(num_reqs,)](
+            self.mrope_positions,
+            self.mrope_positions.stride(0),
+            self.prefill_mrope_positions.gpu,
+            3 * self.max_model_len,
+            self.max_model_len,
+            self.prefill_mrope_delta.gpu,
+            idx_mapping,
+            query_start_loc,
+            prefill_lens,
+            num_computed_tokens,
+            BLOCK_SIZE=1024,
+        )
+
+
+@triton.jit
+def _prepare_mrope_positions_kernel(
+    mrope_positions_ptr,
+    mrope_positions_stride,
+    prefill_mrope_positions_ptr,
+    prefill_mrope_positions_stride0,
+    prefill_mrope_positions_stride1,
+    prefill_mrope_delta_ptr,
+    idx_mapping_ptr,
+    query_start_loc_ptr,
+    prefill_lens_ptr,
+    num_computed_tokens_ptr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
+
+    prefill_len = tl.load(prefill_lens_ptr + req_state_idx)
+    num_computed = tl.load(num_computed_tokens_ptr + req_state_idx)
+    is_prefill = num_computed < prefill_len
+
+    query_start = tl.load(query_start_loc_ptr + batch_idx)
+    query_end = tl.load(query_start_loc_ptr + batch_idx + 1)
+    query_len = query_end - query_start
+
+    mrope_delta = tl.load(prefill_mrope_delta_ptr + req_state_idx)
+    for i in range(0, query_len, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < query_len
+        orig_pos = num_computed + block
+
+        for j in tl.static_range(3):
+            if is_prefill:
+                # Read from pre-computed M-RoPE positions.
+                pos = tl.load(
+                    prefill_mrope_positions_ptr
+                    + req_state_idx * prefill_mrope_positions_stride0
+                    + j * prefill_mrope_positions_stride1
+                    + orig_pos,
+                    mask=mask,
+                )
+            else:
+                # Apply M-RoPE delta.
+                pos = orig_pos + mrope_delta
+            tl.store(
+                mrope_positions_ptr + j * mrope_positions_stride + query_start + block,
+                pos,
+                mask=mask,
+            )

vllm/v1/worker/gpu/pp_utils.py

@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Pipeline Parallelism utils for V2 Model Runner."""
+
+import torch
+
+from vllm.distributed.parallel_state import get_pp_group
+
+
+def pp_broadcast(
+    sampled_token_ids: torch.Tensor,
+    num_sampled: torch.Tensor,
+    num_rejected: torch.Tensor,
+) -> None:
+    pp = get_pp_group()
+    assert pp.is_last_rank
+
+    assert sampled_token_ids.dtype == torch.int64
+    torch.distributed.broadcast(
+        sampled_token_ids.contiguous(), src=pp.last_rank, group=pp.device_group
+    )
+
+    combined = torch.stack((num_sampled, num_rejected), dim=0)
+    torch.distributed.broadcast(combined, src=pp.last_rank, group=pp.device_group)
+
+
+def pp_receive(
+    num_reqs: int, max_sample_len: int = 1
+) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    pp = get_pp_group()
+    assert not pp.is_last_rank
+
+    sampled_tokens = torch.empty(
+        num_reqs, max_sample_len, dtype=torch.int64, device=pp.device
+    )
+    torch.distributed.broadcast(sampled_tokens, src=pp.last_rank, group=pp.device_group)
+
+    combined = torch.empty(2, num_reqs, dtype=torch.int32, device=pp.device)
+    torch.distributed.broadcast(combined, src=pp.last_rank, group=pp.device_group)
+    num_sampled, num_rejected = combined.unbind(dim=0)
+    return sampled_tokens, num_sampled, num_rejected

vllm/v1/worker/gpu/sample/bad_words.py

@@ -0,0 +1,194 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import numpy as np
+import torch
+
+from vllm.sampling_params import SamplingParams
+from vllm.triton_utils import tl, triton
+from vllm.v1.worker.gpu.buffer_utils import StagedWriteTensor, UvaBackedTensor
+from vllm.v1.worker.gpu.states import RequestState
+
+MAX_BAD_WORDS_TOTAL_TOKENS = 1024  # Max total tokens for all bad words per request
+MAX_NUM_BAD_WORDS = 128  # Max number of bad words per request
+
+
+class BadWordsState:
+    def __init__(self, req_states: RequestState):
+        self.req_states = req_states
+        self.max_num_reqs = req_states.max_num_reqs
+        self.device = req_states.device
+
+        # flattened bad word tokens: [max_num_reqs, MAX_BAD_WORDS_TOTAL_TOKENS]
+        self.bad_word_token_ids = StagedWriteTensor(
+            (self.max_num_reqs, MAX_BAD_WORDS_TOTAL_TOKENS),
+            dtype=torch.int32,
+            device=self.device,
+        )
+        # cumulative offsets of bad words: [max_num_reqs, MAX_NUM_BAD_WORDS + 1]
+        self.bad_word_offsets = StagedWriteTensor(
+            (self.max_num_reqs, MAX_NUM_BAD_WORDS + 1),
+            dtype=torch.int32,
+            device=self.device,
+        )
+        # number of bad words per request
+        self.num_bad_words = UvaBackedTensor(self.max_num_reqs, dtype=torch.int32)
+
+    def add_request(self, req_idx: int, sampling_params: SamplingParams) -> None:
+        bad_words_token_ids = sampling_params.bad_words_token_ids
+        if not bad_words_token_ids:
+            self.num_bad_words.np[req_idx] = 0
+            return
+
+        num_bad_words = len(bad_words_token_ids)
+        if num_bad_words > MAX_NUM_BAD_WORDS:
+            raise ValueError(
+                f"Too many bad words: {num_bad_words}. "
+                f"The max number is {MAX_NUM_BAD_WORDS}."
+            )
+
+        # Flatten bad words and compute offsets
+        flattened_tokens: list[int] = []
+        offsets: list[int] = [0]
+        for bad_word in bad_words_token_ids:
+            flattened_tokens.extend(bad_word)
+            offsets.append(len(flattened_tokens))
+
+        if len(flattened_tokens) > MAX_BAD_WORDS_TOTAL_TOKENS:
+            raise ValueError(
+                f"Too many total bad word tokens: {len(flattened_tokens)}. "
+                f"The max is {MAX_BAD_WORDS_TOTAL_TOKENS}."
+            )
+
+        # Stage writes
+        self.bad_word_token_ids.stage_write(req_idx, 0, flattened_tokens)
+        self.bad_word_offsets.stage_write(req_idx, 0, offsets)
+        self.num_bad_words.np[req_idx] = num_bad_words
+
+    def apply_staged_writes(self) -> None:
+        self.num_bad_words.copy_to_uva()
+        self.bad_word_token_ids.apply_write()
+        self.bad_word_offsets.apply_write()
+
+    def apply_bad_words(
+        self,
+        logits: torch.Tensor,
+        idx_mapping: torch.Tensor,
+        idx_mapping_np: np.ndarray,
+        input_ids: torch.Tensor,
+        expanded_local_pos: torch.Tensor,
+    ) -> None:
+        max_num_bad_words = int(self.num_bad_words.np[idx_mapping_np].max())
+        if max_num_bad_words == 0:
+            # No request uses bad words. Skip the kernel launch.
+            return
+
+        apply_bad_words(
+            logits,
+            idx_mapping,
+            self.bad_word_token_ids.gpu,
+            self.bad_word_offsets.gpu,
+            self.num_bad_words.gpu,
+            self.req_states.all_token_ids.gpu,
+            self.req_states.prompt_len.gpu,
+            self.req_states.total_len.gpu,
+            input_ids,
+            expanded_local_pos,
+            max_num_bad_words,
+        )
+
+
+@triton.jit
+def _bad_words_kernel(
+    logits_ptr,
+    logits_stride,
+    expanded_idx_mapping_ptr,
+    bad_word_token_ids_ptr,
+    bad_word_token_ids_stride,
+    bad_word_offsets_ptr,
+    bad_word_offsets_stride,
+    num_bad_words_ptr,
+    all_token_ids_ptr,
+    all_token_ids_stride,
+    prompt_len_ptr,
+    total_len_ptr,
+    input_ids_ptr,
+    expanded_local_pos_ptr,
+):
+    logit_idx = tl.program_id(0)
+    bw_idx = tl.program_id(1)
+
+    req_state_idx = tl.load(expanded_idx_mapping_ptr + logit_idx)
+    num_bad_words = tl.load(num_bad_words_ptr + req_state_idx)
+
+    if bw_idx >= num_bad_words:
+        return
+
+    pos = tl.load(expanded_local_pos_ptr + logit_idx)
+    cur_req_first_pos = logit_idx - pos
+
+    prompt_len = tl.load(prompt_len_ptr + req_state_idx)
+    total_len = tl.load(total_len_ptr + req_state_idx)
+    output_len = total_len - prompt_len
+    effective_len = output_len + pos
+
+    bd_offsets_base = bad_word_offsets_ptr + req_state_idx * bad_word_offsets_stride
+    bd_tokens_base = bad_word_token_ids_ptr + req_state_idx * bad_word_token_ids_stride
+    output_base = all_token_ids_ptr + req_state_idx * all_token_ids_stride + prompt_len
+
+    start = tl.load(bd_offsets_base + bw_idx)
+    end = tl.load(bd_offsets_base + bw_idx + 1)
+    bad_word_len = end - start
+    prefix_len = bad_word_len - 1
+
+    if prefix_len > effective_len:
+        return
+
+    last_token = tl.load(bd_tokens_base + end - 1)
+    match = 1
+    for i in range(prefix_len):
+        expected = tl.load(bd_tokens_base + start + i)
+        actual_pos = effective_len - prefix_len + i
+
+        from_spec_input = actual_pos >= output_len
+        if from_spec_input:
+            spec_offset = actual_pos - output_len
+            actual = tl.load(input_ids_ptr + cur_req_first_pos + spec_offset)
+        else:
+            actual = tl.load(output_base + actual_pos)
+
+        match = match & (expected == actual)
+
+    if match:
+        tl.store(logits_ptr + logit_idx * logits_stride + last_token, -float("inf"))
+
+
+def apply_bad_words(
+    logits: torch.Tensor,
+    expanded_idx_mapping: torch.Tensor,
+    bad_word_token_ids: torch.Tensor,
+    bad_word_offsets: torch.Tensor,
+    num_bad_words: torch.Tensor,
+    all_token_ids: torch.Tensor,
+    prompt_len: torch.Tensor,
+    total_len: torch.Tensor,
+    input_ids: torch.Tensor,
+    expanded_local_pos: torch.Tensor,
+    max_num_bad_words: int,
+) -> None:
+    total_num_tokens = logits.shape[0]
+    _bad_words_kernel[(total_num_tokens, max_num_bad_words)](
+        logits,
+        logits.stride(0),
+        expanded_idx_mapping,
+        bad_word_token_ids,
+        bad_word_token_ids.stride(0),
+        bad_word_offsets,
+        bad_word_offsets.stride(0),
+        num_bad_words,
+        all_token_ids,
+        all_token_ids.stride(0),
+        prompt_len,
+        total_len,
+        input_ids,
+        expanded_local_pos,
+    )

vllm/v1/worker/gpu/sample/gumbel.py

@@ -0,0 +1,149 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def _temperature_kernel(
+    logits_ptr,
+    logits_stride,
+    idx_mapping_ptr,
+    temperature_ptr,
+    vocab_size,
+    BLOCK_SIZE: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
+    temperature = tl.load(temperature_ptr + req_state_idx).to(tl.float32)
+    if temperature == 0.0 or temperature == 1.0:
+        # Early return to avoid loading logits.
+        return
+
+    block_idx = tl.program_id(1)
+    block = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    mask = block < vocab_size
+
+    logits = tl.load(logits_ptr + batch_idx * logits_stride + block, mask=mask)
+    logits = logits.to(tl.float32)
+    logits = logits / temperature
+    tl.store(logits_ptr + batch_idx * logits_stride + block, logits, mask=mask)
+
+
+def apply_temperature(
+    logits: torch.Tensor,
+    idx_mapping: torch.Tensor,
+    temperature: torch.Tensor,
+) -> None:
+    num_reqs, vocab_size = logits.shape
+    BLOCK_SIZE = 8192
+    num_blocks = triton.cdiv(vocab_size, BLOCK_SIZE)
+    _temperature_kernel[(num_reqs, num_blocks)](
+        logits,
+        logits.stride(0),
+        idx_mapping,
+        temperature,
+        vocab_size,
+        BLOCK_SIZE=BLOCK_SIZE,
+    )
+
+
+@triton.jit
+def _gumbel_sample_kernel(
+    local_argmax_ptr,
+    local_argmax_stride,
+    local_max_ptr,
+    local_max_stride,
+    logits_ptr,
+    logits_stride,
+    idx_mapping_ptr,
+    seeds_ptr,
+    pos_ptr,
+    temp_ptr,
+    vocab_size,
+    BLOCK_SIZE: tl.constexpr,
+    APPLY_TEMPERATURE: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
+
+    block_idx = tl.program_id(1)
+    block = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    mask = block < vocab_size
+    logits = tl.load(
+        logits_ptr + batch_idx * logits_stride + block,
+        mask=mask,
+        other=float("-inf"),
+    )
+    logits = logits.to(tl.float32)
+
+    temp = tl.load(temp_ptr + req_state_idx).to(tl.float32)
+    if temp != 0.0:
+        # Calculate the seed for gumbel noise.
+        seed = tl.load(seeds_ptr + req_state_idx)
+        pos = tl.load(pos_ptr + batch_idx)
+        gumbel_seed = tl.randint(seed, pos)
+
+        # Generate gumbel noise in FP32.
+        u = tl.rand(gumbel_seed, block)
+        u = tl.maximum(u, 1e-7)
+        gumbel_noise = -tl.log(-tl.log(u))
+
+        # Apply temperature.
+        if APPLY_TEMPERATURE:
+            # NOTE(woosuk): Match the behavior of _temperature_kernel.
+            # E.g., if the kernel uses tl.div_rn, we should use tl.div_rn here too.
+            logits = logits / temp
+
+        # Apply gumbel noise.
+        logits = tl.where(mask, logits + gumbel_noise, float("-inf"))
+
+    value, idx = tl.max(logits, axis=0, return_indices=True)
+    token_id = block_idx * BLOCK_SIZE + idx
+    tl.store(local_argmax_ptr + batch_idx * local_argmax_stride + block_idx, token_id)
+    tl.store(local_max_ptr + batch_idx * local_max_stride + block_idx, value)
+
+
+def gumbel_sample(
+    logits: torch.Tensor,  # [num_reqs, vocab_size]
+    idx_mapping: torch.Tensor,  # [max_num_reqs]
+    temperature: torch.Tensor,  # [max_num_reqs]
+    seed: torch.Tensor,  # [max_num_reqs]
+    pos: torch.Tensor,  # [num_reqs]
+    apply_temperature: bool,
+) -> torch.Tensor:
+    num_reqs, vocab_size = logits.shape
+    BLOCK_SIZE = 1024
+    num_blocks = triton.cdiv(vocab_size, BLOCK_SIZE)
+    local_argmax = torch.empty(
+        num_reqs,
+        num_blocks,
+        dtype=torch.int64,
+        device=logits.device,
+    )
+    local_max = torch.empty(
+        num_reqs,
+        num_blocks,
+        dtype=torch.float32,
+        device=logits.device,
+    )
+    _gumbel_sample_kernel[(num_reqs, num_blocks)](
+        local_argmax,
+        local_argmax.stride(0),
+        local_max,
+        local_max.stride(0),
+        logits,
+        logits.stride(0),
+        idx_mapping,
+        seed,
+        pos,
+        temperature,
+        vocab_size,
+        BLOCK_SIZE=BLOCK_SIZE,
+        APPLY_TEMPERATURE=apply_temperature,
+    )
+    # NOTE(woosuk): Use int64 for later indexing.
+    max_block_idx = local_max.argmax(dim=-1, keepdim=True)
+    sampled = local_argmax.gather(dim=-1, index=max_block_idx).view(-1)
+    return sampled

vllm/v1/worker/gpu/sample/logit_bias.py

@@ -0,0 +1,280 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import numpy as np
+import torch
+
+from vllm.sampling_params import SamplingParams
+from vllm.triton_utils import tl, triton
+from vllm.v1.worker.gpu.buffer_utils import StagedWriteTensor, UvaBackedTensor
+
+MAX_NUM_ALLOWED_TOKEN_IDS = 1024
+MAX_NUM_LOGIT_BIAS_TOKENS = 1024
+MAX_NUM_STOP_TOKEN_IDS = 128
+
+
+class LogitBiasState:
+    def __init__(self, max_num_reqs: int, device: torch.device):
+        self.max_num_reqs = max_num_reqs
+
+        # Allowed token IDs.
+        self.num_allowed_token_ids = UvaBackedTensor(
+            self.max_num_reqs, dtype=torch.int32
+        )
+        self.allowed_token_ids = StagedWriteTensor(
+            (self.max_num_reqs, MAX_NUM_ALLOWED_TOKEN_IDS),
+            dtype=torch.int32,
+            device=device,
+        )
+        # Logit bias.
+        self.num_logit_bias = UvaBackedTensor(self.max_num_reqs, dtype=torch.int32)
+        self.logit_bias_token_ids = StagedWriteTensor(
+            (self.max_num_reqs, MAX_NUM_LOGIT_BIAS_TOKENS),
+            dtype=torch.int32,
+            device=device,
+        )
+        self.logit_bias = StagedWriteTensor(
+            (self.max_num_reqs, MAX_NUM_LOGIT_BIAS_TOKENS),
+            dtype=torch.float32,
+            device=device,
+        )
+        # Min tokens.
+        self.min_lens = UvaBackedTensor(self.max_num_reqs, dtype=torch.int32)
+        self.num_stop_token_ids = UvaBackedTensor(self.max_num_reqs, dtype=torch.int32)
+        self.stop_token_ids = StagedWriteTensor(
+            (self.max_num_reqs, MAX_NUM_STOP_TOKEN_IDS),
+            dtype=torch.int32,
+            device=device,
+        )
+
+        # Using any of the above.
+        self.use_logit_bias = np.zeros(max_num_reqs, dtype=bool)
+
+    def add_request(
+        self, req_idx: int, prompt_len: int, sampling_params: SamplingParams
+    ) -> None:
+        # Using any logit bias.
+        use_logit_bias = False
+
+        # Allowed token IDs.
+        allowed_token_ids = sampling_params.allowed_token_ids
+        if allowed_token_ids:
+            num_allowed_token_ids = len(allowed_token_ids)
+            if num_allowed_token_ids > MAX_NUM_ALLOWED_TOKEN_IDS:
+                raise ValueError(
+                    f"Too many allowed token IDs: {num_allowed_token_ids}. "
+                    f"The max size is {MAX_NUM_ALLOWED_TOKEN_IDS}."
+                )
+            self.num_allowed_token_ids.np[req_idx] = num_allowed_token_ids
+            self.allowed_token_ids.stage_write(req_idx, 0, allowed_token_ids)
+            use_logit_bias = True
+        else:
+            self.num_allowed_token_ids.np[req_idx] = 0
+
+        # Logit bias.
+        logit_bias = sampling_params.logit_bias
+        if logit_bias:
+            num_logit_bias = len(logit_bias)
+            if num_logit_bias > MAX_NUM_LOGIT_BIAS_TOKENS:
+                raise ValueError(
+                    f"Too many logit bias tokens: {num_logit_bias}. "
+                    f"The max size is {MAX_NUM_LOGIT_BIAS_TOKENS}."
+                )
+            self.num_logit_bias.np[req_idx] = num_logit_bias
+            self.logit_bias_token_ids.stage_write(req_idx, 0, logit_bias.keys())
+            self.logit_bias.stage_write(req_idx, 0, logit_bias.values())
+            use_logit_bias = True
+        else:
+            self.num_logit_bias.np[req_idx] = 0
+
+        # Min tokens.
+        min_tokens = sampling_params.min_tokens
+        min_len = prompt_len + min_tokens
+        self.min_lens.np[req_idx] = min_len
+        stop_token_ids = sampling_params.all_stop_token_ids
+        if min_tokens > 0 and stop_token_ids:
+            num_stop_token_ids = len(stop_token_ids)
+            if num_stop_token_ids > MAX_NUM_STOP_TOKEN_IDS:
+                raise ValueError(
+                    f"Too many stop tokens: {num_stop_token_ids}. "
+                    f"The max size is {MAX_NUM_STOP_TOKEN_IDS}."
+                )
+            self.num_stop_token_ids.np[req_idx] = num_stop_token_ids
+            self.stop_token_ids.stage_write(req_idx, 0, stop_token_ids)
+            use_logit_bias = True
+        else:
+            self.num_stop_token_ids.np[req_idx] = 0
+
+        self.use_logit_bias[req_idx] = use_logit_bias
+
+    def apply_staged_writes(self) -> None:
+        self.num_allowed_token_ids.copy_to_uva()
+        self.allowed_token_ids.apply_write()
+
+        self.num_logit_bias.copy_to_uva()
+        self.logit_bias_token_ids.apply_write()
+        self.logit_bias.apply_write()
+
+        self.min_lens.copy_to_uva()
+        self.num_stop_token_ids.copy_to_uva()
+        self.stop_token_ids.apply_write()
+
+    def apply_logit_bias(
+        self,
+        logits: torch.Tensor,
+        idx_mapping: torch.Tensor,
+        idx_mapping_np: np.ndarray,
+        pos: torch.Tensor,
+    ) -> None:
+        if not np.any(self.use_logit_bias[idx_mapping_np]):
+            # No request uses logit bias. Skip the kernel launch.
+            return
+
+        apply_logit_bias(
+            logits,
+            idx_mapping,
+            pos,
+            self.num_allowed_token_ids.gpu,
+            self.allowed_token_ids.gpu,
+            self.num_logit_bias.gpu,
+            self.logit_bias_token_ids.gpu,
+            self.logit_bias.gpu,
+            self.min_lens.gpu,
+            self.num_stop_token_ids.gpu,
+            self.stop_token_ids.gpu,
+        )
+
+
+@triton.jit
+def _bias_kernel(
+    logits_ptr,
+    logits_stride,
+    vocab_size,
+    idx_mapping_ptr,
+    # Allowed token IDs.
+    num_allowed_token_ids_ptr,
+    allowed_token_ids_ptr,
+    allowed_token_ids_stride,
+    # Logit bias.
+    num_logit_bias_ptr,
+    bias_token_ids_ptr,
+    bias_token_ids_stride,
+    bias_ptr,
+    bias_stride,
+    # Min tokens.
+    pos_ptr,
+    min_lens_ptr,
+    num_stop_token_ids_ptr,
+    stop_token_ids_ptr,
+    stop_token_ids_stride,
+    BLOCK_SIZE: tl.constexpr,
+    LOGITS_BLOCK_SIZE: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
+
+    block = tl.arange(0, BLOCK_SIZE)
+
+    # Allowed token IDs.
+    num_allowed_token_ids = tl.load(num_allowed_token_ids_ptr + req_state_idx)
+    if num_allowed_token_ids > 0:
+        block = tl.arange(0, BLOCK_SIZE)
+        mask = block < num_allowed_token_ids
+
+        # Save logits for allowed token IDs.
+        allowed_token_ids = tl.load(
+            allowed_token_ids_ptr + req_state_idx * allowed_token_ids_stride + block,
+            mask=mask,
+        )
+        logits = tl.load(
+            logits_ptr + batch_idx * logits_stride + allowed_token_ids, mask=mask
+        )
+
+        # Set logits to -inf for all tokens.
+        for i in range(0, vocab_size, LOGITS_BLOCK_SIZE):
+            offset = i + tl.arange(0, LOGITS_BLOCK_SIZE)
+            tl.store(
+                logits_ptr + batch_idx * logits_stride + offset,
+                -float("inf"),
+                mask=offset < vocab_size,
+            )
+
+        # Restore logits for allowed token IDs.
+        tl.store(
+            logits_ptr + batch_idx * logits_stride + allowed_token_ids,
+            logits,
+            mask=mask,
+        )
+
+    # Logit bias.
+    num_logit_bias = tl.load(num_logit_bias_ptr + req_state_idx)
+    if num_logit_bias > 0:
+        mask = block < num_logit_bias
+        token_ids = tl.load(
+            bias_token_ids_ptr + req_state_idx * bias_token_ids_stride + block,
+            mask=mask,
+        )
+        bias = tl.load(bias_ptr + req_state_idx * bias_stride + block, mask=mask)
+        logits = tl.load(logits_ptr + batch_idx * logits_stride + token_ids, mask=mask)
+        logits += bias
+        tl.store(logits_ptr + batch_idx * logits_stride + token_ids, logits, mask=mask)
+
+    # Apply min tokens.
+    num_stop_token_ids = tl.load(num_stop_token_ids_ptr + req_state_idx)
+    pos = tl.load(pos_ptr + batch_idx)
+    min_len = tl.load(min_lens_ptr + req_state_idx)
+    if num_stop_token_ids > 0 and pos < min_len:
+        mask = block < num_stop_token_ids
+        stop_token_ids = tl.load(
+            stop_token_ids_ptr + req_state_idx * stop_token_ids_stride + block,
+            mask=mask,
+        )
+        tl.store(
+            logits_ptr + batch_idx * logits_stride + stop_token_ids,
+            -float("inf"),
+            mask=mask,
+        )
+
+
+def apply_logit_bias(
+    logits: torch.Tensor,
+    idx_mapping: torch.Tensor,
+    pos: torch.Tensor,
+    num_allowed_token_ids: torch.Tensor,
+    allowed_token_ids: torch.Tensor,
+    num_logit_bias: torch.Tensor,
+    logit_bias_token_ids: torch.Tensor,
+    logit_bias: torch.Tensor,
+    min_lens: torch.Tensor,
+    num_stop_token_ids: torch.Tensor,
+    stop_token_ids: torch.Tensor,
+) -> None:
+    num_reqs, vocab_size = logits.shape
+    BLOCK_SIZE = triton.next_power_of_2(
+        max(
+            allowed_token_ids.shape[-1],
+            logit_bias_token_ids.shape[-1],
+            stop_token_ids.shape[-1],
+        )
+    )
+    LOGITS_BLOCK_SIZE = 8192
+    _bias_kernel[(num_reqs,)](
+        logits,
+        logits.stride(0),
+        vocab_size,
+        idx_mapping,
+        num_allowed_token_ids,
+        allowed_token_ids,
+        allowed_token_ids.stride(0),
+        num_logit_bias,
+        logit_bias_token_ids,
+        logit_bias_token_ids.stride(0),
+        logit_bias,
+        logit_bias.stride(0),
+        pos,
+        min_lens,
+        num_stop_token_ids,
+        stop_token_ids,
+        stop_token_ids.stride(0),
+        BLOCK_SIZE=BLOCK_SIZE,
+        LOGITS_BLOCK_SIZE=LOGITS_BLOCK_SIZE,
+    )

vllm/v1/worker/gpu/sample/logprob.py

@@ -0,0 +1,126 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch
+
+from vllm.triton_utils import tl, triton
+from vllm.v1.outputs import LogprobsTensors
+
+
+@triton.jit
+def _topk_log_softmax_kernel(
+    output_ptr,
+    logits_ptr,
+    logits_stride,
+    topk_ids_ptr,
+    topk,
+    vocab_size,
+    BLOCK_SIZE: tl.constexpr,
+    PADDED_TOPK: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+    row_ptr = logits_ptr + req_idx * logits_stride
+
+    max_val = float("-inf")
+    for i in range(0, vocab_size, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        logits = tl.load(row_ptr + block, mask=block < vocab_size, other=float("-inf"))
+        max_val = tl.max(tl.maximum(logits, max_val))
+    max_val = max_val.to(tl.float32)  # type: ignore
+
+    se = 0.0
+    for i in range(0, vocab_size, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        logits = tl.load(row_ptr + block, mask=block < vocab_size, other=0.0)
+        # NOTE(woosuk): Make sure that logits and all following operations use FP32.
+        logits = logits.to(tl.float32)
+        e = tl.exp(logits - max_val)
+        e = tl.where(block < vocab_size, e, 0.0)
+        se += tl.sum(e)
+    lse = tl.log(se)
+
+    k_offset = tl.arange(0, PADDED_TOPK)
+    k_mask = k_offset < topk
+    topk_ids = tl.load(topk_ids_ptr + req_idx * topk + k_offset, mask=k_mask, other=0)
+
+    logits = tl.load(row_ptr + topk_ids, mask=k_mask)
+    logits = logits.to(tl.float32)
+    o = logits - max_val - lse
+    tl.store(output_ptr + req_idx * topk + k_offset, o, mask=k_mask)
+
+
+@triton.jit
+def _ranks_kernel(
+    output_ptr,
+    logits_ptr,
+    logits_stride,
+    token_ids_ptr,
+    vocab_size,
+    BLOCK_SIZE: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+    row_ptr = logits_ptr + req_idx * logits_stride
+
+    token_id = tl.load(token_ids_ptr + req_idx)
+    x = tl.load(row_ptr + token_id)
+
+    n = 0
+    for i in range(0, vocab_size, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        logits = tl.load(row_ptr + block, mask=block < vocab_size, other=float("-inf"))
+        n += tl.sum((logits >= x).to(tl.int32))
+    tl.store(output_ptr + req_idx, n)
+
+
+def compute_token_logprobs(
+    logits: torch.Tensor, token_ids: torch.Tensor
+) -> torch.Tensor:
+    batch_size, vocab_size = logits.shape
+    token_ids = token_ids.to(torch.int64)
+    num_logprobs = token_ids.shape[1]
+    logprobs = logits.new_empty((batch_size, num_logprobs), dtype=torch.float32)
+    _topk_log_softmax_kernel[(batch_size,)](
+        logprobs,
+        logits,
+        logits.stride(0),
+        token_ids,
+        num_logprobs,
+        vocab_size,
+        BLOCK_SIZE=1024,  # type: ignore
+        PADDED_TOPK=triton.next_power_of_2(num_logprobs),
+    )
+    return logprobs
+
+
+def compute_topk_logprobs(
+    logits: torch.Tensor,
+    num_logprobs: int,
+    sampled_token_ids: torch.Tensor,
+    cu_num_logits: list[int] | None = None,
+) -> LogprobsTensors:
+    assert num_logprobs >= 0
+    batch_size, vocab_size = logits.shape
+    logprob_token_ids = sampled_token_ids.unsqueeze(-1)
+    if num_logprobs > 0:
+        topk_indices = torch.topk(logits, num_logprobs, dim=-1).indices
+        logprob_token_ids = torch.cat((logprob_token_ids, topk_indices), dim=1)
+
+    # NOTE(woosuk): Here, to save GPU memory, we do not materialize the full
+    # logprobs tensor. Instead, we only compute and return the logprobs of
+    # the topk + 1 tokens.
+    logprobs = compute_token_logprobs(logits, logprob_token_ids)
+    token_ranks = torch.empty(batch_size, dtype=torch.int64, device=logits.device)
+    _ranks_kernel[(batch_size,)](
+        token_ranks,
+        logits,
+        logits.stride(0),
+        sampled_token_ids,
+        vocab_size,
+        BLOCK_SIZE=8192,  # type: ignore
+    )
+    return LogprobsTensors(
+        logprob_token_ids=logprob_token_ids,
+        logprobs=logprobs,
+        selected_token_ranks=token_ranks,
+        cu_num_generated_tokens=cu_num_logits,
+    )

vllm/v1/worker/gpu/sample/min_p.py

@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def _min_p_kernel(
+    logits_ptr,
+    logits_stride,
+    idx_mapping_ptr,
+    min_p_ptr,
+    vocab_size,
+    BLOCK_SIZE: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + req_idx)
+    min_p = tl.load(min_p_ptr + req_state_idx).to(tl.float32)
+    if min_p == 0.0:
+        return
+
+    max_val = float("-inf")
+    for i in range(0, vocab_size, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < vocab_size
+        logits = tl.load(
+            logits_ptr + req_idx * logits_stride + block, mask=mask, other=float("-inf")
+        )
+        max_val = tl.max(tl.maximum(logits, max_val))
+    max_val = max_val.to(tl.float32)  # type: ignore
+
+    threshold = max_val + tl.log(min_p)
+    for i in range(0, vocab_size, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < vocab_size
+        logits = tl.load(
+            logits_ptr + req_idx * logits_stride + block, mask=mask, other=float("-inf")
+        )
+        logits = tl.where(logits < threshold, float("-inf"), logits)
+        tl.store(logits_ptr + req_idx * logits_stride + block, logits, mask=mask)
+
+
+def apply_min_p(
+    logits: torch.Tensor, idx_mapping: torch.Tensor, min_p: torch.Tensor
+) -> None:
+    num_reqs, vocab_size = logits.shape
+    BLOCK_SIZE = 1024
+    _min_p_kernel[(num_reqs,)](
+        logits,
+        logits.stride(0),
+        idx_mapping,
+        min_p,
+        vocab_size,
+        BLOCK_SIZE=BLOCK_SIZE,
+    )

vllm/v1/worker/gpu/sample/output.py

@@ -0,0 +1,14 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+
+import torch
+
+from vllm.v1.outputs import LogprobsTensors
+
+
+@dataclass
+class SamplerOutput:
+    sampled_token_ids: torch.Tensor
+    logprobs_tensors: LogprobsTensors | None
+    num_nans: torch.Tensor | None

vllm/v1/worker/gpu/sample/penalties.py

@@ -0,0 +1,311 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import numpy as np
+import torch
+
+from vllm.sampling_params import SamplingParams
+from vllm.triton_utils import tl, triton
+from vllm.utils.math_utils import cdiv
+from vllm.utils.torch_utils import async_tensor_h2d
+from vllm.v1.worker.gpu.buffer_utils import UvaBackedTensor
+from vllm.v1.worker.gpu.states import RequestState
+
+
+class PenaltiesState:
+    def __init__(self, req_states: RequestState):
+        self.req_states = req_states
+
+        max_num_reqs = req_states.max_num_reqs
+        self.vocab_size = req_states.vocab_size
+        self.device = req_states.device
+
+        self.repetition_penalty = UvaBackedTensor(max_num_reqs, dtype=torch.float32)
+        self.frequency_penalty = UvaBackedTensor(max_num_reqs, dtype=torch.float32)
+        self.presence_penalty = UvaBackedTensor(max_num_reqs, dtype=torch.float32)
+        self.use_penalty = np.zeros(max_num_reqs, dtype=bool)
+
+        # Initialize repetition penalty manually because 0 is an invalid value for it.
+        self.repetition_penalty.np.fill(1.0)
+        self.repetition_penalty.copy_to_uva()
+
+        # Statistics for penalties.
+        self.prompt_bin_mask = torch.zeros(
+            max_num_reqs,
+            cdiv(self.vocab_size, 32),
+            dtype=torch.int32,
+            device=self.device,
+        )
+        # TODO(woosuk): This tensor is rarely used but can be very large, taking up
+        # GBs of GPU memory. Optimize the memory usage.
+        self.output_bin_counts = torch.zeros(
+            max_num_reqs, self.vocab_size, dtype=torch.int32, device=self.device
+        )
+
+        self._new_penalties_reqs: list[int] = []
+
+    def add_request(self, req_idx: int, sampling_params: SamplingParams) -> None:
+        self.repetition_penalty.np[req_idx] = sampling_params.repetition_penalty
+        self.frequency_penalty.np[req_idx] = sampling_params.frequency_penalty
+        self.presence_penalty.np[req_idx] = sampling_params.presence_penalty
+
+        do_penalty = use_penalty(sampling_params)
+        self.use_penalty[req_idx] = do_penalty
+        if do_penalty:
+            self._new_penalties_reqs.append(req_idx)
+
+    def apply_staged_writes(self) -> None:
+        if self._new_penalties_reqs:
+            idx_mapping = async_tensor_h2d(
+                self._new_penalties_reqs,
+                dtype=torch.int32,
+                target_device=self.device,
+                pin_memory=True,
+            )
+
+            prefill_lens = self.req_states.prefill_len.np[self._new_penalties_reqs]
+            max_prefill_len = int(prefill_lens.max())
+            bincount(
+                idx_mapping,
+                self.req_states.all_token_ids.gpu,
+                self.req_states.prompt_len.gpu,
+                self.req_states.prefill_len.gpu,
+                self.prompt_bin_mask,
+                self.output_bin_counts,
+                max_prefill_len,
+            )
+            self._new_penalties_reqs.clear()
+
+        self.repetition_penalty.copy_to_uva()
+        self.frequency_penalty.copy_to_uva()
+        self.presence_penalty.copy_to_uva()
+
+    def apply_penalties(
+        self,
+        logits: torch.Tensor,
+        idx_mapping: torch.Tensor,
+        idx_mapping_np: np.ndarray,
+        input_ids: torch.Tensor,
+        expanded_local_pos: torch.Tensor,
+        num_speculative_tokens: int,
+    ) -> None:
+        if not np.any(self.use_penalty[idx_mapping_np]):
+            # No request uses penalties. Skip the kernel launch.
+            return
+
+        apply_penalties(
+            logits,
+            idx_mapping,
+            input_ids,
+            expanded_local_pos,
+            self.repetition_penalty.gpu,
+            self.frequency_penalty.gpu,
+            self.presence_penalty.gpu,
+            self.prompt_bin_mask,
+            self.output_bin_counts,
+            num_speculative_tokens,
+        )
+
+
+@triton.jit
+def _penalties_kernel(
+    logits_ptr,
+    logits_stride,
+    idx_mapping_ptr,
+    token_ids_ptr,
+    expanded_local_pos_ptr,
+    repetition_penalty_ptr,
+    frequency_penalty_ptr,
+    presence_penalty_ptr,
+    prompt_bin_mask_ptr,
+    prompt_bin_mask_stride,
+    output_bin_counts_ptr,
+    output_bin_counts_stride,
+    vocab_size,
+    BLOCK_SIZE: tl.constexpr,
+    MAX_SPEC_LEN: tl.constexpr,
+):
+    token_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + token_idx)
+    rep_penalty = tl.load(repetition_penalty_ptr + req_state_idx)
+    freq_penalty = tl.load(frequency_penalty_ptr + req_state_idx)
+    pres_penalty = tl.load(presence_penalty_ptr + req_state_idx)
+
+    use_rep_penalty = rep_penalty != 1.0
+    use_freq_penalty = freq_penalty != 0.0
+    use_pres_penalty = pres_penalty != 0.0
+    use_penalty = use_rep_penalty or use_freq_penalty or use_pres_penalty
+    if not use_penalty:
+        # Early return to avoid loading logits.
+        return
+
+    block_idx = tl.program_id(1)
+    block = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    mask = block < vocab_size
+    logits = tl.load(logits_ptr + token_idx * logits_stride + block, mask=mask)
+    logits = logits.to(tl.float32)
+
+    base_output_counts = tl.load(
+        output_bin_counts_ptr + req_state_idx * output_bin_counts_stride + block,
+        mask=mask,
+        other=0,
+    )
+
+    # Compute cumulative draft_counts from previous positions in this request
+    pos = tl.load(expanded_local_pos_ptr + token_idx)
+    start_idx = token_idx - pos
+    draft_counts = tl.zeros((BLOCK_SIZE,), dtype=tl.int32)
+    for prev_pos in tl.static_range(MAX_SPEC_LEN):
+        if prev_pos < pos:
+            prev_token = tl.load(token_ids_ptr + start_idx + prev_pos + 1)
+            token_match = block == prev_token
+            draft_counts = draft_counts + token_match.to(tl.int32)
+
+    # Total counts = base output counts + cumulative draft counts
+    output_bin_counts = base_output_counts + draft_counts
+    output_bin_mask = output_bin_counts > 0
+
+    # Apply repetition penalties.
+    if use_rep_penalty:
+        packed_block = block_idx * BLOCK_SIZE // 32 + tl.arange(0, BLOCK_SIZE // 32)
+        packed_mask = tl.load(
+            prompt_bin_mask_ptr + req_state_idx * prompt_bin_mask_stride + packed_block,
+            mask=packed_block < tl.cdiv(vocab_size, 32),
+            other=0,
+        )
+        prompt_bin_mask = (packed_mask[:, None] >> (tl.arange(0, 32)[None, :])) & 1
+        prompt_bin_mask = prompt_bin_mask.to(tl.int1)
+        prompt_bin_mask = prompt_bin_mask.reshape(BLOCK_SIZE)
+
+        # If token appears in prompt or output, apply, otherwise use 1.0 for no-op.
+        scale = tl.where(prompt_bin_mask | output_bin_mask, rep_penalty, 1.0)
+        # If logits are positive, divide by penalty, otherwise multiply by penalty.
+        logits *= tl.where(logits > 0, 1.0 / scale, scale)
+
+    # Apply frequency penalties.
+    logits -= freq_penalty * output_bin_counts
+    # Apply presence penalties.
+    logits -= pres_penalty * output_bin_mask
+    # Store back to logits.
+    tl.store(logits_ptr + token_idx * logits_stride + block, logits, mask=mask)
+
+
+def apply_penalties(
+    logits: torch.Tensor,
+    idx_mapping: torch.Tensor,
+    token_ids: torch.Tensor,
+    expanded_local_pos: torch.Tensor,
+    repetition_penalty: torch.Tensor,
+    frequency_penalty: torch.Tensor,
+    presence_penalty: torch.Tensor,
+    prompt_bin_mask: torch.Tensor,
+    output_bin_counts: torch.Tensor,
+    num_speculative_tokens: int,
+) -> None:
+    num_tokens, vocab_size = logits.shape
+    BLOCK_SIZE = 8192
+    num_blocks = triton.cdiv(vocab_size, BLOCK_SIZE)
+    _penalties_kernel[(num_tokens, num_blocks)](
+        logits,
+        logits.stride(0),
+        idx_mapping,
+        token_ids,
+        expanded_local_pos,
+        repetition_penalty,
+        frequency_penalty,
+        presence_penalty,
+        prompt_bin_mask,
+        prompt_bin_mask.stride(0),
+        output_bin_counts,
+        output_bin_counts.stride(0),
+        vocab_size,
+        BLOCK_SIZE=BLOCK_SIZE,
+        MAX_SPEC_LEN=num_speculative_tokens,
+    )
+
+
+@triton.jit
+def _bincount_kernel(
+    idx_mapping_ptr,
+    all_token_ids_ptr,
+    all_token_ids_stride,
+    prompt_len_ptr,
+    prefill_len_ptr,
+    prompt_bin_mask_ptr,
+    prompt_bin_mask_stride,
+    output_bin_counts_ptr,
+    output_bin_counts_stride,
+    BLOCK_SIZE: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+    block_idx = tl.program_id(1)
+    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
+
+    prefill_len = tl.load(prefill_len_ptr + req_state_idx)
+    if block_idx * BLOCK_SIZE >= prefill_len:
+        return
+
+    prompt_len = tl.load(prompt_len_ptr + req_state_idx)
+    block = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    if block_idx * BLOCK_SIZE < prompt_len:
+        mask = block < prompt_len
+        prompt_tokens = tl.load(
+            all_token_ids_ptr + req_state_idx * all_token_ids_stride + block, mask=mask
+        )
+        idx = prompt_tokens // 32
+        bit_idx = prompt_tokens % 32
+        bit = tl.full((BLOCK_SIZE,), 1, tl.int32) << bit_idx
+        tl.atomic_or(
+            prompt_bin_mask_ptr + req_state_idx * prompt_bin_mask_stride + idx,
+            bit,
+            mask=mask,
+        )
+
+    if (block_idx + 1) * BLOCK_SIZE >= prompt_len:
+        mask = block < prefill_len
+        mask &= block >= prompt_len
+        output_tokens = tl.load(
+            all_token_ids_ptr + req_state_idx * all_token_ids_stride + block, mask=mask
+        )
+        tl.atomic_add(
+            output_bin_counts_ptr
+            + req_state_idx * output_bin_counts_stride
+            + output_tokens,
+            1,
+            mask=mask,
+        )
+
+
+def bincount(
+    idx_mapping: torch.Tensor,
+    all_token_ids: torch.Tensor,
+    prompt_len: torch.Tensor,
+    prefill_len: torch.Tensor,
+    prompt_bin_mask: torch.Tensor,
+    output_bin_counts: torch.Tensor,
+    max_prefill_len: int,
+) -> None:
+    prompt_bin_mask[idx_mapping] = 0
+    output_bin_counts[idx_mapping] = 0
+    num_reqs = idx_mapping.shape[0]
+    BLOCK_SIZE = 1024
+    num_blocks = triton.cdiv(max_prefill_len, BLOCK_SIZE)
+    _bincount_kernel[(num_reqs, num_blocks)](
+        idx_mapping,
+        all_token_ids,
+        all_token_ids.stride(0),
+        prompt_len,
+        prefill_len,
+        prompt_bin_mask,
+        prompt_bin_mask.stride(0),
+        output_bin_counts,
+        output_bin_counts.stride(0),
+        BLOCK_SIZE=BLOCK_SIZE,
+    )
+
+
+def use_penalty(sampling_params: SamplingParams) -> bool:
+    return (
+        sampling_params.repetition_penalty != 1.0
+        or sampling_params.frequency_penalty != 0.0
+        or sampling_params.presence_penalty != 0.0
+    )

vllm/v1/worker/gpu/sample/prompt_logprob.py

@@ -0,0 +1,208 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Callable
+
+import numpy as np
+import torch
+
+from vllm.sampling_params import SamplingParams
+from vllm.triton_utils import tl, triton
+from vllm.v1.outputs import LogprobsTensors
+from vllm.v1.worker.gpu.input_batch import InputBatch
+from vllm.v1.worker.gpu.sample.logprob import compute_topk_logprobs
+
+
+class PromptLogprobsWorker:
+    def __init__(self, max_num_reqs: int):
+        self.max_num_reqs = max_num_reqs
+
+        self.uses_prompt_logprobs = np.zeros(self.max_num_reqs, dtype=bool)
+        # req_idx -> list of in-progress LogprobsTensors
+        self.in_progress_prompt_logprobs: dict[str, list[LogprobsTensors]] = {}
+
+    def add_request(self, req_id: str, req_idx: int, sampling_params: SamplingParams):
+        # For now, only support prompt logprobs for the prompt tokens (not top-k).
+        uses_prompt_logprobs = sampling_params.prompt_logprobs is not None
+        self.uses_prompt_logprobs[req_idx] = uses_prompt_logprobs
+        if uses_prompt_logprobs:
+            self.in_progress_prompt_logprobs[req_id] = []
+
+    def remove_request(self, req_id: str) -> None:
+        self.in_progress_prompt_logprobs.pop(req_id, None)
+
+    def compute_prompt_logprobs(
+        self,
+        logits_fn: Callable[[torch.Tensor], torch.Tensor],
+        hidden_states: torch.Tensor,
+        input_batch: InputBatch,
+        # [max_num_reqs, max_model_len]
+        all_token_ids: torch.Tensor,
+        # [max_num_reqs]
+        num_computed_tokens: torch.Tensor,
+        # [max_num_reqs]
+        prompt_lens: np.ndarray,
+        # [max_num_reqs]
+        prefill_lens: np.ndarray,
+        # [max_num_reqs]
+        num_computed_prefill_tokens: np.ndarray,
+    ) -> dict[str, LogprobsTensors]:
+        idx_mapping_np = input_batch.idx_mapping_np
+        needs_prompt_logprobs = self.uses_prompt_logprobs[idx_mapping_np]
+        if not np.any(needs_prompt_logprobs):
+            # Common case: No request asks for prompt logprobs.
+            return {}
+
+        prompt_lens = prompt_lens[idx_mapping_np]
+        # NOTE(woosuk): -1 because the last prompt token's hidden state is not
+        # needed for prompt logprobs.
+        computed_prefill = num_computed_prefill_tokens[idx_mapping_np]
+        includes_prompt = computed_prefill < prompt_lens - 1
+        # NOTE(woosuk): If the request was resumed after preemption, its prompt
+        # logprobs must have been computed before preemption. Skip.
+        resumed_after_prompt = prompt_lens < prefill_lens[idx_mapping_np]
+        needs_prompt_logprobs &= includes_prompt & ~resumed_after_prompt
+        if not np.any(needs_prompt_logprobs):
+            return {}
+
+        # Get the prompt logprobs token_ids.
+        prompt_logprobs_token_ids = get_prompt_logprobs_token_ids(
+            input_batch.num_tokens,
+            input_batch.query_start_loc,
+            input_batch.idx_mapping,
+            num_computed_tokens,
+            all_token_ids,
+        )
+        # Compute the prompt logprobs.
+        prompt_logprobs, prompt_ranks = compute_prompt_logprobs_with_chunking(
+            prompt_logprobs_token_ids,
+            hidden_states[: input_batch.num_tokens],
+            logits_fn,
+        )
+
+        pos_after_step = computed_prefill + input_batch.num_scheduled_tokens
+        is_prompt_chunked = pos_after_step < prompt_lens
+
+        query_start_loc_np = input_batch.query_start_loc_np
+        prompt_token_ids = prompt_logprobs_token_ids.unsqueeze(-1)
+        prompt_logprobs_dict: dict[str, LogprobsTensors] = {}
+        for i, req_id in enumerate(input_batch.req_ids):
+            if not needs_prompt_logprobs[i]:
+                continue
+
+            start_idx = query_start_loc_np[i]
+            end_idx = query_start_loc_np[i + 1]
+            assert start_idx < end_idx, (
+                f"start_idx ({start_idx}) >= end_idx ({end_idx})"
+            )
+            if not is_prompt_chunked[i]:
+                end_idx -= 1
+            logprobs = LogprobsTensors(
+                logprob_token_ids=prompt_token_ids[start_idx:end_idx],
+                logprobs=prompt_logprobs[start_idx:end_idx],
+                selected_token_ranks=prompt_ranks[start_idx:end_idx],
+            )
+
+            prompt_logprobs_list = self.in_progress_prompt_logprobs[req_id]
+            if is_prompt_chunked[i]:
+                # Prompt is chunked. Do not return the logprobs yet.
+                prompt_logprobs_list.append(logprobs)
+                continue
+
+            if prompt_logprobs_list:
+                # Merge the in-progress logprobs.
+                prompt_logprobs_list.append(logprobs)
+                logprobs = LogprobsTensors(
+                    logprob_token_ids=torch.cat(
+                        [x.logprob_token_ids for x in prompt_logprobs_list]
+                    ),
+                    logprobs=torch.cat([x.logprobs for x in prompt_logprobs_list]),
+                    selected_token_ranks=torch.cat(
+                        [x.selected_token_ranks for x in prompt_logprobs_list]
+                    ),
+                )
+                prompt_logprobs_list.clear()
+
+            prompt_logprobs_dict[req_id] = logprobs
+        return prompt_logprobs_dict
+
+
+@triton.jit
+def _prompt_logprobs_token_ids_kernel(
+    prompt_logprobs_token_ids_ptr,
+    query_start_loc_ptr,
+    idx_mapping_ptr,
+    num_computed_tokens_ptr,
+    all_token_ids_ptr,
+    all_token_ids_stride,
+    BLOCK_SIZE: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
+
+    query_start = tl.load(query_start_loc_ptr + batch_idx)
+    query_end = tl.load(query_start_loc_ptr + batch_idx + 1)
+    query_len = query_end - query_start
+
+    num_computed_tokens = tl.load(num_computed_tokens_ptr + req_state_idx)
+    for i in range(0, query_len, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < query_len
+        # NOTE(woosuk): We should shift the pos by one
+        # because the logprob is computed for the next token.
+        target_pos = num_computed_tokens + 1 + block
+        token_ids = tl.load(
+            all_token_ids_ptr + req_state_idx * all_token_ids_stride + target_pos,
+            mask=mask,
+        )
+        tl.store(
+            prompt_logprobs_token_ids_ptr + query_start + block, token_ids, mask=mask
+        )
+
+
+def get_prompt_logprobs_token_ids(
+    num_tokens: int,
+    query_start_loc: torch.Tensor,
+    idx_mapping: torch.Tensor,
+    num_computed_tokens: torch.Tensor,
+    all_token_ids: torch.Tensor,
+) -> torch.Tensor:
+    token_ids = torch.empty(num_tokens, dtype=torch.int64, device=idx_mapping.device)
+    num_reqs = idx_mapping.shape[0]
+    _prompt_logprobs_token_ids_kernel[(num_reqs,)](
+        token_ids,
+        query_start_loc,
+        idx_mapping,
+        num_computed_tokens,
+        all_token_ids,
+        all_token_ids.stride(0),
+        BLOCK_SIZE=1024,
+    )
+    return token_ids
+
+
+def compute_prompt_logprobs_with_chunking(
+    prompt_token_ids: torch.Tensor,
+    prompt_hidden_states: torch.Tensor,
+    logits_fn: Callable[[torch.Tensor], torch.Tensor],
+) -> tuple[torch.Tensor, torch.Tensor]:
+    # Since materializing the full prompt logits can take too much memory,
+    # we compute it in chunks.
+    CHUNK_SIZE = 1024
+    logprobs = []
+    ranks = []
+    prompt_token_ids = prompt_token_ids.to(torch.int64)
+    for start_idx in range(0, prompt_token_ids.shape[0], CHUNK_SIZE):
+        end_idx = start_idx + CHUNK_SIZE
+        # NOTE(woosuk): logits_fn can be slow because it involves all-gather.
+        prompt_logits = logits_fn(prompt_hidden_states[start_idx:end_idx])
+        prompt_logprobs = compute_topk_logprobs(
+            prompt_logits,
+            0,  # num_logprobs
+            prompt_token_ids[start_idx:end_idx],
+        )
+        logprobs.append(prompt_logprobs.logprobs)
+        ranks.append(prompt_logprobs.selected_token_ranks)
+
+    logprobs = torch.cat(logprobs, dim=0) if len(logprobs) > 1 else logprobs[0]
+    ranks = torch.cat(ranks, dim=0) if len(ranks) > 1 else ranks[0]
+    return logprobs, ranks

vllm/v1/worker/gpu/sample/sampler.py

@@ -0,0 +1,155 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import numpy as np
+import torch
+
+import vllm.envs as envs
+from vllm.config.model import LogprobsMode
+from vllm.sampling_params import SamplingParams
+from vllm.v1.worker.gpu.metrics.logits import get_num_nans
+from vllm.v1.worker.gpu.sample.bad_words import BadWordsState
+from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
+from vllm.v1.worker.gpu.sample.logit_bias import LogitBiasState
+from vllm.v1.worker.gpu.sample.logprob import compute_topk_logprobs
+from vllm.v1.worker.gpu.sample.output import SamplerOutput
+from vllm.v1.worker.gpu.sample.penalties import PenaltiesState
+from vllm.v1.worker.gpu.sample.states import NO_LOGPROBS, SamplingStates
+from vllm.v1.worker.gpu.states import RequestState
+
+
+class Sampler:
+    def __init__(
+        self,
+        max_num_reqs: int,
+        vocab_size: int,
+        device: torch.device,
+        req_states: RequestState,
+        logprobs_mode: LogprobsMode = "raw_logprobs",
+        num_speculative_tokens: int = 1,
+    ):
+        if logprobs_mode not in ("processed_logprobs", "raw_logprobs"):
+            raise NotImplementedError(f"Unsupported logprobs_mode: {logprobs_mode}")
+        self.logprobs_mode = logprobs_mode
+        self.compute_nans = envs.VLLM_COMPUTE_NANS_IN_LOGITS  # False by default.
+
+        self.sampling_states = SamplingStates(max_num_reqs, vocab_size)
+        self.penalties_state = PenaltiesState(req_states)
+        self.logit_bias_state = LogitBiasState(max_num_reqs, device)
+        self.bad_words_state = BadWordsState(req_states)
+        self.num_speculative_tokens = num_speculative_tokens
+
+    def add_request(
+        self, req_idx: int, prompt_len: int, sampling_params: SamplingParams
+    ) -> None:
+        self.sampling_states.add_request(req_idx, sampling_params)
+        self.penalties_state.add_request(req_idx, sampling_params)
+        self.logit_bias_state.add_request(req_idx, prompt_len, sampling_params)
+        self.bad_words_state.add_request(req_idx, sampling_params)
+
+    def apply_staged_writes(self) -> None:
+        self.sampling_states.apply_staged_writes()
+        self.penalties_state.apply_staged_writes()
+        self.logit_bias_state.apply_staged_writes()
+        self.bad_words_state.apply_staged_writes()
+
+    def __call__(
+        self,
+        logits: torch.Tensor,
+        idx_mapping: torch.Tensor,
+        idx_mapping_np: np.ndarray,
+        cu_num_logits_np: np.ndarray,
+        pos: torch.Tensor,
+        input_ids: torch.Tensor,
+        expanded_local_pos: torch.Tensor,
+    ) -> SamplerOutput:
+        # NOTE(woosuk): We intentionally compute num_nans before sampling to make clear
+        # that num_nans is computed before applying penalties and temperature.
+        num_nans = get_num_nans(logits) if self.compute_nans else None
+        sampled, processed_logits = self.sample(
+            logits,
+            idx_mapping,
+            idx_mapping_np,
+            pos,
+            input_ids,
+            expanded_local_pos,
+        )
+
+        max_num_logprobs = self.sampling_states.max_num_logprobs(idx_mapping_np)
+        if max_num_logprobs != NO_LOGPROBS:
+            if self.logprobs_mode == "processed_logprobs":
+                logits = processed_logits
+            expanded_logits = logits.shape[0] != idx_mapping_np.shape[0]
+            cu_num_logits = cu_num_logits_np.tolist() if expanded_logits else None
+            logprobs_tensors = compute_topk_logprobs(
+                logits, max_num_logprobs, sampled, cu_num_logits
+            )
+        else:
+            logprobs_tensors = None
+
+        # These are GPU tensors.
+        sampler_output = SamplerOutput(
+            # The sampled tokens are expanded to 2D tensor with shape
+            # [num_requests, 1], where each row represents one generated
+            # token per request.
+            sampled_token_ids=sampled.view(-1, 1),
+            logprobs_tensors=logprobs_tensors,
+            num_nans=num_nans,
+        )
+        return sampler_output
+
+    def sample(
+        self,
+        logits: torch.Tensor,
+        idx_mapping: torch.Tensor,
+        idx_mapping_np: np.ndarray,
+        pos: torch.Tensor,
+        input_ids: torch.Tensor,
+        expanded_local_pos: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Copy logits to a new FP32 tensor.
+        logits = torch.empty_like(logits, dtype=torch.float32).copy_(logits)
+
+        # Apply logit bias (e.g., allowed_token_ids, min_tokens) in place.
+        self.logit_bias_state.apply_logit_bias(logits, idx_mapping, idx_mapping_np, pos)
+
+        # Apply penalties in place.
+        self.penalties_state.apply_penalties(
+            logits,
+            idx_mapping,
+            idx_mapping_np,
+            input_ids,
+            expanded_local_pos,
+            self.num_speculative_tokens,
+        )
+
+        # Apply bad words masking in place.
+        self.bad_words_state.apply_bad_words(
+            logits,
+            idx_mapping,
+            idx_mapping_np,
+            input_ids,
+            expanded_local_pos,
+        )
+
+        # Apply temperature in place.
+        self.sampling_states.apply_temperature(logits, idx_mapping, idx_mapping_np)
+
+        # Apply min_p in place.
+        self.sampling_states.apply_min_p(logits, idx_mapping, idx_mapping_np)
+
+        # Apply top_k and/or top_p. This might or might not return a new tensor.
+        logits = self.sampling_states.apply_top_k_top_p(
+            logits, idx_mapping, idx_mapping_np
+        )
+
+        # Sample the next token.
+        sampled = gumbel_sample(
+            logits,
+            idx_mapping,
+            self.sampling_states.temperature.gpu,
+            self.sampling_states.seeds.gpu,
+            pos,
+            apply_temperature=False,
+        )
+        return sampled, logits

vllm/v1/worker/gpu/sample/states.py

@@ -0,0 +1,104 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import numpy as np
+import torch
+
+from vllm.sampling_params import SamplingParams
+from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
+from vllm.v1.worker.gpu.buffer_utils import UvaBackedTensor
+from vllm.v1.worker.gpu.sample.gumbel import apply_temperature
+from vllm.v1.worker.gpu.sample.min_p import apply_min_p
+
+NO_LOGPROBS = -1
+_NP_INT64_MIN = np.iinfo(np.int64).min
+_NP_INT64_MAX = np.iinfo(np.int64).max
+
+
+class SamplingStates:
+    def __init__(self, max_num_reqs: int, vocab_size: int):
+        self.max_num_reqs = max_num_reqs
+        self.vocab_size = vocab_size
+
+        self.temperature = UvaBackedTensor(max_num_reqs, dtype=torch.float32)
+        self.top_k = UvaBackedTensor(max_num_reqs, dtype=torch.int32)
+        self.top_p = UvaBackedTensor(max_num_reqs, dtype=torch.float32)
+        self.min_p = UvaBackedTensor(max_num_reqs, dtype=torch.float32)
+        self.seeds = UvaBackedTensor(max_num_reqs, dtype=torch.int64)
+
+        # Initialize top_k and top_p manually because 0 is an invalid value for them.
+        self.top_k.np.fill(self.vocab_size)
+        self.top_k.copy_to_uva()
+        self.top_p.np.fill(1.0)
+        self.top_p.copy_to_uva()
+
+        self.num_logprobs = np.empty(self.max_num_reqs, dtype=np.int32)
+        # -1 means no logprobs are requested.
+        self.num_logprobs.fill(NO_LOGPROBS)
+
+    def add_request(self, req_idx: int, sampling_params: SamplingParams) -> None:
+        self.temperature.np[req_idx] = sampling_params.temperature
+        self.top_p.np[req_idx] = sampling_params.top_p
+        top_k = sampling_params.top_k
+        if top_k <= 0 or top_k > self.vocab_size:
+            top_k = self.vocab_size
+        self.top_k.np[req_idx] = top_k
+        self.min_p.np[req_idx] = sampling_params.min_p
+
+        seed = sampling_params.seed
+        if seed is None:
+            seed = np.random.randint(_NP_INT64_MIN, _NP_INT64_MAX)
+        self.seeds.np[req_idx] = seed
+
+        num_logprobs = sampling_params.logprobs
+        if num_logprobs is None:
+            num_logprobs = NO_LOGPROBS
+        self.num_logprobs[req_idx] = num_logprobs
+
+    def apply_staged_writes(self) -> None:
+        self.temperature.copy_to_uva()
+        self.top_p.copy_to_uva()
+        self.top_k.copy_to_uva()
+        self.min_p.copy_to_uva()
+        self.seeds.copy_to_uva()
+
+    def apply_temperature(
+        self,
+        logits: torch.Tensor,
+        idx_mapping: torch.Tensor,
+        idx_mapping_np: np.ndarray,
+    ) -> None:
+        temp_np = self.temperature.np[idx_mapping_np]
+        if np.all((temp_np == 0.0) | (temp_np == 1.0)):
+            # No request requires temperature. Skip the kernel launch.
+            return
+
+        apply_temperature(logits, idx_mapping, self.temperature.gpu)
+
+    def apply_min_p(
+        self,
+        logits: torch.Tensor,
+        idx_mapping: torch.Tensor,
+        idx_mapping_np: np.ndarray,
+    ) -> None:
+        if np.all(self.min_p.np[idx_mapping_np] == 0.0):
+            # No request uses min_p. Skip the kernel launch.
+            return
+        apply_min_p(logits, idx_mapping, self.min_p.gpu)
+
+    def apply_top_k_top_p(
+        self,
+        logits: torch.Tensor,
+        idx_mapping: torch.Tensor,
+        idx_mapping_np: np.ndarray,
+    ) -> torch.Tensor:
+        do_top_k = np.any(self.top_k.np[idx_mapping_np] != self.vocab_size)
+        do_top_p = np.any(self.top_p.np[idx_mapping_np] != 1.0)
+        if not (do_top_k or do_top_p):
+            return logits
+
+        top_k = self.top_k.gpu[idx_mapping] if do_top_k else None
+        top_p = self.top_p.gpu[idx_mapping] if do_top_p else None
+        return apply_top_k_top_p(logits, top_k, top_p)
+
+    def max_num_logprobs(self, idx_mapping_np: np.ndarray) -> int:
+        return int(np.max(self.num_logprobs[idx_mapping_np]))

vllm/v1/worker/gpu/spec_decode/__init__.py

@@ -0,0 +1,15 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+from vllm.config import VllmConfig
+
+
+def init_speculator(vllm_config: VllmConfig, device: torch.device):
+    speculative_config = vllm_config.speculative_config
+    assert speculative_config is not None
+    if speculative_config.use_eagle():
+        from vllm.v1.worker.gpu.spec_decode.eagle.speculator import EagleSpeculator
+
+        return EagleSpeculator(vllm_config, device)
+    raise NotImplementedError(f"{speculative_config.method} is not supported yet.")

vllm/v1/worker/gpu/spec_decode/eagle/cudagraph.py

@@ -0,0 +1,191 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Callable
+from typing import Any
+
+import torch
+
+from vllm.config import VllmConfig
+from vllm.config.compilation import CUDAGraphMode
+from vllm.model_executor.offloader.base import get_offloader
+from vllm.v1.kv_cache_interface import KVCacheConfig
+from vllm.v1.worker.gpu.block_table import BlockTables
+from vllm.v1.worker.gpu.cudagraph_utils import (
+    capture_graphs,
+    get_cudagraph_sizes,
+    prepare_inputs_to_capture,
+)
+from vllm.v1.worker.gpu.dp_utils import make_num_tokens_across_dp
+from vllm.v1.worker.gpu.input_batch import InputBuffers
+from vllm.v1.worker.utils import AttentionGroup
+
+
+class EagleCudaGraphManager:
+    def __init__(self, vllm_config: VllmConfig, device: torch.device):
+        self.vllm_config = vllm_config
+        self.scheduler_config = vllm_config.scheduler_config
+        self.device = device
+
+        self.max_model_len = vllm_config.model_config.max_model_len
+        self.max_num_reqs = self.scheduler_config.max_num_seqs
+        self.max_num_tokens = self.scheduler_config.max_num_batched_tokens
+        self.dp_size = vllm_config.parallel_config.data_parallel_size
+        self.compilation_config = vllm_config.compilation_config
+        assert self.compilation_config is not None
+
+        # NOTE(woosuk): For Eagle, we only use CUDA graphs for decode.
+        self.cudagraph_mode = self.compilation_config.cudagraph_mode.decode_mode()
+
+        # only need to capture uniform decode cudagraph sizes (the 2nd return value)
+        _, self.cudagraph_sizes = get_cudagraph_sizes(
+            self.compilation_config.cudagraph_capture_sizes,
+            self.max_num_reqs,
+            self.max_num_tokens,
+            self.cudagraph_mode,
+            uniform_decode_query_len=1,
+            uniform_decode_cudagraph=True,
+        )
+
+        self.graphs: dict[int, torch.cuda.CUDAGraph] = {}
+        self.pool = None
+        if self.cudagraph_mode != CUDAGraphMode.NONE:
+            self.pool = torch.cuda.graph_pool_handle()
+
+    def get_cudagraph_size(self, num_tokens: int) -> int | None:
+        return self.cudagraph_sizes.get(num_tokens)
+
+    def capture_graph(
+        self,
+        num_tokens: int,
+        capture_cg_mode: CUDAGraphMode,
+        generate_fn: Callable,
+        input_buffers: InputBuffers,
+        block_tables: BlockTables,
+        attn_groups: list[list[AttentionGroup]],
+        kv_cache_config: KVCacheConfig,
+    ) -> None:
+        assert capture_cg_mode in [CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL], (
+            f"Invalid capture_cudagraph_mode for capture: {capture_cg_mode}"
+        )
+        if capture_cg_mode == CUDAGraphMode.PIECEWISE:
+            capture_fn = self._capture_piecewise_graph
+        else:
+            capture_fn = self._capture_full_graph
+
+        num_reqs = min(num_tokens, self.max_num_reqs)
+        attn_metadata, slot_mappings = prepare_inputs_to_capture(
+            num_reqs,
+            num_tokens,
+            input_buffers,
+            block_tables,
+            attn_groups,
+            self.max_model_len,
+            kv_cache_config,
+            uniform_decode_query_len=1,
+        )
+        num_tokens_across_dp = make_num_tokens_across_dp(self.dp_size, num_tokens)
+
+        # Warm up.
+        generate_fn(
+            num_reqs,
+            num_tokens,
+            attn_metadata,
+            slot_mappings,
+            num_tokens_across_dp,
+            CUDAGraphMode.NONE,
+        )
+
+        # Capture the graph.
+        capture_fn(
+            num_reqs=num_reqs,
+            num_tokens=num_tokens,
+            generate_fn=generate_fn,
+            attn_metadata=attn_metadata,
+            slot_mappings=slot_mappings,
+            num_tokens_across_dp=num_tokens_across_dp,
+        )
+
+    def _capture_full_graph(
+        self,
+        num_reqs: int,
+        num_tokens: int,
+        generate_fn: Callable,
+        attn_metadata: dict[str, Any],
+        slot_mappings: dict[str, torch.Tensor],
+        num_tokens_across_dp: torch.Tensor,
+    ) -> None:
+        assert num_tokens not in self.graphs
+        graph = torch.cuda.CUDAGraph()
+
+        # Sync offloader's copy stream before capture.
+        # Ensure any pre-capture prefetches from offloader are complete.
+        get_offloader().sync_prev_onload()
+
+        with torch.cuda.graph(graph, self.pool):
+            generate_fn(
+                num_reqs,
+                num_tokens,
+                attn_metadata,
+                slot_mappings,
+                num_tokens_across_dp,
+                CUDAGraphMode.NONE,
+            )
+            # Join offloader's copy stream after forward to avoid unjoined
+            # stream error. The last layer's start_prefetch forks copy_stream,
+            # but wait_prefetch only happens in the next forward pass.
+            get_offloader().join_after_forward()
+        self.graphs[num_tokens] = graph
+
+    def _capture_piecewise_graph(
+        self,
+        num_reqs: int,
+        num_tokens: int,
+        generate_fn: Callable,
+        attn_metadata: dict[str, Any],
+        slot_mappings: dict[str, torch.Tensor],
+        num_tokens_across_dp: torch.Tensor,
+    ) -> None:
+        generate_fn(
+            num_reqs,
+            num_tokens,
+            attn_metadata,
+            slot_mappings,
+            num_tokens_across_dp,
+            CUDAGraphMode.PIECEWISE,
+        )
+
+    @torch.inference_mode()
+    def capture(
+        self,
+        generate_fn: Callable,
+        input_buffers: InputBuffers,
+        block_tables: BlockTables,
+        attn_groups: list[list[AttentionGroup]],
+        kv_cache_config: KVCacheConfig,
+    ) -> None:
+        if self.cudagraph_mode == CUDAGraphMode.NONE:
+            return
+
+        capture_graphs(
+            self.cudagraph_sizes,
+            self.device,
+            self.capture_graph,
+            capture_cudagraph_mode=self.cudagraph_mode,
+            desc=f"Capturing eagle CUDA graphs ({self.cudagraph_mode.name})",
+            generate_fn=generate_fn,
+            input_buffers=input_buffers,
+            block_tables=block_tables,
+            attn_groups=attn_groups,
+            kv_cache_config=kv_cache_config,
+        )
+
+    def run_fullgraph(self, num_tokens: int) -> None:
+        assert num_tokens in self.graphs
+        # Sync offloader before replay - needed when transitioning from
+        # eager/piecewise to full cudagraph (e.g., prefill → decode).
+        # The previous eager iteration's start_prefetch may have queued
+        # H2D copies on copy_stream that the graph's captured events
+        # cannot see. Without this, replay could overwrite static buffers
+        # while those copies are still in flight.
+        get_offloader().sync_prev_onload()
+        self.graphs[num_tokens].replay()

vllm/v1/worker/gpu/spec_decode/eagle/eagle3_utils.py

@@ -0,0 +1,46 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import cast
+
+import torch.nn as nn
+
+from vllm.config import SpeculativeConfig
+from vllm.logger import init_logger
+from vllm.model_executor.models.interfaces import SupportsEagle3, supports_eagle3
+
+logger = init_logger(__name__)
+
+
+def set_eagle3_aux_hidden_state_layers(
+    model: nn.Module,
+    spec_config: SpeculativeConfig,
+) -> None:
+    if not supports_eagle3(model):
+        raise RuntimeError("Model does not support EAGLE3 interface")
+    # mypy may infer the class-level overload for supports_eagle3.
+    # Narrow explicitly to the runtime protocol instance.
+    if isinstance(model, type):
+        raise RuntimeError("Expected model instance for EAGLE3 configuration")
+    eagle3_model = cast(SupportsEagle3, model)
+
+    aux_layers = get_eagle3_aux_layers_from_config(spec_config)
+    if aux_layers:
+        logger.info("Using Eagle3 auxiliary layers from config: %s", aux_layers)
+    else:
+        aux_layers = eagle3_model.get_eagle3_aux_hidden_state_layers()
+        logger.info("Using Eagle3 auxiliary layers from model: %s", aux_layers)
+    eagle3_model.set_aux_hidden_state_layers(aux_layers)
+
+
+def get_eagle3_aux_layers_from_config(
+    spec_config: SpeculativeConfig,
+) -> tuple[int, ...] | None:
+    if not (spec_config and spec_config.draft_model_config):
+        return None
+    hf_config = spec_config.draft_model_config.hf_config
+    if not hasattr(hf_config, "eagle_aux_hidden_state_layer_ids"):
+        return None
+    layer_ids = hf_config.eagle_aux_hidden_state_layer_ids
+    if layer_ids and isinstance(layer_ids, (list, tuple)):
+        return tuple(layer_ids)
+    return None

vllm/v1/worker/gpu/spec_decode/eagle/speculator.py

@@ -0,0 +1,583 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+from vllm.config import VllmConfig
+from vllm.config.compilation import CUDAGraphMode
+from vllm.forward_context import BatchDescriptor, set_forward_context
+from vllm.logger import init_logger
+from vllm.triton_utils import tl, triton
+from vllm.v1.kv_cache_interface import KVCacheConfig
+from vllm.v1.worker.gpu.attn_utils import (
+    build_attn_metadata,
+    build_slot_mappings_by_layer,
+)
+from vllm.v1.worker.gpu.block_table import BlockTables
+from vllm.v1.worker.gpu.input_batch import InputBatch, InputBuffers
+from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
+from vllm.v1.worker.gpu.spec_decode.eagle.cudagraph import EagleCudaGraphManager
+from vllm.v1.worker.gpu.spec_decode.eagle.utils import load_eagle_model
+from vllm.v1.worker.utils import AttentionGroup
+
+logger = init_logger(__name__)
+
+
+class EagleSpeculator:
+    def __init__(self, vllm_config: VllmConfig, device: torch.device):
+        self.vllm_config = vllm_config
+        self.device = device
+
+        self.speculative_config = vllm_config.speculative_config
+        assert self.speculative_config is not None
+        self.method = self.speculative_config.method
+        self.num_speculative_steps = self.speculative_config.num_speculative_tokens
+        self.draft_model_config = self.speculative_config.draft_model_config
+
+        self.scheduler_config = vllm_config.scheduler_config
+        self.max_num_reqs = self.scheduler_config.max_num_seqs
+        self.max_num_tokens = self.scheduler_config.max_num_batched_tokens
+        self.max_model_len = vllm_config.model_config.max_model_len
+        # 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 = self.draft_model_config.get_hidden_size()
+        self.inputs_embeds_size = self.draft_model_config.get_inputs_embeds_size()
+        self.vocab_size = self.draft_model_config.get_vocab_size()
+        self.dtype = vllm_config.model_config.dtype
+
+        self.input_buffers = InputBuffers(
+            max_num_reqs=self.max_num_reqs,
+            max_num_tokens=self.max_num_tokens,
+            device=device,
+        )
+        self.hidden_states = torch.zeros(
+            self.max_num_tokens, self.hidden_size, dtype=self.dtype, device=device
+        )
+        self.idx_mapping = torch.zeros(
+            self.max_num_reqs, dtype=torch.int32, device=device
+        )
+        self.temperature = torch.zeros(
+            self.max_num_reqs, dtype=torch.float32, device=device
+        )
+        self.seeds = torch.zeros(self.max_num_reqs, dtype=torch.int64, device=device)
+        self.draft_tokens = torch.zeros(
+            self.max_num_reqs,
+            self.num_speculative_steps,
+            dtype=torch.int64,
+            device=device,
+        )
+
+        self.cudagraph_manager = EagleCudaGraphManager(vllm_config, device)
+
+    def load_model(self, target_model: nn.Module) -> None:
+        self.model = load_eagle_model(target_model, self.vllm_config)
+
+    def set_attn(
+        self,
+        kv_cache_config: KVCacheConfig,
+        attn_groups: list[list[AttentionGroup]],
+        block_tables: BlockTables,
+    ) -> None:
+        self.kv_cache_config = kv_cache_config
+        self.attn_groups = attn_groups
+        self.block_tables = block_tables
+
+    @torch.inference_mode()
+    def run_model(
+        self,
+        num_tokens: int,
+        attn_metadata: dict[str, Any] | None,
+        slot_mappings: dict[str, torch.Tensor] | None,
+        num_tokens_across_dp: torch.Tensor | None,
+        cudagraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        batch_descriptor = BatchDescriptor(num_tokens=num_tokens)
+        with set_forward_context(
+            attn_metadata,
+            self.vllm_config,
+            num_tokens=num_tokens,
+            cudagraph_runtime_mode=cudagraph_runtime_mode,
+            num_tokens_across_dp=num_tokens_across_dp,
+            slot_mapping=slot_mappings,
+            batch_descriptor=batch_descriptor,
+        ):
+            ret_hidden_states = self.model(
+                input_ids=self.input_buffers.input_ids[:num_tokens],
+                positions=self.input_buffers.positions[:num_tokens],
+                hidden_states=self.hidden_states[:num_tokens],
+            )
+        if self.method == "mtp":
+            last_hidden_states = ret_hidden_states
+            hidden_states = ret_hidden_states
+        else:
+            last_hidden_states, hidden_states = ret_hidden_states
+        return last_hidden_states, hidden_states
+
+    def generate_draft(
+        self,
+        num_reqs: int,
+        num_tokens_padded: int,
+        attn_metadata: dict[str, Any],
+        slot_mappings: dict[str, torch.Tensor],
+        num_tokens_across_dp: torch.Tensor | None,
+        cudagraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
+    ) -> None:
+        pos = self.input_buffers.positions[:num_reqs]
+        query_start_loc = self.input_buffers.query_start_loc[: num_reqs + 1]
+        idx_mapping = self.idx_mapping[:num_reqs]
+        for step in range(1, self.num_speculative_steps):
+            # Run the eagle model.
+            last_hidden_states, hidden_states = self.run_model(
+                num_tokens_padded,
+                attn_metadata,
+                slot_mappings,
+                num_tokens_across_dp,
+                cudagraph_runtime_mode,
+            )
+            last_hidden_states = last_hidden_states[:num_reqs]
+            hidden_states = hidden_states[:num_reqs]
+            logits = self.model.compute_logits(last_hidden_states)
+
+            # NOTE(woosuk): We must add 1 to the positions to match the Gumbel noise
+            # used for draft and target sampling.
+            draft_tokens = gumbel_sample(
+                logits,
+                idx_mapping,
+                self.temperature,
+                self.seeds,
+                pos + 1,
+                apply_temperature=True,
+            )
+            self.draft_tokens[:num_reqs, step] = draft_tokens
+
+            if step < self.num_speculative_steps - 1:
+                # Update the inputs for the next step.
+                update_eagle_inputs(
+                    draft_tokens,
+                    hidden_states,
+                    self.input_buffers,
+                    self.hidden_states,
+                    self.max_model_len,
+                )
+                self.block_tables.compute_slot_mappings(
+                    idx_mapping, query_start_loc, pos
+                )
+
+    def capture_model(self) -> None:
+        if self.num_speculative_steps == 1:
+            return
+        logger.info("Capturing model for Eagle speculator...")
+        self.cudagraph_manager.capture(
+            self.generate_draft,
+            self.input_buffers,
+            self.block_tables,
+            self.attn_groups,
+            self.kv_cache_config,
+        )
+
+    @torch.inference_mode()
+    def propose(
+        self,
+        input_batch: InputBatch,
+        # [num_tokens, hidden_size]
+        last_hidden_states: torch.Tensor,
+        # num_layers x [num_tokens, hidden_size]
+        aux_hidden_states: list[torch.Tensor] | None,
+        # [num_reqs]
+        num_sampled: torch.Tensor,
+        # [num_reqs]
+        num_rejected: torch.Tensor,
+        # [max_num_reqs]
+        last_sampled: torch.Tensor,
+        # [max_num_reqs]
+        next_prefill_tokens: torch.Tensor,
+        # [max_num_reqs]
+        temperature: torch.Tensor,
+        # [max_num_reqs]
+        seeds: torch.Tensor,
+    ) -> torch.Tensor:
+        # NOTE(woosuk): To avoid CPU-GPU synchronization without CPU knowing the
+        # number of rejected tokens, we maintain the size of eagle's input_ids and
+        # hidden_states the same as the target model's. This means, we pad each
+        # request's query length to include any rejected positions. By doing so,
+        # we can also reuse the attention metadata (e.g., query_start_loc,
+        # seq_lens) of the target model.
+        if aux_hidden_states:
+            assert self.method == "eagle3"
+            hidden_states = self.model.combine_hidden_states(
+                torch.cat(aux_hidden_states, dim=-1)
+            )
+        else:
+            hidden_states = last_hidden_states
+        num_tokens = input_batch.num_tokens_after_padding
+        self.hidden_states[:num_tokens] = hidden_states
+
+        # Get the input ids and last token indices for the speculator.
+        last_token_indices = prepare_eagle_inputs(
+            self.input_buffers,
+            input_batch,
+            num_sampled,
+            num_rejected,
+            last_sampled,
+            next_prefill_tokens,
+        )
+
+        # Prefill: Run the eagle speculator with eager mode.
+        # TODO(woosuk): Support CUDA graph for prefill.
+        last_hidden_states, hidden_states = self.run_model(
+            num_tokens,
+            input_batch.attn_metadata,
+            input_batch.slot_mappings,
+            num_tokens_across_dp=None,  # FIXME
+        )
+        sample_hidden_states = last_hidden_states[last_token_indices]
+        logits = self.model.compute_logits(sample_hidden_states)
+
+        num_reqs = input_batch.num_reqs
+        # NOTE(woosuk): For draft sampling, we only consider the temperature
+        # and ignore the other sampling parameters such as top_k and top_p,
+        # for simplicity and performance.
+        # While this may slightly degrade the acceptance rate, it does not
+        # affect the output distribution after rejection sampling.
+        idx_mapping = self.idx_mapping[:num_reqs]
+        idx_mapping.copy_(input_batch.idx_mapping)
+        self.temperature.copy_(temperature)
+        self.seeds.copy_(seeds)
+        # Gather the values and copy them to the pre-allocated buffers.
+        pos = self.input_buffers.positions[:num_reqs]
+        torch.gather(input_batch.positions, 0, last_token_indices, out=pos)
+        # NOTE(woosuk): We must add 1 to the positions to match the Gumbel noise
+        # used for draft and target sampling.
+        draft_tokens = gumbel_sample(
+            logits,
+            idx_mapping,
+            self.temperature,
+            self.seeds,
+            pos + 1,
+            apply_temperature=True,
+        )
+        if self.num_speculative_steps == 1:
+            # Early exit.
+            return draft_tokens.view(-1, 1)
+
+        # Save the draft tokens for the first step.
+        self.draft_tokens[:num_reqs, 0] = draft_tokens
+        # Prepare the inputs for the decode steps.
+        prepare_eagle_decode(
+            draft_tokens,
+            hidden_states,
+            last_token_indices,
+            input_batch.seq_lens,
+            num_rejected,
+            self.input_buffers,
+            self.hidden_states,
+            self.max_model_len,
+            self.max_num_reqs,
+        )
+        query_start_loc = self.input_buffers.query_start_loc[: num_reqs + 1]
+        slot_mappings = self.block_tables.compute_slot_mappings(
+            idx_mapping, query_start_loc, pos
+        )
+
+        cudagraph_size = self.cudagraph_manager.get_cudagraph_size(num_reqs)
+        cudagraph_mode = self.cudagraph_manager.cudagraph_mode
+        if cudagraph_size is not None and cudagraph_mode == CUDAGraphMode.FULL:
+            # Run full CUDA graph.
+            self.cudagraph_manager.run_fullgraph(cudagraph_size)
+            return self.draft_tokens[:num_reqs]
+
+        # Run eager or piecewise CUDA graph.
+        num_tokens_padded = cudagraph_size if cudagraph_size is not None else num_reqs
+        query_start_loc_cpu = torch.arange(
+            num_reqs + 1, dtype=torch.int32, device="cpu"
+        )
+        block_tables = [x[:num_reqs] for x in self.block_tables.input_block_tables]
+
+        # FIXME(woosuk): This is UNSAFE!!
+        attn_metadata = build_attn_metadata(
+            attn_groups=self.attn_groups,
+            num_reqs=num_reqs,
+            num_tokens=num_reqs,
+            query_start_loc_gpu=query_start_loc,
+            query_start_loc_cpu=query_start_loc_cpu,
+            max_query_len=1,
+            seq_lens=self.input_buffers.seq_lens[:num_reqs],
+            max_seq_len=self.max_model_len,
+            block_tables=block_tables,
+            slot_mappings=slot_mappings,
+            kv_cache_config=self.kv_cache_config,
+        )
+        slot_mappings_by_layer = build_slot_mappings_by_layer(
+            slot_mappings, self.kv_cache_config
+        )
+        self.generate_draft(
+            num_reqs,
+            num_tokens_padded,
+            attn_metadata,
+            slot_mappings_by_layer,
+            num_tokens_across_dp=None,  # FIXME
+            cudagraph_runtime_mode=cudagraph_mode,
+        )
+        return self.draft_tokens[:num_reqs]
+
+
+@triton.jit
+def _prepare_eagle_inputs_kernel(
+    last_token_indices_ptr,
+    eagle_input_ids_ptr,
+    eagle_positions_ptr,
+    target_input_ids_ptr,
+    target_positions_ptr,
+    idx_mapping_ptr,
+    last_sampled_ptr,
+    next_prefill_tokens_ptr,
+    num_sampled_ptr,
+    num_rejected_ptr,
+    query_start_loc_ptr,
+    BLOCK_SIZE: tl.constexpr,
+):
+    batch_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
+
+    query_start = tl.load(query_start_loc_ptr + batch_idx)
+    query_end = tl.load(query_start_loc_ptr + batch_idx + 1)
+    query_len = query_end - query_start
+
+    # Get the true query length and next token after accounting for rejected tokens.
+    num_rejected = tl.load(num_rejected_ptr + batch_idx)
+    query_len -= num_rejected
+
+    num_sampled = tl.load(num_sampled_ptr + batch_idx)
+    if num_sampled > 0:
+        next_token = tl.load(last_sampled_ptr + req_state_idx).to(tl.int32)
+    else:
+        # Chunked prefilling.
+        # Get the next prefill token.
+        next_token = tl.load(next_prefill_tokens_ptr + req_state_idx)
+
+    # Shift target_input_ids by one.
+    for i in range(1, query_len, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < query_len
+        input_ids = tl.load(target_input_ids_ptr + query_start + block, mask=mask)
+        tl.store(eagle_input_ids_ptr + query_start + block - 1, input_ids, mask=mask)
+
+    last_token_index = query_start + query_len - 1
+    tl.store(last_token_indices_ptr + batch_idx, last_token_index)
+    tl.store(eagle_input_ids_ptr + last_token_index, next_token)
+
+    # Copy positions.
+    for i in range(0, query_len, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < query_len
+        target_pos = tl.load(target_positions_ptr + query_start + block, mask=mask)
+        tl.store(eagle_positions_ptr + query_start + block, target_pos, mask=mask)
+
+
+def prepare_eagle_inputs(
+    input_buffers: InputBuffers,
+    input_batch: InputBatch,
+    # [num_reqs]
+    num_sampled: torch.Tensor,
+    # [num_reqs]
+    num_rejected: torch.Tensor,
+    # [max_num_reqs]
+    last_sampled: torch.Tensor,
+    # [max_num_reqs]
+    next_prefill_tokens: torch.Tensor,
+) -> torch.Tensor:
+    num_reqs = input_batch.num_reqs
+    last_token_indices = torch.empty(
+        num_reqs,
+        dtype=torch.int64,
+        device=num_sampled.device,
+    )
+    _prepare_eagle_inputs_kernel[(num_reqs,)](
+        last_token_indices,
+        input_buffers.input_ids,
+        input_buffers.positions,
+        input_batch.input_ids,
+        input_batch.positions,
+        input_batch.idx_mapping,
+        last_sampled,
+        next_prefill_tokens,
+        num_sampled,
+        num_rejected,
+        input_batch.query_start_loc,
+        BLOCK_SIZE=1024,
+    )
+    return last_token_indices
+
+
+@triton.jit
+def _prepare_eagle_docode_kernel(
+    draft_tokens_ptr,
+    output_hidden_states_ptr,
+    output_hidden_states_stride,
+    last_token_indices_ptr,
+    target_seq_lens_ptr,
+    num_rejected_ptr,
+    input_ids_ptr,
+    positions_ptr,
+    input_hidden_states_ptr,
+    input_hidden_states_stride,
+    query_start_loc_ptr,
+    seq_lens_ptr,
+    hidden_size,
+    max_model_len,
+    max_num_reqs,
+    BLOCK_SIZE: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+    num_reqs = tl.num_programs(0) - 1
+    if req_idx == num_reqs:
+        # Compute query_start_loc. Pad it with the last query_start_loc
+        # for CUDA graphs.
+        for i in range(0, max_num_reqs + 1, BLOCK_SIZE):
+            block = i + tl.arange(0, BLOCK_SIZE)
+            q = tl.where(block < num_reqs, block, num_reqs)
+            mask = block < max_num_reqs + 1
+            tl.store(query_start_loc_ptr + block, q, mask=mask)
+        # Pad seq_lens for CUDA graphs.
+        for i in range(req_idx, max_num_reqs, BLOCK_SIZE):
+            block = i + tl.arange(0, BLOCK_SIZE)
+            mask = block < max_num_reqs
+            tl.store(seq_lens_ptr + block, 0, mask=mask)
+        return
+
+    # draft token -> input id.
+    draft_token = tl.load(draft_tokens_ptr + req_idx)
+    tl.store(input_ids_ptr + req_idx, draft_token)
+
+    # output hidden states -> input hidden states.
+    src_idx = tl.load(last_token_indices_ptr + req_idx)
+    for i in range(0, hidden_size, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < hidden_size
+        output_hidden_states = tl.load(
+            output_hidden_states_ptr + src_idx * output_hidden_states_stride + block,
+            mask=mask,
+        )
+        tl.store(
+            input_hidden_states_ptr + req_idx * input_hidden_states_stride + block,
+            output_hidden_states,
+            mask=mask,
+        )
+
+    # Compute position and seq_lens.
+    # NOTE(woosuk): To prevent out-of-range access, we clamp these values
+    # if they reach the max model length.
+    position = tl.load(positions_ptr + req_idx)
+    position = tl.minimum(position + 1, max_model_len - 1)
+    tl.store(positions_ptr + req_idx, position)
+
+    target_seq_len = tl.load(target_seq_lens_ptr + req_idx)
+    num_rejected = tl.load(num_rejected_ptr + req_idx)
+    seq_len = target_seq_len - num_rejected
+    seq_len = tl.minimum(seq_len + 1, max_model_len)
+    tl.store(seq_lens_ptr + req_idx, seq_len)
+
+
+def prepare_eagle_decode(
+    draft_tokens: torch.Tensor,
+    output_hidden_states: torch.Tensor,
+    last_token_indices: torch.Tensor,
+    target_seq_lens: torch.Tensor,
+    num_rejected: torch.Tensor,
+    input_buffers: InputBuffers,
+    input_hidden_states: torch.Tensor,
+    max_model_len: int,
+    max_num_reqs: int,
+):
+    num_reqs = draft_tokens.shape[0]
+    hidden_size = output_hidden_states.shape[-1]
+    _prepare_eagle_docode_kernel[(num_reqs + 1,)](
+        draft_tokens,
+        output_hidden_states,
+        output_hidden_states.stride(0),
+        last_token_indices,
+        target_seq_lens,
+        num_rejected,
+        input_buffers.input_ids,
+        input_buffers.positions,
+        input_hidden_states,
+        input_hidden_states.stride(0),
+        input_buffers.query_start_loc,
+        input_buffers.seq_lens,
+        hidden_size,
+        max_model_len,
+        max_num_reqs,
+        BLOCK_SIZE=1024,
+    )
+
+
+@triton.jit
+def _update_eagle_inputs_kernel(
+    input_ids_ptr,
+    positions_ptr,
+    input_hidden_states_ptr,
+    input_hidden_states_stride,
+    seq_lens_ptr,
+    max_model_len,
+    draft_tokens_ptr,
+    output_hidden_states_ptr,
+    output_hidden_states_stride,
+    hidden_size,
+    BLOCK_SIZE: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+
+    # Draft token -> Input ID.
+    draft_token = tl.load(draft_tokens_ptr + req_idx)
+    tl.store(input_ids_ptr + req_idx, draft_token)
+
+    # Output hidden states -> Input hidden states.
+    for i in range(0, hidden_size, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < hidden_size
+        output_hidden_states = tl.load(
+            output_hidden_states_ptr + req_idx * output_hidden_states_stride + block,
+            mask=mask,
+        )
+        tl.store(
+            input_hidden_states_ptr + req_idx * input_hidden_states_stride + block,
+            output_hidden_states,
+            mask=mask,
+        )
+
+    # Increment position and seq_lens.
+    # NOTE(woosuk): To prevent out-of-range access, we clamp these values
+    # if they reach the max model length.
+    position = tl.load(positions_ptr + req_idx)
+    position = tl.minimum(position + 1, max_model_len - 1)
+    tl.store(positions_ptr + req_idx, position)
+
+    seq_len = tl.load(seq_lens_ptr + req_idx)
+    seq_len = tl.minimum(seq_len + 1, max_model_len)
+    tl.store(seq_lens_ptr + req_idx, seq_len)
+
+
+def update_eagle_inputs(
+    draft_tokens: torch.Tensor,
+    output_hidden_states: torch.Tensor,
+    input_buffers: InputBuffers,
+    hidden_states: torch.Tensor,
+    max_model_len: int,
+):
+    num_reqs, hidden_size = output_hidden_states.shape
+    _update_eagle_inputs_kernel[(num_reqs,)](
+        input_buffers.input_ids,
+        input_buffers.positions,
+        hidden_states,
+        hidden_states.stride(0),
+        input_buffers.seq_lens,
+        max_model_len,
+        draft_tokens,
+        output_hidden_states,
+        output_hidden_states.stride(0),
+        hidden_size,
+        BLOCK_SIZE=1024,
+    )

vllm/v1/worker/gpu/spec_decode/eagle/utils.py

@@ -0,0 +1,52 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch.nn as nn
+
+from vllm.config import VllmConfig
+from vllm.model_executor.model_loader import get_model
+
+
+def load_eagle_model(target_model: nn.Module, vllm_config: VllmConfig) -> nn.Module:
+    from vllm.compilation.backends import set_model_tag
+
+    speculative_config = vllm_config.speculative_config
+    assert speculative_config is not None
+    draft_model_config = speculative_config.draft_model_config
+    with set_model_tag("eagle_head"):
+        eagle_model = get_model(
+            vllm_config=vllm_config, model_config=draft_model_config
+        )
+
+    # Share target embeddings when the draft checkpoint does not include
+    # its own vocab embedding table.
+    share_embeddings = True
+    if hasattr(eagle_model, "has_own_embed_tokens"):
+        share_embeddings = not eagle_model.has_own_embed_tokens
+    if share_embeddings:
+        target_language_model = (
+            target_model.get_language_model()
+            if hasattr(target_model, "get_language_model")
+            else target_model
+        )
+        inner_model = getattr(target_language_model, "model", None)
+        target_embed_tokens = None
+        if inner_model is not None:
+            if hasattr(inner_model, "embed_tokens"):
+                target_embed_tokens = inner_model.embed_tokens
+            elif hasattr(inner_model, "embedding"):
+                target_embed_tokens = inner_model.embedding
+        if target_embed_tokens is not None and hasattr(eagle_model, "model"):
+            if hasattr(eagle_model.model, "embed_tokens"):
+                del eagle_model.model.embed_tokens
+            eagle_model.model.embed_tokens = target_embed_tokens
+
+    # Only share target lm_head when the draft model does not own one.
+    share_lm_head = True
+    if hasattr(eagle_model, "has_own_lm_head"):
+        share_lm_head = not eagle_model.has_own_lm_head
+    if share_lm_head and hasattr(target_model, "lm_head"):
+        if hasattr(eagle_model, "lm_head"):
+            del eagle_model.lm_head
+        eagle_model.lm_head = target_model.lm_head
+
+    return eagle_model

vllm/v1/worker/gpu/spec_decode/rejection_sample.py

@@ -0,0 +1,71 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def _rejection_sample_kernel(
+    sampled_ptr,  # [num_reqs, num_speculative_steps + 1]
+    sampled_stride,
+    num_sampled_ptr,  # [num_reqs]
+    target_sampled_ptr,  # [num_draft_tokens + num_reqs]
+    input_ids_ptr,  # [num_draft_tokens + num_reqs]
+    cu_num_logits_ptr,  # [num_reqs + 1]
+):
+    req_idx = tl.program_id(0)
+    start_idx = tl.load(cu_num_logits_ptr + req_idx)
+    end_idx = tl.load(cu_num_logits_ptr + req_idx + 1)
+    num_tokens = end_idx - start_idx
+
+    num_sampled = 0
+    rejected = False
+    for i in range(num_tokens - 1):
+        if not rejected:
+            target_sampled = tl.load(target_sampled_ptr + start_idx + i)
+            draft_sampled = tl.load(input_ids_ptr + start_idx + i + 1)
+            tl.store(sampled_ptr + req_idx * sampled_stride + i, target_sampled)
+            num_sampled += 1
+            if target_sampled != draft_sampled:
+                rejected = True
+    if not rejected:
+        target_sampled = tl.load(target_sampled_ptr + start_idx + num_tokens - 1)
+        tl.store(
+            sampled_ptr + req_idx * sampled_stride + num_tokens - 1, target_sampled
+        )
+        num_sampled += 1
+    tl.store(num_sampled_ptr + req_idx, num_sampled)
+
+
+def rejection_sample(
+    # [num_draft_tokens + num_reqs]
+    target_sampled: torch.Tensor,
+    # [num_draft_tokens + num_reqs]
+    input_ids: torch.Tensor,
+    # [num_reqs + 1]
+    cu_num_logits: torch.Tensor,
+    num_speculative_steps: int,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    num_reqs = cu_num_logits.shape[0] - 1
+    sampled = torch.empty(
+        num_reqs,
+        num_speculative_steps + 1,
+        dtype=target_sampled.dtype,
+        device=target_sampled.device,
+    )
+    num_sampled = torch.empty(
+        num_reqs,
+        dtype=torch.int32,
+        device=target_sampled.device,
+    )
+    _rejection_sample_kernel[(num_reqs,)](
+        sampled,
+        sampled.stride(0),
+        num_sampled,
+        target_sampled,
+        input_ids,
+        cu_num_logits,
+        num_warps=1,
+    )
+    return sampled, num_sampled

vllm/v1/worker/gpu/spec_decode/utils.py

@@ -0,0 +1,47 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import numpy as np
+import torch
+
+from vllm.v1.outputs import DraftTokenIds
+from vllm.v1.worker.gpu.async_utils import async_copy_to_np
+from vllm.v1.worker.gpu.input_batch import InputBatch
+
+
+class DraftTokensHandler:
+    def __init__(self, device: torch.device | None = None):
+        self.device = device
+        self.copy_stream = torch.cuda.Stream(device)
+        self.copy_event = torch.cuda.Event()
+
+        self.req_ids: list[str] = []
+        self.draft_tokens_np: np.ndarray | None = None
+        self.num_draft_tokens: int = 0
+
+    def set_draft_tokens(
+        self, input_batch: InputBatch, draft_tokens: torch.Tensor
+    ) -> None:
+        self.req_ids = input_batch.req_ids
+        self.num_draft_tokens = draft_tokens.shape[1]
+        if not input_batch.has_structured_output_reqs:
+            # No draft token validation needs to be performed by
+            # the scheduler for this batch.
+            self.draft_tokens_np = None
+            return
+
+        # For spec decoding + structured outputs, we must transfer the
+        # draft tokens back to the scheduler for grammar validation.
+        current_stream = torch.cuda.current_stream(self.device)
+        self.copy_stream.wait_stream(current_stream)
+        with torch.cuda.stream(self.copy_stream):
+            self.draft_tokens_np = async_copy_to_np(draft_tokens)
+            self.copy_event.record()
+
+    def get_draft_tokens(self) -> DraftTokenIds | None:
+        if self.draft_tokens_np is not None:
+            self.copy_event.synchronize()
+            draft_token_ids = self.draft_tokens_np.tolist()
+        else:
+            # This case only happens when async scheduling is disabled.
+            draft_token_ids = [[-1] * self.num_draft_tokens for _ in self.req_ids]
+        return DraftTokenIds(self.req_ids, draft_token_ids)

vllm/v1/worker/gpu/states.py

@@ -0,0 +1,123 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import numpy as np
+import torch
+
+from vllm.v1.worker.gpu.buffer_utils import StagedWriteTensor, UvaBackedTensor
+
+
+class RequestState:
+    def __init__(
+        self,
+        max_num_reqs: int,
+        max_model_len: int,
+        max_num_batched_tokens: int,
+        num_speculative_steps: int,
+        vocab_size: int,
+        device: torch.device,
+    ):
+        self.max_num_reqs = max_num_reqs
+        self.max_model_len = max_model_len
+        self.max_num_batched_tokens = max_num_batched_tokens
+        self.num_speculative_steps = num_speculative_steps
+        self.vocab_size = vocab_size
+        self.device = device
+
+        self.req_id_to_index: dict[str, int] = {}
+        self.index_to_req_id: dict[int, str] = {}
+        self.free_indices = list(range(max_num_reqs))
+
+        # NOTE(woosuk): This tensor can be extremely large (e.g., several GBs)
+        # depending on the configured max_num_reqs and max_model_len.
+        # To save GPU memory, we use UVA instead of GPU for this tensor.
+        self.all_token_ids = StagedWriteTensor(
+            (self.max_num_reqs, self.max_model_len),
+            dtype=torch.int32,
+            device=device,
+            uva_instead_of_gpu=True,
+        )
+        # NOTE(woosuk): Distinguish clearly between prompt_len and prefill_len:
+        # - prompt_len: Number of tokens in the user-provided prompt.
+        # - prefill_len: Number of tokens passed into the model runner.
+        #   This can include the prompt and additional partial output tokens,
+        #   so prefill_len >= prompt_len.
+        # Usually, prefill_len equals prompt_len, but in cases such as resumption after
+        # preemption, prefill_len may be greater. Differentiating between these values
+        # is crucial, as certain features such as prompt logprobs or frequency penalties
+        # must treat prompt and output tokens separately.
+        self.prompt_len = UvaBackedTensor(self.max_num_reqs, dtype=torch.int32)
+        self.prefill_len = UvaBackedTensor(self.max_num_reqs, dtype=torch.int32)
+        # total_len = prompt_len + output_len. It grows as the request progresses.
+        self.total_len = StagedWriteTensor(
+            self.max_num_reqs, dtype=torch.int32, device=device
+        )
+
+        # Number of computed tokens.
+        self.num_computed_prefill_tokens = np.zeros(self.max_num_reqs, dtype=np.int32)
+        self.num_computed_tokens = StagedWriteTensor(
+            self.max_num_reqs, dtype=torch.int32, device=device
+        )
+
+        # Last sampled tokens.
+        self.last_sampled_tokens = torch.zeros(
+            self.max_num_reqs, 1, dtype=torch.int64, device=device
+        )
+
+        # Draft tokens.
+        self.draft_tokens = torch.zeros(
+            self.max_num_reqs,
+            self.num_speculative_steps,
+            dtype=torch.int64,
+            device=device,
+        )
+        self.next_prefill_tokens = torch.zeros(
+            self.max_num_reqs, dtype=torch.int32, device=device
+        )
+
+    @property
+    def num_reqs(self) -> int:
+        return len(self.req_id_to_index)
+
+    def add_request(
+        self,
+        req_id: str,
+        prompt_len: int,
+        all_token_ids: list[int],
+        num_computed_tokens: int,
+    ) -> None:
+        assert len(self.free_indices) > 0, "No free indices"
+        req_idx = self.free_indices.pop()
+        self.req_id_to_index[req_id] = req_idx
+        self.index_to_req_id[req_idx] = req_id
+
+        self.prompt_len.np[req_idx] = prompt_len
+        prefill_len = len(all_token_ids)
+        assert prefill_len >= prompt_len, (
+            f"prefill_len {prefill_len} < prompt_len {prompt_len}"
+        )
+        self.prefill_len.np[req_idx] = prefill_len
+        self.total_len.stage_write_elem(req_idx, prefill_len)
+        self.all_token_ids.stage_write(req_idx, 0, all_token_ids)
+        self.num_computed_prefill_tokens[req_idx] = num_computed_tokens
+        self.num_computed_tokens.stage_write_elem(req_idx, num_computed_tokens)
+
+    def apply_staged_writes(self) -> None:
+        self.prompt_len.copy_to_uva()
+        self.prefill_len.copy_to_uva()
+        self.total_len.apply_write()
+        self.all_token_ids.apply_write()
+        self.num_computed_tokens.apply_write()
+
+    def remove_request(self, req_id: str) -> None:
+        req_idx = self.req_id_to_index.pop(req_id, None)
+        if req_idx is None:
+            # Request not found.
+            return
+        self.index_to_req_id.pop(req_idx, None)
+        self.free_indices.append(req_idx)
+
+    def any_prefills(self, idx_mapping_np: np.ndarray) -> bool:
+        return np.any(
+            self.num_computed_prefill_tokens[idx_mapping_np]
+            < self.prefill_len.np[idx_mapping_np]
+        )

vllm/v1/worker/gpu/structured_outputs.py

@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import numpy as np
+import torch
+
+from vllm.triton_utils import tl, triton
+from vllm.utils.math_utils import cdiv
+from vllm.v1.worker.gpu.buffer_utils import async_copy_to_gpu
+from vllm.v1.worker.gpu.input_batch import InputBatch
+
+
+class StructuredOutputsWorker:
+    def __init__(self, max_num_logits: int, vocab_size: int, device: torch.device):
+        self.logits_indices = torch.zeros(
+            max_num_logits, dtype=torch.int32, device=device
+        )
+        self.grammar_bitmask = torch.zeros(
+            (max_num_logits, cdiv(vocab_size, 32)), dtype=torch.int32, device=device
+        )
+        self.device = device
+        self.copy_stream = torch.cuda.Stream()
+
+    def apply_grammar_bitmask(
+        self,
+        logits: torch.Tensor,
+        input_batch: InputBatch,
+        grammar_req_ids: list[str],
+        grammar_bitmask: np.ndarray,
+    ) -> None:
+        if not grammar_req_ids:
+            return
+
+        # Asynchronously copy the bitmask to GPU.
+        with torch.cuda.stream(self.copy_stream):
+            bitmask = async_copy_to_gpu(
+                grammar_bitmask, out=self.grammar_bitmask[: grammar_bitmask.shape[0]]
+            )
+
+        # Construct bitmask -> logits mapping
+        mapping: list[int] = []
+        req_ids = input_batch.req_ids
+        cu_num_logits = input_batch.cu_num_logits_np.tolist()
+        req_id_to_idx = {req_id: i for i, req_id in enumerate(req_ids)}
+        for grammar_req_id in grammar_req_ids:
+            req_idx = req_id_to_idx[grammar_req_id]
+            logits_start_idx = cu_num_logits[req_idx]
+            logits_end_idx = cu_num_logits[req_idx + 1]
+            mapping.extend(range(logits_start_idx, logits_end_idx))
+
+        # Asynchronously copy the mapping to GPU.
+        with torch.cuda.stream(self.copy_stream):
+            logits_indices = torch.tensor(
+                mapping, dtype=torch.int32, device="cpu", pin_memory=True
+            )
+            logits_indices = self.logits_indices[: len(mapping)].copy_(
+                logits_indices, non_blocking=True
+            )
+
+        # Ensure all async copies are complete before launching the kernel.
+        current_stream = torch.cuda.current_stream()
+        current_stream.wait_stream(self.copy_stream)
+
+        num_masks = bitmask.shape[0]
+        assert num_masks == len(mapping)
+        vocab_size = logits.shape[-1]
+        BLOCK_SIZE = 8192
+        grid = (num_masks, triton.cdiv(vocab_size, BLOCK_SIZE))
+        _apply_grammar_bitmask_kernel[grid](
+            logits,
+            logits.stride(0),
+            logits_indices,
+            bitmask,
+            bitmask.stride(0),
+            vocab_size,
+            BLOCK_SIZE=BLOCK_SIZE,
+        )
+
+        # Ensure the copy stream waits for the device tensors to finish being used
+        # before it re-uses or deallocates them
+        self.copy_stream.wait_stream(current_stream)
+
+
+# Adapted from
+# https://github.com/mlc-ai/xgrammar/blob/main/python/xgrammar/kernels/apply_token_bitmask_inplace_triton.py
+@triton.jit
+def _apply_grammar_bitmask_kernel(
+    logits_ptr,
+    logits_stride,
+    logits_indices_ptr,
+    bitmask_ptr,
+    bitmask_stride,
+    vocab_size,
+    BLOCK_SIZE: tl.constexpr,
+):
+    bitmask_idx = tl.program_id(0)
+    logits_idx = tl.load(logits_indices_ptr + bitmask_idx)
+
+    # Load the bitmask.
+    block_id = tl.program_id(1)
+    bitmask_offset = (block_id * BLOCK_SIZE) // 32 + tl.arange(0, BLOCK_SIZE // 32)
+    packed_bitmask = tl.load(
+        bitmask_ptr + bitmask_idx * bitmask_stride + bitmask_offset,
+        mask=bitmask_offset < bitmask_stride,
+    )
+    # Unpack the bitmask.
+    bitmask = ((packed_bitmask[:, None] >> (tl.arange(0, 32)[None, :])) & 1) == 0
+    bitmask = bitmask.reshape(BLOCK_SIZE)
+
+    # Apply the bitmask to the logits.
+    block_offset = block_id * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    tl.store(
+        logits_ptr + logits_idx * logits_stride + block_offset,
+        -float("inf"),
+        mask=bitmask & (block_offset < vocab_size),
+    )