# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import dataclasses import itertools from collections.abc import Callable from typing import Any import torch from vllm.config import CacheConfig from vllm.model_executor.layers.mamba.mamba_utils import ( MambaStateCopyFunc, get_conv_copy_spec, get_temporal_copy_spec, is_conv_state_dim_first, ) from vllm.triton_utils import tl, triton from vllm.utils.math_utils import cdiv from vllm.v1.core.sched.output import SchedulerOutput from vllm.v1.kv_cache_interface import KVCacheConfig, MambaSpec from vllm.v1.utils import CpuGpuBuffer from vllm.v1.worker.gpu_input_batch import CachedRequestState from vllm.v1.worker.lora_model_runner_mixin import GPUInputBatch @triton.jit def _copy_mamba_state_block( state_idx, bt_row_idx, src_col, dst_col, token_bias, block_table_ptrs_ptr, block_table_stride_req, state_base_addrs_ptr, state_block_strides_ptr, state_elem_sizes_ptr, state_inner_sizes_ptr, state_conv_widths_ptr, state_group_indices_ptr, # DS conv row metadata. Zero keeps the single-region copy path. state_dim_row_count_ptr, state_dim_row_stride_ptr, COPY_BLOCK_SIZE: tl.constexpr, CONV_STATE_DIM_FIRST: tl.constexpr, ): """Copy one (layer, state-type) mamba state block between block columns. Shared copy body of ``postprocess_mamba_fused_kernel`` and ``precopy_mamba_align_fused_kernel``, mirroring the V1 copy specs (``get_conv_copy_spec`` / ``get_temporal_copy_spec``): - conv state (conv_width > 0): shift the window by ``token_bias`` tokens, ``state[bt[src_col], token_bias:] -> state[bt[dst_col], :conv_width - token_bias]`` - temporal state: ``token_bias`` selects the accepted speculative column, ``state[bt[src_col + token_bias]] -> state[bt[dst_col]]`` The caller owns the decision logic (which columns, whether to copy); this device function only performs the byte copy for the given metadata slot. """ state_base_addr = tl.load(state_base_addrs_ptr + state_idx) state_block_stride = tl.load(state_block_strides_ptr + state_idx) state_elem_size = tl.load(state_elem_sizes_ptr + state_idx) state_inner_size = tl.load(state_inner_sizes_ptr + state_idx) conv_width = tl.load(state_conv_widths_ptr + state_idx) # Load the group index for this state, then index into the correct # group's block table. Each mamba group has independently allocated # physical blocks. Reinterpret as int32* since block ids are int32. group_idx = tl.load(state_group_indices_ptr + state_idx).to(tl.int64) group_base_addr = tl.load(block_table_ptrs_ptr + group_idx) block_table_typed = group_base_addr.to(tl.pointer_type(tl.int32)) block_table_base = block_table_typed + bt_row_idx * block_table_stride_req # Widen block ids to int64 before they reach `block_id * state_block_stride` # below: state_block_stride can exceed 2**31 bytes for large mamba caches, # and Triton would otherwise do the multiply in int32 and wrap. dest_block_id = tl.load(block_table_base + dst_col).to(tl.int64) dst_addr = state_base_addr + dest_block_id * state_block_stride is_conv_state = conv_width > 0 if CONV_STATE_DIM_FIRST and is_conv_state: # DS conv layout: state_len is the slide axis; copy per dim row. src_block_id = tl.load(block_table_base + src_col).to(tl.int64) dim_rows = tl.load(state_dim_row_count_ptr + state_idx) row_stride = tl.load(state_dim_row_stride_ptr + state_idx) per_row_bytes = (conv_width - token_bias).to(tl.int64) * state_elem_size bias_bytes = token_bias.to(tl.int64) * state_elem_size src_block_addr = state_base_addr + src_block_id * state_block_stride offsets = tl.arange(0, COPY_BLOCK_SIZE) for d in range(0, dim_rows): row_src = src_block_addr + d * row_stride + bias_bytes row_dst = dst_addr + d * row_stride for i in range(0, per_row_bytes, COPY_BLOCK_SIZE): mask = (i + offsets) < per_row_bytes curr_src = (row_src + i + offsets).to(tl.pointer_type(tl.uint8)) curr_dst = (row_dst + i + offsets).to(tl.pointer_type(tl.uint8)) data = tl.load(curr_src, mask=mask) tl.store(curr_dst, data, mask=mask) return if is_conv_state: # SD conv: copy # state[bt[src_col], token_bias:] -> # state[bt[dst_col], :conv_width - token_bias] src_block_id = tl.load(block_table_base + src_col).to(tl.int64) src_offset = token_bias.to(tl.int64) * state_inner_size * state_elem_size src_addr = state_base_addr + src_block_id * state_block_stride + src_offset num_elems_to_copy = (conv_width - token_bias).to(tl.int64) * state_inner_size copy_size = num_elems_to_copy * state_elem_size else: # Temporal state: copy state[bt[src_col + token_bias]] -> state[bt[dst_col]] actual_src_block_id = tl.load(block_table_base + src_col + token_bias).to( tl.int64 ) src_addr = state_base_addr + actual_src_block_id * state_block_stride # Use natural block data size (inner_size * elem_size), NOT # state_block_stride which is the page stride and can exceed the # actual data when the state tensor uses as_strided page padding. copy_size = state_inner_size * state_elem_size offsets = tl.arange(0, COPY_BLOCK_SIZE) for i in range(0, copy_size, COPY_BLOCK_SIZE): mask = (i + offsets) < copy_size curr_src = (src_addr + i + offsets).to(tl.pointer_type(tl.uint8)) curr_dst = (dst_addr + i + offsets).to(tl.pointer_type(tl.uint8)) data = tl.load(curr_src, mask=mask) tl.store(curr_dst, data, mask=mask) @triton.jit def postprocess_mamba_fused_kernel( # Decision inputs (per-request) num_accepted_tokens_ptr, mamba_state_idx_ptr, num_scheduled_tokens_ptr, num_computed_tokens_ptr, num_draft_tokens_ptr, # Per-group block table base addresses: int64[num_groups]. Each entry is # the data_ptr of that group's persistent [max_reqs, max_blocks] int32 # block table. block_table_ptrs_ptr, block_table_stride_req: tl.int64, # stride between requests (in elements) # Mamba state metadata (per-layer, per-state-type) # These are 1D arrays indexed by (layer_idx * num_state_types + state_type_idx) state_base_addrs_ptr, # base address of each state tensor state_block_strides_ptr, # bytes per block for each state state_elem_sizes_ptr, # element size for each state state_inner_sizes_ptr, # number of elements in inner dimensions state_conv_widths_ptr, # conv width for conv states (0 for temporal) state_group_indices_ptr, # maps state_idx to group index in block table # DS conv row metadata. Zero keeps the single-region copy path. state_dim_row_count_ptr, # int32: per-block dim row count for DS conv state_dim_row_stride_ptr, # int64: bytes between rows for DS conv # Output: num_accepted_tokens update (for src==dst case) num_accepted_tokens_out_ptr, # Optional: batch_idx -> req_idx mapping (V2 model runner / PP). The # per-request decision arrays are in req-state-slot order; the block table # is in batch order, so HAS_IDX_MAPPING splits the two indexings. idx_mapping_ptr, # Runtime parameter (varies per batch - NOT constexpr to avoid recompilation) num_reqs, # Compile-time constants (fixed after model initialization) # block_size: determined by model config, constant for all invocations block_size: tl.constexpr, # COPY_BLOCK_SIZE: fixed tuning parameter for memory copy loop COPY_BLOCK_SIZE: tl.constexpr, CONV_STATE_DIM_FIRST: tl.constexpr, # HAS_IDX_MAPPING: when True, program_id(0) is a batch index resolved to a # req-state slot via idx_mapping_ptr (V2). When False, it is the req index. HAS_IDX_MAPPING: tl.constexpr = False, # PRECOMPUTED_NEW_COMPUTED: when True, num_computed_tokens_ptr already holds # the post-step new_num_computed value (V2 supplies the advanced count). PRECOMPUTED_NEW_COMPUTED: tl.constexpr = False, ): """ Fused GPU kernel for postprocess_mamba that computes decisions AND performs mamba state copies without any CPU-GPU synchronization. Grid: (num_reqs, num_layers * num_state_types) - program_id(0) = request/batch index - program_id(1) = state_idx (flattened index into layer/state_type metadata) Note: num_layers and num_state_types are not passed as kernel parameters because the kernel indexes directly into pre-flattened metadata arrays using program_id(1). The grid dimensions encode the total state count. """ batch_idx = tl.program_id(0) state_idx = tl.program_id(1) # Bounds check if batch_idx >= num_reqs: return if HAS_IDX_MAPPING: req_idx = tl.load(idx_mapping_ptr + batch_idx) if req_idx < 0: return else: req_idx = batch_idx # Compute decision logic (mirrors postprocess_mamba Python reference) num_accepted = tl.load(num_accepted_tokens_ptr + req_idx) src_block_idx = tl.load(mamba_state_idx_ptr + req_idx) if PRECOMPUTED_NEW_COMPUTED: new_num_computed = tl.load(num_computed_tokens_ptr + req_idx) num_tokens_running_state = new_num_computed - num_accepted + 1 else: num_scheduled = tl.load(num_scheduled_tokens_ptr + req_idx) num_computed = tl.load(num_computed_tokens_ptr + req_idx) num_draft = tl.load(num_draft_tokens_ptr + req_idx) num_tokens_running_state = num_computed + num_scheduled - num_draft new_num_computed = num_tokens_running_state + num_accepted - 1 aligned_new_computed = (new_num_computed // block_size) * block_size needs_copy = aligned_new_computed >= num_tokens_running_state if not needs_copy: return # Compute copy parameters accept_token_bias = aligned_new_computed - num_tokens_running_state dest_block_idx = aligned_new_computed // block_size - 1 # Update accepted-token count before early exits (per-request, so only # state_idx == 0 writes). V2 updates in place; V1 writes the _out buffer. if src_block_idx == dest_block_idx and state_idx == 0: if HAS_IDX_MAPPING: tl.store(num_accepted_tokens_ptr + req_idx, 1) else: tl.store(num_accepted_tokens_out_ptr + req_idx, 1) # Skip no-op self-copy. if src_block_idx == dest_block_idx and accept_token_bias == 0: return bt_row_idx = batch_idx if HAS_IDX_MAPPING else req_idx _copy_mamba_state_block( state_idx, bt_row_idx, src_block_idx, dest_block_idx, accept_token_bias, block_table_ptrs_ptr, block_table_stride_req, state_base_addrs_ptr, state_block_strides_ptr, state_elem_sizes_ptr, state_inner_sizes_ptr, state_conv_widths_ptr, state_group_indices_ptr, state_dim_row_count_ptr, state_dim_row_stride_ptr, COPY_BLOCK_SIZE, CONV_STATE_DIM_FIRST, ) @triton.jit def preprocess_mamba_align_fused_kernel( idx_mapping_ptr, state_idx_ptr, num_computed_tokens_ptr, query_start_loc_ptr, num_accepted_tokens_ptr, src_col_ptr, src_off_ptr, num_reqs, BLOCK_SIZE: tl.constexpr, MAMBA_BLOCK_SIZE: tl.constexpr, ): """Fused align preprocess: emit the pre-copy src column/offset AND advance state_idx (with accepted-token reset) in a single launch (V2 align). Per batch_idx (0..num_reqs-1), resolving req slot via idx_mapping: 1. Read pre-advance state_idx and num_accepted (last step's values). 2. Store the pre-copy src columns for ``precopy_mamba_align_fused_kernel``: - src_col = state_idx (the previous running block column) - src_off = max(num_accepted - 1, 0) (the accepted-token bias) 3. Advance state_idx to the new running block, and reset num_accepted to 1 when a block boundary is crossed (so the migrated state, now at the start of the new block, is read with the neutral bias). """ offsets = tl.program_id(0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) mask = offsets < num_reqs req_indices = tl.load(idx_mapping_ptr + offsets, mask=mask, other=0) state_idx = tl.load(state_idx_ptr + req_indices, mask=mask, other=-1) num_accepted = tl.load(num_accepted_tokens_ptr + req_indices, mask=mask, other=1) src_off = tl.maximum(num_accepted - 1, 0) tl.store(src_col_ptr + req_indices, state_idx, mask=mask) tl.store(src_off_ptr + req_indices, src_off, mask=mask) num_computed = tl.load(num_computed_tokens_ptr + req_indices, mask=mask, other=0) query_start = tl.load(query_start_loc_ptr + offsets, mask=mask, other=0) query_end = tl.load(query_start_loc_ptr + offsets + 1, mask=mask, other=0) computed_after = num_computed + query_end - query_start new_state_idx = (computed_after + MAMBA_BLOCK_SIZE - 1) // MAMBA_BLOCK_SIZE - 1 tl.store(state_idx_ptr + req_indices, new_state_idx, mask=mask) should_reset = (state_idx >= 0) & (state_idx != new_state_idx) tl.store(num_accepted_tokens_ptr + req_indices, 1, mask=mask & should_reset) @triton.jit def precopy_mamba_align_fused_kernel( # Per-request-slot inputs (indexed by req_idx via idx_mapping), produced by # the V2 fused align preprocess kernel for the current step: mamba_state_idx_ptr, # post-advance dst block column src_col_ptr, # pre-advance src block column (-1 = fresh) token_bias_ptr, # accepted-token bias = num_accepted - 1 (pre-reset) # Same flattened state-layout metadata as postprocess_mamba_fused_kernel block_table_ptrs_ptr, block_table_stride_req: tl.int64, state_base_addrs_ptr, state_block_strides_ptr, state_elem_sizes_ptr, state_inner_sizes_ptr, state_conv_widths_ptr, state_group_indices_ptr, state_dim_row_count_ptr, state_dim_row_stride_ptr, idx_mapping_ptr, # [num_reqs] batch_idx -> req_state_idx (-1 to skip) num_reqs, COPY_BLOCK_SIZE: tl.constexpr, CONV_STATE_DIM_FIRST: tl.constexpr, ): """Pre-copy mamba "align" state across block boundaries on the V2 runner. Before the forward pass, copy each request's last SSM/conv state from its previous block column into the new window block column, so the kernels read the initial state from the write-side block as usual (V1 align semantics). Same per-(layer, state) copy semantics as ``postprocess_mamba_fused_kernel`` (shared ``_copy_mamba_state_block`` body, i.e. the V1 ``preprocess_mamba`` copy specs), but driven by the GPU-resident src columns so it needs no CPU-GPU sync (async-scheduling safe). Grid: (num_reqs, num_layers * num_state_types); block tables are indexed by batch row, per-request state by req_idx via idx_mapping (V2 layout). """ batch_idx = tl.program_id(0) state_idx = tl.program_id(1) if batch_idx >= num_reqs: return req_idx = tl.load(idx_mapping_ptr + batch_idx) if req_idx < 0: return src_col = tl.load(src_col_ptr + req_idx) dst_col = tl.load(mamba_state_idx_ptr + req_idx) # Fresh state, or still writing the same block: kernels locate the initial # state in-block via num_accepted (preserved when no boundary is crossed), # so there is nothing to copy. if src_col < 0 or src_col == dst_col: return token_bias = tl.load(token_bias_ptr + req_idx) _copy_mamba_state_block( state_idx, batch_idx, src_col, dst_col, token_bias, block_table_ptrs_ptr, block_table_stride_req, state_base_addrs_ptr, state_block_strides_ptr, state_elem_sizes_ptr, state_inner_sizes_ptr, state_conv_widths_ptr, state_group_indices_ptr, state_dim_row_count_ptr, state_dim_row_stride_ptr, COPY_BLOCK_SIZE, CONV_STATE_DIM_FIRST, ) @triton.jit def batch_memcpy_kernel(src_ptrs, dst_ptrs, sizes, BLOCK_SIZE: tl.constexpr): pid = tl.program_id(0) src_ptr = tl.load(src_ptrs + pid) dst_ptr = tl.load(dst_ptrs + pid) size = tl.load(sizes + pid) offsets = tl.arange(0, BLOCK_SIZE) for i in range(0, size, BLOCK_SIZE): mask = (i + offsets) < size curr_src_ptr = (src_ptr + i + offsets).to(tl.pointer_type(tl.uint8)) curr_dst_ptr = (dst_ptr + i + offsets).to(tl.pointer_type(tl.uint8)) data = tl.load(curr_src_ptr, mask=mask) tl.store(curr_dst_ptr, data, mask=mask) def batch_memcpy(src_ptrs, dst_ptrs, sizes): batch = src_ptrs.shape[0] assert dst_ptrs.shape[0] == batch assert sizes.shape[0] == batch grid = (batch,) BLOCK_SIZE = 1024 batch_memcpy_kernel[grid](src_ptrs, dst_ptrs, sizes, BLOCK_SIZE=BLOCK_SIZE) def get_mamba_groups(kv_cache_config: KVCacheConfig) -> tuple[list[int], MambaSpec]: mamba_group_ids: list[int] = [] mamba_specs: list[MambaSpec] = [] for i in range(len(kv_cache_config.kv_cache_groups)): kv_cache_spec = kv_cache_config.kv_cache_groups[i].kv_cache_spec if isinstance(kv_cache_spec, MambaSpec): mamba_group_ids.append(i) mamba_specs.append(kv_cache_spec) assert len(mamba_group_ids) > 0, "no mamba layers in the model" assert all(mamba_specs[0] == spec for spec in mamba_specs) return mamba_group_ids, mamba_specs[0] @dataclasses.dataclass class MambaCopyBuffers: src_ptrs: CpuGpuBuffer dst_ptrs: CpuGpuBuffer sizes: CpuGpuBuffer mamba_group_ids: list[int] mamba_spec: MambaSpec offset: int = 0 @classmethod def create( cls, max_num_reqs: int, kv_cache_config: KVCacheConfig, copy_funcs: tuple[MambaStateCopyFunc, ...], make_buffer: Callable[..., CpuGpuBuffer], ) -> "MambaCopyBuffers": mamba_group_ids, mamba_spec = get_mamba_groups(kv_cache_config) entries_per_req = sum( len(kv_cache_config.kv_cache_groups[gid].layer_names) for gid in mamba_group_ids ) * len(copy_funcs) n = max_num_reqs * entries_per_req return cls( src_ptrs=make_buffer(n, dtype=torch.uint64), dst_ptrs=make_buffer(n, dtype=torch.uint64), sizes=make_buffer(n, dtype=torch.int32), mamba_group_ids=mamba_group_ids, mamba_spec=mamba_spec, ) @dataclasses.dataclass class MambaSpecDecodeGPUContext: """ Context for GPU-side Mamba state copy operations during the fused postprocess path. Only used when speculative decoding is enabled on a hybrid model (and the mamba_cache_config is in align mode). Precomputes memory layout metadata (base addresses, strides, element sizes) so the GPU kernel can perform state copies without CPU-GPU sync. State types are distinguished by conv_width: >0 for conv states (sliding window with offset-based copies), 0 for temporal states (full block copies). """ # Per-state metadata tensors (shape: [num_layers * num_state_types]) # These are populated from forward_context during the first forward pass state_base_addrs: torch.Tensor # int64: base address of each state tensor state_block_strides: torch.Tensor # int64: bytes per block state_elem_sizes: torch.Tensor # int32: element size in bytes state_inner_sizes: torch.Tensor # int64: elements in inner dimensions state_conv_widths: torch.Tensor # int32: conv width (0 for temporal states) state_group_indices: torch.Tensor # int32: maps state_idx to group index # DS conv row metadata. Zero keeps the single-region copy path. state_dim_row_count: torch.Tensor # int32: per-block dim row count state_dim_row_stride: torch.Tensor # int64: bytes between rows # Configuration block_size: int num_layers: int num_state_types: int mamba_group_ids: list[int] num_groups: int # Output buffer for num_accepted_tokens updates num_accepted_tokens_out: torch.Tensor # Per-group block-table base addresses: int64[num_groups]. Populated in # initialize_from_forward_context from the persistent per-group block # table tensors (whose data_ptr is stable across steps). block_table_ptrs: torch.Tensor block_table_stride_req: int = 0 # Per-request staging buffers (CPU+GPU mirrors). The runner stages # values into the CPU view in ``_prepare_inputs`` and the fused kernel # reads the GPU side. These only exist when the postprocess kernel is # enabled (spec decode + hybrid + align mode). mamba_state_idx_buf: CpuGpuBuffer | None = None num_scheduled_tokens_buf: CpuGpuBuffer | None = None num_computed_tokens_buf: CpuGpuBuffer | None = None num_draft_tokens_buf: CpuGpuBuffer | None = None # Flag to track if metadata has been populated is_initialized: bool = False @classmethod def create( cls, max_num_reqs: int, kv_cache_config: KVCacheConfig, num_state_types: int, device: torch.device, make_buffer: Callable[..., CpuGpuBuffer], ) -> "MambaSpecDecodeGPUContext": """Create context with allocated buffers (metadata populated later).""" mamba_group_ids, mamba_spec = get_mamba_groups(kv_cache_config) # Count total layers across all mamba groups num_layers = sum( len(kv_cache_config.kv_cache_groups[gid].layer_names) for gid in mamba_group_ids ) total_states = num_layers * num_state_types return cls( state_base_addrs=torch.zeros( total_states, dtype=torch.int64, device=device ), state_block_strides=torch.zeros( total_states, dtype=torch.int64, device=device ), state_elem_sizes=torch.zeros( total_states, dtype=torch.int32, device=device ), state_inner_sizes=torch.zeros( total_states, dtype=torch.int64, device=device ), state_conv_widths=torch.zeros( total_states, dtype=torch.int32, device=device ), state_group_indices=torch.zeros( total_states, dtype=torch.int32, device=device ), state_dim_row_count=torch.zeros( total_states, dtype=torch.int32, device=device ), state_dim_row_stride=torch.zeros( total_states, dtype=torch.int64, device=device ), block_size=mamba_spec.block_size, num_layers=num_layers, num_state_types=num_state_types, mamba_group_ids=mamba_group_ids, num_groups=len(mamba_group_ids), num_accepted_tokens_out=torch.zeros( max_num_reqs, dtype=torch.int32, device=device ), block_table_ptrs=torch.zeros( len(mamba_group_ids), dtype=torch.int64, device=device ), mamba_state_idx_buf=make_buffer(max_num_reqs, dtype=torch.int32), num_scheduled_tokens_buf=make_buffer(max_num_reqs, dtype=torch.int32), num_computed_tokens_buf=make_buffer(max_num_reqs, dtype=torch.int32), num_draft_tokens_buf=make_buffer(max_num_reqs, dtype=torch.int32), is_initialized=False, ) def initialize_from_forward_context( self, kv_cache_config: KVCacheConfig, forward_context: dict[str, Any], mamba_state_copy_funcs: tuple[MambaStateCopyFunc, ...], block_tables: list[torch.Tensor], ) -> None: """ Extract and cache memory layout metadata from Mamba state tensors. This method populates the pre-allocated metadata tensors with information needed by `postprocess_mamba_fused_kernel` to perform state copies entirely on the GPU without CPU-GPU synchronization. For each Mamba layer and state type, the following metadata is extracted: - state_base_addrs: GPU memory address (data_ptr) of the state tensor - state_block_strides: Bytes between consecutive blocks (stride * elem_size) - state_elem_sizes: Element size in bytes (e.g., 2 for float16) - state_inner_sizes: For conv states, elements per conv position (stride(1)), used to compute offset when slicing state[block, offset:]. For temporal states, this field is unused (set to 1). - state_conv_widths: Conv dimension size for conv states, 0 for temporal states The conv vs temporal state type is detected by inspecting the copy function name: functions containing "conv" are treated as conv states. This method is idempotent - it only executes once (guarded by is_initialized flag) since the metadata is static after model loading. Args: kv_cache_config: Configuration containing KV cache group info and layer name mappings. forward_context: Dictionary mapping layer names to attention objects, populated after the model is loaded. Each attention object must have a `kv_cache` attribute containing the list of state tensors. mamba_state_copy_funcs: Tuple of copy functions (one per state type) used to determine whether each state is a conv or temporal state. block_tables: per-mamba-group persistent block-table tensors, in the same order as `mamba_group_ids`. Their `data_ptr()` / `stride(0)` are captured once for the kernel to index into. """ if self.is_initialized: return idx = 0 for group_local_idx, mamba_group_id in enumerate(self.mamba_group_ids): layer_names = kv_cache_config.kv_cache_groups[mamba_group_id].layer_names for layer_name in layer_names: attention = forward_context[layer_name] kv_caches: list[torch.Tensor] = attention.kv_cache for state_type_idx, state in enumerate(kv_caches): # Base address self.state_base_addrs[idx] = state.data_ptr() # Block stride (bytes between consecutive blocks) # state shape: [num_blocks, ...], stride(0) = elements per block if state.dim() > 1: block_stride_elems = state.stride(0) else: block_stride_elems = state.numel() self.state_block_strides[idx] = ( block_stride_elems * state.element_size() ) # Element size self.state_elem_sizes[idx] = state.element_size() copy_func = mamba_state_copy_funcs[state_type_idx] assert ( copy_func is get_conv_copy_spec or copy_func is get_temporal_copy_spec ), f"unexpected copy func: {copy_func}" if copy_func is get_conv_copy_spec: if state.dim() != 3: raise ValueError( "Expected 3D conv state cache, got " f"shape {tuple(state.shape)}" ) if is_conv_state_dim_first(): # DS layout: state_len is the slide axis. self.state_conv_widths[idx] = state.size(2) self.state_inner_sizes[idx] = 1 self.state_dim_row_count[idx] = state.size(1) self.state_dim_row_stride[idx] = ( state.stride(1) * state.element_size() ) else: # SD layout: dim is contiguous. self.state_conv_widths[idx] = state.size(1) self.state_inner_sizes[idx] = state.stride(1) else: # Temporal state: inner_size = natural elements per # block (prod of inner dims). The kernel uses this # to compute copy_size = inner_size * elem_size, # which gives the correct byte count even when the # state tensor is as_strided with padded page strides # (state_block_stride would be the page size, too big). self.state_conv_widths[idx] = 0 self.state_inner_sizes[idx] = ( state[0].numel() if state.dim() > 1 else 1 ) self.state_group_indices[idx] = group_local_idx idx += 1 # Cache per-group block-table base addresses and per-request stride. # `block_tables[i]` is the persistent 2D int32 block-table tensor for # `mamba_group_ids[i]`; `data_ptr()` / `stride(0)` are stable for the # engine's lifetime, so we capture them once here. assert len(block_tables) == self.num_groups, ( f"expected {self.num_groups} block tables, got {len(block_tables)}" ) strides = {bt.stride(0) for bt in block_tables} assert len(strides) == 1, ( f"all mamba block tables must share stride(0), got {strides}" ) self.block_table_stride_req = int(next(iter(strides))) for i, bt in enumerate(block_tables): self.block_table_ptrs[i] = bt.data_ptr() self.is_initialized = True def run_fused_postprocess( self, num_reqs: int, num_accepted_tokens_gpu: torch.Tensor, mamba_state_idx_gpu: torch.Tensor, num_scheduled_tokens_gpu: torch.Tensor, num_computed_tokens_gpu: torch.Tensor, num_draft_tokens_gpu: torch.Tensor, ) -> None: """ Run the fused postprocess_mamba kernel on GPU. This computes decisions and performs mamba state copies entirely on GPU, eliminating the CPU-GPU sync that was previously needed. Args: num_reqs: Number of active requests num_accepted_tokens_gpu: [num_reqs] accepted token counts mamba_state_idx_gpu: [num_reqs] source block indices num_scheduled_tokens_gpu: [num_reqs] scheduled token counts num_computed_tokens_gpu: [num_reqs] computed token counts num_draft_tokens_gpu: [num_reqs] draft token counts """ if num_reqs == 0 or not self.is_initialized: return # Initialize output to current values (unchanged unless src==dst) self.num_accepted_tokens_out[:num_reqs].copy_( num_accepted_tokens_gpu[:num_reqs] ) total_states = self.num_layers * self.num_state_types grid = (num_reqs, total_states) postprocess_mamba_fused_kernel[grid]( num_accepted_tokens_gpu, mamba_state_idx_gpu, num_scheduled_tokens_gpu, num_computed_tokens_gpu, num_draft_tokens_gpu, self.block_table_ptrs, self.block_table_stride_req, self.state_base_addrs, self.state_block_strides, self.state_elem_sizes, self.state_inner_sizes, self.state_conv_widths, self.state_group_indices, self.state_dim_row_count, self.state_dim_row_stride, self.num_accepted_tokens_out, None, # idx_mapping: V1 decision arrays are already in req order num_reqs, block_size=self.block_size, COPY_BLOCK_SIZE=1024, CONV_STATE_DIM_FIRST=is_conv_state_dim_first(), ) def run_fused_precopy( self, num_reqs: int, state_idx_gpu: torch.Tensor, src_col_gpu: torch.Tensor, token_bias_gpu: torch.Tensor, idx_mapping: torch.Tensor, ) -> None: """Pre-copy each request's previous running block into its new window block before the forward pass (V2 align boundary migration). Args: num_reqs: Number of active requests (batch order). state_idx_gpu: [max_reqs] post-advance dst block column per req slot. src_col_gpu: [max_reqs] pre-advance src block column (-1 = fresh). token_bias_gpu: [max_reqs] accepted-token bias (num_accepted - 1). idx_mapping: [num_reqs] batch_idx -> req_state_idx (-1 to skip). """ if num_reqs == 0 or not self.is_initialized: return total_states = self.num_layers * self.num_state_types grid = (num_reqs, total_states) precopy_mamba_align_fused_kernel[grid]( state_idx_gpu, src_col_gpu, token_bias_gpu, self.block_table_ptrs, self.block_table_stride_req, self.state_base_addrs, self.state_block_strides, self.state_elem_sizes, self.state_inner_sizes, self.state_conv_widths, self.state_group_indices, self.state_dim_row_count, self.state_dim_row_stride, idx_mapping, num_reqs, COPY_BLOCK_SIZE=1024, CONV_STATE_DIM_FIRST=is_conv_state_dim_first(), ) def run_fused_postprocess_align( self, num_reqs: int, num_accepted_tokens_gpu: torch.Tensor, state_idx_gpu: torch.Tensor, new_num_computed_tokens_gpu: torch.Tensor, idx_mapping: torch.Tensor, ) -> None: """V2 align postprocess: save the running state to the block-aligned position after spec-decode acceptance leaves the sequence non-aligned. ``num_accepted_tokens_gpu`` is updated in place (reset to 1 when the accepted position stays in the running block); ``new_num_computed_tokens`` already holds the post-step computed count (PRECOMPUTED_NEW_COMPUTED). ``idx_mapping`` maps batch row -> req-state slot (HAS_IDX_MAPPING). """ if num_reqs == 0 or not self.is_initialized: return total_states = self.num_layers * self.num_state_types grid = (num_reqs, total_states) postprocess_mamba_fused_kernel[grid]( num_accepted_tokens_gpu, state_idx_gpu, None, # num_scheduled: unused under PRECOMPUTED_NEW_COMPUTED new_num_computed_tokens_gpu, None, # num_draft: unused under PRECOMPUTED_NEW_COMPUTED self.block_table_ptrs, self.block_table_stride_req, self.state_base_addrs, self.state_block_strides, self.state_elem_sizes, self.state_inner_sizes, self.state_conv_widths, self.state_group_indices, self.state_dim_row_count, self.state_dim_row_stride, None, # num_accepted_out: V2 updates num_accepted in place idx_mapping, num_reqs, block_size=self.block_size, COPY_BLOCK_SIZE=1024, CONV_STATE_DIM_FIRST=is_conv_state_dim_first(), HAS_IDX_MAPPING=True, PRECOMPUTED_NEW_COMPUTED=True, ) @dataclasses.dataclass class MambaBuffers: """Single owner for all mamba-specific runner buffers. The two sub-objects have different gates: ``preprocess`` is needed whenever ``mamba_cache_mode == "align"``; ``postprocess_align`` is needed only when align is combined with speculative decoding on a hybrid model, and is ``None`` otherwise. """ preprocess: MambaCopyBuffers postprocess_align: MambaSpecDecodeGPUContext | None @classmethod def create( cls, max_num_reqs: int, kv_cache_config: KVCacheConfig, copy_funcs: tuple[MambaStateCopyFunc, ...], make_buffer: Callable[..., CpuGpuBuffer], device: torch.device, with_postprocess_align: bool, ) -> "MambaBuffers": return cls( preprocess=MambaCopyBuffers.create( max_num_reqs, kv_cache_config, copy_funcs, make_buffer ), postprocess_align=( MambaSpecDecodeGPUContext.create( max_num_reqs=max_num_reqs, kv_cache_config=kv_cache_config, num_state_types=len(copy_funcs), device=device, make_buffer=make_buffer, ) if with_postprocess_align else None ), ) def collect_mamba_copy_meta( copy_bufs: MambaCopyBuffers, kv_cache_config: KVCacheConfig, mamba_state_copy_funcs: tuple[MambaStateCopyFunc, ...], mamba_group_ids: list[int], src_block_idx: int, dest_block_idx: int, accept_token_bias: int, req_state: CachedRequestState, forward_context: dict[str, Any], ) -> None: if src_block_idx == dest_block_idx and accept_token_bias == 0: return src_ptrs_np = copy_bufs.src_ptrs.np dst_ptrs_np = copy_bufs.dst_ptrs.np sizes_np = copy_bufs.sizes.np offset = copy_bufs.offset for mamba_group_id in mamba_group_ids: block_ids = req_state.block_ids[mamba_group_id] dest_block_id = block_ids[dest_block_idx] layer_names = kv_cache_config.kv_cache_groups[mamba_group_id].layer_names for layer_name in layer_names: attention = forward_context[layer_name] kv_caches: list[torch.Tensor] = attention.kv_cache for state, state_copy_func in zip(kv_caches, mamba_state_copy_funcs): copy_spec = state_copy_func( state, block_ids, src_block_idx, accept_token_bias + 1 ) src_ptrs_np[offset] = copy_spec.start_addr dst_ptrs_np[offset] = state[dest_block_id].data_ptr() sizes_np[offset] = copy_spec.num_elements * state.element_size() offset += 1 copy_bufs.offset = offset def do_mamba_copy_block(copy_bufs: MambaCopyBuffers): n = copy_bufs.offset if n == 0: return batch_memcpy( copy_bufs.src_ptrs.copy_to_gpu(n), copy_bufs.dst_ptrs.copy_to_gpu(n), copy_bufs.sizes.copy_to_gpu(n), ) def cleanup_mamba_state_idx( scheduler_output: SchedulerOutput, mamba_state_idx: dict[str, int], ) -> None: """Pop stale `mamba_state_idx` entries for finished/preempted/resumed reqs. Force-preempted requests (e.g., during reset_prefix_cache / KV cache flush) appear in resumed_req_ids without a corresponding entry in preempted_req_ids, leaving stale entries that can point to block indices beyond the new (smaller) block allocation. """ finished_req_ids = scheduler_output.finished_req_ids preempted_req_ids = scheduler_output.preempted_req_ids or set() resumed_req_ids = scheduler_output.scheduled_cached_reqs.resumed_req_ids for req_id in itertools.chain(finished_req_ids, preempted_req_ids, resumed_req_ids): mamba_state_idx.pop(req_id, None) def preprocess_mamba( scheduler_output: SchedulerOutput, kv_cache_config: KVCacheConfig, cache_config: CacheConfig, mamba_state_idx: dict[str, int], input_batch: GPUInputBatch, requests: dict[str, CachedRequestState], forward_context: dict[str, Any], mamba_state_copy_funcs: tuple[MambaStateCopyFunc, ...], copy_bufs: MambaCopyBuffers, ): """ Copy the mamba state of previous step to the last (1 + num_speculative_blocks) block. """ mamba_group_ids = copy_bufs.mamba_group_ids mamba_spec = copy_bufs.mamba_spec num_speculative_blocks = mamba_spec.num_speculative_blocks # TODO(Chen): we need to optimize this function a lot assert cache_config.enable_prefix_caching block_size = mamba_spec.block_size cleanup_mamba_state_idx(scheduler_output, mamba_state_idx) copy_bufs.offset = 0 for i, req_id in enumerate(input_batch.req_ids): req_state = requests[req_id] prev_state_idx = mamba_state_idx.get(req_id) if prev_state_idx is None: # new / resumed request, no previous state # if num_computed_tokens is 0, prev_state_idx will be -1 prev_state_idx = (req_state.num_computed_tokens - 1) // block_size num_scheduled_tokens = scheduler_output.num_scheduled_tokens[req_id] num_blocks: int = ( cdiv(req_state.num_computed_tokens + num_scheduled_tokens, block_size) + num_speculative_blocks ) # We always save the current running state at the last # (1 + num_speculative_blocks) block. # A corner case worth mention here: assume we have block_size = 4 and # num_speculative_tokens = 2. The request is [A, B, C] and contains 2 draft # tokens [draft 1, draft 2]. Then we will have: # Block 0: [A, B, C, draft 1] # Block 1: [draft 2, TOFILL, TOFILL, TOFILL] # Block 2: speculative block # Block 3: speculative block # And use block 1 to save the running state. curr_state_idx = num_blocks - 1 - num_speculative_blocks mamba_state_idx[req_id] = curr_state_idx if prev_state_idx != -1 and prev_state_idx != curr_state_idx: collect_mamba_copy_meta( copy_bufs, kv_cache_config, mamba_state_copy_funcs, mamba_group_ids, prev_state_idx, curr_state_idx, input_batch.num_accepted_tokens_cpu[i] - 1, req_state, forward_context, ) input_batch.num_accepted_tokens_cpu[i] = 1 do_mamba_copy_block(copy_bufs) def postprocess_mamba_all( scheduler_output: SchedulerOutput, kv_cache_config: KVCacheConfig, input_batch: GPUInputBatch, requests: dict[str, CachedRequestState], mamba_state_idx: dict[str, int], num_spec_tokens: int, num_reqs: int, ): """All-mode postprocess (only meaningful with num_spec_tokens > 0): record per-request the block index of the last token scheduled this step, so the next step can anchor its in-place writes when accepted drafts leave the sequence at a non-block-aligned position. """ if num_spec_tokens <= 0: return _, mamba_spec = get_mamba_groups(kv_cache_config) block_size = mamba_spec.block_size full_decode_len = 1 + num_spec_tokens scheduled = scheduler_output.num_scheduled_tokens for req_id in input_batch.req_ids[:num_reqs]: num_query = scheduled.get(req_id, 0) if num_query == full_decode_len: req = requests[req_id] seq_len = req.num_computed_tokens + num_query mamba_state_idx[req_id] = max(0, (seq_len - 1) // block_size) else: mamba_state_idx.pop(req_id, None) def preprocess_mamba_all_specdec( scheduler_output: SchedulerOutput, input_batch: GPUInputBatch, mamba_state_idx: dict[str, int], num_reqs: int, prev_last_scheduled_idx_buf: CpuGpuBuffer, ) -> None: cleanup_mamba_state_idx(scheduler_output, mamba_state_idx) np_view = prev_last_scheduled_idx_buf.np for i, req_id in enumerate(input_batch.req_ids[:num_reqs]): np_view[i] = mamba_state_idx.get(req_id, -1) np_view[num_reqs:].fill(-1) prev_last_scheduled_idx_buf.copy_to_gpu() def postprocess_mamba_align_gpu( *, bufs: "MambaBuffers", num_reqs: int, num_accepted_tokens_gpu: torch.Tensor, num_accepted_tokens_cpu_tensor: torch.Tensor, input_batch: GPUInputBatch, kv_cache_config: KVCacheConfig, forward_context: dict[str, Any], mamba_state_copy_funcs: tuple[MambaStateCopyFunc, ...], ) -> None: """GPU-side mamba postprocess for spec decode + hybrid + align mode. Lazily binds the fused-kernel context to the persistent block tables and forward-context state pointers on the first call, runs the fused kernel, and async-copies the per-request accepted-token counts back to the input batch's CPU tensor for the next iteration's preprocess. """ ctx = bufs.postprocess_align # Caller is responsible for gating on spec decode + hybrid; this assert is # a tripwire if those gates ever drift apart. assert ctx is not None assert ctx.mamba_state_idx_buf is not None assert ctx.num_scheduled_tokens_buf is not None assert ctx.num_computed_tokens_buf is not None assert ctx.num_draft_tokens_buf is not None if not ctx.is_initialized: ctx.initialize_from_forward_context( kv_cache_config, forward_context, mamba_state_copy_funcs, [ input_batch.block_table[gid].get_device_tensor(num_reqs) for gid in ctx.mamba_group_ids ], ) ctx.run_fused_postprocess( num_reqs=num_reqs, num_accepted_tokens_gpu=num_accepted_tokens_gpu, mamba_state_idx_gpu=ctx.mamba_state_idx_buf.gpu, num_scheduled_tokens_gpu=ctx.num_scheduled_tokens_buf.gpu, num_computed_tokens_gpu=ctx.num_computed_tokens_buf.gpu, num_draft_tokens_gpu=ctx.num_draft_tokens_buf.gpu, ) # ``num_accepted_tokens_out`` is pre-initialized from # ``num_accepted_tokens_gpu``; the kernel only overwrites entries to 1 # when src_block_idx == dest_block_idx (copy within the same block), so # the original count is preserved for everyone else. num_accepted_tokens_cpu_tensor[:num_reqs].copy_( ctx.num_accepted_tokens_out[:num_reqs], non_blocking=True ) def stage_postprocess_metadata_to_gpu( scheduler_output: SchedulerOutput, req_ids: list[str], num_reqs: int, requests: dict[str, CachedRequestState], num_scheduled_tokens_buf: CpuGpuBuffer, num_computed_tokens_buf: CpuGpuBuffer, num_draft_tokens_buf: CpuGpuBuffer, ) -> None: """Stage per-request postprocess metadata into GPU buffers (non-blocking). Walks ``req_ids[:num_reqs]`` in batch order and writes each request's scheduled/computed/draft token counts into the matching pinned numpy views, then issues three non-blocking H→D copies. These values don't change between ``_prepare_inputs`` and ``_update_states_after_model_execute``. The fused postprocess kernel indexes the resulting GPU tensors by ``req_idx``. """ scheduled_spec_tokens = scheduler_output.scheduled_spec_decode_tokens num_scheduled = scheduler_output.num_scheduled_tokens scheduled_np = num_scheduled_tokens_buf.np computed_np = num_computed_tokens_buf.np draft_np = num_draft_tokens_buf.np for i in range(num_reqs): req_id = req_ids[i] scheduled_np[i] = num_scheduled[req_id] computed_np[i] = requests[req_id].num_computed_tokens draft_np[i] = len(scheduled_spec_tokens.get(req_id, [])) num_scheduled_tokens_buf.copy_to_gpu(num_reqs) num_computed_tokens_buf.copy_to_gpu(num_reqs) num_draft_tokens_buf.copy_to_gpu(num_reqs) def stage_mamba_state_idx_to_gpu( mamba_state_idx: dict[str, int], req_ids: list[str], num_reqs: int, gpu_buf: CpuGpuBuffer, ) -> None: """Materialize ``mamba_state_idx`` into ``gpu_buf`` and copy to GPU. Walks ``req_ids[:num_reqs]`` in batch order, writing each request's block index into the buffer's pinned numpy view, then issues a non-blocking H→D copy. The fused kernel indexes the resulting GPU tensor by ``req_idx``. Invariant: ``preprocess_mamba`` must have run first for the same batch so that every ``req_ids[i]`` has an entry in ``mamba_state_idx``. """ np_view = gpu_buf.np for i in range(num_reqs): req_id = req_ids[i] state_idx = mamba_state_idx.get(req_id) assert state_idx is not None, ( f"mamba_state_idx missing entry for {req_id!r}; " "preprocess_mamba must run before stage_mamba_state_idx_to_gpu" ) np_view[i] = state_idx gpu_buf.copy_to_gpu(num_reqs) def stage_postprocess_inputs_to_gpu( ctx: MambaSpecDecodeGPUContext, scheduler_output: SchedulerOutput, req_ids: list[str], num_reqs: int, requests: dict[str, CachedRequestState], mamba_state_idx: dict[str, int], ) -> None: """Stage all per-request inputs the fused mamba postprocess kernel reads. Bundles ``stage_mamba_state_idx_to_gpu`` and ``stage_postprocess_metadata_to_gpu`` into a single call so the runner has one entry point for postprocess staging. Buffers live on ``ctx`` and only exist when the postprocess kernel is enabled. """ assert ctx.mamba_state_idx_buf is not None assert ctx.num_scheduled_tokens_buf is not None assert ctx.num_computed_tokens_buf is not None assert ctx.num_draft_tokens_buf is not None stage_mamba_state_idx_to_gpu( mamba_state_idx, req_ids, num_reqs, ctx.mamba_state_idx_buf, ) stage_postprocess_metadata_to_gpu( scheduler_output, req_ids, num_reqs, requests, ctx.num_scheduled_tokens_buf, ctx.num_computed_tokens_buf, ctx.num_draft_tokens_buf, )