# Copyright (c) 2024, Tri Dao. # Adapted from https://github.com/Dao-AILab/causal-conv1d/blob/main/causal_conv1d/causal_conv1d_interface.py # and https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/mamba/ops/causal_conv1d.py from typing import List, Optional, Union import torch import triton import triton.language as tl PAD_SLOT_ID = -1 @triton.jit() def _causal_conv1d_fwd_kernel( # continuous batching # Pointers to matrices x_ptr, # (dim, cu_seqlen) holding `batch` of actual sequences + padded sequences w_ptr, # (dim, width) bias_ptr, initial_states_ptr, # conv_states_ptr cache_indices_ptr, # conv_state_indices_ptr has_initial_states_ptr, query_start_loc_ptr, o_ptr, # (dim, seqlen) - actually pointing to x_ptr # Matrix dimensions dim: tl.constexpr, seqlen: tl.int32, # cu_seqlen num_cache_lines: tl.constexpr, # added to support vLLM larger cache lines # Strides stride_x_seq: tl.constexpr, # stride to get to next sequence, stride_x_dim: tl.constexpr, # stride to get to next feature-value, stride_x_token: tl.constexpr, # stride to get to next token (same feature-index, same sequence-index) stride_w_dim: tl.constexpr, # stride to get to next dim-axis value stride_w_width: tl.constexpr, # stride to get to next width-axis value stride_istate_seq: tl.constexpr, stride_istate_dim: tl.constexpr, stride_istate_token: tl.constexpr, stride_o_seq: tl.constexpr, stride_o_dim: tl.constexpr, stride_o_token: tl.constexpr, # others pad_slot_id: tl.constexpr, # Meta-parameters HAS_BIAS: tl.constexpr, KERNEL_WIDTH: tl.constexpr, SILU_ACTIVATION: tl.constexpr, HAS_INITIAL_STATES: tl.constexpr, HAS_CACHE: tl.constexpr, IS_CONTINUOUS_BATCHING: tl.constexpr, USE_PAD_SLOT: tl.constexpr, NP2_STATELEN: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, ): conv_states_ptr = initial_states_ptr conv_state_indices_ptr = cache_indices_ptr stride_conv_state_seq = stride_istate_seq stride_conv_state_dim = stride_istate_dim stride_conv_state_tok = stride_istate_token state_len = ( KERNEL_WIDTH - 1 ) # can be passed via argument if it's not the same as this value # one program handles one chunk in a single sequence # rather than mixing sequences - to make updating initial_states across sequences efficiently # single-sequence id idx_seq = tl.program_id(0) chunk_offset = tl.program_id(1) # BLOCK_N elements along the feature-dimension (channel) idx_feats = tl.program_id(2) * BLOCK_N + tl.arange(0, BLOCK_N) if idx_seq == pad_slot_id: return sequence_start_index = tl.load(query_start_loc_ptr + idx_seq) sequence_end_index = tl.load(query_start_loc_ptr + idx_seq + 1) # find the actual sequence length seqlen = sequence_end_index - sequence_start_index token_offset = BLOCK_M * chunk_offset segment_len = min(BLOCK_M, seqlen - token_offset) if segment_len <= 0: return # base of the sequence x_base = ( x_ptr + sequence_start_index * stride_x_token + idx_feats * stride_x_dim ) # [BLOCK_N,] if IS_CONTINUOUS_BATCHING: # cache_idx conv_state_batch_coord = tl.load(conv_state_indices_ptr + idx_seq).to(tl.int64) else: # cache_idx conv_state_batch_coord = idx_seq if USE_PAD_SLOT: # noqa if conv_state_batch_coord == pad_slot_id: # not processing as this is not the actual sequence return conv_states_base = ( conv_states_ptr + (conv_state_batch_coord * stride_conv_state_seq) + (idx_feats * stride_conv_state_dim) ) # [BLOCK_N,] w_base = w_ptr + (idx_feats * stride_w_dim) # [BLOCK_N,] # Does 2 things: # 1. READ prior-block init-state data - [done by every Triton programs] # 2. update conv_state with new data [only by the Triton program handles chunk_offset=0] if chunk_offset == 0: # read from conv_states load_init_state = False if HAS_INITIAL_STATES: # the new HAS_INITIAL_STATES load_init_state = tl.load(has_initial_states_ptr + idx_seq).to(tl.int1) if load_init_state: # load from conv_states prior_tokens = conv_states_base + (state_len - 1) * stride_conv_state_tok mask_w = idx_feats < dim if KERNEL_WIDTH == 2: conv_states_ptrs = prior_tokens # [BLOCK_N] col0 = tl.load(conv_states_ptrs, mask_w, 0.0) if KERNEL_WIDTH == 3: conv_states_ptrs = prior_tokens # [BLOCK_N] col1 = tl.load(conv_states_ptrs, mask_w, 0.0) conv_states_ptrs = prior_tokens - 1 * stride_conv_state_tok # [BLOCK_N] col0 = tl.load(conv_states_ptrs, mask_w, 0.0) if KERNEL_WIDTH == 4: conv_states_ptrs = prior_tokens # [BLOCK_N] col2 = tl.load(conv_states_ptrs, mask_w, 0.0) conv_states_ptrs = prior_tokens - 1 * stride_conv_state_tok # [BLOCK_N] col1 = tl.load(conv_states_ptrs, mask_w, 0.0) conv_states_ptrs = prior_tokens - 2 * stride_conv_state_tok # [BLOCK_N] col0 = tl.load(conv_states_ptrs, mask_w, 0.0) if KERNEL_WIDTH == 5: conv_states_ptrs = prior_tokens # [BLOCK_N] col3 = tl.load(conv_states_ptrs, mask_w, 0.0) conv_states_ptrs = prior_tokens - 1 * stride_conv_state_tok # [BLOCK_N] col2 = tl.load(conv_states_ptrs, mask_w, 0.0) conv_states_ptrs = prior_tokens - 2 * stride_conv_state_tok # [BLOCK_N] col1 = tl.load(conv_states_ptrs, mask_w, 0.0) conv_states_ptrs = prior_tokens - 3 * stride_conv_state_tok # [BLOCK_N] col0 = tl.load(conv_states_ptrs, mask_w, 0.0) else: # prior-tokens are zeros if KERNEL_WIDTH >= 2: # STRATEGY1 # first chunk and does not have prior-token, so just set to 0 col0 = tl.zeros((BLOCK_N,), dtype=x_ptr.dtype.element_ty) if KERNEL_WIDTH >= 3: # STRATEGY1 col1 = tl.zeros((BLOCK_N,), dtype=x_ptr.dtype.element_ty) if KERNEL_WIDTH >= 4: # STRATEGY1 col2 = tl.zeros((BLOCK_N,), dtype=x_ptr.dtype.element_ty) if KERNEL_WIDTH >= 5: # STRATEGY1 col3 = tl.zeros((BLOCK_N,), dtype=x_ptr.dtype.element_ty) # STEP 2: # here prepare data for updating conv_state if ( state_len <= seqlen ): # SMALL_CACHE=True (only move part of 'x' into conv_state cache) # just read from 'x' # copy 'x' data to conv_state # load only 'x' data (and set 0 before 'x' if seqlen < state_len) idx_tokens_last = (seqlen - state_len) + tl.arange( 0, NP2_STATELEN ) # [BLOCK_M] x_ptrs = ( x_ptr + ((sequence_start_index + idx_tokens_last) * stride_x_token)[:, None] + (idx_feats * stride_x_dim)[None, :] ) # [BLOCK_M,BLOCK_N,] mask_x = ( (idx_tokens_last >= 0)[:, None] & (idx_tokens_last < seqlen)[:, None] & (idx_feats < dim)[None, :] ) # token-index # token-index # feature-index loaded_x = tl.load(x_ptrs, mask_x, 0.0) new_conv_state = tl.load(x_ptrs, mask_x, 0.0) idx_tokens_conv = tl.arange(0, NP2_STATELEN) # [BLOCK_M] conv_states_ptrs_target = ( conv_states_base[None, :] + (idx_tokens_conv * stride_conv_state_tok)[:, None] ) mask = (idx_tokens_conv < state_len)[:, None] & (idx_feats < dim)[None, :] tl.debug_barrier() # NOTE: use this due to bug in Triton compiler tl.store(conv_states_ptrs_target, new_conv_state, mask) else: if load_init_state: # update conv_state by shifting left, i.e. take last few cols from conv_state + cols from 'x' idx_tokens_conv = tl.arange(0, NP2_STATELEN) # [BLOCK_M] conv_states_ptrs_source = ( conv_states_ptr + (conv_state_batch_coord * stride_conv_state_seq) + (idx_feats * stride_conv_state_dim)[None, :] + ((idx_tokens_conv + seqlen) * stride_conv_state_tok)[:, None] ) # [BLOCK_M, BLOCK_N] mask = ( (conv_state_batch_coord < num_cache_lines) & ((idx_tokens_conv + seqlen) < state_len)[:, None] & (idx_feats < dim)[None, :] ) conv_state = tl.load(conv_states_ptrs_source, mask, other=0.0) VAL = state_len - seqlen x_ptrs = ( x_base[None, :] + ((idx_tokens_conv - VAL) * stride_x_token)[:, None] ) # [BLOCK_M, BLOCK_N] mask_x = ( (idx_tokens_conv - VAL >= 0)[:, None] & (idx_tokens_conv - VAL < seqlen)[:, None] & (idx_feats < dim)[None, :] ) # token-index # token-index # feature-index loaded_x = tl.load(x_ptrs, mask_x, 0.0) tl.debug_barrier() # need this due to the bug in tl.where not enforcing this when data is the result of another tl.load new_conv_state = tl.where( mask, conv_state, loaded_x ) # BUG in 'tl.where' which requires a barrier before this conv_states_ptrs_target = ( conv_states_base + (idx_tokens_conv * stride_conv_state_tok)[:, None] ) # [BLOCK_M, BLOCK_N] mask = (idx_tokens_conv < state_len)[:, None] & (idx_feats < dim)[ None, : ] tl.store(conv_states_ptrs_target, new_conv_state, mask) else: # load_init_state == False # update conv_state by shifting left, BUT # set cols prior to 'x' as zeros + cols from 'x' idx_tokens_conv = tl.arange(0, NP2_STATELEN) # [BLOCK_M] VAL = state_len - seqlen x_ptrs = ( x_base[None, :] + ((idx_tokens_conv - VAL) * stride_x_token)[:, None] ) # [BLOCK_M, BLOCK_N] mask_x = ( (idx_tokens_conv - VAL >= 0)[:, None] & (idx_tokens_conv - VAL < seqlen)[:, None] & (idx_feats < dim)[None, :] ) # token-index # token-index # feature-index new_conv_state = tl.load(x_ptrs, mask_x, 0.0) conv_states_ptrs_target = ( conv_states_base + (idx_tokens_conv * stride_conv_state_tok)[:, None] ) # [BLOCK_M, BLOCK_N] mask = (idx_tokens_conv < state_len)[:, None] & (idx_feats < dim)[ None, : ] tl.store(conv_states_ptrs_target, new_conv_state, mask) else: # chunk_offset > 0 # read prior-token data from `x` load_init_state = True prior_tokens = x_base + (token_offset - 1) * stride_x_token mask_w = idx_feats < dim if KERNEL_WIDTH == 2: conv_states_ptrs = prior_tokens # [BLOCK_N] col0 = tl.load(conv_states_ptrs, mask_w, 0.0, cache_modifier=".ca") if KERNEL_WIDTH == 3: conv_states_ptrs = prior_tokens # [BLOCK_N] col1 = tl.load(conv_states_ptrs, mask_w, 0.0, cache_modifier=".ca") conv_states_ptrs = prior_tokens - 1 * stride_x_token # [BLOCK_N] col0 = tl.load(conv_states_ptrs, mask_w, 0.0, cache_modifier=".ca") if KERNEL_WIDTH == 4: conv_states_ptrs = prior_tokens # [BLOCK_N] col2 = tl.load(conv_states_ptrs, mask_w, 0.0, cache_modifier=".ca") conv_states_ptrs = prior_tokens - 1 * stride_x_token # [BLOCK_N] col1 = tl.load(conv_states_ptrs, mask_w, 0.0, cache_modifier=".ca") conv_states_ptrs = prior_tokens - 2 * stride_x_token # [BLOCK_N] col0 = tl.load(conv_states_ptrs, mask_w, 0.0, cache_modifier=".ca") if KERNEL_WIDTH == 5: # ruff: noqa: F841 conv_states_ptrs = prior_tokens # [BLOCK_N] col3 = tl.load(conv_states_ptrs, mask_w, 0.0, cache_modifier=".ca") conv_states_ptrs = prior_tokens - 1 * stride_x_token # [BLOCK_N] col2 = tl.load(conv_states_ptrs, mask_w, 0.0, cache_modifier=".ca") conv_states_ptrs = prior_tokens - 2 * stride_x_token # [BLOCK_N] col1 = tl.load(conv_states_ptrs, mask_w, 0.0, cache_modifier=".ca") conv_states_ptrs = prior_tokens - 3 * stride_x_token # [BLOCK_N] col0 = tl.load(conv_states_ptrs, mask_w, 0.0, cache_modifier=".ca") if HAS_BIAS: bias = bias_ptr + idx_feats mask_bias = idx_feats < dim acc_preload = tl.load(bias, mask=mask_bias, other=0.0).to( tl.float32 ) # [BLOCK_N] else: acc_preload = tl.zeros((BLOCK_N,), dtype=tl.float32) x_base_1d = x_base + token_offset * stride_x_token # starting of chunk # PRE-LOAD WEIGHTS mask_w = idx_feats < dim if KERNEL_WIDTH >= 2: w_ptrs = w_base + (0 * stride_w_width) # [BLOCK_N] tensor w_col0 = tl.load(w_ptrs, mask_w, other=0.0) w_ptrs = w_base + (1 * stride_w_width) # [BLOCK_N] tensor w_col1 = tl.load(w_ptrs, mask_w, other=0.0) if KERNEL_WIDTH >= 3: w_ptrs = w_base + (2 * stride_w_width) # [BLOCK_N] tensor w_col2 = tl.load(w_ptrs, mask_w, other=0.0) if KERNEL_WIDTH >= 4: w_ptrs = w_base + (3 * stride_w_width) # [BLOCK_N] tensor w_col3 = tl.load(w_ptrs, mask_w, other=0.0) mask_x_1d = idx_feats < dim for idx_token in range(segment_len): acc = acc_preload matrix_w = w_col0 matrix_x = col0 for j in tl.static_range(KERNEL_WIDTH): if KERNEL_WIDTH == 2: if j == 1: # KERNEL_WIDTH-1: matrix_w = w_col1 x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N] matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d) elif KERNEL_WIDTH == 3: if j == 1: matrix_w = w_col1 matrix_x = col1 elif j == 2: matrix_w = w_col2 x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N] matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d) elif KERNEL_WIDTH == 4: if j == 1: matrix_w = w_col1 matrix_x = col1 elif j == 2: matrix_w = w_col2 matrix_x = col2 elif j == 3: matrix_w = w_col3 x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N] matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d) acc += matrix_x * matrix_w # [BLOCK_N] if KERNEL_WIDTH == 2: col0 = matrix_x elif KERNEL_WIDTH == 3: col0 = col1 col1 = matrix_x elif KERNEL_WIDTH == 4: col0 = col1 col1 = col2 col2 = matrix_x if SILU_ACTIVATION: acc = acc / (1 + tl.exp(-acc)) mask_1d = (idx_token < segment_len) & ( idx_feats < dim ) # token-index # feature-index o_ptrs = ( o_ptr + (sequence_start_index + token_offset + idx_token) * stride_o_token + (idx_feats * stride_o_dim) ) tl.store(o_ptrs, acc, mask=mask_1d) def causal_conv1d_fn( x: torch.Tensor, weight: torch.Tensor, bias: Union[torch.Tensor, None], conv_states: torch.Tensor, query_start_loc: torch.Tensor, seq_lens_cpu: List[int], cache_indices: Optional[torch.Tensor] = None, has_initial_state: Optional[torch.Tensor] = None, activation: Optional[str] = "silu", pad_slot_id: int = PAD_SLOT_ID, validate_data=False, **kwargs, ): """support varlen + continuous batching when x is 2D tensor x: (dim,cu_seq_len) cu_seq_len = total tokens of all seqs in that batch sequences are concatenated from left to right for varlen weight: (dim, width) conv_states: (...,dim,width - 1) itype updated inplace if provided [it use `cache_indices` to get the index to the cache of conv_state for that sequence conv_state[cache_indices[i]] for seq-i - to be used as initial_state when has_initial_state[i] = True and after that conv_state[cache_indices[i]] need to be shift-left and updated with values from 'x' ] query_start_loc: (batch + 1) int32 The cumulative sequence lengths of the sequences in the batch, used to index into sequence. prepended by 0. if x = [5, 1, 1, 1] <- continuous batching (batch=4) then query_start_loc = [0, 5, 6, 7, 8] <- the starting index of the next sequence; while the last value is the ending index of the last sequence [length(query_start_loc)-1 == batch] for example: query_start_loc = torch.Tensor([0,10,16,17]), x.shape=(dim,17) seq_lens_cpu: (batch) int32 The sequence lengths of the sequences in the batch cache_indices: (batch) int32 indicates the corresponding state index, like so: conv_state = conv_states[cache_indices[batch_id]] has_initial_state: (batch) bool indicates whether should the kernel take the current state as initial state for the calculations [single boolean for each sequence in the batch: True or False] bias: (dim,) activation: either None or "silu" or "swish" or True pad_slot_id: int if cache_indices is passed, lets the kernel identify padded entries that will not be processed, for example: cache_indices = [pad_slot_id, 1, 20, pad_slot_id] in this case, the kernel will not process entries at indices 0 and 3 out: same shape as `x` """ if isinstance(activation, bool) and activation: activation = "silu" out = torch.empty_like(x) is_channel_last = (x.stride(0) == 1) & (x.stride(1) > 1) dim, cu_seqlen = x.shape _, width = weight.shape state_len = width - 1 np2_statelen = triton.next_power_of_2(state_len) stride_x_seq = 0 stride_x_dim = x.stride(0) stride_x_token = x.stride(1) stride_w_dim = weight.stride(0) stride_w_width = weight.stride(1) stride_istate_seq = 0 stride_istate_dim = 0 stride_istate_token = 0 num_cache_lines = 0 if conv_states is not None: # extensions to support vLLM: # 1. conv_states is used to replaced initial_states # 2. conv_states serve as a cache with num cache lines can be larger than batch size # 3. mapping from sequence x[idx] to a cache line at index as specified via cache_indices[idx] # 4. computation can be skipped if cache_indices[idx] == pad_slot_id num_cache_lines = conv_states.size(0) assert ( num_cache_lines == conv_states.shape[0] and dim == conv_states.shape[1] and width - 1 <= conv_states.shape[2] ) stride_istate_seq = conv_states.stride(0) stride_istate_dim = conv_states.stride(1) stride_istate_token = conv_states.stride(2) # assert stride_istate_dim == 1 if out.dim() == 2: stride_o_seq = 0 stride_o_dim = out.stride(0) stride_o_token = out.stride(1) else: stride_o_seq = out.stride(0) stride_o_dim = out.stride(1) stride_o_token = out.stride(2) if validate_data: assert x.dim() == 2 assert query_start_loc is not None assert query_start_loc.dim() == 1 assert x.stride(0) == 1 or x.stride(1) == 1 padded_batch = query_start_loc.size(0) - 1 if bias is not None: assert bias.dim() == 1 assert dim == bias.size(0) if cache_indices is not None: assert cache_indices.dim() == 1 assert padded_batch == cache_indices.size(0) if has_initial_state is not None: assert has_initial_state.size() == (padded_batch,) assert ( conv_states is not None ), "ERROR: `has_initial_state` is used, which needs also `conv_states`" assert weight.stride(1) == 1 assert (dim, width) == weight.shape assert is_channel_last, "Need to run in channel-last layout" def grid(META): max_seq_len = max(seq_lens_cpu) return ( len(seq_lens_cpu), # batch_size (max_seq_len + META["BLOCK_M"] - 1) // META["BLOCK_M"], triton.cdiv(dim, META["BLOCK_N"]), ) _causal_conv1d_fwd_kernel[grid]( # Pointers to matrices x, weight, bias, conv_states, cache_indices, has_initial_state, query_start_loc, out, # Matrix dimensions dim, cu_seqlen, num_cache_lines, # stride stride_x_seq, stride_x_dim, stride_x_token, stride_w_dim, stride_w_width, stride_istate_seq, stride_istate_dim, stride_istate_token, stride_o_seq, stride_o_dim, stride_o_token, # others pad_slot_id, # META HAS_BIAS=bias is not None, KERNEL_WIDTH=width, SILU_ACTIVATION=activation in ["silu", "swish"], HAS_INITIAL_STATES=has_initial_state is not None, HAS_CACHE=conv_states is not None, IS_CONTINUOUS_BATCHING=cache_indices is not None, USE_PAD_SLOT=pad_slot_id is not None, NP2_STATELEN=np2_statelen, # launch_cooperative_grid=True BLOCK_M=8, BLOCK_N=256, num_stages=2, ) return out # HAS_EAGLE_TREE_CUSTOM_ATTN_MASK is added to support eagle tree attention mask # retrieve_next_token_ptr: [N, NP2_T], retrieve_next_sibling_ptr: [N, NP2_T] # e.g. for a sequence of length 4, the eagle tree attention structure is: # retrieve_next_token=[1, 3, -1, -1] -> retrieve_next_token[i]: the 1st child token of token i # retrieve_next_sibling=[-1, 2, -1, -1] -> retrieve_next_sibling[i]: the 1st tree sibling token of token i # retrieve_parent_token=[n/a, 0, 0, 1] -> retrieve_parent_token[i]: the parent token of token i # Tree: # 0 # / \ # 1 2 # / # 3 # When calculating token 3's convolution, it should conv to token 1 (parent) and token 0 (grand-parent) # When calculating token 2's convolution, it should conv to token 0 (parent) # This kernel is a fused kernel which will also produce retrieve_parent_token based on retrieve_next_token & retrieve_next_sibling @triton.jit() def _causal_conv1d_update_kernel( # Pointers to matrices x_ptr, # (batch, dim, seqlen) w_ptr, # (dim, width) bias_ptr, conv_state_ptr, cache_seqlens_ptr, # circular buffer conv_state_indices_ptr, num_accepted_tokens_ptr, intermediate_conv_window_ptr, intermediate_state_indices_ptr, retrieve_next_token_ptr, retrieve_next_sibling_ptr, retrieve_parent_token_ptr, o_ptr, # (batch, dim, seqlen) # Matrix dimensions batch: int, dim: tl.constexpr, seqlen: tl.constexpr, state_len: tl.constexpr, num_cache_lines: tl.constexpr, # added to support vLLM larger cache lines # Strides stride_x_seq: tl.constexpr, stride_x_dim: tl.constexpr, stride_x_token: tl.constexpr, stride_w_dim: tl.constexpr, stride_w_width: tl.constexpr, stride_conv_state_seq: tl.constexpr, stride_conv_state_dim: tl.constexpr, stride_conv_state_tok: tl.constexpr, stride_state_indices: tl.constexpr, stride_inter_seq: tl.constexpr, stride_inter_step: tl.constexpr, stride_inter_dim: tl.constexpr, stride_inter_win: tl.constexpr, stride_intermediate_state_indices: tl.constexpr, stride_retrieve_next_token_seq: tl.constexpr, stride_retrieve_next_token_token: tl.constexpr, stride_retrieve_next_sibling_seq: tl.constexpr, stride_retrieve_next_sibling_token: tl.constexpr, stride_retrieve_parent_token_seq: tl.constexpr, stride_retrieve_parent_token_token: tl.constexpr, stride_o_seq: tl.constexpr, stride_o_dim: tl.constexpr, stride_o_token: tl.constexpr, # others pad_slot_id: tl.constexpr, # Meta-parameters HAS_BIAS: tl.constexpr, KERNEL_WIDTH: tl.constexpr, SILU_ACTIVATION: tl.constexpr, IS_CONTINUOUS_BATCHING: tl.constexpr, IS_SPEC_DECODING: tl.constexpr, NP2_STATELEN: tl.constexpr, NP2_SEQLEN: tl.constexpr, USE_PAD_SLOT: tl.constexpr, BLOCK_N: tl.constexpr, SAVE_INTERMEDIATE: tl.constexpr, HAS_EAGLE_TREE_CUSTOM_ATTN_MASK: tl.constexpr, ): # ruff: noqa: E501 idx_seq = tl.program_id(0) if idx_seq >= batch: return # [BLOCK_N,] elements along the feature-dimension (channel) idx_feats = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N) if IS_CONTINUOUS_BATCHING: # mask = idx_seq < batch conv_state_batch_coord = tl.load( conv_state_indices_ptr + idx_seq * stride_state_indices ).to(tl.int64) if SAVE_INTERMEDIATE: intermediate_state_batch_coord = tl.load( intermediate_state_indices_ptr + idx_seq * stride_intermediate_state_indices ).to(tl.int64) else: conv_state_batch_coord = idx_seq if USE_PAD_SLOT: # noqa if conv_state_batch_coord == pad_slot_id: # not processing as this is not the actual sequence return if IS_SPEC_DECODING: # The rolling of conv state: # # Before forward, the conv_state is: # [history1, history2, ..., historyM]. # # After forward, the conv_state becomes: # [history2, ..., historyM, draft1, draft2, ..., draftN]. # # After acceptance, it becomes: # # - accept 1 tokens: [history2, ..., historyM, draft1] # - accept 2 tokens: [history3, ..., historyM, draft1, draft2] # - and so on. conv_state_token_offset = tl.load(num_accepted_tokens_ptr + idx_seq) - 1 else: conv_state_token_offset = 0 # STEP 1: READ init_state data conv_states_base = ( conv_state_ptr + (conv_state_batch_coord * stride_conv_state_seq) + (idx_feats * stride_conv_state_dim) ) mask_w = idx_feats < dim prior_tokens = conv_states_base + conv_state_token_offset * stride_conv_state_tok if KERNEL_WIDTH >= 2: conv_states_ptrs = prior_tokens # [BLOCK_N] col0 = tl.load(conv_states_ptrs, mask_w, 0.0) if KERNEL_WIDTH >= 3: conv_states_ptrs = prior_tokens + 1 * stride_conv_state_tok # [BLOCK_N] col1 = tl.load(conv_states_ptrs, mask_w, 0.0) if KERNEL_WIDTH >= 4: conv_states_ptrs = prior_tokens + 2 * stride_conv_state_tok # [BLOCK_N] col2 = tl.load(conv_states_ptrs, mask_w, 0.0) if KERNEL_WIDTH == 5: conv_states_ptrs = prior_tokens + 3 * stride_conv_state_tok # [BLOCK_N] col3 = tl.load(conv_states_ptrs, mask_w, 0.0) # STEP 2: assume state_len > seqlen idx_tokens = tl.arange(0, NP2_STATELEN) # [BLOCK_M] # The conv_state updates works in a sliding window manner, # at each forward pass, the tokens are shift by 1, so we # load since idx_tokens + 1. conv_state_ptrs_source = ( conv_state_ptr + (conv_state_batch_coord * stride_conv_state_seq) + conv_state_token_offset * stride_conv_state_tok + (idx_feats * stride_conv_state_dim)[None, :] + ((idx_tokens + (1 if IS_SPEC_DECODING else seqlen)) * stride_conv_state_tok)[ :, None ] ) # [BLOCK_M, BLOCK_N] mask = ( (conv_state_batch_coord < num_cache_lines) & ((idx_tokens + seqlen) < state_len)[:, None] & (idx_feats < dim)[None, :] ) conv_state = tl.load(conv_state_ptrs_source, mask, other=0.0) VAL = state_len - seqlen x_base = x_ptr + (idx_seq * stride_x_seq) + (idx_feats * stride_x_dim) # [BLOCK_N] x_ptrs = ( x_base[None, :] + ((idx_tokens - VAL) * stride_x_token)[:, None] ) # [BLOCK_M, BLOCK_N] mask_x = ( (idx_tokens - VAL >= 0)[:, None] & (idx_tokens - VAL < seqlen)[:, None] & (idx_feats < dim)[None, :] ) # token-index # token-index # feature-index loaded_x = tl.load(x_ptrs, mask_x, 0.0) tl.debug_barrier() new_conv_state = tl.where(mask, conv_state, loaded_x) conv_state_base = ( conv_state_ptr + (conv_state_batch_coord * stride_conv_state_seq) + (idx_feats * stride_conv_state_dim) ) # [BLOCK_N,] conv_state_ptrs_target = ( conv_state_base + (idx_tokens * stride_conv_state_tok)[:, None] ) # [BLOCK_M, BLOCK_N] mask = (idx_tokens < state_len)[:, None] & (idx_feats < dim)[None, :] tl.store(conv_state_ptrs_target, new_conv_state, mask) # STEP 3: init accumulator if HAS_BIAS: bias = bias_ptr + idx_feats mask_bias = idx_feats < dim acc_preload = tl.load(bias, mask=mask_bias, other=0.0).to( tl.float32 ) # [BLOCK_N] else: acc_preload = tl.zeros((BLOCK_N,), dtype=tl.float32) # STEP 4: # PRE-LOAD WEIGHTS # first kernel column, configured for weights to handle BLOCK_N features in range if HAS_EAGLE_TREE_CUSTOM_ATTN_MASK: idx_tokens = tl.arange(0, NP2_SEQLEN) # [BLOCK_M] # Update parent mapping for all tokens at once using vectorized operations mask_retrieve = idx_tokens < seqlen retrieve_next_token_base = ( retrieve_next_token_ptr + (idx_seq * stride_retrieve_next_token_seq) + idx_tokens * stride_retrieve_next_token_token ) retrieve_next_tokens = tl.load(retrieve_next_token_base, mask_retrieve) retrieve_next_sibling_base = ( retrieve_next_sibling_ptr + (idx_seq * stride_retrieve_next_sibling_seq) + idx_tokens * stride_retrieve_next_sibling_token ) retrieve_next_siblings = tl.load(retrieve_next_sibling_base, mask_retrieve) parent_idx_tokens = tl.zeros((NP2_SEQLEN,), dtype=tl.int32) w_base = w_ptr + (idx_feats * stride_w_dim) # [BLOCK_N,] mask_w = idx_feats < dim if KERNEL_WIDTH >= 2: w_ptrs = w_base + (0 * stride_w_width) # [BLOCK_N] tensor w_col0 = tl.load(w_ptrs, mask_w, other=0.0) w_ptrs = w_base + (1 * stride_w_width) # [BLOCK_N] tensor w_col1 = tl.load(w_ptrs, mask_w, other=0.0) if KERNEL_WIDTH >= 3: w_ptrs = w_base + (2 * stride_w_width) # [BLOCK_N] tensor w_col2 = tl.load(w_ptrs, mask_w, other=0.0) if KERNEL_WIDTH >= 4: w_ptrs = w_base + (3 * stride_w_width) # [BLOCK_N] tensor w_col3 = tl.load(w_ptrs, mask_w, other=0.0) x_base_1d = x_base # starting of chunk [BLOCK_N] mask_x_1d = idx_feats < dim # STEP 5: compute each token for idx_token in tl.static_range(seqlen): acc = acc_preload if HAS_EAGLE_TREE_CUSTOM_ATTN_MASK: # set the parent index of the next token in the eagle tree # next token's parent is the current token retrieve_next_token_idx = tl.sum( tl.where(idx_tokens == idx_token, retrieve_next_tokens, 0) ) if retrieve_next_token_idx != -1: # pad slot id parent_idx_tokens = tl.where( idx_tokens == retrieve_next_token_idx, idx_token, parent_idx_tokens, ) # next token's parent is the parent of the current token retrieve_sibling_token_idx = tl.sum( tl.where(idx_tokens == idx_token, retrieve_next_siblings, 0) ) if retrieve_sibling_token_idx != -1: # pad slot id parent_idx_token = tl.sum( tl.where(idx_tokens == idx_token, parent_idx_tokens, 0) ) parent_idx_tokens = tl.where( idx_tokens == retrieve_sibling_token_idx, parent_idx_token, parent_idx_tokens, ) # tl.device_print("am", parent_idx_tokens) _idx_token = idx_token x_ptrs_1d = x_base_1d + _idx_token * stride_x_token # [BLOCK_N] matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d) # convolution operation: itself * wcol[-1] + parent * wcol[-2] + grand-parent * wcol[-3] + ... for j in tl.static_range(KERNEL_WIDTH): if KERNEL_WIDTH == 2: if j == 0: matrix_w = w_col1 else: matrix_w = w_col0 elif KERNEL_WIDTH == 3: if j == 0: matrix_w = w_col2 elif j == 1: matrix_w = w_col1 else: matrix_w = w_col0 elif KERNEL_WIDTH == 4: if j == 0: matrix_w = w_col3 elif j == 1: matrix_w = w_col2 elif j == 2: matrix_w = w_col1 else: matrix_w = w_col0 if SAVE_INTERMEDIATE: # Save the window state after consuming this token # Layout: [seq(cache line), step, dim, win(K-1)] base_ptr = ( intermediate_conv_window_ptr + intermediate_state_batch_coord * stride_inter_seq + idx_token * stride_inter_step + idx_feats * stride_inter_dim ) # store itself in KERNEL_WIDTH-2 slot, parent in KERNEL_WIDTH-3 slot, grand-parent in KERNEL_WIDTH-4 slot, ... if KERNEL_WIDTH - j - 2 >= 0: tl.store( base_ptr + (KERNEL_WIDTH - j - 2) * stride_inter_win, matrix_x, mask=mask_w, ) acc += matrix_x * matrix_w # move to parent for next iteration if _idx_token > 0: _idx_token = tl.sum( tl.where(idx_tokens == _idx_token, parent_idx_tokens, 0) ) x_ptrs_1d = x_base_1d + _idx_token * stride_x_token # [BLOCK_N] matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d) else: # no parent within the current chunk, load from prev conv state: col[-1] (idx 0's parent), col[-2] (idx 0's grand parent), ... if KERNEL_WIDTH == 2: if _idx_token == 0: matrix_x = col0 elif KERNEL_WIDTH == 3: if _idx_token == 0: matrix_x = col1 else: matrix_x = col0 elif KERNEL_WIDTH == 4: if _idx_token == 0: matrix_x = col2 elif _idx_token == -1: matrix_x = col1 else: matrix_x = col0 _idx_token = _idx_token - 1 else: matrix_w = w_col0 matrix_x = col0 for j in tl.static_range(KERNEL_WIDTH): if KERNEL_WIDTH == 2: if j == 1: # KERNEL_WIDTH-1: matrix_w = w_col1 x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N] matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d) elif KERNEL_WIDTH == 3: if j == 1: matrix_w = w_col1 matrix_x = col1 elif j == 2: matrix_w = w_col2 x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N] matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d) elif KERNEL_WIDTH == 4: if j == 1: matrix_w = w_col1 matrix_x = col1 elif j == 2: matrix_w = w_col2 matrix_x = col2 elif j == 3: matrix_w = w_col3 x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N] matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d) acc += matrix_x * matrix_w # [BLOCK_N] if KERNEL_WIDTH == 2: col0 = matrix_x elif KERNEL_WIDTH == 3: col0 = col1 col1 = matrix_x elif KERNEL_WIDTH == 4: col0 = col1 col1 = col2 col2 = matrix_x if SAVE_INTERMEDIATE: # Save the window state after consuming this token # Layout: [seq(cache line), step, dim, win(K-1)] base_ptr = ( intermediate_conv_window_ptr + intermediate_state_batch_coord * stride_inter_seq + idx_token * stride_inter_step + idx_feats * stride_inter_dim ) if KERNEL_WIDTH >= 2: tl.store(base_ptr + 0 * stride_inter_win, col0, mask=mask_w) if KERNEL_WIDTH >= 3: tl.store(base_ptr + 1 * stride_inter_win, col1, mask=mask_w) if KERNEL_WIDTH >= 4: tl.store(base_ptr + 2 * stride_inter_win, col2, mask=mask_w) if SILU_ACTIVATION: acc = acc / (1 + tl.exp(-acc)) mask_1d = (idx_token < seqlen) & ( idx_feats < dim ) # token-index # feature-index o_ptrs = ( o_ptr + (idx_seq) * stride_o_seq + idx_token * stride_o_token + (idx_feats * stride_o_dim) ) tl.store(o_ptrs, acc, mask=mask_1d) # fuse: store calculated retrieve_parent_token to tensor if HAS_EAGLE_TREE_CUSTOM_ATTN_MASK: tl.store( retrieve_parent_token_ptr + idx_seq * stride_retrieve_parent_token_seq + idx_tokens * stride_retrieve_parent_token_token, parent_idx_tokens, mask=mask_retrieve, ) def causal_conv1d_update( x: torch.Tensor, conv_state: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, activation: Union[bool, str, None] = None, cache_seqlens: Optional[torch.Tensor] = None, conv_state_indices: Optional[torch.Tensor] = None, num_accepted_tokens: Optional[torch.Tensor] = None, intermediate_conv_window: Optional[torch.Tensor] = None, intermediate_state_indices: Optional[torch.Tensor] = None, retrieve_next_token: Optional[torch.Tensor] = None, retrieve_next_sibling: Optional[torch.Tensor] = None, retrieve_parent_token: Optional[torch.Tensor] = None, pad_slot_id: int = PAD_SLOT_ID, metadata=None, validate_data=False, ): """ x: (batch, dim) or (batch, dim, seqlen) [shape=2: single token prediction] [shape=3: single or multiple tokens prediction] conv_state: (..., dim, state_len), where state_len >= width - 1 weight: (dim, width) bias: (dim,) cache_seqlens: (batch,), dtype int32. If not None, the conv_state is treated as a circular buffer. The conv_state will be updated by copying x to the conv_state starting at the index @cache_seqlens % state_len. conv_state_indices: (batch,), dtype int32 If not None, the conv_state is a larger tensor along the batch dim, and we are selecting the batch coords specified by conv_state_indices. Useful for a continuous batching scenario. pad_slot_id: int if cache_indices is passed, lets the kernel identify padded entries that will not be processed, for example: cache_indices = [pad_slot_id, 1 ,20 ,pad_slot_id] in this case, the kernel will not process entries at indices 0 and 3 out: (batch, dim) or (batch, dim, seqlen) """ if validate_data: assert cache_seqlens is None # not implemented yet - ok for vLLM assert pad_slot_id is not None assert x.stride(1) == 1 if isinstance(activation, bool): activation = "silu" if activation is True else None elif activation is not None: assert activation in ["silu", "swish"] unsqueeze = x.dim() == 2 if unsqueeze: # make it (batch, dim, seqlen) with seqlen == 1 x = x.unsqueeze(-1) batch, dim, seqlen = x.shape _, width = weight.shape # conv_state: (..., dim, state_len), where state_len >= width - 1 num_cache_lines, _, state_len = conv_state.size() if validate_data: assert dim == weight.size(0) assert ( conv_state.stride(-2) == 1 ), f"ERROR: expect contiguous along feat-dim of conv_state (currently stride={conv_state.stride()})" assert state_len >= width - 1 # when above happens, we don't shift-left to keep any records in conv_state assert dim == conv_state.size(1) if conv_state_indices is None: assert conv_state.size(0) >= batch else: assert (batch,) == conv_state_indices.shape assert intermediate_state_indices is not None assert (batch,) == intermediate_state_indices.shape assert num_cache_lines >= batch assert weight.stride(1) == 1 # Need this assert cache_seqlens is None # not needed for vLLM - circular buffer # adopt the strategy in vLLM that overwrite on 'x' directly, rather than creating a new tensor 'o' out = torch.empty_like(x) stride_w_dim, stride_w_width = weight.stride() stride_x_seq, stride_x_dim, stride_x_token = x.stride() # X (batch, dim, seqlen) stride_o_seq, stride_o_dim, stride_o_token = out.stride() stride_istate_seq, stride_istate_dim, stride_istate_token = conv_state.stride() stride_state_indices = ( conv_state_indices.stride(0) if conv_state_indices is not None else 0 ) stride_intermediate_state_indices = ( intermediate_state_indices.stride(0) if intermediate_state_indices is not None else 0 ) if num_accepted_tokens is not None: state_len = width - 1 + (seqlen - 1) # effective state_len needed else: state_len = width - 1 np2_statelen = triton.next_power_of_2(state_len) np2_seqlen = triton.next_power_of_2(seqlen) def grid(META): return ( batch, triton.cdiv(dim, META["BLOCK_N"]), ) # prepare intermediate buffer strides if provided if intermediate_conv_window is not None: stride_inter_seq, stride_inter_step, stride_inter_dim, stride_inter_win = ( intermediate_conv_window.stride(0), intermediate_conv_window.stride(1), intermediate_conv_window.stride(2), intermediate_conv_window.stride(3), ) else: stride_inter_seq = stride_inter_step = stride_inter_dim = stride_inter_win = 0 # prepare retrieve next token buffer strides if provided if retrieve_next_token is not None: stride_retrieve_next_token_seq, stride_retrieve_next_token_token = ( retrieve_next_token.stride(0), retrieve_next_token.stride(1), ) else: stride_retrieve_next_token_seq = stride_retrieve_next_token_token = 0 # prepare retrieve next sibling buffer strides if provided if retrieve_next_sibling is not None: stride_retrieve_next_sibling_seq, stride_retrieve_next_sibling_token = ( retrieve_next_sibling.stride(0), retrieve_next_sibling.stride(1), ) else: stride_retrieve_next_sibling_seq = stride_retrieve_next_sibling_token = 0 # prepare retrieve parent token buffer strides if provided if retrieve_parent_token is not None: stride_retrieve_parent_token_seq, stride_retrieve_parent_token_token = ( retrieve_parent_token.stride(0), retrieve_parent_token.stride(1), ) else: stride_retrieve_parent_token_seq = stride_retrieve_parent_token_token = 0 _causal_conv1d_update_kernel[grid]( # Pointers to matrices x, weight, bias, conv_state, cache_seqlens, conv_state_indices, num_accepted_tokens, intermediate_conv_window if intermediate_conv_window is not None else x, intermediate_state_indices, retrieve_next_token, retrieve_next_sibling, retrieve_parent_token, out, # Matrix dimensions batch, dim, seqlen, state_len, num_cache_lines, # stride stride_x_seq, stride_x_dim, stride_x_token, stride_w_dim, stride_w_width, stride_istate_seq, stride_istate_dim, stride_istate_token, stride_state_indices, stride_inter_seq, stride_inter_step, stride_inter_dim, stride_inter_win, stride_intermediate_state_indices, stride_retrieve_next_token_seq, stride_retrieve_next_token_token, stride_retrieve_next_sibling_seq, stride_retrieve_next_sibling_token, stride_retrieve_parent_token_seq, stride_retrieve_parent_token_token, stride_o_seq, stride_o_dim, stride_o_token, # others pad_slot_id, # META HAS_BIAS=bias is not None, KERNEL_WIDTH=width, SILU_ACTIVATION=activation in ["silu", "swish"], IS_CONTINUOUS_BATCHING=conv_state_indices is not None, IS_SPEC_DECODING=num_accepted_tokens is not None, NP2_STATELEN=np2_statelen, NP2_SEQLEN=np2_seqlen, USE_PAD_SLOT=pad_slot_id is not None, BLOCK_N=256, SAVE_INTERMEDIATE=intermediate_conv_window is not None, HAS_EAGLE_TREE_CUSTOM_ATTN_MASK=retrieve_next_token is not None, ) if unsqueeze: out = out.squeeze(-1) return out