# Adapted from: https://github.com/vllm-project/vllm/tree/main/vllm/model_executor/layers/mamba/ops/mamba_ssm.py # SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # Copyright (c) 2024, Tri Dao, Albert Gu. # Adapted from https://github.com/state-spaces/mamba/blob/v2.2.4/mamba_ssm/ops/triton/selective_state_update.py import torch import triton import triton.language as tl from packaging import version PAD_SLOT_ID = -1 TRITON3 = version.parse(triton.__version__) >= version.parse("3.0.0") if TRITON3: @triton.jit def softplus(dt): dt = tl.where(dt <= 20.0, tl.math.log(tl.math.exp(dt) + 1), dt) return dt else: @triton.jit def softplus(dt): dt = tl.where(dt <= 20.0, tl.math.log1p(tl.exp(dt)), dt) return dt @triton.heuristics({"HAS_DT_BIAS": lambda args: args["dt_bias_ptr"] is not None}) @triton.heuristics({"HAS_D": lambda args: args["D_ptr"] is not None}) @triton.heuristics({"HAS_Z": lambda args: args["z_ptr"] is not None}) @triton.heuristics( { "HAS_STATE_BATCH_INDICES": lambda args: args["state_batch_indices_ptr"] is not None } ) @triton.heuristics( {"BLOCK_SIZE_DSTATE": lambda args: triton.next_power_of_2(args["dstate"])} ) @triton.heuristics( { "CACHE_INTERMEDIATE_STATES": lambda args: args["intermediate_states_buffer"] is not None } ) @triton.heuristics( { "HAS_EAGLE_TREE_CUSTOM_ATTN_MASK": lambda args: args[ "retrieve_parent_token_ptr" ] is not None } ) @triton.heuristics( { "HAS_INTERMEDIATE_STATE_INDICES": lambda args: args[ "intermediate_state_indices_ptr" ] is not None } ) @triton.jit(do_not_specialize=["T"]) def _selective_scan_update_kernel( # Pointers to matrices state_ptr, x_ptr, dt_ptr, dt_bias_ptr, A_ptr, B_ptr, C_ptr, D_ptr, z_ptr, out_ptr, state_batch_indices_ptr, pad_slot_id, intermediate_states_buffer, cache_steps, retrieve_parent_token_ptr, intermediate_state_indices_ptr, # Matrix dimensions batch, T, nheads, dim, dstate, nheads_ngroups_ratio, # Strides stride_state_batch, stride_state_head, stride_state_dim, stride_state_dstate, stride_x_batch, stride_x_T, stride_x_head, stride_x_dim, stride_dt_batch, stride_dt_T, stride_dt_head, stride_dt_dim, stride_dt_bias_head, stride_dt_bias_dim, stride_A_head, stride_A_dim, stride_A_dstate, stride_B_batch, stride_B_T, stride_B_group, stride_B_dstate, stride_C_batch, stride_C_T, stride_C_group, stride_C_dstate, stride_D_head, stride_D_dim, stride_z_batch, stride_z_T, stride_z_head, stride_z_dim, stride_out_batch, stride_out_T, stride_out_head, stride_out_dim, stride_retrieve_parent_token_batch, stride_retrieve_parent_token_T, # Meta-parameters DT_SOFTPLUS: tl.constexpr, TIE_HDIM: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, HAS_DT_BIAS: tl.constexpr, HAS_D: tl.constexpr, HAS_Z: tl.constexpr, HAS_STATE_BATCH_INDICES: tl.constexpr, DISABLE_STATE_UPDATE: tl.constexpr, CACHE_INTERMEDIATE_STATES: tl.constexpr, HAS_EAGLE_TREE_CUSTOM_ATTN_MASK: tl.constexpr, HAS_INTERMEDIATE_STATE_INDICES: tl.constexpr, BLOCK_SIZE_DSTATE: tl.constexpr, ): pid_m = tl.program_id(axis=0) pid_b = tl.program_id(axis=1) pid_h = tl.program_id(axis=2) # If HAS_STATE_BATCH_INDICES is true, then the ssm state's batch coordinate # is taken from the state_batch_indices_ptr Otherwise, the state coordinate # is the same as the batch id. if HAS_STATE_BATCH_INDICES: state_batch_indices_ptr += pid_b state_batch_idx = tl.load(state_batch_indices_ptr).to(tl.int64) state_ptr += state_batch_idx * stride_state_batch + pid_h * stride_state_head else: state_ptr += pid_b * stride_state_batch + pid_h * stride_state_head x_ptr += pid_b * stride_x_batch + pid_h * stride_x_head dt_ptr += pid_b * stride_dt_batch + pid_h * stride_dt_head if HAS_DT_BIAS: dt_bias_ptr += pid_h * stride_dt_bias_head A_ptr += pid_h * stride_A_head B_ptr += pid_b * stride_B_batch + (pid_h // nheads_ngroups_ratio) * stride_B_group C_ptr += pid_b * stride_C_batch + (pid_h // nheads_ngroups_ratio) * stride_C_group if HAS_Z: z_ptr += pid_b * stride_z_batch + pid_h * stride_z_head out_ptr += pid_b * stride_out_batch + pid_h * stride_out_head offs_m = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_n = tl.arange(0, BLOCK_SIZE_DSTATE) state_ptrs = state_ptr + ( offs_m[:, None] * stride_state_dim + offs_n[None, :] * stride_state_dstate ) mask = (offs_m[:, None] < dim) & (offs_n[None, :] < dstate) if HAS_STATE_BATCH_INDICES: mask &= state_batch_idx != pad_slot_id state = tl.load(state_ptrs, mask=mask, other=0.0).to(tl.float32) if HAS_DT_BIAS: dt_bias_ptrs = dt_bias_ptr + offs_m * stride_dt_bias_dim if HAS_D: D_ptr += pid_h * stride_D_head D_ptrs = D_ptr + offs_m * stride_D_dim A_ptrs = A_ptr + offs_m[:, None] * stride_A_dim + offs_n[None, :] * stride_A_dstate cache_idx = -1 if CACHE_INTERMEDIATE_STATES: if HAS_INTERMEDIATE_STATE_INDICES: intermediate_state_idx = tl.load(intermediate_state_indices_ptr + pid_b).to( tl.int64 ) cache_idx = intermediate_state_idx elif HAS_STATE_BATCH_INDICES: cache_idx = state_batch_idx else: cache_idx = pid_b current_step_idx = 0 for _ in range(T): if HAS_EAGLE_TREE_CUSTOM_ATTN_MASK: if current_step_idx != 0 and cache_idx >= 0: parent_ptr = ( retrieve_parent_token_ptr + pid_b * stride_retrieve_parent_token_batch + current_step_idx * stride_retrieve_parent_token_T ) parent_step_idx = tl.load(parent_ptr).to(tl.int32) if parent_step_idx >= 0 and parent_step_idx < T: step_offset = parent_step_idx * nheads * dim * dstate cache_ptr = ( intermediate_states_buffer + cache_idx * cache_steps * nheads * dim * dstate + step_offset + pid_h * dim * dstate + offs_m[:, None] * dstate + offs_n[None, :] ) state = tl.load(cache_ptr, mask=mask, other=0.0).to(tl.float32) x_ptrs = x_ptr + offs_m * stride_x_dim dt_ptrs = dt_ptr + offs_m * stride_dt_dim B_ptrs = B_ptr + offs_n * stride_B_dstate C_ptrs = C_ptr + offs_n * stride_C_dstate if HAS_Z: z_ptrs = z_ptr + offs_m * stride_z_dim out_ptrs = out_ptr + offs_m * stride_out_dim x = tl.load(x_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32) if not TIE_HDIM: dt = tl.load(dt_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32) if HAS_DT_BIAS: dt += tl.load(dt_bias_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32) if DT_SOFTPLUS: dt = softplus(dt) A = tl.load( A_ptrs, mask=(offs_m[:, None] < dim) & (offs_n[None, :] < dstate), other=0.0, ).to(tl.float32) dA = tl.exp(A * dt[:, None]) else: dt = tl.load(dt_ptr).to(tl.float32) if HAS_DT_BIAS: dt += tl.load(dt_bias_ptr).to(tl.float32) if DT_SOFTPLUS: dt = softplus(dt) A = tl.load(A_ptr).to(tl.float32) dA = tl.exp(A * dt) # scalar, not a matrix B = tl.load(B_ptrs, mask=offs_n < dstate, other=0.0).to(tl.float32) C = tl.load(C_ptrs, mask=offs_n < dstate, other=0.0).to(tl.float32) if HAS_D: D = tl.load(D_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32) if HAS_Z: z = tl.load(z_ptrs, mask=offs_m < dim, other=0.0).to(tl.float32) dB = B[None, :] * dt[:, None] if not TIE_HDIM else B * dt state = state * dA + dB * x[:, None] if CACHE_INTERMEDIATE_STATES: if HAS_STATE_BATCH_INDICES: if state_batch_idx != pad_slot_id: cache_ptr_base = ( intermediate_states_buffer + cache_idx * cache_steps * nheads * dim * dstate + current_step_idx * nheads * dim * dstate + pid_h * dim * dstate ) cache_ptrs = cache_ptr_base + ( offs_m[:, None] * dstate + offs_n[None, :] ) tl.store( cache_ptrs, state.to(cache_ptrs.dtype.element_ty), mask=mask ) out = tl.sum(state * C[None, :], axis=1) if HAS_D: out += x * D if HAS_Z: out *= z * tl.sigmoid(z) tl.store(out_ptrs, out, mask=offs_m < dim) current_step_idx += 1 x_ptr += stride_x_T dt_ptr += stride_dt_T B_ptr += stride_B_T C_ptr += stride_C_T out_ptr += stride_out_T if HAS_Z: z_ptr += stride_z_T if not DISABLE_STATE_UPDATE: tl.store(state_ptrs, state.to(state_ptrs.dtype.element_ty), mask=mask) def selective_state_update( state, x, dt, A, B, C, D=None, z=None, dt_bias=None, dt_softplus=False, state_batch_indices=None, pad_slot_id=PAD_SLOT_ID, out=None, disable_state_update=False, intermediate_states_buffer=None, cache_steps=None, retrieve_parent_token=None, intermediate_state_indices=None, ): """ Argument: state: (batch, dim, dstate) or (batch, nheads, dim, dstate) x: (batch, dim) or (batch, nheads, dim) for single-token or (batch, T, nheads, dim) for multi-token dt: (batch, dim) or (batch, nheads, dim) A: (dim, dstate) or (nheads, dim, dstate) B: (batch, dstate) or (batch, ngroups, dstate) for single-token or (batch, T, ngroups, dstate) for multi-token C: (batch, dstate) or (batch, ngroups, dstate) D: (dim,) or (nheads, dim) z: (batch, dim) or (batch, nheads, dim) dt_bias: (dim,) or (nheads, dim) 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: Preallocated ssm output tensor. Assume same shape as x. In-place updated. disable_state_update: If True, don't write back to state (for speculative verify) intermediate_states_buffer: Buffer to cache intermediate states cache_steps: Total number of steps in the buffer retrieve_parent_token: (batch, T) tensor of parent token indices for EAGLE tree attention intermediate_state_indices: (batch,) tensor of indices for intermediate_states_buffer operations. If provided, uses these indices instead of state_batch_indices for the buffer. """ if state.dim() == 3: state = state.unsqueeze(1) if x.dim() == 2: x = x.unsqueeze(1) if x.dim() == 3: x = x.unsqueeze(1) if dt.dim() == 2: dt = dt.unsqueeze(1) if dt.dim() == 3: dt = dt.unsqueeze(1) if A.dim() == 2: A = A.unsqueeze(0) if B.dim() == 2: B = B.unsqueeze(1) if B.dim() == 3: B = B.unsqueeze(1) if C.dim() == 2: C = C.unsqueeze(1) if C.dim() == 3: C = C.unsqueeze(1) if D is not None and D.dim() == 1: D = D.unsqueeze(0) if z is not None: if z.dim() == 2: z = z.unsqueeze(1) if z.dim() == 3: z = z.unsqueeze(1) if dt_bias is not None and dt_bias.dim() == 1: dt_bias = dt_bias.unsqueeze(0) if out.dim() == 2: out = out.unsqueeze(1) if out.dim() == 3: out = out.unsqueeze(1) _, nheads, dim, dstate = state.shape batch, T, _, _ = x.shape assert x.shape == (batch, T, nheads, dim) assert dt.shape == x.shape assert A.shape == (nheads, dim, dstate) ngroups = B.shape[2] assert nheads % ngroups == 0, "nheads must be divisible by ngroups" assert B.shape == (batch, T, ngroups, dstate) assert C.shape == B.shape if D is not None: assert D.shape == (nheads, dim) if z is not None: assert z.shape == x.shape if dt_bias is not None: assert dt_bias.shape == (nheads, dim) if state_batch_indices is not None: assert state_batch_indices.shape == (batch,) assert out.shape == x.shape grid = lambda META: (triton.cdiv(dim, META["BLOCK_SIZE_M"]), batch, nheads) z_strides = ( (z.stride(0), z.stride(1), z.stride(2), z.stride(3)) if z is not None else (0, 0, 0, 0) ) # We don't want autotune since it will overwrite the state # We instead tune by hand. BLOCK_SIZE_M, num_warps = ( (32, 4) if dstate <= 16 else ( (16, 4) if dstate <= 32 else ((8, 4) if dstate <= 64 else ((4, 4) if dstate <= 128 else ((4, 8)))) ) ) tie_hdim = ( A.stride(-1) == 0 and A.stride(-2) == 0 and dt.stride(-1) == 0 and dt_bias.stride(-1) == 0 ) retrieve_parent_token_strides = ( (retrieve_parent_token.stride(0), retrieve_parent_token.stride(1)) if retrieve_parent_token is not None else (0, 0) ) with torch.cuda.device(x.device.index): _selective_scan_update_kernel[grid]( state, x, dt, dt_bias, A, B, C, D, z, out, state_batch_indices, pad_slot_id, intermediate_states_buffer, cache_steps if cache_steps is not None else 0, retrieve_parent_token, intermediate_state_indices, batch, T, nheads, dim, dstate, nheads // ngroups, state.stride(0), state.stride(1), state.stride(2), state.stride(3), x.stride(0), x.stride(1), x.stride(2), x.stride(3), dt.stride(0), dt.stride(1), dt.stride(2), dt.stride(3), *(dt_bias.stride(0), dt_bias.stride(1)) if dt_bias is not None else 0, A.stride(0), A.stride(1), A.stride(2), B.stride(0), B.stride(1), B.stride(2), B.stride(3), C.stride(0), C.stride(1), C.stride(2), C.stride(3), *(D.stride(0), D.stride(1)) if D is not None else 0, z_strides[0], z_strides[1], z_strides[2], z_strides[3], out.stride(0), out.stride(1), out.stride(2), out.stride(3), retrieve_parent_token_strides[0], retrieve_parent_token_strides[1], dt_softplus, tie_hdim, BLOCK_SIZE_M, DISABLE_STATE_UPDATE=disable_state_update, num_warps=num_warps, )