# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import torch from torch import nn from vllm.config import CacheConfig, ModelConfig, get_current_vllm_config from vllm.distributed import ( divide, get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, tensor_model_parallel_all_gather, tensor_model_parallel_all_reduce, ) from vllm.forward_context import ForwardContext, get_forward_context from vllm.model_executor.custom_op import CustomOp from vllm.model_executor.layers.linear import ( ColumnParallelLinear, MergedColumnParallelLinear, RowParallelLinear, ) from vllm.model_executor.layers.mamba.abstract import MambaBase from vllm.model_executor.layers.mamba.mamba_utils import ( MambaStateDtypeCalculator, MambaStateShapeCalculator, ) from vllm.model_executor.layers.mamba.ops.causal_conv1d import ( causal_conv1d_fn, causal_conv1d_update, ) from vllm.model_executor.layers.mamba.ops.layernorm_gated import rms_norm_gated from vllm.model_executor.layers.mamba.ops.mamba_ssm import selective_state_update from vllm.model_executor.layers.mamba.ops.ssd_combined import ( mamba_chunk_scan_combined_varlen, ) from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.model_loader.weight_utils import ( LoaderFunction, composed_weight_loader, sharded_weight_loader, ) from vllm.model_executor.utils import set_weight_attrs from vllm.utils.torch_utils import direct_register_custom_op from vllm.v1.attention.backend import AttentionMetadata from vllm.v1.attention.backends.mamba2_attn import Mamba2AttentionMetadata # Added by the IBM Team, 2024 # Adapted from transformers.models.mamba2.modeling_mamba2.MambaRMSNormGated # --8<-- [start:mixer2_gated_rms_norm] @CustomOp.register("mixer2_gated_rms_norm") class Mixer2RMSNormGated(CustomOp): # --8<-- [end:mixer2_gated_rms_norm] def __init__( self, full_hidden_size: int, full_n_groups: int, use_rms_norm: bool = True, eps: float = 1e-6, ): super().__init__() self.tp_size = get_tensor_model_parallel_world_size() self.tp_rank = get_tensor_model_parallel_rank() self.full_hidden_size = full_hidden_size self.group_size = full_hidden_size // full_n_groups self.per_rank_hidden_size = full_hidden_size // self.tp_size self.n_groups = full_hidden_size // self.group_size self.variance_epsilon = eps self.use_rms_norm = use_rms_norm if self.use_rms_norm: # Register norm weight only if we're actually applying RMSNorm self.weight = nn.Parameter(torch.ones(self.per_rank_hidden_size)) set_weight_attrs(self.weight, {"weight_loader": sharded_weight_loader(0)}) else: # Avoid checkpoint mismatch by skipping unused parameter self.register_parameter("weight", None) assert self.full_hidden_size % self.tp_size == 0, ( "Tensor parallel world size must divide hidden size." ) def forward_native( self, x: torch.Tensor, gate: torch.Tensor, ): # Three tensor-parallel cases: # 1. n_groups is 1 # In this case we parallelize along the reduction dim. # Each rank computes a local sum of squares followed by AllReduce # 2. tp_size divides n_groups # Each rank only reduces within its local group(s). # No collective ops necessary. # 3. The general case can be pretty complicated so we AllGather # the input and then redundantly compute the RMSNorm. input_dtype = x.dtype x = x * nn.functional.silu(gate.to(torch.float32)) if not self.use_rms_norm: return x.to(input_dtype) if self.n_groups == 1: if self.tp_size > 1: # Compute local sum and then reduce to obtain global sum local_sums = x.pow(2).sum(dim=-1, keepdim=True) global_sums = tensor_model_parallel_all_reduce(local_sums) # Calculate the variance count = self.tp_size * x.shape[-1] variance = global_sums / count else: variance = x.pow(2).mean(-1, keepdim=True) x = x * torch.rsqrt(variance + self.variance_epsilon) else: redundant_tp: bool = self.n_groups % self.tp_size != 0 if redundant_tp: # To handle the general case, redundantly apply the variance x = tensor_model_parallel_all_gather(x, -1) *prefix_dims, hidden_dim = x.shape group_count = hidden_dim // self.group_size x_grouped = x.view(*prefix_dims, group_count, self.group_size) variance = x_grouped.pow(2).mean(-1, keepdim=True) x_grouped = x_grouped * torch.rsqrt(variance + self.variance_epsilon) x = x_grouped.view(*prefix_dims, hidden_dim) if redundant_tp: start = self.per_rank_hidden_size * self.tp_rank end = start + self.per_rank_hidden_size x = x[..., start:end] return self.weight * x.to(input_dtype) def forward_cuda( self, x: torch.Tensor, gate: torch.Tensor, ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]: input_dtype = x.dtype if not self.use_rms_norm: # Keep gate in float32 for numerical stability during silu return x * nn.functional.silu(gate.to(torch.float32)).to(input_dtype) if ((self.n_groups % self.tp_size) != 0) or self.n_groups != 1: return self.forward_native(x, gate) return rms_norm_gated( x, self.weight.data, bias=None, z=gate, eps=self.variance_epsilon, norm_before_gate=False, ) def mamba_v2_sharded_weight_loader( shard_spec: list[tuple[int, int, float]], tp_size: int, tp_rank: int, ) -> LoaderFunction: """Create a weight loader for mamba v2. This ensures that the projections are correctly sharded so that they can be split into x, B, C. It also ensures that all the groups corresponding to a head shard is placed together with it. """ def loader(param: torch.Tensor, loaded_weight: torch.Tensor) -> None: # - track boundary of (sharded) param, and loaded_weight, respectively boundary, loaded_boundary = 0, 0 # - iterate over the shard specs for full_dim, extra, duplicate_groups in shard_spec: # - full dim is the model dim (before TP). # - extra > 0, means there is expected overall increase # of dimensions. This is so because of replication. # - ratio is used map the tp_rank to the actual shard # rank. This is useful when there is replication of # groups to accompany head shards. # - size of the loaded shard shard_size = full_dim // tp_size # - compute the rank into the loaded shard. # - if there is replication, different TP shards will # take from the same rank. # NOTE: currently we only support duplication # in the case where num_groups == 1 rank = 0 if duplicate_groups else tp_rank # - leftmost boundary index into loaded weight. loaded_skip = rank * shard_size loaded_start_idx = loaded_boundary + loaded_skip # - take these many dims from the loaded weight. take = min(shard_size, full_dim - extra - loaded_skip) # - always shard on dim 0 # - the ignore is for a mundane mypy error as it does not # seem to handle slices well. # https://github.com/python/mypy/issues/2410 param.data[ boundary : (boundary + take), ... # type: ignore[misc] ] = loaded_weight[ loaded_start_idx : ( loaded_start_idx + take ) # type: ignore[misc] ] # type: ignore[misc] # move indexing boundaries boundary += shard_size loaded_boundary += full_dim - extra return loader # Adapted from transformers.models.mamba.modeling_mamba.MambaMixer # --8<-- [start:mamba_mixer2] @CustomOp.register("mamba_mixer2") class MambaMixer2(MambaBase, CustomOp): """ Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`. A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective) ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4, and is why Mamba is called **selective** state spaces) """ # --8<-- [end:mamba_mixer2] def __init__( self, hidden_size: int, ssm_state_size: int, conv_kernel_size: int, intermediate_size: int, use_conv_bias: bool, use_bias: bool, n_groups: int = 1, num_heads: int = 128, head_dim: int = 64, rms_norm_eps: float = 1e-5, activation: str = "silu", use_rms_norm: bool = True, model_config: ModelConfig | None = None, cache_config: CacheConfig | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() # For TP, the sharding plan is as follows: # - for the conv modules, since # conv_dim = intermediate_size * 2 * n_groups * ssm_state_size, # we shard intermediate_size and n_groups # - since intermediate_size = n_heads * head_dim, sharding on # intermediate_size is achieved by sharding on n_heads. # - IF, world_size divides groups, then sharding # (n_groups / world_size, n_heads / world_size) # also maintains the invariant n_heads % n_groups == 0 # - HOWEVER IF, world_size DOES NOT divide groups, then we need # to allocate extra space in the shard, such that groups # may be replicated to follow the head shard. # - NOTE: currently for the world size DOES NOT divide groups # case, we only support the case when n_groups == 1 self.tp_size = get_tensor_model_parallel_world_size() tp_rank = get_tensor_model_parallel_rank() assert num_heads % self.tp_size == 0, ( "Tensor parallel world size must divide num heads." ) assert (n_groups % self.tp_size) == 0 or n_groups == 1, ( "If tensor parallel world size does not divide num_groups, " "then num_groups must equal 1." ) assert ( (n_groups % self.tp_size == 0) or self.tp_size == 1 or quant_config is None ), ( "Tensor parallel currently supported for quantized models only " "if tensor parallel world size divides num groups." ) self.ssm_state_size = ssm_state_size self.conv_kernel_size = conv_kernel_size self.activation = activation self.intermediate_size = intermediate_size self.head_dim = head_dim self.num_heads = num_heads self.n_groups = n_groups if n_groups % self.tp_size != 0: # - for TP we shard conv_dim by sharding on n_groups, # - but if n_groups cannot divide tp_size, we need to # extend some extra groups groups = MambaStateShapeCalculator.extra_groups_for_head_shards( n_groups, self.tp_size ) self.n_groups = n_groups + groups self.groups_ssm_state_size = self.n_groups * self.ssm_state_size self.conv_dim = intermediate_size + 2 * self.groups_ssm_state_size if n_groups % self.tp_size == 0: self.conv1d = MergedColumnParallelLinear( input_size=conv_kernel_size, output_sizes=[ intermediate_size, self.groups_ssm_state_size, self.groups_ssm_state_size, ], bias=use_conv_bias, quant_config=None, prefix=f"{prefix}.conv1d", ) self.in_proj = MergedColumnParallelLinear( input_size=hidden_size, output_sizes=[ intermediate_size, intermediate_size, self.groups_ssm_state_size, self.groups_ssm_state_size, self.num_heads, ], bias=use_bias, quant_config=quant_config, prefix=f"{prefix}.in_proj", ) else: # This is the n_groups == 1 case, # where we need to duplicate groups if TP>1. self.conv1d = ColumnParallelLinear( input_size=conv_kernel_size, output_size=self.conv_dim, bias=use_conv_bias, quant_config=None, prefix=f"{prefix}.conv1d", ) self.in_proj = ColumnParallelLinear( input_size=hidden_size, output_size=intermediate_size + self.conv_dim + self.num_heads, bias=use_bias, quant_config=quant_config, prefix=f"{prefix}.in_proj", ) # - because in_proj is a concatenation of 3 weights, we # need to interleave them before sharding # - use the custom weight loader mamba_v2_sharded_weight_loader # for conv1d.bias, covn1d.weight and in_proj.weight # - need to set these settings, to assign the groups # to the head shards group_shard_settings = ( self.groups_ssm_state_size, # expected model size (self.n_groups - n_groups) * self.ssm_state_size, # extra dims assigned n_groups == 1, # if there was only one group ) intermediate_settings = (intermediate_size, 0, False) head_settings = (self.num_heads, 0, False) # - the weight already has a "weight_loader" attribute # which set_weight_attrs will raise if we do not # delete before trying to override it # - ditto for the other two weights below delattr(self.conv1d.bias, "weight_loader") set_weight_attrs( self.conv1d.bias, { "weight_loader": mamba_v2_sharded_weight_loader( [ intermediate_settings, group_shard_settings, group_shard_settings, ], self.tp_size, tp_rank, ) }, ) delattr(self.conv1d.weight, "weight_loader") set_weight_attrs( self.conv1d.weight, { "weight_loader": mamba_v2_sharded_weight_loader( [ intermediate_settings, group_shard_settings, group_shard_settings, ], self.tp_size, tp_rank, ) }, ) if quant_config is None: # - quant layers do not have a weight loader delattr(self.in_proj.weight, "weight_loader") set_weight_attrs( self.in_proj.weight, { "weight_loader": mamba_v2_sharded_weight_loader( [ intermediate_settings, # for gate intermediate_settings, group_shard_settings, group_shard_settings, head_settings, # for dt ], self.tp_size, tp_rank, ) }, ) # unsqueeze to fit conv1d weights shape into the linear weights shape. # Can't do this in `weight_loader` since it already exists in # `ColumnParallelLinear` and `MergedColumnParallelLinear`, # and `set_weight_attrs` doesn't allow to override it self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1) conv_weights = self.conv1d.weight.view( self.conv1d.weight.size(0), self.conv1d.weight.size(2) ) self.register_buffer("conv_weights", conv_weights, persistent=False) # - these are TPed by heads to reduce the size of the # temporal shape self.A = nn.Parameter( torch.empty( divide(num_heads, self.tp_size), dtype=torch.float32, ) ) self.D = nn.Parameter(torch.ones(num_heads // self.tp_size)) self.dt_bias = nn.Parameter(torch.ones(num_heads // self.tp_size)) self.use_rms_norm = use_rms_norm set_weight_attrs(self.D, {"weight_loader": sharded_weight_loader(0)}) a_weight_loader = composed_weight_loader( sharded_weight_loader(0), lambda x: -torch.exp(x.float()) ) set_weight_attrs(self.A, {"weight_loader": a_weight_loader}) set_weight_attrs(self.dt_bias, {"weight_loader": sharded_weight_loader(0)}) self.out_proj = RowParallelLinear( intermediate_size, hidden_size, bias=use_bias, input_is_parallel=True, quant_config=quant_config, prefix=f"{prefix}.out_proj", ) self.norm = Mixer2RMSNormGated( intermediate_size, n_groups, self.use_rms_norm, eps=rms_norm_eps ) # - get hidden_states, B and C after depthwise convolution. self.split_hidden_states_B_C_fn = lambda hidden_states_B_C: torch.split( hidden_states_B_C, [ self.intermediate_size // self.tp_size, self.groups_ssm_state_size // self.tp_size, self.groups_ssm_state_size // self.tp_size, ], dim=-1, ) compilation_config = get_current_vllm_config().compilation_config if prefix in compilation_config.static_forward_context: raise ValueError(f"Duplicate layer name: {prefix}") compilation_config.static_forward_context[prefix] = self # The tuple is (conv_state, ssm_state) self.kv_cache = (torch.tensor([]), torch.tensor([])) self.model_config = model_config self.cache_config = cache_config self.prefix = prefix # Pre-compute sizes for forward pass self.tped_intermediate_size = self.intermediate_size // self.tp_size self.tped_conv_size = self.conv_dim // self.tp_size self.tped_dt_size = self.num_heads // self.tp_size self.split_hidden_states_B_C_fn = lambda hidden_states_B_C: torch.split( hidden_states_B_C, [ self.tped_intermediate_size, self.groups_ssm_state_size // self.tp_size, self.groups_ssm_state_size // self.tp_size, ], dim=-1, ) def forward_native( self, hidden_states: torch.Tensor, mup_vector: torch.Tensor | None = None, ): pass def forward( self, hidden_states: torch.Tensor, mup_vector: torch.Tensor | None = None, ): # 1. Gated MLP's linear projection projected_states, _ = self.in_proj(hidden_states) if mup_vector is not None: projected_states = projected_states * mup_vector # 2. Prepare inputs for conv + SSM ssm_output = torch.empty( [ hidden_states.shape[0], (self.num_heads // self.tp_size) * self.head_dim, ], dtype=hidden_states.dtype, device=hidden_states.device, ) # 3. conv + SSM # (split `projected_states` into hidden_states_B_C, dt in the custom op to # ensure it is not treated as an intermediate tensor by torch compile) torch.ops.vllm.mamba_mixer2( projected_states, ssm_output, self.prefix, ) # 4. gated MLP # GatedRMSNorm internally applying SiLU to the gate # SiLU is applied internally before normalization, unlike standard # norm usage gate = projected_states[..., : self.tped_intermediate_size] hidden_states = self.norm(ssm_output, gate) # 5. Final linear projection output, _ = self.out_proj(hidden_states) return output def conv_ssm_forward( self, projected_states: torch.Tensor, output: torch.Tensor, ): hidden_states_B_C, dt = torch.split( projected_states[..., self.tped_intermediate_size :], [self.tped_conv_size, self.tped_dt_size], dim=-1, ) forward_context = get_forward_context() # attn_metadata contains metadata necessary for the mamba2 triton # kernels to operate in continuous batching and in chunked prefill # modes; they are computed at top-level model forward since they # stay the same and reused for all mamba layers in the same iteration attn_metadata: AttentionMetadata = forward_context.attn_metadata assert self.cache_config is not None mamba_block_size = self.cache_config.mamba_block_size is_mamba_cache_all = self.cache_config.mamba_cache_mode == "all" if attn_metadata is not None: assert isinstance(attn_metadata, dict) attn_metadata = attn_metadata[self.prefix] assert isinstance(attn_metadata, Mamba2AttentionMetadata) self_kv_cache = self.kv_cache[forward_context.virtual_engine] # conv_state = (..., dim, width-1) yet contiguous along 'dim' conv_state = self_kv_cache[0].transpose(-1, -2) ssm_state = self_kv_cache[1] state_indices_tensor = attn_metadata.state_indices_tensor has_initial_states_p = attn_metadata.has_initial_states_p prep_initial_states = attn_metadata.prep_initial_states chunk_size = attn_metadata.chunk_size seq_idx_p = attn_metadata.seq_idx_p query_start_loc_p = attn_metadata.query_start_loc_p cu_chunk_seqlen_p = attn_metadata.cu_chunk_seqlen_p last_chunk_indices_p = attn_metadata.last_chunk_indices_p if attn_metadata is None: # profile run hidden_states_B_C = ( hidden_states_B_C.transpose(0, 1).clone().transpose(0, 1) ).contiguous() hidden_states, _B, _C = self.split_hidden_states_B_C_fn(hidden_states_B_C) return hidden_states num_prefills = attn_metadata.num_prefills # request count num_decodes = attn_metadata.num_decode_tokens # token count (=request) num_prefill_tokens = attn_metadata.num_prefill_tokens # token count has_prefill = num_prefills > 0 has_decode = num_decodes > 0 num_actual_tokens = num_prefill_tokens + num_decodes # Separate prefill and decode by splitting varlen input # Split along token dimension hidden_states_B_C_d, hidden_states_B_C_p = torch.split( hidden_states_B_C[:num_actual_tokens], [num_decodes, num_prefill_tokens], dim=0, ) dt_d, dt_p = torch.split( dt[:num_actual_tokens], [num_decodes, num_prefill_tokens], dim=0, ) # Split along batch dimension state_indices_tensor_d, state_indices_tensor_p = torch.split( state_indices_tensor[:num_actual_tokens], [num_decodes, num_prefills], dim=0, ) if is_mamba_cache_all: # If prefix caching is enabled, retrieve the relevant variables # for prefill and decode block_idx_last_computed_token_d, block_idx_last_computed_token_p = ( torch.split( attn_metadata.block_idx_last_computed_token, [num_decodes, num_prefills], dim=0, ) ) block_idx_last_scheduled_token_d, block_idx_last_scheduled_token_p = ( torch.split( attn_metadata.block_idx_last_scheduled_token, [num_decodes, num_prefills], dim=0, ) ) # Prefill-only variables: block_idx_first_scheduled_token_p = ( attn_metadata.block_idx_first_scheduled_token_p ) num_computed_tokens_p = attn_metadata.num_computed_tokens_p else: block_idx_last_computed_token_d = None block_idx_last_computed_token_p = None block_idx_last_scheduled_token_d = None block_idx_last_scheduled_token_p = None block_idx_first_scheduled_token_p = None num_computed_tokens_p = None preallocated_ssm_out_d, preallocated_ssm_out_p = torch.split( output[:num_actual_tokens], [num_decodes, num_prefill_tokens], dim=0, ) # Process prefill requests if has_prefill: # 2. Convolution sequence transformation # - It will read the initial states for every sequence, # that has "has_initial_states_p" == True, # from "cache_indices", using "state_indices_tensor_p". # - It updates the "conv_state" cache in positions pointed # to by "state_indices_tensor_p". # In particular, it will always write the state at the # sequence end. # In addition, "block_idx_first_scheduled_token_p" and # "block_idx_last_scheduled_token_p" # are provided (which are pointers into # "state_indices_tensor_p"), it will write additional cache # states aligned at "block_size_to_align". x = hidden_states_B_C_p.transpose( 0, 1 ) # this is the form that causal-conv see hidden_states_B_C_p = causal_conv1d_fn( x, self.conv_weights, self.conv1d.bias, activation=self.activation, conv_states=conv_state, has_initial_state=has_initial_states_p, cache_indices=state_indices_tensor_p, block_idx_first_scheduled_token=block_idx_first_scheduled_token_p, block_idx_last_scheduled_token=block_idx_last_scheduled_token_p, initial_state_idx=block_idx_last_computed_token_p, num_computed_tokens=num_computed_tokens_p, block_size_to_align=mamba_block_size, metadata=attn_metadata, query_start_loc=query_start_loc_p, ).transpose(0, 1)[:num_prefill_tokens] hidden_states_p, B_p, C_p = self.split_hidden_states_B_C_fn( hidden_states_B_C_p ) # 3. State Space Model sequence transformation initial_states = None if has_initial_states_p is not None and prep_initial_states: kernel_ssm_indices = state_indices_tensor_p if is_mamba_cache_all: kernel_ssm_indices = state_indices_tensor_p.gather( 1, block_idx_last_computed_token_p.unsqueeze(1) ).squeeze(1) initial_states = torch.where( has_initial_states_p[:, None, None, None], ssm_state[kernel_ssm_indices], 0, ) # NOTE: final output is an in-place update of out tensor varlen_states = mamba_chunk_scan_combined_varlen( hidden_states_p.view( num_prefill_tokens, self.num_heads // self.tp_size, self.head_dim ), dt_p, self.A, B_p.view(num_prefill_tokens, self.n_groups // self.tp_size, -1), C_p.view(num_prefill_tokens, self.n_groups // self.tp_size, -1), chunk_size=chunk_size, D=self.D, z=None, dt_bias=self.dt_bias, seq_idx=seq_idx_p, cu_seqlens=query_start_loc_p, cu_chunk_seqlens=cu_chunk_seqlen_p, last_chunk_indices=last_chunk_indices_p, initial_states=initial_states, return_intermediate_states=is_mamba_cache_all, dt_softplus=True, dt_limit=(0.0, float("inf")), out=preallocated_ssm_out_p.view(num_prefill_tokens, -1, self.head_dim), state_dtype=ssm_state.dtype, ) if is_mamba_cache_all: # The chunk_stride is the number of chunks per mamba block # e.g., if mamba_block_size = 512 and chunk_size = 256, # then chunk_stride = 2 chunk_stride = mamba_block_size // chunk_size # Save state for sequences with more than just final state for seq_idx in range(num_prefills): # Block index for the first scheduled token block_idx_first_scheduled_token = block_idx_first_scheduled_token_p[ seq_idx ] # Block index for the last scheduled token block_idx_last_scheduled_token = block_idx_last_scheduled_token_p[ seq_idx ] # Number of blocks that need to be written n_blocks_to_fill = ( block_idx_last_scheduled_token - block_idx_first_scheduled_token ) # Skip sequences that don't have any blocks to fill if n_blocks_to_fill == 0: continue # Look up the state indices cache_blocks_to_fill = state_indices_tensor_p[ seq_idx, block_idx_first_scheduled_token:block_idx_last_scheduled_token, ] # First chunk index for this sequence if seq_idx == 0: first_chunk = 0 else: first_chunk = 1 + last_chunk_indices_p[seq_idx - 1] # First chunk that is aligned on the mamba block boundary first_aligned_chunk = first_chunk + chunk_stride - 1 # Calculate the number of computed tokens that were not # already cached num_unaligned_computed_tokens = ( num_computed_tokens_p[seq_idx] % mamba_block_size ) if num_unaligned_computed_tokens > 0: # If the number of computed tokens is not block aligned, # then we need to shift the index accordingly first_aligned_chunk -= ( num_unaligned_computed_tokens // chunk_size ) # Get states to write from_where = varlen_states[ first_aligned_chunk : first_aligned_chunk + n_blocks_to_fill * chunk_stride : chunk_stride ] # Write the states ssm_state[cache_blocks_to_fill] = from_where # For all seqs, store the last state (note: might be partial): ssm_state[ state_indices_tensor_p.gather( 1, block_idx_last_scheduled_token_p.unsqueeze(1) ).squeeze(1) ] = varlen_states[last_chunk_indices_p] else: # update ssm states # - varlen state is a (num_prefills, nheads, headdim, dstate) # tensor ssm_state[state_indices_tensor_p] = varlen_states # Process decode requests if has_decode: if is_mamba_cache_all: state_indices_tensor_d_input = state_indices_tensor_d.gather( 1, block_idx_last_computed_token_d.unsqueeze(1) ).squeeze(1) state_indices_tensor_d_output = state_indices_tensor_d.gather( 1, block_idx_last_scheduled_token_d.unsqueeze(1) ).squeeze(1) # for decode: # block_idx_first_scheduled_token_d == # block_idx_last_scheduled_token_d # at block boundaries: # block_idx_first_scheduled_token_d > # block_idx_last_computed_token_d else: # Without caching, read and write in-place to the same blocks: state_indices_tensor_d_input = state_indices_tensor_d state_indices_tensor_d_output = state_indices_tensor_d # 2. Convolution sequence transformation hidden_states_B_C_d = causal_conv1d_update( hidden_states_B_C_d, conv_state, self.conv_weights, self.conv1d.bias, self.activation, conv_state_indices=state_indices_tensor_d, block_idx_last_scheduled_token=block_idx_last_scheduled_token_d, initial_state_idx=block_idx_last_computed_token_d, ) hidden_states_d, B_d, C_d = self.split_hidden_states_B_C_fn( hidden_states_B_C_d ) # 3. State Space Model sequence transformation n_groups = self.n_groups // self.tp_size A_d = ( self.A[:, None, ...][:, :, None] .expand(-1, self.head_dim, self.ssm_state_size) .to(dtype=torch.float32) ) dt_d = dt_d[:, :, None].expand(-1, -1, self.head_dim) dt_bias = self.dt_bias[:, None, ...].expand(-1, self.head_dim) D_d = self.D[:, None, ...].expand(-1, self.head_dim) B_d = B_d.view(-1, n_groups, B_d.shape[1] // n_groups) C_d = C_d.view(-1, n_groups, C_d.shape[1] // n_groups) hidden_states_d = hidden_states_d.view( -1, self.num_heads // self.tp_size, self.head_dim ) # - the hidden is reshaped into (bs, num_heads, head_dim) # - mamba_cache_params.ssm_state's slots will be selected # using state_indices_tensor_d # NOTE: final output is an in-place update of out tensor selective_state_update( ssm_state, hidden_states_d, dt_d, A_d, B_d, C_d, D_d, z=None, dt_bias=dt_bias, dt_softplus=True, state_batch_indices=state_indices_tensor_d_input, dst_state_batch_indices=state_indices_tensor_d_output, out=preallocated_ssm_out_d.view(num_decodes, -1, self.head_dim), ) def get_state_dtype(self) -> tuple[torch.dtype, torch.dtype]: assert self.model_config is not None assert self.cache_config is not None return MambaStateDtypeCalculator.mamba2_state_dtype( self.model_config.dtype, self.cache_config.mamba_cache_dtype, self.cache_config.mamba_ssm_cache_dtype, ) def get_state_shape(self) -> tuple[tuple[int, ...], tuple[int, ...]]: return MambaStateShapeCalculator.mamba2_state_shape( intermediate_size=self.intermediate_size, tp_world_size=get_tensor_model_parallel_world_size(), n_groups=self.n_groups, num_heads=self.num_heads, head_dim=self.head_dim, state_size=self.ssm_state_size, conv_kernel=self.conv_kernel_size, ) @property def mamba_type(self) -> str: return "mamba2" def mamba_mixer2( projected_states: torch.Tensor, output: torch.Tensor, layer_name: str, ) -> None: forward_context: ForwardContext = get_forward_context() self = forward_context.no_compile_layers[layer_name] self.conv_ssm_forward(projected_states=projected_states, output=output) def mamba_mixer2_fake( projected_states: torch.Tensor, output: torch.Tensor, layer_name: str, ) -> None: return direct_register_custom_op( op_name="mamba_mixer2", op_func=mamba_mixer2, mutates_args=["output"], fake_impl=mamba_mixer2_fake, )