# coding=utf-8 """Inference-only Jurassic model.""" from dataclasses import dataclass from typing import Dict, Iterable, List, Optional, Tuple import torch from causal_conv1d import causal_conv1d_fn, causal_conv1d_update from mamba_ssm.ops.selective_scan_interface import selective_scan_fn from mamba_ssm.ops.triton.selective_state_update import selective_state_update from torch import nn from torch.nn.parameter import Parameter from transformers import JambaConfig from vllm.attention.backends.abstract import AttentionMetadata from vllm.attention.layer import Attention from vllm.config import CacheConfig, LoRAConfig from vllm.distributed import (get_tensor_model_parallel_rank, get_tensor_model_parallel_world_size, tensor_model_parallel_all_reduce) from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.fused_moe import fused_moe from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (ColumnParallelLinear, MergedColumnParallelLinear, QKVParallelLinear, ReplicatedLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) from vllm.model_executor.layers.sampler import Sampler from vllm.model_executor.layers.vocab_parallel_embedding import ( DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.model_executor.utils import set_weight_attrs from vllm.sequence import IntermediateTensors, SamplerOutput from vllm.worker.model_runner import _BATCH_SIZES_TO_CAPTURE KVCache = Tuple[torch.Tensor, torch.Tensor] @dataclass class MambaCacheParams: is_prompt: bool = False conv_state: torch.Tensor = torch.Tensor() ssm_state: torch.Tensor = torch.Tensor() # Adapted from transformers.models.mamba.modeling_mamba.MambaMixer class JambaMambaMixer(nn.Module): """ 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) """ def __init__(self, config: JambaConfig, layer_idx): super().__init__() self.config = config self.layer_idx = layer_idx self.hidden_size = config.hidden_size self.ssm_state_size = config.mamba_d_state self.conv_kernel_size = config.mamba_d_conv self.intermediate_size = config.mamba_expand * config.hidden_size self.time_step_rank = config.mamba_dt_rank self.use_conv_bias = config.mamba_conv_bias self.use_bias = config.mamba_proj_bias self.conv1d = ColumnParallelLinear( input_size=self.conv_kernel_size, output_size=self.intermediate_size, bias=self.use_conv_bias, ) # 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 `set_weight_attrs` # doesn't allow to override it self.conv1d.weight.data = self.conv1d.weight.data.unsqueeze(1) self.in_proj = MergedColumnParallelLinear(self.hidden_size, [self.intermediate_size] * 2, bias=self.use_bias) # selective projection used to make dt, B and C input dependent self.x_proj = RowParallelLinear( self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False, ) # time step projection (discretization) - # In the forward we need to apply dt_proj without the bias, # as the bias is added in the selective scan kernel. self.dt_proj = ColumnParallelLinear(self.time_step_rank, self.intermediate_size, bias=True, skip_bias_add=True) def weight_loader(param: Parameter, loaded_weight: torch.Tensor): tp_rank = get_tensor_model_parallel_rank() tp_size = get_tensor_model_parallel_world_size() param.data.copy_( loaded_weight.data.split(loaded_weight.shape[0] // tp_size, dim=0)[tp_rank]) def A_weight_loader(param: Parameter, loaded_weight: torch.Tensor): weight_loader(param, -torch.exp(loaded_weight.float())) tp_size = get_tensor_model_parallel_world_size() self.A = nn.Parameter( torch.empty( self.intermediate_size // tp_size, self.ssm_state_size, dtype=torch.float32, )) self.D = nn.Parameter(torch.ones(self.intermediate_size // tp_size)) set_weight_attrs(self.D, {"weight_loader": weight_loader}) set_weight_attrs(self.A, {"weight_loader": A_weight_loader}) self.out_proj = RowParallelLinear( self.intermediate_size, self.hidden_size, bias=self.use_bias, input_is_parallel=True, ) self.activation = config.hidden_act self.dt_layernorm = RMSNorm(self.time_step_rank, eps=config.rms_norm_eps) self.b_layernorm = RMSNorm(self.ssm_state_size, eps=config.rms_norm_eps) self.c_layernorm = RMSNorm(self.ssm_state_size, eps=config.rms_norm_eps) def mamba_forward(self, hidden_states: torch.Tensor, cache_params: MambaCacheParams = None): # 1. Gated MLP's linear projection projected_states = self.in_proj(hidden_states)[0].transpose(1, 2) hidden_states, gate = projected_states.chunk(2, dim=1) # 2. Convolution sequence transformation conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2)) if cache_params is not None and not cache_params.is_prompt: hidden_states = causal_conv1d_update( hidden_states.squeeze(-1), cache_params.conv_state, conv_weights, self.conv1d.bias, self.activation, ) hidden_states = hidden_states.unsqueeze(-1) else: if cache_params is not None: conv_states = nn.functional.pad( hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0)) cache_params.conv_state.copy_(conv_states) hidden_states = causal_conv1d_fn( hidden_states, conv_weights, self.conv1d.bias, activation=self.activation, ) # 3. State Space Model sequence transformation # 3.a. input varying initialization of time_step, B and C ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))[0] time_step, B, C = torch.split( ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1, ) time_step = self.dt_layernorm(time_step.contiguous()) B = self.b_layernorm(B.contiguous()) C = self.c_layernorm(C.contiguous()) discrete_time_step = self.dt_proj(time_step)[0].transpose(1, 2) # 3.c perform the recurrence y ← SSM(A, B, C)(x) time_proj_bias = (self.dt_proj.bias.float() if hasattr( self.dt_proj, "bias") else None) if cache_params is not None and not cache_params.is_prompt: scan_outputs = selective_state_update( cache_params.ssm_state, hidden_states[..., 0], discrete_time_step[..., 0], self.A, B[:, 0], C[:, 0], self.D, gate[..., 0], time_proj_bias, dt_softplus=True, ).unsqueeze(-1) else: scan_outputs, ssm_state = selective_scan_fn( hidden_states, discrete_time_step, self.A, B.transpose(1, 2), C.transpose(1, 2), self.D.float(), gate, time_proj_bias, delta_softplus=True, return_last_state=True, ) if ssm_state is not None and cache_params is not None: cache_params.ssm_state.copy_(ssm_state) # 4. Final linear projection contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))[0] return contextualized_states def forward( self, hidden_states: torch.Tensor, attn_metadata: AttentionMetadata, conv_state: torch.Tensor, ssm_state: torch.Tensor, ): if attn_metadata.prefill_metadata is not None: offset = 0 for i, prompt_len in enumerate( attn_metadata.prefill_metadata.seq_lens): cache = MambaCacheParams(True, conv_state=conv_state[i].unsqueeze(0), ssm_state=ssm_state[i].unsqueeze(0)) hidden_states[offset:offset + prompt_len].copy_( self.mamba_forward(hidden_states[offset:offset + prompt_len].unsqueeze(0), cache_params=cache)[0]) offset += prompt_len else: cache = MambaCacheParams(False, conv_state=conv_state, ssm_state=ssm_state) hidden_states = self.mamba_forward(hidden_states.unsqueeze(1), cache_params=cache) hidden_states = hidden_states.squeeze(1) return hidden_states class JambaMLP(nn.Module): def __init__( self, config: JambaConfig, quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() hidden_size = config.hidden_size intermediate_size = config.intermediate_size hidden_act = config.hidden_act self.gate_up_proj = MergedColumnParallelLinear( hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config) self.down_proj = RowParallelLinear(intermediate_size, hidden_size, bias=False, quant_config=quant_config) if hidden_act != "silu": raise ValueError(f"Unsupported activation: {hidden_act}. " "Only silu is supported for now.") self.act_fn = SiluAndMul() def forward(self, x): gate_up, _ = self.gate_up_proj(x) x = self.act_fn(gate_up) x, _ = self.down_proj(x) return x class JambaMoE(nn.Module): """A tensor-parallel MoE implementation for Mixtral that shards each expert across all ranks. Each expert's weights are sharded across all ranks and a fused MoE kernel is used for the forward pass, and finally we reduce the outputs across ranks. """ def __init__( self, config: JambaConfig, params_dtype: Optional[torch.dtype] = None, tp_size: Optional[int] = None, quant_config: Optional[QuantizationConfig] = None, ): super().__init__() self.tp_size = tp_size or get_tensor_model_parallel_world_size() self.num_total_experts = config.num_experts self.top_k = config.num_experts_per_tok self.hidden_size = config.hidden_size self.intermediate_size = config.intermediate_size // self.tp_size if params_dtype is None: params_dtype = torch.get_default_dtype() self.params_dtype = params_dtype self.router = ReplicatedLinear(self.hidden_size, self.num_total_experts, bias=False, params_dtype=self.params_dtype) self.ws = nn.Parameter( torch.empty( self.num_total_experts, 2 * self.intermediate_size, self.hidden_size, device="cuda", dtype=self.params_dtype, )) self.w2s = nn.Parameter( torch.empty( self.num_total_experts, self.hidden_size, self.intermediate_size, device="cuda", dtype=self.params_dtype, )) set_weight_attrs( self.ws, { "weight_loader": self.weight_loader, }, ) set_weight_attrs( self.w2s, { "weight_loader": self.weight_loader, }, ) def weight_loader( self, param: nn.Parameter, loaded_weight: torch.Tensor, weight_name: str, expert_id: int, ): tp_rank = get_tensor_model_parallel_rank() param_data = param.data shard_size = self.intermediate_size shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size) if weight_name.endswith("gate_proj.weight"): param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :] if weight_name.endswith("up_proj.weight"): param_data[expert_id, shard_size:2 * shard_size, :] = loaded_weight[shard, :] if weight_name.endswith("down_proj.weight"): param_data[expert_id, :, :] = loaded_weight[:, shard] def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: num_tokens, hidden_size = hidden_states.shape hidden_states = hidden_states.view(-1, self.hidden_size) # router_logits: (batch * sequence_length, n_experts) router_logits, _ = self.router(hidden_states) final_hidden_states = fused_moe( hidden_states, self.ws, self.w2s, router_logits, self.top_k, renormalize= False, # Mixtral normalize the expert probs to 1. We don't! inplace=True, ) if self.tp_size > 1: final_hidden_states = tensor_model_parallel_all_reduce( final_hidden_states) return final_hidden_states.view(num_tokens, hidden_size) class JambaMambaDecoderLayer(nn.Module): def __init__(self, config: JambaConfig, layer_idx: int, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None) -> None: super().__init__() self.layer_idx = layer_idx self.config = config self.mamba = JambaMambaMixer(config, layer_idx) num_experts = config.layers_num_experts[layer_idx] ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP self.feed_forward = ffn_layer_class(config, quant_config=quant_config) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.pre_ff_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, hidden_states: torch.Tensor, attn_metadata: AttentionMetadata, residual: Optional[torch.Tensor], conv_state: torch.Tensor, ssm_state: torch.Tensor, **kwargs, ): if residual is None: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) else: hidden_states, residual = self.input_layernorm( hidden_states, residual) hidden_states = self.mamba(hidden_states, attn_metadata, conv_state, ssm_state) # Fully Connected hidden_states, residual = self.pre_ff_layernorm( hidden_states, residual) hidden_states = self.feed_forward(hidden_states) return hidden_states, residual class JambaAttentionDecoderLayer(nn.Module): def __init__( self, config: JambaConfig, layer_idx: int, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, ) -> None: super().__init__() self.hidden_size = config.hidden_size tp_size = get_tensor_model_parallel_world_size() self.total_num_heads = config.num_attention_heads assert self.total_num_heads % tp_size == 0 self.num_heads = self.total_num_heads // tp_size self.total_num_kv_heads = config.num_key_value_heads if self.total_num_kv_heads >= tp_size: # Number of KV heads is greater than TP size, so we partition # the KV heads across multiple tensor parallel GPUs. assert self.total_num_kv_heads % tp_size == 0 else: # Number of KV heads is less than TP size, so we replicate # the KV heads across multiple tensor parallel GPUs. assert tp_size % self.total_num_kv_heads == 0 self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) self.head_dim = config.hidden_size // self.total_num_heads self.q_size = self.num_heads * self.head_dim self.kv_size = self.num_kv_heads * self.head_dim self.scaling = self.head_dim**-0.5 self.qkv_proj = QKVParallelLinear( config.hidden_size, self.head_dim, self.total_num_heads, self.total_num_kv_heads, bias=False, quant_config=quant_config, ) self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim, config.hidden_size, bias=False, quant_config=quant_config) self.attn = Attention( self.num_heads, self.head_dim, self.scaling, num_kv_heads=self.num_kv_heads, cache_config=cache_config, ) num_experts = config.layers_num_experts[layer_idx] ffn_layer_class = JambaMoE if num_experts > 1 else JambaMLP self.feed_forward = ffn_layer_class(config, quant_config=quant_config) self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.pre_ff_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def self_attention( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, **kwargs, ) -> torch.Tensor: qkv, _ = self.qkv_proj(hidden_states) q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) attn_output = self.attn(q, k, v, kv_cache, attn_metadata) output, _ = self.o_proj(attn_output) return output def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, kv_cache: torch.Tensor, attn_metadata: AttentionMetadata, residual: Optional[torch.Tensor], **kwargs, ): if residual is None: residual = hidden_states hidden_states = self.input_layernorm(hidden_states) else: hidden_states, residual = self.input_layernorm( hidden_states, residual) hidden_states = self.self_attention( positions=positions, hidden_states=hidden_states, kv_cache=kv_cache, attn_metadata=attn_metadata, ) # Fully Connected hidden_states, residual = self.pre_ff_layernorm( hidden_states, residual) hidden_states = self.feed_forward(hidden_states) return hidden_states, residual ALL_DECODER_LAYER_TYPES = { "attention": JambaAttentionDecoderLayer, "mamba": JambaMambaDecoderLayer } class JambaModel(nn.Module): def __init__( self, config: JambaConfig, quant_config: Optional[QuantizationConfig] = None, cache_config: Optional[CacheConfig] = None, lora_config: Optional[LoRAConfig] = None, ) -> None: super().__init__() self.config = config self.padding_idx = config.pad_token_id lora_vocab = ((lora_config.lora_extra_vocab_size * (lora_config.max_loras or 1)) if lora_config else 0) self.vocab_size = config.vocab_size + lora_vocab self.org_vocab_size = config.vocab_size self.embed_tokens = VocabParallelEmbedding( self.vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, ) decoder_layers = [] for i in range(config.num_hidden_layers): layer_class = ALL_DECODER_LAYER_TYPES[config.layers_block_type[i]] decoder_layers.append( layer_class(config, layer_idx=i, cache_config=cache_config, quant_config=quant_config)) self.layers = nn.ModuleList(decoder_layers) self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, conv_state: torch.Tensor, ssm_state: torch.Tensor, ) -> torch.Tensor: hidden_states = self.embed_tokens(input_ids) residual = None for i in range(len(self.layers)): layer = self.layers[i] kv_cache = None current_ssm_state = None current_conv_state = None if isinstance(layer, JambaAttentionDecoderLayer): kv_cache = kv_caches[(i - self.config.attn_layer_offset) // self.config.attn_layer_period] if isinstance(layer, JambaMambaDecoderLayer): current_state_layer = i - (1 + (i - self.config.attn_layer_offset) // self.config.attn_layer_period) current_ssm_state = ssm_state[current_state_layer] current_conv_state = conv_state[current_state_layer] hidden_states, residual = layer( positions=positions, hidden_states=hidden_states, kv_cache=kv_cache, attn_metadata=attn_metadata, residual=residual, conv_state=current_conv_state, ssm_state=current_ssm_state, ) hidden_states, _ = self.final_layernorm(hidden_states, residual) return hidden_states class JambaForCausalLM(nn.Module): packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], } # LoRA specific attributes supported_lora_modules = [ "qkv_proj", "o_proj", "embed_tokens", "lm_head", ] embedding_modules = { "embed_tokens": "input_embeddings", "lm_head": "output_embeddings", } embedding_padding_modules = ["lm_head"] def __init__( self, config: JambaConfig, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, lora_config: Optional[LoRAConfig] = None, ) -> None: super().__init__() self.config = config self.model = JambaModel(config, cache_config=cache_config, quant_config=quant_config, lora_config=lora_config) self.unpadded_vocab_size = config.vocab_size if lora_config: self.unpadded_vocab_size += lora_config.lora_extra_vocab_size self.lm_head = ParallelLMHead( self.unpadded_vocab_size, config.hidden_size, org_num_embeddings=config.vocab_size, padding_size=DEFAULT_VOCAB_PADDING_SIZE # We need bigger padding if using lora for kernel # compatibility if not lora_config else lora_config.lora_vocab_padding_size, ) # Current step used indices self.current_indices: List[int] = [] # Used to track and store by the Mamba cache between steps. self.mamba_cache: Tuple[torch.Tensor, torch.Tensor] = tuple() # Used as an input_buffer for the CUDA graph runs. self.mamba_gc_cache_buffer: Tuple[torch.Tensor, torch.Tensor] = tuple() # Maps between the request id and a dict that maps between the seq_id # and its index inside the self.mamba_cache self.mamba_cache_indices_mapping: Dict[str, Dict[int, int]] = {} self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, config.vocab_size) self.sampler = Sampler() def forward(self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[KVCache], attn_metadata: AttentionMetadata, intermediate_tensors: Optional[IntermediateTensors] = None, **kwargs): if not self.mamba_cache: self._prepare_mamba_cache() if "seqlen_agnostic_capture_inputs" not in kwargs: # We get here only on Prefill/Eager mode runs assert all( key in kwargs for key in ["request_ids_to_seq_ids", "finished_requests_ids"]) request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"] batch_size = input_ids.shape[0] if attn_metadata.prefill_metadata: batch_size = len(request_ids_to_seq_ids) ( current_seqlen_agnostic_cache, indices, ) = self._prepare_current_run_mamba_cache(request_ids_to_seq_ids, batch_size) finished_requests_ids = kwargs["finished_requests_ids"] self._release_mamba_cache(finished_requests_ids) else: # CUDA graph capturing runs current_seqlen_agnostic_cache, indices = ( kwargs["seqlen_agnostic_capture_inputs"], [], ) self.current_indices = indices hidden_states = self.model(input_ids, positions, kv_caches, attn_metadata, current_seqlen_agnostic_cache[0], current_seqlen_agnostic_cache[1]) if "seqlen_agnostic_capture_inputs" not in kwargs: self._copy_mamba_cache_by_indices(self.current_indices, current_seqlen_agnostic_cache) return hidden_states def _copy_mamba_cache_by_indices( self, indices: List[int], current_seqlen_agnostic_cache: Tuple[torch.Tensor, torch.Tensor]): for i, offset in enumerate(indices): self._copy_mamba_cache(offset, i, current_seqlen_agnostic_cache) def _copy_mamba_cache(self, index_to: int, index_from: int, from_buffer: Tuple[torch.Tensor, torch.Tensor]): assert len(self.mamba_cache) > 0 for (cache_t, from_buffer_t) in zip(self.mamba_cache, from_buffer): cache_t[:, index_to].copy_(from_buffer_t[:, index_from], non_blocking=True) def _assign_seq_id_to_mamba_cache(self, cur_rid: str, seqs_id: List[int]) -> List[int]: indices_for_current_run = [] for seq_id in seqs_id: if cur_rid not in self.mamba_cache_indices_mapping: self.mamba_cache_indices_mapping[cur_rid] = {} first_free_index = self._first_free_index_in_mamba_cache() self.mamba_cache_indices_mapping[cur_rid][ seq_id] = first_free_index index_for_current_run = first_free_index ## case of decoding n>1, copy prefill cache to decoding indices elif seq_id not in (seq_ids2indices := self.mamba_cache_indices_mapping[cur_rid]): first_free_index = self._first_free_index_in_mamba_cache() index_exist = list(seq_ids2indices.values())[0] self._copy_mamba_cache(index_from=index_exist, index_to=first_free_index, from_buffer=self.mamba_cache) self.mamba_cache_indices_mapping[cur_rid][ seq_id] = first_free_index index_for_current_run = first_free_index else: index_for_current_run = self.mamba_cache_indices_mapping[ cur_rid][seq_id] indices_for_current_run.append(index_for_current_run) return indices_for_current_run def _prepare_current_run_mamba_cache( self, request_ids_to_seq_ids: Dict[str, list[int]], batch_size: int ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], List[int]]: indices_for_current_run = [] for request_id, seqs_id in request_ids_to_seq_ids.items(): indices_for_current_run += self._assign_seq_id_to_mamba_cache( request_id, seqs_id) ## Pad the batch in case of running batch that was not captured via CG padded_indices = indices_for_current_run.copy() pad_index = self._first_free_index_in_mamba_cache() for _ in range(batch_size - len(indices_for_current_run)): padded_indices.append(pad_index) conv_state = self.mamba_cache[0][:, padded_indices] temporal_state = self.mamba_cache[1][:, padded_indices] return (conv_state, temporal_state), indices_for_current_run def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs): """ Copy the relevant Mamba cache into the CUDA graph input buffer that was provided during the capture runs (JambaForCausalLM.mamba_gc_cache_buffer). """ assert all( key in kwargs for key in ["request_ids_to_seq_ids", "finished_requests_ids"]) request_ids_to_seq_ids = kwargs["request_ids_to_seq_ids"] batch_size = len(request_ids_to_seq_ids) ( current_mamba_cache, indices, ) = self._prepare_current_run_mamba_cache(request_ids_to_seq_ids, batch_size) self.current_indices = indices finished_requests_ids = kwargs["finished_requests_ids"] self._release_mamba_cache(finished_requests_ids) for input_buffer, current_cache_buffer in zip( input_buffers["seqlen_agnostic_capture_inputs"], current_mamba_cache): input_buffer.copy_(current_cache_buffer, non_blocking=True) def copy_outputs_after_cuda_graphs(self, input_buffers, **kwargs): """ Copy the relevant Mamba cache from the CUDA graph input_buffers back to the JambaForCausalLM.mamba_cache after CUDA graph replay run is done. """ self._copy_mamba_cache_by_indices( self.current_indices, input_buffers["seqlen_agnostic_capture_inputs"]) def get_seqlen_agnostic_capture_inputs(self, batch_size: int): """ Provide the CUDA graph capture runs with a buffer in adjusted size. The buffer is used to maintain the Mamba Cache during the CUDA graph replay runs. """ return tuple(buffer[:, :batch_size] for buffer in self.mamba_gc_cache_buffer) def _release_mamba_cache(self, finished_seq_groups_req_ids: List[str]): for req_id in finished_seq_groups_req_ids: if req_id in self.mamba_cache_indices_mapping: self.mamba_cache_indices_mapping.pop(req_id) def _first_free_index_in_mamba_cache(self) -> int: if self.mamba_cache: max_possible_batch_size = self.mamba_cache[0].shape[1] occupied = [ id for seq_ids in self.mamba_cache_indices_mapping.values() for id in seq_ids.values() ] first_free_index = [ i not in occupied for i in range(max_possible_batch_size) ].index(True) return first_free_index return 0 def _get_mamba_cache_shape( self ) -> Tuple[Optional[Tuple[int, int]], Optional[Tuple[int, int]]]: world_size = get_tensor_model_parallel_world_size() hidden_size = self.config.hidden_size conv_state_shape = ( self.config.mamba_expand * hidden_size // world_size, self.config.mamba_d_conv, ) temporal_state_shape = ( self.config.mamba_expand * self.config.hidden_size // world_size, self.config.mamba_d_state, ) return conv_state_shape, temporal_state_shape def _prepare_mamba_cache(self): dtype = self.lm_head.weight.dtype layers_type = self.config.layers_block_type mamba_layers = sum( [layer_type == "mamba" for layer_type in layers_type]) max_batch_size = _BATCH_SIZES_TO_CAPTURE[-1] + 10 conv_state_shape, temporal_state_shape = self._get_mamba_cache_shape() assert conv_state_shape is not None and temporal_state_shape is not None for buffername in ["mamba_cache", "mamba_gc_cache_buffer"]: buffer = (torch.empty(size=(mamba_layers, max_batch_size) + conv_state_shape, dtype=dtype, device="cuda"), torch.empty(size=(mamba_layers, max_batch_size) + temporal_state_shape, dtype=dtype, device="cuda")) setattr(self, buffername, buffer) def compute_logits(self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata) -> torch.Tensor: logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata) return logits def sample( self, logits: Optional[torch.Tensor], sampling_metadata: SamplingMetadata, ) -> Optional[SamplerOutput]: next_tokens = self.sampler(logits, sampling_metadata) return next_tokens def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ("gate_up_proj", "gate_proj", 0), ("gate_up_proj", "up_proj", 1), ] expert_params_mapping = [ # (param_name, weight_name, expert_id) ( "ws" if weight_name in ["gate_proj", "up_proj"] else "w2s", f"experts.{expert_id}.{weight_name}.weight", expert_id, ) for expert_id in range(self.config.num_experts) for weight_name in ["down_proj", "up_proj", "gate_proj"] ] params_dict = dict(self.named_parameters()) for name, loaded_weight in weights: if "rotary_emb.inv_freq" in name: continue if "A_log" in name: name = name.replace("A_log", "A") if ".self_attn." in name: name = name.replace(".self_attn", "") for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue if 'experts' in name: continue name = name.replace(weight_name, param_name) # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: for param_name, weight_name, expert_id in expert_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, weight_name, expert_id=expert_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight)