# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """Inference-only Bamba model.""" # Added by the IBM Team, 2024 from collections.abc import Iterable from typing import Optional import torch from torch import nn from transformers import BambaConfig from vllm import envs from vllm.attention.layer import Attention from vllm.compilation.decorators import support_torch_compile from vllm.config import CacheConfig, ModelConfig, VllmConfig from vllm.distributed import get_tensor_model_parallel_world_size from vllm.distributed.parallel_state import get_pp_group from vllm.forward_context import get_forward_context from vllm.model_executor.layers.activation import SiluAndMul from vllm.model_executor.layers.layernorm import RMSNorm from vllm.model_executor.layers.linear import (MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear) from vllm.model_executor.layers.logits_processor import LogitsProcessor from vllm.model_executor.layers.mamba.mamba2_metadata import ( Mamba2Metadata, prepare_mamba2_metadata) from vllm.model_executor.layers.mamba.mamba_mixer2 import MambaMixer2 from vllm.model_executor.layers.mamba.mamba_utils import ( MambaStateDtypeCalculator, MambaStateShapeCalculator) from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding import get_rope 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.models.mamba_cache import (MambaCacheManager, MambaCacheParams) from vllm.model_executor.sampling_metadata import SamplingMetadata from vllm.sequence import IntermediateTensors from vllm.utils import LayerBlockType from .interfaces import (HasInnerState, IsHybrid, SupportsLoRA, SupportsPP, SupportsQuant) from .utils import (AutoWeightsLoader, is_pp_missing_parameter, make_empty_intermediate_tensors_factory, make_layers, maybe_prefix) class BambaMLP(nn.Module): def __init__( self, config: BambaConfig, quant_config: Optional[QuantizationConfig] = None, bias: bool = False, ) -> None: super().__init__() self.gate_up_proj = MergedColumnParallelLinear( input_size=config.hidden_size, output_sizes=[config.intermediate_size] * 2, bias=bias, quant_config=quant_config, ) self.down_proj = RowParallelLinear( input_size=config.intermediate_size, output_size=config.hidden_size, bias=bias, quant_config=quant_config, ) if config.hidden_act != "silu": raise ValueError(f"Unsupported activation: {config.hidden_act}. " "Only silu is supported for now.") self.act_fn = SiluAndMul() def forward(self, x): x, _ = self.gate_up_proj(x) x = self.act_fn(x) x, _ = self.down_proj(x) return x class BambaMixerDecoderLayer(nn.Module): def __init__(self, config: BambaConfig, layer_idx: int, model_config: Optional[ModelConfig] = None, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "") -> None: super().__init__() self.config = config self.mamba = MambaMixer2(hidden_size= config.hidden_size, ssm_state_size = config.mamba_d_state, conv_kernel_size = config.mamba_d_conv, intermediate_size = config.mamba_expand *\ config.hidden_size, use_conv_bias = config.mamba_conv_bias, use_bias = config.mamba_proj_bias, n_groups=config.mamba_n_groups, num_heads=config.mamba_n_heads, head_dim=config.mamba_d_head, rms_norm_eps=config.rms_norm_eps, activation=config.hidden_act, model_config=model_config, cache_config=cache_config, quant_config=quant_config, prefix=f"{prefix}.mixer") self.feed_forward = BambaMLP(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, residual: Optional[torch.Tensor], mamba_cache_params: MambaCacheParams, mamba2_metadata: Mamba2Metadata, **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) output = torch.empty_like(hidden_states) self.mamba(hidden_states, output, mamba_cache_params, mamba2_metadata) # Fully Connected hidden_states, residual = self.pre_ff_layernorm(output, residual) hidden_states = self.feed_forward(hidden_states) return hidden_states, residual class BambaAttentionDecoderLayer(nn.Module): def __init__( self, config: BambaConfig, layer_idx: int, model_config: Optional[ModelConfig] = None, cache_config: Optional[CacheConfig] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() rope_theta = getattr(config, "rope_theta", 10000) rope_scaling = getattr(config, "rope_scaling", None) max_position_embeddings = getattr(config, "max_position_embeddings", 8192) 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.rope_theta = rope_theta self.max_position_embeddings = max_position_embeddings if hasattr(config, "partial_rotary_factor"): rotary_dim = int(self.head_dim * config.partial_rotary_factor) elif hasattr(config, "attn_rotary_emb"): rotary_dim = config.attn_rotary_emb # for backward compatibility else: rotary_dim = self.head_dim # default self.rotary_emb = get_rope( head_size=self.head_dim, rotary_dim=rotary_dim, max_position=max_position_embeddings, rope_scaling=rope_scaling, base=rope_theta, is_neox_style=True, dtype=torch.get_default_dtype(), # see impl of get_rope ) 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, prefix=f"{prefix}.attn", ) self.feed_forward = BambaMLP(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, **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) q, k = self.rotary_emb(positions, q, k) attn_output = self.attn(q, k, v) output, _ = self.o_proj(attn_output) return output def forward( self, positions: torch.Tensor, hidden_states: torch.Tensor, 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, ) # 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": BambaAttentionDecoderLayer, "mamba": BambaMixerDecoderLayer } @support_torch_compile class BambaModel(nn.Module): def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config: BambaConfig = vllm_config.model_config.hf_config model_config = vllm_config.model_config cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config lora_config = vllm_config.lora_config self.config = config 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, ) def get_layer(prefix: str): layer_idx = int(prefix.rsplit(".", 1)[1]) layer_class = ALL_DECODER_LAYER_TYPES[ config.layers_block_type[layer_idx]] return layer_class( config, layer_idx, model_config, cache_config, quant_config=quant_config, prefix=prefix, ) self.start_layer, self.end_layer, self.layers = make_layers( config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers") self.make_empty_intermediate_tensors = ( make_empty_intermediate_tensors_factory( ["hidden_states", "residual"], config.hidden_size)) self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.embed_tokens(input_ids) def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, mamba_cache_params: MambaCacheParams, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, ) -> torch.Tensor: attn_metadata = get_forward_context().attn_metadata if not envs.VLLM_USE_V1: mamba2_metadata = prepare_mamba2_metadata( chunk_size=self.config.mamba_chunk_size, attn_metadata=attn_metadata, ) else: # v1 get mamba2_metadata from forward_context mamba2_metadata = None if get_pp_group().is_first_rank: if inputs_embeds is not None: hidden_states = inputs_embeds else: hidden_states = self.get_input_embeddings(input_ids) residual = None else: assert intermediate_tensors is not None hidden_states = intermediate_tensors["hidden_states"] residual = intermediate_tensors["residual"] residual = None num_attn = 0 for i in range(len(self.layers)): layer = self.layers[i] if isinstance(layer, BambaAttentionDecoderLayer): num_attn += 1 layer_mamba_cache_params = None if isinstance(layer, BambaMixerDecoderLayer) and mamba_cache_params: layer_mamba_cache_params = mamba_cache_params.at_layer_idx( i - num_attn) hidden_states, residual = layer( positions=positions, hidden_states=hidden_states, residual=residual, mamba_cache_params=layer_mamba_cache_params, mamba2_metadata=mamba2_metadata, ) if not get_pp_group().is_last_rank: return IntermediateTensors({ "hidden_states": hidden_states, "residual": residual }) hidden_states, _ = self.final_layernorm(hidden_states, residual) return hidden_states def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: 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), ] params_dict = dict(self.named_parameters()) loaded_params: set[str] = set() 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 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 # Skip layers on other devices. if is_pp_missing_parameter(name, self): continue param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: # Skip loading extra bias for GPTQ models. if name.endswith(".bias") and name not in params_dict: continue if is_pp_missing_parameter(name, self): continue param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) return loaded_params class BambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP, IsHybrid, SupportsQuant): packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], "gate_up_proj": ["up_proj", "down_proj"] } # LoRA specific attributes embedding_modules = { "embed_tokens": "input_embeddings", "lm_head": "output_embeddings", } embedding_padding_modules = ["lm_head"] @classmethod def get_mamba_state_dtype_from_config( cls, vllm_config: "VllmConfig", ) -> tuple[torch.dtype, torch.dtype]: return MambaStateDtypeCalculator.mamba2_state_dtype( vllm_config.model_config.dtype, vllm_config.cache_config.mamba_cache_dtype, vllm_config.cache_config.mamba_ssm_cache_dtype, ) @classmethod def get_mamba_state_shape_from_config( cls, vllm_config: "VllmConfig", use_v1: bool = True, ) -> tuple[tuple[int, int], tuple[int, int, int]]: """Calculate shapes for Mamba's convolutional and state caches. Args: vllm_config: vLLM config use_v1: Get shapes for V1 (or V0) Returns: Tuple containing: - conv_state_shape: Shape for convolutional state cache - temporal_state_shape: Shape for state space model cache """ parallel_config = vllm_config.parallel_config hf_config = vllm_config.model_config.hf_config intermediate_size = hf_config.mamba_expand * hf_config.hidden_size return MambaStateShapeCalculator.mamba2_state_shape( intermediate_size=intermediate_size, tp_world_size=parallel_config.tensor_parallel_size, n_groups=hf_config.mamba_n_groups, num_heads=hf_config.mamba_n_heads, head_dim=hf_config.mamba_d_head, state_size=hf_config.mamba_d_state, conv_kernel=hf_config.mamba_d_conv, use_v1=use_v1, ) def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): config = vllm_config.model_config.hf_config self.vllm_config = vllm_config self.model_config = vllm_config.model_config cache_config = vllm_config.cache_config lora_config = vllm_config.lora_config scheduler_config = vllm_config.scheduler_config assert not cache_config.enable_prefix_caching, \ "Bamba currently does not support prefix caching" self.quant_config = vllm_config.quant_config super().__init__() self.config = config self.scheduler_config = scheduler_config self.model = BambaModel(vllm_config=vllm_config, prefix=maybe_prefix(prefix, "model")) 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, ) # Used to track and store by the Mamba cache between steps. self.mamba_cache: Optional[MambaCacheManager] = None self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, config.vocab_size) self.make_empty_intermediate_tensors = ( self.model.make_empty_intermediate_tensors) def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor: return self.model.get_input_embeddings(input_ids) def forward(self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, **kwargs): mamba_cache_params = None if not envs.VLLM_USE_V1: if self.mamba_cache is None: num_mamba_layers = \ self.model_config.get_num_layers_by_block_type( self.vllm_config.parallel_config, LayerBlockType.mamba ) mamba_state_shape = \ self.get_mamba_state_shape_from_config( self.vllm_config, use_v1=False) mamba_state_dtype = \ self.get_mamba_state_dtype_from_config( self.vllm_config) self.mamba_cache = MambaCacheManager(self.vllm_config, num_mamba_layers, *mamba_state_shape, *mamba_state_dtype) mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs) hidden_states = self.model(input_ids, positions, mamba_cache_params, intermediate_tensors, inputs_embeds) return hidden_states def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs): return self.mamba_cache.copy_inputs_before_cuda_graphs( input_buffers, **kwargs) def get_seqlen_agnostic_capture_inputs(self, batch_size: int): return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size) def compute_logits( self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[torch.Tensor]: logits = self.logits_processor(self.lm_head, hidden_states, sampling_metadata) return logits def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader(self) return loader.load_weights(weights)