mamba.py

"""PyTorch MAMBA model."""
from typing import Iterable, List, Optional, Set, Tuple

import torch
from torch import nn
from transformers import MambaConfig

from vllm.attention.backends.abstract import AttentionMetadata
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer
from vllm.model_executor.layers.quantization.base_config import (
    QuantizationConfig)
from vllm.model_executor.layers.sampler import SamplerOutput, get_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.models.interfaces import (HasInnerState,
                                                   IsAttentionFree)
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.worker.model_runner import (_BATCH_SIZES_TO_CAPTURE,
                                      _get_graph_batch_size)

from .utils import maybe_prefix

KVCache = Tuple[torch.Tensor, torch.Tensor]


class MambaDecoderLayer(nn.Module):

    def __init__(self,
                 config: MambaConfig,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional[QuantizationConfig] = None) -> None:
        super().__init__()
        self.config = config
        self.is_falcon_mamba = config.model_type == "falcon_mamba"
        mixer_rms_eps = config.mixer_rms_eps if self.is_falcon_mamba else None
        self.mixer = MambaMixer(hidden_size=config.hidden_size,
                                ssm_state_size=config.state_size,
                                conv_kernel_size=config.conv_kernel,
                                intermediate_size=config.intermediate_size,
                                time_step_rank=config.time_step_rank,
                                use_conv_bias=config.use_conv_bias,
                                use_bias=config.use_bias,
                                use_rms_norm=self.is_falcon_mamba,
                                rms_norm_eps=mixer_rms_eps,
                                activation=config.hidden_act)

        self.norm = RMSNorm(config.hidden_size, eps=config.layer_norm_epsilon)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attn_metadata: AttentionMetadata,
        residual: Optional[torch.Tensor],
        mamba_cache_params: MambaCacheParams,
        **kwargs,
    ):
        if residual is None:
            residual = hidden_states
            hidden_states = self.norm(hidden_states)
        else:
            hidden_states, residual = self.norm(hidden_states, residual)

        hidden_states = self.mixer(hidden_states, attn_metadata,
                                   mamba_cache_params)
        return hidden_states, residual


class MambaModel(nn.Module):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config

        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.embeddings = VocabParallelEmbedding(
            self.vocab_size,
            config.hidden_size,
            org_num_embeddings=config.vocab_size,
        )

        decoder_layers = []
        for i in range(config.num_hidden_layers):
            decoder_layers.append(
                MambaDecoderLayer(config,
                                  cache_config=cache_config,
                                  quant_config=quant_config))
        self.layers = nn.ModuleList(decoder_layers)
        self.norm_f = RMSNorm(config.hidden_size,
                              eps=config.layer_norm_epsilon)

    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embeddings(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        attn_metadata: AttentionMetadata,
        mamba_cache_params: MambaCacheParams,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:

        if inputs_embeds is not None:
            hidden_states = inputs_embeds
        else:
            hidden_states = self.get_input_embeddings(input_ids)
        residual = None

        for i in range(len(self.layers)):
            layer = self.layers[i]
            hidden_states, residual = layer(
                positions=positions,
                hidden_states=hidden_states,
                attn_metadata=attn_metadata,
                residual=residual,
                mamba_cache_params=mamba_cache_params.at_layer_idx(i))
        hidden_states, _ = self.norm_f(hidden_states, residual)

        return hidden_states


class MambaForCausalLM(nn.Module, HasInnerState, IsAttentionFree):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        config = vllm_config.model_config.hf_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, \
            "Mamba does not support prefix caching"

        super().__init__()
        self.config = config
        self.scheduler_config = scheduler_config
        self.backbone = MambaModel(vllm_config=vllm_config,
                                   prefix=maybe_prefix(prefix, "backbone"))
        self.unpadded_vocab_size = config.vocab_size
        if lora_config:
            self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
        if config.tie_word_embeddings:
            self.lm_head = self.backbone.embeddings
        else:
            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.sampler = get_sampler()

    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.backbone.get_input_embeddings(input_ids)

    def forward(self,
                input_ids: torch.Tensor,
                positions: torch.Tensor,
                kv_caches: List[KVCache],
                attn_metadata: AttentionMetadata,
                intermediate_tensors: Optional[IntermediateTensors] = None,
                inputs_embeds: Optional[torch.Tensor] = None,
                **kwargs):
        if self.mamba_cache is None:
            max_batch_size = (_get_graph_batch_size(
                self.scheduler_config.max_num_seqs) if self.scheduler_config
                              else max(_BATCH_SIZES_TO_CAPTURE) + 2)
            self.mamba_cache = MambaCacheManager(
                self.lm_head.weight.dtype, self.config.num_hidden_layers,
                max_batch_size, *self._get_mamba_cache_shape())

        (
            mamba_cache_tensors,
            state_indices_tensor,
        ) = self.mamba_cache.current_run_tensors(input_ids, attn_metadata,
                                                 **kwargs)

        mamba_cache_params = MambaCacheParams(mamba_cache_tensors[0],
                                              mamba_cache_tensors[1],
                                              state_indices_tensor)

        hidden_states = self.backbone(input_ids, positions, attn_metadata,
                                      mamba_cache_params, 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 _get_mamba_cache_shape(
            self) -> Tuple[Tuple[int, int], Tuple[int, int]]:
        world_size = get_tensor_model_parallel_world_size()
        conv_state_shape = (
            self.config.intermediate_size // world_size,
            self.config.conv_kernel - 1,
        )
        temporal_state_shape = (
            self.config.intermediate_size // world_size,
            self.config.state_size,
        )
        return conv_state_shape, temporal_state_shape

    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]]) -> Set[str]:
        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()
        for name, loaded_weight in weights:
            if "A_log" in name:
                name = name.replace("A_log", "A")
            # 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)
            loaded_params.add(name)
        return loaded_params