mamba2.py

# SPDX-License-Identifier: Apache-2.0
"""PyTorch MAMBA2 model."""
from typing import Iterable, Optional, Set, Tuple

import torch
from torch import nn
from transformers import MambaConfig

from vllm.attention.backends.abstract import AttentionMetadata
from vllm.config import VllmConfig
from vllm.distributed import divide, 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.layernorm import RMSNorm
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.mamba_mixer2 import (
    MambaMixer2, extra_groups_for_head_shards)
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,
                                                   SupportsV0Only)
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 .utils import (is_pp_missing_parameter,
                    make_empty_intermediate_tensors_factory, make_layers,
                    maybe_prefix)

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


class Mamba2DecoderLayer(nn.Module):

    def __init__(self,
                 config: MambaConfig,
                 quant_config: Optional[QuantizationConfig] = None) -> None:
        super().__init__()
        self.config = config
        self.mixer = MambaMixer2(hidden_size=config.hidden_size,
                                 ssm_state_size=config.state_size,
                                 conv_kernel_size=config.conv_kernel,
                                 intermediate_size=getattr(
                                     config, "intermediate_size",
                                     config.expand * config.hidden_size),
                                 use_conv_bias=config.use_conv_bias,
                                 use_bias=config.use_bias,
                                 n_groups=config.n_groups,
                                 num_heads=config.num_heads,
                                 head_dim=config.head_dim,
                                 rms_norm_eps=config.layer_norm_epsilon,
                                 activation=config.hidden_act,
                                 chunk_size=config.chunk_size,
                                 quant_config=quant_config)

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

    def forward(
        self,
        hidden_states: torch.Tensor,
        residual: Optional[torch.Tensor],
        mamba_cache_params: MambaCacheParams,
        sequence_idx: Optional[torch.Tensor],
        **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, mamba_cache_params,
                                   sequence_idx)
        return hidden_states, residual


class Mamba2Model(nn.Module):

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

        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config
        is_lora_enabled = bool(lora_config)
        assert not is_lora_enabled

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

        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: Mamba2DecoderLayer(config,
                                              quant_config=quant_config),
            prefix=f"{prefix}.layers")

        self.norm_f = RMSNorm(config.hidden_size,
                              eps=config.layer_norm_epsilon)
        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(
                ["hidden_states", "residual"], config.hidden_size))

    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,
        mamba_cache_params: MambaCacheParams,
        intermediate_tensors: Optional[IntermediateTensors] = None,
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        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"]

        # pass a sequence index tensor, that is required for
        # proper continuous batching computation including
        # chunked prefill
        seq_idx = None
        attn_metadata: AttentionMetadata = get_forward_context().attn_metadata
        if attn_metadata.num_prefills > 0:
            seq_idx = torch.zeros_like(input_ids, dtype=torch.int32)
            for i, (srt, end) in enumerate(
                    zip(
                        attn_metadata.query_start_loc,
                        attn_metadata.query_start_loc[1:],
                    )):
                seq_idx[srt:end] = i
            seq_idx.unsqueeze_(0)

        for i in range(len(self.layers)):
            layer = self.layers[i]

            hidden_states, residual = layer(
                positions=positions,
                hidden_states=hidden_states,
                residual=residual,
                mamba_cache_params=mamba_cache_params.at_layer_idx(
                    i - self.start_layer),
                sequence_idx=seq_idx)

        if not get_pp_group().is_last_rank:
            return IntermediateTensors({
                "hidden_states": hidden_states,
                "residual": residual
            })

        hidden_states, _ = self.norm_f(hidden_states, residual)

        return hidden_states


class Mamba2ForCausalLM(nn.Module, HasInnerState, IsAttentionFree,
                        SupportsV0Only):

    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.vllm_config = vllm_config
        self.scheduler_config = scheduler_config
        self.model_config = vllm_config.model_config
        self.backbone = Mamba2Model(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

        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,
        )
        if config.tie_word_embeddings:
            self.lm_head = self.lm_head.tie_weights(self.backbone.embeddings)

        # 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()

        self.make_empty_intermediate_tensors = (
            self.backbone.make_empty_intermediate_tensors)

    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,
                intermediate_tensors: Optional[IntermediateTensors] = None,
                inputs_embeds: Optional[torch.Tensor] = None,
                **kwargs):
        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)
            self.mamba_cache = MambaCacheManager(
                self.vllm_config, self.lm_head.weight.dtype, num_mamba_layers,
                *self._get_mamba_cache_shape())

        mamba_cache_params = self.mamba_cache.current_run_tensors(**kwargs)

        hidden_states = self.backbone(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 _get_mamba_cache_shape(
            self) -> Tuple[Tuple[int, int], Tuple[int, int]]:
        world_size = get_tensor_model_parallel_world_size()

        conv_state_shape, temporal_state_shape = None, None

        intermediate_size = getattr(
            self.config, "intermediate_size",
            self.config.expand * self.config.hidden_size)

        # if n_groups is not divisible by world_size, need to extend the shards
        # to ensure all groups needed by a head is sharded along with it
        n_groups = (
            self.config.n_groups +
            extra_groups_for_head_shards(self.config.n_groups, world_size))

        # - heads and n_groups are TP-ed
        conv_dim = (intermediate_size + 2 * n_groups * self.config.state_size)
        conv_state_shape = (
            divide(conv_dim, world_size),
            self.config.conv_kernel - 1,
        )

        # These are not TP-ed as they depend on A, dt_bias, D
        # - they are typically small
        #   e.g., (h_heads, d_head, d_state) = (128, 64, 128)
        temporal_state_shape = (
            divide(self.config.num_heads, world_size),
            self.config.head_dim,
            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
            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