jamba.py

"""Inference-only Jamba model."""
from typing import Iterable, List, Optional, Set, Tuple

import torch
from torch import nn
from transformers import JambaConfig

from vllm.attention.backends.abstract import AttentionMetadata
from vllm.attention.layer import Attention
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.distributed.parallel_state import get_pp_group
from vllm.model_executor.layers.fused_moe import FusedMoE
from vllm.model_executor.layers.layernorm import RMSNorm
from vllm.model_executor.layers.linear import (QKVParallelLinear,
                                               ReplicatedLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.mamba.mamba_mixer import MambaMixer
from vllm.model_executor.layers.pooler import Pooler, PoolingType
from vllm.model_executor.layers.quantization 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.mamba_cache import (MambaCacheManager,
                                                    MambaCacheParams)
from vllm.model_executor.pooling_metadata import PoolingMetadata
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors, PoolerOutput
from vllm.utils import LayerBlockType

from .interfaces import HasInnerState, IsHybrid, SupportsLoRA, SupportsPP
from .utils import (is_pp_missing_parameter,
                    make_empty_intermediate_tensors_factory, make_layers,
                    maybe_prefix)

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


class JambaMoE(nn.Module):

    def __init__(self,
                 config: JambaConfig,
                 num_experts: Optional[int] = None,
                 top_k: Optional[int] = None,
                 params_dtype: Optional[torch.dtype] = None,
                 tp_size: Optional[int] = None,
                 quant_config: Optional[QuantizationConfig] = None):
        super().__init__()
        self.num_total_experts = num_experts or config.num_experts
        self.top_k = top_k or config.num_experts_per_tok
        self.hidden_size = config.hidden_size
        self.intermediate_size = config.intermediate_size

        if self.num_total_experts > 1:
            self.router = ReplicatedLinear(self.hidden_size,
                                           self.num_total_experts,
                                           bias=False,
                                           quant_config=None,
                                           params_dtype=params_dtype)

        self.experts = FusedMoE(self.num_total_experts,
                                self.top_k,
                                self.hidden_size,
                                self.intermediate_size,
                                tp_size=tp_size,
                                params_dtype=params_dtype,
                                reduce_results=True,
                                renormalize=False,
                                use_grouped_topk=False,
                                quant_config=quant_config)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        orig_shape = hidden_states.shape
        hidden_states = hidden_states.view(-1, self.hidden_size)
        # router_logits: (batch * sequence_length, n_experts)
        if self.num_total_experts > 1:
            router_logits, _ = self.router(hidden_states)
        else:
            router_logits = torch.ones((hidden_states.shape[0], 1),
                                       device=hidden_states.device,
                                       dtype=hidden_states.dtype)
        hidden_states = self.experts(hidden_states, router_logits)
        return hidden_states.view(orig_shape)


class JambaMLP(JambaMoE):

    def __init__(self,
                 config: JambaConfig,
                 params_dtype: Optional[torch.dtype] = None,
                 tp_size: Optional[int] = None,
                 quant_config: Optional[QuantizationConfig] = None):
        super().__init__(config,
                         num_experts=1,
                         top_k=1,
                         params_dtype=params_dtype,
                         tp_size=tp_size,
                         quant_config=quant_config)


class JambaMambaDecoderLayer(nn.Module):

    def __init__(self,
                 config: JambaConfig,
                 layer_idx: int,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = "") -> None:
        super().__init__()
        self.config = config
        self.mamba = MambaMixer(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,
                                time_step_rank = config.mamba_dt_rank,
                                use_conv_bias = config.mamba_conv_bias,
                                use_bias = config.mamba_proj_bias,
                                use_rms_norm=True,
                                rms_norm_eps=config.rms_norm_eps,
                                activation=config.hidden_act)

        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],
        mamba_cache_params: MambaCacheParams,
        **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,
                                   mamba_cache_params)
        # 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,
        prefix: str = "",
    ) -> 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,
            prefix=f"{prefix}.attn",
        )

        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, *, 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.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,
                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,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        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"]

        kv_cache_index = 0
        mamba_cache_index = 0
        for i in range(self.start_layer, self.end_layer):
            layer = self.layers[i]
            kv_cache = None
            layer_mamba_cache_params = None
            if isinstance(layer, JambaAttentionDecoderLayer):
                kv_cache = kv_caches[kv_cache_index]
                kv_cache_index += 1
            if isinstance(layer, JambaMambaDecoderLayer):
                current_state_layer = mamba_cache_index
                layer_mamba_cache_params = mamba_cache_params.at_layer_idx(
                    current_state_layer)
                mamba_cache_index += 1

            hidden_states, residual = layer(
                positions=positions,
                hidden_states=hidden_states,
                kv_cache=kv_cache,
                attn_metadata=attn_metadata,
                residual=residual,
                mamba_cache_params=layer_mamba_cache_params)
        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


class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
                       IsHybrid):
    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, *, 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, \
            "Jamba currently does not support prefix caching"

        super().__init__()
        self.config = config
        self.vllm_config = vllm_config
        self.model_config = vllm_config.model_config
        self.scheduler_config = scheduler_config
        self.model = JambaModel(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.sampler = get_sampler()

        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)
        if self.scheduler_config is not None and \
            not self.model_config.enforce_eager:
            if self.scheduler_config.max_num_seqs > \
                vllm_config.compilation_config.max_capture_size:
                self.max_batch_size = \
                    vllm_config.compilation_config.max_capture_size
            else:
                self.max_batch_size = vllm_config.pad_for_cudagraph(
                    self.scheduler_config.max_num_seqs)
        else:
            self.max_batch_size = 8192 + 2

    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,
                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:

            num_mamba_layers = self.model_config.get_num_layers_by_block_type(
                self.vllm_config.parallel_config, LayerBlockType.mamba)
            self.mamba_cache = MambaCacheManager(
                self.lm_head.weight.dtype, num_mamba_layers,
                self.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.model(input_ids, positions, kv_caches,
                                   attn_metadata, 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()
        hidden_size = self.config.hidden_size
        conv_state_shape = (
            self.config.mamba_expand * hidden_size // world_size,
            self.config.mamba_d_conv - 1,
        )
        temporal_state_shape = (
            self.config.mamba_expand * hidden_size // world_size,
            self.config.mamba_d_state,
        )
        return conv_state_shape, temporal_state_shape

    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 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]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]

        # Params for weights, fp8 weight scales, fp8 activation scales
        # (param_name, weight_name, expert_id, shard_id)
        expert_params_mapping = FusedMoE.make_expert_params_mapping(
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            num_experts=self.config.num_experts)

        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", "")

            if "feed_forward" in name and not _is_moe_layer(name):
                ## map MLP layers to expert with ID=0
                name = name.replace("feed_forward", "feed_forward.experts.0")

            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
                # 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:
                for (
                        param_name,
                        weight_name,
                        expert_id,
                        shard_id,
                ) in expert_params_mapping:
                    if weight_name not in name:
                        continue

                    if is_pp_missing_parameter(name, self):
                        continue
                    name = name.replace(weight_name, param_name)
                    param = params_dict[name]
                    weight_loader = param.weight_loader
                    weight_loader(param,
                                  loaded_weight,
                                  name,
                                  shard_id=shard_id,
                                  expert_id=expert_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


def _is_moe_layer(name: str):
    return any(
        [experts_name in name for experts_name in [
            "experts",
            "router",
        ]])


class JambaForSequenceClassification(JambaForCausalLM):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__(vllm_config=vllm_config, prefix=prefix)
        config = vllm_config.model_config.hf_config
        num_labels: int = config.num_labels
        score_bias: bool = getattr(config, 'score_bias', False)
        self.score = nn.Linear(config.hidden_size, num_labels, bias=score_bias)

        pooler_config = vllm_config.model_config.pooler_config
        self._pooler = Pooler.from_config_with_defaults(
            pooler_config,
            pooling_type=PoolingType.LAST,
            normalize=False,
            softmax=False)

    def pooler(
        self,
        hidden_states: torch.Tensor,
        pooling_metadata: PoolingMetadata,
    ) -> Optional[PoolerOutput]:
        hidden_states = hidden_states.float()
        logits = self.score(hidden_states)
        return self._pooler(logits, pooling_metadata)

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        # TODO: The reward weights themselves have float32 accuracy data, we
        # would like to load them in fp32 to get that extra precision.
        super().load_weights(weights)
        self.score = self.score.float()