vllm.model_executor.models.jamba

Inference-only Jamba model.

ALL_DECODER_LAYER_TYPES module-attribute

ALL_DECODER_LAYER_TYPES = {
    "attention": JambaAttentionDecoderLayer,
    "mamba": JambaMambaDecoderLayer,
}

JambaAttentionDecoderLayer

Bases: Module

Source code in vllm/model_executor/models/jamba.py

class JambaAttentionDecoderLayer(nn.Module):

    def __init__(self,
                 config: JambaConfig,
                 layer_idx: int,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = "",
                 **kwargs) -> 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,
                                            prefix=f"{prefix}.feed_forward")
        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)
        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

attn instance-attribute

attn = Attention(
    num_heads,
    head_dim,
    scaling,
    num_kv_heads=num_kv_heads,
    cache_config=cache_config,
    prefix=f"{prefix}.attn",
)

feed_forward instance-attribute

feed_forward = ffn_layer_class(
    config,
    quant_config=quant_config,
    prefix=f"{prefix}.feed_forward",
)

head_dim instance-attribute

head_dim = hidden_size // total_num_heads

hidden_size instance-attribute

hidden_size = hidden_size

input_layernorm instance-attribute

input_layernorm = RMSNorm(hidden_size, eps=rms_norm_eps)

kv_size instance-attribute

kv_size = num_kv_heads * head_dim

num_heads instance-attribute

num_heads = total_num_heads // tp_size

num_kv_heads instance-attribute

num_kv_heads = max(1, total_num_kv_heads // tp_size)

o_proj instance-attribute

o_proj = RowParallelLinear(
    total_num_heads * head_dim,
    hidden_size,
    bias=False,
    quant_config=quant_config,
)

pre_ff_layernorm instance-attribute

pre_ff_layernorm = RMSNorm(hidden_size, eps=rms_norm_eps)

q_size instance-attribute

q_size = num_heads * head_dim

qkv_proj instance-attribute

qkv_proj = QKVParallelLinear(
    hidden_size,
    head_dim,
    total_num_heads,
    total_num_kv_heads,
    bias=False,
    quant_config=quant_config,
)

scaling instance-attribute

scaling = head_dim ** -0.5

total_num_heads instance-attribute

total_num_heads = num_attention_heads

total_num_kv_heads instance-attribute

total_num_kv_heads = num_key_value_heads

__init__

__init__(
    config: JambaConfig,
    layer_idx: int,
    cache_config: Optional[CacheConfig] = None,
    quant_config: Optional[QuantizationConfig] = None,
    prefix: str = "",
    **kwargs,
) -> None

Source code in vllm/model_executor/models/jamba.py

def __init__(self,
             config: JambaConfig,
             layer_idx: int,
             cache_config: Optional[CacheConfig] = None,
             quant_config: Optional[QuantizationConfig] = None,
             prefix: str = "",
             **kwargs) -> 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,
                                        prefix=f"{prefix}.feed_forward")
    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)

forward

forward(
    positions: Tensor,
    hidden_states: Tensor,
    residual: Optional[Tensor],
    **kwargs,
)

Source code in vllm/model_executor/models/jamba.py

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

self_attention

self_attention(
    positions: Tensor, hidden_states: Tensor, **kwargs
) -> Tensor

Source code in vllm/model_executor/models/jamba.py

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)
    attn_output = self.attn(q, k, v)
    output, _ = self.o_proj(attn_output)
    return output

JambaForCausalLM

Bases: Module, HasInnerState, SupportsLoRA, SupportsPP, IsHybrid, SupportsV0Only

Source code in vllm/model_executor/models/jamba.py

class JambaForCausalLM(nn.Module, HasInnerState, SupportsLoRA, SupportsPP,
                       IsHybrid, SupportsV0Only):
    packed_modules_mapping = {
        "qkv_proj": [
            "q_proj",
            "k_proj",
            "v_proj",
        ],
        "in_proj": ["in_proj"],
    }

    # LoRA specific attributes
    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.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):
        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.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 _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 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

config instance-attribute

config = config

embedding_modules class-attribute instance-attribute

embedding_modules = {
    "embed_tokens": "input_embeddings",
    "lm_head": "output_embeddings",
}

embedding_padding_modules class-attribute instance-attribute

embedding_padding_modules = ['lm_head']

lm_head instance-attribute

lm_head = ParallelLMHead(
    unpadded_vocab_size,
    hidden_size,
    org_num_embeddings=vocab_size,
    padding_size=DEFAULT_VOCAB_PADDING_SIZE
    if not lora_config
    else lora_vocab_padding_size,
)

logits_processor instance-attribute

logits_processor = LogitsProcessor(
    unpadded_vocab_size, vocab_size
)

make_empty_intermediate_tensors instance-attribute

make_empty_intermediate_tensors = (
    make_empty_intermediate_tensors
)

mamba_cache instance-attribute

mamba_cache: Optional[MambaCacheManager] = None

model instance-attribute

model = JambaModel(
    vllm_config=vllm_config,
    prefix=maybe_prefix(prefix, "model"),
)

model_config instance-attribute

model_config = model_config

packed_modules_mapping class-attribute instance-attribute

packed_modules_mapping = {
    "qkv_proj": ["q_proj", "k_proj", "v_proj"],
    "in_proj": ["in_proj"],
}

scheduler_config instance-attribute

scheduler_config = scheduler_config

unpadded_vocab_size instance-attribute

unpadded_vocab_size = vocab_size

vllm_config instance-attribute

vllm_config = vllm_config

__init__

__init__(*, vllm_config: VllmConfig, prefix: str = '')

Source code in vllm/model_executor/models/jamba.py

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.make_empty_intermediate_tensors = (
        self.model.make_empty_intermediate_tensors)

_get_mamba_cache_shape

_get_mamba_cache_shape() -> tuple[
    tuple[int, int], tuple[int, int]
]

Source code in vllm/model_executor/models/jamba.py

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

compute_logits

compute_logits(
    hidden_states: Tensor,
    sampling_metadata: SamplingMetadata,
) -> Optional[Tensor]

Source code in vllm/model_executor/models/jamba.py

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

copy_inputs_before_cuda_graphs

copy_inputs_before_cuda_graphs(input_buffers, **kwargs)

Source code in vllm/model_executor/models/jamba.py

def copy_inputs_before_cuda_graphs(self, input_buffers, **kwargs):
    return self.mamba_cache.copy_inputs_before_cuda_graphs(
        input_buffers, **kwargs)

forward

forward(
    input_ids: Tensor,
    positions: Tensor,
    intermediate_tensors: Optional[
        IntermediateTensors
    ] = None,
    inputs_embeds: Optional[Tensor] = None,
    **kwargs,
)

Source code in vllm/model_executor/models/jamba.py

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.model(input_ids, positions, mamba_cache_params,
                               intermediate_tensors, inputs_embeds)
    return hidden_states

get_input_embeddings

get_input_embeddings(input_ids: Tensor) -> Tensor

Source code in vllm/model_executor/models/jamba.py

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

get_seqlen_agnostic_capture_inputs

get_seqlen_agnostic_capture_inputs(batch_size: int)

Source code in vllm/model_executor/models/jamba.py

def get_seqlen_agnostic_capture_inputs(self, batch_size: int):
    return self.mamba_cache.get_seqlen_agnostic_capture_inputs(batch_size)

load_weights

load_weights(
    weights: Iterable[tuple[str, Tensor]],
) -> set[str]

Source code in vllm/model_executor/models/jamba.py

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

JambaForSequenceClassification

Bases: JambaForCausalLM

Source code in vllm/model_executor/models/jamba.py

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()

_pooler instance-attribute

_pooler = from_config_with_defaults(
    pooler_config,
    pooling_type=LAST,
    normalize=False,
    softmax=False,
)

score instance-attribute

score = Linear(hidden_size, num_labels, bias=score_bias)

__init__

__init__(*, vllm_config: VllmConfig, prefix: str = '')

Source code in vllm/model_executor/models/jamba.py

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)

load_weights

load_weights(weights: Iterable[tuple[str, Tensor]])

Source code in vllm/model_executor/models/jamba.py

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()

pooler

pooler(
    hidden_states: Tensor, pooling_metadata: PoolingMetadata
) -> Optional[PoolerOutput]

Source code in vllm/model_executor/models/jamba.py

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)

JambaMLP

Bases: JambaMoE

Source code in vllm/model_executor/models/jamba.py

class JambaMLP(JambaMoE):

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

__init__

__init__(
    config: JambaConfig,
    params_dtype: Optional[dtype] = None,
    tp_size: Optional[int] = None,
    quant_config: Optional[QuantizationConfig] = None,
    prefix: str = "",
)

Source code in vllm/model_executor/models/jamba.py

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

JambaMambaDecoderLayer

Bases: Module

Source code in vllm/model_executor/models/jamba.py

class JambaMambaDecoderLayer(nn.Module):

    def __init__(self,
                 config: JambaConfig,
                 layer_idx: int,
                 cache_config: Optional[CacheConfig] = None,
                 quant_config: Optional[QuantizationConfig] = None,
                 is_lora_enabled: Optional[bool] = False,
                 prefix: str = "",
                 **kwargs) -> None:
        super().__init__()
        self.config = config
        self.is_lora_enabled = is_lora_enabled
        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,
                                is_lora_enabled = self.is_lora_enabled
                                )

        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,
                                            prefix=f"{prefix}.feed_forward")
        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,
        **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, 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

config instance-attribute

config = config

feed_forward instance-attribute

feed_forward = ffn_layer_class(
    config,
    quant_config=quant_config,
    prefix=f"{prefix}.feed_forward",
)

input_layernorm instance-attribute

input_layernorm = RMSNorm(hidden_size, eps=rms_norm_eps)

is_lora_enabled instance-attribute

is_lora_enabled = is_lora_enabled

mamba instance-attribute

mamba = MambaMixer(
    hidden_size=hidden_size,
    ssm_state_size=mamba_d_state,
    conv_kernel_size=mamba_d_conv,
    intermediate_size=mamba_expand * hidden_size,
    time_step_rank=mamba_dt_rank,
    use_conv_bias=mamba_conv_bias,
    use_bias=mamba_proj_bias,
    use_rms_norm=True,
    rms_norm_eps=rms_norm_eps,
    activation=hidden_act,
    is_lora_enabled=is_lora_enabled,
)

pre_ff_layernorm instance-attribute

pre_ff_layernorm = RMSNorm(hidden_size, eps=rms_norm_eps)

__init__

__init__(
    config: JambaConfig,
    layer_idx: int,
    cache_config: Optional[CacheConfig] = None,
    quant_config: Optional[QuantizationConfig] = None,
    is_lora_enabled: Optional[bool] = False,
    prefix: str = "",
    **kwargs,
) -> None

Source code in vllm/model_executor/models/jamba.py

def __init__(self,
             config: JambaConfig,
             layer_idx: int,
             cache_config: Optional[CacheConfig] = None,
             quant_config: Optional[QuantizationConfig] = None,
             is_lora_enabled: Optional[bool] = False,
             prefix: str = "",
             **kwargs) -> None:
    super().__init__()
    self.config = config
    self.is_lora_enabled = is_lora_enabled
    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,
                            is_lora_enabled = self.is_lora_enabled
                            )

    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,
                                        prefix=f"{prefix}.feed_forward")
    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)

forward

forward(
    hidden_states: Tensor,
    residual: Optional[Tensor],
    mamba_cache_params: MambaCacheParams,
    **kwargs,
)

Source code in vllm/model_executor/models/jamba.py

def forward(
    self,
    hidden_states: torch.Tensor,
    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, 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

JambaMoE

Bases: Module

Source code in vllm/model_executor/models/jamba.py

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,
                 prefix: str = ""):
        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,
                                prefix=f"{prefix}.experts")

    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)

experts instance-attribute

experts = FusedMoE(
    num_total_experts,
    top_k,
    hidden_size,
    intermediate_size,
    tp_size=tp_size,
    params_dtype=params_dtype,
    reduce_results=True,
    renormalize=False,
    use_grouped_topk=False,
    quant_config=quant_config,
    prefix=f"{prefix}.experts",
)

hidden_size instance-attribute

hidden_size = hidden_size

intermediate_size instance-attribute

intermediate_size = intermediate_size

num_total_experts instance-attribute

num_total_experts = num_experts or num_experts

router instance-attribute

router = ReplicatedLinear(
    hidden_size,
    num_total_experts,
    bias=False,
    quant_config=None,
    params_dtype=params_dtype,
)

top_k instance-attribute

top_k = top_k or num_experts_per_tok

__init__

__init__(
    config: JambaConfig,
    num_experts: Optional[int] = None,
    top_k: Optional[int] = None,
    params_dtype: Optional[dtype] = None,
    tp_size: Optional[int] = None,
    quant_config: Optional[QuantizationConfig] = None,
    prefix: str = "",
)

Source code in vllm/model_executor/models/jamba.py

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,
             prefix: str = ""):
    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,
                            prefix=f"{prefix}.experts")

forward

forward(hidden_states: Tensor) -> Tensor

Source code in vllm/model_executor/models/jamba.py

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)

JambaModel

Bases: Module

Source code in vllm/model_executor/models/jamba.py

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
        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,
        )

        extra_kwargs = {"is_lora_enabled": bool(vllm_config.lora_config)}

        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,
                               **extra_kwargs)

        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:
        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 layer in self.layers[self.start_layer:self.end_layer]:
            layer_mamba_cache_params = None
            if isinstance(layer, JambaAttentionDecoderLayer):
                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,
                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

config instance-attribute

config = config

embed_tokens instance-attribute

embed_tokens = VocabParallelEmbedding(
    vocab_size, hidden_size, org_num_embeddings=vocab_size
)

final_layernorm instance-attribute

final_layernorm = RMSNorm(hidden_size, eps=rms_norm_eps)

make_empty_intermediate_tensors instance-attribute

make_empty_intermediate_tensors = (
    make_empty_intermediate_tensors_factory(
        ["hidden_states", "residual"], hidden_size
    )
)

org_vocab_size instance-attribute

org_vocab_size = vocab_size

vocab_size instance-attribute

vocab_size = vocab_size + lora_vocab

__init__

__init__(*, vllm_config: VllmConfig, prefix: str = '')

Source code in vllm/model_executor/models/jamba.py

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
    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,
    )

    extra_kwargs = {"is_lora_enabled": bool(vllm_config.lora_config)}

    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,
                           **extra_kwargs)

    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)

forward

forward(
    input_ids: Tensor,
    positions: Tensor,
    mamba_cache_params: MambaCacheParams,
    intermediate_tensors: Optional[
        IntermediateTensors
    ] = None,
    inputs_embeds: Optional[Tensor] = None,
) -> Tensor

Source code in vllm/model_executor/models/jamba.py

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"]

    kv_cache_index = 0
    mamba_cache_index = 0
    for layer in self.layers[self.start_layer:self.end_layer]:
        layer_mamba_cache_params = None
        if isinstance(layer, JambaAttentionDecoderLayer):
            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,
            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

get_input_embeddings

get_input_embeddings(input_ids: Tensor) -> Tensor

Source code in vllm/model_executor/models/jamba.py

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

_is_moe_layer

_is_moe_layer(name: str)

Source code in vllm/model_executor/models/jamba.py

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