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vllm.model_executor.models.mixtral_quant

Inference-only Mixtral model.

MixtralAttention

Bases: Module

Source code in vllm/model_executor/models/mixtral_quant.py
class MixtralAttention(nn.Module):

    def __init__(
        self,
        config: MixtralConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        max_position: int = 4096 * 32,
        rope_theta: float = 10000,
        quant_config: Optional[QuantizationConfig] = None,
        cache_config: Optional[CacheConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = hidden_size
        tp_size = get_tensor_model_parallel_world_size()
        self.total_num_heads = num_heads
        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
        self.total_num_kv_heads = num_kv_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)
        # MixtralConfig has an optional head_dim argument
        self.head_dim = getattr(config, "head_dim", None)
        if self.head_dim is None:
            self.head_dim = self.hidden_size // self.total_num_heads
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
        self.scaling = self.head_dim**-0.5
        self.rope_theta = rope_theta

        self.qkv_proj = QKVParallelLinear(
            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,
            hidden_size,
            bias=False,
            quant_config=quant_config,
        )
        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position,
            base=int(self.rope_theta),
            is_neox_style=True,
        )
        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
                              num_kv_heads=self.num_kv_heads,
                              cache_config=cache_config,
                              quant_config=quant_config,
                              prefix=f"{prefix}.attn")

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
        q, k = self.rotary_emb(positions, q, k)
        attn_output = self.attn(q, k, v)
        output, _ = self.o_proj(attn_output)
        return output

attn instance-attribute

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

head_dim instance-attribute

head_dim = getattr(config, 'head_dim', None)

hidden_size instance-attribute

hidden_size = hidden_size

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,
)

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,
)

rope_theta instance-attribute

rope_theta = rope_theta

rotary_emb instance-attribute

rotary_emb = get_rope(
    head_dim,
    rotary_dim=head_dim,
    max_position=max_position,
    base=int(rope_theta),
    is_neox_style=True,
)

scaling instance-attribute

scaling = head_dim ** -0.5

total_num_heads instance-attribute

total_num_heads = num_heads

total_num_kv_heads instance-attribute

total_num_kv_heads = num_kv_heads

__init__

__init__(
    config: MixtralConfig,
    hidden_size: int,
    num_heads: int,
    num_kv_heads: int,
    max_position: int = 4096 * 32,
    rope_theta: float = 10000,
    quant_config: Optional[QuantizationConfig] = None,
    cache_config: Optional[CacheConfig] = None,
    prefix: str = "",
) -> None
Source code in vllm/model_executor/models/mixtral_quant.py
def __init__(
    self,
    config: MixtralConfig,
    hidden_size: int,
    num_heads: int,
    num_kv_heads: int,
    max_position: int = 4096 * 32,
    rope_theta: float = 10000,
    quant_config: Optional[QuantizationConfig] = None,
    cache_config: Optional[CacheConfig] = None,
    prefix: str = "",
) -> None:
    super().__init__()
    self.hidden_size = hidden_size
    tp_size = get_tensor_model_parallel_world_size()
    self.total_num_heads = num_heads
    assert self.total_num_heads % tp_size == 0
    self.num_heads = self.total_num_heads // tp_size
    self.total_num_kv_heads = num_kv_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)
    # MixtralConfig has an optional head_dim argument
    self.head_dim = getattr(config, "head_dim", None)
    if self.head_dim is None:
        self.head_dim = self.hidden_size // self.total_num_heads
    self.q_size = self.num_heads * self.head_dim
    self.kv_size = self.num_kv_heads * self.head_dim
    self.scaling = self.head_dim**-0.5
    self.rope_theta = rope_theta

    self.qkv_proj = QKVParallelLinear(
        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,
        hidden_size,
        bias=False,
        quant_config=quant_config,
    )
    self.rotary_emb = get_rope(
        self.head_dim,
        rotary_dim=self.head_dim,
        max_position=max_position,
        base=int(self.rope_theta),
        is_neox_style=True,
    )
    self.attn = Attention(self.num_heads,
                          self.head_dim,
                          self.scaling,
                          num_kv_heads=self.num_kv_heads,
                          cache_config=cache_config,
                          quant_config=quant_config,
                          prefix=f"{prefix}.attn")

forward

forward(positions: Tensor, hidden_states: Tensor) -> Tensor
Source code in vllm/model_executor/models/mixtral_quant.py
def forward(
    self,
    positions: torch.Tensor,
    hidden_states: torch.Tensor,
) -> torch.Tensor:
    qkv, _ = self.qkv_proj(hidden_states)
    q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
    q, k = self.rotary_emb(positions, q, k)
    attn_output = self.attn(q, k, v)
    output, _ = self.o_proj(attn_output)
    return output

MixtralDecoderLayer

Bases: Module

Source code in vllm/model_executor/models/mixtral_quant.py
class MixtralDecoderLayer(nn.Module):

    def __init__(
        self,
        config: MixtralConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        # Requires transformers > 4.32.0
        rope_theta = getattr(config, "rope_theta", 10000)
        self.self_attn = MixtralAttention(
            config=config,
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            max_position=config.max_position_embeddings,
            num_kv_heads=config.num_key_value_heads,
            rope_theta=rope_theta,
            cache_config=cache_config,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )
        self.block_sparse_moe = MixtralMoE(config=config,
                                           quant_config=quant_config)
        self.input_layernorm = RMSNorm(config.hidden_size,
                                       eps=config.rms_norm_eps)
        self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                                eps=config.rms_norm_eps)

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        residual: Optional[torch.Tensor],
    ) -> torch.Tensor:
        # Self Attention
        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_attn(
            positions=positions,
            hidden_states=hidden_states,
        )

        # Fully Connected
        hidden_states, residual = self.post_attention_layernorm(
            hidden_states, residual)
        hidden_states = self.block_sparse_moe(hidden_states)
        return hidden_states, residual

block_sparse_moe instance-attribute

block_sparse_moe = MixtralMoE(
    config=config, quant_config=quant_config
)

hidden_size instance-attribute

hidden_size = hidden_size

input_layernorm instance-attribute

input_layernorm = RMSNorm(hidden_size, eps=rms_norm_eps)

post_attention_layernorm instance-attribute

post_attention_layernorm = RMSNorm(
    hidden_size, eps=rms_norm_eps
)

self_attn instance-attribute

self_attn = MixtralAttention(
    config=config,
    hidden_size=hidden_size,
    num_heads=num_attention_heads,
    max_position=max_position_embeddings,
    num_kv_heads=num_key_value_heads,
    rope_theta=rope_theta,
    cache_config=cache_config,
    quant_config=quant_config,
    prefix=f"{prefix}.self_attn",
)

__init__

__init__(
    config: MixtralConfig,
    cache_config: Optional[CacheConfig] = None,
    quant_config: Optional[QuantizationConfig] = None,
    prefix: str = "",
) -> None
Source code in vllm/model_executor/models/mixtral_quant.py
def __init__(
    self,
    config: MixtralConfig,
    cache_config: Optional[CacheConfig] = None,
    quant_config: Optional[QuantizationConfig] = None,
    prefix: str = "",
) -> None:
    super().__init__()
    self.hidden_size = config.hidden_size
    # Requires transformers > 4.32.0
    rope_theta = getattr(config, "rope_theta", 10000)
    self.self_attn = MixtralAttention(
        config=config,
        hidden_size=self.hidden_size,
        num_heads=config.num_attention_heads,
        max_position=config.max_position_embeddings,
        num_kv_heads=config.num_key_value_heads,
        rope_theta=rope_theta,
        cache_config=cache_config,
        quant_config=quant_config,
        prefix=f"{prefix}.self_attn",
    )
    self.block_sparse_moe = MixtralMoE(config=config,
                                       quant_config=quant_config)
    self.input_layernorm = RMSNorm(config.hidden_size,
                                   eps=config.rms_norm_eps)
    self.post_attention_layernorm = RMSNorm(config.hidden_size,
                                            eps=config.rms_norm_eps)

forward

forward(
    positions: Tensor,
    hidden_states: Tensor,
    residual: Optional[Tensor],
) -> Tensor
Source code in vllm/model_executor/models/mixtral_quant.py
def forward(
    self,
    positions: torch.Tensor,
    hidden_states: torch.Tensor,
    residual: Optional[torch.Tensor],
) -> torch.Tensor:
    # Self Attention
    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_attn(
        positions=positions,
        hidden_states=hidden_states,
    )

    # Fully Connected
    hidden_states, residual = self.post_attention_layernorm(
        hidden_states, residual)
    hidden_states = self.block_sparse_moe(hidden_states)
    return hidden_states, residual

MixtralForCausalLM

Bases: Module, SupportsPP

Source code in vllm/model_executor/models/mixtral_quant.py
class MixtralForCausalLM(nn.Module, SupportsPP):
    fall_back_to_pt_during_load = False

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config
        self.quant_config = quant_config
        self.model = MixtralModel(vllm_config=vllm_config,
                                  prefix=maybe_prefix(prefix, "model"))
        self.lm_head = ParallelLMHead(config.vocab_size,
                                      config.hidden_size,
                                      quant_config=quant_config)
        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.model.embed_tokens.weight
        self.logits_processor = LogitsProcessor(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,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        hidden_states = self.model(input_ids, positions, intermediate_tensors,
                                   inputs_embeds)
        return hidden_states

    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
        logits = self.logits_processor(self.lm_head, hidden_states,
                                       sampling_metadata)
        return logits

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        loader = AutoWeightsLoader(self)
        return loader.load_weights(weights)

config instance-attribute

config = config

fall_back_to_pt_during_load class-attribute instance-attribute

fall_back_to_pt_during_load = False

lm_head instance-attribute

lm_head = ParallelLMHead(
    vocab_size, hidden_size, quant_config=quant_config
)

logits_processor instance-attribute

logits_processor = LogitsProcessor(vocab_size)

make_empty_intermediate_tensors instance-attribute

make_empty_intermediate_tensors = (
    make_empty_intermediate_tensors
)

model instance-attribute

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

quant_config instance-attribute

quant_config = quant_config

__init__

__init__(*, vllm_config: VllmConfig, prefix: str = '')
Source code in vllm/model_executor/models/mixtral_quant.py
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
    super().__init__()
    config = vllm_config.model_config.hf_config
    quant_config = vllm_config.quant_config
    self.config = config
    self.quant_config = quant_config
    self.model = MixtralModel(vllm_config=vllm_config,
                              prefix=maybe_prefix(prefix, "model"))
    self.lm_head = ParallelLMHead(config.vocab_size,
                                  config.hidden_size,
                                  quant_config=quant_config)
    if self.config.tie_word_embeddings:
        self.lm_head.weight = self.model.embed_tokens.weight
    self.logits_processor = LogitsProcessor(config.vocab_size)
    self.make_empty_intermediate_tensors = (
        self.model.make_empty_intermediate_tensors)

compute_logits

compute_logits(
    hidden_states: Tensor,
    sampling_metadata: SamplingMetadata,
) -> Optional[Tensor]
Source code in vllm/model_executor/models/mixtral_quant.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

forward

forward(
    input_ids: Tensor,
    positions: Tensor,
    intermediate_tensors: Optional[
        IntermediateTensors
    ] = None,
    inputs_embeds: Optional[Tensor] = None,
) -> Union[Tensor, IntermediateTensors]
Source code in vllm/model_executor/models/mixtral_quant.py
def forward(
    self,
    input_ids: torch.Tensor,
    positions: torch.Tensor,
    intermediate_tensors: Optional[IntermediateTensors] = None,
    inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
    hidden_states = self.model(input_ids, positions, intermediate_tensors,
                               inputs_embeds)
    return hidden_states

get_input_embeddings

get_input_embeddings(input_ids: Tensor) -> Tensor
Source code in vllm/model_executor/models/mixtral_quant.py
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
    return self.model.get_input_embeddings(input_ids)

load_weights

load_weights(
    weights: Iterable[tuple[str, Tensor]],
) -> set[str]
Source code in vllm/model_executor/models/mixtral_quant.py
def load_weights(self, weights: Iterable[tuple[str,
                                               torch.Tensor]]) -> set[str]:
    loader = AutoWeightsLoader(self)
    return loader.load_weights(weights)

MixtralMLP

Bases: Module

Source code in vllm/model_executor/models/mixtral_quant.py
class MixtralMLP(nn.Module):

    def __init__(
        self,
        num_experts: int,
        hidden_size: int,
        intermediate_size: int,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.num_experts = num_experts
        self.ffn_dim = intermediate_size
        self.hidden_dim = hidden_size

        self.w1 = ReplicatedLinear(self.hidden_dim,
                                   self.ffn_dim,
                                   bias=False,
                                   quant_config=quant_config)
        self.w2 = ReplicatedLinear(self.ffn_dim,
                                   self.hidden_dim,
                                   bias=False,
                                   quant_config=quant_config)
        self.w3 = ReplicatedLinear(self.hidden_dim,
                                   self.ffn_dim,
                                   bias=False,
                                   quant_config=quant_config)

        # TODO: Use vllm's SiluAndMul
        self.act_fn = nn.SiLU()

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        w1_out, _ = self.w1(hidden_states)
        w1_out = self.act_fn(w1_out)
        w3_out, _ = self.w3(hidden_states)
        current_hidden_states = w1_out * w3_out
        current_hidden_states, _ = self.w2(current_hidden_states)
        return current_hidden_states

act_fn instance-attribute

act_fn = SiLU()

ffn_dim instance-attribute

ffn_dim = intermediate_size

hidden_dim instance-attribute

hidden_dim = hidden_size

num_experts instance-attribute

num_experts = num_experts

w1 instance-attribute

w1 = ReplicatedLinear(
    hidden_dim,
    ffn_dim,
    bias=False,
    quant_config=quant_config,
)

w2 instance-attribute

w2 = ReplicatedLinear(
    ffn_dim,
    hidden_dim,
    bias=False,
    quant_config=quant_config,
)

w3 instance-attribute

w3 = ReplicatedLinear(
    hidden_dim,
    ffn_dim,
    bias=False,
    quant_config=quant_config,
)

__init__

__init__(
    num_experts: int,
    hidden_size: int,
    intermediate_size: int,
    quant_config: Optional[QuantizationConfig] = None,
) -> None
Source code in vllm/model_executor/models/mixtral_quant.py
def __init__(
    self,
    num_experts: int,
    hidden_size: int,
    intermediate_size: int,
    quant_config: Optional[QuantizationConfig] = None,
) -> None:
    super().__init__()
    self.num_experts = num_experts
    self.ffn_dim = intermediate_size
    self.hidden_dim = hidden_size

    self.w1 = ReplicatedLinear(self.hidden_dim,
                               self.ffn_dim,
                               bias=False,
                               quant_config=quant_config)
    self.w2 = ReplicatedLinear(self.ffn_dim,
                               self.hidden_dim,
                               bias=False,
                               quant_config=quant_config)
    self.w3 = ReplicatedLinear(self.hidden_dim,
                               self.ffn_dim,
                               bias=False,
                               quant_config=quant_config)

    # TODO: Use vllm's SiluAndMul
    self.act_fn = nn.SiLU()

forward

forward(hidden_states: Tensor) -> Tensor
Source code in vllm/model_executor/models/mixtral_quant.py
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
    w1_out, _ = self.w1(hidden_states)
    w1_out = self.act_fn(w1_out)
    w3_out, _ = self.w3(hidden_states)
    current_hidden_states = w1_out * w3_out
    current_hidden_states, _ = self.w2(current_hidden_states)
    return current_hidden_states

MixtralMoE

Bases: Module

Source code in vllm/model_executor/models/mixtral_quant.py
class MixtralMoE(nn.Module):

    def __init__(
        self,
        config: MixtralConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.config = config
        self.rank = get_tensor_model_parallel_rank()
        self.tp_size = get_tensor_model_parallel_world_size()
        self.num_total_experts = config.num_local_experts
        self.top_k = config.num_experts_per_tok
        if self.tp_size > self.num_total_experts:
            raise ValueError(
                f"Tensor parallel size {self.tp_size} is greater than "
                f"the number of experts {self.num_total_experts}.")
        # Split experts equally between ranks
        self.expert_indices = np.array_split(range(self.num_total_experts),
                                             self.tp_size)[self.rank].tolist()
        if not self.expert_indices:
            raise ValueError(
                f"Rank {self.rank} has no experts assigned to it.")

        self.experts = nn.ModuleList([
            MixtralMLP(self.num_total_experts,
                       config.hidden_size,
                       config.intermediate_size,
                       quant_config=quant_config)
            if idx in self.expert_indices else None
            for idx in range(self.num_total_experts)
        ])
        self.gate = ReplicatedLinear(config.hidden_size,
                                     self.num_total_experts,
                                     bias=False,
                                     quant_config=None)

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        num_tokens, hidden_dim = hidden_states.shape
        hidden_states = hidden_states.view(-1, hidden_dim)
        # router_logits: (num_tokens, n_experts)
        router_logits, _ = self.gate(hidden_states)

        routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
        routing_weights, selected_experts = torch.topk(routing_weights,
                                                       self.top_k,
                                                       dim=-1)
        routing_weights /= routing_weights.sum(dim=-1, keepdim=True)

        final_hidden_states = None
        for expert_idx in self.expert_indices:
            expert_layer = self.experts[expert_idx]
            expert_mask = (selected_experts == expert_idx)
            expert_weights = (routing_weights * expert_mask).sum(dim=-1,
                                                                 keepdim=True)

            current_hidden_states = expert_layer(hidden_states).mul_(
                expert_weights)
            if final_hidden_states is None:
                final_hidden_states = current_hidden_states
            else:
                final_hidden_states.add_(current_hidden_states)

        return tensor_model_parallel_all_reduce(final_hidden_states).view(
            num_tokens, hidden_dim)

config instance-attribute

config = config

expert_indices instance-attribute

expert_indices = tolist()

experts instance-attribute

experts = ModuleList(
    [
        MixtralMLP(
            num_total_experts,
            hidden_size,
            intermediate_size,
            quant_config=quant_config,
        )
        if idx in expert_indices
        else None
        for idx in range(num_total_experts)
    ]
)

gate instance-attribute

gate = ReplicatedLinear(
    hidden_size,
    num_total_experts,
    bias=False,
    quant_config=None,
)

num_total_experts instance-attribute

num_total_experts = num_local_experts

rank instance-attribute

top_k instance-attribute

top_k = num_experts_per_tok

tp_size instance-attribute

__init__

__init__(
    config: MixtralConfig,
    quant_config: Optional[QuantizationConfig] = None,
)
Source code in vllm/model_executor/models/mixtral_quant.py
def __init__(
    self,
    config: MixtralConfig,
    quant_config: Optional[QuantizationConfig] = None,
):
    super().__init__()
    self.config = config
    self.rank = get_tensor_model_parallel_rank()
    self.tp_size = get_tensor_model_parallel_world_size()
    self.num_total_experts = config.num_local_experts
    self.top_k = config.num_experts_per_tok
    if self.tp_size > self.num_total_experts:
        raise ValueError(
            f"Tensor parallel size {self.tp_size} is greater than "
            f"the number of experts {self.num_total_experts}.")
    # Split experts equally between ranks
    self.expert_indices = np.array_split(range(self.num_total_experts),
                                         self.tp_size)[self.rank].tolist()
    if not self.expert_indices:
        raise ValueError(
            f"Rank {self.rank} has no experts assigned to it.")

    self.experts = nn.ModuleList([
        MixtralMLP(self.num_total_experts,
                   config.hidden_size,
                   config.intermediate_size,
                   quant_config=quant_config)
        if idx in self.expert_indices else None
        for idx in range(self.num_total_experts)
    ])
    self.gate = ReplicatedLinear(config.hidden_size,
                                 self.num_total_experts,
                                 bias=False,
                                 quant_config=None)

forward

forward(hidden_states: Tensor) -> Tensor
Source code in vllm/model_executor/models/mixtral_quant.py
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
    num_tokens, hidden_dim = hidden_states.shape
    hidden_states = hidden_states.view(-1, hidden_dim)
    # router_logits: (num_tokens, n_experts)
    router_logits, _ = self.gate(hidden_states)

    routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
    routing_weights, selected_experts = torch.topk(routing_weights,
                                                   self.top_k,
                                                   dim=-1)
    routing_weights /= routing_weights.sum(dim=-1, keepdim=True)

    final_hidden_states = None
    for expert_idx in self.expert_indices:
        expert_layer = self.experts[expert_idx]
        expert_mask = (selected_experts == expert_idx)
        expert_weights = (routing_weights * expert_mask).sum(dim=-1,
                                                             keepdim=True)

        current_hidden_states = expert_layer(hidden_states).mul_(
            expert_weights)
        if final_hidden_states is None:
            final_hidden_states = current_hidden_states
        else:
            final_hidden_states.add_(current_hidden_states)

    return tensor_model_parallel_all_reduce(final_hidden_states).view(
        num_tokens, hidden_dim)

MixtralModel

Bases: Module

Source code in vllm/model_executor/models/mixtral_quant.py
class MixtralModel(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

        self.vocab_size = config.vocab_size

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: MixtralDecoderLayer(
                config, cache_config, quant_config=quant_config, prefix=prefix
            ),
            prefix=f"{prefix}.layers")
        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        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.embed_tokens(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        intermediate_tensors: Optional[IntermediateTensors],
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        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"]
        for layer in self.layers[self.start_layer:self.end_layer]:
            hidden_states, residual = layer(positions, hidden_states, residual)
        if not get_pp_group().is_last_rank:
            return IntermediateTensors({
                "hidden_states": hidden_states,
                "residual": residual
            })
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states

    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
        ]

        params_dict = dict(self.named_parameters())
        loaded_params: set[str] = set()
        for name, loaded_weight in weights:
            if name.endswith("scale"):
                # Remapping the name of FP8 kv-scale.
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue
            for (param_name, weight_name, shard_id) in stacked_params_mapping:
                if weight_name not in name:
                    continue
                name = name.replace(weight_name, param_name)
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = param.weight_loader
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                # Skip loading extra bias for GPTQ models.
                if name.endswith(".bias") and name not in params_dict:
                    continue
                # Skip experts that are not assigned to this worker.
                if ("block_sparse_moe.experts." in name
                        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

embed_tokens instance-attribute

embed_tokens = VocabParallelEmbedding(
    vocab_size, hidden_size
)

make_empty_intermediate_tensors instance-attribute

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

norm instance-attribute

norm = RMSNorm(hidden_size, eps=rms_norm_eps)

vocab_size instance-attribute

vocab_size = vocab_size

__init__

__init__(*, vllm_config: VllmConfig, prefix: str = '')
Source code in vllm/model_executor/models/mixtral_quant.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

    self.vocab_size = config.vocab_size

    self.embed_tokens = VocabParallelEmbedding(
        config.vocab_size,
        config.hidden_size,
    )
    self.start_layer, self.end_layer, self.layers = make_layers(
        config.num_hidden_layers,
        lambda prefix: MixtralDecoderLayer(
            config, cache_config, quant_config=quant_config, prefix=prefix
        ),
        prefix=f"{prefix}.layers")
    self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
    self.make_empty_intermediate_tensors = (
        make_empty_intermediate_tensors_factory(
            ["hidden_states", "residual"], config.hidden_size))

forward

forward(
    input_ids: Tensor,
    positions: Tensor,
    intermediate_tensors: Optional[IntermediateTensors],
    inputs_embeds: Optional[Tensor] = None,
) -> Union[Tensor, IntermediateTensors]
Source code in vllm/model_executor/models/mixtral_quant.py
def forward(
    self,
    input_ids: torch.Tensor,
    positions: torch.Tensor,
    intermediate_tensors: Optional[IntermediateTensors],
    inputs_embeds: Optional[torch.Tensor] = None,
) -> Union[torch.Tensor, IntermediateTensors]:
    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"]
    for layer in self.layers[self.start_layer:self.end_layer]:
        hidden_states, residual = layer(positions, hidden_states, residual)
    if not get_pp_group().is_last_rank:
        return IntermediateTensors({
            "hidden_states": hidden_states,
            "residual": residual
        })
    hidden_states, _ = self.norm(hidden_states, residual)
    return hidden_states

get_input_embeddings

get_input_embeddings(input_ids: Tensor) -> Tensor
Source code in vllm/model_executor/models/mixtral_quant.py
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
    return self.embed_tokens(input_ids)

load_weights

load_weights(
    weights: Iterable[tuple[str, Tensor]],
) -> set[str]
Source code in vllm/model_executor/models/mixtral_quant.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_dict = dict(self.named_parameters())
    loaded_params: set[str] = set()
    for name, loaded_weight in weights:
        if name.endswith("scale"):
            # Remapping the name of FP8 kv-scale.
            name = maybe_remap_kv_scale_name(name, params_dict)
            if name is None:
                continue
        for (param_name, weight_name, shard_id) in stacked_params_mapping:
            if weight_name not in name:
                continue
            name = name.replace(weight_name, param_name)
            # Skip loading extra bias for GPTQ models.
            if name.endswith(".bias") and name not in params_dict:
                continue
            if is_pp_missing_parameter(name, self):
                continue
            param = params_dict[name]
            weight_loader = param.weight_loader
            weight_loader(param, loaded_weight, shard_id)
            break
        else:
            # Skip loading extra bias for GPTQ models.
            if name.endswith(".bias") and name not in params_dict:
                continue
            # Skip experts that are not assigned to this worker.
            if ("block_sparse_moe.experts." in name
                    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