llama.py 24.4 KB
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# SPDX-License-Identifier: Apache-2.0

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# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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"""Inference-only LLaMA model compatible with HuggingFace weights."""
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from typing import Any, Dict, Iterable, List, Optional, Set, Tuple, Type, Union
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import torch
from torch import nn
from transformers import LlamaConfig

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from vllm.attention import Attention, AttentionMetadata
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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                                               QKVParallelLinear,
                                               RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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    default_weight_loader, maybe_remap_kv_scale_name)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
                    is_pp_missing_parameter,
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                    make_empty_intermediate_tensors_factory, make_layers,
                    maybe_prefix)
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class LlamaMLP(nn.Module):
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    def __init__(
        self,
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        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
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        quant_config: Optional[QuantizationConfig] = None,
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        bias: bool = False,
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        prefix: str = "",
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    ) -> None:
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        super().__init__()
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        self.gate_up_proj = MergedColumnParallelLinear(
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            input_size=hidden_size,
            output_sizes=[intermediate_size] * 2,
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            bias=bias,
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            quant_config=quant_config,
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            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            input_size=intermediate_size,
            output_size=hidden_size,
            bias=bias,
            quant_config=quant_config,
            prefix=f"{prefix}.down_proj",
        )
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        if hidden_act != "silu":
            raise ValueError(f"Unsupported activation: {hidden_act}. "
                             "Only silu is supported for now.")
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        self.act_fn = SiluAndMul()
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    def forward(self, x):
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        x, _ = self.gate_up_proj(x)
        x = self.act_fn(x)
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        x, _ = self.down_proj(x)
        return x


class LlamaAttention(nn.Module):

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    def __init__(self,
                 config: LlamaConfig,
                 hidden_size: int,
                 num_heads: int,
                 num_kv_heads: int,
                 rope_theta: float = 10000,
                 rope_scaling: Optional[Dict[str, Any]] = None,
                 max_position_embeddings: int = 8192,
                 quant_config: Optional[QuantizationConfig] = None,
                 bias: bool = False,
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                 bias_o_proj: bool = False,
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                 cache_config: Optional[CacheConfig] = None,
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                 prefix: str = "") -> None:
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        super().__init__()
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        layer_idx = extract_layer_index(prefix)
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        self.hidden_size = hidden_size
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        tp_size = get_tensor_model_parallel_world_size()
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        self.total_num_heads = num_heads
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        assert self.total_num_heads % tp_size == 0
        self.num_heads = self.total_num_heads // tp_size
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        self.total_num_kv_heads = num_kv_heads
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        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)
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        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
        self.head_dim = getattr(config, "head_dim",
                                self.hidden_size // self.total_num_heads)
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        # Phi models introduced a partial_rotary_factor parameter in the config
        partial_rotary_factor = getattr(config, "partial_rotary_factor", 1)
        self.rotary_dim = int(partial_rotary_factor * self.head_dim)
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        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim
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        self.scaling = self.head_dim**-0.5
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        self.rope_theta = rope_theta
        self.max_position_embeddings = max_position_embeddings
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        self.qkv_proj = QKVParallelLinear(
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            hidden_size=hidden_size,
            head_size=self.head_dim,
            total_num_heads=self.total_num_heads,
            total_num_kv_heads=self.total_num_kv_heads,
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            bias=bias,
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            quant_config=quant_config,
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            prefix=f"{prefix}.qkv_proj",
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        )
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        self.o_proj = RowParallelLinear(
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            input_size=self.total_num_heads * self.head_dim,
            output_size=hidden_size,
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            bias=bias_o_proj,
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            quant_config=quant_config,
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            prefix=f"{prefix}.o_proj",
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        )
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        is_neox_style = True
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        is_gguf = quant_config and quant_config.get_name() == "gguf"
        if is_gguf and config.model_type == "llama":
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            is_neox_style = False

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        self.rotary_emb = get_rope(
            self.head_dim,
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            rotary_dim=self.rotary_dim,
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            max_position=max_position_embeddings,
            base=rope_theta,
            rope_scaling=rope_scaling,
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            is_neox_style=is_neox_style,
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        )
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        if hasattr(config, "interleaved_sliding_window"):
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            interleaved_sliding_window = config.interleaved_sliding_window
            if isinstance(interleaved_sliding_window, int):
                sliding_window = interleaved_sliding_window
            elif isinstance(interleaved_sliding_window, list):
                sw_idx = layer_idx % len(interleaved_sliding_window)
                sliding_window = interleaved_sliding_window[sw_idx]
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            else:
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                raise ValueError(
                    f"{type(interleaved_sliding_window)} is not supported.")
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        else:
            sliding_window = None

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        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,
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            per_layer_sliding_window=sliding_window,
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            prefix=f"{prefix}.attn",
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        )
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    def forward(
        self,
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        positions: torch.Tensor,
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        hidden_states: torch.Tensor,
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        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
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    ) -> torch.Tensor:
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        qkv, _ = self.qkv_proj(hidden_states)
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        q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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        q, k = self.rotary_emb(positions, q, k)
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        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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        output, _ = self.o_proj(attn_output)
        return output


class LlamaDecoderLayer(nn.Module):

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    def __init__(
        self,
        config: LlamaConfig,
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        cache_config: Optional[CacheConfig] = None,
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        quant_config: Optional[QuantizationConfig] = None,
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        prefix: str = "",
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    ) -> None:
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        super().__init__()
        self.hidden_size = config.hidden_size
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        rope_theta = getattr(config, "rope_theta", 10000)
        rope_scaling = getattr(config, "rope_scaling", None)
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        if rope_scaling is not None and getattr(
                config, "original_max_position_embeddings", None):
            rope_scaling["original_max_position_embeddings"] = (
                config.original_max_position_embeddings)
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        max_position_embeddings = getattr(config, "max_position_embeddings",
                                          8192)
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        # Support abacusai/Smaug-72B-v0.1 with attention_bias
        # Support internlm/internlm-7b with bias
        attention_bias = getattr(config, "attention_bias", False) or getattr(
            config, "bias", False)
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        bias_o_proj = attention_bias
        # support internlm/internlm3-8b with qkv_bias
        if hasattr(config, 'qkv_bias'):
            attention_bias = config.qkv_bias

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        self.self_attn = LlamaAttention(
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            config=config,
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            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
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            num_kv_heads=getattr(config, "num_key_value_heads",
                                 config.num_attention_heads),
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            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            max_position_embeddings=max_position_embeddings,
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            quant_config=quant_config,
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            bias=attention_bias,
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            bias_o_proj=bias_o_proj,
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            cache_config=cache_config,
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            prefix=f"{prefix}.self_attn",
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        )
        self.mlp = LlamaMLP(
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            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
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            quant_config=quant_config,
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            bias=getattr(config, "mlp_bias", False),
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            prefix=f"{prefix}.mlp",
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        )
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        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)
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    def forward(
        self,
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        positions: torch.Tensor,
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        hidden_states: torch.Tensor,
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        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
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        residual: Optional[torch.Tensor],
    ) -> Tuple[torch.Tensor, torch.Tensor]:
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        # Self Attention
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        if residual is None:
            residual = hidden_states
            hidden_states = self.input_layernorm(hidden_states)
        else:
            hidden_states, residual = self.input_layernorm(
                hidden_states, residual)
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        hidden_states = self.self_attn(positions=positions,
                                       hidden_states=hidden_states,
                                       kv_cache=kv_cache,
                                       attn_metadata=attn_metadata)
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        # Fully Connected
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        hidden_states, residual = self.post_attention_layernorm(
            hidden_states, residual)
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        hidden_states = self.mlp(hidden_states)
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        return hidden_states, residual
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@support_torch_compile
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class LlamaModel(nn.Module):

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    def __init__(self,
                 *,
                 vllm_config: VllmConfig,
                 prefix: str = "",
                 layer_type: Type[LlamaDecoderLayer] = LlamaDecoderLayer):
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        super().__init__()
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        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

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        self.config = config
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        self.quant_config = quant_config
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        self.padding_idx = config.pad_token_id
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        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
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        if get_pp_group().is_first_rank or (config.tie_word_embeddings
                                            and get_pp_group().is_last_rank):
            self.embed_tokens = VocabParallelEmbedding(
                self.vocab_size,
                config.hidden_size,
                org_num_embeddings=config.vocab_size,
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                quant_config=quant_config,
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            )
        else:
            self.embed_tokens = PPMissingLayer()
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        self.start_layer, self.end_layer, self.layers = make_layers(
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            config.num_hidden_layers,
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            lambda prefix: layer_type(config=config,
                                      cache_config=cache_config,
                                      quant_config=quant_config,
                                      prefix=prefix),
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            prefix=f"{prefix}.layers",
        )
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        if get_pp_group().is_last_rank:
            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
        else:
            self.norm = PPMissingLayer()
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        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(
                ["hidden_states", "residual"], config.hidden_size))

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

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    def forward(
        self,
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        input_ids: Optional[torch.Tensor],
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        positions: torch.Tensor,
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        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
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        intermediate_tensors: Optional[IntermediateTensors],
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        inputs_embeds: Optional[torch.Tensor] = None,
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    ) -> 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
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        else:
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            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
            residual = intermediate_tensors["residual"]

        for i in range(self.start_layer, self.end_layer):
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            layer = self.layers[i]
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            hidden_states, residual = layer(positions, hidden_states,
                                            kv_caches[i - self.start_layer],
                                            attn_metadata, residual)
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        if not get_pp_group().is_last_rank:
            return IntermediateTensors({
                "hidden_states": hidden_states,
                "residual": residual
            })

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        hidden_states, _ = self.norm(hidden_states, residual)
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        return hidden_states

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    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
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        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            (".qkv_proj", ".q_proj", "q"),
            (".qkv_proj", ".k_proj", "k"),
            (".qkv_proj", ".v_proj", "v"),
            (".gate_up_proj", ".gate_proj", 0),
            (".gate_up_proj", ".up_proj", 1),
        ]
        params_dict = dict(self.named_parameters())
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        loaded_params: Set[str] = set()
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        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name:
                continue
            if ("rotary_emb.cos_cached" in name
                    or "rotary_emb.sin_cached" in name):
                # Models trained using ColossalAI may include these tensors in
                # the checkpoint. Skip them.
                continue
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            if (self.quant_config is not None and
                (scale_name := self.quant_config.get_cache_scale(name))):
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                # Loading kv cache quantization scales
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                param = params_dict[scale_name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
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                loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
                                 loaded_weight[0])
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                weight_loader(param, loaded_weight)
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                loaded_params.add(scale_name)
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                continue
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            if "scale" in name:
                # Remapping the name of FP8 kv-scale.
                name = maybe_remap_kv_scale_name(name, params_dict)
                if name is None:
                    continue
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            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

                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)
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            loaded_params.add(name)
        return loaded_params
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class LlamaForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
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    packed_modules_mapping = {
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        "qkv_proj": ["q_proj", "k_proj", "v_proj"],
        "gate_up_proj": ["gate_proj", "up_proj"]
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    }

    # LoRA specific attributes
    supported_lora_modules = [
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        "qkv_proj", "o_proj", "gate_up_proj", "down_proj", "embed_tokens",
        "lm_head"
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    ]
    embedding_modules = {
        "embed_tokens": "input_embeddings",
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        "lm_head": "output_embeddings"
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    }
    embedding_padding_modules = ["lm_head"]
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    # Mistral/Llama models can also be loaded with --load-format mistral
    # from consolidated.safetensors checkpoints
    mistral_mapping = {
        "layers": "model.layers",
        "attention": "self_attn",
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        "qscale_act": "input_scale",
        "qscale_weight": "weight_scale",
        "kv_fake_quantizer.qscale_act": "kv_scale",
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        "wq": "q_proj",
        "wk": "k_proj",
        "wv": "v_proj",
        "wo": "o_proj",
        "attention_norm": "input_layernorm",
        "feed_forward": "mlp",
        "w1": "gate_proj",
        "w2": "down_proj",
        "w3": "up_proj",
        "ffn_norm": "post_attention_layernorm",
        "tok_embeddings": "model.embed_tokens",
        "output": "lm_head",
        "norm": "model.norm"
    }
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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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        super().__init__()
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        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        lora_config = vllm_config.lora_config
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        self.config = config
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        self.lora_config = lora_config

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        self.model = self._init_model(vllm_config=vllm_config,
                                      prefix=maybe_prefix(prefix, "model"))

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        if get_pp_group().is_last_rank:
            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,
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                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),
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                quant_config=quant_config,
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                prefix=maybe_prefix(prefix, "lm_head"),
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            )
            if config.tie_word_embeddings:
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                self.lm_head = self.lm_head.tie_weights(
                    self.model.embed_tokens)
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            logit_scale = getattr(config, "logit_scale", 1.0)
            self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
                                                    config.vocab_size,
                                                    logit_scale)
        else:
            self.lm_head = PPMissingLayer()
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        self.sampler = get_sampler()

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        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)
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    def _init_model(self, vllm_config: VllmConfig, prefix: str = ""):
        return LlamaModel(vllm_config=vllm_config, prefix=prefix)

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

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    def forward(
        self,
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        input_ids: torch.Tensor,
        positions: torch.Tensor,
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        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
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        intermediate_tensors: Optional[IntermediateTensors] = None,
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        inputs_embeds: Optional[torch.Tensor] = None,
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    ) -> Union[torch.Tensor, IntermediateTensors]:
        model_output = self.model(input_ids, positions, kv_caches,
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                                  attn_metadata, intermediate_tensors,
                                  inputs_embeds)
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        return model_output
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    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
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        logits = self.logits_processor(self.lm_head, hidden_states,
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                                       sampling_metadata)
        return logits

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    def sample(self, logits: torch.Tensor,
               sampling_metadata: SamplingMetadata) -> Optional[SamplerOutput]:
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        next_tokens = self.sampler(logits, sampling_metadata)
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        return next_tokens

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    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
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        loader = AutoWeightsLoader(
            self,
            skip_prefixes=(["lm_head."]
                           if self.config.tie_word_embeddings else None),
        )
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        return loader.load_weights(
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            self.maybe_remap_mistral(name, loaded_weight)
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            for name, loaded_weight in weights)
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    # This function is used to remap the mistral format as
    # used by Mistral and Llama <=2
    def maybe_remap_mistral(
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        self,
        name: str,
        loaded_weight: torch.Tensor,
    ) -> Tuple[str, torch.Tensor]:
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        def permute(w: torch.Tensor, n_heads: int):
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            attn_in = self.config.head_dim * n_heads
            attn_out = self.config.hidden_size

            return w.view(n_heads, attn_in // n_heads // 2, 2,
                          attn_out).transpose(1, 2).reshape(attn_in, attn_out)

        mapping = self.mistral_mapping
        modules = name.split(".")

        # rotary embeds should be sliced
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        if "wk" in modules and modules[-1] == "weight":
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            loaded_weight = permute(loaded_weight,
                                    self.config.num_key_value_heads)
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        elif "wq" in modules and modules[-1] == "weight":
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            loaded_weight = permute(loaded_weight,
                                    self.config.num_attention_heads)

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        num_modules = len(modules)
        for i in range(num_modules):
            item = modules[i]
            next_item = modules[i + 1] if i < num_modules - 1 else None

            combined_item = (f"{item}.{next_item}"
                             if next_item is not None else None)

            if combined_item in mapping:
                name = name.replace(combined_item, mapping[combined_item])
            elif item in mapping and mapping[item] not in name:
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                name = name.replace(item, mapping[item])

        return name, loaded_weight