llama.py

# Copyright 2023-2024 SGLang Team
# 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.
# ==============================================================================

# Adapted from
# https://github.com/vllm-project/vllm/blob/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/llama.py#L1
"""Inference-only LLaMA model compatible with HuggingFace weights."""

import logging
from typing import Any, Dict, Iterable, Optional, Tuple

import torch
from torch import nn
from transformers import LlamaConfig
from vllm.distributed import get_tensor_model_parallel_world_size
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.model_loader.weight_utils import default_weight_loader

from sglang.srt.layers.activation import SiluAndMul
from sglang.srt.layers.layernorm import RMSNorm
from sglang.srt.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
from sglang.srt.layers.pooler import Pooler, PoolingType
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.torchao_utils import apply_torchao_config_
from sglang.srt.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.utils import make_layers

logger = logging.getLogger(__name__)


class LlamaMLP(nn.Module):
    def __init__(
        self,
        hidden_size: int,
        intermediate_size: int,
        hidden_act: str,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.gate_up_proj = MergedColumnParallelLinear(
            hidden_size,
            [intermediate_size] * 2,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj",
        )
        self.down_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.down_proj",
        )
        if hidden_act != "silu":
            raise ValueError(
                f"Unsupported activation: {hidden_act}. "
                "Only silu is supported for now."
            )
        self.act_fn = SiluAndMul()

    def forward(self, x):
        gate_up, _ = self.gate_up_proj(x)
        x = self.act_fn(gate_up)
        x, _ = self.down_proj(x)
        return x


class LlamaAttention(nn.Module):
    def __init__(
        self,
        config: LlamaConfig,
        hidden_size: int,
        num_heads: int,
        num_kv_heads: int,
        layer_id: int = 0,
        rope_theta: float = 10000,
        rope_scaling: Optional[Dict[str, Any]] = None,
        rope_is_neox_style: bool = True,
        max_position_embeddings: int = 8192,
        quant_config: Optional[QuantizationConfig] = 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)
        # MistralConfig has an optional head_dim introduced by Mistral-Nemo
        self.head_dim = getattr(
            config, "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.max_position_embeddings = max_position_embeddings

        self.qkv_proj = QKVParallelLinear(
            hidden_size,
            self.head_dim,
            self.total_num_heads,
            self.total_num_kv_heads,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.qkv_proj",
        )
        self.o_proj = RowParallelLinear(
            self.total_num_heads * self.head_dim,
            hidden_size,
            bias=False,
            quant_config=quant_config,
            prefix=f"{prefix}.o_proj",
        )

        self.rotary_emb = get_rope(
            self.head_dim,
            rotary_dim=self.head_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
            rope_scaling=rope_scaling,
            is_neox_style=rope_is_neox_style,
        )
        self.attn = RadixAttention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_kv_heads,
            layer_id=layer_id,
        )

    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> 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, forward_batch)
        output, _ = self.o_proj(attn_output)
        return output


class LlamaDecoderLayer(nn.Module):
    def __init__(
        self,
        config: LlamaConfig,
        layer_id: int = 0,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.hidden_size = config.hidden_size
        rope_theta = getattr(config, "rope_theta", 10000)
        rope_scaling = getattr(config, "rope_scaling", None)
        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
            )
        rope_is_neox_style = getattr(config, "rope_is_neox_style", True)
        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
        self.self_attn = LlamaAttention(
            config=config,
            hidden_size=self.hidden_size,
            num_heads=config.num_attention_heads,
            num_kv_heads=config.num_key_value_heads,
            layer_id=layer_id,
            rope_theta=rope_theta,
            rope_scaling=rope_scaling,
            rope_is_neox_style=rope_is_neox_style,
            max_position_embeddings=max_position_embeddings,
            quant_config=quant_config,
            prefix=f"{prefix}.self_attn",
        )
        self.mlp = LlamaMLP(
            hidden_size=self.hidden_size,
            intermediate_size=config.intermediate_size,
            hidden_act=config.hidden_act,
            quant_config=quant_config,
            prefix=f"{prefix}.mlp",
        )
        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,
        forward_batch: ForwardBatch,
        residual: Optional[torch.Tensor],
    ) -> Tuple[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,
            forward_batch=forward_batch,
        )

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


class LlamaModel(nn.Module):
    def __init__(
        self,
        config: LlamaConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ) -> None:
        super().__init__()
        self.config = config
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )
        self.layers = make_layers(
            config.num_hidden_layers,
            lambda idx, prefix: LlamaDecoderLayer(
                config=config, quant_config=quant_config, layer_id=idx, prefix=prefix
            ),
            prefix="model.layers",
        )

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

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: torch.Tensor = None,
    ) -> torch.Tensor:
        if input_embeds is None:
            hidden_states = self.embed_tokens(input_ids)
        else:
            hidden_states = input_embeds
        residual = None
        for i in range(len(self.layers)):
            layer = self.layers[i]
            hidden_states, residual = layer(
                positions,
                hidden_states,
                forward_batch,
                residual,
            )
        hidden_states, _ = self.norm(hidden_states, residual)
        return hidden_states


class LlamaForCausalLM(nn.Module):
    def __init__(
        self,
        config: LlamaConfig,
        quant_config: Optional[QuantizationConfig] = None,
        cache_config=None,
    ) -> None:
        super().__init__()
        self.config = config
        self.quant_config = quant_config
        self.torchao_config = global_server_args_dict["torchao_config"]
        self.model = LlamaModel(config, quant_config=quant_config)
        self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
        self.logits_processor = LogitsProcessor(config)
        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
        self.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),
        ]

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        input_embeds: torch.Tensor = None,
        get_embedding: bool = False,
    ) -> LogitsProcessorOutput:
        hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
        if not get_embedding:
            return self.logits_processor(
                input_ids, hidden_states, self.lm_head.weight, forward_batch
            )
        else:
            return self.pooler(hidden_states, forward_batch)

    def get_hidden_dim(self, module_name):
        # return input_dim, output_dim
        if module_name in ["q_proj", "o_proj", "qkv_proj"]:
            return self.config.hidden_size, self.config.hidden_size
        elif module_name in ["kv_proj"]:
            return self.config.hidden_size, self.config.hidden_size // (
                self.config.num_attention_heads // self.config.num_key_value_heads
            )
        elif module_name == "gate_up_proj":
            return self.config.hidden_size, self.config.intermediate_size
        elif module_name == "down_proj":
            return self.config.intermediate_size, self.config.hidden_size
        else:
            raise NotImplementedError()

    def get_module_name(self, name):
        params_mapping = {
            "q_proj": "qkv_proj",
            "k_proj": "qkv_proj",
            "v_proj": "qkv_proj",
            "gate_proj": "gate_up_proj",
            "up_proj": "gate_up_proj",
        }
        return params_mapping.get(name, name)

    def get_module_name_from_weight_name(self, name):
        for param_name, weight_name, shard_id, num_shard in self.stacked_params_mapping:
            if weight_name in name:
                return (
                    name.replace(weight_name, param_name)[: -len(".weight")],
                    num_shard,
                )
        return name[: -len(".weight")], 1

    def get_num_params(self):
        params_dict = dict(self.named_parameters())
        return len(params_dict)

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        embed_tokens_weight = None
        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())

        load_tie_word_embeddings = (
            hasattr(self.config, "tie_word_embeddings")
            and self.config.tie_word_embeddings
            and "lm_head.weight" in params_dict
        )

        for name, loaded_weight in weights:
            if "rotary_emb.inv_freq" in name or "projector" 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
            if name.startswith("model.vision_tower") and name not in params_dict:
                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
                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 loading kv_scale from ckpts towards new design.
                if name.endswith(".kv_scale") and name not in params_dict:
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader)
                weight_loader(param, loaded_weight)

                if load_tie_word_embeddings and name == "model.embed_tokens.weight":
                    embed_tokens_weight = loaded_weight

        if load_tie_word_embeddings:
            # Tie output embedding layer to input embedding layer, to solve issues where lm_head.weight is missing
            param = self.lm_head.weight
            weight_loader = getattr(param, "weight_loader", default_weight_loader)
            if embed_tokens_weight is not None:
                weight_loader(param, embed_tokens_weight)

        apply_torchao_config_(self, params_dict, set(["proj.weight"]))

    def get_weights_by_name(
        self, name: str, truncate_size: int = 100, tp_size: int = 1
    ) -> Optional[torch.Tensor]:
        """Get the weights of the parameter by its name. Similar to `get_parameter` in Hugging Face.

        Only used for unit test with an unoptimized performance.
        For optimized performance, please use torch.save and torch.load.
        """
        try:
            mapped_name = name
            mapped_shard_id = None
            for param_name, weight_name, shard_id in self.stacked_params_mapping:
                if weight_name in name:
                    mapped_name = name.replace(weight_name, param_name)
                    mapped_shard_id = shard_id
                    break
            params_dict = dict(self.named_parameters())
            if mapped_name in params_dict:
                param = params_dict[mapped_name]
                if mapped_shard_id is not None:
                    if mapped_shard_id in ["q", "k", "v"]:
                        num_heads = self.config.num_attention_heads // tp_size
                        num_kv_heads = self.config.num_key_value_heads // tp_size
                        head_dim = (
                            self.config.hidden_size // self.config.num_attention_heads
                        )
                        if mapped_shard_id == "q":
                            offset = 0
                            size = num_heads * head_dim
                        elif mapped_shard_id == "k":
                            offset = num_heads * head_dim
                            size = num_kv_heads * head_dim
                        elif mapped_shard_id == "v":
                            offset = (num_heads + num_kv_heads) * head_dim
                            size = num_kv_heads * head_dim
                        weight = param.data.narrow(0, offset, size)
                    elif mapped_shard_id in [0, 1]:
                        intermediate_size = self.config.intermediate_size
                        hidden_size = self.config.hidden_size
                        slice_size = intermediate_size // tp_size
                        if mapped_shard_id == 0:  # gate_proj
                            offset = 0
                            size = slice_size
                        elif mapped_shard_id == 1:  # up_proj
                            offset = slice_size
                            size = slice_size

                        weight = param.data.narrow(0, offset, size)
                    else:
                        weight = param.data
                else:
                    weight = param.data
                if tp_size > 1 and ("o_proj" in name or "down_proj" in name):
                    gathered_weights = [
                        torch.zeros_like(weight) for _ in range(tp_size)
                    ]
                    torch.distributed.all_gather(gathered_weights, weight)
                    weight = torch.cat(gathered_weights, dim=1)
                return weight.cpu().to(torch.float32).numpy().tolist()[:truncate_size]
            else:
                return None

        except Exception as e:
            logger.error(
                f"Error getting weights by name {name} in LlamaForCausalLM: {e}"
            )
            return None


class Phi3ForCausalLM(LlamaForCausalLM):
    pass


EntryClass = [LlamaForCausalLM, Phi3ForCausalLM]