srt/models/opt.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.
# ==============================================================================

"""Inference-only OPT model compatible with HuggingFace weights."""
import logging
from collections.abc import Iterable
from typing import Optional, Union

import torch
from torch import nn
from transformers import OPTConfig

from sglang.srt.distributed import (
    get_pp_group,
    get_tensor_model_parallel_rank,
    get_tensor_model_parallel_world_size,
)
from sglang.srt.layers.linear import (
    ColumnParallelLinear,
    QKVParallelLinear,
    ReplicatedLinear,
    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.utils import get_layer_id
from sglang.srt.layers.vocab_parallel_embedding import (
    ParallelLMHead,
    VocabParallelEmbedding,
)
from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
from sglang.srt.model_loader.weight_utils import (
    default_weight_loader,
    kv_cache_scales_loader,
)
from sglang.srt.utils import add_prefix, make_layers
from sglang.utils import get_exception_traceback

logger = logging.getLogger(__name__)


def get_activation(name="relu"):
    """Select an activation function by name

    Args:
        name: str
            activation function name,
            one of ["relu", "gelu", "swish", "sigmoid"],
            default "relu".
    """
    name = name.lower()
    if name == "relu":
        return nn.ReLU()
    if name == "gelu":
        return nn.GELU()
    if name == "sigmoid":
        return torch.nn.Sigmoid()
    return nn.Identity()


class OPTLearnedPositionalEmbedding(nn.Embedding):

    def __init__(self, num_embeddings: int, embedding_dim: int):
        # OPT is set up so that if padding_idx is specified then offset the
        # embedding ids by 2 and adjust num_embeddings appropriately. Other
        # models don't have this hack
        self.offset = 2
        super().__init__(num_embeddings + self.offset, embedding_dim)

    def forward(self, positions: torch.Tensor):
        return super().forward(positions + self.offset)


class OPTAttention(nn.Module):

    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        layer_id: int = 0,
        bias: bool = True,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()
        self.embed_dim = embed_dim
        tensor_model_parallel_world_size = get_tensor_model_parallel_world_size()
        total_num_heads = num_heads
        assert num_heads % tensor_model_parallel_world_size == 0
        self.num_heads = total_num_heads // tensor_model_parallel_world_size
        self.head_dim = embed_dim // total_num_heads
        self.scaling = self.head_dim**-0.5

        self.qkv_proj = QKVParallelLinear(
            embed_dim,
            self.head_dim,
            total_num_heads,
            bias=bias,
            quant_config=quant_config,
            prefix=add_prefix("qkv_proj", prefix),
        )
        self.out_proj = RowParallelLinear(
            embed_dim,
            embed_dim,
            bias=bias,
            quant_config=quant_config,
            prefix=add_prefix("o_proj", prefix),
        )

        self.attn = RadixAttention(
            self.num_heads,
            self.head_dim,
            self.scaling,
            num_kv_heads=self.num_heads,
            layer_id=layer_id,
            quant_config=quant_config,
            prefix=add_prefix("attn", prefix),
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        qkv, _ = self.qkv_proj(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        attn_output = self.attn(q, k, v, forward_batch)
        output, _ = self.out_proj(attn_output)
        return output


class OPTDecoderLayer(nn.Module):

    def __init__(
        self,
        config: OPTConfig,
        layer_id: int = 0,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.self_attn = OPTAttention(
            embed_dim=self.embed_dim,
            num_heads=config.num_attention_heads,
            layer_id=layer_id,
            bias=config.enable_bias,
            quant_config=quant_config,
            prefix=add_prefix("self_attn", prefix),
        )
        self.do_layer_norm_before = config.do_layer_norm_before

        self.self_attn_layer_norm = nn.LayerNorm(
            self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
        )
        self.fc1 = ColumnParallelLinear(
            self.embed_dim,
            config.ffn_dim,
            bias=config.enable_bias,
            quant_config=quant_config,
            prefix=add_prefix("fc1", prefix),
        )
        self.activation_fn = get_activation(config.activation_function)
        self.fc2 = RowParallelLinear(
            config.ffn_dim,
            self.embed_dim,
            bias=config.enable_bias,
            quant_config=quant_config,
            prefix=add_prefix("fc2", prefix),
        )
        self.final_layer_norm = nn.LayerNorm(
            self.embed_dim, elementwise_affine=config.layer_norm_elementwise_affine
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        # Self Attention
        residual = hidden_states
        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
        if self.do_layer_norm_before:
            hidden_states = self.self_attn_layer_norm(hidden_states)
        hidden_states = self.self_attn(
            hidden_states=hidden_states, forward_batch=forward_batch
        )
        hidden_states = residual + hidden_states
        # 350m applies layer norm AFTER attention
        if not self.do_layer_norm_before:
            hidden_states = self.self_attn_layer_norm(hidden_states)

        # Fully Connected
        residual = hidden_states
        # 125m, 1.7B, ..., 175B applies layer norm BEFORE attention
        if self.do_layer_norm_before:
            hidden_states = self.final_layer_norm(hidden_states)
        hidden_states, _ = self.fc1(hidden_states)
        hidden_states = self.activation_fn(hidden_states)
        hidden_states, _ = self.fc2(hidden_states)
        hidden_states = residual + hidden_states
        # 350m applies layer norm AFTER attention
        if not self.do_layer_norm_before:
            hidden_states = self.final_layer_norm(hidden_states)
        return hidden_states


class OPTDecoder(nn.Module):

    def __init__(
        self,
        config: OPTConfig,
        layer_id: int = 0,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        self.max_target_positions = config.max_position_embeddings
        self.vocab_size = config.vocab_size

        self.pp_group = get_pp_group()

        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.word_embed_proj_dim,
            prefix=add_prefix("embed_tokens", prefix),
        )
        # Positional embeddings are replicated (not sharded).
        self.embed_positions = OPTLearnedPositionalEmbedding(
            config.max_position_embeddings, config.hidden_size
        )

        # Project out & in will be replicated if they exist.
        if config.word_embed_proj_dim != config.hidden_size:
            self.project_out = ReplicatedLinear(
                config.hidden_size,
                config.word_embed_proj_dim,
                bias=False,
                quant_config=quant_config,
                prefix=add_prefix("project_out", prefix),
            )
        else:
            self.project_out = None

        if config.word_embed_proj_dim != config.hidden_size:
            self.project_in = ReplicatedLinear(
                config.word_embed_proj_dim,
                config.hidden_size,
                bias=False,
                quant_config=quant_config,
                prefix=add_prefix("project_in", prefix),
            )
        else:
            self.project_in = None

        # Note that the only purpose of `config._remove_final_layer_norm` is to
        # keep backward compatibility with checkpoints that have been fine-tuned
        # before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        if config.do_layer_norm_before and not config._remove_final_layer_norm:
            self.final_layer_norm = nn.LayerNorm(
                config.hidden_size,
                elementwise_affine=config.layer_norm_elementwise_affine,
            )
        else:
            self.final_layer_norm = None

        self.layers, self.start_layer, self.end_layer = make_layers(
            config.num_hidden_layers,
            lambda idx, prefix: OPTDecoderLayer(
                config=config, layer_id=idx, quant_config=quant_config, prefix=prefix
            ),
            pp_rank=self.pp_group.rank_in_group,
            pp_size=self.pp_group.world_size,
            prefix="model.layers",
        )

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        pp_proxy_tensors: Optional[PPProxyTensors] = None,
        input_embeds: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, PPProxyTensors]:
        if self.pp_group.is_first_rank:
            if input_embeds is None:
                input_embeds = self.embed_tokens(input_ids)
            pos_embeds = self.embed_positions(positions)
            if self.project_in is not None:
                input_embeds, _ = self.project_in(input_embeds)
            hidden_states = input_embeds + pos_embeds
        else:
            assert pp_proxy_tensors is not None
            hidden_states = pp_proxy_tensors["hidden_states"]

        for layer in self.layers[self.start_layer : self.end_layer]:
            hidden_states = layer(
                hidden_states=hidden_states, forward_batch=forward_batch
            )
        if not self.pp_group.is_last_rank:
            return PPProxyTensors({"hidden_states": hidden_states})
        if self.final_layer_norm is not None:
            hidden_states = self.final_layer_norm(hidden_states)
            # 没有经过这里
        if self.project_out is not None:
            hidden_states, _ = self.project_out(hidden_states)
        return hidden_states


class OPTModel(nn.Module):

    def __init__(
        self,
        config: OPTConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ) -> None:
        super().__init__()

        # config = vllm_config.model_config.hf_config
        # quant_config = vllm_config.quant_config
        self.config = config
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size
        self.pp_group = get_pp_group()

        self.decoder = OPTDecoder(
            config=config,
            quant_config=quant_config,
            prefix=add_prefix("decoder", prefix),
        )

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        pp_proxy_tensors: Optional[PPProxyTensors],
        input_embeds: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, PPProxyTensors]:
        return self.decoder(
            input_ids,
            positions,
            pp_proxy_tensors=pp_proxy_tensors,
            input_embeds=input_embeds,
            forward_batch=forward_batch,
        )

    def load_kv_cache_scales(self, quantization_param_path: str) -> None:
        tp_size = get_tensor_model_parallel_world_size()
        tp_rank = get_tensor_model_parallel_rank()
        for layer_idx, scaling_factor in kv_cache_scales_loader(
            quantization_param_path,
            tp_rank,
            tp_size,
            self.config.num_hidden_layers,
            self.config.__class__.model_type,
        ):
            if not isinstance(self.decoder.layers[layer_idx], nn.Identity):
                layer_self_attn = self.decoder.layers[layer_idx].self_attn

            if hasattr(layer_self_attn.attn, "k_scale"):
                layer_self_attn.attn.k_scale = scaling_factor
                layer_self_attn.attn.v_scale = scaling_factor
            else:
                raise RuntimeError(
                    "Self attention has no KV cache scaling " "factor attribute!"
                )


class OPTForCausalLM(nn.Module):
    # BitandBytes specific attributes
    # in TP, these weights are partitioned along the column dimension (dim=-1)
    column_parallel_weights_modules = [".down_proj.", ".o_proj."]

    def __init__(
        self,
        config: OPTConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        self.quant_config = quant_config

        self.model = OPTModel(
            config=config, quant_config=quant_config, prefix=add_prefix("model", prefix)
        )
        if self.config.tie_word_embeddings:
            self.lm_head = self.model.decoder.embed_tokens
        else:
            self.lm_head = ParallelLMHead(
                config.vocab_size,
                config.word_embed_proj_dim,
                prefix=add_prefix("lm_head", prefix),
            )
        self.logits_processor = LogitsProcessor(config)
        self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
        self.capture_aux_hidden_states = False
        self.pp_group = get_pp_group()
        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"),
        ]

    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
        pp_proxy_tensors: Optional[PPProxyTensors] = None,
        input_embeds: Optional[torch.Tensor] = None,
        get_embedding: bool = False,
    ) -> LogitsProcessorOutput:
        hidden_states = self.model(
            input_ids=input_ids,
            positions=positions,
            forward_batch=forward_batch,
            input_embeds=input_embeds,
            pp_proxy_tensors=pp_proxy_tensors,
        )
        aux_hidden_states = None
        if self.capture_aux_hidden_states:
            hidden_states, aux_hidden_states = hidden_states

        if self.pp_group.is_last_rank:
            if not get_embedding:
                return self.logits_processor(
                    input_ids,
                    hidden_states,
                    self.lm_head,
                    forward_batch,
                    aux_hidden_states=aux_hidden_states,
                )
            else:
                return self.pooler(hidden_states, forward_batch)
        else:
            return hidden_states

    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> 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"),
        ]
        params_dict = dict(self.named_parameters(remove_duplicate=False))

        for name, loaded_weight in weights:
            if name.startswith("decoder"):
                name = name.replace("decoder.", "model.decoder.")
            layer_id = get_layer_id(name)
            if (
                layer_id is not None
                and hasattr(self.model, "start_layer")
                and (
                    layer_id < self.model.start_layer
                    or layer_id >= self.model.end_layer
                )
            ):
                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
                # if is_pp_missing_parameter(name, self):
                #     continue
                if name not in params_dict:
                    continue
                if name in params_dict.keys():
                    param = params_dict[name]
                    weight_loader = getattr(
                        param, "weight_loader", default_weight_loader
                    )
                    weight_loader(param, loaded_weight)
                else:
                    logger.warning(f"Parameter {name} not found in params_dict")

    @property
    def start_layer(self):
        return self.model.start_layer

    @property
    def end_layer(self):
        return self.model.end_layer

    def get_input_embeddings(self) -> nn.Embedding:
        return self.model.embed_tokens

    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 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:
            if name == "lm_head.weight" and self.config.tie_word_embeddings:
                logger.info(
                    "word embedding is tied for this model, return embed_tokens.weight as lm_head.weight."
                )
                return (
                    self.model.embed_tokens.weight.cpu()
                    .to(torch.float32)
                    .numpy()
                    .tolist()[:truncate_size]
                )

            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())
            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_attention_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.ffn_dim
                    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]

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

    def get_embed_and_head(self):
        return self.model.embed_tokens.weight, self.lm_head.weight

    def set_embed_and_head(self, embed, head):
        del self.model.embed_tokens.weight
        del self.lm_head.weight
        self.model.embed_tokens.weight = embed
        self.lm_head.weight = head
        torch.cuda.empty_cache()
        torch.cuda.synchronize()

    def get_embed(self):
        return self.model.embed_tokens.weight

    def set_embed(self, embed):
        # NOTE: If draft hidden size != target hidden size, the embed weight cannot be shared for EAGLE3
        if (
            hasattr(self.config, "target_hidden_size")
            and self.config.target_hidden_size != self.config.hidden_size
        ):
            return
        del self.model.embed_tokens.weight
        self.model.embed_tokens.weight = embed
        torch.cuda.empty_cache()
        torch.cuda.synchronize()

    def load_kv_cache_scales(self, quantization_param_path: str) -> None:
        self.model.load_kv_cache_scales(quantization_param_path)


EntryClass = [OPTForCausalLM]