sgl-project--sglang/python/sglang/srt/models/gpt_bigcode.py

# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project

# 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/07eb6f19f3b0ee9f7adf6eb689607028aa40bfd5/vllm/model_executor/models/gpt_bigcode.py
"""Inference-only GPTBigCode model compatible with HuggingFace weights."""

from typing import Iterable, Optional, Tuple

import torch
from torch import nn
from transformers import GPTBigCodeConfig

from sglang.srt.layers.activation import get_act_fn
from sglang.srt.layers.linear import (
    ColumnParallelLinear,
    QKVParallelLinear,
    RowParallelLinear,
)
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.quantization.base_config import QuantizationConfig
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.layers.vocab_parallel_embedding import VocabParallelEmbedding
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.model_loader.weight_utils import default_weight_loader
from sglang.srt.runtime_context import get_parallel
from sglang.srt.utils import add_prefix


class GPTBigCodeAttention(nn.Module):

    def __init__(
        self,
        layer_id: int,
        config: GPTBigCodeConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.hidden_size = config.hidden_size
        total_num_heads = config.num_attention_heads
        self.tensor_model_parallel_world_size = get_parallel().tp_size
        assert total_num_heads % self.tensor_model_parallel_world_size == 0
        self.num_heads = total_num_heads // self.tensor_model_parallel_world_size
        self.head_dim = self.hidden_size // total_num_heads
        self.scale = self.head_dim**-0.5

        self.multi_query = config.multi_query
        if self.multi_query:
            total_num_kv_heads = 1
            self.num_kv_heads = 1
        else:
            total_num_kv_heads = total_num_heads
            self.num_kv_heads = self.num_heads
        self.kv_dim = self.head_dim * self.num_kv_heads
        self.c_attn = QKVParallelLinear(
            self.hidden_size,
            self.head_dim,
            total_num_heads,
            total_num_kv_heads,
            bias=True,
            quant_config=quant_config,
            prefix=add_prefix("c_attn", prefix),
        )

        self.c_proj = RowParallelLinear(
            self.hidden_size,
            self.hidden_size,
            bias=True,
            quant_config=quant_config,
            prefix=add_prefix("c_proj", prefix),
        )
        self.attn = RadixAttention(
            self.num_heads,
            self.head_dim,
            scaling=self.scale,
            num_kv_heads=self.num_kv_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.c_attn(hidden_states)
        q, k, v = qkv.split(
            [
                self.hidden_size // self.tensor_model_parallel_world_size,
                self.kv_dim,
                self.kv_dim,
            ],
            dim=-1,
        )
        attn_output = self.attn(q, k, v, forward_batch)
        attn_output, _ = self.c_proj(attn_output)
        return attn_output


class GPTBigMLP(nn.Module):

    def __init__(
        self,
        intermediate_size: int,
        config: GPTBigCodeConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        hidden_size = config.hidden_size
        self.c_fc = ColumnParallelLinear(
            hidden_size,
            intermediate_size,
            bias=True,
            quant_config=quant_config,
            prefix=add_prefix("c_fc", prefix),
        )
        self.c_proj = RowParallelLinear(
            intermediate_size,
            hidden_size,
            bias=True,
            quant_config=quant_config,
            prefix=add_prefix("c_proj", prefix),
        )
        self.act = get_act_fn(
            config.activation_function, quant_config, intermediate_size
        )

    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states, _ = self.c_fc(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states, _ = self.c_proj(hidden_states)
        return hidden_states


class GPTBigCodeBlock(nn.Module):

    def __init__(
        self,
        layer_id: int,
        config: GPTBigCodeConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        hidden_size = config.hidden_size
        inner_dim = config.n_inner if config.n_inner is not None else 4 * hidden_size

        self.ln_1 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.attn = GPTBigCodeAttention(
            layer_id, config, quant_config, prefix=add_prefix("attn", prefix)
        )
        self.ln_2 = nn.LayerNorm(hidden_size, eps=config.layer_norm_epsilon)
        self.mlp = GPTBigMLP(
            inner_dim, config, quant_config, prefix=add_prefix("mlp", prefix)
        )

    def forward(
        self,
        hidden_states: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        residual = hidden_states
        hidden_states = self.ln_1(hidden_states)
        attn_output = self.attn(
            hidden_states=hidden_states, forward_batch=forward_batch
        )
        # residual connection
        hidden_states = attn_output + residual

        residual = hidden_states
        hidden_states = self.ln_2(hidden_states)
        feed_forward_hidden_states = self.mlp(hidden_states)
        # residual connection
        hidden_states = residual + feed_forward_hidden_states
        return hidden_states


class GPTBigCodeModel(nn.Module):

    def __init__(
        self,
        config: GPTBigCodeConfig,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.config = config
        assert not config.add_cross_attention

        self.embed_dim = config.hidden_size
        lora_vocab = 0
        self.vocab_size = config.vocab_size + lora_vocab
        self.wte = VocabParallelEmbedding(
            self.vocab_size,
            self.embed_dim,
            org_num_embeddings=config.vocab_size,
            prefix=add_prefix("wte", prefix),
        )
        self.wpe = nn.Embedding(config.max_position_embeddings, self.embed_dim)
        self.h = nn.ModuleList(
            [
                GPTBigCodeBlock(
                    i, config, quant_config, prefix=add_prefix(f"h.{i}", prefix)
                )
                for i in range(config.num_hidden_layers)
            ]
        )
        self.ln_f = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_epsilon)

    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        inputs_embeds = self.wte(input_ids)
        position_embeds = self.wpe(position_ids)
        hidden_states = inputs_embeds + position_embeds

        for i in range(len(self.h)):
            layer = self.h[i]
            hidden_states = layer(hidden_states, forward_batch)

        hidden_states = self.ln_f(hidden_states)
        return hidden_states


class GPTBigCodeForCausalLM(nn.Module):
    packed_modules_mapping = {"c_attn": ["c_attn"]}

    supported_lora_modules = ["c_fc", "c_proj", "wte", "c_attn"]

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

    embedding_padding_modules = []

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

        self.config = config

        self.quant_config = quant_config
        self.transformer = GPTBigCodeModel(
            config, quant_config, prefix=add_prefix("transformer", prefix)
        )
        self.lm_head = self.transformer.wte
        self.unpadded_vocab_size = config.vocab_size
        self.logits_processor = LogitsProcessor(config)

    @torch.no_grad()
    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
        forward_batch: ForwardBatch,
    ) -> torch.Tensor:
        hidden_states = self.transformer(input_ids, positions, forward_batch)
        return self.logits_processor(
            input_ids, hidden_states, self.lm_head, forward_batch
        )

    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
        params_dict = dict(self.named_parameters(remove_duplicate=False))
        for name, loaded_weight in weights:
            if "lm_head.weight" in name:
                continue
            if ".attn.bias" in name:
                # Skip attention mask.
                # NOTE: "c_attn.bias" should not be skipped.
                continue
            param = params_dict[name]
            weight_loader = getattr(param, "weight_loader", default_weight_loader)
            # TODO (@robertgshaw2-neuralmagic): move to fp8 linear method
            if "c_attn.input_scale" in name or "c_attn.weight_scale" in name:
                weight_loader(param, loaded_weight, "q")
                weight_loader(param, loaded_weight, "k")
                weight_loader(param, loaded_weight, "v")
            else:
                weight_loader(param, loaded_weight)


EntryClass = GPTBigCodeForCausalLM