FastDeploy/fastdeploy/model_executor/models/glm4_moe.py

"""
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
"""

from __future__ import annotations

import re

import paddle
from paddle import nn

from fastdeploy.config import FDConfig
from fastdeploy.distributed.communication import tensor_model_parallel_all_reduce
from fastdeploy.model_executor.forward_meta import ForwardMeta
from fastdeploy.model_executor.graph_optimization.decorator import (
    support_graph_optimization,
)
from fastdeploy.model_executor.layers.activation import SiluAndMul
from fastdeploy.model_executor.layers.attention.attention import Attention
from fastdeploy.model_executor.layers.embeddings import VocabParallelEmbedding
from fastdeploy.model_executor.layers.linear import (
    MergedColumnParallelLinear,
    QKVParallelLinear,
    ReplicatedLinear,
    RowParallelLinear,
)
from fastdeploy.model_executor.layers.lm_head import ParallelLMHead
from fastdeploy.model_executor.layers.moe.moe import FusedMoE
from fastdeploy.model_executor.layers.normalization import RMSNorm
from fastdeploy.model_executor.models.model_base import ModelForCasualLM


class Glm4MoeMLP(nn.Layer):
    """ """

    def __init__(
        self,
        fd_config: FDConfig,
        intermediate_size: int,
        prefix: str = "",
        reduce_results: bool = True,
    ) -> None:
        super().__init__()

        self.up_gate_proj = MergedColumnParallelLinear(
            fd_config=fd_config,
            prefix=f"{prefix}.up_gate_proj",
            input_size=fd_config.model_config.hidden_size,
            output_size=intermediate_size * 2,
            with_bias=False,
            activation=fd_config.model_config.hidden_act,
        )

        self.down_proj = RowParallelLinear(
            fd_config=fd_config,
            prefix=f"{prefix}.down_proj",
            input_size=intermediate_size,
            output_size=fd_config.model_config.hidden_size,
            with_bias=False,
            reduce_results=reduce_results,
        )

        self.act_fn = SiluAndMul(
            fd_config=fd_config,
            bias=None,
            act_method=fd_config.model_config.hidden_act,
        )

    def forward(self, x):
        """ """
        gate_up_out = self.up_gate_proj(x)
        act_out = self.act_fn(gate_up_out)
        down_out = self.down_proj(act_out)
        return down_out


class Glm4Moe(nn.Layer):
    def __init__(
        self,
        fd_config: FDConfig,
        layer_id: int,
        prefix: str = "",
    ) -> None:
        super().__init__()

        self.expert_parallel_size = fd_config.parallel_config.expert_parallel_size
        self.tensor_parallel_size = fd_config.parallel_config.tensor_parallel_size
        self.tensor_parallel_rank = fd_config.parallel_config.tensor_parallel_rank
        self.tp_group = fd_config.parallel_config.tp_group

        self.use_ep = self.expert_parallel_size > 1
        self.use_tp = self.tensor_parallel_size > 1

        self.n_routed_experts: int = fd_config.model_config.n_routed_experts
        self.n_shared_experts: int = fd_config.model_config.n_shared_experts

        weight_key_map = {
            "gate_correction_bias_key": f"{prefix}.gate.e_score_correction_bias",
            "up_gate_proj_expert_weight_key": f"{prefix}.experts.{{}}.up_gate_proj.weight",
            "down_proj_expert_weight_key": f"{prefix}.experts.{{}}.down_proj.weight",
        }

        self.gate = ReplicatedLinear(
            fd_config=fd_config,
            prefix=f"{prefix}.gate",
            input_size=fd_config.model_config.hidden_size,
            output_size=fd_config.model_config.n_routed_experts,
            with_bias=False,
            skip_quant=True,
            weight_dtype="float32",
        )
        self.gate.e_score_correction_bias = self.create_parameter(
            shape=[1, fd_config.model_config.n_routed_experts],
            dtype="float32",
            default_initializer=paddle.nn.initializer.Constant(0),
        )

        self.experts = FusedMoE(
            fd_config,
            reduce_results=False,
            moe_intermediate_size=fd_config.model_config.moe_intermediate_size,
            num_experts=fd_config.model_config.n_routed_experts,
            top_k=fd_config.model_config.num_experts_per_tok,
            topk_method="noaux_tc",
            topk_group=fd_config.model_config.topk_group,
            n_group=fd_config.model_config.n_group,
            routed_scaling_factor=fd_config.model_config.routed_scaling_factor,
            layer_idx=layer_id,
            gate_correction_bias=self.gate.e_score_correction_bias,
            weight_key_map=weight_key_map,
        )

        shared_experts_intermediate_size = self.n_shared_experts * fd_config.model_config.moe_intermediate_size

        self.shared_experts = Glm4MoeMLP(
            fd_config=fd_config,
            intermediate_size=shared_experts_intermediate_size,
            prefix=f"{prefix}.shared_experts",
            reduce_results=False,
        )

    def forward(self, x):
        shared_experts_out = self.shared_experts(x)
        out = self.experts(x, self.gate)
        out = out + shared_experts_out
        # We do to TP all reduce after the sum of experts.
        if self.tensor_parallel_size > 1:
            tensor_model_parallel_all_reduce(out, self.tp_group)
        return out


class Glm4MoeAttention(nn.Layer):
    """ """

    def __init__(self, fd_config: FDConfig, layer_id: int, prefix: str = "") -> None:
        super().__init__()

        tp_size = fd_config.parallel_config.tensor_parallel_size
        self.fd_config = fd_config
        self.head_dim = fd_config.model_config.head_dim
        self.num_heads = fd_config.model_config.num_attention_heads // tp_size
        self.num_kv_heads = fd_config.model_config.num_key_value_heads // tp_size
        self.attention_bias = fd_config.model_config.attention_bias
        self.use_qk_norm = fd_config.model_config.use_qk_norm
        self.q_size = self.num_heads * self.head_dim
        self.kv_size = self.num_kv_heads * self.head_dim

        self.qkv_proj = QKVParallelLinear(fd_config, prefix=f"{prefix}.qkv_proj", with_bias=self.attention_bias)

        self.o_proj = RowParallelLinear(
            fd_config,
            prefix=f"{prefix}.o_proj",
            input_size=fd_config.model_config.num_attention_heads * fd_config.model_config.head_dim,
            output_size=fd_config.model_config.hidden_size,
        )

        self.attn = Attention(
            fd_config,
            layer_id=layer_id,
            prefix=prefix,
            use_neox_rotary_style=True,
            rms_norm_eps=fd_config.model_config.rms_norm_eps,
        )
        if self.use_qk_norm:
            self.q_norm = RMSNorm(
                fd_config,
                hidden_size=self.head_dim,
                eps=fd_config.model_config.rms_norm_eps,
                prefix=f"{prefix}.q_norm",
                begin_norm_axis=2,
            )
            self.k_norm = RMSNorm(
                fd_config,
                hidden_size=self.head_dim,
                eps=fd_config.model_config.rms_norm_eps,
                prefix=f"{prefix}.k_norm",
                begin_norm_axis=2,
            )

    def forward(
        self,
        forward_meta: ForwardMeta,
        hidden_states: paddle.Tensor,
    ):
        """ """
        qkv_out = self.qkv_proj(hidden_states)

        if self.use_qk_norm:
            q, k, v = qkv_out.split([self.q_size, self.kv_size, self.kv_size], axis=-1)
            q = self.q_norm(q.reshape([-1, self.num_heads, self.head_dim])).reshape(q.shape)
            k = self.k_norm(k.reshape([-1, self.num_kv_heads, self.head_dim])).reshape(k.shape)
            qkv_out = paddle.concat([q, k, v], axis=-1)

        atten_out = self.attn(
            qkv=qkv_out,
            forward_meta=forward_meta,
        )
        output = self.o_proj(atten_out)
        return output


class Glm4MoeDecoderLayer(nn.Layer):
    """ """

    def __init__(
        self,
        fd_config: FDConfig,
        prefix: str = "",
    ) -> None:
        super().__init__()

        layer_id = int(prefix.split(sep=".")[-1])
        self.self_attn = Glm4MoeAttention(
            fd_config=fd_config,
            layer_id=layer_id,
            prefix=f"{prefix}.self_attn",
        )

        if (
            fd_config.model_config.n_routed_experts is not None
            and layer_id >= fd_config.model_config.first_k_dense_replace
        ):
            self.mlp = Glm4Moe(fd_config, layer_id, prefix=f"{prefix}.mlp")
        else:
            self.mlp = Glm4MoeMLP(
                fd_config,
                intermediate_size=fd_config.model_config.intermediate_size,
                prefix=f"{prefix}.mlp",
            )

        self.input_layernorm = RMSNorm(
            fd_config,
            hidden_size=fd_config.model_config.hidden_size,
            eps=fd_config.model_config.rms_norm_eps,
            prefix=f"{prefix}.input_layernorm",
        )

        self.post_attention_layernorm = RMSNorm(
            fd_config,
            hidden_size=fd_config.model_config.hidden_size,
            eps=fd_config.model_config.rms_norm_eps,
            prefix=f"{prefix}.post_attention_layernorm",
        )

    def forward(
        self,
        forward_meta: ForwardMeta,
        hidden_states: paddle.Tensor,
        residual: paddle.Tensor = None,
    ):
        """ """
        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(
            hidden_states=hidden_states,
            forward_meta=forward_meta,
        )

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

        hidden_states = self.mlp(hidden_states)

        return hidden_states, residual


@support_graph_optimization
class Glm4MoeModel(nn.Layer):
    """ """

    def __init__(
        self,
        fd_config: FDConfig = None,
    ):
        """
        Initializer for the Qwen2Model class.

        Args:

        """
        super().__init__()

        self.num_layers = fd_config.model_config.num_hidden_layers
        fd_config.model_config.pretrained_config.prefix_name = "model"

        self.embed_tokens = VocabParallelEmbedding(
            fd_config,
            num_embeddings=fd_config.model_config.vocab_size,
            embedding_dim=fd_config.model_config.hidden_size,
            params_dtype=paddle.get_default_dtype,
            prefix=(f"{fd_config.model_config.pretrained_config.prefix_name}.embed_tokens"),
        )

        self.layers = nn.LayerList(
            [
                Glm4MoeDecoderLayer(
                    fd_config,
                    prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.layers.{i}",
                )
                for i in range(self.num_layers)
            ]
        )

        self.norm = RMSNorm(
            fd_config,
            hidden_size=fd_config.model_config.hidden_size,
            eps=fd_config.model_config.rms_norm_eps,
            prefix=f"{fd_config.model_config.pretrained_config.prefix_name}.norm",
        )

    def forward(
        self,
        ids_remove_padding: paddle.Tensor,
        forward_meta: ForwardMeta,
    ):
        """ """
        hidden_states = self.embed_tokens(ids_remove_padding=ids_remove_padding)

        residual = None

        for i in range(self.num_layers):
            hidden_states, residual = self.layers[i](forward_meta, hidden_states, residual)
        hidden_states = hidden_states + residual

        out = self.norm(hidden_states)

        return out


class Glm4MoeForCausalLM(ModelForCasualLM):
    """
    Glm4MoeForCausalLM
    """

    def __init__(self, fd_config: FDConfig):
        """
        Args:
            fd_config (FDConfig): Configurations for the LLM model.
        """
        super(Glm4MoeForCausalLM, self).__init__(fd_config)

        self.model = Glm4MoeModel(fd_config)

        self.ori_vocab_size = fd_config.model_config.ori_vocab_size

        self.lm_head = ParallelLMHead(
            fd_config,
            embedding_dim=fd_config.model_config.hidden_size,
            num_embeddings=fd_config.model_config.vocab_size,
            prefix="lm_head",
        )

    @classmethod
    def name(self):
        """ """
        return "Glm4MoeForCausalLM"

    @paddle.no_grad()
    def load_weights(self, weights_iterator) -> None:
        """
        Load model parameters from a given weights_iterator object.

        Args:
            weights_iterator (Iterator): An iterator yielding (name, weight) pairs.
        """

        from fastdeploy.model_executor.utils import (
            default_weight_loader,
            process_weights_after_loading,
        )

        stacked_params_mapping = [
            # (param_name, shard_name, shard_id)
            ("qkv_proj", "q_proj", "q"),
            ("qkv_proj", "k_proj", "k"),
            ("qkv_proj", "v_proj", "v"),
            ("up_gate_proj", "gate_proj", "gate"),
            ("up_gate_proj", "up_proj", "up"),
            ("embed_tokens.embeddings", "embed_tokens", None),
            ("lm_head.linear", "lm_head", None),
            ("experts.gate_correction_bias", "gate.e_score_correction_bias", None),
        ]
        # (param_name, weight_name, expert_id, shard_id)
        expert_params_mapping = FusedMoE.make_expert_params_mapping(
            num_experts=self.fd_config.model_config.n_routed_experts,
            ckpt_gate_proj_name="gate_proj",
            ckpt_down_proj_name="down_proj",
            ckpt_up_proj_name="up_proj",
            param_gate_up_proj_name="experts.up_gate_proj_",
            param_down_proj_name="experts.down_proj_",
        )
        params_dict = dict(self.named_parameters())
        process_weights_after_loading_fn = process_weights_after_loading(dict(self.named_sublayers()))
        for loaded_weight_name, loaded_weight in weights_iterator:
            for param_name, weight_name, shard_id in stacked_params_mapping:
                if weight_name not in loaded_weight_name:
                    continue
                if "mlp.experts" in loaded_weight_name:
                    continue
                model_param_name = loaded_weight_name.replace(weight_name, param_name)
                if model_param_name not in params_dict:
                    continue
                param = params_dict[model_param_name]
                weight_loader = getattr(param, "weight_loader", default_weight_loader(self.fd_config))
                weight_loader(param, loaded_weight, shard_id)
                break
            else:
                for mapping in expert_params_mapping:
                    param_name, weight_name, expert_id, shard_id = mapping
                    if weight_name not in loaded_weight_name:
                        continue
                    model_param_name = loaded_weight_name.replace(weight_name, param_name)
                    if model_param_name not in params_dict:
                        continue
                    param = params_dict[model_param_name]
                    weight_loader = param.weight_loader
                    weight_loader(param, loaded_weight, shard_id=shard_id, expert_id=expert_id)
                    break
                else:
                    model_param_name = loaded_weight_name
                    if model_param_name not in params_dict:
                        continue
                    param = params_dict[model_param_name]
                    weight_loader = getattr(param, "weight_loader", default_weight_loader(self.fd_config))
                    weight_loader(param, loaded_weight)

            model_sublayer_name = re.sub(r"\.(up_gate_proj_weight|down_proj_weight|weight)$", "", model_param_name)
            process_weights_after_loading_fn(model_sublayer_name, param)

    @paddle.no_grad()
    def set_state_dict(self, state_dict):
        """
        glm4_moe only support loader_v1.
        """
        assert False, "glm4_moe only support --load_choices default_v1."

    def compute_logits(self, hidden_states: paddle.Tensor):
        """ """
        logits = self.lm_head(hidden_states)
        logits = logits.astype(paddle.float32)
        logits[:, self.ori_vocab_size :] = -float("inf")

        return logits

    def forward(
        self,
        ids_remove_padding: paddle.Tensor,
        forward_meta: ForwardMeta,
    ):
        """ """
        hidden_states = self.model(ids_remove_padding=ids_remove_padding, forward_meta=forward_meta)

        return hidden_states

    def clear_grpah_opt_backend(self):
        """Clear graph optimization backend, the captured cuda graph will be cleaned"""
        self.model.clear_grpah_opt_backend(fd_config=self.fd_config)