Support MiniCPM3 (#1371)

README.md

@@ -259,6 +259,7 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3-8B-Instruct
 - ChatGLM
 - InternLM 2
 - Exaone 3
+- MiniCPM / MiniCPM 3
 
 **Embedding Models**
 

python/sglang/srt/layers/decode_attention.py

@@ -483,11 +483,14 @@ def _decode_grouped_att_m_fwd(
     # shape constraints
     Lq, Lk = q.shape[-1], k_buffer.shape[-1]
     assert Lq == Lk
-    assert Lk in {16, 32, 64, 96, 128, 256, 576}
+    assert Lk in {16, 32, 64, 96, 128, 256, 576, 288}
 
     if Lk == 576:
         BLOCK_DMODEL = 512
         BLOCK_DPE = 64
+    elif Lk == 288:
+        BLOCK_DMODEL = 256
+        BLOCK_DPE = 32
     else:
         BLOCK_DMODEL = triton.next_power_of_2(Lk)
         BLOCK_DPE = 0

python/sglang/srt/layers/extend_attention.py

@@ -280,12 +280,15 @@ def extend_attention_fwd(
     assert Lq == Lk and Lv == Lo
 
     # TODO: is the assertion necessary?
-    assert Lq in {16, 32, 64, 96, 128, 256, 576}
+    assert Lq in {16, 32, 64, 96, 128, 256, 576, 288}
     assert Lv in {16, 32, 64, 96, 128, 256, 512}
 
     if Lq == 576:
         BLOCK_DMODEL = 512
         BLOCK_DPE = 64
+    elif Lq == 288:
+        BLOCK_DMODEL = 256
+        BLOCK_DPE = 32
     else:
         BLOCK_DMODEL = triton.next_power_of_2(Lq)
         BLOCK_DPE = 0

python/sglang/srt/model_config.py

@@ -64,6 +64,11 @@ class ModelConfig:
             self.attention_arch = AttentionArch.MLA
             self.kv_lora_rank = self.hf_config.kv_lora_rank
             self.qk_rope_head_dim = self.hf_config.qk_rope_head_dim
+        elif "MiniCPM3ForCausalLM" in self.hf_config.architectures:
+            self.head_dim = 128
+            self.attention_arch = AttentionArch.MLA
+            self.kv_lora_rank = self.hf_config.kv_lora_rank
+            self.qk_rope_head_dim = self.hf_config.qk_rope_head_dim
         else:
             self.attention_arch = AttentionArch.MHA
 

python/sglang/srt/models/minicpm3.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 MiniCPM3 model compatible with HuggingFace weights."""
+
+import math
+from typing import Any, Dict, Iterable, Optional, Tuple
+
+import torch
+from flashinfer import bmm_fp8
+from torch import nn
+from transformers import PretrainedConfig
+from vllm.config import CacheConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    ReplicatedLinear,
+    RowParallelLinear,
+)
+from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
+from vllm.model_executor.layers.rotary_embedding import get_rope
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    ParallelLMHead,
+    VocabParallelEmbedding,
+)
+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.logits_processor import LogitsProcessor
+from sglang.srt.layers.radix_attention import RadixAttention
+from sglang.srt.layers.sampler import Sampler
+from sglang.srt.managers.schedule_batch import global_server_args_dict
+from sglang.srt.model_executor.forward_batch_info import InputMetadata
+
+
+class MiniCPM3MLP(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size,
+            [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.down_proj = RowParallelLinear(
+            intermediate_size,
+            hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+        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
+
+
+def input_to_float8(x, dtype=torch.float8_e4m3fn):
+    finfo = torch.finfo(dtype)
+    min_val, max_val = x.aminmax()
+    amax = torch.maximum(min_val.abs(), max_val.abs()).clamp(min=1e-12)
+    scale = finfo.max / amax
+    x_scl_sat = (x * scale).clamp(min=finfo.min, max=finfo.max)
+    return x_scl_sat.to(dtype).contiguous(), scale.float().reciprocal()
+
+
+class MiniCPM3Attention(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        hidden_size: int,
+        num_heads: int,
+        qk_nope_head_dim: int,
+        qk_rope_head_dim: int,
+        v_head_dim: int,
+        q_lora_rank: int,
+        kv_lora_rank: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        layer_id=None,
+    ) -> None:
+        super().__init__()
+        self.layer_id = layer_id
+        self.hidden_size = hidden_size
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.q_lora_rank = q_lora_rank
+        self.kv_lora_rank = kv_lora_rank
+        self.num_heads = num_heads
+        tp_size = get_tensor_model_parallel_world_size()
+        assert num_heads % tp_size == 0
+        self.num_local_heads = num_heads // tp_size
+        self.scaling = self.qk_head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        if self.q_lora_rank is not None:
+            self.q_a_proj = ReplicatedLinear(
+                self.hidden_size,
+                self.q_lora_rank,
+                bias=False,
+                quant_config=quant_config,
+            )
+            self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
+            self.q_b_proj = ColumnParallelLinear(
+                q_lora_rank,
+                self.num_heads * self.qk_head_dim,
+                bias=False,
+                quant_config=quant_config,
+            )
+        else:
+            self.q_proj = ColumnParallelLinear(
+                self.hidden_size,
+                self.num_heads * self.qk_head_dim,
+                bias=False,
+                quant_config=quant_config,
+            )
+
+        self.kv_a_proj_with_mqa = ReplicatedLinear(
+            self.hidden_size,
+            self.kv_lora_rank + self.qk_rope_head_dim,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
+        self.kv_b_proj = ColumnParallelLinear(
+            self.kv_lora_rank,
+            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
+            bias=False,
+            quant_config=quant_config,
+        )
+        # O projection.
+        self.o_proj = RowParallelLinear(
+            self.num_heads * self.v_head_dim,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.rotary_emb = get_rope(
+            qk_rope_head_dim,
+            rotary_dim=qk_rope_head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+
+        # TODO support head_size 96
+        self.attn = RadixAttention(
+            self.num_local_heads,
+            128,
+            self.scaling,
+            num_kv_heads=self.num_local_heads,
+            layer_id=layer_id,
+        )
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        if self.q_lora_rank is not None:
+            q = self.q_a_proj(hidden_states)[0]
+            q = self.q_a_layernorm(q)
+            q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
+        else:
+            q = self.q_proj(hidden_states)[0].view(
+                -1, self.num_local_heads, self.qk_head_dim
+            )
+        _, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+        latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
+        kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
+        latent_cache = latent_cache.unsqueeze(1)
+        kv_a = self.kv_a_layernorm(kv_a.contiguous())
+        kv = self.kv_b_proj(kv_a)[0]
+        kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
+        k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+        k_pe = latent_cache[:, :, self.kv_lora_rank :]
+        original_shapes = [q_pe.shape, k_pe.shape]
+        q_pe, k_pe = self.rotary_emb(
+            positions, q_pe.reshape(q_pe.shape[0], -1), k_pe.reshape(k_pe.shape[0], -1)
+        )
+        q_pe, k_pe = q_pe.view(original_shapes[0]), k_pe.view(original_shapes[1])
+        q[..., self.qk_nope_head_dim :] = q_pe
+        k = torch.empty_like(q)
+        k[..., : self.qk_nope_head_dim] = k_nope
+        k[..., self.qk_nope_head_dim :] = k_pe
+        q = torch.nn.functional.pad(q, [0, 128 - self.qk_head_dim], value=0).view(
+            -1, self.num_local_heads * 128
+        )
+        k = torch.nn.functional.pad(k, [0, 128 - self.qk_head_dim], value=0).view(
+            -1, self.num_local_heads * 128
+        )
+        v = torch.nn.functional.pad(v, [0, 128 - self.v_head_dim], value=0).view(
+            -1, self.num_local_heads * 128
+        )
+        attn_output = self.attn(q, k, v, input_metadata)
+        attn_output = attn_output.view(-1, self.num_local_heads, 128)[
+            ..., : self.v_head_dim
+        ].reshape(-1, self.num_local_heads * self.v_head_dim)
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MiniCPM3AttentionMLA(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        hidden_size: int,
+        num_heads: int,
+        qk_nope_head_dim: int,
+        qk_rope_head_dim: int,
+        v_head_dim: int,
+        q_lora_rank: int,
+        kv_lora_rank: int,
+        rope_theta: float = 10000,
+        rope_scaling: Optional[Dict[str, Any]] = None,
+        max_position_embeddings: int = 8192,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        layer_id=None,
+    ) -> None:
+        super().__init__()
+        self.layer_id = layer_id
+        self.hidden_size = hidden_size
+        self.qk_nope_head_dim = qk_nope_head_dim
+        self.qk_rope_head_dim = qk_rope_head_dim
+        self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
+        self.v_head_dim = v_head_dim
+        self.q_lora_rank = q_lora_rank
+        self.kv_lora_rank = kv_lora_rank
+        self.num_heads = num_heads
+        tp_size = get_tensor_model_parallel_world_size()
+        assert num_heads % tp_size == 0
+        self.num_local_heads = num_heads // tp_size
+        self.scaling = self.qk_head_dim**-0.5
+        self.rope_theta = rope_theta
+        self.max_position_embeddings = max_position_embeddings
+
+        if self.q_lora_rank is not None:
+            self.q_a_proj = ReplicatedLinear(
+                self.hidden_size,
+                self.q_lora_rank,
+                bias=False,
+                quant_config=quant_config,
+            )
+            self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
+            self.q_b_proj = ColumnParallelLinear(
+                q_lora_rank,
+                self.num_heads * self.qk_head_dim,
+                bias=False,
+                quant_config=quant_config,
+            )
+        else:
+            self.q_proj = ColumnParallelLinear(
+                self.hidden_size,
+                self.num_heads * self.qk_head_dim,
+                bias=False,
+                quant_config=quant_config,
+            )
+
+        self.kv_a_proj_with_mqa = ReplicatedLinear(
+            self.hidden_size,
+            self.kv_lora_rank + self.qk_rope_head_dim,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
+        self.kv_b_proj = ColumnParallelLinear(
+            self.kv_lora_rank,
+            self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
+            bias=False,
+            quant_config=quant_config,
+        )
+        # O projection.
+        self.o_proj = RowParallelLinear(
+            self.num_heads * self.v_head_dim,
+            self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+        )
+        self.rotary_emb = get_rope(
+            qk_rope_head_dim,
+            rotary_dim=qk_rope_head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+
+        self.attn = RadixAttention(
+            self.num_local_heads,
+            self.kv_lora_rank + self.qk_rope_head_dim,
+            self.scaling,
+            num_kv_heads=1,
+            layer_id=layer_id,
+            v_head_dim=self.kv_lora_rank,
+        )
+
+        self.w_kc = None
+        self.w_vc = None
+        self.w_scale = None
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        input_metadata: InputMetadata,
+    ) -> torch.Tensor:
+        q_len = hidden_states.shape[0]
+        q_input = hidden_states.new_empty(
+            q_len, self.num_local_heads, self.kv_lora_rank + self.qk_rope_head_dim
+        )
+        if self.q_lora_rank is not None:
+            q = self.q_a_proj(hidden_states)[0]
+            q = self.q_a_layernorm(q)
+            q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
+        else:
+            q = self.q_proj(hidden_states)[0].view(
+                -1, self.num_local_heads, self.qk_head_dim
+            )
+        q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+
+        if self.w_kc.dtype == torch.float8_e4m3fn:
+            q_nope_val, q_nope_scale = input_to_float8(
+                q_nope.transpose(0, 1), torch.float8_e4m3fn
+            )
+            q_nope_out = bmm_fp8(
+                q_nope_val, self.w_kc, q_nope_scale, self.w_scale, torch.bfloat16
+            )
+        else:
+            q_nope_out = torch.bmm(q_nope.transpose(0, 1), self.w_kc)
+        q_input[..., : self.kv_lora_rank] = q_nope_out.transpose(0, 1)
+
+        latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
+        v_input = latent_cache[..., : self.kv_lora_rank]
+        v_input = self.kv_a_layernorm(v_input.contiguous()).unsqueeze(1)
+        k_input = latent_cache.unsqueeze(1)
+        k_input[..., : self.kv_lora_rank] = v_input
+        k_pe = k_input[..., self.kv_lora_rank :]
+
+        original_shapes = [q_pe.shape, k_pe.shape]
+        q_pe, k_pe = self.rotary_emb(
+            positions, q_pe.reshape(q_pe.shape[0], -1), k_pe.reshape(k_pe.shape[0], -1)
+        )
+        q_pe, k_pe = q_pe.view(original_shapes[0]), k_pe.view(original_shapes[1])
+        q_input[..., self.kv_lora_rank :] = q_pe
+        k_input[..., self.kv_lora_rank :] = k_pe
+
+        attn_output = self.attn(q_input, k_input, v_input, input_metadata)
+        attn_output = attn_output.view(-1, self.num_local_heads, self.kv_lora_rank)
+
+        if self.w_vc.dtype == torch.float8_e4m3fn:
+            attn_output_val, attn_output_scale = input_to_float8(
+                attn_output.transpose(0, 1), torch.float8_e4m3fn
+            )
+            attn_bmm_output = bmm_fp8(
+                attn_output_val,
+                self.w_vc,
+                attn_output_scale,
+                self.w_scale,
+                torch.bfloat16,
+            )
+        else:
+            attn_bmm_output = torch.bmm(attn_output.transpose(0, 1), self.w_vc)
+        attn_output = attn_bmm_output.transpose(0, 1).flatten(1, 2)
+        output, _ = self.o_proj(attn_output)
+
+        return output
+
+
+class MiniCPM3DecoderLayer(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        layer_id: int,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        rope_theta = getattr(config, "rope_theta", 10000)
+        rope_scaling = getattr(config, "rope_scaling", None)
+        max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
+        if global_server_args_dict["enable_mla"]:
+            self.self_attn = MiniCPM3AttentionMLA(
+                config=config,
+                hidden_size=self.hidden_size,
+                num_heads=config.num_attention_heads,
+                qk_nope_head_dim=config.qk_nope_head_dim,
+                qk_rope_head_dim=config.qk_rope_head_dim,
+                v_head_dim=self.hidden_size // config.num_attention_heads,
+                q_lora_rank=(
+                    config.q_lora_rank if hasattr(config, "q_lora_rank") else None
+                ),
+                kv_lora_rank=config.kv_lora_rank,
+                rope_theta=rope_theta,
+                rope_scaling=rope_scaling,
+                max_position_embeddings=max_position_embeddings,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                layer_id=layer_id,
+            )
+        else:
+            self.self_attn = MiniCPM3Attention(
+                config=config,
+                hidden_size=self.hidden_size,
+                num_heads=config.num_attention_heads,
+                qk_nope_head_dim=config.qk_nope_head_dim,
+                qk_rope_head_dim=config.qk_rope_head_dim,
+                v_head_dim=self.hidden_size // config.num_attention_heads,
+                q_lora_rank=(
+                    config.q_lora_rank if hasattr(config, "q_lora_rank") else None
+                ),
+                kv_lora_rank=config.kv_lora_rank,
+                rope_theta=rope_theta,
+                rope_scaling=rope_scaling,
+                max_position_embeddings=max_position_embeddings,
+                cache_config=cache_config,
+                quant_config=quant_config,
+                layer_id=layer_id,
+            )
+        self.mlp = MiniCPM3MLP(
+            hidden_size=self.hidden_size,
+            intermediate_size=config.intermediate_size,
+            hidden_act=config.hidden_act,
+            quant_config=quant_config,
+        )
+        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,
+        input_metadata: InputMetadata,
+        residual: Optional[torch.Tensor],
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # Self Attention
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        hidden_states = self.self_attn(
+            positions=positions,
+            hidden_states=hidden_states,
+            input_metadata=input_metadata,
+        )
+        hidden_states = residual + hidden_states * (
+            self.config.scale_depth / math.sqrt(self.config.num_hidden_layers)
+        )
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states * (
+            self.config.scale_depth / math.sqrt(self.config.num_hidden_layers)
+        )
+
+        return hidden_states, None
+
+
+class MiniCPM3Model(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        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(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+        self.layers = nn.ModuleList(
+            [
+                MiniCPM3DecoderLayer(
+                    config, i, cache_config=cache_config, quant_config=quant_config
+                )
+                for i in range(config.num_hidden_layers)
+            ]
+        )
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        input_metadata: InputMetadata,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        if input_embeds is None:
+            hidden_states = self.embed_tokens(input_ids) * self.config.scale_emb
+        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,
+                input_metadata,
+                residual,
+            )
+        hidden_states = self.norm(hidden_states)
+        return hidden_states
+
+
+class MiniCPM3ForCausalLM(nn.Module):
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+    ) -> None:
+        super().__init__()
+        self.config = config
+
+        self.num_experts = getattr(self.config, "num_experts", 0)
+        self.quant_config = quant_config
+        self.model = MiniCPM3Model(
+            config, cache_config=cache_config, quant_config=quant_config
+        )
+        # self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
+        if not self.config.tie_word_embeddings:
+            self.lm_head = ParallelLMHead(
+                config.vocab_size,
+                config.hidden_size,
+                org_num_embeddings=config.vocab_size,
+            )
+
+        self.scale_width = self.config.hidden_size / self.config.dim_model_base
+
+        self.logits_processor = LogitsProcessor(config)
+        self.sampler = Sampler()
+
+    @torch.no_grad()
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        input_metadata: InputMetadata,
+        input_embeds: torch.Tensor = None,
+    ) -> torch.Tensor:
+        if input_embeds is not None:
+            input_embeds = input_embeds * self.config.scale_emb
+        hidden_states = self.model(input_ids, positions, input_metadata, input_embeds)
+        hidden_states = hidden_states / self.scale_width
+        if self.config.tie_word_embeddings:
+            lm_head_weight = self.model.embed_tokens.weight
+        else:
+            lm_head_weight = self.lm_head.weight
+        logits_output = self.logits_processor(
+            input_ids, hidden_states, lm_head_weight, input_metadata
+        )
+        sample_output = self.sampler(logits_output, input_metadata.sampling_info)
+        return sample_output, logits_output
+
+    def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("gate_up_proj", "gate_proj", 0),
+            ("gate_up_proj", "up_proj", 1),
+        ]
+        expert_params_mapping = [
+            # (param_name, weight_name, expert_id)
+            (
+                "ws" if weight_name in ["w1", "w3"] else "w2s",
+                f"experts.{expert_id}.{weight_name}.weight",
+                expert_id,
+            )
+            for expert_id in range(self.num_experts)
+            for weight_name in ["w1", "w2", "w3"]
+        ]
+        params_dict = dict(self.named_parameters())
+        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
+
+            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:
+                for param_name, weight_name, expert_id in expert_params_mapping:
+                    if weight_name not in name:
+                        continue
+                    name = name.replace(weight_name, param_name)
+                    param = params_dict[name]
+                    weight_loader = param.weight_loader
+                    weight_loader(
+                        param, loaded_weight, weight_name, expert_id=expert_id
+                    )
+                    break
+                else:
+                    # Skip loading extra bias for GPTQ models.
+                    if name.endswith(".bias") 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 global_server_args_dict["enable_mla"]:
+            for layer_id in range(self.config.num_hidden_layers):
+                self_attn = self.model.layers[layer_id].self_attn
+                w_kc, w_vc = self_attn.kv_b_proj.weight.unflatten(
+                    0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
+                ).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
+                self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
+                self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
+                if hasattr(self_attn.kv_b_proj, "weight_scale"):
+                    self_attn.w_scale = self_attn.kv_b_proj.weight_scale
+                del self_attn.kv_b_proj
+
+
+EntryClass = MiniCPM3ForCausalLM