[Model] support minicpm3

docs/source/models/supported_models.rst

@@ -107,6 +107,10 @@ Decoder-only Language Models
     - MiniCPM
     - :code:`openbmb/MiniCPM-2B-sft-bf16`, :code:`openbmb/MiniCPM-2B-dpo-bf16`, etc.
     -
+  * - :code:`MiniCPM3ForCausalLM`
+    - MiniCPM3
+    - :code:`openbmb/MiniCPM3-4B`
+    -
   * - :code:`MistralForCausalLM`
     - Mistral, Mistral-Instruct
     - :code:`mistralai/Mistral-7B-v0.1`, :code:`mistralai/Mistral-7B-Instruct-v0.1`, etc.

vllm/model_executor/models/__init__.py

@@ -43,6 +43,7 @@
     "MptForCausalLM": ("mpt", "MPTForCausalLM"),
     "MPTForCausalLM": ("mpt", "MPTForCausalLM"),
     "MiniCPMForCausalLM": ("minicpm", "MiniCPMForCausalLM"),
+    "MiniCPM3ForCausalLM": ("minicpm3", "MiniCPM3ForCausalLM"),
     "NemotronForCausalLM": ("nemotron", "NemotronForCausalLM"),
     "OlmoForCausalLM": ("olmo", "OlmoForCausalLM"),
     "OPTForCausalLM": ("opt", "OPTForCausalLM"),

vllm/model_executor/models/minicpm.py

@@ -270,38 +270,47 @@ def __init__(
     ) -> None:
         super().__init__()
         self.config = config
+        self.cache_config = cache_config
+        self.quant_config = quant_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)
+        self.rope_theta = getattr(config, "rope_theta", 10000)
+        self.rope_scaling = getattr(config, "rope_scaling", None)
+        self.max_position_embeddings = getattr(config,
+                                               "max_position_embeddings", 8192)
+        self._init_attn_block()
+        self._init_ffn_block()
+
+    def _init_attn_block(self):
+        self.input_layernorm = RMSNorm(self.config.hidden_size,
+                                       eps=self.config.rms_norm_eps)
         self.self_attn = MiniCPMAttention(
             hidden_size=self.hidden_size,
-            num_heads=config.num_attention_heads,
-            num_kv_heads=config.num_key_value_heads,
-            rope_theta=rope_theta,
-            rope_scaling=rope_scaling,
-            max_position_embeddings=max_position_embeddings,
-            cache_config=cache_config,
-            quant_config=quant_config,
+            num_heads=self.config.num_attention_heads,
+            num_kv_heads=self.config.num_key_value_heads,
+            rope_theta=self.rope_theta,
+            rope_scaling=self.rope_scaling,
+            max_position_embeddings=self.max_position_embeddings,
+            cache_config=self.cache_config,
+            quant_config=self.quant_config,
         )
+
+    def _init_ffn_block(self):
+        self.post_attention_layernorm = RMSNorm(self.config.hidden_size,
+                                                eps=self.config.rms_norm_eps)
         self.num_experts = getattr(self.config, "num_experts", 0)
         if self.num_experts == 0:
             self.mlp = MiniCPMMLP(
                 hidden_size=self.hidden_size,
-                intermediate_size=config.intermediate_size,
-                hidden_act=config.hidden_act,
-                quant_config=quant_config,
+                intermediate_size=self.config.intermediate_size,
+                hidden_act=self.config.hidden_act,
+                quant_config=self.quant_config,
             )
         else:
-            self.mlp = MiniCPMMoE(num_experts=config.num_experts,
-                                  top_k=config.num_experts_per_tok,
-                                  hidden_size=config.hidden_size,
-                                  intermediate_size=config.intermediate_size)
-        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)
+            self.mlp = MiniCPMMoE(
+                num_experts=self.config.num_experts,
+                top_k=self.config.num_experts_per_tok,
+                hidden_size=self.config.hidden_size,
+                intermediate_size=self.config.intermediate_size)
 
     def forward(
         self,
@@ -344,6 +353,8 @@ def __init__(
     ) -> None:
         super().__init__()
         self.config = config
+        self.cache_config = cache_config
+        self.quant_config = quant_config
         self.padding_idx = config.pad_token_id
         lora_vocab = (lora_config.lora_extra_vocab_size *
                       (lora_config.max_loras or 1)) if lora_config else 0
@@ -354,11 +365,15 @@ def __init__(
             config.hidden_size,
             org_num_embeddings=config.vocab_size,
         )
+        self._init_layers()
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def _init_layers(self):
         self.layers = nn.ModuleList([
-            MiniCPMDecoderLayer(config, cache_config, quant_config)
-            for _ in range(config.num_hidden_layers)
+            MiniCPMDecoderLayer(self.config, self.cache_config,
+                                self.quant_config)
+            for _ in range(self.config.num_hidden_layers)
         ])
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
 
     def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
         embedding = self.embed_tokens(input_ids)
@@ -431,13 +446,11 @@ def __init__(
 
         self.config = config
         self.lora_config = lora_config
+        self.cache_config = cache_config
+        self.quant_config = quant_config
 
         self.num_experts = getattr(self.config, "num_experts", 0)
-        self.quant_config = quant_config
-        self.model = MiniCPMModel(config,
-                                  cache_config,
-                                  quant_config,
-                                  lora_config=lora_config)
+        self._init_model()
         unpadded_vocab_size = config.vocab_size
         if lora_config:
             unpadded_vocab_size += lora_config.lora_extra_vocab_size
@@ -458,6 +471,12 @@ def __init__(
                                                 config.vocab_size)
         self.sampler = Sampler()
 
+    def _init_model(self):
+        self.model = MiniCPMModel(config=self.config,
+                                  cache_config=self.cache_config,
+                                  quant_config=self.quant_config,
+                                  lora_config=self.lora_config)
+
     def forward(
         self,
         input_ids: torch.Tensor,

vllm/model_executor/models/minicpm3.py

@@ -0,0 +1,216 @@
+# coding=utf-8
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# Copyright 2024 The ModelBest team.
+# Copyright 2023 The vLLM team.
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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."""
+from typing import Any, Dict, Optional
+
+import torch
+from torch import nn
+
+from vllm.attention import Attention, AttentionMetadata
+from vllm.config import CacheConfig
+from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               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.models.minicpm import (MiniCPMDecoderLayer,
+                                                MiniCPMForCausalLM,
+                                                MiniCPMModel)
+
+
+class MiniCPM3Attention(nn.Module):
+
+    def __init__(
+        self,
+        config,
+        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,
+    ) -> None:
+        super().__init__()
+        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 self.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
+
+        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)
+
+        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(
+            self.qk_rope_head_dim,
+            rotary_dim=self.qk_rope_head_dim,
+            max_position=max_position_embeddings,
+            base=rope_theta,
+            rope_scaling=rope_scaling,
+        )
+        self.attn = Attention(self.num_local_heads,
+                              self.qk_head_dim,
+                              self.scaling,
+                              num_kv_heads=self.num_local_heads,
+                              cache_config=cache_config,
+                              quant_config=quant_config)
+
+    def forward(
+        self,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        kv_cache: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        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)
+        _, 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:]
+
+        q_pe, k_pe = self.rotary_emb(
+            positions,
+            q_pe.reshape(-1, self.num_local_heads * self.qk_rope_head_dim),
+            k_pe.reshape(-1, self.qk_rope_head_dim))
+        q_pe = q_pe.view(-1, self.num_local_heads, self.qk_rope_head_dim)
+        k_pe = k_pe.view(-1, 1, self.qk_rope_head_dim)
+
+        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 = q.reshape(-1, self.num_local_heads * self.qk_head_dim)
+        k = k.view(-1, self.num_local_heads * self.qk_head_dim)
+        v = torch.nn.functional.pad(
+            v, [0, self.qk_head_dim - self.v_head_dim],
+            value=0).view(-1, self.num_local_heads * self.qk_head_dim)
+
+        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
+        attn_output = attn_output.view(
+            -1, self.num_local_heads,
+            self.qk_head_dim)[..., :self.v_head_dim].reshape(
+                -1, self.num_local_heads * self.v_head_dim)
+
+        output, _ = self.o_proj(attn_output)
+        return output
+
+
+class MiniCPM3DecoderLayer(MiniCPMDecoderLayer):
+
+    def _init_attn_block(self):
+        self.input_layernorm = RMSNorm(self.config.hidden_size,
+                                       eps=self.config.rms_norm_eps)
+        self.self_attn = MiniCPM3Attention(
+            config=self.config,
+            hidden_size=self.hidden_size,
+            num_heads=self.config.num_attention_heads,
+            qk_nope_head_dim=self.config.qk_nope_head_dim,
+            qk_rope_head_dim=self.config.qk_rope_head_dim,
+            v_head_dim=self.config.v_head_dim,
+            q_lora_rank=self.config.q_lora_rank,
+            kv_lora_rank=self.config.kv_lora_rank,
+            rope_theta=self.rope_theta,
+            rope_scaling=self.rope_scaling,
+            max_position_embeddings=self.max_position_embeddings,
+            cache_config=self.cache_config,
+            quant_config=self.quant_config,
+        )
+
+
+class MiniCPM3Model(MiniCPMModel):
+
+    def _init_layers(self):
+        self.layers = nn.ModuleList([
+            MiniCPM3DecoderLayer(self.config, self.cache_config,
+                                 self.quant_config)
+            for _ in range(self.config.num_hidden_layers)
+        ])
+
+
+class MiniCPM3ForCausalLM(MiniCPMForCausalLM):
+
+    def _init_model(self):
+        self.model = MiniCPM3Model(config=self.config,
+                                   cache_config=self.cache_config,
+                                   quant_config=self.quant_config,
+                                   lora_config=self.lora_config)