[Feature] minicpm eagle support (#18943)

Signed-off-by: huangyuxiang03 <huangyx0321@gmail.com>
Co-authored-by: huangyuxiang03 <huangyx0321@gmail.com>

tests/models/registry.py

@@ -434,6 +434,11 @@ _SPECULATIVE_DECODING_EXAMPLE_MODELS = {
                                             trust_remote_code=True,
                                             speculative_model="yuhuili/EAGLE3-LLaMA3.1-Instruct-8B",
                                             tokenizer="meta-llama/Llama-3.1-8B-Instruct"),
+    "EagleMiniCPMForCausalLM": _HfExamplesInfo("openbmb/MiniCPM-1B-sft-bf16",
+                                            trust_remote_code=True,
+                                            is_available_online=False,
+                                            speculative_model="openbmb/MiniCPM-2B-sft-bf16",
+                                            tokenizer="openbmb/MiniCPM-2B-sft-bf16"),
     "MiMoMTPModel": _HfExamplesInfo("XiaomiMiMo/MiMo-7B-RL",
                                     trust_remote_code=True,
                                     speculative_model="XiaomiMiMo/MiMo-7B-RL")

vllm/model_executor/models/minicpm.py

@@ -242,6 +242,7 @@ class MiniCPMAttention(nn.Module):
             base=rope_theta,
             rope_scaling=rope_scaling,
         )
+
         self.attn = Attention(self.num_heads,
                               self.head_dim,
                               self.scaling,
@@ -444,6 +445,7 @@ class MiniCPMModel(nn.Module):
             for weight_name in ["w1", "w2", "w3"]
         ]
         params_dict = dict(self.named_parameters())
+
         loaded_params: set[str] = set()
         for name, loaded_weight in weights:
             if "rotary_emb.inv_freq" in name:
@@ -567,7 +569,7 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         inputs_embeds: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, IntermediateTensors]:
         hidden_states = self.model(input_ids, positions, intermediate_tensors,
-                                   inputs_embeds)
+                                   inputs_embeds) / self.scale_width
         return hidden_states
 
     def compute_logits(
@@ -575,7 +577,6 @@ class MiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
         hidden_states: torch.Tensor,
         sampling_metadata: SamplingMetadata,
     ) -> Optional[torch.Tensor]:
-        hidden_states = hidden_states / self.scale_width
         logits = self.logits_processor(self.lm_head, hidden_states,
                                        sampling_metadata)
         return logits

vllm/model_executor/models/minicpm_eagle.py

+# SPDX-License-Identifier: Apache-2.0
+
+# Adapted from
+# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
+# 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 EagleMiniCPM model compatible with HuggingFace weights."""
+import math
+from collections.abc import Iterable
+from typing import Optional, Union
+
+import torch
+from torch import nn
+from transformers import PretrainedConfig
+
+from vllm.compilation.decorators import support_torch_compile
+from vllm.config import CacheConfig, VllmConfig
+from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.logits_processor import LogitsProcessor
+from vllm.model_executor.layers.quantization import QuantizationConfig
+from vllm.model_executor.layers.vocab_parallel_embedding import (
+    DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
+from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.sampling_metadata import SamplingMetadata
+from vllm.sequence import IntermediateTensors
+
+from .interfaces import SupportsLoRA, SupportsPP
+from .minicpm import MiniCPMAttention as EagleMiniCPMAttention
+from .minicpm import MiniCPMMLP as EagleMiniCPMMLP
+from .minicpm import MiniCPMMoE as EagleMiniCPMMoE
+from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
+                    make_empty_intermediate_tensors_factory, maybe_prefix)
+
+
+class EagleMiniCPMDecoderLayer(nn.Module):
+
+    def __init__(
+        self,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.config = config
+        self.cache_config = cache_config
+        self.quant_config = quant_config
+        self.hidden_size = config.hidden_size
+        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.prefix = prefix
+        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 = EagleMiniCPMAttention(
+            hidden_size=self.hidden_size,
+            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,
+            prefix=f"{self.prefix}.self_attn",
+        )
+
+    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 = EagleMiniCPMMLP(
+                hidden_size=self.hidden_size,
+                intermediate_size=self.config.intermediate_size,
+                hidden_act=self.config.hidden_act,
+                hidden_act_param=getattr(self.config, "hidden_act_param", 0.),
+                quant_config=self.quant_config,
+            )
+        else:
+            self.mlp = EagleMiniCPMMoE(
+                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,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+        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,
+        )
+        hidden_states = residual + hidden_states * \
+            (self.config.scale_depth / math.sqrt(self.config.mup_denominator))
+
+        # 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.mup_denominator))
+
+        return hidden_states, None
+
+
+@support_torch_compile
+class EagleMiniCPMModel(nn.Module):
+
+    def __init__(self,
+                 *,
+                 vllm_config: VllmConfig,
+                 prefix: str = "",
+                 start_layer: int = 0):
+        super().__init__()
+
+        config = vllm_config.speculative_config.draft_model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.config = config
+        self.cache_config = cache_config
+        self.quant_config = quant_config
+        lora_vocab = (lora_config.lora_extra_vocab_size *
+                      (lora_config.max_loras or 1)) if lora_config else 0
+        self.vocab_size = config.vocab_size + lora_vocab
+        self.org_vocab_size = config.vocab_size
+        self.fc = torch.nn.Linear(self.config.hidden_size * 2,
+                                  self.config.hidden_size,
+                                  bias=False)
+        self.input_norm1 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.input_norm2 = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.embed_tokens = VocabParallelEmbedding(
+            self.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+        )
+        self.num_experts = getattr(self.config, "num_experts", 0)
+        self._init_layers(prefix, config, cache_config, quant_config,
+                          start_layer)
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.make_empty_intermediate_tensors = (
+            make_empty_intermediate_tensors_factory(
+                ["hidden_states", "residual"], self.config.hidden_size))
+
+    def _init_layers(
+        self,
+        prefix: str,
+        config: PretrainedConfig,
+        cache_config: Optional[CacheConfig],
+        quant_config: Optional[QuantizationConfig],
+        start_layer: int,
+    ):
+        self.eagle_layers = nn.ModuleList([
+            EagleMiniCPMDecoderLayer(
+                config,
+                cache_config,
+                quant_config,
+                f"{prefix}.eagle_layers.{i + start_layer}",
+            ) for i in range(self.config.num_hidden_layers)
+        ])
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        embedding = self.embed_tokens(input_ids)
+        return embedding * self.config.scale_emb
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> Union[torch.Tensor, IntermediateTensors]:
+        input_embeds = self.get_input_embeddings(input_ids)
+        input_embeds = self.input_norm1(input_embeds)
+        hidden_states = self.input_norm2(hidden_states)
+
+        hidden_states = self.fc(
+            torch.cat((input_embeds, hidden_states), dim=-1))
+        residual = None
+        for layer in self.eagle_layers:
+            hidden_states, residual = layer(
+                positions,
+                hidden_states,
+                residual,
+            )
+
+        return hidden_states, hidden_states
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        stacked_params_mapping = [
+            # (param_name, shard_name, shard_id)
+            ("qkv_proj", "q_proj", "q"),
+            ("qkv_proj", "k_proj", "k"),
+            ("qkv_proj", "v_proj", "v"),
+            ("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())
+
+        loaded_params: set[str] = set()
+        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
+                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:
+                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)
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    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
+                    if is_pp_missing_parameter(name, self):
+                        continue
+                    param = params_dict[name]
+                    weight_loader = getattr(param, "weight_loader",
+                                            default_weight_loader)
+
+                    weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params
+
+
+class EagleMiniCPMForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
+    packed_modules_mapping = {
+        "qkv_proj": [
+            "q_proj",
+            "k_proj",
+            "v_proj",
+        ],
+        "gate_up_proj": [
+            "gate_proj",
+            "up_proj",
+        ],
+    }
+
+    # LoRA specific attributes
+    embedding_modules = {
+        "embed_tokens": "input_embeddings",
+        "lm_head": "output_embeddings",
+    }
+    embedding_padding_modules = ["lm_head"]
+
+    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
+        super().__init__()
+        config = vllm_config.speculative_config.draft_model_config.hf_config
+        cache_config = vllm_config.cache_config
+        quant_config = vllm_config.quant_config
+        lora_config = vllm_config.lora_config
+
+        self.prefix = prefix
+        self.vllm_config = vllm_config
+        self.config = config
+        self.lora_config = lora_config
+        self.cache_config = cache_config
+        self.quant_config = quant_config
+
+        target_layer_num = vllm_config.model_config.get_num_layers(
+            vllm_config.parallel_config)
+
+        self.model = self._init_model(vllm_config=vllm_config,
+                                      prefix=maybe_prefix(prefix, "model"),
+                                      start_layer=target_layer_num)
+
+        unpadded_vocab_size = config.vocab_size
+        if lora_config:
+            unpadded_vocab_size += lora_config.lora_extra_vocab_size
+        self.lm_head = ParallelLMHead(
+            unpadded_vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size,
+            padding_size=DEFAULT_VOCAB_PADDING_SIZE
+            # We need bigger padding if using lora for kernel
+            # compatibility
+            if not lora_config else lora_config.lora_vocab_padding_size,
+            quant_config=quant_config,
+        )
+        if config.tie_word_embeddings:
+            self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
+        self.scale_width = self.config.hidden_size / self.config.dim_model_base
+
+        self.logits_processor = LogitsProcessor(unpadded_vocab_size,
+                                                config.vocab_size)
+        self.make_empty_intermediate_tensors = (
+            self.model.make_empty_intermediate_tensors)
+
+    def _init_model(self,
+                    *,
+                    vllm_config: VllmConfig,
+                    prefix: str = "",
+                    start_layer: int = 0):
+        return EagleMiniCPMModel(vllm_config=vllm_config,
+                                 prefix=prefix,
+                                 start_layer=start_layer)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.model.get_input_embeddings(input_ids)
+
+    def forward(
+        self,
+        input_ids: torch.Tensor,
+        positions: torch.Tensor,
+        hidden_states: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        hidden_states, hidden_states2 = self.model(input_ids, positions,
+                                                   hidden_states)
+        hidden_states = hidden_states / self.scale_width
+        hidden_states2 = hidden_states2 / self.scale_width
+        return hidden_states, hidden_states2
+
+    def compute_logits(
+        self,
+        hidden_states: torch.Tensor,
+        sampling_metadata: SamplingMetadata,
+    ) -> Optional[torch.Tensor]:
+        logits = self.logits_processor(self.lm_head, hidden_states,
+                                       sampling_metadata)
+        return logits
+
+    def load_weights(self, weights: Iterable[tuple[str,
+                                                   torch.Tensor]]) -> set[str]:
+        loader = AutoWeightsLoader(
+            self,
+            skip_prefixes=(["lm_head."]
+                           if self.config.tie_word_embeddings else None),
+        )
+        return loader.load_weights(weights)

vllm/model_executor/models/registry.py

@@ -223,6 +223,7 @@ _SPECULATIVE_DECODING_MODELS = {
     "MiMoMTPModel": ("mimo_mtp", "MiMoMTP"),
     "EAGLEModel": ("eagle", "EAGLE"),
     "EagleLlamaForCausalLM": ("llama_eagle", "EagleLlamaForCausalLM"),
+    "EagleMiniCPMForCausalLM": ("minicpm_eagle", "EagleMiniCPMForCausalLM"),
     "Eagle3LlamaForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"),
     "DeepSeekMTPModel": ("deepseek_mtp", "DeepSeekMTP"),
     "MedusaModel": ("medusa", "Medusa"),