From 322f2c81854fe724b3c2f0d36e36382661df6bc7 Mon Sep 17 00:00:00 2001 From: wangpeng66 Date: Tue, 29 Apr 2025 12:52:45 +0000 Subject: [PATCH] [Model]: support mimo model Signed-off-by: wp-alpha --- docs/source/models/supported_models.md | 5 + tests/models/registry.py | 2 + vllm/config.py | 20 +- vllm/model_executor/models/mimo.py | 190 +++++++++++++++++ vllm/model_executor/models/mimo_mtp.py | 283 +++++++++++++++++++++++++ vllm/model_executor/models/registry.py | 2 + vllm/worker/worker.py | 6 +- 7 files changed, 504 insertions(+), 4 deletions(-) create mode 100644 vllm/model_executor/models/mimo.py create mode 100644 vllm/model_executor/models/mimo_mtp.py diff --git a/docs/source/models/supported_models.md b/docs/source/models/supported_models.md index 98b7d76313d..12700143850 100644 --- a/docs/source/models/supported_models.md +++ b/docs/source/models/supported_models.md @@ -585,6 +585,11 @@ See [this page](#generative-models) for more information on how to use generativ * `Zyphra/Zamba2-7B-instruct`, `Zyphra/Zamba2-2.7B-instruct`, `Zyphra/Zamba2-1.2B-instruct`, etc. * * +- * `MiMoForCausalLM` + * MiMo + * `XiaomiMiMo/MiMo-7B-RL`, etc. + * + * ::: :::{note} diff --git a/tests/models/registry.py b/tests/models/registry.py index a3c5bc865d0..70ad26717ec 100644 --- a/tests/models/registry.py +++ b/tests/models/registry.py @@ -242,6 +242,8 @@ def check_available_online( is_available_online=False, trust_remote_code=True), "Zamba2ForCausalLM": _HfExamplesInfo("Zyphra/Zamba2-7B-instruct"), + "MiMoForCausalLM": _HfExamplesInfo("XiaomiMiMo/MiMo-7B-RL", + trust_remote_code=True), # [Encoder-decoder] "BartModel": _HfExamplesInfo("facebook/bart-base"), "BartForConditionalGeneration": _HfExamplesInfo("facebook/bart-large-cnn"), diff --git a/vllm/config.py b/vllm/config.py index c1c72846d93..28263a95e8a 100644 --- a/vllm/config.py +++ b/vllm/config.py @@ -1107,7 +1107,8 @@ def get_num_attention_heads(self, def get_layers_start_end_indices( self, parallel_config: "ParallelConfig") -> tuple[int, int]: from vllm.distributed.utils import get_pp_indices - if self.hf_text_config.model_type == "deepseek_mtp": + if (self.hf_text_config.model_type == "deepseek_mtp" + or self.hf_config.model_type == "mimo_mtp"): total_num_hidden_layers = getattr(self.hf_text_config, "num_nextn_predict_layers", 0) else: @@ -2290,6 +2291,17 @@ def hf_config_override(hf_config: PretrainedConfig) -> PretrainedConfig: "n_predict": n_predict, "architectures": ["DeepSeekMTPModel"] }) + + if hf_config.architectures[0] == "MiMoForCausalLM": + hf_config.model_type = "mimo_mtp" + n_predict = getattr(hf_config, "num_nextn_predict_layers", None) + hf_config.update({ + "num_hidden_layers": 0, + "n_predict": n_predict, + "architectures": ["MiMoMTPModel"] + }) + return hf_config + return hf_config def __post_init__(self): @@ -2306,8 +2318,10 @@ def __post_init__(self): # TODO(Shangming): Refactor mtp configuration logic when supporting # mtp acceleration for more models besides deepseek_v3 if self.target_model_config and \ - self.target_model_config.hf_text_config.model_type \ - == "deepseek_v3": + (self.target_model_config.hf_text_config.model_type \ + == "deepseek_v3" or + self.target_model_config.hf_text_config.model_type \ + == "mimo"): # use the draft model from the same model: self.model = self.target_model_config.model elif self.method in ("ngram", "[ngram]"): diff --git a/vllm/model_executor/models/mimo.py b/vllm/model_executor/models/mimo.py new file mode 100644 index 00000000000..b882aeebb08 --- /dev/null +++ b/vllm/model_executor/models/mimo.py @@ -0,0 +1,190 @@ +# SPDX-License-Identifier: Apache-2.0 + +# Adapted from +# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/qwen2/modeling_qwen2.py +# Copyright 2025 Xiaomi Corporation. +# Copyright 2024 The Qwen 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 MiMo model compatible with HuggingFace weights.""" +from typing import Iterable, Optional, Set, Tuple, Union + +import torch +import torch.nn as nn + +from vllm.compilation.decorators import support_torch_compile +from vllm.config import VllmConfig +from vllm.distributed import get_pp_group +from vllm.logger import init_logger +from vllm.model_executor.layers.logits_processor import LogitsProcessor +from vllm.model_executor.layers.sampler import get_sampler +from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead +from vllm.model_executor.model_loader.weight_utils import ( + default_weight_loader, maybe_remap_kv_scale_name) +from vllm.model_executor.models.qwen2 import Qwen2ForCausalLM, Qwen2Model +from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.sequence import IntermediateTensors + +from .utils import PPMissingLayer, is_pp_missing_parameter, maybe_prefix + +logger = init_logger(__name__) + + +@support_torch_compile( + dynamic_arg_dims={ + "input_ids": 0, + "positions": -1, + "intermediate_tensors": 0, + "inputs_embeds": 0, + }) +class MiMoModel(Qwen2Model): + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + intermediate_tensors: Optional[IntermediateTensors] = None, + inputs_embeds: Optional[torch.Tensor] = None, + ) -> Union[torch.Tensor, IntermediateTensors]: + if get_pp_group().is_first_rank: + if inputs_embeds is not None: + hidden_states = inputs_embeds + else: + hidden_states = self.get_input_embeddings(input_ids) + residual = None + else: + assert intermediate_tensors is not None + hidden_states = intermediate_tensors["hidden_states"] + residual = intermediate_tensors["residual"] + for layer in self.layers[self.start_layer:self.end_layer]: + hidden_states, residual = layer( + positions, + hidden_states, + residual, + ) + if not get_pp_group().is_last_rank: + return IntermediateTensors({ + "hidden_states": hidden_states, + "residual": residual + }) + hidden_states = hidden_states + residual + return hidden_states + + def load_weights(self, weights: Iterable[Tuple[str, + torch.Tensor]]) -> Set[str]: + stacked_params_mapping = [ + ("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), + ] + params_dict = dict(self.named_parameters(remove_duplicate=False)) + loaded_params: Set[str] = set() + for name, loaded_weight in weights: + if "mtp_layers" in name: + continue + if "rotary_emb.inv_freq" in name: + continue + if (self.quant_config is not None and + (scale_name := self.quant_config.get_cache_scale(name))): + # Loading kv cache quantization scales + param = params_dict[scale_name] + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else + loaded_weight[0]) + weight_loader(param, loaded_weight) + loaded_params.add(scale_name) + 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: + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + # Remapping the name of FP8 kv-scale. + name = maybe_remap_kv_scale_name(name, params_dict) + if name is None: + 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 MiMoForCausalLM(Qwen2ForCausalLM, nn.Module): + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + nn.Module.__init__(self) + config = vllm_config.model_config.hf_config + quant_config = vllm_config.quant_config + lora_config = vllm_config.lora_config + + self.config = config + self.lora_config = lora_config + + self.quant_config = quant_config + + self.model = MiMoModel(vllm_config=vllm_config, + prefix=maybe_prefix(prefix, "model")) + + if get_pp_group().is_last_rank: + if config.tie_word_embeddings: + self.lm_head = self.model.embed_tokens + else: + self.lm_head = ParallelLMHead(config.vocab_size, + config.hidden_size, + quant_config=quant_config, + prefix=maybe_prefix( + prefix, "lm_head")) + else: + self.lm_head = PPMissingLayer() + + self.logits_processor = LogitsProcessor(config.vocab_size) + self.sampler = get_sampler() + + self.make_empty_intermediate_tensors = ( + self.model.make_empty_intermediate_tensors) + + def compute_logits( + self, + hidden_states: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> Optional[torch.Tensor]: + hidden_states = self.model.norm(hidden_states) + logits = self.logits_processor(self.lm_head, hidden_states, + sampling_metadata) + return logits diff --git a/vllm/model_executor/models/mimo_mtp.py b/vllm/model_executor/models/mimo_mtp.py new file mode 100644 index 00000000000..c2f1cf4112f --- /dev/null +++ b/vllm/model_executor/models/mimo_mtp.py @@ -0,0 +1,283 @@ +# SPDX-License-Identifier: Apache-2.0 + +# Adapted from +# https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/model_executor/models/deepseek_mtp.py +# Copyright 2025 Xiaomi Corporation. +# Copyright 2023 The vLLM team. +# Copyright 2024 DeepSeek-AI 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 MiMo-MTP model.""" +from typing import Iterable, Optional, Set, Tuple + +import torch +import torch.nn as nn +from transformers import PretrainedConfig + +from vllm.config import CacheConfig, ModelConfig, 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.sampler import SamplerOutput, get_sampler +from vllm.model_executor.layers.vocab_parallel_embedding import ( + ParallelLMHead, VocabParallelEmbedding) +from vllm.model_executor.model_loader.weight_utils import default_weight_loader +from vllm.model_executor.models.qwen2 import Qwen2DecoderLayer +from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.sequence import IntermediateTensors + +from .utils import maybe_prefix + + +class MiMoMultiTokenPredictorLayer(nn.Module): + + def __init__( + self, + config: PretrainedConfig, + prefix: str, + model_config: ModelConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + ) -> None: + super().__init__() + + self.token_layernorm = RMSNorm(config.hidden_size, + eps=config.rms_norm_eps) + self.hidden_layernorm = RMSNorm(config.hidden_size, + eps=config.rms_norm_eps) + self.input_proj = nn.Linear(config.hidden_size * 2, + config.hidden_size, + bias=False) + self.mtp_block = Qwen2DecoderLayer(config=config, + cache_config=cache_config, + quant_config=quant_config, + prefix=prefix) + self.final_layernorm = RMSNorm(config.hidden_size, + eps=config.rms_norm_eps) + + def forward( + self, + inputs_embeds: torch.Tensor, + positions: torch.Tensor, + previous_hidden_states: torch.Tensor, + spec_step_index: int = 0, + ) -> torch.Tensor: + assert inputs_embeds is not None + # masking inputs at position 0, as not needed by MTP + inputs_embeds[positions == 0] = 0 + inputs_embeds = self.token_layernorm(inputs_embeds) + previous_hidden_states = self.hidden_layernorm(previous_hidden_states) + + hidden_states = self.input_proj( + torch.cat([previous_hidden_states, inputs_embeds], dim=-1)) + + hidden_states, residual = self.mtp_block(positions=positions, + hidden_states=hidden_states, + residual=None) + hidden_states = residual + hidden_states + return self.final_layernorm(hidden_states) + + +class MiMoMultiTokenPredictor(nn.Module): + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__() + + config = vllm_config.model_config.hf_config + self.mtp_start_layer_idx = config.num_hidden_layers + self.num_mtp_layers = config.num_nextn_predict_layers + + self.embed_tokens = VocabParallelEmbedding( + config.vocab_size, + config.hidden_size, + ) + + self.mtp_layers = torch.nn.ModuleDict({ + str(idx): + MiMoMultiTokenPredictorLayer( + config, + f"{prefix}.layers.{idx}", + model_config=vllm_config.model_config, + cache_config=vllm_config.cache_config, + quant_config=vllm_config.quant_config, + ) + for idx in range(self.mtp_start_layer_idx, + self.mtp_start_layer_idx + self.num_mtp_layers) + }) + + self.logits_processor = LogitsProcessor(config.vocab_size) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + previous_hidden_states: torch.Tensor, + inputs_embeds: Optional[torch.Tensor] = None, + spec_step_idx: int = 0, + ) -> torch.Tensor: + + if inputs_embeds is None: + inputs_embeds = self.embed_tokens(input_ids) + return self.mtp_layers[str(self.mtp_start_layer_idx + spec_step_idx)]( + inputs_embeds, + positions, + previous_hidden_states, + spec_step_idx, + ) + + def compute_logits( + self, + hidden_states: torch.Tensor, + lm_head: ParallelLMHead, + sampling_metadata: SamplingMetadata, + spec_step_idx: int = 0, + ) -> torch.Tensor: + self.mtp_layers[str(self.mtp_start_layer_idx + spec_step_idx)] + logits = self.logits_processor(lm_head, hidden_states, + sampling_metadata) + return logits + + +class MiMoMTP(nn.Module): + + def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): + super().__init__() + self.config = vllm_config.model_config.hf_config + self.model = MiMoMultiTokenPredictor(vllm_config=vllm_config, + prefix=maybe_prefix( + prefix, "model")) + self.lm_head = ParallelLMHead(self.config.vocab_size, + self.config.hidden_size) + + self.sampler = get_sampler() + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + previous_hidden_states: torch.Tensor, + intermediate_tensors: Optional[IntermediateTensors] = None, + inputs_embeds: Optional[torch.Tensor] = None, + spec_step_idx: int = 0, + ) -> torch.Tensor: + assert spec_step_idx == 0, "mimo_mtp only support predict one token now" + hidden_states = self.model(input_ids, positions, + previous_hidden_states, inputs_embeds, + spec_step_idx) + return hidden_states + + def compute_logits( + self, + hidden_states: torch.Tensor, + sampling_metadata: SamplingMetadata, + spec_step_idx: int = 0, + ) -> Optional[torch.Tensor]: + return self.model.compute_logits(hidden_states, self.lm_head, + sampling_metadata, spec_step_idx) + + def sample( + self, + logits: torch.Tensor, + sampling_metadata: SamplingMetadata, + ) -> Optional[SamplerOutput]: + next_tokens = self.sampler(logits, sampling_metadata) + return next_tokens + + def load_weights(self, weights: Iterable[Tuple[str, + torch.Tensor]]) -> Set[str]: + stacked_params_mapping = [ + ("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), + ] + + 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 + name = self.map_model_name_to_mtp_param_name(name) + + for (param_name, weight_name, shard_id) in stacked_params_mapping: + # Skip non-stacked layers and experts (experts handled below). + if weight_name not in name: + continue + if "mtp_layers" not in name: + break + # We have mlp.experts[0].gate_proj in the checkpoint. + # Since we handle the experts below in expert_params_mapping, + # we need to skip here BEFORE we update the name, otherwise + # name will be updated to mlp.experts[0].gate_up_proj, which + # will then be updated below in expert_params_mapping + # for mlp.experts[0].gate_gate_up_proj, which breaks load. + if (("mlp.experts." in name) and name not in params_dict): + 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: + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + if "mtp_layers" not in name and ("embed_tokens" not in name + and "lm_head" not in name): + 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 + + def map_model_name_to_mtp_param_name(self, name: str) -> str: + import re + name_without_prefix = [ + "token_layernorm", "hidden_layernorm", "input_proj", + "final_layernorm" + ] + for sub_name in name_without_prefix: + if sub_name in name: + return name + pattern = r"model.mtp_layers.(\d+)." + group = re.match(pattern, name) + if group is not None: + name = name.replace(group.group(), group.group() + "mtp_block.") + return name + + def _rewrite_spec_layer_name(self, spec_layer: int, name: str) -> str: + """ + Rewrite the weight name to match the format of the original model. + Add .mtp_block for modules in transformer layer block for spec layer + """ + spec_layer_weight_names = [ + "embed_tokens", "enorm", "hnorm", "eh_proj", "shared_head" + ] + spec_layer_weight = False + for weight_name in spec_layer_weight_names: + if weight_name in name: + spec_layer_weight = True + break + if not spec_layer_weight: + # treat rest weights as weights for transformer layer block + name = name.replace(f"model.layers.{spec_layer}.", + f"model.layers.{spec_layer}.mtp_block.") + return name diff --git a/vllm/model_executor/models/registry.py b/vllm/model_executor/models/registry.py index df5b2323212..8b055f69414 100644 --- a/vllm/model_executor/models/registry.py +++ b/vllm/model_executor/models/registry.py @@ -88,6 +88,7 @@ # transformers's mpt class has lower case "MptForCausalLM": ("mpt", "MPTForCausalLM"), "MPTForCausalLM": ("mpt", "MPTForCausalLM"), + "MiMoForCausalLM": ("mimo", "MiMoForCausalLM"), "NemotronForCausalLM": ("nemotron", "NemotronForCausalLM"), "OlmoForCausalLM": ("olmo", "OlmoForCausalLM"), "Olmo2ForCausalLM": ("olmo2", "Olmo2ForCausalLM"), @@ -214,6 +215,7 @@ } _SPECULATIVE_DECODING_MODELS = { + "MiMoMTPModel": ("mimo_mtp", "MiMoMTP"), "EAGLEModel": ("eagle", "EAGLE"), "EagleLlamaForCausalLM": ("llama_eagle", "EagleLlamaForCausalLM"), "Eagle3LlamaForCausalLM": ("llama_eagle3", "Eagle3LlamaForCausalLM"), diff --git a/vllm/worker/worker.py b/vllm/worker/worker.py index 78ea990de82..3ef1a003396 100644 --- a/vllm/worker/worker.py +++ b/vllm/worker/worker.py @@ -71,7 +71,11 @@ def __init__( or (speculative_config.draft_model_config.hf_config.model_type == model_config.hf_config.model_type) \ or (speculative_config.draft_model_config.hf_config.model_type - not in ("medusa", "mlp_speculator", "eagle", "deepseek_mtp")) \ + not in ("medusa", + "mlp_speculator", + "eagle", + "deepseek_mtp", + "mimo_mtp")) \ else {"return_hidden_states": True} ModelRunnerClass: Type[GPUModelRunnerBase] = ModelRunner