forked from EngineX-MetaX/enginex-c_series-vllm
[gpt-oss] Add gpt-oss bf16 support
This commit is contained in:
285
vllm/model_executor/models/mimo_mtp.py
Normal file
285
vllm/model_executor/models/mimo_mtp.py
Normal file
@@ -0,0 +1,285 @@
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
||||
|
||||
# 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 collections.abc import Iterable
|
||||
from typing import Optional
|
||||
|
||||
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 regex as 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
|
||||
Reference in New Issue
Block a user