205 lines
7.1 KiB
Python
205 lines
7.1 KiB
Python
# Adapted from https://github.com/vllm-project/vllm/pull/17433/files and deepseek_nextn.py
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from functools import partial
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from typing import Any, Dict, Iterable, Optional, Tuple
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from sglang.srt.distributed import get_tensor_model_parallel_world_size
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.model_loader.weight_utils import default_weight_loader
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from sglang.srt.models.qwen2 import Qwen2DecoderLayer
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class MiMoMultiTokenPredictorLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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prefix: str,
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quant_config: Optional[QuantizationConfig] = None,
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) -> None:
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super().__init__()
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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)
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self.token_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.hidden_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.input_proj = nn.Linear(
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config.hidden_size * 2, config.hidden_size, bias=False
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)
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self.mtp_block = Qwen2DecoderLayer(
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config=config, quant_config=quant_config, prefix=prefix
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)
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self.final_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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) -> torch.Tensor:
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if input_embeds is None:
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hidden_states = self.embed_tokens(input_ids)
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else:
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hidden_states = input_embeds
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# masking inputs at position 0, as not needed by MTP
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hidden_states[positions == 0] = 0
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hidden_states = self.input_proj(
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torch.cat(
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(
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self.hidden_layernorm(forward_batch.spec_info.hidden_states),
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self.token_layernorm(hidden_states),
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),
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dim=-1,
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)
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)
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hidden_states, residual = self.mtp_block(
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positions=positions,
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hidden_states=hidden_states,
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forward_batch=forward_batch,
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residual=None,
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)
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hidden_states = residual + hidden_states
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states
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class MiMoMTP(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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nn.Module.__init__(self)
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self.config = config
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self.tp_size = get_tensor_model_parallel_world_size()
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self.quant_config = quant_config
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self.model = MiMoMultiTokenPredictorLayer(
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config,
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prefix,
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quant_config,
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)
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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)
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self.logits_processor = LogitsProcessor(config)
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@torch.no_grad()
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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) -> torch.Tensor:
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hidden_states = self.model(input_ids, positions, forward_batch)
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return self.logits_processor(
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input_ids, hidden_states, self.lm_head, forward_batch
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)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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("qkv_proj", "q_proj", "q"),
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("qkv_proj", "k_proj", "k"),
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("qkv_proj", "v_proj", "v"),
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("gate_up_proj", "gate_proj", 0),
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("gate_up_proj", "up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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for name, loaded_weight in weights:
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if "rotary_emb.inv_freq" in name or "projector" in name:
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continue
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if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
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# Models trained using ColossalAI may include these tensors in
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# the checkpoint. Skip them.
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continue
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if self.config.tie_word_embeddings and "lm_head.weight" in name:
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continue
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if name.startswith("model.vision_tower") and name not in params_dict:
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continue
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name = self.map_model_name_to_mtp_param_name(name)
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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if "mtp_block" not in name:
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break
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if "mtp_block" not in name and (
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"embed_tokens" not in name
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and "lm_head" not in name
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and "token_layernorm" not in name
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and "hidden_layernorm" not in name
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and "input_proj" not in name
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and "final_layernorm" not in name
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):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader)
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weight_loader(param, loaded_weight)
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def map_model_name_to_mtp_param_name(self, name: str) -> str:
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import re
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name_without_prefix = [
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"token_layernorm",
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"hidden_layernorm",
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"input_proj",
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"final_layernorm",
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]
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pattern = r"model.mtp_layers.(\d+)."
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group = re.match(pattern, name)
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if group is not None:
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for sub_name in name_without_prefix:
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if sub_name in name:
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name = name.replace(group.group(), "model.")
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return name
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name = name.replace(group.group(), "model.mtp_block.")
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return name
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def get_embed_and_head(self):
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return self.model.embed_tokens.weight, self.lm_head.weight
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def set_embed_and_head(self, embed, head):
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del self.model.embed_tokens.weight
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del self.lm_head.weight
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self.model.embed_tokens.weight = embed
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self.lm_head.weight = head
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torch.cuda.empty_cache()
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torch.cuda.synchronize()
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EntryClass = MiMoMTP
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