Add ernie4.py for ERNIE-4.5 (#7657)
This commit is contained in:
Wenbo Yang
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# Copyright 2023-2025 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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""" Ernie4.5 MTP model compatible with baidu/ERNIE-4.5-*-PT weights. """
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from typing import Iterable, Optional, Tuple
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import torch
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from torch import nn
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from transformers.models.ernie4_5_moe.configuration_ernie4_5_moe import (
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Ernie4_5_MoeConfig,
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)
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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.ernie4 import Ernie4_5_ForCausalLM, Ernie4DecoderLayer
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from sglang.srt.utils import add_prefix
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class Ernie4ModelMTP(nn.Module):
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def __init__(
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self,
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config: Ernie4_5_MoeConfig,
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layer_id: int,
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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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quant_config=quant_config,
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prefix=add_prefix("embed_tokens", prefix),
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)
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self.mtp_emb_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.mtp_hidden_norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.mtp_linear_proj = nn.Linear(
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config.hidden_size * 2, config.hidden_size, bias=config.use_bias
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)
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self.mtp_block = Ernie4DecoderLayer(
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config=config,
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layer_id=layer_id,
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quant_config=quant_config,
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prefix=add_prefix("mtp_block", prefix),
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is_mtp=True,
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)
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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.mtp_linear_proj(
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torch.cat(
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(
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self.mtp_emb_norm(hidden_states),
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self.mtp_hidden_norm(forward_batch.spec_info.hidden_states),
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),
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dim=-1,
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)
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)
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residual = None
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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=residual,
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)
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hidden_states = residual + hidden_states
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return hidden_states
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class Ernie4_5_MoeForCausalLMMTP(nn.Module):
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def __init__(
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self,
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config: Ernie4_5_MoeConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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mtp_layer_id: int = 0,
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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.mtp_layer_id = mtp_layer_id
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self.model = Ernie4ModelMTP(
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config=config,
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layer_id=self.mtp_layer_id,
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quant_config=quant_config,
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prefix=add_prefix("model", prefix),
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)
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if config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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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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prefix="lm_head",
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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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mtp_layer_found = False
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mtp_weight_patterns = [
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f"mtp_block.{self.mtp_layer_id}",
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f"mtp_emb_norm.{self.mtp_layer_id}",
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f"mtp_hidden_norm.{self.mtp_layer_id}",
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f"mtp_linear_proj.{self.mtp_layer_id}",
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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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# Only name matched patterns should be loaded
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for layer_pattern in mtp_weight_patterns:
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if layer_pattern in name:
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mtp_layer_found = True
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break
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else:
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continue
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# But strip mtp_layer_id before loading, because each MTP layer is a MTP model.
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name = name.replace(f".{self.mtp_layer_id}.", ".")
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for (
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param_name,
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weight_name,
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shard_id,
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) in Ernie4_5_ForCausalLM.stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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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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if name in params_dict.keys():
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param = params_dict[name]
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weight_loader = getattr(
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param, "weight_loader", default_weight_loader
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)
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weight_loader(param, loaded_weight)
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else:
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raise KeyError(f"Parameter '{name}' not found in MTP model.")
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if not mtp_layer_found:
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raise KeyError(
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f"MTP layers 'mtp_*.{self.mtp_layer_id}.*' not found in weights."
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)
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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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self.model.embed_tokens.weight = embed
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if self.config.tie_word_embeddings:
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self.lm_head = self.model.embed_tokens
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else:
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del self.lm_head.weight
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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 = [Ernie4_5_MoeForCausalLMMTP]
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