107 lines
3.7 KiB
Python
107 lines
3.7 KiB
Python
# Copyright 2025 The LG AI Research Team
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# Copyright 2023-2024 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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# Adapted from the vLLM version of EXAONE-MoE MTP
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"""Inference-only ExaoneMoE MTP Speculative Decoding."""
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import logging
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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 import PretrainedConfig
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from sglang.srt.distributed import get_pp_group
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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 ParallelLMHead
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch
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from sglang.srt.models.exaone_moe import ExaoneMoEForCausalLM, ExaoneMoEModel
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from sglang.srt.runtime_context import get_parallel
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from sglang.srt.utils import add_prefix
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logger = logging.getLogger(__name__)
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class ExaoneMoEForCausalLMMTP(ExaoneMoEForCausalLM):
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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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config.num_hidden_layers = 1
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self.tp_size = get_parallel().tp_size
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self.quant_config = quant_config
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self.pp_group = get_pp_group()
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self.fc = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False)
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self.pre_fc_norm_embedding = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.pre_fc_norm_hidden = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.model = ExaoneMoEModel(
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config, quant_config, prefix=add_prefix("model", prefix)
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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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prefix=add_prefix("lm_head", prefix),
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use_attn_tp_group=get_parallel().config.enable_dp_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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input_embeds: Optional[torch.Tensor] = None,
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**kwargs,
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):
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if input_embeds is None:
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input_embeds = self.model.embed_tokens(input_ids)
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hidden_states = forward_batch.spec_info.hidden_states
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if not forward_batch.forward_mode.is_idle():
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input_embeds = self.pre_fc_norm_embedding(input_embeds)
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hidden_states = self.pre_fc_norm_hidden(hidden_states)
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hidden_states = self.fc(torch.cat((input_embeds, hidden_states), dim=-1))
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hidden_states = self.model(
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input_ids,
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positions,
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forward_batch,
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hidden_states,
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)
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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(
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self, weights: Iterable[Tuple[str, torch.Tensor]], is_mtp: bool = False
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):
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super().load_weights(weights, is_mtp=True)
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EntryClass = ExaoneMoEForCausalLMMTP
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