370 lines
13 KiB
Python
370 lines
13 KiB
Python
# 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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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_tensor_model_parallel_world_size
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from sglang.srt.eplb.expert_distribution import get_global_expert_distribution_recorder
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from sglang.srt.layers.communicator import (
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LayerCommunicator,
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LayerScatterModes,
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enable_moe_dense_fully_dp,
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)
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from sglang.srt.layers.dp_attention import (
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get_attention_tp_rank,
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is_dp_attention_enabled,
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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.mimo_v2_flash import (
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MiMoV2Attention,
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MiMoV2FlashForCausalLM,
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MiMoV2MLP,
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)
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from sglang.srt.server_args import get_global_server_args
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from sglang.srt.utils import add_prefix
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MiMoV2FlashConfig = None
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logger = logging.getLogger(__name__)
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class MiMoV2MTPLayer(nn.Module):
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def __init__(
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self,
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config: MiMoV2FlashConfig,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.config = config
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self.hidden_size = config.hidden_size
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rope_theta = getattr(config, "rope_theta", 1000000)
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rope_scaling = getattr(config, "rope_scaling", None)
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max_position_embeddings = getattr(config, "max_position_embeddings", 32768)
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self.self_attn = MiMoV2Attention(
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hidden_size=self.hidden_size,
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num_heads=config.swa_num_attention_heads,
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num_kv_heads=config.swa_num_key_value_heads,
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head_dim=config.swa_head_dim,
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v_head_dim=getattr(config, "swa_v_head_dim", None),
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v_scale=getattr(config, "attention_value_scale", None),
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sliding_window_size=config.sliding_window_size,
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attention_bias=config.attention_bias,
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attention_sink_bias=getattr(config, "add_swa_attention_sink_bias", False),
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layer_id=layer_id,
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rope_theta=getattr(config, "swa_rope_theta", rope_theta),
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rope_scaling=rope_scaling,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
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prefix=add_prefix("self_attn", prefix),
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)
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self.is_layer_sparse = False
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is_previous_layer_sparse = True
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is_next_layer_sparse = False
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if enable_moe_dense_fully_dp():
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mlp_tp_rank, mlp_tp_size = 0, 1
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else:
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mlp_tp_rank, mlp_tp_size = None, None
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self.mlp = MiMoV2MLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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prefix=add_prefix("mlp", prefix),
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tp_rank=mlp_tp_rank,
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tp_size=mlp_tp_size,
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.layernorm_epsilon
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)
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self.layer_scatter_modes = LayerScatterModes.init_new(
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layer_id=layer_id,
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num_layers=1,
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is_layer_sparse=self.is_layer_sparse,
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is_previous_layer_sparse=is_previous_layer_sparse,
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is_next_layer_sparse=is_next_layer_sparse,
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)
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self.layer_communicator = LayerCommunicator(
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layer_scatter_modes=self.layer_scatter_modes,
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input_layernorm=self.input_layernorm,
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post_attention_layernorm=self.post_attention_layernorm,
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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hidden_states, residual = self.layer_communicator.prepare_attn(
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hidden_states, residual, forward_batch
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)
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if hidden_states.shape[0] != 0:
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hidden_states = self.self_attn(
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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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)
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hidden_states, residual = self.layer_communicator.prepare_mlp(
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hidden_states, residual, forward_batch
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)
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with get_global_expert_distribution_recorder().disable_this_region():
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hidden_states = self.mlp(hidden_states)
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hidden_states, residual = self.layer_communicator.postprocess_layer(
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hidden_states, residual, forward_batch
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)
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return hidden_states, residual
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class MiMoV2ModelNextN(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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super().__init__()
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self.vocab_size = config.vocab_size
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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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use_attn_tp_group=is_dp_attention_enabled(),
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prefix=add_prefix("embed_tokens", prefix),
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)
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self.enorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
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self.hnorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
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self.eh_proj = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False)
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self.mtp_block = MiMoV2MTPLayer(
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config,
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0,
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quant_config=quant_config,
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prefix=add_prefix("decoder", prefix),
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)
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self.final_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
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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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if hidden_states.shape[0] > 0:
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hidden_states = self.eh_proj(
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torch.cat(
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(
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self.enorm(hidden_states),
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self.hnorm(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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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_before_norm = None
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if not forward_batch.forward_mode.is_idle():
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if forward_batch.return_hidden_states_before_norm:
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hidden_states_before_norm = (
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hidden_states if residual is None else hidden_states + residual
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)
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if residual is not None:
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hidden_states, _ = self.final_layernorm(hidden_states, residual)
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else:
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hidden_states = self.final_layernorm(hidden_states)
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return hidden_states, hidden_states_before_norm
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class MiMoV2MTP(MiMoV2FlashForCausalLM):
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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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draft_model_idx: Optional[int] = 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 = MiMoV2ModelNextN(
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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_global_server_args().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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) -> torch.Tensor:
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hidden_states, hidden_states_before_norm = self.model(
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input_ids, positions, forward_batch
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)
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return self.logits_processor(
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input_ids,
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hidden_states,
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self.lm_head,
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forward_batch,
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hidden_states_before_norm=hidden_states_before_norm,
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)
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def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]], is_nextn=False):
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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 "enorm" not in name
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and "hnorm" not in name
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and "eh_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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if name in params_dict.keys():
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param = params_dict[name]
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if "attention_sink_bias" in name:
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start = get_attention_tp_rank() * param.numel()
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param.data.copy_(loaded_weight[start : start + param.numel()])
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else:
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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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logger.warning(f"Parameter {name} not found in params_dict")
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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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if "pre_mlp_layernorm" in name:
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name = name.replace("pre_mlp_layernorm", "post_attention_layernorm")
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name_without_prefix = [
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"enorm",
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"hnorm",
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"eh_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 = MiMoV2MTP
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