987 lines
35 KiB
Python
987 lines
35 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 Any, Dict, Iterable, List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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from torch import nn
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from sglang.srt.distributed import (
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get_moe_expert_parallel_world_size,
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get_pp_group,
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get_tensor_model_parallel_world_size,
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tensor_model_parallel_all_reduce,
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)
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from sglang.srt.eplb.expert_location import ModelConfigForExpertLocation
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from sglang.srt.eplb.expert_location_dispatch import ExpertLocationDispatchInfo
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from sglang.srt.layers.activation import SiluAndMul
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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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get_attention_tp_size,
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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.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.moe import (
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get_moe_a2a_backend,
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get_moe_runner_backend,
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should_use_flashinfer_cutlass_moe_fp4_allgather,
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)
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from sglang.srt.layers.moe.ep_moe.layer import DeepEPMoE, get_moe_impl_class
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from sglang.srt.layers.moe.topk import TopK, TopKOutputFormat
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
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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, PPProxyTensors
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from sglang.srt.model_loader.weight_utils import (
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default_weight_loader,
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kv_cache_scales_loader,
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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 LazyValue, add_prefix, make_layers
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MiMoV2FlashConfig = None
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logger = logging.getLogger(__name__)
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class MiMoV2MLP(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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hidden_act: str,
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quant_config: Optional[QuantizationConfig] = None,
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reduce_results: bool = True,
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prefix: str = "",
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tp_rank: Optional[int] = None,
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tp_size: Optional[int] = None,
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) -> None:
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super().__init__()
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self.tp_size = tp_size
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config,
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prefix=add_prefix("gate_up_proj", prefix),
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tp_rank=tp_rank,
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tp_size=tp_size,
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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reduce_results=reduce_results,
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prefix=add_prefix("down_proj", prefix),
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tp_rank=tp_rank,
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tp_size=tp_size,
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)
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if hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(
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self,
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x,
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forward_batch: ForwardBatch = None,
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should_allreduce_fusion: bool = False,
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use_reduce_scatter: bool = False,
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):
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if (self.tp_size == 1) and x.shape[0] == 0:
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return x
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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(
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x, skip_all_reduce=should_allreduce_fusion or use_reduce_scatter
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)
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return x
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class MoEGate(nn.Module):
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def __init__(
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self,
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config,
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quant_config,
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prefix: str = "",
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is_nextn: bool = False,
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):
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super().__init__()
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self.is_nextn = is_nextn
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self.dtype = torch.float32
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self.weight = nn.Parameter(
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torch.empty((config.n_routed_experts, config.hidden_size), dtype=self.dtype)
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)
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if config.topk_method == "noaux_tc":
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correction_bias_dtype = (
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torch.bfloat16
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if quant_config is not None
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and quant_config.get_name() == "modelopt_fp4"
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and get_moe_runner_backend().is_flashinfer_trtllm()
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else self.dtype
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)
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self.e_score_correction_bias = nn.Parameter(
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torch.empty((config.n_routed_experts), dtype=correction_bias_dtype)
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)
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else:
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self.e_score_correction_bias = None
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def forward(self, hidden_states):
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logits = F.linear(hidden_states.to(self.dtype), self.weight, None)
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return logits
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class MiMoV2MoE(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,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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is_nextn: bool = False,
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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.config = config
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self.layer_id = layer_id
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if self.tp_size > config.n_routed_experts:
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raise ValueError(
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f"Tensor parallel size {self.tp_size} is greater than "
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f"the number of experts {config.n_routed_experts}."
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)
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if config.hidden_act != "silu":
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raise ValueError(
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f"Unsupported activation: {config.hidden_act}. "
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"Only silu is supported for now."
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)
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self.gate = MoEGate(
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config=config,
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quant_config=quant_config,
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prefix=add_prefix("gate", prefix),
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is_nextn=is_nextn,
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)
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experts_type = get_moe_impl_class(quant_config)
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self.experts = experts_type(
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num_experts=config.n_routed_experts
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+ get_global_server_args().ep_num_redundant_experts,
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top_k=config.num_experts_per_tok,
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hidden_size=config.hidden_size,
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intermediate_size=config.moe_intermediate_size,
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layer_id=self.layer_id,
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quant_config=quant_config,
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routed_scaling_factor=1.0,
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prefix=add_prefix("experts", prefix),
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)
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self.topk = TopK(
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top_k=config.num_experts_per_tok,
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renormalize=config.norm_topk_prob,
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use_grouped_topk=True,
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num_expert_group=config.n_group,
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topk_group=config.topk_group,
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correction_bias=self.gate.e_score_correction_bias,
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quant_config=quant_config,
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routed_scaling_factor=1.0,
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apply_routed_scaling_factor_on_output=self.experts.should_fuse_routed_scaling_factor_in_topk,
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# Some Fp4 MoE backends require the output format to be bypassed but the MTP layers are unquantized
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# and requires the output format to be standard. We use quant_config to determine the output format.
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output_format=TopKOutputFormat.STANDARD if quant_config is None else None,
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)
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# todo : implement tbo forward needed
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if get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mooncake():
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# TODO: we will support tp < ep in the future
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self.ep_size = get_moe_expert_parallel_world_size()
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self.num_experts = (
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config.n_routed_experts
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+ get_global_server_args().ep_num_redundant_experts
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)
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self.renormalize = config.norm_topk_prob
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self.topk_group = config.topk_group
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self.num_expert_group = config.n_group
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self.correction_bias = (
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self.gate.e_score_correction_bias.data
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if self.gate.e_score_correction_bias is not None
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else None
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)
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self._enable_a2a_moe = (
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get_moe_a2a_backend().is_deepep() or get_moe_a2a_backend().is_mooncake()
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)
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def get_moe_weights(self):
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return [
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x.data
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for name, x in self.experts.named_parameters()
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if name not in ["correction_bias"]
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]
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def forward(
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self,
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hidden_states: torch.Tensor,
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forward_batch: Optional[ForwardBatch] = None,
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should_allreduce_fusion: bool = False,
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use_reduce_scatter: bool = False,
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) -> torch.Tensor:
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if not self._enable_a2a_moe:
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return self.forward_normal(
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hidden_states,
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should_allreduce_fusion,
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use_reduce_scatter,
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)
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else:
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return self.forward_deepep(hidden_states, forward_batch)
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def forward_normal(
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self,
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hidden_states: torch.Tensor,
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should_allreduce_fusion: bool = False,
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use_reduce_scatter: bool = False,
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) -> torch.Tensor:
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if hidden_states.shape[0] > 0:
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# router_logits: (num_tokens, n_experts)
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router_logits = self.gate(hidden_states)
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topk_output = self.topk(hidden_states, router_logits)
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else:
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topk_output = self.topk.empty_topk_output(hidden_states.device)
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final_hidden_states = self.experts(hidden_states, topk_output)
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if (
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self.tp_size > 1
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and not should_allreduce_fusion
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and not use_reduce_scatter
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and not should_use_flashinfer_cutlass_moe_fp4_allgather()
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):
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final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
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return final_hidden_states
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def forward_deepep(
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self, hidden_states: torch.Tensor, forward_batch: ForwardBatch
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) -> torch.Tensor:
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if hidden_states.shape[0] > 0:
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# router_logits: (num_tokens, n_experts)
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router_logits = self.gate(hidden_states)
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topk_output = self.topk(
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hidden_states,
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router_logits,
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num_token_non_padded=forward_batch.num_token_non_padded,
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expert_location_dispatch_info=ExpertLocationDispatchInfo.init_new(
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layer_id=self.layer_id,
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),
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)
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else:
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topk_output = self.topk.empty_topk_output(hidden_states.device)
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final_hidden_states = self.experts(
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hidden_states=hidden_states, topk_output=topk_output
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)
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return final_hidden_states
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class MiMoV2Attention(nn.Module):
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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head_dim: Optional[int] = None,
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v_head_dim: Optional[int] = None,
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v_scale: Optional[float] = None,
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sliding_window_size: int = -1, # if is -1 ,normal attention,else ,window attention
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attention_bias: bool = False,
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attention_sink_bias: bool = False,
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layer_id: int = 0,
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rope_theta: float = 1000000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 32768,
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quant_config: Optional[QuantizationConfig] = None,
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partial_rotary_factor: float = 1.0,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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attn_tp_rank = get_attention_tp_rank()
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attn_tp_size = get_attention_tp_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % attn_tp_size == 0
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self.num_heads = self.total_num_heads // attn_tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= attn_tp_size:
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# Number of KV heads is greater than TP size, so we partition
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# the KV heads across multiple tensor parallel GPUs.
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assert self.total_num_kv_heads % attn_tp_size == 0
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else:
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# Number of KV heads is less than TP size, so we replicate
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# the KV heads across multiple tensor parallel GPUs.
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assert attn_tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // attn_tp_size)
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self.head_dim = head_dim
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self.v_head_dim = v_head_dim if v_head_dim is not None else head_dim
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self.q_size = self.num_heads * self.head_dim
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self.k_size = self.num_kv_heads * self.head_dim
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self.v_size = self.num_kv_heads * self.v_head_dim
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self.v_scale = v_scale
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self.scaling = self.head_dim**-0.5
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self.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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v_head_size=self.v_head_dim,
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bias=attention_bias,
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quant_config=quant_config,
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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prefix=add_prefix("qkv_proj", prefix),
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skip_block_quant_check=True,
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.v_head_dim,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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tp_rank=attn_tp_rank,
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tp_size=attn_tp_size,
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reduce_results=False,
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prefix=add_prefix("o_proj", prefix),
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=max_position_embeddings,
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base=rope_theta,
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rope_scaling=rope_scaling,
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partial_rotary_factor=partial_rotary_factor,
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)
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self.attn = RadixAttention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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num_kv_heads=self.num_kv_heads,
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layer_id=layer_id,
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v_head_dim=self.v_head_dim,
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sliding_window_size=sliding_window_size, # if is -1 ,normal attention,else ,window attention
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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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)
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self.attention_sink_bias = (
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torch.nn.Parameter(torch.empty(self.num_heads), requires_grad=False)
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if attention_sink_bias
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else None
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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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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.k_size, self.v_size], dim=-1)
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# [t, h, dr]
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q, k = self.rotary_emb(positions, q, k)
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# [t, h, d]
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if self.v_scale is not None:
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v = v * self.v_scale
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attn_output = self.attn(q, k, v, forward_batch, sinks=self.attention_sink_bias)
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output, _ = self.o_proj(attn_output)
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return output
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class MiMoV2DecoderLayer(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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self.layer_id = layer_id
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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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if self.is_swa_layer():
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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(
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config, "add_swa_attention_sink_bias", False
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),
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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,
|
|
partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
|
|
prefix=add_prefix("self_attn", prefix),
|
|
)
|
|
else:
|
|
self.self_attn = MiMoV2Attention(
|
|
hidden_size=self.hidden_size,
|
|
num_heads=self.config.num_attention_heads,
|
|
num_kv_heads=config.num_key_value_heads,
|
|
head_dim=config.head_dim,
|
|
v_head_dim=getattr(config, "v_head_dim", None),
|
|
v_scale=getattr(config, "attention_value_scale", None),
|
|
sliding_window_size=-1, # normal attention
|
|
attention_bias=config.attention_bias,
|
|
attention_sink_bias=getattr(
|
|
config, "add_full_attention_sink_bias", False
|
|
),
|
|
layer_id=layer_id,
|
|
rope_theta=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
max_position_embeddings=max_position_embeddings,
|
|
quant_config=quant_config,
|
|
partial_rotary_factor=getattr(config, "partial_rotary_factor", 1.0),
|
|
prefix=add_prefix("self_attn", prefix),
|
|
)
|
|
|
|
self.is_layer_sparse = self.is_moe_layer(layer_id)
|
|
is_previous_layer_sparse = self.is_moe_layer(layer_id - 1)
|
|
is_next_layer_sparse = self.is_moe_layer(layer_id + 1)
|
|
|
|
if self.is_layer_sparse:
|
|
self.mlp = MiMoV2MoE(
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("mlp", prefix),
|
|
layer_id=layer_id,
|
|
)
|
|
else:
|
|
if enable_moe_dense_fully_dp():
|
|
mlp_tp_rank, mlp_tp_size = 0, 1
|
|
else:
|
|
mlp_tp_rank, mlp_tp_size = None, None
|
|
self.mlp = MiMoV2MLP(
|
|
hidden_size=self.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("mlp", prefix),
|
|
tp_rank=mlp_tp_rank,
|
|
tp_size=mlp_tp_size,
|
|
)
|
|
|
|
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
|
|
self.post_attention_layernorm = RMSNorm(
|
|
config.hidden_size, eps=config.layernorm_epsilon
|
|
)
|
|
|
|
self.layer_scatter_modes = LayerScatterModes.init_new(
|
|
layer_id=layer_id,
|
|
num_layers=config.num_hidden_layers,
|
|
is_layer_sparse=self.is_layer_sparse,
|
|
is_previous_layer_sparse=is_previous_layer_sparse,
|
|
is_next_layer_sparse=is_next_layer_sparse,
|
|
)
|
|
self.layer_communicator = LayerCommunicator(
|
|
layer_scatter_modes=self.layer_scatter_modes,
|
|
input_layernorm=self.input_layernorm,
|
|
post_attention_layernorm=self.post_attention_layernorm,
|
|
allow_reduce_scatter=True,
|
|
is_last_layer=(self.layer_id == self.config.num_hidden_layers - 1),
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
residual: Optional[torch.Tensor],
|
|
captured_last_layer_outputs: Optional[List[torch.Tensor]] = None,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
# Self Attention
|
|
hidden_states, residual = (
|
|
self.layer_communicator.prepare_attn_and_capture_last_layer_outputs(
|
|
hidden_states,
|
|
residual,
|
|
forward_batch,
|
|
captured_last_layer_outputs=captured_last_layer_outputs,
|
|
)
|
|
)
|
|
|
|
if hidden_states.shape[0] != 0:
|
|
hidden_states = self.self_attn(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
forward_batch=forward_batch,
|
|
)
|
|
|
|
hidden_states, residual = self.layer_communicator.prepare_mlp(
|
|
hidden_states, residual, forward_batch
|
|
)
|
|
|
|
should_allreduce_fusion = (
|
|
self.layer_communicator.should_fuse_mlp_allreduce_with_next_layer(
|
|
forward_batch
|
|
)
|
|
)
|
|
|
|
# For DP with padding, reduce scatter can be used instead of all-reduce.
|
|
use_reduce_scatter = self.layer_communicator.should_use_reduce_scatter(
|
|
forward_batch
|
|
)
|
|
|
|
hidden_states = self.mlp(
|
|
hidden_states, forward_batch, should_allreduce_fusion, use_reduce_scatter
|
|
)
|
|
|
|
if should_allreduce_fusion:
|
|
hidden_states._sglang_needs_allreduce_fusion = True
|
|
else:
|
|
hidden_states, residual = self.layer_communicator.postprocess_layer(
|
|
hidden_states, residual, forward_batch
|
|
)
|
|
|
|
return hidden_states, residual
|
|
|
|
def is_moe_layer(self, layer_idx: int) -> bool:
|
|
return (
|
|
hasattr(self.config, "moe_layer_freq")
|
|
and 0 <= layer_idx < len(self.config.moe_layer_freq)
|
|
and not isinstance(self.config.moe_layer_freq, int)
|
|
and self.config.moe_layer_freq[layer_idx]
|
|
)
|
|
|
|
def is_swa_layer(self) -> bool:
|
|
return self.config.hybrid_layer_pattern[self.layer_id] == 1
|
|
|
|
|
|
class MiMoV2Model(nn.Module):
|
|
def __init__(
|
|
self,
|
|
config: MiMoV2FlashConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
decoder_layer_type: type[nn.Module] = MiMoV2DecoderLayer,
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.padding_idx = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
self.pp_group = get_pp_group()
|
|
|
|
if self.pp_group.is_first_rank:
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
use_attn_tp_group=is_dp_attention_enabled(),
|
|
prefix=add_prefix("embed_tokens", prefix),
|
|
)
|
|
else:
|
|
self.embed_tokens = PPMissingLayer()
|
|
|
|
# Use the provided decoder layer type or default to MiMoV2DecoderLayer
|
|
decoder_layer_type = decoder_layer_type or MiMoV2DecoderLayer
|
|
self.layers, self.start_layer, self.end_layer = make_layers(
|
|
config.num_hidden_layers,
|
|
layer_fn=lambda idx, prefix: decoder_layer_type(
|
|
layer_id=idx,
|
|
config=config,
|
|
quant_config=quant_config,
|
|
prefix=prefix,
|
|
),
|
|
pp_rank=self.pp_group.rank_in_group,
|
|
pp_size=self.pp_group.world_size,
|
|
prefix=add_prefix("layers", prefix),
|
|
)
|
|
if self.pp_group.is_last_rank:
|
|
self.norm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon)
|
|
else:
|
|
self.norm = PPMissingLayer(return_tuple=True)
|
|
|
|
def get_input_embedding(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
if hasattr(self.config, "scale_emb"):
|
|
return self.get_input_embeddings()(input_ids) * self.config.scale_emb
|
|
else:
|
|
return self.get_input_embeddings()(input_ids)
|
|
|
|
def get_input_embeddings(self) -> nn.Embedding:
|
|
return self.embed_tokens
|
|
|
|
def set_eagle3_layers_to_capture(self, layers_to_capture: List[int]):
|
|
self.layers_to_capture = layers_to_capture
|
|
for layer_id in self.layers_to_capture:
|
|
setattr(self.layers[layer_id], "_is_layer_to_capture", True)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
|
) -> Union[torch.Tensor, PPProxyTensors]:
|
|
if self.pp_group.is_first_rank:
|
|
if input_embeds is None:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
else:
|
|
hidden_states = input_embeds
|
|
residual = None
|
|
else:
|
|
assert pp_proxy_tensors is not None
|
|
hidden_states = pp_proxy_tensors["hidden_states"]
|
|
residual = pp_proxy_tensors["residual"]
|
|
|
|
aux_hidden_states = []
|
|
for i in range(self.start_layer, self.end_layer):
|
|
layer = self.layers[i]
|
|
hidden_states, residual = layer(
|
|
positions,
|
|
hidden_states,
|
|
forward_batch,
|
|
residual,
|
|
captured_last_layer_outputs=(
|
|
aux_hidden_states
|
|
if getattr(layer, "_is_layer_to_capture", False)
|
|
else None
|
|
),
|
|
)
|
|
|
|
hidden_states_before_norm = None
|
|
if not self.pp_group.is_last_rank:
|
|
return PPProxyTensors(
|
|
{
|
|
"hidden_states": hidden_states,
|
|
"residual": residual,
|
|
}
|
|
)
|
|
else:
|
|
if hidden_states.shape[0] > 0:
|
|
if forward_batch.return_hidden_states_before_norm:
|
|
hidden_states_before_norm = (
|
|
hidden_states if residual is None else hidden_states + residual
|
|
)
|
|
if residual is None:
|
|
hidden_states = self.norm(hidden_states)
|
|
else:
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
|
|
return hidden_states, hidden_states_before_norm
|
|
|
|
# If this function is called, it should always initialize KV cache scale
|
|
# factors (or else raise an exception). Thus, handled exceptions should
|
|
# make sure to leave KV cache scale factors in a known good (dummy) state
|
|
def load_kv_cache_scales(self, quantization_param_path: str) -> None:
|
|
attn_tp_rank = get_attention_tp_rank()
|
|
attn_tp_size = get_attention_tp_size()
|
|
for layer_idx, scaling_factor in kv_cache_scales_loader(
|
|
quantization_param_path,
|
|
attn_tp_rank,
|
|
attn_tp_size,
|
|
self.config.num_hidden_layers,
|
|
self.config.__class__.model_type,
|
|
):
|
|
if not isinstance(self.layers[layer_idx], nn.Identity):
|
|
layer_self_attn = self.layers[layer_idx].self_attn
|
|
if hasattr(layer_self_attn.attn, "k_scale"):
|
|
layer_self_attn.attn.k_scale = scaling_factor
|
|
layer_self_attn.attn.v_scale = scaling_factor
|
|
else:
|
|
raise RuntimeError(
|
|
"Self attention has no KV cache scaling " "factor attribute!"
|
|
)
|
|
|
|
|
|
class MiMoV2FlashForCausalLM(nn.Module):
|
|
# BitandBytes specific attributes
|
|
default_bitsandbytes_target_modules = [
|
|
".gate_proj.",
|
|
".down_proj.",
|
|
".up_proj.",
|
|
".q_proj.",
|
|
".k_proj.",
|
|
".v_proj.",
|
|
".o_proj.",
|
|
]
|
|
bitsandbytes_stacked_params_mapping = {
|
|
# shard_name, weight_name, index
|
|
"q_proj": ("qkv_proj", 0),
|
|
"k_proj": ("qkv_proj", 1),
|
|
"v_proj": ("qkv_proj", 2),
|
|
"gate_proj": ("gate_up_proj", 0),
|
|
"up_proj": ("gate_up_proj", 1),
|
|
}
|
|
|
|
def __init__(
|
|
self,
|
|
config: MiMoV2FlashConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
prefix: str = "",
|
|
) -> None:
|
|
super().__init__()
|
|
self.pp_group = get_pp_group()
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.model = MiMoV2Model(
|
|
config, quant_config=quant_config, prefix=add_prefix("model", prefix)
|
|
)
|
|
|
|
if self.pp_group.is_last_rank:
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
quant_config=quant_config,
|
|
prefix=add_prefix("lm_head", prefix),
|
|
use_attn_tp_group=get_global_server_args().enable_dp_lm_head,
|
|
)
|
|
else:
|
|
# ranks other than the last rank will have a placeholder layer
|
|
self.lm_head = PPMissingLayer()
|
|
|
|
self.logits_processor = LogitsProcessor(config)
|
|
|
|
self._routed_experts_weights_of_layer = LazyValue(
|
|
lambda: {
|
|
layer_id: layer.mlp.get_moe_weights()
|
|
for layer_id, layer in enumerate(self.model.layers)
|
|
if isinstance(layer.mlp, MiMoV2MoE)
|
|
}
|
|
)
|
|
|
|
@property
|
|
def routed_experts_weights_of_layer(self):
|
|
return self._routed_experts_weights_of_layer.value
|
|
|
|
def get_input_embedding(self, input_ids: torch.Tensor) -> torch.Tensor:
|
|
return self.model.get_input_embedding(input_ids)
|
|
|
|
def get_input_embeddings(self) -> nn.Embedding:
|
|
return self.model.embed_tokens
|
|
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
input_embeds: torch.Tensor = None,
|
|
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
|
) -> torch.Tensor:
|
|
hidden_states, hidden_states_before_norm = self.model(
|
|
input_ids,
|
|
positions,
|
|
forward_batch,
|
|
input_embeds,
|
|
pp_proxy_tensors=pp_proxy_tensors,
|
|
)
|
|
|
|
if self.pp_group.is_last_rank:
|
|
return self.logits_processor(
|
|
input_ids,
|
|
hidden_states,
|
|
self.lm_head,
|
|
forward_batch,
|
|
hidden_states_before_norm=hidden_states_before_norm,
|
|
)
|
|
else:
|
|
return hidden_states
|
|
|
|
@property
|
|
def start_layer(self):
|
|
return self.model.start_layer
|
|
|
|
@property
|
|
def end_layer(self):
|
|
return self.model.end_layer
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("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),
|
|
]
|
|
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
expert_params_mapping = DeepEPMoE.make_expert_params_mapping(
|
|
ckpt_gate_proj_name="gate_proj",
|
|
ckpt_down_proj_name="down_proj",
|
|
ckpt_up_proj_name="up_proj",
|
|
num_experts=self.config.n_routed_experts,
|
|
)
|
|
|
|
params_dict = dict(self.named_parameters())
|
|
|
|
for name, loaded_weight in weights:
|
|
layer_id = get_layer_id(name)
|
|
if (
|
|
layer_id is not None
|
|
and hasattr(self.model, "start_layer")
|
|
and (
|
|
layer_id < self.model.start_layer
|
|
or layer_id >= self.model.end_layer
|
|
)
|
|
):
|
|
continue
|
|
|
|
if "rotary_emb.inv_freq" in name or "projector" in name:
|
|
continue
|
|
if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
|
|
# Models trained using ColossalAI may include these tensors in
|
|
# the checkpoint. Skip them.
|
|
continue
|
|
|
|
if self.config.tie_word_embeddings and "lm_head.weight" in name:
|
|
if self.pp_group.world_size > 1 and self.pp_group.is_last_rank:
|
|
# Handle pp weight tying here
|
|
# find the embed_tokens.weight in the weights
|
|
embed_token_weights = next(
|
|
filter(lambda x: x[0] == "model.embed_tokens.weight", weights)
|
|
)[1]
|
|
loaded_weight = embed_token_weights
|
|
else:
|
|
continue
|
|
|
|
# TODO: skip mtp weights for now, need to implement mtp
|
|
if "mtp" in name:
|
|
continue
|
|
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
if weight_name not in name:
|
|
continue
|
|
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:
|
|
for mapping in expert_params_mapping:
|
|
param_name, weight_name, expert_id, shard_id = mapping
|
|
if weight_name not in name:
|
|
continue
|
|
name = name.replace(weight_name, param_name)
|
|
param = params_dict[name]
|
|
weight_loader = param.weight_loader
|
|
weight_loader(
|
|
param,
|
|
loaded_weight,
|
|
name,
|
|
shard_id=shard_id,
|
|
expert_id=expert_id,
|
|
)
|
|
break
|
|
else:
|
|
# Skip loading extra bias for GPTQ models.
|
|
if name.endswith(".bias") and name not in params_dict:
|
|
continue
|
|
|
|
if name in params_dict.keys():
|
|
param = params_dict[name]
|
|
if "attention_sink_bias" in name:
|
|
start = get_attention_tp_rank() * param.numel()
|
|
param.data.copy_(
|
|
loaded_weight[start : start + param.numel()]
|
|
)
|
|
else:
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
else:
|
|
logger.warning(f"Parameter {name} not found in params_dict")
|
|
|
|
def get_embed_and_head(self):
|
|
return self.model.embed_tokens.weight, self.lm_head.weight
|
|
|
|
def set_embed_and_head(self, embed, head):
|
|
del self.model.embed_tokens.weight
|
|
del self.lm_head.weight
|
|
self.model.embed_tokens.weight = embed
|
|
self.lm_head.weight = head
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.synchronize()
|
|
|
|
def load_kv_cache_scales(self, quantization_param_path: str) -> None:
|
|
self.model.load_kv_cache_scales(quantization_param_path)
|
|
|
|
@classmethod
|
|
def get_model_config_for_expert_location(cls, config):
|
|
return ModelConfigForExpertLocation(
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num_layers=config.num_hidden_layers,
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num_logical_experts=getattr(config, "n_routed_experts", 1),
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num_groups=getattr(config, "n_group", None),
|
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)
|
|
|
|
|
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EntryClass = MiMoV2FlashForCausalLM
|