440 lines
17 KiB
Python
440 lines
17 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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"""Inference-only DeepSeek NextN 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_tensor_model_parallel_world_size
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from sglang.srt.layers.layernorm import RMSNorm
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from sglang.srt.layers.linear import ReplicatedLinear
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from sglang.srt.layers.logits_processor import LogitsProcessor
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from sglang.srt.layers.moe.ep_moe.layer import EPMoE
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from sglang.srt.layers.moe.fused_moe_triton import FusedMoE
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.quantization.fp8_utils import (
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block_quant_to_tensor_quant,
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normalize_e4m3fn_to_e4m3fnuz,
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)
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from sglang.srt.layers.quantization.int8_utils import (
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block_dequant as int8_block_dequant,
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)
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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.managers.schedule_batch import global_server_args_dict
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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.deepseek_v2 import DeepseekV2DecoderLayer, DeepseekV3ForCausalLM
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from sglang.srt.utils import BumpAllocator, add_prefix, is_cuda, is_hip
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_is_hip = is_hip()
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_is_cuda = is_cuda()
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if _is_cuda:
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from sgl_kernel import awq_dequantize
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else:
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from vllm._custom_ops import awq_dequantize
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logger = logging.getLogger(__name__)
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class DeepseekModelNextN(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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enable_tp=not global_server_args_dict["enable_dp_attention"],
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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.rms_norm_eps)
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self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.eh_proj = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False)
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self.decoder = DeepseekV2DecoderLayer(
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config,
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0,
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quant_config=quant_config,
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is_nextn=True,
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prefix=add_prefix("decoder", prefix),
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)
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self.shared_head = nn.Module()
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self.shared_head.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def forward(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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forward_batch: ForwardBatch,
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input_embeds: torch.Tensor = None,
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) -> torch.Tensor:
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zero_allocator = BumpAllocator(
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buffer_size=2,
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dtype=torch.float32,
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device=(
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input_embeds.device if input_embeds is not None else input_ids.device
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),
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)
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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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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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residual = None
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hidden_states, residual = self.decoder(
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positions, hidden_states, forward_batch, residual, zero_allocator
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)
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if not forward_batch.forward_mode.is_idle():
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hidden_states, _ = self.shared_head.norm(hidden_states, residual)
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return hidden_states
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class DeepseekV3ForCausalLMNextN(DeepseekV3ForCausalLM):
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def __init__(
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self,
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config: PretrainedConfig,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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nn.Module.__init__(self)
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self.config = config
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self.tp_size = get_tensor_model_parallel_world_size()
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self.quant_config = quant_config
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self.determine_n_share_experts_fusion("DeepseekV3ForCausalLMNextN")
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self.model = DeepseekModelNextN(
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config, quant_config, prefix=add_prefix("model", prefix)
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)
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if global_server_args_dict["enable_dp_attention"]:
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self.lm_head = ReplicatedLinear(
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config.hidden_size,
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config.vocab_size,
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bias=False,
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prefix=add_prefix("model.shared_head.head", prefix),
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)
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self.logits_processor = LogitsProcessor(config, skip_all_gather=True)
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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=add_prefix("model.shared_head.head", prefix),
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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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if hasattr(self.config, "num_nextn_predict_layers"):
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num_nextn_layers = self.config.num_nextn_predict_layers
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assert num_nextn_layers == 1, "Only 1 nextn layer is supportted"
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assert num_nextn_layers == self.config.num_hidden_layers
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else:
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raise ValueError("num_nextn_predict_layers is not in the config")
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stacked_params_mapping = [
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# (param_name, shard_name, shard_id)
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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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if self.n_share_experts_fusion > 0:
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logger.info(
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f"Cloning {self.n_share_experts_fusion} "
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"replicas of the shared expert into MoE for DeepseekV3ForCausalLMNextN"
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)
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weights_list = list(weights)
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weights_dict = dict(weights_list)
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if self.quant_config is None or self.quant_config.get_name() == "w8a8_int8":
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suffix_list = [
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"down_proj.weight",
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"down_proj.weight_scale",
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"gate_proj.weight",
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"gate_proj.weight_scale",
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"up_proj.weight",
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"up_proj.weight_scale",
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]
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else:
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suffix_list = [
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"down_proj.weight",
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"down_proj.weight_scale_inv",
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"gate_proj.weight",
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"gate_proj.weight_scale_inv",
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"up_proj.weight",
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"up_proj.weight_scale_inv",
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]
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names_to_remove = []
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for num_repeat in range(self.n_share_experts_fusion):
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for suffix in suffix_list:
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shared_expert_weight_name = (
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f"model.layers.0.mlp.shared_experts.{suffix}"
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)
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weights_list.append(
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(
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f"model.layers.0."
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f"mlp.experts."
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f"{self.config.n_routed_experts + num_repeat}"
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f".{suffix}",
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weights_dict[shared_expert_weight_name],
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)
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)
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names_to_remove += [shared_expert_weight_name]
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weights = [w for w in weights_list if w[0] not in names_to_remove]
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# Params for weights, fp8 weight scales, fp8 activation scales
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# (param_name, weight_name, expert_id, shard_id)
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MoEImpl = EPMoE if global_server_args_dict["enable_ep_moe"] else FusedMoE
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expert_params_mapping = MoEImpl.make_expert_params_mapping(
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ckpt_gate_proj_name="gate_proj",
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ckpt_down_proj_name="down_proj",
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ckpt_up_proj_name="up_proj",
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num_experts=self.config.n_routed_experts + self.n_share_experts_fusion,
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)
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# Fuse q_a_proj and kv_a_proj_with_mqa along output dimension when q_lora_rank is not None
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fuse_qkv_a_proj = hasattr(self.config, "q_lora_rank") and (
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self.config.q_lora_rank is not None
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)
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cached_a_proj = {} if fuse_qkv_a_proj else None
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nextn_layer_prefix = "model.layers.0"
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nextn_spec_weight_names = [
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"shared_head.norm",
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"eh_proj",
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"enorm",
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"hnorm",
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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 not name.startswith(nextn_layer_prefix):
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continue
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# Use shared head and embed weights from target model
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if "shared_head.head" in name or "embed_tokens" in name:
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continue
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is_decoder = True
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# For nextn specific weights
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for weight_name in nextn_spec_weight_names:
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if weight_name in name:
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name = name.replace(nextn_layer_prefix, "model")
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is_decoder = False
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break
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# For decoder layer weights
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if is_decoder:
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name = name.replace(nextn_layer_prefix, "model.decoder")
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if "rotary_emb.inv_freq" in name:
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continue
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for param_name, weight_name, shard_id in stacked_params_mapping:
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# Skip non-stacked layers and experts (experts handled below).
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if weight_name not in name:
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continue
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# We have mlp.experts[0].gate_proj in the checkpoint.
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# Since we handle the experts below in expert_params_mapping,
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# we need to skip here BEFORE we update the name, otherwise
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# name will be updated to mlp.experts[0].gate_up_proj, which
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# will then be updated below in expert_params_mapping
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# for mlp.experts[0].gate_gate_up_proj, which breaks load.
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if ("mlp.experts." in name) and name not in params_dict:
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continue
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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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for mapping in expert_params_mapping:
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param_name, weight_name, expert_id, shard_id = 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(
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param,
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loaded_weight,
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name,
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shard_id=shard_id,
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expert_id=expert_id,
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)
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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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# Handle fused_qkv_a_proj
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if fuse_qkv_a_proj and (
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"q_a_proj" in name or "kv_a_proj_with_mqa" in name
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):
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cached_a_proj[name] = loaded_weight
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q_a_proj_name = (
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name
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if "q_a_proj" in name
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else name.replace("kv_a_proj_with_mqa", "q_a_proj")
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)
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kv_a_proj_name = (
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name
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if "kv_a_proj_with_mqa" in name
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else name.replace("q_a_proj", "kv_a_proj_with_mqa")
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)
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# When both q_a_proj and kv_a_proj_with_mqa has been cached, load the fused weight to parameter
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if (
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q_a_proj_name in cached_a_proj
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and kv_a_proj_name in cached_a_proj
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):
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q_a_proj_weight = cached_a_proj[q_a_proj_name]
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kv_a_proj_weight = cached_a_proj[kv_a_proj_name]
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fused_weight = torch.cat(
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[q_a_proj_weight, kv_a_proj_weight], dim=0
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)
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param_name = name.replace(
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"q_a_proj", "fused_qkv_a_proj_with_mqa"
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)
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param = params_dict[param_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, fused_weight)
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cached_a_proj.pop(q_a_proj_name)
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cached_a_proj.pop(kv_a_proj_name)
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else:
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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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self_attn = self.model.decoder.self_attn
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if hasattr(self_attn.kv_b_proj, "qweight"):
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# AWQ compatible
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if _is_cuda:
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w = awq_dequantize(
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self_attn.kv_b_proj.qweight,
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self_attn.kv_b_proj.scales,
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self_attn.kv_b_proj.qzeros,
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).T
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else:
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w = awq_dequantize(
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self_attn.kv_b_proj.qweight,
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self_attn.kv_b_proj.scales,
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self_attn.kv_b_proj.qzeros,
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0,
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0,
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0,
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).T
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else:
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w = self_attn.kv_b_proj.weight
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# NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`.
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# This may affect the accuracy of fp8 model.
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if hasattr(self.quant_config, "weight_block_size") and w.dtype in (
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torch.float8_e4m3fn,
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torch.float8_e4m3fnuz,
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):
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weight_block_size = self.quant_config.weight_block_size
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if weight_block_size is not None:
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assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
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if _is_hip:
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weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
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weight=w,
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weight_scale=self_attn.kv_b_proj.weight_scale_inv,
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input_scale=None,
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)
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else:
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weight = w
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weight_scale = self_attn.kv_b_proj.weight_scale_inv
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w, scale = block_quant_to_tensor_quant(
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weight, weight_scale, weight_block_size
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)
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self_attn.w_scale = scale
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if w.dtype == torch.int8:
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if hasattr(self.quant_config, "weight_block_size"):
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# block-wise int8 need it
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weight_block_size = self.quant_config.weight_block_size
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if weight_block_size is not None:
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assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
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weight = w
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weight_scale = self_attn.kv_b_proj.weight_scale_inv
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w = int8_block_dequant(weight, weight_scale, weight_block_size).to(
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torch.bfloat16
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)
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else:
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# channel-wise int8 need it
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assert hasattr(self_attn.kv_b_proj, "weight_scale")
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w = w.to(torch.bfloat16) * self_attn.kv_b_proj.weight_scale.to(
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torch.bfloat16
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)
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w_kc, w_vc = w.unflatten(
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0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
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).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
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self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
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self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
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if hasattr(self_attn.kv_b_proj, "weight_scale") and self_attn.w_scale is None:
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self_attn.w_scale = self_attn.kv_b_proj.weight_scale
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if _is_hip:
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self_attn.w_scale *= 2.0
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EntryClass = [DeepseekV3ForCausalLMNextN]
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