1011 lines
38 KiB
Python
1011 lines
38 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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# Adapted from:
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# https://github.com/vllm-project/vllm/blob/fb6af8bc086328ca6659e72d11ffd4309ce4de22/vllm/model_executor/models/deepseek_v2.py
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"""Inference-only DeepseekV2 model."""
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from typing import Any, Dict, Iterable, Optional, Tuple
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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 transformers import PretrainedConfig
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from vllm import _custom_ops as ops
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from sglang.srt.distributed import (
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get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size,
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get_tp_group,
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tensor_model_parallel_all_reduce,
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)
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from sglang.srt.layers.activation import SiluAndMul
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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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ColumnParallelLinear,
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MergedColumnParallelLinear,
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ReplicatedLinear,
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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.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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input_to_float8,
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normalize_e4m3fn_to_e4m3fnuz,
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)
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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, get_rope_wrapper
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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.utils import is_cuda_available, is_hip
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is_hip_ = is_hip()
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if is_cuda_available():
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from sgl_kernel import bmm_fp8
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class DeepseekV2MLP(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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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size, [intermediate_size] * 2, bias=False, quant_config=quant_config
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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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)
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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(self, 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(x)
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return x
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class MoEGate(nn.Module):
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def __init__(self, config):
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super().__init__()
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self.weight = nn.Parameter(
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torch.empty((config.n_routed_experts, config.hidden_size))
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)
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if config.topk_method == "noaux_tc":
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self.e_score_correction_bias = nn.Parameter(
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torch.empty((config.n_routed_experts))
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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, self.weight, None)
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return logits
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class DeepseekV2MoE(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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):
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super().__init__()
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self.tp_size = get_tensor_model_parallel_world_size()
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self.routed_scaling_factor = config.routed_scaling_factor
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self.n_shared_experts = config.n_shared_experts
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self.routed_scaling_factor = config.routed_scaling_factor
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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(config=config)
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MoEImpl = EPMoE if global_server_args_dict["enable_ep_moe"] else FusedMoE
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self.experts = MoEImpl(
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num_experts=config.n_routed_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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renormalize=config.norm_topk_prob,
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quant_config=quant_config,
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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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)
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if config.n_shared_experts is not None:
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intermediate_size = config.moe_intermediate_size * config.n_shared_experts
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self.shared_experts = DeepseekV2MLP(
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hidden_size=config.hidden_size,
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intermediate_size=intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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reduce_results=False,
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)
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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num_tokens, hidden_dim = hidden_states.shape
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hidden_states = hidden_states.view(-1, hidden_dim)
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if self.n_shared_experts is not None:
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shared_output = self.shared_experts(hidden_states)
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# router_logits: (num_tokens, n_experts)
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router_logits = self.gate(hidden_states)
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final_hidden_states = (
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self.experts(hidden_states=hidden_states, router_logits=router_logits)
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* self.routed_scaling_factor
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)
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if shared_output is not None:
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final_hidden_states = final_hidden_states + shared_output
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if self.tp_size > 1:
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final_hidden_states = tensor_model_parallel_all_reduce(final_hidden_states)
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return final_hidden_states.view(num_tokens, hidden_dim)
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def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
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import math
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if scale <= 1:
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return 1.0
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return 0.1 * mscale * math.log(scale) + 1.0
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class DeepseekV2Attention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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qk_nope_head_dim: int,
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qk_rope_head_dim: int,
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v_head_dim: int,
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q_lora_rank: int,
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kv_lora_rank: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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layer_id=None,
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) -> None:
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super().__init__()
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self.layer_id = layer_id
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self.hidden_size = hidden_size
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self.qk_nope_head_dim = qk_nope_head_dim
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self.qk_rope_head_dim = qk_rope_head_dim
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self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.q_lora_rank = q_lora_rank
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self.kv_lora_rank = kv_lora_rank
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self.num_heads = num_heads
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tp_size = get_tensor_model_parallel_world_size()
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assert num_heads % tp_size == 0
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self.num_local_heads = num_heads // tp_size
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self.scaling = self.qk_head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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if self.q_lora_rank is not None:
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self.q_a_proj = ReplicatedLinear(
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self.hidden_size,
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self.q_lora_rank,
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bias=False,
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quant_config=quant_config,
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)
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self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
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self.q_b_proj = ColumnParallelLinear(
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q_lora_rank,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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else:
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self.q_proj = ColumnParallelLinear(
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self.hidden_size,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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self.kv_a_proj_with_mqa = ReplicatedLinear(
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self.hidden_size,
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self.kv_lora_rank + self.qk_rope_head_dim,
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bias=False,
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quant_config=quant_config,
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# FIXME: quick fix for skip quantization
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prefix=f"self_attn.kv_a_proj_with_mqa",
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)
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self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
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self.kv_b_proj = ColumnParallelLinear(
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self.kv_lora_rank,
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self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
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bias=False,
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quant_config=quant_config,
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)
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# O projection.
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self.o_proj = RowParallelLinear(
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self.num_heads * self.v_head_dim,
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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)
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rope_scaling["rope_type"] = "deepseek_yarn"
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self.rotary_emb = get_rope_wrapper(
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qk_rope_head_dim,
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rotary_dim=qk_rope_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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is_neox_style=False,
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device=global_server_args_dict["device"],
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)
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if rope_scaling:
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mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
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scaling_factor = rope_scaling["factor"]
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mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
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self.scaling = self.scaling * mscale * mscale
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# TODO, support head_size 192
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self.attn = RadixAttention(
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self.num_local_heads,
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256,
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self.scaling,
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num_kv_heads=self.num_local_heads,
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layer_id=layer_id,
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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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if self.q_lora_rank is not None:
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q = self.q_a_proj(hidden_states)[0]
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q = self.q_a_layernorm(q)
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q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
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else:
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q = self.q_proj(hidden_states)[0].view(
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-1, self.num_local_heads, self.qk_head_dim
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)
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_, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
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latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
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kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
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latent_cache = latent_cache.unsqueeze(1)
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kv_a = self.kv_a_layernorm(kv_a.contiguous())
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kv = self.kv_b_proj(kv_a)[0]
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kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
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k_nope, v = kv.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
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k_pe = latent_cache[:, :, self.kv_lora_rank :]
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q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
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q[..., self.qk_nope_head_dim :] = q_pe
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k = torch.empty_like(q)
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k[..., : self.qk_nope_head_dim] = k_nope
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k[..., self.qk_nope_head_dim :] = k_pe
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q = torch.nn.functional.pad(q, [0, 256 - self.qk_head_dim], value=0).view(
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-1, self.num_local_heads * 256
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)
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k = torch.nn.functional.pad(k, [0, 256 - self.qk_head_dim], value=0).view(
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-1, self.num_local_heads * 256
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)
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v = torch.nn.functional.pad(v, [0, 256 - self.v_head_dim], value=0).view(
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-1, self.num_local_heads * 256
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)
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attn_output = self.attn(q, k, v, forward_batch)
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attn_output = attn_output.view(-1, self.num_local_heads, 256)[
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..., : self.v_head_dim
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].reshape(-1, self.num_local_heads * self.v_head_dim)
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output, _ = self.o_proj(attn_output)
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return output
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class DeepseekV2AttentionMLA(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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qk_nope_head_dim: int,
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qk_rope_head_dim: int,
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v_head_dim: int,
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q_lora_rank: int,
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kv_lora_rank: int,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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layer_id=None,
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use_dp=False,
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) -> None:
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super().__init__()
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self.layer_id = layer_id
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self.hidden_size = hidden_size
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self.qk_nope_head_dim = qk_nope_head_dim
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self.qk_rope_head_dim = qk_rope_head_dim
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self.qk_head_dim = qk_nope_head_dim + qk_rope_head_dim
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self.v_head_dim = v_head_dim
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self.q_lora_rank = q_lora_rank
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self.kv_lora_rank = kv_lora_rank
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self.num_heads = num_heads
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tp_size = get_tensor_model_parallel_world_size()
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assert num_heads % tp_size == 0
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self.num_local_heads = num_heads if use_dp else num_heads // tp_size
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self.scaling = self.qk_head_dim**-0.5
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self.rope_theta = rope_theta
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self.max_position_embeddings = max_position_embeddings
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if use_dp:
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# For data parallel attention
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if self.q_lora_rank is not None:
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self.q_a_proj = ReplicatedLinear(
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self.hidden_size,
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self.q_lora_rank,
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bias=False,
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quant_config=quant_config,
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)
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self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
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self.q_b_proj = ReplicatedLinear(
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q_lora_rank,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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else:
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self.q_proj = ReplicatedLinear(
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self.hidden_size,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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self.kv_b_proj = ReplicatedLinear(
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self.kv_lora_rank,
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self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
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bias=False,
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quant_config=quant_config,
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)
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# O projection.
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self.o_proj = ReplicatedLinear(
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self.num_heads * self.v_head_dim,
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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)
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else:
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# For tensor parallel attention
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if self.q_lora_rank is not None:
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self.q_a_proj = ReplicatedLinear(
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self.hidden_size,
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self.q_lora_rank,
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bias=False,
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quant_config=quant_config,
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)
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self.q_a_layernorm = RMSNorm(self.q_lora_rank, eps=config.rms_norm_eps)
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self.q_b_proj = ColumnParallelLinear(
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q_lora_rank,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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else:
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self.q_proj = ColumnParallelLinear(
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self.hidden_size,
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self.num_heads * self.qk_head_dim,
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bias=False,
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quant_config=quant_config,
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)
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self.kv_b_proj = ColumnParallelLinear(
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self.kv_lora_rank,
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self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
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bias=False,
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quant_config=quant_config,
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)
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# O projection.
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self.o_proj = RowParallelLinear(
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self.num_heads * self.v_head_dim,
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self.hidden_size,
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bias=False,
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quant_config=quant_config,
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)
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self.kv_a_proj_with_mqa = ReplicatedLinear(
|
|
self.hidden_size,
|
|
self.kv_lora_rank + self.qk_rope_head_dim,
|
|
bias=False,
|
|
quant_config=quant_config,
|
|
# FIXME: quick fix for skip quantization
|
|
prefix=f"self_attn.kv_a_proj_with_mqa",
|
|
)
|
|
self.kv_a_layernorm = RMSNorm(self.kv_lora_rank, eps=config.rms_norm_eps)
|
|
|
|
if rope_scaling:
|
|
rope_scaling["rope_type"] = "deepseek_yarn"
|
|
|
|
self.rotary_emb = get_rope(
|
|
qk_rope_head_dim,
|
|
rotary_dim=qk_rope_head_dim,
|
|
max_position=max_position_embeddings,
|
|
base=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
is_neox_style=False,
|
|
)
|
|
|
|
if rope_scaling:
|
|
mscale_all_dim = rope_scaling.get("mscale_all_dim", False)
|
|
scaling_factor = rope_scaling["factor"]
|
|
mscale = yarn_get_mscale(scaling_factor, float(mscale_all_dim))
|
|
self.scaling = self.scaling * mscale * mscale
|
|
else:
|
|
self.rotary_emb.forward = self.rotary_emb.forward_native
|
|
|
|
self.attn_mqa = RadixAttention(
|
|
self.num_local_heads,
|
|
self.kv_lora_rank + self.qk_rope_head_dim,
|
|
self.scaling,
|
|
num_kv_heads=1,
|
|
layer_id=layer_id,
|
|
v_head_dim=self.kv_lora_rank,
|
|
)
|
|
|
|
self.attn_mha = RadixAttention(
|
|
self.num_local_heads,
|
|
self.qk_nope_head_dim + self.qk_rope_head_dim,
|
|
self.scaling,
|
|
num_kv_heads=self.num_local_heads,
|
|
layer_id=layer_id,
|
|
v_head_dim=self.v_head_dim,
|
|
)
|
|
|
|
self.w_kc = None
|
|
self.w_vc = None
|
|
self.w_scale = None
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
if global_server_args_dict["enable_flashinfer_mla"]:
|
|
if forward_batch.forward_mode.is_extend():
|
|
return self.forward_normal(positions, hidden_states, forward_batch)
|
|
else:
|
|
return self.forward_absorb(positions, hidden_states, forward_batch)
|
|
else:
|
|
# Triton: Use normal computation for prefill and use weight absorption for extend/decode
|
|
if (
|
|
forward_batch.forward_mode.is_extend()
|
|
and not forward_batch.forward_mode.is_target_verify()
|
|
and not forward_batch.forward_mode.is_draft_extend()
|
|
and forward_batch.extend_prefix_lens.sum() == 0
|
|
):
|
|
return self.forward_normal(positions, hidden_states, forward_batch)
|
|
else:
|
|
return self.forward_absorb(positions, hidden_states, forward_batch)
|
|
|
|
def forward_normal(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
if self.q_lora_rank is not None:
|
|
q = self.q_a_proj(hidden_states)[0]
|
|
q = self.q_a_layernorm(q)
|
|
q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
|
|
else:
|
|
q = self.q_proj(hidden_states)[0].view(
|
|
-1, self.num_local_heads, self.qk_head_dim
|
|
)
|
|
_, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
|
|
latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
|
|
kv_a, _ = latent_cache.split([self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
|
latent_cache = latent_cache.unsqueeze(1)
|
|
kv_a = self.kv_a_layernorm(kv_a.contiguous())
|
|
kv = self.kv_b_proj(kv_a)[0]
|
|
kv = kv.view(-1, self.num_local_heads, self.qk_nope_head_dim + self.v_head_dim)
|
|
k_nope = kv[..., : self.qk_nope_head_dim]
|
|
v = kv[..., self.qk_nope_head_dim :]
|
|
k_pe = latent_cache[:, :, self.kv_lora_rank :]
|
|
q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
|
|
q[..., self.qk_nope_head_dim :] = q_pe
|
|
k = torch.empty_like(q)
|
|
k[..., : self.qk_nope_head_dim] = k_nope
|
|
k[..., self.qk_nope_head_dim :] = k_pe
|
|
|
|
latent_cache[:, :, : self.kv_lora_rank] = kv_a.unsqueeze(1)
|
|
latent_cache[:, :, self.kv_lora_rank :] = k_pe
|
|
|
|
# Save latent cache
|
|
forward_batch.token_to_kv_pool.set_kv_buffer(
|
|
self.attn_mha, forward_batch.out_cache_loc, latent_cache, None
|
|
)
|
|
attn_output = self.attn_mha(q, k, v, forward_batch, save_kv_cache=False)
|
|
attn_output = attn_output.reshape(-1, self.num_local_heads * self.v_head_dim)
|
|
output, _ = self.o_proj(attn_output)
|
|
return output
|
|
|
|
def forward_absorb(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
q_len = hidden_states.shape[0]
|
|
q_input = hidden_states.new_empty(
|
|
q_len, self.num_local_heads, self.kv_lora_rank + self.qk_rope_head_dim
|
|
)
|
|
if self.q_lora_rank is not None:
|
|
q = self.q_a_proj(hidden_states)[0]
|
|
q = self.q_a_layernorm(q)
|
|
q = self.q_b_proj(q)[0].view(-1, self.num_local_heads, self.qk_head_dim)
|
|
else:
|
|
q = self.q_proj(hidden_states)[0].view(
|
|
-1, self.num_local_heads, self.qk_head_dim
|
|
)
|
|
q_nope, q_pe = q.split([self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
|
|
|
|
if self.w_kc.dtype == torch.float8_e4m3fnuz:
|
|
# TODO(kernel): add bmm_fp8 for torch.float8_e4m3fnuz
|
|
q_nope_out = torch.bmm(
|
|
q_nope.to(torch.bfloat16).transpose(0, 1),
|
|
self.w_kc.to(torch.bfloat16) * self.w_scale,
|
|
)
|
|
elif self.w_kc.dtype == torch.float8_e4m3fn:
|
|
q_nope_val, q_nope_scale = input_to_float8(
|
|
q_nope.transpose(0, 1), torch.float8_e4m3fn
|
|
)
|
|
q_nope_out = bmm_fp8(
|
|
q_nope_val, self.w_kc, q_nope_scale, self.w_scale, torch.bfloat16
|
|
)
|
|
else:
|
|
q_nope_out = torch.bmm(q_nope.transpose(0, 1), self.w_kc)
|
|
q_input[..., : self.kv_lora_rank] = q_nope_out.transpose(0, 1)
|
|
|
|
latent_cache = self.kv_a_proj_with_mqa(hidden_states)[0]
|
|
v_input = latent_cache[..., : self.kv_lora_rank]
|
|
v_input = self.kv_a_layernorm(v_input.contiguous()).unsqueeze(1)
|
|
k_input = latent_cache.unsqueeze(1)
|
|
k_input[..., : self.kv_lora_rank] = v_input
|
|
k_pe = k_input[..., self.kv_lora_rank :]
|
|
|
|
q_pe, k_pe = self.rotary_emb(positions, q_pe, k_pe)
|
|
q_input[..., self.kv_lora_rank :] = q_pe
|
|
k_input[..., self.kv_lora_rank :] = k_pe
|
|
|
|
attn_output = self.attn_mqa(q_input, k_input, v_input, forward_batch)
|
|
attn_output = attn_output.view(-1, self.num_local_heads, self.kv_lora_rank)
|
|
|
|
if self.w_vc.dtype == torch.float8_e4m3fnuz:
|
|
# TODO(kernel): add bmm_fp8 for torch.float8_e4m3fnuz
|
|
attn_bmm_output = torch.bmm(
|
|
attn_output.to(torch.bfloat16).transpose(0, 1),
|
|
self.w_vc.to(torch.bfloat16) * self.w_scale,
|
|
)
|
|
elif self.w_vc.dtype == torch.float8_e4m3fn:
|
|
attn_output_val, attn_output_scale = input_to_float8(
|
|
attn_output.transpose(0, 1), torch.float8_e4m3fn
|
|
)
|
|
attn_bmm_output = bmm_fp8(
|
|
attn_output_val,
|
|
self.w_vc,
|
|
attn_output_scale,
|
|
self.w_scale,
|
|
torch.bfloat16,
|
|
)
|
|
else:
|
|
attn_bmm_output = torch.bmm(attn_output.transpose(0, 1), self.w_vc)
|
|
attn_output = attn_bmm_output.transpose(0, 1).flatten(1, 2)
|
|
output, _ = self.o_proj(attn_output)
|
|
|
|
return output
|
|
|
|
|
|
def all_gather(
|
|
input_tensor: torch.Tensor, forward_batch: ForwardBatch, rank, world_size, group
|
|
):
|
|
if world_size == 1:
|
|
return input_tensor
|
|
|
|
all_lens = forward_batch.global_num_tokens
|
|
max_len = max(forward_batch.global_num_tokens)
|
|
|
|
padded_tensor = torch.nn.functional.pad(
|
|
input_tensor, (0, 0, 0, max_len - input_tensor.shape[0])
|
|
)
|
|
|
|
torch.distributed.all_gather_into_tensor(
|
|
forward_batch.gathered_buffer, padded_tensor, group=group
|
|
)
|
|
|
|
gathered_tensors = torch.concat(
|
|
[
|
|
forward_batch.gathered_buffer[i * max_len : i * max_len + all_lens[i]]
|
|
for i in range(world_size)
|
|
]
|
|
)
|
|
|
|
start_index = 0 if rank == 0 else sum(all_lens[:rank])
|
|
end_index = start_index + all_lens[rank]
|
|
|
|
return gathered_tensors, start_index, end_index
|
|
|
|
|
|
class DeepseekV2DecoderLayer(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
layer_id: int,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
is_nextn: bool = False,
|
|
) -> None:
|
|
super().__init__()
|
|
self.hidden_size = config.hidden_size
|
|
rope_theta = getattr(config, "rope_theta", 10000)
|
|
rope_scaling = getattr(config, "rope_scaling", None)
|
|
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
|
|
self.enable_dp_attention = (
|
|
not global_server_args_dict["disable_mla"]
|
|
and global_server_args_dict["enable_dp_attention"]
|
|
)
|
|
if self.enable_dp_attention:
|
|
self.tp_rank = get_tensor_model_parallel_rank()
|
|
self.tp_size = get_tensor_model_parallel_world_size()
|
|
self.tp_group = get_tp_group().device_group
|
|
if not global_server_args_dict["disable_mla"]:
|
|
self.self_attn = DeepseekV2AttentionMLA(
|
|
config=config,
|
|
hidden_size=self.hidden_size,
|
|
num_heads=config.num_attention_heads,
|
|
qk_nope_head_dim=config.qk_nope_head_dim,
|
|
qk_rope_head_dim=config.qk_rope_head_dim,
|
|
v_head_dim=config.v_head_dim,
|
|
q_lora_rank=(
|
|
config.q_lora_rank if hasattr(config, "q_lora_rank") else None
|
|
),
|
|
kv_lora_rank=config.kv_lora_rank,
|
|
rope_theta=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
max_position_embeddings=max_position_embeddings,
|
|
quant_config=quant_config,
|
|
layer_id=layer_id,
|
|
use_dp=self.enable_dp_attention,
|
|
)
|
|
else:
|
|
self.self_attn = DeepseekV2Attention(
|
|
config=config,
|
|
hidden_size=self.hidden_size,
|
|
num_heads=config.num_attention_heads,
|
|
qk_nope_head_dim=config.qk_nope_head_dim,
|
|
qk_rope_head_dim=config.qk_rope_head_dim,
|
|
v_head_dim=config.v_head_dim,
|
|
q_lora_rank=(
|
|
config.q_lora_rank if hasattr(config, "q_lora_rank") else None
|
|
),
|
|
kv_lora_rank=config.kv_lora_rank,
|
|
rope_theta=rope_theta,
|
|
rope_scaling=rope_scaling,
|
|
max_position_embeddings=max_position_embeddings,
|
|
quant_config=quant_config,
|
|
layer_id=layer_id,
|
|
)
|
|
if is_nextn or (
|
|
config.n_routed_experts is not None
|
|
and layer_id >= config.first_k_dense_replace
|
|
and layer_id % config.moe_layer_freq == 0
|
|
):
|
|
self.mlp = DeepseekV2MoE(config=config, quant_config=quant_config)
|
|
else:
|
|
self.mlp = DeepseekV2MLP(
|
|
hidden_size=config.hidden_size,
|
|
intermediate_size=config.intermediate_size,
|
|
hidden_act=config.hidden_act,
|
|
quant_config=quant_config,
|
|
)
|
|
self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
self.post_attention_layernorm = RMSNorm(
|
|
config.hidden_size, eps=config.rms_norm_eps
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
positions: torch.Tensor,
|
|
hidden_states: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
residual: Optional[torch.Tensor],
|
|
) -> torch.Tensor:
|
|
# Self Attention
|
|
if not forward_batch.forward_mode.is_idle():
|
|
if residual is None:
|
|
residual = hidden_states
|
|
hidden_states = self.input_layernorm(hidden_states)
|
|
else:
|
|
hidden_states, residual = self.input_layernorm(hidden_states, residual)
|
|
|
|
hidden_states = self.self_attn(
|
|
positions=positions,
|
|
hidden_states=hidden_states,
|
|
forward_batch=forward_batch,
|
|
)
|
|
hidden_states, residual = self.post_attention_layernorm(
|
|
hidden_states, residual
|
|
)
|
|
|
|
# Fully Connected
|
|
if self.enable_dp_attention:
|
|
hidden_states, start_idx, end_idx = all_gather(
|
|
hidden_states, forward_batch, self.tp_rank, self.tp_size, self.tp_group
|
|
)
|
|
hidden_states = self.mlp(hidden_states)
|
|
hidden_states = hidden_states[start_idx:end_idx]
|
|
else:
|
|
hidden_states = self.mlp(hidden_states)
|
|
|
|
return hidden_states, residual
|
|
|
|
|
|
class DeepseekV2Model(nn.Module):
|
|
|
|
fall_back_to_pt_during_load = False
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.padding_id = config.pad_token_id
|
|
self.vocab_size = config.vocab_size
|
|
|
|
self.embed_tokens = VocabParallelEmbedding(
|
|
config.vocab_size,
|
|
config.hidden_size,
|
|
enable_tp=not global_server_args_dict["enable_dp_attention"],
|
|
)
|
|
self.layers = nn.ModuleList(
|
|
[
|
|
DeepseekV2DecoderLayer(
|
|
config,
|
|
layer_id,
|
|
quant_config=quant_config,
|
|
)
|
|
for layer_id in range(config.num_hidden_layers)
|
|
]
|
|
)
|
|
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
|
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.embed_tokens(input_ids)
|
|
residual = None
|
|
for i in range(len(self.layers)):
|
|
layer = self.layers[i]
|
|
hidden_states, residual = layer(
|
|
positions, hidden_states, forward_batch, residual
|
|
)
|
|
if not forward_batch.forward_mode.is_idle():
|
|
hidden_states, _ = self.norm(hidden_states, residual)
|
|
return hidden_states
|
|
|
|
|
|
class DeepseekV2ForCausalLM(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
config: PretrainedConfig,
|
|
quant_config: Optional[QuantizationConfig] = None,
|
|
) -> None:
|
|
super().__init__()
|
|
self.config = config
|
|
self.quant_config = quant_config
|
|
self.model = DeepseekV2Model(config, quant_config)
|
|
if global_server_args_dict["enable_dp_attention"]:
|
|
self.lm_head = ReplicatedLinear(
|
|
config.hidden_size,
|
|
config.vocab_size,
|
|
bias=False,
|
|
)
|
|
self.logits_processor = LogitsProcessor(config, skip_all_gather=True)
|
|
else:
|
|
self.lm_head = ParallelLMHead(
|
|
config.vocab_size, config.hidden_size, quant_config=quant_config
|
|
)
|
|
self.logits_processor = LogitsProcessor(config)
|
|
|
|
@torch.no_grad()
|
|
def forward(
|
|
self,
|
|
input_ids: torch.Tensor,
|
|
positions: torch.Tensor,
|
|
forward_batch: ForwardBatch,
|
|
) -> torch.Tensor:
|
|
hidden_states = self.model(input_ids, positions, forward_batch)
|
|
return self.logits_processor(
|
|
input_ids, hidden_states, self.lm_head, forward_batch
|
|
)
|
|
|
|
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
|
stacked_params_mapping = [
|
|
# (param_name, shard_name, shard_id)
|
|
("gate_up_proj", "gate_proj", 0),
|
|
("gate_up_proj", "up_proj", 1),
|
|
]
|
|
|
|
# Params for weights, fp8 weight scales, fp8 activation scales
|
|
# (param_name, weight_name, expert_id, shard_id)
|
|
MoEImpl = EPMoE if global_server_args_dict["enable_ep_moe"] else FusedMoE
|
|
expert_params_mapping = MoEImpl.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:
|
|
# TODO(HandH1998): Modify it when nextn is supported.
|
|
if hasattr(self.config, "num_nextn_predict_layers"):
|
|
num_nextn_layers = self.config.num_nextn_predict_layers
|
|
if num_nextn_layers > 0 and name.startswith("model.layers"):
|
|
name_list = name.split(".")
|
|
if (
|
|
len(name_list) >= 3
|
|
and int(name_list[2]) >= self.config.num_hidden_layers
|
|
):
|
|
continue
|
|
if "rotary_emb.inv_freq" in name:
|
|
continue
|
|
for param_name, weight_name, shard_id in stacked_params_mapping:
|
|
# Skip non-stacked layers and experts (experts handled below).
|
|
if weight_name not in name:
|
|
continue
|
|
# We have mlp.experts[0].gate_proj in the checkpoint.
|
|
# Since we handle the experts below in expert_params_mapping,
|
|
# we need to skip here BEFORE we update the name, otherwise
|
|
# name will be updated to mlp.experts[0].gate_up_proj, which
|
|
# will then be updated below in expert_params_mapping
|
|
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
|
|
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
|
|
|
|
param = params_dict[name]
|
|
weight_loader = getattr(
|
|
param, "weight_loader", default_weight_loader
|
|
)
|
|
weight_loader(param, loaded_weight)
|
|
|
|
if not global_server_args_dict["disable_mla"]:
|
|
for layer_id in range(self.config.num_hidden_layers):
|
|
self_attn = self.model.layers[layer_id].self_attn
|
|
if hasattr(self_attn.kv_b_proj, "qweight"):
|
|
# AWQ compatible
|
|
w = ops.awq_dequantize(
|
|
self_attn.kv_b_proj.qweight,
|
|
self_attn.kv_b_proj.scales,
|
|
self_attn.kv_b_proj.qzeros,
|
|
0,
|
|
0,
|
|
0,
|
|
).T
|
|
else:
|
|
w = self_attn.kv_b_proj.weight
|
|
# NOTE(HandH1998): Since `bmm_fp8` only supports per-tensor scale, we have to requantize `self_attn.kv_b_proj`.
|
|
# This may affect the accuracy of fp8 model.
|
|
if hasattr(self.quant_config, "weight_block_size") and w.dtype in (
|
|
torch.float8_e4m3fn,
|
|
torch.float8_e4m3fnuz,
|
|
):
|
|
weight_block_size = self.quant_config.weight_block_size
|
|
if weight_block_size is not None:
|
|
assert hasattr(self_attn.kv_b_proj, "weight_scale_inv")
|
|
if is_hip_:
|
|
weight, weight_scale, _ = normalize_e4m3fn_to_e4m3fnuz(
|
|
weight=w,
|
|
weight_scale=self_attn.kv_b_proj.weight_scale_inv,
|
|
input_scale=None,
|
|
)
|
|
else:
|
|
weight = w
|
|
weight_scale = self_attn.kv_b_proj.weight_scale_inv
|
|
|
|
w, scale = block_quant_to_tensor_quant(
|
|
weight, weight_scale, weight_block_size
|
|
)
|
|
self_attn.w_scale = scale
|
|
w_kc, w_vc = w.unflatten(
|
|
0, (-1, self_attn.qk_nope_head_dim + self_attn.v_head_dim)
|
|
).split([self_attn.qk_nope_head_dim, self_attn.v_head_dim], dim=1)
|
|
self_attn.w_kc = w_kc.transpose(1, 2).contiguous().transpose(1, 2)
|
|
self_attn.w_vc = w_vc.contiguous().transpose(1, 2)
|
|
if (
|
|
hasattr(self_attn.kv_b_proj, "weight_scale")
|
|
and self_attn.w_scale is None
|
|
):
|
|
self_attn.w_scale = self_attn.kv_b_proj.weight_scale
|
|
if is_hip_:
|
|
self_attn.w_scale *= 2.0
|
|
|
|
|
|
class DeepseekV3ForCausalLM(DeepseekV2ForCausalLM):
|
|
pass
|
|
|
|
|
|
EntryClass = [DeepseekV2ForCausalLM, DeepseekV3ForCausalLM]
|