Add Tencent HunYuanMoEV1 model support (#7549)
This commit is contained in:
Meng, Peng
@@ -890,6 +890,43 @@ class Llama4VisionRotaryEmbedding(RotaryEmbedding):
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return query_out.type_as(query), key_out.type_as(key)
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class DynamicNTKAlphaRotaryEmbedding(RotaryEmbedding):
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"""RotaryEmbedding extended with Dynamic NTK scaling.
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Credits to the Reddit users /u/bloc97 and /u/emozilla
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"""
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def __init__(
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self,
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head_size: int,
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rotary_dim: int,
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max_position_embeddings: int,
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base: int,
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is_neox_style: bool,
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scaling_alpha: float,
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dtype: torch.dtype,
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) -> None:
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self.scaling_alpha = scaling_alpha
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super().__init__(
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head_size, rotary_dim, max_position_embeddings, base, is_neox_style, dtype
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)
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def _compute_cos_sin_cache(self) -> torch.Tensor:
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max_len = self.max_position_embeddings
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base = self.base * self.scaling_alpha ** (
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self.rotary_dim / (self.rotary_dim - 2)
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)
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inv_freq = self._compute_inv_freq(base)
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t = torch.arange(max_len, dtype=torch.float)
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freqs = torch.einsum("i,j -> ij", t, inv_freq)
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cos = freqs.cos()
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sin = freqs.sin()
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cache = torch.cat((cos, sin), dim=-1)
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return cache
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class MRotaryEmbedding(RotaryEmbedding):
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"""Rotary Embedding with Multimodal Sections."""
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@@ -1234,15 +1271,26 @@ def get_rope(
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)
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elif scaling_type == "dynamic":
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scaling_factor = rope_scaling["factor"]
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rotary_emb = DynamicNTKScalingRotaryEmbedding(
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head_size,
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rotary_dim,
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max_position,
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base,
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is_neox_style,
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scaling_factor,
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dtype,
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)
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if "alpha" in rope_scaling:
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rotary_emb = DynamicNTKAlphaRotaryEmbedding(
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head_size,
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rotary_dim,
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max_position,
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base,
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is_neox_style,
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rope_scaling["alpha"],
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dtype,
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)
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else:
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rotary_emb = DynamicNTKScalingRotaryEmbedding(
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head_size,
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rotary_dim,
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max_position,
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base,
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is_neox_style,
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scaling_factor,
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dtype,
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)
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elif scaling_type == "yarn":
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scaling_factor = rope_scaling["factor"]
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original_max_position = rope_scaling["original_max_position_embeddings"]
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771
python/sglang/srt/models/hunyuan.py
Normal file
771
python/sglang/srt/models/hunyuan.py
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@@ -0,0 +1,771 @@
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# coding=utf-8
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# Copyright 2024 The HunYuan 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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"""Inference-only HunYuan model compatible with HuggingFace weights."""
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import logging
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import re
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from dataclasses import dataclass
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from enum import Enum, auto
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from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
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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 (
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get_pp_group,
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get_tensor_model_parallel_rank,
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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.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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QKVParallelLinear,
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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.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.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.sampler import Sampler
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from sglang.srt.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE,
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.managers.expert_distribution import ExpertDistributionRecorder
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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 (
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default_weight_loader,
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kv_cache_scales_loader,
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maybe_remap_kv_scale_name,
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)
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from sglang.srt.utils import add_prefix, is_hip
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expert_distribution_recorder = ExpertDistributionRecorder()
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def _is_moe(config: PretrainedConfig) -> bool:
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if getattr(config, "num_experts", None) and (
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(isinstance(config.num_experts, int) and config.num_experts > 1)
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or (isinstance(config.num_experts, list) and max(config.num_experts) > 1)
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):
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return True
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else:
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return False
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def _get_cla_factor(config: PretrainedConfig) -> int:
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if not getattr(config, "use_cla", False):
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return 1
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return getattr(config, "cla_share_factor", 1)
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class HunYuanMLP(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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bias: bool = False,
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prefix: str = "",
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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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input_size=hidden_size,
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output_sizes=[intermediate_size] * 2,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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input_size=intermediate_size,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.down_proj",
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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 HunYuanSparseMoeBlock(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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layer_id: int = -1,
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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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if self.tp_size > config.num_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.num_experts}."
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)
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# Get layer_id topk if config.moe_topk is a list
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if isinstance(config.moe_topk, list):
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assert layer_id >= 0
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assert len(config.moe_topk) > layer_id
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top_k = config.moe_topk[layer_id]
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else:
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top_k = config.moe_topk
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# If it is moe, moe_intermediate_size is preferred
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intermediate_size = config.intermediate_size
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if config.moe_intermediate_size is not None:
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intermediate_size = (
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config.moe_intermediate_size
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if isinstance(config.moe_intermediate_size, int)
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else config.moe_intermediate_size[layer_id]
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)
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self.experts = FusedMoE(
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num_experts=config.num_experts,
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top_k=top_k,
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hidden_size=config.hidden_size,
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intermediate_size=intermediate_size,
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reduce_results=False,
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renormalize=True if top_k > 1 else False,
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quant_config=quant_config,
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)
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self.gate = ReplicatedLinear(
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config.hidden_size, config.num_experts, bias=False, quant_config=None
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)
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if config.use_mixed_mlp_moe > 0:
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# Get layer_id num_shared_expert if config.num_shared_expert is a list
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if isinstance(config.num_shared_expert, list):
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assert layer_id >= 0
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assert len(config.num_shared_expert) > layer_id
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num_shared_expert = config.num_shared_expert[layer_id]
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else:
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num_shared_expert = config.num_shared_expert
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self.shared_mlp = HunYuanMLP(
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hidden_size=config.hidden_size,
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intermediate_size=config.intermediate_size * num_shared_expert,
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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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else:
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self.shared_mlp = None
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def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
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# NOTE: hidden_states can have either 1D or 2D shape.
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orig_shape = hidden_states.shape
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hidden_dim = hidden_states.shape[-1]
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hidden_states = hidden_states.view(-1, hidden_dim)
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shared_output = None
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if self.shared_mlp is not None:
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shared_output = self.shared_mlp(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 = self.experts(
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hidden_states=hidden_states, router_logits=router_logits
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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(orig_shape)
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class HunYuanAttention(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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num_kv_heads: 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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bias: bool = False,
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prefix: str = "",
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attention_type: str = "self",
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layer_id: int = -1,
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) -> None:
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super().__init__()
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self.hidden_size = hidden_size
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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = num_heads
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assert self.total_num_heads % tp_size == 0
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self.num_heads = self.total_num_heads // tp_size
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self.total_num_kv_heads = num_kv_heads
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if self.total_num_kv_heads >= 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 % 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 tp_size % self.total_num_kv_heads == 0
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self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
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# MistralConfig has an optional head_dim introduced by Mistral-Nemo
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self.head_dim = getattr(
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config, "head_dim", self.hidden_size // self.total_num_heads
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)
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self.q_size = self.num_heads * self.head_dim
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self.kv_size = self.num_kv_heads * self.head_dim
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self.scaling = self.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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self.use_qk_norm = getattr(config, "use_qk_norm", False)
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self.attention_type = attention_type
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self.layer_id = layer_id
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if attention_type == "self":
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self.qkv_proj = QKVParallelLinear(
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hidden_size=hidden_size,
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head_size=self.head_dim,
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total_num_heads=self.total_num_heads,
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total_num_kv_heads=self.total_num_kv_heads,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.qkv_proj",
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)
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elif attention_type == "cross":
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self.q_proj = ColumnParallelLinear(
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hidden_size,
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hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.q_proj",
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)
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else:
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raise RuntimeError("Not support attnention type")
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self.o_proj = RowParallelLinear(
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input_size=self.total_num_heads * self.head_dim,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.o_proj",
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)
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is_neox_style = True
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if quant_config is not None and quant_config.get_name() == "gguf":
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is_neox_style = False
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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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is_neox_style=is_neox_style,
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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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prefix=f"{prefix}.attn",
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)
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if self.use_qk_norm:
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self.query_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.key_layernorm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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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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kv_states: Optional[Tuple[torch.Tensor]] = None,
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) -> torch.Tensor:
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if self.attention_type == "self":
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q, k = self.rotary_emb(positions, q, k)
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ori_k = k
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if self.use_qk_norm:
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# q = self.query_layernorm(q.view(-1, self.num_heads, self.head_dim).contiguous())
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# k = self.key_layernorm(k.view(-1, self.num_kv_heads, self.head_dim).contiguous())
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q = self.query_layernorm(q.reshape(-1, self.head_dim).contiguous())
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k = self.key_layernorm(k.reshape(-1, self.head_dim).contiguous())
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elif self.attention_type == "cross":
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assert kv_states is not None
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ori_k, v = kv_states # use last layer kv,
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k = ori_k
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q, _ = self.q_proj(hidden_states)
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k_tmp = torch.empty_like(k) # Todo: reduant rotary embedding
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q, _ = self.rotary_emb(positions, q, k_tmp)
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if self.use_qk_norm:
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q = self.query_layernorm(
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q.view(-1, self.num_heads, self.head_dim).contiguous()
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)
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k = self.key_layernorm(
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k.view(-1, self.num_kv_heads, self.head_dim).contiguous()
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)
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else:
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raise RuntimeError("Not support attnention type")
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attn_output = self.attn(q, k, v, forward_batch)
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output, _ = self.o_proj(attn_output)
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return output, (ori_k, v)
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class HunYuanDecoderLayer(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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layer_id: int = -1,
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) -> None:
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super().__init__()
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assert layer_id >= 0
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self.layer_id = layer_id
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self.hidden_size = config.hidden_size
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self.intermediate_size = (
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config.intermediate_size
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if isinstance(config.intermediate_size, int)
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else config.intermediate_size[layer_id]
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)
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rope_theta = getattr(config, "rope_theta", 10000)
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rope_scaling = getattr(config, "rope_scaling", None)
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if rope_scaling is not None and getattr(
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||||
config, "original_max_position_embeddings", None
|
||||
):
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rope_scaling["original_max_position_embeddings"] = (
|
||||
config.original_max_position_embeddings
|
||||
)
|
||||
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
|
||||
# Support abacusai/Smaug-72B-v0.1 with attention_bias
|
||||
# Support internlm/internlm-7b with bias
|
||||
attention_bias = getattr(config, "attention_bias", False) or getattr(
|
||||
config, "bias", False
|
||||
)
|
||||
cla_factor = _get_cla_factor(config)
|
||||
attention_type = (
|
||||
"cross" if layer_id >= 0 and layer_id % cla_factor != 0 else "self"
|
||||
)
|
||||
self.self_attn = HunYuanAttention(
|
||||
config=config,
|
||||
hidden_size=self.hidden_size,
|
||||
num_heads=config.num_attention_heads,
|
||||
num_kv_heads=getattr(
|
||||
config, "num_key_value_heads", config.num_attention_heads
|
||||
),
|
||||
rope_theta=rope_theta,
|
||||
rope_scaling=rope_scaling,
|
||||
max_position_embeddings=max_position_embeddings,
|
||||
quant_config=quant_config,
|
||||
bias=attention_bias,
|
||||
prefix=f"{prefix}.self_attn",
|
||||
attention_type=attention_type,
|
||||
layer_id=layer_id,
|
||||
)
|
||||
if _is_moe(config):
|
||||
self.mlp = HunYuanSparseMoeBlock(
|
||||
config=config,
|
||||
quant_config=quant_config,
|
||||
layer_id=layer_id,
|
||||
)
|
||||
else:
|
||||
self.mlp = HunYuanMLP(
|
||||
hidden_size=self.hidden_size,
|
||||
intermediate_size=self.intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
quant_config=quant_config,
|
||||
bias=getattr(config, "mlp_bias", False),
|
||||
prefix=f"{prefix}.mlp",
|
||||
)
|
||||
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],
|
||||
kv_states: Optional[Tuple[torch.Tensor]] = None,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
# Self Attention
|
||||
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, ori_kv_states = self.self_attn(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
forward_batch=forward_batch,
|
||||
kv_states=kv_states,
|
||||
)
|
||||
|
||||
# Fully Connected
|
||||
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
return hidden_states, residual, ori_kv_states
|
||||
|
||||
|
||||
class HunYuanModel(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
self.org_vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
self.vocab_size,
|
||||
config.hidden_size,
|
||||
)
|
||||
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
HunYuanDecoderLayer(
|
||||
config=config,
|
||||
layer_id=layer_id,
|
||||
quant_config=quant_config,
|
||||
# prefix=prefix
|
||||
)
|
||||
for layer_id in range(config.num_hidden_layers)
|
||||
]
|
||||
)
|
||||
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.embed_tokens(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: Optional[torch.Tensor],
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
input_embeds: Optional[torch.Tensor] = None,
|
||||
) -> torch.Tensor:
|
||||
if input_embeds is not None:
|
||||
hidden_states = input_embeds
|
||||
else:
|
||||
hidden_states = self.get_input_embeddings(input_ids)
|
||||
residual = None
|
||||
|
||||
cla_factor = _get_cla_factor(self.config)
|
||||
prev_kv_states = None
|
||||
for i in range(len(self.layers)):
|
||||
layer = self.layers[i]
|
||||
hidden_states, residual, kv_states = layer(
|
||||
positions,
|
||||
hidden_states,
|
||||
forward_batch,
|
||||
residual,
|
||||
prev_kv_states,
|
||||
)
|
||||
|
||||
if False: # (i - self.start_layer) % cla_factor == 0:
|
||||
prev_kv_states = kv_states
|
||||
else:
|
||||
prev_kv_states = None
|
||||
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class HunYuanMoEV1ForCausalLM(nn.Module):
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": [
|
||||
"q_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
],
|
||||
"gate_up_proj": [
|
||||
"gate_proj",
|
||||
"up_proj",
|
||||
],
|
||||
}
|
||||
|
||||
embedding_modules = {
|
||||
"embed_tokens": "input_embeddings",
|
||||
"lm_head": "output_embeddings",
|
||||
}
|
||||
embedding_padding_modules = ["lm_head"]
|
||||
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: PretrainedConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
|
||||
self.config = config
|
||||
|
||||
self.model = HunYuanModel(config, quant_config, prefix="model")
|
||||
self.unpadded_vocab_size = config.vocab_size
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
if config.tie_word_embeddings:
|
||||
self.lm_head.weight = self.model.embed_tokens.weight
|
||||
|
||||
logit_scale = getattr(config, "logit_scale", 1.0)
|
||||
self.logits_processor = LogitsProcessor(config, logit_scale=logit_scale)
|
||||
self.sampler = Sampler()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
input_embeds: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
|
||||
return self.logits_processor(
|
||||
input_ids, hidden_states, self.lm_head, forward_batch
|
||||
)
|
||||
|
||||
def _split_qkv_weight(self, qkv: torch.Tensor):
|
||||
num_attention_heads = self.config.num_attention_heads
|
||||
num_kv_heads = getattr(
|
||||
self.config, "num_key_value_heads", self.config.num_attention_heads
|
||||
)
|
||||
num_key_value_groups = num_attention_heads // num_kv_heads
|
||||
hidden_size = self.config.hidden_size
|
||||
attention_head_dim = self.config.hidden_size // num_attention_heads
|
||||
|
||||
qkv = qkv.reshape(
|
||||
num_kv_heads, num_key_value_groups + 2, attention_head_dim, hidden_size
|
||||
)
|
||||
q, k, v = torch.split(qkv, (num_key_value_groups, 1, 1), dim=1)
|
||||
q = q.reshape(-1, hidden_size)
|
||||
k = k.reshape(-1, hidden_size)
|
||||
v = v.reshape(-1, hidden_size)
|
||||
return torch.concat((q, k, v))
|
||||
# return qkv.reshape((num_kv_heads, num_key_value_groups+2 , attention_head_dim, hidden_size)).permute((1,0,2,3)).reshape((-1, hidden_size)),
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
cla_factor = _get_cla_factor(self.config)
|
||||
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),
|
||||
]
|
||||
|
||||
num_attention_heads = self.config.num_attention_heads
|
||||
num_kv_heads = getattr(
|
||||
self.config, "num_key_value_heads", self.config.num_attention_heads
|
||||
)
|
||||
split_params_mapping = [
|
||||
(".gate_up_proj", ".gate_and_up_proj", 2, [(1, 1), (0, 1)], None),
|
||||
(
|
||||
".qkv_proj",
|
||||
".qkv_proj",
|
||||
num_attention_heads + num_kv_heads * 2,
|
||||
[("q", num_attention_heads), ("k", num_kv_heads), ("v", num_kv_heads)],
|
||||
self._split_qkv_weight,
|
||||
),
|
||||
]
|
||||
|
||||
if _is_moe(self.config):
|
||||
# Params for weights, fp8 weight scales, fp8 activation scales
|
||||
# (param_name, weight_name, expert_id, shard_id)
|
||||
expert_params_mapping = FusedMoE.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.num_experts,
|
||||
)
|
||||
else:
|
||||
expert_params_mapping = {}
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
if "gate_proj_bias" in name:
|
||||
name = name.replace("gate_proj_bias", "gate_proj.bias")
|
||||
if "up_proj_bias" in name:
|
||||
name = name.replace("up_proj_bias", "up_proj.bias")
|
||||
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
|
||||
# With tie_word_embeddings, we can skip lm_head.weight
|
||||
# The weight might appear unnecessarily in the files if the model is
|
||||
# processed with quantization, LoRA, fine-tuning, etc.
|
||||
if self.config.tie_word_embeddings and "lm_head.weight" in name:
|
||||
continue
|
||||
|
||||
is_found = False
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
if "mlp.experts" in name:
|
||||
continue
|
||||
# cross layer only have q_proj, skip qkv pack
|
||||
if weight_name == ".q_proj":
|
||||
match = re.search(r"layers\.\d+", name)
|
||||
if match:
|
||||
layer_id = int(match.group(0).split(".")[-1])
|
||||
if cla_factor > 1 and layer_id % cla_factor != 0:
|
||||
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)
|
||||
|
||||
is_found = True
|
||||
break
|
||||
if is_found:
|
||||
continue
|
||||
|
||||
for param_name, weight_name, den, split_param, func in split_params_mapping:
|
||||
if weight_name not in name:
|
||||
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
|
||||
|
||||
assert loaded_weight.shape[0] % den == 0
|
||||
units = loaded_weight.shape[0] // den
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
offset = 0
|
||||
for shard_id, num in split_param:
|
||||
new_offset = offset + num * units
|
||||
if func:
|
||||
weight_loader(
|
||||
param, func(loaded_weight)[offset:new_offset], shard_id
|
||||
)
|
||||
else:
|
||||
weight_loader(param, loaded_weight[offset:new_offset], shard_id)
|
||||
offset = new_offset
|
||||
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
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)
|
||||
# Skip layers on other devices.
|
||||
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:
|
||||
# Remapping the name of FP8 kv-scale.
|
||||
name = maybe_remap_kv_scale_name(name, params_dict)
|
||||
if name is None:
|
||||
continue
|
||||
|
||||
if "mlp.gate.wg." in name:
|
||||
name = name.replace("wg.", "")
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
# 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:
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
tp_rank = get_tensor_model_parallel_rank()
|
||||
for layer_idx, scaling_factor in kv_cache_scales_loader(
|
||||
quantization_param_path,
|
||||
tp_rank,
|
||||
tp_size,
|
||||
self.config.num_hidden_layers,
|
||||
self.config.__class__.model_type,
|
||||
):
|
||||
if not isinstance(self.model.layers[layer_idx], nn.Identity):
|
||||
layer_self_attn = self.model.layers[layer_idx].self_attn
|
||||
|
||||
if is_hip():
|
||||
# The scaling factor convention we are assuming is
|
||||
# quantized_value * scaling_factor ~= true_value
|
||||
# which is consistent with the practice of setting
|
||||
# scaling_factor = tensor_amax / FPtype_max
|
||||
scaling_factor *= 2
|
||||
if hasattr(layer_self_attn, "kv_scale"):
|
||||
layer_self_attn.attn._kv_scale = scaling_factor
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"Self attention has no KV cache scaling " "factor attribute!"
|
||||
)
|
||||
|
||||
|
||||
EntryClass = HunYuanMoEV1ForCausalLM
|
||||
Reference in New Issue
Block a user