model: support Apertus (#9774)
This commit is contained in:
EduardDurech
@@ -171,6 +171,115 @@ class QuickGELU(CustomOp):
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return torch_npu.npu_fast_gelu(x)
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class XIELU(CustomOp):
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"""
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Applies the xIELU activation function introduced in https://arxiv.org/abs/2411.13010
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If the user has installed the nickjbrowning/XIELU, we import xIELU CUDA
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Otherwise, we emit a single warning and use xIELU Python
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"""
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def __init__(
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self,
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alpha_p_init: float = 0.8,
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alpha_n_init: float = 0.8,
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beta: float = 0.5,
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eps: float = -1e-6,
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dtype: torch.dtype = torch.bfloat16,
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with_vector_loads: bool = False,
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):
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super().__init__()
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self.alpha_p = nn.Parameter(
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torch.log(torch.exp(torch.tensor(alpha_p_init, dtype=dtype)) - 1).unsqueeze(
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0
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)
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)
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self.alpha_n = nn.Parameter(
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torch.log(
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torch.exp(torch.tensor(alpha_n_init - beta, dtype=dtype)) - 1
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).unsqueeze(0)
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)
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self.register_buffer("beta", torch.tensor(beta, dtype=dtype))
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self.register_buffer("eps", torch.tensor(eps, dtype=dtype))
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self.with_vector_loads = with_vector_loads
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# Temporary until xIELU CUDA fully implemented
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self._beta_scalar = float(self.beta.detach().cpu().float().item())
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self._eps_scalar = float(self.eps.detach().cpu().float().item())
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self._xielu_cuda_obj = None
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try:
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import xielu.ops # noqa: F401
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self._xielu_cuda_obj = torch.classes.xielu.XIELU()
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msg = "Using experimental xIELU CUDA."
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try:
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from torch._dynamo import allow_in_graph
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self._xielu_cuda_fn = allow_in_graph(self._xielu_cuda)
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msg += " Enabled torch._dynamo for xIELU CUDA."
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except Exception as err:
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msg += (
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f" Could not enable torch._dynamo for xIELU ({err}) - "
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"this may result in slower performance."
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)
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self._xielu_cuda_fn = self._xielu_cuda
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logger.warning_once(msg)
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except Exception as err:
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logger.warning_once(
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"CUDA-fused xIELU not available (%s) –"
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" falling back to a Python version.\n"
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"For CUDA xIELU (experimental), `pip install git+https://github.com/nickjbrowning/XIELU`",
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str(err),
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)
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def _xielu_python(self, x: torch.Tensor) -> torch.Tensor:
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alpha_p = nn.functional.softplus(self.alpha_p)
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alpha_n = self.beta + nn.functional.softplus(self.alpha_n)
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return torch.where(
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x > 0,
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alpha_p * x * x + self.beta * x,
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(torch.expm1(torch.min(x, self.eps)) - x) * alpha_n + self.beta * x,
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)
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def _xielu_cuda(self, x: torch.Tensor) -> torch.Tensor:
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"""Firewall function to prevent torch.compile from seeing .item()"""
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assert self._xielu_cuda_obj is not None, "XIELU CUDA object must not be None"
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original_shape = x.shape
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# CUDA kernel expects 3D tensors, reshape if needed
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while x.dim() < 3:
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x = x.unsqueeze(0)
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if x.dim() > 3:
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x = x.view(-1, 1, x.size(-1))
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if original_shape != x.shape:
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logger.warning_once(
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"Warning: xIELU input tensor expects 3 dimensions"
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" but got (shape: %s). Reshaping to (shape: %s).\n"
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"Note: For SGLang this may be expected if sending"
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"[B*S,D] instead of [B,S,D].",
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original_shape,
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x.shape,
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)
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result = self._xielu_cuda_obj.forward(
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x,
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self.alpha_p,
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self.alpha_n,
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# Temporary until xIELU CUDA fully implemented -> self.{beta,eps}.item()
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self._beta_scalar,
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self._eps_scalar,
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self.with_vector_loads,
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)
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return result.view(original_shape)
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def forward(self, input: torch.Tensor) -> torch.Tensor:
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if self._xielu_cuda_obj is not None and input.is_cuda:
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if not torch._dynamo.is_compiling():
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return self._xielu_cuda_fn(input)
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else:
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logger.warning_once(
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"torch._dynamo is compiling, using Python version of xIELU."
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)
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return self._xielu_python(input)
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class ScaledActivation(nn.Module):
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"""An activation function with post-scale parameters.
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@@ -218,6 +327,7 @@ _ACTIVATION_REGISTRY = {
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"gelu_pytorch_tanh": nn.GELU(approximate="tanh"),
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"gelu_new": NewGELU(),
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"relu2": ReLU2(),
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"xielu": XIELU(),
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}
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686
python/sglang/srt/models/apertus.py
Normal file
686
python/sglang/srt/models/apertus.py
Normal file
@@ -0,0 +1,686 @@
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# Copyright 2025 The SwissAI Initiative
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# 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/c7f2cf2b7f67bce5842fedfdba508440fe257375/vllm/model_executor/models/llama.py#L1
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"""Inference-only Apertus model compatible with HuggingFace weights."""
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import logging
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from typing import Any, Dict, Iterable, List, Optional, Tuple, Union
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import torch
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from torch import nn
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from transformers import ApertusConfig
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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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)
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from sglang.srt.layers.activation import XIELU
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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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QKVParallelLinear,
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RowParallelLinear,
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)
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from sglang.srt.layers.logits_processor import LogitsProcessor, LogitsProcessorOutput
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from sglang.srt.layers.pooler import Pooler, PoolingType
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from sglang.srt.layers.quantization.base_config import QuantizationConfig
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from sglang.srt.layers.radix_attention import RadixAttention
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from sglang.srt.layers.rotary_embedding import get_rope
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from sglang.srt.layers.utils import PPMissingLayer, get_layer_id
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from sglang.srt.layers.vocab_parallel_embedding import (
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from sglang.srt.managers.schedule_batch import global_server_args_dict
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from sglang.srt.model_executor.forward_batch_info import ForwardBatch, PPProxyTensors
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from sglang.srt.model_loader.weight_utils import (
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default_weight_loader,
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kv_cache_scales_loader,
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maybe_remap_kv_scale_name,
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)
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from sglang.srt.utils import add_prefix, make_layers
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from sglang.utils import get_exception_traceback
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logger = logging.getLogger(__name__)
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class ApertusMLP(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.up_proj = ColumnParallelLinear(
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hidden_size,
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intermediate_size,
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bias=bias,
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quant_config=quant_config,
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prefix=add_prefix("up_proj", prefix),
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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=bias,
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quant_config=quant_config,
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prefix=add_prefix("down_proj", prefix),
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reduce_results=reduce_results,
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)
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if hidden_act != "xielu":
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raise ValueError(
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f"Unsupported activation: {hidden_act}. "
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"Only xIELU is supported for now."
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)
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self.act_fn = XIELU()
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def forward(
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self,
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x,
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forward_batch=None,
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use_reduce_scatter: bool = False,
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):
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# note: with xielu, there's no gate_proj
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x, _ = self.up_proj(x)
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x = self.act_fn(x)
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x, _ = self.down_proj(
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x,
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skip_all_reduce=use_reduce_scatter,
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)
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return x
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class ApertusAttention(nn.Module):
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def __init__(
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self,
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config: ApertusConfig,
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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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layer_id: int = 0,
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rope_theta: float = 10000,
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rope_scaling: Optional[Dict[str, Any]] = None,
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rope_is_neox_style: bool = True,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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bias: bool = False,
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bias_o_proj: bool = 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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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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partial_rotary_factor = getattr(config, "partial_rotary_factor", 1)
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self.rotary_dim = int(partial_rotary_factor * self.head_dim)
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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.qkv_proj = QKVParallelLinear(
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hidden_size,
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self.head_dim,
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self.total_num_heads,
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self.total_num_kv_heads,
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bias=bias,
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quant_config=quant_config,
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prefix=add_prefix("qkv_proj", prefix),
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)
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self.o_proj = RowParallelLinear(
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self.total_num_heads * self.head_dim,
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hidden_size,
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bias=bias_o_proj,
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quant_config=quant_config,
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prefix=add_prefix("o_proj", prefix),
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)
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.rotary_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=rope_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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quant_config=quant_config,
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prefix=add_prefix("attn", prefix),
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)
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self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)
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self.k_norm = 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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) -> torch.Tensor:
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qkv, _ = self.qkv_proj(hidden_states)
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q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
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q = self.q_norm(q.contiguous().view(-1, self.head_dim)).view_as(q)
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k = self.k_norm(k.contiguous().view(-1, self.head_dim)).view_as(k)
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q, k = self.rotary_emb(positions, q, k)
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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
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class ApertusDecoderLayer(nn.Module):
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def __init__(
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self,
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config: ApertusConfig,
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layer_id: int = 0,
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quant_config: Optional[QuantizationConfig] = None,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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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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):
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rope_scaling["original_max_position_embeddings"] = (
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config.original_max_position_embeddings
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)
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rope_is_neox_style = getattr(config, "rope_is_neox_style", True)
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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# Support llamafy/Qwen-Qwen2.5-7B-Instruct-llamafied with attention_bias
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# Support internlm/internlm-7b with bias
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attention_bias = getattr(config, "attention_bias", False) or getattr(
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config, "bias", False
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)
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bias_o_proj = attention_bias
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# support internlm/internlm3-8b with qkv_bias
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if hasattr(config, "qkv_bias"):
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attention_bias = config.qkv_bias
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self.self_attn = ApertusAttention(
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config=config,
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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num_kv_heads=config.num_key_value_heads,
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layer_id=layer_id,
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rope_theta=rope_theta,
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rope_scaling=rope_scaling,
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rope_is_neox_style=rope_is_neox_style,
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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prefix=add_prefix("self_attn", prefix),
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bias=attention_bias,
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bias_o_proj=bias_o_proj,
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)
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self.mlp = ApertusMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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hidden_act=config.hidden_act,
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quant_config=quant_config,
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bias=getattr(config, "mlp_bias", False),
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prefix=add_prefix("mlp", prefix),
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)
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self.attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.feedforward_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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def forward(
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self,
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positions: torch.Tensor,
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hidden_states: torch.Tensor,
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forward_batch: ForwardBatch,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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# Self Attention
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if residual is None:
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residual = hidden_states
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hidden_states = self.attention_layernorm(hidden_states)
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else:
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hidden_states, residual = self.attention_layernorm(hidden_states, residual)
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hidden_states = self.self_attn(
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positions=positions,
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hidden_states=hidden_states,
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forward_batch=forward_batch,
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)
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# Fully Connected
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hidden_states, residual = self.feedforward_layernorm(hidden_states, residual)
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hidden_states = self.mlp(hidden_states)
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return hidden_states, residual
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class ApertusModel(nn.Module):
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def __init__(
|
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self,
|
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config: ApertusConfig,
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quant_config: Optional[QuantizationConfig] = None,
|
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prefix: str = "",
|
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) -> None:
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super().__init__()
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self.quant_config = quant_config
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self.config = config
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self.padding_idx = config.pad_token_id
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self.vocab_size = config.vocab_size
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self.org_vocab_size = config.vocab_size
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self.pp_group = get_pp_group()
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if self.pp_group.is_first_rank:
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self.embed_tokens = VocabParallelEmbedding(
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config.vocab_size,
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config.hidden_size,
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||||
quant_config=quant_config,
|
||||
prefix=add_prefix("embed_tokens", prefix),
|
||||
)
|
||||
else:
|
||||
self.embed_tokens = PPMissingLayer()
|
||||
|
||||
self.layers, self.start_layer, self.end_layer = make_layers(
|
||||
config.num_hidden_layers,
|
||||
lambda idx, prefix: ApertusDecoderLayer(
|
||||
config=config, quant_config=quant_config, layer_id=idx, prefix=prefix
|
||||
),
|
||||
pp_rank=self.pp_group.rank_in_group,
|
||||
pp_size=self.pp_group.world_size,
|
||||
prefix="model.layers",
|
||||
)
|
||||
|
||||
if self.pp_group.is_last_rank:
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
else:
|
||||
self.norm = PPMissingLayer(return_tuple=True)
|
||||
self.layers_to_capture = []
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
input_embeds: torch.Tensor = None,
|
||||
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
||||
) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]], PPProxyTensors]:
|
||||
if self.pp_group.is_first_rank:
|
||||
if input_embeds is None:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
else:
|
||||
hidden_states = input_embeds
|
||||
residual = None
|
||||
else:
|
||||
assert pp_proxy_tensors is not None
|
||||
# FIXME(@ying): reduce the number of proxy tensors by not fusing layer norms
|
||||
hidden_states = pp_proxy_tensors["hidden_states"]
|
||||
residual = pp_proxy_tensors["residual"]
|
||||
deferred_norm = None
|
||||
|
||||
aux_hidden_states = []
|
||||
for i in range(self.start_layer, self.end_layer):
|
||||
if i in self.layers_to_capture:
|
||||
aux_hidden_states.append(hidden_states + residual)
|
||||
layer = self.layers[i]
|
||||
hidden_states, residual = layer(
|
||||
positions,
|
||||
hidden_states,
|
||||
forward_batch,
|
||||
residual,
|
||||
)
|
||||
|
||||
if not self.pp_group.is_last_rank:
|
||||
return PPProxyTensors(
|
||||
{
|
||||
"hidden_states": hidden_states,
|
||||
"residual": residual,
|
||||
}
|
||||
)
|
||||
else:
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
|
||||
if len(aux_hidden_states) == 0:
|
||||
return hidden_states
|
||||
|
||||
return hidden_states, aux_hidden_states
|
||||
|
||||
# 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.layers[layer_idx], nn.Identity):
|
||||
layer_self_attn = self.layers[layer_idx].self_attn
|
||||
|
||||
if hasattr(layer_self_attn.attn, "k_scale"):
|
||||
layer_self_attn.attn.k_scale = scaling_factor
|
||||
layer_self_attn.attn.v_scale = scaling_factor
|
||||
else:
|
||||
raise RuntimeError(
|
||||
"Self attention has no KV cache scaling " "factor attribute!"
|
||||
)
|
||||
|
||||
|
||||
class ApertusForCausalLM(nn.Module):
|
||||
# LoRA specific attributes
|
||||
embedding_modules = {
|
||||
"embed_tokens": "input_embeddings",
|
||||
"lm_head": "output_embeddings",
|
||||
}
|
||||
embedding_padding_modules = ["lm_head"]
|
||||
# BitandBytes specific attributes
|
||||
default_bitsandbytes_target_modules = [
|
||||
".down_proj.",
|
||||
".up_proj.",
|
||||
".q_proj.",
|
||||
".k_proj.",
|
||||
".v_proj.",
|
||||
".o_proj.",
|
||||
]
|
||||
# in TP, these weights are partitioned along the column dimension (dim=-1)
|
||||
column_parallel_weights_modules = [".down_proj.", ".o_proj."]
|
||||
bitsandbytes_stacked_params_mapping = {
|
||||
# shard_name, weight_name, index
|
||||
".q_proj": (".qkv_proj", 0),
|
||||
".k_proj": (".qkv_proj", 1),
|
||||
".v_proj": (".qkv_proj", 2),
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: ApertusConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.pp_group = get_pp_group()
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
self.model = self._init_model(config, quant_config, add_prefix("model", prefix))
|
||||
if self.config.tie_word_embeddings:
|
||||
self.lm_head = self.model.embed_tokens
|
||||
else:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
quant_config=quant_config,
|
||||
prefix=add_prefix("lm_head", prefix),
|
||||
use_attn_tp_group=global_server_args_dict["enable_dp_lm_head"],
|
||||
)
|
||||
self.logits_processor = LogitsProcessor(config)
|
||||
self.pooler = Pooler(pooling_type=PoolingType.LAST, normalize=True)
|
||||
self.stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
(".qkv_proj", ".q_proj", "q"),
|
||||
(".qkv_proj", ".k_proj", "k"),
|
||||
(".qkv_proj", ".v_proj", "v"),
|
||||
]
|
||||
|
||||
self.capture_aux_hidden_states = False
|
||||
|
||||
def _init_model(
|
||||
self,
|
||||
config: ApertusConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
):
|
||||
return ApertusModel(config, quant_config=quant_config, prefix=prefix)
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
input_embeds: torch.Tensor = None,
|
||||
get_embedding: bool = False,
|
||||
pp_proxy_tensors: Optional[PPProxyTensors] = None,
|
||||
) -> LogitsProcessorOutput:
|
||||
hidden_states = self.model(
|
||||
input_ids,
|
||||
positions,
|
||||
forward_batch,
|
||||
input_embeds,
|
||||
pp_proxy_tensors=pp_proxy_tensors,
|
||||
)
|
||||
|
||||
aux_hidden_states = None
|
||||
if self.capture_aux_hidden_states:
|
||||
hidden_states, aux_hidden_states = hidden_states
|
||||
|
||||
if self.pp_group.is_last_rank:
|
||||
if not get_embedding:
|
||||
return self.logits_processor(
|
||||
input_ids,
|
||||
hidden_states,
|
||||
self.lm_head,
|
||||
forward_batch,
|
||||
aux_hidden_states,
|
||||
)
|
||||
else:
|
||||
return self.pooler(hidden_states, forward_batch)
|
||||
else:
|
||||
return hidden_states
|
||||
|
||||
@torch.no_grad()
|
||||
def forward_split_prefill(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
split_interval: Tuple[int, int], # [start, end) 0-based
|
||||
input_embeds: torch.Tensor = None,
|
||||
) -> Optional[LogitsProcessorOutput]:
|
||||
start, end = split_interval
|
||||
# embed
|
||||
if start == 0:
|
||||
if input_embeds is None:
|
||||
forward_batch.hidden_states = self.model.embed_tokens(input_ids)
|
||||
else:
|
||||
forward_batch.hidden_states = input_embeds
|
||||
# decoder layer
|
||||
for i in range(start, end):
|
||||
layer = self.model.layers[i]
|
||||
forward_batch.hidden_states, forward_batch.residual = layer(
|
||||
positions,
|
||||
forward_batch.hidden_states,
|
||||
forward_batch,
|
||||
forward_batch.residual,
|
||||
)
|
||||
|
||||
if end == self.model.config.num_hidden_layers:
|
||||
# norm
|
||||
hidden_states, _ = self.model.norm(
|
||||
forward_batch.hidden_states, forward_batch.residual
|
||||
)
|
||||
forward_batch.hidden_states = hidden_states
|
||||
# logits process
|
||||
result = self.logits_processor(
|
||||
input_ids, forward_batch.hidden_states, self.lm_head, forward_batch
|
||||
)
|
||||
else:
|
||||
result = None
|
||||
|
||||
return result
|
||||
|
||||
@property
|
||||
def start_layer(self):
|
||||
return self.model.start_layer
|
||||
|
||||
@property
|
||||
def end_layer(self):
|
||||
return self.model.end_layer
|
||||
|
||||
def get_input_embeddings(self) -> nn.Embedding:
|
||||
return self.model.embed_tokens
|
||||
|
||||
def get_module_name_from_weight_name(self, name):
|
||||
for param_name, weight_name, shard_id, num_shard in self.stacked_params_mapping:
|
||||
if weight_name in name:
|
||||
return (
|
||||
name.replace(weight_name, param_name)[: -len(".weight")],
|
||||
num_shard,
|
||||
)
|
||||
return name[: -len(".weight")], 1
|
||||
|
||||
def get_num_params(self):
|
||||
params_dict = dict(self.named_parameters())
|
||||
return len(params_dict)
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
(".qkv_proj", ".q_proj", "q"),
|
||||
(".qkv_proj", ".k_proj", "k"),
|
||||
(".qkv_proj", ".v_proj", "v"),
|
||||
]
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
|
||||
for name, buffer in self.named_buffers():
|
||||
if name.endswith(".beta") or name.endswith(".eps"):
|
||||
params_dict[name] = buffer
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
layer_id = get_layer_id(name)
|
||||
if (
|
||||
layer_id is not None
|
||||
and hasattr(self.model, "start_layer")
|
||||
and (
|
||||
layer_id < self.model.start_layer
|
||||
or layer_id >= self.model.end_layer
|
||||
)
|
||||
):
|
||||
continue
|
||||
if "rotary_emb.inv_freq" in name or "projector" in name:
|
||||
continue
|
||||
if "rotary_emb.cos_cached" in name or "rotary_emb.sin_cached" in name:
|
||||
# Models trained using ColossalAI may include these tensors in
|
||||
# the checkpoint. Skip them.
|
||||
continue
|
||||
if name.startswith("model.vision_tower") and name not in params_dict:
|
||||
continue
|
||||
if self.config.tie_word_embeddings and "lm_head.weight" in name:
|
||||
continue
|
||||
# Handle FP8 kv-scale remapping
|
||||
if "scale" in name:
|
||||
name = maybe_remap_kv_scale_name(name, params_dict)
|
||||
if name is None:
|
||||
continue
|
||||
|
||||
for param_name, weight_name, shard_id in stacked_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
|
||||
if name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
break
|
||||
else:
|
||||
# Skip loading extra bias for GPTQ models.
|
||||
if name.endswith(".bias") and name not in params_dict:
|
||||
continue
|
||||
# Skip loading kv_scale from ckpts towards new design.
|
||||
if name.endswith(".kv_scale") and name not in params_dict:
|
||||
continue
|
||||
if name in params_dict.keys():
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(
|
||||
param, "weight_loader", default_weight_loader
|
||||
)
|
||||
weight_loader(param, loaded_weight)
|
||||
else:
|
||||
logger.warning(f"Parameter {name} not found in params_dict")
|
||||
|
||||
def get_embed_and_head(self):
|
||||
return self.model.embed_tokens.weight, self.lm_head.weight
|
||||
|
||||
def set_embed_and_head(self, embed, head):
|
||||
del self.model.embed_tokens.weight
|
||||
del self.lm_head.weight
|
||||
self.model.embed_tokens.weight = embed
|
||||
self.lm_head.weight = head
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.synchronize()
|
||||
|
||||
def get_embed(self):
|
||||
return self.model.embed_tokens.weight
|
||||
|
||||
def set_embed(self, embed):
|
||||
# NOTE: If draft hidden size != target hidden size, the embed weight cannot be shared for EAGLE3
|
||||
if (
|
||||
hasattr(self.config, "target_hidden_size")
|
||||
and self.config.target_hidden_size != self.config.hidden_size
|
||||
):
|
||||
return
|
||||
del self.model.embed_tokens.weight
|
||||
self.model.embed_tokens.weight = embed
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.synchronize()
|
||||
|
||||
def load_kv_cache_scales(self, quantization_param_path: str) -> None:
|
||||
self.model.load_kv_cache_scales(quantization_param_path)
|
||||
|
||||
def set_eagle3_layers_to_capture(self, layer_ids: Optional[List[int]] = None):
|
||||
if not self.pp_group.is_last_rank:
|
||||
return
|
||||
|
||||
if layer_ids is None:
|
||||
self.capture_aux_hidden_states = True
|
||||
num_layers = self.config.num_hidden_layers
|
||||
self.model.layers_to_capture = [2, num_layers // 2, num_layers - 3]
|
||||
else:
|
||||
self.capture_aux_hidden_states = True
|
||||
# we plus 1 here because in sglang, for the ith layer, it takes the output
|
||||
# of the (i-1)th layer as aux hidden state
|
||||
self.model.layers_to_capture = [val + 1 for val in layer_ids]
|
||||
|
||||
|
||||
EntryClass = [ApertusForCausalLM]
|
||||
Reference in New Issue
Block a user