[MODEL] Apertus and XIELU (#23068)
Signed-off-by: EduardDurech <39579228+EduardDurech@users.noreply.github.com> Co-authored-by: AllenHaoHuang <allenhuangdd@gmail.com>
This commit is contained in:
EduardDurech
GitHub
@ -92,7 +92,8 @@ AITER_MODEL_LIST = [
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pytest.param(
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"allenai/OLMoE-1B-7B-0924-Instruct",
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marks=[pytest.mark.cpu_model],
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)
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),
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pytest.param("swiss-ai/Apertus-8B"), # apertus
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])
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@pytest.mark.parametrize("max_tokens", [32])
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@pytest.mark.parametrize("num_logprobs", [5])
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@ -137,6 +137,9 @@ class _HfExamplesInfo:
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# yapf: disable
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_TEXT_GENERATION_EXAMPLE_MODELS = {
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# [Decoder-only]
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"ApertusForCausalLM": _HfExamplesInfo("swiss-ai/Apertus-8B",
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min_transformers_version="4.56.0",
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trust_remote_code=True),
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"AquilaModel": _HfExamplesInfo("BAAI/AquilaChat-7B",
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trust_remote_code=True),
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"AquilaForCausalLM": _HfExamplesInfo("BAAI/AquilaChat2-7B",
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@ -24,6 +24,9 @@ from .registry import HF_EXAMPLE_MODELS
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@pytest.mark.parametrize("model_arch", ModelRegistry.get_supported_archs())
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def test_registry_imports(model_arch):
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# Skip if transformers version is incompatible
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model_info = HF_EXAMPLE_MODELS.get_hf_info(model_arch)
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model_info.check_transformers_version(on_fail="skip")
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# Ensure all model classes can be imported successfully
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model_cls = ModelRegistry._try_load_model_cls(model_arch)
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assert model_cls is not None
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@ -10,11 +10,14 @@ import torch.nn.functional as F
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from vllm.distributed import (divide, get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size)
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from vllm.logger import init_logger
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from vllm.model_executor.custom_op import CustomOp
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from vllm.model_executor.utils import set_weight_attrs
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from vllm.platforms import current_platform
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from vllm.utils import LazyDict
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logger = init_logger(__name__)
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@CustomOp.register("fatrelu_and_mul")
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class FatreluAndMul(CustomOp):
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@ -363,6 +366,112 @@ class ReLUSquaredActivation(CustomOp):
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return self.forward_native(x)
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@CustomOp.register("xielu")
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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)) -
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1).unsqueeze(0))
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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)) -
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1).unsqueeze(0))
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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 += (f" Could not enable torch._dynamo for xIELU ({err}) - "
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"this may result in slower performance.")
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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 +
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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, (
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"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).",
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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 ->
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# 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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@ -426,6 +535,8 @@ _ACTIVATION_REGISTRY = LazyDict({
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lambda: nn.Tanh(),
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"sigmoid":
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lambda: nn.Sigmoid(),
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"xielu":
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lambda: XIELU(),
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})
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576
vllm/model_executor/models/apertus.py
Normal file
576
vllm/model_executor/models/apertus.py
Normal file
@ -0,0 +1,576 @@
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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# Adapted from
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# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py
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# Copyright 2025 The Swiss AI Initiative.
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# Copyright 2023 The vLLM team.
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# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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#
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# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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# and OPT implementations in this library. It has been modified from its
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# original forms to accommodate the architectural differences made by
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# the Swiss AI Initiative that trained the model.
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#
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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 Apertus model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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from typing import Any, Optional, 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 vllm.attention import Attention, AttentionType
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from vllm.attention.layers.encoder_only_attention import EncoderOnlyAttention
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from vllm.compilation.decorators import support_torch_compile
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import XIELU
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (ColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader, maybe_remap_kv_scale_name)
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP
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from .utils import (AutoWeightsLoader, PPMissingLayer, extract_layer_index,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory, make_layers,
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maybe_prefix)
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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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input_size=hidden_size,
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output_size=intermediate_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.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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reduce_results=reduce_results,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "xielu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only xIELU is supported for now.")
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self.act_fn = XIELU()
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def forward(self, x):
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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(x)
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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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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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bias_o_proj: bool = False,
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cache_config: Optional[CacheConfig] = None,
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prefix: str = "",
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attn_type: str = AttentionType.DECODER,
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) -> None:
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super().__init__()
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layer_idx = extract_layer_index(prefix)
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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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head_dim = getattr(config, "head_dim", None)
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if head_dim is None:
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head_dim = self.hidden_size // self.total_num_heads
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self.head_dim = head_dim
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# Phi models introduced a partial_rotary_factor parameter in the config
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self.partial_rotary_factor = getattr(config, "partial_rotary_factor",
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1)
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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=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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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_o_proj,
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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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self._init_rotary_emb(config,
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rope_scaling=rope_scaling,
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quant_config=quant_config)
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sliding_window = None
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if layer_types := getattr(config, "layer_types", None):
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is_sliding = layer_types[layer_idx] == "sliding_attention"
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if is_sliding:
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sliding_window = config.sliding_window
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attn_cls = (EncoderOnlyAttention
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if attn_type == AttentionType.ENCODER_ONLY else Attention)
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self.attn = attn_cls(
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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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cache_config=cache_config,
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quant_config=quant_config,
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per_layer_sliding_window=sliding_window,
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attn_type=attn_type,
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prefix=f"{prefix}.attn",
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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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) -> 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)
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output, _ = self.o_proj(attn_output)
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return output
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def _init_rotary_emb(self, config: ApertusConfig,
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rope_scaling: Optional[dict[str, Any]],
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quant_config: Optional[QuantizationConfig]) -> None:
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is_neox_style = True
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is_gguf = quant_config and quant_config.get_name() == "gguf"
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if is_gguf and config.model_type == "apertus":
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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=int(self.partial_rotary_factor * self.head_dim),
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max_position=self.max_position_embeddings,
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base=self.rope_theta,
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rope_scaling=rope_scaling,
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is_neox_style=is_neox_style,
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partial_rotary_factor=self.partial_rotary_factor,
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)
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|
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class ApertusDecoderLayer(nn.Module):
|
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|
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def __init__(
|
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self,
|
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config: ApertusConfig,
|
||||
cache_config: Optional[CacheConfig] = None,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> 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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rope_scaling["original_max_position_embeddings"] = (
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config.original_max_position_embeddings)
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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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# 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)
|
||||
bias_o_proj = attention_bias
|
||||
# support internlm/internlm3-8b with qkv_bias
|
||||
if hasattr(config, 'qkv_bias'):
|
||||
attention_bias = config.qkv_bias
|
||||
|
||||
# Apertus defaults to causal attention as it is a decoder-only model.
|
||||
# You can override the HF config with `is_causal=False` to enable
|
||||
# bidirectional attention, which is used in some embedding models
|
||||
# (e.g. parasail-ai/GritLM-7B-vllm)
|
||||
if getattr(config, "is_causal", True):
|
||||
attn_type = AttentionType.DECODER
|
||||
else:
|
||||
attn_type = AttentionType.ENCODER_ONLY
|
||||
|
||||
self.self_attn = ApertusAttention(
|
||||
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,
|
||||
bias_o_proj=bias_o_proj,
|
||||
cache_config=cache_config,
|
||||
prefix=f"{prefix}.self_attn",
|
||||
attn_type=attn_type,
|
||||
)
|
||||
self.mlp = ApertusMLP(
|
||||
hidden_size=self.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
quant_config=quant_config,
|
||||
bias=getattr(config, "mlp_bias", False),
|
||||
prefix=f"{prefix}.mlp",
|
||||
)
|
||||
self.attention_layernorm = RMSNorm(config.hidden_size,
|
||||
eps=config.rms_norm_eps)
|
||||
self.feedforward_layernorm = RMSNorm(config.hidden_size,
|
||||
eps=config.rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
residual: Optional[torch.Tensor],
|
||||
) -> tuple[torch.Tensor, torch.Tensor]:
|
||||
# Self Attention
|
||||
if residual is None:
|
||||
residual = hidden_states
|
||||
hidden_states = self.attention_layernorm(hidden_states)
|
||||
else:
|
||||
hidden_states, residual = self.attention_layernorm(
|
||||
hidden_states, residual)
|
||||
hidden_states = self.self_attn(positions=positions,
|
||||
hidden_states=hidden_states)
|
||||
|
||||
# Fully Connected
|
||||
hidden_states, residual = self.feedforward_layernorm(
|
||||
hidden_states, residual)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
return hidden_states, residual
|
||||
|
||||
|
||||
@support_torch_compile
|
||||
class ApertusModel(nn.Module):
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
vllm_config: VllmConfig,
|
||||
prefix: str = "",
|
||||
layer_type: type[nn.Module] = ApertusDecoderLayer):
|
||||
super().__init__()
|
||||
|
||||
config = vllm_config.model_config.hf_config
|
||||
cache_config = vllm_config.cache_config
|
||||
quant_config = vllm_config.quant_config
|
||||
lora_config = vllm_config.lora_config
|
||||
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
lora_vocab = (lora_config.lora_extra_vocab_size *
|
||||
(lora_config.max_loras or 1)) if lora_config else 0
|
||||
self.vocab_size = config.vocab_size + lora_vocab
|
||||
self.org_vocab_size = config.vocab_size
|
||||
if get_pp_group().is_first_rank or (config.tie_word_embeddings
|
||||
and get_pp_group().is_last_rank):
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
self.vocab_size,
|
||||
config.hidden_size,
|
||||
org_num_embeddings=config.vocab_size,
|
||||
quant_config=quant_config,
|
||||
)
|
||||
else:
|
||||
self.embed_tokens = PPMissingLayer()
|
||||
self.start_layer, self.end_layer, self.layers = make_layers(
|
||||
config.num_hidden_layers,
|
||||
lambda prefix: layer_type(config=config,
|
||||
cache_config=cache_config,
|
||||
quant_config=quant_config,
|
||||
prefix=prefix),
|
||||
prefix=f"{prefix}.layers",
|
||||
)
|
||||
if get_pp_group().is_last_rank:
|
||||
self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
else:
|
||||
self.norm = PPMissingLayer()
|
||||
|
||||
self.aux_hidden_state_layers = tuple[int, ...]()
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
make_empty_intermediate_tensors_factory(
|
||||
["hidden_states", "residual"], config.hidden_size))
|
||||
|
||||
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,
|
||||
intermediate_tensors: Optional[IntermediateTensors],
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
) -> Union[torch.Tensor, IntermediateTensors, tuple[torch.Tensor,
|
||||
list[torch.Tensor]]]:
|
||||
if get_pp_group().is_first_rank:
|
||||
if inputs_embeds is not None:
|
||||
hidden_states = inputs_embeds
|
||||
else:
|
||||
hidden_states = self.get_input_embeddings(input_ids)
|
||||
residual = None
|
||||
else:
|
||||
assert intermediate_tensors is not None
|
||||
hidden_states = intermediate_tensors["hidden_states"]
|
||||
residual = intermediate_tensors["residual"]
|
||||
|
||||
aux_hidden_states = []
|
||||
for idx, layer in enumerate(
|
||||
self.layers[self.start_layer:self.end_layer]):
|
||||
if idx in self.aux_hidden_state_layers:
|
||||
aux_hidden_states.append(hidden_states + residual)
|
||||
hidden_states, residual = layer(positions, hidden_states, residual)
|
||||
|
||||
if not get_pp_group().is_last_rank:
|
||||
return IntermediateTensors({
|
||||
"hidden_states": hidden_states,
|
||||
"residual": residual
|
||||
})
|
||||
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
|
||||
if len(aux_hidden_states) > 0:
|
||||
return hidden_states, aux_hidden_states
|
||||
return hidden_states
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str,
|
||||
torch.Tensor]]) -> set[str]:
|
||||
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())
|
||||
loaded_params: set[str] = set()
|
||||
for name, loaded_weight in weights:
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
if ("rotary_emb.cos_cached" in name
|
||||
or "rotary_emb.sin_cached" in name):
|
||||
# Models trained using ColossalAI may include these tensors in
|
||||
# the checkpoint. Skip them.
|
||||
continue
|
||||
if (self.quant_config is not None and
|
||||
(scale_name := self.quant_config.get_cache_scale(name))):
|
||||
# Loading kv cache quantization scales
|
||||
param = params_dict[scale_name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
loaded_weight = (loaded_weight if loaded_weight.dim() == 0 else
|
||||
loaded_weight[0])
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(scale_name)
|
||||
continue
|
||||
if "scale" in name:
|
||||
# Remapping the name of FP8 kv-scale.
|
||||
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 is_pp_missing_parameter(name, self):
|
||||
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
|
||||
|
||||
if is_pp_missing_parameter(name, self):
|
||||
continue
|
||||
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader",
|
||||
default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
|
||||
class ApertusForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
|
||||
packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]}
|
||||
|
||||
# LoRA specific attributes
|
||||
embedding_modules = {
|
||||
"embed_tokens": "input_embeddings",
|
||||
"lm_head": "output_embeddings"
|
||||
}
|
||||
embedding_padding_modules = ["lm_head"]
|
||||
|
||||
def __init__(self,
|
||||
*,
|
||||
vllm_config: VllmConfig,
|
||||
prefix: str = "",
|
||||
layer_type: type[nn.Module] = ApertusDecoderLayer):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
lora_config = vllm_config.lora_config
|
||||
self.config = config
|
||||
self.lora_config = lora_config
|
||||
|
||||
self.model = self._init_model(vllm_config=vllm_config,
|
||||
prefix=maybe_prefix(prefix, "model"),
|
||||
layer_type=layer_type)
|
||||
|
||||
if get_pp_group().is_last_rank:
|
||||
self.unpadded_vocab_size = config.vocab_size
|
||||
if lora_config:
|
||||
self.unpadded_vocab_size += lora_config.lora_extra_vocab_size
|
||||
self.lm_head = ParallelLMHead(
|
||||
self.unpadded_vocab_size,
|
||||
config.hidden_size,
|
||||
org_num_embeddings=config.vocab_size,
|
||||
padding_size=(
|
||||
DEFAULT_VOCAB_PADDING_SIZE
|
||||
# We need bigger padding if using lora for kernel
|
||||
# compatibility
|
||||
if not lora_config else
|
||||
lora_config.lora_vocab_padding_size),
|
||||
quant_config=quant_config,
|
||||
prefix=maybe_prefix(prefix, "lm_head"),
|
||||
)
|
||||
if config.tie_word_embeddings:
|
||||
self.lm_head = self.lm_head.tie_weights(
|
||||
self.model.embed_tokens)
|
||||
|
||||
logit_scale = getattr(config, "logit_scale", 1.0)
|
||||
self.logits_processor = LogitsProcessor(self.unpadded_vocab_size,
|
||||
config.vocab_size,
|
||||
logit_scale)
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.model.make_empty_intermediate_tensors)
|
||||
|
||||
def set_aux_hidden_state_layers(self, layers: tuple[int, ...]) -> None:
|
||||
self.model.aux_hidden_state_layers = layers
|
||||
|
||||
def get_eagle3_aux_hidden_state_layers(self) -> tuple[int, ...]:
|
||||
num_layers = len(self.model.layers)
|
||||
return (2, num_layers // 2, num_layers - 3)
|
||||
|
||||
def _init_model(self,
|
||||
vllm_config: VllmConfig,
|
||||
prefix: str = "",
|
||||
layer_type: type[nn.Module] = ApertusDecoderLayer):
|
||||
return ApertusModel(vllm_config=vllm_config,
|
||||
prefix=prefix,
|
||||
layer_type=layer_type)
|
||||
|
||||
def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.get_input_embeddings(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: Optional[IntermediateTensors] = None,
|
||||
inputs_embeds: Optional[torch.Tensor] = None,
|
||||
) -> Union[torch.Tensor, IntermediateTensors]:
|
||||
model_output = self.model(input_ids, positions, intermediate_tensors,
|
||||
inputs_embeds)
|
||||
return model_output
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> Optional[torch.Tensor]:
|
||||
logits = self.logits_processor(self.lm_head, hidden_states,
|
||||
sampling_metadata)
|
||||
return logits
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str,
|
||||
torch.Tensor]]) -> set[str]:
|
||||
loader = AutoWeightsLoader(
|
||||
self,
|
||||
skip_prefixes=(["lm_head."]
|
||||
if self.config.tie_word_embeddings else None),
|
||||
)
|
||||
return loader.load_weights(weights)
|
||||
@ -39,6 +39,7 @@ logger = init_logger(__name__)
|
||||
# yapf: disable
|
||||
_TEXT_GENERATION_MODELS = {
|
||||
# [Decoder-only]
|
||||
"ApertusForCausalLM": ("apertus", "ApertusForCausalLM"),
|
||||
"AquilaModel": ("llama", "LlamaForCausalLM"),
|
||||
"AquilaForCausalLM": ("llama", "LlamaForCausalLM"), # AquilaChat2
|
||||
"ArceeForCausalLM": ("arcee", "ArceeForCausalLM"),
|
||||
|
||||
Reference in New Issue
Block a user