Sync from v0.13
This commit is contained in:
@@ -1,4 +1,6 @@
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# coding=utf-8
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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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# 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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@@ -18,38 +20,57 @@
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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 BaiChuan model compatible with HuggingFace weights."""
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import math
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from typing import Iterable, List, Optional, Tuple
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from collections.abc import Iterable
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from itertools import islice
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import torch
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from torch import nn
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from transformers import PretrainedConfig
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from vllm.attention import Attention, AttentionMetadata
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from vllm.config import LoRAConfig
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from vllm.distributed import (get_tensor_model_parallel_rank,
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get_tensor_model_parallel_world_size)
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from vllm.attention.layer import Attention
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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 (
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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 vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.layernorm import RMSNorm
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear)
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from vllm.model_executor.layers.linear import (
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MergedColumnParallelLinear,
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QKVParallelLinear,
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RowParallelLinear,
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)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization.base_config import (
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QuantizationConfig)
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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.sampler import Sampler
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from vllm.model_executor.layers.vocab_parallel_embedding import (
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ParallelLMHead, VocabParallelEmbedding)
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from vllm.model_executor.model_loader.weight_utils import default_weight_loader
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from vllm.model_executor.sampling_metadata import SamplingMetadata
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from vllm.sequence import SamplerOutput
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ParallelLMHead,
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VocabParallelEmbedding,
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)
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from vllm.model_executor.model_loader.weight_utils import (
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default_weight_loader,
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row_parallel_weight_loader,
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)
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from vllm.sequence import IntermediateTensors
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from .interfaces import SupportsLoRA, SupportsPP, SupportsQuant
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from .utils import (
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AutoWeightsLoader,
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is_pp_missing_parameter,
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make_empty_intermediate_tensors_factory,
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make_layers,
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maybe_prefix,
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)
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def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
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closest_power_of_2 = 2**math.floor(math.log2(total_num_heads))
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closest_power_of_2 = 2 ** math.floor(math.log2(total_num_heads))
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base = torch.tensor(
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2**(-(2**-(math.log2(closest_power_of_2) - 3))),
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2 ** (-(2 ** -(math.log2(closest_power_of_2) - 3))),
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dtype=torch.float32,
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)
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powers = torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32)
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@@ -57,41 +78,47 @@ def _get_alibi_slopes(total_num_heads: int) -> torch.Tensor:
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if closest_power_of_2 != total_num_heads:
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extra_base = torch.tensor(
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2**(-(2**-(math.log2(2 * closest_power_of_2) - 3))),
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2 ** (-(2 ** -(math.log2(2 * closest_power_of_2) - 3))),
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dtype=torch.float32,
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)
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num_remaining_heads = min(closest_power_of_2,
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total_num_heads - closest_power_of_2)
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extra_powers = torch.arange(start=1,
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end=1 + 2 * num_remaining_heads,
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step=2,
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dtype=torch.int32)
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slopes = torch.cat(
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[slopes, torch.pow(extra_base, extra_powers)], dim=0)
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num_remaining_heads = min(
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closest_power_of_2, total_num_heads - closest_power_of_2
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)
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extra_powers = torch.arange(
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start=1, end=1 + 2 * num_remaining_heads, step=2, dtype=torch.int32
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)
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slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
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return slopes
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class BaiChuanMLP(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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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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hidden_size, [intermediate_size] * 2,
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hidden_size,
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[intermediate_size] * 2,
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bias=False,
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quant_config=quant_config)
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self.down_proj = RowParallelLinear(intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config)
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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intermediate_size,
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hidden_size,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "silu":
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raise ValueError(f"Unsupported activation: {hidden_act}. "
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"Only silu is supported for now.")
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raise ValueError(
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f"Unsupported activation: {hidden_act}. Only silu is supported for now."
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)
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self.act_fn = SiluAndMul()
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def forward(self, x):
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@@ -109,21 +136,20 @@ class BaiChuanAttention(nn.Module):
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hidden_size: int,
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num_heads: int,
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position_embedding: str,
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rope_theta: float = 10000,
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rope_parameters: dict,
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max_position_embeddings: int = 8192,
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quant_config: Optional[QuantizationConfig] = None,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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super().__init__()
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self.hidden_size = hidden_size
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tensor_model_parallel_world_size = get_tensor_model_parallel_world_size(
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)
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tensor_model_parallel_world_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 % tensor_model_parallel_world_size == 0
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self.num_heads = (self.total_num_heads //
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tensor_model_parallel_world_size)
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self.num_heads = self.total_num_heads // tensor_model_parallel_world_size
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self.head_dim = hidden_size // self.total_num_heads
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self.postion_embedding = position_embedding
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self.rope_theta = rope_theta
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self.position_embedding = position_embedding
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self.max_position_embeddings = max_position_embeddings
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# pylint: disable=invalid-name
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@@ -134,15 +160,17 @@ class BaiChuanAttention(nn.Module):
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self.total_num_heads,
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bias=False,
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quant_config=quant_config,
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prefix=f"{prefix}.W_pack",
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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=False,
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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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# Create the alibi slopes and slice them.
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if self.postion_embedding == "ALIBI":
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if self.position_embedding == "ALIBI":
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tp_rank = get_tensor_model_parallel_rank()
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head_start = tp_rank * self.num_heads
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head_end = (tp_rank + 1) * self.num_heads
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@@ -150,225 +178,191 @@ class BaiChuanAttention(nn.Module):
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alibi_slopes = alibi_slopes[head_start:head_end].tolist()
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scaling = self.head_dim**-0.5
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self.attn = Attention(self.num_heads,
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self.head_dim,
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scaling,
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alibi_slopes=alibi_slopes)
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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scaling,
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alibi_slopes=alibi_slopes,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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)
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else:
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self.rotary_emb = get_rope(
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self.head_dim,
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rotary_dim=self.head_dim,
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max_position=self.max_position_embeddings,
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base=self.rope_theta,
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rope_parameters=rope_parameters,
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)
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self.scaling = self.head_dim**-0.5
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self.attn = Attention(self.num_heads, self.head_dim, self.scaling)
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self.attn = Attention(
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self.num_heads,
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self.head_dim,
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self.scaling,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.attn",
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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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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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) -> torch.Tensor:
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qkv, _ = self.W_pack(hidden_states)
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q, k, v = qkv.chunk(chunks=3, dim=-1)
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if self.postion_embedding != "ALIBI":
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if self.position_embedding != "ALIBI":
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q, k = self.rotary_emb(positions, q, k)
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attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
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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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class BaiChuanDecoderLayer(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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position_embedding: str,
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quant_config: Optional[QuantizationConfig] = None):
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def __init__(
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self,
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config: PretrainedConfig,
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position_embedding: str,
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cache_config: CacheConfig | None = None,
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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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):
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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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max_position_embeddings = getattr(config, "max_position_embeddings",
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8192)
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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self.self_attn = BaiChuanAttention(
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hidden_size=self.hidden_size,
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num_heads=config.num_attention_heads,
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position_embedding=position_embedding,
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rope_theta=rope_theta,
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rope_parameters=getattr(config, "rope_parameters", None),
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max_position_embeddings=max_position_embeddings,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=f"{prefix}.self_attn",
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)
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self.mlp = BaiChuanMLP(
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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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prefix=f"{prefix}.mlp",
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)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(
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config.hidden_size, eps=config.rms_norm_eps
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)
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self.input_layernorm = RMSNorm(config.hidden_size,
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eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size,
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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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kv_cache: torch.Tensor,
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attn_metadata: AttentionMetadata,
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residual: Optional[torch.Tensor],
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) -> Tuple[torch.Tensor, torch.Tensor]:
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residual: torch.Tensor | None,
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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.input_layernorm(hidden_states)
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else:
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hidden_states, residual = self.input_layernorm(
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hidden_states, residual)
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hidden_states, residual = self.input_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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kv_cache=kv_cache,
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attn_metadata=attn_metadata,
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)
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# Fully Connected
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hidden_states, residual = self.post_attention_layernorm(
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hidden_states, residual)
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hidden_states, residual = self.post_attention_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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@support_torch_compile
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class BaiChuanModel(nn.Module):
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def __init__(self,
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config: PretrainedConfig,
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position_embedding: str,
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quant_config: Optional[QuantizationConfig] = None):
|
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def __init__(
|
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self,
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vllm_config: VllmConfig,
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prefix: str = "",
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position_embedding: str = "ROPE",
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) -> None:
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super().__init__()
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config = vllm_config.model_config.hf_config
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cache_config = vllm_config.cache_config
|
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quant_config = vllm_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.embed_tokens = VocabParallelEmbedding(
|
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config.vocab_size,
|
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config.hidden_size,
|
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)
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self.layers = nn.ModuleList([
|
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BaiChuanDecoderLayer(config, position_embedding, quant_config)
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for _ in range(config.num_hidden_layers)
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])
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self.start_layer, self.end_layer, self.layers = make_layers(
|
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config.num_hidden_layers,
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lambda prefix: BaiChuanDecoderLayer(
|
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config, position_embedding, cache_config, quant_config, prefix=prefix
|
||||
),
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prefix=f"{prefix}.layers",
|
||||
)
|
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
|
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["hidden_states", "residual"], config.hidden_size
|
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)
|
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|
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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
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return self.embed_tokens(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
kv_caches: List[torch.Tensor],
|
||||
attn_metadata: AttentionMetadata,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
residual = None
|
||||
for i in range(len(self.layers)):
|
||||
layer = self.layers[i]
|
||||
intermediate_tensors: IntermediateTensors | None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
if get_pp_group().is_first_rank:
|
||||
if inputs_embeds is not None:
|
||||
hidden_states = inputs_embeds
|
||||
else:
|
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hidden_states = self.embed_input_ids(input_ids)
|
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residual = None
|
||||
else:
|
||||
assert intermediate_tensors is not None
|
||||
hidden_states = intermediate_tensors["hidden_states"]
|
||||
residual = intermediate_tensors["residual"]
|
||||
for layer in islice(self.layers, self.start_layer, self.end_layer):
|
||||
hidden_states, residual = layer(
|
||||
positions,
|
||||
hidden_states,
|
||||
kv_caches[i],
|
||||
attn_metadata,
|
||||
residual,
|
||||
)
|
||||
if not get_pp_group().is_last_rank:
|
||||
return IntermediateTensors(
|
||||
{
|
||||
"hidden_states": hidden_states,
|
||||
"residual": residual,
|
||||
}
|
||||
)
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class BaiChuanBaseForCausalLM(nn.Module):
|
||||
packed_modules_mapping = {
|
||||
"W_pack": ["W_pack"],
|
||||
"gate_up_proj": [
|
||||
"gate_proj",
|
||||
"up_proj",
|
||||
],
|
||||
}
|
||||
# LoRA specific attributes
|
||||
supported_lora_modules = [
|
||||
"W_pack",
|
||||
"o_proj",
|
||||
"gate_up_proj",
|
||||
"down_proj",
|
||||
]
|
||||
embedding_modules = {}
|
||||
embedding_padding_modules = []
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config,
|
||||
position_embedding: str,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
lora_config: Optional[LoRAConfig] = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
self.model = BaiChuanModel(config, position_embedding, quant_config)
|
||||
self.lm_head = ParallelLMHead(config.vocab_size, config.hidden_size)
|
||||
self.logits_processor = LogitsProcessor(config.vocab_size)
|
||||
self.sampler = Sampler()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
kv_caches: List[torch.Tensor],
|
||||
attn_metadata: AttentionMetadata,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.model(input_ids, positions, kv_caches,
|
||||
attn_metadata)
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(self, hidden_states: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata) -> torch.Tensor:
|
||||
logits = self.logits_processor(self.lm_head.weight, hidden_states,
|
||||
sampling_metadata)
|
||||
return logits
|
||||
|
||||
def sample(
|
||||
self,
|
||||
logits: torch.Tensor,
|
||||
sampling_metadata: SamplingMetadata,
|
||||
) -> Optional[SamplerOutput]:
|
||||
next_tokens = self.sampler(logits, sampling_metadata)
|
||||
return next_tokens
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
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 name == "lm_head.weight":
|
||||
# Unlike Baichuan, Baichuan2 normalizes the head weights.
|
||||
# Refer to:
|
||||
# https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/84603cde5ebffb6084e476cfaeceaf0b8b91fe54/modeling_baichuan.py#L508
|
||||
# Distinguish between Baichuan and Baichuan2 by checking the
|
||||
# vocab size. This is suggested by
|
||||
# https://github.com/vllm-project/vllm/pull/1022#discussion_r1325652704
|
||||
is_baichuan2 = self.config.vocab_size == 125696
|
||||
if is_baichuan2:
|
||||
loaded_weight = torch.nn.functional.normalize(
|
||||
loaded_weight)
|
||||
|
||||
for (param_name, weight_name, shard_id) in stacked_params_mapping:
|
||||
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)
|
||||
@@ -377,34 +371,123 @@ class BaiChuanBaseForCausalLM(nn.Module):
|
||||
# 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 = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
loaded_params.add(name)
|
||||
return loaded_params
|
||||
|
||||
|
||||
class BaiChuanBaseForCausalLM(nn.Module, SupportsLoRA, SupportsPP, SupportsQuant):
|
||||
packed_modules_mapping = {
|
||||
"W_pack": ["W_pack"],
|
||||
"gate_up_proj": [
|
||||
"gate_proj",
|
||||
"up_proj",
|
||||
],
|
||||
}
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
*,
|
||||
vllm_config: VllmConfig,
|
||||
prefix: str = "",
|
||||
position_embedding: str = "ROPE",
|
||||
):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
|
||||
self.config = config
|
||||
|
||||
self.tp_size = get_tensor_model_parallel_world_size()
|
||||
self.quant_config = quant_config
|
||||
self.model = BaiChuanModel(
|
||||
vllm_config=vllm_config,
|
||||
prefix=prefix,
|
||||
position_embedding=position_embedding,
|
||||
)
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
quant_config=quant_config,
|
||||
prefix=maybe_prefix(prefix, "lm_head"),
|
||||
)
|
||||
self.lm_head.weight.weight_loader = self.lm_head_weight_loader
|
||||
if self.config.tie_word_embeddings:
|
||||
self.lm_head.weight = self.model.embed_tokens.weight
|
||||
self.logits_processor = LogitsProcessor(config.vocab_size)
|
||||
self.make_empty_intermediate_tensors = (
|
||||
self.model.make_empty_intermediate_tensors
|
||||
)
|
||||
|
||||
def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
|
||||
return self.model.embed_input_ids(input_ids)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
intermediate_tensors: IntermediateTensors | None = None,
|
||||
inputs_embeds: torch.Tensor | None = None,
|
||||
) -> torch.Tensor | IntermediateTensors:
|
||||
hidden_states = self.model(
|
||||
input_ids, positions, intermediate_tensors, inputs_embeds
|
||||
)
|
||||
return hidden_states
|
||||
|
||||
def compute_logits(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
) -> torch.Tensor | None:
|
||||
logits = self.logits_processor(self.lm_head, hidden_states)
|
||||
return logits
|
||||
|
||||
def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
||||
loader = AutoWeightsLoader(self)
|
||||
return loader.load_weights(weights)
|
||||
|
||||
def lm_head_weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
|
||||
# Unlike Baichuan, Baichuan2 normalizes the head weights.
|
||||
# Refer to:
|
||||
# https://huggingface.co/baichuan-inc/Baichuan2-7B-Chat/blob/84603cde5ebffb6084e476cfaeceaf0b8b91fe54/modeling_baichuan.py#L508
|
||||
# Distinguish between Baichuan and Baichuan2 by checking the
|
||||
# vocab size. This is suggested by
|
||||
# https://github.com/vllm-project/vllm/pull/1022#discussion_r1325652704
|
||||
is_baichuan2 = self.config.vocab_size == 125696
|
||||
if is_baichuan2:
|
||||
loaded_weight = torch.nn.functional.normalize(loaded_weight)
|
||||
if self.tp_size > 1:
|
||||
row_parallel_weight_loader(param, loaded_weight)
|
||||
else:
|
||||
default_weight_loader(param, loaded_weight)
|
||||
|
||||
|
||||
class BaichuanForCausalLM(BaiChuanBaseForCausalLM):
|
||||
"""Baichuan 13B and Baichuan2 7B/13B."""
|
||||
"""Baichuan 13B and Baichuan2 7B/13B.
|
||||
NOTE: the class name has a lower case 'c'.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
lora_config: Optional[LoRAConfig] = None,
|
||||
):
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
config = vllm_config.model_config.hf_config
|
||||
if config.hidden_size == 4096: # baichuan2 7b
|
||||
super().__init__(config, "ROPE", quant_config, lora_config)
|
||||
super().__init__(
|
||||
vllm_config=vllm_config, prefix=prefix, position_embedding="ROPE"
|
||||
)
|
||||
else: # baichuan 13b, baichuan2 13b
|
||||
super().__init__(config, "ALIBI", quant_config, lora_config)
|
||||
super().__init__(
|
||||
vllm_config=vllm_config, prefix=prefix, position_embedding="ALIBI"
|
||||
)
|
||||
|
||||
|
||||
class BaiChuanForCausalLM(BaiChuanBaseForCausalLM):
|
||||
"""Baichuan 7B."""
|
||||
"""Baichuan 7B.
|
||||
NOTE: the class name has an upper case 'C'.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
lora_config: Optional[LoRAConfig] = None,
|
||||
):
|
||||
super().__init__(config, "ROPE", quant_config, lora_config)
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__(
|
||||
vllm_config=vllm_config, prefix=prefix, position_embedding="ROPE"
|
||||
)
|
||||
|
||||
Reference in New Issue
Block a user