Sync from v0.13
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vllm/model_executor/models/solar.py
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484
vllm/model_executor/models/solar.py
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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 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 minor architectural differences compared
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# to GPT-NeoX and OPT used by the Meta AI team 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 Solar model compatible with HuggingFace weights."""
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from collections.abc import Iterable
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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.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 get_pp_group, get_tensor_model_parallel_world_size
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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 (
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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 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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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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maybe_remap_kv_scale_name,
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)
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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 (
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AutoWeightsLoader,
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PPMissingLayer,
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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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class SolarMLP(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: QuantizationConfig | None = None,
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bias: bool = False,
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prefix: str = "",
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) -> None:
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super().__init__()
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self.gate_up_proj = MergedColumnParallelLinear(
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input_size=hidden_size,
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output_sizes=[intermediate_size] * 2,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.gate_up_proj",
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)
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self.down_proj = RowParallelLinear(
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input_size=intermediate_size,
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output_size=hidden_size,
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bias=bias,
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quant_config=quant_config,
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prefix=f"{prefix}.down_proj",
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)
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if hidden_act != "silu":
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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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gate_up, _ = self.gate_up_proj(x)
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x = self.act_fn(gate_up)
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x, _ = self.down_proj(x)
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return x
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class SolarAttention(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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max_position_embeddings: int = 8192,
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quant_config: QuantizationConfig | None = None,
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bias: bool = False,
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cache_config: CacheConfig | None = 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 = 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(config, "head_dim", None)
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if self.head_dim is None:
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self.head_dim = self.hidden_size // self.total_num_heads
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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.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,
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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.rotary_emb = get_rope(
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self.head_dim,
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max_position=max_position_embeddings,
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rope_parameters=config.rope_parameters,
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)
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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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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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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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) -> 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, 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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class SolarDecoderLayer(nn.Module):
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def __init__(
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self,
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config: PretrainedConfig,
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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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) -> None:
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super().__init__()
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self.hidden_size = config.hidden_size
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max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
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# Support abacusai/Smaug-72B-v0.1 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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self.self_attn = SolarAttention(
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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=getattr(
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config, "num_key_value_heads", config.num_attention_heads
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),
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max_position_embeddings=max_position_embeddings,
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quant_config=quant_config,
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bias=attention_bias,
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cache_config=cache_config,
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prefix=f"{prefix}.self_attn",
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)
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self.mlp = SolarMLP(
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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=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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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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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(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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)
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# Fully Connected
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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 SolarModel(nn.Module):
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def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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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.quant_config = quant_config
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self.vocab_size = config.vocab_size
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if get_pp_group().is_first_rank or (
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config.tie_word_embeddings and get_pp_group().is_last_rank
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):
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self.embed_tokens = VocabParallelEmbedding(
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self.vocab_size,
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config.hidden_size,
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)
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else:
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self.embed_tokens = PPMissingLayer()
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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: SolarDecoderLayer(
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config=config,
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cache_config=cache_config,
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quant_config=quant_config,
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prefix=prefix,
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),
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prefix=f"{prefix}.layers",
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)
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if get_pp_group().is_last_rank:
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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else:
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self.norm = PPMissingLayer()
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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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def embed_input_ids(self, input_ids: torch.Tensor) -> torch.Tensor:
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return self.embed_tokens(input_ids)
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def forward(
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self,
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input_ids: torch.Tensor | None,
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positions: torch.Tensor,
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intermediate_tensors: IntermediateTensors | None,
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inputs_embeds: torch.Tensor | None = None,
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) -> torch.Tensor | IntermediateTensors:
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if get_pp_group().is_first_rank:
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if inputs_embeds is not None:
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hidden_states = inputs_embeds
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else:
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hidden_states = self.embed_input_ids(input_ids)
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residual = None
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else:
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assert intermediate_tensors is not None
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hidden_states = intermediate_tensors["hidden_states"]
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residual = intermediate_tensors["residual"]
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bskcn_h_1 = None
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bskcn_h_2 = None
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bskcn_r_1 = None
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bskcn_r_2 = None
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bskcn_tv = self.config.bskcn_tv[0] if self.training else self.config.bskcn_tv[1]
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for i in range(self.start_layer, self.end_layer):
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if i in self.config.bskcn_1:
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bskcn_h_1 = hidden_states.clone()
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bskcn_r_1 = residual.clone()
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if i in self.config.bskcn_2:
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bskcn_h_2 = hidden_states.clone()
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bskcn_r_2 = residual.clone()
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if i in self.config.bskcn_3:
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hidden_states = bskcn_h_1 * bskcn_tv + hidden_states * (1 - bskcn_tv)
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residual = bskcn_r_1 * bskcn_tv + residual * (1 - bskcn_tv)
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if i in self.config.bskcn_4:
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hidden_states = bskcn_h_2 * bskcn_tv + hidden_states * (1 - bskcn_tv)
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residual = bskcn_r_2 * bskcn_tv + residual * (1 - bskcn_tv)
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layer = self.layers[i]
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hidden_states, residual = layer(
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positions,
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hidden_states,
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residual,
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)
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if not get_pp_group().is_last_rank:
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return IntermediateTensors(
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{"hidden_states": hidden_states, "residual": residual}
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||||
)
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||||
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hidden_states, _ = self.norm(hidden_states, residual)
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return hidden_states
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
|
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stacked_params_mapping = [
|
||||
# (param_name, shard_name, shard_id)
|
||||
(".qkv_proj", ".q_proj", "q"),
|
||||
(".qkv_proj", ".k_proj", "k"),
|
||||
(".qkv_proj", ".v_proj", "v"),
|
||||
(".gate_up_proj", ".gate_proj", 0),
|
||||
(".gate_up_proj", ".up_proj", 1),
|
||||
]
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||||
params_dict = dict(self.named_parameters())
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||||
loaded_params: set[str] = set()
|
||||
for name, loaded_weight in weights:
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||||
if self.quant_config is not None and (
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||||
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
|
||||
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
|
||||
# Remapping the name of FP8 kv-scale.
|
||||
name = maybe_remap_kv_scale_name(name, params_dict)
|
||||
if name is None:
|
||||
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 SolarForCausalLM(nn.Module, SupportsLoRA, SupportsPP):
|
||||
packed_modules_mapping = {
|
||||
"qkv_proj": [
|
||||
"q_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
],
|
||||
"gate_up_proj": [
|
||||
"gate_proj",
|
||||
"up_proj",
|
||||
],
|
||||
}
|
||||
|
||||
# LoRA specific attributes
|
||||
embedding_modules = {
|
||||
"embed_tokens": "input_embeddings",
|
||||
"lm_head": "output_embeddings",
|
||||
}
|
||||
|
||||
def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
|
||||
super().__init__()
|
||||
config = vllm_config.model_config.hf_config
|
||||
quant_config = vllm_config.quant_config
|
||||
|
||||
self.config = config
|
||||
|
||||
self.quant_config = quant_config
|
||||
|
||||
self.model = SolarModel(
|
||||
vllm_config=vllm_config,
|
||||
prefix=maybe_prefix(prefix, "model"),
|
||||
)
|
||||
if get_pp_group().is_last_rank:
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
quant_config=quant_config,
|
||||
prefix=maybe_prefix(prefix, "lm_head"),
|
||||
)
|
||||
if config.tie_word_embeddings:
|
||||
self.lm_head.weight = self.model.embed_tokens.weight
|
||||
|
||||
logit_scale = getattr(config, "logit_scale", 1.0)
|
||||
self.logits_processor = LogitsProcessor(
|
||||
config.vocab_size, scale=logit_scale
|
||||
)
|
||||
else:
|
||||
self.lm_head = PPMissingLayer()
|
||||
|
||||
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:
|
||||
model_output = self.model(
|
||||
input_ids, positions, intermediate_tensors, inputs_embeds
|
||||
)
|
||||
return model_output
|
||||
|
||||
def compute_logits(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
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)
|
||||
Reference in New Issue
Block a user