[Model] Support Step1 Model (#32511)
Signed-off-by: xieli <xieli@stepfun.com>
This commit is contained in:
Li Xie
415
vllm/model_executor/models/step1.py
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415
vllm/model_executor/models/step1.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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"""Shared Step decoder blocks and the Step1 text model."""
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from __future__ import annotations
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import math
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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 vllm.attention.layer import Attention, AttentionType
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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 (
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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.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 default_weight_loader
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from vllm.model_executor.models.interfaces import SupportsPP
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from vllm.model_executor.models.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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from vllm.sequence import IntermediateTensors
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STEP_PACKED_MODULES_MAPPING = {
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"qkv_proj": ["q_proj", "k_proj", "v_proj"],
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"gate_up_proj": ["gate_proj", "up_proj"],
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}
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def _get_step_alibi_slopes(total_num_heads: int) -> torch.Tensor:
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"""Reference ALiBi slopes used by Step models."""
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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 ** (-8.0 / closest_power_of_2),
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dtype=torch.float32,
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)
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slopes = torch.pow(
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base,
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torch.arange(1, 1 + closest_power_of_2, dtype=torch.int32),
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)
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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 ** (-4.0 / closest_power_of_2),
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dtype=torch.float32,
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)
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num_remaining_heads = total_num_heads - closest_power_of_2
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extra_powers = torch.arange(
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1,
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1 + 2 * num_remaining_heads,
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2,
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dtype=torch.int32,
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)
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slopes = torch.cat(
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[slopes, torch.pow(extra_base, extra_powers)],
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dim=0,
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)
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return slopes
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class StepAttention(nn.Module):
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def __init__(
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self,
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config,
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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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tp_size = get_tensor_model_parallel_world_size()
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self.total_num_heads = config.num_attention_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.head_dim = self.hidden_size // self.total_num_heads
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total_num_kv_heads = getattr(
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config, "num_attention_groups", getattr(config, "num_key_value_heads", 1)
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)
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if total_num_kv_heads is None or total_num_kv_heads <= 0:
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total_num_kv_heads = 1
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self.total_num_kv_heads = total_num_kv_heads
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if self.total_num_kv_heads >= tp_size:
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assert self.total_num_kv_heads % tp_size == 0
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else:
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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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self.qkv_proj = QKVParallelLinear(
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hidden_size=self.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=getattr(config, "attention_bias", False),
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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.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.o_proj = RowParallelLinear(
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input_size=self.total_num_heads * self.head_dim,
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output_size=self.hidden_size,
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bias=getattr(config, "attention_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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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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alibi_slopes = _get_step_alibi_slopes(self.total_num_heads)[head_start:head_end]
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alibi_slopes = alibi_slopes.tolist()
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self.scale = self.head_dim**-0.5
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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.scale,
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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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alibi_slopes=alibi_slopes,
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prefix=f"{prefix}.attn",
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use_alibi_sqrt=True,
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attn_type=AttentionType.DECODER,
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)
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def forward(
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self,
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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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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 StepMLP(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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quant_config: QuantizationConfig | None = None,
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prefix: str = "",
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bias: bool = False,
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):
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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, intermediate_size],
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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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self.act_fn = SiluAndMul()
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x, _ = self.gate_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 StepDecoderLayer(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.hidden_size = config.hidden_size
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self.self_attn = StepAttention(
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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=f"{prefix}.self_attn",
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)
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self.mlp = StepMLP(
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hidden_size=self.hidden_size,
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intermediate_size=config.intermediate_size,
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quant_config=quant_config,
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prefix=f"{prefix}.mlp",
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bias=getattr(config, "mlp_bias", False),
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)
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self.input_layernorm = RMSNorm(
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self.hidden_size,
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eps=config.rms_norm_eps,
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)
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self.post_attention_layernorm = RMSNorm(
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self.hidden_size,
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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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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(hidden_states=hidden_states)
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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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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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stacked_params_mapping = [
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(".qkv_proj", ".q_proj", "q"),
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(".qkv_proj", ".k_proj", "k"),
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(".qkv_proj", ".v_proj", "v"),
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(".gate_up_proj", ".gate_proj", 0),
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(".gate_up_proj", ".up_proj", 1),
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]
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params_dict = dict(self.named_parameters())
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loaded_params: set[str] = set()
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for name, loaded_weight in weights:
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for param_name, weight_name, shard_id in stacked_params_mapping:
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if weight_name not in name:
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continue
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name = name.replace(weight_name, param_name)
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = param.weight_loader
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weight_loader(param, loaded_weight, shard_id)
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break
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else:
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# Skip loading extra bias for GPTQ models.
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if name.endswith(".bias") and name not in params_dict:
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continue
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if is_pp_missing_parameter(name, self):
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continue
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param = params_dict[name]
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weight_loader = getattr(param, "weight_loader", default_weight_loader) # type: ignore[name-defined]
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weight_loader(param, loaded_weight)
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loaded_params.add(name)
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return loaded_params
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class StepDecoderModel(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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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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# Need embed_tokens on first rank, and also on last rank if tie_word_embeddings
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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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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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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: StepDecoderLayer(vllm_config=vllm_config, prefix=prefix),
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prefix=maybe_prefix(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.aux_hidden_state_layers: tuple[int, ...] = getattr(
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config, "aux_hidden_state_layers", ()
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)
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self.make_empty_intermediate_tensors = make_empty_intermediate_tensors_factory(
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["hidden_states", "residual"],
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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 | tuple[torch.Tensor, list[torch.Tensor]]:
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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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assert input_ids is not None
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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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aux_hidden_states = []
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for idx, layer in enumerate(self.layers[self.start_layer : self.end_layer]):
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if idx in self.aux_hidden_state_layers:
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if residual is None:
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aux_hidden_states.append(hidden_states)
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else:
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aux_hidden_states.append(hidden_states + residual)
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hidden_states, residual = layer(positions, hidden_states, residual)
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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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hidden_states, _ = self.norm(hidden_states, residual)
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if aux_hidden_states:
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return hidden_states, aux_hidden_states
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return hidden_states
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class Step1ForCausalLM(nn.Module, SupportsPP):
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packed_modules_mapping = STEP_PACKED_MODULES_MAPPING
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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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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.model = StepDecoderModel(
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vllm_config=vllm_config,
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prefix=maybe_prefix(prefix, "model"),
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)
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if get_pp_group().is_last_rank:
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self.lm_head = ParallelLMHead(
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config.vocab_size,
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config.hidden_size,
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quant_config=quant_config,
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prefix=maybe_prefix(prefix, "lm_head"),
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)
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if getattr(config, "tie_word_embeddings", True):
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self.lm_head = self.lm_head.tie_weights(self.model.embed_tokens)
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self.logits_processor = LogitsProcessor(config.vocab_size)
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else:
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self.lm_head = PPMissingLayer()
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self.logits_processor = None # type: ignore[assignment]
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self.make_empty_intermediate_tensors = (
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self.model.make_empty_intermediate_tensors
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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.model.embed_input_ids(input_ids)
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def forward(
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self,
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input_ids: torch.LongTensor | 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 | tuple[torch.Tensor, list[torch.Tensor]]:
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return self.model(
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input_ids,
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positions,
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intermediate_tensors,
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inputs_embeds=inputs_embeds,
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)
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def compute_logits(
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self,
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hidden_states: torch.Tensor,
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) -> torch.Tensor | None:
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if not get_pp_group().is_last_rank:
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return None
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return self.logits_processor(self.lm_head, hidden_states)
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def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
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loader = AutoWeightsLoader(self)
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return loader.load_weights(weights)
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