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Merge branch 'develop' of https://github.com/PaddlePaddle/GraphNet into develop
2 parents 6fa0a5d + c9a2358 commit 03a3103

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‎graph_net/torch/backend/unstable_to_stable_backend.py‎

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import os
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import torch
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import sys
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import inspect
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from .graph_compiler_backend import GraphCompilerBackend
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class UnstableToStableBackend(GraphCompilerBackend):
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def __call__(self, model):
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# Perform unstable API check before running the model
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self.model = model
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self.unstable_to_stable()
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self.check_unstable_api()
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return self.model
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unstable_api = os.getenv("DISALLOWED_UNSTABLE_API", "").strip()
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self.unstable_api = unstable_api
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def my_backend(gm, sample_inputs):
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gm = self.unstable_to_stable(gm)
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self.check_unstable_api(gm)
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return gm.forward
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return torch.compile(backend=my_backend)(model)
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"""
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TODO: 实现将 self.model 中的不稳定（unstable）API 转换为稳定（stable）API 的逻辑。
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该 API 负责遍历 self.model，并将其中调用的实验性或不稳定接口替换为对应的稳定版本。
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注意：此逻辑属于模型编译安全机制的重要组成部分，请勿随意修改或删除。
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api命名规范：
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<unstable>_to_<stable>
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stable api链接：
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TODO: Implement logic to convert unstable APIs in `self.model` into their stable counterparts.
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This API is responsible for traversing `self.model` and replacing any calls to experimental or unstable interfaces with the corresponding stable versions.
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Note: This logic is a critical component of the model compilation safety mechanism—do not modify or remove it without caution.
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**API naming convention:**
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`<unstable>_to_<stable>`
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**Stable API reference link:**
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"""
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def unstable_to_stable(self):
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return
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def unstable_to_stable(self, gm):
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# TODO
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return gm
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def check_unstable_api(self):
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def check_unstable_api(self, gm):
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"""
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Check whether gm contains the API specified in the environment
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variable DISALLOWED_UNSTABLE_API. If it does, raise an exception and stop
@@ -35,29 +43,15 @@ def check_unstable_api(self):
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This logic is part of the GraphNet compiler safety mechanism.
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Do NOT modify, remove, or bypass this check under any circumstances.
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"""
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unstable_api = os.getenv("DISALLOWED_UNSTABLE_API", "").strip()
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if not unstable_api:
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return # Skip check if no environment variable is set
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from torch.fx import symbolic_trace
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try:
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# Convert the model into a static computation graph (FX IR)
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traced = symbolic_trace(self.model)
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graph_text = str(traced.graph)
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except Exception as e:
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# In case tracing fails, fallback to textual model dump
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graph_text = str(self.model)
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graph_text = gm.code
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# Search for the unstable API substring
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if unstable_api in graph_text:
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count = graph_text.count(unstable_api)
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raise RuntimeError(
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f"❌ Detected unstable API '{unstable_api}' '{count}' times in model graph.\n"
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f"Please replace it with a stable API before proceeding.\n"
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)
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if self.unstable_api in graph_text:
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count = graph_text.count(self.unstable_api)
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print(f"❌unstable_api:{self.unstable_api} occurs {count} times")
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sys.exit(-1)
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else:
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print(f"✅ Model passed: no occurrence of '{unstable_api}' found.")
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print(f"✅ Model passed: no occurrence of '{self.unstable_api}' found.")
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def synchronize(self):
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# Synchronize CUDA operations if available

‎plot_unstable_to_stable.sh‎

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#!/bin/bash
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# 批量运行 GraphNet benchmark for unstable_to_stable (_add_batch_dim)
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# 从文件列表中读取模型路径执行编译测试，并将 log 转换为 JSON
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if [ -z "$DISALLOWED_UNSTABLE_API" ]; then
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echo "❌ 环境变量 DISALLOWED_UNSTABLE_API 未设置！"
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echo "请使用： export DISALLOWED_UNSTABLE_API=<target_unstable_api>"
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exit 1
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fi
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if [ -z "$GRAPH_NET_WORKSPACE" ]; then
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echo "❌ 环境变量 GRAPH_NET_WORKSPACE 未设置！"
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echo "请使用： export GRAPH_NET_WORKSPACE=/path/to/GraphNet"
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exit 1
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fi
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# === 配置区 ===
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root_dir="${GRAPH_NET_WORKSPACE}/todo_works/unstable_api_to_stable_api/${DISALLOWED_UNSTABLE_API}"
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file_list="${root_dir}/${DISALLOWED_UNSTABLE_API}_files.txt"
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log_file="${root_dir}/log.log"
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json_output_dir="${root_dir}/JSON_results"
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# 设置环境变量（benchmark 路径）
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export GRAPH_NET_BENCHMARK_PATH="$root_dir"
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# === 检查输入文件 ===
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if [ ! -f "$file_list" ]; then
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echo "❌ 文件不存在: $file_list"
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exit 1
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fi
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# === 执行 benchmark ===
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echo "🚀 开始执行 benchmark..."
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echo "日志将写入: $log_file"
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# echo "--------------------------------------" > "$log_file"
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if [ -f "$log_file" ]; then
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echo "🧹 删除旧的日志文件: $log_file"
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rm "$log_file"
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fi
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while IFS= read -r model_path; do
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[ -z "$model_path" ] && continue
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echo "▶️ 运行模型: $model_path"
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echo ">>> Running model: $model_path"
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python -m graph_net.torch.test_compiler \
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--model-path "/root/GraphNet/${model_path}/" \
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--compiler unstable_to_stable \
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>> "$log_file" 2>&1
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echo "✅ 完成: $model_path"
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echo "--------------------------------------"
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done < "$file_list"
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echo "🎯 所有模型运行完成，日志保存在: $log_file"
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# === 转换 log 为 JSON ===
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echo "📦 正在将日志转换为 JSON..."
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if [ -d "$json_output_dir" ]; then
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echo "🧹 删除旧的 JSON 输出目录: $json_output_dir"
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rm -rf "$json_output_dir"
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fi
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mkdir -p "$json_output_dir"
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python -m graph_net.log2json \
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--log-file "$log_file" \
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--output-dir "$json_output_dir"
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if [ $? -eq 0 ]; then
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echo "✅ JSON 文件已生成: $json_output_dir"
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else
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echo "⚠️ log2json 执行失败，请检查 log.log"
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fi
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echo "📦 正在将JSON转换为结果图"
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python -m graph_net.plot_ESt \
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--benchmark-path $GRAPH_NET_BENCHMARK_PATH/JSON_results/ \
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--output-dir $GRAPH_NET_BENCHMARK_PATH \
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if [ $? -eq 0 ]; then
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echo "✅ 结果图 文件已生成: $GRAPH_NET_BENCHMARK_PATH"
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else
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echo "❌结果图生成失败"
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fi

‎todo_works/unstable_api_to_stable_api/_add_batch_dim/_add_batch_dim_files.txt‎

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samples/transformers-auto-model/AceInstruct-1.5B
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samples/transformers-auto-model/AlphaMaze-v0.2-1.5B
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samples/transformers-auto-model/Biggie-SmoLlm-0.4B
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samples/transformers-auto-model/EXAONE-4.0-1.2B
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samples/transformers-auto-model/SmolLM3-3B
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samples/transformers-auto-model/ZR1-1.5B
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samples/transformers-auto-model/gemma-3-1b-pt
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samples/transformers-auto-model/jhu-clsp_ettin-decoder-150m
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samples/transformers-auto-model/jinaai_jina-reranker-m0
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samples/transformers-auto-model/openai_whisper-base

‎todo_works/unstable_api_to_stable_api/_log_api_usage_once/_log_api_usage_once_files.txt‎

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samples/torchvision/convnext_base
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samples/torchvision/convnext_large
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samples/torchvision/convnext_small
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samples/torchvision/convnext_tiny
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samples/torchvision/efficientnet_b0
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samples/torchvision/efficientnet_b1
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samples/torchvision/efficientnet_b2
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samples/torchvision/efficientnet_b3
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samples/torchvision/efficientnet_b4
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samples/torchvision/efficientnet_b5
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samples/torchvision/efficientnet_b6
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samples/torchvision/efficientnet_b7
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samples/torchvision/efficientnet_v2_l
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samples/torchvision/efficientnet_v2_m
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samples/torchvision/efficientnet_v2_s
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samples/torchvision/maxvit_t
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samples/torchvision/swin_b
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samples/torchvision/swin_v2_b
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samples/torchvision/swin_v2_s
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samples/torchvision/swin_v2_t
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samples/transformers-auto-model/42dot_LLM-SFT-1.3B
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samples/transformers-auto-model/AceInstruct-1.5B
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samples/transformers-auto-model/AlphaMaze-v0.2-1.5B
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samples/transformers-auto-model/Biggie-SmoLlm-0.4B
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samples/transformers-auto-model/HuggingFaceTB/SmolLM3-3B
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samples/transformers-auto-model/Intel_zoedepth-kitti
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samples/transformers-auto-model/Intel_zoedepth-nyu
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samples/transformers-auto-model/LFM2-350M
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samples/transformers-auto-model/LLaMmlein_120M
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samples/transformers-auto-model/NDugar_deberta-v2-xlarge-mnli
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samples/transformers-auto-model/Qwen1.5-0.5B
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samples/transformers-auto-model/Qwen2.5-0.5B
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samples/transformers-auto-model/Qwen3-Embedding-0.6B
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samples/transformers-auto-model/SmolLM3-3B
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samples/transformers-auto-model/TinyLlama-1.1B-step-50K-105b
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samples/transformers-auto-model/TinyLlama/TinyLlama-1.1B-Chat-v0.4
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samples/transformers-auto-model/Tucano-2b4
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samples/transformers-auto-model/ZR1-1.5B
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samples/transformers-auto-model/baidu/ERNIE-4.5-0.3B-PT
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samples/transformers-auto-model/deepseek-ai/deepseek-coder-1.3b-base
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samples/transformers-auto-model/facebook_dpt-dinov2-giant-nyu
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samples/transformers-auto-model/facebook_dpt-dinov2-small-kitti
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samples/transformers-auto-model/gemma-3-1b-pt
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samples/transformers-auto-model/google/gemma-1.1-2b-it
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samples/transformers-auto-model/google/gemma-2b-it
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samples/transformers-auto-model/google/gemma-3-1b-it
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samples/transformers-auto-model/google/gemma-3-270m
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samples/transformers-auto-model/hf-tiny-model-private_tiny-random-BlipModel
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samples/transformers-auto-model/hf-tiny-model-private_tiny-random-CanineForSequenceClassification
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samples/transformers-auto-model/hf-tiny-model-private_tiny-random-PerceiverForImageClassificationConvProcessing
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samples/transformers-auto-model/hf-tiny-model-private_tiny-random-PerceiverForImageClassificationFourier
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samples/transformers-auto-model/hf-tiny-model-private_tiny-random-PerceiverForImageClassificationLearned
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samples/transformers-auto-model/microsoft/Phi-3-mini-4k-instruct
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samples/transformers-auto-model/microsoft/Phi-3.5-mini-instruct
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samples/transformers-auto-model/microsoft/Phi-4-mini-instruct
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samples/transformers-auto-model/microsoft/phi-1
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samples/transformers-auto-model/microsoft/phi-1_5
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samples/transformers-auto-model/microsoft/phi-2
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samples/transformers-auto-model/mooncakex_img2
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samples/transformers-auto-model/nli-deberta-v3-base
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samples/transformers-auto-model/nli-deberta-v3-small
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samples/transformers-auto-model/nli-deberta-v3-xsmall
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samples/transformers-auto-model/orca_mini_3b
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samples/transformers-auto-model/sarvam-0.5

‎todo_works/unstable_api_to_stable_api/_remove_batch_dim/_remove_batch_dim_files.txt‎

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samples/transformers-auto-model/AceInstruct-1.5B
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samples/transformers-auto-model/AlphaMaze-v0.2-1.5B
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samples/transformers-auto-model/Biggie-SmoLlm-0.4B
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samples/transformers-auto-model/EXAONE-4.0-1.2B
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samples/transformers-auto-model/SmolLM3-3B
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samples/transformers-auto-model/ZR1-1.5B
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samples/transformers-auto-model/gemma-3-1b-pt
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samples/transformers-auto-model/jhu-clsp_ettin-decoder-150m
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samples/transformers-auto-model/jinaai_jina-reranker-m0
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samples/transformers-auto-model/openai_whisper-base

‎todo_works/unstable_api_to_stable_api/_set_grad_enabled/_set_grad_enabled_files.txt‎

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samples/transformers-auto-model/EleutherAI_pythia-1b
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samples/transformers-auto-model/HuggingFaceTB/SmolLM3-3B
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samples/transformers-auto-model/TinyLlama/TinyLlama-1.1B-Chat-v0.4
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samples/transformers-auto-model/baidu/ERNIE-4.5-0.3B-PT
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samples/transformers-auto-model/deepseek-ai/deepseek-coder-1.3b-base
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samples/transformers-auto-model/google/gemma-1.1-2b-it
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samples/transformers-auto-model/google/gemma-2b-it
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samples/transformers-auto-model/google/gemma-3-1b-it
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samples/transformers-auto-model/google/gemma-3-270m
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samples/transformers-auto-model/microsoft/Phi-3-mini-4k-instruct
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samples/transformers-auto-model/microsoft/phi-1
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samples/transformers-auto-model/microsoft/phi-1_5
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samples/transformers-auto-model/microsoft/phi-2

‎todo_works/unstable_api_to_stable_api/_vmap_decrement_nesting/_vmap_decrement_nesting_files.txt‎

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samples/transformers-auto-model/AceInstruct-1.5B
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samples/transformers-auto-model/AlphaMaze-v0.2-1.5B
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samples/transformers-auto-model/Biggie-SmoLlm-0.4B
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samples/transformers-auto-model/EXAONE-4.0-1.2B
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samples/transformers-auto-model/SmolLM3-3B
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samples/transformers-auto-model/ZR1-1.5B
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samples/transformers-auto-model/gemma-3-1b-pt
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samples/transformers-auto-model/jhu-clsp_ettin-decoder-150m
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samples/transformers-auto-model/jinaai_jina-reranker-m0
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samples/transformers-auto-model/openai_whisper-base

‎todo_works/unstable_api_to_stable_api/_vmap_increment_nesting/_vmap_increment_nesting_files.txt‎

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samples/transformers-auto-model/AceInstruct-1.5B
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samples/transformers-auto-model/AlphaMaze-v0.2-1.5B
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samples/transformers-auto-model/Biggie-SmoLlm-0.4B
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samples/transformers-auto-model/EXAONE-4.0-1.2B
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samples/transformers-auto-model/SmolLM3-3B
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samples/transformers-auto-model/ZR1-1.5B
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samples/transformers-auto-model/gemma-3-1b-pt
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samples/transformers-auto-model/jhu-clsp_ettin-decoder-150m
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samples/transformers-auto-model/jinaai_jina-reranker-m0
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samples/transformers-auto-model/openai_whisper-base

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