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[v0.10.0 -> v0.13.0] Task14 (#231)
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@Anleeos
Anleeos authored Dec 1, 2023
1 parent dd0f0e1 commit a38de9e
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21 changes: 13 additions & 8 deletions docs/how_to/autotune/01-tuning_nvidia.md
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Expand Up @@ -166,15 +166,20 @@ measure_option = autotvm.measure_option(
runner=autotvm.LocalRunner(repeat=3, min_repeat_ms=100, timeout=4),
)

record_file = None
# 开始调优，将 log 记录到 `conv2d.log`
# 在调优过程中，会尝试很多无效的配置，所以你应该
# 查看许多错误报告。只要能看到非零的 GFLOPS 就可以。
tuner = autotvm.tuner.XGBTuner(task)
tuner.tune(
n_trial=20,
measure_option=measure_option,
callbacks=[autotvm.callback.log_to_file("conv2d.log")],
)
# 我们不再在服务器上运行调优，因为太耗时间了
# 去掉下面的注释，自己运行

# tuner = autotvm.tuner.XGBTuner(task)
# record_file = "conv2d.log"
# tuner.tune(
# n_trial=5,
# measure_option=measure_option,
# callbacks=[autotvm.callback.log_to_file(record_file)],
# )
```

输出结果：
Expand Down Expand Up @@ -1839,13 +1844,13 @@ tvm.autotvm.task.space.InstantiationError: Skipped because of invalid gpu kernel
``` python
# 检查最佳配置
dispatch_context = autotvm.apply_history_best("conv2d.log")
dispatch_context = autotvm.apply_history_best(record_file)
best_config = dispatch_context.query(task.target, task.workload)
print("\nBest config:")
print(best_config)

# 从日志文件中应用历史最好记录
with autotvm.apply_history_best("conv2d.log"):
with autotvm.apply_history_best(record_file):
with tvm.target.Target("cuda"):
s, arg_bufs = conv2d_no_batching(N, H, W, CO, CI, KH, KW, strides, padding)
func = tvm.build(s, arg_bufs)
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24 changes: 23 additions & 1 deletion docs/how_to/autotune/02-autotuning_nvidia.md
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Expand Up @@ -158,8 +158,30 @@ def tune_tasks(
prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))

# 创建调优器
if tuner == "xgb" or tuner == "xgb-rank":
if tuner == "xgb":
tuner_obj = XGBTuner(tsk, loss_type="reg")
elif tuner == "xgb_knob":
tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="knob")
elif tuner == "xgb_itervar":
tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="itervar")
elif tuner == "xgb_curve":
tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="curve")
elif tuner == "xgb_rank":
tuner_obj = XGBTuner(tsk, loss_type="rank")
elif tuner == "xgb_rank_knob":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
elif tuner == "xgb_rank_itervar":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="itervar")
elif tuner == "xgb_rank_curve":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="curve")
elif tuner == "xgb_rank_binary":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary")
elif tuner == "xgb_rank_binary_knob":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="knob")
elif tuner == "xgb_rank_binary_itervar":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="itervar")
elif tuner == "xgb_rank_binary_curve":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="curve")
elif tuner == "ga":
tuner_obj = GATuner(tsk, pop_size=100)
elif tuner == "random":
Expand Down
24 changes: 23 additions & 1 deletion docs/how_to/autotune/03-autotuning_x86.md
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Original file line number Diff line number Diff line change
Expand Up @@ -122,8 +122,30 @@ def tune_kernels(
prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))

# 创建调优器
if tuner == "xgb" or tuner == "xgb-rank":
if tuner == "xgb":
tuner_obj = XGBTuner(task, loss_type="reg")
elif tuner == "xgb_knob":
tuner_obj = XGBTuner(task, loss_type="reg", feature_type="knob")
elif tuner == "xgb_itervar":
tuner_obj = XGBTuner(task, loss_type="reg", feature_type="itervar")
elif tuner == "xgb_curve":
tuner_obj = XGBTuner(task, loss_type="reg", feature_type="curve")
elif tuner == "xgb_rank":
tuner_obj = XGBTuner(task, loss_type="rank")
elif tuner == "xgb_rank_knob":
tuner_obj = XGBTuner(task, loss_type="rank", feature_type="knob")
elif tuner == "xgb_rank_itervar":
tuner_obj = XGBTuner(task, loss_type="rank", feature_type="itervar")
elif tuner == "xgb_rank_curve":
tuner_obj = XGBTuner(task, loss_type="rank", feature_type="curve")
elif tuner == "xgb_rank_binary":
tuner_obj = XGBTuner(task, loss_type="rank-binary")
elif tuner == "xgb_rank_binary_knob":
tuner_obj = XGBTuner(task, loss_type="rank-binary", feature_type="knob")
elif tuner == "xgb_rank_binary_itervar":
tuner_obj = XGBTuner(task, loss_type="rank-binary", feature_type="itervar")
elif tuner == "xgb_rank_binary_curve":
tuner_obj = XGBTuner(task, loss_type="rank-binary", feature_type="curve")
elif tuner == "ga":
tuner_obj = GATuner(task, pop_size=50)
elif tuner == "random":
Expand Down
24 changes: 20 additions & 4 deletions docs/how_to/autotune/04-autotuning_arm.md
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Original file line number Diff line number Diff line change
Expand Up @@ -216,14 +216,30 @@ def tune_tasks(
prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))

# 创建调优器
if tuner == "xgb" or tuner == "xgb-rank":
tuner_obj = XGBTuner(tsk, loss_type="rank")
if tuner == "xgb":
tuner_obj = XGBTuner(tsk, loss_type="reg")
elif tuner == "xgb_knob":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="knob")
elif tuner == "xgb_itervar":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="itervar")
tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="itervar")
elif tuner == "xgb_curve":
tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="curve")
elif tuner == "xgb_rank":
tuner_obj = XGBTuner(tsk, loss_type="rank")
elif tuner == "xgb_rank_knob":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
elif tuner == "xgb_rank_itervar":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="itervar")
elif tuner == "xgb_rank_curve":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="curve")
elif tuner == "xgb_rank_binary":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary")
elif tuner == "xgb_rank_binary_knob":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="knob")
elif tuner == "xgb_rank_binary_itervar":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="itervar")
elif tuner == "xgb_rank_binary_curve":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="curve")
elif tuner == "ga":
tuner_obj = GATuner(tsk, pop_size=50)
elif tuner == "random":
Expand Down
24 changes: 23 additions & 1 deletion docs/how_to/autotune/05-autotuning_mobile.md
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Original file line number Diff line number Diff line change
Expand Up @@ -213,8 +213,30 @@ def tune_tasks(
prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))

# 创建调优器
if tuner == "xgb" or tuner == "xgb-rank":
if tuner == "xgb":
tuner_obj = XGBTuner(tsk, loss_type="reg")
elif tuner == "xgb_knob":
tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="knob")
elif tuner == "xgb_itervar":
tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="itervar")
elif tuner == "xgb_curve":
tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="curve")
elif tuner == "xgb_rank":
tuner_obj = XGBTuner(tsk, loss_type="rank")
elif tuner == "xgb_rank_knob":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
elif tuner == "xgb_rank_itervar":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="itervar")
elif tuner == "xgb_rank_curve":
tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="curve")
elif tuner == "xgb_rank_binary":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary")
elif tuner == "xgb_rank_binary_knob":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="knob")
elif tuner == "xgb_rank_binary_itervar":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="itervar")
elif tuner == "xgb_rank_binary_curve":
tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="curve")
elif tuner == "ga":
tuner_obj = GATuner(tsk, pop_size=50)
elif tuner == "random":
Expand Down

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