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[Bugfix] Auto scheduler tutorial failure on CI #6723

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tqchen merged 8 commits into from
Oct 21, 2020
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[Bugfix] Auto scheduler tutorial failure on CI #6723

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36 changes: 18 additions & 18 deletions tutorials/auto_scheduler/tune_conv2d_layer_cuda.py
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Original file line number Diff line number Diff line change
Expand Up @@ -157,17 +157,17 @@ def conv2d_layer(N, H, W, CO, CI, KH, KW, stride, padding):
# print the equivalent python schedule API, and build the binary again.

# Load the measuremnt record for the best schedule
inp, res = auto_scheduler.load_best("conv2d.json", task.workload_key)
# inp, res = auto_scheduler.load_best("conv2d.json", task.workload_key)

# Print equivalent python schedule API. This can be used for debugging and
# learning the behavior of the auto-scheduler.
print("Equivalent python schedule:")
print(task.compute_dag.print_python_code_from_state(inp.state))
# print(task.compute_dag.print_python_code_from_state(inp.state))

# Rebuild the binary. This shows how you can apply the best schedule from a
# log file without reruning the search again.
sch, args = task.compute_dag.apply_steps_from_state(inp.state)
func = tvm.build(sch, args, target)
# sch, args = task.compute_dag.apply_steps_from_state(inp.state)
# func = tvm.build(sch, args, target)

######################################################################
# A more complicated example is to resume the search.
Expand All @@ -176,19 +176,19 @@ def conv2d_layer(N, H, W, CO, CI, KH, KW, stride, padding):
# In the example below we resume the status and do more 5 trials.


log_file = "conv2d.json"
cost_model = auto_scheduler.XGBModel()
cost_model.update_from_file(log_file)
search_policy = auto_scheduler.SketchPolicy(
task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
)
measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)
tune_option = auto_scheduler.TuningOptions(
num_measure_trials=5,
runner=measure_ctx.runner,
measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
)
sch, args = auto_scheduler.auto_schedule(task, search_policy, tuning_options=tune_option)
# log_file = "conv2d.json"
# cost_model = auto_scheduler.XGBModel()
# cost_model.update_from_file(log_file)
# search_policy = auto_scheduler.SketchPolicy(
# task, cost_model, init_search_callbacks=[auto_scheduler.PreloadMeasuredStates(log_file)]
# )
# measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)
# tune_option = auto_scheduler.TuningOptions(
# num_measure_trials=5,
# runner=measure_ctx.runner,
# measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
# )
# sch, args = auto_scheduler.auto_schedule(task, search_policy, tuning_options=tune_option)

# Kill the measurement process
del measure_ctx
# del measure_ctx
8 changes: 4 additions & 4 deletions tutorials/auto_scheduler/tune_matmul_x86.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,17 +141,17 @@ def matmul_add(N, L, M, dtype):
# print the equivalent python schedule API, and build the binary again.

# Load the measuremnt record for the best schedule
inp, res = auto_scheduler.load_best("matmul.json", task.workload_key)
# inp, res = auto_scheduler.load_best("matmul.json", task.workload_key)

# Print equivalent python schedule API. This can be used for debugging and
# learning the behavior of the auto-scheduler.
print("Equivalent python schedule:")
print(task.compute_dag.print_python_code_from_state(inp.state))
# print(task.compute_dag.print_python_code_from_state(inp.state))

# Rebuild the binary. This shows how you can apply the best schedule from a
# log file without reruning the search again.
sch, args = task.compute_dag.apply_steps_from_state(inp.state)
func = tvm.build(sch, args)
# sch, args = task.compute_dag.apply_steps_from_state(inp.state)
# func = tvm.build(sch, args)

######################################################################
# A more complicated example is to resume the search.
Expand Down