FrozenGene commented on a change in pull request #7053:
URL: https://github.com/apache/tvm/pull/7053#discussion_r538060242
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File path: python/tvm/auto_scheduler/auto_schedule.py
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@@ -210,6 +253,35 @@ def auto_schedule(task, search_policy=None,
tuning_options=TuningOptions()):
if search_policy is None:
cost_model = XGBModel()
search_policy = SketchPolicy(task, cost_model)
+
+ if tuning_options.check_correctness == True:
+ empty_sch, args = task.compute_dag.apply_steps_from_state(
+ task.compute_dag.get_init_state(), layout_rewrite=True)
+ cpu_func = build_module.build(
+ empty_sch, args, target="llvm",
target_host=task.target_host
+ )
+ buffer_path = os.path.join(tuning_options.working_dir, "buffer.pkl")
+ if os.path.exists(buffer_path) is True:
+ with open(buffer_path, "rb") as fi:
+ buffer = pickle.load(fi)
+ if len(buffer) == len(args):
+ # we skip check each arg shape here
+ pass
+ elif len(buffer) == len(args) - 1:
+ # assume only one output
+ np_args =
np.zeros(size=get_const_tuple(args[-1].shape)).astype(args[-1].dtype)
+ cpu_args = [v for _, v in buffer.items()] +
[ndarray.array(np_args, ctx=tvm.cpu())]
+ cpu_func(*cpu_args)
+ ### save cpu result
+ answer = [x.asnumpy() for x in cpu_args]
+ tuning_options.register_buffer(args[-1].name, answer[-1])
+ else:
+ np_args = [np.random.uniform(-0.1, 0.1,
size=get_const_tuple(x.shape)).astype(x.dtype) for x in args]
Review comment:
We should use `random_fill` function as it help us handle different
types. For example, for quantized uint8 dtype, [-0.1, 0.1] of
`np.random.uniform(-0.1, 0.1, size=get_const_tuple(x.shape)).astype(x.dtype)
for x in args` will be all zeros, which is not we want.
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