[GitHub] [tvm] FrozenGene commented on a change in pull request #7053: [auto_scheduler] buffer support, correctness check

[GitBox <gi...@apache.org>]

FrozenGene commented on a change in pull request #7053:
URL: https://github.com/apache/tvm/pull/7053#discussion_r538060242



##########
File path: python/tvm/auto_scheduler/auto_schedule.py
##########
@@ -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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