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Siyuan Feng
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update for ci
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docs/reference/api/python/ndarray.rst

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docs/reference/api/python/runtime/runtime.rst

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@@ -19,5 +19,4 @@ tvm.runtime
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-----------
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.. automodule:: tvm.runtime
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:members:
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:imported-members:
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:exclude-members: NDArray

python/tvm/relax/transform/transform.py

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@@ -391,8 +391,8 @@ def ConvertToDataflow(min_size: int = 2) -> tvm.ir.transform.Pass:
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Note: ConvertToDataflow may need to be called first.
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Params
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------
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Parameters
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----------
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min_size: int
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The minimum number of consecutive dataflow bindings
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the pass needs to extract a new block.
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func_name: str
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The function name to be bound
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params : Dict[
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Union[str,relax.Var],
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Union[tvm.runtime.NDArray, np.ndarray],
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]
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The map from parameter or parameter name to constant
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tensors.
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params: Dict[Union[str,relax.Var], Union[tvm.runtime.NDArray, np.ndarray]]
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The map from parameter or parameter name to constant tensors.
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Returns
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-------
@@ -994,16 +989,16 @@ def LiftTransformParams(shared_transform: Union[bool, List[str]] = False) -> tvm
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Indicates how the parameter transformation function will be produced
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- `False` (default): A separate parameter transformation function will be
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produced for each function with the `"num_input"` attribute.
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produced for each function with the `"num_input"` attribute.
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- `True`: A single parameter transformation function will be produced,
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containing the preprocessing steps common across all functions with
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the `"num_input"` attribute.
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containing the preprocessing steps common across all functions with
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the `"num_input"` attribute.
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- List[str]: A single parameter transformation function will be produced,
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containing the preprocessing steps common across each function whose
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name is in the list. Passing a list of all functions with the `"num_input"`
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attribute or an empty list is equivalent to passing `True`.
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containing the preprocessing steps common across each function whose
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name is in the list. Passing a list of all functions with the `"num_input"`
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attribute or an empty list is equivalent to passing `True`.
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Returns
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-------
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maximum number of trials per task
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op_names: Optional[List[str]]
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A list of operator names to specify which op to tune. When it is None, all operators
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are tuned.
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are tuned.
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Returns
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-------

python/tvm/runtime/profiling/__init__.py

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@@ -230,6 +230,7 @@ def profile_function(mod, dev, collectors, func_name=None, warmup_iters=10):
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-------
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.. code-block: python
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f = tvm.build(my_func, target="llvm", name="my_func")
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prof = tvm.runtime.profiling.profile_function(
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f,
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Device to run the function on.
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collectors: List[MetricCollector]
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:py:class:`MetricCollector`s which will collect performance information.
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:py:class:`MetricCollector` which will collect performance information.
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func_name: Optional[str]
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Name of the function in `mod` to profile. Defaults to the `entry_name` of `mod`.
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warmup_iters: int

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