295 lines
9.4 KiB
Python
295 lines
9.4 KiB
Python
#################################################################################################
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#
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# Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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#
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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#
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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#
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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#
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#################################################################################################
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"""
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Load nodes and implementations
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"""
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import ctypes
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from cutlass_cppgen.backend.c_types import tuple_factory
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from cutlass_cppgen.backend.epilogue import dtype2ctype, to_ctype_value
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from cutlass_cppgen.backend.evt.ir.node import NodeBase, ImplBase
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class LoadImplBase(ImplBase):
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"""
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Base class for load node implementations
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"""
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reserved_names = ["accum", "C"]
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def __init__(self, node) -> None:
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super().__init__(node)
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self.element = node.element
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self.element_output = node.element_output
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self.stride = node.tensor.stride
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class AccumulatorImpl(LoadImplBase):
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"""
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Accumulator node implementation
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"""
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@staticmethod
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def match(node, problem_size: tuple):
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return node.name == "accum" and node.tensor.shape == problem_size
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class LoadSrcImpl(LoadImplBase):
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"""
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Load C implementation
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"""
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@property
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def name_camel(self) -> str:
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return "TensorC"
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@property
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def argument_type_c(self):
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stride_mnl = self.get_stride_mnl()
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tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
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class _Argument(ctypes.Structure):
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_fields_ = [
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("ptr_C", ctypes.c_void_p),
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("stride_C", tuple_type)
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]
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def __init__(self, ptr) -> None:
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self.ptr_C = ptr
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self.stride_C = tuple_type(stride_mnl)
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return _Argument
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@staticmethod
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def match(node, problem_size: tuple):
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return node.name == "C" and node.tensor.shape == problem_size
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class AuxLoadImpl(LoadImplBase):
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"""
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Load arbitrary tensor
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"""
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@property
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def argument_type(self):
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stride_mnl = self.get_stride_mnl()
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name = self.name
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tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
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element_type = self.element
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class _Argument(ctypes.Structure):
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_fields_ = [
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("ptr_aux", ctypes.c_void_p),
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("null_default", dtype2ctype[element_type]),
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("dAux", tuple_type)
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]
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def __init__(self, kwargs) -> None:
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ptr = kwargs[name]
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self.ptr_aux = ptr
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self.null_default = to_ctype_value(0, element_type)
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self.dAux = tuple_type(stride_mnl)
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return _Argument
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@staticmethod
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def match(node, problem_size: tuple):
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if node.name in LoadImplBase.reserved_names:
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return False
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strideMN = node.tensor.stride[-2:]
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if (strideMN[0] == 1 and strideMN[1] != 0 or
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strideMN[0] != 0 and strideMN[1] == 1 ):
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return True
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else:
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return False
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class RowBroadcastImpl(LoadImplBase):
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"""
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Broadcast a row vector
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"""
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def __init__(self, node) -> None:
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super().__init__(node)
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self.stride_dtype = "int"
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@property
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def argument_type(self):
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stride_mnl = self.get_stride_mnl()
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name = self.name
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tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
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element_type = self.element
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class _Argument(ctypes.Structure):
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_fields_ = [
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("ptr_row", ctypes.c_void_p),
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("null_default", dtype2ctype[element_type]),
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("dRow", tuple_type)
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]
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def __init__(self, kwargs) -> None:
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ptr = kwargs[name]
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self.ptr_row = ptr
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self.null_default = to_ctype_value(0, element_type)
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self.dRow = tuple_type(stride_mnl)
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return _Argument
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@staticmethod
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def match(node, problem_size: tuple):
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if node.name in LoadImplBase.reserved_names:
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return False
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strideMN = node.tensor.stride[-2:]
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if strideMN == (0, 1):
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return True
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else:
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return False
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class ColumnBroadcastImpl(LoadImplBase):
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"""
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Broadcast a column vector
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"""
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def __init__(self, node) -> None:
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super().__init__(node)
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self.stride_dtype = "int"
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@property
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def argument_type(self):
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stride_mnl = self.get_stride_mnl()
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name = self.name
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tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
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element_type = self.element
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class _Argument(ctypes.Structure):
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_fields_ = [
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("ptr_col", ctypes.c_void_p),
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("null_default", dtype2ctype[element_type]),
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("dCol", tuple_type)
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]
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def __init__(self, kwargs) -> None:
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ptr = kwargs[name]
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self.ptr_col = int(ptr)
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self.null_default = to_ctype_value(0, element_type)
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self.dCol = tuple_type(stride_mnl)
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return _Argument
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@staticmethod
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def match(node, problem_size: tuple):
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if node.name in LoadImplBase.reserved_names:
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return False
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strideMN = node.tensor.stride[-2:]
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if strideMN == (1, 0):
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return True
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else:
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return False
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class ScalarBroadcastImpl(LoadImplBase):
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"""
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Broadcast a scalar
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"""
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def __init__(self, node) -> None:
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super().__init__(node)
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self.stride_dtype = "int"
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@property
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def argument_type(self):
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stride_mnl = self.get_stride_mnl()
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name = self.name
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tuple_type = tuple_factory(stride_mnl, self.stride_dtype)
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element_type = self.element
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if self.tensor.is_constant:
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value = self.tensor.value
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class _Argument(ctypes.Structure):
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_fields_ = [
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("scalars", dtype2ctype[element_type]),
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("scalar_ptrs", ctypes.c_void_p),
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("dScalar", tuple_type)
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]
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def __init__(self, kwargs) -> None:
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self.scalars = to_ctype_value(value, element_type)
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self.scalar_ptrs = 0
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self.dScalar = tuple_type(stride_mnl)
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else:
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class _Argument(ctypes.Structure):
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_fields_ = [
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("scalars", dtype2ctype[element_type]),
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("scalar_ptrs", ctypes.c_void_p),
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("dScalar", tuple_type)
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]
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def __init__(self, kwargs) -> None:
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scalar_or_ptr = kwargs[name]
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if isinstance(scalar_or_ptr, float):
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self.scalars = to_ctype_value(scalar_or_ptr, element_type)
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self.scalar_ptrs = 0
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else:
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self.scalar_ptrs = int(scalar_or_ptr)
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self.dScalar = tuple_type(stride_mnl)
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return _Argument
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@staticmethod
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def match(node, problem_size: tuple):
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if node.name in LoadImplBase.reserved_names:
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return False
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strideMN = node.tensor.stride[-2:]
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if strideMN == (0, 0):
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return True
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else:
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return False
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class LoadNode(NodeBase):
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"""
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Load Node
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"""
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cnt = 0
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possible_impls = [
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AccumulatorImpl, LoadSrcImpl, AuxLoadImpl,
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RowBroadcastImpl, ColumnBroadcastImpl,
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ScalarBroadcastImpl
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]
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def __init__(self, name: str) -> None:
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if name is None:
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name = f"load{LoadNode.cnt}"
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LoadNode.cnt += 1
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super().__init__(name)
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self.op = "load"
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def type_propagation(self, *args, **kwargs):
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"""
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Load node loads tensor under type `tensor.element` and returns an array of type `tensor.element`.
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"""
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if self.tensor is None:
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raise RuntimeError(f"The tensor of node {self.name} is unknown.")
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self.element = self.tensor.element
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self.element_output = self.tensor.element
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