################################################################################################# # # Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: BSD-3-Clause # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, this # list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its # contributors may be used to endorse or promote products derived from # this software without specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE # DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE # FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL # DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR # SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER # CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, # OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. # ################################################################################################# """ Infer the underlying implement of each node. While the frontend only distinguish between Load/Store/Compute Node, each of these nodes can have different underlying implementation based on their layout. For instance, a LoadNode can be AuxLoad, Row/Col/Scalar broadcast, etc. This pass infers the underlying impl of each node """ import cutlass_cppgen.backend.evt.backend as evt_backend from cutlass_cppgen.backend.evt.ir import DAGIR, LoadNode from cutlass_cppgen.backend.evt.passes.pass_fix_element_d import PassFixElementD from cutlass_cppgen.backend.evt.passes.pass_manager import EVTPassBase from cutlass_cppgen.backend.evt.passes.pass_no_op_elimination import PassNoOpElimination from cutlass_cppgen.backend.evt.passes.pass_shape_type_propagation import PassShapeTypePropagation from cutlass_cppgen.backend.evt.passes.util import cc_map class PassGetImpl(EVTPassBase): """ While the frontend only distinguish between Load/Store/Compute Node, each of these nodes can have different underlying implementation based on their layout. For instance, a LoadNode can be AuxLoad, Row/Col/Scalar broadcast, etc. This pass infers the underlying impl of each node """ dependencies = [ PassShapeTypePropagation, # The shape and type info are required for inference PassFixElementD ] def __init__(self, dag_ir: DAGIR) -> None: super().__init__(dag_ir) self.no_op_elimination = PassNoOpElimination(dag_ir) def requires(self) -> None: # Verify "accum" is in the arg list if not self.dag_ir.has_node("accum"): raise SyntaxError("Cannot find 'accum' in the argument list.") def call(self): # The loop structure of the epilogue is determined by the # accumulator shape accumulator: LoadNode = self.dag_ir.get_node_meta("accum") problem_size = accumulator.tensor.shape for node_meta in self.dag_ir.node_metas_topological_order(): node_meta.get_underlying_impl(problem_size) def ensures(self) -> None: # Some nodes will be lowered to NoOp, eliminate them self.no_op_elimination() # Lower to cc-specific impl for node_meta in self.dag_ir.nodes_meta: node_impl_ccs = getattr(evt_backend, f"sm{cc_map[self.cc]}_nodes") node_meta.underlying_impl = getattr( node_impl_ccs, f"Sm{cc_map[self.cc]}" + node_meta.underlying_impl.__class__.__name__ )(node_meta)