# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""
Operator definition for gemm_rcr_softmax.
"""
from aitemplate.compiler.base import Tensor
from aitemplate.compiler.ops.gemm_universal.gemm_rcr import gemm_rcr
from aitemplate.compiler.tensor_accessor import TensorAccessor
# pylint: disable=C0103,W0223,W0221,W0613
[docs]class gemm_rcr_softmax(gemm_rcr):
"""gemm_rcr_softmax operator."""
def __init__(self):
"""Initializes gemm_rcr_softmax."""
super().__init__()
self._attrs["op"] = "gemm_rcr_softmax"
def __call__(self, a: Tensor, b: Tensor) -> Tensor:
"""Performs sanity checks, offline shape inference and returns an output tensor."""
a, b = self._align_ab(a, b)
self._attrs["inputs"] = [a, b]
self._sanity_check(a, b)
output_shape = self._infer_shapes(a, b)
self._extract_epilogue_alignment(output_shape)
self._attrs["input_accessors"] = [
TensorAccessor(tensor) for tensor in self._attrs["inputs"]
]
self._set_depth()
output = Tensor(output_shape, src_ops={self}, dtype=a._attrs["dtype"])
self._attrs["outputs"] = [output]
self._attrs["output_accessors"] = [TensorAccessor(output)]
return output