Source code for aitemplate.compiler.ops.gemm_epilogue_vistor.gemm_rcr_bias_softmax

#  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,
#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#  See the License for the specific language governing permissions and
#  limitations under the License.
#
"""
Operator definition for gemm_rcr_bias_softmax.
"""
from aitemplate.compiler.base import Tensor
from aitemplate.compiler.ops.gemm_epilogue_vistor.gemm_rcr_softmax import (
    gemm_rcr_softmax,
)
from aitemplate.compiler.tensor_accessor import TensorAccessor

# pylint: disable=C0103,R1711,W0102,W0221,E1120,W0223


[docs]class gemm_rcr_bias_softmax(gemm_rcr_softmax): """gemm_rcr_bias_softmax operator.""" def __init__(self): """Initializes gemm_rcr_bias_softmax.""" super().__init__() self._attrs["op"] = "gemm_rcr_bias_softmax" def _infer_shapes(self, a: Tensor, b: Tensor, bias: Tensor): """Infers output shapes from input tensors.""" bias_shape = bias._attrs["shape"] if len(bias_shape) != 1: raise RuntimeError("Bias should be 1D vector ") bias_shape_value = bias_shape[0]._attrs["values"] if len(bias_shape_value) != 1: raise RuntimeError("Bias should be fixed 1D vector") bias_dim = bias_shape_value[0] outshape = super()._infer_shapes(a, b) if outshape[1]._attrs["values"][0] != bias_dim: raise RuntimeError("GEMM/Bias shape doesn't match") return outshape def __call__(self, a: Tensor, b: Tensor, bias: 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, bias] self._sanity_check(a, b) output_shape = self._infer_shapes(a, b, bias) 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