# Copyright (c) Meta Platforms, Inc. and affiliates.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
#
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"""
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