# 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.
#
"""
GEMM Specialization for A[RowMajor], B[RowMajor], C[RowMajor]
This is special in template based gemm solution
This is used for `torch.nn.functional.linear`
When use for `linear`, need set A->Data, B->Weight
"""
from typing import Tuple
from aitemplate.compiler.base import IntImm, IntVar, Tensor
from aitemplate.compiler.ops.common.view_ops import reshape
from aitemplate.compiler.ops.gemm_universal import gemm_rcr
from aitemplate.compiler.tensor_accessor import TensorAccessor
from aitemplate.testing import detect_target
# pylint: disable=C0103,W0223,W0221,W0613
[docs]class gemm_rcr_permute(gemm_rcr):
def __init__(self, shape: Tuple[int], layout="20314"):
super().__init__()
if layout == "20314" or layout == "0213":
self._attrs["op"] = "gemm_rcr_permute"
elif layout == "m2n3":
self._attrs["op"] = "gemm_rcr_permute_m2n3"
else:
raise NotImplementedError("{} is not implemented!".format(layout))
self._attrs["shape"] = shape
if layout == "0213":
self._attrs["layout"] = "Permute4D_{}".format(layout)
else:
self._attrs["layout"] = "Permute5D_{}".format(layout)
self._attrs["permute_shape"] = "_".join(map(str, shape))
def __call__(self, a: Tensor, b: Tensor) -> Tensor:
a, b = self._align_ab(a, b)
self._attrs["inputs"] = [a, b]
self._attrs["input_accessors"] = [TensorAccessor(a), TensorAccessor(b)]
self._set_depth()
self._sanity_check(a, b)
output_shape = self._infer_shapes(a, b)
output = Tensor(output_shape, src_ops={self}, dtype=a.dtype())
self._attrs["outputs"] = [output]
self._attrs["output_accessors"] = [TensorAccessor(output)]
if self._attrs["layout"] == "Permute5D_20314" or (
self._attrs["layout"] == "Permute5D_m2n3"
and detect_target().name() == "rocm"
):
m, n = output_shape
t1, t2, t3 = self._attrs["shape"]
output_shape = [t2, m.value() // t1, t3, t1, n.value() // t2 // t3]
# output alignment needs to be calculated based on the reshaped last dim
self._extract_epilogue_alignment(output_shape)
return reshape()(output, output_shape)
elif (
self._attrs["layout"] == "Permute4D_0213"
and detect_target().name() == "cuda"
):
m, n = output_shape
t1, t2 = self._attrs["shape"]
if not isinstance(m, IntImm):
vals = []
for val in m._attrs["values"]:
assert val % t1 == 0
vals.append(val // t1)
dim0 = IntVar(vals)
else:
assert m.value() % t1 == 0
dim0 = m.value() // t1
output_shape = [dim0, t2, t1, n.value() // t2]
# output alignment needs to be calculated based on the reshaped last dim
self._extract_epilogue_alignment(output_shape)
return reshape()(output, output_shape)
else:
raise NotImplementedError(
"{} is not implemented!".format(self._attrs["layout"])
)
def _get_op_attributes(self):
return {
"layout": self._attrs["layout"].split("_")[-1],
"shape": self._attrs["shape"],
}