Source code for aitemplate.compiler.ops.gemm_universal.gemm_rrr_permute

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"""
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 Tensor
from aitemplate.compiler.ops.common.view_ops import reshape

from aitemplate.compiler.ops.gemm_universal import gemm_rrr
from aitemplate.compiler.tensor_accessor import TensorAccessor

# pylint: disable=C0103,W0223,W0221,W0613


[docs]class gemm_rrr_permute(gemm_rrr): def __init__(self, shape: Tuple[int], layout="20314"): super().__init__() self._attrs["op"] = "gemm_rrr_permute" self._attrs["shape"] = shape 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": m, n = output_shape t1, t2, t3 = self._attrs["shape"] output_shape = [t2, m.value() // t1, t3, t1, n.value() // t2 // t3] 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"], }