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

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"""
gemm rcr with bias + permute
"""

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_rcr_bias
from aitemplate.compiler.tensor_accessor import TensorAccessor

from aitemplate.testing import detect_target

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


[docs]class gemm_rcr_bias_permute(gemm_rcr_bias): def __init__(self, shape: Tuple[int], layout="20314"): super().__init__() if layout == "20314": self._attrs["op"] = "gemm_rcr_bias_permute" elif layout == "m2n3": self._attrs["op"] = "gemm_rcr_bias_permute_m2n3" elif layout == "m3n2": self._attrs["op"] = "gemm_rcr_bias_permute_m3n2" else: raise NotImplementedError("{} is not implemented!".format(layout)) 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, bias: Tensor) -> Tensor: a, b = self._align_ab(a, b) self._attrs["inputs"] = [a, b, bias] self._attrs["input_accessors"] = [ TensorAccessor(tensor) for tensor in self._attrs["inputs"] ] self._set_depth() self._sanity_check(a, b) output_shape = self._infer_shapes(a, b, bias) output = Tensor(output_shape, src_ops={self}, dtype=a.dtype()) self._attrs["outputs"] = [output] self._attrs["output_accessors"] = [TensorAccessor(output)] m, n = output_shape t1, t2, t3 = self._attrs["shape"] if ( self._attrs["layout"] == "Permute5D_20314" and detect_target().name() == "rocm" ) or self._attrs["layout"] == "Permute5D_m3n2": output_shape = [t2, m.value() // t1 // t2, t3, t1, n.value() // t3] else: output_shape = [t2, m.value() // t1, t3, t1, n.value() // t3 // t2] self._extract_epilogue_alignment(output_shape) return reshape()(output, output_shape) def _get_op_attributes(self): return { "layout": self._attrs["layout"].split("_")[-1], "shape": self._attrs["shape"], }