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

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"""
Batch GEMM specialization: BMM_RRR(A, B0) / BMM_RRR(A, B1)
"""
from aitemplate.compiler.base import Tensor
from aitemplate.compiler.ops.gemm_universal import bmm_rrr
from aitemplate.compiler.tensor_accessor import TensorAccessor

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


[docs]class dual_bmm_rrr_div(bmm_rrr): """Batch GEMM specialization: BMM_RRR(A, B0) / BMM_RRR(A, B1) This operator is equivalent to the following pytorch code: .. highlight:: python .. code-block:: python A = torch.randn(B, M, K) B0 = torch.randn(B, K, N) B1 = torch.randn(B, K, N) D0 = torch.bmm(A, B0) D1 = torch.bmm(A, B1) D2 = D0 / D1 If the last dim of B1 is 1 (while the last dim of B0 isn't), B1 is broadcasted to the same shape as B0 before computing the right gemm A @ B1. """ def __init__(self): super().__init__() self._attrs["op"] = "dual_bmm_rrr_div" self._attrs["epilogue2"] = "Div" def __call__(self, a: Tensor, b: Tensor, bias: Tensor) -> Tensor: output = super().__call__(a, b) self._attrs["inputs"].append(bias) self._attrs["input_accessors"] = [ TensorAccessor(tensor) for tensor in self._attrs["inputs"] ] self._set_depth() if b._attrs["shape"][-1] != 1 and bias._attrs["shape"][-1] == 1: self._attrs["broadcast_b1"] = True return output