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

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"""
GEMM Specialization: SILU(GEMM_RCR(A, B)) * GEMM_RCR(A, B1)
"""
from aitemplate.compiler.base import Tensor
from aitemplate.compiler.ops.gemm_universal.gemm_rcr import gemm_rcr
from aitemplate.compiler.tensor_accessor import TensorAccessor

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


[docs]class dual_gemm_rcr_silu(gemm_rcr): """GEMM Specialization: SILU(GEMM_RCR(A, B)) * GEMM_RCR(A, B1) This operator is equivalent to the following pytorch code: .. highlight:: python .. code-block:: python A = torch.randn(M, K) W = torch.randn(N, K) B = torch.randn(N, K) Y1 = torch.nn.functional.linear(A, W) Y2 = torch.nn.functional.linear(A, B) Y = torch.nn.functional.silu(Y1) * Y2 """ def __init__(self): super().__init__() self._attrs["op"] = "dual_gemm_rcr_silu" self._attrs["epilogue2"] = "LeftSiLUAndMul" def _infer_shapes(self, a: Tensor, b: Tensor, bias: Tensor): """Infers output shapes for gemm_rcr_bas. Parameters ---------- a : Tensor Input tensor A. b : Tensor Input tensor B. bias : Tensor Input tensor bias. Must be a 1D vector. Returns ------- List[IntVar] Output tensor shape. """ return super()._infer_shapes(a, b) 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) self._extract_epilogue_alignment(output_shape) output = Tensor( output_shape, src_ops={self}, dtype=self._attrs["inputs"][0]._attrs["dtype"], ) self._attrs["outputs"] = [output] self._attrs["output_accessors"] = [TensorAccessor(output)] if b._attrs["shape"][-2] != 1 and bias._attrs["shape"][-2] == 1: self._attrs["broadcast_b1"] = True return output