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# Licensed under the Apache License, Version 2.0 (the "License");
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# http://www.apache.org/licenses/LICENSE-2.0
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"""Grouped GEMM Specialization: ReLU(GEMM_RCR(A, B) + Bias)
"""
from aitemplate.compiler.ops.gemm_universal import group_gemm_rcr_bias
# pylint: disable=C0103,W0223
[docs]class group_gemm_rcr_bias_relu(group_gemm_rcr_bias):
"""Grouped GEMM Specialization: ReLU(GEMM_RCR(A, B) + Bias)
This operator is equivalent to the following pytorch code:
.. highlight:: python
.. code-block:: python
# group 1
A1 = torch.randn(M1, K1).cuda().half()
B1 = torch.randn(N1, K1).cuda().half()
Bias1 = torch.randn(N1).cuda().half()
linear1 = torch.nn.functional.linear(A1, B1, bias=Bias1)
y1 = torch.nn.ReLU(linear1)
...
# group n
An = torch.randn(Mn, Kn).cuda().half()
Bn = torch.randn(Nn, Kn).cuda().half()
Biasn = torch.randn(Nn).cuda().half()
linearn = torch.nn.functional.linear(An, Bn, bias=Biasn)
yn = torch.nn.ReLU(linearn)
"""
def __init__(self):
super().__init__()
self._attrs["op"] = "group_gemm_rcr_bias_relu"
self._attrs["epilogue"] = "LinearCombinationRelu"