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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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"""
gemm rrr with bias
"""
from aitemplate.compiler.base import IntImm, Tensor
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_bias(gemm_rrr):
"""GEMM Specialization: GEMM_RRR(A, B) + Bias
A[RowMajor], B[RowMajor], Bias[RowMajor], C[RowMajor]
This operator is equivalent to the following pytorch code:
.. highlight:: python
.. code-block:: python
A = torch.randn(M, K).cuda().half()
B = torch.randn(K, N).cuda().half()
Bias = torch.randn(N).cuda().half()
y = torch.nn.functional.linear(A, B.t(), bias=Bias)
"""
def __init__(self):
super().__init__()
self._attrs["op"] = "gemm_rrr_bias"
@staticmethod
def is_valid_inputs(X: Tensor, W: Tensor, bias: Tensor):
msg = ""
bias_shapes = bias._attrs["shape"]
if len(bias_shapes) != 1:
msg = f"Bias should be 1D vector! Current bias shape: {bias_shapes}"
return False, msg
bias_shape = bias_shapes[0]
if not isinstance(bias_shape, IntImm):
msg = f"Bias should be fixed 1D vector! Current bias shape: {bias_shape}"
return False, msg
outshape = gemm_rrr()._infer_shapes(X, W)
if outshape[-1] != bias_shape:
msg = f"GEMM/Bias shape doesn't match! Gemm shape: {outshape}, bias shape: {bias_shape}"
return False, msg
return True, msg
def _infer_shapes(self, a: Tensor, b: Tensor, bias: Tensor):
"""Infers output shapes for gemm_rrr_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.
"""
is_valid_inputs, msg = self.is_valid_inputs(a, b, bias)
if not is_valid_inputs:
raise RuntimeError(msg)
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=a.dtype())
self._attrs["outputs"] = [output]
self._attrs["output_accessors"] = [TensorAccessor(output)]
return output