# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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"""
GEMM Specialization: (A.permute(0, 2, 1)[col] @ B[col] + Bias)
"""
from aitemplate.compiler.base import Tensor
from aitemplate.compiler.ops.gemm_universal import perm021fc_ccr
from aitemplate.compiler.tensor_accessor import TensorAccessor
# pylint: disable=C0103, W0223, W0221
[docs]class perm021fc_ccr_bias(perm021fc_ccr):
"""GEMM Specialization: (A.permute(0, 2, 1) @ B + Bias)
This op is equivalent to the following PyTorch code:
.. highlight:: python
.. code-block:: python
X_pt = torch.randn(B, K, M).cuda().half()
W_pt = torch.randn(N, K).cuda().half()
Bias_pt = torch.randn(N).cuda().half()
XT = X_pt.permute(0, 2, 1)
XT = torch.reshape(XT, (-1, K))
Y_pt = torch.nn.functional.linear(XT, W_pt, bias=B_pt)
Y_pt = torch.reshape(Y_pt, (B, M, N))
"""
def __init__(self):
"""Constructor for perm021fc_ccr_bias"""
super().__init__()
self._attrs["op"] = "perm021fc_ccr_bias"
def _infer_shapes(self, a: Tensor, b: Tensor, bias: Tensor):
bias_shape = bias._attrs["shape"]
if len(bias_shape) != 1:
raise RuntimeError("Bias should be 1D vector ")
bias_shape_value = bias_shape[0]._attrs["values"]
if len(bias_shape_value) != 1:
raise RuntimeError("Bias should be fixed 1D vector")
bias_dim = bias_shape_value[0]
outshape = super()._infer_shapes(a, b)
if outshape[2]._attrs["values"][0] != bias_dim:
raise RuntimeError("GEMM/Bias shape doesn't match")
return outshape
def __call__(self, a: Tensor, b: Tensor, bias: Tensor) -> Tensor:
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._attrs["dtype"])
self._attrs["outputs"] = [output]
self._attrs["output_accessors"] = [
TensorAccessor(tensor) for tensor in self._attrs["outputs"]
]
return output