94155a3934
Updates LLVM usage to match [1b97cdf885d6](https://github.com/llvm/llvm-project/commit/1b97cdf885d6) PiperOrigin-RevId: 348587513 Change-Id: I853d197b33c5df08c00c99ddc8cf8b2681bed708
160 lines
4.6 KiB
Python
160 lines
4.6 KiB
Python
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# 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.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
"""Intermediate between python bindings for MLIR and mlir generation for tensorflow program.
|
|
|
|
This passes most of the mlir classes as is, but adds a few new operations and
|
|
the basic structure for a tensorflow program.
|
|
"""
|
|
|
|
# pylint: disable=invalid-name
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
from tensorflow.compiler.mlir.python.mlir_wrapper import mlir_wrapper as mlir
|
|
|
|
# Class Definitions
|
|
OpBuilder = mlir.OpBuilder
|
|
Block = mlir.Block
|
|
|
|
# Types
|
|
Type = mlir.Type
|
|
IntegerType = mlir.IntegerType
|
|
FloatType = mlir.FloatType
|
|
RankedTensorType = mlir.RankedTensorType
|
|
UnrankedTensorType = mlir.UnrankedTensorType
|
|
IntegerAttr = mlir.IntegerAttr
|
|
|
|
# Standard Ops
|
|
ReturnOp = mlir.ReturnOp
|
|
|
|
# TF Dialect Ops
|
|
Tf_AnyOp = mlir.Tf_AnyOp
|
|
Tf_AddV2Op = mlir.Tf_AddV2Op
|
|
Tf_ConstOp = mlir.Tf_ConstOp
|
|
Tf_EqualOp = mlir.Tf_EqualOp
|
|
Tf_GreaterEqualOp = mlir.Tf_GreaterEqualOp
|
|
Tf_GreaterOp = mlir.Tf_GreaterOp
|
|
Tf_LegacyCallOp = mlir.Tf_LegacyCallOp
|
|
Tf_LessEqualOp = mlir.Tf_LessEqualOp
|
|
Tf_LessOp = mlir.Tf_LessOp
|
|
Tf_NegOp = mlir.Tf_NegOp
|
|
Tf_NotEqualOp = mlir.Tf_NotEqualOp
|
|
Tf_SubOp = mlir.Tf_SubOp
|
|
|
|
|
|
class IfOp(object):
|
|
"""
|
|
tfp.if(cond) ({body}, {orelse}) : type If `cond` is true, `body` is
|
|
executed, otherwise `orelse` is executed.
|
|
"""
|
|
|
|
@classmethod
|
|
def create(cls, opb, loc, cond, outputs):
|
|
state = mlir.OperationState(loc, "tfp.If")
|
|
state.addOperands([cond])
|
|
state.addTypes(outputs)
|
|
state.addRegion().push_back(Block.new()) # body region
|
|
state.addRegion().push_back(Block.new()) # orelse region
|
|
return opb.createOperation(state)
|
|
|
|
|
|
class OrOp(object):
|
|
"""
|
|
tfp.Or(ops...) This is like tf.Any, except that the first dimension is opened
|
|
into `ops`.
|
|
|
|
Returns a tensor of 1-bit integers which is "Logical OR" of the
|
|
coressponding elements in ops...
|
|
"""
|
|
|
|
@classmethod
|
|
def create(cls, opb, loc, values):
|
|
state = mlir.OperationState(loc, "tfp.Or")
|
|
state.addTypes(
|
|
[UnrankedTensorType.get(IntegerType.get(opb.getContext(), 1))])
|
|
state.addOperands(values)
|
|
return opb.createOperation(state)
|
|
|
|
|
|
class AndOp(object):
|
|
"""
|
|
tfp.And(ops...) This is like tf.All, except that the first dimension is opened
|
|
to `ops`.
|
|
|
|
Returns a tensor of 1-bit integers which is "Logical AND" of the
|
|
coressponding elements in ops...
|
|
"""
|
|
|
|
@classmethod
|
|
def create(cls, opb, loc, values):
|
|
state = mlir.OperationState(loc, "tfp.And")
|
|
state.addTypes(
|
|
[UnrankedTensorType.get(IntegerType.get(opb.getContext(), 1))])
|
|
state.addOperands(values)
|
|
return opb.createOperation(state)
|
|
|
|
|
|
class WhileOp(object):
|
|
"""tfp.While(init-vals, {
|
|
|
|
^bb1(cond-args):
|
|
cond-region
|
|
return cond
|
|
}, {
|
|
^bb1(body-args):
|
|
body-region
|
|
})
|
|
As long as `cond-region` returns a "true"-like value, the body-region
|
|
is executed and the arguments are replaced by its return values for the next
|
|
iteration.
|
|
"""
|
|
|
|
@classmethod
|
|
def create(cls, opb, loc, inputs, outputs):
|
|
state = mlir.OperationState(loc, "tfp.While")
|
|
state.addOperands(inputs)
|
|
state.addTypes(outputs)
|
|
state.addRegion().push_back(Block.new()) # cond region
|
|
state.addRegion().push_back(Block.new()) # body region
|
|
return opb.createOperation(state)
|
|
|
|
|
|
class TFProgram(object):
|
|
"""Python wrap for a Tensorflow Program (essentially an mlir Module)."""
|
|
|
|
def __init__(self):
|
|
self.ctx = mlir.MLIRContext()
|
|
mlir.preloadTensorFlowDialects(self.ctx)
|
|
self.builder = mlir.Builder(self.ctx)
|
|
self.module = mlir.ModuleOp.create(mlir.UnknownLoc.get(self.ctx))
|
|
self.curr_func = None
|
|
|
|
def add_function(self, name, func_type):
|
|
self.curr_func = mlir.FuncOp.create(
|
|
mlir.UnknownLoc.get(self.ctx), name, func_type)
|
|
self.module.push_back(self.curr_func)
|
|
return self.curr_func
|
|
|
|
def get_function_type(self, inputs, outputs):
|
|
return self.builder.getFunctionType(inputs, outputs)
|
|
|
|
def dump(self):
|
|
self.module.dump()
|
|
|
|
def __str__(self):
|
|
return self.module.getAsStr()
|