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parser.py
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parser.py
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you 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.
"""Hybrid Script Parser"""
import ast
import operator
import logging
import sys
import types
import numbers
from enum import Enum
from tvm.ir import Array, Range
import tvm.runtime
import tvm.tir
import tvm.te
import tvm.te._ffi_api
import tvm.arith
from tvm.tir import expr as _expr
from tvm.tir import stmt as _stmt
from tvm.te.tensor import Tensor, Operation
from tvm.tir import all as _all
from tvm.tir import any as _any
from .util import _internal_assert
from . import calls
from . import util
from .preprocessor import determine_variable_usage
def concat_list_to_block(lst):
"""Concatenate a list of Python IR nodes to HalideIR Block"""
if not lst:
return util.make_nop()
n = len(lst)
if n == 1:
return lst[0]
return _stmt.SeqStmt(lst)
def visit_list_to_block(visit, lst):
"""Visit and concatenate a list of Python IR nodes to HalideIR Block"""
lst = [visit(stmt) for stmt in lst if not util.is_docstring(stmt)]
lst = [stmt for stmt in lst if not tvm.ir.structural_equal(stmt, util.make_nop())]
if not lst:
return util.make_nop()
return concat_list_to_block(lst)
class Symbol(Enum):
"""Enumerates types in the symbol table"""
Callable = 0
Input = 1
OutputBuffer = 2
GlobalBuffer = 3
LocalBuffer = 4
SharedBuffer = 5
ConstVar = 6
BufferVar = 7
LoopVar = 8
ConstLoopVar = 9
ThreadBind = 10
def _floordiv(x, y):
if isinstance(x, _expr.ExprOp) or isinstance(y, _expr.ExprOp):
return tvm.tir.floordiv(x, y)
return operator.floordiv(x, y)
def _floormod(x, y):
if isinstance(x, _expr.ExprOp) or isinstance(y, _expr.ExprOp):
return tvm.tir.floormod(x, y)
return operator.mod(x, y)
class HybridParser(ast.NodeVisitor):
"""Python AST visitor pass which finally lowers it to HalideIR"""
_binop_maker = {
ast.Add : operator.add,
ast.Sub : operator.sub,
ast.Mult : operator.mul,
ast.Div : operator.div if sys.version_info[0] == 2 else operator.truediv,
ast.FloorDiv: _floordiv,
ast.Mod : _floormod,
ast.BitOr : operator.or_,
ast.BitAnd : operator.and_,
ast.BitXor : operator.xor,
ast.Gt : operator.gt,
ast.GtE : operator.ge,
ast.Lt : operator.lt,
ast.LtE : operator.le,
ast.Eq : operator.eq,
ast.NotEq : operator.ne,
ast.And : _all,
ast.Or : _any,
}
_unaryop_maker = {
ast.USub : operator.neg,
ast.Invert : operator.invert,
ast.Not : operator.not_
}
def __init__(self, args, usage, symbols, closure_vars, func_name=None):
"""
Parameters
----------
args: A list of tvm.te.placeholder or te.var
Provided by the user, the argument list of the function to be lowered.
usage: A dict of variables used in last in this function
Provided by last lower pass, which collects this information
symbols : list of str
The symbol list of the global context of the function.
closure_vars: dict
A dict of external name reference captured by this function.
Returns
-------
func_name: str
The name of the function to be lowered; if not provided,
the compiler will use the name in the AST
"""
self.args = list(args)
self.usage = usage.copy()
self.symbols = {} # Symbol table
for k, v in symbols.items():
if isinstance(v, types.FunctionType):
self.add_symbol(k, Symbol.Callable, v)
self.closure_vars = closure_vars
self.binds = {} # Thread binds
self.device = 0 # Is it generating device
self.func_name = func_name # The name of the function to be lowered
self.outputs = [] # Output tensors' name
self.side_effect = set() # Tensors with side effects
self.parsed_body = None # The parsed HalideIR body
self.analyzer = tvm.arith.Analyzer()
self.returned = False # If this function has a valid return
def add_symbol(self, key, ty, val): #pylint: disable=invalid-name
"""Add value to the symbol table context"""
if key in self.symbols.keys():
old = str(self.symbols[key])
new = str((ty, val))
_internal_assert(False,
"Name conflict in symbol table! [%s] %s -> %s" % (key, old, new))
self.symbols[key] = ty, val
if ty == Symbol.ThreadBind:
if val.var.name not in self.binds.keys():
self.binds[val.var.name] = val
return
val_ = self.binds[val.var.name]
_internal_assert(tvm.tir.analysis.expr_deep_equal(val_.dom.extent, val.dom.extent),
"Thread extents should be uniform!")
self.symbols[key] = ty, val_
def wrap_up_realize(self, node, body):
"""Wrap up all the variables which will no longer be used"""
to_pop = []
for key, val in self.usage.items():
_, level, _ = val
if key not in self.symbols:
# don't realize the symbols that are never visited
continue
if level != node:
continue
_internal_assert(key in self.symbols.keys(), "Unknown symbol %s!" % key)
ty, entry = self.symbols[key] #pylint: disable=invalid-name
if ty in [Symbol.Input, Symbol.OutputBuffer]:
continue
if 'Buffer' in ty.name:
_buf = entry
_scope = 'global' if ty is Symbol.BufferVar else ty.name[:-6].lower()
to_pop.append(key)
else:
continue
if _scope == 'global':
body = self.wrap_up_binds(body)
_domain = [Range.make_by_min_extent(0, i) for i in _buf.shape]
_dtype = _buf.dtype
_true = tvm.runtime.convert(True)
body = tvm.tir.ProducerRealize(_buf, _domain, _true, body)
body = tvm.tir.AttrStmt(_buf.op, 'realize_scope', tvm.runtime.convert(_scope), body)
for elem in to_pop:
self.symbols.pop(elem)
return body
def wrap_up_binds(self, body):
for _, iter_var in self.binds.items():
ext = iter_var.dom.extent
body = tvm.tir.AttrStmt(iter_var, 'thread_extent', ext, body)
self.binds = {}
return body
#pylint: disable=invalid-name, missing-docstring
def visit_Module(self, node):
_internal_assert(len(node.body) == 1, \
"Only one-function source code will be fed to this parser!")
return self.visit(node.body[0])
def visit_FunctionDef(self, node):
_internal_assert(len(node.args.args) == len(self.args), \
"The number of arguments passed to the \
function should be the same as it is defined!")
if self.func_name is None:
self.func_name = node.name
for idx, arg in enumerate(node.args.args):
_attr = 'id' if sys.version_info[0] < 3 else 'arg' # To make py2 and 3 compatible
self.add_symbol(getattr(arg, _attr), Symbol.Input, self.args[idx])
res = visit_list_to_block(self.visit, node.body)
res = self.wrap_up_realize(node, res)
return self.wrap_up_binds(res)
def visit_Expr(self, node):
return self.visit(node.value)
def visit_Name(self, node):
name = node.id
if sys.version_info[0] == 2 and name in ['True', 'False']:
return tvm.runtime.convert(ast.literal_eval(name))
if name in self.closure_vars:
return tvm.runtime.convert(self.closure_vars[name])
ty, entry = self.symbols[name]
_internal_assert(name in self.symbols, "Unknown symbol %s!" % name)
if ty in [Symbol.LoopVar, Symbol.Input, Symbol.ConstLoopVar]:
return entry
if ty is Symbol.ThreadBind:
return entry.var
if ty is Symbol.ConstVar:
return entry if isinstance(node.ctx, ast.Load) else None
if ty is Symbol.BufferVar:
if isinstance(node.ctx, ast.Load):
return tvm.tir.ProducerLoad(entry, [tvm.runtime.const(0, 'int32')])
return entry, [tvm.runtime.const(0, 'int32')]
# Do I need any assertion here?
return entry
def visit_Num(self, node):
if isinstance(node.n, numbers.Integral):
dtype = "int32"
elif isinstance(node.n, float):
dtype = "float32"
else:
_internal_assert(isinstance(node.n, bool),
"The data type should be one of (int, float, bool)")
dtype = "bool"
return tvm.runtime.const(node.n, dtype)
def visit_NameConstant(self, node):
return tvm.runtime.convert(node.value)
def visit_AugAssign(self, node):
buf = self.visit(node.target)
rhs = self.visit(node.value)
if isinstance(buf, tuple):
_internal_assert(len(buf) == 2, "LHS is supposed to be (buf, args)!")
buf, args = buf
else:
args = [tvm.runtime.const(0, 'int32')]
_internal_assert(isinstance(buf, Tensor), "LHS is supposed to be Tensor!")
read = tvm.tir.ProducerLoad(buf, args)
value = HybridParser._binop_maker[type(node.op)](read, rhs)
return tvm.tir.ProducerStore(buf, value, args)
def visit_Assign(self, node):
rhs = self.visit(node.value)
if isinstance(rhs, Operation):
rmap = {}
_internal_assert(len(node.targets) == rhs.num_outputs, \
"Unable to detuple the outs to targets")
for i in range(rhs.num_outputs):
_internal_assert(isinstance(node.targets[i], ast.Name),
"You should bind a pure name to the tensors")
self.add_symbol(node.targets[i].id, Symbol.GlobalBuffer, rhs.output(i))
rmap[rhs.outputs[i].op] = rhs.output(i)
return util.replace_io(rhs.body, rmap)
_internal_assert(len(node.targets) == 1, "So far only one-valued assignment is supported!")
lhs = node.targets[0]
if isinstance(rhs, _expr.PrimExpr):
rhs = self.analyzer.simplify(rhs)
if isinstance(lhs, ast.Name):
#TODO: support defined intermediate buffer later
lhs_ = lhs
lhs = lhs.id
if lhs in self.symbols.keys():
ty, _ = self.symbols[lhs]
_internal_assert(ty != Symbol.LoopVar, \
"Loop variable cannot be overwritten!")
decl, _, rw = self.usage[lhs]
if decl == lhs_:
_internal_assert(lhs not in self.symbols.keys(),
"This value should not be defined before this point!")
if isinstance(rhs, tuple):
shape, dtype, scope = rhs
ph = tvm.te.placeholder(shape, dtype=dtype, name=lhs)
self.add_symbol(lhs, getattr(Symbol, scope.title() + "Buffer"), ph)
if scope == 'output':
self.outputs.append(lhs)
return util.make_nop()
if isinstance(rhs, util.halide_imm_types) and ast.Store not in rw:
self.add_symbol(lhs, Symbol.ConstVar, rhs)
else:
_internal_assert(self.device == 0,
"Single variable not supported in devices' side!\n" + \
"If you are using GPU, please allocate a 'local' spad " + \
"outside the bind body")
ph = tvm.te.placeholder((1, ), dtype=rhs.dtype, name=lhs)
self.add_symbol(lhs, Symbol.BufferVar, ph)
lhs = self.visit(lhs_)
if lhs is not None:
buf, args = lhs
return tvm.tir.ProducerStore(buf, rhs, args)
return util.make_nop()
lhs, args = self.visit(lhs)
_internal_assert(isinstance(lhs, Tensor), \
"An array access's LHS is expected to be a expr.Call!")
res = tvm.tir.ProducerStore(lhs, rhs, args)
return res
def visit_Index(self, node):
if isinstance(node.value, ast.Tuple):
return self.visit(node.value)
return [self.visit(node.value)]
def visit_Attribute(self, node):
buf = self.visit(node.value)
return getattr(buf, node.attr)
def visit_Subscript(self, node):
args = self.visit(node.slice)
arr = self.visit(node.value)
if isinstance(arr, Array):
for i in args:
if isinstance(i, numbers.Integral):
arr = arr[i]
else:
_internal_assert(isinstance(i, (_expr.IntImm,)), \
"All indices are supposed to be constants")
arr = arr[i.value]
return arr
if isinstance(node.ctx, ast.Load):
return tvm.tir.ProducerLoad(arr, args)
return arr, args
def visit_With(self, node):
if sys.version_info[0] < 3:
context = node.context_expr
option = node.optional_vars
else:
_internal_assert(len(node.items) == 1, "Only one with element is supported so far!")
context = node.items[0].context_expr
option = node.items[0].optional_vars
_internal_assert(isinstance(context, ast.Call), "The object must be a Python func call!")
_internal_assert(isinstance(option, ast.Name), "The object after 'as' must be an id!")
self.annotation[option.id] = context.func.id
return visit_list_to_block(self.visit, node.body)
def visit_If(self, node):
cond = self.analyzer.simplify(self.visit(node.test))
# Return no IfThenElse if proven
if isinstance(cond, _expr.IntImm):
if cond.value:
return visit_list_to_block(self.visit, node.body)
if node.orelse:
return visit_list_to_block(self.visit, node.orelse)
return util.make_nop()
if_body = visit_list_to_block(self.visit, node.body)
if node.orelse:
else_body = visit_list_to_block(self.visit, node.orelse)
else:
else_body = None
return tvm.tir.IfThenElse(cond, if_body, else_body)
def visit_IfExp(self, node):
cond = self.visit(node.test)
if_body = self.visit(node.body)
else_body = self.visit(node.orelse)
return tvm.tir.Select(cond, if_body, else_body)
def visit_Compare(self, node):
_internal_assert(len(node.ops) == len(node.comparators),
"#compare ops != #comparators")
ops = [self.visit(node.left)]
ops += [self.visit(i) for i in node.comparators]
res = []
for i in range(len(node.ops)):
lhs = ops[i]
rhs = ops[i + 1]
res.append(HybridParser._binop_maker[type(node.ops[i])](lhs, rhs))
return _all(*res)
def visit_BoolOp(self, node):
n = len(node.values)
if n == 1:
_internal_assert(isinstance(node.op, ast.Not), \
"Unary is supposed to be not!")
return operator.not_(self.visit(node.values[0]))
_internal_assert(isinstance(node.op, (ast.And, ast.Or)), \
"Binary is supposed to be and/or!")
values = [self.visit(i) for i in node.values]
return HybridParser._binop_maker[type(node.op)](*values)
def visit_UnaryOp(self, node):
operand = self.visit(node.operand)
return HybridParser._unaryop_maker[type(node.op)](operand)
def visit_BinOp(self, node):
lhs = self.visit(node.left)
rhs = self.visit(node.right)
return HybridParser._binop_maker[type(node.op)](lhs, rhs)
def visit_Call(self, node):
# Yet, no function pointer supported
_internal_assert(isinstance(node.func, ast.Name), \
"Only id-function function call is supported so far!")
func_id = node.func.id
args = [self.visit(i) for i in node.args]
# Intrinsics'
if hasattr(calls, func_id):
return getattr(calls, func_id)(func_id, args)
# Contexts'
_internal_assert(func_id in self.symbols.keys(), \
"The function called (%s) is not in the context either!" % func_id)
ty, entry = self.symbols[func_id]
_internal_assert(ty is Symbol.Callable, \
"Are you sure what you call is a function?!")
outs = entry(*args)
op = outs.op if isinstance(outs, Tensor) else outs[0].op
return op
def visit_For(self, node):
iter_var, low, ext, for_type = self.visit(node.iter)
_internal_assert(isinstance(node.target, ast.Name), \
"The loop iterator should be a variable!")
_name = node.target.id
if isinstance(for_type, tuple):
low = self.analyzer.simplify(low)
ext = self.analyzer.simplify(ext)
_internal_assert(isinstance(low, _expr.ConstExpr) and
isinstance(ext, _expr.ConstExpr), \
"Const range should start from a const " + \
"and iterate const times")
low, ext = low.value, ext.value
if ext > 114514:
logging.log(logging.CRITICAL, \
'[Warning] Are you sure to unroll a large loop in Python?')
bodies = []
for i in range(low, low + ext):
self.add_symbol(_name, Symbol.ConstLoopVar, i)
body = visit_list_to_block(self.visit, node.body)
body = self.wrap_up_realize(node, body)
bodies.append(body)
self.symbols.pop(_name)
return concat_list_to_block(bodies)
if iter_var is None:
_internal_assert(for_type is not None, "The loop iterating function parse error!")
offset = iter_var = tvm.te.var(_name)
if not tvm.tir.analysis.expr_deep_equal(low, tvm.runtime.const(0, 'int32')):
offset = iter_var + low
self.add_symbol(_name, Symbol.LoopVar, offset)
_body = visit_list_to_block(self.visit, node.body)
else:
_internal_assert(for_type is None, "The loop bind function parse error!")
self.add_symbol(_name, Symbol.ThreadBind, iter_var)
self.device += 1
_body = visit_list_to_block(self.visit, node.body)
self.device -= 1
_body = self.wrap_up_realize(node, _body)
if for_type is None:
res = _body
else:
_internal_assert(not isinstance(for_type, tuple), \
"Micro expansion should be handled before!")
res = tvm.tir.For(iter_var, tvm.runtime.const(0, 'int32'), ext, for_type, 0, _body)
self.symbols.pop(_name)
return res
def visit_Return(self, node):
_internal_assert(all(ty != Symbol.LoopVar for ty, _ in self.symbols.values()), \
"Return should not be in a loop body!")
ids = []
if isinstance(node.value, ast.Name):
ids = [node.value.id]
else:
_internal_assert(isinstance(node.value, ast.Tuple), \
"You should return either a single tensor or a tuple")
_internal_assert(all(isinstance(i, ast.Name) for i in node.value.elts), \
"What do you return?")
ids = [i.id for i in node.value.elts]
_internal_assert(len(set(ids)) == len(ids), "Duplicated tensors in the return tuples")
if len(ids) < len(self.outputs):
logging.log(logging.CRITICAL, '[Warning] Not all the output buffers returned!')
self.outputs = [self.symbols[i][1] for i in ids]
self.returned = True
return util.make_nop()
def visit_Tuple(self, node):
return tuple(self.visit(i) for i in node.elts)
def visit_Str(self, node):
return node.s
def visit_Assert(self, node):
test = self.visit(node.test)
mesg = tvm.runtime.convert(self.visit(node.msg))
return tvm.tir.AssertStmt(test, mesg, util.make_nop())
def parse_python(src, args, symbols, closure_vars):
"""The helper function of calling the AST visitor
Parameters
----------
src : ast.node or str
If an ast.node, then directly lower it.
If a str, then parse it to ast and lower it.
args : list of Tensors or Vars
The argument lists to the function.
It is NOT encouraged to write a function without arguments.
It is NOT encouraged to write a function with side effect.
symbols : list of str
The symbol list of the global context of the function.
closure_vars: dict
A dict of external name reference captured by this function.
Returns
-------
root : Stmt
The result Halide IR and the parser class instance.
"""
root = ast.parse(src) if isinstance(src, str) else src
_internal_assert(root, ast.AST)
var_usage = determine_variable_usage(root, args, symbols, closure_vars)
parser = HybridParser(args, var_usage, symbols, closure_vars)
parser.parsed_body = parser.visit(root)
_internal_assert(parser.returned, 'No valid return found in the function body!')
return parser
def source_to_op(src, args, symbols, closure_vars):
"""Another level of wrapper
Parameters
----------
src : ast.node or str
If an ast.node, then directly lower it.
If a str, then parse it to ast and lower it.
args : list of Tensors or Vars
The argument lists to the function.
It is NOT encouraged to write a function without arguments.
It is NOT encouraged to write a function with side effect.
symbols : list of str
The symbol list of the global context of the function.
closure_vars: dict
A dict of external name reference captured by this function.
Returns
-------
res : list of output tensors
The result of output tensors of the formed OpNode.
"""
parser = parse_python(src, args, symbols, closure_vars)
input_tensors = []
def get_input_tensors(arg):
if isinstance(arg, Tensor):
input_tensors.append(arg)
elif isinstance(arg, Array):
for i in arg:
get_input_tensors(i)
for i in args:
get_input_tensors(i)
op = tvm.te._ffi_api.HybridOp(parser.func_name, "HybridOp", None, input_tensors,
parser.outputs, parser.parsed_body)
res = [op.output(i) for i in range(len(parser.outputs))]
return res[0] if len(res) == 1 else res