"""The build utils in python. This module provides the functions to transform schedule to LoweredFunc and compiled Module. """ from __future__ import absolute_import as _abs import warnings import types from ._ffi.node import NodeBase, register_node from . import api from . import _api_internal from . import tensor from . import schedule from . import expr from . import ir_pass from . import stmt as _stmt from . import container from . import module from . import codegen from . import ndarray from . import target as _target from . import make class DumpIR(object): """ Dump IR for each pass. With it, you can dump ir just like gcc/llvm. How to use: ----------- .. code-block:: python with tvm.build_config(dump_pass_ir=True) run() """ scope_level = 0 def __init__(self): self._pass_id = 0 self._recover_list = [] def decorate(self, func): """ decorate the pass function""" def dump(*args, **kwargs): """dump function""" retv = func(*args, **kwargs) if not isinstance(retv, (_stmt.Stmt, container.LoweredFunc, container.Array)): return retv fname = func.func_name if hasattr(func, 'func_name') else func.__name__ pname = str(self._pass_id) + "_" + fname + "_ir.cc" with open(pname, "a") as f: out = retv.body if isinstance(retv, container.LoweredFunc) else retv f.write(str(out)) if isinstance(retv, container.Array): for x in retv: out = x.body if isinstance(x, container.LoweredFunc) else x f.write("---------%s\n%s\n-----------\n"%(x.name, str(out))) self._pass_id += 1 return retv return dump def decorate_irpass(self): """decorate ir_pass and ScheduleOps""" self._old_sgpass = schedule.ScheduleOps schedule.ScheduleOps = self.decorate(schedule.ScheduleOps) vset = vars(ir_pass) k = v = 0 def recover(): vset[k] = v for k, v in vset.items(): self._recover_list.append(recover) vset[k] = self.decorate(v) if isinstance(v, types.FunctionType) else v def decorate_custompass(self, custom_pass): """decorate given list of custom passes, and return decorated passes""" custom_pass = custom_pass if custom_pass else [] pass_list = [] for idx, x in enumerate(custom_pass): x[1].__name__ = "custom{}_phase{}".format(idx, x[0]) pass_list += [(x[0], self.decorate(x[1]))] return pass_list def enter(self): """only decorate outermost nest""" if DumpIR.scope_level > 0: return self.decorate_irpass() self._pass_id = 0 DumpIR.scope_level += 1 def exit(self): """recover outermost nest""" if DumpIR.scope_level > 1: return # recover decorated functions for f in self._recover_list: f() schedule.ScheduleOps = self._old_sgpass DumpIR.scope_level -= 1 @register_node class BuildConfig(NodeBase): """Configuration scope to set a build config option. Note ---- This object is backed by node system in C++, with arguments that can be exchanged between python and C++. Do not construct directly, use build_config instead. The fields that are backed by the C++ node are immutable once an instance is constructed. See _node_defaults for the fields. """ _node_defaults = { "auto_unroll_max_step": 0, "auto_unroll_max_depth": 8, "auto_unroll_max_extent": 0, "unroll_explicit": True, "detect_global_barrier": False, "partition_const_loop": False, "offset_factor": 0, "data_alignment": -1, "restricted_func": True, "double_buffer_split_loop": 1, "dump_pass_ir": False } _dump_ir = DumpIR() # pylint: disable=no-member def __init__(self, handle): """Initialize the function with handle Parameters ---------- handle : SymbolHandle the handle to the underlying C++ Symbol """ super(BuildConfig, self).__init__(handle) self.handle = handle @property def add_lower_pass(self): size = _api_internal._BuildConfigGetAddLowerPassInfo(self) result = [] for i in range(size): phase = _api_internal._BuildConfigGetAddLowerPassInfo(self, i, True) func = _api_internal._BuildConfigGetAddLowerPassInfo(self, i, False) result += [(phase, func)] return result @add_lower_pass.setter def add_lower_pass(self, value): add_lower_pass_args = [] for x in value: add_lower_pass_args += [x[0], x[1]] _api_internal._BuildConfigSetAddLowerPass(self, *add_lower_pass_args) def __enter__(self): # pylint: disable=protected-access _api_internal._EnterBuildConfigScope(self) if self.dump_pass_ir: BuildConfig._dump_ir.enter() return self def __exit__(self, ptype, value, trace): if self.dump_pass_ir: BuildConfig._dump_ir.exit() _api_internal._ExitBuildConfigScope() def __setattr__(self, name, value): if name in BuildConfig._node_defaults: raise AttributeError( "'%s' object cannot set attribute '%s'" % (str(type(self)), name)) return super(BuildConfig, self).__setattr__(name, value) def current_build_config(): """Get the current build configuration.""" return _api_internal._GetCurrentBuildConfig() def build_config(**kwargs): """Configure the build behavior by setting config variables. Parameters ---------- auto_unroll_max_step: int, default=0 Threshold of number of steps in the loop to be automatically unrolled. This takes inner loop count into consideration. auto_unroll_max_depth: int, default=8 The maximum nested level of loops that can be automatically unrolled. unroll_explicit: bool, default=True Whether explicitly unroll the loop, if set false, the unroll hint will be passed to the CodeGen phase, which may generate pragma unroll hint. Set this to be true if CodeGen support unroll pragma and when we want to be more readable. detect_global_barrier: bool, default=True Whether detect global barrier. partition_const_loop: bool, default=False Whether partition const loop data_alignment: int, optional The alignment of data pointer in bytes. If -1 is passed, the alignment will be set to TVM's internal default. offset_factor: int, default=0 The factor used in default buffer declaration. If specified as 0, offset field is not used. restricted_func: bool, default=True Whether build restricted function. That is each buffer argument to the function are guaranteed not to overlap. This enables more optimization. Corresponds to restricted keyword in C99 double_buffer_split_loop: int, default=2 Whether split the loop with factor. If it is zero, no splitting will happen. It it is bigger than one, the logic will do a split with factor equals the integer and unroll the inner loop. This allows the buffer fetching won't contain condition. add_lower_pass: list of tuple (phase, function(Stmt->Stmt)), default=None phase contains an integer on which optimization pass we apply the pass. Additional lowering passes to be applied before make_api. dump_pass_ir: dump ir of each pass into file idx_passname_ir.cc, default=False Returns ------- config: BuildConfig The build configuration """ node_args = {k: v if k not in kwargs else kwargs[k] for k, v in BuildConfig._node_defaults.items()} config = make.node("BuildConfig", **node_args) if "add_lower_pass" in kwargs: config.add_lower_pass = kwargs["add_lower_pass"] return config def get_binds(args, binds=None): """Internal function to get binds and arg_list given arguments. Parameters ---------- args : list of Buffer or Tensor or Var The argument lists to the function. binds : dict of :any:`Tensor` to :any:`Buffer`, optional Dictionary that maps the Tensor to Buffer which specified the data layout requirement of the function. By default, a new compact buffer is created for each tensor in the argument. Returns ------- binds: dict The bind specification arg_list: list The list of symbolic buffers of arguments. """ binds = {} if binds is None else binds.copy() cfg = current_build_config() arg_list = [] for x in args: if isinstance(x, tensor.Tensor): if x not in binds: buf = api.decl_buffer(x.shape, dtype=x.dtype, name=x.name, data_alignment=cfg.data_alignment, offset_factor=cfg.offset_factor) binds[x] = buf arg_list.append(buf) else: arg_list.append(binds[x]) elif isinstance(x, schedule.Buffer): arg_list.append(x) elif isinstance(x, expr.Var): arg_list.append(x) else: raise ValueError("args must be Tensor, Buffer or Var") return binds, arg_list def lower(sch, args, name="default_function", binds=None, simple_mode=False): """Lowering step before build into target. Parameters ---------- sch : tvm.Schedule The schedule to be builded args : list of Buffer or Tensor or Var The argument lists to the function. name : str, optional The name of result function. binds : dict of :any:`Tensor` to :any:`Buffer`, optional Dictionary that maps the Tensor to Buffer which specified the data layout requirement of the function. By default, a new compact buffer is created for each tensor in the argument. simple_mode : bool, optional Whether only output simple and compact statement, this will skip LoopPartition, api wrapper generation and Unrolling. Returns ------- f : LoweredFunc or Stmt The result function, if with_api_wrapper=False Then the Stmt before make api is returned. """ binds, arg_list = get_binds(args, binds) cfg = current_build_config() add_lower_pass = cfg.add_lower_pass if cfg.add_lower_pass else [] if cfg.dump_pass_ir: add_lower_pass = BuildConfig._dump_ir.decorate_custompass(add_lower_pass) lower_phase0 = [x[1] for x in add_lower_pass if x[0] == 0] lower_phase1 = [x[1] for x in add_lower_pass if x[0] == 1] lower_phase2 = [x[1] for x in add_lower_pass if x[0] == 2] lower_phase3 = [x[1] for x in add_lower_pass if x[0] > 2] # Phase 0 if isinstance(sch, schedule.Schedule): # normalize schedule first sch = sch.normalize() bounds = schedule.InferBound(sch) stmt = schedule.ScheduleOps(sch, bounds) stmt = ir_pass.InjectPrefetch(stmt) else: #So far there is no op for hybrid script, so a plain ir body is given if not isinstance(sch, _stmt.Stmt): raise ValueError("sch should be either a Schedule or a Stmt") stmt = sch for f in lower_phase0: stmt = f(stmt) # Phase 1 stmt = ir_pass.StorageFlatten(stmt, binds, 64) stmt = ir_pass.CanonicalSimplify(stmt) for f in lower_phase1: stmt = f(stmt) # Phase 2 if not simple_mode: stmt = ir_pass.LoopPartition(stmt, cfg.partition_const_loop) stmt = ir_pass.VectorizeLoop(stmt) stmt = ir_pass.InjectVirtualThread(stmt) stmt = ir_pass.InjectDoubleBuffer(stmt, cfg.double_buffer_split_loop) stmt = ir_pass.StorageRewrite(stmt) stmt = ir_pass.UnrollLoop( stmt, cfg.auto_unroll_max_step, cfg.auto_unroll_max_depth, cfg.auto_unroll_max_extent, cfg.unroll_explicit) for f in lower_phase2: stmt = f(stmt) # Phase 2 stmt = ir_pass.Simplify(stmt) stmt = ir_pass.LowerStorageAccessInfo(stmt) stmt = ir_pass.RemoveNoOp(stmt) stmt = ir_pass.RewriteUnsafeSelect(stmt) for f in lower_phase3: stmt = f(stmt) if simple_mode: return stmt return ir_pass.MakeAPI(stmt, name, arg_list, 0, cfg.restricted_func) def build(sch, args=None, target=None, target_host=None, name="default_function", binds=None): """Build a function with arguments as signiture. Parameters ---------- sch : tvm.Schedule, or LoweredFunc The schedule to be builded args : list of Buffer or Tensor or Var, optional The argument lists to the function. target : str or :any:`tvm.target.Target`, optional The target and option of the compilation. target_host : str or :any:`tvm.target.Target` optional Host compilation target, if target is device. When TVM compiles device specific program such as CUDA, we also need host(CPU) side code to interact with the driver setup the dimensions and parameters correctly. target_host is used to specify the host side codegen target. By default, llvm is used if it is enabled, otherwise a stackvm intepreter is used. name : str, optional The name of result function. binds : dict, optional Dictionary that maps the binding of symbolic buffer to Tensor. By default, a new buffer is created for each tensor in the argument. Returns ------- f : Function, or pair of functions The result function. Note ---- See the note on :any:`tvm.target` on target string format. """ if isinstance(sch, schedule.Schedule): if args is None: raise ValueError("args must be given for build from schedule") flist = lower(sch, args, name=name, binds=binds) if isinstance(flist, container.LoweredFunc): flist = [flist] elif isinstance(sch, container.LoweredFunc): if args: raise ValueError("args must be done when build from LoweredFunc") flist = [sch] elif isinstance(sch, (list, tuple, container.Array)): flist = sch else: raise ValueError("sch have to be Schedule, LoweredFunc or list of LoweredFunc") fname_set = set() for x in flist: if not isinstance(x, container.LoweredFunc): raise ValueError("sch have to be Schedule, LoweredFunc or list of LoweredFunc") if x.name in fname_set: raise ValueError("Duplicate function name %s" % x.name) fname_set.add(x.name) target = _target.current_target() if target is None else target target = _target.create(target) if target else _target.create("llvm") device_type = ndarray.context(target.target_name, 0).device_type fhost = [] fdevice = [] for func in flist: if not ir_pass.VerifyMemory(func, device_type): raise ValueError( "Direct host side access to device memory is detected in %s. " "Did you forget to bind?" % func.name) if func.func_type == container.LoweredFunc.MixedFunc: if current_build_config().detect_global_barrier: func = ir_pass.ThreadSync(func, "global") func = ir_pass.ThreadSync(func, "shared") func = ir_pass.ThreadSync(func, "warp") warp_size = target.thread_warp_size func = ir_pass.LowerThreadAllreduce(func, warp_size) fsplits = [s for s in ir_pass.SplitHostDevice(func)] fhost.append(fsplits[0]) for x in fsplits[1:]: fdevice.append(x) elif func.func_type == container.LoweredFunc.HostFunc: fhost.append(func) elif func.func_type == container.LoweredFunc.DeviceFunc: fdevice.append(func) else: raise ValueError("unknown function type %d" % func.func_type) for i, func in enumerate(fdevice): warp_size = target.thread_warp_size fdevice[i] = ir_pass.LowerWarpMemory(func, warp_size) if "gpu" in target.keys and not fdevice: warnings.warn( "Specified target %s, but cannot find device code, did you do bind?" % target) fhost = [ir_pass.BindDeviceType(x, device_type) for x in fhost] fhost = [ir_pass.LowerTVMBuiltin(x) for x in fhost] if not target_host: if device_type == ndarray.cpu(0).device_type: target_host = target assert not fdevice else: target_host = "llvm" if module.enabled("llvm") else "stackvm" target_host = _target.create(target_host) target_device = target fdevice = [ir_pass.LowerIntrin(x, target_device.target_name) for x in fdevice] fhost = [ir_pass.LowerIntrin(x, target_host.target_name) for x in fhost] fhost = [ir_pass.CombineContextCall(x) for x in fhost] mhost = codegen.build_module(fhost, str(target_host)) if fdevice: mdev = codegen.build_module(fdevice, str(target_device)) mhost.import_module(mdev) return mhost