import atexit import collections import contextlib import copy import cProfile import dataclasses import datetime import dis import enum import functools import gc import inspect import itertools import linecache import logging import math import operator import os import pstats import sys import textwrap import time import types import typing import weakref from contextlib import contextmanager from functools import lru_cache, wraps from typing import Any, Dict, Optional, Tuple, Union try: import numpy as np except ModuleNotFoundError: np = None import torch._logging import torch._numpy as tnp from torch._guards import detect_fake_mode # noqa: F401 from torch._logging import LazyString from . import config # NOTE: Make sure `NP_SUPPORTED_MODULES` and `NP_TO_TNP_MODULE` are in sync. if np: NP_SUPPORTED_MODULES = (np, np.fft, np.linalg, np.random) NP_TO_TNP_MODULE = { np: tnp, np.fft: tnp.fft, np.linalg: tnp.linalg, np.random: tnp.random, } else: NP_SUPPORTED_MODULES = {} NP_TO_TNP_MODULE = {} import importlib import torch import torch._functorch.config import torch.fx.experimental.symbolic_shapes from torch import fx from torch._dispatch.python import enable_python_dispatcher from torch._subclasses.fake_tensor import FakeTensor, is_fake from torch.nn.modules.lazy import LazyModuleMixin from torch.utils._pytree import tree_map counters = collections.defaultdict(collections.Counter) troubleshooting_url = "https://pytorch.org/docs/master/compile/troubleshooting.html" nnmodule_doc_url = "https://pytorch.org/docs/master/compile/nn-module.html" nnmodule_doc_url_msg = f"See {nnmodule_doc_url} for more information and limitations." log = logging.getLogger(__name__) # profiling compilation time by function compilation_time_metrics = collections.OrderedDict() # profiling compilation time by frame phase frame_phase_timing = collections.OrderedDict() timer_counter = itertools.count() def tabulate(rows, headers): try: import tabulate return tabulate.tabulate(rows, headers=headers) except ImportError: return "\n".join( ", ".join(map(str, row)) for row in itertools.chain([headers], rows) ) def dynamo_profiled(func): @wraps(func) def profile_wrapper(*args, **kwargs): global timer_counter datafn = ( func.__name__ + f"{next(timer_counter)}.profile" ) # Name the data file sensibly prof = cProfile.Profile() prof.enable() retval = prof.runcall(func, *args, **kwargs) prof.disable() print(f"### Cprofile for {func.__name__} iter {next(timer_counter)} ###") ps = pstats.Stats(prof) ps.sort_stats(pstats.SortKey.TIME).print_stats(20) ps.sort_stats(pstats.SortKey.CUMULATIVE).print_stats(20) prof.dump_stats(datafn) return retval return profile_wrapper curr_frame = 0 # Note: Called for you by dynamo - you almost never ever want to invoke this yourself. def increment_frame(): global curr_frame curr_frame = curr_frame + 1 # Note: Called for you by dynamo - you almost never ever want to invoke this yourself. def reset_frame_count(): global curr_frame frame_phase_timing.clear() compilation_time_metrics.clear() curr_frame = 0 op_count = 0 def increment_op_count(cnt): global op_count op_count += cnt # Print a report of time spent so far # Ex: # TIMING: # entire_frame_compile:8.574629999999999 # backend_compile:5.26806 def print_time_report(): total = 0 total_by_key = {} for timings in frame_phase_timing.values(): for key, timing in timings.items(): total += timing if key not in total_by_key: total_by_key[key] = timing else: total_by_key[key] += timing out = "TIMING:" for key, value in total_by_key.items(): out = f"{out} {key}:{round(value, 5)}" print(out) # dynamo_timed API works as a function decorator # By wrapping a function in dynamo_timed, we can store a record in compilation_time_metrics # where the key is the functions name. # For example: # # @dynamo_timed # def _foo(...): # # Would show up as an entry in our timing dict: # OrderedDict([('bar.._foo', [0.083690, 0.23949, 3.1425e-05])]) # This is extremely useful for granular debugging. # # For a higher-level mode, pass a phase_name into dynamo_timed # phase_names record an extra record into a separate compilation timing structure, # one keyed on frame+name rather than function. # The frame is incremented outside of this function, in def increment_frame() above. def dynamo_timed(original_function=None, phase_name=None): def dynamo_timed_inner(func): @wraps(func) def time_wrapper(*args, **kwargs): key = func.__qualname__ if key not in compilation_time_metrics: compilation_time_metrics[key] = [] with torch.profiler.record_function(f"{key} (dynamo_timed)"): t0 = time.time() r = func(*args, **kwargs) time_spent = time.time() - t0 compilation_time_metrics[key].append(time_spent) if phase_name: frame_key = str(curr_frame) if frame_key not in frame_phase_timing: frame_phase_timing[frame_key] = {} assert ( phase_name not in frame_phase_timing[frame_key] ), f"Duplicate phase name {phase_name} for frame {frame_key}" frame_phase_timing[frame_key][phase_name] = time_spent return r return time_wrapper if original_function: return dynamo_timed_inner(original_function) return dynamo_timed_inner def compile_times(repr="str", aggregate=False): """ Get metrics about torchdynamo frontend/backend compilation times. Accumulates information from functions tagged with `@dynamo_timed`. repr='str' returns a printable string for user interaction, and 'csv' returns headers, rows which can be logged for output aggregate causes values from multiple compilations (e.g. split graphs) to be accumulated into one value. If false, expect more than one value per metric. """ def fmt_fn(values, item_fn=lambda x: x): if aggregate: return item_fn(sum(values)) return ", ".join(map(item_fn, values)) if repr == "str": rows = [ (k, fmt_fn(compilation_time_metrics[k], item_fn=lambda x: f"{x:.4f}")) for k in compilation_time_metrics ] out = "TorchDynamo compilation metrics:\n" out += tabulate(rows, headers=("Function", "Runtimes (s)")) return out elif repr == "csv": values = [ fmt_fn(v, item_fn=lambda x: f"{x:.6f}") for v in compilation_time_metrics.values() ] headers = list(compilation_time_metrics.keys()) return headers, values @atexit.register def dump_compile_times(): log.info(compile_times(repr="str", aggregate=True)) tensortype_to_dtype = { torch.FloatTensor: (torch.float32, torch.float), torch.DoubleTensor: (torch.float64, torch.double), torch.HalfTensor: (torch.float16, torch.half), torch.BFloat16Tensor: (torch.bfloat16,), torch.ByteTensor: (torch.uint8,), torch.CharTensor: (torch.int8,), torch.LongTensor: (torch.int64, torch.long), torch.IntTensor: (torch.int32, torch.int), torch.ShortTensor: (torch.int16, torch.short), torch.BoolTensor: (torch.bool,), } class DuplicateWarningChecker: def __init__(self, maxsize=4096): self.maxsize = maxsize self.reset() def reset(self): self.set = collections.OrderedDict() def add(self, key): if key in self.set: self.set.move_to_end(key, last=True) if not config.verbose: return False else: self.set[key] = None while len(self.set) > self.maxsize: self.set.popitem(last=False) return True graph_break_dup_warning_checker = DuplicateWarningChecker() def setup_compile_debug(): compile_debug = os.environ.get("TORCH_COMPILE_DEBUG", "0") == "1" if compile_debug: torch._logging.set_logs( dynamo=logging.DEBUG, aot=logging.DEBUG, inductor=logging.DEBUG, output_code=True, # this is off by default ) return add_file_handler() return contextlib.ExitStack() def reset_graph_break_dup_checker(): graph_break_dup_warning_checker.reset() def add_file_handler(): log_path = os.path.join(get_debug_dir(), "torchdynamo") if not os.path.exists(log_path): os.makedirs(log_path) log_file_handler = logging.FileHandler(os.path.join(log_path, "debug.log")) logger = logging.getLogger("torch._dynamo") logger.addHandler(log_file_handler) exitstack = contextlib.ExitStack() exitstack.callback(lambda: logger.removeHandler(log_file_handler)) return exitstack def setup_log_file(): exitstack = contextlib.ExitStack() if config.log_file_name is not None: log_file_handler = logging.FileHandler(config.log_file_name) for logger in logging.get_loggers(): logger.addHandler(log_file_handler) exitstack.callback(lambda: logger.removeHandler(log_file_handler)) return exitstack return exitstack def gen_record_file_name(exc, code): return f"{get_debug_dir()}/error_recordings/\ {code.co_name}_{type(exc).__name__}_{code.co_firstlineno}.rec" def write_record_to_file(filename, exec_record): try: if os.path.exists(filename): log.warning( "Unable to write execution record %s; file already exists.", filename ) else: os.makedirs(os.path.dirname(filename), exist_ok=True) with open(filename, "wb") as f: exec_record.dump(f) except Exception: log.error("Unable to write execution record %s", filename, exc_info=1) def count_calls(g: fx.Graph): c = 0 for n in g.nodes: if "call" in n.op: c += 1 return c def identity(x): return x def nothing(*args, **kwargs): pass class ExactWeakKeyDictionary: """Similar to weakref.WeakKeyDictionary, but use `is`/`id` rather than `==` to compare equality""" def __init__(self): self.values = dict() self.refs = dict() def __getitem__(self, key): return self.values[id(key)] def get(self, key, default=None): return self.values.get(id(key), default) def __contains__(self, key): return id(key) in self.values def __setitem__(self, key, value): idx = id(key) if idx not in self.refs: self.refs[idx] = weakref.ref(key, lambda ref: self._remove_id(idx)) self.values[idx] = value def _remove_id(self, idx): if idx in self.values: del self.values[idx] if idx in self.refs: del self.refs[idx] def clear(self): self.refs.clear() self.values.clear() def istype(obj, allowed_types): """isinstance() without subclasses""" if isinstance(allowed_types, (tuple, list, set)): return type(obj) in allowed_types return type(obj) is allowed_types def is_typing(value): if sys.version_info < (3, 9): return isinstance(value, typing._GenericAlias) else: return isinstance( value, (typing._SpecialGenericAlias, typing._UnionGenericAlias) ) def is_numpy_int_type(value): if not np: return False return istype( value, ( np.int8, np.int16, np.int32, np.int64, np.uint8, np.uint16, np.uint32, np.uint64, ), ) def is_numpy_float_type(value): if not np: return False return istype( value, ( np.float16, np.float32, np.float64, ), ) def is_numpy_ndarray(value): if not np: return False return istype(value, np.ndarray) def istensor(obj): """Check of obj is a tensor""" tensor_list = ( torch.Tensor, torch.nn.Parameter, *config.traceable_tensor_subclasses, ) tensor_list = tensor_list + (torch._subclasses.FakeTensor,) return istype(obj, tensor_list) def is_lazy_module(mod): return isinstance(mod, LazyModuleMixin) @functools.lru_cache(4096) def print_once(*args): print(*args) def make_cell(val=None): """Some black magic to create a cell object that usually only exists in a closure""" x = val def f(): return x assert len(f.__closure__) == 1 return f.__closure__[0] def proxy_args_kwargs(args, kwargs): try: proxy_args = tuple(arg.as_proxy() for arg in args) proxy_kwargs = {key: arg.as_proxy() for key, arg in kwargs.items()} return proxy_args, proxy_kwargs except NotImplementedError as e: from .exc import unimplemented from .variables.base import typestr raise unimplemented( f"call_function args: {typestr(*args)} {typestr(*list(kwargs.values()))}" ) from e @dataclasses.dataclass class CompilationMetrics: frame_key: str co_name: str co_filename: str co_firstlineno: int cache_size: int guard_count: Optional[int] graph_op_count: Optional[int] graph_node_count: Optional[int] graph_input_count: Optional[int] entire_frame_compile_time_s: Optional[float] backend_compile_time_s: Optional[float] fail_reason: Optional[str] @dataclasses.dataclass class CleanupHook: """Remove a global variable when hook is called""" scope: Dict[str, Any] name: str def __call__(self, *args): CleanupManager.count -= 1 del self.scope[self.name] @staticmethod def create(scope, name, val): assert name not in scope CleanupManager.count += 1 scope[name] = val return CleanupHook(scope, name) class CleanupManager(ExactWeakKeyDictionary): count = 0 def _remove_id(self, idx): for hook in self.values[idx]: hook() super()._remove_id(idx) CleanupManager.instance = CleanupManager() def clone_tensor(x): """Clone the tensor and its gradient""" y = x.clone().requires_grad_(x.requires_grad) if x.is_leaf and x.grad is not None: y.grad = x.grad.clone() return y def clone_input(x, *, dtype=None): """copy while preserving strides""" # TODO: this is questionable if isinstance(x, torch._subclasses.FakeTensor): # this func fails on fake tensors in __torch_dispatch__ return x def torch_clone(x): y = torch.clone(x) if x.is_leaf: y.requires_grad_(x.requires_grad) if x.is_leaf and x.grad is not None: y.grad = clone_input(x.grad, dtype=dtype) if hasattr(x, "_dynamo_dynamic_indices"): y._dynamo_dynamic_indices = x._dynamo_dynamic_indices.copy() return y with torch.no_grad(): if x.device.type == "xla": # Access data_ptr() for a xla tensor will cause crash return torch_clone(x) needed_size = sum( (shape - 1) * stride for shape, stride in zip(x.size(), x.stride()) ) if x.is_quantized: result = torch.empty_quantized((needed_size + 32,), x) else: result = torch.empty( needed_size + 32, dtype=dtype or x.dtype, device=x.device ) cache_line_offset = ( (x.data_ptr() - result.data_ptr()) % 32 ) // x.element_size() result.as_strided_(x.size(), x.stride(), cache_line_offset) try: result.copy_(x.clone()) if x.is_leaf: result.requires_grad_(x.requires_grad) if x.is_leaf and x.grad is not None: result.grad = clone_input(x.grad, dtype=dtype) except RuntimeError: # RuntimeError: unsupported operation: more than one element of the written-to # tensor refers to a single memory location. Please clone() the tensor before # performing the operation. return torch_clone(x) if hasattr(x, "_dynamo_dynamic_indices"): result._dynamo_dynamic_indices = x._dynamo_dynamic_indices.copy() return result def clone_inputs(example_inputs): if type(example_inputs) is dict: res = dict(example_inputs) for key, value in res.items(): if isinstance(value, tuple): res[key] = clone_inputs(value) else: assert isinstance(value, torch.Tensor), type(value) res[key] = clone_input(value) return res res = list(example_inputs) for i in range(len(res)): if isinstance(res[i], torch.Tensor): res[i] = clone_input(res[i]) return res @contextmanager def preserve_rng_state(): with torch.utils._python_dispatch._disable_current_modes(): rng_state = torch.clone(torch.random.get_rng_state()) if torch.cuda.is_available(): cuda_rng_state = torch.clone(torch.cuda.get_rng_state()) try: yield finally: with torch.utils._python_dispatch._disable_current_modes(): torch.random.set_rng_state(rng_state) if torch.cuda.is_available(): torch.cuda.set_rng_state(cuda_rng_state) def is_jit_model(model0): return isinstance( model0, ( torch.jit._trace.TopLevelTracedModule, torch.jit._script.RecursiveScriptModule, torch.jit.ScriptFunction, torch.jit.ScriptModule, ), ) def torchscript(model, example_inputs, verbose=False): if is_jit_model(model): # already done? return model try: return torch.jit.trace(model, example_inputs) except Exception: try: return torch.jit.script(model) except Exception: if verbose: log.exception("jit error") else: log.error("Both torch.jit.trace and torch.jit.script failed") return None def getfile(obj): try: return inspect.getfile(obj) except TypeError: return None def is_namedtuple(obj): """Test if an object is a namedtuple or a torch.return_types.* quasi-namedtuple""" return is_namedtuple_cls(type(obj)) def is_namedtuple_cls(cls): """Test if an object is a namedtuple or a torch.return_types.* quasi-namedtuple""" try: if issubclass(cls, tuple): bases = getattr(cls, "__bases__", []) or [None] module = getattr(cls, "__module__", None) return module == "torch.return_types" or ( bases[0] is tuple and hasattr(cls, "_make") and hasattr(cls, "_fields") ) except TypeError: pass return False @functools.lru_cache(1) def namedtuple_fields(cls): """Get the fields of a namedtuple or a torch.return_types.* quasi-namedtuple""" if cls is slice: return ["start", "stop", "step"] assert issubclass(cls, tuple) if hasattr(cls, "_fields"): # normal namedtuples return cls._fields @dataclasses.dataclass class Marker: index: int # frustrating ones e.g. torch.return_types.max assert cls.__module__ == "torch.return_types" obj = cls(map(Marker, range(cls.n_fields))) fields = [None] * cls.n_fields for name in dir(obj): if name[0] != "_" and isinstance(getattr(obj, name), Marker): fields[getattr(obj, name).index] = name return fields def checkpoint_params(gm): with torch.no_grad(): rng_state = torch.clone(torch.random.get_rng_state()) if torch.cuda.is_available(): cuda_rng_state = torch.clone(torch.cuda.get_rng_state()) saved_state = [] for param in itertools.chain(gm.parameters(), gm.buffers()): saved_state.append((param, param._version, torch.clone(param))) def restore(): with torch.no_grad(): torch.random.set_rng_state(rng_state) if torch.cuda.is_available(): torch.cuda.set_rng_state(cuda_rng_state) for param, version, original_value in saved_state: if param._version != version: param.copy_(original_value) return restore def timed(model, example_inputs, times=1): if torch.cuda.is_available(): synchronize = torch.cuda.synchronize else: synchronize = nothing synchronize() gc.collect() torch.manual_seed(1337) t0 = time.perf_counter() for _ in range(times): result = model(*example_inputs) synchronize() t1 = time.perf_counter() return result, t1 - t0 def check_is_cuda(gm, example_inputs): return all(x.is_cuda for x in itertools.chain(example_inputs, gm.parameters(True))) @lru_cache(32) def rot_n_helper(n): assert n > 1 vars = [f"v{i}" for i in range(n)] rotated = reversed(vars[-1:] + vars[:-1]) fn = eval(f"lambda {','.join(vars)}: ({','.join(rotated)})") fn.__name__ = f"rot_{n}_helper" return fn def is_safe_constant(v): if istype(v, (tuple, frozenset)): return all(map(is_safe_constant, v)) return isinstance(v, (enum.Enum, type)) or istype( v, ( types.CodeType, int, float, bool, str, bytes, type(None), slice, type(type), torch.device, torch.dtype, ), ) def guard_if_dyn(arg): from .variables import ConstantVariable, SymNodeVariable if isinstance(arg, SymNodeVariable): # This is because SymNodeVariable intentionally doesn't define # as_python_constant to avoid shunting down some codepaths # that expect consts. In this case, we know we definitely # want to specialize though. return arg.evaluate_expr() elif isinstance(arg, ConstantVariable): return arg.as_python_constant() return arg def check_constant_args(args, kwargs): return all(x.is_python_constant() for x in itertools.chain(args, kwargs.values())) def check_unspec_python_args(args, kwargs): from .variables.constant import ConstantVariable from .variables.tensor import UnspecializedPythonVariable unspec_count = 0 for x in itertools.chain(args, kwargs.values()): if isinstance(x, UnspecializedPythonVariable): unspec_count += 1 elif not isinstance(x, (UnspecializedPythonVariable, ConstantVariable)): return False else: pass return unspec_count > 0 def check_numpy_ndarray_args(args, kwargs): from .variables.tensor import NumpyNdarrayVariable return any( isinstance(x, NumpyNdarrayVariable) for x in itertools.chain(args, kwargs.values()) ) def specialize_args_kwargs(tx, args, kwargs): specialized_args = [] specialized_kwargs = {} for x in args: specialized_args.append(x.as_specialized(tx)) for k, v in kwargs.items(): specialized_kwargs.update({k: v.as_specialized(tx)}) return specialized_args, specialized_kwargs dict_values = type(dict().values()) odict_values = type(collections.OrderedDict().values()) tuple_iterator = type(iter(tuple())) tuple_iterator_len = tuple_iterator.__length_hint__ object_new = object.__new__ def nn_module_new(cls): obj = object_new(cls) torch.nn.Module.__init__(obj) return obj def product(it): return functools.reduce(operator.mul, it, 1) def tuple_iterator_getitem(it, index): _, (obj,), start = it.__reduce__() return obj[start + index] def enum_repr(value, local): # enum class can override __str__ method. Use __class__ and name attribute # to extract the class name and key name. name = value.__class__.__name__ val = value.name scope = "L" if local else "G" local_name = f'{scope}["{name}"].{val}' return local_name def dict_param_key_ids(value): return { id(k) for k in value.keys() if isinstance(k, (torch.nn.Parameter, torch.Tensor)) } def dict_const_keys(value): return { k for k in value.keys() if not isinstance(k, (torch.nn.Parameter, torch.Tensor)) } def dict_const_keys_repr(const_keys, *, local): if any(isinstance(k, enum.Enum) for k in const_keys): # To workaround repr(Enum) returning invalid global reference before python 3.11 # by calling enum_repr and removing quotes to render enum in guard code. const_keys_str = f"{ {enum_repr(k, local=local) if isinstance(k, enum.Enum) else repr(k) for k in const_keys} }".replace( "'", "" ) else: const_keys_str = f"{const_keys!r}" return const_keys_str def global_key_name(key): return f"__dict_key_{id(key)}" from torch._subclasses import ( # noqa: F401 FakeTensorMode, UnsupportedFakeTensorException, ) def wrap_fake_exception(fn): try: return fn() except UnsupportedFakeTensorException as e: from .exc import unimplemented msg = f"Unsupported: {e.reason} with fake tensor propagation." log.warning(msg) raise unimplemented(msg) from e def deepcopy_to_fake_tensor(obj, fake_mode): with torch._subclasses.fake_tensor.FakeCopyMode(fake_mode): return wrap_fake_exception(lambda: copy.deepcopy(obj)) def rmse(ref, res): """ Calculate root mean squared error """ return torch.sqrt(torch.mean(torch.square(ref - res))) def same( ref, res, fp64_ref=None, cos_similarity=False, tol=1e-4, equal_nan=False, exact_dtype=True, relax_numpy_equality=False, ignore_non_fp=False, log_error=log.error, ): """Check correctness to see if ref and res match""" if fp64_ref is None: fp64_ref = ref if isinstance(ref, (list, tuple, torch.nn.ParameterList, torch.Size)): assert isinstance(res, (list, tuple)), f"type mismatch {type(ref)} {type(res)}" if len(ref) != len(res): log_error("Length mismatch") return False return len(ref) == len(res) and all( same( ai, bi, fp64_refi, cos_similarity, tol, equal_nan, exact_dtype, relax_numpy_equality, ignore_non_fp, log_error=log_error, ) for ai, bi, fp64_refi in zip(ref, res, fp64_ref) ) elif isinstance(ref, dict): assert isinstance(res, dict) assert set(ref.keys()) == set( res.keys() ), f"keys mismatch {set(ref.keys())} == {set(res.keys())}" for k in sorted(ref.keys()): if not ( same( ref[k], res[k], fp64_ref[k], cos_similarity=cos_similarity, tol=tol, equal_nan=equal_nan, exact_dtype=exact_dtype, relax_numpy_equality=relax_numpy_equality, ignore_non_fp=ignore_non_fp, log_error=log_error, ) ): log_error("Accuracy failed for key name %s", k) return False return True elif isinstance(ref, torch.Tensor): assert not isinstance(ref, torch._subclasses.FakeTensor) assert not isinstance(res, torch._subclasses.FakeTensor) if ref.is_sparse: assert res.is_sparse ref = ref.to_dense() res = res.to_dense() assert isinstance(res, torch.Tensor), f"type mismatch {type(ref)} {type(res)}" if exact_dtype: if ref.dtype != res.dtype: log_error("dtype mismatch %s, %s", ref.dtype, res.dtype) return False if ref.dtype == torch.bool: if ignore_non_fp: return True # triton stores bool as int8, so add this for more accurate checking r = torch.allclose( ref.to(dtype=torch.uint8), res.to(dtype=torch.uint8), atol=tol, rtol=tol, equal_nan=equal_nan, ) if not r: log_error("Accuracy failed: uint8 tensor did not match") return r if cos_similarity: ref = ref.flatten().to(torch.float32) res = res.flatten().to(torch.float32) if torch.allclose(ref, res, atol=tol, rtol=tol, equal_nan=True): # early exit that handles zero/nan better # cosine_similarity(zeros(10), zeros(10), dim=0) is 0 return True score = torch.nn.functional.cosine_similarity(ref, res, dim=0, eps=1e-6) if score < 0.99: log.warning("Similarity score=%s", score.cpu().detach().item()) return score >= 0.99 else: if not exact_dtype: ref = ref.to(res.dtype) # First try usual allclose if torch.allclose(ref, res, atol=tol, rtol=tol, equal_nan=equal_nan): return True # Check error from fp64 version if fp64_ref.dtype == torch.float64: ref_error = rmse(fp64_ref, ref).item() res_error = rmse(fp64_ref, res).item() multiplier = 2.0 if ( fp64_ref.numel() < 1000 or (ref.ndim == 4 and ref.shape[-1] == ref.shape[-2] == 1) # large tol means a benchmark has been specified as REQUIRE_HIGHER_TOLERANCE or tol >= 2 * 1e-2 ): # In the presence of noise, noise might dominate our error # metric for smaller tensors. # Similary, for 1x1 kernels, there seems to be high noise with amp. multiplier = 3.0 passes_test = res_error <= (multiplier * ref_error + tol / 10.0) if not passes_test: log_error( "RMSE (res-fp64): %.5f, (ref-fp64): %.5f and shape=%s", res_error, ref_error, res.size(), ) # import pdb; pdb.set_trace() return passes_test if ignore_non_fp: return True log_error("Accuracy failed: allclose not within tol=%s", tol) return False elif isinstance(ref, (str, int, type(None), bool, torch.device)): if ignore_non_fp: return True r = ref == res if not r: log_error("Accuracy failed (%s): %s != %s", type(ref), ref, res) return r elif isinstance(ref, float): r = math.isclose(ref, res, rel_tol=tol, abs_tol=tol) if not r: log_error( "Accuracy failed (float): %s != %s (within tol=%s)", ref, res, tol ) return r elif is_numpy_int_type(ref) or is_numpy_float_type(ref): if relax_numpy_equality and not ( is_numpy_int_type(res) or is_numpy_float_type(res) ): ref = ref.item() r = (type(ref) is type(res)) and (ref == res) if not r: log_error("Accuracy failed (numpy): %s != %s", ref, res) return r elif is_numpy_ndarray(ref): return (type(ref) is type(res)) and same( torch.as_tensor(ref), torch.as_tensor(res), fp64_ref, cos_similarity=cos_similarity, tol=tol, equal_nan=equal_nan, exact_dtype=exact_dtype, relax_numpy_equality=relax_numpy_equality, ignore_non_fp=ignore_non_fp, log_error=log_error, ) elif type(ref).__name__ in ( "MaskedLMOutput", "Seq2SeqLMOutput", "CausalLMOutputWithCrossAttentions", "LongformerMaskedLMOutput", "Instances", "SquashedNormal", "Boxes", "Normal", "TanhTransform", "Foo", "Variable", ): assert type(ref) is type(res) return all( same( getattr(ref, key), getattr(res, key), getattr(fp64_ref, key), cos_similarity=cos_similarity, tol=tol, equal_nan=equal_nan, exact_dtype=exact_dtype, relax_numpy_equality=relax_numpy_equality, ignore_non_fp=ignore_non_fp, log_error=log_error, ) for key in ref.__dict__.keys() ) else: raise RuntimeError(f"unsupported type: {type(ref).__name__}") def format_func_info(code): short_filename = code.co_filename.split("/")[-1] return f"'{code.co_name}' ({short_filename}:{code.co_firstlineno})" @contextlib.contextmanager def disable_cache_limit(): prior = config.cache_size_limit config.cache_size_limit = sys.maxsize try: yield finally: config.cache_size_limit = prior # map from transformed code back to original user code orig_code_map = ExactWeakKeyDictionary() # keep a record of code_obj -> list of guard failure reasons for logging guard_failures = collections.defaultdict(list) # Keep a record of graph break reasons for logging graph_break_reasons = list() # keep record of compiled code, if we are in "error if recompile" # to track code that dynamo has compiled previously seen_code_map = ExactWeakKeyDictionary() class CompileProfiler: """Utility for profiling how and what dynamo would compile. Can be used for * diagnosing recompilation issues * determining an appropriate compile cache limit * (TODO)confirming which functions got compiled/skipped """ def __init__(self): self.frame_count = 0 self.op_count = 0 self.backend_ctx_ctor = lambda: disable_cache_limit() def __call__(self, gm: torch.fx.GraphModule, example_inputs): self.frame_count += 1 for node in gm.graph.nodes: if "call" in node.op: self.op_count += 1 return gm.forward def __enter__(self): self.old_report_guard_failure = config.report_guard_failures config.report_guard_failures = True return self def __exit__(self, typ, val, traceback): config.report_guard_failures = self.old_report_guard_failure def get_metrics(self): return {"guard_failures": guard_failures} def report(self): metrics = self.get_metrics() gf = metrics["guard_failures"] def num_recompiles(code): return len(gf[code]) def recompile_reasons(code): return "\n".join([str(x) for x in gf[code]]) summarized_gf = [ [format_func_info(code), num_recompiles(code), recompile_reasons(code)] for code in gf ] def graph_break_report(): if "graph_break" in counters: graph_breaks = counters["graph_break"] return tabulate( [[msg, graph_breaks[msg]] for msg in graph_breaks], headers=["Graph Break Reason", "Count"], ) def recompilation_report(): if len(gf): max_recompiles = max([num_recompiles(code) for code in gf]) recomp_table = tabulate( summarized_gf, headers=["Function", "Recompiles", "Recompile Reasons"], ) return recomp_table + textwrap.dedent( f""" Set torch._dynamo.config.cache_size_limit to {max_recompiles} to avoid being cache limited. """ ) report = textwrap.dedent( """ Torchdynamo Profiler Report =========================== Graph Breaks ------------ Graph breaks happen when torchdynamo encounters code it can't safely trace. If you want to find out why breaks are happening, check below for each break reason You may gain additional insight by passing `fullgraph=True` to torch.compile, to stop at the first break. """ ) report += graph_break_report() or "No graph breaks detected." report += textwrap.dedent( """ Recompilation ------------- These subgraphs were recompiled more than once due to guard failures Guard failures indicate some condition assumed to be static by the tracer changed, making it unsafe to reuse the compiled program. """ ) report += recompilation_report() or "No recompilation detected.\n" return report # return same dir unless user changes config between calls @functools.lru_cache(None) def _get_debug_dir(root_dir): dir_name = ( "run_" + datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S_%f") # use pid to avoid conflicts among ranks + "-pid_" + str(os.getpid()) ) return os.path.join(root_dir, dir_name) def get_debug_dir(): debug_root = config.debug_dir_root return _get_debug_dir(debug_root) def get_fake_value(node, tx): """ Run the computation represented by `node` using fake tensors and return the result. """ from .exc import ( TorchRuntimeError, unimplemented, Unsupported, UserError, UserErrorType, ) op = node.op def fake_wrapper(e): if isinstance(e, torch.Tensor): assert is_fake(e) return e def visit(n: torch.fx.Node): return n.meta["example_value"] args, kwargs = torch.fx.node.map_arg((node.args, node.kwargs), visit) args = tree_map(fake_wrapper, args) kwargs = tree_map(fake_wrapper, kwargs) nnmodule = None if op == "call_method" and len(args) > 0 and isinstance(args[0], torch.nn.Module): # If the first argument is nn.Module, should copy to fake mode. args = (deepcopy_to_fake_tensor(args[0], tx.fake_mode),) + tuple(args[1:]) if op == "call_module": nnmodule = tx.output.nn_modules[node.target] if is_lazy_module(nnmodule) and hasattr(nnmodule, "_initialize_hook"): # In the case of a lazy module, we want to run # the pre-hooks which initialize it. # Afterwards, lazy module deletes its pre-hooks # to avoid treating it as lazy on subsequent recompile. nnmodule._infer_parameters(nnmodule, args) # no matter it's lazy module or not, we should copy to fake mode. nnmodule = deepcopy_to_fake_tensor(nnmodule, tx.fake_mode) try: with tx.fake_mode, enable_python_dispatcher(): return wrap_fake_exception( lambda: run_node(tx.output, node, args, kwargs, nnmodule) ) except Unsupported: raise except RuntimeError as e: cause = e if e.__cause__ is not None: cause = e.__cause__ if isinstance( cause, torch._subclasses.fake_tensor.DataDependentOutputException ): unimplemented(f"data dependent operator: {cause.func}") elif isinstance( cause, torch._subclasses.fake_tensor.DynamicOutputShapeException ): unimplemented(f"dynamic shape operator: {cause.func}") elif isinstance( cause, torch._subclasses.fake_tensor.UnsupportedOperatorException ): unimplemented( f"unsupported operator: {cause.func} (see " "https://docs.google.com/document/d/1GgvOe7C8_NVOMLOCwDaYV1mXXyHMXY7ExoewHqooxrs/edit#heading=h.64r4npvq0w0" " for how to fix)" ) elif isinstance( cause, torch.fx.experimental.symbolic_shapes.GuardOnDataDependentSymNode ): raise UserError( UserErrorType.CONSTRAIN_VIOLATION, "Tried to use data-dependent value in the subsequent computation. " "This can happen when we encounter unbounded dynamic value that is unknown during tracing time." "You will need to explicitly give hint to the compiler. Please take a look at " "constrain_as_value OR constrain_as_size APIs", case_name="constrain_as_size_example", ) elif isinstance(cause, torch.utils._sympy.value_ranges.ValueRangeError): raise UserError(UserErrorType.CONSTRAIN_VIOLATION, e.args[0]) from e raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None def run_node(tracer, node, args, kwargs, nnmodule): """ Runs a given node, with the given args and kwargs. Behavior is dicatated by a node's op. run_node is useful for extracting real values out of nodes. See get_real_value for more info on common usage. Note: The tracer arg is only used for 'get_attr' ops Note: The nnmodule arg is only used for 'call_module' ops Nodes that are not call_function, call_method, call_module, or get_attr will raise an AssertionError. """ op = node.op try: if op == "call_function": return node.target(*args, **kwargs) elif op == "call_method": return getattr(args[0], node.target)(*args[1:], **kwargs) elif op == "call_module": assert nnmodule is not None return nnmodule(*args, **kwargs) elif op == "get_attr": return tracer.get_submodule(node.target) elif op == "placeholder": assert "example_value" in node.meta return node.meta["example_value"] except Exception as e: fn_str = f"Failed running {op} {node.target}(*{args}, **{kwargs}):\n" raise RuntimeError(fn_str + str(e)).with_traceback(e.__traceback__) from e raise AssertionError(op) def get_real_value(node, tracer): """ Run the actual computation represented by `node` and return the result. This will execute any dependent nodes in the graph as well. """ from .exc import TorchRuntimeError cache = tracer.real_value_cache if node in cache: return cache[node] op = node.op args, kwargs = torch.fx.node.map_arg( (node.args, node.kwargs), lambda n: get_real_value(n, tracer), ) if op == "call_module": nn_module = tracer.output_graph.nn_modules[node.target] if not is_lazy_module(nn_module): nn_module = copy.deepcopy(nn_module) else: # In the case of a lazy module, we want to run # the pre-hooks which initialize it nn_module(*args, **kwargs) else: nn_module = None try: real_value = run_node(tracer, node, args, kwargs, nn_module) cache[node] = real_value except RuntimeError as e: raise TorchRuntimeError(str(e)).with_traceback(e.__traceback__) from None return real_value def assert_no_fake_params_or_buffers(gm): from torch._subclasses.fake_tensor import FakeTensorConfig def stack_or_hint(t): if FakeTensorConfig.debug: import traceback return f"FAKE TENSOR CREATION TRACEBACK: \n {traceback.format_list(t._debug_trace)}" else: return "Enable TORCH_FAKE_TENSOR_DEBUG=1 to get creation stack traces on fake tensors." for name, buffer in gm.named_buffers(): assert not isinstance( buffer, torch._subclasses.FakeTensor ), f"Unexpected fake buffer {name} {stack_or_hint(buffer)}" for name, param in gm.named_parameters(): assert not isinstance( param, torch._subclasses.FakeTensor ), f"Unexpected fake param {name} {stack_or_hint(param)}" def fqn(obj: Any): """ Returns the fully qualified name of the object. """ return f"{obj.__module__}.{obj.__qualname__}" def ifdynstaticdefault(count1, count2): if torch._dynamo.config.assume_static_by_default: return count1 else: return count2 def import_submodule(mod: types.ModuleType): """ Ensure all the files in a given submodule are imported """ for filename in sorted(os.listdir(os.path.dirname(mod.__file__))): if filename.endswith(".py") and filename[0] != "_": importlib.import_module(f"{mod.__name__}.{filename[:-3]}") def object_has_getattribute(value: Any): try: if isinstance( inspect.getattr_static(type(value), "__getattribute__"), types.FunctionType, ): return True except AttributeError: pass return False def get_custom_getattr(value: Any): try: getattr_fn = inspect.getattr_static(type(value), "__getattr__") except AttributeError: getattr_fn = None if getattr_fn is torch.nn.Module.__getattr__: # ignore this case of getattr getattr_fn = None return getattr_fn class TensorStaticReason(enum.Enum): PARAMETER = 2 NOT_TENSOR = 4 NN_MODULE_PROPERTY = 5 def tensor_static_reason_to_message(reason: TensorStaticReason): if reason == TensorStaticReason.PARAMETER: return "mark_dynamic on parameter, parameters are always static today." if reason == TensorStaticReason.NOT_TENSOR: return "mark_dynamic on a non tensor, how did this happen?" if reason == TensorStaticReason.NN_MODULE_PROPERTY: return "tensor is static because it is nn module associated." raise AssertionError(f"Illegal reason {reason}") def tensor_always_has_static_shape( tensor: Union[torch.Tensor, Any], is_tensor: bool, guard_source: "GuardSource" ) -> Tuple[bool, TensorStaticReason]: """ Given a tensor, source, and is_tensor flag, determine if a shape should be static. Args: tensor - the real tensor to evaluate, parameters force a static shape. is_tensor - internal dynamo check, esentially "is_tensor": target_cls is TensorVariable, tensors not in a TensorVariable for whatever reason are forced static. Returns a tuple, where the first element is the bool of whether or not this tensor should have a static shape. The second element is a TensorStaticReason, useful for passing to tensor_static_reason_to_message if needed. """ if guard_source.is_nn_module() and config.force_nn_module_property_static_shapes: return True, TensorStaticReason.NN_MODULE_PROPERTY if type(tensor) is torch.nn.Parameter and config.force_parameter_static_shapes: return True, TensorStaticReason.PARAMETER if not is_tensor: return True, TensorStaticReason.NOT_TENSOR return False, None def lazy_format_graph_code(name, gm, maybe_id=None): def format_name(): if maybe_id is not None: return f"{name} {maybe_id}" else: return name return LazyString( lambda: _format_graph_code( f"===== {format_name()} =====\n", gm.forward.__code__.co_filename, gm.print_readable(print_output=False), ) ) def _format_graph_code(name, filename, graph_str): return f"TRACED GRAPH\n {name} {filename} {graph_str}\n" def lazy_format_graph_tabular(fn_name, gm): def inner(): try: from tabulate import tabulate # TODO: Check that this is installed except ImportError: return ( "Tabulate module missing, please install tabulate to log the graph in tabular format, logging code instead:\n" + str(lazy_format_graph_code(fn_name, gm)) ) node_specs = [ [n.op, n.name, n.target, n.args, n.kwargs] for n in gm.graph.nodes ] graph_str = tabulate( node_specs, headers=["opcode", "name", "target", "args", "kwargs"] ) return _format_graph_code(fn_name, gm.forward.__code__.co_filename, graph_str) return LazyString(inner) def format_bytecode(prefix, name, filename, line_no, code): return f"{prefix} {name} {filename} line {line_no} \n{dis.Bytecode(code).dis()}\n" forward_hook_names = ["_forward_pre_hooks", "_forward_hooks"] backward_hook_names = ["_backward_pre_hooks", "_backward_hooks"] state_dict_hook_names = [ "_state_dict_pre_hooks", "_state_dict_hooks", "_load_state_dict_pre_hooks", "_load_state_dict_post_hooks", ] all_hook_names = forward_hook_names + backward_hook_names + state_dict_hook_names def nn_module_get_all_hooks( mod, check_forward_hooks=False, check_backward_hooks=False, check_state_dict_hooks=False, ): reset_code = torch._C._dynamo.eval_frame.reset_code """ Sometimes its useful to differentiate between types of hooks such as forward/backward/pre hooks executed during module.__call__, and state_dict hooks which are executed separately. """ hook_dicts_to_check = [] check_all_hooks = ( not check_forward_hooks and not check_backward_hooks and not check_state_dict_hooks ) if check_forward_hooks or check_all_hooks: hook_dicts_to_check.extend(forward_hook_names) if check_backward_hooks or check_all_hooks: hook_dicts_to_check.extend(backward_hook_names) if check_state_dict_hooks: hook_dicts_to_check.extend(state_dict_hook_names) all_hooks = [] for hook_dict_name in hook_dicts_to_check: hooks = getattr(mod, hook_dict_name, []) for hook_name in hooks: hook = hooks[hook_name] all_hooks.append(hook) return all_hooks def nnmodule_has_hooks( mod, check_forward_hooks=False, check_backward_hooks=False, check_state_dict_hooks=False, ): """ Helper function to check if a module has any hooks attached to it. """ hooks = nn_module_get_all_hooks( mod, check_forward_hooks=check_forward_hooks, check_backward_hooks=check_backward_hooks, check_state_dict_hooks=check_state_dict_hooks, ) return bool(hooks) def to_numpy_helper(value): """Convert tensor and tnp.ndarray to numpy.ndarray.""" if isinstance(value, tnp.ndarray): return to_numpy_helper(value.tensor) elif isinstance(value, torch.Tensor): return value.cpu().numpy() elif isinstance(value, (tuple, list)): return type(value)(to_numpy_helper(obj) for obj in value) else: return value def numpy_to_tensor(value): """Convert tnp.ndarray to tensor, leave other types intact. If a list/tuple, loop through it to convert.""" if isinstance(value, np.ndarray): return torch.as_tensor(value) if isinstance(value, tnp.ndarray): return value.tensor elif isinstance(value, (tuple, list)): return type(value)(numpy_to_tensor(obj) for obj in value) else: return value class numpy_to_tensor_wrapper: def __init__(self, f): self.f = f self.__name__ = "wrapped_" + self.f.__name__ def __repr__(self): return f">" def __call__(self, *args, **kwargs): out = self.f(*args, **kwargs) return numpy_to_tensor(out) def numpy_attr_wrapper(obj, name): if isinstance(obj, tnp.ndarray): out = getattr(obj, name) return numpy_to_tensor(out) elif isinstance(obj, torch.Tensor): out = getattr(tnp.ndarray(obj), name) return numpy_to_tensor(out) class numpy_method_wrapper: """Convert obj from torch.Tensor to tnp.ndarray and call method. Then convert result back to torch.Tensor.""" def __init__(self, method: str): self.method = method self.__name__ = "wrapped_" + self.method def __repr__(self): return f">" def __call__(self, *args, **kwargs): obj = args[0] if isinstance(obj, torch.Tensor): obj = tnp.ndarray(obj) method_callable = getattr(obj, self.method) out = method_callable(*args[1:], **kwargs) return numpy_to_tensor(out) class numpy_operator_wrapper: """Implements dunder methods for tnp.ndarray via functions from the operator library""" def __init__(self, op: str): self.op = op self.__name__ = f"wrapped_{op.__name__}" def __repr__(self): return f">" def __call__(self, *args, **kwargs): assert not kwargs args = ( tnp.ndarray(arg) if isinstance(arg, torch.Tensor) else arg for arg in args ) out = self.op(*args) return numpy_to_tensor(out) def defake(x): if not isinstance(x, FakeTensor): return x if x._has_symbolic_sizes_strides: size = [ s.node.shape_env.size_hint(s.node.expr) if isinstance(s, torch.SymInt) else s for s in x.size() ] stride = [ s.node.shape_env.size_hint(s.node.expr) if isinstance(s, torch.SymInt) else s for s in x.stride() ] else: size = x.size() stride = x.stride() y = torch.empty_strided( size, stride, dtype=x.dtype, device=x.device, requires_grad=x.requires_grad, ) y.zero_() return y def is_utils_checkpoint(obj): # Lazy import to avoid circular dependenices import torch.utils.checkpoint return obj is torch.utils.checkpoint.checkpoint def build_checkpoint_variable(**options): import torch._higher_order_ops.wrap as higher_order_ops from .variables.higher_order_ops import TorchHigherOrderOperatorVariable # TODO - This is a temporary sitaution where we have two versions of # checkpointing implemetation. We will converge on one and remove the other. activation_checkpoint_op = higher_order_ops.tag_activation_checkpoint if torch._functorch.config.functionalize_rng_ops: activation_checkpoint_op = higher_order_ops.wrap_activation_checkpoint return TorchHigherOrderOperatorVariable.make( activation_checkpoint_op, **options, ) def is_compile_supported(device_type): from .eval_frame import is_dynamo_supported compile_supported = is_dynamo_supported() if device_type == "cpu": pass elif device_type == "cuda" and compile_supported: from torch._inductor.utils import has_triton compile_supported = has_triton() else: compile_supported = False return compile_supported # The following 3.11 source code functions are adapted from # https://github.com/python/cpython/blob/v3.11.4/Lib/traceback.py # in order to output source code corresponding to bytecode in 3.11+. # We need our own versions since we want to support multiline expressions. def _fix_offset(str: str, offset: int) -> int: """ Convert byte offset `offset` of `str` into character offset. Byte offset is used for 3.11+ instruction column data. Takes things like unicode characters into consideration. Unchanged from CPython implementation. """ as_utf8 = str.encode("utf-8") return len(as_utf8[:offset].decode("utf-8", errors="replace")) @dataclasses.dataclass class _Anchors: # inclusive left_end_lineno: int left_end_offset: int right_start_lineno: int # exclusive right_start_offset: int def _extract_anchors_from_expr(segment: str) -> Optional[_Anchors]: """ Given source code `segment` corresponding to a bytecode instruction, determine: - for binary ops, the location of the binary op - for indexing, the location of the brackets. `segment` is expected to be a valid Python expression """ assert sys.version_info >= (3, 11) import ast try: # Without brackets, `segment` is parsed as a statement. # We expect an expression, so wrap `segment` in # brackets to handle multi-line expressions. tree = ast.parse("(\n" + segment + "\n)") except SyntaxError: return None if len(tree.body) != 1: return None lines = segment.split("\n") # get character index given byte offset def normalize(lineno, offset): return _fix_offset(lines[lineno], offset) # Gets the next valid character index in `lines`, if # the current location is not valid. Handles empty lines. def next_valid_char(lineno, col): while lineno < len(lines) and col >= len(lines[lineno]): col = 0 lineno += 1 assert lineno < len(lines) and col < len(lines[lineno]) return lineno, col # Get the next valid character index in `lines`. def increment(lineno, col): col += 1 lineno, col = next_valid_char(lineno, col) assert lineno < len(lines) and col < len(lines[lineno]) return lineno, col # Get the next valid character at least on the next line def nextline(lineno, col): col = 0 lineno += 1 lineno, col = next_valid_char(lineno, col) assert lineno < len(lines) and col < len(lines[lineno]) return lineno, col statement = tree.body[0] if isinstance(statement, ast.Expr): expr = statement.value if isinstance(expr, ast.BinOp): # ast gives locations for BinOp subexpressions, e.g. # ( left_expr ) + ( right_expr ) # left^^^^^ right^^^^^ # -2 since end_lineno is 1-indexed and because we added an extra # bracket to `segment` when calling ast.parse cur_lineno = expr.left.end_lineno - 2 cur_col = normalize(cur_lineno, expr.left.end_col_offset) cur_lineno, cur_col = next_valid_char(cur_lineno, cur_col) # Heuristic to find the operator character. # The original CPython implementation did not look for ), \, or #, # leading to incorrect anchor location, e.g. # (x) + (y) # ~~^~~~~~~ while (ch := lines[cur_lineno][cur_col]).isspace() or ch in ")\\#": if ch in "\\#": cur_lineno, cur_col = nextline(cur_lineno, cur_col) else: cur_lineno, cur_col = increment(cur_lineno, cur_col) # binary op is 1 or 2 characters long, on the same line right_col = cur_col + 1 if ( right_col < len(lines[cur_lineno]) and not (ch := lines[cur_lineno][right_col]).isspace() and ch not in "\\#" ): right_col += 1 # right_col can be invalid since it is exclusive return _Anchors(cur_lineno, cur_col, cur_lineno, right_col) elif isinstance(expr, ast.Subscript): # ast gives locations for value and slice subexpressions, e.g. # ( value_expr ) [ slice_expr ] # value^^^^^ slice^^^^^ # subscript^^^^^^^^^^^^^^^^^^^^ # find left bracket (first '[' after value) left_lineno = expr.value.end_lineno - 2 left_col = normalize(left_lineno, expr.value.end_col_offset) left_lineno, left_col = next_valid_char(left_lineno, left_col) while lines[left_lineno][left_col] != "[": left_lineno, left_col = increment(left_lineno, left_col) # find right bracket (final character of expression) right_lineno = expr.end_lineno - 2 right_col = normalize(right_lineno, expr.end_col_offset) return _Anchors(left_lineno, left_col, right_lineno, right_col) elif isinstance(expr, ast.Call): # ( func_expr ) (args, kwargs) # func^^^^^ # call^^^^^^^^^^^^^^^^^^^^^^^^ # find left bracket (first '(' after func) left_lineno = expr.func.end_lineno - 2 left_col = normalize(left_lineno, expr.func.end_col_offset) left_lineno, left_col = next_valid_char(left_lineno, left_col) while lines[left_lineno][left_col] != "(": left_lineno, left_col = increment(left_lineno, left_col) # find right bracket (final character of expression) right_lineno = expr.end_lineno - 2 right_col = normalize(right_lineno, expr.end_col_offset) return _Anchors(left_lineno, left_col, right_lineno, right_col) return None def get_instruction_source_311(code: types.CodeType, inst: dis.Instruction) -> str: """ Python 3.11+ only. Returns lines of source code (from code object `code`) corresponding to `inst`'s location data, and underlines relevant code to `inst`. Example: CALL on `g`: f(g( ^^ h(x))) ^^^^^ We need our own implementation since `format_frame_summary` in Python's `traceback` module doesn't handle multi-line expressions (and their anchor extraction code is not completely correct). """ if inst.positions.lineno is None: return "" # The rstrip + "\n" pattern is used throughout this function to handle # linecache.getline errors. Error lines are treated as empty strings "", but we want # to treat them as blank lines "\n". first_line = linecache.getline(code.co_filename, inst.positions.lineno).rstrip() if inst.positions.end_lineno is None: return first_line if inst.positions.col_offset is None or inst.positions.end_col_offset is None: return first_line # character index of the start of the instruction start_offset = _fix_offset(first_line, inst.positions.col_offset) # character index of the end of the instruction # compute later since end may be a different line end_offset = None # expression corresponding to the instruction so we can get anchors segment = "" # underline markers to be printed - start with `~` marker and replace with `^` later markers = [] # Compute segment and initial markers if inst.positions.end_lineno == inst.positions.lineno: end_offset = _fix_offset(first_line, inst.positions.end_col_offset) segment = first_line[start_offset:end_offset] markers.append(" " * start_offset + "~" * (end_offset - start_offset)) else: segment = first_line[start_offset:] + "\n" markers.append(" " * start_offset + "~" * (len(first_line) - start_offset)) last_line = linecache.getline( code.co_filename, inst.positions.end_lineno ).rstrip() end_offset = _fix_offset(last_line, inst.positions.end_col_offset) for lineno in range(inst.positions.lineno + 1, inst.positions.end_lineno): line = linecache.getline(code.co_filename, lineno).rstrip() segment += line + "\n" # don't underline leading spaces num_spaces = len(line) - len(line.lstrip()) markers.append(" " * num_spaces + "~" * (len(line) - num_spaces)) segment += last_line[:end_offset] num_spaces = len(last_line) - len(last_line.lstrip()) markers.append(" " * num_spaces + "~" * (end_offset - num_spaces)) anchors: Optional[_Anchors] = None try: anchors = _extract_anchors_from_expr(segment) except AssertionError: pass # replace `~` markers with `^` where necessary if anchors is None: markers = [marker.replace("~", "^") for marker in markers] else: # make markers mutable markers = [list(marker) for marker in markers] # anchor positions do not take start_offset into account if anchors.left_end_lineno == 0: anchors.left_end_offset += start_offset if anchors.right_start_lineno == 0: anchors.right_start_offset += start_offset # Turn `~`` markers between anchors to `^` for line in range(len(markers)): for col in range(len(markers[line])): if line < anchors.left_end_lineno: continue if line == anchors.left_end_lineno and col < anchors.left_end_offset: continue if ( line == anchors.right_start_lineno and col >= anchors.right_start_offset ): continue if line > anchors.right_start_lineno: continue if markers[line][col] == "~": markers[line][col] = "^" # make markers into strings again markers = ["".join(marker) for marker in markers] result = "" for i in range(len(markers)): result += ( linecache.getline(code.co_filename, inst.positions.lineno + i).rstrip() + "\n" ) result += markers[i] + "\n" return result def is_guard_failure_reporting_enabled(): return ( config.report_guard_failures or torch._logging._internal.log_state.is_artifact_enabled("recompiles") ) def get_static_address_type(t): if isinstance(t, torch.Tensor): return getattr(t, "_dynamo_static_input_type", None) return None