# mypy: allow-untyped-defs from __future__ import annotations import collections import contextlib import dataclasses import enum import functools import inspect import io import itertools import logging import math import operator import os import platform import shutil import sys import tempfile import textwrap import time import unittest from datetime import datetime from io import StringIO from typing import ( Any, Callable, Dict, Generic, Iterable, List, NamedTuple, Optional, Protocol, Sequence, Set, TypeVar, Union, ValuesView, ) from typing_extensions import Concatenate, ParamSpec from unittest import mock import sympy import torch GPU_TYPES = ["cuda", "xpu"] # defines here before import torch._dynamo is for avoiding circular import # when get_gpu_type is imported from dynamo @functools.lru_cache(None) def get_gpu_type(): avail_gpus = [x for x in GPU_TYPES if getattr(torch, x).is_available()] assert len(avail_gpus) <= 1 gpu_type = "cuda" if len(avail_gpus) == 0 else avail_gpus.pop() return gpu_type from torch._dynamo.device_interface import get_interface_for_device from torch._dynamo.utils import detect_fake_mode from torch.autograd import DeviceType from torch.autograd.profiler_util import EventList from torch.fx.passes.graph_transform_observer import GraphTransformObserver from torch.fx.passes.shape_prop import ShapeProp from torch.utils._sympy.functions import ( CeilDiv, CleanDiv, FloorDiv, Identity, ModularIndexing, ) from torch.utils._sympy.symbol import make_symbol, SymT from torch.utils._sympy.value_ranges import bound_sympy, ValueRanges from . import config from .runtime.runtime_utils import ceildiv as runtime_ceildiv _IS_WINDOWS = sys.platform == "win32" log = logging.getLogger(__name__) _T = TypeVar("_T") VarRanges = Dict[sympy.Expr, sympy.Expr] InputType = Union[torch.Tensor, int] GPU_ALIGN_BYTES = 16 ALIGNMENT = 16 ALIGN_BYTES = 64 assert (ALIGN_BYTES & (ALIGN_BYTES - 1)) == 0 and ALIGN_BYTES >= 8, "must be power of 2" def _align(nbytes): """Round up to the nearest multiple of ALIGN_BYTES""" return (nbytes + ALIGN_BYTES - 1) & -ALIGN_BYTES def _is_aligned(v: sympy.Expr): """v can be statically proven to be a multiple of ALIGN_BYTES""" if isinstance(v, (sympy.Add, sympy.Max)): return all(map(_is_aligned, v.args)) return isinstance(v, align) or sympy.gcd(v, ALIGN_BYTES) == ALIGN_BYTES class align(sympy.Function): """Symbolically round up to the nearest multiple of ALIGN_BYTES""" nargs = (1,) is_integer = True @classmethod def eval(cls, value): if isinstance(value, (int, sympy.Integer)): return _align(int(value)) if _is_aligned(value): return value def do_bench_using_profiling(fn: Callable[[], Any], warmup=25, rep=100) -> float: """ Returns benchmark results by examining torch profiler events. This could be more accurate as it doesn't count CPU side overhead. However, this also requires manually excluding irrelevant event, e.g. vectorized_elementwise_kernel which is used to fill L2 cache, various CUDA events, etc, so could also be fragile. """ fn() torch.cuda.synchronize() cache = torch.empty(int(256e6 // 4), dtype=torch.int, device="cuda") # Estimate the runtime of the function start_event = torch.cuda.Event(enable_timing=True) end_event = torch.cuda.Event(enable_timing=True) start_event.record() for _ in range(5): cache.zero_() fn() end_event.record() torch.cuda.synchronize() estimate_ms = start_event.elapsed_time(end_event) / 5 # compute number of warmup and repeat n_warmup = max(1, int(warmup / estimate_ms)) n_repeat = max(1, int(rep / estimate_ms)) # Warm-up for _ in range(n_warmup): fn() with torch.profiler.profile( activities=[ torch.profiler.ProfilerActivity.CUDA, ] ) as p: # Benchmark for i in range(n_repeat): # we clear the L2 cache before each run cache.zero_() # record time of `fn` fn() # Record clocks torch.cuda.synchronize() log.debug("raw events") log.debug(p.key_averages().table(sort_by="self_device_time_total", row_limit=-1)) filtered_events = EventList( [ event for event in p.events() if event.device_type == DeviceType.CUDA and event.name != "Context Sync" ] ) if len(filtered_events) % n_repeat != 0: raise RuntimeError( "Failed to divide all profiling events into #repeat groups. " "#CUDA events: %d, #repeats: %s", len(filtered_events), n_repeat, ) num_event_per_group = len(filtered_events) / n_repeat actual_events = EventList( [ event for i, event in enumerate(filtered_events) if i % num_event_per_group != 0 ] ) actual_events._build_tree() actual_events = actual_events.key_averages() log.debug("profiling time breakdown") log.debug(actual_events.table(row_limit=-1)) res = sum(event.device_time_total for event in actual_events) / 1000.0 / n_repeat log.debug("profiling results: %s ms", res) return res @functools.lru_cache(None) def has_torchvision_roi_align() -> bool: try: from torchvision.ops import roi_align # noqa: F401 torch._C._dispatch_has_kernel_for_dispatch_key("torchvision::nms", "Meta") return roi_align is not None and hasattr( getattr(torch.ops, "torchvision", None), "roi_align" ) except ImportError: return False except RuntimeError as e: assert "torchvision::nms does not exist" in str(e) return False def decode_device(device: Union[Optional[torch.device], str]) -> torch.device: if device is None: return torch.tensor(0.0).device # default device if isinstance(device, str): device = torch.device(device) if device.type not in ("cpu", "meta") and device.index is None: device_interface = get_interface_for_device(device.type) return torch.device(device.type, index=device_interface.Worker.current_device()) return device def sympy_product(it): return functools.reduce(operator.mul, it, sympy.Integer(1)) def sympy_dot(seq1, seq2): assert len(seq1) == len(seq2) return sympy.expand(sum(a * b for a, b in zip(seq1, seq2))) def unique(it: Iterable[_T]) -> ValuesView[_T]: return {id(x): x for x in it}.values() def ceildiv( numer: Union[int, sympy.Expr], denom: Union[int, sympy.Expr] ) -> Union[int, sympy.Expr]: if isinstance(numer, sympy.Expr) or isinstance(denom, sympy.Expr): return CeilDiv(sympy.sympify(numer), sympy.sympify(denom)) # TODO: There is a bug in a call to this function, to repro: # python benchmarks/dynamo/huggingface.py --inductor -d cuda --accuracy # --amp --only YituTechConvBert --dynamic-shapes assert isinstance(numer, int) and isinstance( denom, int ), f"{numer}: {type(numer)}, {denom}: {type(denom)}" return runtime_ceildiv(numer, denom) def _type_of(key): # Use the function here to get rid of dependencies on the Triton during the codegen. # Refer to Triton implementation here: # https://github.com/openai/triton/blob/98b5945d2aef679e00ebca8e07c35c3658ec76de/python/triton/runtime/jit.py#L238 # `None` is nullptr. Implicitly convert to *i8. if key is None: return "*i8" dtype_str = str(key).split(".")[-1] tys = { "bool": "i1", "float8e4nv": "fp8e4nv", "float8e5": "fp8e5", "float8e4b15": "fp8e4b15", "float8e4b15x4": "fp8e4b15x4", "float8_e4m3fn": "fp8e4nv", "float8_e5m2": "fp8e5", "float16": "fp16", "bfloat16": "bf16", "float32": "fp32", "float64": "fp64", "int8": "i8", "int16": "i16", "int32": "i32", "int64": "i64", "uint8": "u8", "uint16": "u16", "uint32": "u32", "uint64": "u64", } # reinterpret can create triton type for v in list(tys.values()): tys[v] = v return key if isinstance(key, str) else f"*{tys[dtype_str]}" def convert_shape_to_inductor( lst: Iterable[Union[int, torch.SymInt]] ) -> List[sympy.Expr]: """ Gets the shape and stride of a tensor. For non-symbolic tensors, this is trivial. But for symbolic tensors, we need to map from SymIntNode into sympy.Expr. """ return [sympy.sympify(i) for i in lst] def convert_shape_to_symint( lst: Iterable[Union[int, sympy.Expr]] ) -> List[Union[int, torch.SymInt]]: """ Takes a list of shapes from Inductor and converts them into symints (or just ints if all shapes are static). """ from .virtualized import V return [ i if isinstance(i, int) else int(i) if isinstance(i, sympy.Integer) else V.graph.sizevars.shape_env.create_symintnode(i, hint=None) for i in lst ] def is_view(op: torch._ops.OpOverload): """ Does this op overload have aliasing """ assert isinstance(op, torch._ops.OpOverload) return any(a.alias_info is not None for a in op._schema.arguments) def is_pointwise_use( use, is_pointwise_fn: Optional[Callable[[torch._ops.OpOverload], bool]] = None ): """ Do all uses of this op have torch.Tag.pointwise or return True for optional `is_pointwise_fn` Uses in views ops will follow the views uses """ if not use.op == "call_function": return False if not ( isinstance(use.target, torch._ops.OpOverload) or use.target is operator.getitem ): return False if use.target is operator.getitem or is_view(use.target): return all(is_pointwise_use(u, is_pointwise_fn) for u in use.users) return torch.Tag.pointwise in use.target.tags or ( is_pointwise_fn is not None and is_pointwise_fn(use.target) ) def gen_gm_and_inputs(target, args, kwargs): g = torch.fx.Graph() g_args = [] a_args = [] for n, arg in enumerate(args): if isinstance(arg, torch.Tensor): g_args.append(g.placeholder(f"arg{n}")) a_args.append(arg) else: g_args.append(arg) assert all(not isinstance(x, torch.Tensor) for x in kwargs.values()) node = g.call_function(target, tuple(g_args), kwargs) if ( len(target._schema.returns) == 1 and str(target._schema.returns[0].type) == "Tensor" ): node = (node,) # type: ignore[assignment] g.output(node) gm = torch.fx.GraphModule({}, g) return gm, a_args def synchronize(device: str = "cuda"): if device == "cpu": return device_interface = get_interface_for_device(device) if device_interface.is_available(): device_interface.synchronize() def timed( model: Callable[..., Any], example_inputs, times: int = 1, device: str = "cuda" ) -> float: synchronize(device) torch.manual_seed(1337) t0 = time.perf_counter() for _ in range(times): result = model(*example_inputs) synchronize(device) t1 = time.perf_counter() # GC the result after timing assert result is not None # type: ignore[possibly-undefined] return t1 - t0 def print_performance( fn, args=(), times=10, repeat=10, baseline=1.0, device: str = "cuda" ): timings = torch.tensor([timed(fn, args, times, device) for _ in range(repeat)]) took = torch.median(timings) / times print(f"{took / baseline:.6f}") return took def precompute_method(obj: Any, method: str): """Replace obj.method() with a new method that returns a precomputed constant.""" result = getattr(obj, method)() setattr(obj, method, lambda: result) def precompute_methods(obj: Any, methods: List[str]): """Replace methods with new methods that returns a precomputed constants.""" for method in methods: precompute_method(obj, method) def cmp(a, b) -> int: return int(a > b) - int(a < b) def pad_listlike(x, size): if len(x) == 1: return type(x)([x[0]]) * size else: return x # Used to ensure that iterating over a set is deterministic def tuple_sorted(x): if len(x) == 0: return [] def sort_func(elem): if isinstance(elem, str): return elem else: # We expect `elem` to be `scheduler.BaseSchedulerNode` type here, # but we are not able to do isinstance assert because of circular dependency return elem.get_name() return sorted(x, key=sort_func) P = ParamSpec("P") RV = TypeVar("RV", covariant=True) class CachedMethod(Protocol, Generic[P, RV]): @staticmethod def clear_cache(self) -> None: ... def __call__(self, *args: P.args, **kwargs: P.kwargs) -> RV: ... # See https://github.com/python/mypy/issues/13222#issuecomment-1193073470 to understand the type signature def cache_on_self(fn: Callable[Concatenate[Any, P], RV]) -> CachedMethod[P, RV]: key = f"__{fn.__name__}_cache" @functools.wraps(fn) def wrapper(self): if not hasattr(self, key): setattr(self, key, fn(self)) return getattr(self, key) def clear_cache(self): if hasattr(self, key): delattr(self, key) wrapper.clear_cache = clear_cache # type: ignore[attr-defined] return wrapper # type: ignore[return-value] def aggregate_origins(node_schedule): from . import ir if isinstance(node_schedule, list): return functools.reduce( operator.or_, [ node.node.origins for node in node_schedule if hasattr(node, "node") and node.node ], set(), ) elif isinstance(node_schedule, ir.ExternKernel): return node_schedule.origins else: return set() def get_fused_kernel_name(node_schedule, descriptive_names): all_origins = aggregate_origins(node_schedule) if descriptive_names == "original_aten": # Bases the kernel name off of the top-level aten operator (i.e. pre-decompositions) sources = [ origin.meta["original_aten"]._overloadpacket.__name__ for origin in all_origins if origin.op == "call_function" and "original_aten" in origin.meta and origin.meta["original_aten"] is not None ] sources = sorted(set(sources)) elif descriptive_names == "torch": # Bases the kernel name off of the top-level "torch" operator (i.e. post-dynamo graph) sources = [] for origin in all_origins: if origin.op == "call_function" and "source_fn_stack" in origin.meta: source_fn = origin.meta["source_fn_stack"][-1] if isinstance(source_fn[1], str): sources.append(source_fn[1]) else: sources.append(source_fn[1].__name__) sources = sorted(set(sources)) elif descriptive_names == "inductor_node": sources = [ origin.name for origin in all_origins if origin.op == "call_function" ] else: raise NotImplementedError sources = sources return "_".join(["fused"] + sources) def get_kernel_metadata(node_schedule, wrapper): all_origins = aggregate_origins(node_schedule) inductor_nodes = [origin for origin in all_origins if origin.op == "call_function"] from_node_dict = collections.defaultdict(list) original_aten_dict = collections.defaultdict(list) # Attempt to sort `inductor_nodes` topologically. Note that the case # where `inductor_nodes` contains nodes from multiple graph instances # is not supported. An example of this is conditional statements. single_graph = None if len(inductor_nodes): unique_graphs = {n.graph for n in inductor_nodes} if len(unique_graphs) == 1: single_graph = inductor_nodes[0].graph # create a map of idx -> node and cache it if not hasattr(single_graph, "_inductor_kernel_metadata_node_to_idx_map"): node_to_idx_map = {} for idx, n in enumerate(single_graph.nodes): node_to_idx_map[n] = idx single_graph._inductor_kernel_metadata_node_to_idx_map = node_to_idx_map inductor_nodes.sort( key=lambda n: single_graph._inductor_kernel_metadata_node_to_idx_map[n] ) for node in inductor_nodes: if "original_aten" in node.meta and node.meta["original_aten"] is not None: key = str(node.meta["original_aten"]._overloadpacket) original_aten_dict[key].append(node.name) if "from_node" in node.meta: key = node.meta["from_node"][0][0] from_node_dict[key].append(node.name) sort_str = "Topologically Sorted" if single_graph is not None else "Unsorted" metadata = ( f"{wrapper.comment} {sort_str} Source Nodes: [{', '.join(from_node_dict.keys())}], " f"Original ATen: [{', '.join(original_aten_dict.keys())}]" ) # trace back to original node here detailed_metadata = [f"{wrapper.comment} Source node to ATen node mapping:"] for original_node, nodes in sorted(from_node_dict.items()): detailed_metadata.append( f"{wrapper.comment} {original_node} => {', '.join(sorted(nodes))}" ) # print the aot_autograd graph fragment if single_graph is not None: detailed_metadata.append(f"{wrapper.comment} Graph fragment:") for n in inductor_nodes: # TODO(future): maybe refactor torch/fx/graph.py to make it easy to # generate python code for graph fragments detailed_metadata.append(f"{wrapper.comment} {n.format_node()}") return metadata, "\n".join(detailed_metadata) def dominated_nodes( initial_queue: Iterable[torch.fx.Node], skip_filter=None ) -> Set[torch.fx.Node]: """Returns the set of nodes whose values depend on those within initial_queue""" initial_queue = list(initial_queue) dominated_set = set(initial_queue) while initial_queue: node = initial_queue.pop() for user in node.users: if skip_filter and skip_filter(user): continue if user not in dominated_set: dominated_set.add(user) initial_queue.append(user) return dominated_set def gather_origins(args, kwargs): import itertools from . import ir def is_unrealized_node(n): if isinstance(n, ir.TensorBox): return is_unrealized_node(n.data) if isinstance(n, ir.StorageBox): return is_unrealized_node(n.data) return isinstance(n, ir.IRNode) and isinstance(n, ir.Pointwise) kwarg_origins = [val.origins for val in kwargs.values() if is_unrealized_node(val)] arg_origins = [arg.origins for arg in args if is_unrealized_node(arg)] return set(itertools.chain(*arg_origins, *kwarg_origins)) def sympy_str(expr: sympy.Expr) -> str: """ Normal sympy str is very slow, this is a lot faster. The result are somewhat worse, as it doesn't do as much simplification. So don't use this for final codegen. """ if isinstance(expr, sympy.Symbol): return expr.name if isinstance(expr, sympy.Add): return " + ".join(map(sympy_str, expr.args)) if isinstance(expr, sympy.Mul): return " * ".join(map(sympy_str, expr.args)) if isinstance(expr, (ModularIndexing, CleanDiv, FloorDiv, Identity)): return f"{expr.func.__name__}({', '.join(map(sympy_str, expr.args))})" return str(expr) def get_bounds_index_expr(index): from .virtualized import V # If this expression does not come from an FX node, we compute its bounds if ( config.compute_all_bounds and (fx_node := getattr(V.interpreter, "current_node", None)) and fx_node.target != "index_expr" ): return bound_sympy(index) else: return ValueRanges.unknown() def sympy_index_symbol_with_prefix(prefix: SymT, idx: int) -> sympy.Symbol: """ Used to generate an integer-nonnegative symbol. """ # This should never be used for creating shape/stride symbols, as those # should all be allocated before Inductor. assert prefix != SymT.SIZE # NOTE: shape symbols are positive (> 0), but index variables are only # non-negative (>= 0). return make_symbol(prefix, idx, integer=True, nonnegative=True) def generate_assert(check): return (check or config.debug_index_asserts) and config.assert_indirect_indexing def sympy_index_symbol(name: str) -> sympy.Symbol: """ Used to generate an integer-nonnegative symbol. """ # This should never be used for creating shape/stride symbols, as those # should all be allocated before Inductor. assert name[0] != "s" # NOTE: shape symbols are positive (> 0), but index variables are only # non-negative (>= 0). return sympy.Symbol(name, integer=True, nonnegative=True) def sympy_subs(expr: sympy.Expr, replacements: Dict[sympy.Expr, Any]) -> sympy.Expr: """ When the passed replacement symbol v is a string, it is converted to a symbol with name v that have the same replaced expression integer and nonnegative properties. """ def to_symbol(replaced, replacement): assert isinstance(replaced, sympy.Expr) if isinstance(replacement, str): return sympy.Symbol( replacement, integer=replaced.is_integer, # type: ignore[attr-defined] nonnegative=replaced.is_nonnegative, # type: ignore[attr-defined] ) else: return replacement # xreplace is faster than subs, but is way more picky return sympy.sympify(expr).xreplace( {k: to_symbol(k, v) for k, v in replacements.items()} ) def is_symbolic(a: Any) -> bool: return isinstance(a, torch.SymInt) or ( isinstance(a, torch.Tensor) and any(is_symbolic(x) for x in itertools.chain(a.size(), a.stride())) ) def any_is_symbolic(*args: Any) -> bool: return any(is_symbolic(a) for a in args) def get_first_incompatible_cudagraph_node(gm): from torch.fx.experimental.symbolic_shapes import free_unbacked_symbols forbidden_set = { "aten._fused_moving_avg_obs_fq_helper.default", "aten._fused_moving_avg_obs_fq_helper_functional.default", "aten.multinomial.default", "fbgemm.dense_to_jagged.default", "fbgemm.jagged_to_padded_dense.default", "run_and_save_rng_state", "run_with_rng_state", "aten._local_scalar_dense", # Technically, it's not necessary to ban this, because an # assert_scalar with constant arguments can be validly run # with CUDA graphs, but the operator is also pointless with # constant arguments, so might as well ban "aten._assert_scalar", } if torch.are_deterministic_algorithms_enabled(): forbidden_set.update( { "aten._unsafe_index_put.default", "aten._unsafe_masked_index_put_accumulate.default", "aten.index_put.default", "aten.index_put_.default", "aten.scatter.src", "aten.scatter.reduce", "aten.scatter.value_reduce", "aten.scatter_add_", "aten.scatter_add.default", "aten.scatter_reduce.two", "aten.scatter_reduce_.two", "aten.scatter_reduce.two_out", } ) for node in gm.graph.nodes: if str(node.target) in forbidden_set: return node if (val := node.meta.get("val")) is not None and free_unbacked_symbols(val): return node return None def has_incompatible_cudagraph_ops(gm): return get_first_incompatible_cudagraph_node(gm) is not None def output_node(gm: torch.fx.GraphModule): """Get the output node from an FX graph""" last_node = next(iter(reversed(gm.graph.nodes))) assert last_node.op == "output" return last_node _registered_caches: List[Any] = [] def clear_on_fresh_inductor_cache(obj: Any): """ Use this decorator to register any caches that should be cache_clear'd with fresh_inductor_cache(). """ if not hasattr(obj, "cache_clear") or not callable(obj.cache_clear): raise AttributeError(f"{obj} does not have a cache_clear method") _registered_caches.append(obj) return obj def clear_inductor_caches(): """ Clear all registered caches. """ for obj in _registered_caches: obj.cache_clear() @contextlib.contextmanager def fresh_inductor_cache(cache_entries=None, dir=None, delete=True): """ Contextmanager that provides a clean tmp cachedir for inductor. Optionally, pass a dict as 'cache_entries' to get a list of filenames and sizes generated with this cache instance. """ clear_inductor_caches() inductor_cache_dir = tempfile.mkdtemp(dir=dir) try: with mock.patch.dict( os.environ, {"TORCHINDUCTOR_CACHE_DIR": inductor_cache_dir} ): log.debug("Using inductor cache dir %s", inductor_cache_dir) triton_cache_dir = os.path.join(inductor_cache_dir, "triton") with mock.patch.dict(os.environ, {"TRITON_CACHE_DIR": triton_cache_dir}): yield if isinstance(cache_entries, dict): assert len(cache_entries) == 0, "expected empty cache_entries dict" if os.path.exists(triton_cache_dir): files = os.listdir(triton_cache_dir) cache_entries.update( { f: os.path.getsize(os.path.join(triton_cache_dir, f)) for f in files if ".lock" not in f } ) if delete: shutil.rmtree(inductor_cache_dir) except Exception: if not _IS_WINDOWS: """ Windows can't delete the loaded modules, because the modules binaries are opened. TODO: discuss if have better solution to handle this issue. """ log.warning("on error, temporary cache dir kept at %s", inductor_cache_dir) raise finally: clear_inductor_caches() def argsort(seq) -> List[int]: # preserve original order for equal strides getter = seq.__getitem__ a_r = range(len(seq)) return list(reversed(sorted(a_r, key=getter, reverse=True))) # noqa: C413 @functools.lru_cache(8) def get_dtype_size(dtype): return torch.empty((), dtype=dtype).element_size() class LineContext(NamedTuple): context: Any class IndentedBuffer: tabwidth = 4 def __init__(self, initial_indent=0): self._lines = [] self._indent = initial_indent def getvaluewithlinemap(self) -> tuple[str, list[tuple[int, LineContext]]]: buf = StringIO() p = 1 linemap = [] for line in self._lines: if isinstance(line, DeferredLineBase): line = line() if line is None: continue elif isinstance(line, LineContext): linemap.append((p, line.context)) continue assert isinstance(line, str) buf.write(line) buf.write("\n") p += 1 + line.count("\n") return buf.getvalue(), linemap def getvalue(self) -> str: v, _ = self.getvaluewithlinemap() return v def getrawvalue(self) -> str: buf = StringIO() for line in self._lines: if isinstance(line, DeferredLineBase): line = line() if line is None: continue elif isinstance(line, LineContext): continue assert isinstance(line, str) # backslash implies line continuation if line.endswith("\\"): buf.write(line[:-1]) else: buf.write(line) buf.write("\n") return buf.getvalue() def clear(self): self._lines.clear() def __bool__(self): return bool(self._lines) def prefix(self): return " " * (self._indent * self.tabwidth) def newline(self): self.writeline("\n") def writeline(self, line): if isinstance(line, LineContext): self._lines.append(line) elif isinstance(line, DeferredLineBase): self._lines.append(line.with_prefix(self.prefix())) elif line.strip(): self._lines.append(f"{self.prefix()}{line}") else: self._lines.append("") def writelines(self, lines): for line in lines: self.writeline(line) def indent(self, offset=1): @contextlib.contextmanager def ctx(): self._indent += offset try: yield finally: self._indent -= offset return ctx() def do_indent(self, offset=1): self._indent += offset def do_unindent(self, offset=1): self._indent -= offset def splice(self, other_code, strip=False): if isinstance(other_code, IndentedBuffer): dedent = float("inf") for line in other_code._lines: if not isinstance(line, LineContext) and line: dedent = min(dedent, len(line) - len(line.lstrip())) if math.isinf(dedent): dedent = 0 for line in other_code._lines: if isinstance(line, LineContext): self._lines.append(line) else: IndentedBuffer.writeline(self, line[int(dedent) :]) else: other_code = textwrap.dedent(other_code) if strip: other_code = other_code.lstrip() if not other_code: return other_code = other_code.rstrip() for line in other_code.split("\n"): self.writeline(line) def map(self, func: Callable[[Any], Any]) -> IndentedBuffer: res = IndentedBuffer(initial_indent=self._indent) res._lines = [func(line) for line in self._lines] return res def __repr__(self): return f"{type(self)}({self.getvalue()})" def __add__(self, other): assert self._indent == other._indent res = IndentedBuffer(initial_indent=self._indent) res.writelines(self._lines) res.writelines(other._lines) return res class FakeIndentedBuffer(IndentedBuffer): def __init__(self) -> None: super().__init__() def __getattribute__(self, name): if name == "__class__": # Allow access to the class attribute return object.__getattribute__(self, name) raise RuntimeError( f"Tried to call self.{name} on FakeIndentedBuffer. This buffer" "is currently used on TritonTemplateKernel to prevent actual" "writes to the body without explicitly specifying the body with" "`TritonTemplateKernel.set_subgraph_body(name)`" ) @contextlib.contextmanager def restore_stdout_stderr(initial_stdout, initial_stderr): try: yield finally: sys.stdout = initial_stdout sys.stderr = initial_stderr class DeferredLineBase: """A line that can be 'unwritten' at a later time""" def __init__(self, line): if not line.strip(): line = "" self.line = line def __call__(self) -> Optional[str]: """Returns either self.line or None to indicate the line has been 'unwritten'""" raise NotImplementedError def _new_line(self, line: str) -> DeferredLineBase: """Returns a new deferred line with the same condition""" raise NotImplementedError def with_prefix(self, prefix): return self._new_line(f"{prefix}{self.line}") def lstrip(self): return self._new_line(self.line.lstrip()) def __getitem__(self, index): return self._new_line(self.line[index]) def __bool__(self): return bool(self.line) def __len__(self): return len(self.line) @functools.lru_cache(None) def is_big_gpu(index) -> bool: min_sms = 68 # 3080 avail_sms = torch.cuda.get_device_properties(index).multi_processor_count if avail_sms < min_sms: log.warning( "Not enough SMs to use max_autotune_gemm mode", extra={"min_sms": min_sms, "avail_sms": avail_sms}, ) return False return True def use_max_autotune() -> bool: return config.max_autotune or config.max_autotune_gemm def _use_template_for_cuda(layout, allowed_layout_dtypes: List[torch.dtype]) -> bool: return ( use_max_autotune() and layout.device.type == "cuda" and layout.dtype in allowed_layout_dtypes and is_big_gpu(layout.device.index or 0) ) def _use_autotune_backend(backend: str) -> bool: return backend.upper() in [ x.strip() for x in config.max_autotune_gemm_backends.upper().split(",") ] def _use_conv_autotune_backend(backend: str) -> bool: return backend.upper() in [ x.strip() for x in config.max_autotune_conv_backends.upper().split(",") ] def use_triton_template(layout, *, enable_int32=False, enable_float8=False): from .codegen.common import BackendFeature, has_backend_feature layout_dtypes = [torch.float16, torch.bfloat16, torch.float32] if enable_int32: layout_dtypes = [torch.float16, torch.bfloat16, torch.float32, torch.int32] if enable_float8: layout_dtypes.extend([torch.float8_e4m3fn, torch.float8_e5m2]) return ( _use_template_for_cuda(layout, layout_dtypes) and _use_autotune_backend("TRITON") and has_backend_feature(layout.device, BackendFeature.TRITON_TEMPLATES) ) def use_cutlass_template(layout, m, n, k): from .virtualized import V gemm_size = V.graph.sizevars.size_hint(m * n * k, fallback=-1) if gemm_size <= 0 or gemm_size < config.cuda.cutlass_backend_min_gemm_size: return False from .codegen.cuda.cutlass_utils import try_import_cutlass # Do not use cutlass template on ROCm if torch.version.hip: return False layout_dtypes = [torch.float16, torch.bfloat16, torch.float32, torch.int32] res = _use_template_for_cuda(layout, layout_dtypes) and _use_autotune_backend( "CUTLASS" ) if res: if not try_import_cutlass(): log.warning( "Failed to import CUTLASS lib. Please check whether " "_inductor.config.cuda.cutlass_dir is set correctly. " "Skipping CUTLASS backend for now." ) return False return res @functools.lru_cache(None) def _rocm_native_device_arch_name(device): return torch.cuda.get_device_properties(device).gcnArchName @functools.lru_cache(None) def try_import_ck_lib(): try: import ck4inductor # type: ignore[import] from ck4inductor.universal_gemm.gen_instances import ( # type: ignore[import] gen_ops_library, gen_ops_preselected, ) from ck4inductor.universal_gemm.op import ( # type: ignore[import] CKGemmOperation, ) package_dirname = os.path.dirname(ck4inductor.__file__) except ImportError: def gen_ops_library(): return [] def gen_ops_preselected(): return [] class CKGemmOperation: # type: ignore[no-redef] pass package_dirname = None return package_dirname, gen_ops_library, gen_ops_preselected, CKGemmOperation def use_ck_template(layout, m, n, k): # config knobs check 1 if not use_max_autotune(): return False # config knobs check 2 if not _use_autotune_backend("CK"): return False # platform check if not torch.version.hip: return False # tensors must be on GPU if not layout.device.type == "cuda": return False # hardware check # if config arch list is not specified, get the native arch from the device properties native_arch = _rocm_native_device_arch_name(layout.device) requested_archs = {k.split(":")[0]: k for k in config.rocm.arch} or { native_arch.split(":")[0]: native_arch } requested_supported_archs = [ requested_archs[k] for k in requested_archs.keys() & config.rocm.ck_supported_arch ] if not requested_supported_archs: return False # supported input dtypes if layout.dtype not in [torch.float16, torch.bfloat16]: return False # TBD: investigate if we need to disable backend based on number of available CUs similar to `is_big_gpu` # check if shape is static and gemm size is not 0 from .virtualized import V gemm_size = V.graph.sizevars.size_hint(m * n * k, fallback=-1) if gemm_size <= 0: return False # TBD: investigate if backend needs to be disabled for small gemms similar to CUTLASS ck_package_dirname, _, _, _ = try_import_ck_lib() if not ck_package_dirname: log.warning("Please pip install Composable Kernel package") return False if not config.rocm.ck_dir: log.warning("Please set TORCHINDUCTOR_CK_DIR env variable") return False if ck_package_dirname != config.rocm.ck_dir: log.warning("Invalid path to CK library") return False return True def _use_template_for_cpu(layout): return use_max_autotune() and layout.device.type == "cpu" def use_cpp_packed_gemm_template(layout, mat1, mat2, mat2_transposed=False): from . import ir from .codegen.cpp_micro_gemm import create_micro_gemm from .codegen.cpp_utils import get_gemm_template_output_and_compute_dtype from .kernel.mm_common import mm_args if not _use_template_for_cpu(layout) or not _use_autotune_backend("CPP"): return False if not config.cpp.weight_prepack: return False int8_gemm = mat1.get_dtype() == torch.uint8 layout_dtypes = [torch.float32, torch.bfloat16, torch.half, torch.uint8] m, n, k, layout, mat1, mat2 = mm_args( mat1, mat2, out_dtype=layout.dtype if int8_gemm else None, mat2_transposed=mat2_transposed, ) # TODO(jgong5): support dynamic shapes for n or k if has_free_symbols((n, k)): return False if isinstance(mat2, ir.BaseView): mat2 = mat2.unwrap_view() output_dtype, _ = get_gemm_template_output_and_compute_dtype(mat1.get_dtype()) micro_gemm = create_micro_gemm( "micro_gemm", m, n, k, input_dtype=mat1.get_dtype(), input2_dtype=mat2.get_dtype(), output_dtype=output_dtype, num_threads=parallel_num_threads(), ) def is_last_dim_stride1(x): x.freeze_layout() return x.get_stride()[-1] == 1 return ( layout.dtype in layout_dtypes and micro_gemm is not None and is_last_dim_stride1(mat1) # TODO(jgong5): support transposed input and isinstance(mat2, ir.StorageBox) and mat2.is_module_buffer() ) def use_aten_gemm_kernels(): return not use_max_autotune() or _use_autotune_backend("ATEN") class DebugDirManager: counter = itertools.count(0) prev_debug_name: str def __init__(self) -> None: self.id = next(DebugDirManager.counter) def __enter__(self): self.prev_debug_name = torch._dynamo.config.debug_dir_root self.new_name = f"{self.prev_debug_name}_tmp_{self.id}" torch._dynamo.config.debug_dir_root = self.new_name def __exit__(self, *args): shutil.rmtree(self.new_name) torch._dynamo.config.debug_dir_root = self.prev_debug_name def run_and_get_code(fn, *args, **kwargs): from .graph import GraphLowering source_codes: List[str] = [] def save_output_code(code: str): source_codes.append(code) with mock.patch.object(GraphLowering, "save_output_code", save_output_code): torch._dynamo.reset() result = fn(*args, **kwargs) return result, source_codes def run_fw_bw_and_get_code(fn): def run_with_backward(): result = fn() result.sum().backward() return result return run_and_get_code(run_with_backward) def get_code(fn, *args, **kwargs): """Get the inductor-generated code, but skip any actual compilation or running.""" from .graph import GraphLowering source_codes: List[str] = [] def save_output_code(code: str): source_codes.append(code) def patched_compile_to_module(self: GraphLowering): class DummyModule: """This is empty to replace the generated triton module""" def __init__(self) -> None: pass def call(self, *args, **kwargs): # Don't do anything when called pass code, _ = ( self.codegen_with_cpp_wrapper() if self.cpp_wrapper else self.codegen() ) # Skip all the actual compiling. nonlocal save_output_code save_output_code(code) return DummyModule() with mock.patch.object( GraphLowering, "compile_to_module", patched_compile_to_module ), mock.patch.object(GraphLowering, "save_output_code", save_output_code): torch._dynamo.reset() # Note the return here is None _ = fn(*args, **kwargs) return source_codes def get_triton_code(fn, *args, **kwargs): source_codes = get_code(fn, *args, **kwargs) # Can have two outputs if backwards was eagerly compiled assert ( 1 <= len(source_codes) <= 2 ), f"expected one or two code outputs got {len(source_codes)}" return source_codes[0] def run_and_get_triton_code(fn, *args, **kwargs): _, source_codes = run_and_get_code(fn, *args, **kwargs) # Can have two outputs if backwards was eagerly compiled assert ( 1 <= len(source_codes) <= 2 ), f"expected one or two code outputs got {len(source_codes)}" return source_codes[0] def run_and_get_graph_lowering(fn, *args, **kwargs): from torch._inductor.codecache import CompiledFxGraph from torch._inductor.graph import GraphLowering real_init = CompiledFxGraph.__init__ graph_lowerings = [] def fake_init(*args, **kwargs): real_init(*args, **kwargs) graph = args[2] assert isinstance(graph, GraphLowering) graph_lowerings.append(graph) with mock.patch.object(CompiledFxGraph, "__init__", fake_init): result = fn(*args, **kwargs) return result, graph_lowerings @contextlib.contextmanager def override_lowering(aten_op, override_fn): """ Override the lowering of aten_op with override_fn. The first argument of override_fn is the original lowering fn. """ from torch._inductor import lowering orig_fn = lowering.lowerings[aten_op] try: lowering.lowerings[aten_op] = functools.partial(override_fn, orig_fn) yield finally: lowering.lowerings[aten_op] = orig_fn def add_scheduler_init_hook(pre_fn, post_fn=None): """ Add hook functions to be called at the beginning and end of Scheduler.__init__. Used for unit tests. """ from torch._inductor.scheduler import Scheduler orig_fn = Scheduler.__init__ def wrapper(scheduler, nodes): pre_fn(scheduler, nodes) out = orig_fn(scheduler, nodes) if post_fn: post_fn(scheduler, nodes) return out return unittest.mock.patch.object(Scheduler, "__init__", wrapper) def developer_warning(msg): """ Warnings that will be actionable for PyTorch developers, but not end users. Allows us to easily disable them in stable releases but keep them on for nightly builds. """ if config.developer_warnings: log.warning(msg) else: log.info(msg) def get_benchmark_name(): """ An experimental API used only when config.benchmark_kernel is true. The benchmark name is only available at codegen time. So we can not directly call it in benchmark_all_kernels which is run after codegen. The function assumes the argument after --only is the benchmark name. It works for torchbench.py/hugginface.py/timm_models.py. But for ad-hoc scripts, this function may return None. There are 2 flavors of --only argument we need handle: 1. --only model_name 2. --only=model_name """ try: idx = sys.argv.index("--only") if ( idx + 1 < len(sys.argv) and len(sys.argv[idx + 1]) > 0 and sys.argv[idx + 1][0] != "-" ): return sys.argv[idx + 1] except ValueError: pass for arg in sys.argv: if arg.startswith("--only="): return arg[len("--only=") :] def is_ones(items): return all(x == 1 for x in items) def is_zeros(items): return all(x == 0 for x in items) def is_cpu_device(inputs): return all( item.device == torch.device("cpu") for item in inputs if isinstance(item, torch.Tensor) ) def get_sympy_Expr_dtype(val: sympy.Expr) -> torch.dtype: assert isinstance( val, sympy.Expr ), "only support sympy.Expr as input to get_sympy_Expr_dtype" if val.is_integer: # type: ignore[attr-defined] return torch.int64 else: return torch.float64 @contextlib.contextmanager def maybe_profile(should_profile, *args, **kwargs): if should_profile: with torch.profiler.profile(*args, **kwargs) as p: yield p else: yield def parallel_num_threads(): threads = config.cpp.threads if threads < 1: threads = torch.get_num_threads() return threads @functools.lru_cache(None) def get_device_tflops(dtype): from triton.testing import get_max_simd_tflops, get_max_tensorcore_tflops assert dtype in (torch.float16, torch.bfloat16, torch.float32) if inspect.signature(get_max_simd_tflops).parameters.get("clock_rate"): # Triton API change in https://github.com/openai/triton/pull/2293 from torch._utils_internal import max_clock_rate sm_clock = max_clock_rate() if dtype in (torch.float16, torch.bfloat16): return get_max_tensorcore_tflops(dtype, sm_clock) if torch.backends.cuda.matmul.allow_tf32: return get_max_tensorcore_tflops(torch.float32, sm_clock) else: return get_max_simd_tflops(torch.float32, sm_clock) else: if dtype in (torch.float16, torch.bfloat16): return get_max_tensorcore_tflops(dtype) if torch.backends.cuda.matmul.allow_tf32: return get_max_tensorcore_tflops(torch.float32) else: return get_max_simd_tflops(torch.float32) @functools.lru_cache(None) def get_gpu_dram_gbps(): from triton.testing import get_dram_gbps return get_dram_gbps() def get_gpu_shared_memory(): from triton.runtime import driver return driver.active.utils.get_device_properties(0).get("max_shared_mem", 0) def is_welford_reduction(reduction_type): return reduction_type.startswith("welford") def reduction_num_outputs(reduction_type): return 3 if is_welford_reduction(reduction_type) else 1 def is_linux() -> bool: return platform.system() == "Linux" def is_windows(): return sys.platform == "win32" def has_free_symbols(itr: Iterable[Any]): return any(isinstance(x, sympy.Expr) and not x.is_number for x in itr) def is_dynamic(*args): from . import ir for t in args: if isinstance(t, ir.TensorBox): if has_free_symbols(t.data.get_size()) or ( hasattr(t.data, "get_stride") and has_free_symbols(t.data.get_stride()) ): return True elif isinstance(t, (ir.StorageBox, ir.BaseView, ir.ComputedBuffer)): assert hasattr(t, "get_size") and hasattr(t, "get_stride") if has_free_symbols(t.get_size()) or has_free_symbols(t.get_stride()): return True elif not isinstance(t, ir.IRNode): continue else: raise TypeError(f"unexpected type for is_dynamic {type(t)}") return False # Placeholder strings used in triton codegen. class Placeholder(enum.Enum): # The placeholder for the actual name of a triton kernel. # e.g. for "def triton_" it would be "triton_" KERNEL_NAME = "KERNEL_NAME" # The descriptive name of the triton kernel; when unique_kernel_names = False, this # placeholder will be replaced with a string with more information. DESCRIPTIVE_NAME = "DESCRIPTIVE_NAME" def pass_execution_and_save(func, gm, inp, msg): from .pattern_matcher import stable_topological_sort with tempfile.NamedTemporaryFile( mode="w", encoding="utf-8", delete=False, ) as f: before_io = io.StringIO() after_io = io.StringIO() ShapeProp(gm=gm, fake_mode=detect_fake_mode(inp)).propagate(*inp) print(f"Before:\n{gm.graph}", file=f) print(gm.graph, file=before_io) start_time = datetime.now() with GraphTransformObserver(gm, msg, config.trace.log_url_for_graph_xform): func(gm.graph) time_elapsed = datetime.now() - start_time # recompile graph stable_topological_sort(gm.graph) gm.graph.lint() gm.recompile() print(f"After:\n{gm.graph}", file=f) print(gm.graph, file=after_io) t = before_io.getvalue() == after_io.getvalue() log.info( "%s, save before/after graph to %s, graph before/after are the same = %s, time elapsed = %s", msg, f.name, t, time_elapsed, ) def is_collective(node, op=None): from . import ir return type(node) == ir._CollectiveKernel and (op is None or node.op_overload is op) def is_wait(node): from . import ir return type(node) == ir._WaitKernel def contains_collective(snode): from torch._inductor.scheduler import BaseSchedulerNode, GroupedSchedulerNode assert isinstance(snode, BaseSchedulerNode) if isinstance(snode, GroupedSchedulerNode): return any(contains_collective(x) for x in snode.snodes) else: return is_collective(snode.node) def contains_wait(snode): from torch._inductor.scheduler import BaseSchedulerNode, GroupedSchedulerNode assert isinstance(snode, BaseSchedulerNode) if isinstance(snode, GroupedSchedulerNode): return any(contains_wait(x) for x in snode.snodes) else: return is_wait(snode.node) def is_fallback_op(node, op): from . import ir if isinstance(op, torch._ops.OpOverload): op = {op} return isinstance(node, ir.FallbackKernel) and node.op_overload in op def buf_name_to_fused_snode(buf_name, name_to_buf, name_to_fused_node): return name_to_fused_node[name_to_buf[buf_name].defining_op.get_name()] def find_recursive_deps_of_node( snode, collected_node_set, name_to_buf, name_to_fused_node, criteria_cb=None ): if criteria_cb and criteria_cb(snode): return collected_node_set.add(snode) for dep in snode.unmet_dependencies: defining_op_for_dep = buf_name_to_fused_snode( dep.name, name_to_buf, name_to_fused_node ) if defining_op_for_dep in collected_node_set: continue find_recursive_deps_of_node( defining_op_for_dep, collected_node_set, name_to_buf, name_to_fused_node, criteria_cb=criteria_cb, ) def find_recursive_users_of_node( snode, collected_node_set, name_to_buf, name_to_fused_node, criteria_cb=None ): if criteria_cb and criteria_cb(snode): return collected_node_set.add(snode) for o in snode.get_outputs(): for user in o.users: assert user.node is not None if user.node.get_name() == "OUTPUT": continue if user.node.get_name() not in name_to_fused_node: continue user_op = name_to_fused_node[user.node.get_name()] if user_op in collected_node_set: continue find_recursive_users_of_node( user_op, collected_node_set, name_to_buf, name_to_fused_node, criteria_cb=criteria_cb, ) def num_fw_fixed_arguments(dynamo_gm_num_inputs: int, aot_fw_gm_num_inputs: int): "Computes the number of inputs to the aot fw graph which have fixed addresses (params and buffers)" num_rng_seed_offset_inputs = ( 2 if torch._functorch.config.functionalize_rng_ops else 0 ) # AOT won't lift any parameters if we're inlining NN Modules # however desugaring subclasses will still add arguments # resulted in extra fixed inputs https://github.com/pytorch/pytorch/issues/130502 if ( torch._dynamo.config.inline_inbuilt_nn_modules and not torch._dynamo.utils.is_parameter_freezing() ): return 0 return aot_fw_gm_num_inputs - dynamo_gm_num_inputs - num_rng_seed_offset_inputs def count_tangents(fx_g: torch.fx.GraphModule): """ Infers which inputs are static for a backwards graph """ def is_saved_tensor(x): return ( "tangents" not in x.name and "bwd_seed" not in x.name and "bwd_base_offset" not in x.name ) arg_count = 0 static_arg_idxs = [] for n in fx_g.graph.nodes: if n.op == "placeholder": if is_saved_tensor(n): static_arg_idxs.append(arg_count) arg_count += 1 assert static_arg_idxs == list(range(len(static_arg_idxs))) return len(static_arg_idxs) @dataclasses.dataclass class BoxedBool: value: bool def __bool__(self): return self.value @staticmethod def disable(obj): if isinstance(obj, BoxedBool): obj.value = False return obj return False @contextlib.contextmanager def collect_defined_kernels(kernel_list): from .codegen.wrapper import WrapperCodeGen orig_define_kernel = WrapperCodeGen.define_kernel def new_define_kernel(wrapper, name, kernel_code, metadata, *args, **kwargs): nonlocal kernel_list kernel_list.append(kernel_code) return orig_define_kernel(wrapper, name, kernel_code, metadata, *args, **kwargs) with unittest.mock.patch.object(WrapperCodeGen, "define_kernel", new_define_kernel): yield def get_cloned_parameter_buffer_name(name: str): return name + "__original__" def is_gpu(device: str): assert isinstance(device, str) or device is None, device return device in ["cuda", "xpu"] def device_need_guard(device: str): assert isinstance(device, str) return is_gpu(device) def needs_fallback_due_to_atomic_add_limitations(dtype): # tl.atomic_add does NOT support the following types return dtype in {torch.int64, torch.bool, torch.bfloat16} def use_scatter_fallback( op_overload: torch._ops.OpOverload, reduction_type, self_dtype, src_dtype, src_device_type, src_is_tensor, ): if ( op_overload.overloadpacket in (torch.ops.aten.scatter_reduce_, torch.ops.aten.scatter_reduce) and reduction_type is None ): return False reduce_ty = ( "add" if op_overload.overloadpacket == torch.ops.aten.scatter_ else "sum" ) return ( reduction_type not in {None, reduce_ty} or ( src_is_tensor and is_gpu(src_device_type) and needs_fallback_due_to_atomic_add_limitations(src_dtype) ) or ( op_overload.overloadpacket == torch.ops.aten.scatter_reduce_ and reduction_type == "sum" and src_is_tensor and src_device_type == "cpu" and config.cpp.fallback_scatter_reduce_sum and (config.cpp.dynamic_threads or parallel_num_threads() != 1) ) or (reduction_type == reduce_ty and self_dtype in {torch.bool, torch.int64}) or torch.are_deterministic_algorithms_enabled() ) def dump_node_schedule(node_schedule): """ An API that can be used in pdb to dump a node_schedule. Right mainly dump the read/write dependencies but can add more as needed. """ from torch._inductor.codegen.simd import DisableReduction, EnableReduction from torch._inductor.scheduler import SchedulerNode print(f"Node schedule with {len(node_schedule)} nodes") for idx, node in enumerate(node_schedule): print(f" {idx:3}:") if node is EnableReduction: print("enable reduction") elif node is DisableReduction: print("disable reduction") elif isinstance(node, SchedulerNode): is_red = node.is_reduction() print(f"{'red' if is_red else 'pw'} scheduler node") if is_red: assert node.node is not None print(f"original reduction hint {node.node.data.reduction_hint}") # type: ignore[attr-defined] print("ReadDep:") for dep in node.read_writes.reads: print(dep) print("WriteDep:") for dep in node.read_writes.writes: print(dep) else: raise RuntimeError(f"Unrecognized node type: {type(node)}") def tensor_is_aligned(tensor: torch.Tensor): # See Note: [Input Alignment handling in Inductor] # Right now, we don't try to guard on the alignment of the storage offset. # When this comment was written, non-symbolic storage_offsets are not guarded on # but symbolic storage_offsets are. For consistency, we suppress guard creation # upon performing this check: that ensures that we don't add recompiles when we # add this logic. from torch.fx.experimental.symbolic_shapes import statically_known_true return statically_known_true( (tensor.storage_offset() * get_dtype_size(tensor.dtype)) % GPU_ALIGN_BYTES == 0 ) def should_assume_input_aligned(example_input: torch.Tensor): # See Note: [Input Alignment handling in Inductor] # right now, we only care about alignment for cuda tensors. if not is_gpu(example_input.device.type): return False return config.assume_aligned_inputs or tensor_is_aligned(example_input) def maybe_get_suppress_shape_guards_ctx(): # Try to get TracingContext.try_get().fake_mode.shape_env.suppress_guards() # If it's not available, return a nullcontext. # If we're dealing with cudagraphs, we might not have a tracing_context tracing_context = torch._guards.TracingContext.try_get() if not tracing_context: return contextlib.nullcontext() # In standalone inductor compile mode, we might not have a shape_env attached to the fake mode shape_env = tracing_context.fake_mode.shape_env if not shape_env: return contextlib.nullcontext() return shape_env.suppress_guards() def run_and_get_cpp_code(fn, *args, **kwargs): # We use the patch context manager instead of using it as a decorator. # In this way, we can ensure that the attribute is patched and unpatched correctly # even if this run_and_get_cpp_code function is called multiple times. with unittest.mock.patch.object(config, "debug", True): torch._dynamo.reset() import io import logging log_capture_string = io.StringIO() ch = logging.StreamHandler(log_capture_string) from torch._inductor.codecache import output_code_log output_code_log.addHandler(ch) prev_level = output_code_log.level output_code_log.setLevel(logging.DEBUG) result = fn(*args, **kwargs) s = log_capture_string.getvalue() output_code_log.setLevel(prev_level) output_code_log.removeHandler(ch) return result, s def shape_env_from_inputs(inputs: List[torch.Tensor]): shape_env = None fake_mode = detect_fake_mode(inputs) # TODO(voz): It would be nice to enable this assert, but there are lots of tests that # pass in real inputs for now. # if len(inputs) > 0: # assert fake_mode is not None, breakpoint() if fake_mode is not None: return fake_mode.shape_env # When there are no tensor inputs, get shape_env from the first SymInt. for input in inputs: if isinstance(input, torch.SymInt): return input.node.shape_env # TODO(voz): Should we always have one anyway? return None def align_inputs_from_check_idxs( model: Callable[[List[InputType]], Any], inputs_to_check: Sequence[int], ) -> Callable[[List[InputType]], Any]: if len(inputs_to_check) == 0: return model def run(new_inputs: List[InputType]): copy_misaligned_inputs(new_inputs, inputs_to_check) return model(new_inputs) return run def clone_preserve_strides(x: torch.Tensor): needed_size = ( sum((shape - 1) * stride for shape, stride in zip(x.size(), x.stride())) + 1 ) buffer = torch.as_strided(x, (needed_size,), (1,)).clone() return torch.as_strided(buffer, x.size(), x.stride()) def copy_misaligned_inputs( new_inputs: List[InputType], check_inputs_idxs: Sequence[int] ) -> None: for i in check_inputs_idxs: _inp = new_inputs[i] assert isinstance(_inp, torch.Tensor) if _inp.data_ptr() % ALIGNMENT: new_inputs[i] = clone_preserve_strides(_inp) def remove_unaligned_input_idxs( inputs: List[InputType], static_input_idxs: Sequence[int], ): """ We require all inputs to be aligned, so introduce a copy for any that aren't. """ aligned_static_input_idxs = [] for idx in static_input_idxs: input = inputs[idx] if isinstance(input, torch.Tensor) and (input.data_ptr() % ALIGNMENT) == 0: aligned_static_input_idxs.append(idx) if len(aligned_static_input_idxs) != len(static_input_idxs): return aligned_static_input_idxs return static_input_idxs def set_tracing_context_output_strides(example_inputs, compiled_graph): # Return the output strides to the caller via TracingContext context = torch._guards.TracingContext.try_get() if context is not None and context.output_strides is not None: assert len(context.output_strides) == 0 shape_env = shape_env_from_inputs(example_inputs) for exprs in compiled_graph.output_strides: if exprs is None: context.output_strides.append(None) else: context.output_strides.append( tuple( ( shape_env.evaluate_symexpr(e) if shape_env is not None else int(e) ) for e in exprs ) )