Lightstar/venv/Lib/site-packages/torch/_dynamo/compiled_autograd.py

# mypy: allow-untyped-defs
import contextlib
import functools
from typing import Dict, List, Optional, TYPE_CHECKING

import torch
from torch._dynamo.external_utils import call_backward, call_hook
from torch._dynamo.source import GetItemSource, LocalSource
from torch._dynamo.utils import counters, lazy_format_graph_code, set_locals_to_steal
from torch._logging import getArtifactLogger, trace_structured
from torch._prims_common import clone_preserve_strides
from torch._subclasses import FakeTensorMode
from torch.fx import GraphModule
from torch.fx.experimental._backward_state import BackwardState
from torch.fx.experimental.proxy_tensor import (
    decompose,
    disable_autocast_cache,
    disable_proxy_modes_tracing,
    fetch_object_proxy,
    ProxyTorchDispatchMode,
    PythonKeyTracer,
    track_tensor_tree,
)
from torch.fx.experimental.symbolic_shapes import DimDynamic, ShapeEnv
from torch.fx.traceback import preserve_node_meta, set_stack_trace
from torch.utils._traceback import CapturedTraceback

if TYPE_CHECKING:
    from torch.fx.proxy import Proxy

compiled_autograd_log = getArtifactLogger(__name__, "compiled_autograd")
verbose_log = getArtifactLogger(__name__, "compiled_autograd_verbose")


def snapshot_verbose_logging_enabled():
    return torch._logging._internal.log_state.is_artifact_enabled(
        "compiled_autograd_verbose"
    )


def cpp_verbose_log_fn(msg: str) -> None:
    verbose_log.debug(msg)


def snapshot_cudagraph_enabled():
    return torch._inductor.config.triton.cudagraphs


def maybe_clone(x):
    if x is not None:
        return clone_preserve_strides(x)
    return x


class AutogradCompilerInstance:
    def __init__(self, compiler_fn) -> None:
        self.compiler_fn = compiler_fn
        self.stack = contextlib.ExitStack()
        self.close = self.stack.close
        self.shape_env = ShapeEnv()
        self.fake_tensor_mode = FakeTensorMode(
            allow_fallback_kernels=True,
            allow_non_fake_inputs=True,
            shape_env=self.shape_env,
        )
        self.fx_tracer = PythonKeyTracer()
        self.proxy_mode = ProxyTorchDispatchMode(self.fx_tracer, "symbolic")
        self.hooks_proxy: Optional[Proxy] = None

    def wrap_fake(self, x, source):
        assert isinstance(x, torch.Tensor)
        return self.fake_tensor_mode.from_tensor(x, source=source)

    @staticmethod
    def source(name, idx) -> GetItemSource:
        return GetItemSource(LocalSource(name), idx)

    def begin_capture(self, inputs: List[torch.Tensor], sizes: List[int]):
        counters["compiled_autograd"]["captures"] += 1
        self.fx_tracer.root = torch.nn.Module()
        self.fx_tracer.graph = torch.fx.Graph(tracer_cls=PythonKeyTracer)
        self.fx_tracer.tensor_attrs = {}
        args_proxy = self.fx_tracer.create_proxy("placeholder", "inputs", (), {})
        sizes_proxy = self.fx_tracer.create_proxy("placeholder", "sizes", (), {})
        self.hooks_proxy = self.fx_tracer.create_proxy("placeholder", "hooks", (), {})

        # tensor inputs to fake tensors
        inputs = [
            self.wrap_fake(x, self.source("inputs", idx))
            for idx, x in enumerate(inputs)
        ]
        proxies = [args_proxy[i] for i in range(len(inputs))]
        self.bind_tensors_to_proxies(inputs, proxies)

        # size inputs to symints
        sizes = [
            self.shape_env.create_unspecified_symint_and_symbol(
                val,
                self.source("sizes", idx),
                DimDynamic.DYNAMIC,
            )
            for idx, val in enumerate(sizes)
        ]
        self.bind_tensors_to_proxies(sizes, sizes_proxy)

        # TODO(jansel): are all these modes needed?
        self.stack.enter_context(decompose({}))
        self.stack.enter_context(self.fake_tensor_mode)
        self.stack.enter_context(self.proxy_mode.sym_mode)
        self.stack.enter_context(self.proxy_mode)
        self.stack.enter_context(disable_autocast_cache())
        self.stack.enter_context(preserve_node_meta())
        return inputs, sizes

    def proxy_call_backward(
        self,
        inputs,
        output_metadatas,
        saved_tensors,
        backward_idx: int,
    ):
        assert self.hooks_proxy is not None
        backward_c_function = self.hooks_proxy[backward_idx]  # type: ignore[index]
        proxies = self.fx_tracer.create_proxy(
            kind="call_function",
            target=call_backward,
            args=(
                backward_c_function,
                self.to_proxy(saved_tensors),
                *self.to_proxy(inputs),
            ),
            kwargs={},
        )

        with disable_proxy_modes_tracing():
            # create fake Tensors
            grad_ins: List[Optional[torch.Tensor]] = []
            for output_metadata in output_metadatas:
                if output_metadata is None:
                    grad_ins.append(None)
                    continue

                layout, device, dtype, size = output_metadata
                grad_ins.append(
                    torch.empty(size=size, dtype=dtype, layout=layout, device=device)
                )
            self.bind_tensors_to_proxies(grad_ins, proxies)
        return tuple(grad_ins)

    def proxy_call_hook(self, hook, *args):
        return self.fx_tracer.create_proxy(
            "call_function",
            call_hook,
            (
                hook,
                *[self.to_proxy(x) for x in args],
            ),
            {},
        )

    def tensor_pre_hook(self, inputs, hook_id, i: int):
        assert self.hooks_proxy is not None
        hook = self.hooks_proxy[hook_id]  # type: ignore[index]
        proxy = self.proxy_call_hook(
            hook,
            inputs[i],
        )
        with disable_proxy_modes_tracing():
            inputs[i] = maybe_clone(inputs[i])
            self.bind_tensors_to_proxies([inputs[i]], [proxy])
        return inputs

    def pre_hook(self, inputs, hook_id):
        assert self.hooks_proxy is not None
        hook = self.hooks_proxy[hook_id]  # type: ignore[index]
        proxies = self.proxy_call_hook(
            hook,
            inputs,
        )
        with disable_proxy_modes_tracing():
            inputs = [maybe_clone(x) for x in inputs]
            self.bind_tensors_to_proxies(inputs, proxies)
        return inputs

    def post_hook(self, outputs, inputs, hook_id):
        assert self.hooks_proxy is not None
        hook = self.hooks_proxy[hook_id]  # type: ignore[index]
        proxies = self.proxy_call_hook(
            hook,
            outputs,
            inputs,
        )
        with disable_proxy_modes_tracing():
            outputs = [maybe_clone(x) for x in outputs]
            self.bind_tensors_to_proxies(outputs, proxies)
        return outputs

    def post_acc_grad_hook(self, input, hook_id):
        assert isinstance(input, torch.Tensor)
        assert self.hooks_proxy is not None
        hook = self.hooks_proxy[hook_id]  # type: ignore[index]
        proxies = self.proxy_call_hook(
            hook,
            input,
        )
        with disable_proxy_modes_tracing():
            input = [maybe_clone(input)]
            self.bind_tensors_to_proxies(input, proxies)
        return input

    # Note: [Compiled autograd and cudagraphs]
    # Eager autograd backward implements scalars as 0-dim tensors, see DivBackward0::other_.
    # When compiled autograd traces those nodes, it lifts the scalar tensors, resulting in a graph
    # with some cpu 0-dim tensor inputs. To prevent the entire graph from skipping cudagraph, we move the
    # scalars tensors to cuda. This works because ATen/prims ops will accept cuda 0-dim tensors too.
    def move_graph_nodes_to_cuda(self, graph) -> List[int]:
        to_move: Dict[int, torch.fx.Node] = {}
        has_cuda_inputs = False
        nodes = list(graph.nodes)
        assert nodes[0].target == "inputs"
        inputs = nodes[0]
        inputs_users = list(inputs.users.keys())
        # the ordering of the nodes should always [inputs, sizes, hooks, getitem, getitem1, ...]
        # where getitemi accesses inputs[i]
        first_getitem_idx = 3
        assert nodes[first_getitem_idx] == inputs_users[0]
        last_getitem_idx = first_getitem_idx + len(inputs_users) - 1
        assert nodes[last_getitem_idx] == inputs_users[-1]
        for i, node in enumerate(inputs_users):
            if not has_cuda_inputs and node.meta["val"].device.type == "cuda":
                has_cuda_inputs = True
                continue

            is_cpu = node.meta["val"].device.type == "cpu"
            is_scalar = len(node.meta["val"].size()) == 0
            if is_cpu and is_scalar:
                node_users = list(node.users.keys())
                if all(
                    isinstance(user.target, torch._ops.OpOverload)
                    and user.target.namespace in ("prims", "aten")
                    for user in node_users
                ):
                    # all users are prims/aten, can move safely
                    to_move[i] = node

        # only move cpu scalars to cuda if there were cuda activations in this graph,
        # this is to handle the case where cudagraphs is enabled on a cpu-only graph
        if has_cuda_inputs:
            for node in to_move.values():
                node.meta["val"] = node.meta["val"].cuda()

            # return runtime indices we need to move to cuda
            return list(to_move.keys())

        return []

    def end_capture(self, outputs):
        self.stack.close()
        self.fx_tracer.create_node(
            "output",
            "output",
            (self.fx_tracer.create_arg(self.to_proxy(outputs)),),
            {},
        )
        self.reorder_accumulate_grad_nodes()
        runtime_inputs_to_move: List[int] = []
        if snapshot_cudagraph_enabled():
            runtime_inputs_to_move = self.move_graph_nodes_to_cuda(self.fx_tracer.graph)

        graph = GraphModule(
            self.fx_tracer.root, self.fx_tracer.graph, "CompiledAutograd"
        )
        set_locals_to_steal(graph, ["inputs"])
        compiled_autograd_log.info(
            "%s", lazy_format_graph_code("Compiled autograd graph", graph)
        )
        verbose_log.debug(
            "%s",
            lazy_format_graph_code(
                "Compiled autograd graph", graph, include_device=True
            ),
        )
        trace_structured(
            "compiled_autograd_graph",
            payload_fn=lambda: graph.print_readable(print_output=False),
        )

        def runtime_wrapper(compiled_fn, inputs, sizes, hooks):
            for i in runtime_inputs_to_move:
                inputs[i] = inputs[i].cuda()

            return compiled_fn(inputs, sizes, hooks)

        return runtime_wrapper, self.compiler_fn(graph)

    def reorder_accumulate_grad_nodes(self):
        """
        Usage of AOTAutograd causes all the accumulate_grad_ nodes to get pushed to the end of
        the graph.  This differs from eager mode, which schedules them as soon as possible. This
        pass attempts to reorder the graph to mimic eager behavior.
        """
        for node in self.fx_tracer.graph.find_nodes(
            op="call_function", target=torch.ops.inductor.accumulate_grad_.default
        ):
            arg = max(node.args)  # last arg
            if arg is not node.prev and arg.op != "placeholder":
                arg.append(node)

    def to_proxy(self, t):
        if t is None:
            return None
        if isinstance(t, list):
            return [self.to_proxy(x) for x in t]
        if isinstance(t, tuple):
            return tuple(self.to_proxy(x) for x in t)
        assert isinstance(t, (torch.Tensor, torch.SymInt))
        return fetch_object_proxy(self.fx_tracer)(t).proxy

    def bind_tensors_to_proxies(self, tensors, proxies):
        if isinstance(proxies, torch.fx.Proxy):
            proxies = [proxies[i] for i in range(len(tensors))]
        assert len(tensors) == len(proxies)
        track_tensor_tree(tensors, proxies, constant=None, tracer=self.fx_tracer)

    def bind_backward_state(self, index: int):
        assert self.hooks_proxy is not None
        proxy = self.hooks_proxy[index]  # type: ignore[index]
        bw_state = BackwardState()
        track_tensor_tree(bw_state, proxy, constant=None, tracer=self.fx_tracer)
        return bw_state

    def set_node_origin(self, node_name, node_index):
        raw_stack_trace = CapturedTraceback.extract().format()[-1]
        new_code = f"{node_name} (NodeCall {node_index})"
        new_stack_trace = raw_stack_trace.replace(
            "raw_stack_trace = CapturedTraceback.extract().format()[-1]", new_code
        )
        set_stack_trace(new_stack_trace)


compiled_autograd_enabled = False

# We may have code like:
# with enable(compiler_fn):
#   ...
#   with disable():
#     ...
#   ...
# The disable() call just want to disable compiled autograd temporarily.
# But overall the feature is enabled.
#
# The code covered by the disable context manager has no way to know if
# compiled autograd is overall eanbled. Use another variable
# compiled_autograd_enabled_count to indicate how many times compiled
# autograd has been enabled in the call stack for this purpose.
compiled_autograd_enabled_count = 0


@contextlib.contextmanager
def enable(compiler_fn):
    prior = torch._C._dynamo.compiled_autograd.set_autograd_compiler(
        functools.partial(AutogradCompilerInstance, compiler_fn)
    )
    if snapshot_verbose_logging_enabled():
        torch._C._dynamo.compiled_autograd.set_verbose_logger(cpp_verbose_log_fn)
    global compiled_autograd_enabled, compiled_autograd_enabled_count
    compiled_autograd_enabled = True
    compiled_autograd_enabled_count += 1
    try:
        with torch.autograd.set_multithreading_enabled(False):
            yield
    finally:
        compiled_autograd_enabled_count -= 1
        if not prior:
            compiled_autograd_enabled = False
        torch._C._dynamo.compiled_autograd.set_autograd_compiler(prior)


@contextlib.contextmanager
def disable():
    prior = torch._C._dynamo.compiled_autograd.set_autograd_compiler(None)
    global compiled_autograd_enabled
    compiled_autograd_enabled = False
    try:
        yield
    finally:
        if prior:
            compiled_autograd_enabled = True
        torch._C._dynamo.compiled_autograd.set_autograd_compiler(prior)


# return to starting state of a new process
def reset() -> None:
    compiled_autograd_enable = False
    assert compiled_autograd_enabled_count == 0
    torch._C._dynamo.compiled_autograd.set_autograd_compiler(None)
    torch._C._dynamo.compiled_autograd.set_verbose_logger(None)