compile_fx.py

import dataclasses
import functools
import itertools
import logging
import sys
import warnings
from typing import Any, Callable, Dict, List, Optional

import functorch
from functorch.compile import min_cut_rematerialization_partition

import torch._dynamo.config as dynamo_config

import torch.fx
import torch.utils._pytree as pytree

from torch._dynamo import logging as dynamo_logging, utils as dynamo_utils
from torch._dynamo.utils import fake_mode_from_tensors
from torch._functorch.aot_autograd import make_boxed_func
from torch._ops import OpOverload
from torch._subclasses.fake_tensor import FakeTensor
from .._dynamo.backends.common import aot_autograd
from ..fx.graph import _PyTreeCodeGen
from . import config, metrics, overrides, pattern_matcher
from .debug import DebugContext
from .decomposition import select_decomp_table
from .graph import GraphLowering
from .mkldnn import convert_outplace_to_inplace
from .utils import developer_warning, get_dtype_size, has_incompatible_cudagraph_ops
from .virtualized import V

log = logging.getLogger(__name__)
ALIGNMENT = 16


@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


# copy_ fails when trying to write to tensors with memory overlap,
# for expanded dimensions (a dimension which used to have size 1 -> ?)
# we can select one element from that dimension and write to it
# to achieve writing to all values of that dimension of the input tensor
def get_expanded_dims(t):
    return [i for i in range(t.ndim) if t.stride(i) == 0 and t.size(i) != 1]


def index_expanded_dims(t, expanded_dims):
    for expanded_dim in expanded_dims:
        t = torch.ops.aten.slice(t, expanded_dim, 0, 1)
    return t


def complex_memory_overlap(t):
    # if torch._debug_has_internal_overlap thinks this tensor potentially has
    # memory overlap internally, let's dig deeper to find out whether it's true.
    if torch._debug_has_internal_overlap(t) != 0:
        strides = t.stride()
        sizes = t.shape
        indices = list(range(len(strides)))
        indices = [x for _, x in sorted(zip(strides, indices))]
        for i in range(len(strides)):
            prev_stride = 1 if i == 0 else strides[indices[i - 1]]
            prev_size = 1 if i == 0 else sizes[indices[i - 1]]
            if strides[indices[i]] < prev_stride * prev_size:
                return True
    return False


@functools.lru_cache(None)
def _step_logger():
    return dynamo_logging.get_step_logger(log)


@functools.lru_cache(None)
def _warn_tf32_disabled():
    if (
        torch.cuda.is_available()
        and not torch.backends.cuda.matmul.allow_tf32
        and torch.cuda.get_device_capability() >= (8, 0)
    ):
        warnings.warn(
            "TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. "
            "Consider setting `torch.set_float32_matmul_precision('high')` for better performance."
        )


def is_tf32_warning_applicable(gm: torch.fx.GraphModule):
    aten = torch.ops.aten
    tf32_ops = {
        aten.mm.default,
        aten.addmm.default,
        aten.bmm.default,
        aten.baddbmm.default,
    }
    for node in gm.graph.nodes:
        if (
            node.op == "call_function"
            and node.target in tf32_ops
            and isinstance(node.meta.get("val", None), torch.Tensor)
            and node.meta["val"].dtype == torch.float32
            and node.meta["val"].device.type == "cuda"
        ):
            return True
    return False


@DebugContext.wrap
def count_bytes_inner(gm, example_inputs, num_fixed=0, **kwargs):
    shape_env = _shape_env_from_inputs(example_inputs)

    graph = GraphLowering(gm, shape_env=shape_env, num_static_inputs=num_fixed)
    with V.set_graph_handler(graph):
        graph.run(*example_inputs)
        num_bytes, nodes_num_elem = graph.count_bytes()
        metrics.num_bytes_accessed += num_bytes
        metrics.nodes_num_elem += nodes_num_elem
    return make_boxed_func(gm.forward)


@DebugContext.wrap
@torch.utils._python_dispatch._disable_current_modes()
def compile_fx_inner(
    gm: torch.fx.GraphModule,
    example_inputs: List[torch.Tensor],
    cudagraphs=None,
    num_fixed=0,
    is_backward=False,
    graph_id=None,
):
    if is_tf32_warning_applicable(gm):
        _warn_tf32_disabled()

    if dynamo_utils.count_calls(gm.graph) == 0:
        return make_boxed_func(gm.forward)

    # lift the maximum depth of the Python interpreter stack
    # to adapt large/deep models
    sys.setrecursionlimit(max(sys.getrecursionlimit(), 2000))

    _step_logger()(
        logging.INFO,
        "torchinductor compiling "
        f"{'BACKWARDS' if is_backward else 'FORWARDS'} "
        f"graph {graph_id}",
    )
    V.debug.fx_graph(gm, example_inputs)

    if cudagraphs is None:
        cudagraphs = config.triton.cudagraphs

    shape_env = _shape_env_from_inputs(example_inputs)
    fake_mode = fake_mode_from_tensors(
        example_inputs
    ) or torch._subclasses.FakeTensorMode(allow_non_fake_inputs=True)

    with V.set_fake_mode(fake_mode):
        pattern_matcher.fx_passes(gm)
        V.debug.fx_graph_transformed(gm, example_inputs)

        graph = GraphLowering(
            gm,
            shape_env=shape_env,
            num_static_inputs=num_fixed,
            graph_id=graph_id,
        )
        with V.set_graph_handler(graph):
            graph.run(*example_inputs)
            compiled_fn = graph.compile_to_fn()

    if cudagraphs:
        complex_memory_overlap_inputs = any(
            complex_memory_overlap(t) for t in example_inputs
        )

        if (
            set(graph.device_types) == {"cuda"}
            and not graph.mutated_inputs
            and not has_incompatible_cudagraph_ops(gm)
            and not complex_memory_overlap_inputs
        ):
            compiled_fn = cudagraphify(
                compiled_fn, example_inputs, static_input_idxs=range(num_fixed)
            )
        else:
            BoxedBool.disable(cudagraphs)

            if len(set(graph.device_types)) > 1:
                developer_warning("skipping cudagraphs due to multiple devices")
            elif set(graph.device_types) == {"cuda"}:
                if graph.mutated_inputs:
                    developer_warning("skipping cudagraphs due to input mutation")
                elif complex_memory_overlap_inputs:
                    developer_warning(
                        "skipping cudagraphs due to complex input striding"
                    )

    result = align_inputs(compiled_fn, example_inputs, range(num_fixed))
    _step_logger()(
        logging.INFO,
        "torchinductor done compiling "
        f"{'BACKWARDS' if is_backward else 'FORWARDS'} "
        f"graph {graph_id}",
    )

    # aot autograd needs to know to pass in inputs as a list
    result._boxed_call = True
    return result


def clone_preserve_strides(x):
    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 align_inputs(model, inputs, static_input_idxs=()):
    def is_aligned(storage_offset, dtype):
        return (storage_offset * get_dtype_size(dtype)) % ALIGNMENT == 0

    check_inputs = [
        i
        for i in range(len(inputs))
        if isinstance(inputs[i], torch.Tensor)
        and (
            i not in static_input_idxs
            or not is_aligned(inputs[i].storage_offset(), inputs[i].dtype)
        )
        and inputs[i].device.type == "cuda"
    ]

    if len(check_inputs) == 0:
        return model

    def run(new_inputs):
        for i in check_inputs:
            if new_inputs[i].data_ptr() % ALIGNMENT:
                new_inputs[i] = clone_preserve_strides(new_inputs[i])
        return model(new_inputs)

    return run


@dynamo_utils.dynamo_timed
def cudagraphify(model, inputs, static_input_idxs=()):
    # if using fake tensors, defer cudagraphs until we get real inputs at runtime
    if not any(isinstance(inp, FakeTensor) for inp in inputs):
        return cudagraphify_impl(model, inputs, static_input_idxs)

    compiled_fn = None

    def run(new_inputs):
        nonlocal compiled_fn
        if compiled_fn is None:
            with dynamo_utils.preserve_rng_state():
                compiled_fn = cudagraphify_impl(model, new_inputs, static_input_idxs)

        return compiled_fn(new_inputs)

    return run


def remove_unaligned_input_idxs(inputs, static_input_idxs):
    """
    We require all inputs to be aligned, so introduce a copy for any
    that aren't.
    """
    aligned_static_input_idxs = {
        idx for idx in static_input_idxs if (inputs[idx].data_ptr() % ALIGNMENT) == 0
    }
    if len(aligned_static_input_idxs) != len(static_input_idxs):
        return aligned_static_input_idxs
    return static_input_idxs


def cudagraphify_impl(model, inputs, static_input_idxs=()):
    """
    Assumes inputs[static_input_idxs[i]] are always the same memory address
    """
    static_input_idxs = remove_unaligned_input_idxs(inputs, static_input_idxs)

    def static_input(x):
        """
        Copy and input while preserving strides
        """
        # TODO(jansel): figure out why this version doesn't work:
        # return torch.empty_strided(x.size(), x.stride(), dtype=x.dtype, device=x.device)
        needed_size = (
            sum((shape - 1) * stride for shape, stride in zip(x.size(), x.stride())) + 1
        )
        buffer = torch.zeros(needed_size, dtype=x.dtype, device=x.device)
        return torch.as_strided(buffer, x.size(), x.stride())

    assert isinstance(inputs, (list, tuple))
    static_inputs = [
        static_input(x) if idx not in static_input_idxs else x.detach()
        for idx, x in enumerate(inputs)
    ]

    inps_expanded_dims = [
        get_expanded_dims(x) if idx not in static_input_idxs else []
        for idx, x in enumerate(inputs)
    ]

    # warmup
    torch.cuda.synchronize()
    stream = torch.cuda.Stream()
    stream.wait_stream(torch.cuda.current_stream())
    # copy static_inputs because it will be cleared in model
    with torch.cuda.stream(stream):
        model(list(static_inputs))
    stream.synchronize()
    torch.cuda.current_stream().wait_stream(stream)
    torch.cuda.synchronize()

    # record
    graph = torch.cuda.CUDAGraph()
    with torch.cuda.graph(graph, stream=stream):
        static_outputs = model(list(static_inputs))
    if not isinstance(static_outputs, (list, tuple)):
        static_outputs = (static_outputs,)

    if config.size_asserts:

        def run(new_inputs):
            assert len(static_inputs) == len(new_inputs)
            for idx, (dst, src, expanded_dims) in enumerate(
                zip(static_inputs, new_inputs, inps_expanded_dims)
            ):
                if idx in static_input_idxs:
                    assert dst.data_ptr() == src.data_ptr()
                else:
                    # TODO - could make one single op of multiple slices
                    # and avoid dispatch.
                    # Could also pre-index the `dst` tensors
                    dst = index_expanded_dims(dst, expanded_dims)
                    src = index_expanded_dims(src, expanded_dims)
                    dst.copy_(src)
            new_inputs.clear()
            graph.replay()
            return static_outputs

    else:
        copy_indices = [
            idx for idx in range(len(static_inputs)) if idx not in static_input_idxs
        ]

        def run(new_inputs):
            for idx in copy_indices:
                src = index_expanded_dims(static_inputs[idx], inps_expanded_dims[idx])
                dst = index_expanded_dims(new_inputs[idx], inps_expanded_dims[idx])
                dst.copy_(src)
            new_inputs.clear()
            graph.replay()
            return static_outputs

    return run


def count_tangents(fx_g: torch.fx.GraphModule):
    """
    Infers which inputs are static for a backwards graph
    """

    def is_not_gradout(x):
        return "tangents" not in x.name

    arg_count = 0
    static_arg_idxs = []
    for n in fx_g.graph.nodes:
        if n.op == "placeholder":
            if is_not_gradout(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)


_graph_counter = itertools.count(0)


def compile_fx(
    model_: torch.fx.GraphModule,
    example_inputs_: List[torch.Tensor],
    inner_compile=compile_fx_inner,
    config_patches: Optional[Dict[str, Any]] = None,
    decompositions: Optional[Dict[OpOverload, Callable]] = None,
):
    """Main entrypoint to a compile given FX graph"""
    if config_patches:
        with config.patch(config_patches):
            return compile_fx(
                model_,
                example_inputs_,
                # need extra layer of patching as backwards is compiled out of scope
                inner_compile=config.patch(config_patches)(inner_compile),
                decompositions=decompositions,
            )
    recursive_compile_fx = functools.partial(
        compile_fx,
        inner_compile=inner_compile,
        decompositions=decompositions,
    )

    if not graph_returns_tuple(model_):
        return make_graph_return_tuple(
            model_,
            example_inputs_,
            recursive_compile_fx,
        )

    if isinstance(model_, torch.fx.GraphModule):
        with overrides.patch_functions():
            model_ = overrides.replace_fx(model_)
            model_ = overrides.fuse_fx(model_, example_inputs_)

        if isinstance(model_.graph._codegen, _PyTreeCodeGen):
            # this graph is the result of dynamo.export()
            return handle_dynamo_export_graph(
                model_,
                example_inputs_,
                recursive_compile_fx,
            )

    if any(isinstance(x, (list, tuple, dict)) for x in example_inputs_):
        return flatten_graph_inputs(
            model_,
            example_inputs_,
            recursive_compile_fx,
        )

    assert not config._raise_error_for_testing
    functorch.compile.config.use_functionalize = True
    functorch.compile.config.use_fake_tensor = True
    num_example_inputs = len(example_inputs_)
    cudagraphs = BoxedBool(
        config.triton.cudagraphs and not dynamo_config.dynamic_shapes
    )
    graph_id = next(_graph_counter)

    @dynamo_utils.dynamo_timed
    def fw_compiler(model: torch.fx.GraphModule, example_inputs):
        fixed = len(example_inputs) - num_example_inputs
        # Why convert outplace op to inplace? Inductor can support inplace operations well and for custom
        # inplace ops which are lowered as ExternKernel, it is beneficial to performance when the inplace
        # implementation is used if available.
        model = convert_outplace_to_inplace(model)
        return inner_compile(
            model,
            example_inputs,
            num_fixed=fixed,
            cudagraphs=cudagraphs,
            graph_id=graph_id,
        )

    @dynamo_utils.dynamo_timed
    def bw_compiler(model: torch.fx.GraphModule, example_inputs):
        fixed = count_tangents(model)
        return inner_compile(
            model,
            example_inputs,
            num_fixed=fixed,
            cudagraphs=cudagraphs,
            is_backward=True,
            graph_id=graph_id,
        )

    with overrides.patch_functions():
        if decompositions is None:
            decompositions = select_decomp_table()
        # TODO: can add logging before/after the call to create_aot_dispatcher_function
        # in torch._functorch/aot_autograd.py::aot_module_simplified::aot_function_simplified::new_func
        # once torchdynamo is merged into pytorch
        return aot_autograd(
            fw_compiler=fw_compiler,
            bw_compiler=bw_compiler,
            decompositions=decompositions,
            partition_fn=functools.partial(
                min_cut_rematerialization_partition, compiler="inductor"
            ),
            keep_inference_input_mutations=True,
        )(model_, example_inputs_)


def _shape_env_from_inputs(inputs):
    shape_env = None
    fake_mode = fake_mode_from_tensors(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

    # TODO(voz): Should we always have one anyway?
    return 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


def graph_returns_tuple(gm: torch.fx.GraphModule):
    """True if a FX graph returns a tuple"""
    if not isinstance(gm, torch.fx.GraphModule):
        return True  # can't check this, assume true
    (rv,) = output_node(gm).args
    if isinstance(rv, (list, tuple)):
        return True
    return False


def make_graph_return_tuple(gm: torch.fx.GraphModule, inputs, compile_gm):
    """
    Mutate gm so it returns a tuple.  This is only needed for graphs
    not created by torchdynamo that return non-tuples.
    """
    node = output_node(gm)
    (rv,) = node.args
    rv, spec = pytree.tree_flatten(rv)
    with gm.graph.inserting_before(node):
        gm.graph.output(rv)
    gm.graph.erase_node(node)
    assert graph_returns_tuple(gm)

    compiled_fn = compile_gm(gm, inputs)

    @functools.wraps(compiled_fn)
    def wrapper(*args, **kwargs):
        return pytree.tree_unflatten(compiled_fn(*args, **kwargs), spec)

    return wrapper


def flatten_graph_inputs(gm: torch.fx.GraphModule, inputs, compile_gm):
    """
    Mutate inputs so that they are flat and wrap gm such that it
    accepts those inputs.  This is only needed for graphs not created
    by torchdynamo that take bumpy inputs.
    """
    inputs, spec = pytree.tree_flatten(inputs)

    class GmWrapper(torch.nn.Module):
        def __init__(self):
            super().__init__()
            self.gm = gm

        def forward(self, *args):
            return self.gm(*pytree.tree_unflatten(args, spec))

    compiled_fn = compile_gm(GmWrapper(), inputs)

    @functools.wraps(compiled_fn)
    def wrapper(*args):
        # note this doesn't check the spec, assuming it is the same
        return compiled_fn(*pytree.tree_flatten(args)[0])

    return wrapper


def handle_dynamo_export_graph(gm, inputs, compile_gm):
    """
    `torch._dynamo.export` embeds pytrees in the FX graph codgen object,
    convert that to a normal FX graph so inductor can compile it.
    """
    codegen = gm.graph._codegen
    gm.graph._codegen = torch.fx.graph.CodeGen()
    gm.recompile()

    compiled_fn = compile_gm(gm, codegen.process_inputs(*inputs))

    @functools.wraps(compiled_fn)
    def wrapper(*args):
        return codegen.process_outputs(compiled_fn(*codegen.process_inputs(*args)))

    return wrapper