Source code for functorch._src.partitioners

import torch
import torch.fx as fx
import operator
import math
import torch.utils._pytree as pytree
import copy
import os
from collections import defaultdict
from torch.fx.passes import graph_drawer
from typing import Tuple
from .compile_utils import fx_graph_cse, get_aten_target
from . import config

AOT_PARTITIONER_DEBUG = config.debug_partitioner



class InvalidNodeBase(object):
    def __repr__(self):
        return "Invalid Node"


InvalidNode = InvalidNodeBase()


def _extract_graph_with_inputs_outputs(joint_graph, inputs, outputs):
    """
    Given a graph, extracts out a subgraph that takes the specified nodes as
    inputs and returns the specified outputs.

    This includes specifying non-placeholder nodes as inputs.

    The general strategy is to initialize all inputs with proxies as we
    encounter them, and trace through the graph, only keeping values which take
    in valid proxies. Then, all dead code is eliminated.
    """
    new_graph = fx.Graph()
    env = {}

    # Add new placeholder nodes in the order specified by the inputs
    for node in inputs:
        new_node = new_graph.placeholder(node.name)
        # Can't use node_copy here as we may be turning previous call_function into placeholders
        new_node.meta = node.meta
        env[node] = new_node

    for node in joint_graph.nodes:
        if node in inputs:
            continue
        elif node.op == 'placeholder':
            env[node] = InvalidNode
        elif node.op == 'call_function':
            all_args = pytree.tree_flatten((node.args, node.kwargs))[0]
            all_args = [isinstance(env[x], InvalidNodeBase) for x in all_args if isinstance(x, fx.Node)]
            if any(all_args):
                env[node] = InvalidNode
                continue
            env[node] = new_graph.node_copy(node, lambda x: env[x])
        elif node.op == 'get_attr':
            env[node] = new_graph.node_copy(node, lambda x: env[x])
        elif node.op == 'output':
            pass
    output_values = []
    for x in outputs:
        if isinstance(x, fx.Node):
            if x not in env:
                raise RuntimeError(f"Node {x} couldn't be found in env")
            output_values.append(env[x])
        else:
            output_values.append(x)
    new_graph.output(output_values)

    new_graph.eliminate_dead_code()
    new_graph.lint()
    return new_graph


def _is_primal(node):
    return node.op == "placeholder" and "tangents" not in node.target


def _is_tangent(node):
    return node.op == "placeholder" and "tangents" in node.target


def _extract_fwd_bwd_outputs(joint_module: fx.GraphModule):
    num_fwd_outputs = joint_module._out_spec.children_specs[0].num_leaves
    outputs = pytree.tree_flatten([node.args for node in joint_module.graph.nodes if node.op == 'output'])[0]
    fwd_outputs = outputs[:num_fwd_outputs]
    bwd_outputs = outputs[num_fwd_outputs:]
    return fwd_outputs, bwd_outputs


def _extract_fwd_bwd_modules(joint_module: fx.GraphModule, saved_values):
    fwd_outputs, bwd_outputs = _extract_fwd_bwd_outputs(joint_module)
    primal_inputs = list(filter(_is_primal, joint_module.graph.nodes))
    tangent_inputs = list(filter(_is_tangent, joint_module.graph.nodes))
    # Construct the forward module
    fwd_graph = _extract_graph_with_inputs_outputs(joint_module.graph, primal_inputs, fwd_outputs + saved_values)
    bwd_graph = _extract_graph_with_inputs_outputs(joint_module.graph, saved_values + tangent_inputs, bwd_outputs)

    # This is to filter out saved values that don't actually end up being used by the backwards pass
    for node in bwd_graph.nodes:
        if node.op == 'placeholder' and not node.users:
            for saved_value in saved_values:
                if saved_value.name == node.name:
                    saved_values.remove(saved_value)
                    break

    # Now, we re-generate the fwd/bwd graphs.
    # NB: This might increase compilation time, but I doubt it matters
    fwd_graph = _extract_graph_with_inputs_outputs(joint_module.graph, primal_inputs, fwd_outputs + saved_values)
    bwd_graph = _extract_graph_with_inputs_outputs(joint_module.graph, saved_values + tangent_inputs, bwd_outputs)

    fwd_module = fx.GraphModule(joint_module, fwd_graph)
    bwd_module = fx.GraphModule(joint_module, bwd_graph)
    return fwd_module, bwd_module


def default_partition(
    joint_module: fx.GraphModule, _joint_inputs
) -> Tuple[fx.GraphModule, fx.GraphModule]:
    """
    Partitions the :attr:`joint_module` in a manner that closely resembles the
    behavior observed in the original ``.forward()`` and ``.backward()`` of the
    callable, i.e., the resulting forward graph contains those operators that
    are executed in the original ``.forward()`` callable passed to
    :func:`aot_function`.

    The default partitioner collects the operators that are between the forward
    inputs and the forward outputs. This helps in finding the tensors which have
    to be stashed for the backward pass. These stashed tensors become the output
    of the generated forward graph. The remaining operators are then placed in
    the backward graph.

    .. warning::
        This API is experimental and likely to change.

    Args:
        joint_module(fx.GraphModule): The joint forward and backward graph. This
            is the result of AOT Autograd tracing.

    Returns:
        Returns the generated forward and backward Fx graph modules.
    """
    primal_inputs = list(filter(_is_primal, joint_module.graph.nodes))
    fwd_outputs, bwd_outputs = _extract_fwd_bwd_outputs(joint_module)
    forward_only_graph = _extract_graph_with_inputs_outputs(joint_module.graph, primal_inputs, fwd_outputs)
    forward_node_names = {node.name for node in forward_only_graph.nodes if node.op != 'output'}
    saved_values = []
    for node in joint_module.graph.nodes:
        if node.name not in forward_node_names:
            continue
        # Since we can't save tuple of tensor values, we need to flatten out what we're saving
        if 'tensor_meta' not in node.meta and node.op == 'call_function':
            users = node.users
            assert all(user.target == operator.getitem for user in users)
            for user in users:
                saved_values.append(user)
        else:
            saved_values.append(node)
    saved_values = list(set(saved_values))

    return _extract_fwd_bwd_modules(joint_module, saved_values)


def _prod(x):
    s = 1
    for i in x:
        s *= i
    return s


def _size_of(metadata):
    sizes = {
        torch.float: 4,
        torch.float16: 2,
        torch.bfloat16: 2,
        torch.float32: 4,
        torch.float64: 8,
        torch.int: 4,
        torch.int8: 1,
        torch.int16: 2,
        torch.int32: 4,
        torch.int64: 8,
        torch.uint8: 1,
        torch.bool: 1,
    }

    numel = _prod(metadata.shape)
    dtype = metadata.dtype

    if dtype not in sizes:
        raise NotImplementedError("Don't know the size of dtype ", dtype)

    return numel * sizes[dtype]


# Used for some investigative purposes
def _count_ops(graph):
    from collections import defaultdict
    cnt = defaultdict(int)
    for node in graph.nodes:
        if node.op == 'call_function':
            cnt[node.target.__name__] += 1
    print(sorted(cnt.items(), key=lambda x: x[1], reverse=True))


def min_cut_rematerialization_partition(
    joint_module: fx.GraphModule, _joint_inputs, compiler="nvfuser"
) -> Tuple[fx.GraphModule, fx.GraphModule]:
    """
    Partitions the joint graph such that the backward recomputes the forward.
    Recomputing helps in trading off memory bandwidth with computation.

    To create the fwd and bwd graph, we copy the joint graph, manually set the
    outputs to just original forward or backward outputs. And then we run the
    resulting graphs through dead code elimintation.

    .. warning::
        This API is experimental and likely to change.

    Args:
        joint_module(fx.GraphModule): The joint forward and backward graph. This
            is the result of AOT Autograd tracing.

    Returns:
        Returns the generated forward and backward Fx graph modules.
    """
    try:
        import networkx as nx
    except ImportError:
        raise RuntimeError("Need networkx installed to perform smart recomputation heuristics")

    joint_module.graph.eliminate_dead_code()
    joint_module.recompile()
    fx_g = joint_module.graph

    #  add the CSE pass
    cse_graph = fx_graph_cse(fx_g)
    joint_module.graph = cse_graph
    full_bw_graph = joint_module.graph

    name_to_node = {}
    for node in joint_module.graph.nodes:
        name_to_node[node.name] = node

    def classify_nodes(joint_module):
        required_bw_nodes = set()
        for node in joint_module.graph.nodes:
            if node.op == 'placeholder' and "tangents" in node.target:
                required_bw_nodes.add(node)
            if node in required_bw_nodes:
                for user in node.users:
                    required_bw_nodes.add(user)

        primal_inputs = list(filter(_is_primal, joint_module.graph.nodes))
        fwd_outputs, _ = _extract_fwd_bwd_outputs(joint_module)
        forward_only_graph = _extract_graph_with_inputs_outputs(joint_module.graph, primal_inputs, fwd_outputs)
        required_fw_nodes = {name_to_node[node.name] for node in forward_only_graph.nodes
                             if node.op != 'output'}
        unclaimed_nodes = {node for node in joint_module.graph.nodes
                           if node not in required_fw_nodes and node not in required_bw_nodes}
        return required_fw_nodes, required_bw_nodes, unclaimed_nodes

    required_fw_nodes, required_bw_nodes, unclaimed_nodes = classify_nodes(joint_module)
    for node in reversed(joint_module.graph.nodes):
        if node not in required_fw_nodes:
            node.dist_from_bw = 0
        else:
            node.dist_from_bw = int(1e9)
            for user in node.users:
                node.dist_from_bw = min(node.dist_from_bw, user.dist_from_bw + 1)

    aten = torch.ops.aten
    prims = torch.ops.prims

    recomputable_ops = [aten.add, aten.sub, aten.div, aten.atan2, aten.mul, aten.max, aten.min, aten.pow, aten.remainder, aten.fmod, aten.__and__, aten.__or__, aten.__xor__, aten.__lshift__, aten.__rshift__, aten.eq, aten.ne, aten.ge, aten.gt, aten.le, aten.lt, aten.abs, aten.bitwise_not, aten.ceil, aten.floor, aten.frac, aten.neg, aten.relu, aten.round, aten.silu, aten.trunc, aten.log, aten.log10, aten.log1p, aten.log2, aten.lgamma, aten.exp, aten.expm1, aten.erf, aten.erfc, aten.cos, aten.acos, aten.cosh, aten.sin, aten.asin, aten.sinh, aten.tan, aten.atan, aten.tanh, aten.atanh, aten.sqrt, aten.rsqrt, aten.reciprocal, aten.sigmoid, aten.softplus, aten.threshold, aten.threshold_backward, aten.clamp, aten.where, aten.lerp, aten.addcmul, aten.gelu, aten.gelu_backward, aten.alias, aten.softmax, aten._softmax, aten._softmax_backward_data, aten.sum, aten.mean, aten._grad_sum_to_size, aten.sum_to_size, aten.amax, aten.to, aten.type_as, operator.getitem, aten.squeeze, aten.unsqueeze]  # noqa: E501
    if compiler == "inductor":
        recomputable_ops += [prims.div, prims.convert_element_type, aten.sign, aten.clone, aten._to_copy, aten.full_like, prims.var, prims.sum, aten.var, aten.std, prims.broadcast_in_dim, aten.select, aten.permute, aten._unsafe_view, aten.view, aten.expand, aten.slice, aten.reshape, aten.broadcast_tensors, aten.scalar_tensor, aten.ones, aten.new_zeros, aten.lift_fresh_copy, aten.minimum, aten.arange, aten.bitwise_and, aten.triu, aten.var_mean, aten.isinf, aten.any, aten.isnan, aten.full, aten.as_strided, aten.zeros, aten.argmax, aten.maximum, aten.bitwise_or, aten.logical_and, aten.logical_or]  # noqa: E501
        # Natalia said that we should allow recomputing indexing :)
        recomputable_ops += [aten.index]

    recomputable_ops = set(recomputable_ops)

    random_ops = [aten.native_dropout, aten.rand_like, aten.randn_like]
    compute_intensive_ops = [aten.mm, aten.convolution, aten.convolution_backward, aten.bmm, aten.addmm, aten.upsample_bilinear2d]  # noqa: E501

    unrecomputable_ops = random_ops + compute_intensive_ops

    fusible_ops = recomputable_ops | set(random_ops)
    if AOT_PARTITIONER_DEBUG:
        joint_module_ops = set(
            str(node.target._overloadpacket)
            for node in joint_module.graph.nodes
            if node.op == "call_function" and hasattr(node.target, "_overloadpacket")
        )
        ops_ignored = joint_module_ops - set([str(i) for i in recomputable_ops])
        print("Ops banned from rematerialization: ", ops_ignored)
        print()

    AGGRESSIVE_RECOMPUTATION = False

    def _maybe_size_of(node):
        if 'tensor_meta' in node.meta:
            return _size_of(node.meta['tensor_meta'])
        return 0

    def ban_recomputation(node):
        if AGGRESSIVE_RECOMPUTATION:
            return (node.op == 'call_function' and get_aten_target(node) in unrecomputable_ops)
        else:
            if node.op != 'call_function':
                return False
            if get_aten_target(node) not in recomputable_ops:
                return True
            if node.target == operator.getitem:
                return False
            if compiler == "inductor" and node.dist_from_bw > 4:
                return True
            # If the output of an op is 4x smaller (arbitrary choice),
            # then we don't allow recomputation.
            if 'tensor_meta' not in node.meta:
                return False
            input_tensors_size = sum(_maybe_size_of(i) for i in node.args if isinstance(i, fx.Node))
            output_size = _size_of(node.meta['tensor_meta'])
            return (output_size * 4 < input_tensors_size)

    def is_fusible(a, b):
        return get_aten_target(a) in fusible_ops and get_aten_target(b) in fusible_ops

    def is_materialized(node):
        if node.op == 'placeholder':
            return True

        return not all(is_fusible(node, user) for user in node.users)

    def get_node_weight(node):
        mem_sz = _size_of(node.meta['tensor_meta'])

        # Heuristic to bias towards nodes closer to the backwards pass
        # Complete guess about current value
        mem_sz = int(mem_sz * (1.1 ** max(min(node.dist_from_bw, 100), 1)))
        # mem_sz = int(mem_sz + node.dist_from_bw)

        if is_materialized(node):
            return mem_sz
        else:
            return mem_sz * 2

    nx_graph = nx.DiGraph()
    for node in full_bw_graph.nodes:
        if node.op == 'output':
            continue

        if node in required_bw_nodes:
            nx_graph.add_edge(node.name + "_in", "sink", capacity=math.inf)
            continue

        if node.op == 'placeholder' and "primals" in node.target:
            nx_graph.add_edge("source", node.name + "_in", capacity=math.inf)

        # If a node can't be recomputed (too expensive or involves randomness),
        # we prevent it from being recomputed by adding an inf edge to the source
        # We only need to ban nodes in the fw pass, as those are the only ones that would be recomputed.
        if ban_recomputation(node) and node in required_fw_nodes:
            nx_graph.add_edge("source", node.name + "_in", capacity=math.inf)

        if 'tensor_meta' not in node.meta:
            weight = math.inf
        else:
            weight = get_node_weight(node)

        # Creates the weights on the "node" edge
        nx_graph.add_edge(node.name + "_in", node.name + "_out", capacity=weight)
        for user in node.users:
            nx_graph.add_edge(node.name + "_out", user.name + "_in", capacity=math.inf)

    cut_value, partition = nx.minimum_cut(nx_graph, "source", "sink")
    reachable, non_reachable = partition
    cutset = set()
    for u, nbrs in ((n, nx_graph[n]) for n in reachable):
        cutset.update((u, v) for v in nbrs if v in non_reachable)

    cut_nodes = set()
    for node_in, node_out in cutset:
        assert node_in[:-3] == node_out[:-4]
        node_name = node_in[:-3]
        cut_nodes.add(node_name)

    # To make this stuff deterministic
    node_idx = {node: idx for idx, node in enumerate(joint_module.graph.nodes)}
    saved_values = sorted((name_to_node[node] for node in cut_nodes), key=lambda x: node_idx[x])
    fw_module, bw_module = _extract_fwd_bwd_modules(joint_module, saved_values)
    if AOT_PARTITIONER_DEBUG:
        print("Theoretical Activations Stored: ", sum([_size_of(i.meta['tensor_meta']) for i in saved_values]) / 1e9)
        fw_module_nodes = set([node.name for node in fw_module.graph.nodes if node.op == 'call_function'])
        bw_module_nodes = set([node.name for node in bw_module.graph.nodes if node.op == 'call_function'])
        remat_nodes = fw_module_nodes & bw_module_nodes

        counts = defaultdict(int)
        for node in fw_module.graph.nodes:
            if node.name in remat_nodes and hasattr(node.target, '_overloadpacket'):
                counts[str(node.target._overloadpacket)] += 1
        print("# nodes rematerialized: ", len(remat_nodes))
        print("Count of Ops Rematerialized: ", sorted(counts.items(), key=lambda x: x[1], reverse=True))
    return fw_module, bw_module


def draw_graph(traced: torch.fx.GraphModule, fname: str, figname: str = "fx_graph", clear_meta=True):
    if clear_meta:
        new_graph = copy.deepcopy(traced.graph)
        traced = fx.GraphModule(traced, new_graph)
        for node in traced.graph.nodes:
            node.meta = {}
    base, ext = os.path.splitext(fname)
    if not ext:
        ext = ".svg"
    print(f"Writing FX graph to file: {base}{ext}")
    g = graph_drawer.FxGraphDrawer(traced, figname)
    x = g.get_main_dot_graph()
    getattr(x, "write_" + ext.lstrip("."))(f"{base}{ext}")


def draw_joint_graph(graph, joint_inputs, file_name="full_graph.png"):
    draw_graph(graph, file_name)
    return default_partition(graph, joint_inputs)