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 torch.fx.passes import graph_drawer
from typing import Tuple

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(
        # 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:
        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
            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':
    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")

    return new_graph

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

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

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 ==

    # 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

[docs]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 = set([ for node in forward_only_graph.nodes if node.op != 'output']) def node_saved(node): return in forward_node_names and 'tensor_meta' in node.meta saved_values = [node for node in joint_module.graph.nodes if node_saved(node)] 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, 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]
[docs]def min_cut_rematerialization_partition( joint_module: fx.GraphModule, _joint_inputs ) -> 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") # draw_graph(joint_module, "joint.svg") full_bw_graph = joint_module.graph nx_graph = nx.DiGraph() tangent_closure = set() name_to_node = {} for node in full_bw_graph.nodes: name_to_node[] = node if node.op == 'placeholder' and "tangents" in tangent_closure.add(node) if node in tangent_closure: for user in node.users: tangent_closure.add(user) aten = torch.ops.aten pointwise_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.le,, 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] # noqa: E501 misc_ops = [, aten.type_as, operator.getitem] # Ban reductions for now due to it being unnecessary/running into pathological situations # todo(chilli): add a heuristic to allow reduction only if output node is much smaller than input node reduction_ops = [aten.softmax, aten._softmax, aten._softmax_backward_data, aten.sum, aten.mean, aten._grad_sum_to_size, aten.sum_to_size, aten.amax] # noqa: E501 # not recomputed by default since these are kinda expensive/hard to fuse into # norm_ops = [aten.instance_norm, aten._batch_norm_impl_index, aten.native_batch_norm, aten.batch_norm, aten._batch_norm_impl_index_backward, aten.native_layer_norm, aten.layer_norm, aten.native_layer_norm_backward] # noqa: E501 # Not used by default since NVFuser can't fuse view ops # view_ops = [aten.expand, aten.clone, aten.transpose, aten.t, aten.view, aten._unsafe_view, aten.permute, aten.transpose, aten.t, aten._reshape_alias, aten.squeeze, aten.unsqueeze, aten.reshape,, aten.slice, aten.split,, aten.repeat] # noqa: E501 unrecomputable_ops = [, aten.convolution, aten.convolution_backward, aten.bmm, aten.addmm, aten.native_dropout, aten.rand_like, aten.randn_like, aten.upsample_bilinear2d] # noqa: E501 recomputable_ops = set( pointwise_ops + misc_ops # + reduction_ops # + norm_ops # + view_ops ) # ops = set([ for i in joint_module.graph.nodes if i.op == 'call_function']) # print(ops - recomputable_ops) AGGRESSIVE_RECOMPUTATION = False def ban_recomputation(node): if AGGRESSIVE_RECOMPUTATION: return (node.op == 'call_function' and in unrecomputable_ops) else: if node.op != 'call_function': return False if not in recomputable_ops: return True # If the output of the reduction is 4x smaller (arbitrary choice), # then we don't allow recomputation. if in reduction_ops: input_tensors_size = sum([_size_of(i.meta['tensor_meta']) 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) return False def get_node_weight(node): mem_sz = _size_of(node.meta['tensor_meta']) if node.op == 'placeholder' and "primals" in return mem_sz else: return mem_sz * 2 for node in full_bw_graph.nodes: if node in tangent_closure and node.op != 'output': nx_graph.add_edge("_in", "sink", capacity=math.inf) continue if node.op == 'placeholder' and "primals" in nx_graph.add_edge("source","_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 if ban_recomputation(node): nx_graph.add_edge("source","_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("_in","_out", capacity=weight) for user in node.users: nx_graph.add_edge("_out","_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) # print(len(cut_nodes), sorted(list(cut_nodes))) saved_values = [name_to_node[node] for node in cut_nodes] return _extract_fwd_bwd_modules(joint_module, saved_values)
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)