pytorch/torch/_lazy/extract_compiled_graph.py

import torch._lazy.metrics as metrics
from torch._lazy.tensor_factory_functions import tensor_factory_functions
from torch._lazy import computation
from torch._lazy import debug as lazy_debug
import torch._lazy as lazy
import dataclasses
from typing import List, Dict, Any, Callable
import copy
from torch import fx
import torch
import itertools
import os

debug = os.environ.get("debug_extract_compiled_graph") is not None

@dataclasses.dataclass
class GraphInputMatcher:
    """
    The GraphInputMatcher class setup the graph inputs for future calls after lazy tracing.
    Specifically, those graph inputs corresponding to method parameters should be replaced with the
    arguments for the current call.

    tensor_id_to_arg_idx maps the tensor id to the parameter index.
    graph_input_tensor_ids, graph_input_ivalues list the tensor_id and ivalue for each of the
    TS/XLA graph inputs.
    """
    tensor_id_to_arg_idx: Dict[int, int]
    graph_input_tensor_ids: List[int]
    # there are 2 categories of graph_input_tensors.
    # Category 1: those whose id are not found in tensor_id_to_arg_idx. These are
    # most likely const tensors and we can get its content from graph_input_tensors
    # Category 2: those whose id are found in tensor_id_to_arg_idx. We should get
    #  the tensor from method arguments
    graph_input_ivalues: List[Any]

    # get the real graph input tensors
    def __call__(self, args):
        real_input = []
        for tensor_id, traced_ivalue in zip(self.graph_input_tensor_ids, self.graph_input_ivalues):
            arg_idx = self.tensor_id_to_arg_idx.get(tensor_id, None)
            if arg_idx is None:
                inp = traced_ivalue
            else:
                inp = args[arg_idx]
            real_input.append(inp)
        return real_input

class ReturnValueHandler:
    r"""
    When ltc_sync_multi is called on multi tensors, the compiled graph
    will contain output only for unique tensors - if a tensor appears multiple
    times in the input to _ltc_sync_multi, only the first occurance matters.

    However from python level, we still expect multi tensors returned with duplciation
    even if the TS graph dedup the output. e.g. for method:

      def forward(self, a):
        return a, a

    the TS graph captured by LTC will return a single tensor, but Python method expects 2.

    This class dedup the lazy tensors first to get the index that will be used
    to duplicate the eager tensors later.
    """
    def __init__(self, lazy_out_list):
        self.index: List[List[int]] = []
        self.total_count = len(lazy_out_list)

        tensor_id_to_idx: Dict[int, int] = dict()
        for dup_idx, lazy_tensor in enumerate(lazy_out_list):
            uniq_idx = tensor_id_to_idx.get(id(lazy_tensor), None)
            if uniq_idx is not None:
                self.index[uniq_idx].append(dup_idx)
            else:
                uniq_idx = len(self.index)
                self.index.append([dup_idx])
                tensor_id_to_idx[id(lazy_tensor)] = uniq_idx

    def duplicate_eager_tensors(self, eager_tensor_list):
        duplicated_list = [None] * self.total_count
        assert len(eager_tensor_list) == len(self.index)

        for uniq_idx, eager_tensor in enumerate(eager_tensor_list):
            for dup_idx in self.index[uniq_idx]:
                duplicated_list[dup_idx] = eager_tensor
        return duplicated_list

def force_lazy_device(model: fx.GraphModule):
    """
    Factory methods in a Fx graph may create tensors for a specific eager devices.
    If we take no actions, those eager tensors will be mixed with lazy tensors and
    cause crash. This method overwrite those eager device to lazy device.
    """
    def tolazydevice(dev):
        if isinstance(dev, torch.device):
            return torch.device("lazy", index=dev.index)
        return dev

    def hasDeviceArg(args, kwargs):
        return any(isinstance(arg, torch.device) for arg in itertools.chain(args, kwargs.values()))

    for nd in model.graph.nodes:
        nd.args = tuple(tolazydevice(arg) for arg in nd.args)
        nd.kwargs = {k: tolazydevice(v) for k, v in nd.kwargs.items()}

        # For torchbench like yolov3, hf_Bart, dynamo generates Fx graph that return
        # eager tensors on the default device
        # (check https://gist.github.com/shunting314/eabdf6c769c59bc384469717b8f9bb7f for yolove,
        # and https://gist.github.com/shunting314/8d5e2d9348a3258959d3954186c48814 for hf_Bart).
        # To force those tensors on the lazy device, we can not simply override
        # the device argument since there is no explicit device argument.
        # What we are doing here is, for the list of covered tensor factory methods
        # we add a lazy device argument explicity.
        #
        # TODO: This solution is no ideal since we may miss some factory methods. In future
        # when we support lazy mode, this method can be replaced by that.
        if nd.target in tensor_factory_functions and not hasDeviceArg(nd.args, nd.kwargs):
            kwargs = dict(nd.kwargs)  # nd.kwargs is immutable. make a mutable copy.
            kwargs["device"] = torch.device("lazy")
            nd.kwargs = kwargs

    model.recompile()

def get_fallback_ops():
    fallback_ops = []
    for opname in metrics.counter_names():
        if "aten::" not in opname:
            continue
        val = int(metrics.counter_value(opname))
        if val > 0:
            fallback_ops.append(f"{opname}={val}")

    return fallback_ops

def extract_compiled_graph(model: fx.GraphModule, example_inputs) -> Callable:
    """
    Optimize an eager model with LTC and returns a wrapper to execute the
    compiled graph directly without retracing. It depends on other mechanisms
    like TorchDynamo guards to guarantee the returned wrapper is only called
    when it's safe.
    """
    lazy_args = [arg.to(device="lazy") for arg in example_inputs]
    args_tensor_ids = [lazy.get_tensor_id(lazy_arg) for lazy_arg in lazy_args]
    tensor_id_to_arg_idx = {tensor_id: i for i, tensor_id in enumerate(args_tensor_ids)}
    lazy_model = copy.deepcopy(model).to(device=torch.device("lazy"))
    force_lazy_device(lazy_model)

    # This line executes lazy tracing and enable us extracting compiled graph later
    metrics.reset()
    lazy_out = lazy_model(*lazy_args)
    fallback_ops = get_fallback_ops()
    metrics.reset()

    if len(fallback_ops) > 0:
        raise RuntimeError(f"Fail to extact the compiled graph because of fallback: {','.join(fallback_ops)}")

    if not isinstance(lazy_out, (tuple, list)):
        lazy_out = (lazy_out,)

    args_and_out = tuple(lazy_args) + tuple(lazy_out)
    return_value_handler = ReturnValueHandler(args_and_out)
    if debug:
        print("Fx code:\n", model.code)
        print("LTC IR:", lazy_debug.dump_ir(args_and_out, "text"))

    # TODO: this part is TS backend specific for now and will be generalized to
    # support XLA
    graph_input_tensor_ids, graph_input_ivalues = computation.get_tensors_ts_device_data_node(args_and_out)
    assert len(graph_input_tensor_ids) == len(graph_input_ivalues)
    graph_input_matcher = GraphInputMatcher(tensor_id_to_arg_idx, graph_input_tensor_ids, graph_input_ivalues)

    graph_hash = computation.get_graph_hash(args_and_out)

    if debug:
        print("graph_hash", graph_hash)
        print(f"args_tensor_ids {args_tensor_ids}")
        print("tensor ids from device data:", graph_input_tensor_ids)

    # sync the list of output tensors so the computation graph for these
    # tensors will be cached. Those computation graphs can be retrieved
    # by graph hash later.
    lazy.sync_multi(args_and_out, [])

    def optimized_mod(*args):
        if len(args_and_out) == 0:
            return ()
        graph_input = graph_input_matcher(args)
        res = return_value_handler.duplicate_eager_tensors(computation.run_cached_graph(graph_hash, graph_input))

        assert len(res) == len(args_and_out)
        for i, arg in enumerate(args):
            # only copy those tensors that get inplace updated
            if arg is not res[i]:
                arg.copy_(res[i])

        # skip the args
        return res[len(args):]

    return optimized_mod