mirror of
https://github.com/zebrajr/pytorch.git
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ec3c8abb54
Summary: to reduce the peak memory consumption Pull Request resolved: https://github.com/pytorch/pytorch/pull/100275 Approved by: https://github.com/jansel
848 lines
28 KiB
Python
848 lines
28 KiB
Python
import dataclasses
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import functools
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import itertools
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import logging
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import sys
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import warnings
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from copy import deepcopy
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from typing import Any, Callable, Dict, List, Optional
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from functorch.compile import min_cut_rematerialization_partition
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import torch._dynamo.config as dynamo_config
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import torch.fx
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import torch.utils._pytree as pytree
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from torch._dynamo import logging as dynamo_logging, utils as dynamo_utils
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from torch._dynamo.utils import detect_fake_mode
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from torch._functorch.aot_autograd import make_boxed_func
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from torch._ops import OpOverload
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from torch._subclasses.fake_tensor import FakeTensor
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from torch.fx.passes.fake_tensor_prop import FakeTensorProp
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from torch.utils._mode_utils import no_dispatch
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from .._dynamo.backends.common import aot_autograd
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from ..fx.graph import _PyTreeCodeGen
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from . import config, metrics, overrides
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from .debug import DebugContext
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from .decomposition import select_decomp_table
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from .fx_passes.joint_graph import joint_graph_passes
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from .fx_passes.post_grad import post_grad_passes
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from .fx_passes.pre_grad import pre_grad_passes
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from .graph import GraphLowering
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from .utils import developer_warning, get_dtype_size, has_incompatible_cudagraph_ops
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from .virtualized import V
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log = logging.getLogger(__name__)
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ALIGNMENT = 16
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@dataclasses.dataclass
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class BoxedBool:
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value: bool
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def __bool__(self):
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return self.value
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@staticmethod
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def disable(obj):
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if isinstance(obj, BoxedBool):
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obj.value = False
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return obj
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return False
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@dataclasses.dataclass
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class BoxedDeviceIndex:
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value: Optional[int]
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def set(self, device_idx):
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assert device_idx is None or isinstance(device_idx, int)
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self.value = device_idx
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# copy_ fails when trying to write to tensors with memory overlap,
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# for expanded dimensions (a dimension which used to have size 1 -> ?)
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# we can select one element from that dimension and write to it
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# to achieve writing to all values of that dimension of the input tensor
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def get_expanded_dims(t):
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return [i for i in range(t.ndim) if t.stride(i) == 0 and t.size(i) != 1]
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def index_expanded_dims(t, expanded_dims):
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for expanded_dim in expanded_dims:
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t = torch.ops.aten.slice(t, expanded_dim, 0, 1)
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return t
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def complex_memory_overlap(t):
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# if torch._debug_has_internal_overlap thinks this tensor potentially has
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# memory overlap internally, let's dig deeper to find out whether it's true.
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t = index_expanded_dims(t, get_expanded_dims(t))
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if torch._debug_has_internal_overlap(t) != 0:
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strides = t.stride()
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sizes = t.shape
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indices = list(range(len(strides)))
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indices = [x for _, x in sorted(zip(strides, indices))]
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for i in range(len(strides)):
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prev_stride = 1 if i == 0 else strides[indices[i - 1]]
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prev_size = 1 if i == 0 else sizes[indices[i - 1]]
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if strides[indices[i]] < prev_stride * prev_size:
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return True
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return False
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@functools.lru_cache(None)
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def _step_logger():
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return dynamo_logging.get_step_logger(log)
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@functools.lru_cache(None)
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def _warn_tf32_disabled():
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if (
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torch.cuda.is_available()
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and not torch.backends.cuda.matmul.allow_tf32
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and torch.cuda.get_device_capability() >= (8, 0)
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):
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warnings.warn(
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"TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. "
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"Consider setting `torch.set_float32_matmul_precision('high')` for better performance."
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)
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def is_tf32_warning_applicable(gm: torch.fx.GraphModule):
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aten = torch.ops.aten
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tf32_ops = {
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aten.mm.default,
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aten.addmm.default,
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aten.bmm.default,
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aten.baddbmm.default,
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}
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for node in gm.graph.nodes:
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if (
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node.op == "call_function"
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and node.target in tf32_ops
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and isinstance(node.meta.get("val", None), torch.Tensor)
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and node.meta["val"].dtype == torch.float32
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and node.meta["val"].device.type == "cuda"
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):
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return True
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return False
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@DebugContext.wrap
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def count_bytes_inner(gm, example_inputs, num_fixed=0, **kwargs):
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shape_env = _shape_env_from_inputs(example_inputs)
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graph = GraphLowering(gm, shape_env=shape_env, num_static_inputs=num_fixed)
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with V.set_graph_handler(graph):
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graph.run(*example_inputs)
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num_bytes, nodes_num_elem = graph.count_bytes()
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metrics.num_bytes_accessed += num_bytes
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metrics.nodes_num_elem += nodes_num_elem
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return make_boxed_func(gm.forward)
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@DebugContext.wrap
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@torch.utils._python_dispatch._disable_current_modes()
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def compile_fx_inner(
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gm: torch.fx.GraphModule,
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example_inputs: List[torch.Tensor],
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cudagraphs=None,
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num_fixed=0,
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is_backward=False,
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graph_id=None,
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cpp_wrapper=False,
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aot_mode=False,
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is_inference=False,
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boxed_forward_device_index=None,
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):
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if is_tf32_warning_applicable(gm):
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_warn_tf32_disabled()
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if dynamo_utils.count_calls(gm.graph) == 0:
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return make_boxed_func(gm.forward)
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# lift the maximum depth of the Python interpreter stack
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# to adapt large/deep models
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sys.setrecursionlimit(max(sys.getrecursionlimit(), 2000))
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_step_logger()(
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logging.INFO,
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"torchinductor compiling "
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f"{'BACKWARDS' if is_backward else 'FORWARDS'} "
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f"graph {graph_id}",
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)
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V.debug.fx_graph(gm, example_inputs)
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if cudagraphs is None:
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cudagraphs = config.triton.cudagraphs
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shape_env = _shape_env_from_inputs(example_inputs)
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fake_mode = detect_fake_mode(example_inputs)
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if not fake_mode:
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fake_mode = torch._subclasses.FakeTensorMode(allow_non_fake_inputs=True)
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FakeTensorProp(gm, mode=fake_mode).propagate(*example_inputs)
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else:
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FakeTensorProp(gm, mode=fake_mode).propagate_dont_convert_inputs(
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*example_inputs
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)
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# pattern matcher passes might not preserve striding information
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# on node.meta["val"]. if in the future we rely on these being
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# correct we will need to fix.
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with V.set_fake_mode(fake_mode):
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post_grad_passes(gm)
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V.debug.fx_graph_transformed(gm, example_inputs)
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with V.set_fake_mode(fake_mode):
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graph = GraphLowering(
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gm,
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shape_env=shape_env,
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num_static_inputs=num_fixed,
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graph_id=graph_id,
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cpp_wrapper=cpp_wrapper,
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aot_mode=aot_mode,
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)
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with V.set_graph_handler(graph):
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graph.run(*example_inputs)
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compiled_fn = graph.compile_to_fn()
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if aot_mode:
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return compiled_fn
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if cudagraphs:
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# output args are tuple of first argument
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output = list(gm.graph.nodes)[-1]
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assert len(output.args) == 1
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stack_traces = [
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(arg.stack_trace if isinstance(arg, torch.fx.node.Node) else None)
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for arg in output.args[0]
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]
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complex_memory_overlap_inputs = any(
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complex_memory_overlap(t)
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for t in example_inputs
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if isinstance(t, torch.Tensor)
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)
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if (
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set(graph.device_types) == {"cuda"}
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and not graph.mutated_inputs
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and not has_incompatible_cudagraph_ops(gm)
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and not complex_memory_overlap_inputs
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and all(isinstance(t, torch.Tensor) for t in example_inputs)
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and (len(graph.device_idxs) == 1 or not config.triton.cudagraph_trees)
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):
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if (
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boxed_forward_device_index is not None
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and not is_inference
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and not is_backward
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):
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boxed_forward_device_index.set(next(iter(graph.device_idxs)))
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compiled_fn = cudagraphify(
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compiled_fn,
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example_inputs,
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static_input_idxs=range(num_fixed),
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device_index=next(iter(graph.device_idxs)),
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stack_traces=stack_traces,
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is_backward=is_backward,
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is_inference=is_inference,
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)
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else:
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BoxedBool.disable(cudagraphs)
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# See [Backward Generation Handling]
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# if cudagraph'd the forward and set the device, we need to let the cudagraph manager
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# know we are we running the backward even if we will not run it in cudagraphs
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if is_backward and config.triton.cudagraph_trees:
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assert boxed_forward_device_index.value is not None
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compiled_fn_inner = compiled_fn
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manager = torch._inductor.cudagraph_trees.get_manager(
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boxed_forward_device_index.value, create_if_none_exists=False
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)
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# should already exist from forward
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assert manager is not None
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def compiled_fn(new_inputs):
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manager.set_to_running_backward()
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return compiled_fn_inner(new_inputs)
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if len(set(graph.device_types)) > 1:
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developer_warning("skipping cudagraphs due to multiple devices")
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elif set(graph.device_types) == {"cuda"}:
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if graph.mutated_inputs:
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developer_warning("skipping cudagraphs due to input mutation")
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elif complex_memory_overlap_inputs:
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developer_warning(
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"skipping cudagraphs due to complex input striding"
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)
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elif len(graph.device_idxs) > 1 and config.triton.cudagraph_trees:
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developer_warning(
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"skipping cudagraphs due to multiple device indexes"
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)
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result = align_inputs(compiled_fn, example_inputs, range(num_fixed))
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_step_logger()(
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logging.INFO,
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"torchinductor done compiling "
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f"{'BACKWARDS' if is_backward else 'FORWARDS'} "
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f"graph {graph_id}",
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)
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# aot autograd needs to know to pass in inputs as a list
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result._boxed_call = True
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return result
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def clone_preserve_strides(x):
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needed_size = (
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sum((shape - 1) * stride for shape, stride in zip(x.size(), x.stride())) + 1
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)
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buffer = torch.as_strided(x, (needed_size,), (1,)).clone()
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return torch.as_strided(buffer, x.size(), x.stride())
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def align_inputs(model, inputs, static_input_idxs=()):
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def is_aligned(storage_offset, dtype):
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return (storage_offset * get_dtype_size(dtype)) % ALIGNMENT == 0
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check_inputs = [
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i
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for i in range(len(inputs))
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if isinstance(inputs[i], torch.Tensor)
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and (
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i not in static_input_idxs
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or not is_aligned(inputs[i].storage_offset(), inputs[i].dtype)
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)
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and inputs[i].device.type == "cuda"
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]
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if len(check_inputs) == 0:
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return model
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def run(new_inputs):
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for i in check_inputs:
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if new_inputs[i].data_ptr() % ALIGNMENT:
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new_inputs[i] = clone_preserve_strides(new_inputs[i])
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return model(new_inputs)
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return run
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@dynamo_utils.dynamo_timed
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def cudagraphify(
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model,
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inputs,
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static_input_idxs=(),
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*,
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device_index: int,
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stack_traces: List[Optional[str]],
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is_backward: bool,
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is_inference: bool,
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):
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from torch._inductor.cudagraph_trees import (
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cudagraphify_impl as new_cudagraphify_impl,
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)
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if config.triton.cudagraph_trees:
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cudagraphify_fn = functools.partial(
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new_cudagraphify_impl,
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device_index=device_index,
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stack_traces=stack_traces,
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is_backward=is_backward,
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is_inference=is_inference,
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)
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else:
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cudagraphify_fn = cudagraphify_impl
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# if using fake tensors, defer cudagraphs until we get real inputs at runtime
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if not any(isinstance(inp, FakeTensor) for inp in inputs):
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return cudagraphify_fn(model, inputs, static_input_idxs)
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compiled_fn = None
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def run(new_inputs):
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nonlocal compiled_fn
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if compiled_fn is None:
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with dynamo_utils.preserve_rng_state():
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compiled_fn = cudagraphify_fn(model, new_inputs, static_input_idxs)
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return compiled_fn(new_inputs)
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return run
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def remove_unaligned_input_idxs(inputs, static_input_idxs):
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"""
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We require all inputs to be aligned, so introduce a copy for any
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that aren't.
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"""
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aligned_static_input_idxs = {
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idx for idx in static_input_idxs if (inputs[idx].data_ptr() % ALIGNMENT) == 0
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}
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if len(aligned_static_input_idxs) != len(static_input_idxs):
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return aligned_static_input_idxs
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return static_input_idxs
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def static_input(x):
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"""
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Copy and input while preserving strides
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"""
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# TODO(jansel): figure out why this version doesn't work:
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# return torch.empty_strided(x.size(), x.stride(), dtype=x.dtype, device=x.device)
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needed_size = (
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sum((shape - 1) * stride for shape, stride in zip(x.size(), x.stride())) + 1
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)
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buffer = torch.empty(needed_size, dtype=x.dtype, device=x.device)
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return torch.as_strided(buffer, x.size(), x.stride())
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def index_expanded_dims_and_copy_(dst, src, expanded_dims):
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"Index into expanded dimensions of both dst and src then copy_"
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dst = index_expanded_dims(dst, expanded_dims)
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src = index_expanded_dims(src, expanded_dims)
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dst.copy_(src)
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def cudagraphify_impl(model, inputs, static_input_idxs=()):
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"""
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Assumes inputs[static_input_idxs[i]] are always the same memory address
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"""
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static_input_idxs = remove_unaligned_input_idxs(inputs, static_input_idxs)
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assert isinstance(inputs, (list, tuple))
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inps_expanded_dims = [
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get_expanded_dims(x) if idx not in static_input_idxs else []
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for idx, x in enumerate(inputs)
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]
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# allocate static tensor inputs
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static_inputs = [
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static_input(x) if idx not in static_input_idxs else x.detach()
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for idx, x in enumerate(inputs)
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]
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# copy over input values for fresh allocations
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for idx, (x, expanded_dims) in enumerate(zip(inputs, inps_expanded_dims)):
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if idx not in static_input_idxs:
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index_expanded_dims_and_copy_(static_inputs[idx], x, expanded_dims)
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# warmup
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torch.cuda.synchronize()
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stream = torch.cuda.Stream()
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stream.wait_stream(torch.cuda.current_stream())
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# copy static_inputs because it will be cleared in model
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with torch.cuda.stream(stream):
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model(list(static_inputs))
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stream.synchronize()
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torch.cuda.current_stream().wait_stream(stream)
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torch.cuda.synchronize()
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# record
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graph = torch.cuda.CUDAGraph()
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with torch.cuda.graph(graph, stream=stream):
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static_outputs = model(list(static_inputs))
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if not isinstance(static_outputs, (list, tuple)):
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static_outputs = (static_outputs,)
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|
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if config.size_asserts:
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|
|
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def run(new_inputs):
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assert len(static_inputs) == len(new_inputs)
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for idx, (dst, src, expanded_dims) in enumerate(
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zip(static_inputs, new_inputs, inps_expanded_dims)
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):
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if idx in static_input_idxs:
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assert dst.data_ptr() == src.data_ptr()
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else:
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# TODO - could make one single op of multiple slices
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# and avoid dispatch.
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# Could also pre-index the `dst` tensors
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index_expanded_dims_and_copy_(dst, src, expanded_dims)
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new_inputs.clear()
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graph.replay()
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return static_outputs
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else:
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copy_indices = [
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idx for idx in range(len(static_inputs)) if idx not in static_input_idxs
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]
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|
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def run(new_inputs):
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for idx in copy_indices:
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expanded_dims = inps_expanded_dims[idx]
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index_expanded_dims_and_copy_(
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static_inputs[idx], new_inputs[idx], expanded_dims
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)
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new_inputs.clear()
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graph.replay()
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return static_outputs
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return run
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|
|
|
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def count_tangents(fx_g: torch.fx.GraphModule):
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"""
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Infers which inputs are static for a backwards graph
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"""
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|
|
|
def is_not_gradout(x):
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return "tangents" not in x.name
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|
|
arg_count = 0
|
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static_arg_idxs = []
|
|
for n in fx_g.graph.nodes:
|
|
if n.op == "placeholder":
|
|
if is_not_gradout(n):
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static_arg_idxs.append(arg_count)
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arg_count += 1
|
|
|
|
assert static_arg_idxs == list(range(len(static_arg_idxs)))
|
|
return len(static_arg_idxs)
|
|
|
|
|
|
def compile_fx_with_cpp_wrapper(
|
|
module: torch.fx.GraphModule,
|
|
example_inputs: List[torch.Tensor],
|
|
inner_compile,
|
|
decompositions: Optional[Dict[OpOverload, Callable]] = None,
|
|
aot_mode=False,
|
|
):
|
|
"""
|
|
Compile into cpp wrapper:
|
|
For CPU, this is currently done in one pass.
|
|
For GPU, this is done in two passes: JIT-compile the model with python wrapper code
|
|
and run it to generate autotuned kernel binaries in the first pass; and then generate
|
|
cpp wrapper code and compile it to a dynamic library in the second pass.
|
|
"""
|
|
# Turns off cpp_wrapper before calling back into compile_fx
|
|
config_patches = {"cpp_wrapper": False}
|
|
devices = (
|
|
{t.device.type for t in module.parameters()}
|
|
| {t.device.type for t in module.buffers()}
|
|
| {t.device.type for t in example_inputs if isinstance(t, torch.Tensor)}
|
|
)
|
|
|
|
if "cuda" not in devices:
|
|
with config.patch(config_patches):
|
|
return compile_fx(
|
|
module,
|
|
example_inputs,
|
|
inner_compile=functools.partial(
|
|
inner_compile, cpp_wrapper=True, aot_mode=aot_mode
|
|
),
|
|
decompositions=decompositions,
|
|
)
|
|
else:
|
|
config_patches.update(
|
|
{
|
|
"triton.cudagraphs": False,
|
|
"triton.store_cubin": True,
|
|
}
|
|
)
|
|
with config.patch(config_patches):
|
|
# first pass
|
|
compiled = compile_fx(
|
|
module,
|
|
example_inputs,
|
|
inner_compile=functools.partial(
|
|
inner_compile, cpp_wrapper=False, aot_mode=False
|
|
),
|
|
decompositions=decompositions,
|
|
)
|
|
if detect_fake_mode(example_inputs):
|
|
with no_dispatch():
|
|
|
|
def to_real_tensor(e):
|
|
if isinstance(e, FakeTensor):
|
|
out = torch.zeros_like(e, device=e.fake_device)
|
|
return out
|
|
return e
|
|
|
|
inputs_real = [to_real_tensor(t) for t in example_inputs]
|
|
else:
|
|
inputs_real = deepcopy(example_inputs)
|
|
|
|
compiled(*inputs_real)
|
|
del inputs_real
|
|
|
|
# second pass
|
|
return compile_fx(
|
|
module,
|
|
example_inputs,
|
|
inner_compile=functools.partial(
|
|
inner_compile, cpp_wrapper=True, aot_mode=aot_mode
|
|
),
|
|
decompositions=decompositions,
|
|
)
|
|
|
|
|
|
def compile_fx_aot(
|
|
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,
|
|
):
|
|
if config_patches:
|
|
with config.patch(config_patches):
|
|
return compile_fx_aot(
|
|
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,
|
|
)
|
|
return compile_fx_with_cpp_wrapper(
|
|
model_, example_inputs_, inner_compile, decompositions, aot_mode=True
|
|
)
|
|
|
|
|
|
_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,
|
|
)
|
|
|
|
if config.cpp_wrapper:
|
|
return compile_fx_with_cpp_wrapper(
|
|
model_,
|
|
example_inputs_,
|
|
inner_compile=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):
|
|
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,
|
|
)
|
|
|
|
# Since handle_dynamo_export_graph will trigger compile_fx again,
|
|
# Move these passes after handle_dynamo_export_graph to avoid repeated calls.
|
|
model_ = pre_grad_passes(model_, example_inputs_)
|
|
|
|
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
|
|
num_example_inputs = len(example_inputs_)
|
|
cudagraphs = BoxedBool(
|
|
config.triton.cudagraphs and not dynamo_config.dynamic_shapes
|
|
)
|
|
forward_device = BoxedDeviceIndex(None)
|
|
|
|
graph_id = next(_graph_counter)
|
|
|
|
@dynamo_utils.dynamo_timed
|
|
def fw_compiler_base(model: torch.fx.GraphModule, example_inputs, is_inference):
|
|
if is_inference:
|
|
# partition_fn won't be called
|
|
joint_graph_passes(model)
|
|
|
|
fixed = len(example_inputs) - num_example_inputs
|
|
return inner_compile(
|
|
model,
|
|
example_inputs,
|
|
num_fixed=fixed,
|
|
cudagraphs=cudagraphs,
|
|
graph_id=graph_id,
|
|
is_inference=is_inference,
|
|
boxed_forward_device_index=forward_device,
|
|
)
|
|
|
|
fw_compiler = functools.partial(fw_compiler_base, is_inference=False)
|
|
inference_compiler = functools.partial(fw_compiler_base, is_inference=True)
|
|
|
|
def partition_fn(graph, joint_inputs, **kwargs):
|
|
joint_graph_passes(graph)
|
|
return min_cut_rematerialization_partition(
|
|
graph, joint_inputs, **kwargs, compiler="inductor"
|
|
)
|
|
|
|
# Save and restore dynamic shapes setting for backwards, as it is
|
|
# sometimes done as a context manager which won't be set when we
|
|
# hit backwards compile
|
|
dynamic_shapes = dynamo_config.dynamic_shapes
|
|
|
|
@dynamo_utils.dynamo_timed
|
|
def bw_compiler(model: torch.fx.GraphModule, example_inputs):
|
|
with dynamo_config.patch(dynamic_shapes=dynamic_shapes):
|
|
fixed = count_tangents(model)
|
|
return inner_compile(
|
|
model,
|
|
example_inputs,
|
|
num_fixed=fixed,
|
|
cudagraphs=cudagraphs,
|
|
is_backward=True,
|
|
graph_id=graph_id,
|
|
boxed_forward_device_index=forward_device,
|
|
)
|
|
|
|
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,
|
|
inference_compiler=inference_compiler,
|
|
decompositions=decompositions,
|
|
partition_fn=partition_fn,
|
|
keep_inference_input_mutations=True,
|
|
)(model_, example_inputs_)
|
|
|
|
|
|
def _shape_env_from_inputs(inputs):
|
|
shape_env = None
|
|
fake_mode = detect_fake_mode(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
|
|
|
|
# When there are no tensor inputs, get shape_env from the first SymInt.
|
|
for input in inputs:
|
|
if isinstance(input, torch.SymInt):
|
|
return input.node.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
|
|
if (
|
|
isinstance(rv, torch.fx.node.Node)
|
|
and hasattr(rv.target, "_schema")
|
|
and len(rv.target._schema.returns) > 1
|
|
and all(str(ret.type) == "Tensor" for ret in rv.target._schema.returns)
|
|
):
|
|
# for graphs whose result is one node with multiple outputs
|
|
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 codegen 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
|