mirror of
https://github.com/pytorch/pytorch.git
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571f96bf59
CUDA Graph Trees Design doc: https://docs.google.com/document/d/1ZrxLGWz7T45MSX6gPsL6Ln4t0eZCSfWewtJ_qLd_D0E/edit Not currently implemented : - Right now, we are using weak tensor refs from outputs to check if a tensor has dies. This doesn't work because a) aliasing, and b) aot_autograd detaches tensors (see note [Detaching saved tensors in AOTAutograd]). Would need either https://github.com/pytorch/pytorch/issues/91395 to land to use storage weak refs or manually add a deleter fn that does what I want. This is doable but theres some interactions with the caching allocator checkpointing so saving for a stacked pr. - Reclaiming memory from the inputs during model recording. This isn't terribly difficult but deferring to another PR. You would need to write over the input memory during warmup, and therefore copy the inputs to cpu. Saving for a stacked pr. - Warning on overwriting previous generation outputs. and handling nested torch.compile() calls in generation tracking Differential Revision: [D43999887](https://our.internmc.facebook.com/intern/diff/D43999887) Pull Request resolved: https://github.com/pytorch/pytorch/pull/89146 Approved by: https://github.com/ezyang
642 lines
20 KiB
Python
642 lines
20 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 typing import Any, Callable, Dict, List, Optional
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import functorch
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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 fake_mode_from_tensors
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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 .._dynamo.backends.common import aot_autograd
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from ..fx.graph import _PyTreeCodeGen
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from . import config, metrics, overrides, pattern_matcher
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from .debug import DebugContext
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from .decomposition import select_decomp_table
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from .graph import GraphLowering
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from .mkldnn import convert_outplace_to_inplace
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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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# 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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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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aot_mode=False,
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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 = fake_mode_from_tensors(
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example_inputs
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) or torch._subclasses.FakeTensorMode(allow_non_fake_inputs=True)
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with V.set_fake_mode(fake_mode):
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pattern_matcher.fx_passes(gm)
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V.debug.fx_graph_transformed(gm, example_inputs)
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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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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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complex_memory_overlap_inputs = any(
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complex_memory_overlap(t) for t in example_inputs
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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 (len(graph.device_idxs) == 1 or not config.triton.cudagraph_trees)
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):
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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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)
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else:
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BoxedBool.disable(cudagraphs)
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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(model, inputs, static_input_idxs=(), *, device_index: int):
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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, device_index=device_index
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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 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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static_inputs = [
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static_input(x).zero_() 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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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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# 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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if config.size_asserts:
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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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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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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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def run(new_inputs):
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for idx in copy_indices:
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src = index_expanded_dims(static_inputs[idx], inps_expanded_dims[idx])
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dst = index_expanded_dims(new_inputs[idx], inps_expanded_dims[idx])
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dst.copy_(src)
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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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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 = []
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for n in fx_g.graph.nodes:
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if n.op == "placeholder":
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if is_not_gradout(n):
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static_arg_idxs.append(arg_count)
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arg_count += 1
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assert static_arg_idxs == list(range(len(static_arg_idxs)))
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return len(static_arg_idxs)
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_graph_counter = itertools.count(0)
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def compile_fx(
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model_: torch.fx.GraphModule,
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example_inputs_: List[torch.Tensor],
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inner_compile=compile_fx_inner,
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config_patches: Optional[Dict[str, Any]] = None,
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decompositions: Optional[Dict[OpOverload, Callable]] = None,
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aot_mode=False,
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):
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"""Main entrypoint to a compile given FX graph"""
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if config_patches:
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with config.patch(config_patches):
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return compile_fx(
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model_,
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example_inputs_,
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# need extra layer of patching as backwards is compiled out of scope
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inner_compile=config.patch(config_patches)(inner_compile),
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decompositions=decompositions,
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aot_mode=aot_mode,
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)
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if aot_mode:
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aot_config_patches = {
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"cpp_wrapper": True,
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"debug": True,
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"triton.cudagraphs": False,
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}
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with config.patch(aot_config_patches):
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return compile_fx(
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model_,
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example_inputs_,
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inner_compile=functools.partial(inner_compile, aot_mode=aot_mode),
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decompositions=decompositions,
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)
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recursive_compile_fx = functools.partial(
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compile_fx,
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inner_compile=inner_compile,
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decompositions=decompositions,
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)
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if not graph_returns_tuple(model_):
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return make_graph_return_tuple(
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model_,
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example_inputs_,
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recursive_compile_fx,
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)
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if isinstance(model_, torch.fx.GraphModule):
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with overrides.patch_functions():
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model_ = overrides.replace_fx(model_)
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model_ = overrides.fuse_fx(model_, example_inputs_)
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if isinstance(model_.graph._codegen, _PyTreeCodeGen):
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# this graph is the result of dynamo.export()
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return handle_dynamo_export_graph(
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model_,
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example_inputs_,
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recursive_compile_fx,
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)
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if any(isinstance(x, (list, tuple, dict)) for x in example_inputs_):
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return flatten_graph_inputs(
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model_,
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example_inputs_,
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recursive_compile_fx,
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)
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assert not config._raise_error_for_testing
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functorch.compile.config.use_functionalize = True
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functorch.compile.config.use_fake_tensor = True
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num_example_inputs = len(example_inputs_)
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cudagraphs = BoxedBool(
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config.triton.cudagraphs and not dynamo_config.dynamic_shapes
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|
)
|
|
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,
|
|
)
|
|
|
|
# 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,
|
|
)
|
|
|
|
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
|