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01ab5a3104
In the next PR I force `set_()` input mutation to require always been in the graph. It's a lot easier to do this if we make our other debugging backends allow input mutations in the graph. Input mutations are relatively hardened at this point, so I'd rather just have our debugging backends consistently allow input mutations. Pull Request resolved: https://github.com/pytorch/pytorch/pull/123646 Approved by: https://github.com/ezyang ghstack dependencies: #122433
301 lines
9.5 KiB
Python
301 lines
9.5 KiB
Python
# mypy: ignore-errors
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import dataclasses
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import functools
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from importlib import import_module
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from typing import Any, List, Optional
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from functorch.compile import min_cut_rematerialization_partition
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import torch
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from torch import _guards
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from torch._functorch import config as functorch_config
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from torch._functorch.compilers import ts_compile
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from .common import aot_autograd
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from .registry import register_debug_backend as register_backend
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"""
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This file contains TorchDynamo backends intended for debugging uses.
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"""
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@register_backend
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def eager(gm, fake_tensor_inputs):
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return gm
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@register_backend
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def pre_dispatch_eager(gm, fake_tensor_inputs):
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from torch.fx.experimental.proxy_tensor import make_fx
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def runnable_gm(*args):
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return torch.fx.Interpreter(gm).run(*args)
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pre_dispatch_gm = make_fx(runnable_gm, pre_dispatch=True)(*fake_tensor_inputs)
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pre_dispatch_gm.print_readable()
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return pre_dispatch_gm
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@register_backend
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def eager_debug(gm, fake_tensor_inputs):
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from torch._subclasses.schema_check_mode import SchemaCheckMode
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# We could add more debugging bits here.
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# Right now, this backend can be used to check for and error on
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# custom dispatcher ops that have incorrect schemas.
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def inner(*args):
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with SchemaCheckMode():
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return torch.fx.Interpreter(gm).run(*args)
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return inner
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@register_backend(name="ts")
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def torchscript(gm, fake_tensor_inputs):
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return torch.jit.script(gm)
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# used boxed call to discard inputs when they are no longer needed
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def boxed_nop(fx_g, example_inputs):
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def run(args):
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return torch.fx.Interpreter(fx_g).boxed_run(args)
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run._boxed_call = True
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return run
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# Useful for debugging purpose
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# aot_eager uses AOT Autograd backend with nop compiler. It is helpful in debugging.
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aot_eager = aot_autograd(
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fw_compiler=boxed_nop,
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partition_fn=min_cut_rematerialization_partition,
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keep_inference_input_mutations=True,
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)
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register_backend(name="aot_eager", compiler_fn=aot_eager)
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aot_eager_default_partitioner = aot_autograd(
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fw_compiler=boxed_nop, keep_inference_input_mutations=True
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)
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register_backend(
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name="aot_eager_default_partitioner", compiler_fn=aot_eager_default_partitioner
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)
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# Uses TorchInductor AOT Autograd decomps and partitioner to isolate aot vs
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# inductor problems.
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# aot_eager_decomp_partition just replaces the inductor compiler with nop to help
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# isolate inductor vs aot_eager errors
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def aot_eager_decomp_partition(gm, fake_tensor_inputs):
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with functorch_config.patch(unlift_effect_tokens=True):
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return aot_autograd(
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# these are taken from memory_efficient_fusion()
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fw_compiler=boxed_nop,
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bw_compiler=boxed_nop,
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# NB: lambda here is to delay import of inductor
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decompositions=lambda: import_module(
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"torch._inductor.compile_fx"
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).select_decomp_table(),
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partition_fn=functools.partial(
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min_cut_rematerialization_partition, compiler="inductor"
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),
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)(gm, fake_tensor_inputs)
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register_backend(
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name="aot_eager_decomp_partition", compiler_fn=aot_eager_decomp_partition
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)
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# AOT Autograd with torchscript backend. Default partitioner.
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# aot_ts uses torchscript backend. We can use this with both nnc and nvfuser
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# by using the relevant fuser with torch.jit.fuser(...)
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aot_ts = aot_autograd(fw_compiler=ts_compile)
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register_backend(name="aot_ts", compiler_fn=aot_ts)
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# These buggy backends are used for inducing bugs so that we can test
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# our repro extraction / minifier scripts
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class ReluCompileError(Exception):
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pass
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class TestingOnlyCompileError(Exception):
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pass
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@register_backend
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def relu_compile_error_TESTING_ONLY(gm: torch.fx.GraphModule, example_inputs):
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for node in gm.graph.nodes:
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if node.target == torch.relu:
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raise ReluCompileError()
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return gm
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@register_backend
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def relu_runtime_error_TESTING_ONLY(gm: torch.fx.GraphModule, example_inputs):
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for node in gm.graph.nodes:
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if node.target == torch.relu:
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node.target = torch._assert
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node.args = (False, "ReluRuntimeError")
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gm.recompile()
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return gm
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@register_backend
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def relu_accuracy_error_TESTING_ONLY(gm: torch.fx.GraphModule, example_inputs):
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for node in gm.graph.nodes:
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if node.target == torch.relu:
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node.target = torch.add
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node.args = (node.args[0], 1)
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gm.recompile()
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return gm
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@register_backend
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def non_leaf_compile_error_TESTING_ONLY(gm: torch.fx.GraphModule, example_inputs):
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# Require at least one non-trivial thing in the graph,
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# see https://github.com/pytorch/pytorch/issues/102898
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for node in gm.graph.nodes:
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if node.op == "call_function":
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break
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else:
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return gm
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for t in example_inputs:
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if not t.is_leaf:
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raise TestingOnlyCompileError()
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return gm
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@dataclasses.dataclass
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class ExplainOutput:
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"""
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This is the output of :func:`torch._dynamo.explain()`
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There is no reason to create this class directly.
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"""
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graphs: List[torch.fx.GraphModule]
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graph_count: int
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graph_break_count: int
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break_reasons: List[
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Any
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] # Type is GraphCompileReason but doesn't matter for this purpose
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op_count: int
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ops_per_graph: Optional[List[torch.fx.Node]] = None
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out_guards: Optional[List[_guards.Guard]] = None
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compile_times: Optional[str] = None
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def __str__(self):
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output = f"Graph Count: {self.graph_count}\n"
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output += f"Graph Break Count: {self.graph_break_count}\n"
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output += f"Op Count: {self.op_count}\n"
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output += "Break Reasons:\n"
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for idx, break_reason in enumerate(self.break_reasons):
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output += f" Break Reason {idx+1}:\n"
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output += f" Reason: {break_reason.reason}\n"
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output += " User Stack:\n"
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for frame_summary in break_reason.user_stack:
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output += f" {frame_summary}\n"
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if self.ops_per_graph is not None:
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output += "Ops per Graph:\n"
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for idx, ops in enumerate(self.ops_per_graph):
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output += f" Ops {idx+1}:\n"
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for op in ops:
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output += f" {op}\n"
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if self.out_guards is not None:
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output += "Out Guards:\n"
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for i, guard in enumerate(self.out_guards):
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output += f" Guard {i+1}:\n"
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output += f" {str(guard)}"
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if self.compile_times is not None:
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output += f"Compile Times: {self.compile_times}\n"
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return output
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def _explain_graph_detail(
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gm: torch.fx.GraphModule, graphs, op_count, ops_per_graph, break_reasons
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):
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"""
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This function is a utility which processes a torch.fx.GraphModule and
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accumulates information about its ops, graph breaks, and other details. It
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is intended to be used by the ExplainWithBackend class and
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`torch._dynamo.explain()` to provide details from Dynamo's graph capture.
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Parameters:
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gm (torch.fx.GraphModule): The GraphModule to be processed.
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graphs (list): A list that accumulates all the GraphModules processed.
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op_count (int): The total count of operations in all GraphModules processed so far.
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ops_per_graph (list): A list that accumulates the operations of each GraphModule.
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break_reasons (list): A list that accumulates the reasons for breaks in each GraphModule.
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Returns:
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tuple: A tuple containing the processed GraphModule, the updated lists of graphs,
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operations per graph, and break reasons, and the updated operation count.
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"""
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graphs.append(gm)
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ops = [node.target for node in gm.graph.nodes if node.op == "call_function"]
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op_count += len(ops)
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ops_per_graph.append(ops)
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if gm.compile_subgraph_reason.graph_break:
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break_reasons.append(gm.compile_subgraph_reason)
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return gm, graphs, op_count, ops_per_graph, break_reasons
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class ExplainWithBackend:
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"""
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This class is intended to be used as a backend for `torch.compile`. It is
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composable with other backends. When used in this way, it accumulates
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information about graph breaks, ops, and other info and provides a string
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representation summarizing this information.
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Attributes:
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backend (str): The name of the backend to use for optimization.
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graphs (list): A list of the graphs captured by TorchDynamo.
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op_count (int): The total number of operations in all optimized graphs.
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break_reasons (list): A list of graph break reasons with stack traces.
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Example Usage:
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def fn(x):
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x = torch.sigmoid(x)
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return x
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torch._dynamo.reset()
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eb = ExplainWithBackend("inductor")
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optimized_fn = torch.compile(fn, backend=eb)
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result = optimized_fn(torch.randn(5))
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print(eb.output())
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"""
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def __init__(self, backend):
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from .registry import lookup_backend
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self.backend = lookup_backend(backend)
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self.graphs = []
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self.op_count = 0
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self.break_reasons = []
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def __call__(self, gm: torch.fx.GraphModule, example_inputs):
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gm, self.graphs, self.op_count, _, self.break_reasons = _explain_graph_detail(
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gm, self.graphs, self.op_count, [], self.break_reasons
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)
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return self.backend(gm, example_inputs)
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def output(self) -> ExplainOutput:
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graph_count = len(self.graphs)
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output = ExplainOutput(
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self.graphs,
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graph_count,
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graph_count - 1,
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self.break_reasons,
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self.op_count,
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)
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return output
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