(beta) Using TORCH_LOGS python API with torch.compile

Author: Michael Lazos

import logging

This tutorial introduces the TORCH_LOGS environment variable, as well as the Python API, and demonstrates how to apply it to observe the phases of torch.compile.

참고

This tutorial requires PyTorch 2.2.0 or later.

Setup

In this example, we’ll set up a simple Python function which performs an elementwise add and observe the compilation process with TORCH_LOGS Python API.

참고

There is also an environment variable TORCH_LOGS, which can be used to change logging settings at the command line. The equivalent environment variable setting is shown for each example.

import torch

# exit cleanly if we are on a device that doesn't support torch.compile
if torch.cuda.get_device_capability() < (7, 0):
    print("Skipping because torch.compile is not supported on this device.")
else:
    @torch.compile()
    def fn(x, y):
        z = x + y
        return z + 2


    inputs = (torch.ones(2, 2, device="cuda"), torch.zeros(2, 2, device="cuda"))


# print separator and reset dynamo
# between each example
    def separator(name):
        print(f"==================={name}=========================")
        torch._dynamo.reset()


    separator("Dynamo Tracing")
# View dynamo tracing
# TORCH_LOGS="+dynamo"
    torch._logging.set_logs(dynamo=logging.DEBUG)
    fn(*inputs)

    separator("Traced Graph")
# View traced graph
# TORCH_LOGS="graph"
    torch._logging.set_logs(graph=True)
    fn(*inputs)

    separator("Fusion Decisions")
# View fusion decisions
# TORCH_LOGS="fusion"
    torch._logging.set_logs(fusion=True)
    fn(*inputs)

    separator("Output Code")
# View output code generated by inductor
# TORCH_LOGS="output_code"
    torch._logging.set_logs(output_code=True)
    fn(*inputs)

    separator("")

Traceback (most recent call last):
  File "/workspace/tutorials-kr/recipes_source/torch_logs.py", line 59, in <module>
    fn(*inputs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py", line 451, in _fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 921, in catch_errors
    return callback(frame, cache_entry, hooks, frame_state, skip=1)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 786, in _convert_frame
    result = inner_convert(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 400, in _convert_frame_assert
    return _compile(
  File "/usr/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 676, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 535, in compile_inner
    out_code = transform_code_object(code, transform)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/bytecode_transformation.py", line 1036, in transform_code_object
    transformations(instructions, code_options)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 165, in _fn
    return fn(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/convert_frame.py", line 500, in transform
    tracer.run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2149, in run
    super().run()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 810, in run
    and self.step()
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 773, in step
    getattr(self, inst.opname)(inst)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/symbolic_convert.py", line 2268, in RETURN_VALUE
    self.output.compile_subgraph(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py", line 981, in compile_subgraph
    self.compile_and_call_fx_graph(tx, list(reversed(stack_values)), root)
  File "/usr/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py", line 1178, in compile_and_call_fx_graph
    compiled_fn = self.call_user_compiler(gm)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py", line 1251, in call_user_compiler
    raise BackendCompilerFailed(self.compiler_fn, e).with_traceback(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/output_graph.py", line 1232, in call_user_compiler
    compiled_fn = compiler_fn(gm, self.example_inputs())
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/repro/after_dynamo.py", line 117, in debug_wrapper
    compiled_gm = compiler_fn(gm, example_inputs)
  File "/usr/local/lib/python3.10/dist-packages/torch/__init__.py", line 1731, in __call__
    return compile_fx(model_, inputs_, config_patches=self.config)
  File "/usr/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py", line 1330, in compile_fx
    return aot_autograd(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/backends/common.py", line 58, in compiler_fn
    cg = aot_module_simplified(gm, example_inputs, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py", line 903, in aot_module_simplified
    compiled_fn = create_aot_dispatcher_function(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py", line 628, in create_aot_dispatcher_function
    compiled_fn = compiler_fn(flat_fn, fake_flat_args, aot_config, fw_metadata=fw_metadata)
  File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 443, in aot_wrapper_dedupe
    return compiler_fn(flat_fn, leaf_flat_args, aot_config, fw_metadata=fw_metadata)
  File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 648, in aot_wrapper_synthetic_base
    return compiler_fn(flat_fn, flat_args, aot_config, fw_metadata=fw_metadata)
  File "/usr/local/lib/python3.10/dist-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 119, in aot_dispatch_base
    compiled_fw = compiler(fw_module, updated_flat_args)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py", line 1257, in fw_compiler_base
    return inner_compile(
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/repro/after_aot.py", line 83, in debug_wrapper
    inner_compiled_fn = compiler_fn(gm, example_inputs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_inductor/debug.py", line 304, in inner
    return fn(*args, **kwargs)
  File "/usr/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/usr/lib/python3.10/contextlib.py", line 79, in inner
    return func(*args, **kwds)
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py", line 438, in compile_fx_inner
    compiled_graph = fx_codegen_and_compile(
  File "/usr/local/lib/python3.10/dist-packages/torch/_inductor/compile_fx.py", line 714, in fx_codegen_and_compile
    compiled_fn = graph.compile_to_fn()
  File "/usr/local/lib/python3.10/dist-packages/torch/_inductor/graph.py", line 1307, in compile_to_fn
    return self.compile_to_module().call
  File "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/utils.py", line 262, in time_wrapper
    r = func(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/_inductor/graph.py", line 1254, in compile_to_module
    mod = PyCodeCache.load_by_key_path(
  File "/usr/local/lib/python3.10/dist-packages/torch/_inductor/codecache.py", line 2160, in load_by_key_path
    exec(code, mod.__dict__, mod.__dict__)
  File "/tmp/torchinductor_root/7y/c7ygyi5w7g7iibo7dunmqen7o2aqqi63qm4kimhfb23leusd5zee.py", line 71, in <module>
    async_compile.wait(globals())
  File "/usr/local/lib/python3.10/dist-packages/torch/_inductor/codecache.py", line 2715, in wait
    scope[key] = result.result()
  File "/usr/local/lib/python3.10/dist-packages/torch/_inductor/codecache.py", line 2522, in result
    self.future.result()
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 458, in result
    return self.__get_result()
  File "/usr/lib/python3.10/concurrent/futures/_base.py", line 403, in __get_result
    raise self._exception
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
CalledProcessError: Command '['/usr/bin/gcc', '/tmp/tmps0smf6jv/main.c', '-O3', '-I/usr/local/lib/python3.10/dist-packages/triton/common/../third_party/cuda/include', '-I/usr/include/python3.10', '-I/tmp/tmps0smf6jv', '-shared', '-fPIC', '-lcuda', '-o', '/tmp/tmps0smf6jv/triton_.cpython-310-x86_64-linux-gnu.so', '-L/lib/x86_64-linux-gnu', '-L/lib/x86_64-linux-gnu']' returned non-zero exit status 1.

Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information


You can suppress this exception and fall back to eager by setting:
    import torch._dynamo
    torch._dynamo.config.suppress_errors = True

Conclusion

In this tutorial we introduced the TORCH_LOGS environment variable and python API by experimenting with a small number of the available logging options. To view descriptions of all available options, run any python script which imports torch and set TORCH_LOGS to 《help》.

Alternatively, you can view the torch._logging documentation to see descriptions of all available logging options.

For more information on torch.compile, see the torch.compile tutorial.