cpu  eval  resnet50                           
ERROR:common:Backend dynamo failed in warmup()
Traceback (most recent call last):
  File "/workspace/pytorch/benchmarks/dynamo/common.py", line 2604, in warmup
    fn(model, example_inputs)
  File "/workspace/pytorch/torch/_dynamo/eval_frame.py", line 409, in _fn
    return fn(*args, **kwargs)
  File "/workspace/pytorch/torch/_dynamo/eval_frame.py", line 570, in catch_errors
    return callback(frame, cache_entry, hooks, frame_state)
  File "/workspace/pytorch/torch/_dynamo/convert_frame.py", line 670, in _convert_frame
    result = inner_convert(frame, cache_entry, hooks, frame_state)
  File "/workspace/pytorch/torch/_dynamo/convert_frame.py", line 377, in _convert_frame_assert
    return _compile(
  File "/workspace/pytorch/torch/_dynamo/convert_frame.py", line 594, in _compile
    guarded_code = compile_inner(code, one_graph, hooks, transform)
  File "/workspace/pytorch/torch/_dynamo/utils.py", line 222, in time_wrapper
    r = func(*args, **kwargs)
  File "/workspace/pytorch/torch/_dynamo/convert_frame.py", line 511, in compile_inner
    out_code = transform_code_object(code, transform)
  File "/workspace/pytorch/torch/_dynamo/bytecode_transformation.py", line 1033, in transform_code_object
    transformations(instructions, code_options)
  File "/workspace/pytorch/torch/_dynamo/convert_frame.py", line 150, in _fn
    return fn(*args, **kwargs)
  File "/workspace/pytorch/torch/_dynamo/convert_frame.py", line 476, in transform
    tracer.run()
  File "/workspace/pytorch/torch/_dynamo/symbolic_convert.py", line 2120, in run
    super().run()
  File "/workspace/pytorch/torch/_dynamo/symbolic_convert.py", line 815, in run
    and self.step()
  File "/workspace/pytorch/torch/_dynamo/symbolic_convert.py", line 778, in step
    getattr(self, inst.opname)(inst)
  File "/workspace/pytorch/torch/_dynamo/symbolic_convert.py", line 2235, in RETURN_VALUE
    self.output.compile_subgraph(
  File "/workspace/pytorch/torch/_dynamo/output_graph.py", line 893, in compile_subgraph
    self.compile_and_call_fx_graph(tx, list(reversed(stack_values)), root)
  File "/opt/conda/lib/python3.8/contextlib.py", line 75, in inner
    return func(*args, **kwds)
  File "/workspace/pytorch/torch/_dynamo/output_graph.py", line 1038, in compile_and_call_fx_graph
    compiled_fn = self.call_user_compiler(gm)
  File "/workspace/pytorch/torch/_dynamo/utils.py", line 222, in time_wrapper
    r = func(*args, **kwargs)
  File "/workspace/pytorch/torch/_dynamo/output_graph.py", line 1109, in call_user_compiler
    raise BackendCompilerFailed(self.compiler_fn, e).with_traceback(
  File "/workspace/pytorch/torch/_dynamo/output_graph.py", line 1090, in call_user_compiler
    compiled_fn = compiler_fn(gm, self.example_inputs())
  File "/workspace/pytorch/torch/_dynamo/repro/after_dynamo.py", line 117, in debug_wrapper
    compiled_gm = compiler_fn(gm, example_inputs)
  File "/workspace/pytorch/torch/_dynamo/backends/inductor.py", line 9, in inductor
    return compile_fx(*args, **kwargs)
  File "/workspace/pytorch/torch/_inductor/compile_fx.py", line 1226, in compile_fx
    return aot_autograd(
  File "/workspace/pytorch/torch/_dynamo/backends/common.py", line 55, in compiler_fn
    cg = aot_module_simplified(gm, example_inputs, **kwargs)
  File "/workspace/pytorch/torch/_functorch/aot_autograd.py", line 4837, in aot_module_simplified
    compiled_fn = create_aot_dispatcher_function(
  File "/workspace/pytorch/torch/_dynamo/utils.py", line 222, in time_wrapper
    r = func(*args, **kwargs)
  File "/workspace/pytorch/torch/_functorch/aot_autograd.py", line 4376, in create_aot_dispatcher_function
    compiled_fn = compiler_fn(flat_fn, fake_flat_args, aot_config, fw_metadata=fw_metadata)
  File "/workspace/pytorch/torch/_functorch/aot_autograd.py", line 2724, in aot_wrapper_dedupe
    return compiler_fn(flat_fn, leaf_flat_args, aot_config, fw_metadata=fw_metadata)
  File "/workspace/pytorch/torch/_functorch/aot_autograd.py", line 2911, in aot_wrapper_synthetic_base
    return compiler_fn(flat_fn, flat_args, aot_config, fw_metadata=fw_metadata)
  File "/workspace/pytorch/torch/_functorch/aot_autograd.py", line 2073, in aot_dispatch_base
    compiled_fw = compiler(fw_module, updated_flat_args)
  File "/workspace/pytorch/torch/_inductor/compile_fx.py", line 975, in fw_compiler_freezing
    optimized_function = inner_compile(
  File "/workspace/pytorch/torch/_dynamo/repro/after_aot.py", line 83, in debug_wrapper
    inner_compiled_fn = compiler_fn(gm, example_inputs)
  File "/workspace/pytorch/torch/_inductor/debug.py", line 303, in inner
    return fn(*args, **kwargs)
  File "/opt/conda/lib/python3.8/contextlib.py", line 75, in inner
    return func(*args, **kwds)
  File "/workspace/pytorch/torch/_inductor/compile_fx.py", line 390, in compile_fx_inner
    compiled_graph = fx_codegen_and_compile(
  File "/workspace/pytorch/torch/_inductor/compile_fx.py", line 600, in fx_codegen_and_compile
    graph.run(*example_inputs)
  File "/workspace/pytorch/torch/_dynamo/utils.py", line 222, in time_wrapper
    r = func(*args, **kwargs)
  File "/workspace/pytorch/torch/_inductor/graph.py", line 444, in run
    return super().run(*args)
  File "/workspace/pytorch/torch/fx/interpreter.py", line 138, in run
    self.env[node] = self.run_node(node)
  File "/workspace/pytorch/torch/_inductor/graph.py", line 754, in run_node
    result = super().run_node(n)
  File "/workspace/pytorch/torch/fx/interpreter.py", line 195, in run_node
    return getattr(self, n.op)(n.target, args, kwargs)
  File "/workspace/pytorch/torch/_inductor/graph.py", line 584, in call_function
    return target(*args, **kwargs)
  File "/workspace/pytorch/torch/_inductor/fx_passes/mkldnn_fusion.py", line 234, in fn
    return L[computation_op](*computation_args)
  File "/workspace/pytorch/torch/_inductor/lowering.py", line 288, in wrapped
    out = decomp_fn(*args, **kwargs)
  File "/workspace/pytorch/torch/_inductor/lowering.py", line 1266, in convolution_unary
    ir.ConvolutionUnary.create(
  File "/workspace/pytorch/torch/_inductor/ir.py", line 4897, in create
    (inputs, constant_args, kernel_layout, _) = _prepare_convolution_fusion_create(
  File "/workspace/pytorch/torch/_inductor/ir.py", line 4717, in _prepare_convolution_fusion_create
    assert 0 < len(padding) <= dims
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
AssertionError: 

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