I am unable to use torch compile with pytorch using the inductor backend.
I am not sure how to interpret this error BackendCompilerFailed: backend='inductor' raised: RuntimeError: Triton Error [CUDA]: device kernel image is invalid
Code to reproduce the bug:
# file: torch_compile_error.py
import torch
def add_tensors(a, b):
c = a + b
return c
a = torch.tensor([1, 2, 3]).to("cuda")
b = torch.tensor([1, 2, 3]).to("cuda")
c = add_tensors(a, b)
print(f"{a=}, {b=}, {c=}")
# Compile
add_tensors_compiled = torch.compile(add_tensors)
c_compiled = add_tensors_compiled(a, b)
print("{c_compiled=}")
Full output when running python torch_compile_error.py:
a=tensor([1, 2, 3], device='cuda:0'), b=tensor([1, 2, 3], device='cuda:0'), c=tensor([2, 4, 6], device='cuda:0')
/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/onnxruntime/capi/onnxruntime_validation.py:114: UserWarning: WARNING: failed to get cudart_version from onnxruntime build info.
warnings.warn("WARNING: failed to get cudart_version from onnxruntime build info.")
Traceback (most recent call last):
File "/mnt/pv/notebooks/smishra/torch_compile_error.py", line 17, in <module>
c_compiled = add_tensors_compiled(a, b)
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 489, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py", line 655, in catch_errors
return callback(frame, cache_entry, hooks, frame_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 727, in _convert_frame
result = inner_convert(frame, cache_entry, hooks, frame_state)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 383, in _convert_frame_assert
compiled_product = _compile(
^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 646, in _compile
guarded_code = compile_inner(code, one_graph, hooks, transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/utils.py", line 244, in time_wrapper
r = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 562, in compile_inner
out_code = transform_code_object(code, transform)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/bytecode_transformation.py", line 1033, in transform_code_object
transformations(instructions, code_options)
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 151, in _fn
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/convert_frame.py", line 527, in transform
tracer.run()
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 2128, in run
super().run()
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 818, in run
and self.step()
^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 781, in step
getattr(self, inst.opname)(inst)
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/symbolic_convert.py", line 2243, in RETURN_VALUE
self.output.compile_subgraph(
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/output_graph.py", line 919, in compile_subgraph
self.compile_and_call_fx_graph(tx, list(reversed(stack_values)), root)
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/contextlib.py", line 81, in inner
return func(*args, **kwds)
^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/output_graph.py", line 1087, in compile_and_call_fx_graph
compiled_fn = self.call_user_compiler(gm)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/utils.py", line 244, in time_wrapper
r = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/output_graph.py", line 1159, in call_user_compiler
raise BackendCompilerFailed(self.compiler_fn, e).with_traceback(
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/output_graph.py", line 1140, in call_user_compiler
compiled_fn = compiler_fn(gm, self.example_inputs())
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/repro/after_dynamo.py", line 117, in debug_wrapper
compiled_gm = compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/__init__.py", line 1662, in __call__
return compile_fx(model_, inputs_, config_patches=self.config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/compile_fx.py", line 1168, in compile_fx
return aot_autograd(
^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/backends/common.py", line 55, in compiler_fn
cg = aot_module_simplified(gm, example_inputs, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py", line 887, in aot_module_simplified
compiled_fn = create_aot_dispatcher_function(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/utils.py", line 244, in time_wrapper
r = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_functorch/aot_autograd.py", line 600, in create_aot_dispatcher_function
compiled_fn = compiler_fn(flat_fn, fake_flat_args, aot_config, fw_metadata=fw_metadata)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 425, in aot_wrapper_dedupe
return compiler_fn(flat_fn, leaf_flat_args, aot_config, fw_metadata=fw_metadata)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/runtime_wrappers.py", line 630, in aot_wrapper_synthetic_base
return compiler_fn(flat_fn, flat_args, aot_config, fw_metadata=fw_metadata)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py", line 97, in aot_dispatch_base
compiled_fw = compiler(fw_module, updated_flat_args)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/utils.py", line 244, in time_wrapper
r = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/compile_fx.py", line 1100, in fw_compiler_base
return inner_compile(
^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/repro/after_aot.py", line 83, in debug_wrapper
inner_compiled_fn = compiler_fn(gm, example_inputs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/debug.py", line 305, in inner
return fn(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/contextlib.py", line 81, in inner
return func(*args, **kwds)
^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/compile_fx.py", line 320, in compile_fx_inner
compiled_graph = fx_codegen_and_compile(
^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/compile_fx.py", line 550, in fx_codegen_and_compile
compiled_fn = graph.compile_to_fn()
^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/graph.py", line 1116, in compile_to_fn
return self.compile_to_module().call
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_dynamo/utils.py", line 244, in time_wrapper
r = func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/graph.py", line 1070, in compile_to_module
mod = PyCodeCache.load_by_key_path(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/codecache.py", line 1892, in load_by_key_path
exec(code, mod.__dict__, mod.__dict__)
File "/tmp/torchinductor_ray/ou/cou5lpnmw3pu2wttvcehhpf7x4qtl47sgysbgnpxel3l6uecylhj.py", line 63, in <module>
async_compile.wait(globals())
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/codecache.py", line 2471, in wait
scope[key] = result.result()
^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/codecache.py", line 2315, in result
kernel = self.kernel = _load_kernel(self.kernel_name, self.source_code)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/codecache.py", line 2291, in _load_kernel
kernel.precompile()
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/triton_heuristics.py", line 188, in precompile
compiled_binary, launcher = self._precompile_config(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/torch/_inductor/triton_heuristics.py", line 308, in _precompile_config
binary._init_handles()
File "/home/ray/anaconda3/envs/pytorch-temp/lib/python3.11/site-packages/triton/compiler/compiler.py", line 683, in _init_handles
mod, func, n_regs, n_spills = fn_load_binary(self.metadata["name"], self.asm[bin_path], self.shared, device)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
torch._dynamo.exc.BackendCompilerFailed: backend='inductor' raised:
RuntimeError: Triton Error [CUDA]: device kernel image is invalid
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
I am used the collect_env.py downloaded using:
curl -OL https://raw.githubusercontent.com/pytorch/pytorch/main/torch/utils/collect_env.py
Output of python collect_env.py
Collecting environment information...
PyTorch version: 2.2.0+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.16.3
Libc version: glibc-2.31
Python version: 3.11.7 (main, Dec 15 2023, 18:12:31) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.4.254-170.358.amzn2.x86_64-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA A100-SXM4-80GB
GPU 1: NVIDIA A100-SXM4-80GB
GPU 2: NVIDIA A100-SXM4-80GB
GPU 3: NVIDIA A100-SXM4-80GB
GPU 4: NVIDIA A100-SXM4-80GB
GPU 5: NVIDIA A100-SXM4-80GB
GPU 6: NVIDIA A100-SXM4-80GB
GPU 7: NVIDIA A100-SXM4-80GB
Nvidia driver version: 470.182.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
Address sizes: 46 bits physical, 48 bits virtual
CPU(s): 96
On-line CPU(s) list: 0-95
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 2
NUMA node(s): 2
Vendor ID: GenuineIntel
CPU family: 6
Model: 85
Model name: Intel(R) Xeon(R) Platinum 8275CL CPU @ 3.00GHz
Stepping: 7
CPU MHz: 2962.577
BogoMIPS: 5999.99
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 1.5 MiB
L1i cache: 1.5 MiB
L2 cache: 48 MiB
L3 cache: 71.5 MiB
NUMA node0 CPU(s): 0-23,48-71
NUMA node1 CPU(s): 24-47,72-95
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit: KVM: Vulnerable
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed: Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves ida arat pku ospke
Versions of relevant libraries:
[pip3] numpy==1.26.3
[pip3] onnx==1.15.0
[pip3] onnxruntime-training==1.16.3
[pip3] onnxscript==0.1.0.dev20240201
[pip3] torch==2.2.0+cu118
[pip3] triton==2.2.0
[conda] numpy 1.26.3 pypi_0 pypi
[conda] torch 2.2.0+cu118 pypi_0 pypi
[conda] triton 2.2.0 pypi_0 pypi