ValueError("arange's arguments must be of type tl.constexpr")

[Sine7812]

Does anyone know how to view the model's architecture diagram? Whether it's using TensorBoard or attempting to convert to ONNX format, I'm encountering errors.Traceback (most recent call last): File "/mnt/e/code/VideoMamba-main/videomamba/video_sm/run_class_finetuning.py", line 744, in main(opts, ds_init) File "/mnt/e/code/VideoMamba-main/videomamba/video_sm/run_class_finetuning.py", line 674, in main save_model_and_onnx(args, model_without_ddp, epoch, model_ema) File "/mnt/e/code/VideoMamba-main/videomamba/video_sm/run_class_finetuning.py", line 249, in save_model_and_onnx
torch.onnx.export(model, dummy_input, onnx_path, export_params=True, opset_version=11, File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/onnx/utils.py", line 516, in export _export( File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/onnx/utils.py", line 1596, in _export graph, params_dict, torch_out = _model_to_graph( File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/onnx/utils.py", line 1135, in _model_to_graph graph, params, torch_out, module = _create_jit_graph(model, args) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/onnx/utils.py", line 1011, in _create_jit_graph graph, torch_out = _trace_and_get_graph_from_model(model, args) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/onnx/utils.py", line 915, in _trace_and_get_graph_from_model trace_graph, torch_out, inputs_states = torch.jit._get_trace_graph( File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/jit/_trace.py", line 1285, in _get_trace_graph outs = ONNXTracedModule( File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, kwargs) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, kwargs) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/jit/_trace.py", line 133, in forward graph, out = torch._C._create_graph_by_tracing( File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/jit/_trace.py", line 124, in wrapper outs.append(self.inner(trace_inputs)) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(args, kwargs) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(*args, kwargs) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1508, in _slow_forward result = self.forward(*input, kwargs) File "/mnt/e/code/VideoMamba-main/videomamba/video_sm/models/videomamba.py", line 417, in forward x = self.forward_features(x, inference_params) File "/mnt/e/code/VideoMamba-main/videomamba/video_sm/models/videomamba.py", line 390, in forward_features hidden_states, residual = layer( File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl return self._call_impl(*args, *kwargs) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1527, in _call_impl return forward_call(args, kwargs) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1508, in _slow_forward result = self.forward(*input, kwargs) File "/mnt/e/code/VideoMamba-main/videomamba/video_sm/models/videomamba.py", line 123, in forward hidden_states, residual = fused_add_norm_fn( File "/mnt/e/code/VideoMamba-main/mamba/mamba_ssm/ops/triton/layernorm.py", line 478, in rms_norm_fn return LayerNormFn.apply(x, weight, bias, residual, eps, prenorm, residual_in_fp32, True) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/torch/autograd/function.py", line 539, in apply
return super().apply(*args, *kwargs) # type: ignore[misc] File "/mnt/e/code/VideoMamba-main/mamba/mamba_ssm/ops/triton/layernorm.py", line 411, in forward y, mean, rstd, residual_out = _layer_norm_fwd( File "/mnt/e/code/VideoMamba-main/mamba/mamba_ssm/ops/triton/layernorm.py", line 155, in _layer_norm_fwd _layer_norm_fwd_1pass_kernel[(M,)]( File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 100, in run
timings = {config: self._bench(args, config=config, kwargs) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 100, in timings = {config: self._bench(*args, config=config, *kwargs) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 83, in _bench
return do_bench(kernel_call, warmup=self.warmup, rep=self.rep, quantiles=(0.5, 0.2, 0.8)) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/triton/testing.py", line 104, in do_bench fn() File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/triton/runtime/autotuner.py", line 81, in kernel_call self.fn.run(args, num_warps=config.num_warps, num_stages=config.num_stages, **current) File "", line 63, in _layer_norm_fwd_1pass_kernel File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/triton/compiler/compiler.py", line 476, in compile
next_module = compile_kernel(module) File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/triton/compiler/compiler.py", line 381, in lambda src: optimize_ttir(ast_to_ttir(src, signature, configs[0], constants, debug=debug, arch=arch), arch))
File "/home/jcz/anaconda3/envs/mamba/lib/python3.10/site-packages/triton/compiler/code_generator.py", line 1133, in ast_to_ttir raise CompilationError(fn.src, node, repr(e)) from e triton.compiler.errors.CompilationError: at 31:24: HAS_BIAS: tl.constexpr, ):

Map the program id to the row of X and Y it should compute.

row = tl.program_id(0)
X += row * stride_x_row
Y += row * stride_y_row
if HAS_RESIDUAL:
    RESIDUAL += row * stride_res_row
if STORE_RESIDUAL_OUT:
    RESIDUAL_OUT += row * stride_res_out_row
# Compute mean and variance
cols = tl.arange(0, BLOCK_N)
                    ^

ValueError("arange's arguments must be of type tl.constexpr")

[SophieOstmeier]

I have the same issue. Does anyone have a solution? Help is greatly appreciated.