Export escn_l6_m2_lay12_2M_s2ef_export_cuda_9182024 using aot_inductor

[ipcamit]

What would you like to report?

I want to use the exported escn_l6_m2_lay12_2M_s2ef_export_cuda_9182024.pt2 as a shared library using torch._export.aot_compile. I tried the naive attempt as following:

model = torch.export.load("./escn_l6_m2_lay12_2M_s2ef_export_cuda_9182024.pt2")

inputs = sim_export_input(batch_size=1, device="cuda")

atoms_dim = Dim("atoms_dim", min=2, max=1000)
edges_dim = Dim("edges_dim", min=2, max=10000)

dynamic_shapes = {
            "pos": {0: atoms_dim, 1: None},
            "batch_idx": {0: atoms_dim},
            "natoms": {0: None},
            "atomic_numbers": {0: atoms_dim},
            "edge_index": {0: None, 1: edges_dim},
            "edge_distance": {0: edges_dim},
            "edge_distance_vec": {0: edges_dim, 1: None},
        }


with torch.inference_mode():
    exported_prog = export(module_fx, args=inputs_cpu, dynamic_shapes=dynamic_shapes)
    export_path = os.path.join("./", "escn_l6_m2_lay12_2M_s2ef_export_cuda_9182024.pt2")
    torch.export.save(exported_prog, export_path)

so_path = os.path.join("./", "escn_l6_m2_lay12_2M_s2ef_export_cpu_9182024.so")
aot_compile_options = {"aot_inductor.output_path": so_path}
if device == "cuda":
    aot_compile_options.update({"max_autotune": True})

so_path = torch._export.aot_compile(
    model,
    args=inputs,
    dynamic_shapes=dynamic_shapes,
    options=aot_compile_options,
)

But i get the following error:

ValueError: Node keys mismatch; missing key(s): {'arg_5', 'arg_1', 'arg_4', 'arg_3', 'arg_6', 'arg_2', 'arg_0'}; extra key(s): {'edge_distance', 'batch_idx', 'pos', 'natoms', 'atomic_numbers', 'edge_index', 'edge_distance_vec'}.

I tried different variations of it such as:

dynamic_shapes = {
            "args_0": {0: atoms_dim, 1: None},
            "args_1": {0: atoms_dim},
            "args_2": {0: None},
            "args_3": {0: atoms_dim},
            "args_4": {0: None, 1: edges_dim},
            "args_5": {0: edges_dim},
            "args_6": {0: edges_dim, 1: None},
        }

But i keep getting variations of above error with permutations of arg_*.

ValueError: Node keys mismatch; missing key(s): {'arg_0', 'arg_4', 'arg_2', 'arg_3', 'arg_1', 'arg_5', 'arg_6'}; extra key(s): {'args_5', 'args_4', 'args_6', 'args_2', 'args_3', 'args_1', 'args_0'}.

What is easiest way to convert this model to a .so file?

Also there seem to be a mismatch between escn_l6_m2_lay12_2M_s2ef.pt and escn_l6_m2_lay12_2M_s2ef_export_cuda_9182024.pt2. pt2 file lists number of parameters as

model = torch.export.load("escn_l6_m2_lay12_2M_s2ef_export_cuda_9182024.pt2")
torch.sum(torch.tensor([p.numel() for p in model.module().parameters() if p.requires_grad]))
>>> tensor(51869952)

but the escn_l6_m2_lay12_2M_s2ef.pt checkpoint only has 51852408 parameters.

ckpt = torch.load("escn_l6_m2_lay12_2M_s2ef.pt", map_location=torch.device("cpu"))
n_total = 0
for p in ckpt["state_dict"]:
    n_total += ckpt["state_dict"][p].numel()
n_total
>>> 51852408

[ipcamit]

I also tried recreating the model from the config file as:

from fairchem.core.models.escn.escn_exportable import eSCN

model_new = eSCN(
    num_layers = 12,
    max_neighbors = 20,
    max_num_elements = 100,
    cutoff = 12.0,
    sphere_channels = 128,
    hidden_channels = 256,
    lmax = 6,
    mmax = 2,
    num_sphere_samples = 128,
    distance_function = "gaussian",
    basis_width_scalar = 2.0,
    export = True,
    distance_resolution = 0.02,
    edge_channels = 128)

Now while the number of traininable parameters match original model:

torch.sum(torch.tensor([p.numel() for p in model_new.parameters() if p.requires_grad]))
>> tensor(51869952)

The state dict are different

len(list(model_new.state_dict().keys()))
>>> 4547

len(list(model.module().state_dict().keys()))
>>> 521

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