This document is relevant for: Inf2, Trn1, Trn1n

PyTorch NeuronX Analyze API for Inference#

torch_neuronx.analyze(func, example_inputs, compiler_workdir=None)#

Checks the support of the operations in the func by checking each operator against neuronx-cc.

  • func (Module,callable) – The function/module that that will be run using the example_inputs arguments in order to record the computation graph.

  • example_inputs (Tensor,tuple[Tensor]) – A tuple of example inputs that will be passed to the func while tracing.

Keyword Arguments
  • compiler_workdir (str) – Work directory used by neuronx-cc. This can be useful for debugging and/or inspecting intermediary neuronx-cc outputs

  • additional_ignored_ops (set) – A set of aten operators to not analyze. Default is an empty set.

  • max_workers (int) – The max number of workers threads to spawn. The default is 4.

  • is_hf_transformers (bool) – If the model is a huggingface transformers model, it is recommended to enable this option to prevent deadlocks. Default is False.

  • cleanup (bool) – Specifies whether to delete the compiler artifact directories generated after running analyze. Default is False.


A JSON like Dict with the supported operators and their count, and unsupported operators with the failure mode and location of the operator in the python code.

Return type



Fully supported model

import json

import torch
import torch.nn as nn
import torch_neuronx

class MLP(nn.Module):
   def __init__(self, input_size=28*28, output_size=10, layers=[120,84]):
      super(MLP, self).__init__()
      self.fc1 = nn.Linear(input_size, layers[0])
      self.relu = nn.ReLU()
      self.fc2 = nn.Linear(layers[0], layers[1])
   def forward(self, x):
      f1 = self.fc1(x)
      r1 = self.relu(f1)
      f2 = self.fc2(r1)
      r2 = self.relu(f2)
      f3 = self.fc3(r2)
      return torch.log_softmax(f3, dim=1)

model = MLP()
ex_input = torch.rand([32,784])

model_support = torch_neuronx.analyze(model,ex_input)
    "torch_neuronx_version": "",
    "neuronx_cc_version": "",
    "support_percentage": "100.00%",
    "supported_operators": {
       "aten::linear": 3,
    "aten::relu": 2,
    "aten::log_softmax": 1
    "unsupported_operators": []

Unsupported Model/Operator

import json
import torch
import torch_neuronx

def fft(x):
   return torch.fft.fft(x)

model = fft
ex_input = torch.arange(4)

model_support = torch_neuronx.analyze(model,ex_input)
   "torch_neuronx_version": "",
   "neuronx_cc_version": "",
   "support_percentage": "0.00%",
   "supported_operators": {},
   "unsupported_operators": [
         "kind": "aten::fft_fft",
         "failureAt": "neuronx-cc",
         "call": " fft\n/home/ubuntu/testdir/venv/lib/python3.8/site-packages/torch_neuronx/xla_impl/ forward\n/home/ubuntu/testdir/venv/lib/python3.8/site-packages/torch/nn/modules/ _slow_forward\n/home/ubuntu/testdir/venv/lib/python3.8/site-packages/torch/nn/modules/ _call_impl\n/home/ubuntu/testdir/venv/lib/python3.8/site-packages/torch/jit/ trace_module\n/home/ubuntu/testdir/venv/lib/python3.8/site-packages/torch/jit/ trace\n/home/ubuntu/testdir/venv/lib/python3.8/site-packages/torch_neuronx/xla_impl/ analyze\ <module>\n",
         "opGraph": "graph(%x : Long(4, strides=[1], requires_grad=0, device=cpu),\n      %neuron_4 : NoneType,\n      %neuron_5 : int,\n      %neuron_6 : NoneType):\n  %neuron_7 : ComplexFloat(4, strides=[1], requires_grad=0, device=cpu) = aten::fft_fft(%x, %neuron_4, %neuron_5, %neuron_6)\n  return (%neuron_7)\n"

Note: the failureAt field can either be “neuronx-cc” or “Lowering to HLO”. If the field is “neuronx-cc”, then it indicates that the provided operator configuration failed to be compiled with neuronx-cc. This could either indicate that the operator configuration is unsupported, or there is a bug with that operator configuration.

This document is relevant for: Inf2, Trn1, Trn1n