justinchuby

Metadata-Version: 2.2
Name: onnxscript
Version: 0.1.0.dev20250113
Summary: Naturally author ONNX functions and models using a subset of Python
Author-email: Microsoft Corporation <[email protected]>
License: MIT License
        
        Copyright (c) Microsoft Corporation
        
        Permission is hereby granted, free of charge, to any person obtaining a copy

justinchuby

import os
import torch
import transformers

device = "cpu"
config = {
        "_name_or_path": "/fsx/loubna/checkpoints/cosmo2_1T/500000",
        "architectures": ["LlamaForCausalLM"],
        "attention_bias": False,
        "attention_dropout": 0.0,

justinchuby

import time

import onnx
import onnxscript.optimizer
import requests
import torch
from PIL import Image
from transformers import (
    AutoProcessor,
    MllamaConfig,

justinchuby

{
    "input_model": {
        "type": "PyTorchModel",
        "model_script": "model_loader.py",
        "model_loader": "load_model",
        "model_path": "meta-llama/Llama-3.2-11B-Vision",
        "io_config": {
            "input_names": [
                "input_ids",
                "attention_mask",

justinchuby

import requests
import torch
from PIL import Image
from transformers import MllamaForConditionalGeneration, AutoProcessor
import onnxscript.optimizer

model_id = "meta-llama/Llama-3.2-11B-Vision"

model = MllamaForConditionalGeneration.from_pretrained(
    model_id,

justinchuby

import os
from typing import Sequence
import torch

import torch_onnx
import torch_onnx.tools.diff_model
from onnxscript import ir
import onnxscript
import onnxscript.rewriter.pattern as orp

justinchuby

E0816 19:43:22.023000 57436 torch/_subclasses/fake_tensor.py:1975] failed while attempting to run meta for aten.group_norm.default
E0816 19:43:22.023000 57436 torch/_subclasses/fake_tensor.py:1975] Traceback (most recent call last):
E0816 19:43:22.023000 57436 torch/_subclasses/fake_tensor.py:1975]   File "/home/justinchu/anaconda3/envs/onnx/lib/python3.11/site-packages/torch/_subclasses/fake_tensor.py", line 1971, in _dispatch_impl
E0816 19:43:22.023000 57436 torch/_subclasses/fake_tensor.py:1975]     r = func(*args, **kwargs)
E0816 19:43:22.023000 57436 torch/_subclasses/fake_tensor.py:1975]         ^^^^^^^^^^^^^^^^^^^^^
E0816 19:43:22.023000 57436 torch/_subclasses/fake_tensor.py:1975]   File "/home/justinchu/anaconda3/envs/onnx/lib/python3.11/site-packages/torch/_ops.py", line 713, in __call__
E0816 19:43:22.023000 57436 torch/_subclasses/fake_tensor.py:1975]     return self._op(*args, **kwargs)
E0816 19:43:22.023000 57436 torch/_subclasses/fake_tensor.py:1975]            ^^^^^^^^^^^^^^^^^^^^^^^^^
E0816 1

justinchuby

def save_node_data_for_model_explorer(verification_infos: Collection[VerificationInfo], node_names: list[str], model_name: str = "model"
):
    # https://github.com/google-ai-edge/model-explorer/wiki/4.-API-Guide#create-custom-node-data
    # This API is unstable and may change in the future.
    from model_explorer import node_data_builder as ndb

    for field in ("max_abs_diff", "max_rel_diff"):
        # Populate values for the main graph in a model.
        main_graph_results: dict[str, ndb.NodeDataResult] = {}
        for info, node_name in zip(verification_infos, node_names):

justinchuby

import torch

class UpsampleModel(torch.nn.Module):
    def forward(self, x):
        return torch.nn.functional.upsample_bilinear(x, scale_factor=2)


model = UpsampleModel()
ep = torch.export.export(model, (torch.randn(1, 3, 224, 224),))
ep.run_decompositions()

justinchuby

import torch


class M(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = torch.nn.Linear(5, 10)

    def forward(self, x):
        return self.linear(x)