0.7.0

• #149: supports for StaticCache
• #147: simplified log processing
• #146: patch for IdeficsAttention, IdeficsEmbedding
• #145: patch for _compute_dynamic_ntk_parameters (Phi3RotaryEmbedding)
• #144: support for second inputs with different dimension,
  rename test_helper into validate,
  support interpolate_pos_encoding for VitModel,
  update model builder helpers for this PR
  Use ONNX IR for model builder
• #143: compares intermediate results,

0.6.3

• #140: improves command line find
• #131: support for custom kernels in TorchOnnxEvaluator

0.6.1

• #128: patch for Phi3RotaryEmbedding
• #126: add repeat and warmup to command line validate
• #125: handles sequences in TorchOnnxEvaluator
• #123: add subgraphs to TorchOnnxEvaluator
• #122: add local functions to TorchOnnxEvaluator
• #120: enables TorchOnnxEvaluator in command line python -m onnx_diagnostic validate ...
• #115, #116, #117, #118, #119, #127: first steps for TorchOnnxEvaluator
• #114: extends the list of known rewritings
• #113: fixes a couple of issues with ModelBuilder

0.6.0

• #111: support ModelBuilder with command line validate
• #108, #109, #110: first version of an algorithm rendering small onnx graph in ascii, patch for torch.vmap

0.5.0

• #105: more options to tune control flow rewriting
• #104: add summarization task, add rewrite to command line validate
• #101: first draft to rewrite loops
• #100: implements a context to automatically rewrite methods or function with control flows
• #96: implements is_stealing, steal_append to complement steal_forward
• #95: fixzq Scan implementation for OnnxruntimeEvaluator
• #93: introduces patched expressions to get around annoying export issues
• #92: supports errors distribution in max_diff
• #91: enables strings in guess_dynamic_shapes
• #88`, #89: extends steal_forward to dump input, outputs in onnx models
• #83, #85: improves the automated rewriting of control flow (test)

0.4.4

• #82: exposes register_flattening_functions, add option --subfolder
• #81: fixes missing intermediate_size in configuration
• #79: implements task object-detection
• #78: uses onnx-weekly instead of onnx to avoid conflicts with onnxscript

0.4.3

• #75: renames bypass_export_some_patches into torch_export_patches, keeps the old name
• #74: increases the list of class/architectures
• #73: supports MambaCache in max_diff, torch_deepcopy

0.4.1

• #72: fix change_dynamic_dimension for custom classes, second set of inputs for every task
• #70: support model options in command lines (--mop, --iop)

0.4.0

• #65: support SlidingWindowCache
  *#63: support option --trained
• #61: improves dynamic shapes for EncoderDecoderCache
• #58: add function use_dyn_not_str to replace string by torch.export.Dim.DYNAMIC,
  use string instead of torch.export.Dim.DYNAMIC when returning the dynamic shapes
  for a specific models, it is a valid definition for torch.onnx.export
  which can reuse the names
• #55: add support for text-classification
• #54: add support for fill-mask, refactoring
• #52: add support for zero-shot-image-classification
• #50: add support for onnxruntime fusion
• #48: add support for EncoderDecoderCache, test with openai/whisper-tiny
• #45: improve change_dynamic_dimension to fix some dimensions

0.3.0

• #43: uses custom patches
• #38: uses the registered serialization functions when it is available
• #30, #31: adds command to test a model id, validate the export
• #29: adds helpers to measure the memory peak and run benchmark on different processes
• #28: adds command line to print out the configuration for a model id, support image-text-to-text
• #26: creates a folder helpers to gather all the functions used in many places
• #25: improve patches for DynamicCache (issue with register_pytree_flatten_spec being deprecated)
• #24: dummy inputs for text2text-generation, add new function convert_dynamic_axes_into_dynamic_shapes to convert dynamic axes into dynamic shapes, add support for T5ForConditionalGeneration
• #23: dummy inputs for image-classification
• #22, #27: api to create untrained model copying the architecture of the trained models and dummy inputs for them, support for text-generation