# Model debugging toolboxes This page lists all the debugging and model adding tools used by the library, as well as the utility functions it provides for it. Most of those are only useful if you are adding new models in the library. ## Model addition debuggers ### Model addition debugger - context manager for model adders This context manager is a power user tool intended for model adders. It tracks all forward calls within a model forward and logs a slice of each input and output on a nested Json. To note, this context manager enforces `torch.no_grad()`. ### Rationale Because when porting models to transformers, even from python to python, model adders often have to do a lot of manual operations, involving saving and loading tensors, comparing dtypes, etc. This small tool can hopefully shave off some time. ### Usage Add this context manager as follows to debug a model: ```python import torch from PIL import Image import requests from transformers import LlavaProcessor, LlavaForConditionalGeneration from transformers.model_debugging_utils import model_addition_debugger_context torch.random.manual_seed(673) # load pretrained model and processor model_id = "llava-hf/llava-1.5-7b-hf" processor = LlavaProcessor.from_pretrained(model_id) model = LlavaForConditionalGeneration.from_pretrained(model_id, low_cpu_mem_usage=True) # create random image input random_image = Image.fromarray(torch.randint(0, 256, (224, 224, 3), dtype=torch.uint8).numpy()) # prompt prompt = "Describe this image." # process inputs inputs = processor(text=prompt, images=random_image, return_tensors="pt") # call forward method (not .generate!) with model_addition_debugger_context( model, debug_path="optional_path_to_your_directory", do_prune_layers=False # This will output ALL the layers of a model. ): output = model.forward(**inputs) ``` ### Reading results The debugger generates two files from the forward call, both with the same base name, but ending either with `_SUMMARY.json` or with `_FULL_TENSORS.json`. The first one will contain a summary of each module's _input_ and _output_ tensor values and shapes. ```json { "module_path": "MolmoForConditionalGeneration", "inputs": { "args": [], "kwargs": { "input_ids": { "shape": "torch.Size([1, 589])", "dtype": "torch.int64" }, "attention_mask": { "shape": "torch.Size([1, 589])", "dtype": "torch.int64" }, "pixel_values": { "shape": "torch.Size([1, 5, 576, 588])", "dtype": "torch.float32", "mean": "tensor(-8.9514e-01, device='cuda:0')", "std": "tensor(9.2586e-01, device='cuda:0')", "min": "tensor(-1.7923e+00, device='cuda:0')", "max": "tensor(1.8899e+00, device='cuda:0')" } }, "children": [ { "module_path": "MolmoForConditionalGeneration.language_model.model.embed_tokens", "inputs": { "args": [ { "shape": "torch.Size([1, 589])", "dtype": "torch.int64" } ] }, "outputs": { "shape": "torch.Size([1, 589, 3584])", "dtype": "torch.float32", "mean": "tensor(6.5460e-06, device='cuda:0')", "std": "tensor(2.3807e-02, device='cuda:0')", "min": "tensor(-3.3398e-01, device='cuda:0')", "max": "tensor(3.9453e-01, device='cuda:0')" } }, { "module_path": "MolmoForConditionalGeneration.vision_tower", "inputs": { "args": [ { "shape": "torch.Size([5, 1, 576, 588])", "dtype": "torch.float32", "mean": "tensor(-8.9514e-01, device='cuda:0')", "std": "tensor(9.2586e-01, device='cuda:0')", "min": "tensor(-1.7923e+00, device='cuda:0')", "max": "tensor(1.8899e+00, device='cuda:0')" } ], "kwargs": { "output_hidden_states": "True" } }, "children": [ { ... and so on ``` The `_FULL_TENSORS.json` file will display a full view of all tensors, which is useful for comparing two files. ```json "pixel_values": { "shape": "torch.Size([1, 5, 576, 588])", "dtype": "torch.float32", "value": [ "tensor([[[[-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " ...,", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00]],", "", " [[-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " ...,", " [-1.4857e+00, -1.4820e+00, -1.2100e+00, ..., -6.0979e-01, -5.9650e-01, -3.8527e-01],", " [-1.6755e+00, -1.7221e+00, -1.4518e+00, ..., -7.5577e-01, -7.4658e-01, -5.5592e-01],", " [-7.9957e-01, -8.2162e-01, -5.7014e-01, ..., -1.3689e+00, -1.3169e+00, -1.0678e+00]],", "", " [[-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " ...,", " [-3.0322e-01, -5.0645e-01, -5.8436e-01, ..., -6.2439e-01, -7.9160e-01, -8.1188e-01],", " [-4.4921e-01, -6.5653e-01, -7.2656e-01, ..., -3.4702e-01, -5.2146e-01, -5.1326e-01],", " [-3.4702e-01, -5.3647e-01, -5.4170e-01, ..., -1.0915e+00, -1.1968e+00, -1.0252e+00]],", "", " [[-1.1207e+00, -1.2718e+00, -1.0678e+00, ..., 1.2013e-01, -1.3126e-01, -1.7197e-01],", " [-6.9738e-01, -9.1166e-01, -8.5454e-01, ..., -5.5050e-02, -2.8134e-01, -4.2793e-01],", " [-3.4702e-01, -5.5148e-01, -5.8436e-01, ..., 1.9312e-01, -8.6235e-02, -2.1463e-01],", " ...,", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00]],", "", " [[-1.0039e+00, -9.5669e-01, -6.5546e-01, ..., -1.4711e+00, -1.4219e+00, -1.1389e+00],", " [-1.0039e+00, -9.5669e-01, -6.5546e-01, ..., -1.7193e+00, -1.6771e+00, -1.4091e+00],", " [-1.6317e+00, -1.6020e+00, -1.2669e+00, ..., -1.2667e+00, -1.2268e+00, -8.9720e-01],", " ...,", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00],", " [-1.7923e+00, -1.7521e+00, -1.4802e+00, ..., -1.7923e+00, -1.7521e+00, -1.4802e+00]]]], device='cuda:0')" ], "mean": "tensor(-8.9514e-01, device='cuda:0')", "std": "tensor(9.2586e-01, device='cuda:0')", "min": "tensor(-1.7923e+00, device='cuda:0')", "max": "tensor(1.8899e+00, device='cuda:0')" }, ``` ### Comparing between implementations Once the forward passes of two models have been traced by the debugger, one can compare the `json` output files. See below: we can see slight differences between these two implementations' key projection layer. Inputs are mostly identical, but not quite. Looking through the file differences makes it easier to pinpoint which layer is wrong.  ### Limitations and scope This feature will only work for torch-based models, and would require more work and case-by-case approach for say `jax`-based models that are usually compiled. Models relying heavily on external kernel calls may work, but trace will probably miss some things. Regardless, any python implementation that aims at mimicking another implementation can be traced once instead of reran N times with breakpoints. If you pass `do_prune_layers=False` to your model debugger, ALL the layers will be outputted to `json`. Else, only the first and last layer will be shown. This is useful when some layers (typically cross-attention) appear only after N layers. [[autodoc]] model_addition_debugger_context