o shB @sddlZddlZddlZddlZddlmZmZddlmZddl m Z ddl m Z m Z e rEddlZddlmZejrDddlZdZnd Zdd lmZeeZd d Zed ZdedefddZddZ d/de ede de efddZ!d/de ede de efddZ"dej#fddZ$ddZ%ed Z&d!d"Z'd#d$Z(d%d&Z) '  d0de ed(e e de fd)d*Z*e d+d,e   d1de ed(e e de e fd-d.Z+dS)2N)contextmanagerredirect_stdout)StringIO)Optional)is_torch_availablerequires) save_fileTF)loggingcCs trtjs dStjdkS)z7Return True if rank=0 or we aren't running distributed.Tr)_torch_distributed_availabletorch distributedis_initializedget_rankrr`/var/www/html/alpaca_bot/venv/lib/python3.10/site-packages/transformers/model_debugging_utils.py _is_rank_zero,srzobject at 0x[0-9A-Fa-f]+x_strreturncCs td|S)z Replace memory addresses in an object's repr with a stable placeholder so that beautiful JSON diffs won't be ruined by ephemeral addresses. zobject at 0xXXXXXXXX)MEMORY_ADDRESS_REGEXsub)rrrr_sanitize_repr_for_diff6s rcCstr dt|jSdS)z@Return a stable string representation for a DTensor-like object.zDTensor (rank0) -> zDTensor(non-rank0))rrepr _local_tensor)xrrr _dtensor_repr>sr debug_pathuse_repr path_to_valuec Cstjdd|r t|}n4|r6|ds|d7}|r!tj||n|}td| i|d|}n t d|d|dt |j t |j|d }|jtjtjtjhvrz|tt |tt |tt |tt |d |S) a Converts Tensors and DTensors to a JSON-serializable dictionary representation. 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T)sci_modez .safetensorsdataz./z use_repr=z and path_to_value=z cannot both be falsy.)shapedtypevalue)meanstdminmax)r set_printoptions _repr_to_listendswithospathjoinr contiguousdetachcpu ValueErrorrr!r"float16float32bfloat16updaterr$r%r&r')r#rrr value_outfilepathoutrrr_serialize_tensor_like_ioEs.    r9cst|ttfrfddt|DSt|tr&fdd|DSt|dr4t|jdSt|t j rBt|dSt t |S)a Recursively build a JSON-serializable Python structure from `value`. Tensors and DTensors become either sanitized repr strings, or are saved to disk as SafeTensors files and their relative paths are recorded in the returned Python structure. Lists/tuples/dicts are recursed into. All memory addresses are replaced with a stable placeholder. Args: value: Any Python object, often including torch Tensors, lists, dicts, etc. debug_path (`str`, *optional*, defaults to `None`): Directory to dump debug JSON and SafeTensors files. use_repr (bool, *optional*, defaults to `True`): Whether to save a `repr()`-ized version of the tensors as the `value` property in the asscoiated FULL_TENSORS.json file, or to store full tensors in separate SafeTensors files and store the relative path to that file in the `value` property. path_to_value (`str`, *optional*, defaults to `None`): The file name for the SafeTensors file holding the full tensor value if `use_repr=False`. Returns: A nested Python structure (list, dict, or sanitized string) that is safe to json.dump. c s*g|]\}}t|d|dqS_rrr _serialize_io).0ivrrrrr sz!_serialize_io..c s,i|]\}}|t|d|dqSr:r=)r?krArBrr sz!_serialize_io..rr<) isinstancelisttuple enumeratedictitemshasattrr9rr Tensorrr)r#rrrrrBrr>vs     r>r#c Csxtjdddt$}t|t||}Wdn1s"wYWdn1s1wYt|S)z Converts a tensor into a sanitized multi-line string representation. Args: value (`torch.Tensor`): The tensor to represent. Returns: `list[str]`: List of string lines representing the tensor. Tx)r linewidthN)r r(rrprintgetvaluer splitlines)r#bufrawrrrr)s   r)cCs4|dr|dd|dD]}t|qdSdS)Nchildrenoutputs)getpopprune_outputs_if_childrennodechildrrrrYs    rYz (.*)\.(\d+)$csHt|dd}|r|dsdS|dtfdd|dDS)z Checks whether a node represents a layer block with submodules. Args: node (`dict`): A node from the call tree. 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This records structured inputs and outputs during the forward pass into a call tree. Args: model (`PreTrainedModel`, `nn.Module`): Model to wrap. debug_path (`str`): Optional directory to dump debug JSON files. do_prune_layers (`bool`, *optional*, defaults to `True`): Whether to prune intermediate layers. use_repr (bool, *optional*, defaults to `True`): Whether to save a `repr()`-ized version of the tensors as the `value` property in the asscoiated FULL_TENSORS.json file, or to store full tensors in separate SafeTensors files and store the relative path to that file in the `value` property. Nr]r|rVrUTrqrtr`cs0jtfdd}|_dS)Ncstr'||dfddDtdddgd}j|t|i|}Wdn1s=wYtrtddDd krWd|d <n t|d d|d <j}|d sr|d jrjd d ||S)Nargskwargscs&i|]}t|dkr||qS)r)rnr?rD dict_inputsrrrE5s&zY_attach_debugger_logic..wrap_forward..wrapped_forward.._inputsr<rcss|]}dVqdS)rNr)r?r;rrrrdFszX_attach_debugger_logic..wrap_forward..wrapped_forward..rrV_outputsrUrk) rr>_debugger_model_call_stackappendr no_gradsumnamed_childrenrX)inpskwsr[r8finished)rrrmodule orig_forwardrrrwrapped_forward1s@     zE_attach_debugger_logic..wrap_forward..wrapped_forward)forward functoolswraps)rrr)rrr)rrrr wrap_forward.s 'z,_attach_debugger_logic..wrap_forwardr^cstrdt||ddddgd}j||i|}trnjrnt|dd|d<j}|djd<|djd<|d jd <fd d tjDrhtjt d |S) Nz (top-level)rrr<rrrVr|rUcs$g|]}j|sj|dqS)N)rrXr)rrrrCs$zG_attach_debugger_logic..top_wrapped_forward..)rr) rr>rrrXrrGkeysror)rrtop_noder8r class_namerrrreal_top_forwardrrrtop_wrapped_forwardds:     z3_attach_debugger_logic..top_wrapped_forward) __class____name__rrrr+rrr1 named_modulesrrr) rrrrrrname submodulerrrr_attach_debugger_logics(. %r)r )backendsc csldd|D}|j||<t||||z|VW|D]\}}||_qdS|D]\}}||_q-w)a # 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 file. If `use_repr=True` (the default), the JSON file will record a `repr()`-ized version of the tensors as a list of strings. If `use_repr=False`, the full tensors will be stored in separate SafeTensors files and the JSON file will provide a relative path to that file. To note, this context manager enforces `torch.no_grad()`. ## Usage add the context manager to a model to debug ```python import torch from PIL import Image from transformers import LlavaProcessor, LlavaForConditionalGeneration, 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) # 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="Your_debug_path", do_prune_layers=False): output = model.forward(**inputs) ``` cSsi|]\}}||jqSr)r)r?r;mrrrrEsz3model_addition_debugger_context..N)rrrrK)rrrr orig_forwardsmodule_instanceforward_methodrrrmodel_addition_debugger_contexts3 r)NTN)r`TT)NTT),rrr+re contextlibrriortypingrutils.import_utilsrrr safetensors.torchr r is_availabletorch.distributed.tensorr utilsr get_loggerrrrcompilerstrrrboolr9r>rMr)rYrerirorrrrrrrsv        1+ -