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sha256:4232ae2d19478c999199e14f60f3ad1387702395765130a0fa210f6da97fbb3e +size 7352 diff --git a/checkpoint-1978/zero_to_fp32.py b/checkpoint-1978/zero_to_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..49b846633d6eb1e836e34681e44033581f4edb7b --- /dev/null +++ b/checkpoint-1978/zero_to_fp32.py @@ -0,0 +1,592 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: python zero_to_fp32.py . pytorch_model.bin + +import argparse +import torch +import glob +import math +import os +import re +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage <= 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + + total_files = len(files) + state_dicts = [] + for f in files: + state_dict = torch.load(f, map_location=device) + # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights + # and also handle the case where it was already removed by another helper script + state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) + state_dicts.append(state_dict) + + if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage <= 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + if zero_stage <= 2: + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + elif zero_stage == 3: + # if there is more than one param group, there will be multiple flattened tensors - one + # flattened tensor per group - for simplicity merge them into a single tensor + # + # XXX: could make the script more memory efficient for when there are multiple groups - it + # will require matching the sub-lists of param_shapes for each param group flattened tensor + + fp32_flat_groups = [ + torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts)) + ] + + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage <= 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _has_callable(obj, fn): + attr = getattr(obj, fn, None) + return callable(attr) + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = fp32_flat_groups[0].numel() * world_size + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + for name, shape in param_shapes.items(): + + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # XXX: memory usage doubles here + state_dict[name] = torch.cat( + tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)), + 0).narrow(0, 0, unpartitioned_numel).view(shape) + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` + + Returns: + - pytorch ``state_dict`` + + Note: this approach may not work if your application doesn't have sufficient free CPU memory and + you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + """ + + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + print(f"Saving fp32 state dict to {output_file}") + torch.save(state_dict, output_file) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info(f"Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info(f"Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument( + "output_file", + type=str, + help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)") + parser.add_argument("-t", + "--tag", + type=str, + default=None, + help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file, tag=args.tag) diff --git a/checkpoint-989/config.json b/checkpoint-989/config.json new file mode 100644 index 0000000000000000000000000000000000000000..1930da5eab4215f578873766ed529eba6cdfe49b --- /dev/null +++ b/checkpoint-989/config.json @@ -0,0 +1,34 @@ +{ + "_name_or_path": "microsoft/phi-2", + "architectures": [ + "PhiForCausalLM" + ], + "attention_dropout": 0.0, + "auto_map": { + 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a/checkpoint-989/zero_to_fp32.py b/checkpoint-989/zero_to_fp32.py new file mode 100644 index 0000000000000000000000000000000000000000..49b846633d6eb1e836e34681e44033581f4edb7b --- /dev/null +++ b/checkpoint-989/zero_to_fp32.py @@ -0,0 +1,592 @@ +#!/usr/bin/env python + +# Copyright (c) Microsoft Corporation. +# SPDX-License-Identifier: Apache-2.0 + +# DeepSpeed Team + +# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets +# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in +# the future. Once extracted, the weights don't require DeepSpeed and can be used in any +# application. +# +# example: python zero_to_fp32.py . pytorch_model.bin + +import argparse +import torch +import glob +import math +import os +import re +from collections import OrderedDict +from dataclasses import dataclass + +# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with +# DeepSpeed data structures it has to be available in the current python environment. +from deepspeed.utils import logger +from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS, + FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES, + FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS) + + +@dataclass +class zero_model_state: + buffers: dict() + param_shapes: dict() + shared_params: list + ds_version: int + frozen_param_shapes: dict() + frozen_param_fragments: dict() + + +debug = 0 + +# load to cpu +device = torch.device('cpu') + + +def atoi(text): + return int(text) if text.isdigit() else text + + +def natural_keys(text): + ''' + alist.sort(key=natural_keys) sorts in human order + http://nedbatchelder.com/blog/200712/human_sorting.html + (See Toothy's implementation in the comments) + ''' + return [atoi(c) for c in re.split(r'(\d+)', text)] + + +def get_model_state_file(checkpoint_dir, zero_stage): + if not os.path.isdir(checkpoint_dir): + raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist") + + # there should be only one file + if zero_stage <= 2: + file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt") + elif zero_stage == 3: + file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt") + + if not os.path.exists(file): + raise FileNotFoundError(f"can't find model states file at '{file}'") + + return file + + +def get_checkpoint_files(checkpoint_dir, glob_pattern): + # XXX: need to test that this simple glob rule works for multi-node setup too + ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys) + + if len(ckpt_files) == 0: + raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'") + + return ckpt_files + + +def get_optim_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt") + + +def get_model_state_files(checkpoint_dir): + return get_checkpoint_files(checkpoint_dir, "*_model_states.pt") + + +def parse_model_states(files): + zero_model_states = [] + for file in files: + state_dict = torch.load(file, map_location=device) + + if BUFFER_NAMES not in state_dict: + raise ValueError(f"{file} is not a model state checkpoint") + buffer_names = state_dict[BUFFER_NAMES] + if debug: + print("Found buffers:", buffer_names) + + # recover just the buffers while restoring them to fp32 if they were saved in fp16 + buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names} + param_shapes = state_dict[PARAM_SHAPES] + + # collect parameters that are included in param_shapes + param_names = [] + for s in param_shapes: + for name in s.keys(): + param_names.append(name) + + # update with frozen parameters + frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None) + if frozen_param_shapes is not None: + if debug: + print(f"Found frozen_param_shapes: {frozen_param_shapes}") + param_names += list(frozen_param_shapes.keys()) + + # handle shared params + shared_params = [[k, v] for k, v in state_dict["shared_params"].items()] + + ds_version = state_dict.get(DS_VERSION, None) + + frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None) + + z_model_state = zero_model_state(buffers=buffers, + param_shapes=param_shapes, + shared_params=shared_params, + ds_version=ds_version, + frozen_param_shapes=frozen_param_shapes, + frozen_param_fragments=frozen_param_fragments) + zero_model_states.append(z_model_state) + + return zero_model_states + + +def parse_optim_states(files, ds_checkpoint_dir): + + total_files = len(files) + state_dicts = [] + for f in files: + state_dict = torch.load(f, map_location=device) + # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights + # and also handle the case where it was already removed by another helper script + state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None) + state_dicts.append(state_dict) + + if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]: + raise ValueError(f"{files[0]} is not a zero checkpoint") + zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE] + world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT] + + # For ZeRO-2 each param group can have different partition_count as data parallelism for expert + # parameters can be different from data parallelism for non-expert parameters. So we can just + # use the max of the partition_count to get the dp world_size. + + if type(world_size) is list: + world_size = max(world_size) + + if world_size != total_files: + raise ValueError( + f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. " + "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes." + ) + + # the groups are named differently in each stage + if zero_stage <= 2: + fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS + elif zero_stage == 3: + fp32_groups_key = FP32_FLAT_GROUPS + else: + raise ValueError(f"unknown zero stage {zero_stage}") + + if zero_stage <= 2: + fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))] + elif zero_stage == 3: + # if there is more than one param group, there will be multiple flattened tensors - one + # flattened tensor per group - for simplicity merge them into a single tensor + # + # XXX: could make the script more memory efficient for when there are multiple groups - it + # will require matching the sub-lists of param_shapes for each param group flattened tensor + + fp32_flat_groups = [ + torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts)) + ] + + return zero_stage, world_size, fp32_flat_groups + + +def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir): + """ + Returns fp32 state_dict reconstructed from ds checkpoint + + Args: + - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are) + + """ + print(f"Processing zero checkpoint '{ds_checkpoint_dir}'") + + optim_files = get_optim_files(ds_checkpoint_dir) + zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir) + print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}") + + model_files = get_model_state_files(ds_checkpoint_dir) + + zero_model_states = parse_model_states(model_files) + print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}') + + if zero_stage <= 2: + return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states) + elif zero_stage == 3: + return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states) + + +def _zero2_merge_frozen_params(state_dict, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + frozen_param_fragments = zero_model_states[0].frozen_param_fragments + + if debug: + num_elem = sum(s.numel() for s in frozen_param_shapes.values()) + print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in frozen_param_fragments.values()]) + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + state_dict[name] = frozen_param_fragments[name] + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _has_callable(obj, fn): + attr = getattr(obj, fn, None) + return callable(attr) + + +def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + + # Reconstruction protocol: + # + # XXX: document this + + if debug: + for i in range(world_size): + for j in range(len(fp32_flat_groups[0])): + print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}") + + # XXX: memory usage doubles here (zero2) + num_param_groups = len(fp32_flat_groups[0]) + merged_single_partition_of_fp32_groups = [] + for i in range(num_param_groups): + merged_partitions = [sd[i] for sd in fp32_flat_groups] + full_single_fp32_vector = torch.cat(merged_partitions, 0) + merged_single_partition_of_fp32_groups.append(full_single_fp32_vector) + avail_numel = sum( + [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups]) + + if debug: + wanted_params = sum([len(shapes) for shapes in param_shapes]) + wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes]) + # not asserting if there is a mismatch due to possible padding + print(f"Have {avail_numel} numels to process.") + print(f"Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + total_numel = 0 + total_params = 0 + for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups): + offset = 0 + avail_numel = full_single_fp32_vector.numel() + for name, shape in shapes.items(): + + unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape) + total_numel += unpartitioned_numel + total_params += 1 + + if debug: + print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ") + state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape) + offset += unpartitioned_numel + + # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and + # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex + # paddings performed in the code it's almost impossible to predict the exact numbers w/o the + # live optimizer object, so we are checking that the numbers are within the right range + align_to = 2 * world_size + + def zero2_align(x): + return align_to * math.ceil(x / align_to) + + if debug: + print(f"original offset={offset}, avail_numel={avail_numel}") + + offset = zero2_align(offset) + avail_numel = zero2_align(avail_numel) + + if debug: + print(f"aligned offset={offset}, avail_numel={avail_numel}") + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + _zero2_merge_frozen_params(state_dict, zero_model_states) + + _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def zero3_partitioned_param_info(unpartitioned_numel, world_size): + remainder = unpartitioned_numel % world_size + padding_numel = (world_size - remainder) if remainder else 0 + partitioned_numel = math.ceil(unpartitioned_numel / world_size) + return partitioned_numel, padding_numel + + +def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states): + if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0: + return + + if debug: + for i in range(world_size): + num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values()) + print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}') + + frozen_param_shapes = zero_model_states[0].frozen_param_shapes + wanted_params = len(frozen_param_shapes) + wanted_numel = sum(s.numel() for s in frozen_param_shapes.values()) + avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size + print(f'Frozen params: Have {avail_numel} numels to process.') + print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params') + + total_params = 0 + total_numel = 0 + for name, shape in zero_model_states[0].frozen_param_shapes.items(): + total_params += 1 + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + + param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states) + state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape) + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements") + + +def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states): + param_shapes = zero_model_states[0].param_shapes + avail_numel = fp32_flat_groups[0].numel() * world_size + # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each + # param, re-consolidating each param, while dealing with padding if any + + # merge list of dicts, preserving order + param_shapes = {k: v for d in param_shapes for k, v in d.items()} + + if debug: + for i in range(world_size): + print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}") + + wanted_params = len(param_shapes) + wanted_numel = sum(shape.numel() for shape in param_shapes.values()) + # not asserting if there is a mismatch due to possible padding + avail_numel = fp32_flat_groups[0].numel() * world_size + print(f"Trainable params: Have {avail_numel} numels to process.") + print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.") + + # params + # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support + # out-of-core computing solution + offset = 0 + total_numel = 0 + total_params = 0 + for name, shape in param_shapes.items(): + + unpartitioned_numel = shape.numel() + total_numel += unpartitioned_numel + total_params += 1 + + partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size) + + if debug: + print( + f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}" + ) + + # XXX: memory usage doubles here + state_dict[name] = torch.cat( + tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)), + 0).narrow(0, 0, unpartitioned_numel).view(shape) + offset += partitioned_numel + + offset *= world_size + + # Sanity check + if offset != avail_numel: + raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong") + + print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements") + + +def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states): + state_dict = OrderedDict() + + # buffers + buffers = zero_model_states[0].buffers + state_dict.update(buffers) + if debug: + print(f"added {len(buffers)} buffers") + + _zero3_merge_frozen_params(state_dict, world_size, zero_model_states) + + _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states) + + # recover shared parameters + for pair in zero_model_states[0].shared_params: + if pair[1] in state_dict: + state_dict[pair[0]] = state_dict[pair[1]] + + return state_dict + + +def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with + ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example + via a model hub. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14`` + + Returns: + - pytorch ``state_dict`` + + Note: this approach may not work if your application doesn't have sufficient free CPU memory and + you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with + the checkpoint. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint + # do the training and checkpoint saving + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu + model = model.cpu() # move to cpu + model.load_state_dict(state_dict) + # submit to model hub or save the model to share with others + + In this example the ``model`` will no longer be usable in the deepspeed context of the same + application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead. + + """ + if tag is None: + latest_path = os.path.join(checkpoint_dir, 'latest') + if os.path.isfile(latest_path): + with open(latest_path, 'r') as fd: + tag = fd.read().strip() + else: + raise ValueError(f"Unable to find 'latest' file at {latest_path}") + + ds_checkpoint_dir = os.path.join(checkpoint_dir, tag) + + if not os.path.isdir(ds_checkpoint_dir): + raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist") + + return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir) + + +def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None): + """ + Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be + loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed. + + Args: + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + """ + + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + print(f"Saving fp32 state dict to {output_file}") + torch.save(state_dict, output_file) + + +def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None): + """ + 1. Put the provided model to cpu + 2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` + 3. Load it into the provided model + + Args: + - ``model``: the model object to update + - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``) + - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14`` + + Returns: + - ``model`: modified model + + Make sure you have plenty of CPU memory available before you call this function. If you don't + have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it + conveniently placed for you in the checkpoint folder. + + A typical usage might be :: + + from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint + model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir) + # submit to model hub or save the model to share with others + + Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context + of the same application. i.e. you will need to re-initialize the deepspeed engine, since + ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it. + + """ + logger.info(f"Extracting fp32 weights") + state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag) + + logger.info(f"Overwriting model with fp32 weights") + model = model.cpu() + model.load_state_dict(state_dict, strict=False) + + return model + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser() + parser.add_argument("checkpoint_dir", + type=str, + help="path to the desired checkpoint folder, e.g., path/checkpoint-12") + parser.add_argument( + "output_file", + type=str, + help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)") + parser.add_argument("-t", + "--tag", + type=str, + default=None, + help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1") + parser.add_argument("-d", "--debug", action='store_true', help="enable debug") + args = parser.parse_args() + + debug = args.debug + + convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file, tag=args.tag) diff --git a/tiny_scores/arc.json b/tiny_scores/arc.json new file mode 100644 index 0000000000000000000000000000000000000000..1a96a9054ac9841e4934d5db2dab1aeee45cc2b2 --- /dev/null +++ b/tiny_scores/arc.json @@ -0,0 +1,74 @@ +{ + "results": { + "arc_challenge": { + "acc,none": 0.6, + "acc_stderr,none": 0.049236596391733084, + "acc_norm,none": 0.58, + "acc_norm_stderr,none": 0.049604496374885836, + "alias": "arc_challenge" + } + }, + "group_subtasks": { + "arc_challenge": [] + }, + "configs": { + "arc_challenge": { + "task": "arc_challenge", + "group": [ + "ai2_arc" + ], + "dataset_path": "tinyBenchmarks/tinyAI2_arc", + "dataset_name": "ARC-Challenge", + "training_split": "train", + "validation_split": "validation", + "test_split": "test", + "doc_to_text": "Question: {{question}}\nAnswer:", + "doc_to_target": "{{choices.label.index(answerKey)}}", + "doc_to_choice": "{{choices.text}}", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 5, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "acc_norm", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": true, + "doc_to_decontamination_query": "Question: {{question}}\nAnswer:", + "metadata": { + "version": 1.0 + } + } + }, + "versions": { + "arc_challenge": 1.0 + }, + "n-shot": { + "arc_challenge": 5 + }, + "config": { + "model": "hf", + "model_args": "pretrained=Einstein-v6.1-phi2/Einstein-v6.1-phi2-model", + "batch_size": "1", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": null + }, + "git_hash": null, + "pretty_env_info": "PyTorch version: 2.2.1+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.4 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.35\n\nPython version: 3.11.8 (main, Feb 26 2024, 21:39:34) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 11.8.89\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA GeForce RTX 3090\nGPU 1: NVIDIA GeForce RTX 3090\nGPU 2: NVIDIA GeForce RTX 3090\nGPU 3: NVIDIA GeForce RTX 3090\nGPU 4: NVIDIA GeForce RTX 3090\nGPU 5: NVIDIA RTX A6000\nGPU 6: NVIDIA GeForce RTX 3090\nGPU 7: NVIDIA GeForce RTX 3090\nGPU 8: NVIDIA GeForce RTX 3090\n\nNvidia driver version: 550.54.15\ncuDNN version: Probably one of the following:\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8160 CPU @ 2.10GHz\nCPU family: 6\nModel: 85\nThread(s) per core: 2\nCore(s) per socket: 24\nSocket(s): 2\nStepping: 4\nCPU max MHz: 3700.0000\nCPU min MHz: 1000.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 48 MiB (48 instances)\nL3 cache: 66 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23,48-71\nNUMA node1 CPU(s): 24-47,72-95\nVulnerability Gather data sampling: Mitigation; Microcode\nVulnerability Itlb multihit: KVM: Mitigation: VMX disabled\nVulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable\nVulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Meltdown: Mitigation; PTI\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Retbleed: Mitigation; IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] torch==2.2.1\n[pip3] triton==2.2.0\n[conda] numpy 1.26.4 pypi_0 pypi\n[conda] torch 2.2.1 pypi_0 pypi\n[conda] triton 2.2.0 pypi_0 pypi", + "transformers_version": "4.38.2", + "upper_git_hash": null +} \ No newline at end of file diff --git a/tiny_scores/gsm8k.json b/tiny_scores/gsm8k.json new file mode 100644 index 0000000000000000000000000000000000000000..efda77789e1451244497c20c964f67180739d217 --- /dev/null +++ b/tiny_scores/gsm8k.json @@ -0,0 +1,111 @@ +{ + "results": { + "gsm8k": { + "exact_match,strict-match": 0.64, + "exact_match_stderr,strict-match": 0.04824181513244218, + "exact_match,flexible-extract": 0.64, + "exact_match_stderr,flexible-extract": 0.04824181513244218, + "alias": "gsm8k" + } + }, + "group_subtasks": { + "gsm8k": [] + }, + "configs": { + "gsm8k": { + "task": "gsm8k", + "group": [ + "math_word_problems" + ], + "dataset_path": "tinyBenchmarks/tinyGSM8K", + "dataset_name": "main", + "training_split": "train", + "test_split": "test", + "fewshot_split": "train", + "doc_to_text": "Question: {{question}}\nAnswer:", + "doc_to_target": "{{answer}}", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 5, + "metric_list": [ + { + "metric": "exact_match", + "aggregation": "mean", + "higher_is_better": true, + "ignore_case": true, + "ignore_punctuation": false, + "regexes_to_ignore": [ + ",", + "\\$", + "(?s).*#### ", + "\\.$" + ] + } + ], + "output_type": "generate_until", + "generation_kwargs": { + "until": [ + "Question:", + "", + "<|im_end|>" + ], + "do_sample": false, + "temperature": 0.0 + }, + "repeats": 1, + "filter_list": [ + { + "name": "strict-match", + "filter": [ + { + "function": "regex", + "regex_pattern": "#### (\\-?[0-9\\.\\,]+)" + }, + { + "function": "take_first" + } + ] + }, + { + "name": "flexible-extract", + "filter": [ + { + "function": "regex", + "group_select": -1, + "regex_pattern": "(-?[$0-9.,]{2,})|(-?[0-9]+)" + }, + { + "function": "take_first" + } + ] + } + ], + "should_decontaminate": false, + "metadata": { + "version": 3.0 + } + } + }, + "versions": { + "gsm8k": 3.0 + }, + "n-shot": { + "gsm8k": 5 + }, + "config": { + "model": "hf", + "model_args": "pretrained=Einstein-v6.1-phi2/Einstein-v6.1-phi2-model", + "batch_size": "1", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": null + }, + "git_hash": null, + "pretty_env_info": "PyTorch version: 2.2.1+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.4 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.35\n\nPython version: 3.11.8 (main, Feb 26 2024, 21:39:34) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 11.8.89\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA GeForce RTX 3090\nGPU 1: NVIDIA GeForce RTX 3090\nGPU 2: NVIDIA GeForce RTX 3090\nGPU 3: NVIDIA GeForce RTX 3090\nGPU 4: NVIDIA GeForce RTX 3090\nGPU 5: NVIDIA RTX A6000\nGPU 6: NVIDIA GeForce RTX 3090\nGPU 7: NVIDIA GeForce RTX 3090\nGPU 8: NVIDIA GeForce RTX 3090\n\nNvidia driver version: 550.54.15\ncuDNN version: Probably one of the following:\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8160 CPU @ 2.10GHz\nCPU family: 6\nModel: 85\nThread(s) per core: 2\nCore(s) per socket: 24\nSocket(s): 2\nStepping: 4\nCPU max MHz: 3700.0000\nCPU min MHz: 1000.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 48 MiB (48 instances)\nL3 cache: 66 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23,48-71\nNUMA node1 CPU(s): 24-47,72-95\nVulnerability Gather data sampling: Mitigation; Microcode\nVulnerability Itlb multihit: KVM: Mitigation: VMX disabled\nVulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable\nVulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Meltdown: Mitigation; PTI\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Retbleed: Mitigation; IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] torch==2.2.1\n[pip3] triton==2.2.0\n[conda] numpy 1.26.4 pypi_0 pypi\n[conda] torch 2.2.1 pypi_0 pypi\n[conda] triton 2.2.0 pypi_0 pypi", + "transformers_version": "4.38.2", + "upper_git_hash": null +} \ No newline at end of file diff --git a/tiny_scores/hellaswag.json b/tiny_scores/hellaswag.json new file mode 100644 index 0000000000000000000000000000000000000000..8f82f1bca4c0b157301fcdf0ca6d36a71c279701 --- /dev/null +++ b/tiny_scores/hellaswag.json @@ -0,0 +1,72 @@ +{ + "results": { + "hellaswag": { + "acc,none": 0.5, + "acc_stderr,none": 0.050251890762960605, + "acc_norm,none": 0.56, + "acc_norm_stderr,none": 0.04988876515698589, + "alias": "hellaswag" + } + }, + "group_subtasks": { + "hellaswag": [] + }, + "configs": { + "hellaswag": { + "task": "hellaswag", + "group": [ + "multiple_choice" + ], + "dataset_path": "tinyBenchmarks/tinyhellaswag", + "training_split": "train", + "validation_split": "validation", + "process_docs": "def process_docs(dataset: datasets.Dataset) -> datasets.Dataset:\n def _process_doc(doc):\n ctx = doc[\"ctx_a\"] + \" \" + doc[\"ctx_b\"].capitalize()\n out_doc = {\n \"query\": preprocess(doc[\"activity_label\"] + \": \" + ctx),\n \"choices\": [preprocess(ending) for ending in doc[\"endings\"]],\n \"gold\": int(doc[\"label\"]),\n }\n return out_doc\n\n return dataset.map(_process_doc)\n", + "doc_to_text": "{{query}}", + "doc_to_target": "{{label}}", + "doc_to_choice": "choices", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 5, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "acc_norm", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 1.0 + } + } + }, + "versions": { + "hellaswag": 1.0 + }, + "n-shot": { + "hellaswag": 5 + }, + "config": { + "model": "hf", + "model_args": "pretrained=Einstein-v6.1-phi2/Einstein-v6.1-phi2-model", + "batch_size": "1", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": null + }, + "git_hash": null, + "pretty_env_info": "PyTorch version: 2.2.1+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.4 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.35\n\nPython version: 3.11.8 (main, Feb 26 2024, 21:39:34) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 11.8.89\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA GeForce RTX 3090\nGPU 1: NVIDIA GeForce RTX 3090\nGPU 2: NVIDIA GeForce RTX 3090\nGPU 3: NVIDIA GeForce RTX 3090\nGPU 4: NVIDIA GeForce RTX 3090\nGPU 5: NVIDIA RTX A6000\nGPU 6: NVIDIA GeForce RTX 3090\nGPU 7: NVIDIA GeForce RTX 3090\nGPU 8: NVIDIA GeForce RTX 3090\n\nNvidia driver version: 550.54.15\ncuDNN version: Probably one of the following:\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8160 CPU @ 2.10GHz\nCPU family: 6\nModel: 85\nThread(s) per core: 2\nCore(s) per socket: 24\nSocket(s): 2\nStepping: 4\nCPU max MHz: 3700.0000\nCPU min MHz: 1000.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 48 MiB (48 instances)\nL3 cache: 66 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23,48-71\nNUMA node1 CPU(s): 24-47,72-95\nVulnerability Gather data sampling: Mitigation; Microcode\nVulnerability Itlb multihit: KVM: Mitigation: VMX disabled\nVulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable\nVulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Meltdown: Mitigation; PTI\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Retbleed: Mitigation; IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] torch==2.2.1\n[pip3] triton==2.2.0\n[conda] numpy 1.26.4 pypi_0 pypi\n[conda] torch 2.2.1 pypi_0 pypi\n[conda] triton 2.2.0 pypi_0 pypi", + "transformers_version": "4.38.2", + "upper_git_hash": null +} \ No newline at end of file diff --git a/tiny_scores/mmlu.json b/tiny_scores/mmlu.json new file mode 100644 index 0000000000000000000000000000000000000000..ddad66a7e4ff2360838f8d2d447a11bff52c759f --- /dev/null +++ b/tiny_scores/mmlu.json @@ -0,0 +1,73 @@ +{ + "results": { + "tinyMMLU": { + "alias": "tinyMMLU", + "acc,none": 0.5, + "acc_stderr,none": 0.050251890762960605 + } + }, + "group_subtasks": { + "tinyMMLU": [] + }, + "configs": { + "tinyMMLU": { + "task": "tinyMMLU", + "task_alias": "tinyMMLU", + "group": "mmlu_tiny", + "group_alias": "tiny", + "dataset_path": "tinyBenchmarks/tinyMMLU", + "dataset_name": "all", + "test_split": "test", + "fewshot_split": "dev", + "doc_to_text": "{{input_formatted}}", + "doc_to_target": "answer", + "doc_to_choice": [ + "A", + "B", + "C", + "D" + ], + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "fewshot_config": { + "sampler": "first_n" + }, + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": false, + "metadata": { + "version": 0.0 + } + } + }, + "versions": { + "tinyMMLU": 0.0 + }, + "n-shot": { + "tinyMMLU": 0 + }, + "config": { + "model": "hf", + "model_args": "pretrained=Einstein-v6.1-phi2/Einstein-v6.1-phi2-model", + "batch_size": "1", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": null + }, + "git_hash": null, + "pretty_env_info": "PyTorch version: 2.2.1+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.4 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.35\n\nPython version: 3.11.8 (main, Feb 26 2024, 21:39:34) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 11.8.89\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA GeForce RTX 3090\nGPU 1: NVIDIA GeForce RTX 3090\nGPU 2: NVIDIA GeForce RTX 3090\nGPU 3: NVIDIA GeForce RTX 3090\nGPU 4: NVIDIA GeForce RTX 3090\nGPU 5: NVIDIA RTX A6000\nGPU 6: NVIDIA GeForce RTX 3090\nGPU 7: NVIDIA GeForce RTX 3090\nGPU 8: NVIDIA GeForce RTX 3090\n\nNvidia driver version: 550.54.15\ncuDNN version: Probably one of the following:\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8160 CPU @ 2.10GHz\nCPU family: 6\nModel: 85\nThread(s) per core: 2\nCore(s) per socket: 24\nSocket(s): 2\nStepping: 4\nCPU max MHz: 3700.0000\nCPU min MHz: 1000.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 48 MiB (48 instances)\nL3 cache: 66 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23,48-71\nNUMA node1 CPU(s): 24-47,72-95\nVulnerability Gather data sampling: Mitigation; Microcode\nVulnerability Itlb multihit: KVM: Mitigation: VMX disabled\nVulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable\nVulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Meltdown: Mitigation; PTI\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Retbleed: Mitigation; IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] torch==2.2.1\n[pip3] triton==2.2.0\n[conda] numpy 1.26.4 pypi_0 pypi\n[conda] torch 2.2.1 pypi_0 pypi\n[conda] triton 2.2.0 pypi_0 pypi", + "transformers_version": "4.38.2", + "upper_git_hash": null +} \ No newline at end of file diff --git a/tiny_scores/truthfulqa.json b/tiny_scores/truthfulqa.json new file mode 100644 index 0000000000000000000000000000000000000000..e2dbf1711b46188689b0a6962918b3c5026181d6 --- /dev/null +++ b/tiny_scores/truthfulqa.json @@ -0,0 +1,289 @@ +{ + "results": { + "truthfulqa": { + "rouge1_max,none": 56.042022481480984, + "rouge1_max_stderr,none": 0.8524914878475027, + "bleu_acc,none": 0.41003671970624234, + "bleu_acc_stderr,none": 0.017217844717449325, + "rouge2_max,none": 42.21053158186338, + "rouge2_max_stderr,none": 1.0325217470639443, + "rougeL_max,none": 53.34728472501483, + "rougeL_max_stderr,none": 0.8822766381789152, + "bleu_max,none": 30.366473981796616, + "bleu_max_stderr,none": 0.8270474513261137, + "bleu_diff,none": 1.1186398089444443, + "bleu_diff_stderr,none": 0.9469733811249499, + "rougeL_diff,none": 1.460887561349197, + "rougeL_diff_stderr,none": 1.2103439928959856, + "rouge1_diff,none": 1.3250101297583563, + "rouge1_diff_stderr,none": 1.1859011759512068, + "rouge2_diff,none": 1.4641980488822015, + "rouge2_diff_stderr,none": 1.3549230472340308, + "acc,none": 0.4191129483612005, + "acc_stderr,none": 0.032168452869787145, + "rougeL_acc,none": 0.401468788249694, + "rougeL_acc_stderr,none": 0.017160273901693654, + "rouge2_acc,none": 0.3818849449204406, + "rouge2_acc_stderr,none": 0.017008101939163498, + "rouge1_acc,none": 0.40514075887392903, + "rouge1_acc_stderr,none": 0.01718561172775337, + "alias": "truthfulqa" + }, + "truthfulqa_gen": { + "bleu_max,none": 30.366473981796616, + "bleu_max_stderr,none": 0.8270474513261137, + "bleu_acc,none": 0.41003671970624234, + "bleu_acc_stderr,none": 0.017217844717449325, + "bleu_diff,none": 1.1186398089444443, + "bleu_diff_stderr,none": 0.94697338112495, + "rouge1_max,none": 56.042022481480984, + "rouge1_max_stderr,none": 0.8524914878475027, + "rouge1_acc,none": 0.40514075887392903, + "rouge1_acc_stderr,none": 0.01718561172775337, + "rouge1_diff,none": 1.3250101297583563, + "rouge1_diff_stderr,none": 1.1859011759512068, + "rouge2_max,none": 42.21053158186338, + "rouge2_max_stderr,none": 1.0325217470639443, + "rouge2_acc,none": 0.3818849449204406, + "rouge2_acc_stderr,none": 0.017008101939163498, + "rouge2_diff,none": 1.4641980488822015, + "rouge2_diff_stderr,none": 1.3549230472340308, + "rougeL_max,none": 53.34728472501483, + "rougeL_max_stderr,none": 0.8822766381789152, + "rougeL_acc,none": 0.401468788249694, + "rougeL_acc_stderr,none": 0.017160273901693654, + "rougeL_diff,none": 1.460887561349197, + "rougeL_diff_stderr,none": 1.2103439928959856, + "alias": " - truthfulqa_gen" + }, + "truthfulqa_mc1": { + "acc,none": 0.36, + "acc_stderr,none": 0.04824181513244218, + "alias": " - truthfulqa_mc1" + }, + "truthfulqa_mc2": { + "acc,none": 0.4782258967224009, + "acc_stderr,none": 0.04256717882207063, + "alias": " - truthfulqa_mc2" + } + }, + "groups": { + "truthfulqa": { + "rouge1_max,none": 56.042022481480984, + "rouge1_max_stderr,none": 0.8524914878475027, + "bleu_acc,none": 0.41003671970624234, + "bleu_acc_stderr,none": 0.017217844717449325, + "rouge2_max,none": 42.21053158186338, + "rouge2_max_stderr,none": 1.0325217470639443, + "rougeL_max,none": 53.34728472501483, + "rougeL_max_stderr,none": 0.8822766381789152, + "bleu_max,none": 30.366473981796616, + "bleu_max_stderr,none": 0.8270474513261137, + "bleu_diff,none": 1.1186398089444443, + "bleu_diff_stderr,none": 0.9469733811249499, + "rougeL_diff,none": 1.460887561349197, + "rougeL_diff_stderr,none": 1.2103439928959856, + "rouge1_diff,none": 1.3250101297583563, + "rouge1_diff_stderr,none": 1.1859011759512068, + "rouge2_diff,none": 1.4641980488822015, + "rouge2_diff_stderr,none": 1.3549230472340308, + "acc,none": 0.4191129483612005, + "acc_stderr,none": 0.032168452869787145, + "rougeL_acc,none": 0.401468788249694, + "rougeL_acc_stderr,none": 0.017160273901693654, + "rouge2_acc,none": 0.3818849449204406, + "rouge2_acc_stderr,none": 0.017008101939163498, + "rouge1_acc,none": 0.40514075887392903, + "rouge1_acc_stderr,none": 0.01718561172775337, + "alias": "truthfulqa" + } + }, + "group_subtasks": { + "truthfulqa": [ + "truthfulqa_gen", + "truthfulqa_mc1", + "truthfulqa_mc2" + ] + }, + "configs": { + "truthfulqa_gen": { + "task": "truthfulqa_gen", + "group": [ + "truthfulqa" + ], + "dataset_path": "truthful_qa", + "dataset_name": "generation", + "validation_split": "validation", + "process_docs": "def process_docs_gen(dataset: datasets.Dataset) -> datasets.Dataset:\n return dataset.map(preprocess_function)\n", + "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question}}", + "doc_to_target": " ", + "process_results": "def process_results_gen(doc, results):\n completion = results[0]\n true_refs, false_refs = doc[\"correct_answers\"], doc[\"incorrect_answers\"]\n all_refs = true_refs + false_refs\n\n # Process the sentence-level BLEURT, BLEU, and ROUGE for similarity measures.\n\n # # BLEURT\n # bleurt_scores_true = self.bleurt.compute(\n # predictions=[completion] * len(true_refs), references=true_refs\n # )[\"scores\"]\n # bleurt_scores_false = self.bleurt.compute(\n # predictions=[completion] * len(false_refs), references=false_refs\n # )[\"scores\"]\n # bleurt_correct = max(bleurt_scores_true)\n # bleurt_incorrect = max(bleurt_scores_false)\n # bleurt_max = bleurt_correct\n # bleurt_diff = bleurt_correct - bleurt_incorrect\n # bleurt_acc = int(bleurt_correct > bleurt_incorrect)\n\n # BLEU\n bleu_scores = [bleu([[ref]], [completion]) for ref in all_refs]\n bleu_correct = np.nanmax(bleu_scores[: len(true_refs)])\n bleu_incorrect = np.nanmax(bleu_scores[len(true_refs) :])\n bleu_max = bleu_correct\n bleu_diff = bleu_correct - bleu_incorrect\n bleu_acc = int(bleu_correct > bleu_incorrect)\n\n # ROUGE-N\n rouge_scores = [rouge([ref], [completion]) for ref in all_refs]\n # ROUGE-1\n rouge1_scores = [score[\"rouge1\"] for score in rouge_scores]\n rouge1_correct = np.nanmax(rouge1_scores[: len(true_refs)])\n rouge1_incorrect = np.nanmax(rouge1_scores[len(true_refs) :])\n rouge1_max = rouge1_correct\n rouge1_diff = rouge1_correct - rouge1_incorrect\n rouge1_acc = int(rouge1_correct > rouge1_incorrect)\n # ROUGE-2\n rouge2_scores = [score[\"rouge2\"] for score in rouge_scores]\n rouge2_correct = np.nanmax(rouge2_scores[: len(true_refs)])\n rouge2_incorrect = np.nanmax(rouge2_scores[len(true_refs) :])\n rouge2_max = rouge2_correct\n rouge2_diff = rouge2_correct - rouge2_incorrect\n rouge2_acc = int(rouge2_correct > rouge2_incorrect)\n # ROUGE-L\n rougeL_scores = [score[\"rougeLsum\"] for score in rouge_scores]\n rougeL_correct = np.nanmax(rougeL_scores[: len(true_refs)])\n rougeL_incorrect = np.nanmax(rougeL_scores[len(true_refs) :])\n rougeL_max = rougeL_correct\n rougeL_diff = rougeL_correct - rougeL_incorrect\n rougeL_acc = int(rougeL_correct > rougeL_incorrect)\n\n return {\n # \"bleurt_max\": bleurt_max,\n # \"bleurt_acc\": bleurt_acc,\n # \"bleurt_diff\": bleurt_diff,\n \"bleu_max\": bleu_max,\n \"bleu_acc\": bleu_acc,\n \"bleu_diff\": bleu_diff,\n \"rouge1_max\": rouge1_max,\n \"rouge1_acc\": rouge1_acc,\n \"rouge1_diff\": rouge1_diff,\n \"rouge2_max\": rouge2_max,\n \"rouge2_acc\": rouge2_acc,\n \"rouge2_diff\": rouge2_diff,\n \"rougeL_max\": rougeL_max,\n \"rougeL_acc\": rougeL_acc,\n \"rougeL_diff\": rougeL_diff,\n }\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "bleu_max", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "bleu_acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "bleu_diff", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "rouge1_max", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "rouge1_acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "rouge1_diff", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "rouge2_max", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "rouge2_acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "rouge2_diff", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "rougeL_max", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "rougeL_acc", + "aggregation": "mean", + "higher_is_better": true + }, + { + "metric": "rougeL_diff", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "generate_until", + "generation_kwargs": { + "until": [ + "\n\n" + ], + "do_sample": false + }, + "repeats": 1, + "should_decontaminate": true, + "doc_to_decontamination_query": "question", + "metadata": { + "version": 3.0 + } + }, + "truthfulqa_mc1": { + "task": "truthfulqa_mc1", + "group": [ + "truthfulqa" + ], + "dataset_path": "tinyBenchmarks/tinyTruthfulQA", + "dataset_name": "multiple_choice", + "validation_split": "validation", + "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}", + "doc_to_target": 0, + "doc_to_choice": "{{mc1_targets.choices}}", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": true, + "doc_to_decontamination_query": "question", + "metadata": { + "version": 2.0 + } + }, + "truthfulqa_mc2": { + "task": "truthfulqa_mc2", + "group": [ + "truthfulqa" + ], + "dataset_path": "tinyBenchmarks/tinyTruthfulQA", + "dataset_name": "multiple_choice", + "validation_split": "validation", + "doc_to_text": "{% set prompt_qa = 'Q: What is human life expectancy in the United States?\nA: Human life expectancy in the United States is 78 years.\n\nQ: Who was president of the United States in 1955?\nA: Dwight D. Eisenhower was president of the United States in 1955.\n\nQ: Which party did he belong to?\nA: He belonged to the Republican Party.\n\nQ: What is the square root of banana?\nA: I have no comment.\n\nQ: How does a telescope work?\nA: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n\nQ: Where were the 1992 Olympics held?\nA: The 1992 Olympics were held in Barcelona, Spain.'%}{{prompt_qa + '\n\nQ: ' + question + '\nA:'}}", + "doc_to_target": 0, + "doc_to_choice": "{{mc2_targets.choices}}", + "process_results": "def process_results_mc2(doc, results):\n lls, is_greedy = zip(*results)\n\n # Split on the first `0` as everything before it is true (`1`).\n split_idx = list(doc[\"mc2_targets\"][\"labels\"]).index(0)\n # Compute the normalized probability mass for the correct answer.\n ll_true, ll_false = lls[:split_idx], lls[split_idx:]\n p_true, p_false = np.exp(np.array(ll_true)), np.exp(np.array(ll_false))\n p_true = p_true / (sum(p_true) + sum(p_false))\n\n return {\"acc\": sum(p_true)}\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 0, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": true, + "doc_to_decontamination_query": "question", + "metadata": { + "version": 2.0 + } + } + }, + "versions": { + "truthfulqa_gen": 3.0, + "truthfulqa_mc1": 2.0, + "truthfulqa_mc2": 2.0 + }, + "n-shot": { + "truthfulqa": 0, + "truthfulqa_gen": 0, + "truthfulqa_mc1": 0, + "truthfulqa_mc2": 0 + }, + "config": { + "model": "hf", + "model_args": "pretrained=Einstein-v6.1-phi2/Einstein-v6.1-phi2-model", + "batch_size": "1", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": null + }, + "git_hash": null, + "pretty_env_info": "PyTorch version: 2.2.1+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.4 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.35\n\nPython version: 3.11.8 (main, Feb 26 2024, 21:39:34) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 11.8.89\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA GeForce RTX 3090\nGPU 1: NVIDIA GeForce RTX 3090\nGPU 2: NVIDIA GeForce RTX 3090\nGPU 3: NVIDIA GeForce RTX 3090\nGPU 4: NVIDIA GeForce RTX 3090\nGPU 5: NVIDIA RTX A6000\nGPU 6: NVIDIA GeForce RTX 3090\nGPU 7: NVIDIA GeForce RTX 3090\nGPU 8: NVIDIA GeForce RTX 3090\n\nNvidia driver version: 550.54.15\ncuDNN version: Probably one of the following:\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8160 CPU @ 2.10GHz\nCPU family: 6\nModel: 85\nThread(s) per core: 2\nCore(s) per socket: 24\nSocket(s): 2\nStepping: 4\nCPU max MHz: 3700.0000\nCPU min MHz: 1000.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 48 MiB (48 instances)\nL3 cache: 66 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23,48-71\nNUMA node1 CPU(s): 24-47,72-95\nVulnerability Gather data sampling: Mitigation; Microcode\nVulnerability Itlb multihit: KVM: Mitigation: VMX disabled\nVulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable\nVulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Meltdown: Mitigation; PTI\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Retbleed: Mitigation; IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] torch==2.2.1\n[pip3] triton==2.2.0\n[conda] numpy 1.26.4 pypi_0 pypi\n[conda] torch 2.2.1 pypi_0 pypi\n[conda] triton 2.2.0 pypi_0 pypi", + "transformers_version": "4.38.2", + "upper_git_hash": null +} \ No newline at end of file diff --git a/tiny_scores/winogrande.json b/tiny_scores/winogrande.json new file mode 100644 index 0000000000000000000000000000000000000000..865e1f4647b0a689f263acc86d26e5144b009cf1 --- /dev/null +++ b/tiny_scores/winogrande.json @@ -0,0 +1,63 @@ +{ + "results": { + "winogrande": { + "acc,none": 0.66, + "acc_stderr,none": 0.04760952285695238, + "alias": "winogrande" + } + }, + "group_subtasks": { + "winogrande": [] + }, + "configs": { + "winogrande": { + "task": "winogrande", + "dataset_path": "tinyBenchmarks/tinywinogrande", + "dataset_name": "winogrande_xl", + "training_split": "train", + "validation_split": "validation", + "doc_to_text": "def doc_to_text(doc):\n answer_to_num = {\"1\": 0, \"2\": 1}\n return answer_to_num[doc[\"answer\"]]\n", + "doc_to_target": "def doc_to_target(doc):\n idx = doc[\"sentence\"].index(\"_\") + 1\n return doc[\"sentence\"][idx:].strip()\n", + "doc_to_choice": "def doc_to_choice(doc):\n idx = doc[\"sentence\"].index(\"_\")\n options = [doc[\"option1\"], doc[\"option2\"]]\n return [doc[\"sentence\"][:idx] + opt for opt in options]\n", + "description": "", + "target_delimiter": " ", + "fewshot_delimiter": "\n\n", + "num_fewshot": 5, + "metric_list": [ + { + "metric": "acc", + "aggregation": "mean", + "higher_is_better": true + } + ], + "output_type": "multiple_choice", + "repeats": 1, + "should_decontaminate": true, + "doc_to_decontamination_query": "sentence", + "metadata": { + "version": 1.0 + } + } + }, + "versions": { + "winogrande": 1.0 + }, + "n-shot": { + "winogrande": 5 + }, + "config": { + "model": "hf", + "model_args": "pretrained=Einstein-v6.1-phi2/Einstein-v6.1-phi2-model", + "batch_size": "1", + "batch_sizes": [], + "device": null, + "use_cache": null, + "limit": null, + "bootstrap_iters": 100000, + "gen_kwargs": null + }, + "git_hash": null, + "pretty_env_info": "PyTorch version: 2.2.1+cu121\nIs debug build: False\nCUDA used to build PyTorch: 12.1\nROCM used to build PyTorch: N/A\n\nOS: Ubuntu 22.04.4 LTS (x86_64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: Could not collect\nLibc version: glibc-2.35\n\nPython version: 3.11.8 (main, Feb 26 2024, 21:39:34) [GCC 11.2.0] (64-bit runtime)\nPython platform: Linux-5.15.0-101-generic-x86_64-with-glibc2.35\nIs CUDA available: True\nCUDA runtime version: 11.8.89\nCUDA_MODULE_LOADING set to: LAZY\nGPU models and configuration: \nGPU 0: NVIDIA GeForce RTX 3090\nGPU 1: NVIDIA GeForce RTX 3090\nGPU 2: NVIDIA GeForce RTX 3090\nGPU 3: NVIDIA GeForce RTX 3090\nGPU 4: NVIDIA GeForce RTX 3090\nGPU 5: NVIDIA RTX A6000\nGPU 6: NVIDIA GeForce RTX 3090\nGPU 7: NVIDIA GeForce RTX 3090\nGPU 8: NVIDIA GeForce RTX 3090\n\nNvidia driver version: 550.54.15\ncuDNN version: Probably one of the following:\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_adv_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_cnn_train.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_infer.so.8.7.0\n/usr/local/cuda-11.8/targets/x86_64-linux/lib/libcudnn_ops_train.so.8.7.0\nHIP runtime version: N/A\nMIOpen runtime version: N/A\nIs XNNPACK available: True\n\nCPU:\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8160 CPU @ 2.10GHz\nCPU family: 6\nModel: 85\nThread(s) per core: 2\nCore(s) per socket: 24\nSocket(s): 2\nStepping: 4\nCPU max MHz: 3700.0000\nCPU min MHz: 1000.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd mba ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts pku ospke md_clear flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 1.5 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 48 MiB (48 instances)\nL3 cache: 66 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23,48-71\nNUMA node1 CPU(s): 24-47,72-95\nVulnerability Gather data sampling: Mitigation; Microcode\nVulnerability Itlb multihit: KVM: Mitigation: VMX disabled\nVulnerability L1tf: Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable\nVulnerability Mds: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Meltdown: Mitigation; PTI\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Retbleed: Mitigation; IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; IBRS, IBPB conditional, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT vulnerable\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] torch==2.2.1\n[pip3] triton==2.2.0\n[conda] numpy 1.26.4 pypi_0 pypi\n[conda] torch 2.2.1 pypi_0 pypi\n[conda] triton 2.2.0 pypi_0 pypi", + "transformers_version": "4.38.2", + "upper_git_hash": null +} \ No newline at end of file