prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Implement 1-Bit Adam
  Integrate 1-bit Adam into our model. The DeepSpeed tutorial can be found [here](https://www.deepspeed.ai/tutorials/onebit-adam/)
  
  </issue>
  <code>
  [start of train_pipeline.py]
  1 import random
  2 import deepspeed
  3 import torch
  4 from torch.utils.data import DataLoader
  5 from tqdm.auto import trange
  6 import torch.distributed as distributed
  7 
  8 from gpt_neox import (GPTNeoX_Pipe, AutoregressiveWrapper, GPT2Dataset, extract_tarfile,
  9                       prepare_optimizer_parameters, get_tokenizer, is_main, prepare_data)
  10 
  11 from gpt_neox.utils import get_args, get_params
  12 
  13 import GPUtil
  14 
  15 # arguments
  16 train_args = get_args()
  17 params = get_params(train_args.model)
  18 
  19 # tokenizer
  20 tokenizer = get_tokenizer(tokenizer_type=params["tokenizer"].get("type", None),
  21                           from_pretrained=params["tokenizer"].get("from_pretrained", True),
  22                           add_padding_token=params["tokenizer"].get("add_padding_token", False))
  23 vocab_size = len(tokenizer) if params["vocab_size"] is None else params["vocab_size"]
  24 
  25 # model
  26 deepspeed.init_distributed(dist_backend='nccl')
  27 torch.distributed.barrier()  # barrier will force processes to stop until *all* processes have reached the barrier
  28 
  29 def loss_function(x, y):
  30     losses = torch.nn.functional.cross_entropy(x, y, reduction='none')
  31     loss = losses.mean()
  32     return loss
  33         
  34 model = GPTNeoX_Pipe(
  35     num_tokens=params["vocab_size"],
  36     dim=params["hidden_dim"],
  37     seq_len=params["seq_len"],
  38     depth=params["n_layers"],
  39     heads=params["n_heads"],
  40     dim_head=params["dim_head"],
  41     loss_fn = loss_function,#torch.nn.CrossEntropyLoss(),
  42     num_stages = params.get("pipeline_num_stages", 2)
  43 )
  44 model = AutoregressiveWrapper(model)
  45 
  46 # optimizer
  47 ds_model_params = prepare_optimizer_parameters(model)
  48 optim = torch.optim.Adam(model.parameters(), lr=params["learning_rate"])
  49 
  50 # prepare data
  51 dset_params = params["dataset"]
  52 assert dset_params is not None
  53 
  54 if is_main(train_args):
  55     prepare_data(dset_params["name"])
  56     torch.distributed.barrier()  # barrier will force processes to stop until *all* processes have reached the barrier
  57 else:
  58     torch.distributed.barrier()
  59     
  60 # data loading
  61 train_dataset = GPT2Dataset(glob_pattern=dset_params["train_path"],
  62                             seq_len=params["seq_len"],
  63                             train=True,
  64                             **dset_params)
  65 train_loader = model_engine.deepspeed_io(train_dataset, pin_memory=params.get("pin_memory", False))
  66 
  67 eval_dataset = GPT2Dataset(glob_pattern=dset_params["eval_path"],
  68                            seq_len=params["seq_len"],
  69                            train=False,
  70                            **dset_params)
  71 
  72 val_loader = DataLoader(eval_dataset, batch_size=params["eval_batch_size"])
  73 val_loader = iter(val_loader)
  74 
  75 # deepspeed loader
  76 model_engine, optim, train_loader, _ = deepspeed.initialize(args=train_args,
  77                                                             model=model,
  78                                                             optimizer=optim,
  79                                                             model_parameters=ds_model_params,
  80                                                             training_data=train_dataset)
  81 
  82 
  83 batches_to_train = 10000
  84 
  85 pbar = trange(params["num_epochs"], mininterval=10., desc='Training Model', dynamic_ncols=True)
  86 for _ in pbar:
  87     for i in range(batches_to_train):
  88 
  89         is_main = model_engine.local_rank == 0
  90 
  91         loss = model_engine.train_batch()
  92 
  93         pbar.set_description(f'Training Loss: {loss.item():.4f}')
  94         pbar.update()
  95
  [end of train_pipeline.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/train_pipeline.py b/train_pipeline.py
  --- a/train_pipeline.py
  +++ b/train_pipeline.py
  @@ -1,16 +1,21 @@
  +import argparse
  +import json
   import random
  +from collections import defaultdict
  +import os
   import deepspeed
   import torch
   from torch.utils.data import DataLoader
   from tqdm.auto import trange
  -import torch.distributed as distributed
   
  -from gpt_neox import (GPTNeoX_Pipe, AutoregressiveWrapper, GPT2Dataset, extract_tarfile,
  -                      prepare_optimizer_parameters, get_tokenizer, is_main, prepare_data)
  +from gpt_neox import (GPTNeoX, AutoregressiveWrapper, TextSamplerDataset,
  +                      cycle, prepare_optimizer_parameters, decode_tokens, prepare_data,
  +                      GPTNeoX_Pipe)
  +from gpt_neox.datasets import GPT2Dataset
  +from gpt_neox.utils import is_main
  +import gpt_neox
   
  -from gpt_neox.utils import get_args, get_params
  -
  -import GPUtil
  +WORLD_SIZE = os.getenv('WORLD_SIZE')
   
   # arguments
   train_args = get_args()
  
verification_info: {"golden_diff": "diff --git a/train_pipeline.py b/train_pipeline.py\n--- a/train_pipeline.py\n+++ b/train_pipeline.py\n@@ -1,16 +1,21 @@\n+import argparse\n+import json\n import random\n+from collections import defaultdict\n+import os\n import deepspeed\n import torch\n from torch.utils.data import DataLoader\n from tqdm.auto import trange\n-import torch.distributed as distributed\n \n-from gpt_neox import (GPTNeoX_Pipe, AutoregressiveWrapper, GPT2Dataset, extract_tarfile,\n-                      prepare_optimizer_parameters, get_tokenizer, is_main, prepare_data)\n+from gpt_neox import (GPTNeoX, AutoregressiveWrapper, TextSamplerDataset,\n+                      cycle, prepare_optimizer_parameters, decode_tokens, prepare_data,\n+                      GPTNeoX_Pipe)\n+from gpt_neox.datasets import GPT2Dataset\n+from gpt_neox.utils import is_main\n+import gpt_neox\n \n-from gpt_neox.utils import get_args, get_params\n-\n-import GPUtil\n+WORLD_SIZE = os.getenv('WORLD_SIZE')\n \n # arguments\n train_args = get_args()\n", "issue": "Implement 1-Bit Adam\nIntegrate 1-bit Adam into our model. The DeepSpeed tutorial can be found [here](https://www.deepspeed.ai/tutorials/onebit-adam/)\n", "before_files": [{"content": "import random\nimport deepspeed\nimport torch\nfrom torch.utils.data import DataLoader\nfrom tqdm.auto import trange\nimport torch.distributed as distributed\n\nfrom gpt_neox import (GPTNeoX_Pipe, AutoregressiveWrapper, GPT2Dataset, extract_tarfile,\n                      prepare_optimizer_parameters, get_tokenizer, is_main, prepare_data)\n\nfrom gpt_neox.utils import get_args, get_params\n\nimport GPUtil\n\n# arguments\ntrain_args = get_args()\nparams = get_params(train_args.model)\n\n# tokenizer\ntokenizer = get_tokenizer(tokenizer_type=params[\"tokenizer\"].get(\"type\", None),\n                          from_pretrained=params[\"tokenizer\"].get(\"from_pretrained\", True),\n                          add_padding_token=params[\"tokenizer\"].get(\"add_padding_token\", False))\nvocab_size = len(tokenizer) if params[\"vocab_size\"] is None else params[\"vocab_size\"]\n\n# model\ndeepspeed.init_distributed(dist_backend='nccl')\ntorch.distributed.barrier()  # barrier will force processes to stop until *all* processes have reached the barrier\n\ndef loss_function(x, y):\n    losses = torch.nn.functional.cross_entropy(x, y, reduction='none')\n    loss = losses.mean()\n    return loss\n        \nmodel = GPTNeoX_Pipe(\n    num_tokens=params[\"vocab_size\"],\n    dim=params[\"hidden_dim\"],\n    seq_len=params[\"seq_len\"],\n    depth=params[\"n_layers\"],\n    heads=params[\"n_heads\"],\n    dim_head=params[\"dim_head\"],\n    loss_fn = loss_function,#torch.nn.CrossEntropyLoss(),\n    num_stages = params.get(\"pipeline_num_stages\", 2)\n)\nmodel = AutoregressiveWrapper(model)\n\n# optimizer\nds_model_params = prepare_optimizer_parameters(model)\noptim = torch.optim.Adam(model.parameters(), lr=params[\"learning_rate\"])\n\n# prepare data\ndset_params = params[\"dataset\"]\nassert dset_params is not None\n\nif is_main(train_args):\n    prepare_data(dset_params[\"name\"])\n    torch.distributed.barrier()  # barrier will force processes to stop until *all* processes have reached the barrier\nelse:\n    torch.distributed.barrier()\n    \n# data loading\ntrain_dataset = GPT2Dataset(glob_pattern=dset_params[\"train_path\"],\n                            seq_len=params[\"seq_len\"],\n                            train=True,\n                            **dset_params)\ntrain_loader = model_engine.deepspeed_io(train_dataset, pin_memory=params.get(\"pin_memory\", False))\n\neval_dataset = GPT2Dataset(glob_pattern=dset_params[\"eval_path\"],\n                           seq_len=params[\"seq_len\"],\n                           train=False,\n                           **dset_params)\n\nval_loader = DataLoader(eval_dataset, batch_size=params[\"eval_batch_size\"])\nval_loader = iter(val_loader)\n\n# deepspeed loader\nmodel_engine, optim, train_loader, _ = deepspeed.initialize(args=train_args,\n                                                            model=model,\n                                                            optimizer=optim,\n                                                            model_parameters=ds_model_params,\n                                                            training_data=train_dataset)\n\n\nbatches_to_train = 10000\n\npbar = trange(params[\"num_epochs\"], mininterval=10., desc='Training Model', dynamic_ncols=True)\nfor _ in pbar:\n    for i in range(batches_to_train):\n\n        is_main = model_engine.local_rank == 0\n\n        loss = model_engine.train_batch()\n\n        pbar.set_description(f'Training Loss: {loss.item():.4f}')\n        pbar.update()\n", "path": "train_pipeline.py"}]}