We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Should `get_linear_schedule_with_warmup` consider `gradient_accumulation_steps`?
I think `num_training_steps` should be `len(train_dataloader) * num_epochs // gradient_accumulation_steps`

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `examples/nlp_example.py`
Content:
```
1 # coding=utf-8
2 # Copyright 2021 The HuggingFace Inc. team. All rights reserved.
3 #
4 # Licensed under the Apache License, Version 2.0 (the "License");
5 # you may not use this file except in compliance with the License.
6 # You may obtain a copy of the License at
7 #
8 #     http://www.apache.org/licenses/LICENSE-2.0
9 #
10 # Unless required by applicable law or agreed to in writing, software
11 # distributed under the License is distributed on an "AS IS" BASIS,
12 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 # See the License for the specific language governing permissions and
14 # limitations under the License.
15 import argparse
16 
17 import torch
18 from torch.utils.data import DataLoader
19 
20 from accelerate import Accelerator, DistributedType
21 from datasets import load_dataset, load_metric
22 from transformers import (
23     AdamW,
24     AutoModelForSequenceClassification,
25     AutoTokenizer,
26     get_linear_schedule_with_warmup,
27     set_seed,
28 )
29 
30 
31 ########################################################################
32 # This is a fully working simple example to use Accelerate
33 #
34 # This example trains a Bert base model on GLUE MRPC
35 # in any of the following settings (with the same script):
36 #   - single CPU or single GPU
37 #   - multi GPUS (using PyTorch distributed mode)
38 #   - (multi) TPUs
39 #   - fp16 (mixed-precision) or fp32 (normal precision)
40 #
41 # To run it in each of these various modes, follow the instructions
42 # in the readme for examples:
43 # https://github.com/huggingface/accelerate/tree/main/examples
44 #
45 ########################################################################
46 
47 
48 MAX_GPU_BATCH_SIZE = 16
49 EVAL_BATCH_SIZE = 32
50 
51 
52 def training_function(config, args):
53     # Initialize accelerator
54     accelerator = Accelerator(fp16=args.fp16, cpu=args.cpu)
55 
56     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
57     lr = config["lr"]
58     num_epochs = int(config["num_epochs"])
59     correct_bias = config["correct_bias"]
60     seed = int(config["seed"])
61     batch_size = int(config["batch_size"])
62 
63     tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
64     datasets = load_dataset("glue", "mrpc")
65     metric = load_metric("glue", "mrpc")
66 
67     def tokenize_function(examples):
68         # max_length=None => use the model max length (it's actually the default)
69         outputs = tokenizer(examples["sentence1"], examples["sentence2"], truncation=True, max_length=None)
70         return outputs
71 
72     # Apply the method we just defined to all the examples in all the splits of the dataset
73     tokenized_datasets = datasets.map(
74         tokenize_function,
75         batched=True,
76         remove_columns=["idx", "sentence1", "sentence2"],
77     )
78 
79     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
80     # transformers library
81     tokenized_datasets.rename_column_("label", "labels")
82 
83     # If the batch size is too big we use gradient accumulation
84     gradient_accumulation_steps = 1
85     if batch_size > MAX_GPU_BATCH_SIZE:
86         gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE
87         batch_size = MAX_GPU_BATCH_SIZE
88 
89     def collate_fn(examples):
90         # On TPU it's best to pad everything to the same length or training will be very slow.
91         if accelerator.distributed_type == DistributedType.TPU:
92             return tokenizer.pad(examples, padding="max_length", max_length=128, return_tensors="pt")
93         return tokenizer.pad(examples, padding="longest", return_tensors="pt")
94 
95     # Instantiate dataloaders.
96     train_dataloader = DataLoader(
97         tokenized_datasets["train"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size
98     )
99     eval_dataloader = DataLoader(
100         tokenized_datasets["validation"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE
101     )
102 
103     set_seed(seed)
104 
105     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
106     model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", return_dict=True)
107 
108     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
109     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
110     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
111     model = model.to(accelerator.device)
112 
113     # Instantiate optimizer
114     optimizer = AdamW(params=model.parameters(), lr=lr, correct_bias=correct_bias)
115 
116     # Prepare everything
117     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
118     # prepare method.
119     model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(
120         model, optimizer, train_dataloader, eval_dataloader
121     )
122 
123     # Instantiate learning rate scheduler after preparing the training dataloader as the prepare method
124     # may change its length.
125     lr_scheduler = get_linear_schedule_with_warmup(
126         optimizer=optimizer,
127         num_warmup_steps=100,
128         num_training_steps=len(train_dataloader) * num_epochs,
129     )
130 
131     # Now we train the model
132     for epoch in range(num_epochs):
133         model.train()
134         for step, batch in enumerate(train_dataloader):
135             # We could avoid this line since we set the accelerator with `device_placement=True`.
136             batch.to(accelerator.device)
137             outputs = model(**batch)
138             loss = outputs.loss
139             loss = loss / gradient_accumulation_steps
140             accelerator.backward(loss)
141             if step % gradient_accumulation_steps == 0:
142                 optimizer.step()
143                 lr_scheduler.step()
144                 optimizer.zero_grad()
145 
146         model.eval()
147         for step, batch in enumerate(eval_dataloader):
148             # We could avoid this line since we set the accelerator with `device_placement=True`.
149             batch.to(accelerator.device)
150             with torch.no_grad():
151                 outputs = model(**batch)
152             predictions = outputs.logits.argmax(dim=-1)
153             metric.add_batch(
154                 predictions=accelerator.gather(predictions),
155                 references=accelerator.gather(batch["labels"]),
156             )
157 
158         eval_metric = metric.compute()
159         # Use accelerator.print to print only on the main process.
160         accelerator.print(f"epoch {epoch}:", eval_metric)
161 
162 
163 def main():
164     parser = argparse.ArgumentParser(description="Simple example of training script.")
165     parser.add_argument("--fp16", action="store_true", help="If passed, will use FP16 training.")
166     parser.add_argument("--cpu", action="store_true", help="If passed, will train on the CPU.")
167     args = parser.parse_args()
168     config = {"lr": 2e-5, "num_epochs": 3, "correct_bias": True, "seed": 42, "batch_size": 16}
169     training_function(config, args)
170 
171 
172 if __name__ == "__main__":
173     main()
174
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/examples/nlp_example.py b/examples/nlp_example.py
--- a/examples/nlp_example.py
+++ b/examples/nlp_example.py
@@ -125,7 +125,7 @@
     lr_scheduler = get_linear_schedule_with_warmup(
         optimizer=optimizer,
         num_warmup_steps=100,
-        num_training_steps=len(train_dataloader) * num_epochs,
+        num_training_steps=(len(train_dataloader) * num_epochs) // gradient_accumulation_steps,
     )
 
     # Now we train the model

{"golden_diff": "diff --git a/examples/nlp_example.py b/examples/nlp_example.py\n--- a/examples/nlp_example.py\n+++ b/examples/nlp_example.py\n@@ -125,7 +125,7 @@\n     lr_scheduler = get_linear_schedule_with_warmup(\n         optimizer=optimizer,\n         num_warmup_steps=100,\n-        num_training_steps=len(train_dataloader) * num_epochs,\n+        num_training_steps=(len(train_dataloader) * num_epochs) // gradient_accumulation_steps,\n     )\n \n     # Now we train the model\n", "issue": "Should `get_linear_schedule_with_warmup` consider `gradient_accumulation_steps`?\nI think `num_training_steps` should be `len(train_dataloader) * num_epochs // gradient_accumulation_steps`\n", "before_files": [{"content": "# coding=utf-8\n# Copyright 2021 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport argparse\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom accelerate import Accelerator, DistributedType\nfrom datasets import load_dataset, load_metric\nfrom transformers import (\n    AdamW,\n    AutoModelForSequenceClassification,\n    AutoTokenizer,\n    get_linear_schedule_with_warmup,\n    set_seed,\n)\n\n\n########################################################################\n# This is a fully working simple example to use Accelerate\n#\n# This example trains a Bert base model on GLUE MRPC\n# in any of the following settings (with the same script):\n#   - single CPU or single GPU\n#   - multi GPUS (using PyTorch distributed mode)\n#   - (multi) TPUs\n#   - fp16 (mixed-precision) or fp32 (normal precision)\n#\n# To run it in each of these various modes, follow the instructions\n# in the readme for examples:\n# https://github.com/huggingface/accelerate/tree/main/examples\n#\n########################################################################\n\n\nMAX_GPU_BATCH_SIZE = 16\nEVAL_BATCH_SIZE = 32\n\n\ndef training_function(config, args):\n    # Initialize accelerator\n    accelerator = Accelerator(fp16=args.fp16, cpu=args.cpu)\n\n    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs\n    lr = config[\"lr\"]\n    num_epochs = int(config[\"num_epochs\"])\n    correct_bias = config[\"correct_bias\"]\n    seed = int(config[\"seed\"])\n    batch_size = int(config[\"batch_size\"])\n\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n    datasets = load_dataset(\"glue\", \"mrpc\")\n    metric = load_metric(\"glue\", \"mrpc\")\n\n    def tokenize_function(examples):\n        # max_length=None => use the model max length (it's actually the default)\n        outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n        return outputs\n\n    # Apply the method we just defined to all the examples in all the splits of the dataset\n    tokenized_datasets = datasets.map(\n        tokenize_function,\n        batched=True,\n        remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n    )\n\n    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n    # transformers library\n    tokenized_datasets.rename_column_(\"label\", \"labels\")\n\n    # If the batch size is too big we use gradient accumulation\n    gradient_accumulation_steps = 1\n    if batch_size > MAX_GPU_BATCH_SIZE:\n        gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE\n        batch_size = MAX_GPU_BATCH_SIZE\n\n    def collate_fn(examples):\n        # On TPU it's best to pad everything to the same length or training will be very slow.\n        if accelerator.distributed_type == DistributedType.TPU:\n            return tokenizer.pad(examples, padding=\"max_length\", max_length=128, return_tensors=\"pt\")\n        return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n\n    # Instantiate dataloaders.\n    train_dataloader = DataLoader(\n        tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size\n    )\n    eval_dataloader = DataLoader(\n        tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE\n    )\n\n    set_seed(seed)\n\n    # Instantiate the model (we build the model here so that the seed also control new weights initialization)\n    model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\", return_dict=True)\n\n    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).\n    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer\n    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).\n    model = model.to(accelerator.device)\n\n    # Instantiate optimizer\n    optimizer = AdamW(params=model.parameters(), lr=lr, correct_bias=correct_bias)\n\n    # Prepare everything\n    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the\n    # prepare method.\n    model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n        model, optimizer, train_dataloader, eval_dataloader\n    )\n\n    # Instantiate learning rate scheduler after preparing the training dataloader as the prepare method\n    # may change its length.\n    lr_scheduler = get_linear_schedule_with_warmup(\n        optimizer=optimizer,\n        num_warmup_steps=100,\n        num_training_steps=len(train_dataloader) * num_epochs,\n    )\n\n    # Now we train the model\n    for epoch in range(num_epochs):\n        model.train()\n        for step, batch in enumerate(train_dataloader):\n            # We could avoid this line since we set the accelerator with `device_placement=True`.\n            batch.to(accelerator.device)\n            outputs = model(**batch)\n            loss = outputs.loss\n            loss = loss / gradient_accumulation_steps\n            accelerator.backward(loss)\n            if step % gradient_accumulation_steps == 0:\n                optimizer.step()\n                lr_scheduler.step()\n                optimizer.zero_grad()\n\n        model.eval()\n        for step, batch in enumerate(eval_dataloader):\n            # We could avoid this line since we set the accelerator with `device_placement=True`.\n            batch.to(accelerator.device)\n            with torch.no_grad():\n                outputs = model(**batch)\n            predictions = outputs.logits.argmax(dim=-1)\n            metric.add_batch(\n                predictions=accelerator.gather(predictions),\n                references=accelerator.gather(batch[\"labels\"]),\n            )\n\n        eval_metric = metric.compute()\n        # Use accelerator.print to print only on the main process.\n        accelerator.print(f\"epoch {epoch}:\", eval_metric)\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Simple example of training script.\")\n    parser.add_argument(\"--fp16\", action=\"store_true\", help=\"If passed, will use FP16 training.\")\n    parser.add_argument(\"--cpu\", action=\"store_true\", help=\"If passed, will train on the CPU.\")\n    args = parser.parse_args()\n    config = {\"lr\": 2e-5, \"num_epochs\": 3, \"correct_bias\": True, \"seed\": 42, \"batch_size\": 16}\n    training_function(config, args)\n\n\nif __name__ == \"__main__\":\n    main()\n", "path": "examples/nlp_example.py"}], "after_files": [{"content": "# coding=utf-8\n# Copyright 2021 The HuggingFace Inc. team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\nimport argparse\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom accelerate import Accelerator, DistributedType\nfrom datasets import load_dataset, load_metric\nfrom transformers import (\n    AdamW,\n    AutoModelForSequenceClassification,\n    AutoTokenizer,\n    get_linear_schedule_with_warmup,\n    set_seed,\n)\n\n\n########################################################################\n# This is a fully working simple example to use Accelerate\n#\n# This example trains a Bert base model on GLUE MRPC\n# in any of the following settings (with the same script):\n#   - single CPU or single GPU\n#   - multi GPUS (using PyTorch distributed mode)\n#   - (multi) TPUs\n#   - fp16 (mixed-precision) or fp32 (normal precision)\n#\n# To run it in each of these various modes, follow the instructions\n# in the readme for examples:\n# https://github.com/huggingface/accelerate/tree/main/examples\n#\n########################################################################\n\n\nMAX_GPU_BATCH_SIZE = 16\nEVAL_BATCH_SIZE = 32\n\n\ndef training_function(config, args):\n    # Initialize accelerator\n    accelerator = Accelerator(fp16=args.fp16, cpu=args.cpu)\n\n    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs\n    lr = config[\"lr\"]\n    num_epochs = int(config[\"num_epochs\"])\n    correct_bias = config[\"correct_bias\"]\n    seed = int(config[\"seed\"])\n    batch_size = int(config[\"batch_size\"])\n\n    tokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n    datasets = load_dataset(\"glue\", \"mrpc\")\n    metric = load_metric(\"glue\", \"mrpc\")\n\n    def tokenize_function(examples):\n        # max_length=None => use the model max length (it's actually the default)\n        outputs = tokenizer(examples[\"sentence1\"], examples[\"sentence2\"], truncation=True, max_length=None)\n        return outputs\n\n    # Apply the method we just defined to all the examples in all the splits of the dataset\n    tokenized_datasets = datasets.map(\n        tokenize_function,\n        batched=True,\n        remove_columns=[\"idx\", \"sentence1\", \"sentence2\"],\n    )\n\n    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the\n    # transformers library\n    tokenized_datasets.rename_column_(\"label\", \"labels\")\n\n    # If the batch size is too big we use gradient accumulation\n    gradient_accumulation_steps = 1\n    if batch_size > MAX_GPU_BATCH_SIZE:\n        gradient_accumulation_steps = batch_size // MAX_GPU_BATCH_SIZE\n        batch_size = MAX_GPU_BATCH_SIZE\n\n    def collate_fn(examples):\n        # On TPU it's best to pad everything to the same length or training will be very slow.\n        if accelerator.distributed_type == DistributedType.TPU:\n            return tokenizer.pad(examples, padding=\"max_length\", max_length=128, return_tensors=\"pt\")\n        return tokenizer.pad(examples, padding=\"longest\", return_tensors=\"pt\")\n\n    # Instantiate dataloaders.\n    train_dataloader = DataLoader(\n        tokenized_datasets[\"train\"], shuffle=True, collate_fn=collate_fn, batch_size=batch_size\n    )\n    eval_dataloader = DataLoader(\n        tokenized_datasets[\"validation\"], shuffle=False, collate_fn=collate_fn, batch_size=EVAL_BATCH_SIZE\n    )\n\n    set_seed(seed)\n\n    # Instantiate the model (we build the model here so that the seed also control new weights initialization)\n    model = AutoModelForSequenceClassification.from_pretrained(\"bert-base-cased\", return_dict=True)\n\n    # We could avoid this line since the accelerator is set with `device_placement=True` (default value).\n    # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer\n    # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).\n    model = model.to(accelerator.device)\n\n    # Instantiate optimizer\n    optimizer = AdamW(params=model.parameters(), lr=lr, correct_bias=correct_bias)\n\n    # Prepare everything\n    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the\n    # prepare method.\n    model, optimizer, train_dataloader, eval_dataloader = accelerator.prepare(\n        model, optimizer, train_dataloader, eval_dataloader\n    )\n\n    # Instantiate learning rate scheduler after preparing the training dataloader as the prepare method\n    # may change its length.\n    lr_scheduler = get_linear_schedule_with_warmup(\n        optimizer=optimizer,\n        num_warmup_steps=100,\n        num_training_steps=(len(train_dataloader) * num_epochs) // gradient_accumulation_steps,\n    )\n\n    # Now we train the model\n    for epoch in range(num_epochs):\n        model.train()\n        for step, batch in enumerate(train_dataloader):\n            # We could avoid this line since we set the accelerator with `device_placement=True`.\n            batch.to(accelerator.device)\n            outputs = model(**batch)\n            loss = outputs.loss\n            loss = loss / gradient_accumulation_steps\n            accelerator.backward(loss)\n            if step % gradient_accumulation_steps == 0:\n                optimizer.step()\n                lr_scheduler.step()\n                optimizer.zero_grad()\n\n        model.eval()\n        for step, batch in enumerate(eval_dataloader):\n            # We could avoid this line since we set the accelerator with `device_placement=True`.\n            batch.to(accelerator.device)\n            with torch.no_grad():\n                outputs = model(**batch)\n            predictions = outputs.logits.argmax(dim=-1)\n            metric.add_batch(\n                predictions=accelerator.gather(predictions),\n                references=accelerator.gather(batch[\"labels\"]),\n            )\n\n        eval_metric = metric.compute()\n        # Use accelerator.print to print only on the main process.\n        accelerator.print(f\"epoch {epoch}:\", eval_metric)\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Simple example of training script.\")\n    parser.add_argument(\"--fp16\", action=\"store_true\", help=\"If passed, will use FP16 training.\")\n    parser.add_argument(\"--cpu\", action=\"store_true\", help=\"If passed, will train on the CPU.\")\n    args = parser.parse_args()\n    config = {\"lr\": 2e-5, \"num_epochs\": 3, \"correct_bias\": True, \"seed\": 42, \"batch_size\": 16}\n    training_function(config, args)\n\n\nif __name__ == \"__main__\":\n    main()\n", "path": "examples/nlp_example.py"}]}