Added basic unit test for train method

tests/test_train_utils.py

+import torch
+
+from llama_recipes.utils.train_utils import train
+
+def test_gradient_accumulation(mocker):
+    # import sys
+    # sys.path.append('/home/ubuntu/llama-recipes/')
+    
+    model = mocker.MagicMock(name="model")
+    model().loss.__truediv__().detach.return_value = torch.tensor(1)
+    batch = {"input": torch.zeros(1)}
+    train_dataloader = [batch, batch, batch, batch, batch]
+    eval_dataloader = None
+    tokenizer = mocker.MagicMock()
+    optimizer = mocker.MagicMock()
+    lr_scheduler = mocker.MagicMock()
+    gradient_accumulation_steps = 1
+    train_config = mocker.MagicMock()
+    train_config.enable_fsdp = False
+    train_config.use_fp16 = False
+    train_config.run_validation = False
+    
+    train(
+        model,
+        train_dataloader,
+        eval_dataloader,
+        tokenizer,
+        optimizer,
+        lr_scheduler,
+        gradient_accumulation_steps,
+        train_config,
+    )
+    
+    assert optimizer.zero_grad.call_count == 5
+    optimizer.zero_grad.reset_mock()
+    
+    gradient_accumulation_steps = 2
+    train(
+        model,
+        train_dataloader,
+        eval_dataloader,
+        tokenizer,
+        optimizer,
+        lr_scheduler,
+        gradient_accumulation_steps,
+        train_config,
+    )
+    assert optimizer.zero_grad.call_count == 3
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