Increase the default batch size of alexnet (#919)

Summary:
The default batch size for alexnet is too small and is not a proper workload for GPU. Some performance issues (e.g., pytorch/pytorch#76970) only affects small batch size inputs and I believe they are insignificant in real settings.
This PR increases the default batch size for alexnet on GPU.

Although this PR will also increase CPU batch size, we are working on a PR that uses separate batch sizes for different devices, which will use small batch size for CPU, and large batch size for GPU.

Pull Request resolved: #919

Reviewed By: jansel

Differential Revision: D36549692

Pulled By: xuzhao9

fbshipit-source-id: 03552c1f5617670797b7ea9069be926293da4f34

torchbenchmark/models/alexnet/__init__.py

@@ -6,7 +6,7 @@ class Model(TorchVisionModel):
     # Train batch size: use the smallest example batch of 128 (assuming only 1 worker)
     # Source: https://arxiv.org/pdf/1404.5997.pdf
     DEFAULT_TRAIN_BSIZE = 128
-    DEFAULT_EVAL_BSIZE = 16
+    DEFAULT_EVAL_BSIZE = 128
 
     def __init__(self, test, device, jit, batch_size=None, extra_args=[]):
         super().__init__(model_name="alexnet", test=test, device=device, jit=jit,