remove numpy dependency

According to the `setup.py` file, only dependencies are torch and einops. But the `bert_padding.py` file requires `numpy` only to multiply the elements of a `torch.Size` object. This change aims at allowing the use of FlashAttention without numpy.

flash_attn/bert_padding.py

 # Adapted from https://github.com/mlcommons/training_results_v1.1/blob/main/NVIDIA/benchmarks/bert/implementations/pytorch/padding.py
 
-import numpy as np
-
 import torch
 import torch.nn.functional as F
 
@@ -15,7 +13,7 @@ class IndexFirstAxis(torch.autograd.Function):
         ctx.save_for_backward(indices)
         assert input.ndim >= 2
         ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:]
-        second_dim = np.prod(other_shape)
+        second_dim = other_shape.numel()
         # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
         # return input[indices]
         return torch.gather(rearrange(input, 'b ... -> b (...)'), 0,
@@ -71,7 +69,7 @@ class IndexFirstAxisResidual(torch.autograd.Function):
         ctx.save_for_backward(indices)
         assert input.ndim >= 2
         ctx.first_axis_dim, other_shape = input.shape[0], input.shape[1:]
-        second_dim = np.prod(other_shape)
+        second_dim = other_shape.numel()
         # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing.
         output = input[indices]
         # We don't want to reshape input (b ... -> b (...)) since it could change the channel_last