fix tests

memory_efficient_attention_pytorch/memory_efficient_attention.py

@@ -50,7 +50,7 @@ def attention(
 
 # memory efficient attention
 
-def summarize_qkv_chunk(q, k, v, mask, attn_bias_chunk, causal, qk_start_indices, dropout=0., training=False):
+def summarize_qkv_chunk(q, k, v, mask, attn_bias_chunk, causal, qk_start_indices, dropout):
     q_start_index, k_start_index, q_chunk_size, k_chunk_size, device = *qk_start_indices, q.shape[-2], k.shape[-2], q.device
 
     weight = einsum('b h i d, b h j d -> b h i j', q, k)
@@ -72,8 +72,9 @@ def summarize_qkv_chunk(q, k, v, mask, attn_bias_chunk, causal, qk_start_indices
     weight = weight - weight_max
 
     exp_weight = weight.exp()
-    if training:
-        exp_weight = F.dropout(exp_weight, p=dropout, training=training)
+
+    exp_weight = F.dropout(exp_weight, p = dropout)
+
     weighted_value = einsum('b h i j, b h j d -> b h i d', exp_weight, v)
 
     return exp_weight.sum(dim = -1), weighted_value, rearrange(weight_max, '... 1 -> ...')
@@ -137,8 +138,7 @@ def memory_efficient_attention(
                 attn_bias_chunk,
                 causal,
                 (q_start_index, k_start_index),
-                dropout = dropout,
-                training = training
+                dropout if training else 0.
             )
 
             exp_weights.append(exp_weight_chunk)

setup.py

@@ -3,7 +3,7 @@
 setup(
   name = 'memory-efficient-attention-pytorch',
   packages = find_packages(exclude=[]),
-  version = '0.1.0',
+  version = '0.1.1',
   license='MIT',
   description = 'Memory Efficient Attention - Pytorch',
   long_description_content_type = 'text/markdown',