Fix linting error (#437)

* fix: linting error and testing error;

.github/workflows/testing_ci.yml

@@ -42,7 +42,6 @@ jobs:
               run: |
                   which python
                   which pip
-                  pip install --upgrade
                   pip install numpy==1.24 torch==${{ matrix.pytorch-version }} -f https://download.pytorch.org/whl/cpu
                   python -c "import torch; print('PyTorch:', torch.__version__)"
 

pypots/nn/modules/reformer/local_attention.py

@@ -43,10 +43,6 @@ def apply_rotary_pos_emb(q, k, freqs, scale=1):
     return q, k
 
 
-def exists(val):
-    return val is not None
-
-
 def default(value, d):
     return d if not exists(value) else value
 
@@ -186,7 +182,6 @@ def forward(
         ), "cannot perform window size extrapolation if xpos is not turned on"
 
         (
-            shape,
             autopad,
             pad_value,
             window_size,
@@ -195,7 +190,6 @@ def forward(
             look_forward,
             shared_qk,
         ) = (
-            q.shape,
             self.autopad,
             -1,
             default(window_size, self.window_size),
@@ -216,7 +210,7 @@ def forward(
                 lambda t: pad_to_multiple(t, self.window_size, dim=-2), (q, k, v)
             )
 
-        b, n, dim_head, device, dtype = *q.shape, q.device, q.dtype
+        b, n, dim_head, device = *q.shape, q.device
 
         scale = default(self.scale, dim_head**-0.5)
 

pypots/nn/modules/reformer/lsh_attention.py

@@ -139,9 +139,9 @@ def merge_dims(ind_from, ind_to, tensor):
 
 def split_at_index(dim, index, t):
     pre_slices = (slice(None),) * dim
-    l = (*pre_slices, slice(None, index))
-    r = (*pre_slices, slice(index, None))
-    return t[l], t[r]
+    l_ = (*pre_slices, slice(None, index))
+    r_ = (*pre_slices, slice(index, None))
+    return t[l_], t[r_]
 
 
 class FullQKAttention(nn.Module):
@@ -608,10 +608,9 @@ def forward(
         **kwargs,
     ):
         device, dtype = x.device, x.dtype
-        b, t, e, h, dh, m, l_h = (
+        b, t, e, h, m, l_h = (
             *x.shape,
             self.heads,
-            self.dim_head,
             self.num_mem_kv,
             self.n_local_attn_heads,
         )