rename ambiguous variable

colossalai/autochunk/chunk_selector.py

@@ -126,14 +126,14 @@ class ChunkSelector(object):
         )
         return chunk_info
 
-    def _chunk_size_binary_search(self, l, r, chunk_region_dict, chunk_infos):
+    def _chunk_size_binary_search(self, left, right, chunk_region_dict, chunk_infos):
-        if l >= 16:
+        if left >= 16:
             gap = 4
         else:
             gap = 1
         chunk_info = chunk_region_dict["reorder_chunk_info"]
-        while r >= l + gap:
+        while right >= left + gap:
-            mid = int((l + r) / 2 + 0.5)
+            mid = int((left + right) / 2 + 0.5)
             chunk_info["chunk_size"] = mid
             cur_chunk_infos = chunk_infos + [chunk_info]
             cur_mem_peak = self.memory_estimator.estimate_chunk_inference_mem(
@@ -143,10 +143,10 @@ class ChunkSelector(object):
                 cur_mem_peak[chunk_info["region"][0] : chunk_info["region"][1] + 1]
             )
             if cur_chunk_max_mem >= self.max_memory:
-                r = mid - gap
+                right = mid - gap
             else:
-                l = mid + gap
+                left = mid + gap
-        return l
+        return left
 
     def _get_compute_node_num(self, start, end):
         count = 0

tests/test_autochunk/evoformer/ops.py

@@ -67,10 +67,10 @@ class OutProductMean(nn.Module):
         left_act = self.linear_a(M)
         right_act = self.linear_b(M)
 
-        O = torch.einsum('bsid,bsje->bijde', left_act, right_act).contiguous()
+        o = torch.einsum('bsid,bsje->bijde', left_act, right_act).contiguous()
         # O = rearrange(O, 'b i j d e -> b i j (d e)')
-        O = O.reshape(O.shape[0], O.shape[1], O.shape[2], -1)
+        o = o.reshape(o.shape[0], o.shape[1], o.shape[2], -1)
-        Z = self.o_linear(O)
+        Z = self.o_linear(o)
 
         return Z
 

tests/test_autochunk/openfold/tensor_utils.py

@@ -157,12 +157,12 @@ def _get_minimal_slice_set(
     # start_edges and end_edges both indicate whether, starting from any given
     # dimension, the start/end index is at the top/bottom edge of the
     # corresponding tensor, modeled as a tree
-    def reduce_edge_list(l):
+    def reduce_edge_list(ll):
         tally = 1
-        for i in range(len(l)):
+        for i in range(len(ll)):
             reversed_idx = -1 * (i + 1)
-            l[reversed_idx] *= tally
+            ll[reversed_idx] *= tally
-            tally = l[reversed_idx]
+            tally = ll[reversed_idx]
 
     if(start_edges is None):
         start_edges = [s == 0 for s in start]