popxl.ops.repeat - remove 'see also' links (#4)

Removing "See also" links in `popxl.ops.repeat`.

Author: jayniep-gc

python/popxl/python_files/ops/repeat.py

@@ -54,8 +54,6 @@ def build(self, x):
                 # repeat 8 times
                 y0, w0 = ops.repeat(add_weight_graph0, 8, x0, inputs_dict={add_weight0.w: w0})
 
-    See also `PyTorch Tensor.repeat <https://pytorch.org/docs/stable/generated/torch.Tensor.repeat.html>`__, `NumPy repeat <https://numpy.org/doc/stable/reference/generated/numpy.repeat.html>`__.
-
     Args:
         graph (Graph): User defined graph to repeat `repeat_count` times.
         repeat_count (int): Number of times to repeat calling the graph.
@@ -74,7 +72,10 @@ def build(self, x):
         Tuple[Tensor, ...]:
             Tuple of the output tensors of the call in the parent graph.
     """
-    loop_info = repeat_with_info(graph, repeat_count, *inputs, inputs_dict=inputs_dict)
+    loop_info = repeat_with_info(graph,
+                                 repeat_count,
+                                 *inputs,
+                                 inputs_dict=inputs_dict)
 
     out_tensors = loop_info.outputs
     return out_tensors
@@ -193,8 +194,7 @@ def build(self, x):
     if total_inputs < total_outputs:
         raise ValueError(
             f"To repeat the subgraph ({graph.id}) the number of inputs must be greater than or equal to the number of outputs."
-            f" {total_inputs} < {total_outputs}"
-        )
+            f" {total_inputs} < {total_outputs}")
 
     # For clarity, we rename our graphs:
     # - Bottom: The user provided bottom level graph. We call this with a call op. This has gone
@@ -215,16 +215,14 @@ def build(self, x):
 
     # Create the middle graph, call and loop ops
     pb_middle_graph, pb_callop, pb_loop_op = _setup_call_and_repeat(
-        pb_ir, pb_top_graph, pb_bottom_graph
-    )
+        pb_ir, pb_top_graph, pb_bottom_graph)
 
     # set the number of times to loop
     pb_loop_op.setTripCountValue(repeat_count)
 
     # Prep and validate inputs
-    inputs_all = _prep_and_validate_inputs(
-        check_inputs, top_graph, graph, "called", inputs, inputs_dict
-    )
+    inputs_all = _prep_and_validate_inputs(check_inputs, top_graph, graph,
+                                           "called", inputs, inputs_dict)
 
     # 1, 2. Connect inputs.
     _setup_inputs(
@@ -236,9 +234,8 @@ def build(self, x):
     )
 
     # 3. Connect outputs.
-    _ = _setup_outputs(
-        pb_top_graph, pb_bottom_graph, pb_middle_graph, pb_callop, pb_loop_op
-    )
+    _ = _setup_outputs(pb_top_graph, pb_bottom_graph, pb_middle_graph,
+                       pb_callop, pb_loop_op)
 
     pb_callop.setup()
     pb_loop_op.setup()
@@ -250,13 +247,14 @@ def build(self, x):
     loop_carried_inputs = pb_loop_op.getNumExplicitInputs()
     for bottom_t in bottom_graph._by_ref_inputs:
         middle_t = c_info.graph_to_parent(bottom_t)
-        loop_carried = pb_middle_graph.getInputIndex(middle_t.id) < loop_carried_inputs
+        loop_carried = pb_middle_graph.getInputIndex(
+            middle_t.id) < loop_carried_inputs
         # If a tensor was set as a by_ref_input, we should also do the same for the looped subgraph.
         c_info.set_parent_input_modified(
-            middle_t, infer_modified_regions=not loop_carried
-        )
+            middle_t, infer_modified_regions=not loop_carried)
         top_t = r_info.graph_to_parent(middle_t)
-        r_info.set_parent_input_modified(top_t, infer_modified_regions=not loop_carried)
+        r_info.set_parent_input_modified(
+            top_t, infer_modified_regions=not loop_carried)
         r_info.called_graph._by_ref_inputs.add(middle_t)
 
     return r_info
@@ -280,34 +278,35 @@ def _setup_call_and_repeat(
     # This is the graph we will repeat.
     pb_middle_graph = pb_ir.createGraph(
         _ir.GraphId(
-            pb_ir.createUniqueSubgraphId(f"{pb_bottom_graph.id.str()}__loop_wrapper")
-        )
-    )
+            pb_ir.createUniqueSubgraphId(
+                f"{pb_bottom_graph.id.str()}__loop_wrapper")))
 
-    opid = _ir.OperatorIdentifier("ai.graphcore", "Call", 1, _ir.NumInputs(), 0)
+    opid = _ir.OperatorIdentifier("ai.graphcore", "Call", 1, _ir.NumInputs(),
+                                  0)
     op_name = pb_middle_graph.id.str() + "__call__" + pb_bottom_graph.id.str()
 
     ctx = get_current_context()
     # Call the bottom_graph
-    pb_callop = pb_middle_graph.createOp_CallOp(
-        opid, pb_bottom_graph, ctx._get_op_settings(op_name)
-    )
+    pb_callop = pb_middle_graph.createOp_CallOp(opid, pb_bottom_graph,
+                                                ctx._get_op_settings(op_name))
 
     opid = _ir.OperatorIdentifier("ai.onnx", "Loop", 11, _ir.NumInputs(), 0)
     op_name = pb_top_graph.id.str() + "__loop__" + pb_middle_graph.id.str()
 
     # Loop the middle_graph
-    pb_loop_op = pb_top_graph.createOp_LoopOp(
-        opid, ctx._get_op_settings(op_name), pb_middle_graph
-    )
+    pb_loop_op = pb_top_graph.createOp_LoopOp(opid,
+                                              ctx._get_op_settings(op_name),
+                                              pb_middle_graph)
 
     # Add mandatory loop iterator tensor to graph (is not an output)
     repeatIterId = _ir.addScope(pb_middle_graph, "Iterator___")
-    pb_middle_graph.addInput(repeatIterId, _ir.TensorInfo(_ir.DataType.INT32, ()))
+    pb_middle_graph.addInput(repeatIterId,
+                             _ir.TensorInfo(_ir.DataType.INT32, ()))
 
     # Add mandatory loop condition tensor to graph (is also an output)
     repeatCondId = _ir.addScope(pb_middle_graph, "LoopCond___")
-    pb_middle_graph.addInput(repeatCondId, _ir.TensorInfo(_ir.DataType.BOOL, ()))
+    pb_middle_graph.addInput(repeatCondId,
+                             _ir.TensorInfo(_ir.DataType.BOOL, ()))
     pb_middle_graph.markAsOutput(repeatCondId)
 
     return pb_middle_graph, pb_callop, pb_loop_op
@@ -354,8 +353,7 @@ def _setup_inputs(
             False,
         )
         pb_callop.connectInTensor(
-            call_in_idx, _ir.addScope(pb_middle_graph, parent_tensor.name)
-        )
+            call_in_idx, _ir.addScope(pb_middle_graph, parent_tensor.name))
 
 
 def _setup_outputs(
@@ -385,33 +383,31 @@ def _setup_outputs(
 
     for pb_subgraph_out_id in pb_bottom_graph.getOutputIds():
         top_tensor_id = _ir.addScope(
-            pb_top_graph, _ir.removeScope(pb_bottom_graph, pb_subgraph_out_id)
-        )
+            pb_top_graph, _ir.removeScope(pb_bottom_graph, pb_subgraph_out_id))
         # Already has scope added
         middle_tensor_id = _ir.removeScope(pb_bottom_graph, pb_subgraph_out_id)
         bottom_tensor_id = _ir.addScope(
-            pb_bottom_graph, _ir.removeScope(pb_bottom_graph, pb_subgraph_out_id)
-        )
+            pb_bottom_graph,
+            _ir.removeScope(pb_bottom_graph, pb_subgraph_out_id))
 
         sgOutIdx = pb_bottom_graph.getOutputIndex(bottom_tensor_id)
         callOutIdx = pb_callop.subgraphOutToOpOutIndex(sgOutIdx)
 
         # Avoid tensor name collisions
         middle_tensor_id = pb_middle_graph.getIr().createIntermediateTensorId(
-            middle_tensor_id
-        )
+            middle_tensor_id)
         pb_callop.createAndConnectOutTensor(callOutIdx, middle_tensor_id)
 
         pb_middle_graph.markAsOutput(middle_tensor_id)
         sgOutIdx = pb_middle_graph.getOutputIndex(middle_tensor_id)
         repeatOutIdx = pb_loop_op.subgraphOutToOpOutIndex(sgOutIdx)
         # Avoid tensor name collisions
         top_tensor_id = pb_middle_graph.getIr().createIntermediateTensorId(
-            top_tensor_id
-        )
+            top_tensor_id)
         # We overwrite here as we added the middle_tensor_id as an output above, but we want to make
         # sure the loop op is setup correctly.
-        pb_loop_op.addLoopOutput(repeatOutIdx, top_tensor_id, middle_tensor_id, True)
+        pb_loop_op.addLoopOutput(repeatOutIdx, top_tensor_id, middle_tensor_id,
+                                 True)
 
         outnames.append(top_tensor_id)
     return outnames