[GitHub] [tvm] manupa-arm commented on a change in pull request #10062: [microNPU][5] Convert Proposals to te.Schedules

[GitBox <gi...@apache.org>]

manupa-arm commented on a change in pull request #10062:
URL: https://github.com/apache/tvm/pull/10062#discussion_r814732971



##########
File path: python/tvm/contrib/ethosu/cascader/scheduler.py
##########
@@ -0,0 +1,232 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""Scheduler for cascader which converts Proposals into Schedules."""
+from typing import Tuple, List, Dict, DefaultDict
+from collections import defaultdict
+import numpy as np
+
+from tvm import te
+from tvm import tir
+from .cascader_options import CascaderOptions
+from .graph import CascaderGraph, Part, Tensor, TESubgraph
+from .tensor_config import MemoryRegion
+from .proposal import Proposal
+from .proposal_generator import generate_proposals
+from .graph import create_cascader_graph
+from .device_config import EthosuDeviceConfig
+
+
+def tile_nd(
+    sch: te.Schedule, tensor: te.Tensor, tile: Tuple[int, ...]
+) -> Tuple[List[tir.IterVar], List[tir.IterVar]]:
+    """Scheduling utility to perform N-dimensional tiling.
+
+    Parameters
+    ----------
+    sch : te.Schedule
+        The schedule to apply the tiling to.
+    tensor : te.Tensor
+        The tensor to apply the tiling to.
+    tile : Tuple[int, ...]
+        The N-dimensional tile size.
+
+    Returns
+    -------
+    outer_indices : List[tir.IterVar]
+        The outer iteration variables.
+    inner_indices : List[tir.IterVar]
+        The inner iteration variables.
+
+    """
+    outer_indices = []
+    inner_indices = []
+    for i, size in enumerate(tile):
+        outer, inner = sch[tensor].split(tensor.op.axis[i], size)
+        outer_indices.append(outer)
+        inner_indices.append(inner)
+
+    sch[tensor].reorder(*outer_indices, *inner_indices)
+    return outer_indices, inner_indices
+
+
+def stripe_part(
+    part: Part, stripe_shape: Tuple[int, ...], sch: te.Schedule
+) -> Tuple[te.Stage, tir.IterVar]:
+    """Apply a striping schedule to the TE subgraph represented by a Part."""
+    te_subgraph = part.subgraph
+    te_output_tensor = te_subgraph.output_tensor
+    outer_indices, _ = tile_nd(sch, te_output_tensor, stripe_shape)
+    g = sch.create_group(
+        outputs=te_output_tensor.op.input_tensors,
+        inputs=te_subgraph.input_tensors,
+        include_inputs=False,
+    )
+    g.compute_at(sch[te_output_tensor], outer_indices[-1])
+    for ax in outer_indices:
+        sch[te_output_tensor].unroll(ax)
+
+    return sch[te_output_tensor], outer_indices[-1]
+
+
+def cascade_part(
+    part: Part, stripe_stage: te.Stage, stripe_axis: tir.IterVar, sch: te.Schedule
+) -> None:
+    """Schedule a Part into a cascade indicated by a stripe Stage."""
+    te_subgraph = part.subgraph
+    g = sch.create_group(
+        outputs=te_subgraph.output_tensor, inputs=te_subgraph.input_tensors, include_inputs=False
+    )
+    g.compute_at(stripe_stage, stripe_axis)
+
+
+def update_readers(part: Part, readers: DefaultDict[te.Tensor, List[te.Tensor]]) -> None:
+    """Update a dictionary which stores the te.Tensors that need to be read in order to produce a given te.Tensor."""
+    visited = set()
+
+    def _visit(tensor):
+        if tensor is not visited and tensor not in part.subgraph.input_tensors:

Review comment:
       ```suggestion
           if tensor is not in visited and tensor not in part.subgraph.input_tensors:
   ```




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