[GitHub] [tvm] FrozenGene commented on a change in pull request #6987: [AutoScheduler] Support layout rewrite for whole networks

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

FrozenGene commented on a change in pull request #6987:
URL: https://github.com/apache/tvm/pull/6987#discussion_r533889606



##########
File path: python/tvm/topi/nn/conv2d.py
##########
@@ -371,8 +379,29 @@ def conv2d_nhwc(Input, Filter, stride, padding, dilation, out_dtype="float32"):
     else:
         dilation_h, dilation_w = dilation
 
+    if auto_scheduler_rewritten_layout:
+        # Infer shape for the rewritten layout

Review comment:
       As discussed, we need extract it. If conveniently, we could mark this one todo.

##########
File path: tests/python/relay/test_auto_scheduler_layout_rewrite.py
##########
@@ -0,0 +1,122 @@
+# 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.
+"""Test layout rewrite support for whole neural networks"""
+import tempfile
+
+import numpy as np
+
+import tvm
+from tvm import relay, auto_scheduler
+from tvm.contrib import graph_runtime
+import tvm.testing
+
+
+def get_np_array(var, dtype):
+    return np.random.randn(*[int(x) for x in var.type_annotation.shape]).astype(dtype)
+
+
+def get_relay_conv2d(
+    outc=128,
+    inc=64,
+    height=14,
+    width=14,
+    kh=3,
+    kw=3,
+    batch=1,
+    pad=0,
+    stride=1,
+    dilation=1,
+    layout="NHWC",
+):
+    dtype = "float32"
+    if layout == "NHWC":
+        kernel_layout = "HWIO"
+        d = relay.var("data", shape=(batch, height, width, inc), dtype=dtype)
+        w = relay.var("weight", shape=(kh, kw, inc, outc), dtype=dtype)
+    elif layout == "NCHW":
+        kernel_layout = "OIHW"
+        d = relay.var("data", shape=(batch, inc, height, width), dtype=dtype)
+        w = relay.var("weight", shape=(outc, inc, kh, kw), dtype=dtype)
+
+    y = relay.nn.conv2d(
+        d,
+        w,
+        padding=pad,
+        kernel_size=(kh, kw),
+        strides=(stride, stride),
+        dilation=(dilation, dilation),
+        channels=outc,
+        groups=1,
+        data_layout=layout,
+        kernel_layout=kernel_layout,
+    )
+    mod = tvm.IRModule()
+    mod["main"] = relay.Function([d, w], y)
+    data, weight = get_np_array(d, dtype), get_np_array(w, dtype)
+    return mod, data, weight
+
+
+def tune_and_check(mod, data, weight):
+    # Extract tasks from a relay program
+    target = tvm.target.Target("llvm")
+    tasks, task_weights = auto_scheduler.extract_tasks(mod, target=target, params={})
+
+    with tempfile.NamedTemporaryFile() as fp:
+        log_file = fp.name
+        # log_file = "test_layout_rewrite.json"

Review comment:
       remove it. If we want to merge it soon, could leave it in the clean up pr.




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