tkonolige commented on code in PR #11341:
URL: https://github.com/apache/tvm/pull/11341#discussion_r876347585
##########
src/relay/op/tensor/transform.cc:
##########
@@ -346,7 +346,7 @@ RELAY_REGISTER_OP("concatenate")
.set_support_level(1)
.add_type_rel("Concatenate", ConcatenateRel<ConcatenateAttrs>)
.set_attr<FInferCorrectLayout>("FInferCorrectLayout", ConcatenateLayout)
- .set_attr<FTVMCompute>("FTVMCompute", ConcatenateCompute)
+ // .set_attr<FTVMCompute>("FTVMCompute", ConcatenateCompute)
Review Comment:
nit: uncomment or delete
##########
src/te/schedule/schedule_dataflow_rewrite.cc:
##########
@@ -511,6 +511,23 @@ void InjectInline(ScheduleNode* sch, bool
feature_extraction_mode) {
std::vector<bool> changed(sch->stages.size(), false);
std::vector<Stmt> new_hybrid_body(sch->stages.size());
std::vector<bool> hybrid_changed(sch->stages.size(), false);
+ // (sshtin): this workaround allows to inline extern ops.
+ // All inputs for extern op should not be inlined because inlining happens
+ // before generation of TE script for particular extern op. That may lead to
+ // crash during lowering or building stages.
+ std::unordered_map<Operation, Operation> ext_ops;
Review Comment:
Can you document how this workaround works and in what cases it might fail?
##########
tests/python/relay/test_op_level1.py:
##########
@@ -430,6 +430,112 @@ def test_batch_norm():
)
+def do_concat_test(shapes, t_shape, dtype, axis, dev, target):
+ varsToConcat = []
+ inputData = []
+ pos = 0
+ for s in shapes:
+ varsToConcat.append(relay.var("x{}".format(pos), shape=s))
+ inputData.append(np.random.rand(*s).astype(dtype))
+ pos += 1
+ t = relay.var("z", shape=t_shape, dtype=dtype)
+ z = relay.concatenate(varsToConcat, axis=axis)
+ z = relay.add(z, t)
+ params = varsToConcat
+ params.append(t)
+ func = relay.Function(params, z)
+ t_data = np.random.uniform(low=-10, high=10, size=t_shape).astype(dtype)
+ ref_res = np.concatenate((tuple(inputData)), axis=axis) + t_data
+ mod = tvm.IRModule.from_expr(func)
+
+ executor = relay.create_executor("graph", mod=mod, device=dev,
target=target)
+ op_res1 = executor.evaluate()(*inputData, t_data)
+
+ tvm.testing.assert_allclose(op_res1.numpy(), ref_res, rtol=0.000001)
+ op_res2 = relay.create_executor("debug", device=dev,
target=target).evaluate(func)(
+ *inputData, t_data
+ )
+ tvm.testing.assert_allclose(op_res2.numpy(), ref_res, rtol=0.000001)
+
+
[email protected]_gpu
+def test_concatenate1():
+ for target, dev in tvm.testing.enabled_targets():
+ if target != "llvm":
+ continue
Review Comment:
```suggestion
@tvm.testing.parameterize_targets("llvm")
def test_concatenate1(target, dev):
```
##########
tests/python/relay/test_op_level1.py:
##########
@@ -430,6 +430,112 @@ def test_batch_norm():
)
+def do_concat_test(shapes, t_shape, dtype, axis, dev, target):
+ varsToConcat = []
+ inputData = []
+ pos = 0
+ for s in shapes:
+ varsToConcat.append(relay.var("x{}".format(pos), shape=s))
+ inputData.append(np.random.rand(*s).astype(dtype))
+ pos += 1
+ t = relay.var("z", shape=t_shape, dtype=dtype)
+ z = relay.concatenate(varsToConcat, axis=axis)
+ z = relay.add(z, t)
+ params = varsToConcat
+ params.append(t)
+ func = relay.Function(params, z)
+ t_data = np.random.uniform(low=-10, high=10, size=t_shape).astype(dtype)
+ ref_res = np.concatenate((tuple(inputData)), axis=axis) + t_data
+ mod = tvm.IRModule.from_expr(func)
+
+ executor = relay.create_executor("graph", mod=mod, device=dev,
target=target)
+ op_res1 = executor.evaluate()(*inputData, t_data)
+
+ tvm.testing.assert_allclose(op_res1.numpy(), ref_res, rtol=0.000001)
+ op_res2 = relay.create_executor("debug", device=dev,
target=target).evaluate(func)(
+ *inputData, t_data
+ )
+ tvm.testing.assert_allclose(op_res2.numpy(), ref_res, rtol=0.000001)
+
+
[email protected]_gpu
+def test_concatenate1():
+ for target, dev in tvm.testing.enabled_targets():
+ if target != "llvm":
+ continue
+ np.random.seed(471)
+ maxNumDimensions = 6
+ shape = [4, 32, 16, 1, 31, 20, 21, 8, 28, 7] # just randomly selected
10 numbers
+ for dtype in ["float32"]:
+ for dimsNum in range(1, maxNumDimensions):
+ np.random.shuffle(shape)
+ for axis in range(0, dimsNum): # range should be (-dimsNum +
1, dimsNum)
+ numToConcat = np.random.uniform(low=2, high=10,
size=(1)).astype("int64")[0]
+ shapes = []
+ # the code below to normalize axes index. For some reasons
tvm notifies about error if the axis is negative
+ normalizedAxis = axis
+ if axis < 0:
+ normalizedAxis += dimsNum
+ finalSize = 0
+ for i in range(0, numToConcat):
+ shp = tuple(shape[:dimsNum])
+ finalSize += shape[(i % len(shape))]
+ shapes.append(
+ shp[:normalizedAxis]
+ + tuple([shape[(i % len(shape))]])
+ + shp[normalizedAxis + 1 :]
+ )
+ t_shape = shp[:normalizedAxis] + tuple([finalSize]) +
shp[normalizedAxis + 1 :]
+ do_concat_test(shapes, t_shape, dtype, axis, dev, target)
+
+
[email protected]_gpu
+def test_concatenate2():
+ # test to cover cases (1, .. , x, 1, .. , 1)
+ for target, dev in tvm.testing.enabled_targets():
+ if target != "llvm":
+ continue
Review Comment:
```suggestion
@tvm.testing.parametrize_targets("llvm")
def test_concatenate2(target, dev):
# test to cover cases (1, .. , x, 1, .. , 1)
```
##########
python/tvm/topi/x86/concat.py:
##########
@@ -0,0 +1,134 @@
+# 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.
+# pylint: disable=invalid-name,unused-variable,unused-argument,invalid-name
Review Comment:
It would be better to not disable all these lints.
##########
tests/python/relay/test_op_level1.py:
##########
@@ -430,6 +430,112 @@ def test_batch_norm():
)
+def do_concat_test(shapes, t_shape, dtype, axis, dev, target):
+ varsToConcat = []
+ inputData = []
+ pos = 0
+ for s in shapes:
+ varsToConcat.append(relay.var("x{}".format(pos), shape=s))
+ inputData.append(np.random.rand(*s).astype(dtype))
+ pos += 1
+ t = relay.var("z", shape=t_shape, dtype=dtype)
+ z = relay.concatenate(varsToConcat, axis=axis)
+ z = relay.add(z, t)
+ params = varsToConcat
+ params.append(t)
+ func = relay.Function(params, z)
+ t_data = np.random.uniform(low=-10, high=10, size=t_shape).astype(dtype)
+ ref_res = np.concatenate((tuple(inputData)), axis=axis) + t_data
+ mod = tvm.IRModule.from_expr(func)
+
+ executor = relay.create_executor("graph", mod=mod, device=dev,
target=target)
+ op_res1 = executor.evaluate()(*inputData, t_data)
+
+ tvm.testing.assert_allclose(op_res1.numpy(), ref_res, rtol=0.000001)
+ op_res2 = relay.create_executor("debug", device=dev,
target=target).evaluate(func)(
+ *inputData, t_data
+ )
+ tvm.testing.assert_allclose(op_res2.numpy(), ref_res, rtol=0.000001)
+
+
[email protected]_gpu
+def test_concatenate1():
+ for target, dev in tvm.testing.enabled_targets():
+ if target != "llvm":
+ continue
+ np.random.seed(471)
+ maxNumDimensions = 6
+ shape = [4, 32, 16, 1, 31, 20, 21, 8, 28, 7] # just randomly selected
10 numbers
+ for dtype in ["float32"]:
+ for dimsNum in range(1, maxNumDimensions):
+ np.random.shuffle(shape)
+ for axis in range(0, dimsNum): # range should be (-dimsNum +
1, dimsNum)
+ numToConcat = np.random.uniform(low=2, high=10,
size=(1)).astype("int64")[0]
+ shapes = []
+ # the code below to normalize axes index. For some reasons
tvm notifies about error if the axis is negative
+ normalizedAxis = axis
+ if axis < 0:
+ normalizedAxis += dimsNum
+ finalSize = 0
+ for i in range(0, numToConcat):
+ shp = tuple(shape[:dimsNum])
+ finalSize += shape[(i % len(shape))]
+ shapes.append(
+ shp[:normalizedAxis]
+ + tuple([shape[(i % len(shape))]])
+ + shp[normalizedAxis + 1 :]
+ )
+ t_shape = shp[:normalizedAxis] + tuple([finalSize]) +
shp[normalizedAxis + 1 :]
+ do_concat_test(shapes, t_shape, dtype, axis, dev, target)
+
+
[email protected]_gpu
+def test_concatenate2():
+ # test to cover cases (1, .. , x, 1, .. , 1)
+ for target, dev in tvm.testing.enabled_targets():
+ if target != "llvm":
+ continue
+ np.random.seed(13)
+ maxNumDimensions = 6
+ shape = [8, 3, 25, 33, 12, 29, 5, 11, 29, 11] # just randomly
selected 10 numbers
+ ind = 0
+ for dtype in ["float32"]:
+ for dimsNum in range(2, maxNumDimensions):
+ np.random.shuffle(shape)
+ for axis in range(-dimsNum + 1, dimsNum): # range should be
(-dimsNum + 1, dimsNum)
+ numToConcat = np.random.uniform(low=2, high=10,
size=(1)).astype("int64")[0]
+ shapes = []
+ # the code below to normalize axes index. For some reasons
tvm notifies about error if the axis is negative
+ normalizedAxis = axis
+ if axis < 0:
+ normalizedAxis += dimsNum
+ finalSize = 0
+ for i in range(0, numToConcat):
+ axisVal = [1] * dimsNum
+ axisVal[axis] = shape[(ind % len(shape))]
+ ind += 1
+ finalSize += axisVal[axis]
+ shapes.append(tuple(axisVal))
+ temp = [1] * dimsNum
+ temp[axis] = finalSize
+ t_shape = tuple(temp)
+ do_concat_test(shapes, t_shape, dtype, axis, dev, target)
+
+
[email protected]_gpu
+def test_concatenate3():
+ for target, dev in tvm.testing.enabled_targets():
+ if target != "llvm":
+ continue
Review Comment:
```suggestion
@tvm.testing.parametrize_targets("llvm")
def test_concatenate3(target, dev):
```
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