shtinsa commented on code in PR #11341:
URL: https://github.com/apache/tvm/pull/11341#discussion_r877135712
##########
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:
Done
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