def testTwoOutputs(self):
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='I')
program1 = Program('two_outputs', [I, I])
self.assertMultiLineEqual(
str(program1), '''function (
I[I_0]
) -> (
_X1,
_X0
) {
_X0 = ident(I);
_X1 = ident(I);
}
''')
outputs = plaidml_exec.run(program1, [(I, np.array([(1, 2, 3)]))])
self.assertEqual(outputs[0].tolist(), [1, 2, 3])
self.assertEqual(outputs[1].tolist(), [1, 2, 3])
O1 = I
O2 = I
program2 = Program('two_outputs', [O1, O2])
self.assertMultiLineEqual(str(program1), str(program2))
outputs = plaidml_exec.run(program2, [(I, np.array([(1, 2, 3)]))])
self.assertEqual(outputs[0].tolist(), [1, 2, 3])
self.assertEqual(outputs[1].tolist(), [1, 2, 3])
def test_identity(self):
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='I')
program = Program('identity', [I])
if USE_MLIR():
self.assertMultiLineEqual(
str(program), '''
module {
func @identity(%arg0: tensor<3x!eltwise.fp32> {tile.name = "I"}) -> tensor<3x!eltwise.fp32> {
%0 = "eltwise.ident"(%arg0) {type = !eltwise.fp32} : (tensor<3x!eltwise.fp32>) -> tensor<3x!eltwise.fp32>
return %0 : tensor<3x!eltwise.fp32>
}
}
''')
else:
self.assertMultiLineEqual(
str(program), '''function (
I[I_0]
) -> (
_X0
) {
_X0 = ident(I);
}
''')
outputs = plaidml_exec.run(program, [(I, np.array([(1, 2, 3)]))])
self.assertEqual(outputs[0].tolist(), [1, 2, 3])
def testAssignmentExceptions(self):
A = Tensor(LogicalShape(plaidml.DType.FLOAT32, [5, 1]), name='A')
B = Tensor(LogicalShape(plaidml.DType.FLOAT32, [1, 5]), name='B')
L, M, N = TensorDims(3)
i, j, k = TensorIndexes(3)
A.bind_dims(L, M)
B.bind_dims(M, N)
O = TensorOutput(L, N)
O[i, j] = A[i, k] * B[k, j]
program = Program('assignment_non_exception', [O])
self.assertMultiLineEqual(
str(program), '''function (
A[A_0, A_1],
B[B_0, B_1]
) -> (
_X0
) {
_X0[x0, x2 : 5, 5] = =(A[x0, x1] * B[x1, x2]);
}
''')
outputs = plaidml_exec.run(program, [(A, np.array([[1], [2], [3], [4], [5]])),
(B, np.array([1, 2, 3, 4, 5]))])
self.assertEqual(outputs[0].tolist(),
[[1., 2., 3., 4., 5.], [2., 4., 6., 8., 10.], [3., 6., 9., 12., 15.],
[4., 8., 12., 16., 20.], [5., 10., 15., 20., 25.]])
O = TensorOutput(L, N)
O[i, j] = B[i, k] * A[k, j]
with self.assertRaises(plaidml.Error) as cm:
program = Program('assignment_exception', [O])
self.assertTrue("illegal assignment aggregation" in str(cm.exception))
def testFunkyLayerNames(self):
'''Exercises fix for plaidml bug #241
Now that we emit keras layer names as 'pid' attribute values, in order
to help link tile code back to its origin while debugging, we must
reformat those names as valid tile identifiers. If we are doing that,
this test will pass, otherwise we'll get a syntax error.'''
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='I')
K = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='K')
i, j = TensorIndexes(2)
O = TensorOutput(5)
O[i] += (I[(i - j + 1) // 2] * K[j])
program = Program('this-is-not an identifier', [O])
self.assertMultiLineEqual(
str(program), '''function (
I[I_0],
K[K_0]
) -> (
_X0
) {
_X0[x0 : 5] = +(I[1/2 + 1/2*x0 - 1/2*x1] * K[x1]);
}
''')
outputs = plaidml_exec.run(program, [(I, np.array([1, 2, 3])), (K, np.array([1, 2, 3]))])
self.assertEqual(outputs[0].tolist(), [2, 5, 4, 9, 6])
def test_funky_names(self):
'''Exercises fix for plaidml bug #241
Now that we emit keras layer names as 'pid' attribute values, in order
to help link tile code back to its origin while debugging, we must
reformat those names as valid tile identifiers. If we are doing that,
this test will pass, otherwise we'll get a syntax error.'''
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='I')
K = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='K')
i, j = TensorIndexes(2)
O = TensorOutput(5)
O[i] += (I[(i - j + 1) // 2] * K[j])
program = Program('this-is-not an identifier', [O])
if USE_MLIR():
self.assertMultiLineEqual(
str(program), '''
module {
func @"this-is-not an identifier"(%arg0: tensor<3x!eltwise.fp32> {tile.name = "I"}, %arg1: tensor<3x!eltwise.fp32> {tile.name = "K"}) -> tensor<5x!eltwise.fp32> {
%c1 = "tile.affine_const"() {value = 1 : i64} : () -> !eltwise.i32
%c2 = "tile.affine_const"() {value = 2 : i64} : () -> !eltwise.i32
%c5 = "tile.affine_const"() {value = 5 : i64} : () -> !eltwise.i32
%0 = "tile.domain"() ( {
^bb0(%arg2: !eltwise.i32, %arg3: !eltwise.i32): // no predecessors
%1 = "tile.affine_sub"(%arg3, %arg2) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%2 = "tile.affine_add"(%1, %c1) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%3 = "tile.affine_div"(%2, %c2) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%4 = "tile.src_idx_map"(%arg0, %3) : (tensor<3x!eltwise.fp32>, !eltwise.i32) -> !tile.imap
%5 = "tile.src_idx_map"(%arg1, %arg2) : (tensor<3x!eltwise.fp32>, !eltwise.i32) -> !tile.imap
%6 = "tile.sink_idx_map"(%arg3) : (!eltwise.i32) -> !tile.imap
%7 = "tile.size_map"(%c5) : (!eltwise.i32) -> !tile.smap
"tile.+(x*y)"(%7, %4, %5, %6) : (!tile.smap, !tile.imap, !tile.imap, !tile.imap) -> ()
}) {idx_names = ["x0", "x1"]} : () -> tensor<5x!eltwise.fp32>
return %0 : tensor<5x!eltwise.fp32>
}
}
''')
else:
self.assertMultiLineEqual(
str(program), '''function (
I[I_0],
K[K_0]
) -> (
_X0
) {
_X0[x0 : 5] = +(I[1/2 + 1/2*x0 - 1/2*x1] * K[x1]);
}
''')
outputs = plaidml_exec.run(program, [(I, np.array([1, 2, 3])),
(K, np.array([1, 2, 3]))])
self.assertEqual(outputs[0].tolist(), [2, 5, 4, 9, 6])
def testTileIdentity(self):
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='I')
program = Program('tile_identity', [I])
self.assertMultiLineEqual(str(program), '''function (
I[I_0]
) -> (
_X0
) {
_X0 = ident(I);
}
''')
outputs = plaidml_exec.run(program, [(I, np.array([(1, 2, 3)]))])
self.assertEqual(outputs[0].tolist(), [1, 2, 3])
def test_two_outputs(self):
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='I')
program1 = Program('two_outputs', [I, I])
if USE_MLIR():
self.assertMultiLineEqual(
str(program1), '''
module {
func @two_outputs(%arg0: tensor<3x!eltwise.fp32> {tile.name = "I"}) -> (tensor<3x!eltwise.fp32>, tensor<3x!eltwise.fp32>) {
%0 = "eltwise.ident"(%arg0) {type = !eltwise.fp32} : (tensor<3x!eltwise.fp32>) -> tensor<3x!eltwise.fp32>
%1 = "eltwise.ident"(%arg0) {type = !eltwise.fp32} : (tensor<3x!eltwise.fp32>) -> tensor<3x!eltwise.fp32>
return %0, %1 : tensor<3x!eltwise.fp32>, tensor<3x!eltwise.fp32>
}
}
''')
else:
self.assertMultiLineEqual(
str(program1), '''function (
I[I_0]
) -> (
_X1,
_X0
) {
_X0 = ident(I);
_X1 = ident(I);
}
''')
outputs = plaidml_exec.run(program1, [(I, np.array([(1, 2, 3)]))])
self.assertEqual(outputs[0].tolist(), [1, 2, 3])
self.assertEqual(outputs[1].tolist(), [1, 2, 3])
O1 = I
O2 = I
program2 = Program('two_outputs', [O1, O2])
self.assertMultiLineEqual(str(program1), str(program2))
outputs = plaidml_exec.run(program2, [(I, np.array([(1, 2, 3)]))])
self.assertEqual(outputs[0].tolist(), [1, 2, 3])
self.assertEqual(outputs[1].tolist(), [1, 2, 3])
def testDefractShort(self):
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='I')
i, j = TensorIndexes(2)
O = TensorOutput(6)
O[i] += (I[(i - 1) // 2])
program = Program('defract_short_test', [O])
self.assertMultiLineEqual(
str(program), '''function (
I[I_0]
) -> (
_X0
) {
_X0[x0 : 6] = +(I[-1/2 + 1/2*x0]);
}
''')
outputs = plaidml_exec.run(program, [(I, np.array([1, 2, 3]))])
self.assertEqual(outputs[0].tolist(), [0, 1, 0, 2, 0, 3])
def test_defract(self):
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='I')
K = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='K')
i, j = TensorIndexes(2)
O = TensorOutput(5)
O[i] += (I[(i - j + 1) // 2] * K[j])
program = Program('defract_test', [O])
if USE_MLIR():
self.assertMultiLineEqual(
str(program), '''
module {
func @defract_test(%arg0: tensor<3x!eltwise.fp32> {tile.name = "I"}, %arg1: tensor<3x!eltwise.fp32> {tile.name = "K"}) -> tensor<5x!eltwise.fp32> {
%c1 = "tile.affine_const"() {value = 1 : i64} : () -> !eltwise.i32
%c2 = "tile.affine_const"() {value = 2 : i64} : () -> !eltwise.i32
%c5 = "tile.affine_const"() {value = 5 : i64} : () -> !eltwise.i32
%0 = "tile.domain"() ( {
^bb0(%arg2: !eltwise.i32, %arg3: !eltwise.i32): // no predecessors
%1 = "tile.affine_sub"(%arg3, %arg2) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%2 = "tile.affine_add"(%1, %c1) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%3 = "tile.affine_div"(%2, %c2) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%4 = "tile.src_idx_map"(%arg0, %3) : (tensor<3x!eltwise.fp32>, !eltwise.i32) -> !tile.imap
%5 = "tile.src_idx_map"(%arg1, %arg2) : (tensor<3x!eltwise.fp32>, !eltwise.i32) -> !tile.imap
%6 = "tile.sink_idx_map"(%arg3) : (!eltwise.i32) -> !tile.imap
%7 = "tile.size_map"(%c5) : (!eltwise.i32) -> !tile.smap
"tile.+(x*y)"(%7, %4, %5, %6) : (!tile.smap, !tile.imap, !tile.imap, !tile.imap) -> ()
}) {idx_names = ["x0", "x1"]} : () -> tensor<5x!eltwise.fp32>
return %0 : tensor<5x!eltwise.fp32>
}
}
''')
else:
self.assertMultiLineEqual(
str(program), '''function (
I[I_0],
K[K_0]
) -> (
_X0
) {
_X0[x0 : 5] = +(I[1/2 + 1/2*x0 - 1/2*x1] * K[x1]);
}
''')
outputs = plaidml_exec.run(program, [(I, np.array([1, 2, 3])),
(K, np.array([1, 2, 3]))])
self.assertEqual(outputs[0].tolist(), [2, 5, 4, 9, 6])
def testDefract(self):
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='I')
K = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='K')
i, j = TensorIndexes(2)
O = TensorOutput(5)
O[i] += (I[(i - j + 1) // 2] * K[j])
program = Program('defract_test', [O])
self.assertMultiLineEqual(
str(program), '''function (
I[I_0],
K[K_0]
) -> (
_X0
) {
_X0[x0 : 5] = +(I[1/2 + 1/2*x0 - 1/2*x1] * K[x1]);
}
''')
outputs = plaidml_exec.run(program, [(I, np.array([1, 2, 3])), (K, np.array([1, 2, 3]))])
self.assertEqual(outputs[0].tolist(), [2, 5, 4, 9, 6])
def testDefractLong(self):
shape = [1, 3, 3, 1]
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, shape), name='I')
K = Tensor(LogicalShape(plaidml.DType.FLOAT32, shape), name='K')
n, x0, x1, c0, c1, co, ci, k0, k1 = TensorIndexes(9)
O = TensorOutput(1, 5, 5, 1)
O[n, x0, x1, co] += (I[n, (x0 + k0 - 1) // 2,
(x1 + k1 - 1) // 2, ci] * K[2 - k0, 2 - k1, co, ci])
program = Program('defract_long', [O])
self.assertMultiLineEqual(
str(program), '''function (
I[I_0, I_1, I_2, I_3],
K[K_0, K_1, K_2, K_3]
) -> (
_X0
) {
_X0[x0, x1, x3, x6 : 1, 5, 5, 1] = +(I[x0, -1/2 + 1/2*x1 + 1/2*x2, -1/2 + 1/2*x3 + 1/2*x4, x5] * K[2 - x2, 2 - x4, x6, x5]);
}
''')
outputs = plaidml_exec.run(program, [
(I, np.array([[
[[1], [3], [-1]],
[[0], [2], [4]],
[[1], [-1], [-2]],
]])),
(K, np.array([[
[[2], [3], [4]],
[[6], [-3], [-1]],
[[-1], [-2], [1]],
]])),
])
np.testing.assert_array_equal(
outputs[0],
np.array([[
[[0], [0], [0], [0], [0]],
[[0], [4], [12], [6], [24]],
[[0], [0], [0], [0], [0]],
[[6], [-3], [-6], [-3], [-12]],
[[0], [0], [0], [0], [0]],
]]))
def test_defract_short(self):
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, [3]), name='I')
i, j = TensorIndexes(2)
O = TensorOutput(6)
O[i] += (I[(i - 1) // 2])
program = Program('defract_short_test', [O])
if USE_MLIR():
self.assertMultiLineEqual(
str(program), '''
module {
func @defract_short_test(%arg0: tensor<3x!eltwise.fp32> {tile.name = "I"}) -> tensor<6x!eltwise.fp32> {
%c1 = "tile.affine_const"() {value = 1 : i64} : () -> !eltwise.i32
%c2 = "tile.affine_const"() {value = 2 : i64} : () -> !eltwise.i32
%c6 = "tile.affine_const"() {value = 6 : i64} : () -> !eltwise.i32
%0 = "tile.domain"() ( {
^bb0(%arg1: !eltwise.i32): // no predecessors
%1 = "tile.affine_sub"(%arg1, %c1) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%2 = "tile.affine_div"(%1, %c2) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%3 = "tile.src_idx_map"(%arg0, %2) : (tensor<3x!eltwise.fp32>, !eltwise.i32) -> !tile.imap
%4 = "tile.sink_idx_map"(%arg1) : (!eltwise.i32) -> !tile.imap
%5 = "tile.size_map"(%c6) : (!eltwise.i32) -> !tile.smap
"tile.+(x)"(%5, %3, %4) : (!tile.smap, !tile.imap, !tile.imap) -> ()
}) {idx_names = ["x0"]} : () -> tensor<6x!eltwise.fp32>
return %0 : tensor<6x!eltwise.fp32>
}
}
''')
else:
self.assertMultiLineEqual(
str(program), '''function (
I[I_0]
) -> (
_X0
) {
_X0[x0 : 6] = +(I[-1/2 + 1/2*x0]);
}
''')
outputs = plaidml_exec.run(program, [(I, np.array([1, 2, 3]))])
self.assertEqual(outputs[0].tolist(), [0, 1, 0, 2, 0, 3])
def main():
print("""
PlaidML Setup ({0})
Thanks for using PlaidML!
Some Notes:
* Bugs and other issues: https://github.com/plaidml/plaidml
* Questions: https://stackoverflow.com/questions/tagged/plaidml
* Say hello: https://groups.google.com/forum/#!forum/plaidml-dev
* PlaidML is licensed under the Apache License 2.0
""".format(plaidml.__version__))
devices = sorted(plaidml_exec.list_devices())
targets = sorted(plaidml_exec.list_targets())
if not devices:
print("""
No OpenCL devices found. Check driver installation.
Read the helpful, easy driver installation instructions from our README:
http://github.com/plaidml/plaidml
""")
sys.exit(-1)
dev_idx = 0
if len(devices) > 1:
print("""
Multiple devices detected (You can override by setting PLAIDML_DEVICE).
Please choose a default device:
""")
for i, device in enumerate(devices):
print(" {} : {}".format(i + 1, device))
choices = [str(i + 1) for i in range(len(devices))]
dev_idx = int(choice_prompt("\nDefault device", choices, "1"))
plaidml_settings.set('PLAIDML_DEVICE', devices[dev_idx - 1])
device = plaidml_settings.get('PLAIDML_DEVICE')
print()
print("Selected device:")
print(" {}".format(device))
print()
print("A target determines the compiler configuration and should be matched with your device.")
print("Please choose a default target:")
for i, target in enumerate(targets):
print(" {} : {}".format(i + 1, target))
choices = [str(i + 1) for i in range(len(targets))]
tgt_idx = int(choice_prompt("\nDefault target", choices, "1"))
plaidml_settings.set('PLAIDML_TARGET', targets[tgt_idx - 1])
target = plaidml_settings.get('PLAIDML_TARGET')
print()
print("Selected target:")
print(" {}".format(target))
print()
print("Almost done. Multiplying some matrices...")
print("Tile code:")
print(" function (B[X, Z], C[Z, Y]) -> (A) { A[x, y : X, Y] = +(B[x, z] * C[z, y]); }")
shape = edsl.LogicalShape(plaidml.DType.FLOAT32, [3, 3])
B = edsl.Tensor(shape)
C = edsl.Tensor(shape)
X, Y, Z = edsl.TensorDims(3)
x, y, z = edsl.TensorIndexes(3)
B.bind_dims(X, Z)
C.bind_dims(Z, Y)
A = edsl.TensorOutput(X, Y)
A[x, y] += B[x, z] * C[z, y]
program = edsl.Program('plaidml_setup', [A])
plaidml_exec.run(program, [(B, np.random.rand(3, 3)), (C, np.random.rand(3, 3))])
print("Whew. That worked.")
print()
settings_path = plaidml_settings.get('PLAIDML_SETTINGS')
save = choice_prompt("Save settings to {0}".format(settings_path), ["y", "n"], "y")
if save == "y":
plaidml_settings.save()
print("Success!")
print()
def test_defract_long(self):
shape = [1, 3, 3, 1]
I = Tensor(LogicalShape(plaidml.DType.FLOAT32, shape), name='I')
K = Tensor(LogicalShape(plaidml.DType.FLOAT32, shape), name='K')
n, x0, x1, c0, c1, co, ci, k0, k1 = TensorIndexes(9)
O = TensorOutput(1, 5, 5, 1)
O[n, x0, x1,
co] += (I[n, (x0 + k0 - 1) // 2,
(x1 + k1 - 1) // 2, ci] * K[2 - k0, 2 - k1, co, ci])
program = Program('defract_long', [O])
if USE_MLIR():
self.assertMultiLineEqual(
str(program), '''
module {
func @defract_long(%arg0: tensor<1x3x3x1x!eltwise.fp32> {tile.name = "I"}, %arg1: tensor<1x3x3x1x!eltwise.fp32> {tile.name = "K"}) -> tensor<1x5x5x1x!eltwise.fp32> {
%c2 = "tile.affine_const"() {value = 2 : i64} : () -> !eltwise.i32
%c5 = "tile.affine_const"() {value = 5 : i64} : () -> !eltwise.i32
%c1 = "tile.affine_const"() {value = 1 : i64} : () -> !eltwise.i32
%0 = "tile.domain"() ( {
^bb0(%arg2: !eltwise.i32, %arg3: !eltwise.i32, %arg4: !eltwise.i32, %arg5: !eltwise.i32, %arg6: !eltwise.i32, %arg7: !eltwise.i32, %arg8: !eltwise.i32): // no predecessors
%1 = "tile.affine_add"(%arg4, %arg3) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%2 = "tile.affine_sub"(%1, %c1) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%3 = "tile.affine_div"(%2, %c2) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%4 = "tile.affine_add"(%arg6, %arg5) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%5 = "tile.affine_sub"(%4, %c1) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%6 = "tile.affine_div"(%5, %c2) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%7 = "tile.src_idx_map"(%arg0, %arg7, %6, %3, %arg2) : (tensor<1x3x3x1x!eltwise.fp32>, !eltwise.i32, !eltwise.i32, !eltwise.i32, !eltwise.i32) -> !tile.imap
%8 = "tile.affine_sub"(%c2, %arg3) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%9 = "tile.affine_sub"(%c2, %arg5) : (!eltwise.i32, !eltwise.i32) -> !eltwise.i32
%10 = "tile.src_idx_map"(%arg1, %9, %8, %arg8, %arg2) : (tensor<1x3x3x1x!eltwise.fp32>, !eltwise.i32, !eltwise.i32, !eltwise.i32, !eltwise.i32) -> !tile.imap
%11 = "tile.sink_idx_map"(%arg7, %arg6, %arg4, %arg8) : (!eltwise.i32, !eltwise.i32, !eltwise.i32, !eltwise.i32) -> !tile.imap
%12 = "tile.size_map"(%c1, %c5, %c5, %c1) : (!eltwise.i32, !eltwise.i32, !eltwise.i32, !eltwise.i32) -> !tile.smap
"tile.+(x*y)"(%12, %7, %10, %11) : (!tile.smap, !tile.imap, !tile.imap, !tile.imap) -> ()
}) {idx_names = ["x0", "x1", "x2", "x3", "x4", "x5", "x6"]} : () -> tensor<1x5x5x1x!eltwise.fp32>
return %0 : tensor<1x5x5x1x!eltwise.fp32>
}
}
''')
else:
self.assertMultiLineEqual(
str(program), '''function (
I[I_0, I_1, I_2, I_3],
K[K_0, K_1, K_2, K_3]
) -> (
_X0
) {
_X0[x0, x1, x3, x6 : 1, 5, 5, 1] = +(I[x0, -1/2 + 1/2*x1 + 1/2*x2, -1/2 + 1/2*x3 + 1/2*x4, x5] * K[2 - x2, 2 - x4, x6, x5]);
}
''')
outputs = plaidml_exec.run(program, [
(I,
np.array([[
[[1], [3], [-1]],
[[0], [2], [4]],
[[1], [-1], [-2]],
]])),
(K,
np.array([[
[[2], [3], [4]],
[[6], [-3], [-1]],
[[-1], [-2], [1]],
]])),
])
np.testing.assert_array_equal(
outputs[0],
np.array([[
[[0], [0], [0], [0], [0]],
[[0], [4], [12], [6], [24]],
[[0], [0], [0], [0], [0]],
[[6], [-3], [-6], [-3], [-12]],
[[0], [0], [0], [0], [0]],
]]))
