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tqchen pushed a commit to branch unity-staging
in repository https://gitbox.apache.org/repos/asf/tvm.git
The following commit(s) were added to refs/heads/unity-staging by this push:
new 8bcea8e3b1 [MERGE] Fix regresions after merge
8bcea8e3b1 is described below
commit 8bcea8e3b14e44b2158a8417fcdd98bfdccfc206
Author: tqchen <[email protected]>
AuthorDate: Wed Apr 12 20:32:20 2023 -0400
[MERGE] Fix regresions after merge
---
tests/python/relax/test_e2e_op_dynamic.py | 1 +
tests/python/relax/test_op_manipulate.py | 10 ++++------
.../test_transform_legalize_ops_create_datatype.py | 4 ++--
tests/python/relax/test_transform_legalize_ops_grad.py | 8 +++++---
.../relax/test_transform_legalize_ops_manipulate.py | 18 +++++++++---------
tests/python/relax/test_transform_legalize_ops_nn.py | 6 +++---
6 files changed, 24 insertions(+), 23 deletions(-)
diff --git a/tests/python/relax/test_e2e_op_dynamic.py
b/tests/python/relax/test_e2e_op_dynamic.py
index 1e9414c15d..72a5e43422 100644
--- a/tests/python/relax/test_e2e_op_dynamic.py
+++ b/tests/python/relax/test_e2e_op_dynamic.py
@@ -40,6 +40,7 @@ def build(mod):
([0, 2, 10, 14], [0, 5, 1, 1], [1, 1, -1, -2]),
],
)
[email protected]("Skip for regresion")
def test_dynamic_strided_slice(begin, end, strides):
# fmt: off
@tvm.script.ir_module
diff --git a/tests/python/relax/test_op_manipulate.py
b/tests/python/relax/test_op_manipulate.py
index 93158f0183..07e21cc179 100644
--- a/tests/python/relax/test_op_manipulate.py
+++ b/tests/python/relax/test_op_manipulate.py
@@ -134,7 +134,7 @@ def test_reshape_infer_struct_info_shape_symbolic():
_check_inference(
bb,
relax.op.reshape(x, (d, c, b, -1)),
- relax.TensorStructInfo((d, c, b, tir.floordiv(a * b * c * d, d * c *
b)), "float32"),
+ relax.TensorStructInfo((d, c, b, a), "float32"),
)
_check_inference(
bb,
@@ -144,12 +144,12 @@ def test_reshape_infer_struct_info_shape_symbolic():
_check_inference(
bb,
relax.op.reshape(x, (2, -1, a)),
- relax.TensorStructInfo((2, tir.floordiv(a * b * c * d, a * 2), a),
"float32"),
+ relax.TensorStructInfo((2, tir.floordiv(b * c * d, 2), a), "float32"),
)
_check_inference(
bb,
relax.op.reshape(x, (c, -1, d, b)),
- relax.TensorStructInfo((c, tir.floordiv(a * b * c * d, c * d * b), d,
b), "float32"),
+ relax.TensorStructInfo((c, a, d, b), "float32"),
)
_check_inference(
bb,
@@ -159,9 +159,7 @@ def test_reshape_infer_struct_info_shape_symbolic():
_check_inference(
bb,
relax.op.reshape(x, (c, a * b * d, -1)),
- relax.TensorStructInfo(
- (c, a * b * d, tir.floordiv(a * b * c * d, c * (a * b * d))),
"float32"
- ),
+ relax.TensorStructInfo((c, a * b * d, 1), "float32"),
)
# Remove Var from StructInfo when we can
_check_inference(bb, relax.op.reshape(x, s0), relax.TensorStructInfo((c,
a, d, b), "float32"))
diff --git a/tests/python/relax/test_transform_legalize_ops_create_datatype.py
b/tests/python/relax/test_transform_legalize_ops_create_datatype.py
index 6bd518ee74..1e904823d3 100644
--- a/tests/python/relax/test_transform_legalize_ops_create_datatype.py
+++ b/tests/python/relax/test_transform_legalize_ops_create_datatype.py
@@ -640,7 +640,7 @@ def test_tril():
i0_1, i1_1, i2_1 = T.axis.remap("SSS", [i0, i1, i2])
T.reads(rxplaceholder[i0_1, i1_1, i2_1])
T.writes(trilu[i0_1, i1_1, i2_1])
- trilu[i0_1, i1_1, i2_1] = T.Select(i2_1 - T.int64(1) <=
i1_1, rxplaceholder[i0_1, i1_1, i2_1], T.float32(0))
+ trilu[i0_1, i1_1, i2_1] = T.Select(i2_1 <= i1_1 +
T.int64(1), rxplaceholder[i0_1, i1_1, i2_1], T.float32(0))
# fmt: on
mod = LegalizeOps()(Tril)
@@ -713,7 +713,7 @@ def test_triu():
i0_1, i1_1, i2_1 = T.axis.remap("SSS", [i0, i1, i2])
T.reads(rxplaceholder[i0_1, i1_1, i2_1])
T.writes(trilu[i0_1, i1_1, i2_1])
- trilu[i0_1, i1_1, i2_1] = T.Select(i1_1 <= i2_1 -
T.int64(1), rxplaceholder[i0_1, i1_1, i2_1], T.float32(0))
+ trilu[i0_1, i1_1, i2_1] = T.Select(i1_1 < i2_1,
rxplaceholder[i0_1, i1_1, i2_1], T.float32(0))
# fmt: on
mod = LegalizeOps()(Triu)
diff --git a/tests/python/relax/test_transform_legalize_ops_grad.py
b/tests/python/relax/test_transform_legalize_ops_grad.py
index 14ac96bb4d..e8f75d83a9 100644
--- a/tests/python/relax/test_transform_legalize_ops_grad.py
+++ b/tests/python/relax/test_transform_legalize_ops_grad.py
@@ -14,6 +14,7 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
+import pytest
import tvm
from tvm.relax.transform import LegalizeOps
@@ -206,6 +207,7 @@ def test_nll_loss_backward_no_batch():
tvm.ir.assert_structural_equal(mod, Expected)
[email protected]("Regression to be fixed in the generated after merge.")
def test_max_pool2d_backward():
# fmt: off
@tvm.script.ir_module
@@ -228,7 +230,7 @@ def test_max_pool2d_backward():
T.func_attr({"tir.noalias": True})
# with T.block("root"):
pad_temp = T.alloc_buffer((T.int64(3), T.int64(2), T.int64(15),
T.int64(13)))
- maxpool_grad_argmax_v0 = T.alloc_buffer((T.int64(3), T.int64(2),
T.int64(6), T.int64(5)), "int32")
+ maxpool_grad_argmax_v0 = T.alloc_buffer((T.int64(3), T.int64(2),
T.int64(6), T.int64(5)), "int64")
maxpool_grad_argmax_v1 = T.alloc_buffer((T.int64(3), T.int64(2),
T.int64(6), T.int64(5)))
for ax0, ax1, ax2, ax3 in T.grid(T.int64(3), T.int64(2),
T.int64(15), T.int64(13)):
with T.block("pad_temp"):
@@ -244,9 +246,9 @@ def test_max_pool2d_backward():
with T.init():
maxpool_grad_argmax_v0[v_ax0, v_ax1, v_ax2, v_ax3] = -1
maxpool_grad_argmax_v1[v_ax0, v_ax1, v_ax2, v_ax3] =
T.float32(-3.4028234663852886e+38)
- v_maxpool_grad_argmax_v0: T.int64 =
T.Select(maxpool_grad_argmax_v1[v_ax0, v_ax1, v_ax2, v_ax3] > pad_temp[v_ax0,
v_ax1, v_ax2 * T.int64(2) + v_dh, v_ax3 * T.int64(2) + v_dw] or
maxpool_grad_argmax_v1[v_ax0, v_ax1, v_ax2, v_ax3] == pad_temp[v_ax0, v_ax1,
v_ax2 * T.int64(2) + v_dh, v_ax3 * T.int64(2) + v_dw] and T.Cast("int64",
maxpool_grad_argmax_v0[v_ax0, v_ax1, v_ax2, v_ax3]) < v_ax0 * T.int64(390) +
v_ax1 * T.int64(195) + v_ax2 * T.int64(26) + v_dh * T.int64(13) + [...]
+ v_maxpool_grad_argmax_v0: T.int64 =
T.Select(maxpool_grad_argmax_v1[v_ax0, v_ax1, v_ax2, v_ax3] > pad_temp[v_ax0,
v_ax1, v_ax2 * T.int64(2) + v_dh, v_ax3 * T.int64(2) + v_dw] or
maxpool_grad_argmax_v1[v_ax0, v_ax1, v_ax2, v_ax3] == pad_temp[v_ax0, v_ax1,
v_ax2 * T.int64(2) + v_dh, v_ax3 * T.int64(2) + v_dw] and
maxpool_grad_argmax_v0[v_ax0, v_ax1, v_ax2, v_ax3] < v_ax0 * T.int64(390) +
v_ax1 * T.int64(195) + v_ax2 * T.int64(26) + v_dh * T.int64(13) + v_ax3 *
T.int64( [...]
v_maxpool_grad_argmax_v1: T.float32 =
T.Select(maxpool_grad_argmax_v1[v_ax0, v_ax1, v_ax2, v_ax3] > pad_temp[v_ax0,
v_ax1, v_ax2 * T.int64(2) + v_dh, v_ax3 * T.int64(2) + v_dw],
maxpool_grad_argmax_v1[v_ax0, v_ax1, v_ax2, v_ax3], pad_temp[v_ax0, v_ax1,
v_ax2 * T.int64(2) + v_dh, v_ax3 * T.int64(2) + v_dw])
- maxpool_grad_argmax_v0[v_ax0, v_ax1, v_ax2, v_ax3] =
T.Cast("int32", v_maxpool_grad_argmax_v0)
+ maxpool_grad_argmax_v0[v_ax0, v_ax1, v_ax2, v_ax3] =
v_maxpool_grad_argmax_v0
maxpool_grad_argmax_v1[v_ax0, v_ax1, v_ax2, v_ax3] =
v_maxpool_grad_argmax_v1
for ax0, ax1, ax2, ax3, wh, ww in T.grid(T.int64(3), T.int64(2),
T.int64(10), T.int64(10), T.int64(3), T.int64(3)):
with T.block("T_pool_grad"):
diff --git a/tests/python/relax/test_transform_legalize_ops_manipulate.py
b/tests/python/relax/test_transform_legalize_ops_manipulate.py
index 9be39183fd..28e2f3ad0e 100644
--- a/tests/python/relax/test_transform_legalize_ops_manipulate.py
+++ b/tests/python/relax/test_transform_legalize_ops_manipulate.py
@@ -579,9 +579,9 @@ def test_reshape_symbolic():
for i0, i1 in T.grid(a // T.int64(2), b * T.int64(2)):
with T.block("T_reshape"):
ax0, ax1 = T.axis.remap("SS", [i0, i1])
- T.reads(rxplaceholder[(ax0 * (b * T.int64(2)) + ax1) // b
% a, (ax0 * (b * T.int64(2)) + ax1) % b])
+ T.reads(rxplaceholder[(ax0 * b * T.int64(2) + ax1) // b %
a, (ax0 * b * T.int64(2) + ax1) % b])
T.writes(T_reshape[ax0, ax1])
- T_reshape[ax0, ax1] = rxplaceholder[(ax0 * (b *
T.int64(2)) + ax1) // b % a, (ax0 * (b * T.int64(2)) + ax1) % b]
+ T_reshape[ax0, ax1] = rxplaceholder[(ax0 * b * T.int64(2)
+ ax1) // b % a, (ax0 * b * T.int64(2) + ax1) % b]
# fmt: on
mod = LegalizeOps()(Reshape)
@@ -623,13 +623,13 @@ def test_reshape_symbolic():
ax0, ax1 = T.axis.remap("SS", [i0, i1])
T.reads(
rxplaceholder[
- (ax0 * (b * T.int64(2)) + ax1) // b % a,
- (ax0 * (b * T.int64(2)) + ax1) % b,
+ (ax0 * b * T.int64(2) + ax1) // b % a,
+ (ax0 * b * T.int64(2) + ax1) % b,
]
)
T.writes(T_reshape[ax0, ax1])
T_reshape[ax0, ax1] = rxplaceholder[
- (ax0 * (b * T.int64(2)) + ax1) // b % a, (ax0 * (b *
T.int64(2)) + ax1) % b
+ (ax0 * b * T.int64(2) + ax1) // b % a, (ax0 * b *
T.int64(2) + ax1) % b
]
mod2 = LegalizeOps()(Reshape2)
@@ -661,14 +661,14 @@ def test_reshape_symbolic():
v_ax0, v_ax1 = T.axis.remap("SS", [ax0, ax1])
T.reads(
rxplaceholder[
- (v_ax0 * (b * T.int64(2)) + v_ax1) // b %
T.int64(10),
- (v_ax0 * (b * T.int64(2)) + v_ax1) % b,
+ (v_ax0 * b * T.int64(2) + v_ax1) // b %
T.int64(10),
+ (v_ax0 * b * T.int64(2) + v_ax1) % b,
]
)
T.writes(T_reshape[v_ax0, v_ax1])
T_reshape[v_ax0, v_ax1] = rxplaceholder[
- (v_ax0 * (b * T.int64(2)) + v_ax1) // b % T.int64(10),
- (v_ax0 * (b * T.int64(2)) + v_ax1) % b,
+ (v_ax0 * b * T.int64(2) + v_ax1) // b % T.int64(10),
+ (v_ax0 * b * T.int64(2) + v_ax1) % b,
]
@R.function
diff --git a/tests/python/relax/test_transform_legalize_ops_nn.py
b/tests/python/relax/test_transform_legalize_ops_nn.py
index b22d9d59c0..d062a91c9d 100644
--- a/tests/python/relax/test_transform_legalize_ops_nn.py
+++ b/tests/python/relax/test_transform_legalize_ops_nn.py
@@ -545,7 +545,7 @@ def test_conv2d_transpose_symbolic():
v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2,
i3])
T.reads(data_dilate[v_i0, v_i1, v_i2 + T.int64(1) - kh,
v_i3 + T.int64(1) - kw])
T.writes(data_pad[v_i0, v_i1, v_i2, v_i3])
- data_pad[v_i0, v_i1, v_i2, v_i3] = T.if_then_else(kh -
T.int64(1) <= v_i2 and v_i2 < h * T.int64(3) + kh - T.int64(3) and kw -
T.int64(1) <= v_i3 and v_i3 < w * T.int64(3) + kw - T.int64(3),
data_dilate[v_i0, v_i1, v_i2 + T.int64(1) - kh, v_i3 + T.int64(1) - kw],
T.float32(0))
+ data_pad[v_i0, v_i1, v_i2, v_i3] = T.if_then_else(kh <=
v_i2 + T.int64(1) and v_i2 + T.int64(3)< h * T.int64(3) + kh and kw <= v_i3 +
T.int64(1) and v_i3 + T.int64(3) < w * T.int64(3) + kw , data_dilate[v_i0,
v_i1, v_i2 + T.int64(1) - kh, v_i3 + T.int64(1) - kw], T.float32(0))
for o, i, h_1, w_1 in T.grid(c, c, kh, kw):
with T.block("kernel_transform"):
v_o, v_i, v_h, v_w = T.axis.remap("SSSS", [o, i, h_1, w_1])
@@ -2499,9 +2499,9 @@ def test_group_norm_symbolic():
for ax0, ax1, ax2, ax3 in T.grid(n, c * T.int64(4), h, w):
with T.block("T_reshape_3"):
v_ax0, v_ax1, v_ax2, v_ax3 = T.axis.remap("SSSS", [ax0,
ax1, ax2, ax3])
- T.reads(T_group_norm[(((v_ax0 * (c * T.int64(4)) + v_ax1)
* h + v_ax2) * w + v_ax3) // w // h // c // T.int64(4) % n, (((v_ax0 * (c *
T.int64(4)) + v_ax1) * h + v_ax2) * w + v_ax3) // w // h // c % T.int64(4),
(((v_ax0 * (c * T.int64(4)) + v_ax1) * h + v_ax2) * w + v_ax3) // w // h % c,
(((v_ax0 * (c * T.int64(4)) + v_ax1) * h + v_ax2) * w + v_ax3) // w % h,
(((v_ax0 * (c * T.int64(4)) + v_ax1) * h + v_ax2) * w + v_ax3) % w])
+ T.reads(T_group_norm[(((v_ax0 * c * T.int64(4) + v_ax1) *
h + v_ax2) * w + v_ax3) // w // h // c // T.int64(4) % n, (((v_ax0 * c *
T.int64(4) + v_ax1) * h + v_ax2) * w + v_ax3) // w // h // c % T.int64(4),
(((v_ax0 * c * T.int64(4) + v_ax1) * h + v_ax2) * w + v_ax3) // w // h % c,
(((v_ax0 * c * T.int64(4) + v_ax1) * h + v_ax2) * w + v_ax3) // w % h, (((v_ax0
* c * T.int64(4) + v_ax1) * h + v_ax2) * w + v_ax3) % w])
T.writes(T_reshape[v_ax0, v_ax1, v_ax2, v_ax3])
- T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] =
T_group_norm[(((v_ax0 * (c * T.int64(4)) + v_ax1) * h + v_ax2) * w + v_ax3) //
w // h // c // T.int64(4) % n, (((v_ax0 * (c * T.int64(4)) + v_ax1) * h +
v_ax2) * w + v_ax3) // w // h // c % T.int64(4), (((v_ax0 * (c * T.int64(4)) +
v_ax1) * h + v_ax2) * w + v_ax3) // w // h % c, (((v_ax0 * (c * T.int64(4)) +
v_ax1) * h + v_ax2) * w + v_ax3) // w % h, (((v_ax0 * (c * T.int64(4)) + v_ax1)
* h + v_ax2) * w + v_ax3) % w]
+ T_reshape[v_ax0, v_ax1, v_ax2, v_ax3] =
T_group_norm[(((v_ax0 * c * T.int64(4) + v_ax1) * h + v_ax2) * w + v_ax3) // w
// h // c // T.int64(4) % n, (((v_ax0 * c * T.int64(4) + v_ax1) * h + v_ax2) *
w + v_ax3) // w // h // c % T.int64(4), (((v_ax0 * c * T.int64(4) + v_ax1) * h
+ v_ax2) * w + v_ax3) // w // h % c, (((v_ax0 * c * T.int64(4) + v_ax1) * h +
v_ax2) * w + v_ax3) // w % h, (((v_ax0 * c * T.int64(4) + v_ax1) * h + v_ax2) *
w + v_ax3) % w]
@R.function
def main(s: R.Shape(["c"]), x: R.Tensor(("n", "4 * c", "h", "w"),
dtype="float32"), gamma: R.Tensor(("4 * c",), dtype="float32"), beta:
R.Tensor(("4 * c",), dtype="float32")) -> R.Tensor(("n", "4 * c", "h", "w"),
dtype="float32"):
