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Posted to commits@tvm.apache.org by le...@apache.org on 2022/04/11 09:51:14 UTC
[tvm] 01/01: [CI] Bump black version to 22.3.0
This is an automated email from the ASF dual-hosted git repository.
leandron pushed a commit to branch ci-docker-staging
in repository https://gitbox.apache.org/repos/asf/tvm.git
commit ef2137bc39fbacfcc0e9dcbd4dc9bcc7e3eeb392
Author: Leandro Nunes <le...@arm.com>
AuthorDate: Mon Apr 11 10:46:17 2022 +0100
[CI] Bump black version to 22.3.0
* Make all required adjusts in the code to comply with the new version
* Upadte ci-lint to v0.71, based on tlcpackstaging/ci_lint:20220411-060305-45f3d4a52
---
Jenkinsfile | 4 +-
apps/topi_recipe/gemm/android_gemm_square.py | 2 +-
jenkins/Jenkinsfile.j2 | 2 +-
python/tvm/autotvm/task/space.py | 2 +-
python/tvm/contrib/debugger/debug_result.py | 2 +-
python/tvm/relay/frontend/paddlepaddle.py | 2 +-
python/tvm/relay/frontend/pytorch.py | 2 +-
python/tvm/relay/qnn/op/canonicalizations.py | 2 +-
python/tvm/relay/quantize/_calibrate.py | 2 +-
python/tvm/relay/testing/tf.py | 2 +-
python/tvm/testing/utils.py | 2 +-
python/tvm/tir/schedule/_type_checker.py | 1 -
python/tvm/topi/gpu/dense.py | 4 +-
python/tvm/topi/random/kernel.py | 4 +-
python/tvm/topi/testing/correlation_nchw_python.py | 2 +-
tests/python/contrib/test_cmsisnn/utils.py | 2 +-
.../contrib/test_ethosu/cascader/conftest.py | 6 +--
tests/python/frontend/pytorch/test_forward.py | 2 +-
tests/python/relay/test_op_grad_level1.py | 12 ++---
tests/python/topi/python/test_topi_prng.py | 4 +-
tests/python/topi/python/test_topi_transform.py | 4 +-
.../unittest/test_arith_canonical_simplify.py | 2 +-
.../unittest/test_auto_scheduler_compute_dag.py | 10 ++---
.../python/unittest/test_auto_scheduler_feature.py | 4 +-
tests/python/unittest/test_autotvm_space.py | 2 +-
tests/python/unittest/test_format_si_prefix.py | 2 +-
.../python/unittest/test_target_codegen_c_host.py | 2 +-
tests/python/unittest/test_target_codegen_rocm.py | 2 +-
.../unittest/test_tir_transform_narrow_datatype.py | 52 +++++++++++-----------
.../unittest/test_tir_transform_vectorize.py | 2 +-
.../unittest/test_tir_usmp_algo_hill_climb.py | 11 ++---
.../python/integration/test_benchmark_gemm.py | 18 ++++----
.../integration/test_benchmark_topi_conv2d.py | 2 +-
.../test_benchmark_topi_conv2d_transpose.py | 2 +-
.../integration/test_benchmark_topi_dense.py | 2 +-
.../test_benchmark_topi_group_conv2d.py | 2 +-
36 files changed, 88 insertions(+), 92 deletions(-)
diff --git a/Jenkinsfile b/Jenkinsfile
index b0e263c513..7e21dc08eb 100755
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -45,11 +45,11 @@
// 'python3 jenkins/generate.py'
// Note: This timestamp is here to ensure that updates to the Jenkinsfile are
// always rebased on main before merging:
-// Generated at 2022-04-07T13:50:22.427152
+// Generated at 2022-04-11T10:45:26.226802
import org.jenkinsci.plugins.pipeline.modeldefinition.Utils
// NOTE: these lines are scanned by docker/dev_common.sh. Please update the regex as needed. -->
-ci_lint = 'tlcpack/ci-lint:v0.69'
+ci_lint = 'tlcpack/ci-lint:v0.71'
ci_gpu = 'tlcpack/ci-gpu:v0.84'
ci_cpu = 'tlcpack/ci-cpu:v0.83'
ci_wasm = 'tlcpack/ci-wasm:v0.73'
diff --git a/apps/topi_recipe/gemm/android_gemm_square.py b/apps/topi_recipe/gemm/android_gemm_square.py
index 2d50dd7e8d..5f13d88707 100644
--- a/apps/topi_recipe/gemm/android_gemm_square.py
+++ b/apps/topi_recipe/gemm/android_gemm_square.py
@@ -34,7 +34,7 @@ target = "llvm -mtriple=%s-linux-android" % arch
def ngflops(N):
- return 2.0 * float(N * N * N) / (10 ** 9)
+ return 2.0 * float(N * N * N) / (10**9)
dtype = "float32"
diff --git a/jenkins/Jenkinsfile.j2 b/jenkins/Jenkinsfile.j2
index 1a61d140c3..6b306e99e7 100644
--- a/jenkins/Jenkinsfile.j2
+++ b/jenkins/Jenkinsfile.j2
@@ -51,7 +51,7 @@ import org.jenkinsci.plugins.pipeline.modeldefinition.Utils
{% import 'jenkins/macros.j2' as m with context -%}
// NOTE: these lines are scanned by docker/dev_common.sh. Please update the regex as needed. -->
-ci_lint = 'tlcpack/ci-lint:v0.69'
+ci_lint = 'tlcpack/ci-lint:v0.71'
ci_gpu = 'tlcpack/ci-gpu:v0.84'
ci_cpu = 'tlcpack/ci-cpu:v0.83'
ci_wasm = 'tlcpack/ci-wasm:v0.73'
diff --git a/python/tvm/autotvm/task/space.py b/python/tvm/autotvm/task/space.py
index 8a707b8721..4d6b23162a 100644
--- a/python/tvm/autotvm/task/space.py
+++ b/python/tvm/autotvm/task/space.py
@@ -187,7 +187,7 @@ def get_pow2s(n):
factors: list
List of all power-of-two numbers
"""
- return [2 ** x for x in range(math.floor(math.log2(n)) + 1)]
+ return [2**x for x in range(math.floor(math.log2(n)) + 1)]
class SplitSpace(TransformSpace):
diff --git a/python/tvm/contrib/debugger/debug_result.py b/python/tvm/contrib/debugger/debug_result.py
index e53aa298a0..8185391e35 100644
--- a/python/tvm/contrib/debugger/debug_result.py
+++ b/python/tvm/contrib/debugger/debug_result.py
@@ -154,7 +154,7 @@ class DebugResult(object):
"""Dump the trace to the Chrome trace.json format."""
def s_to_us(t):
- return t * 10 ** 6
+ return t * 10**6
starting_times = np.zeros(len(self._time_list) + 1)
starting_times[1:] = np.cumsum([times[0] for times in self._time_list])
diff --git a/python/tvm/relay/frontend/paddlepaddle.py b/python/tvm/relay/frontend/paddlepaddle.py
index 1084826911..d85f98a847 100644
--- a/python/tvm/relay/frontend/paddlepaddle.py
+++ b/python/tvm/relay/frontend/paddlepaddle.py
@@ -658,7 +658,7 @@ def convert_gelu(g, op, block):
x = g.get_node(op.input("X")[0])
out = x * (
_expr.const(0.5, dtype="float32")
- + _op.erf(x * _expr.const(0.5 ** 0.5, dtype="float32")) * _expr.const(0.5, dtype="float32")
+ + _op.erf(x * _expr.const(0.5**0.5, dtype="float32")) * _expr.const(0.5, dtype="float32")
)
g.add_node(op.output("Out")[0], out)
diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index 361b4f86c0..9984a4454a 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -827,7 +827,7 @@ class PyTorchOpConverter:
# with tanh and third order polynomials, but this is "true" gelu
return data * (
_expr.const(0.5, dtype=dtype)
- + _op.erf(data * _expr.const(0.5 ** 0.5, dtype=dtype)) * _expr.const(0.5, dtype=dtype)
+ + _op.erf(data * _expr.const(0.5**0.5, dtype=dtype)) * _expr.const(0.5, dtype=dtype)
)
def selu(self, inputs, input_types):
diff --git a/python/tvm/relay/qnn/op/canonicalizations.py b/python/tvm/relay/qnn/op/canonicalizations.py
index 95e0cb6036..1f2c57c6da 100644
--- a/python/tvm/relay/qnn/op/canonicalizations.py
+++ b/python/tvm/relay/qnn/op/canonicalizations.py
@@ -75,7 +75,7 @@ def create_integer_lookup_table(
# inputs_quantized = np.array(range(dtype_info.min, dtype_info.max + 1)).astype(in_dtype)
# First generate a list of all num_bit integer patterns
- inputs_quantized = np.array(range(0, 2 ** num_bits), dtype=f"uint{num_bits}")
+ inputs_quantized = np.array(range(0, 2**num_bits), dtype=f"uint{num_bits}")
# Reinterpret bits as the real datatype
# Note what we are doing here is a bit tricky, the canonical view of our lookup table
diff --git a/python/tvm/relay/quantize/_calibrate.py b/python/tvm/relay/quantize/_calibrate.py
index ae3a846c11..4b2d55ebe8 100644
--- a/python/tvm/relay/quantize/_calibrate.py
+++ b/python/tvm/relay/quantize/_calibrate.py
@@ -159,7 +159,7 @@ def _set_params(mod, input_scale_func, weight_scale_func):
def _make_const(val):
return _expr.const(val, "float32")
- valid_range = 2 ** valid_bit
+ valid_range = 2**valid_bit
const_params[ndom_scale] = _make_const(scale / valid_range)
const_params[nclip_min] = _make_const(-(valid_range - 1))
const_params[nclip_max] = _make_const((valid_range - 1))
diff --git a/python/tvm/relay/testing/tf.py b/python/tvm/relay/testing/tf.py
index b711208597..e09111a205 100644
--- a/python/tvm/relay/testing/tf.py
+++ b/python/tvm/relay/testing/tf.py
@@ -321,7 +321,7 @@ def pick_from_weight(weight, pows=1.0):
"""Identify token from Softmax output.
This token will be mapped to word in the vocabulary.
"""
- weight = weight ** pows
+ weight = weight**pows
t = np.cumsum(weight)
s = np.sum(weight)
return int(np.searchsorted(t, 0.5 * s))
diff --git a/python/tvm/testing/utils.py b/python/tvm/testing/utils.py
index 3043dabbed..eeb9c35b4a 100644
--- a/python/tvm/testing/utils.py
+++ b/python/tvm/testing/utils.py
@@ -218,7 +218,7 @@ def check_numerical_grads(
wrong_percentage = int(100 * len(wrong_positions) / np.prod(grad.shape))
dist = np.sqrt(np.sum((ngrad - grad) ** 2))
- grad_norm = np.sqrt(np.sum(ngrad ** 2))
+ grad_norm = np.sqrt(np.sum(ngrad**2))
if not (np.isfinite(dist) and np.isfinite(grad_norm)):
raise ValueError(
diff --git a/python/tvm/tir/schedule/_type_checker.py b/python/tvm/tir/schedule/_type_checker.py
index c815282b74..1b86c4aa30 100644
--- a/python/tvm/tir/schedule/_type_checker.py
+++ b/python/tvm/tir/schedule/_type_checker.py
@@ -57,7 +57,6 @@ if hasattr(typing, "_GenericAlias"):
return list(subtypes)
return None
-
elif hasattr(typing, "_Union"):
class _Subtype: # type: ignore
diff --git a/python/tvm/topi/gpu/dense.py b/python/tvm/topi/gpu/dense.py
index 4dce6eec90..5f2f36c46b 100644
--- a/python/tvm/topi/gpu/dense.py
+++ b/python/tvm/topi/gpu/dense.py
@@ -153,8 +153,8 @@ def _schedule_dense_large_batch(cfg, s, C):
# create tuning space
try:
block_cand = [64, 128]
- vthread_cand = [2 ** x for x in range(1, 7)]
- n_thread_cand = [2 ** x for x in range(3, 7)]
+ vthread_cand = [2**x for x in range(1, 7)]
+ n_thread_cand = [2**x for x in range(3, 7)]
cfg.define_split(
"tile_x",
batch,
diff --git a/python/tvm/topi/random/kernel.py b/python/tvm/topi/random/kernel.py
index 64afcf066c..11c2480d3d 100644
--- a/python/tvm/topi/random/kernel.py
+++ b/python/tvm/topi/random/kernel.py
@@ -233,7 +233,7 @@ def threefry_generate(gen, out_shape):
for s in out_shape:
out_len *= s
assert (
- out_len.value <= 2 ** 64 - 1
+ out_len.value <= 2**64 - 1
), f"Can only generate up to 2^64 random numbers, but {out_len} were requested."
def gen_ir(gen_ptr, out_gen_ptr, out_array_ptr):
@@ -264,7 +264,7 @@ def threefry_generate(gen, out_shape):
# Max value for counter should be 2**64-2 because we need to reserve a special value to
# indicate the counter is used up.
- with irb.if_scope(gen[7] < tir.const(2 ** 64 - 1, dtype=gen.dtype) - out_len):
+ with irb.if_scope(gen[7] < tir.const(2**64 - 1, dtype=gen.dtype) - out_len):
for i in range(10):
tmp[i] = gen[i]
with irb.else_scope():
diff --git a/python/tvm/topi/testing/correlation_nchw_python.py b/python/tvm/topi/testing/correlation_nchw_python.py
index ac12e81bc6..bab5f2dc52 100644
--- a/python/tvm/topi/testing/correlation_nchw_python.py
+++ b/python/tvm/topi/testing/correlation_nchw_python.py
@@ -103,5 +103,5 @@ def correlation_nchw_python(
pad_data2[nbatch, channel, y2 + h, x2 + w],
)
- out /= float(kernel_size ** 2 * data1.shape[1])
+ out /= float(kernel_size**2 * data1.shape[1])
return out
diff --git a/tests/python/contrib/test_cmsisnn/utils.py b/tests/python/contrib/test_cmsisnn/utils.py
index 18e3d4e53f..6bd375db1f 100644
--- a/tests/python/contrib/test_cmsisnn/utils.py
+++ b/tests/python/contrib/test_cmsisnn/utils.py
@@ -290,7 +290,7 @@ def generate_ref_data_tflite(model):
def create_conv2d_tflite_model(ifm_shape, kernel_shape, strides, dilation, padding, activation):
- """ This method prepares TFlite graph with a single Conv2d layer """
+ """This method prepares TFlite graph with a single Conv2d layer"""
import tensorflow as tf
class Model(tf.Module):
diff --git a/tests/python/contrib/test_ethosu/cascader/conftest.py b/tests/python/contrib/test_ethosu/cascader/conftest.py
index 1d55067929..74063ba343 100644
--- a/tests/python/contrib/test_ethosu/cascader/conftest.py
+++ b/tests/python/contrib/test_ethosu/cascader/conftest.py
@@ -29,7 +29,7 @@ import tvm.contrib.ethosu.cascader as cs
def FLASH():
return cs.MemoryRegion(
name="FLASH",
- size=10 ** 7,
+ size=10**7,
read_bandwidth=4,
write_bandwidth=4,
read_latency=0,
@@ -42,7 +42,7 @@ def FLASH():
def DRAM():
return cs.MemoryRegion(
name="DRAM",
- size=10 ** 9,
+ size=10**9,
read_bandwidth=8,
write_bandwidth=8,
read_latency=0,
@@ -55,7 +55,7 @@ def DRAM():
def SRAM():
return cs.MemoryRegion(
name="SRAM",
- size=10 ** 6,
+ size=10**6,
read_bandwidth=16,
write_bandwidth=16,
read_latency=0,
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index 285d857ca6..c3fca80838 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -2193,7 +2193,7 @@ def test_vgg11_bn():
def test_custom_conversion_map():
def get_roi_align():
pool_size = 5
- n_channels = 2 * (pool_size ** 2)
+ n_channels = 2 * (pool_size**2)
x = torch.rand(2, n_channels, 10, 10)
rois = torch.tensor(
[
diff --git a/tests/python/relay/test_op_grad_level1.py b/tests/python/relay/test_op_grad_level1.py
index bab709f2b8..a31191a42c 100644
--- a/tests/python/relay/test_op_grad_level1.py
+++ b/tests/python/relay/test_op_grad_level1.py
@@ -56,11 +56,11 @@ class TestUnaryOp:
"log10": (tvm.relay.log10, lambda x, g: g * (1 / (np.log(10) * x))),
"cosh": (tvm.relay.cosh, lambda x, g: g * (np.sinh(x))),
"sinh": (tvm.relay.sinh, lambda x, g: g * (np.cosh(x))),
- "asin": (tvm.relay.asin, lambda x, g: g * (1.0 / (1.0 - x ** 2) ** (1.0 / 2.0))),
- "acos": (tvm.relay.acos, lambda x, g: g * (-1.0 / (1.0 - x ** 2.0) ** (1.0 / 2.0))),
- "acosh": (tvm.relay.acosh, lambda x, g: g * (1.0 / (x ** 2 - 1.0) ** (1.0 / 2.0))),
- "asinh": (tvm.relay.asinh, lambda x, g: g * (1.0 / (x ** 2 + 1.0) ** (1.0 / 2.0))),
- "atanh": (tvm.relay.atanh, lambda x, g: g * (-1.0 / (x ** 2 - 1.0))),
+ "asin": (tvm.relay.asin, lambda x, g: g * (1.0 / (1.0 - x**2) ** (1.0 / 2.0))),
+ "acos": (tvm.relay.acos, lambda x, g: g * (-1.0 / (1.0 - x**2.0) ** (1.0 / 2.0))),
+ "acosh": (tvm.relay.acosh, lambda x, g: g * (1.0 / (x**2 - 1.0) ** (1.0 / 2.0))),
+ "asinh": (tvm.relay.asinh, lambda x, g: g * (1.0 / (x**2 + 1.0) ** (1.0 / 2.0))),
+ "atanh": (tvm.relay.atanh, lambda x, g: g * (-1.0 / (x**2 - 1.0))),
}
relay_op, ref_func = tvm.testing.parameters(*config.values(), ids=config.keys())
@@ -136,7 +136,7 @@ class TestBinaryOp:
"add": (relay.add, lambda x, y: [np.ones_like(x), np.ones_like(y)]),
"subtract": (relay.subtract, lambda x, y: [np.ones_like(x), -np.ones_like(y)]),
"multiply": (relay.multiply, lambda x, y: [y, x]),
- "divide": (relay.divide, lambda x, y: [1 / y, -x / (y ** 2)]),
+ "divide": (relay.divide, lambda x, y: [1 / y, -x / (y**2)]),
}
relay_op, ref_func = tvm.testing.parameters(*config.values(), ids=config.keys())
diff --git a/tests/python/topi/python/test_topi_prng.py b/tests/python/topi/python/test_topi_prng.py
index 60ef7b3b23..d431679444 100644
--- a/tests/python/topi/python/test_topi_prng.py
+++ b/tests/python/topi/python/test_topi_prng.py
@@ -120,14 +120,14 @@ def test_threefry_generate(target, dev):
# test enough generates to go over generate limit
gen = np.array(
- [0, 0, 0, 0, 0, 0, 0, 2 ** 64 - 2, 1 << 63, 0], dtype="uint64"
+ [0, 0, 0, 0, 0, 0, 0, 2**64 - 2, 1 << 63, 0], dtype="uint64"
) # make counter large
a, rands = threefry_generate(target, dev, gen, (2048,))
assert gen[4] != a[4], "Overflow of counter should trigger path change"
assert a[7] == 2048, "Overflow of counter should still update counter"
# check generate with path at length limit
- gen = np.array([0, 0, 0, 0, 0, 0, 0, 2 ** 64 - 2, 0, 0], dtype="uint64") # make counter large
+ gen = np.array([0, 0, 0, 0, 0, 0, 0, 2**64 - 2, 0, 0], dtype="uint64") # make counter large
a, rands = threefry_generate(target, dev, gen, (2048,))
assert (
gen[0:4] != a[0:4]
diff --git a/tests/python/topi/python/test_topi_transform.py b/tests/python/topi/python/test_topi_transform.py
index 730d22cba1..180f267650 100644
--- a/tests/python/topi/python/test_topi_transform.py
+++ b/tests/python/topi/python/test_topi_transform.py
@@ -861,10 +861,10 @@ def test_reinterpret():
(1000,), "int16", "uint16", lambda shape: np.random.randint(-1000, 1000, size=shape)
)
verify_reinterpret(
- (1000,), "uint32", "int32", lambda shape: np.random.randint(0, 2 ** 32 - 1, size=shape)
+ (1000,), "uint32", "int32", lambda shape: np.random.randint(0, 2**32 - 1, size=shape)
)
verify_reinterpret(
- (1000,), "uint32", "int32", lambda shape: np.random.randint(0, 2 ** 32 - 1, size=shape)
+ (1000,), "uint32", "int32", lambda shape: np.random.randint(0, 2**32 - 1, size=shape)
)
diff --git a/tests/python/unittest/test_arith_canonical_simplify.py b/tests/python/unittest/test_arith_canonical_simplify.py
index 6dc91d7804..74c8bcb5fd 100644
--- a/tests/python/unittest/test_arith_canonical_simplify.py
+++ b/tests/python/unittest/test_arith_canonical_simplify.py
@@ -331,7 +331,7 @@ def test_simplify_cast():
# cast(i32, i + j - 100)
i = te.var("i", dtype="int64")
j = te.var("j", dtype="int64")
- ck.analyzer.update(i, tvm.arith.ConstIntBound(0, 2 ** 31 - 1))
+ ck.analyzer.update(i, tvm.arith.ConstIntBound(0, 2**31 - 1))
ck.analyzer.update(j, tvm.arith.ConstIntBound(0, 10))
res = tcast("int32", i + j - 100)
ck.verify(res, res)
diff --git a/tests/python/unittest/test_auto_scheduler_compute_dag.py b/tests/python/unittest/test_auto_scheduler_compute_dag.py
index 81ee5cabbf..d3b618d675 100644
--- a/tests/python/unittest/test_auto_scheduler_compute_dag.py
+++ b/tests/python/unittest/test_auto_scheduler_compute_dag.py
@@ -47,25 +47,25 @@ def test_estimate_flop():
N = 512
A, B, C = matmul_auto_scheduler_test(N, N, N)
dag = auto_scheduler.ComputeDAG([A, B, C])
- assert abs(dag.flop_ct - 2 * N ** 3) < 0.5
+ assert abs(dag.flop_ct - 2 * N**3) < 0.5
D = topi.nn.relu(C)
dag = auto_scheduler.ComputeDAG([A, B, D])
- assert abs(dag.flop_ct - (2 * N ** 3 + N * N)) < 0.5
+ assert abs(dag.flop_ct - (2 * N**3 + N * N)) < 0.5
# should not count the comparison operations in padding
E = topi.nn.pad(C, [1, 1])
dag = auto_scheduler.ComputeDAG([A, B, E])
- assert abs(dag.flop_ct - 2 * N ** 3) < 0.5
+ assert abs(dag.flop_ct - 2 * N**3) < 0.5
F = te.compute((N, N), lambda i, j: E[i, j], name="F", attrs={"FLOP": 1234})
dag = auto_scheduler.ComputeDAG([A, B, F])
- assert abs(dag.flop_ct - (2 * N ** 3 + 1234)) < 0.5
+ assert abs(dag.flop_ct - (2 * N**3 + 1234)) < 0.5
A = te.placeholder((N, N), dtype="float32", name="A")
F = te.compute((N, N), lambda i, j: te.if_then_else(A[i, j] > 0, A[i, j], 0))
dag = auto_scheduler.ComputeDAG([A, F])
- assert abs(dag.flop_ct - N ** 2) < 0.5
+ assert abs(dag.flop_ct - N**2) < 0.5
def test_stage_order():
diff --git a/tests/python/unittest/test_auto_scheduler_feature.py b/tests/python/unittest/test_auto_scheduler_feature.py
index e11496e8ca..084f23db51 100644
--- a/tests/python/unittest/test_auto_scheduler_feature.py
+++ b/tests/python/unittest/test_auto_scheduler_feature.py
@@ -78,8 +78,8 @@ def test_cpu_matmul():
"""
# check touched memory in bytes, touched unique memory in bytes, reuse distance, etc.
- assert fequal(fea_dict[c_name + ".bytes"], math.log2(512 ** 3 * 4 + 1))
- assert fequal(fea_dict[b_name + ".unique_bytes"], math.log2(512 ** 2 * 4 + 1))
+ assert fequal(fea_dict[c_name + ".bytes"], math.log2(512**3 * 4 + 1))
+ assert fequal(fea_dict[b_name + ".unique_bytes"], math.log2(512**2 * 4 + 1))
assert fequal(fea_dict[c_name + ".reuse_dis_iter"], math.log2(8 * 16 + 1))
assert fequal(fea_dict[c_name + ".reuse_dis_bytes"], math.log2((8 * 16 + 8 + 16) * 4 + 1))
assert fequal(fea_dict[c_name + ".reuse_ct"], math.log2(512 + 1))
diff --git a/tests/python/unittest/test_autotvm_space.py b/tests/python/unittest/test_autotvm_space.py
index d56ca9e072..d9f2b528e4 100644
--- a/tests/python/unittest/test_autotvm_space.py
+++ b/tests/python/unittest/test_autotvm_space.py
@@ -76,7 +76,7 @@ def test_split():
# test overflow
n = 25
cfg = ConfigSpace()
- cfg.define_split("x", cfg.axis(2 ** n), policy="factors", num_outputs=4)
+ cfg.define_split("x", cfg.axis(2**n), policy="factors", num_outputs=4)
# count4(25) is 3276.
assert len(cfg.space_map["x"]) == count4(n)
diff --git a/tests/python/unittest/test_format_si_prefix.py b/tests/python/unittest/test_format_si_prefix.py
index 4df5c2b8cd..e0276ce022 100644
--- a/tests/python/unittest/test_format_si_prefix.py
+++ b/tests/python/unittest/test_format_si_prefix.py
@@ -30,7 +30,7 @@ def test_format_si_prefix():
for i, prefix in enumerate(SI_PREFIXES):
integer, decimal = random.randint(0, 1000), random.randint(0, 1000)
exp = -24 + 3 * i # 0th prefix (yocto) is 10^-24
- number = integer * (10 ** exp) + decimal * (10 ** (exp - 3))
+ number = integer * (10**exp) + decimal * (10 ** (exp - 3))
expected = integer + decimal / 1000
assert isclose(utils.format_si_prefix(number, prefix), expected)
diff --git a/tests/python/unittest/test_target_codegen_c_host.py b/tests/python/unittest/test_target_codegen_c_host.py
index 95cd967dd2..fc7d62b393 100644
--- a/tests/python/unittest/test_target_codegen_c_host.py
+++ b/tests/python/unittest/test_target_codegen_c_host.py
@@ -111,7 +111,7 @@ def test_reinterpret():
fadd = m["test_reinterpret"]
dev = tvm.cpu(0)
n = nn
- a = tvm.nd.array(np.random.randint(-(2 ** 30), 2 ** 30, size=n).astype(A.dtype), dev)
+ a = tvm.nd.array(np.random.randint(-(2**30), 2**30, size=n).astype(A.dtype), dev)
b = tvm.nd.array(np.zeros(n, dtype=B.dtype), dev)
fadd(a, b)
tvm.testing.assert_allclose(b.numpy(), (2 + a.numpy()).view("float32"))
diff --git a/tests/python/unittest/test_target_codegen_rocm.py b/tests/python/unittest/test_target_codegen_rocm.py
index 894c8ecd0a..3e286f6ebf 100644
--- a/tests/python/unittest/test_target_codegen_rocm.py
+++ b/tests/python/unittest/test_target_codegen_rocm.py
@@ -105,7 +105,7 @@ def test_rocm_copy():
dtype = np.random.choice(["float32", "float16", "int8", "int32"])
logN = np.random.randint(1, 15)
peturb = np.random.uniform(low=0.5, high=1.5)
- check_rocm(dtype, int(peturb * (2 ** logN)))
+ check_rocm(dtype, int(peturb * (2**logN)))
@tvm.testing.requires_rocm
diff --git a/tests/python/unittest/test_tir_transform_narrow_datatype.py b/tests/python/unittest/test_tir_transform_narrow_datatype.py
index 51c3823098..9909262a44 100644
--- a/tests/python/unittest/test_tir_transform_narrow_datatype.py
+++ b/tests/python/unittest/test_tir_transform_narrow_datatype.py
@@ -67,13 +67,13 @@ def test_basic():
# i32 -> i32
check(2, 2, 32, "int32")
# i32 + i32 is not promoted to i64 even if overflow
- check(2 ** 16, 2 ** 16, 32, "int32")
+ check(2**16, 2**16, 32, "int32")
# i64 -> i32
check(const(2, dtype="int64"), const(2, dtype="int64"), 32, "int32")
- check(const(2 ** 16, dtype="int64"), const(2 ** 16, dtype="int64"), 32, "int64")
+ check(const(2**16, dtype="int64"), const(2**16, dtype="int64"), 32, "int64")
# i32 -> i16
check(2, 2, 16, "int16")
- check(2 ** 10, 2 ** 10, 16, "int32")
+ check(2**10, 2**10, 16, "int32")
# symbolic shape
check(te.size_var(name="m", dtype="int32"), te.size_var(name="n", dtype="int32"), 32, "int32")
@@ -100,7 +100,7 @@ def test_thread_axis():
# i32 -> i32
check(2, 32, target_bits=32, target_dtype="int32")
check(
- 2 ** 30,
+ 2**30,
32, # i32 + i32 is not promoted to i64 even in the case of overflow
target_bits=32,
target_dtype="int32",
@@ -108,14 +108,14 @@ def test_thread_axis():
# i64 -> i32
check(const(2, dtype="int64"), const(32, dtype="int64"), target_bits=32, target_dtype="int32")
check(
- const(2 ** 30, dtype="int64"),
+ const(2**30, dtype="int64"),
const(32, dtype="int64"),
target_bits=32,
target_dtype="int64",
)
# i32 -> i16
check(2, 32, target_bits=16, target_dtype="int16")
- check(2 ** 14, 32, target_bits=16, target_dtype="int32")
+ check(2**14, 32, target_bits=16, target_dtype="int32")
def test_multilanes():
@@ -133,14 +133,14 @@ def test_multilanes():
assert stmt.seq[0].loop_var.dtype == target_dtype
# i32 -> i32
- check(const(2 ** 10, dtype="int32"), 2, target_bits=32, target_dtype="int32")
- check(const(2 ** 32, dtype="int32"), 2, target_bits=32, target_dtype="int32")
+ check(const(2**10, dtype="int32"), 2, target_bits=32, target_dtype="int32")
+ check(const(2**32, dtype="int32"), 2, target_bits=32, target_dtype="int32")
# i64 -> i32
- check(const(2 ** 10, dtype="int64"), 2, target_bits=32, target_dtype="int32")
- check(const(2 ** 32, dtype="int64"), 2, target_bits=32, target_dtype="int64")
+ check(const(2**10, dtype="int64"), 2, target_bits=32, target_dtype="int32")
+ check(const(2**32, dtype="int64"), 2, target_bits=32, target_dtype="int64")
# i32 -> i16
- check(const(2 ** 10, dtype="int32"), 2, target_bits=16, target_dtype="int16")
- check(const(2 ** 16, dtype="int32"), 2, target_bits=16, target_dtype="int32")
+ check(const(2**10, dtype="int32"), 2, target_bits=16, target_dtype="int16")
+ check(const(2**16, dtype="int32"), 2, target_bits=16, target_dtype="int32")
def test_reduce():
@@ -158,7 +158,7 @@ def test_reduce():
check(const(64, dtype="int64"), 32, "int32")
# i32 -> i16
check(const(64, dtype="int32"), 16, "int16")
- check(const(2 ** 16, dtype="int32"), 16, "int32")
+ check(const(2**16, dtype="int32"), 16, "int32")
# symbolic
check(te.var("n", dtype="int32"), 32, "int32")
check(te.var("n", dtype="int64"), 32, "int64")
@@ -181,10 +181,10 @@ def test_slice():
assert stmt.body.loop_var.dtype == target_dtype
# The maximum index is (2**15 * 2**15 - 1) * 2 <= 2**31 - 1
- check(const(2 ** 15, "int64"), const(2 ** 15, "int64"), target_bits=32, target_dtype="int32")
+ check(const(2**15, "int64"), const(2**15, "int64"), target_bits=32, target_dtype="int32")
# The maximum index is (2**15 * 2**15 - 1 + 2**15) * 2 > 2**31 - 1
check(
- const(2 ** 15, "int64"), const((2 ** 15 + 1), "int64"), target_bits=32, target_dtype="int64"
+ const(2**15, "int64"), const((2**15 + 1), "int64"), target_bits=32, target_dtype="int64"
)
@@ -208,23 +208,23 @@ def test_relay_basic():
assert stmt.body.loop_var.dtype == target_dtype
check(
- (const(2 ** 16, "int64"), const(2 ** 15 + 1, "int64")),
- (1, const(2 ** 15 + 1, "int64")),
+ (const(2**16, "int64"), const(2**15 + 1, "int64")),
+ (1, const(2**15 + 1, "int64")),
target_bits=32,
target_dtype="int64",
)
check(
- (const(2 ** 16, "int64"), const(2 ** 15, "int64")),
- (1, const(2 ** 15, "int64")),
+ (const(2**16, "int64"), const(2**15, "int64")),
+ (1, const(2**15, "int64")),
target_bits=32,
target_dtype="int32",
)
check(
- (const(2 ** 31, "int64"),), (const(2 ** 31, "int64"),), target_bits=32, target_dtype="int32"
+ (const(2**31, "int64"),), (const(2**31, "int64"),), target_bits=32, target_dtype="int32"
)
check(
- (const(2 ** 31 + 1, "int64"),),
- (const(2 ** 31 + 1, "int64"),),
+ (const(2**31 + 1, "int64"),),
+ (const(2**31 + 1, "int64"),),
target_bits=32,
target_dtype="int64",
)
@@ -245,14 +245,14 @@ def test_relay_take():
assert stmt.value.indices[0].dtype == target_dtype
check(
- (const(2 ** 16, "int64"), const(2 ** 15 + 1, "int64")),
+ (const(2**16, "int64"), const(2**15 + 1, "int64")),
relay.const(0, dtype="int64"),
target_bits=32,
target_dtype="int32",
)
check(
- (const(2 ** 16, "int64"), const(2 ** 15 + 1, "int64")),
- relay.const(2 ** 31, dtype="int64"),
+ (const(2**16, "int64"), const(2**15 + 1, "int64")),
+ relay.const(2**31, dtype="int64"),
target_bits=32,
target_dtype="int64",
)
@@ -271,7 +271,7 @@ def test_ramp_dtype_consistency():
"""
n = tvm.tir.IntImm("int64", 4)
m = tvm.tir.IntImm("int64", 2)
- A = te.compute((n, m), lambda i, j: tvm.tir.Cast("int64", 2 ** 31 - 1) * i, name="A")
+ A = te.compute((n, m), lambda i, j: tvm.tir.Cast("int64", 2**31 - 1) * i, name="A")
s = te.create_schedule(A.op)
s[A].vectorize(A.op.axis[1])
lower_sch(s, [A], 32, extra_passes=[tvm.tir.transform.VectorizeLoop()])
diff --git a/tests/python/unittest/test_tir_transform_vectorize.py b/tests/python/unittest/test_tir_transform_vectorize.py
index 5b6f7de97b..2448fffe89 100644
--- a/tests/python/unittest/test_tir_transform_vectorize.py
+++ b/tests/python/unittest/test_tir_transform_vectorize.py
@@ -220,7 +220,7 @@ def test_vectorize_while_fail():
def test_vectorize_dtype_mismatch():
n = tvm.tir.IntImm("int64", 4)
- A = te.compute((n,), lambda i: tvm.tir.IntImm("int64", 2 ** 31 - 1) + i, name="A")
+ A = te.compute((n,), lambda i: tvm.tir.IntImm("int64", 2**31 - 1) + i, name="A")
s = te.create_schedule(A.op)
s[A].vectorize(A.op.axis[0])
tvm.lower(s, [A], "llvm", simple_mode=True)
diff --git a/tests/python/unittest/test_tir_usmp_algo_hill_climb.py b/tests/python/unittest/test_tir_usmp_algo_hill_climb.py
index a5f1158a90..863b0a566c 100644
--- a/tests/python/unittest/test_tir_usmp_algo_hill_climb.py
+++ b/tests/python/unittest/test_tir_usmp_algo_hill_climb.py
@@ -45,13 +45,10 @@ def _verify_conflicts(buffer_info, pool_allocation, buffer_info_map):
if conflict_pool_allocation.pool_info == pool_allocation.pool_info:
assert conflict_pool_allocation.byte_offset != pool_allocation.byte_offset
- l2 = (
- max(
- conflict_pool_allocation.byte_offset + conflict.size_bytes,
- pool_allocation.byte_offset + buffer_info.size_bytes,
- )
- - min(conflict_pool_allocation.byte_offset, pool_allocation.byte_offset)
- )
+ l2 = max(
+ conflict_pool_allocation.byte_offset + conflict.size_bytes,
+ pool_allocation.byte_offset + buffer_info.size_bytes,
+ ) - min(conflict_pool_allocation.byte_offset, pool_allocation.byte_offset)
assert (
conflict.size_bytes + buffer_info.size_bytes <= l2
), 'Conflicting: \n"{} @{}"\n"{} @{}"'.format(
diff --git a/vta/tests/python/integration/test_benchmark_gemm.py b/vta/tests/python/integration/test_benchmark_gemm.py
index 3bc3520d86..6290ca436f 100644
--- a/vta/tests/python/integration/test_benchmark_gemm.py
+++ b/vta/tests/python/integration/test_benchmark_gemm.py
@@ -174,7 +174,7 @@ def test_gemm():
env.dma_copy,
print_ir,
)
- gops = (num_ops / cost.mean) / float(10 ** 9)
+ gops = (num_ops / cost.mean) / float(10**9)
print(header)
print("\tTime cost = %g sec/op, %g GOPS" % (cost.mean, gops))
@@ -189,7 +189,7 @@ def test_gemm():
cost = run_schedule(
mock.dma_copy, mock.dma_copy, env.gemm, mock.alu, mock.dma_copy, print_ir
)
- gops = (num_ops / cost.mean) / float(10 ** 9)
+ gops = (num_ops / cost.mean) / float(10**9)
print(header)
print("\tTime cost = %g sec/op, %g GOPS" % (cost.mean, gops))
@@ -204,7 +204,7 @@ def test_gemm():
cost = run_schedule(
mock.dma_copy, mock.dma_copy, mock.gemm, env.alu, mock.dma_copy, print_ir
)
- gops = (num_ops / cost.mean) / float(10 ** 9)
+ gops = (num_ops / cost.mean) / float(10**9)
print(header)
print("\tTime cost = %g sec/op, %g GOPS" % (cost.mean, gops))
@@ -220,8 +220,8 @@ def test_gemm():
cost = run_schedule(
env.dma_copy, mock.dma_copy, mock.gemm, mock.alu, mock.dma_copy, print_ir
)
- gops = (num_ops / cost.mean) / float(10 ** 9)
- bandwith = (batch_size * channel * env.INP_WIDTH / cost.mean) / float(10 ** 9)
+ gops = (num_ops / cost.mean) / float(10**9)
+ bandwith = (batch_size * channel * env.INP_WIDTH / cost.mean) / float(10**9)
print(header)
print(
"\tTime cost = %g sec/op, %g GOPS, bandwidth=%g Gbits"
@@ -240,8 +240,8 @@ def test_gemm():
cost = run_schedule(
mock.dma_copy, env.dma_copy, mock.gemm, mock.alu, mock.dma_copy, print_ir
)
- gops = (num_ops / cost.mean) / float(10 ** 9)
- bandwith = (channel * channel * env.WGT_WIDTH / cost.mean) / float(10 ** 9)
+ gops = (num_ops / cost.mean) / float(10**9)
+ bandwith = (channel * channel * env.WGT_WIDTH / cost.mean) / float(10**9)
print(header)
print(
"\tTime cost = %g sec/op, %g GOPS, bandwidth=%g Gbits"
@@ -260,8 +260,8 @@ def test_gemm():
cost = run_schedule(
mock.dma_copy, mock.dma_copy, mock.gemm, mock.alu, env.dma_copy, print_ir
)
- gops = (num_ops / cost.mean) / float(10 ** 9)
- bandwith = (batch_size * channel * env.OUT_WIDTH / cost.mean) / float(10 ** 9)
+ gops = (num_ops / cost.mean) / float(10**9)
+ bandwith = (batch_size * channel * env.OUT_WIDTH / cost.mean) / float(10**9)
print(header)
print(
"\tTime cost = %g sec/op, %g GOPS, bandwidth=%g Gbits"
diff --git a/vta/tests/python/integration/test_benchmark_topi_conv2d.py b/vta/tests/python/integration/test_benchmark_topi_conv2d.py
index 672c113488..64f9ec2deb 100644
--- a/vta/tests/python/integration/test_benchmark_topi_conv2d.py
+++ b/vta/tests/python/integration/test_benchmark_topi_conv2d.py
@@ -283,7 +283,7 @@ def run_conv2d(env, remote, wl, target, check_correctness=True, print_ir=False,
res_ref = res_ref.astype(env.out_dtype)
correct = np.allclose(res_orig, res_ref)
- gops = (num_ops / cost.mean) / float(10 ** 9)
+ gops = (num_ops / cost.mean) / float(10**9)
status = "PASSED" if correct else "FAILED"
if "arm_cpu" in target.keys:
device = "CPU"
diff --git a/vta/tests/python/integration/test_benchmark_topi_conv2d_transpose.py b/vta/tests/python/integration/test_benchmark_topi_conv2d_transpose.py
index 65c861ba46..b0ea2fc113 100644
--- a/vta/tests/python/integration/test_benchmark_topi_conv2d_transpose.py
+++ b/vta/tests/python/integration/test_benchmark_topi_conv2d_transpose.py
@@ -270,7 +270,7 @@ def run_conv2d_transpose(
res_ref = res_ref.astype(env.out_dtype)
correct = np.allclose(res_orig, res_ref)
- gops = (num_ops / cost.mean) / float(10 ** 9)
+ gops = (num_ops / cost.mean) / float(10**9)
status = "PASSED" if correct else "FAILED"
if "arm_cpu" in target.keys:
device = "CPU"
diff --git a/vta/tests/python/integration/test_benchmark_topi_dense.py b/vta/tests/python/integration/test_benchmark_topi_dense.py
index 133cbf506e..45a400b24e 100644
--- a/vta/tests/python/integration/test_benchmark_topi_dense.py
+++ b/vta/tests/python/integration/test_benchmark_topi_dense.py
@@ -184,7 +184,7 @@ def run_gemm(
res_ref = res_ref.astype(env.out_dtype)
correct = np.allclose(res_orig, res_ref)
- gops = (num_ops / cost.mean) / float(10 ** 9)
+ gops = (num_ops / cost.mean) / float(10**9)
status = "PASSED" if correct else "FAILED"
if "arm_cpu" in target.keys:
device = "CPU"
diff --git a/vta/tests/python/integration/test_benchmark_topi_group_conv2d.py b/vta/tests/python/integration/test_benchmark_topi_group_conv2d.py
index 66de6d9a54..bc9efa05f3 100644
--- a/vta/tests/python/integration/test_benchmark_topi_group_conv2d.py
+++ b/vta/tests/python/integration/test_benchmark_topi_group_conv2d.py
@@ -277,7 +277,7 @@ def run_group_conv2d(env, remote, wl, target, check_correctness=True, print_ir=F
res_ref = res_ref.astype(env.out_dtype)
correct = np.allclose(res_orig, res_ref)
- gops = (num_ops / cost.mean) / float(10 ** 9)
+ gops = (num_ops / cost.mean) / float(10**9)
status = "PASSED" if correct else "FAILED"
if "arm_cpu" in target.keys:
device = "CPU"