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[Pylint] fix pylint issues for cblas #16015

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merged 2 commits into from
Oct 31, 2023
Merged

[Pylint] fix pylint issues for cblas #16015

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841e06b
Update pylint.sh
tlopex Oct 30, 2023
da27b5f
Update test_cblas.py
tlopex Oct 30, 2023
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1 change: 1 addition & 0 deletions tests/lint/pylint.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,7 @@ python3 -m pylint tests/python/relay/aot/*.py --rcfile="$(dirname "$0")"/pylintr
python3 -m pylint tests/python/ci --rcfile="$(dirname "$0")"/pylintrc
python3 -m pylint tests/python/integration/ --rcfile="$(dirname "$0")"/pylintrc
python3 -m pylint tests/python/conftest.py --rcfile="$(dirname "$0")"/pylintrc
python3 -m pylint tests/python/contrib/test_cblas.py --rcfile="$(dirname "$0")"/pylintrc

# tests/python/contrib/test_hexagon tests
python3 -m pylint tests/python/contrib/test_hexagon/*.py --rcfile="$(dirname "$0")"/pylintrc
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165 changes: 102 additions & 63 deletions tests/python/contrib/test_cblas.py
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Expand Up @@ -14,35 +14,41 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
"""Configure pytest"""
import pytest
import numpy as np
import tvm
from tvm import te
import numpy as np
import tvm.topi.testing
from tvm.contrib import cblas
from tvm.contrib import mkl
from tvm.contrib import dnnl
import tvm.testing
import tvm.topi.testing


def verify_matmul_add(m, l, n, lib, transa=False, transb=False, dtype="float32"):
def verify_matmul_add(
matrix_m, matrix_l, matrix_n, lib, transa=False, transb=False, dtype="float32"
):
"""Tests matmul+add op"""
bias = te.var("bias", dtype=dtype)
ashape = (l, n) if transa else (n, l)
bshape = (m, l) if transb else (l, m)
A = te.placeholder(ashape, name="A", dtype=dtype)
B = te.placeholder(bshape, name="B", dtype=dtype)
C = lib.matmul(A, B, transa, transb)
D = te.compute(C.shape, lambda i, j: C[i, j] + bias, name="D")
s = te.create_schedule(D.op)

def get_numpy(a, b, bb, transa, transb):
ashape = (matrix_l, matrix_n) if transa else (matrix_n, matrix_l)
bshape = (matrix_m, matrix_l) if transb else (matrix_l, matrix_m)
input1_data = te.placeholder(ashape, name="input1_data", dtype=dtype)
input2_data = te.placeholder(bshape, name="input2_data", dtype=dtype)
matmul_result = lib.matmul(input1_data, input2_data, transa, transb)
final_result = te.compute(
matmul_result.shape, lambda i, j: matmul_result[i, j] + bias, name="final_result"
)
s = te.create_schedule(final_result.op)

def get_numpy(a, b, matrix_bias, transa, transb):
if transa:
a = a.transpose()
if transb:
b = b.transpose()
return np.dot(a, b) + bb
return np.dot(a, b) + matrix_bias

def compile(f, name="test_matmul_add", ext=".so"):
def compiling(f, name="test_matmul_add", ext=".so"):
path = name + ext
f.export_library(path)
mod = tvm.runtime.load_module(path)
Expand All @@ -58,23 +64,26 @@ def verify(target="llvm"):
return
dev = tvm.cpu(0)
name = "test_matmul_add"
f = tvm.build(s, [A, B, D, bias], target, name=name)
f = tvm.build(s, [input1_data, input2_data, final_result, bias], target, name=name)
if target == "c":
f = compile(f, name)
a = tvm.nd.array(np.random.uniform(size=ashape).astype(A.dtype), dev)
b = tvm.nd.array(np.random.uniform(size=bshape).astype(B.dtype), dev)
d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), dev)
bb = 10.0
f(a, b, d, bb)
f = compiling(f, name)
matrix_input1 = tvm.nd.array(np.random.uniform(size=ashape).astype(input1_data.dtype), dev)
matrix_input2 = tvm.nd.array(np.random.uniform(size=bshape).astype(input2_data.dtype), dev)
matrix_result = tvm.nd.array(np.zeros((matrix_n, matrix_m), dtype=final_result.dtype), dev)
matrix_bias = 10.0
f(matrix_input1, matrix_input2, matrix_result, matrix_bias)
tvm.testing.assert_allclose(
d.numpy(), get_numpy(a.numpy(), b.numpy(), bb, transa, transb), rtol=1e-5
matrix_result.numpy(),
get_numpy(matrix_input1.numpy(), matrix_input2.numpy(), matrix_bias, transa, transb),
rtol=1e-5,
)

verify("llvm")
verify("c")


def test_matmul_add():
"""Tests of matmul+add op"""
verify_matmul_add(235, 128, 1024, cblas)
verify_matmul_add(235, 128, 1024, cblas, True, False)
verify_matmul_add(235, 128, 1024, cblas, False, True)
Expand All @@ -101,27 +110,30 @@ def test_matmul_add():
verify_matmul_add(1, 16, 3, dnnl, True, True)


def verify_quantized_matmul_add(m, l, n, transa=False, transb=False):
def verify_quantized_matmul_add(matrix_m, matrix_l, matrix_n, transa=False, transb=False):
"""Tests quantized matmul+add op"""
if not tvm.get_global_func("tvm.contrib.mkl.matmul_u8s8s32", True):
pytest.skip("Quantized dense is supported only for MKL. TVM GPU CI uses openblas")
data_dtype = "uint8"
kernel_dtype = "int8"
out_dtype = "int32"
bias = te.var("bias", dtype=out_dtype)
ashape = (l, n) if transa else (n, l)
bshape = (m, l) if transb else (l, m)
A = te.placeholder(ashape, name="A", dtype=data_dtype)
B = te.placeholder(bshape, name="B", dtype=kernel_dtype)
C = mkl.matmul_u8s8s32(A, B, transa, transb, dtype=out_dtype)
D = te.compute(C.shape, lambda i, j: C[i, j] + bias, name="D")
s = te.create_schedule(D.op)

def get_numpy(a, b, bb, transa, transb):
ashape = (matrix_l, matrix_n) if transa else (matrix_n, matrix_l)
bshape = (matrix_m, matrix_l) if transb else (matrix_l, matrix_m)
input1_data = te.placeholder(ashape, name="input1_data", dtype=data_dtype)
input2_data = te.placeholder(bshape, name="input2_data", dtype=kernel_dtype)
matmul_result = mkl.matmul_u8s8s32(input1_data, input2_data, transa, transb, dtype=out_dtype)
final_result = te.compute(
matmul_result.shape, lambda i, j: matmul_result[i, j] + bias, name="final_result"
)
s = te.create_schedule(final_result.op)

def get_numpy(a, b, matrix_bias, transa, transb):
if transa:
a = a.transpose()
if transb:
b = b.transpose()
return np.dot(a, b) + bb
return np.dot(a, b) + matrix_bias

def verify(target="llvm"):
if not tvm.testing.device_enabled(target):
Expand All @@ -131,22 +143,33 @@ def verify(target="llvm"):
print("skip because extern function is not available")
return
dev = tvm.cpu(0)
f = tvm.build(s, [A, B, D, bias], target)
a = tvm.nd.array(np.random.randint(low=0, high=50, size=ashape).astype(A.dtype), dev)
b = tvm.nd.array(np.random.randint(low=0, high=50, size=bshape).astype(B.dtype), dev)
d = tvm.nd.array(np.zeros((n, m), dtype=D.dtype), dev)
bb = 10
f(a, b, d, bb)
f = tvm.build(s, [input1_data, input2_data, final_result, bias], target)
matrix_input1 = tvm.nd.array(
np.random.randint(low=0, high=50, size=ashape).astype(input1_data.dtype), dev
)
matrix_input2 = tvm.nd.array(
np.random.randint(low=0, high=50, size=bshape).astype(input2_data.dtype), dev
)
matrix_result = tvm.nd.array(np.zeros((matrix_n, matrix_m), dtype=final_result.dtype), dev)
matrix_bias = 10
f(matrix_input1, matrix_input2, matrix_result, matrix_bias)
tvm.testing.assert_allclose(
d.numpy(),
get_numpy(a.numpy().astype("int32"), b.numpy().astype("int32"), bb, transa, transb),
matrix_result.numpy(),
get_numpy(
matrix_input1.numpy().astype("int32"),
matrix_input2.numpy().astype("int32"),
matrix_bias,
transa,
transb,
),
rtol=1e-5,
)

verify()


def test_quantized_matmul_add():
"""Tests of quantized matmul+add op"""
verify_quantized_matmul_add(235, 128, 1024)
verify_quantized_matmul_add(235, 128, 1024, True, False)
verify_quantized_matmul_add(235, 128, 1024, False, True)
Expand All @@ -158,16 +181,27 @@ def test_quantized_matmul_add():


def verify_batch_matmul(
batch_a, batch_b, m, l, n, lib, transa=False, transb=False, iterative=False, dtype="float32"
batch_a,
batch_b,
matrix_m,
matrix_l,
matrix_n,
lib,
transa=False,
transb=False,
dtype="float32",
):
"""Tests matmul op where matrices are in batch"""
batch = max(batch_a, batch_b)
ashape = (batch_a, l, n) if transa else (batch_a, n, l)
bshape = (batch_b, m, l) if transb else (batch_b, l, m)
A = te.placeholder(ashape, name="A", dtype=dtype)
B = te.placeholder(bshape, name="B", dtype=dtype)
C = lib.batch_matmul(A, B, transa, transb)
D = te.compute(C.shape, lambda k, i, j: C[k, i, j], name="D")
s = te.create_schedule(D.op)
ashape = (batch_a, matrix_l, matrix_n) if transa else (batch_a, matrix_n, matrix_l)
bshape = (batch_b, matrix_m, matrix_l) if transb else (batch_b, matrix_l, matrix_m)
input1_data = te.placeholder(ashape, name="input1_data", dtype=dtype)
input2_data = te.placeholder(bshape, name="input2_data", dtype=dtype)
matmul_result = lib.batch_matmul(input1_data, input2_data, transa, transb)
final_result = te.compute(
matmul_result.shape, lambda k, i, j: matmul_result[k, i, j], name="final_result"
)
s = te.create_schedule(final_result.op)

def get_numpy(a, b, transa, transb):
if transa:
Expand All @@ -176,7 +210,7 @@ def get_numpy(a, b, transa, transb):
b = b.transpose(0, 2, 1)
return tvm.topi.testing.batch_matmul(a, b)

def compile(f, name="test_batch_matmul", ext=".so"):
def compiling(f, name="test_batch_matmul", ext=".so"):
path = name + ext
f.export_library(path)
mod = tvm.runtime.load_module(path)
Expand All @@ -192,22 +226,27 @@ def verify(target="llvm"):
return
dev = tvm.cpu(0)
name = "test_batch_matmul"
f = tvm.build(s, [A, B, D], target, name=name)
f = tvm.build(s, [input1_data, input2_data, final_result], target, name=name)
if target == "c":
f = compile(f, name)
a = tvm.nd.array(np.random.uniform(size=ashape).astype(A.dtype), dev)
b = tvm.nd.array(np.random.uniform(size=bshape).astype(B.dtype), dev)
d = tvm.nd.array(np.zeros((batch, n, m), dtype=D.dtype), dev)
f(a, b, d)
f = compiling(f, name)
matrix_input1 = tvm.nd.array(np.random.uniform(size=ashape).astype(input1_data.dtype), dev)
matrix_input2 = tvm.nd.array(np.random.uniform(size=bshape).astype(input2_data.dtype), dev)
matrix_result = tvm.nd.array(
np.zeros((batch, matrix_n, matrix_m), dtype=final_result.dtype), dev
)
f(matrix_input1, matrix_input2, matrix_result)
tvm.testing.assert_allclose(
d.numpy(), get_numpy(a.numpy(), b.numpy(), transa, transb), rtol=1e-5
matrix_result.numpy(),
get_numpy(matrix_input1.numpy(), matrix_input2.numpy(), transa, transb),
rtol=1e-5,
)

verify("llvm")
verify("c")


def test_batch_matmul():
"""Tests of matmul op where matrices are in batch"""
verify_batch_matmul(16, 16, 235, 128, 1024, cblas)
verify_batch_matmul(16, 16, 235, 128, 1024, cblas, True, False)
verify_batch_matmul(16, 16, 235, 128, 1024, cblas, False, True)
Expand All @@ -218,22 +257,22 @@ def test_batch_matmul():
verify_batch_matmul(16, 16, 235, 128, 1024, mkl, True, True)
verify_batch_matmul(16, 1, 235, 128, 1024, cblas)
verify_batch_matmul(1, 16, 235, 128, 1024, cblas)
verify_batch_matmul(16, 1, 235, 128, 1024, cblas, iterative=True)
verify_batch_matmul(1, 16, 235, 128, 1024, cblas, iterative=True)
verify_batch_matmul(16, 1, 235, 128, 1024, cblas)
verify_batch_matmul(1, 16, 235, 128, 1024, cblas)
verify_batch_matmul(16, 1, 235, 128, 1024, mkl)
verify_batch_matmul(1, 16, 235, 128, 1024, mkl)
verify_batch_matmul(16, 1, 235, 128, 1024, mkl)
verify_batch_matmul(1, 16, 235, 128, 1024, mkl)
verify_batch_matmul(16, 1, 235, 128, 1024, mkl, iterative=True)
verify_batch_matmul(1, 16, 235, 128, 1024, mkl, iterative=True)
verify_batch_matmul(1, 1, 1, 16, 3, cblas)
verify_batch_matmul(1, 1, 1, 16, 3, cblas, True, False)
verify_batch_matmul(1, 1, 1, 16, 3, cblas, False, False)
verify_batch_matmul(1, 1, 1, 16, 3, cblas, True, True)
verify_batch_matmul(1, 1, 1, 16, 3, cblas, iterative=True)
verify_batch_matmul(1, 1, 1, 16, 3, cblas)
verify_batch_matmul(1, 1, 1, 16, 3, mkl)
verify_batch_matmul(1, 1, 1, 16, 3, mkl, True, False)
verify_batch_matmul(1, 1, 1, 16, 3, mkl, False, False)
verify_batch_matmul(1, 1, 1, 16, 3, mkl, True, True)
verify_batch_matmul(1, 1, 1, 16, 3, mkl, iterative=True)
verify_batch_matmul(1, 1, 1, 16, 3, mkl)


if __name__ == "__main__":
Expand Down