# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
import os, unittest, time
import torch
import torch.cuda
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import tensor_comprehensions as tc
from tensor_comprehensions.mapping_options import Options
from common import TestCase, run_tests
tc.SetDebugFlags(dump_cuda=False)
MATMUL_LANG = """
def matmul(float(M,N) A, float(N,K) B) -> (output) {
output(i, j) +=! A(i, kk) * B(kk, j)
}
"""
MATMUL_ABS_LANG = """
def matmul(float(M,N) A, float(N,K) B) -> (output) {
output(i, j) +=! A(i, kk) * B(kk, j)
}
def abs(float(M, N) A) -> (O1) {
O1(m, n) = fabs(A(m, n))
}
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
import tensor_comprehensions as tc
import torch
import unittest
# enable this to dump cuda code generated whenever tc layer runs: simple run or
# autotuner run
tc.SetDebugFlags(dump_cuda=True)
class TestDumpCuda(unittest.TestCase):
def test_dump_cuda(self):
LANG = """
def matmul(float(M,N) A, float(N,K) B) -> (output) {
output(i, j) +=! A(i, kk) * B(kk, j)
}
"""
matmul = tc.define(LANG, name="matmul")
mat1, mat2 = torch.randn(3, 4).cuda(), torch.randn(4, 5).cuda()
out = matmul(mat1, mat2)
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
import os, unittest
import torch
import torch.cuda
import tensor_comprehensions as tc
tc.SetDebugFlags(dump_cuda=True, debug_tc_mapper=True)
class TestDebugInit(unittest.TestCase):
def test_debug_init(self):
lang = """
def matmul(float(M,N) A, float(N,K) B) -> (output) {
output(i, j) +=! A(i, kk) * B(kk, j)
}
"""
matmul = tc.define(lang, name="matmul")
mat1, mat2 = torch.randn(3, 4).cuda(), torch.randn(4, 5).cuda()
out = matmul(mat1, mat2)
if __name__ == '__main__':
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
##############################################################################
import tensor_comprehensions as tc
import torch
import torch.cuda
import unittest
tc.SetDebugFlags(debug_tuner=False, debug_tc_mapper=False)
class TestLayerNorm(unittest.TestCase):
def test_layernorm(self):
# NOTE: take note of use of {{ }} below for handling TC with scalars
lang = """
def layernorm(float(T, B, C) I) -> (O, mean, centered, var)
{{
mean(t, b) +=! I(t, b, c) / C
centered(t, b, c) = I(t, b, c) - mean(t, b)
var(t, b) +=! centered(t, b, c) * centered(t, b, c)
var(t, b) = (var(t, b) + {eps}) / C
O(t, b, c) = centered(t, b, c) / rsqrt(var(t, b))
}}
