def test_rocm_cross_thread_reduction():
if not tvm.rocm(0).exist or not tvm.module.enabled("rocm"):
print("skip because rocm is not enabled..")
return
# based on the reduction tutorial
n = tvm.var("n")
m = tvm.var("m")
A = tvm.placeholder((n, m), name='A')
k = tvm.reduce_axis((0, m), "k")
B = tvm.compute((n, ), lambda i: tvm.sum(A[i, k], axis=k), name="B")
s = tvm.create_schedule(B.op)
ko, ki = s[B].split(B.op.reduce_axis[0], factor=16)
BF = s.rfactor(B, ki)
xo, xi = s[B].split(s[B].op.axis[0], factor=32)
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
s[B].bind(xi, tvm.thread_axis("threadIdx.y"))
tx = tvm.thread_axis("threadIdx.x")
s[B].bind(s[B].op.reduce_axis[0], tx)
s[BF].compute_at(s[B], s[B].op.reduce_axis[0])
s[B].set_store_predicate(tx.var.equal(0))
frocm = tvm.build(s, [A, B], "rocm")
nn = 128
ctx = tvm.rocm(0)
a = tvm.nd.array(np.random.uniform(size=(nn, nn)).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(nn, dtype=B.dtype), ctx)
frocm(a, b)
tvm.testing.assert_allclose(b.asnumpy(),
np.sum(a.asnumpy(), axis=1),
rtol=1e-4)
def test_rocm_cross_thread_reduction():
# based on the reduction tutorial
n = te.size_var("n")
m = te.size_var("m")
A = te.placeholder((n, m), name="A")
k = te.reduce_axis((0, m), "k")
B = te.compute((n, ), lambda i: te.sum(A[i, k], axis=k), name="B")
s = te.create_schedule(B.op)
ko, ki = s[B].split(B.op.reduce_axis[0], factor=16)
BF = s.rfactor(B, ki)
xo, xi = s[B].split(s[B].op.axis[0], factor=32)
s[B].bind(xo, bx)
s[B].bind(xi, ty)
s[B].bind(s[B].op.reduce_axis[0], tx)
s[BF].compute_at(s[B], s[B].op.reduce_axis[0])
s[B].set_store_predicate(tx.var.equal(0))
frocm = tvm.build(s, [A, B], "rocm")
nn = 128
ctx = tvm.rocm(0)
a = tvm.nd.array(np.random.uniform(size=(nn, nn)).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(nn, dtype=B.dtype), ctx)
frocm(a, b)
tvm.testing.assert_allclose(b.asnumpy(),
np.sum(a.asnumpy(), axis=1),
rtol=1e-4)
def check_rocm(dtype, n):
A = tvm.placeholder((n, ), name='A', dtype=dtype)
ctx = tvm.rocm(0)
a_np = np.random.uniform(size=(n, )).astype(A.dtype)
a = tvm.nd.empty((n, ), A.dtype, ctx).copyfrom(a_np)
b_np = a.asnumpy()
tvm.testing.assert_allclose(a_np, b_np)
tvm.testing.assert_allclose(a_np, a.asnumpy())
def enabled_ctx_list():
ctx_list = [('cpu', tvm.cpu(0)), ('gpu', tvm.gpu(0)),
('cl', tvm.opencl(0)), ('metal', tvm.metal(0)),
('rocm', tvm.rocm(0)), ('vpi', tvm.vpi(0))]
for k, v in ctx_list:
assert tvm.context(k, 0) == v
ctx_list = [x[1] for x in ctx_list if x[1].exist]
return ctx_list
def enabled_ctx_list():
ctx_list = [('cpu', tvm.cpu(0)),
('gpu', tvm.gpu(0)),
('cl', tvm.opencl(0)),
('metal', tvm.metal(0)),
('rocm', tvm.rocm(0)),
('vulkan', tvm.vulkan(0)),
('vpi', tvm.vpi(0))]
for k, v in ctx_list:
assert tvm.context(k, 0) == v
ctx_list = [x[1] for x in ctx_list if x[1].exist]
return ctx_list
def verify(target="rocm"):
if not tvm.get_global_func("tvm.contrib.rocblas.matmul", True):
print("skip because extern function is not available")
return
ctx = tvm.rocm(0)
f = tvm.build(s, [A, B, C], target)
a = tvm.nd.array(np.random.uniform(size=(n, l)).astype(A.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx)
f(a, b, c)
tvm.testing.assert_allclose(c.asnumpy(),
np.dot(a.asnumpy(), b.asnumpy()),
rtol=1e-5)
def check_rocm(dtype, n, lanes):
A = te.placeholder((n,), name="A", dtype="%sx%d" % (dtype, lanes))
B = te.compute((n,), lambda i: A[i] + tvm.tir.const(1, A.dtype), name="B")
s = te.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=num_thread)
s[B].bind(xo, bx)
s[B].bind(xi, tx)
fun = tvm.build(s, [A, B], "rocm")
dev = tvm.rocm(0)
a = tvm.nd.empty((n,), A.dtype, dev).copyfrom(np.random.uniform(size=(n, lanes)))
c = tvm.nd.empty((n,), B.dtype, dev)
fun(a, c)
tvm.testing.assert_allclose(c.numpy(), a.numpy() + 1)
def verify(target="rocm"):
if not tvm.module.enabled(target):
print("skip because %s is not enabled..." % target)
return
if not tvm.get_global_func("tvm.contrib.rocblas.matmul", True):
print("skip because extern function is not available")
return
ctx = tvm.rocm(0)
f = tvm.build(s, [A, B, C], target)
a = tvm.nd.array(np.random.uniform(size=(n, l)).astype(A.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=(l, m)).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros((n, m), dtype=C.dtype), ctx)
f(a, b, c)
tvm.testing.assert_allclose(
c.asnumpy(), np.dot(a.asnumpy(), b.asnumpy()), rtol=1e-5)
def verify():
ctx = tvm.rocm(0)
f = tvm.build(s, [X, W, Y], "rocm", target_host="llvm", name="conv2d")
x = tvm.nd.array(np.random.uniform(-1, 1, xshape).astype(np.float32), ctx)
w = tvm.nd.array(np.random.uniform(-1, 1, wshape).astype(np.float32), ctx)
y = tvm.nd.array(np.random.uniform(-1, 1, yshape).astype(np.float32), ctx)
f(x, w, y)
Y_ref = topi.nn.conv2d_nchw(X, W, (stride_h, stride_w), (pad_h, pad_w), (dilation_h, dilation_w))
with tvm.target.rocm():
s_ref = topi.generic.schedule_conv2d_nchw([Y_ref])
f_ref = tvm.build(s_ref, [X, W, Y_ref], "rocm")
y_ref = tvm.nd.array(np.random.uniform(-1, 1, yshape).astype(np.float32), ctx)
f_ref(x, w, y_ref)
print("Max abs diff:", np.max(np.abs(y.asnumpy() - y_ref.asnumpy())))
tvm.testing.assert_allclose(y.asnumpy(), y_ref.asnumpy(), atol=1e-3)
def verify():
ctx = tvm.rocm(0)
f = tvm.build(s, [X, W, Y], "rocm", target_host="llvm", name="conv2d")
x = tvm.nd.array(np.random.uniform(-1, 1, xshape).astype(np.float32), ctx)
w = tvm.nd.array(np.random.uniform(-1, 1, wshape).astype(np.float32), ctx)
y = tvm.nd.array(np.random.uniform(-1, 1, yshape).astype(np.float32), ctx)
f(x, w, y)
Y_ref = topi.nn.conv2d_nchw(X, W, (stride_h, stride_w), (pad_h, pad_w))
with tvm.target.rocm():
s_ref = topi.generic.schedule_conv2d_nchw([Y_ref])
f_ref = tvm.build(s_ref, [X, W, Y_ref], "rocm")
y_ref = tvm.nd.array(np.random.uniform(-1, 1, yshape).astype(np.float32), ctx)
f_ref(x, w, y_ref)
print("Max abs diff:", np.max(np.abs(y.asnumpy() - y_ref.asnumpy())))
tvm.testing.assert_allclose(y.asnumpy(), y_ref.asnumpy(), atol=1e-3)
def verify_softmax(shape, axis, dtype="float32", log_softmax=False):
miopen_op = miopen.log_softmax if log_softmax else miopen.softmax
testing_op = (tvm.topi.testing.log_softmax_python
if log_softmax else tvm.topi.testing.softmax_python)
A = te.placeholder(shape, dtype=dtype, name="A")
B = miopen_op(A, axis)
s = te.create_schedule([B.op])
dev = tvm.rocm(0)
a_np = np.random.uniform(size=shape).astype(dtype)
b_np = testing_op(a_np)
a = tvm.nd.array(a_np, dev)
b = tvm.nd.array(b_np, dev)
f = tvm.build(s, [A, B], target="rocm --host=llvm", name="softmax")
f(a, b)
tvm.testing.assert_allclose(b.numpy(), b_np, rtol=1e-3)
def verify(target="rocm"):
if not tvm.testing.device_enabled(target):
print("skip because %s is not enabled..." % target)
return
if not tvm.get_global_func(lib.__name__ + ".batch_matmul", True):
print("skip because extern function is not available")
return
dev = tvm.rocm(0)
f = tvm.build(s, [A, B, C], target)
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)
c = tvm.nd.array(np.zeros((batch, m, n), dtype=C.dtype), dev)
f(a, b, c)
tvm.testing.assert_allclose(c.numpy(),
get_numpy(a.numpy(), b.numpy(), transa,
transb),
rtol=1e-5)
def verify():
dev = tvm.rocm(0)
f = tvm.build(s, [X, W, Y], "rocm --host=llvm", name="conv2d")
x = tvm.nd.array(
np.random.uniform(-1, 1, xshape).astype(np.float32), dev)
w = tvm.nd.array(
np.random.uniform(-1, 1, wshape).astype(np.float32), dev)
y = tvm.nd.array(
np.random.uniform(-1, 1, yshape).astype(np.float32), dev)
f(x, w, y)
Y_ref = topi.nn.conv2d_nchw(X, W, (stride_h, stride_w), (pad_h, pad_w),
(dilation_h, dilation_w))
s_ref = te.create_schedule(Y_ref.op)
f_ref = tvm.build(s_ref, [X, W, Y_ref], "rocm --host=llvm")
y_ref = tvm.nd.array(
np.random.uniform(-1, 1, yshape).astype(np.float32), dev)
f_ref(x, w, y_ref)
print("Max abs diff:", np.max(np.abs(y.numpy() - y_ref.numpy())))
tvm.testing.assert_allclose(y.numpy(), y_ref.numpy(), atol=1e-3)
def requires_gpu(*args):
"""Mark a test as requiring a GPU to run.
Tests with this mark will not be run unless a gpu is present.
Parameters
----------
f : function
Function to mark
"""
_requires_gpu = [
pytest.mark.skipif(
not tvm.cuda().exist and not tvm.rocm().exist
and not tvm.opencl().exist and not tvm.metal().exist
and not tvm.vulkan().exist,
reason="No GPU present",
),
*uses_gpu(),
]
return _compose(args, _requires_gpu)
def test_rocm_inf_nan():
def check_inf_nan(ctx, n, value, dtype):
A = tvm.placeholder((n, ), name='A', dtype=dtype)
inf_value = tvm.const(value, dtype=dtype)
C = tvm.compute((n, ), lambda i: inf_value, name='C')
s = tvm.create_schedule(C.op)
s[C].bind(s[C].op.axis[0], tx)
fun = tvm.build(s, [A, C], "rocm")
a = tvm.nd.empty((n, ), A.dtype, ctx)
c = tvm.nd.empty((n, ), A.dtype, ctx)
# Only need to test compiling here
fun(a, c)
ctx = tvm.rocm(0)
check_inf_nan(ctx, 1, -float('inf'), 'float32')
check_inf_nan(ctx, 1, -float('inf'), 'float64')
check_inf_nan(ctx, 1, float('inf'), 'float32')
check_inf_nan(ctx, 1, float('inf'), 'float64')
check_inf_nan(ctx, 1, float('nan'), 'float32')
check_inf_nan(ctx, 1, float('nan'), 'float64')
def test_rocm_inf_nan():
def check_inf_nan(dev, n, value, dtype):
A = te.placeholder((n,), name="A", dtype=dtype)
inf_value = tvm.tir.const(value, dtype=dtype)
C = te.compute((n,), lambda i: inf_value, name="C")
s = te.create_schedule(C.op)
s[C].bind(s[C].op.axis[0], tx)
fun = tvm.build(s, [A, C], "rocm")
a = tvm.nd.empty((n,), A.dtype, dev)
c = tvm.nd.empty((n,), A.dtype, dev)
# Only need to test compiling here
fun(a, c)
dev = tvm.rocm(0)
check_inf_nan(dev, 1, -float("inf"), "float32")
check_inf_nan(dev, 1, -float("inf"), "float64")
check_inf_nan(dev, 1, float("inf"), "float32")
check_inf_nan(dev, 1, float("inf"), "float64")
check_inf_nan(dev, 1, float("nan"), "float32")
check_inf_nan(dev, 1, float("nan"), "float64")
# 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 tvm
from tvm import te
import numpy as np
import unittest
tx = te.thread_axis("threadIdx.x")
ty = te.thread_axis("threadIdx.y")
bx = te.thread_axis("blockIdx.x")
by = te.thread_axis("blockIdx.y")
@unittest.skipIf(not tvm.rocm(0).exist or not tvm.runtime.enabled("rocm"), "skip because rocm is not enabled..")
def test_rocm_cross_thread_reduction():
# based on the reduction tutorial
n = te.size_var("n")
m = te.size_var("m")
A = te.placeholder((n, m), name='A')
k = te.reduce_axis((0, m), "k")
B = te.compute((n,), lambda i: te.sum(A[i, k], axis=k), name="B")
s = te.create_schedule(B.op)
ko, ki = s[B].split(B.op.reduce_axis[0], factor=16)
BF = s.rfactor(B, ki)
xo, xi = s[B].split(s[B].op.axis[0], factor=32)
s[B].bind(xo, bx)
s[B].bind(xi, ty)
s[B].bind(s[B].op.reduce_axis[0], tx)
s[BF].compute_at(s[B], s[B].op.reduce_axis[0])
# inspect the best config
dispatch_context = autotvm.apply_history_best("conv2d.log")
best_config = dispatch_context.query(task.target, task.workload)
print("\nBest config:")
print(best_config)
# apply history best from log file
with autotvm.apply_history_best('conv2d.log'):
with tvm.target.create("rocm"):
s, arg_bufs = conv2d_no_batching(N, H, W, CO, CI, KH, KW, strides,
padding)
func = tvm.build(s, arg_bufs)
# check correctness
a_np = np.random.uniform(size=(N, CI, H, W)).astype(np.float32)
w_np = np.random.uniform(size=(CO, CI, KH, KW)).astype(np.float32)
c_np = conv2d_nchw_python(a_np, w_np, strides, padding)
ctx = tvm.rocm()
a_tvm = tvm.nd.array(a_np, ctx=ctx)
w_tvm = tvm.nd.array(w_np, ctx=ctx)
c_tvm = tvm.nd.empty(c_np.shape, ctx=ctx)
func(a_tvm, w_tvm, c_tvm)
tvm.testing.assert_allclose(c_np, c_tvm.asnumpy(), rtol=1e-2)
# Evaluate running time. Here we choose a large repeat number (400) to reduce the noise
# and the overhead of kernel launch. You can also use nvprof to validate the result.
evaluator = func.time_evaluator(func.entry_name, ctx, number=400)
print('Time cost of this operator: %f' % evaluator(a_tvm, w_tvm, c_tvm).mean)
# 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 tvm
import numpy as np
import unittest
tx = tvm.thread_axis("threadIdx.x")
ty = tvm.thread_axis("threadIdx.y")
bx = tvm.thread_axis("blockIdx.x")
by = tvm.thread_axis("blockIdx.y")
@unittest.skipIf(not tvm.rocm(0).exist or not tvm.module.enabled("rocm"),
"skip because rocm is not enabled..")
def test_rocm_cross_thread_reduction():
# based on the reduction tutorial
n = tvm.size_var("n")
m = tvm.size_var("m")
A = tvm.placeholder((n, m), name='A')
k = tvm.reduce_axis((0, m), "k")
B = tvm.compute((n, ), lambda i: tvm.sum(A[i, k], axis=k), name="B")
s = tvm.create_schedule(B.op)
ko, ki = s[B].split(B.op.reduce_axis[0], factor=16)
BF = s.rfactor(B, ki)
xo, xi = s[B].split(s[B].op.axis[0], factor=32)
s[B].bind(xo, bx)
s[B].bind(xi, ty)
s[B].bind(s[B].op.reduce_axis[0], tx)
#
xo, xi = s[B].split(s[B].op.axis[0], factor=32)
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
s[B].bind(xi, tvm.thread_axis("threadIdx.y"))
tx = tvm.thread_axis("threadIdx.x")
s[B].bind(s[B].op.reduce_axis[0], tx)
s[BF].compute_at(s[B], s[B].op.reduce_axis[0])
s[B].set_store_predicate(tx.var.equal(0))
frocm = tvm.build(s, [A, B], "rocm")
print(frocm.get_source())
######################################################################
# Verify the correctness of result kernel by comparing it to numpy.
#
nn = 128
ctx = tvm.rocm(0)
a = tvm.nd.array(np.random.uniform(size=(nn, nn)).astype(A.dtype), ctx)
b = tvm.nd.array(np.zeros(nn, dtype=B.dtype), ctx)
frocm(a, b)
tvm.testing.assert_allclose(
b.asnumpy(), np.sum(a.asnumpy(), axis=1), rtol=1e-4)
######################################################################
# Describe Convolution via 2D Reduction
# -------------------------------------
# In TVM, we can describe convolution via 2D reduction in a simple way.
# Here is an example for 2D convolution with filter size = [3, 3] and strides = [1, 1].
#
n = tvm.var('n')
Input = tvm.placeholder((n, n), name='Input')
Filter = tvm.placeholder((3, 3), name='Filter')
# 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 tvm
import numpy as np
import unittest
tx = tvm.thread_axis("threadIdx.x")
ty = tvm.thread_axis("threadIdx.y")
bx = tvm.thread_axis("blockIdx.x")
by = tvm.thread_axis("blockIdx.y")
@unittest.skipIf(not tvm.rocm(0).exist or not tvm.runtime.enabled("rocm"),
"skip because rocm is not enabled..")
def test_rocm_cross_thread_reduction():
# based on the reduction tutorial
n = tvm.size_var("n")
m = tvm.size_var("m")
A = tvm.placeholder((n, m), name='A')
k = tvm.reduce_axis((0, m), "k")
B = tvm.compute((n, ), lambda i: tvm.sum(A[i, k], axis=k), name="B")
s = tvm.create_schedule(B.op)
ko, ki = s[B].split(B.op.reduce_axis[0], factor=16)
BF = s.rfactor(B, ki)
xo, xi = s[B].split(s[B].op.axis[0], factor=32)
s[B].bind(xo, bx)
s[B].bind(xi, ty)
s[B].bind(s[B].op.reduce_axis[0], tx)
