def test_fp16_build():
dtype = "float16"
dev = tvm.cuda(0)
if dtype == "float16" and not have_fp16(dev.compute_version):
print("skip because gpu does not support fp16")
return
x = relay.var("x", dtype=dtype, shape=(4, 4))
y = relay.var("y", dtype=dtype, shape=(4, 4))
z = x + y
func = relay.Function([x, y], z)
X = tvm.nd.array(np.random.uniform(-1, 1, (4, 4)).astype(dtype), device=dev)
Y = tvm.nd.array(np.random.uniform(-1, 1, (4, 4)).astype(dtype), device=dev)
params = {
"x": X,
"y": Y,
}
# build
g_json, mmod, params = relay.build(func, "cuda", params=params)
# test
rt = tvm.contrib.graph_executor.create(g_json, mmod, dev)
rt.load_params(runtime.save_param_dict(params))
rt.run()
out = rt.get_output(0)
np.testing.assert_allclose(out.asnumpy(), X.asnumpy() + Y.asnumpy(), atol=1e-5, rtol=1e-5)
def check_cuda(dtype, m=32, n=32):
if not tvm.gpu(0).exist or not tvm.runtime.enabled("cuda"):
print("skip because cuda is not enabled..")
return
if dtype == "float16" and not have_fp16(tvm.gpu(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
a = te.placeholder((m, n), name="a", dtype=dtype)
b = te.placeholder((m, n), name="b", dtype=dtype)
c = a + b
d = a * b
e = topi.elemwise_sum([c, d])
g = topi.sum(e)
with tvm.target.cuda():
sg = topi.cuda.schedule_reduce(g)
ctx = tvm.gpu(0)
func = tvm.build(sg, [a, b, g], 'cuda')
a_np = np.random.uniform(size=(m, n)).astype(a.dtype)
b_np = np.random.uniform(size=(m, n)).astype(b.dtype)
g_np = np.sum(np.add(a_np * b_np, a_np + b_np))
a_nd = tvm.nd.array(a_np, ctx)
b_nd = tvm.nd.array(b_np, ctx)
g_nd = tvm.nd.array(np.zeros(g_np.shape, dtype=g_np.dtype), ctx)
func(a_nd, b_nd, g_nd)
tvm.testing.assert_allclose(g_nd.asnumpy(), g_np, rtol=1e-3)
def check_single_op(opfunc, ref, dtype):
shape = (10, 4)
dtype = dtype
tp = relay.TensorType(shape)
x = relay.var("x", tp, dtype=dtype)
y = opfunc(x)
# test printer
assert ("{}(%x)".format(y.op.name)) in y.astext()
# test type inference
yy = run_infer_type(y)
assert yy.checked_type == tp
if ref is not None:
data = np.random.rand(*shape).astype(dtype)
ref_res = ref(data)
func = relay.Function([x], y)
for target, ctx in tvm.testing.enabled_targets():
# use graph by execuor default for testing, as we need
# create function explicitly to avoid constant-folding.
if dtype == 'float16' and target == 'cuda' and not have_fp16(
tvm.gpu(0).compute_version):
continue
intrp = relay.create_executor("graph", ctx=ctx, target=target)
op_res = intrp.evaluate(func)(data)
np.testing.assert_allclose(op_res.asnumpy(),
ref_res,
rtol=0.01)
def check_cuda(dtype):
if dtype == "float16" and not have_fp16(tvm.gpu(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
n, m = 16, 16
A = te.placeholder((n, m,), name='A', dtype=dtype)
B = te.compute((n, m,), lambda j, i: A[j, (i + 1) % m], name='B')
cuda_target = tvm.target.create("cuda")
assert cuda_target.thread_warp_size == 2 * m
with cuda_target:
s = te.create_schedule(B.op)
tx = te.thread_axis("threadIdx.x")
ty = te.thread_axis("threadIdx.y")
bx = te.thread_axis("blockIdx.x")
AA = s.cache_read(A, "warp", [B])
y, x = B.op.axis
z, y = s[B].split(y, nparts=2)
s[B].bind(x, tx)
s[B].bind(y, ty)
s[B].bind(z, bx)
s[AA].compute_at(s[B], y)
_, x = AA.op.axis
s[AA].bind(x, tx)
ctx = tvm.gpu(0)
func = tvm.build(s, [A, B], "cuda")
A_np = np.array([list(range(i, m + i)) for i in range(n)], dtype=dtype)
B_np = np.array([list(range(1 + i, m + i)) + [i] for i in range(n)], dtype=dtype)
A_nd = tvm.nd.array(A_np, ctx)
B_nd = tvm.nd.array(np.zeros(B_np.shape, dtype=B_np.dtype), ctx)
func(A_nd, B_nd)
tvm.testing.assert_allclose(B_nd.asnumpy(), B_np, rtol=1e-3)
def check_conversion(tgt, ctx):
if not tvm.runtime.enabled(tgt):
print("skip because {} is not enabled.".format(tgt))
return
elif tgt == "cuda" and ctx.exist and not have_fp16(
ctx.compute_version):
print("skip because gpu does not support fp16")
return
n = 10
for (src, dst) in [('float32', 'float16'), ('float16', 'float32')]:
x = relay.var("x", relay.TensorType((n, ), src))
y = x.astype(dst)
func = relay.Function([x], y)
# init input
X = tvm.nd.array(n * np.random.randn(n).astype(src) - n / 2)
# build
with relay.build_config(opt_level=1):
g_json, mmod, params = relay.build(
tvm.IRModule.from_expr(func), tgt)
# test
rt = tvm.contrib.graph_runtime.create(g_json, mmod, ctx)
rt.set_input("x", X)
rt.run()
out = rt.get_output(0)
np.testing.assert_allclose(out.asnumpy(),
X.asnumpy().astype(dst),
atol=1e-5,
rtol=1e-5)
def check(t0, t1, factor):
if (t0 == "float16" or t1 == "float16") and not have_fp16(tvm.cuda(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
# compute
n = 128
A = te.placeholder((n,), dtype=t0, name="A")
B = te.placeholder((n,), dtype=t1, name="B")
C = te.compute((n,), lambda i: A[i] + topi.cast(B[i], A.dtype), name="C")
# schedule
s = tvm.te.create_schedule(C.op)
ob, ib = s[C].split(s[C].op.axis[0], factor=factor)
s[C].vectorize(ib)
s[C].bind(ob, tx)
func = tvm.build(s, [A, B, C], "cuda")
# correctness
dev = tvm.cuda(0)
low, high = (0, 20) if t0.startswith("u") or t1.startswith("u") else (-10, 10)
a_np = np.random.randint(low, high, size=n).astype(A.dtype)
b_np = np.random.randint(low, high, size=n).astype(B.dtype)
c_np = (a_np + b_np).astype(A.dtype)
a_nd = tvm.nd.array(a_np, dev)
b_nd = tvm.nd.array(b_np, dev)
c_nd = tvm.nd.array(np.zeros(c_np.shape, dtype=c_np.dtype), dev)
func(a_nd, b_nd, c_nd)
tvm.testing.assert_allclose(c_nd.numpy(), c_np, rtol=1e-3)
def check_binary_op(opfunc, ref, dtype):
# TODO(@jroesch): this piece of code improperly uses type variables.
n = tvm.var("n")
s1 = (5, n, 5)
s2 = (n, 1)
t1 = relay.TensorType(s1)
t2 = relay.TensorType(s2)
x = relay.var("x", t1, dtype=dtype)
y = relay.var("y", t2, dtype=dtype)
z = opfunc(x, y)
# test printer
assert ("{}(%x, %y)".format(z.op.name)) in z.astext()
zz = run_infer_type(z)
assert zz.checked_type == t1
if ref is not None:
t1 = relay.TensorType((5, 10, 5))
t2 = relay.TensorType((5, 10, 5))
x = relay.var("x", t1, dtype=dtype)
y = relay.var("y", t2, dtype=dtype)
z = opfunc(x, y)
x_data = np.random.rand(5, 10, 5).astype(dtype)
y_data = np.random.rand(5, 10, 5).astype(dtype)
ref_res = ref(x_data, y_data)
func = relay.Function([x, y], z)
for target, ctx in ctx_list():
# use graph by execuor default for testing, as we need
# create function explicitly to avoid constant-folding.
if dtype == 'float16' and target == 'cuda' and not have_fp16(tvm.gpu(0).compute_version):
continue
intrp = relay.create_executor("graph", ctx=ctx, target=target)
op_res = intrp.evaluate(func)(x_data, y_data)
np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=0.01)
def test_bias_add():
for dtype in ["float16", "float32"]:
xshape = (10, 2, 3, 4)
bshape = (2,)
rtol = 1e-2 if dtype == "float16" else 1e-5
x = relay.var("x", shape=xshape, dtype=dtype)
bias = relay.var("bias", dtype=dtype)
z = relay.nn.bias_add(x, bias)
zz = run_infer_type(z)
assert "axis=" not in zz.astext()
assert zz.args[1].checked_type == relay.TensorType(bshape, dtype)
func = relay.Function([x, bias], z)
x_data = np.random.uniform(size=xshape).astype(dtype)
y_data = np.random.uniform(size=bshape).astype(dtype)
ref_res = x_data + y_data.reshape((2, 1, 1))
for target, dev in tvm.testing.enabled_targets():
if (
dtype == "float16"
and target == "cuda"
and not have_fp16(tvm.cuda(0).compute_version)
):
continue
op_res = relay.create_executor("graph", device=dev, target=target).evaluate(func)(
x_data, y_data
)
np.testing.assert_allclose(op_res.numpy(), ref_res, rtol=rtol)
def run_test(tvm_intrin, np_func, dtype):
if dtype == "float16" and not have_fp16(tvm.cuda(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
# set of intrinsics does not support fp16 yet.
skip_set = {
tvm.tir.abs,
tvm.tir.round,
tvm.tir.tan,
tvm.tir.atan,
tvm.tir.tanh,
tvm.tir.cosh,
tvm.tir.sinh,
}
if dtype == "float16" and tvm_intrin in skip_set:
print("Skip because '{0}' does not support fp16 yet".format(tvm_intrin.__name__))
return
n = 128
A = te.placeholder((n,), dtype=dtype, name="A")
B = te.compute((n,), lambda *i: tvm_intrin(A(*i)), name="B")
s = sched(B)
f = tvm.build(s, [A, B], "cuda")
dev = tvm.cuda(0)
a = tvm.nd.array(np.random.uniform(0, 1, size=n).astype(A.dtype), dev)
b = tvm.nd.array(np.zeros(shape=(n,)).astype(A.dtype), dev)
f(a, b)
tvm.testing.assert_allclose(b.numpy(), np_func(a.numpy()), atol=1e-3, rtol=1e-3)
def check_cuda(dtype, n, l, padding, lanes):
if dtype == "float16" and not have_fp16(tvm.cuda(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
dev = tvm.cuda(0)
A = tvm.te.placeholder((n, l), name="A", dtype=dtype)
B = tvm.te.compute(
(n // lanes, l + 2 * padding, lanes),
lambda i, j, k: tvm.te.if_then_else(
tvm.te.any(j < padding, j >= l + padding),
tvm.runtime.convert(0).astype(dtype),
A[i * lanes + k, j - padding],
),
name="B",
)
s = te.create_schedule(B.op)
block, thread, vectorize = s[B].op.axis
s[B].bind(block, bx)
s[B].bind(thread, tx)
s[B].vectorize(vectorize)
fun = tvm.build(s, [A, B], "cuda", name="vector_load_permute_pad")
np_a = np.random.randint(low=-128, high=127, size=(n, l)).astype(A.dtype)
a = tvm.nd.empty((n, l), A.dtype, dev).copyfrom(np_a)
b = tvm.nd.empty((n // lanes, l + padding * 2, lanes), B.dtype, dev)
fun(a, b)
np_a_reshape = np_a.reshape(n // lanes, lanes, l).transpose(0, 2, 1)
ref = np.pad(
np_a_reshape, ((0, 0), (padding, padding), (0, 0)), mode="constant", constant_values=0
)
tvm.testing.assert_allclose(b.numpy(), ref)
def test_fp16_conversion(target, dev):
if target == "cuda" and not have_fp16(dev.compute_version):
print("skip because gpu does not support fp16")
return
n = 10
for (src, dst) in [("float32", "float16"), ("float16", "float32")]:
x = relay.var("x", relay.TensorType((n,), src))
y = x.astype(dst)
func = relay.Function([x], y)
# init input
X = tvm.nd.array(n * np.random.randn(n).astype(src) - n / 2)
# build
with tvm.transform.PassContext(opt_level=1):
g_json, mmod, params = relay.build(tvm.IRModule.from_expr(func), target)
# test
rt = tvm.contrib.graph_executor.create(g_json, mmod, dev)
rt.set_input("x", X)
rt.run()
out = rt.get_output(0)
np.testing.assert_allclose(out.asnumpy(), X.asnumpy().astype(dst), atol=1e-5, rtol=1e-5)
def check_cuda(dtype, n, lanes):
if not tvm.gpu(0).exist or not tvm.module.enabled("cuda"):
print("skip because cuda is not enabled..")
return
if dtype == "float16" and not have_fp16(tvm.gpu(0).compute_version):
print("skip because gpu does not support fp16")
return
if dtype == "int8" and not have_int8(tvm.gpu(0).compute_version):
print("skip because gpu does not support int8")
return
A = tvm.placeholder((n, ), name='A', dtype="%sx%d" % (dtype, lanes))
B = tvm.compute((n, ),
lambda i: A[i] + tvm.const(1, A.dtype),
name='B')
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=num_thread)
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
fun = tvm.build(s, [A, B], "cuda")
ctx = tvm.gpu(0)
a = tvm.nd.empty((n, ), A.dtype,
ctx).copyfrom(np.random.uniform(size=(n, lanes)))
c = tvm.nd.empty((n, ), B.dtype, ctx)
fun(a, c)
tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + 1)
def test_concatenate():
for dtype in ["float16", "float32"]:
n, t, d = te.size_var("n"), te.size_var("t"), 100
x = relay.var("x", shape=(n, t, d))
y = relay.var("y", shape=(n, t, d))
z = relay.concatenate((x, y), axis=-1)
assert "axis=" in z.astext()
zz = run_infer_type(z)
assert zz.checked_type == relay.TensorType((n, t, 200))
x = relay.exp(x)
z = relay.concatenate((x, y), axis=2)
zz = run_infer_type(z)
assert zz.checked_type == relay.TensorType((n, t, 200))
z = relay.concatenate((x, y), axis=1)
zz = run_infer_type(z)
assert zz.checked_type == relay.TensorType((n, t + t, 100))
# check shape mismatches (the following case is expected to raise tvm._ffi.base.TVMError.
try:
x = relay.var("p1", shape=(2, 5))
y = relay.var("p2", shape=(2, 3))
c = relay.concatenate([x, y], axis=0)
func = relay.Function([x, y], c)
zz = run_infer_type(func)
except tvm._ffi.base.TVMError:
pass
else:
assert False
x = relay.var("x", shape=(10, 5), dtype=dtype)
y = relay.var("y", shape=(10, 5), dtype=dtype)
t = relay.var("z", shape=(), dtype=dtype)
z = relay.concatenate((x, y), axis=1)
z = relay.add(z, t)
# Check result.
func = relay.Function([x, y, t], z)
x_data = np.random.rand(10, 5).astype(dtype)
y_data = np.random.rand(10, 5).astype(dtype)
t_data = np.random.uniform(size=()).astype(dtype)
ref_res = np.concatenate((x_data, y_data), axis=1) + t_data
for target, dev in tvm.testing.enabled_targets():
if (
dtype == "float16"
and target == "cuda"
and not have_fp16(tvm.cuda(0).compute_version)
):
continue
op_res1 = relay.create_executor("graph", device=dev, target=target).evaluate(func)(
x_data, y_data, t_data
)
tvm.testing.assert_allclose(op_res1.numpy(), ref_res, rtol=0.01)
op_res2 = relay.create_executor("debug", device=dev, target=target).evaluate(func)(
x_data, y_data, t_data
)
tvm.testing.assert_allclose(op_res2.numpy(), ref_res, rtol=0.01)
def verify_expand_dims(dshape, dtype, oshape, axis, num_newaxis):
x = relay.Var("x", relay.TensorType(dshape, dtype))
func = relay.Function([x], relay.expand_dims(x, axis, num_newaxis))
for target, ctx in ctx_list():
if dtype == 'float16' and target == 'cuda' and not have_fp16(tvm.gpu(0).compute_version):
continue
data = np.random.uniform(size=dshape).astype(dtype)
ref_res = data.reshape(oshape)
intrp = relay.create_executor("graph", ctx=ctx, target=target)
op_res = intrp.evaluate(func)(data)
np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=0.01)
def verify_expand_dims(dshape, dtype, oshape, axis, num_newaxis):
x = relay.Var("x", relay.TensorType(dshape, dtype))
func = relay.Function([x], relay.expand_dims(x, axis, num_newaxis))
for target, dev in tvm.testing.enabled_targets():
if (dtype == "float16" and target == "cuda"
and not have_fp16(tvm.cuda(0).compute_version)):
continue
data = np.random.uniform(size=dshape).astype(dtype)
ref_res = data.reshape(oshape)
op_res = relay.create_executor("graph", device=dev,
target=target).evaluate(func)(data)
np.testing.assert_allclose(op_res.numpy(), ref_res, rtol=0.01)
def check_target(target, dev):
if dtype == "float16" and target == "cuda" and not have_fp16(tvm.gpu(0).compute_version):
print("Skip because %s does not have fp16 support" % target)
return
print("Running on target: %s" % target)
with tvm.target.Target(target):
s = tvm.topi.testing.get_elemwise_schedule(target)(B)
a = tvm.nd.array(a_np, dev)
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
foo = tvm.build(s, [A, B], target, name="relu")
foo(a, b)
tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
def check_cuda(dtype):
if dtype == "float16" and not have_fp16(tvm.cuda(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
n, m = 16, 16
A = te.placeholder(
(
n,
m,
),
name="A",
dtype=dtype,
)
B = te.compute(
(
n,
m,
),
lambda j, i: A[j, (i + 1) % m],
name="B",
)
cuda_target = tvm.target.Target("cuda")
assert cuda_target.thread_warp_size == 2 * m
with cuda_target:
s = te.create_schedule(B.op)
tx = te.thread_axis("threadIdx.x")
ty = te.thread_axis("threadIdx.y")
bx = te.thread_axis("blockIdx.x")
AA = s.cache_read(A, "warp", [B])
y, x = B.op.axis
z, y = s[B].split(y, nparts=2)
s[B].bind(x, tx)
s[B].bind(y, ty)
s[B].bind(z, bx)
s[AA].compute_at(s[B], y)
_, x = AA.op.axis
s[AA].bind(x, tx)
dev = tvm.cuda(0)
# building with the CSE pass disabled as otherwise it would do some commoning
with tvm.transform.PassContext(opt_level=3, disabled_pass=["tir.CommonSubexprElimTIR"]):
func = tvm.build(s, [A, B], "cuda")
A_np = np.array([list(range(i, m + i)) for i in range(n)], dtype=dtype)
B_np = np.array([list(range(1 + i, m + i)) + [i] for i in range(n)], dtype=dtype)
A_nd = tvm.nd.array(A_np, dev)
B_nd = tvm.nd.array(np.zeros(B_np.shape, dtype=B_np.dtype), dev)
func(A_nd, B_nd)
tvm.testing.assert_allclose(B_nd.numpy(), B_np, rtol=1e-3)
def check_device(device, ctx):
if in_dtype == "float16" and device == "cuda" and not have_fp16(ctx.compute_version):
print("Skip because %s does not have fp16 support" % device)
return
print("Running on target: %s" % device)
with tvm.target.Target(device):
s = tvm.topi.testing.get_elemwise_schedule(device)(B)
foo = tvm.build(s, [A, B], device, name="reinterpret")
data_npy = generator(in_shape).astype(in_dtype)
out_npy = data_npy.view(B.dtype)
data_nd = tvm.nd.array(data_npy, ctx)
out_nd = tvm.nd.array(np.empty(in_shape).astype(B.dtype), ctx)
foo(data_nd, out_nd)
np.testing.assert_equal(out_nd.asnumpy(), out_npy)
def test_unary_op(self, target, dev, relay_op, ref_func, supports_fp16,
dtype):
target = tvm.target.Target(target)
if dtype == "float16":
if target.kind.name == "cuda":
if not have_fp16(tvm.cuda(0).compute_version):
pytest.xfail(
"No float16 support on local cuda device (compute_version != 5.3 and < 6.0)"
)
elif target.kind.name == "vulkan" and not target.attrs.get(
"supports_float16", False):
pytest.xfail(
"No float16 support on vulkan target (supports_float16=False)"
)
elif not supports_fp16:
pytest.xfail(
f"No float16 support on {target.kind.name} target")
if target.kind.name == "vulkan" and relay_op in [
tvm.relay.erf,
tvm.relay.tan,
tvm.relay.atan,
]:
pytest.xfail(f"Vulkan runtime doesn't yet support {relay_op}")
shape = (10, 4)
dtype = dtype
tp = relay.TensorType(shape, dtype=dtype)
x = relay.var("x", type_annotation=tp)
y = relay_op(x)
# test printer
assert ("{}(%x)".format(y.op.name)) in y.astext()
# test type inference
yy = run_infer_type(y)
assert yy.checked_type == tp
if ref_func is not None:
data = np.random.rand(*shape).astype(dtype)
ref_res = ref_func(data).astype(dtype)
func = relay.Function([x], y)
# use graph by execuor default for testing, as we need
# create function explicitly to avoid constant-folding.
op_res = relay.create_executor("graph", device=dev,
target=target).evaluate(func)(data)
tolerance = 1e-2 if dtype == "float16" else 1e-5
np.testing.assert_allclose(op_res.numpy(), ref_res, rtol=tolerance)
def check_cuda(dtype):
if not tvm.gpu(0).exist or not tvm.runtime.enabled("cuda"):
print("skip because cuda is not enabled..")
return
if dtype == "float16" and not have_fp16(tvm.gpu(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
m = 32
A = te.placeholder((m, ), name='A', dtype=dtype)
B = te.placeholder((m, ), name='B', dtype=dtype)
C = te.compute((m, ),
lambda i: A[(i + 1) % m] + B[(i + 1) % m],
name='C')
cuda_target = tvm.target.create("cuda")
assert m <= cuda_target.thread_warp_size
with cuda_target:
s = te.create_schedule(C.op)
tx = te.thread_axis("threadIdx.x")
bx = te.thread_axis("blockIdx.x")
AA = s.cache_read(A, "warp", [C])
BB = s.cache_read(B, "warp", [C])
xo, xi = s[C].split(C.op.axis[0], nparts=1)
s[C].bind(xi, tx)
s[C].bind(xo, bx)
s[AA].compute_at(s[C], xo)
s[BB].compute_at(s[C], xo)
xo, xi = s[AA].split(s[AA].op.axis[0], nparts=1)
s[AA].bind(xo, bx)
s[AA].bind(xi, tx)
xo, xi = s[BB].split(s[BB].op.axis[0], nparts=1)
s[BB].bind(xo, bx)
s[BB].bind(xi, tx)
ctx = tvm.gpu(0)
func = tvm.build(s, [A, B, C], "cuda")
AB_np = np.array(list(range(m)), dtype=dtype)
C_np = np.array(list(range(1, m)) + [0], dtype=dtype) * 2
A_nd = tvm.nd.array(AB_np, ctx)
B_nd = tvm.nd.array(AB_np, ctx)
C_nd = tvm.nd.array(np.zeros(C_np.shape, dtype=C_np.dtype), ctx)
func(A_nd, B_nd, C_nd)
tvm.testing.assert_allclose(C_nd.asnumpy(), C_np, rtol=1e-3)
def check_device(device):
if not tvm.testing.device_enabled(device):
print("Skip because %s is not enabled" % device)
return
if dtype == "float16" and device == "cuda" and not have_fp16(tvm.gpu(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
with tvm.target.Target(device):
ctx = tvm.context(device, 0)
A = te.placeholder((n, m), name="A", dtype=dtype)
B = te.compute((n, m), lambda i, j: A[i, j] + tvm.tir.const(1, A.dtype), name="B")
S = tvm.topi.testing.get_elemwise_schedule(device)(B)
fun = tvm.build(S, [A, B], device)
np_A = tvm.nd.empty((n, m), A.dtype, ctx).copyfrom(np.random.uniform(size=(n, m)))
np_B = tvm.nd.empty((n, m), B.dtype, ctx)
fun(np_A, np_B)
tvm.testing.assert_allclose(np_B.asnumpy(), np_A.asnumpy() + 1, rtol=1e-5)
def check_cuda(dtype, n, lanes):
if dtype == "float16" and not have_fp16(tvm.cuda(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
if dtype == "int8" and not have_int8(tvm.cuda(0).compute_version):
print("skip because gpu does not support int8")
return
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], "cuda")
dev = tvm.cuda(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 check_device(device):
ctx = tvm.context(device, 0)
if not ctx.exist:
print("Skip because %s is not enabled" % device)
return
if dtype == "float16" and device == "cuda" and not have_fp16(
tvm.gpu(0).compute_version):
print("Skip because %s does not have fp16 support" % device)
return
print("Running on target: %s" % device)
with tvm.target.create(device):
s = topi.testing.get_elemwise_schedule(device)(B)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype),
ctx)
foo = tvm.build(s, [A, B], device, name="relu")
foo(a, b)
tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
def test_relu(target, dev, m, n, dtype):
A = te.placeholder((m, n), name="A", dtype=dtype)
B = topi.nn.relu(A)
a_np = np.random.uniform(low=-1.0, high=1.0, size=get_const_tuple(A.shape)).astype(A.dtype)
b_np = a_np * (a_np > 0)
if dtype == "float16" and target == "cuda" and not have_fp16(tvm.cuda(0).compute_version):
pytest.skip("Skip because %s does not have fp16 support" % target)
print("Running on target: %s" % target)
with tvm.target.Target(target):
s = tvm.topi.testing.get_elemwise_schedule(target)(B)
a = tvm.nd.array(a_np, dev)
b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), dev)
foo = tvm.build(s, [A, B], target, name="relu")
foo(a, b)
tvm.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
def check_cuda(dtype):
if dtype == "float16" and not have_fp16(tvm.cuda(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
m = 128
A = te.placeholder((m,), name="A", dtype=dtype)
B = te.compute((m,), lambda i: A[i // 32 * 32 + (i + 1) % 32], name="B")
cuda_target = tvm.target.Target("cuda")
assert cuda_target.thread_warp_size == 32
with cuda_target:
s = te.create_schedule(B.op)
AA = s.cache_read(A, "warp", [B])
xo, xi = s[B].split(B.op.axis[0], 64)
xi0, xi1 = s[B].split(xi, factor=32)
tx = te.thread_axis("threadIdx.x")
s[B].bind(xi1, tx)
s[B].bind(xo, te.thread_axis("blockIdx.x"))
s[AA].compute_at(s[B], xo)
xo, xi = s[AA].split(s[AA].op.axis[0], 32)
s[AA].bind(xi, tx)
dev = tvm.cuda(0)
# building with the CSE pass disabled as otherwise it would do some commoning
with tvm.transform.PassContext(opt_level=3, disabled_pass=["tir.CommonSubexprElimTIR"]):
func = tvm.build(s, [A, B], "cuda")
A_np = np.array(list(range(m)), dtype=dtype)
B_np = np.array(
list(range(1, 32))
+ [0]
+ list(range(33, 64))
+ [32]
+ list(range(65, 96))
+ [64]
+ list(range(97, 128))
+ [96],
dtype=dtype,
)
A_nd = tvm.nd.array(A_np, dev)
B_nd = tvm.nd.array(np.zeros(B_np.shape, dtype=B_np.dtype), dev)
func(A_nd, B_nd)
tvm.testing.assert_allclose(B_nd.numpy(), B_np, rtol=1e-3)
def check_cuda(dtype):
if dtype == "float16" and not have_fp16(tvm.cuda(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
m = 32
A = te.placeholder((m,), name="A", dtype=dtype)
B = te.placeholder((m,), name="B", dtype=dtype)
C = te.compute((m,), lambda i: A[(i + 1) % m] + B[(i + 1) % m], name="C")
cuda_target = tvm.target.Target("cuda")
assert m <= cuda_target.thread_warp_size
with cuda_target:
s = te.create_schedule(C.op)
tx = te.thread_axis("threadIdx.x")
bx = te.thread_axis("blockIdx.x")
AA = s.cache_read(A, "warp", [C])
BB = s.cache_read(B, "warp", [C])
xo, xi = s[C].split(C.op.axis[0], nparts=1)
s[C].bind(xi, tx)
s[C].bind(xo, bx)
s[AA].compute_at(s[C], xo)
s[BB].compute_at(s[C], xo)
xo, xi = s[AA].split(s[AA].op.axis[0], nparts=1)
s[AA].bind(xo, bx)
s[AA].bind(xi, tx)
xo, xi = s[BB].split(s[BB].op.axis[0], nparts=1)
s[BB].bind(xo, bx)
s[BB].bind(xi, tx)
dev = tvm.cuda(0)
# building with the CSE pass disabled as otherwise it would do some commoning
with tvm.transform.PassContext(opt_level=3, disabled_pass=["tir.CommonSubexprElimTIR"]):
func = tvm.build(s, [A, B, C], "cuda")
AB_np = np.array(list(range(m)), dtype=dtype)
C_np = np.array(list(range(1, m)) + [0], dtype=dtype) * 2
A_nd = tvm.nd.array(AB_np, dev)
B_nd = tvm.nd.array(AB_np, dev)
C_nd = tvm.nd.array(np.zeros(C_np.shape, dtype=C_np.dtype), dev)
func(A_nd, B_nd, C_nd)
tvm.testing.assert_allclose(C_nd.numpy(), C_np, rtol=1e-3)
def check(device, dtype, m=32, n=32):
if not tvm.testing.device_enabled(device):
print("Skipping", device)
return
dev = tvm.device(device, 0)
if dtype == "float16" and not have_fp16(dev.compute_version):
print("Skip because gpu does not have fp16 support")
return
a = tvm.te.placeholder((m, n), name="a", dtype=dtype)
b = topi.sum(a)
with tvm.target.Target(device):
sb = tvm.te.create_schedule(b.op)
i, _ = b.op.reduce_axis
sb[b].bind(i, tvm.te.thread_axis("threadIdx.x"))
func = tvm.build(sb, [a, b], device)
a_np = np.random.uniform(size=(m, n)).astype(a.dtype)
b_np = np.sum(a_np)
a_nd = tvm.nd.array(a_np, dev)
b_nd = tvm.nd.array(np.zeros(b_np.shape, dtype=b_np.dtype), dev)
func(a_nd, b_nd)
tvm.testing.assert_allclose(b_nd.asnumpy(), b_np, rtol=1e-3)
def test_bias_add():
for dtype in ['float16', 'float32']:
xshape=(10, 2, 3, 4)
bshape=(2,)
rtol = 1e-2 if dtype == 'float16' else 1e-5
x = relay.var("x", shape=xshape, dtype=dtype)
bias = relay.var("bias", dtype=dtype)
z = relay.nn.bias_add(x, bias)
zz = run_infer_type(z)
assert "axis=" not in zz.astext()
assert zz.args[1].checked_type == relay.TensorType(bshape, dtype)
func = relay.Function([x, bias], z)
x_data = np.random.uniform(size=xshape).astype(dtype)
y_data = np.random.uniform(size=bshape).astype(dtype)
ref_res = x_data + y_data.reshape((2, 1, 1))
for target, ctx in ctx_list():
if dtype == 'float16' and target == 'cuda' and not have_fp16(tvm.gpu(0).compute_version):
continue
intrp = relay.create_executor("graph", ctx=ctx, target=target)
op_res = intrp.evaluate(func)(x_data, y_data)
np.testing.assert_allclose(op_res.asnumpy(), ref_res, rtol=rtol)
def check_cuda(dtype):
if not tvm.gpu(0).exist or not tvm.runtime.enabled("cuda"):
print("skip because cuda is not enabled..")
return
if dtype == "float16" and not have_fp16(tvm.gpu(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
m = 128
A = te.placeholder((m, ), name='A', dtype=dtype)
B = te.compute((m, ),
lambda i: A[i // 32 * 32 + (i + 1) % 32],
name='B')
cuda_target = tvm.target.create("cuda")
assert cuda_target.thread_warp_size == 32
with cuda_target:
s = te.create_schedule(B.op)
AA = s.cache_read(A, "warp", [B])
xo, xi = s[B].split(B.op.axis[0], 64)
xi0, xi1 = s[B].split(xi, factor=32)
tx = te.thread_axis("threadIdx.x")
s[B].bind(xi1, tx)
s[B].bind(xo, te.thread_axis("blockIdx.x"))
s[AA].compute_at(s[B], xo)
xo, xi = s[AA].split(s[AA].op.axis[0], 32)
s[AA].bind(xi, tx)
ctx = tvm.gpu(0)
func = tvm.build(s, [A, B], "cuda")
A_np = np.array(list(range(m)), dtype=dtype)
B_np = np.array(list(range(1, 32)) + [0] + list(range(33, 64)) +
[32] + list(range(65, 96)) + [64] +
list(range(97, 128)) + [96],
dtype=dtype)
A_nd = tvm.nd.array(A_np, ctx)
B_nd = tvm.nd.array(np.zeros(B_np.shape, dtype=B_np.dtype), ctx)
func(A_nd, B_nd)
tvm.testing.assert_allclose(B_nd.asnumpy(), B_np, rtol=1e-3)
def check_cuda(dtype, m=32, n=32):
if not tvm.gpu(0).exist or not tvm.runtime.enabled("cuda"):
print("skip because cuda is not enabled..")
return
if dtype == "float16" and not have_fp16(tvm.gpu(0).compute_version):
print("Skip because gpu does not have fp16 support")
return
a = tvm.te.placeholder((m, n), name="a", dtype=dtype)
b = topi.sum(a)
with tvm.target.cuda():
sb = tvm.te.create_schedule(b.op)
i, _ = b.op.reduce_axis
sb[b].bind(i, tvm.te.thread_axis("threadIdx.x"))
ctx = tvm.gpu(0)
func = tvm.build(sb, [a, b], 'cuda')
a_np = np.random.uniform(size=(m, n)).astype(a.dtype)
b_np = np.sum(a_np)
a_nd = tvm.nd.array(a_np, ctx)
b_nd = tvm.nd.array(np.zeros(b_np.shape, dtype=b_np.dtype), ctx)
func(a_nd, b_nd)
tvm.testing.assert_allclose(b_nd.asnumpy(), b_np, rtol=1e-3)
def check_cuda(dtype, n, lanes):
if not tvm.gpu(0).exist or not tvm.module.enabled("cuda"):
print("skip because cuda is not enabled..")
return
if dtype == "float16" and not have_fp16(tvm.gpu(0).compute_version):
print("skip because gpu does not support fp16")
return
if dtype == "int8" and not have_int8(tvm.gpu(0).compute_version):
print("skip because gpu does not support int8")
return
A = tvm.placeholder((n,), name='A', dtype="%sx%d" % (dtype, lanes))
B = tvm.compute((n,), lambda i: A[i]+tvm.const(1, A.dtype), name='B')
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=num_thread)
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
fun = tvm.build(s, [A, B], "cuda")
ctx = tvm.gpu(0)
a = tvm.nd.empty((n,), A.dtype, ctx).copyfrom(
np.random.uniform(size=(n, lanes)))
c = tvm.nd.empty((n,), B.dtype, ctx)
fun(a, c)
tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + 1)