def test_static_tensor():
dtype = "float32"
stype = "csr"
target = "llvm"
ctx = tvm.context(target, 0)
m = te.size_var("m")
n = te.size_var("n")
A = tvmsp.placeholder(shape=(m, n), name="A", dtype=dtype)
assert A.stype == "csr"
n = 3
a = np.maximum(np.random.uniform(size=(n, n)).astype(dtype) - 0.6, 0.0)
a = tvmsp.array(a, ctx)
A.data = te.placeholder(a.data.shape, dtype, name="A_data")
Ab = tvm.tir.decl_buffer(a.data.shape, dtype, name="A_data")
binds = {A.data: Ab}
C = te.compute(A.data.shape, lambda i: A.data[i] * 2.0, tag="cs_scatter")
s = te.create_schedule(C.op)
f = tvm.build(s, [A.data, C], target, binds=binds)
c = tvmsp.array(np.zeros((n, n), dtype), ctx)
c.data = tvm.nd.empty(a.data.shape, dtype)
c.indices = a.indices
c.indptr = a.indptr
f(a.data, c.data)
tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() * 2.0, rtol=1e-5)
def test_tensor_intrin_scalar_params():
n = te.size_var("n")
x = te.placeholder((n, ), name="x")
v = te.size_var("v")
w = te.size_var("w")
z = te.compute((n, ), lambda i: x[i] * v + w, name="z")
def intrin_func(ins, outs, sp):
assert isinstance(ins[0], tvm.te.schedule.Buffer)
assert ins[0].shape[0] == n
assert sp[0] == v
assert sp[1] == w
return tvm.tir.call_packed("hw_func", ins[0].data, outs[0].data, sp[0],
sp[1])
intrin = te.decl_tensor_intrin(z.op,
intrin_func,
scalar_params=[v, w],
default_buffer_params={"offset_factor": 1})
assert intrin.op == z.op
assert intrin.reduce_init is None
assert tuple(intrin.inputs) == tuple(z.op.input_tensors)
assert intrin.buffers[0].shape[0] == n
assert tuple(intrin.scalar_params) == tuple((v, w))
A = te.placeholder((10, 10), name="A")
# Pass scalar inputs to the TensorIntrin, interleaved with tensor inputs
C = te.compute((10, 10),
lambda i, j: intrin(i * i, A[i, j], i + j),
name="C")
s = te.create_schedule(C.op)
stmt = tvm.lower(s, [A, C])["main"].body
assert isinstance(stmt.body.body, tvm.tir.Evaluate)
assert len(stmt.body.body.value.args) == 5
assert str(stmt.body.body.value.args[3]) == "(i: int32*i)"
assert str(stmt.body.body.value.args[4]) == "(i: int32 + j: int32)"
def test_batch_matmul(executor_kind):
b, m, n, k = te.size_var("b"), te.size_var("m"), te.size_var(
"n"), te.size_var("k")
x = relay.var("x", relay.TensorType((b, m, k), "float32"))
y = relay.var("y", relay.TensorType((b, n, k), "float32"))
z = relay.nn.batch_matmul(x, y)
zz = run_infer_type(z)
assert zz.checked_type == relay.TensorType((b, m, n), "float32")
verify_batch_matmul(executor_kind, (1, 16, 32), (1, 16, 32), (1, 16, 16),
trans_x=False,
trans_y=True)
verify_batch_matmul(executor_kind, (5, 16, 32), (5, 16, 32), (5, 16, 16),
trans_x=False,
trans_y=True)
verify_batch_matmul(executor_kind, (5, 16, 32), (5, 20, 32), (5, 16, 20),
trans_x=False,
trans_y=True)
verify_batch_matmul(executor_kind, (30, 16, 32), (30, 20, 32),
(30, 16, 20),
trans_x=False,
trans_y=True)
verify_batch_matmul(executor_kind, (1, 32, 16), (1, 16, 32), (1, 16, 16),
trans_x=True,
trans_y=True)
verify_batch_matmul(executor_kind, (5, 16, 32), (5, 32, 16), (5, 16, 16),
trans_x=False,
trans_y=False)
verify_batch_matmul(executor_kind, (5, 32, 16), (5, 32, 20), (5, 16, 20),
trans_x=True,
trans_y=False)
x_np = np.random.randn(10, 27, 64).astype("float32")
x = relay.var("x", shape=x_np.shape)
verify_batch_matmul_with_inputs(executor_kind, x, x, x_np, x_np,
(10, 27, 27))
def test_thread_storage_sync():
m = te.size_var('m')
l = te.size_var('l')
A = te.placeholder((m, l), name='A')
A1 = te.compute((m, l), lambda i, j: A[i, j], name='A1')
A2 = te.compute((m, l), lambda i, j: A1[i, j] + 3, name='A2')
s = te.create_schedule(A2.op)
xo, xi = s[A2].split(A2.op.axis[0], factor=8)
s[A2].bind(xo, te.thread_axis("blockIdx.x"))
s[A1].compute_at(s[A2], xo)
s[A1].set_scope("shared")
bounds = tvm.te.schedule.InferBound(s)
assert isinstance(bounds, tvm.container.Map)
stmt = tvm.te.schedule.ScheduleOps(s, bounds)
func = tvm.te.schedule.SchedulePostProcToPrimFunc([A, A2], stmt, None)
mod = tvm.IRModule.from_expr(func)
mod = tvm.tir.transform.StorageFlatten(64)(mod._move())
cuda_target = tvm.target.create("cuda")
mod = tvm.tir.transform.Apply(lambda f: f.with_attr({
"global_symbol": "test",
"target": cuda_target
}))(mod._move())
fdevice = tvm.tir.transform.SplitHostDevice()(mod)["test_kernel0"]
mod = tvm.IRModule.from_expr(fdevice)
cuda_target = tvm.target.create("cuda")
f = tvm.tir.transform.ThreadSync("shared")(mod)["test_kernel0"]
body_list = tvm.tir.stmt_list(f.body.body.body.body)
assert (body_list[1].value.op.same_as(
tvm.ir.Op.get("tir.tvm_storage_sync")))
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
dev = tvm.rocm(0)
a = tvm.nd.array(np.random.uniform(size=(nn, nn)).astype(A.dtype), dev)
b = tvm.nd.array(np.zeros(nn, dtype=B.dtype), dev)
frocm(a, b)
tvm.testing.assert_allclose(b.numpy(), np.sum(a.numpy(), axis=1), rtol=1e-4)
def test_basic():
n = te.size_var('n')
A = te.placeholder((n, ), name='A')
B = te.placeholder((n, ), name='B')
T = te.compute((n, ), lambda i: A[i]+B[i])
s = te.create_schedule(T.op)
xo, xi = s[T].split(T.op.axis[0], factor=4)
bounds = tvm.te.schedule.InferBound(s)
stmt = tvm.te.schedule.ScheduleOps(s, bounds)
stmt = tvm.tir.ir_pass.LoopPartition(stmt, False)
stmt = tvm.tir.ir_pass.Simplify(stmt)
assert('if' not in str(stmt.body.body.body[0]))
assert('if' in str(stmt.body.body.body[1]))
def test_in_bounds_llvm():
n = te.size_var("n")
A = te.placeholder((n,), name="A")
B = te.placeholder((n,), name="B")
C = te.compute(A.shape, lambda i: A[i] + B[i], name="C")
s = te.create_schedule(C.op)
tgt = "llvm"
tgt_host = "llvm"
stmt = tvm.lower(s, [A, B, C], simple_mode=True)
fadd = tvm.build(s, [A, B, C], tgt, target_host=tgt_host, name="myadd")
ctx = tvm.context(tgt, 0)
a = tvm.nd.array(np.random.uniform(size=1024).astype(A.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=1024).astype(B.dtype), ctx)
c = tvm.nd.array(np.zeros(1024, dtype=C.dtype), ctx)
fadd(a, b, c)
def test_hoisting_block_scope_1():
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.op].bind(xo, te.thread_axis("blockIdx.x"))
s[B.op].bind(xi, te.thread_axis("threadIdx.y"))
s[B].bind(s[B].op.reduce_axis[0], te.thread_axis("threadIdx.x"))
s[BF].compute_at(s[B], s[B].op.reduce_axis[0])
func = tvm.driver.build_module.schedule_to_module(s, [A, B], "main", None)["main"]
stmt = func.body
new_stmt = tvm.tir.transform.HoistIfThenElse()(tvm.IRModule.from_expr(func))["main"].body
tvm.ir.assert_structural_equal(new_stmt, stmt)
with tvm.transform.PassContext(
config={"tir.HoistIfThenElse": {"support_block_scope_hosting": True}}
):
new_stmt = tvm.tir.transform.HoistIfThenElse()(tvm.IRModule.from_expr(func))["main"].body
assert not tvm.ir.structural_equal(new_stmt, stmt)
def test_basic_likely_elimination():
n = te.size_var('n')
X = te.placeholder(shape=(n, ), name="x")
W = te.placeholder(shape=(n + 1, ), dtype="int32", name="w")
def f(i):
start = W[i]
extent = W[i + 1] - W[i]
rv = te.reduce_axis((0, extent))
return te.sum(X[rv + start], axis=rv)
Y = te.compute(X.shape, f, name="y")
s = te.create_schedule([Y.op])
stmt = tvm.lower(s, [X, W, Y], simple_mode=True)
assert ('if' not in str(stmt))
def test_multi_if():
ib = tvm.tir.ir_builder.create()
m = te.size_var("m")
n = te.size_var("n")
with ib.for_range(0, 4, "i") as i:
with ib.for_range(0, n, "j") as j:
with ib.for_range(0, m, "k") as k:
with ib.if_scope(ib.likely(i * m + j + k < n)):
ib.emit(tvm.tir.Evaluate(m))
with ib.else_scope():
ib.emit(tvm.tir.Evaluate(n))
with ib.if_scope(ib.likely(i * m + j - k < n)):
ib.emit(tvm.tir.Evaluate(m))
with ib.else_scope():
ib.emit(tvm.tir.Evaluate(n))
stmt = ib.get()
mod = tvm.IRModule.from_expr(tvm.tir.PrimFunc([], stmt))
mod = tvm.tir.transform.LoopPartition()(mod)
stmt = tvm.tir.transform.Simplify()(mod)["main"].body
assert not any(
collect_visit(stmt.body[0],
lambda x: isinstance(x, tvm.tir.IfThenElse)))
def test_thread_extent_simplify():
ib = tvm.tir.ir_builder.create()
A = ib.pointer("float32", name="A")
C = ib.pointer("float32", name="C")
n = te.size_var("n")
tx = te.thread_axis("threadIdx.x")
ty = te.thread_axis("threadIdx.y")
ib.scope_attr(tx, "thread_extent", n)
ib.scope_attr(tx, "thread_extent", n)
ib.scope_attr(ty, "thread_extent", 1)
with ib.if_scope(tx + ty < 12):
A[tx] = C[tx + ty]
body = tvm.tir.LetStmt(n, 10, ib.get())
body = tvm.tir.ir_pass.CanonicalSimplify(body)
assert isinstance(body.body.body.body, tvm.tir.Store)
def test_dyn_shared_reuse_and_merge():
n = 64
A = te.placeholder((n, ), name="A", dtype="float32")
B = te.placeholder((n, ), name="B", dtype="float32")
C = te.placeholder((te.size_var("n_dyn"), ), name="C", dtype="float32")
def test_device_ir(A, B, C, D):
ib = tvm.tir.ir_builder.create()
tx = te.thread_axis("threadIdx.x")
ib.scope_attr(tx, "thread_extent", n)
A_sh = ib.allocate(A.dtype, (n, ), scope="shared.dyn", name="A_sh")
B_sh = ib.allocate(B.dtype, (n, ), scope="shared.dyn", name="B_sh")
C_sh = ib.allocate(C.dtype, (C.shape[0], ),
scope="shared.dyn",
name="C_sh")
Aptr = ib.buffer_ptr(A)
Bptr = ib.buffer_ptr(B)
Cptr = ib.buffer_ptr(C)
Dptr = ib.buffer_ptr(D)
A_sh[tx] = Aptr[tx]
Dptr[tx] = A_sh[tx]
B_sh[tx] = Bptr[tx]
Dptr[tx] += B_sh[tx]
C_sh[tx] = Cptr[
tx] # C cannot reuse other buffers since it size is dynamic
Dptr[tx] += C_sh[tx]
return ib.get()
D = te.extern(
(n, ),
[A, B, C],
lambda ins, outs: test_device_ir(ins[0], ins[1], ins[2], outs[0]),
name="vadd",
dtype="float32",
)
s = te.create_schedule(D.op)
mod = run_passes(s, [A, B, C, D])
# merged allocation
# allocate(buf_dyn_shmem: Pointer(shared.dyn uint8), uint8, [((n_dyn*4) + 256)]);
verify_single_allocation(mod["main"].body)
def test_thread_axis2():
n = tvm.runtime.convert(4096)
m = te.size_var('m')
A = te.placeholder((n, ), name='A')
B = te.placeholder((n, ), name='B')
C = te.compute(A.shape, lambda i: A[i] + B[i], name='C')
s = te.create_schedule(C.op)
num_thread = 32
bx, x = s[C].split(C.op.axis[0], factor=32)
tx, x = s[C].split(x, nparts=num_thread)
_, x = s[C].split(x, factor=m)
s[C].bind(bx, te.thread_axis("blockIdx.x"))
s[C].bind(tx, te.thread_axis("threadIdx.x"))
stmt = lower(s, [A, B])
for_body = stmt.body.body.body.body[0]
assert ('threadIdx' not in str(for_body.extent))
def test_thread_extent_simplify():
ib = tvm.tir.ir_builder.create()
A = ib.pointer("float32", name="A")
C = ib.pointer("float32", name="C")
n = te.size_var("n")
tx = te.thread_axis("threadIdx.x")
ty = te.thread_axis("threadIdx.y")
ib.scope_attr(tx, "thread_extent", n)
ib.scope_attr(tx, "thread_extent", n)
ib.scope_attr(ty, "thread_extent", 1)
with ib.if_scope(tx + ty < 12):
A[tx] = C[tx + ty]
body = tvm.tir.LetStmt(n, 10, ib.get())
mod = tvm.IRModule.from_expr(tvm.tir.PrimFunc([A, C, n], body))
body = tvm.tir.transform.Simplify()(mod)["main"].body
assert isinstance(body.body.body.body, tvm.tir.BufferStore)
def get_shape(shape):
"""Convert the shape to correct dtype and vars."""
ret = []
for dim in shape:
if isinstance(dim, tvm.tir.IntImm):
if libinfo()["INDEX_DEFAULT_I64"] == "ON":
ret.append(dim)
else:
val = int(dim)
assert val <= np.iinfo(np.int32).max
ret.append(tvm.tir.IntImm("int32", val))
elif isinstance(dim, tvm.tir.Any):
ret.append(te.size_var("any_dim", "int32"))
else:
ret.append(dim)
return ret
def test_out_of_bounds_loop_partition_basic_llvm(index_a, index_b):
n = te.size_var("n")
A = te.placeholder((n, ), name="A")
B = te.placeholder((n, ), name="B")
T = te.compute((n, ), lambda i: A[i + index_a] + B[i + index_b])
s = te.create_schedule(T.op)
xo, xi = s[T].split(T.op.axis[0], factor=4)
lowered_func = tvm.lower(s, [A, B, T], "llvm", simple_mode=False)
dev = tvm.cpu(0)
f = tvm.build(s, [A, B, T], "llvm")
a = tvm.nd.array(np.random.uniform(size=(32, )).astype(A.dtype), dev)
b = tvm.nd.array(np.random.uniform(size=(32, )).astype(B.dtype), dev)
t = tvm.nd.empty((32, ), T.dtype, dev)
f(a, b, t)
def test_in_bounds_loop_partition_basic_llvm():
n = te.size_var('n')
A = te.placeholder((n, ), name='A')
B = te.placeholder((n, ), name='B')
T = te.compute((n, ), lambda i: A[i] + B[i])
s = te.create_schedule(T.op)
xo, xi = s[T].split(T.op.axis[0], factor=4)
lowered_func = tvm.lower(s, [A, B, T], "llvm", simple_mode=False)
ctx = tvm.cpu(0)
f = tvm.build(s, [A, B, T], "llvm")
a = tvm.nd.array(np.random.uniform(size=(32, )).astype(A.dtype), ctx)
b = tvm.nd.array(np.random.uniform(size=(32, )).astype(B.dtype), ctx)
t = tvm.nd.empty((32, ), T.dtype, ctx)
f(a, b, t)
def test_for():
ib = tvm.tir.ir_builder.create()
n = te.size_var("n")
A = ib.allocate("float32", n, name="A", scope="global")
with ib.for_range(0, n, name="i") as i:
A[i] = A[i] + 1
with ib.for_range(0, 10, name="j") as j:
A[j] = A[j] + 2
body = ib.get()
assert isinstance(body, tvm.tir.Allocate)
body = body.body
assert isinstance(body, tvm.tir.For)
body = body.body
assert isinstance(body, tvm.tir.SeqStmt)
assert isinstance(body[1], tvm.tir.For)
def test_stack_vm_basic():
a = tvm.nd.array(np.zeros(10, dtype='float32'))
@tvm.register_func
def tvm_call_back_get_shape(shape0):
print(shape0)
assert shape0 == a.shape[0]
n = te.size_var('n')
Ab = tvm.tir.decl_buffer((n, ), "float32")
stmt = tvm.tir.Evaluate(
tvm.tir.call_packed("tvm_call_back_get_shape", Ab.shape[0]))
fapi = tvm.tir.ir_pass.MakeAPI(stmt, "print_shape", [Ab], 0, True)
fapi = tvm.tir.ir_pass.LowerTVMBuiltin(fapi)
fapi = tvm.tir.ir_pass.LowerIntrin(fapi, "stackvm")
run_jit(fapi, lambda f: f(a))
def test_buffer_broadcast():
m0, m1, m2 = te.size_var("m0"), te.size_var("m1"), te.size_var("m2")
n0, n1, n2 = te.size_var("n0"), te.size_var("n1"), te.size_var("n2")
o0, o1, o2 = te.size_var("o0"), te.size_var("o1"), te.size_var("o2")
A = te.placeholder((m0, m1, m2), name="A")
B = te.placeholder((n0, n1, n2), name="B")
C = te.compute((o0, o1, o2),
lambda i, j, k: A[i, j, k] + B[i, j, k],
name="C")
Ab = tvm.tir.decl_buffer(A.shape,
A.dtype,
name="Ab",
buffer_type="auto_broadcast")
Bb = tvm.tir.decl_buffer(B.shape,
B.dtype,
name="Bb",
buffer_type="auto_broadcast")
s = te.create_schedule(C.op)
def check():
fadd = tvm.build(s, [A, B, C],
target="llvm",
name="bcast_add",
binds={
A: Ab,
B: Bb
})
dev = tvm.cpu(0)
a = tvm.nd.array(
np.random.uniform(size=(2, 4, 3)).astype(A.dtype), dev)
b = tvm.nd.array(
np.random.uniform(size=(2, 1, 1)).astype(B.dtype), dev)
c = tvm.nd.array(np.zeros((2, 4, 3), dtype=C.dtype), dev)
fadd(a, b, c)
tvm.testing.assert_allclose(c.numpy(), a.numpy() + b.numpy())
check()
def test_buffer_broadcast_expr():
n0, m0, x = te.size_var("n0"), te.size_var("m0"), te.size_var("x")
n1, m1 = te.size_var("n1"), te.size_var("m1")
o0, o1 = te.size_var("o0"), te.size_var("o1")
A = te.placeholder((m0, n0), name="A")
B = te.placeholder((m1, n1), name="B")
C = te.compute((o0, o1 // x), lambda i, j: A[i, j] + B[i, j], name="C")
Ab = tvm.tir.decl_buffer(A.shape, A.dtype, name="Ab", buffer_type="auto_broadcast")
Bb = tvm.tir.decl_buffer(B.shape, B.dtype, name="Bb", buffer_type="auto_broadcast")
Cc = tvm.tir.decl_buffer(C.shape, C.dtype, name="Cc", buffer_type="auto_broadcast")
s = te.create_schedule(C.op)
def check_stride():
fadd = tvm.build(
s, [A, B, C, o1, x], target="llvm", name="bcast_add", binds={A: Ab, B: Bb, C: Cc}
)
dev = tvm.cpu(0)
a = tvm.nd.array(np.random.uniform(size=(2, 4)).astype(A.dtype), dev)
b = tvm.nd.array(np.random.uniform(size=(2, 4)).astype(B.dtype), dev)
c = tvm.nd.array(np.zeros((2, 4), dtype=C.dtype), dev)
fadd(a, b, c, 4, 1)
tvm.testing.assert_allclose(c.numpy(), a.numpy() + b.numpy())
def check_no_stride():
fadd = tvm.build(
s, [A, B, C, o1, x], target="llvm", name="bcast_add", binds={A: Ab, B: Bb, C: Cc}
)
dev = tvm.cpu(0)
a = tvm.nd.array(np.random.uniform(size=(1, 4)).astype(A.dtype), dev)
b = tvm.nd.array(np.random.uniform(size=(2, 4)).astype(B.dtype), dev)
c = tvm.nd.array(np.zeros((2, 4), dtype=C.dtype), dev)
fadd(a, b, c, 4, 1)
tvm.testing.assert_allclose(c.numpy(), a.numpy() + b.numpy())
def check_auto_bind():
# Let build bind buffers
fadd = tvm.build(s, [A, B, C, o1, x], target="llvm", name="bcast_add")
dev = tvm.cpu(0)
a = tvm.nd.array(np.random.uniform(size=(1, 4)).astype(A.dtype), dev)
b = tvm.nd.array(np.random.uniform(size=(2, 4)).astype(B.dtype), dev)
c = tvm.nd.array(np.zeros((2, 4), dtype=C.dtype), dev)
fadd(a, b, c, 4, 1)
tvm.testing.assert_allclose(c.numpy(), a.numpy() + b.numpy())
check_stride()
check_no_stride()
check_auto_bind()
def verify_tensor_scalar_bop(shape, typ="add"):
"""Verify non-constant Tensor and scalar binary operations."""
sh = [te.size_var("n%d" % i) for i in range(0, len(shape))]
k = te.var("k")
A = te.placeholder(sh, name="A")
if typ == "add":
B = A + k
elif typ == "sub":
B = A - k
elif typ == "mul":
B = A * k
elif typ == "div":
B = A / k
else:
raise NotImplementedError()
def check_device(device):
if not tvm.testing.device_enabled(device):
print("Skip because %s is not enabled" % device)
return
ctx = tvm.context(device, 0)
print("Running on target: %s" % device)
with tvm.target.Target(device):
s = tvm.topi.testing.get_elemwise_schedule(device)(B)
k_ = 2
foo = tvm.build(s, [A, B, k] + sh, device, name="tensor_scalar_" + typ)
a_npy = np.random.uniform(size=shape).astype(A.dtype)
if typ == "add":
b_npy = a_npy + k_
elif typ == "sub":
b_npy = a_npy - k_
elif typ == "mul":
b_npy = a_npy * k_
elif typ == "div":
b_npy = a_npy / k_
else:
raise NotImplementedError()
a_nd = tvm.nd.array(a_npy, ctx)
b_nd = tvm.nd.array(np.empty(b_npy.shape).astype(B.dtype), ctx)
foo(a_nd, b_nd, k_, *shape)
tvm.testing.assert_allclose(b_nd.asnumpy(), b_npy, rtol=1e-5)
for device in ["llvm", "cuda", "opencl", "metal", "rocm", "vulkan"]:
check_device(device)
def test_if_likely():
ib = tvm.tir.ir_builder.create()
A = ib.pointer("float32", name="A")
C = ib.pointer("float32", name="C")
n = te.size_var("n")
tx = te.thread_axis("threadIdx.x")
ty = te.thread_axis("threadIdx.y")
ib.scope_attr(tx, "thread_extent", 32)
ib.scope_attr(ty, "thread_extent", 32)
with ib.if_scope(ib.likely(tx * 32 + ty < n)):
with ib.if_scope(ib.likely(tx * 32 + ty < n)):
A[tx] = C[tx * 32 + ty]
body = ib.get()
mod = tvm.IRModule.from_expr(tvm.tir.PrimFunc([A, C, n], body))
body = tvm.tir.transform.Simplify()(mod)["main"].body
assert isinstance(body.body.body, tvm.tir.IfThenElse)
assert not isinstance(body.body.body.then_case, tvm.tir.IfThenElse)
def test_meta_data():
n, c, h, w = te.size_var("n"), 10, 224, 224
x = relay.var("x", shape=(n, c, h, w))
w = relay.var("w")
z = relay.nn.conv2d(x, w, kernel_size=(3, 3), padding=(1, 1), channels=2)
f = relay.Function([x, w], z)
text = astext(f, unify_free_vars=True)
text_no_meta = str(f)
assert "channels=2" in text
assert "channels=2" in text_no_meta
assert "meta[tir.SizeVar][0]" in text
assert "meta[tir.SizeVar][0]" in text_no_meta
assert "type_key" in text
assert "type_key" not in text_no_meta
text = astext(relay.const([1, 2, 3]))
assert "meta[relay.Constant][0]" in text
def test_unroll_fake_loop():
ib = tvm.tir.ir_builder.create()
dtype = 'int32'
n = te.size_var('n')
Ab = tvm.tir.decl_buffer((n, ), dtype)
Aptr = ib.buffer_ptr(Ab)
# for i in 0 to n-1:
with ib.for_range(0, 1, name="i") as i:
Aptr[i * 2] = 3
with ib.for_range(0, 10, name="j") as j:
Aptr[j + 1] = Aptr[i] + 1
stmt = ib.get()
mod = tvm.IRModule.from_expr(tvm.tir.PrimFunc([Ab], stmt))
ret = tvm.tir.transform.UnrollLoop(8, 0, 1, False)(mod)["main"].body
assert isinstance(ret[0], tvm.tir.Store)
def test_stack_vm_basic():
a = tvm.nd.array(np.zeros(10, dtype="float32"))
@tvm.register_func
def tvm_call_back_get_shape(shape0):
print(shape0)
assert shape0 == a.shape[0]
n = te.size_var("n")
Ab = tvm.tir.decl_buffer((n, ), "float32")
stmt = tvm.tir.Evaluate(
tvm.tir.call_packed("tvm_call_back_get_shape", Ab.shape[0]))
mod = tvm.IRModule.from_expr(
tvm.tir.PrimFunc([Ab], stmt).with_attr("global_symbol", "print_shape"))
run_jit(mod, lambda f: f(a))
def test_inline():
m = te.size_var('m')
A = te.placeholder((m, ), name='A')
T = te.compute((m, ), lambda i, : A[i] + 10, name='T')
stmt = tvm.tir.Evaluate(T[10] + 11 * T[100])
stmt = tvm.tir.ir_pass.Inline(stmt, T.op, [x.var for x in T.op.axis],
T.op.body[0])
print(stmt)
assert (tvm.tir.ir_pass.VerifySSA(stmt))
try:
# pass in int array(wrong argument type)
# must raise an error
stmt = tvm.tir.ir_pass.Inline(T.op, [1, 2, 3], T.op.body, stmt)
assert False
except tvm.error.TVMError:
pass
def save_object(names):
n = te.size_var('n')
Ab = tvm.tir.decl_buffer((n, ), dtype)
i = te.var('i')
# for i in 0 to n-1:
stmt = tvm.tir.For(
i, 0, n - 1, 0, 0,
tvm.tir.Store(Ab.data,
tvm.tir.Load(dtype, Ab.data, i) + 1,
i + 1))
mod = tvm.IRModule.from_expr(
tvm.tir.PrimFunc([Ab], stmt).with_attr(
"global_symbol", "main")
)
m = tvm.driver.build(mod, target="llvm")
for name in names:
m.save(name)
def run(dtype):
# graph
n = te.size_var("n")
A = te.placeholder((n, ), name="A", dtype=dtype)
B = te.placeholder((n, ), name="B", dtype=dtype)
bias = te.var("bias", dtype=dtype)
scale = te.var("scale", dtype=dtype)
C = te.compute(A.shape, lambda *i: A(*i) + B(*i), name="C")
# schedule
s = te.create_schedule(C.op)
# create iter var and assign them tags.
num_thread = 16
bx, x = s[C].split(C.op.axis[0], factor=num_thread * 4)
tx, x = s[C].split(x, nparts=num_thread)
_, x = s[C].split(x, factor=4)
s[C].bind(bx, te.thread_axis("blockIdx.x"))
s[C].bind(tx, te.thread_axis("threadIdx.x"))
s[C].vectorize(x)
# one line to build the function.
def check_device(device):
ctx = tvm.context(device, 0)
if not tvm.testing.device_enabled(device):
print("skip because %s is not enabled.." % device)
return
fadd = tvm.build(s, [A, B, C], device, name="myadd")
# launch the kernel.
n = 1024
a = tvm.nd.array((np.random.uniform(size=n) * 256).astype(A.dtype),
ctx)
b = tvm.nd.array((np.random.uniform(size=n) * 256).astype(B.dtype),
ctx)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), ctx)
ftimer = fadd.time_evaluator(fadd.entry_name, ctx, number=1)
tcost = ftimer(a, b, c).mean
tvm.testing.assert_allclose(c.asnumpy(),
a.asnumpy() + b.asnumpy(),
rtol=1e-6)
check_device("opencl")
check_device("cuda")
if dtype == "float32":
check_device("metal")
check_device("vulkan")
def check_llvm():
# Specifically allow offset to test codepath when offset is available
Ab = tvm.tir.decl_buffer(A.shape,
A.dtype,
elem_offset=te.size_var("Aoffset"),
offset_factor=8,
name="A")
binds = {A: Ab}
# BUILD and invoke the kernel.
f = tvm.build(s, [A, B, C], "llvm", binds=binds)
dev = tvm.cpu(0)
# launch the kernel.
n = nn
a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), dev)
b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), dev)
c = tvm.nd.array(np.zeros(n, dtype=C.dtype), dev)
f(a, b, c)
tvm.testing.assert_allclose(c.asnumpy(), a.asnumpy() + b.asnumpy())
