def create_diamond(x, branch_len):
x1 = x
x2 = x
for _ in range(branch_len):
x1 = relay.exp(x1)
x2 = relay.exp(x2)
return relay.add(x1, x2)
def expected():
x = relay.var("p", shape=(10, 20))
y = x
for i in range(max_fused_ops):
y = relay.exp(y)
f1 = relay.Function([x], y)
x = relay.var("x", shape=(10, 20))
z = relay.Call(f1, [x])
xx = relay.var("pp", shape=(10, 20))
yy = xx
for i in range(n - max_fused_ops):
yy = relay.exp(yy)
f2 = relay.Function([xx], yy)
zz = relay.Call(f2, [z])
return relay.Function([x], zz)
def build_export_vm(device):
"""relay build & export graph"""
x = relay.var("x", shape=(10, 5))
y = relay.var("y", shape=(1, 5))
z = relay.add(x, y)
z = relay.exp(z)
func = relay.Function([x, y], z)
x_data = np.random.rand(10, 5).astype("float32")
y_data = np.random.rand(1, 5).astype("float32")
pt_device = torch.device(device)
if pt_device.type == "cuda":
target = "cuda"
ctx = tvm.cuda(pt_device.index)
else:
target = "llvm"
ctx = tvm.cpu(0)
exe = relay.vm.compile(tvm.IRModule.from_expr(func),
target=target,
params={})
code, lib = exe.save()
export_dir = tempfile.mkdtemp("tvm_export")
# export to tempdir
lib.export_library(os.path.join(export_dir, TVM_ASSETS[0]))
with open(os.path.join(export_dir, TVM_ASSETS[1]), "wb") as fout:
fout.write(code)
vm = tvm.runtime.vm.VirtualMachine(exe, ctx)
res = vm.run(x_data, y_data)
ref_res = np.exp(y_data + x_data)
tvm.testing.assert_allclose(res.numpy(), ref_res, atol=1e-5, rtol=1e-5)
return export_dir
def expected():
x = relay.var("x", shape=(8, 8))
y = relay.var("y", shape=(8, 8))
x0 = relay.var("x0", shape=(8, 8))
y0 = relay.var("y0", shape=(8, 8))
add = x0 + y0
# Function that uses C compiler
func = relay.Function([x0, y0], add)
func = func.set_attribute("Primitive", tvm.expr.IntImm("int32", 1))
func = func.set_attribute("Compiler",
tvm.expr.StringImm("ccompiler"))
func = func.set_attribute("ExternalSymbol",
tvm.expr.StringImm("ccompiler_0"))
add_call = relay.Call(func, [x, y])
# Function that uses default compiler. Ops are fused in this function.
p0 = relay.var("p0", shape=(8, 8))
log = relay.log(p0)
exp = relay.exp(p0)
concat = relay.concatenate([log, exp], axis=0)
fused_func = relay.Function([p0], concat)
fused_func = fused_func.set_attribute("Primitive",
tvm.expr.IntImm("int32", 1))
fused_call = relay.Call(fused_func, [add_call])
main = relay.Function([x, y], fused_call)
mod = relay.Module()
mod["main"] = main
return mod
def test_concatenate():
n, t, d = tvm.var("n"), tvm.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 = relay.ir_pass.infer_type(z)
assert zz.checked_type == relay.TensorType((n, t, 200))
x = relay.exp(x)
z = relay.concatenate((x, y), axis=2)
zz = relay.ir_pass.infer_type(z)
assert zz.checked_type == relay.TensorType((n, t, 200))
z = relay.concatenate((x, y), axis=1)
zz = relay.ir_pass.infer_type(z)
assert zz.checked_type == relay.TensorType((n, t + t, 100))
x = relay.var("x", shape=(10, 5))
y = relay.var("y", shape=(10, 5))
z = relay.concatenate((x, y), axis=1)
# Check result.
func = relay.Function([x, y], z)
x_data = np.random.rand(10, 5).astype('float32')
y_data = np.random.rand(10, 5).astype('float32')
ref_res = np.concatenate((x_data, y_data), axis=1)
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(x_data, y_data)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=0.01)
op_res2 = intrp2.evaluate(func)(x_data, y_data)
tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=0.01)
def expected():
mod = tvm.IRModule()
x = relay.var("x", shape=(8, 8))
y = relay.var("y", shape=(8, 8))
x0 = relay.var("x0", shape=(8, 8))
y0 = relay.var("y0", shape=(8, 8))
add = x0 + y0
# Function that uses C compiler
func = relay.Function([x0, y0], add)
func = set_func_attr(func, "ccompiler", "ccompiler_0")
glb_0 = relay.GlobalVar("ccompiler_0")
mod[glb_0] = func
add_call = relay.Call(glb_0, [x, y])
# Function that uses default compiler. Ops are fused in this function.
p0 = relay.var("p0", shape=(8, 8))
log = relay.log(p0)
exp = relay.exp(p0)
concat = relay.concatenate([log, exp], axis=0)
fused_func = relay.Function([p0], concat)
fused_func = fused_func.with_attr("Primitive",
tvm.tir.IntImm("int32", 1))
fused_call = relay.Call(fused_func, [add_call])
main = relay.Function([x, y], fused_call)
mod["main"] = main
return mod
def before(dim):
X = relay.var("X", shape=(1, dim))
W = relay.var("W", shape=(3 * dim, dim))
matmul = relay.nn.dense(X, W)
splitted = relay.split(matmul, indices_or_sections=3, axis=1)
out = relay.sigmoid(splitted[0]) + relay.tanh(splitted[1]) * relay.exp(splitted[2])
return relay.Function([X, W], out)
def get_inner_func_3():
x = relay.var("x", shape=(1, 4, 5, 6), dtype="int8")
x = relay.abs(x)
x = relay.nn.relu(x)
x = relay.exp(x)
x = _create_primitive_function(x)
return x
def before(dim):
X = relay.var("X", shape=(1, dim))
W = relay.var("W", shape=(3 * dim, dim))
matmul = relay.nn.dense(X, W)
splitted = relay.split(matmul, indices_or_sections=3, axis=1)
out = relay.sigmoid(splitted[0]) + relay.tanh(splitted[1]) * relay.exp(splitted[2])
return relay.Function([X, W], out)
def before():
x = relay.var("x", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.exp(y)
w = relay.squeeze(z)
mod = tvm.IRModule()
mod["main"] = relay.Function([x], w)
return mod
def expected():
x = relay.var("p", shape=(10, 20))
y = x
for i in range(max_fused_ops):
y = relay.exp(y)
f1 = relay.Function([x], y)
f1 = f1.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=(10, 20))
z = relay.Call(f1, [x])
xx = relay.var("pp", shape=(10, 20))
yy = xx
for i in range(n - max_fused_ops):
yy = relay.exp(yy)
f2 = relay.Function([xx], yy)
f2 = f2.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
zz = relay.Call(f2, [z])
return relay.Function([x], zz)
def expected():
x = relay.var("p", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.exp(y)
f1 = relay.Function([x], z)
x = relay.var("x", shape=(10, 20))
y = relay.Call(f1, [x])
return relay.Function([x], y)
def expected():
x = relay.var("p", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.exp(y)
f1 = relay.Function([x], z)
x = relay.var("x", shape=(10, 20))
y = relay.Call(f1, [x])
return relay.Function([x], y)
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 test1():
x = relay.Var('x')
y = relay.exp(x)
print('y', type(y), y)
print('y.__dir__()', y.__dir__())
print('y.op.__dir__()', y.op.__dir__())
lib.test_call_node(y.handle)
print('done')
def get_func():
add = relay.add(x, y)
sqrt = relay.sqrt(add)
log = relay.log(add)
subtract = relay.subtract(sqrt, log)
exp = relay.exp(subtract)
func = relay.Function([x, y], exp)
return func
def get_func():
add = relay.add(x, y)
sqrt = relay.sqrt(add)
log = relay.log(add)
subtract = relay.subtract(sqrt, log)
exp = relay.exp(subtract)
func = relay.Function([x, y], exp)
return func
def test_fuse_simple():
"""Simple testcase."""
x = relay.var("x", shape=(10, 20))
y = relay.add(x, x)
z = relay.exp(y)
z = relay.ir_pass.infer_type(z)
zz = relay.ir_pass.fuse_ops(z)
zz = relay.ir_pass.fuse_ops(zz)
zz = relay.ir_pass.infer_type(zz)
zz.astext()
def safe_exp(w):
slope = relay.const(np.exp(1, dtype=np.float32))
lin_bool = w > slope
lin_region = relay.cast(lin_bool, "float32")
lin_out = slope * w
exp_out = relay.exp(relay.where(lin_bool, relay.zeros_like(w), w))
out = lin_region * lin_out + (relay.const(1.) - lin_region) * exp_out
return out
def expected():
x = relay.var("p", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.exp(y)
w = relay.squeeze(z)
f1 = relay.Function([x], w)
f1 = f1.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=(10, 20))
y = relay.Call(f1, [x])
return relay.Function([x], y)
def expected():
add = relay.add(x, y)
sqrt = relay.sqrt(add)
log = relay.log(add)
subtract = relay.subtract(sqrt, log)
copy_sub_exp = relay.device_copy(subtract, dev_ctx, cpu_ctx)
exp = relay.exp(copy_sub_exp)
func = relay.Function([x, y], exp)
return func
def expected():
add = relay.add(x, y)
sqrt = relay.sqrt(add)
log = relay.log(add)
subtract = relay.subtract(sqrt, log)
copy_sub_exp = relay.device_copy(subtract, dev_dev, cpu_dev)
exp = relay.exp(copy_sub_exp)
func = relay.Function([x, y], exp)
return func
def expected():
x = relay.var("p", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.exp(y)
w = relay.squeeze(z)
f1 = relay.Function([x], w)
x = relay.var("x", shape=(10, 20))
y = relay.Call(f1, [x])
mod = relay.module.Module()
mod["main"] = relay.Function([x], y)
return mod
def annotated():
add = relay.add(x, y)
sqrt = relay.sqrt(add)
log = relay.log(add)
subtract = relay.subtract(sqrt, log)
exp = relay.exp(subtract)
_exp = relay.annotation.on_device(exp, cpu_dev)
func = relay.Function([x, y], _exp)
func = run_opt_pass(func, transform.RewriteAnnotatedOps(dev_dev.device_type))
return func
def expected():
x = relay.var("p", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.exp(y)
w = relay.squeeze(z)
f1 = relay.Function([x], w)
f1 = f1.set_attribute("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=(10, 20))
y = relay.Call(f1, [x])
mod = tvm.IRModule()
mod["main"] = relay.Function([x], y)
return mod
def expected():
add = relay.add(x, y)
copy_add_sqrt = relay.device_copy(add, cpu_ctx, dev_ctx)
sqrt = relay.sqrt(copy_add_sqrt)
log = relay.log(add)
copy_sqrt_subtract = relay.device_copy(sqrt, dev_ctx, cpu_ctx)
subtract = relay.subtract(copy_sqrt_subtract, log)
copy_sub_exp = relay.device_copy(subtract, cpu_ctx, dev_ctx)
exp = relay.exp(copy_sub_exp)
func = relay.Function([x, y], exp)
return func
def test_plan_memory():
# it is sufficient to cycle through two memories.
x = relay.var("x", shape=(10, ))
y = relay.var("x", shape=(1, ))
y2 = relay.exp(y)
z = relay.add(x, y2)
z = relay.exp(z)
z = relay.exp(z)
z = relay.exp(z)
z = relay.exp(z)
z = relay.exp(z)
func = relay.Function([x, y], z)
func = relay.ir_pass.infer_type(func)
func = relay.ir_pass.fuse_ops(func, opt_level=0)
func = relay.ir_pass.infer_type(func)
smap = relay.backend._backend.GraphPlanMemory(func)
storage_ids = set()
for k, v in smap.items():
for x in v:
storage_ids.add(x.value)
# Current rule requires vars have unique storage id
# because we don't do inplace, we will need another
# two alternating temporary space.
assert len(storage_ids) == 4
def test_plan_memory():
# it is sufficient to cycle through two memories.
x = relay.var("x", shape=(10,))
y = relay.var("x", shape=(1,))
y2 = relay.exp(y)
z = relay.add(x, y2)
z = relay.exp(z)
z = relay.exp(z)
z = relay.exp(z)
z = relay.exp(z)
z = relay.exp(z)
func = relay.Function([x, y], z)
func = relay.ir_pass.infer_type(func)
func = relay.ir_pass.fuse_ops(func, opt_level=0)
func = relay.ir_pass.infer_type(func)
smap = relay.backend._backend.GraphPlanMemory(func)
storage_ids = set()
device_types = set()
for k, v in smap.items():
assert len(v) == 2
for x in v[0]:
storage_ids.add(x.value)
for x in v[1]:
device_types.add(x.value)
# Current rule requires vars have unique storage id
# because we don't do inplace, we will need another
# two alternating temporary space.
assert len(storage_ids) == 4
assert len(device_types) == 1
def test_plan_memory():
# it is sufficient to cycle through two memories.
x = relay.var("x", shape=(10, ))
y = relay.var("x", shape=(1, ))
y2 = relay.exp(y)
z = relay.add(x, y2)
z = relay.exp(z)
z = relay.exp(z)
z = relay.exp(z)
z = relay.exp(z)
z = relay.exp(z)
func = relay.Function([x, y], z)
mod = tvm.IRModule.from_expr(func)
mod = relay.transform.FuseOps(0)(mod)
func = mod["main"]
smap = relay.backend._backend.GraphPlanMemory(func)
storage_ids = set()
device_types = set()
for k, v in smap.items():
assert len(v) == 2
for x in v[0]:
storage_ids.add(x.value)
for x in v[1]:
device_types.add(x.value)
# Current rule requires vars have unique storage id
# because we don't do inplace, we will need another
# two alternating temporary space.
assert len(storage_ids) == 4
assert len(device_types) == 1
def test_extern_ccompiler_default_ops():
def expected():
mod = tvm.IRModule()
x = relay.var("x", shape=(8, 8))
y = relay.var("y", shape=(8, 8))
x0 = relay.var("x0", shape=(8, 8))
y0 = relay.var("y0", shape=(8, 8))
add = x0 + y0
# Function that uses C compiler
func = relay.Function([x0, y0], add)
func = func.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
func = func.with_attr("Inline", tvm.tir.IntImm("int32", 1))
func = func.with_attr("Compiler", tvm.tir.StringImm("ccompiler"))
func = func.with_attr("ExternalSymbol",
tvm.tir.StringImm("ccompiler_0"))
glb_0 = relay.GlobalVar("ccompiler_0")
mod[glb_0] = func
add_call = relay.Call(glb_0, [x, y])
# Function that uses default compiler. Ops are fused in this function.
p0 = relay.var("p0", shape=(8, 8))
log = relay.log(p0)
exp = relay.exp(p0)
concat = relay.concatenate([log, exp], axis=0)
fused_func = relay.Function([p0], concat)
fused_func = fused_func.with_attr("Primitive",
tvm.tir.IntImm("int32", 1))
fused_call = relay.Call(fused_func, [add_call])
main = relay.Function([x, y], fused_call)
mod["main"] = main
return mod
x = relay.var("x", shape=(8, 8))
y = relay.var("y", shape=(8, 8))
add = x + y
log = relay.log(add)
exp = relay.exp(add)
concat = relay.concatenate([log, exp], axis=0)
f = relay.Function([x, y], concat)
mod = tvm.IRModule()
mod["main"] = f
mod = WhiteListAnnotator(["add", "subtract", "multiply"], "ccompiler")(mod)
mod = transform.PartitionGraph()(mod)
fused_mod = transform.FuseOps(2)(mod)
expected_mod = expected()
assert relay.alpha_equal(fused_mod, expected_mod)
x_data = np.random.rand(8, 8).astype('float32')
y_data = np.random.rand(8, 8).astype('float32')
np_add = x_data + y_data
res = np.concatenate([np.log(np_add), np.exp(np_add)])
check_result(mod, {"x": x_data, "y": y_data}, (16, 8), res)
def expected(dshape):
x = relay.var("p0", shape=dshape)
y = relay.add(x, relay.const(1, "float32"))
f1 = relay.Function([x], y)
x = relay.var("p01", shape=dshape)
y = relay.exp(x)
f2 = relay.Function([x], y)
x = relay.var("x", shape=dshape)
y = relay.Call(f1, [x])
z = relay.Call(f2, [y])
return relay.Function([x], z)
def test_exp():
x = relay.var("x", shape=(1, 16, 16, 16), dtype="float32")
y = relay.exp(x)
func = relay.Function([x], y)
mod = tvm.IRModule.from_expr(func)
fast_mod = FastMath()(mod)
assert "fast_exp" in fast_mod.astext()
# Check that FastMath option works for relay.build.
with relay.build_config(opt_level=3, required_pass=['FastMath']):
fast_mod = relay.optimize(mod, target='llvm', params=None)
assert "fast_exp" in fast_mod[0].astext()
def expected(dshape):
x = relay.var("p0", shape=dshape)
y = relay.add(x, relay.const(1, "float32"))
f1 = relay.Function([x], y)
x = relay.var("p01", shape=dshape)
y = relay.exp(x)
f2 = relay.Function([x], y)
x = relay.var("x", shape=dshape)
y = relay.Call(f1, [x])
z = relay.Call(f2, [y])
return relay.Function([x], z)
def annotated():
add = relay.add(x, y)
sqrt = relay.sqrt(add)
log = relay.log(add)
subtract = relay.subtract(sqrt, log)
exp = relay.exp(subtract)
_exp = relay.annotation.on_device(exp, cpu_ctx)
func = relay.Function([x, y], _exp)
func = relay.ir_pass.infer_type(func)
func = relay.ir_pass.rewrite_annotated_ops(func,
dev_ctx.device_type)
return func
def test_concatenate():
n, t, d = tvm.var("n"), tvm.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))
y = relay.var("y", shape=(10, 5))
t = relay.var("z", shape=())
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('float32')
y_data = np.random.rand(10, 5).astype('float32')
t_data = np.random.uniform(size=()).astype('float32')
ref_res = np.concatenate((x_data, y_data), axis=1) + t_data
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(x_data, y_data, t_data)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=0.01)
op_res2 = intrp2.evaluate(func)(x_data, y_data, t_data)
tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=0.01)
def expected(dshape):
x = relay.var("p0", shape=dshape)
y = relay.add(x, relay.const(1, "float32"))
f1 = relay.Function([x], y)
f1 = f1.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("p01", shape=dshape)
y = relay.exp(x)
f2 = relay.Function([x], y)
f2 = f2.with_attr("Primitive", tvm.tir.IntImm("int32", 1))
x = relay.var("x", shape=dshape)
y = relay.Call(f1, [x])
z = relay.Call(f2, [y])
return relay.Function([x], z)
def expected(dim):
p0 = relay.var("p0", shape=(1, dim))
p1 = relay.var("p1", shape=(3 * dim, dim))
matmul = relay.nn.dense(p0, p1)
f0 = relay.Function([p0, p1], matmul)
p01 = relay.var("p01", shape=(1, 3 * dim))
splitted = relay.split(p01, indices_or_sections=3, axis=1)
out = relay.sigmoid(splitted[0]) + relay.tanh(splitted[1]) * relay.exp(splitted[2])
f1 = relay.Function([p01], out)
X = relay.var("X", shape=(1, dim))
W = relay.var("W", shape=(3 * dim, dim))
y = relay.Call(f0, [X, W])
z = relay.Call(f1, [y])
return relay.Function([X, W], z)
def annotated():
add = relay.add(x, y)
sqrt = relay.sqrt(add)
log = relay.log(add)
subtract = relay.subtract(sqrt, log)
exp = relay.exp(subtract)
_exp = relay.annotation.on_device(exp, cpu_ctx)
func = relay.Function([x, y],
relay.Tuple(tvm.convert([_exp, exp])))
func = relay.ir_pass.infer_type(func)
func = relay.ir_pass.rewrite_annotated_ops(func,
dev_ctx.device_type)
func = relay.ir_pass.infer_type(func)
return relay.Function(relay.ir_pass.free_vars(func.body[1]),
func.body[1])
def test_with_params():
x = relay.var('x', shape=(10, 5))
y = relay.var('y', shape=(1, 5))
z = relay.add(x, y)
z = relay.exp(z)
func = relay.Function([x, y], z)
x_data = np.random.rand(10, 5).astype('float32')
y_data = np.random.rand(1, 5).astype('float32')
params = {"y": y_data}
graph, lib, params = relay.build(func, "llvm", params=params)
mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
mod.set_input(**params)
mod.set_input(x=x_data)
mod.run()
res = mod.get_output(0).asnumpy()
ref_res = np.exp(y_data + x_data)
tvm.testing.assert_allclose(res, ref_res)
def test_concatenate():
n, t, d = tvm.var("n"), tvm.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 = relay.ir_pass.infer_type(z)
assert zz.checked_type == relay.TensorType((n, t, 200))
x = relay.exp(x)
z = relay.concatenate((x, y), axis=2)
zz = relay.ir_pass.infer_type(z)
assert zz.checked_type == relay.TensorType((n, t, 200))
z = relay.concatenate((x, y), axis=1)
zz = relay.ir_pass.infer_type(z)
assert zz.checked_type == relay.TensorType((n, t + t, 100))
x = relay.var("x", shape=(10, 5))
y = relay.var("y", shape=(10, 5))
t = relay.var("z", shape=())
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('float32')
y_data = np.random.rand(10, 5).astype('float32')
t_data = np.random.uniform(size=()).astype('float32')
ref_res = np.concatenate((x_data, y_data), axis=1) + t_data
for target, ctx in ctx_list():
intrp1 = relay.create_executor("graph", ctx=ctx, target=target)
intrp2 = relay.create_executor("debug", ctx=ctx, target=target)
op_res1 = intrp1.evaluate(func)(x_data, y_data, t_data)
tvm.testing.assert_allclose(op_res1.asnumpy(), ref_res, rtol=0.01)
op_res2 = intrp2.evaluate(func)(x_data, y_data, t_data)
tvm.testing.assert_allclose(op_res2.asnumpy(), ref_res, rtol=0.01)
def before():
x = relay.var("x", shape=(10, 20))
y = relay.add(x, relay.const(1, "float32"))
z = relay.exp(y)
w = relay.squeeze(z)
return relay.Function([x], w)
def before(dshape):
x = relay.var("x", shape=dshape)
y = relay.add(x, relay.const(1, "float32"))
y = relay.annotation.stop_fusion(y)
z = relay.exp(y)
return relay.Function([x], z)
