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[Tests] Replace the Relay interpreter with the VM in the op tests (#1…
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…1386)
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@gigiblender
gigiblender authored May 23, 2022
1 parent 83c9ee1 commit df632ba
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Showing 18 changed files with 645 additions and 569 deletions.
9 changes: 7 additions & 2 deletions python/tvm/relay/testing/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -82,6 +82,7 @@ def check_grad(
mean=0,
mode="higher_order",
target_devices=None,
executor_kind="debug",
):
"""Perform numerical gradient checking given a relay function.
Expand Down Expand Up @@ -146,8 +147,12 @@ def check_grad(
for target, dev in target_devices:
# Eval the backward and forward functions
# TODO(mbs): Evaluate a pair of functions so can share preparation between them.
bwd_func_compiled = relay.create_executor(device=dev, target=target).evaluate(bwd_func)
fwd_func_compiled = relay.create_executor(device=dev, target=target).evaluate(fwd_func)
bwd_func_compiled = relay.create_executor(
executor_kind, device=dev, target=target
).evaluate(bwd_func)
fwd_func_compiled = relay.create_executor(
executor_kind, device=dev, target=target
).evaluate(fwd_func)

# Get analytic gradients.
_, grads = bwd_func_compiled(*inputs)
Expand Down
54 changes: 27 additions & 27 deletions tests/python/relay/dyn/test_dynamic_op_level10.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,9 +27,11 @@
import random
import tvm.testing

executor_kind = tvm.testing.parameter("debug", "vm")


@tvm.testing.uses_gpu
def test_broadcast_to():
def test_broadcast_to(executor_kind):
def verify_more_dynamic_broadcast_to(x_shape, out_shape):
rank = len(out_shape)
dtype = "float32"
Expand All @@ -45,12 +47,13 @@ def verify_more_dynamic_broadcast_to(x_shape, out_shape):
x = np.random.uniform(size=np.prod(x_shape)).astype(dtype)
ref_res = np.broadcast_to(np.reshape(x, x_shape), out_shape)
for target, dev in tvm.testing.enabled_targets():
for kind in ["vm", "debug"]:
mod = tvm.ir.IRModule.from_expr(func)
op_res = relay.create_executor(kind, mod=mod, device=dev, target=target).evaluate(
func
)(x, np.array(x_shape).astype(shape_type), np.array(out_shape).astype(shape_type))
tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-5)
mod = tvm.ir.IRModule.from_expr(func)
op_res = relay.create_executor(
executor_kind, mod=mod, device=dev, target=target
).evaluate(func)(
x, np.array(x_shape).astype(shape_type), np.array(out_shape).astype(shape_type)
)
tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-5)

verify_more_dynamic_broadcast_to((4, 3), (3, 4, 3))

Expand All @@ -70,20 +73,19 @@ def verify_broadcast_to(x_shape, out_shape):
x = np.random.uniform(size=x_shape).astype(dtype)
ref_res = np.broadcast_to(x, out_shape)
for target, dev in tvm.testing.enabled_targets():
for kind in ["vm", "debug"]:
mod = tvm.ir.IRModule.from_expr(func)
op_res = relay.create_executor(kind, mod=mod, device=dev, target=target).evaluate(
func
)(x, np.array(out_shape).astype(shape_type))
tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-5)
mod = tvm.ir.IRModule.from_expr(func)
op_res = relay.create_executor(
executor_kind, mod=mod, device=dev, target=target
).evaluate(func)(x, np.array(out_shape).astype(shape_type))
tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-5)

verify_broadcast_to((1,), (1, 1, 1))
verify_broadcast_to((1, 1), (4, 1, 1))
verify_broadcast_to((4, 1), (1, 4, 3))


@tvm.testing.uses_gpu
def test_dyn_broadcast_to():
def test_dyn_broadcast_to(executor_kind):
dtype = "uint8"
rank = 3
shape_type = "int64"
Expand All @@ -101,16 +103,15 @@ def test_dyn_broadcast_to():
dyn_shape = (1,) * rank
ref_res = np.broadcast_to(x, dyn_shape)
for target, dev in tvm.testing.enabled_targets():
for kind in ["vm", "debug"]:
mod = tvm.ir.IRModule.from_expr(func)
op_res = relay.create_executor(kind, mod=mod, device=dev, target=target).evaluate(func)(
x, np.array(dyn_shape).astype(shape_type)
)
tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-5)
mod = tvm.ir.IRModule.from_expr(func)
op_res = relay.create_executor(executor_kind, mod=mod, device=dev, target=target).evaluate(
func
)(x, np.array(dyn_shape).astype(shape_type))
tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-5)


@tvm.testing.uses_gpu
def test_dyn_one_hot():
def test_dyn_one_hot(executor_kind):
def _get_oshape(indices_shape, depth, axis):
oshape = []
true_axis = len(indices_shape) if axis == -1 else axis
Expand All @@ -135,12 +136,11 @@ def _verify(indices_shape, depth, on_value, off_value, axis, dtype):
indices_np = np.random.randint(0, depth, size=indices_shape).astype("int32")
out_np = tvm.topi.testing.one_hot(indices_np, on_value, off_value, depth, axis, dtype)
for target, dev in tvm.testing.enabled_targets():
for kind in ["vm", "debug"]:
mod = tvm.ir.IRModule.from_expr(func)
out_relay = relay.create_executor(
kind, mod=mod, device=dev, target=target
).evaluate()(indices_np, np.array(depth).astype("int32"))
tvm.testing.assert_allclose(out_relay.numpy(), out_np)
mod = tvm.ir.IRModule.from_expr(func)
out_relay = relay.create_executor(
executor_kind, mod=mod, device=dev, target=target
).evaluate()(indices_np, np.array(depth).astype("int32"))
tvm.testing.assert_allclose(out_relay.numpy(), out_np)

_verify((3,), 3, 1, 0, -1, "int32")
_verify((3,), 3, 1.0, 0.0, -1, "float32")
Expand Down
46 changes: 26 additions & 20 deletions tests/python/relay/dyn/test_dynamic_op_level2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,9 +27,11 @@
import tvm.topi.testing
from tvm.relay.testing import run_infer_type

executor_kind = tvm.testing.parameter("debug", "vm")


@tvm.testing.uses_gpu
def test_dyn_upsampling_run():
def test_dyn_upsampling_run(executor_kind):
def verify_upsampling(dshape, scale_h, scale_w, layout, method, align_corners=False):

if layout == "NCHW":
Expand Down Expand Up @@ -58,12 +60,13 @@ def verify_upsampling(dshape, scale_h, scale_w, layout, method, align_corners=Fa
func = relay.Function([x, scale_h_var, scale_w_var], z)

for target, dev in tvm.testing.enabled_targets():
for kind in ["vm", "debug"]:
mod = tvm.ir.IRModule.from_expr(func)
op_res = relay.create_executor(kind, mod=mod, device=dev, target=target).evaluate()(
x_data, np.array(scale_h).astype("float32"), np.array(scale_w).astype("float32")
)
tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-4, atol=1e-6)
mod = tvm.ir.IRModule.from_expr(func)
op_res = relay.create_executor(
executor_kind, mod=mod, device=dev, target=target
).evaluate()(
x_data, np.array(scale_h).astype("float32"), np.array(scale_w).astype("float32")
)
tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-4, atol=1e-6)

verify_upsampling((1, 16, 32, 32), 3, 2.0, "NCHW", "nearest_neighbor")
verify_upsampling((1, 16, 32, 32), 5, 2.0, "NCHW", "bilinear", True)
Expand All @@ -85,7 +88,7 @@ def test_dyn_upsampling_infer_type_const():


@tvm.testing.uses_gpu
def test_dyn_upsampling3d_run():
def test_dyn_upsampling3d_run(executor_kind):
def verify_upsampling3d(
dshape, scale_d, scale_h, scale_w, layout, method, coord_trans="asymmetric"
):
Expand Down Expand Up @@ -124,15 +127,16 @@ def verify_upsampling3d(
func = relay.Function([x, scale_d_var, scale_h_var, scale_w_var], z)

for target, dev in enabled_targets():
for kind in ["vm", "debug"]:
mod = tvm.ir.IRModule.from_expr(func)
op_res = relay.create_executor(kind, mod=mod, device=dev, target=target).evaluate()(
x_data,
np.array(scale_d).astype("float32"),
np.array(scale_h).astype("float32"),
np.array(scale_w).astype("float32"),
)
tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-4, atol=1e-6)
mod = tvm.ir.IRModule.from_expr(func)
op_res = relay.create_executor(
executor_kind, mod=mod, device=dev, target=target
).evaluate()(
x_data,
np.array(scale_d).astype("float32"),
np.array(scale_h).astype("float32"),
np.array(scale_w).astype("float32"),
)
tvm.testing.assert_allclose(op_res.numpy(), ref_res, rtol=1e-4, atol=1e-6)

verify_upsampling3d((1, 1, 1, 1, 1), 2, 3, 4, "NCDHW", "nearest_neighbor")
verify_upsampling3d((1, 8, 16, 16, 16), 2.0, 3.0, 4.0, "NCDHW", "nearest_neighbor")
Expand Down Expand Up @@ -163,7 +167,7 @@ def test_dyn_upsampling3d_infer_type_const():


@tvm.testing.uses_gpu
def test_dyn_pad():
def test_dyn_pad(executor_kind):
def verify_pad(dshape, pad_width, pad_val, dtype):
x = relay.var("x", relay.TensorType(dshape, dtype))
ndim = len(dshape)
Expand All @@ -178,7 +182,9 @@ def verify_pad(dshape, pad_width, pad_val, dtype):
ref_res = np.pad(data, pad_width, "constant", constant_values=(((pad_val,) * 2),) * ndim)
pad_width = np.array(pad_width).astype("int64")

verify_func(func, [data, pad_width, np.array(pad_val).astype(dtype)], ref_res)
verify_func(
executor_kind, func, [data, pad_width, np.array(pad_val).astype(dtype)], ref_res
)

def verify_pad_default_fill(dshape, pad_width, dtype):
x = relay.var("x", relay.TensorType(dshape, dtype))
Expand All @@ -193,7 +199,7 @@ def verify_pad_default_fill(dshape, pad_width, dtype):
ref_res = np.pad(data, pad_width)
pad_width = np.array(pad_width).astype("int64")

verify_func(func, [data, pad_width], ref_res)
verify_func(executor_kind, func, [data, pad_width], ref_res)

verify_pad((4, 10, 7, 7), ((1, 1), (2, 2), (3, 3), (4, 4)), 2.0, "int32")
verify_pad((2, 7), ((1, 4), (2, 2)), 4.0, "float64")
Expand Down
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