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331 lines (271 loc) · 10.8 KB
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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# 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 importlib.util
import numpy as np
import pytest
import tvm
import tvm.testing
from tvm import relax
target, dev = "llvm", tvm.cpu()
def _has_xcode():
try:
import tvm.support.xcode
tvm.support.xcode.xcrun([])
return True
except FileNotFoundError:
pass
return False
requires_coreml_runtime = pytest.mark.skipif(
not (importlib.util.find_spec("coremltools") and _has_xcode()),
reason="coreml is not enabled.",
)
def test_partition_for_coreml_uses_current_relax_passes():
from tvm.relax.backend.metal.coreml import partition_for_coreml
x = relax.Var("x", relax.TensorType([10, 10], "float32"))
y = relax.Var("y", relax.TensorType([10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x, y]):
with bb.dataflow():
lv0 = bb.emit(relax.op.add(x, y))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
partitioned = partition_for_coreml(bb.get())
relax.analysis.well_formed(partitioned)
assert any(
getattr(func, "attrs", None) is not None
and "Codegen" in func.attrs
and str(func.attrs["Codegen"]) == "coreml"
for func in partitioned.functions.values()
)
def verify(mod, inputs):
from tvm.relax.backend.metal.coreml import partition_for_coreml
mod1 = partition_for_coreml(mod)
mod1 = relax.transform.RunCodegen()(mod1)
relax.analysis.well_formed(mod1)
assert mod1.attrs, "Should exist if offloaded successfully."
assert "external_mods" in mod1.attrs, "Should exist if offloaded successfully."
mod1 = relax.transform.LegalizeOps()(mod1)
relax.analysis.well_formed(mod1)
ex1 = tvm.compile(mod1, target=target)
vm1 = relax.VirtualMachine(ex1, dev)
out1 = vm1["main"](*inputs)
mod2 = relax.transform.LegalizeOps()(mod)
ex2 = tvm.compile(mod2, target=target)
vm2 = relax.VirtualMachine(ex2, dev)
out2 = vm2["main"](*inputs)
tvm.testing.assert_allclose(out1.numpy(), out2.numpy(), rtol=1e-3, atol=1e-3)
@requires_coreml_runtime
def test_add():
x = relax.Var("x", relax.TensorType([10, 10], "float32"))
y = relax.Var("y", relax.TensorType([10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x, y]):
with bb.dataflow():
lv0 = bb.emit(relax.op.add(x, y))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
y_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
verify(mod, [x_data, y_data])
@requires_coreml_runtime
def test_add_const():
x = relax.Var("x", relax.TensorType([10, 10], "float32"))
y = relax.const(np.ones([10, 10]), "float32")
bb = relax.BlockBuilder()
with bb.function("main", [x]):
with bb.dataflow():
lv0 = bb.emit(relax.op.add(x, y))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
verify(mod, [x_data])
@requires_coreml_runtime
def test_multiply():
x = relax.Var("x", relax.TensorType([10, 10], "float32"))
y = relax.Var("y", relax.TensorType([10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x, y]):
with bb.dataflow():
lv0 = bb.emit(relax.op.multiply(x, y))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
y_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
verify(mod, [x_data, y_data])
@requires_coreml_runtime
def test_matmul():
x = relax.Var("x", relax.TensorType([8, 10], "float32"))
y = relax.Constant(tvm.runtime.tensor(np.random.rand(10, 8).astype("float32"), dev))
bb = relax.BlockBuilder()
with bb.function("main", [x]):
with bb.dataflow():
lv0 = bb.emit(relax.op.matmul(x, y))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(8, 10).astype("float32"), dev)
verify(mod, [x_data])
x = relax.Var("x", relax.TensorType([8, 10], "float32"))
y = relax.Var("y", relax.TensorType([10, 8], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x, y]):
with bb.dataflow():
lv0 = bb.emit(relax.op.matmul(x, y))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(8, 10).astype("float32"), dev)
y_data = tvm.runtime.tensor(np.random.rand(10, 8).astype("float32"), dev)
verify(mod, [x_data, y_data])
@requires_coreml_runtime
def test_clip():
x = relax.Var("x", relax.TensorType([10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x]):
with bb.dataflow():
lv0 = bb.emit(relax.op.clip(x, 0, 4))
gv0 = bb.emit_output(lv0)
bb.emit_func_output(gv0)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
verify(mod, [x_data])
x = relax.Var("x", relax.TensorType([10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x]):
with bb.dataflow():
lv0 = bb.emit(relax.op.clip(x, 0, 4))
lv1 = bb.emit(relax.op.clip(x, 1, 3))
gv0 = bb.emit_output(lv0)
gv1 = bb.emit_output(lv1)
bb.emit_func_output([gv0, gv1])
x_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
verify(mod, [x_data])
@requires_coreml_runtime
def test_expand_dims():
def get_mod(axis):
x = relax.Var("x", relax.TensorType([10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x]):
with bb.dataflow():
lv0 = bb.emit(relax.op.expand_dims(x, axis=axis))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
return bb.get()
x_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
verify(get_mod(axis=0), [x_data])
verify(get_mod(axis=1), [x_data])
@requires_coreml_runtime
def test_relu():
x = relax.Var("x", relax.TensorType([10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x]):
with bb.dataflow():
lv0 = bb.emit(relax.op.nn.relu(x))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
verify(mod, [x_data])
@requires_coreml_runtime
def test_batch_flatten():
x = relax.Var("x", relax.TensorType([10, 10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x]):
with bb.dataflow():
lv0 = bb.emit(relax.op.nn.batch_flatten(x))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(10, 10, 10).astype("float32"), dev)
verify(mod, [x_data])
@requires_coreml_runtime
def test_softmax():
x = relax.Var("x", relax.TensorType([10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x]):
with bb.dataflow():
lv0 = bb.emit(relax.op.nn.softmax(x))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
verify(mod, [x_data])
@requires_coreml_runtime
def test_conv2d():
x = relax.Var("x", relax.TensorType([1, 3, 224, 224], "float32"))
w = relax.const(np.zeros((16, 3, 3, 3), dtype="float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x]):
with bb.dataflow():
lv0 = bb.emit(relax.op.nn.conv2d(x, w, strides=[2, 2], padding=[1, 1, 1, 1]))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(1, 3, 224, 224).astype("float32"), dev)
verify(mod, [x_data])
@requires_coreml_runtime
def test_global_avg_pool2d():
x = relax.Var("x", relax.TensorType([1, 1, 10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x]):
with bb.dataflow():
lv0 = bb.emit(relax.op.nn.avg_pool2d(x))
gv = bb.emit_output(lv0)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(1, 1, 10, 10).astype("float32"), dev)
verify(mod, [x_data])
@requires_coreml_runtime
def test_subgraph1():
x = relax.Var("x", relax.TensorType([10, 10], "float32"))
y = relax.Var("y", relax.TensorType([10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x, y]):
with bb.dataflow():
lv0 = bb.emit(relax.op.multiply(x, y))
lv1 = bb.emit(relax.op.nn.softmax(lv0))
gv = bb.emit_output(lv1)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
y_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
verify(mod, [x_data, y_data])
@requires_coreml_runtime
def test_subgraph2():
x = relax.Var("x", relax.TensorType([10, 10], "float32"))
y = relax.Var("y", relax.TensorType([10, 10], "float32"))
bb = relax.BlockBuilder()
with bb.function("main", [x, y]):
with bb.dataflow():
# multiply+relu will be offloaded to coreml
lv0 = bb.emit(relax.op.multiply(x, y))
lv1 = bb.emit(relax.op.nn.relu(lv0))
# gelu wouldn't be offloaded to coreml
lv2 = bb.emit(relax.op.nn.gelu(lv1))
# relu would be offloaded to coreml
lv3 = bb.emit(relax.op.nn.relu(lv2))
gv = bb.emit_output(lv3)
bb.emit_func_output(gv)
mod = bb.get()
x_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
y_data = tvm.runtime.tensor(np.random.rand(10, 10).astype("float32"), dev)
verify(mod, [x_data, y_data])
if __name__ == "__main__":
pytest.main([__file__])
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