Tianqi Chen
ffea531107
## Summary Lifts 10 host-toolchain / CLI / process / utility modules from `python/tvm/contrib/` to a new `python/tvm/support/` package, and deletes two dead contrib shims. `tvm.support` is the home for Python helpers that integrate TVM with external CLIs and host-side tools — compilers, archivers, subprocess pools, and build-info queries. These are load-bearing internal pieces that TVM's compile/link/run paths depend on. `tvm.contrib` is reserved for optional vendor SDK integrations and experimental features. The distinction is documented in the `tvm.support` package docstring. Moved (one commit each): - `tvm.contrib.cc` → `tvm.support.cc` - `tvm.contrib.nvcc` → `tvm.support.nvcc` - `tvm.contrib.rocm` → `tvm.support.rocm` - `tvm.contrib.ndk` → `tvm.support.ndk` - `tvm.contrib.xcode` → `tvm.support.xcode` - `tvm.contrib.clang` → `tvm.support.clang` - `tvm.contrib.emcc` → `tvm.support.emcc` - `tvm.contrib.popen_pool` → `tvm.support.popen_pool` - `tvm.contrib.utils` → `tvm.support.utils` - `tvm.contrib.tar` → `tvm.support.tar` Deleted: - `tvm.contrib.spirv` — single `optimize()` wrapping `spirv-opt`; zero importers. - `tvm.contrib.rpc` — self-deprecation shim with "removed in 0.5" banner; honoring it. Package conversion: - `python/tvm/support.py` → `python/tvm/support/__init__.py` with inclusion-rule docstring. - `libinfo()` extracted into `python/tvm/support/libinfo.py`. - `FrontendTestModule` dropped (audit confirmed zero callers outside its own definition). ## Compatibility Hard break — no `tvm.contrib.<mod>` re-export shims. All callers updated in this PR. C++-side FFI registry keys (`tvm.contrib.nvcc.*`, etc.) are unchanged — only the Python module path moves. Renaming the FFI keys is a separate follow-up.
109 lines
3.9 KiB
Python
109 lines
3.9 KiB
Python
# Licensed to the Apache Software Foundation (ASF) under one
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# or more contributor license agreements. See the NOTICE file
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# distributed with this work for additional information
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# regarding copyright ownership. The ASF licenses this file
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# to you under the Apache License, Version 2.0 (the
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# "License"); you may not use this file except in compliance
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# with the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing,
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# software distributed under the License is distributed on an
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# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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# KIND, either express or implied. See the License for the
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# specific language governing permissions and limitations
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# under the License.
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# ruff: noqa: F401
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import os
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import numpy as np
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import pytest
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import tvm
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from tvm import rpc, te
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from tvm.contrib import coreml_runtime
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from tvm.support import utils, xcode
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proxy_host = os.environ.get("TVM_IOS_RPC_PROXY_HOST", "127.0.0.1")
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proxy_port = os.environ.get("TVM_IOS_RPC_PROXY_PORT", 9090)
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destination = os.environ.get("TVM_IOS_RPC_DESTINATION", "")
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key = "iphone"
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@pytest.mark.skip("skip because coremltools is not available in CI")
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def test_coreml_runtime():
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import coremltools
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from coremltools.models.neural_network import NeuralNetworkBuilder
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def create_coreml_model():
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shape = (2,)
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alpha = 2
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inputs = [
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("input0", coremltools.models.datatypes.Array(*shape)),
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("input1", coremltools.models.datatypes.Array(*shape)),
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]
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outputs = [
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("output0", coremltools.models.datatypes.Array(*shape)),
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("output1", coremltools.models.datatypes.Array(*shape)),
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]
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builder = NeuralNetworkBuilder(inputs, outputs)
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builder.add_elementwise(
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name="Add", input_names=["input0", "input1"], output_name="output0", mode="ADD"
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)
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builder.add_elementwise(
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name="Mul", alpha=alpha, input_names=["input0"], output_name="output1", mode="MULTIPLY"
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)
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return coremltools.models.MLModel(builder.spec)
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def verify(coreml_model, model_path, dev):
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coreml_model = create_coreml_model()
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out_spec = coreml_model.output_description._fd_spec
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out_names = [spec.name for spec in out_spec]
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# inference via coremltools
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inputs = {}
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for in_spec in coreml_model.input_description._fd_spec:
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name = in_spec.name
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shape = in_spec.type.multiArrayType.shape
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inputs[name] = np.random.random_sample(shape)
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coreml_outputs = [coreml_model.predict(inputs)[name] for name in out_names]
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# inference via tvm coreml runtime
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runtime = coreml_runtime.create("main", model_path, dev)
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for name in inputs:
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runtime.set_input(name, tvm.runtime.tensor(inputs[name], dev))
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runtime.invoke()
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tvm_outputs = [runtime.get_output(i).numpy() for i in range(runtime.get_num_outputs())]
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for c_out, t_out in zip(coreml_outputs, tvm_outputs):
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np.testing.assert_almost_equal(c_out, t_out, 3)
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def check_remote(coreml_model):
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temp = utils.tempdir()
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compiled_model = xcode.compile_coreml(coreml_model, out_dir=temp.temp_dir)
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xcode.popen_test_rpc(
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proxy_host, proxy_port, key, destination=destination, libs=[compiled_model]
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)
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compiled_model = os.path.basename(compiled_model)
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remote = rpc.connect(proxy_host, proxy_port, key=key)
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dev = remote.cpu(0)
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verify(coreml_model, compiled_model, dev)
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def check_local(coreml_model):
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temp = utils.tempdir()
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compiled_model = xcode.compile_coreml(coreml_model, out_dir=temp.temp_dir)
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dev = tvm.cpu(0)
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verify(coreml_model, compiled_model, dev)
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coreml_model = create_coreml_model()
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check_remote(coreml_model)
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check_local(coreml_model)
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if __name__ == "__main__":
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test_coreml_runtime()
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