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Infra for tvm op runtime dispatch (apache#16100)
* infra for dispatch tvm op * fix ci and sanity error * disable shape with hint and fix coding style * rename to avoid conflict with original dot * update tvm and use soft link * config file moves to lib/ when using Makefile * add tvmop.conf to ci * fix rebase * fix rebase * use inspect to detect dispatchable func
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,57 @@ | ||
| # 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 time | ||
| import mxnet as mx | ||
| import numpy as _np | ||
| from mxnet import np, npx | ||
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| def measure_cost(repeat, func_name, *args, **kwargs): | ||
| """Measure time cost of running a function | ||
| """ | ||
| mx.nd.waitall() | ||
| start = time.time() | ||
| for _ in range(repeat): | ||
| func_name(*args, **kwargs) | ||
| mx.nd.waitall() | ||
| end = time.time() | ||
| diff = end - start | ||
| return diff / repeat | ||
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| def test_tvm_dot(): | ||
| # benchmark | ||
| for i in list(range(1000, 1100, 4)): | ||
| m = i | ||
| k = i | ||
| n = i | ||
| print("{} * {} X {} * {}".format(m, k, k, n)) | ||
| a = mx.nd.random.uniform(shape=(m, k), dtype='float32') | ||
| b = mx.nd.random.uniform(shape=(k, n), dtype='float32') | ||
| cost = measure_cost(2, mx.nd.contrib.tvm_dot, a, b) | ||
| print("dispatch cost: {} ms".format(cost * 1000)) | ||
| a = mx.nd.random.uniform(shape=(m, k), dtype='float32') | ||
| b = mx.nd.random.uniform(shape=(k, n), dtype='float32') | ||
| cost = measure_cost(2, mx.nd.contrib.tvm_dot_fallback, a, b) | ||
| print("fallback cost: {} ms".format(cost * 1000)) | ||
| a = mx.nd.random.uniform(shape=(m, k), dtype='float32') | ||
| b = mx.nd.random.uniform(shape=(k, n), dtype='float32') | ||
| cost = measure_cost(2, mx.nd.dot, a, b) | ||
| print("dot cost: {} ms".format(cost * 1000)) | ||
|
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| if __name__ == "__main__": | ||
| test_tvm_dot() |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,53 @@ | ||
| # 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. | ||
|
|
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| # coding: utf-8 | ||
| import tvm | ||
| from tvm import autotvm | ||
| from .. import defop, AllTypes | ||
| from .. import assign_by_req, reduce_axes | ||
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| def compute_dot(A, B): | ||
| M = A.shape[0] | ||
| K = A.shape[1] | ||
| N = B.shape[1] | ||
| k = tvm.reduce_axis((0, K), 'k') | ||
| C = tvm.compute((M, N), | ||
| lambda x, y: tvm.sum(A[x, k] * B[k, y], axis=k), | ||
| name='C') | ||
| return C | ||
|
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| @defop(name="dot", target="cpu", dtype=AllTypes) | ||
| def dot(dtype, fallback): | ||
| cfg = autotvm.get_config() | ||
| cfg.define_knob("bn", [64] if fallback else [64, 32]) | ||
| cfg.define_knob("factor", [4] if fallback else [4]) | ||
| M = tvm.var("M") | ||
| K = tvm.var("K") | ||
| N = tvm.var("N") | ||
| A = tvm.placeholder((M, K), name='A', dtype=dtype) | ||
| B = tvm.placeholder((K, N), name='B', dtype=dtype) | ||
| C = compute_dot(A, B) | ||
| s = tvm.create_schedule(C.op) | ||
| # Blocking by loop tiling | ||
| xo, yo, xi, yi = s[C].tile(C.op.axis[0], C.op.axis[1], cfg["bn"].val, cfg["bn"].val) | ||
| k, = s[C].op.reduce_axis | ||
| ko, ki = s[C].split(k, factor=cfg["factor"].val) | ||
| # Hoist reduction domain outside the blocking loop | ||
| s[C].reorder(xo, yo, ko, ki, xi, yi) | ||
| return s, [A, B, C] |
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