jcf94 commented on a change in pull request #5962: URL: https://github.com/apache/incubator-tvm/pull/5962#discussion_r448174640
########## File path: python/tvm/ansor/workload_registry.py ########## @@ -0,0 +1,268 @@ +# 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. + +""" +Workload registration and serialization. + +We use a json string to represent a workload (a compute dag). +The format of the string is `[func_name, [args...]]`. +The dag should be the return value of this `func_name(*args)`. + +Rationale: The workload is actually a compute dag defined by tvm dsl. But serializing compute dags +and matching them efficiently is not easy. Therefore, we use the above string to encode a compute +dag. +These strings are efficient for serialization/matching and wont' be too long. +When we need the dag, we decode the string and call the function, which will return the dag. +""" + +from typing import Hashable +import pickle +import json +import hashlib + +import tvm._ffi +from ..te import Tensor, PlaceholderOp, ComputeOp, placeholder +from .utils import get_const_tuple +from .compute_dag import ComputeDAG + +WORKLOAD_FUNC_REGISTRY = {} + + +def register_workload_func(func): + """Register a workload generation function + The input function should take hashable and jsonable arguments + (int, float, tuple of int, tvm.tensor.Tensor, ...) and return a list of tvm.tensor.Tensor. + + Examples + -------- + @register_workload_func + def matmul(N, M, K): + A = te.placeholder((N, K), name='A') + B = te.placeholder((K, M), name='B') + k = te.reduce_axis((0, K), name='k') + C = te.compute((N, M), lambda i, j: tvm.sum(A[i][k] * B[k][j], axis=[k]), name='C') + return [A, B, C] + """ + func_name = func.__name__ + if func_name in WORKLOAD_FUNC_REGISTRY: + raise RuntimeError('%s has been registered already' % func_name) + WORKLOAD_FUNC_REGISTRY[func_name] = func + return func + + +def compute_dag_hash(dag): + """ Get hash value for a ComputeDAG. + + Parameters + ---------- + dag : ComputeDAG + The target ComputeDAG. + + Returns + ------- + hash_value : Str + The hash value of this ComputeDAG in hex digest. + """ + # todo: implement this more carefully and move this to c++ as a member function of ComputeDAG + str_key = '' + for op in dag.ops: + t = op.output(0) + if isinstance(op, PlaceholderOp): + str_key += 'placeholder,' + str_key += str(get_const_tuple(t.shape)) + ',' + str_key += t.dtype + ';' + elif isinstance(op, ComputeOp): + str_key += str(t.op.body) + ',' + str_key += str(get_const_tuple(t.shape)) + ',' + str_key += t.dtype + ';' + else: + raise ValueError("Invalid op: " + op) + + str_key = str_key.encode(encoding='utf-8') + return hashlib.md5(str_key).hexdigest() + + +def register_workload_bufs(bufs): + """ Directly register buffers of a workload and return the workload_key. + + The buffers can be looked up with workload_key_to_tensors by the workload_key. + + Parameters + ---------- + bufs : List[Tensor] + A list of Tensors for the target compute declaration. + + Returns + ------- + workload_key : Str + A workload key mapping to the registered compute declaration. + """ + dag = ComputeDAG(bufs) + key = compute_dag_hash(dag) + WORKLOAD_FUNC_REGISTRY[key] = bufs + return json.dumps((key,)) + + +def list_to_tuple(x): + """Convert a list to a tuple recursively""" + assert isinstance(x, list) + return tuple(list_to_tuple(y) if isinstance(y, list) else y for y in x) + + +def serialize_args(args): + """ + Serialize arguments of a function to a hashable and jsonable tuple. + Currently this is mainly used for tvm.tensor.Tensor + """ + ret = [] + for t in args: + if isinstance(t, Tensor): + t = ('TENSOR', get_const_tuple(t.shape), t.dtype) + elif isinstance(t, list): + t = list_to_tuple(t) + + assert isinstance(t, Hashable), str(t) + " is not hashable" + ret.append(t) + + return tuple(ret) + + +def deserialize_args(args): + """The inverse function of :code:`serialize_args`""" + ret = [] + for t in args: + if isinstance(t, (tuple, list)) and t[0] == 'TENSOR': + ret.append(placeholder(shape=t[1], dtype=t[2])) + else: + ret.append(t) + return ret + + +@tvm._ffi.register_func("ansor.workload_key_to_tensors") +def workload_key_to_tensors(workload_key): + """ Decode a workload key to the input/output tensors. + + Parameters + ---------- + workload_key : Str + The target workload key. + + Returns + ------- + tensors : List[Tensor] + The registered compute declaration Tensors. + """ + workload = json.loads(workload_key) + name = workload[0] Review comment: This confused me at first.... The workload_key is a string of `'["matmul_ansor_test", 128, 128, 128]'`, while the workload is a list `["matmul_ansor_test", 128, 128, 128]`. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org
