comaniac commented on a change in pull request #7313: URL: https://github.com/apache/tvm/pull/7313#discussion_r566325374
########## File path: tutorials/auto_scheduler/tune_sparse_x86.py ########## @@ -0,0 +1,331 @@ +# 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. +""" +Auto-scheduling Sparse Matrix Multiplication for CPU by Custom Sketch Rule +========================================================================== +**Author**: `Chengfan Jia <https://github.com/jcf94/>`_ + +This is a tutorial on how to use the auto-scheduler to tune a sparse matrix multiplication for +CPUs. + +Auto-scheduler is designed to explore the schedule with best performance for a given computation +declaration automatically. While sometimes, we may have a demand to try some special ops which may +not been well supported by auto-scheduler's default search policy. Auto-scheduler currently allows +user to provide a CustomSketch to cover these cases. + +We use sparse matrix multiplication as an example in this tutorial. + +Note that this tutorial will not run on Windows or recent versions of macOS. To +get it to run, you will need to wrap the body of this tutorial in a :code:`if +__name__ == "__main__":` block. +""" + +import os +import itertools + +import numpy as np +import tvm +from tvm import te, auto_scheduler, topi +from tvm.auto_scheduler import _ffi_api +from tvm.topi.utils import get_const_tuple + +import scipy.sparse as sp + +###################################################################### +# Define the computation +# ^^^^^^^^^^^^^^^^^^^^^^ +# To begin with, let us define the computation of a sparse matmul with several relu and bias add. +# The function should return the list of input/output tensors. +# From these tensors, the auto-scheduler can get the whole computational graph. + +# We use this function to generate a random bsr matrix +def random_bsr_matrix(M, N, BS_R, BS_C, density, dtype): + import itertools + + Y = np.zeros((M, N), dtype=dtype) + assert M % BS_R == 0 + assert N % BS_C == 0 + nnz = int(density * M * N) + num_blocks = int(nnz / (BS_R * BS_C)) + 1 + candidate_blocks = np.asarray(list(itertools.product(range(0, M, BS_R), range(0, N, BS_C)))) + assert candidate_blocks.shape[0] == M // BS_R * N // BS_C + chosen_blocks = candidate_blocks[ + np.random.choice(candidate_blocks.shape[0], size=num_blocks, replace=False) + ] + for i in range(len(chosen_blocks)): + r, c = chosen_blocks[i] + Y[r : r + BS_R, c : c + BS_C] = np.random.randn(BS_R, BS_C) + s = sp.bsr_matrix(Y, blocksize=(BS_R, BS_C)) + assert s.data.shape == (num_blocks, BS_R, BS_C) + assert s.indices.shape == (num_blocks,) + assert s.indptr.shape == (M // BS_R + 1,) + return s + +@auto_scheduler.register_workload +def sparse_dense(M, N, K, w_data_shape, w_indices_shape, w_indptr_shape, dtype): + X = te.placeholder(shape=(M, K), dtype=dtype) + W_data = te.placeholder(shape=w_data_shape, dtype=dtype) + W_indices = te.placeholder(shape=w_indices_shape, dtype="int32") + W_indptr = te.placeholder(shape=w_indptr_shape, dtype="int32") + B = te.placeholder(shape=(M, N), dtype=dtype) + + out = topi.nn.sparse_dense( + topi.nn.relu(X), W_data, W_indices, W_indptr + ) + out = te.compute((M, N), lambda i, j: out[i, j] + B[i, j], name="BiasAdd") + out = topi.nn.relu(out) + + return [X, W_data, W_indices, W_indptr, B, out] + +###################################################################### +# Special step for sparse workload +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# During schedule tuning, auto-scheduler will use random inputs to measure the performance of a +# generated schedule. While we cannot directly use a random array as the input of a sparse op, for +# the "indices" and "indptr" array are meaningful for the computation. +# +# To solve this problem, we register these as special buffers, and load them when process program +# measuring. +# See the :any:`auto_scheduler.measure` code for more details. Review comment: Sounds good to me. ---------------------------------------------------------------- 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
