From commits-return-9245-archive-asf-public=cust-asf.ponee.io@tvm.apache.org Mon Mar 23 11:31:11 2020 Return-Path: X-Original-To: archive-asf-public@cust-asf.ponee.io Delivered-To: archive-asf-public@cust-asf.ponee.io Received: from mail.apache.org (hermes.apache.org [207.244.88.153]) by mx-eu-01.ponee.io (Postfix) with SMTP id 7BA6418064F for ; Mon, 23 Mar 2020 12:31:11 +0100 (CET) Received: (qmail 55473 invoked by uid 500); 23 Mar 2020 11:31:10 -0000 Mailing-List: contact commits-help@tvm.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Reply-To: dev@tvm.apache.org Delivered-To: mailing list commits@tvm.apache.org Received: (qmail 55464 invoked by uid 99); 23 Mar 2020 11:31:10 -0000 Received: from ec2-52-202-80-70.compute-1.amazonaws.com (HELO gitbox.apache.org) (52.202.80.70) by apache.org (qpsmtpd/0.29) with ESMTP; Mon, 23 Mar 2020 11:31:10 +0000 From: GitBox To: commits@tvm.apache.org Subject: [GitHub] [incubator-tvm] Shawn-Inspur commented on a change in pull request #5099: [TOPI][Tensor Core] Conv2d and Dense ops support on Tensor Core Message-ID: <158496307081.9024.715488359117045937.gitbox@gitbox.apache.org> References: In-Reply-To: Date: Mon, 23 Mar 2020 11:31:10 -0000 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 8bit Shawn-Inspur commented on a change in pull request #5099: [TOPI][Tensor Core] Conv2d and Dense ops support on Tensor Core URL: https://github.com/apache/incubator-tvm/pull/5099#discussion_r396384057 ########## File path: topi/python/topi/cuda/dense_tensorcore.py ########## @@ -0,0 +1,290 @@ +# 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. +# pylint: disable=invalid-name, too-many-locals, too-many-statements, unused-argument +"""Compute and Schedule definition for dense tensorcore with cuda backend""" +from __future__ import absolute_import as _abs +import tvm +from tvm import te +import tvm.autotvm as autotvm +from .. import tag +from ..util import traverse_inline, get_const_tuple +from .tensor_intrin import intrin_wmma_load_matrix_A, \ + intrin_wmma_load_matrix_W, intrin_wmma_store_matrix + + +@autotvm.register_topi_compute("dense_tensorcore.cuda") +def dense_tensorcore(cfg, data, weight, bias=None, out_dtype=None): + """Dense tensorcore operator on CUDA""" + matmul = dense_tensorcore_cuda(data, weight, bias, out_dtype) + return matmul + + +@autotvm.register_topi_schedule("dense_tensorcore.cuda") +def schedule_dense_tensorcore(cfg, outs): + """Schedule dense operator using Tensorcore""" + outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs + s = te.create_schedule([x.op for x in outs]) + + def _callback(op): + if op.tag == 'dense_tensorcore': + _schedule_dense_tensorcore(cfg, s, op.output(0)) + traverse_inline(s, outs[0].op, _callback) + return s + + +def dense_tensorcore_cuda(data, weight, bias=None, out_dtype=None): + """Dense tensorcore operator on CUDA""" + assert len(data.shape) == 2 and len(weight.shape) == 2, \ + "only support 2-dim dense" + if bias is not None: + assert len(bias.shape) == 1 + if out_dtype is None: + out_dtype = data.dtype + batch, in_dim = data.shape + out_dim, _ = weight.shape + k = te.reduce_axis((0, in_dim), name='k') + data_16 = te.compute((batch, in_dim), lambda b, i: data[b, i].astype('float16')) + weight_16 = te.compute((out_dim, in_dim), lambda o, i: weight[o, i].astype('float16')) + matmul = te.compute((batch, out_dim), \ + lambda i, j: te.sum(data_16[i, k].astype(out_dtype) * \ + weight_16[j, k].astype(out_dtype), axis=k), \ + name='T_dense', tag='dense_tensorcore') + if bias is not None: + matmul = te.compute((batch, out_dim), \ + lambda i, j: matmul[i, j] + bias[j].astype(out_dtype), \ + tag=tag.BROADCAST) + return matmul + + +def _schedule_dense_tensorcore(cfg, s, C): + """Schedule dense operator using Tensorcore""" + A, B = s[C].op.input_tensors + batch, out_dim = get_const_tuple(C.shape) + out_dtype = C.dtype + s[A].compute_inline() + s[B].compute_inline() + + # Explicit memory access + AS = s.cache_read(A, 'shared', [C]) + BS = s.cache_read(B, 'shared', [C]) + AF = s.cache_read(AS, 'wmma.matrix_a', [C]) + BF = s.cache_read(BS, 'wmma.matrix_b', [C]) + CF = s.cache_write(C, 'wmma.accumulator') + CS = s.cache_read(CF, 'shared', [C]) + + # fallback support + target = tvm.target.Target.current() + if cfg.is_fallback: + ref_log = autotvm.tophub.load_reference_log( + target.target_name, target.model, 'dense_tensorcore.cuda') + cfg.fallback_with_reference_log(ref_log) + + # Deal with op fusion, such as bias and relu + if C.op not in s.outputs: + s[C].compute_inline() + C = s.outputs[0].output(0) + + # create tuning space + cfg.define_knob("block_row_warps", [1, 2, 4]) + cfg.define_knob("block_col_warps", [1, 2, 4]) + cfg.define_knob("warp_row_tiles", [1, 2, 4]) + cfg.define_knob("warp_col_tiles", [1, 2, 4]) + cfg.define_knob("chunk", [1, 2, 4, 8]) + cfg.define_knob("offset", [0, 8]) + cfg.define_knob("offsetCS", [0, 8]) + cfg.define_knob("vec", [1, 2, 4, 8]) + + #Make it available by default Review comment: It means the default parameters are applicable when users do not take autoTVM to fine tune the parameters. 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