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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2022/02/01 11:39:30 UTC
[GitHub] [incubator-mxnet] bartekkuncer commented on a change in pull request #20853: Improve MaskedSoftmax by oneDNN
bartekkuncer commented on a change in pull request #20853:
URL: https://github.com/apache/incubator-mxnet/pull/20853#discussion_r796503011
##########
File path: src/operator/nn/dnnl/dnnl_masked_softmax.cc
##########
@@ -0,0 +1,195 @@
+/*
+ * 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.
+ */
+
+/*!
+ * \file dnnl_maskedsoftmax.cc
+ */
+
+#include "dnnl_masked_softmax-inl.h"
+#include "dnnl_softmax-inl.h"
+
+#if MXNET_USE_ONEDNN == 1
+namespace mxnet {
+namespace op {
+
+bool SupportDNNLMaskedSoftmax(const MaskedSoftmaxParam& param,
+ const std::vector<NDArray>& inputs,
+ const NDArray& output) {
+ CHECK_EQ(inputs.size(), 2);
+ const auto mask = inputs[1];
+ SoftmaxParam softmax_param;
+ softmax_param.axis = param.axis;
+ softmax_param.dtype = inputs[0].dtype();
+ softmax_param.temperature = param.temperature;
+ return mask.dtype() == mshadow::kBool && SupportDNNLSoftmax(softmax_param, inputs[0], output);
+}
+
+inline static dnnl::memory::dims GetOneDNNDims(const NDArray& arr) {
+ return dnnl::memory::dims(arr.shape().begin(), arr.shape().end());
+}
+
+typedef ParamOpSign<MaskedSoftmaxParam> MaskedSoftmaxSignature;
+
+DNNLMaskedSoftmaxFwd& DNNLMaskedSoftmaxFwd::GetCached(
+ const MaskedSoftmaxParam& param,
+ const DNNLMaskedSoftmaxFwd::Tensors& tensors) {
+ using maskedsoftmax_fwd_map =
+ std::unordered_map<MaskedSoftmaxSignature, DNNLMaskedSoftmaxFwd, OpHash>;
+#if DMLC_CXX11_THREAD_LOCAL
+ static thread_local maskedsoftmax_fwd_map fwds;
+#else
+ static MX_THREAD_LOCAL maskedsoftmax_fwd_map fwds;
+#endif
+ MaskedSoftmaxSignature key(param);
+ key.AddSign(tensors.input);
+ key.AddSign(tensors.mask);
+ key.AddSign(tensors.output);
+
+ auto it = fwds.find(key);
+ if (it == fwds.end()) {
+ const DNNLMaskedSoftmaxFwd fwd(param, tensors);
+ it = AddToCache(&fwds, key, fwd);
+ }
+ return it->second;
+}
+
+std::shared_ptr<Primitives> DNNLMaskedSoftmaxFwd::CreatePrimitives(const MaskedSoftmaxParam& param,
+ const Tensors& tensors) {
+ const dnnl::engine engine = CpuEngine::Get()->get_engine();
+
+ auto prim = std::make_shared<Primitives>();
+
+ auto mem_format =
+ static_cast<dnnl::memory::format_tag>(GetDefaultFormat(tensors.input.shape().ndim()));
+ dnnl::memory::dims input_dims = GetOneDNNDims(tensors.input);
+ // output_desc is the same
+ dnnl::memory::desc input_desc =
+ dnnl::memory::desc(input_dims, dnnl::memory::data_type::f32, mem_format);
+
+ dnnl::memory::dims mask_dims = GetOneDNNDims(tensors.mask);
+ dnnl::memory::desc mask_desc =
+ dnnl::memory::desc(mask_dims, dnnl::memory::data_type::s8, mem_format);
+
+ // (mask - 1)
+ auto minusone_desc = dnnl::eltwise_forward::desc(dnnl::prop_kind::forward_scoring,
+ dnnl::algorithm::eltwise_linear,
+ mask_desc,
+ 1.0f, // multiply factor
+ -1.0f); // minus one
+ prim->minusone_pd = dnnl::eltwise_forward::primitive_desc(minusone_desc, engine);
+ prim->minusone = dnnl::eltwise_forward(prim->minusone_pd);
+
+ // input * mask
+ auto mask_input_desc =
+ dnnl::binary::desc(dnnl::algorithm::binary_add,
+ input_desc,
+ dnnl::memory::desc(mask_dims, dnnl::memory::data_type::f32, mem_format),
+ input_desc);
+ prim->mask_input_pd = dnnl::binary::primitive_desc(mask_input_desc, engine);
+ prim->mask_input = dnnl::binary(prim->mask_input_pd);
+
+ // output * mask
+ auto mask_output_desc =
+ dnnl::binary::desc(dnnl::algorithm::binary_mul, input_desc, mask_desc, input_desc);
+ prim->mask_output_pd = dnnl::binary::primitive_desc(mask_output_desc, engine);
+ prim->mask_output = dnnl::binary(prim->mask_output_pd);
+
+ return prim;
+}
+
+void DNNLMaskedSoftmaxForward(const nnvm::NodeAttrs& attrs,
+ const OpContext& ctx,
+ const std::vector<NDArray>& inputs,
+ const std::vector<OpReqType>& req,
+ const std::vector<NDArray>& outputs) {
+ TmpMemMgr::Get()->Init(ctx.requested[0]);
+ const auto& param = nnvm::get<MaskedSoftmaxParam>(attrs.parsed);
+ const auto tensors = DNNLMaskedSoftmaxFwd::Tensors(inputs, outputs);
+ const auto& fwd = DNNLMaskedSoftmaxFwd::GetCached(param, tensors);
+ fwd.Execute(tensors, req, param, ctx.is_train);
+}
+
+void DNNLMaskedSoftmaxFwd::Execute(const Tensors& tensors,
+ const std::vector<OpReqType>& req,
+ const MaskedSoftmaxParam& param,
+ bool is_train) const {
+ using namespace mxnet::op;
+ using namespace mxnet::op::mxnet_op;
+ using namespace dnnl;
+ /*
+ 1. out = input * [(mask - 1) * inf]
Review comment:
Maybe add a title to this e.g. Three steps of masked softmax.
##########
File path: src/operator/nn/dnnl/dnnl_softmax.cc
##########
@@ -195,6 +218,24 @@ DNNLSoftmaxBwd& DNNLSoftmaxBwd::GetCached(const SoftmaxParam& param, const Tenso
return it->second;
}
+DNNLSoftmaxBwd::DNNLSoftmaxBwd(const SoftmaxParam& param, const Tensors& tensors) {
+ const float temperature = param.temperature.has_value() ? param.temperature.value() : 1.0f;
+ const int axis = CheckAxis(param.axis, tensors.out.shape().ndim());
+ const auto out_grad_mem = tensors.out_grad.GetDNNLData();
+ const auto out_mem = tensors.out.GetDNNLData();
+ const auto softmax_fwd_pd = DNNLSoftmaxFwd::GetSoftmaxFwdPd(*out_mem, axis, /*is_train*/ true);
+
+ softmax_bwd_pd = std::make_shared<softmax_bwd_pd_t>(
+ GetSoftmaxBwdPd(*out_grad_mem, *out_mem, axis, softmax_fwd_pd));
+ softmax_bwd = std::make_shared<softmax_bwd_t>(*softmax_bwd_pd);
+
+ if (temperature != 1.0f) { // avoid dividing by 1
Review comment:
Should we not check somewhere if temperature == 0?
##########
File path: src/operator/nn/masked_softmax.cc
##########
@@ -0,0 +1,141 @@
+/*
+ * 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.
+ */
+
+/*!
+ * \file masked_softmax.cc
+ */
+
+#include "./softmax-inl.h"
+#include "../tensor/elemwise_unary_op.h"
Review comment:
Please avoid dots in includes.
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