update embedding_eltwise_layernorm fuse and kernel. test=develop (#23114)
update embedding_eltwise_layernorm fuse pass and fused kernel, to support multi inputrevert-23830-2.0-beta
Wilber
5 years ago
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/* Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/framework/ir/embedding_eltwise_layernorm_fuse_pass.h"
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#include <gtest/gtest.h>
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#include "paddle/fluid/framework/ir/pass_tester_helper.h"
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namespace paddle {
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namespace framework {
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namespace ir {
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TEST(SkipLayerNormFusePass, basic) {
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// inputs operator output
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// --------------------------------------------------------------------
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// (x, y) elementwise_add -> elementwise_out
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// (elementwise_out, scale, bias) layer_norm -> layer_norm_out...
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Layers layers;
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auto* x0 = layers.data("x0", {1, 256, 1});
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auto* x1 = layers.data("x1", {1, 256, 1});
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auto* x2 = layers.data("x2", {1, 256, 1});
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auto* x3 = layers.data("x3", {1, 256, 1});
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auto* emb0 = layers.data("emb0", {18000, 768}, true);
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auto* emb1 = layers.data("emb1", {4, 768}, true);
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auto* emb2 = layers.data("emb2", {513, 768}, true);
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auto* emb3 = layers.data("emb3", {3, 768}, true);
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auto* lkt0 = layers.embedding(x0, emb0);
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auto* lkt1 = layers.embedding(x1, emb1);
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auto* lkt2 = layers.embedding(x2, emb2);
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auto* lkt3 = layers.embedding(x3, emb3);
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auto* elementwise_out1 = layers.elementwise_add(lkt0, lkt2);
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auto* elementwise_out2 = layers.elementwise_add(elementwise_out1, lkt1);
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auto* elementwise_out3 = layers.elementwise_add(elementwise_out2, lkt3);
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auto* scale = layers.data("scale", {768}, true);
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auto* bias = layers.data("bias", {768}, true);
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layers.layer_norm(elementwise_out3, scale, bias);
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auto* y0 = layers.data("y0", {1, 256, 1});
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auto* y1 = layers.data("y1", {1, 256, 1});
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auto* y2 = layers.data("y2", {1, 256, 1});
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auto* emb0y = layers.data("emb0y", {18000, 768}, true);
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auto* emb1y = layers.data("emb1y", {4, 768}, true);
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auto* emb2y = layers.data("emb2y", {513, 768}, true);
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auto* lkt0y = layers.embedding(y0, emb0y);
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auto* lkt1y = layers.embedding(y1, emb1y);
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auto* lkt2y = layers.embedding(y2, emb2y);
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auto* elementwise_out1y = layers.elementwise_add(lkt0y, lkt2y);
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auto* elementwise_out2y = layers.elementwise_add(elementwise_out1y, lkt1y);
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auto* scaley = layers.data("scaley", {768}, true);
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auto* biasy = layers.data("biasy", {768}, true);
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layers.layer_norm(elementwise_out2y, scaley, biasy);
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std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program()));
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auto pass =
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PassRegistry::Instance().Get("embedding_eltwise_layernorm_fuse_pass");
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int num_nodes_before = graph->Nodes().size();
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VLOG(3) << DebugString(graph);
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graph.reset(pass->Apply(graph.release()));
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int num_nodes_after = graph->Nodes().size();
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int num_fused_nodes_after =
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GetNumOpNodes(graph, "fused_embedding_eltwise_layernorm");
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VLOG(3) << DebugString(graph);
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PADDLE_ENFORCE_EQ(num_nodes_before, num_nodes_after + 28,
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platform::errors::PreconditionNotMet(
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"The number of nodes before and after the fuse does "
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"not meet expectations"));
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PADDLE_ENFORCE_EQ(
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num_fused_nodes_after, 2,
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platform::errors::PreconditionNotMet(
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"The number of fusion nodes does not meet expectations after fuse"));
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}
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} // namespace ir
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} // namespace framework
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} // namespace paddle
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USE_PASS(embedding_eltwise_layernorm_fuse_pass);
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