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paddle/fluid/framework/ir/fuse_multi_transformer_layer_pass_tester.cc
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| /* Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | ||
| Licensed 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. */ | ||
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| #include <gtest/gtest.h> | ||
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| #include "paddle/fluid/framework/ir/fuse_multi_transformer_layer_pass.h" | ||
| #include "paddle/fluid/framework/ir/pass_tester_helper.h" | ||
| #include "paddle/fluid/framework/op_version_registry.h" | ||
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| #define DEF_INPUT_DATA \ | ||
| Layers layers; \ | ||
| int num_layers = 3; \ | ||
| auto* x = layers.data("x", {1, 128, 1024}); \ | ||
| auto* src_mask = layers.data("src_mask", {1, 16, 128, 128}); \ | ||
| auto* ln_scale = layers.data("ln_scale", {1024}, true); \ | ||
| auto* ln_bias = layers.data("ln_bias", {1024}, true); \ | ||
| auto* ffn_ln_scale = layers.data("ffn_ln_scale", {1024}, true); \ | ||
| auto* ffn_ln_bias = layers.data("ffn_ln_bias", {1024}, true); \ | ||
| auto* qkv_w = layers.data("qkv_w", {3, 16, 64, 1024}, true); \ | ||
| auto* out_linear_w = layers.data("out_linear_w", {1024, 1024}, true); \ | ||
| auto* ffn1_w = layers.data("ffn1_w", {1024, 4096}, true); \ | ||
| auto* ffn2_w = layers.data("ffn2_w", {4096, 1024}, true); \ | ||
| auto* qkv_bias = layers.data("qkv_bias", {3072}, true); \ | ||
| auto* out_linear_bias = layers.data("out_linear_bias", {1024}, true); \ | ||
| auto* ffn1_bias = layers.data("ffn1_bias", {4096}, true); \ | ||
| auto* ffn2_bias = layers.data("ffn2_bias", {1024}, true); | ||
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| namespace paddle { | ||
| namespace framework { | ||
| namespace ir { | ||
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| void AddVarToScope(Scope* param_scope, | ||
| const std::string& name, | ||
| const DDim& dims) { | ||
| auto* tensor = param_scope->Var(name)->GetMutable<phi::DenseTensor>(); | ||
| tensor->Resize(dims); | ||
| tensor->mutable_data<float>(platform::CPUPlace()); | ||
| } | ||
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| Scope* CreateParamScope() { | ||
| auto param_scope = new Scope(); | ||
| AddVarToScope(param_scope, "ln_scale", {1024}); | ||
| AddVarToScope(param_scope, "ln_bias", {1024}); | ||
| AddVarToScope(param_scope, "ffn_ln_scale", {1024}); | ||
| AddVarToScope(param_scope, "ffn_ln_bias", {1024}); | ||
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| AddVarToScope(param_scope, "qkv_w", {3, 16, 64, 1024}); | ||
| AddVarToScope(param_scope, "out_linear_w", {1024, 1024}); | ||
| AddVarToScope(param_scope, "ffn1_w", {1024, 4096}); | ||
| AddVarToScope(param_scope, "ffn2_w", {4096, 1024}); | ||
| AddVarToScope(param_scope, "qkv_bias", {3072}); | ||
| AddVarToScope(param_scope, "out_linear_bias", {1024}); | ||
| AddVarToScope(param_scope, "ffn1_bias", {4096}); | ||
| AddVarToScope(param_scope, "ffn2_bias", {1024}); | ||
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| return param_scope; | ||
| } | ||
| TEST(FuseMultiTransformerLayerPass, encoder_fp) { | ||
| // Layers layers; | ||
| // int num_layers = 3; | ||
| // // Vars | ||
| // auto* x = layers.data("x", {1, 128, 1024}); | ||
| // auto* src_mask = layers.data("src_mask", {1, 16, 128, 128}); | ||
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| // auto* ln_scale = layers.data("ln_scale", {1024}, true); | ||
| // auto* ln_bias = layers.data("ln_bias", {1024}, true); | ||
| // auto* ffn_ln_scale = layers.data("ffn_ln_scale", {1024}, true); | ||
| // auto* ffn_ln_bias = layers.data("ffn_ln_bias", {1024}, true); | ||
| // auto* qkv_w = layers.data("qkv_w", {3, 16, 64, 1024}, true); | ||
| // auto* out_linear_w = layers.data("out_linear_w", {1024, 1024}, true); | ||
| // auto* ffn1_w = layers.data("ffn1_w", {1024, 4096}, true); | ||
| // auto* ffn2_w = layers.data("ffn2_w", {4096, 1024}, true); | ||
| // auto* qkv_bias = layers.data("qkv_bias", {3072}, true); | ||
| // auto* out_linear_bias = layers.data("out_linear_bias", {1024}, true); | ||
| // auto* ffn1_bias = layers.data("ffn1_bias", {4096}, true); | ||
| // auto* ffn2_bias = layers.data("ffn2_bias", {1024}, true); | ||
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| DEF_INPUT_DATA | ||
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| // Layers | ||
| for (int i = 0; i < num_layers; ++i) { | ||
| std::cout << "begin to add fill const layer " << i << std::endl; | ||
| auto* cache_kv = layers.fill_constant_batch_size_like( | ||
| x, | ||
| static_cast<int>(proto::VarType::FP32), | ||
| 0, | ||
| 1, | ||
| {2, -1, 16, 1024, 64}, | ||
| 0); | ||
| std::cout << "begin to add fused_multi_transformer layer " << i | ||
| << std::endl; | ||
| auto* out = layers.fused_multi_transformer(x, | ||
| cache_kv, | ||
| src_mask, | ||
| qkv_w, | ||
| qkv_bias, | ||
| out_linear_w, | ||
| out_linear_bias, | ||
| ffn1_w, | ||
| ffn1_bias, | ||
| ffn2_w, | ||
| ffn2_bias, | ||
| ln_scale, | ||
| ln_bias, | ||
| ffn_ln_scale, | ||
| ffn_ln_bias, | ||
| 0.1, | ||
| 1e-12); | ||
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| x = out; | ||
| } | ||
| std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program())); | ||
| graph->Set("__param_scope__", CreateParamScope()); | ||
| graph->Set(kFusedMultiTransformerEncoderFusionCount, new int(num_layers)); | ||
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| auto pass = PassRegistry::Instance().Get("fuse_multi_transformer_layer_pass"); | ||
| if (pass.get() == nullptr) | ||
| LOG(INFO) << "get fuse_multi_transformer_layer_pass failed"; | ||
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| graph.reset(pass->Apply(graph.release())); | ||
| int num_nodes_after = GetNumOpNodes(graph, "fused_multi_transformer"); | ||
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| PADDLE_ENFORCE_EQ( | ||
| num_nodes_after, | ||
| 1, | ||
| platform::errors::InvalidArgument( | ||
| "After the fuse_multi_transformer_layer_pass, " | ||
| "The node num in graph should be 1, but the result is %d", | ||
| num_nodes_after)); | ||
| } | ||
| TEST(FuseMultiTransformerLayerPass, decoder_fp) { | ||
| DEF_INPUT_DATA | ||
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| x = layers.data("x", {1, 1, 1024}); | ||
| auto* cache_kv = layers.data("cache_kv", {2, 1, 16, 1024, 64}, true); | ||
| src_mask = layers.data("src_mask", {1, 16, 1, 129}); | ||
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| // Layers | ||
| for (int i = 0; i < num_layers; ++i) { | ||
| auto* shape_out = layers.shape(src_mask); | ||
| auto* time_stamp = layers.slice(shape_out, {0}, {3}, {4}); | ||
| std::cout << "begin to add fused_multi_transformer layer " << i | ||
| << std::endl; | ||
| auto* out = layers.fused_multi_transformer(x, | ||
| cache_kv, | ||
| src_mask, | ||
| qkv_w, | ||
| qkv_bias, | ||
| out_linear_w, | ||
| out_linear_bias, | ||
| ffn1_w, | ||
| ffn1_bias, | ||
| ffn2_w, | ||
| ffn2_bias, | ||
| ln_scale, | ||
| ln_bias, | ||
| ffn_ln_scale, | ||
| ffn_ln_bias, | ||
| 0.1, | ||
| 1e-12, | ||
| time_stamp); | ||
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| x = out; | ||
| } | ||
| std::unique_ptr<ir::Graph> graph(new ir::Graph(layers.main_program())); | ||
| auto param_scope = CreateParamScope(); | ||
| AddVarToScope(param_scope, "cache_kv", {2, 1, 16, 1024, 64}); | ||
| graph->Set("__param_scope__", param_scope); | ||
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| graph->Set(kFusedMultiTransformerDecoderFusionCount, new int(num_layers)); | ||
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| auto pass = PassRegistry::Instance().Get("fuse_multi_transformer_layer_pass"); | ||
| if (pass.get() == nullptr) | ||
| LOG(INFO) << "get fuse_multi_transformer_layer_pass failed"; | ||
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| graph.reset(pass->Apply(graph.release())); | ||
| int num_nodes_after = GetNumOpNodes(graph, "fused_multi_transformer"); | ||
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| PADDLE_ENFORCE_EQ( | ||
| num_nodes_after, | ||
| 1, | ||
| platform::errors::InvalidArgument( | ||
| "After the fuse_multi_transformer_layer_pass, " | ||
| "The node num in graph should be 1, but the result is %d", | ||
| num_nodes_after)); | ||
| } | ||
| } // namespace ir | ||
| } // namespace framework | ||
| } // namespace paddle | ||
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| USE_PASS(fuse_multi_transformer_layer_pass); |
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