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[LITE][NPU] Add layer_norm op bridge (#2767)
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| // Copyright (c) 2019 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 "lite/kernels/npu/bridges/graph.h" | ||
| #include "lite/kernels/npu/bridges/registry.h" | ||
| #include "lite/kernels/npu/bridges/utility.h" | ||
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| namespace paddle { | ||
| namespace lite { | ||
| namespace subgraph { | ||
| namespace npu { | ||
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| int LayerNormConverter(void* ctx, OpLite* op, KernelBase* kernel) { | ||
| CHECK(ctx != nullptr); | ||
| CHECK(op != nullptr); | ||
| auto graph = static_cast<Graph*>(ctx); | ||
| auto op_info = op->op_info(); | ||
| auto op_type = op_info->Type(); | ||
| auto scope = op->scope(); | ||
| VLOG(3) << "[NPU] Converting " + op_type + "..."; | ||
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| // Get input and output vars and op attributes | ||
| auto x_name = op_info->Input("X").front(); | ||
| auto x_type = kernel->GetInputDeclType("X"); | ||
| CHECK(x_type->precision() == PRECISION(kFloat)); | ||
| CHECK(x_type->layout() == DATALAYOUT(kNCHW)); | ||
| auto x = scope->FindMutableTensor(x_name); | ||
| auto x_dims = x->dims(); | ||
| auto padded_x_shape = CvtShape(x_dims); | ||
| auto x_rank = static_cast<int>(x_dims.size()); | ||
| CHECK(x_rank >= 2 && x_rank <= 4); | ||
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| auto y_name = op_info->Output("Y").front(); | ||
| auto y_type = kernel->GetOutputDeclType("Y"); | ||
| CHECK(y_type->precision() == PRECISION(kFloat)); | ||
| CHECK(y_type->layout() == DATALAYOUT(kNCHW)); | ||
| auto y = scope->FindMutableTensor(y_name); | ||
| auto y_dims = y->dims(); | ||
| auto padded_y_shape = CvtShape(y_dims); | ||
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| auto epsilon = op_info->GetAttr<float>("epsilon"); | ||
| auto begin_norm_axis = op_info->GetAttr<int>("begin_norm_axis"); | ||
| if (begin_norm_axis < 0) { | ||
| begin_norm_axis += x_rank; | ||
| } | ||
| CHECK(begin_norm_axis >= 1 && begin_norm_axis < x_rank); | ||
| auto x_mat_dims = x_dims.Flatten2D(begin_norm_axis); | ||
| auto left = x_mat_dims[0]; | ||
| auto right = x_mat_dims[1]; | ||
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| // X node | ||
| std::shared_ptr<Node> x_node = nullptr; | ||
| if (graph->Has(x_name)) { | ||
| x_node = graph->Get(x_name); | ||
| } else { | ||
| x_node = graph->Add(x_name, *x, padded_x_shape); | ||
| } | ||
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| // Reshaped X node if needs | ||
| bool reshape = false; | ||
| if (!(x_rank == 4 && begin_norm_axis == 1)) { | ||
| reshape = true; | ||
| // Only the input shape 4-D(n, c, h, w) and axis=1 is supported | ||
| // by HiAI DDK, So the input shape need to be padded to 4-D if it is less | ||
| // than 4 or axis!=1. For example: | ||
| // (1) (n, c, h, w), axis=1 -> no need | ||
| // (2) (n, c, h, w), axis=2 -> (n * c, h, w, 1) | ||
| // (3) (n, c, h, w), axis=3 -> (n * c * h, w, 1) | ||
| // (4) (n, h, w), axis=1 -> (n, h, w, 1) | ||
| // (5) (n, h, w), axis=2 -> (n * h, w, 1, 1) | ||
| // (6) (h, w), axis=1 -> (h, w, 1, 1) | ||
| padded_x_shape = {left}; | ||
| for (int i = begin_norm_axis; i < x_rank; i++) { | ||
| padded_x_shape.push_back(x_dims[i]); | ||
| } | ||
| auto remain = 4 - padded_x_shape.size(); | ||
| for (int i = 0; i < remain; i++) { | ||
| padded_x_shape.push_back(1); | ||
| } | ||
| auto reshaped_x_node = graph->Add<ge::op::Reshape>( | ||
| x_name + "/reshape", x_node->precision(), x_node->layout()); | ||
| auto reshaped_x_op = reshaped_x_node->data<ge::op::Reshape>(); | ||
| reshaped_x_op->set_input_tensor(*x_node->data()); | ||
| reshaped_x_op->set_attr_shape(padded_x_shape); | ||
| x_node = reshaped_x_node; | ||
| } | ||
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| // Bias node | ||
| auto scale_bias_dims = | ||
| DDim({1, padded_x_shape[1], padded_x_shape[2], padded_x_shape[3]}); | ||
| std::shared_ptr<Node> bias_node = nullptr; | ||
| if (HasInputArg(op_info, scope, "Bias")) { | ||
| auto bias_name = op_info->Input("Bias").front(); | ||
| auto bias_type = kernel->GetInputDeclType("Bias"); | ||
| CHECK(bias_type->precision() == PRECISION(kFloat)); | ||
| CHECK(bias_type->layout() == DATALAYOUT(kNCHW)); | ||
| auto bias = scope->FindMutableTensor(bias_name); | ||
| auto bias_dims = bias->dims(); | ||
| CHECK_EQ(bias_dims.size(), 1); | ||
| CHECK_EQ(bias_dims.production(), right); | ||
| if (!bias->persistable()) { | ||
| LOG(WARNING) << "[NPU] Only supporting persistable bias tensor."; | ||
| return FAILED; | ||
| } | ||
| bias_node = graph->Add(bias_name, *bias, scale_bias_dims); | ||
| } else { | ||
| bias_node = graph->Add(y_name + "/bias", 0.0f, scale_bias_dims); | ||
| } | ||
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| // Scale node | ||
| std::shared_ptr<Node> scale_node = nullptr; | ||
| if (HasInputArg(op_info, scope, "Scale")) { | ||
| auto scale_name = op_info->Input("Scale").front(); | ||
| auto scale_type = kernel->GetInputDeclType("Scale"); | ||
| CHECK(scale_type->precision() == PRECISION(kFloat)); | ||
| CHECK(scale_type->layout() == DATALAYOUT(kNCHW)); | ||
| auto scale = scope->FindMutableTensor(scale_name); | ||
| auto scale_dims = scale->dims(); | ||
| CHECK_EQ(scale_dims.size(), 1); | ||
| CHECK_EQ(scale_dims.production(), right); | ||
| if (!scale->persistable()) { | ||
| LOG(WARNING) << "[NPU] Only supporting persistable scale tensor."; | ||
| return FAILED; | ||
| } | ||
| scale_node = graph->Add(scale_name, *scale, scale_bias_dims); | ||
| } else { | ||
| scale_node = graph->Add(y_name + "/scale", 1.0f, scale_bias_dims); | ||
| } | ||
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| // LayerNorm node | ||
| auto layer_norm_node = graph->Add<ge::op::InstanceNorm>(y_name); | ||
| auto layer_norm_op = layer_norm_node->data<ge::op::InstanceNorm>(); | ||
| layer_norm_op->set_input_x(*x_node->data()); | ||
| layer_norm_op->set_input_scale(*scale_node->data()); | ||
| layer_norm_op->set_input_bias(*bias_node->data()); | ||
| layer_norm_op->set_attr_reduction_indices(ge::AttrValue::LIST_INT({3})); | ||
| layer_norm_op->set_attr_epsilon(epsilon); | ||
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| // Reshaped Y node if needs | ||
| if (reshape) { | ||
| auto reshaped_y_node = graph->Add<ge::op::Reshape>( | ||
| y_name, layer_norm_node->precision(), layer_norm_node->layout()); | ||
| auto reshaped_y_op = reshaped_y_node->data<ge::op::Reshape>(); | ||
| reshaped_y_op->set_input_tensor(*layer_norm_node->data()); | ||
| reshaped_y_op->set_attr_shape(padded_y_shape); | ||
| } | ||
| return REBUILD_WHEN_SHAPE_CHANGED; | ||
| } | ||
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| } // namespace npu | ||
| } // namespace subgraph | ||
| } // namespace lite | ||
| } // namespace paddle | ||
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| REGISTER_SUBGRAPH_BRIDGE(layer_norm, | ||
| kNPU, | ||
| paddle::lite::subgraph::npu::LayerNormConverter); |
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