import hugectr
from hugectr.tools import DataGenerator, DataGeneratorParams
from mpi4py import MPI

# Raw data generation
data_generator_params = DataGeneratorParams(
  format = hugectr.DataReaderType_t.Raw,
  label_dim = 1,
  dense_dim = 13,
  num_slot = 26,
  i64_input_key = False,
  source = "./dlrm_raw/train_data.bin",
  eval_source = "./dlrm_raw/test_data.bin",
  slot_size_array = [203931, 18598, 14092, 7012, 18977, 4, 6385, 1245, 49, 186213, 71328, 67288, 11, 2168, 7338, 61, 4, 932, 15, 204515, 141526, 199433, 60919, 9137, 71, 34],
  nnz_array = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
)
data_generator = DataGenerator(data_generator_params)
data_generator.generate()


# DLRM train
solver = hugectr.CreateSolver(max_eval_batches = 1280,
                              batchsize_eval = 1024,
                              batchsize = 1024,
                              lr = 0.5,
                              warmup_steps = 500,
                              vvgpu = [[0]],
                              repeat_dataset = True)
reader = hugectr.DataReaderParams(data_reader_type = data_generator_params.format,
                                  source = [data_generator_params.source],
                                  eval_source = data_generator_params.eval_source,
                                  num_samples = data_generator_params.num_samples,
                                  eval_num_samples = data_generator_params.eval_num_samples,
                                  check_type = data_generator_params.check_type)
optimizer = hugectr.CreateOptimizer(optimizer_type = hugectr.Optimizer_t.SGD,
                                    update_type = hugectr.Update_t.Local,
                                    atomic_update = True)
model = hugectr.Model(solver, reader, optimizer)
model.add(hugectr.Input(label_dim = data_generator_params.label_dim, label_name = "label",
                        dense_dim = data_generator_params.dense_dim, dense_name = "dense",
                        data_reader_sparse_param_array = 
                        [hugectr.DataReaderSparseParam("data1", 1, True, data_generator_params.num_slot)]))
model.add(hugectr.SparseEmbedding(embedding_type = hugectr.Embedding_t.LocalizedSlotSparseEmbeddingOneHot, 
                            slot_size_array = data_generator_params.slot_size_array,
                            workspace_size_per_gpu_in_mb = 800,
                            embedding_vec_size = 128,
                            combiner = "sum",
                            sparse_embedding_name = "sparse_embedding1",
                            bottom_name = "data1",
                            optimizer = optimizer))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["dense"],
                            top_names = ["fc1"],
                            num_output=512))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc1"],
                            top_names = ["relu1"]))                           
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu1"],
                            top_names = ["fc2"],
                            num_output=256))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc2"],
                            top_names = ["relu2"]))                            
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu2"],
                            top_names = ["fc3"],
                            num_output=128))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc3"],
                            top_names = ["relu3"]))                              
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Interaction,
                            bottom_names = ["relu3","sparse_embedding1"],
                            top_names = ["interaction1"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["interaction1"],
                            top_names = ["fc4"],
                            num_output=1024))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc4"],
                            top_names = ["relu4"]))                              
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu4"],
                            top_names = ["fc5"],
                            num_output=1024))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc5"],
                            top_names = ["relu5"]))                              
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu5"],
                            top_names = ["fc6"],
                            num_output=512))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc6"],
                            top_names = ["relu6"]))                               
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu6"],
                            top_names = ["fc7"],
                            num_output=256))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc7"],
                            top_names = ["relu7"]))                                                                              
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu7"],
                            top_names = ["fc8"],
                            num_output=1))                                                                                           
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.BinaryCrossEntropyLoss,
                            bottom_names = ["fc8", "label"],
                            top_names = ["loss"]))
model.compile()
model.summary()
model.fit(max_iter = 5120, display = 200, eval_interval = 1000)
import hugectr
from hugectr.tools import DataGenerator, DataGeneratorParams
from mpi4py import MPI

# Raw data generation
data_generator_params = DataGeneratorParams(
  format = hugectr.DataReaderType_t.Raw,
  label_dim = 1,
  dense_dim = 13,
  num_slot = 26,
  i64_input_key = False,
  source = "./dlrm_raw/train_data.bin",
  eval_source = "./dlrm_raw/test_data.bin",
  slot_size_array = [203931, 18598, 14092, 7012, 18977, 4, 6385, 1245, 49, 186213, 71328, 67288, 11, 2168, 7338, 61, 4, 932, 15, 204515, 141526, 199433, 60919, 9137, 71, 34],
  nnz_array = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
)
data_generator = DataGenerator(data_generator_params)
data_generator.generate()


# DLRM train
solver = hugectr.CreateSolver(max_eval_batches = 1280,
                              batchsize_eval = 1024,
                              batchsize = 1024,
                              lr = 0.5,
                              warmup_steps = 500,
                              vvgpu = [[0]],
                              repeat_dataset = True)
reader = hugectr.DataReaderParams(data_reader_type = data_generator_params.format,
                                  source = [data_generator_params.source],
                                  eval_source = data_generator_params.eval_source,
                                  num_samples = data_generator_params.num_samples,
                                  eval_num_samples = data_generator_params.eval_num_samples,
                                  check_type = data_generator_params.check_type)
optimizer = hugectr.CreateOptimizer(optimizer_type = hugectr.Optimizer_t.SGD,
                                    update_type = hugectr.Update_t.Local,
                                    atomic_update = True)
model = hugectr.Model(solver, reader, optimizer)
model.add(hugectr.Input(label_dim = data_generator_params.label_dim, label_name = "label",
                        dense_dim = data_generator_params.dense_dim, dense_name = "dense",
                        data_reader_sparse_param_array = 
                        [hugectr.DataReaderSparseParam("data1", 1, True, data_generator_params.num_slot)]))
model.add(hugectr.SparseEmbedding(embedding_type = hugectr.Embedding_t.LocalizedSlotSparseEmbeddingOneHot, 
                            slot_size_array = data_generator_params.slot_size_array,
                            workspace_size_per_gpu_in_mb = 800,
                            embedding_vec_size = 128,
                            combiner = "sum",
                            sparse_embedding_name = "sparse_embedding1",
                            bottom_name = "data1",
                            optimizer = optimizer))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["dense"],
                            top_names = ["fc1"],
                            num_output=512))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc1"],
                            top_names = ["relu1"]))                           
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu1"],
                            top_names = ["fc2"],
                            num_output=256))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc2"],
                            top_names = ["relu2"]))                            
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu2"],
                            top_names = ["fc3"],
                            num_output=128))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc3"],
                            top_names = ["relu3"]))                              
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.Interaction,
                            bottom_names = ["relu3","sparse_embedding1"],
                            top_names = ["interaction1"]))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["interaction1"],
                            top_names = ["fc4"],
                            num_output=1024))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc4"],
                            top_names = ["relu4"]))                              
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu4"],
                            top_names = ["fc5"],
                            num_output=1024))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc5"],
                            top_names = ["relu5"]))                              
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu5"],
                            top_names = ["fc6"],
                            num_output=512))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc6"],
                            top_names = ["relu6"]))                               
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu6"],
                            top_names = ["fc7"],
                            num_output=256))
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.ReLU,
                            bottom_names = ["fc7"],
                            top_names = ["relu7"]))                                                                              
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.InnerProduct,
                            bottom_names = ["relu7"],
                            top_names = ["fc8"],
                            num_output=1))                                                                                           
model.add(hugectr.DenseLayer(layer_type = hugectr.Layer_t.BinaryCrossEntropyLoss,
                            bottom_names = ["fc8", "label"],
                            top_names = ["loss"]))
model.compile()
model.summary()
model.fit(max_iter = 5120, display = 200, eval_interval = 1000)
# Raw data generation # DLRM train