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dlrm_raw_generate_train.py
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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)