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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
import torch.nn as nn
from fairscale.optim import GradScaler
class Offload_Transformer:
def get_model_config():
return {
"vocab_size": 10000,
"ninp": 2048, # embedding dimension
"nhid": 2048, # the dimension of the feedforward network model in nn.TransformerEncoder
"nhead": 32, # the number of heads in the multiheadattention models
"dropout": 0,
"initrange": 0.1,
"scaler": GradScaler(),
"clip_value": 0.05,
"num_decoder_layers": 10,
"seq_len": 32,
}
def get_benchmark_config(checkpoint_activation=True):
return {
"epochs": 1,
"lr": 0.001, # learning rate
"batch_size": 8,
"criterion": nn.CrossEntropyLoss(),
"checkpoint_activation": checkpoint_activation,
"num_microbatches": 1,
"slices": 3,
}
def get_golden_real_stats():
return {
"avg_wps": 192.105,
"std_dev_wps": 39.56,
"peak_mem_usage": 1180848128,
}
class Offload_Sequential:
def get_model_config():
return {
"inputs": 100,
"outputs": 5,
"hidden": 1000,
"layers": 100,
"clip_value": 0.05,
}
def get_benchmark_config():
return {
"epochs": 1,
"lr": 0.001, # learning rate
"batch_size": 8,
"criterion": nn.CrossEntropyLoss(),
"slices": 3,
"checkpoint_activation": True,
"num_microbatches": 1,
}
class FSDP:
def get_model_config():
return {
"vocab_size": 10000,
"ninp": 2048, # embedding dimension
"nhid": 2048, # the dimension of the feedforward network model in nn.TransformerEncoder
"nhead": 32, # the number of heads in the multiheadattention models
"dropout": 0,
"initrange": 0.1,
"scaler": GradScaler(),
"clip_value": 0.05,
"num_decoder_layers": 10,
"seq_len": 32,
}
def get_benchmark_config():
return {
"epochs": 1,
"lr": 0.001, # learning rate
"batch_size": 8,
"criterion": nn.CrossEntropyLoss(),
}
def get_golden_real_stats():
raise NotImplementedError("Synthetic data benchmarks are not supported.")
def get_golden_synthetic_stats():
return {
"avg_wps": 486.303,
"std_dev_wps": 71.307,
"peak_mem_usage": [5.5055 * 2**30, 5.5055 * 2**30, 5.5055 * 2**30, 5.5055 * 2**30],
}
class Pipe:
def get_model_config():
return {
"vocab_size": 10000,
"ninp": 2048, # embedding dimension
"nhid": 2048, # the dimension of the feedforward network model in nn.TransformerEncoder
"nhead": 32, # the number of heads in the multiheadattention models
"dropout": 0,
"initrange": 0.1,
"scaler": GradScaler(),
"clip_value": 0.05,
"num_decoder_layers": 10,
"seq_len": 32,
}
def get_benchmark_config():
return {
"epochs": 1,
"lr": 0.001, # learning rate
"batch_size": 8,
"criterion": nn.CrossEntropyLoss(),
}
def get_golden_real_stats():
return {
"avg_wps": 703.778,
"std_dev_wps": 5.732,
"peak_mem_usage": [2320996352, 1396742144, 1396742144, 2340010496],
}
def get_golden_synthetic_stats():
# TODO(anj-s): Add support for synthetic regression benchmarks
raise NotImplementedError("Synthetic data benchmarks are not supported.")
class MOE:
def get_model_config():
return {
"vocab_size": 10000,
"ninp": 1024, # embedding dimension
"nhid": 4096, # the dimension of the feedforward network model in nn.TransformerEncoder
"nhead": 32, # the number of heads in the multiheadattention models
"dropout": 0,
"initrange": 0.1,
"scaler": GradScaler(),
"clip_value": 0.05,
"num_decoder_layers": 20,
"seq_len": 33, # (seq_len - 1) needs to be divisible by num_local_experts
"is_moe": True,
"num_local_experts": 2,
}
def get_benchmark_config():
return {
"epochs": 1,
"lr": 0.001, # learning rate
"batch_size": 32,
"criterion": nn.CrossEntropyLoss(),
}
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