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# Zero Redundancy Optimizer (ZeRO)
def estimate_zero1_model_states_mem_needs(total_params,
num_gpus_per_node=1,
num_nodes=1,
cpu_offload=True,
additional_buffer_factor=1.5,
precision_fac = 2, # half precision
params_fac = 4 # 4 bytes per float32 model parameter type
):
# TODO: check if params_fac is needed during full fp32 training.
# Normally, mixed precision training results in 1.5x memory compared to FP32.
# Currently, we are assuming 2x memory for FP32, as deepspeed's ZeRO-2 is optimized for FP16 training.
total_gpus = num_nodes * num_gpus_per_node
master_params_fac = 4
variance_fac = 4
momentum_fac = 4
grads_fac = 4
optimizer_fac = variance_fac + momentum_fac # Adam optimizer
total_gpus = num_nodes * num_gpus_per_node
if cpu_offload:
gpu_mem = (precision_fac * total_params) + (precision_fac * total_params)
cpu_mem = total_params * max(params_fac * total_gpus, (master_params_fac+optimizer_fac+grads_fac)) * additional_buffer_factor
else:
gpu_mem = (precision_fac * total_params) + (precision_fac * total_params) + int((precision_fac + optimizer_fac + master_params_fac + precision_fac) * total_params / total_gpus)
cpu_mem = total_params * params_fac * num_gpus_per_node * additional_buffer_factor
return int(cpu_mem), int(gpu_mem)
def estimate_zero2_model_states_mem_needs(total_params,
num_gpus_per_node=1,
num_nodes=1,
cpu_offload=True,
additional_buffer_factor=1.5,
precision_fac = 2, # half precision
params_fac = 4 # 4 bytes per float32 model parameter type
):
# TODO: check if params_fac is needed during full fp32 training.
# Normally, mixed precision training results in 1.5x memory compared to FP32.
# Currently, we are assuming 2x memory for FP32, as deepspeed's ZeRO-2 is optimized for FP16 training.
total_gpus = num_nodes * num_gpus_per_node
master_params_fac = 4
variance_fac = 4
momentum_fac = 4
grads_fac = 4
optimizer_fac = variance_fac + momentum_fac # Adam optimizer
total_gpus = num_nodes * num_gpus_per_node
if cpu_offload:
gpu_mem = precision_fac * total_params
cpu_mem = total_params * max(params_fac * total_gpus, (master_params_fac+optimizer_fac+grads_fac)) * additional_buffer_factor
else:
gpu_mem = precision_fac * total_params + int((precision_fac + grads_fac + optimizer_fac + master_params_fac + precision_fac) * total_params / total_gpus)
cpu_mem = total_params * params_fac * num_gpus_per_node * additional_buffer_factor
return int(cpu_mem), int(gpu_mem)
def estimate_zero3_model_states_mem_needs(total_params,
largest_layer_params,
num_gpus_per_node=1,
num_nodes=1,
cpu_offload=True,
cpu_offload_params=True,
zero_init=True,
additional_buffer_factor=1.5,
precision_fac = 2, # half precision
params_fac = 4 # 4 bytes per float32 model parameter type
):
# TODO: check if params_fac is needed during full fp32 training.
# Normally, mixed precision training results in 1.5x memory compared to FP32.
# Currently, we are assuming 2x memory for FP32, as deepspeed's ZeRO-2 is optimized for FP16 training.
total_gpus = num_nodes * num_gpus_per_node
gpus_factor = 1 / num_nodes
master_params_fac = 4
variance_fac = 4
momentum_fac = 4
grads_fac = 4
optimizer_fac = variance_fac + momentum_fac # Adam optimizer
largest_layer_memory = (2 * precision_fac) * largest_layer_params # params + grads = (2 * modifier)
if cpu_offload:
if cpu_offload_params:
gpu_mem = largest_layer_memory
if zero_init:
cpu_mem = total_params * (master_params_fac + grads_fac + optimizer_fac + params_fac) * gpus_factor * additional_buffer_factor
else:
cpu_mem = total_params * max(params_fac * num_gpus_per_node, (master_params_fac + grads_fac + optimizer_fac + params_fac) * gpus_factor) * additional_buffer_factor
else:
gpu_mem = largest_layer_memory + int(precision_fac * total_params / total_gpus)
if zero_init:
cpu_mem = total_params * (master_params_fac + grads_fac + optimizer_fac) * gpus_factor * additional_buffer_factor
else:
cpu_mem = total_params * max(params_fac * num_gpus_per_node, (master_params_fac + grads_fac + optimizer_fac) * gpus_factor) * additional_buffer_factor
else:
gpu_mem = largest_layer_memory + int((master_params_fac + grads_fac + optimizer_fac + precision_fac) * total_params / total_gpus)
# 2b for fp16 params, 4b master params, 4b grads, 4b momentum and 4b variance per parameter = 18
if zero_init:
cpu_mem = largest_layer_params * params_fac * num_gpus_per_node * additional_buffer_factor
else:
cpu_mem = total_params * params_fac * num_gpus_per_node * additional_buffer_factor
return int(cpu_mem), int(gpu_mem), largest_layer_memory
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