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Allocating CPU memory directly on CPU without transfering them from GPU #360

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merged 2 commits into from
Sep 4, 2020
Merged

Allocating CPU memory directly on CPU without transfering them from GPU #360

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04a9459
Allocating CPU memory directly on CPU without transfering them from GPU
samyam Sep 4, 2020
882136e
formatting fixes
samyam Sep 4, 2020
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2 changes: 1 addition & 1 deletion deepspeed/ops/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,4 +4,4 @@
if __installed_ops__['sparse-attn']:
from . import sparse_attention
if __installed_ops__['cpu-adam']:
from . import adam
from . import adam
8 changes: 4 additions & 4 deletions deepspeed/runtime/zero/stage2.py
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Original file line number Diff line number Diff line change
Expand Up @@ -263,7 +263,7 @@ def __init__(self,
# a partition of the fp32 master weights that will be updated by this process
self.single_partition_of_fp32_groups.append(
self.parallel_partitioned_fp16_groups[i]
[partition_id].clone().float().detach().to(self.device))
[partition_id].to(self.device).clone().float().detach())

# modify optimizer of have flat master weight
self.single_partition_of_fp32_groups[
Expand Down Expand Up @@ -330,7 +330,7 @@ def __init__(self,
self.local_overflow = False
self.grad_position = {}
self.temp_grad_buffer_for_cpu_offload = torch.zeros(
largest_param_numel).half().pin_memory()
largest_param_numel, device=self.device).half().pin_memory()
self.temp_grad_buffer_for_gpu_offload = torch.zeros(
largest_param_numel,
device=torch.cuda.current_device()).half()
Expand Down Expand Up @@ -787,7 +787,7 @@ def async_accumulate_grad_in_cpu(self, param):
if param_id not in self.accumulated_grads_in_cpu:
self.accumulated_grads_in_cpu[param_id] = torch.zeros(
param.numel(),
dtype=param.dtype).pin_memory()
dtype=param.dtype, device=self.device).pin_memory()

self.accumulated_grads_in_cpu[param_id].add_(dest_buffer)

Expand All @@ -803,7 +803,7 @@ def async_accumulate_grad_in_cpu_via_gpu(self, param):
if param_id not in self.accumulated_grads_in_cpu:
self.accumulated_grads_in_cpu[param_id] = torch.zeros(
param.numel(),
dtype=param.dtype).pin_memory()
dtype=param.dtype, device=self.device).pin_memory()

if self.micro_step_id > 0:
dest_buffer.copy_(self.accumulated_grads_in_cpu[param_id].view(-1),
Expand Down