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solve the RuntimeError: Tensors must be CUDA and dense #33

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solve the RuntimeError: Tensors must be CUDA and dense #33

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30906f9
Update parallel_state.py
13416157913 Sep 25, 2023
95b5744
Update overlapped_dist_optimizer.py
13416157913 Sep 25, 2023
b93da19
Update distrib_optimizer.py
13416157913 Sep 25, 2023
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9 changes: 8 additions & 1 deletion megatron/core/parallel_state.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,6 +115,10 @@ def initialize_model_parallel(
# Get world size and rank. Ensure some consistencies.
assert torch.distributed.is_initialized()
world_size: int = torch.distributed.get_world_size()

# Get world size and rank. Ensure some consistencies.
assert torch.distributed.is_initialized()
world_size: int = torch.distributed.get_world_size()

if world_size % (tensor_model_parallel_size * pipeline_model_parallel_size) != 0:
raise RuntimeError(
Expand Down Expand Up @@ -157,7 +161,10 @@ def initialize_model_parallel(
ranks = range(start_rank + j, end_rank, tensor_model_parallel_size)
all_data_parallel_group_ranks.append(list(ranks))
group = torch.distributed.new_group(ranks)
group_gloo = torch.distributed.new_group(ranks, backend="gloo")
if world_size <= 8:
group_gloo = torch.distributed.new_group(ranks, backend="gloo")
else:
group_gloo = torch.distributed.new_group(ranks, backend="nccl")
if rank in ranks:
_DATA_PARALLEL_GROUP = group
_DATA_PARALLEL_GROUP_GLOO = group_gloo
Expand Down
41 changes: 30 additions & 11 deletions megatron/optimizer/distrib_optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -567,7 +567,10 @@ def save_parameter_state(self, filename):
buffers.
- Save world buffers to disk (i.e., distrib_opt.pt).
"""

# Order to judge cpu or cuda
assert torch.distributed.is_initialized()
world_size: int = torch.distributed.get_world_size()

# Data parallelism variables.
data_parallel_world_size = mpu.get_data_parallel_world_size()
data_parallel_rank = mpu.get_data_parallel_rank()
Expand All @@ -587,10 +590,16 @@ def save_parameter_state(self, filename):
model = self.models[model_idx]
gbuf_world_numel = model._grad_buffers[dtype].numel_padded
gbuf_local_numel = int(gbuf_world_numel / data_parallel_world_size)
local_shards = {key: torch.empty((gbuf_local_numel,),
dtype=torch.float32,
device="cpu")
for key in ("param", "exp_avg", "exp_avg_sq")}
if world_size <=8:
local_shards = {key: torch.empty((gbuf_local_numel,),
dtype=torch.float32,
device="cpu")
for key in ("param", "exp_avg", "exp_avg_sq")}
else:
local_shards = {key: torch.empty((gbuf_local_numel,),
dtype=torch.float32,
device="cuda")
for key in ("param", "exp_avg", "exp_avg_sq")}

# Build contiguous DP rank shards (for param + optim states).
for model_param, param_range_map in \
Expand All @@ -612,19 +621,29 @@ def save_parameter_state(self, filename):
gbuf_local_start = param_range_map["gbuf_local"].start
gbuf_local_end = param_range_map["gbuf_local"].end
for key in local_shards:
local_shards[key][gbuf_local_start:gbuf_local_end] \
.data.copy_(tensors[key].detach().cpu())
if world_size <=8:
local_shards[key][gbuf_local_start:gbuf_local_end] \
.data.copy_(tensors[key].detach().cpu())
else:
local_shards[key][gbuf_local_start:gbuf_local_end] \
.data.copy_(tensors[key].detach().cuda())

# Gather contiguous shards on DP rank 0.
world_tensors = {}
for key, send_tensor in local_shards.items():

# Gather tensor list.
if data_parallel_rank == 0:
recv_tensors = [torch.empty((gbuf_local_numel,),
dtype=torch.float32,
device="cpu")
for _ in range(data_parallel_world_size)]
if world_size <=8:
recv_tensors = [torch.empty((gbuf_local_numel,),
dtype=torch.float32,
device="cpu")
for _ in range(data_parallel_world_size)]
else:
recv_tensors = [torch.empty((gbuf_local_numel,),
dtype=torch.float32,
device="cuda")
for _ in range(data_parallel_world_size)]
else:
recv_tensors = None

Expand Down
52 changes: 37 additions & 15 deletions megatron/optimizer/overlapped_dist_optimizer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -953,7 +953,10 @@ def save_parameter_state(self, filename):
buffers.
- Save world buffers to disk (i.e., distrib_opt.pt).
"""

# Order to judge cpu or cuda
assert torch.distributed.is_initialized()
world_size: int = torch.distributed.get_world_size()

# Data parallelism variables.
data_parallel_world_size = mpu.get_data_parallel_world_size()
data_parallel_rank = mpu.get_data_parallel_rank()
Expand All @@ -970,30 +973,49 @@ def save_parameter_state(self, filename):
optim_state = self.optimizer.state[inner_param]
# copy to CPU buffer
num_elements = self._param_buffer[i].get_partitioned_size()
local_shards = {
key: torch.empty(
num_elements,
dtype=torch.float32,
device="cpu",
)
for key in ("param", "exp_avg", "exp_avg_sq")
}
if world_size <=8:
local_shards = {
key: torch.empty(
num_elements,
dtype=torch.float32,
device="cpu",
)
for key in ("param", "exp_avg", "exp_avg_sq")
}
else:
local_shards = {
key: torch.empty(
num_elements,
dtype=torch.float32,
device="cuda",
)
for key in ("param", "exp_avg", "exp_avg_sq")
}
tensors = {
"param": self._param_buffer[i].get_fp32_partitioned_param(),
**optim_state,
}
for key in local_shards:
local_shards[key].data.copy_(tensors[key].detach().cpu())
if world_size <=8:
local_shards[key].data.copy_(tensors[key].detach().cpu())
else:
local_shards[key].data.copy_(tensors[key].detach().cuda())

# Gather contiguous shards on DP rank 0.
world_tensors = {}
for key, send_tensor in local_shards.items():
# Gather tensor list.
if data_parallel_rank == 0:
recv_tensors = [torch.empty((num_elements,),
dtype=torch.float32,
device="cpu")
for _ in range(data_parallel_world_size)]
if world_size <=8:
recv_tensors = [torch.empty((num_elements,),
dtype=torch.float32,
device="cpu")
for _ in range(data_parallel_world_size)]
else:
recv_tensors = [torch.empty((num_elements,),
dtype=torch.float32,
device="cuda")
for _ in range(data_parallel_world_size)]
else:
recv_tensors = None
# Gather.
Expand Down Expand Up @@ -1150,4 +1172,4 @@ def load_parameter_state_in_parallel(self, filename):
tensors[key].data.copy_(local_shards[key])

self.copy_updated_parameters()
self.gather_parameters()
self.gather_parameters()