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[CP] test load-balance on llama3-8B #1897

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[CP] test load-balance on llama3-8B #1897

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18 changes: 15 additions & 3 deletions torchtitan/distributed/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -434,9 +434,7 @@ def _clip_grad_norm_with_ep(
if math.isinf(norm_type):
total_norm = torch.maximum(ep_grads_total_norm, non_ep_grads_total_norm)
else:
total_norm = (
ep_grads_total_norm**norm_type + non_ep_grads_total_norm**norm_type
)
total_norm = ep_grads_total_norm**norm_type + non_ep_grads_total_norm**norm_type
total_norm **= 1.0 / norm_type

if pp_mesh is not None:
Expand All @@ -462,9 +460,23 @@ def cp_shard(
order_sensitive_buffers_seq_dims: dict[str, int],
):
from torch.distributed.tensor.experimental._attention import _context_parallel_shard
from torch.distributed.tensor.experimental._load_balancer import (
_HeadTailLoadBalancer,
_PTRRLoadBalancer,
)
from torch.nn.attention.flex_attention import BlockMask

load_balancer = None
"""
seq_length = inputs.shape[1]
load_balancer = _HeadTailLoadBalancer(
seq_length, cp_mesh.size(0), cp_mesh.device_type
)

assert isinstance(attention_masks, BlockMask)
load_balancer = _PTRRLoadBalancer(attention_masks, cp_mesh.size(0))
"""

inputs, labels = _context_parallel_shard(
mesh=cp_mesh,
buffers=(inputs, labels),
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