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f0d6538 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 | # Getting Started with Abbie
This guide covers integrating Abbie into existing PyTorch distributed training scripts.
## Quick Setup
Initialize the distributed environment and Device Mesh Manager (DMM):
```python
import os
import torch
import torch.distributed as dist
from abbie.device_mesh_manager import DMM
# Standard PyTorch distributed setup
rank = int(os.environ["RANK"])
local_rank = int(os.environ["LOCAL_RANK"])
world_size = int(os.environ["WORLD_SIZE"])
torch.cuda.set_device(local_rank)
dist.init_process_group(backend="nccl", init_method="env://")
# Initialize Abbie's device mesh
DMM.initialize(pp_size=2, ep_size=1, sp_size=1)
```
- `pp_size`: Pipeline parallelism degree (layers split across ranks)
- `ep_size`: Expert parallelism degree (for MoE models)
- `sp_size`: Sequence parallelism degree (sequence split within DP)
## Loading a Model
```python
from abbie.models import load_pretrained_hf_model
model = load_pretrained_hf_model(
pretrained_path="path/to/hf_model",
max_batch_size=micro_batch_size * num_chunks,
max_seq_len=4096,
)
```
Supported models: Qwen2, Qwen3, Qwen3-MoE, Qwen2.5-VL, Qwen3-VL, Qwen3-VL-MoE.
Additional options exist for selective recomputation (`recompute_attn`, `recompute_mlp`), activation offloading, and token dispatch methods.
## Creating an Optimizer
```python
from abbie.gargantua.causal_lm import make_model_optimizer
optimizer = make_model_optimizer(
model=model,
num_training_steps=10000,
num_warmup_steps=500,
lr=1e-5,
betas=(0.9, 0.999),
weight_decay=0.01,
lr_schedule="cosine",
)
```
For VL models, `visual_lr` allows a separate learning rate for the vision encoder. The optimizer automatically handles parameter grouping for LLM, experts, and visual components.
## Training Loop
Key difference from standard PyTorch: `model.step()` performs both forward and backward passes together. This enables Abbie's overlapped scheduling.
```python
for batch in dataloader:
input_ids = batch["input_ids"].cuda()
attention_mask = batch["attention_mask"].cuda()
position_ids = batch["position_ids"].cuda()
labels = batch["labels"].cuda()
optimizer.zero_grad()
outputs = model.step(
num_chunks=num_chunks,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
labels=labels,
return_outputs=True,
)
optimizer.step()
if outputs.loss is not None:
loss = outputs.loss.sum().item()
print(f"Loss: {loss:.4f}")
```
`num_chunks` is the number of microbatches in one training step. The input tensors should contain `micro_batch_size * num_chunks` samples concatenated together.
## Checkpointing
### Saving
```python
# Save model in HuggingFace format
model.save_pretrained("path/to/checkpoint/hf_model")
# Save optimizer state (sharded per rank)
torch.save(
optimizer.state_dict(),
f"path/to/checkpoint/optimizer/{DMM.global_rank}-of-{DMM.world_size}.pt"
)
```
### Resuming
```python
# Load optimizer state
optimizer.load_state_dict(
torch.load(f"path/to/checkpoint/optimizer/{DMM.global_rank}-of-{DMM.world_size}.pt")
)
# Restore model weights from optimizer's FP32 master weights
for _, base_optimizer in optimizer.optimizers.items():
base_optimizer.reload_original_from_fp32_param()
```
## Complete Example
```python
import os
import torch
import torch.distributed as dist
from abbie.device_mesh_manager import DMM
from abbie.models import load_pretrained_hf_model
from abbie.gargantua.causal_lm import make_model_optimizer
# Setup
rank = int(os.environ["RANK"])
local_rank = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
dist.init_process_group(backend="nccl", init_method="env://")
DMM.initialize(pp_size=2, ep_size=1, sp_size=1)
# Model and optimizer
model = load_pretrained_hf_model(
pretrained_path="Qwen/Qwen2-7B",
max_batch_size=8,
max_seq_len=4096,
)
optimizer = make_model_optimizer(
model=model,
num_training_steps=10000,
num_warmup_steps=500,
lr=1e-5,
)
# Training loop
for step, batch in enumerate(dataloader):
batch = {k: v.cuda() for k, v in batch.items()}
optimizer.zero_grad()
outputs = model.step(
num_chunks=4,
input_ids=batch["input_ids"],
attention_mask=batch["attention_mask"],
position_ids=batch["position_ids"],
labels=batch["labels"],
)
optimizer.step()
if DMM.is_global_rank0 and step % 10 == 0:
loss = outputs.loss.sum().item() if outputs.loss is not None else 0
DMM.log_rank0(f"Step {step}: loss={loss:.4f}")
# Cleanup
dist.destroy_process_group()
```
Launch with torchrun:
```bash
torchrun --nproc_per_node=8 train.py
```
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