File size: 3,936 Bytes
46b9eea | 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 | import os
import sys
import time
import torch
# Add recipe path to sys.path
_RECIPE_ROOT = os.path.dirname(os.path.abspath(__file__))
sys.path.insert(0, _RECIPE_ROOT)
from modeling_aha_qwen3 import AHAQwen3ForCausalLM, AHAQwen3Config
from router_training_utils import RowWiseAdamW
def main():
AHAQwen3Config.register_for_auto_class()
AHAQwen3ForCausalLM.register_for_auto_class("AutoModelForCausalLM")
# The repo root is the parent directory of recipe
model_path = os.path.dirname(_RECIPE_ROOT)
print("Loading model in BF16...", flush=True)
model = AHAQwen3ForCausalLM.from_pretrained_qwen3(
model_path,
aha_window_size=128,
aha_lambda=3e-4,
aha_distill_weight=0.0,
aha_ce_weight=1.0,
aha_gate_target=1.0,
aha_reg_weight=0.01,
aha_mode="dynamic",
aha_router_granularity="token",
duo_sink_size=64,
duo_recent_size=256,
duo_alpha_init=1.0,
torch_dtype=torch.bfloat16,
attn_implementation="sdpa",
)
# Freeze embeddings and LM head as in stage 2 SFT training
for param in model.model.embed_tokens.parameters():
param.requires_grad = False
for param in model.lm_head.parameters():
param.requires_grad = False
print("Moving model to CUDA...", flush=True)
model = model.to("cuda")
# Configure RowWiseAdamW optimizer
num_heads = model.config.num_attention_heads
head_dim = getattr(model.config, "head_dim", model.config.hidden_size // num_heads)
q_rows = num_heads * head_dim
q_row_scale = 3e-7 / 3e-6 # backbone_lr / gate_lr
gate_params = []
gate_param_ids = set()
row_scales = []
for layer in model.model.layers:
q_proj = layer.self_attn.q_proj
for p in (q_proj.weight, q_proj.bias):
if p is None or not p.requires_grad:
continue
gate_params.append(p)
gate_param_ids.add(id(p))
row_scales.append((p, q_rows, q_row_scale))
backbone_params = [
p for p in model.parameters()
if p.requires_grad and id(p) not in gate_param_ids
]
param_groups = [{"params": gate_params, "lr": 3e-6}]
if backbone_params:
param_groups.append({"params": backbone_params, "lr": 3e-7})
optimizer = RowWiseAdamW(
param_groups,
row_scales=row_scales,
weight_decay=0.0,
)
# Allocate a batch of seq_len=8192
seq_len = 8192
print(f"Allocating dummy batch: batch_size=1, seq_len={seq_len}", flush=True)
input_ids = torch.randint(0, model.config.vocab_size, (1, seq_len), device="cuda")
labels = input_ids.clone()
# Enable gradient checkpointing
model.gradient_checkpointing_enable(gradient_checkpointing_kwargs={"use_reentrant": False})
# Warmup step (GPU caching, model trace creation, etc.)
print("Warmup step...", flush=True)
outputs = model(input_ids=input_ids, labels=labels)
loss = outputs.loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
# Reset peak memory stats and time the next step
print("Starting measured smoke test step...", flush=True)
torch.cuda.reset_peak_memory_stats()
torch.cuda.synchronize()
start_time = time.time()
outputs = model(input_ids=input_ids, labels=labels)
loss = outputs.loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
torch.cuda.synchronize()
step_time = time.time() - start_time
peak_mem = torch.cuda.max_memory_allocated() / (1024 ** 3)
reserved_mem = torch.cuda.memory_reserved() / (1024 ** 3)
print("=== SMOKE TEST RESULTS ===", flush=True)
print(f"Peak VRAM: {peak_mem:.4f} GB", flush=True)
print(f"Reserved VRAM: {reserved_mem:.4f} GB", flush=True)
print(f"Step time: {step_time:.4f} seconds", flush=True)
print("==========================", flush=True)
if __name__ == '__main__':
main()
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