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model parallel llama (#11588)

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MP=8 GRADIENT_ACC_STEPS=3 BS=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=70B SEQLEN=512 PYTHONPATH=. MODEL=llama3 python3 examples/mlperf/model_train.py
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chenyu
2025-08-09 13:54:27 -07:00
committed by GitHub
parent 09bc377da3
commit 45baec1aab
1 changed files with 19 additions and 18 deletions
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examples/mlperf/model_train.py
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@@ -1323,24 +1323,22 @@ def train_llama3():
for v in get_parameters(model): for v in get_parameters(model):
v.shard_(device, axis=None) v.shard_(device, axis=None)
# TODO: MP if (MP := getenv("MP", 1)) > 1:
# if (GPUS := getenv("GPUS", 1)) > 1: device = tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))
# device = tuple(f"{Device.DEFAULT}:{i}" for i in range(GPUS)) for k,v in get_state_dict(model).items():
# for k,v in get_state_dict(model).items(): if 'scale' in k: v.shard_(device, axis=None) # from quantized
# if 'scale' in k: v.shard_(device, axis=None) # from quantized elif '.attention.wq' in k: v.shard_(device, axis=0)
# # elif '.attention.wq' in k: v.shard_(device, axis=0) elif '.attention.wk' in k: v.shard_(device, axis=0)
# # elif '.attention.wk' in k: v.shard_(device, axis=0) elif '.attention.wv' in k: v.shard_(device, axis=0)
# # elif '.attention.wv' in k: v.shard_(device, axis=0) elif '.attention.wo' in k: v.shard_(device, axis=1)
# # elif '.attention.wo' in k: v.shard_(device, axis=1) elif '.feed_forward.w1.' in k: v.shard_(device, axis=0)
# # elif '.feed_forward.w1.' in k: v.shard_(device, axis=0) elif '.feed_forward.w2.' in k: v.shard_(device, axis=1)
# # elif '.feed_forward.w2.' in k: v.shard_(device, axis=1) elif '.feed_forward.w3.' in k: v.shard_(device, axis=0)
# # elif '.feed_forward.w3.' in k: v.shard_(device, axis=0) elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0)
# # elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0) elif 'output.weight' in k: v.shard_(device, axis=0)
# elif 'output.weight' in k: v.shard_(device, axis=0) # 243.32 else:
# else: # attention_norm, ffn_norm, norm
# # print(k) v.shard_(device, axis=None)
# # attention_norm, ffn_norm, norm
# v.shard_(device, axis=None)
optim = AdamW(get_parameters(model), lr=0.0, optim = AdamW(get_parameters(model), lr=0.0,
b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay) b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay)
@@ -1355,6 +1353,9 @@ def train_llama3():
if (DP := getenv("DP", 1)) > 1: if (DP := getenv("DP", 1)) > 1:
device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP)) device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
batch = batch.shard(device, 0) batch = batch.shard(device, 0)
if (MP := getenv("MP", 1)) > 1:
device = tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))
batch = batch.shard(device)
logits:Tensor = model(batch[:, :-1], start_pos=0, temperature=math.nan) logits:Tensor = model(batch[:, :-1], start_pos=0, temperature=math.nan)
loss = logits.sparse_categorical_crossentropy(batch[:, 1:]) loss = logits.sparse_categorical_crossentropy(batch[:, 1:])
loss.backward() loss.backward()