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feat: Enable amp with autocast (fix poor bf16 convergence on GRPO #26

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Merged
SahilJain314 merged 2 commits into from
Mar 21, 2025
Merged

feat: Enable amp with autocast (fix poor bf16 convergence on GRPO #26

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5bd9894
Enable amp with autocast
SahilJain314 Mar 21, 2025
e60d53a
Merge branch 'main' into sahilj/real_amp
SahilJain314 Mar 21, 2025
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40 changes: 21 additions & 19 deletions nemo_reinforcer/models/policy/hf_policy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,22 +73,22 @@ def __init__(
world_size = torch.distributed.get_world_size()
model_name = self.cfg["model_name"]
if self.cfg["precision"] == "float32":
dtype = torch.float32
self.dtype = torch.float32
elif self.cfg["precision"] == "bfloat16":
dtype = torch.bfloat16
self.dtype = torch.bfloat16
else:
raise ValueError(f"Unknown precision: {self.cfg['precision']}")

print(f"[Rank {rank}] Loading model {model_name} on CPU...")
self.model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="cpu", # load weights onto CPU initially
torch_dtype=dtype, # use full precision in sft until https://github.com/NVIDIA/reinforcer/issues/13 is fixed
torch_dtype=torch.float32, # use full precision in sft until https://github.com/NVIDIA/reinforcer/issues/13 is fixed
)
self.reference_model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="cpu", # load weights onto CPU initially
torch_dtype=dtype, # use full precision in sft until https://github.com/NVIDIA/reinforcer/issues/13 is fixed
torch_dtype=torch.float32, # use full precision in sft until https://github.com/NVIDIA/reinforcer/issues/13 is fixed
)

self.tokenizer = AutoTokenizer.from_pretrained(model_name)
Expand Down Expand Up @@ -272,16 +272,17 @@ def train(
# For right-padded sequence, set 1s at the beginning of the sequence
attention_mask[i, :length] = 1

outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
use_cache=False,
)
# Get logprobs
if not hasattr(outputs, "logits"):
logits = self.model.lm_head(outputs.last_hidden_state)
else:
logits = outputs.logits
with torch.autocast(device_type="cuda", dtype=self.dtype):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
use_cache=False,
)
# Get logprobs
if not hasattr(outputs, "logits"):
logits = self.model.lm_head(outputs.last_hidden_state)
else:
logits = outputs.logits

loss, loss_metrics = loss_fn(logits, mb)

Expand Down Expand Up @@ -358,11 +359,12 @@ def get_logprobs(self, data: BatchedDataDict) -> BatchedDataDict:
attention_mask[i, :length] = 1

# Process with the model directly using right-padded inputs
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
use_cache=False,
)
with torch.autocast(device_type="cuda", dtype=self.dtype):
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
use_cache=False,
)
log_probs = torch.nn.functional.log_softmax(
outputs.logits.to(torch.float32), dim=-1
)
Expand Down