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Track entropy at training time #178

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Jun 30, 2025
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Track entropy at training time #178

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30 changes: 25 additions & 5 deletions src/art/unsloth/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,7 +83,7 @@ def compute_loss(
seq_len % chunk_size == 0
), f"Sequence length ({seq_len}) must be evenly divisible by chunk size ({chunk_size})"
os.environ["UNSLOTH_RETURN_HIDDEN_STATES"] = "1"
new_logprobs = calculate_logprobs(
new_logprobs, entropies = calculate_logprobs(
autocast_dtype,
trainer,
inputs["tokens"],
Expand All @@ -94,7 +94,7 @@ def compute_loss(
reference_logprobs=False,
)
if config.beta > 0.0:
ref_logprobs = calculate_logprobs(
ref_logprobs, _ = calculate_logprobs(
autocast_dtype,
trainer,
inputs["tokens"],
Expand Down Expand Up @@ -143,9 +143,14 @@ def compute_loss(
kl_div = kl_div * weights * assistant_mask
mean_policy_loss = policy_loss.sum() / (assistant_mask.sum() + 1e-6)
mean_kl = kl_div.sum() / (assistant_mask.sum() + 1e-6)

# Compute mean entropy for the current step
shifted_entropies = shift_tensor(entropies, 0.0)
mean_entropy = (shifted_entropies * weights * assistant_mask).sum() / (assistant_mask.sum() + 1e-6)

trainer._metrics["learning_rate"].append(config.learning_rate)
trainer._metrics["policy_loss"].append(mean_policy_loss.item())
trainer._metrics["entropy"].append(mean_entropy.item())
if config.beta > 0.0:
trainer._metrics["kl_div"].append(mean_kl.item())
return mean_policy_loss + config.beta * mean_kl
Expand Down Expand Up @@ -235,7 +240,7 @@ def calculate_logprobs(
lm_head_t: torch.Tensor,
chunk_size: int,
reference_logprobs: bool,
) -> torch.Tensor: # Returns shape [B, S]
) -> tuple[torch.Tensor, torch.Tensor]: # Returns (log_probs, entropy) both shape [B, S]
with (
torch.amp.autocast_mode.autocast(device_type="cuda", dtype=autocast_dtype),
torch.inference_mode() if reference_logprobs else nullcontext(),
Expand All @@ -258,14 +263,19 @@ def _calculate_logprobs(
hidden_states: torch.Tensor, # Shape [B, S, H]
next_input_ids: torch.Tensor, # Shape [B, S]
chunk_size: int,
) -> torch.Tensor: # Returns shape [B, S]
) -> tuple[torch.Tensor, torch.Tensor]: # Returns (log_probs, entropy) both shape [B, S]
batch_size, seq_len, _ = hidden_states.shape
# Output shape is [B, S]
log_probs = torch.empty(
(batch_size, seq_len),
dtype=hidden_states.dtype,
device=hidden_states.device,
)
entropy = torch.empty(
(batch_size, seq_len),
dtype=hidden_states.dtype,
device=hidden_states.device,
)
# Ensure lm_head_t is in the same dtype as hidden_states
lm_head_t = lm_head_t.to(hidden_states.dtype)

Expand All @@ -281,15 +291,25 @@ def _calculate_logprobs(
) # [B, chunk_size]
chunk_logsumexp = torch.logsumexp(chunk_logits, dim=-1) # [B, chunk_size]
log_probs[:, i : i + chunk_size] = chunk_selected_logits - chunk_logsumexp

# Compute entropy for the chunk
log_probs_full = chunk_logits - chunk_logsumexp.unsqueeze(-1)
chunk_entropy = (-torch.exp(log_probs_full) * log_probs_full).sum(
dim=-1
) # [B, chunk_size]
entropy[:, i : i + chunk_size] = chunk_entropy

del (
chunk_hs,
chunk_input_ids,
chunk_logits,
chunk_selected_logits,
chunk_logsumexp,
log_probs_full,
chunk_entropy,
)
del hidden_states
return log_probs
return log_probs, entropy


def shift_tensor(tensor: torch.Tensor, pad: int | float | bool) -> torch.Tensor:
Expand Down