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Custom rl loss patch 2 (with key detection) #4

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Custom rl loss patch 2 (with key detection) #4

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21 changes: 19 additions & 2 deletions src/tinker/lib/public_interfaces/training_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -263,7 +263,24 @@ async def forward_backward_custom_async(
import torch

# First do a forward pass and get logprobs
forward_future = await self.forward_async(data, "cross_entropy")
_loss_fn_input_keys = data[0].loss_fn_inputs.keys()
if "advantages" in _loss_fn_input_keys:
# Check for RL (`importance_sampling`) loss inputs
assert "advantages" in _loss_fn_input_keys, "advantages must be in loss_fn_inputs"
assert "target_tokens" in _loss_fn_input_keys, "target_tokens must be in loss_fn_inputs"
assert "logprobs" in _loss_fn_input_keys, "logprobs must be in loss_fn_inputs"
_loss_fn = "importance_sampling"

elif "weights" in _loss_fn_input_keys:
# Check for supervised learning loss inputs
assert "weights" in _loss_fn_input_keys, "weights must be in loss_fn_inputs"
assert "target_tokens" in _loss_fn_input_keys, "target_tokens must be in loss_fn_inputs"
_loss_fn = "cross_entropy"
else:
assert False, "Invalid loss function inputs"

# Compute on-policy logprobs
forward_future = await self.forward_async(data, _loss_fn)
forward_result = await forward_future.result_async()
logprobs_list: List[torch.Tensor] = []
for out in forward_result.loss_fn_outputs:
Expand All @@ -280,7 +297,7 @@ async def forward_backward_custom_async(
grads.append(logprob.grad)

linear_loss_data = []
for datum, grad in zip(data, grads):
for datum, grad in zip(data, grads, strict=True):
loss_fn_inputs: Any = {
"target_tokens": datum.loss_fn_inputs["target_tokens"],
"weights": -grad, # Pass PyTorch tensor directly (will be converted to TensorData)
Expand Down