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Merged
pan-x-c merged 13 commits into from
Jul 28, 2025
Merged

Standardize Experience and Sample Strategy #141

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46c5bf7
simplify experiences
pan-x-c Jul 24, 2025
bb0678e
fix vllm patch
pan-x-c Jul 24, 2025
4eef9ec
move sample strategy to trainer
pan-x-c Jul 24, 2025
6bf9aee
fix monitor
pan-x-c Jul 24, 2025
85251e4
fix trainer test
pan-x-c Jul 24, 2025
44d622b
refactor sample strategy
pan-x-c Jul 25, 2025
29eb584
fix doc
pan-x-c Jul 25, 2025
e60dd6f
add converter
pan-x-c Jul 25, 2025
3e626a1
fix comments
pan-x-c Jul 25, 2025
fcbe4fb
fix tests
pan-x-c Jul 25, 2025
22596b5
fix vllm tests
pan-x-c Jul 25, 2025
01af0e5
fix tests
pan-x-c Jul 25, 2025
b59bffe
clean code
pan-x-c Jul 25, 2025
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82 changes: 48 additions & 34 deletions tests/common/experience_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,14 +43,14 @@ def test_eid_properties(self):
class TestExperience(unittest.TestCase):
def test_single_turn_experience(self):
tokens = torch.tensor([10, 11, 12], dtype=torch.int32)
logprobs = torch.tensor([0.1, 0.2, 0.3], dtype=torch.float32)
logprobs = torch.tensor([0.2, 0.3], dtype=torch.float32)
exp = Experience(tokens=tokens, logprobs=logprobs, reward=1.0, prompt_length=1)
self.assertEqual(exp.experience_type.name, "SINGLE_TURN")
self.assertTrue(torch.equal(exp.tokens, tokens))
self.assertTrue(torch.equal(exp.logprobs, logprobs))
self.assertEqual(exp.reward, 1.0)
self.assertEqual(exp.prompt_length, 1)
self.assertTrue(torch.equal(exp.action_mask, torch.tensor([0, 1, 1], dtype=torch.bool)))
self.assertTrue(torch.equal(exp.action_mask, torch.tensor([1, 1], dtype=torch.bool)))

def test_multi_turn_experience(self):
tokens = torch.tensor([1, 2, 3, 4])
Expand Down Expand Up @@ -171,13 +171,17 @@ def test_batch_conversion(self):
tokens=torch.tensor([1, 2]),
prompt_length=1,
reward=float(0.1),
logprobs=torch.tensor([0, 0.1]),
logprobs=torch.tensor([0.1]),
advantages=torch.tensor([0.1]),
returns=torch.tensor([0.4]),
),
Experience(
tokens=torch.tensor([1, 2, 3]),
prompt_length=2,
reward=float(0.2),
logprobs=torch.tensor([0, 0, 0.1]),
logprobs=torch.tensor([0.1]),
advantages=torch.tensor([0.3]),
returns=torch.tensor([0.2]),
),
]
batch = Experiences.gather_experiences(exps)
Expand All @@ -199,45 +203,53 @@ def test_batch_conversion(self):
)
self.assertTrue(
torch.all(
batch.logprobs[i][
prompt_length
- exps[i].prompt_length : prompt_length
+ exps[i].tokens.size(0)
- exps[i].prompt_length
]
batch.logprobs[i][: exps[i].tokens.size(0) - exps[i].prompt_length]
== exps[i].logprobs
)
)
self.assertTrue(
torch.all(
batch.action_masks[i][
prompt_length
- exps[i].prompt_length : prompt_length
- exps[i].prompt_length
+ exps[i].action_mask.size(0)
]
batch.action_masks[i][: exps[i].tokens.size(0) - exps[i].prompt_length]
== exps[i].action_mask
)
)
self.assertTrue(
torch.all(
batch.advantages[i][: exps[i].tokens.size(0) - exps[i].prompt_length]
== exps[i].advantages
)
)
self.assertTrue(
torch.all(
batch.returns[i][: exps[i].tokens.size(0) - exps[i].prompt_length]
== exps[i].returns
)
)

def test_multiturn_experience_batch_converstion(self):
exps = [
Experience(
tokens=torch.tensor([1, 2, 3, 4]),
tokens=torch.tensor([1, 2, 3, 4, 5, 6]),
reward=float(0.3),
logprobs=torch.tensor([0, 0, 0.1, 0.2]),
action_mask=torch.tensor([1, 0, 1, 0]),
logprobs=torch.tensor([0, 0.1, 0.2, 0.3]),
prompt_length=2,
action_mask=torch.tensor([1, 0, 1, 1]),
advantages=torch.tensor([0.1, 0, 0.2, 0.3]),
returns=torch.tensor([0.5, 0, 0.7, 0.8]),
),
Experience(
tokens=torch.tensor([1, 2, 3, 4]),
reward=float(0.4),
logprobs=torch.tensor([0, 0, 0, 0.1]),
action_mask=torch.tensor([1, 0, 0, 1]),
logprobs=torch.tensor([0, 0.1]),
prompt_length=2,
action_mask=torch.tensor([1, 1]),
advantages=torch.tensor([0.2, 0.3]),
returns=torch.tensor([0.6, 0.9]),
),
]
batch = Experiences.gather_experiences(exps)
self.assertEqual(batch.batch_size, 2)
self.assertEqual(batch.prompt_length, 1)
self.assertEqual(batch.prompt_length, 2)
prompt_length = batch.prompt_length
for i in range(batch.batch_size):
self.assertEqual(batch.rewards[i], exps[i].reward)
Expand All @@ -254,26 +266,28 @@ def test_multiturn_experience_batch_converstion(self):
)
self.assertTrue(
torch.all(
batch.logprobs[i][
prompt_length
- exps[i].prompt_length : prompt_length
+ exps[i].tokens.size(0)
- exps[i].prompt_length
]
batch.logprobs[i][: exps[i].tokens.size(0) - exps[i].prompt_length]
== exps[i].logprobs
)
)
self.assertTrue(
torch.all(
batch.action_masks[i][
prompt_length
- exps[i].prompt_length : prompt_length
- exps[i].prompt_length
+ exps[i].action_mask.size(0)
]
batch.action_masks[i][: exps[i].tokens.size(0) - exps[i].prompt_length]
== exps[i].action_mask
)
)
self.assertTrue(
torch.all(
batch.advantages[i][: exps[i].tokens.size(0) - exps[i].prompt_length]
== exps[i].advantages
)
)
self.assertTrue(
torch.all(
batch.returns[i][: exps[i].tokens.size(0) - exps[i].prompt_length]
== exps[i].returns
)
)

def test_dpo_experience_batch_conversion(self):
exps = [
Expand Down
5 changes: 1 addition & 4 deletions tests/common/vllm_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -159,10 +159,7 @@ async def test_generate(
self.assertEqual(exp.prompt_length, history_exp.prompt_length)
self.assertEqual(exp.logprobs.tolist(), history_exp.logprobs.tolist())
for result in results:
input_logprobs = result.logprobs[: result.prompt_length]
output_logprobs = result.logprobs[result.prompt_length :]
self.assertTrue(torch.all(input_logprobs == 0))
self.assertTrue(torch.any(output_logprobs != 0))
self.assertTrue(torch.any(result.logprobs != 0))
if self.use_async:
logprobs = await self.model_wrapper.logprobs_async(results[0].tokens.tolist())
else:
Expand Down
6 changes: 3 additions & 3 deletions trinity/algorithm/sample_strategy/mix_sample_strategy.py
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Original file line number Diff line number Diff line change
Expand Up @@ -57,7 +57,7 @@ def sample(self, step: int) -> Tuple[Any, Dict, List]:
expert_exp_list = self.expert_exp_buffer.read()
for exp in expert_exp_list:
exp.reward = 0.0
exp.logprobs = torch.zeros_like(exp.tokens, dtype=torch.float32)
exp.logprobs = torch.zeros_like(exp.tokens[exp.prompt_length:], dtype=torch.float32)
if exp.info is None:
exp.info = {}
exp.info["is_expert"] = True
Expand Down Expand Up @@ -98,7 +98,7 @@ def to_data_proto_mix(experiences: Experiences, is_expert_mask: torch.tensor):
"responses": experiences.tokens[:, experiences.prompt_length :].long(),
"attention_mask": attention_mask.long(),
"response_mask": (
experiences.action_masks[:, experiences.prompt_length :].long()
experiences.action_masks.long()
if hasattr(experiences, "action_masks") and experiences.action_masks is not None
else attention_mask[:, experiences.prompt_length :].long()
),
Expand All @@ -114,7 +114,7 @@ def to_data_proto_mix(experiences: Experiences, is_expert_mask: torch.tensor):
batch_dict.update(
{
"token_level_scores": token_level_rewards,
"old_log_probs": experiences.logprobs[:, experiences.prompt_length :], # type: ignore
"old_log_probs": experiences.logprobs, # type: ignore
}
)
return DataProto.from_single_dict(batch_dict)
4 changes: 2 additions & 2 deletions trinity/algorithm/sample_strategy/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,7 @@ def to_data_proto(experiences: Experiences) -> DataProto:
"responses": experiences.tokens[:, experiences.prompt_length :].long(),
"attention_mask": attention_mask.long(),
"response_mask": (
experiences.action_masks[:, experiences.prompt_length :].long()
experiences.action_masks.long()
if hasattr(experiences, "action_masks") and experiences.action_masks is not None
else attention_mask[:, experiences.prompt_length :].long()
),
Expand All @@ -35,7 +35,7 @@ def to_data_proto(experiences: Experiences) -> DataProto:
batch_dict.update(
{
"token_level_scores": token_level_rewards,
"old_log_probs": experiences.logprobs[:, experiences.prompt_length :], # type: ignore
"old_log_probs": experiences.logprobs, # type: ignore
}
)
return DataProto.from_single_dict(batch_dict)
Expand Down
3 changes: 2 additions & 1 deletion trinity/buffer/schema/sql_schema.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,14 +85,15 @@ def from_messages(
"""Convert a list of messages into a single instance of SFT data."""
from trinity.common.models.utils import tokenize_and_mask_messages_hf

tokens, action_mask = tokenize_and_mask_messages_hf(
tokens, action_mask, prompt_length = tokenize_and_mask_messages_hf(
tokenizer=tokenizer,
messages=messages,
chat_template=chat_template,
)
exp = Experience(
tokens=tokens,
action_mask=action_mask,
prompt_length=prompt_length,
info={"response_num": sum([1 if m["role"] == "assistant" else 0 for m in messages])},
)
return cls(
Expand Down
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