test&implement(dcy): add unit tests for GRPO and RLOO

- Add test_grpo_rlhf.py for GRPO unit tests
- Add test_rloo_rlhf.py for RLOO unit tests
- Update GRPO implementation
- Update RLOO implementation

ding/rl_utils/grpo.py

@@ -10,9 +10,76 @@
 def grpo_policy_error(
         data: namedtuple,
         clip_ratio: float = 0.2,
+        beta: float = 0.1,  # KL散度的权重系数
 ) -> Tuple[namedtuple, namedtuple]:
+
     """
     .. note::
         Each element in this input data is a group of response samples from the same prompt.
     """
+    """计算GRPO (Generalized Reward-Conditioned Policy Optimization) 的策略损失
+
+      Args:
+          data (grpo_policy_data): 包含以下字段的数据:
+              - logit_new: 当前策略的logits [batch_size, seq_len, vocab_size]
+              - logit_old: 旧策略的logits [batch_size, seq_len, vocab_size]
+              - logit_ref: 参考策略的logits [batch_size, seq_len, vocab_size]
+              - action: 实际采取的动作 [batch_size, seq_len]
+              - adv: 优势值 [batch_size]
+              - weight: 注意力掩码 [batch_size, seq_len]
+          clip_ratio (float): PPO截断比率，默认0.2
+          beta (float): KL散度的权重系数，默认0.1
+
+      Returns:
+          Tuple[namedtuple, namedtuple]:
+              - 第一个namedtuple包含policy_loss
+              - 第二个namedtuple包含额外的指标信息
+      """
+    # 计算每个token的log概率
+    log_prob_new = torch.log_softmax(data.logit_new, dim=-1)
+    log_prob_old = torch.log_softmax(data.logit_old, dim=-1)
+    log_prob_ref = torch.log_softmax(data.logit_ref, dim=-1)
+
+    # 获取选定动作的log概率
+    action = data.action.unsqueeze(-1)  # [batch_size, seq_len, 1]
+    per_token_logps = torch.gather(log_prob_new, -1, action).squeeze(-1)  # [batch_size, seq_len]
+    per_token_old_logps = torch.gather(log_prob_old, -1, action).squeeze(-1)
+    per_token_ref_logps = torch.gather(log_prob_ref, -1, action).squeeze(-1)
+
+    # 计算KL散度: exp(q-p) - (q-p) - 1，其中p是当前策略，q是参考策略
+    per_token_kl = torch.exp(per_token_ref_logps - per_token_logps) - \
+                   (per_token_ref_logps - per_token_logps) - 1
+
+    # 计算策略比率
+    ratio = torch.exp(per_token_logps - per_token_old_logps)
+    ratio_clipped = torch.clamp(ratio, 1 - clip_ratio, 1 + clip_ratio)
+
+    # 计算每个token的损失
+    advantages = data.adv.unsqueeze(1)  # [batch_size, 1]
+    per_token_loss_unclipped = ratio * advantages
+    per_token_loss_clipped = ratio_clipped * advantages
+    per_token_loss = -torch.min(per_token_loss_unclipped, per_token_loss_clipped)
+
+    # 添加KL散度正则化项
+    per_token_loss = per_token_loss + beta * per_token_kl
+
+    # 使用weight计算平均损失
+    weight = data.weight if data.weight is not None else torch.ones_like(per_token_loss)
+    loss = ((per_token_loss * weight).sum(dim=1) / weight.sum(dim=1)).mean()
+
+    # 计算额外的指标
+    metrics = {
+        'mean_kl': ((per_token_kl * weight).sum(dim=1) / weight.sum(dim=1)).mean().item(),
+        'mean_ratio': ((ratio * weight).sum(dim=1) / weight.sum(dim=1)).mean().item(),
+        'mean_clipped': (ratio > (1 + clip_ratio)).float().mean().item() + \
+                        (ratio < (1 - clip_ratio)).float().mean().item(),
+    }
+
+    # 创建返回的namedtuple对象
+    loss_info = namedtuple('LossInfo', ['policy_loss'])(policy_loss=loss)
+    metric_info = namedtuple('MetricInfo', list(metrics.keys()))(**metrics)
+
+    return loss_info, metric_info
+
+
     raise NotImplementedError

ding/rl_utils/rloo.py

@@ -11,8 +11,63 @@ def rloo_policy_error(
         data: namedtuple,
         clip_ratio: float = 0.2,
 ) -> Tuple[namedtuple, namedtuple]:
+
     """
     .. note::
         Each element in this input data is a group of response samples from the same prompt.
     """
+    """计算RLOO (Rejection Learning with Optimistic Optimization) 的策略损失
+
+       Args:
+           data (rloo_policy_data): 包含以下字段的数据:
+               - logit_new: 当前策略的logits [batch_size, seq_len, vocab_size]
+               - logit_old: 旧策略的logits [batch_size, seq_len, vocab_size]
+               - action: 实际采取的动作 [batch_size, seq_len]
+               - adv: 优势值 [batch_size]
+               - weight: 注意力掩码 [batch_size, seq_len]
+           clip_ratio (float): PPO截断比率，默认0.2
+
+       Returns:
+           Tuple[namedtuple, namedtuple]:
+               - 第一个namedtuple包含policy_loss
+               - 第二个namedtuple包含额外的指标信息
+       """
+    # 计算每个token的log概率
+    log_prob_new = torch.log_softmax(data.logit_new, dim=-1)
+    log_prob_old = torch.log_softmax(data.logit_old, dim=-1)
+
+    # 获取选定动作的log概率
+    action = data.action.unsqueeze(-1)  # [batch_size, seq_len, 1]
+    per_token_logps = torch.gather(log_prob_new, -1, action).squeeze(-1)  # [batch_size, seq_len]
+    per_token_old_logps = torch.gather(log_prob_old, -1, action).squeeze(-1)
+
+    # 计算策略比率
+    ratio = torch.exp(per_token_logps - per_token_old_logps)
+    ratio_clipped = torch.clamp(ratio, 1 - clip_ratio, 1 + clip_ratio)
+
+    # 计算每个token的损失
+    advantages = data.adv.unsqueeze(1)  # [batch_size, 1]
+    per_token_loss_unclipped = ratio * advantages
+    per_token_loss_clipped = ratio_clipped * advantages
+    per_token_loss = -torch.min(per_token_loss_unclipped, per_token_loss_clipped)
+
+    # 使用weight计算平均损失
+    weight = data.weight if data.weight is not None else torch.ones_like(per_token_loss)
+    loss = ((per_token_loss * weight).sum(dim=1) / weight.sum(dim=1)).mean()
+
+    # 计算额外的指标
+    metrics = {
+        'mean_ratio': ((ratio * weight).sum(dim=1) / weight.sum(dim=1)).mean().item(),
+        'mean_clipped': (ratio > (1 + clip_ratio)).float().mean().item() + \
+                        (ratio < (1 - clip_ratio)).float().mean().item(),
+        'mean_advantage': advantages.mean().item(),
+    }
+
+    # 创建返回的namedtuple对象
+    loss_info = namedtuple('LossInfo', ['policy_loss'])(policy_loss=loss)
+    metric_info = namedtuple('MetricInfo', list(metrics.keys()))(**metrics)
+
+    return loss_info, metric_info
+
+
     raise NotImplementedError

ding/rl_utils/tests/test_grpo_rlhf.py

@@ -0,0 +1,113 @@
+import pytest
+import numpy as np
+import torch
+from ding.rl_utils.grpo import grpo_policy_data, grpo_policy_error  # 导入GRPO相关函数
+
+
+@pytest.fixture
+def batch_size():
+    return 4
+
+
+@pytest.fixture
+def seq_length():
+    return 8
+
+
+@pytest.fixture
+def dictionary_num():
+    return 1000
+
+
+
+@pytest.mark.unittest
+def test_grpo_policy_loss_with_mask(batch_size: int = 4, seq_length: int = 8, vocab_size: int = 1000):
+    """测试GRPO策略损失的计算"""
+    # 1. 创建测试数据
+    logit_new = torch.randn(batch_size, seq_length, vocab_size).requires_grad_(True)  # 当前策略的logits
+    logit_old = logit_new + torch.randn_like(logit_new) * 0.1  # 旧策略的logits（稍微偏离当前策略）
+    logit_ref = logit_new + torch.randn_like(logit_new) * 0.2  # 参考策略的logits
+    action = torch.randint(0, vocab_size, (batch_size, seq_length))  # 随机采样的token
+    adv = torch.randn(batch_size)  # 每个序列的优势值
+    weight = torch.ones(batch_size, seq_length)  # 掩码
+    weight[:, -2:] = 0  # 设置最后两个时间步为padding
+
+    # 2. 创建grpo_policy_data实例
+    data = grpo_policy_data(
+        logit_new=logit_new,  # 当前策略的输出
+        logit_old=logit_old,  # 旧策略的输出
+        logit_ref=logit_ref,  # 参考策略的输出
+        action=action,  # 实际采样的token
+        adv=adv,  # 优势值
+        weight=weight  # 掩码
+    )
+
+    # 3. 计算GRPO损失
+    loss, info = grpo_policy_error(
+        data=data,
+        clip_ratio=0.2,  # PPO截断比率
+        beta=0.1  # KL散度权重
+    )
+
+    # 4. 验证输出
+    assert isinstance(loss.policy_loss, torch.Tensor)
+    assert loss.policy_loss.shape == torch.Size([])  # 确保是标量
+    assert not torch.isnan(loss.policy_loss)
+    assert not torch.isinf(loss.policy_loss)
+
+    # 5. 测试梯度
+    assert logit_new.grad is None
+    loss.policy_loss.backward()
+    assert isinstance(logit_new.grad, torch.Tensor)
+
+    # 6. 验证指标
+    assert 'mean_kl' in info._asdict()
+    assert 'mean_ratio' in info._asdict()
+    assert 'mean_clipped' in info._asdict()
+    assert all([np.isscalar(v) for v in info._asdict().values()])
+
+
+@pytest.mark.unittest
+def test_grpo_policy_loss_without_mask(batch_size: int = 4, seq_length: int = 8, vocab_size: int = 1000):
+    """测试GRPO策略损失的计算"""
+    # 1. 创建测试数据
+    logit_new = torch.randn(batch_size, seq_length, vocab_size).requires_grad_(True)  # 当前策略的logits
+    logit_old = logit_new + torch.randn_like(logit_new) * 0.1  # 旧策略的logits（稍微偏离当前策略）
+    logit_ref = logit_new + torch.randn_like(logit_new) * 0.2  # 参考策略的logits
+    action = torch.randint(0, vocab_size, (batch_size, seq_length))  # 随机采样的token
+    adv = torch.randn(batch_size)  # 每个序列的优势值
+
+
+    # 2. 创建grpo_policy_data实例
+    data = grpo_policy_data(
+        logit_new=logit_new,  # 当前策略的输出
+        logit_old=logit_old,  # 旧策略的输出
+        logit_ref=logit_ref,  # 参考策略的输出
+        action=action,  # 实际采样的token
+        adv=adv,  # 优势值
+        weight=None  # 掩码
+    )
+
+    # 3. 计算GRPO损失
+    loss, info = grpo_policy_error(
+        data=data,
+        clip_ratio=0.2,  # PPO截断比率
+        beta=0.1  # KL散度权重
+    )
+
+    # 4. 验证输出
+    assert isinstance(loss.policy_loss, torch.Tensor)
+    assert loss.policy_loss.shape == torch.Size([])  # 确保是标量
+    assert not torch.isnan(loss.policy_loss)
+    assert not torch.isinf(loss.policy_loss)
+
+    # 5. 测试梯度
+    assert logit_new.grad is None
+    loss.policy_loss.backward()
+    assert isinstance(logit_new.grad, torch.Tensor)
+
+    # 6. 验证指标
+    assert 'mean_kl' in info._asdict()
+    assert 'mean_ratio' in info._asdict()
+    assert 'mean_clipped' in info._asdict()
+    assert all([np.isscalar(v) for v in info._asdict().values()])

ding/rl_utils/tests/test_rloo_rlhf.py

@@ -0,0 +1,88 @@
+import pytest
+import numpy as np
+import torch
+from ding.rl_utils.rloo import rloo_policy_data, rloo_policy_error
+
+
+@pytest.fixture
+def batch_size():
+    return 4
+
+
+@pytest.fixture
+def seq_length():
+    return 8
+
+
+@pytest.fixture
+def dictionary_num():
+    return 1000
+
+
+@pytest.mark.unittest
+def test_rloo_policy_loss_without_mask(batch_size: int, seq_length: int, dictionary_num: int):
+    """测试不带掩码的RLOO策略损失计算"""
+    # 创建测试数据
+    logit_new = torch.randn(batch_size, seq_length, dictionary_num).requires_grad_(True)
+    logit_old = logit_new + torch.randn_like(logit_new) * 0.1  # 稍微偏离当前策略
+    action = torch.randint(0, dictionary_num, (batch_size, seq_length))
+    advantages = torch.randn(batch_size)  # RLOO中每个序列只有一个优势值
+
+    # 计算损失
+    data = rloo_policy_data(
+        logit_new=logit_new,
+        logit_old=logit_old,
+        action=action,
+        adv=advantages,
+        weight=None
+    )
+    loss, info = rloo_policy_error(data, clip_ratio=0.2)
+
+    # 验证输出
+    assert isinstance(loss.policy_loss, torch.Tensor)
+    assert loss.policy_loss.shape == torch.Size([])  # 标量
+    assert not torch.isnan(loss.policy_loss)
+    assert not torch.isinf(loss.policy_loss)
+    assert logit_new.grad is None
+    loss.policy_loss.backward()
+    assert isinstance(logit_new.grad, torch.Tensor)
+    assert all([np.isscalar(v) for v in info._asdict().values()])
+
+
+@pytest.mark.unittest
+def test_rloo_policy_loss_with_mask(batch_size: int, seq_length: int, dictionary_num: int):
+    """测试带掩码的RLOO策略损失计算"""
+    # 创建测试数据
+    logit_new = torch.randn(batch_size, seq_length, dictionary_num).requires_grad_(True)
+    logit_old = logit_new + torch.randn_like(logit_new) * 0.1  # 稍微偏离当前策略
+    action = torch.randint(0, dictionary_num, (batch_size, seq_length))
+    advantages = torch.randn(batch_size)  # RLOO中每个序列只有一个优势值
+    action_mask = torch.ones(batch_size, seq_length)
+    action_mask[:, -2:] = 0  # 设置最后两个时间步为padding
+
+    # 计算损失
+    data = rloo_policy_data(
+        logit_new=logit_new,
+        logit_old=logit_old,
+        action=action,
+        adv=advantages,
+        weight=action_mask
+    )
+    loss, info = rloo_policy_error(data, clip_ratio=0.2)
+
+    # 验证输出
+    assert isinstance(loss.policy_loss, torch.Tensor)
+    assert loss.policy_loss.shape == torch.Size([])  # 标量
+    assert not torch.isnan(loss.policy_loss)
+    assert not torch.isinf(loss.policy_loss)
+    assert logit_new.grad is None
+    loss.policy_loss.backward()
+    assert isinstance(logit_new.grad, torch.Tensor)
+
+    # 验证指标
+    assert 'mean_ratio' in info._asdict()
+    assert 'mean_clipped' in info._asdict()
+    assert 'mean_advantage' in info._asdict()
+    assert all([np.isscalar(v) for v in info._asdict().values()])
+
+