|
| 1 | +import copy |
| 2 | + |
| 3 | +import torch |
| 4 | +from coati.distributed.loss import PolicyLoss |
| 5 | +from torch.testing import assert_close |
| 6 | + |
| 7 | +from colossalai.testing import parameterize |
| 8 | +from colossalai.utils import set_seed |
| 9 | + |
| 10 | + |
| 11 | +@parameterize( |
| 12 | + "test_config", |
| 13 | + [ |
| 14 | + { |
| 15 | + "precision": torch.bfloat16, |
| 16 | + "device": "npu", |
| 17 | + }, |
| 18 | + ], |
| 19 | +) |
| 20 | +def run_policy_loss_fn(test_config): |
| 21 | + dtype = test_config["precision"] |
| 22 | + device = test_config["device"] |
| 23 | + set_seed(42) |
| 24 | + policy_loss_fn = PolicyLoss() |
| 25 | + |
| 26 | + ############ |
| 27 | + # init npu tensor |
| 28 | + ############ |
| 29 | + action_log_probs = torch.rand(8, 2048, dtype=dtype, device=device) # float [8, 2048] |
| 30 | + old_action_log_probs = torch.rand(8, 2048, dtype=dtype, device=device) # float [8, 2048] |
| 31 | + advantages = torch.rand(8, dtype=dtype, device=device) # float [8] |
| 32 | + per_token_kl = torch.rand(8, 2048, dtype=dtype, device=device) # float [8, 2048] |
| 33 | + action_mask = torch.randint( |
| 34 | + low=0, high=2, size=(8, 2048), dtype=torch.int32, device=device |
| 35 | + ) # torch.int32 [8, 2048] in range(0,1) |
| 36 | + |
| 37 | + loss, skip_update, _ = policy_loss_fn( |
| 38 | + action_log_probs, |
| 39 | + old_action_log_probs, |
| 40 | + advantages.unsqueeze(dim=-1).repeat_interleave(action_log_probs.size(-1), dim=-1), |
| 41 | + per_token_kl, |
| 42 | + action_mask, |
| 43 | + ) |
| 44 | + |
| 45 | + ############ |
| 46 | + # init cpu tensor |
| 47 | + ############ |
| 48 | + action_log_probs_cpu = copy.deepcopy(action_log_probs.cpu()) |
| 49 | + old_action_log_probs_cpu = copy.deepcopy(old_action_log_probs.cpu()) |
| 50 | + advantages_cpu = copy.deepcopy(advantages.cpu()) |
| 51 | + per_token_kl_cpu = copy.deepcopy(per_token_kl.cpu()) |
| 52 | + action_mask_cpu = copy.deepcopy(action_mask.cpu()) |
| 53 | + |
| 54 | + loss_cpu, skip_update_cpu, _ = policy_loss_fn( |
| 55 | + action_log_probs_cpu, |
| 56 | + old_action_log_probs_cpu, |
| 57 | + advantages_cpu.unsqueeze(dim=-1).repeat_interleave(action_log_probs.size(-1), dim=-1), |
| 58 | + per_token_kl_cpu, |
| 59 | + action_mask_cpu, |
| 60 | + ) |
| 61 | + |
| 62 | + # assert close |
| 63 | + assert_close( |
| 64 | + loss.to("cpu"), |
| 65 | + loss_cpu, |
| 66 | + rtol=5e-4, |
| 67 | + atol=5e-4, |
| 68 | + # msg=f"NPU/CPU {test_config['precision']} not close" |
| 69 | + ) |
| 70 | + |
| 71 | + |
| 72 | +def test_loss_func(): |
| 73 | + run_policy_loss_fn() |
| 74 | + |
| 75 | + |
| 76 | +if __name__ == "__main__": |
| 77 | + test_loss_func() |
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