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## Summary This PR adds the Simple Preference Optimization Loss function. The only difference between SimPO and CPO is a margin term `gamma` which specifies that the preferred response should be atleast gamma logit points better than the losing response. $$SimPOLoss = -\log(\sigma(\beta\log(\pi_\theta(y_c|x)) - \beta\log(\pi_\theta(y_r|x)) - \gamma))$$ Note that SimPO explicitly specifies that $$\pi_\theta(y|x)$$ needs to be normalized by length, unlike DPO. This corresponds to Eq 6 in the [paper](https://arxiv.org/pdf/2405.14734). ## Testing Done GPU A100-80G-SXM   - Hardware Type: <BLANK> - [X] run `make test` to ensure correctness - [X] run `make checkstyle` to ensure code style - [X] run `make test-convergence` to ensure convergence --------- Co-authored-by: Byron Hsu <byronhsu1230@gmail.com>
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,191 @@ | ||
| import os | ||
| import sys | ||
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| import torch | ||
| import triton | ||
| from utils import ( | ||
| QUANTILES, | ||
| SingleBenchmarkRunInput, | ||
| SingleBenchmarkRunOutput, | ||
| _test_memory, | ||
| parse_benchmark_script_args, | ||
| run_benchmarks, | ||
| ) | ||
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| from liger_kernel.chunked_loss.simpo_loss import LigerFusedLinearSimPOFunction | ||
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| sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))) | ||
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| class TorchLMHeadSimPO(torch.nn.Module): | ||
| """Ground truth implementation of the linear fused with torch based cross entropy loss. | ||
| :param H: hidden size | ||
| :param V: vocab size | ||
| :param ignore_index: index to ignore | ||
| :param reduction: reduction method | ||
| """ | ||
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| def __init__(self, H: int, V: int, dtype: torch.dtype, ignore_index: int = -100): | ||
| from test.chunked_loss.test_cpo_loss import HFCPOLoss | ||
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| super().__init__() | ||
| self.lin = torch.nn.Linear( | ||
| in_features=H, out_features=V, bias=False, dtype=dtype | ||
| ) | ||
| self.simpo_loss = HFCPOLoss(loss_type="simpo").get_batch_loss_metrics | ||
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| def forward(self, x, y): | ||
| return self.simpo_loss(x, self.lin.weight, y) | ||
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| class LigerLMHeadSimPO(torch.nn.Module): | ||
| def __init__(self, H: int, V: int, dtype: torch.dtype, ignore_index: int = -100): | ||
| super().__init__() | ||
| self.lin = torch.nn.Linear( | ||
| in_features=H, out_features=V, bias=False, dtype=dtype | ||
| ) | ||
| self.simpo_loss = LigerFusedLinearSimPOFunction.apply | ||
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| def forward(self, x, y): | ||
| return self.simpo_loss(x, self.lin.weight, y) | ||
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| ############################################################################# | ||
| # Test the memory consumption of the linear fused cross entropy loss | ||
| ############################################################################# | ||
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| def bench_memory_fused_linear_simpo_loss( | ||
| input: SingleBenchmarkRunInput, | ||
| ) -> SingleBenchmarkRunOutput: | ||
| B = input.x | ||
| T = input.extra_benchmark_config["T"] | ||
| H = input.extra_benchmark_config["H"] | ||
| V = input.extra_benchmark_config["V"] | ||
| dtype = input.extra_benchmark_config["dtype"] | ||
| provider = input.kernel_provider | ||
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| device = "cuda" | ||
| torch_lm_head_simpo = TorchLMHeadSimPO(H=H, V=V, dtype=dtype).to(device) | ||
| liger_lm_head_simpo = LigerLMHeadSimPO(H=H, V=V, dtype=dtype).to(device) | ||
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| _input = torch.randn(B, T, H, requires_grad=True, dtype=dtype, device=device) | ||
| target = torch.randint(V, (B, T), dtype=torch.long, device=device) | ||
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| def fwd(): | ||
| if provider == "liger": | ||
| return liger_lm_head_simpo(_input, target) | ||
| elif provider == "huggingface": | ||
| return torch_lm_head_simpo(_input, target) | ||
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| def full(): | ||
| y = fwd() | ||
| y.backward() | ||
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| mem_50, mem_20, mem_80 = _test_memory(full, _iter=10, quantiles=QUANTILES) | ||
| return SingleBenchmarkRunOutput( | ||
| y_20=mem_20, | ||
| y_50=mem_50, | ||
| y_80=mem_80, | ||
| ) | ||
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| # ############################################################################# | ||
| # # Test the speed of the fused linear cross entropy loss | ||
| # ############################################################################# | ||
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| def bench_speed_fused_linear_simpo_loss( | ||
| input: SingleBenchmarkRunInput, | ||
| ) -> SingleBenchmarkRunOutput: | ||
| B = input.x | ||
| T = input.extra_benchmark_config["T"] | ||
| H = input.extra_benchmark_config["H"] | ||
| V = input.extra_benchmark_config["V"] | ||
| dtype = input.extra_benchmark_config["dtype"] | ||
| provider = input.kernel_provider | ||
| mode = input.kernel_operation_mode | ||
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| device = "cuda" | ||
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| torch_lm_head_simpo = TorchLMHeadSimPO(H=H, V=V, dtype=dtype).to(device) | ||
| liger_lm_head_simpo = LigerLMHeadSimPO(H=H, V=V, dtype=dtype).to(device) | ||
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| _input = torch.randn(B, T, H, requires_grad=True, dtype=dtype, device=device) | ||
| target = torch.randint(V, (B, T), dtype=torch.long, device=device) | ||
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| def fwd(): | ||
| if provider == "liger": | ||
| return liger_lm_head_simpo(_input, target) | ||
| elif provider == "huggingface": | ||
| return torch_lm_head_simpo(_input, target) | ||
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| if mode == "forward": | ||
| ms_50, ms_20, ms_80 = triton.testing.do_bench( | ||
| fwd, | ||
| rep=100, | ||
| quantiles=QUANTILES, | ||
| ) | ||
| elif mode == "backward": | ||
| y = fwd() | ||
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| ms_50, ms_20, ms_80 = triton.testing.do_bench( | ||
| lambda: y.backward(retain_graph=True), | ||
| grad_to_none=[_input], | ||
| rep=100, | ||
| quantiles=QUANTILES, | ||
| ) | ||
| elif mode == "full": | ||
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| def full(): | ||
| y = fwd() | ||
| y.backward() | ||
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| ms_50, ms_20, ms_80 = triton.testing.do_bench( | ||
| full, | ||
| rep=100, | ||
| quantiles=QUANTILES, | ||
| ) | ||
| return SingleBenchmarkRunOutput( | ||
| y_20=ms_20, | ||
| y_50=ms_50, | ||
| y_80=ms_80, | ||
| ) | ||
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| if __name__ == "__main__": | ||
| args = parse_benchmark_script_args() | ||
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| common_configs = { | ||
| "kernel_name": "fused_linear_simpo_loss", | ||
| "x_name": "B", | ||
| "x_label": "B", | ||
| "x_values": [2**i for i in range(1, 5)], | ||
| "kernel_providers": ["liger", "huggingface"], | ||
| "extra_benchmark_configs": [ | ||
| { | ||
| "T": 1024, | ||
| "H": 4096, | ||
| "V": 128256, | ||
| "mode": "forward", | ||
| "dtype": torch.bfloat16, | ||
| } | ||
| ], | ||
| "overwrite": args.overwrite, | ||
| } | ||
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| run_benchmarks( | ||
| bench_test_fn=bench_speed_fused_linear_simpo_loss, | ||
| kernel_operation_modes=["forward", "full"], | ||
| metric_name="speed", | ||
| metric_unit="ms", | ||
| **common_configs | ||
| ) | ||
| run_benchmarks( | ||
| bench_test_fn=bench_memory_fused_linear_simpo_loss, | ||
| kernel_operation_modes=["full"], | ||
| metric_name="memory", | ||
| metric_unit="MB", | ||
| **common_configs | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,64 @@ | ||
| import torch.nn.functional as F | ||
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| from liger_kernel.chunked_loss.fused_linear_preference import ( | ||
| LigerFusedLinearPreferenceBase, | ||
| ) | ||
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| class LigerFusedLinearSimPOFunction(LigerFusedLinearPreferenceBase): | ||
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| @staticmethod | ||
| def preference_loss_fn(chosen_logps, rejected_logps, beta=0.1, gamma=0.5): | ||
| """ | ||
| Compute odds-ratio loss. | ||
| Args: | ||
| chosen_logps (torch.Tensor): Avg log probabilities of chosen tokens. Shape: (batch_size,). | ||
| rejected_logps (torch.Tensor): Avg log probabilities of rejected tokens. Shape: (batch_size,). | ||
| beta (float): Weight for the odds ratio loss. | ||
| gamma (float): The simpo gamma, margin term. | ||
| """ | ||
| logits = beta * (chosen_logps - rejected_logps) - gamma | ||
| loss = F.logsigmoid(logits).mean() | ||
| return loss | ||
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| @staticmethod | ||
| def forward( | ||
| ctx, | ||
| _input, | ||
| weight, | ||
| target, | ||
| bias=None, | ||
| ignore_index=-100, | ||
| beta=0.1, | ||
| alpha=1.0, | ||
| compute_nll_loss=False, | ||
| compiled=True, | ||
| gamma=0.5, | ||
| ): | ||
| """ | ||
| Fused linear layer with SimPO (Simple Preference Optimization) loss. https://arxiv.org/pdf/2405.14734 | ||
| Handles both the forward and backward pass of the final linear layer with SimPO loss. | ||
| Inspired from LigerFusedLinearCrossEntropyFunction (https://arxiv.org/abs/2410.10989) which fuses final linear layer and CE loss. | ||
| """ | ||
|
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| return LigerFusedLinearPreferenceBase.forward( | ||
| ctx, | ||
| _input, | ||
| weight, | ||
| target, | ||
| bias, | ||
| loss_fn=LigerFusedLinearSimPOFunction.preference_loss_fn, | ||
| compute_nll_loss=compute_nll_loss, | ||
| ignore_index=ignore_index, | ||
| alpha=alpha, | ||
| beta=beta, | ||
| compiled=compiled, | ||
| gamma=gamma, | ||
| ) | ||
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| @staticmethod | ||
| def backward(ctx, grad_output): | ||
| # Get gradients for _input, weight, bias, and target from the base class | ||
| grads = LigerFusedLinearPreferenceBase.backward(ctx, grad_output)[:4] | ||
| # Return these gradients, followed by None for the remaining inputs | ||
| return *grads, None, None, None, None, None, None |
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