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Add TVD Loss Kernel #324
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Add TVD Loss Kernel #324
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da24657
Feature/tvd loss fused (#1)
saurabhkoshatwar 7736e32
Add TVD to README.md
saurabhkoshatwar a45f6ce
checkstyle fixes
saurabhkoshatwar bc906d8
Merge branch 'main' into main
saurabhkoshatwar 0097d15
Merge branch 'main' into main
lancerts bd5f976
Merge branch 'main' into main
saurabhkoshatwar 18fa8b7
init and backward pass reduction
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,136 @@ | ||
| import torch | ||
| import triton | ||
| from utils import ( | ||
| QUANTILES, | ||
| SingleBenchmarkRunInput, | ||
| SingleBenchmarkRunOutput, | ||
| _test_memory, | ||
| parse_benchmark_script_args, | ||
| run_benchmarks, | ||
| ) | ||
|
|
||
| from liger_kernel.transformers.tvd import LigerTVDLoss | ||
|
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||
|
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||
| class TorchTVDLoss(torch.nn.Module): | ||
| def __init__(self, reduction="batchmean"): | ||
| super(TorchTVDLoss, self).__init__() | ||
| self.reduction = reduction | ||
|
|
||
| def forward(self, p, q): | ||
| tvd = torch.abs(p - q) / 2.0 | ||
| if self.reduction == "mean": | ||
| return torch.sum(tvd) / (p.size(0) * p.size(1)) | ||
| elif self.reduction == "sum": | ||
| return torch.sum(tvd) | ||
| elif self.reduction == "none": | ||
| return tvd | ||
| elif self.reduction == "batchmean": | ||
| return torch.sum(tvd) / p.size(0) | ||
| else: | ||
| raise ValueError("Invalid reduction type.") | ||
|
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||
|
|
||
| S, E = 12, 18 | ||
|
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||
|
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| def bench_speed_tvd(input: SingleBenchmarkRunInput) -> SingleBenchmarkRunOutput: | ||
| reduction = "batchmean" | ||
| V = input.x | ||
| B, T = input.extra_benchmark_config["B"], input.extra_benchmark_config["T"] | ||
| torch_tvd = TorchTVDLoss(reduction=reduction) | ||
| liger_tvd = LigerTVDLoss(reduction=reduction) | ||
|
|
||
| _input = torch.randn(B * T, V, requires_grad=True, device="cuda").softmax(dim=-1) | ||
| target = torch.randn(B * T, V, device="cuda").softmax(dim=-1) | ||
|
|
||
| def fwd(): | ||
| if input.kernel_provider == "liger": | ||
| return liger_tvd(_input, target) | ||
| else: | ||
| return torch_tvd(_input, target) | ||
|
|
||
| if input.kernel_operation_mode == "forward": | ||
| ms_50, ms_20, ms_80 = triton.testing.do_bench(fwd, quantiles=QUANTILES, rep=100) | ||
| elif input.kernel_operation_mode == "backward": | ||
| y = fwd() | ||
|
|
||
| ms_50, ms_20, ms_80 = triton.testing.do_bench( | ||
| lambda: y.backward(retain_graph=True), | ||
| quantiles=QUANTILES, | ||
| grad_to_none=[_input], | ||
| rep=100, | ||
| ) | ||
| elif input.kernel_operation_mode == "full": | ||
|
|
||
| def full(): | ||
| y = fwd() | ||
| y.backward(retain_graph=True) | ||
|
|
||
| ms_50, ms_20, ms_80 = triton.testing.do_bench( | ||
| full, quantiles=QUANTILES, rep=100 | ||
| ) | ||
| return SingleBenchmarkRunOutput( | ||
| y_20=ms_20, | ||
| y_50=ms_50, | ||
| y_80=ms_80, | ||
| ) | ||
|
|
||
|
|
||
| def bench_memory_tvd(input: SingleBenchmarkRunInput) -> SingleBenchmarkRunOutput: | ||
| reduction = "batchmean" | ||
| torch_tvd = TorchTVDLoss(reduction=reduction) | ||
| liger_tvd = LigerTVDLoss(reduction=reduction) | ||
|
|
||
| V = input.x | ||
| B, T = input.extra_benchmark_config["B"], input.extra_benchmark_config["T"] | ||
|
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| _input = torch.randn(B * T, V, requires_grad=True, device="cuda").softmax(dim=-1) | ||
| target = torch.randn(B * T, V, device="cuda").softmax(dim=-1) | ||
|
|
||
| def fwd(): | ||
| if input.kernel_provider == "liger": | ||
| return liger_tvd(_input, target) | ||
| else: | ||
| return torch_tvd(_input, target) | ||
|
|
||
| def full(): | ||
| y = fwd() | ||
| y.backward(retain_graph=True) | ||
|
|
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| mem_50, mem_20, mem_80 = _test_memory(full, quantiles=QUANTILES) | ||
|
|
||
| return SingleBenchmarkRunOutput( | ||
| y_20=mem_20, | ||
| y_50=mem_50, | ||
| y_80=mem_80, | ||
| ) | ||
|
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||
|
|
||
| if __name__ == "__main__": | ||
| args = parse_benchmark_script_args() | ||
| common_args = { | ||
| "kernel_name": "tvd", | ||
| "x_name": "V", | ||
| "x_label": "vocab size", | ||
| "x_values": [2**i for i in range(12, 18)], | ||
| "kernel_providers": ["liger", "torch"], | ||
| "extra_benchmark_configs": [{"B": 8, "T": 2048}], | ||
| "overwrite": args.overwrite, | ||
| } | ||
|
|
||
| run_benchmarks( | ||
| bench_test_fn=bench_memory_tvd, | ||
| kernel_operation_modes=["full"], | ||
| metric_name="memory", | ||
| metric_unit="MB", | ||
| **common_args, | ||
| ) | ||
|
|
||
| run_benchmarks( | ||
| bench_test_fn=bench_speed_tvd, | ||
| kernel_operation_modes=["forward", "full"], | ||
| metric_name="speed", | ||
| metric_unit="ms", | ||
| **common_args, | ||
| ) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,183 @@ | ||
| from typing import Literal | ||
|
|
||
| import torch | ||
| import triton | ||
| import triton.language as tl | ||
|
|
||
| from liger_kernel.ops.utils import ensure_contiguous | ||
|
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||
| MAX_FUSED_SIZE = 65536 // 4 | ||
|
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||
| REDUCTION_LITERAL = Literal["none", "sum", "mean", "batchmean"] | ||
|
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||
| _REDUCTION_MODE_NONE = tl.constexpr(0) | ||
| _REDUCTION_MODE_SUM = tl.constexpr(1) | ||
| _REDUCTION_MODE_MEAN = tl.constexpr(2) | ||
| _REDUCTION_MODE_BATCHMEAN = tl.constexpr(3) | ||
|
|
||
| _str_to_reduction_mode = { | ||
| "none": _REDUCTION_MODE_NONE.value, | ||
| "sum": _REDUCTION_MODE_SUM.value, | ||
| "mean": _REDUCTION_MODE_MEAN.value, | ||
| "batchmean": _REDUCTION_MODE_BATCHMEAN.value, | ||
| } | ||
|
|
||
|
|
||
| def get_num_warps(BLOCK_SIZE): | ||
| num_warps = 4 | ||
| if BLOCK_SIZE >= 32768: | ||
| num_warps = 32 | ||
| elif BLOCK_SIZE >= 8192: | ||
| num_warps = 16 | ||
| elif BLOCK_SIZE >= 2048: | ||
| num_warps = 8 | ||
|
|
||
| return num_warps | ||
|
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||
|
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||
| @triton.jit | ||
| def _tv_distance_kernel( | ||
| p_ptr, | ||
| p_stride, | ||
| q_ptr, | ||
| q_stride, | ||
| loss_ptr, | ||
| loss_stride, | ||
| grads_ptr, | ||
| grads_stride, | ||
| n_cols, | ||
| BLOCK_SIZE: tl.constexpr, | ||
| reduction: tl.constexpr = _REDUCTION_MODE_BATCHMEAN, | ||
| ): | ||
| pid = tl.program_id(0).to(tl.int64) | ||
| p_ptr += pid * p_stride | ||
| q_ptr += pid * q_stride | ||
| loss_ptr += pid * loss_stride | ||
| grads_ptr += pid * grads_stride | ||
|
|
||
| base_offsets = tl.arange(0, BLOCK_SIZE) | ||
|
|
||
| loss_sum = 0.0 | ||
| for i in range(0, n_cols, BLOCK_SIZE): | ||
| offsets = i + base_offsets | ||
| mask = offsets < n_cols | ||
|
|
||
| p = tl.load(p_ptr + offsets, mask=mask, other=0.0) | ||
| q = tl.load(q_ptr + offsets, mask=mask, other=0.0) | ||
|
|
||
| # TVD(P || Q) = 0.5 * |P - Q| | ||
| tv_loss = 0.5 * tl.abs(p - q) | ||
|
|
||
| grad_res = tl.where(p > q, 0.5, -0.5) | ||
|
|
||
| tl.store(grads_ptr + offsets, grad_res, mask=mask) | ||
|
|
||
| if reduction == _REDUCTION_MODE_NONE: | ||
| tl.store(loss_ptr + offsets, tv_loss, mask=mask) | ||
| else: | ||
| loss_sum += tl.sum(tv_loss, axis=0) | ||
|
|
||
| if reduction != _REDUCTION_MODE_NONE: | ||
| tl.store(loss_ptr, loss_sum) | ||
|
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||
|
|
||
| def tv_distance_forward_triton(p, q, reduction): | ||
| BT, V = p.shape | ||
|
|
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| BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(V)) | ||
| num_warps = get_num_warps(BLOCK_SIZE) | ||
|
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||
| grid = (BT,) | ||
|
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| reduction = _str_to_reduction_mode[reduction] | ||
|
|
||
| out_size = (BT, V) if reduction == _REDUCTION_MODE_NONE.value else (BT,) | ||
| output_tensor = torch.zeros(out_size, device=p.device, dtype=torch.float32) | ||
| grads = torch.empty_like(p) | ||
|
|
||
| _tv_distance_kernel[grid]( | ||
| p, | ||
| p.stride(0), | ||
| q, | ||
| q.stride(0), | ||
| output_tensor, | ||
| output_tensor.stride(0), | ||
| grads, | ||
| grads.stride(0), | ||
| V, | ||
| BLOCK_SIZE=BLOCK_SIZE, | ||
| num_warps=num_warps, | ||
| reduction=reduction, | ||
| ) | ||
|
|
||
| if reduction == _REDUCTION_MODE_BATCHMEAN.value: | ||
| return output_tensor.sum() / BT, grads | ||
| elif reduction == _REDUCTION_MODE_SUM.value: | ||
| return output_tensor.sum(dim=0), grads | ||
| elif reduction == _REDUCTION_MODE_MEAN.value: | ||
| return output_tensor.sum() / (BT * V), grads | ||
| else: | ||
| return output_tensor, grads | ||
|
|
||
|
|
||
| def tvd_backward_triton(grad_output, grads): | ||
|
|
||
| # If cross entropy is the last layer, grad_output is 1.0. Skip the mul then. | ||
| if torch.equal(grad_output, torch.tensor(1.0, device=grad_output.device)): | ||
| return grads | ||
|
|
||
| return grads * grad_output | ||
|
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||
|
|
||
| class LigerTVDLossFunction(torch.autograd.Function): | ||
| """ | ||
| Class implementing the forward and backward pass for the Total Variation Distance Loss using Triton. | ||
| """ | ||
|
|
||
| @staticmethod | ||
| @ensure_contiguous | ||
| def forward( | ||
| ctx, | ||
| p: torch.Tensor, | ||
| q: torch.Tensor, | ||
| reduction: REDUCTION_LITERAL = "batchmean", | ||
| ) -> torch.Tensor: | ||
| """A forward pass for the Total Variation Distance Loss. | ||
|
|
||
| Args: | ||
| ctx: Torch autograd context | ||
| p (torch.Tensor): A tensor of shape (BT, V) containing the first distribution. | ||
| q (torch.Tensor): A tensor of shape (BT, V) containing the second distribution. | ||
| reduction (REDUCTION_LITERAL, optional): The reduction method to be applied. Defaults to "batchmean". | ||
|
|
||
| Returns: | ||
| torch.Tensor: The computed Total Variation Distance Loss. | ||
| """ | ||
| loss, grads = tv_distance_forward_triton(p, q, reduction) | ||
| ctx.save_for_backward(grads) | ||
| ctx.reduction = reduction | ||
| return loss | ||
|
|
||
| @staticmethod | ||
| @ensure_contiguous | ||
| def backward(ctx, grad_output: torch.Tensor) -> torch.Tensor: | ||
| """A backward pass for the Total Variation Distance Loss. | ||
|
|
||
| Args: | ||
| ctx: Torch autograd context | ||
| grad_output (torch.Tensor): The gradient of the loss with respect to the output. | ||
|
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||
| Returns: | ||
| tuple[torch.Tensor, None, None]: The gradient of the loss with respect to the inputs. | ||
| """ | ||
| (grads,) = ctx.saved_tensors | ||
| BT, V = grads.shape | ||
|
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| grads = tvd_backward_triton(grad_output, grads) | ||
|
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||
| if ctx.reduction == "batchmean": | ||
| grads /= BT | ||
| elif ctx.reduction == "mean": | ||
| grads /= BT * V | ||
|
|
||
| return grads, None, None | ||
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since we're doing gradients calculation in forward pass already, we can divide gradients by BT (BT * V) based on the reduction mode here to avoid extra calculations in backward pass and saving reduction mode in ctx