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josc146
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finetune/lora/v6/fla/modules/l2norm.py
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216
finetune/lora/v6/fla/modules/l2norm.py
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# -*- coding: utf-8 -*-
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import math
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import torch
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import torch.nn.functional as F
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from torch.cuda.amp import custom_fwd, custom_bwd
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import triton
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import triton.language as tl
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@triton.autotune(
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configs=[
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triton.Config({}, num_warps=1),
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triton.Config({}, num_warps=2),
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triton.Config({}, num_warps=4),
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triton.Config({}, num_warps=8),
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triton.Config({}, num_warps=16),
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triton.Config({}, num_warps=32),
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],
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key=["N"],
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)
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# @triton.heuristics({"HAS_BIAS": lambda args: args["B"] is not None})
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# @triton.heuristics({"HAS_RESIDUAL": lambda args: args["RESIDUAL"] is not None})
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@triton.jit
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def _l2_norm_fwd_1pass_kernel(
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X, # pointer to the input
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Y, # pointer to the output
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stride_x_row, # how much to increase the pointer when moving by 1 row
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N, # number of columns in X
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eps, # epsilon to avoid division by zero
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BLOCK_N: tl.constexpr,
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):
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# Map the program id to the row of X and Y it should compute.
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row = tl.program_id(0)
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X += row * stride_x_row
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Y += row * stride_x_row
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# Compute mean and variance
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cols = tl.arange(0, BLOCK_N)
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x = tl.load(X + cols, mask=cols < N, other=0.0).to(tl.float32)
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xbar = tl.where(cols < N, x, 0.0)
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var = tl.sum(xbar * xbar, axis=0)
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rstd = 1 / tl.sqrt(var + eps)
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# tl.store(Rstd + row, rstd)
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# Normalize and apply linear transformation
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mask = cols < N
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y = x * rstd
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# Write output
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tl.store(Y + cols, y, mask=mask)
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@triton.autotune(
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configs=[
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triton.Config({}, num_warps=1),
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triton.Config({}, num_warps=2),
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triton.Config({}, num_warps=4),
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triton.Config({}, num_warps=8),
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triton.Config({}, num_warps=16),
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triton.Config({}, num_warps=32),
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],
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key=["N"],
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)
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# @triton.heuristics({"HAS_BIAS": lambda args: args["B"] is not None})
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# @triton.heuristics({"HAS_DRESIDUAL": lambda args: args["DRESIDUAL"] is not None})
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# @triton.heuristics({"STORE_DRESIDUAL": lambda args: args["DRESIDUAL_IN"] is not None})
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# @triton.heuristics({"RECOMPUTE_OUTPUT": lambda args: args["Y"] is not None})
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@triton.jit
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def _l2_norm_bwd_kernel(
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X, # pointer to the input
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# Y, # pointer to the output to be recomputed
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DY, # pointer to the output gradient
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DX, # pointer to the input gradient
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stride_x_row, # how much to increase the pointer when moving by 1 row
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N, # number of columns in X
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eps, # epsilon to avoid division by zero
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BLOCK_N: tl.constexpr,
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):
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# Map the program id to the elements of X, DX, and DY it should compute.
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# Map the program id to the row of X and Y it should compute.
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row = tl.program_id(0)
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X += row * stride_x_row
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DX += row * stride_x_row
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DY += row * stride_x_row
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# Y += row * stride_y_row
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cols = tl.arange(0, BLOCK_N)
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x = tl.load(X + cols, mask=cols < N, other=0.0).to(tl.float32)
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x = tl.where(cols < N, x, 0.0)
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var = tl.sum(x * x)
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rstd = 1 / tl.sqrt(var + eps)
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# tl.store(Rstd + row, rstd)
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# Normalize and apply linear transformation
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mask = cols < N
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# y = x * rstd
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dy = tl.load(DY + cols, mask=cols < N, other=0.0).to(tl.float32)
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dy = tl.where(cols < N, dy, 0.0)
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# dx = dy * rstd - tl.sum(dy * x) * (1 / (var+eps)) * rstd * x
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dx = dy * rstd - tl.sum(dy * x) * (1 / (var+eps)) * rstd * x
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tl.store(DX + cols, dx, mask=mask)
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def _l2_norm_fwd(
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x, eps=1e-6
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):
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x_shape_og = x.shape
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x = x.reshape(-1, x.shape[-1])
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if x.stride(-1) != 1:
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x = x.contiguous()
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M, N = x.shape
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assert x.stride(-1) == 1
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# allocate output
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y = torch.empty_like(x)
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assert y.stride(-1) == 1
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N = x.shape[-1]
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M = x.shape[0]
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# rstd = torch.empty((M,), dtype=torch.float32, device="cuda")
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# Less than 64KB per feature: enqueue fused kernel
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MAX_FUSED_SIZE = 65536 // x.element_size()
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BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
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if N > BLOCK_N:
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raise RuntimeError(
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"This layer norm doesn't support feature dim >= 64KB.")
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# heuristics for number of warps
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with torch.cuda.device(x.device.index):
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_l2_norm_fwd_1pass_kernel[(M,)](
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x,
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y,
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x.stride(0),
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N,
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eps,
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# is_rms_norm,
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BLOCK_N,
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# residual is not None,
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# residual_out is not None,
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# bias is not None,
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)
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return y.reshape(x_shape_og)
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def _l2_norm_bwd(
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x, dy, eps=1e-5,
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):
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x_shape_og = x.shape
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x = x.reshape(-1, dy.shape[-1])
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dy = dy.reshape(-1, dy.shape[-1])
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if dy.stride(-1) != 1:
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dy = dy.contiguous()
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assert dy.shape == x.shape
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# allocate output
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dx = torch.empty_like(x)
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N = x.shape[-1]
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M = x.shape[0]
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assert x.stride(-1) == 1
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assert dy.stride(-1) == 1
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# rstd = torch.empty((M,), dtype=torch.float32, device="cuda")
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# Less than 64KB per feature: enqueue fused kernel
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MAX_FUSED_SIZE = 65536 // x.element_size()
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BLOCK_N = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
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if N > BLOCK_N:
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raise RuntimeError(
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"This layer norm doesn't support feature dim >= 64KB.")
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# heuristics for number of warps
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with torch.cuda.device(x.device.index):
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_l2_norm_bwd_kernel[(M,)](
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x,
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dy,
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dx,
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x.stride(0),
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N,
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eps,
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BLOCK_N,
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)
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return dx.reshape(x_shape_og)
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class L2NormFN(torch.autograd.Function):
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@staticmethod
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def forward(
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ctx,
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x,
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eps=1e-6,
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):
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# reshape input data into 2D tensor
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y = _l2_norm_fwd(x, eps)
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ctx.x_shape_og = x_shape_og
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ctx.eps = eps
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ctx.x_dtype = x.dtype
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ctx.save_for_backward(x)
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return y
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@staticmethod
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def backward(ctx, dy, *args):
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x, = ctx.saved_tensors
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dx = _l2_norm_bwd(
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x,
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dy,
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ctx.eps,
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)
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return (
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dx,
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None
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)
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l2_norm_fn = L2NormFN.apply
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if __name__ == '__main__':
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x = torch.rand(10, 10, 100).cuda().requires_grad_(True)
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y = torch.nn.functional.normalize(x, dim=-1, p=2)
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dy = torch.rand_like(y)
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y.backward(dy, retain_graph=True)
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x_grad, x.grad = x.grad, None
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y2 = l2_norm_fn(x, 1e-6)
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print((y-y2).abs().max())
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y2.backward(dy, retain_graph=True)
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x_grad2, x.grad = x.grad, None
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print((x_grad2-x_grad).abs().max())
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breakpoint()
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