[DOCS] Added layer norm example (#326)
This commit is contained in:
Philippe Tillet
GitHub
246
python/tutorials/05-layer-norm.py
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246
python/tutorials/05-layer-norm.py
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import torch
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import triton.language as tl
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import triton
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# Forward Pass
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@triton.jit
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def _layer_norm_fwd_fused(X, Y, W, B, M, V, stride, N, eps, **META):
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BLOCK_SIZE = META['BLOCK_SIZE']
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# position of elements processed by this program
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row = tl.program_id(0)
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cols = tl.arange(0, BLOCK_SIZE)
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mask = cols < N
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# offset data pointers to start at the row of interest
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X += row * stride
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Y += row * stride
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# load data and cast to float32
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x = tl.load(X + cols, mask=mask, other=0).to(tl.float32)
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# compute mean
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mean = tl.sum(x, axis=0) / N
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# compute std
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xmean = tl.where(mask, x - mean, 0.)
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var = tl.sum(xmean * xmean, axis=0) / N
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rstd = 1 / tl.sqrt(var + eps)
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xhat = xmean*rstd
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# write-back mean/rstd
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tl.store(M + row, mean)
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tl.store(V + row, rstd)
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# multiply by weight and add bias
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w = tl.load(W + cols, mask=mask)
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b = tl.load(B + cols, mask=mask)
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y = xhat * w + b
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# write-back
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tl.store(Y + cols, y, mask=mask)
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# Backward pass (DX + partial DW + partial DB)
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@triton.jit
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def _layer_norm_bwd_dx_fused(DX, DY, DW, DB, X, W, B, M, V, Lock,
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stride, N, eps,
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**META):
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GROUP_SIZE_M = META['GROUP_SIZE_M']
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BLOCK_SIZE_N = META['BLOCK_SIZE_N']
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# position of elements processed by this program
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row = tl.program_id(0)
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cols = tl.arange(0, BLOCK_SIZE_N)
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mask = cols < N
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# offset data pointers to start at the row of interest
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X += row * stride
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DY += row * stride
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DX += row * stride
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# offset locks and weight/bias gradient pointer
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# each kernel instance accumulates partial sums for
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# DW and DB into one of GROUP_SIZE_M independent buffers
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# these buffers stay in the L2, which allow this kernel
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# to be fast
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lock_id = row % GROUP_SIZE_M
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Lock += lock_id
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Count = Lock + GROUP_SIZE_M
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DW = DW + lock_id*N + cols
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DB = DB + lock_id*N + cols
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# load data to SRAM
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x = tl.load(X + cols, mask=mask, other=0).to(tl.float32)
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dy = tl.load(DY + cols, mask=mask, other=0).to(tl.float32)
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w = tl.load(W + cols, mask=mask).to(tl.float32)
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mean = tl.load(M + row)
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rstd = tl.load(V + row)
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# compute dx
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xhat = (x - mean)*rstd
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wdy = w * dy
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xhat = tl.where(mask, xhat, 0.)
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wdy = tl.where(mask, wdy , 0.)
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mean1 = tl.sum(xhat * wdy, axis=0) / N
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mean2 = tl.sum(wdy, axis=0) / N
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dx = (wdy - (xhat*mean1 + mean2))*rstd
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# write-back dx
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tl.store(DX + cols, dx, mask=mask)
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# accumulate partial sums for dw/db
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partial_dw = (dy*xhat).to(w.dtype)
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partial_db = (dy).to(w.dtype)
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while tl.atomic_cas(Lock, 0, 1) == 1:
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pass
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count = tl.load(Count)
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# first store doesn't accumulate
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if count == 0:
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tl.atomic_xchg(Count, 1)
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else:
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partial_dw += tl.load(DW, mask=mask)
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partial_db += tl.load(DB, mask=mask)
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tl.store(DW, partial_dw, mask=mask)
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tl.store(DB, partial_db, mask=mask)
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# release lock
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tl.atomic_xchg(Lock, 0)
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# Backward pass (total DW + total DB)
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@triton.jit
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def _layer_norm_bwd_dwdb(DW, DB, FINAL_DW, FINAL_DB, M, N, **meta):
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pid = tl.program_id(0)
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BLOCK_SIZE_M = meta['BLOCK_SIZE_M']
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BLOCK_SIZE_N = meta['BLOCK_SIZE_N']
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cols = pid*BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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dw = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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db = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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for i in range(0, M, BLOCK_SIZE_M):
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rows = i + tl.arange(0, meta['BLOCK_SIZE_M'])
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mask = (rows[:, None] < M) & (cols[None, :] < N)
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offs = rows[:, None]*N + cols[None, :]
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dw += tl.load(DW + offs, mask=mask, other=0.)
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db += tl.load(DB + offs, mask=mask, other=0.)
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sum_dw = tl.sum(dw, axis=0)
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sum_db = tl.sum(db, axis=0)
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tl.store(FINAL_DW + cols, sum_dw, mask=cols<N)
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tl.store(FINAL_DB + cols, sum_db, mask=cols<N)
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class LayerNorm(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, normalized_shape, weight, bias, eps):
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# allocate output
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y = torch.empty_like(x)
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# reshape input data into 2D tensor
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x_arg = x.reshape(-1, x.shape[-1])
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M, N = x_arg.shape
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mean = torch.empty((M, ), dtype=torch.float32, device='cuda')
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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_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
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if N > BLOCK_SIZE:
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raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
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# heuristics for number of warps
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num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
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# enqueue kernel
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_layer_norm_fwd_fused[(M,)](x_arg, y, weight, bias, mean, rstd,
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x_arg.stride(0), N, eps,
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BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps)
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ctx.save_for_backward(x, weight, bias, mean, rstd)
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ctx.BLOCK_SIZE = BLOCK_SIZE
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ctx.num_warps = num_warps
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ctx.eps = eps
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return y
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@staticmethod
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def backward(ctx, dy):
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x, w, b, m, v = ctx.saved_tensors
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# heuristics for amount of parallel reduction stream for DG/DB
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N = w.shape[0]
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GROUP_SIZE_M = 64
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if N <= 8192: GROUP_SIZE_M = 96
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if N <= 4096: GROUP_SIZE_M = 128
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if N <= 1024: GROUP_SIZE_M = 256
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# allocate output
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locks = torch.zeros(2*GROUP_SIZE_M, dtype=torch.int32, device='cuda')
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_dw = torch.empty((GROUP_SIZE_M, w.shape[0]), dtype=x.dtype, device=w.device)
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_db = torch.empty((GROUP_SIZE_M, w.shape[0]), dtype=x.dtype, device=w.device)
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dw = torch.empty((w.shape[0],), dtype=w.dtype, device=w.device)
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db = torch.empty((w.shape[0],), dtype=w.dtype, device=w.device)
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dx = torch.empty_like(dy)
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# enqueue kernel using forward pass heuristics
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# also compute partial sums for DW and DB
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x_arg = x.reshape(-1, x.shape[-1])
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M, N = x_arg.shape
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_layer_norm_bwd_dx_fused[(M,)](dx, dy, _dw, _db, x, w, b, m, v, locks,
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x_arg.stride(0), N, ctx.eps,
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BLOCK_SIZE_N=ctx.BLOCK_SIZE,
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GROUP_SIZE_M=GROUP_SIZE_M,
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num_warps=ctx.num_warps)
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grid = lambda meta: [triton.cdiv(N, meta['BLOCK_SIZE_N'])]
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# accumulate partial sums in separate kernel
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_layer_norm_bwd_dwdb[grid](_dw, _db, dw, db, GROUP_SIZE_M, N,
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BLOCK_SIZE_M = 32,
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BLOCK_SIZE_N = 128)
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return dx, None, dw, db, None
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layer_norm = LayerNorm.apply
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def test_layer_norm(M, N, dtype, eps=1e-5, device='cuda'):
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# create data
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x_shape = (M, N)
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w_shape = (x_shape[-1], )
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weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
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bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
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x = -2.3 + 0.5*torch.randn(x_shape, dtype=dtype, device='cuda')
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dy = .1*torch.randn_like(x)
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x.requires_grad_(True)
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# forward pass
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y_tri = layer_norm(x, w_shape, weight, bias, eps)
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y_ref = torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps).to(dtype)
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# backward pass (triton)
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y_tri.backward(dy, retain_graph=True)
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dx_tri, dw_tri, db_tri = [_.grad.clone() for _ in [x, weight, bias]]
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x.grad, weight.grad, bias.grad = None, None, None
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# backward pass (torch)
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y_ref.backward(dy, retain_graph=True)
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dx_ref, dw_ref, db_ref = [_.grad.clone() for _ in [x, weight, bias]]
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# compare
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triton.testing.assert_almost_equal(y_tri, y_ref)
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triton.testing.assert_almost_equal(dx_tri, dx_ref)
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triton.testing.assert_almost_equal(db_tri, db_ref, decimal=1)
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triton.testing.assert_almost_equal(dw_tri, dw_ref, decimal=1)
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@triton.testing.perf_report(
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triton.testing.Benchmark(
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x_names=['N'],
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x_vals=[512 * i for i in range(2, 32)],
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line_arg='provider',
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line_vals=['triton', 'torch', 'apex'],
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line_names=['Triton', 'Torch', 'Apex'],
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styles=[('blue', '-'), ('green', '-'), ('orange', '-')],
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ylabel='GB/s',
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plot_name='layer-norm-backward',
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args={'M': 4096, 'dtype': torch.float16, 'mode': 'backward'}
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)
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)
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def bench_layer_norm(M, N, dtype, provider, mode='backward',eps=1e-5, device='cuda'):
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# create data
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x_shape = (M, N)
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w_shape = (x_shape[-1], )
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weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
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bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
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x = -2.3 + 0.5*torch.randn(x_shape, dtype=dtype, device='cuda')
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dy = .1*torch.randn_like(x)
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x.requires_grad_(True)
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# utility functions
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if provider == 'triton':
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y_fwd = lambda: layer_norm(x, w_shape, weight, bias, eps)
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if provider == 'torch':
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y_fwd = lambda: torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps)
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if provider == 'apex':
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import apex
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apex_layer_norm = apex.normalization.FusedLayerNorm(w_shape).to(x.device).to(x.dtype)
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y_fwd = lambda: apex_layer_norm(x)
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# forward pass
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if mode == 'forward':
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gbps = lambda ms: 2*x.numel()*x.element_size()/ms*1e-6
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ms, min_ms, max_ms = triton.testing.do_bench(y_fwd, rep=500)
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# backward pass
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if mode == 'backward':
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gbps = lambda ms: 3*x.numel()*x.element_size()/ms*1e-6
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y = y_fwd()
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ms, min_ms, max_ms = triton.testing.do_bench(lambda: y.backward(dy, retain_graph=True),
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grad_to_none=[x], rep=500)
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return gbps(ms), gbps(max_ms), gbps(min_ms)
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bench_layer_norm.run(save_path='.', print_data=True)
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