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triton/master/.doctrees/getting-started/tutorials/05-layer-norm.doctree

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hhhh<03>source<63><65>j/tmp/tmp51lver4g/9b2bc88d11af01b26f0bf7e74631f095e00f36d1/docs/getting-started/tutorials/05-layer-norm.rst<73><74>line<6E>Kubh )<29><>}<7D>(h<05>8THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.<2E>h]<5D>h<11>8THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.<2E><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhh)ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
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to download the full example code<64>hhnubeh}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(K hhhubah}<7D>(h]<5D>h]<5D><>sphx-glr-download-link-note<74>ah]<5D>h]<5D>h!]<5D>uh%hfhhchhh&h'h(Nubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D><>expr<70><72>html<6D>uh%hahhh&h'h(Khhubh <09>target<65><74><EFBFBD>)<29><>}<7D>(h<05>8.. _sphx_glr_getting-started_tutorials_05-layer-norm.py:<3A>h]<5D>h}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D><>refid<69><64>3sphx-glr-getting-started-tutorials-05-layer-norm-py<70>uh%h<>h(Khhhhh&h'ubh <09>section<6F><6E><EFBFBD>)<29><>}<7D>(hhh]<5D>(h <09>title<6C><65><EFBFBD>)<29><>}<7D>(h<05>Layer Normalization<6F>h]<5D>h<11>Layer Normalization<6F><6E><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hh<>hhh&Nh(Nubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%h<>hh<>hhh&h'h(Kubh )<29><>}<7D>(h<05>(GENERATED FROM PYTHON SOURCE LINES 5-316<31>h]<5D>h<11>(GENERATED FROM PYTHON SOURCE LINES 5-316<31><36><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhh<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$uh%h
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hh<>hhh&h'h(Kubh <09>image<67><65><EFBFBD>)<29><>}<7D>(h<05><>.. image:: /getting-started/tutorials/images/sphx_glr_05-layer-norm_001.png
:alt: 05 layer norm
:class: sphx-glr-single-img
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candidates<EFBFBD>}<7D><>*<2A>h<EFBFBD>suh%h<>hh<>hhh&h'h(Nubhm)<29><>}<7D>(h<05>Out:<3A>h]<5D>h<11>Out:<3A><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hh<>hh<>hhh&Nh(Nubah}<7D>(h]<5D>h]<5D><>sphx-glr-script-out<75>ah]<5D>h]<5D>h!]<5D>uh%hlh&h'h(K#hh<>hhubh <09> literal_block<63><6B><EFBFBD>)<29><>}<7D>(hX<>layer-norm:
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N Triton Torch Apex
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29 15872.0 447.887117 406.974373 376.225175<EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhjubah}<7D>(h]<5D>h]<5D>jah]<5D>h]<5D>h!]<5D>h#h$<24>force<63><65><EFBFBD>language<67><65>none<6E><65>highlight_args<67>}<7D>uh%j h&h'h(K%hh<>hhubh <09>
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line_block<EFBFBD><EFBFBD><EFBFBD>)<29><>}<7D>(hhh]<5D>h h(<28><>)<29><>}<7D>(hhh]<5D>h}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%h(hj#hhh&h'h(K<00>indent<6E>Kubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%j!hh<>hhh&h'h(KMubj )<29><>}<7D>(hX<>+import torch
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import triton
import triton.language as tl
try:
# This is https://github.com/NVIDIA/apex, NOT the apex on PyPi, so it
# should not be added to extras_require in setup.py.
import apex
HAS_APEX = True
except ModuleNotFoundError:
HAS_APEX = False
@triton.jit
def _layer_norm_fwd_fused(
Out,
A,
Weight,
Bias,
Mean, Rstd,
stride, N, eps,
BLOCK_SIZE: tl.constexpr,
):
# position of elements processed by this program
row = tl.program_id(0)
Out += row * stride
A += row * stride
# compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(A + cols, mask=cols < N, other=0., eviction_policy="evict_last").to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(A + cols, mask=cols < N, other=0., eviction_policy="evict_last").to(tl.float32)
a = tl.where(cols < N, a - mean, 0.)
_var += a * a
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# write-back mean/rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# multiply by weight and add bias
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
weight = tl.load(Weight + cols, mask=mask)
bias = tl.load(Bias + cols, mask=mask)
a = tl.load(A + cols, mask=mask, other=0., eviction_policy="evict_first").to(tl.float32)
a_hat = (a - mean) * rstd
out = a_hat * weight + bias
# # write-back
tl.store(Out + cols, out, mask=mask)
# Backward pass (DA + partial DW + partial DB)
@triton.jit
def _layer_norm_bwd_dx_fused(
_DA,
_DOut,
_A,
Weight,
Mean, Rstd,
stride, NumRows, NumCols, eps,
BLOCK_SIZE_N: tl.constexpr,
):
# position of elements processed by this program
pid = tl.program_id(0)
row = pid
A = _A + row * stride
DOut = _DOut + row * stride
DA = _DA + row * stride
mean = tl.load(Mean + row)
rstd = tl.load(Rstd + row)
# load data to SRAM
_mean1 = tl.zeros([BLOCK_SIZE_N], dtype=tl.float32)
_mean2 = tl.zeros([BLOCK_SIZE_N], dtype=tl.float32)
for off in range(0, NumCols, BLOCK_SIZE_N):
cols = off + tl.arange(0, BLOCK_SIZE_N)
mask = cols < NumCols
a = tl.load(A + cols, mask=mask, other=0).to(tl.float32)
dout = tl.load(DOut + cols, mask=mask, other=0).to(tl.float32)
weight = tl.load(Weight + cols, mask=mask, other=0).to(tl.float32)
a_hat = (a - mean) * rstd
wdout = weight * dout
_mean1 += a_hat * wdout
_mean2 += wdout
mean1 = tl.sum(_mean1, axis=0) / NumCols
mean2 = 0.
mean2 = tl.sum(_mean2, axis=0) / NumCols
for off in range(0, NumCols, BLOCK_SIZE_N):
cols = off + tl.arange(0, BLOCK_SIZE_N)
mask = cols < NumCols
a = tl.load(A + cols, mask=mask, other=0).to(tl.float32)
dout = tl.load(DOut + cols, mask=mask, other=0).to(tl.float32)
weight = tl.load(Weight + cols, mask=mask, other=0).to(tl.float32)
a_hat = (a - mean) * rstd
wdout = weight * dout
da = (wdout - (a_hat * mean1 + mean2)) * rstd
# write-back dx
tl.store(DA + cols, da, mask=mask)
# Backward pass (total DW + total DB)
@triton.jit
def _layer_norm_bwd_dwdb(
A, DOut,
Mean, Var,
DW,
DB,
M, N,
BLOCK_SIZE_M: tl.constexpr,
BLOCK_SIZE_N: tl.constexpr,
):
pid = tl.program_id(0)
cols = pid * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
dw = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
db = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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UNROLL: tl.constexpr = 4
for i in range(0, M, BLOCK_SIZE_M * UNROLL):
for j in range(UNROLL):
rows = i + j * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
mask = (rows[:, None] < M) & (cols[None, :] < N)
offs = rows[:, None] * N + cols[None, :]
a = tl.load(A + offs, mask=mask, other=0.).to(tl.float32)
dout = tl.load(DOut + offs, mask=mask, other=0.).to(tl.float32)
mean = tl.load(Mean + rows, mask=rows < M, other=0.)
rstd = tl.load(Var + rows, mask=rows < M, other=0.)
a_hat = (a - mean[:, None]) * rstd[:, None]
dw += dout * a_hat
db += dout
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sum_dw = tl.sum(dw, axis=0)
sum_db = tl.sum(db, axis=0)
tl.store(DW + cols, sum_dw, mask=cols < N)
tl.store(DB + cols, sum_db, mask=cols < N)
class LayerNorm(torch.autograd.Function):
@staticmethod
def forward(ctx, a, normalized_shape, weight, bias, eps):
# allocate output
out = torch.empty_like(a)
# reshape input data into 2D tensor
a_arg = a.reshape(-1, a.shape[-1])
M, N = a_arg.shape
mean = torch.empty((M,), dtype=torch.float32, device="cuda")
rstd = torch.empty((M,), dtype=torch.float32, device="cuda")
# Less than 64KB per feature: enqueue fused kernel
MAX_FUSED_SIZE = 65536 // a.element_size()
BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
BLOCK_SIZE = max(BLOCK_SIZE, 128)
BLOCK_SIZE = min(BLOCK_SIZE, 4096)
# heuristics for number of warps
num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
_layer_norm_fwd_fused[(M,)](
out,
a_arg,
weight,
bias,
mean, rstd,
a_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE,
num_warps=num_warps,
)
ctx.save_for_backward(
a, weight, bias, mean, rstd,
)
ctx.BLOCK_SIZE = BLOCK_SIZE
ctx.num_warps = num_warps
ctx.eps = eps
if hasattr(bias, "config"):
assert bias.config.grad_scale_name == weight.config.grad_scale_name
grad_scale_name = bias.config.grad_scale_name
else:
grad_scale_name = None
ctx.grad_scale_gain_bias_name = grad_scale_name
return out
@staticmethod
def backward(ctx, dout):
assert dout.is_contiguous()
a, weight, bias, mean, var = ctx.saved_tensors
# heuristics for amount of parallel reduction stream for DG/DB
N = weight.shape[0]
# allocate output
da = torch.empty_like(dout)
# enqueue kernel using forward pass heuristics
# also compute partial sums for DW and DB
x_arg = a.reshape(-1, a.shape[-1])
M, N = x_arg.shape
dweight = torch.empty((weight.shape[0],), dtype=weight.dtype, device=weight.device)
dbias = torch.empty((weight.shape[0],), dtype=weight.dtype, device=weight.device)
_layer_norm_bwd_dx_fused[(M,)](
da,
dout,
a,
weight,
mean, var,
x_arg.stride(0), M, N,
ctx.eps,
BLOCK_SIZE_N=ctx.BLOCK_SIZE,
num_warps=ctx.num_warps,
)
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if N > 10240:
BLOCK_SIZE_N = 128
BLOCK_SIZE_M = 32
num_warps = 4
else:
# maximize occupancy for small N
BLOCK_SIZE_N = 16
BLOCK_SIZE_M = 16
num_warps = 8
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grid = lambda meta: [triton.cdiv(N, meta["BLOCK_SIZE_N"])]
_layer_norm_bwd_dwdb[grid](
a, dout,
mean, var,
dweight,
dbias,
M,
N,
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BLOCK_SIZE_M=BLOCK_SIZE_M,
BLOCK_SIZE_N=BLOCK_SIZE_N,
num_warps=num_warps
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)
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return (da, None, dweight, dbias, None)
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def layer_norm(a, normalized_shape, weight, bias, eps):
return LayerNorm.apply(a, normalized_shape, weight, bias, eps)
def test_layer_norm(M, N, dtype, eps=1e-5, device='cuda'):
torch.manual_seed(0)
# create data
x_shape = (M, N)
w_shape = (x_shape[-1], )
weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')
dy = .1 * torch.randn_like(x)
x.requires_grad_(True)
# forward pass
y_tri = layer_norm(x, w_shape, weight, bias, eps)
y_ref = torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps).to(dtype)
# backward pass (triton)
y_tri.backward(dy, retain_graph=True)
dx_tri, dw_tri, db_tri = [_.grad.clone() for _ in [x, weight, bias]]
x.grad, weight.grad, bias.grad = None, None, None
# backward pass (torch)
y_ref.backward(dy, retain_graph=True)
dx_ref, dw_ref, db_ref = [_.grad.clone() for _ in [x, weight, bias]]
# compare
triton.testing.assert_almost_equal(y_tri, y_ref)
triton.testing.assert_almost_equal(dx_tri, dx_ref)
triton.testing.assert_almost_equal(db_tri, db_ref, decimal=1)
triton.testing.assert_almost_equal(dw_tri, dw_ref, decimal=1)
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=['N'],
x_vals=[512 * i for i in range(2, 32)],
line_arg='provider',
line_vals=['triton', 'torch'] + (['apex'] if HAS_APEX else []),
line_names=['Triton', 'Torch'] + (['Apex'] if HAS_APEX else []),
styles=[('blue', '-'), ('green', '-'), ('orange', '-')],
ylabel='GB/s',
plot_name='layer-norm',
args={'M': 4096, 'dtype': torch.float16, 'mode': 'forward'}
)
)
def bench_layer_norm(M, N, dtype, provider, mode, eps=1e-5, device='cuda'):
# create data
x_shape = (M, N)
w_shape = (x_shape[-1], )
weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')
dy = .1 * torch.randn_like(x)
x.requires_grad_(True)
# utility functions
if provider == 'triton':
y_fwd = lambda: layer_norm(x, w_shape, weight, bias, eps)
if provider == 'torch':
y_fwd = lambda: torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps)
if provider == 'apex':
apex_layer_norm = apex.normalization.FusedLayerNorm(w_shape).to(x.device).to(x.dtype)
y_fwd = lambda: apex_layer_norm(x)
# forward pass
if mode == 'forward':
gbps = lambda ms: 2 * x.numel() * x.element_size() / ms * 1e-6
ms, min_ms, max_ms = triton.testing.do_bench(y_fwd, rep=500)
# backward pass
if mode == 'backward':
gbps = lambda ms: 3 * x.numel() * x.element_size() / ms * 1e-6
y = y_fwd()
ms, min_ms, max_ms = triton.testing.do_bench(lambda: y.backward(dy, retain_graph=True),
grad_to_none=[x], rep=500)
return gbps(ms), gbps(max_ms), gbps(min_ms)
# test_layer_norm(1151, 8192, torch.float16)
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bench_layer_norm.run(save_path='.', print_data=True)<29>h]<5D>hX<>+import torch
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import triton
import triton.language as tl
try:
# This is https://github.com/NVIDIA/apex, NOT the apex on PyPi, so it
# should not be added to extras_require in setup.py.
import apex
HAS_APEX = True
except ModuleNotFoundError:
HAS_APEX = False
@triton.jit
def _layer_norm_fwd_fused(
Out,
A,
Weight,
Bias,
Mean, Rstd,
stride, N, eps,
BLOCK_SIZE: tl.constexpr,
):
# position of elements processed by this program
row = tl.program_id(0)
Out += row * stride
A += row * stride
# compute mean
mean = 0
_mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(A + cols, mask=cols < N, other=0., eviction_policy="evict_last").to(tl.float32)
_mean += a
mean = tl.sum(_mean, axis=0) / N
# compute variance
_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
a = tl.load(A + cols, mask=cols < N, other=0., eviction_policy="evict_last").to(tl.float32)
a = tl.where(cols < N, a - mean, 0.)
_var += a * a
var = tl.sum(_var, axis=0) / N
rstd = 1 / tl.sqrt(var + eps)
# write-back mean/rstd
tl.store(Mean + row, mean)
tl.store(Rstd + row, rstd)
# multiply by weight and add bias
for off in range(0, N, BLOCK_SIZE):
cols = off + tl.arange(0, BLOCK_SIZE)
mask = cols < N
weight = tl.load(Weight + cols, mask=mask)
bias = tl.load(Bias + cols, mask=mask)
a = tl.load(A + cols, mask=mask, other=0., eviction_policy="evict_first").to(tl.float32)
a_hat = (a - mean) * rstd
out = a_hat * weight + bias
# # write-back
tl.store(Out + cols, out, mask=mask)
# Backward pass (DA + partial DW + partial DB)
@triton.jit
def _layer_norm_bwd_dx_fused(
_DA,
_DOut,
_A,
Weight,
Mean, Rstd,
stride, NumRows, NumCols, eps,
BLOCK_SIZE_N: tl.constexpr,
):
# position of elements processed by this program
pid = tl.program_id(0)
row = pid
A = _A + row * stride
DOut = _DOut + row * stride
DA = _DA + row * stride
mean = tl.load(Mean + row)
rstd = tl.load(Rstd + row)
# load data to SRAM
_mean1 = tl.zeros([BLOCK_SIZE_N], dtype=tl.float32)
_mean2 = tl.zeros([BLOCK_SIZE_N], dtype=tl.float32)
for off in range(0, NumCols, BLOCK_SIZE_N):
cols = off + tl.arange(0, BLOCK_SIZE_N)
mask = cols < NumCols
a = tl.load(A + cols, mask=mask, other=0).to(tl.float32)
dout = tl.load(DOut + cols, mask=mask, other=0).to(tl.float32)
weight = tl.load(Weight + cols, mask=mask, other=0).to(tl.float32)
a_hat = (a - mean) * rstd
wdout = weight * dout
_mean1 += a_hat * wdout
_mean2 += wdout
mean1 = tl.sum(_mean1, axis=0) / NumCols
mean2 = 0.
mean2 = tl.sum(_mean2, axis=0) / NumCols
for off in range(0, NumCols, BLOCK_SIZE_N):
cols = off + tl.arange(0, BLOCK_SIZE_N)
mask = cols < NumCols
a = tl.load(A + cols, mask=mask, other=0).to(tl.float32)
dout = tl.load(DOut + cols, mask=mask, other=0).to(tl.float32)
weight = tl.load(Weight + cols, mask=mask, other=0).to(tl.float32)
a_hat = (a - mean) * rstd
wdout = weight * dout
da = (wdout - (a_hat * mean1 + mean2)) * rstd
# write-back dx
tl.store(DA + cols, da, mask=mask)
# Backward pass (total DW + total DB)
@triton.jit
def _layer_norm_bwd_dwdb(
A, DOut,
Mean, Var,
DW,
DB,
M, N,
BLOCK_SIZE_M: tl.constexpr,
BLOCK_SIZE_N: tl.constexpr,
):
pid = tl.program_id(0)
cols = pid * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
dw = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
db = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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UNROLL: tl.constexpr = 4
for i in range(0, M, BLOCK_SIZE_M * UNROLL):
for j in range(UNROLL):
rows = i + j * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
mask = (rows[:, None] < M) & (cols[None, :] < N)
offs = rows[:, None] * N + cols[None, :]
a = tl.load(A + offs, mask=mask, other=0.).to(tl.float32)
dout = tl.load(DOut + offs, mask=mask, other=0.).to(tl.float32)
mean = tl.load(Mean + rows, mask=rows < M, other=0.)
rstd = tl.load(Var + rows, mask=rows < M, other=0.)
a_hat = (a - mean[:, None]) * rstd[:, None]
dw += dout * a_hat
db += dout
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sum_dw = tl.sum(dw, axis=0)
sum_db = tl.sum(db, axis=0)
tl.store(DW + cols, sum_dw, mask=cols < N)
tl.store(DB + cols, sum_db, mask=cols < N)
class LayerNorm(torch.autograd.Function):
@staticmethod
def forward(ctx, a, normalized_shape, weight, bias, eps):
# allocate output
out = torch.empty_like(a)
# reshape input data into 2D tensor
a_arg = a.reshape(-1, a.shape[-1])
M, N = a_arg.shape
mean = torch.empty((M,), dtype=torch.float32, device="cuda")
rstd = torch.empty((M,), dtype=torch.float32, device="cuda")
# Less than 64KB per feature: enqueue fused kernel
MAX_FUSED_SIZE = 65536 // a.element_size()
BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
BLOCK_SIZE = max(BLOCK_SIZE, 128)
BLOCK_SIZE = min(BLOCK_SIZE, 4096)
# heuristics for number of warps
num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
_layer_norm_fwd_fused[(M,)](
out,
a_arg,
weight,
bias,
mean, rstd,
a_arg.stride(0), N, eps,
BLOCK_SIZE=BLOCK_SIZE,
num_warps=num_warps,
)
ctx.save_for_backward(
a, weight, bias, mean, rstd,
)
ctx.BLOCK_SIZE = BLOCK_SIZE
ctx.num_warps = num_warps
ctx.eps = eps
if hasattr(bias, "config"):
assert bias.config.grad_scale_name == weight.config.grad_scale_name
grad_scale_name = bias.config.grad_scale_name
else:
grad_scale_name = None
ctx.grad_scale_gain_bias_name = grad_scale_name
return out
@staticmethod
def backward(ctx, dout):
assert dout.is_contiguous()
a, weight, bias, mean, var = ctx.saved_tensors
# heuristics for amount of parallel reduction stream for DG/DB
N = weight.shape[0]
# allocate output
da = torch.empty_like(dout)
# enqueue kernel using forward pass heuristics
# also compute partial sums for DW and DB
x_arg = a.reshape(-1, a.shape[-1])
M, N = x_arg.shape
dweight = torch.empty((weight.shape[0],), dtype=weight.dtype, device=weight.device)
dbias = torch.empty((weight.shape[0],), dtype=weight.dtype, device=weight.device)
_layer_norm_bwd_dx_fused[(M,)](
da,
dout,
a,
weight,
mean, var,
x_arg.stride(0), M, N,
ctx.eps,
BLOCK_SIZE_N=ctx.BLOCK_SIZE,
num_warps=ctx.num_warps,
)
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if N > 10240:
BLOCK_SIZE_N = 128
BLOCK_SIZE_M = 32
num_warps = 4
else:
# maximize occupancy for small N
BLOCK_SIZE_N = 16
BLOCK_SIZE_M = 16
num_warps = 8
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grid = lambda meta: [triton.cdiv(N, meta["BLOCK_SIZE_N"])]
_layer_norm_bwd_dwdb[grid](
a, dout,
mean, var,
dweight,
dbias,
M,
N,
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BLOCK_SIZE_M=BLOCK_SIZE_M,
BLOCK_SIZE_N=BLOCK_SIZE_N,
num_warps=num_warps
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)
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return (da, None, dweight, dbias, None)
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def layer_norm(a, normalized_shape, weight, bias, eps):
return LayerNorm.apply(a, normalized_shape, weight, bias, eps)
def test_layer_norm(M, N, dtype, eps=1e-5, device='cuda'):
torch.manual_seed(0)
# create data
x_shape = (M, N)
w_shape = (x_shape[-1], )
weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')
dy = .1 * torch.randn_like(x)
x.requires_grad_(True)
# forward pass
y_tri = layer_norm(x, w_shape, weight, bias, eps)
y_ref = torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps).to(dtype)
# backward pass (triton)
y_tri.backward(dy, retain_graph=True)
dx_tri, dw_tri, db_tri = [_.grad.clone() for _ in [x, weight, bias]]
x.grad, weight.grad, bias.grad = None, None, None
# backward pass (torch)
y_ref.backward(dy, retain_graph=True)
dx_ref, dw_ref, db_ref = [_.grad.clone() for _ in [x, weight, bias]]
# compare
triton.testing.assert_almost_equal(y_tri, y_ref)
triton.testing.assert_almost_equal(dx_tri, dx_ref)
triton.testing.assert_almost_equal(db_tri, db_ref, decimal=1)
triton.testing.assert_almost_equal(dw_tri, dw_ref, decimal=1)
@triton.testing.perf_report(
triton.testing.Benchmark(
x_names=['N'],
x_vals=[512 * i for i in range(2, 32)],
line_arg='provider',
line_vals=['triton', 'torch'] + (['apex'] if HAS_APEX else []),
line_names=['Triton', 'Torch'] + (['Apex'] if HAS_APEX else []),
styles=[('blue', '-'), ('green', '-'), ('orange', '-')],
ylabel='GB/s',
plot_name='layer-norm',
args={'M': 4096, 'dtype': torch.float16, 'mode': 'forward'}
)
)
def bench_layer_norm(M, N, dtype, provider, mode, eps=1e-5, device='cuda'):
# create data
x_shape = (M, N)
w_shape = (x_shape[-1], )
weight = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
bias = torch.rand(w_shape, dtype=dtype, device='cuda', requires_grad=True)
x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device='cuda')
dy = .1 * torch.randn_like(x)
x.requires_grad_(True)
# utility functions
if provider == 'triton':
y_fwd = lambda: layer_norm(x, w_shape, weight, bias, eps)
if provider == 'torch':
y_fwd = lambda: torch.nn.functional.layer_norm(x, w_shape, weight, bias, eps)
if provider == 'apex':
apex_layer_norm = apex.normalization.FusedLayerNorm(w_shape).to(x.device).to(x.dtype)
y_fwd = lambda: apex_layer_norm(x)
# forward pass
if mode == 'forward':
gbps = lambda ms: 2 * x.numel() * x.element_size() / ms * 1e-6
ms, min_ms, max_ms = triton.testing.do_bench(y_fwd, rep=500)
# backward pass
if mode == 'backward':
gbps = lambda ms: 3 * x.numel() * x.element_size() / ms * 1e-6
y = y_fwd()
ms, min_ms, max_ms = triton.testing.do_bench(lambda: y.backward(dy, retain_graph=True),
grad_to_none=[x], rep=500)
return gbps(ms), gbps(max_ms), gbps(min_ms)
# test_layer_norm(1151, 8192, torch.float16)
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bench_layer_norm.run(save_path='.', print_data=True)<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj7ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>h#h$j<00>j<00>default<6C>j}<7D>uh%j h&h'h(KOhh<>hhubhm)<29><>}<7D>(h<05>B**Total running time of the script:** ( 5 minutes 28.983 seconds)<29>h]<5D>(h <09>strong<6E><67><EFBFBD>)<29><>}<7D>(h<05>%**Total running time of the script:**<2A>h]<5D>h<11>!Total running time of the script:<3A><><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhjMubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%jKhjGubh<11> ( 5 minutes 28.983 seconds)<29><><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05> ( 5 minutes 28.983 seconds)<29>hjGhhh&Nh(Nubeh}<7D>(h]<5D>h]<5D><>sphx-glr-timing<6E>ah]<5D>h]<5D>h!]<5D>uh%hlh&h'h(M<>hh<>hhubh<62>)<29><>}<7D>(h<05>A.. _sphx_glr_download_getting-started_tutorials_05-layer-norm.py:<3A>h]<5D>h}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>hČ<sphx-glr-download-getting-started-tutorials-05-layer-norm-py<70>uh%h<>h(M<>hh<>hhh&h'ubhb)<29><>}<7D>(hhh]<5D>h <09> container<65><72><EFBFBD>)<29><>}<7D>(hX.. container:: sphx-glr-download sphx-glr-download-python
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:download:`Download Python source code: 05-layer-norm.py <05-layer-norm.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
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:download:`Download Jupyter notebook: 05-layer-norm.ipynb <05-layer-norm.ipynb>`<60>h]<5D>(jv)<29><>}<7D>(h<05>L:download:`Download Python source code: 05-layer-norm.py <05-layer-norm.py>`<60>h]<5D>hm)<29><>}<7D>(hj}h]<5D>h<00>download_reference<63><65><EFBFBD>)<29><>}<7D>(hj}h]<5D>h <09>literal<61><6C><EFBFBD>)<29><>}<7D>(hj}h]<5D>h<11>-Download Python source code: 05-layer-norm.py<70><79><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>(h<><68>download<61>eh]<5D>h]<5D>h!]<5D>uh%j<>hj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D><>refdoc<6F>h<EFBFBD><68> refdomain<69>h<06>reftype<70>j<EFBFBD><00> refexplicit<69><74><EFBFBD>refwarn<72><6E>h<EFBFBD><68>05-layer-norm.py<70><79>filename<6D><65>1935c0dd0fbeb4b2e69588471cbb2d4b2/05-layer-norm.py<70>uh%j<>h&h'h(M<>hjubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(M<>hj{ubah}<7D>(h]<5D>h]<5D>(<28>sphx-glr-download<61><64>sphx-glr-download-python<6F>eh]<5D>h]<5D>h!]<5D>uh%juhjwubjv)<29><>}<7D>(h<05>P:download:`Download Jupyter notebook: 05-layer-norm.ipynb <05-layer-norm.ipynb>`<60>h]<5D>hm)<29><>}<7D>(hj<>h]<5D>j<EFBFBD>)<29><>}<7D>(hj<>h]<5D>j<EFBFBD>)<29><>}<7D>(hj<>h]<5D>h<11>.Download Jupyter notebook: 05-layer-norm.ipynb<6E><62><EFBFBD><EFBFBD><EFBFBD>}<7D>(hhhj<>ubah}<7D>(h]<5D>h]<5D>(h<><68>download<61>eh]<5D>h]<5D>h!]<5D>uh%j<>hj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D><>refdoc<6F>h<EFBFBD><68> refdomain<69>h<06>reftype<70>j<EFBFBD><00> refexplicit<69><74><EFBFBD>refwarn<72><6E>h<EFBFBD><68>05-layer-norm.ipynb<6E>j<EFBFBD><00>4ae7fff29e1b574187bc930ed94bcc353/05-layer-norm.ipynb<6E>uh%j<>h&h'h(M<>hj<>ubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D>uh%hlh&h'h(M<>hj<>ubah}<7D>(h]<5D>h]<5D>(<28>sphx-glr-download<61><64>sphx-glr-download-jupyter<65>eh]<5D>h]<5D>h!]<5D>uh%juhjwubeh}<7D>(h]<5D>h]<5D>(<28>sphx-glr-footer<65><72>class<73><73>sphx-glr-footer-example<6C>eh]<5D>h]<5D>h!]<5D>uh%juhjrhhh&Nh(Nubah}<7D>(h]<5D>jqah]<5D>h]<5D><><sphx_glr_download_getting-started_tutorials_05-layer-norm.py<70>ah]<5D>h!]<5D>h<EFBFBD><68>html<6D>uh%hahhh&h'h(M<>hhȌexpect_referenced_by_name<6D>}<7D>j<EFBFBD>jgs<>expect_referenced_by_id<69>}<7D>jqjgsubhb)<29><>}<7D>(hhh]<5D>hm)<29><>}<7D>(h<05>I`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_<>h]<5D>(h <09> reference<63><65><EFBFBD>)<29><>}<7D>(hjh]<5D>h<11>#Gallery generated by Sphinx-Gallery<72><79><EFBFBD><EFBFBD><EFBFBD>}<7D>(h<05>#Gallery generated by Sphinx-Gallery<72>hjubah}<7D>(h]<5D>h]<5D>h]<5D>h]<5D>h!]<5D><>name<6D><65>#Gallery generated by Sphinx-Gallery<72><79>refuri<72><69> https://sphinx-gallery.github.io<69>uh%jhj<>ubh<62>)<29><>}<7D>(h<05># <https://sphinx-gallery.github.io><3E>h]<5D>h}<7D>(h]<5D><>#gallery-generated-by-sphinx-gallery<72>ah]<5D>h]<5D><>#gallery generated by sphinx-gallery<72>ah]<5D>h!]<5D><>refuri<72>juh%h<><68>
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[GH-PAGES] Updated website
2022-06-05 21:05:02 +00:00
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[GH-PAGES] Updated website
2022-07-14 07:22:19 +00:00
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[GH-PAGES] Updated website
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