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|
# mypy: ignore-errors
import unittest
from torch.testing._internal.inductor_utils import HAS_CUDA, HAS_GPU
from torch.utils._triton import has_triton
requires_cuda = unittest.skipUnless(HAS_CUDA, "requires cuda")
requires_gpu = unittest.skipUnless(HAS_GPU, "requires gpu")
if has_triton():
import triton
from triton import language as tl
# Define here so that multiple tests can take advantage of it
@triton.jit
def add_kernel(
in_ptr0,
in_ptr1,
out_ptr,
n_elements,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
y = tl.load(in_ptr1 + offsets, mask=mask)
output = x + y
tl.store(out_ptr + offsets, output, mask=mask)
@triton.jit
def sub_kernel(
in_ptr0,
in_ptr1,
out_ptr,
n_elements,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
y = tl.load(in_ptr1 + offsets, mask=mask)
output = x - y
tl.store(out_ptr + offsets, output, mask=mask)
@triton.jit
def add_kernel_with_optional_param(
in_ptr0,
in_ptr1,
out_ptr,
n_elements,
ARGS_PASSED: "tl.constexpr",
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
if ARGS_PASSED == "two":
y = tl.load(in_ptr1 + offsets, mask=mask)
output = x + y
else:
output = x
tl.store(out_ptr + offsets, output, mask=mask)
@triton.autotune(
configs=[
triton.Config({"BLOCK_SIZE": 128}, num_stages=3, num_warps=8),
triton.Config({"BLOCK_SIZE": 128}, num_stages=4, num_warps=4),
triton.Config({"BLOCK_SIZE": 64}, num_stages=3, num_warps=8),
triton.Config({"BLOCK_SIZE": 64}, num_stages=4, num_warps=4),
],
key=[],
)
@triton.jit
def add_kernel_autotuned(
in_ptr0,
in_ptr1,
out_ptr,
n_elements,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
y = tl.load(in_ptr1 + offsets, mask=mask)
output = x + y
tl.store(out_ptr + offsets, output, mask=mask)
@triton.autotune(
configs=[
triton.Config({"BLOCK_SIZE": 16}, num_stages=2, num_warps=2),
],
key=[],
)
@triton.jit
def add_kernel_autotuned_weird_param_order(
in_ptr0,
in_ptr1,
n_elements,
BLOCK_SIZE: "tl.constexpr",
out_ptr,
):
# out_ptr is after an autotuned param that's declared as tl.constexpr.
# This param ordering can create bugs if not handled correctly.
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
y = tl.load(in_ptr1 + offsets, mask=mask)
output = x + y
tl.store(out_ptr + offsets, output, mask=mask)
@triton.autotune(
configs=[
triton.Config(
{"BLOCK_SIZE_X": 128, "BLOCK_SIZE_Y": 128}, num_stages=3, num_warps=8
),
triton.Config(
{"BLOCK_SIZE_X": 128, "BLOCK_SIZE_Y": 128}, num_stages=4, num_warps=4
),
triton.Config(
{"BLOCK_SIZE_X": 64, "BLOCK_SIZE_Y": 64}, num_stages=3, num_warps=8
),
triton.Config(
{"BLOCK_SIZE_X": 64, "BLOCK_SIZE_Y": 64}, num_stages=4, num_warps=4
),
],
key=[],
)
@triton.jit
def add_kernel_2d_autotuned(
in_ptr0,
in_ptr1,
out_ptr,
x_elements,
y_elements,
BLOCK_SIZE_X: "tl.constexpr",
BLOCK_SIZE_Y: "tl.constexpr",
):
xoffset = tl.program_id(0) * BLOCK_SIZE_X
xindex = xoffset + tl.arange(0, BLOCK_SIZE_X)[:, None]
xmask = xindex < x_elements
yoffset = tl.program_id(1) * BLOCK_SIZE_Y
yindex = yoffset + tl.arange(0, BLOCK_SIZE_Y)[None, :]
ymask = yindex < y_elements
x1 = xindex
y0 = yindex
tmp0 = tl.load(in_ptr0 + (x1 + (x_elements * y0)), xmask & ymask)
tmp1 = tl.load(in_ptr0 + (y0 + (y_elements * x1)), xmask & ymask)
tmp2 = tmp0 + tmp1
tl.store(out_ptr + (x1 + (x_elements * y0)), tmp2, xmask & ymask)
def _dummy_early_config_prune(configs, *_, **__):
return configs
@triton.autotune(
configs=[
triton.Config({"BLOCK_SIZE": 128}, num_stages=3, num_warps=8),
triton.Config({"BLOCK_SIZE": 64}, num_stages=4, num_warps=4),
],
key=[],
warmup=10,
rep=20,
prune_configs_by={"early_config_prune": _dummy_early_config_prune},
)
@triton.jit
def add_kernel_autotuned_with_unsupported_args(
in_ptr0,
in_ptr1,
out_ptr,
n_elements,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
y = tl.load(in_ptr1 + offsets, mask=mask)
output = x + y
tl.store(out_ptr + offsets, output, mask=mask)
@triton.jit
def add_kernel_with_scaling(
in_ptr0,
in_ptr1,
out_ptr,
n_elements,
scaling_factor,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
y = tl.load(in_ptr1 + offsets, mask=mask)
output = (x + y) * scaling_factor
tl.store(out_ptr + offsets, output, mask=mask)
@triton.jit
def add_kernel_with_tma_1d(
in_desc_ptr0,
in_desc_ptr1,
out_desc_ptr,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
offset = pid * BLOCK_SIZE
a = tl._experimental_descriptor_load(
in_desc_ptr0,
[offset],
[BLOCK_SIZE],
tl.float32,
)
b = tl._experimental_descriptor_load(
in_desc_ptr1,
[offset],
[BLOCK_SIZE],
tl.float32,
)
output = a + b
tl._experimental_descriptor_store(
out_desc_ptr,
output,
[offset],
)
@triton.jit
def add_kernel_with_tma_2d(
in_desc_ptr0,
in_desc_ptr1,
out_desc_ptr,
BLOCK_SIZE_X: "tl.constexpr",
BLOCK_SIZE_Y: "tl.constexpr",
):
pid_x = tl.program_id(axis=0)
pid_y = tl.program_id(axis=1)
offset_x = pid_x * BLOCK_SIZE_X
offset_y = pid_y * BLOCK_SIZE_Y
x = tl._experimental_descriptor_load(
in_desc_ptr0,
[offset_x, offset_y],
[BLOCK_SIZE_X, BLOCK_SIZE_Y],
tl.float32,
)
y = tl._experimental_descriptor_load(
in_desc_ptr1,
[offset_x, offset_y],
[BLOCK_SIZE_X, BLOCK_SIZE_Y],
tl.float32,
)
output = x + y
tl._experimental_descriptor_store(
out_desc_ptr,
output,
[offset_x, offset_y],
)
@triton.jit
def mul2_kernel(
in_ptr0,
out_ptr,
n_elements,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
output = 2 * x
tl.store(out_ptr + offsets, output, mask=mask)
@triton.jit
def mul2_inplace_kernel(
ptr,
n_elements,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(ptr + offsets, mask=mask)
output = 2 * x
tl.store(ptr + offsets, output, mask=mask)
@triton.jit
def zero_negs(x):
return tl.where(x >= 0, x, 0)
@triton.jit
def indirection_kernel(
in_ptr0,
out_ptr,
n_elements,
BLOCK_SIZE: "tl.constexpr",
ACTIVATION: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
if ACTIVATION == "mul2_inplace_kernel":
mul2_inplace_kernel(in_ptr0, n_elements, BLOCK_SIZE=BLOCK_SIZE)
elif ACTIVATION == "add_kernel":
add_kernel(in_ptr0, in_ptr0, out_ptr, n_elements, BLOCK_SIZE=BLOCK_SIZE)
x = tl.load(in_ptr0 + offsets, mask=mask)
tl.store(out_ptr + offsets, x, mask=mask)
@triton.jit
def double_strided_kernel(
in_ptr,
out_ptr,
in_y_stride,
out_y_stride,
X_BLOCK_SIZE: "tl.constexpr",
Y_BLOCK_SIZE: "tl.constexpr",
):
xid = tl.program_id(axis=0)
yid = tl.program_id(axis=1)
x_start = xid * X_BLOCK_SIZE
y_start = yid * Y_BLOCK_SIZE
x_offsets = x_start + tl.arange(0, X_BLOCK_SIZE)
y_offsets = y_start + tl.arange(0, Y_BLOCK_SIZE)
src_offsets = y_offsets[:, None] * in_y_stride + x_offsets[None, :]
dst_offsets = y_offsets[:, None] * out_y_stride + x_offsets[None, :]
src = tl.load(in_ptr + src_offsets)
tl.store(out_ptr + dst_offsets, src * 2.0)
@triton.jit
def inline_asm_kernel(X, Y, Z, n: "tl.constexpr", BLOCK: "tl.constexpr"):
x = tl.load(X + tl.arange(0, BLOCK))
y = tl.load(Y + tl.arange(0, BLOCK))
s = tl.full([BLOCK], n, tl.int32)
z = tl.inline_asm_elementwise(
"shf.l.wrap.b32 $0, $1, $2, $3;",
"=r,r, r, r",
[x, y, s],
dtype=tl.int32,
is_pure=True,
pack=1,
)
tl.store(Z + tl.arange(0, BLOCK), z)
@triton.jit
def add_kernel_with_block_ptr(
x_ptr,
y_ptr,
output_ptr,
n_elements,
BLOCK_SIZE: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
x = tl.load(
tl.make_block_ptr(
base=x_ptr,
shape=[n_elements],
strides=[1],
offsets=[block_start],
block_shape=[BLOCK_SIZE],
order=[0],
),
boundary_check=[0],
)
y = tl.load(
tl.make_block_ptr(
base=y_ptr,
shape=[n_elements],
strides=[1],
offsets=[block_start],
block_shape=[BLOCK_SIZE],
order=[0],
),
boundary_check=[0],
)
output = x + y
tl.store(
tl.make_block_ptr(
base=output_ptr,
shape=[n_elements],
strides=[1],
offsets=[block_start],
block_shape=[BLOCK_SIZE],
order=[0],
),
output,
boundary_check=[0],
)
@triton.jit
def kernel_with_block_ptr_2d(
x_ptr,
output_ptr,
n_elements,
BLOCK_SIZE: tl.constexpr,
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
x = tl.load(
tl.make_block_ptr(
base=x_ptr,
shape=[n_elements, 1],
strides=[1, 1],
offsets=[block_start, 0],
block_shape=[BLOCK_SIZE, 1],
order=[1, 0],
),
boundary_check=[0],
)
output = x
tl.store(
tl.make_block_ptr(
base=output_ptr,
shape=[n_elements, 1],
strides=[1, 1],
offsets=[block_start, 0],
block_shape=[BLOCK_SIZE, 1],
order=[1, 0],
),
output,
boundary_check=[0],
)
from triton.language import load, store
@triton.jit
def add_kernel_with_import(
in_ptr0,
in_ptr1,
out_ptr,
n_elements,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = load(in_ptr0 + offsets, mask=mask)
y = load(in_ptr1 + offsets, mask=mask)
output = x + y
store(out_ptr + offsets, output, mask=mask)
@triton.jit
def cond_op_kernel(
in_ptr0,
in_ptr1,
out_ptr,
n_elements,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
y = tl.load(in_ptr1 + offsets, mask=mask)
if tl.program_id(0) == 0:
output = x + y
else:
output = x * y
tl.store(out_ptr + offsets, output, mask=mask)
@triton.jit
def atomic_add_kernel(
in_ptr0,
in_ptr1,
out_ptr,
n_elements,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
y = tl.load(in_ptr1 + offsets, mask=mask)
output = x + y
tl.atomic_add(out_ptr + offsets, output, mask=mask)
@triton.jit
def add_4_times_kernel(
in_ptr0,
in_ptr1,
out_ptr,
n_elements,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
y = tl.load(in_ptr1 + offsets, mask=mask)
for i in range(2):
output = x + y
tl.store(out_ptr + offsets, output, mask=mask)
i = 2
while i > 0:
i -= 1
output = x + y
tl.store(out_ptr + offsets, output, mask=mask)
@triton.jit
def add_kernel_out_of_order_fn2(
in_ptr0,
in_ptr1,
n_elements,
out_ptr,
BLOCK_SIZE: "tl.constexpr",
):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(in_ptr0 + offsets, mask=mask)
y = tl.load(in_ptr1 + offsets, mask=mask)
output = x + y
tl.store(out_ptr + offsets, output, mask=mask)
|
