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support mm and autotune

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Signed-off-by: jinjieliu <jinjie.liu@usc.edu>
This commit is contained in:
jinjieliu
2026-02-07 00:41:23 +08:00
parent f6c7a48c1b
commit 2298b6f8c8
7 changed files with 486 additions and 56 deletions
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7
examples/add/add.cc
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@@ -5,8 +5,7 @@
#include <tvm/ffi/tvm_ffi.h>
#ifndef ADD_KERNEL_STUB
#define ADD_KERNEL_STUB(grid, stream, numWarps, numStages, x, y, output, \
numel, BLOCK_SIZE)
#define ADD_KERNEL_STUB(grid, stream, numWarps, numStages, args, kwargs)
#endif
#ifndef ADD_NAME
@@ -27,7 +26,9 @@ tvm::ffi::Tensor Add(tvm::ffi::Tensor x, tvm::ffi::Tensor y) {
tvm::ffi::Optional<int32_t> numWarps = std::nullopt, numStages = std::nullopt;
DLDevice device = x.device();
void *stream = TVMFFIEnvGetStream(device.device_type, device.device_id);
ADD_KERNEL_STUB(grid, stream, numWarps, numStages, x, y, output, numel, 1024);
tvm::ffi::Array<tvm::ffi::Any> args = {x, y, output, numel, 1024};
tvm::ffi::Map<tvm::ffi::String, tvm::ffi::Any> kwargs = {};
ADD_KERNEL_STUB(grid, stream, numWarps, numStages, args, kwargs);
return output;
}

56
examples/mm/mm.cc Normal file
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@@ -0,0 +1,56 @@
#include <ATen/DLConvertor.h>
#include <ATen/dlpack.h>
#include <tvm/ffi/extra/cuda/cubin_launcher.h>
#include <tvm/ffi/tvm_ffi.h>
#ifndef MATMUL_KERNEL_STUB
#define MATMUL_KERNEL_STUB(grid, stream, numWarps, numStages, args, kwargs)
#endif
#ifndef MATMUL_NAME
#define MATMUL_NAME ""
#endif
tvm::ffi::Tensor Matmul(tvm::ffi::Tensor a, tvm::ffi::Tensor b,
tvm::ffi::String activation) {
at::Tensor atorch = at::fromDLPack(a.ToDLPack()),
btorch = at::fromDLPack(b.ToDLPack());
const int32_t M = atorch.size(0), K = atorch.size(1), N = btorch.size(1);
at::Tensor ctorch = at::empty({M, N}, atorch.options());
tvm::ffi::Function grid = tvm::ffi::Function::FromTyped(
[M, N](const tvm::ffi::Map<tvm::ffi::String, tvm::ffi::Any> &meta)
-> tvm::ffi::Tuple<int32_t, int32_t, int32_t> {
const int32_t BLOCK_SIZE_M = meta["BLOCK_SIZE_M"].cast<int32_t>(),
BLOCK_SIZE_N = meta["BLOCK_SIZE_N"].cast<int32_t>();
return tvm::ffi::Tuple<int32_t, int32_t, int32_t>{
(M + BLOCK_SIZE_M - 1) / BLOCK_SIZE_M *
((N + BLOCK_SIZE_N - 1) / BLOCK_SIZE_N),
1, 1};
});
tvm::ffi::Optional<int32_t> numWarps = std::nullopt, numStages = std::nullopt;
DLDevice device = a.device();
void *stream = TVMFFIEnvGetStream(device.device_type, device.device_id);
tvm::ffi::Tensor c = tvm::ffi::Tensor::FromDLPack(at::toDLPack(ctorch));
tvm::ffi::Array<tvm::ffi::Any> args = {a,
b,
c,
M,
N,
K,
atorch.stride(0),
atorch.stride(1),
btorch.stride(0),
btorch.stride(1),
ctorch.stride(0),
ctorch.stride(1)};
tvm::ffi::Map<tvm::ffi::String, tvm::ffi::Any> kwargs = {
{"ACTIVATION", activation},
};
MATMUL_KERNEL_STUB(grid, stream, numWarps, numStages, args, kwargs);
return c;
}
TVM_FFI_STATIC_INIT_BLOCK() {
namespace refl = tvm::ffi::reflection;
refl::GlobalDef().def(MATMUL_NAME, Matmul);
}

307
examples/mm/mm.py Normal file
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@@ -0,0 +1,307 @@
from pathlib import Path
import time
import torch
import triton
import triton.language as tl
import triton_tvm_ffi
DEVICE = triton.runtime.driver.active.get_active_torch_device()
def get_autotune_config():
return [
triton.Config(
{
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
},
num_stages=3,
num_warps=8,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=5,
num_warps=2,
),
triton.Config(
{
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 32,
"GROUP_SIZE_M": 8,
},
num_stages=5,
num_warps=2,
),
triton.Config(
{
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
},
num_stages=3,
num_warps=8,
),
triton.Config(
{
"BLOCK_SIZE_M": 256,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
},
num_stages=3,
num_warps=8,
),
triton.Config(
{
"BLOCK_SIZE_M": 256,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
triton.Config(
{
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
},
num_stages=4,
num_warps=4,
),
]
@triton_tvm_ffi.jit
@triton.autotune(
configs=get_autotune_config(),
key=["M", "N", "K"],
)
@triton.jit
def matmul_kernel(
a_ptr,
b_ptr,
c_ptr,
M,
N,
K,
stride_am,
stride_ak,
stride_bk,
stride_bn,
stride_cm,
stride_cn,
BLOCK_SIZE_M: tl.constexpr,
BLOCK_SIZE_N: tl.constexpr,
BLOCK_SIZE_K: tl.constexpr,
GROUP_SIZE_M: tl.constexpr,
ACTIVATION: tl.constexpr,
):
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
num_pid_in_group = GROUP_SIZE_M * num_pid_n
group_id = pid // num_pid_in_group
first_pid_m = group_id * GROUP_SIZE_M
group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m)
pid_n = (pid % num_pid_in_group) // group_size_m
tl.assume(pid_m >= 0)
tl.assume(pid_n >= 0)
tl.assume(stride_am > 0)
tl.assume(stride_ak > 0)
tl.assume(stride_bn > 0)
tl.assume(stride_bk > 0)
tl.assume(stride_cm > 0)
tl.assume(stride_cn > 0)
offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
accumulator = tl.dot(a, b, accumulator)
a_ptrs += BLOCK_SIZE_K * stride_ak
b_ptrs += BLOCK_SIZE_K * stride_bk
if ACTIVATION == "leaky_relu":
accumulator = leaky_relu(accumulator)
c = accumulator.to(tl.float16)
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
@triton.jit
def leaky_relu(x):
return tl.where(x >= 0, x, 0.01 * x)
def matmul_triton(a, b, activation=""):
assert a.shape[1] == b.shape[0], "Incompatible dimensions"
assert a.is_contiguous(), "Matrix A must be contiguous"
M, K = a.shape
K, N = b.shape
c = torch.empty((M, N), device=a.device, dtype=torch.float16)
matmul_kernel[
lambda META: (
triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
)
](
a,
b,
c,
M,
N,
K,
a.stride(0),
a.stride(1),
b.stride(0),
b.stride(1),
c.stride(0),
c.stride(1),
ACTIVATION=activation,
)
return c
@triton_tvm_ffi.torch_wrap(
[matmul_kernel],
Path(__file__).parent / "mm.cc",
)
def matmul(a: torch.Tensor, b: torch.Tensor, activation: str = "") -> torch.Tensor: ...
if __name__ == "__main__":
torch.manual_seed(0)
a = torch.rand((512, 512), device=DEVICE, dtype=torch.float16) - 0.5
b = torch.rand((512, 512), device=DEVICE, dtype=torch.float16) - 0.5
torch_output = torch.matmul(a, b)
triton_output = matmul_triton(a, b, "")
tvm_ffi_output = matmul(a, b, "")
assert torch.allclose(torch_output, triton_output, atol=1e-2, rtol=1e-2)
assert torch.allclose(torch_output, tvm_ffi_output, atol=1e-2, rtol=1e-2)
tvm_ffi_output = matmul(a, b, "")
assert torch.allclose(torch_output, tvm_ffi_output, atol=1e-2, rtol=1e-2)
round = 1000
cp0 = time.perf_counter_ns()
for _ in range(round):
a @ b
cp1 = time.perf_counter_ns()
for _ in range(round):
matmul_triton(a, b, "")
cp2 = time.perf_counter_ns()
for _ in range(round):
matmul(a, b, "")
cp3 = time.perf_counter_ns()
print(
f"PyTorch matmul: {(cp1 - cp0) / round * 1e-6:.3f} ms\nTriton matmul: {(cp2 - cp1) / round * 1e-6:.3f} ms\nTVM FFI matmul: {(cp3 - cp2) / round * 1e-6:.3f} ms"
)

13
examples/softmax/softmax.cc
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@@ -4,9 +4,7 @@
#include <tvm/ffi/tvm_ffi.h>
#ifndef SOFTMAX_KERNEL_STUB
#define SOFTMAX_KERNEL_STUB(grid, stream, numWarps, numStages, output, input, \
inputStride, outputStride, nRows, nCols, \
BLOCK_SIZE)
#define SOFTMAX_KERNEL_STUB(grid, stream, numWarps, numStages, args, kwargs)
#endif
#ifndef SOFTMAX_NAME
@@ -23,9 +21,12 @@ tvm::ffi::Tensor Softmax(tvm::ffi::Tensor x) {
tvm::ffi::Tensor y = tvm::ffi::Tensor::FromDLPack(at::toDLPack(ytorch));
tvm::ffi::Tuple<int32_t, int32_t, int32_t> grid{nRows / 1024, 1, 1};
DLDevice device = x.device();
void *stream = TVMFFIEnvGetStream(device.device_type, device.device_id);
SOFTMAX_KERNEL_STUB(grid, stream, numWarps, numStages, y, x, xStride, yStride,
nRows, nCols, BLOCK_SIZE);
void* stream =
TVMFFIEnvGetStream(device.device_type, device.device_id);
tvm::ffi::Array<tvm::ffi::Any> args = {y, x, xStride, yStride,
nRows, nCols, BLOCK_SIZE};
tvm::ffi::Map<tvm::ffi::String, tvm::ffi::Any> kwargs = {};
SOFTMAX_KERNEL_STUB(grid, stream, numWarps, numStages, args, kwargs);
return y;
}

69
python/triton_tvm_ffi/jit.py
View File

@@ -2,24 +2,38 @@ from __future__ import annotations
from functools import cached_property
import inspect
from typing import Any, Callable, Dict, Final, List, Optional, Tuple, Union
from typing import (
Any,
Callable,
Dict,
Final,
Iterator,
List,
Mapping,
Optional,
Sequence,
Tuple,
Union,
)
import torch
from triton.compiler import CompiledKernel
from triton.runtime import JITFunction
from triton.runtime import Autotuner, JITFunction
import tvm_ffi
from .utils import type_canonicalize
class TVMFFIJITFunction(object):
def __init__(self, fn: JITFunction, *args, **kwargs) -> None:
def __init__(self, fn: Union[Autotuner, JITFunction], *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.fn: Final[JITFunction] = fn
self.signature: Optional[List[str]] = None
self.fn: Final[Union[Autotuner, JITFunction]] = fn
self.signature: List[str] = [*inspect.signature(self.basefn).parameters.keys()]
self.best_config: Optional[Dict[str, Any]] = None
self.ctypes: Optional[List[Optional[str]]] = None
self.kernel: Optional[bytes] = None
self.num_warps: Optional[int] = None
self.shmem: int = 0
@tvm_ffi.register_global_func(self.fullname)
def _(
@@ -29,22 +43,23 @@ class TVMFFIJITFunction(object):
_: int,
num_warps: Optional[int],
num_stages: Optional[int],
*args,
**kwargs,
args: Sequence[Any],
kwargs: Mapping[str, Any],
):
args: List[Any] = map(self.canonicalize, args)
args: Iterator[Any] = map(self.canonicalize, args)
kwargs: Dict[str, Any] = {
k: self.canonicalize(v) for k, v in kwargs.items()
}
k: v for k, v in zip(self.signature, args) if v is not None
} | {k: self.canonicalize(v) for k, v in kwargs.items()}
if num_warps is not None:
kwargs["num_warps"] = num_warps
if num_stages is not None:
kwargs["num_stages"] = num_stages
kernel: CompiledKernel = self.fn[grid](*args, **kwargs)
self.num_warps, _, _ = kernel.packed_metadata
self.signature = [*inspect.signature(self.fn.fn).parameters.keys()]
self.num_warps, _, self.shmem = kernel.packed_metadata
self.ctypes = [type_canonicalize(v) for v in kernel.src.signature.values()]
self.kernel = kernel.kernel
if isinstance(self.fn, Autotuner):
self.best_config = self.fn.best_config.all_kwargs()
return kernel
def __getitem__(
@@ -55,6 +70,10 @@ class TVMFFIJITFunction(object):
):
return self.fn[grid]
@cached_property
def basefn(self) -> Callable:
return self.jitfn.fn
@property
def cache_hash(self) -> int:
return self.ctypes_hash ^ self.kernel_hash
@@ -63,21 +82,35 @@ class TVMFFIJITFunction(object):
def ctypes_hash(self) -> int:
return hash(tuple(self.ctypes) if self.ctypes is not None else None)
@property
def kernel_hash(self) -> int:
return hash(self.kernel)
@cached_property
def fnname(self) -> str:
return self.fn.fn.__name__
return self.basefn.__name__
@cached_property
def fullname(self) -> str:
return f"triton.{self.name}"
@cached_property
def jitfn(self) -> JITFunction:
fn: Union[Autotuner, JITFunction] = self.fn
while not isinstance(fn, JITFunction):
fn = fn.fn
return fn
@property
def kernel_hash(self) -> int:
return hash(self.kernel)
@property
def kernel_cstr(self) -> Optional[str]:
if self.kernel is not None:
return "".join(f"\\x{byte:02x}" for byte in self.kernel)
else:
return None
@cached_property
def name(self) -> str:
return f"{self.fnname}_{hash(self.fn.fn)}"
return f"{self.fnname}_{hash(self.basefn)}"
@staticmethod
def canonicalize(val: Any) -> Any:

80
python/triton_tvm_ffi/templates/gendef.cc.j2
View File

@@ -1,4 +1,6 @@
#include <cassert>
#include <cuda.h>
#include <optional>
#include <tvm/ffi/extra/cuda/cubin_launcher.h>
#include <tvm/ffi/function.h>
@@ -9,32 +11,60 @@
{% if fn.ctypes is none %}
#define {{ fn.fnname | upper }}_STUB tvm::ffi::Function::GetGlobalRequired("{{ fn.fullname }}")
{% else %}
TVM_FFI_EMBED_CUBIN(triton_{{ fn.fnname }});
#define {{ fn.fnname | upper}}_STUB(__grid, __stream, __numWarps, __numStages{% for ctype in fn.ctypes %}, {{ "__arg" ~ loop.index0 }}{% endfor %}) do { \
const tvm::ffi::Map<tvm::ffi::String, tvm::ffi::Any> __meta = { \
{% for name in fn.signature %}
{ "{{ name }}", __arg{{ loop.index0 }} }, \
static const char __cubin[] = "{{ fn.kernel_cstr }}";
#define __CUDA_CHECK(code) assert((code) == CUDA_SUCCESS)
static CUfunction __Get{{ fn.fnname }}Kernel() {
static std::optional<CUfunction> function = std::nullopt;
if (!function) {
CUmodule module;
CUfunction func;
__CUDA_CHECK(cuModuleLoadData(&module, __cubin));
__CUDA_CHECK(cuModuleGetFunction(&func, module, "{{ fn.fnname }}"));
{% if fn.shmem > 49152 %}
int shared_optin, shared_static;
__CUDA_CHECK(cuDeviceGetAttribute(&shared_optin, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK_OPTIN, /* TODO: we assume the device id is 0 here, but this may not work on devices with more than one gpu */0));
if (shared_optin >= 49152) {
__CUDA_CHECK(cuFuncGetAttribute(&shared_static, CU_FUNC_ATTRIBUTE_SHARED_SIZE_BYTES, func));
__CUDA_CHECK(cuFuncSetAttribute(func, CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, shared_optin - shared_static));
}
{% endif %}
function = func;
}
return *function;
}
#define {{ fn.fnname | upper }}_STUB(__grid, __stream, __numWarps, __numStages, __args, __kwargs) do { \
const char *__signature[] = { "{{ fn.signature | join("\", \"") }}" }; \
tvm::ffi::Map<tvm::ffi::String, tvm::ffi::Any> __meta = { \
{% if fn.best_config != none %}
{% for k, v in fn.best_config.items() %}
{ "{{ k }}", {{ v }} }, \
{% endfor %}
}; \
static auto __kernel = TVM_FFI_EMBED_CUBIN_GET_KERNEL(triton_{{ fn.fnname }}, "{{ fn.fnname }}"); \
tvm::ffi::dim3 __gridDim = MakeGridDim(__grid, __meta); \
tvm::ffi::dim3 __block({% if fn.num_warps != none %}{{ fn.num_warps }}{% else %}__numWarps{% endif %} * 32, 1, 1); \
void *dummy = nullptr
{%- for ctype in fn.ctypes -%}
{%- if ctype == "CUdeviceptr" -%}
, *__arg{{ loop.index0 }}_ptr=__arg{{ loop.index0 }}.data_ptr()
{%- endif -%}
{%- endfor -%}; \
void *__params[] = {
{%- for ctype in fn.ctypes -%}
{%- if ctype != none -%}
&__arg{{ loop.index0 }}
{%- if ctype == "CUdeviceptr" -%}
_ptr
{%- endif -%},
{%- endif -%}
{%- endfor -%}&dummy, &dummy }; \
TVM_FFI_CHECK_CUBIN_LAUNCHER_CUDA_ERROR(__kernel.Launch(__params, __gridDim, __block, static_cast<tvm::ffi::cuda_api::StreamHandle>(__stream))); \
{% endif %}
}; \
for (size_t __i = 0, __size_args = __args.size(); __i < sizeof(__signature) / sizeof(const char *); ++__i) { \
if (__i < __size_args) { \
__meta.Set(__signature[__i], __args[__i]); \
} else if (auto __val = __kwargs.Get(__signature[__i])) { \
__meta.Set(__signature[__i], *__val); \
} \
} \
CUfunction __function = __Get{{ fn.fnname }}Kernel(); \
tvm::ffi::Tuple<int32_t, int32_t, int32_t> __gridDim = MakeGridDim(__grid, __meta); \
void *dummy = nullptr; \
{% for ctype in fn.ctypes %}
{% if ctype != none %}
{% if ctype == "CUdeviceptr" %}
void *__arg{{ loop.index0 }} = __args[{{ loop.index0 }}].cast<tvm::ffi::TensorView>().data_ptr(); \
{% else %}
{{ ctype }} __arg{{ loop.index0 }} = __args[{{ loop.index0 }}].cast<{{ ctype }}>(); \
{% endif %}
{% endif %}
{% endfor %}
void *__params[] = { {% for ctype in fn.ctypes %}{% if ctype != none %}&__arg{{ loop.index0 }}, {% endif %}{% endfor %}&dummy, &dummy }; \
__CUDA_CHECK(cuLaunchKernel(__function, __gridDim.get<0>(), __gridDim.get<1>(), __gridDim.get<2>(), 32 * {{ fn.num_warps }}, 1, 1, {{ fn.shmem }}, reinterpret_cast<CUstream>(__stream), __params, nullptr)); \
} while (false)
{% endif %}
{% endfor %}

10
python/triton_tvm_ffi/templates/grid.h
View File

@@ -6,19 +6,21 @@
#include <tvm/ffi/tvm_ffi.h>
template <typename T>
inline tvm::ffi::dim3
inline tvm::ffi::Tuple<int32_t, int32_t, int32_t>
MakeGridDim(const T &grid,
const tvm::ffi::Map<tvm::ffi::String, tvm::ffi::Any> &meta);
template <>
inline tvm::ffi::dim3 MakeGridDim<tvm::ffi::Tuple<int32_t, int32_t, int32_t>>(
inline tvm::ffi::Tuple<int32_t, int32_t, int32_t>
MakeGridDim<tvm::ffi::Tuple<int32_t, int32_t, int32_t>>(
const tvm::ffi::Tuple<int32_t, int32_t, int32_t> &grid,
const tvm::ffi::Map<tvm::ffi::String, tvm::ffi::Any> &) {
return tvm::ffi::dim3(grid.get<0>(), grid.get<1>(), grid.get<2>());
return grid;
}
template <>
inline tvm::ffi::dim3 MakeGridDim<tvm::ffi::Function>(
inline tvm::ffi::Tuple<int32_t, int32_t, int32_t>
MakeGridDim<tvm::ffi::Function>(
const tvm::ffi::Function &grid,
const tvm::ffi::Map<tvm::ffi::String, tvm::ffi::Any> &meta) {
tvm::ffi::Tuple<int32_t, int32_t, int32_t> tuple =