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205 changes: 205 additions & 0 deletions src/native/cambricon/ops/swiglu/kernel.mlu
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#include "swiglu.h"

__nram__ char nram_buffer[NRAM_MAX_SIZE];

namespace infini::ops {

template <typename T>
__mlu_device__ void ComputeSwiglu(const T* input, const T* gate, T* output,
size_t n) {
if constexpr (std::is_same_v<T, float>) {
for (size_t i = 0; i < n; ++i) {
float g = gate[i];
output[i] = input[i] * g / (1.0f + expf(-g));
}
} else if constexpr (std::is_same_v<T, __half>) {
auto* out_h = reinterpret_cast<half*>(output);
auto* in_h = reinterpret_cast<const half*>(input);
auto* gate_h = reinterpret_cast<const half*>(gate);
__bang_active_sigmoid(out_h, gate_h, n);
__bang_mul(out_h, out_h, gate_h, n);
__bang_mul(out_h, out_h, in_h, n);
} else {
__bang_active_sigmoid(output, gate, n);
__bang_mul(output, output, gate, n);
__bang_mul(output, output, input, n);
}
}

template <typename T>
__mlu_global__ void SwigluKernel(
const T* input, const T* gate, T* output, const size_t* out_shape,
const ptrdiff_t* out_strides, const size_t* input_shape,
const ptrdiff_t* input_strides, const size_t* gate_shape,
const ptrdiff_t* gate_strides, size_t output_size, int ndim, bool fast_path,
bool out_contiguous) {
size_t elements_per_task = (output_size + taskDim - 1) / taskDim;
size_t start = taskId * elements_per_task;
size_t end = start + elements_per_task;
if (end > output_size) end = output_size;
size_t num_elements = end > start ? end - start : 0;
if (num_elements == 0) return;

size_t nram_usable = NRAM_MAX_SIZE - 256;
size_t block_size = nram_usable / (3 * sizeof(T));
block_size = (block_size / 64) * 64;
if (block_size == 0) block_size = 64;

T* input_buf = reinterpret_cast<T*>(nram_buffer);
T* gate_buf = input_buf + block_size;
T* output_buf = gate_buf + block_size;

size_t processed = 0;

if (fast_path) {
while (processed < num_elements) {
size_t curr = block_size;
if (curr > num_elements - processed) curr = num_elements - processed;

__memcpy(input_buf, input + start + processed, curr * sizeof(T),
GDRAM2NRAM);
__memcpy(gate_buf, gate + start + processed, curr * sizeof(T),
GDRAM2NRAM);
ComputeSwiglu<T>(input_buf, gate_buf, output_buf, curr);
__memcpy(output + start + processed, output_buf, curr * sizeof(T),
NRAM2GDRAM);

processed += curr;
}
return;
}

// General path: handle non-contiguous tensors and broadcasting.
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这里 general path 里已经处理了 broadcasting / non-contiguous offset,不过现有 test_swiglu.py 主要还是同 shape 的 contiguous/strided case。能否补一个 broadcast 或 expanded tensor 的测试用例?比如 input/gate 其中一个维度为 1,或通过 expand 得到 stride=0 的场景,这样可以覆盖 coord 映射到 broadcast 维度时的行为。

while (processed < num_elements) {
size_t curr = block_size;
if (curr > num_elements - processed) curr = num_elements - processed;

for (size_t i = 0; i < curr; ++i) {
size_t flat_idx = start + processed + i;

// Compute `input` offset.
{
size_t tmp = flat_idx;
ptrdiff_t offset = 0;
for (int d = ndim - 1; d >= 0; --d) {
size_t coord = tmp % out_shape[d];
tmp /= out_shape[d];
size_t c = coord < input_shape[d] ? coord : 0;
offset += static_cast<ptrdiff_t>(c) * input_strides[d];
}
input_buf[i] = input[offset];
}

// Compute `gate` offset.
{
size_t tmp = flat_idx;
ptrdiff_t offset = 0;
for (int d = ndim - 1; d >= 0; --d) {
size_t coord = tmp % out_shape[d];
tmp /= out_shape[d];
size_t c = coord < gate_shape[d] ? coord : 0;
offset += static_cast<ptrdiff_t>(c) * gate_strides[d];
}
gate_buf[i] = gate[offset];
}
}

ComputeSwiglu<T>(input_buf, gate_buf, output_buf, curr);

if (out_contiguous) {
__memcpy(output + start + processed, output_buf, curr * sizeof(T),
NRAM2GDRAM);
} else {
for (size_t i = 0; i < curr; ++i) {
size_t flat_idx = start + processed + i;
size_t tmp = flat_idx;
ptrdiff_t offset = 0;
for (int d = ndim - 1; d >= 0; --d) {
size_t coord = tmp % out_shape[d];
offset += static_cast<ptrdiff_t>(coord) * out_strides[d];
tmp /= out_shape[d];
}
output[offset] = output_buf[i];
}
}

processed += curr;
}
}

template <typename T>
void SwigluUnion(void* workspace, int core_per_cluster, int cluster_count,
cnrtQueue_t queue, void* out, const void* input,
const void* gate, const size_t* out_shape,
const ptrdiff_t* out_strides, const size_t* input_shape,
const ptrdiff_t* input_strides, const size_t* gate_shape,
const ptrdiff_t* gate_strides, size_t output_size, int ndim,
bool fast_path, bool out_contiguous) {
cnrtDim3_t kernel_dim;
cnrtFunctionType_t kernel_type;

kernel_dim.x = core_per_cluster;
kernel_dim.y = cluster_count;
kernel_dim.z = 1;
kernel_type = cnrtFuncTypeUnion1;

auto out_ = reinterpret_cast<T*>(out);
auto input_ = reinterpret_cast<const T*>(input);
auto gate_ = reinterpret_cast<const T*>(gate);

char* tmp = reinterpret_cast<char*>(workspace);
size_t* mlu_out_shape = reinterpret_cast<size_t*>(tmp);
size_t* mlu_input_shape = mlu_out_shape + ndim;
size_t* mlu_gate_shape = mlu_input_shape + ndim;
ptrdiff_t* mlu_out_strides =
reinterpret_cast<ptrdiff_t*>(mlu_gate_shape + ndim);
ptrdiff_t* mlu_input_strides = mlu_out_strides + ndim;
ptrdiff_t* mlu_gate_strides = mlu_input_strides + ndim;

CNRT_CHECK(cnrtMemcpyAsync(mlu_out_shape, const_cast<size_t*>(out_shape),
ndim * sizeof(size_t), queue,
cnrtMemcpyHostToDev));
CNRT_CHECK(cnrtMemcpyAsync(mlu_input_shape, const_cast<size_t*>(input_shape),
ndim * sizeof(size_t), queue,
cnrtMemcpyHostToDev));
CNRT_CHECK(cnrtMemcpyAsync(mlu_gate_shape, const_cast<size_t*>(gate_shape),
ndim * sizeof(size_t), queue,
cnrtMemcpyHostToDev));
CNRT_CHECK(
cnrtMemcpyAsync(mlu_out_strides, const_cast<ptrdiff_t*>(out_strides),
ndim * sizeof(ptrdiff_t), queue, cnrtMemcpyHostToDev));
CNRT_CHECK(
cnrtMemcpyAsync(mlu_input_strides, const_cast<ptrdiff_t*>(input_strides),
ndim * sizeof(ptrdiff_t), queue, cnrtMemcpyHostToDev));
CNRT_CHECK(
cnrtMemcpyAsync(mlu_gate_strides, const_cast<ptrdiff_t*>(gate_strides),
ndim * sizeof(ptrdiff_t), queue, cnrtMemcpyHostToDev));

SwigluKernel<T><<<kernel_dim, kernel_type, queue>>>(
input_, gate_, out_, mlu_out_shape, mlu_out_strides, mlu_input_shape,
mlu_input_strides, mlu_gate_shape, mlu_gate_strides, output_size, ndim,
fast_path, out_contiguous);

cnrtQueueSync(queue);
}

template void SwigluUnion<__half>(void*, int, int, cnrtQueue_t, void*,
const void*, const void*, const size_t*,
const ptrdiff_t*, const size_t*,
const ptrdiff_t*, const size_t*,
const ptrdiff_t*, size_t, int, bool, bool);

template void SwigluUnion<__bang_bfloat16>(void*, int, int, cnrtQueue_t, void*,
const void*, const void*,
const size_t*, const ptrdiff_t*,
const size_t*, const ptrdiff_t*,
const size_t*, const ptrdiff_t*,
size_t, int, bool, bool);

template void SwigluUnion<float>(void*, int, int, cnrtQueue_t, void*,
const void*, const void*, const size_t*,
const ptrdiff_t*, const size_t*,
const ptrdiff_t*, const size_t*,
const ptrdiff_t*, size_t, int, bool, bool);

} // namespace infini::ops
66 changes: 66 additions & 0 deletions src/native/cambricon/ops/swiglu/swiglu.h
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#ifndef INFINI_OPS_CAMBRICON_SWIGLU_H_
#define INFINI_OPS_CAMBRICON_SWIGLU_H_

#include "base/swiglu.h"
#include "native/cambricon/common.h"
#include "native/cambricon/data_type_.h"

namespace infini::ops {

template <typename T>
void SwigluUnion(void* workspace, int core_per_cluster, int cluster_count,
cnrtQueue_t queue, void* out, const void* input,
const void* gate, const size_t* out_shape,
const ptrdiff_t* out_strides, const size_t* input_shape,
const ptrdiff_t* input_strides, const size_t* gate_shape,
const ptrdiff_t* gate_strides, size_t output_size, int ndim,
bool fast_path, bool out_contiguous);

template <>
class Operator<Swiglu, Device::Type::kCambricon> : public Swiglu {
public:
Operator(const Tensor input, const Tensor gate, Tensor out)
: Swiglu{input, gate, out} {
cnrt_utils::GetLaunchConfig(input.device(), &core_per_cluster,
&cluster_count);
cnrtMalloc(&default_workspace_, workspace_size_in_bytes());
}

void operator()(const Tensor input, const Tensor gate,
Tensor out) const override {
auto queue = static_cast<cnrtQueue_t>(stream_ ? stream_ : 0);
auto workspace{workspace_ ? workspace_ : default_workspace_};

bool fast_path = is_input_contiguous_ && is_gate_contiguous_ &&
is_out_contiguous_ && input_shape_ == out_shape_ &&
gate_shape_ == out_shape_;

DispatchFunc<
List<DataType::kFloat16, DataType::kBFloat16, DataType::kFloat32>>(
{static_cast<int64_t>(out_type_)},
[&](auto tag) {
using T = TypeMapType<Device::Type::kCambricon, ListGet<0>(tag)>;
SwigluUnion<T>(workspace, core_per_cluster, cluster_count, queue,
out.data(), input.data(), gate.data(),
out_shape_.data(), out_strides_.data(),
input_shape_.data(), input_strides_.data(),
gate_shape_.data(), gate_strides_.data(), output_size_,
ndim_, fast_path, is_out_contiguous_);
},
"CambriconSwiglu::operator() - output dispatch");
}

~Operator() { cnrtFree(default_workspace_); }

std::size_t workspace_size_in_bytes() const override {
return ndim_ * (3 * sizeof(size_t) + 3 * sizeof(ptrdiff_t));
}

void* default_workspace_{nullptr};
int core_per_cluster = 0;
int cluster_count = 0;
};

} // namespace infini::ops

#endif
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