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SYCL: Remove misleading ggml_sycl_op_flatten function #12387

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SYCL: Remove misleading ggml_sycl_op_flatten function #12387

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113d803
SYCL: Remove misleading ggml_sycl_op_flatten function
qnixsynapse Mar 4, 2025
73f1849
remove trailing whitespace
qnixsynapse Mar 14, 2025
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35 changes: 0 additions & 35 deletions ggml/src/ggml-sycl/common.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -66,41 +66,6 @@ int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block
return sycl_down_blk_size;
}

void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst,
const ggml_sycl_op_flatten_t op) try {

const bool use_src1 = src1 != nullptr;
if(use_src1)
GGML_ASSERT(strcmp(src1->buffer->buft->iface.get_name(src1->buffer->buft), GGML_SYCL_NAME "_Split") != 0);
GGML_ASSERT(strcmp(dst->buffer->buft->iface.get_name(dst->buffer->buft), GGML_SYCL_NAME "_Split") != 0);

// dd = data device
float * src0_ddf = (float *) src0->data;
float * src1_ddf = use_src1 ? (float *) src1->data : nullptr;
float * dst_ddf = (float *) dst->data;
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@qnixsynapse qnixsynapse Mar 14, 2025 •
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This function typecasted src0, src1 and dst tensors to float regardless of whatever types they were. I think we don't want this behaviour in the long run.

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@acbits acbits Mar 14, 2025 •
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Just curious, why we don't use auto? You could avoid the casting and if the datatype changes in future, you don't have to rework it again?

Modern C++ code should use auto where ever it is possible.

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Do we need to create a separate pointer variable when we are passing the ggml_tensor itself anyways to the kernel OP function which contains everything needed to perform the OP?

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ggml_sycl_pool_alloc<float> src0_f(ctx.pool());
ggml_sycl_pool_alloc<float> src1_f(ctx.pool());
ggml_sycl_pool_alloc<float> dst_f(ctx.pool());

ggml_sycl_set_device(ctx.device);
queue_ptr main_stream = ctx.stream();
// GGML_SYCL_DEBUG("ctx.device=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n",
// ctx.device, main_stream, src0_on_device, src1_on_device, dst_on_device);

// do the computation
op(ctx, src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream);
// print_ggml_tensor("tensor", dst);
}
catch (sycl::exception const &exc) {

std::cerr << exc.what() << "Exception caught at file:" << __FILE__
<< ", line:" << __LINE__ << std::endl;
std::exit(1);
}


void release_extra_gpu(ggml_tensor_extra_gpu * extra, std::vector<queue_ptr> streams) {
for (int i = 0; i < ggml_sycl_info().device_count; ++i) {
for (int64_t is = 0; is < GGML_SYCL_MAX_STREAMS; ++is) {
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29 changes: 9 additions & 20 deletions ggml/src/ggml-sycl/common.hpp
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Expand Up @@ -463,12 +463,6 @@ static __dpct_inline__ Tp* get_pointer(sycl::local_accessor<Tp, dim> acc) {

int64_t downsample_sycl_global_range(int64_t accumulate_block_num, int64_t block_size);

typedef void (*ggml_sycl_op_flatten_t)(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1,
ggml_tensor *dst, const float *src0_dd,
const float *src1_dd, float *dst_dd,
const queue_ptr &main_stream);

template<float (*bin_op)(const float, const float), typename src0_t, typename src1_t, typename dst_t>
static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst,
int ne0, int ne1, int ne2, int ne3,
Expand Down Expand Up @@ -691,24 +685,23 @@ struct bin_bcast_sycl {

template <class op>
inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst,
const float *src0_dd, const float *src1_dd,
float *dst_dd,
const queue_ptr &main_stream) {
const ggml_tensor *src1, ggml_tensor *dst) {
/*TODO: Refactor bbincast */
dpct::queue_ptr main_stream = ctx.stream();

if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
op()(ctx, src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream);
op()(ctx, src0, src1, dst, (const float *)src0->data, (const float *)src1->data, (float *)dst->data, main_stream);
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) {
op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd,
(sycl::half *)dst_dd, main_stream);
op()(ctx, src0, src1, dst, (const sycl::half *)src0->data, (const float *)src1->data,
(sycl::half *)dst->data, main_stream);
} else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) {
op()(ctx, src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, dst_dd,
op()(ctx, src0, src1, dst, (const sycl::half *)src0->data, (const float *)src1->data, (float *)dst->data,
main_stream);
} else if (src0->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) {
op()(ctx, src0, src1, dst, (const int32_t *)src0_dd, (const int32_t *)src1_dd, (int32_t *)dst_dd,
op()(ctx, src0, src1, dst, (const int32_t *)src0->data, (const int32_t *)src1->data, (int32_t *)dst->data,
main_stream);
} else if (src0->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) {
op()(ctx, src0, src1, dst, (const int16_t *)src0_dd, (const int16_t *)src1_dd, (int16_t *)dst_dd,
op()(ctx, src0, src1, dst, (const int16_t *)src0->data, (const int16_t *)src1->data, (int16_t *)dst->data,
main_stream);
} else {
fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__,
Expand All @@ -718,8 +711,4 @@ inline void ggml_sycl_op_bin_bcast(ggml_backend_sycl_context & ctx, const ggml_t
}

bool gpu_has_xmx(sycl::device &dev);

void ggml_sycl_op_flatten(ggml_backend_sycl_context & ctx, const ggml_tensor *src0,
const ggml_tensor *src1, ggml_tensor *dst,
const ggml_sycl_op_flatten_t op);
#endif // GGML_SYCL_COMMON_HPP
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