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CANN: Refactor to reduce duplicate code (#12731)

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* CANN: Refactor to reduce duplicate code

* CANN: fix review comment
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hipudding 2025-04-07 17:10:36 +08:00 committed by GitHub
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1297
ggml/src/ggml-cann/aclnn_ops.cpp
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356
ggml/src/ggml-cann/aclnn_ops.h
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@ -31,20 +31,25 @@
* IN THE SOFTWARE.
*/
#include <aclnnop/aclnn_add.h>
#include <aclnnop/aclnn_abs.h>
#include <aclnnop/aclnn_neg.h>
#include <aclnnop/aclnn_exp.h>
#include <aclnnop/aclnn_arange.h>
#include <aclnnop/aclnn_argsort.h>
#include <aclnnop/aclnn_cat.h>
#include <aclnnop/aclnn_clamp.h>
#include <aclnnop/aclnn_div.h>
#include <aclnnop/aclnn_gelu.h>
#include <aclnnop/aclnn_gelu_v2.h>
#include <aclnnop/aclnn_sigmoid.h>
#include <aclnnop/aclnn_hardsigmoid.h>
#include <aclnnop/aclnn_hardswish.h>
#include <aclnnop/aclnn_leaky_relu.h>
#include <aclnnop/aclnn_mul.h>
#include <aclnnop/aclnn_relu.h>
#include <aclnnop/aclnn_silu.h>
#include <aclnnop/aclnn_tanh.h>
#include <aclnnop/aclnn_sqrt.h>
#include <aclnnop/aclnn_sin.h>
#include <aclnnop/aclnn_cos.h>
#include "acl_tensor.h"
#include "common.h"
@ -63,23 +68,6 @@
*/
void ggml_cann_repeat(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Adds two ggml tensors using the CANN backend.
*
* @details This function performs an element-wise addition of two tensors. In
* case the tensors do not have the same shape, one or both tensors
* will be broadcasted to match the shape of the other before the
* addition is performed.The formula for the operation is given by:
* \f[
* \text{dst} = \text{acl_src0} + \alpha \cdot \text{acl_src1}
* \f]
*
* @param ctx The CANN context used for operations.
* @param dst The ggml tensor representing the destination, result of the
* addition is stored at dst->data, and dst->op is `GGML_OP_ADD`
*/
void ggml_cann_add(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Applies the Leaky ReLU activation function to a tensor using the CANN
* backend.
@ -131,19 +119,6 @@ void ggml_cann_concat(ggml_backend_cann_context& ctx, ggml_tensor* dst);
*/
void ggml_cann_arange(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Computes the square of the elements of a ggml tensor using the CANN
* backend.
* @details The function sets the second source tensor of the destination
* tensor `dst` to be equal to the first source tensor. This is
* effectively squaring the elements since the multiplication becomes
* `element * element`.
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the squared values will be stored，
* which dst->op is `GGML_OP_SQR`.
*/
void ggml_cann_sqr(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Applies a clamp operation to the elements of a ggml tensor using the
* CANN backend.
@ -275,6 +250,20 @@ void ggml_cann_acc(ggml_backend_cann_context& ctx, ggml_tensor* dst);
*/
void ggml_cann_sum_rows(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Computes the sum of elements in a ggml tensor.
*
* @details This function performs a reduction sum operation along the last
* dimension of the input tensor `src`. The result of the sum is stored
* in the destination tensor `dst`.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the reduced values will be stored。
*
*/
void ggml_cann_sum(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Upsamples a ggml tensor using nearest neighbor interpolation using
* the CANN backend.
@ -500,128 +489,247 @@ void ggml_cann_rope(ggml_backend_cann_context& ctx, ggml_tensor* dst);
void ggml_cann_argmax(ggml_backend_cann_context& ctx, ggml_tensor* dst);
/**
* @brief Computes the cosine of each element in a ggml tensor using the CANN backend.
* @brief Adds two tensors element-wise and stores the result in a destination
* tensor.
*
* @details This function applies the cosine function element-wise to the input tensor.
* The computed cosine values are stored in the destination tensor `dst`.
* The operation is optimized using the CANN backend for improved performance.
* This function performs the operation:
* \f[
* dst = acl\_src0 + alpha \times acl\_src1
* \f]
* where alpha is a scalar value and defaults to 1.0f.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the cosine values will be stored.
* dst->op is `GGML_OP_COS`.
* @param ctx The context for the CANN backend operations.
* @param acl_src0 The first source tensor.
* @param acl_src1 The second source tensor.
* @param acl_dst The destination tensor where the result will be stored.
*/
void ggml_cann_cos(ggml_backend_cann_context& ctx, ggml_tensor* dst);
void aclnn_add(ggml_backend_cann_context& ctx, aclTensor* acl_src0,
aclTensor* acl_src1, aclTensor* acl_dst = nullptr);
/**
* @brief Computes the sine of each element in a ggml tensor using the CANN backend.
* @brief Sub two tensors element-wise and stores the result in a destination
* tensor.
*
* @details This function applies the sine function element-wise to the input tensor.
* The computed sine values are stored in the destination tensor `dst`.
* The operation is optimized using the CANN backend for improved performance.
* This function performs the operation:
* \f[
* dst = acl\_src0 - alpha \times acl\_src1
* \f]
* where alpha is a scalar value and defaults to 1.0f.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor where the sine values will be stored.
* dst->op is `GGML_OP_SIN`.
* @param ctx The context for the CANN backend operations.
* @param acl_src0 The first source tensor.
* @param acl_src1 The second source tensor.
* @param acl_dst The destination tensor where the result will be stored.
*/
void ggml_cann_sin(ggml_backend_cann_context& ctx, ggml_tensor* dst);
void aclnn_sub(ggml_backend_cann_context& ctx, aclTensor* acl_src0,
aclTensor* acl_src1, aclTensor* acl_dst = nullptr);
template <aclnnStatus getWorkspaceSize(const aclTensor*, const aclTensor*,
aclTensor*, uint64_t*, aclOpExecutor**),
aclnnStatus execute(void*, uint64_t, aclOpExecutor*, aclrtStream)>
void ggml_cann_mul_div(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
/**
* @brief Performs element-wise multiplication of two tensors and stores the
* result in a destination tensor.
*
* This function performs element-wise multiplication of the tensors `acl_src`
* and `acl_other` and stores the result in the destination tensor `acl_dst`.
* The operation is defined as:
* \f[
* \text {acl_dst }_i=\text {acl_src }_i \times \text {acl_other }_i
* \f]
*
* @param ctx The context for the CANN backend operations.
* @param acl_src The first tensor for element-wise multiplication.
* @param acl_other The second tensor for element-wise multiplication.
* @param acl_dst The destination tensor where the result will be stored.
*/
void aclnn_mul(ggml_backend_cann_context& ctx, aclTensor* acl_src,
aclTensor* acl_other, aclTensor* acl_dst = nullptr);
/**
* @brief Matrix division, optionally in-place.
*
* This function division each element of the source tensor `acl_src` by the
* tensor `acl_other` and stores the result in the destination tensor `acl_dst`.
* If `inplace` is true, `acl_dst` will not be used and the operation is
* performed in-place on `acl_src`. The operation is defined as: \f[
* \text{dst}_i = \frac{\text{acl_src}_i}{\text{acl_other}_i}
* \f]
*
* @param ctx The context for the CANN backend operations.
* @param acl_src Numerator tensor..
* @param acl_other Denominator tensor.
* @param acl_dst The destination tensor where the result will be stored if
* `inplace` is false.
* @param inplace Flag indicating whether to perform the operation in-place on
* `acl_src`.
*/
void aclnn_div(ggml_backend_cann_context& ctx, aclTensor* acl_src,
aclTensor* acl_other, aclTensor* acl_dst = nullptr);
/**
* @brief Applies element-wise cosine function to the elements of a tensor.
*
* This function computes the cosine of each element in the source tensor
* `acl_src` and stores the result in the destination tensor `acl_dst`. The
* operation is defined as: \f[ \text {acl_dst }_i=\cos \left(\text {acl_src
* }_i\right) \f]
*
* @param ctx The context for the CANN backend operations.
* @param acl_src The source tensor on which the cosine function will be
* applied.
* @param acl_dst The destination tensor where the cosine results will be
* stored.
*/
void aclnn_cos(ggml_backend_cann_context& ctx, aclTensor* acl_src,
aclTensor* acl_dst);
/**
* @brief Applies element-wise sine function to the elements of a tensor.
*
* This function computes the sine of each element in the source tensor
`acl_src`
* and stores the result in the destination tensor `acl_dst`.
* The operation is defined as:
* \f[
* \text {acl_dst }_i=\sin \left(\text {acl_src }_i\right)
* \f]
* @param ctx The context for the CANN backend operations.
* @param acl_src The source tensor on which the sine function will be applied.
* @param acl_dst The destination tensor where the sine results will be stored.
*/
void aclnn_sin(ggml_backend_cann_context& ctx, aclTensor* acl_src,
aclTensor* acl_dst);
/**
* @brief Launches an asynchronous task using the memory allocator.
*
* This macro submit an asynchronous task on the specified stream.
* The task uses memory allocated by the allocator. It is guaranteed
* that the memory will not be accessed by other tasks until this task
* completes, due to the sequential execution order within the same stream.
*
* @param OP_NAME aclnn operator name.
* @param args Additional arguments required by the task.
*
* @note
* Memory from the allocator will be "freed" immediately and can be
* reallocated to other pointers. However, it won't be accessed by any
* other task before this asynchronous task ends, because all tasks in the
* same stream are executed in queue order.
*/
#define GGML_CANN_CALL_ACLNN_OP(OP_NAME, ...) \
do { \
uint64_t workspaceSize = 0; \
aclOpExecutor * executor; \
void * workspaceAddr = nullptr; \
\
ACL_CHECK(aclnn##OP_NAME##GetWorkspaceSize(__VA_ARGS__, &workspaceSize, &executor)); \
\
if (workspaceSize > 0) { \
ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize); \
workspaceAddr = workspace_allocator.get(); \
} \
ACL_CHECK(aclnn##OP_NAME(workspaceAddr, workspaceSize, executor, ctx.stream())); \
} while (0)
/**
* @brief Prepares broadcast-compatible ACL tensors for two input tensors and one output tensor.
*
* This function checks whether broadcasting is needed between `src0` and `src1`.
* If broadcasting is required, it calculates the proper shapes and creates
* ACL tensors with broadcast parameters. Otherwise, it directly creates ACL tensors
* based on the original tensor shapes.
*
* @param src0 The first input tensor (reference shape).
* @param src1 The second input tensor (possibly broadcasted).
* @param dst The destination/output tensor.
* @param acl_src0 Output pointer to the created ACL tensor corresponding to src0.
* @param acl_src1 Output pointer to the created ACL tensor corresponding to src1.
* @param acl_dst Output pointer to the created ACL tensor corresponding to dst.
*/
void bcast_shape(ggml_tensor * src0, ggml_tensor * src1, ggml_tensor * dst, aclTensor ** acl_src0,
aclTensor ** acl_src1, aclTensor ** acl_dst);
/**
* @brief Applies a element-wise operation to two input tensors using the CANN backend.
*
* This templated function takes a binary operator and applies it to two source tensors
* associated with the destination tensor. The function handles broadcasting as needed.
*
* @tparam binary_op A callable object (e.g., lambda or function pointer) representing
* the binary operation to be performed. It must take three arguments:
* (ggml_backend_cann_context&, aclTensor*, aclTensor*, aclTensor*).
*
* @param ctx The CANN backend context used to manage execution and resources.
* @param dst The destination tensor.
*/
template <auto binary_op>
void ggml_cann_binary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
ggml_tensor* src0 = dst->src[0];
ggml_tensor* src1 = dst->src[1];
GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
aclTensor* acl_src0;
aclTensor* acl_src1;
aclTensor* acl_dst;
// Need bcast
if (!ggml_are_same_shape(src0, src1) && ggml_cann_need_bcast(src0, src1)) {
BCAST_SHAPE(src0, src1)
acl_src0 = ggml_cann_create_tensor(src0, BCAST_PARAM(src0));
acl_src1 = ggml_cann_create_tensor(src1, BCAST_PARAM(src1));
acl_dst = ggml_cann_create_tensor(dst, BCAST_PARAM(src0));
} else {
acl_src0 = ggml_cann_create_tensor(src0);
acl_src1 = ggml_cann_create_tensor(src1);
acl_dst = ggml_cann_create_tensor(dst);
}
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
void* workspaceAddr = nullptr;
ACL_CHECK(getWorkspaceSize(acl_src0, acl_src1, acl_dst, &workspaceSize,
&executor));
if (workspaceSize > 0) {
ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
workspaceAddr = workspace_allocator.get();
}
aclrtStream main_stream = ctx.stream();
ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
bcast_shape(src0, src1, dst, &acl_src0, &acl_src1, &acl_dst);
binary_op(ctx, acl_src0, acl_src1, acl_dst);
ACL_CHECK(aclDestroyTensor(acl_src0));
ACL_CHECK(aclDestroyTensor(acl_src1));
ACL_CHECK(aclDestroyTensor(acl_dst));
}
// Activation functions template.
template <aclnnStatus getWorkspaceSize(const aclTensor*, aclTensor*, uint64_t*,
aclOpExecutor**),
aclnnStatus execute(void*, uint64_t, aclOpExecutor*,
const aclrtStream)>
void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
/**
* @brief Applies a unary operation to an input tensor using the CANN backend.
*
* This templated function applies a unary operator to the source tensor of `dst`
* and stores the result in the destination tensor.
*
* @tparam unary_op A callable with the signature:
* void(ggml_backend_cann_context&, aclTensor*, aclTensor*)
* where the first aclTensor is the source and the second is the destination.
*
* @param ctx The CANN backend context for managing resources and execution.
* @param dst The destination tensor. Its src[0] is treated as the input tensor.
*/
template <void unary_op(ggml_backend_cann_context&, aclTensor*, aclTensor*)>
void ggml_cann_unary_op(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
ggml_tensor* src = dst->src[0];
aclTensor* acl_src = ggml_cann_create_tensor(src);
aclTensor* acl_dst = ggml_cann_create_tensor(dst);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
void* workspaceAddr = nullptr;
ACL_CHECK(getWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
if (workspaceSize > 0) {
ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
workspaceAddr = workspace_allocator.get();
}
aclrtStream main_stream = ctx.stream();
ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
unary_op(ctx, acl_src, acl_dst);
ACL_CHECK(aclDestroyTensor(acl_src));
ACL_CHECK(aclDestroyTensor(acl_dst));
}
// Activation functions template for const aclTensors.
template <aclnnStatus getWorkspaceSize(const aclTensor*, const aclTensor*,
uint64_t*, aclOpExecutor**),
aclnnStatus execute(void*, uint64_t, aclOpExecutor*,
const aclrtStream)>
void ggml_cann_activation(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
ggml_tensor* src = dst->src[0];
aclTensor* acl_src = ggml_cann_create_tensor(src);
aclTensor* acl_dst = ggml_cann_create_tensor(dst);
uint64_t workspaceSize = 0;
aclOpExecutor* executor;
void* workspaceAddr = nullptr;
ACL_CHECK(getWorkspaceSize(acl_src, acl_dst, &workspaceSize, &executor));
if (workspaceSize > 0) {
ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
workspaceAddr = workspace_allocator.get();
}
aclrtStream main_stream = ctx.stream();
ACL_CHECK(execute(workspaceAddr, workspaceSize, executor, main_stream));
ACL_CHECK(aclDestroyTensor(acl_src));
ACL_CHECK(aclDestroyTensor(acl_dst));
}
/**
* @brief Helper macro to invoke a unary ACL operation using ggml_cann_unary_op.
*
* This macro defines an inline lambda wrapping a specific ACL operation name,
* and passes it to the templated ggml_cann_unary_op function. It simplifies
* calling unary ops by hiding the lambda boilerplate.
*
* Internally, the lambda will call:
* @code
* GGML_CANN_CALL_ACLNN_OP(OP_NAME, acl_src, acl_dst);
* @endcode
*
* @param OP_NAME The name of the ACL unary operator to invoke via GGML_CANN_CALL_ACLNN_OP.
*
* @see ggml_cann_unary_op
* @see GGML_CANN_CALL_ACLNN_OP
*/
#define GGML_CANN_CALL_UNARY_OP(OP_NAME) \
do { \
auto lambda = [](auto ctx, auto acl_src, auto acl_dst) { \
GGML_CANN_CALL_ACLNN_OP(OP_NAME, acl_src, acl_dst); \
}; \
ggml_cann_unary_op<lambda>(ctx, dst); \
} \
while (0)
#endif // CANN_ACLNN_OPS

74
ggml/src/ggml-cann/ggml-cann.cpp
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@ -1300,47 +1300,59 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
ggml_cann_dup(ctx, dst);
break;
case GGML_OP_ADD:
ggml_cann_add(ctx, dst);
case GGML_OP_ADD1:
ggml_cann_binary_op<aclnn_add>(ctx, dst);
break;
case GGML_OP_SUB:
ggml_cann_binary_op<aclnn_sub>(ctx, dst);
break;
case GGML_OP_ACC:
ggml_cann_acc(ctx, dst);
break;
case GGML_OP_MUL:
ggml_cann_mul_div<aclnnMulGetWorkspaceSize, aclnnMul>(ctx, dst);
ggml_cann_binary_op<aclnn_mul>(ctx, dst);
break;
case GGML_OP_DIV:
ggml_cann_mul_div<aclnnDivGetWorkspaceSize, aclnnDiv>(ctx, dst);
ggml_cann_binary_op<aclnn_div>(ctx, dst);
break;
case GGML_OP_UNARY:
switch (ggml_get_unary_op(dst)) {
case GGML_UNARY_OP_ABS:
GGML_CANN_CALL_UNARY_OP(Abs);
break;
case GGML_UNARY_OP_NEG:
GGML_CANN_CALL_UNARY_OP(Neg);
break;
case GGML_UNARY_OP_GELU:
ggml_cann_activation<aclnnGeluGetWorkspaceSize, aclnnGelu>(
ctx, dst);
GGML_CANN_CALL_UNARY_OP(Gelu);
break;
case GGML_UNARY_OP_SILU:
ggml_cann_activation<aclnnSiluGetWorkspaceSize, aclnnSilu>(
ctx, dst);
GGML_CANN_CALL_UNARY_OP(Silu);
break;
// TODO: Use faster gelu??
case GGML_UNARY_OP_GELU_QUICK:
ggml_cann_activation<aclnnGeluGetWorkspaceSize, aclnnGelu>(
ctx, dst);
case GGML_UNARY_OP_GELU_QUICK: {
auto lambda = [](auto ctx, auto acl_src, auto acl_dst) {
GGML_CANN_CALL_ACLNN_OP(GeluV2, acl_src, 0, acl_dst);
};
ggml_cann_unary_op<lambda>(ctx, dst);
}
break;
case GGML_UNARY_OP_TANH:
ggml_cann_activation<aclnnTanhGetWorkspaceSize, aclnnTanh>(
ctx, dst);
GGML_CANN_CALL_UNARY_OP(Tanh);
break;
case GGML_UNARY_OP_RELU:
ggml_cann_activation<aclnnReluGetWorkspaceSize, aclnnRelu>(
ctx, dst);
GGML_CANN_CALL_UNARY_OP(Relu);
break;
case GGML_UNARY_OP_SIGMOID:
GGML_CANN_CALL_UNARY_OP(Sigmoid);
break;
case GGML_UNARY_OP_HARDSIGMOID:
ggml_cann_activation<aclnnHardsigmoidGetWorkspaceSize,
aclnnHardsigmoid>(ctx, dst);
GGML_CANN_CALL_UNARY_OP(Hardsigmoid);
break;
case GGML_UNARY_OP_HARDSWISH:
ggml_cann_activation<aclnnHardswishGetWorkspaceSize,
aclnnHardswish>(ctx, dst);
GGML_CANN_CALL_UNARY_OP(Hardswish);
break;
case GGML_UNARY_OP_EXP:
GGML_CANN_CALL_UNARY_OP(Exp);
break;
default:
return false;
@ -1382,7 +1394,12 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
ggml_cann_scale(ctx, dst);
break;
case GGML_OP_SQR:
ggml_cann_sqr(ctx, dst);
GGML_ASSERT(dst->src[1] == nullptr);
dst->src[1] = dst->src[0];
ggml_cann_binary_op<aclnn_mul>(ctx, dst);
break;
case GGML_OP_SQRT:
GGML_CANN_CALL_UNARY_OP(Sqrt);
break;
case GGML_OP_CLAMP:
ggml_cann_clamp(ctx, dst);
@ -1414,6 +1431,9 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
case GGML_OP_POOL_2D:
ggml_cann_pool2d(ctx, dst);
break;
case GGML_OP_SUM:
ggml_cann_sum(ctx, dst);
break;
case GGML_OP_SUM_ROWS:
ggml_cann_sum_rows(ctx, dst);
break;
@ -1424,11 +1444,11 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context& ctx,
ggml_cann_argmax(ctx, dst);
break;
case GGML_OP_COS:
ggml_cann_cos(ctx, dst);
ggml_cann_unary_op<aclnn_cos>(ctx, dst);
break;
case GGML_OP_SIN:
ggml_cann_sin(ctx, dst);
break;
ggml_cann_unary_op<aclnn_sin>(ctx, dst);
break;
default:
return false;
}
@ -1679,13 +1699,17 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
switch (op->op) {
case GGML_OP_UNARY:
switch (ggml_get_unary_op(op)) {
case GGML_UNARY_OP_ABS:
case GGML_UNARY_OP_NEG:
case GGML_UNARY_OP_GELU:
case GGML_UNARY_OP_SILU:
case GGML_UNARY_OP_RELU:
case GGML_UNARY_OP_SIGMOID:
case GGML_UNARY_OP_HARDSIGMOID:
case GGML_UNARY_OP_HARDSWISH:
case GGML_UNARY_OP_GELU_QUICK:
case GGML_UNARY_OP_TANH:
case GGML_UNARY_OP_EXP:
return true;
default:
return false;
@ -1784,6 +1808,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
// value of paddingW should be at most half of kernelW
return (p0 <= (k0 / 2)) && (p1 <= (k1 / 2));
}
case GGML_OP_SUM:
case GGML_OP_DUP:
case GGML_OP_IM2COL:
case GGML_OP_CONCAT:
@ -1795,11 +1820,14 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
case GGML_OP_TRANSPOSE:
case GGML_OP_NORM:
case GGML_OP_ADD:
case GGML_OP_ADD1:
case GGML_OP_SUB:
case GGML_OP_MUL:
case GGML_OP_DIV:
case GGML_OP_RMS_NORM:
case GGML_OP_SCALE:
case GGML_OP_SQR:
case GGML_OP_SQRT:
case GGML_OP_CLAMP:
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_SOFT_MAX: