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CANN: fix acl_tensor_ptr usage in ASCEND_310P ROPE (#17347)

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* cann: fix acl_tensor_ptr usage in ASCEND_310P ROPE implementation

Fix compilation errors in the ASCEND_310P-specific ROPE operation code
by adding .get() calls when passing acl_tensor_ptr smart pointers to
functions expecting raw aclTensor* pointers.

This fixes the code that was missed in the previous refactoring commit
(8981848) which changed ggml_cann_create_tensor() return type from
aclTensor* to acl_tensor_ptr.

* cann: format code
This commit is contained in:
Chenguang Li
2025-11-18 16:41:52 +08:00
committed by GitHub
parent 97cb3fd5ae
commit bc4064cfea
1 changed files with 13 additions and 12 deletions
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25
ggml/src/ggml-cann/aclnn_ops.cpp
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@@ -2544,7 +2544,7 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
int64_t shifts[] = { 1 }; int64_t shifts[] = { 1 };
int64_t dims[] = { 3 }; int64_t dims[] = { 3 };
aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims); aclnn_roll(ctx, acl_input_tensor.get(), acl_input_roll_tensor.get(), shifts, dims);
// init [-1, 1, -1, 1, ...] // init [-1, 1, -1, 1, ...]
minus_one_scale_buffer = minus_one_scale_allocator.get(); minus_one_scale_buffer = minus_one_scale_allocator.get();
@@ -2564,7 +2564,7 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
} }
int64_t index_num = src0->ne[0]; int64_t index_num = src0->ne[0];
float value = -1; float value = -1;
aclnn_index_fill_tensor(ctx, acl_minus_one_tensor, dim, index, index_num, value); aclnn_index_fill_tensor(ctx, acl_minus_one_tensor.get(), dim, index, index_num, value);
} else { } else {
// roll input: [q0,q1,q2,...] -> // roll input: [q0,q1,q2,...] ->
// [q_half,q_half+1,...,q_end,q0,q1,...q_half-1] // [q_half,q_half+1,...,q_end,q0,q1,...q_half-1]
@@ -2576,7 +2576,7 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
int64_t shifts[] = { src0->ne[0] / 2 }; int64_t shifts[] = { src0->ne[0] / 2 };
int64_t dims[] = { 3 }; int64_t dims[] = { 3 };
aclnn_roll(ctx, acl_input_tensor, acl_input_roll_tensor, shifts, dims); aclnn_roll(ctx, acl_input_tensor.get(), acl_input_roll_tensor.get(), shifts, dims);
// init [-1, -1, -1, 1, 1，1，...] // init [-1, -1, -1, 1, 1，1，...]
minus_one_scale_buffer = minus_one_scale_allocator.get(); minus_one_scale_buffer = minus_one_scale_allocator.get();
@@ -2599,7 +2599,7 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
first_half_ne, first_half_nb, GGML_MAX_DIMS); first_half_ne, first_half_nb, GGML_MAX_DIMS);
bool inplace = true; bool inplace = true;
float scale = -1; float scale = -1;
aclnn_muls(ctx, acl_first_half_tensor, scale, nullptr, inplace); aclnn_muls(ctx, acl_first_half_tensor.get(), scale, nullptr, inplace);
} }
// TODO: n_dims < ne0 // TODO: n_dims < ne0
@@ -2620,14 +2620,15 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
ggml_cann_create_tensor(input_roll_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type), ggml_cann_create_tensor(input_roll_buffer, ggml_cann_type_mapping(src0->type), ggml_type_size(src0->type),
src0->ne, input_nb, GGML_MAX_DIMS); src0->ne, input_nb, GGML_MAX_DIMS);
aclnn_mul(ctx, acl_input_roll_reshape_tensor, acl_minus_one_tensor, acl_input_roll_mul_scale_tensor); aclnn_mul(ctx, acl_input_roll_reshape_tensor.get(), acl_minus_one_tensor.get(),
acl_input_roll_mul_scale_tensor.get());
// output // output
void * output_fp32_buffer; void * output_fp32_buffer;
if (src0->type == GGML_TYPE_F32) { if (src0->type == GGML_TYPE_F32) {
aclnn_mul(ctx, acl_src, acl_cos_reshape_tensor); aclnn_mul(ctx, acl_src.get(), acl_cos_reshape_tensor.get());
aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor); aclnn_mul(ctx, acl_input_roll_mul_scale_tensor.get(), acl_sin_reshape_tensor.get());
aclnn_add(ctx, acl_src, acl_input_roll_mul_scale_tensor, acl_dst); aclnn_add(ctx, acl_src.get(), acl_input_roll_mul_scale_tensor.get(), acl_dst.get());
// TODO: ne0 != n_dims in mode2 // TODO: ne0 != n_dims in mode2
} else if (src0->type == GGML_TYPE_F16) { } else if (src0->type == GGML_TYPE_F16) {
size_t input_fp32_nb[GGML_MAX_DIMS]; size_t input_fp32_nb[GGML_MAX_DIMS];
@@ -2648,10 +2649,10 @@ void ggml_cann_rope(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
output_fp32_buffer = fp32_allocator.get(); output_fp32_buffer = fp32_allocator.get();
acl_tensor_ptr output_fp32_tensor = ggml_cann_create_tensor(output_fp32_buffer, ACL_FLOAT, sizeof(float), acl_tensor_ptr output_fp32_tensor = ggml_cann_create_tensor(output_fp32_buffer, ACL_FLOAT, sizeof(float),
dst->ne, input_fp32_nb, GGML_MAX_DIMS); dst->ne, input_fp32_nb, GGML_MAX_DIMS);
aclnn_mul(ctx, acl_src, acl_cos_reshape_tensor, input_fp32_tensor1); aclnn_mul(ctx, acl_src.get(), acl_cos_reshape_tensor.get(), input_fp32_tensor1.get());
aclnn_mul(ctx, acl_input_roll_mul_scale_tensor, acl_sin_reshape_tensor, input_fp32_tensor2); aclnn_mul(ctx, acl_input_roll_mul_scale_tensor.get(), acl_sin_reshape_tensor.get(), input_fp32_tensor2.get());
aclnn_add(ctx, input_fp32_tensor1, input_fp32_tensor2, output_fp32_tensor); aclnn_add(ctx, input_fp32_tensor1.get(), input_fp32_tensor2.get(), output_fp32_tensor.get());
aclnn_cast(ctx, output_fp32_tensor, acl_dst, ACL_FLOAT16); aclnn_cast(ctx, output_fp32_tensor.get(), acl_dst.get(), ACL_FLOAT16);
} }
return; return;
#endif #endif