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https://github.com/ggerganov/whisper.cpp.git
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CANN: GGML_OP_CPY optimization (llama/15070)
Signed-off-by: noemotiovon <757486878@qq.com>
This commit is contained in:
committed by
Georgi Gerganov
parent
8e2ddfec31
commit
0effaad964
@@ -753,69 +753,55 @@ static void cann_copy(ggml_backend_cann_context& ctx, aclTensor* acl_src,
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void ggml_cann_dup(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
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ggml_tensor* src0 = dst->src[0];
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aclTensor* acl_src = ggml_cann_create_tensor(src0);
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aclTensor* acl_dst = ggml_cann_create_tensor(dst);
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if (ggml_are_same_shape(src0, dst)) {
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aclTensor* acl_src = ggml_cann_create_tensor(src0);
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aclTensor* acl_dst = ggml_cann_create_tensor(dst);
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if (dst->type == src0->type) {
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cann_copy(ctx, acl_src, acl_dst);
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} else {
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aclnn_cast(ctx, acl_src, acl_dst, ggml_cann_type_mapping(dst->type));
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}
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ggml_cann_release_resources(ctx, acl_src, acl_dst);
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} else {
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if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst)) {
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if (dst->type == src0->type) {
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size_t cpy_size = ggml_nbytes(dst);
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ggml_cann_async_memcpy(ctx, dst->data, src0->data, cpy_size,
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ACL_MEMCPY_DEVICE_TO_DEVICE);
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return;
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} else {
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ggml_cann_pool_alloc src_buffer_allocator(
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ctx.pool(),
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ggml_nelements(dst) * ggml_type_size(dst->type));
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void* src_trans_buffer = src_buffer_allocator.get();
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size_t src_trans_nb[GGML_MAX_DIMS];
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src_trans_nb[0] = ggml_type_size(dst->type);
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for (int i = 1; i < GGML_MAX_DIMS; i++) {
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src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
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}
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aclTensor* src_trans_tensor = ggml_cann_create_tensor(
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src_trans_buffer, ggml_cann_type_mapping(dst->type),
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ggml_type_size(dst->type), src0->ne, src_trans_nb,
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GGML_MAX_DIMS);
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aclnn_cast(ctx, acl_src, src_trans_tensor, ggml_cann_type_mapping(dst->type));
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size_t cpy_size = ggml_nbytes(dst);
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ggml_cann_async_memcpy(ctx, dst->data, src_trans_buffer, cpy_size,
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ACL_MEMCPY_DEVICE_TO_DEVICE);
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ggml_cann_release_resources(ctx, src_trans_tensor);
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return;
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}
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} else if (ggml_is_contiguous(dst)) {
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ggml_cann_pool_alloc src_buffer_allocator(
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ctx.pool(), ggml_nelements(dst) * ggml_type_size(dst->type));
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void* src_trans_buffer = src_buffer_allocator.get();
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void* src_trans_buffer = src0->data;
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ggml_cann_pool_alloc src_buffer_allocator;
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if (!ggml_is_contiguous(src0)) {
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aclTensor* acl_src = ggml_cann_create_tensor(src0);
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src_buffer_allocator.alloc(ctx.pool(),
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ggml_nelements(src0) * ggml_type_size(src0->type));
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src_trans_buffer = src_buffer_allocator.get();
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size_t src_trans_nb[GGML_MAX_DIMS];
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src_trans_nb[0] = ggml_type_size(dst->type);
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src_trans_nb[0] = ggml_type_size(src0->type);
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for (int i = 1; i < GGML_MAX_DIMS; i++) {
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src_trans_nb[i] = src_trans_nb[i - 1] * src0->ne[i - 1];
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}
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aclTensor* src_trans_tensor = ggml_cann_create_tensor(
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src_trans_buffer, ggml_cann_type_mapping(dst->type),
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ggml_type_size(dst->type), src0->ne, src_trans_nb,
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src_trans_buffer, ggml_cann_type_mapping(src0->type),
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ggml_type_size(src0->type), src0->ne, src_trans_nb,
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GGML_MAX_DIMS);
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aclnn_cast(ctx, acl_src, src_trans_tensor, ggml_cann_type_mapping(dst->type));
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size_t cpy_size = ggml_nbytes(dst);
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ggml_cann_async_memcpy(ctx, dst->data, src_trans_buffer, cpy_size,
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ACL_MEMCPY_DEVICE_TO_DEVICE);
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ggml_cann_release_resources(ctx, src_trans_tensor);
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return;
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} else {
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GGML_ABORT("Unsupport dst is not contiguous.");
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cann_copy(ctx, acl_src, src_trans_tensor);
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ggml_cann_release_resources(ctx, acl_src, src_trans_tensor);
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}
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size_t src_reshape_nb[GGML_MAX_DIMS];
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src_reshape_nb[0] = ggml_type_size(src0->type);
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for (int i = 1; i < GGML_MAX_DIMS; i++) {
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src_reshape_nb[i] = src_reshape_nb[i - 1] * dst->ne[i - 1];
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}
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aclTensor* trans_acl_src = ggml_cann_create_tensor(src_trans_buffer,
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ggml_cann_type_mapping(src0->type),ggml_type_size(src0->type),
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dst->ne, src_reshape_nb, GGML_MAX_DIMS, ACL_FORMAT_ND);
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aclTensor* acl_dst = ggml_cann_create_tensor(dst);
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if (dst->type == src0->type) {
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cann_copy(ctx, trans_acl_src, acl_dst);
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} else {
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aclnn_cast(ctx, trans_acl_src, acl_dst, ggml_cann_type_mapping(dst->type));
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}
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ggml_cann_release_resources(ctx, trans_acl_src, acl_dst);
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}
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ggml_cann_release_resources(ctx, acl_src, acl_dst);
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return;
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}
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/**
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@@ -2391,7 +2391,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
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// only support F32 and F16.
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return false;
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}
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return ggml_is_contiguous(op);
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return true;
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} break;
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case GGML_OP_CONT: {
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// TODO: support GGML_TYPE_BF16
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@@ -2457,7 +2457,6 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev,
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return (p0 <= (k0 / 2)) && (p1 <= (k1 / 2));
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}
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case GGML_OP_DUP:
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return ggml_is_contiguous(op);
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case GGML_OP_SUM:
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case GGML_OP_IM2COL:
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case GGML_OP_CONCAT:
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