CANN: Improve the Inferencing Performance for Ascend NPU Device (#10454)
* improve inferencing performance for ascend npu. Co-authored-by: Frank Mai <thxCode@thxcode0824@gmail.com> * some modification after review * some modifications after review * restore some modifications * restore some modifications --------- Co-authored-by: shanshan shen <shanshanshen333@gmail.com> Co-authored-by: Frank Mai <thxCode@thxcode0824@gmail.com>
This commit is contained in:
Shanshan Shen
GitHub
parent
7066b4cce2
commit
9a4b79bcfa
3 changed files with 266 additions and 102 deletions
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@ -33,6 +33,8 @@
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#include <aclnnop/aclnn_group_norm.h>
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#include <aclnnop/aclnn_index_fill_tensor.h>
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#include <aclnnop/aclnn_layer_norm.h>
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#include <aclnnop/aclnn_mm.h>
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#include <aclnnop/aclnn_batch_matmul.h>
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#include <aclnnop/aclnn_matmul.h>
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#include <aclnnop/aclnn_max_pool.h>
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#include <aclnnop/aclnn_permute.h>
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@ -2423,7 +2425,6 @@ static void aclnn_mat_mul(ggml_backend_cann_context& ctx, aclTensor* acl_input,
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aclTensor* acl_weight, aclTensor* acl_dst) {
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int8_t cube_math_type = 1; // ALLOW_FP32_DOWN_PRECISION, when input is
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// fp32, atlas a2 will transpose it to HFLOAT32.
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uint64_t workspaceSize = 0;
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aclOpExecutor* executor;
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void* workspaceAddr = nullptr;
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@ -2441,6 +2442,80 @@ static void aclnn_mat_mul(ggml_backend_cann_context& ctx, aclTensor* acl_input,
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aclnnMatmul(workspaceAddr, workspaceSize, executor, ctx.stream()));
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}
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/**
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* @brief Performs matrix multiplication of two 2D tensors.
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*
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* This function computes the matrix multiplication of the input tensor
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* `acl_input` and the weight tensor `acl_weight`, and stores the result in the
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* destination tensor `acl_dst`.
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* The operation is defined as:
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* \f[
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* \text {acl_dst}=\text {acl_input@acl_weight}
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* \f]
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*
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* @param ctx The context for the CANN backend operations.
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* @param acl_input The input tensor for the matrix multiplication.
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* @param acl_weight The weight tensor for the matrix multiplication.
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* @param acl_dst The destination tensor where the result of the matrix
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* multiplication will be stored.
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*/
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static void aclnn_mat_mul_2d(ggml_backend_cann_context& ctx, aclTensor* acl_input,
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aclTensor* acl_weight, aclTensor* acl_dst) {
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int8_t cube_math_type = 2;
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uint64_t workspaceSize = 0;
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aclOpExecutor* executor;
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void* workspaceAddr = nullptr;
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ACL_CHECK(aclnnMmGetWorkspaceSize(acl_input, acl_weight, acl_dst,
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cube_math_type, &workspaceSize,
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&executor));
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if (workspaceSize > 0) {
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ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
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workspaceAddr = workspace_allocator.get();
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}
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ACL_CHECK(
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aclnnMm(workspaceAddr, workspaceSize, executor, ctx.stream()));
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}
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/**
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* @brief Performs matrix multiplication of two 3D tensors.
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*
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* This function computes the matrix multiplication of the input tensor
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* `acl_input` and the weight tensor `acl_weight`, and stores the result in the
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* destination tensor `acl_dst`.
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* The operation is defined as:
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* \f[
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* \text {acl_dst}=\text {acl_input@acl_weight}
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* \f]
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*
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* @param ctx The context for the CANN backend operations.
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* @param acl_input The input tensor for the matrix multiplication.
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* @param acl_weight The weight tensor for the matrix multiplication.
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* @param acl_dst The destination tensor where the result of the matrix
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* multiplication will be stored.
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*/
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static void aclnn_mat_mul_3d(ggml_backend_cann_context& ctx, aclTensor* acl_input,
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aclTensor* acl_weight, aclTensor* acl_dst) {
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int8_t cube_math_type = 2;
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uint64_t workspaceSize = 0;
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aclOpExecutor* executor;
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void* workspaceAddr = nullptr;
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ACL_CHECK(aclnnBatchMatMulGetWorkspaceSize(acl_input, acl_weight, acl_dst,
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cube_math_type, &workspaceSize,
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&executor));
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if (workspaceSize > 0) {
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ggml_cann_pool_alloc workspace_allocator(ctx.pool(), workspaceSize);
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workspaceAddr = workspace_allocator.get();
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}
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ACL_CHECK(
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aclnnBatchMatMul(workspaceAddr, workspaceSize, executor, ctx.stream()));
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}
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/**
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* @brief Performs matrix multiplication with floating-point precision on
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* tensors using the CANN backend.
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@ -2462,20 +2537,43 @@ static void ggml_cann_mat_mul_fp(ggml_backend_cann_context& ctx,
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// broadcast, when weight ne2 or ne3 is not 1, weight need repeat.
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BCAST_MUL_MAT_SHAPE(input, weight, dst);
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// transpose weight: [1,2,3,4] -> [1,2,4,3]
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int64_t transpose_ne[] = {bcast_weight_ne[1], bcast_weight_ne[0],
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bcast_weight_ne[2], bcast_weight_ne[3],
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bcast_weight_ne[4], bcast_weight_ne[5]};
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size_t transpose_nb[] = {bcast_weight_nb[1], bcast_weight_nb[0],
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bcast_weight_nb[2], bcast_weight_nb[3],
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bcast_weight_nb[4], bcast_weight_nb[5]};
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int64_t n_dims = bcast_dims;
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if (bcast_input_ne[3] == bcast_weight_ne[3] && bcast_input_ne[3] == 1) {
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if (bcast_input_ne[2] == 1 && bcast_weight_ne[2] == 1) {
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n_dims = 2;
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} else if (bcast_input_ne[2] == 1) {
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n_dims = 3;
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}
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}
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aclTensor* acl_weight_tensor =
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ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, bcast_dims);
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aclTensor* acl_input_tensor =
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ggml_cann_create_tensor(input, BCAST_MUL_MAT_PARAM(input));
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aclTensor* acl_dst = ggml_cann_create_tensor(dst, BCAST_MUL_MAT_PARAM(dst));
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aclnn_mat_mul(ctx, acl_input_tensor, acl_weight_tensor, acl_dst);
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ggml_cann_create_tensor(input, bcast_input_ne, bcast_input_nb, n_dims);
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int64_t transpose_ne[] = {
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bcast_weight_ne[1], bcast_weight_ne[0],
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bcast_weight_ne[2], bcast_weight_ne[3],
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bcast_weight_ne[4], bcast_weight_ne[5]
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};
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size_t transpose_nb[] = {
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bcast_weight_nb[1], bcast_weight_nb[0],
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bcast_weight_nb[2], bcast_weight_nb[3],
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bcast_weight_nb[4], bcast_weight_nb[5]
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};
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aclTensor* acl_weight_tensor =
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ggml_cann_create_tensor(weight, transpose_ne, transpose_nb, n_dims);
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aclTensor* acl_dst =
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ggml_cann_create_tensor(dst, bcast_dst_ne, bcast_dst_nb, n_dims);
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switch (n_dims) {
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case 2:
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aclnn_mat_mul_2d(ctx, acl_input_tensor, acl_weight_tensor, acl_dst);
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break;
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case 3:
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aclnn_mat_mul_3d(ctx, acl_input_tensor, acl_weight_tensor, acl_dst);
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break;
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default:
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aclnn_mat_mul(ctx, acl_input_tensor, acl_weight_tensor, acl_dst);
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break;
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}
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ACL_CHECK(aclDestroyTensor(acl_weight_tensor));
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ACL_CHECK(aclDestroyTensor(acl_input_tensor));
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@ -2501,46 +2599,40 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx,
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ggml_tensor* src0 = dst->src[0]; // weight
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ggml_tensor* src1 = dst->src[1]; // input
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// The shape of the weight is NCHW. Matrix multiplication uses HW dims. HC
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// is regarded as batch. weight need transpose.
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int64_t weight_ne[] = {src0->ne[1], src0->ne[0]};
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// The shape of the weight is NCHW.
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// Matrix multiplication uses HW dims.
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// HC is regarded as batch.
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// weight need transpose.
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float weight_elem_size;
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if (type == GGML_TYPE_Q4_0) {
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weight_elem_size = float(sizeof(uint8_t)) / 2;
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}
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else if (type == GGML_TYPE_Q8_0) {
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} else if (type == GGML_TYPE_Q8_0) {
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weight_elem_size = float(sizeof(uint8_t));
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}
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else {
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} else {
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GGML_ABORT("Only support Q4_0 and Q8_0 MUL_MAT");
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}
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float weight_nb[] = {weight_elem_size * src0->ne[0], weight_elem_size};
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// size of one matrix is element_size * height * width.
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size_t weight_stride = weight_elem_size * src0->ne[0] * src0->ne[1];
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float weight_nb[] = {src0->ne[0] * weight_elem_size, weight_elem_size};
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size_t weight_stride = src0->ne[1] * src0->ne[0] * weight_elem_size;
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size_t weight_size = weight_stride * src0->ne[2] * src0->ne[3];
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// scale stored at the end of weight. Also need transpose.
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GGML_ASSERT(QK4_0 == QK8_0);
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int64_t scale_ne[] = {src0->ne[1], src0->ne[0] / QK8_0};
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size_t scale_elem_size = sizeof(uint16_t);
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size_t scale_nb[] = {src0->ne[0] / QK8_0 * scale_elem_size,
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scale_elem_size};
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size_t scale_stride = scale_elem_size * src0->ne[0] * src0->ne[1] / QK8_0;
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size_t scale_nb[] = {src0->ne[0] / QK8_0 * scale_elem_size, scale_elem_size};
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size_t scale_stride = src0->ne[1] * src0->ne[0] / QK8_0 * scale_elem_size;
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char* scale_offset = (char*)src0->data + weight_size;
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// input
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void* input_buffer;
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size_t input_elem_size = sizeof(uint16_t);
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int64_t input_ne[] = {src1->ne[0], src1->ne[1]};
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size_t input_nb[] = {input_elem_size, input_elem_size * src1->ne[0]};
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size_t input_stride = input_elem_size * src1->ne[0] * src1->ne[1];
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size_t input_nb[] = {input_elem_size, input_ne[0] * input_elem_size};
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size_t input_stride = input_ne[0] * input_ne[1] * input_elem_size;
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ggml_cann_pool_alloc input_alloctor(ctx.pool());
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void* input_buffer = src1->data;
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// case in
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if (src1->type != GGML_TYPE_F16) {
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aclTensor* acl_src1_tensor = ggml_cann_create_tensor(src1);
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input_alloctor.alloc(ggml_nelements(src1) * input_elem_size);
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input_buffer = input_alloctor.get();
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input_buffer = input_alloctor.alloc(ggml_nelements(src1) * input_elem_size);
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int64_t* input_cast_ne = src1->ne;
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size_t input_cast_nb[GGML_MAX_DIMS];
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@ -2550,88 +2642,139 @@ static void ggml_cann_mul_mat_quant(ggml_backend_cann_context& ctx,
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}
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aclTensor* acl_input_tensor = ggml_cann_create_tensor(
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input_buffer, ACL_FLOAT16, input_elem_size, input_cast_ne,
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input_cast_nb, GGML_MAX_DIMS);
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input_buffer,
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ACL_FLOAT16,
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input_elem_size, input_cast_ne, input_cast_nb, GGML_MAX_DIMS);
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aclnn_cast(ctx, acl_src1_tensor, acl_input_tensor, ACL_FLOAT16);
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ACL_CHECK(aclDestroyTensor(acl_input_tensor));
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ACL_CHECK(aclDestroyTensor(acl_src1_tensor));
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} else {
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input_buffer = src1->data;
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}
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// output
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size_t output_elem_size = sizeof(uint16_t);
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int64_t output_ne[] = {dst->ne[0], dst->ne[1]};
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size_t output_nb[] = {output_elem_size, output_elem_size * dst->ne[0]};
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ggml_cann_pool_alloc output_alloctor(
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ctx.pool(), ggml_nelements(dst) * output_elem_size);
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void* output_buffer = output_alloctor.get();
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size_t output_stride = output_elem_size * dst->ne[0] * dst->ne[1];
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size_t output_nb[] = {output_elem_size, dst->ne[0] * output_elem_size};
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ggml_cann_pool_alloc output_allocator(ctx.pool());
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void* output_buffer = output_allocator.alloc(ggml_nelements(dst) * output_elem_size);
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size_t output_stride = dst->ne[0] * dst->ne[1] * output_elem_size;
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// aclnn
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int64_t max_elem_size = 65535;
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int64_t split_size = (src0->ne[1] / max_elem_size) + 1;
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ggml_cann_pool_alloc workspace_allocator(ctx.pool());
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aclOpExecutor* executor = nullptr;
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uint64_t workspaceSize = 0;
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aclOpExecutor* executor;
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void* workspaceAddr = nullptr;
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for (int64_t n1 = 0; n1 < src1->ne[3]; n1++) {
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for (int64_t c1 = 0; c1 < src1->ne[2]; c1++) {
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int64_t n0 = n1 / (src1->ne[3] / src0->ne[3]);
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int64_t c0 = c1 / (src1->ne[2] / src0->ne[2]);
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int64_t batch1 = n1 * src1->ne[2] + c1;
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int64_t batch0 = n0 * src0->ne[2] + c0;
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int64_t batch1 = (n1 * src1->ne[2]) + c1;
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int64_t batch0 = (n0 * src0->ne[2]) + c0;
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aclTensor* acl_input_tensor = ggml_cann_create_tensor(
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(char*)input_buffer + batch1 * input_stride, ACL_FLOAT16,
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input_elem_size, input_ne, input_nb, 2);
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// first split
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int64_t weight_ne_offset = 0;
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int64_t weight_ne[2] = {max_elem_size > src0->ne[1] ? src0->ne[1] : max_elem_size, src0->ne[0]};
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int64_t scale_ne_offset = 0;
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int64_t scale_ne[2] = {weight_ne[0], weight_ne[1] / QK8_0};
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int64_t output_ne_offset = 0;
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int64_t output_ne[2] = {weight_ne[0], dst->ne[1]};
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aclTensor* acl_weight_tensor = ggml_cann_create_tensor(
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(char*)src0->data + batch0 * weight_stride,
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ggml_cann_type_mapping(type), weight_elem_size, weight_ne,
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weight_nb, 2);
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ggml_cann_type_mapping(type),
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weight_elem_size, weight_ne, weight_nb, 2,
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ACL_FORMAT_ND, weight_ne_offset);
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aclTensor* acl_scale_tensor = ggml_cann_create_tensor(
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scale_offset + batch0 * scale_stride, ACL_FLOAT16,
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scale_elem_size, scale_ne, scale_nb, 2);
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scale_offset + batch0 * scale_stride,
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ACL_FLOAT16,
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scale_elem_size, scale_ne, scale_nb, 2,
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ACL_FORMAT_ND, scale_ne_offset);
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aclTensor* acl_output_tensor = ggml_cann_create_tensor(
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(char*)output_buffer + batch1 * output_stride, ACL_FLOAT16,
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output_elem_size, output_ne, output_nb, 2);
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(char*)output_buffer + batch1 * output_stride,
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ACL_FLOAT16,
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output_elem_size, output_ne, output_nb, 2,
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ACL_FORMAT_ND, output_ne_offset);
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ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize(
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acl_input_tensor, acl_weight_tensor, acl_scale_tensor, nullptr,
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nullptr, nullptr, nullptr, QK8_0, acl_output_tensor,
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&workspaceSize, &executor));
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if (workspaceSize > 0 && workspaceAddr == nullptr) {
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ggml_cann_pool_alloc workspace_allocator(ctx.pool(),
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workspaceSize);
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workspaceAddr = workspace_allocator.get();
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acl_input_tensor, acl_weight_tensor, acl_scale_tensor,
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nullptr, nullptr, nullptr, nullptr, QK8_0,
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acl_output_tensor, &workspaceSize, &executor));
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if (workspaceAddr == nullptr) {
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workspaceAddr = workspace_allocator.alloc(workspaceSize);
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}
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ACL_CHECK(aclnnWeightQuantBatchMatmulV2(
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workspaceAddr, workspaceSize, executor, ctx.stream()));
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ACL_CHECK(aclDestroyTensor(acl_input_tensor));
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ACL_CHECK(aclDestroyTensor(acl_weight_tensor));
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ACL_CHECK(aclDestroyTensor(acl_scale_tensor));
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ACL_CHECK(aclDestroyTensor(acl_output_tensor));
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// other splits
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for (int64_t split = 1; split < split_size; split++) {
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weight_ne_offset += weight_elem_size * weight_ne[0] * weight_ne[1];
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weight_ne[0] = max_elem_size * (split + 1) > src0->ne[1] ? src0->ne[1] - (max_elem_size * split) : max_elem_size;
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scale_ne_offset += scale_elem_size * scale_ne[0] * scale_ne[1];
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scale_ne[0] = weight_ne[0];
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output_ne_offset += output_elem_size * output_ne[0] * output_ne[1];
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output_ne[0] = weight_ne[0];
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acl_weight_tensor = ggml_cann_create_tensor(
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(char*)src0->data + batch0 * weight_stride,
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ggml_cann_type_mapping(type),
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weight_elem_size, weight_ne, weight_nb, 2,
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ACL_FORMAT_ND, weight_ne_offset);
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acl_scale_tensor = ggml_cann_create_tensor(
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scale_offset + batch0 * scale_stride,
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ACL_FLOAT16,
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scale_elem_size, scale_ne, scale_nb, 2,
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ACL_FORMAT_ND, scale_ne_offset);
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acl_output_tensor = ggml_cann_create_tensor(
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(char*)output_buffer + batch1 * output_stride,
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ACL_FLOAT16,
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output_elem_size, output_ne, output_nb, 2,
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ACL_FORMAT_ND, output_ne_offset);
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ACL_CHECK(aclnnWeightQuantBatchMatmulV2GetWorkspaceSize(
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acl_input_tensor, acl_weight_tensor, acl_scale_tensor,
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nullptr, nullptr, nullptr, nullptr, QK8_0,
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acl_output_tensor, &workspaceSize, &executor));
|
||||
ACL_CHECK(aclnnWeightQuantBatchMatmulV2(
|
||||
workspaceAddr, workspaceSize, executor, ctx.stream()));
|
||||
|
||||
ACL_CHECK(aclDestroyTensor(acl_weight_tensor));
|
||||
ACL_CHECK(aclDestroyTensor(acl_scale_tensor));
|
||||
ACL_CHECK(aclDestroyTensor(acl_output_tensor));
|
||||
}
|
||||
|
||||
ACL_CHECK(aclDestroyTensor(acl_input_tensor));
|
||||
}
|
||||
}
|
||||
|
||||
// cast out
|
||||
int64_t* output_cast_ne = dst->ne;
|
||||
size_t output_cast_nb[GGML_MAX_DIMS];
|
||||
output_cast_nb[0] = sizeof(uint16_t);
|
||||
for (int i = 1; i < GGML_MAX_DIMS; i++) {
|
||||
output_cast_nb[i] = output_cast_nb[i - 1] * output_cast_ne[i - 1];
|
||||
if (dst->type != GGML_TYPE_F16) {
|
||||
int64_t* output_cast_ne = dst->ne;
|
||||
size_t output_cast_nb[GGML_MAX_DIMS];
|
||||
output_cast_nb[0] = sizeof(uint16_t);
|
||||
for (int i = 1; i < GGML_MAX_DIMS; i++) {
|
||||
output_cast_nb[i] = output_cast_nb[i - 1] * output_cast_ne[i - 1];
|
||||
}
|
||||
|
||||
aclTensor* acl_output_tensor = ggml_cann_create_tensor(
|
||||
output_buffer,
|
||||
ACL_FLOAT16,
|
||||
output_elem_size, output_cast_ne, output_cast_nb, GGML_MAX_DIMS);
|
||||
aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst);
|
||||
aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, ggml_cann_type_mapping(dst->type));
|
||||
|
||||
ACL_CHECK(aclDestroyTensor(acl_output_tensor));
|
||||
ACL_CHECK(aclDestroyTensor(acl_dst_tensor));
|
||||
}
|
||||
|
||||
aclTensor* acl_output_tensor =
|
||||
ggml_cann_create_tensor(output_buffer, ACL_FLOAT16, output_elem_size,
|
||||
output_cast_ne, output_cast_nb, GGML_MAX_DIMS);
|
||||
aclTensor* acl_dst_tensor = ggml_cann_create_tensor(dst);
|
||||
aclnn_cast(ctx, acl_output_tensor, acl_dst_tensor, ACL_FLOAT);
|
||||
|
||||
ACL_CHECK(aclDestroyTensor(acl_output_tensor));
|
||||
ACL_CHECK(aclDestroyTensor(acl_dst_tensor));
|
||||
}
|
||||
|
||||
void ggml_cann_mul_mat(ggml_backend_cann_context& ctx, ggml_tensor* dst) {
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue