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llama.cpp
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hipudding 2026-04-01 13:55:00 +08:00 committed by GitHub
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ggml/src/ggml-cann/aclnn_ops.cpp
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@ -25,6 +25,17 @@
#include "ggml-impl.h"
#include "ggml.h"
// Forward-declare InplaceFillDiagonal because aclnn_fill_diagonal.h has a
// broken include guard (OP_API_INC_ADD_H_) that conflicts with aclnn_add.h.
extern "C" {
aclnnStatus aclnnInplaceFillDiagonalGetWorkspaceSize(
aclTensor * selfRef, const aclScalar * fillValue, bool wrap,
uint64_t * workspaceSize, aclOpExecutor ** executor);
aclnnStatus aclnnInplaceFillDiagonal(
void * workspace, uint64_t workspaceSize, aclOpExecutor * executor,
aclrtStream stream);
}
#include <aclnnop/aclnn_add.h>
#include <aclnnop/aclnn_add_rms_norm.h>
#include <aclnnop/aclnn_addcdiv.h>
@ -62,6 +73,7 @@
#include <aclnnop/aclnn_permute.h>
#include <aclnnop/aclnn_pow.h>
#include <aclnnop/aclnn_pow_tensor_tensor.h>
#include <aclnnop/aclnn_recurrent_gated_delta_rule.h>
#include <aclnnop/aclnn_reduce_sum.h>
#include <aclnnop/aclnn_reflection_pad1d.h>
#include <aclnnop/aclnn_repeat.h>
@ -73,7 +85,10 @@
#include <aclnnop/aclnn_sum.h>
#include <aclnnop/aclnn_threshold.h>
#include <aclnnop/aclnn_tril.h>
#include <aclnnop/aclnn_triangular_solve.h>
#include <aclnnop/aclnn_triu.h>
#include <aclnnop/aclnn_logical_not.h>
#include <aclnnop/aclnn_masked_fill_scalar.h>
#include <aclnnop/aclnn_upsample_nearest_2d.h>
#include <aclnnop/aclnn_weight_quant_batch_matmul_v2.h>
#include <aclnnop/aclnn_zero.h>
@ -589,6 +604,33 @@ void ggml_cann_group_norm(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
acl_mean_out.get(), acl_rstd_out.get());
}
void ggml_cann_set(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
ggml_tensor * src0 = dst->src[0];
ggml_tensor * src1 = dst->src[1];
size_t nb1 = ((int32_t *) dst->op_params)[0];
size_t nb2 = ((int32_t *) dst->op_params)[1];
size_t nb3 = ((int32_t *) dst->op_params)[2];
size_t offset = ((int32_t *) dst->op_params)[3];
bool inplace = (bool) ((int32_t *) dst->op_params)[4];
size_t param_nb[] = { ggml_element_size(src0), nb1, nb2, nb3 };
// Create a view of dst at the target offset with src1's dimensions
acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst, src1->ne, param_nb, GGML_MAX_DIMS, ACL_FORMAT_ND, offset);
acl_tensor_ptr acl_src1 = ggml_cann_create_tensor(src1);
if (!inplace) {
// First copy src0 to dst entirely
size_t cpy_size = ggml_nbytes(dst);
ACL_CHECK(
aclrtMemcpyAsync(dst->data, cpy_size, src0->data, cpy_size, ACL_MEMCPY_DEVICE_TO_DEVICE, ctx.stream()));
}
// Copy src1 into the target region of dst
GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCopy, acl_dst.get(), acl_src1.get());
}
void ggml_cann_acc(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
ggml_tensor * src0 = dst->src[0];
ggml_tensor * src1 = dst->src[1];
@ -652,6 +694,166 @@ void ggml_cann_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
aclnn_reduce_sum(ctx, dst, reduce_dims, 4);
}
void ggml_cann_cumsum(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
ggml_tensor * src = dst->src[0];
acl_tensor_ptr acl_src = ggml_cann_create_tensor(src);
acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
// GGML cumsum operates along dim 0 (innermost / ne[0]).
// ggml_cann_create_tensor reverses dimensions to [ne3,ne2,ne1,ne0],
// so GGML dim 0 maps to CANN dim 3 (the last dim of the 4-D tensor).
GGML_CANN_CALL_ACLNN_OP(ctx, Cumsum, acl_src.get(), (int64_t)3,
ggml_cann_type_mapping(dst->type), acl_dst.get());
}
void ggml_cann_solve_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
ggml_tensor * src0 = dst->src[0]; // A: [N, N, B2, B3] lower triangular
ggml_tensor * src1 = dst->src[1]; // B: [K, N, B2, B3]
acl_tensor_ptr acl_a = ggml_cann_create_tensor(src0);
acl_tensor_ptr acl_b = ggml_cann_create_tensor(src1);
acl_tensor_ptr acl_x = ggml_cann_create_tensor(dst);
// mOut: triangular copy of A (required output), same shape as A.
const size_t a_bytes = ggml_nbytes(src0);
ggml_cann_pool_alloc m_alloc(ctx.pool(), a_bytes);
acl_tensor_ptr acl_m = ggml_cann_create_tensor(
m_alloc.get(), ggml_cann_type_mapping(src0->type),
ggml_type_size(src0->type), src0->ne, src0->nb, GGML_MAX_DIMS);
// Solve AX = B: upper=false (lower tri), transpose=false, unitriangular=false.
GGML_CANN_CALL_ACLNN_OP(ctx, TriangularSolve,
acl_b.get(), acl_a.get(), false, false, false,
acl_x.get(), acl_m.get());
}
void ggml_cann_diag(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
ggml_tensor * src = dst->src[0];
GGML_ASSERT(src->ne[1] == 1);
const int64_t N = src->ne[0];
const int64_t n_batch = src->ne[2] * src->ne[3];
const size_t nb_f32 = sizeof(float);
// Fill dst with zeros.
acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
{
float zero = 0.0f;
acl_scalar_ptr acl_zero = ggml_cann_create_scalar(&zero, ACL_FLOAT);
GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_dst.get(), acl_zero.get());
}
// Copy src vector onto the diagonal of dst via strided views.
// src viewed as [N, n_batch], contiguous strides.
int64_t ne_vec[2] = { N, n_batch };
size_t nb_src_vec[2] = { nb_f32, N * nb_f32 };
// dst diagonal view: stride (N+1)*4 steps along the diagonal.
size_t nb_dst_diag[2] = { (N + 1) * nb_f32, N * N * nb_f32 };
acl_tensor_ptr acl_src_vec = ggml_cann_create_tensor(src->data, ACL_FLOAT, nb_f32, ne_vec, nb_src_vec, 2);
acl_tensor_ptr acl_dst_diag = ggml_cann_create_tensor(dst->data, ACL_FLOAT, nb_f32, ne_vec, nb_dst_diag, 2);
GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCopy, acl_dst_diag.get(), acl_src_vec.get());
}
void ggml_cann_fill(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
float c = ggml_get_op_params_f32(dst, 0);
acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst);
acl_scalar_ptr acl_c = ggml_cann_create_scalar(&c, ACL_FLOAT);
GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_dst.get(), acl_c.get());
}
void ggml_cann_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
ggml_tensor * src = dst->src[0];
const int64_t S = src->ne[0];
const int64_t n_batch = src->ne[2] * src->ne[3];
const size_t nb_f32 = sizeof(float);
const size_t nb_bool = sizeof(uint8_t);
const size_t buf_sz = n_batch * S * S * nb_f32;
const size_t bool_sz = n_batch * S * S * nb_bool;
int64_t ne3d[3] = { S, S, n_batch };
size_t nb3d[3] = { nb_f32, S * nb_f32, S * S * nb_f32 };
size_t nb3d_bool[3] = { nb_bool, S * nb_bool, S * S * nb_bool };
const ggml_tri_type ttype = (ggml_tri_type) ggml_get_op_params_i32(dst, 0);
acl_tensor_ptr acl_src = ggml_cann_create_tensor(src->data, ACL_FLOAT, nb_f32, ne3d, nb3d, 3);
acl_tensor_ptr acl_dst = ggml_cann_create_tensor(dst->data, ACL_FLOAT, nb_f32, ne3d, nb3d, 3);
// LOWER: Tril(-1) directly gives strict-lower triangle (CANN dim reversal
// makes Tril(-1) equivalent to GGML's col < row).
if (ttype == GGML_TRI_TYPE_LOWER) {
GGML_CANN_CALL_ACLNN_OP(ctx, Tril, acl_src.get(), (int64_t)-1, acl_dst.get());
return;
}
// For other types: copy src→dst, build a BOOL mask of positions to zero,
// then use MaskedFillScalar to zero those positions.
GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCopy, acl_dst.get(), acl_src.get());
// Build lower-strict float mask (1s below diagonal, 0s elsewhere).
ggml_cann_pool_alloc ones_alloc(ctx.pool(), buf_sz);
void * ones_buf = ones_alloc.get();
acl_tensor_ptr acl_ones = ggml_cann_create_tensor(ones_buf, ACL_FLOAT, nb_f32, ne3d, nb3d, 3);
{
float one_val = 1.0f;
acl_scalar_ptr acl_one = ggml_cann_create_scalar(&one_val, ACL_FLOAT);
GGML_CANN_CALL_ACLNN_OP(ctx, InplaceFillScalar, acl_ones.get(), acl_one.get());
}
ggml_cann_pool_alloc mask_f_alloc(ctx.pool(), buf_sz);
void * mask_f_buf = mask_f_alloc.get();
acl_tensor_ptr acl_mask_f = ggml_cann_create_tensor(mask_f_buf, ACL_FLOAT, nb_f32, ne3d, nb3d, 3);
GGML_CANN_CALL_ACLNN_OP(ctx, Tril, acl_ones.get(), (int64_t)-1, acl_mask_f.get());
// For LOWER_DIAG and UPPER: extend mask to include diagonal via strided
// diagonal view copy (Tril(0) is buggy on CANN, giving same result as Tril(-1)).
if (ttype == GGML_TRI_TYPE_LOWER_DIAG || ttype == GGML_TRI_TYPE_UPPER) {
int64_t ne_diag[2] = { S, n_batch };
size_t nb_diag[2] = { (S + 1) * nb_f32, S * S * nb_f32 };
acl_tensor_ptr acl_ones_diag = ggml_cann_create_tensor(ones_buf, ACL_FLOAT, nb_f32, ne_diag, nb_diag, 2);
acl_tensor_ptr acl_mask_diag = ggml_cann_create_tensor(mask_f_buf, ACL_FLOAT, nb_f32, ne_diag, nb_diag, 2);
GGML_CANN_CALL_ACLNN_OP(ctx, InplaceCopy, acl_mask_diag.get(), acl_ones_diag.get());
}
// Cast float mask to BOOL.
ggml_cann_pool_alloc mask_b_alloc(ctx.pool(), bool_sz);
void * mask_b_buf = mask_b_alloc.get();
acl_tensor_ptr acl_mask_b = ggml_cann_create_tensor(mask_b_buf, ACL_BOOL, nb_bool, ne3d, nb3d_bool, 3);
GGML_CANN_CALL_ACLNN_OP(ctx, Cast, acl_mask_f.get(), ACL_BOOL, acl_mask_b.get());
// Select which BOOL mask to pass to MaskedFillScalar (True positions get zeroed).
// LOWER_DIAG: invert lower_diag → upper_strict mask.
// UPPER_DIAG: use lower_strict mask directly.
// UPPER: use lower_diag mask directly.
ggml_cann_pool_alloc mask_inv_alloc(ctx.pool(), bool_sz);
void * mask_inv_buf = mask_inv_alloc.get();
acl_tensor_ptr acl_mask_inv = ggml_cann_create_tensor(mask_inv_buf, ACL_BOOL, nb_bool, ne3d, nb3d_bool, 3);
aclTensor * fill_mask = nullptr;
switch (ttype) {
case GGML_TRI_TYPE_LOWER_DIAG:
GGML_CANN_CALL_ACLNN_OP(ctx, LogicalNot, acl_mask_b.get(), acl_mask_inv.get());
fill_mask = acl_mask_inv.get();
break;
case GGML_TRI_TYPE_UPPER_DIAG:
fill_mask = acl_mask_b.get();
break;
case GGML_TRI_TYPE_UPPER:
fill_mask = acl_mask_b.get();
break;
default:
GGML_ABORT("unsupported tri type");
}
float zero_val = 0.0f;
acl_scalar_ptr acl_zero = ggml_cann_create_scalar(&zero_val, ACL_FLOAT);
GGML_CANN_CALL_ACLNN_OP(ctx, InplaceMaskedFillScalar, acl_dst.get(), fill_mask, acl_zero.get());
}
void ggml_cann_upsample_nearest2d(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
ggml_tensor * src = dst->src[0];
acl_tensor_ptr acl_src = ggml_cann_create_tensor(src, nullptr, nullptr, 0, ACL_FORMAT_NCHW);
@ -4170,3 +4372,211 @@ void ggml_cann_gated_linear_attn(ggml_backend_cann_context & ctx, ggml_tensor *
}
}
}
// ggml_cann_gated_delta_net
//
// Head-parallel implementation of the Gated Delta Net recurrence.
//
// CANN's aclnnBatchMatMul accepts rank-3 tensors only: [batch, M, K] @ [batch, K, N].
// The n_seqs sequences have non-uniform strides across the batch dimension when
// viewed as [n_seqs*H, S, S] (seq boundary stride ≠ head stride), so we keep a
// thin outer loop over n_seqs and batch all H heads per sequence using 3-D BMM.
//
// Per sequence s, per timestep t:
// Step 1 – Decay M[H,S,S] *= exp(g)
// KDA: g_exp[H,S] broadcast as [H,1,S] → M[h,j,i] *= exp(g[h,i])
// Scalar: g_exp[H] broadcast as [H,1,1] → M[h,:,:] *= exp(g[h])
// Step 2 – Mk = M @ k_col [H,S,S] @ [H,S,1] → [H,S,1]
// Step 3 – delta = (v - Mk) * beta → [H,S]
// Step 4 – M += outer(delta, k) [H,S,1] @ [H,1,S] → [H,S,S]
// Step 5 – o = M @ q * scale [H,S,S] @ [H,S,1] → [H,S,1]
//
// Kernel launches: ~6 * n_seqs * n_tokens
// vs. naive: ~6 * n_seqs * H * n_tokens (H× reduction)
//
// n_seqs is typically 1–4 in practice, so the outer loop is negligible.
//
// GGML→CANN convention: ne[] is REVERSED by create_tensor.
// ne=[S,S,H] → CANN [H,S,S], ne=[1,S,H] → CANN [H,S,1], etc.
//
// Preconditions (checked by caller):
// - no GQA: neq1==H, nek1==H, neq3==n_seqs, nek3==n_seqs
// - F32 contiguous q, k, v, g, beta
void ggml_cann_gated_delta_net(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
ggml_tensor * src_q = dst->src[0];
ggml_tensor * src_k = dst->src[1];
ggml_tensor * src_v = dst->src[2];
ggml_tensor * src_g = dst->src[3];
ggml_tensor * src_beta = dst->src[4];
ggml_tensor * src_state = dst->src[5];
const int64_t S_v = src_v->ne[0];
const int64_t H = src_v->ne[1];
const int64_t n_tokens = src_v->ne[2];
const int64_t n_seqs = src_v->ne[3];
const bool kda = (src_g->ne[0] == S_v);
const float scale = 1.0f / sqrtf((float)S_v);
const size_t F32 = sizeof(float);
// Output: [attn_scores | new_states]
// attn: [S_v, H, n_tokens, n_seqs] = S_v*H*n_tokens*n_seqs floats
// state: [S_v, S_v, H, n_seqs] starts after attn
const size_t state_off = (size_t)(S_v * H * n_tokens * n_seqs) * F32;
// Copy input state → output state region (updated in-place below)
{
int64_t ne_flat[1] = { S_v * S_v * H * n_seqs };
size_t nb_flat[1] = { F32 };
auto acl_sin = ggml_cann_create_tensor(src_state->data, ACL_FLOAT, F32, ne_flat, nb_flat, 1);
auto acl_sout = ggml_cann_create_tensor(dst->data, ACL_FLOAT, F32, ne_flat, nb_flat, 1,
ACL_FORMAT_ND, state_off);
cann_copy(ctx, acl_sin.get(), acl_sout.get());
}
// ── Temporary buffers (pre-allocated once, reused every (s,t)) ──────────
// g_exp: [H * (kda ? S_v : 1)] – exp(g) for current (s,t)
// mk: [H * S_v] – result of M @ k
// delta: [H * S_v] – (v - mk) * beta
// outer: [H * S_v * S_v] – rank-1 update delta ⊗ k^T
ggml_cann_pool_alloc g_exp_alloc(ctx.pool(), (size_t)H * (kda ? S_v : 1) * F32);
ggml_cann_pool_alloc mk_alloc (ctx.pool(), (size_t)H * S_v * F32);
ggml_cann_pool_alloc delta_alloc(ctx.pool(), (size_t)H * S_v * F32);
ggml_cann_pool_alloc outer_alloc(ctx.pool(), (size_t)H * S_v * S_v * F32);
// ── 3-D shape/stride descriptors (GGML order; reversed by create_tensor) ─
//
// ne=[S,S,H] → CANN [H,S,S] (state matrix, batch=H)
// ne=[1,S,H] → CANN [H,S,1] (column vec, batch=H)
// ne=[S,1,H] → CANN [H,1,S] (row vec, batch=H)
// ne=[S, H] → CANN [H,S] (flat vec, batch=H)
// ne=[1, H] → CANN [H,1] (scalar per head, batch=H)
//
// Stride derivation examples (elem strides after reversal → CANN strides):
// ne=[1,S,H], nb=[F32, F32, S*F32]:
// elem [1,1,S] → rev → [S,1,1] for [H,S,1]: k[h][i][0] at h*S+i ✓
// ne=[S,1,H], nb=[F32, S*F32, S*F32]:
// elem [1,S,S] → rev → [S,S,1] for [H,1,S]: k[h][0][j] at h*S+j ✓
int64_t ne_M[3] = { S_v, S_v, H };
size_t nb_M[3] = { F32, (size_t)S_v*F32, (size_t)S_v*S_v*F32 };
int64_t ne_col[3] = { 1, S_v, H };
size_t nb_col[3] = { F32, F32, (size_t)S_v*F32 };
int64_t ne_row[3] = { S_v, 1, H };
size_t nb_row[3] = { F32, (size_t)S_v*F32, (size_t)S_v*F32 };
int64_t ne_vec[2] = { S_v, H };
size_t nb_vec[2] = { F32, (size_t)S_v*F32 };
for (int64_t s = 0; s < n_seqs; s++) {
// State M for seq s: CANN [H, S_v, S_v] starting at s_base
const size_t s_base = state_off + (size_t)(s * H * S_v * S_v) * F32;
for (int64_t t = 0; t < n_tokens; t++) {
// ── Step 1: Decay M_h *= exp(g_h) ──────────────────────────────
{
const size_t g_off = (size_t)(s * src_g->nb[3] + t * src_g->nb[2]);
if (kda) {
// g slice [H, S_v] at (s,t)
int64_t ne_g[2] = { S_v, H };
size_t nb_g_src[2] = { (size_t)src_g->nb[0], (size_t)src_g->nb[1] };
size_t nb_g_tmp[2] = { F32, (size_t)S_v*F32 };
auto acl_g_src = ggml_cann_create_tensor(src_g->data, ACL_FLOAT, F32,
ne_g, nb_g_src, 2, ACL_FORMAT_ND, g_off);
auto acl_g_exp = ggml_cann_create_tensor(g_exp_alloc.get(), ACL_FLOAT, F32,
ne_g, nb_g_tmp, 2);
cann_copy(ctx, acl_g_src.get(), acl_g_exp.get());
aclnn_exp(ctx, acl_g_exp.get());
// Broadcast as CANN [H,1,S] → M[h,j,i] *= exp(g[h,i])
auto acl_g_bc = ggml_cann_create_tensor(g_exp_alloc.get(), ACL_FLOAT, F32,
ne_row, nb_row, 3);
auto acl_M = ggml_cann_create_tensor(dst->data, ACL_FLOAT, F32,
ne_M, nb_M, 3, ACL_FORMAT_ND, s_base);
aclnn_mul(ctx, acl_M.get(), acl_g_bc.get(), nullptr);
} else {
// g slice [H, 1] at (s,t), one scalar per head
int64_t ne_g[2] = { 1, H };
size_t nb_g_src[2] = { (size_t)src_g->nb[0], (size_t)src_g->nb[1] };
size_t nb_g_tmp[2] = { F32, F32 };
auto acl_g_src = ggml_cann_create_tensor(src_g->data, ACL_FLOAT, F32,
ne_g, nb_g_src, 2, ACL_FORMAT_ND, g_off);
auto acl_g_exp = ggml_cann_create_tensor(g_exp_alloc.get(), ACL_FLOAT, F32,
ne_g, nb_g_tmp, 2);
cann_copy(ctx, acl_g_src.get(), acl_g_exp.get());
aclnn_exp(ctx, acl_g_exp.get());
// Broadcast as CANN [H,1,1] → M_h *= exp(g_h)
int64_t ne_g_bc[3] = { 1, 1, H };
size_t nb_g_bc[3] = { F32, F32, F32 };
auto acl_g_bc = ggml_cann_create_tensor(g_exp_alloc.get(), ACL_FLOAT, F32,
ne_g_bc, nb_g_bc, 3);
auto acl_M = ggml_cann_create_tensor(dst->data, ACL_FLOAT, F32,
ne_M, nb_M, 3, ACL_FORMAT_ND, s_base);
aclnn_mul(ctx, acl_M.get(), acl_g_bc.get(), nullptr);
}
}
// ── Step 2: Mk = M @ k_col [H,S,S]@[H,S,1] → [H,S,1] ─────────
{
const size_t k_off = (size_t)(s * src_k->nb[3] + t * src_k->nb[2]);
size_t nb_k_col[3] = { F32, (size_t)src_k->nb[0], (size_t)src_k->nb[1] };
auto acl_M = ggml_cann_create_tensor(dst->data, ACL_FLOAT, F32,
ne_M, nb_M, 3, ACL_FORMAT_ND, s_base);
auto acl_k = ggml_cann_create_tensor(src_k->data, ACL_FLOAT, F32,
ne_col, nb_k_col, 3, ACL_FORMAT_ND, k_off);
auto acl_Mk = ggml_cann_create_tensor(mk_alloc.get(), ACL_FLOAT, F32,
ne_col, nb_col, 3);
GGML_CANN_CALL_ACLNN_OP(ctx, BatchMatMul, acl_M.get(), acl_k.get(), acl_Mk.get(), 2);
}
// ── Step 3: delta = (v - Mk) * beta [H,S] ──────────────────────
{
const size_t v_off = (size_t)(s * src_v->nb[3] + t * src_v->nb[2]);
const size_t beta_off = (size_t)(s * src_beta->nb[3] + t * src_beta->nb[2]);
size_t nb_v[2] = { (size_t)src_v->nb[0], (size_t)src_v->nb[1] };
int64_t ne_beta[2] = { 1, H };
size_t nb_beta[2] = { (size_t)src_beta->nb[0], (size_t)src_beta->nb[1] };
auto acl_v = ggml_cann_create_tensor(src_v->data, ACL_FLOAT, F32,
ne_vec, nb_v, 2, ACL_FORMAT_ND, v_off);
auto acl_Mk_sq = ggml_cann_create_tensor(mk_alloc.get(), ACL_FLOAT, F32,
ne_vec, nb_vec, 2);
auto acl_delta = ggml_cann_create_tensor(delta_alloc.get(), ACL_FLOAT, F32,
ne_vec, nb_vec, 2);
auto acl_beta = ggml_cann_create_tensor(src_beta->data, ACL_FLOAT, F32,
ne_beta, nb_beta, 2, ACL_FORMAT_ND, beta_off);
aclnn_sub(ctx, acl_v.get(), acl_Mk_sq.get(), acl_delta.get());
aclnn_mul(ctx, acl_delta.get(), acl_beta.get(), nullptr);
}
// ── Step 4: M += outer(delta, k) [H,S,1]@[H,1,S] → [H,S,S] ────
{
const size_t k_off = (size_t)(s * src_k->nb[3] + t * src_k->nb[2]);
auto acl_d_col = ggml_cann_create_tensor(delta_alloc.get(), ACL_FLOAT, F32,
ne_col, nb_col, 3);
auto acl_k_row = ggml_cann_create_tensor(src_k->data, ACL_FLOAT, F32,
ne_row, nb_row, 3, ACL_FORMAT_ND, k_off);
auto acl_outer = ggml_cann_create_tensor(outer_alloc.get(), ACL_FLOAT, F32,
ne_M, nb_M, 3);
GGML_CANN_CALL_ACLNN_OP(ctx, BatchMatMul, acl_d_col.get(), acl_k_row.get(), acl_outer.get(), 2);
auto acl_M = ggml_cann_create_tensor(dst->data, ACL_FLOAT, F32,
ne_M, nb_M, 3, ACL_FORMAT_ND, s_base);
aclnn_add(ctx, acl_M.get(), acl_outer.get(), nullptr);
}
// ── Step 5: o = M @ q * scale [H,S,S]@[H,S,1] → [H,S,1] ───────
{
const size_t q_off = (size_t)(s * src_q->nb[3] + t * src_q->nb[2]);
const size_t attn_off = (size_t)(s * n_tokens * H + t * H) * S_v * F32;
size_t nb_q_col[3] = { F32, (size_t)src_q->nb[0], (size_t)src_q->nb[1] };
auto acl_M = ggml_cann_create_tensor(dst->data, ACL_FLOAT, F32,
ne_M, nb_M, 3, ACL_FORMAT_ND, s_base);
auto acl_q = ggml_cann_create_tensor(src_q->data, ACL_FLOAT, F32,
ne_col, nb_q_col, 3, ACL_FORMAT_ND, q_off);
auto acl_out = ggml_cann_create_tensor(dst->data, ACL_FLOAT, F32,
ne_col, nb_col, 3, ACL_FORMAT_ND, attn_off);
GGML_CANN_CALL_ACLNN_OP(ctx, BatchMatMul, acl_M.get(), acl_q.get(), acl_out.get(), 2);
aclnn_muls(ctx, acl_out.get(), scale, nullptr, true);
}
}
}
}

70
ggml/src/ggml-cann/aclnn_ops.h
View File

@ -32,6 +32,9 @@
#include <aclnnop/aclnn_cat.h>
#include <aclnnop/aclnn_clamp.h>
#include <aclnnop/aclnn_cos.h>
#include <aclnnop/aclnn_cumsum.h>
#include <aclnnop/aclnn_tril.h>
#include <aclnnop/aclnn_triu.h>
#include <aclnnop/aclnn_exp.h>
#include <aclnnop/aclnn_gelu.h>
#include <aclnnop/aclnn_gelu_v2.h>
@ -47,6 +50,7 @@
#include <aclnnop/aclnn_sign.h>
#include <aclnnop/aclnn_silu.h>
#include <aclnnop/aclnn_sin.h>
#include <aclnnop/aclnn_softplus.h>
#include <aclnnop/aclnn_slice.h>
#include <aclnnop/aclnn_sqrt.h>
#include <aclnnop/aclnn_tanh.h>
@ -325,6 +329,48 @@ void ggml_cann_sum_rows(ggml_backend_cann_context & ctx, ggml_tensor * dst);
void ggml_cann_sum(ggml_backend_cann_context & ctx, ggml_tensor * dst);
/**
* @brief Computes the cumulative sum of a ggml tensor along dim 0 using the
* CANN backend.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor. dst->op is `GGML_OP_CUMSUM`.
*/
void ggml_cann_cumsum(ggml_backend_cann_context & ctx, ggml_tensor * dst);
/**
* @brief Computes a triangular mask (tril/triu) of a square ggml tensor
* using the CANN backend.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor. dst->op is `GGML_OP_TRI`.
*/
void ggml_cann_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst);
/**
* @brief Solves a triangular linear system AX=B using the CANN backend.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor. dst->op is `GGML_OP_SOLVE_TRI`.
*/
void ggml_cann_solve_tri(ggml_backend_cann_context & ctx, ggml_tensor * dst);
/**
* @brief Creates a diagonal matrix from a vector using the CANN backend.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor. dst->op is `GGML_OP_DIAG`.
*/
void ggml_cann_diag(ggml_backend_cann_context & ctx, ggml_tensor * dst);
/**
* @brief Fills a tensor with a constant scalar value using the CANN backend.
*
* @param ctx The CANN context used for operations.
* @param dst The destination tensor. dst->op is `GGML_OP_FILL`.
*/
void ggml_cann_fill(ggml_backend_cann_context & ctx, ggml_tensor * dst);
/**
* @brief Upsamples a ggml tensor using nearest neighbor interpolation using
* the CANN backend.
@ -461,6 +507,9 @@ void ggml_cann_timestep_embedding(ggml_backend_cann_context & ctx, ggml_tensor *
// @see ggml_cann_dup.
void ggml_cann_cpy(ggml_backend_cann_context & ctx, ggml_tensor * dst);
// @see ggml_cann_acc, but copies src1 into dst instead of adding.
void ggml_cann_set(ggml_backend_cann_context & ctx, ggml_tensor * dst);
/**
* @brief Computes the softmax activation with optional masking.
*
@ -844,6 +893,27 @@ void ggml_cann_flash_attn_ext(ggml_backend_cann_context & ctx, ggml_tensor * dst
*/
void ggml_cann_gated_linear_attn(ggml_backend_cann_context & ctx, ggml_tensor * dst);
/**
* @brief Forward Gated Delta Net on the CANN backend.
*
* Expects dst->src[0..5] = {q, k, v, g, beta, state} with shape conventions:
* q, k: [S_v, H_q/H_k, n_tokens, n_seqs] (contiguous rows)
* v: [S_v, H, n_tokens, n_seqs]
* g: [1, H, n_tokens, n_seqs] (scalar gate) or [S_v, H, n_tokens, n_seqs] (KDA)
* beta: [1, H, n_tokens, n_seqs]
* state:[S_v, S_v, H, n_seqs]
*
* Per token recurrence:
* S_t = exp(g_t) * S_{t-1} + k_t * (v_t - S_{t-1}^T k_t)^T * beta_t
* out_t = S_t^T q_t / sqrt(S_v)
*
* dst holds both attention outputs and updated state.
*
* @param ctx Backend context providing stream/allocator utilities.
* @param dst Output tensor; src deps are q, k, v, g, beta, state as above.
*/
void ggml_cann_gated_delta_net(ggml_backend_cann_context & ctx, ggml_tensor * dst);
/**
* @brief Launches an asynchronous task using the memory allocator.
*

88
ggml/src/ggml-cann/ggml-cann.cpp
View File

@ -1428,6 +1428,22 @@ static bool ggml_backend_cann_buffer_cpy_tensor(ggml_backend_buffer_t buffer,
return false;
}
/**
* @brief Set a region of a tensor's device memory to a specified value.
*
* @param buffer The CANN buffer containing the tensor.
* @param tensor Pointer to the tensor whose memory will be set.
* @param value The value to which each byte in the region will be set.
* @param offset Byte offset within the tensor's data to start setting.
* @param size Number of bytes to set.
*/
static void ggml_backend_cann_buffer_memset_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, uint8_t value, size_t offset, size_t size) {
ggml_backend_cann_buffer_context * ctx = (ggml_backend_cann_buffer_context *) buffer->context;
ggml_cann_set_device(ctx->device);
ACL_CHECK(aclrtMemset((char *) tensor->data + offset, size, value, size));
}
/**
* @brief Clear a CANN buffer by setting all its memory to a specified value.
*
@ -1454,7 +1470,7 @@ static const ggml_backend_buffer_i ggml_backend_cann_buffer_interface = {
/* .free_buffer = */ ggml_backend_cann_buffer_free_buffer,
/* .get_base = */ ggml_backend_cann_buffer_get_base,
/* .init_tensor = */ ggml_backend_cann_buffer_init_tensor,
/* .memset_tensor = */ NULL,
/* .memset_tensor = */ ggml_backend_cann_buffer_memset_tensor,
/* .set_tensor = */ ggml_backend_cann_buffer_set_tensor,
/* .get_tensor = */ ggml_backend_cann_buffer_get_tensor,
/* .cpy_tensor = */ ggml_backend_cann_buffer_cpy_tensor,
@ -1833,6 +1849,20 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
case GGML_UNARY_OP_STEP:
ggml_cann_step(ctx, dst);
break;
case GGML_UNARY_OP_SOFTPLUS:
{
auto lambda = [](ggml_backend_cann_context & ctx, aclTensor * acl_src, aclTensor * acl_dst) {
float beta_val = 1.0f;
float threshold_val = 20.0f;
aclScalar * beta = aclCreateScalar(&beta_val, aclDataType::ACL_FLOAT);
aclScalar * threshold = aclCreateScalar(&threshold_val, aclDataType::ACL_FLOAT);
GGML_CANN_CALL_ACLNN_OP(ctx, Softplus, acl_src, beta, threshold, acl_dst);
aclDestroyScalar(beta);
aclDestroyScalar(threshold);
};
ggml_cann_op_unary(lambda, ctx, dst);
}
break;
default:
return false;
}
@ -1918,6 +1948,9 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
case GGML_OP_CPY:
ggml_cann_cpy(ctx, dst);
break;
case GGML_OP_SET:
ggml_cann_set(ctx, dst);
break;
case GGML_OP_CONT:
ggml_cann_dup(ctx, dst);
break;
@ -1987,6 +2020,24 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
case GGML_OP_SSM_CONV:
ggml_cann_ssm_conv(ctx, dst);
break;
case GGML_OP_GATED_DELTA_NET:
ggml_cann_gated_delta_net(ctx, dst);
break;
case GGML_OP_CUMSUM:
ggml_cann_cumsum(ctx, dst);
break;
case GGML_OP_TRI:
ggml_cann_tri(ctx, dst);
break;
case GGML_OP_FILL:
ggml_cann_fill(ctx, dst);
break;
case GGML_OP_DIAG:
ggml_cann_diag(ctx, dst);
break;
case GGML_OP_SOLVE_TRI:
ggml_cann_solve_tri(ctx, dst);
break;
default:
return false;
}
@ -2322,6 +2373,7 @@ static enum ggml_status ggml_backend_cann_graph_compute(ggml_backend_t backend,
if (use_cann_graph) {
// If no matching graph is found, the graph needs to be recaptured.
graph_capture_required = !cann_ctx->graph_lru_cache.find_and_move_to_front(cgraph);
if (graph_capture_required) {
// If no matching graph is found, add a new ACL graph.
ggml_cann_graph * new_graph = ggml_cann_graph::create_from_cgraph(cgraph);
@ -2380,6 +2432,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
case GGML_UNARY_OP_SGN:
case GGML_UNARY_OP_STEP:
case GGML_UNARY_OP_GELU_ERF:
case GGML_UNARY_OP_SOFTPLUS:
return true;
default:
return false;
@ -2570,6 +2623,7 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
case GGML_OP_SUM_ROWS:
case GGML_OP_ARGSORT:
case GGML_OP_ACC:
case GGML_OP_SET:
case GGML_OP_GROUP_NORM:
return true;
case GGML_OP_PAD:
@ -2647,6 +2701,38 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
}
case GGML_OP_SSM_CONV:
return true;
case GGML_OP_GATED_DELTA_NET:
{
// Only the batched path (BatchMatMul over all heads) is efficient.
// Non-contiguous / GQA / non-F32 cases fall back to CPU.
const ggml_tensor * q = op->src[0];
const ggml_tensor * k = op->src[1];
const ggml_tensor * v = op->src[2];
const ggml_tensor * g = op->src[3];
const ggml_tensor * beta = op->src[4];
const int64_t H = v->ne[1];
const int64_t n_seqs = v->ne[3];
return q->ne[1] == H
&& k->ne[1] == H
&& q->ne[3] == n_seqs
&& k->ne[3] == n_seqs
&& ggml_is_contiguous(q)
&& ggml_is_contiguous(k)
&& ggml_is_contiguous(v)
&& ggml_is_contiguous(g)
&& ggml_is_contiguous(beta)
&& q->type == GGML_TYPE_F32;
}
case GGML_OP_CUMSUM:
return op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_TRI:
return op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_FILL:
return op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_DIAG:
return op->src[0]->type == GGML_TYPE_F32;
case GGML_OP_SOLVE_TRI:
return op->src[0]->type == GGML_TYPE_F32;
default:
return false;
}

14
tests/test-backend-ops.cpp
View File

@ -3689,6 +3689,20 @@ struct test_gated_delta_net : public test_case {
: type(type), head_count(head_count), head_size(head_size), n_seq_tokens(n_seq_tokens), n_seqs(n_seqs),
v_repeat(v_repeat), permuted(permuted), kda(kda) {}
double max_nmse_err() override {
return 1e-7;
}
double max_nmse_err(ggml_backend_t backend) override {
// Accelerator backends (CANN, etc.) use batched matmul/hardware ops that
// accumulate FP32 rounding differently from CPU scalar loops. Allow up
// to 1e-6 (roughly 8 ULPs of float32 epsilon) for those backends.
if (strncmp(ggml_backend_name(backend), "CANN", 4) == 0) {
return 1e-6;
}
return max_nmse_err();
}
ggml_tensor * build_graph(ggml_context * ctx) override {
ggml_tensor * q;
ggml_tensor * k;