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llama.cpp
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CANN: implement SSM_CONV operator 

Co-authored-by: Aleksei Lobanov, <zeromarblectm@gmail.com>
Co-authored-by: Sujin Kang, <waterjin326@gmail.com>
This commit is contained in:
Aleksei Lobanov 2025-11-20 14:30:42 +08:00
parent b3e3060f4e
commit df6a560c10
4 changed files with 137 additions and 0 deletions
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123
ggml/src/ggml-cann/aclnn_ops.cpp
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@ -3484,3 +3484,126 @@ void ggml_cann_out_prod(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
break;
}
}
void ggml_cann_ssm_conv(ggml_backend_cann_context & ctx, ggml_tensor * dst) {
ggml_tensor * src0 = dst->src[0]; // conv_x
ggml_tensor * src1 = dst->src[1]; // conv1d.weight
// This op is currently defined only for F32 in ggml_cpu
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
// Shapes follow ggml_compute_forward_ssm_conv_f32
const int64_t nc = src1->ne[0]; // d_conv
const int64_t ncs = src0->ne[0]; // d_conv - 1 + n_t
const int64_t nr = src0->ne[1]; // d_inner
const int64_t n_s = src0->ne[2]; // n_seqs
const int64_t n_t = dst->ne[1]; // tokens per sequence
GGML_ASSERT(dst->ne[0] == nr); // dst: {d_inner, n_t, n_s}
GGML_ASSERT(src1->ne[1] == nr); // weight: {d_conv, d_inner}
GGML_ASSERT(ncs == nc - 1 + n_t); // conv_x: {d_conv - 1 + n_t, d_inner, n_s}
GGML_ASSERT(src0->nb[0] == sizeof(float));
GGML_ASSERT(src1->nb[0] == sizeof(float));
// --- Build CANN tensors ---
// 1) Input: conv_x as NCL
//
// src0->ne = { ncs, nr, n_s, 1 } // {L_in, C, N}
// Passing ACL_FORMAT_NCL here means:
// reversed dims -> [N, C, L_in] = [n_s, nr, ncs]
acl_tensor_ptr acl_x = ggml_cann_create_tensor(src0, src0->ne, src0->nb, 3, ACL_FORMAT_NCL);
// 2) Weights: depthwise conv kernel, view src1 as {K, 1, C}
//
// src1 original: ne = { nc, nr, 1, 1 } // [K, C, 1, 1]
// we want a view: ne_w = { nc, 1, nr } // [K, 1, C]
// so that reversed dims -> [C, 1, K] which matches
// [out_channels, in_channels/groups, kernel_size]
int64_t w_ne[GGML_MAX_DIMS] = { 0 };
size_t w_nb[GGML_MAX_DIMS] = { 0 };
w_ne[0] = nc; // K
w_ne[1] = 1; // 1 input channel per group
w_ne[2] = nr; // C groups
w_ne[3] = 1;
// Layout: src1 data is [K, C] with
// offset(k, c) = k*nb0 + c*nb1
// We want offset_w(k, 0, c) = k*nb0 + c*nb1,
// so we can reuse nb0 and nb1, and set nb2 = nb1.
w_nb[0] = src1->nb[0]; // sizeof(float)
w_nb[1] = src1->nb[1]; // nc * sizeof(float)
w_nb[2] = src1->nb[1]; // same stride for each (fake) "channel"
w_nb[3] = src1->nb[3];
acl_tensor_ptr acl_w = ggml_cann_create_tensor(
src1->data, ggml_cann_type_mapping(src1->type), ggml_type_size(src1->type), w_ne, w_nb, 3, ACL_FORMAT_NCL);
// 3) Output: dst is { d_inner, n_t, n_s } (CLN)
//
// We need an NCL view of the same buffer:
// desired NCL logical shape: { L_out = n_t, C = nr, N = n_s }
//
// Original CLN layout:
// dst->ne = { nr, n_t, n_s }
// dst->nb[0] = sizeof(float)
// dst->nb[1] = nr * sizeof(float)
// dst->nb[2] = nr * n_t * sizeof(float)
//
// We want offset_new(L, C, N) = offset_orig(C, L, N).
// Choose:
// nb_y[0] = nr * sizeof(float); // step in L
// nb_y[1] = sizeof(float); // step in C
// nb_y[2] = nr * n_t * sizeof(float); // step in N
int64_t y_ne[GGML_MAX_DIMS] = { 0 };
size_t y_nb[GGML_MAX_DIMS] = { 0 };
y_ne[0] = n_t; // L_out
y_ne[1] = nr; // C
y_ne[2] = n_s; // N
y_ne[3] = 1;
y_nb[0] = dst->ne[0] * sizeof(float); // nr * sizeof(float)
y_nb[1] = sizeof(float);
y_nb[2] = dst->ne[0] * dst->ne[1] * sizeof(float); // nr * n_t * sizeof(float)
y_nb[3] = dst->nb[3];
acl_tensor_ptr acl_y = ggml_cann_create_tensor(
dst->data, ggml_cann_type_mapping(dst->type), ggml_type_size(dst->type), y_ne, y_nb, 3, ACL_FORMAT_NCL);
// --- Conv1d parameters: depthwise, stride 1, no padding ("valid") ---
int64_t strideVal[1] = { 1 };
int64_t paddingVal[1] = { 0 };
int64_t dilationVal[1] = { 1 };
acl_int_array_ptr stride = ggml_cann_create_int_array(strideVal, 1);
acl_int_array_ptr padding = ggml_cann_create_int_array(paddingVal, 1);
acl_int_array_ptr dilation = ggml_cann_create_int_array(dilationVal, 1);
const bool transposed = false;
const int64_t groups = nr; // depthwise: one group per inner dim
int8_t cubeMathType = 0;
#ifdef ASCEND_310P
cubeMathType = 1;
#endif
GGML_CANN_CALL_ACLNN_OP(ctx,
Convolution,
acl_x.get(), // input: N, C, L_in = ncs
acl_w.get(), // weight: [C, 1, K] with groups=nr
nullptr, // bias
stride.get(),
padding.get(),
dilation.get(),
transposed,
padding.get(), // output padding (unused for non-transposed)
groups,
acl_y.get(),
cubeMathType);
}

2
ggml/src/ggml-cann/aclnn_ops.h
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@ -1032,6 +1032,8 @@ void ggml_cann_op_unary(std::function<void(ggml_backend_cann_context &, aclTenso
ggml_backend_cann_context & ctx,
ggml_tensor * dst);
void ggml_cann_ssm_conv(ggml_backend_cann_context & ctx, ggml_tensor * dst);
/**
* @brief Applies a gated (GLU-style) unary operation using the CANN backend.
*

4
ggml/src/ggml-cann/ggml-cann.cpp
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@ -1888,6 +1888,8 @@ static bool ggml_cann_compute_forward(ggml_backend_cann_context & ctx, struct gg
break;
case GGML_OP_OUT_PROD:
ggml_cann_out_prod(ctx, dst);
case GGML_OP_SSM_CONV:
ggml_cann_ssm_conv(ctx, dst);
break;
default:
return false;
@ -2625,6 +2627,8 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten
}
return true;
}
case GGML_OP_SSM_CONV:
return true;
default:
return false;
}

8
tests/test-backend-ops.cpp
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@ -3379,6 +3379,14 @@ struct test_ssm_conv : public test_case {
ggml_tensor * out = ggml_ssm_conv(ctx, a, b);
return out;
}
// for CANN Ascend310P3:
// this card requires setting cubeMathType=1 (ALLOW_FP32_DOWN_PRECISION)
// so the inputs are converted from f32
// and tests fail with NMSE = 0.000000114 > 0.000000100
double max_nmse_err() override {
return 1e-6;
}
};
// GGML_OP_SSM_SCAN