llama-cpp-sys-4 0.3.2

Low Level Bindings to llama.cpp
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218

#include "conv3d.hpp"

static inline int64_t ggml_sycl_conv3d_calc_patch_total(const ggml_tensor * dst, int32_t n) {
    return (int64_t) n * dst->ne[0] * dst->ne[1] * dst->ne[2];
}

static inline int64_t ggml_sycl_conv3d_calc_knl_n_total(const ggml_tensor * src0, int32_t c) {
    return (int64_t) src0->ne[0] * src0->ne[1] * src0->ne[2] * c;
}

static inline void ggml_sycl_conv3d_write_output(
        const ggml_tensor * dst,
        const float * src, float * dst_data,
        int64_t patch_total, int64_t oc,
        int64_t dst_w, int64_t dst_h, int64_t dst_d,
        dpct::queue_ptr stream) {
    const int64_t dst_nb0 = dst->nb[0];
    const int64_t dst_nb1 = dst->nb[1];
    const int64_t dst_nb2 = dst->nb[2];
    const int64_t dst_nb3 = dst->nb[3];
    const int64_t total = patch_total * oc;
    const int64_t block_size = 256;
    const int64_t num_work_items = ((total + block_size - 1) / block_size) * block_size;

    stream->parallel_for(sycl::range<1>(num_work_items), [=](sycl::id<1> id) {
        const int64_t i = id[0];
        if (i >= total) {
            return;
        }

        const int64_t patch_idx = i / oc;
        const int64_t out_ch = i % oc;
        const int64_t p_in_batch = patch_idx % (dst_w * dst_h * dst_d);
        const int64_t batch_idx = patch_idx / (dst_w * dst_h * dst_d);
        const int64_t dst_z = p_in_batch / (dst_w * dst_h);
        const int64_t dst_y = (p_in_batch % (dst_w * dst_h)) / dst_w;
        const int64_t dst_x = p_in_batch % dst_w;
        const int64_t ocn_idx = batch_idx * oc + out_ch;

        const int64_t dst_offset = dst_x * dst_nb0 + dst_y * dst_nb1 + dst_z * dst_nb2 + ocn_idx * dst_nb3;
        // `src` is a column-major (m x n) GEMM output where m == patch_total, n == oc.
        // GEMM stores element (row, col) at index `row + col*m`, so compute index accordingly.
        const int64_t src_index = patch_idx + out_ch * patch_total;
        const float value = src[src_index];
        *(float *)((char *)dst_data + dst_offset) = value;
    });
}

void ggml_sycl_op_conv_3d(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);

    const ggml_tensor * src0 = dst->src[0];
    const ggml_tensor * src1 = dst->src[1];

    GGML_ASSERT(src0->type == GGML_TYPE_F16 || src0->type == GGML_TYPE_F32);
    GGML_ASSERT(src1->type == GGML_TYPE_F32);
    GGML_ASSERT(dst->type == GGML_TYPE_F32);
    GGML_ASSERT(ggml_is_contiguous(src0));
    GGML_ASSERT(ggml_is_contiguous(src1));

    const int32_t * opts = (const int32_t *) dst->op_params;
    const int32_t s0 = opts[0];
    const int32_t s1 = opts[1];
    const int32_t s2 = opts[2];
    const int32_t p0 = opts[3];
    const int32_t p1 = opts[4];
    const int32_t p2 = opts[5];
    const int32_t d0 = opts[6];
    const int32_t d1 = opts[7];
    const int32_t d2 = opts[8];
    const int32_t c  = opts[9];
    const int32_t n  = opts[10];
    const int32_t oc = opts[11];

    const int64_t knl_w = src0->ne[0];
    const int64_t knl_h = src0->ne[1];
    const int64_t knl_d = src0->ne[2];

    const int64_t patch_total = ggml_sycl_conv3d_calc_patch_total(dst, n);
    const int64_t knl_n_total = ggml_sycl_conv3d_calc_knl_n_total(src0, c);

    const size_t kernel_type_size = ggml_element_size(src0);

    ggml_sycl_pool_alloc<float> gemm_output(ctx.pool());
    gemm_output.alloc((size_t) patch_total * oc);

    ggml_tensor dst_mat = {};
    dst_mat.type = GGML_TYPE_F32;
    dst_mat.ne[0] = patch_total;
    dst_mat.ne[1] = oc;
    dst_mat.ne[2] = 1;
    dst_mat.ne[3] = 1;
    dst_mat.nb[0] = sizeof(float);
    dst_mat.nb[1] = dst_mat.nb[0] * dst_mat.ne[0];
    dst_mat.nb[2] = dst_mat.nb[1];
    dst_mat.nb[3] = dst_mat.nb[2];
    dst_mat.data = gemm_output.get();
    dst_mat.buffer = dst->buffer;
    dst_mat.extra = dst->extra;

    dpct::queue_ptr stream = ctx.stream();

    // allocate packed arrays: A_packed (k x m), B_packed (k x n)
    ggml_sycl_pool_alloc<float> A_packed_alloc(ctx.pool());
    ggml_sycl_pool_alloc<float> B_packed_alloc(ctx.pool());
    A_packed_alloc.alloc((size_t) knl_n_total * patch_total * sizeof(float));
    B_packed_alloc.alloc((size_t) knl_n_total * oc * sizeof(float));

    float * A_packed = A_packed_alloc.get();
    float * B_packed = B_packed_alloc.get();

    const int m = (int) patch_total;
    const int n_gemm = (int) oc;
    const int k = (int) knl_n_total;

    // Combined kernel: im2col -> pack A, and pack B simultaneously
    const char * src1_base = (const char *) src1->data;
    const int64_t src1_nb0 = src1->nb[0];
    const int64_t src1_nb1 = src1->nb[1];
    const int64_t src1_nb2 = src1->nb[2];
    const int64_t src1_nb3 = src1->nb[3];

    // Compute correct strides for src0 as (knl_n_total, oc) matrix
    const int64_t src0_packed_nb0 = kernel_type_size;
    const int64_t src0_packed_nb1 = kernel_type_size * knl_n_total;

    const int64_t KW = knl_w;
    const int64_t KH = knl_h;
    const int64_t KD = knl_d;
    const int64_t PW = dst->ne[0];
    const int64_t PH = dst->ne[1];
    const int64_t PD = dst->ne[2];

    // Pack A (with inline im2col): for each (row, col) in k x m matrix
    const int64_t A_total = (int64_t)k * m;
    const int64_t A_block_size = 256;
    const int64_t A_num_work = ((A_total + A_block_size - 1) / A_block_size) * A_block_size;

    stream->parallel_for(sycl::range<1>(A_num_work), [=](sycl::id<1> id) {
        const int64_t t = id[0];
        if (t >= A_total) return;

        const int64_t row = t % k;
        const int64_t col = t / k;

        // Inline im2col for this element
        const int64_t k_index = row;
        const int64_t patch_idx = col;

        const int64_t ic = k_index / (KD * KH * KW);
        const int64_t rem = k_index - ic * (KD * KH * KW);
        const int64_t kz = rem / (KH * KW);
        const int64_t rem2 = rem - kz * (KH * KW);
        const int64_t ky = rem2 / KW;
        const int64_t kx = rem2 % KW;

        const int64_t p_in_batch = patch_idx % (PW * PH * PD);
        const int64_t batch_idx = patch_idx / (PW * PH * PD);
        const int64_t dst_z = p_in_batch / (PW * PH);
        const int64_t dst_y = (p_in_batch % (PW * PH)) / PW;
        const int64_t dst_x = p_in_batch % PW;

        const int64_t sx = dst_x * s0 + kx * d0 - p0;
        const int64_t sy = dst_y * s1 + ky * d1 - p1;
        const int64_t sz = dst_z * s2 + kz * d2 - p2;

        float val = 0.0f;
        if (sx >= 0 && sx < src1->ne[0] && sy >= 0 && sy < src1->ne[1] && sz >= 0 && sz < src1->ne[2]) {
            const int64_t channel_idx = batch_idx * c + ic;
            const char * ptr = src1_base + sx * src1_nb0 + sy * src1_nb1 + sz * src1_nb2 + channel_idx * src1_nb3;
            val = *(const float *) ptr;
        }
        A_packed[row + col * (int64_t)k] = val;
    });

    // Pack B: for each (row, col) in k x n_gemm matrix
    const int64_t B_total = (int64_t)k * n_gemm;
    const int64_t B_block_size = 256;
    const int64_t B_num_work = ((B_total + B_block_size - 1) / B_block_size) * B_block_size;

    stream->parallel_for(sycl::range<1>(B_num_work), [=](sycl::id<1> id) {
        const int64_t t = id[0];
        if (t >= B_total) return;

        const int64_t row = t % k;
        const int64_t col = t / k;
        const char * src_ptr = (const char *) src0->data + row * src0_packed_nb0 + col * src0_packed_nb1;
        float v;
        if (src0->type == GGML_TYPE_F32) {
            v = *(const float *) src_ptr;
        } else {
            v = sycl::vec<sycl::half, 1>(*(const sycl::half *) src_ptr).convert<float, sycl::rounding_mode::automatic>()[0];
        }
        B_packed[row + col * (int64_t)k] = v;
    });

    // GEMM: C = A^T * B where A is (k x m), B is (k x n), C is (m x n)
    const float alpha = 1.0f;
    const float beta  = 0.0f;
    const int lda = k;
    const int ldb = k;
    const int ldc = m;

    SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm(
        *stream, oneapi::mkl::transpose::trans, oneapi::mkl::transpose::nontrans,
        m, n_gemm, k,
        dpct::get_value(&alpha, *stream),
        (const float *) A_packed, lda,
        (const float *) B_packed, ldb,
        dpct::get_value(&beta, *stream),
        (float *) dst_mat.data, ldc)));

    const float * gemm_data = (const float *) dst_mat.data;
    float * dst_data = (float *) dst->data;

    ggml_sycl_conv3d_write_output(dst, gemm_data, dst_data, patch_total, oc,
                                  dst->ne[0], dst->ne[1], dst->ne[2], stream);
}