mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-12-25 10:54:36 +00:00
ggml : add ggml_pool_1d and ggml_pool_2d
This commit is contained in:
Georgi Gerganov
parent
680e6f9177
commit
4523d10d0c
283
ggml.c
283
ggml.c
@ -3787,6 +3787,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
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"CLAMP",
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"CONV_1D",
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"CONV_2D",
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"POOL_1D",
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"POOL_2D",
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"FLASH_ATTN",
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"FLASH_FF",
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@ -3805,7 +3807,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
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"CROSS_ENTROPY_LOSS_BACK",
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};
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static_assert(GGML_OP_COUNT == 66, "GGML_OP_COUNT != 66");
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static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68");
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static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"none",
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@ -3865,6 +3867,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"clamp(x)",
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"conv_1d(x)",
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"conv_2d(x)",
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"pool_1d(x)",
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"pool_2d(x)",
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"flash_attn(x)",
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"flash_ff(x)",
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@ -3883,7 +3887,9 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
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"cross_entropy_loss_back(x,y)",
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};
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static_assert(GGML_OP_COUNT == 66, "GGML_OP_COUNT != 66");
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static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68");
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static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
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static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
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static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
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@ -7214,6 +7220,98 @@ struct ggml_tensor* ggml_conv_1d_ph(
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return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d);
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}
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// ggml_pool_*
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static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) {
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return (ins + 2 * p - ks) / s + 1;
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}
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// ggml_pool_2d
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struct ggml_tensor* ggml_pool_1d(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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enum ggml_op_pool op,
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int k0,
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int s0,
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int p0) {
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bool is_node = false;
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if (a->grad) {
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GGML_ASSERT(false); // TODO: implement backward
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is_node = true;
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}
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const int64_t ne[3] = {
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ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
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a->ne[1],
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};
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struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne);
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ggml_scratch_save(ctx);
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struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 4);
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((int32_t*)c->data)[0] = op;
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((int32_t*)c->data)[1] = k0;
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((int32_t*)c->data)[2] = s0;
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((int32_t*)c->data)[3] = p0;
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ggml_scratch_load(ctx);
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result->op = GGML_OP_POOL_1D;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src[0] = a;
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result->src[1] = c;
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return result;
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}
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// ggml_pool_2d
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struct ggml_tensor* ggml_pool_2d(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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enum ggml_op_pool op,
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int k0,
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int k1,
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int s0,
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int s1,
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int p0,
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int p1) {
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bool is_node = false;
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if (a->grad) {
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GGML_ASSERT(false); // TODO: implement backward
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is_node = true;
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}
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const int64_t ne[3] = {
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ggml_calc_pool_output_size(a->ne[0], k0, s0, p0),
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ggml_calc_pool_output_size(a->ne[1], k1, s1, p1),
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a->ne[2],
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};
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struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
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ggml_scratch_save(ctx);
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struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 7);
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((int32_t*)c->data)[0] = op;
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((int32_t*)c->data)[1] = k0;
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((int32_t*)c->data)[2] = k1;
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((int32_t*)c->data)[3] = s0;
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((int32_t*)c->data)[4] = s1;
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((int32_t*)c->data)[5] = p0;
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((int32_t*)c->data)[6] = p1;
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ggml_scratch_load(ctx);
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result->op = GGML_OP_POOL_2D;
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result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
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result->src[0] = a;
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result->src[1] = c;
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return result;
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}
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// ggml_flash_attn
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struct ggml_tensor * ggml_flash_attn(
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@ -13013,6 +13111,166 @@ static void ggml_compute_forward_conv_2d(
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};
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}
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// ggml_compute_forward_pool_1d_sk_p0
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static void ggml_compute_forward_pool_1d_sk_p0(
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const struct ggml_compute_params * params,
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const enum ggml_op_pool op,
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const struct ggml_tensor * src,
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const int k,
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struct ggml_tensor * dst) {
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assert(src->type == GGML_TYPE_F32);
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assert(params->ith == 0);
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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const char * cdata = (const char *)src->data;
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const char * const data_end = cdata + ggml_nbytes(src);
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float * drow = (float *)dst->data;
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const int64_t rs = dst->ne[0];
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while (cdata < data_end) {
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const float * const srow = (const float *)cdata;
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int j = 0;
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for (int64_t i = 0; i < rs; ++i) {
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switch (op) {
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case GGML_OP_POOL_AVG: drow[i] = 0; break;
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case GGML_OP_POOL_MAX: drow[i] = -FLT_MAX; break;
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case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
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}
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for (int ki = 0; ki < k; ++ki) {
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switch (op) {
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case GGML_OP_POOL_AVG: drow[i] += srow[j]; break;
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case GGML_OP_POOL_MAX: if (srow[j] > drow[i]) drow[i] = srow[j]; break;
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case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
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}
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++j;
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}
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switch (op) {
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case GGML_OP_POOL_AVG: drow[i] /= k; break;
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case GGML_OP_POOL_MAX: break;
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case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
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}
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}
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cdata += src->nb[1];
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drow += rs;
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}
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}
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// ggml_compute_forward_pool_1d
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static void ggml_compute_forward_pool_1d(
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const struct ggml_compute_params* params,
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const struct ggml_tensor* src0,
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const struct ggml_tensor* opt0,
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struct ggml_tensor* dst) {
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GGML_ASSERT(opt0->ne[0] == 4);
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const int* opts = (const int*)opt0->data;
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enum ggml_op_pool op = opts[0];
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const int k0 = opts[1];
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const int s0 = opts[2];
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const int p0 = opts[3];
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GGML_ASSERT(p0 == 0); // padding not supported
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GGML_ASSERT(k0 == s0); // only s = k supported
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ggml_compute_forward_pool_1d_sk_p0(params, op, src0, k0, dst);
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}
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// ggml_compute_forward_pool_2d_sk_p0
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static void ggml_compute_forward_pool_2d_sk_p0(
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const struct ggml_compute_params * params,
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const enum ggml_op_pool op,
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const struct ggml_tensor * src,
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const int k0,
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const int k1,
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struct ggml_tensor * dst) {
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assert(src->type == GGML_TYPE_F32);
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assert(params->ith == 0);
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if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
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return;
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}
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const char * cdata = (const char*)src->data;
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const char * const data_end = cdata + ggml_nbytes(src);
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const int64_t px = dst->ne[0];
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const int64_t py = dst->ne[1];
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const int64_t pa = px * py;
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float * dplane = (float *)dst->data;
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const int ka = k0 * k1;
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while (cdata < data_end) {
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for (int oy = 0; oy < py; ++oy) {
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float * const drow = dplane + oy * px;
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for (int ox = 0; ox < px; ++ox) {
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float * const out = drow + ox;
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switch (op) {
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case GGML_OP_POOL_AVG: *out = 0; break;
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case GGML_OP_POOL_MAX: *out = -FLT_MAX; break;
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case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
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}
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const int ix = ox * k0;
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const int iy = oy * k1;
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for (int ky = 0; ky < k1; ++ky) {
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const float * const srow = (const float *)(cdata + src->nb[1] * (iy + ky));
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for (int kx = 0; kx < k0; ++kx) {
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int j = ix + kx;
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switch (op) {
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case GGML_OP_POOL_AVG: *out += srow[j]; break;
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case GGML_OP_POOL_MAX: if (srow[j] > *out) *out = srow[j]; break;
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case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
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}
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}
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}
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switch (op) {
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case GGML_OP_POOL_AVG: *out /= ka; break;
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case GGML_OP_POOL_MAX: break;
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case GGML_OP_POOL_COUNT: GGML_ASSERT(false); break;
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}
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}
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}
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cdata += src->nb[2];
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dplane += pa;
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}
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}
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// ggml_compute_forward_pool_2d
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static void ggml_compute_forward_pool_2d(
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const struct ggml_compute_params * params,
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const struct ggml_tensor * src0,
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const struct ggml_tensor * opt0,
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struct ggml_tensor * dst) {
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GGML_ASSERT(opt0->ne[0] == 7);
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const int* opts = (const int*)opt0->data;
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enum ggml_op_pool op = opts[0];
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const int k0 = opts[1];
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const int k1 = opts[2];
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const int s0 = opts[3];
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const int s1 = opts[4];
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const int p0 = opts[5];
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const int p1 = opts[6];
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GGML_ASSERT(p0 == 0);
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GGML_ASSERT(p1 == 0); // padding not supported
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GGML_ASSERT(k0 == s0);
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GGML_ASSERT(k1 == s1); // only s = k supported
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ggml_compute_forward_pool_2d_sk_p0(params, op, src0, k0, k1, dst);
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}
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// ggml_compute_forward_flash_attn
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@ -14794,6 +15052,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
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{
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ggml_compute_forward_conv_2d(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor);
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} break;
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case GGML_OP_POOL_1D:
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{
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ggml_compute_forward_pool_1d(params, tensor->src[0], tensor->src[1], tensor);
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} break;
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case GGML_OP_POOL_2D:
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{
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ggml_compute_forward_pool_2d(params, tensor->src[0], tensor->src[1], tensor);
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} break;
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case GGML_OP_FLASH_ATTN:
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{
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const int32_t t = ggml_get_i32_1d(tensor->src[3], 0);
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@ -15494,6 +15760,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
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{
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GGML_ASSERT(false); // TODO: not implemented
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} break;
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case GGML_OP_POOL_1D:
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{
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GGML_ASSERT(false); // TODO: not implemented
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} break;
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case GGML_OP_POOL_2D:
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{
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GGML_ASSERT(false); // TODO: not implemented
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} break;
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case GGML_OP_FLASH_ATTN:
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{
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struct ggml_tensor * flash_grad = NULL;
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@ -16315,6 +16589,11 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
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work_size = MAX(work_size, cur);
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} break;
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case GGML_OP_POOL_1D:
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case GGML_OP_POOL_2D:
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{
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n_tasks = 1;
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} break;
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case GGML_OP_FLASH_ATTN:
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{
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n_tasks = n_threads;
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27
ggml.h
27
ggml.h
@ -368,6 +368,8 @@ extern "C" {
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GGML_OP_CLAMP,
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GGML_OP_CONV_1D,
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GGML_OP_CONV_2D,
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GGML_OP_POOL_1D,
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GGML_OP_POOL_2D,
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GGML_OP_FLASH_ATTN,
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GGML_OP_FLASH_FF,
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@ -1173,6 +1175,31 @@ extern "C" {
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int s,
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int d);
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enum ggml_op_pool {
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GGML_OP_POOL_MAX,
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GGML_OP_POOL_AVG,
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GGML_OP_POOL_COUNT,
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};
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GGML_API struct ggml_tensor* ggml_pool_1d(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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enum ggml_op_pool op,
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int k0, // kernel size
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int s0, // stride
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int p0); // padding
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GGML_API struct ggml_tensor* ggml_pool_2d(
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struct ggml_context * ctx,
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struct ggml_tensor * a,
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enum ggml_op_pool op,
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int k0,
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int k1,
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int s0,
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int s1,
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int p0,
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int p1);
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GGML_API struct ggml_tensor * ggml_flash_attn(
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struct ggml_context * ctx,
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struct ggml_tensor * q,
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