Merge branch 'master' into gg/flash-attn
This commit is contained in:
Georgi Gerganov
commit
2ddc9bbef1
30 changed files with 1758 additions and 1597 deletions
135
ggml.c
135
ggml.c
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@ -5413,7 +5413,7 @@ GGML_API struct ggml_tensor * ggml_conv_1d(
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int s0,
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int p0,
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int d0) {
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struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false); // [N, OL, IC * K]
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struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K]
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struct ggml_tensor * result =
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ggml_mul_mat(ctx,
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@ -5491,16 +5491,15 @@ struct ggml_tensor * ggml_conv_depthwise_2d(
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int p1,
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int d0,
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int d1) {
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struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]);
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struct ggml_tensor * im2col = ggml_im2col(ctx, new_a,
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ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]),
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s0, s1, p0, p1, d0, d1, true); // [N * IC, OH, OW, KH * KW]
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struct ggml_tensor * result =
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ggml_mul_mat(ctx,
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ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1), // [OC，1, KH, KW] => [1, OC, 1, KH * KW]
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ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3])); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW]
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s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW]
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struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW]
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new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC，1, KH, KW] => [1, OC, 1, KH * KW]
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struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b);
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result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW]
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return result;
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@ -5521,7 +5520,8 @@ struct ggml_tensor * ggml_im2col(
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int p1,
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int d0,
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int d1,
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bool is_2D) {
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bool is_2D,
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enum ggml_type dst_type) {
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if(is_2D) {
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GGML_ASSERT(a->ne[2] == b->ne[2]);
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@ -5545,7 +5545,7 @@ struct ggml_tensor * ggml_im2col(
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is_2D ? b->ne[3] : 1,
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};
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struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne);
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struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 4, ne);
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int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) };
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ggml_set_op_params(result, params, sizeof(params));
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@ -5570,7 +5570,7 @@ struct ggml_tensor * ggml_conv_2d(
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int p1,
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int d0,
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int d1) {
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struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true); // [N, OH, OW, IC * KH * KW]
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struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N, OH, OW, IC * KH * KW]
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struct ggml_tensor * result =
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ggml_mul_mat(ctx,
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@ -5696,12 +5696,13 @@ struct ggml_tensor * ggml_pool_2d(
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is_node = true;
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}
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struct ggml_tensor * result;
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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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result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne);
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int32_t params[] = { op, k0, k1, s0, s1, p0, p1 };
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ggml_set_op_params(result, params, sizeof(params));
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@ -5709,7 +5710,6 @@ struct ggml_tensor * ggml_pool_2d(
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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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return result;
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}
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@ -12608,6 +12608,92 @@ static void ggml_compute_forward_conv_transpose_1d(
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}
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}
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// src0: kernel [OC, IC, KH, KW]
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// src1: image [N, IC, IH, IW]
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// dst: result [N, OH, OW, IC*KH*KW]
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static void ggml_compute_forward_im2col_f32(
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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 * src1,
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struct ggml_tensor * dst) {
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GGML_ASSERT(src0->type == GGML_TYPE_F16);
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GGML_ASSERT(src1->type == GGML_TYPE_F32);
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GGML_ASSERT( dst->type == GGML_TYPE_F32);
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int64_t t0 = ggml_perf_time_us();
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UNUSED(t0);
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GGML_TENSOR_BINARY_OP_LOCALS;
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const int32_t s0 = ((const int32_t *)(dst->op_params))[0];
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const int32_t s1 = ((const int32_t *)(dst->op_params))[1];
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const int32_t p0 = ((const int32_t *)(dst->op_params))[2];
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const int32_t p1 = ((const int32_t *)(dst->op_params))[3];
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const int32_t d0 = ((const int32_t *)(dst->op_params))[4];
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const int32_t d1 = ((const int32_t *)(dst->op_params))[5];
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const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1;
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const int ith = params->ith;
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const int nth = params->nth;
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const int64_t N = is_2D ? ne13 : ne12;
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const int64_t IC = is_2D ? ne12 : ne11;
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const int64_t IH = is_2D ? ne11 : 1;
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const int64_t IW = ne10;
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const int64_t KH = is_2D ? ne01 : 1;
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const int64_t KW = ne00;
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const int64_t OH = is_2D ? ne2 : 1;
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const int64_t OW = ne1;
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int ofs0 = is_2D ? nb13 : nb12;
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int ofs1 = is_2D ? nb12 : nb11;
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GGML_ASSERT(nb00 == sizeof(ggml_fp16_t));
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GGML_ASSERT(nb10 == sizeof(float));
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if (params->type == GGML_TASK_INIT) {
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return;
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}
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if (params->type == GGML_TASK_FINALIZE) {
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return;
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}
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// im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW]
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{
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float * const wdata = (float *) dst->data;
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for (int64_t in = 0; in < N; in++) {
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for (int64_t ioh = 0; ioh < OH; ioh++) { // 1
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for (int64_t iow = 0; iow < OW; iow++) {
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for (int64_t iic = ith; iic < IC; iic += nth) {
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// micro kernel
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float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW]
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const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW]
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for (int64_t ikh = 0; ikh < KH; ikh++) { // 1
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for (int64_t ikw = 0; ikw < KW; ikw++) {
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const int64_t iiw = iow*s0 + ikw*d0 - p0;
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const int64_t iih = ioh*s1 + ikh*d1 - p1;
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if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) {
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dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0;
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} else {
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dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]);
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}
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}
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}
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}
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}
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}
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}
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}
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}
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// src0: kernel [OC, IC, KH, KW]
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// src1: image [N, IC, IH, IW]
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// dst: result [N, OH, OW, IC*KH*KW]
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@ -12698,14 +12784,14 @@ static void ggml_compute_forward_im2col(
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const struct ggml_tensor * src0,
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const struct ggml_tensor * src1,
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struct ggml_tensor * dst) {
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switch (src0->type) {
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switch (dst->type) {
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case GGML_TYPE_F16:
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{
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ggml_compute_forward_im2col_f16(params, src0, src1, dst);
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} break;
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case GGML_TYPE_F32:
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{
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GGML_ASSERT(false);
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ggml_compute_forward_im2col_f32(params, src0, src1, dst);
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} break;
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default:
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{
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@ -12896,8 +12982,8 @@ 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 * src,
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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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GGML_ASSERT(src->type == GGML_TYPE_F32);
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GGML_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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@ -17297,12 +17383,16 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
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struct ggml_cplan cplan;
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memset(&cplan, 0, sizeof(struct ggml_cplan));
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int max_tasks = 1;
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// thread scheduling for the different operations + work buffer size estimation
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for (int i = 0; i < cgraph->n_nodes; i++) {
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struct ggml_tensor * node = cgraph->nodes[i];
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const int n_tasks = ggml_get_n_tasks(node, n_threads);
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max_tasks = MAX(max_tasks, n_tasks);
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size_t cur = 0;
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switch (node->op) {
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@ -17475,7 +17565,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa
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work_size += CACHE_LINE_SIZE*(n_threads - 1);
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}
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cplan.n_threads = n_threads;
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cplan.n_threads = MIN(max_tasks, n_threads);
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cplan.work_size = work_size;
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cplan.work_data = NULL;
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@ -20791,6 +20881,14 @@ int ggml_cpu_has_vulkan(void) {
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#endif
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}
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int ggml_cpu_has_kompute(void) {
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#if defined(GGML_USE_KOMPUTE)
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return 1;
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#else
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return 0;
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#endif
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}
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int ggml_cpu_has_sycl(void) {
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#if defined(GGML_USE_SYCL)
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return 1;
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@ -20800,7 +20898,8 @@ int ggml_cpu_has_sycl(void) {
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}
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int ggml_cpu_has_gpublas(void) {
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return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_sycl();
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return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() ||
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ggml_cpu_has_sycl();
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}
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int ggml_cpu_has_sse3(void) {
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