diff --git a/ggml.c b/ggml.c
index 0571777d1..01604ec95 100644
--- a/ggml.c
+++ b/ggml.c
@@ -3339,9 +3339,12 @@ static const char * GGML_OP_LABEL[GGML_OP_COUNT] = {
 
     "MAP_UNARY",
     "MAP_BINARY",
+
+    "CROSS_ENTROPY_LOSS",
+    "CROSS_ENTROPY_LOSS_BACK",
 };
 
-static_assert(GGML_OP_COUNT == 53, "GGML_OP_COUNT != 53");
+static_assert(GGML_OP_COUNT == 55, "GGML_OP_COUNT != 55");
 
 static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
     "none",
@@ -3402,9 +3405,12 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
 
     "f(x)",
     "f(x,y)",
+
+    "cross_entropy_loss(x,y)",
+    "cross_entropy_loss_back(x,y)",
 };
 
-static_assert(GGML_OP_COUNT == 53, "GGML_OP_COUNT != 53");
+static_assert(GGML_OP_COUNT == 55, "GGML_OP_COUNT != 55");
 
 static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
 static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@@ -6347,6 +6353,50 @@ struct ggml_tensor * ggml_map_binary_inplace_f32(
     return ggml_map_binary_impl_f32(ctx, a, b, fun, true);
 }
 
+// ggml_cross_entropy_loss
+
+struct ggml_tensor * ggml_cross_entropy_loss(
+        struct ggml_context         * ctx,
+        struct ggml_tensor          * a,
+        struct ggml_tensor          * b) {
+    GGML_ASSERT(ggml_are_same_shape(a, b));
+    bool is_node = false;
+
+    if (a->grad || b->grad) {
+        is_node = true;
+    }
+
+    struct ggml_tensor * result = ggml_new_tensor_1d(ctx, a->type, 1);
+
+    result->op   = GGML_OP_CROSS_ENTROPY_LOSS;
+    result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
+    result->src0 = a;
+    result->src1 = b;
+
+    return result;
+}
+
+// ggml_cross_entropy_loss_back
+
+struct ggml_tensor * ggml_cross_entropy_loss_back(
+        struct ggml_context         * ctx,
+        struct ggml_tensor          * a,
+        struct ggml_tensor          * b,
+        struct ggml_tensor          * c) {
+    GGML_ASSERT(ggml_are_same_shape(a, b));
+    GGML_ASSERT(ggml_is_scalar(c));
+
+    struct ggml_tensor * result = ggml_dup_tensor(ctx, a);
+
+    result->op   = GGML_OP_CROSS_ENTROPY_LOSS_BACK;
+    result->grad = NULL;
+    result->src0 = a;
+    result->src1 = b;
+    result->opt[0] = c;
+
+    return result;
+}
+
 ////////////////////////////////////////////////////////////////////////////////
 
 void ggml_set_param(
@@ -12831,6 +12881,287 @@ static void ggml_compute_forward_map_binary(
     }
 }
 
+// ggml_compute_forward_cross_entropy_loss
+
+static void ggml_compute_forward_cross_entropy_loss_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+    GGML_ASSERT(ggml_is_scalar(dst));
+    GGML_ASSERT(ggml_are_same_shape(src0, src1));
+
+    const int ith = params->ith;
+    const int nth = params->nth;
+
+    float * sums = (float *) params->wdata;
+
+    // TODO: handle transposed/permuted matrices
+    const int nc = src0->ne[0];
+    const int nr = ggml_nrows(src0);
+
+    if (params->type == GGML_TASK_INIT) {
+        if (ith == 0) {
+            memset(sums, 0, sizeof(float) * (nth + nth * nc));
+        }
+        return;
+    }
+
+    if (params->type == GGML_TASK_FINALIZE) {
+        if (ith == 0) {
+            float * dp = (float *) dst->data;
+            ggml_vec_sum_f32(nth, dp, sums);
+            dp[0] *= -1.0f;
+        }
+        return;
+    }
+
+    const float eps = 1e-9f;
+
+
+    // rows per thread
+    const int dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int ir0 = dr*ith;
+    const int ir1 = MIN(ir0 + dr, nr);
+
+    for (int i1 = ir0; i1 < ir1; i1++) {
+        float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]);
+        float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]);
+        float * st = (float *) params->wdata + nth + ith*nc;
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            //printf("p[%d] = %f\n", i, p[i]);
+            assert(!isnan(s0[i]));
+            assert(!isnan(s1[i]));
+        }
+#endif
+        // soft_max
+        ggml_float sum = 0.0;
+        {
+            float max = -INFINITY;
+            ggml_vec_max_f32(nc, &max, s0);
+
+            uint16_t scvt;
+            for (int i = 0; i < nc; i++) {
+                if (s0[i] == -INFINITY) {
+                    st[i] = 0.0f;
+                } else {
+                    // const float val = (s0[i] == -INFINITY) ? 0.0 : exp(s0[i] - max);
+                    ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max);
+                    memcpy(&scvt, &s, sizeof(scvt));
+                    const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
+                    sum += (ggml_float)val;
+                    st[i] = val;
+                }
+            }
+
+            assert(sum > 0.0);
+            sum = 1.0/sum;
+        }
+        // avoid log(0) by rescaling from [0..1] to [eps..1]
+        sum = sum * (1.0f - eps);
+        ggml_vec_scale_f32(nc, st, sum);
+        ggml_vec_add1_f32(nc, st, st, eps);
+        ggml_vec_log_f32(nc, st, st);
+        ggml_vec_mul_f32(nc, st, st, s1);
+
+        ggml_vec_sum_f32(nc, sums + ith, st);
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            assert(!isnan(st[i]));
+            assert(!isinf(st[i]));
+        }
+#endif
+    }
+
+}
+
+static void ggml_compute_forward_cross_entropy_loss(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_cross_entropy_loss_f32(params, src0, src1, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+// ggml_compute_forward_cross_entropy_loss_back
+
+static void ggml_compute_forward_cross_entropy_loss_back_f32(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        const struct ggml_tensor * opt0,
+        struct ggml_tensor * dst) {
+    GGML_ASSERT(ggml_is_contiguous(dst));
+    GGML_ASSERT(ggml_is_contiguous(src0));
+    GGML_ASSERT(ggml_is_contiguous(src1));
+    GGML_ASSERT(ggml_is_contiguous(opt0));
+    GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
+
+    const int64_t ith = params->ith;
+    const int64_t nth = params->nth;
+
+    float * sums = (float *) params->wdata;
+
+    if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
+        return;
+    }
+
+    const float eps = 1e-9f;
+
+    // TODO: handle transposed/permuted matrices
+    const int64_t nc = src0->ne[0];
+    const int64_t nr = ggml_nrows(src0);
+
+    // rows per thread
+    const int64_t dr = (nr + nth - 1)/nth;
+
+    // row range for this thread
+    const int64_t ir0 = dr*ith;
+    const int64_t ir1 = MIN(ir0 + dr, nr);
+
+    float * d   = (float *) opt0->data;
+
+    for (int64_t i1 = ir0; i1 < ir1; i1++) {
+        float * ds0 = (float *)((char *) dst->data  + i1*dst->nb[1]);
+        float * s0  = (float *)((char *) src0->data + i1*src0->nb[1]);
+        float * s1  = (float *)((char *) src1->data + i1*src1->nb[1]);
+        float * sm  = (float *) params->wdata + ith*nc;
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            //printf("p[%d] = %f\n", i, p[i]);
+            assert(!isnan(s0[i]));
+            assert(!isnan(s1[i]));
+        }
+#endif
+        // step by step explanation:
+        {
+            // forward pass with annotated gradients from backward pass
+            // (built by going in reverse operation order, adding to gradients of current operation args)
+            // st0 = exp(s0-max(s0))                                                       grad[st0] = grad[st1]*(1.0 - eps)/sum
+                                                          // from softmax_back:            grad[s0]  = st1_k * (grad[st1]_k - dot(st1, grad[st1]))
+            // ggml_vec_scale_f32(nc, st, sum);           // st1 = st0*/sum = softmax(s0)  grad[st1] = grad[st2]*(1.0 - eps)
+            // ggml_vec_scale_f32(nc, st, (1.0f - eps));  // st2 = st1*(1.0 - eps)         grad[st2] = grad[st3]
+            // ggml_vec_add1_f32(nc, st, st, eps);        // st3 = st2 + eps               grad[st3] = grad[st4]/st3
+            // ggml_vec_log_f32(nc, st, st);              // st4 = log(st3)                grad[st4] = grad[st5] * s1
+            // ggml_vec_mul_f32(nc, st, st, s1);          // st5 = st4 * s1                grad[st5] = grad[sums[ith]]
+            // ggml_vec_sum_f32(nc, sums + ith, st);      // sums[ith] = st5               grad[sums[ith]] = grad[cross_entropy_loss] = -grad[cel]
+
+            // substitute into grad[st1], because we can reuse softmax_back from this point on
+            // grad[st1] = -grad[cel]*s1*(1.0 - eps)/(eps + softmax(s0)*(1.0 - eps))
+            // postorder:
+            // grad[st1] := softmax(s0)
+            // grad[st1] := grad[st1]*(1.0 - eps)
+            // grad[st1] := grad[st1] + eps
+            // grad[st1] := s1 / grad[st1]
+            // grad[st1] := grad[st1]*(1.0-eps)*-grad[cel]
+
+            // src0 gradients by going through softmax_back
+            // grad[s0] = st1_k * (grad[st1]_k - dot(st1, grad[st1]))
+            //   from softmax_back:
+            //   dxk = yk * (dyk - dot(y, dy))
+            //   dot_y_dy := dot(y, dy)
+            //   dx := dy
+            //   dx := dx - dot_y_dy
+            //   dx := dx * y
+            //   postorder:
+            //   dot_st1_dst1 := dot(st1, grad[st1])
+            //   grad[s0] := grad[st1]
+            //   grad[s0] := grad[s0] - dot_st1_dst1
+            //   grad[s0] := grad[s0] * st1
+
+            // prepend postorder from grad[st1] directly using grad[s0] as memory location, as we will grad[s0] := grad[st1]
+            // sm           := softmax(s0)
+            // grad[s0]     := sm*(1.0 - eps)
+            // grad[s0]     := grad[s0] + eps
+            // grad[s0]     := s1 / grad[s0]
+            // grad[s0]     := grad[s0]*(1.0-eps)*-grad[cel]
+            // dot_st1_dst1 := dot(sm, grad[s0])
+            // grad[s0]     := grad[s0] - dot_st1_dst1
+            // grad[s0]     := grad[s0] * sm
+        }
+
+        // soft_max
+        ggml_float sum = 0.0;
+        {
+            float max = -INFINITY;
+            ggml_vec_max_f32(nc, &max, s0);
+
+            uint16_t scvt;
+            for (int i = 0; i < nc; i++) {
+                if (s0[i] == -INFINITY) {
+                    sm[i] = 0.0f;
+                } else {
+                    // const float val = (s0[i] == -INFINITY) ? 0.0 : exp(s0[i] - max);
+                    ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max);
+                    memcpy(&scvt, &s, sizeof(scvt));
+                    const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]);
+                    sum += (ggml_float)val;
+                    sm[i] = val;
+                }
+            }
+
+            assert(sum > 0.0);
+            sum = 1.0/sum;
+        }
+
+        float dot_st1_dst1 = 0;
+        ggml_vec_scale_f32(nc, sm, sum);
+        ggml_vec_cpy_f32  (nc, ds0, sm);
+        ggml_vec_scale_f32(nc, ds0, (1.0 - eps));
+        ggml_vec_add1_f32 (nc, ds0, ds0, eps);
+        ggml_vec_div_f32  (nc, ds0, s1, ds0);
+        ggml_vec_scale_f32(nc, ds0, -(1.0 - eps)*d[0]);
+        ggml_vec_dot_f32  (nc, &dot_st1_dst1, sm, ds0);
+        ggml_vec_acc1_f32 (nc, ds0, -dot_st1_dst1);
+        ggml_vec_mul_f32  (nc, ds0, ds0, sm);
+
+#ifndef NDEBUG
+        for (int i = 0; i < nc; ++i) {
+            assert(!isnan(sm[i]));
+            assert(!isinf(sm[i]));
+            assert(!isnan(ds0[i]));
+            assert(!isinf(ds0[i]));
+        }
+#endif
+    }
+}
+
+static void ggml_compute_forward_cross_entropy_loss_back(
+        const struct ggml_compute_params * params,
+        const struct ggml_tensor * src0,
+        const struct ggml_tensor * src1,
+        const struct ggml_tensor * opt0,
+        struct ggml_tensor * dst) {
+    switch (src0->type) {
+        case GGML_TYPE_F32:
+            {
+                ggml_compute_forward_cross_entropy_loss_back_f32(params, src0, src1, opt0, dst);
+            } break;
+        default:
+            {
+                GGML_ASSERT(false);
+            } break;
+    }
+}
+
+
 /////////////////////////////////
 
 static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) {
@@ -13052,6 +13383,16 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
                 ggml_compute_forward_map_binary(params, tensor->src0, tensor->src1, tensor, fun);
             }
             break;
+        case GGML_OP_CROSS_ENTROPY_LOSS:
+            {
+                ggml_compute_forward_cross_entropy_loss(params, tensor->src0, tensor->src1, tensor);
+            }
+            break;
+        case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
+            {
+                ggml_compute_forward_cross_entropy_loss_back(params, tensor->src0, tensor->src1, tensor->opt[0], tensor);
+            }
+            break;
         case GGML_OP_NONE:
             {
                 // nop
@@ -13677,6 +14018,22 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
             {
                 GGML_ASSERT(false); // not supported
             } break;
+        case GGML_OP_CROSS_ENTROPY_LOSS:
+            {
+                if (src0->grad) {
+                    src0->grad = ggml_add_impl(ctx,
+                                src0->grad,
+                                ggml_cross_entropy_loss_back(ctx,
+                                    src0,
+                                    src1,
+                                    tensor->grad),
+                                inplace);
+                }
+            } break;
+        case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
+            {
+                GGML_ASSERT(false); // not supported
+            } break;
         case GGML_OP_NONE:
             {
                 // nop
@@ -14225,6 +14582,22 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph)
                     {
                         node->n_tasks = 1;
                     } break;
+                case GGML_OP_CROSS_ENTROPY_LOSS:
+                    {
+                        node->n_tasks = n_threads;
+
+                        size_t cur = ggml_type_size(node->type)*(node->n_tasks + node->src0->ne[0]*node->n_tasks);
+
+                        work_size = MAX(work_size, cur);
+                    } break;
+                case GGML_OP_CROSS_ENTROPY_LOSS_BACK:
+                    {
+                        node->n_tasks = n_threads;
+
+                        size_t cur = ggml_type_size(node->type)*node->src0->ne[0]*node->n_tasks;
+
+                        work_size = MAX(work_size, cur);
+                    } break;
                 case GGML_OP_NONE:
                     {
                         node->n_tasks = 1;
diff --git a/ggml.h b/ggml.h
index 711d34e78..ba60588d6 100644
--- a/ggml.h
+++ b/ggml.h
@@ -322,6 +322,9 @@ extern "C" {
         GGML_OP_MAP_UNARY,
         GGML_OP_MAP_BINARY,
 
+        GGML_OP_CROSS_ENTROPY_LOSS,
+        GGML_OP_CROSS_ENTROPY_LOSS_BACK,
+
         GGML_OP_COUNT,
     };
 
@@ -972,6 +975,19 @@ extern "C" {
             struct ggml_tensor          * b,
                    ggml_binary_op_f32_t   fun);
 
+    // loss function
+
+    GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
+            struct ggml_context         * ctx,
+            struct ggml_tensor          * a,
+            struct ggml_tensor          * b);
+
+    GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back(
+            struct ggml_context         * ctx,
+            struct ggml_tensor          * a,
+            struct ggml_tensor          * b,
+            struct ggml_tensor          * c);
+
     //
     // automatic differentiation
     //