From 95a6c595e7ca8dbe47ccf8824e04213e10357f9a Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 23 Jul 2023 14:36:02 +0200 Subject: [PATCH] ggml: move op parameters from tensors to ggml_tensor::op_params (#2333) * ggml: move op parameters from tensors to ggml_tensor::op_params * alibi: use memcpy for float params * remove `src[1] = NULL` in ops --- ggml-cuda.cu | 21 +- ggml-metal.m | 20 +- ggml.c | 667 +++++++++++++++------------------------------------ ggml.h | 4 + 4 files changed, 226 insertions(+), 486 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 720447440..6fb55d838 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2742,6 +2742,7 @@ inline void ggml_cuda_op_mul( (void) dst; (void) src0_ddq_i; (void) i02; + (void) i1; } inline void ggml_cuda_op_gelu( @@ -3037,15 +3038,15 @@ inline void ggml_cuda_op_rope( const int64_t ne00 = src0->ne[0]; const int64_t i01_diff = i01_high - i01_low; - const int n_past = ((int32_t *) src1->data)[0]; - const int n_dims = ((int32_t *) src1->data)[1]; - const int mode = ((int32_t *) src1->data)[2]; - const int n_ctx = ((int32_t *) src1->data)[3]; - + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx = ((int32_t *) dst->op_params)[3]; // RoPE alteration for extended context + float freq_base, freq_scale; - memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); + memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); const float theta_scale = powf(freq_base, -2.0f/n_dims); const float p = (((mode & 1) == 0 ? n_past + i02 : i02)) * freq_scale; @@ -3061,6 +3062,7 @@ inline void ggml_cuda_op_rope( rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p, theta_scale, cudaStream_main); } + (void) src1; (void) dst; (void) src0_ddq_i; (void) src1_ddf_i; @@ -3079,11 +3081,12 @@ inline void ggml_cuda_op_diag_mask_inf( const int64_t ne01 = src0->ne[1]; const int64_t i01_diff = i01_high - i01_low; - const int n_past = ((int32_t *) src1->data)[0]; + const int n_past = ((int32_t *) dst->op_params)[0]; // compute diag_mask_inf_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, ne01, n_past, cudaStream_main); + (void) src1; (void) dst; (void) src0_ddq_i; (void) src1_ddf_i; @@ -3803,7 +3806,7 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bo char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; size_t offset = 0; if (tensor->op == GGML_OP_VIEW) { - memcpy(&offset, tensor->src[2]->data, sizeof(size_t)); + memcpy(&offset, tensor->op_params, sizeof(size_t)); } extra = ggml_cuda_alloc_temp_tensor_extra(); extra->data_device[g_main_device] = src0_ddc + offset; diff --git a/ggml-metal.m b/ggml-metal.m index 78a3b65f1..bf3f68fe4 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -585,7 +585,7 @@ void ggml_metal_graph_compute( encoder = [command_buffer computeCommandEncoder]; } - const int n_past = ((int32_t *)(src1->data))[0]; + const int n_past = ((int32_t *)(dst->op_params))[0]; [encoder setComputePipelineState:ctx->pipeline_diag_mask_inf]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; @@ -850,9 +850,10 @@ void ggml_metal_graph_compute( GGML_ASSERT((src0t == GGML_TYPE_F32)); - const int n_past = ((int32_t *) src1->data)[0]; UNUSED(n_past); - const int n_head = ((int32_t *) src1->data)[1]; - const float max_bias = ((float *) src1->data)[2]; + const int n_past = ((int32_t *) dst->op_params)[0]; UNUSED(n_past); + const int n_head = ((int32_t *) dst->op_params)[1]; + float max_bias; + memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); if (__builtin_popcount(n_head) != 1) { GGML_ASSERT(false && "only power-of-two n_head implemented"); @@ -890,15 +891,14 @@ void ggml_metal_graph_compute( encoder = [command_buffer computeCommandEncoder]; } - const int n_dims = ((int32_t *) src1->data)[1]; - const int mode = ((int32_t *) src1->data)[2]; - - const int n_past = ((int32_t *)(src1->data))[0]; + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; float freq_base; float freq_scale; - memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); + memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); [encoder setComputePipelineState:ctx->pipeline_rope]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; diff --git a/ggml.c b/ggml.c index 6055da867..747a39241 100644 --- a/ggml.c +++ b/ggml.c @@ -4590,6 +4590,7 @@ struct ggml_tensor * ggml_new_tensor_impl( /*.ne =*/ { 1, 1, 1, 1 }, /*.nb =*/ { 0, 0, 0, 0 }, /*.op =*/ GGML_OP_NONE, + /*.op_params =*/ {0}, /*.is_param =*/ false, /*.grad =*/ NULL, /*.src =*/ { NULL }, @@ -4969,6 +4970,11 @@ struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * return tensor; } +static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) { + assert(params_size <= GGML_MAX_OP_PARAMS); + memcpy(tensor->op_params, params, params_size); +} + struct ggml_tensor * ggml_view_tensor( struct ggml_context * ctx, const struct ggml_tensor * src) { @@ -5019,7 +5025,6 @@ struct ggml_tensor * ggml_dup_impl( result->op = GGML_OP_DUP; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5143,23 +5148,13 @@ struct ggml_tensor * ggml_acc_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 5); - - ((int32_t *) c->data)[0] = nb1; - ((int32_t *) c->data)[1] = nb2; - ((int32_t *) c->data)[2] = nb3; - ((int32_t *) c->data)[3] = offset; - ((int32_t *) c->data)[4] = inplace ? 1 : 0; - - ggml_scratch_load(ctx); + int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 }; + ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_ACC; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; - result->src[2] = c; return result; } @@ -5332,7 +5327,6 @@ struct ggml_tensor * ggml_sqr_impl( result->op = GGML_OP_SQR; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5366,7 +5360,6 @@ struct ggml_tensor * ggml_sqrt_impl( result->op = GGML_OP_SQRT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5401,7 +5394,6 @@ struct ggml_tensor * ggml_log_impl( result->op = GGML_OP_LOG; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5434,7 +5426,6 @@ struct ggml_tensor * ggml_sum( result->op = GGML_OP_SUM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5461,7 +5452,6 @@ struct ggml_tensor * ggml_sum_rows( result->op = GGML_OP_SUM_ROWS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5484,7 +5474,6 @@ struct ggml_tensor * ggml_mean( result->op = GGML_OP_MEAN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5508,7 +5497,6 @@ struct ggml_tensor * ggml_argmax( result->op = GGML_OP_ARGMAX; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5586,7 +5574,6 @@ struct ggml_tensor * ggml_abs_impl( result->op = GGML_OP_ABS; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5621,7 +5608,6 @@ struct ggml_tensor * ggml_sgn_impl( result->op = GGML_OP_SGN; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5655,7 +5641,6 @@ struct ggml_tensor * ggml_neg_impl( result->op = GGML_OP_NEG; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5689,7 +5674,6 @@ struct ggml_tensor * ggml_step_impl( result->op = GGML_OP_STEP; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5723,7 +5707,6 @@ struct ggml_tensor * ggml_tanh_impl( result->op = GGML_OP_TANH; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5757,7 +5740,6 @@ struct ggml_tensor * ggml_elu_impl( result->op = GGML_OP_ELU; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5791,7 +5773,6 @@ struct ggml_tensor * ggml_relu_impl( result->op = GGML_OP_RELU; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5825,7 +5806,6 @@ struct ggml_tensor * ggml_gelu_impl( result->op = GGML_OP_GELU; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5859,7 +5839,6 @@ struct ggml_tensor * ggml_gelu_quick_impl( result->op = GGML_OP_GELU_QUICK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5893,7 +5872,6 @@ struct ggml_tensor * ggml_silu_impl( result->op = GGML_OP_SILU; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -5948,10 +5926,11 @@ struct ggml_tensor * ggml_norm_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + // TODO: maybe store epsilon here? + result->op = GGML_OP_NORM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; // TODO: maybe store epsilon here? return result; } @@ -5980,10 +5959,11 @@ struct ggml_tensor * ggml_rms_norm_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + // TODO: maybe store epsilon here? + result->op = GGML_OP_RMS_NORM; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; // TODO: maybe store epsilon here? return result; } @@ -6136,23 +6116,13 @@ struct ggml_tensor * ggml_set_impl( // make a view of the destination struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 5); - - (( int32_t * ) c->data)[0] = nb1; - (( int32_t * ) c->data)[1] = nb2; - (( int32_t * ) c->data)[2] = nb3; - (( int32_t * ) c->data)[3] = offset; - (( int32_t * ) c->data)[4] = inplace ? 1 : 0; - - ggml_scratch_load(ctx); + int32_t params[] = { nb1, nb2, nb3, offset, inplace ? 1 : 0 }; + ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_SET; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; - result->src[2] = c; return result; } @@ -6277,7 +6247,6 @@ struct ggml_tensor * ggml_cont_impl( result->op = GGML_OP_CONT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -6321,7 +6290,6 @@ struct ggml_tensor * ggml_reshape( result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -6346,7 +6314,6 @@ struct ggml_tensor * ggml_reshape_1d( result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -6372,7 +6339,6 @@ struct ggml_tensor * ggml_reshape_2d( result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -6399,7 +6365,6 @@ struct ggml_tensor * ggml_reshape_3d( result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -6428,7 +6393,6 @@ struct ggml_tensor * ggml_reshape_4d( result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -6450,19 +6414,11 @@ struct ggml_tensor * ggml_view_1d( struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, &ne0, (char *) a->data + offset); ggml_format_name(result, "%s (view)", a->name); - ggml_scratch_save(ctx); - - struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2); - ggml_set_name(offs, "offset"); - memcpy(offs->data, &offset, 2*sizeof(int32_t)); - - ggml_scratch_load(ctx); + ggml_set_op_params(result, &offset, sizeof(offset)); result->op = GGML_OP_VIEW; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; - result->src[2] = offs; return result; } @@ -6488,13 +6444,7 @@ struct ggml_tensor * ggml_view_2d( struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, (char *) a->data + offset); ggml_format_name(result, "%s (view)", a->name); - ggml_scratch_save(ctx); - - struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2); - ggml_set_name(offs, "offset"); - memcpy(offs->data, &offset, 2*sizeof(int32_t)); - - ggml_scratch_load(ctx); + ggml_set_op_params(result, &offset, sizeof(offset)); result->nb[1] = nb1; result->nb[2] = result->nb[1]*ne1; @@ -6503,8 +6453,6 @@ struct ggml_tensor * ggml_view_2d( result->op = GGML_OP_VIEW; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; - result->src[2] = offs; return result; } @@ -6532,13 +6480,7 @@ struct ggml_tensor * ggml_view_3d( struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, (char *) a->data + offset); ggml_format_name(result, "%s (view)", a->name); - ggml_scratch_save(ctx); - - struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2); - ggml_set_name(offs, "offset"); - memcpy(offs->data, &offset, 2*sizeof(int32_t)); - - ggml_scratch_load(ctx); + ggml_set_op_params(result, &offset, sizeof(offset)); result->nb[1] = nb1; result->nb[2] = nb2; @@ -6547,8 +6489,6 @@ struct ggml_tensor * ggml_view_3d( result->op = GGML_OP_VIEW; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; - result->src[2] = offs; return result; } @@ -6578,13 +6518,7 @@ struct ggml_tensor * ggml_view_4d( struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, (char *) a->data + offset); ggml_format_name(result, "%s (view)", a->name); - ggml_scratch_save(ctx); - - struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2); - ggml_set_name(offs, "offset"); - memcpy(offs->data, &offset, 2*sizeof(int32_t)); - - ggml_scratch_load(ctx); + ggml_set_op_params(result, &offset, sizeof(offset)); result->nb[1] = nb1; result->nb[2] = nb2; @@ -6593,8 +6527,6 @@ struct ggml_tensor * ggml_view_4d( result->op = GGML_OP_VIEW; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; - result->src[2] = offs; return result; } @@ -6655,22 +6587,9 @@ struct ggml_tensor * ggml_permute( result->op = GGML_OP_PERMUTE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; - if (is_node) { - ggml_scratch_save(ctx); - - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 4); - - ((int32_t *) b->data)[0] = axis0; - ((int32_t *) b->data)[1] = axis1; - ((int32_t *) b->data)[2] = axis2; - ((int32_t *) b->data)[3] = axis3; - - ggml_scratch_load(ctx); - - result->src[2] = b; - } + int32_t params[] = { axis0, axis1, axis2, axis3 }; + ggml_set_op_params(result, ¶ms, sizeof(params)); return result; } @@ -6698,7 +6617,6 @@ struct ggml_tensor * ggml_transpose( result->op = GGML_OP_TRANSPOSE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -6776,7 +6694,6 @@ struct ggml_tensor * ggml_diag( result->op = GGML_OP_DIAG; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -6797,19 +6714,12 @@ struct ggml_tensor * ggml_diag_mask_inf_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2); - - ((int32_t *) b->data)[0] = n_past; - ((int32_t *) b->data)[1] = inplace ? 1 : 0; - - ggml_scratch_load(ctx); + int32_t params[] = { n_past, inplace ? 1 : 0 }; + ggml_set_op_params(result, ¶ms, sizeof(params)); result->op = GGML_OP_DIAG_MASK_INF; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = b; return result; } @@ -6844,20 +6754,12 @@ struct ggml_tensor * ggml_diag_mask_zero_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2); - ggml_set_name(b, "n_past, inplace"); - - ((int32_t *) b->data)[0] = n_past; - ((int32_t *) b->data)[1] = inplace ? 1 : 0; - - ggml_scratch_load(ctx); + int32_t params[] = { n_past, inplace ? 1 : 0 }; + ggml_set_op_params(result, ¶ms, sizeof(params)); result->op = GGML_OP_DIAG_MASK_ZERO; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = b; return result; } @@ -6893,7 +6795,6 @@ struct ggml_tensor * ggml_soft_max_impl( result->op = GGML_OP_SOFT_MAX; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; return result; } @@ -6969,23 +6870,14 @@ struct ggml_tensor * ggml_rope_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 6); - - ((int32_t *) b->data)[0] = n_past; - ((int32_t *) b->data)[1] = n_dims; - ((int32_t *) b->data)[2] = mode; - ((int32_t *) b->data)[3] = n_ctx; - memcpy((int32_t *) b->data + 4, &freq_base, sizeof(float)); - memcpy((int32_t *) b->data + 5, &freq_scale, sizeof(float)); - - ggml_scratch_load(ctx); + int32_t params[6] = { n_past, n_dims, mode, n_ctx }; + memcpy(params + 4, &freq_base, sizeof(float)); + memcpy(params + 5, &freq_scale, sizeof(float)); + ggml_set_op_params(result, ¶ms, sizeof(params)); result->op = GGML_OP_ROPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = b; return result; } @@ -7042,22 +6934,12 @@ struct ggml_tensor * ggml_rope_back( struct ggml_tensor * result = ggml_dup_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 4); - ggml_set_name(b, "n_past, n_dims, mode"); - - ((int32_t *) b->data)[0] = n_past; - ((int32_t *) b->data)[1] = n_dims; - ((int32_t *) b->data)[2] = mode; - ((int32_t *) b->data)[3] = n_ctx; - - ggml_scratch_load(ctx); + int32_t params[] = { n_past, n_dims, mode, n_ctx }; + ggml_set_op_params(result, ¶ms, sizeof(params)); result->op = GGML_OP_ROPE_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = b; return result; } @@ -7082,21 +6964,13 @@ struct ggml_tensor * ggml_alibi( //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); struct ggml_tensor * result = ggml_view_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3); - - ((int32_t *) b->data)[0] = n_past; - ((int32_t *) b->data)[1] = n_head; - GGML_ASSERT(sizeof(float) == sizeof(int32_t)); - (((float *) b->data)[2]) = bias_max; - - ggml_scratch_load(ctx); + int32_t op_params[3] = { n_past, n_head }; + memcpy(op_params + 2, &bias_max, sizeof(float)); + ggml_set_op_params(result, &op_params, sizeof(op_params)); result->op = GGML_OP_ALIBI; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = b; return result; } @@ -7118,19 +6992,12 @@ struct ggml_tensor * ggml_clamp( // TODO: when implement backward, fix this: struct ggml_tensor * result = ggml_view_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 2); - - ((float *) b->data)[0] = min; - ((float *) b->data)[1] = max; - - ggml_scratch_load(ctx); + float params[] = { min, max }; + ggml_set_op_params(result, ¶ms, sizeof(params)); result->op = GGML_OP_CLAMP; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = b; return result; } @@ -7163,18 +7030,13 @@ GGML_API struct ggml_tensor * ggml_conv_1d( }; struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); - ggml_scratch_save(ctx); - struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3); - ((int32_t*)c->data)[0] = s0; - ((int32_t*)c->data)[1] = p0; - ((int32_t*)c->data)[2] = d0; - ggml_scratch_load(ctx); + int32_t params[] = { s0, p0, d0 }; + ggml_set_op_params(result, ¶ms, sizeof(params)); result->op = GGML_OP_CONV_1D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; - result->src[2] = c; return result; } @@ -7207,21 +7069,13 @@ struct ggml_tensor* ggml_conv_2d( }; struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - ggml_scratch_save(ctx); - struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 6); - ((int32_t*)c->data)[0] = s0; - ((int32_t*)c->data)[1] = s1; - ((int32_t*)c->data)[2] = p0; - ((int32_t*)c->data)[3] = p1; - ((int32_t*)c->data)[4] = d0; - ((int32_t*)c->data)[5] = d1; - ggml_scratch_load(ctx); + int32_t params[] = { s0, s1, p0, p1, d0, d1 }; + ggml_set_op_params(result, ¶ms, sizeof(params)); result->op = GGML_OP_CONV_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; - result->src[2] = c; return result; @@ -7245,7 +7099,7 @@ static int64_t ggml_calc_pool_output_size(int64_t ins, int ks, int s, int p) { return (ins + 2 * p - ks) / s + 1; } -// ggml_pool_2d +// ggml_pool_1d struct ggml_tensor* ggml_pool_1d( struct ggml_context * ctx, @@ -7268,18 +7122,12 @@ struct ggml_tensor* ggml_pool_1d( }; struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); - ggml_scratch_save(ctx); - struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 4); - ((int32_t*)c->data)[0] = op; - ((int32_t*)c->data)[1] = k0; - ((int32_t*)c->data)[2] = s0; - ((int32_t*)c->data)[3] = p0; - ggml_scratch_load(ctx); + int32_t params[] = { op, k0, s0, p0 }; + ggml_set_op_params(result, ¶ms, sizeof(params)); result->op = GGML_OP_POOL_1D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = c; return result; } @@ -7311,21 +7159,12 @@ struct ggml_tensor* ggml_pool_2d( }; struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); - ggml_scratch_save(ctx); - struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 7); - ((int32_t*)c->data)[0] = op; - ((int32_t*)c->data)[1] = k0; - ((int32_t*)c->data)[2] = k1; - ((int32_t*)c->data)[3] = s0; - ((int32_t*)c->data)[4] = s1; - ((int32_t*)c->data)[5] = p0; - ((int32_t*)c->data)[6] = p1; - ggml_scratch_load(ctx); + int32_t params[] = { op, k0, k1, s0, s1, p0, p1 }; + ggml_set_op_params(result, ¶ms, sizeof(params)); result->op = GGML_OP_POOL_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = c; return result; } @@ -7484,21 +7323,12 @@ struct ggml_tensor * ggml_win_part( struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - ggml_scratch_save(ctx); - - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3); - - ((int32_t *) b->data)[0] = npx; - ((int32_t *) b->data)[1] = npy; - ((int32_t *) b->data)[2] = w; - - ggml_scratch_load(ctx); + int32_t params[] = { npx, npy, w }; + ggml_set_op_params(result, ¶ms, sizeof(params)); result->op = GGML_OP_WIN_PART; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; - result->src[2] = b; return result; } @@ -7523,19 +7353,12 @@ struct ggml_tensor * ggml_win_unpart( const int64_t ne[4] = { a->ne[0], w0, h0, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); - ggml_scratch_save(ctx); - - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); - - ((int32_t *) b->data)[0] = w; - - ggml_scratch_load(ctx); + int32_t params[] = { w }; + ggml_set_op_params(result, ¶ms, sizeof(params)); result->op = GGML_OP_WIN_UNPART; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[1] = NULL; - result->src[2] = b; return result; } @@ -7553,19 +7376,13 @@ struct ggml_tensor * ggml_map_unary_impl_f32( is_node = true; } - struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); - *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; - - ggml_scratch_load(ctx); + ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_UNARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[2] = addr_tensor; return result; } @@ -7600,20 +7417,14 @@ struct ggml_tensor * ggml_map_binary_impl_f32( is_node = true; } - struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); - *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; - - ggml_scratch_load(ctx); + ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_BINARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; - result->src[2] = addr_tensor; return result; } @@ -7647,19 +7458,13 @@ struct ggml_tensor * ggml_map_custom1_impl_f32( is_node = true; } - struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); - *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; - - ggml_scratch_load(ctx); + ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_CUSTOM1; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - result->src[2] = addr_tensor; return result; } @@ -7692,20 +7497,14 @@ struct ggml_tensor * ggml_map_custom2_impl_f32( is_node = true; } - struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); - *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; - - ggml_scratch_load(ctx); + ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_CUSTOM2; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; - result->src[2] = addr_tensor; return result; } @@ -7741,21 +7540,15 @@ struct ggml_tensor * ggml_map_custom3_impl_f32( is_node = true; } - struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - ggml_scratch_save(ctx); - - struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); - *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; - - ggml_scratch_load(ctx); + ggml_set_op_params(result, (const void *) &fun, sizeof(fun)); result->op = GGML_OP_MAP_CUSTOM3; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; - result->src[2] = addr_tensor; - result->src[3] = c; + result->src[2] = c; return result; } @@ -8983,21 +8776,17 @@ static void ggml_compute_forward_acc_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_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - GGML_ASSERT(opt0->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(opt0) == 5); - // view src0 and dst with these strides and data offset inbytes during acc // nb0 is implicitely element_size because src0 and dst are contiguous - size_t nb1 = ((int32_t *) opt0->data)[0]; - size_t nb2 = ((int32_t *) opt0->data)[1]; - size_t nb3 = ((int32_t *) opt0->data)[2]; - size_t offset = ((int32_t *) opt0->data)[3]; - bool inplace = (bool) ((int32_t *) opt0->data)[4]; + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; if (!inplace && (params->type == GGML_TASK_INIT)) { // memcpy needs to be synchronized across threads to avoid race conditions. @@ -9066,13 +8855,12 @@ static void ggml_compute_forward_acc( 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_acc_f32(params, src0, src1, opt0, dst); + ggml_compute_forward_acc_f32(params, src0, src1, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_Q4_0: @@ -11092,21 +10880,17 @@ static void ggml_compute_forward_set_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_are_same_shape(src0, dst)); GGML_ASSERT(ggml_is_contiguous(dst) && ggml_is_contiguous(src0)); - GGML_ASSERT(opt0->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(opt0) == 5); - // view src0 and dst with these strides and data offset inbytes during set // nb0 is implicitely element_size because src0 and dst are contiguous - size_t nb1 = ((int32_t *) opt0->data)[0]; - size_t nb2 = ((int32_t *) opt0->data)[1]; - size_t nb3 = ((int32_t *) opt0->data)[2]; - size_t offset = ((int32_t *) opt0->data)[3]; - bool inplace = (bool) ((int32_t *) opt0->data)[4]; + size_t nb1 = ((int32_t *) dst->op_params)[0]; + size_t nb2 = ((int32_t *) dst->op_params)[1]; + size_t nb3 = ((int32_t *) dst->op_params)[2]; + size_t offset = ((int32_t *) dst->op_params)[3]; + bool inplace = (bool) ((int32_t *) dst->op_params)[4]; if (!inplace && (params->type == GGML_TASK_INIT)) { // memcpy needs to be synchronized across threads to avoid race conditions. @@ -11166,13 +10950,12 @@ static void ggml_compute_forward_set( 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_set_f32(params, src0, src1, opt0, dst); + ggml_compute_forward_set_f32(params, src0, src1, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_Q4_0: @@ -11568,17 +11351,14 @@ static void ggml_compute_forward_diag( static void ggml_compute_forward_diag_mask_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst, const float value) { - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(src1) == 2); const int ith = params->ith; const int nth = params->nth; - const int n_past = ((int32_t *) src1->data)[0]; - const bool inplace = (bool)((int32_t *) src1->data)[1]; + const int n_past = ((int32_t *) dst->op_params)[0]; + const bool inplace = (bool)((int32_t *) dst->op_params)[1]; GGML_ASSERT(n_past >= 0); @@ -11621,12 +11401,11 @@ static void ggml_compute_forward_diag_mask_f32( static void ggml_compute_forward_diag_mask_inf( 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_diag_mask_f32(params, src0, src1, dst, -INFINITY); + ggml_compute_forward_diag_mask_f32(params, src0, dst, -INFINITY); } break; default: { @@ -11638,12 +11417,11 @@ static void ggml_compute_forward_diag_mask_inf( static void ggml_compute_forward_diag_mask_zero( 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_diag_mask_f32(params, src0, src1, dst, 0); + ggml_compute_forward_diag_mask_f32(params, src0, dst, 0); } break; default: { @@ -11841,20 +11619,17 @@ static void ggml_compute_forward_soft_max_back( static void ggml_compute_forward_alibi_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(src1) == 3); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } - const int n_past = ((int32_t *) src1->data)[0]; - const int n_head = ((int32_t *) src1->data)[1]; - const float max_bias = ((float *) src1->data)[2]; + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_head = ((int32_t *) dst->op_params)[1]; + float max_bias; + memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); assert(n_past >= 0); @@ -11907,20 +11682,17 @@ static void ggml_compute_forward_alibi_f32( static void ggml_compute_forward_alibi_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(src1) == 3); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } - const int n_past = ((int32_t *) src1->data)[0]; - const int n_head = ((int32_t *) src1->data)[1]; - const float max_bias = ((float *) src1->data)[2]; + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_head = ((int32_t *) dst->op_params)[1]; + float max_bias; + memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); assert(n_past >= 0); @@ -11973,16 +11745,15 @@ static void ggml_compute_forward_alibi_f16( static void ggml_compute_forward_alibi( 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_F16: { - ggml_compute_forward_alibi_f16(params, src0, src1, dst); + ggml_compute_forward_alibi_f16(params, src0, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_alibi_f32(params, src0, src1, dst); + ggml_compute_forward_alibi_f32(params, src0, dst); } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -12012,19 +11783,17 @@ static void ggml_compute_forward_alibi( static void ggml_compute_forward_clamp_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(ggml_nelements(src1) == 2); - if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } - const float min = ((float *) src1->data)[0]; - const float max = ((float *) src1->data)[1]; + float min; + float max; + memcpy(&min, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); const int ith = params->ith; const int nth = params->nth; @@ -12054,12 +11823,11 @@ static void ggml_compute_forward_clamp_f32( static void ggml_compute_forward_clamp( 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_clamp_f32(params, src0, src1, dst); + ggml_compute_forward_clamp_f32(params, src0, dst); } break; case GGML_TYPE_F16: case GGML_TYPE_Q4_0: @@ -12089,10 +11857,7 @@ static void ggml_compute_forward_clamp( static void ggml_compute_forward_rope_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(src1) == 6); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; @@ -12101,12 +11866,12 @@ static void ggml_compute_forward_rope_f32( float freq_base; float freq_scale; - const int n_past = ((int32_t *) src1->data)[0]; - const int n_dims = ((int32_t *) src1->data)[1]; - const int mode = ((int32_t *) src1->data)[2]; - const int n_ctx = ((int32_t *) src1->data)[3]; - memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx = ((int32_t *) dst->op_params)[3]; + memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); assert(n_past >= 0); @@ -12221,10 +11986,7 @@ static void ggml_compute_forward_rope_f32( static void ggml_compute_forward_rope_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(src1) == 6); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; @@ -12233,12 +11995,12 @@ static void ggml_compute_forward_rope_f16( float freq_base; float freq_scale; - const int n_past = ((int32_t *) src1->data)[0]; - const int n_dims = ((int32_t *) src1->data)[1]; - const int mode = ((int32_t *) src1->data)[2]; - const int n_ctx = ((int32_t *) src1->data)[3]; - memcpy(&freq_base, (int32_t *) src1->data + 4, sizeof(float)); - memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float)); + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx = ((int32_t *) dst->op_params)[3]; + memcpy(&freq_base, (int32_t *) dst->op_params + 4, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float)); assert(n_past >= 0); @@ -12353,16 +12115,15 @@ static void ggml_compute_forward_rope_f16( static void ggml_compute_forward_rope( 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_F16: { - ggml_compute_forward_rope_f16(params, src0, src1, dst); + ggml_compute_forward_rope_f16(params, src0, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_rope_f32(params, src0, src1, dst); + ggml_compute_forward_rope_f32(params, src0, dst); } break; default: { @@ -12376,10 +12137,7 @@ static void ggml_compute_forward_rope( static void ggml_compute_forward_rope_back_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { - assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 4); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; @@ -12389,9 +12147,9 @@ static void ggml_compute_forward_rope_back_f32( // dx = rope_back(dy, src1) // src0 is dy, src1 contains options - const int n_past = ((int32_t *) src1->data)[0]; - const int n_dims = ((int32_t *) src1->data)[1]; - const int mode = ((int32_t *) src1->data)[2]; + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; assert(n_past >= 0); @@ -12475,10 +12233,7 @@ static void ggml_compute_forward_rope_back_f32( static void ggml_compute_forward_rope_back_f16( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * src1, struct ggml_tensor * dst) { - assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 3); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; @@ -12488,9 +12243,9 @@ static void ggml_compute_forward_rope_back_f16( // dx = rope_back(dy, src1) // src0 is dy, src1 contains options - const int n_past = ((int32_t *) src1->data)[0]; - const int n_dims = ((int32_t *) src1->data)[1]; - const int mode = ((int32_t *) src1->data)[2]; + const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; assert(n_past >= 0); @@ -12574,16 +12329,15 @@ static void ggml_compute_forward_rope_back_f16( static void ggml_compute_forward_rope_back( 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_F16: { - ggml_compute_forward_rope_back_f16(params, src0, src1, dst); + ggml_compute_forward_rope_back_f16(params, src0, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_rope_back_f32(params, src0, src1, dst); + ggml_compute_forward_rope_back_f32(params, src0, dst); } break; default: { @@ -12780,7 +12534,7 @@ static void ggml_compute_forward_conv_1d_s1_ph( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, - struct ggml_tensor * dst) { + struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { @@ -12983,7 +12737,7 @@ static void ggml_compute_forward_conv_1d_s2_ph( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, - struct ggml_tensor * dst) { + struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { @@ -13003,14 +12757,13 @@ static void ggml_compute_forward_conv_1d_s2_ph( // ggml_compute_forward_conv_1d static void ggml_compute_forward_conv_1d( - 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) { - const int32_t s0 = ((const int32_t*)(opt0->data))[0]; - const int32_t p0 = ((const int32_t*)(opt0->data))[1]; - const int32_t d0 = ((const int32_t*)(opt0->data))[2]; + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + const int32_t p0 = ((const int32_t*)(dst->op_params))[1]; + const int32_t d0 = ((const int32_t*)(dst->op_params))[2]; GGML_ASSERT(d0 == 1); // dilation not supported GGML_ASSERT(p0 == src0->ne[0]/2); // only half padding supported if (s0 == 1) { @@ -13028,7 +12781,6 @@ static void ggml_compute_forward_conv_2d_f16_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(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -13048,12 +12800,12 @@ static void ggml_compute_forward_conv_2d_f16_f32( // size of the convolution row - the kernel size unrolled across all channels const int ew0 = nk0*nk1*ne02; - const int32_t s0 = ((const int32_t*)(opt0->data))[0]; - const int32_t s1 = ((const int32_t*)(opt0->data))[1]; - const int32_t p0 = ((const int32_t*)(opt0->data))[2]; - const int32_t p1 = ((const int32_t*)(opt0->data))[3]; - const int32_t d0 = ((const int32_t*)(opt0->data))[4]; - const int32_t d1 = ((const int32_t*)(opt0->data))[5]; + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t*)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t*)(dst->op_params))[5]; GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); @@ -13125,17 +12877,15 @@ static void ggml_compute_forward_conv_2d( 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 - ) { + struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_conv_2d_f16_f32(params, src0, src1, opt0, dst); + ggml_compute_forward_conv_2d_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { - //ggml_compute_forward_conv_2d_f32(params, src0, src1, opt0, dst); + //ggml_compute_forward_conv_2d_f32(params, src0, src1, dst); GGML_ASSERT(false); } break; default: @@ -13200,12 +12950,11 @@ static void ggml_compute_forward_pool_1d_sk_p0( // ggml_compute_forward_pool_1d static void ggml_compute_forward_pool_1d( - const struct ggml_compute_params* params, - const struct ggml_tensor* src0, - const struct ggml_tensor* opt0, - struct ggml_tensor* dst) { - GGML_ASSERT(opt0->ne[0] == 4); - const int* opts = (const int*)opt0->data; + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + + const int32_t* opts = (const int32_t*)dst->op_params; enum ggml_op_pool op = opts[0]; const int k0 = opts[1]; const int s0 = opts[2]; @@ -13219,12 +12968,12 @@ static void ggml_compute_forward_pool_1d( // ggml_compute_forward_pool_2d_sk_p0 static void ggml_compute_forward_pool_2d_sk_p0( - const struct ggml_compute_params * params, - const enum ggml_op_pool op, - const struct ggml_tensor * src, - const int k0, - const int k1, - struct ggml_tensor * dst) { + const struct ggml_compute_params * params, + const enum ggml_op_pool op, + const struct ggml_tensor * src, + const int k0, + const int k1, + struct ggml_tensor * dst) { assert(src->type == GGML_TYPE_F32); assert(params->ith == 0); @@ -13284,12 +13033,11 @@ static void ggml_compute_forward_pool_2d_sk_p0( // ggml_compute_forward_pool_2d static void ggml_compute_forward_pool_2d( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * opt0, - struct ggml_tensor * dst) { - GGML_ASSERT(opt0->ne[0] == 7); - const int* opts = (const int*)opt0->data; + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + + const int32_t * opts = (const int32_t *)dst->op_params; enum ggml_op_pool op = opts[0]; const int k0 = opts[1]; const int k1 = opts[2]; @@ -13314,7 +13062,7 @@ static void ggml_compute_forward_flash_attn_f32( const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked, - struct ggml_tensor * dst) { + struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); @@ -13492,7 +13240,7 @@ static void ggml_compute_forward_flash_attn_f16( const struct ggml_tensor * k, const struct ggml_tensor * v, const bool masked, - struct ggml_tensor * dst) { + struct ggml_tensor * dst) { int64_t t0 = ggml_perf_time_us(); UNUSED(t0); @@ -14257,7 +14005,6 @@ static void ggml_compute_forward_flash_attn_back( static void ggml_compute_forward_win_part_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * opt0, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; @@ -14266,9 +14013,9 @@ static void ggml_compute_forward_win_part_f32( GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); - const int32_t nep0 = ((const int32_t *)(opt0->data))[0]; - const int32_t nep1 = ((const int32_t *)(opt0->data))[1]; - const int32_t w = ((const int32_t *)(opt0->data))[2]; + const int32_t nep0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t nep1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t w = ((const int32_t *)(dst->op_params))[2]; assert(ne00 == ne0); assert(ne3 == nep0*nep1); @@ -14302,12 +14049,11 @@ static void ggml_compute_forward_win_part_f32( static void ggml_compute_forward_win_part( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * opt0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_win_part_f32(params, src0, opt0, dst); + ggml_compute_forward_win_part_f32(params, src0, dst); } break; default: { @@ -14321,7 +14067,6 @@ static void ggml_compute_forward_win_part( static void ggml_compute_forward_win_unpart_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * opt0, struct ggml_tensor * dst) { if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; @@ -14330,7 +14075,7 @@ static void ggml_compute_forward_win_unpart_f32( GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); - const int32_t w = ((const int32_t *)(opt0->data))[0]; + const int32_t w = ((const int32_t *)(dst->op_params))[0]; // padding const int px = (w - ne1%w)%w; @@ -14364,12 +14109,11 @@ static void ggml_compute_forward_win_unpart_f32( static void ggml_compute_forward_win_unpart( const struct ggml_compute_params * params, const struct ggml_tensor * src0, - const struct ggml_tensor * opt0, struct ggml_tensor * dst) { switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_win_unpart_f32(params, src0, opt0, dst); + ggml_compute_forward_win_unpart_f32(params, src0, dst); } break; default: { @@ -14888,7 +14632,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_ACC: { - ggml_compute_forward_acc(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); + ggml_compute_forward_acc(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_SUB: { @@ -15008,7 +14752,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_SET: { - ggml_compute_forward_set(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); + ggml_compute_forward_set(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_CPY: { @@ -15048,11 +14792,11 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_DIAG_MASK_INF: { - ggml_compute_forward_diag_mask_inf(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_diag_mask_inf(params, tensor->src[0], tensor); } break; case GGML_OP_DIAG_MASK_ZERO: { - ggml_compute_forward_diag_mask_zero(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_diag_mask_zero(params, tensor->src[0], tensor); } break; case GGML_OP_SOFT_MAX: { @@ -15064,35 +14808,35 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_ROPE: { - ggml_compute_forward_rope(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_rope(params, tensor->src[0], tensor); } break; case GGML_OP_ROPE_BACK: { - ggml_compute_forward_rope_back(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_rope_back(params, tensor->src[0], tensor); } break; case GGML_OP_ALIBI: { - ggml_compute_forward_alibi(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_alibi(params, tensor->src[0], tensor); } break; case GGML_OP_CLAMP: { - ggml_compute_forward_clamp(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_clamp(params, tensor->src[0], tensor); } break; case GGML_OP_CONV_1D: { - ggml_compute_forward_conv_1d(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); + ggml_compute_forward_conv_1d(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_CONV_2D: { - ggml_compute_forward_conv_2d(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); + ggml_compute_forward_conv_2d(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_POOL_1D: { - ggml_compute_forward_pool_1d(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_pool_1d(params, tensor->src[0], tensor); } break; case GGML_OP_POOL_2D: { - ggml_compute_forward_pool_2d(params, tensor->src[0], tensor->src[1], tensor); + ggml_compute_forward_pool_2d(params, tensor->src[0], tensor); } break; case GGML_OP_FLASH_ATTN: { @@ -15114,40 +14858,45 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_WIN_PART: { - ggml_compute_forward_win_part(params, tensor->src[0], tensor->src[2], tensor); + ggml_compute_forward_win_part(params, tensor->src[0], tensor); } break; case GGML_OP_WIN_UNPART: { - ggml_compute_forward_win_unpart(params, tensor->src[0], tensor->src[2], tensor); + ggml_compute_forward_win_unpart(params, tensor->src[0], tensor); } break; case GGML_OP_MAP_UNARY: { - const ggml_unary_op_f32_t fun = *((ggml_unary_op_f32_t *)tensor->src[2]->data); + ggml_unary_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_unary(params, tensor->src[0], tensor, fun); } break; case GGML_OP_MAP_BINARY: { - const ggml_binary_op_f32_t fun = *((ggml_binary_op_f32_t *)tensor->src[2]->data); + ggml_binary_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_binary(params, tensor->src[0], tensor->src[1], tensor, fun); } break; case GGML_OP_MAP_CUSTOM1: { - const ggml_custom1_op_f32_t fun = *((ggml_custom1_op_f32_t *)tensor->src[2]->data); + ggml_custom1_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_custom1(params, tensor->src[0], tensor, fun); } break; case GGML_OP_MAP_CUSTOM2: { - const ggml_custom2_op_f32_t fun = *((ggml_custom2_op_f32_t *)tensor->src[2]->data); + ggml_custom2_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); ggml_compute_forward_map_custom2(params, tensor->src[0], tensor->src[1], tensor, fun); } break; case GGML_OP_MAP_CUSTOM3: { - const ggml_custom3_op_f32_t fun = *((ggml_custom3_op_f32_t *)tensor->src[2]->data); - ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[3], tensor, fun); + ggml_custom3_op_f32_t fun; + memcpy(&fun, tensor->op_params, sizeof(fun)); + ggml_compute_forward_map_custom3(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor, fun); } break; case GGML_OP_CROSS_ENTROPY_LOSS: @@ -15211,12 +14960,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor src0->grad = ggml_add_impl(ctx, src0->grad, tensor->grad, inplace); } if (src1->grad) { - GGML_ASSERT(ggml_nelements(tensor->src[2]) == 5); - GGML_ASSERT(tensor->src[2]->type == GGML_TYPE_I32); - const size_t nb1 = (( int32_t * ) tensor->src[2]->data)[0]; - const size_t nb2 = (( int32_t * ) tensor->src[2]->data)[1]; - const size_t nb3 = (( int32_t * ) tensor->src[2]->data)[2]; - const size_t offset = (( int32_t * ) tensor->src[2]->data)[3]; + const size_t nb1 = ((int32_t *) tensor->op_params)[0]; + const size_t nb2 = ((int32_t *) tensor->op_params)[1]; + const size_t nb3 = ((int32_t *) tensor->op_params)[2]; + const size_t offset = ((int32_t *) tensor->op_params)[3]; struct ggml_tensor * tensor_grad_view = ggml_view_4d(ctx, tensor->grad, @@ -15524,12 +15271,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor } break; case GGML_OP_SET: { - GGML_ASSERT(ggml_nelements(tensor->src[2]) == 5); - GGML_ASSERT(tensor->src[2]->type == GGML_TYPE_I32); - const size_t nb1 = (( int32_t * ) tensor->src[2]->data)[0]; - const size_t nb2 = (( int32_t * ) tensor->src[2]->data)[1]; - const size_t nb3 = (( int32_t * ) tensor->src[2]->data)[2]; - const size_t offset = (( int32_t * ) tensor->src[2]->data)[3]; + const size_t nb1 = ((int32_t *) tensor->op_params)[0]; + const size_t nb2 = ((int32_t *) tensor->op_params)[1]; + const size_t nb3 = ((int32_t *) tensor->op_params)[2]; + const size_t offset = ((int32_t *) tensor->op_params)[3]; struct ggml_tensor * tensor_grad_view = NULL; @@ -15606,8 +15351,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor if (src0->grad) { size_t offset; - GGML_ASSERT(sizeof(offset) <= ggml_nbytes(tensor->src[2])); - memcpy(&offset, tensor->src[2]->data, sizeof(offset)); + memcpy(&offset, tensor->op_params, sizeof(offset)); size_t nb1 = tensor->nb[1]; size_t nb2 = tensor->nb[2]; @@ -15634,7 +15378,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { // necessary for llama if (src0->grad) { - int32_t * axes = (int32_t *) tensor->src[2]->data; + int32_t * axes = (int32_t *) tensor->op_params; int axis0 = axes[0] & 0x3; int axis1 = axes[1] & 0x3; int axis2 = axes[2] & 0x3; @@ -15690,9 +15434,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { // necessary for llama if (src0->grad) { - assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 2); - const int n_past = ((int32_t *) src1->data)[0]; + const int n_past = ((int32_t *) tensor->op_params)[0]; src0->grad = ggml_add_impl(ctx, src0->grad, ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), @@ -15706,9 +15448,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { // necessary for llama if (src0->grad) { - assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 2); - const int n_past = ((int32_t *) src1->data)[0]; + const int n_past = ((int32_t *) tensor->op_params)[0]; src0->grad = ggml_add_impl(ctx, src0->grad, ggml_diag_mask_zero_impl(ctx, tensor->grad, n_past, false), @@ -15737,12 +15477,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { // necessary for llama if (src0->grad) { - assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 6); - const int n_past = ((int32_t *) src1->data)[0]; - const int n_dims = ((int32_t *) src1->data)[1]; - const int mode = ((int32_t *) src1->data)[2]; - const int n_ctx = ((int32_t *) src1->data)[3]; + const int n_past = ((int32_t *) tensor->op_params)[0]; + const int n_dims = ((int32_t *) tensor->op_params)[1]; + const int mode = ((int32_t *) tensor->op_params)[2]; + const int n_ctx = ((int32_t *) tensor->op_params)[3]; src0->grad = ggml_add_impl(ctx, src0->grad, ggml_rope_back(ctx, @@ -15760,12 +15498,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor case GGML_OP_ROPE_BACK: { if (src0->grad) { - assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 4); - const int n_past = ((int32_t *) src1->data)[0]; - const int n_dims = ((int32_t *) src1->data)[1]; - const int mode = ((int32_t *) src1->data)[2]; - const int n_ctx = ((int32_t *) src1->data)[3]; + const int n_past = ((int32_t *) tensor->op_params)[0]; + const int n_dims = ((int32_t *) tensor->op_params)[1]; + const int mode = ((int32_t *) tensor->op_params)[2]; + const int n_ctx = ((int32_t *) tensor->op_params)[3]; src0->grad = ggml_add_impl(ctx, src0->grad, ggml_rope(ctx, @@ -16543,9 +16279,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { case GGML_OP_GET_ROWS_BACK: case GGML_OP_DIAG: case GGML_OP_DIAG_MASK_ZERO: - { - n_tasks = 1; - } break; case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_SOFT_MAX_BACK: @@ -17289,7 +17022,7 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** tensor = ggml_view_4d(*ctx_eval, args[0], ne[0], ne[1], ne[2], ne[3], 0, 0, 0, 0); uint64_t offs; - memcpy(&offs, args[2]->data, sizeof(offs)); + memcpy(&offs, tensor->op_params, sizeof(offs)); tensor->data = ((char *) tensor->data) + offs; } break; diff --git a/ggml.h b/ggml.h index 5023b1652..871c85a89 100644 --- a/ggml.h +++ b/ggml.h @@ -199,6 +199,7 @@ #define GGML_MAX_CONTEXTS 64 #define GGML_MAX_SRC 6 #define GGML_MAX_NAME 48 +#define GGML_MAX_OP_PARAMS 32 #define GGML_DEFAULT_N_THREADS 4 @@ -418,6 +419,9 @@ extern "C" { // compute data enum ggml_op op; + // op params - allocated as int32_t for alignment + int32_t op_params[GGML_MAX_OP_PARAMS / sizeof(uint32_t)]; + bool is_param; struct ggml_tensor * grad;