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etnaviv/ml: Add support for tensor padding operations
Just one more TP operation, at least for the pad modes supported. Reviewed-by: Philipp Zabel <p.zabel@pengutronix.de> Part-of: <https://gitlab.freedesktop.org/mesa/mesa/-/merge_requests/32509>
This commit is contained in:
Tomeu Vizoso
Marge Bot
parent
02e92bbcea
commit
3e74234450
5 changed files with 218 additions and 0 deletions
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@ -389,6 +389,30 @@ lower_operations(struct etna_ml_subgraph *subgraph,
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break;
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}
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case PIPE_ML_OPERATION_TYPE_PAD: {
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unsigned input_tensor = poperation->input_tensors[0]->index;
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if (needs_transpose(poperations, count, poperation)) {
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struct etna_operation *operation = calloc(1, sizeof(*operation));
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etna_ml_lower_transpose(subgraph, poperation->input_tensors[0], operation, &input_tensor);
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list_addtail(&operation->link, etna_operations);
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}
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ML_DBG("Adding pad operation.\n");
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struct etna_operation *operation = calloc(1, sizeof(*operation));
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etna_ml_lower_pad(subgraph, poperation, operation);
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operation->input_tensors[0] = input_tensor;
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list_addtail(&operation->link, etna_operations);
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if (needs_detranspose(poperations, count, poperation)) {
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struct etna_operation *detranspose = calloc(1, sizeof(*operation));
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etna_ml_lower_detranspose(subgraph, operation, detranspose);
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operation->output_tensors[0] = detranspose->input_tensors[0];
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list_addtail(&detranspose->link, etna_operations);
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}
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break;
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}
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default:
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unreachable("Unsupported ML operation type");
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}
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@ -469,6 +493,7 @@ count_tensors(const struct pipe_ml_operation *poperations,
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tensor_count = MAX2(tensor_count, poperation->conv.weight_tensor->index);
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tensor_count = MAX2(tensor_count, poperation->conv.bias_tensor->index);
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break;
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case PIPE_ML_OPERATION_TYPE_PAD:
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case PIPE_ML_OPERATION_TYPE_ADD:
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case PIPE_ML_OPERATION_TYPE_CONCATENATION:
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case PIPE_ML_OPERATION_TYPE_SPLIT:
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@ -33,6 +33,7 @@ enum etna_ml_tp_type {
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ETNA_ML_TP_TRANSPOSE,
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ETNA_ML_TP_DETRANSPOSE,
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ETNA_ML_TP_RESHUFFLE,
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ETNA_ML_TP_PAD,
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};
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struct etna_ml_subgraph {
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@ -48,6 +49,7 @@ struct etna_ml_subgraph {
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struct etna_vip_instruction {
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enum etna_job_type type;
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enum etna_ml_tp_type tp_type;
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struct etna_bo *configs[MAX_CONFIG_BOS];
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struct etna_bo *coefficients;
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@ -9,6 +9,7 @@
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#include "etnaviv_context.h"
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#include "etnaviv_debug.h"
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#include "etnaviv_emit.h"
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#include "etnaviv_ml.h"
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#include "etnaviv_ml_nn.h"
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#define ETNA_NN_INT8 0
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@ -3,11 +3,13 @@
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* SPDX-License-Identifier: MIT
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*/
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#include "hw/state.xml.h"
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#include "util/u_inlines.h"
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#include "etnaviv_context.h"
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#include "etnaviv_debug.h"
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#include "etnaviv_emit.h"
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#include "etnaviv_ml.h"
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#include "etnaviv_ml_tp.h"
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#define FIELD(field, bits) uint32_t field : bits;
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@ -538,6 +540,141 @@ create_reshuffle_config(struct etna_ml_subgraph *subgraph, const struct etna_ope
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return bo;
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}
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static void
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split_pad(struct etna_ml_subgraph *subgraph, const struct etna_operation *operation,
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unsigned tp_core, unsigned tp_cores_used, unsigned *in_dims, unsigned *out_dims)
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{
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unsigned remaining_in_size;
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unsigned dim_to_split = 2;
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remaining_in_size = in_dims[dim_to_split];
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for (unsigned i = 0; i <= tp_core; i++) {
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unsigned size = DIV_ROUND_UP(remaining_in_size, (tp_cores_used - i));
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if (i < tp_cores_used - 1) {
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in_dims[dim_to_split] = size;
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remaining_in_size -= in_dims[dim_to_split];
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} else
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in_dims[dim_to_split] = remaining_in_size;
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out_dims[dim_to_split] = size;
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}
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}
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static struct etna_bo *
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create_pad_config(struct etna_ml_subgraph *subgraph, const struct etna_operation *operation,
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unsigned tp_core, unsigned tp_cores_used)
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{
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struct pipe_context *pctx = subgraph->base.context;
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struct etna_bo *bo = etna_ml_create_bo(pctx, sizeof(struct etna_tp_params));
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unsigned input_width = operation->input_width;
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unsigned input_height = operation->input_height;
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unsigned input_channels = operation->input_channels;
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unsigned output_width = operation->output_width;
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unsigned output_height = operation->output_height;
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unsigned output_channels = operation->output_channels;
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unsigned in_dims[3];
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unsigned out_dims[3];
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SWAP(input_width, input_height);
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SWAP(output_width, output_height);
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etna_bo_cpu_prep(bo, DRM_ETNA_PREP_WRITE);
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struct etna_tp_params *map = etna_bo_map(bo);
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set_default_tp_config(map);
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in_dims[0] = input_width;
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in_dims[1] = input_height;
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in_dims[2] = input_channels;
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out_dims[0] = output_width;
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out_dims[1] = output_height;
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out_dims[2] = output_channels;
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split_pad(subgraph, operation, tp_core, tp_cores_used, in_dims, out_dims);
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map->in_image_x_size = in_dims[0];
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map->in_image_y_size = in_dims[1];
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map->in_image_z_size = in_dims[2];
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map->in_image_stride = input_width;
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map->in_image_slice = input_width * input_height;
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map->in_window_x_start = 0xffff;
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map->in_window_y_start = 0xffff;
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map->in_window_x_end = in_dims[0];
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map->in_window_y_end = in_dims[1];
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map->in_tile_x_size = out_dims[0];
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map->in_tile_x_inc = out_dims[0];
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map->in_tile_y_size = out_dims[1];
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map->in_tile_y_inc = out_dims[1];
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struct pipe_resource *input = etna_ml_get_tensor(subgraph, operation->input_tensors[0]);
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unsigned offset = etna_ml_get_offset(subgraph, operation->input_tensors[0]);
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map->in_image_base_address = etna_bo_gpu_va(etna_resource(input)->bo) + offset;
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struct pipe_resource *output = etna_ml_get_tensor(subgraph, operation->output_tensors[0]);
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offset = etna_ml_get_offset(subgraph, operation->output_tensors[0]);
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map->out_image_base_address = etna_bo_gpu_va(etna_resource(output)->bo) + offset;
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for (unsigned i = 0; i < tp_core; i++) {
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unsigned in_dims[3];
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unsigned out_dims[3];
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unsigned in_offset = 0;
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unsigned out_offset = 0;
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in_dims[0] = input_width;
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in_dims[1] = input_height;
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in_dims[2] = input_channels;
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out_dims[0] = output_width;
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out_dims[1] = output_height;
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out_dims[2] = output_channels;
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split_pad(subgraph, operation, i, tp_cores_used, in_dims, out_dims);
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in_offset = map->in_image_slice * in_dims[2];
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out_offset = out_dims[2];
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out_offset *= map->in_tile_x_size * map->in_tile_y_size;
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map->in_image_base_address += in_offset;
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map->out_image_base_address += out_offset;
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}
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map->out_loop_1_reset = 0x0;
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map->out_loop_2_reset = 0x0;
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map->out_loop_3_reset = 0x0;
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map->out_loop_0_inc = 0x0;
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map->out_loop_1_inc = 0x1;
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map->out_loop_0_count = 0x1;
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map->out_loop_1_count = out_dims[0];
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map->out_loop_2_count = out_dims[1];
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map->out_loop_3_count = 0x1;
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map->out_loop_2_inc = out_dims[0];
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map->out_loop_3_inc = 0x0;
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map->out_loop_6_inc = out_dims[0] * out_dims[1];
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map->in_zp = operation->input_zero_point;
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map->out_zp = operation->output_zero_point;
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if (tp_cores_used > 1)
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map->no_flush = tp_core < tp_cores_used - 1;
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map->in_image_circular_buf_size = 0x0;
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map->in_image_circular_buf_end_address_plus_1 = 0xFFFFFFFF >> 6;
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map->out_image_circular_buf_size = 0x0;
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map->out_image_circular_buf_end_address_plus_1 = 0xFFFFFFFF >> 6;
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etna_bo_cpu_fini(bo);
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return bo;
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}
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static inline uint8_t
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etna_tensor_zero_point(const struct pipe_tensor *tensor)
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{
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@ -663,6 +800,37 @@ etna_ml_lower_reshuffle(struct etna_ml_subgraph *subgraph,
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}
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}
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void
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etna_ml_lower_pad(struct etna_ml_subgraph *subgraph,
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const struct pipe_ml_operation *pad,
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struct etna_operation *operation)
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{
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operation->type = ETNA_JOB_TYPE_TP;
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operation->tp_type = ETNA_ML_TP_PAD;
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operation->stride = 1;
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operation->input_tensors[0] = pad->input_tensors[0]->index;
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operation->input_count = 1;
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operation->input_width = pad->input_tensors[0]->dims[1];
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operation->input_height = pad->input_tensors[0]->dims[2];
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operation->input_channels = pad->input_tensors[0]->dims[3];
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operation->input_tensor_sizes[0] = operation->input_width *
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operation->input_height *
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operation->input_channels;
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operation->input_zero_point = pad->input_tensors[0]->zero_point;
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operation->input_scale = pad->input_tensors[0]->scale;
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operation->output_tensors[0] = pad->output_tensors[0]->index;
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operation->output_width = pad->output_tensors[0]->dims[1];
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operation->output_height = pad->output_tensors[0]->dims[2];
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operation->output_channels = pad->output_tensors[0]->dims[3];
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operation->output_zero_point = pad->output_tensors[0]->zero_point;
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operation->output_scale = pad->output_tensors[0]->scale;
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operation->output_tensor_sizes[0] = operation->output_width *
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operation->output_height *
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operation->output_channels;
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}
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void
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etna_ml_compile_operation_tp(struct etna_ml_subgraph *subgraph,
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const struct etna_operation *operation,
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@ -704,8 +872,18 @@ etna_ml_compile_operation_tp(struct etna_ml_subgraph *subgraph,
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}
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break;
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}
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case ETNA_ML_TP_PAD: {
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unsigned tp_cores_used = etna_ml_get_core_info(ctx)->tp_core_count;
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ML_DBG("pad: input_width %d tp_cores_used %d\n", operation->input_width, tp_cores_used);
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for (unsigned i = 0; i < tp_cores_used; i++) {
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instruction->configs[i] = create_pad_config(subgraph, operation, i, tp_cores_used);
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}
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break;
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}
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}
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instruction->type = ETNA_JOB_TYPE_TP;
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instruction->tp_type = operation->tp_type;
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}
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void
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@ -728,6 +906,13 @@ etna_ml_emit_operation_tp(struct etna_ml_subgraph *subgraph,
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etna_set_state(stream, VIVS_GL_OCB_REMAP_START, 0x0);
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etna_set_state(stream, VIVS_GL_OCB_REMAP_END, 0x0);
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etna_set_state(stream, VIVS_GL_TP_CONFIG, 0x0);
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if (operation->tp_type == ETNA_ML_TP_PAD) {
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etna_set_state(stream, VIVS_GL_UNK03950, j < tp_core_count - 1 ? 0x8 : 0x0);
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} else {
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etna_set_state(stream, VIVS_GL_UNK03950, 0x0);
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}
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etna_set_state_reloc(stream, VIVS_PS_TP_INST_ADDR, &(struct etna_reloc) {
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.bo = operation->configs[j],
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.flags = ETNA_RELOC_READ,
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@ -22,6 +22,11 @@ etna_ml_lower_reshuffle(struct etna_ml_subgraph *subgraph,
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struct etna_operation *operation,
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unsigned *output_tensor);
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void
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etna_ml_lower_pad(struct etna_ml_subgraph *subgraph,
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const struct pipe_ml_operation *pad,
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struct etna_operation *operation);
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void
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etna_ml_compile_operation_tp(struct etna_ml_subgraph *subgraph,
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const struct etna_operation *operation,
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