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etnaviv/ml: Make use of the new depthwise support in V8

Browse source 
The V8 hardware supports a faster way of executing depthwise
convolutions, instead of having to fully lower them to regular
convolutions.

Reviewed-by: Philipp Zabel <p.zabel@pengutronix.de>
Part-of: <https://gitlab.freedesktop.org/mesa/mesa/-/merge_requests/31842>
This commit is contained in:
Tomeu Vizoso 2024-10-23 13:42:14 +02:00 • committed by Marge Bot
parent 63a10f770c
commit 459da82db6
1 changed files with 77 additions and 14 deletions
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91
src/gallium/drivers/etnaviv/etnaviv_ml_nn.c
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@ -242,24 +242,52 @@ expand_depthwise(struct etna_ml_subgraph *subgraph, struct etna_operation *opera
operation->weight_tensor = output_res;
}
static void
reorder_for_hw_depthwise(struct etna_ml_subgraph *subgraph, struct etna_operation *operation)
{
struct pipe_context *context = subgraph->base.context;
uint8_t *input = map_resource(operation->weight_tensor);
struct pipe_resource *output_res = pipe_buffer_create(context->screen, 0, PIPE_USAGE_DEFAULT,
pipe_buffer_size(operation->weight_tensor));
uint8_t (*output)[operation->weight_width * operation->weight_height] = (void *)map_resource(output_res);
for (int i = 0; i < operation->weight_height * operation->weight_width * operation->output_channels; i++) {
unsigned out_channel = i % operation->output_channels;
output[out_channel][i / operation->output_channels] = input[i];
}
pipe_resource_reference(&operation->weight_tensor, NULL);
operation->weight_tensor = output_res;
}
static void
transpose(struct etna_ml_subgraph *subgraph, struct etna_operation *operation)
{
struct pipe_context *context = subgraph->base.context;
unsigned nn_core_version = etna_context(context)->screen->specs.nn_core_version;
void *map = map_resource(operation->weight_tensor);
unsigned new_size = operation->output_channels * operation->weight_width * \
operation->weight_height * operation->input_channels;
struct pipe_resource *output_res = pipe_buffer_create(context->screen, 0, PIPE_USAGE_DEFAULT,
new_size);
uint8_t *output = map_resource(output_res);
unsigned new_size;
struct pipe_resource *output_res;
uint8_t *output;
unsigned output_channels = operation->output_channels;
unsigned input_channels = operation->input_channels;
if (nn_core_version == 8 && operation->depthwise)
input_channels = 1;
else
input_channels = operation->input_channels;
if (operation->addition) {
output_channels = 1;
input_channels = 2;
}
new_size = operation->output_channels * operation->weight_width * \
operation->weight_height * input_channels;
output_res = pipe_buffer_create(context->screen, 0, PIPE_USAGE_DEFAULT, new_size);
output = map_resource(output_res);
uint8_t (*input)[operation->weight_width][operation->weight_height][input_channels] = map;
unsigned i = 0;
for (unsigned d0 = 0; d0 < output_channels; d0++)
@ -361,11 +389,44 @@ strided_to_normal(struct etna_ml_subgraph *subgraph, struct etna_operation *oper
operation->weight_tensor = output_res;
}
static bool
calc_pooling_first_pixel(struct etna_ml_subgraph *subgraph,
const struct pipe_ml_operation *poperation)
{
struct pipe_context *context = subgraph->base.context;
unsigned nn_core_version = etna_context(context)->screen->specs.nn_core_version;
unsigned input_width = poperation->input_tensor->dims[1];
unsigned input_channels = poperation->input_tensor->dims[3];
if (poperation->conv.stride_x == 1)
return false;
if (poperation->conv.depthwise)
return true;
if (nn_core_version < 8) {
if (poperation->conv.pointwise)
return true;
} else {
if (poperation->conv.pointwise && input_width >= 3 && input_channels > 1)
return true;
if (poperation->conv.pointwise && poperation->conv.padding_same)
return true;
}
return false;
}
void
etna_ml_lower_convolution(struct etna_ml_subgraph *subgraph,
const struct pipe_ml_operation *poperation,
struct etna_operation *operation)
{
struct pipe_context *context = subgraph->base.context;
struct etna_context *ctx = etna_context(context);
unsigned nn_core_version = ctx->screen->specs.nn_core_version;
/* TODO: Support stride_x != stride_y */
assert(poperation->conv.stride_x == poperation->conv.stride_y);
assert(poperation->type == PIPE_ML_OPERATION_TYPE_CONVOLUTION);
@ -374,8 +435,7 @@ etna_ml_lower_convolution(struct etna_ml_subgraph *subgraph,
operation->addition = false;
operation->depthwise = poperation->conv.depthwise;
operation->pointwise = poperation->conv.pointwise;
operation->pooling_first_pixel = poperation->conv.stride_x > 1 && \
(poperation->conv.depthwise || poperation->conv.pointwise);
operation->pooling_first_pixel = calc_pooling_first_pixel(subgraph, poperation);
operation->padding_same = poperation->conv.padding_same;
operation->stride = poperation->conv.stride_x;
@ -404,12 +464,13 @@ etna_ml_lower_convolution(struct etna_ml_subgraph *subgraph,
if (operation->pointwise && operation->input_channels == 1)
pointwise_to_2x2(subgraph, operation);
if (operation->depthwise && (operation->output_channels > 1 || operation->stride > 1)) {
if (operation->input_width < 8 && operation->input_width > 2)
operation->pooling_first_pixel = false;
expand_depthwise(subgraph, operation);
if (operation->depthwise) {
if (nn_core_version < 8 && (operation->output_channels > 1 || operation->stride > 1)) {
if (operation->input_width < 8 && operation->input_width > 2)
operation->pooling_first_pixel = false;
expand_depthwise(subgraph, operation);
} else if (operation->output_channels > 1)
reorder_for_hw_depthwise(subgraph, operation);
}
if (operation->stride > 1 && !operation->pooling_first_pixel)
@ -595,7 +656,9 @@ create_nn_config(struct etna_ml_subgraph *subgraph, const struct etna_operation
map->no_flush = nn_core_version == 8;
map->rounding_mode = 0x1;
map->partial_cache_data_unit = 0x0;
map->depthwise = 0x0;
if (nn_core_version == 8 && operation->depthwise)
map->depthwise = 0x1;
map->unused0 = 0x0;
map->unused1 = 0x0;