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Merge pull request from advaitjain:fusion-f1-svdf

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PiperOrigin-RevId: 357813167
Change-Id: I20771b069117c8fdbe0644b3cbcb8cf2a0371abc
This commit is contained in:
TensorFlower Gardener 2021-02-16 15:09:33 -08:00
commit c8e9451ab4
1 changed files with 93 additions and 15 deletions

108
tensorflow/lite/micro/kernels/xtensa/svdf.cc
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@ -51,14 +51,14 @@ constexpr int kOutputTensor = 0;
* Note: passing OpData by value might seem like an oversight but it helps
* reduce the latency. See b/155656675 for more details.
*/
void EvalIntegerSVDF(TfLiteContext* context, TfLiteNode* node,
const TfLiteEvalTensor* input_tensor,
const TfLiteEvalTensor* weights_feature_tensor,
const TfLiteEvalTensor* weights_time_tensor,
const TfLiteEvalTensor* bias_tensor,
const TfLiteSVDFParams* params,
TfLiteEvalTensor* activation_state_tensor,
TfLiteEvalTensor* output_tensor, OpData data) {
void EvalIntegerSvdfHifimini(TfLiteContext* context, TfLiteNode* node,
const TfLiteEvalTensor* input_tensor,
const TfLiteEvalTensor* weights_feature_tensor,
const TfLiteEvalTensor* weights_time_tensor,
const TfLiteEvalTensor* bias_tensor,
const TfLiteSVDFParams* params,
TfLiteEvalTensor* activation_state_tensor,
TfLiteEvalTensor* output_tensor, OpData data) {
const int n_rank = params->rank;
const int n_batch = input_tensor->dims->data[0];
const int n_input = input_tensor->dims->data[1];
@ -243,7 +243,76 @@ void EvalIntegerSVDF(TfLiteContext* context, TfLiteNode* node,
}
}
}
#endif
#elif defined(FUSION_F1)
TfLiteStatus EvalIntegerSvdfHifi4(
TfLiteContext* context, TfLiteNode* node,
const TfLiteEvalTensor* input_tensor,
const TfLiteEvalTensor* weights_feature_tensor,
const TfLiteEvalTensor* weights_time_tensor,
const TfLiteEvalTensor* bias_tensor, const TfLiteSVDFParams* params,
TfLiteEvalTensor* activation_state_tensor, TfLiteEvalTensor* output_tensor,
const OpData& data) {
const int n_rank = params->rank;
const int n_batch = input_tensor->dims->data[0];
const int n_input = input_tensor->dims->data[1];
const int n_filter = weights_feature_tensor->dims->data[0];
const int n_unit = n_filter / n_rank;
const int n_memory = weights_time_tensor->dims->data[1];
TFLITE_DCHECK(context != nullptr);
TFLITE_DCHECK(context->GetScratchBuffer != nullptr);
// Shift states.
int16_t* const state_ptr =
tflite::micro::GetTensorData<int16_t>(activation_state_tensor);
// Left shift the activation_state.
int num_bytes = sizeof(*state_ptr) * (n_batch * n_filter * n_memory - 1);
xa_nn_memmove_16(state_ptr, state_ptr + 1, num_bytes);
// Note: no need to clear the latest activation, matmul is not accumulative.
// Feature matmul.
const int8_t* input = tflite::micro::GetTensorData<int8_t>(input_tensor);
const int8_t* weight_feature =
tflite::micro::GetTensorData<int8_t>(weights_feature_tensor);
int16_t* result_in_batch = state_ptr + (n_memory - 1);
for (int b = 0; b < n_batch; b++) {
TF_LITE_ENSURE_EQ(context,
xa_nn_matXvec_out_stride_sym8sxasym8s_16(
&result_in_batch[b * n_filter * n_memory],
weight_feature, &input[b * n_input], NULL, n_filter,
n_input, n_input, n_memory, -data.input_zero_point,
(data.effective_scale_1_a), data.effective_scale_1_b),
0);
}
// Time weights dot product + activation
for (int b = 0; b < n_batch; ++b) {
const int16_t* vector1_ptr =
tflite::micro::GetTensorData<int16_t>(weights_time_tensor);
const int16_t* vector2_ptr =
tflite::micro::GetTensorData<int16_t>(activation_state_tensor) +
b * n_memory * n_filter;
const int32_t* bias_ptr =
tflite::micro::GetTensorData<int32_t>(bias_tensor);
int8_t* output_ptr =
tflite::micro::GetTensorData<int8_t>(output_tensor) + b * n_unit;
TF_LITE_ENSURE_EQ(
context,
xa_nn_dot_prod_16x16_asym8s(
output_ptr, vector1_ptr, vector2_ptr, bias_ptr, n_memory * n_rank,
(data.effective_scale_2_a), data.effective_scale_2_b,
data.output_zero_point, n_unit),
0);
}
return kTfLiteOk;
}
#endif // defined(FUSION_F1) || defined(HIFIMINI)
void* Init(TfLiteContext* context, const char* buffer, size_t length) {
TFLITE_DCHECK(context != nullptr);
@ -274,11 +343,14 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
const int rank = params->rank;
const int input_size = input->dims->data[1];
const int batch_size = input->dims->data[0];
#if defined(HIFIMINI)
// Ensure the input size is a multiple of two. This is necessary since
// optimized kernels access the memory in chunks of two, and all accesses
// must be aligned to 16 bits.
// TODO(b/153202598): Remove when padding is allowed in TFLite tensors.
TF_LITE_ENSURE_EQ(context, input_size % 2, 0);
#endif // defined(HIFIMINI)
const int num_filters = weights_feature->dims->data[0];
TF_LITE_ENSURE_EQ(context, num_filters % rank, 0);
@ -339,9 +411,10 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) {
static_cast<double>(activation_state->params.scale *
weights_time->params.scale / output->params.scale);
TF_LITE_ENSURE_EQ(context, static_cast<double>(bias->params.scale),
static_cast<double>(activation_state->params.scale *
weights_time->params.scale));
TF_LITE_ENSURE_NEAR(context, static_cast<double>(bias->params.scale),
static_cast<double>(activation_state->params.scale *
weights_time->params.scale),
1e-5);
TFLITE_DCHECK(node->user_data != nullptr);
OpData* data = static_cast<OpData*>(node->user_data);
@ -396,13 +469,18 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) {
const OpData& data = *(static_cast<const OpData*>(node->user_data));
#if defined(HIFIMINI)
EvalIntegerSVDF(context, node, input, weights_feature, weights_time, bias,
params, activation_state, output, data);
EvalIntegerSvdfHifimini(context, node, input, weights_feature, weights_time,
bias, params, activation_state, output, data);
return kTfLiteOk;
#elif defined(FUSION_F1)
return EvalIntegerSvdfHifi4(context, node, input, weights_feature,
weights_time, bias, params, activation_state,
output, data);
#else
EvalIntegerSvdfReference(context, node, input, weights_feature, weights_time,
bias, params, activation_state, output, data);
#endif
return kTfLiteOk;
#endif
}
} // namespace