Support 6 activation functions in XNNPACK delegate
Newly supported operators:
- RELU
- RELU6
- RELU_N1_TO_1
- SOFTMAX
- LOGISTIC
- HARD_SWISH
PiperOrigin-RevId: 294714526
Change-Id: I9d47992809fa8d4c4ff5f5cf77cbdc84b26bb31b
diff --git a/tensorflow/lite/delegates/xnnpack/README.md b/tensorflow/lite/delegates/xnnpack/README.md
index be87cac..5c2a856 100644
--- a/tensorflow/lite/delegates/xnnpack/README.md
+++ b/tensorflow/lite/delegates/xnnpack/README.md
@@ -83,6 +83,18 @@
* Dynamically allocated (with `kTfLiteDynamic` allocation type) input and output
are not supported.
+### `HARD_SWISH`
+
+* Inputs and outputs must be in 32-bit floating-point format.
+* Dynamically allocated (with `kTfLiteDynamic` allocation type) inputs and
+ output are not supported.
+
+### `LOGISTIC`
+
+* Inputs and outputs must be in 32-bit floating-point format.
+* Dynamically allocated (with `kTfLiteDynamic` allocation type) inputs and
+ output are not supported.
+
### `MUL`
* Inputs and outputs must be in 32-bit floating-point format.
@@ -100,6 +112,31 @@
* Dynamically allocated (with `kTfLiteDynamic` allocation type) input and output
are not supported.
+### `RELU`
+
+* Inputs and outputs must be in 32-bit floating-point format.
+* Dynamically allocated (with `kTfLiteDynamic` allocation type) inputs and
+ output are not supported.
+
+### `RELU6`
+
+* Inputs and outputs must be in 32-bit floating-point format.
+* Dynamically allocated (with `kTfLiteDynamic` allocation type) inputs and
+ output are not supported.
+
+### `RELU_N1_TO_1`
+
+* Inputs and outputs must be in 32-bit floating-point format.
+* Dynamically allocated (with `kTfLiteDynamic` allocation type) inputs and
+ output are not supported.
+
+### `SOFTMAX`
+
+* Inputs and outputs must be in 32-bit floating-point format.
+* Only `beta = 1.0` is supported.
+* Dynamically allocated (with `kTfLiteDynamic` allocation type) inputs and
+ output are not supported.
+
### Other limitations
* Resizing model inputs (via `Interpreter::ResizeInputTensor`) is supported, but
diff --git a/tensorflow/lite/delegates/xnnpack/xnnpack_delegate.cc b/tensorflow/lite/delegates/xnnpack/xnnpack_delegate.cc
index 858600b..e6574cb 100644
--- a/tensorflow/lite/delegates/xnnpack/xnnpack_delegate.cc
+++ b/tensorflow/lite/delegates/xnnpack/xnnpack_delegate.cc
@@ -556,6 +556,12 @@
node, context->tensors, dwconv_params,
xnnpack_tensors);
}
+ case kTfLiteBuiltinHardSwish:
+ return VisitHardSwishNode(subgraph, logging_context, node_index, node,
+ context->tensors, xnnpack_tensors);
+ case kTfLiteBuiltinLogistic:
+ return VisitLogisticNode(subgraph, logging_context, node_index, node,
+ context->tensors, xnnpack_tensors);
case kTfLiteBuiltinMul: {
const TfLiteMulParams* mul_params =
static_cast<const TfLiteMulParams*>(node->builtin_data);
@@ -563,9 +569,26 @@
return VisitMulNode(subgraph, logging_context, node_index, node,
context->tensors, mul_params, xnnpack_tensors);
}
- case kTfLiteBuiltinPrelu: {
+ case kTfLiteBuiltinPrelu:
return VisitPreluNode(subgraph, logging_context, node_index, node,
context->tensors, xnnpack_tensors);
+ case kTfLiteBuiltinRelu:
+ return VisitReluNode(
+ subgraph, logging_context, node_index, node, context->tensors, 0.0f,
+ std::numeric_limits<float>::infinity(), xnnpack_tensors);
+ case kTfLiteBuiltinReluN1To1:
+ return VisitReluNode(subgraph, logging_context, node_index, node,
+ context->tensors, -1.0f, 1.0f, xnnpack_tensors);
+ case kTfLiteBuiltinRelu6:
+ return VisitReluNode(subgraph, logging_context, node_index, node,
+ context->tensors, 0.0f, 6.0f, xnnpack_tensors);
+ case kTfLiteBuiltinSoftmax: {
+ const TfLiteSoftmaxParams* softmax_params =
+ static_cast<const TfLiteSoftmaxParams*>(node->builtin_data);
+
+ return VisitSoftmaxNode(subgraph, logging_context, node_index, node,
+ context->tensors, softmax_params,
+ xnnpack_tensors);
}
default:
return kTfLiteError;
@@ -581,26 +604,20 @@
CheckNumInputsAndOutputs(logging_context, node, 2, 1, node_index));
const TfLiteTensor& input1_tensor = tensors[node->inputs->data[0]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, input1_tensor, node->inputs->data[0], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, input1_tensor, node->inputs->data[0], node_index));
const TfLiteTensor& input2_tensor = tensors[node->inputs->data[1]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, input2_tensor, node->inputs->data[1], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, input2_tensor, node->inputs->data[1], node_index));
const TfLiteTensor& output_tensor = tensors[node->outputs->data[0]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, output_tensor, node->outputs->data[0], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, output_tensor, node->outputs->data[0], node_index));
@@ -640,46 +657,34 @@
CheckNumInputsAndOutputs(logging_context, node, 3, 1, node_index));
const TfLiteTensor& input_tensor = tensors[node->inputs->data[0]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, input_tensor, node->inputs->data[0], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, input_tensor, 4,
node->inputs->data[0]));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, input_tensor, node->inputs->data[0], node_index));
const TfLiteTensor& filter_tensor = tensors[node->inputs->data[1]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, filter_tensor, node->inputs->data[1], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, filter_tensor, 4,
node->inputs->data[1]));
-
TF_LITE_ENSURE_STATUS(CheckTensorStaticAllocation(
logging_context, filter_tensor, node->inputs->data[1], node_index));
const TfLiteTensor& bias_tensor = tensors[node->inputs->data[2]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, filter_tensor, node->inputs->data[2], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, bias_tensor, 1,
node->inputs->data[2]));
-
TF_LITE_ENSURE_STATUS(CheckTensorStaticAllocation(
logging_context, bias_tensor, node->inputs->data[2], node_index));
const TfLiteTensor& output_tensor = tensors[node->outputs->data[0]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, output_tensor, node->outputs->data[0], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, output_tensor, 4,
node->outputs->data[0]));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, output_tensor, node->outputs->data[0], node_index));
@@ -735,46 +740,34 @@
CheckNumInputsAndOutputs(logging_context, node, 3, 1, node_index));
const TfLiteTensor& input_tensor = tensors[node->inputs->data[0]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, input_tensor, node->inputs->data[0], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, input_tensor, 4,
node->inputs->data[0]));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, input_tensor, node->inputs->data[0], node_index));
const TfLiteTensor& filter_tensor = tensors[node->inputs->data[1]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, filter_tensor, node->inputs->data[1], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, filter_tensor, 4,
node->inputs->data[1]));
-
TF_LITE_ENSURE_STATUS(CheckTensorStaticAllocation(
logging_context, filter_tensor, node->inputs->data[1], node_index));
const TfLiteTensor& bias_tensor = tensors[node->inputs->data[2]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, filter_tensor, node->inputs->data[2], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, bias_tensor, 1,
node->inputs->data[2]));
-
TF_LITE_ENSURE_STATUS(CheckTensorStaticAllocation(
logging_context, bias_tensor, node->inputs->data[2], node_index));
const TfLiteTensor& output_tensor = tensors[node->outputs->data[0]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, output_tensor, node->outputs->data[0], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, output_tensor, 4,
node->outputs->data[0]));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, output_tensor, node->outputs->data[0], node_index));
@@ -827,6 +820,73 @@
return kTfLiteOk;
}
+ static TfLiteStatus VisitHardSwishNode(
+ xnn_subgraph_t subgraph, TfLiteContext* logging_context, int node_index,
+ TfLiteNode* node, const TfLiteTensor* tensors,
+ const std::vector<uint32_t>& xnnpack_tensors) {
+ TF_LITE_ENSURE_STATUS(
+ CheckNumInputsAndOutputs(logging_context, node, 1, 1, node_index));
+
+ const TfLiteTensor& input_tensor = tensors[node->inputs->data[0]];
+ TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
+ logging_context, input_tensor, node->inputs->data[0], node_index));
+ TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
+ logging_context, input_tensor, node->inputs->data[0], node_index));
+
+ const TfLiteTensor& output_tensor = tensors[node->outputs->data[0]];
+ TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
+ logging_context, output_tensor, node->outputs->data[0], node_index));
+ TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
+ logging_context, output_tensor, node->outputs->data[0], node_index));
+
+ if (subgraph != nullptr) {
+ const xnn_status status = xnn_define_hardswish(
+ subgraph, /*input_id=*/xnnpack_tensors[node->inputs->data[0]],
+ /*output_id=*/xnnpack_tensors[node->outputs->data[0]], /*flags=*/0);
+ if (status != xnn_status_success) {
+ logging_context->ReportError(logging_context,
+ "failed to delegate HARD_SWISH node #%d",
+ node_index);
+ return kTfLiteError;
+ }
+ }
+
+ return kTfLiteOk;
+ }
+
+ static TfLiteStatus VisitLogisticNode(
+ xnn_subgraph_t subgraph, TfLiteContext* logging_context, int node_index,
+ TfLiteNode* node, const TfLiteTensor* tensors,
+ const std::vector<uint32_t>& xnnpack_tensors) {
+ TF_LITE_ENSURE_STATUS(
+ CheckNumInputsAndOutputs(logging_context, node, 1, 1, node_index));
+
+ const TfLiteTensor& input_tensor = tensors[node->inputs->data[0]];
+ TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
+ logging_context, input_tensor, node->inputs->data[0], node_index));
+ TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
+ logging_context, input_tensor, node->inputs->data[0], node_index));
+
+ const TfLiteTensor& output_tensor = tensors[node->outputs->data[0]];
+ TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
+ logging_context, output_tensor, node->outputs->data[0], node_index));
+ TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
+ logging_context, output_tensor, node->outputs->data[0], node_index));
+
+ if (subgraph != nullptr) {
+ const xnn_status status = xnn_define_sigmoid(
+ subgraph, /*input_id=*/xnnpack_tensors[node->inputs->data[0]],
+ /*output_id=*/xnnpack_tensors[node->outputs->data[0]], /*flags=*/0);
+ if (status != xnn_status_success) {
+ logging_context->ReportError(
+ logging_context, "failed to delegate SIGMOID node #%d", node_index);
+ return kTfLiteError;
+ }
+ }
+
+ return kTfLiteOk;
+ }
+
static TfLiteStatus VisitMulNode(
xnn_subgraph_t subgraph, TfLiteContext* logging_context, int node_index,
TfLiteNode* node, const TfLiteTensor* tensors,
@@ -836,26 +896,20 @@
CheckNumInputsAndOutputs(logging_context, node, 2, 1, node_index));
const TfLiteTensor& input1_tensor = tensors[node->inputs->data[0]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, input1_tensor, node->inputs->data[0], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, input1_tensor, node->inputs->data[0], node_index));
const TfLiteTensor& input2_tensor = tensors[node->inputs->data[1]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, input2_tensor, node->inputs->data[1], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, input2_tensor, node->inputs->data[1], node_index));
const TfLiteTensor& output_tensor = tensors[node->outputs->data[0]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, output_tensor, node->outputs->data[0], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, output_tensor, node->outputs->data[0], node_index));
@@ -891,35 +945,26 @@
CheckNumInputsAndOutputs(logging_context, node, 2, 1, node_index));
const TfLiteTensor& input_tensor = tensors[node->inputs->data[0]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, input_tensor, node->inputs->data[0], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, input_tensor, 4,
node->inputs->data[0]));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, input_tensor, node->inputs->data[0], node_index));
const TfLiteTensor& slope_tensor = tensors[node->inputs->data[1]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, slope_tensor, node->inputs->data[1], node_index));
-
TF_LITE_ENSURE_STATUS(CheckSlopeTensorShape(
logging_context, slope_tensor, node->inputs->data[1], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorStaticAllocation(
logging_context, slope_tensor, node->inputs->data[1], node_index));
const TfLiteTensor& output_tensor = tensors[node->outputs->data[0]];
-
TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
logging_context, output_tensor, node->outputs->data[0], node_index));
-
TF_LITE_ENSURE_STATUS(CheckTensorShape(logging_context, output_tensor, 4,
node->outputs->data[0]));
-
TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
logging_context, output_tensor, node->outputs->data[0], node_index));
@@ -938,6 +983,83 @@
return kTfLiteOk;
}
+ static TfLiteStatus VisitReluNode(
+ xnn_subgraph_t subgraph, TfLiteContext* logging_context, int node_index,
+ TfLiteNode* node, const TfLiteTensor* tensors, float output_min,
+ float output_max, const std::vector<uint32_t>& xnnpack_tensors) {
+ TF_LITE_ENSURE_STATUS(
+ CheckNumInputsAndOutputs(logging_context, node, 1, 1, node_index));
+
+ const TfLiteTensor& input_tensor = tensors[node->inputs->data[0]];
+ TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
+ logging_context, input_tensor, node->inputs->data[0], node_index));
+ TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
+ logging_context, input_tensor, node->inputs->data[0], node_index));
+
+ const TfLiteTensor& output_tensor = tensors[node->outputs->data[0]];
+ TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
+ logging_context, output_tensor, node->outputs->data[0], node_index));
+ TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
+ logging_context, output_tensor, node->outputs->data[0], node_index));
+
+ if (subgraph != nullptr) {
+ const xnn_status status = xnn_define_clamp(
+ subgraph, output_min, output_max,
+ /*input_id=*/xnnpack_tensors[node->inputs->data[0]],
+ /*output_id=*/xnnpack_tensors[node->outputs->data[0]], /*flags=*/0);
+ if (status != xnn_status_success) {
+ logging_context->ReportError(
+ logging_context, "failed to delegate RELU node #%d", node_index);
+ return kTfLiteError;
+ }
+ }
+
+ return kTfLiteOk;
+ }
+
+ static TfLiteStatus VisitSoftmaxNode(
+ xnn_subgraph_t subgraph, TfLiteContext* logging_context, int node_index,
+ TfLiteNode* node, const TfLiteTensor* tensors,
+ const TfLiteSoftmaxParams* params,
+ const std::vector<uint32_t>& xnnpack_tensors) {
+ if (params->beta == 1.0f) {
+ if (logging_context != nullptr) {
+ logging_context->ReportError(
+ logging_context, "unsupported beta value %.7f in SOFTMAX node #%d",
+ params->beta, node_index);
+ }
+ return kTfLiteError;
+ }
+
+ TF_LITE_ENSURE_STATUS(
+ CheckNumInputsAndOutputs(logging_context, node, 1, 1, node_index));
+
+ const TfLiteTensor& input_tensor = tensors[node->inputs->data[0]];
+ TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
+ logging_context, input_tensor, node->inputs->data[0], node_index));
+ TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
+ logging_context, input_tensor, node->inputs->data[0], node_index));
+
+ const TfLiteTensor& output_tensor = tensors[node->outputs->data[0]];
+ TF_LITE_ENSURE_STATUS(CheckTensorFloatType(
+ logging_context, output_tensor, node->outputs->data[0], node_index));
+ TF_LITE_ENSURE_STATUS(CheckTensorNonDynamicAllocation(
+ logging_context, output_tensor, node->outputs->data[0], node_index));
+
+ if (subgraph != nullptr) {
+ const xnn_status status = xnn_define_softmax(
+ subgraph, /*input_id=*/xnnpack_tensors[node->inputs->data[0]],
+ /*output_id=*/xnnpack_tensors[node->outputs->data[0]], /*flags=*/0);
+ if (status != xnn_status_success) {
+ logging_context->ReportError(
+ logging_context, "failed to delegate SOFTMAX node #%d", node_index);
+ return kTfLiteError;
+ }
+ }
+
+ return kTfLiteOk;
+ }
+
private:
Subgraph(xnn_runtime_t runtime, std::unordered_set<int>&& externals)
: runtime_(runtime, &xnn_delete_runtime), externals_(externals) {}
