Add FFT2d to the reference model
Includes:
* FFT2d reference implementation
* Basic TOSA tests
Change-Id: Ie79fcb713542345d550ec013646810c1e890e388
Signed-off-by: Luke Hutton <luke.hutton@arm.com>
diff --git a/reference_model/src/ops/tensor_ops.cc b/reference_model/src/ops/tensor_ops.cc
index 4663c47..af808e8 100644
--- a/reference_model/src/ops/tensor_ops.cc
+++ b/reference_model/src/ops/tensor_ops.cc
@@ -238,6 +238,86 @@
return 0;
}
+int check_fft_shape(const std::vector<int32_t>& in_real,
+ const std::vector<int32_t>& in_imag,
+ const std::vector<int32_t>& out_real,
+ const std::vector<int32_t>& out_imag,
+ std::string& msg) {
+ const bool is_rfft = in_imag.empty();
+ auto is_power_of_two = [](int32_t n) -> bool
+ {
+ return (n & (n-1)) == 0 && n > 0;
+ };
+
+ if (!is_power_of_two(in_real[1]) || !is_power_of_two(in_real[2]))
+ {
+ msg = "Input height and width must be a power of two";
+ return 1;
+ }
+
+ // RFFT does not have a second input
+ if (!is_rfft)
+ {
+ bool input_check = true;
+ for (size_t i = 0; i < in_real.size(); i++)
+ {
+ if (in_real[i] != in_imag[i])
+ {
+ input_check = false;
+ break;
+ }
+ }
+ if (!input_check)
+ {
+ msg = "Mismatch between real input shape and imaginary input shape";
+ return 1;
+ }
+ }
+
+ bool output_check = true;
+ for (size_t i = 0; i < out_real.size(); i++)
+ {
+ if (out_real[i] != out_imag[i])
+ {
+ output_check = false;
+ break;
+ }
+ }
+ if (!output_check)
+ {
+ msg = "Mismatch between real output shape and imaginary output shape";
+ return 1;
+ }
+
+ if (in_real[0] != out_real[0])
+ {
+ msg = "Input and output batch size don't match";
+ return 1;
+ }
+ if (in_real[1] != out_real[1])
+ {
+ msg = "Input and output height don't match";
+ return 1;
+ }
+
+ if (is_rfft)
+ {
+ if (in_real[2] / 2 + 1 != out_real[2])
+ {
+ msg = "Output width is expected to match input width / 2 + 1";
+ return 1;
+ }
+ } else {
+ if (in_real[2] != out_real[2])
+ {
+ msg = "Input and output width don't match";
+ return 1;
+ }
+ }
+
+ return 0;
+}
+
template <int Rank, DType Dtype>
OpArgMax<Rank, Dtype>::OpArgMax(SubgraphTraverser* sgt_,
TosaAttributeBase* attribute_,
@@ -1448,6 +1528,124 @@
}
template <DType Dtype>
+OpFFT2d<Dtype>::OpFFT2d(SubgraphTraverser* sgt_,
+ TosaAttributeBase* attribute_,
+ uint64_t id_)
+ : GraphNode(sgt_, Op_FFT2D, id_)
+{
+ setRequiredOperands(2, 2);
+ setRequiredRank(3);
+
+ INIT_ATTRIBUTE(FFT);
+}
+
+template <DType Dtype>
+OpFFT2d<Dtype>::~OpFFT2d() {
+ if (attribute)
+ delete attribute;
+}
+
+
+template <DType Dtype>
+int OpFFT2d<Dtype>::checkTensorAttributes()
+{
+ if (validateRequiredOperands())
+ return 1;
+
+ if (validateRequiredRank(inputs[0]) || validateRequiredRank(inputs[1]) ||
+ validateRequiredRank(outputs[0]) || validateRequiredRank(outputs[1]))
+ {
+ return 1;
+ }
+
+ if (inputs[0]->matchType(*outputs[0]) || inputs[1]->matchType(*outputs[1]) ||
+ inputs[0]->matchType(*inputs[1]))
+ {
+ printNodeValidationError("OpFFT2d: input and output tensor type mismatch");
+ return 1;
+ }
+
+ in_real = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
+ in_imag = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[1]);
+ out_real = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
+ out_imag = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[1]);
+
+ ASSERT_MEM(in_real && in_imag && out_real && out_imag);
+
+ std::string msg;
+ if (check_fft_shape(in_real->getShape(), in_imag->getShape(),
+ out_real->getShape(), out_imag->getShape(), msg))
+ {
+ msg = "OpFFT2d: " + msg;
+ printNodeValidationError(msg.c_str());
+ return 1;
+ }
+
+ return 0;
+}
+
+template <DType Dtype>
+int OpFFT2d<Dtype>::eval()
+{
+ int in_real_batch = this->in_real->getShape()[0];
+ int in_real_height = this->in_real->getShape()[1];
+ int in_real_width = this->in_real->getShape()[2];
+
+ int in_imag_batch = this->in_imag->getShape()[0];
+ int in_imag_height = this->in_imag->getShape()[1];
+ int in_imag_width = this->in_imag->getShape()[2];
+
+ int out_real_batch = this->out_real->getShape()[0];
+ int out_real_height = this->out_real->getShape()[1];
+ int out_real_width = this->out_real->getShape()[2];
+
+ int out_imag_batch = this->out_imag->getShape()[0];
+ int out_imag_height = this->out_imag->getShape()[1];
+ int out_imag_width = this->out_imag->getShape()[2];
+
+ DEBUG_INFO(OP,
+ "perform OpFFT2d, input.shapes=[[%d,%d,%d],[%d,%d,%d]], output.shapes=[[%d,%d,%d],[%d,%d,%d]]",
+ in_real_batch, in_real_height, in_real_width,
+ in_imag_batch, in_imag_height, in_imag_width,
+ out_real_batch, out_real_height, out_real_width,
+ out_imag_batch, out_imag_height, out_imag_width);
+
+ OutEigenType sum_real, sum_imag, a, sign_val = 1.0;
+
+ if (attribute->inverse()) {
+ sign_val = -1.0;
+ }
+
+ for (int n = 0; n < in_real_batch; n++)
+ {
+ for (int oy = 0; oy < out_real_height; oy++)
+ {
+ for (int ox = 0; ox < out_real_width; ox++)
+ {
+ sum_real = 0.0;
+ sum_imag = 0.0;
+ for (int iy = 0; iy < in_real_height; iy++)
+ {
+ for (int ix = 0; ix < in_real_width; ix++)
+ {
+ OutEigenType val_real = this->in_real->getTensor()(n, iy, ix);
+ OutEigenType val_imag = this->in_imag->getTensor()(n, iy, ix);
+ // Use explicit cast to ensure intermmediate calculations are completed using OutEigenType
+ a = sign_val * 2 * M_PI * ((iy * (OutEigenType)oy) / in_real_height + (ix * (OutEigenType)ox) / in_real_width);
+ sum_real += val_real * cos(a) + val_imag * sin(a);
+ sum_imag += -val_real * sin(a) + val_imag * cos(a);
+ }
+ }
+ this->out_real->getTensor()(n, oy, ox) = sum_real;
+ this->out_imag->getTensor()(n, oy, ox) = sum_imag;
+ }
+ }
+ }
+
+ return GraphNode::eval();
+}
+
+template <DType Dtype>
OpRFFT2d<Dtype>::OpRFFT2d(SubgraphTraverser* sgt_,
TosaAttributeBase* attribute_,
uint64_t id_)
@@ -1485,45 +1683,12 @@
ASSERT_MEM(in && out_real && out_imag);
- auto is_power_of_two = [](int32_t n) -> bool
+ std::string msg;
+ if (check_fft_shape(in->getShape(), {},
+ out_real->getShape(), out_imag->getShape(), msg))
{
- return (n & (n-1)) == 0 && n > 0;
- };
-
- // Input shape: [N, H, W]
- if (!is_power_of_two(in->getShape()[1]) || !is_power_of_two(in->getShape()[2]))
- {
- printNodeValidationError("OpRFFT2d: input height and width must be a power of two");
- return 1;
- }
-
- // Output shape: [N, H, W / 2 + 1]
- bool output_check = true;
- for (int32_t i = 0; i < out_real->getRank(); i++)
- {
- if (out_real->getShape()[i] != out_imag->getShape()[i])
- {
- output_check = false;
- break;
- }
- }
- if (!output_check)
- {
- printNodeValidationError(
- "OpRFFT2d: Mismatch between real output shape and imaginary output shape");
- return 1;
- }
-
- if (in->getShape()[0] != out_real->getShape()[0]) {
- printNodeValidationError("OpRFFT2d: input and output batch size don't match");
- return 1;
- }
- if (in->getShape()[1] != out_real->getShape()[1]) {
- printNodeValidationError("OpRFFT2d: input and output height don't match");
- return 1;
- }
- if (in->getShape()[2] / 2 + 1 != out_real->getShape()[2]) {
- printNodeValidationError("OpRFFT2d: output width is expected to match input width / 2 + 1");
+ msg = "OpRFFT2d: " + msg;
+ printNodeValidationError(msg.c_str());
return 1;
}
@@ -1843,6 +2008,8 @@
DEF_INSTANTIATE_THREE_TYPE(OpDepthwiseConv2d, INT8, INT8, INT32);
DEF_INSTANTIATE_THREE_TYPE(OpDepthwiseConv2d, INT16, INT8, INT48);
+DEF_INSTANTIATE_ONE_TYPE(OpFFT2d, FP32);
+
DEF_INSTANTIATE_THREE_TYPE(OpFullyConnected, FP16, FP16, FP16);
DEF_INSTANTIATE_THREE_TYPE(OpFullyConnected, FP16, FP16, FP32);
DEF_INSTANTIATE_THREE_TYPE(OpFullyConnected, BF16, BF16, FP32);