Align the serialization schema with TOSA 0.24.0 specification
The operators are pool, conv, reshape, slice, transpose, and table.
Signed-off-by: TatWai Chong <tatwai.chong@arm.com>
Change-Id: I13f8d626df59be14361068222746347ba69d2fb5
diff --git a/reference_model/src/ops/data_layout.cc b/reference_model/src/ops/data_layout.cc
index 24c86ed..df7084d 100644
--- a/reference_model/src/ops/data_layout.cc
+++ b/reference_model/src/ops/data_layout.cc
@@ -201,6 +201,9 @@
case DType_FLOAT:
pad_value = (InEigenType)attribute->pad_const_fp();
break;
+ default:
+ printNodeValidationError("Unsupported data type");
+ break;
}
if (this->qinfo && Dtype == DType_INT8)
@@ -256,7 +259,7 @@
for (uint32_t d = 0; d < OutRank; d++)
{
- ERROR_IF(attribute->shape()[d] != outputs[0]->getShape()[d],
+ ERROR_IF(attribute->new_shape()[d] != outputs[0]->getShape()[d],
"OpReshape: new_shape doesn't match output shape");
}
@@ -271,7 +274,7 @@
{
for (int32_t d = 0; d < OutRank; d++)
{
- array_shape[d] = attribute->shape()[OutRank - 1 - d];
+ array_shape[d] = attribute->new_shape()[OutRank - 1 - d];
out_reverser[d] = OutRank - 1 - d;
}
@@ -418,13 +421,13 @@
in = dynamic_cast<TosaReference::TensorTemplate<TIn>*>(inputs[0]);
out = dynamic_cast<TosaReference::TensorTemplate<TOut>*>(outputs[0]);
- ERROR_IF((int32_t)attribute->begin().size() != in->getRank(),
+ ERROR_IF((int32_t)attribute->start().size() != in->getRank(),
"OpSlice: begin array length needs to be rank(input)");
ERROR_IF((int32_t)attribute->size().size() != in->getRank(), "OpSlice: size array length needs to be rank(input)");
for (int32_t i = 0; i < in->getRank(); i++)
{
- int32_t b = attribute->begin()[i];
+ int32_t b = attribute->start()[i];
int32_t s = attribute->size()[i];
ERROR_IF(b < 0 || b >= in->getShape()[i], "OpSlice: start out of boundary");
ERROR_IF((b + s) < 0 || (b + s) > in->getShape()[i], "OpSlice: (start+size) out of boundary");
@@ -629,13 +632,13 @@
ASSERT_MEM(in && out);
- ERROR_IF(attribute->perm().size() != Rank, "OpTranspose: perm array size needs to match rank(input)");
+ ERROR_IF(attribute->perms().size() != Rank, "OpTranspose: perms array size needs to match rank(input)");
std::array<bool, Rank> index_used;
index_used.fill(false);
for (int32_t d = 0; d < Rank; d++)
{
- int32_t index = attribute->perm()[d];
+ int32_t index = attribute->perms()[d];
ERROR_IF(index < 0 or index >= Rank, "OpTranspose: index out of boundary");
ERROR_IF(index_used[index], "OpTranspose: index duplicated in perm attribute");
index_used[index] = true;
diff --git a/reference_model/src/ops/tensor_ops.cc b/reference_model/src/ops/tensor_ops.cc
index 732480c..6144dbc 100644
--- a/reference_model/src/ops/tensor_ops.cc
+++ b/reference_model/src/ops/tensor_ops.cc
@@ -26,7 +26,7 @@
std::vector<int32_t> output_shape,
std::string& msg)
{
- if (attribute->padding().size() != 4)
+ if (attribute->pad().size() != 4)
{
msg = "illegal size for attribute padding";
return 1;
@@ -44,7 +44,7 @@
return 1;
}
- for (int32_t i : attribute->padding())
+ for (int32_t i : attribute->pad())
{
if (i < 0)
{
@@ -76,10 +76,10 @@
int32_t OH = output_shape[1];
int32_t OW = output_shape[2];
- int32_t pad_top = attribute->padding()[0];
- int32_t pad_bottom = attribute->padding()[1];
- int32_t pad_left = attribute->padding()[2];
- int32_t pad_right = attribute->padding()[3];
+ int32_t pad_top = attribute->pad()[0];
+ int32_t pad_bottom = attribute->pad()[1];
+ int32_t pad_left = attribute->pad()[2];
+ int32_t pad_right = attribute->pad()[3];
int32_t stride_y = attribute->stride()[0];
int32_t stride_x = attribute->stride()[1];
@@ -125,9 +125,9 @@
DType WeightDtype,
std::string& msg)
{
- if (attribute->padding().size() != (2 * conv_dimension))
+ if (attribute->pad().size() != (2 * conv_dimension))
{
- msg = "Illegal size for attribute padding";
+ msg = "Illegal size for attribute pad";
return 1;
}
@@ -143,7 +143,7 @@
return 1;
}
- for (int32_t i : attribute->padding())
+ for (int32_t i : attribute->pad())
{
if (i < 0)
{
@@ -191,12 +191,12 @@
int32_t dilation_x = attribute->dilation()[1 + offset_d];
offset_d *= 2;
- int32_t pad_d0 = conv_dimension == 3 ? attribute->padding()[0] : 0;
- int32_t pad_d1 = conv_dimension == 3 ? attribute->padding()[1] : 0;
- int32_t pad_top = attribute->padding()[0 + offset_d];
- int32_t pad_bottom = attribute->padding()[1 + offset_d];
- int32_t pad_left = attribute->padding()[2 + offset_d];
- int32_t pad_right = attribute->padding()[3 + offset_d];
+ int32_t pad_d0 = conv_dimension == 3 ? attribute->pad()[0] : 0;
+ int32_t pad_d1 = conv_dimension == 3 ? attribute->pad()[1] : 0;
+ int32_t pad_top = attribute->pad()[0 + offset_d];
+ int32_t pad_bottom = attribute->pad()[1 + offset_d];
+ int32_t pad_left = attribute->pad()[2 + offset_d];
+ int32_t pad_right = attribute->pad()[3 + offset_d];
int32_t full_D = ID - 1 + pad_d0 + pad_d1 - (kernel_d - 1) * dilation_d;
int32_t full_H = IH - 1 + pad_top + pad_bottom - (kernel_h - 1) * dilation_y;
@@ -442,10 +442,10 @@
ERROR_IF(in_batch != out_batch, "OpAvgPool2d: tensor batch mismatch %d != %d", in_batch, out_batch);
ERROR_IF(in_channels != out_channels, "OpAvgPool2d: tensor channel mismatch %d != %d", in_channels, out_channels);
- int padding_top = this->attribute->padding()[0];
- int padding_bottom = this->attribute->padding()[1];
- int padding_left = this->attribute->padding()[2];
- int padding_right = this->attribute->padding()[3];
+ int pad_top = this->attribute->pad()[0];
+ int pad_bottom = this->attribute->pad()[1];
+ int pad_left = this->attribute->pad()[2];
+ int pad_right = this->attribute->pad()[3];
int kernel_h = this->attribute->kernel()[0];
int kernel_w = this->attribute->kernel()[1];
int stride_h = this->attribute->stride()[0];
@@ -453,9 +453,9 @@
DEBUG_INFO(OP,
"perform AvgPool2d, input.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], kernel=[%d,%d], "
- "stride=[%d,%d], padding=[%d,%d,%d,%d]",
+ "stride=[%d,%d], pad=[%d,%d,%d,%d]",
in_batch, in_height, in_width, in_channels, out_batch, out_height, out_width, out_channels, kernel_h,
- kernel_w, stride_h, stride_w, padding_top, padding_bottom, padding_left, padding_right);
+ kernel_w, stride_h, stride_w, pad_top, pad_bottom, pad_left, pad_right);
Eigen::array<Eigen::Index, 2> im2col_input_dims;
im2col_input_dims[0] = kernel_h * kernel_w;
@@ -467,11 +467,11 @@
col2im_output_dims[2] = out_width;
col2im_output_dims[3] = out_channels;
- Eigen::array<std::pair<int32_t, int32_t>, 4> padding;
- padding[0] = std::make_pair(0, 0);
- padding[1] = std::make_pair(padding_top, padding_bottom);
- padding[2] = std::make_pair(padding_left, padding_right);
- padding[3] = std::make_pair(0, 0);
+ Eigen::array<std::pair<int32_t, int32_t>, 4> pad;
+ pad[0] = std::make_pair(0, 0);
+ pad[1] = std::make_pair(pad_top, pad_bottom);
+ pad[2] = std::make_pair(pad_left, pad_right);
+ pad[3] = std::make_pair(0, 0);
ETensor4<InEigenType> input_val = this->in->getTensor();
if (this->qinfo)
@@ -479,7 +479,7 @@
input_val = input_val - (InEigenType)this->qinfo->input_zp();
}
- ETensor4<InEigenType> input_padded = input_val.pad(padding);
+ ETensor4<InEigenType> input_padded = input_val.pad(pad);
// assuming input and output have same scales
// so input and output scaling is not required
@@ -511,8 +511,8 @@
// calculate 1d height/width div_map (number of elements this pooling window covers)
// and outer product to get 2d div_map, then reshape/broadcast to [N, H, W, C]
- ETensor1<int32_t> div_map_h = calculate_div_map_1d(in_height, out_height, kernel_h, stride_h, padding_top, padding_bottom);
- ETensor1<int32_t> div_map_w = calculate_div_map_1d(in_width, out_width, kernel_w, stride_w, padding_left, padding_right);
+ ETensor1<int32_t> div_map_h = calculate_div_map_1d(in_height, out_height, kernel_h, stride_h, pad_top, pad_bottom);
+ ETensor1<int32_t> div_map_w = calculate_div_map_1d(in_width, out_width, kernel_w, stride_w, pad_left, pad_right);
Eigen::array<Eigen::IndexPair<Eigen::Index>, 1> contract_dims = { Eigen::IndexPair<Eigen::Index>(1, 0) };
Eigen::array<Eigen::Index, 4> bcast{ out_batch, 1, 1, out_channels };
@@ -636,10 +636,11 @@
out_channels);
ERROR_IF(b_out_channels != out_channels, "OpConv2d: bias channel mismatch %d != %d", b_out_channels, out_channels);
- int padding_top = this->attribute->padding()[0];
- int padding_bottom = this->attribute->padding()[1];
- int padding_left = this->attribute->padding()[2];
- int padding_right = this->attribute->padding()[3];
+ int pad_top = this->attribute->pad()[0];
+ int pad_bottom = this->attribute->pad()[1];
+ int pad_left = this->attribute->pad()[2];
+ int pad_right = this->attribute->pad()[3];
+
int stride_h = this->attribute->stride()[0];
int stride_w = this->attribute->stride()[1];
int dilation_h = this->attribute->dilation()[0];
@@ -647,10 +648,10 @@
DEBUG_INFO(OP,
"perform OpConv2d, input.shape=[%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], "
- "stride=[%d,%d], dilation=[%d,%d], padding=[%d,%d,%d,%d]",
+ "stride=[%d,%d], dilation=[%d,%d], pad=[%d,%d,%d,%d]",
in_batch, in_height, in_width, in_channels, f_height, f_width, f_in_channels, f_out_channels, out_batch,
- out_height, out_width, out_channels, stride_h, stride_w, dilation_h, dilation_w, padding_top,
- padding_bottom, padding_left, padding_right);
+ out_height, out_width, out_channels, stride_h, stride_w, dilation_h, dilation_w, pad_top,
+ pad_bottom, pad_left, pad_right);
// GEMM-conv2d, left matrix is input, right matrix is weight
Eigen::array<Eigen::Index, 2> im2col_input_dims;
@@ -682,11 +683,11 @@
Eigen::array<Eigen::IndexPair<Eigen::Index>, 1> contract_dims = { Eigen::IndexPair<Eigen::Index>(1, 0) };
- Eigen::array<std::pair<int32_t, int32_t>, 4> padding;
- padding[0] = std::make_pair(0, 0);
- padding[1] = std::make_pair(padding_top, padding_bottom);
- padding[2] = std::make_pair(padding_left, padding_right);
- padding[3] = std::make_pair(0, 0);
+ Eigen::array<std::pair<int32_t, int32_t>, 4> pad;
+ pad[0] = std::make_pair(0, 0);
+ pad[1] = std::make_pair(pad_top, pad_bottom);
+ pad[2] = std::make_pair(pad_left, pad_right);
+ pad[3] = std::make_pair(0, 0);
TIn input_val = this->input->getTensor();
TWeight weight_val = this->weight->getTensor();
@@ -696,7 +697,7 @@
weight_val = weight_val - (WeightEigenType)this->qinfo->weight_zp();
}
- ETensor4<InEigenType> input_padded = input_val.pad(padding);
+ ETensor4<InEigenType> input_padded = input_val.pad(pad);
// extract_image_patches() output [N, KH, KW, H * W, C]
// need to transpose to [N, H * W, KH, KW, C]
@@ -825,15 +826,17 @@
out_channels);
ERROR_IF(b_out_channels != out_channels, "OpConv3d: bias channel mismatch %d != %d", b_out_channels, out_channels);
- int padding_d0 = this->attribute->padding()[0];
- int padding_d1 = this->attribute->padding()[1];
- int padding_top = this->attribute->padding()[2];
- int padding_bottom = this->attribute->padding()[3];
- int padding_left = this->attribute->padding()[4];
- int padding_right = this->attribute->padding()[5];
+ int pad_d0 = this->attribute->pad()[0];
+ int pad_d1 = this->attribute->pad()[1];
+ int pad_top = this->attribute->pad()[2];
+ int pad_bottom = this->attribute->pad()[3];
+ int pad_left = this->attribute->pad()[4];
+ int pad_right = this->attribute->pad()[5];
+
int stride_d = this->attribute->stride()[0];
int stride_h = this->attribute->stride()[1];
int stride_w = this->attribute->stride()[2];
+
int dilation_d = this->attribute->dilation()[0];
int dilation_h = this->attribute->dilation()[1];
int dilation_w = this->attribute->dilation()[2];
@@ -841,17 +844,17 @@
DEBUG_INFO(
OP,
"perform OpConv3d, input.shape=[%d,%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d,%d], output.shape=[%d,%d,%d,%d,%d], "
- "stride=[%d,%d,%d], dilation=[%d,%d,%d], padding=[%d,%d,%d,%d,%d,%d]",
+ "stride=[%d,%d,%d], dilation=[%d,%d,%d], pad=[%d,%d,%d,%d,%d,%d]",
in_batch, in_depth, in_height, in_width, in_channels, f_out_channels, f_depth, f_height, f_width, f_in_channels,
out_batch, out_depth, out_height, out_width, out_channels, stride_d, stride_h, stride_w, dilation_d, dilation_h,
- dilation_w, padding_d0, padding_d1, padding_top, padding_bottom, padding_left, padding_right);
+ dilation_w, pad_d0, pad_d1, pad_top, pad_bottom, pad_left, pad_right);
- Eigen::array<std::pair<int32_t, int32_t>, 5> padding;
- padding[0] = std::make_pair(0, 0);
- padding[1] = std::make_pair(padding_d0, padding_d1);
- padding[2] = std::make_pair(padding_top, padding_bottom);
- padding[3] = std::make_pair(padding_left, padding_right);
- padding[4] = std::make_pair(0, 0);
+ Eigen::array<std::pair<int32_t, int32_t>, 5> pad;
+ pad[0] = std::make_pair(0, 0);
+ pad[1] = std::make_pair(pad_d0, pad_d1);
+ pad[2] = std::make_pair(pad_top, pad_bottom);
+ pad[3] = std::make_pair(pad_left, pad_right);
+ pad[4] = std::make_pair(0, 0);
TIn input_val = this->input->getTensor();
TWeight weight_val = this->weight->getTensor();
@@ -861,7 +864,7 @@
weight_val = weight_val - (WeightEigenType)this->qinfo->weight_zp();
}
- ETensor5<InEigenType> input_padded = input_val.pad(padding);
+ ETensor5<InEigenType> input_padded = input_val.pad(pad);
// 1. initialize with bias
Eigen::array<Eigen::Index, 5> reshape_dim;
@@ -1013,10 +1016,11 @@
ERROR_IF(b_out_channels != out_channels, "OpDepthwiseConv2d: bias channels mismatch %d != %d", b_out_channels,
out_channels);
- int padding_top = this->attribute->padding()[0];
- int padding_bottom = this->attribute->padding()[1];
- int padding_left = this->attribute->padding()[2];
- int padding_right = this->attribute->padding()[3];
+ int pad_top = this->attribute->pad()[0];
+ int pad_bottom = this->attribute->pad()[1];
+ int pad_left = this->attribute->pad()[2];
+ int pad_right = this->attribute->pad()[3];
+
int stride_h = this->attribute->stride()[0];
int stride_w = this->attribute->stride()[1];
int dilation_h = this->attribute->dilation()[0];
@@ -1024,16 +1028,16 @@
DEBUG_INFO(OP,
"perform OpDepthwiseConv2d, input.shape=[%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d], "
- "output.shape=[%d,%d,%d,%d], stride=[%d,%d], dilation=[%d,%d], padding=[%d,%d,%d,%d]",
+ "output.shape=[%d,%d,%d,%d], stride=[%d,%d], dilation=[%d,%d], pad=[%d,%d,%d,%d]",
in_batch, in_height, in_width, in_channels, f_height, f_width, f_in_channels, f_multiplier, out_batch,
- out_height, out_width, out_channels, stride_h, stride_w, dilation_h, dilation_w, padding_top,
- padding_bottom, padding_left, padding_right);
+ out_height, out_width, out_channels, stride_h, stride_w, dilation_h, dilation_w, pad_top,
+ pad_bottom, pad_left, pad_right);
- Eigen::array<std::pair<int32_t, int32_t>, 4> padding;
- padding[0] = std::make_pair(0, 0);
- padding[1] = std::make_pair(padding_top, padding_bottom);
- padding[2] = std::make_pair(padding_left, padding_right);
- padding[3] = std::make_pair(0, 0);
+ Eigen::array<std::pair<int32_t, int32_t>, 4> pad;
+ pad[0] = std::make_pair(0, 0);
+ pad[1] = std::make_pair(pad_top, pad_bottom);
+ pad[2] = std::make_pair(pad_left, pad_right);
+ pad[3] = std::make_pair(0, 0);
TIn input_val = this->input->getTensor();
TWeight weight_val = this->weight->getTensor();
@@ -1043,10 +1047,10 @@
weight_val = weight_val - (WeightEigenType)this->qinfo->weight_zp();
}
- ETensor4<InEigenType> input_padded = input_val.pad(padding);
+ ETensor4<InEigenType> input_padded = input_val.pad(pad);
// GEMM doesn't fit well with DepthwiseConv2d
- // 1. use extract_image_patches() to handle stride/dilation/padding
+ // 1. use extract_image_patches() to handle stride/dilation/pad
// 2. perform direct convolution
// 1. extract_image_patches() output [N, KH, KW, OH * OW, IC]
@@ -1411,10 +1415,11 @@
ERROR_IF(in_batch != out_batch, "OpMaxPool2d: tensor batch mismatch %d != %d", in_batch, out_batch);
ERROR_IF(in_channels != out_channels, "OpMaxPool2d: tensor channel mismatch %d != %d", in_channels, out_channels);
- int padding_top = this->attribute->padding()[0];
- int padding_bottom = this->attribute->padding()[1];
- int padding_left = this->attribute->padding()[2];
- int padding_right = this->attribute->padding()[3];
+ int pad_top = this->attribute->pad()[0];
+ int pad_bottom = this->attribute->pad()[1];
+ int pad_left = this->attribute->pad()[2];
+ int pad_right = this->attribute->pad()[3];
+
int kernel_h = this->attribute->kernel()[0];
int kernel_w = this->attribute->kernel()[1];
int stride_h = this->attribute->stride()[0];
@@ -1422,9 +1427,9 @@
DEBUG_INFO(OP,
"perform MaxPool2d, input.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], kernel=[%d,%d], "
- "stride=[%d,%d], padding=[%d,%d,%d,%d]",
+ "stride=[%d,%d], pad=[%d,%d,%d,%d]",
in_batch, in_height, in_width, in_channels, out_batch, out_height, out_width, out_channels, kernel_h,
- kernel_w, stride_h, stride_w, padding_top, padding_bottom, padding_left, padding_right);
+ kernel_w, stride_h, stride_w, pad_top, pad_bottom, pad_left, pad_right);
Eigen::array<Eigen::Index, 2> im2col_input_dims;
im2col_input_dims[0] = kernel_h * kernel_w;
@@ -1436,13 +1441,13 @@
col2im_output_dims[2] = out_width;
col2im_output_dims[3] = out_channels;
- Eigen::array<std::pair<int32_t, int32_t>, 4> padding;
- padding[0] = std::make_pair(0, 0);
- padding[1] = std::make_pair(padding_top, padding_bottom);
- padding[2] = std::make_pair(padding_left, padding_right);
- padding[3] = std::make_pair(0, 0);
+ Eigen::array<std::pair<int32_t, int32_t>, 4> pad;
+ pad[0] = std::make_pair(0, 0);
+ pad[1] = std::make_pair(pad_top, pad_bottom);
+ pad[2] = std::make_pair(pad_left, pad_right);
+ pad[3] = std::make_pair(0, 0);
- ETensor4<InEigenType> input_padded = this->in->getTensor().pad(padding, std::numeric_limits<InEigenType>::lowest());
+ ETensor4<InEigenType> input_padded = this->in->getTensor().pad(pad, std::numeric_limits<InEigenType>::lowest());
// extract_image_patches() output [N, KH, KW, H * W, C]
// transpose to [KH, KW, N, H * W, C]
diff --git a/thirdparty/serialization_lib b/thirdparty/serialization_lib
index 4102773..7be7165 160000
--- a/thirdparty/serialization_lib
+++ b/thirdparty/serialization_lib
@@ -1 +1 @@
-Subproject commit 4102773d83e236448130b43b1747621ace00160f
+Subproject commit 7be7165ca5168d768a08841658c805dd1bda49c7