Add level checking to TOSA Ref model
Signed-off-by: Jerry Ge <jerry.ge@arm.com>
Change-Id: I5689d7c6b902a319a68fa4628b59e0bcc23aeca4
diff --git a/reference_model/src/ops/tensor_ops.cc b/reference_model/src/ops/tensor_ops.cc
index af808e8..ab3919d 100644
--- a/reference_model/src/ops/tensor_ops.cc
+++ b/reference_model/src/ops/tensor_ops.cc
@@ -515,21 +515,32 @@
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];
- int stride_w = this->attribute->stride()[1];
+ int kernel_y = this->attribute->kernel()[0];
+ int kernel_x = this->attribute->kernel()[1];
+ int stride_y = this->attribute->stride()[0];
+ int stride_x = this->attribute->stride()[1];
+
+ // Check Tosa Level
+ auto tosa_level = g_func_config.tosa_level;
+ LEVEL_CHECK(kernel_y <= tosa_level.MAX_KERNEL, "kernel_y should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(kernel_x <= tosa_level.MAX_KERNEL, "kernel_x should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(stride_y <= tosa_level.MAX_STRIDE, "stride_y should be smaller than or equal to MAX_STRIDE");
+ LEVEL_CHECK(stride_x <= tosa_level.MAX_STRIDE, "stride_x should be smaller than or equal to MAX_STRIDE");
+ LEVEL_CHECK(pad_top <= tosa_level.MAX_KERNEL, "pad_top should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_bottom <= tosa_level.MAX_KERNEL, "pad_bottom should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_left <= tosa_level.MAX_KERNEL, "pad_left should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_right <= tosa_level.MAX_KERNEL, "pad_right should be smaller than or equal to MAX_KERNEL");
tosa::DType accum_dtype = (tosa::DType)this->attribute->accum_dtype();
DEBUG_INFO(OP,
"perform AvgPool2d, input.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], kernel=[%d,%d], "
"stride=[%d,%d], pad=[%d,%d,%d,%d], accum_dtype=%s",
- in_batch, in_height, in_width, in_channels, out_batch, out_height, out_width, out_channels, kernel_h,
- kernel_w, stride_h, stride_w, pad_top, pad_bottom, pad_left, pad_right, EnumNamesDType()[accum_dtype]);
+ in_batch, in_height, in_width, in_channels, out_batch, out_height, out_width, out_channels, kernel_y,
+ kernel_x, stride_y, stride_x, pad_top, pad_bottom, pad_left, pad_right, EnumNamesDType()[accum_dtype]);
Eigen::array<Eigen::Index, 2> im2col_input_dims;
- im2col_input_dims[0] = kernel_h * kernel_w;
+ im2col_input_dims[0] = kernel_y * kernel_x;
im2col_input_dims[1] = out_batch * out_height * out_width * out_channels;
Eigen::array<Eigen::Index, 4> col2im_output_dims;
@@ -560,7 +571,7 @@
// transpose to [KH, KW, N, H * W, C]
// reshape to [KH * KW, N * H * W * C]
ETensor2<InEigenType> input_extract_patches =
- input_padded.extract_image_patches(kernel_h, kernel_w, stride_h, stride_w, 1, 1, Eigen::PADDING_VALID)
+ input_padded.extract_image_patches(kernel_y, kernel_x, stride_y, stride_x, 1, 1, Eigen::PADDING_VALID)
.shuffle(Eigen::array<Eigen::Index, 5>{ 1, 2, 0, 3, 4 })
.reshape(im2col_input_dims);
@@ -571,7 +582,7 @@
// sum pool
for (size_t i = 0; i < this->out->getElementCount(); i++)
{
- for (int32_t j = 0; j < kernel_h * kernel_w; j++)
+ for (int32_t j = 0; j < kernel_y * kernel_x; j++)
{
out_1d(i) += (AccEigenType)input_extract_patches(j, i);
}
@@ -582,8 +593,8 @@
// 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, 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);
+ ETensor1<int32_t> div_map_h = calculate_div_map_1d(in_height, out_height, kernel_y, stride_x, pad_top, pad_bottom);
+ ETensor1<int32_t> div_map_w = calculate_div_map_1d(in_width, out_width, kernel_x, stride_x, 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 };
@@ -709,16 +720,27 @@
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];
- int dilation_w = this->attribute->dilation()[1];
+ int stride_y = this->attribute->stride()[0];
+ int stride_x = this->attribute->stride()[1];
+ int dilation_y = this->attribute->dilation()[0];
+ int dilation_x = this->attribute->dilation()[1];
+
+ // Check Tosa Level
+ auto tosa_level = g_func_config.tosa_level;
+ LEVEL_CHECK(dilation_y * f_height <= tosa_level.MAX_KERNEL, "dilation_y * KH should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(dilation_x * f_width <= tosa_level.MAX_KERNEL, "dilation_x * KW should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_top <= tosa_level.MAX_KERNEL, "pad_top should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_bottom <= tosa_level.MAX_KERNEL, "pad_bottom should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_left <= tosa_level.MAX_KERNEL, "pad_left should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_right <= tosa_level.MAX_KERNEL, "pad_right should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(stride_y <= tosa_level.MAX_STRIDE, "stride_y should be smaller than or equal to MAX_STRIDE");
+ LEVEL_CHECK(stride_x <= tosa_level.MAX_STRIDE, "stride_x should be smaller than or equal to MAX_STRIDE");
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], 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, pad_top,
+ out_height, out_width, out_channels, stride_y, stride_x, dilation_y, dilation_x, pad_top,
pad_bottom, pad_left, pad_right);
// GEMM-conv2d, left matrix is input, right matrix is weight
@@ -771,7 +793,7 @@
// need to transpose to [N, H * W, KH, KW, C]
ETensor5<InEigenType> input_extract_patches =
input_padded
- .extract_image_patches(f_height, f_width, stride_h, stride_w, dilation_h, dilation_w, Eigen::PADDING_VALID)
+ .extract_image_patches(f_height, f_width, stride_y, stride_x, dilation_y, dilation_x, Eigen::PADDING_VALID)
.shuffle(Eigen::array<Eigen::Index, 5>{ 0, 3, 1, 2, 4 });
// reshape input to [N * H * W, KH * KW * C]
@@ -898,20 +920,35 @@
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 stride_y = this->attribute->stride()[1];
+ int stride_x = 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];
+ int dilation_y = this->attribute->dilation()[1];
+ int dilation_x = this->attribute->dilation()[2];
+
+ // Check Tosa Level
+ auto tosa_level = g_func_config.tosa_level;
+ LEVEL_CHECK(dilation_d * f_depth <= tosa_level.MAX_KERNEL, "dilation_d * KD should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(dilation_y * f_height <= tosa_level.MAX_KERNEL, "dilation_y * KH should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(dilation_x * f_width <= tosa_level.MAX_KERNEL, "dilation_x * KW should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_d0 <= tosa_level.MAX_KERNEL, "pad_d0 should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_d1 <= tosa_level.MAX_KERNEL, "pad_d1 should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_top <= tosa_level.MAX_KERNEL, "pad_top should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_bottom <= tosa_level.MAX_KERNEL, "pad_bottom should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_left <= tosa_level.MAX_KERNEL, "pad_left should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_right <= tosa_level.MAX_KERNEL, "pad_right should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(stride_y <= tosa_level.MAX_STRIDE, "stride_y should be smaller than or equal to MAX_STRIDE");
+ LEVEL_CHECK(stride_x <= tosa_level.MAX_STRIDE, "stride_x should be smaller than or equal to MAX_STRIDE");
+ LEVEL_CHECK(stride_d <= tosa_level.MAX_STRIDE, "stride_d should be smaller than or equal to MAX_STRIDE");
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], 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, pad_d0, pad_d1, pad_top, pad_bottom, pad_left, pad_right);
+ out_batch, out_depth, out_height, out_width, out_channels, stride_d, stride_y, stride_x, dilation_d, dilation_y,
+ dilation_x, pad_d0, pad_d1, pad_top, pad_bottom, pad_left, pad_right);
Eigen::array<std::pair<int32_t, int32_t>, 5> pad;
pad[0] = std::make_pair(0, 0);
@@ -964,10 +1001,10 @@
d_idx = od * stride_d + fd * dilation_d;
for (int fh = 0; fh < f_height; fh++)
{
- h_idx = oh * stride_h + fh * dilation_h;
+ h_idx = oh * stride_y + fh * dilation_y;
for (int fw = 0; fw < f_width; fw++)
{
- w_idx = ow * stride_w + fw * dilation_w;
+ w_idx = ow * stride_x + fw * dilation_x;
for (int ic = 0; ic < in_channels; ic++)
{
acc += ((AccEigenType)input_padded(ob, d_idx, h_idx, w_idx, ic) *
@@ -1081,16 +1118,27 @@
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];
- int dilation_w = this->attribute->dilation()[1];
+ int stride_y = this->attribute->stride()[0];
+ int stride_x = this->attribute->stride()[1];
+ int dilation_y = this->attribute->dilation()[0];
+ int dilation_x = this->attribute->dilation()[1];
+
+ // Check Tosa Level
+ auto tosa_level = g_func_config.tosa_level;
+ LEVEL_CHECK(dilation_y * f_height <= tosa_level.MAX_KERNEL, "dilation_y * KH should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(dilation_x * f_width <= tosa_level.MAX_KERNEL, "dilation_x * KW should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_top <= tosa_level.MAX_KERNEL, "pad_top should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_bottom <= tosa_level.MAX_KERNEL, "pad_bottom should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_left <= tosa_level.MAX_KERNEL, "pad_left should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_right <= tosa_level.MAX_KERNEL, "pad_right should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(stride_y <= tosa_level.MAX_STRIDE, "stride_y should be smaller than or equal to MAX_STRIDE");
+ LEVEL_CHECK(stride_x <= tosa_level.MAX_STRIDE, "stride_x should be smaller than or equal to MAX_STRIDE");
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], 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, pad_top,
+ out_height, out_width, out_channels, stride_y, stride_x, dilation_y, dilation_x, pad_top,
pad_bottom, pad_left, pad_right);
Eigen::array<std::pair<int32_t, int32_t>, 4> pad;
@@ -1115,7 +1163,7 @@
// 1. extract_image_patches() output [N, KH, KW, OH * OW, IC]
ETensor5<InEigenType> input_extract_patches = input_padded.extract_image_patches(
- f_height, f_width, stride_h, stride_w, dilation_h, dilation_w, Eigen::PADDING_VALID);
+ f_height, f_width, stride_y, stride_x, dilation_y, dilation_x, Eigen::PADDING_VALID);
Eigen::array<Eigen::Index, 4> reshape_dim;
reshape_dim.fill(1);
@@ -1466,19 +1514,30 @@
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];
- int stride_w = this->attribute->stride()[1];
+ int kernel_y = this->attribute->kernel()[0];
+ int kernel_x = this->attribute->kernel()[1];
+ int stride_y = this->attribute->stride()[0];
+ int stride_x = this->attribute->stride()[1];
+
+ // Check Tosa Level
+ auto tosa_level = g_func_config.tosa_level;
+ LEVEL_CHECK(kernel_y <= tosa_level.MAX_KERNEL, "kernel_y should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(kernel_x <= tosa_level.MAX_KERNEL, "kernel_x should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(stride_y <= tosa_level.MAX_STRIDE, "stride_y should be smaller than or equal to MAX_STRIDE");
+ LEVEL_CHECK(stride_x <= tosa_level.MAX_STRIDE, "stride_x should be smaller than or equal to MAX_STRIDE");
+ LEVEL_CHECK(pad_top <= tosa_level.MAX_KERNEL, "pad_top should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_bottom <= tosa_level.MAX_KERNEL, "pad_bottom should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_left <= tosa_level.MAX_KERNEL, "pad_left should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(pad_right <= tosa_level.MAX_KERNEL, "pad_right should be smaller than or equal to MAX_KERNEL");
DEBUG_INFO(OP,
"perform MaxPool2d, input.shape=[%d,%d,%d,%d], output.shape=[%d,%d,%d,%d], kernel=[%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, pad_top, pad_bottom, pad_left, pad_right);
+ in_batch, in_height, in_width, in_channels, out_batch, out_height, out_width, out_channels, kernel_y,
+ kernel_x, stride_y, stride_x, pad_top, pad_bottom, pad_left, pad_right);
Eigen::array<Eigen::Index, 2> im2col_input_dims;
- im2col_input_dims[0] = kernel_h * kernel_w;
+ im2col_input_dims[0] = kernel_y * kernel_x;
im2col_input_dims[1] = out_batch * out_height * out_width * out_channels;
Eigen::array<Eigen::Index, 4> col2im_output_dims;
@@ -1504,7 +1563,7 @@
// to or smaller than the actual maximum in the KH x KW patch.
ETensor2<InEigenType> input_extract_patches =
input_padded
- .extract_image_patches(kernel_h, kernel_w, stride_h, stride_w, 1, 1, Eigen::PADDING_VALID,
+ .extract_image_patches(kernel_y, kernel_x, stride_y, stride_x, 1, 1, Eigen::PADDING_VALID,
std::numeric_limits<InEigenType>::lowest())
.shuffle(Eigen::array<Eigen::Index, 5>{ 1, 2, 0, 3, 4 })
.reshape(im2col_input_dims);
@@ -1603,6 +1662,11 @@
int out_imag_height = this->out_imag->getShape()[1];
int out_imag_width = this->out_imag->getShape()[2];
+ // Check Tosa Level
+ auto tosa_level = g_func_config.tosa_level;
+ LEVEL_CHECK(in_real_height <= tosa_level.MAX_KERNEL, "H should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(in_real_width <= tosa_level.MAX_KERNEL, "W should be smaller than or equal to MAX_KERNEL");
+
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,
@@ -1710,6 +1774,11 @@
int32_t out_imag_height = out_imag->getShape()[1];
int32_t out_imag_width = out_imag->getShape()[2];
+ // Check Tosa Level
+ auto tosa_level = g_func_config.tosa_level;
+ LEVEL_CHECK(in_height <= tosa_level.MAX_KERNEL, "H should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(in_width <= tosa_level.MAX_KERNEL, "W should be smaller than or equal to MAX_KERNEL");
+
DEBUG_INFO(OP,
"perform OpRFFT2d, input.shape=[%d,%d,%d], output_real.shape=[%d,%d,%d], "
"output_imag.shape=[%d,%d,%d]",
@@ -1885,8 +1954,8 @@
int out_pad_left = this->attribute->out_pad()[2];
int out_pad_right = this->attribute->out_pad()[3];
- int stride_h = this->attribute->stride()[0];
- int stride_w = this->attribute->stride()[1];
+ int stride_y = this->attribute->stride()[0];
+ int stride_x = this->attribute->stride()[1];
ERROR_IF(in_batch != out_batch, "OpTransposeConv2d: tensor batch mismatch %d != %d", in_batch, out_batch);
ERROR_IF(f_in_channels != in_channels, "OpTransposeConv2d: tensor input channel mismatch %d != %d", f_in_channels,
@@ -1896,11 +1965,22 @@
ERROR_IF(b_out_channels != out_channels, "OpDepthwiseConv2d: bias channels mismatch %d != %d", b_out_channels,
out_channels);
+ // Check Tosa Level
+ auto tosa_level = g_func_config.tosa_level;
+ LEVEL_CHECK(f_height <= tosa_level.MAX_KERNEL, "KH should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(f_width <= tosa_level.MAX_KERNEL, "KW should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(out_pad_top <= tosa_level.MAX_KERNEL, "out_pad_top should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(out_pad_bottom <= tosa_level.MAX_KERNEL, "out_pad_bottom should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(out_pad_left <= tosa_level.MAX_KERNEL, "out_pad_left should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(out_pad_right <= tosa_level.MAX_KERNEL, "out_pad_right should be smaller than or equal to MAX_KERNEL");
+ LEVEL_CHECK(stride_y <= tosa_level.MAX_STRIDE, "stride_y should be smaller than or equal to MAX_STRIDE");
+ LEVEL_CHECK(stride_x <= tosa_level.MAX_STRIDE, "stride_x should be smaller than or equal to MAX_STRIDE");
+
DEBUG_INFO(OP,
"perform OpTransposeConv2d, input.shape=[%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d], "
"output.shape=[%d,%d,%d,%d], stride=[%d,%d], out_pad=[%d,%d,%d,%d]",
in_batch, in_height, in_width, in_channels, f_height, f_width, f_out_channels, f_in_channels,
- out_batch, out_height, out_width, out_channels, stride_h, stride_w, out_pad_top,
+ out_batch, out_height, out_width, out_channels, stride_y, stride_x, out_pad_top,
out_pad_bottom, out_pad_left, out_pad_right);
TIn input_val = this->input->getTensor();
@@ -1934,8 +2014,8 @@
{
for (int iw = 0; iw < in_width; iw++)
{
- out_x_origin = iw * stride_w + out_pad_left;
- out_y_origin = ih * stride_h + out_pad_top;
+ out_x_origin = iw * stride_x + out_pad_left;
+ out_y_origin = ih * stride_y + out_pad_top;
for (int ic = 0; ic < in_channels; ic++)
{
for (int fh = 0; fh < f_height; fh++)