Support new RESCALE attributes
input_unsigned and output_unsigned were added to the specification.
Older TOSA files with uint data types are still supported.
Signed-off-by: Eric Kunze <eric.kunze@arm.com>
Change-Id: I059484086887c3bd5e1af5b1aac0dacf0703f827
diff --git a/reference_model/include/operators.h b/reference_model/include/operators.h
index b0a3227..2c31c30 100644
--- a/reference_model/include/operators.h
+++ b/reference_model/include/operators.h
@@ -329,6 +329,8 @@
const int32_t client_shift[],
const bool client_scale32,
const bool client_double_round,
+ const bool client_input_unsigned,
+ const bool client_output_unsigned,
const bool client_per_channel);
tosa_status_t tosa_run_identity(tosa_tensor_t client_input1, tosa_tensor_t client_output);
@@ -337,4 +339,4 @@
}
#endif /* __cplusplus */
-#endif // OPERATORS_H_
\ No newline at end of file
+#endif // OPERATORS_H_
diff --git a/reference_model/src/operators.cc b/reference_model/src/operators.cc
index ae5963d..cb71ae8 100644
--- a/reference_model/src/operators.cc
+++ b/reference_model/src/operators.cc
@@ -2339,6 +2339,8 @@
const int32_t client_shift[],
const bool client_scale32,
const bool client_double_round,
+ const bool client_input_unsigned,
+ const bool client_output_unsigned,
const bool client_per_channel)
{
// Create operator attributes
@@ -2346,10 +2348,13 @@
const int32_t output_zp = client_output_zp;
const std::vector<int32_t> multiplier(&client_multiplier[0], &client_multiplier[0] + client_multiplier_len);
const std::vector<int32_t> shift(&client_shift[0], &client_shift[0] + client_shift_len);
- const bool scale32 = client_scale32;
- const bool double_round = client_double_round;
- const bool per_channel = client_per_channel;
- TosaRescaleAttribute attr(input_zp, output_zp, multiplier, shift, scale32, double_round, per_channel);
+ const bool scale32 = client_scale32;
+ const bool double_round = client_double_round;
+ const bool per_channel = client_per_channel;
+ const bool input_unsigned = client_input_unsigned;
+ const bool output_unsigned = client_output_unsigned;
+ TosaRescaleAttribute attr(input_zp, output_zp, multiplier, shift, scale32, double_round, per_channel,
+ input_unsigned, output_unsigned);
// Create tensors
tosa::TosaSerializationTensor* input = translate_client_tensor(client_input, "input");
diff --git a/reference_model/src/ops/type_conversion.cc b/reference_model/src/ops/type_conversion.cc
index 9464fd9..0135d1b 100644
--- a/reference_model/src/ops/type_conversion.cc
+++ b/reference_model/src/ops/type_conversion.cc
@@ -102,6 +102,18 @@
return 0;
}
+// helpers to convert types
+static int64_t zero_extend(int8_t val)
+{
+ uint8_t* rval = reinterpret_cast<uint8_t*>(&val);
+ return static_cast<int64_t>(*rval);
+}
+static int64_t zero_extend(int16_t val)
+{
+ uint16_t* rval = reinterpret_cast<uint16_t*>(&val);
+ return static_cast<int64_t>(*rval);
+}
+
template <int Rank, TOSA_REF_TYPE InDtype, TOSA_REF_TYPE OutDtype>
int OpRescale<Rank, InDtype, OutDtype>::eval()
{
@@ -112,6 +124,8 @@
bool scale32 = attribute->scale32();
bool double_round = attribute->double_round();
bool per_channel = attribute->per_channel();
+ bool input_unsigned = attribute->input_unsigned();
+ bool output_unsigned = attribute->output_unsigned();
// reshape [d0, d1, ..., dn] into [d0 * d1 ..., dn]
Eigen::array<Eigen::Index, 2> shape_2d;
@@ -143,22 +157,68 @@
{
for (int32_t i = 0; i < shape_2d[1]; i++)
{
- begin = Eigen::array<Eigen::Index, 2>({ 0, i });
- curr_channel_slice_prescaled = input_reshaped.slice(begin, size);
- channel_multiplier = multiplier[i];
- channel_shift = shift[i];
- curr_channel_slice_postscaled =
- curr_channel_slice_prescaled.unaryExpr([input_zp, output_zp, channel_multiplier, channel_shift,
- double_round, scale32](InEigenType in_val) -> OutEigenType {
- InEigenType input_zp_shifted = in_val - (InEigenType)input_zp;
+ begin = Eigen::array<Eigen::Index, 2>({ 0, i });
+ curr_channel_slice_prescaled = input_reshaped.slice(begin, size);
+ channel_multiplier = multiplier[i];
+ channel_shift = shift[i];
+ curr_channel_slice_postscaled = curr_channel_slice_prescaled.unaryExpr(
+ [input_zp, output_zp, channel_multiplier, channel_shift, double_round, scale32, input_unsigned,
+ output_unsigned](InEigenType in_val) -> OutEigenType {
+ int64_t input_zp_shifted;
+ if (input_unsigned)
+ {
+ int64_t in_val64;
+ int64_t in_zp64;
+ switch (GetNumBits<InDtype>::value)
+ {
+ case 8:
+ in_val64 = zero_extend(static_cast<int8_t>(in_val));
+ in_zp64 = zero_extend(static_cast<int8_t>(input_zp));
+ break;
+ case 16:
+ in_val64 = zero_extend(static_cast<int16_t>(in_val));
+ in_zp64 = zero_extend(static_cast<int16_t>(input_zp));
+ break;
+ default:
+ in_val64 = static_cast<int64_t>(in_val);
+ in_zp64 = static_cast<int64_t>(input_zp);
+ break;
+ }
+ input_zp_shifted = in_val64 - in_zp64;
+ }
+ else
+ {
+ input_zp_shifted = in_val - input_zp;
+ }
int32_t scaled;
if (scale32)
- scaled = TosaReference::QuantUtil::apply_scale_32(input_zp_shifted, channel_multiplier,
- channel_shift, double_round);
+ scaled = TosaReference::QuantUtil::apply_scale_32(static_cast<int32_t>(input_zp_shifted),
+ channel_multiplier, channel_shift,
+ double_round);
else
scaled = TosaReference::QuantUtil::apply_scale_16(input_zp_shifted, channel_multiplier,
channel_shift);
- int64_t res_in_64 = static_cast<int64_t>(scaled) + output_zp;
+ int64_t output_zp_extended;
+ if (output_unsigned)
+ {
+ switch (GetNumBits<OutDtype>::value)
+ {
+ case 8:
+ output_zp_extended = zero_extend(static_cast<int8_t>(output_zp));
+ break;
+ case 16:
+ output_zp_extended = zero_extend(static_cast<int16_t>(output_zp));
+ break;
+ default:
+ output_zp_extended = static_cast<int64_t>(output_zp);
+ break;
+ }
+ }
+ else
+ {
+ output_zp_extended = static_cast<int64_t>(output_zp);
+ }
+ int64_t res_in_64 = static_cast<int64_t>(scaled) + output_zp_extended;
int64_t i32_max_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::max());
int64_t i32_min_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::min());
if (res_in_64 > i32_max_in_64 || res_in_64 < i32_min_in_64)
@@ -190,31 +250,78 @@
int32_t tensor_shift = shift[0];
try
{
- output_2d = input_reshaped.unaryExpr([input_zp, output_zp, tensor_multiplier, tensor_shift, double_round,
- scale32](InEigenType in_val) -> OutEigenType {
- InEigenType input_zp_shifted = in_val - (InEigenType)input_zp;
- int32_t scaled;
- if (scale32)
- scaled = TosaReference::QuantUtil::apply_scale_32(input_zp_shifted, tensor_multiplier, tensor_shift,
- double_round);
- else
- scaled =
- TosaReference::QuantUtil::apply_scale_16(input_zp_shifted, tensor_multiplier, tensor_shift);
- int64_t res_in_64 = static_cast<int64_t>(scaled) + output_zp;
- int64_t i32_max_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::max());
- int64_t i32_min_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::min());
- if (res_in_64 > i32_max_in_64 || res_in_64 < i32_min_in_64)
- {
- std::string desc = "scaling result [" + std::to_string(scaled) + "] plus output_zp [" +
- std::to_string(output_zp) + "] not in i32 range";
- throw desc;
- }
+ output_2d =
+ input_reshaped.unaryExpr([input_zp, output_zp, tensor_multiplier, tensor_shift, double_round, scale32,
+ input_unsigned, output_unsigned](InEigenType in_val) -> OutEigenType {
+ int64_t input_zp_shifted;
+ if (input_unsigned)
+ {
+ int64_t in_val64;
+ int64_t in_zp64;
+ switch (GetNumBits<InDtype>::value)
+ {
+ case 8:
+ in_val64 = zero_extend(static_cast<int8_t>(in_val));
+ in_zp64 = zero_extend(static_cast<int8_t>(input_zp));
+ break;
+ case 16:
+ in_val64 = zero_extend(static_cast<int16_t>(in_val));
+ in_zp64 = zero_extend(static_cast<int16_t>(input_zp));
+ break;
+ default:
+ in_val64 = static_cast<int64_t>(in_val);
+ in_zp64 = static_cast<int64_t>(input_zp);
+ break;
+ }
+ input_zp_shifted = in_val64 - in_zp64;
+ }
+ else
+ {
+ input_zp_shifted = in_val - input_zp;
+ }
+ int32_t scaled;
+ if (scale32)
+ scaled = TosaReference::QuantUtil::apply_scale_32(input_zp_shifted, tensor_multiplier,
+ tensor_shift, double_round);
+ else
+ scaled =
+ TosaReference::QuantUtil::apply_scale_16(input_zp_shifted, tensor_multiplier, tensor_shift);
- OutEigenType out_val = static_cast<OutEigenType>(res_in_64);
- out_val = std::max<OutEigenType>(out_val, QMin);
- out_val = std::min<OutEigenType>(out_val, QMax);
- return out_val;
- });
+ int64_t output_zp_extended;
+ if (output_unsigned)
+ {
+ switch (GetNumBits<OutDtype>::value)
+ {
+ case 8:
+ output_zp_extended = zero_extend(static_cast<int8_t>(output_zp));
+ break;
+ case 16:
+ output_zp_extended = zero_extend(static_cast<int16_t>(output_zp));
+ break;
+ default:
+ output_zp_extended = static_cast<int64_t>(output_zp);
+ break;
+ }
+ }
+ else
+ {
+ output_zp_extended = static_cast<int64_t>(output_zp);
+ }
+ int64_t res_in_64 = static_cast<int64_t>(scaled) + output_zp_extended;
+ int64_t i32_max_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::max());
+ int64_t i32_min_in_64 = static_cast<int64_t>(std::numeric_limits<int32_t>::min());
+ if (res_in_64 > i32_max_in_64 || res_in_64 < i32_min_in_64)
+ {
+ std::string desc = "scaling result [" + std::to_string(scaled) + "] plus output_zp [" +
+ std::to_string(output_zp) + "] not in i32 range";
+ throw desc;
+ }
+
+ OutEigenType out_val = static_cast<OutEigenType>(res_in_64);
+ out_val = std::max<OutEigenType>(out_val, QMin);
+ out_val = std::min<OutEigenType>(out_val, QMax);
+ return out_val;
+ });
}
catch (std::string desc)
{
diff --git a/thirdparty/serialization_lib b/thirdparty/serialization_lib
index 54fcf85..29c294c 160000
--- a/thirdparty/serialization_lib
+++ b/thirdparty/serialization_lib
@@ -1 +1 @@
-Subproject commit 54fcf8538291b16eb2038097059e517cf704a3ff
+Subproject commit 29c294c4e74b1b48967cd4831f6f508500b1f1d1