Main Compliance testing support for ARGMAX, REDUCE_SUM/MAX/MIN
Add extra tests for FP32 REDUCE_SUM that meet MIN_DOT_PRODUCTS.
Plus improved dot product test generation skip information.
Signed-off-by: Jeremy Johnson <jeremy.johnson@arm.com>
Change-Id: Ia8198a9500ddddfc86c5bb84230b9a4edf5ffd50
diff --git a/reference_model/src/generate/generate_dot_product.cc b/reference_model/src/generate/generate_dot_product.cc
index e6815ad..c1934dd 100644
--- a/reference_model/src/generate/generate_dot_product.cc
+++ b/reference_model/src/generate/generate_dot_product.cc
@@ -189,6 +189,48 @@
return false;
}
}
+//---------------------------------------------------------------------------//
+// Reduce Sum //
+//---------------------------------------------------------------------------//
+
+bool generateReduceSum(const TosaReference::GenerateConfig& cfg,
+ TosaReference::IDotProductGenerator& generator,
+ void* data,
+ size_t size)
+{
+ if (cfg.dataType != DType::DType_FP32)
+ {
+ WARNING("[Generator][DP][ReduceSum] Only supports FP32.");
+ return false;
+ }
+ if (cfg.inputPos != 0)
+ {
+ WARNING("[Generator][DP][ReduceSum] Invalid input tensor slot position to operator.");
+ return false;
+ }
+ if (cfg.dotProductInfo.axis < 0 || static_cast<size_t>(cfg.dotProductInfo.axis) >= cfg.shape.size())
+ {
+ WARNING("[Generator][DP][ReduceSum] Invalid axis %d.", cfg.dotProductInfo.axis);
+ return false;
+ }
+
+ float* input = reinterpret_cast<float*>(data);
+ const int64_t T = TosaReference::numElementsFromShape(cfg.shape);
+ const uint32_t axis = cfg.dotProductInfo.axis;
+
+ for (int64_t t = 0; t < T; ++t)
+ {
+ uint64_t k = t;
+ for (uint32_t d = cfg.shape.size() - 1; d > axis; --d)
+ {
+ k = k / cfg.shape[d];
+ }
+ k = k % cfg.shape[axis];
+
+ input[t] = generator(static_cast<int32_t>(k));
+ }
+ return true;
+}
} // namespace
namespace TosaReference
@@ -210,6 +252,8 @@
return generateMatMul(cfg, *generator, data, size);
case tosa::Op_CONV2D:
return generateConv2D(cfg, *generator, data, size);
+ case tosa::Op_REDUCE_SUM:
+ return generateReduceSum(cfg, *generator, data, size);
default:
WARNING("[Generator][DP] Unsupported operator.");
return false;
diff --git a/reference_model/src/generate/generate_utils.cc b/reference_model/src/generate/generate_utils.cc
index 8ae889a..e410436 100644
--- a/reference_model/src/generate/generate_utils.cc
+++ b/reference_model/src/generate/generate_utils.cc
@@ -39,6 +39,7 @@
{
{ Op::Op_UNKNOWN, "UNKNOWN" },
{ Op::Op_ADD, "ADD" },
+ { Op::Op_ARGMAX, "ARGMAX" },
{ Op::Op_CONV2D, "CONV2D" },
{ Op::Op_MATMUL, "MATMUL" },
{ Op::Op_MAXIMUM, "MAXIMUM" },
@@ -46,6 +47,9 @@
{ Op::Op_MINIMUM, "MINIMUM" },
{ Op::Op_MUL, "MUL" },
{ Op::Op_PAD, "PAD" },
+ { Op::Op_REDUCE_MAX, "REDUCE_MAX" },
+ { Op::Op_REDUCE_MIN, "REDUCE_MIN" },
+ { Op::Op_REDUCE_SUM, "REDUCE_SUM" },
{ Op::Op_SUB, "SUB" },
})
diff --git a/reference_model/test/generate_tests.cpp b/reference_model/test/generate_tests.cpp
index 6173372..40dd59f 100644
--- a/reference_model/test/generate_tests.cpp
+++ b/reference_model/test/generate_tests.cpp
@@ -509,4 +509,75 @@
check_not_output<float>(bufferP0, bufferP1);
}
}
+void reduce_sum_test_FP32(const std::string tosaName,
+ const size_t tosaElements,
+ const std::string templateJsonCfg,
+ const std::string setStr,
+ const std::vector<uint32_t> expected)
+{
+ std::string jsonCfg = templateJsonCfg;
+ update_json_template(jsonCfg, "_SET_", setStr);
+
+ std::vector<float> buffer(tosaElements);
+ REQUIRE(tgd_generate_data(jsonCfg.c_str(), tosaName.c_str(), (void*)buffer.data(), tosaElements * 4));
+ // Choose different generator values to test at positions 6, 7 & 8
+ std::vector<float> mid_three(buffer.begin() + 6, buffer.begin() + 9);
+ check_output<float>(mid_three, expected);
+}
+
+TEST_CASE("positive - FP32 reduce_sum dot product (values 6,7 & 8)")
+{
+ std::string templateJsonCfg = R"({
+ "tensors" : {
+ "input" : {
+ "generator": "DOT_PRODUCT",
+ "data_type": "FP32",
+ "input_type": "VARIABLE",
+ "shape" : [ 5, 3, 7 ],
+ "input_pos": 0,
+ "op" : "REDUCE_SUM",
+ "dot_product_info": {
+ "s": _SET_,
+ "ks": 3,
+ "acc_type": "FP32",
+ "axis": 1
+ }
+ }
+ }
+ })";
+
+ const std::string tosaName = "input";
+ const size_t tosaElements = 5 * 3 * 7;
+
+ SUBCASE("reduce_sum, set 0, param 0")
+ {
+ std::vector<uint32_t> expected = { 0x3df2e612, 0x3f59255f, 0x0 };
+ reduce_sum_test_FP32(tosaName, tosaElements, templateJsonCfg, "0", expected);
+ }
+ SUBCASE("reduce_sum, set 1, param 0")
+ {
+ std::vector<uint32_t> expected = { 0x5edaa175, 0x5edb84c1, 0x5ea3c765 };
+ reduce_sum_test_FP32(tosaName, tosaElements, templateJsonCfg, "1", expected);
+ }
+ SUBCASE("reduce_sum, set 2, param 0")
+ {
+ std::vector<uint32_t> expected = { 0x3f800000, 0x3e73f143, 0x3f12cef8 };
+ reduce_sum_test_FP32(tosaName, tosaElements, templateJsonCfg, "2", expected);
+ }
+ SUBCASE("reduce_sum, set 3, param 0")
+ {
+ std::vector<uint32_t> expected = { 0x41800000, 0xbe9f659e, 0xbfaca78c };
+ reduce_sum_test_FP32(tosaName, tosaElements, templateJsonCfg, "3", expected);
+ }
+ SUBCASE("reduce_sum, set 4, param 0")
+ {
+ std::vector<uint32_t> expected = { 0x5e1e6f12, 0x3f000000, 0x3f000000 };
+ reduce_sum_test_FP32(tosaName, tosaElements, templateJsonCfg, "4", expected);
+ }
+ SUBCASE("reduce_sum, set 5, param 0")
+ {
+ std::vector<uint32_t> expected = { 0x5d2790c5, 0xdec3dadc, 0xdea1486e };
+ reduce_sum_test_FP32(tosaName, tosaElements, templateJsonCfg, "5", expected);
+ }
+}
TEST_SUITE_END(); // generate
diff --git a/verif/conformance/tosa_main_profile_ops_info.json b/verif/conformance/tosa_main_profile_ops_info.json
index 254f5e7..faccf75 100644
--- a/verif/conformance/tosa_main_profile_ops_info.json
+++ b/verif/conformance/tosa_main_profile_ops_info.json
@@ -131,6 +131,7 @@
"profile": [
"tosa-mi"
],
+ "support_for": [ "lazy_data_gen" ],
"generation": {
"standard": {
"generator_args": [
@@ -142,7 +143,7 @@
"--target-dtype",
"bf16",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--tensor-dim-range",
"32,64",
"--target-rank",
@@ -162,7 +163,7 @@
"--target-dtype",
"bf16",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--tensor-dim-range",
"1,32",
"--target-rank",
@@ -174,7 +175,7 @@
"--target-dtype",
"fp32",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--target-shape",
"1,3,65535,1",
"--target-shape",
@@ -612,7 +613,7 @@
"--target-dtype",
"bf16",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--target-shape",
"1,34,19,27",
"--target-shape",
@@ -625,7 +626,7 @@
"--target-dtype",
"fp32",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--target-shape",
"1,65537,1,3",
"--target-shape",
@@ -2378,6 +2379,7 @@
"profile": [
"tosa-mi"
],
+ "support_for": [ "lazy_data_gen" ],
"generation": {
"standard": {
"negative_dim_range": "1,10",
@@ -2390,7 +2392,7 @@
"--target-dtype",
"bf16",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--tensor-dim-range",
"1,32"
],
@@ -2398,7 +2400,7 @@
"--target-dtype",
"fp32",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--target-shape",
"1,1,1,65531",
"--target-shape",
@@ -2423,6 +2425,7 @@
"profile": [
"tosa-mi"
],
+ "support_for": [ "lazy_data_gen" ],
"generation": {
"standard": {
"negative_dim_range": "1,10",
@@ -2435,7 +2438,7 @@
"--target-dtype",
"bf16",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--tensor-dim-range",
"1,32"
],
@@ -2443,7 +2446,7 @@
"--target-dtype",
"fp32",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--target-shape",
"1,1,65531,1",
"--target-shape",
@@ -2510,6 +2513,7 @@
"profile": [
"tosa-mi"
],
+ "support_for": [ "lazy_data_gen" ],
"generation": {
"standard": {
"generator_args": [
@@ -2521,13 +2525,25 @@
"--target-dtype",
"bf16",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--tensor-dim-range",
"1,37"
],
[
"--target-dtype",
+ "fp32",
+ "--fp-values-range",
+ "-max,max",
+ "--target-shape",
+ "1001",
+ "--target-shape",
+ "50,200"
+ ],
+ [
+ "--target-dtype",
"bf16",
+ "--fp-values-range",
+ "-max,max",
"--target-shape",
"1,3,65529,1",
"--target-shape",
diff --git a/verif/generator/tosa_arg_gen.py b/verif/generator/tosa_arg_gen.py
index 1be243c..4014656 100644
--- a/verif/generator/tosa_arg_gen.py
+++ b/verif/generator/tosa_arg_gen.py
@@ -1078,27 +1078,26 @@
return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
@staticmethod
- def tvgConcat(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
+ def tvgConcat(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
count = len(shapeList) - testGen.args.num_const_inputs_concat
if count < 1:
count = 1
if testGen.args.num_const_inputs_concat == 0:
count = len(shapeList)
- # Ensure axis is an int
- testArgs[0] = int(testArgs[0])
-
shapeList = TosaTensorGen.tgConcatConstInput(
- testGen, shapeList, testArgs[0], error_name
+ testGen, shapeList, argsDict["axis"], error_name
)
- tens = []
- tens.extend(
+ tens_ser_list = []
+ tens_ser_list.extend(
testGen.buildPlaceholderTensors(shapeList[0:count], dtypeList[0:count])
)
- tens.extend(testGen.buildConstTensors(shapeList[count:], dtypeList[count:]))
+ tens_ser_list.extend(
+ testGen.buildConstTensors(shapeList[count:], dtypeList[count:])
+ )
- return tens
+ return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
@staticmethod
def tvgLogicalShift(
@@ -1164,8 +1163,9 @@
)
@staticmethod
- def tvgReduceSum(testGen, op, dtypeList, shapeList, testArgs, error_name=None):
+ def tvgReduceSum(testGen, opName, dtypeList, shapeList, argsDict, error_name=None):
if dtypeList[0] == DType.INT32:
+ op = testGen.TOSA_OP_LIST[opName]
pCount, cCount = op["operands"]
assert (
pCount == 1 and cCount == 0
@@ -1176,14 +1176,15 @@
values_arr = np.int32(
testGen.rng.integers(low=-range_val, high=range_val, size=shapeList[0])
)
- placeholders = []
- placeholders.append(
+ tens_ser_list = []
+ tens_ser_list.append(
testGen.ser.addPlaceholder(shapeList[0], dtypeList[0], values_arr)
)
- return placeholders
+ return TosaTensorValuesGen.TVGInfo(tens_ser_list, None)
else:
- return TosaTensorValuesGen.tvgDefault(
- testGen, op, dtypeList, shapeList, testArgs, error_name
+ # ERROR_IF or dot product floating point test
+ return TosaTensorValuesGen.tvgLazyGenDefault(
+ testGen, opName, dtypeList, shapeList, argsDict, error_name
)
@@ -1228,12 +1229,18 @@
# Extra tests for each dot product test set
dot_products = args_dict["dot_products"]
if dot_products < testGen.TOSA_MI_DOT_PRODUCT_MIN:
+ shape_info = (
+ " ({})".format(testGen.shapeStr(args_dict["shape"]))
+ if "shape" in args_dict
+ else ""
+ )
print(
- f"Skipping {opName} dot product test as too few calculations {dot_products} < {testGen.TOSA_MI_DOT_PRODUCT_MIN}"
+ f"Skipping {opName}{shape_info} dot product test as too few calculations {dot_products} < {testGen.TOSA_MI_DOT_PRODUCT_MIN}"
)
continue
- # KS is required by all dot product generators
+ # KS and acc_type is required by all dot product generators
assert "ks" in args_dict
+ assert "acc_type" in args_dict
for s in testGen.TOSA_MI_DOT_PRODUCT_TEST_SETS:
new_arg_str = f"{arg_str}_s{s}"
@@ -1260,20 +1267,40 @@
@staticmethod
def agAxis(testGen, opName, shapeList, dtype, error_name=None):
"""Build the axis argument for operators that take a single axis"""
- axes = []
+ arg_list = []
shape = shapeList[0]
if error_name == ErrorIf.AxisSmallerZero:
- small_axis = testGen.rng.integers(-5, 0)
- axes.append(("axis{}".format(small_axis), [small_axis]))
+ # Set too small axis
+ axes = [testGen.rng.integers(-5, 0)]
elif error_name == ErrorIf.AxisLargerRank:
- large_axis = testGen.rng.integers(len(shape) + 1, len(shape) + 10)
- axes.append(("axis{}".format(large_axis), [large_axis]))
+ # Set too large axis
+ axes = [testGen.rng.integers(len(shape) + 1, len(shape) + 10)]
else:
- for a in range(0, len(shape)):
- axes.append(("axis{}".format(a), [a]))
+ # Create tests for each dimension
+ axes = range(0, len(shape))
- return axes
+ opid = testGen.TOSA_OP_LIST[opName]["op"]
+
+ for a in axes:
+ args_dict = {"axis": int(a)}
+ if opid == Op.REDUCE_SUM:
+ args_dict["dot_products"] = gtu.product(shape)
+ args_dict["shape"] = shape
+ args_dict["ks"] = int(shape[a]) if a >= 0 and a < len(shape) else 1
+ args_dict["acc_type"] = dtype if dtype != DType.BF16 else DType.FP32
+
+ arg_list.append(("axis{}".format(a), args_dict))
+
+ arg_list = TosaArgGen._add_data_generators(
+ testGen,
+ opName,
+ dtype,
+ arg_list,
+ error_name,
+ )
+ # Return list of tuples: (arg_str, args_dict)
+ return arg_list
@staticmethod
def _calculate_sparsity(num_tests, sparsity_factor):
@@ -1483,6 +1510,7 @@
"kernel": k_shape,
"ks": k_size,
"dot_products": dots,
+ "shape": ifm_shape,
}
# Support for larger values than 9 needs different delimiter
@@ -1558,6 +1586,7 @@
"dot_products": gtu.product(
(shapeList[0][0], shapeList[0][1], shapeList[1][2])
),
+ "shape": shapeList[0],
}
# Create arg tuple of string and dict
@@ -1886,7 +1915,7 @@
else "st{}_kern{}_pad{}"
)
- def get_arg_list_element(accum, stride, pad, kern, dot_products=0):
+ def get_arg_list_element(accum, stride, pad, kern, dot_products=0, shape=[]):
# Return tuple containing the formatted argument string and
# the corresponding argument values in a dictionary
@@ -1902,6 +1931,7 @@
"pad": pad,
"kernel": kern,
"dot_products": dot_products, # Ignored for error tests
+ "shape": shape,
"ks": gtu.product(kern), # avg_pool2d: KS = KX*KY
}
@@ -1926,7 +1956,7 @@
)
if None not in [sNew, pNew, kNew] and n % sparsity == 0:
arg_list.append(
- get_arg_list_element(a, sNew, pNew, kNew)
+ get_arg_list_element(a, sNew, pNew, kNew, shape)
)
elif (
n % sparsity == 0
@@ -1965,7 +1995,9 @@
dp = gtu.product(
(shape[0], output_h, output_w, shape[3])
)
- arg_list.append(get_arg_list_element(a, s, p, k, dp))
+ arg_list.append(
+ get_arg_list_element(a, s, p, k, dp, shape)
+ )
n += 1
# Now add data generator types
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 556a0d8..3180cf5 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -658,12 +658,17 @@
)
return result_tens
- def build_argmax(self, op, a, axis, validator_fcns, error_name):
- result_tens = OutputShaper.argmaxOp(self.ser, self.rng, a, axis, error_name)
+ def build_argmax(
+ self, op, inputs, args_dict, validator_fcns, error_name, qinfo=None
+ ):
+ assert len(inputs) == 1
+ a = inputs[0]
+ axis = args_dict["axis"]
+ result_tensor = OutputShaper.argmaxOp(self.ser, self.rng, a, axis, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name]
- output_list = [result_tens.name]
+ output_list = [result_tensor.name]
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
@@ -678,9 +683,9 @@
axis=axis,
input_shape=a.shape,
input_dtype=a.dtype,
- output_shape=result_tens.shape,
- output_dtype=result_tens.dtype,
- result_tensors=[result_tens],
+ output_shape=result_tensor.shape,
+ output_dtype=result_tensor.dtype,
+ result_tensors=[result_tensor],
input_list=input_list,
output_list=output_list,
num_operands=num_operands,
@@ -691,7 +696,11 @@
attr.AxisAttribute(axis)
self.ser.addOperator(op["op"], input_list, output_list, attr)
- return result_tens
+
+ compliance = self.tensorComplianceMetaData(
+ op, inputs[0].dtype, args_dict, result_tensor, error_name
+ )
+ return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_pool2d(
self,
@@ -1173,12 +1182,17 @@
return TosaTestGen.BuildInfo(result_tensor, compliance)
- def build_reduce(self, op, a, axis, validator_fcns, error_name=None):
- result_tens = OutputShaper.reduceOp(self.ser, self.rng, a, axis, error_name)
+ def build_reduce(
+ self, op, inputs, args_dict, validator_fcns, error_name=None, qinfo=None
+ ):
+ assert len(inputs) == 1
+ a = inputs[0]
+ axis = args_dict["axis"]
+ result_tensor = OutputShaper.reduceOp(self.ser, self.rng, a, axis, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name]
- output_list = [result_tens.name]
+ output_list = [result_tensor.name]
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
@@ -1192,10 +1206,10 @@
op=op,
axis=axis,
input_shape=a.shape,
- output_shape=result_tens.shape,
+ output_shape=result_tensor.shape,
input_dtype=a.dtype,
- output_dtype=result_tens.dtype,
- result_tensors=[result_tens],
+ output_dtype=result_tensor.dtype,
+ result_tensors=[result_tensor],
input_list=input_list,
output_list=output_list,
num_operands=num_operands,
@@ -1206,7 +1220,16 @@
attr.AxisAttribute(axis)
self.ser.addOperator(op["op"], input_list, output_list, attr)
- return result_tens
+
+ if op["op"] == Op.REDUCE_PRODUCT:
+ # TODO: Add compliance support!
+ compliance = None
+ else:
+ compliance = self.tensorComplianceMetaData(
+ op, a.dtype, args_dict, result_tensor, error_name
+ )
+
+ return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_clamp(self, op, a, validator_fcns=None, error_name=None):
result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
@@ -1373,25 +1396,24 @@
self.ser.addOperator(op["op"], input_list, output_list)
return result_tens
- def build_concat(self, op, *a, validator_fcns=None, error_name=None):
+ def build_concat(
+ self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ ):
+ axis = args_dict["axis"]
if error_name != ErrorIf.WrongInputType:
- assert type(a[-1]) == int
+ assert type(axis) == int
- # To store variable length list of input tensors we need to store axis along with it
- axis = a[-1]
- a = a[:-1]
-
- result_tens = OutputShaper.concatOp(
- self.ser, self.rng, axis, *a, error_name=error_name
+ result_tensor = OutputShaper.concatOp(
+ self.ser, self.rng, axis, inputs, error_name=error_name
)
input_tensor_names = []
- for tensor in a:
+ for tensor in inputs:
input_tensor_names.append(tensor.name)
# Invalidate Input/Output list for error if checks.
input_list = input_tensor_names
- output_list = [result_tens.name]
+ output_list = [result_tensor.name]
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
@@ -1404,12 +1426,12 @@
error_name,
op=op,
axis=axis,
- input_shape=a[0].shape,
- output_shape=result_tens.shape,
- input_dtype=a[0].dtype,
- output_dtype=result_tens.dtype,
- inputs=a,
- result_tensors=[result_tens],
+ input_shape=inputs[0].shape,
+ output_shape=result_tensor.shape,
+ input_dtype=inputs[0].dtype,
+ output_dtype=result_tensor.dtype,
+ inputs=inputs,
+ result_tensors=[result_tensor],
input_list=input_list,
output_list=output_list,
num_operands=num_operands,
@@ -1420,7 +1442,7 @@
attr.AxisAttribute(axis)
self.ser.addOperator(op["op"], input_list, output_list, attr)
- return result_tens
+ return TosaTestGen.BuildInfo(result_tensor, None)
def build_pad(
self,
@@ -1483,17 +1505,20 @@
def build_dim(
self,
op,
- a,
- axis,
+ inputs,
+ args_dict,
validator_fcns=None,
error_name=None,
qinfo=None,
):
- result_tens = OutputShaper.dimOp(self.ser, self.rng, a, axis, error_name)
+ assert len(inputs) == 1
+ a = inputs[0]
+ axis = args_dict["axis"]
+ result_tensor = OutputShaper.dimOp(self.ser, self.rng, a, axis, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name]
- output_list = [result_tens.name]
+ output_list = [result_tensor.name]
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
@@ -1508,9 +1533,9 @@
axis=axis,
input_shape=a.shape,
input_dtype=a.dtype,
- output_shape=result_tens.shape,
- output_dtype=result_tens.dtype,
- result_tensors=[result_tens],
+ output_shape=result_tensor.shape,
+ output_dtype=result_tensor.dtype,
+ result_tensors=[result_tensor],
input_list=input_list,
output_list=output_list,
num_operands=num_operands,
@@ -1521,7 +1546,7 @@
attr.AxisAttribute(axis)
self.ser.addOperator(op["op"], input_list, output_list, attr)
- return result_tens
+ return TosaTestGen.BuildInfo(result_tensor, None)
def build_reshape(self, op, a, newShape, validator_fcns=None, error_name=None):
result_tens = OutputShaper.reshapeOp(
@@ -1559,12 +1584,17 @@
self.ser.addOperator(op["op"], input_list, output_list, attr)
return result_tens
- def build_reverse(self, op, a, axis, validator_fcns=None, error_name=None):
- result_tens = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
+ def build_reverse(
+ self, op, inputs, args_dict, validator_fcns=None, error_name=None, qinfo=None
+ ):
+ assert len(inputs) == 1
+ a = inputs[0]
+ axis = args_dict["axis"]
+ result_tensor = OutputShaper.unaryOp(self.ser, self.rng, a, error_name)
# Invalidate Input/Output list for error if checks.
input_list = [a.name]
- output_list = [result_tens.name]
+ output_list = [result_tensor.name]
pCount, cCount = op["operands"]
num_operands = pCount + cCount
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
@@ -1578,10 +1608,10 @@
op=op,
axis=axis,
input_shape=a.shape,
- output_shape=result_tens.shape,
+ output_shape=result_tensor.shape,
input_dtype=a.dtype,
- output_dtype=result_tens.dtype,
- result_tensors=[result_tens],
+ output_dtype=result_tensor.dtype,
+ result_tensors=[result_tensor],
input_list=input_list,
output_list=output_list,
num_operands=num_operands,
@@ -1592,7 +1622,7 @@
attr.AxisAttribute(axis)
self.ser.addOperator(op["op"], input_list, output_list, attr)
- return result_tens
+ return TosaTestGen.BuildInfo(result_tensor, None)
def build_transpose(self, op, a, perms, validator_fcns=None, error_name=None):
result_tens = OutputShaper.transposeOp(self.ser, self.rng, a, perms, error_name)
@@ -2898,7 +2928,7 @@
"build_fcn": (
build_argmax,
TosaTensorGen.tgBasic,
- TosaTensorValuesGen.tvgDefault,
+ TosaTensorValuesGen.tvgLazyGenDefault,
TosaArgGen.agAxis,
),
"types": TYPE_NARROW_INT_FP,
@@ -2913,6 +2943,9 @@
TosaErrorValidator.evWrongInputList,
TosaErrorValidator.evWrongOutputList,
),
+ "data_gen": {
+ "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+ },
},
"avg_pool2d": {
"op": Op.AVG_POOL2D,
@@ -3853,7 +3886,7 @@
"build_fcn": (
build_reduce,
TosaTensorGen.tgBasic,
- TosaTensorValuesGen.tvgDefault,
+ TosaTensorValuesGen.tvgLazyGenDefault,
TosaArgGen.agAxis,
),
"types": TYPE_BOOL,
@@ -3875,7 +3908,7 @@
"build_fcn": (
build_reduce,
TosaTensorGen.tgBasic,
- TosaTensorValuesGen.tvgDefault,
+ TosaTensorValuesGen.tvgLazyGenDefault,
TosaArgGen.agAxis,
),
"types": TYPE_BOOL,
@@ -3897,7 +3930,7 @@
"build_fcn": (
build_reduce,
TosaTensorGen.tgBasic,
- TosaTensorValuesGen.tvgDefault,
+ TosaTensorValuesGen.tvgLazyGenDefault,
TosaArgGen.agAxis,
),
"types": TYPE_INT_FP,
@@ -3911,6 +3944,9 @@
TosaErrorValidator.evWrongInputList,
TosaErrorValidator.evWrongOutputList,
),
+ "data_gen": {
+ "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+ },
},
"reduce_min": {
"op": Op.REDUCE_MIN,
@@ -3919,7 +3955,7 @@
"build_fcn": (
build_reduce,
TosaTensorGen.tgBasic,
- TosaTensorValuesGen.tvgDefault,
+ TosaTensorValuesGen.tvgLazyGenDefault,
TosaArgGen.agAxis,
),
"types": TYPE_INT_FP,
@@ -3933,6 +3969,9 @@
TosaErrorValidator.evWrongInputList,
TosaErrorValidator.evWrongOutputList,
),
+ "data_gen": {
+ "fp": (gtu.DataGenType.PSEUDO_RANDOM,),
+ },
},
"reduce_product": {
"op": Op.REDUCE_PRODUCT,
@@ -3941,7 +3980,7 @@
"build_fcn": (
build_reduce,
TosaTensorGen.tgBasic,
- TosaTensorValuesGen.tvgDefault,
+ TosaTensorValuesGen.tvgLazyGenDefault,
TosaArgGen.agAxis,
),
"types": TYPE_FP,
@@ -3977,6 +4016,9 @@
TosaErrorValidator.evWrongInputList,
TosaErrorValidator.evWrongOutputList,
),
+ "data_gen": {
+ "fp": (gtu.DataGenType.DOT_PRODUCT,),
+ },
},
# Data layout operators
"concat": {
@@ -4030,7 +4072,7 @@
"build_fcn": (
build_dim,
TosaTensorGen.tgBasic,
- TosaTensorValuesGen.tvgDefault,
+ TosaTensorValuesGen.tvgLazyGenDefault,
TosaArgGen.agAxis,
),
"types": TYPE_FIB,
@@ -4069,7 +4111,7 @@
"build_fcn": (
build_reverse,
TosaTensorGen.tgBasic,
- TosaTensorValuesGen.tvgDefault,
+ TosaTensorValuesGen.tvgLazyGenDefault,
TosaArgGen.agAxis,
),
"types": TYPE_FIB,
@@ -4892,9 +4934,9 @@
return ser.addOutput(output_shape, out_dtype)
@staticmethod
- def concatOp(ser, rng, axis, *a, error_name=None):
- input1 = a[0]
- remaining_inputs = a[1:]
+ def concatOp(ser, rng, axis, inputs, error_name=None):
+ input1 = inputs[0]
+ remaining_inputs = inputs[1:]
# calculate the output shape, if possible, otherwise just use the first input shape
output_shape = input1.shape.copy()