Main Compliance testing for CONV3D
Signed-off-by: evacha01 <evan.chandler@arm.com>
Change-Id: Ie05f88db15cd07fd5483ab669329d7048bd3349c
diff --git a/reference_model/src/generate/generate_dot_product.cc b/reference_model/src/generate/generate_dot_product.cc
index 046007e..7337969 100644
--- a/reference_model/src/generate/generate_dot_product.cc
+++ b/reference_model/src/generate/generate_dot_product.cc
@@ -736,6 +736,147 @@
return false;
}
}
+
+//---------------------------------------------------------------------------//
+// Conv3D //
+//---------------------------------------------------------------------------//
+
+template <typename DataType>
+bool generateConv3DInput(const TosaReference::GenerateConfig& cfg,
+ TosaReference::IDotProductGenerator& generator,
+ DataType* data,
+ size_t size)
+{
+ if (cfg.dotProductInfo.kernel.size() != 3 || cfg.dotProductInfo.kernel[0] <= 0 ||
+ cfg.dotProductInfo.kernel[1] <= 0 || cfg.dotProductInfo.kernel[2] <= 0)
+ {
+ WARNING("[Generator][DP][Conv3D][Input] Missing or incorrect kernel size information.");
+ return false;
+ }
+ if (cfg.shape.size() != 5)
+ {
+ WARNING("[Generator][DP][Conv3D][Input] Tensor shape expected 5 dimensions.");
+ return false;
+ }
+
+ const int64_t T = TosaReference::numElementsFromShape(cfg.shape);
+ const uint32_t ID = cfg.shape[1];
+ const uint32_t IH = cfg.shape[2];
+ const uint32_t IW = cfg.shape[3];
+ const uint32_t IC = cfg.shape[4];
+ const uint32_t KD = cfg.dotProductInfo.kernel[0];
+ const uint32_t KH = cfg.dotProductInfo.kernel[1];
+ const uint32_t KW = cfg.dotProductInfo.kernel[2];
+
+ for (int64_t t = 0; t < T; ++t)
+ {
+ uint32_t ic = t % IC;
+ uint32_t ix = (t / IC) % IW;
+ uint32_t iy = ((t / IC) / IW) % IH;
+ uint32_t id = (((t / IC) / IW) / IH) % ID;
+ uint32_t k = (((id % KD) * KH + (iy % KH)) * KW + (ix % KW)) * IC + ic;
+
+ data[t] = static_cast<DataType>(generator(k));
+ }
+ return true;
+}
+
+template <typename DataType>
+bool generateConv3DWeight(const TosaReference::GenerateConfig& cfg,
+ TosaReference::IDotProductGenerator& generator,
+ DataType* data,
+ size_t size)
+{
+ if (cfg.shape.size() != 5)
+ {
+ WARNING("[Generator][DP][Conv3D][Weight] Tensor shape expected 5 dimensions.");
+ return false;
+ }
+
+ const int64_t T = TosaReference::numElementsFromShape(cfg.shape);
+ const uint32_t KD = cfg.shape[0];
+ const uint32_t KH = cfg.shape[1];
+ const uint32_t KW = cfg.shape[2];
+ const uint32_t IC = cfg.shape[3];
+
+ for (int64_t t = 0; t < T; ++t)
+ {
+ uint32_t ic = t % IC;
+ uint32_t kx = (t / IC) % KW;
+ uint32_t ky = ((t / IC) / KW) % KH;
+ uint32_t kd = (((t / IC) / KW) / KH) % KD;
+ uint32_t k = ((kd * KH + ky) * KW + kx) * IC + ic;
+
+ data[t] = static_cast<DataType>(generator(k));
+ }
+ return true;
+}
+
+template <typename DataType>
+bool generateConv3DBias(const TosaReference::GenerateConfig& cfg,
+ TosaReference::IDotProductGenerator& generator,
+ DataType* data,
+ size_t size)
+{
+ if (cfg.shape.size() != 1)
+ {
+ WARNING("[Generator][DP][Conv3D][Bias] Tensor shape expected 1 dimension.");
+ return false;
+ }
+
+ const uint32_t T = cfg.shape[0];
+
+ for (uint32_t t = 0; t < T; ++t)
+ {
+ data[t] = static_cast<DataType>(generator(2));
+ }
+ return true;
+}
+
+bool generateConv3D(const TosaReference::GenerateConfig& cfg,
+ TosaReference::IDotProductGenerator& generator,
+ void* data,
+ size_t size)
+{
+ switch (cfg.dataType)
+ {
+ case DType::DType_FP32: {
+ float* outData = reinterpret_cast<float*>(data);
+ switch (cfg.inputPos)
+ {
+ case 0:
+ return generateConv3DInput(cfg, generator, outData, size);
+ case 1:
+ return generateConv3DWeight(cfg, generator, outData, size);
+ case 2:
+ return generateConv3DBias(cfg, generator, outData, size);
+ default:
+ WARNING("[Generator][DP][Conv3D] Invalid input tensor slot position to operator.");
+ return false;
+ }
+ break;
+ }
+ case DType::DType_FP16: {
+ half_float::half* outData = reinterpret_cast<half_float::half*>(data);
+ switch (cfg.inputPos)
+ {
+ case 0:
+ return generateConv3DInput(cfg, generator, outData, size);
+ case 1:
+ return generateConv3DWeight(cfg, generator, outData, size);
+ case 2:
+ return generateConv3DBias(cfg, generator, outData, size);
+ default:
+ WARNING("[Generator][DP][Conv3D] Invalid input tensor slot position to operator.");
+ return false;
+ }
+ break;
+ }
+ default:
+ WARNING("[Generator][DP][Conv3D] Only supports FP32 or FP16.");
+ return false;
+ }
+}
//---------------------------------------------------------------------------//
// FFT2D //
//---------------------------------------------------------------------------//
@@ -858,6 +999,8 @@
return generateDepthwiseConv2D(cfg, *generator, data, size);
case tosa::Op_TRANSPOSE_CONV2D:
return generateTransposeConv2D(cfg, *generator, data, size);
+ case tosa::Op_CONV3D:
+ return generateConv3D(cfg, *generator, data, size);
case tosa::Op_FFT2D:
return generateFFT2D(cfg, *generator, data, size);
default:
diff --git a/reference_model/src/generate/generate_utils.cc b/reference_model/src/generate/generate_utils.cc
index 2e40b04..c495fb6 100644
--- a/reference_model/src/generate/generate_utils.cc
+++ b/reference_model/src/generate/generate_utils.cc
@@ -50,6 +50,7 @@
{ Op::Op_CONST, "CONST" },
{ Op::Op_CONV2D, "CONV2D" },
{ Op::Op_DEPTHWISE_CONV2D, "DEPTHWISE_CONV2D" },
+ { Op::Op_CONV3D, "CONV3D" },
{ Op::Op_EQUAL, "EQUAL" },
{ Op::Op_ERF, "ERF" },
{ Op::Op_EXP, "EXP" },
diff --git a/reference_model/test/generate_tests.cpp b/reference_model/test/generate_tests.cpp
index 4f62ede..3be402c 100644
--- a/reference_model/test/generate_tests.cpp
+++ b/reference_model/test/generate_tests.cpp
@@ -1224,6 +1224,164 @@
}
}
+void conv3d_test_FP16(const std::string tosaName[3],
+ const size_t tosaElements[3],
+ const std::string templateJsonCfg,
+ const std::string setStr,
+ int32_t param,
+ const std::vector<uint16_t> expected)
+{
+ std::string jsonCfg = templateJsonCfg;
+ update_json_template(jsonCfg, "_SET_", setStr);
+
+ std::vector<half_float::half> buffer(tosaElements[param]);
+ REQUIRE(tgd_generate_data(jsonCfg.c_str(), tosaName[param].c_str(), (void*)buffer.data(), tosaElements[param] * 2));
+ check_output<half_float::half>(buffer, expected);
+}
+
+TEST_CASE("positive - FP16 conv3d dot product (first 3 values)")
+{
+ std::string templateJsonCfg = R"({
+ "tensors" : {
+ "input" : {
+ "generator": "DOT_PRODUCT",
+ "data_type": "FP16",
+ "input_type": "VARIABLE",
+ "shape" : [1, 3, 2, 2, 3],
+ "input_pos": 0,
+ "op" : "CONV3D",
+ "dot_product_info": {
+ "s": _SET_,
+ "ks": 27,
+ "acc_type": "FP16",
+ "kernel": [3, 1, 3]
+ }
+ },
+ "weight" : {
+ "generator": "DOT_PRODUCT",
+ "data_type": "FP16",
+ "input_type": "CONSTANT",
+ "shape" : [4, 3, 1, 3, 3],
+ "input_pos": 1,
+ "op" : "CONV3D",
+ "dot_product_info": {
+ "s": _SET_,
+ "ks": 27,
+ "acc_type": "FP16"
+ }
+ },
+ "bias" : {
+ "generator": "DOT_PRODUCT",
+ "data_type": "FP16",
+ "input_type": "CONSTANT",
+ "shape" : [ 4 ],
+ "input_pos": 2,
+ "op" : "CONV3D",
+ "dot_product_info": {
+ "s": _SET_,
+ "ks": 27,
+ "acc_type": "FP16"
+ }
+ }
+
+ }
+ })";
+
+ const std::string tosaName[3] = { "input", "weight", "bias" };
+ const size_t tosaElements[3] = { (1 * 3 * 2 * 2 * 3), (4 * 3 * 1 * 3 * 3), 4 };
+
+ SUBCASE("conv3d, set 0, param 0")
+ {
+ std::vector<uint16_t> expected = { 0xbb33, 0xbb9b, 0x0 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "0", 0, expected);
+ }
+ SUBCASE("conv3d, set 0, param 1")
+ {
+ std::vector<uint16_t> expected = { 0x0, 0x0, 0x39a8 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "0", 1, expected);
+ }
+ SUBCASE("conv3d, set 0, param 2")
+ {
+ std::vector<uint16_t> expected = { 0x0, 0x0, 0x0 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "0", 2, expected);
+ }
+ SUBCASE("conv3d, set 1, param 0")
+ {
+ std::vector<uint16_t> expected = { 0x4e37, 0x4ed1, 0x4f87 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "1", 0, expected);
+ }
+ SUBCASE("conv3d, set 1, param 1")
+ {
+ std::vector<uint16_t> expected = { 0x51fe, 0x5104, 0x4fbf };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "1", 1, expected);
+ }
+ SUBCASE("conv3d, set 1, param 2")
+ {
+ std::vector<uint16_t> expected = { 0x6498, 0x66e0, 0x687d };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "1", 2, expected);
+ }
+ SUBCASE("conv3d, set 2, param 0")
+ {
+ std::vector<uint16_t> expected = { 0x3c00, 0x2bdb, 0xad62 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "2", 0, expected);
+ }
+ SUBCASE("conv3d, set 2, param 1")
+ {
+ std::vector<uint16_t> expected = { 0x3c00, 0x1814, 0x31be };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "2", 1, expected);
+ }
+ SUBCASE("conv3d, set 2, param 2")
+ {
+ std::vector<uint16_t> expected = { 0x0, 0x0, 0x0 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "2", 2, expected);
+ }
+ SUBCASE("conv3d, set 3, param 0")
+ {
+ std::vector<uint16_t> expected = { 0x4c00, 0xb92b, 0x30f4 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "3", 0, expected);
+ }
+ SUBCASE("conv3d, set 3, param 1")
+ {
+ std::vector<uint16_t> expected = { 0x4c00, 0x3a2e, 0x3bf5 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "3", 1, expected);
+ }
+ SUBCASE("conv3d, set 3, param 2")
+ {
+ std::vector<uint16_t> expected = { 0x0, 0x0, 0x0 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "3", 2, expected);
+ }
+ SUBCASE("conv3d, set 4, param 0")
+ {
+ std::vector<uint16_t> expected = { 0x0, 0x0, 0x5110 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "4", 0, expected);
+ }
+ SUBCASE("conv3d, set 4, param 1")
+ {
+ std::vector<uint16_t> expected = { 0x4384, 0xd1de, 0x0 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "4", 1, expected);
+ }
+ SUBCASE("conv3d, set 4, param 2")
+ {
+ std::vector<uint16_t> expected = { 0x0, 0x0, 0x0 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "4", 2, expected);
+ }
+ SUBCASE("conv3d, set 5, param 0")
+ {
+ std::vector<uint16_t> expected = { 0x490c, 0x4ccf, 0x5046 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "5", 0, expected);
+ }
+ SUBCASE("conv3d, set 5, param 1")
+ {
+ std::vector<uint16_t> expected = { 0xc994, 0x4ca4, 0x4f9f };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "5", 1, expected);
+ }
+ SUBCASE("conv3d, set 5, param 2")
+ {
+ std::vector<uint16_t> expected = { 0x0, 0x0, 0x0 };
+ conv3d_test_FP16(tosaName, tosaElements, templateJsonCfg, "5", 2, expected);
+ }
+}
+
void fft2d_test_FP32(const std::string tosaName,
const size_t tosaElements,
const std::string templateJsonCfg,
diff --git a/verif/conformance/tosa_main_profile_ops_info.json b/verif/conformance/tosa_main_profile_ops_info.json
index 067fab7..a53d0c7 100644
--- a/verif/conformance/tosa_main_profile_ops_info.json
+++ b/verif/conformance/tosa_main_profile_ops_info.json
@@ -684,6 +684,7 @@
"profile": [
"tosa-mi"
],
+ "support_for": [ "lazy_data_gen" ],
"generation": {
"standard": {
"negative_dim_range": "1,10",
@@ -696,7 +697,7 @@
"--target-dtype",
"bf16",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--target-shape",
"1,7,18,5,4",
"--target-shape",
@@ -709,7 +710,7 @@
"--target-dtype",
"fp32",
"--fp-values-range",
- "-2.0,2.0",
+ "-max,max",
"--target-shape",
"1,2,65539,1,2",
"--target-shape",
diff --git a/verif/generator/tosa_test_gen.py b/verif/generator/tosa_test_gen.py
index 68a4e94..9c3cd32 100644
--- a/verif/generator/tosa_test_gen.py
+++ b/verif/generator/tosa_test_gen.py
@@ -325,6 +325,7 @@
Op.FULLY_CONNECTED,
Op.DEPTHWISE_CONV2D,
Op.TRANSPOSE_CONV2D,
+ Op.CONV3D,
)
if (
errorName
@@ -952,7 +953,7 @@
dilations = args_dict["dilation"]
assert len(padding) == 6
- result_tens = OutputShaper.conv3dOp(
+ result_tensor = OutputShaper.conv3dOp(
self.ser,
self.rng,
ifm,
@@ -971,12 +972,12 @@
):
qinfo = [
TosaQuantGen.getZeroPoint(self, ifm.dtype),
- TosaQuantGen.getZeroPoint(self, result_tens.dtype),
+ TosaQuantGen.getZeroPoint(self, result_tensor.dtype),
]
# Invalidate Input/Output list for error_if checks.
input_list = [ifm.name, filter.name, bias.name]
- output_list = [result_tens.name]
+ output_list = [result_tensor.name]
num_operands = sum(op["operands"])
input_list, output_list = TosaErrorIfArgGen.eiInvalidateInputOutputList(
self, error_name, input_list, output_list
@@ -989,7 +990,7 @@
op=op,
input_dtype=ifm.dtype,
weight_dtype=filter.dtype,
- output_dtype=result_tens.dtype,
+ output_dtype=result_tensor.dtype,
qinfo=qinfo,
input_list=input_list,
num_operands=num_operands,
@@ -999,7 +1000,7 @@
dilation=dilations,
input_shape=ifm.shape,
weight_shape=filter.shape,
- output_shape=result_tens.shape,
+ output_shape=result_tensor.shape,
):
return None
@@ -1010,7 +1011,12 @@
attr.ConvAttribute(padding, strides, dilations, qinfo[0], qinfo[1], local_bound)
self.ser.addOperator(op["op"], input_list, output_list, attr)
- return result_tens
+
+ compliance = self.tensorComplianceMetaData(
+ op, ifm.dtype, args_dict, result_tensor, error_name
+ )
+
+ return TosaTestGen.BuildInfo(result_tensor, compliance)
def build_transpose_conv2d(
self,
@@ -3254,6 +3260,9 @@
TosaErrorValidator.evConvOutputShapeMismatch,
TosaErrorValidator.evConvOutputShapeNonInteger,
),
+ "data_gen": {
+ "fp": (gtu.DataGenType.DOT_PRODUCT,),
+ },
"template": True,
},
# Templated operator. Filled in by createDynamicOpLists