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author | Kevin Cheng <kevin.cheng@arm.com> | 2021-06-29 15:32:19 -0700 |
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committer | Kevin Cheng <kevin.cheng@arm.com> | 2021-08-20 18:07:06 +0100 |
commit | acb550f4410ae861e53cae27a9feb4b11d45769f (patch) | |
tree | ae2f4ec558c2cdf1afa020b80a09d7ab4be5ef6d /reference_model/src/ops/tensor_ops.cc | |
parent | 68e7aee65bda5ac03fa7def753b7dc7462554793 (diff) | |
download | reference_model-acb550f4410ae861e53cae27a9feb4b11d45769f.tar.gz |
Replace node level check ASSERT_MSG_NODE()/FATAL_ERROR_NODE() with REQUIRE() or ERROR_IF()
- Adding return code enum class: {VALID, UNPREDICTABLE, ERROR}
- Runtime errors (e.g. memory allocation failure) will abort immediately, or will return one of the three return codes
Part of the codes are re-written to pass REQUIRE() to the top-level (e.g. apply_scale_32/16())
- Update setExpectedFailure() to setExpectedReturnCode() on test generation script
- Update test regression script to interface with reference model change
Signed-off-by: Kevin Cheng <kevin.cheng@arm.com>
Change-Id: Ia063c936bcb2a54d6e379a5bb6801aa72d1186f1
Diffstat (limited to 'reference_model/src/ops/tensor_ops.cc')
-rw-r--r-- | reference_model/src/ops/tensor_ops.cc | 118 |
1 files changed, 70 insertions, 48 deletions
diff --git a/reference_model/src/ops/tensor_ops.cc b/reference_model/src/ops/tensor_ops.cc index 0007553..045c0a5 100644 --- a/reference_model/src/ops/tensor_ops.cc +++ b/reference_model/src/ops/tensor_ops.cc @@ -22,8 +22,11 @@ using namespace Eigen; using namespace tosa; template <int Rank, DType Dtype> -OpArgMax<Rank, Dtype>::OpArgMax(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) - : GraphNode(Op_ARGMAX, id_) +OpArgMax<Rank, Dtype>::OpArgMax(SubgraphTraverser* sgt_, + TosaAttributeBase* attribute_, + TosaQuantInfoBase* qinfo_, + uint64_t id_) + : GraphNode(sgt_, Op_ARGMAX, id_) { setRequiredOperands(1, 1); setRequiredRank(0, 6); @@ -66,8 +69,11 @@ int OpArgMax<Rank, Dtype>::eval() } template <DType Dtype> -OpAvgPool2d<Dtype>::OpAvgPool2d(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) - : GraphNode(Op_AVG_POOL2D, id_) +OpAvgPool2d<Dtype>::OpAvgPool2d(SubgraphTraverser* sgt_, + TosaAttributeBase* attribute_, + TosaQuantInfoBase* qinfo_, + uint64_t id_) + : GraphNode(sgt_, Op_AVG_POOL2D, id_) { setRequiredOperands(1, 1); setRequiredRank(4); @@ -142,9 +148,6 @@ ETensor1<int32_t> OpAvgPool2d<Dtype>::calculate_div_map_1d(int in_size, int out_ int32_t left_index = pad_left / stride; int32_t right_index = pad_right / stride; - // not handle ultra small activation yet - ASSERT_MSG_NODE((out_size - 1 - right_index) >= left_index, "AvgPool2d: Small activations not supported yet"); - // minus the number of pad bit this index cover while (left_index >= 0) { @@ -176,7 +179,8 @@ int OpAvgPool2d<Dtype>::eval() int out_width = this->out->getShape()[2]; int out_channels = this->out->getShape()[3]; - ASSERT_MSG_NODE(in_batch == out_batch, "OpAvgPool2d: tensor batch mismatch %d != %d", in_batch, out_batch); + 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]; @@ -260,12 +264,19 @@ int OpAvgPool2d<Dtype>::eval() if (Dtype != DType_FLOAT) { - this->out->getTensor() = sum.binaryExpr(div_map, [](AccEigenType value, int32_t div) -> OutEigenType { - int32_t multiplier, shift; - TosaReference::QuantUtil::reciprocal_scale(div, multiplier, shift); + try + { + this->out->getTensor() = sum.binaryExpr(div_map, [](AccEigenType value, int32_t div) -> OutEigenType { + int32_t multiplier, shift; + TosaReference::QuantUtil::reciprocal_scale(div, multiplier, shift); - return (OutEigenType)TosaReference::QuantUtil::apply_scale_32(value, multiplier, shift, false); - }); + return (OutEigenType)TosaReference::QuantUtil::apply_scale_32(value, multiplier, shift, false); + }); + } + catch (std::string desc) + { + REQUIRE(false, "OpAvgPool2d apply_scale_32() fails: %s.", desc.c_str()); + } this->out->getTensor() = this->out->getTensor() + (OutEigenType)(this->qinfo->output_zp()); this->out->getTensor() = this->out->getTensor().cwiseMax((OutEigenType)QMin); this->out->getTensor() = this->out->getTensor().cwiseMin((OutEigenType)QMax); @@ -279,8 +290,11 @@ int OpAvgPool2d<Dtype>::eval() } template <DType InDtype, DType WeightDtype> -OpConv2d<InDtype, WeightDtype>::OpConv2d(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) - : GraphNode(Op_CONV2D, id_) +OpConv2d<InDtype, WeightDtype>::OpConv2d(SubgraphTraverser* sgt_, + TosaAttributeBase* attribute_, + TosaQuantInfoBase* qinfo_, + uint64_t id_) + : GraphNode(sgt_, Op_CONV2D, id_) { setRequiredOperands(3, 1); setRequiredRank(4); @@ -361,13 +375,12 @@ int OpConv2d<InDtype, WeightDtype>::eval() int out_width = this->output->getShape()[2]; int out_channels = this->output->getShape()[3]; - ASSERT_MSG_NODE(in_batch == out_batch, "OpConv2d: tensor batch mismatch %d != %d", in_batch, out_batch); - ASSERT_MSG_NODE(f_in_channels == in_channels, "OpConv2d: tensor input channel mismatch %d != %d", f_in_channels, - in_channels); - ASSERT_MSG_NODE(f_out_channels == out_channels, "OpConv2d: tensor output channel mismatch %d != %d", f_out_channels, - out_channels); - ASSERT_MSG_NODE(b_out_channels == out_channels, "OpConv2d: tensor output channel mismatch %d != %d", b_out_channels, - out_channels); + ERROR_IF(in_batch != out_batch, "OpConv2d: tensor batch mismatch %d != %d", in_batch, out_batch); + ERROR_IF(f_in_channels != in_channels, "OpConv2d: tensor input channel mismatch %d != %d", f_in_channels, + in_channels); + ERROR_IF(f_out_channels != out_channels, "OpConv2d: tensor output channel mismatch %d != %d", f_out_channels, + 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]; @@ -469,10 +482,11 @@ int OpConv2d<InDtype, WeightDtype>::eval() } template <DType InDtype, DType WeightDtype> -OpDepthwiseConv2d<InDtype, WeightDtype>::OpDepthwiseConv2d(TosaAttributeBase* attribute_, +OpDepthwiseConv2d<InDtype, WeightDtype>::OpDepthwiseConv2d(SubgraphTraverser* sgt_, + TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) - : GraphNode(Op_DEPTHWISE_CONV2D, id_) + : GraphNode(sgt_, Op_DEPTHWISE_CONV2D, id_) { setRequiredOperands(3, 1); setRequiredRank(4); @@ -553,14 +567,13 @@ int OpDepthwiseConv2d<InDtype, WeightDtype>::eval() int out_width = this->output->getShape()[2]; int out_channels = this->output->getShape()[3]; - ASSERT_MSG_NODE(in_batch == out_batch, "OpDepthwiseConv2d: tensor batch mismatch %d != %d", in_batch, out_batch); - ASSERT_MSG_NODE(f_in_channels == in_channels, "OpDepthwiseConv2d: tensor input channel mismatch %d != %d", - f_in_channels, in_channels); - ASSERT_MSG_NODE(in_channels * f_multiplier == out_channels, - "OpDepthwiseConv2d: tensor output channel mismatch %d != %d", in_channels * f_multiplier, - out_channels); - ASSERT_MSG_NODE(b_out_channels == out_channels, "OpDepthwiseConv2d: tensor b_out_channels mismatch %d != %d", - b_out_channels, out_channels); + ERROR_IF(in_batch != out_batch, "OpDepthwiseConv2d: tensor batch mismatch %d != %d", in_batch, out_batch); + ERROR_IF(f_in_channels != in_channels, "OpDepthwiseConv2d: tensor input channel mismatch %d != %d", f_in_channels, + in_channels); + ERROR_IF(in_channels * f_multiplier != out_channels, "OpDepthwiseConv2d: tensor output channel mismatch %d != %d", + in_channels * f_multiplier, out_channels); + 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]; @@ -651,10 +664,11 @@ int OpDepthwiseConv2d<InDtype, WeightDtype>::eval() } template <DType InDtype, DType WeightDtype> -OpFullyConnected<InDtype, WeightDtype>::OpFullyConnected(TosaAttributeBase* attribute_, +OpFullyConnected<InDtype, WeightDtype>::OpFullyConnected(SubgraphTraverser* sgt_, + TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) - : GraphNode(Op_FULLY_CONNECTED, id_) + : GraphNode(sgt_, Op_FULLY_CONNECTED, id_) { setRequiredOperands(3, 1); setRequiredRank(2); @@ -738,8 +752,11 @@ int OpFullyConnected<InDtype, WeightDtype>::eval() } template <DType Dtype> -OpMatMul<Dtype>::OpMatMul(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) - : GraphNode(Op_MATMUL, id_) +OpMatMul<Dtype>::OpMatMul(SubgraphTraverser* sgt_, + TosaAttributeBase* attribute_, + TosaQuantInfoBase* qinfo_, + uint64_t id_) + : GraphNode(sgt_, Op_MATMUL, id_) { setRequiredOperands(2, 1); setRequiredRank(3); @@ -866,8 +883,11 @@ int OpMatMul<Dtype>::eval() } template <DType Dtype> -OpMaxPool2d<Dtype>::OpMaxPool2d(TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) - : GraphNode(Op_MAX_POOL2D, id_) +OpMaxPool2d<Dtype>::OpMaxPool2d(SubgraphTraverser* sgt_, + TosaAttributeBase* attribute_, + TosaQuantInfoBase* qinfo_, + uint64_t id_) + : GraphNode(sgt_, Op_MAX_POOL2D, id_) { setRequiredOperands(1, 1); setRequiredRank(4); @@ -936,7 +956,8 @@ int OpMaxPool2d<Dtype>::eval() int out_width = this->out->getShape()[2]; int out_channels = this->out->getShape()[3]; - ASSERT_MSG_NODE(in_batch == out_batch, "OpMaxPool2d: tensor batch mismatch %d != %d", in_batch, out_batch); + 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]; @@ -1004,10 +1025,11 @@ int OpMaxPool2d<Dtype>::eval() } template <DType InDtype, DType OutDtype> -OpTransposeConv2d<InDtype, OutDtype>::OpTransposeConv2d(TosaAttributeBase* attribute_, +OpTransposeConv2d<InDtype, OutDtype>::OpTransposeConv2d(SubgraphTraverser* sgt_, + TosaAttributeBase* attribute_, TosaQuantInfoBase* qinfo_, uint64_t id_) - : GraphNode(Op_TRANSPOSE_CONV2D, id_) + : GraphNode(sgt_, Op_TRANSPOSE_CONV2D, id_) { setRequiredOperands(3, 1); setRequiredRank(4); @@ -1104,13 +1126,13 @@ int OpTransposeConv2d<InDtype, OutDtype>::eval() int dilation_h = this->attribute->dilation()[0]; int dilation_w = this->attribute->dilation()[1]; - ASSERT_MSG_NODE(in_batch == out_batch, "OpTransposeConv2d: tensor batch mismatch %d != %d", in_batch, out_batch); - ASSERT_MSG_NODE(f_in_channels == in_channels, "OpTransposeConv2d: tensor input channel mismatch %d != %d", - f_in_channels, in_channels); - ASSERT_MSG_NODE(f_out_channels == out_channels, "OpTransposeConv2d: tensor output channel mismatch %d != %d", - f_out_channels, out_channels); - ASSERT_MSG_NODE(b_out_channels == out_channels, "OpDepthwiseConv2d: tensor b_out_channels mismatch %d != %d", - b_out_channels, out_channels); + 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, + in_channels); + ERROR_IF(f_out_channels != out_channels, "OpTransposeConv2d: tensor output channel mismatch %d != %d", + f_out_channels, out_channels); + ERROR_IF(b_out_channels != out_channels, "OpDepthwiseConv2d: bias channels mismatch %d != %d", b_out_channels, + out_channels); DEBUG_INFO(OP, "perform OpTransposeConv2d, input.shape=[%d,%d,%d,%d], weight.shape=[%d,%d,%d,%d], " |