[ETHOSN] Add support for transpose convolution

Adds support for offloading transpose convolution with an optional bias
to the NPU.

Co-authored-by: Samuel Panijel <samuel.panijel@arm.com>
Co-authored-by: Leo Blonk <leo.blonk@arm.com>
Change-Id: I2534fc45a1498679b4701f41b8f7a1f79dda4e79

python/tvm/relay/op/contrib/ethosn.py

@@ -233,6 +233,16 @@ def qnn_add_pattern():
 
         return input_is_left | input_is_right | two_inputs
 
+    def qnn_conv2d_transpose_pattern():
+        pattern = is_op("qnn.conv2d_transpose")(
+            wildcard(), is_constant(), is_constant(), is_constant(), is_constant(), is_constant()
+        ).has_attr({"data_layout": "NHWC"})
+        pattern = pattern.optional(lambda x: is_op("nn.bias_add")(x, is_constant()))
+        pattern = is_op("qnn.requantize")(
+            pattern, is_constant(), is_constant(), is_constant(), is_constant()
+        )
+        return pattern
+
     def check_conv2d(extract):
         """Check if a conv2d is supported by Ethos-N."""
         if not ethosn_available():
@@ -261,6 +271,13 @@ def check_mean(extract):
 
         return _ethosn.mean(extract)
 
+    def check_conv2d_transpose(extract):
+        """Check if mean is supported by Ethos-N."""
+        if not ethosn_available():
+            return False
+
+        return _ethosn.conv2d_transpose(extract)
+
     def check_sigmoid(extract):
         """Check if a sigmoid is supported by Ethos-N."""
         if not ethosn_available():
@@ -326,6 +343,7 @@ def check_add(extract):
         ("ethos-n.qnn_mul", qnn_mul_pattern(), check_mul),
         ("ethos-n.qnn_add", qnn_add_pattern(), check_add),
         ("ethos-n.qnn_conv2d", qnn_conv_pattern(), check_conv2d),
+        ("ethos-n.qnn_conv2d_transpose", qnn_conv2d_transpose_pattern(), check_conv2d_transpose),
         ("ethos-n.qnn_avg_pool2d", qnn_avg_pool2d_pattern(), check_avg_pool2d),
         ("ethos-n.qnn_sigmoid", qnn_sigmoid_pattern(), check_sigmoid),
         ("ethos-n.qnn_fc", qnn_fc_pattern(), check_fc),

src/relay/backend/contrib/ethosn/codegen.cc

@@ -125,6 +125,10 @@ void InferTensorsVisitor::InferCall(const CallNode* cn) {
     LeakyReLUParams params;
     err += EthosnAPI::LeakyReLU(cn->op.as<FunctionNode>()->body, &params);
     tensor_table_[cn->args[0]] = {params.input_info};
+  } else if (IsEthosnFunc(call, "ethos-n.qnn_conv2d_transpose")) {
+    QnnConv2dTransposeParams params;
+    err += EthosnAPI::QnnConv2dTranspose(cn->op.as<FunctionNode>()->body, &params);
+    tensor_table_[cn->args[0]] = {params.input_info};
   } else if (IsEthosnOp(call, "qnn.concatenate")) {
     ConcatenateParams params;
     err = EthosnAPI::Concatenate(call, &params);
@@ -311,6 +315,9 @@ sl::TensorsAndId ConstructNetworkVisitor::HandleCall(const CallNode* cn) {
   } else if (IsEthosnFunc(call, "ethos-n.qnn_leaky_relu")) {
     if ((err = MakeLeakyReLULayer(call, &tensor))) ReportFatalError(call, err);
     return MakeOps(tensor);
+  } else if (IsEthosnFunc(call, "ethos-n.qnn_conv2d_transpose")) {
+    if ((err = MakeConv2DTransposeLayer(call, &tensor))) ReportFatalError(call, err);
+    return MakeOps(tensor);
   } else if (IsEthosnOp(call, "qnn.concatenate")) {
     if ((err = MakeConcatenateLayer(call, &tensor))) ReportFatalError(call, err);
     return MakeOps(tensor);
@@ -537,6 +544,24 @@ EthosnError ConstructNetworkVisitor::MakeLeakyReLULayer(const Call& call,
   return EthosnError();
 }
 
+EthosnError ConstructNetworkVisitor::MakeConv2DTransposeLayer(const Call& call,
+                                                              sl::TensorAndId<sl::Operand>* out) {
+  QnnConv2dTransposeParams params;
+  if (auto err = EthosnAPI::QnnConv2dTranspose(call->op.as<FunctionNode>()->body, &params)) {
+    return err;
+  }
+
+  auto activation = operand_table_[call->args[0]][0];
+  auto weights = AddConstant(network_, params.weights_info, params.raw_weights->data).tensor;
+  auto bias = AddConstant(network_, params.bias_info, params.raw_bias->data).tensor;
+  try {
+    *out = AddTransposeConvolution(network_, *activation, *bias, *weights, params.conv_info);
+  } catch (const sl::NotSupportedException& e) {
+    return EthosnError(e.what());
+  }
+  return EthosnError();
+}
+
 EthosnError ConstructNetworkVisitor::MakeConcatenateLayer(const Call& call,
                                                           sl::TensorAndId<sl::Operand>* out) {
   ConcatenateParams params;
@@ -913,6 +938,20 @@ TVM_REGISTER_GLOBAL("relay.ethos-n.support.leaky_relu")
       err += EthosnError(reason);
     });
 
+TVM_REGISTER_GLOBAL("relay.ethos-n.support.conv2d_transpose")
+    .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
+      Call call = args[0];
+      QnnConv2dTransposeParams params;
+      auto err = EthosnAPI::QnnConv2dTranspose(call, &params);
+      err += EthosnCompiler::SupportedSetup();
+      char reason[kReasonMaxLength];
+      reason[0] = '\0';
+      *rv = !err && EthosnCompiler::GetSupported()->IsTransposeConvolutionSupported(
+                        params.bias_info, params.weights_info, params.conv_info, params.input_info,
+                        &params.output_info, reason, sizeof(reason));
+      err += EthosnError(reason);
+    });
+
 TVM_REGISTER_GLOBAL("relay.ethos-n.support.concatenate")
     .set_body([](tvm::TVMArgs args, tvm::TVMRetValue* rv) {
       Call call = args[0];

src/relay/backend/contrib/ethosn/codegen_ethosn.h

@@ -206,6 +206,7 @@ class ConstructNetworkVisitor : public MixedModeVisitor, private ErrorReportingP
   EthosnError MakeSigmoidLayer(const Call& call, sl::TensorAndId<sl::Operand>* out);
   EthosnError MakeMeanLayer(const Call& call, sl::TensorAndId<sl::Operand>* out);
   EthosnError MakeTanhLayer(const Call& call, sl::TensorAndId<sl::Operand>* out);
+  EthosnError MakeConv2DTransposeLayer(const Call& call, sl::TensorAndId<sl::Operand>* out);
   EthosnError MakeConcatenateLayer(const Call& call, sl::TensorAndId<sl::Operand>* out);
   EthosnError MakeSplitLayer(const Call& call, sl::TensorsAndId* outs);
   EthosnError MakeDepthToSpaceLayer(const Call& call, sl::TensorAndId<sl::Operand>* out);

src/relay/backend/contrib/ethosn/convert_equivalent.cc

@@ -32,26 +32,13 @@
 #include "../../../qnn/utils.h"
 #include "../../../transforms/pattern_utils.h"
 #include "../../../transforms/simplify_expr.h"
+#include "ethosn_api.h"
 
 namespace tvm {
 namespace relay {
 namespace contrib {
 namespace ethosn {
 
-/*!
- * \brief Apply constant folding on an expression.
- *
- * \param expr The expression to fold.
- * \param fold_qnn Whether to fold constants for QNN operations.
- * \returns The new folded expression.
- */
-Expr FoldConstantExpr(const Expr& expr, bool fold_qnn = true) {
-  auto mod = IRModule::FromExpr(expr);
-  mod = transform::FoldConstant(fold_qnn)(mod);
-  auto entry_func = Downcast<Function>(mod->Lookup("main"));
-  return expr.as<FunctionNode>() == nullptr ? entry_func->body : entry_func;
-}
-
 /*!
  * \brief Converts qnn.mul to mathematically equivalent
  * qnn.conv2d depthwise operation.

src/relay/backend/contrib/ethosn/ethosn_api.cc

@@ -23,6 +23,7 @@
 
 #include "ethosn_api.h"
 
+#include <tvm/relay/analysis.h>
 #include <tvm/relay/attrs/image.h>
 #include <tvm/relay/attrs/nn.h>
 #include <tvm/relay/expr.h>
@@ -37,6 +38,9 @@
 #include <utility>
 #include <vector>
 
+#include "../../../op/make_op.h"
+#include "../../../transforms/pattern_utils.h"
+#include "../../../transforms/simplify_expr.h"
 #include "ethosn_support_library/Support.hpp"
 #include "ethosn_support_library/SupportQueries.hpp"
 #include "tvm/relay/qnn/attrs.h"
@@ -445,6 +449,121 @@ EthosnError EthosnAPI::Mean(const Expr& expr, MeanParams* params) {
   return err;
 }
 
+Constant TransposeWeights(const Constant& data, const std::string& input_layout) {
+  int pos_h = input_layout.find("H");
+  int pos_w = input_layout.find("W");
+  int pos_i = input_layout.find("I");
+  int pos_o = input_layout.find("O");
+
+  // Currently the expected target layout is HWIO only.
+  Array<Integer> target_shape = {pos_h, pos_w, pos_i, pos_o};
+
+  Expr transpose = MakeTranspose(data, target_shape);
+  transpose = InferType(FoldConstantExpr(transpose));
+  Constant transposed_data = Downcast<Constant>(transpose);
+  return transposed_data;
+}
+
+EthosnError EthosnAPI::QnnConv2dTranspose(const Expr& expr, QnnConv2dTransposeParams* params) {
+  Call requantize = Downcast<Call>(expr);
+  Call bias;
+  Call conv2d_transpose;
+  if (requantize->args[0]->IsInstance<CallNode>() &&
+      Downcast<Call>(requantize->args[0])->op == Op::Get("nn.bias_add")) {
+    bias = Downcast<Call>(requantize->args[0]);
+    conv2d_transpose = Downcast<Call>(bias->args[0]);
+  } else {
+    conv2d_transpose = Downcast<Call>(requantize->args[0]);
+  }
+  const auto& conv_attr = conv2d_transpose->attrs.as<Conv2DTransposeAttrs>();
+  ICHECK(conv_attr) << "Expected type Conv2DTransposeAttrs but was "
+                    << conv2d_transpose->attrs->GetTypeKey();
+
+  int input_zero_point;
+  int kernel_zero_point;
+  int output_zero_point;
+  std::valarray<float> input_scale;
+  std::valarray<float> kernel_scale;
+  float output_scale;
+  unsigned int qaxis = conv_attr->kernel_layout.find("O");
+
+  EthosnError err = AsConstant(conv2d_transpose->args[2], &input_zero_point);
+  err += AsConstant(conv2d_transpose->args[3], &kernel_zero_point);
+  err += AsConstant(requantize->args[4], &output_zero_point);
+  err += AsConstant(conv2d_transpose->args[4], &input_scale);
+  err += AsConstant(conv2d_transpose->args[5], &kernel_scale);
+  err += AsConstant(requantize->args[3], &output_scale);
+
+  // Convert quantization params
+  sl::QuantizationInfo input_q_info;
+  sl::QuantizationInfo weights_q_info;
+  sl::QuantizationInfo bias_q_info;
+  sl::QuantizationInfo output_q_info;
+  err += Tvm2Npu(input_zero_point, input_scale, qaxis, &input_q_info);
+  err += Tvm2Npu(kernel_zero_point, kernel_scale, qaxis, &weights_q_info);
+  std::valarray<float> bias_scales = input_q_info.GetScales() * weights_q_info.GetScales();
+  err += Tvm2Npu(0, bias_scales, 3, &bias_q_info);
+  err += Tvm2Npu(output_zero_point, output_scale, &output_q_info);
+
+  // Convert convolution attributes
+  sl::Padding padding;
+  err += Tvm2Npu(conv_attr->padding, &padding);
+  sl::Stride stride;
+  err += Tvm2Npu(conv_attr->strides, &stride);
+  // Dilation is not supported
+  std::array<uint32_t, 2> dilation = {1, 1};
+  AsArray(conv_attr->dilation, &dilation);
+  if (conv_attr->dilation.size() != 2 || dilation[0] != 1 || dilation[1] != 1) {
+    err +=
+        EthosnError(ErrStrm() << "dilation=" << conv_attr->dilation << ", dilation must = [1, 1]");
+  }
+
+  // Create convolution info
+  params->conv_info = sl::ConvolutionInfo(padding, stride, output_q_info);
+
+  // Create input info
+  sl::TensorInfo input_tensor_info;
+  err += Tvm2Npu(conv2d_transpose->args[0]->checked_type(), &input_tensor_info);
+  input_tensor_info.m_QuantizationInfo = input_q_info;
+  params->input_info = input_tensor_info;
+
+  // Create weights info
+  Constant weights_data = Downcast<Constant>(conv2d_transpose->args[1]);
+  if (conv_attr->kernel_layout != "HWIO") {
+    weights_data = TransposeWeights(weights_data, conv_attr->kernel_layout);
+  }
+  const auto* weights_ttype = weights_data->checked_type().as<TensorTypeNode>();
+  sl::TensorShape weights_tensor_shape;
+  sl::DataType weights_data_type;
+  sl::DataFormat weights_data_format;
+  // Ignore the error here because weights don't have a batch axis
+  Tvm2Npu(weights_ttype->shape, &weights_tensor_shape);
+  err += Tvm2Npu(weights_ttype->dtype, &weights_data_type);
+  err += Tvm2Npu("HWIO", &weights_data_format);
+  params->weights_info =
+      sl::TensorInfo(weights_tensor_shape, weights_data_type, weights_data_format, weights_q_info);
+
+  params->raw_weights = weights_data->data;
+
+  // Create bias info
+  unsigned int out_channels = Downcast<IntImm>(conv_attr->channels)->value;
+  params->bias_info = sl::TensorInfo({1, 1, 1, out_channels}, sl::DataType::INT32_QUANTIZED,
+                                     sl::DataFormat::NHWC, bias_q_info);
+  if (bias.defined()) {
+    params->raw_bias = Downcast<Constant>(bias->args[1])->data;
+  } else {
+    params->raw_bias = MakeConstantZeros(tvm::DataType::Int(32), {1, 1, 1, out_channels})->data;
+  }
+
+  // Create output info
+  sl::TensorInfo output_tensor_info;
+  err += Tvm2Npu(requantize->checked_type(), &output_tensor_info);
+  output_tensor_info.m_QuantizationInfo = output_q_info;
+  params->output_info = output_tensor_info;
+
+  return err;
+}
+
 EthosnError EthosnAPI::Tanh(const Expr& expr, TanhParams* params) {
   Call quantize = Downcast<Call>(expr);
   Call tanh = Downcast<Call>(quantize->args[0]);
@@ -920,6 +1039,13 @@ EthosnError EthosnAPI::AsConstant(const Expr& expr, T* out) {
   return EthosnError();
 }
 
+Expr FoldConstantExpr(const Expr& expr, bool fold_qnn) {
+  auto mod = IRModule::FromExpr(expr);
+  mod = transform::FoldConstant(fold_qnn)(mod);
+  auto entry_func = Downcast<Function>(mod->Lookup("main"));
+  return expr.as<FunctionNode>() == nullptr ? entry_func->body : entry_func;
+}
+
 }  // namespace ethosn
 }  // namespace contrib
 }  // namespace relay

src/relay/backend/contrib/ethosn/ethosn_api.h

@@ -24,6 +24,7 @@
 #ifndef TVM_RELAY_BACKEND_CONTRIB_ETHOSN_ETHOSN_API_H_
 #define TVM_RELAY_BACKEND_CONTRIB_ETHOSN_ETHOSN_API_H_
 
+#include <tvm/relay/attrs/nn.h>
 #include <tvm/relay/expr.h>
 #include <tvm/relay/expr_functor.h>
 #include <tvm/relay/transform.h>
@@ -115,6 +116,16 @@ struct LeakyReLUParams {
   sl::TensorInfo output_info;
 };
 
+struct QnnConv2dTransposeParams {
+  sl::ConvolutionInfo conv_info;
+  sl::TensorInfo input_info;
+  sl::TensorInfo weights_info;
+  sl::TensorInfo bias_info;
+  sl::TensorInfo output_info;
+  runtime::NDArray raw_weights;
+  runtime::NDArray raw_bias;
+};
+
 struct ConcatenateParams {
   sl::QuantizationInfo qInfo;
   sl::ConcatenationInfo concat_info = sl::ConcatenationInfo(1, qInfo);
@@ -237,6 +248,9 @@ class EthosnAPI {
   static EthosnError Tanh(const Expr& expr, TanhParams* params);
   /*! \brief Extract the Support Library leaky relu params from an ethos-n leaky relu Relu call. */
   static EthosnError LeakyReLU(const Expr& expr, LeakyReLUParams* params);
+  /*! \brief Extract the Support Library transpose params from a Relay
+   * ethos-n.qnn_conv2d_transpose func */
+  static EthosnError QnnConv2dTranspose(const Expr& expr, QnnConv2dTransposeParams* params);
   /*! \brief Extract the Support Library concatenate params from a Relay qnn.concatenate call */
   static EthosnError Concatenate(const Expr& expr, ConcatenateParams* params);
   /*! \brief Extract the Support Library split params from a Relay split call */
@@ -294,6 +308,15 @@ class EthosnAPI {
   static EthosnError AsConstant(const Expr& expr, std::valarray<float>* out);
 };
 
+/*!
+ * \brief Apply constant folding on an expression.
+ *
+ * \param expr The expression to fold.
+ * \param fold_qnn Whether to fold constants for QNN operations.
+ * \returns The new folded expression.
+ */
+Expr FoldConstantExpr(const Expr& expr, bool fold_qnn = true);
+
 }  // namespace ethosn
 }  // namespace contrib
 }  // namespace relay