[QNN] Conv2D operator

docs/langref/relay_op.rst

@@ -211,6 +211,7 @@ This level supports dialect operators.
    :nosignatures:
 
    tvm.relay.qnn.op.requantize
+   tvm.relay.qnn.op.conv2d
 
 
 Level 1 Definitions
@@ -357,3 +358,4 @@ Level 10 Definitions
 Level 11 Definitions
 --------------------
 .. autofunction:: tvm.relay.qnn.op.requantize
+.. autofunction:: tvm.relay.qnn.op.conv2d

include/tvm/relay/qnn/attrs.h

@@ -125,6 +125,68 @@ struct QnnConcatenateAttrs : public tvm::AttrsNode<QnnConcatenateAttrs> {
   }
 };  // struct QnnConcatenateAttrs
 
+/*! \brief Attribute for QNN Conv2d operator */
+struct QnnConv2DAttrs : public tvm::AttrsNode<QnnConv2DAttrs> {
+  // Traditional conv2d attributes.
+  Array<IndexExpr> strides;
+  Array<IndexExpr> padding;
+  Array<IndexExpr> dilation;
+  int groups;
+  IndexExpr channels;
+  Array<IndexExpr> kernel_size;
+  std::string data_layout;
+  std::string kernel_layout;
+  std::string out_layout;
+  DataType out_dtype;
+
+  // Quantization related attributes.
+  int32_t input_zero_point;
+  int32_t kernel_zero_point;
+
+  TVM_DECLARE_ATTRS(QnnConv2DAttrs, "relay.attrs.QnnConv2DAttrs") {
+    TVM_ATTR_FIELD(strides).set_default(Array<IndexExpr>({1, 1}))
+        .describe("Specifies the strides of the convolution.");
+    TVM_ATTR_FIELD(padding).set_default(Array<IndexExpr>({0, 0}))
+        .describe("If padding is non-zero, then the input is implicitly zero-padded"
+                  "on both sides for padding number of points");
+    TVM_ATTR_FIELD(dilation).set_default(Array<IndexExpr>({1, 1}))
+        .describe("Specifies the dilation rate to use for dilated convolution.");
+    TVM_ATTR_FIELD(groups).set_default(1)
+        .describe("Controls the connections between inputs and outputs."
+                  "At groups=1, all inputs are convolved to all outputs."
+                  "At groups=2, the operation becomes equivalent to having two convolution"
+                  "layers side by side, each seeing half the input channels, and producing"
+                  "half the output channels, and both subsequently concatenated.");
+    TVM_ATTR_FIELD(channels)
+        .describe("The number of output channels in the convolution."
+                  " If it is not set, inferred by shape of the weight.")
+        .set_default(NullValue<IndexExpr>());
+    TVM_ATTR_FIELD(kernel_size)
+        .describe("Specifies the dimensions of the convolution window.")
+        .set_default(NullValue<Array<IndexExpr> >());
+    TVM_ATTR_FIELD(data_layout).set_default("NCHW")
+        .describe("Dimension ordering of input data. Can be 'NCHW', 'NHWC', etc."
+                  "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                  "dimensions respectively. Convolution is applied on the 'H' and"
+                  "'W' dimensions.");
+    TVM_ATTR_FIELD(kernel_layout).set_default("OIHW")
+        .describe("Dimension ordering of weight. Can be 'OIHW', 'OIHW16o16i', etc."
+                  "'O', 'I', 'H', 'W' stands for num_filter, input_channel, height, and width"
+                  "dimensions respectively.");
+    TVM_ATTR_FIELD(out_layout).set_default("")
+        .describe("Dimension ordering of output. Can be 'NCHW', 'NHWC', etc."
+                  "'N', 'C', 'H', 'W' stands for batch, channel, height, and width"
+                  "dimensions respectively. Default to be same as input layout.");
+    TVM_ATTR_FIELD(out_dtype)
+        .set_default(NullValue<DataType>())
+        .describe("Output data type, set to explicit type under mixed precision setting");
+    TVM_ATTR_FIELD(input_zero_point)
+        .describe("The zero point of the input tensor.");
+    TVM_ATTR_FIELD(kernel_zero_point)
+        .describe("The zero point of the kernel tensor.");
+  }
+};
+
 }  // namespace qnn
 }  // namespace relay
 }  // namespace tvm

python/tvm/relay/qnn/op/qnn.py

@@ -183,3 +183,83 @@ def concatenate(data,
                              output_scale,
                              output_zero_point,
                              axis)
+
+
+def conv2d(data,
+           kernel,
+           input_zero_point,
+           kernel_zero_point,
+           strides=(1, 1),
+           padding=(0, 0),
+           dilation=(1, 1),
+           groups=1,
+           channels=None,
+           kernel_size=None,
+           data_layout="NCHW",
+           kernel_layout="OIHW",
+           out_layout="",
+           out_dtype="int32"):
+    r"""Quantized 2D convolution.
+
+    This operator convolves quantized data with quantized kernel. The scale of
+    the output quantized tensor is the product of the kernel_scale and
+    input_scale of the input quantized tensors. The zero point of the output
+    quantized tensor is 0. By default, the dtype of output is int32. Please also
+    refer to Requantize operator to understand how to scale back the int32
+    output to (u)int8.
+
+    Parameters
+    ----------
+    data : tvm.relay.Expr
+        The input data to the operator.
+
+    kernel : tvm.relay.Expr
+        The kernel expressions.
+
+    input_zero_point: int
+           The zero point of the data distribution.
+
+    kernel_zero_point: int
+           The zero point of the quantized_kernel distribution.
+
+    strides : tuple of int, optional
+        The strides of convolution.
+
+    padding : tuple of int, optional
+        The padding of convolution on both sides of inputs before convolution.
+
+    dilation : tuple of int, optional
+        Specifies the dilation rate to be used for dilated convolution.
+
+    groups : int, optional
+        Number of groups for grouped convolution.
+
+    channels : int, optional
+        Number of output channels of this convolution.
+
+    kernel_size : tuple of int, optional
+        The spatial of the convolution kernel.
+
+    data_layout : str, optional
+        Layout of the input.
+
+    kernel_layout : str, optional
+        Layout of the kernel.
+
+    out_layout : str, optional
+        Layout of the output, by default, out_layout is the same as data_layout
+
+    out_dtype : str, optional
+        Specifies the output data type for mixed precision conv2d.
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The computed result.
+    """
+
+    return _make.conv2d(data, kernel,
+                        input_zero_point, kernel_zero_point,
+                        strides, padding, dilation,
+                        groups, channels, kernel_size,
+                        data_layout, kernel_layout, out_layout, out_dtype)

src/relay/pass/pattern_util.h

@@ -415,6 +415,71 @@ static inline Expr Full(Expr fill_value,
   return CallNode::make(op, {fill_value}, Attrs(attrs), {});
 }
 
+static inline Expr Conv2D(Expr data, Expr weight, Array<IndexExpr> strides,
+                          Array<IndexExpr> padding, Array<IndexExpr> dilation, int groups,
+                          IndexExpr channels, Array<IndexExpr> kernel_size, std::string data_layout,
+                          std::string kernel_layout, std::string out_layout, DataType out_dtype) {
+  auto attrs = make_node<Conv2DAttrs>();
+  attrs->strides = std::move(strides);
+  attrs->padding = std::move(padding);
+  attrs->dilation = std::move(dilation);
+  attrs->groups = groups;
+  attrs->channels = std::move(channels);
+  attrs->kernel_size = std::move(kernel_size);
+  attrs->data_layout = std::move(data_layout);
+  attrs->kernel_layout = std::move(kernel_layout);
+  attrs->out_layout = std::move(out_layout);
+  attrs->out_dtype = std::move(out_dtype);
+  static const Op& op = Op::Get("nn.conv2d");
+  return CallNode::make(op, {data, weight}, Attrs(attrs), {});
+}
+
+static inline Expr Sum(Expr data, Array<Integer> axis, bool keepdims, bool exclude) {
+  auto attrs = make_node<ReduceAttrs>();
+  attrs->axis = std::move(axis);
+  attrs->keepdims = keepdims;
+  attrs->exclude = exclude;
+  static const Op& op = Op::Get("sum");
+  return CallNode::make(op, {data}, Attrs(attrs), {});
+}
+
+static inline Expr Reshape(Expr data, Array<Integer> newshape) {
+  auto attrs = make_node<ReshapeAttrs>();
+  attrs->newshape = std::move(newshape);
+  attrs->reverse = false;
+  static const Op& op = Op::Get("reshape");
+  return CallNode::make(op, {data}, Attrs(attrs), {});
+}
+
+static inline Expr AvgPool2D(Expr data, Array<IndexExpr> pool_size, Array<IndexExpr> strides,
+                             Array<IndexExpr> padding, std::string layout, bool ceil_mode,
+                             bool count_include_pad) {
+  auto attrs = make_node<AvgPool2DAttrs>();
+  attrs->pool_size = std::move(pool_size);
+  attrs->strides = std::move(strides);
+  attrs->padding = std::move(padding);
+  attrs->layout = std::move(layout);
+  attrs->ceil_mode = ceil_mode;
+  attrs->count_include_pad = count_include_pad;
+  static const Op& op = Op::Get("nn.avg_pool2d");
+  return CallNode::make(op, {data}, Attrs(attrs), {});
+}
+
+static inline Expr Pad(Expr data, Array<Array<IndexExpr>> pad_width, double pad_value) {
+  auto attrs = make_node<PadAttrs>();
+  attrs->pad_value = pad_value;
+  attrs->pad_width = std::move(pad_width);
+  static const Op& op = Op::Get("nn.pad");
+  return CallNode::make(op, {data}, Attrs(attrs), {});
+}
+
+static inline Expr Tile(Expr data, Array<Integer> reps) {
+  auto attrs = make_node<TileAttrs>();
+  attrs->reps = reps;
+  static const Op& op = Op::Get("tile");
+  return CallNode::make(op, {data}, Attrs(attrs), {});
+}
+
 Expr MakeConcatenate(Expr data, int axis);
 
 Expr MakeStridedSlice(Expr data, Array<Integer> begin, Array<Integer> end, Array<Integer> strides);