[Relay] Add support for relay expressions as pad value for static pad

include/tvm/relay/attrs/nn.h

@@ -1044,13 +1044,10 @@ struct UpSampling3DAttrs : public tvm::AttrsNode<UpSampling3DAttrs> {
 
 /*! \brief Attributes used for the padding operator */
 struct PadAttrs : public tvm::AttrsNode<PadAttrs> {
-  double pad_value;
   Array<Array<Integer>> pad_width;
   std::string pad_mode;
 
   TVM_DECLARE_ATTRS(PadAttrs, "relay.attrs.PadAttrs") {
-    TVM_ATTR_FIELD(pad_value).set_default(0.0).describe(
-        "The value used for padding when mode is 'constant'.");
     TVM_ATTR_FIELD(pad_width).describe(
         "Number of values padded to the edges of each axis, "
         "in the format of ((before_1, after_1), ..., (before_N, after_N))");

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

@@ -17,16 +17,15 @@
 # pylint: disable=invalid-name, unused-argument
 """Arm Compute Library supported operators."""
 import tvm
-
 from tvm import relay
 from tvm._ffi import register_func
-from tvm.relay.expr import const
 from tvm.relay import transform
 from tvm.relay.build_module import bind_params_by_name
+from tvm.relay.expr import const
 
-from ...dataflow_pattern import wildcard, is_op, is_constant, is_expr
-from .register import register_pattern_table
+from ...dataflow_pattern import is_constant, is_expr, is_op, wildcard
 from ..strategy.generic import is_depthwise_conv2d
+from .register import register_pattern_table
 
 
 def is_arm_compute_runtime_enabled():
@@ -140,7 +139,7 @@ def conv_pattern():
         pattern : dataflow_pattern.AltPattern
             Denotes the convolution pattern.
         """
-        pattern = is_op("nn.pad")(wildcard()) | wildcard()
+        pattern = is_op("nn.pad")(wildcard(), wildcard()) | wildcard()
         pattern = is_op("nn.conv2d")(pattern, is_constant())
         pattern = pattern.optional(lambda x: is_op("nn.bias_add")(x, is_constant()))
         pattern = pattern.optional(is_op("nn.relu"))
@@ -154,7 +153,7 @@ def qnn_conv_pattern():
         pattern : dataflow_pattern.AltPattern
             Denotes the convolution pattern.
         """
-        pattern = is_op("nn.pad")(wildcard()) | wildcard()
+        pattern = is_op("nn.pad")(wildcard(), wildcard()) | wildcard()
         pattern = is_op("qnn.conv2d")(
             pattern, is_constant(), is_constant(), is_constant(), is_constant(), is_constant()
         )

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

@@ -22,10 +22,10 @@
 from tvm.relay import transform
 from tvm.relay.build_module import bind_params_by_name
 
-from ...dataflow_pattern import wildcard, is_op, is_constant
 from ... import qnn as _qnn
-from .register import register_pattern_table
+from ...dataflow_pattern import is_constant, is_op, wildcard
 from . import _ethosn as support
+from .register import register_pattern_table
 
 
 class Available(Enum):
@@ -82,7 +82,7 @@ def pattern_table():
     """Get the Ethos-N compiler pattern table."""
 
     def qnn_conv_pattern():
-        pattern = is_op("nn.pad")(wildcard()) | wildcard()
+        pattern = is_op("nn.pad")(wildcard(), wildcard()) | wildcard()
         pattern = is_op("qnn.conv2d")(
             pattern, is_constant(), is_constant(), is_constant(), is_constant(), is_constant()
         )

python/tvm/relay/op/nn/nn.py

@@ -18,10 +18,10 @@
 """Neural network operations."""
 from tvm.relay import expr
 
-from . import _make
+from ...expr import Constant, Expr, const
 from ..dyn.nn import _make as _dyn_make
+from . import _make
 from .utils import get_pad_tuple1d, get_pad_tuple2d, get_pad_tuple3d
-from ...expr import const, Expr, Constant
 
 
 def conv1d(
@@ -1606,15 +1606,11 @@ def pad(data, pad_width, pad_value=0, pad_mode="constant"):
     result : tvm.relay.Expr
         The computed result.
     """
-    if isinstance(pad_value, Constant):
-        pad_value = pad_value.data.asnumpy().item()
     if isinstance(pad_width, Constant):
         pad_width = [list(i) for i in pad_width.data.asnumpy()]
-    if isinstance(pad_width, Expr) or (isinstance(pad_value, Expr)):
-        if not isinstance(pad_width, Expr):
-            pad_width = const(list(pad_width))
-        if not isinstance(pad_value, Expr):
-            pad_value = const(pad_value)
+    if not isinstance(pad_value, Expr):
+        pad_value = const(pad_value)
+    if isinstance(pad_width, Expr):
         return _dyn_make.pad(data, pad_width, pad_value, pad_mode)
     return _make.pad(data, pad_width, pad_value, pad_mode)
 

src/relay/op/make_op.h

@@ -58,7 +58,7 @@ Expr MakeAutoSchedulerLayoutTransform(Expr data, String src_layout, String dst_l
 
 Expr MakeOnes(Array<Integer> shape, DataType dtype);
 
-Expr MakePad(Expr data, Array<Array<Integer>> pad_width, double pad_value, String pad_mode);
+Expr MakePad(Expr data, Array<Array<Integer>> pad_width, Expr pad_value, String pad_mode);
 
 Expr MakeReduce(Expr data, Array<Integer> axis, bool keepdims, bool exclude, String op_name);
 

src/relay/op/nn/pad.cc

@@ -25,6 +25,7 @@
 #include <tvm/relay/op.h>
 #include <tvm/tir/data_layout.h>
 #include <tvm/tir/op.h>
+#include <tvm/topi/elemwise.h>
 #include <tvm/topi/nn.h>
 
 #include <vector>
@@ -44,7 +45,7 @@ Array<Array<Layout>> PadInferCorrectLayout(const Attrs& attrs, const Array<Layou
   // NOTE: Discard "const" qualifier here.
   PadAttrs* params = const_cast<PadAttrs*>(attrs.as<PadAttrs>());
 
-  Layout ret;
+  Layout ret_data;
   // If new_in_layouts are defined, this code tries to modify the layout.
   bool is_layout_modified = new_in_layouts.defined();
   if (new_in_layouts.defined()) {
@@ -55,8 +56,8 @@ Array<Array<Layout>> PadInferCorrectLayout(const Attrs& attrs, const Array<Layou
     // 1) Create a map from axis to param_width using old layout.
     std::map<std::string, tvm::Array<Integer>> axis_pad_width;
     int index_counter = 0;
-    ICHECK_EQ(new_in_layouts.size(), 1);
-    ICHECK_EQ(old_in_layouts.size(), 1);
+    ICHECK_EQ(new_in_layouts.size(), 2);
+    ICHECK_EQ(old_in_layouts.size(), 2);
     for (auto iter_var : old_in_layouts[0]->axes) {
       const auto& old_layout_axis = LayoutAxis::Get(iter_var);
       axis_pad_width.emplace(old_layout_axis.name(), params->pad_width[index_counter]);
@@ -95,26 +96,29 @@ Array<Array<Layout>> PadInferCorrectLayout(const Attrs& attrs, const Array<Layou
     // If the above conditions satisfied, we can set the newly created pad_width and use the new
     // layout.
     if (is_layout_modified) {
-      ret = new_in_layouts[0];
+      ret_data = new_in_layouts[0];
       params->pad_width = new_pad_width;
     }
   }
 
   if (!is_layout_modified) {
     if (old_in_layouts.defined()) {
-      ICHECK_EQ(old_in_layouts.size(), 1);
-      ret = old_in_layouts[0];
+      ICHECK_EQ(old_in_layouts.size(), 2);
+      ret_data = old_in_layouts[0];
     } else {
-      ret = Layout::Undef();
+      ret_data = Layout::Undef();
     }
   }
 
-  return Array<Array<Layout>>{{ret}, {ret}};
+  // The pad value is always a scalar
+  Layout ret_pad_value = Layout("1");
+  return Array<Array<Layout>>{{ret_data, ret_pad_value}, {ret_data}};
 }
 
 bool PadRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
             const TypeReporter& reporter) {
-  ICHECK_EQ(types.size(), 2);
+  // types = [pad_data_type, pad_value_type, ret_type]
+  ICHECK_EQ(types.size(), 3);
   const auto* data = types[0].as<TensorTypeNode>();
   if (data == nullptr) return false;
 
@@ -151,7 +155,7 @@ bool PadRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
     }
   }
 
-  reporter->Assign(types[1], TensorType(Array<IndexExpr>(oshape), data->dtype));
+  reporter->Assign(types[2], TensorType(Array<IndexExpr>(oshape), data->dtype));
   return true;
 }
 
@@ -170,20 +174,19 @@ Array<te::Tensor> PadCompute(const Attrs& attrs, const Array<te::Tensor>& inputs
   for (size_t i = 0; i < pad_width.size(); ++i) {
     pad_after.push_back(pad_width[i][1]);
   }
-  const auto* out_ttype = out_type.as<TensorTypeNode>();
-  return Array<te::Tensor>{topi::pad(inputs[0], pad_before, pad_after,
-                                     tvm::tir::make_const(out_ttype->dtype, param->pad_value),
-                                     "T_pad", topi::kElementWise, param->pad_mode)};
+  te::Tensor cast_pad_value = topi::cast(inputs[1], inputs[0]->dtype);
+  const PrimExpr& pad_value = cast_pad_value(Array<PrimExpr>());
+  return Array<te::Tensor>{topi::pad(inputs[0], pad_before, pad_after, pad_value, "T_pad",
+                                     topi::kElementWise, param->pad_mode)};
 }
 
 // Handler to create a call to the padding op used by front-end FFI
-Expr MakePad(Expr data, Array<Array<Integer>> pad_width, double pad_value, String pad_mode) {
+Expr MakePad(Expr data, Array<Array<Integer>> pad_width, Expr pad_value, String pad_mode) {
   auto attrs = make_object<PadAttrs>();
-  attrs->pad_value = pad_value;
   attrs->pad_width = std::move(pad_width);
   attrs->pad_mode = std::move(pad_mode);
   static const Op& op = Op::Get("nn.pad");
-  return Call(op, {data}, Attrs(attrs), {});
+  return Call(op, {data, pad_value}, Attrs(attrs), {});
 }
 
 TVM_REGISTER_GLOBAL("relay.op.nn._make.pad").set_body_typed(MakePad);
@@ -193,8 +196,9 @@ RELAY_REGISTER_OP("nn.pad")
 
 )code" TVM_ADD_FILELINE)
     .set_attrs_type<PadAttrs>()
-    .set_num_inputs(1)
+    .set_num_inputs(2)
     .add_argument("data", "Tensor", "The input tensor.")
+    .add_argument("pad_val", "Tensor", "The value to fill the padded area with")
     .set_support_level(2)
     .add_type_rel("Pad", PadRel)
     .set_attr<FInferCorrectLayout>("FInferCorrectLayout", PadInferCorrectLayout)

src/relay/qnn/op/convolution.cc

@@ -234,8 +234,7 @@ Expr Conv2DPadInput(const Expr& data, const Expr& input_zero_point, const Conv2D
     } else {
       LOG(FATAL) << "qnn.conv2d does not support " << param->data_layout << " layout";
     }
-    auto pad_value = GetScalarFromConstant<int>(input_zero_point);
-    padded_data = Pad(data, pad_width, pad_value, "constant");
+    padded_data = Pad(data, pad_width, input_zero_point, "constant");
   }
   return padded_data;
 }

src/relay/transforms/dynamic_to_static.cc

@@ -179,7 +179,9 @@ class DynamicToStaticMutator : public MixedModeMutator {
 
              const PadAttrs* param = call_node->attrs.as<PadAttrs>();
              ICHECK(param);
-             return MakePad(call_node->args[0], ToMatrix(pad_width->data), ToScalar(pad_fill->data),
+
+             Expr pad_value = args[2];
+             return MakePad(call_node->args[0], ToMatrix(pad_width->data), pad_value,
                             param->pad_mode);
            }
            return Expr(nullptr);

src/relay/transforms/fold_explicit_padding.cc

@@ -45,7 +45,7 @@ class SimplifyConvPad {
   SimplifyConvPad() {
     x_ = IsWildcard();
     w_ = IsWildcard();
-    pad_ = IsOp("nn.pad")({x_});
+    pad_ = IsOp("nn.pad")({x_, IsWildcard()});
     conv1d_ = IsOp("nn.conv1d");
     conv2d_ = IsOp("nn.conv2d");
     conv3d_ = IsOp("nn.conv3d");
@@ -119,7 +119,11 @@ class SimplifyConvPad {
     ICHECK(pad_node);
     const PadAttrs* param = pad_node->attrs.as<PadAttrs>();
     ICHECK(param);
-    if (param->pad_mode == "constant" && param->pad_value == 0.0) {
+    Array<Expr> args = pad_node->args;
+
+    // Possibly perform more optimizations if the pad_value is 0
+    const ConstantNode* pad_value = args[1].as<ConstantNode>();
+    if (param->pad_mode == "constant" && pad_value && ToScalar(pad_value->data) == 0.0) {
       Attrs attrs;
       if (node_map.count(conv1d_)) {
         attrs = GetAttrs(param, call_node->attrs.as<Conv1DAttrs>());

src/relay/transforms/pattern_utils.h

@@ -676,7 +676,7 @@ static inline Expr AvgPool2D(Expr data, Array<IndexExpr> pool_size, Array<IndexE
                                      count_include_pad, "nn.avg_pool2d");
 }
 
-static inline Expr Pad(Expr data, Array<Array<IndexExpr>> pad_width, double pad_value,
+static inline Expr Pad(Expr data, Array<Array<IndexExpr>> pad_width, Expr pad_value,
                        std::string pad_mode) {
   Array<Array<Integer>> pad_width_int;
   for (size_t i = 0; i < pad_width.size(); ++i) {

tests/python/relay/test_op_level2.py

@@ -18,15 +18,13 @@
 """
 import numpy as np
 import tvm
-from tvm import te
-from tvm import autotvm
-from tvm import relay
+import tvm.testing
+import tvm.topi.testing
+from tvm import autotvm, relay, te
+from tvm.contrib import utils
 from tvm.relay import transform
 from tvm.relay.testing import run_infer_type
-from tvm.contrib import utils
-import tvm.topi.testing
 from tvm.topi.cuda.conv3d_winograd import _infer_tile_size
-import tvm.testing
 
 
 @tvm.testing.uses_gpu
@@ -1197,6 +1195,30 @@ def test_pad_infer_type():
     yy = run_infer_type(y)
     assert yy.checked_type == relay.TensorType((n + (-2), c + (-4), h + (-2), w + 8), "float32")
 
+    # dealing with dynamic vals
+    n, c, h, w = te.size_var("n"), 2, 3, te.size_var("w")
+    t = relay.var("t", relay.TensorType((n, c, h, w), "float32"))
+    y = relay.nn.pad(
+        t, ((1, 1), (2, 2), (3, 3), (4, 4)), pad_value=relay.var("pad_value", "float32")
+    )
+    yy = run_infer_type(y)
+    assert yy.checked_type == relay.TensorType((n + 2, 6, 9, w + 8), "float32")
+
+
+def _get_numpy_pad(dshape, data, pad, pad_value=0):
+    mod_pad = []
+    for axis, (pad_x, pad_y) in enumerate(pad):
+        indices = range(dshape[axis])
+        if pad_x < 0:
+            indices = indices[abs(pad_x) :]
+            pad_x = 0
+        if pad_y < 0:
+            indices = indices[:pad_y]
+            pad_y = 0
+        data = np.take(data, indices, axis)
+        mod_pad.append((pad_x, pad_y))
+    return np.pad(data, tuple(mod_pad), "constant", constant_values=pad_value)
+
 
 @tvm.testing.uses_gpu
 def test_pad_run():
@@ -1209,20 +1231,7 @@ def _test_run(dtype):
             y = relay.nn.pad(x, pad)
             func = relay.Function([x], y)
             data = np.random.uniform(size=dshape).astype(dtype)
-            mod_pad = []
-            mod_data = data
-            for axis, (pad_x, pad_y) in enumerate(pad):
-                indices = range(dshape[axis])
-                if pad_x < 0:
-                    indices = indices[abs(pad_x) :]
-                    pad_x = 0
-                if pad_y < 0:
-                    indices = indices[:pad_y]
-                    pad_y = 0
-                mod_data = np.take(mod_data, indices, axis)
-                mod_pad.append((pad_x, pad_y))
-
-            ref_res = np.pad(mod_data, tuple(mod_pad), "constant")
+            ref_res = _get_numpy_pad(dshape, data, pad)
             for target, dev in tvm.testing.enabled_targets():
                 intrp1 = relay.create_executor("graph", device=dev, target=target)
                 op_res1 = intrp1.evaluate(func)(data)
@@ -1232,6 +1241,30 @@ def _test_run(dtype):
     _test_run("int32")
 
 
+@tvm.testing.uses_gpu
+def test_pad_run_dynamic_pad_value():
+    def _test_run(dtype):
+        dshape = (4, 6, 3, 5)
+        pad = ((-1, -1), (2, -2), (0, -2), (4, 4))
+
+        data = relay.var("data", shape=dshape, dtype=dtype)
+        pad_value = relay.var("pad_value", dtype)
+        pad_data = relay.nn.pad(data, pad, pad_value=pad_value)
+        f = relay.Function([data, pad_value], pad_data)
+
+        data_arr = np.random.uniform(-10, 10, size=dshape).astype(dtype)
+        pad_value_arr = 2.0
+        ref_res = _get_numpy_pad(dshape, data_arr, pad, pad_value=pad_value_arr)
+
+        for target, dev in tvm.testing.enabled_targets():
+            intrp = relay.create_executor(kind="graph", device=dev, target=target)
+            result = intrp.evaluate(f)(data_arr, pad_value_arr)
+            tvm.testing.assert_allclose(result.asnumpy(), ref_res, rtol=1e-5, atol=1e-5)
+
+    _test_run("float32")
+    _test_run("int32")
+
+
 @tvm.testing.uses_gpu
 def test_lrn():
     n, c, h, w = te.size_var("n"), te.size_var("c"), te.size_var("h"), te.size_var("w")
@@ -1766,6 +1799,7 @@ def _test_correlation(
     test_flatten_infer_type()
     test_pad_infer_type()
     test_pad_run()
+    test_pad_run_dynamic_pad_value()
     test_conv3d_transpose_infer_type()
     test_conv3d_transpose_ncdhw_run()
     test_conv2d_transpose_infer_type()