[RELAY/PASS] Simplify inference.

nnvm/tests/python/compiler/test_simplify_inference.py

@@ -10,7 +10,6 @@ def simple_bn(x, gamma, beta, moving_mean, moving_var,
         scale = sym.elemwise_mul(1 / sym.sqrt(moving_var + epsilon), gamma)
         shift = sym.elemwise_add(
             sym.elemwise_mul(sym.negative(moving_mean), scale), beta)
-        shape = [-1 if i == axis else 1 for i in range(len(shape))]
         # for 2D
         num_newaxis=len(shape) - axis - 1
         if num_newaxis:

python/tvm/relay/expr.py

@@ -1,6 +1,7 @@
 # pylint: disable=no-else-return, unidiomatic-typecheck, invalid-name
 """The expression nodes of Relay."""
 from __future__ import absolute_import
+from numbers import Number as _Number
 
 import numpy as _np
 from .base import RelayNode, register_relay_node
@@ -11,6 +12,8 @@
 from .. import nd as _nd
 from .. import convert
 
+# will be registered afterwards
+_op_make = None
 
 class Expr(RelayNode):
     """The base type for all Relay expressions."""
@@ -48,6 +51,62 @@ def astype(self, dtype):
         """
         return _make.dtype_cast(self, dtype)
 
+    def __add__(self, other):
+        if isinstance(other, Expr):
+            return _op_make.add(self, other)
+        elif isinstance(other, _Number):
+            raise TypeError('convert "%s" with `const` first' % str(other))
+        else:
+            raise TypeError("type %s not supported" % str(type(other)))
+
+    def __radd__(self, other):
+        return self.__add__(other)
+
+    def __sub__(self, other):
+        if isinstance(other, Expr):
+            return _op_make.subtract(self, other)
+        elif isinstance(other, _Number):
+            raise TypeError('convert "%s" with `const` first' % str(other))
+        else:
+            raise TypeError("type %s not supported" % str(type(other)))
+
+    def __rsub__(self, other):
+        if isinstance(other, _Number):
+            raise TypeError('convert "%s" with `const` first' % str(other))
+        else:
+            raise TypeError("type %s not supported" % str(type(other)))
+
+    def __mul__(self, other):
+        if isinstance(other, Expr):
+            return _op_make.multiply(self, other)
+        elif isinstance(other, _Number):
+            raise TypeError('convert "%s" with `const` first' % str(other))
+        else:
+            raise TypeError("type %s not supported" % str(type(other)))
+
+    def __rmul__(self, other):
+        return self.__mul__(other)
+
+    def __div__(self, other):
+        if isinstance(other, Expr):
+            return _op_make.divide(self, other)
+        elif isinstance(other, _Number):
+            raise TypeError('convert "%s" with `const` first' % str(other))
+        else:
+            raise TypeError("type %s not supported" % str(type(other)))
+
+    def __rdiv__(self, other):
+        if isinstance(other, _Number):
+            raise TypeError('convert "%s" with `const` first' % str(other))
+        else:
+            raise TypeError("type %s not supported" % str(type(other)))
+
+    def __truediv__(self, other):
+        return self.__div__(other)
+
+    def __rtruediv__(self, other):
+        return self.__rdiv__(other)
+
 
 @register_relay_node
 class Constant(Expr):
@@ -305,7 +364,7 @@ def __len__(self):
 
     def __repr__(self):
         return ("TupleWrapper(" + self.tuple_value.__repr__() +
-                ", " + self.size + ")")
+                ", " + str(self.size) + ")")
 
     def astype(self, _):
         raise TypeError("astype cannot be used on tuple")

python/tvm/relay/ir_pass.py

@@ -160,6 +160,21 @@ def free_type_vars(expr):
     """
     return _ir_pass.free_type_vars(expr)
 
+def simplify_inference(expr):
+    """ Simplify the data-flow graph for inference phase.
+
+    Parameters
+    ----------
+    e: tvm.relay.Expr
+        The input Expression
+
+    Returns
+    -------
+    result: tvm.relay.Expr
+        An expression which is semantically equal to the input expression,
+        but with some simplification
+    """
+    return _ir_pass.simplify_inference(expr)
 
 def dead_code_elimination(expr):
     """ Remove expressions which does not effect the program result (dead code).

python/tvm/relay/op/__init__.py

@@ -15,3 +15,11 @@
 from . import _tensor
 from ..expr import Expr
 from ..base import register_relay_node
+
+
+def _register_op_make():
+    from . import _make
+    from .. import expr
+    expr._op_make = _make
+
+_register_op_make()

src/relay/pass/pattern_util.h

@@ -120,6 +120,40 @@ inline bool IsDepthwiseConv2D(const Call& call,
 }
 
 
+/*!
+ * \brief Create a Constant with a scalar
+ *
+ * \param dtype The data type.
+ * \param value The value of the scalar.
+ * \return A Constant.
+ */
+template<typename T>
+inline Constant MakeConstantScalar(DataType dtype, T value) {
+  CHECK_EQ(sizeof(T) * 8, dtype.bits()) << "data type mismatch";
+  runtime::NDArray arr = runtime::NDArray::Empty({}, Type2TVMType(dtype), {kDLCPU, 0});
+  *static_cast<T*>(arr->data) = value;
+  return ConstantNode::make(arr);
+}
+
+
+inline Expr Negative(Expr x) {
+  static const Op& op = Op::Get("negative");
+  return CallNode::make(op, {x}, Attrs(), {});
+}
+
+
+inline Expr Sqrt(Expr x) {
+  static const Op& op = Op::Get("sqrt");
+  return CallNode::make(op, {x}, Attrs(), {});
+}
+
+
+inline Expr Add(Expr lhs, Expr rhs) {
+  static const Op& op = Op::Get("add");
+  return CallNode::make(op, {lhs, rhs}, Attrs(), {});
+}
+
+
 inline Expr Multiply(Expr lhs, Expr rhs) {
   static const Op& op = Op::Get("multiply");
   return CallNode::make(op, {lhs, rhs}, Attrs(), {});

src/relay/pass/simplify_inference.cc

@@ -0,0 +1,77 @@
+/*!
+ * Copyright (c) 2018 by Contributors
+ * \file simplify_inference.cc
+ */
+#include <tvm/relay/pass.h>
+#include <tvm/relay/expr_functor.h>
+#include <tvm/relay/attrs/nn.h>
+#include "./pattern_util.h"
+
+namespace tvm {
+namespace relay {
+
+Expr BatchNormToInferUnpack(const Attrs attrs,
+                            Expr data,
+                            Expr gamma,
+                            Expr beta,
+                            Expr moving_mean,
+                            Expr moving_var) {
+  const auto param = attrs.as<BatchNormAttrs>();
+  Expr epsilon = MakeConstantScalar(Float(32), static_cast<float>(param->epsilon));
+  Expr var_add_eps = Add(moving_var, epsilon);
+  Expr sqrt_var = Sqrt(var_add_eps);
+  Expr scale = Divide(MakeConstantScalar(Float(32), 1.0f), sqrt_var);
+
+  if (param->scale) {
+    scale = Multiply(scale, gamma);
+  }
+  Expr neg_mean = Negative(moving_mean);
+  Expr shift = Multiply(neg_mean, scale);
+  if (param->center) {
+    shift = Add(shift, beta);
+  }
+
+  int axis = param->axis;
+  const auto* tdata = data->type_as<TensorTypeNode>();
+  scale = ExpandBiasToMatchAxis(scale, tdata->shape.size(), {axis});
+  shift = ExpandBiasToMatchAxis(shift, tdata->shape.size(), {axis});
+
+  Expr out = Multiply(data, scale);
+  out = Add(out, shift);
+  return out;
+}
+
+class InferenceSimplifier : public ExprMutator {
+ public:
+  Expr VisitExpr_(const TupleGetItemNode* n) final {
+    static const Op& batch_norm = Op::Get("nn.batch_norm");
+    static const Op& dropout = Op::Get("nn.dropout");
+
+    Expr new_e = ExprMutator::VisitExpr_(n);
+    const auto* new_n = new_e.as<TupleGetItemNode>();
+    if (new_n->index != 0) {
+      return new_e;
+    }
+    if (const auto* call = new_n->tuple.as<CallNode>()) {
+      if (call->op.same_as(batch_norm)) {
+        return BatchNormToInferUnpack(call->attrs,
+          call->args[0], call->args[1], call->args[2], call->args[3], call->args[4]);
+      } else if (call->op.same_as(dropout)) {
+        return call->args[0];
+      }
+    }
+    return new_e;
+  }
+};
+
+Expr SimplifyInference(const Expr& e) {
+  return InferenceSimplifier().Mutate(e);
+}
+
+TVM_REGISTER_API("relay._ir_pass.simplify_inference")
+.set_body([](TVMArgs args, TVMRetValue* ret) {
+    *ret = SimplifyInference(args[0]);
+  });
+
+}  // namespace relay
+}  // namespace tvm

tests/python/relay/test_pass_simplify_inference.py

@@ -0,0 +1,47 @@
+from tvm import relay as rly
+from tvm.relay.ir_pass import simplify_inference, alpha_equal
+
+def test_simplify_batchnorm():
+    def simple_bn(x, gamma, beta, moving_mean, moving_var,
+                  axis=1, epsilon=1e-5, shape=None):
+        # expect = (x - moving_mean) / sqrt(moving_var + eps) * gamma + beta
+        scale = rly.multiply(rly.const(1, 'float32') /
+                rly.sqrt(moving_var + rly.const(epsilon, 'float32')), gamma)
+        shift = rly.add(
+            rly.multiply(rly.negative(moving_mean), scale), beta)
+        num_newaxis = len(shape) - (axis + 1)
+        if num_newaxis:
+            scale = rly.expand_dims(scale, axis=1, num_newaxis=num_newaxis)
+            shift = rly.expand_dims(shift, axis=1, num_newaxis=num_newaxis)
+        return x * scale + shift
+
+    def check(dim, axis, nstep):
+        eps = 0.01
+        ttype1 = rly.TensorType(tuple(10 for i in range(dim)), 'float32')
+        ttype2 = rly.TensorType((10,), 'float32')
+        x = rly.var("x", ttype1)
+        beta = rly.var("beta", ttype2)
+        gamma = rly.var("gamma", ttype2)
+        moving_var = rly.var("moving_var", ttype2)
+        moving_mean = rly.var("moving_mean", ttype2)
+        y1, y2 = x, x
+
+        for _ in range(nstep):
+            y1, _, _ = rly.nn.batch_norm(y1 + rly.const(1, 'float32'),
+                gamma, beta, moving_mean, moving_var, epsilon=eps, axis=axis)
+            y1 = rly.nn.dropout(y1)
+            y1 = rly.ir_pass.infer_type(y1)
+            y1 = simplify_inference(y1)
+
+            y2 = simple_bn(y2 + rly.const(1, 'float32'),
+                           gamma, beta, moving_mean, moving_var,
+                           epsilon=eps, axis=axis, shape=ttype1.shape)
+        assert rly.ir_pass.graph_equal(y1, y2)
+
+    check(2, 1, 1)
+    check(4, 1, 1)
+    check(4, 0, 3)
+
+
+if __name__ == "__main__":
+    test_simplify_batchnorm()