[Fix] Fix a few bugs when dtype is fp16 (apache#4088)

* Fix layer norm for fp16

* [Fix] Fix arange for fp16

* [Fix] Fix mxnet frontend for fp16

* [Fix] Fix arange for fp16

* remove comments

* x

* fix nnvm

python/tvm/relay/frontend/mxnet.py

@@ -615,12 +615,17 @@ def _mx_arange(inputs, attrs):
     if attrs.get_int("repeat", 1) != 1:
         raise tvm.error.OpAttributeUnimplemented(
             'Attribute "repeat" is not supported in operator arange.')
-    new_attrs = {}
-    new_attrs["start"] = _expr.const(attrs.get_float("start", 0.0))
+    dtype = attrs.get_str("dtype", "float32")
     stop = attrs.get_str("stop", "None")
-    new_attrs["stop"] = None if stop == "None" else _expr.const(float(stop))
-    new_attrs["step"] = _expr.const(attrs.get_float("step", 1.0))
-    new_attrs["dtype"] = attrs.get_str("dtype", "float32")
+    if stop == "None":
+        stop = None
+    else:
+        stop = _expr.const(float(stop), dtype=dtype)
+    new_attrs = {}
+    new_attrs["start"] = _expr.const(attrs.get_float("start", 0.0), dtype=dtype)
+    new_attrs["stop"] = stop
+    new_attrs["step"] = _expr.const(attrs.get_float("step", 1.0), dtype=dtype)
+    new_attrs["dtype"] = dtype
     return _op.arange(**new_attrs)
 
 
@@ -863,7 +868,8 @@ def _mx_contrib_div_sqrt_dim(inputs, _):
     assert len(inputs) == 1
     ndim = len(_infer_type(inputs[0]).checked_type.shape)
     dim = _op.take(_op.shape_of(inputs[0]), _expr.const(ndim-1, dtype="int32"))
-    sqrt_dim = _op.sqrt(dim.astype('float32'))
+    dtype = _infer_type(inputs[0]).checked_type.dtype
+    sqrt_dim = _op.sqrt(dim.astype(dtype))
     out = inputs[0] / sqrt_dim
     return out
 

python/tvm/relay/frontend/nnvm_common.py

@@ -21,6 +21,7 @@
 from .. import expr as _expr
 from .. import op as _op
 from .common import get_relay_op
+from .common import infer_type as _infer_type
 
 def _warn_not_used(attr, op='nnvm'):
     import warnings
@@ -123,20 +124,22 @@ def _elemwise_sum(inputs, _, _dtype='float32'):
 
 
 def _binop_scalar(new_op):
-    def _impl(inputs, attrs, odtype='float32'):
+    def _impl(inputs, attrs, odtype=None):
         assert len(inputs) == 1
         scalar = attrs.get_float("scalar")
-        # Note: binary scalar only works for float op for now
+        if odtype is None:
+            odtype = _infer_type(inputs[0]).checked_type.dtype
         scalar = _expr.const(scalar, dtype=odtype)
         return new_op(inputs[0], scalar)
     return _impl
 
 
 def _rbinop_scalar(new_op):
-    def _impl(inputs, attrs, odtype='float32'):
+    def _impl(inputs, attrs, odtype=None):
         assert len(inputs) == 1
         scalar = attrs.get_float("scalar")
-        # Note: binary scalar only works for float op for now
+        if odtype is None:
+            odtype = _infer_type(inputs[0]).checked_type.dtype
         scalar = _expr.const(scalar, dtype=odtype)
         return new_op(scalar, inputs[0])
     return _impl

src/relay/op/tensor/transform.cc

@@ -28,6 +28,7 @@
 #include <tvm/expr_operator.h>
 #include <tvm/ir.h>
 #include <tvm/data_layout.h>
+#include <tvm/runtime/packed_func.h>
 #include <topi/transform.h>
 #include <topi/elemwise.h>
 #include <topi/broadcast.h>
@@ -1139,11 +1140,41 @@ and type as the input array.
 TVM_REGISTER_NODE_TYPE(ArangeAttrs);
 
 double ToScalar(const runtime::NDArray& array) {
-  if (array->dtype.code == kDLInt || array->dtype.code == kDLUInt) {
-    return reinterpret_cast<int32_t*>(array->data)[0];
-  } else {
-    return reinterpret_cast<float*>(array->data)[0];
+  if (array->dtype.code == kDLInt) {
+    if (array->dtype.bits == 8) {
+      return reinterpret_cast<int8_t*>(array->data)[0];
+    } else if (array->dtype.bits == 16) {
+      return reinterpret_cast<int16_t*>(array->data)[0];
+    } else if (array->dtype.bits == 32) {
+      return reinterpret_cast<int32_t*>(array->data)[0];
+    } else if (array->dtype.bits == 64) {
+      return reinterpret_cast<int64_t*>(array->data)[0];
+    }
+  } else if (array->dtype.code == kDLUInt) {
+    if (array->dtype.bits == 8) {
+      return reinterpret_cast<uint8_t*>(array->data)[0];
+    } else if (array->dtype.bits == 16) {
+      return reinterpret_cast<uint16_t*>(array->data)[0];
+    } else if (array->dtype.bits == 32) {
+      return reinterpret_cast<uint32_t*>(array->data)[0];
+    } else if (array->dtype.bits == 64) {
+      return reinterpret_cast<uint64_t*>(array->data)[0];
+    }
+  } else if (array->dtype.code == kDLFloat) {
+#if (__ARM_FP16_FORMAT_IEEE == 1)
+    if (array->dtype.bits == 16) {
+      return reinterpret_cast<__fp16*>(array->data)[0];
+    }
+#endif
+    if (array->dtype.bits == 32) {
+      return reinterpret_cast<float*>(array->data)[0];
+    } else if (array->dtype.bits == 64) {
+      return reinterpret_cast<double*>(array->data)[0];
+    }
   }
+  LOG(FATAL) << "Unknown data type: " << tvm::runtime::TVMType2String(array->dtype);
+  // make compiler happy
+  return -std::numeric_limits<double>::infinity();
 }
 
 bool ArangeRel(const Array<Type>& types,

src/relay/pass/simplify_inference.cc

@@ -75,7 +75,7 @@ Expr LayerNormToInferUnpack(const Attrs attrs,
   const auto param = attrs.as<LayerNormAttrs>();
   CHECK(param);
 
-  Expr epsilon = MakeConstantScalar(Float(32), static_cast<float>(param->epsilon));
+  Expr epsilon = MakeConstantScalar(ttype->dtype, static_cast<float>(param->epsilon));
   Expr mean = Mean(data, {param->axis}, true, false);
   Expr var = Variance(data, mean, {param->axis}, true, false);
   Expr denom = Sqrt(Add(var, epsilon));