more cleanup

Signed-off-by: Yuan Yao <[email protected]>

docs/IR.md

@@ -421,7 +421,7 @@ It is common to represent a tensor as a nested list. This generally works fine,
 
 |Group|Types|Description|
 |---|---|---|
-Floating Point Types|float16, float32, float64, bfloat16, float8e4m3fn, float8e5m2, float8e4m3fnuz, float8e5m2fnuz|Values adhering to the IEEE 754-2008 standard representation of floating-point data or defined in papers [FP8 Formats for Deep Learning](https://arxiv.org/abs/2209.05433) and [8-bit Numerical Formats for Deep Neural Networks](https://arxiv.org/abs/2206.02915)
+Floating Point Types|float16, float32, float64, bfloat16, float8e4m3fn, float8e5m2, float8e4m3fnuz, float8e5m2fnuz, float4e2m1|Values adhering to the IEEE 754-2008 standard representation of floating-point data or defined in papers [FP8 Formats for Deep Learning](https://arxiv.org/abs/2209.05433) and [8-bit Numerical Formats for Deep Neural Networks](https://arxiv.org/abs/2206.02915)
 Signed Integer Types|int4, int8, int16, int32, int64|Signed integers are supported for 4-64 bit widths.
 Unsigned Integer Types|uint4, uint8, uint16, uint32, uint64|Unsigned integers are supported for 4-64 bit widths.
 Complex Types|complex64, complex128|A complex number with either 32- or 64-bit real and imaginary parts.

docs/docsgen/source/api/numpy_helper.md

@@ -33,7 +33,7 @@
 .. autofunction:: onnx.numpy_helper.to_array
 ```
 
-As numpy does not support all the types defined in ONNX (float 8 types, blofat16, int4, uint4),
+As numpy does not support all the types defined in ONNX (float 8 types, blofat16, int4, uint4, float4e2m1),
 these two functions use a custom dtype defined in :mod:`onnx._custom_element_types`.
 
 ## sequence

onnx/common/ir_pb_converter.cc

@@ -51,7 +51,8 @@ Tensor tensorProtoToTensor(const ONNX_NAMESPACE::TensorProto& tp) {
     case ONNX_NAMESPACE::TensorProto_DataType_FLOAT8E4M3FN:
     case ONNX_NAMESPACE::TensorProto_DataType_FLOAT8E4M3FNUZ:
     case ONNX_NAMESPACE::TensorProto_DataType_FLOAT8E5M2:
-    case ONNX_NAMESPACE::TensorProto_DataType_FLOAT8E5M2FNUZ: {
+    case ONNX_NAMESPACE::TensorProto_DataType_FLOAT8E5M2FNUZ:
+    case ONNX_NAMESPACE::TensorProto_DataType_FLOAT4E2M1: {
       ret.int32s().reserve(tp.int32_data_size());
       for (int i = 0; i < tp.int32_data_size(); i++) {
         ret.int32s().push_back(tp.int32_data(i));

onnx/defs/parser.cc

@@ -473,6 +473,7 @@ Status OnnxParser::Parse(TensorProto& tensorProto, const TypeProto& tensorTypePr
           case TensorProto::DataType::TensorProto_DataType_FLOAT8E5M2:
           case TensorProto::DataType::TensorProto_DataType_FLOAT8E5M2FNUZ:
           case TensorProto::DataType::TensorProto_DataType_BOOL:
+          case TensorProto::DataType::TensorProto_DataType_FLOAT4E2M1:
             PARSE_TOKEN(intval);
             // TODO: check values are in the correct range.
             tensorProto.add_int32_data(intval);

onnx/defs/parser.h

@@ -96,6 +96,7 @@ class PrimitiveTypeNameMap : public StringIntMap<PrimitiveTypeNameMap> {
     map_["float8e5m2fnuz"] = TensorProto_DataType_FLOAT8E5M2FNUZ;
     map_["uint4"] = TensorProto_DataType_UINT4;
     map_["int4"] = TensorProto_DataType_INT4;
+    map_["float4e2m1"] = TensorProto_DataType_FLOAT4E2M1;
   }
 
   static bool IsTypeName(const std::string& dtype) {

onnx/numpy_helper.py

@@ -229,7 +229,7 @@ def evaluate_float4e2m1_from_bits(x: np.uint8) -> np.float32:
         x: a uint8 element representing a float4e2m1 (using the 4 LSB)
 
     Returns:
-        Packed array with size `ceil(farray.size/2)` (single dimension).
+        A float32 element representing the value of the float4e2m1 input.
     """
     # x is stored in 4 LSB of int
     S = -1 if bool(x & 0x08) else 1
@@ -619,10 +619,13 @@ def from_array(tensor: np.ndarray, name: str | None = None) -> TensorProto:
     elif dt == custom_np_types.uint4 and dt.descr[0][0] == "uint4":
         to = TensorProto.UINT4
         dt_to = np.uint8  # type: ignore[assignment]
+    elif dt == custom_np_types.float4e2m1 and dt.descr[0][0] == "float4e2m1":
+        to = TensorProto.FLOAT4E2M1
+        dt_to = np.uint8
     else:
         return _from_array(tensor, name)
 
-    if to in (TensorProto.UINT4, TensorProto.INT4):
+    if to in (TensorProto.UINT4, TensorProto.INT4, TensorProto.FLOAT4E2M1):
         value = tensor.astype(dt_to).ravel()
         if value.size % 2 == 1:
             raise ValueError(

onnx/reference/custom_element_types.py

@@ -12,6 +12,7 @@
 
 from onnx._custom_element_types import (
     bfloat16,
+    float4e2m1,
     float8e4m3fn,
     float8e4m3fnuz,
     float8e5m2,
@@ -22,6 +23,7 @@
 
 _supported_types = [
     (bfloat16, "bfloat16", "bfloat16"),
+    (float4e2m1, "float4e2m1", "float4_e2m1"),
     (float8e4m3fn, "e4m3fn", "float8_e4m3fn"),
     (float8e4m3fnuz, "e4m3fnuz", "float8_e4m3fnuz"),
     (float8e5m2, "e5m2", "float8_e5m2"),

onnx/reference/ops/op_cast.py

@@ -140,7 +140,7 @@ def cast_to(x, to, saturate):  # noqa: PLR0911
 
     if to == TensorProto.FLOAT4E2M1:
         xf = x.astype(np.float32)
-        y = subbyte.float32_to_float4e2m1_unpacked(xf)
+        y = subbyte.float32_to_float4e2m1_unpacked(xf).astype(float4e2m1)
         return y.reshape(x.shape)
 
     if to == TensorProto.STRING:

onnx/test/numpy_helper_test.py

@@ -648,6 +648,7 @@ def test_to_array_from_array(self, att):
     def test_to_array_from_array_subtype(self):
         self._to_array_from_array(onnx.TensorProto.INT4)
         self._to_array_from_array(onnx.TensorProto.UINT4)
+        self._to_array_from_array(onnx.TensorProto.FLOAT4E2M1)
 
     def test_to_array_from_array_string(self):
         self._to_array_from_array(onnx.TensorProto.STRING, False)

onnx/test/parser_test.py

@@ -292,6 +292,7 @@ def test_parse_various_float_values(self, test_literal, expect_exception):
             ("uint16", TensorProto.UINT16),
             ("uint32", TensorProto.UINT32),
             ("uint64", TensorProto.UINT64),
+            ("float4e2m1", TensorProto.FLOAT4E2M1),
         ]
     )
     def test_parse_graph_types(self, name, itype) -> None: