Generate opset 22 impl (#1923)

Dependent on #1924

Author: justinchuby

onnxscript/onnx_opset/__init__.py

@@ -37,10 +37,13 @@
 from onnxscript.onnx_opset._impl.opset18 import Opset18
 from onnxscript.onnx_opset._impl.opset19 import Opset19
 from onnxscript.onnx_opset._impl.opset20 import Opset20
+from onnxscript.onnx_opset._impl.opset21 import Opset21
+from onnxscript.onnx_opset._impl.opset22 import Opset22
 from onnxscript.onnx_opset._impl.opset_ai_onnx_ml1 import Opset_ai_onnx_ml1
 from onnxscript.onnx_opset._impl.opset_ai_onnx_ml2 import Opset_ai_onnx_ml2
 from onnxscript.onnx_opset._impl.opset_ai_onnx_ml3 import Opset_ai_onnx_ml3
 from onnxscript.onnx_opset._impl.opset_ai_onnx_ml4 import Opset_ai_onnx_ml4
+from onnxscript.onnx_opset._impl.opset_ai_onnx_ml5 import Opset_ai_onnx_ml5
 from onnxscript.onnx_opset._impl.opset_ai_onnx_preview_training1 import (
     Opset_ai_onnx_preview_training1,
 )
@@ -68,10 +71,13 @@
     "opset18",
     "opset19",
     "opset20",
+    "opset21",
+    "opset22",
     "opset_ai_onnx_ml1",
     "opset_ai_onnx_ml2",
     "opset_ai_onnx_ml3",
     "opset_ai_onnx_ml4",
+    "opset_ai_onnx_ml5",
     "opset_ai_onnx_preview_training1",
 ]
 
@@ -102,10 +108,13 @@
 opset18 = Opset18()
 opset19 = Opset19()
 opset20 = Opset20()
+opset21 = Opset21()
+opset22 = Opset22()
 opset_ai_onnx_ml1 = Opset_ai_onnx_ml1()
 opset_ai_onnx_ml2 = Opset_ai_onnx_ml2()
 opset_ai_onnx_ml3 = Opset_ai_onnx_ml3()
 opset_ai_onnx_ml4 = Opset_ai_onnx_ml4()
+opset_ai_onnx_ml5 = Opset_ai_onnx_ml5()
 opset_ai_onnx_preview_training1 = Opset_ai_onnx_preview_training1()
 all_opsets: Mapping[Tuple[str, int], Opset] = {
     (
@@ -188,6 +197,14 @@
         "",
         20,
     ): opset20,
+    (
+        "",
+        21,
+    ): opset21,
+    (
+        "",
+        22,
+    ): opset22,
     (
         "ai.onnx.ml",
         1,
@@ -204,6 +221,10 @@
         "ai.onnx.ml",
         4,
     ): opset_ai_onnx_ml4,
+    (
+        "ai.onnx.ml",
+        5,
+    ): opset_ai_onnx_ml5,
     (
         "ai.onnx.preview.training",
         1,

onnxscript/onnx_opset/_impl/opset1.py

@@ -2171,7 +2171,7 @@ def MatMul(self, A: T_MatMul, B: T_MatMul) -> T_MatMul:
         r"""[🌐 MatMul(1)](https://onnx.ai/onnx/operators/onnx__MatMul.html#matmul-1 "Online Documentation")
 
 
-        Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
+        Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).
 
 
         Args:
@@ -3538,7 +3538,7 @@ def Slice(
 
 
         Produces a slice of the input tensor along multiple axes. Similar to numpy:
-        https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+        https://numpy.org/doc/stable/reference/routines.indexing.html
         Slices uses `axes`, `starts` and `ends` attributes to specify the start and end
         dimension for each axis in the list of axes, it uses this information to
         slice the input `data` tensor. If a negative value is passed for any of the

onnxscript/onnx_opset/_impl/opset10.py

@@ -346,7 +346,7 @@ def MatMulInteger(
         r"""[🌐 MatMulInteger(10)](https://onnx.ai/onnx/operators/onnx__MatMulInteger.html#matmulinteger-10 "Online Documentation")
 
 
-        Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
+        Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).
         The production MUST never overflow. The accumulation may overflow if and only if in 32 bits.
 
 
@@ -749,7 +749,7 @@ def QLinearMatMul(
         r"""[🌐 QLinearMatMul(10)](https://onnx.ai/onnx/operators/onnx__QLinearMatMul.html#qlinearmatmul-10 "Online Documentation")
 
 
-        Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html.
+        Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).
         It consumes two quantized input tensors, their scales and zero points, scale and zero point of output,
         and computes the quantized output. The quantization formula is y = saturate((x / y_scale) + y_zero_point).
         For (x / y_scale), it is rounding to nearest ties to even. Refer to https://en.wikipedia.org/wiki/Rounding for details.
@@ -1067,7 +1067,7 @@ def Slice(
 
 
         Produces a slice of the input tensor along multiple axes. Similar to numpy:
-        https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+        https://numpy.org/doc/stable/reference/routines.indexing.html
         Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end
         dimension and step for each axis in the list of axes, it uses this information to
         slice the input `data` tensor. If a negative value is passed for any of the

onnxscript/onnx_opset/_impl/opset11.py

@@ -3481,7 +3481,7 @@ def Slice(
 
 
         Produces a slice of the input tensor along multiple axes. Similar to numpy:
-        https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html
+        https://numpy.org/doc/stable/reference/routines.indexing.html
         Slices uses `starts`, `ends`, `axes` and `steps` inputs to specify the start and end
         dimension and step for each axis in the list of axes, it uses this information to
         slice the input `data` tensor. If a negative value is passed for any of the

onnxscript/onnx_opset/_impl/opset12.py

@@ -674,7 +674,7 @@ def MaxPool(
          ```
          output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)
          ```
-         if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored.
+         if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`.
 
          `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:
          ```

onnxscript/onnx_opset/_impl/opset13.py

@@ -1762,7 +1762,7 @@ def MatMul(self, A: T_MatMul, B: T_MatMul) -> T_MatMul:
         r"""[🌐 MatMul(13)](https://onnx.ai/onnx/operators/onnx__MatMul.html#matmul-13 "Online Documentation")
 
 
-        Matrix product that behaves like numpy.matmul: https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.matmul.html
+        Matrix product that behaves like [numpy.matmul](https://numpy.org/doc/stable/reference/generated/numpy.matmul.html).
 
 
         Args:

onnxscript/onnx_opset/_impl/opset19.py

@@ -80,7 +80,7 @@ def AveragePool(
          ```
          output_spatial_shape[i] = ceil((input_spatial_shape[i] + pad_shape[i] - dilation[i] * (kernel_shape[i] - 1) - 1) / strides_spatial_shape[i] + 1)
          ```
-         if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`. Sliding windows that would start in the right padded region are ignored.
+         if ceil_mode is enabled. `pad_shape[i]` is the sum of pads along axis `i`.
 
          `auto_pad` is a DEPRECATED attribute. If you are using them currently, the output spatial shape will be following when ceil_mode is enabled:
          ```
@@ -566,9 +566,10 @@ def DequantizeLinear(
                 It's optional. Zero point is 0 when it's not specified.
 
             axis: (Optional) The axis of the dequantizing dimension of the input tensor.
-                Ignored for per-tensor quantization. Negative value means counting
-                dimensions from the back. Accepted range is [-r, r-1] where r =
-                rank(input).
+                Used only for per-axis quantization. Negative value means counting
+                dimensions from the back. Accepted range is `[-r, r-1]` where `r =
+                rank(input)`. When the rank of the input is 1, per-tensor quantization
+                is applied, rendering the axis unnecessary in this scenario.
         """
 
         schema = get_schema("DequantizeLinear", 19, "")

onnxscript/onnx_opset/_impl/opset2.py

@@ -19,6 +19,7 @@
 
 from onnxscript.onnx_opset._impl.opset1 import Opset1
 from onnxscript.onnx_types import (
+    BFLOAT16,
     BOOL,
     COMPLEX64,
     COMPLEX128,
@@ -42,7 +43,7 @@ class Opset2(Opset1):
     def __new__(cls):
         return Opset.__new__(cls, "", 2)
 
-    T_GlobalLpPool = TypeVar("T_GlobalLpPool", DOUBLE, FLOAT, FLOAT16)
+    T_GlobalLpPool = TypeVar("T_GlobalLpPool", BFLOAT16, DOUBLE, FLOAT, FLOAT16)
 
     def GlobalLpPool(self, X: T_GlobalLpPool, *, p: int = 2) -> T_GlobalLpPool:
         r"""[🌐 GlobalLpPool(2)](https://onnx.ai/onnx/operators/onnx__GlobalLpPool.html#globallppool-2 "Online Documentation")