Remove proto2text | chore! (#1193)

Remove proto2text because it is simply an alias of
`onnx.printer.to_text`.

---------

Co-authored-by: Ti-Tai Wang <[email protected]>

Author: justinchuby

.lintrunner.toml

@@ -8,7 +8,6 @@ include_patterns = [
     '**/*.pyi',
 ]
 exclude_patterns = [
-    'docs/**',
     'onnxscript/tests/models/**',
 ]
 command = [

README.md

@@ -65,9 +65,8 @@ pytest onnxscript
 import onnx
 
 # We use ONNX opset 15 to define the function below.
-from onnxscript import FLOAT
+from onnxscript import FLOAT, script
 from onnxscript import opset15 as op
-from onnxscript import script
 
 
 # We use the script decorator to indicate that

docs/examples/01_plot_selu.py

@@ -32,6 +32,6 @@ def Selu(X, alpha: float, gamma: float):
 
 # %%
 # Let's see what the translated function looks like:
-from onnxscript import proto2text  # noqa: E402
+import onnx  # noqa: E402
 
-print(proto2text(onnx_fun))
+print(onnx.printer.to_text(onnx_fun))

docs/examples/02_plot_square_loss.py

@@ -14,9 +14,8 @@
 import onnx
 from onnxruntime import InferenceSession
 
-from onnxscript import FLOAT
+from onnxscript import FLOAT, script
 from onnxscript import opset15 as op
-from onnxscript import proto2text, script
 
 
 @script()
@@ -32,7 +31,7 @@ def square_loss(X: FLOAT["N", 1], Y: FLOAT["N", 1]) -> FLOAT[1, 1]:  # noqa: F82
 
 # %%
 # Let's see what the generated model looks like.
-print(proto2text(model))
+print(onnx.printer.to_text(model))
 
 # %%
 # We can run shape-inference and type-check the model using the standard ONNX API.

docs/examples/03_export_lib.py

@@ -8,9 +8,8 @@
 **This is preliminary. Proto extensions are required to fully support LibProto.**
 """
 
-from onnxscript import export_onnx_lib
+from onnxscript import export_onnx_lib, script
 from onnxscript import opset15 as op
-from onnxscript import script
 from onnxscript.values import Opset
 
 # %%

docs/examples/04_plot_eager_mode_evaluation.py

@@ -12,9 +12,8 @@
 """
 import numpy as np
 
-from onnxscript import FLOAT
+from onnxscript import FLOAT, script
 from onnxscript import opset15 as op
-from onnxscript import script
 
 
 @script()

docs/examples/05_plot_model_props.py

@@ -15,9 +15,10 @@
 # %%
 # First, we define the implementation of a square-loss function in onnxscript.
 
-from onnxscript import FLOAT
+import onnx
+
+from onnxscript import FLOAT, script
 from onnxscript import opset15 as op
-from onnxscript import proto2text, script
 
 
 @script(ir_version=7, producer_name="OnnxScript", producer_version="0.1")
@@ -29,4 +30,4 @@ def square_loss(X: FLOAT["N"], Y: FLOAT["N"]) -> FLOAT[1]:  # noqa: F821
 # %%
 # Let's see what the generated model looks like.
 model = square_loss.to_model_proto()
-print(proto2text(model))
+print(onnx.printer.to_text(model))

docs/examples/06_plot_model_local_funs.py

@@ -14,9 +14,10 @@
 # %%
 # First, let us define an ONNXScript function that calls other ONNXScript functions.
 
-from onnxscript import FLOAT
+import onnx
+
+from onnxscript import FLOAT, script
 from onnxscript import opset15 as op
-from onnxscript import proto2text, script
 from onnxscript.values import Opset
 
 # A dummy opset used for model-local functions
@@ -43,17 +44,17 @@ def l2norm(x: FLOAT["N"], y: FLOAT["N"]) -> FLOAT[1]:  # noqa: F821
 # Let's see what the generated model looks like by default:
 
 model = l2norm.to_model_proto()
-print(proto2text(model))
+print(onnx.printer.to_text(model))
 
 # %%
 # Let's now explicitly specify which functions to include.
 # First, generate a model with no model-local functions:
 
 model = l2norm.to_model_proto(functions=[])
-print(proto2text(model))
+print(onnx.printer.to_text(model))
 
 # %%
 # Now, generate a model with one model-local function:
 
 model = l2norm.to_model_proto(functions=[sum])
-print(proto2text(model))
+print(onnx.printer.to_text(model))

docs/tutorial/examples/forloop.py

@@ -6,7 +6,7 @@
 def sumprod(x, N):
     sum = op.Identity(x)
     prod = op.Identity(x)
-    for i in range(N):
+    for _ in range(N):
         sum = sum + x
         prod = prod * x
     return sum, prod

docs/tutorial/examples/forwhileloop.py

@@ -6,7 +6,7 @@
 def sumprod_break(x, N):
     sum = op.Identity(x)
     prod = op.Identity(x)
-    for i in range(N):
+    for _ in range(N):
         sum = sum + x
         prod = prod * x
         cond = op.ReduceSum(prod) > 1e7