6.0 Release (#1600)

* 6.0 Release

* Fix error message

* Update torch in warning message

* Skip iOS16 target unit test on macOS 12 or lower

* Fix unit test

* Skip a test on macOS 12

BUILDING.md

@@ -19,7 +19,7 @@ Follow these steps:
 1. Fork and clone the GitHub [coremltools repository](https://github.com/apple/coremltools).
 
 2. Run the [build.sh](scripts/build.sh) script to build `coremltools`. 
-	* By default this script uses Python 3.7, but you can include `--python=3.6` (or `3.7`, `3.8`, `3.9`) as a argument to change the Python version.
+	* By default this script uses Python 3.7, but you can include `--python=3.6` (or `3.7`, `3.8`, `3.9`, `3.10`) as a argument to change the Python version.
 	* The script creates a new `build` folder with the coremltools distribution, and a `dist` folder with Python wheel files.
 
 3. Run the [test.sh](scripts/test.sh) script to test the build.
@@ -45,7 +45,7 @@ The following build targets help you configure the development environment. If y
 * `test_slow` | Run all non-fast tests.
 * `wheel` | Build wheels in release mode.
 
-The script uses Python 3.7, but you can include `--python=3.6` (or `3.7`, `3.8`, `3.9`) as a argument to change the Python version.
+The script uses Python 3.7, but you can include `--python=3.6` (or `3.7`, `3.8`, `3.9`, `3.10`) as a argument to change the Python version.
 
 ## Resources
 

coremltools/_deps/__init__.py

@@ -46,7 +46,7 @@ def _warn_if_above_max_supported_version(package_name, package_version, max_supp
 _HAS_SKLEARN = True
 _SKLEARN_VERSION = None
 _SKLEARN_MIN_VERSION = "0.17"
-_SKLEARN_MAX_VERSION = "0.19.2"
+_SKLEARN_MAX_VERSION = "1.1.2"
 
 
 def __get_sklearn_version(version):
@@ -98,7 +98,7 @@ def __get_sklearn_version(version):
 _HAS_TF_1 = False
 _HAS_TF_2 = False
 _TF_1_MIN_VERSION = "1.12.0"
-_TF_1_MAX_VERSION = "1.15.0"
+_TF_1_MAX_VERSION = "1.15.4"
 _TF_2_MIN_VERSION = "2.1.0"
 _TF_2_MAX_VERSION = "2.8.0"
 
@@ -145,7 +145,7 @@ def __get_sklearn_version(version):
 
 # ---------------------------------------------------------------------------------------
 _HAS_TORCH = True
-_TORCH_MAX_VERSION = "1.11.0"
+_TORCH_MAX_VERSION = "1.12.1"
 try:
     import torch
     _warn_if_above_max_supported_version("Torch", torch.__version__, _TORCH_MAX_VERSION)

coremltools/converters/_converters_entry.py

@@ -484,7 +484,7 @@ def _set_default_specification_version(target):
         return _LOWEST_ALLOWED_SPECIFICATION_VERSION_FOR_NEURALNETWORK
     elif target == "mlprogram":
         return _LOWEST_ALLOWED_SPECIFICATION_VERSION_FOR_MILPROGRAM
-    elif target == "milinternal":
+    elif target in ("milinternal", "milpython"):
         return None
     else:
         raise NotImplementedError("Backend converter {} not implemented".format(target))

coremltools/converters/mil/__init__.py

@@ -3,57 +3,31 @@
 #  Use of this source code is governed by a BSD-3-clause license that can be
 #  found in the LICENSE.txt file or at https://opensource.org/licenses/BSD-3-Clause
 
-# This import should be pruned rdar://84519338
 from .mil import (
-    block,
     Block,
-    BoolInputType,
-    BoolTensorInputType,
     builder,
     Builder,
     curr_block,
     DefaultInputs,
-    FloatInputType,
-    FloatTensorInputType,
     Function,
     get_existing_symbol,
     get_new_symbol,
     get_new_variadic_symbol,
-    input_type,
     InputSpec,
-    IntInputType,
-    IntOrFloatInputType,
-    IntOrFloatOrBoolInputType,
-    IntTensorInputType,
-    InternalInputType,
-    InternalScalarOrTensorInputType,
-    InternalStringInputType,
     InternalVar,
     ListInputType,
-    ListOrScalarOrTensorInputType,
     ListVar,
     mil_list,
-    operation,
     Operation,
-    ops,
     Placeholder,
-    precondition,
-    program,
     Program,
-    PyFunctionInputType,
     register_op,
     SPACES,
     SUPPORT_FLOAT_TYPES,
     SUPPORT_INT_TYPES,
-    ScalarOrTensorInputType,
-    StringInputType,
     Symbol,
-    TensorInputType,
     TupleInputType,
-    types,
-    var,
     Var,
-    visitors
 )
 
 from .frontend.torch import register_torch_op

coremltools/converters/mil/backend/mil/load.py

@@ -197,6 +197,12 @@ def feeds_to_only_constexprs(op):
             # Classify's "classes" param should be serialized as a value literal bound
             # directly to the param, rather than as a const-generated variable.
             proto_ops.append(translate_generic_op(op, parameters, blob_writer, ["classes"]))
+        elif op_cls_name == "reshape_like":
+            # The reshape_like should also be able to take value from a const op
+            # This is a workaround solution
+            # rdar://98689808 (Reshape_like should also accept const value from non literal input)
+            literal_params = ["begins", "ends", "end_masks"]
+            proto_ops.append(translate_generic_op(op, parameters, blob_writer, literal_params))
         else:
             proto_ops.append(translate_generic_op(op, parameters, blob_writer))
 

coremltools/converters/mil/backend/mil/passes/__init__.py

@@ -6,7 +6,6 @@
 from . import (
     adjust_io_to_supported_types,
     fuse_activation_silu,
-    homogenize_input_dtypes,
     insert_image_preprocessing_op,
     sanitize_name_strings
 )

coremltools/converters/mil/backend/mil/passes/adjust_io_to_supported_types.py

@@ -105,8 +105,8 @@ def _adjust_main_inputs(func):
                 # This is some other dtype. Change the type to fp32 and add a cast.
                 # This is only a limitation of main--other functions do not represent CoreML model inputs
                 # and do not have the same limitation on input types.
-                supported_dtypes = "{int32, fp32, fp64}" if func.opset_version < target.iOS16 else \
-                                    "{int32, fp16, fp32, fp64}"
+                supported_dtypes = "{int32, fp32}" if func.opset_version < target.iOS16 else \
+                                    "{int32, fp16, fp32}"
                 msg = "\nInput '{}' is of dtype {}. The " +\
                                "CoreML runtime does not support inputs with this dtype " +\
                                "(supported dtypes are: {}). This input will be assigned a dtype of " +\
@@ -135,8 +135,8 @@ def _adjust_main_outputs(func):
             and (func.opset_version < target.iOS16 or output_var.dtype != types.fp16):
             # since fp16 is a valid output type for coreml from ios16 spec onwards, no need to cast
             output_dtype_str = types.builtin_to_string(output_var.dtype)
-            supported_dtypes = "{int32, fp32, fp64}" if func.opset_version < target.iOS16 else \
-                                "{int32, fp16, fp32, fp64}"
+            supported_dtypes = "{int32, fp32}" if func.opset_version < target.iOS16 else \
+                                "{int32, fp16, fp32}"
             msg = "\nOutput '{}' is of dtype {}. The " +\
                            "CoreML runtime does not support outputs with this dtype " +\
                            "(supported dtypes are: {}). This output will be assigned a dtype " +\
@@ -192,74 +192,12 @@ def _adjust_func_inputs(func):
     for input_name, input_var in func.inputs.items():
         _adjust_var(input_var)
 
-def _adjust_block_inputs(block):
-    for input_var in block.inputs:
-        _adjust_var(input_var)
-
-@block_context_manager
-def _adjust_ops(block):
-    len_block = len(block.operations)
-    i = 0
-    while i < len_block:
-        op = block.operations[i]
-
-        # Classifier is a special exception to this rule. It can output 64 bit integer labels.
-        # Classifier should be inserted after running this pass.
-        if op.op_type == "classify":
-            raise ValueError("ML Program backend pass adjust_to_supported_types does not support programs" +\
-                             " that have already added a classify op.")
-
-        for subblock in op.blocks:
-            _adjust_block_inputs(subblock)
-            _adjust_ops(subblock)
-
-        for var in op.outputs:
-            _adjust_var(var)
-
-        # Cast ops have a param (dtype) that should match the output dtype.
-        # If the output dtype or input dtype was previously adjusted,
-        # the cast op must change or be removed in kind.
-        if op.op_type == "cast":
-            output_type_str = types.builtin_to_string(op.outputs[0].dtype)
-            if op.outputs[0].dtype == op.x.dtype:
-                # The type of the input or output of this cast op was changed per the rules
-                # defined in the top level comment for adjust_io_to_supported_types.
-                #
-                # That changed output type is the same type as the input to the cast
-                # op. Therefore, regardless of whether the user created this cast or
-                # not, it is now redundant (noop), and should be removed.
-                #
-                # The removal isn't covered by the main cast
-                # optimization pass since that pass runs before this pass.
-                block.replace_uses_of_var_after_op(
-                    anchor_op=op, old_var=op.outputs[0], new_var=op.x
-                )
-                block.remove_ops([op])
-                len_block = len(block.operations)
-                i -= 1
-            elif output_type_str != op.dtype.val:
-                # The type of the output of this cast op was changed per the rules
-                # defined in the top level comment for adjust_io_to_supported_types.
-                #
-                # This cast is meaningful, and the "dtype" param now differs from the output
-                # type. Replace the dtype cast with a new cast op with a matching dtype param.
-                new_cast_out = mb.cast(x=op.x, dtype=output_type_str, before_op=op)
-                block.replace_uses_of_var_after_op(
-                    anchor_op=op, old_var=op.outputs[0], new_var=new_cast_out
-                )
-                block.remove_ops([op])
-                len_block = len(block.operations)
-        i = i + 1
-    return block
-
 #####
 # The Pass
 #####
 def _adjust_io_to_supported_types(func, is_main):
     if is_main:
         _adjust_main_inputs(func)
-        _adjust_ops(func)
         _adjust_main_outputs(func)
     else:
         _adjust_func_inputs(func)
-        _adjust_ops(func)

coremltools/converters/mil/backend/mil/passes/mil_passes.py

@@ -17,13 +17,7 @@ def mil_backend_passes(prog):
         "mil_backend::insert_image_preprocessing_ops",
         "mil_backend::fuse_activation_silu",
         "common::const_elimination", # rank0_expand_dims_swap might introduce some new const tensor
-        # TODO: Right now, "const elimination" pass CANNOT be done after the "homogenize_input_dtypes" pass.
-        # Remove this requirement in rdar://76032946.
-        # Right now due to a bug in the PYMIL const op, which is that it can only produce FP32 and INT32 types tensors (e.g. it can't produce int64),
-        # therefore invoking const elimination after the var type promotion that happens in "homogenize_input_dtypes" will lead to issues if a
-        # const var (on const propagation through cast op) has to be promoted to int64 dtype.
-        "mil_backend::homogenize_input_dtypes",
-        "common::cast_optimization",  # Need to run after homogenize_input_dtypes
+        "common::cast_optimization",
         "common::dead_code_elimination",
         "mil_backend::sanitize_name_strings",
         "common::dedup_op_and_var_names",