7.0 Release (#1977)

Author: TobyRoseman

cmake/coreml-utils.cmake

@@ -37,7 +37,7 @@ function(coreml_add_build_proto proto_fn target_suffix)
             ${CMAKE_CURRENT_BINARY_DIR}/format/${proto_fn}_enum.h
         COMMENT "Generating c++ enums from ${proto_fn}.proto into ${CMAKE_CURRENT_BINARY_DIR}/format/"
         COMMAND ${CMAKE_BINARY_DIR}/deps/protobuf/cmake/protoc
-            --plugin=protoc-gen-enum=mlmodel${target_suffix}/enumgen
+            --plugin=protoc-gen-enum=mlmodel/enumgen
             --enum_out=${CMAKE_CURRENT_BINARY_DIR}/format/
             -I${CMAKE_CURRENT_SOURCE_DIR}/format/
             ${CMAKE_CURRENT_SOURCE_DIR}/format/${proto_fn}.proto
@@ -77,7 +77,7 @@ function(coreml_add_build_proto proto_fn target_suffix)
         add_custom_target(tgt_${proto_fn}_enums ALL
             COMMENT "Generating c++ enums from ${proto_fn}.proto into ${CMAKE_CURRENT_SOURCE_DIR}/build/format/"
             COMMAND ${CMAKE_BINARY_DIR}/deps/protobuf/cmake/protoc
-                --plugin=protoc-gen-enum=mlmodel${target_suffix}/enumgen
+                --plugin=protoc-gen-enum=mlmodel/enumgen
                 --enum_out=${CMAKE_CURRENT_SOURCE_DIR}/build/format/
                 -I${CMAKE_CURRENT_SOURCE_DIR}/format/
                 ${CMAKE_CURRENT_SOURCE_DIR}/format/${proto_fn}.proto

coremltools/converters/mil/_deployment_compatibility.py

@@ -23,8 +23,6 @@ class AvailableTarget(IntEnum):
     iOS17 = _SPECIFICATION_VERSION_IOS_17
 
     # macOS versions (aliases of iOS versions)
-    macOS15 = _SPECIFICATION_VERSION_IOS_13
-    macOS16 = _SPECIFICATION_VERSION_IOS_14
     macOS10_15 = _SPECIFICATION_VERSION_IOS_13
     macOS10_16 = _SPECIFICATION_VERSION_IOS_14
     macOS11 = _SPECIFICATION_VERSION_IOS_14

coremltools/converters/mil/backend/mil/helper.py

@@ -19,6 +19,9 @@
 from coremltools.converters.mil.mil.types.type_mapping import np_val_to_py_type
 from coremltools.models.utils import _WEIGHTS_DIR_NAME, _WEIGHTS_FILE_NAME
 
+# For immediate values, those types are stored in bytes (MIL parser reads those types from bytes).
+IMMEDIATE_VALUE_TYPES_IN_BYTES = (types.fp16, types.int8, types.uint8, types.uint32)
+
 
 def create_valuetype_scalar(data_type):
     """
@@ -105,7 +108,7 @@ def _tensor_field_by_type(tensor_val, builtin_type):
     elif types.is_int(builtin_type):
         if builtin_type == types.int64 or builtin_type == types.uint64:
             return tensor_val.longInts.values
-        if builtin_type in (types.int8, types.uint8, types.uint32):
+        if builtin_type in IMMEDIATE_VALUE_TYPES_IN_BYTES:
             return tensor_val.bytes.values
         if builtin_type == types.int16 or builtin_type == types.uint16:
             # TODO (rdar://111797203): Serialize to byte after MIL changes to read from byte field.
@@ -132,7 +135,7 @@ def _set_empty_tensor_field_by_type(tensor_val, builtin_type):
     elif types.is_int(builtin_type):
         if (builtin_type == types.int64 or builtin_type == types.uint64):
             tensor_val.longInts.SetInParent()
-        elif builtin_type in (types.int8, types.uint8, types.uint32):
+        elif builtin_type in IMMEDIATE_VALUE_TYPES_IN_BYTES:
             tensor_val.bytes.SetInParent()
         else:
             tensor_val.ints.SetInParent()
@@ -167,7 +170,7 @@ def create_tensor_value(np_tensor):
         if builtin_type == types.str:
             for x in np.nditer(np_tensor):
                 t_field.append(x.encode("utf-8"))
-        elif builtin_type in (types.fp16, types.int8, types.uint8, types.uint32):
+        elif builtin_type in IMMEDIATE_VALUE_TYPES_IN_BYTES:
             val.immediateValue.tensor.bytes.values = np_val_to_py_type(np_tensor)
         else:
             for x in np_tensor.flatten():
@@ -189,7 +192,7 @@ def create_scalar_value(py_scalar):
 
     # Set the tensor value
     t_field = _tensor_field_by_type(t_val, builtin_type)
-    if builtin_type in (types.fp16, types.int8, types.uint8, types.uint32):
+    if builtin_type in IMMEDIATE_VALUE_TYPES_IN_BYTES:
         # Serialize to bytes because MIL read them from the "bytes" field in TensorValue.
         val.immediateValue.tensor.bytes.values = np_val_to_py_type(py_scalar)
     else:
@@ -295,7 +298,7 @@ def types_to_proto(valuetype):
         return create_valuetype_scalar(types_to_proto_primitive(valuetype))
 
 
-def create_file_value(output_var, blob_writer):
+def _get_offset_by_writing_data(output_var, blob_writer):
     if output_var.val.dtype.kind == 'f' and output_var.val.dtype.itemsize == 4:
         offset = blob_writer.write_float_data(np.ascontiguousarray(output_var.val.flatten()))
     elif output_var.val.dtype.kind == "f" and output_var.val.dtype.itemsize == 2:
@@ -316,6 +319,12 @@ def create_file_value(output_var, blob_writer):
     else:
         raise TypeError("Unsupported type, {}, for net buffer serialization.".format(output_var.val.dtype))
 
+    return offset
+
+
+def create_file_value(output_var, blob_writer):
+    offset = _get_offset_by_writing_data(output_var, blob_writer)
+
     return create_file_value_tensor(
         file_name=os.path.join(os.path.join('@model_path', _WEIGHTS_DIR_NAME), _WEIGHTS_FILE_NAME),
         offset=offset,

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

@@ -42,7 +42,8 @@
 
 try:
     from coremltools.libmilstoragepython import _BlobStorageWriter as BlobWriter
-except:
+except Exception as e:
+    logger.warning(f"Fail to import BlobWriter from libmilstoragepython. {e}")
     BlobWriter = None
 
 

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

@@ -1088,13 +1088,13 @@ def program(x):
                 x = mb.pow(x=x, y=2.0)
                 x = mb.sqrt(x=x)
                 x = mb.reduce_argmax(x=x)
-                x = mb.reshape(x=x, shape=[*x_shape])
+                x = mb.reshape(x=x, shape=[*x_shape[:-1]])
             else:
                 x = mb.mul(x=x, y=x)
                 x = mb.sqrt(x=x)
                 x = mb.pow(x=x, y=2.0)
                 x = mb.reduce_argmax(x=x)
-                x = mb.reshape(x=x, shape=[*x_shape])
+                x = mb.reshape(x=x, shape=[*x_shape[:-1]])
             return x
 
         prev_prog, _, block = apply_pass_and_basic_check(
@@ -1108,5 +1108,5 @@ def program(x):
             program=program,
             inputs={"x": x_shape},
             backend=("mlprogram", "fp32"),
-            expected_output_shapes={block.outputs[0].name: tuple(x_shape)},
+            expected_output_shapes={block.outputs[0].name: tuple(x_shape[:-1])},
         )

coremltools/converters/mil/converter.py

@@ -288,7 +288,7 @@ def mil_convert_to_proto(
 
     PassPipelineManager.apply_pipeline(prog, main_pipeline)
 
-    prog._check_invalid_tensor_rank()
+    prog._check_invalid_program()
 
     if convert_to == 'milinternal':
         return None, prog

coremltools/converters/mil/frontend/milproto/__init__.py

@@ -3,4 +3,4 @@
 # 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
 
-from .load import load
+from . import load

coremltools/converters/mil/frontend/milproto/load.py

@@ -8,24 +8,31 @@
 import numpy as np
 
 from coremltools import _logger as logger
-from coremltools.converters.mil._deployment_compatibility import \
-    AvailableTarget as _target
+from coremltools.converters.mil._deployment_compatibility import AvailableTarget as _target
+from coremltools.converters.mil.backend.mil import helper
 from coremltools.converters.mil.mil import Block
 from coremltools.converters.mil.mil import Builder as mb
-from coremltools.converters.mil.mil import (Function, ListVar, Placeholder,
-                                            Program, TupleInputType, Var,
-                                            mil_list, types)
+from coremltools.converters.mil.mil import (
+    Function,
+    ListVar,
+    Placeholder,
+    Program,
+    TupleInputType,
+    Var,
+    mil_list,
+    types,
+)
 from coremltools.converters.mil.mil.block import curr_block
-from coremltools.converters.mil.mil.ops.registry import \
-    SSAOpRegistry as _SSAOpRegistry
+from coremltools.converters.mil.mil.ops.registry import SSAOpRegistry as _SSAOpRegistry
 from coremltools.proto import MIL_pb2 as pm
 from coremltools.proto import Model_pb2 as ml
 
 from .helper import proto_to_types
 
 try:
     from coremltools.libmilstoragepython import _BlobStorageReader as BlobReader
-except:
+except Exception as e:
+    logger.warning(f"Fail to import BlobReader from libmilstoragepython. {e}")
     BlobReader = None
 
 
@@ -145,7 +152,7 @@ def _load_value(context, value_spec):
         else:
             value = _load_file_value(context, value_spec.blobFileValue, dtype)
 
-        if dtype in (types.fp16, types.int8, types.uint8, types.uint32):
+        if dtype in helper.IMMEDIATE_VALUE_TYPES_IN_BYTES:
             value = np.frombuffer(value, types.nptype_from_builtin(dtype)).reshape(
                 shape
             )
@@ -246,20 +253,23 @@ def _dummy_false_fn(*loop_vars):
         inputs["_false_fn"] = _dummy_false_fn
 
 
+def _load_const_op(context, op_spec):
+    inputs = {k: _load_value(context, v) for k, v in op_spec.attributes.items()}
+    pymil_var = getattr(mb, op_spec.type)(**inputs)
+    context.register_var_with_name(op_spec.outputs[0].name, pymil_var)
+
+
 def _load_operation(context, op_spec):
     if not isinstance(op_spec, pm.Operation):
         raise TypeError("Invalid Operation spec object")
 
     op_type = op_spec.type
-    if op_type == "const" or op_type.startswith("constexpr_"):
+    if op_type == "const" or "constexpr_" in op_type:
         if op_spec.blocks:
             raise ValueError("const / constexpr operation can't have any block")
         if op_spec.inputs:
             raise ValueError("const / constexpr operation can't have any input")
-
-        inputs = {k: _load_value(context, v) for k, v in op_spec.attributes.items()}
-        pymil_var = getattr(mb, op_type)(**inputs)
-        context.register_var_with_name(op_spec.outputs[0].name, pymil_var)
+        _load_const_op(context, op_spec)
 
     else:
         if op_type == "custom_layer":
@@ -402,11 +412,19 @@ def _load_function(context, func_spec, spec_version):
 
 
 def load(model_spec, specification_version, file_weights_dir="", **kwargs):
+    """
+    Load MILProto to Pymil.
+
+    Set force_spec_version to force override the spec version.
+    """
     if not isinstance(model_spec, ml.Model):
         raise TypeError("Invalid Model sepc object")
 
     if specification_version < model_spec.specificationVersion:
-        raise ValueError("specification_version must be greater or equal to the input model spec version")
+        if not kwargs.get("force_spec_version", False):
+            raise ValueError(
+                "specification_version must be greater or equal to the input model spec version"
+            )
 
     if model_spec.WhichOneof("Type") != "mlProgram":
         raise ValueError("Only MIL proto based mlmodels can be loaded")

coremltools/converters/mil/frontend/tensorflow/test/test_load.py

@@ -158,7 +158,7 @@ def build_model(x):
 
     @pytest.mark.parametrize(
         "target",
-        [ct.target.iOS13, ct.target.macOS15, ct.target.watchOS6, ct.target.tvOS13],
+        [ct.target.iOS13, ct.target.macOS10_15, ct.target.watchOS6, ct.target.tvOS13],
     )
     def test_invalid_deployment_target_cumsum(self, target):
         x_shape = (3, 4, 5)
@@ -179,7 +179,7 @@ def build_model(x):
 
     @pytest.mark.parametrize(
         "target",
-        [ct.target.iOS14, ct.target.macOS16, ct.target.watchOS7, ct.target.tvOS14],
+        [ct.target.iOS14, ct.target.macOS10_16, ct.target.watchOS7, ct.target.tvOS14],
     )
     def test_valid_deployment_target_cumsum(self, target):
         x_shape = (3, 4, 5)

coremltools/converters/mil/frontend/torch/converter.py

@@ -19,6 +19,7 @@
 from .._utils import get_output_names
 from .internal_graph import InternalTorchIRGraph, InternalTorchIRNode
 from .ops import convert_nodes
+from .quantization_ops import _dequantized_weight
 from .torch_op_registry import _TORCH_OPS_REGISTRY
 from .torchir_passes import (
     flatten_graph_input_values,
@@ -147,8 +148,6 @@ def get_dequantized_var(self, name: str, dequantized_name: str = None):
             # the MIL op.
             if dequantized_name is not None:
                 self._context.add(original_var, dequantized_name)
-            if self._quant_dtype is None:
-                raise AssertionError("Trying to dequantize without quantization info")
             return original_var, self._quant_dtype
 
         quant_params = self.get_quantization_info(name)
@@ -429,6 +428,10 @@ def convert_const(self):
             if isinstance(val, torch._C.ScriptObject):
                 logger.info(f"Encountered constant {name} of type _torch._C.ScriptObject")
                 continue
+            elif isinstance(val, torch.Tensor) and val.is_quantized:
+                const = _dequantized_weight(val.cpu(), name)
+                self.context.add(const)
+                continue
             elif not isinstance(val, np.ndarray):
                 raise ValueError(f"unsupported class for {name} in PyTorch graph: {type(val)}")
             # TODO (rdar://107718371): support uint8 quantization
@@ -623,10 +626,10 @@ def _lower_graph_block(graph):
                 if is_tensor or is_quantized_tensor:
                     if is_tensor and prefix in state_dict:
                         assert torch.equal(
-                            module, state_dict[prefix]
+                            module.cpu(), state_dict[prefix].cpu()
                         ), "tensor value not consistent between torch ir and state_dict"
                     if prefix in params_dict:
-                        assert torch.equal(module, params_dict[prefix])
+                        assert torch.equal(module.cpu(), params_dict[prefix].cpu())
                         replace_input[_output] = first_node_with_prefix[prefix]
                     else:
                         params_dict[prefix] = module