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* 5.0b2 Release * Fixed reference to rmtree
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coremltools/converters/mil/backend/mil/passes/adjust_io_to_supported_types.py
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| # -*- coding: utf-8 -*- | ||
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| # Copyright (c) 2021, Apple Inc. All rights reserved. | ||
| # | ||
| # 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 | ||
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| from coremltools.converters.mil.mil import Builder as _mb | ||
| from coremltools.converters.mil.mil import types as _types | ||
| from coremltools.converters.mil.mil.ops import defs as _ops | ||
| from coremltools.converters.mil.mil.passes.pass_registry import register_pass as _register_pass | ||
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| import warnings as _warnings | ||
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| @_register_pass(namespace="mil_backend") | ||
| def adjust_io_to_supported_types(prog): | ||
| """ | ||
| Converts all dTypes to types that are supported by the CoreML runtime. | ||
| The runtime supports only fp16, fp32, int32, str, and bool variables. | ||
| General rules: | ||
| * Integer vars that are not 32 bit are replaced with int32 types. | ||
| * All other types not in the list of runtime supported types are replaced with the fp32 dtype. | ||
| No casts are inserted; the previous type is replaced. The assumption is that all remaining | ||
| types are numerical and can be reasonably replaced with 32 bit float types. | ||
| The "main" function has additional rules since its I/O is mapped to CoreML model I/O: | ||
| * Fp16 I/O is replaced with fp32 I/O. | ||
| Casts (fp32 input -> fp16) are inserted at the beginning of the program to preserve 16 bit inputs. | ||
| Casts (fp16 -> fp32 output) are inserted at the end of the program to preserve 16 bit computations. | ||
| * All non-integer I/O that is not fp32 is replaced with fp32 I/O. | ||
| A cast (prev input type -> fp32) is inserted at the beginning of the program to preserve non-fp32 inputs. | ||
| A cast (prev type -> fp32 out) is inserted at the end of the program to preserve non-fp32 computations. | ||
| The assumption is that all remaining types are numerical and it is valid to cast them to/from fp32. | ||
| * The only exception: Int64 outputs are allowed for the classifier op. This is to keep consistency with | ||
| the CoreML API, which uses 64 bit integers to represent classifier labels. | ||
| ------ | ||
| func main(bool x, int32 y, fp32 z) { | ||
| bool out = logical_not(x) | ||
| } -> (out, y, z) | ||
| becomes | ||
| func main(fp32 x, int32 y, fp32 z) { | ||
| bool x_casted = cast(x) | ||
| bool out__pre__output__fp32__cast = logical_not(x_casted) | ||
| fp32 out = cast(out__pre__output__fp32__cast) | ||
| } -> (out, y, z) | ||
| ------ | ||
| func not_main(bool x, int32 y, fp32 z) { | ||
| bool out = logical_not(x) | ||
| } -> (out, y, z) | ||
| is unchanged. | ||
| """ | ||
| for name, func in prog.functions.items(): | ||
| _adjust_io_to_supported_types(func, name == "main") | ||
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| __RUNTIME_SUPPORTED_TYPES = [_types.fp16, _types.fp32, _types.int32, _types.str, _types.bool] | ||
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| ##### | ||
| # Main Function | ||
| ##### | ||
| def _adjust_main_inputs(func): | ||
| first_op = func.operations[0] if len(func.operations) > 0 else None | ||
| for input_name, input_var in func.inputs.items(): | ||
| if (_types.is_tensor(input_var.sym_type) or _types.is_scalar(input_var.sym_type)) \ | ||
| and input_var.dtype != _types.fp32 \ | ||
| and input_var.dtype != _types.int32: | ||
| input_dtype_str = _types.builtin_to_string(input_var.dtype) | ||
| if _types.is_int(input_var.dtype): | ||
| # Replace non-int32 input type with int32. | ||
| _warnings.warn("Input" + input_var.name + " is of dType " + input_dtype_str +\ | ||
| ". Only integer variables of bit width 32 are supported by the CoreML runtime. " +\ | ||
| "This input will be assigned a dType of int32. " +\ | ||
| "No cast will be inserted; the previous dtype will be replaced.") | ||
| input_var._sym_type = _types.tensor(_types.int32, input_var.sym_type.get_shape()) | ||
| elif input_var.dtype == _types.fp64: | ||
| # Replace float64 input type with fp32. | ||
| _warnings.warn("Input" + input_var.name + " is of dtype fp64. 64 bit float inputs are " +\ | ||
| "not supported by ML program models. This input will be assigned a dType " +\ | ||
| "of fp32. No cast will be inserted; the previous dtype will be replaced.") | ||
| input_var._sym_type = _types.tensor(_types.fp32, input_var.sym_type.get_shape()) | ||
| else: | ||
| # 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. | ||
| _warnings.warn("Input" + input_var.name + " is of dType " + input_dtype_str + ". The " +\ | ||
| "CoreML runtime does not support inputs with this dType (only fp32 and " +\ | ||
| "int32 inputs are supported). This input will be assigned a dType of " +\ | ||
| "fp32. A cast will be inserted at the beginning of the program to " +\ | ||
| "convert the input to the originally defined dType.") | ||
| with func: | ||
| casted_input_var = _mb.cast(x=input_var, dtype=input_dtype_str, before_op=first_op) | ||
| func.replace_uses_of_var_after_op(anchor_op=casted_input_var.op, old_var=input_var, new_var=casted_input_var) | ||
| input_var._sym_type = _types.tensor(_types.fp32, input_var.sym_type.get_shape()) | ||
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| def _adjust_main_outputs(func): | ||
| new_outputs = [] | ||
| for output_var in func.outputs: | ||
| output_type = output_var.sym_type | ||
| if (_types.is_tensor(output_type) or _types.is_scalar(output_type)) \ | ||
| and output_var.dtype != _types.fp32 \ | ||
| and output_var.dtype != _types.int32: | ||
| output_dtype_str = _types.builtin_to_string(output_var.dtype) | ||
| _warnings.warn("Output" + output_var.name + " is of dType " + output_dtype_str + ". The " +\ | ||
| "CoreML runtime does not support outputs with this dType (only int32 and " +\ | ||
| "fp32 are supported for outputs). This output will be assigned a dType " +\ | ||
| "of fp32. A cast will be inserted at the end of the program to convert" +\ | ||
| "the original output dType to the dType supported by the CoreML runtime.") | ||
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| output_var_name = output_var.name | ||
| output_var.set_name(output_var_name + "__pre__output__fp32__cast") | ||
| # Convert the output to fp32, and add a cast. | ||
| with func: | ||
| output_var = _mb.cast(x=output_var, dtype="fp32") | ||
| output_var.set_name(output_var_name) | ||
| new_outputs.append(output_var) | ||
| func.set_outputs(new_outputs) | ||
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| ##### | ||
| # General Functions and Blocks | ||
| ##### | ||
| def _adjust_var(var): | ||
| """ | ||
| Changes the dtype of the provided variable according | ||
| to the rules outlined in the top level pass comment | ||
| (see adjust_io_to_supported_types). | ||
| """ | ||
| if (_types.is_tensor(var.sym_type) or _types.is_scalar(var.sym_type)) \ | ||
| and var.dtype not in __RUNTIME_SUPPORTED_TYPES: | ||
| dtype_str = _types.builtin_to_string(var.dtype) | ||
| if _types.is_int(var.dtype): | ||
| # Replace non-int32 input type with int32. | ||
| _warnings.warn("Input" + var.name + " is of dType " + dtype_str +\ | ||
| ". Only integer variables of bit width 32 are supported by the CoreML runtime. " +\ | ||
| "This input will be assigned a dType of int32. " +\ | ||
| "No cast will be inserted; the previous dtype will be replaced.") | ||
| var._sym_type = _types.tensor(_types.int32, var.sym_type.get_shape()) | ||
| else: | ||
| # This is some other unsupported dType. Change the input type to fp32. | ||
| _warnings.warn("Var " + var.name + " is of dType " + dtype_str + ". The CoreML runtime " +\ | ||
| "does not support this dType (only fp16, fp32, bool, and int32 are supported). " +\ | ||
| "This input will be assigned a dType of fp32. No cast will be inserted; " +\ | ||
| "the previous dtype will be replaced.") | ||
| var._sym_type = _types.tensor(_types.fp32, var.sym_type.get_shape()) | ||
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| def _adjust_func_inputs(func): | ||
| for input_name, input_var in func.inputs.items(): | ||
| _adjust_var(input_var) | ||
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| def _adjust_block_inputs(block): | ||
| for input_var in block.inputs: | ||
| _adjust_var(input_var) | ||
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| def _adjust_ops(block): | ||
| len_block = len(block.operations) | ||
| i = 0 | ||
| while i < len_block: | ||
| op = block.operations[i] | ||
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| # 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.") | ||
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| for subblock in op.blocks: | ||
| _adjust_block_inputs(subblock) | ||
| _adjust_ops(subblock) | ||
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| for var in op.outputs: | ||
| _adjust_var(var) | ||
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| # 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. | ||
| with block: | ||
| 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 | ||
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| ##### | ||
| # 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) |
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