114 lines
4.9 KiB
Python
114 lines
4.9 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import logging as log
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import numpy as np
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from mo.graph.graph import Node, Graph
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from mo.middle.passes.convert_data_type import np_data_type_to_precision, convert_blob, \
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np_data_type_to_destination_type, packed_I4, packed_U4
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from mo.ops.op import Op
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from mo.utils.utils import refer_to_faq_msg
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class Cast(Op):
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op = 'Cast'
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enabled = False
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def __init__(self, graph: Graph, attrs: dict):
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mandatory_props = {
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'op': self.op,
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'type': 'Convert',
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'version': 'opset1',
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'infer': self.infer,
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'type_infer': self.type_infer,
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'dst_type': None,
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'in_ports_count': 1,
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'out_ports_count': 1,
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}
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super().__init__(graph, mandatory_props, attrs)
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def backend_attrs(self):
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return [('destination_type', lambda node: np_data_type_to_destination_type(node.dst_type))]
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@staticmethod
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def type_infer(node: Node):
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assert node.has_valid('dst_type'), 'Destination type of "Cast" operation should be extracted earlier'
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node.out_port(0).set_data_type(node.dst_type)
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@staticmethod
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def helper_value_propagation(node_name, value, dst_type):
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new_blob, finite_match_count, zero_match_count = convert_blob(value, dst_type)
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if finite_match_count:
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log.error("{} elements of {} were clipped to infinity while converting an input blob for node '{}' to {}."
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" ".format(finite_match_count, new_blob.size, node_name, dst_type) + refer_to_faq_msg(76))
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if zero_match_count:
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log.warning("{} elements of {} were clipped to zero while converting an input blob for node '{}' to {}."
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" ".format(zero_match_count, new_blob.size, node_name, dst_type) + refer_to_faq_msg(77))
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return new_blob
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@staticmethod
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def custom_type_casting_and_packing(node: Node, value, dst_type):
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"""
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Custom types are not supported by numpy but we still need to write it to the .bin file in a compact way.
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To do so we prepare bit representation of int4/uint4 values and store them in a numpy friendly data type.
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We pack int4/uint4 values into uint8 type (two int4/uint4 numbers fit in uint8).
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If the number of elements in the blob is odd we pad them with zero value to be able to fit the bit sequence
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into the uint8 array.
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Example: we need to represent 5 elements of int4 dtype
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we would pad them to 6 element with the last element as zero and we would pack them into 3 uint8 values
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"""
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assert dst_type in [packed_U4, packed_I4]
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minimum_regular_dtype = np.uint8 if dst_type == packed_U4 else np.int8
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# initial casing from the source type to the numpy-friendly type which could absorb all the values of dst_type
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casted_to_regular_type = Cast.helper_value_propagation(
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node.soft_get('name', node.id), value, minimum_regular_dtype)
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# packing the values
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data_shape = node.out_port(0).data.get_shape()
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assert data_shape is not None
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data_size = np.prod(data_shape)
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num_bits = 4
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assert num_bits < 8 and 8 % num_bits == 0, "Packing algorithm for the data types stored in 1, 2 or 4 bits"
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num_values_fitting_into_uint8 = 8 // num_bits
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pad = (-data_size) % num_values_fitting_into_uint8
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flattened = casted_to_regular_type.flatten()
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padded = np.concatenate((flattened, np.zeros([pad], dtype=minimum_regular_dtype)))
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assert np.prod(padded.shape) % num_values_fitting_into_uint8 == 0
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bit_order_little = (padded[:, None] & (1 << np.arange(num_bits)) > 0).astype(np.uint8)
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bit_order_big = np.flip(bit_order_little, axis=1)
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bit_order_big_flattened = bit_order_big.flatten()
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packed = np.packbits(bit_order_big_flattened)
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node.out_node(0)['force_shape'] = data_shape.copy()
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node.out_node(0)['force_type'] = np_data_type_to_precision(dst_type)
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node.out_port(0).data.set_value(packed)
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@staticmethod
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def infer(node: Node):
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node_name = node.soft_get('name', node.id)
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dst_type = node.soft_get('dst_type', None)
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assert dst_type is not None, \
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'Destination type of "Cast" operation should be extracted earlier, but it`s not for node: ' + node_name
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input_shape = node.in_port(0).data.get_shape()
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assert input_shape is not None
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node.out_port(0).data.set_shape(input_shape)
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value = node.in_port(0).data.get_value()
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if value is None or node.has_and_set('stop_value_propagation'):
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return
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if dst_type in [packed_U4, packed_I4]: # custom types conversion
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Cast.custom_type_casting_and_packing(node, value, dst_type)
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else:
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node.out_port(0).data.set_value(
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Cast.helper_value_propagation(node_name, value, dst_type))
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