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305921734a
Test Plan: revert-hammer Differential Revision: D22013026 Original commit changeset: 714b938f25c1 fbshipit-source-id: 6230bc8060892e6485159ca88cc3ad49217791a2
89 lines
4.1 KiB
Python
89 lines
4.1 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import enum
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import torch
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from .qconfig import QConfig
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from torch.jit._recursive import wrap_cpp_module
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# Quantization type (dynamic quantization, static quantization).
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# Should match the c++ enum in quantization_type.h
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class QuantType(enum.IntEnum):
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DYNAMIC = 0
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STATIC = 1
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def _check_is_script_module(model):
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if not isinstance(model, torch.jit.ScriptModule):
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raise ValueError('input must be a script module, got: ' + str(type(model)))
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def _check_forward_method(model):
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if not model._c._has_method('forward'):
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raise ValueError('input script module does not have forward method')
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def script_qconfig(qconfig):
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return QConfig(
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activation=torch.jit.script(qconfig.activation())._c,
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weight=torch.jit.script(qconfig.weight())._c)
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def script_qconfig_dict(qconfig_dict):
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return {k: script_qconfig(v) if v else None for k, v in qconfig_dict.items()}
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def _prepare_script(model, qconfig_dict, inplace=False, quant_type=QuantType.STATIC):
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assert not inplace, "The inplace support is still in development"
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_check_is_script_module(model)
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_check_forward_method(model)
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if not all(isinstance(x, str) for x in qconfig_dict.keys()):
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raise ValueError('qconfig_dict should only contain names(str) as keys.')
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scripted_qconfig_dict = script_qconfig_dict(qconfig_dict)
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model = wrap_cpp_module(torch._C._jit_pass_fold_convbn(model._c))
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return wrap_cpp_module(torch._C._jit_pass_insert_observers(model._c,
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'forward',
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scripted_qconfig_dict,
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inplace,
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quant_type))
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def prepare_script(model, qconfig_dict, inplace=False):
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return _prepare_script(model, qconfig_dict, inplace, quant_type=QuantType.STATIC)
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def prepare_dynamic_script(model, qconfig_dict, inplace=False):
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return _prepare_script(model, qconfig_dict, inplace, quant_type=QuantType.DYNAMIC)
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def _convert_script(model, inplace=False, debug=False, quant_type=QuantType.STATIC):
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assert not inplace, "The inplace support is still in development"
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_check_is_script_module(model)
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model.eval()
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model = wrap_cpp_module(torch._C._jit_pass_insert_quant_dequant(model._c, 'forward', inplace, quant_type))
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if not debug:
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model = wrap_cpp_module(torch._C._jit_pass_quant_finalize(model._c, quant_type))
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return model
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def convert_script(model, inplace=False, debug=False):
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return _convert_script(model, inplace, debug, quant_type=QuantType.STATIC)
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def convert_dynamic_script(model, inplace=False, debug=False):
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return _convert_script(model, inplace, debug, quant_type=QuantType.DYNAMIC)
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def _quantize_script(model, qconfig_dict, run_fn=None, run_args=None, inplace=False, debug=False, quant_type=QuantType.STATIC):
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assert not inplace, "We don't support inplace right now"
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# Always do inplace convert because the Tensor is already
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# copied in prepare_script when inplace is False
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if quant_type == QuantType.DYNAMIC:
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model = prepare_dynamic_script(model, qconfig_dict, inplace)
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# TODO: change inplace to True
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model = convert_dynamic_script(model, False, debug)
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else:
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assert run_fn, "Must provide calibration function for post training static quantization"
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assert run_args, "Must provide calibration dataset for post training static quantization"
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model = prepare_script(model, qconfig_dict, inplace)
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run_fn(model, *run_args)
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# TODO: change inplace to True
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model = convert_script(model, False, debug)
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return model
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def quantize_script(model, qconfig_dict, run_fn, run_args, inplace=False, debug=False):
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return _quantize_script(model, qconfig_dict, run_fn, run_args, inplace, debug, quant_type=QuantType.STATIC)
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def quantize_dynamic_script(model, qconfig_dict, inplace=False, debug=False):
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return _quantize_script(model, qconfig_dict, inplace=inplace, debug=debug, quant_type=QuantType.DYNAMIC)
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