THIS IS A TEST INSTANCE. ALL YOUR CHANGES WILL BE LOST!!!!
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def convert_model(sym, arg_params, aux_params, target_dtype="float16", target_dtype_ops=None, fp32_ops=None, widest_dtype_ops=None, conditional_fp32_ops=None, excluded_sym_names=None): """API for converting a model from FP32 model to a mixed precision model. MXNet tries to convert the FP32 model to mixed precision model by adding cast layers using amp_cast and amp_multicast operators. The decision on which cast layer to add is based on hardcoded lists for Automatic Mixed Precision in MXNet. These lists can be overridden by the user by providing their own lists using : targe_precision_ops, fp32_ops, widest_precision_ops, conditional_fp32_ops Parameters ---------- sym : str or Symbol Defines the structure of a neural network for FP32 types. arg_params : dict Dictionary of name to `NDArray`. aux_params : dict Dictionary of name to `NDArray`. target_dtype : str Currently only supports float16. The target dtype indicates to add cast layers when possible so that lower precision computation can be leveraged. target_dtype_ops : list of strs Override the list of operator names casted to target_dtype. If None, uses the framework's default list to be casted to target dtype. fp32_ops : list of strs Override the lists of operator names casted to FP32. If None, uses the framework's default list to be casted to FP32. widestconditional_dtypefp32_ops : list of strs A (string, string, list of op names provided by user which should run in widest precision among its inputs. If None, uses the framework's default list of widest_precision_ops. conditional_fp32_ops : list of (string, string, list of string) Override the list of operatorsstring) Override the list of operators to be casted to FP32. The format of the list is (name of the function, name of the parameter, list of values of the parameter that make the operator to be casted to FP32. The format of the list is (name of the function, name of the parameter, list of values of the parameter that make the operator to be casted to fp32) excluded_sym_names : list of strs A list of strings that represent the names of symbols that users want to exclude from being quantized. """ fp32) excluded_sym_names : list of strs A list of strings that represent the names of symbols that users want to exclude from being quantized. """ |
target_dtype should decide target_dtype should decide which lists need to be overridden.
For example, in the future bfloat16 support may be added in which case these lists for operators running in bfloat16 will also be added to AMP.
In this case, target_dtype will allow users to choose the right dtype for the mixed precision model.
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def convert_block(block, target_dtype="float16", target_dtype_ops=None,
fp32_ops=None, widest_dtype_ops=None, conditional_fp32_ops=None,
excluded_sym_names=None, input_names=['data']):
"""Given a hybrid block/symbol block representing a neural network of data type FP32 and target_dtype,
return a block with mixed precision support
Parameters
----------
block : HybridBlock or SymbolBlock object
FP32 HybridBlock or SymbolBlock object
target_dtype : str or numpy
currently only supports float16. The target dtype indicates to add cast layers
when possible so that lower precision computation can be leveraged.
target_precision_ops : list of strs
Override the list of operator names casted to target_dtype.
If None, uses the framework's default list to be casted to target dtype.
fp32_ops : list of strs
Override the lists of operator names casted to FP32.
If None, uses the framework's default list to be casted to FP32.
widestconditional_precisionfp32_ops : list of (string, string, list of strsstring)
Override the list of operatorfunctions namescasted whichto shouldFP32.
run in widest precision among its
The format of the list is
input arguments.
(name of Ifthe Nonefunction, name usesof the framework's default list of widest_precision_ops.
parameter,
conditional_fp32_ops : list of (string, string, list of string)
Override the list of functions casted to FP32.values of the parameter that make the operator to be casted to
fp32)
excluded_sym_names : list of Thestrs
format of the list is
(nameA list of thestrings function, name ofthat represent the parameter,
listnames of values of the parametersymbols that makeusers the operatorwant to beexclude
casted to
from being fp32)quantized.
excluded_syminput_names : list of strs
A list of strings that represent the names of symbols that users want to exclude
from being quantized.
input_names : list of strs
A list of strings representingrepresenting the names of input variables
""" |
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Setup
EC2 Instance: p3.8xlarge
CUDNN: 7.4.2
CUDA: 10.0
Commit Hash: b3b952f9d5490ee2707209ab866e6c3f094e2046 (PoC changes made on top of this built from source)
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imagenet1k-resnet-152: JSON File, Params File
Results
| Model (Samples/sec) | Batch Size | Original Model (Samples/sec) | Mixed Precision Model (Samples/sec) | Original Model with Implicit Type Conversion (MXNET_CUDA_TENSOR_OP_MATH_ALLOW_CONVERSION=1) (Samples/sec) |
|---|---|---|---|---|
imagenet1k-resnet-152 | 1 | 85 | 72 | 72 |
| 2 | 140 | 140 | 142 | |
| 4 | 240 | 270 | 228 | |
| 8 | 320 | 470 | 261 | |
| 16 | 405 | 680 | 315 | |
resnet50_v1 | 1 | 215 | 165 | 205 |
| 2 | 370 | 330 | 365 | |
| 4 | 560 | 600 | 545 | |
| 8 | 760 | 980 | 635 | |
| 16 | 935 | 1400 |
| 790 |
FAQ
Will the arg_params and aux_params be casted to fp16 ?
Depends on the whitelists provided. The default whitelists have been selected in a way to avoid casting of the params, for commonly used convnet networks. If the whitelist is such that the type inference decides that certain param needs to be float16 then it will be castedInputs of ops in FP16 will be casted. Other params may or may not be casted based on the type inference logic.
How is this different from casting inputs to FP16 and casting params to FP16 in Gluon ?
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