Neural network quantization parameter determination method and related products

1. A method for quantizing data in a neural network using neural network quantization parameters, comprising:

obtaining an analyzing result of each type of the data to be quantized, wherein the data includes at least one type of neurons, weights, gradients, or biases of the neural network;

determining a corresponding quantization parameter according to the analyzing result of each type of the data to be quantized and a data bit width corresponding to the data to be quantized;

quantizing the data using the corresponding quantization parameter to obtain quantized data;

performing inverse quantization on the quantized data to obtain inverse quantized data, wherein a data format of the inverse quantized data is the same as that of the corresponding pre-quantized data; and

determining a quantization error based on the quantized data and the inverse quantized data.

2. The method of claim 1 , wherein the neural network operation process includes at least one operation of neural network training, neural network inference, or neural network fine-tuning,

wherein the analyzing result includes a maximum value and a minimum value, or includes a maximum absolute value, of each type of data to be quantized,

wherein the maximum absolute value is determined according to the maximum value and the minimum value of each type of data to be quantized, and

wherein the quantization parameter is determined according to the data bit width along with either the maximum value and the minimum value of each type of the data to be quantized or the maximum absolute value of each type of the data to be quantized.

3. The method of claim 1 , wherein the data bit width is adjusted according to the quantization error by comparing the quantization error with a threshold to obtain a comparison result, and adjusting the data bit width according to the comparison result, wherein the threshold includes at least one of a first threshold or a second threshold.

4. The method of claim 3 , wherein the adjusting of the data bit width includes:

increasing the data bit width if the quantization error is greater than or equal to the first threshold, or

reducing the data bit width if the quantization error is less than or equal to the second threshold, or

keeping the data bit width unchanged if the quantization error is between the first threshold and the second threshold.

5. The method of claim 3 , wherein a method for obtaining the quantization error includes:

determining a quantization interval according to the data bit width, and

determining the quantization error according to the quantization interval, the number of the quantized data, and the corresponding pre-quantized data.

6. The method of claim 3 , wherein the pre-quantized data is the data to be quantized in the weight update iteration within a target iteration interval, and wherein the target iteration interval includes at least one weight update iteration and the same quantization parameter is used in the quantization process within the same target iteration interval.

7. The method of claim 6 , wherein the determining the target iteration interval includes:

at a predicted time point, determining a variation trend value of a point position parameters of data to be quantized involved in the weight update iteration, wherein the predicted time point is used to determine whether the quantization parameter needs to be adjusted or not, and the predicted time point corresponds to the time point when the weight update iteration is completed, and

determining the corresponding target iteration interval according to the variation trend value of the point position parameter.

8. The method of claim 3 , wherein the pre-quantized data is data to be quantized involved in weight update iteration within a target iteration interval, wherein the target iteration interval includes at least one weight update iteration, and the same data bit width is used in the quantization process within the same target iteration interval.

9. The method of claim 8 , wherein the determining the target iteration interval includes:

at a predicted time point, determining a variation trend value of a point position parameter and a variation trend value of data bit width corresponding to the data to be quantized involved in the weight iteration process, wherein the predicted time point is configured to determine whether the data bit width needs to be adjusted or not, and the predicted time point corresponds to the time point when the weight update iteration is completed, and

determining the corresponding target iteration interval according to the variation trend value of the point position parameter and the variation trend value of the data bit width.

10. The method of claim 9 , wherein the variation trend value of the data bit width is determined according to the corresponding quantization error.

11. The method of claim 8 , wherein the determining of the target iteration interval includes:

at a predicted time point, determining a variation trend value of a point position parameter of data to be quantized involved in the weight update iteration, wherein the predicted time point is configured to determine whether the data bit width needs to be adjusted or not, and the predicted time point corresponds to the time point when the weight update iteration is completed, and

determining the corresponding target iteration interval according to the variation trend value of the point position parameter.

12. The method of claim 11 , wherein the predicted time point includes a first predicted time point, wherein the first predicted time point is determined according to the target iteration interval.

13. The method of claim 12 , wherein the predicted time point further includes a second predicted time point, wherein the second predicted time point is determined according to a curve of data variation range, wherein the curve of data variation range is obtained by analyzing the data variation range in the process of weight update iteration.

14. The method of claim 11 , wherein the variation trend value of the point position parameter is determined according to a moving average value of the point position parameter corresponding to a current predicted time point and a moving average value of the point position parameter corresponding to a previous predicted time point, or is determined according to the point position parameter corresponding to the current predicted time point and the moving average value of the corresponding point position parameter corresponding to the previous predicted time point.

15. The method of claim 14 , wherein the determining of a moving average value of a point position parameter corresponding to the current predicted time point includes:

determining the point position parameter corresponding to the current predicted time point according to a point position parameter corresponding to a previous predicted time point and an adjusted value of the data bit width,

adjusting a moving average value of a point position parameter corresponding to the previous predicted time point according to the adjusted value of the data bit width to obtain an adjusted result, and

determining the moving average value of the point position parameter corresponding to the current predicted time point according to the point position parameter corresponding to the current predicted time point and the adjusted result.

16. The method of claim 14 , wherein the determining of the moving average value of the point position parameter corresponding to the current predicted time point include:

determining an intermediate result of the moving average value of the point position parameter corresponding to the current predicted time point according to the point position parameter corresponding to the previous predicted time point and the moving average value of the point position parameter corresponding to the previous predicted time point, and

determining the moving average value of the point position parameter corresponding to the current predicted time point according to the intermediate result of the moving average value of the point position parameter corresponding to the current predicted time point and the adjusted value of the data bit width.

17. The method of claim 11 , wherein the determining data bit width used in the quantization process within the target iteration interval includes:

determining a corresponding quantization error, wherein pre-quantized data corresponding to the quantization error is the data to be quantized involved in the weight update iteration corresponding to the predicted time point, and

determining the data bit width used in the quantization process within the target iteration interval according to the corresponding quantization error.

18. The method of claim 17 , wherein the determining data bit width used in the quantization process within the target iteration interval includes:

comparing the quantization error with the threshold to obtain a comparison result, and adjusting the data bit width used in the quantization process within the previous target iteration interval according to the comparison result to obtain an adjusted result, wherein the adjusted result is taken as the data bit width used in the quantization process within a current target iteration interval.

19. A neural network quantization parameter determination device comprising a non-transitory memory and a processor, wherein a computer program that can be run on the processor is stored on the memory, and the processor implements the method of claim 1 when executing the computer program.

20. A non-transitory computer readable storage medium, on which a computer program is stored, wherein the method of claim 1 is implemented when the computer program is executed.