pytorch

77a72857 - add more Python interface functions to make quantization simpler (#18246)

add more Python interface functions to make quantization simpler (#18246) Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/18246 Simplifies histogram collection and quantization process. Histogram collection before this diff was something like this ``` from caffe2.quantization.server import dnnlowp_pybind11 ... dnnlowp_pybind11.ObserveHistogramOfOutput(hist_file) for ... workspace.RunNet(predict_net) dnnlowp_pybind11.ClearNetObservers() # This is to trigger Stop function in the observer to dump out histogram file but this can have unintended consequence of also clearing all the other useful observers we attached ``` After this diff we can ``` workspace.CreateNet(predict_net) # Note we need to create net to have a net to attach observer histogram_observer = dnnlowp_pybind11.AddHistogramObserver(predic_net, hist_file) for ... workspace.RunNet(predict_net) predict_net.RemoveObserver(histogram_observer) ``` Choosing quantization parameters of weights before this diff was something like this ``` dnnlowp_pybind11.ObserveHistogramOfOutput(weight_hist_file) workspace.RunNetOnce(init_net) dnnlowp_pybind11.ClearNetObservers() # Has same issue as the histogram collection example above dnnlowp_pybind11.RegisterQuantizationParamsWithHistogram( weight_hist_file, is_weight=True, qparams_output_file_name=qparams_file ) workspace.CreateNet(init_net, overwrite=True) dnnlowp_pybind11.ClearNetObservers() logger.info("Loading quantization params from {}".format(qparams_file)) blobs_to_qparams = {} with open(qparams_file) as f: lines = f.readlines() for line in lines: op_id, op_type, output_id, tensor_name, mini, maxi, scale, zero_point, precision = ( line.split() ) op_id = int(op_id) output_id = int(output_id) op = net.Proto().op[op_id] if op_type != op.type or op.output[output_id] != tensor_name: print( "Corrupt qparams file {} {} {} {} {}".format( qparams_file, op_type, op.type, op.output[output_id], tensor_name ) ) blobs_to_qparams[tensor_name] = QuantizationParam(float(scale), int(zero_point)) ``` After this diff this can be simplified to ``` blobs_to_qparams = {} for op in init_net.Proto().op: for output in op.output: scale, zero_point = dnnlowp_pybind11.ChooseQuantizationParams(output) blobs_to_qparams[output] = QuantizationParam(scale, zero_point) ``` Reviewed By: dskhudia Differential Revision: D14544694 fbshipit-source-id: 4fd06cd63256201e2e9d15c39f503138d1be53c2