New PyTorch and Keras interfaces (#30)

Native Torch and Keras Interfaces for CrossSim and small bugfixes.

.gitignore

@@ -14,6 +14,7 @@ applications/dnn/training/cross_sim_models/
 applications/dnn/training/sweep_results/
 applications/dnn/training/weight_update_stats/
 applications/dnn/inference/errorloop_outputs/
+applications/dnn/torch/cifar10_resnet/cifar-10-batches-py/
 tutorial/.ipynb_checkpoints/
 tutorial/params_64.json
 CrossSim.egg-info/

applications/dnn/calibration.py

@@ -0,0 +1,262 @@
+#
+# Copyright 2024 National Technology & Engineering Solutions of Sandia, LLC
+# (NTESS). Under the terms of Contract DE-NA0003525 with NTESS, the U.S.
+# Government retains certain rights in this software.
+#
+# See LICENSE for full license details
+#
+
+"""
+Utility functions for calibrating input ranges for crossbar inputs and ADCs
+based on profiled data. Compatible with both PyTorch and Keras interfaces.
+These calibration methods are not guaranteed to be optimal.
+"""
+
+import os
+import numpy.typing as npt
+import numpy as np
+from scipy.optimize import minimize
+from simulator.parameters.core_parameters import CoreStyle
+from simulator.backend import ComputeBackend
+xp = ComputeBackend()
+
+
+def calibrate_input_limits(
+    all_xbar_inputs: list,
+    Nbits: int = 0,
+    norm_ord: float = 1.0,
+) -> npt.ArrayLike:
+    """Optimizes the input range for all layers in a network given profiled
+    input values. This function is intended for use with ResNet CNNs where
+    all but the first layer is precded by a ReLU, so inputs are strictly positive.
+
+    Args:
+        all_xbar_inputs: list of arrays, each array contains profiled input
+            values for a layer
+        Nbits: quantization resolution used in optimizer
+            Set to 0 to set range based on max profiled value
+        norm_ord: power of the error norm used for the loss function in optimizer
+    Returns:
+        NumPy array containing the (min, max) range for the inputs of every layer
+    """
+
+    n_layers = len(all_xbar_inputs)
+    input_limits = np.zeros((n_layers, 2))
+
+    for k in range(n_layers):
+
+        xbar_inputs_k = xp.asarray(all_xbar_inputs[k])
+
+        if Nbits > 0:
+            eta0 = -4
+            # Optimize the input percentile
+            eta = minimize(
+                quantizationError_ReLU,
+                eta0,
+                args=(xbar_inputs_k, Nbits, norm_ord),
+                method="nelder-mead",
+                tol=0.1,
+            )
+            percentile_max = 100 * (1 - pow(10, eta.x[0]))
+            xmax = xp.percentile(xbar_inputs_k, percentile_max)
+        else:
+            xmax = xp.max(all_xbar_inputs[k])
+
+        input_limits[k, :] = np.array([0, float(xmax)])
+
+    return input_limits
+
+
+def calibrate_adc_limits(
+    analog_layers: list,
+    all_adc_inputs: list,
+    Nbits: int = 0,
+    norm_ord: float = 1.0,
+    bitslice_pct: float = 99.99,
+) -> npt.ArrayLike:
+    """Optimizes the ADC input range for all layers in a network given profiled
+    input values.
+
+    Args:
+        analog_layers: list of Torch analog modules or Keras analog layers containing 
+            params that will be used to decide how to calibrate
+        all_adc_inputs: list of arrays, each array contains profiled input
+            values for a layer
+        Nbits: quantization resolution used in optimizer
+            Set to 0 to set range based on max profiled value
+        norm_ord: power of the error norm used for the loss function in optimizer
+            (Used for unsliced core only)
+        bitslice_pct: desired percentile coverage of input distribution that is used to
+            find ADC ranges. (Used for bitsliced core only)
+    Returns:
+        NumPy array containing the (min, max) range for the inputs of every layer
+    """
+
+
+    n_layers = len(all_adc_inputs)
+    if analog_layers[0].params.core.style != CoreStyle.BITSLICED:
+        adc_limits = np.zeros((n_layers, 2))
+    else:
+        # Allows non-uniform bit slice width across layers
+        adc_limits = [None] * n_layers
+
+    k = 0
+    for layer in analog_layers:
+        adc_inputs_k = xp.asarray(all_adc_inputs[k])
+
+        if layer.params.core.style != CoreStyle.BITSLICED:
+            adc_limits[k, :] = optimize_adc_limits_unsliced(
+                adc_inputs_k, Nbits=Nbits, norm_ord=norm_ord
+            )
+        else:
+            num_slices = layer.params.core.bit_sliced.num_slices
+            adc_limits[k] = optimize_adc_limits_bitsliced(
+                adc_inputs_k,
+                num_slices,
+                style = layer.params.core.bit_sliced.style,
+                Nrows = Nrows,
+                pct = bitslice_pct,
+            )
+        k += 1
+
+    return adc_limits
+
+
+def optimize_adc_limits_unsliced(
+    adc_inputs_k: npt.ArrayLike,
+    Nbits: int = 0,
+    norm_ord: float = 1.0,
+) -> npt.ArrayLike:
+    """Optimizes the ADC input range for one layer which does not using weight bit slicing."""
+
+    # Although input bit slices are profiled separately, the current calibration
+    # method does not resolve data by input bit
+    adc_inputs_k = adc_inputs_k.flatten()
+
+    if Nbits > 0:
+        etas0 = (-4, -4)
+        # Optimize the input percentile
+        etas = minimize(
+            quantizationError_minMax,
+            etas0,
+            args=(adc_inputs_k, Nbits, norm_ord),
+            method="nelder-mead",
+            tol=0.1,
+        )
+        percentile_min = 100 * pow(10, etas.x[0])
+        percentile_max = 100 * (1 - pow(10, etas.x[1]))
+        xmin = xp.percentile(adc_inputs_k, percentile_min)
+        xmax = xp.percentile(adc_inputs_k, percentile_max)
+    else:
+        xmin = xp.min(adc_inputs_k)
+        xmax = xp.max(adc_inputs_k)
+
+    adc_limits_k = np.array([float(xmin), float(xmax)])
+
+    return adc_limits_k
+
+
+def optimize_adc_limits_bitsliced(
+    adc_inputs_k: npt.ArrayLike,
+    num_slices: int = 2,
+    style: int = BitSlicedCoreStyle.BALANCED,
+    Nrows: int = 1,
+    pct: float = 99.99,
+) -> npt.ArrayLike:
+    """
+    Optimizes the ADC input range for one layer which uses weight bit slicing.
+    To reduce the overhead of bit slice digital post-processing, this method ensures
+    that the ratio of the ADC limits of any two bit slices must be a power of 2.
+    """
+
+    # NOTE: Although input bit slices are profiled separately, the current calibration
+    # method does not resolve data by input bit
+
+    adc_limits_k = np.zeros((num_slices, 2))
+
+    if style == BitSlicedCoreStyle.OFFSET:
+        raise NotImplementedError(
+            "ADC limits auto-calibration with weight bit slicing OFFSET "
+            + "style not been implemented yet."
+        )
+
+    for i_slice in range(num_slices):
+        adc_inputs_ik = adc_inputs_k[i_slice,:,:].flatten()
+        adc_inputs_ik /= Nrows
+
+        # Find the percentile extreme values of the ADC input distribution
+        p_neg = xp.percentile(adc_inputs_ik, 100-pct)
+        p_pos = xp.percentile(adc_inputs_ik, pct)
+        p_out = xp.maximum(xp.abs(p_neg),xp.abs(p_pos))
+
+        # Compute how much the ADC limits can be divided from the maximum possible,
+        # and still cover the percentile extreme values
+        clip_power_i = xp.floor(xp.log2(1/p_out)).astype(int)
+        adc_limits_k[i_slice,0] = -Nrows / 2**clip_power_i
+        adc_limits_k[i_slice,1] = Nrows / 2**clip_power_i
+
+    return adc_limits_k
+
+
+
+def quantizationError_ReLU(eta, x, Nbits, norm_ord):
+    """Quantizes values over a range from the minimum value to a high
+    percentile value of the data. The percentile is only applied on
+    large positive values, assuming ReLU activation is used.
+
+    Args:
+        eta: parameter that controls the percentile used for clipping
+               (to be optimized)
+        x: data values to be quantized
+        Nbits: quantization resolution in bits
+        norm_ord: power of the error norm used for the loss function
+    """
+
+    # Clip
+    P = 100 * (1 - pow(10, eta))
+    P = xp.clip(P, 0, 100)
+    x_min = 0  # assume ReLU
+    x_maxP = xp.percentile(x, P)
+    x_Q = x.copy()
+    x_Q = x_Q.clip(x_min, x_maxP)
+
+    # Quantize
+    qmult = (2**Nbits - 1) / (x_maxP - x_min)
+    x_Q = (x_Q - x_min) * qmult
+    x_Q = xp.rint(x_Q, out=x_Q)
+    x_Q /= qmult
+    x_Q += x_min
+    err = xp.linalg.norm(x - x_Q, ord=norm_ord)
+    return float(err)
+
+
+def quantizationError_minMax(etas, x, Nbits, norm_ord):
+    """Quantizes values over a range by optimizing the upper and lower
+    percentiles of the range.
+
+    Args:
+        etas: tuple of two parameters that control the lower and upper percentile
+            used for clipping (to be optimized)
+        x: data values to be quantized
+        Nbits: quantization resolution in bits
+        norm_ord: power of the error norm used for the loss function
+    """
+    # Clip
+    etaMin, etaMax = etas
+    P_min = 100 * pow(10, etaMin)
+    P_max = 100 * (1 - pow(10, etaMax))
+    P_min = xp.clip(P_min, 0, 100)
+    P_max = xp.clip(P_max, 0, 100)
+    x_min = xp.percentile(x, P_min)
+    x_max = xp.percentile(x, P_max)
+    x_Q = x.copy()
+    x_Q = x_Q.clip(x_min, x_max)
+
+    # Quantize
+    qmult = (2**Nbits - 1) / (x_max - x_min)
+    x_Q = (x_Q - x_min) * qmult
+    x_Q = xp.rint(x_Q, out=x_Q)
+    x_Q /= qmult
+    x_Q += x_min
+    err = xp.linalg.norm(x - x_Q, ord=norm_ord)
+    return float(err)