crude visualization for narrow act

model.py

@@ -70,6 +70,18 @@ def _init_layers(self):
     self.output.weight = nn.Parameter(output_weight)
     self.output.bias = nn.Parameter(output_bias)
 
+  def get_narrow_preactivations(self, x, ls_indices):
+    # precompute and cache the offset for gathers
+    if self.idx_offset == None or self.idx_offset.shape[0] != x.shape[0]:
+      self.idx_offset = torch.arange(0,x.shape[0]*self.count,self.count, device=ls_indices.device)
+
+    indices = ls_indices.flatten() + self.idx_offset
+
+    l1s_ = self.l1(x).reshape((-1, self.count, L2))
+    l1f_ = self.l1_fact(x)
+    l1c_ = l1s_.view(-1, L2)[indices]
+    return l1c_ + l1f_
+
   def forward(self, x, ls_indices):
     # precompute and cache the offset for gathers
     if self.idx_offset == None or self.idx_offset.shape[0] != x.shape[0]:
@@ -241,6 +253,23 @@ def set_feature_set(self, new_feature_set):
     else:
       raise Exception('Cannot change feature set from {} to {}.'.format(self.feature_set.name, new_feature_set.name))
 
+  def get_narrow_preactivations(self, us, them, white_indices, white_values, black_indices, black_values, psqt_indices, layer_stack_indices):
+    wp, bp = self.input(white_indices, white_values, black_indices, black_values)
+    w, wpsqt = torch.split(wp, L1, dim=1)
+    b, bpsqt = torch.split(bp, L1, dim=1)
+    l0_ = (us * torch.cat([w, b], dim=1)) + (them * torch.cat([b, w], dim=1))
+    # clamp here is used as a clipped relu to (0.0, 1.0)
+    l0_ = torch.clamp(l0_, 0.0, 1.0)
+
+    psqt_indices_unsq = psqt_indices.unsqueeze(dim=1)
+    wpsqt = wpsqt.gather(1, psqt_indices_unsq)
+    bpsqt = bpsqt.gather(1, psqt_indices_unsq)
+    preact = self.layer_stacks.get_narrow_preactivations(l0_, layer_stack_indices)
+    bucketed_preact = []
+    for i in range(self.num_ls_buckets):
+      bucketed_preact.append(torch.masked_select(preact, (layer_stack_indices==i).repeat(preact.shape[1], 1).t()).reshape((-1, L2)))
+    return bucketed_preact
+
   def forward(self, us, them, white_indices, white_values, black_indices, black_values, psqt_indices, layer_stack_indices):
     wp, bp = self.input(white_indices, white_values, black_indices, black_values)
     w, wpsqt = torch.split(wp, L1, dim=1)

visualize_narrow_preactivation.py

@@ -0,0 +1,150 @@
+import argparse
+import chess
+import features
+import nnue_dataset
+import model as M
+import numpy as np
+import torch
+import matplotlib.pyplot as plt
+from matplotlib.gridspec import GridSpec
+
+from serialize import NNUEReader
+
+
+class NNUEVisualizer():
+    def __init__(self, model, args):
+        self.model = model
+        self.args = args
+
+        self.model.cuda()
+
+        import matplotlib as mpl
+        self.dpi = 100
+        mpl.rcParams["figure.figsize"] = (
+            self.args.default_width//self.dpi, self.args.default_height//self.dpi)
+        mpl.rcParams["figure.dpi"] = self.dpi
+
+    def _process_fig(self, name, fig=None):
+        if self.args.save_dir:
+            from os.path import join
+            destname = join(
+                self.args.save_dir, "{}{}.jpg".format("" if self.args.label is None else self.args.label + "_", name))
+            print("Saving {}".format(destname))
+            if fig is not None:
+                fig.savefig(destname)
+            else:
+                plt.savefig(destname)
+
+    def get_data(self, count, batch_size):
+        fen_batch_provider = nnue_dataset.FenBatchProvider(self.args.data, True, 1, batch_size, False, 10)
+
+        activations_by_bucket = [[] for i in range(self.model.num_ls_buckets)]
+        i = 0
+        while i < count:
+            fens = next(fen_batch_provider)
+            batch = nnue_dataset.make_sparse_batch_from_fens(self.model.feature_set, fens, [0] * len(fens), [1] * len(fens), [0] * len(fens))
+            us, them, white_indices, white_values, black_indices, black_values, outcome, score, psqt_indices, layer_stack_indices = batch.contents.get_tensors('cuda')
+            bucketed_preact = self.model.get_narrow_preactivations(us, them, white_indices, white_values, black_indices, black_values, psqt_indices, layer_stack_indices)
+
+            for a, b in zip(activations_by_bucket, bucketed_preact):
+                a.append(b.cpu().detach().numpy().clip(0, 1))
+
+            i += batch_size
+            print('{}/{}'.format(i, count))
+
+        return activations_by_bucket
+
+    def plot(self):
+        bucketed_preact = self.get_data(self.args.count, self.args.batch_size)
+        for i, d in enumerate(bucketed_preact):
+            print('Bucket {} has {} entries.'.format(i, sum(a.shape[0] for a in d)))
+
+        fig, axs = plt.subplots(M.L2, self.model.num_ls_buckets, sharex=True, sharey=True, figsize=(20, 20), dpi=100)
+
+        for bucket_id, preact in enumerate(bucketed_preact):
+            for i in range(M.L2):
+                acts = np.concatenate([v[:,i] for v in preact]).flatten()
+
+                ax = axs[bucket_id, i]
+                ax.hist(acts, density=True, log=True, bins=128)
+                ax.set_xlim([0, 1])
+                if i == 0:
+                    ax.set_ylabel('Bucket {}'.format(bucket_id))
+                if bucket_id == 0:
+                    ax.set_xlabel('Neuron {}'.format(i))
+                    ax.xaxis.set_label_position('top')
+
+        fig.show()
+
+def load_model(filename, feature_set):
+    if filename.endswith(".pt") or filename.endswith(".ckpt"):
+        if filename.endswith(".pt"):
+            model = torch.load(filename)
+        else:
+            model = M.NNUE.load_from_checkpoint(
+                filename, feature_set=feature_set)
+        model.eval()
+    elif filename.endswith(".nnue"):
+        with open(filename, 'rb') as f:
+            reader = NNUEReader(f, feature_set)
+        model = reader.model
+    else:
+        raise Exception("Invalid filetype: " + str(filename))
+
+    return model
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Visualizes networks in ckpt, pt and nnue format.")
+    parser.add_argument(
+        "model", help="Source model (can be .ckpt, .pt or .nnue)")
+    parser.add_argument(
+        "--default-width", default=1600, type=int,
+        help="Default width of all plots (in pixels).")
+    parser.add_argument(
+        "--count", default=1000000, type=int,
+        help="")
+    parser.add_argument(
+        "--batch_size", default=5000, type=int,
+        help="")
+    parser.add_argument(
+        "--default-height", default=900, type=int,
+        help="Default height of all plots (in pixels).")
+    parser.add_argument(
+        "--save-dir", type=str, required=False,
+        help="Save the plots in this directory.")
+    parser.add_argument(
+        "--dont-show", action="store_true",
+        help="Don't show the plots.")
+    parser.add_argument("--data", type=str, help="path to a .bin or .binpack dataset")
+    parser.add_argument(
+        "--label", type=str, required=False,
+        help="Override the label used in plot titles and as prefix of saved files.")
+    features.add_argparse_args(parser)
+    args = parser.parse_args()
+
+    supported_features = ('HalfKAv2_hm', 'HalfKAv2_hm^')
+    assert args.features in supported_features
+    feature_set = features.get_feature_set_from_name(args.features)
+
+    from os.path import basename
+    label = basename(args.model)
+
+    model = load_model(args.model, feature_set)
+
+    print("Visualizing {}".format(args.model))
+
+    if args.label is None:
+        args.label = label
+
+    visualizer = NNUEVisualizer(model, args)
+
+    visualizer.plot()
+
+    if not args.dont_show:
+        plt.show()
+
+
+if __name__ == '__main__':
+    main()