fix config

.notebook/position_embeding_check.ipynb

@@ -2,10 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 2,
    "id": "designing-borough",
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/workspace/DeepSpeech-2.x/tools/venv/lib/python3.7/site-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
+      "  and should_run_async(code)\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -61,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 5,
    "id": "swiss-referral",
    "metadata": {},
    "outputs": [
@@ -74,13 +82,69 @@
       "False\n",
       "False\n",
       "False\n",
-      "False\n"
+      "False\n",
+      "[[ 1.          1.          1.         ...  1.          1.\n",
+      "   1.        ]\n",
+      " [ 0.5403023   0.59737533  0.6479059  ...  1.          1.\n",
+      "   1.        ]\n",
+      " [-0.41614684 -0.28628543 -0.1604359  ...  0.99999994  1.\n",
+      "   1.        ]\n",
+      " ...\n",
+      " [-0.92514753 -0.66694194 -0.67894876 ...  0.9999276   0.99993724\n",
+      "   0.9999457 ]\n",
+      " [-0.81928825 -0.9959641  -0.999139   ...  0.99992603  0.999936\n",
+      "   0.99994457]\n",
+      " [ 0.03982088 -0.52298605 -0.6157435  ...  0.99992454  0.9999347\n",
+      "   0.99994344]]\n",
+      "----\n",
+      "[[ 1.          1.          1.         ...  1.          1.\n",
+      "   1.        ]\n",
+      " [ 0.54030234  0.59737533  0.6479059  ...  1.          1.\n",
+      "   1.        ]\n",
+      " [-0.41614684 -0.28628543 -0.1604359  ...  1.          1.\n",
+      "   1.        ]\n",
+      " ...\n",
+      " [-0.92514753 -0.66694194 -0.67894876 ...  0.9999276   0.9999373\n",
+      "   0.9999457 ]\n",
+      " [-0.81928825 -0.9959641  -0.999139   ...  0.99992603  0.999936\n",
+      "   0.99994457]\n",
+      " [ 0.03982088 -0.5229861  -0.6157435  ...  0.99992454  0.9999347\n",
+      "   0.99994344]]\n",
+      ")))))))\n",
+      "[[ 0.0000000e+00  0.0000000e+00  0.0000000e+00 ...  0.0000000e+00\n",
+      "   0.0000000e+00  0.0000000e+00]\n",
+      " [ 8.4147096e-01  8.0196178e-01  7.6172036e-01 ...  1.2409373e-04\n",
+      "   1.1547816e-04  1.0746076e-04]\n",
+      " [ 9.0929741e-01  9.5814437e-01  9.8704624e-01 ...  2.4818745e-04\n",
+      "   2.3095631e-04  2.1492151e-04]\n",
+      " ...\n",
+      " [ 3.7960774e-01  7.4510968e-01  7.3418564e-01 ...  1.2036801e-02\n",
+      "   1.1201146e-02  1.0423505e-02]\n",
+      " [-5.7338190e-01 -8.9752287e-02 -4.1488394e-02 ...  1.2160885e-02\n",
+      "   1.1316618e-02  1.0530960e-02]\n",
+      " [-9.9920684e-01 -8.5234123e-01 -7.8794664e-01 ...  1.2284970e-02\n",
+      "   1.1432089e-02  1.0638415e-02]]\n",
+      "----\n",
+      "[[ 0.0000000e+00  0.0000000e+00  0.0000000e+00 ...  0.0000000e+00\n",
+      "   0.0000000e+00  0.0000000e+00]\n",
+      " [ 8.4147096e-01  8.0196178e-01  7.6172036e-01 ...  1.2409373e-04\n",
+      "   1.1547816e-04  1.0746076e-04]\n",
+      " [ 9.0929741e-01  9.5814437e-01  9.8704624e-01 ...  2.4818745e-04\n",
+      "   2.3095631e-04  2.1492151e-04]\n",
+      " ...\n",
+      " [ 3.7960774e-01  7.4510968e-01  7.3418564e-01 ...  1.2036801e-02\n",
+      "   1.1201146e-02  1.0423505e-02]\n",
+      " [-5.7338190e-01 -8.9752287e-02 -4.1488394e-02 ...  1.2160885e-02\n",
+      "   1.1316618e-02  1.0530960e-02]\n",
+      " [-9.9920684e-01 -8.5234123e-01 -7.8794664e-01 ...  1.2284970e-02\n",
+      "   1.1432089e-02  1.0638415e-02]]\n"
      ]
     }
    ],
    "source": [
     "import paddle\n",
-    "ppe = paddle.zeros([max_len, d_model])\n",
+    "paddle.set_device('cpu')\n",
+    "ppe = paddle.zeros((max_len, d_model), dtype='float32')\n",
     "position = paddle.arange(0, max_len,\n",
     "                        dtype='float32').unsqueeze(1)\n",
     "print(np.allclose(position.numpy(), toruch_position))\n",
@@ -98,12 +162,19 @@
     "ppe[:, 0::2] = p_sin\n",
     "ppe[:, 1::2] = p_cos\n",
     "print(np.allclose(p_sin.numpy(), torhc_sin.cpu().detach().numpy()))\n",
-    "print(np.allclose(p_cos.numpy(), torhc_cos.cpu().detach().numpy()))"
+    "print(np.allclose(p_cos.numpy(), torhc_cos.cpu().detach().numpy()))\n",
+    "print(p_cos.numpy())\n",
+    "print(\"----\")\n",
+    "print(torhc_cos.cpu().detach().numpy())\n",
+    "print(\")))))))\")\n",
+    "print(p_sin.numpy())\n",
+    "print(\"----\")\n",
+    "print(torhc_sin.cpu().detach().numpy())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 4,
    "id": "integrated-boards",
    "metadata": {},
    "outputs": [

deepspeech/utils/layer_tools.py

@@ -31,7 +31,8 @@ def summary(layer: nn.Layer, print_func=print):
         num_elements += np.prod(param.shape)
         num_params += 1
     if print_func:
-        print_func(f"Total parameters: {num_params}, {num_elements} elements.")
+        num_elements = num_elements / 1024**3
+        print_func(f"Total parameters: {num_params}, {num_elements}G elements.")
 
 
 def print_grads(model, print_func=print):
@@ -54,7 +55,8 @@ def print_params(model, print_func=print):
         if print_func:
             print_func(msg)
     if print_func:
-        print_func(f"Total parameters: {num_params}, {total} elements.")
+        total = total / 1024**3
+        print_func(f"Total parameters: {num_params}, {total}G elements.")
 
 
 def gradient_norm(layer: nn.Layer):

examples/aishell/s1/conf/conformer.yaml

@@ -84,7 +84,7 @@ training:
   scheduler_conf:
     warmup_steps: 25000
     lr_decay: 1.0
-  log_interval: 1
+  log_interval: 100
 
 
 decoding: