fix bug in attention probe dropout, fix bug in None noise_key passed …

…in the probing jit function, add the spliting of noise_keys to two dropout in two cross attention

ngclearn/utils/analysis/attentive_probe.py

@@ -70,7 +70,7 @@ def cross_attention(dkey, params: tuple, x1: jax.Array, x2: jax.Array, mask: jax
     score = jax.nn.softmax(score, axis=-1) # (B, H, T, S)
     score = score.astype(q.dtype) # (B, H, T, S)
     if dropout_rate > 0.:
-        score, _ = drop_out(dkey, input=score, rate=dropout_rate) ## NOTE: normally you apply dropout here
+        score, _ = drop_out(dkey, score, rate=dropout_rate) ## NOTE: normally you apply dropout here
     attention = jnp.einsum("BHTS,BHSE->BHTE", score, v) # (B, T, H, E)
     attention = attention.transpose([0, 2, 1, 3]).reshape((B, T, -1)) # (B, T, H, E) => (B, T, D)
     return attention @ Wout + bout # (B, T, Dq)
@@ -105,6 +105,8 @@ def run_attention_probe(
     Returns:
         output scores/probabilities, cross-attention (hidden) features
     """
+    # Two separate dkeys for each dropout in two cross attention
+    dkey1, dkey2 = random.split(dkey, 2)
     # encoded_image_feature: (B, hw, dim)
     #learnable_query, *_params) = params
     learnable_query, Wq, bq, Wk, bk, Wv, bv, Wout, bout,\
@@ -116,14 +118,14 @@ def run_attention_probe(
     if use_LN_input:
         learnable_query = layer_normalize(learnable_query, ln_in_mu, ln_in_scale)
         encodings = layer_normalize(encodings, ln_in_mu2, ln_in_scale2)
-    features = cross_attention(dkey, cross_attn_params, learnable_query, encodings, mask, n_heads, dropout)
+    features = cross_attention(dkey1, cross_attn_params, learnable_query, encodings, mask, n_heads, dropout)
     # Perform a single self-attention block here
     # Self-Attention
     self_attn_params = (Wqs, bqs, Wks, bks, Wvs, bvs, Wouts, bouts)
     skip = features
     if use_LN:
         features = layer_normalize(features, Wlnattn_mu, Wlnattn_scale)
-    features = cross_attention(dkey, self_attn_params, features, features, None, n_heads, dropout)
+    features = cross_attention(dkey2, self_attn_params, features, features, None, n_heads, dropout)
     features = features + skip
     features = features[:, 0]  # (B, 1, dim) => (B, dim)
     # MLP
@@ -222,7 +224,7 @@ def __init__(
         super().__init__(dkey, batch_size, **kwargs)
         assert attn_dim % num_heads == 0, f"`attn_dim` must be divisible by `num_heads`. Got {attn_dim} and {num_heads}."
         assert learnable_query_dim % num_heads == 0, f"`learnable_query_dim` must be divisible by `num_heads`. Got {learnable_query_dim} and {num_heads}."
-        self.dkey, *subkeys = random.split(self.dkey, 25)
+        self.dkey, *subkeys = random.split(self.dkey, 26)
         self.num_heads = num_heads
         self.source_seq_length = source_seq_length
         self.input_dim = input_dim
@@ -287,8 +289,12 @@ def __init__(
         self.optim_params = adam.adam_init(self.probe_params)
         self.eta = 0.0002 #0.001
 
+        # Finally, the dkey for the noise_key
+        self.noise_key = subkeys[24]
+
     def process(self, embeddings, dkey=None):
-        noise_key = None
+        # noise_key = None
+        noise_key = self.noise_key
         if dkey is not None:
             dkey, *subkeys = random.split(dkey, 2)
             noise_key = subkeys[0]
@@ -299,7 +305,8 @@ def process(self, embeddings, dkey=None):
         return outs
 
     def update(self, embeddings, labels, dkey=None):
-        noise_key = None
+        # noise_key = None
+        noise_key = self.noise_key
         if dkey is not None:
             dkey, *subkeys = random.split(dkey, 2)
             noise_key = subkeys[0]