Merge 1a0ba385eb into d6d9447e2d

model.py

@@ -1002,7 +1002,7 @@ class DenseBlock(hk.Module):
             sharding=P("model", "data"),
             mesh=self.mesh,
             shard_axis=1,
-        )(h_w1 * h_v)
+        )(h_w1 * h_v) # TODO: Document why this isn't sequential and whether it should be.
 
         return h_dense
 
@@ -1036,13 +1036,10 @@ class DecoderLayer(hk.Module):
     ) -> DecoderOutput:
         """Transforms input embedding sequences to output embedding sequences."""
 
-        def layer_norm(x):
-            return hk_rms_norm(x)
-
+        sharding = P(self.data_axis, None)
         if self.shard_activations:
             sharding = P(self.data_axis, None, self.model_axis)
-        else:
-            sharding = P(self.data_axis, None)
+            
         h = with_sharding_constraint(inputs, sharding)
 
         attn_output = MHABlock(
@@ -1054,8 +1051,8 @@ class DecoderLayer(hk.Module):
             data_axis=self.data_axis,
             model_axis=self.model_axis,
         )(layer_norm(h), mask, layer_memory)
-        h_attn = attn_output.embeddings
 
+        h_attn = attn_output.embeddings
         h_attn = layer_norm(h_attn)
         h += h_attn
         h = with_sharding_constraint(h, sharding)
@@ -1165,15 +1162,17 @@ class LanguageModelConfig:
     _initialized = False
 
     def initialize(self):
-        # We cannot specify [] as a default value (it is mutable), hence None.
         model_config = self.model
-        assert self.init_scale_override is None, (
-            "Overriding model initialize scale is supported only for predefined models."
-        )
+
+        if self.model is None: # We cannot specify [] as a default value (it is mutable), hence None.
+            raise ValueError("Model configuration is not set.")
+        if self.init_scale_override is not None:
+            raise ValueError("Overriding model initialize scale is supported only for predefined models.")
+
         if self.model_size == 0:
             self.model_size = model_config.emb_size
-        assert self.model is not None, "Model could not be initialized."
         self._initialized = True
+
         return self
 
     def make(self, *args, **kwargs):
@@ -1194,7 +1193,7 @@ class LanguageModelConfig:
         return LM_PARTITION_RULES + self.model.partition_rules()
 
 
-def layer_norm(x, model):
+def layer_norm(x):
     return hk_rms_norm(x)
 
 
@@ -1213,17 +1212,12 @@ class LanguageModel(hk.Module):
         tokens: jax.Array,
         memory: Optional[Memory] = None,
         *,
-        batch: Dict[str, jax.Array] = {},
         last_hid_only: bool = False,
         length: Optional[jax.Array] = None,
     ) -> LanguageModelOutput:
         """Forward pass, producing a sequence of logits."""
-        del batch  # Unused.
-
         config = self.config
 
-        input_mask = jnp.greater(tokens, config.pad_token)
-
         # Embed the input tokens and positions.
         in_out_embed = InOutEmbed(
             self.config.vocab_size,
@@ -1235,6 +1229,7 @@ class LanguageModel(hk.Module):
             input_embeddings, P("data", None, self.model.model_axis)
         )
         input_embeddings *= config.embedding_multiplier_scale
+        input_mask = jnp.not_equal(tokens, config.pad_token)
 
         model_output = self.model(
             input_embeddings,
@@ -1242,15 +1237,15 @@ class LanguageModel(hk.Module):
             memory=memory,
         )  # [B, T, D]
         embeddings, model_state = model_output.embeddings, model_output.memory
+        if embeddings.dtype != self.fprop_dtype:
+            raise ValueError(f"Expected forward propagation dtype {self.fprop_dtype} but got {embeddings.dtype} in embeddings.")
+
         if self.model.shard_activations:
-            embeddings = with_sharding_constraint(
-                embeddings, P("data", None, self.model.model_axis)
-            )
+            embeddings = with_sharding_constraint(embeddings, P("data", None, self.model.model_axis))
         else:
             embeddings = with_sharding_constraint(embeddings, P("data", None))
         rank_logger.debug(f"Final embedding shape: {embeddings.shape}")
-        embeddings = layer_norm(embeddings, self.model)
-        assert embeddings.dtype == self.fprop_dtype
+        embeddings = layer_norm(embeddings)
 
         if last_hid_only:
             last_step = jnp.maximum(jnp.sum(input_mask.astype(jnp.int32), axis=1) - 1, 0)