Fix some TF GPT-J CI testings (#16454)

* Fix for test_mixed_precision

* Fix test_saved_model_creation by using shape_list instead of shape

* skit test_model_from_pretrained on GPU for now to avoid GPU OOM

* skip test_gptj_sample_max_time for now
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>

src/transformers/models/gptj/modeling_tf_gptj.py

@@ -59,13 +59,13 @@ GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST = [
 
 
 def fixed_pos_embedding(x: tf.Tensor, seq_dim: int = 1, seq_len: Optional[int] = None) -> Tuple[tf.Tensor, tf.Tensor]:
-    dim = x.shape[-1]
+    dim = shape_list(x)[-1]
     if seq_len is None:
-        seq_len = x.shape[seq_dim]
+        seq_len = shape_list(x)[seq_dim]
     inv_freq = tf.cast(1.0 / (10000 ** (tf.range(0, dim, 2) / dim)), tf.float32)
     seq_len_range = tf.cast(tf.range(seq_len), tf.float32)
     sinusoid_inp = tf.cast(tf.einsum("i , j -> i j", seq_len_range, inv_freq), tf.float32)
-    return tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)
+    return tf.cast(tf.sin(sinusoid_inp), dtype=x.dtype), tf.cast(tf.cos(sinusoid_inp), dtype=x.dtype)
 
 
 def rotate_every_two(x: tf.Tensor) -> tf.Tensor:
@@ -77,8 +77,8 @@ def rotate_every_two(x: tf.Tensor) -> tf.Tensor:
 
 def apply_rotary_pos_emb(x: tf.Tensor, sincos: tf.Tensor, offset: int = 0) -> tf.Tensor:
     sin_pos, cos_pos = sincos
-    sin_pos = tf.repeat(sin_pos[None, offset : x.shape[1] + offset, None, :], 2, 3)
-    cos_pos = tf.repeat(cos_pos[None, offset : x.shape[1] + offset, None, :], 2, 3)
+    sin_pos = tf.repeat(sin_pos[None, offset : shape_list(x)[1] + offset, None, :], 2, 3)
+    cos_pos = tf.repeat(cos_pos[None, offset : shape_list(x)[1] + offset, None, :], 2, 3)
     return (x * cos_pos) + (rotate_every_two(x) * sin_pos)
 
 
@@ -173,7 +173,7 @@ class TFGPTJAttention(tf.keras.layers.Layer):
         head_mask: Optional[tf.Tensor] = None,
     ) -> Tuple[tf.Tensor, tf.Tensor]:
         # compute causal mask from causal mask buffer
-        query_length, key_length = query.shape[-2], key.shape[-2]
+        query_length, key_length = shape_list(query)[-2], shape_list(key)[-2]
         causal_mask = self.get_causal_mask(key_length, query_length)
 
         # Keep the attention weights computation in fp32 to avoid overflow issues
@@ -218,11 +218,11 @@ class TFGPTJAttention(tf.keras.layers.Layer):
         key = self._split_heads(key, True)
         value = self._split_heads(value, False)
 
-        seq_len = key.shape[1]
+        seq_len = shape_list(key)[1]
         offset = 0
 
         if layer_past is not None:
-            offset = layer_past[0].shape[-2]
+            offset = shape_list(layer_past[0])[-2]
             seq_len += offset
 
         if self.rotary_dim is not None:

tests/gptj/test_modeling_tf_gptj.py

@@ -345,6 +345,10 @@ class TFGPTJModelTest(TFModelTesterMixin, TFCoreModelTesterMixin, unittest.TestC
                 assert name is None
 
     @slow
+    @unittest.skipIf(
+        not is_tf_available() or len(tf.config.list_physical_devices("GPU")) > 0,
+        "skip testing on GPU for now to avoid GPU OOM.",
+    )
     def test_model_from_pretrained(self):
         model = TFGPTJModel.from_pretrained("EleutherAI/gpt-j-6B", from_pt=True)
         self.assertIsNotNone(model)
@@ -395,6 +399,7 @@ class TFGPTJModelLanguageGenerationTest(unittest.TestCase):
         )  # token_type_ids should change output
 
     @slow
+    @unittest.skip(reason="TF generate currently has no time-based stopping criteria")
     def test_gptj_sample_max_time(self):
         tokenizer = AutoTokenizer.from_pretrained("anton-l/gpt-j-tiny-random")
         model = TFGPTJForCausalLM.from_pretrained("anton-l/gpt-j-tiny-random", from_pt=True)