Llama: fix batched generation

src/transformers/models/llama/modeling_llama.py

@@ -101,11 +101,34 @@ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim))
         self.register_buffer("inv_freq", inv_freq, persistent=False)
 
+    @property
+    def sin_cached(self):
+        logger.warning_once(
+            "The sin_cached attribute will be removed in 4.40. Bear in mind that its contents changed in v4.38. Use "
+            "the forward method of RoPE from now on instead."
+        )
+        return self._sin_cached
+
+    @property
+    def cos_cached(self):
+        logger.warning_once(
+            "The cos_cached attribute will be removed in 4.40. Bear in mind that its contents changed in v4.38. Use "
+            "the forward method of RoPE from now on instead."
+        )
+        return self._cos_cached
+
     def forward(self, x, position_ids, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
-        freqs = (self.inv_freq[:, None].float().expand(-1, position_ids.shape[0]) @ (position_ids.float())).t()
+        inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2)
         emb = torch.cat((freqs, freqs), dim=-1)
-        return emb.cos().to(dtype=x.dtype), emb.sin().to(dtype=x.dtype)
+        cos = emb.cos().to(dtype=x.dtype)
+        sin = emb.sin().to(dtype=x.dtype)
+        # backwards compatibility
+        self._cos_cached = cos
+        self._sin_cached = sin
+        return cos, sin
 
 
 class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):
@@ -181,6 +204,8 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
     Returns:
         `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding.
     """
+    cos = cos.unsqueeze(unsqueeze_dim)
+    sin = sin.unsqueeze(unsqueeze_dim)
     q_embed = (q * cos) + (rotate_half(q) * sin)
     k_embed = (k * cos) + (rotate_half(k) * sin)
     return q_embed, k_embed
@@ -1033,6 +1058,7 @@ def _update_causal_mask(self, attention_mask, input_tensor):
 
         batch_size, seq_length = input_tensor.shape[:2]
         dtype = input_tensor.dtype
+        device = input_tensor.device
 
         # support going beyond cached `max_position_embedding`
         if seq_length > self.causal_mask.shape[-1]:
@@ -1048,8 +1074,9 @@ def _update_causal_mask(self, attention_mask, input_tensor):
                 (self.config.max_position_embeddings, self.config.max_position_embeddings),
                 fill_value=torch.finfo(dtype).min,
             )
-            causal_mask = torch.triu(mask, diagonal=1).to(dtype)
+            causal_mask = torch.triu(mask, diagonal=1)
 
+        causal_mask = causal_mask.to(dtype=dtype, device=device)
         if attention_mask is not None and attention_mask.dim() == 2:
             mask_length = attention_mask.shape[-1]
             padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0)

tests/test_cache_utils.py

@@ -293,7 +293,7 @@ def test_sink_cache_iterative_prompts(self):
     @parameterized.expand(["eager", "sdpa", "flash_attention_2"])
     def test_static_cache_greedy_sampling_pad_left(self, attn_implementation):
         EXPECTED_GENERATION = [
-            "The best color is the one that complements the subject you are photograph",
+            "The best color is the one that complements the skin tone of the",
             "We should not undermind the issues at hand.\nWe should not undermind the issues",
         ]
 
@@ -333,18 +333,18 @@ def test_static_cache_greedy_sampling_pad_left(self, attn_implementation):
     @parameterized.expand(["eager", "sdpa", "flash_attention_2"])
     def test_static_cache_greedy_sampling_pad_right(self, attn_implementation):
         EXPECTED_GENERATION = [
-            "The best color is\n\n\n\n\n\n\n\n\n\n",
-            "We should not undermind the issues at hand, but address them head on.\nI think",
+            "The best color isЋ the one that complements the skin tone of",
+            "We should not undermind the issues at hand.\nWe should not undermind the issues",
         ]
 
         tokenizer = AutoTokenizer.from_pretrained(
-            "NousResearch/Llama-2-7b-chat-hf", padding_side="left", pad_token="<s>"
+            "NousResearch/Llama-2-7b-chat-hf", padding_side="right", pad_token="<s>"
         )
         model = AutoModelForCausalLM.from_pretrained(
             "NousResearch/Llama-2-7b-chat-hf",
             torch_dtype=torch.bfloat16,
             attn_implementation=attn_implementation,
-        ).to("cuda:1")
+        ).to(torch_device)
         inputs = tokenizer(
             ["The best color is", "We should not undermind the issues at hand"], padding=True, return_tensors="pt"
         ).to(model.device)