RoPE loses precision for Llama / Gemma + Gemma logits.float() (#29285)

* Update modeling_llama.py

Llama - Force float32 since bfloat16 loses precision on long contexts

* Update modeling_llama.py

* Update modeling_gemma.py

Fix RoPE and logits.float()

* @torch.no_grad()

* @torch.no_grad()

* Cos, Sin to float32

* cos, sin to float32

* Update src/transformers/models/gemma/modeling_gemma.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Update src/transformers/models/llama/modeling_llama.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Resolve PR conflicts

* Fix RoPE for llama

* Revert "Fix RoPE for llama"

This reverts commit b860a22.

* Fix RoPE for llama

* RoPE device

* Autocast device type

* RoPE

* RoPE isinstance

---------

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

src/transformers/models/gemma/modeling_gemma.py

@@ -101,18 +101,25 @@ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
         self.base = base
         self.register_buffer("inv_freq", None, persistent=False)
 
+    @torch.no_grad()
     def forward(self, x, position_ids, seq_len=None):
         # x: [bs, num_attention_heads, seq_len, head_size]
         if self.inv_freq is None:
             self.inv_freq = 1.0 / (
                 self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64, device=x.device).float() / self.dim)
             )
-
         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)
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
 
 
 # Copied from transformers.models.llama.modeling_llama.rotate_half
@@ -1082,7 +1089,7 @@ def forward(
 
         hidden_states = outputs[0]
         logits = self.lm_head(hidden_states)
-
+        logits = logits.float()
         loss = None
         if labels is not None:
             # Shift so that tokens < n predict n

src/transformers/models/llama/modeling_llama.py

@@ -126,16 +126,24 @@ def cos_cached(self):
         )
         return self._cos_cached
 
+    @torch.no_grad()
     def forward(self, x, position_ids, seq_len=None):
         if seq_len is not None:
             logger.warning_once("The `seq_len` argument is deprecated and unused. It will be removed in v4.39.")
 
         # x: [bs, num_attention_heads, seq_len, head_size]
         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)
+        # Force float32 since bfloat16 loses precision on long contexts
+        # See https://github.com/huggingface/transformers/pull/29285
+        device_type = x.device.type
+        device_type = device_type if isinstance(device_type, str) else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos()
+            sin = emb.sin()
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
 
 
 class LlamaLinearScalingRotaryEmbedding(LlamaRotaryEmbedding):