XGLM - Fix Softmax NaNs when using FP16 (#18057)

* fix fp16 for xglm

* Removed misleading comment

* Fix undefined variable
Co-authored-by: Gabriele Sarti <gsarti@amazon.com>
Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
Co-authored-by: Younes Belkada <49240599+younesbelkada@users.noreply.github.com>

src/transformers/models/xglm/modeling_xglm.py

@@ -235,7 +235,6 @@ class XGLMSinusoidalPositionalEmbedding(nn.Module):
         return position_ids.unsqueeze(0).expand(input_shape).contiguous() + past_key_values_length
 
 
-# Copied from transformers.models.bart.modeling_bart.BartAttention with Bart->XGLM
 class XGLMAttention(nn.Module):
     """Multi-headed attention from 'Attention Is All You Need' paper"""
 
@@ -338,8 +337,13 @@ class XGLMAttention(nn.Module):
                     f"Attention mask should be of size {(bsz, 1, tgt_len, src_len)}, but is {attention_mask.size()}"
                 )
             attn_weights = attn_weights.view(bsz, self.num_heads, tgt_len, src_len) + attention_mask
+            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
             attn_weights = attn_weights.view(bsz * self.num_heads, tgt_len, src_len)
 
+        # upcast to fp32 if the weights are in fp16. Please see https://github.com/huggingface/transformers/pull/17437
+        if attn_weights.dtype == torch.float16:
+            attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(torch.float16)
+        else:
             attn_weights = nn.functional.softmax(attn_weights, dim=-1)
 
         if layer_head_mask is not None:

tests/models/xglm/test_modeling_xglm.py

@@ -18,7 +18,7 @@ import math
 import unittest
 
 from transformers import XGLMConfig, is_torch_available
-from transformers.testing_utils import require_torch, slow, torch_device
+from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
 
 from ...generation.test_generation_utils import GenerationTesterMixin
 from ...test_configuration_common import ConfigTester
@@ -468,3 +468,22 @@ class XGLMModelLanguageGenerationTest(unittest.TestCase):
         model.generate(input_ids, do_sample=False, max_time=None, max_length=256)
         duration = datetime.datetime.now() - start
         self.assertGreater(duration, datetime.timedelta(seconds=1.25 * MAX_TIME))
+
+    @require_torch_gpu
+    def test_batched_nan_fp16(self):
+        model_name = "facebook/xglm-564M"
+        tokenizer = XGLMTokenizer.from_pretrained(model_name, use_fast=False, padding_side="left")
+
+        model = XGLMForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, use_cache=True).cuda()
+        model = model.eval()
+
+        batch = tokenizer(["Who are you?", "Joe Biden is the president of"], padding=True, return_tensors="pt")
+
+        input_ids = batch["input_ids"].cuda()
+        attention_mask = batch["attention_mask"].cuda()
+
+        with torch.no_grad():
+            outputs = model(input_ids, attention_mask=attention_mask)
+            self.assertFalse(
+                torch.isnan(outputs.logits[0]).any().item()
+            )  # the first logits could contain NaNs if it fails