after_fix_log.txt

Check hf-internal-testing/tiny-random-roberta ...
--------------------------Checking logits match--------------------------
Flax logits shape: (2, 64, 1000), PyTorch logits shape: torch.Size([2, 64, 1000])
✅ Difference between Flax and PyTorch is 1.7881393432617188e-07 (< 0.01)
--------------------------Checking losses match--------------------------
Flax loss: 6.887884140014648, PyTorch loss: 6.887884616851807
✅ Difference between Flax and PyTorch is 4.76837158203125e-07 (< 0.01)
--------------------------Checking gradients match--------------------------
✅ All grads pass
--------------------------Checking rel gradients match--------------------------
❌ Layer ('roberta', 'encoder', 'layer', '0', 'attention', 'self', 'key', 'bias') has PT grad norm 7.584575871001642e-13 and flax grad norm 6.388195094436666e-13.
...
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Check hf-internal-testing/tiny-random-bert ...
--------------------------Checking logits match--------------------------
Flax logits shape: (2, 64, 1124), PyTorch logits shape: torch.Size([2, 64, 1124])
✅ Difference between Flax and PyTorch is 1.7881393432617188e-07 (< 0.01)
--------------------------Checking losses match--------------------------
Flax loss: 7.036032199859619, PyTorch loss: 7.036032676696777
✅ Difference between Flax and PyTorch is 4.76837158203125e-07 (< 0.01)
--------------------------Checking gradients match--------------------------
✅ All grads pass
--------------------------Checking rel gradients match--------------------------
❌ Layer ('bert', 'encoder', 'layer', '0', 'attention', 'self', 'key', 'bias') has PT grad norm 5.234438642080785e-13 and flax grad norm 4.935363641205004e-13.
...
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Check hf-internal-testing/tiny-random-t5 ...
--------------------------Checking logits match--------------------------
Flax logits shape: (2, 64, 1103), PyTorch logits shape: torch.Size([2, 64, 1103])
✅ Difference between Flax and PyTorch is 3.725290298461914e-09 (< 0.01)
--------------------------Checking losses match--------------------------
Flax loss: 7.006012916564941, PyTorch loss: 7.006012916564941
✅ Difference between Flax and PyTorch is 0.0 (< 0.01)
--------------------------Checking gradients match--------------------------
✅ All grads pass
--------------------------Checking rel gradients match--------------------------
✅ All rel grads pass
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Check hf-internal-testing/tiny-random-bart ...
--------------------------Checking logits match--------------------------
Flax logits shape: (2, 64, 1000), PyTorch logits shape: torch.Size([2, 64, 1000])
✅ Difference between Flax and PyTorch is 8.940696716308594e-08 (< 0.01)
--------------------------Checking losses match--------------------------
Flax loss: 6.919522285461426, PyTorch loss: 6.919522285461426
✅ Difference between Flax and PyTorch is 0.0 (< 0.01)
--------------------------Checking gradients match--------------------------
✅ All grads pass
--------------------------Checking rel gradients match--------------------------
❌ Layer ('final_logits_bias',) has PT grad norm 0.0 and flax grad norm 0.0.
❌ Layer ('model', 'decoder', 'layers', '0', 'encoder_attn', 'k_proj', 'bias') has PT grad norm 1.1293364247239035e-13 and flax grad norm 7.444291358479557e-14.
❌ Layer ('model', 'decoder', 'layers', '0', 'self_attn', 'k_proj', 'bias') has PT grad norm 1.9028742882613858e-13 and flax grad norm 1.0847509820726894e-13.
...
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