wandb/run-20210713_005751-1wnn0lyf/files/output.log

/home/dat/pino/lib/python3.8/site-packages/jax/lib/xla_bridge.py:382: UserWarning: jax.host_count has been renamed to jax.process_count. This alias will eventually be removed; please update your code.
  warnings.warn(
/home/dat/pino/lib/python3.8/site-packages/jax/lib/xla_bridge.py:369: UserWarning: jax.host_id has been renamed to jax.process_index. This alias will eventually be removed; please update your code.
  warnings.warn(
Epoch ... (1/5):   0%|                                    | 0/5 [00:00<?, ?it/s]
Traceback (most recent call last):                    | 0/46383 [00:00<?, ?it/s]
  File "/usr/lib/python3.8/threading.py", line 932, in _bootstrap_inner
    self.run()
  File "/usr/lib/python3.8/threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "/home/dat/pino/lib/python3.8/site-packages/wandb/sdk/wandb_run.py", line 183, in check_network_status
    status_response = self._interface.communicate_network_status()
  File "/home/dat/pino/lib/python3.8/site-packages/wandb/sdk/interface/interface.py", line 755, in communicate_network_status
    resp = self._communicate(req, timeout=timeout, local=True)
  File "/home/dat/pino/lib/python3.8/site-packages/wandb/sdk/interface/interface.py", line 545, in _communicate
    return self._communicate_async(rec, local=local).get(timeout=timeout)
  File "/home/dat/pino/lib/python3.8/site-packages/wandb/sdk/interface/interface.py", line 550, in _communicate_async
    raise Exception("The wandb backend process has shutdown")
Exception: The wandb backend process has shutdown
Training...:   0%|                                    | 0/46383 [01:25<?, ?it/s]
Epoch ... (1/5):   0%|                                    | 0/5 [02:13<?, ?it/s]
Traceback (most recent call last):
  File "/home/dat/pino/lib/python3.8/site-packages/jax/_src/traceback_util.py", line 183, in reraise_with_filtered_traceback
    return fun(*args, **kwargs)
  File "/home/dat/pino/lib/python3.8/site-packages/jax/_src/api.py", line 1647, in f_pmapped
    out = pxla.xla_pmap(
  File "/home/dat/pino/lib/python3.8/site-packages/jax/core.py", line 1620, in bind
    return call_bind(self, fun, *args, **params)
  File "/home/dat/pino/lib/python3.8/site-packages/jax/core.py", line 1551, in call_bind
    outs = primitive.process(top_trace, fun, tracers, params)
  File "/home/dat/pino/lib/python3.8/site-packages/jax/core.py", line 1623, in process
    return trace.process_map(self, fun, tracers, params)
  File "/home/dat/pino/lib/python3.8/site-packages/jax/core.py", line 606, in process_call
    return primitive.impl(f, *tracers, **params)
  File "/home/dat/pino/lib/python3.8/site-packages/jax/interpreters/pxla.py", line 624, in xla_pmap_impl
    compiled_fun, fingerprint = parallel_callable(fun, backend, axis_name, axis_size,
  File "/home/dat/pino/lib/python3.8/site-packages/jax/linear_util.py", line 262, in memoized_fun
    ans = call(fun, *args)
  File "/home/dat/pino/lib/python3.8/site-packages/jax/interpreters/pxla.py", line 899, in parallel_callable
    compiled = xla.backend_compile(backend, built, compile_options)
  File "/home/dat/pino/lib/python3.8/site-packages/jax/interpreters/xla.py", line 360, in backend_compile
    return backend.compile(built_c, compile_options=options)
RuntimeError: Resource exhausted: Ran out of memory in memory space hbm. Used 20.30G of 15.48G hbm. Exceeded hbm capacity by 4.82G.
Total hbm usage >= 20.82G:
    reserved        530.00M
    program          20.30G
    arguments            0B
Output size 0B; shares 0B with arguments.
Program hbm requirement 20.30G:
    global           660.0K
    scoped           125.0K
    HLO temp         20.30G (63.5% utilization: Unpadded (12.44G) Padded (19.60G), 3.5% fragmentation (717.54M))
  Largest program allocations in hbm:
  1. Size: 1.54G
     Operator: op_type="dot_general" op_name="pmap(train_step)/dot_general[ dimension_numbers=(((2,), (0,)), ((), ()))\n                              precision=None\n                              preferred_element_type=None ]" source_file="/home/dat/pino/lib/python3.8/site-packages/flax/linen/linear.py" source_line=175
     Shape: bf16[4,4096,50358]{1,2,0:T(8,128)(2,1)}
     Unpadded size: 1.54G
     Extra memory due to padding: 64.0K (1.0x expansion)
     XLA label: %fusion.1304.remat4 = bf16[4,4096,50358]{1,2,0:T(8,128)(2,1)} fusion(bf16[50358,768]{1,0:T(8,128)(2,1)} %copy.16213, f32[768]{0:T(1024)} %fusion.8859, f32[768]{0:T(1024)} %fusion.8860, f32[4,4096]{1,0:T(4,128)} %get-tuple-element.16597, f32[4,4096]{1,0:T(4...
     Allocation type: HLO temp
     ==========================
  2. Size: 360.00M
     Operator: op_type="div" op_name="pmap(train_step)/div" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=619
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.135 = bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)} fusion(f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.485, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.5710, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} %get-tuple-element.16812, f32[4,12,60,64,192]{3,4,2,1,0...
     Allocation type: HLO temp
     ==========================
  3. Size: 360.00M
     Operator: op_type="div" op_name="pmap(train_step)/div" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=619
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.144.remat = (bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.494, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.5719, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} ...
     Allocation type: HLO temp
     ==========================
  4. Size: 360.00M
     Operator: op_type="div" op_name="pmap(train_step)/div" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=619
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.143.remat = (bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.493, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.5718, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} ...
     Allocation type: HLO temp
     ==========================
  5. Size: 360.00M
     Operator: op_type="div" op_name="pmap(train_step)/div" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=619
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.142.remat = (bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.492, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.5717, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} ...
     Allocation type: HLO temp
     ==========================
  6. Size: 360.00M
     Operator: op_type="div" op_name="pmap(train_step)/div" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=619
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.141.remat = (bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.491, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.5716, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} ...
     Allocation type: HLO temp
     ==========================
  7. Size: 360.00M
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.134.remat_uncompressed = bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)} copy(bf16[4,12,60,64,512]{4,3,2,1,0:T(8,128)(2,1)} %fusion.134.remat_compressed)
     Allocation type: HLO temp
     ==========================
  8. Size: 360.00M
     Operator: op_type="div" op_name="pmap(train_step)/div" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=619
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.140.remat = (bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.490, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.5715, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} ...
     Allocation type: HLO temp
     ==========================
  9. Size: 360.00M
     Operator: op_type="div" op_name="pmap(train_step)/div" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=619
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.139.remat = (bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.489, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.5714, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} ...
     Allocation type: HLO temp
     ==========================
  10. Size: 360.00M
     Operator: op_type="div" op_name="pmap(train_step)/div" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=619
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.138.remat = (bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.488, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.5713, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} ...
     Allocation type: HLO temp
     ==========================
  11. Size: 360.00M
     Operator: op_type="div" op_name="pmap(train_step)/div" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=619
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.137.remat = (bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.487, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.5712, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} ...
     Allocation type: HLO temp
     ==========================
  12. Size: 360.00M
     Operator: op_type="div" op_name="pmap(train_step)/div" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=619
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.136.remat = (bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.486, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.5711, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} ...
     Allocation type: HLO temp
     ==========================
  13. Size: 360.00M
     Shape: bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)}
     Unpadded size: 180.00M
     Extra memory due to padding: 180.00M (2.0x expansion)
     XLA label: %fusion.133.remat_uncompressed = bf16[4,12,60,64,512]{3,4,2,1,0:T(8,128)(2,1)} copy(bf16[4,12,60,64,512]{4,3,2,1,0:T(8,128)(2,1)} %fusion.133.remat_compressed)
     Allocation type: HLO temp
     ==========================
  14. Size: 270.00M
     Operator: op_type="dot_general" op_name="pmap(train_step)/jit(jvp(_einsum))/dot_general[ dimension_numbers=(((4,), (4,)), ((0, 1, 2), (0, 1, 2)))\n                                                precision=None\n                                                preferred_element_type=None ]" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=584
     Shape: f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)}
     Unpadded size: 135.00M
     Extra memory due to padding: 135.00M (2.0x expansion)
     XLA label: %fusion.378.remat5 = f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)} fusion(f32[4,60,64,192]{2,3,1,0:T(8,128)} %get-tuple-element.17038, bf16[4,12,64,64,64]{4,3,2,1,0:T(8,128)(2,1)} %copy.14428, bf16[4,12,60,192,64]{3,2,4,1,0:T(8,128)(2,1)} %fusion.655), kind=kOut...
     Allocation type: HLO temp
     ==========================
  15. Size: 270.00M
     Shape: f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)}
     Unpadded size: 135.00M
     Extra memory due to padding: 135.00M (2.0x expansion)
     XLA label: %fusion.310.remat_uncompressed = f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)} copy(f32[4,12,60,64,192]{4,3,2,1,0:T(8,128)} %fusion.310.remat_compressed)
     Allocation type: HLO temp
     ==========================
  16. Size: 270.00M
     Operator: op_type="dot_general" op_name="pmap(train_step)/jit(jvp(_einsum))/dot_general[ dimension_numbers=(((4,), (4,)), ((0, 1, 2), (0, 1, 2)))\n                                                precision=None\n                                                preferred_element_type=None ]" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=584
     Shape: f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)}
     Unpadded size: 135.00M
     Extra memory due to padding: 135.00M (2.0x expansion)
     XLA label: %fusion.386.remat6 = f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)} fusion(f32[4,60,64,192]{2,3,1,0:T(8,128)} %get-tuple-element.17038, bf16[4,12,64,64,64]{4,3,2,1,0:T(8,128)(2,1)} %copy.13900, bf16[4,12,60,192,64]{3,2,4,1,0:T(8,128)(2,1)} %fusion.639), kind=kOut...
     Allocation type: HLO temp
     ==========================
  17. Size: 270.00M
     Shape: f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)}
     Unpadded size: 135.00M
     Extra memory due to padding: 135.00M (2.0x expansion)
     XLA label: %fusion.326.remat_uncompressed.remat2 = f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)} copy(f32[4,12,60,64,192]{4,3,2,1,0:T(8,128)} %fusion.326.remat_compressed)
     Allocation type: HLO temp
     ==========================
  18. Size: 270.00M
     Operator: op_type="dot_general" op_name="pmap(train_step)/jit(jvp(_einsum))/dot_general[ dimension_numbers=(((4,), (4,)), ((0, 1, 2), (0, 1, 2)))\n                                                precision=None\n                                                preferred_element_type=None ]" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=591
     Shape: f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)}
     Unpadded size: 135.00M
     Extra memory due to padding: 135.00M (2.0x expansion)
     XLA label: %fusion.10361 = (f32[4,12,60,64]{3,2,1,0:T(8,128)}, f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)}) fusion(s32[4,12,62,64,192]{3,4,2,1,0:T(8,128)} %get-tuple-element.18295, bf16[4,12,64,64,64]{4,3,2,1,0:T(8,128)(2,1)} %copy.14494, bf16[4,12,60,192,64]{3,2,1,0,4:T...
     Allocation type: HLO temp
     ==========================
  19. Size: 270.00M
     Operator: op_type="dot_general" op_name="pmap(train_step)/jit(jvp(_einsum))/dot_general[ dimension_numbers=(((4,), (4,)), ((0, 1, 2), (0, 1, 2)))\n                                                precision=None\n                                                preferred_element_type=None ]" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=584
     Shape: f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)}
     Unpadded size: 135.00M
     Extra memory due to padding: 135.00M (2.0x expansion)
     XLA label: %fusion.380.remat5 = f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)} fusion(f32[4,60,64,192]{2,3,1,0:T(8,128)} %get-tuple-element.17038, bf16[4,12,64,64,64]{4,3,2,1,0:T(8,128)(2,1)} %copy.14296, bf16[4,12,60,192,64]{3,2,4,1,0:T(8,128)(2,1)} %fusion.651), kind=kOut...
     Allocation type: HLO temp
     ==========================
  20. Size: 270.00M
     Operator: op_type="dot_general" op_name="pmap(train_step)/jit(jvp(_einsum))/dot_general[ dimension_numbers=(((4,), (4,)), ((0, 1, 2), (0, 1, 2)))\n                                                precision=None\n                                                preferred_element_type=None ]" source_file="/home/dat/transformers/src/transformers/models/big_bird/modeling_flax_big_bird.py" source_line=584
     Shape: f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)}
     Unpadded size: 135.00M
     Extra memory due to padding: 135.00M (2.0x expansion)
     XLA label: %fusion.379.remat3 = f32[4,12,60,64,192]{3,4,2,1,0:T(8,128)} fusion(f32[4,60,64,192]{2,3,1,0:T(8,128)} %get-tuple-element.17038, bf16[4,12,64,64,64]{4,3,2,1,0:T(8,128)(2,1)} %copy.14362, bf16[4,12,60,192,64]{3,2,4,1,0:T(8,128)(2,1)} %fusion.653), kind=kOut...
     Allocation type: HLO temp
     ==========================
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
  File "./run_mlm_flax.py", line 709, in <module>
    state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs)
  File "/home/dat/pino/lib/python3.8/site-packages/jax/_src/traceback_util.py", line 183, in reraise_with_filtered_traceback
    return fun(*args, **kwargs)
KeyboardInterrupt