dat
Saving weights and logs at step 1252
f291f93
raw
history blame
31.5 kB
[21:01:12] - INFO - absl - A polynomial schedule was set with a non-positive `transition_steps` value; this results in a constant schedule with value `init_value`.
/home/dat/pino/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py:3132: UserWarning: Explicitly requested dtype <class 'jax._src.numpy.lax_numpy.int64'> requested in zeros is not available, and will be truncated to dtype int32. To enable more dtypes, set the jax_enable_x64 configuration option or the JAX_ENABLE_X64 shell environment variable. See https://github.com/google/jax#current-gotchas for more.
lax._check_user_dtype_supported(dtype, "zeros")
/home/dat/pino/lib/python3.8/site-packages/jax/lib/xla_bridge.py:386: 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:373: UserWarning: jax.host_id has been renamed to jax.process_index. This alias will eventually be removed; please update your code.
warnings.warn(
Epoch ... (1/3): 0%| | 0/3 [00:00<?, ?it/s][21:01:13] - INFO - __main__ - Skipping to epoch 0 step 0
Epoch ... (1/3): 0%| | 0/3 [01:17<?, ?it/s]
Traceback (most recent call last):
File "./run_mlm_flax.py", line 790, 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)
File "/home/dat/pino/lib/python3.8/site-packages/jax/_src/api.py", line 1669, 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 906, 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)
jax._src.traceback_util.UnfilteredStackTrace: RuntimeError: Resource exhausted: Ran out of memory in memory space hbm. Used 17.79G of 15.48G hbm. Exceeded hbm capacity by 2.31G.
Total hbm usage >= 18.31G:
reserved 530.00M
program 17.79G
arguments 0B
Output size 0B; shares 0B with arguments.
Program hbm requirement 17.79G:
global 884.0K
scoped 253.0K
HLO temp 17.79G (97.6% utilization: Unpadded (17.27G) Padded (17.68G), 0.6% fragmentation (106.34M))
Largest program allocations in hbm:
1. Size: 3.07G
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: f32[4,4096,50358]{1,2,0:T(8,128)}
Unpadded size: 3.07G
Extra memory due to padding: 128.0K (1.0x expansion)
XLA label: %fusion.1233.remat4 = f32[4,4096,50358]{1,2,0:T(8,128)} fusion(f32[50358]{0:T(1024)} %get-tuple-element.21733, f32[768,50358,1]{0,1,2:T(8,128)} %bitcast.4927, f32[768]{0:T(1024)} %get-tuple-element.21734, f32[768]{0:T(1024)} %get-tuple-element.21735, f32[4...
Allocation type: HLO temp
==========================
2. Size: 336.00M
Shape: bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.12188 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1904, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8899, f32[4,12,28,128,128]{3,4,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
3. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1304.remat6 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1906, f32[4,12,28,128]{3,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
4. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1304.remat6 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1906, f32[4,12,28,128]{3,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
5. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1306.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1908, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8903, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
6. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1307.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1909, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8904, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
7. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1308.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1910, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8905, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
8. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1309.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1911, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8906, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
9. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1310.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1912, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8907, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
10. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1311.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1913, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8908, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
11. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1312.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1914, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8909, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
12. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1305 = bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)} fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1907, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8902, f32[4,12,28,128,128]{3,4,2,1,0:T(8,128)} %get-tuple-element.19534, f32[4,12,28,128,384]{...
Allocation type: HLO temp
==========================
13. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1301.remat6 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1903, f32[4,12,28,128]{3,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
14. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1301.remat6 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1903, f32[4,12,28,128]{3,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
15. Size: 336.00M
Shape: bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.12187 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1905, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8900, f32[4,12,28,128,128]{3,4,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
16. Size: 252.00M
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[4,12,28,128,384]{3,4,2,1,0:T(8,128)}
Unpadded size: 252.00M
XLA label: %fusion.10998 = (f32[4,12,28,128]{3,2,1,0:T(8,128)}, f32[4,12,28,128,384]{3,4,2,1,0:T(8,128)}) fusion(s32[4,12,30,128,384]{3,4,2,1,0:T(8,128)} %get-tuple-element.23248, bf16[4,12,28,384,64]{3,2,1,0,4:T(8,128)(2,1)} %slice.28551, f32[4,12,32,128,64]{3,2,4,1...
Allocation type: HLO temp
==========================
17. Size: 252.00M
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[4,12,28,128,384]{3,4,2,1,0:T(8,128)}
Unpadded size: 252.00M
XLA label: %fusion.11022 = (f32[4,12,28,128]{3,2,1,0:T(8,128)}, f32[4,12,28,128,384]{3,4,2,1,0:T(8,128)}) fusion(s32[4,12,30,128,384]{3,4,2,1,0:T(8,128)} %get-tuple-element.23245, bf16[4,12,28,384,64]{3,2,1,0,4:T(8,128)(2,1)} %slice.28656.remat_uncompressed, f32[4,12...
Allocation type: HLO temp
==========================
18. Size: 252.00M
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[4,12,28,128,384]{3,4,2,1,0:T(8,128)}
Unpadded size: 252.00M
XLA label: %fusion.11014 = (f32[4,12,28,128]{3,2,1,0:T(8,128)}, f32[4,12,28,128,384]{3,4,2,1,0:T(8,128)}) fusion(s32[4,12,30,128,384]{3,4,2,1,0:T(8,128)} %get-tuple-element.23246, bf16[4,12,28,384,64]{3,2,1,0,4:T(8,128)(2,1)} %slice.28621.remat_uncompressed, f32[4,12...
Allocation type: HLO temp
==========================
19. Size: 252.00M
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[4,12,28,128,384]{3,4,2,1,0:T(8,128)}
Unpadded size: 252.00M
XLA label: %fusion.11006 = (f32[4,12,28,128]{3,2,1,0:T(8,128)}, f32[4,12,28,128,384]{3,4,2,1,0:T(8,128)}) fusion(s32[4,12,30,128,384]{3,4,2,1,0:T(8,128)} %get-tuple-element.23247, bf16[4,12,28,384,64]{3,2,1,0,4:T(8,128)(2,1)} %slice.28586.remat_uncompressed, f32[4,12...
Allocation type: HLO temp
==========================
20. Size: 252.00M
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[4,12,28,128,384]{3,4,2,1,0:T(8,128)}
Unpadded size: 252.00M
XLA label: %fusion.10934 = (f32[4,12,28,128]{3,2,1,0:T(8,128)}, f32[4,12,28,128,384]{3,4,2,1,0:T(8,128)}) fusion(s32[4,12,30,128,384]{3,4,2,1,0:T(8,128)} %get-tuple-element.19864, bf16[4,12,28,384,64]{3,2,1,0,4:T(8,128)(2,1)} %slice.28270, f32[4,12,32,128,64]{3,2,4,1...
Allocation type: HLO temp
==========================
The stack trace below excludes JAX-internal frames.
The preceding is the original exception that occurred, unmodified.
--------------------
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "./run_mlm_flax.py", line 790, 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/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 17.79G of 15.48G hbm. Exceeded hbm capacity by 2.31G.
Total hbm usage >= 18.31G:
reserved 530.00M
program 17.79G
arguments 0B
Output size 0B; shares 0B with arguments.
Program hbm requirement 17.79G:
global 884.0K
scoped 253.0K
HLO temp 17.79G (97.6% utilization: Unpadded (17.27G) Padded (17.68G), 0.6% fragmentation (106.34M))
Largest program allocations in hbm:
1. Size: 3.07G
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: f32[4,4096,50358]{1,2,0:T(8,128)}
Unpadded size: 3.07G
Extra memory due to padding: 128.0K (1.0x expansion)
XLA label: %fusion.1233.remat4 = f32[4,4096,50358]{1,2,0:T(8,128)} fusion(f32[50358]{0:T(1024)} %get-tuple-element.21733, f32[768,50358,1]{0,1,2:T(8,128)} %bitcast.4927, f32[768]{0:T(1024)} %get-tuple-element.21734, f32[768]{0:T(1024)} %get-tuple-element.21735, f32[4...
Allocation type: HLO temp
==========================
2. Size: 336.00M
Shape: bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.12188 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1904, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8899, f32[4,12,28,128,128]{3,4,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
3. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1304.remat6 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1906, f32[4,12,28,128]{3,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
4. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1304.remat6 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1906, f32[4,12,28,128]{3,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
5. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1306.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1908, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8903, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
6. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1307.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1909, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8904, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
7. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1308.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1910, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8905, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
8. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1309.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1911, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8906, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
9. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1310.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1912, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8907, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
10. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1311.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1913, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8908, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
11. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1312.remat = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1914, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8909, f32[4,12,28,128,128]{3,4,2,1,0:...
Allocation type: HLO temp
==========================
12. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1305 = bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)} fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1907, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8902, f32[4,12,28,128,128]{3,4,2,1,0:T(8,128)} %get-tuple-element.19534, f32[4,12,28,128,384]{...
Allocation type: HLO temp
==========================
13. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1301.remat6 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1903, f32[4,12,28,128]{3,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
14. Size: 336.00M
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[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.1301.remat6 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1903, f32[4,12,28,128]{3,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
15. Size: 336.00M
Shape: bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}
Unpadded size: 336.00M
XLA label: %fusion.12187 = (bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}, bf16[4,12,28,128,1024]{3,4,2,1,0:T(8,128)(2,1)}) fusion(f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.1905, f32[4,12,28,128]{3,2,1,0:T(8,128)} %fusion.8900, f32[4,12,28,128,128]{3,4,2,1,0:T(8,1...
Allocation type: HLO temp
==========================
16. Size: 252.00M
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[4,12,28,128,384]{3,4,2,1,0:T(8,128)}
Unpadded size: 252.00M
XLA label: %fusion.10998 = (f32[4,12,28,128]{3,2,1,0:T(8,128)}, f32[4,12,28,128,384]{3,4,2,1,0:T(8,128)}) fusion(s32[4,12,30,128,384]{3,4,2,1,0:T(8,128)} %get-tuple-element.23248, bf16[4,12,28,384,64]{3,2,1,0,4:T(8,128)(2,1)} %slice.28551, f32[4,12,32,128,64]{3,2,4,1...
Allocation type: HLO temp
==========================
17. Size: 252.00M
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[4,12,28,128,384]{3,4,2,1,0:T(8,128)}
Unpadded size: 252.00M
XLA label: %fusion.11022 = (f32[4,12,28,128]{3,2,1,0:T(8,128)}, f32[4,12,28,128,384]{3,4,2,1,0:T(8,128)}) fusion(s32[4,12,30,128,384]{3,4,2,1,0:T(8,128)} %get-tuple-element.23245, bf16[4,12,28,384,64]{3,2,1,0,4:T(8,128)(2,1)} %slice.28656.remat_uncompressed, f32[4,12...
Allocation type: HLO temp
==========================
18. Size: 252.00M
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[4,12,28,128,384]{3,4,2,1,0:T(8,128)}
Unpadded size: 252.00M
XLA label: %fusion.11014 = (f32[4,12,28,128]{3,2,1,0:T(8,128)}, f32[4,12,28,128,384]{3,4,2,1,0:T(8,128)}) fusion(s32[4,12,30,128,384]{3,4,2,1,0:T(8,128)} %get-tuple-element.23246, bf16[4,12,28,384,64]{3,2,1,0,4:T(8,128)(2,1)} %slice.28621.remat_uncompressed, f32[4,12...
Allocation type: HLO temp
==========================
19. Size: 252.00M
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[4,12,28,128,384]{3,4,2,1,0:T(8,128)}
Unpadded size: 252.00M
XLA label: %fusion.11006 = (f32[4,12,28,128]{3,2,1,0:T(8,128)}, f32[4,12,28,128,384]{3,4,2,1,0:T(8,128)}) fusion(s32[4,12,30,128,384]{3,4,2,1,0:T(8,128)} %get-tuple-element.23247, bf16[4,12,28,384,64]{3,2,1,0,4:T(8,128)(2,1)} %slice.28586.remat_uncompressed, f32[4,12...
Allocation type: HLO temp
==========================
20. Size: 252.00M
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[4,12,28,128,384]{3,4,2,1,0:T(8,128)}
Unpadded size: 252.00M
XLA label: %fusion.10934 = (f32[4,12,28,128]{3,2,1,0:T(8,128)}, f32[4,12,28,128,384]{3,4,2,1,0:T(8,128)}) fusion(s32[4,12,30,128,384]{3,4,2,1,0:T(8,128)} %get-tuple-element.19864, bf16[4,12,28,384,64]{3,2,1,0,4:T(8,128)(2,1)} %slice.28270, f32[4,12,32,128,64]{3,2,4,1...
Allocation type: HLO temp
==========================