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/home/dat/pino/lib/python3.8/site-packages/jax/_src/numpy/lax_numpy.py:3114: 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: |
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lax._check_user_dtype_supported(dtype, "zeros") |
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/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. |
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warnings.warn( |
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/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. |
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warnings.warn( |
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Epoch ... (1/5): 0%| | 0/5 [00:00<?, ?it/s] |
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Epoch ... (1/5): 0%| | 0/5 [02:23<?, ?it/s] |
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Traceback (most recent call last): |
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File "./run_mlm_flax.py", line 709, in <module> |
|
state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs) |
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File "/home/dat/pino/lib/python3.8/site-packages/jax/_src/traceback_util.py", line 183, in reraise_with_filtered_traceback |
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return fun(*args, **kwargs) |
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File "/home/dat/pino/lib/python3.8/site-packages/jax/_src/api.py", line 1647, in f_pmapped |
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out = pxla.xla_pmap( |
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File "/home/dat/pino/lib/python3.8/site-packages/jax/core.py", line 1620, in bind |
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return call_bind(self, fun, *args, **params) |
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File "/home/dat/pino/lib/python3.8/site-packages/jax/core.py", line 1551, in call_bind |
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outs = primitive.process(top_trace, fun, tracers, params) |
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File "/home/dat/pino/lib/python3.8/site-packages/jax/core.py", line 1623, in process |
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return trace.process_map(self, fun, tracers, params) |
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File "/home/dat/pino/lib/python3.8/site-packages/jax/core.py", line 606, in process_call |
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return primitive.impl(f, *tracers, **params) |
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File "/home/dat/pino/lib/python3.8/site-packages/jax/interpreters/pxla.py", line 624, in xla_pmap_impl |
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compiled_fun, fingerprint = parallel_callable(fun, backend, axis_name, axis_size, |
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File "/home/dat/pino/lib/python3.8/site-packages/jax/linear_util.py", line 262, in memoized_fun |
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ans = call(fun, *args) |
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File "/home/dat/pino/lib/python3.8/site-packages/jax/interpreters/pxla.py", line 899, in parallel_callable |
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compiled = xla.backend_compile(backend, built, compile_options) |
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File "/home/dat/pino/lib/python3.8/site-packages/jax/interpreters/xla.py", line 360, in backend_compile |
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return backend.compile(built_c, compile_options=options) |
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jax._src.traceback_util.UnfilteredStackTrace: RuntimeError: Resource exhausted: Ran out of memory in memory space hbm. Used 20.61G of 15.48G hbm. Exceeded hbm capacity by 5.13G. |
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Total hbm usage >= 21.13G: |
|
reserved 530.00M |
|
program 20.61G |
|
arguments 0B |
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Output size 0B; shares 0B with arguments. |
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Program hbm requirement 20.61G: |
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global 900.0K |
|
scoped 924.0K |
|
HLO temp 20.61G (63.0% utilization: Unpadded (12.43G) Padded (19.71G), 4.4% fragmentation (918.84M)) |
|
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.3615.remat4 = bf16[4,4096,50358]{1,2,0:T(8,128)(2,1)} fusion(bf16[50358,768]{1,0:T(8,128)(2,1)} %get-tuple-element.22628, f32[768]{0:T(1024)} %fusion.10158, f32[768]{0:T(1024)} %fusion.10159, f32[4,4096]{1,0:T(4,128)} %get-tuple-element.20129, f32[... |
|
Allocation type: HLO temp |
|
========================== |
|
2. 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.2444.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.2444.remat_compressed) |
|
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.2454.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.2804, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7916, 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.2453.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.2803, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7915, 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.2452.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.2802, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7914, 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.2451.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.2801, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7913, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)... |
|
Allocation type: HLO temp |
|
========================== |
|
7. 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.2445 = 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.2795, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7907, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} %get-tuple-element.20342, f32[4,12,60,64,192]{3,4,2,1... |
|
Allocation type: HLO temp |
|
========================== |
|
8. 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.2443.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.2443.remat_compressed) |
|
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.2450.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.2800, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7912, 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.2449.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.2799, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7911, 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.2448.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.2798, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7910, 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.2447.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.2797, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7909, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)... |
|
Allocation type: HLO temp |
|
========================== |
|
13. 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.2446.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.2796, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7908, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)... |
|
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.2689.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.20556, 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.2964), kind=kO... |
|
Allocation type: HLO temp |
|
========================== |
|
15. 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.2690.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.20556, 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.2962), kind=kO... |
|
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.2688.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.20556, 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.2966), kind=kO... |
|
Allocation type: HLO temp |
|
========================== |
|
17. 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.2691.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.20556, bf16[4,12,64,64,64]{4,3,2,1,0:T(8,128)(2,1)} %copy.14230, bf16[4,12,60,192,64]{3,2,4,1,0:T(8,128)(2,1)} %fusion.2960), kind=kO... |
|
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=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.2692.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.20556, bf16[4,12,64,64,64]{4,3,2,1,0:T(8,128)(2,1)} %copy.14164, bf16[4,12,60,192,64]{3,2,4,1,0:T(8,128)(2,1)} %fusion.2958), kind=kO... |
|
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.2693.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.20556, bf16[4,12,64,64,64]{4,3,2,1,0:T(8,128)(2,1)} %copy.14098, bf16[4,12,60,192,64]{3,2,4,1,0:T(8,128)(2,1)} %fusion.2956), kind=kO... |
|
Allocation type: HLO temp |
|
========================== |
|
20. 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.2616.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.2616.remat_compressed) |
|
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 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/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 20.61G of 15.48G hbm. Exceeded hbm capacity by 5.13G. |
|
Total hbm usage >= 21.13G: |
|
reserved 530.00M |
|
program 20.61G |
|
arguments 0B |
|
Output size 0B; shares 0B with arguments. |
|
Program hbm requirement 20.61G: |
|
global 900.0K |
|
scoped 924.0K |
|
HLO temp 20.61G (63.0% utilization: Unpadded (12.43G) Padded (19.71G), 4.4% fragmentation (918.84M)) |
|
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.3615.remat4 = bf16[4,4096,50358]{1,2,0:T(8,128)(2,1)} fusion(bf16[50358,768]{1,0:T(8,128)(2,1)} %get-tuple-element.22628, f32[768]{0:T(1024)} %fusion.10158, f32[768]{0:T(1024)} %fusion.10159, f32[4,4096]{1,0:T(4,128)} %get-tuple-element.20129, f32[... |
|
Allocation type: HLO temp |
|
========================== |
|
2. 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.2444.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.2444.remat_compressed) |
|
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.2454.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.2804, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7916, 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.2453.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.2803, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7915, 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.2452.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.2802, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7914, 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.2451.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.2801, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7913, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)... |
|
Allocation type: HLO temp |
|
========================== |
|
7. 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.2445 = 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.2795, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7907, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)} %get-tuple-element.20342, f32[4,12,60,64,192]{3,4,2,1... |
|
Allocation type: HLO temp |
|
========================== |
|
8. 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.2443.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.2443.remat_compressed) |
|
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.2450.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.2800, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7912, 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.2449.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.2799, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7911, 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.2448.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.2798, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7910, 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.2447.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.2797, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7909, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)... |
|
Allocation type: HLO temp |
|
========================== |
|
13. 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.2446.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.2796, f32[4,12,60,64]{3,2,1,0:T(8,128)} %fusion.7908, f32[4,12,60,64,64]{3,4,2,1,0:T(8,128)... |
|
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.2689.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.20556, 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.2964), kind=kO... |
|
Allocation type: HLO temp |
|
========================== |
|
15. 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.2690.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.20556, 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.2962), kind=kO... |
|
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.2688.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.20556, 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.2966), kind=kO... |
|
Allocation type: HLO temp |
|
========================== |
|
17. 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.2691.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.20556, bf16[4,12,64,64,64]{4,3,2,1,0:T(8,128)(2,1)} %copy.14230, bf16[4,12,60,192,64]{3,2,4,1,0:T(8,128)(2,1)} %fusion.2960), kind=kO... |
|
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=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.2692.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.20556, bf16[4,12,64,64,64]{4,3,2,1,0:T(8,128)(2,1)} %copy.14164, bf16[4,12,60,192,64]{3,2,4,1,0:T(8,128)(2,1)} %fusion.2958), kind=kO... |
|
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.2693.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.20556, bf16[4,12,64,64,64]{4,3,2,1,0:T(8,128)(2,1)} %copy.14098, bf16[4,12,60,192,64]{3,2,4,1,0:T(8,128)(2,1)} %fusion.2956), kind=kO... |
|
Allocation type: HLO temp |
|
========================== |
|
20. 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.2616.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.2616.remat_compressed) |
|
Allocation type: HLO temp |
|
========================== |
