Enabled auxilillary loss free load balancing and sequence wise load b… by dipakg-lang · Pull Request #4233 · AI-Hypercomputer/maxtext

dipakg-lang · 2026-06-22T23:09:54Z

Description

Enabled auxilillary loss free load balancing and sequence wise load balancing for Deepseek.

The rest of the description includes relevant details and context, examples:

We require these changes to be compliant with the specification and also this will help us to have better use to hardware due to the load balancing between the experts.

FIXES: b/509933890
FIXES: b/521990776

Tests

Tested by running training loop with new tiny Deeepseek V4 model added as part of the commit,
here are the logs for testing and commands used for this :

export JAX_PLATFORM_NAME=cpu & export XLA_FLAGS=--xla_force_host_platform_device_count=8 & python3 -m maxtext.trainers.pre_train.train src/maxtext/configs/base.yml override_model_config=True model_name=deepseek4-tiny enable_checkpointing=False base_output_directory=/tmp/maxtext_output/ dataset_type=synthetic hardware=cpu skip_jax_distributed_system=True attention=dot_product per_device_batch_size=1 steps=100 max_target_length=256 async_checkpointing=false dtype=bfloat16 weight_dtype=bfloat16 megablox=False sparse_matmul=False ici_expert_parallelism=-1 sharding_tolerance=1.0 ici_fsdp_parallelism=1 indexer_topk=16 routed_bias=True load_balance_loss_weight=0.0001 routed_bias_update_rate=0.001

Without load balancing active logs : https://paste.googleplex.com/6421399878107136

with load balancing logs : https://paste.googleplex.com/6551357300539392

Here are the results actived for reducing the varience :

=== DeepSeek V4 Load Balancing Variance Analysis (Step 0 vs Step 20) ===

Layer Index	Routing Type	Step 0 Var (Baseline)	Step 20 Var (Run A)	Step 20 Var (Run B)	Improvement (A vs B)
0	Hash Routed	3932160.00	3932160.00	3932160.00	0.00%
1	Hash Routed	3932160.00	3932160.00	3932160.00	0.00%
2	Hash Routed	3932160.00	3932160.00	3932160.00	0.00%
3	Top-K Routed	7409.38	7509.25	3672.12	51.10%
4	Top-K Routed	3158.38	3230.12	1216.00	62.35%
5	Top-K Routed	5713.38	5772.75	2359.38	59.13%
6	Top-K Routed	8295.25	8082.50	3674.12	54.54%
7	Top-K Routed	4765.62	4614.62	1212.75	73.72%
8	Top-K Routed	4960.75	4923.12	1663.50	66.21%
9	Top-K Routed	3905.50	3816.25	1316.88	65.49%
10	Top-K Routed	5057.00	4981.12	2257.75	54.67%
11	Top-K Routed	10446.62	10381.62	5565.75	46.39%
12	Top-K Routed	9538.50	9529.25	5319.12	44.18%
13	Top-K Routed	7031.38	7131.25	3270.25	54.14%
14	Top-K Routed	4852.00	4900.12	1906.88	61.09%
15	Top-K Routed	9306.12	9342.88	4733.75	49.33%
16	Top-K Routed	5811.25	5749.50	2110.88	63.29%
17	Top-K Routed	6715.62	6874.25	2664.12	61.24%
18	Top-K Routed	8145.50	7869.25	3383.75	57.00%
19	Top-K Routed	6042.12	5908.62	2353.00	60.18%
20	Top-K Routed	8559.88	8158.25	4333.38	46.88%
21	Top-K Routed	11742.25	11943.62	7563.50	36.67%
22	Top-K Routed	4959.62	5014.88	1998.62	60.15%
23	Top-K Routed	7717.12	7751.88	3879.88	49.95%
24	Top-K Routed	9017.75	9307.88	4702.75	49.48%
25	Top-K Routed	14127.12	14111.25	8079.25	42.75%
26	Top-K Routed	5074.25	5194.12	1675.50	67.74%
27	Top-K Routed	11919.50	11204.38	6470.75	42.25%
28	Top-K Routed	12241.75	12998.62	7624.12	41.35%
29	Top-K Routed	9384.50	9005.00	5052.00	43.90%
30	Top-K Routed	9698.62	9678.25	5231.75	45.94%
31	Top-K Routed	12244.25	12392.75	7249.25	41.50%
32	Top-K Routed	10030.00	9972.62	4755.50	52.31%
33	Top-K Routed	7265.00	6973.62	3271.75	53.08%
34	Top-K Routed	11945.50	11940.62	6076.88	49.11%
35	Top-K Routed	12917.50	13740.00	7210.62	47.52%
36	Top-K Routed	15011.62	15083.00	8870.62	41.19%
37	Top-K Routed	10294.12	10176.25	5907.50	41.95%
38	Top-K Routed	8928.62	9236.00	5136.62	44.38%
39	Top-K Routed	15633.62	15171.00	9684.75	36.16%
40	Top-K Routed	7687.75	7658.12	4521.25	40.96%
41	Top-K Routed	12485.12	12270.38	6933.25	43.50%
42	Top-K Routed	17641.25	17163.50	10974.12	36.06%
-------------	--------------	-----------------------	---------------------	---------------------	----------------------
TOTAL/AVG	Top-K Only	357681.12	356762.50	185883.62	47.90%

Raw data collected for this analysis:

https://paste.googleplex.com/5060754624610304

https://paste.googleplex.com/5473518849490944

Checklist

Before submitting this PR, please make sure (put X in square brackets):

I have performed a self-review of my code. For an optional AI review, add the gemini-review label.
I have necessary comments in my code, particularly in hard-to-understand areas.
I have run end-to-end tests tests and provided workload links above if applicable.
I have made or will make corresponding changes to the doc if needed, including adding new documentation pages to the relevant Table of Contents (toctree directive) as explained in our documentation.

codecov · 2026-06-22T23:16:30Z

Codecov Report

❌ Patch coverage is 84.12698% with 10 lines in your changes missing coverage. Please review.

Files with missing lines	Patch %	Lines
src/maxtext/trainers/pre_train/train.py	82.97%	5 Missing and 3 partials ⚠️
src/maxtext/optimizers/optimizers.py	85.71%	1 Missing and 1 partial ⚠️

📢 Thoughts on this report? Let us know!

parambole

Hey @dipakg-lang, on a high level, the changes look good.
a) Can you add test cases for the dsv4-like structure?
b) Also, could you add the bug number for sequence load balancing loss?

dipakg-lang · 2026-07-06T18:45:39Z

Hey @dipakg-lang, on a high level, the changes look good. a) Can you add test cases for the dsv4-like structure? b) Also, could you add the bug number for sequence load balancing loss?

The sequence wise changes are already present in the code they are enabled with the multiplier : load_balance_loss_weight

parambole

LGTM. Thank you for adding these changes.

RissyRan · 2026-07-08T23:42:14Z

Note: This is a fork repo, so unfortunately Gemini review doesn't work due to the API key thing.

RissyRan · 2026-07-09T00:03:53Z

Thanks for the change! I have a few high level comments:

based on previous discussion, it seems nnx.variable is the right way to go. Did you meet some issues to leverage it?
baed on your tests, it seems the loss doesn't change, do you happen to know why? Even use synthetic dataset, we should see the decrease
we are very actively working on DS v3 optimization. would like to ensure there is no perf regression with current change. Could you help have a run using small/tiny v3 configs (should have in configs) to sanity check before/after your changes and see if tflops/s/device is the same? regular DS v3 using pure FSDP should be sufficient

RissyRan · 2026-07-08T23:44:36Z

+adamw_mask: [".*gate.*bias.*"]
+
+# --- Attention configuration ---
+attention: 'dot_product'


We usually just put model configs in this yml file. The experimental config (like dot_product or flash, etc) usually is passed from cmd.

okey, would you suggest we skip them and have the user pass it instead? as we only support the dot product right now, I felt it would be safer to have that value present here and later change when we remove it from there when we start supporting more options, thoughts?

In this case, we commonly put assertion like this to indicate which case is not supported:

maxtext/src/maxtext/configs/types.py

Lines 2567 to 2572 in 7d8d169

if self.use_ring_of_experts and not self.use_ragged_sort:

raise ValueError(

"Ragged buffer factor is currently only supported with:\n"

" 1. Ragged A2A approach (use_ring_of_experts=False)\n"

" 2. Ragged sort with ring of experts (use_ring_of_experts=True AND use_ragged_sort=True)"

)

This already addressed in PR4153

This attention: 'dot_product' has been removed from deepseek4-284b.yml

types check is already added

maxtext/src/maxtext/configs/types.py

Lines 3135 to 3136 in 8e7ff2d

if self.decoder_block == DecoderBlockType.DEEPSEEK4 and self.attention != "dot_product":

raise ValueError("DeepSeek4 decoder block currently only supports dot_product attention.")

Please remove attention: 'dot_product' here as well.

Removed dot produce here as well

It seems it still here?

RissyRan · 2026-07-08T23:47:10Z

+
+# Tiny model config for DeepSeek V4 for CPU execution and testing
+
+base_emb_dim: 64


Do you know why those model configs (if from v4) are not included in the 284B model? if using default value from function, it's risky - https://screenshot.googleplex.com/4na5Jwtha49zfZM

adding @parambole to this conversation as well who has added the 284B model, so that we can resolve this.

I think deepseek4-284b.yml from main branch is the up-to-date version. In particular, it has been updated in PR4153 based on review.

Should make this tiny version consistent with 284b when possible.

Made consistent with 284b perfectly only scaled down version of it.

Thanks! Didn't see qk_rope_head_dim in 284-version configs?

RissyRan · 2026-07-08T23:53:51Z

      pre_bias_logits = output

    if self.use_bias:
+      # Architectural Note:  Bias is an nnx.Param rather than nnx.Variable due to Linen/NNX state


I think we have turned on NNX by default. Shall we actually use nnx.variable for a cleaner solution? It seems nnx.param + stop_gradient is a shortcut/hack. Using nnx.variable, I would expect we don't need adamw_mask: [".*gate.*bias.*"]. Please let me know your thoughts!

cc @shuningjin involved discussion before

Yes, I had the same suggestion earlier, but then we will have to manage the overhead of managing this variable ourselves that is why we have gone through this route, please let me know if you strongly feel about going through the variable route then I will have to create a plan on this variable management.

we will have to manage the overhead of managing this variable

Oh I didn't realize that. I thought we were thinking of nnx.variable in this thread. Please let me know if I missed something.

Gemini says "Yes, the DeepSeek router bias is an absolute textbook use case for nnx.Variable"

I would prefer nnx.variable to avoid manipulation of optimizer mask. Could you elaborate what is the "overhead of managing this variable"?

Declaring the bias as an nnx.Variable instead of an nnx.Param introduces framework overhead because JAX requires custom state to be manually threaded through the train_step pipeline
(passed as inputs and returned as outputs) on every iteration. Additionally, because MaxText's sharding and checkpointing are built for the params tree, using a new state collection
would require writing custom PartitionSpec logic to distribute the bias across the mesh and custom serialization code to save and restore it. This is what motivated me to do design it in this way, otherwise if you remember I was the one pointing out need for this to be a variable. Please let me know if you would like to have discussion on this.

RissyRan · 2026-07-08T23:54:57Z

        lambda params: jax.tree_util.tree_map(lambda x: "frozen" if x else "trainable", freeze_mask_fn(params)),
    )

+  if getattr(config, "routed_bias", False) and getattr(config, "routed_bias_update_rate", 0.0) > 0.0:


This is needed if using nnx.variable?

No, this part would be saved then. but we will have own variable management separately if not param

dipakg-lang · 2026-07-10T21:05:08Z

Thanks for the change! I have a few high level comments:

based on previous discussion, it seems nnx.variable is the right way to go. Did you meet some issues to leverage it?

baed on your tests, it seems the loss doesn't change, do you happen to know why? Even use synthetic dataset, we should see the decrease

we are very actively working on DS v3 optimization. would like to ensure there is no perf regression with current change. Could you help have a run using small/tiny v3 configs (should have in configs) to sanity check before/after your changes and see if tflops/s/device is the same? regular DS v3 using pure FSDP should be sufficient

Explained in the other comment happy to do if we feel strongly about it
I have extensively tested this change for longer time, I was able to see loss constantly going down and similar behavior to code without this change.
can you share the recipe with me for V3 so that I can make sure there are no regressions there, but as this code change is only enabled through these two constants, I don't think there will any regression for v3 as I don't see them being used in v3's config., but good to test it.

Auxiliary loss free load balancing : routed_bias_update_rate.

Sequence wise load balancing : load_balance_loss_weight

RissyRan · 2026-07-11T04:34:35Z

Thanks for the change! I have a few high level comments:

based on previous discussion, it seems nnx.variable is the right way to go. Did you meet some issues to leverage it?

baed on your tests, it seems the loss doesn't change, do you happen to know why? Even use synthetic dataset, we should see the decrease

we are very actively working on DS v3 optimization. would like to ensure there is no perf regression with current change. Could you help have a run using small/tiny v3 configs (should have in configs) to sanity check before/after your changes and see if tflops/s/device is the same? regular DS v3 using pure FSDP should be sufficient

Explained in the other comment happy to do if we feel strongly about it

I have extensively tested this change for longer time, I was able to see loss constantly going down and similar behavior to code without this change.

can you share the recipe with me for V3 so that I can make sure there are no regressions there, but as this code change is only enabled through these two constants, I don't think there will any regression for v3 as I don't see them being used in v3's config., but good to test it.
Auxiliary loss free load balancing : routed_bias_update_rate.
Sequence wise load balancing : load_balance_loss_weight

I see. It seems strange, as we usually see obvious loss decreasing within every 10 steps. One example. So I feel your test seems interesting.
Thanks for the sanity check. You could just this script. But without load_parameters_path (so pre-training) and without mtp_num_layers=1 mtp_loss_scaling_factor=0.1 for simplicity. If you'd like to run it in local like v5p-8, you could manually reduce # of layers in v3 yml config.

shuningjin

Thanks for the feature! I agree with @RissyRan in many aspects.

More testing with Deepseek4 & Deepseek3
loss-free lb - Routed bias implementation:

it currently uses: nnx.Param + stop gradient + optimizer mask for adamw and muon
Can we simplify via nnx.Variable to avoid optimizer mask?

(additionally) loss-free lb - Routed bias update:

in train.py uses complicated logic based on path.
Can we simplify?

For lb loss, perhaps the existing load_balance_loss is already sequence-wise. I added some notes in b/521990776

shuningjin · 2026-07-13T08:06:11Z

      pre_bias_logits = output

    if self.use_bias:
+      # Architectural Note:  Bias is an nnx.Param rather than nnx.Variable due to Linen/NNX state


I would prefer nnx.variable to avoid manipulation of optimizer mask. Could you elaborate what is the "overhead of managing this variable"?

shuningjin · 2026-07-13T08:12:30Z

+
+# Tiny model config for DeepSeek V4 for CPU execution and testing
+
+base_emb_dim: 64


I think deepseek4-284b.yml from main branch is the up-to-date version. In particular, it has been updated in PR4153 based on review.

Should make this tiny version consistent with 284b when possible.

shuningjin · 2026-07-13T08:15:33Z

+adamw_mask: [".*gate.*bias.*"]
+
+# --- Attention configuration ---
+attention: 'dot_product'


This already addressed in PR4153

This attention: 'dot_product' has been removed from deepseek4-284b.yml

types check is already added

maxtext/src/maxtext/configs/types.py

Lines 3135 to 3136 in 8e7ff2d

if self.decoder_block == DecoderBlockType.DEEPSEEK4 and self.attention != "dot_product":

raise ValueError("DeepSeek4 decoder block currently only supports dot_product attention.")

Please remove attention: 'dot_product' here as well.

shuningjin · 2026-07-13T08:20:27Z

-      # Updates the shape to be aligned with state.
-      moe_bias_updates = jnp.array(moe_bias_updates[0]).transpose()
-      new_state = maxtext_utils.update_state_param(new_state, target_path, moe_bias_updates)
+    # Apply updates for Auxiliary-Loss-Free load balancing for the DeepSeek family.


The logic of routed bias update (in loss-free load balancing) seems really complicated. Is it possible to simplify?

I think you are talking about this code which is about locating the bias in the tree, considering the nature of path of the bias, we have to have logic to locate it in such way that we don't miss it and the code continue to work for any other models as well is resulting in this code, if you have any ideas we can always incorporate them!

… balancing for Deepseek. Tested by running training loop with new tiny Deeepseek V4 model added as part of the commit, here are the logs for testing Without load balancing active logs : https://paste.googleplex.com/6421399878107136 with load balancing logs : https://paste.googleplex.com/6551357300539392 Here are the results actived for reducing the varience : 1 === DeepSeek V4 Load Balancing Variance Analysis (Step 0 vs Step 20) === 2 3 | Layer Index | Routing Type | Step 0 Var (Baseline) | Step 20 Var (Run A) | Step 20 Var (Run B) | Improvement (A vs B) | 4 |-------------|--------------|-----------------------|---------------------|---------------------|----------------------| 5 | 0 | Hash Routed | 3932160.00 | 3932160.00 | 3932160.00 | 0.00% | 6 | 1 | Hash Routed | 3932160.00 | 3932160.00 | 3932160.00 | 0.00% | 7 | 2 | Hash Routed | 3932160.00 | 3932160.00 | 3932160.00 | 0.00% | 8 | 3 | Top-K Routed | 7409.38 | 7509.25 | 3672.12 | 51.10% | 9 | 4 | Top-K Routed | 3158.38 | 3230.12 | 1216.00 | 62.35% | 10 | 5 | Top-K Routed | 5713.38 | 5772.75 | 2359.38 | 59.13% | 11 | 6 | Top-K Routed | 8295.25 | 8082.50 | 3674.12 | 54.54% | 12 | 7 | Top-K Routed | 4765.62 | 4614.62 | 1212.75 | 73.72% | 13 | 8 | Top-K Routed | 4960.75 | 4923.12 | 1663.50 | 66.21% | 14 | 9 | Top-K Routed | 3905.50 | 3816.25 | 1316.88 | 65.49% | 15 | 10 | Top-K Routed | 5057.00 | 4981.12 | 2257.75 | 54.67% | 16 | 11 | Top-K Routed | 10446.62 | 10381.62 | 5565.75 | 46.39% | 17 | 12 | Top-K Routed | 9538.50 | 9529.25 | 5319.12 | 44.18% | 18 | 13 | Top-K Routed | 7031.38 | 7131.25 | 3270.25 | 54.14% | 19 | 14 | Top-K Routed | 4852.00 | 4900.12 | 1906.88 | 61.09% | 20 | 15 | Top-K Routed | 9306.12 | 9342.88 | 4733.75 | 49.33% | 21 | 16 | Top-K Routed | 5811.25 | 5749.50 | 2110.88 | 63.29% | 22 | 17 | Top-K Routed | 6715.62 | 6874.25 | 2664.12 | 61.24% | 23 | 18 | Top-K Routed | 8145.50 | 7869.25 | 3383.75 | 57.00% | 24 | 19 | Top-K Routed | 6042.12 | 5908.62 | 2353.00 | 60.18% | 25 | 20 | Top-K Routed | 8559.88 | 8158.25 | 4333.38 | 46.88% | 26 | 21 | Top-K Routed | 11742.25 | 11943.62 | 7563.50 | 36.67% | 27 | 22 | Top-K Routed | 4959.62 | 5014.88 | 1998.62 | 60.15% | 28 | 23 | Top-K Routed | 7717.12 | 7751.88 | 3879.88 | 49.95% | 29 | 24 | Top-K Routed | 9017.75 | 9307.88 | 4702.75 | 49.48% | 30 | 25 | Top-K Routed | 14127.12 | 14111.25 | 8079.25 | 42.75% | 31 | 26 | Top-K Routed | 5074.25 | 5194.12 | 1675.50 | 67.74% | 32 | 27 | Top-K Routed | 11919.50 | 11204.38 | 6470.75 | 42.25% | 33 | 28 | Top-K Routed | 12241.75 | 12998.62 | 7624.12 | 41.35% | 34 | 29 | Top-K Routed | 9384.50 | 9005.00 | 5052.00 | 43.90% | 35 | 30 | Top-K Routed | 9698.62 | 9678.25 | 5231.75 | 45.94% | 36 | 31 | Top-K Routed | 12244.25 | 12392.75 | 7249.25 | 41.50% | 37 | 32 | Top-K Routed | 10030.00 | 9972.62 | 4755.50 | 52.31% | 38 | 33 | Top-K Routed | 7265.00 | 6973.62 | 3271.75 | 53.08% | 39 | 34 | Top-K Routed | 11945.50 | 11940.62 | 6076.88 | 49.11% | 40 | 35 | Top-K Routed | 12917.50 | 13740.00 | 7210.62 | 47.52% | 41 | 36 | Top-K Routed | 15011.62 | 15083.00 | 8870.62 | 41.19% | 42 | 37 | Top-K Routed | 10294.12 | 10176.25 | 5907.50 | 41.95% | 43 | 38 | Top-K Routed | 8928.62 | 9236.00 | 5136.62 | 44.38% | 44 | 39 | Top-K Routed | 15633.62 | 15171.00 | 9684.75 | 36.16% | 45 | 40 | Top-K Routed | 7687.75 | 7658.12 | 4521.25 | 40.96% | 46 | 41 | Top-K Routed | 12485.12 | 12270.38 | 6933.25 | 43.50% | 47 | 42 | Top-K Routed | 17641.25 | 17163.50 | 10974.12 | 36.06% | 48 |-------------|--------------|-----------------------|---------------------|---------------------|----------------------| 49 | TOTAL/AVG | Top-K Only | 357681.12 | 356762.50 | 185883.62 | 47.90% | Raw data collected for this analysis: https://paste.googleplex.com/5060754624610304 https://paste.googleplex.com/5473518849490944

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dipakg-lang force-pushed the dsv4_load_balancing branch 3 times, most recently from 6d108ec to bce41ae Compare June 23, 2026 17:33

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Comment thread tests/unit/deepseek_routed_bias_test.py

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	if self.use_ring_of_experts and not self.use_ragged_sort:
	raise ValueError(
	"Ragged buffer factor is currently only supported with:\n"
	" 1. Ragged A2A approach (use_ring_of_experts=False)\n"
	" 2. Ragged sort with ring of experts (use_ring_of_experts=True AND use_ragged_sort=True)"
	)

	if self.decoder_block == DecoderBlockType.DEEPSEEK4 and self.attention != "dot_product":
	raise ValueError("DeepSeek4 decoder block currently only supports dot_product attention.")


		# Tiny model config for DeepSeek V4 for CPU execution and testing

		base_emb_dim: 64

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dipakg-lang commented Jun 22, 2026 •

edited by shuningjin

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codecov Bot commented Jun 22, 2026 •

edited

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shuningjin left a comment •

edited

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