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Merged
copybara-service merged 3 commits into from
Apr 8, 2026
Merged

Remove no exp usage from logical rule Part I #3578

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8752555
remove no exp
NuojCheng Apr 6, 2026
83f4af0
remove activation_length_no_exp
NuojCheng Apr 6, 2026
ef90d9b
refresh dump sharding test
NuojCheng Apr 6, 2026
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2 changes: 0 additions & 2 deletions src/maxtext/common/common_types.py
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Expand Up @@ -32,13 +32,11 @@
AxisIdxes = tuple[int, ...]

BATCH = "activation_batch"
BATCH_NO_EXP = "activation_batch_no_exp"

ATTN_LENGTH = "activation_attn_length"
ATTN_LENGTH_NO_EXP = "activation_attn_length_no_exp"

LENGTH = "activation_length"
LENGTH_NO_EXP = "activation_length_no_exp"
PREFILL_LENGTH = "prefill_activation_length"
Q_LENGTH = "activation_q_length"
Q_LENGTH_NO_EXP = "activation_q_length_no_exp"
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17 changes: 6 additions & 11 deletions src/maxtext/configs/base.yml
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Expand Up @@ -450,22 +450,17 @@ mesh_axes: ['diloco', 'data', 'stage', 'fsdp', 'fsdp_transpose', 'sequence', 'co
logical_axis_rules: [
['activation_batch', ['data', 'fsdp', 'fsdp_transpose', 'expert']],
['activation_batch_moe', ['data', 'fsdp', 'fsdp_transpose', 'expert']],
['activation_batch_no_exp', ['data', 'fsdp', 'fsdp_transpose']],
['activation_batch_no_exp_moe', ['data', 'fsdp', 'fsdp_transpose']],
['activation_embed_and_logits_batch', ['data', 'stage', 'fsdp', 'fsdp_transpose', 'expert']],
['activation_embed_and_logits_batch_sequence', ['data', 'stage', 'fsdp', 'fsdp_transpose', 'sequence', 'context', 'expert']],
['activation_heads', ['tensor', 'tensor_transpose', 'sequence','tensor_sequence','autoregressive']],
['activation_kv_heads', ['tensor', 'tensor_transpose', 'sequence','tensor_sequence']],
['activation_length', ['sequence', 'context', 'expert']],
['activation_length', ['context', 'expert']],
['activation_attn_length', ['sequence', 'context', 'expert']],
['activation_attn_length', ['context', 'expert']],
['activation_attn_length_no_exp', ['sequence', 'context']],
['activation_attn_length_no_exp', ['context']],
['activation_length_no_exp', ['sequence', 'context']],
['activation_length_no_exp', ['context']],
['activation_length_no_exp_moe', ['sequence', 'context']],
['activation_length_no_exp_moe', ['context']],
['activation_length', ['sequence', 'context']],
['activation_length', ['context']],
['activation_attn_length', ['sequence', 'context']],
['activation_attn_length', ['context']],
['activation_length_moe', ['sequence', 'context']],
['activation_length_moe', ['context']],
['activation_norm_length', ['tensor_sequence', 'context', 'sequence']],
['activation_norm_length_moe', ['tensor_sequence', 'context', 'sequence']],
['activation_q_length', ['context', 'expert']],
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1 change: 0 additions & 1 deletion src/maxtext/configs/custom_mesh_and_rule/pipeline-large-moe.yml
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Expand Up @@ -31,7 +31,6 @@ data_sharding: [['data', 'stage', 'fsdp', 'context', 'tensor', 'expert']]
logical_axis_rules: [
['activation_batch', ['data', 'fsdp', 'expert']],
['activation_batch_moe', ['data', 'fsdp', 'expert']],
['activation_batch_no_exp', ['data', 'fsdp']],
['activation_batch_no_exp_moe', ['data', 'fsdp']],
['activation_embed_and_logits_batch', ['data', 'stage', 'fsdp', 'expert']],
['activation_embed_and_logits_batch_sequence', ['data', 'stage', 'fsdp', 'context', 'expert']],
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1 change: 0 additions & 1 deletion src/maxtext/configs/custom_mesh_and_rule/pure-fsdp.yml
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Expand Up @@ -18,7 +18,6 @@ mesh_axes: ['fsdp']
data_sharding: [['fsdp']]
logical_axis_rules: [
['activation_batch', ['fsdp']],
['activation_batch_no_exp', ['fsdp']],
['activation_batch_moe', ['fsdp']],
['activation_batch_no_exp_moe', ['fsdp']],
['activation_embed_and_logits_batch', ['fsdp']],
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1 change: 0 additions & 1 deletion src/maxtext/configs/inference/inference.yml
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Expand Up @@ -2,7 +2,6 @@ base_config: "base.yml"

logical_axis_rules: [
['activation_batch', ['data', 'fsdp', 'fsdp_transpose', 'expert']],
['activation_batch_no_exp', ['data', 'fsdp', 'fsdp_transpose']],
['activation_embed_and_logits_batch', ['data', 'stage', 'fsdp', 'fsdp_transpose', 'expert']],
['activation_heads', ['tensor', 'tensor_transpose', 'sequence','tensor_sequence']],
['activation_kv_heads', ['tensor', 'tensor_transpose', 'sequence','tensor_sequence']],
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6 changes: 2 additions & 4 deletions src/maxtext/configs/inference/vllm.yml
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Expand Up @@ -31,18 +31,16 @@ mesh_axes: ['data', 'attn_dp', 'model', 'expert', 'attn_dp_expert']
logical_axis_rules: [
['activation_batch', ['data']],
['activation_batch_moe', []],
['activation_batch_no_exp', []],
['activation_batch_no_exp_moe', []],
['activation_embed_and_logits_batch', ['data', 'expert']],
['activation_embed_and_logits_batch_sequence', ['data', 'expert']],
['activation_heads', ['model', 'expert']],
['activation_kv_heads', ['model', 'expert']],
['activation_attn_length', ['expert']],
['activation_attn_length_no_exp', []],
['activation_length', ['data', 'expert']],
['activation_length', ['data']],
['activation_length_moe', ['data', 'expert']],
['activation_length_no_exp', 'data'],
['activation_length_no_exp_moe', 'data'],
['activation_length_moe', 'data'],
['activation_q_length', ['expert', 'attn_dp_expert']],
['activation_attn_embed', 'model'],
['activation_embed', ['model', 'attn_dp']],
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1 change: 0 additions & 1 deletion src/maxtext/configs/post_train/rl_mt_jt.yml
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Expand Up @@ -18,7 +18,6 @@ logical_axis_rules: [
['prefill_activation_length', ['data']],
['prefill_activation_norm_length', ['data']],
['activation_batch', ['data', 'fsdp', 'fsdp_transpose', 'expert']],
['activation_batch_no_exp', ['data', 'fsdp', 'fsdp_transpose']],
['activation_embed_and_logits_batch', ['data', 'stage', 'fsdp', 'fsdp_transpose', 'expert']],
['activation_heads', ['tensor', 'tensor_transpose', 'sequence','tensor_sequence']],
['activation_kv_heads', ['tensor', 'tensor_transpose', 'sequence','tensor_sequence']],
Expand Down
58 changes: 13 additions & 45 deletions src/maxtext/layers/attention_mla.py
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Expand Up @@ -37,7 +37,6 @@
AxisIdxes,
AxisNames,
BATCH,
BATCH_NO_EXP,
CACHE_BATCH,
CACHE_BATCH_PREFILL,
CACHE_SEQUENCE,
Expand All @@ -53,12 +52,10 @@
HEAD,
Q_LORA_UP_PROJ,
KV_BATCH,
KV_BATCH_NO_EXP,
KV_HEAD,
KV_HEAD_DIM,
KV_LORA_UP_PROJ,
LENGTH,
LENGTH_NO_EXP,
MODEL_MODE_PREFILL,
MODEL_MODE_TRAIN,
PREFILL_KV_BATCH,
Expand Down Expand Up @@ -425,16 +422,11 @@ def mla_as_linen(
prefill_query_axis_names: AxisNames = (PREFILL_KV_BATCH, PREFILL_LENGTH, KV_HEAD, KV_HEAD_DIM),
prefill_key_axis_names: AxisNames = (PREFILL_KV_BATCH, PREFILL_LENGTH, KV_HEAD, KV_HEAD_DIM),
prefill_value_axis_names: AxisNames = (PREFILL_KV_BATCH, PREFILL_LENGTH, KV_HEAD, KV_HEAD_DIM),
query_axis_names: AxisNames = (KV_BATCH, LENGTH_NO_EXP, KV_HEAD, KV_HEAD_DIM),
key_axis_names: AxisNames = (KV_BATCH, LENGTH_NO_EXP, KV_HEAD, KV_HEAD_DIM),
value_axis_names: AxisNames = (KV_BATCH, LENGTH_NO_EXP, KV_HEAD, KV_HEAD_DIM),
ep_query_axis_names: AxisNames = (KV_BATCH_NO_EXP, LENGTH, KV_HEAD, KV_HEAD_DIM),
ep_key_axis_names: AxisNames = (KV_BATCH_NO_EXP, LENGTH, KV_HEAD, KV_HEAD_DIM),
ep_value_axis_names: AxisNames = (KV_BATCH_NO_EXP, LENGTH, KV_HEAD, KV_HEAD_DIM),
input_axis_names: AxisNames = (BATCH, LENGTH_NO_EXP, EMBED),
ep_input_axis_names: AxisNames = (BATCH_NO_EXP, LENGTH, EMBED),
out_axis_names: AxisNames = (BATCH, LENGTH_NO_EXP, HEAD, D_KV),
ep_out_axis_names: AxisNames = (BATCH_NO_EXP, LENGTH, HEAD, D_KV),
query_axis_names: AxisNames = (KV_BATCH, LENGTH, KV_HEAD, KV_HEAD_DIM),
key_axis_names: AxisNames = (KV_BATCH, LENGTH, KV_HEAD, KV_HEAD_DIM),
value_axis_names: AxisNames = (KV_BATCH, LENGTH, KV_HEAD, KV_HEAD_DIM),
input_axis_names: AxisNames = (BATCH, LENGTH, EMBED),
out_axis_names: AxisNames = (BATCH, LENGTH, HEAD, D_KV),
prefill_input_axis_names: AxisNames = (PREFILL_KV_BATCH, PREFILL_LENGTH, EMBED),
decode_input_axis_names: AxisNames = (DECODE_BATCH, DECODE_LENGTH, EMBED),
prefill_out_axis_names: AxisNames = (PREFILL_KV_BATCH, PREFILL_LENGTH, HEAD, D_KV),
Expand Down Expand Up @@ -499,13 +491,8 @@ def mla_as_linen(
query_axis_names=query_axis_names,
key_axis_names=key_axis_names,
value_axis_names=value_axis_names,
ep_query_axis_names=ep_query_axis_names,
ep_key_axis_names=ep_key_axis_names,
ep_value_axis_names=ep_value_axis_names,
input_axis_names=input_axis_names,
ep_input_axis_names=ep_input_axis_names,
out_axis_names=out_axis_names,
ep_out_axis_names=ep_out_axis_names,
prefill_input_axis_names=prefill_input_axis_names,
decode_input_axis_names=decode_input_axis_names,
prefill_out_axis_names=prefill_out_axis_names,
Expand Down Expand Up @@ -573,16 +560,11 @@ def __init__(
prefill_query_axis_names: AxisNames = (PREFILL_KV_BATCH, PREFILL_LENGTH, KV_HEAD, KV_HEAD_DIM),
prefill_key_axis_names: AxisNames = (PREFILL_KV_BATCH, PREFILL_LENGTH, KV_HEAD, KV_HEAD_DIM),
prefill_value_axis_names: AxisNames = (PREFILL_KV_BATCH, PREFILL_LENGTH, KV_HEAD, KV_HEAD_DIM),
query_axis_names: AxisNames = (KV_BATCH, LENGTH_NO_EXP, KV_HEAD, KV_HEAD_DIM),
key_axis_names: AxisNames = (KV_BATCH, LENGTH_NO_EXP, KV_HEAD, KV_HEAD_DIM),
value_axis_names: AxisNames = (KV_BATCH, LENGTH_NO_EXP, KV_HEAD, KV_HEAD_DIM),
ep_query_axis_names: AxisNames = (KV_BATCH_NO_EXP, LENGTH, KV_HEAD, KV_HEAD_DIM),
ep_key_axis_names: AxisNames = (KV_BATCH_NO_EXP, LENGTH, KV_HEAD, KV_HEAD_DIM),
ep_value_axis_names: AxisNames = (KV_BATCH_NO_EXP, LENGTH, KV_HEAD, KV_HEAD_DIM),
input_axis_names: AxisNames = (BATCH, LENGTH_NO_EXP, EMBED),
ep_input_axis_names: AxisNames = (BATCH_NO_EXP, LENGTH, EMBED),
out_axis_names: AxisNames = (BATCH, LENGTH_NO_EXP, HEAD, D_KV),
ep_out_axis_names: AxisNames = (BATCH_NO_EXP, LENGTH, HEAD, D_KV),
query_axis_names: AxisNames = (KV_BATCH, LENGTH, KV_HEAD, KV_HEAD_DIM),
key_axis_names: AxisNames = (KV_BATCH, LENGTH, KV_HEAD, KV_HEAD_DIM),
value_axis_names: AxisNames = (KV_BATCH, LENGTH, KV_HEAD, KV_HEAD_DIM),
input_axis_names: AxisNames = (BATCH, LENGTH, EMBED),
out_axis_names: AxisNames = (BATCH, LENGTH, HEAD, D_KV),
prefill_input_axis_names: AxisNames = (PREFILL_KV_BATCH, PREFILL_LENGTH, EMBED),
decode_input_axis_names: AxisNames = (DECODE_BATCH, DECODE_LENGTH, EMBED),
prefill_out_axis_names: AxisNames = (PREFILL_KV_BATCH, PREFILL_LENGTH, HEAD, D_KV),
Expand Down Expand Up @@ -664,13 +646,8 @@ def __init__(
query_axis_names=query_axis_names,
key_axis_names=key_axis_names,
value_axis_names=value_axis_names,
ep_query_axis_names=ep_query_axis_names,
ep_key_axis_names=ep_key_axis_names,
ep_value_axis_names=ep_value_axis_names,
input_axis_names=input_axis_names,
ep_input_axis_names=ep_input_axis_names,
out_axis_names=out_axis_names,
ep_out_axis_names=ep_out_axis_names,
prefill_input_axis_names=prefill_input_axis_names,
decode_input_axis_names=decode_input_axis_names,
prefill_out_axis_names=prefill_out_axis_names,
Expand Down Expand Up @@ -882,12 +859,9 @@ def mla_query_projection(
if model_mode == MODEL_MODE_PREFILL:
query_logical_name = self.prefill_query_axis_names
wqa_logical_name = (PREFILL_KV_BATCH, PREFILL_LENGTH, Q_LORA_UP_PROJ)
elif model_mode == MODEL_MODE_TRAIN and self.config.expert_shard_attention_option == EP_AS_CONTEXT:
query_logical_name = self.ep_query_axis_names
wqa_logical_name = (KV_BATCH_NO_EXP, LENGTH, Q_LORA_UP_PROJ)
else:
query_logical_name = self.query_axis_names
wqa_logical_name = (KV_BATCH, LENGTH_NO_EXP, Q_LORA_UP_PROJ)
wqa_logical_name = (KV_BATCH, LENGTH, Q_LORA_UP_PROJ)
query_sharding = create_sharding(self.mesh, query_logical_name)
wqa_out_sharding = create_sharding(self.mesh, wqa_logical_name)
# Set softmax scaling.
Expand Down Expand Up @@ -1038,10 +1012,8 @@ def mla_kv_projection(self, inputs: Array, inputs_positions: Array, decoder_segm
"""MLA key/value projection with integrated rotary embedding."""
if model_mode == MODEL_MODE_PREFILL:
wka_logical_name = (PREFILL_KV_BATCH, PREFILL_LENGTH, KV_LORA_UP_PROJ)
elif model_mode == MODEL_MODE_TRAIN and self.config.expert_shard_attention_option == EP_AS_CONTEXT:
wka_logical_name = (KV_BATCH_NO_EXP, LENGTH, KV_LORA_UP_PROJ)
else:
wka_logical_name = (KV_BATCH, LENGTH_NO_EXP, KV_LORA_UP_PROJ)
wka_logical_name = (KV_BATCH, LENGTH, KV_LORA_UP_PROJ)
wkva_out_sharding = create_sharding(self.mesh, wka_logical_name)
low_rank = self.wkv_a(inputs, out_sharding=wkva_out_sharding)
low_rank = checkpoint_name(low_rank, "kv_wa_proj")
Expand Down Expand Up @@ -1178,14 +1150,10 @@ def __call__(
inputs_q = self._maybe_shard_with_logical(inputs_q, self.prefill_input_axis_names)
inputs_kv = self._maybe_shard_with_logical(inputs_kv, self.prefill_input_axis_names)
out_logical_name = (PREFILL_KV_BATCH, PREFILL_LENGTH, HEAD, D_KV)
elif model_mode == MODEL_MODE_TRAIN and self.config.expert_shard_attention_option == EP_AS_CONTEXT:
inputs_q = self._maybe_shard_with_logical(inputs_q, self.ep_input_axis_names)
inputs_kv = self._maybe_shard_with_logical(inputs_kv, self.ep_input_axis_names)
out_logical_name = (BATCH_NO_EXP, LENGTH, HEAD, D_KV)
else:
inputs_q = self._maybe_shard_with_logical(inputs_q, self.input_axis_names)
inputs_kv = self._maybe_shard_with_logical(inputs_kv, self.input_axis_names)
out_logical_name = (BATCH, LENGTH_NO_EXP, HEAD, D_KV)
out_logical_name = (BATCH, LENGTH, HEAD, D_KV)

if model_mode != MODEL_MODE_TRAIN and decoder_segment_ids is None:
decoder_segment_ids = jnp.ones(inputs_q.shape[:2], dtype=jnp.int32)
Expand Down
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