Add flops calculation for DeepSeek v3.2

[RissyRan]

Description

Add flops calculation for DeepSeek v3.2, and this PR depends on this change

• Add indexer flops helper function
• Add option to combine indexer flops inside of MLA
• Added a unit test to rough estimate tflops

Tests

• Tested DS v2-16b, no impact

# Before the change
Per train step:
 Total TFLOPs: 593.27 
 split as 81.24% learnable weight flops and 18.76% attention flops
before the change: 593.272422531072

# After the change
Per train step:
 Total TFLOPs: 593.27 
 split as 81.24% learnable weight flops and 18.76% attention flops
after change: 593.272422531072

• Tested DS v3.2, with indexer (expected)

# Enable this feature
Per train step:
 Total TFLOPs: 4162.71 
 split as 88.50% learnable weight flops and 11.50% attention flops
enable use_sparse_indexer: 4162.71

# Disable this feature like v3
Per train step:
 Total TFLOPs: 4103.37 
 split as 87.74% learnable weight flops and 12.26% attention flops
disable use_sparse_indexer: 4103.370952409088

Checklist

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Codecov Report

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[shuningjin]

Thanks for the careful calculation! Here is the summary of my understanding and questions. Would appreciate your confirmation and clarification (could be in-line).

Let B=batch_size, T=max_target_length, K=index_topk

Indexer Flops

b=B, s=t=T, h=index_n_heads, d=index_head_dim

# indexer linear flop
I_1 = q_proj + k_proj + head_proj
# Q = RoPE(Wq @ q_lora),  [b, t, q_lora_rank] x [b, t, q_lora_rank, h * d] -> [b, t, h * d]
q_proj = 2 * B * T * index_n_heads * index_head_dim * q_lora_rank  
# K = RoPE(Norm(Wk @ X)), [b, s, embed_dim] x [b, s, emb_dim, d] -> [b, s, d]
k_proj = 2 * B * T * index_head_dim * emb_dim 
# Head_Weights = (W_proj @ X), [b, t, embed_dim] x [b, t, emb_dim, h] -> [b, t, h]
head_proj = 2 * B * T * index_n_heads * emb_dim 

# indexer quadratic flop (causal factor 0.5 applied)
I_2 = qk_product + index_score
# Logits = ReLU(Q @ K.T), "bthd, bsd -> btsh"
qk_product = B * T^2 * index_n_heads * index_head_dim 
# Score = Sum_head(Logits * Head_Weights), "btsh, bth -> bts"
index_score = B * T^2 * index_n_heads

[Remark] Your code looks correct. Might be good to clarify in the comments, especially about causality.

Regular causal attention: B * H * D * T^2, where H = num_query_heads and D = qk_head_dim_sum + v_head_dim (MLA specific)

Sparse causal attention ($T\le K$):

• Current logic: regular_causal_attention_flop + indexer_flop
• [Question 1] Shall we keep or skip indexer_flop? In our actual implementation, we skip the indexer computation in this case.

Sparse causal attention ($T > K$):

• Current logic: 2 * B * H * D * (T * K) + indexer_flop.
• [Question 2] This seems an approximation, overestimating the cost during the "warm-up" phase (first K tokens). Shall we use the exact formula: 2 * B * H * D * (T * K - K^2 / 2) + indexer_flop?

[Question 3] Should the Indexer cost be accounted inside MLA cost or outside?

[shuningjin]

Thanks for the clarification and visualization! LGTM.