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design.py declares Q/O as (heads, S_q_pad, d) but K/V as (num_KV_heads, S_kv_pad * d). get_arg_spec used num_heads for all four, so under GQA K and V are over by num_heads/num_KV_heads: 8388608 against 2097152 on the GQA param already in test.py, a factor of 4. Two things hid it. num_kv_heads=0 normalizes to heads, which makes the old formula accidentally right for plain MHA, and run_test reads only spec.direction, never spec.shape. It surfaces in an OperatorSequence, where compilation asserts the MLIR arg count equals the computed one.
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| Under GQA the two disagree on K and V by num_heads/num_KV_heads. Nothing catches | ||
| it today: run_test sizes input buffers from the supplied tensor rather than from | ||
| the spec, so only a fused OperatorSequence -- which asserts the MLIR arg count | ||
| equals the computed one -- would notice. |
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design.pydeclares Q and O as(heads, S_q_pad, d)but K and V as(num_KV_heads, S_kv_pad * d).get_arg_spec()sized all four fromnum_heads, sounder GQA the K/V specs are over by
num_heads / num_KV_heads. On the GQA paramalready in
test.py(seq_len=16384, d=64, heads=8, pipelines=8, kv_heads=2) that is8388608 against 2097152, a factor of 4. How to create a product launching plan smartsheet
Grand stage setup for telecom product launch event premium ai Two things kept it hidden.
num_kv_heads=0normalizes toheadsindesign.py, whichmakes the old formula accidentally correct for standard MHA, and
run_testreads onlyspec.direction, taking input buffers from the supplied tensor rather than fromspec.shape. So nothing consults the wrong value today. It surfaces inside anOperatorSequence, wherecompilation/sequence.pyasserts the MLIR arg count equalsthe one computed here. Enterprise ai stack 2026 mcp a2a and domain models
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iron/operators/mha/test.py:test_arg_spec_matches_design_shapes, covering GQA andstandard MHA. It asserts against the shapes
design.pydeclares rather than againstliterals, so it fails if either side drifts. Device-free, and deliberately not reusing
get_params()-- the GQA case there is markedextensive, which is half of why thiswent unnoticed.
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iron/operators/mha/op.py:get_arg_spec()sizes Q/O fromnum_headsand K/V fromnum_KV_heads, applying the same0 -> num_headsnormalizationdesign.pydoes.Free Blog Post Templat Picture Of Indoor Product Launch Stage
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_calculate_seq_paddinghardcodes the block size to 64, whiledesign.pyderivesS_q_padandS_kv_padfromB_qandB_kvseparately. That is correct today onlybecause both default to 64 and
op.pynever overrides them, and__post_init__setsself.B_q/self.B_kvwhich the helper ignores. Left alone here to keep this to onedefect. Product launch stage búsqueda de google stage backdrop design