↓ 1 callersMethodto_underlying_arguments(
problem_shape_mnkl,
tile_shape,
cluster_shape,
hw_info,
arguments,
cutlass.py/tile_scheduler.py:275
↓ 1 callersFunctiontorch_impl(rq, iq, rk, ik, rv, iv, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex.py:232
↓ 1 callersFunctiontorch_impl(rq, iq, rk, ik, rv, iv, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_tile_k_tile_n.py:270
↓ 1 callersFunctiontorch_impl(rq, iq, rk, ik, rv, iv, rgo, igo, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_bwd_bind_bhm.py:347
↓ 1 callersFunctiontorch_impl(rq, iq, rk, ik, rv, iv, rgo, igo, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_bwd_bind_bh.py:413
↓ 1 callersFunctiontorch_impl(rq, iq, rk, ik, rv, iv, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_tile_k.py:247
↓ 1 callersFunctiontorch_impl(rq, iq, rk, ik, rv, iv, rgo, igo, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_bwd_bind_bhm_v2.py:401
↓ 1 callersFunctionverify_matmul(
func, M, N, K, ref_target="cuda", sm=80, atol=1e-5, rtol=1e-5
)
examples/matmul/tvm/relay_matmul_cutlass.py:56