↓ 1 callersMethodinitialize(
self,
problem_shape: HyperCube, # m, n, k, l
tile_shape: HyperCube,
cluster
cutlass.py/tile_scheduler.py:29
↓ 1 callersFunctionlinear_attention_bwd(rq, iq, rk, ik, rv, iv, rgo, igo, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_bwd_bind_bhm.py:296
↓ 1 callersFunctionlinear_attention_bwd(rq, iq, rk, ik, rv, iv, rgo, igo, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_bwd_bind_bhm_v2.py:350
↓ 1 callersFunctionlinear_attention_fwd(rq, iq, rk, ik, rv, iv, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_tile_k_tile_n.py:222
↓ 1 callersFunctionlinear_attention_fwd(rq, iq, rk, ik, rv, iv, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_tile_k.py:199
↓ 1 callersFunctionmain(batch_size, num_heads, seq_len, model_k, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex.py:212
↓ 1 callersFunctionmain(batch_size, num_heads, seq_len, model_k, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_tile_k_tile_n.py:250
↓ 1 callersFunctionmain(batch_size, num_heads, seq_len, model_k, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_bwd_bind_bhm.py:330
↓ 1 callersFunctionmain(batch_size, num_heads, seq_len, model_k, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_bwd_bind_bh.py:396
↓ 1 callersFunctionmain(batch_size, num_heads, seq_len, model_k, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_tile_k.py:227
↓ 1 callersFunctionmain(batch_size, num_heads, seq_len, model_k, r_scale, i_scale)
examples/attention/triton/fused_linear_attention_complex_bwd_bind_bhm_v2.py:384
↓ 1 callersFunctionmain(M, N, K, in_dtype, out_dtype, only_once, trials)
examples/matmul/tvm/tensorir_meta_schedule.py:95
↓ 1 callersFunctionprofile_and_build(mod, params, sm, tmp_dir="./tmp", lib_path="compile.so", use_fast_math=False)
examples/matmul/tvm/relay_matmul_cutlass.py:29
↓ 1 callersFunctionrelay_matmul(
M, N, K, in_dtype="float16", acc_dtype="float32", target="cuda"
)
examples/matmul/tvm/relay_matmul_cublas.py:9
↓ 1 callersFunctionrelay_matmul(
M, N, K, in_dtype="float16", acc_dtype="float32", target="cuda"
)
examples/matmul/tvm/relay_matmul_cutlass.py:16