uslumt opened a new issue, #14906: URL: https://github.com/apache/tvm/issues/14906
Thanks for participating in the TVM community! We use https://discuss.tvm.ai for any general usage questions and discussions. The issue tracker is used for actionable items such as feature proposals discussion, roadmaps, and bug tracking. You are always welcomed to post on the forum first :smile_cat: Issues that are inactive for a period of time may get closed. We adopt this policy so that we won't lose track of actionable issues that may fall at the bottom of the pile. Feel free to reopen a new one if you feel there is an additional problem that needs attention when an old one gets closed. ### Expected behavior Splitting loops over multiple compute units(Cores) parallel. ### Actual behavior Error message: The queried subtree root tir.For#0 in SRef tree does not have compact dataflow, because its child block tir.Block#1 on SRef tree is neither a local complete block nor a local reduction block. ### Environment tvm - 0.9.dev0 ### Steps to reproduce @tvm.script.ir_module class Convolution: @T.prim_func def main(inpt: T.handle, kernl: T.handle, reslt: T.handle): T.func_attr({"global_symbol": "main", "tir.noalias": True}) input = T.match_buffer(inpt, (10, 3, 128, 128), "float32") kernel = T.match_buffer(kernl, (2, 3, 3, 3), "float32") result = T.match_buffer(reslt, (10, 2, 124, 124), "float32") result_compute = T.match_buffer(reslt, (10, 2, 124, 124), "float32") for b, o, h, w in T.grid(10, 2, 124, 124): for kc, kh, kw in T.grid(3, 3, 3): with T.block("compute"): b, o, h, w, kc, kh, kw = T.axis.remap("RRRRRRR", [b, o, h, w, kc, kh, kw]) result_compute[b, o, h, w] += input[b, kc, h+kh, w+kw] * kernel[o, kc, kh, kw] for b, o, h, w in T.grid(10, 2, 124, 124): with T.block("result"): vb = T.axis.reduce(10, b) vc_o = T.axis.reduce(2, o) vh = T.axis.reduce(124, h) vw = T.axis.reduce(124, w) result[vb, vc_o, vh, vw] = result_compute[vb, vc_o, vh, vw] written_ir = Convolution sch = tvm.tir.Schedule(written_ir) b_i, o_i, h_i, w_I, kc_i, kh_i, kw_i = sch.get_loops(sch.get_block("compute")) sch.parallel(b_i) Best regards -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
