[GitHub] [tvm] zxybazh commented on issue #10900: [Bug] `Compute_inline` Accepts Invalid Block Causing Error

[GitBox <gi...@apache.org>]

zxybazh commented on issue #10900:
URL: https://github.com/apache/tvm/issues/10900#issuecomment-1088153208

   Here is the generated tir:
   ```
   
   # from tvm.script import tir as T
   @tvm.script.ir_module
   class Module:
       @T.prim_func
       def main(placeholder: T.Buffer[(1, 512, 7, 7), "float32"], tensor: T.Buffer[(1, 512, 1, 1), "float32"]) -> None:
           # function attr dict
           T.func_attr({"global_symbol": "main", "tir.noalias": True})
           # buffer definition
           tensor_1_rf = T.buffer_decl([1, 512, 1, 1, 49], dtype="float32")
           # body
           with T.block("root"):
               T.reads()
               T.writes()
               T.block_attr({"meta_schedule.parallel":64, "meta_schedule.unroll_explicit":0, "meta_schedule.vectorize":64})
               tensor_1 = T.alloc_buffer([1, 512, 1, 1], dtype="float32")
               for i0, i1 in T.grid(1, 512):
                   for ax0, ax1, ax2, ax3, ax4 in T.grid(49, 1, 1, 1, 1):
                       with T.block("tensor"):
                           vi4_i5_fused_0_i4_i5_fused_1_fused_0, ax0_1 = T.axis.remap("RS", [ax0, ax1])
                           ax1_1 = T.axis.spatial(512, i1 + ax2)
                           ax2_1, ax3_1 = T.axis.remap("SS", [ax3, ax4])
                           T.reads(tensor_1_rf[ax0_1, ax1_1, ax2_1, ax3_1, vi4_i5_fused_0_i4_i5_fused_1_fused_0], placeholder[ax0_1, ax1_1, ax2_1 * 7 + vi4_i5_fused_0_i4_i5_fused_1_fused_0 // 7, ax3_1 * 7 + vi4_i5_fused_0_i4_i5_fused_1_fused_0 % 7])
                           T.writes(tensor_1[ax0_1, ax1_1, ax2_1, ax3_1])
                           with T.init():
                               tensor_1[ax0_1, ax1_1, ax2_1, ax3_1] = T.float32(0)
                           tensor_1[ax0_1, ax1_1, ax2_1, ax3_1] = tensor_1[ax0_1, ax1_1, ax2_1, ax3_1] + (tensor_1_rf[ax0_1, ax1_1, ax2_1, ax3_1, vi4_i5_fused_0_i4_i5_fused_1_fused_0] + placeholder[ax0_1, ax1_1, ax2_1 * 7 + vi4_i5_fused_0_i4_i5_fused_1_fused_0 // 7, ax3_1 * 7 + vi4_i5_fused_0_i4_i5_fused_1_fused_0 % 7])
                   for i2, i3 in T.grid(1, 1):
                       with T.block("tensor_1"):
                           ax0, ax1, ax2, ax3 = T.axis.remap("SSSS", [i0, i1, i2, i3])
                           T.reads(tensor_1[ax0, ax1, ax2, ax3])
                           T.writes(tensor[ax0, ax1, ax2, ax3])
                           tensor[ax0, ax1, ax2, ax3] = tensor_1[ax0, ax1, ax2, ax3] * T.float32(0.020408163265306121)
   ```


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