[GitHub] [tvm] funnyApe opened a new issue #7599: [BUG][conv3d]output shape error when using "tvm.topi.nn.conv3d_ndhwc( )" in auto_scheduler.

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

funnyApe opened a new issue #7599:
URL: https://github.com/apache/tvm/issues/7599


   This bug was encountered when I was trying to use auto_scheduler method to optimize the conv3d op.  I referred to the script "/tvm/tutorials/auto_scheduler/tune_conv2d_layer_cuda.py" and did a little modification to implement the optimization of conv3d with format "NDHWC" by call function topi.nn.conv3d_ndhwc(). I set input as  a tensor with layout (N, D, H, W, CI)， and kernel with (KD, KH, KW, CI, CO)。when I used (1, 128, 64, 32, 32) as input,  (16, 5, 5, 32, 3) as kernel,  (1, 1, 1) as stride, 1 as dilation and "SAME" as padding to call topi.nn.conv3d_ndhwc()，I expected an output with shape (1, 128, 64, 32,  3)  while I got an actual output shape(1, 117, 75, 32, 3).
   
   **Environment**
   
   - OS:  Ubuntu 16.04 LTS
   
   - TVM:  0.8.dev0
   
   - CUDA: 10.1
   
   **Code to generate the result**
   
   `import os
   import numpy as np
   import tvm
   from tvm import te, auto_scheduler, topi
   from tvm.topi.testing import conv2d_nchw_python
   
   ######################################################################
   # Define the computation
   # ^^^^^^^^^^^^^^^^^^^^^^
   # To begin with, let us define the computation of a convolution layer.
   # The function should return the list of input/output tensors.
   # From these tensors, the auto-scheduler can get the whole computational graph.
   
   @auto_scheduler.register_workload
   def conv3d_layer(N, D, H, W, CO, CI, KD, KH, KW, stride, padding):
       data = te.placeholder((N, D, H, W, CI), name="data")
       kernel = te.placeholder((KD, KH, KW, CI, CO), name="kernel")
       out = topi.nn.conv3d_ndhwc(data, kernel, stride, padding, dilation=1, out_dtype="float32")
       print(out.shape)
       return [data, kernel, out]
   
   ######################################################################
   # Create the search task
   target = tvm.target.Target("cuda")
   
   # Use the last layer in ResNet-50
   N, D, H, W, CO, CI, KD, KH, KW, strides, padding = 1, 128, 64, 32, 3, 32, 16, 5, 5, (1,1,1), "SAME"
   task = auto_scheduler.SearchTask(
       func=conv3d_layer, args=(N, D, H, W, CO, CI, KD, KH, KW, strides, padding), target=target
   )
   
   # Inspect the computational graph
   print("Computational DAG:")
   print(task.compute_dag)
   
   log_file = "conv3d.json"
   measure_ctx = auto_scheduler.LocalRPCMeasureContext(min_repeat_ms=300)
   tune_option = auto_scheduler.TuningOptions(
       num_measure_trials=10,  # change this to 1000 to achieve the best performance
       runner=measure_ctx.runner,
       measure_callbacks=[auto_scheduler.RecordToFile(log_file)],
       verbose=2,
   )
   
   # Run auto-tuning (search)
   task.tune(tune_option)
   # Apply the best schedule
   sch, args = task.apply_best(log_file)
   
   # Kill the measurement process
   del measure_ctx
   
   print("Lowered TIR:")
   print(tvm.lower(sch, args, simple_mode=True))
   func = tvm.build(sch, args, target)`
   
   I checked the TVM's source code, and found that  topi.nn.conv3d_ndhwc( ) and topi.nn.conv3d_ncdhw( )  get error results when calling function "get_pad_tuple3d()". When "padding==SAME", pad_h and pad_d are interchanged. 
   The following code is from TVM' source code.
   
   `def get_pad_tuple3d(padding, kernel):
         ...
           elif padding == "SAME":
               **pad_h = kernel[0] - 1
               pad_w = kernel[1] - 1
               pad_d = kernel[2] - 1**
   
   def conv3d_ncdhw(Input, Filter, stride, padding, dilation, out_dtype=None):
       ...
           pad_front, pad_top, pad_left, pad_back, pad_down, pad_right = get_pad_tuple3d(
               padding, (**dilated_kernel_d, dilated_kernel_h, dilated_kernel_w**))
   
   def conv3d_ndhwc(
       Input,
       Filter,
       stride,
       padding,
       dilation,
       out_dtype="float32",
       auto_scheduler_rewritten_layout="",
   ):
      ...
          pad_front, pad_top, pad_left, pad_back, pad_down, pad_right = get_pad_tuple3d(
               padding, (**dilated_kernel_d, dilated_kernel_h, dilated_kernel_w**) )
   
   `
   
   
   
   


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[GitHub] [tvm] jcf94 commented on issue #7599: [BUG][conv3d]output shape error when using "tvm.topi.nn.conv3d_ndhwc( )" in auto_scheduler.

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

jcf94 commented on issue #7599:
URL: https://github.com/apache/tvm/issues/7599#issuecomment-794934071


   Is this correct without auto_scheduler?


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[GitHub] [tvm] tqchen closed issue #7599: [BUG][conv3d]output shape error when using "tvm.topi.nn.conv3d_ndhwc( )" in auto_scheduler.

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

tqchen closed issue #7599:
URL: https://github.com/apache/tvm/issues/7599


   


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