1D-CNN's problem.yaml configuration

[xpww]

Hello! I recently started working with timeloop, and I noticed that both construct_workloads.py and cnn_layers.py in timeloop-accelergy-exercises/workspace/baseline_designs/scripts are configuration files for CONV2D.

I consider CONV1D as a special case of CONV2D. For a one-dimensional convolution with ifmap=(1,3600), kernel size=(8,7), stride=2, and ofmap=(8,1802), I configured it in cnn_layers.py as follows:

# W, H, C, N, M, S, R, Wpad, Hpad, Wstride, Hstride
cnn_layers = [ (3600, 1, 1, 1, 8, 1, 7, 5, 0, 2, 0) ]

My question is, is it appropriate to set H and S to 1, and Hpad and Hstride to 0? Because in order to make this code run, I had to change the original code in construct_workloads.py from:

q = int((w - s + 2 * wpad) / wstride) + 1
p = int((h - r + 2 * hpad) / hstride) + 1

to:

p = int((w - r + 2 * wpad) / wstride) + 1
if hstride != 0:
    q = int((h - s + 2 * hpad) / hstride) + 1
else:
    q = 1

In addition, the reason why q = int((w - s + 2 * wpad) / wstride) + 1 is changed to q = int((h - s + 2 * hpad) / hstride) + 1 is to match the official tutorial corresponds to the pictures in the tutorial.pdf.In fact, if do not make any changes and just add the judgment condition if hstride != 0, then run python3 run_example_designs.py --architecture eyeriss_like --problem 1d_cnn_layers.yaml, the original 3600 inputs will be recognized as 7 inputs. mistake in timeloop-mapper.map.txt.

I am concerned that changing the source code provided by the official repository may introduce errors.

[angshuman-parashar]

Some of those scripts (e.g., construct_workloads.py) are just helpers scripts and aren't as well-tested as the main codebase. Please feel free to modify them and submit bugfixes.

That said, I don't believe we have stride=0 in any of our regression tests for the main codebase either. The recommended way to model 1D conv is to have a separate problem spec. However, you are correct that configuring it as a special case of 2D conv should work. Does it work after your script changes? You can also try directly invoking the Timeloop binary from hand-written YAMLs.

[xpww]

Some of those scripts (e.g., construct_workloads.py) are just helpers scripts and aren't as well-tested as the main codebase. Please feel free to modify them and submit bugfixes.

That said, I don't believe we have stride=0 in any of our regression tests for the main codebase either. The recommended way to model 1D conv is to have a separate problem spec. However, you are correct that configuring it as a special case of 2D conv should work. Does it work after your script changes? You can also try directly invoking the Timeloop binary from hand-written YAMLs.

hi, sfter modifying construct_workloads.py, the generated yaml file is as follows:

problem:
  instance:
    C: 1
    Hdilation: 1
    Hstride: 0
    M: 8
    N: 1
    P: 1802
    Q: 1
    R: 7
    S: 1
    Wdilation: 1
    Wstride: 2
  shape:
    coefficients:
    - default: 1
      name: Wstride
    - default: 1
      name: Hstride
    - default: 1
      name: Wdilation
    - default: 1
      name: Hdilation
    data_spaces:
    - name: Weights
      projection:
      - - - C
      - - - M
      - - - R
      - - - S
    - name: Inputs
      projection:
      - - - N
      - - - C
      - - - R
          - Wdilation
        - - P
          - Wstride
      - - - S
          - Hdilation
        - - Q
          - Hstride
    - name: Outputs
      projection:
      - - - N
      - - - M
      - - - Q
      - - - P
      read_write: true
    dimensions:
    - C
    - M
    - R
    - S
    - N
    - P
    - Q
    name: CNN_Layer
  version: 0.4

After executing python3 run_example_designs.py --clear_outputs --architecture eyeriss_like --problem 1dcnn.yaml on this yaml file, the content of the generated timeloop-mapper.map.txt file is as follows:

DRAM [ Weights:56 (56) Inputs:3609 (3609) Outputs:14416 (14416) ] 
-----------------------------------------------------------------
| for P in [0:1802)

shared_glb [ Inputs:7 (7) Outputs:8 (8) ] 
inter_PE_column_spatial [ ] 
---------------------------
|   for M in [0:2) (Spatial-X)

inter_PE_spatial [ ] 
--------------------
|     for M in [0:4) (Spatial-Y)

ifmap_spad [ Inputs:7 (7) ] 
weights_spad [ Weights:7 (7) ] 
------------------------------
|       for R in [0:7)

psum_spad [ Outputs:1 (1) ] 
---------------------------
|         << Compute >>

I can't quite understand the other generated files at the moment, but as far as timeloop-mapper.map.txt is concerned, the number of weight, input, ofmap, and Loop mapping modifications seem to be correct.