Merge pull request #441 from FluxML/bc/dw-conv-debug

Remove threading from all `∇*conv_filter` and re-enable old tests

src/dim_helpers/ConvDims.jl

@@ -77,6 +77,25 @@ function im2col_dims(c::ConvDims)
     )
 end
 
+"""
+    ∇filter_im2col_dims(c::ConvDims)
+
+Like [`im2col_dims`](@ref), but saves some memory because multiple (Julia) threads are
+not required for the filter gradient calculation.
+
+Note: in the future, this may return `Dims{2}` instead of `Dims{3}`.
+"""
+function ∇filter_im2col_dims(c::ConvDims)
+    return (
+        # Output size
+        prod(output_size(c)),
+        # Size of single dotproduct within convolution
+        prod(kernel_size(c))*channels_in(c),
+        # No threading, this is just here for backwards compat
+        1
+    )
+end
+
 # Protect your skin, kids.  Also do common validation of stride, padding, etc...
 function check_spdf(x_size::NTuple{N}, w_size::NTuple{N}, stride, padding, dilation) where {N}
     # Number of spatial dimensions in `x` and `w`.

src/impl/conv_im2col.jl

@@ -60,15 +60,16 @@ function conv_im2col!(
 end
 
 """
-    ∇conv_filter_im2col!(dw, x, dy, cdims, col=similar(dw); alpha=1, beta=0)
+    ∇conv_filter_im2col!(dw, x, dy, cdims, col=similar(dw, ∇filter_im2col_dims(cdims));
+                         alpha=1, beta=0)
 
 Conv backward pass onto the weights using im2col and GEMM; stores the result in `dw`.
-See the documentation for `conv_im2col!()` for explanation of optional parameters.
+See [`conv_im2col!`](@ref) for explanation of optional parameters.
 """
 function ∇conv_filter_im2col!(
                 dw::AbstractArray{T,5}, x::AbstractArray{T,5},
                 dy::AbstractArray{T,5}, cdims::DenseConvDims;
-                col::AbstractArray{T,3} = similar(dw, im2col_dims(cdims)),
+                col::AbstractArray{T,3} = similar(dw, ∇filter_im2col_dims(cdims)),
                 alpha::T=T(1), beta::T=T(0)) where {T}
     check_dims(size(x), size(dw), size(dy), cdims)
 
@@ -115,7 +116,7 @@ end
     ∇conv_data_im2col!(dx, w, dy, cdims, col=similar(dx); alpha=1, beta=0)
 
 Conv2d backward pass onto the input using im2col and GEMM; stores the result in `dx`.
-See the documentation for `conv_im2col!()` for explanation of other parameters.
+See [`conv_im2col!`](@ref) for explanation of optional parameters.
 """
 function ∇conv_data_im2col!(
                 dx::AbstractArray{T,5}, dy::AbstractArray{T,5},

src/impl/depthwiseconv_im2col.jl

@@ -5,8 +5,7 @@
     depthwiseconv_im2col!(y, x, w, cdims, col=similar(x); alpha=1, beta=0)
 
 Perform a depthwise convolution using im2col and GEMM, store the result in `y`.
-
-See `conv_im2col!()` for an explanation of optional parameters.
+See [`conv_im2col!`](@ref) for explanation of optional parameters.
 """
 depthwiseconv_im2col!
 
@@ -48,27 +47,32 @@ function depthwiseconv_im2col!(
 end
 
 """
-    ∇depthwiseconv_filter_im2col!(dw, w, dy, cdims, col=similar(dw); alpha=1, beta)
+    ∇depthwiseconv_filter_im2col!(dw, w, dy, cdims, col=similar(dw, ∇filter_im2col_dims(cdims));
+                                  alpha=1, beta=0)
 
-Depthwise conv2d backward pass onto the weights using im2col and GEMM.
-See the documentation for `conv_im2col!()` for explanation of optional parameters.
+Depthwise conv backward pass onto the weights using im2col and GEMM.
+See [`conv_im2col!`](@ref) for explanation of optional parameters.
 """
 ∇depthwiseconv_filter_im2col!
 
 function ∇depthwiseconv_filter_im2col!(
                 dw::AbstractArray{T,5}, x::AbstractArray{T,5},
                 dy::AbstractArray{T,5}, cdims::DepthwiseConvDims;
-                col::AbstractArray{T,3} = similar(dw, im2col_dims(cdims)),
+                col::AbstractArray{T,3} = similar(dw, ∇filter_im2col_dims(cdims)),
                 alpha::T=T(1), beta::T=T(0)) where T
     check_dims(size(x), size(dw), size(dy), cdims)
 
     M = prod(kernel_size(cdims))
     N = channel_multiplier(cdims)
     K = prod(output_size(cdims))
 
-    @threads for batch_idx in 1:size(x)[end]
+    for batch_idx in 1:size(x, 5)
+        # Because we accumulate over batches in this loop, we must set `beta` equal
+        # to `1.0` after the first sample.
+        beta′ = batch_idx == 1 ? beta : T(1)
+
         # col_slice is a thread-local workspace
-        col_slice = view(col, :, :, threadid())
+        col_slice = view(col, :, :, 1)
         im2col!(col_slice, view(x, :, :, :, :, batch_idx), cdims)
 
         # We do a separate convolution for each channel in x, as we must
@@ -78,22 +82,18 @@ function ∇depthwiseconv_filter_im2col!(
                 col_ptr = pointer(col_slice, (c_in - 1)*M*K + 1)
                 dy_ptr = pointer(dy, (batch_idx - 1)*N*K*channels_in(cdims) + (c_in - 1)*K*N + 1)
                 dw_ptr = pointer(dw, (c_in - 1)*M*N + 1)
-                gemm!(Val(true), Val(false), M, N, K, alpha, col_ptr, dy_ptr, beta, dw_ptr)
+                gemm!(Val(true), Val(false), M, N, K, alpha, col_ptr, dy_ptr, beta′, dw_ptr)
             end
         end
-
-        # Because we accumulate over batches in this loop, we must set `beta` equal
-        # to `1.0` from this point on.
-        beta = T(1)
     end
     return dw
 end
 
 """
-    depthwiseconv2d_Δx_im2col!(dx, w, dy, cdims, col=similar(dx); alpha=1, beta=0)
+    ∇depthwiseconv_data_im2col!(dx, w, dy, cdims, col=similar(dx); alpha=1, beta=0)
 
 Depwthwise conv2d backward pass onto the input using im2col and GEMM.
-See the documentation for `conv_im2col!()` for explanation of optional parameters.
+See [`conv_im2col!`](@ref) for explanation of optional parameters.
 """
 ∇depthwiseconv_data_im2col!
 

test/conv.jl

@@ -737,7 +737,7 @@ end
 end
 
 # https://github.com/FluxML/NNlib.jl/pull/171
-@testset "conv_direct! - Check Sizes" begin 
+@testset "conv_direct! - Check Sizes" begin
     x_size = (6, 7, 8, 5, 3)
     y_size = (5, 6, 7, 4, 3)
     w_size = (2, 2, 2, 5, 4)
@@ -759,25 +759,15 @@ end
 
   y = conv(x, w, cdims)
   gradtest((y, w) -> ∇conv_data(y, w, cdims), y, w)
-  # if spatial_rank == 3
-  #   @test_broken gradtest((y, w) -> sum(∇conv_data(y, w, cdims)), y, w)
-  # else
-    gradtest((y, w) -> sum(∇conv_data(y, w, cdims)), y, w)
-  # end
-    gradtest((x, y) -> ∇conv_filter(x, y, cdims), x, y)
-    if spatial_rank < 3
-        gradtest((x, y) -> sum(∇conv_filter(x, y, cdims)), x, y)
-    end
+  gradtest((y, w) -> sum(∇conv_data(y, w, cdims)), y, w)
+  gradtest((x, y) -> ∇conv_filter(x, y, cdims), x, y)
+  gradtest((x, y) -> sum(∇conv_filter(x, y, cdims)), x, y)
 
   dcdims = DepthwiseConvDims(x, w)
   gradtest((x, w) -> depthwiseconv(x, w, dcdims), x, w)
 
   # FIXME fails
-  # y = depthwiseconv(x, w, dcdims)
-  # gradtest((y, w) -> ∇depthwiseconv_data(y, w, dcdims), y, w)
-  # if spatial_rank == 3
-  #   @test_broken gradtest((y, w) -> sum(∇depthwiseconv_data(y, w, dcdims)), y, w)
-  # else
-  @test_skip gradtest((y, w) -> sum(∇depthwiseconv_data(y, w, dcdims)), y, w)
-  # end
+  y = depthwiseconv(x, w, dcdims)
+  gradtest((y, w) -> ∇depthwiseconv_data(y, w, dcdims), y, w)
+  gradtest((y, w) -> sum(∇depthwiseconv_data(y, w, dcdims)), y, w)
 end