Merge pull request #216 from FluxML/cl/nn

nnpack no more default for conv
FluxML · Jul 2, 2020 · be66282 · be66282 · CarloLucibello · Jul 2, 2020
2 parents 2729369 + b2abb7c
commit be66282
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Showing 2 changed files with 21 additions and 20 deletions.
diff --git a/Project.toml b/Project.toml
@@ -1,6 +1,6 @@
 name = "NNlib"
 uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-version = "0.7.0"
+version = "0.7.1"
 
 [deps]
 Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"

diff --git a/src/conv.jl b/src/conv.jl
@@ -211,23 +211,24 @@ for front_name in (:conv, :∇conv_data, :∇conv_filter,
 end
 
 # Use NNPACK if it is available and the operation is supported
-if is_nnpack_available()
-    function conv(x::Array{Float32, 4}, w::Array{Float32, 4},
-                  cdims::DenseConvDims{2, K, C_in, C_out, (1, 1), P, (1, 1), F};
-                  kwargs...) where {K, C_in, C_out, P, F}
-        return conv_nnpack(x, w, cdims; kwargs...)
-    end
-
-    function ∇conv_data(dy::Array{Float32, 4}, w::Array{Float32, 4},
-                cdims::DenseConvDims{2, K, C_in, C_out, (1, 1), P, (1, 1), F};
-                kwargs...) where {K, C_in, C_out, P, F}
-        return ∇conv_data_nnpack(dy, w, cdims; kwargs...)
-    end
-
-    function ∇conv_filter(x::Array{Float32, 4}, dy::Array{Float32, 4},
-                cdims::DenseConvDims{2, K, C_in, C_out, (1, 1), P, (1, 1), F};
-                kwargs...) where {K, C_in, C_out, P, F}
-        return ∇conv_filter_nnpack(x, dy, cdims; kwargs...)
-    end
-end
+# commented out 'till proper benchmarking and more correctness test are performed
+# if is_nnpack_available()
+#     function conv(x::Array{Float32, 4}, w::Array{Float32, 4},
+#                   cdims::DenseConvDims{2, K, C_in, C_out, (1, 1), P, (1, 1), F};
+#                   kwargs...) where {K, C_in, C_out, P, F}
+#         return conv_nnpack(x, w, cdims; kwargs...)
+#     end
+
+#     function ∇conv_data(dy::Array{Float32, 4}, w::Array{Float32, 4},
+#                 cdims::DenseConvDims{2, K, C_in, C_out, (1, 1), P, (1, 1), F};
+#                 kwargs...) where {K, C_in, C_out, P, F}
+#         return ∇conv_data_nnpack(dy, w, cdims; kwargs...)
+#     end
+
+#     function ∇conv_filter(x::Array{Float32, 4}, dy::Array{Float32, 4},
+#                 cdims::DenseConvDims{2, K, C_in, C_out, (1, 1), P, (1, 1), F};
+#                 kwargs...) where {K, C_in, C_out, P, F}
+#         return ∇conv_filter_nnpack(x, dy, cdims; kwargs...)
+#     end
+# end
 ########################################################