rm threadpool arg

Author: MikeInnes

src/nnpack/interface.jl

@@ -12,17 +12,17 @@ function softmax!(x::A) where A<:AbstractVecOrMat{Float64}
 end
 
 softmax!(x::A) where A<:AbstractVecOrMat{Float32} =
-    nnp_softmax_output(x, x, threadpool = shared_threadpool[])
+    nnp_softmax_output(x, x)
 
 softmax!(y::A, x::A) where A<:AbstractVecOrMat{Float64} = softmax!(Float32.(y), Float32.(x))
 
 softmax!(y::A, x::A) where A<:AbstractVecOrMat{Float32} =
-    nnp_softmax_output(x, y, threadpool = shared_threadpool[])
+    nnp_softmax_output(x, y)
 
 softmax(x::A) where A<:AbstractVecOrMat{Float64} = softmax(Float32.(x))
 
 softmax(x::A) where A<:AbstractVecOrMat{Float32} =
-    nnp_softmax_output(x, similar(x), threadpool = shared_threadpool[])
+    nnp_softmax_output(x, similar(x))
 
 maxpool(x::A, k; pad = map(_->0,k), stride = k) where A<:AbstractArray{Float64, 4} =
     maxpool(Float32.(x), k, pad = pad, stride = stride)
@@ -40,7 +40,7 @@ maxpool!(y::A, x::A, k; pad = map(_->0,k), stride = k) where A<:AbstractArray{Fl
     maxpool!(Float32.(y), Float32.(x), k, pad = pad, stride = stride)
 
 maxpool!(y::A, x::A, k; pad = map(_->0,k), stride = k) where A<:AbstractArray{Float32, 4} =
-    nnp_max_pooling_output(x, y, k, padding = expand(Val{length(k)}, pad), stride = expand(Val{length(k)}, stride), threadpool = shared_threadpool[])
+    nnp_max_pooling_output(x, y, k, padding = expand(Val{length(k)}, pad), stride = expand(Val{length(k)}, stride))
 
 conv(x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AbstractArray{Float64, 4} =
     conv(Float32.(x), Float32.(w), pad = pad, stride = stride, dilation = dilation, algo = algo)
@@ -88,7 +88,7 @@ function conv!(y::A1, x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, al
     if flipkernel == 0
         w = reverse(reverse(w, dims=1), dims=2)
     end
-    nnp_convolution_output(y, x, w, b, algo = algo, padding = pad, stride = stride, threadpool = shared_threadpool[])
+    nnp_convolution_output(y, x, w, b, algo = algo, padding = pad, stride = stride)
 end
 
 crosscor!(y::A1, x::A1, w::A1, b::A2; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where {A1<:AbstractArray{Float64, 4}, A2<:AbstractArray{Float64, 1}} =
@@ -104,7 +104,7 @@ function ∇conv_data(dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo
     pad_, stride_, fallback = check_support(x, (size(w, 1), size(w, 2)), pad, stride, dilation)
     if fallback
         conv2d_grad_x!(zeros(Float32, size(x)), x, w, dy, padding = pad_, stride = stride_, dilation = dilation)
-    else  
+    else
         ∇conv_data!(zeros(Float32, size(x)), dy, x, w; pad = pad_, stride = stride_, dilation = dilation, algo = UInt32(algo))
     end
 end
@@ -114,7 +114,7 @@ end
 
 function ∇conv_data!(dx::A, dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where A<:AbstractArray{Float32, 4}
     flipkernel == 0 && (w = reverse(reverse(w, dims=1), dims=2))
-    nnp_convolution_input_gradient(dx, x, dy, w, padding = pad, stride = stride, algo = algo, threadpool = shared_threadpool[])
+    nnp_convolution_input_gradient(dx, x, dy, w, padding = pad, stride = stride, algo = algo)
 end
 
 ∇conv_filter(dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0)) where A<:AbstractArray{Float64, 4} =
@@ -124,7 +124,7 @@ function ∇conv_filter(dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, al
     pad_, stride_, fallback = check_support(x, (size(w, 1), size(w, 2)), pad, stride, dilation)
     if fallback
         conv2d_grad_w!(zeros(Float32, size(w)), x, w, dy, padding = pad_, stride = stride_, dilation = dilation)
-    else 
+    else
         ∇conv_filter!(zeros(Float32, size(w)), dy, x, w; pad = pad_, stride = stride_, dilation = dilation, algo = UInt32(algo))
     end
 end
@@ -134,6 +134,6 @@ end
 
 function ∇conv_filter!(dw::A, dy::A, x::A, w::A; pad = 0, stride = 1, dilation = 1, algo = UInt32(0), flipkernel = 0) where A<:AbstractArray{Float32, 4}
     flipkernel == 0 && (w = reverse(reverse(w, dims=1), dims=2))
-    dw .= nnp_convolution_kernel_gradient(dw, x, dy, w, padding = pad, stride = stride, algo = algo, threadpool = shared_threadpool[])
+    dw .= nnp_convolution_kernel_gradient(dw, x, dy, w, padding = pad, stride = stride, algo = algo)
     flipkernel == 0 ? reverse(reverse(dw, dims=1), dims=2) : dw
 end