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Results 1 - 10 of 62 for CONV (0.04 sec)
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tensorflow/compiler/mlir/lite/tests/dilated-conv.mlir
// CHECK-NEXT: [[CONV:%.*]] = "tf.Conv2D"([[INPUT]], [[FILTER]]) <{dilations = [1, 2, 2, 1], padding = "SAME", strides = [1, 1, 1, 1]}> : (tensor<1x128x128x3xf32>, tensor<5x5x3x8xf32>) -> tensor<1x128x128x8xf32> // CHECK-NEXT: [[RESULT:%.*]] = "tf.BiasAdd"([[CONV]], [[BIAS]]) : (tensor<1x128x128x8xf32>, tensor<8xf32>) -> tensor<1x128x128x8xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 44.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/replace_cast_hacks_with_tf_xla_ops.td
// support non-constant weights. def ConvertTFConv2DToXLAConvOp : Pat< (TF_Conv2DOp:$conv (TF_SubOp (TF_CastOp $input, $truncate), $input_zp), (TF_CastOp (TF_IdentityOp $filter), $truncate1), $strides, $use_cudnn, $padding, $explicit_padding, IsDataFormatNHWC:$data_format, $dilations), (CreateXLAConvOpFromTFConv2DOp $input, $filter, $input_zp, $conv, $strides, $dilations, $padding, $explicit_padding),
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sun Dec 10 05:52:02 UTC 2023 - 21.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/bridge/optimize.mlir
%zp_offset: tensor<?x2x2x1xi32>, %bias: tensor<1xi32> ) -> tensor<?x2x2x1xi32> { // CHECK-DAG: %[[conv:.*]] = mhlo.convolution // CHECK-DAG: %[[combined:.*]] = chlo.broadcast_add %[[zp_offset:.*]], %[[bias:.*]] // CHECK-DAG: %[[result:.*]] = chlo.broadcast_add %[[conv]], %[[combined]] // CHECK: return %[[result]] %0 = mhlo.convolution(%lhs, %rhs) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Feb 24 02:26:47 UTC 2024 - 10.7K bytes - Viewed (0) -
src/cmd/compile/internal/walk/builtin.go
return mkcall("countrunes", n.Type(), init, typecheck.Conv(n.X.(*ir.ConvExpr).X, types.Types[types.TSTRING])) } if isByteCount(n) { conv := n.X.(*ir.ConvExpr) walkStmtList(conv.Init()) init.Append(ir.TakeInit(conv)...) _, len := backingArrayPtrLen(cheapExpr(conv.X, init)) return len } if isChanLenCap(n) { name := "chanlen" if n.Op() == ir.OCAP {
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Fri Mar 08 22:35:22 UTC 2024 - 31.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize-dynamic-range.mlir
%b = arith.constant dense<-1.23697901> : tensor<64xf32> %conv = "tfl.conv_2d"(%arg0, %w, %b) {dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 2 : i32, stride_w = 2 : i32} : (tensor<1x224x224x3xf32>, tensor<64x3x3x3xf32>, tensor<64xf32>) -> tensor<1x112x112x64xf32> func.return %conv : tensor<1x112x112x64xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 23 21:09:00 UTC 2024 - 23.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/pipelines/process_nchw_tensor.mlir
// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[TRANSPOSE_0]], %[[CONST]]) dim_numbers = [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f], window = {pad = {{\[\[}}1, 1], [1, 1]]} {batch_group_count = 1 : i64, feature_group_count = 1 : i64} : (tensor<1x4x4x8xf32>, tensor<3x3x8x8xf32>) -> tensor<1x4x4x8xf32> // CHECK: %[[TRANSPOSE_1:.+]] = stablehlo.transpose %[[CONV]], dims = [0, 3, 1, 2] : (tensor<1x4x4x8xf32>) -> tensor<1x8x4x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 20:32:46 UTC 2024 - 12.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/components/tf_to_stablehlo.mlir
// CHECK-DAG: %[[CONV:.*]] = stablehlo.convolution(%[[ARG]], %[[CONST_1]]) {{.*}} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<1x3x2x2xf32> // CHECK-DAG: %[[BROADCAST:.*]] = stablehlo.broadcast_in_dim %[[CONST_0]], dims = [3] : (tensor<2xf32>) -> tensor<1x3x2x2xf32> // CHECK-DAG: %[[ADD:.*]] = stablehlo.add %[[CONV]], %[[BROADCAST]] : tensor<1x3x2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 08 20:05:12 UTC 2024 - 13.6K bytes - Viewed (0) -
src/cmd/compile/internal/walk/compare.go
cmplw := ir.Node(ir.NewIndexExpr(base.Pos, cmpl, ir.NewInt(base.Pos, i))) cmplw = typecheck.Conv(cmplw, elemType) // convert to unsigned cmplw = typecheck.Conv(cmplw, convType) // widen cmprw := ir.Node(ir.NewIndexExpr(base.Pos, cmpr, ir.NewInt(base.Pos, i))) cmprw = typecheck.Conv(cmprw, elemType) cmprw = typecheck.Conv(cmprw, convType) // For code like this: uint32(s[0]) | uint32(s[1])<<8 | uint32(s[2])<<16 ...
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Wed May 24 21:55:14 UTC 2023 - 16.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/transforms/fused_kernel_matcher.cc
// attribute which is shared. bool AreFuseCompatible(Conv2DOp conv, BiasAddOp bias_add, PatternRewriter &rewriter) const override { // Verify that the data formats match and are valid for fusion. if (conv.getDataFormat() != bias_add.getDataFormat()) { (void)rewriter.notifyMatchFailure(conv, [&](Diagnostic &diag) { diag << "data format does not match Conv2D data format ("
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 14.9K bytes - Viewed (0) -
src/cmd/compile/internal/walk/convert.go
init.Append(as) return res } // Returns the data word (the second word) used to represent conv.X in // an interface. func dataWord(conv *ir.ConvExpr, init *ir.Nodes) ir.Node { pos, n := conv.Pos(), conv.X fromType := n.Type() // If it's a pointer, it is its own representation. if types.IsDirectIface(fromType) { return n }
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Oct 09 17:28:22 UTC 2023 - 18.2K bytes - Viewed (0)