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Results 1 - 10 of 109 for CONV (0.03 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/lite/stablehlo/tests/legalize-tfl-stablehlo-conv.mlir
Michael Levesque-Dion <******@****.***> 1706075999 -0800
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jan 24 06:08:43 UTC 2024 - 1.6K 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) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/bridge/optimize.td
def IsNull : Constraint<CPred<"!$0">>; // This pattern optimizes: // conv/dot_general + a + b -> conv/dot_general + (a + b) // conv/dot_general - a - b -> conv/dot_general - (a + b) // conv/dot_general + a - b -> conv/dot_general + (a - b) // conv/dot_general - a + b -> conv/dot_general - (a - b) foreach OpsTuple = [ [CHLO_BroadcastAddOp, CHLO_BroadcastAddOp, CHLO_BroadcastAddOp],
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Feb 24 02:26:47 UTC 2024 - 2K 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/tensorflow/tests/optimize.mlir
// CHECK-DAG: %[[cst:.*]] = "tf.Const{{.*}} dense<8.000000e+00> : tensor<3x3x3x16xf32> // CHECK-DAG: %[[cst_0:.*]] = "tf.Const{{.*}} dense<1.200000e+01> : tensor<16xf32> // CHECK-NEXT: %[[conv:.*]] = "tf.Conv2D"(%arg0, %[[cst]]) // CHECK-NEXT: %[[bias:.*]] = "tf.AddV2"(%[[conv]], %[[cst_0]]) // CHECK-NEXT: return %[[bias]] : tensor<256x8x7x16xf32> } // CHECK-LABEL: convaddv2mul
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 3.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tests/tf_to_quant_4bit.mlir
// CHECK: %[[DEQUANTIZE:.*]] = "quantfork.dcast"(%[[QUANTIZE]]) // CHECK: %[[CONV:.*]] = "tf.Conv2D"(%arg0, %[[DEQUANTIZE]]) // CHECK: return %[[CONV]] } // CHECK-LABEL: perChannelFakeQuantWithConv2D func.func @perChannelFakeQuantWithConv2D(tensor<256x32x32x3xf32>) -> (tensor<256x8x7x16xf32>) { ^bb0(%arg: tensor<256x32x32x3xf32>) :
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 9.4K bytes - Viewed (0)