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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) -
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) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/lift_quantizable_spots_as_functions.mlir
// CHECK: } // CHECK-LABEL: private @composite_conv_with_bias_dynamic_fn_1 // CHECK: %[[CONV:.*]] = stablehlo.convolution(%arg0, %arg1) // CHECK: %[[SHAPE_OF:.*]] = shape.shape_of %[[CONV]] // CHECK: %[[DYNAMIC_BROADCAST_IN_DIM:.*]] = stablehlo.dynamic_broadcast_in_dim %arg2, %[[SHAPE_OF]] // CHECK: %[[ADD:.*]] = stablehlo.add %[[CONV]], %[[DYNAMIC_BROADCAST_IN_DIM]] // CHECK: return %[[ADD]] : tensor<?x28x28x16xf32> // CHECK: }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 10 04:07:09 UTC 2024 - 49.8K 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/quantization/tensorflow/tests/cast_bf16_ops_to_f32.mlir
// CHECK: %[[cast:.*]] = "tf.Cast"(%[[cst]]) <{Truncate = false}> : (tensor<2x3x3x2xbf16>) -> tensor<2x3x3x2xf32> // CHECK: %[[conv:.*]] = "tf.Conv2D"(%arg0, %[[cast]]) // CHECK: %[[identity:.*]] = "tf.IdentityN"(%[[conv]]) {device = ""} : (tensor<1x3x2x2xf32>) -> tensor<1x3x2x2xf32> // CHECK: return %[[identity]] : tensor<1x3x2x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 8.4K 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) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/nchw_convolution_to_nhwc.mlir
// CHECK: %[[CONV:.+]] = stablehlo.convolution(%[[TRANSPOSE_0]], %[[TRANSPOSE_1]]) 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_2:.+]] = stablehlo.transpose %[[CONV]], dims = [0, 3, 1, 2] : (tensor<1x4x4x8xf32>) -> tensor<1x8x4x4xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Mar 25 23:00:47 UTC 2024 - 5.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_lifting.td
[(HasRankOf<1> $add_rhs_value), (HasEqualElementSize<[-1], [0]> $conv_out, $add_rhs)], [], (addBenefit -1)>; // Convert conv+sub+mul pattern to conv+mul+add. // (conv - sub) * mul -> conv * mul + (-sub) * mul // // This is needed to support Conv+BatchNorm pattern from Jax models converted // using jax2tf w/o native serialization. Note that Jax2tf patterns always
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 03:24:59 UTC 2024 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/fuse_convolution_pass.cc
"non-broadcastable operands"; }); } filter_value = filter.getValue(); mul_value = multiplier.getValue(); // In MHLO, Conv filter is in HWIO format, Depthwise conv filter is in HW1O // format and backprop input conv filter is in HWOI format. // Only fuses multiplier if all dimensions other than the out channel // dimension are equal to 1. if (!TFL::IsDimensionsDegenerateExceptLastOne(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 22:21:19 UTC 2024 - 8.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize.mlir
%conv = "tf.Conv2D"(%dq_input, %dq_weight) {attr_map = "0:strides,1:use_cudnn_on_gpu,2:padding,3:explicit_paddings,4:dilations", data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "VALID", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x2xf32>) -> tensor<*xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 6.4K bytes - Viewed (0)