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Results 21 - 30 of 49 for conv4 (0.09 sec)
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tensorflow/compiler/mlir/lite/experimental/tac/transforms/device_transform_patterns.cc
weight_new_shape, &rewriter); // Replace the fc with conv. // The output would be [1, 1, width, output]. auto conv_output_type = RankedTensorType::get({1, 1, width, output_size}, output_type.getElementType()); auto conv = rewriter.create<TFL::Conv2DOp>( fc_op.getLoc(), conv_output_type, reshaped_input, reshaped_weight,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 25.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/quantize_preprocess.cc
mlir::mhlo::createLegalizeDotToDotGeneralPass()); // Unfuse mhlo BatchNorm to primitive ops. pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createUnfuseBatchNormPass()); // Fuse Conv + Mul to Conv. pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createFuseConvolutionPass()); // Fold broadcast_in_dim + Mul. pm.addNestedPass<mlir::func::FuncOp>(mlir::odml::createFoldBroadcastPass());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 12:49:45 UTC 2024 - 9.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_lifting.mlir
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 03:24:59 UTC 2024 - 33.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize.mlir
// CHECK: %[[cst1:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 1.000000e-01>>, value = dense<1> : tensor<32x3x3x3xi8>}> // CHECK: %[[conv:.*]] = "tfl.conv_2d"(%arg0, %[[cst1]], %[[cst0]]) // CHECK: return %[[conv]] : tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>> } // CHECK-LABEL: QuantizeConv2D4Bit
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 23:10:13 UTC 2024 - 39.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_without_identity.pbtxt
# MLIR: %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} # MLIR: %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_without_identity_4bit.pbtxt
# MLIR: %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} # MLIR: %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/passes.td
"MLIR dump file name.">, Option<"merge_fusion_with_dequantize_", "merge-fusion-with-dequantize", "bool", /*default=*/"false", "Whether to merge quantized conv/dot_general fusion with subsequent dequantize.">, ]; let dependentDialects = [ "mlir::arith::ArithDialect", "mlir::stablehlo::StablehloDialect", "mlir::quant::QuantizationDialect",
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 06:31:57 UTC 2024 - 10.3K bytes - Viewed (0) -
src/cmd/compile/internal/walk/expr.go
if types.IsComplex[et] && n.Op() == ir.ODIV { t := n.Type() call := mkcall("complex128div", types.Types[types.TCOMPLEX128], init, typecheck.Conv(n.X, types.Types[types.TCOMPLEX128]), typecheck.Conv(n.Y, types.Types[types.TCOMPLEX128])) return typecheck.Conv(call, t) } // Nothing to do for float divisions. if types.IsFloat[et] { return n } // rewrite 64-bit div and mod on 32-bit architectures.
Registered: Wed Jun 12 16:32:35 UTC 2024 - Last Modified: Mon Mar 04 17:34:01 UTC 2024 - 27.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_per_channel_4bit.pbtxt
# MLIR: %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} # MLIR: %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_per_channel.pbtxt
# MLIR: %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32} # MLIR: %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 18.1K bytes - Viewed (0)