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Results 131 - 140 of 193 for Quantile (0.14 sec)
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tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/prepare_quantize/prepare_quantize_per_channel.mlir
// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-prepare-quantize=enable-per-channel-quantized-weight=true -verify-diagnostics | FileCheck %s // ----- module { // CHECK-LABEL: conv_with_bias_and_relu func.func private @conv_with_bias_and_relu(%arg0: tensor<1x3x2x3xf32>) -> tensor<1x2x2x2xf32> { %cst = "tf.Const"() {device = "", value = dense<[7.11401462, 7.05456924]> : tensor<2xf32>} : () -> tensor<2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 26 07:48:15 UTC 2024 - 8.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/lite/quantize_weights.cc
mlir::TFL::PassConfig(quant_specs), pm); if (failed(pm.run(module.get()))) { absl::string_view err = statusHandler.ConsumeStatus().message(); LOG(ERROR) << "Failed to quantize: " << err; return kTfLiteError; } // Export the results to the builder std::string result; tflite::FlatbufferExportOptions options; options.toco_flags.set_force_select_tf_ops(false);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Jun 12 23:15:24 UTC 2024 - 9.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize_composite_functions_weight_only.mlir
// RUN: stablehlo-quant-opt %s -split-input-file -verify-diagnostics \ // RUN: -stablehlo-quantize-composite-functions | FileCheck --check-prefix=CHECK %s // Test that per-tensor weight-only quantized dot_general op is produced when // empty `weight_only_ptq` is provided. module attributes {tf_saved_model.semantics} { func.func private @quantize_dot_general_per_tensor(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> attributes {tf._original_func_name = "main_0"} {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 05:56:10 UTC 2024 - 9.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/prepare_quantize/prepare_quantize.mlir
// RUN: stablehlo-quant-opt %s -split-input-file -stablehlo-prepare-quantize=enable-per-channel-quantized-weight=false -verify-diagnostics | FileCheck %s // ----- // CHECK-LABEL: func @dot // CHECK-SAME: (%[[ARG_0:.*]]: tensor<?x3xf32>) -> tensor<?x2xf32> func.func @dot(%arg0: tensor<?x3xf32>) -> tensor<?x2xf32> { // CHECK: %[[cst:.*]] = stablehlo.constant // CHECK: %[[q1:.*]] = "quantfork.qcast"(%[[cst]])
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Feb 22 19:52:06 UTC 2024 - 8.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization_utils.cc
// asymmetric range. For a state tensor, assigning correct quantization // parameters is sufficient, and for constants with asymmetric range it's // not correctly quantized by legacy quantizer so call the new Quantize. return Quantize(real_value, tensor_type); } else if (width == 16) { if (const auto uniform_type = dyn_cast<UniformQuantizedType>(q_type)) { const auto quantized_values =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 02:10:16 UTC 2024 - 43.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization.td
quantization.}], "bool", "GetDynamicRangeQuantKernelSupport", (ins), [{}], [{return false;}]>, InterfaceMethod< [{Returns whether the op requires asymmetric quantize input attribute setting.}], "bool", "RequireAsymmetricQuantizeInputsAttr", (ins), [{}], [{return false;}]>, ]; } // Specify this trait if the op has a fixed output value range.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 05 07:39:40 UTC 2024 - 8.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/modify_io_nodes.cc
returned_type = quant::ConvertSignedQuantizedToUnsigned( dequantize_input.getType(), dequantize_op.getLoc()); // replace the dequantize op by a quantize op TypeAttr type_attr = TypeAttr::get(returned_type); auto quantize_op = builder.create<QuantizeOp>( dequantize_op.getLoc(), returned_type, dequantize_input, type_attr);
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_patterns.td
class UsedBy<string op> : Constraint< CPred<"llvm::isa<mlir::TFL::" # op # "Op>(*$0.getUsers().begin())">>; // When the op is passing-through, the output types of the quantized ops need // to be updated as well. Since the quantize op manages its own type by the // "qtype" attribute, we should update the type shape in this attribute. def ReorderTransposeDequantQuant : Pat<(TF_TransposeOp:$old_value
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 30 00:40:15 UTC 2024 - 10.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize_composite_functions_drq.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-insert-quantized-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' -quant-quantize-composite-functions='quantization-method=drq target-opset=UNIFORM_QUANTIZED' -symbol-dce | FileCheck %s module { // TODO(b/260020937): Support transpose_a, transpose_b for matmul. func.func @matmul(%arg0: tensor<2x12xf32>) -> (tensor<*xf32>) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 9.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/quantization_patterns.cc
// `stablehlo.convolution` assumes the following format: // [b, 0, 1, f]x[0, 1, i, o]->[b, 0, 1, f] // `stablehlo.dot_general` can take various formats. We only per-channel // quantize non-batch ops. // `stablehlo.dot_general` legalizable to `tfl.fully_connected` has a // filter rank of 2 with the last dimension as the channel dimension. const int64_t quantization_dimension =
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri May 03 06:04:36 UTC 2024 - 41.7K bytes - Viewed (0)