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Results 41 - 49 of 49 for Quantile (0.18 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/quantization/common/attrs_and_constraints.cc
if (dot_general_op == nullptr) return std::nullopt; const int64_t filter_rank = mlir::dyn_cast<ShapedType>(dot_general_op.getOperand(1).getType()) .getRank(); // To quantize rhs per-channel, we currently only consider the case where // `stablehlo.dot_general` is legalizable to `tfl.fully_connected`. const bool is_per_axis_quantizable = IsDotGeneralFullyConnected(dot_general_op).value();
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.8K 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.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/tests/optimize_batch_matmul.mlir
// CHECK-NOT: "tfl.batch_matmul" func.func @Batchmatmul2FullyconnectedQDQ(%arg0: tensor<4x128x2xf32>, %arg1: tensor<2x1xf32>) -> (tensor<4x128x1xf32>) { %0 = arith.constant dense<[[1.0], [2.0]]> : tensor<2x1xf32> %1 = "tfl.quantize"(%0) {qtype = tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>} : (tensor<2x1xf32>) -> tensor<2x1x!quant.uniform<i8:f32, 0.024986599940879671:92>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 9K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/lift_quantizable_spots_as_functions_with_quantization_specs.mlir
func.func @main(%arg0: tensor<1x2xf32>) -> tensor<1x2xf32> { %0 = stablehlo.add %arg0, %arg0 : tensor<1x2xf32> return %0 : tensor<1x2xf32> } // Tests that `composite_add_fn_1` does not quantize when quantizing // only compute-heavy ops. // STATIC-RANGE-PTQ-TO-COMPUTE-HEAVY: %[[CONST:.+]] = stablehlo.constant dense<2.000000e+00>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Apr 02 18:09:38 UTC 2024 - 8.1K bytes - Viewed (0)