- Sort Score
- Result 10 results
- Languages All
Results 51 - 60 of 152 for requantize (0.28 sec)
-
tensorflow/compiler/mlir/lite/transforms/passes.td
]; } def DecomposeHybridQuantizationPass : Pass<"tfl-decompose-hybrid-quantization", "mlir::func::FuncOp"> { let summary = "Decomposes hybridge quantization to explicit quantize / dequantize"; let description = [{ Decomposes (with explicit quantize/dequantize ops) selected math operations which exist in the model with hybrid quantization (some arguments/results left in floating point). }];
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Apr 24 20:30:06 UTC 2024 - 22.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/passes/prepare_quantize_drq.cc
llvm::cl::desc("Whether enable per-channel quantized weights.")}; }; // If the weight is applicable to dynamic range quantization, insert Quantize // and Dequantize ops with per-tensor scale. class PrepareDRQQuantizableOp : public OpRewritePattern<arith::ConstantOp> { public: explicit PrepareDRQQuantizableOp(MLIRContext* context,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 11.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/prepare_quantize_dynamic_range.cc
quant::QuantizationSpecs quant_specs_; }; #include "tensorflow/compiler/mlir/lite/utils/generated_op_quant_spec_getters.inc" // If the weight is applicable to dynamic range quantization, insert Quantize // and Dequantize ops with either per-axis or per-tensor scale. class PrepareDynamicRangeQuantizableOp : public OpRewritePattern<arith::ConstantOp> { public: explicit PrepareDynamicRangeQuantizableOp(
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 20.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/uniform_quantized_types.cc
// `tfl.quantize` or `tfl.dequantize` ops. ui8, i8 and i16 are supported. bool IsSupportedByTfliteQuantizeOrDequantizeOps(IntegerType storage_type) { if (storage_type.getWidth() == 8 || (storage_type.isSigned() && storage_type.getWidth() == 16)) { return true; } LLVM_DEBUG(llvm::dbgs() << "Uniform quantize / dequantize op only supports ui8, i8 or "
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/decompose-hybrid-quantization.mlir
// CHECK-DAG: %[[VAL2:.+]] = "tfl.dequantize"(%[[VAL0]]) // CHECK-DAG: %[[VAL3:.+]] = "tfl.dequantize"(%[[VAL1]]) // CHECK-DAG: %[[VAL4:.+]] = "tfl.conv_2d"(%arg0, %[[VAL2]], %[[VAL3]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 13.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/common/quantization_lib/quantization.td
left as is for weight-only which means the weight is dequantized at runtime. For example, if the kernel does not support dynamic range quantization the graph will be converted into the following IR: %q_w = "tfl.pseudo_qconst"() { qtype = tensor<64x3x3x3x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>> %w = "tfl.dequantize"(%q_w) :
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/tf_tfl_translate_cl.cc
// going forward. // NOLINTNEXTLINE llvm::cl::list<std::string> custom_opdefs( "tf-custom-opdefs", llvm::cl::desc("List of custom opdefs when importing " "graphdef")); // Quantize and Dequantize ops pair can be optionally emitted before and after // the quantized model as the adaptors to receive and produce floating point // type data with the quantized model. Set this to `false` if the model input is
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue Mar 05 20:53:17 UTC 2024 - 7.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/experimental/tac/tests/get-alternative-subgraph.mlir
// CHECK-DAG: %[[VAL_8:.*]] = "tfl.pseudo_const"(){{.*}}dense<[384, 128]> : tensor<2xi32> // CHECK: %[[VAL_9:.*]] = "tfl.dequantize"(%[[VAL_0]]) {tac.device = "GPU", tac.inference_type = "FLOAT"} : (tensor<384x512x!quant.uniform<i8:f32, 1.000000e-01>>) -> tensor<384x512xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 20.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/transforms/passes.td
* A tensor is dequantized using a `func::FuncOp` whose name contains "uniform_dequantize". The first argument is the tensor to be quantized, the second argument is the zero point constant (element type: int) and the third argument is the inverse scale constant (element type: float). * Inputs to the target quantized op is quantized and the outputs are dequantized.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 21:59:06 UTC 2024 - 5.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/quantize_variables.cc
llvm::make_early_inc_range(var_handle_op.getResult().getUsers())) { auto read_variable_op = dyn_cast_or_null<ReadVariableOp>(var_handle_user); if (!read_variable_op) continue; // Add dequantize. builder.setInsertionPointAfter(read_variable_op); auto new_read_variable_op = builder.create<ReadVariableOp>(read_variable_op.getLoc(), ref_qtype,
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.5K bytes - Viewed (0)