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tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize/quantize.mlir
return %7 : tensor<1x3xf32> } // Test that the inputs and output of the tf.XlaCallModule op has been replaced // by quantized types, and the corresponding quantfork.dcast ops that turned // those quantized types back to float types are removed. // CHECK: %[[CONST_0:.+]] = stablehlo.constant dense<1.000000e+00> : tensor<4x3xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 18 01:38:40 UTC 2024 - 6.3K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/quantize.mlir
// RUN: tf-quant-opt %s -split-input-file -quant-lift-quantizable-spots-as-functions -quant-quantize -verify-each=false | FileCheck %s func.func private @conv(%input: tensor<1x3x4x3xf32> {tf._user_specified_name = "input_tensor"}) -> tensor<*xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf_type.shape<1x3x4x3>]} { %weight = arith.constant dense_resource<__elided__> : tensor<2x3x3x2xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed May 08 19:32:28 UTC 2024 - 6.4K bytes - Viewed (0) -
tensorflow/compiler/aot/quantize.h
Jake Harmon <******@****.***> 1694027275 -0700
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Sep 06 19:12:29 UTC 2023 - 1.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/passes/quantize.cc
patterns.add<StableHloQuantization, StableHloQuantizationReverse>(&ctx); PopulateCommonQuantizationPatterns(ctx, patterns, enable_per_channel_quantized_weight_); // Quantize all quantizable ops, including ops that are not compute-heavy. PopulateAllQuantizablePatterns(ctx, patterns); if (failed(applyPatternsAndFoldGreedily(module_op, std::move(patterns)))) {
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Apr 22 07:08:19 UTC 2024 - 5K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/quantize-dynamic-range-float16.mlir
// CHECK: %[[DQ_1:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32> // CHECK: %[[DQ_2:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32> // CHECK: %[[DQ_3:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32> // CHECK: %[[DQ_4:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32> // CHECK: %[[DQ_5:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 4.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/stablehlo/tests/tf-tfl-translate-tf-quantize.mlir
A. Unique TensorFlower <******@****.***> 1713119208 -0700
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sun Apr 14 18:33:43 UTC 2024 - 1.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/utils/fake_quant_utils.h
// dequantize ops, and insert them between the tf.FakeQuantWithMinMaxVarsOp // and its users. Value value = tf_op.getOutputs(); auto quantize = rewriter.create<TFL::QuantizeOp>( tf_op.getLoc(), qtype.getValue(), value, qtype); auto dequantize = rewriter.create<TFL::DequantizeOp>( tf_op.getLoc(), res_type, quantize.getOutput());
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 6.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/default_quant_params.mlir
// CHECK: %[[q0:.*]] = "tfl.quantize"(%arg1) <{qtype = tensor<2x1x!quant.uniform<u8:f32, 0.0078431372549019607:128>>}> // CHECK: %[[q1:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<2x2x!quant.uniform<u8:f32, 1.000000e+00:128>>}> // CHECK: %[[add:.*]] = tfl.add(%[[q1]], %[[q0]]) <{fused_activation_function = "NONE"}> : (tensor<2x2x!quant.uniform<u8:f32, 1.000000e+00:128>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 8.8K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/quantization/tensorflow/tf_to_quant.cc
} }; // Inserts a "tfl.quantize" and "tfl.dequantize" op pair (QDQs) after the // "tf.FakeQuantWithMinMaxVarsOp" to be constant folded. Since the constant // folding logic will use a "arith.constant" op to replace the // "tf.FakeQuantWithMinMaxVarsOp", the "tfl.quantize" op is used to preserve // the quantization parameters as a TypeAttr and "tfl.dequantize" op used to
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Apr 25 16:01:03 UTC 2024 - 8.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/transforms/quantize_patterns.td
include "tensorflow/compiler/mlir/lite/ir/tfl_ops.td" // Quantize attribute $0 by using quantization parameter from %1. def QuantizeByQuantizedType : NativeCodeCall<"quant::Quantize($0, $1.getValue())">; def F32ElementsAttr : ElementsAttrBase< CPred<"$_self.cast<ElementsAttr>().getShapedType().getElementType().isF32()">, "float constant tensor">; // Squash tfl.dequantize and tfl.quantize pairs.
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 28 23:10:13 UTC 2024 - 2.3K bytes - Viewed (0)