wehub-resource-sync
93 lines
5.3 KiB
MLIR
93 lines
5.3 KiB
MLIR
// Copyright 2026 The TensorFlow Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// ==============================================================================
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// RUN: litert-opt %s -tfl-prepare-quantize-dynamic-range="enable-float16-quantization" -tfl-quantize="enable-dynamic-range-quantization=true" | FileCheck --check-prefix=CHECK %s
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// CHECK-LABEL: QuantizeUnidirectionalLstm
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func.func @QuantizeUnidirectionalLstm(%arg0: tensor<1x2x3xf32>) -> (tensor<1x2x3xf32>) {
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%1 = "tfl.pseudo_const"() {value = dense<[[0.1]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
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%2 = "tfl.pseudo_const"() {value = dense<[[0.2]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
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%3 = "tfl.pseudo_const"() {value = dense<[[0.3]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
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%4 = "tfl.pseudo_const"() {value = dense<[[0.4]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
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%5 = "tfl.pseudo_const"() {value = dense<[[0.5]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
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%6 = "tfl.pseudo_const"() {value = dense<[[0.6]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
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%7 = "tfl.pseudo_const"() {value = dense<[[0.7]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
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%8 = "tfl.pseudo_const"() {value = dense<[[0.8]]> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
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%9 = "tfl.no_value"() {value} : () -> none
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%10 = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
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%11 = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<3xf32>} : () -> tensor<3xf32>
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%recurrent_input = "tfl.pseudo_const"() {value = dense<0.000000e+00> : tensor<1x3xf32>} : () -> tensor<1x3xf32>
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%cell_input = "tfl.pseudo_const"() {value = dense<1.000000e+00> : tensor<1x3xf32>} : () -> tensor<1x3xf32>
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%16 = "tfl.unidirectional_sequence_lstm"(
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%arg0,
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%1, %2, %3, %4,
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%5, %6, %7, %8,
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%9, %9, %9,
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%10, %11,
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%10, %10,
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%9, %9,
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%recurrent_input, %cell_input,
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%9, %9, %9, %9) {
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cell_clip = 1.000000e+01 : f32,
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fused_activation_function = "TANH",
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proj_clip = 0.000000e+00 : f32,
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time_major = false} : (
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tensor<1x2x3xf32>,
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tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>,
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tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>,
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none, none, none,
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tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>,
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none, none,
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tensor<1x3xf32>, tensor<1x3xf32>,
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none, none, none, none) -> tensor<1x2x3xf32>
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%17 = "quantfork.stats"(%16) {layerStats = dense<[-0.1, 0.1]> : tensor<2xf32>} : (tensor<1x2x3xf32>) -> tensor<1x2x3xf32>
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func.return %17 : tensor<1x2x3xf32>
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// CHECK: %[[NONE:.*]] = "tfl.no_value"() <{value}> : () -> none
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// CHECK: %[[DQ_1:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
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// CHECK: %[[DQ_2:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
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// CHECK: %[[DQ_3:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
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// CHECK: %[[DQ_4:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
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// CHECK: %[[DQ_5:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
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// CHECK: %[[DQ_6:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
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// CHECK: %[[DQ_7:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
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// CHECK: %[[DQ_8:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x1xf16>) -> tensor<1x1xf32>
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// CHECK: %[[DQ_9:.*]] = "tfl.dequantize"({{.*}}) : (tensor<3xf16>) -> tensor<3xf32>
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// CHECK: %[[DQ_10:.*]] = "tfl.dequantize"({{.*}}) : (tensor<3xf16>) -> tensor<3xf32>
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// CHECK: %[[DQ_11:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x3xf16>) -> tensor<1x3xf32>
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// CHECK: %[[DQ_12:.*]] = "tfl.dequantize"({{.*}}) : (tensor<1x3xf16>) -> tensor<1x3xf32>
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// CHECK: %[[lstm:.*]] = "tfl.unidirectional_sequence_lstm"(
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// CHECK-SAME: %arg0,
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// CHECK-SAME: %[[DQ_1]], %[[DQ_2]], %[[DQ_3]], %[[DQ_4]],
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// CHECK-SAME: %[[DQ_5]], %[[DQ_6]], %[[DQ_7]], %[[DQ_8]],
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// CHECK-SAME: %[[NONE]], %[[NONE]], %[[NONE]],
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// CHECK-SAME: %[[DQ_9]], %[[DQ_10]], %[[DQ_9]], %[[DQ_9]],
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// CHECK-SAME: %[[NONE]], %[[NONE]],
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// CHECK-SAME: %[[DQ_11]], %[[DQ_12]],
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// CHECK-SAME: %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]]) <{
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// CHECK-SAME: cell_clip = 1.000000e+01 : f32,
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// CHECK-SAME: fused_activation_function = "TANH",
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// CHECK-SAME: proj_clip = 0.000000e+00 : f32,
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// CHECK-SAME: time_major = false}> : (
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// CHECK-SAME: tensor<1x2x3xf32>,
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// CHECK-SAME: tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>,
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// CHECK-SAME: tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>, tensor<1x1xf32>,
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// CHECK-SAME: none, none, none,
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// CHECK-SAME: tensor<3xf32>, tensor<3xf32>, tensor<3xf32>, tensor<3xf32>,
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// CHECK-SAME: none, none,
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// CHECK-SAME: tensor<1x3xf32>, tensor<1x3xf32>,
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// CHECK-SAME: none, none, none, none)
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// CHECK-SAME: -> tensor<1x2x3xf32>
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}
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