wehub-resource-sync
113 lines
12 KiB
MLIR
113 lines
12 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 -tfl-quantize="enable-dynamic-range-quantization=true" -tfl-post-quantize | FileCheck %s
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// RUN: litert-opt %s -tfl-prepare-quantize-dynamic-range="enable-custom-op-quantization=CustomTestOp=1" -tfl-quantize="enable-dynamic-range-quantization=true enable-custom-op-weight-only=CustomTestOp=false" -tfl-post-quantize="enable-no-side-effect=CustomTestOp=false" | FileCheck --check-prefix=NotPrune %s
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// RUN: litert-opt %s -tfl-prepare-quantize-dynamic-range="enable-custom-op-quantization=CustomTestOp=1" -tfl-quantize="enable-dynamic-range-quantization=true enable-custom-op-weight-only=CustomTestOp=false" -tfl-post-quantize="enable-no-side-effect=CustomTestOp=true" | FileCheck --check-prefix=NoSideEffect %s
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// RUN: litert-opt %s -tfl-prepare-quantize-dynamic-range="enable-custom-op-quantization=CustomTestOp=1" -tfl-quantize="enable-dynamic-range-quantization=true enable-custom-op-weight-only=CustomTestOp=true" -tfl-post-quantize="enable-no-side-effect=CustomTestOp=true" | FileCheck --check-prefix=NoSideEffectWeightOnly %s
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// CHECK-LABEL: PruneUnusedCustomOp
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func.func @PruneUnusedCustomOp(%arg0: tensor<1x1x1x1xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input", outputs = "custom_op"}} {
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%q_w = "tfl.pseudo_qconst"() {qtype = tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<1024x1x1x1xi8>} : () -> tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
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%dq_w = "tfl.dequantize"(%q_w) : (tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<1024x1x1x1xf32>
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%custom_1 = "tfl.custom"(%arg0, %dq_w) {custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<1024x1x1x1xf32>) -> tensor<*xf32>
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%custom_2 = "tfl.custom"(%arg0, %dq_w) {custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<1024x1x1x1xf32>) -> tensor<*xf32>
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%custom_3 = "tfl.custom"(%arg0, %dq_w) {custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<1024x1x1x1xf32>) -> tensor<*xf32>
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func.return %custom_3 : tensor<*xf32>
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// CHECK: %[[q_w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<1024x1x1x1xi8>}> : () -> tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
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// CHECK: %[[dq_w:.*]] = "tfl.dequantize"(%[[q_w:.*]]) : (tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<1024x1x1x1xf32>
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// CHECK: %[[custom_3:.*]] = "tfl.custom"(%arg0, %[[dq_w:.*]]) <{custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">}> : (tensor<1x1x1x1xf32>, tensor<1024x1x1x1xf32>) -> tensor<*xf32>
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// CHECK: return %[[custom_3:.*]]
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}
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// CHECK-LABEL: NotPruneUnusedCustomOp
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func.func @NotPruneUnusedCustomOp(%arg0: tensor<1x1x1x1xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input", outputs = "custom_op"}} {
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%q_w = "tfl.pseudo_qconst"() {qtype = tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<1024x1x1x1xi8>} : () -> tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
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%dq_w = "tfl.dequantize"(%q_w) : (tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<1024x1x1x1xf32>
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%custom_1 = "tfl.custom"(%arg0, %dq_w) {custom_code = "CustomTestOp2", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<1024x1x1x1xf32>) -> tensor<*xf32>
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%custom_2 = "tfl.custom"(%arg0, %dq_w) {custom_code = "CustomTestOp2", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<1024x1x1x1xf32>) -> tensor<*xf32>
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%custom_3 = "tfl.custom"(%arg0, %dq_w) {custom_code = "CustomTestOp2", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<1024x1x1x1xf32>) -> tensor<*xf32>
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func.return %custom_3 : tensor<*xf32>
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// CHECK: %[[q_w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<1024x1x1x1xi8>}> : () -> tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
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// CHECK: %[[dq_w:.*]] = "tfl.dequantize"(%[[q_w:.*]]) : (tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<1024x1x1x1xf32>
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// CHECK: %[[custom_1:.*]] = "tfl.custom"(%arg0, %[[dq_w:.*]]) <{custom_code = "CustomTestOp2", custom_option = #tfl<const_bytes : "0x">}> : (tensor<1x1x1x1xf32>, tensor<1024x1x1x1xf32>) -> tensor<*xf32>
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// CHECK: %[[custom_2:.*]] = "tfl.custom"(%arg0, %[[dq_w:.*]]) <{custom_code = "CustomTestOp2", custom_option = #tfl<const_bytes : "0x">}> : (tensor<1x1x1x1xf32>, tensor<1024x1x1x1xf32>) -> tensor<*xf32>
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// CHECK: %[[custom_3:.*]] = "tfl.custom"(%arg0, %[[dq_w:.*]]) <{custom_code = "CustomTestOp2", custom_option = #tfl<const_bytes : "0x">}> : (tensor<1x1x1x1xf32>, tensor<1024x1x1x1xf32>) -> tensor<*xf32>
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// CHECK: return %[[custom_3:.*]]
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}
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// CHECK-LABEL: PruneQuantizedCustomOp
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// NotPrune-LABEL: PruneQuantizedCustomOp
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// NoSideEffect-LABEL: PruneQuantizedCustomOp
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// NoSideEffectWeightOnly-LABEL: PruneQuantizedCustomOp
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func.func @PruneQuantizedCustomOp(%arg0: tensor<1x1x1x1xf32>) -> tensor<*xf32> attributes {tf.entry_function = {inputs = "input", outputs = "custom_op"}} {
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%0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>} : (tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32>
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%w = arith.constant dense<127.0> : tensor<1024x1x1x1xf32>
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%custom = "tfl.custom"(%0, %w) {custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<1024x1x1x1xf32>) -> tensor<*xf32>
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func.return %custom : tensor<*xf32>
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// CHECK: %[[w:.*]] = arith.constant dense<1.270000e+02> : tensor<1024x1x1x1xf32>
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// CHECK: %[[custom:.*]] = "tfl.custom"(%arg0, %[[w:.*]]) <{custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">}>
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// CHECK: return %[[custom:.*]]
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// NotPrune: %[[w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
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// NotPrune: %[[dq_w:.*]] = "tfl.dequantize"(%[[w:.*]]) : (tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<1024x1x1x1xf32>
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// NotPrune: %[[custom:.*]] = "tfl.custom"(%arg0, %[[dq_w:.*]]) <{custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">}>
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// NotPrune: %[[custom_1:.*]] = "tfl.custom"(%arg0, %[[w:.*]]) <{custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">}>
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// NotPrune: %[[custom_2:.*]] = "tfl.custom"(%arg0, %[[w:.*]]) <{custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">}>
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// NoSideEffect: %[[q_w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
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// NoSideEffect: %[[custom:.*]] = "tfl.custom"(%arg0, %[[q_w:.*]]) <{custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">}
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// NoSideEffect: return %[[custom:.*]]
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// NoSideEffectWeightOnly: %[[q_w:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
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// NoSideEffectWeightOnly: %[[dq_w:.*]] = "tfl.dequantize"(%[[q_w:.*]]) : (tensor<1024x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<1024x1x1x1xf32>
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// NoSideEffectWeightOnly: %[[custom:.*]] = "tfl.custom"(%arg0, %[[dq_w:.*]]) <{custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">}>
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// NoSideEffectWeightOnly: return %[[custom:.*]]
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}
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// CHECK-LABEL: QuantizeCustomOp
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// CustomOp-LABEL: QuantizeCustomOp
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func.func @QuantizeCustomOp(%arg0: tensor<1x1x1x1xf32>) -> (tensor<*xf32>, tensor<*xf32>, tensor<*xf32>) attributes {tf.entry_function = {inputs = "input", outputs = "custom_op"}} {
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%0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 2.550000e+02]> : tensor<2xf32>} : (tensor<1x1x1x1xf32>) -> tensor<1x1x1x1xf32>
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%w_1 = arith.constant dense<127.0> : tensor<4096x1x1x1xf32>
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%w_2 = arith.constant dense<127.0> : tensor<128x1x1x1xf32>
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%b = arith.constant dense<127.0> : tensor<2048x1x1x1xf32>
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%custom_1 = "tfl.custom"(%0, %w_1, %w_2, %b) {custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<4096x1x1x1xf32>, tensor<128x1x1x1xf32>, tensor<2048x1x1x1xf32>) -> tensor<*xf32>
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%custom_2 = "tfl.custom"(%0, %w_1, %w_2, %b) {custom_code = "CustomTestOp2", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<4096x1x1x1xf32>, tensor<128x1x1x1xf32>, tensor<2048x1x1x1xf32>) -> tensor<*xf32>
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%custom_3 = "tfl.custom"(%0, %w_1, %w_2, %b) {custom_code = "CustomTestOp3", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<4096x1x1x1xf32>, tensor<128x1x1x1xf32>, tensor<2048x1x1x1xf32>) -> tensor<*xf32>
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func.return %custom_1, %custom_2, %custom_3 : tensor<*xf32>, tensor<*xf32>, tensor<*xf32>
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// CHECK: %[[w_1:.*]] = arith.constant dense<1.270000e+02> : tensor<4096x1x1x1xf32>
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// CHECK: %[[w_2:.*]] = arith.constant dense<1.270000e+02> : tensor<128x1x1x1xf32>
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// CHECK: %[[b:.*]] = arith.constant dense<1.270000e+02> : tensor<2048x1x1x1xf32>
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// CHECK: %[[custom_1:.*]] = "tfl.custom"(%arg0, %[[w_1]], %[[w_2]], %[[b]]) <{custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">}> : (tensor<1x1x1x1xf32>, tensor<4096x1x1x1xf32>, tensor<128x1x1x1xf32>, tensor<2048x1x1x1xf32>) -> tensor<*xf32>
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// CHECK: %[[custom_2:.*]] = "tfl.custom"(%arg0, %[[w_1]], %[[w_2]], %[[b]]) <{custom_code = "CustomTestOp2", custom_option = #tfl<const_bytes : "0x">}> : (tensor<1x1x1x1xf32>, tensor<4096x1x1x1xf32>, tensor<128x1x1x1xf32>, tensor<2048x1x1x1xf32>) -> tensor<*xf32>
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// CHECK: %[[custom_3:.*]] = "tfl.custom"(%arg0, %[[w_1]], %[[w_2]], %[[b]]) <{custom_code = "CustomTestOp3", custom_option = #tfl<const_bytes : "0x">}> : (tensor<1x1x1x1xf32>, tensor<4096x1x1x1xf32>, tensor<128x1x1x1xf32>, tensor<2048x1x1x1xf32>) -> tensor<*xf32>
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// CHECK: return %[[custom_1:.*]], %[[custom_2:.*]], %[[custom_3:.*]]
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// CustomOpWeightOnly: %[[w_1:.*]] = arith.constant dense<1.270000e+02> : tensor<4096x1x1x1xf32>
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// CustomOpWeightOnly: %[[q_w1:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<4096x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
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// CustomOpWeightOnly: %[[dq_w1:.*]] = "tfl.dequantize"(%[[q_w1]]) : (tensor<4096x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<4096x1x1x1xf32>
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// CustomOpWeightOnly: %[[w_2:.*]] = arith.constant dense<1.270000e+02> : tensor<128x1x1x1xf32>
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// CustomOpWeightOnly: %[[q_b:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<2048x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
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// CustomOpWeightOnly: %[[dq_b:.*]] = "tfl.dequantize"(%[[q_b]]) : (tensor<2048x1x1x1x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<2048x1x1x1xf32>
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// CustomOpWeightOnly: %[[custom_1:.*]] = "tfl.custom"(%arg0, %[[dq_w1]], %[[w_2]], %[[dq_b]]) {custom_code = "CustomTestOp", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<4096x1x1x1xf32>, tensor<128x1x1x1xf32>, tensor<2048x1x1x1xf32>) -> tensor<*xf32>
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// CustomOpWeightOnly: %[[custom_2:.*]] = "tfl.custom"(%arg0, %[[w_1]], %[[w_2]], %[[b]]) {custom_code = "CustomTestOp2", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<4096x1x1x1xf32>, tensor<128x1x1x1xf32>, tensor<2048x1x1x1xf32>) -> tensor<*xf32>
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// CustomOpWeightOnly: %[[custom_3:.*]] = "tfl.custom"(%arg0, %[[w_1]], %[[w_2]], %[[q_b]]) {custom_code = "CustomTestOp3", custom_option = #tfl<const_bytes : "0x">} : (tensor<1x1x1x1xf32>, tensor<4096x1x1x1xf32>, tensor<128x1x1x1xf32>, tensor<2048x1x1x1xf32>) -> tensor<*xf32>
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// CustomOpWeightOnly: return %[[custom_1:.*]], %[[custom_2:.*]], %[[custom_3:.*]]
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}
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