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chore: import upstream snapshot with attribution
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wehub-resource-sync
2026-07-13 12:14:16 +08:00
commit 8a852e4b4e
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tensorflow/compiler/mlir/lite/tests/end2end/BUILD
+45
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@@ -0,0 +1,45 @@
load("//tensorflow:tensorflow.default.bzl", "filegroup")
load("//tensorflow/compiler/mlir:glob_lit_test.bzl", "glob_lit_tests")
# copybara:uncomment package(default_applicable_licenses = ["//tensorflow:LICENSE"])
licenses(["notice"])
glob_lit_tests(
name = "all_tests",
data = [
":quant_stats_files",
":test_utilities",
],
driver = "@llvm-project//mlir:run_lit.sh",
size_override = {
"quant_stats.pbtxt": "medium",
},
tags_override = {
"add.pbtxt": ["no_rocm"],
"fake_quant_per_channel.pbtxt": ["no_rocm"],
},
test_file_exts = [
"pbtxt",
],
)
# Bundle together all of the test utilities that are used by tests.
filegroup(
name = "test_utilities",
testonly = True,
data = [
"//tensorflow/compiler/mlir/lite:flatbuffer_to_string",
"//tensorflow/compiler/mlir/lite:tf_tfl_translate",
"@llvm-project//llvm:FileCheck",
"@llvm-project//llvm:not",
],
)
# Bundle together all the quant stats files that are used by the tests.
filegroup(
name = "quant_stats_files",
srcs = glob(
["**/*.stats"],
),
)
tensorflow/compiler/mlir/lite/tests/end2end/add.pbtxt
+104
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@@ -0,0 +1,104 @@
# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 -tf-input-shapes=4:4 -tf-input-data-types=DT_INT32,DT_INT32 -tf-output-arrays=Add %s -o - | flatbuffer_to_string - | FileCheck %s
# Add two tensor<4xi32> inputs and return the result
node {
name: "Add"
op: "Add"
input: "input0"
input: "input1"
attr {
key: "T"
value {
type: DT_INT32
}
}
}
node {
name: "input0"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
}
node {
name: "input1"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
}
versions {
producer: 27
}
# CHECK: {
# CHECK-NEXT: version: 3,
# CHECK-NEXT: operator_codes: [ {
# CHECK-NEXT: version: 1
# CHECK-NEXT: } ],
# CHECK-NEXT: subgraphs: [ {
# CHECK-NEXT: tensors: [ {
# CHECK-NEXT: shape: [ 4 ],
# CHECK-NEXT: type: INT32,
# CHECK-NEXT: buffer: 1,
# CHECK-NEXT: name: "input0",
# CHECK-NEXT: quantization: {
# CHECK-EMPTY:
# CHECK-NEXT: },
# CHECK-NEXT: has_rank: true
# CHECK-NEXT: }, {
# CHECK-NEXT: shape: [ 4 ],
# CHECK-NEXT: type: INT32,
# CHECK-NEXT: buffer: 2,
# CHECK-NEXT: name: "input1",
# CHECK-NEXT: quantization: {
# CHECK-EMPTY:
# CHECK-NEXT: },
# CHECK-NEXT: has_rank: true
# CHECK-NEXT: }, {
# CHECK-NEXT: shape: [ 4 ],
# CHECK-NEXT: type: INT32,
# CHECK-NEXT: buffer: 3,
# CHECK-NEXT: name: "Add",
# CHECK-NEXT: quantization: {
# CHECK-EMPTY:
# CHECK-NEXT: },
# CHECK-NEXT: has_rank: true
# CHECK-NEXT: } ],
# CHECK-NEXT: inputs: [ 0, 1 ],
# CHECK-NEXT: outputs: [ 2 ],
# CHECK-NEXT: operators: [ {
# CHECK-NEXT: inputs: [ 0, 1 ],
# CHECK-NEXT: outputs: [ 2 ],
# CHECK-NEXT: builtin_options_type: AddOptions,
# CHECK-NEXT: builtin_options: {
# CHECK-EMPTY:
# CHECK-NEXT: }
# CHECK-NEXT: } ]
# CHECK-NEXT: name: "main"
# CHECK-NEXT: } ],
# CHECK-NEXT: description: "MLIR Converted.",
# CHECK-NEXT: buffers: [ {
# CHECK-EMPTY:
# CHECK-NEXT: }, {
# CHECK-EMPTY:
# CHECK-NEXT: }, {
# CHECK-EMPTY:
# CHECK-NEXT: }, {
# CHECK-EMPTY:
# CHECK-NEXT: }, {
# CHECK-NEXT: data: [ 49, 46, 53, 46, 48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
# CHECK-NEXT: } ],
# CHECK-NEXT: metadata: [ {
# CHECK-NEXT: name: "min_runtime_version",
# CHECK-NEXT: buffer: 4
# CHECK-NEXT: } ],
# CHECK-NEXT: signature_defs: [ ]
# CHECK-NEXT: }
tensorflow/compiler/mlir/lite/tests/end2end/control_flow_v1.pbtxt
+257
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@@ -0,0 +1,257 @@
# RUN: not tf_tfl_translate -tf-upgrade-legacy=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=1,2:1 -tf-output-arrays=cond/Merge -tf-enable-shape-inference-on-import=false -mlir-print-debuginfo -output-mlir %s -o - 2>&1 | FileCheck %s
# CHECK: error: The graph has Control Flow V1 ops. TFLite converter doesn't support Control Flow V1 ops. Consider using Control Flow V2 ops instead.
node {
name: "Const"
op: "Const"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_FLOAT
tensor_shape {
dim {
size: 2
}
dim {
size: 2
}
}
tensor_content: "\315\314\314=\315\314L>\232\231\231>\315\314\314>"
}
}
}
}
node {
name: "Placeholder"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "shape"
value {
shape {
dim {
size: -1
}
dim {
size: 2
}
}
}
}
}
node {
name: "Placeholder_1"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_BOOL
}
}
attr {
key: "shape"
value {
shape {
}
}
}
}
node {
name: "cond/Switch"
op: "Switch"
input: "Placeholder_1"
input: "Placeholder_1"
attr {
key: "T"
value {
type: DT_BOOL
}
}
}
node {
name: "cond/switch_t"
op: "Identity"
input: "cond/Switch:1"
attr {
key: "T"
value {
type: DT_BOOL
}
}
}
node {
name: "cond/switch_f"
op: "Identity"
input: "cond/Switch"
attr {
key: "T"
value {
type: DT_BOOL
}
}
}
node {
name: "cond/pred_id"
op: "Identity"
input: "Placeholder_1"
attr {
key: "T"
value {
type: DT_BOOL
}
}
}
node {
name: "cond/MatMul"
op: "MatMul"
input: "cond/MatMul/Switch:1"
input: "cond/MatMul/Switch_1:1"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "transpose_a"
value {
b: false
}
}
attr {
key: "transpose_b"
value {
b: false
}
}
}
node {
name: "cond/MatMul/Switch"
op: "Switch"
input: "Placeholder"
input: "cond/pred_id"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "_class"
value {
list {
s: "loc:@Placeholder"
}
}
}
}
node {
name: "cond/MatMul/Switch_1"
op: "Switch"
input: "Const"
input: "cond/pred_id"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "_class"
value {
list {
s: "loc:@Const"
}
}
}
}
node {
name: "cond/Add"
op: "Add"
input: "cond/Add/Switch"
input: "cond/Add/Switch_1"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
}
node {
name: "cond/Add/Switch"
op: "Switch"
input: "Placeholder"
input: "cond/pred_id"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "_class"
value {
list {
s: "loc:@Placeholder"
}
}
}
}
node {
name: "cond/Add/Switch_1"
op: "Switch"
input: "Const"
input: "cond/pred_id"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "_class"
value {
list {
s: "loc:@Const"
}
}
}
}
node {
name: "cond/Merge"
op: "Merge"
input: "cond/Add"
input: "cond/MatMul"
attr {
key: "N"
value {
i: 2
}
}
attr {
key: "T"
value {
type: DT_FLOAT
}
}
}
node {
name: "init"
op: "NoOp"
}
versions {
producer: 134
}
tensorflow/compiler/mlir/lite/tests/end2end/custom_opdef.pbtxt
+47
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@@ -0,0 +1,47 @@
# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 -tf-input-shapes=4:4 -tf-input-data-types=DT_INT32,DT_INT32 -tf-output-arrays=output %s -o - --output-mlir -tf-custom-opdefs="name: 'BannaPotatoSaladWithColeslaw' input_arg: { name: 'a' type: DT_INT32 } input_arg: { name: 'b' type: DT_INT32 } output_arg: { name: 'c' type: DT_INT32 }" | FileCheck %s
node {
name: "output"
op: "BannaPotatoSaladWithColeslaw"
input: "input0"
input: "input1"
attr {
key: "T"
value {
type: DT_INT32
}
}
}
node {
name: "input0"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
}
node {
name: "input1"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
}
versions {
producer: 27
}
# CHECK-LABEL: func @main(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>) -> tensor<*xi32>
# CHECK: attributes {tf.entry_function = {control_outputs = "", inputs = "input0,input1", outputs = "output"}} {
# CHECK-NEXT: %[[CUSTOM:.*]] = "tfl.custom_tf"(%arg0, %arg1) ({
# CHECK-NEXT: ^bb0(%arg2: tensor<4xi32>, %arg3: tensor<4xi32>):
# CHECK-NEXT: %[[OUTPUTS:.*]] = "tf.BannaPotatoSaladWithColeslaw"(%arg2, %arg3) {T = i32, device = ""} : (tensor<4xi32>, tensor<4xi32>) -> tensor<*xi32>
# CHECK-NEXT: "tfl.yield"(%[[OUTPUTS]]) : (tensor<*xi32>) -> ()
# CHECK-NEXT: }) {T = i32, device = ""} : (tensor<4xi32>, tensor<4xi32>) -> tensor<*xi32>
# CHECK-NEXT: return %[[CUSTOM]] : tensor<*xi32>
# CHECK-NEXT: }
tensorflow/compiler/mlir/lite/tests/end2end/disallow_stateful_partitioned_call.pbtxt
+195
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@@ -0,0 +1,195 @@
# RUN: not tf_tfl_translate -tf-input-arrays=input0 -tf-input-shapes=-1 -tf-input-data-types=DT_FLOAT -tf-output-arrays=add %s 2>&1 | FileCheck %s
# CHECK: error: The Graph contains unsupported `StatefulPartionedCallOp`(s)
node {
name: "input0"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
}
node {
name: "args_0"
op: "_Arg"
attr {
key: "T"
value {
type: DT_RESOURCE
}
}
attr {
key: "index"
value {
i: 0
}
}
}
node {
name: "spc1"
op: "StatefulPartitionedCall"
input: "input0"
input: "args_0"
attr {
key: "Tin"
value {
list {
type: DT_FLOAT
type: DT_RESOURCE
}
}
}
attr {
key: "Tout"
value {
list {
type: DT_FLOAT
type: DT_RESOURCE
}
}
}
attr {
key: "config"
value {
s: ""
}
}
attr {
key: "config_proto"
value {
s: ""
}
}
attr {
key: "executor_type"
value {
s: ""
}
}
attr {
key: "f"
value {
func {
name: "function"
}
}
}
}
node {
name: "spc2"
op: "StatefulPartitionedCall"
input: "input0"
input: "args_0"
attr {
key: "Tin"
value {
list {
type: DT_FLOAT
type: DT_RESOURCE
}
}
}
attr {
key: "Tout"
value {
list {
type: DT_FLOAT
type: DT_RESOURCE
}
}
}
attr {
key: "config"
value {
s: ""
}
}
attr {
key: "config_proto"
value {
s: ""
}
}
attr {
key: "executor_type"
value {
s: ""
}
}
attr {
key: "f"
value {
func {
name: "function"
}
}
}
}
node {
name: "add"
op: "Add"
input: "spc1"
input: "spc2"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
}
library {
function {
signature {
name: "function"
input_arg {
name: "inputs"
type: DT_FLOAT
}
input_arg {
name: "statefulpartitionedcall_args_1"
type: DT_RESOURCE
}
output_arg {
name: "identity"
type: DT_FLOAT
}
is_stateful: true
}
node_def {
name: "Identity"
op: "Identity"
input: "inputs"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
}
ret {
key: "identity"
value: "Identity:output:0"
}
arg_attr {
key: 0
value {
attr {
key: "_user_specified_name"
value {
s: "inputs"
}
}
}
}
arg_attr {
key: 1
value {
}
}
}
}
versions {
producer: 121
}
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_per_channel.pbtxt
+559
View File
@@ -0,0 +1,559 @@
# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=1,1,1,256 -tf-input-data-types=DT_FLOAT -tf-inference-type=DT_QINT8 -tf-input-min-values='-33.614346' -tf-input-max-values='21.54917' -tf-output-arrays=output %s -o - --output-mlir 2>&1 | FileCheck --check-prefix=MLIR %s
# TODO(fengliuai): Figure out what to do here. A "golden" end-to-end test is overly fragile.
# DISABLED_RUN: true || tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=1,1,1,256 -tf-input-data-types=DT_FLOAT -tf-inference-type=DT_QINT8 -tf-input-min-values='-33.614346' -tf-input-max-values='21.54917' -tf-output-arrays=output %s -o - | flatbuffer_to_string - | FileCheck %s
node {
name: "input"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "shape"
value {
shape {
dim {
size: 1
}
dim {
size: 1
}
dim {
size: 1
}
dim {
size: 256
}
}
}
}
}
node {
name: "BoxPredictor_4/ClassPredictor/weights"
op: "Const"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_FLOAT
tensor_shape {
dim {
size: 1
}
dim {
size: 1
}
dim {
size: 256
}
dim {
size: 186
}
}
float_val: 1.0
}
}
}
}
node {
name: "BoxPredictor_4/ClassPredictor/weights/read"
op: "Identity"
input: "BoxPredictor_4/ClassPredictor/weights"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "_class"
value {
list {
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}
}
}
node {
name: "BoxPredictor_4/ClassPredictor/weights_quant/min"
op: "Const"
attr {
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value {
type: DT_FLOAT
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attr {
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value {
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tensor_shape {
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node {
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attr {
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node {
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op: "Const"
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attr {
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tensor_shape {
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# MLIR-LABEL: func @main
# MLIR-SAME: (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME: control_outputs = ""
# MLIR-SAME: inputs = "input"
# MLIR-SAME: outputs = "output"
# MLIR: %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>
# MLIR: %[[bias:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x!quant.uniform<i32:f32:0
# MLIR: %[[weight:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x1x1x256x!quant.uniform<i8<-127:127>:f32:0, {0.12581039038230116,
# MLIR: %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}
# MLIR: %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)
# MLIR: return %[[reshape]] : tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR: }
# CHECK-LABEL: {
# CHECK: version: 3,
# CHECK: operator_codes: [ {
# CHECK: deprecated_builtin_code: 3,
# CHECK: version: 3,
# CHECK: builtin_code: CONV_2D
# CHECK: }, {
# CHECK: deprecated_builtin_code: 22,
# CHECK: version: 1
# CHECK: builtin_code: RESHAPE
# CHECK: } ],
# CHECK: subgraphs: [ {
# CHECK: tensors: [ {
# CHECK: shape: [ 1, 1, 1, 256 ],
# CHECK: type: INT8,
# CHECK: buffer: 1,
# CHECK: name: "input",
# CHECK: quantization: {
# CHECK: scale: [ 0.216328 ],
# CHECK: zero_point: [ 27 ]
# CHECK: }
# CHECK: }, {
# CHECK: shape: [ 3 ],
# CHECK: type: INT32,
# CHECK: buffer: 2,
# CHECK: name: "BoxPredictor_4/Reshape_1/shape",
# CHECK: quantization: {
# CHECK-EMPTY
# CHECK: }
# CHECK: }, {
# CHECK: shape: [ 186 ],
# CHECK: type: INT32,
# CHECK: buffer: 3,
# CHECK: name: "BoxPredictor_4/ClassPredictor/BiasAdd;BoxPredictor_4/ClassPredictor/Conv2D;BoxPredictor_4/ClassPredictor/biases",
# CHECK: quantization: {
# CHECK: scale: [ 0.027216, 0.00038, 0.000413, 0.000426, 0.001607,
# CHECK: zero_point: [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# CHECK: }
# CHECK: }, {
# CHECK: shape: [ 186, 1, 1, 256 ],
# CHECK: type: INT8,
# CHECK: buffer: 4,
# CHECK: name: "BoxPredictor_4/ClassPredictor/Conv2D;BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel",
# CHECK: quantization: {
# CHECK: scale: [ 0.12581, 0.001755, 0.001908, 0.001967, 0.007431,
# CHECK: zero_point: [ 0, 0, 0, 0, 0, 0, 0, 0, 0,
# CHECK: }
# CHECK: }, {
# CHECK: shape: [ 1, 1, 1, 186 ],
# CHECK: type: INT8,
# CHECK: buffer: 5,
# CHECK: name: "BoxPredictor_4/ClassPredictor/BiasAdd;BoxPredictor_4/ClassPredictor/Conv2D;BoxPredictor_4/ClassPredictor/biases1",
# CHECK: quantization: {
# CHECK: scale: [ 0.093635 ],
# CHECK: zero_point: [ 22 ]
# CHECK: }
# CHECK: }, {
# CHECK: shape: [ 1, 6, 31 ],
# CHECK: type: INT8,
# CHECK: buffer: 6,
# CHECK: name: "output",
# CHECK: quantization: {
# CHECK: scale: [ 0.093635 ],
# CHECK: zero_point: [ 22 ]
# CHECK: }
# CHECK: } ],
# CHECK: inputs: [ 0 ],
# CHECK: outputs: [ 5 ],
# CHECK: operators: [ {
# CHECK: inputs: [ 0, 3, 2 ],
# CHECK: outputs: [ 4 ],
# CHECK: builtin_options_type: Conv2DOptions,
# CHECK: builtin_options: {
# CHECK: stride_w: 1,
# CHECK: stride_h: 1
# CHECK: }
# CHECK: }, {
# CHECK: opcode_index: 1,
# CHECK: inputs: [ 4, 1 ],
# CHECK: outputs: [ 5 ]
# CHECK: } ],
# CHECK: name: "main"
# CHECK: } ],
# CHECK: description: "MLIR Converted.",
# CHECK: buffers: [ {
# CHECK-EMPTY
# CHECK: }, {
# CHECK-EMPTY
# CHECK: }, {
# CHECK: data: [ 1, 0, 0, 0, 255, 255, 255, 255, 31, 0, 0, 0 ]
# CHECK: }, {
# CHECK: data: [ 245, 255, 255, 255, 186, 254, 255, 255, 213, 254, 255, 255,
# CHECK: }, {
# CHECK: data: [ 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
# CHECK: }, {
# CHECK-EMPTY
# CHECK: }, {
# CHECK-EMPTY
# CHECK: } ]
# CHECK: }
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_per_channel_4bit.pbtxt
+559
View File
@@ -0,0 +1,559 @@
# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=1,1,1,256 -tf-input-data-types=DT_FLOAT -tf-inference-type=DT_QINT8 -tf-input-min-values='-33.614346' -tf-input-max-values='21.54917' -tf-output-arrays=output %s -o - --output-mlir 2>&1 | FileCheck --check-prefix=MLIR %s
# TODO(fengliuai): Figure out what to do here. A "golden" end-to-end test is overly fragile.
# DISABLED_RUN: true || tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=1,1,1,256 -tf-input-data-types=DT_FLOAT -tf-inference-type=DT_QINT8 -tf-input-min-values='-33.614346' -tf-input-max-values='21.54917' -tf-output-arrays=output %s -o - | flatbuffer_to_string - | FileCheck %s
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# MLIR-LABEL: func @main
# MLIR-SAME: (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME: control_outputs = ""
# MLIR-SAME: inputs = "input"
# MLIR-SAME: outputs = "output"
# MLIR: %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>
# MLIR: %[[bias:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x!quant.uniform<i32:f32:0
# MLIR: %[[weight:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x1x1x256x!quant.uniform<i4<-7:7>:f32:0, {2.2825599397931779,
# MLIR: %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}
# MLIR: %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)
# MLIR: return %[[reshape]] : tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR: }
# CHECK-LABEL: {
# CHECK: version: 3,
# CHECK: operator_codes: [ {
# CHECK: deprecated_builtin_code: 3,
# CHECK: version: 3,
# CHECK: builtin_code: CONV_2D
# CHECK: }, {
# CHECK: deprecated_builtin_code: 22,
# CHECK: version: 1
# CHECK: builtin_code: RESHAPE
# CHECK: } ],
# CHECK: subgraphs: [ {
# CHECK: tensors: [ {
# CHECK: shape: [ 1, 1, 1, 256 ],
# CHECK: type: INT8,
# CHECK: buffer: 1,
# CHECK: name: "input",
# CHECK: quantization: {
# CHECK: scale: [ 0.216328 ],
# CHECK: zero_point: [ 27 ]
# CHECK: }
# CHECK: }, {
# CHECK: shape: [ 3 ],
# CHECK: type: INT32,
# CHECK: buffer: 2,
# CHECK: name: "BoxPredictor_4/Reshape_1/shape",
# CHECK: quantization: {
# CHECK-EMPTY
# CHECK: }
# CHECK: }, {
# CHECK: shape: [ 186 ],
# CHECK: type: INT32,
# CHECK: buffer: 3,
# CHECK: name: "BoxPredictor_4/ClassPredictor/BiasAdd;BoxPredictor_4/ClassPredictor/Conv2D;BoxPredictor_4/ClassPredictor/biases",
# CHECK: quantization: {
# CHECK: scale: [ 0.027216, 0.00038, 0.000413, 0.000426, 0.001607,
# CHECK: zero_point: [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
# CHECK: }
# CHECK: }, {
# CHECK: shape: [ 186, 1, 1, 256 ],
# CHECK: type: INT8,
# CHECK: buffer: 4,
# CHECK: name: "BoxPredictor_4/ClassPredictor/Conv2D;BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVarsPerChannel",
# CHECK: quantization: {
# CHECK: scale: [ 0.12581, 0.001755, 0.001908, 0.001967, 0.007431,
# CHECK: zero_point: [ 0, 0, 0, 0, 0, 0, 0, 0, 0,
# CHECK: }
# CHECK: }, {
# CHECK: shape: [ 1, 1, 1, 186 ],
# CHECK: type: INT8,
# CHECK: buffer: 5,
# CHECK: name: "BoxPredictor_4/ClassPredictor/BiasAdd;BoxPredictor_4/ClassPredictor/Conv2D;BoxPredictor_4/ClassPredictor/biases1",
# CHECK: quantization: {
# CHECK: scale: [ 0.093635 ],
# CHECK: zero_point: [ 22 ]
# CHECK: }
# CHECK: }, {
# CHECK: shape: [ 1, 6, 31 ],
# CHECK: type: INT8,
# CHECK: buffer: 6,
# CHECK: name: "output",
# CHECK: quantization: {
# CHECK: scale: [ 0.093635 ],
# CHECK: zero_point: [ 22 ]
# CHECK: }
# CHECK: } ],
# CHECK: inputs: [ 0 ],
# CHECK: outputs: [ 5 ],
# CHECK: operators: [ {
# CHECK: inputs: [ 0, 3, 2 ],
# CHECK: outputs: [ 4 ],
# CHECK: builtin_options_type: Conv2DOptions,
# CHECK: builtin_options: {
# CHECK: stride_w: 1,
# CHECK: stride_h: 1
# CHECK: }
# CHECK: }, {
# CHECK: opcode_index: 1,
# CHECK: inputs: [ 4, 1 ],
# CHECK: outputs: [ 5 ]
# CHECK: } ],
# CHECK: name: "main"
# CHECK: } ],
# CHECK: description: "MLIR Converted.",
# CHECK: buffers: [ {
# CHECK-EMPTY
# CHECK: }, {
# CHECK-EMPTY
# CHECK: }, {
# CHECK: data: [ 1, 0, 0, 0, 255, 255, 255, 255, 31, 0, 0, 0 ]
# CHECK: }, {
# CHECK: data: [ 245, 255, 255, 255, 186, 254, 255, 255, 213, 254, 255, 255,
# CHECK: }, {
# CHECK: data: [ 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
# CHECK: }, {
# CHECK-EMPTY
# CHECK: }, {
# CHECK-EMPTY
# CHECK: } ]
# CHECK: }
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_without_identity.pbtxt
+418
View File
@@ -0,0 +1,418 @@
# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=1,1,1,256 -tf-input-data-types=DT_FLOAT -tf-inference-type=DT_QINT8 -tf-input-min-values='-33.614346' -tf-input-max-values='21.54917' -tf-output-arrays=output %s -o - --output-mlir 2>&1 | FileCheck --check-prefix=MLIR %s
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# MLIR-LABEL: func @main
# MLIR-SAME: (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME: control_outputs = ""
# MLIR-SAME: inputs = "input"
# MLIR-SAME: outputs = "output"
# MLIR: %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>
# MLIR: %[[bias:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x!quant.uniform<i32:f32:0
# MLIR: %[[weight:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x1x1x256x!quant.uniform<i8<-127:127>:f32:0, {0.12581039038230116,
# MLIR: %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}
# MLIR: %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)
# MLIR: return %[[reshape]] : tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR: }
tensorflow/compiler/mlir/lite/tests/end2end/fake_quant_without_identity_4bit.pbtxt
+418
View File
@@ -0,0 +1,418 @@
# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=1,1,1,256 -tf-input-data-types=DT_FLOAT -tf-inference-type=DT_QINT8 -tf-input-min-values='-33.614346' -tf-input-max-values='21.54917' -tf-output-arrays=output %s -o - --output-mlir 2>&1 | FileCheck --check-prefix=MLIR %s
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}
}
}
node {
name: "BoxPredictor_4/ClassPredictor/biases/read"
op: "Identity"
input: "BoxPredictor_4/ClassPredictor/biases"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "_class"
value {
list {
s: "loc:@BoxPredictor_4/ClassPredictor/biases"
}
}
}
}
node {
name: "BoxPredictor_4/ClassPredictor/BiasAdd"
op: "BiasAdd"
input: "BoxPredictor_4/ClassPredictor/Conv2D"
input: "BoxPredictor_4/ClassPredictor/biases/read"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "data_format"
value {
s: "NHWC"
}
}
}
node {
name: "BoxPredictor_4/ClassPredictor/act_quant/min"
op: "Const"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_FLOAT
tensor_shape {
}
float_val: -14.066669
}
}
}
}
node {
name: "BoxPredictor_4/ClassPredictor/act_quant/min/read"
op: "Identity"
input: "BoxPredictor_4/ClassPredictor/act_quant/min"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "_class"
value {
list {
s: "loc:@BoxPredictor_4/ClassPredictor/act_quant/min"
}
}
}
}
node {
name: "BoxPredictor_4/ClassPredictor/act_quant/max"
op: "Const"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_FLOAT
tensor_shape {
}
float_val: 9.810242
}
}
}
}
node {
name: "BoxPredictor_4/ClassPredictor/act_quant/max/read"
op: "Identity"
input: "BoxPredictor_4/ClassPredictor/act_quant/max"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "_class"
value {
list {
s: "loc:@BoxPredictor_4/ClassPredictor/act_quant/max"
}
}
}
}
node {
name: "BoxPredictor_4/ClassPredictor/act_quant/FakeQuantWithMinMaxVars"
op: "FakeQuantWithMinMaxVars"
input: "BoxPredictor_4/ClassPredictor/BiasAdd"
input: "BoxPredictor_4/ClassPredictor/act_quant/min/read"
input: "BoxPredictor_4/ClassPredictor/act_quant/max/read"
attr {
key: "narrow_range"
value {
b: false
}
}
attr {
key: "num_bits"
value {
i: 8
}
}
}
node {
name: "BoxPredictor_4/Reshape_1/shape"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
dim {
size: 3
}
}
tensor_content: "\001\000\000\000\377\377\377\377\037\000\000\000"
}
}
}
}
node {
name: "output"
op: "Reshape"
input: "BoxPredictor_4/ClassPredictor/act_quant/FakeQuantWithMinMaxVars"
input: "BoxPredictor_4/Reshape_1/shape"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "Tshape"
value {
type: DT_INT32
}
}
}
# MLIR-LABEL: func @main
# MLIR-SAME: (%[[ARG_0:[a-z0-9]+]]: tensor<1x1x1x256x!quant.uniform<i8:f32, 0.21632751372549019:27>>) -> tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR-SAME: control_outputs = ""
# MLIR-SAME: inputs = "input"
# MLIR-SAME: outputs = "output"
# MLIR: %[[shape:.*]] = arith.constant dense<[1, -1, 31]> : tensor<3xi32>
# MLIR: %[[bias:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x!quant.uniform<i32:f32:0
# MLIR: %[[weight:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<186x1x1x256x!quant.uniform<i4<-7:7>:f32:0, {2.2825599397931779,
# MLIR: %[[conv:.*]] = "tfl.conv_2d"(%[[ARG_0]], %[[weight]], %[[bias]]) <{dilation_h_factor = 1 : i32, dilation_w_factor = 1 : i32, fused_activation_function = "NONE", padding = "SAME", stride_h = 1 : i32, stride_w = 1 : i32}
# MLIR: %[[reshape:.*]] = "tfl.reshape"(%[[conv]], %[[shape]]) : (tensor<1x1x1x186x!quant.uniform<i8:f32, 0.09363494573854933:22>>, tensor<3xi32>)
# MLIR: return %[[reshape]] : tensor<1x6x31x!quant.uniform<i8:f32, 0.09363494573854933:22>>
# MLIR: }
tensorflow/compiler/mlir/lite/tests/end2end/graph-input-node.pbtxt
+56
View File
@@ -0,0 +1,56 @@
# RUN: tf_tfl_translate -tf-input-arrays=input -tf-input-shapes=4 -tf-input-data-types=DT_INT32 -tf-output-arrays=output %s -o - --output-mlir | FileCheck %s
node {
name: "default"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
}
int_val: 0
}
}
}
}
node {
name: "input"
op: "Identity"
input: "default"
attr {
key: "T"
value {
type: DT_INT32
}
}
}
node {
name: "output"
op: "Identity"
input: "input"
attr {
key: "T"
value {
type: DT_INT32
}
}
}
versions {
producer: 27
}
# CHECK-LABEL: func @main
# CHECK-SAME: (%[[ARG_0:[a-z0-9]+]]: tensor<4xi32>) -> tensor<4xi32>
# CHECK-SAME: control_outputs = ""
# CHECK-SAME: inputs = "input"
# CHECK-SAME: outputs = "output"
# CHECK-NEXT: return %[[ARG_0]] : tensor<4xi32>
# CHECK-NEXT: }
tensorflow/compiler/mlir/lite/tests/end2end/graph_with_placeholder_with_default.pbtxt
+148
View File
@@ -0,0 +1,148 @@
# RUN: tf_tfl_translate -tf-input-arrays=unranked -tf-input-shapes=1,8,8,2 -tf-input-data-types=DT_INT32 -tf-output-arrays=unranked,static,static_10 %s -o - --output-mlir | FileCheck %s
node {
name: "tf.Const"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
dim {
size: 10
}
}
int_val: 0
}
}
}
experimental_debug_info {
}
}
node {
name: "tf.Const1"
op: "Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_INT32
tensor_shape {
}
int_val: 0
}
}
}
experimental_debug_info {
}
}
node {
name: "unranked"
op: "PlaceholderWithDefault"
input: "tf.Const1"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "shape"
value {
shape {
unknown_rank: true
}
}
}
attr {
key: "type"
value {
type: DT_INT32
}
}
experimental_debug_info {
}
}
node {
name: "static"
op: "PlaceholderWithDefault"
input: "tf.Const1"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "shape"
value {
shape {
}
}
}
attr {
key: "type"
value {
type: DT_INT32
}
}
experimental_debug_info {
}
}
node {
name: "static_10"
op: "PlaceholderWithDefault"
input: "tf.Const"
attr {
key: "dtype"
value {
type: DT_INT32
}
}
attr {
key: "shape"
value {
shape {
dim {
size: 10
}
}
}
}
attr {
key: "type"
value {
type: DT_INT32
}
}
experimental_debug_info {
}
}
library {
}
versions {
producer: 198
}
# CHECK-LABEL: func @main
# CHECK-SAME: ([[VAL_0:%.*]]: tensor<1x8x8x2xi32>) -> (tensor<1x8x8x2xi32>, tensor<i32>, tensor<10xi32>)
# CHECK-SAME: control_outputs = ""
# CHECK-SAME: inputs = "unranked"
# CHECK-SAME: outputs = "unranked,static,static_10"
# CHECK-DAG: [[VAL_2:%.*]] = arith.constant dense<0> : tensor<10xi32>
# CHECK-DAG: [[VAL_1:%.*]] = arith.constant dense<0> : tensor<i32>
# CHECK: return [[VAL_0]], [[VAL_1]], [[VAL_2]] : tensor<1x8x8x2xi32>, tensor<i32>, tensor<10xi32>
# CHECK: }
tensorflow/compiler/mlir/lite/tests/end2end/if_op.pbtxt
+421
View File
@@ -0,0 +1,421 @@
# RUN: tf_tfl_translate -tf-input-arrays=a,b -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-input-shapes=: -tf-output-arrays=StatefulIf,StatelessIf %s -o - --output-mlir | FileCheck %s
node {
name: "tf.Less"
op: "Less"
input: "a"
input: "b"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
experimental_debug_info {
}
}
node {
name: "my_equal"
op: "Equal"
input: "a"
input: "b"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
experimental_debug_info {
}
}
node {
name: "cst0"
op: "Const"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_FLOAT
tensor_shape {
dim {
size: 4
}
}
float_val: 1.0
float_val: 2.0
float_val: 3.0
float_val: 4.0
}
}
}
}
node {
name: "cst1"
op: "Const"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "value"
value {
tensor {
dtype: DT_FLOAT
tensor_shape {
dim {
size: 4
}
}
float_val: 5.0
float_val: 6.0
float_val: 7.0
float_val: 8.0
}
}
}
}
node {
name: "StatefulIf"
op: "If"
input: "tf.Less"
input: "a"
input: "b"
input: "cst0"
input: "cst1"
attr {
key: "Tcond"
value {
type: DT_BOOL
}
}
attr {
key: "Tin"
value {
list {
type: DT_FLOAT
type: DT_FLOAT
type: DT_FLOAT
type: DT_FLOAT
}
}
}
attr {
key: "Tout"
value {
list {
type: DT_FLOAT
}
}
}
attr {
key: "else_branch"
value {
func {
name: "cond_false"
}
}
}
attr {
key: "then_branch"
value {
func {
name: "cond_true"
}
}
}
experimental_debug_info {
}
}
node {
name: "StatelessIf"
op: "StatelessIf"
input: "my_equal"
input: "a"
input: "b"
attr {
key: "Tcond"
value {
type: DT_BOOL
}
}
attr {
key: "Tin"
value {
list {
type: DT_FLOAT
type: DT_FLOAT
}
}
}
attr {
key: "Tout"
value {
list {
type: DT_FLOAT
}
}
}
attr {
key: "else_branch"
value {
func {
name: "cond_false_1"
}
}
}
attr {
key: "then_branch"
value {
func {
name: "cond_true_1"
}
}
}
experimental_debug_info {
}
}
node {
name: "main"
op: "_Retval"
input: "StatefulIf"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "index"
value {
i: 0
}
}
}
node {
name: "main1"
op: "_Retval"
input: "StatelessIf"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "index"
value {
i: 1
}
}
}
node {
name: "a"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
experimental_debug_info {
}
}
node {
name: "b"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
experimental_debug_info {
}
}
library {
function {
signature {
name: "cond_true"
input_arg {
name: "cond_true_arg0"
type: DT_FLOAT
}
input_arg {
name: "cond_true_arg1"
type: DT_FLOAT
}
input_arg {
name: "cond_true_arg2"
type: DT_FLOAT
}
input_arg {
name: "cond_true_arg3"
type: DT_FLOAT
}
output_arg {
name: "cond_true_ret"
type: DT_FLOAT
}
}
node_def {
name: "tf.Add"
op: "Add"
input: "cond_true_arg2"
input: "cond_true_arg3"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
experimental_debug_info {
original_node_names: "tf.Add"
}
}
ret {
key: "cond_true_ret"
value: "tf.Add:z:0"
}
}
function {
signature {
name: "cond_false"
input_arg {
name: "cond_false_arg0"
type: DT_FLOAT
}
input_arg {
name: "cond_false_arg1"
type: DT_FLOAT
}
input_arg {
name: "cond_false_arg2"
type: DT_FLOAT
}
input_arg {
name: "cond_false_arg3"
type: DT_FLOAT
}
output_arg {
name: "cond_false_ret"
type: DT_FLOAT
}
}
node_def {
name: "tf.Mul"
op: "Mul"
input: "cond_false_arg0"
input: "cond_false_arg3"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
experimental_debug_info {
original_node_names: "tf.Mul"
}
}
ret {
key: "cond_false_ret"
value: "tf.Mul:z:0"
}
}
function {
signature {
name: "cond_true_1"
input_arg {
name: "cond_true_arg0"
type: DT_FLOAT
}
input_arg {
name: "cond_true_arg1"
type: DT_FLOAT
}
output_arg {
name: "cond_true_ret"
type: DT_FLOAT
}
}
node_def {
name: "tf.Sub"
op: "Sub"
input: "cond_true_arg0"
input: "cond_true_arg1"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
experimental_debug_info {
original_node_names: "tf.Sub"
}
}
ret {
key: "cond_true_ret"
value: "tf.Sub:z:0"
}
}
function {
signature {
name: "cond_false_1"
input_arg {
name: "cond_false_arg0"
type: DT_FLOAT
}
input_arg {
name: "cond_false_arg1"
type: DT_FLOAT
}
output_arg {
name: "cond_false_ret"
type: DT_FLOAT
}
}
node_def {
name: "tf.Div"
op: "Div"
input: "cond_false_arg0"
input: "cond_false_arg1"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
experimental_debug_info {
original_node_names: "tf.Div"
}
}
ret {
key: "cond_false_ret"
value: "tf.Div:z:0"
}
}
}
versions {
producer: 115
min_consumer: 12
}
# CHECK: func @main
# CHECK-DAG: constant dense<[1.000000e+00, 2.000000e+00, 3.000000e+00, 4.000000e+00]>
# CHECK-DAG: constant dense<[5.000000e+00, 6.000000e+00, 7.000000e+00, 8.000000e+00]>
# CHECK: "tf.If"{{.+}}else_branch = @cond_false_10{{.+}}is_stateless = true{{.+}}then_branch = @cond_true_10
# CHECK: "tf.If"{{.+}}else_branch = @cond_false0{{.+}}is_stateless = false{{.+}}then_branch = @cond_true0
# CHECK: func private @cond_false_10
# CHECK-NEXT: tfl.div
# CHECK: func private @cond_true_10
# CHECK-NEXT: tfl.sub
# CHECK: func private @cond_false0
# CHECK-NEXT: tfl.mul
# CHECK: func private @cond_true0
# CHECK-NEXT: tfl.add
tensorflow/compiler/mlir/lite/tests/end2end/quant_stats.pbtxt
+124
View File
@@ -0,0 +1,124 @@
# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 \
# RUN: -tf-input-shapes=4:4 \
# RUN: -tf-input-data-types=DT_FLOAT,DT_FLOAT \
# RUN: -tf-output-arrays=Add \
# RUN: -tf-inference-type=DT_QUINT8 \
# RUN: -tf-input-min-values='-2,-3' \
# RUN: -tf-input-max-values='2,3' \
# RUN: --quant-stats=%s.stats \
# RUN: %s -o - --output-mlir 2>&1 \
# RUN: | FileCheck --check-prefix=MLIR %s
# RUN: tf_tfl_translate -tf-input-arrays=input0,input1 \
# RUN: -tf-input-shapes=4:4 \
# RUN: -tf-input-data-types=DT_FLOAT,DT_FLOAT \
# RUN: -tf-output-arrays=Add \
# RUN: -tf-inference-type=DT_QUINT8 \
# RUN: -tf-input-min-values='-2,-3' \
# RUN: -tf-input-max-values='2,3' \
# RUN: --quant-stats=%s.stats \
# RUN: %s -o - \
# RUN: | flatbuffer_to_string - \
# RUN: | FileCheck %s
node {
name: "Add"
op: "Add"
input: "input0"
input: "input1"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
}
node {
name: "input0"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
}
node {
name: "input1"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
}
versions {
producer: 27
}
# MLIR-LABEL: func @main(%arg0: tensor<4x!quant.uniform<u8:f32, 0.015686274509803921:128>>, %arg1: tensor<4x!quant.uniform<u8:f32, 0.023529411764705882:128>>) -> tensor<4x!quant.uniform<u8:f32, 0.0078431372549019607:128>>
# MLIR-SAME: attributes {tf.entry_function = {control_outputs = "", inputs = "input0,input1", outputs = "Add"}} {
# MLIR-NEXT: %[[add:.*]] = tfl.add(%arg0, %arg1) <{fused_activation_function = "NONE"}> : (tensor<4x!quant.uniform<u8:f32, 0.015686274509803921:128>>, tensor<4x!quant.uniform<u8:f32, 0.023529411764705882:128>>) -> tensor<4x!quant.uniform<u8:f32, 0.0078431372549019607:128>>
# MLIR-NEXT: return %[[add]] : tensor<4x!quant.uniform<u8:f32, 0.0078431372549019607:128>>
# MLIR-NEXT: }
# CHECK-LABEL: {
# CHECK-NEXT: version: 3,
# CHECK-NEXT: operator_codes: [ {
# CHECK-NEXT: version: 1
# CHECK-NEXT: } ],
# CHECK-NEXT: subgraphs: [ {
# CHECK-NEXT: tensors: [ {
# CHECK-NEXT: shape: [ 4 ],
# CHECK-NEXT: type: UINT8,
# CHECK-NEXT: buffer: 1,
# CHECK-NEXT: name: "input0",
# CHECK-NEXT: quantization: {
# CHECK-NEXT: scale: [ 0.015686 ],
# CHECK-NEXT: zero_point: [ 128 ]
# CHECK-NEXT: },
# CHECK-NEXT: has_rank: true
# CHECK-NEXT: }, {
# CHECK-NEXT: shape: [ 4 ],
# CHECK-NEXT: type: UINT8,
# CHECK-NEXT: buffer: 2,
# CHECK-NEXT: name: "input1",
# CHECK-NEXT: quantization: {
# CHECK-NEXT: scale: [ 0.023529 ],
# CHECK-NEXT: zero_point: [ 128 ]
# CHECK-NEXT: },
# CHECK-NEXT: has_rank: true
# CHECK-NEXT: }, {
# CHECK-NEXT: shape: [ 4 ],
# CHECK-NEXT: type: UINT8,
# CHECK-NEXT: buffer: 3,
# CHECK-NEXT: name: "Add",
# CHECK-NEXT: quantization: {
# CHECK-NEXT: scale: [ 0.007843 ],
# CHECK-NEXT: zero_point: [ 128 ]
# CHECK-NEXT: },
# CHECK-NEXT: has_rank: true
# CHECK-NEXT: } ],
# CHECK-NEXT: inputs: [ 0, 1 ],
# CHECK-NEXT: outputs: [ 2 ],
# CHECK-NEXT: operators: [ {
# CHECK-NEXT: inputs: [ 0, 1 ],
# CHECK-NEXT: outputs: [ 2 ],
# CHECK-NEXT: builtin_options_type: AddOptions,
# CHECK-NEXT: builtin_options: {
# CHECK-EMPTY:
# CHECK-NEXT: }
# CHECK-NEXT: } ],
# CHECK-NEXT: name: "main"
# CHECK-NEXT: } ],
# CHECK-NEXT: description: "MLIR Converted.",
# CHECK-NEXT: buffers: [ {
# CHECK-EMPTY:
# CHECK-NEXT: }, {
# CHECK-EMPTY:
# CHECK-NEXT: }, {
# CHECK-EMPTY:
# CHECK-NEXT: }, {
# CHECK: data: [ 49, 46, 53, 46, 48, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 ]
# CHECK-NEXT: } ]
tensorflow/compiler/mlir/lite/tests/end2end/quant_stats.pbtxt.stats
+9
View File
@@ -0,0 +1,9 @@
entries {
name: "Add"
params {
min_max {
min: -1
max: 1
}
}
}
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul.pbtxt
+90
View File
@@ -0,0 +1,90 @@
# RUN: tf_tfl_translate -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -unfold_batchmatmul=true -output-mlir %s -o - 2>&1 | FileCheck %s
node {
name: "Placeholder"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "shape"
value {
shape {
dim {
size: 2
}
dim {
size: 5
}
dim {
size: 3
}
}
}
}
}
node {
name: "Placeholder_1"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "shape"
value {
shape {
dim {
size: 3
}
dim {
size: 7
}
}
}
}
}
node {
name: "MatMul"
op: "BatchMatMulV2"
input: "Placeholder"
input: "Placeholder_1"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "adj_x"
value {
b: false
}
}
attr {
key: "adj_y"
value {
b: false
}
}
}
versions {
producer: 175
}
# CHECK: func @main(%[[VAL_0:.*]]: tensor<2x5x3xf32>, %[[VAL_1:.*]]: tensor<3x7xf32>) -> tensor<2x5x7xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "Placeholder,Placeholder_1", outputs = "MatMul"}} {
# CHECK-DAG: %[[VAL_2:.*]] = arith.constant dense<[1, 0]> : tensor<2xi32>
# CHECK-DAG: %[[VAL_3:.*]] = "tfl.no_value"() <{value}> : () -> none
# CHECK-DAG: %[[VAL_6:.*]] = arith.constant dense<0> : tensor<i32>
# CHECK: %[[VAL_7:.*]]:2 = "tfl.split"(%[[VAL_6]], %[[VAL_0]]) <{num_splits = 2 : i32}> : (tensor<i32>, tensor<2x5x3xf32>) -> (tensor<1x5x3xf32>, tensor<1x5x3xf32>)
# CHECK: %[[VAL_9:.*]] = "tfl.transpose"(%[[VAL_1]], %[[VAL_2]]) : (tensor<3x7xf32>, tensor<2xi32>) -> tensor<7x3xf32>
# CHECK: %[[VAL_10:.*]] = "tfl.fully_connected"(%[[VAL_7]]#0, %[[VAL_9]], %[[VAL_3]]) <{fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"}> : (tensor<1x5x3xf32>, tensor<7x3xf32>, none) -> tensor<5x7xf32>
# CHECK: %[[VAL_11:.*]] = "tfl.fully_connected"(%[[VAL_7]]#1, %[[VAL_9]], %[[VAL_3]]) <{fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"}> : (tensor<1x5x3xf32>, tensor<7x3xf32>, none) -> tensor<5x7xf32>
# CHECK: %[[VAL_12:.*]] = "tfl.pack"(%[[VAL_10]], %[[VAL_11]]) <{axis = 0 : i32, values_count = 2 : i32}> : (tensor<5x7xf32>, tensor<5x7xf32>) -> tensor<2x5x7xf32>
# CHECK: return %[[VAL_12]] : tensor<2x5x7xf32>
# CHECK: }
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul_disabled.pbtxt
+83
View File
@@ -0,0 +1,83 @@
# RUN: tf_tfl_translate -unfold_batchmatmul=false -tf-input-arrays=Placeholder,Placeholder_1 -tf-input-shapes=2,5,3:3,7 -tf-input-data-types=DT_FLOAT,DT_FLOAT -tf-output-arrays=MatMul -output-mlir %s -o - 2>&1 | FileCheck %s
node {
name: "Placeholder"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "shape"
value {
shape {
dim {
size: 2
}
dim {
size: 5
}
dim {
size: 3
}
}
}
}
}
node {
name: "Placeholder_1"
op: "Placeholder"
attr {
key: "dtype"
value {
type: DT_FLOAT
}
}
attr {
key: "shape"
value {
shape {
dim {
size: 3
}
dim {
size: 7
}
}
}
}
}
node {
name: "MatMul"
op: "BatchMatMulV2"
input: "Placeholder"
input: "Placeholder_1"
attr {
key: "T"
value {
type: DT_FLOAT
}
}
attr {
key: "adj_x"
value {
b: false
}
}
attr {
key: "adj_y"
value {
b: false
}
}
}
versions {
producer: 175
}
# CHECK: func @main(%[[VAL_0:.*]]: tensor<2x5x3xf32>, %[[VAL_1:.*]]: tensor<3x7xf32>) -> tensor<2x5x7xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "Placeholder,Placeholder_1", outputs = "MatMul"}} {
# CHECK: %[[VAL_2:.*]] = "tfl.batch_matmul"(%[[VAL_0]], %[[VAL_1]]) <{adj_x = false, adj_y = false}> : (tensor<2x5x3xf32>, tensor<3x7xf32>) -> tensor<2x5x7xf32>
# CHECK: return %[[VAL_2]] : tensor<2x5x7xf32>
# CHECK: }