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/external/tensorflow/tensorflow/compiler/mlir/tensorflow/tests/
Dlayout_optimization_to_nhwc.mlir13 // it to NCHW before padding, and does all computations in NCHW (this is the
19 // Padding in spatial dimension (NCHW)
22 // Reduce over spatial dimensions (NCHW)
25 // Transpose input: NHWC -> NCHW
48 data_format = "NCHW",
63 data_format = "NCHW",
75 data_format = "NCHW",
91 data_format = "NCHW",
104 data_format = "NCHW",
118 data_format = "NCHW",
[all …]
Dlayout_optimization_layout_assignment_gpu_cc_70.mlir9 // cuDNN prefers NCHW data format for spatial convolutions.
11 // CHECK-SAME: data_format = "NCHW"
30 data_format = "NCHW",
38 // CHECK-SAME: data_format = "NCHW"
41 data_format = "NCHW",
57 data_format = "NCHW",
74 // CHECK-SAME: data_format = "NCHW"
97 data_format = "NCHW",
114 // CHECK-SAME: data_format = "NCHW"
137 data_format = "NCHW",
[all …]
Dlayout_optimization_to_nchw.mlir1 // RUN: tf-opt %s -tf-layout-optimization=force-data-format=NCHW -verify-diagnostics | FileCheck %s…
6 // Convert input: NCHW -> NHWC
19 // Convert result back: NHWC -> NCHW
23 // Check that Conv2D computed in NCHW format, and all redundant transpose
27 // CHECK-SAME: data_format = "NCHW"
Dlayout_optimization_layout_assignment_gpu_cc_60.mlir9 // cuDNN prefers NCHW data format for spatial convolutions in f16 before
13 // CHECK-SAME: data_format = "NCHW"
33 // CHECK-SAME: data_format = "NCHW"
53 // CHECK-SAME: data_format = "NCHW"
73 // CHECK-SAME: data_format = "NCHW"
98 // CHECK-SAME: data_format = "NCHW"
124 // CHECK-SAME: data_format = "NCHW"
150 // CHECK-SAME: data_format = "NCHW"
Dlayout_optimization_layout_assignment_to_nchw.mlir1 // RUN: tf-opt %s -tf-layout-assignment=force-data-format=NCHW -verify-diagnostics | FileCheck %s -…
14 // CHECK-SAME: data_format = "NCHW"
45 // CHECK-SAME: data_format = "NCHW"
77 // CHECK-SAME: dst_format = "NCHW"
86 // CHECK-SAME: data_format = "NCHW"
117 // CHECK-SAME: dst_format = "NCHW"
125 // CHECK-SAME: data_format = "NCHW"
162 // CHECK-SAME: data_format = "NCHW"
202 // CHECK-SAME: data_format = "NCHW"
Dlayout_optimization_move_transposes_end.mlir56 // must use NCHW data format with updated kernel size and strides.
59 …// CHECK: %[[MAX_POOL:[0-9]*]] = "tf.MaxPool"(%arg0) {data_format = "NCHW", ksize = [1, 1, 3, 3], …
63 // Transpose NCHW -> NHWC
92 // Transpose NCHW -> NHWC
107 // CHECK: "tf.FusedBatchNormV3"(%arg0, {{.*}} {data_format = "NCHW"
111 // Transpose NCHW -> NHWC
/external/tensorflow/tensorflow/core/ops/compat/ops_history_v2/
DAvgPool.pbtxt42 s: "NCHW"
99 s: "NCHW"
156 s: "NCHW"
213 s: "NCHW"
DBiasAddGrad.pbtxt42 s: "NCHW"
90 s: "NCHW"
139 s: "NCHW"
188 s: "NCHW"
DBiasAdd.pbtxt46 s: "NCHW"
98 s: "NCHW"
151 s: "NCHW"
204 s: "NCHW"
DAvgPoolGrad.pbtxt46 s: "NCHW"
107 s: "NCHW"
168 s: "NCHW"
229 s: "NCHW"
DMaxPool.pbtxt55 s: "NCHW"
121 s: "NCHW"
188 s: "NCHW"
257 s: "NCHW"
335 s: "NCHW"
DConv2D.pbtxt55 s: "NCHW"
115 s: "NCHW"
188 s: "NCHW"
270 s: "NCHW"
353 s: "NCHW"
DMaxPoolGrad.pbtxt50 s: "NCHW"
117 s: "NCHW"
191 s: "NCHW"
267 s: "NCHW"
344 s: "NCHW"
430 s: "NCHW"
DConv2DBackpropInput.pbtxt59 s: "NCHW"
123 s: "NCHW"
200 s: "NCHW"
286 s: "NCHW"
373 s: "NCHW"
DDepthwiseConv2dNativeBackpropInput.pbtxt95 s: "NCHW"
152 s: "NCHW"
222 s: "NCHW"
301 s: "NCHW"
DMaxPoolGradV2.pbtxt46 s: "NCHW"
116 s: "NCHW"
188 s: "NCHW"
261 s: "NCHW"
/external/tensorflow/tensorflow/core/ops/compat/ops_history_v1/
DAvgPool.pbtxt42 s: "NCHW"
99 s: "NCHW"
156 s: "NCHW"
213 s: "NCHW"
DBiasAddGrad.pbtxt42 s: "NCHW"
90 s: "NCHW"
139 s: "NCHW"
188 s: "NCHW"
DAvgPoolGrad.pbtxt46 s: "NCHW"
107 s: "NCHW"
168 s: "NCHW"
229 s: "NCHW"
DBiasAdd.pbtxt46 s: "NCHW"
98 s: "NCHW"
151 s: "NCHW"
204 s: "NCHW"
DConv2D.pbtxt55 s: "NCHW"
115 s: "NCHW"
188 s: "NCHW"
270 s: "NCHW"
353 s: "NCHW"
DConv2DBackpropInput.pbtxt59 s: "NCHW"
123 s: "NCHW"
200 s: "NCHW"
286 s: "NCHW"
373 s: "NCHW"
DMaxPool.pbtxt55 s: "NCHW"
121 s: "NCHW"
188 s: "NCHW"
257 s: "NCHW"
DMaxPoolGrad.pbtxt50 s: "NCHW"
117 s: "NCHW"
191 s: "NCHW"
267 s: "NCHW"
344 s: "NCHW"
DMaxPoolGradV2.pbtxt46 s: "NCHW"
116 s: "NCHW"
188 s: "NCHW"
261 s: "NCHW"

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