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/external/pytorch/functorch/op_analysis/
Dannotated_ops39 as_strided, view/reshape
44 atleast_1d, view/reshape
45 atleast_2d, view/reshape
46 atleast_3d, view/reshape
62 broadcast_tensors, view/reshape
63 broadcast_to, view/reshape
64 cat, view/reshape
65 block_diag, view/reshape
68 unsafe_chunk, view/reshape
69 chunk, view/reshape
[all …]
/external/tensorflow/tensorflow/compiler/xla/service/
Ddynamic_update_slice_test.cc108 reshape.23 = s32[1]{0} reshape(slice.18) in XLA_TEST_F()
109 reshape.4 = s32[4]{0} reshape(dynamic-slice) in XLA_TEST_F()
110 slice.19 = s32[3]{0} slice(reshape.4), slice={[1:4]} in XLA_TEST_F()
112 concatenate.1 = s32[5]{0} concatenate(reshape.23, slice.19, constant.6), dimensions={0} in XLA_TEST_F()
120 reshape.24 = s32[] reshape(slice.18) in XLA_TEST_F()
121 slice.26 = s32[1]{0} slice(reshape.4), slice={[1:2]} in XLA_TEST_F()
122 reshape.10 = s32[] reshape(slice.26) in XLA_TEST_F()
123 slice.27 = s32[1]{0} slice(reshape.4), slice={[2:3]} in XLA_TEST_F()
124 reshape.11 = s32[] reshape(slice.27) in XLA_TEST_F()
125 slice.28 = s32[1]{0} slice(reshape.4), slice={[3:4]} in XLA_TEST_F()
[all …]
Dreshape_mover.cc21 // %reshape.A = NewShape reshape(%param.A)
22 // %reshape.B = NewShape reshape(%param.B)
23 // %instruction = NewShape instruction(%reshape.A, %reshape.B)
28 // %reshape = NewShape reshape(%instruction)
57 // NOTE: Technically a sequence of reshape(reshape(constant)) is also in CanTriviallyChangeShape()
61 // But it's not that simple. E.g. reshape(reshape(rng)) is only trivially in CanTriviallyChangeShape()
63 // reshape(scalar) isn't trivial at all if the reshape itself isn't scalar. in CanTriviallyChangeShape()
71 // A constant can trivially reshape the literal it holds. in CanTriviallyChangeShape()
93 // Returns true iff `instruction` is a reshape/transpose instruction for which
101 // Finds the first operand of an instruction that is a non-trivial reshape or
[all …]
Dreshape_mover_test.cc58 op::Add(op::Reshape(param0), op::Reshape(param1))); in TEST_F()
63 op::Add(op::Reshape(param0), op::Reshape(param1))); in TEST_F()
76 // Verifies that the reshape is not moved, since rng0 is trivially reshapable
101 op::Add(op::Reshape(rng0), const1));
106 op::Add(op::Reshape(rng0), const1));
127 op::Add(op::Reshape(param0), op::Reshape(param1))); in TEST_F()
133 op::Add(op::Reshape(op::Parameter()), op::Reshape(op::Parameter()))); in TEST_F()
154 op::Add(op::Reshape(param0), op::Reshape(param1))); in TEST_F()
158 op::Reshape(op::Add(param0, param1))); in TEST_F()
213 op::Reshape(op::Select(op::Reshape(const0), param1, param2)));
[all …]
Ddynamic_dimension_simplifier.cc92 // Reshape(Broadcast(A, []->[1]), [1]->[]) ==> A
93 StatusOr<bool> ReshapeBroadcastForwarding(HloInstruction* reshape) { in ReshapeBroadcastForwarding() argument
94 if (reshape->opcode() != HloOpcode::kReshape) { in ReshapeBroadcastForwarding()
97 auto broadcast = reshape->mutable_operand(0); in ReshapeBroadcastForwarding()
102 if (reshape->shape().rank() != 0) { in ReshapeBroadcastForwarding()
115 reshape->ReplaceAllUsesWith(broadcast->mutable_operand(0))); in ReshapeBroadcastForwarding()
120 // Reshape(Reshape(A, []->[1]), [1]->[]) ==> A
121 StatusOr<bool> ReshapeReshapeForwarding(HloInstruction* reshape) { in ReshapeReshapeForwarding() argument
122 if (reshape->opcode() != HloOpcode::kReshape) { in ReshapeReshapeForwarding()
125 auto reshape_2 = reshape->mutable_operand(0); in ReshapeReshapeForwarding()
[all …]
Dreshape_decomposer.cc28 Status HandleReshape(HloInstruction* reshape) override { in HandleReshape() argument
29 HloInstruction* operand = reshape->mutable_operand(0); in HandleReshape()
30 auto s = reshape->shape(); in HandleReshape()
38 VLOG(3) << "Decomposing reshape into reshape-bitcast and a physical " in HandleReshape()
43 TF_RETURN_IF_ERROR(ReplaceInstruction(reshape, r)); in HandleReshape()
51 VLOG(3) << "Decomposing reshape into reshape-bitcast and a physical " in HandleReshape()
54 TF_RETURN_IF_ERROR(ReplaceInstruction(reshape, copied_result)); in HandleReshape()
56 VLOG(3) << "Both input and output of reshape are not alignable, create " in HandleReshape()
60 auto c1 = MakeCopyHlo(reshape->mutable_operand(0), s0_normalized); in HandleReshape()
66 TF_RETURN_IF_ERROR(ReplaceInstruction(reshape, c2)); in HandleReshape()
Dbatch_dot_simplification_test.cc48 op::Reshape(op::Dot( in TEST_F()
49 op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)), in TEST_F()
72 op::Reshape(op::Dot( in TEST_F()
73 op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)), in TEST_F()
96 op::Reshape(op::Dot( in TEST_F()
97 op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)), in TEST_F()
120 op::Reshape(op::Dot( in TEST_F()
121 op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)), in TEST_F()
144 op::Reshape(op::Dot( in TEST_F()
145 op::Reshape(op::Parameter(0)), op::Reshape(op::Parameter(1)), in TEST_F()
[all …]
Dspace_to_batch_converter_test.cc57 auto reshape = root->operand(0)->operand(0); in TEST_F() local
58 EXPECT_THAT(reshape, op::Reshape()); in TEST_F()
59 auto previous_reshape = reshape->operand(0); in TEST_F()
60 EXPECT_THAT(previous_reshape, op::Reshape()); in TEST_F()
94 auto reshape = root->operand(0)->operand(0); in TEST_F() local
95 EXPECT_THAT(reshape, op::Reshape()); in TEST_F()
96 auto previous_reshape = reshape->operand(0); in TEST_F()
97 EXPECT_THAT(previous_reshape, op::Reshape()); in TEST_F()
200 auto reshape = root->operand(0)->operand(0); in TEST_F() local
201 EXPECT_THAT(reshape, op::Reshape()); in TEST_F()
[all …]
/external/tensorflow/tensorflow/compiler/xla/tests/
Dptxas_bug_120501638.cc41 reshape.2 = f32[2,5,2]{2,1,0} reshape(arg0.1) in TEST_F()
43 pad.4 = f32[2,6,2]{2,1,0} pad(reshape.2, constant.3), padding=0_0x0_1x0_0 in TEST_F()
44 reshape.5 = f32[2,3,2,2]{3,2,1,0} reshape(pad.4) in TEST_F()
45 transpose.6 = f32[2,2,3,2]{3,0,2,1} transpose(reshape.5), dimensions={2,0,1,3} in TEST_F()
46 reshape.7 = f32[4,3,2]{2,1,0} reshape(transpose.6) in TEST_F()
47 reshape.8 = f32[4,1,3,2]{3,2,1,0} reshape(reshape.7) in TEST_F()
48 transpose.9 = f32[4,2,1,3]{1,3,2,0} transpose(reshape.8), dimensions={0,3,1,2} in TEST_F()
60 reshape.24 = f32[4,3,2]{2,1,0} reshape(transpose.23) in TEST_F()
61 reshape.25 = f32[2,2,3,2]{3,2,1,0} reshape(reshape.24) in TEST_F()
62 transpose.26 = f32[2,3,2,2]{3,1,0,2} transpose(reshape.25), dimensions={1,2,0,3} in TEST_F()
[all …]
/external/tensorflow/tensorflow/python/kernel_tests/array_ops/
Dreshape_op_test.py35 np_ans = x.reshape(y)
36 tf_ans = array_ops.reshape(x, y)
43 tf_ans = array_ops.reshape(x, y64)
50 y = array_ops.reshape(x, shape)
56 y = array_ops.reshape(x, shape64)
65 x = np.arange(1., 7.).reshape([1, 6]) > 3
69 x = np.arange(1., 7.).reshape([1, 6]).astype(np.float32)
73 x = np.arange(1., 7.).reshape([1, 6]).astype(np.float64)
77 x = np.arange(1., 7.).reshape([1, 6]).astype(np.int32)
81 x = np.arange(1., 7.).reshape([1, 6]).astype(np.complex64)
[all …]
Dweights_broadcast_test.py29 return np.reshape(np.cumsum(np.ones(shape), dtype=np.int32), newshape=shape)
58 weights=np.asarray((5,)).reshape((1, 1, 1)),
64 weights=np.asarray((5, 7, 11, 3)).reshape((1, 1, 4)),
70 weights=np.asarray((5, 11)).reshape((1, 2, 1)),
76 weights=np.asarray((5, 7, 11, 3, 2, 13, 7, 5)).reshape((1, 2, 4)),
82 weights=np.asarray((5, 7, 11)).reshape((3, 1, 1)),
89 5, 7, 11, 3, 2, 12, 7, 5, 2, 17, 11, 3)).reshape((3, 1, 4)),
97 2, 17, 11, 3, 5, 7, 11, 3, 2, 12, 7, 5)).reshape((3, 2, 4)),
122 weights=np.asarray((5,)).reshape((1, 1)),
128 weights=np.asarray((5, 7, 11, 3, 2, 12)).reshape((3, 2)),
[all …]
/external/tensorflow/tensorflow/compiler/xla/service/spmd/
Dcanonicalize_all_gather_for_cse_test.cc59 resh = s32[1,8]{1,0} reshape(param0) in TEST_F()
66 const HloInstruction* const reshape = in TEST_F() local
68 EXPECT_THAT(reshape, in TEST_F()
69 AllOf(op::Reshape(op::AllGather(_)), op::Shape("s32[2,8]"))); in TEST_F()
78 resh = s32[1,8]{1,0} reshape(param0) in TEST_F()
79 resh2 = s32[1,8,1,1]{3,2,1,0} reshape(resh) in TEST_F()
86 const HloInstruction* const reshape = in TEST_F() local
88 EXPECT_THAT(reshape, op::Reshape(op::AllGather(op::Parameter()))); in TEST_F()
97 resh = s32[8,1,1]{2,1,0} reshape(param0) in TEST_F()
98 resh2 = s32[1,8,1,1]{3,2,1,0} reshape(resh) in TEST_F()
[all …]
/external/tensorflow/tensorflow/compiler/mlir/xla/tests/translate/
Dfully_connected_reference_model.hlotxt14 // CHECK-NEXT: %[[VAL_2:.*]] = mhlo.reshape %[[VAL_0]] : (tensor<1x300xf32>) -> tensor<1x300xf32>
15 %reshape.3 = f32[1,300] reshape(%arg0.1)
18 %transpose.27 = f32[300,1] transpose(%reshape.3), dimensions={1,0}
20 …// CHECK-NEXT: %[[VAL_4:.*]] = mhlo.reshape %[[VAL_3]] : (tensor<300x1xf32>) -> tensor<300x1x1xf32>
21 %reshape.28 = f32[300,1,1] reshape(%transpose.27)
23 …// CHECK-NEXT: %[[VAL_5:.*]] = mhlo.reshape %[[VAL_4]] : (tensor<300x1x1xf32>) -> tensor<300x1xf32>
24 %reshape.29 = f32[300,1] reshape(%reshape.28)
27 %broadcast.30 = f32[300,1,5] broadcast(%reshape.29), dimensions={0,1}
62 …// CHECK-NEXT: %[[VAL_18:.*]] = mhlo.reshape %[[VAL_17]] : (tensor<1x300x3x1xf32>) -> tensor<1x300…
63 %reshape.4 = f32[1,300,3,1] reshape(%copy.1)
[all …]
/external/tensorflow/tensorflow/python/compiler/tensorrt/test/
Dreshape_transpose_test.py32 # reshape with scalar input will be filtered out of the segment before
38 incompatible_reshape = array_ops.reshape(inp, shape)
39 reshape_back = array_ops.reshape(incompatible_reshape, orig_shape)
43 compatible_reshape = array_ops.reshape(
44 inp, [-1, 24 * 24, 2], name="reshape-0")
45 compatible_reshape = array_ops.reshape(
46 compatible_reshape, [100, 24, -1], name="reshape-1")
47 compatible_reshape = array_ops.reshape(
48 compatible_reshape, [100, 24 * 2, 24], name="reshape-2")
49 compatible_reshape = array_ops.reshape(
[all …]
Dbiasadd_matmul_test.py49 x2 = gen_array_ops.reshape(x2, [4, -1])
58 x4 = gen_array_ops.reshape(x4, [4, -1])
66 # TODO(b/154672994): Put the reshape back when the bug is fixed.
67 # x5 = gen_array_ops.reshape(x5, [4, -1])
69 x6 = gen_array_ops.reshape(x, [4, 24, 6])
72 x6 = gen_array_ops.reshape(x6, [4, -1])
74 x7 = gen_array_ops.reshape(x, [4, 12, 4, 3])
77 x7 = gen_array_ops.reshape(x7, [4, -1])
79 x8 = gen_array_ops.reshape(x, [4, 4, 3, 2, 6])
82 x8 = gen_array_ops.reshape(x8, [4, -1])
[all …]
/external/tensorflow/tensorflow/core/kernels/
Dbatch_norm_op.h46 output.reshape(rest_by_depth).device(d) = in operator()
47 (input.reshape(rest_by_depth) - in operator()
48 mean.reshape(one_by_depth).broadcast(rest_by_one)) * in operator()
51 .reshape(one_by_depth) in operator()
53 beta.reshape(one_by_depth).broadcast(rest_by_one); in operator()
55 output.reshape(rest_by_depth).device(d) = in operator()
56 (input.reshape(rest_by_depth) - in operator()
57 mean.reshape(one_by_depth).broadcast(rest_by_one)) * in operator()
60 .reshape(one_by_depth) in operator()
62 beta.reshape(one_by_depth).broadcast(rest_by_one); in operator()
[all …]
Dreshape_op.cc21 REGISTER_KERNEL_BUILDER(Name("Reshape")
26 REGISTER_KERNEL_BUILDER(Name("Reshape")
33 REGISTER_KERNEL_BUILDER(Name("Reshape") \
39 REGISTER_KERNEL_BUILDER(Name("Reshape") \
54 REGISTER_KERNEL_BUILDER(Name("Reshape")
62 REGISTER_KERNEL_BUILDER(Name("Reshape")
73 REGISTER_KERNEL_BUILDER(Name("Reshape") \
79 REGISTER_KERNEL_BUILDER(Name("Reshape") \
89 REGISTER_KERNEL_BUILDER(Name("Reshape")
97 REGISTER_KERNEL_BUILDER(Name("Reshape")
/external/tensorflow/tensorflow/compiler/xla/mlir_hlo/tests/Dialect/mhlo/canonicalize/
Dreshape.mlir7 %0 = "mhlo.reshape"(%cst) : (tensor<1x1xi32>) -> tensor<i32>
18 %0 = "mhlo.reshape"(%cst) : (tensor<1x2xi32>) -> tensor<2xi32>
29 %0 = "mhlo.reshape"(%cst) : (tensor<i32>) -> tensor<1xi32>
40 %0 = "mhlo.reshape"(%cst) : (tensor<4x4xi64>) -> tensor<16xi64>
51 %0 = "mhlo.reshape"(%cst) : (tensor<4x4xi64>) -> tensor<16xi64>
62 %0 = "mhlo.reshape"(%cst) : (tensor<3x2xi32>) -> tensor<6xi32>
75 %0 = "mhlo.reshape"(%cst) : (tensor<6xi32>) -> tensor<2x3xi32>
86 %0 = "mhlo.reshape"(%cst) : (tensor<4x4xf64>) -> tensor<16xf64>
97 %0 = "mhlo.reshape"(%arg) : (tensor<2x3xi32>) -> tensor<2x3xi32>
106 // CHECK-NEXT: mhlo.reshape [[ARG]] : (tensor<2x3xi32>) -> tensor<3x2xi32>
[all …]
/external/tensorflow/tensorflow/python/training/
Dcheckpoint_ops_test.py45 np.reshape(np.linspace(0.0, 79, 5 * 16), (5, 16)))
109 np.reshape([18, 34, 50, self.init_val, self.init_val], [5, 1]),
110 np.reshape([16, 32, 48, self.init_val, self.init_val], [5, 1]),
111 np.reshape([self.init_val] * 5, [5, 1]),
112 np.reshape([17, 33, 49, self.init_val, self.init_val], [5, 1]),
113 np.reshape([self.init_val] * 5, [5, 1])
141 np.reshape([2, 18, 34, 50, self.init_val, self.init_val], [6, 1]),
142 np.reshape([0, 16, 32, 48, self.init_val, self.init_val], [6, 1]),
143 np.reshape([self.init_val] * 6, [6, 1]),
144 np.reshape([1, 17, 33, 49, self.init_val, self.init_val], [6, 1]),
[all …]
/external/tensorflow/tensorflow/compiler/xla/service/gpu/tests/
Dgpu_reduce_scatter_creator_test.cc82 %reshape = s32[] reshape(%id) in TEST_F()
84 %offset = s32[] multiply(%reshape, %slice_size) in TEST_F()
120 %reshape = s32[] reshape(%id) in TEST_F()
122 %offset = s32[] multiply(%reshape, %slice_size) in TEST_F()
124 %reshape.1 = f32[32,16,64] reshape(%all-reduce) in TEST_F()
125 ROOT %dynamic-slice = f32[4,16,64] dynamic-slice(%reshape.1, %offset, %zero, %zero), in TEST_F()
135 op::Reshape(op::ReduceScatter(op::Parameter(0)))); in TEST_F()
157 %reshape.1 = f32[4,84,1024] reshape(%all-reduce) in TEST_F()
158 ROOT %dynamic-slice = f32[4,84,128] dynamic-slice(%reshape.1, %zero, %zero, %offset), in TEST_F()
168 op::Reshape(op::ReduceScatter(op::Parameter(0)))); in TEST_F()
[all …]
/external/tensorflow/tensorflow/compiler/xla/mlir_hlo/lib/Dialect/mhlo/transforms/
Drestrict_max_rank.cc43 // Rewrites Reshape -> Transpose -> Reshape sequence of ops originating from
46 // Input to the first Reshape is Tensor in NHWC format in 4D or 5D.
48 // The first reshape splits spatial dimensions to generated two dimensions for
50 // split dimensions to the beginning. The final reshape op combines the first
53 // reshape(NxHxWxC) -> (Nx(H/B1)xB1x(W/B2)xB2xC)
55 // reshape(B1xB2xNx(H/B1)x(W/B2)xC) -> ((B1*B2*N)x(H/B1)x(W/B2)xC)
83 op, "defining op for operand is not reshape"); in matchAndRewrite()
88 "user of the result is not reshape"); in matchAndRewrite()
95 op, "reshape op input or output type is not static"); in matchAndRewrite()
101 op, "reshape op input and output rank are different"); in matchAndRewrite()
[all …]
/external/tensorflow/tensorflow/compiler/xla/mlir_hlo/tests/Dialect/mhlo/
Drestrict_max_rank.mlir7 // CHECK: %[[OUT0:.*]] = mhlo.reshape %arg0 : (tensor<17x6x35x13xf32>) -> tensor<17x6x5x7x13xf32>
9 …// CHECK: %[[OUT2:.*]] = mhlo.reshape %[[OUT1]] : (tensor<7x17x6x5x13xf32>) -> tensor<119x2x3x5x13…
11 …// CHECK: %[[OUT4:.*]] = mhlo.reshape %[[OUT3]] : (tensor<3x119x2x5x13xf32>) -> tensor<357x2x5x13x…
14 %0 = "mhlo.reshape"(%arg0) : (tensor<17x6x35x13xf32>) -> tensor<17x2x3x5x7x13xf32>
16 %2 = "mhlo.reshape"(%1) : (tensor<3x7x17x2x5x13xf32>) -> tensor<357x2x5x13xf32>
25 …// CHECK: %[[OUT0:.*]] = mhlo.reshape %arg0 : (tensor<17x6x35x15x13xf32>) -> tensor<17x6x35x3x5x13…
27 …// CHECK: %[[OUT2:.*]] = mhlo.reshape %[[OUT1]] : (tensor<5x17x6x35x3x13xf32>) -> tensor<85x6x5x7x…
29 …// CHECK: %[[OUT4:.*]] = mhlo.reshape %[[OUT3]] : (tensor<7x85x6x5x3x13xf32>) -> tensor<595x2x3x5x…
31 …// CHECK: %[[OUT6:.*]] = mhlo.reshape %[[OUT5]] : (tensor<3x595x2x5x3x13xf32>) -> tensor<1785x2x5x…
34 %0 = "mhlo.reshape"(%arg0) : (tensor<17x6x35x15x13xf32>) -> tensor<17x2x3x5x7x3x5x13xf32>
[all …]
/external/tensorflow/tensorflow/python/kernel_tests/math_ops/
Dtranspose_op_test.py142 vector = np.arange(0, 2).reshape((1, 1, 1, 2, 1))
164 1, total_size + 1, dtype=datatype).reshape(input_shape)
187 1, total_size + 1, dtype=np.float32).reshape(input_shape)
224 1, total_size + 1, dtype=np.float32).reshape(input_shape)
249 1, total_size + 1, dtype=datatype).reshape(input_shape)
272 1, total_size + 1, dtype=np.float32).reshape(input_shape)
336 self._compareCpu(np.arange(0, 6).reshape([3, 2]).astype(np.float32), [0, 1])
340 np.arange(0, 8).reshape([2, 4]).astype(np.float32),
346 x = np.arange(0, 8).reshape([2, 4]).astype(np.float32)
358 self._compare(np.arange(0, 21).reshape([3, 7]).astype(np.float16))
[all …]
/external/tensorflow/tensorflow/compiler/mlir/tensorflow/tests/
Dunroll-batch-matmul.mlir17 …// CHECK: %[[LHS_RESHAPED:.*]] = "tf.Reshape"(%arg0, %[[LHS_RESHAPED_SHAPE]]) : (tensor<2x3x4x5xf3…
19 …// CHECK: %[[LHS_1:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#0, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
20 …// CHECK: %[[LHS_2:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#1, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
21 …// CHECK: %[[LHS_3:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#2, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
22 …// CHECK: %[[LHS_4:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#3, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
23 …// CHECK: %[[LHS_5:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#4, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
24 …// CHECK: %[[LHS_6:.*]] = "tf.Reshape"(%[[LHS_SPLIT]]#5, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf3…
26 …// CHECK: %[[RHS_RESHAPED:.*]] = "tf.Reshape"(%arg1, %[[RHS_RESHAPED_SHAPE]]) : (tensor<2x3x5x6xf3…
28 …// CHECK: %[[RHS_1:.*]] = "tf.Reshape"(%[[RHS_SPLIT]]#0, %[[MATMUL_RHS_SHAPE]]) : (tensor<1x5x6xf3…
29 …// CHECK: %[[RHS_2:.*]] = "tf.Reshape"(%[[RHS_SPLIT]]#1, %[[MATMUL_RHS_SHAPE]]) : (tensor<1x5x6xf3…
[all …]
Dtf_optimize.mlir40 …%98 = "tf.Reshape"(%97, %cst_2) : (tensor<1x8x6x1x6x1x1x18xbf16>, tensor<4xi64>) -> tensor<8x6x6x1…
45 …// CHECK: %[[RESHAPE:.*]] = "tf.Reshape"(%arg0, %[[CST]]) : (tensor<1x8x1x1x1x1x1x18xbf16>, tensor…
46 …// CHECK: %[[BROADCAST:.*]] = "tf.BroadcastTo"(%[[RESHAPE]], %[[CST1]]) : (tensor<8x1x1x18xbf16>, …
55 …%98 = "tf.Reshape"(%97, %cst_2) : (tensor<7x1x8x6x1x6x1x1x18xbf16>, tensor<5xi64>) -> tensor<7x8x6…
60 …// CHECK: %[[RESHAPE:.*]] = "tf.Reshape"(%arg0, %[[CST]]) : (tensor<1x8x1x1x1x1x1x18xbf16>, tensor…
61 …// CHECK: %[[BROADCAST:.*]] = "tf.BroadcastTo"(%[[RESHAPE]], %[[CST1]]) : (tensor<1x8x1x1x18xbf16>…
70 …%98 = "tf.Reshape"(%97, %cst_2) : (tensor<1x1x6x1x6x1x1x18xbf16>, tensor<5xi64>) -> tensor<1x6x1x6…
75 …// CHECK: %[[RESHAPE:.*]] = "tf.Reshape"(%arg0, %[[CST]]) : (tensor<1x1x1x1x1x1x1x18xbf16>, tensor…
76 …// CHECK: %[[BROADCAST:.*]] = "tf.BroadcastTo"(%[[RESHAPE]], %[[CST1]]) : (tensor<1x1x1x1x18xbf16>…
85 …%98 = "tf.Reshape"(%97, %cst_2) : (tensor<1x8x6x1x6x1x1x18xbf16>, tensor<4xi64>) -> tensor<8x6x6x1…
[all …]

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