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Results 1 - 10 of 25 for 3x5x7xf32 (0.16 sec)
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tensorflow/compiler/mlir/tensorflow/tests/fold-broadcast.mlir
%1 = "tf.Mul"(%arg0, %0) : (tensor<5x7xf32>, tensor<3x5x7xf32>) -> tensor<3x5x7xf32> func.return %1 : tensor<3x5x7xf32> // CHECK: %[[C0:.*]] = arith.constant dense<[3, 5, 7]> : tensor<3xi32> // CHECK: %[[V0:.*]] = "tf.BroadcastTo"(%arg1, %[[C0]]) : (tensor<5xf32>, tensor<3xi32>) -> tensor<3x5x7xf32> // CHECK: %[[V1:.*]] = "tf.Mul"(%arg0, %[[V0]]) : (tensor<5x7xf32>, tensor<3x5x7xf32>) -> tensor<3x5x7xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Mon Oct 30 06:52:55 UTC 2023 - 6.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/einsum.mlir
%0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "ijk,ikm->ijm"}: (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32> func.return %0 : tensor<3x4x6xf32> // CHECK-LABEL: einsum_basic // CHECK: "tf.BatchMatMulV2"(%arg0, %arg1) <{adj_x = false, adj_y = false}> : (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Fri Jan 05 18:35:42 UTC 2024 - 25.9K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/unroll-batch-matmul.mlir
// CHECK: %[[RHS_SPLIT:.*]]:6 = "tf.Split"(%[[SPLITTING_AXIS]], %[[RHS_RESHAPED]]) : (tensor<i32>, tensor<6x5x6xf32>) -> (tensor<1x5x6xf32>, tensor<1x5x6xf32>, tensor<1x5x6xf32>, tensor<1x5x6xf32>, tensor<1x5x6xf32>, tensor<1x5x6xf32>) // CHECK: %[[RHS_1:.*]] = "tf.Reshape"(%[[RHS_SPLIT]]#0, %[[MATMUL_RHS_SHAPE]]) : (tensor<1x5x6xf32>, tensor<2xi64>) -> tensor<5x6xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Dec 06 18:42:28 UTC 2023 - 63.7K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul.pbtxt
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 2.6K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-BatchMatMulV2.mlir
func.func @batchmatmulv2_basic(%arg0: tensor<1x4x2xf32>, %arg1: tensor<3x2x4xf32>) -> tensor<3x4x4xf32> { // CHECK-LABEL: func @batchmatmulv2_basic // CHECK-SAME: ([[LHS:%.*]]: tensor<1x4x2xf32>, [[RHS:%.*]]: tensor<3x2x4xf32>) -> tensor<3x4x4xf32> // CHECK: [[LHSSHAPE:%.*]] = shape.shape_of [[LHS]] : tensor<1x4x2xf32> // CHECK: [[RHSSHAPE:%.*]] = shape.shape_of [[RHS]] : tensor<3x2x4xf32> // CHECK: [[CM2:%.*]] = arith.constant -2 : index
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Sat Apr 06 15:32:52 UTC 2024 - 5.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tensorflow/tests/canonicalize.mlir
// CHECK: "tf.BatchMatMulV2"(%arg0, %arg1) <{adj_x = false, adj_y = false, grad_x = false, grad_y = false}> {device = "/job:localhost/replica:0/task:0/device:GPU:0"} %0 = "tf.BatchMatMul"(%arg0, %arg1) <{adj_x = false, adj_y = false}> {device = "/job:localhost/replica:0/task:0/device:GPU:0"} : (tensor<2x3x5xf32>, tensor<2x5x7xf32>) -> tensor<2x3x7xf32> func.return %0: tensor<2x3x7xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 09 22:07:10 UTC 2024 - 132.1K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/stablehlo/tests/passes/quantize/quantize_same_scale.mlir
// CHECK-SAME: %[[ARG0:.*]]: tensor<3x4x5xf32> // CHECK-SAME: %[[ARG1:.*]]: tensor<3x5x2xf32> // CHECK-SAME: %[[ARG2:.*]]: tensor<2x3x2xi64> func.func private @composite_and_gather(%arg0: tensor<3x4x5xf32>, %arg1: tensor<3x5x2xf32>, %arg2: tensor<2x3x2xi64>) -> tensor<2x3x2x2xf32> { // CHECK: %[[Q1:.*]] = "quantfork.qcast"(%[[ARG0]]) {volatile} : (tensor<3x4x5xf32>) -> tensor<3x4x5x!quant.uniform<i8:f32, 5.000000e-03>>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Tue May 14 17:10:32 UTC 2024 - 35.4K bytes - Viewed (0) -
tensorflow/compiler/mlir/lite/tests/end2end/unroll_batch_matmul_disabled.pbtxt
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>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu May 02 09:41:17 UTC 2024 - 1.5K bytes - Viewed (0) -
tensorflow/compiler/mlir/tf2xla/tests/legalize-tf-include-tf2xla-fallback.mlir
%0 = "tf.BatchMatMulV2"(%arg0, %arg1) {T = f32, adj_x = false, adj_y = false, grad_x = false, grad_y = false, device = ""} : (tensor<1x4x2xf32>, tensor<3x2x4xf32>) -> tensor<3x4x4xf32> func.return %0 : tensor<3x4x4xf32> }
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Thu Nov 16 19:04:03 UTC 2023 - 3.2K bytes - Viewed (0) -
tensorflow/compiler/mlir/quantization/tensorflow/tests/prepare_lifting.mlir
func.func @lower_einsum(%arg0: tensor<3x4x5xf32>, %arg1: tensor<3x5x6xf32>) -> tensor<3x4x6xf32> { %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "ijk,ikm->ijm"}: (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32> func.return %0 : tensor<3x4x6xf32> } // CHECK-LABEL: lower_einsum // CHECK: "tf.BatchMatMulV2"(%arg0, %arg1) <{adj_x = false, adj_y = false}> : (tensor<3x4x5xf32>, tensor<3x5x6xf32>) -> tensor<3x4x6xf32>
Registered: Sun Jun 16 05:45:23 UTC 2024 - Last Modified: Wed Feb 14 03:24:59 UTC 2024 - 33.3K bytes - Viewed (0)