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torch-mlir/test/Conversion/TorchToTosa/basic.mlir

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Add a basic TOSA E2E backend. We lower through linalg-on-tensors and use RefBackend to run it. This adds enough support for a "tanh" op. Adding more ops should be fairly mechanical now that things are wired up. Run with: ``` ./tools/torchscript_e2e_test.sh -c tosa ``` The backend structure is very similar to linalg-on-tensors based E2E backends and is a nice parallel (see `tosa_backend.py`). Actually, this forced a nice refactoring to the layering here. We removed `torchscript-module-to-linalg-on-tensors-backend-pipeline` and instead require separately running ``` torchscript-function-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline ``` This highlights the step that lowers to the "torch backend contract" of cleaned up `torch` dialect ops is a critical step in the lowering. Going forward, that is the key load-bearing contract of the torch-mlir project, not the linalg-on-tensors backend contract. Recommended review order: - `TorchToTosa.cpp` / `TorchToTosa/basic.mlir` - `python/torch_mlir_e2e_test/torchscript/configs/tosa_backend.py` and the new `utils.py` file there. - `python/torch_mlir_e2e_test/tosa_backends/linalg_on_tensors.py` and `abc.py` in that directory for the TOSA backend e2e interface. - other misc mechanical changes
2021-10-08 10:07:03 +08:00
// RUN: torch-mlir-opt <%s -convert-torch-to-tosa -split-input-file -verify-diagnostics | FileCheck %s
// CHECK-LABEL: func @torch.aten.tanh$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.tanh"(%[[ARG_BUILTIN]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func @torch.aten.tanh$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.tanh %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
[tosa] Add Torch.sigmoid fp32 to TOSA (#386) * [tosa] Add Torch.sigmoid fp32 to TOSA Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-10-29 01:09:12 +08:00
// -----
// CHECK-LABEL: func @torch.aten.sigmoid$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.sigmoid"(%[[ARG_BUILTIN]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func @torch.aten.sigmoid$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.sigmoid %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
[tosa] Add structure for eltwise ops Add a bunch of op legalizations. Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-11-11 11:03:36 +08:00
// -----
// CHECK-LABEL: func @torch.aten.relu$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.clamp"(%[[ARG_BUILTIN]]) {max_fp = 3.40282347E+38 : f32, max_int = 2147483647 : i64, min_fp = 0.000000e+00 : f32, min_int = 0 : i64} : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func @torch.aten.relu$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.relu %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func @torch.aten.log$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.log"(%[[ARG_BUILTIN]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func @torch.aten.log$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.log %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func @torch.aten.exp$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.exp"(%[[ARG_BUILTIN]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func @torch.aten.exp$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.exp %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func @torch.aten.neg$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.negate"(%[[ARG_BUILTIN]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func @torch.aten.neg$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.neg %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func @torch.aten.floor$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.floor"(%[[ARG_BUILTIN]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func @torch.aten.floor$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.floor %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func @torch.aten.bitwise_not$basic(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.bitwise_not"(%[[ARG_BUILTIN]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func @torch.aten.bitwise_not$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.bitwise_not %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
* [tosa] Support for AtenRsubScalarOp for scalar constants (#531) * [tosa] Support for AtenCeilOp and AtenReciprocalOp * [tosa] Support for comparator ops, Aten[Gt|Lt|Eq][Tensor|Scalar]Op with scalar constant * [tosa] Support for Scalar variants of Aten[Mul|Div|Add|Sub] Ops with scalar constants Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-21 02:58:30 +08:00
// CHECK-LABEL: func @torch.aten.ceil$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = "tosa.ceil"(%[[VAL_1]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func @torch.aten.ceil$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.ceil %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func @torch.aten.reciprocal$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = "tosa.reciprocal"(%[[VAL_1]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func @torch.aten.reciprocal$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.reciprocal %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
[tosa] Add structure for eltwise ops Add a bunch of op legalizations. Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-11-11 11:03:36 +08:00
// CHECK-LABEL: func @torch.aten.add$basic(
* [tosa] Support for AtenRsubScalarOp for scalar constants (#531) * [tosa] Support for AtenCeilOp and AtenReciprocalOp * [tosa] Support for comparator ops, Aten[Gt|Lt|Eq][Tensor|Scalar]Op with scalar constant * [tosa] Support for Scalar variants of Aten[Mul|Div|Add|Sub] Ops with scalar constants Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-21 02:58:30 +08:00
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = torch.constant.int 1
// CHECK: %[[VAL_5:.*]] = "tosa.const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[VAL_6:.*]] = "tosa.mul"(%[[VAL_3]], %[[VAL_5]]) {shift = 0 : i32} : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_7:.*]] = "tosa.add"(%[[VAL_2]], %[[VAL_6]]) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[?,?],f32>
// CHECK: }
[tosa] Add structure for eltwise ops Add a bunch of op legalizations. Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-11-11 11:03:36 +08:00
func @torch.aten.add$basic(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],f32>) -> !torch.vtensor<[?, ?],f32> {
%int1 = torch.constant.int 1
%0 = torch.aten.add.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],f32>, !torch.int -> !torch.vtensor<[?, ?],f32>
return %0 : !torch.vtensor<[?, ?],f32>
}
// -----
// CHECK-LABEL: func @torch.aten.sub$basic(
* [tosa] Support for AtenRsubScalarOp for scalar constants (#531) * [tosa] Support for AtenCeilOp and AtenReciprocalOp * [tosa] Support for comparator ops, Aten[Gt|Lt|Eq][Tensor|Scalar]Op with scalar constant * [tosa] Support for Scalar variants of Aten[Mul|Div|Add|Sub] Ops with scalar constants Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-21 02:58:30 +08:00
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = torch.constant.int 1
// CHECK: %[[VAL_5:.*]] = "tosa.const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[VAL_6:.*]] = "tosa.mul"(%[[VAL_3]], %[[VAL_5]]) {shift = 0 : i32} : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_7:.*]] = "tosa.sub"(%[[VAL_2]], %[[VAL_6]]) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[?,?],f32>
// CHECK: }
[tosa] Add structure for eltwise ops Add a bunch of op legalizations. Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-11-11 11:03:36 +08:00
func @torch.aten.sub$basic(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],f32>) -> !torch.vtensor<[?, ?],f32> {
%int1 = torch.constant.int 1
%0 = torch.aten.sub.Tensor %arg0, %arg1, %int1 : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],f32>, !torch.int -> !torch.vtensor<[?, ?],f32>
return %0 : !torch.vtensor<[?, ?],f32>
}
[mlir][tosa] Refactor conversions to use templates (#416) - Remove use of conversion construction macros - Add mul and div op conversions - Add corresponding tests Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-11-12 08:15:58 +08:00
// -----
// CHECK-LABEL: func @torch.aten.mul$basic(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG0_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[ARG1_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.mul"(%[[ARG0_BUILTIN]], %[[ARG1_BUILTIN]]) {shift = 0 : i32} : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func @torch.aten.mul$basic(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],f32>) -> !torch.vtensor<[?, ?],f32> {
%0 = torch.aten.mul.Tensor %arg0, %arg1 : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],f32> -> !torch.vtensor<[?, ?],f32>
return %0 : !torch.vtensor<[?, ?],f32>
}
// -----
// CHECK-LABEL: func @torch.aten.div$basic(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[ARG1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[ARG0_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[ARG1_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[RCP:.*]] = "tosa.reciprocal"(%[[ARG1_BUILTIN]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.mul"(%[[ARG0_BUILTIN]], %[[RCP]]) {shift = 0 : i32} : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?],f32>
func @torch.aten.div$basic(%arg0: !torch.vtensor<[?, ?],f32>, %arg1: !torch.vtensor<[?, ?],f32>) -> !torch.vtensor<[?, ?],f32> {
%0 = torch.aten.div.Tensor %arg0, %arg1 : !torch.vtensor<[?, ?],f32>, !torch.vtensor<[?, ?],f32> -> !torch.vtensor<[?, ?],f32>
return %0 : !torch.vtensor<[?, ?],f32>
}
[tosa] Add Torch reduction operators - Supports variants with multiple dims, one dim, all dime - Leverages legalize_common and legalize_utils code from TensorFlow-TOSA work Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-12-03 08:52:01 +08:00
// -----
// CHECK-LABEL: func @test_reduce_mean_dim$basic(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?],f32> {
// CHECK: %[[ARG0_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK: %[[ARG1:.*]] = torch.constant.int 0
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[ARG1_BUILTIN:.*]] = torch.prim.ListConstruct %[[ARG1]] : (!torch.int) -> !torch.list<int>
[tosa] Add Torch reduction operators - Supports variants with multiple dims, one dim, all dime - Leverages legalize_common and legalize_utils code from TensorFlow-TOSA work Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-12-03 08:52:01 +08:00
// CHECK: %[[ARG2_BUILTIN:.*]] = torch.constant.bool false
// CHECK: %[[ARG3_BUILTIN:.*]] = torch.constant.none
// CHECK: %[[SUM:.*]] = "tosa.reduce_sum"(%[[ARG0_BUILTIN]]) {axis = 0 : i64} : (tensor<?x?x?x?xf32>) -> tensor<1x?x?x?xf32>
// CHECK: %[[RESHAPE_SUM:.*]] = "tosa.reshape"(%[[SUM]]) {new_shape = [-1, -1, -1]} : (tensor<1x?x?x?xf32>) -> tensor<?x?x?xf32>
// CHECK: %[[CONST:.*]] = "tosa.const"() {value = dense<-1.000000e+00> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.mul"(%[[RESHAPE_SUM]], %[[CONST]]) {shift = 0 : i32} : (tensor<?x?x?xf32>, tensor<f32>) -> tensor<?x?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?,?],f32>
func @test_reduce_mean_dim$basic(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?],f32> {
%dim0 = torch.constant.int 0
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
%reducedims = torch.prim.ListConstruct %dim0 : (!torch.int) -> !torch.list<int>
[tosa] Add Torch reduction operators - Supports variants with multiple dims, one dim, all dime - Leverages legalize_common and legalize_utils code from TensorFlow-TOSA work Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-12-03 08:52:01 +08:00
%keepdims = torch.constant.bool false
%dtype = torch.constant.none
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
%0 = torch.aten.mean.dim %arg0, %reducedims, %keepdims, %dtype : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[?,?,?],f32>
[tosa] Add Torch reduction operators - Supports variants with multiple dims, one dim, all dime - Leverages legalize_common and legalize_utils code from TensorFlow-TOSA work Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-12-03 08:52:01 +08:00
return %0 : !torch.vtensor<[?,?,?],f32>
}
// -----
// CHECK-LABEL: func @test_reduce_sum_dims$basic(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?],f32> {
// CHECK: %[[ARG0_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK: %[[ARG1_BUILTIN:.*]] = torch.constant.none
// CHECK: %[[ARG2_BUILTIN:.*]] = torch.constant.bool false
// CHECK: %[[ARG3:.*]] = torch.constant.int 0
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[ARG3_BUILTIN:.*]] = torch.prim.ListConstruct %[[ARG3]] : (!torch.int) -> !torch.list<int>
[tosa] Add Torch reduction operators - Supports variants with multiple dims, one dim, all dime - Leverages legalize_common and legalize_utils code from TensorFlow-TOSA work Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-12-03 08:52:01 +08:00
// CHECK: %[[SUM:.*]] = "tosa.reduce_sum"(%[[ARG0_BUILTIN]]) {axis = 0 : i64} : (tensor<?x?x?x?xf32>) -> tensor<1x?x?x?xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.reshape"(%[[SUM]]) {new_shape = [-1, -1, -1]} : (tensor<1x?x?x?xf32>) -> tensor<?x?x?xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?x?xf32> -> !torch.vtensor<[?,?,?],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?,?],f32>
func @test_reduce_sum_dims$basic(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?,?,?],f32> {
%none = torch.constant.none
%false = torch.constant.bool false
%int0 = torch.constant.int 0
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
%0 = torch.prim.ListConstruct %int0 : (!torch.int) -> !torch.list<int>
%1 = torch.aten.sum.dim_IntList %arg0, %0, %false, %none : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int>, !torch.bool, !torch.none -> !torch.vtensor<[?,?,?],f32>
[tosa] Add Torch reduction operators - Supports variants with multiple dims, one dim, all dime - Leverages legalize_common and legalize_utils code from TensorFlow-TOSA work Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
2021-12-03 08:52:01 +08:00
return %1 : !torch.vtensor<[?,?,?],f32>
}
// -----
// CHECK-LABEL: func @test_reduce_sum$basic(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[1],f32> {
// CHECK: %[[ARG0_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK: %[[ARG1_BUILTIN:.*]] = torch.constant.none
// CHECK: %[[REDUCE1:.*]] = "tosa.reduce_sum"(%[[ARG0_BUILTIN]]) {axis = 0 : i64} : (tensor<?x?x?x?xf32>) -> tensor<1x?x?x?xf32>
// CHECK: %[[REDUCE2:.*]] = "tosa.reduce_sum"(%[[REDUCE1]]) {axis = 1 : i64} : (tensor<1x?x?x?xf32>) -> tensor<1x1x?x?xf32>
// CHECK: %[[REDUCE3:.*]] = "tosa.reduce_sum"(%[[REDUCE2]]) {axis = 2 : i64} : (tensor<1x1x?x?xf32>) -> tensor<1x1x1x?xf32>
// CHECK: %[[REDUCE4:.*]] = "tosa.reduce_sum"(%[[REDUCE3]]) {axis = 3 : i64} : (tensor<1x1x1x?xf32>) -> tensor<1x1x1x1xf32>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.reshape"(%[[REDUCE4]]) {new_shape = [1]} : (tensor<1x1x1x1xf32>) -> tensor<1xf32>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<1xf32> -> !torch.vtensor<[1],f32>
// CHECK: return %[[RESULT]] : !torch.vtensor<[1],f32>
func @test_reduce_sum$basic(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[1],f32> {
%none = torch.constant.none
%0 = torch.aten.sum %arg0, %none : !torch.vtensor<[?,?,?,?],f32>, !torch.none -> !torch.vtensor<[1],f32>
return %0 : !torch.vtensor<[1],f32>
}
// -----
// CHECK-LABEL: func @test_reduce_all$basic(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?,?,?],i1>) -> !torch.vtensor<[1],i1> {
// CHECK: %[[ARG0_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?,?,?],i1> -> tensor<?x?x?x?xi1>
// CHECK: %[[REDUCE1:.*]] = "tosa.reduce_all"(%[[ARG0_BUILTIN]]) {axis = 0 : i64} : (tensor<?x?x?x?xi1>) -> tensor<1x?x?x?xi1>
// CHECK: %[[REDUCE2:.*]] = "tosa.reduce_all"(%[[REDUCE1]]) {axis = 1 : i64} : (tensor<1x?x?x?xi1>) -> tensor<1x1x?x?xi1>
// CHECK: %[[REDUCE3:.*]] = "tosa.reduce_all"(%[[REDUCE2]]) {axis = 2 : i64} : (tensor<1x1x?x?xi1>) -> tensor<1x1x1x?xi1>
// CHECK: %[[REDUCE4:.*]] = "tosa.reduce_all"(%[[REDUCE3]]) {axis = 3 : i64} : (tensor<1x1x1x?xi1>) -> tensor<1x1x1x1xi1>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.reshape"(%[[REDUCE4]]) {new_shape = [1]} : (tensor<1x1x1x1xi1>) -> tensor<1xi1>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<1xi1> -> !torch.vtensor<[1],i1>
// CHECK: return %[[RESULT]] : !torch.vtensor<[1],i1>
func @test_reduce_all$basic(%arg0: !torch.vtensor<[?,?,?,?],i1>) -> !torch.vtensor<[1],i1> {
%0 = torch.aten.all %arg0 : !torch.vtensor<[?,?,?,?],i1> -> !torch.vtensor<[1],i1>
return %0 : !torch.vtensor<[1],i1>
}
// -----
// CHECK-LABEL: func @test_reduce_any_dim$basic(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?,?,?],i1>) -> !torch.vtensor<[?,?,?],i1> {
// CHECK: %[[ARG0_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?,?,?],i1> -> tensor<?x?x?x?xi1>
// CHECK: %[[ARG1:.*]] = torch.constant.int 0
// CHECK: %[[ARG2:.*]] = torch.constant.bool false
// CHECK: %[[REDUCE:.*]] = "tosa.reduce_any"(%[[ARG0_BUILTIN]]) {axis = 0 : i64} : (tensor<?x?x?x?xi1>) -> tensor<1x?x?x?xi1>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.reshape"(%[[REDUCE]]) {new_shape = [-1, -1, -1]} : (tensor<1x?x?x?xi1>) -> tensor<?x?x?xi1>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<?x?x?xi1> -> !torch.vtensor<[?,?,?],i1>
// CHECK: return %[[RESULT]] : !torch.vtensor<[?,?,?],i1>
func @test_reduce_any_dim$basic(%arg0: !torch.vtensor<[?,?,?,?],i1>) -> !torch.vtensor<[?,?,?],i1> {
%int0 = torch.constant.int 0
%false = torch.constant.bool false
%0 = torch.aten.any.dim %arg0, %int0, %false : !torch.vtensor<[?,?,?,?],i1>, !torch.int, !torch.bool -> !torch.vtensor<[?,?,?],i1>
return %0 : !torch.vtensor<[?,?,?],i1>
}
// -----
// CHECK-LABEL: func @test_reduce_any$basic(
// CHECK-SAME: %[[ARG0:.*]]: !torch.vtensor<[?,?,?,?],i1>) -> !torch.vtensor<[1],i1> {
// CHECK: %[[ARG0_BUILTIN:.*]] = torch_c.to_builtin_tensor %[[ARG0]] : !torch.vtensor<[?,?,?,?],i1> -> tensor<?x?x?x?xi1>
// CHECK: %[[REDUCE1:.*]] = "tosa.reduce_any"(%[[ARG0_BUILTIN]]) {axis = 0 : i64} : (tensor<?x?x?x?xi1>) -> tensor<1x?x?x?xi1>
// CHECK: %[[REDUCE2:.*]] = "tosa.reduce_any"(%[[REDUCE1]]) {axis = 1 : i64} : (tensor<1x?x?x?xi1>) -> tensor<1x1x?x?xi1>
// CHECK: %[[REDUCE3:.*]] = "tosa.reduce_any"(%[[REDUCE2]]) {axis = 2 : i64} : (tensor<1x1x?x?xi1>) -> tensor<1x1x1x?xi1>
// CHECK: %[[REDUCE4:.*]] = "tosa.reduce_any"(%[[REDUCE3]]) {axis = 3 : i64} : (tensor<1x1x1x?xi1>) -> tensor<1x1x1x1xi1>
// CHECK: %[[RESULT_BUILTIN:.*]] = "tosa.reshape"(%[[REDUCE4]]) {new_shape = [1]} : (tensor<1x1x1x1xi1>) -> tensor<1xi1>
// CHECK: %[[RESULT:.*]] = torch_c.from_builtin_tensor %[[RESULT_BUILTIN]] : tensor<1xi1> -> !torch.vtensor<[1],i1>
// CHECK: return %[[RESULT]] : !torch.vtensor<[1],i1>
func @test_reduce_any$basic(%arg0: !torch.vtensor<[?,?,?,?],i1>) -> !torch.vtensor<[1],i1> {
%0 = torch.aten.any %arg0 : !torch.vtensor<[?,?,?,?],i1> -> !torch.vtensor<[1],i1>
return %0 : !torch.vtensor<[1],i1>
}
* [tosa] Support for Rsqrt legalization (#480) Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2021-12-15 02:03:58 +08:00
// -----
// CHECK-LABEL: func @torch.aten.rsqrt$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = "tosa.rsqrt"(%[[VAL_1]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_2]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func @torch.aten.rsqrt$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.rsqrt %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
* [tosa] Support for Maximum and Minimum Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>
2021-12-16 03:19:25 +08:00
// -----
// CHECK-LABEL: func @torch.aten.maximum$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = "tosa.maximum"(%[[VAL_2]], %[[VAL_3]]) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func @torch.aten.maximum$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.maximum %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func @torch.aten.minimum$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = "tosa.minimum"(%[[VAL_2]], %[[VAL_3]]) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func @torch.aten.minimum$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.minimum %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
* [tosa] Support for AtenPowTensorScalarOp with constant Scalar as input Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-12 04:49:17 +08:00
// -----
// CHECK-LABEL: func @torch.aten.pow.Tensor_Scalar$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.float 3.123400e+00
// CHECK: %[[VAL_3:.*]] = "tosa.const"() {value = dense<3.123400e+00> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[VAL_4:.*]] = "tosa.pow"(%[[VAL_1]], %[[VAL_3]]) : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func @torch.aten.pow.Tensor_Scalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%fp0 = torch.constant.float 3.123400e+00
%0 = torch.aten.pow.Tensor_Scalar %arg0, %fp0 : !torch.vtensor<[?,?],f32>, !torch.float -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
* [tosa] Support for AtenRsubScalarOp for scalar constants (#531) * [tosa] Support for AtenCeilOp and AtenReciprocalOp * [tosa] Support for comparator ops, Aten[Gt|Lt|Eq][Tensor|Scalar]Op with scalar constant * [tosa] Support for Scalar variants of Aten[Mul|Div|Add|Sub] Ops with scalar constants Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-21 02:58:30 +08:00
// -----
// CHECK-LABEL: func @torch.aten.rsub.Scalar$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.float 3.123400e+00
// CHECK: %[[VAL_3:.*]] = torch.constant.float 6.432100e+00
// CHECK: %[[VAL_4:.*]] = "tosa.const"() {value = dense<3.123400e+00> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[VAL_5:.*]] = "tosa.const"() {value = dense<6.432100e+00> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[VAL_6:.*]] = "tosa.mul"(%[[VAL_1]], %[[VAL_5]]) {shift = 0 : i32} : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_7:.*]] = "tosa.sub"(%[[VAL_4]], %[[VAL_6]]) : (tensor<f32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func @torch.aten.rsub.Scalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%other = torch.constant.float 3.123400e+00
%alpha = torch.constant.float 6.432100e+00
%0 = torch.aten.rsub.Scalar %arg0, %other, %alpha : !torch.vtensor<[?,?],f32>, !torch.float, !torch.float -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func @torch.aten.rsub.Scalar$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.float 3.123400e+00
// CHECK: %[[VAL_3:.*]] = torch.constant.int 1
// CHECK: %[[VAL_4:.*]] = "tosa.const"() {value = dense<3.123400e+00> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[VAL_5:.*]] = "tosa.const"() {value = dense<1.000000e+00> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[VAL_6:.*]] = "tosa.mul"(%[[VAL_1]], %[[VAL_5]]) {shift = 0 : i32} : (tensor<?x?xf32>, tensor<f32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_7:.*]] = "tosa.sub"(%[[VAL_4]], %[[VAL_6]]) : (tensor<f32>, tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func @torch.aten.rsub.Scalar$basic(%arg0: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
%other = torch.constant.float 3.123400e+00
%alpha = torch.constant.int 1
%0 = torch.aten.rsub.Scalar %arg0, %other, %alpha : !torch.vtensor<[?,?],f32>, !torch.float, !torch.int -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
// CHECK-LABEL: func @torch.aten.gt.Tensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = "tosa.greater"(%[[VAL_2]], %[[VAL_3]]) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
// CHECK: }
func @torch.aten.gt.Tensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
%0 = torch.aten.gt.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],i1>
return %0 : !torch.vtensor<[?,?],i1>
}
// -----
// CHECK-LABEL: func @torch.aten.lt.Tensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = "tosa.greater"(%[[VAL_3]], %[[VAL_2]]) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
// CHECK: }
func @torch.aten.lt.Tensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
%0 = torch.aten.lt.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],i1>
return %0 : !torch.vtensor<[?,?],i1>
}
// -----
// CHECK-LABEL: func @torch.aten.eq.Tensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = "tosa.equal"(%[[VAL_2]], %[[VAL_3]]) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],i1>
// CHECK: }
func @torch.aten.eq.Tensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
%0 = torch.aten.eq.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],i1>
return %0 : !torch.vtensor<[?,?],i1>
}
* [tosa] Support for AtenReshapeOp (#543) * [tosa] Support for AtenBatchNormOp Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-28 06:38:59 +08:00
// -----
// CHECK-LABEL: func @torch.aten.reshape$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?,?,?],f32> -> tensor<?x?x?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int -1
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %[[VAL_2]] : (!torch.int) -> !torch.list<int>
* [tosa] Support for AtenReshapeOp (#543) * [tosa] Support for AtenBatchNormOp Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-28 06:38:59 +08:00
// CHECK: %[[VAL_4:.*]] = "tosa.reshape"(%[[VAL_1]]) {new_shape = [-1]} : (tensor<?x?x?x?xf32>) -> tensor<?xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?xf32> -> !torch.vtensor<[?],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?],f32>
// CHECK: }
func @torch.aten.reshape$basic(%arg0: !torch.vtensor<[?,?,?,?],f32>) -> !torch.vtensor<[?],f32> {
%dim0 = torch.constant.int -1
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
%shape = torch.prim.ListConstruct %dim0 : (!torch.int) -> !torch.list<int>
%0 = torch.aten.reshape %arg0, %shape : !torch.vtensor<[?,?,?,?],f32>, !torch.list<int> -> !torch.vtensor<[?],f32>
* [tosa] Support for AtenReshapeOp (#543) * [tosa] Support for AtenBatchNormOp Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-28 06:38:59 +08:00
return %0 : !torch.vtensor<[?],f32>
}
// -----
[tosa] Support for Aten[Unsqueeze|Contiguous|Dropout|Reshape|View] ops (#700)
2022-03-26 05:15:07 +08:00
// CHECK-LABEL: func @torch.aten.native_batch_norm$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[10,4,3],f32>) -> !torch.vtensor<[10,4,3],f32> {
* [tosa] Support for AtenReshapeOp (#543) * [tosa] Support for AtenBatchNormOp Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-28 06:38:59 +08:00
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[10,4,3],f32> -> tensor<10x4x3xf32>
// CHECK: %[[VAL_2:.*]] = "tosa.const"() {value = dense<[5.000000e-01, 4.000000e-01, 3.000000e-01, 6.000000e-01]> : tensor<4xf32>} : () -> tensor<4xf32>
// CHECK: %[[VAL_3:.*]] = "tosa.const"() {value = dense<[3.000000e+00, 2.000000e+00, 4.000000e+00, 5.000000e+00]> : tensor<4xf32>} : () -> tensor<4xf32>
// CHECK: %[[VAL_4:.*]] = torch.constant.float 1.000000e-01
// CHECK: %[[VAL_5:.*]] = torch.constant.float 1.000000e-05
// CHECK: %[[VAL_6:.*]] = torch.constant.bool true
// CHECK: %[[VAL_7:.*]] = torch.constant.bool false
// CHECK: %[[VAL_8:.*]] = "tosa.reshape"(%[[VAL_2]]) {new_shape = [4, 1]} : (tensor<4xf32>) -> tensor<4x1xf32>
// CHECK: %[[VAL_9:.*]] = "tosa.reshape"(%[[VAL_3]]) {new_shape = [4, 1]} : (tensor<4xf32>) -> tensor<4x1xf32>
// CHECK: %[[VAL_10:.*]] = "tosa.reshape"(%[[VAL_3]]) {new_shape = [4, 1]} : (tensor<4xf32>) -> tensor<4x1xf32>
// CHECK: %[[VAL_11:.*]] = "tosa.reshape"(%[[VAL_2]]) {new_shape = [4, 1]} : (tensor<4xf32>) -> tensor<4x1xf32>
// CHECK: %[[VAL_12:.*]] = "tosa.const"() {value = dense<9.99999974E-6> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[VAL_13:.*]] = "tosa.sub"(%[[VAL_1]], %[[VAL_8]]) : (tensor<10x4x3xf32>, tensor<4x1xf32>) -> tensor<10x4x3xf32>
// CHECK: %[[VAL_14:.*]] = "tosa.add"(%[[VAL_9]], %[[VAL_12]]) : (tensor<4x1xf32>, tensor<f32>) -> tensor<4x1xf32>
// CHECK: %[[VAL_15:.*]] = "tosa.rsqrt"(%[[VAL_14]]) : (tensor<4x1xf32>) -> tensor<4x1xf32>
// CHECK: %[[VAL_16:.*]] = "tosa.mul"(%[[VAL_13]], %[[VAL_15]]) {shift = 0 : i32} : (tensor<10x4x3xf32>, tensor<4x1xf32>) -> tensor<10x4x3xf32>
// CHECK: %[[VAL_17:.*]] = "tosa.mul"(%[[VAL_16]], %[[VAL_10]]) {shift = 0 : i32} : (tensor<10x4x3xf32>, tensor<4x1xf32>) -> tensor<10x4x3xf32>
// CHECK: %[[VAL_18:.*]] = "tosa.add"(%[[VAL_17]], %[[VAL_11]]) : (tensor<10x4x3xf32>, tensor<4x1xf32>) -> tensor<10x4x3xf32>
// CHECK: %[[VAL_19:.*]] = torch_c.from_builtin_tensor %[[VAL_18]] : tensor<10x4x3xf32> -> !torch.vtensor<[10,4,3],f32>
// CHECK: return %[[VAL_19]] : !torch.vtensor<[10,4,3],f32>
// CHECK: }
[tosa] Support for Aten[Unsqueeze|Contiguous|Dropout|Reshape|View] ops (#700)
2022-03-26 05:15:07 +08:00
func @torch.aten.native_batch_norm$basic(%arg0: !torch.vtensor<[10,4,3],f32> ) -> !torch.vtensor<[10,4,3],f32> {
* [tosa] Support for AtenReshapeOp (#543) * [tosa] Support for AtenBatchNormOp Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-01-28 06:38:59 +08:00
%0 = torch.vtensor.literal(dense<[5.000000e-01, 4.000000e-01, 3.000000e-01, 6.000000e-01]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%1 = torch.vtensor.literal(dense<[3.000000e+00, 2.000000e+00, 4.000000e+00, 5.000000e+00]> : tensor<4xf32>) : !torch.vtensor<[4],f32>
%float1.000000e-01 = torch.constant.float 1.000000e-01
%float1.000000e-05 = torch.constant.float 1.000000e-05
%true = torch.constant.bool true
%false = torch.constant.bool false
%2 = torch.aten.batch_norm %arg0, %1, %0, %0, %1, %false, %float1.000000e-01, %float1.000000e-05, %true : !torch.vtensor<[10,4,3],f32>, !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32>, !torch.vtensor<[4],f32>, !torch.bool, !torch.float, !torch.float, !torch.bool -> !torch.vtensor<[10,4,3],f32>
return %2 : !torch.vtensor<[10,4,3],f32>
}
* [tosa] Support for AtenFlattenUsingIntsOp (#548)
2022-01-29 13:38:56 +08:00
// -----
// CHECK-LABEL: func @forward(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[10,3,8,9,3,4],f32>) -> !torch.vtensor<[10,3,?,4],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[10,3,8,9,3,4],f32> -> tensor<10x3x8x9x3x4xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 4
// CHECK: %[[VAL_3:.*]] = torch.constant.int 2
// CHECK: %[[VAL_4:.*]] = "tosa.reshape"(%[[VAL_1]]) {new_shape = [10, 3, 216, 4]} : (tensor<10x3x8x9x3x4xf32>) -> tensor<10x3x216x4xf32>
// CHECK: %[[VAL_5:.*]] = tensor.cast %[[VAL_4]] : tensor<10x3x216x4xf32> to tensor<10x3x?x4xf32>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<10x3x?x4xf32> -> !torch.vtensor<[10,3,?,4],f32>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[10,3,?,4],f32>
// CHECK: }
func @forward(%arg0: !torch.vtensor<[10,3,8,9,3,4],f32> ) -> !torch.vtensor<[10,3,?,4],f32> {
%int4 = torch.constant.int 4
%int2 = torch.constant.int 2
%0 = torch.aten.flatten.using_ints %arg0, %int2, %int4 : !torch.vtensor<[10,3,8,9,3,4],f32>, !torch.int, !torch.int -> !torch.vtensor<[10,3,?,4],f32>
return %0 : !torch.vtensor<[10,3,?,4],f32>
}
* [tosa] Support for AtenNativeLayerNormOp * [tosa] Support for AtenPermuteOp Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-04 06:08:19 +08:00
// -----
// CHECK-LABEL: func @forward(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[5,2,2,3],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[2,2,3],f32>,
// CHECK-SAME: %[[VAL_2:.*]]: !torch.vtensor<[2,2,3],f32>) -> !torch.vtensor<[5,2,2,3],f32> {
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[5,2,2,3],f32> -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_4:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[2,2,3],f32> -> tensor<2x2x3xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.to_builtin_tensor %[[VAL_2]] : !torch.vtensor<[2,2,3],f32> -> tensor<2x2x3xf32>
// CHECK: %[[VAL_6:.*]] = torch.constant.float 5.000000e-01
// CHECK: %[[VAL_7:.*]] = torch.constant.int 3
// CHECK: %[[VAL_8:.*]] = torch.constant.int 2
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[VAL_9:.*]] = torch.prim.ListConstruct %[[VAL_8]], %[[VAL_8]], %[[VAL_7]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
* [tosa] Support for AtenNativeLayerNormOp * [tosa] Support for AtenPermuteOp Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-04 06:08:19 +08:00
// CHECK: %[[VAL_10:.*]] = "tosa.const"() {value = dense<1.200000e+01> : tensor<1xf32>} : () -> tensor<1xf32>
// CHECK: %[[VAL_11:.*]] = "tosa.reciprocal"(%[[VAL_10]]) : (tensor<1xf32>) -> tensor<1xf32>
// CHECK: %[[VAL_12:.*]] = "tosa.reduce_sum"(%[[VAL_3]]) {axis = 3 : i64} : (tensor<5x2x2x3xf32>) -> tensor<5x2x2x1xf32>
// CHECK: %[[VAL_13:.*]] = "tosa.reduce_sum"(%[[VAL_12]]) {axis = 2 : i64} : (tensor<5x2x2x1xf32>) -> tensor<5x2x1xf32>
// CHECK: %[[VAL_14:.*]] = "tosa.reduce_sum"(%[[VAL_13]]) {axis = 1 : i64} : (tensor<5x2x1xf32>) -> tensor<5x1xf32>
// CHECK: %[[VAL_15:.*]] = "tosa.reshape"(%[[VAL_14]]) {new_shape = [5, 1, 1, 1]} : (tensor<5x1xf32>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_16:.*]] = "tosa.mul"(%[[VAL_15]], %[[VAL_11]]) {shift = 0 : i32} : (tensor<5x1x1x1xf32>, tensor<1xf32>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_17:.*]] = "tosa.sub"(%[[VAL_3]], %[[VAL_16]]) : (tensor<5x2x2x3xf32>, tensor<5x1x1x1xf32>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_18:.*]] = "tosa.mul"(%[[VAL_17]], %[[VAL_17]]) {shift = 0 : i32} : (tensor<5x2x2x3xf32>, tensor<5x2x2x3xf32>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_19:.*]] = "tosa.reduce_sum"(%[[VAL_18]]) {axis = 3 : i64} : (tensor<5x2x2x3xf32>) -> tensor<5x2x2x1xf32>
// CHECK: %[[VAL_20:.*]] = "tosa.reduce_sum"(%[[VAL_19]]) {axis = 2 : i64} : (tensor<5x2x2x1xf32>) -> tensor<5x2x1xf32>
// CHECK: %[[VAL_21:.*]] = "tosa.reduce_sum"(%[[VAL_20]]) {axis = 1 : i64} : (tensor<5x2x1xf32>) -> tensor<5x1xf32>
// CHECK: %[[VAL_22:.*]] = "tosa.reshape"(%[[VAL_21]]) {new_shape = [5, 1, 1, 1]} : (tensor<5x1xf32>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_23:.*]] = "tosa.mul"(%[[VAL_22]], %[[VAL_11]]) {shift = 0 : i32} : (tensor<5x1x1x1xf32>, tensor<1xf32>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_24:.*]] = "tosa.reshape"(%[[VAL_4]]) {new_shape = [1, 2, 2, 3]} : (tensor<2x2x3xf32>) -> tensor<1x2x2x3xf32>
// CHECK: %[[VAL_25:.*]] = "tosa.reshape"(%[[VAL_5]]) {new_shape = [1, 2, 2, 3]} : (tensor<2x2x3xf32>) -> tensor<1x2x2x3xf32>
// CHECK: %[[VAL_26:.*]] = "tosa.const"() {value = dense<5.000000e-01> : tensor<f32>} : () -> tensor<f32>
// CHECK: %[[VAL_27:.*]] = "tosa.sub"(%[[VAL_3]], %[[VAL_16]]) : (tensor<5x2x2x3xf32>, tensor<5x1x1x1xf32>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_28:.*]] = "tosa.add"(%[[VAL_23]], %[[VAL_26]]) : (tensor<5x1x1x1xf32>, tensor<f32>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_29:.*]] = "tosa.rsqrt"(%[[VAL_28]]) : (tensor<5x1x1x1xf32>) -> tensor<5x1x1x1xf32>
// CHECK: %[[VAL_30:.*]] = "tosa.mul"(%[[VAL_27]], %[[VAL_29]]) {shift = 0 : i32} : (tensor<5x2x2x3xf32>, tensor<5x1x1x1xf32>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_31:.*]] = "tosa.mul"(%[[VAL_30]], %[[VAL_24]]) {shift = 0 : i32} : (tensor<5x2x2x3xf32>, tensor<1x2x2x3xf32>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_32:.*]] = "tosa.add"(%[[VAL_31]], %[[VAL_25]]) : (tensor<5x2x2x3xf32>, tensor<1x2x2x3xf32>) -> tensor<5x2x2x3xf32>
// CHECK: %[[VAL_33:.*]] = torch_c.from_builtin_tensor %[[VAL_32]] : tensor<5x2x2x3xf32> -> !torch.vtensor<[5,2,2,3],f32>
// CHECK: return %[[VAL_33]] : !torch.vtensor<[5,2,2,3],f32>
// CHECK: }
func @forward(%arg0: !torch.vtensor<[5,2,2,3],f32> , %arg1: !torch.vtensor<[2,2,3],f32> , %arg2: !torch.vtensor<[2,2,3],f32> ) -> !torch.vtensor<[5,2,2,3],f32> {
%float5.000000e-01 = torch.constant.float 5.000000e-01
%int3 = torch.constant.int 3
%int2 = torch.constant.int 2
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
%0 = torch.prim.ListConstruct %int2, %int2, %int3 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%result0, %result1, %result2 = torch.aten.native_layer_norm %arg0, %0, %arg1, %arg2, %float5.000000e-01 : !torch.vtensor<[5,2,2,3],f32>, !torch.list<int>, !torch.vtensor<[2,2,3],f32>, !torch.vtensor<[2,2,3],f32>, !torch.float -> !torch.vtensor<[5,2,2,3],f32>, !torch.vtensor<[3],f32>, !torch.vtensor<[3],f32>
* [tosa] Support for AtenNativeLayerNormOp * [tosa] Support for AtenPermuteOp Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-04 06:08:19 +08:00
return %result0 : !torch.vtensor<[5,2,2,3],f32>
}
// -----
[tosa] Support for some ops and fix for Issue #532 (#575) * [tosa] Support for AtenNe[Tensor|Scalar]Op, AtenLog2Op, AtenBitwiseAndTensorOp, AtenSquareOp and AtenThresholdOp * Fix for Issue #532 - Mixed input types for few ops and updated few tests to use i32 instead of i64 Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-12 04:30:02 +08:00
// CHECK-LABEL: func @torch.aten.ne.Tensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_4:.*]] = "tosa.equal"(%[[VAL_2]], %[[VAL_3]]) : (tensor<?x?xf32>, tensor<?x?xf32>) -> tensor<?x?xi1>
// CHECK: %[[VAL_5:.*]] = "tosa.logical_not"(%[[VAL_4]]) : (tensor<?x?xi1>) -> tensor<?x?xi1>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xi1> -> !torch.vtensor<[?,?],i1>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[?,?],i1>
// CHECK: }
func @torch.aten.ne.Tensor$basic(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],i1> {
%0 = torch.aten.ne.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],i1>
return %0 : !torch.vtensor<[?,?],i1>
}
// -----
* [tosa] Support for AtenNativeLayerNormOp * [tosa] Support for AtenPermuteOp Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-04 06:08:19 +08:00
// CHECK-LABEL: func @forward(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[3,4,2],f32>) -> !torch.vtensor<[3,2,4],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[3,4,2],f32> -> tensor<3x4x2xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 1
// CHECK: %[[VAL_3:.*]] = torch.constant.int 2
// CHECK: %[[VAL_4:.*]] = torch.constant.int 0
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[VAL_5:.*]] = torch.prim.ListConstruct %[[VAL_4]], %[[VAL_3]], %[[VAL_2]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
* [tosa] Support for AtenNativeLayerNormOp * [tosa] Support for AtenPermuteOp Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-04 06:08:19 +08:00
// CHECK: %[[VAL_6:.*]] = "tosa.const"() {value = dense<[0, 2, 1]> : tensor<3xi64>} : () -> tensor<3xi64>
// CHECK: %[[VAL_7:.*]] = "tosa.transpose"(%[[VAL_1]], %[[VAL_6]]) : (tensor<3x4x2xf32>, tensor<3xi64>) -> tensor<3x2x4xf32>
// CHECK: %[[VAL_8:.*]] = torch_c.from_builtin_tensor %[[VAL_7]] : tensor<3x2x4xf32> -> !torch.vtensor<[3,2,4],f32>
// CHECK: return %[[VAL_8]] : !torch.vtensor<[3,2,4],f32>
// CHECK: }
func @forward(%arg0: !torch.vtensor<[3,4,2],f32> ) -> !torch.vtensor<[3,2,4],f32> {
%int1 = torch.constant.int 1
%int2 = torch.constant.int 2
%int0 = torch.constant.int 0
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
%0 = torch.prim.ListConstruct %int0, %int2, %int1 : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.permute %arg0, %0 : !torch.vtensor<[3,4,2],f32>, !torch.list<int> -> !torch.vtensor<[3,2,4],f32>
* [tosa] Support for AtenNativeLayerNormOp * [tosa] Support for AtenPermuteOp Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-04 06:08:19 +08:00
return %1 : !torch.vtensor<[3,2,4],f32>
}
[tosa] Support for some ops and fix for Issue #532 (#575) * [tosa] Support for AtenNe[Tensor|Scalar]Op, AtenLog2Op, AtenBitwiseAndTensorOp, AtenSquareOp and AtenThresholdOp * Fix for Issue #532 - Mixed input types for few ops and updated few tests to use i32 instead of i64 Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-12 04:30:02 +08:00
// -----
// CHECK-LABEL: func @torch.aten.bitwise_and.Tensor$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],si32>,
// CHECK-SAME: %[[VAL_1:.*]]: !torch.vtensor<[?,?],si32>) -> !torch.vtensor<[?,?],si32> {
// CHECK: %[[VAL_2:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],si32> -> tensor<?x?xi32>
// CHECK: %[[VAL_3:.*]] = torch_c.to_builtin_tensor %[[VAL_1]] : !torch.vtensor<[?,?],si32> -> tensor<?x?xi32>
// CHECK: %[[VAL_4:.*]] = "tosa.bitwise_and"(%[[VAL_2]], %[[VAL_3]]) : (tensor<?x?xi32>, tensor<?x?xi32>) -> tensor<?x?xi32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xi32> -> !torch.vtensor<[?,?],si32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],si32>
// CHECK: }
func @torch.aten.bitwise_and.Tensor$basic(%arg0: !torch.vtensor<[?,?],si32>, %arg1: !torch.vtensor<[?,?],si32>) -> !torch.vtensor<[?,?],si32> {
%0 = torch.aten.bitwise_and.Tensor %arg0, %arg1 : !torch.vtensor<[?,?],si32>, !torch.vtensor<[?,?],si32> -> !torch.vtensor<[?,?],si32>
return %0 : !torch.vtensor<[?,?],si32>
}
// -----
// CHECK-LABEL: func @torch.aten.log2$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = "tosa.const"() {value = dense<0.693147182> : tensor<1x1xf32>} : () -> tensor<1x1xf32>
// CHECK: %[[VAL_3:.*]] = "tosa.reciprocal"(%[[VAL_2]]) : (tensor<1x1xf32>) -> tensor<1x1xf32>
// CHECK: %[[VAL_4:.*]] = "tosa.log"(%[[VAL_1]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_5:.*]] = "tosa.mul"(%[[VAL_4]], %[[VAL_3]]) {shift = 0 : i32} : (tensor<?x?xf32>, tensor<1x1xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func @torch.aten.log2$basic(%arg0: !torch.vtensor<[?,?],f32> ) -> !torch.vtensor<[?,?],f32> {
%0 = torch.aten.log2 %arg0 : !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
[tosa] Support for Aten[Zeros|Ones|Fill_Scalar] ops (#604) Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-17 01:53:51 +08:00
// -----
// CHECK-LABEL: func @torch.aten.zeros$basic() -> !torch.vtensor<[3,4],f32> {
// CHECK: %[[VAL_0:.*]] = torch.constant.int 4
// CHECK: %[[VAL_1:.*]] = torch.constant.int 3
// CHECK: %[[VAL_2:.*]] = torch.constant.none
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %[[VAL_1]], %[[VAL_0]] : (!torch.int, !torch.int) -> !torch.list<int>
[tosa] Support for Aten[Zeros|Ones|Fill_Scalar] ops (#604) Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-17 01:53:51 +08:00
// CHECK: %[[VAL_4:.*]] = "tosa.const"() {value = dense<0> : tensor<3x4xi32>} : () -> tensor<3x4xi32>
// CHECK: %[[VAL_5:.*]] = "tosa.cast"(%[[VAL_4]]) : (tensor<3x4xi32>) -> tensor<3x4xf32>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<3x4xf32> -> !torch.vtensor<[3,4],f32>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[3,4],f32>
// CHECK: }
func @torch.aten.zeros$basic() -> !torch.vtensor<[3,4],f32> {
%int4 = torch.constant.int 4
%int3 = torch.constant.int 3
%none = torch.constant.none
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
%0 = torch.prim.ListConstruct %int3, %int4 : (!torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.zeros %0, %none, %none, %none, %none : !torch.list<int>, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[3,4],f32>
[tosa] Support for Aten[Zeros|Ones|Fill_Scalar] ops (#604) Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-17 01:53:51 +08:00
return %1 : !torch.vtensor<[3,4],f32>
}
// -----
[tosa] Support for Aten[Unsqueeze|Contiguous|Dropout|Reshape|View] ops (#700)
2022-03-26 05:15:07 +08:00
// CHECK-LABEL: func @torch.aten.unsqueeze$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[4,3],si32>) -> !torch.vtensor<[4,1,3],si32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[4,3],si32> -> tensor<4x3xi32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 1
// CHECK: %[[VAL_3:.*]] = "tosa.reshape"(%[[VAL_1]]) {new_shape = [4, 1, 3]} : (tensor<4x3xi32>) -> tensor<4x1x3xi32>
// CHECK: %[[VAL_4:.*]] = torch_c.from_builtin_tensor %[[VAL_3]] : tensor<4x1x3xi32> -> !torch.vtensor<[4,1,3],si32>
// CHECK: return %[[VAL_4]] : !torch.vtensor<[4,1,3],si32>
// CHECK: }
func @torch.aten.unsqueeze$basic(%arg0: !torch.vtensor<[4,3],si32> ) -> !torch.vtensor<[4,1,3],si32> {
%int1 = torch.constant.int 1
%0 = torch.aten.unsqueeze %arg0, %int1 : !torch.vtensor<[4,3],si32>, !torch.int -> !torch.vtensor<[4,1,3],si32>
return %0 : !torch.vtensor<[4,1,3],si32>
}
// -----
// CHECK-LABEL: func @torch.aten.contiguous$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.int 0
// CHECK: %[[VAL_3:.*]] = torch_c.from_builtin_tensor %[[VAL_1]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_3]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func @torch.aten.contiguous$basic(%arg0: !torch.vtensor<[?,?],f32> ) -> !torch.vtensor<[?,?],f32> {
%int0 = torch.constant.int 0
%0 = torch.aten.contiguous %arg0, %int0 : !torch.vtensor<[?,?],f32>, !torch.int -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}
// -----
[tosa] Support for Aten[Zeros|Ones|Fill_Scalar] ops (#604) Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-17 01:53:51 +08:00
// CHECK-LABEL: func @torch.aten.ones$basic() -> !torch.vtensor<[3,4],f32> {
// CHECK: %[[VAL_0:.*]] = torch.constant.int 4
// CHECK: %[[VAL_1:.*]] = torch.constant.int 3
// CHECK: %[[VAL_2:.*]] = torch.constant.none
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
// CHECK: %[[VAL_3:.*]] = torch.prim.ListConstruct %[[VAL_1]], %[[VAL_0]] : (!torch.int, !torch.int) -> !torch.list<int>
[tosa] Support for Aten[Zeros|Ones|Fill_Scalar] ops (#604) Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-17 01:53:51 +08:00
// CHECK: %[[VAL_4:.*]] = "tosa.const"() {value = dense<1> : tensor<3x4xi32>} : () -> tensor<3x4xi32>
// CHECK: %[[VAL_5:.*]] = "tosa.cast"(%[[VAL_4]]) : (tensor<3x4xi32>) -> tensor<3x4xf32>
// CHECK: %[[VAL_6:.*]] = torch_c.from_builtin_tensor %[[VAL_5]] : tensor<3x4xf32> -> !torch.vtensor<[3,4],f32>
// CHECK: return %[[VAL_6]] : !torch.vtensor<[3,4],f32>
// CHECK: }
func @torch.aten.ones$basic() -> !torch.vtensor<[3,4],f32> {
%int4 = torch.constant.int 4
%int3 = torch.constant.int 3
%none = torch.constant.none
Elide `!torch.` prefix in nested dialect types. This leads to much more succinct types in many cases: ``` !torch.list<!torch.int> !torch.list<int> !torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>> !torch.tuple<list<int>, list<int>> !torch.optional<!torch.list<!torch.int>> !torch.optional<list<int>> !torch.list<list<list<tensor>>> !torch.list<!torch.list<!torch.list<!torch.tensor>>> ``` I would like to take this further and allow omitting the `!torch.` prefix in all cases, but that's harder -- for example, we currently use `FuncOp` for functions, and so I don't think we can customize the printing there. It seems like it will be a longer road to getting that level of customization.
2022-03-16 07:22:56 +08:00
%0 = torch.prim.ListConstruct %int3, %int4 : (!torch.int, !torch.int) -> !torch.list<int>
%1 = torch.aten.ones %0, %none, %none, %none, %none : !torch.list<int>, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[3,4],f32>
[tosa] Support for Aten[Zeros|Ones|Fill_Scalar] ops (#604) Signed-off-by: Anup Gangwar <anup.gangwar@arm.com> Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-17 01:53:51 +08:00
return %1 : !torch.vtensor<[3,4],f32>
}
[tosa] Support for Aten[Unsqueeze|Contiguous|Dropout|Reshape|View] ops (#700)
2022-03-26 05:15:07 +08:00
// -----
// CHECK-LABEL: func @torch.aten.dropout$basic(
// CHECK-SAME: %[[VAL_0:.*]]: !torch.vtensor<[?,?],f32>) -> !torch.vtensor<[?,?],f32> {
// CHECK: %[[VAL_1:.*]] = torch_c.to_builtin_tensor %[[VAL_0]] : !torch.vtensor<[?,?],f32> -> tensor<?x?xf32>
// CHECK: %[[VAL_2:.*]] = torch.constant.float 0.000000e+00
// CHECK: %[[VAL_3:.*]] = torch.constant.bool false
// CHECK: %[[VAL_4:.*]] = "tosa.cast"(%[[VAL_1]]) : (tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: %[[VAL_5:.*]] = torch_c.from_builtin_tensor %[[VAL_4]] : tensor<?x?xf32> -> !torch.vtensor<[?,?],f32>
// CHECK: return %[[VAL_5]] : !torch.vtensor<[?,?],f32>
// CHECK: }
func @torch.aten.dropout$basic(%arg0: !torch.vtensor<[?,?],f32> ) -> !torch.vtensor<[?,?],f32> {
%float0.000000e00 = torch.constant.float 0.000000e+00
%false = torch.constant.bool false
%0 = torch.aten.dropout %arg0, %float0.000000e00, %false : !torch.vtensor<[?,?],f32>, !torch.float, !torch.bool -> !torch.vtensor<[?,?],f32>
return %0 : !torch.vtensor<[?,?],f32>
}