#5044: Add optional output ,cq_id to addalpha

tests/tt_eager/python_api_testing/sweep_tests/op_map.py

@@ -524,6 +524,10 @@
         "tt_op": tt_lib_ops.eltwise_addalpha,
         "pytorch_op": pytorch_ops.addalpha,
     },
+    "eltwise-addalpha-optional": {
+        "tt_op": tt_lib_ops.eltwise_addalpha_optional,
+        "pytorch_op": pytorch_ops.addalpha,
+    },
     "lamb-optimizer": {
         "tt_op": tt_lib_ops.lamb_optimizer,
         "pytorch_op": pytorch_ops.lamb_optimizer,

tests/tt_eager/python_api_testing/sweep_tests/pytests/tt_dnn/test_addalpha.py

@@ -0,0 +1,124 @@
+# SPDX-FileCopyrightText: © 2023 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+import torch
+from functools import partial
+import tt_lib as ttl
+import numpy as np
+
+
+from tests.tt_eager.python_api_testing.sweep_tests import (
+    comparison_funcs,
+    generation_funcs,
+)
+from tests.tt_eager.python_api_testing.sweep_tests.run_pytorch_ci_tests import (
+    run_single_pytorch_test,
+)
+from models.utility_functions import is_wormhole_b0
+
+shapes = [
+    [[1, 1, 32, 32], [1, 1, 32, 32]],  # Single core
+    [[1, 1, 32, 32], [32, 1, 32, 32]],  # Single core
+    [[64, 1, 32, 32], [1, 1, 32, 32]],  # Single core
+    [[1, 1, 320, 384], [1, 1, 320, 384]],  # Multi core
+    [[1, 3, 320, 384], [1, 3, 320, 384]],  # Multi core
+]
+
+input_mem_cfgs = generation_funcs.supported_mem_configs
+output_mem_cfgs = generation_funcs.supported_mem_configs
+
+if is_wormhole_b0():
+    shapes = [
+        shapes[0],
+    ]
+    input_mem_cfgs = [
+        input_mem_cfgs[0],
+    ]
+
+
+@pytest.mark.parametrize(
+    "input_shapes",
+    shapes,
+)
+@pytest.mark.parametrize("input_mem_config", input_mem_cfgs)
+@pytest.mark.parametrize("output_mem_config", output_mem_cfgs)
+@pytest.mark.parametrize("fn_kind", ["addalpha"])
+def test_run_addalpha(
+    input_shapes,
+    fn_kind,
+    input_mem_config,
+    output_mem_config,
+    device,
+    function_level_defaults,
+):
+    datagen_func = [
+        generation_funcs.gen_func_with_cast(partial(generation_funcs.gen_rand, low=-100, high=100), torch.float32)
+    ] * len(input_shapes)
+    test_args = list(generation_funcs.gen_default_dtype_layout_device(input_shapes))[0]
+    test_args.update(
+        {
+            "input_mem_config": [input_mem_config, input_mem_config],
+            "output_mem_config": output_mem_config,
+            "alpha": np.random.randint(1, 100),
+        }
+    )
+    comparison_func = comparison_funcs.comp_pcc
+    run_single_pytorch_test(
+        f"eltwise-{fn_kind}",
+        input_shapes,
+        datagen_func,
+        comparison_func,
+        device,
+        test_args,
+    )
+
+
+shapes_w_output = [
+    [[1, 1, 32, 32], [1, 1, 32, 32], [1, 1, 32, 32]],  # Single core
+    [[1, 1, 32, 32], [32, 1, 32, 32], [32, 1, 32, 32]],  # Single core
+    [[64, 1, 32, 32], [1, 1, 32, 32], [64, 1, 32, 32]],  # Single core
+    [[1, 1, 320, 384], [1, 1, 320, 384], [1, 1, 320, 384]],  # Multi core
+    [[1, 3, 320, 384], [1, 3, 320, 384], [1, 3, 320, 384]],  # Multi core
+]
+
+
+@pytest.mark.parametrize(
+    "input_shapes",
+    shapes_w_output,
+)
+@pytest.mark.parametrize("input_mem_config", input_mem_cfgs)
+@pytest.mark.parametrize("fn_kind", ["addalpha"])
+@pytest.mark.parametrize("pass_qid", [True, False])
+def test_run_addalpha_optional_output(
+    input_shapes,
+    fn_kind,
+    input_mem_config,
+    device,
+    pass_qid,
+    function_level_defaults,
+):
+    datagen_func = [
+        generation_funcs.gen_func_with_cast(partial(generation_funcs.gen_rand, low=-100, high=100), torch.float32)
+    ] * len(input_shapes)
+    datagen_func.append(
+        generation_funcs.gen_func_with_cast(partial(generation_funcs.gen_rand, low=-80, high=80), torch.bfloat16)
+    )
+    test_args = list(generation_funcs.gen_default_dtype_layout_device(input_shapes))[0]
+    test_args.update(
+        {
+            "input_mem_config": [input_mem_config, input_mem_config, input_mem_config],
+            "alpha": np.random.randint(1, 100),
+            "queue_id": pass_qid,
+        }
+    )
+    comparison_func = comparison_funcs.comp_pcc
+    run_single_pytorch_test(
+        f"eltwise-{fn_kind}-optional",
+        input_shapes,
+        datagen_func,
+        comparison_func,
+        device,
+        test_args,
+    )

tests/tt_eager/python_api_testing/sweep_tests/tt_lib_ops.py

@@ -1026,6 +1026,33 @@ def eltwise_addalpha(
     return tt2torch_tensor(t2)
 
 
+@setup_host_and_device
+def eltwise_addalpha_optional(
+    x,
+    y,
+    z,
+    *args,
+    alpha,
+    device,
+    dtype,
+    layout,
+    queue_id,
+    input_mem_config,
+    **kwargs,
+):
+    t0 = setup_tt_tensor(x, device, layout[0], input_mem_config[0], dtype[0])
+    t1 = setup_tt_tensor(y, device, layout[1], input_mem_config[1], dtype[1])
+    t2 = setup_tt_tensor(z, device, layout[2], input_mem_config[2], dtype[2])
+    cq_id = 0
+
+    if queue_id:
+        ttl.tensor.addalpha(cq_id, t0, t1, alpha, output_tensor=t2)
+    else:
+        ttl.tensor.addalpha(t0, t1, alpha, output_tensor=t2)
+
+    return tt2torch_tensor(t2)
+
+
 @setup_host_and_device
 def eltwise_div(
     x,

tt_eager/tt_dnn/op_library/composite/composite_ops.cpp

@@ -701,12 +701,52 @@ Tensor subalpha(const Tensor& input_a, const Tensor& input_b, float alpha, const
 }
 
 // addalpha(input, other, alpha) = input + (alpha * other)
-Tensor _addalpha(const Tensor& input_a, const Tensor& input_b, float alpha, const MemoryConfig& output_mem_config) {
-    Tensor result = add(mul_unary(input_b, alpha, output_mem_config), input_a, std::nullopt, output_mem_config);
-    return result;
+Tensor _addalpha(
+    const Tensor& input_a,
+    const Tensor& input_b,
+    float alpha,
+    const MemoryConfig& output_mem_config,
+    std::optional<Tensor> output_tensor) {
+    if (output_tensor.has_value()) {
+        add(mul_unary(input_b, alpha, output_mem_config), input_a, std::nullopt, operation::DEFAULT_OUTPUT_MEMORY_CONFIG, std::nullopt, output_tensor.value());
+        return output_tensor.value();
+    }
+
+    return add(mul_unary(input_b, alpha, output_mem_config), input_a, std::nullopt, output_mem_config);
+}
+Tensor addalpha(
+    const Tensor& input_a,
+    const Tensor& input_b,
+    float alpha,
+    const MemoryConfig& output_mem_config,
+    std::optional<Tensor> output_tensor) {
+    return operation::decorate_as_composite(__func__, _addalpha)(
+        input_a, input_b, alpha, output_mem_config, output_tensor);
 }
-Tensor addalpha(const Tensor& input_a, const Tensor& input_b, float alpha, const MemoryConfig& output_mem_config) {
-    return operation::decorate_as_composite(__func__, _addalpha)(input_a, input_b, alpha, output_mem_config);
+
+Tensor _addalpha_overload(
+    uint8_t cq_id,
+    const Tensor& input_a,
+    const Tensor& input_b,
+    float alpha,
+    const MemoryConfig& output_mem_config,
+    std::optional<Tensor> output_tensor) {
+    if (output_tensor.has_value()) {
+        add(mul_unary(input_b, alpha, output_mem_config), input_a, std::nullopt, operation::DEFAULT_OUTPUT_MEMORY_CONFIG, std::nullopt, output_tensor.value());
+        return output_tensor.value();
+    }
+
+    return add(mul_unary(input_b, alpha, output_mem_config), input_a, std::nullopt, output_mem_config);
+}
+Tensor addalpha(
+    uint8_t cq_id,
+    const Tensor& input_a,
+    const Tensor& input_b,
+    float alpha,
+    const MemoryConfig& output_mem_config,
+    std::optional<Tensor> output_tensor) {
+    return operation::decorate_as_composite(__func__, _addalpha_overload)(
+        cq_id, input_a, input_b, alpha, output_mem_config, output_tensor);
 }
 
 // repeat interleave supports repeats as 1 to inf, dim between 0 to 2

tt_eager/tt_dnn/op_library/composite/composite_ops.hpp

@@ -114,9 +114,7 @@ Tensor selu(
     const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG);
 
 Tensor celu(
-    const Tensor& x,
-    float alpha,
-    const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG);
+    const Tensor& x, float alpha, const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG);
 
 // Function Swish = same as SILU
 // use transformation y = x * sigmoid( x ) by broadcast
@@ -230,14 +228,13 @@ Tensor logical_noti(
     float immediate,
     const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG);
 
-//prod
+// prod
 Tensor prod(
     const Tensor& input_a,
     bool all_dimensions = false,
     int64_t dim = 0,
     const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG);
 
-
 /*
 Returns a new tensor with the signed angles in radians between vectors
 
@@ -267,7 +264,16 @@ Tensor addalpha(
     const Tensor& input_a,
     const Tensor& input_b,
     float alpha,
-    const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG);
+    const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG,
+    std::optional<Tensor> output_tensor = std::nullopt);
+
+Tensor addalpha(
+    uint8_t cq_id,
+    const Tensor& input_a,
+    const Tensor& input_b,
+    float alpha,
+    const MemoryConfig& output_mem_config = operation::DEFAULT_OUTPUT_MEMORY_CONFIG,
+    std::optional<Tensor> output_tensor = std::nullopt);
 
 // repeat interleave
 Tensor repeat_interleave(