Add new ttnn sweeps (#13239)

* #11512: Add log_sigmoid and logical_not_sweeps * #11512: Add sweeps to workflow * #11512: Add new sweeps logaddexp and lgamma * #11512: Add new sweeps logaddexp and lgamma * #11512: Add new sweeps ldexp * #11512: Add erf sweep tests * #11512: Add erfinv sweeps * #11512: Update run nightly pipline * #11512: Add hypot and i0 sweeps * #11512: Add run config for sweeps * #11512: Add sigmoid and sigmoid_accurate tests * #11512: Add glu and silu * #11512: Fix run config * #11512: Fix suites --------- Co-authored-by: “Nenad <“[email protected]”>
tenstorrent · Oct 3, 2024 · 844ca1c · 844ca1c
1 parent a4a9a4c
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diff --git a/.github/workflows/ttnn-run-sweeps.yaml b/.github/workflows/ttnn-run-sweeps.yaml
@@ -45,6 +45,16 @@ on:
           - eltwise.unary.bitwise.bitwise_or
           - eltwise.unary.bitwise.bitwise_right_shift
           - eltwise.unary.bitwise.bitwise_xor
+          - eltwise.unary.log_sigmoid.log_sigmoid
+          - eltwise.unary.logical_not_.logical_not_
+          - eltwise.unary.erf.erf
+          - eltwise.unary.erfinv.erfinv
+          - eltwise.unary.i0.i0
+          - eltwise.unary.silu.silu
+          - eltwise.unary.glu.glu
+          - eltwise.unary.lgamma.lgamma
+          - eltwise.unary.sigmoid.sigmoid
+          - eltwise.unary.sigmoid_accurate.sigmoid_accurate
           - eltwise.binary.subtract.subtract
           - eltwise.binary.multiply.multiply
           - eltwise.binary.div.div
@@ -56,6 +66,9 @@ on:
           - eltwise.binary.bcast.bcast_h_sharded
           - eltwise.binary.bcast.bcast
           - eltwise.binary.fmod.fmod
+          - eltwise.binary.logaddexp.logaddexp
+          - eltwise.binary.ldexp.ldexp
+          - eltwise.binary.hypot.hypot
           - eltwise.composite.binary.addalpha.addalpha
           - eltwise.composite.binary.subalpha.subalpha
           - eltwise.composite.binary.minimum.minimum

diff --git a/tests/sweep_framework/sweeps/eltwise/binary/hypot/hypot.py b/tests/sweep_framework/sweeps/eltwise/binary/hypot/hypot.py
@@ -0,0 +1,86 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import torch
+import random
+import ttnn
+from tests.sweep_framework.utils import gen_shapes
+from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
+
+from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
+from models.utility_functions import torch_random
+
+# Override the default timeout in seconds for hang detection.
+TIMEOUT = 30
+
+random.seed(0)
+
+# Parameters provided to the test vector generator are defined here.
+# They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
+# Each suite has a key name (in this case "suite_1") which will associate the test vectors to this specific suite of inputs.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "xfail": {
+        "input_shape": gen_shapes([1, 1, 32, 32], [6, 12, 256, 256], [1, 1, 32, 32], 16)
+        + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
+        + gen_shapes([32, 32], [256, 256], [32, 32], 16),
+        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_b_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_a_layout": [ttnn.TILE_LAYOUT],
+        "input_b_layout": [ttnn.TILE_LAYOUT],
+        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_b_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+    },
+}
+
+
+# This is the run instructions for the test, defined by the developer.
+# The run function must take the above-defined parameters as inputs.
+# The runner will call this run function with each test vector, and the returned results from this function will be stored.
+# If you defined a device_mesh_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
+def run(
+    input_shape,
+    input_a_dtype,
+    input_b_dtype,
+    input_a_layout,
+    input_b_layout,
+    input_a_memory_config,
+    input_b_memory_config,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_a_dtype
+    )(input_shape)
+    torch_input_tensor_b = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_b_dtype
+    )(input_shape)
+    torch_output_tensor = torch.hypot(torch_input_tensor_a, torch_input_tensor_b)
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_a_layout,
+        device=device,
+        memory_config=input_a_memory_config,
+    )
+    input_tensor_b = ttnn.from_torch(
+        torch_input_tensor_b,
+        dtype=input_b_dtype,
+        layout=input_b_layout,
+        device=device,
+        memory_config=input_b_memory_config,
+    )
+    start_time = start_measuring_time()
+    result = ttnn.hypot(input_tensor_a, input_tensor_b)
+    output_tensor = ttnn.to_torch(result)
+    e2e_perf = stop_measuring_time(start_time)
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]
diff --git a/tests/sweep_framework/sweeps/eltwise/binary/ldexp/ldexp.py b/tests/sweep_framework/sweeps/eltwise/binary/ldexp/ldexp.py
@@ -0,0 +1,86 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import torch
+import random
+import ttnn
+from tests.sweep_framework.utils import gen_shapes
+from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
+
+from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
+from models.utility_functions import torch_random
+
+# Override the default timeout in seconds for hang detection.
+TIMEOUT = 30
+
+random.seed(0)
+
+# Parameters provided to the test vector generator are defined here.
+# They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
+# Each suite has a key name (in this case "suite_1") which will associate the test vectors to this specific suite of inputs.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "nightly": {
+        "input_shape": gen_shapes([1, 1, 32, 32], [6, 12, 256, 256], [1, 1, 32, 32], 16)
+        + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
+        + gen_shapes([32, 32], [256, 256], [32, 32], 16),
+        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_b_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_a_layout": [ttnn.TILE_LAYOUT],
+        "input_b_layout": [ttnn.TILE_LAYOUT],
+        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_b_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+    },
+}
+
+
+# This is the run instructions for the test, defined by the developer.
+# The run function must take the above-defined parameters as inputs.
+# The runner will call this run function with each test vector, and the returned results from this function will be stored.
+# If you defined a device_mesh_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
+def run(
+    input_shape,
+    input_a_dtype,
+    input_b_dtype,
+    input_a_layout,
+    input_b_layout,
+    input_a_memory_config,
+    input_b_memory_config,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(torch_random, low=-10, high=10, dtype=torch.float32), input_a_dtype
+    )(input_shape)
+    torch_input_tensor_b = gen_func_with_cast_tt(
+        partial(torch_random, low=-10, high=10, dtype=torch.float32), input_b_dtype
+    )(input_shape)
+    torch_output_tensor = torch.ldexp(torch_input_tensor_a, torch_input_tensor_b)
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_a_layout,
+        device=device,
+        memory_config=input_a_memory_config,
+    )
+    input_tensor_b = ttnn.from_torch(
+        torch_input_tensor_b,
+        dtype=input_b_dtype,
+        layout=input_b_layout,
+        device=device,
+        memory_config=input_b_memory_config,
+    )
+    start_time = start_measuring_time()
+    result = ttnn.ldexp(input_tensor_a, input_tensor_b)
+    output_tensor = ttnn.to_torch(result)
+    e2e_perf = stop_measuring_time(start_time)
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]
diff --git a/tests/sweep_framework/sweeps/eltwise/binary/logaddexp/logaddexp.py b/tests/sweep_framework/sweeps/eltwise/binary/logaddexp/logaddexp.py
@@ -0,0 +1,86 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import torch
+import random
+import ttnn
+from tests.sweep_framework.utils import gen_shapes
+from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
+
+from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
+from models.utility_functions import torch_random
+
+# Override the default timeout in seconds for hang detection.
+TIMEOUT = 30
+
+random.seed(0)
+
+# Parameters provided to the test vector generator are defined here.
+# They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
+# Each suite has a key name (in this case "suite_1") which will associate the test vectors to this specific suite of inputs.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "nightly": {
+        "input_shape": gen_shapes([1, 1, 32, 32], [6, 12, 256, 256], [1, 1, 32, 32], 16)
+        + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
+        + gen_shapes([32, 32], [256, 256], [32, 32], 16),
+        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_b_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_a_layout": [ttnn.TILE_LAYOUT],
+        "input_b_layout": [ttnn.TILE_LAYOUT],
+        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "input_b_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+    },
+}
+
+
+# This is the run instructions for the test, defined by the developer.
+# The run function must take the above-defined parameters as inputs.
+# The runner will call this run function with each test vector, and the returned results from this function will be stored.
+# If you defined a device_mesh_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
+def run(
+    input_shape,
+    input_a_dtype,
+    input_b_dtype,
+    input_a_layout,
+    input_b_layout,
+    input_a_memory_config,
+    input_b_memory_config,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(torch_random, low=-64, high=64, dtype=torch.float32), input_a_dtype
+    )(input_shape)
+    torch_input_tensor_b = gen_func_with_cast_tt(
+        partial(torch_random, low=-64, high=64, dtype=torch.float32), input_b_dtype
+    )(input_shape)
+    torch_output_tensor = torch.logaddexp(torch_input_tensor_a, torch_input_tensor_b)
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_a_layout,
+        device=device,
+        memory_config=input_a_memory_config,
+    )
+    input_tensor_b = ttnn.from_torch(
+        torch_input_tensor_b,
+        dtype=input_b_dtype,
+        layout=input_b_layout,
+        device=device,
+        memory_config=input_b_memory_config,
+    )
+    start_time = start_measuring_time()
+    result = ttnn.logaddexp(input_tensor_a, input_tensor_b)
+    output_tensor = ttnn.to_torch(result)
+    e2e_perf = stop_measuring_time(start_time)
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]
diff --git a/tests/sweep_framework/sweeps/eltwise/unary/erf/erf.py b/tests/sweep_framework/sweeps/eltwise/unary/erf/erf.py
@@ -0,0 +1,73 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import torch
+import random
+import ttnn
+from tests.sweep_framework.utils import gen_shapes
+from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
+
+from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
+from models.utility_functions import torch_random
+
+# Override the default timeout in seconds for hang detection.
+TIMEOUT = 30
+
+random.seed(0)
+
+# Parameters provided to the test vector generator are defined here.
+# They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
+# Each suite has a key name (in this case "suite_1") which will associate the test vectors to this specific suite of inputs.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "nightly": {
+        "input_shape": gen_shapes([1, 1, 32, 32], [6, 12, 256, 256], [1, 1, 32, 32], 16)
+        + gen_shapes([1, 32, 32], [12, 256, 256], [1, 32, 32], 16)
+        + gen_shapes([32, 32], [256, 256], [32, 32], 32),
+        "input_a_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_a_layout": [ttnn.TILE_LAYOUT],
+        "input_a_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+    },
+}
+
+
+# This is the run instructions for the test, defined by the developer.
+# The run function must take the above-defined parameters as inputs.
+# The runner will call this run function with each test vector, and the returned results from this function will be stored.
+# If you defined a device_mesh_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
+def run(
+    input_shape,
+    input_a_dtype,
+    input_a_layout,
+    input_a_memory_config,
+    output_memory_config,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    torch_input_tensor_a = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float16), input_a_dtype
+    )(input_shape)
+    torch_output_tensor = torch.erf(torch_input_tensor_a)
+
+    input_tensor_a = ttnn.from_torch(
+        torch_input_tensor_a,
+        dtype=input_a_dtype,
+        layout=input_a_layout,
+        device=device,
+        memory_config=input_a_memory_config,
+    )
+
+    start_time = start_measuring_time()
+    result = ttnn.erf(input_tensor_a, memory_config=output_memory_config)
+    output_tensor = ttnn.to_torch(result)
+    e2e_perf = stop_measuring_time(start_time)
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]