#7516: Remove reshat pe on to_memory_config

tests/ttnn/integration_tests/vit/test_performance_ttnn_optim_sharded_vit.py

@@ -41,6 +41,8 @@ def get_expected_times(functional_vit):
 @pytest.mark.parametrize("sequence_size", [196])  ## padded from 197 to 224
 @pytest.mark.parametrize("functional_vit", [ttnn_optimized_sharded_vit])
 def test_performance_vit_encoder(device, use_program_cache, model_name, batch_size, sequence_size, functional_vit):
+    ttnn.dump_stack_trace_on_segfault()
+
     config = transformers.ViTConfig.from_pretrained(model_name)
     config.num_hidden_layers = 12
     model = transformers.ViTForImageClassification.from_pretrained(
@@ -127,6 +129,8 @@ def test_performance_vit_encoder(device, use_program_cache, model_name, batch_si
 def test_performance_vit_e2e(
     device, use_program_cache, model_name, batch_size, image_size, sequence_size, functional_vit
 ):
+    ttnn.dump_stack_trace_on_segfault()
+
     config = transformers.ViTConfig.from_pretrained(model_name)
     config.num_hidden_layers = 12
     model = transformers.ViTForImageClassification.from_pretrained("google/vit-base-patch16-224", config=config)

tests/ttnn/unit_tests/test_async.py

@@ -0,0 +1,46 @@
+# SPDX-FileCopyrightText: © 2023 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+import torch
+
+import ttnn
+
+from tests.ttnn.utils_for_testing import assert_with_pcc
+
+
+@pytest.mark.parametrize("scalar", [3])
+@pytest.mark.parametrize("size", [64])
+def test_add_1D_tensor_and_scalar(device, scalar, size):
+    device.enable_async(True)
+
+    torch.manual_seed(0)
+
+    torch_input_tensor = torch.rand((size,), dtype=torch.bfloat16)
+    torch_output_tensor = torch_input_tensor + scalar
+
+    input_tensor = ttnn.from_torch(torch_input_tensor, layout=ttnn.TILE_LAYOUT, device=device)
+    output_tensor = input_tensor + scalar
+    output_tensor = ttnn.to_torch(output_tensor, torch_rank=1)
+
+    assert ttnn.pearson_correlation_coefficient(torch_output_tensor, output_tensor) >= 0.99988
+    assert output_tensor.shape == (size,)
+
+
+@pytest.mark.parametrize("h", [32])
+@pytest.mark.parametrize("w", [64])
+def test_add_2D_tensors(device, h, w):
+    device.enable_async(True)
+
+    torch_input_tensor_a = torch.rand((h, w), dtype=torch.bfloat16)
+    torch_input_tensor_b = torch.rand((h, w), dtype=torch.bfloat16)
+    torch_output_tensor = torch.add(torch_input_tensor_a, torch_input_tensor_b)
+
+    input_tensor_a = ttnn.from_torch(torch_input_tensor_a, layout=ttnn.TILE_LAYOUT, device=device)
+    input_tensor_b = ttnn.from_torch(torch_input_tensor_b, layout=ttnn.TILE_LAYOUT, device=device)
+    output = ttnn.add(input_tensor_a, input_tensor_b)
+    output = ttnn.to_torch(output)
+
+    assert_with_pcc(torch_output_tensor, output, 0.9999)

tt_eager/tt_dnn/op_library/operation.hpp

@@ -85,6 +85,9 @@ struct function_traits<fn<TReturn, TArgs...> T::*> {
 template <class FnPtr>
 using last_arg_of_function_t = typename function_traits<FnPtr>::last_arg_t;
 
+template <class FnPtr>
+using return_arg_of_function_t = typename function_traits<FnPtr>::return_t;
+
 template<typename, typename = std::void_t<>>
 struct has_create_program : std::false_type {};
 

tt_eager/tt_dnn/op_library/run_operation.cpp

@@ -25,7 +25,7 @@ inline bool any_tensor_on_multi_device(const Tensors& tensors) {
     return std::any_of(tensors.begin(), tensors.end(), [](const Tensor& tensor) { return tensor.storage_type() == StorageType::MULTI_DEVICE; });
 }
 
-static Device* get_device(const Tensors& input_tensors, const OptionalConstTensors& optional_input_tensors = {}) {
+Device* get_device(const Tensors& input_tensors, const OptionalConstTensors& optional_input_tensors) {
     for (auto& input_tensor : input_tensors) {
         if (input_tensor.storage_type() == StorageType::DEVICE) {
             return input_tensor.device();
@@ -657,12 +657,18 @@ void launch_op(
     std::function<std::vector<Tensor>(const std::vector<Tensor>&, const std::vector<std::optional<const Tensor>>&)>&& op_func,
     const std::vector<Tensor> input_tensors,
     std::vector<Tensor>& output_tensors,
-    const std::vector<std::optional<const Tensor>> optional_input_tensors
+    const std::vector<std::optional<const Tensor>> optional_input_tensors,
+    bool enable_autoformat_device
 ) {
     // Send host side op compile and run to the worker queue
     // Assert to ensure that worker threads are specified.
     ZoneScopedN("LaunchOp");
     auto& workers = output_tensors.at(0).workers;
+    if (not enable_autoformat_device and workers.empty()) {
+        // Run on the host
+        output_tensors = op_func(input_tensors, optional_input_tensors);
+        return;
+    }
     for (auto& output_tensor : output_tensors) {
         TT_FATAL(output_tensor.workers.size(), "Worker threads must be specified for outputs populated by launch_op. This API can only be used for creating output tensors on device.");
         TT_FATAL(output_tensor.workers == workers, "Worker threads must be consistent across all outputs populated by launch_op.");
@@ -815,7 +821,10 @@ void validate_workers_and_storage(const std::vector<Tensor>& inputs, const std::
     }
 }
 
-std::vector<Device*> get_workers_for_op_output(const std::vector<Tensor>&& inputs, const std::vector<std::optional<const Tensor>>&& optional_inputs) {
+std::vector<Device*> get_workers_for_op_output(
+    const std::vector<Tensor>&& inputs,
+    const std::vector<std::optional<const Tensor>>&& optional_inputs,
+    bool enable_autoformat_device) {
     std::vector<Device*> workers_for_op = {};
     // Infer output workers from inputs. For multi-device tensors the number
     // of workers used for the op (and assigned to the ouput) is the minimum
@@ -844,11 +853,10 @@ std::vector<Device*> get_workers_for_op_output(const std::vector<Tensor>&& input
     validate_workers_and_storage(inputs, optional_inputs, workers_for_op);
     // Workers not specified - inputs are on host and not multi-device.
     // Use the default device from autoformat.
-    if (not workers_for_op.size()) {
+    if (enable_autoformat_device and not workers_for_op.size()) {
         TT_FATAL(AutoFormat::GetDefaultDevice(), "Default device must be specified using AutoFormat::SetDefaultDevice, if workers are not specified for inputs to op.");
         workers_for_op = {AutoFormat::GetDefaultDevice()};
     }
     return workers_for_op;
 }
-
 }

tt_eager/tt_dnn/op_library/run_operation.hpp

@@ -43,6 +43,7 @@ auto generic_create_output_tensors(
 
 namespace run_operation_state {
 namespace detail {
+
 struct RunOperationState {
 
     RunOperationState() {}
@@ -96,6 +97,7 @@ inline const auto& get_composite_parent_names() {
 
 
 namespace detail {
+
 template<typename ReturnType, typename... Args>
 struct CompositeOperation {
 
@@ -387,8 +389,8 @@ void launch_op(
     std::function<std::vector<Tensor>(const Tensors&, const OptionalConstTensors&)>&& op_func,
     const std::vector<Tensor> input_tensors,
     std::vector<Tensor>& output_tensors,
-    const std::vector<std::optional<const Tensor>> optional_input_tensors = {}
-);
+    const std::vector<std::optional<const Tensor>> optional_input_tensors = {},
+    bool enable_autoformat_device = true);
 
 void launch_with_autoformat(
     std::function<std::vector<Tensor>(const std::vector<Tensor>&, const std::vector<std::optional<const Tensor>>&)>&& op_func,
@@ -397,7 +399,14 @@ void launch_with_autoformat(
     const std::vector<std::optional<const Tensor>> optional_input_tensors = {}
 );
 
-std::vector<Device*> get_workers_for_op_output(const std::vector<Tensor>&& inputs, const std::vector<std::optional<const Tensor>>&& optional_inputs = {});
+std::vector<Device*> get_workers_for_op_output(
+    const std::vector<Tensor>&& inputs,
+    const std::vector<std::optional<const Tensor>>&& optional_inputs = {},
+    bool enable_autoformat_device = true);
+
+namespace detail{
+    Device* get_device(const Tensors& input_tensors, const OptionalConstTensors& optional_input_tensors = {});
+}
 
 } //namespace operation
 

ttnn/cpp/pybind11/core.hpp

@@ -33,6 +33,7 @@ void py_module(py::module& module) {
 
     module.def("get_memory_config", &ttnn::get_memory_config);
     module.def("set_printoptions", &ttnn::set_printoptions, py::kw_only(), py::arg("profile"));
+    module.def("dump_stack_trace_on_segfault", &ttnn::core::dump_stack_trace_on_segfault);
 }
 
 }  // namespace core

ttnn/cpp/ttnn/core.hpp

@@ -10,6 +10,7 @@
 #include "tt_eager/tensor/tensor_impl.hpp"  // TTNN_TENSOR_PRINT_PROFILE
 #include "tt_eager/tensor/types.hpp"
 #include "ttnn/types.hpp"
+#include <csignal>
 
 namespace ttnn {
 using tt::tt_metal::any_tensor_on_multi_device;
@@ -97,6 +98,18 @@ inline void set_printoptions(const std::string& profile) {
         }).value();
 }
 
+inline void segfault_handler(int sig) {
+    std::cerr << tt::assert::backtrace_to_string() << std::endl;
+    exit(EXIT_FAILURE);
+}
+
+inline void dump_stack_trace_on_segfault() {
+    if (std::signal(SIGSEGV, segfault_handler) == SIG_ERR) {
+        std::cerr << "Error: cannot handle SIGSEGV" << std::endl;
+        exit(EXIT_FAILURE);
+    }
+}
+
 }  // namespace core
 
 using core::CONFIG;