Fix regression on conv1d auto-shard

Add conv1d pytorch sweep to nightly test,
to prevent further regressions.

tests/sweep_framework/sweeps/conv2d/short/conv2d_short_sweep.py

@@ -1636,3 +1636,19 @@ def test_conv2d_localrun_fail_only(device, input_spec):
         device,
     )[0]
     assert pcc, messsage
+
+
+@pytest.mark.parametrize("input_spec", parameters["short_sweep_suite_conv1d"]["input_specs"])
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 16384}], indirect=True)
+def test_conv2d_localrun_conv1d(device, input_spec):
+    pcc, messsage = run_conv1d_short_sweep(
+        input_spec,
+        device,
+    )[0]
+    assert pcc, messsage
+
+
+failing_parameters_conv1d = [
+    # [batch_size, output_channels, input_channels, input_length, kernel_size, stride, pad, groups, dilation, bias]
+    [1, 768, 768, 3000, 3, 2, 1, 1, 1, True],
+]

tests/ttnn/nightly/unit_tests/operations/conv2d/test_conv1d_sweeps.py

@@ -0,0 +1,32 @@
+# SPDX-FileCopyrightText: © 2025 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from tests.sweep_framework.sweep_utils.conv2d_common import run_conv1d_short_sweep
+from tests.sweep_framework.sweeps.conv2d.short.conv2d_short_sweep import parameters as parameters_ttnn_pytorch
+from tests.sweep_framework.sweeps.conv2d.short.conv2d_short_sweep import (
+    failing_parameters_conv1d as failing_parameters_ttnn_pytorch,
+)
+
+from models.utility_functions import (
+    is_wormhole_b0,
+)
+
+import pytest
+
+
+@pytest.mark.parametrize("input_spec", parameters_ttnn_pytorch["short_sweep_suite_conv1d"]["input_specs"])
+@pytest.mark.parametrize("device_params", [{"l1_small_size": 16384}], indirect=True)
+def test_ttnn_pytorch_sweep(device, input_spec):
+    if device.core_grid.y != 8 and is_wormhole_b0():
+        pytest.skip("Needs 8x8 grid for wormhole_b0")
+
+    # Check if input_spec is in failing_parameters
+    if input_spec in failing_parameters_ttnn_pytorch:
+        pytest.skip(f"Skipping test for failing input_spec: {input_spec}")
+
+    pcc, messsage = run_conv1d_short_sweep(
+        input_spec,
+        device,
+    )[0]
+    assert pcc, messsage

ttnn/cpp/ttnn/operations/conv/conv2d/conv2d_utils.cpp

@@ -372,11 +372,12 @@ bool use_matmul_for_1x1_conv(
            padding[1] == 0 && dilation[0] == 1 && dilation[1] == 1 && groups == 1 && (not is_width_sharded);
 }
 
+bool is_1d_conv(uint32_t kernel_width, uint32_t image_width) { return kernel_width == 1 && image_width == 1; }
+
 bool is_1d_deptwise_conv(
     uint32_t groups, uint32_t input_channels, uint32_t output_channels, uint32_t kernel_width, uint32_t image_width) {
     bool is_depthwise_conv = groups == input_channels && groups == output_channels;
-    bool is_conv1d = kernel_width == 1 && image_width == 1;
-    return is_depthwise_conv && is_conv1d;
+    return is_depthwise_conv && is_1d_conv(kernel_width, image_width);
 }
 
 template <typename DeviceType>
@@ -711,6 +712,7 @@ Conv2dConfig determine_conv_config_for_auto_shard(
         Conv2dConfig conv_config;
     };
 
+    const bool conv_is_1d = is_1d_conv(kernel_size[1], input_width);
     const bool conv_is_1d_deptwise =
         is_1d_deptwise_conv(groups, in_channels, out_channels, kernel_size[1], input_width);
 
@@ -753,7 +755,7 @@ Conv2dConfig determine_conv_config_for_auto_shard(
             compute_grid_size,
             shard_orientation,
             !is_mm_conv,
-            is_out_tiled,
+            true,
             conv_config.act_block_h_override);
 
         const ParallelConfig output_parallel_config = determine_output_parallel_config(
@@ -817,7 +819,7 @@ Conv2dConfig determine_conv_config_for_auto_shard(
     core_count_and_size height = get_l1_usage_for_sharding(TensorMemoryLayout::HEIGHT_SHARDED, conv_config);
 
     // 1d deptwise convs support only height sharding
-    if (conv_is_1d_deptwise) {
+    if (conv_is_1d) {
         return height.conv_config;
     }
 

ttnn/cpp/ttnn/operations/conv/conv2d/conv2d_utils.hpp

@@ -35,6 +35,8 @@ bool use_matmul_for_1x1_conv(
     uint32_t groups,
     const Conv2dConfig& conv_config);
 
+bool is_1d_conv(uint32_t kernel_width, uint32_t image_width);
+
 bool is_1d_deptwise_conv(
     uint32_t groups, uint32_t input_channels, uint32_t output_channels, uint32_t kernel_width, uint32_t image_width);
 sliding_window::ParallelConfig determine_parallel_config(

ttnn/cpp/ttnn/operations/conv/conv2d/device/conv2d_op_sharded_program_factory.cpp

@@ -621,7 +621,7 @@ tt::tt_metal::operation::ProgramWithCallbacks multi_core_optimized_conv_sharded_
 
     uint32_t input_width = ashape[2];
     uint32_t input_channels = ashape[3];
-    bool is_conv1d = filter_w == 1 && input_width == 1;
+    bool is_conv1d = is_1d_conv(filter_w, input_width);
     bool is_conv_1d_depthwise_conv =
         is_1d_deptwise_conv(groups, input_channels, output_channels, filter_w, input_width);