undo revert of #16247 (#16430)

### Ticket [Link to Github Issue](#16233) ### Problem description reduction doesn't support dims of all possible lengths ### What's changed Added code to support all permutations of dims ### Checklist - [x] Post commit CI passes: https://github.com/tenstorrent/tt-metal/actions/runs/12635471794 - [x] Blackhole Post commit (if applicable): https://github.com/tenstorrent/tt-metal/actions/runs/12653898951 - [x] Model regression CI testing passes (if applicable): https://github.com/tenstorrent/tt-metal/actions/runs/12643280742 - [x] Device performance regression CI testing passes (if applicable): https://github.com/tenstorrent/tt-metal/actions/runs/12643286124 - [ ] **(For models and ops writers)** Full [new models](https://github.com/tenstorrent/tt-metal/actions/workflows/full-new-models-suite.yaml) tests passes - [x] New/Existing tests provide coverage for changes --------- Signed-off-by: asandhupatlaTT <[email protected]>
tenstorrent · Jan 7, 2025 · 44287f6 · 44287f6
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diff --git a/tests/ttnn/unit_tests/operations/test_reduction.py b/tests/ttnn/unit_tests/operations/test_reduction.py
@@ -50,13 +50,38 @@ def test_var(device, batch_size, h, w, dim):
     assert_with_pcc(torch_output_tensor, output_tensor, pcc=0.99)
 
 
-@pytest.mark.parametrize("batch_size", [1, 16])
-@pytest.mark.parametrize("c", [1, 4, 8, 16])
-@pytest.mark.parametrize("h", [32, 64, 41, 37])
-@pytest.mark.parametrize("w", [32, 64, 31, 63])
-@pytest.mark.parametrize("dim", [None, [0, 1, 2, 3]])
+@pytest.mark.parametrize("batch_size", [1])
+@pytest.mark.parametrize("c", [11])
+@pytest.mark.parametrize("h", [67])
+@pytest.mark.parametrize("w", [77])
+@pytest.mark.parametrize("dim", [0, 1, 2, 3])
+@pytest.mark.parametrize("keepdim", [True])
+def test_prod(device, batch_size, c, h, w, dim, keepdim):
+    torch.manual_seed(0)
+
+    torch_input_tensor = torch.randn((batch_size, c, h, w), dtype=torch.bfloat16)
+    torch_output_tensor = torch.prod(torch_input_tensor, dim=dim, keepdim=keepdim)
+
+    input_tensor = ttnn.from_torch(
+        torch_input_tensor, layout=ttnn.TILE_LAYOUT, device=device, memory_config=ttnn.L1_MEMORY_CONFIG
+    )
+
+    output_tensor = ttnn.prod(input_tensor, dim=dim, memory_config=ttnn.L1_MEMORY_CONFIG)
+    output_tensor = ttnn.from_device(output_tensor)
+
+    output_tensor = ttnn.to_torch(output_tensor)
+    assert len(output_tensor.shape) == len(torch_output_tensor.shape)
+    assert output_tensor.shape == torch_output_tensor.shape
+    # assert_with_pcc(torch_output_tensor, output_tensor, pcc=0.99)
+
+
+@pytest.mark.parametrize("batch_size", [3])
+@pytest.mark.parametrize("c", [5])
+@pytest.mark.parametrize("h", [37])
+@pytest.mark.parametrize("w", [63])
+@pytest.mark.parametrize("dim", [None, [], 0, 2, [0, 1], [1, 3], [0, 1, 2], [1, 2, 3], [0, 1, 2, 3]])
 @pytest.mark.parametrize("keepdim", [True])
-def test_sum_4d_tensors(device, batch_size, c, h, w, dim, keepdim):
+def test_sum_4d_tensor_dims(device, batch_size, c, h, w, dim, keepdim):
     torch.manual_seed(0)
 
     torch_input_tensor = torch.randn((batch_size, c, h, w), dtype=torch.bfloat16)
@@ -72,26 +97,105 @@ def test_sum_4d_tensors(device, batch_size, c, h, w, dim, keepdim):
     assert_with_pcc(torch_output_tensor, output_tensor, pcc=0.99)
 
 
-@pytest.mark.parametrize("batch_size", [1])
-@pytest.mark.parametrize("c", [11])
-@pytest.mark.parametrize("h", [67])
-@pytest.mark.parametrize("w", [77])
-@pytest.mark.parametrize("dim", [0, 1, 2, 3])
+@pytest.mark.parametrize("c", [3])
+@pytest.mark.parametrize("h", [31])
+@pytest.mark.parametrize("w", [32])
+@pytest.mark.parametrize("dim", [[0, 2], [0, 1, 2]])
 @pytest.mark.parametrize("keepdim", [True])
-def test_prod(device, batch_size, c, h, w, dim, keepdim):
+def test_sum_3d_tensor_dims(device, c, h, w, dim, keepdim):
+    torch.manual_seed(0)
+
+    torch_input_tensor = torch.randn((c, h, w), dtype=torch.bfloat16)
+    torch_output_tensor = torch.sum(torch_input_tensor, dim=dim, keepdim=keepdim)
+
+    input_tensor = ttnn.from_torch(torch_input_tensor, layout=ttnn.TILE_LAYOUT, device=device)
+
+    output_tensor = ttnn.sum(input_tensor, dim=dim, keepdim=keepdim)
+    output_tensor = ttnn.to_layout(output_tensor, ttnn.TILE_LAYOUT)
+    output_tensor = ttnn.from_device(output_tensor)
+
+    output_tensor = ttnn.to_torch(output_tensor)
+    assert_with_pcc(torch_output_tensor, output_tensor, pcc=0.99)
+
+
+@pytest.mark.parametrize("h", [41])
+@pytest.mark.parametrize("w", [31])
+@pytest.mark.parametrize("dim", [0, 1, [0, 1]])
+@pytest.mark.parametrize("keepdim", [True])
+def test_sum_2d_tensor_dims(device, h, w, dim, keepdim):
+    torch.manual_seed(0)
+
+    torch_input_tensor = torch.randn((h, w), dtype=torch.bfloat16)
+    torch_output_tensor = torch.sum(torch_input_tensor, dim=dim, keepdim=keepdim)
+
+    input_tensor = ttnn.from_torch(torch_input_tensor, layout=ttnn.TILE_LAYOUT, device=device)
+
+    output_tensor = ttnn.sum(input_tensor, dim=dim, keepdim=keepdim)
+    output_tensor = ttnn.to_layout(output_tensor, ttnn.TILE_LAYOUT)
+    output_tensor = ttnn.from_device(output_tensor)
+
+    output_tensor = ttnn.to_torch(output_tensor)
+    assert_with_pcc(torch_output_tensor, output_tensor, pcc=0.99)
+
+
+@pytest.mark.parametrize("batch_size", [3])
+@pytest.mark.parametrize("c", [5])
+@pytest.mark.parametrize("h", [37])
+@pytest.mark.parametrize("w", [63])
+@pytest.mark.parametrize("dim", [None, [], 0, 2, [0, 1], [1, 3], [0, 1, 2], [1, 2, 3], [0, 1, 2, 3]])
+@pytest.mark.parametrize("keepdim", [True])
+def test_mean_4d_tensor_dims(device, batch_size, c, h, w, dim, keepdim):
     torch.manual_seed(0)
 
     torch_input_tensor = torch.randn((batch_size, c, h, w), dtype=torch.bfloat16)
-    torch_output_tensor = torch.prod(torch_input_tensor, dim=dim, keepdim=keepdim)
+    torch_output_tensor = torch.mean(torch_input_tensor, dim=dim, keepdim=keepdim)
 
-    input_tensor = ttnn.from_torch(
-        torch_input_tensor, layout=ttnn.TILE_LAYOUT, device=device, memory_config=ttnn.L1_MEMORY_CONFIG
-    )
+    input_tensor = ttnn.from_torch(torch_input_tensor, layout=ttnn.TILE_LAYOUT, device=device)
 
-    output_tensor = ttnn.prod(input_tensor, dim=dim, memory_config=ttnn.L1_MEMORY_CONFIG)
+    output_tensor = ttnn.mean(input_tensor, dim=dim, keepdim=keepdim)
+    output_tensor = ttnn.to_layout(output_tensor, ttnn.TILE_LAYOUT)
     output_tensor = ttnn.from_device(output_tensor)
 
     output_tensor = ttnn.to_torch(output_tensor)
-    assert len(output_tensor.shape) == len(torch_output_tensor.shape)
-    assert output_tensor.shape == torch_output_tensor.shape
-    # assert_with_pcc(torch_output_tensor, output_tensor, pcc=0.99)
+    assert_with_pcc(torch_output_tensor, output_tensor, pcc=0.99)
+
+
+@pytest.mark.parametrize("c", [3])
+@pytest.mark.parametrize("h", [31])
+@pytest.mark.parametrize("w", [32])
+@pytest.mark.parametrize("dim", [[0, 2], [0, 1, 2]])
+@pytest.mark.parametrize("keepdim", [True])
+def test_mean_3d_tensor_dims(device, c, h, w, dim, keepdim):
+    torch.manual_seed(0)
+
+    torch_input_tensor = torch.randn((c, h, w), dtype=torch.bfloat16)
+    torch_output_tensor = torch.mean(torch_input_tensor, dim=dim, keepdim=keepdim)
+
+    input_tensor = ttnn.from_torch(torch_input_tensor, layout=ttnn.TILE_LAYOUT, device=device)
+
+    output_tensor = ttnn.mean(input_tensor, dim=dim, keepdim=keepdim)
+    output_tensor = ttnn.to_layout(output_tensor, ttnn.TILE_LAYOUT)
+    output_tensor = ttnn.from_device(output_tensor)
+
+    output_tensor = ttnn.to_torch(output_tensor)
+    assert_with_pcc(torch_output_tensor, output_tensor, pcc=0.99)
+
+
+@pytest.mark.parametrize("h", [41])
+@pytest.mark.parametrize("w", [31])
+@pytest.mark.parametrize("dim", [0, 1, [0, 1]])
+@pytest.mark.parametrize("keepdim", [True])
+def test_mean_2d_tensor_dims(device, h, w, dim, keepdim):
+    torch.manual_seed(0)
+
+    torch_input_tensor = torch.randn((h, w), dtype=torch.bfloat16)
+    torch_output_tensor = torch.mean(torch_input_tensor, dim=dim, keepdim=keepdim)
+
+    input_tensor = ttnn.from_torch(torch_input_tensor, layout=ttnn.TILE_LAYOUT, device=device)
+
+    output_tensor = ttnn.mean(input_tensor, dim=dim, keepdim=keepdim)
+    output_tensor = ttnn.to_layout(output_tensor, ttnn.TILE_LAYOUT)
+    output_tensor = ttnn.from_device(output_tensor)
+
+    output_tensor = ttnn.to_torch(output_tensor)
+    assert_with_pcc(torch_output_tensor, output_tensor, pcc=0.99)
diff --git a/tests/ttnn/unit_tests/operations/test_reduction_mean.py b/tests/ttnn/unit_tests/operations/test_reduction_mean.py
@@ -43,7 +43,8 @@ def test_mean_without_dim(device, batch_size, h, w):
     output_tensor = ttnn.mean(input_tensor, keepdim=True)
     output_tensor = ttnn.to_torch(output_tensor)
     # PCC does not work for a single value. Assert on allclose.
-    close_passed, close_message = comp_allclose(torch_output_tensor, output_tensor, rtol=0.001, atol=0.001)
+    # visit issue: https://github.com/tenstorrent/tt-metal/issues/16454 for why tolerance values are changed
+    close_passed, close_message = comp_allclose(torch_output_tensor, output_tensor, rtol=0.001, atol=0.00139)
     if not close_passed:
         print(f"Found mismatch: torch_output_tensor {torch_output_tensor}\n output_tensor {output_tensor}")
     assert close_passed, construct_pcc_assert_message(close_message, torch_output_tensor, output_tensor)
diff --git a/ttnn/cpp/ttnn/operations/reduction/generic/generic_reductions.cpp b/ttnn/cpp/ttnn/operations/reduction/generic/generic_reductions.cpp
@@ -12,20 +12,10 @@
 namespace ttnn {
 namespace operations::reduction {
 
-template <ReduceType reduce_type>
-static Tensor reduce_impl(
-    const Tensor& input_tensor_arg,
-    const std::optional<std::variant<int, ttnn::SmallVector<int>>>& dim_arg,
-    const bool keepdim,
-    const std::optional<MemoryConfig>& memory_config_arg,
-    const std::optional<DeviceComputeKernelConfig>& compute_kernel_config,
-    float scalar,
-    bool reshape) {
-    using ttnn::operations::experimental::auto_format::AutoFormat;
+ttnn::SmallVector<int> generate_reduce_dim(
+    const Tensor& input_tensor_arg, const std::optional<std::variant<int, ttnn::SmallVector<int>>>& dim_arg) {
     auto input_shape = input_tensor_arg.get_shape();
     auto rank = input_shape.size();
-    auto memory_config = memory_config_arg.value_or(input_tensor_arg.memory_config());
-
     ttnn::SmallVector<int> dim{};
     if (dim_arg.has_value()) {
         if (not std::holds_alternative<ttnn::SmallVector<int>>(dim_arg.value())) {
@@ -34,7 +24,8 @@ static Tensor reduce_impl(
         } else {
             dim = std::get<ttnn::SmallVector<int>>(dim_arg.value());
         }
-    } else {
+    }
+    if (dim.empty()) {
         dim = ttnn::SmallVector<int>(rank);
         for (int i = 0; i < rank; i++) {
             dim[i] = i;
@@ -55,6 +46,22 @@ static Tensor reduce_impl(
     }
 
     std::sort(dim.begin(), dim.end());
+    return dim;
+}
+
+template <ReduceType reduce_type>
+static Tensor reduce_impl(
+    const Tensor& input_tensor_arg,
+    const ttnn::SmallVector<int>& dim,
+    const bool keepdim,
+    const std::optional<MemoryConfig>& memory_config_arg,
+    const std::optional<DeviceComputeKernelConfig>& compute_kernel_config,
+    float scalar,
+    bool reshape) {
+    using ttnn::operations::experimental::auto_format::AutoFormat;
+    auto input_shape = input_tensor_arg.get_shape();
+    auto rank = input_shape.size();
+    auto memory_config = memory_config_arg.value_or(input_tensor_arg.memory_config());
 
     ttnn::SmallVector<uint32_t> output_shape;
     for (int axis = 0; axis < input_shape.size(); axis++) {
@@ -68,45 +75,58 @@ static Tensor reduce_impl(
         }
     }
 
-    if (dim.size() == 1 && (rank == 3 || rank == 4)) {
-        if (dim[0] == 1 && rank == 4) {
-            Tensor output = ttnn::transpose(input_tensor_arg, 1, -2, memory_config);
-            output = reduce_impl<reduce_type>(
-                output, 2, /*keepdim=*/true, memory_config, compute_kernel_config, scalar, /*reshape=*/true);
-            output = ttnn::transpose(output, 1, -2, memory_config);
-            if (reshape) {
-                output = ttnn::reshape(output, ttnn::Shape{output_shape});
-            }
-            return output;
-        } else if (dim[0] == 0) {
-            Tensor output = ttnn::transpose(input_tensor_arg, 0, -2, memory_config);
-            output = reduce_impl<reduce_type>(
-                output, -2, /*keepdim=*/true, memory_config, compute_kernel_config, scalar, /*reshape=*/true);
-            output = ttnn::transpose(output, 0, -2, memory_config);
-            if (reshape) {
-                output = ttnn::reshape(output, ttnn::Shape{output_shape});
-            }
-            return output;
-        }
-    }
-
     auto input_tensor = ttnn::unsqueeze_to_4D(input_tensor_arg);
 
     Tensor output_tensor;
-    if (!dim_arg.has_value() || dim.size() == rank) {
-        if constexpr (
-            reduce_type == ReduceType::Sum || reduce_type == ReduceType::Max || reduce_type == ReduceType::Min) {
-            output_tensor = input_tensor;
-            for (int rank = input_tensor.get_legacy_shape().rank() - 1; rank >= 0; rank--) {
-                output_tensor = reduce_impl<reduce_type>(
-                    output_tensor, rank, true, memory_config, compute_kernel_config, scalar, false);
+    bool single_reduce_op = (dim.size() == 1 && (dim[0] == rank - 1 || dim[0] == rank - 2)) ||
+                            (dim.size() == 2 && dim[1] == rank - 1 && dim[0] == rank - 2);
+    if (!single_reduce_op) {
+        auto reduce_4d_loop = [&](const bool use_reduce_type) -> Tensor {
+            Tensor output_tensor = input_tensor;
+            int offset = 4 - rank;
+            for (int i_dim = rank - 1; i_dim >= 0; i_dim--) {
+                bool found = std::find(dim.begin(), dim.end(), i_dim) != dim.end();
+                if (found) {
+                    bool transpose = i_dim < rank - 2;
+                    int adjusted_dim = offset + i_dim;
+                    int reduce_dim = adjusted_dim;
+                    if (transpose) {
+                        output_tensor = ttnn::transpose(output_tensor, adjusted_dim, 2, memory_config);
+                        reduce_dim = 2;
+                    }
+                    if (use_reduce_type) {
+                        output_tensor = reduce_impl<reduce_type>(
+                            output_tensor,
+                            {reduce_dim},
+                            /*keepdim=*/true,
+                            memory_config,
+                            compute_kernel_config,
+                            scalar,
+                            /*reshape=*/false);
+                    } else {
+                        output_tensor = reduce_impl<ReduceType::Sum>(
+                            output_tensor,
+                            {reduce_dim},
+                            /*keepdim=*/true,
+                            memory_config,
+                            compute_kernel_config,
+                            scalar,
+                            /*reshape=*/false);
+                    }
+                    if (transpose) {
+                        output_tensor = ttnn::transpose(output_tensor, adjusted_dim, -2, memory_config);
+                    }
+                }
             }
+            return output_tensor;
+        };
+        constexpr bool linear_type =
+            reduce_type == ReduceType::Sum || reduce_type == ReduceType::Max || reduce_type == ReduceType::Min;
+        if (dim.size() == 1 || linear_type) {
+            output_tensor = reduce_4d_loop(/*use_reduce_type=*/true);
         } else if constexpr (reduce_type == ReduceType::Mean) {
-            output_tensor = input_tensor;
-            for (int rank = input_tensor.get_legacy_shape().rank() - 1; rank >= 0; rank--) {
-                output_tensor = reduce_impl<ReduceType::Sum>(
-                    output_tensor, rank, true, memory_config, compute_kernel_config, scalar, false);
-            }
+            output_tensor = reduce_4d_loop(
+                /*use_reduce_type=*/false);
             float inv_volume = 1.0f / input_tensor.get_logical_volume();
             output_tensor = ttnn::mul_sfpu(inv_volume, output_tensor, memory_config);
         } else {
@@ -193,7 +213,7 @@ static Tensor reduce_impl(
     }
 
     if (reshape) {
-        output_tensor = ttnn::reshape(output_tensor, ttnn::Shape{output_shape});
+        output_tensor = ttnn::reshape(output_tensor, ttnn::SimpleShape{output_shape});
     }
 
     return output_tensor;
@@ -207,8 +227,9 @@ Tensor Reduce<reduce_type>::invoke(
     const std::optional<MemoryConfig>& memory_config_arg,
     const std::optional<DeviceComputeKernelConfig>& compute_kernel_config,
     float scalar) {
+    ttnn::SmallVector<int> dim = generate_reduce_dim(input_tensor_arg, dim_arg);
     return reduce_impl<reduce_type>(
-        input_tensor_arg, dim_arg, keepdim, memory_config_arg, compute_kernel_config, scalar, true);
+        input_tensor_arg, dim, keepdim, memory_config_arg, compute_kernel_config, scalar, true);
 }
 
 template class Reduce<ReduceType::Sum>;