#0: update documentation and asserts

docs/source/ttnn/ttnn/api.rst

@@ -193,12 +193,10 @@ Pointwise Unary
    ttnn.tanhshrink
    ttnn.threshold
    ttnn.trunc
-   ttnn.mul_bw
    ttnn.clamp_bw
    ttnn.hardtanh_bw
    ttnn.threshold_bw
    ttnn.softplus_bw
-   ttnn.div_bw
    ttnn.rdiv_bw
    ttnn.bias_gelu_bw
    ttnn.pow_bw
@@ -207,7 +205,6 @@ Pointwise Unary
    ttnn.sqrt_bw
    ttnn.assign_bw
    ttnn.multigammaln_bw
-   ttnn.add_bw
    ttnn.lgamma_bw
    ttnn.fill_bw
    ttnn.hardsigmoid_bw
@@ -216,7 +213,6 @@ Pointwise Unary
    ttnn.acos_bw
    ttnn.atan_bw
    ttnn.rad2deg_bw
-   ttnn.sub_bw
    ttnn.frac_bw
    ttnn.trunc_bw
    ttnn.log_sigmoid_bw
@@ -344,7 +340,9 @@ Pointwise Binary
    ttnn.polyval
    ttnn.scatter
    ttnn.atan2
+   ttnn.add_bw
    ttnn.atan2_bw
+   ttnn.div_bw
    ttnn.embedding_bw
    ttnn.addalpha_bw
    ttnn.subalpha_bw
@@ -353,6 +351,8 @@ Pointwise Binary
    ttnn.ldexp_bw
    ttnn.logaddexp_bw
    ttnn.logaddexp2_bw
+   ttnn.mul_bw
+   ttnn.sub_bw
    ttnn.squared_difference_bw
    ttnn.concat_bw
    ttnn.rsub_bw

tests/ttnn/unit_tests/operations/eltwise/backward/test_backward_add.py

@@ -5,7 +5,11 @@
 import torch
 import pytest
 import ttnn
-from tests.ttnn.unit_tests.operations.eltwise.backward.utility_funcs import data_gen_with_range, compare_pcc
+from tests.ttnn.unit_tests.operations.eltwise.backward.utility_funcs import (
+    data_gen_with_range,
+    compare_pcc,
+    data_gen_with_range_dtype,
+)
 
 
 @pytest.mark.parametrize(
@@ -30,6 +34,28 @@ def test_bw_add(input_shapes, device):
     assert status
 
 
+@pytest.mark.parametrize(
+    "input_shapes",
+    (
+        (torch.Size([1, 1, 32, 32])),
+        (torch.Size([1, 1, 320, 384])),
+        (torch.Size([1, 3, 320, 384])),
+    ),
+)
+def test_bw_add_bf8b(input_shapes, device):
+    in_data, input_tensor = data_gen_with_range_dtype(input_shapes, -100, 100, device, True, False, ttnn.bfloat8_b)
+    other_data, other_tensor = data_gen_with_range_dtype(input_shapes, -100, 100, device, True, False, ttnn.bfloat8_b)
+    grad_data, grad_tensor = data_gen_with_range_dtype(input_shapes, -100, 100, device, False, False, ttnn.bfloat8_b)
+
+    tt_output_tensor_on_device = ttnn.add_bw(grad_tensor, input_tensor, other_tensor)
+
+    golden_function = ttnn.get_golden_function(ttnn.add_bw)
+    golden_tensor = golden_function(grad_data, in_data, other_data)
+
+    status = compare_pcc(tt_output_tensor_on_device, golden_tensor)
+    assert status
+
+
 @pytest.mark.parametrize(
     "input_shapes",
     (

tests/ttnn/unit_tests/operations/eltwise/backward/test_backward_assign.py

@@ -5,7 +5,11 @@
 import torch
 import pytest
 import ttnn
-from tests.ttnn.unit_tests.operations.eltwise.backward.utility_funcs import data_gen_with_range, compare_pcc
+from tests.ttnn.unit_tests.operations.eltwise.backward.utility_funcs import (
+    data_gen_with_range,
+    compare_pcc,
+    data_gen_with_range_dtype,
+)
 
 
 @pytest.mark.parametrize(
@@ -51,6 +55,27 @@ def test_bw_binary_assign(input_shapes, device):
     assert status
 
 
+@pytest.mark.parametrize(
+    "input_shapes",
+    (
+        (torch.Size([1, 1, 32, 32])),
+        (torch.Size([1, 1, 320, 384])),
+        (torch.Size([1, 3, 320, 384])),
+    ),
+)
+def test_bw_binary_assign_bf8b(input_shapes, device):
+    in_data, input_tensor = data_gen_with_range_dtype(input_shapes, -100, 100, device, True, False, ttnn.bfloat8_b)
+    other_data, other_tensor = data_gen_with_range_dtype(input_shapes, -100, 100, device, True, False, ttnn.bfloat8_b)
+    grad_data, grad_tensor = data_gen_with_range_dtype(input_shapes, -100, 100, device, False, False, ttnn.bfloat8_b)
+
+    tt_output_tensor_on_device = ttnn.assign_bw(grad_tensor, input_tensor, other_tensor)
+
+    golden_function = ttnn.get_golden_function(ttnn.assign_bw)
+    golden_tensor = golden_function(grad_data, in_data, other_data)
+    status = compare_pcc(tt_output_tensor_on_device, golden_tensor)
+    assert status
+
+
 @pytest.mark.parametrize(
     "input_shapes",
     (
@@ -78,6 +103,33 @@ def test_bw_unary_assign_opt_output(input_shapes, device):
     assert status
 
 
+@pytest.mark.parametrize(
+    "input_shapes",
+    (
+        (torch.Size([1, 1, 32, 32])),
+        (torch.Size([1, 1, 320, 384])),
+        (torch.Size([1, 3, 320, 384])),
+    ),
+)
+def test_bw_unary_assign_opt_output_rm(input_shapes, device):
+    in_data, input_tensor = data_gen_with_range(input_shapes, -100, 100, device, True, True)
+    grad_data, grad_tensor = data_gen_with_range(input_shapes, -100, 100, device, False, True)
+    opt_tensor = torch.zeros(input_shapes, dtype=torch.bfloat16)
+    input_grad = ttnn.from_torch(
+        opt_tensor, ttnn.bfloat16, layout=ttnn.ROW_MAJOR_LAYOUT, device=device, memory_config=ttnn.L1_MEMORY_CONFIG
+    )
+    pages_before = ttnn._ttnn.reports.get_buffer_pages()
+    ttnn.assign_bw(grad_tensor, input_tensor, input_a_grad=input_grad, queue_id=0)
+    assert len(pages_before) == len(ttnn._ttnn.reports.get_buffer_pages())
+
+    tt_output_tensor_on_device = [input_grad]
+    golden_function = ttnn.get_golden_function(ttnn.assign_bw)
+    golden_tensor = golden_function(grad_data, in_data)
+
+    status = compare_pcc(tt_output_tensor_on_device, golden_tensor)
+    assert status
+
+
 @pytest.mark.parametrize(
     "input_shapes",
     (

tests/ttnn/unit_tests/operations/eltwise/backward/test_backward_div.py

@@ -8,6 +8,7 @@
 from tests.ttnn.unit_tests.operations.eltwise.backward.utility_funcs import (
     data_gen_with_range,
     data_gen_with_val,
+    data_gen_with_range_dtype,
     compare_pcc,
 )
 from models.utility_functions import (
@@ -182,6 +183,28 @@ def test_bw_unary_div_default(input_shapes, scalar, device):
     assert status
 
 
+@pytest.mark.parametrize(
+    "input_shapes",
+    (
+        (torch.Size([1, 1, 32, 32])),
+        (torch.Size([1, 1, 320, 384])),
+        (torch.Size([1, 3, 320, 384])),
+    ),
+)
+@pytest.mark.parametrize("scalar", [0.05, 1.0, 0.5, 0.12, 0.0, -0.05, -1.0, -0.5, -0.12])
+def test_bw_unary_div_bf8b(input_shapes, scalar, device):
+    in_data, input_tensor = data_gen_with_range_dtype(input_shapes, -100, 100, device, True, False, ttnn.bfloat8_b)
+    grad_data, grad_tensor = data_gen_with_range_dtype(input_shapes, -1, 1, device, False, False, ttnn.bfloat8_b)
+
+    tt_output_tensor_on_device = ttnn.div_bw(grad_tensor, input_tensor, scalar)
+
+    golden_function = ttnn.get_golden_function(ttnn.div_bw)
+    golden_tensor = golden_function(grad_data, in_data, scalar)
+
+    status = compare_pcc(tt_output_tensor_on_device, golden_tensor)
+    assert status
+
+
 @pytest.mark.parametrize(
     "input_shapes",
     (

tests/ttnn/unit_tests/operations/eltwise/backward/test_backward_ldexp.py

@@ -5,7 +5,11 @@
 import torch
 import pytest
 import ttnn
-from tests.ttnn.unit_tests.operations.eltwise.backward.utility_funcs import data_gen_with_range, compare_pcc
+from tests.ttnn.unit_tests.operations.eltwise.backward.utility_funcs import (
+    data_gen_with_range,
+    compare_pcc,
+    data_gen_with_range_dtype,
+)
 
 
 @pytest.mark.parametrize(
@@ -28,3 +32,25 @@ def test_bw_ldexp(input_shapes, device):
     golden_tensor = golden_function(grad_data, in_data, other_data)
     comp_pass = compare_pcc(tt_output_tensor_on_device, golden_tensor)
     assert comp_pass
+
+
+@pytest.mark.parametrize(
+    "input_shapes",
+    (
+        (torch.Size([1, 1, 32, 32])),
+        (torch.Size([1, 1, 320, 384])),
+        (torch.Size([1, 3, 320, 384])),
+    ),
+)
+def test_bw_ldexp_bf8b(input_shapes, device):
+    in_data, input_tensor = data_gen_with_range_dtype(input_shapes, -10, 10, device, True, False, ttnn.bfloat8_b)
+    other_data, other_tensor = data_gen_with_range_dtype(input_shapes, -20, 20, device, True, False, ttnn.bfloat8_b)
+
+    grad_data, grad_tensor = data_gen_with_range_dtype(input_shapes, -5, 5, device, False, False, ttnn.bfloat8_b)
+
+    tt_output_tensor_on_device = ttnn.ldexp_bw(grad_tensor, input_tensor, other_tensor)
+
+    golden_function = ttnn.get_golden_function(ttnn.ldexp_bw)
+    golden_tensor = golden_function(grad_data, in_data, other_data)
+    comp_pass = compare_pcc(tt_output_tensor_on_device, golden_tensor)
+    assert comp_pass

tests/ttnn/unit_tests/operations/eltwise/backward/test_backward_mul.py

@@ -5,7 +5,11 @@
 import torch
 import pytest
 import ttnn
-from tests.ttnn.unit_tests.operations.eltwise.backward.utility_funcs import data_gen_with_range, compare_pcc
+from tests.ttnn.unit_tests.operations.eltwise.backward.utility_funcs import (
+    data_gen_with_range,
+    compare_pcc,
+    data_gen_with_range_dtype,
+)
 
 
 @pytest.mark.parametrize(
@@ -100,6 +104,28 @@ def test_bw_mul_scalar(input_shapes, scalar, device):
     assert status
 
 
+@pytest.mark.parametrize(
+    "input_shapes",
+    (
+        (torch.Size([1, 1, 32, 32])),
+        (torch.Size([1, 1, 320, 384])),
+        (torch.Size([1, 3, 320, 384])),
+    ),
+)
+@pytest.mark.parametrize("scalar", [0.05, 1.0, 0.5, 0.12, 0.0, -0.05, -1.0, -0.5, -0.12])
+def test_bw_mul_scalar_bf8b(input_shapes, scalar, device):
+    in_data, input_tensor = data_gen_with_range_dtype(input_shapes, -100, 100, device, True, False, ttnn.bfloat8_b)
+    grad_data, grad_tensor = data_gen_with_range_dtype(input_shapes, -5, 5, device, False, False, ttnn.bfloat8_b)
+
+    tt_output_tensor_on_device = ttnn.mul_bw(grad_tensor, input_tensor, scalar)
+
+    golden_function = ttnn.get_golden_function(ttnn.mul_bw)
+    golden_tensor = golden_function(grad_data, in_data, scalar)
+
+    status = compare_pcc(tt_output_tensor_on_device, golden_tensor)
+    assert status
+
+
 @pytest.mark.parametrize(
     "input_shapes",
     (

tests/ttnn/unit_tests/operations/eltwise/backward/test_backward_sub.py

@@ -5,7 +5,11 @@
 import torch
 import pytest
 import ttnn
-from tests.ttnn.unit_tests.operations.eltwise.backward.utility_funcs import data_gen_with_range, compare_pcc
+from tests.ttnn.unit_tests.operations.eltwise.backward.utility_funcs import (
+    data_gen_with_range,
+    compare_pcc,
+    data_gen_with_range_dtype,
+)
 
 
 @pytest.mark.parametrize(
@@ -30,6 +34,28 @@ def test_bw_sub(input_shapes, device):
     assert status
 
 
+@pytest.mark.parametrize(
+    "input_shapes",
+    (
+        (torch.Size([1, 1, 32, 32])),
+        (torch.Size([1, 1, 320, 384])),
+        (torch.Size([1, 3, 320, 384])),
+    ),
+)
+def test_bw_sub_bf8b(input_shapes, device):
+    in_data, input_tensor = data_gen_with_range_dtype(input_shapes, -100, 100, device, True, False, ttnn.bfloat8_b)
+    other_data, other_tensor = data_gen_with_range_dtype(input_shapes, -100, 100, device, True, False, ttnn.bfloat8_b)
+    grad_data, grad_tensor = data_gen_with_range_dtype(input_shapes, -100, 100, device, False, False, ttnn.bfloat8_b)
+
+    tt_output_tensor_on_device = ttnn.sub_bw(grad_tensor, input_tensor, other_tensor)
+
+    golden_function = ttnn.get_golden_function(ttnn.sub_bw)
+    golden_tensor = golden_function(grad_data, in_data, other_data)
+
+    status = compare_pcc(tt_output_tensor_on_device, golden_tensor)
+    assert status
+
+
 @pytest.mark.parametrize(
     "input_shapes",
     (

tests/ttnn/unit_tests/operations/eltwise/backward/utility_funcs.py

@@ -29,6 +29,19 @@ def data_gen_with_range(input_shapes, low, high, device, required_grad=False, is
     return pt_tensor, tt_tensor
 
 
+def data_gen_with_range_dtype(
+    input_shapes, low, high, device, required_grad=False, is_row_major=False, ttnn_dtype=ttnn.bfloat16
+):
+    assert high > low, "Incorrect range provided"
+    torch.manual_seed(213919)
+    pt_tensor = torch.rand(input_shapes, requires_grad=required_grad).bfloat16() * (high - low) + low
+    if is_row_major:
+        tt_tensor = ttnn.Tensor(pt_tensor, ttnn_dtype).to(ttnn.ROW_MAJOR_LAYOUT).to(device)
+    else:
+        tt_tensor = ttnn.Tensor(pt_tensor, ttnn_dtype).to(ttnn.TILE_LAYOUT).to(device)
+    return pt_tensor, tt_tensor
+
+
 def data_gen_with_range_int(input_shapes, low, high, device, required_grad=False, is_row_major=False):
     assert high > low, "Incorrect range provided"
     torch.manual_seed(213919)