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#14879: Remove unnecessary usage of creation ops #14887

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#14879: Remove unnecessary usage of creation ops #14887

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14 changes: 7 additions & 7 deletions ttnn/cpp/ttnn/operations/eltwise/binary/device/binary_composite_op.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,7 @@ Tensor _hypot(const Tensor& input_a, const Tensor& input_b, const std::optional<

// xlogy(x,y)=x*log(y)
Tensor _xlogy(const Tensor& input_a, const Tensor& input_b, const std::optional<MemoryConfig>& output_mem_config) {
Tensor t_nan = ttnn::full_like(input_b, std::nanf(" "));
float t_nan = std::nanf(" ");
Tensor result = ttnn::multiply(input_a, ttnn::log(input_b, output_mem_config), std::nullopt, output_mem_config);
result = ttnn::where(
ttnn::logical_or(
Expand Down Expand Up @@ -254,7 +254,7 @@ Tensor ExecuteDiv::invoke(const Tensor& input_a, const Tensor& input_b, bool acc

Tensor _div_no_nan_overload(const Tensor& input_a, float value, const std::optional<MemoryConfig>& output_mem_config) {
if (value == 0)
return ttnn::full_like(input_a, 0.0f);
return ttnn::zeros_like(input_a);
else
return ttnn::multiply(input_a, (1.0f/value));
}
Expand All @@ -274,7 +274,7 @@ Tensor ExecuteBinaryRemainder::invoke(const Tensor& input_a, const Tensor& input
Tensor result = ttnn::subtract(a, ttnn::multiply(b, ttnn::div(input_a, input_b, true, "floor", output_mem_config), std::nullopt, output_mem_config), std::nullopt, output_mem_config);
result = ttnn::where(ttnn::ge(result, b), ttnn::subtract(result, b), result);
result = ttnn::where(ttnn::ltz(b), ttnn::add(result, b), result);
result = ttnn::where(ttnn::eq(a, b, std::nullopt, output_mem_config), ttnn::full_like(input_a, 0.0f), result);
result = ttnn::where(ttnn::eq(a, b, std::nullopt, output_mem_config), 0.0f, result);
return typecast(result, input_dtype);
}

Expand All @@ -291,7 +291,7 @@ Tensor ExecuteBinaryFmod::invoke(const Tensor& input_a, const Tensor& input_b, c
Tensor b = typecast(input_b, DataType::FLOAT32);
Tensor div_res = typecast(ttnn::div(input_a, input_b, true, "trunc", output_mem_config), DataType::FLOAT32);
Tensor result = ttnn::subtract(a, ttnn::multiply(div_res, b, std::nullopt, output_mem_config), std::nullopt, output_mem_config);
result = ttnn::where(ttnn::eq(a, b, std::nullopt, output_mem_config), ttnn::full_like(input_a, 0.0f), result);
result = ttnn::where(ttnn::eq(a, b, std::nullopt, output_mem_config), 0.0f, result);
return typecast(result, input_dtype);
}

Expand All @@ -303,12 +303,12 @@ Tensor _floor_div_overload(const Tensor& input_a, float value, const std::option
auto arch = input_a.device()->arch();
TT_FATAL(arch == tt::ARCH::WORMHOLE_B0, "Op is only supported on Wormhole");
if (value == 0) {
Tensor t_inf = ttnn::full_like(input_a, std::numeric_limits<float>::infinity());
Tensor t_nan = ttnn::full_like(input_a, std::nanf(""));
float t_inf = std::numeric_limits<float>::infinity();
float t_nan = std::nanf("");
return ttnn::where(
ttnn::eqz(input_a, output_mem_config),
t_nan,
ttnn::multiply(t_inf, ttnn::sign(input_a, output_mem_config), std::nullopt, output_mem_config));
ttnn::multiply(ttnn::sign(input_a, output_mem_config), t_inf, std::nullopt, output_mem_config));
}
Tensor temp = ttnn::multiply(input_a, (1.0f/value), std::nullopt, output_mem_config);
return ttnn::floor(temp);
Expand Down
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