APIs

Device

ttnn.open_device	Open a device with the given device_id.
ttnn.close_device	Close the device and remove it from the device cache.
ttnn.manage_device	Context manager for opening and closing a device.
ttnn.synchronize_device	Overloaded function.
ttnn.SetDefaultDevice	Sets the default device to use for operations when inputs are not on the device.
ttnn.GetDefaultDevice	Gets the default device to use for ops when inputs aren't on device.
ttnn.format_input_tensor	Formats tensor to target layout and pads to padded shape.
ttnn.format_output_tensor	Formats tensor to target layout and unpads to shape.
ttnn.pad_to_tile_shape	Pads the given shape to tile shape based on specified padding options.

Memory Config

ttnn.create_sharded_memory_config

Creates a MemoryConfig object with a sharding spec, required for sharded ops.

Operations

Core

ttnn.as_tensor	Converts the torch.Tensor tensor into a ttnn.Tensor.
ttnn.from_torch	Converts the torch.Tensor tensor into a ttnn.Tensor.
ttnn.to_torch	Converts the ttnn.Tensor tensor into a torch.Tensor.
ttnn.to_device	Overloaded function.
ttnn.from_device	Copy tensor from device to host.
ttnn.to_layout	Organizes the ttnn.Tensor tensor into either ttnn.ROW_MAJOR_LAYOUT or ttnn.TILE_LAYOUT.
ttnn.dump_tensor	Dump tensor to a file.
ttnn.load_tensor	Load tensor from a file.
ttnn.deallocate	Deallocates device tensor.
ttnn.reallocate	Deallocates device tensor and returns a reallocated tensor.
ttnn.to_memory_config	Converts a tensor to the desired memory configuration.

Tensor Creation

ttnn.arange	Creates a tensor with values ranging from start (inclusive) to end (exclusive) with a specified step size.
ttnn.empty	Creates a device tensor with uninitialized values of the specified shape, data type, layout, and memory configuration.
ttnn.empty_like	Creates a new tensor with the same shape as the given reference, but without initializing its values.
ttnn.zeros	Creates a tensor with the specified shape and fills it with the value of 0.0.
ttnn.zeros_like	Creates a tensor of the same shape as the input tensor and fills it with the value of 0.0.
ttnn.ones	Creates a tensor with the specified shape and fills it with the value of 1.0.
ttnn.ones_like	Creates a tensor of the same shape as the input tensor and fills it with the value of 1.0.
ttnn.full	Creates a tensor of the specified shape and fills it with the specified scalar value.
ttnn.full_like	Creates a tensor of the same shape as the input tensor and fills it with the specified scalar value.

Matrix Multiplication

ttnn.matmul	Returns the matrix product of two tensors.
ttnn.linear	Returns the linear transformation of the inputs.

Pointwise Unary

ttnn.abs	Applies abs to input_tensor element-wise.
ttnn.acos	Applies acos to input_tensor element-wise.
ttnn.acosh	Performs acosh function on input_tensor.
ttnn.asin	Applies asin to input_tensor element-wise.
ttnn.asinh	Performs asinh function on input_tensor.
ttnn.atan	Applies atan to input_tensor element-wise.
ttnn.atanh	Performs atanh function on input_tensor.
ttnn.bitwise_not	Applies bitwise_not to input_tensor element-wise.
ttnn.bitwise_left_shift	Perform bitwise_left_shift operation on input_tensor_a by input_tensor_b and returns the tensor with the same layout as input_tensor_a.
ttnn.bitwise_right_shift	Perform bitwise_right_shift operation on input_tensor_a by input_tensor_b and returns the tensor with the same layout as input_tensor_a.
ttnn.cbrt	Performs cbrt function on input_tensor.
ttnn.ceil	Applies ceil to input_tensor element-wise.
ttnn.celu	Performs celu function on input_tensor, alpha.
ttnn.clamp	Performs clamp function on input_tensor, min, max.
ttnn.clip	Performs clip function on input_tensor, min, max.
ttnn.clone	Clones the input, creating a copy with the specified memory_config and converting its data type to dtype.
ttnn.cos	Applies cos to input_tensor element-wise.
ttnn.cosh	Performs cosh function on input_tensor.
ttnn.deg2rad	Performs deg2rad function on input_tensor.
ttnn.digamma	Performs digamma function on input_tensor.
ttnn.experimental.dropout	Applies dropout to input_tensor element-wise.
ttnn.experimental.gelu_bw	Applies the backward pass of the GELU function using ttnn experimental kernels.
ttnn.elu	Applies elu to input_tensor element-wise with alpha.
ttnn.eqz	Applies eqz to input_tensor element-wise.
ttnn.erf	Applies erf to input_tensor element-wise.
ttnn.erfc	Applies erfc to input_tensor element-wise.
ttnn.erfinv	Applies erfinv to input_tensor element-wise.
ttnn.exp	Applies exp to input_tensor element-wise.
ttnn.exp2	Applies exp2 to input_tensor element-wise.
ttnn.expm1	Applies expm1 to input_tensor element-wise.
ttnn.fill	Applies fill to input_tensor element-wise with fill_value.
ttnn.floor	Applies floor to input_tensor element-wise.
ttnn.frac	Performs frac function on input_tensor.
ttnn.geglu	Applies geglu to input_tensor element-wise.
ttnn.gelu	Applies gelu to input_tensor element-wise.
ttnn.glu	Applies glu to input_tensor element-wise.
ttnn.gez	Applies gez to input_tensor element-wise.
ttnn.gtz	Applies gtz to input_tensor element-wise.
ttnn.hardshrink	Performs hardshrink function on input_tensor, lambd.
ttnn.hardsigmoid	Performs hardsigmoid function on input_tensor, scale, shift.
ttnn.hardswish	Performs hardswish function on input_tensor, scale, shift.
ttnn.hardtanh	Performs hardtanh function on input_tensor, min_val, max_val.
ttnn.heaviside	Applies heaviside to input_tensor element-wise with value.
ttnn.i0	Applies i0 to input_tensor element-wise.
ttnn.identity	Returns a copy of the input_tensor; useful for profiling the SFPU.
ttnn.isfinite	Applies isfinite to input_tensor element-wise.
ttnn.isinf	Applies isinf to input_tensor element-wise.
ttnn.isnan	Applies isnan to input_tensor element-wise.
ttnn.isneginf	Applies isneginf to input_tensor element-wise.
ttnn.isposinf	Applies isposinf to input_tensor element-wise.
ttnn.leaky_relu	Applies leaky_relu to input_tensor element-wise with negative_slope.
ttnn.lez	Applies lez to input_tensor element-wise.
ttnn.lgamma	Performs lgamma function on input_tensor.
ttnn.log	Applies log to input_tensor element-wise.
ttnn.log10	Applies log10 to input_tensor element-wise.
ttnn.log1p	Performs log1p function on input_tensor.
ttnn.log2	Applies log2 to input_tensor element-wise.
ttnn.log_sigmoid	Applies log_sigmoid to input_tensor element-wise.
ttnn.logical_not	Applies logical_not to input_tensor element-wise.
ttnn.logical_not_	Performs logical_not inplace function on input_tensor.
ttnn.logit	Performs logit function on input_tensor, eps.
ttnn.ltz	Applies ltz to input_tensor element-wise.
ttnn.mish	Performs mish function on input_tensor.
ttnn.multigammaln	Performs multigammaln function on input_tensor.
ttnn.neg	Applies neg to input_tensor element-wise.
ttnn.nez	Applies nez to input_tensor element-wise.
ttnn.normalize_global	Performs normalize_global function on input_tensor.
ttnn.normalize_hw	Performs normalize_hw function on input_tensor.
ttnn.polygamma	Performs polygamma function on input_tensor, decimals.
ttnn.prelu	Perform an eltwise-prelu operation.
ttnn.rad2deg	Performs rad2deg function on input_tensor.
ttnn.rdiv	Performs the element-wise division of a scalar value by a tensor input and rounds the result using round_mode.
ttnn.reciprocal	Applies reciprocal to input_tensor element-wise.
ttnn.reglu	Applies reglu to input_tensor element-wise.
ttnn.relu	Applies relu to input_tensor element-wise.
ttnn.relu_max	Applies relu_max to input_tensor element-wise with upper_limit.
ttnn.relu_min	Applies relu_min to input_tensor element-wise with lower_limit.
ttnn.relu6	Applies relu6 to input_tensor element-wise.
ttnn.remainder	Performs an eltwise-modulus operation.
ttnn.round	Applies round to input_tensor element-wise with decimals.
ttnn.rsqrt	Applies rsqrt to input_tensor element-wise.
ttnn.selu	Performs selu function on input_tensor, scale, alpha.
ttnn.sigmoid	Applies sigmoid to input_tensor element-wise.
ttnn.sigmoid_accurate	Applies sigmoid_accurate to input_tensor element-wise.
ttnn.sign	Applies sign to input_tensor element-wise.
ttnn.signbit	Applies signbit to input_tensor element-wise.
ttnn.silu	Applies silu to input_tensor element-wise.
ttnn.sin	Applies sin to input_tensor element-wise.
ttnn.sinh	Performs sinh function on input_tensor.
ttnn.softmax	Compute softmax over input_tensor along dim.
ttnn.softplus	Applies softplus to input_tensor element-wise.
ttnn.softshrink	Performs softshrink function on input_tensor, lambd.
ttnn.softsign	Performs softsign function on input_tensor.
ttnn.sqrt	Applies sqrt to input_tensor element-wise.
ttnn.square	Applies square to input_tensor element-wise.
ttnn.swiglu	Applies swiglu to input_tensor element-wise.
ttnn.swish	Performs swish function on input_tensor.
ttnn.tan	Applies tan to input_tensor element-wise.
ttnn.tanh	Applies tanh to input_tensor element-wise.
ttnn.tanhshrink	Performs tanhshrink function on input_tensor.
ttnn.threshold	Performs threshold function on input_tensor, threshold, value.
ttnn.tril	Performs tril function on input_tensor, diagonal.
ttnn.triu	Performs triu function on input_tensor, diagonal.
ttnn.trunc	Applies trunc to input_tensor element-wise.
ttnn.unary_chain	Applies unary_chain to input_tensor element-wise.
ttnn.clamp_bw	Performs backward operations for clamp on input_tensor, min, max with given grad_tensor.
ttnn.clip_bw	Performs backward operations for clip on input_tensor, min, max with given grad_tensor.
ttnn.hardtanh_bw	Performs backward operations for hardtanh activation function on input_tensor, min, max with given grad_tensor.
ttnn.threshold_bw	Performs backward operations for threshold on input_tensor, threshold, value with given grad_tensor.
ttnn.softplus_bw	Performs backward operations for softplus on input_tensor, beta, threshold with given grad_tensor.
ttnn.rdiv_bw	Performs backward operations for Unary rdiv on input_tensor, scalar with given grad_tensor using given round_mode.
ttnn.pow_bw	Performs backward operations for power on input_tensor, exponent with given grad_tensor.
ttnn.exp_bw	Performs backward operations for exponential function on input_tensor with given grad_tensor.
ttnn.tanh_bw	Performs backward operations for hyperbolic tangent (tanh) function on input_tensor with given grad_tensor.
ttnn.sqrt_bw	Performs backward operations for square-root on input_tensor with given grad_tensor.
ttnn.multigammaln_bw	Performs backward operations for multivariate logarithmic gamma function (also referred to as mvlgamma) on input_tensor with given grad_tensor.
ttnn.lgamma_bw	Performs backward operations for lgamma on input_tensor with given grad_tensor.
ttnn.fill_bw	Performs backward operations for fill on input_tensor with given grad_tensor.
ttnn.hardsigmoid_bw	Performs backward operations for hardsigmoid on input_tensor with given grad_tensor.
ttnn.cos_bw	Performs backward operations for cosine on input_tensor with given grad_tensor.
ttnn.acosh_bw	Performs backward operations for inverse hyperbolic cosine (acosh) on input_tensor with given grad_tensor.
ttnn.acos_bw	Performs backward operations for inverse cosine (acos) on input_tensor with given grad_tensor.
ttnn.atan_bw	Performs backward operations for inverse tangenr (atan) on input_tensor with given grad_tensor.
ttnn.rad2deg_bw	Performs backward operations for radian to degree conversion (rad2deg) on input_tensor with given grad_tensor.
ttnn.frac_bw	Performs backward operations for frac on input_tensor with given grad_tensor.
ttnn.trunc_bw	Performs backward operations for truncation on input_tensor with given grad_tensor.
ttnn.log_sigmoid_bw	Performs backward operations for log sigmoid on input_tensor with given grad_tensor.
ttnn.fill_zero_bw	Performs backward operations for fill zero on input_tensor with given grad_tensor.
ttnn.i0_bw	Performs backward operations for i0 on input_tensor with given grad_tensor.
ttnn.tan_bw	Performs backward operations for tan on input_tensor with given grad_tensor.
ttnn.sigmoid_bw	Performs backward operations for sigmoid on input_tensor with given grad_tensor.
ttnn.rsqrt_bw	Performs backward operations for reciprocal of square-root on input_tensor with given grad_tensor.
ttnn.neg_bw	Performs backward operations for neg on input_tensor with given grad_tensor.
ttnn.relu_bw	Performs backward operations for relu on input_tensor with given grad_tensor.
ttnn.logit_bw	Performs backward operations for logit on input_tensor with given grad_tensor.
ttnn.hardshrink_bw	Performs backward operations for hardshrink on input_tensor, lambd, with given grad_tensor.
ttnn.softshrink_bw	Performs backward operations for softshrink on input_tensor, lambd, with given grad_tensor.
ttnn.leaky_relu_bw	Performs backward operations for leaky_relu on input_tensor, negative_slope, with given grad_tensor.
ttnn.elu_bw	Performs backward operations for elu on input_tensor, alpha, with given grad_tensor.
ttnn.celu_bw	Performs backward operations for celu on input_tensor, alpha, with given grad_tensor.
ttnn.rpow_bw	Performs backward operations for rpow on input_tensor, exponent with given grad_tensor.
ttnn.floor_bw	Performs backward operations for floor on input_tensor with given grad_tensor
ttnn.round_bw	Performs backward operations for round on input_tensor with given grad_tensor.
ttnn.log_bw	Performs backward operations for logarithm on input_tensor with given grad_tensor
ttnn.relu6_bw	Performs backward operations for relu6 on input_tensor with given grad_tensor
ttnn.abs_bw	Performs backward operations for abs on input_tensor with given grad_tensor
ttnn.silu_bw	Performs backward operations for silu on input_tensor with given grad_tensor
ttnn.selu_bw	Performs backward operations for selu on input_tensor with given grad_tensor
ttnn.square_bw	Performs backward operations for square on input_tensor with given grad_tensor
ttnn.prod_bw	Performs backward operations for prod on input_tensor with given grad_tensor along all_dimensions or a particular dim.
ttnn.hardswish_bw	Performs backward operations for hardswish on input_tensor with given grad_tensor
ttnn.tanhshrink_bw	Performs backward operations for tanhshrink on input_tensor with given grad_tensor
ttnn.atanh_bw	Performs backward operations for inverse hyperbolic tangent (atanh) on input_tensor with given grad_tensor
ttnn.asin_bw	Performs backward operations for inverse sine (asin) on input_tensor with given grad_tensor
ttnn.asinh_bw	Performs backward operations for inverse hyperbolic sine (asinh) on input_tensor with given grad_tensor
ttnn.sin_bw	Performs backward operations for sin on input_tensor with given grad_tensor
ttnn.sinh_bw	Performs backward operations for hyperbolic sine (sinh) on input_tensor with given grad_tensor
ttnn.log10_bw	Performs backward operations for log10 on input_tensor with given grad_tensor
ttnn.log1p_bw	Performs backward operations for log1p on input_tensor with given grad_tensor
ttnn.erfc_bw	Performs backward operations for erfc on input_tensor with given grad_tensor
ttnn.ceil_bw	Performs backward operations for ceil on input_tensor with given grad_tensor
ttnn.softsign_bw	Performs backward operations for softsign on input_tensor with given grad_tensor
ttnn.cosh_bw	Performs backward operations for hyperbolic cosine (cosh) on input_tensor with given grad_tensor.
ttnn.logiteps_bw	Performs backward operations for logiteps on input_tensor, eps, with given grad_tensor.
ttnn.log2_bw	Performs backward operations for log2 on input_tensor with given grad_tensor.
ttnn.sign_bw	Performs backward operations for sign on input_tensor with given grad_tensor.
ttnn.div_no_nan_bw	Performs backward operations for div_no_nan on input_tensor, scalar with given grad_tensor.
ttnn.exp2_bw	Performs backward operations for exp2 on input_tensor with given grad_tensor
ttnn.expm1_bw	Performs backward operations for expm1 on input_tensor with given grad_tensor
ttnn.reciprocal_bw	Performs backward operations for reciprocal on input_tensor with given grad_tensor
ttnn.digamma_bw	Performs backward operations for digamma on input_tensor with given grad_tensor
ttnn.erfinv_bw	Performs backward operations for erfinv on input_tensor with given grad_tensor
ttnn.erf_bw	Performs backward operations for erf on input_tensor with given grad_tensor
ttnn.deg2rad_bw	Performs backward operations for degree to radian conversion (deg2rad) on input_tensor with given grad_tensor
ttnn.polygamma_bw	Performs backward operations for polygamma on input_tensor, scalar with given grad_tensor.
ttnn.gelu_bw	Performs backward operations for gelu on input_tensor, with given grad_tensor using given approximate mode.
ttnn.repeat_bw	Performs backward operations for repeat on input_tensor, with given grad_tensor using given shape.
ttnn.real	Performs complex operations for real of input_tensor.
ttnn.imag	Performs complex operations for imag of input_tensor.
ttnn.angle	Performs complex operations for angle of input_tensor.
ttnn.is_imag	Returns boolean tensor if value of input_tensor is imag.
ttnn.is_real	Returns boolean tensor if value of input_tensor is real.
ttnn.polar_bw	Performs backward operations for complex polar function on input_tensor with given grad_tensor.
ttnn.imag_bw	Performs backward operations for complex imaginary function on input_tensor with given grad_tensor.
ttnn.real_bw	Performs backward operations for complex real function on input_tensor with given grad_tensor.
ttnn.angle_bw	Performs backward operations for complex angle function on input_tensor with given grad_tensor.
ttnn.conj_bw	Performs backward operations for complex conj function on input_tensor with given grad_tensor.
ttnn.conj	Returns complex conjugate value of complex tensor input_tensor.
ttnn.polar	Perform an polar to Cartesian transformation on input_tensor, input_tensor.real(r), input_tensor.imag(theta) into x + i*y generating a complex tensor.

Pointwise Binary

ttnn.add	Adds input_tensor_a to input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.addalpha	Computes addalpha for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a.
ttnn.subalpha	Computes subalpha for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.multiply	Multiplies input_tensor_a by input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.subtract	Subtracts input_tensor_b from input_tensor_a and returns the tensor with the same layout as input_tensor_a
ttnn.div	Computes div for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.div_no_nan	Computes div_no_nan for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.floor_div	Computes floor division for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.remainder	Performs an eltwise-modulus operation.
ttnn.fmod	Performs an eltwise-fmod operation.
ttnn.gcd	Computes Greatest common divisor of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a.
ttnn.lcm	Computes Least common multiple of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a.
ttnn.logical_and_	Computes inplace logical AND of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.logical_or_	Computes inplace logical OR of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.logical_xor_	Computes inplace logical XOR of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.rpow	Performs rpow function on input_tensor, exponent.
ttnn.rsub	Subtracts input_tensor_a from input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.ldexp	Computes ldexp of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.logical_and	Computes logical AND of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.logical_or	Computes logical OR of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.logical_xor	Compute logical_xor input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.bitwise_and	Perform bitwise_and operation on input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.bitwise_or	Perform bitwise_or operation on input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.bitwise_xor	Perform bitwise_xor operation on input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.logaddexp	Computes logaddexp of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.logaddexp2	Computes logaddexp2 of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.hypot	Computes hypot input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.xlogy	Computes xlogy input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.squared_difference	Computes squared difference of input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.gt	Compares if input_tensor_a is greater than input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.gt_	Performs Greater than in-place operation on input_a and input_b and returns the tensor with the same layout as input_tensor
ttnn.lt_	Performs Less than in-place operation on input_a and input_b and returns the tensor with the same layout as input_tensor
ttnn.ge_	Performs Greater than or equal to in-place operation on input_a and input_b and returns the tensor with the same layout as input_tensor
ttnn.le_	Performs Less than or equal to in-place operation on input_a and input_b and returns the tensor with the same layout as input_tensor
ttnn.eq_	Performs Equal to in-place operation on input_a and input_b and returns the tensor with the same layout as input_tensor
ttnn.ne_	Performs Not equal to in-place operation on input_a and input_b and returns the tensor with the same layout as input_tensor
ttnn.ge	Compares if input_tensor_a is greater than or equal to input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.lt	Compares if input_tensor_a is less than input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.le	Compares if input_tensor_a is less than or equal to input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.eq	Compares if input_tensor_a is equal to input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.ne	Compares if input_tensor_a is not equal to input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.isclose	Computes isclose for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.nextafter	Computes nextafter input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.maximum	Computes maximum for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.minimum	Computes minimum for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.outer	Computes outer for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.pow	Perform element-wise pow operation on input_tensor with exponent.
ttnn.polyval	Computes polyval of all elements of input_tensor_a with coefficients coeffs and returns the tensor with the same layout as input_tensor_a
ttnn.scatter	Computes scatter for input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.atan2	Computes atan2 input_tensor_a and input_tensor_b and returns the tensor with the same layout as input_tensor_a
ttnn.add_bw	Performs backward operations for add of input_tensor_a and input_tensor_b or scalar with given grad_tensor.
ttnn.assign_bw	Performs backward operations for assign of input_tensor_a, input_tensor_b with given grad_tensor.
ttnn.atan2_bw	Performs backward operations for atan2 of input_tensor_a and input_tensor_b with given grad_tensor.
ttnn.bias_gelu_bw	Performs backward operations for bias_gelu on input_tensor_a and input_tensor_b or input_tensor and bias, with given grad_tensor using given approximate mode.
ttnn.div_bw	Performs backward operations for divide on input_tensor, alpha or input_tensor_a, input_tensor_b, round_mode, with given grad_tensor.
ttnn.embedding_bw	Returns the input gradients of the output gradients tensor with respect to the input indices.
ttnn.fmod_bw	Performs backward operations for fmod of input_tensor_a, scalar or input_tensor_b with given grad_tensor.
ttnn.remainder_bw	Performs backward operations for remainder of input_tensor_a, scalar or input_tensor_b with given grad_tensor.
ttnn.addalpha_bw	Performs backward operations for addalpha on input_tensor_b , input_tensor_a and alpha with given grad_tensor.
ttnn.subalpha_bw	Performs backward operations for subalpha of input_tensor_a and input_tensor_b with given grad_tensor.
ttnn.xlogy_bw	Performs backward operations for xlogy of input_tensor_a and input_tensor_b with given grad_tensor.
ttnn.hypot_bw	Performs backward operations for hypot of input_tensor_a and input_tensor_b with given grad_tensor.
ttnn.ldexp_bw	Performs backward operations for ldexp of input_tensor_a and input_tensor_b with given grad_tensor.
ttnn.logaddexp_bw	Performs backward operations for logaddexp of input_tensor_a and input_tensor_b with given grad_tensor.
ttnn.logaddexp2_bw	Performs backward operations for logaddexp2 of input_tensor_a and input_tensor_b with given grad_tensor.
ttnn.mul_bw	Performs backward operations for multiply on input_tensor_a, input_tensor_b, with given grad_tensor.
ttnn.sub_bw	Performs backward operations for subtract of input_tensor_a and input_tensor_b or scalar with given grad_tensor.
ttnn.squared_difference_bw	Performs backward operations for squared_difference of input_tensor_a and input_tensor_b with given grad_tensor.
ttnn.concat_bw	Performs backward operations for concat on input_tensor_a and input_tensor_b with given grad_tensor.
ttnn.rsub_bw	Performs backward operations for subraction of input_tensor_a from input_tensor_b with given grad_tensor (reversed order of subtraction operator).
ttnn.min_bw	Performs backward operations for minimum of input_tensor_a and input_tensor_b with given grad_tensor.
ttnn.max_bw	Performs backward operations for maximum of input_tensor_a and input_tensor_b with given grad_tensor.

Pointwise Ternary

ttnn.addcdiv	Computes Addcdiv on input_tensor_a, input_tensor_b and input_tensor_c and returns the tensor with the same layout as input_tensor_a
ttnn.addcmul	Computes Addcmul on input_tensor_a, input_tensor_b and input_tensor_c and returns the tensor with the same layout as input_tensor_a
ttnn.mac	Computes Mac on input_tensor_a, input_tensor_b and input_tensor_c and returns the tensor with the same layout as input_tensor_a
ttnn.where	Computes Where on input_tensor_a, input_tensor_b and input_tensor_c and returns the tensor with the same layout as input_tensor_a
ttnn.lerp	Computes Lerp on input, end and weight and returns the tensor with the same layout as input
ttnn.addcmul_bw	Performs backward operations for addcmul of input_tensor_a, input_tensor_b and input_tensor_c with given grad_tensor.
ttnn.addcdiv_bw	Performs backward operations for addcdiv of input_tensor_a, input_tensor_b and input_tensor_c with given grad_tensor.
ttnn.where_bw	Performs backward operations for where of input_tensor_a, input_tensor_b and input_tensor_c with given grad_tensor.
ttnn.lerp_bw	Performs backward operations for lerp of input_tensor_a, input_tensor_b and input_tensor_c or scalar with given grad_tensor.

Losses

ttnn.l1_loss	Returns mean absolute error loss function for input_reference and input_prediction
ttnn.mse_loss	Returns mean squared error loss function for input_reference and input_prediction

Reduction

ttnn.max	param input_a: the input tensor.
ttnn.mean	param input_a: the input tensor.
ttnn.min	param input_a: the input tensor.
ttnn.std	param input_a: the input tensor.
ttnn.sum	param input_a: the input tensor.
ttnn.var	param input_a: the input tensor.
ttnn.argmax	Returns the indices of the maximum value of elements in the input tensor If no dim is provided, it will return the indices of maximum value of all elements in given input
ttnn.prod	Computes the prod function along specified dim or all dimensions on the input tensor.
ttnn.topk	Returns the k largest or k smallest elements of the given input tensor along a given dimension.

Data Movement

ttnn.concat	param input_tensor: the input tensors.
ttnn.nonzero	Returns the number of elements (N) that are non-zero as well as a tensor of the same shape as input where the first N elements are the indices of non-zero elements.
ttnn.pad	Returns a padded tensor, with a specified value at the specified location.
ttnn.permute	Permutes the dimensions of the input tensor according to the specified permutation.
ttnn.reshape	Note: for a 0 cost view, the following conditions must be met:
ttnn.repeat	Returns a new tensor filled with repetition of input input_tensor according to number of times specified in shape.
ttnn.repeat_interleave	Repeats elements of a tensor in the given dim.
ttnn.slice	Returns a sliced tensor.
ttnn.tilize	Changes data layout of input tensor to TILE.
ttnn.tilize_with_val_padding	Changes data layout of input tensor to TILE.
ttnn.fill_rm	Generates an NCHW row-major tensor and fill it with high values up to hOnes, wOnes in each HW tile with the rest padded with high values.
ttnn.fill_ones_rm	Same as fill_rm, but val_hi is set to 1 and val_lo is 0.
ttnn.untilize	Changes data layout of input tensor to ROW_MAJOR.
ttnn.untilize_with_unpadding	Changes data layout of input tensor to ROW_MAJOR and unpads/removes elements from the tensor.
ttnn.indexed_fill	Replaces batch of input in input_b denoted by batch_ids into input_a.

Normalization

ttnn.group_norm	Compute group_norm over input_tensor.
ttnn.layer_norm	Compute layer_norm over input_tensor.
ttnn.rms_norm	Compute rms_norm over input_tensor.
ttnn.batch_norm	Applies Spatial Batch Normalization over each channel on input_tensor. Inputs must be must be tilized and interleaved.

Moreh Operations

ttnn.moreh_sum

Moreh Sum Operation

Transformer

ttnn.transformer.split_query_key_value_and_split_heads	Splits input_tensor of shape [batch_size, sequence_size, 3 * hidden_size] into 3 tensors (Query, Key, Value) of shape [batch_size, sequence_size, hidden_size].
ttnn.transformer.concatenate_heads	Takes in a tensor of shape [batch_size, num_heads, sequence_size, head_size], concatenates heads back along the width dimension and returns the tensor of shape [batch_size, sequence_size, num_heads * head_size]
ttnn.transformer.attention_softmax	Divides tensor by the square root of head_size, adds attention_mask (optionally) and computes softmax.
ttnn.transformer.attention_softmax_	In-Place divides tensor by the square root of head_size, adds attention_mask (optionally) and computes softmax.
ttnn.experimental.rotary_embedding	Applies the rotary embedding to the input_tensor tensor using the cos_cache and sin_cache tensors.
ttnn.transformer.scaled_dot_product_attention	Causal scaled dot product attention.
ttnn.transformer.scaled_dot_product_attention_decode	A version of scaled dot product attention specifically for decode.

CCL

ttnn.all_gather	Performs an all-gather operation on multi-device input_tensor across all devices.
ttnn.reduce_scatter	Performs an reduce_scatter operation on multi-device input_tensor across all devices.
ttnn.experimental.all_reduce	Performs an all_reduce operation on multi-device input_tensor across all devices.

Embedding

ttnn.embedding

Retrieves word embeddings using input_tensor.

Pooling

ttnn.global_avg_pool2d	Applies global_avg_pool2d to input_tensor by performing a 2D adaptive average pooling over an input signal composed of several input planes.
ttnn.max_pool2d	Applies a max pool convolution to the input tensor.

Vision

ttnn.upsample	Upsamples a given multi-channel 2D (spatial) data.
ttnn.downsample	Downsamples a given multi-channel 2D (spatial) data.

KV Cache

ttnn.kv_cache.fill_cache_for_user_	Populates the cache tensor in-place with values sourced from input at batch_index.
ttnn.kv_cache.update_cache_for_token_	Updates the cache tensor in-place with values from input at update_index and batch_offset.

Model Conversion

ttnn.model_preprocessing.preprocess_model	Preprocess modules and parameters of a given model.
ttnn.model_preprocessing.preprocess_model_parameters	Preprocess parameters of a given model.

Reports

ttnn.set_printoptions

Set print options for tensor output.

Operation Hooks

ttnn.register_pre_operation_hook	register_pre_operation_hook is a context manager that registers a pre-operation hook.
ttnn.register_post_operation_hook	register_post_operation_hook is a context manager that registers a post-operation hook.