aclStft

Applicable Products

Function

• Description: Computes the Fourier transform of the input within a sliding window.

• Formula:

  • When normalized=false:

    $$X[w,m]=\sum_{k=0}^{winLength-1}window[k]*self[m*hopLength+k]*exp(-j*\frac{2{\pi}wk}{nFft})$$

  • When normalized=true:

    $$X[w,m]=\frac{1}{\sqrt{nFft}}(\sum_{k=0}^{winLength-1}window[k]*self[m*hopLength+k]*exp(-j*\frac{2{\pi}wk}{nFft}))$$

  Where:

  • $w$ is the frequency bin of the FFT.

  • $m$ is the index of the sliding window.

  • $self$ is a 1D or 2D tensor. When $self$ is 1D, it represents a single time-series sampling sequence. When $self$ is 2D, it represents multiple time-series sampling sequences.

  • $hopLength$ is the sliding window size.

  • $window$ is a 1D tensor, serving as the window function for STFT (e.g., hann_window), with a length of $winLength$.

  • $exp(-j*\frac{2{\pi}wk}{nFft})$ is the twiddle factor.

Function Prototype

Each operator is divided into a two-phase API. The "aclStftGetWorkspaceSize" API must be called first to obtain the workspace size required for computation and the executor that encapsulates the operator computation flow. Then, the "aclStft" API is called to perform the computation.

aclnnStatus aclStftGetWorkspaceSize(
  const aclTensor *self,
  const aclTensor *windowOptional,
  aclTensor       *out,
  int64_t          nFft,
  int64_t          hopLength,
  int64_t          winLength,
  bool             normalized,
  bool             onesided,
  bool             returnComplex,
  uint64_t        *workspaceSize,
  aclOpExecutor  **executor)

aclnnStatus aclStft(
  void          *workspace,
  uint64_t       workspaceSize,
  aclOpExecutor *executor,
  aclrtStream    stream)

aclStftGetWorkspaceSize

• Parameters:

  Parameter	Input/Output	Description	Instruction	Data Type	Data Format	Dimension (Shape)	Non-contiguous Tensor
  self	Input	The input to be computed, corresponding to `self` in the formula.	Null Tensor is not supported. The shape is [L] or [B, L], where L is the length of the time-domain sampling sequence and B is the number of time-domain sampling sequences. Inf, -Inf, or NaN input is not supported.	FLOAT32, DOUBLE, COMPLEX64, COMPLEX128	ND	1-2	×
  windowOptional	Input	Must be a 1D tensor, corresponding to `window` in the formula.	Null not supported. The data type must be consistent with `self`. The shape is [winLength], where winLength is the length of the STFT window function.	FLOAT32, DOUBLE, COMPLEX64, COMPLEX128	ND	1	×
  out	Output	Fourier transform result of self within the window, corresponding to `X` in the formula.	Null not supported. If returnComplex=True, `out` is a complex tensor of shape [N, T] or [B, N, T]. If returnComplex=False, `out` is a real tensor of shape [N, T, 2] or [B, N, T, 2]. Where N = nFft (onesided=False) or (nFft // 2 + 1) (onesided=True); T is the number of sliding windows, T = (L - nFft) // hopLength + 1.	FLOAT32, DOUBLE, COMPLEX64, COMPLEX128	ND	3-4	×
  nFft	Input	Number of FFT points (greater than 0), corresponding to `nFft` in the formula.	-	INT64	-	-	-
  hopLength	Input	Represents the interval of the sliding window (greater than 0), corresponding to `hopLength` in the formula.	-	INT64	-	-	-
  winLength	Input	Indicates the window size (greater than 0), corresponding to `winLength` in the formula.	-	INT64	-	-	-
  normalized	Input	Indicates whether to normalize the Fourier transform result.	-	BOOL	-	-	-
  onesided	Input	Indicates whether to return the full result or half of the result.	When the data type of the input `self` is COMPLEX64 or COMPLEX128, only False is supported.	BOOL	-	-	-
  returnComplex	Input	Indicates whether the return value is a complex tensor or separate real and imaginary tensors.	-	BOOL	-	-	-
  workspaceSize	Output	Returns the workspace size to be allocated on the Device side.	-	-	-	-	-
  executor	Output	Returns the operator executor, which contains the operator computation flow.	-	-	-	-	-

• Return value

  aclnnStatus: return code. For details, see aclnn Return Codes.

  The first-phase API performs input parameter validation and returns an error in the following scenarios:

  Return Code	Error Code	Description
  ACLNN_ERR_PARAM_NULLPTR	161001	The passed self or out is a null pointer.
  ACLNN_ERR_PARAM_INVALID	161002	The data format of self is not within the supported range.
  		The data types of self and windowOptional are inconsistent.
  		The data types of self, windowOptional, and out are not within the supported range of the platform.
  		Invalid values are input for nFft, hopLength, or winLength.
  		The dimensions of self, windowOptional, and out are not within the supported range.
  		When the data type of the input self is COMPLEX64 or COMPLEX128, the value of onesided is True.

aclStft

• Parameters:

  Parameter	Input/Output	Description
  workspace	Input	Address of the workspace memory applied for on the Device side.
  workspaceSize	Input	Size of the workspace applied for on the Device side, obtained from the first-phase API aclStftGetWorkspaceSize.
  executor	Input	Operator executor, which contains the operator computation flow.
  stream	Input	Specifies the stream that executes the task.

• Return value

  aclnnStatus: return code. For details, see aclnn Return Codes.

Constraints

• Difference between the input self and the PyTorch API: The input self of the PyTorch API is the original input; the input self of aclStftGetWorkspaceSize is the result obtained after the original input is padded by the frontend PyTorch.

• When the shape of the input self is [B, L], the current API call may time out if the result of the following formula is large.

  $$B * ((L - nFft) / hopLength + 1) * nFft$$

• nFft <= L.

• winLength <= nFft.

• When normalized = True,

  $$STFT(w,m)=\frac{1}{\sqrt{N}}X[w,m]$$

• The correspondence between the data types of self, windowOptional, returnComplex, and out is as follows:

  self	windowOptional	returnComplex	out
  FLOAT32	FLOAT32	True	COMPLEX64
  DOUBLE	DOUBLE	True	COMPLEX128
  COMPLEX64	COMPLEX64	True	COMPLEX64
  COMPLEX128	COMPLEX128	True	COMPLEX128
  FLOAT32	FLOAT32	False	FLOAT32
  DOUBLE	DOUBLE	False	DOUBLE
  COMPLEX64	COMPLEX64	False	FLOAT32
  COMPLEX128	COMPLEX128	False	DOUBLE

• Deterministic computation:

  • aclStft defaults to a deterministic implementation.

Example

The following provides example code for reference only. For details about compilation and running, see Compile and Run Samples.

#include <iostream>
#include <vector>
#include "acl/acl.h"
#include "aclnnop/acl_stft.h"

#define CHECK_RET(cond, return_expr) \
    do {                             \
        if (!(cond)) {               \
            return_expr;             \
        }                            \
    } while (0)
#define LOG_PRINT(message, ...)         \
    do {                                \
        printf(message, ##__VA_ARGS__); \
    } while (0)
int64_t GetShapeSize(const std::vector<int64_t>& shape)
{
    int64_t shapeSize = 1;
    for (auto i : shape) {
        shapeSize *= i;
    }
    return shapeSize;
}
int Init(int32_t deviceId, aclrtStream* stream)
{
    // Boilerplate: resource initialization.
    auto ret = aclInit(nullptr);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclInit failed. ERROR: %d\n", ret); return ret);
    ret = aclrtSetDevice(deviceId);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSetDevice failed. ERROR: %d\n", ret); return ret);
    ret = aclrtCreateStream(stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtCreateStream failed. ERROR: %d\n", ret); return ret);
    return 0;
}
template <typename T>
int CreateAclTensor(
    const std::vector<T>& hostData, const std::vector<int64_t>& shape, void** deviceAddr, aclDataType dataType,
    aclTensor** tensor)
{
    auto size = GetShapeSize(shape) * sizeof(T);
    // Call aclrtMalloc to allocate memory on the device side.
    auto ret = aclrtMalloc(deviceAddr, size, ACL_MEM_MALLOC_HUGE_FIRST);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMalloc failed. ERROR: %d\n", ret); return ret);
    // Call aclrtMemcpy to copy data from the host side to the device memory.
    ret = aclrtMemcpy(*deviceAddr, size, hostData.data(), size, ACL_MEMCPY_HOST_TO_DEVICE);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtMemcpy failed. ERROR: %d\n", ret); return ret);
    // Calculate the strides of a contiguous tensor.
    std::vector<int64_t> strides(shape.size(), 1);
    for (int64_t i = shape.size() - 2; i >= 0; i--) {
        strides[i] = shape[i + 1] * strides[i + 1];
    }
    // Call aclCreateTensor to create an aclTensor.
    *tensor = aclCreateTensor(
        shape.data(), shape.size(), dataType, strides.data(), 0, aclFormat::ACL_FORMAT_ND, shape.data(), shape.size(),
        *deviceAddr);
    return 0;
}
int main()
{
    // 1. (Boilerplate) Initialize device/stream. See the acl external API list.
    // Fill in deviceId based on your actual device.
    int32_t deviceId = 0;
    aclrtStream stream;
    auto ret = Init(deviceId, &stream);
    // Handle the check as needed.
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("Init acl failed. ERROR: %d\n", ret); return ret);
    // 2. Construct inputs and outputs. Customize the construction based on the API.
    std::vector<int64_t> selfShape = {5};
    std::vector<int64_t> windowShape = {4};
    std::vector<int64_t> outShape = {3, 1, 2};
    void* selfDeviceAddr = nullptr;
    void* windowDeviceAddr = nullptr;
    void* outDeviceAddr = nullptr;
    aclTensor* self = nullptr;
    aclTensor* window = nullptr;
    aclTensor* out = nullptr;
    std::vector<float> selfHostData = {1, 6, 8, 5, 7};
    std::vector<float> windowHostData = {1, 1, 1, 1};
    std::vector<float> outHostData = {0, 0, 0, 0, 0, 0};
    // Create self aclTensor.
    ret = CreateAclTensor(selfHostData, selfShape, &selfDeviceAddr, aclDataType::ACL_FLOAT, &self);
    // Create window aclTensor.
    ret = CreateAclTensor(windowHostData, windowShape, &windowDeviceAddr, aclDataType::ACL_FLOAT, &window);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    // Create out aclTensor.
    ret = CreateAclTensor(outHostData, outShape, &outDeviceAddr, aclDataType::ACL_FLOAT, &out);
    CHECK_RET(ret == ACL_SUCCESS, return ret);
    int n_fft = 4;
    int hop_length = 2;
    int win_length = 4;
    bool normalized = false;
    bool onesided = true;
    bool returnComplex = false;
    // 3. Call the CANN operator library API. Replace with the specific API name.
    uint64_t workspaceSize = 0;
    aclOpExecutor* executor;
    // Call the first-phase API of aclStft.
    ret = aclStftGetWorkspaceSize(
        self, window, out, n_fft, hop_length, win_length, normalized, onesided, returnComplex, &workspaceSize,
        &executor);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclStftGetWorkspaceSize failed. ERROR: %d\n", ret); return ret);
    // Apply for device memory based on the workspaceSize calculated by the first-phase API.
    void* workspaceAddr = nullptr;
    if (workspaceSize > 0) {
        ret = aclrtMalloc(&workspaceAddr, workspaceSize, ACL_MEM_MALLOC_HUGE_FIRST);
        CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("allocate workspace failed. ERROR: %d\n", ret); return ret);
    }
    // Call the aclStft second-phase API.
    ret = aclStft(workspaceAddr, workspaceSize, executor, stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclStft failed. ERROR: %d\n", ret); return ret);
    // 4. (Boilerplate) Synchronously wait for task execution to complete.
    ret = aclrtSynchronizeStream(stream);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("aclrtSynchronizeStream failed. ERROR: %d\n", ret); return ret);
    // 5. Obtain the output value and copy the result from the device memory to the host side. Modify based on the specific API definition.
    auto size = GetShapeSize(outShape);
    std::vector<float> resultData(size, 0);
    ret = aclrtMemcpy(
        resultData.data(), resultData.size() * sizeof(resultData[0]), outDeviceAddr, size * sizeof(resultData[0]),
        ACL_MEMCPY_DEVICE_TO_HOST);
    CHECK_RET(ret == ACL_SUCCESS, LOG_PRINT("copy result from device to host failed. ERROR: %d\n", ret); return ret);
    for (int64_t i = 0; i < size; i++) {
        LOG_PRINT("result[%ld] is: %f\n", i, resultData[i]);
    }
    // 6. Release aclTensor. Modify based on the specific API definition.
    aclDestroyTensor(self);
    aclDestroyTensor(window);
    aclDestroyTensor(out);
    // 7. Release device resources. Modify based on the specific API definition.
    aclrtFree(selfDeviceAddr);
    aclrtFree(windowDeviceAddr);
    aclrtFree(outDeviceAddr);
    if (workspaceSize > 0) {
        aclrtFree(workspaceAddr);
    }
    aclrtDestroyStream(stream);
    aclrtResetDevice(deviceId);
    aclFinalize();
    return 0;
}