def mul(self, a, b, out, alpha=1.0, beta=0.0):
# Ensure the matrices are compatible
if a.nrow != out.nrow or a.ncol != b.nrow or b.ncol != out.ncol:
raise ValueError('Incompatible matrices for out = a*b')
# Check that A is constant
if 'const' not in a.tags:
raise NotSuitableError('GiMMiK requires a constant a matrix')
# Check that A is reasonably sparse
if np.count_nonzero(a.get()) > self.max_nnz:
raise NotSuitableError('Matrix too dense for GiMMiK')
# Generate
src = generate_mm(a.get(),
dtype=a.dtype,
platform='cuda',
alpha=alpha,
beta=beta)
# Build
fun = self._build_kernel('gimmik_mm', src, 'iPiPi')
# Determine the grid/block
block = (128, 1, 1)
grid = get_grid_for_block(block, b.ncol)
class MulKernel(ComputeKernel):
def run(self, queue):
fun.prepared_async_call(grid, block, queue.cuda_stream_comp,
b.ncol, b, b.leaddim, out, out.leaddim)
return MulKernel()
def mul(self, a, b, out, alpha=1.0, beta=0.0):
# Ensure the matrices are compatible
if a.nrow != out.nrow or a.ncol != b.nrow or b.ncol != out.ncol:
raise ValueError('Incompatible matrices for out = a*b')
# Check that A is constant
if 'const' not in a.tags:
raise NotSuitableError('GiMMiK requires a constant a matrix')
# Check that A is reasonably sparse
if np.count_nonzero(a.get()) > self.max_nnz:
raise NotSuitableError('Matrix too dense for GiMMiK')
# Generate
src = generate_mm(a.get(), dtype=a.dtype, platform='opencl',
alpha=alpha, beta=beta)
# Build
fun = self._build_kernel('gimmik_mm', src,
[np.int32] + [np.intp, np.int32]*2)
fun.set_args(b.ncol, b, b.leaddim, out, out.leaddim)
class MulKernel(Kernel):
def run(self, queue):
fun.exec_async(queue.cmd_q, (b.ncol,), None)
return MulKernel(mats=[b, out])
def mul(self, a, b, out, alpha=1.0, beta=0.0):
# Ensure the matrices are compatible
if a.nrow != out.nrow or a.ncol != b.nrow or b.ncol != out.ncol:
raise ValueError('Incompatible matrices for out = a*b')
# Check that A is constant
if 'const' not in a.tags:
raise NotSuitableError('GiMMiK requires a constant a matrix')
# Fetch the matrix and tally up the number of non-zeros
arr = a.get()
nnz, nuq = np.count_nonzero(arr), len(np.unique(np.abs(arr)))
# Check that A is suitable
if nuq > 28 and nnz / arr.size > 0.15:
raise NotSuitableError('Matrix is inappropriate for GiMMiK')
# Generate
src = generate_mm(arr, dtype=a.dtype, platform='cuda',
alpha=alpha, beta=beta)
# Build
fun = self._build_kernel('gimmik_mm', src,
[np.int32, np.intp]*2 + [np.int32])
# Determine the grid/block
block = (128, 1, 1)
grid = get_grid_for_block(block, b.ncol)
class MulKernel(ComputeKernel):
def run(self, queue):
fun.exec_async(grid, block, queue.stream_comp, b.ncol, b,
b.leaddim, out, out.leaddim)
return MulKernel()
def mul(self, a, b, out, alpha=1.0, beta=0.0):
# Ensure the matrices are compatible
if a.nrow != out.nrow or a.ncol != b.nrow or b.ncol != out.ncol:
raise ValueError("Incompatible matrices for out = a*b")
# Check that A is constant
if "const" not in a.tags:
raise NotSuitableError("GiMMiK requires a constant a matrix")
# Check that A is reasonably sparse
if np.count_nonzero(a.get()) > self.max_nnz:
raise NotSuitableError("Matrix too dense for GiMMiK")
# Generate
src = generate_mm(a.get(), dtype=a.dtype, platform="cuda", alpha=alpha, beta=beta)
# Build
fun = self._build_kernel("gimmik_mm", src, "iPiPi")
# Determine the grid/block
block = (128, 1, 1)
grid = get_grid_for_block(block, b.ncol)
class MulKernel(ComputeKernel):
def run(self, queue):
fun.prepared_async_call(grid, block, queue.cuda_stream_comp, b.ncol, b, b.leaddim, out, out.leaddim)
return MulKernel()
def mul(self, a, b, out, alpha=1.0, beta=0.0):
# Ensure the matrices are compatible
if a.nrow != out.nrow or a.ncol != b.nrow or b.ncol != out.ncol:
raise ValueError('Incompatible matrices for out = a*b')
# Check that A is constant
if 'const' not in a.tags:
raise NotSuitableError('GiMMiK requires a constant a matrix')
# Check that A is reasonably sparse
if np.count_nonzero(a.get()) > self.max_nnz:
raise NotSuitableError('Matrix too dense for GiMMiK')
# Generate
src = generate_mm(a.get(), dtype=a.dtype, platform='opencl',
alpha=alpha, beta=beta)
# Build
fun = self._build_kernel('gimmik_mm', src,
[np.int32] + [np.intp, np.int32]*2)
class MulKernel(ComputeKernel):
def run(self, queue):
fun(queue.cl_queue_comp, (b.ncol,), None, b.ncol,
b.data, b.leaddim, out.data, out.leaddim)
return MulKernel()
def mul(self, a, b, out, alpha=1.0, beta=0.0):
# Ensure the matrices are compatible
if a.nrow != out.nrow or a.ncol != b.nrow or b.ncol != out.ncol:
raise ValueError('Incompatible matrices for out = a*b')
# Check that A is constant
if 'const' not in a.tags:
raise NotSuitableError('GiMMiK requires a constant a matrix')
# Check that A is reasonably sparse
if np.count_nonzero(a.get()) > self.max_nnz:
raise NotSuitableError('Matrix too dense for GiMMiK')
# Generate the GiMMiK kernel
src = generate_mm(a.get(),
dtype=a.dtype,
platform='c-omp',
alpha=alpha,
beta=beta)
gimmik_mm = self._build_kernel('gimmik_mm', src,
[np.int32] + [np.intp, np.int32] * 2)
class MulKernel(ComputeKernel):
def run(self, queue):
gimmik_mm(b.ncol, b, b.leaddim, out, out.leaddim)
return MulKernel()
def get_mm_kernel(mat, alpha=1., beta=0., tol=1e-15):
matSrc = generate_mm(filter_tol(mat, tol=tol),
dtype=mat.dtype,
platform='cuda',
alpha=alpha,
beta=beta)
matMod = compiler.SourceModule(matSrc)
matKern = get_kernel(matMod, "gimmik_mm", 'iPiPi')
return matKern
def mul(self, a, b, out, alpha=1.0, beta=0.0):
# Ensure the matrices are compatible
if a.nrow != out.nrow or a.ncol != b.nrow or b.ncol != out.ncol:
raise ValueError('Incompatible matrices for out = a*b')
# Check that A is constant
if 'const' not in a.tags:
raise NotSuitableError('GiMMiK requires a constant a matrix')
# Check that A is reasonably sparse
if np.count_nonzero(a.get()) > self.max_nnz:
raise NotSuitableError('Matrix too dense for GiMMiK')
# Generate the GiMMiK kernel
src = generate_mm(a.get(),
dtype=a.dtype,
platform='c',
alpha=alpha,
beta=beta)
gimmik_mm = self._build_kernel('gimmik_mm', src,
[np.int32] + [np.intp, np.int32] * 2)
gimmik_ptr = cast(gimmik_mm, c_void_p).value
tplargs = {'lib': 'gimmik'}
# Render our parallel wrapper kernel
src = self.backend.lookup.get_template('batch-gemm').render(**tplargs)
# Argument types for par_gimmik
argt = [np.intp] + [np.int32] * 2 + [np.intp, np.int32] * 2
# Build
batch_gemm = self._build_kernel('batch_gemm', src, argt)
class MulKernel(ComputeKernel):
def run(self, queue):
batch_gemm(gimmik_ptr, b.leaddim, b.nblocks, b, b.blocksz, out,
out.blocksz)
return MulKernel()
def mul(self, a, b, out, alpha=1.0, beta=0.0):
# Ensure the matrices are compatible
if a.nrow != out.nrow or a.ncol != b.nrow or b.ncol != out.ncol:
raise ValueError('Incompatible matrices for out = a*b')
# Check that A is constant
if 'const' not in a.tags:
raise NotSuitableError('GiMMiK requires a constant a matrix')
# Check that A is reasonably sparse
if np.count_nonzero(a.get()) > self.max_nnz:
raise NotSuitableError('Matrix too dense for GiMMiK')
# Generate and compile the GiMMiK function
src = generate_mm(a.get(),
dtype=a.dtype,
platform='c',
alpha=alpha,
beta=beta)
gimmik_mm = self._build_function('gimmik_mm', src, 'iPiPi')
gimmik_ptr = cast(gimmik_mm, c_void_p).value
# Render our parallel wrapper kernel
src = self.backend.lookup.get_template('batch-gemm').render(
lib='gimmik')
# Build
batch_gemm = self._build_kernel('batch_gemm', src, 'PiiPiPi')
batch_gemm.set_args(gimmik_ptr, b.leaddim, b.nblocks, b, b.blocksz,
out, out.blocksz)
class MulKernel(Kernel):
def run(self, queue):
batch_gemm()
return MulKernel(mats=[b, out], misc=[gimmik_mm])
