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Merge pull request #141 from avolkov1/cusignal_example
Cusignal nodes for Greenflow.
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| ## Greenflow Cusignal Plugin | ||
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| Greenflow plugin that includes a set of nodes for Cusignal library. | ||
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| ### Install the greenflowlab JupyterLab plugin | ||
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| First create a Python enviroment or use one with RAPIDS cuSignal library. Tip, | ||
| use mamba to resolve dependencies quicker. | ||
| ```bash | ||
| conda create -n rapids_cusignal -c conda-forge mamba python=3.8 | ||
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| conda activate rapids_cusignal | ||
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| mamba install -c rapidsai -c nvidia -c conda-forge \ | ||
| cusignal=21.06 python=3.8 cudatoolkit=11.2 | ||
| ``` | ||
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| Then install `greenflowlab` JupyterLab plugin, make sure `nodejs` of version | ||
| [12^14^15] is installed. E.g: | ||
| ```bash | ||
| mamba install -c conda-forge python-graphviz nodejs=12.4.0 pydot | ||
| ``` | ||
| Then install the `greenflowlab`: | ||
| ```bash | ||
| pip install greenflowlab | ||
| ``` | ||
| Or install `greenflowlab` at the greenflowlab directory: | ||
| ```bash | ||
| pip install . | ||
| ``` | ||
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| ### Install the cusignal plugin | ||
| Install the plugin directly from the plugin diretory. | ||
| ```bash | ||
| pip install . | ||
| ``` |
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gQuant/plugins/cusignal_plugin/greenflow_cusignal_plugin/convolution/__init__.py
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| from .convolve import * | ||
| from .correlate import * | ||
| from .fftconvolve import * | ||
| from .convolve2d import * | ||
| from .correlate2d import * |
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gQuant/plugins/cusignal_plugin/greenflow_cusignal_plugin/convolution/convolve.py
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| import numpy as np | ||
| import cupy as cp | ||
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| from cusignal.convolution import convolve as cuconv | ||
| from scipy.signal import convolve as siconv | ||
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| from greenflow.dataframe_flow import (Node, PortsSpecSchema, ConfSchema) | ||
| from greenflow.dataframe_flow.template_node_mixin import TemplateNodeMixin | ||
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| __all__ = ['CusignalConvolveNode'] | ||
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| _CONV_DESC = '''Convolve two N-dimensional arrays. | ||
| Convolve `in1` and `in2`, with the output size determined by the | ||
| `mode` argument. | ||
| Returns: | ||
| convolve : array | ||
| An N-dimensional array containing a subset of the discrete linear | ||
| convolution of `in1` with `in2`. | ||
| ''' | ||
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| _CONV_MODE_DESC = '''mode : str {'full', 'valid', 'same'}, optional | ||
| A string indicating the size of the output: | ||
| ``full`` | ||
| The output is the full discrete linear convolution | ||
| of the inputs. (Default) | ||
| ``valid`` | ||
| The output consists only of those elements that do not | ||
| rely on the zero-padding. In 'valid' mode, either `in1` or `in2` | ||
| must be at least as large as the other in every dimension. | ||
| ``same`` | ||
| The output is the same size as `in1`, centered | ||
| with respect to the 'full' output. | ||
| ''' | ||
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| _CONV_METHOD_DESC = '''method : str {'auto', 'direct', 'fft'}, optional | ||
| A string indicating which method to use to calculate the convolution. | ||
| ``direct`` | ||
| The convolution is determined directly from sums, the definition of | ||
| convolution. | ||
| ``fft`` | ||
| The Fourier Transform is used to perform the convolution by calling | ||
| `fftconvolve`. | ||
| ``auto`` | ||
| Automatically chooses direct or Fourier method based on an estimate | ||
| of which is faster (default). | ||
| ''' | ||
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| class CusignalConvolveNode(TemplateNodeMixin, Node): | ||
| def init(self): | ||
| TemplateNodeMixin.init(self) | ||
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| port_type = PortsSpecSchema.port_type | ||
| inports = { | ||
| 'in1': {port_type: [cp.ndarray, np.ndarray]}, | ||
| 'in2': {port_type: [cp.ndarray, np.ndarray]} | ||
| } | ||
| outports = { | ||
| 'convolve': {port_type: [cp.ndarray, np.ndarray]}, | ||
| } | ||
| self.template_ports_setup(in_ports=inports, out_ports=outports) | ||
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| meta_outports = {'convolve': {}} | ||
| self.template_meta_setup(out_ports=meta_outports) | ||
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| def conf_schema(self): | ||
| mode_enum = ['full', 'valid', 'same'] | ||
| method_enum = ['direct', 'fft', 'auto'] | ||
| json = { | ||
| 'title': 'Cusignal Convolution Node', | ||
| 'type': 'object', | ||
| 'description': _CONV_DESC, | ||
| 'properties': { | ||
| 'mode': { | ||
| 'type': 'string', | ||
| 'description': _CONV_MODE_DESC, | ||
| 'enum': mode_enum, | ||
| 'default': 'full' | ||
| }, | ||
| 'method': { | ||
| 'type': 'string', | ||
| 'description': _CONV_METHOD_DESC, | ||
| 'enum': method_enum, | ||
| 'default': 'auto' | ||
| }, | ||
| 'normalize': { | ||
| 'type': 'boolean', | ||
| 'description': 'Scale convolutioni by in2 (typically a ' | ||
| 'window) i.e. convolve(in1, in2) / sum(in2). ' | ||
| 'Default False.', | ||
| 'default': False | ||
| }, | ||
| 'use_cpu': { | ||
| 'type': 'boolean', | ||
| 'description': 'Use CPU for computation via ' | ||
| 'scipy::signal.convolve. Default is False and runs on ' | ||
| 'GPU via cusignal.', | ||
| 'default': False | ||
| }, | ||
| }, | ||
| } | ||
| return ConfSchema(json=json) | ||
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| def process(self, inputs): | ||
| mode = self.conf.get('mode', 'full') | ||
| method = self.conf.get('method', 'auto') | ||
| normalize = self.conf.get('normalize', False) | ||
| use_cpu = self.conf.get('use_cpu', False) | ||
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| in1 = inputs['in1'] | ||
| in2 = inputs['in2'] | ||
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| if use_cpu: | ||
| conv = siconv(in1, in2, mode=mode, method=method) | ||
| if normalize: | ||
| scale = np.sum(in2) | ||
| else: | ||
| conv = cuconv(in1, in2, mode=mode, method=method) | ||
| if normalize: | ||
| scale = cp.sum(in2) | ||
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| if normalize: | ||
| conv = conv if scale == 1 else conv / scale | ||
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| return {'convolve': conv} |
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