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How to use transpose method in hypothesis

Best Python code snippet using hypothesis

eliminate_transposes.py

Source:eliminate_transposes.py

...17                                         find_compatible_transpose)18LOG = logging.getLogger("nntool." + __name__)19def reverse_reshape(trans, from_shape, to_shape):20    """reverses the effect of this reshape on the transpose"""21    return find_compatible_transpose(find_combination(from_shape, to_shape), trans)22def reverses_transpose(trans1, trans2, dim=None):23    """Checks if one transpose reverses another. If a dim is provided then24    look if the transpose sequence produces an equivalent dim to cope with 1s in25    dimensions."""26    if trans1 is None or trans2 is None:27        return False28    if dim and dim.layout_shape == dim.calc_transpose(trans1).calc_transpose(trans2).layout_shape:29        return True30    for idx, val in enumerate(trans1):31        if trans2[val] != idx:32            return False33    return True34def search_up_for_reverse(G, visited_edges, node, out_idx, transpose, edge_list):35    """Search up the graph for transpose sequences"""36    LOG.debug("looking up at %s", node.name)37    if not isinstance(node, Transposable) and (len(G.out_edges(node.name)) > 1 or isinstance(node, SensitiveToOrder)):38        LOG.debug("rejected %s - sensitive to order or multi output", node.name)39        return []40    if isinstance(node, Transposable) and node.transpose_out:41        if reverses_transpose(node.transpose_out[out_idx], transpose, node.out_dims[out_idx]):42            LOG.debug("accepted %s - transpose out", node.name)43            return [(node, edge_list, 'out', out_idx)]44        else:45            LOG.debug("rejected %s - transpose out - does not reverse", node.name)46            return []47    # if the node is a concat/split then we cannot proceed further since the48    # concat/split must happen on axis 0 and the transposes were already set up for49    # this to happen50    if isinstance(node, (ConcatParameters, SplitParameters)):51        LOG.debug("rejected %s - concat/split", node.name)52        return []53    if isinstance(node, ReshapeParameters):54        new_transpose = reverse_reshape(transpose, node.shape, node.old_shape)55        if new_transpose is None:56            LOG.debug("rejected %s - transpose in - does not reverse", node.name)57            return []58        transpose = new_transpose59        if node.transpose_in and reverses_transpose(node.transpose_in[0], transpose):60            LOG.debug("accepted %s - transpose in", node.name)61            return [(node, edge_list, "in", 0)]62    if isinstance(node, Transposable) and node.transpose_in:63        LOG.debug("rejected %s - transposable", node.name)64        return []65    return search_up_edges(G, visited_edges, node, transpose, edge_list)66def search_up_edges(G, visited_edges, node, transpose, edge_list, start_edge=None):67    all_nodes = []68    for edge in ([start_edge] if start_edge else G.variable_in_edges(node.name)):69        if edge in visited_edges:70            return []71        next_res = search_up_for_reverse(72            G, visited_edges | {edge},73            edge.from_node,74            edge.from_idx,75            transpose,76            edge_list + [edge])77        if not next_res:78            return []79        all_nodes += next_res80    return all_nodes81def search_down_for_reverse(G, visited_edges, node, in_idx, transpose, edge_list=None):82    """Search down the graph for transpose sequences"""83    LOG.debug("looking down at %s", node.name)84    if not isinstance(node, (Transposable)):85        if len(G.variable_in_edges(node.name)) > 1 or isinstance(node, SensitiveToOrder):86            LOG.debug("rejected %s - sensitive to order or multi input", node.name)87            return []88    if edge_list is None:89        edge_list = []90    if isinstance(node, Transposable):91        if node.transpose_in:92            if reverses_transpose(transpose, node.transpose_in[in_idx], node.in_dims[in_idx]):93                LOG.debug("accepted %s - transpose in", node.name)94                return [(node, edge_list, "in", in_idx)]95            else:96                LOG.debug("rejected %s - transpose in - does not reverse", node.name)97                return []98        elif len(transpose) == 1:99            LOG.debug("accepted %s transpose length 1 - transpose in", node.name)100            return [(node, edge_list, "in", in_idx)]101    # if the node is a concat/split then we cannot proceed further since the102    # concat/split must happen on axis 0 and the transposes were already set up for103    # this to happen104    if isinstance(node, (ConcatParameters, SplitParameters)):105        LOG.debug("rejected %s - concat/split", node.name)106        return []107    # if there is a reshape then the dimensionality of the transpose108    # that we are examining may change and that may or may not be compatible109    # with reversing the transpose110    if isinstance(node, ReshapeParameters):111        new_transpose = reverse_reshape(transpose, node.old_shape, node.shape)112        if new_transpose is None:113            LOG.debug("rejected %s - transpose out - does not reverse", node.name)114            return []115        transpose = new_transpose116        if node.transpose_out:117            if reverses_transpose(transpose, node.transpose_out[0]):118                LOG.debug("accepted %s - transpose out", node.name)119                return [(node, edge_list, "out", 0)]120            elif len(transpose) == 1:121                LOG.debug("accepted %s transpose length 1 - transpose out", node.name)122                return [(node, edge_list, "out", 0)]123    if isinstance(node, Transposable) and node.transpose_out:124        LOG.debug("rejected %s - transposable", node.name)125        return []126    return search_down_edges(G, visited_edges, node, transpose, edge_list)127def search_down_edges(G, visited_edges, node, transpose, edge_list, start_edge=None):128    all_nodes = []129    for edge in ([start_edge] if start_edge else G.out_edges(node.name)):130        if edge in visited_edges:131            return []132        next_res = search_down_for_reverse(133            G, visited_edges | {edge}, edge.to_node, edge.to_idx, transpose, edge_list + [edge])134        if not next_res:135            return []136        all_nodes += next_res137    return all_nodes138def search_for_reverses(G):139    results = []140    # visited edges contains all edges included in found transpose pairs141    visited_edges = set()142    for transpose_node in [node for node in G.nodes() if isinstance(node, Transposable)]:143        # for each transpose node we look up and down from the transpose in and transpose out144        # respectively to see if another transpose reverses this one with nothing145        # inbetween that is transpose sensitive146        if transpose_node.transpose_in:147            for edge in G.in_edges(transpose_node.name):148                # this can be true in the case where a node has constant inputs149                # it probably should be eliminated and all nodes transposed uniformly150                if edge.to_idx >= len(transpose_node.transpose_in):151                    continue152                trans = transpose_node.transpose_in[edge.to_idx]153                if trans is None:154                    continue155                result = search_up_edges(G, visited_edges, transpose_node,156                                         transpose_node.transpose_in[edge.to_idx], [], start_edge=edge)157                for r in result:158                    visited_edges |= set(r[1])159                    # result is (from_node, from_transpose_dir, from_idx), (to_node, to_transpose_dir, to_idx),160                    # edge list, transpose (from_node)161                    results.append(162                        (163                            (r[0], r[2], r[3]),164                            (transpose_node, 'in', edge.to_idx),165                            r[1][::-1],166                            getattr(r[0], "transpose_" + r[2])[r[3]]167                        )168                    )169        if transpose_node.transpose_out:170            for edge in G.out_edges(transpose_node.name):171                trans = transpose_node.transpose_out[edge.from_idx]172                if trans is None:173                    continue174                result = search_down_edges(G, visited_edges, transpose_node,175                                           trans, [], start_edge=edge)176                for r in result:177                    visited_edges |= set(r[1])178                    results.append(179                        (180                            (transpose_node, 'out', edge.from_idx),181                            (r[0], r[2], r[3]),182                            r[1],183                            transpose_node.transpose_out[edge.from_idx]184                        )185                    )186    return results187def process_result(res):188    LOG.info("eliminating transpose between %s[%s] and %s[%s]",189             res[0][0].name, res[0][1], res[1][0].name, res[1][1]190             )191    transpose = res[3]192    for edge in res[2]:193        to_node = edge.to_node194        if isinstance(to_node, ReshapeParameters) and not to_node.transpose_in:195            LOG.info("eliminating input transpose on %s", to_node.name)196            transpose = reverse_reshape(transpose, to_node.old_shape, to_node.shape)197            to_node.shape.transpose(transpose)198        elif isinstance(to_node, StridedSliceParameters):199            LOG.info("transpose strided slice %s", to_node.name)200            to_node.act_slice = [to_node.act_slice[idx] for idx in transpose]201            to_node.out_shape = [to_node.out_shape[idx] for idx in transpose]202    for node, direction, edge_idx in [res[idx] for idx in range(2)]:203        trans = getattr(node, "transpose_"+direction)204        # This transpose node may have been selected because it only has205        # one dimension in which case there may not actually be a transpose206        if trans:207            trans[edge_idx] = None208            if all(elem is None for elem in trans):209                setattr(node, "transpose_"+direction, None)210def eliminate_transposes(G):211    """Eliminates unnecessary transposes from the graph. Valid transposes are those that have no...

sparse_csr_matrix_grad.py

Source:sparse_csr_matrix_grad.py

...53          sparse_csr_matrix_ops.sparse_matrix_mul(grad, beta), None, None)54@ops.RegisterGradient("SparseMatrixTranspose")55def _SparseMatrixTransposeGrad(op, grad):56  """Gradient for sparse_matrix_transpose op."""57  return sparse_csr_matrix_ops.sparse_matrix_transpose(58      grad, type=op.get_attr("type"), conjugate=op.get_attr("conjugate"))59@ops.RegisterGradient("SparseMatrixSoftmax")60def _SparseMatrixSoftmaxGrad(op, grad_softmax):61  """Gradient for sparse_matrix_softmax op."""62  softmax = op.outputs[0]63  return sparse_csr_matrix_ops.sparse_matrix_softmax_grad(64      softmax, grad_softmax, type=op.get_attr("type"))65@ops.RegisterGradient("SparseMatrixMatMul")66def _SparseMatrixMatMulGrad(op, grad):67  """Gradient for sparse_matrix_mat_mul op."""68  # input to sparse_matrix_mat_mul is (A, B) with CSR A and dense B.69  # Output is dense:70  #   C = opA(A) . opB(B) if transpose_output = false71  #   C = (opA(A) . opB(B))' = opB(B)' . opA(A)' if transpose_output = true....

matmul_benchmark.py

Source:matmul_benchmark.py

1# Copyright 2015 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Benchmark for Matmul operator."""16from __future__ import absolute_import17from __future__ import division18from __future__ import print_function19import itertools20import time21import numpy as np22from tensorflow.python.client import session as session_lib23from tensorflow.python.framework import ops24from tensorflow.python.ops import control_flow_ops25from tensorflow.python.ops import math_ops26from tensorflow.python.ops import random_ops27from tensorflow.python.ops import variables28from tensorflow.python.platform import test29def build_graph(device, n, m, k, transpose_a, transpose_b, dtype):30  """Build a graph containing a sequence of matmul operations.31  Args:32    device: String, the device to run on.33    n: tensor A's first dimension size.34    m: tensor A's second dimension size.35    k: tensor B's second dimension size.36    transpose_a: boolean value to show if tensor A is transposed.37    transpose_b: boolean value to show if tensor B is transposed.38    dtype: numpy data type of the input tensor.39  Returns:40    A matmul operation to run()41  """42  with ops.device('%s' % device):43    if not transpose_a:44      x = variables.Variable(random_ops.random_uniform([n, m], dtype=dtype))45    else:46      x = variables.Variable(random_ops.random_uniform([m, n], dtype=dtype))47    if not transpose_b:48      y = variables.Variable(random_ops.random_uniform([m, k], dtype=dtype))49    else:50      y = variables.Variable(random_ops.random_uniform([k, m], dtype=dtype))51    z = math_ops.matmul(x, y, transpose_a=transpose_a, transpose_b=transpose_b)52    return control_flow_ops.group(z)53class MatmulBenchmark(test.Benchmark):54  """Benchmark matmul!"""55  def run_graph(self, device, n, m, k, transpose_a, transpose_b, num_iters,56                dtype):57    """Run the graph and print its execution time.58    Args:59      device: String, the device to run on.60      n: tensor A's first dimension size.61      m: tensor A's second dimension size.62      k: tensor B's second dimension size.63      transpose_a: boolean value to show if tensor A is transposed.64      transpose_b: boolean value to show if tensor B is transposed.65      num_iters: number of iterations to run the benchmark.66      dtype: numpy data type of the input tensor.67    Returns:68      The duration of the run in seconds.69    """70    graph = ops.Graph()71    with graph.as_default():72      output = build_graph(device, n, m, k, transpose_a, transpose_b, dtype)73      with session_lib.Session(graph=graph) as session:74        variables.global_variables_initializer().run()75        for _ in range(500):76          session.run(output)77        start_time = time.time()78        for _ in range(num_iters):79          session.run(output)80        duration = (time.time() - start_time)81        num_items = n * m * k * 282        throughput = num_items * num_iters / duration / 1e983        print('%s %s input_info:%s %d %.4fsec, %.4fGitems/s.' %84              (device, str(dtype), str(n) + 'x' + str(m) + 'x' + str(k) +85               ',ta:' + str(transpose_a) + '.tb:' + str(transpose_b), num_iters,86               duration, throughput))87    name_template = ('matmul_{device}_{dtype}_input_info_{inputinfo}')88    self.report_benchmark(89        name=name_template.format(90            device=device,91            dtype=str(dtype).replace(' ', ''),92            inputinfo=str(n) + 'x' + str(m) + 'x' + str(k) + ',ta:' +93            str(transpose_a) + ',tb:' + str(transpose_b)).replace(' ', ''),94        iters=num_iters,95        wall_time=duration)96    return duration97  def run_test_gpu(self, n, m, k, transpose_a, transpose_b, dtype, num_iters):98    self.run_graph(test.gpu_device_name(), n, m, k, transpose_a, transpose_b,99                   num_iters, dtype)100  def test_round(self, num_iters):101    dtypes = [np.float32, np.float64]102    for dtype in dtypes:103      for n, m, (transpose_a, transpose_b) in itertools.product(104          [512, 1024], [1, 8, 16, 128], [(False, False), (True, False),105                                         (False, True)]):106        k = n107        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)108      for n, m, k, (transpose_a, transpose_b) in itertools.product(109          [200], [1, 8, 20], [10000], [(False, False), (True, False),110                                       (False, True)]):111        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)112      for (n, m, k), (transpose_a, transpose_b) in itertools.product(113          [(200, 20, 20000), (1, 10000, 200)], [(False, False), (True, False),114                                                (False, True)]):115        self.run_test_gpu(n, m, k, transpose_a, transpose_b, dtype, num_iters)116  def benchmark_matmul(self):117    self.test_round(num_iters=200)118if __name__ == '__main__':...

transpose-test.js

Source:transpose-test.js

...7    topic: function() {8      return d3.transpose;9    },10    "transposes a square matrix": function(transpose) {11      assert.deepEqual(d3.transpose([[1, 2], [3, 4]]), [[1, 3], [2, 4]]);12    },13    "transposes a non-square matrix": function(transpose) {14      assert.deepEqual(d3.transpose([[1, 2, 3, 4, 5], [2, 4, 6, 8, 10]]), [[1, 2], [2, 4], [3, 6], [4, 8], [5, 10]]);15    },16    "transposes a single-row matrix": function(transpose) {17      assert.deepEqual(d3.transpose([[1, 2, 3, 4, 5]]), [[1], [2], [3], [4], [5]]);18    },19    "transposes an empty matrix": function(transpose) {20      assert.deepEqual(d3.transpose([]), []);21    },22    "ignores extra elements given an irregular matrix": function(transpose) {23      assert.deepEqual(d3.transpose([[1, 2], [3, 4], [5, 6, 7]]), [[1, 3, 5], [2, 4, 6]]);24    }25  }26});...

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