from __future__ import absolute_import from __future__ import division from __future__ import print_function import numpy as np import copy import time from functools import partial, reduce from future.utils import viewitems, viewkeys from hypothesis import assume, given, settings, HealthCheck import hypothesis.strategies as st import unittest import os from caffe2.python import core, workspace, tt_core, dyndep import caffe2.python.hypothesis_test_util as hu from caffe2.proto import caffe2_pb2 dyndep.InitOpsLibrary('@/caffe2/caffe2/fb/optimizers:sgd_simd_ops') def sigmoid(x): return 1.0 / (1.0 + np.exp(-x)) @st.composite def _tensor_and_prefix(draw, dtype, elements, min_dim=1, max_dim=4, **kwargs): dims_ = draw( st.lists(hu.dims(**kwargs), min_size=min_dim, max_size=max_dim)) extra_ = draw( st.lists(hu.dims(**kwargs), min_size=min_dim, max_size=max_dim)) assume(len(dims_) + len(extra_) < max_dim) return (draw(hu.arrays(dims_ + extra_, dtype, elements)), draw(hu.arrays(extra_, dtype, elements))) def _tensor_and_indices(min_dim=1, max_dim=4, dtype=np.float32, elements=None, **kwargs): """ generates a tensor and a list of indices of larger tensor of same dim""" data_dims_ = st.lists(hu.dims(**kwargs), min_size=min_dim, max_size=max_dim) original_dim = st.integers(min_value=2, max_value=10) return st.tuples(data_dims_, original_dim).flatmap(lambda pair: st.tuples( st.just(pair[1]), # original dimension hu.arrays(pair[0], dtype, elements), # data tensor hu.arrays(pair[0][0], dtype=np.int64, elements=st.integers( min_value=0, max_value=pair[1] - 1)), )) _NUMPY_TYPE_TO_ENUM = { np.float32: core.DataType.FLOAT, np.int32: core.DataType.INT32, np.bool: core.DataType.BOOL, np.uint8: core.DataType.UINT8, np.int8: core.DataType.INT8, np.uint16: core.DataType.UINT16, np.int16: core.DataType.INT16, np.int64: core.DataType.INT64, np.float64: core.DataType.DOUBLE, } def _dtypes(dtypes=None): dtypes = dtypes if dtypes else [np.int32, np.int64, np.float32] return st.sampled_from(dtypes) def _test_binary(name, ref, filter_=None, gcs=hu.gcs, test_gradient=False, allow_inplace=False, dtypes=_dtypes): @given( inputs=dtypes().flatmap( lambda dtype: hu.tensors( n=2, dtype=dtype, elements=hu.elements_of_type(dtype, filter_=filter_))), out=st.sampled_from(('Y', 'X1', 'X2') if allow_inplace else ('Y',)), **gcs) @settings(max_examples=3, timeout=100) def test_binary(self, inputs, out, gc, dc): op = core.CreateOperator(name, ["X1", "X2"], [out]) X1, X2 = inputs self.assertDeviceChecks(dc, op, [X1, X2], [0]) # We only do gradient check with float32 types. if test_gradient and X1.dtype == np.float32: self.assertGradientChecks(gc, op, [X1, X2], 0, [0]) self.assertReferenceChecks(gc, op, [X1, X2], ref) return test_binary def _test_binary_broadcast(name, ref, filter_=None, gcs=hu.gcs, allow_inplace=False, dtypes=_dtypes): @given( inputs=dtypes().flatmap(lambda dtype: _tensor_and_prefix( dtype=dtype, elements=hu.elements_of_type(dtype, filter_=filter_))), in_place=(st.booleans() if allow_inplace else st.just(False)), **gcs) @settings(max_examples=3, timeout=100) def test_binary_broadcast(self, inputs, in_place, gc, dc): op = core.CreateOperator( name, ["X1", "X2"], ["X1" if in_place else "Y"], broadcast=1) X1, X2 = inputs self.assertDeviceChecks(dc, op, [X1, X2], [0]) def cast_ref(x, y): return (np.array(ref(x, y)[0], dtype=x.dtype), ) # gradient not implemented yet # self.assertGradientChecks(gc, op, [X1, X2], 0, [0]) self.assertReferenceChecks(gc, op, [X1, X2], cast_ref) return test_binary_broadcast class TestOperators(hu.HypothesisTestCase): def test_comparison_ops(self): ops = {"LT": lambda x1, x2: [x1 < x2], "LE": lambda x1, x2: [x1 <= x2], "GT": lambda x1, x2: [x1 > x2], "GE": lambda x1, x2: [x1 >= x2]} for name, ref in viewitems(ops): _test_binary(name, ref, gcs=hu.gcs_cpu_only)(self) _test_binary_broadcast(name, ref, gcs=hu.gcs_cpu_only)(self) @given(inputs=hu.tensors(n=2), in_place=st.booleans(), **hu.gcs) def test_sum(self, inputs, in_place, gc, dc): op = core.CreateOperator("Sum", ["X1", "X2"], ["Y" if not in_place else "X1"]) X1, X2 = inputs self.assertDeviceChecks(dc, op, [X1, X2], [0]) self.assertGradientChecks(gc, op, [X1, X2], 0, [0]) @given(inputs=hu.tensors(n=2, min_dim=2, max_dim=2), **hu.gcs_cpu_only) def test_row_mul(self, inputs, gc, dc): op = core.CreateOperator("RowMul", ["X1", "X2"], ["Y"]) X1, Xtmp = inputs X2 = Xtmp[:, 0] def ref(x, y): ret = np.zeros(shape=x.shape, dtype=x.dtype) for i in range(y.size): ret[i, ] = x[i, ] * y[i] return [ret] self.assertDeviceChecks(dc, op, [X1, X2], [0]) for i in range(2): self.assertGradientChecks(gc, op, [X1, X2], i, [0]) self.assertReferenceChecks(gc, op, [X1, X2], ref) @given(inputs=hu.tensors(n=2), **hu.gcs_cpu_only) def test_max(self, inputs, gc, dc): op = core.CreateOperator("Max", ["X1", "X2"], ["Y"]) X1, X2 = inputs # Make X1 and X2 far from each other, since X1=X2 is not differentiable # and the step size of gradient checker is 0.05 X1[np.logical_and(X1 >= X2 - 0.05, X1 <= X2)] -= 0.05 X1[np.logical_and(X1 <= X2 + 0.05, X1 >= X2)] += 0.05 self.assertDeviceChecks(dc, op, [X1, X2], [0]) for i in range(2): self.assertGradientChecks(gc, op, [X1, X2], i, [0]) def elementwise_max(X, Y): return [np.maximum(X, Y)] self.assertReferenceChecks(gc, op, [X1, X2], elementwise_max) def test_add(self): def not_overflow(x): if not isinstance(x, float): return abs(x) < (1 << 30) - 1 return True def ref(x, y): return (x + y, ) _test_binary("Add", ref, filter_=not_overflow, test_gradient=True)(self) _test_binary_broadcast("Add", ref, filter_=not_overflow)(self) def test_sub(self): def ref(x, y): return (x - y, ) # TODO(jiayq): enable gradient test when implemented. _test_binary("Sub", ref, test_gradient=True)(self) _test_binary_broadcast("Sub", ref)(self) def test_mul(self): def not_overflow(x): if not isinstance(x, float): return abs(x) < (1 << 15) - 1 return True def ref(x, y): return (x * y, ) _test_binary("Mul", ref, filter_=not_overflow, test_gradient=True)(self) _test_binary_broadcast("Mul", ref, filter_=not_overflow)(self) def test_div(self): def ref(x, y): return (x / y, ) def non_zero(x): return abs(x) > 1e-2 def div_dtypes(): return st.sampled_from([np.float32, np.float64]) _test_binary( "Div", ref, filter_=non_zero, test_gradient=True, dtypes=div_dtypes, gcs=hu.gcs_cpu_only )(self) _test_binary( "Div", ref, filter_=non_zero, test_gradient=False, dtypes=div_dtypes )(self) _test_binary_broadcast( "Div", ref, filter_=non_zero, dtypes=div_dtypes)(self) @given(X=hu.tensor(), in_place=st.booleans(), **hu.gcs) def test_negative(self, X, in_place, gc, dc): op = core.CreateOperator("Negative", ["X"], ["Y" if not in_place else "X"]) self.assertDeviceChecks(dc, op, [X], [0]) self.assertGradientChecks(gc, op, [X], 0, [0]) @given(X=hu.tensor(), **hu.gcs) def test_tanh(self, X, gc, dc): op = core.CreateOperator("Tanh", "X", "Y") self.assertDeviceChecks(dc, op, [X], [0]) self.assertGradientChecks(gc, op, [X], 0, [0]) @given(X=hu.tensor(), **hu.gcs) def test_averaged_loss(self, X, gc, dc): op = core.CreateOperator("AveragedLoss", ["X"], ["loss"]) self.assertDeviceChecks(dc, op, [X], [0]) self.assertGradientChecks(gc, op, [X], 0, [0]) @given(X=hu.tensor(), inplace=st.booleans(), **hu.gcs) def test_softsign(self, X, inplace, gc, dc): op = core.CreateOperator("Softsign", ["X"], ["X" if inplace else "Y"]) def softsign(X): return (X / (1 + np.abs(X)),) self.assertDeviceChecks(dc, op, [X], [0]) self.assertReferenceChecks(gc, op, [X], softsign) if inplace: with self.assertRaises(Exception): self.assertGradientChecks(gc, op, [X], 0, [0]) else: self.assertGradientChecks(gc, op, [X], 0, [0]) @given( device_options=st.lists( min_size=2, max_size=4, elements=st.sampled_from(hu.expanded_device_options)), set_seed=st.booleans()) def test_random_seed_behaviour(self, device_options, set_seed): # Assume we are always operating on CUDA or CPU, since RNG is # inconsistent between CPU and GPU. device_options = copy.deepcopy(device_options) assume(len({do.device_type for do in device_options}) == 1) if set_seed: for do in device_options: do.random_seed = 1000 def run(do): # Reset each time because 'Y' may already exist in the workspace # on a different device workspace.ResetWorkspace() ws = workspace.C.Workspace() op = core.CreateOperator( "XavierFill", [], ["Y"], device_option=do, shape=[2]) ws.run(op) return ws.blobs["Y"].fetch() ys = [run(do) for do in device_options] for y in ys[1:]: if set_seed: np.testing.assert_array_equal(ys[0], y) else: with self.assertRaises(AssertionError): np.testing.assert_array_equal(ys[0], y) @given(axis=st.integers(min_value=1, max_value=4), num_output=st.integers(min_value=4, max_value=8), engine=st.sampled_from(["", "PACKED"]), **hu.gcs) def test_fully_connected_axis(self, axis, num_output, engine, gc, dc): np.random.seed(1) X = np.random.randn(1, 2, 3, 2, 1).astype(np.float32) def prod(xs): p = 1 for x in xs: p *= x return p K = prod(list(X.shape)[axis:]) N = num_output W = np.random.randn(N, K).astype(np.float32) b = np.random.randn(N).astype(np.float32) op = core.CreateOperator( "FC", ["X", "W", "b"], ["Y"], engine=engine, axis=axis) for name, param in [("X", X), ("W", W), ("b", b)]: self.ws.create_blob(name).feed(param) self.ws.run(op) Y = self.ws.blobs["Y"].fetch() self.assertEqual(list(Y.shape), list(X.shape)[:axis] + [N]) inputs = [X, W, b] self.assertDeviceChecks(dc, op, inputs, [0]) for param, _ in enumerate(inputs): self.assertGradientChecks(gc, op, inputs, param, [0]) @unittest.skipIf(not workspace.has_gpu_support and not workspace.has_hip_support, "Skipping test due to no gpu present.") @given(hidden_size=st.integers(min_value=1, max_value=3), num_layers=st.integers(min_value=1, max_value=3), bidirectional=st.booleans(), rnn_mode=st.sampled_from(["lstm"]), # TODO: "gru" input_mode=st.sampled_from(["linear"]), dropout=st.floats(min_value=1.0, max_value=1.0), T=st.integers(min_value=2, max_value=6), N=st.integers(min_value=1, max_value=4), D=st.integers(min_value=1, max_value=4)) def test_recurrent(self, hidden_size, num_layers, bidirectional, rnn_mode, input_mode, dropout, T, N, D): #there's a bug in miopen for N=1 which would be resolved in the next release. if workspace.has_hip_support: assume(N>1) # Random seed, this one happens to pass seed = 1234 np.random.seed(seed) # set device option if workspace.has_hip_support: device_option = hu.hip_do engine = 'MIOPEN' else: device_option = hu.gpu_do engine = 'CUDNN' input_weight_size = hidden_size * D upper_layer_input_weight_size = hidden_size * hidden_size if bidirectional: upper_layer_input_weight_size *= 2 recurrent_weight_size = hidden_size * hidden_size input_bias_size = hidden_size recurrent_bias_size = hidden_size num_directions = 2 if bidirectional else 1 first_layer_sz = input_weight_size + recurrent_weight_size + \ input_bias_size + recurrent_bias_size upper_layer_sz = upper_layer_input_weight_size + \ recurrent_weight_size + input_bias_size + \ recurrent_bias_size total_sz = 4 * (first_layer_sz + (num_layers - 1) * upper_layer_sz) total_sz *= num_directions W = np.random.rand(total_sz).astype(np.float32) self.ws.create_blob("WEIGHT").feed(W, device_option=device_option) op = core.CreateOperator( "Recurrent", ["INPUT", "HIDDEN_INPUT", "CELL_INPUT", "WEIGHT"], ["OUTPUT", "HIDDEN_OUTPUT", "CELL_OUTPUT", "RNN_SCRATCH", "DROPOUT_STATES"], hidden_size=hidden_size, bidirectional=bidirectional, rnn_mode=rnn_mode, dropout=dropout, input_mode=input_mode, num_layers=num_layers, seed=seed, engine=engine) X = np.random.randn(T, N, D).astype(np.float32) self.ws.create_blob("INPUT").feed(X, device_option=device_option) W = self.ws.blobs["WEIGHT"].fetch() H = np.random.randn( num_layers, N, hidden_size * num_directions).astype( np.float32) C = np.random.randn( num_layers, N, hidden_size * num_directions).astype( np.float32) if rnn_mode == "lstm" else \ np.empty((1,)).astype(np.float32) # unused in GRU inputs = [X, H, C, W] input_idxs = [i for (i, _) in enumerate(inputs)] \ if rnn_mode == "lstm" else [0, 1, 3] # ignore C for input_idx in input_idxs: self.assertGradientChecks( device_option, op, inputs, input_idx, [0], stepsize=0.01, threshold=0.01) @given(ndim=st.integers(1, 4), axis=st.integers(0, 3), add_axis=st.integers(0, 1), num_inputs=st.integers(2, 4), **hu.gcs) def test_depth_concat(self, ndim, axis, add_axis, num_inputs, gc, dc): assume(axis < ndim) input_names = ['X0', 'X1', 'X2', 'X3'][:num_inputs] shape = [2, 3, 5, 7][:ndim] individual_dims = [1, 2, 3, 4, 5][:num_inputs] inputs = [] for i in range(num_inputs): if add_axis == 0: # Sets a unique dim and create the input. shape[axis] = individual_dims[i] inputs.append(np.random.randn(*shape).astype(np.float32)) op = core.CreateOperator("Concat", input_names, ["Y", "Y_dims"], axis=axis, add_axis=add_axis) self.assertDeviceChecks(dc, op, inputs, [0]) for i in range(num_inputs): self.assertGradientChecks(gc, op, inputs, i, [0]) # Reference def depth_concat(*inputs): inputs = list(inputs) if add_axis: for i in range(len(inputs)): inputs[i] = np.expand_dims(inputs[i], axis) input_dims = np.array([np.shape(x)[axis] for x in inputs]) return [np.concatenate(inputs, axis=axis), input_dims] self.assertReferenceChecks(gc, op, inputs, depth_concat) @given(num_inputs=st.integers(2, 4), order=st.sampled_from([("NCHW", 1), ("NHWC", 3)]), **hu.gcs) def test_depth_concat_with_order(self, num_inputs, order, gc, dc): input_names = ['X0', 'X1', 'X2', 'X3'][:num_inputs] shape = [2, 3, 5, 7] individual_dims = [1, 2, 3, 4][:num_inputs] inputs = [] for i in range(num_inputs): # Sets a unique dim and create the input. shape[order[1]] = individual_dims[i] inputs.append(np.random.rand(*shape).astype(np.float32)) op = core.CreateOperator("Concat", input_names, ["Y", "Y_dims"], order=order[0]) self.assertDeviceChecks(dc, op, inputs, [0]) for i in range(num_inputs): self.assertGradientChecks(gc, op, inputs, i, [0]) # Reference def depth_concat_with_order(*inputs): inputs = list(inputs) axis = order[1] input_dims = np.array([np.shape(x)[axis] for x in inputs]) return [np.concatenate(inputs, axis=axis), input_dims] self.assertReferenceChecks(gc, op, inputs, depth_concat_with_order) @given(X=hu.arrays(dims=[5, 2], elements=st.floats(min_value=1.0, max_value=10.0)), **hu.gcs_cpu_only) def test_last_n_windows(self, X, gc, dc): workspace.FeedBlob('input', X) workspace.FeedBlob('next', np.array(0, dtype=np.int32)) workspace.CreateBlob('output') collect_net = core.Net('collect_net') collect_net.LastNWindowCollector( ['output', 'next', 'input'], ['output', 'next'], num_to_collect=7, ) plan = core.Plan('collect_data') plan.AddStep(core.execution_step('collect_data', [collect_net], num_iter=2)) workspace.RunPlan(plan) output = workspace.FetchBlob('output') inputs = workspace.FetchBlob('input') new_output = np.zeros([7, inputs.shape[1]]) for i in range(inputs.shape[0] * 2): new_output[i % 7] = inputs[i % inputs.shape[0]] import numpy.testing as npt npt.assert_almost_equal(output, new_output, decimal=5) @given(dtype=st.sampled_from([np.float32, np.float64, np.int32, np.bool])) def test_print(self, dtype): data = np.random.permutation(6).astype(dtype) self.ws.create_blob("data").feed(data) op = core.CreateOperator("Print", "data", []) self.ws.run(op) @given(inputs=hu.tensors(n=2), in_place=st.booleans(), momentum=st.floats(min_value=0.1, max_value=0.9), nesterov=st.booleans(), lr=st.floats(min_value=0.1, max_value=0.9), **hu.gcs) def test_momentum_sgd( self, inputs, in_place, momentum, nesterov, lr, gc, dc): grad, m = inputs lr = np.asarray([lr], dtype=np.float32) op = core.CreateOperator( "MomentumSGD", ["grad", "m", "lr"], ["grad" if in_place else "grad_o", "m" if in_place else "m_o"], momentum=momentum, nesterov=int(nesterov), device_option=gc) self.assertDeviceChecks( dc, op, [grad, m, lr], [0]) # Reference def momentum_sgd(grad, m, lr): lr = lr[0] if not nesterov: adjusted_gradient = lr * grad + momentum * m return (adjusted_gradient, adjusted_gradient) else: m_new = momentum * m + lr * grad return ((1 + momentum) * m_new - momentum * m, m_new) self.assertReferenceChecks(gc, op, [grad, m, lr], momentum_sgd) @given(inputs=hu.tensors(n=3), in_place=st.booleans(), decay=st.floats(min_value=0.1, max_value=0.9), momentum=st.floats(min_value=0.1, max_value=0.9), lr=st.floats(min_value=0.1, max_value=0.9), epsilon=st.floats(min_value=1e-5, max_value=1e-2), **hu.gcs) def test_rmsprop_sgd(self, inputs, in_place, decay, momentum, lr, epsilon, gc, dc): grad, ms, mom = inputs ms = np.abs(ms) + 0.01 lr = np.asarray([lr], dtype=np.float32) op = core.CreateOperator( "RmsProp", ["grad", "ms", "mom", "lr"], ["grad" if in_place else "grad_o", "ms" if in_place else "ms_o", "mom" if in_place else "mom_o"], momentum=momentum, decay=decay, epsilon=epsilon, device_option=gc) self.assertDeviceChecks(dc, op, [grad, ms, mom, lr], [0]) def rmsprop(grad, ms, mom, lr): lr = lr[0] ms_o = ms + (1. - decay) * (np.square(grad) - ms) mom_o = momentum * mom + lr * grad / np.sqrt(epsilon + ms_o) grad_o = mom_o return (grad_o, ms_o, mom_o) self.assertReferenceChecks(gc, op, [grad, ms, mom, lr], rmsprop) # Reference @staticmethod def _dense_ftrl(alpha, beta, lambda1, lambda2, w, nz, g): if isinstance(alpha, np.ndarray): alpha = np.asscalar(alpha) n = np.take(nz, 0, axis=-1) z = np.take(nz, 1, axis=-1) # python port of Sigrid's implementation g2 = g * g sigma = (np.sqrt(n + g2) - np.sqrt(n)) / alpha z += g - sigma * w n += g2 w = (np.sign(z) * lambda1 - z) / ( (beta + np.sqrt(n)) / alpha + lambda2) w[np.abs(z) <= lambda1] = 0 return (w, np.stack([n, z], axis=-1)) @given(inputs=hu.tensors(n=4), in_place=st.booleans(), alpha=st.floats(min_value=0.01, max_value=0.1), beta=st.floats(min_value=0.1, max_value=0.9), lambda1=st.floats(min_value=0.001, max_value=0.1), lambda2=st.floats(min_value=0.001, max_value=0.1), engine=st.sampled_from([None, "SIMD"]), **hu.gcs_cpu_only) def test_ftrl_sgd(self, inputs, in_place, alpha, beta, lambda1, lambda2, engine, gc, dc): var, n, z, grad = inputs n = np.abs(n) nz = np.stack([n, z], axis=-1) op = core.CreateOperator( "Ftrl", ["var", "nz", "grad"], ["var" if in_place else "var_o", "nz" if in_place else "nz_o"], alpha=alpha, beta=beta, lambda1=lambda1, lambda2=lambda2, engine=engine, device_option=gc) self.assertDeviceChecks( dc, op, [var, nz, grad], [0]) self.assertReferenceChecks( gc, op, [var, nz, grad], partial(self._dense_ftrl, alpha, beta, lambda1, lambda2)) # Reference @staticmethod def _dense_gftrl(alpha, beta, lambda1, lambda2, w, nz, g): if isinstance(alpha, np.ndarray): alpha = np.asscalar(alpha) old_shape = g.shape n = np.take(nz, 0, axis=-1) z = np.take(nz, 1, axis=-1) output_dim = g.shape[0] w = w.reshape(output_dim, -1) g = g.reshape(output_dim, -1) n = n.reshape(output_dim, -1) z = z.reshape(output_dim, -1) input_dim = g.shape[1] g2 = g * g sigma = (np.sqrt(n + g2) - np.sqrt(n)) / alpha z += g - sigma * w n += g2 z_norms = np.linalg.norm(z, 2, axis=0) z_norms = z_norms + 1e-6 w = z * ((lambda1 * np.sqrt(output_dim)) / z_norms - 1) / \ ((beta + np.sqrt(n)) / alpha + lambda2) for i in range(input_dim): if z_norms[i] <= lambda1 * np.sqrt(output_dim): w[:, i] = 0 w = w.reshape(old_shape) n = n.reshape(old_shape) z = z.reshape(old_shape) return (w, np.stack([n, z], axis=-1)) @given(inputs=hu.tensors(n=4), in_place=st.booleans(), alpha=st.floats(min_value=0.01, max_value=0.1), beta=st.floats(min_value=0.1, max_value=0.9), lambda1=st.floats(min_value=0.001, max_value=0.1), lambda2=st.floats(min_value=0.001, max_value=0.1), engine=st.sampled_from([None, "SIMD"]), **hu.gcs_cpu_only) def test_gftrl_sgd(self, inputs, in_place, alpha, beta, lambda1, lambda2, engine, gc, dc): var, n, z, grad = inputs n = np.abs(n) nz = np.stack([n, z], axis=-1) op = core.CreateOperator( "GFtrl", ["var", "nz", "grad"], ["var" if in_place else "var_o", "nz" if in_place else "nz_o"], alpha=alpha, beta=beta, lambda1=lambda1, lambda2=lambda2, engine=engine, device_option=gc) self.assertDeviceChecks( dc, op, [var, nz, grad], [0]) self.assertReferenceChecks( gc, op, [var, nz, grad], partial(self._dense_gftrl, alpha, beta, lambda1, lambda2)) @given(inputs=hu.tensors(n=4), alpha=st.floats(min_value=0.01, max_value=0.1), beta=st.floats(min_value=0.1, max_value=0.9), lambda1=st.floats(min_value=0.001, max_value=0.1), lambda2=st.floats(min_value=0.001, max_value=0.1), engine=st.sampled_from([None, "SIMD"]), **hu.gcs_cpu_only) def test_sparse_ftrl_sgd(self, inputs, alpha, beta, lambda1, lambda2, engine, gc, dc): var, n, z, grad = inputs # generate fake subset manually because hypothesis is too complicated :) indices = np.arange(var.shape[0]) indices = indices[indices % 2 == 0] grad = grad[indices] n = np.abs(n) nz = np.stack([n, z], axis=-1) op = core.CreateOperator( "SparseFtrl", ["var", "nz", "indices", "grad"], ["var", "nz"], alpha=alpha, beta=beta, lambda1=lambda1, lambda2=lambda2, engine=engine, device_option=gc) self.assertDeviceChecks( dc, op, [var, nz, indices, grad], [0]) # Reference def ftrl(w, nz, i, g): sw, snz = self._dense_ftrl(alpha, beta, lambda1, lambda2, w[i], nz[i], g) w[i] = sw nz[i] = snz return (w, nz) self.assertReferenceChecks(gc, op, [var, nz, indices, grad], ftrl) # Reference @staticmethod def _dense_ftrl_send_alpha_by_input(beta, lambda1, lambda2, w, nz, g, alpha): return TestOperators._dense_ftrl(alpha, beta, lambda1, lambda2, w, nz, g) @given(inputs=hu.tensors(n=4), in_place=st.booleans(), alpha=st.floats(min_value=0.01, max_value=0.1), beta=st.floats(min_value=0.1, max_value=0.9), lambda1=st.floats(min_value=0.001, max_value=0.1), lambda2=st.floats(min_value=0.001, max_value=0.1), engine=st.sampled_from([None, "SIMD"]), **hu.gcs_cpu_only) def test_ftrl_sgd_send_alpha_by_input(self, inputs, in_place, alpha, beta, lambda1, lambda2, engine, gc, dc): var, n, z, grad = inputs n = np.abs(n) nz = np.stack([n, z], axis=-1) alpha = np.array(alpha).astype(np.float32) op = core.CreateOperator( "Ftrl", ["var", "nz", "grad", "alpha"], ["var" if in_place else "var_o", "nz" if in_place else "nz_o"], beta=beta, lambda1=lambda1, lambda2=lambda2, engine=engine, device_option=gc) self.assertDeviceChecks( dc, op, [var, nz, grad, alpha], [0]) self.assertReferenceChecks( gc, op, [var, nz, grad, alpha], partial(self._dense_ftrl_send_alpha_by_input, beta, lambda1, lambda2)) @given(inputs=hu.tensors(n=4), alpha=st.floats(min_value=0.01, max_value=0.1), beta=st.floats(min_value=0.1, max_value=0.9), lambda1=st.floats(min_value=0.001, max_value=0.1), lambda2=st.floats(min_value=0.001, max_value=0.1), engine=st.sampled_from([None, "SIMD"]), **hu.gcs_cpu_only) def test_sparse_ftrl_sgd_send_alpha_by_input(self, inputs, alpha, beta, lambda1, lambda2, engine, gc, dc): var, n, z, grad = inputs # generate fake subset manually because hypothesis is too complicated :) indices = np.arange(var.shape[0]) indices = indices[indices % 2 == 0] grad = grad[indices] n = np.abs(n) nz = np.stack([n, z], axis=-1) alpha = np.array(alpha).astype(np.float32) op = core.CreateOperator( "SparseFtrl", ["var", "nz", "indices", "grad", "alpha"], ["var", "nz"], beta=beta, lambda1=lambda1, lambda2=lambda2, engine=engine, device_option=gc) self.assertDeviceChecks( dc, op, [var, nz, indices, grad, alpha], [0]) # Reference def ftrl(w, nz, i, g, alpha): sw, snz = self._dense_ftrl_send_alpha_by_input(beta, lambda1, lambda2, w[i], nz[i], g, alpha) w[i] = sw nz[i] = snz return (w, nz) self.assertReferenceChecks(gc, op, [var, nz, indices, grad, alpha], ftrl) # TODO: (bddppq) test_unique keeps running into segfault on rocm 1.8.2 @given(input=hu.tensor(max_value=20, max_dim=1, dtype=np.int32, elements=st.integers(min_value=0, max_value=10)), with_remapping=st.booleans(), **hu.gcs_no_hip) def test_unique(self, input, with_remapping, gc, dc): op = core.CreateOperator( "Unique", ["input"], ["unique"] + (["remapping"] if with_remapping else []), device_option=gc) self.assertDeviceChecks(dc, op, [input], [0]) # Validator def unique_valid(input, unique, remapping=None): self.assertEqual(unique.size, len(set(input))) self.assertEqual(sorted(unique), sorted(set(input))) if with_remapping: self.assertEqual(remapping.shape, input.shape) remapped = [unique[remapping[i]] for i in range(len(input))] np.testing.assert_array_equal(remapped, input) self.assertValidationChecks(gc, op, [input], unique_valid) @given(prediction=hu.arrays(dims=[10, 3], elements=st.floats(allow_nan=False, allow_infinity=False, min_value=0, max_value=1)), labels=hu.arrays(dims=[10], dtype=np.int32, elements=st.integers(min_value=0, max_value=3 - 1)), top_k=st.integers(min_value=1, max_value=3), **hu.gcs) def test_accuracy(self, prediction, labels, top_k, gc, dc): if(top_k > 1): gc = hu.cpu_do op = core.CreateOperator( "Accuracy", ["prediction", "labels"], ["accuracy"], top_k=top_k, device_option=gc ) def op_ref(prediction, labels, top_k): N = prediction.shape[0] correct = 0 for i in range(0, len(prediction)): pred_sorted = sorted( ([item, j] for j, item in enumerate(prediction[i])), key=lambda x: x[0], reverse=True ) max_ids = [x[1] for x in pred_sorted[0:top_k]] for m in max_ids: if m == labels[i]: correct += 1 accuracy = correct / N return (accuracy,) self.assertReferenceChecks( device_option=gc, op=op, inputs=[prediction, labels, top_k], reference=op_ref) @given(target_probabilities=hu.arrays( dims=[10], elements=st.floats(allow_nan=False, allow_infinity=False, min_value=0.01, max_value=1)), **hu.gcs) def test_perplexity(self, target_probabilities, gc, dc): op = core.CreateOperator( "Perplexity", ["target_probabilities"], ["perplexity"] ) def op_ref(target_probabilities): N = target_probabilities.shape[0] perplexities = np.power(target_probabilities, -1.0 / N) perplexity = reduce(lambda x, y: x * y, perplexities) return (perplexity,) self.assertReferenceChecks( device_option=gc, op=op, inputs=[target_probabilities], reference=op_ref) @given(lengths=st.lists(st.integers(min_value=0, max_value=10), min_size=0, max_size=10), **hu.gcs_cpu_only) def test_lengths_to_segment_ids(self, lengths, gc, dc): op = core.CreateOperator( "LengthsToSegmentIds", ["lengths"], ["segment_ids"]) def op_ref(lengths): sids = [] for i, l in enumerate(lengths): sids.extend(l * [i]) return (np.array(sids, dtype=np.int32), ) self.assertReferenceChecks( device_option=gc, op=op, inputs=[np.array(lengths, dtype=np.int32)], reference=op_ref) @given(lengths=st.lists(st.integers(min_value=0, max_value=10), min_size=0, max_size=10), **hu.gcs_cpu_only) def test_lengths_range_fill(self, lengths, gc, dc): op = core.CreateOperator( "LengthsRangeFill", ["lengths"], ["increasing_seq"]) def op_ref(lengths): sids = [] for _, l in enumerate(lengths): sids.extend(list(range(l))) return (np.array(sids, dtype=np.int32), ) self.assertReferenceChecks( device_option=gc, op=op, inputs=[np.array(lengths, dtype=np.int32)], reference=op_ref) @given(**hu.gcs_cpu_only) def test_segment_ids_to_ranges(self, gc, dc): lengths = [4, 6, 3, 2, 0, 4] op = core.CreateOperator( "SegmentIdsToRanges", ["segment_ids"], ["ranges"]) def op_ref(segment_ids): ranges = [np.array([0, 0], dtype=np.int32)] prev = 0 for i, sid in enumerate(segment_ids): while sid != prev: prev += 1 ranges.append(np.array([i, 0], dtype=np.int32)) ranges[-1][1] += 1 return (np.array(ranges, dtype=np.int32), ) def lengths_to_segment_ids(lengths): sids = [] for i, l in enumerate(lengths): sids.extend(l * [i]) return (np.array(sids, dtype=np.int32), ) self.assertReferenceChecks( device_option=gc, op=op, inputs=np.array(lengths_to_segment_ids(lengths), dtype=np.int32), reference=op_ref) @given(lengths=st.lists(st.integers(min_value=0, max_value=10), min_size=0, max_size=10), **hu.gcs_cpu_only) def test_lengths_to_ranges(self, lengths, gc, dc): op = core.CreateOperator( "LengthsToRanges", ["lengths"], ["ranges"]) def op_ref(x): if not x.size: return (x.reshape((0, 2)), ) return (np.column_stack((np.concatenate(([0], np.cumsum(x)[:-1])), x)), ) self.assertReferenceChecks( device_option=gc, op=op, inputs=[np.array(lengths, dtype=np.int32)], reference=op_ref) @given(prediction=hu.arrays(dims=[10, 3], elements=st.floats(allow_nan=False, allow_infinity=False, min_value=0, max_value=1)), labels=hu.arrays(dims=[10], dtype=np.int32, elements=st.integers(min_value=0, max_value=3 - 1)), **hu.gcs) def test_multi_class_accuracy(self, prediction, labels, gc, dc): op = core.CreateOperator( "MultiClassAccuracy", ["prediction", "labels"], ["accuracies", "amounts"] ) def op_ref(prediction, labels): N = prediction.shape[0] D = prediction.shape[1] accuracies = np.empty(D, dtype=float) accuracies.fill(0) amounts = np.empty(D, dtype=int) amounts.fill(0) max_ids = np.argmax(prediction, axis=1) for i in range(0, N): max_id = max_ids[i] label_id = labels[i] if max_id == label_id: accuracies[label_id] += 1 amounts[label_id] += 1 for i in range(0, D): amount = amounts[i] if amount: accuracies[i] /= amount return (accuracies, amounts,) self.assertReferenceChecks( device_option=gc, op=op, inputs=[prediction, labels], reference=op_ref) @given(lengths=st.lists(st.integers(min_value=0, max_value=10), min_size=0, max_size=10), **hu.gcs_cpu_only) def test_segment_ids_to_lengths(self, lengths, gc, dc): op = core.CreateOperator( "SegmentIdsToLengths", ["segment_ids"], ["lengths"]) def lengths_to_ids(lengths): sids = [] for i, l in enumerate(lengths): sids.extend(l * [i]) return sids segment_ids = lengths_to_ids(lengths) def ids_to_lengths(ids): ids_length = len(ids) if ids_length == 0: return (np.array([], dtype=np.int32),) lengths = [] # segment id starts with 0 prev_id = -1 tmp_length = 0 for idx in range(ids_length): cur_id = ids[idx] if cur_id != prev_id: if idx != 0: lengths.append(tmp_length) while prev_id + 1 != cur_id: lengths.append(0) prev_id += 1 prev_id = cur_id tmp_length = 0 tmp_length += 1 lengths.append(tmp_length) return (np.array(lengths, dtype=np.int32),) self.assertReferenceChecks( device_option=gc, op=op, inputs=[np.array(segment_ids, dtype=np.int32)], reference=ids_to_lengths) @given(lengths=st.lists(st.integers(min_value=1, max_value=10), min_size=0, max_size=10), power=st.sampled_from([0.5, 1.0, 1.5, 2.0]), **hu.gcs_cpu_only) def test_lengths_to_weights(self, lengths, power, gc, dc): op = core.CreateOperator( "LengthsToWeights", ["lengths"], ["weights"], power=power) def lengths_to_weights(lengths): weighted_length = [] for l in lengths: weighted_length.extend(l * [1 / pow(l, power)]) return (np.array(weighted_length, dtype=float),) self.assertReferenceChecks( device_option=gc, op=op, inputs=[np.array(lengths, dtype=np.int32)], reference=lengths_to_weights) @given(input_tensor=hu.arrays( dims=[10], elements=st.floats(allow_nan=False, allow_infinity=False)), **hu.gcs) def test_abs(self, input_tensor, gc, dc): op = core.CreateOperator( "Abs", ["input"], ["output"] ) def abs_ref(input_tensor): return (np.abs(input_tensor),) self.assertReferenceChecks( device_option=gc, op=op, inputs=[input_tensor], reference=abs_ref) @given(input_tensor=hu.arrays( dims=[10], elements=st.floats(min_value=-10, max_value=10)), **hu.gcs) def test_cos(self, input_tensor, gc, dc): op = core.CreateOperator( "Cos", ["input"], ["output"] ) def cos_ref(input_tensor): return (np.cos(input_tensor),) self.assertReferenceChecks( device_option=gc, op=op, inputs=[input_tensor], reference=cos_ref) @given(input_tensor=hu.arrays( dims=[10], elements=st.floats(min_value=-10, max_value=10)), **hu.gcs) def test_sin(self, input_tensor, gc, dc): op = core.CreateOperator( "Sin", ["input"], ["output"] ) def sin_ref(input_tensor): return (np.sin(input_tensor),) self.assertReferenceChecks( device_option=gc, op=op, inputs=[input_tensor], reference=sin_ref) @given(input_tensor=hu.arrays( dims=[10], elements=st.floats(allow_nan=False, allow_infinity=False)), **hu.gcs) def test_exp(self, input_tensor, gc, dc): op = core.CreateOperator( "Exp", ["input"], ["output"] ) def exp_ref(input_tensor): return (np.exp(input_tensor),) self.assertReferenceChecks( device_option=gc, op=op, inputs=[input_tensor], reference=exp_ref) @given(input_tensor=hu.arrays( dims=[10], elements=st.floats(min_value=1, max_value=10000)), **hu.gcs_cpu_only) def test_log(self, input_tensor, gc, dc): op = core.CreateOperator( "Log", ["input"], ["output"] ) def log_ref(input_tensor): return (np.log(input_tensor),) self.assertReferenceChecks( device_option=gc, op=op, inputs=[input_tensor], reference=log_ref) self.assertGradientChecks(gc, op, [input_tensor], 0, [0]) def test_blobs_dequeue_timeout(self): op = core.CreateOperator( "CreateBlobsQueue", [], ["queue"], capacity=5, num_blobs=1) self.ws.run(op) t = time.time() op = core.CreateOperator( "DequeueBlobs", ["queue"], ["out"], timeout_secs=0.2) self.assertRaises(RuntimeError, lambda: self.ws.run(op)) t = time.time() - t self.assertGreater(t, 0.19) @given(num_threads=st.integers(1, 10), # noqa num_elements=st.integers(1, 100), capacity=st.integers(1, 5), num_blobs=st.integers(1, 3), do=st.sampled_from(hu.device_options)) def test_blobs_queue_threading(self, num_threads, num_elements, capacity, num_blobs, do): """ - Construct matrices of size N x D - Start K threads - Push all N rows into the queue of capacity C - Pull all N rows out of the queue. - Verify that the output matrices are permutation of the rows of the original matrices. """ import threading try: import queue except ImportError: # Py3 import Queue as queue op = core.CreateOperator( "CreateBlobsQueue", [], ["queue"], capacity=capacity, num_blobs=num_blobs, device_option=do) self.ws.run(op) xs = [np.random.randn(num_elements, 5).astype(np.float32) for _ in range(num_blobs)] q = queue.Queue() for i in range(num_elements): q.put([x[i] for x in xs]) def enqueue(t): while True: feed_blobs = ["x_{}_{}".format(i, t) for i in range(num_blobs)] op = core.CreateOperator( "EnqueueBlobs", ["queue"] + feed_blobs, feed_blobs, device_option=do) try: elems = q.get_nowait() for elem, feed_blob in zip(elems, feed_blobs): self.ws.create_blob(feed_blob).feed( elem, device_option=do) self.ws.run(op) except queue.Empty: return # Create all blobs before racing on multiple threads # (blob creation is not threadsafe) for t in range(num_threads): for i in range(num_blobs): self.ws.create_blob("x_{}_{}".format(i, t)) threads = [threading.Thread(target=enqueue, args=(t,)) for t in range(num_threads)] for thread in threads: thread.start() for n in range(num_elements): dequeue_blobs = ["y_{}_{}".format(i, n) for i in range(num_blobs)] op = core.CreateOperator( "DequeueBlobs", ["queue"], dequeue_blobs, device_option=do) self.ws.run(op) for thread in threads: thread.join() op = core.CreateOperator("CloseBlobsQueue", ["queue"], []) self.ws.run(op) ys = [np.vstack([self.ws.blobs["y_{}_{}".format(i, n)].fetch() for n in range(num_elements)]) for i in range(num_blobs)] for i in range(num_blobs): self.assertEqual(ys[i].shape, xs[i].shape) for j in range(num_elements): # Verify that the rows of the returned blob are a # permutation. The order may be different due to # different threads racing. self.assertTrue( any(np.array_equal(xs[i][j], ys[i][k]) for k in range(num_elements))) @given(num_producers=st.integers(1, 10), num_consumers=st.integers(1, 10), capacity=st.integers(1, 5), num_blobs=st.integers(1, 3), do=st.sampled_from(hu.device_options)) def test_safe_blobs_queue(self, num_producers, num_consumers, capacity, num_blobs, do): init_net = core.Net('init_net') queue = init_net.CreateBlobsQueue( [], 1, capacity=capacity, num_blobs=num_blobs) producer_steps = [] truth = 0 for i in range(num_producers): name = 'producer_%d' % i net = core.Net(name) blobs = [net.ConstantFill([], 1, value=1.0, run_once=False) for times in range(num_blobs)] status = net.NextName() net.SafeEnqueueBlobs([queue] + blobs, blobs + [status]) count = (i + 1) * 10 step = core.execution_step(name, net, num_iter=count) truth += count producer_steps.append(step) producer_exit_net = core.Net('producer_exit_net') producer_exit_net.CloseBlobsQueue([queue], 0) producer_step = core.execution_step('producer', [ core.execution_step( 'producers', producer_steps, concurrent_substeps=True), core.execution_step('producer_exit', producer_exit_net)] ) consumer_steps = [] counters = [] const_1 = init_net.ConstantFill([], 1, value=1.0) for i in range(num_consumers): name = 'consumer_%d' % i net1 = core.Net(name) blobs = net1.SafeDequeueBlobs([queue], num_blobs + 1) status = blobs[-1] net2 = core.Net(name + '_counter') counter = init_net.ConstantFill([], 1, value=0.0) counters.append(counter) net2.Add([counter, const_1], counter) consumer_steps.append(core.execution_step( name, [net1, net2], should_stop_blob=status)) consumer_step = core.execution_step( 'consumer', consumer_steps, concurrent_substeps=True) init_step = core.execution_step('init', init_net) worker_step = core.execution_step( 'worker', [consumer_step, producer_step], concurrent_substeps=True) plan = core.Plan('test') plan.AddStep(init_step) plan.AddStep(worker_step) self.ws.run(plan) v = 0 for counter in counters: v += self.ws.blobs[str(counter)].fetch().tolist() self.assertEqual(v, truth) @given(num_queues=st.integers(1, 5), num_iter=st.integers(5, 10), capacity=st.integers(1, 5), num_blobs=st.integers(1, 3)) def test_weighted_sample_blobs_queue( self, num_queues, num_iter, capacity, num_blobs ): # Create BlobsQueue for each input queue print("num_queues", num_queues) init_net = core.Net('init_net') queues = [ init_net.CreateBlobsQueue( [], 1, capacity=capacity, num_blobs=num_blobs ) for _ in range(num_queues) ] # Create multiple producer nets and one producer exist net producer_steps = [] producer_exit_nets = [] for i in range(num_queues): name = 'producer_%d' % i net = core.Net(name) blobs = [net.ConstantFill([], 1, value=1.0, run_once=False) for _ in range(num_blobs)] status = net.NextName() net.SafeEnqueueBlobs([queues[i]] + blobs, blobs + [status]) exit_net = core.Net('producer_exit_%d' % i) exit_net.CloseBlobsQueue(queues[i], 0) producer_exit_nets.append(exit_net) step = core.execution_step( name, [ core.execution_step( 'producer_%d' % i, [net], num_iter=num_iter ), core.execution_step('producer_exit_%d' % i, [exit_net]), ] ) producer_steps.append(step) producer_step = core.execution_step( 'producer', [ core.execution_step( 'producers', producer_steps, concurrent_substeps=True, ), ] ) status_lst = [] def append(ins, outs): status_lst.append(ins) # Create one consumer dequeue net and one consumer exist net consumer_net = core.Net('weight_sample_dequeue_net') table_idx_blob = np.random.randint(low=-1, high=num_blobs, size=1) blobs = consumer_net.WeightedSampleDequeueBlobs( queues, num_blobs + 1, weights=np.random.uniform(low=0.0, high=1.0, size=(num_queues,)), table_idx_blob=table_idx_blob[0], ) status = blobs[-1] consumer_net.Python(append)(status) consumer_step = core.execution_step( 'consumer', [ core.execution_step( 'consumer', [consumer_net], should_stop_blob=status ), core.execution_step('producer_exit', producer_exit_nets) ] ) init_step = core.execution_step('init', init_net) worker_step = core.execution_step( 'worker', [producer_step, consumer_step], concurrent_substeps=True) plan = core.Plan('test') plan.AddStep(init_step) plan.AddStep(worker_step) self.ws.run(plan) assert len(status_lst) >= num_iter + 1 assert len(status_lst) <= num_iter * num_queues + 1 @given( data=hu.tensor(), **hu.gcs_cpu_only) def test_squeeze_expand_dims(self, data, gc, dc): dims = [0, 0] if len(data.shape) > 2: dims.append(2) op = core.CreateOperator( "ExpandDims", ["data"], ["expanded"], dims=dims) def expand_dims_ref(data, *args, **kw): inc_dims = list(set(dims)) inc_dims.sort() r = data for dim in inc_dims: r = np.expand_dims(r, axis=dim) return (r, ) def squeeze_ref(data, *args, **kw): dec_dims = list(set(dims)) dec_dims.sort(reverse=True) r = data for dim in dec_dims: r = np.squeeze(r, axis=dim) return (r, ) self.assertReferenceChecks( device_option=gc, op=op, inputs=[data], reference=expand_dims_ref, output_to_grad='expanded', grad_reference=squeeze_ref) @given(**hu.gcs_cpu_only) def test_tt_layer(self, gc, dc): seed = 1234 np.random.seed(seed) inp_sizes = [2, 2, 2, 2] out_sizes = [2, 2, 2, 2] tt_ranks = [1, 3, 3, 3, 1] op = core.CreateOperator( "TT", ["X", "b", "cores"], ["Y"], inp_sizes=inp_sizes, out_sizes=out_sizes, tt_ranks=tt_ranks, ) X = np.expand_dims( np.random.rand(16).astype(np.float32), axis=0) b = np.array([0] * 16).astype(np.float32) cores = tt_core.init_tt_cores(inp_sizes, out_sizes, tt_ranks) self.ws.create_blob("X").feed(X) self.ws.create_blob("b").feed(b) self.ws.create_blob("cores").feed(cores) self.ws.run(op) Y = self.ws.blobs[("Y")].fetch() Y = Y.reshape([16]) golden = np.array([-9.51763490e-07, -1.28442286e-06, -2.86281141e-07, 2.28865644e-07, -1.96180017e-06, -1.78920531e-06, 9.31094666e-07, -2.04273989e-07, 1.70017107e-06, 1.64845711e-06, -1.06099132e-06, -4.69111137e-07, 6.57552358e-08, -1.28942040e-08, -2.29114004e-07, -1.04262714e-06]) # This golden array is dependent on the specified inp_sizes, out_sizes, # tt_ranks, and seed. Changing these will cause the test to fail. self.assertAlmostEqual(np.linalg.norm(golden - Y), 0, delta=1e-10) @given(num_workers=st.integers(1, 10), net_type=st.sampled_from( ["simple", "dag"] + (["async_dag"] if workspace.has_gpu_support else [])), do=st.sampled_from(hu.device_options)) def test_dag_net_forking(self, net_type, num_workers, do): from caffe2.python.model_helper import ModelHelper from caffe2.python import brew m = ModelHelper(name="test_model") n = 10 d = 2 depth = 2 iters = 5 np.random.seed(1701) # Build a binary tree of FC layers, summing at each node. for i in reversed(range(depth)): for j in range(2 ** i): bottom_1 = "{}_{}".format(i + 1, 2 * j) bottom_2 = "{}_{}".format(i + 1, 2 * j + 1) mid_1 = "{}_{}_m".format(i + 1, 2 * j) mid_2 = "{}_{}_m".format(i + 1, 2 * j + 1) top = "{}_{}".format(i, j) brew.fc( m, bottom_1, mid_1, dim_in=d, dim_out=d, weight_init=('ConstantFill', dict(value=np.random.randn())), bias_init=('ConstantFill', dict(value=np.random.randn()))) brew.fc( m, bottom_2, mid_2, dim_in=d, dim_out=d, weight_init=('ConstantFill', dict(value=np.random.randn())), bias_init=('ConstantFill', dict(value=np.random.randn()))) m.net.Sum([mid_1, mid_2], top) m.net.SquaredL2Distance(["0_0", "label"], "xent") m.net.AveragedLoss("xent", "loss") input_to_grad = m.AddGradientOperators(["loss"]) m.Proto().device_option.CopyFrom(do) m.param_init_net.Proto().device_option.CopyFrom(do) m.Proto().type = net_type m.Proto().num_workers = num_workers self.ws.run(m.param_init_net) print(str(m.Proto())) def run(): import numpy as np np.random.seed(1701) input_blobs = ["{}_{}".format(depth, j) for j in range(2 ** depth)] for input_blob in input_blobs: self.ws.create_blob(input_blob).feed( np.random.randn(n, d).astype(np.float32), device_option=do) self.ws.create_blob("label").feed( np.random.randn(n, d).astype(np.float32), device_option=do) self.ws.run(m.net) gradients = [ self.ws.blobs[str(input_to_grad[input_blob])].fetch() for input_blob in input_blobs] return gradients outputs = [run() for _ in range(iters)] for output in outputs[1:]: np.testing.assert_array_equal(outputs[0], output) self.assertAlmostEqual(np.sum(np.square(output)), 91.81752, delta=1e-2) @given(input=hu.tensor(min_dim=2, max_dim=6, dtype=np.int32, elements=st.integers(min_value=0, max_value=2**32 - 1)), slice_dim=st.integers(), a=st.integers(), b=st.integers(), is_empty=st.booleans(), **hu.gcs_cpu_only) def test_slice(self, input, slice_dim, a, b, is_empty, gc, dc): slice_dim = slice_dim % len(input.shape) if (is_empty): input = np.random.rand(*([0] + list(input.shape))).astype(np.int32) slice_dim += 1 a = a % input.shape[slice_dim] b = b % input.shape[slice_dim] + 1 start_vec = np.zeros(len(input.shape), dtype=np.int32) end_vec = np.ones(len(input.shape), dtype=np.int32) * -1 start_vec[slice_dim] = min(a, b) end_vec[slice_dim] = max(a, b) op = core.CreateOperator( "Slice", ["input", "start", "end"], ["output"]) def slice_ref(x, s, e): if len(s.shape) == 0: return x slc = [slice(si, None if ei == -1 else ei) for si, ei in zip(s, e)] return (x[slc], ) self.assertReferenceChecks(gc, op, [input, start_vec, end_vec], slice_ref) @given(data=hu.tensor(), **hu.gcs_cpu_only) def test_shape(self, data, gc, dc): op = core.CreateOperator("Shape", ["data"], ["shape"]) self.assertReferenceChecks(gc, op, [data], lambda x: (x.shape, )) @given(data=hu.tensor(), **hu.gcs_cpu_only) def test_shape_with_axes(self, data, gc, dc): def shape_ref(x, y): return ([x.shape[i] for i in y],) axes = np.random.randint(len(data.shape), size=10).tolist() op = core.CreateOperator("Shape", ["data"], ["shape"], axes=axes) self.assertReferenceChecks(gc, op, [data, axes], shape_ref) @given(data=hu.tensor(), **hu.gcs_cpu_only) def test_has_elements(self, data, gc, dc): op = core.CreateOperator("HasElements", ["data"], ["has_elements"]) self.assertReferenceChecks(gc, op, [data], lambda x: (len(x) > 0, )) op = core.CreateOperator("IsEmpty", ["data"], ["is_empty"]) self.assertReferenceChecks(gc, op, [data], lambda x: (len(x) == 0, )) @given(initial_iters=st.integers(0, 100), max_iters=st.integers(0, 100)) def test_should_stop_as_criteria_net_execution_step( self, initial_iters, max_iters): net = core.Net("net") net.Iter(["iter"], ["iter"]) self.ws.create_blob("iter").feed( np.asarray([initial_iters]).astype(np.int64)) self.ws.create_blob("num_iters").feed( np.asarray([max_iters]).astype(np.int64)) criteria_net = core.Net("criteria") criteria_net.GE(["iter", "num_iters"], ["stop"]) criteria_net.Proto().external_output.extend(["stop"]) plan = core.Plan('plan') plan.AddStep(core.execution_step( 'step', [criteria_net, net], should_stop_blob=core.BlobReference("stop"))) self.ws.run(plan) iters = self.ws.blobs[("iter")].fetch() self.assertEqual(iters.dtype, np.int64) self.assertEqual(iters[0], max(initial_iters, max_iters)) def test_disabled_execution_step(self): def createNets(i, disabled): should_stop = 'should_stop_{}'.format(i) output = 'output_{}'.format(i) # init content and stop signal init = core.Net("init_{}".format(i)) init.ConstantFill( [], [output], shape=[1], value=0.0 ) init.Cast([output], [should_stop], to='bool') # decide if disabled or not criterion = core.Net("criterion_{}".format(i)) tmp = criterion.ConstantFill( [], shape=[1], value=1.0 if disabled else 0.0 ) criterion.Cast([tmp], [should_stop], to='bool') criterion.Proto().external_output.extend([should_stop]) # the body net is just to turn a 0 blob to 1 net = core.Net("net_{}".format(i)) net.ConstantFill( [], [output], shape=[1], value=1.0 ) # always end the loop ender = core.Net("ender_{}".format(i)) tmp = ender.ConstantFill( [], shape=[1], value=1.0 ) ender.Cast([tmp], [should_stop], to='bool') ender.Proto().external_output.extend([should_stop]) return [init, criterion, net, ender] nets = [createNets(1, False), createNets(2, True), createNets(3, False)] steps = [ core.execution_step( 'step_1', nets[0], should_stop_blob=core.BlobReference('should_stop_1')), core.execution_step( 'step_2', nets[1], should_stop_blob=core.BlobReference('should_stop_2')), core.execution_step('step_3', nets[2]) ] expected = [1.0, 0.0, 1.0] plan = core.Plan('plan') plan.AddStep(core.execution_step('all_steps', steps, num_iter=3)) self.ws.run(plan) for i, _ in enumerate(nets): self.assertEqual( self.ws.blobs['output_{}'.format(i + 1)].fetch()[0], expected[i]) @given(initial_iters=st.integers(0, 100), num_iters=st.integers(0, 100)) def test_iter_count_with_execution_step(self, initial_iters, num_iters): net = core.Net("net") net.Iter(["iter"], ["iter"]) self.ws.create_blob("iter").feed( np.asarray([initial_iters]).astype(np.int64)) step = core.ExecutionStep("step", [net]) step.SetIter(num_iters) plan = core.Plan("plan") plan.AddStep(step) self.ws.run(plan) iters = self.ws.blobs[("iter")].fetch() self.assertEqual(iters.dtype, np.int64) self.assertEqual(iters[0], initial_iters + num_iters) @given(initial_iters=st.integers(0, 100), num_iters=st.integers(0, 100), num_nets=st.integers(0, 5)) def test_atomic_iter_with_concurrent_steps(self, initial_iters, num_iters, num_nets): init_net = core.Net("init_net") iter_mutex = init_net.CreateMutex([], ["iter_mutex"]) self.ws.create_blob("iter").feed( np.asarray([initial_iters]).astype(np.int64)) concurrent_steps = core.ExecutionStep("concurrent_steps", num_iter=num_iters) for i in range(num_nets): net = core.Net("net_{}".format(i)) net.AtomicIter([iter_mutex, "iter"], ["iter"]) step = core.ExecutionStep("step", [net]) concurrent_steps.AddSubstep(step) concurrent_steps.SetConcurrentSubsteps(True) plan = core.Plan("plan") plan.AddStep(concurrent_steps) stats_net = core.Net("stats_net") stats_net.StatRegistryExport([], ["stats_key", "stats_val", "stats_ts"]) self.ws.run(init_net) self.ws.run(plan) self.ws.run(stats_net) iters = self.ws.blobs[("iter")].fetch() self.assertEqual(iters.dtype, np.int64) self.assertEqual(iters[0], initial_iters + num_iters * num_nets) if num_iters * num_nets > 0: stats_key = self.ws.blobs[("stats_key")].fetch() atomic_iter_key = b'atomic_iter/stats/iter/num_iter' self.assertTrue(atomic_iter_key in stats_key) stat_val = self.ws.blobs[("stats_val")].fetch() self.assertEqual(num_iters * num_nets, stat_val[list(stats_key).index(atomic_iter_key)]) @given(a=hu.tensor(), src=st.sampled_from(list(viewkeys(_NUMPY_TYPE_TO_ENUM))), dst=st.sampled_from(list(viewkeys(_NUMPY_TYPE_TO_ENUM))), use_name=st.booleans(), **hu.gcs) def test_cast(self, a, src, dst, use_name, gc, dc): a = a.astype(src) # Casting from a float type outside the range of the integral # type is UB. ftypes = [np.float32, np.float64] if src in ftypes and dst not in ftypes and dst is not np.bool: info = np.iinfo(dst) a = np.clip(a, info.min, info.max) def ref(data): return [data.astype(dst)] to = _NUMPY_TYPE_TO_ENUM[dst] if use_name: to = caffe2_pb2.TensorProto.DataType.Name(to).lower() op = core.CreateOperator('Cast', ["X"], ["Y"], to=to) self.assertDeviceChecks(dc, op, [a], [0]) out, = self.assertReferenceChecks(gc, op, [a], ref) self.assertEqual(dst, out.dtype) @given(a=hu.tensor(), eps=st.floats(min_value=1e-4, max_value=1e-2), a_grad=hu.tensor(elements=st.floats(min_value=0.01, max_value=0.99)), eps_grad=st.floats(min_value=1e-4, max_value=1e-3), **hu.gcs) def test_logit(self, a, eps, a_grad, eps_grad, gc, dc): def ref(data): data = np.clip(data, eps, 1.0 - eps) return (np.log(data / (1 - data)), ) # forward testing carried out in the full range of input # to ensure original test coverage. # gradient test carried out with reduced input range # because the sharp increase of the logit curve at 0 and 1 # error increases dramtically when input is close to 0 or 1 # and it will fail the test. # So we only run gradient test in the range of (0.01, 0.99) # very occationally, test may fail due to random accumulated error # reduce test range to (0.02, 0.98) will improve test stability op = core.CreateOperator('Logit', ["X"], ["Y"], eps=eps) self.assertDeviceChecks(dc, op, [a], [0]) self.assertReferenceChecks(gc, op, [a], ref) op_grad = core.CreateOperator('Logit', ["X"], ["Y"], eps=eps_grad) self.assertGradientChecks(gc, op_grad, [a_grad], 0, [0], threshold=0.04, stepsize=2e-3) @given(a=hu.tensor(elements=st.floats(allow_nan=True)), value=st.floats(min_value=-10, max_value=10), **hu.gcs) def test_replace_nan(self, a, value, gc, dc): def ref(data): out = np.copy(data) out[np.isnan(data)] = value return (out, ) op = core.CreateOperator('ReplaceNaN', ["X"], ["Y"], value=value) self.assertDeviceChecks(dc, op, [a], [0]) self.assertReferenceChecks(gc, op, [a], ref) @given(data=_dtypes(dtypes=[np.int32, np.int64, np.float32, np.bool]). flatmap(lambda dtype: hu.tensor( min_dim=1, dtype=dtype, elements=hu.elements_of_type(dtype))), has_input=st.booleans(), has_extra_shape=st.booleans(), extra_shape=st.lists( min_size=1, max_size=5, elements=st.integers(1, 5)), **hu.gcs) def test_constant_fill(self, data, has_input, has_extra_shape, extra_shape, gc, dc): dtype = data.dtype.type # in opt mode, np.bool is converted into np.bool_ if data.dtype == np.dtype(np.bool): dtype = np.bool value = data.item(0) gt_shape = data.shape inputs = [data] enum_type = _NUMPY_TYPE_TO_ENUM[dtype] if has_input: if has_extra_shape: op = core.CreateOperator('ConstantFill', ["X"], ["Y"], dtype=enum_type, extra_shape=extra_shape, value=value) gt_shape += tuple(extra_shape) else: op = core.CreateOperator('ConstantFill', ["X"], ["Y"], dtype=enum_type, value=value) else: op = core.CreateOperator('ConstantFill', [], ["Y"], dtype=enum_type, value=value, shape=list(gt_shape)) inputs = [] def ref(inputs=None): outputs = np.full(shape=gt_shape, fill_value=value, dtype=dtype) return [outputs] self.assertDeviceChecks(dc, op, inputs, [0]) out, = self.assertReferenceChecks(gc, op, inputs, ref) self.assertEqual(dtype, out.dtype) @given(t=st.integers(1, 5), n=st.integers(1, 5), d=st.integers(1, 5)) def test_elman_recurrent_network(self, t, n, d): from caffe2.python import model_helper, brew np.random.seed(1701) step_net = model_helper.ModelHelper(name="Elman") # TODO: name scope external inputs and outputs step_net.Proto().external_input.extend( ["input_t", "seq_lengths", "timestep", "hidden_t_prev", "gates_t_w", "gates_t_b"]) step_net.Proto().type = "simple" step_net.Proto().external_output.extend(["hidden_t", "gates_t"]) brew.fc(step_net, "hidden_t_prev", "gates_t", dim_in=d, dim_out=d, axis=2) step_net.net.Sum(["gates_t", "input_t"], ["gates_t"]) step_net.net.Sigmoid(["gates_t"], ["hidden_t"]) # Initialize params for step net in the parent net for op in step_net.param_init_net.Proto().op: workspace.RunOperatorOnce(op) backward_ops, backward_mapping = core.GradientRegistry.GetBackwardPass( step_net.Proto().op, {"hidden_t": "hidden_t_grad"}) backward_mapping = { str(k): str(v) for k, v in viewitems(backward_mapping) } backward_step_net = core.Net("ElmanBackward") del backward_step_net.Proto().op[:] backward_step_net.Proto().op.extend(backward_ops) assert backward_mapping["input_t"] == "gates_t_grad" links = [ ("hidden_t_prev", "hidden", 0), ("hidden_t", "hidden", 1), ("input_t", "input", 0), ] link_internal, link_external, link_offset = zip(*links) backward_links = [ ("hidden_t_prev_grad", "hidden_grad", 0), ("hidden_t_grad", "hidden_grad", 1), ("gates_t_grad", "input_grad", 0), ] backward_link_internal, backward_link_external, backward_link_offset = \ zip(*backward_links) backward_step_net.Proto().external_input.extend(["hidden_t_grad"]) backward_step_net.Proto().external_input.extend( step_net.Proto().external_input) backward_step_net.Proto().external_input.extend( step_net.Proto().external_output) inputs = ["input", "seq_lengths", "gates_t_w", "gates_t_b", "hidden_input"] recurrent_inputs = ["hidden_input"] op = core.CreateOperator( "RecurrentNetwork", inputs, ["output", "hidden", "hidden_output", "step_workspaces"], alias_src=["hidden", "hidden"], alias_dst=["output", "hidden_output"], alias_offset=[1, -1], recurrent_states=["hidden"], initial_recurrent_state_ids=[ inputs.index(i) for i in recurrent_inputs ], link_internal=link_internal, link_external=link_external, link_offset=link_offset, backward_link_internal=backward_link_internal, backward_link_external=backward_link_external, backward_link_offset=backward_link_offset, param=[inputs.index(p) for p in step_net.params], step_net=step_net.Proto(), backward_step_net=backward_step_net.Proto(), outputs_with_grads=[0], ) workspace.FeedBlob( "input", np.random.randn(t, n, d).astype(np.float32)) workspace.FeedBlob( "hidden_input", np.random.randn(1, n, d).astype(np.float32)) workspace.FeedBlob( "seq_lengths", np.random.randint(0, t, size=(n,)).astype(np.int32)) def reference(input, seq_lengths, gates_w, gates_b, hidden_input): T = input.shape[0] N = input.shape[1] D = input.shape[2] hidden = np.zeros(shape=(T + 1, N, D)) assert hidden.shape[0] == T + 1 assert hidden.shape[1] == N assert hidden.shape[2] == D hidden[0, :, :] = hidden_input for t in range(T): input_t = input[t].reshape(1, N, D) hidden_t_prev = hidden[t].reshape(1, N, D) gates = np.dot(hidden_t_prev, gates_w.T) gates = gates.reshape(1, N, D) + input_t.reshape(1, N, D) hidden[t + 1] = sigmoid(gates) return hidden[1:], hidden, hidden[-1].reshape(1, N, D) self.assertReferenceChecks( hu.cpu_do, op, [workspace.FetchBlob(name) for name in ["input", "seq_lengths", "gates_t_w", "gates_t_b", "hidden_input"]], reference, outputs_to_check=[0, 1, 2]) for param in [0, 2, 3]: self.assertGradientChecks( hu.cpu_do, op, [workspace.FetchBlob(name) for name in ["input", "seq_lengths", "gates_t_w", "gates_t_b", "hidden_input"]], param, [0]) @settings(suppress_health_check=[HealthCheck.filter_too_much]) @given(n=st.integers(1, 5), c=st.integers(1, 5), h=st.integers(1, 5), w=st.integers(1, 5), pad=st.integers(0, 2), block_size=st.integers(2, 3), **hu.gcs) def test_space_to_batch(self, n, c, h, w, pad, block_size, gc, dc): assume((h + 2 * pad) % block_size == 0) assume((w + 2 * pad) % block_size == 0) X = np.random.randn(n, c, h, w).astype(np.float32) op = core.CreateOperator("SpaceToBatch", ["X"], ["Y"], pad=pad, block_size=block_size) self.assertDeviceChecks(dc, op, [X], [0]) self.assertGradientChecks(gc, op, [X], 0, [0]) @settings(suppress_health_check=[HealthCheck.filter_too_much]) @given(n=st.integers(1, 5), c=st.integers(1, 5), h=st.integers(1, 5), w=st.integers(1, 5), pad=st.integers(0, 2), block_size=st.integers(2, 3), **hu.gcs) def test_batch_to_space(self, n, c, h, w, pad, block_size, gc, dc): assume((h + 2 * pad) % block_size == 0) assume((w + 2 * pad) % block_size == 0) X = np.random.randn( n * block_size * block_size, c, (h + 2 * pad) // block_size, (w + 2 * pad) // block_size).astype(np.float32) op = core.CreateOperator("BatchToSpace", ["X"], ["Y"], pad=pad, block_size=block_size) self.assertDeviceChecks(dc, op, [X], [0]) self.assertGradientChecks(gc, op, [X], 0, [0]) @given(X=hu.tensor(), in_place=st.booleans(), scale=st.floats(min_value=-2.0, max_value=2.0), **hu.gcs) def test_scale(self, X, in_place, scale, gc, dc): op = core.CreateOperator( "Scale", ["X"], ["Y" if not in_place else "X"], scale=scale) self.assertDeviceChecks(dc, op, [X], [0]) self.assertGradientChecks(gc, op, [X], 0, [0]) @given(s=st.text()) def test_string_serde(self, s): s = s.encode('ascii', 'ignore') self.ws.create_blob("a").feed(s) serialized = self.ws.blobs["a"].serialize("a") self.ws.create_blob("b").deserialize(serialized) self.assertEqual(s, self.ws.blobs[("a")].fetch()) self.assertEqual(s, self.ws.blobs[("b")].fetch()) @given(pad=st.integers(0, 3), size=st.integers(1, 10), input_channels=st.integers(1, 5), batch_size=st.integers(1, 5), order=st.sampled_from(["NCHW", "NHWC"]), mode=st.sampled_from(["constant", "reflect", "edge"]), **hu.gcs) def test_same_pad_image(self, pad, size, input_channels, batch_size, order, mode, gc, dc): assume(size > pad) op = core.CreateOperator( "PadImage", ["X"], ["Y"], pad=pad, mode=mode, order=order, ) if order == "NHWC": X = np.random.rand( batch_size, size, size, input_channels).astype(np.float32) - 0.5 def numpy_pad_ref(x): return (np.pad( x, ((0, 0), (pad, pad), (pad, pad), (0, 0)), mode),) else: X = np.random.rand( batch_size, input_channels, size, size).astype(np.float32) - 0.5 def numpy_pad_ref(x): return (np.pad( x, ((0, 0), (0, 0), (pad, pad), (pad, pad)), mode),) self.assertReferenceChecks(gc, op, [X], numpy_pad_ref) self.assertDeviceChecks(dc, op, [X], [0]) self.assertGradientChecks(gc, op, [X], 0, [0]) @given(pad_t=st.integers(0, 3), pad_l=st.integers(0, 3), pad_b=st.integers(0, 3), pad_r=st.integers(0, 3), size=st.integers(1, 10), input_channels=st.integers(1, 5), batch_size=st.integers(1, 5), order=st.sampled_from(["NCHW", "NHWC"]), mode=st.sampled_from(["constant", "reflect", "edge"]), **hu.gcs) def test_pad_image(self, pad_t, pad_l, pad_b, pad_r, size, input_channels, batch_size, order, mode, gc, dc): assume(size > max(pad_b, pad_r, pad_t, pad_l)) op = core.CreateOperator( "PadImage", ["X"], ["Y"], pad_t=pad_t, pad_l=pad_l, pad_b=pad_b, pad_r=pad_r, mode=mode, order=order, ) if order == "NHWC": X = np.random.rand( batch_size, size, size, input_channels).astype(np.float32) - 0.5 def numpy_pad_ref(x): return (np.pad( x, ((0, 0), (pad_t, pad_b), (pad_l, pad_r), (0, 0)), mode),) else: X = np.random.rand( batch_size, input_channels, size, size).astype(np.float32) - 0.5 def numpy_pad_ref(x): return (np.pad( x, ((0, 0), (0, 0), (pad_t, pad_b), (pad_l, pad_r)), mode),) self.assertReferenceChecks(gc, op, [X], numpy_pad_ref) self.assertDeviceChecks(dc, op, [X], [0]) self.assertGradientChecks(gc, op, [X], 0, [0]) @given(size=st.integers(7, 10), input_channels=st.integers(1, 10), batch_size=st.integers(1, 3), order=st.sampled_from(["NCHW", "NHWC"]), epsilon=st.floats(min_value=1e-4, max_value=1e-2), **hu.gcs_cpu_only) def test_instance_norm(self, size, input_channels, batch_size, order, epsilon, gc, dc): op = core.CreateOperator( "InstanceNorm", ["X", "scale", "bias"], ["Y"], order=order, epsilon=epsilon, ) np.random.seed(1701) scale = np.random.rand(input_channels).astype(np.float32) + 0.5 bias = np.random.rand(input_channels).astype(np.float32) - 0.5 X = np.random.rand( batch_size, input_channels, size, size).astype(np.float32) - 0.5 if order == "NHWC": X = X.swapaxes(1, 2).swapaxes(2, 3) def ref_nchw(x, scale, bias): x = x.reshape(batch_size * input_channels, size * size) y = (x - x.mean(1)[:, np.newaxis]) y /= np.sqrt(x.var(1) + epsilon)[:, np.newaxis] y = y.reshape(batch_size, input_channels, size, size) y = y * scale.reshape(1, input_channels, 1, 1) y = y + bias.reshape(1, input_channels, 1, 1) return (y, ) def ref_nhwc(x, scale, bias): x = x.swapaxes(2, 3).swapaxes(1, 2) y = ref_nchw(x, scale, bias)[0] return (y.swapaxes(1, 2).swapaxes(2, 3), ) self.assertReferenceChecks( gc, op, [X, scale, bias], ref_nchw if order == "NCHW" else ref_nhwc) # TODO(jiayq): when there are backward and GPU implementations, enable # these two. # self.assertDeviceChecks(dc, op, [X, scale, bias], [0]) # self.assertGradientChecks(gc, op, [X, scale, bias], 0, [0]) ws = workspace.C.Workspace() feeds = [("X", X), ("scale", scale), ("bias", bias)] for blob, arr in feeds: ws.create_blob(blob).feed(arr) for _ in range(100): ws.run(op) for blob, arr in feeds: np.testing.assert_array_equal(ws.blobs[blob].fetch(), arr) @given(inp=_dtypes().flatmap(lambda dt: _tensor_and_indices( elements=st.floats(min_value=0, max_value=1), dtype=dt)), **hu.gcs) def test_sparse_to_dense(self, inp, gc, dc): first_dim, X, I = inp if X.dtype != np.dtype('float32') and gc.device_type in {caffe2_pb2.CUDA, caffe2_pb2.HIP} : # Cuda only support 32 bit float print("Bailout {}".format(X.dtype)) return if gc.device_type in {caffe2_pb2.CUDA, caffe2_pb2.HIP}: # Cuda version only support int32 I = I.astype(np.int32) # values don't matter D = np.zeros((first_dim,) + X.shape[1:]).astype(X.dtype) op = core.CreateOperator("SparseToDense", ["I", "X", "D"], ["Y"]) def sparse_to_dense(I, X, D): O = np.zeros(D.shape) for i, p in enumerate(I): O[p] += X[i] return [O] self.assertReferenceChecks(gc, op, [I, X, D], sparse_to_dense) X = X.astype(np.float32) self.assertGradientChecks(gc, op, [I, X, D], 1, [0]) @given(inputs=hu.tensors(n=2, min_dim=2, max_dim=2), **hu.gcs_cpu_only) def test_dot_product(self, inputs, gc, dc): X, Y = inputs op = core.CreateOperator("DotProduct", ["X", "Y"], 'out') def dotproduct(X, Y): return (np.sum(X * Y, axis=1), ) self.assertReferenceChecks(gc, op, [X, Y], dotproduct) self.assertDeviceChecks(dc, op, [X, Y], [0]) self.assertGradientChecks(gc, op, [X, Y], 0, [0]) self.assertGradientChecks(gc, op, [X, Y], 1, [0]) @given(N=st.integers(min_value=2, max_value=10), M=st.integers(min_value=2, max_value=10), K=st.integers(min_value=2, max_value=10), pad_value=st.floats(min_value=0.1, max_value=1.0), **hu.gcs_cpu_only) def test_dot_product_with_padding(self, N, M, K, pad_value, gc, dc): X = np.random.rand(N, M).astype(np.float32) - 0.5 Y = np.random.rand(N, K).astype(np.float32) - 0.5 op = core.CreateOperator("DotProductWithPadding", ["X", "Y"], 'out', pad_value=pad_value) def dotproduct(X, Y): Z = np.ones((N, max(M, K))).astype(np.float32) * pad_value if M < K: Z[:, :M] = X return (np.sum(Z * Y, axis=1), ) else: Z[:, :K] = Y return (np.sum(Z * X, axis=1), ) self.assertReferenceChecks(gc, op, [X, Y], dotproduct) self.assertDeviceChecks(dc, op, [X, Y], [0]) self.assertGradientChecks(gc, op, [X, Y], 0, [0]) self.assertGradientChecks(gc, op, [X, Y], 1, [0]) @given(N=st.integers(min_value=2, max_value=10), M=st.integers(min_value=2, max_value=10), pad_value=st.floats(min_value=0.1, max_value=1.0), **hu.gcs_cpu_only) def test_dot_product_with_rep_padding(self, N, M, pad_value, gc, dc): K = 2 * M X = np.random.rand(N, M).astype(np.float32) - 0.5 Y = np.random.rand(N, K).astype(np.float32) - 0.5 op = core.CreateOperator("DotProductWithPadding", ["X", "Y"], 'out', replicate=True, pad_value=pad_value) def dotproduct(X, Y): import numpy.matlib as npm if M < K: Z = npm.repmat(X, 1, K // M) return (np.sum(Z * Y, axis=1), ) else: Z = npm.repmat(Y, 1, M // K) return (np.sum(Z * X, axis=1), ) self.assertReferenceChecks(gc, op, [X, Y], dotproduct) self.assertDeviceChecks(dc, op, [X, Y], [0]) self.assertGradientChecks(gc, op, [X, Y], 0, [0]) self.assertGradientChecks(gc, op, [X, Y], 1, [0]) @given(N=st.integers(min_value=2, max_value=10), M=st.integers(min_value=2, max_value=10), **hu.gcs_cpu_only) def test_ensure_dense(self, N, M, gc, dc): # in place X = np.random.rand(N, M).astype(np.float32) - 0.5 op = core.CreateOperator("EnsureDense", ["X"], "X") self.assertReferenceChecks(gc, op, [X], lambda x: [x]) self.assertDeviceChecks(dc, op, [X], [0]) # or not X = np.random.rand(N, M).astype(np.float32) - 0.5 op = core.CreateOperator("EnsureDense", ["X"], "out") self.assertReferenceChecks(gc, op, [X], lambda x: [x]) self.assertDeviceChecks(dc, op, [X], [0]) @given(N=st.integers(min_value=10, max_value=100), M=st.integers(min_value=2, max_value=10), num_buckets=st.integers(min_value=1, max_value=5), **hu.gcs_cpu_only) def test_accumulate_histogram_op(self, N, M, num_buckets, gc, dc): X = np.random.rand(N, M).astype(np.float32) lower_bound, upper_bound = 0.1, 0.9 op = core.CreateOperator("AccumulateHistogram", ["X"], ['cur_hist', 'acc_hist'], lower_bound=lower_bound, upper_bound=upper_bound, num_buckets=num_buckets) def histogram(X): hist = np.zeros((num_buckets + 2, ), dtype=np.int32) segment = (upper_bound - lower_bound) / num_buckets Y = np.zeros((N, M), dtype=np.int32) Y[X < lower_bound] = 0 Y[X >= upper_bound] = num_buckets + 1 Y[(X >= lower_bound) & (X < upper_bound)] = \ ((X[(X >= lower_bound) & (X < upper_bound)] - lower_bound) / segment + 1).astype(np.int32) for i in range(Y.shape[0]): for j in range(Y.shape[1]): hist[Y[i][j]] += 1 cur_hist, acc_hist = hist, hist return [cur_hist, acc_hist] self.assertDeviceChecks(dc, op, [X], [0, 1]) self.assertReferenceChecks(gc, op, [X], histogram) if __name__ == "__main__": unittest.main()