import functools import hypothesis from hypothesis import given import hypothesis.strategies as st import numpy as np import unittest from caffe2.python import core, workspace import caffe2.python.hypothesis_test_util as hu class TestAdam(hu.HypothesisTestCase): @staticmethod def ref_adam(param, mom1, mom2, grad, LR, ITER, beta1, beta2, epsilon, output_grad=False): t = ITER + 1 corrected_local_rate = np.sqrt(1 - np.power(beta2, t)) / \ (1 - np.power(beta1, t)) mom1_out = (beta1 * mom1) + (1 - beta1) * grad mom2_out = (beta2 * mom2) + (1 - beta2) * np.square(grad) grad_out = corrected_local_rate * mom1_out / \ (np.sqrt(mom2_out) + epsilon) param_out = param + LR * grad_out if output_grad: return param_out, mom1_out, mom2_out, grad_out else: return param_out, mom1_out, mom2_out @staticmethod def ref_smart_decay_adam(param, mom1, mom2, last_seen, grad, LR, ITER, beta1, beta2, epsilon): for name in ('param', 'mom1', 'mom2', 'last_seen', 'grad', 'LR', 'ITER', 'beta1', 'beta2', 'epsilon'): print("{} {} {}".format(name, locals()['name'], type(locals()['name']))) t = ITER + 1 k = t - last_seen k = k.flatten()[0] last_seen_out = t * np.ones_like(last_seen) # Make up for lost minibatches. mom2_out = (beta2**k * mom2) + (1 - beta2) * np.square(grad) param_out = param mom1_out = mom1 # For catchup assert k >= 1 for i in range(k): mom1_out *= beta1 if i == k - 1: mom1_out += grad * (1 - beta1) param_out += LR * mom1_out / (np.sqrt(mom2_out) + epsilon) grad_out = mom1_out / (np.sqrt(mom2_out) + epsilon) return param_out, mom1_out, mom2_out, last_seen_out @staticmethod def ref_row_wise_adam(param, mom1, mom2, grad, LR, ITER, beta1, beta2, epsilon, output_grad=False): t = ITER + 1 corrected_local_rate = np.sqrt(1 - np.power(beta2, t)) / \ (1 - np.power(beta1, t)) mom1_out = (beta1 * mom1) + (1 - beta1) * grad mom2_out = (beta2 * mom2) + (1 - beta2) * np.mean(np.square(grad)) grad_out = corrected_local_rate * mom1_out / (np.sqrt(mom2_out) + epsilon) param_out = param + LR * grad_out if output_grad: return param_out, mom1_out, mom2_out, grad_out else: return param_out, mom1_out, mom2_out @given(inputs=hu.tensors(n=4), ITER=st.integers(min_value=0, max_value=10000), LR=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta1=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta2=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), epsilon=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), **hu.gcs) def test_adam(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc): param, mom1, mom2, grad = inputs mom2 = np.abs(mom2) ITER = np.array([ITER], dtype=np.int64) LR = np.array([LR], dtype=np.float32) op = core.CreateOperator( "Adam", ["param", "mom1", "mom2", "grad", "lr", "iter"], ["output_param", "output_mom1", "output_mom2"], beta1=beta1, beta2=beta2, epsilon=epsilon) # Iter lives on the CPU input_device_options = {'iter': hu.cpu_do} self.assertReferenceChecks( gc, op, [param, mom1, mom2, grad, LR, ITER], functools.partial( self.ref_adam, beta1=beta1, beta2=beta2, epsilon=epsilon), input_device_options=input_device_options) @given(inputs=hu.tensors(n=4), ITER=st.integers(min_value=0, max_value=10000), LR=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta1=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta2=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), epsilon=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), **hu.gcs_cpu_only) def test_adam_output_grad(self, inputs, ITER, LR, beta1, beta2, epsilon, gc, dc): param, mom1, mom2, grad = inputs mom2 = np.abs(mom2) ITER = np.array([ITER], dtype=np.int64) LR = np.array([LR], dtype=np.float32) op = core.CreateOperator( "Adam", ["param", "mom1", "mom2", "grad", "lr", "iter"], ["output_param", "output_mom1", "output_mom2", "output_grad"], beta1=beta1, beta2=beta2, epsilon=epsilon) # Iter lives on the CPU input_device_options = {'iter': hu.cpu_do} self.assertReferenceChecks( gc, op, [param, mom1, mom2, grad, LR, ITER], functools.partial( self.ref_adam, beta1=beta1, beta2=beta2, epsilon=epsilon, output_grad=True), input_device_options=input_device_options) @given(inputs=hu.tensors(n=4), ITER=st.integers(min_value=0, max_value=10000), LR=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta1=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta2=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), epsilon=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), data_strategy=st.data(), **hu.gcs) def test_sparse_adam(self, inputs, ITER, LR, beta1, beta2, epsilon, data_strategy, gc, dc): param, mom1, mom2, grad = inputs mom2 = np.absolute(mom2) ITER = np.array([ITER], dtype=np.int64) LR = np.array([LR], dtype=np.float32) # Create an indexing array containing values which index into grad indices = data_strategy.draw( hu.tensor( max_dim=1, min_value=1, max_value=grad.shape[0], dtype=np.int64, elements=st.sampled_from(np.arange(grad.shape[0])), ), ) # Verify that the generated indices are unique hypothesis.assume( np.array_equal( np.unique(indices.flatten()), np.sort(indices.flatten()))) # Sparsify grad grad = grad[indices] op = core.CreateOperator( "SparseAdam", ["param", "mom1", "mom2", "indices", "grad", "lr", "iter"], ["param", "mom1", "mom2"], beta1=beta1, beta2=beta2, epsilon=epsilon) def ref_sparse(param, mom1, mom2, indices, grad, LR, ITER): param_out = np.copy(param) mom1_out = np.copy(mom1) mom2_out = np.copy(mom2) for i, index in enumerate(indices): param_out[index], mom1_out[index], mom2_out[index] = \ self.ref_adam(param[index], mom1[index], mom2[index], grad[i], LR, ITER, beta1, beta2, epsilon) return (param_out, mom1_out, mom2_out) # Iter lives on the CPU input_device_options = {'iter': hu.cpu_do} self.assertReferenceChecks( gc, op, [param, mom1, mom2, indices, grad, LR, ITER], ref_sparse, input_device_options=input_device_options) @unittest.skipIf(not workspace.has_cuda_support, "no cuda support") @given(inputs=hu.tensors(n=4), ITER=st.integers(min_value=0, max_value=10), LR=st.floats(min_value=0.000001, max_value=0.1, allow_nan=False, allow_infinity=False), beta1=st.floats(min_value=0.0, max_value=0.99999, allow_nan=False, allow_infinity=False), beta2=st.floats(min_value=0.9, max_value=0.999999, allow_nan=False, allow_infinity=False), epsilon=st.floats(min_value=0.00001, max_value=0.99, allow_nan=False, allow_infinity=False), data_strategy=st.data(), **hu.gcs) def test_smart_decay_sparse_adam(self, inputs, ITER, LR, beta1, beta2, epsilon, data_strategy, gc, dc): param, mom1, mom2, grad = inputs mom2 = np.absolute(mom2) _iter, _lr = ITER, LR # Keep the scalar types for reference ITER = np.array([ITER], dtype=np.int64) LR = np.array([LR], dtype=np.float32) # Here we will define the last_seen tensor as being randomly from 0 to ITER # (the value of t to be tested will be ITER+1) last_seen = data_strategy.draw( hypothesis.extra.numpy.arrays( dtype=np.int64, shape=(param.shape[0],), elements=st.integers(min_value=0, max_value=_iter), unique=False, ) ) # Create an indexing array containing values which index into grad indices = data_strategy.draw( hu.tensor( max_dim=1, min_value=1, max_value=grad.shape[0], dtype=np.int64, elements=st.sampled_from(np.arange(grad.shape[0])), ), ) # Verify that the generated indices are unique hypothesis.assume( np.array_equal( np.unique(indices.flatten()), np.sort(indices.flatten()))) # Sparsify grad grad = grad[indices] op = core.CreateOperator( "SmartDecaySparseAdam", ["param", "mom1", "mom2", "last_seen", "indices", "grad", "lr", "iter"], ["param", "mom1", "mom2", "last_seen"], beta1=beta1, beta2=beta2, epsilon=epsilon) def ref_sparse(param, mom1, mom2, last_seen, indices, grad, LR, ITER): param_out = np.copy(param) mom1_out = np.copy(mom1) mom2_out = np.copy(mom2) last_seen_out = np.copy(last_seen) for i, index in enumerate(indices): param_out[index], mom1_out[index], mom2_out[index], last_seen_out[index] = \ self.ref_smart_decay_adam(param[index], mom1[index], mom2[index], last_seen[index], grad[i], LR, ITER, beta1, beta2, epsilon) return (param_out, mom1_out, mom2_out, last_seen_out) # Iter lives on the CPU input_device_options = {'iter': hu.cpu_do} self.assertReferenceChecks( gc, op, [param, mom1, mom2, last_seen, indices, grad, LR, ITER], ref_sparse, input_device_options=input_device_options) @given(inputs=hu.tensors(n=4), ITER=st.integers(min_value=0, max_value=10000), LR=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta1=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta2=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), epsilon=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), data_strategy=st.data(), **hu.gcs) def test_sparse_adam_output_grad(self, inputs, ITER, LR, beta1, beta2, epsilon, data_strategy, gc, dc): param, mom1, mom2, grad = inputs mom2 = np.absolute(mom2) ITER = np.array([ITER], dtype=np.int64) LR = np.array([LR], dtype=np.float32) # Create an indexing array containing values which index into grad indices = data_strategy.draw( hu.tensor( max_dim=1, min_value=1, max_value=grad.shape[0], dtype=np.int64, elements=st.sampled_from(np.arange(grad.shape[0])), ), ) # Verify that the generated indices are unique hypothesis.assume( np.array_equal( np.unique(indices.flatten()), np.sort(indices.flatten()))) # Sparsify grad grad = grad[indices] op = core.CreateOperator( "SparseAdam", ["param", "mom1", "mom2", "indices", "grad", "lr", "iter"], ["param", "mom1", "mom2", "output_grad"], beta1=beta1, beta2=beta2, epsilon=epsilon) def ref_sparse_output_grad(param, mom1, mom2, indices, grad, LR, ITER, beta1, beta2, epsilon, output_grad): param_out = np.copy(param) mom1_out = np.copy(mom1) mom2_out = np.copy(mom2) grad_out = np.copy(grad) for i, index in enumerate(indices): param_out[index], mom1_out[index], mom2_out[index], grad_out[i] = \ self.ref_adam(param[index], mom1[index], mom2[index], grad[i], LR, ITER, beta1, beta2, epsilon, output_grad) return (param_out, mom1_out, mom2_out, grad_out) # Iter lives on the CPU input_device_options = {'iter': hu.cpu_do} self.assertReferenceChecks( gc, op, [param, mom1, mom2, indices, grad, LR, ITER], functools.partial( ref_sparse_output_grad, beta1=beta1, beta2=beta2, epsilon=epsilon, output_grad=True), input_device_options=input_device_options) @given(inputs=hu.tensors(n=3), ITER=st.integers(min_value=0, max_value=10000), LR=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta1=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta2=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), epsilon=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), data_strategy=st.data(), **hu.gcs) def test_row_wise_sparse_adam(self, inputs, ITER, LR, beta1, beta2, epsilon, data_strategy, gc, dc): param, mom1, grad = inputs ITER = np.array([ITER], dtype=np.int64) LR = np.array([LR], dtype=np.float32) # Create a 1D row-wise average 2nd moment tensor. mom2 = data_strategy.draw( hu.tensor1d(min_len=param.shape[0], max_len=param.shape[0], elements=hu.elements_of_type(dtype=np.float32)) ) mom2 = np.absolute(mom2) # Create an indexing array containing values which index into grad indices = data_strategy.draw( hu.tensor( max_dim=1, min_value=1, max_value=grad.shape[0], dtype=np.int64, elements=st.sampled_from(np.arange(grad.shape[0])), ), ) # Note that unlike SparseAdam, RowWiseSparseAdam uses a moment # tensor that is strictly 1-dimensional and equal in length to the # first dimension of the parameters, so indices must also be # 1-dimensional. indices = indices.flatten() hypothesis.note('indices.shape: %s' % str(indices.shape)) # Verify that the generated indices are unique hypothesis.assume(np.array_equal(np.unique(indices), np.sort(indices))) # Sparsify grad grad = grad[indices] op = core.CreateOperator( "RowWiseSparseAdam", ["param", "mom1", "mom2", "indices", "grad", "lr", "iter"], ["param", "mom1", "mom2"], beta1=beta1, beta2=beta2, epsilon=epsilon) def ref_row_wise_sparse(param, mom1, mom2, indices, grad, LR, ITER): param_out = np.copy(param) mom1_out = np.copy(mom1) mom2_out = np.copy(mom2) for i, index in enumerate(indices): param_out[index], mom1_out[index], mom2_out[index] = \ self.ref_row_wise_adam(param[index], mom1[index], mom2[index], grad[i], LR, ITER, beta1, beta2, epsilon) return (param_out, mom1_out, mom2_out) # Iter lives on the CPU input_device_options = {'iter': hu.cpu_do} self.assertDeviceChecks( dc, op, [param, mom1, mom2, indices, grad, LR, ITER], [0, 1, 2], input_device_options=input_device_options) self.assertReferenceChecks( gc, op, [param, mom1, mom2, indices, grad, LR, ITER], ref_row_wise_sparse, input_device_options=input_device_options) @given(inputs=hu.tensors(n=3), ITER=st.integers(min_value=0, max_value=10000), LR=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta1=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), beta2=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), epsilon=st.floats(min_value=0.01, max_value=0.99, allow_nan=False, allow_infinity=False), data_strategy=st.data(), **hu.gcs) def test_row_wise_sparse_adam_output_grad(self, inputs, ITER, LR, beta1, beta2, epsilon, data_strategy, gc, dc): param, mom1, grad = inputs ITER = np.array([ITER], dtype=np.int64) LR = np.array([LR], dtype=np.float32) # Create a 1D row-wise average 2nd moment tensor. mom2 = data_strategy.draw( hu.tensor1d(min_len=param.shape[0], max_len=param.shape[0], elements=hu.elements_of_type(dtype=np.float32)) ) mom2 = np.absolute(mom2) # Create an indexing array containing values which index into grad indices = data_strategy.draw( hu.tensor( max_dim=1, min_value=1, max_value=grad.shape[0], dtype=np.int64, elements=st.sampled_from(np.arange(grad.shape[0])), ), ) # Note that unlike SparseAdam, RowWiseSparseAdam uses a moment # tensor that is strictly 1-dimensional and equal in length to the # first dimension of the parameters, so indices must also be # 1-dimensional. indices = indices.flatten() hypothesis.note('indices.shape: %s' % str(indices.shape)) # Verify that the generated indices are unique hypothesis.assume(np.array_equal(np.unique(indices), np.sort(indices))) # Sparsify grad grad = grad[indices] op = core.CreateOperator( "RowWiseSparseAdam", ["param", "mom1", "mom2", "indices", "grad", "lr", "iter"], ["param", "mom1", "mom2", "output_grad"], beta1=beta1, beta2=beta2, epsilon=epsilon) def ref_row_wise_sparse_output_grad(param, mom1, mom2, indices, grad, LR, ITER, beta1, beta2, epsilon, output_grad): param_out = np.copy(param) mom1_out = np.copy(mom1) mom2_out = np.copy(mom2) grad_out = np.copy(grad) for i, index in enumerate(indices): param_out[index], mom1_out[index], mom2_out[index], grad_out[i] = \ self.ref_row_wise_adam(param[index], mom1[index], mom2[index], grad[i], LR, ITER, beta1, beta2, epsilon, output_grad) return (param_out, mom1_out, mom2_out, grad_out) # Iter lives on the CPU input_device_options = {'iter': hu.cpu_do} self.assertDeviceChecks( dc, op, [param, mom1, mom2, indices, grad, LR, ITER], [0, 1, 2, 3], input_device_options=input_device_options) self.assertReferenceChecks( gc, op, [param, mom1, mom2, indices, grad, LR, ITER], functools.partial( ref_row_wise_sparse_output_grad, beta1=beta1, beta2=beta2, epsilon=epsilon, output_grad=True), input_device_options=input_device_options) if __name__ == "__main__": import unittest unittest.main()