Cython acados and minor (#23835)

* acados_ocp_solver_pyx.pyx: implement get_stats for timings and ints

* long_mpc: use acados timers

* acados_ocp_solver_pyx.pyx: fix dynamics_get

* acados_ocp_solver_pyx.pyx: get statistics

* use acados_ocp_solver_pyx.pyx from commaai/cython2 branch

* acados_ocp_solver_pyx.pyx: implement store_iterate

* acados_ocp_solver_pyx.pyx: implement get_residuals

* acados_ocp_solver_pyx.pyx: fix set() for empty fields

* acados_ocp_solver_pyx.pyx: load_iterate

* cython acados: add print_statistics

* test_following_distance: fix typo

* test_longitudinal: unique names for test maneuvers

* longitudinal MPC: comments for evaluation

* longitudinal MPC: add comments to eval acados residuals

* long_mpc: use qp_solver_cond_N = 1

* long MPC: comments, simplify set_cur_state

* update acados version in build script

* longitudinal mpc: weigh a_change in 1 place only

* update ref

* Update ref

Co-authored-by: Harald Schafer <harald.the.engineer@gmail.com>

pyextra/acados_template/acados_ocp_solver_pyx.pyx

@@ -44,16 +44,17 @@ cimport acados_solver
 cimport numpy as cnp
 
 import os
+import json
+from datetime import datetime
 import numpy as np
 
 
 cdef class AcadosOcpSolverFast:
     """
     Class to interact with the acados ocp solver C object.
 
-        :param acados_ocp: type AcadosOcp - description of the OCP for acados
-        :param json_file: name for the json file used to render the templated code - default: acados_ocp_nlp.json
-        :param simulink_opts: Options to configure Simulink S-function blocks, mainly to activate possible Inputs and Outputs
+        :param model_name:
+        :param N:
     """
 
     cdef acados_solver.nlp_solver_capsule *capsule
@@ -68,7 +69,7 @@ cdef class AcadosOcpSolverFast:
     cdef int N
     cdef bint solver_created
 
-    def __cinit__(self, str model_name, int N, str code_export_dir):
+    def __cinit__(self, str model_name, int N):
         self.model_name = model_name
         self.N = N
 
@@ -168,7 +169,7 @@ cdef class AcadosOcpSolverFast:
             - qp_res_ineq: residual wrt inequality constraints (constraints)  of the last QP solution
             - qp_res_comp: residual wrt complementarity conditions of the last QP solution
         """
-        raise NotImplementedError()
+        acados_solver.acados_print_stats(self.capsule)
 
 
     def store_iterate(self, filename='', overwrite=False):
@@ -178,14 +179,48 @@ cdef class AcadosOcpSolverFast:
             :param filename: if not set, use model_name + timestamp + '.json'
             :param overwrite: if false and filename exists add timestamp to filename
         """
-        raise NotImplementedError()
+        if filename == '':
+            filename += self.model_name + '_' + 'iterate' + '.json'
+
+        if not overwrite:
+            # append timestamp
+            if os.path.isfile(filename):
+                filename = filename[:-5]
+                filename += datetime.utcnow().strftime('%Y-%m-%d-%H:%M:%S.%f') + '.json'
+
+        # get iterate:
+        solution = dict()
+
+        for i in range(self.N+1):
+            solution['x_'+str(i)] = self.get(i,'x')
+            solution['u_'+str(i)] = self.get(i,'u')
+            solution['z_'+str(i)] = self.get(i,'z')
+            solution['lam_'+str(i)] = self.get(i,'lam')
+            solution['t_'+str(i)] = self.get(i, 't')
+            solution['sl_'+str(i)] = self.get(i, 'sl')
+            solution['su_'+str(i)] = self.get(i, 'su')
+        for i in range(self.N):
+            solution['pi_'+str(i)] = self.get(i,'pi')
+
+        # save
+        with open(filename, 'w') as f:
+            json.dump(solution, f, default=lambda x: x.tolist(), indent=4, sort_keys=True)
+        print("stored current iterate in ", os.path.join(os.getcwd(), filename))
 
 
     def load_iterate(self, filename):
         """
         Loads the iterate stored in json file with filename into the ocp solver.
         """
-        raise NotImplementedError()
+        if not os.path.isfile(filename):
+            raise Exception('load_iterate: failed, file does not exist: ' + os.path.join(os.getcwd(), filename))
+
+        with open(filename, 'r') as f:
+            solution = json.load(f)
+
+        for key in solution.keys():
+            (field, stage) = key.split('_')
+            self.set(int(stage), field, np.array(solution[key]))
 
 
     def get_stats(self, field_):
@@ -194,7 +229,67 @@ cdef class AcadosOcpSolverFast:
 
             :param field: string in ['statistics', 'time_tot', 'time_lin', 'time_sim', 'time_sim_ad', 'time_sim_la', 'time_qp', 'time_qp_solver_call', 'time_reg', 'sqp_iter']
         """
-        raise NotImplementedError()
+        fields = ['time_tot',  # total cpu time previous call
+                  'time_lin',  # cpu time for linearization
+                  'time_sim',  # cpu time for integrator
+                  'time_sim_ad',  # cpu time for integrator contribution of external function calls
+                  'time_sim_la',  # cpu time for integrator contribution of linear algebra
+                  'time_qp',   # cpu time qp solution
+                  'time_qp_solver_call',  # cpu time inside qp solver (without converting the QP)
+                  'time_qp_xcond',
+                  'time_glob',  # cpu time globalization
+                  'time_reg',  # cpu time regularization
+                  'sqp_iter',  # number of SQP iterations
+                  'qp_iter',  # vector of QP iterations for last SQP call
+                  'statistics',  # table with info about last iteration
+                  'stat_m',
+                  'stat_n',]
+
+        field = field_
+        field = field.encode('utf-8')
+
+        if (field_ not in fields):
+            raise Exception('AcadosOcpSolver.get_stats(): {} is not a valid argument.\
+                    \n Possible values are {}. Exiting.'.format(fields, fields))
+
+        if field_ in ['sqp_iter', 'stat_m', 'stat_n']:
+            return self.__get_stat_int(field)
+
+        elif field_.startswith('time'):
+            return self.__get_stat_double(field)
+
+        elif field_ == 'statistics':
+            sqp_iter = self.get_stats("sqp_iter")
+            stat_m = self.get_stats("stat_m")
+            stat_n = self.get_stats("stat_n")
+            min_size = min([stat_m, sqp_iter+1])
+            return self.__get_stat_matrix(field, stat_n+1, min_size)
+
+        elif field_ == 'qp_iter':
+            NotImplementedError("TODO! Cython not aware if SQP or SQP_RTI")
+            full_stats = self.get_stats('statistics')
+            if self.acados_ocp.solver_options.nlp_solver_type == 'SQP':
+                out = full_stats[6, :]
+            elif self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI':
+                out = full_stats[2, :]
+        else:
+            NotImplementedError("TODO!")
+
+
+    def __get_stat_int(self, field):
+        cdef int out
+        acados_solver_common.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, <void *> &out)
+        return out
+
+    def __get_stat_double(self, field):
+        cdef cnp.ndarray[cnp.float64_t, ndim=1] out = np.zeros((1,))
+        acados_solver_common.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, <void *> out.data)
+        return out
+
+    def __get_stat_matrix(self, field, n, m):
+        cdef cnp.ndarray[cnp.float64_t, ndim=2] out_mat = np.ascontiguousarray(np.zeros((n, m)), dtype=np.float64)
+        acados_solver_common.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, <void *> out_mat.data)
+        return out_mat
 
 
     def get_cost(self):
@@ -217,7 +312,32 @@ cdef class AcadosOcpSolverFast:
         """
         Returns an array of the form [res_stat, res_eq, res_ineq, res_comp].
         """
-        raise NotImplementedError()
+        # TODO: check if RTI, only eval then
+        # if self.acados_ocp.solver_options.nlp_solver_type == 'SQP_RTI':
+        # compute residuals if RTI
+        acados_solver_common.ocp_nlp_eval_residuals(self.nlp_solver, self.nlp_in, self.nlp_out)
+
+        # create output array
+        cdef cnp.ndarray[cnp.float64_t, ndim=1] out = np.ascontiguousarray(np.zeros((4,), dtype=np.float64))
+        cdef double double_value
+
+        field = "res_stat".encode('utf-8')
+        acados_solver_common.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, <void *> &double_value)
+        out[0] = double_value
+
+        field = "res_eq".encode('utf-8')
+        acados_solver_common.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, <void *> &double_value)
+        out[1] = double_value
+
+        field = "res_ineq".encode('utf-8')
+        acados_solver_common.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, <void *> &double_value)
+        out[2] = double_value
+
+        field = "res_comp".encode('utf-8')
+        acados_solver_common.ocp_nlp_get(self.nlp_config, self.nlp_solver, field, <void *> &double_value)
+        out[3] = double_value
+
+        return out
 
 
     # Note: this function should not be used anymore, better use cost_set, constraints_set
@@ -246,12 +366,11 @@ cdef class AcadosOcpSolverFast:
 
         field = field_.encode('utf-8')
 
-        cdef double[::1] value
+        cdef cnp.ndarray[cnp.float64_t, ndim=1] value = np.ascontiguousarray(value_, dtype=np.float64)
 
         # treat parameters separately
         if field_ == 'p':
-            value = np.ascontiguousarray(value_, dtype=np.double)
-            assert acados_solver.acados_update_params(self.capsule, stage, <double *> &value[0], value.shape[0]) == 0
+            assert acados_solver.acados_update_params(self.capsule, stage, <double *> value.data, value.shape[0]) == 0
         else:
             if field_ not in constraints_fields + cost_fields + out_fields:
                 raise Exception("AcadosOcpSolver.set(): {} is not a valid argument.\
@@ -266,16 +385,15 @@ cdef class AcadosOcpSolverFast:
                 msg += 'with dimension {} (you have {})'.format(dims, value_.shape[0])
                 raise Exception(msg)
 
-            value = np.ascontiguousarray(value_, dtype=np.double)
             if field_ in constraints_fields:
                 acados_solver_common.ocp_nlp_constraints_model_set(self.nlp_config,
-                    self.nlp_dims, self.nlp_in, stage, field, <void *> &value[0])
+                    self.nlp_dims, self.nlp_in, stage, field, <void *> value.data)
             elif field_ in cost_fields:
                 acados_solver_common.ocp_nlp_cost_model_set(self.nlp_config,
-                    self.nlp_dims, self.nlp_in, stage, field, <void *> &value[0])
+                    self.nlp_dims, self.nlp_in, stage, field, <void *> value.data)
             elif field_ in out_fields:
                 acados_solver_common.ocp_nlp_out_set(self.nlp_config,
-                    self.nlp_dims, self.nlp_out, stage, field, <void *> &value[0])
+                    self.nlp_dims, self.nlp_out, stage, field, <void *> value.data)
 
 
     def cost_set(self, int stage, str field_, value_):
@@ -361,7 +479,7 @@ cdef class AcadosOcpSolverFast:
         acados_solver_common.ocp_nlp_dynamics_dims_get_from_attr(self.nlp_config, self.nlp_dims, self.nlp_out, stage, field, &dims[0])
 
         # create output data
-        out = np.zeros((dims[0], dims[1]), order='F', dtype=np.float64)
+        cdef cnp.ndarray[cnp.float64_t, ndim=2] out = np.zeros((dims[0], dims[1]), order='F')
 
         # call getter
         acados_solver_common.ocp_nlp_get_at_stage(self.nlp_config, self.nlp_dims, self.nlp_solver, stage, field, <void *> out.data)

selfdrive/controls/lib/lateral_mpc_lib/lat_mpc.py

@@ -117,7 +117,7 @@ def gen_lat_mpc_solver():
 
 class LateralMpc():
   def __init__(self, x0=np.zeros(X_DIM)):
-    self.solver = AcadosOcpSolverFast('lat', N, EXPORT_DIR)
+    self.solver = AcadosOcpSolverFast('lat', N)
     self.reset(x0)
 
   def reset(self, x0=np.zeros(X_DIM)):

selfdrive/controls/lib/longitudinal_mpc_lib/long_mpc.py

@@ -24,7 +24,7 @@
 
 X_DIM = 3
 U_DIM = 1
-PARAM_DIM= 4
+PARAM_DIM = 4
 COST_E_DIM = 5
 COST_DIM = COST_E_DIM + 1
 CONSTR_DIM = 4
@@ -34,7 +34,7 @@
 V_EGO_COST = 0.
 A_EGO_COST = 0.
 J_EGO_COST = 5.0
-A_CHANGE_COST = .5
+A_CHANGE_COST = 200.
 DANGER_ZONE_COST = 100.
 CRASH_DISTANCE = .5
 LIMIT_COST = 1e6
@@ -136,7 +136,7 @@ def gen_long_mpc_solver():
            x_ego,
            v_ego,
            a_ego,
-           20*(a_ego - prev_a),
+           a_ego - prev_a,
            j_ego]
   ocp.model.cost_y_expr = vertcat(*costs)
   ocp.model.cost_y_expr_e = vertcat(*costs[:-1])
@@ -176,7 +176,7 @@ def gen_long_mpc_solver():
   ocp.solver_options.hessian_approx = 'GAUSS_NEWTON'
   ocp.solver_options.integrator_type = 'ERK'
   ocp.solver_options.nlp_solver_type = 'SQP_RTI'
-  ocp.solver_options.qp_solver_cond_N = N//4
+  ocp.solver_options.qp_solver_cond_N = 1
 
   # More iterations take too much time and less lead to inaccurate convergence in
   # some situations. Ideally we would run just 1 iteration to ensure fixed runtime.
@@ -197,7 +197,7 @@ def __init__(self, e2e=False):
     self.source = SOURCES[2]
 
   def reset(self):
-    self.solver = AcadosOcpSolverFast('long', N, EXPORT_DIR)
+    self.solver = AcadosOcpSolverFast('long', N)
     self.v_solution = np.zeros(N+1)
     self.a_solution = np.zeros(N+1)
     self.prev_a = np.array(self.a_solution)
@@ -215,7 +215,11 @@ def reset(self):
     self.status = False
     self.crash_cnt = 0.0
     self.solution_status = 0
+    # timers
     self.solve_time = 0.0
+    self.time_qp_solution = 0.0
+    self.time_linearization = 0.0
+    self.time_integrator = 0.0
     self.x0 = np.zeros(X_DIM)
     self.set_weights()
 
@@ -232,6 +236,7 @@ def set_weights_for_lead_policy(self, prev_accel_constraint=True):
     a_change_cost = A_CHANGE_COST if prev_accel_constraint else 0
     W = np.asfortranarray(np.diag([X_EGO_OBSTACLE_COST, X_EGO_COST, V_EGO_COST, A_EGO_COST, a_change_cost, J_EGO_COST]))
     for i in range(N):
+      # reduce the cost on (a-a_prev) later in the horizon.
       W[4,4] = a_change_cost * np.interp(T_IDXS[i], [0.0, 1.0, 2.0], [1.0, 1.0, 0.0])
       self.solver.cost_set(i, 'W', W)
     # Setting the slice without the copy make the array not contiguous,
@@ -257,14 +262,12 @@ def set_weights_for_xva_policy(self):
       self.solver.cost_set(i, 'Zl', Zl)
 
   def set_cur_state(self, v, a):
-    if abs(self.x0[1] - v) > 2.:
-      self.x0[1] = v
-      self.x0[2] = a
+    v_prev = self.x0[1]
+    self.x0[1] = v
+    self.x0[2] = a
+    if abs(v_prev - v) > 2.: # probably only helps if v < v_prev
       for i in range(0, N+1):
         self.solver.set(i, 'x', self.x0)
-    else:
-      self.x0[1] = v
-      self.x0[2] = a
 
   @staticmethod
   def extrapolate_lead(x_lead, v_lead, a_lead, a_lead_tau):
@@ -355,9 +358,17 @@ def run(self):
     self.solver.constraints_set(0, "lbx", self.x0)
     self.solver.constraints_set(0, "ubx", self.x0)
 
-    t = sec_since_boot()
     self.solution_status = self.solver.solve()
-    self.solve_time = sec_since_boot() - t
+    self.solve_time = float(self.solver.get_stats('time_tot')[0])
+    self.time_qp_solution = float(self.solver.get_stats('time_qp')[0])
+    self.time_linearization = float(self.solver.get_stats('time_lin')[0])
+    self.time_integrator = float(self.solver.get_stats('time_sim')[0])
+
+    # qp_iter = self.solver.get_stats('statistics')[-1][-1] # SQP_RTI specific
+    # print(f"long_mpc timings: tot {self.solve_time:.2e}, qp {self.time_qp_solution:.2e}, lin {self.time_linearization:.2e}, integrator {self.time_integrator:.2e}, qp_iter {qp_iter}")
+    # res = self.solver.get_residuals()
+    # print(f"long_mpc residuals: {res[0]:.2e}, {res[1]:.2e}, {res[2]:.2e}, {res[3]:.2e}")
+    # self.solver.print_statistics()
 
     for i in range(N+1):
       self.x_sol[i] = self.solver.get(i, 'x')
@@ -370,6 +381,7 @@ def run(self):
 
     self.prev_a = np.interp(T_IDXS + 0.05, T_IDXS, self.a_solution)
 
+    t = sec_since_boot()
     if self.solution_status != 0:
       if t > self.last_cloudlog_t + 5.0:
         self.last_cloudlog_t = t

selfdrive/controls/tests/test_following_distance.py

@@ -21,7 +21,7 @@ def run_following_distance_simulation(v_lead, t_end=100.0):
 
 
 class TestFollowingDistance(unittest.TestCase):
-  def test_following_distanc(self):
+  def test_following_distance(self):
     for speed in np.arange(0, 40, 5):
       print(f'Testing {speed} m/s')
       v_lead = float(speed)

selfdrive/test/longitudinal_maneuvers/test_longitudinal.py

@@ -9,7 +9,7 @@
 # TODO: make new FCW tests
 maneuvers = [
   Maneuver(
-    'approach stopped car at 20m/s',
+    'approach stopped car at 20m/s, initial distance: 120m',
     duration=20.,
     initial_speed=25.,
     lead_relevancy=True,
@@ -18,7 +18,7 @@
     breakpoints=[0., 1.],
   ),
   Maneuver(
-    'approach stopped car at 20m/s',
+    'approach stopped car at 20m/s, initial distance 90m',
     duration=20.,
     initial_speed=20.,
     lead_relevancy=True,
@@ -65,7 +65,7 @@
     breakpoints=[2., 2.01, 8.8],
   ),
   Maneuver(
-    "approach stopped car at 20m/s",
+    "approach stopped car at 20m/s, with prob_lead_values",
     duration=30.,
     initial_speed=20.,
     lead_relevancy=True,

selfdrive/test/process_replay/ref_commit

@@ -1 +1 @@
-67c8f283858998b75ac28879e1350a589a968e5d
+7e6072a254791e4106a15ecbf94c16f40d54b459