Fix implicit type conversion error and enable FP32 training

rapidsai · Nov 28, 2020 · bda29cc · bda29cc
1 parent 0bf55d4
commit bda29cc
Showing 1 changed file with 8 additions and 6 deletions.
diff --git a/python/cuml/experimental/linear_model/lars.pyx b/python/cuml/experimental/linear_model/lars.pyx
@@ -161,15 +161,16 @@ class Lars(Base, RegressorMixin):
         """ Remove mean and scale each feature column. """
         x_mean = cp.zeros(self.n_cols, dtype=self.dtype)
         x_scale = cp.ones(self.n_cols, dtype=self.dtype)
-        y_mean = 0.0
+        y_mean = self.dtype.type(0.0)
         X = cp.asarray(X_m)
         y = cp.asarray(y_m)
         if self.fit_intercept:
             y_mean = cp.mean(y)
             y = y - y_mean
             if self.normalize:
                 x_mean = cp.mean(X, axis=0)
-                x_scale = cp.sqrt(cp.var(X, axis=0) * X.shape[0])
+                x_scale = cp.sqrt(cp.var(X, axis=0) *
+                                  self.dtype.type(X.shape[0]))
                 x_scale[x_scale==0] = 1
                 X = (X - x_mean) / x_scale
         return X, y, x_mean, x_scale, y_mean
@@ -204,10 +205,12 @@ class Lars(Base, RegressorMixin):
     def _fit_cpp(self, X, y, Gram, x_scale):
         """ Fit lars model using cpp solver"""
         cdef handle_t* handle_ = <handle_t*><size_t>self.handle.getHandle()
-        X_m, _, _, _ = input_to_cuml_array(X, order='F')
+        X_m, _, _, _ = input_to_cuml_array(X, check_dtype=self.dtype,
+                                           order='F')
         cdef uintptr_t X_ptr = X_m.ptr
         cdef int n_rows = X.shape[0]
-        cdef uintptr_t y_ptr = input_to_cuml_array(y).array.ptr
+        cdef uintptr_t y_ptr = \
+            input_to_cuml_array(y, check_dtype=self.dtype).array.ptr
         cdef int max_iter = self.n_nonzero_coefs
         self.beta_ = CumlArray.zeros(max_iter, dtype=self.dtype)
         cdef uintptr_t beta_ptr = self.beta_.ptr
@@ -269,8 +272,7 @@ class Lars(Base, RegressorMixin):
         self._set_output_type(X)
 
         X_m, n_rows, self.n_cols, self.dtype = input_to_cuml_array(
-            X, check_dtype=[np.float32, np.float64], order='F',
-            convert_to_dtype=np.float64)
+            X, check_dtype=[np.float32, np.float64], order='F')
 
         conv_dtype = self.dtype if convert_dtype else None
         y_m, _, _, _ = input_to_cuml_array(