Use vmap throughout opt savings 2 (#155)

* misc

* misc

* misc

Author: jstac

lectures/opt_savings_1.md

@@ -120,11 +120,15 @@ def create_consumption_model(R=1.01,                    # Gross interest rate
     A function that takes in parameters and returns parameters and grids 
     for the optimal savings problem.
     """
+    # Build grids and transition probabilities
     w_grid = np.linspace(w_min, w_max, w_size)
     mc = qe.tauchen(n=y_size, rho=ρ, sigma=ν)
     y_grid, Q = np.exp(mc.state_values), mc.P
+    # Pack and return
+    params = β, R, γ
     sizes = w_size, y_size
-    return (β, R, γ), sizes, (w_grid, y_grid, Q)
+    arrays = w_grid, y_grid, Q
+    return params, sizes, arrays
 ```
 
 (The function returns sizes of arrays because we use them later to help

lectures/opt_savings_2.md

@@ -100,121 +100,155 @@ def create_consumption_model(R=1.01,                    # Gross interest rate
     A function that takes in parameters and returns parameters and grids 
     for the optimal savings problem.
     """
+    # Build grids and transition probabilities
     w_grid = jnp.linspace(w_min, w_max, w_size)
     mc = qe.tauchen(n=y_size, rho=ρ, sigma=ν)
-    y_grid, Q = jnp.exp(mc.state_values), jax.device_put(mc.P)
+    y_grid, Q = jnp.exp(mc.state_values), mc.P
+    # Pack and return
+    params = β, R, γ
     sizes = w_size, y_size
-    return (β, R, γ), sizes, (w_grid, y_grid, Q)
+    arrays = w_grid, y_grid, jnp.array(Q)
+    return params, sizes, arrays
 ```
 
 Here's the right hand side of the Bellman equation:
 
 ```{code-cell} ipython3
-def B(v, params, sizes, arrays):
+def _B(v, params, arrays, i, j, ip):
     """
-    A vectorized version of the right-hand side of the Bellman equation
-    (before maximization), which is a 3D array representing
+    The right-hand side of the Bellman equation before maximization, which takes
+    the form
 
         B(w, y, w′) = u(Rw + y - w′) + β Σ_y′ v(w′, y′) Q(y, y′)
 
-    for all (w, y, w′).
+    The indices are (i, j, ip) -> (w, y, w′).
     """
-
-    # Unpack
     β, R, γ = params
-    w_size, y_size = sizes
     w_grid, y_grid, Q = arrays
-
-    # Compute current rewards r(w, y, wp) as array r[i, j, ip]
-    w  = jnp.reshape(w_grid, (w_size, 1, 1))    # w[i]   ->  w[i, j, ip]
-    y  = jnp.reshape(y_grid, (1, y_size, 1))    # z[j]   ->  z[i, j, ip]
-    wp = jnp.reshape(w_grid, (1, 1, w_size))    # wp[ip] -> wp[i, j, ip]
+    w, y, wp  = w_grid[i], y_grid[j], w_grid[ip]
     c = R * w + y - wp
+    EV = jnp.sum(v[ip, :] * Q[j, :]) 
+    return jnp.where(c > 0, c**(1-γ)/(1-γ) + β * EV, -jnp.inf)
+```
 
-    # Calculate continuation rewards at all combinations of (w, y, wp)
-    v = jnp.reshape(v, (1, 1, w_size, y_size))  # v[ip, jp] -> v[i, j, ip, jp]
-    Q = jnp.reshape(Q, (1, y_size, 1, y_size))  # Q[j, jp]  -> Q[i, j, ip, jp]
-    EV = jnp.sum(v * Q, axis=3)                 # sum over last index jp
+Now we successively apply `vmap` to vectorize $B$ by simulating nested loops.
 
-    # Compute the right-hand side of the Bellman equation
-    return jnp.where(c > 0, c**(1-γ)/(1-γ) + β * EV, -jnp.inf)
+```{code-cell} ipython3
+B_1    = jax.vmap(_B,  in_axes=(None, None, None, None, None, 0))
+B_2    = jax.vmap(B_1, in_axes=(None, None, None, None, 0,    None))
+B_vmap = jax.vmap(B_2, in_axes=(None, None, None, 0,    None, None))
+```
+
+Here's a fully vectorized version of $B$.
+
+```{code-cell} ipython3
+def B(v, params, sizes, arrays):
+    w_size, y_size = sizes
+    w_indices, y_indices = jnp.arange(w_size), jnp.arange(y_size)
+    return B_vmap(v, params, arrays, w_indices, y_indices, w_indices)
+
+B = jax.jit(B, static_argnums=(2,))
 ```
 
 ## Operators
 
+
+Here's the Bellman operator $T$
+
+```{code-cell} ipython3
+def T(v, params, sizes, arrays):
+    "The Bellman operator."
+    return jnp.max(B(v, params, sizes, arrays), axis=-1)
+
+T = jax.jit(T, static_argnums=(2,))
+```
+
+The next function computes a $v$-greedy policy given $v$
+
+```{code-cell} ipython3
+def get_greedy(v, params, sizes, arrays):
+    "Computes a v-greedy policy, returned as a set of indices."
+    return jnp.argmax(B(v, params, sizes, arrays), axis=-1)
+
+get_greedy = jax.jit(get_greedy, static_argnums=(2,))
+
+```
+
 We define a function to compute the current rewards $r_\sigma$ given policy $\sigma$,
 which is defined as the vector
 
 $$
-r_\sigma(w, y) := r(w, y, \sigma(w, y)) 
+    r_\sigma(w, y) := r(w, y, \sigma(w, y)) 
 $$
 
 ```{code-cell} ipython3
-def compute_r_σ(σ, params, sizes, arrays):
+def _compute_r_σ(σ, params, arrays, i, j):
     """
-    Compute the array r_σ[i, j] = r[i, j, σ[i, j]], which gives current
-    rewards given policy σ.
+    With indices (i, j) -> (w, y) and wp = σ[i, j], compute 
+        
+        r_σ[i, j] = u(Rw + y - wp)   
+
+    which gives current rewards under policy σ.
     """
 
     # Unpack model
     β, R, γ = params
-    w_size, y_size = sizes
     w_grid, y_grid, Q = arrays
-
     # Compute r_σ[i, j]
-    w = jnp.reshape(w_grid, (w_size, 1))
-    y = jnp.reshape(y_grid, (1, y_size))
-    wp = w_grid[σ]
+    w, y, wp = w_grid[i], y_grid[j], w_grid[σ[i, j]]
     c = R * w + y - wp
     r_σ = c**(1-γ)/(1-γ)
 
     return r_σ
 ```
 
-Now we define the policy operator $T_\sigma$
+Now we successively apply `vmap` to simulate nested loops.
 
 ```{code-cell} ipython3
-def T_σ(v, σ, params, sizes, arrays):
-    "The σ-policy operator."
+r_1 = jax.vmap(_compute_r_σ,  in_axes=(None, None, None, None, 0))
+r_σ_vmap = jax.vmap(r_1,      in_axes=(None, None, None, 0,    None))
+```
 
-    # Unpack model
-    β, R, γ = params
+Here's a fully vectorized version of $r_\sigma$.
+
+```{code-cell} ipython3
+def compute_r_σ(σ, params, sizes, arrays):
     w_size, y_size = sizes
-    w_grid, y_grid, Q = arrays
+    w_indices, y_indices = jnp.arange(w_size), jnp.arange(y_size)
+    return r_σ_vmap(σ, params, arrays, w_indices, y_indices)
 
-    r_σ = compute_r_σ(σ, params, sizes, arrays)
+compute_r_σ = jax.jit(compute_r_σ, static_argnums=(2,))
+```
+
+Now we define the policy operator $T_\sigma$ going through similar steps
 
-    # Compute the array v[σ[i, j], jp]
-    yp_idx = jnp.arange(y_size)
-    yp_idx = jnp.reshape(yp_idx, (1, 1, y_size))
-    σ = jnp.reshape(σ, (w_size, y_size, 1))
-    V = v[σ, yp_idx]
+```{code-cell} ipython3
+def _T_σ(v, σ, params, arrays, i, j):
+    "The σ-policy operator."
 
-    # Convert Q[j, jp] to Q[i, j, jp]
-    Q = jnp.reshape(Q, (1, y_size, y_size))
+    # Unpack model
+    β, R, γ = params
+    w_grid, y_grid, Q = arrays
 
+    r_σ  = _compute_r_σ(σ, params, arrays, i, j)
     # Calculate the expected sum Σ_jp v[σ[i, j], jp] * Q[i, j, jp]
-    EV = jnp.sum(V * Q, axis=2)
+    EV = jnp.sum(v[σ[i, j], :] * Q[j, :])
 
     return r_σ + β * EV
-```
 
-and the Bellman operator $T$
 
-```{code-cell} ipython3
-def T(v, params, sizes, arrays):
-    "The Bellman operator."
-    return jnp.max(B(v, params, sizes, arrays), axis=2)
-```
+T_1 = jax.vmap(_T_σ,      in_axes=(None, None, None, None, None, 0))
+T_σ_vmap = jax.vmap(T_1,  in_axes=(None, None, None, None, 0,    None))
 
-The next function computes a $v$-greedy policy given $v$
+def T_σ(v, σ, params, sizes, arrays):
+    w_size, y_size = sizes
+    w_indices, y_indices = jnp.arange(w_size), jnp.arange(y_size)
+    return T_σ_vmap(v, σ, params, arrays, w_indices, y_indices)
 
-```{code-cell} ipython3
-def get_greedy(v, params, sizes, arrays):
-    "Computes a v-greedy policy, returned as a set of indices."
-    return jnp.argmax(B(v, params, sizes, arrays), axis=2)
+T_σ = jax.jit(T_σ, static_argnums=(3,))
 ```
 
+
 The function below computes the value $v_\sigma$ of following policy $\sigma$.
 
 This lifetime value is a function $v_\sigma$ that satisfies
@@ -248,29 +282,28 @@ JAX allows us to solve linear systems defined in terms of operators; the first
 step is to define the function $L_{\sigma}$.
 
 ```{code-cell} ipython3
-def L_σ(v, σ, params, sizes, arrays):
+def _L_σ(v, σ, params, arrays, i, j):
     """
     Here we set up the linear map v -> L_σ v, where 
 
         (L_σ v)(w, y) = v(w, y) - β Σ_y′ v(σ(w, y), y′) Q(y, y′)
 
     """
-
+    # Unpack
     β, R, γ = params
-    w_size, y_size = sizes
     w_grid, y_grid, Q = arrays
+    # Compute and return v[i, j] - β Σ_jp v[σ[i, j], jp] * Q[j, jp]
+    return v[i, j]  - β * jnp.sum(v[σ[i, j], :] * Q[j, :])
 
-    # Set up the array v[σ[i, j], jp]
-    zp_idx = jnp.arange(y_size)
-    zp_idx = jnp.reshape(zp_idx, (1, 1, y_size))
-    σ = jnp.reshape(σ, (w_size, y_size, 1))
-    V = v[σ, zp_idx]
+L_1 = jax.vmap(_L_σ,      in_axes=(None, None, None, None, None, 0))
+L_σ_vmap = jax.vmap(L_1,  in_axes=(None, None, None, None, 0,    None))
 
-    # Expand Q[j, jp] to Q[i, j, jp]
-    Q = jnp.reshape(Q, (1, y_size, y_size))
+def L_σ(v, σ, params, sizes, arrays):
+    w_size, y_size = sizes
+    w_indices, y_indices = jnp.arange(w_size), jnp.arange(y_size)
+    return L_σ_vmap(v, σ, params, arrays, w_indices, y_indices)
 
-    # Compute and return v[i, j] - β Σ_jp v[σ[i, j], jp] * Q[j, jp]
-    return v - β * jnp.sum(V * Q, axis=2)
+L_σ = jax.jit(L_σ, static_argnums=(3,))
 ```
 
 Now we can define a function to compute $v_{\sigma}$
@@ -290,20 +323,16 @@ def get_value(σ, params, sizes, arrays):
     partial_L_σ = lambda v: L_σ(v, σ, params, sizes, arrays)
 
     return jax.scipy.sparse.linalg.bicgstab(partial_L_σ, r_σ)[0]
-```
-
-## JIT compiled versions
 
-```{code-cell} ipython3
-B = jax.jit(B, static_argnums=(2,))
-compute_r_σ = jax.jit(compute_r_σ, static_argnums=(2,))
-T = jax.jit(T, static_argnums=(2,))
-get_greedy = jax.jit(get_greedy, static_argnums=(2,))
 get_value = jax.jit(get_value, static_argnums=(2,))
-T_σ = jax.jit(T_σ, static_argnums=(3,))
-L_σ = jax.jit(L_σ, static_argnums=(3,))
+
 ```
 
+
+
+## Iteration
+
+
 We use successive approximation for VFI.
 
 ```{code-cell} ipython3