Merge pull request #111 from graphcore-research/combine-sparse-symmet…

…ric-with-eri-computation

Move ERI computation to the IPU, keeping sparse symmetric einsum functionality

pyscf_ipu/nanoDFT/compute_eri_utils.py

@@ -0,0 +1,396 @@
+# Copyright (c) 2023 Graphcore Ltd. All rights reserved.
+import numpy as np
+import jax 
+import jax.numpy as jnp 
+
+def reconstruct_ERI(ERI, nonzero_idx, N, sym=True):
+    i, j, k, l = nonzero_idx[:, 0], nonzero_idx[:, 1], nonzero_idx[:, 2], nonzero_idx[:, 3]
+    rec_ERI = np.zeros((N, N, N, N))
+    rec_ERI[i, j, k, l] = ERI[i, j, k, l]
+    if sym:
+        rec_ERI[j, i, k, l] = ERI[j, i, k, l]
+        rec_ERI[i, j, l, k] = ERI[i, j, l, k]
+        rec_ERI[j, i, l, k] = ERI[j, i, l, k]
+        rec_ERI[k, l, i, j] = ERI[k, l, i, j]
+        rec_ERI[k, l, j, i] = ERI[k, l, j, i]
+        rec_ERI[l, k, i, j] = ERI[l, k, i, j]
+        rec_ERI[l, k, j, i] = ERI[l, k, j, i]
+
+    return rec_ERI
+
+def inverse_permutation(a):
+    b = np.arange(a.shape[0])
+    b[a] = b.copy()
+    return b
+
+def get_shapes(input_ijkl, bas):
+    i_sh, j_sh, k_sh, l_sh  = input_ijkl[0]
+    BAS_SLOTS = 8
+    NPRIM_OF = 2
+    NCTR_OF = 3
+    ANG_OF = 1
+    GSHIFT = 4
+
+    i_prim  = bas.reshape(-1)[BAS_SLOTS*i_sh + NPRIM_OF]
+    j_prim  = bas.reshape(-1)[BAS_SLOTS*j_sh + NPRIM_OF]
+    k_prim  = bas.reshape(-1)[BAS_SLOTS*k_sh + NPRIM_OF]
+    l_prim  = bas.reshape(-1)[BAS_SLOTS*l_sh + NPRIM_OF]
+
+    i_ctr   = bas.reshape(-1)[BAS_SLOTS * i_sh + NCTR_OF]
+    j_ctr   = bas.reshape(-1)[BAS_SLOTS * j_sh + NCTR_OF]
+    k_ctr   = bas.reshape(-1)[BAS_SLOTS * k_sh + NCTR_OF]
+    l_ctr   = bas.reshape(-1)[BAS_SLOTS * l_sh + NCTR_OF]
+
+    i_l     = bas.reshape(-1)[BAS_SLOTS * i_sh + ANG_OF]
+    j_l     = bas.reshape(-1)[BAS_SLOTS * j_sh + ANG_OF]
+    k_l     = bas.reshape(-1)[BAS_SLOTS * k_sh + ANG_OF]
+    l_l     = bas.reshape(-1)[BAS_SLOTS * l_sh + ANG_OF]
+
+    nfi  = (i_l+1)*(i_l+2)/2
+    nfj  = (j_l+1)*(j_l+2)/2
+    nfk  = (k_l+1)*(k_l+2)/2
+    nfl  = (l_l+1)*(l_l+2)/2
+    nf = nfi * nfk * nfl * nfj;
+    n_comp = 1
+
+    nc = i_ctr * j_ctr * k_ctr * l_ctr;
+    lenl = nf * nc * n_comp;
+    lenk = nf * i_ctr * j_ctr * k_ctr * n_comp;
+    lenj = nf * i_ctr * j_ctr * n_comp;
+    leni = nf * i_ctr * n_comp;
+    len0 = nf * n_comp;
+
+    ng = [0, 0, 0, 0, 0, 1, 1, 1];
+
+    IINC=0
+    JINC=1
+    KINC=2
+    LINC=3
+
+    li_ceil = i_l + ng[IINC]
+    lj_ceil = j_l + ng[JINC]
+    lk_ceil = k_l + ng[KINC]
+    ll_ceil = l_l + ng[LINC]
+    nrys_roots = (li_ceil + lj_ceil + lk_ceil + ll_ceil)/2 + 1
+
+
+    ibase = li_ceil > lj_ceil;
+    kbase = lk_ceil > ll_ceil;
+    if (nrys_roots <= 2):
+        ibase = 0;
+        kbase = 0;
+    if (kbase) :
+        dlk = lk_ceil + ll_ceil + 1;
+        dll = ll_ceil + 1;
+    else:
+        dlk = lk_ceil + 1;
+        dll = lk_ceil + ll_ceil + 1;
+
+    if (ibase) :
+        dli = li_ceil + lj_ceil + 1;
+        dlj = lj_ceil + 1;
+    else :
+        dli = li_ceil + 1;
+        dlj = li_ceil + lj_ceil + 1;
+
+    g_stride_i = nrys_roots;
+    g_stride_k = nrys_roots * dli;
+    g_stride_l = nrys_roots * dli * dlk;
+    g_stride_j = nrys_roots * dli * dlk * dll;
+    g_size     = nrys_roots * dli * dlk * dll * dlj;
+    gbits        = ng[GSHIFT];
+    leng = g_size*3*((1<<gbits)+1);
+
+    len = leng + lenl + lenk + lenj + leni + len0;
+
+    di = i_l * 2 + 1;
+    dj = j_l * 2 + 1;
+    dk = k_l * 2 + 1;
+    dl = l_l * 2 + 1;
+
+    ni = (i_l*2+1) * i_ctr;
+    nj = (j_l*2+1) * j_ctr;
+    nk = (k_l*2+1) * k_ctr;
+    nl = (l_l*2+1) * l_ctr;
+    nfik = nfi * nfk;
+    nfikl = nfik * nfl;
+    dlj = dl * dj;
+    ofj = ni * dj;
+
+    ofk = ni * nj * dk;
+    ofl = ni * nj * nk * dl;
+    buflen = nfikl*dj;
+
+    return len, nf, buflen
+
+def prescreening(mol, itol, dtype=jnp.int16):
+    assert dtype in [jnp.int16, jnp.int32, jnp.uint32]
+
+    # get molecule info
+    bas, env   = mol._bas, mol._env
+    n_bas, N = bas.shape[0], mol.nao_nr()
+
+    tolerance = itol
+    print('computing ERI s8 to sample N*(N+1)/2 values... ', end='')
+    ERI_s8 = mol.intor('int2e_sph', aosym='s8')
+    print('done')
+
+    # find max value
+    I_max = 0
+    tril_idx = np.tril_indices(N)
+    for a, b in zip(tril_idx[0], tril_idx[1]):
+        index_ab_s8 = a*(a+1)//2 + b
+        index_s8 = index_ab_s8*(index_ab_s8+3)//2
+        abab = np.abs(ERI_s8[index_s8])
+        if abab > I_max:
+            I_max = abab
+
+    # collect candidate pairs for s8
+    considered_indices = []
+    tril_idx = np.tril_indices(N)
+    for a, b in zip(tril_idx[0], tril_idx[1]):
+        index_ab_s8 = a*(a+1)//2 + b
+        index_s8 = index_ab_s8*(index_ab_s8+3)//2
+        abab = np.abs(ERI_s8[index_s8])
+        if abab*I_max>=tolerance**2:
+            considered_indices.append((a, b)) # collect candidate pairs for s8
+
+    screened_indices_s8_4d = np.zeros(((len(considered_indices)*(len(considered_indices)+1)//2), 4), dtype=dtype)
+
+    # generate s8 indices
+    sid = 0
+    for index, ab in enumerate(considered_indices):
+        a, b = ab
+        for cd in considered_indices[index:]:
+            c, d = cd
+            screened_indices_s8_4d[sid, :] = (a, b, c, d)
+            sid += 1
+
+    return screened_indices_s8_4d
+
+def remove_ortho(arr, nonzero_pattern, output_size, dtype=jnp.int16):
+    assert dtype in [jnp.int16, jnp.int32, jnp.uint32]
+    if dtype == jnp.int16: reinterpret_dtype = jnp.float16
+    else: reinterpret_dtype = None
+
+    def condition(i, j, k, l):
+        return ~(nonzero_pattern[i] ^ nonzero_pattern[j]) ^ (nonzero_pattern[k] ^ nonzero_pattern[l])
+
+    def body_fun(carry, x):
+        results, counter = carry
+        x_reinterpret = jax.lax.bitcast_convert_type(x, dtype).astype(jnp.uint32)
+        i, j, k, l = x_reinterpret
+
+        def update_vals(carry):
+            res, count, v = carry
+            res = res.at[count].set(v)
+            count = count + 1
+            return res, count
+
+        results, counter = jax.lax.cond(condition(i, j, k, l), (results, counter, x), update_vals, (results, counter), lambda x: x)
+        return (results, counter), ()
+
+
+    init_results = jnp.zeros((output_size, arr.shape[1]), dtype=dtype)
+    init_count = jnp.array(0, dtype=jnp.int32)
+
+    if reinterpret_dtype is not None:
+        init_results = jax.lax.bitcast_convert_type(init_results, reinterpret_dtype)
+        arr = jax.lax.bitcast_convert_type(arr, reinterpret_dtype)
+
+    (final_results, _), _ = jax.lax.scan(body_fun, (init_results, init_count), arr)
+
+    final_results = jax.lax.bitcast_convert_type(final_results, dtype)
+
+    return final_results
+vmap_remove_ortho = jax.vmap(remove_ortho, in_axes=(0, None, None), out_axes=0)
+
+def prepare_integrals_2_inputs(mol, itol):
+    # Shapes/sizes.
+    atm, bas, env   = mol._atm, mol._bas, mol._env
+    n_atm, n_bas, N = atm.shape[0], bas.shape[0], mol.nao_nr()
+    ao_loc          = np.cumsum(np.concatenate([np.zeros(1), (bas[:,1]*2+1) * bas[:,3] ])).astype(np.int32)
+    n_ao_loc        = np.prod(ao_loc.shape)
+    shls_slice      = (0, n_bas, 0, n_bas, 0, n_bas, 0, n_bas)
+    shape           = [1, N, N, N, N]
+
+    # Initialize tensors for CPU libcint computation.
+    buf     = np.zeros(np.prod(shape)*2)
+    out     = np.zeros(shape)
+    eri     = np.zeros(shape).reshape(-1)
+    ipu_eri = np.zeros(shape).reshape(-1)
+
+    dtype = np.float32 #hardcoded
+    buf, out, eri, ipu_eri, env = buf.astype(dtype), out.astype(dtype), eri.astype(dtype), ipu_eri.astype(dtype), env.astype(dtype)
+
+    # The padded shape used to store output from all tiles.
+    n_buf, n_eri, n_env = 81, 81, np.prod(env.shape)
+    if mol.basis == "6-31G*": # has known error; please open github issue if you want to use 6-31G*
+            n_buf = 5**4
+            n_eri = 5**4
+
+    # Compute how many distinct integrals after 8x symmetry.
+    num_calls = 0
+    for i in range(n_bas):
+        for j in range(i+1):
+            for k in range(i, n_bas):
+                for l in range(k+1):
+                    # almost all 8-fold symmetry rules (except horizontal fade)
+                    num_calls+=1
+    print('num_calls', num_calls)
+
+    # Input/outputs for calling the IPU vertex.
+    input_ijkl   = np.zeros((num_calls, 4),     dtype=np.int32)
+    cpu_output   = np.zeros((num_calls, n_eri), dtype=np.float32)
+    output_sizes = np.zeros((num_calls, 5))
+
+    USE_TOLERANCE_THRESHOLD = True
+    screened_indices_s8_4d = []
+
+    if USE_TOLERANCE_THRESHOLD:
+        tolerance = itol
+        print('computing ERI s8 to sample N*(N+1)/2 values... ', end='')
+        ERI_s8 = mol.intor('int2e_sph', aosym='s8')
+        print('done')
+
+        # sample symmetry pattern and do safety check
+        # if N % 2 == 0:
+        #     nonzero_seed = ERI[N-1, N-1, :N//2, 0] != 0
+        #     nonzero_seed = np.concatenate([nonzero_seed, np.flip(nonzero_seed)])
+        # else:
+        #     nonzero_seed = ERI[N-1, N-1, :(N+1)//2, 0] != 0
+        #     nonzero_seed = np.concatenate([nonzero_seed, np.flip(nonzero_seed[:-1])])
+        # if not np.equal(nonzero_seed, ERI[N-1, N-1, :, 0]!=0).all():
+        #     print('# -------------------------------------------------------------- #')
+        #     print('# WARNING: Experimental symmetry pattern sample is inconsistent. #')
+        #     print('pred', nonzero_seed)
+        #     print('real', ERI[N-1, N-1, :, 0]!=0)
+        #     print('# -------------------------------------------------------------- #')
+
+    # hardcoded symmetry pattern
+    sym_pattern =  np.array([(i+3)%5!=0 for i in range(N)])
+    nonzero_seed = sym_pattern
+
+    if USE_TOLERANCE_THRESHOLD:
+        # find max value
+        I_max = 0
+        tril_idx = np.tril_indices(N)
+        for a, b in zip(tril_idx[0], tril_idx[1]):
+            index_ab_s8 = a*(a+1)//2 + b
+            index_s8 = index_ab_s8*(index_ab_s8+3)//2
+            abab = np.abs(ERI_s8[index_s8])
+            if abab > I_max:
+                I_max = abab
+
+   # collect candidate pairs for s8
+    considered_indices = []
+    tril_idx = np.tril_indices(N)
+    for a, b in zip(tril_idx[0], tril_idx[1]):
+        if USE_TOLERANCE_THRESHOLD:
+            index_ab_s8 = a*(a+1)//2 + b
+            index_s8 = index_ab_s8*(index_ab_s8+3)//2
+            abab = np.abs(ERI_s8[index_s8])
+            if abab*I_max>=tolerance**2:
+                considered_indices.append((a, b)) # collect candidate pairs for s8
+        else:
+            considered_indices.append((a, b)) # collect candidate pairs for s8
+    considered_indices = set(considered_indices)
+
+    print('n_bas', n_bas)
+    print('ao_loc', ao_loc)
+
+    # Fill input_ijkl and output_sizes with the necessary indices.
+    n_items = 0
+    n_all_integrals = 0
+    n_new_integrals = 0
+    for i in range(n_bas):
+        for j in range(i+1):
+            for k in range(i, n_bas):
+                for l in range(k+1):
+                    di = ao_loc[i+1] - ao_loc[i]
+                    dj = ao_loc[j+1] - ao_loc[j]
+                    dk = ao_loc[k+1] - ao_loc[k]
+                    dl = ao_loc[l+1] - ao_loc[l]
+
+                    ia, ib = ao_loc[i], ao_loc[i+1]
+                    ja, jb = ao_loc[j], ao_loc[j+1]
+                    ka, kb = ao_loc[k], ao_loc[k+1]
+                    la, lb = ao_loc[l], ao_loc[l+1]
+
+                    n_all_integrals += di*dj*dk*dl
+
+                    found_nonzero = False
+                    # check i,j boxes
+                    for bi in range(ia, ib):
+                        for bj in range(ja, jb):
+                            if (bi, bj) in considered_indices: # if ij box is considered
+                                # check if kl pairs are considered
+                                for bk in range(ka, kb):
+                                    if bk>=bi: # apply symmetry - tril fade vertical
+                                        mla = la
+                                        if bk == bi:
+                                            mla = max(bj, la) # apply symmetry - tril fade horizontal
+                                        for bl in range(mla, lb):
+                                            if (bk, bl) in considered_indices:
+                                                # apply grid pattern to find final nonzeros
+                                                if ~(nonzero_seed[bi] ^ nonzero_seed[bj]) ^ (nonzero_seed[bk] ^ nonzero_seed[bl]):
+                                                    found_nonzero = True
+                                                    break
+                                    if found_nonzero: break
+                            if found_nonzero: break
+                        if found_nonzero: break
+                    if not found_nonzero: continue 
+
+                    n_new_integrals += di*dj*dk*dl
+
+                    input_ijkl[n_items] = [i, j, k, l]
+
+                    output_sizes[n_items] = [di, dj, dk, dl, di*dj*dk*dl]
+
+                    n_items += 1
+    print('!!! saved', num_calls - n_items, 'calls i.e.', num_calls, '->', n_items)
+    print('!!! saved', n_all_integrals - n_new_integrals, 'integrals i.e.', n_all_integrals, '->', n_new_integrals)
+
+    num_calls = n_items
+    input_ijkl = input_ijkl[:num_calls, :]
+    cpu_output = cpu_output[:num_calls, :]
+    output_sizes = output_sizes[:num_calls, :]
+
+    # Prepare IPU inputs.
+    # Merge all int/float inputs in seperate arrays.
+    input_floats = env.reshape(1, -1)
+    input_ints   = np.zeros((1, 6+n_ao_loc +n_atm*6+n_bas*8), dtype=np.int32)
+    start, stop = 0, 6
+    input_ints[:, start:stop] = np.array( [n_eri, n_buf, n_atm, n_bas, n_env, n_ao_loc] )
+    start, stop = start+6, stop+n_ao_loc
+    input_ints[:, start:stop] = ao_loc.reshape(-1)
+    start, stop = start+n_ao_loc, stop + n_atm*6
+    input_ints[:, start:stop] = atm.reshape(-1)
+    start, stop = start+n_atm*6, stop + n_bas*8
+    input_ints[:, start:stop] = bas.reshape(-1)
+
+    sizes, counts  = np.unique(output_sizes[:, -1], return_counts=True)
+    sizes, counts = sizes.astype(np.int32), counts.astype(np.int32)
+
+    indxs               = np.argsort(output_sizes[:, -1])
+    sorted_output_sizes = output_sizes[indxs]
+    input_ijkl          = input_ijkl[indxs]
+
+    sizes, counts  = np.unique(output_sizes[:, -1], return_counts=True)
+    sizes, counts = sizes.astype(np.int32), counts.astype(np.int32)
+    start_index = 0
+    inputs = []
+    shapes = []
+    for i, (size, count) in enumerate(zip(sizes, counts)):
+        a = input_ijkl[start_index: start_index+count]
+        tuples = tuple(map(tuple, a))
+        inputs.append(tuples)
+        start_index += count
+
+    tuple_ijkl = tuple(inputs)
+    input_ijkl = inputs
+
+    for i in range(len(sizes)):
+        shapes.append(get_shapes(input_ijkl[i], bas))
+
+    return input_floats, input_ints, tuple_ijkl, tuple(shapes), tuple(sizes.tolist()), counts.tolist(), ao_loc, num_calls