model-compression-777

Pytorch Sparse Model Compression

This package provides several functions related to sparse weight compression and size evaluation for pytorch models.

Installation

pip install model-compression-777 -i https://pypi.org/simple

Usage

Important note: to use this, you must first prune your model, for which the methods vary from model to model. Model compression is only efficient if the weights are very sparse.

All functions contain docstrings. They are listed here for convenience (along with some notes).

Model Loading

load_pruned(path):

load a pruned pytorch state file by applying weight mask. returns a dict where the keys are the array names (e.g. encoder.0.2.bias)

load_unpruned(path):

loads pytorch state file into a dict.

Compression / Decompression

to_relative_csr(m, index_bits):

converts m into the column-relative CSR format. m must be a 1D or 2D NUMPY array; use .numpy() on pytorch tensors first. index_bits is the bit width of relative column spacing; try around 2~8. returns (nonzero values (v), column offsets (c), row indices (r)).

from_relative_csr(v, c, r, width):

utility function that converts CSR format back into normal format. the purpose of this function is mostly for testing; note that using this for sparse matrix operations can be very inefficient.

compress(vec, data_bits=4, fc_idx_bits=4, conv_idx_bits=5, def_idx_bits=4, row_bits=32):

compresses common weights. for convolution, when kernel is size 1, it is seen as a fully-connected layer. returns a tuple containing compressed format and size of the compressed weight in bytes.

Model Size Evaluation

get_csr_size_in_bytes(v, c, r, v_width, c_width, r_width):

returns sum of array sizes, where each array element corresponds to the size input (x_width) in bits

print_weight_info(weights_normalized):

prints some info about a weights dict. for each weight matrix, this prints its min, max, total number of elements, and sparsity. returns global sparsity.

Example

First, we load the weights dictionary from a state file.

weights = load_pruned("outputs/exp_dset=verso_ssd,prune_preset=verso2/best.th")

We get some basic info about these weights.

print_weight_info(weights)

encoder.0.0.bias 	-0.1661		0.3005		48
encoder.0.0.weight 	-0.4365		0.3742		384
			52.3438%
encoder.0.2.bias 	-0.4893		0.8613		96
encoder.0.2.weight 	-0.7753		1.5334		4608
			23.7413%
encoder.1.0.bias 	-0.0760		0.2138		96
encoder.1.0.weight 	-0.6905		0.7302		36864
			7.7474%
encoder.1.2.bias 	-0.4025		1.1158		192
encoder.1.2.weight 	-0.9252		0.9893		18432
			15.1204%
encoder.2.0.bias 	-0.1144		0.2732		192
encoder.2.0.weight 	-0.6058		0.6952		147456
			8.4371%
encoder.2.2.bias 	-0.3144		1.1645		384
encoder.2.2.weight 	-0.7645		0.8168		73728
			12.1487%
encoder.3.0.bias 	-0.2279		0.3234		384
encoder.3.0.weight 	-0.7191		0.6821		589824
			9.6261%
encoder.3.2.bias 	-0.3796		0.6909		768
encoder.3.2.weight 	-0.9781		0.9608		294912
			14.5667%
encoder.4.0.bias 	-0.2516		0.1453		768
encoder.4.0.weight 	-0.6874		0.8081		2359296
			10.0274%
encoder.4.2.bias 	-0.2767		0.3749		1536
encoder.4.2.weight 	-1.0705		0.9233		1179648
			14.0047%
decoder.0.0.bias 	-0.4838		0.4492		1536
decoder.0.0.weight 	-1.0980		1.1172		1179648
			12.3450%
decoder.0.2.bias 	-0.5981		0.9222		384
decoder.0.2.weight 	-0.9818		0.7783		2359296
			6.3385%
decoder.1.0.bias 	-0.7151		0.4123		768
decoder.1.0.weight 	-1.4589		1.4236		294912
			9.6459%
decoder.1.2.bias 	-0.4267		0.8702		192
decoder.1.2.weight 	-1.0842		1.5031		589824
			5.3640%
decoder.2.0.bias 	-1.0682		0.7818		384
decoder.2.0.weight 	-1.3639		1.6044		73728
			11.8056%
decoder.2.2.bias 	-0.0633		0.5356		96
decoder.2.2.weight 	-1.9591		1.6501		147456
			6.6155%
decoder.3.0.bias 	-1.1293		0.3498		192
decoder.3.0.weight 	-1.3212		1.9597		18432
			18.1532%
decoder.3.2.bias 	-0.0459		0.1919		48
decoder.3.2.weight 	-0.8982		0.6510		36864
			9.1092%
decoder.4.0.bias 	-0.3222		0.4334		96
decoder.4.0.weight 	-0.9638		1.0617		4608
			24.0885%
decoder.4.2.bias 	-0.0128		-0.0128		1
decoder.4.2.weight 	-0.3609		0.3434		384
			48.4375%
lstm.lstm.bias_ih_l0 	-0.1892		0.5374		3072
lstm.lstm.bias_hh_l0 	-0.2168		0.5108		3072
lstm.lstm.bias_ih_l1 	-0.1490		0.4970		3072
lstm.lstm.bias_hh_l1 	-0.1674		0.4793		3072
lstm.lstm.weight_ih_l0 	-0.8711		0.8251		2359296
			8.8214%
lstm.lstm.weight_ih_l1 	-0.9791		1.0533		2359296
			8.8558%
lstm.lstm.weight_hh_l0 	-0.7069		0.7139		2359296
			7.3889%
lstm.lstm.weight_hh_l1 	-0.8010		0.8255		2359296
			6.0804%
0.0873541874651943
0.9126458125348057

This is a unet architecture with 5 levels of encoding and decoding, which are conv1d layers. At the center is 2 layers of LSTM. After pruning, the model has a global sparsity of 91.26%, indicating only 8.74% of the values are nonzero. There are approximately 18M parameters.

We then try to compress all of the weights, excluding biases. In the end, the compressed size is printed.

compressed = {}
size = 0
for name, vec in weights.items():
    if name.find("activation") > -1: continue
    # print(name, end="\t")
    vec = vec.numpy()
    c, s = compress(vec, fc_idx_bits=4, conv_idx_bits=5, def_idx_bits=5)
    compressed[name] = c
    size += s
print("\n%.2f KiB" % (size / 1024,))

2108.52 KiB

This is approximately 1 / 9 of the original model, if the original model were to be stored in 8 bits.

Now, we try to run inference on this set of compressed weights. First, we add some extra functions for the decoder and encoder.

def beco_matmul(At, B):
    """
    high level simulation of beco matrix multiply behavior. note that beco
    assumes the first matrix is stored in memory transposed, and both matrices
    (when stored) need to have a multiple of 4 as its width.
    """
    return (At.T @ B)

def convolve(W, m, bias, stride):
    """
    efficient convolution algorithm with a batch size B on a NORMAL weight matrix.
    this is to 1. be used for testing and
               2. demonstrate the underlying principles of sparse convolution.
    """
    # assuming K > stride
    B = 8 # batch size, number of rows to multiply every time

    out_ch, K, in_ch = W.shape
    
    in_len = m.shape[0]
    out = np.zeros((((in_len - stride) // (K - stride)) if K > stride else in_len // K, out_ch))
    
    for o in range(out_ch):
        queue = []
        out_idx = 0
    
        for row in range(0, in_len, B):
            curr_matrix = m[row : row + B]
            c = beco_matmul(curr_matrix.T, W[o].T)
            assert(len(c.shape) == 2)
            queue.extend(c)
            
            while (len(queue) >= K):
                s = sum([queue[k][k] for k in range(K)]) # take the kth element of the kth row in queue
                queue = queue[stride:]
                out[out_idx][o], out_idx = s + bias[o], out_idx + 1
    
    return out

def convolve_sparse(W, bias, m, in_len, out_ch, in_ch, k, stride):
    """
    performs convolution on input matrix, where each row is a channel.
    weights W is an array of CSR matrix (v, c, r) pairs, each corresponding to a output channel.
    returns an output matrix where each row is a channel, and is thus chainable.
    
    pseudocode: (only for reference, might not completely match code)
    
    C <- zeros(out_ch*out_len)
    for each output channel:
        W <- weights matrix corr. to this output channel (dim=k*in_ch)
        for each batch of L rows in W:
            At <- zeros(0*in_len)
            B <- zeros(0*L)
            for each l of the L rows keep a head pointer p_l, and current column c_l
            while we have not exhausted all head pointers:
                find minimum col num of all heads
                reconstruct column at that index from W
                transpose this column and vstack it to matrix B
                pick out corr. row in input matrix and vstack to At
                increment p_l and c_l for all rows that have the minimum col num
            # at this point, should have At=(x*in_len) and B=(x*L), where x is nonzero count
            call beco matmul to obtain U_l = At.T @ B (dim=in_len*L)
        hstack all U_l's to form matrix U (dim=in_len*k)
        for i = 0 to in_len step stride:
            s <- sum of U[i, 0] to U[i + k, k]
            C[out channel idx][j++] <- s
    return C
            
    TODO: support batch sizes that are not the entire length of input
    NOTE TO SELF: when transcribing to C, fix & unroll L
    """
    L = 4    
    out_len = ((in_len - stride) // (k - stride)) if k > stride else in_len // k
    # C = np.zeros((out_len, out_ch)) # calloc
    C = np.zeros((out_ch, out_len)) # calloc
    for o in range(out_ch):
        v, c, r = W[o] # v's, c's, r's are stored as separate arrays
        U_l = []
        for l in range(0, k, L):
            At = np.zeros((0, in_len)) # prealloc as in_ch * in_len
            B = np.zeros((0, L)) # prealloc as in_ch * L
            ll = min(k - l, L) # tail condition can be written separately
            p_l = [r[l + i] for i in range(ll)] # need to be uint32; length prealloc to L
            c_l = [c[p_l[i]] - 1 if p_l[i] < r[l + i + 1] else 9999 for i in range(ll)] # index starts at -1
            while np.any([p_l[i] < r[l + i + 1] for i in range(ll)]): # a merge-sort-like operation
                min_val = 9999
                for i in range(ll):
                    if p_l[i] < r[l + i + 1]:
                        min_val = min(min_val, c_l[i])
                rc = np.zeros((L,)) # likely reusable with memset 0
                for i in range(ll):
                    if (c_l[i] == min_val):
                        rc[i] = v[p_l[i]] # fill nonzero to reconstructed vec
                        p_l[i] += 1 # advance to next nonzero
                        if p_l[i] < r[l + i + 1]: c_l[i] += c[p_l[i]]
                B = np.vstack([B, rc]) # in C should just be memcpy
                At = np.vstack([At, m[min_val]]) # add corr. input row
                # At = np.vstack([At, m[:, min_val]]) # would probably require a transpose step
            U_l.append(beco_matmul(At, B))
        U = np.hstack(U_l) # in C skip this step and modify convolution sampling instead

        j = 0
        for i in range(0, in_len - k + 1, stride): # convolve with stride
            s = np.sum([U[i + kk, kk] for kk in range(k)])
            C[o][j], j = s + bias[o], j + 1

    return C # obviously skipped in C since array writes are in-place
    
def fc_sparse(W, bias, m, out_ch, in_len):
    """
    calculates fully connected layer on input matrix, where each row is a channel.
    weights W is a tuple (v, c, r).
    returns an output matrix where each column is a channel, and is thus chainable.
    
    pseudocode:
    
    for each batch of L rows in weight matrix W (dim=out_ch*in_ch):
        At <- zeros(0*L)
        B <- zeros(0*in_len)
        for each l of the L rows keep a head pointer p_l, and current column c_l
        while we have not exhausted all head pointers:
            find minimum col num of all heads
            reconstruct column at that index from the current L-row submatrix of W
            transpose this column and vstack it to matrix At
            pick out corr. row in input matrix and vstack to B
            increment p_l and c_l for all rows that have the minimum col num
        # at this point, should have At=(x*L) and B=(x*in_len), where x is nonzero count
        call beco matmul to obtain U_l = At.T @ B (dim=L*in_len)
    vstack all U_l's to form matrix C (dim=out_ch*in_len)
    return C
    """
    L = 4
    v, c, r = W
    U_l = []
    for l in range(0, out_ch, L):
        At = np.zeros((0, L)) # prealloc as in_ch * L
        B = np.zeros((0, in_len)) # prealloc as in_ch * in_len
        ll = min(out_ch - l, L)
        p_l = [r[l + i] for i in range(ll)]
        c_l = [c[p_l[i]] - 1 if p_l[i] < r[l + i + 1] else 9999 for i in range(ll)]
        while np.any([p_l[i] < r[l + i + 1] for i in range(ll)]):
            min_val = 9999
            for i in range(ll):
                if p_l[i] < r[l + i + 1]:
                    min_val = min(min_val, c_l[i])
            rc = np.zeros((L,))
            for i in range(ll):
                if (c_l[i] == min_val):
                    rc[i] = v[p_l[i]]
                    p_l[i] += 1
                    if p_l[i] < r[l + i + 1]: c_l[i] += c[p_l[i]]
            At = np.vstack([At, rc])
            B = np.vstack([B, m[min_val]])
        U_l.append(beco_matmul(At, B))
    C = np.vstack(U_l) + np.expand_dims(bias, 1)

    return C
    
GLU = lambda x: x[:x.shape[0] // 2] / (1 + np.exp(-x[x.shape[0] // 2:]))
ReLU = lambda x: np.maximum(0, x)

Now we attempt to run the encoding process for an input of length 2560.

layers = [
    (compressed["encoder." + str(i) + ".0.weight"], compressed["encoder." + str(i) + ".0.bias"],
     compressed["encoder." + str(i) + ".2.weight"], compressed["encoder." + str(i) + ".2.bias"])
    for i in range(5)
]

chs = [(1, 48), (48, 96), (96, 192), (192, 384), (384, 768)]

v = np.random.rand(1, 2560)

m = v
for (l1, b1, l2, b2), (ch1, ch2) in zip(layers, chs):
    m = convolve_sparse(l1, b1, m, in_len=m.shape[1],
                        out_ch=ch2, in_ch=ch1, k=8, stride=4)
    m = ReLU(m)
    m = fc_sparse(l2, b2, m, ch2 * 2, m.shape[1])
    m = GLU(m)
print(m)

[[ 2.16402284e-05]
 [-6.86438298e-01]
 [ 7.45622957e-04]
 [ 1.01900820e-01]
 [ 1.54519461e+00]
 [-1.25690016e-03]
 [-1.94400923e-01]
 [ 3.04551532e-01]
 [-1.82606233e+00]
 [-1.41211974e-02]
 [ 1.53868863e-02]
 [ 1.29516874e-02]
 [-1.50087096e-04]
 [ 1.49187872e-01]
 [-3.98359375e-04]
 [-1.05189119e-03]
 [ 4.18792611e-06]
 [ 2.80498345e-03]
 [-2.91582238e-04]
 [-2.22297634e+00]
 [ 8.78154058e-04]
 [ 2.28849150e-03]
 [-1.13989553e-01]
 [-1.64408021e-04]
 [ 4.07686757e-06]
 [ 2.78668457e-05]
 [-2.11741295e-02]
 [-3.63812103e+00]
 [ 2.82300887e-02]
 [-1.02316345e-04]
 [-6.79898742e+00]
 [ 3.79131864e-02]
 [ 1.72463322e-04]
 [ 1.19783254e-02]
 [-8.29995804e-01]
 [ 5.08629907e-02]
 [ 2.59044820e-04]
 [-1.62236813e-02]
 [ 5.15336508e+00]
 [ 4.12606848e-01]
 [ 6.84838350e+00]
 [-4.60875202e-02]
 [ 1.33798626e-06]
 [ 6.69906784e-04]
 [-1.61797997e-02]
 [-5.05738175e-05]
 [ 1.18792637e-02]
 [ 2.01824466e-08]
 [-2.02370504e-02]
 [ 8.79980400e-05]
 [-2.79446803e-03]
 [ 4.29067547e-03]
 [ 9.52100311e-06]
 [-3.99108471e-01]
 [ 5.08875769e-03]
 [ 3.10131139e-03]
 [ 1.00726565e+00]
 [ 9.37067682e-03]
 [-1.82911806e-04]
 [-1.51223576e-04]
 [-4.77106715e-03]
 [ 5.57939170e-05]
 [-1.26068642e+00]
 [-4.72148217e-02]
 [ 5.22163521e-02]
 [ 1.12867614e+01]
 [-5.06816381e-01]
 [-6.20266107e-03]
 [ 1.56053440e-04]
 [ 1.78338047e-01]
 [-1.06517999e-01]
 [ 3.33904488e+00]
 [ 3.25012307e-04]
 [-5.29629197e-01]
 [ 1.25449269e-02]
 [-8.83551951e-04]
 [ 1.54179887e-02]
 [-5.81193718e-01]
 [ 1.96658746e-05]
 [ 1.75131692e-02]
 [ 2.26822252e+00]
 [ 2.14782461e-04]
 [-4.18358514e-02]
 [-1.58986807e-03]
 [ 1.51929710e-04]
 [ 5.18822202e-02]
 [ 1.19674115e-03]
 [ 1.55362947e-04]
 [ 3.29111685e+00]
 [ 1.69947926e-04]
 [-8.83893177e-01]
 [-1.52082086e-01]
 [ 3.66975067e-01]
 [ 4.57957368e-06]
 [-9.59310654e+00]
 [-2.22869251e-02]
 [-1.77374137e-02]
 [-2.87703342e-04]
 [-3.81423295e-03]
 [ 1.65667095e-01]
 [ 2.63715392e+00]
 [ 3.63110961e-04]
 [ 7.71832435e+00]
 [-1.67599192e-03]
 [-1.60511397e-04]
 [ 6.53566806e-01]
 [ 2.07551465e-01]
 [-4.38386192e-02]
 [-7.98142110e-01]
 [ 7.43911643e-03]
 [ 3.03291535e-02]
 [ 1.05395086e-05]
 [-1.36504051e-03]
 [-1.44864165e+00]
 [ 6.43612086e-01]
 [-5.11755452e-03]
 [ 1.34076761e-05]
 [-4.80616965e-05]
 [-1.65409687e-03]
 [-1.22409953e-01]
 [ 3.79309625e-07]
 [ 4.90687141e-01]
 [ 4.17284066e-02]
 [ 1.37489995e-05]
 [-5.32538015e-05]
 [ 1.46352930e-03]
 [ 1.16743909e-01]
 [ 1.12893540e-05]
 [ 5.80462330e-05]
 [-1.49926878e+00]
 [ 3.26890142e-03]
 [-6.13356936e-02]
 [-3.06065654e+00]
 [-1.53681671e-04]
 [ 1.53803482e-01]
 [-2.14382184e-05]
 [-4.82365244e-01]
 [-1.40984863e-02]
 [-4.33194789e-01]
 [ 1.27913601e-01]
 [-2.53693934e-04]
 [ 3.53165355e-04]
 [-2.16453835e-02]
 [-1.27677791e-03]
 [ 1.46852580e-01]
 [-6.30717622e-01]
 [-3.50185824e-02]
 [-4.35849041e-05]
 [ 1.01112814e-02]
 [ 1.18353492e-02]
 [ 2.02767793e-03]
 [ 2.80404036e-02]
 [-1.53773016e+00]
 [ 9.11765761e-04]
 [-1.30189308e-02]
 [-2.07427172e-03]
 [ 1.64920323e-05]
 [-2.33390860e-01]
 [-2.23327682e-01]
 [ 6.70775256e-03]
 [ 1.45987808e+00]
 [ 3.66868477e-04]
 [-2.04600061e-03]
 [ 2.14193515e-04]
 [-3.58027862e-01]
 [-6.01612444e-03]
 [ 3.61380677e-03]
 [ 2.50839046e+00]
 [ 5.04686941e-06]
 [-1.25481575e-01]
 [ 1.27662593e-01]
 [ 2.12404785e-04]
 [ 1.63952821e+00]
 [-1.10040230e-02]
 [ 3.17762399e-04]
 [ 9.73954321e-04]
 [ 6.28515455e-06]
 [ 7.98612850e-03]
 [ 1.49517229e+00]
 [-1.57429942e+00]
 [-9.13901410e-06]
 [ 2.71168130e-03]
 [ 1.94714109e-03]
 [-9.87627215e-04]
 [-2.40376381e+00]
 [ 4.64995931e-03]
 [-1.04441135e-01]
 [ 4.10489906e+00]
 [-4.99242858e-03]
 [-3.61056066e+00]
 [-2.63041184e-03]
 [-5.48211854e-01]
 [ 1.14669233e-04]
 [-1.89198456e-05]
 [-3.03931360e-01]
 [-6.10429951e-04]
 [-2.15477387e+01]
 [-4.62507162e-06]
 [ 5.88701186e-01]
 [-5.83893290e-01]
 [-1.07007164e-01]
 [-1.40714500e-04]
 [-1.98141217e-05]
 [-3.09879236e+00]
 [-3.55130663e-02]
 [ 1.74155030e+00]
 [ 2.87856661e-02]
 [ 2.13640412e-05]
 [-4.04480756e-03]
 [-1.97696281e-01]
 [ 5.39750972e-02]
 [ 7.76877425e-01]
 [-2.10957728e-02]
 [-2.77669274e-01]
 [ 2.57012836e-07]
 [ 2.81173993e+00]
 [-4.28314976e-02]
 [ 7.65993752e-03]
 [ 2.03518215e-02]
 [-3.24292221e-01]
 [-2.45970421e-01]
 [ 2.56945635e-01]
 [-2.62702870e-06]
 [-1.12670145e-05]
 [ 5.15164221e-01]
 [-4.49232940e-02]
 [ 5.59408103e-02]
 [ 9.71112813e-03]
 [-5.17330042e-06]
 [ 5.48824564e-01]
 [-5.83347712e-03]
 [ 1.10468682e-05]
 [ 3.17283761e+00]
 [ 7.43085723e-03]
 [ 9.36713378e-04]
 [ 1.29352082e-01]
 [ 3.23289907e-02]
 [ 3.21018030e-03]
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