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precomputable_biaffine: avoid concatenation (#10911)

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The `forward` of `precomputable_biaffine` performs matrix multiplication
and then `vstack`s the result with padding. This creates a temporary
array used for the output of matrix concatenation.

This change avoids the temporary by pre-allocating an array that is
large enough for the output of matrix multiplication plus padding and
fills the array in-place.

This gave me a small speedup (a bit over 100 WPS) on de_core_news_lg on
M1 Max (after changing thinc-apple-ops to support in-place gemm as BLIS
does).
This commit is contained in:
Daniël de Kok 2022-06-10 18:12:28 +02:00 committed by GitHub
parent 97e8a5041b
commit a83a501195
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1 changed files with 4 additions and 2 deletions
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6
spacy/ml/_precomputable_affine.py
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@ -22,9 +22,11 @@ def forward(model, X, is_train):
nP = model.get_dim("nP")
nI = model.get_dim("nI")
W = model.get_param("W")
Yf = model.ops.gemm(X, W.reshape((nF * nO * nP, nI)), trans2=True)
# Preallocate array for layer output, including padding.
Yf = model.ops.alloc2f(X.shape[0] + 1, nF * nO * nP, zeros=False)
model.ops.gemm(X, W.reshape((nF * nO * nP, nI)), trans2=True, out=Yf[1:])
Yf = Yf.reshape((Yf.shape[0], nF, nO, nP))
Yf = model.ops.xp.vstack((model.get_param("pad"), Yf))
Yf[0] = model.get_param("pad")
def backward(dY_ids):
# This backprop is particularly tricky, because we get back a different