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CPU/GPU compat
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@ -256,7 +256,7 @@ cdef class Parser:
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self._cost_batch(costs, is_valid, states, golds)
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self._cost_batch(costs, is_valid, states, golds)
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self._set_gradient(d_scores, scores, is_valid, costs)
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self._set_gradient(d_scores, scores, is_valid, costs)
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losses.append(numpy.abs(d_scores).sum())
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losses.append(self.model.ops.xp.abs(d_scores).sum())
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if force_gold:
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if force_gold:
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softmaxed *= costs <= 0
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softmaxed *= costs <= 0
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return finish_update(d_scores, sgd=sgd)
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return finish_update(d_scores, sgd=sgd)
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@ -312,9 +312,9 @@ cdef class Parser:
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n = gradients.shape[0]
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n = gradients.shape[0]
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scores = scores * is_valid
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scores = scores * is_valid
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g_scores = scores * is_valid * (costs <= 0.)
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g_scores = scores * is_valid * (costs <= 0.)
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exps = numpy.exp(scores - scores.max(axis=1).reshape((n, 1)))
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exps = self.model.ops.xp.exp(scores - scores.max(axis=1).reshape((n, 1)))
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exps *= is_valid
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exps *= is_valid
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g_exps = numpy.exp(g_scores - g_scores.max(axis=1).reshape((n, 1)))
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g_exps = self.model.ops.xp.exp(g_scores - g_scores.max(axis=1).reshape((n, 1)))
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g_exps *= costs <= 0.
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g_exps *= costs <= 0.
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g_exps *= is_valid
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g_exps *= is_valid
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gradients[:] = exps / exps.sum(axis=1).reshape((n, 1))
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gradients[:] = exps / exps.sum(axis=1).reshape((n, 1))
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