Robotic Assisted Feeding (RAF) addresses the fundamental need for individuals with mobility impairments to regain autonomy in feeding themselves. The goal of RAF is to use a robot arm to acquire and transfer food to individuals from the table. Existing RAF methods primarily focus on solid foods, leaving a gap in manipulation strategies for semisolid and deformable foods. We present Long-horizon Visual Action-based (LAVA) food acquisition of liquid, semisolid, and deformable foods. Long-horizon refers to the goal of "clearing the bowl" by sequentially acquiring the food from the bowl. LAVA is hierarchical: (1) At the highest level, we determine primitives using ScoopNet. (2) At the mid-level, LAVA finds parameters for the low-level primitives. (3) At the lowest level, LAVA carries out action execution using behavior cloning. We validate LAVA on real-world acquisition trials involving granular, liquid, semisolid, and deformable foods along with fruit chunks and soup. Across 46 bowls, LAVA acquires much more efficiently than baselines with a success rate of 89±4%, and generalizes across realistic plate variations such as varying positions, varieties, and amount of food in the bowl. Datasets and supplementary materials can be found on our website.