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"""Code for serving bloom blocks via hivemind-server""" |
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from typing import Sequence, Tuple |
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import torch |
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from hivemind.moe.server.module_backend import ModuleBackend |
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from hivemind.moe.server.task_pool import TaskPool |
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from src.bloom.from_pretrained import BloomBlock |
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from src.server.cache import MemoryCache |
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MAX_LENGTH = 2048 |
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class TransformerBackend(ModuleBackend): |
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"""A wrapper for BloomBlock that can process requests for bloom layer forward, forward_incremental, and backward""" |
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def __init__(self, *args, memory_cache: MemoryCache, **kwargs): |
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super().__init__(*args, **kwargs) |
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assert isinstance(self.module, BloomBlock) |
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self.memory_cache = memory_cache |
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for name, param in self.module.named_parameters(): |
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assert not param.requires_grad, f"Bloom layer parameters must not accumulate gradients, but {name} does" |
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for name, buf in self.module.named_buffers(): |
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assert not buf.requires_grad, f"Bloom layer parameters must not accumulate gradients, but {name} does" |
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self.inference_pool = TaskPool(self.inference_step, max_batch_size=1, name=f"{self.name}_inference") |
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def inference_step(self, cache_metadata: torch.IntTensor, *inputs: torch.Tensor) -> Tuple[torch.Tensor, ...]: |
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with torch.inference_mode(): |
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attention_cache_handle = int(cache_metadata[0, 0].item()) |
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prefix_length = int(cache_metadata[0, 1].item()) |
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hidden_states = inputs[0] |
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assert ( |
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hidden_states.ndim == 3 |
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), "expected hidden states to be 3-dimensional: [batch_size, seq_len, hid_size]" |
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with self.memory_cache.use_cache(attention_cache_handle) as cache: |
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assert isinstance(self.module, BloomBlock) and cache.shape[0] == 2 and cache.ndim == 5 |
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layer_past = past_k, past_v = cache[0, :, :prefix_length], cache[1, :, :prefix_length] |
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print("METADATA:", cache_metadata, past_k.shape, past_v.shape) |
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hidden_states, (new_k, new_v) = self.module.forward( |
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hidden_states, layer_past=layer_past, use_cache=True |
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) |
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new_length = new_v.shape[1] |
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assert new_length > prefix_length |
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assert new_k.shape[0] == past_k.shape[0] and new_v.shape[0] == past_v.shape[0] |
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assert new_k.shape[1] == new_length and new_v.shape[1] == new_length |
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assert new_k.shape[2:] == past_k.shape[2:] and new_v.shape[2:] == past_v.shape[2:] |
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assert torch.allclose(new_v[:, : past_v.shape[1]], past_v) |
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assert torch.allclose(new_k[:, : past_k.shape[1]], past_k) |
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cache[0, :, prefix_length:new_length, :] = new_k[:, prefix_length:new_length] |
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cache[1, :, prefix_length:new_length, :] = new_v[:, prefix_length:new_length] |
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return (hidden_states,) |
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def get_pools(self) -> Sequence[TaskPool]: |
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return self.forward_pool, self.backward_pool, self.inference_pool |
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