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"""Code for serving bloom blocks via hivemind-server"""
from typing import Any, Dict, Optional, Sequence, Tuple
import torch
from hivemind import BatchTensorDescriptor, use_hivemind_log_handler
from hivemind.moe.server.module_backend import ModuleBackend
from hivemind.utils import get_logger
from src.bloom.from_pretrained import BloomBlock
from src.server.cache import MemoryCache
from src.server.task_pool import PrioritizedTaskPool
from src.utils.misc import is_dummy
use_hivemind_log_handler("in_root_logger")
logger = get_logger(__file__)
class TransformerBackend(ModuleBackend):
"""A wrapper for BloomBlock that can process requests for bloom layer forward, forward_incremental, and backward"""
def __init__(self, *args, memory_cache: MemoryCache, backend_dtype: Optional[torch.dtype] = None, **kwargs):
super().__init__(*args, **kwargs)
assert isinstance(self.module, BloomBlock)
self.memory_cache = memory_cache
for name, param in self.module.named_parameters():
assert not param.requires_grad, f"Bloom layer parameters must not accumulate gradients, but {name} does"
for name, buf in self.module.named_buffers():
assert not buf.requires_grad, f"Bloom layer parameters must not accumulate gradients, but {name} does"
max_batch_size = self.forward_pool.max_batch_size
self.inference_pool = PrioritizedTaskPool(
self.inference_step, max_batch_size=max_batch_size, name=f"{self.name}_inference"
)
self.forward_pool = PrioritizedTaskPool(
self.forward, max_batch_size=max_batch_size, name=f"{self.name}_forward"
)
self.backward_pool = PrioritizedTaskPool(
self.backward, max_batch_size=max_batch_size, name=f"{self.name}_backward"
)
self.dtype = backend_dtype if backend_dtype else self.module.input_layernorm.weight.dtype
self.inference_schema = (
(
*self.args_schema,
BatchTensorDescriptor((), dtype=self.dtype),
BatchTensorDescriptor((), dtype=torch.int64),
),
self.kwargs_schema,
)
def inference_step(self, cache_metadata: torch.IntTensor, *inputs: torch.Tensor) -> Tuple[torch.Tensor, ...]:
with torch.inference_mode():
attention_cache_handle = int(cache_metadata[0, 0].item())
prefix_length = int(cache_metadata[0, 1].item())
(hidden_states, hypo_ids) = inputs
assert (
hidden_states.ndim == 3
), "expected hidden states to be 3-dimensional: [batch_size, seq_len, hid_size]"
with self.memory_cache.use_cache(attention_cache_handle) as cache:
assert isinstance(self.module, BloomBlock) and cache.shape[0] == 2 and cache.ndim == 5
if not is_dummy(hypo_ids):
cache[:, :] = cache[:, hypo_ids] # in-place reorder cache by hypo ids
layer_past = past_k, past_v = cache[0, :, :prefix_length], cache[1, :, :prefix_length]
logger.debug(f"Metadata: {cache_metadata}, past_k.shape={past_k.shape}, past_v.shape={past_v.shape}")
hidden_states, (new_k, new_v) = self.module.forward(
hidden_states, layer_past=layer_past, use_cache=True
)
# todo remove these asserts once we pass all tests
new_length = new_v.shape[1]
assert new_length > prefix_length
assert new_k.shape[0] == past_k.shape[0] and new_v.shape[0] == past_v.shape[0]
assert new_k.shape[1] == new_length and new_v.shape[1] == new_length
assert new_k.shape[2:] == past_k.shape[2:] and new_v.shape[2:] == past_v.shape[2:]
cache[0, :, prefix_length:new_length, :] = new_k[:, prefix_length:new_length]
cache[1, :, prefix_length:new_length, :] = new_v[:, prefix_length:new_length]
return (hidden_states,)
def get_pools(self) -> Sequence[PrioritizedTaskPool]:
return self.forward_pool, self.backward_pool, self.inference_pool
def get_info(self) -> Dict[str, Any]:
"""Get module parameters and stats. Used by RemoteExpert to check shapes and for DMoE orchestration."""
return dict(super().get_info(), inference_schema=self.inference_schema)
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