fix block init

src/petals/server/block_utils.py

@@ -4,7 +4,7 @@ import torch
 from accelerate import init_empty_weights
 from transformers import PretrainedConfig
 
-from petals.utils.convert_block import QuantType
+from petals.utils.convert_block import QuantType, get_model_block
 from petals.utils.misc import get_size_in_bytes
 
 
@@ -32,7 +32,7 @@ def get_block_size(
         ), 'get_block_size(..., location="memory") requires to specify dtype and quant_type for calculations'
 
     with init_empty_weights(include_buffers=True):
-        block = config.block_class(config)
+        block = get_model_block(config)
         n_params = sum(param.numel() for param in block.parameters())
 
     if location == "memory":

src/petals/server/from_pretrained.py

@@ -26,6 +26,7 @@ from petals.constants import DTYPE_MAP
 from petals.models.mixtral import WrappedMixtralBlock
 from petals.server.block_utils import resolve_block_dtype
 from petals.utils.auto_config import AutoDistributedConfig
+from petals.utils.convert_block import get_model_block
 from petals.utils.disk_cache import DEFAULT_CACHE_DIR, allow_cache_reads, allow_cache_writes, free_disk_space_for
 from petals.utils.hf_auth import always_needs_auth
 
@@ -52,11 +53,7 @@ def load_pretrained_block(
     torch_dtype = resolve_block_dtype(config, torch_dtype)
 
     with init_empty_weights():
-        if config.block_class == WrappedMixtralBlock:
-            config = PreTrainedModel._autoset_attn_implementation(config)
-            block = config.block_class(config, block_index)
-        else:
-            block = config.block_class(config)
+        block = get_model_block(config, layer_idx=block_index)
 
     block_prefix = f"{config.block_prefix}.{block_index}."
     state_dict = _load_state_dict_from_repo(

src/petals/server/throughput.py

@@ -14,7 +14,7 @@ from hivemind.utils.logging import get_logger
 from transformers import PretrainedConfig
 
 from petals.server.block_utils import resolve_block_dtype
-from petals.utils.convert_block import QuantType, convert_block
+from petals.utils.convert_block import QuantType, convert_block, get_model_block
 from petals.utils.disk_cache import DEFAULT_CACHE_DIR
 
 logger = get_logger(__name__)
@@ -201,7 +201,8 @@ def measure_compute_rps(
     if not tensor_parallel_devices:
         tensor_parallel_devices = (device,)
     with torch.inference_mode():
-        block = config.block_class(config).to(dtype)
+        block = get_model_block(config)
+        block = block.to(dtype)
         block = convert_block(block, 0, config, tensor_parallel_devices, device, quant_type=quant_type, freeze=True)
 
         cache = None

src/petals/utils/convert_block.py

@@ -12,6 +12,8 @@ from hivemind.utils.logging import get_logger, use_hivemind_log_handler
 from tensor_parallel.slicing_configs import get_bloom_config
 from transformers import PretrainedConfig
 
+from petals.models.mixtral import WrappedMixtralBlocks
+
 use_hivemind_log_handler("in_root_logger")
 logger = get_logger(__name__)
 
@@ -154,3 +156,15 @@ def check_device_balance(devices: Sequence[torch.device]):
             f"GPU devices have highly uneven memory, {wasted_memory_rate * 100:.2f}% memory is wasted. "
             f"Consider running high-memory GPUs in a separate server."
         )
+
+
+def get_model_block(config, **kwargs):
+    """
+    The function to create a model block based on the block class
+    kwargs argument **only** is necessary for specific classes, like Mixtral.
+    They will not be passed to other block constructors.
+    """
+    if config.block_class == WrappedMixtralBlocks:
+        PreTrainedModel._autoset_attn_implementation(config)
+        return config.block_class(config, kwargs.get("layer_idx", 0))
+    return config.block_class(config)