fix cache and throughput

src/petals/models/bloom/block.py

@@ -9,6 +9,8 @@ import torch
 from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
 from transformers.models.bloom.modeling_bloom import BloomBlock, BloomModel, build_alibi_tensor
 
+from petals.utils.misc import is_dummy
+
 
 class WrappedBloomBlock(BloomBlock):
     def forward(
@@ -22,6 +24,10 @@ class WrappedBloomBlock(BloomBlock):
     ):
         assert attention_mask is None, "Non-causal attention masks are not supported yet"
         batch_size, seq_length = hidden_states.shape[:2]
+        if layer_past is not None and is_dummy(layer_past[0]):
+            # Bloom cannot use cache if it was misconsctructed(e.g. Dummy tensors)
+            # In this case, fallback to the old code:
+            layer_past = None
         past_length = 0 if layer_past is None else layer_past[0].shape[-1]
         seq_length_with_past = seq_length + past_length
         attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)

src/petals/server/throughput.py

@@ -16,6 +16,7 @@ from transformers import PretrainedConfig
 from petals.server.block_utils import resolve_block_dtype, get_model_block
 from petals.utils.convert_block import QuantType, convert_block
 from petals.utils.disk_cache import DEFAULT_CACHE_DIR
+from petals.utils.misc import DUMMY_KEY_PAST
 
 logger = get_logger(__name__)
 
@@ -205,15 +206,21 @@ def measure_compute_rps(
         block = block.to(dtype)
         block = convert_block(block, 0, config, tensor_parallel_devices, device, quant_type=quant_type, freeze=True)
 
-        cache = None
+        cache = (DUMMY_KEY_PAST, DUMMY_KEY_PAST)
         elapsed = 0
         dummy_input = torch.randn(1, n_tokens, config.hidden_size, device=device, dtype=dtype)
-        _, cache = block.forward(dummy_input, use_cache=True)  # Skip the 1st step to exclude the initialization time
+
+        def step(cache_):
+            outputs = block.forward(dummy_input, use_cache=inference, layer_past=cache_ if inference else None)
+            return outputs[1] if inference else None
+
+        cache = step(cache)
+        # Skip the 1st step to exclude the initialization time
         synchronize(device)
 
         start_time = time.perf_counter()
         for _ in range(n_steps):
-            _, cache = block.forward(dummy_input, use_cache=True, layer_past=cache if inference else None)
+            cache = step(cache)
         synchronize(device)
         elapsed = time.perf_counter() - start_time
         device_rps = n_steps * n_tokens / elapsed

src/petals/utils/misc.py

@@ -4,6 +4,8 @@ DUMMY = torch.empty(0)  # dummy tensor that replaces empty prompt or adapter par
 
 DUMMY_INT64 = torch.empty(0, dtype=torch.int64)
 
+DUMMY_KEY_PAST = torch.empty((0, 0, 0))
+
 
 def is_dummy(tensor: torch.Tensor) -> bool:
     return tensor.numel() == 0