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225 lines
9.2 KiB
Python
225 lines
9.2 KiB
Python
from typing import Optional, Tuple
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import pytest
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import torch
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from transformers.cache_utils import DynamicCache
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from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
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from transformers.models.falcon.modeling_falcon import FalconDecoderLayer, FalconModel, build_alibi_tensor
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from transformers.models.llama.modeling_llama import LlamaDecoderLayer, LlamaModel
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from petals.server.block_utils import get_model_block
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from petals.utils.auto_config import AutoDistributedConfig
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from petals.utils.convert_block import QuantType, convert_block
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from test_utils import MODEL_NAME
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KVCache = Tuple[torch.Tensor, torch.Tensor]
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class UnoptimizedWrappedFalconBlock(FalconDecoderLayer):
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def forward(
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self,
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hidden_states: torch.Tensor,
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*args,
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attention_mask: Optional[torch.Tensor] = None,
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alibi: Optional[torch.Tensor] = None,
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layer_past: Optional[KVCache] = None,
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use_cache: bool = False,
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**kwargs,
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):
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batch_size, seq_length = hidden_states.shape[:2]
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if layer_past is not None:
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layer_past = self._reorder_cache_from_bloom_to_falcon(layer_past)
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past_length = 0 if layer_past is None else layer_past[0].shape[1]
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seq_length_with_past = seq_length + past_length
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attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
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if alibi is None and self.config.alibi:
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alibi = build_alibi_tensor(attention_mask, num_heads=self.num_heads, dtype=hidden_states.dtype)
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attention_mask = FalconModel._prepare_attn_mask(attention_mask, (batch_size, seq_length), past_length)
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outputs = super().forward(
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hidden_states,
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*args,
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attention_mask=attention_mask,
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alibi=alibi,
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layer_past=layer_past,
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use_cache=use_cache,
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**kwargs,
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)
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if use_cache:
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present_key_value = outputs[-1]
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present_key_value = self._reorder_cache_from_falcon_to_bloom(present_key_value)
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outputs = outputs[:-1] + (present_key_value,)
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return outputs
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def _reorder_cache_from_bloom_to_falcon(self, key_value: KVCache) -> KVCache:
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key_states, value_states = key_value
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key_states = key_states.permute(0, 2, 1)
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assert key_states.shape == value_states.shape # Both are [batch_size * num_kv_heads, seq_len, head_dim]
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if self.config.new_decoder_architecture:
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key_states = self._expand_states(key_states)
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value_states = self._expand_states(value_states)
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return (key_states, value_states)
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def _reorder_cache_from_falcon_to_bloom(self, key_value: KVCache) -> KVCache:
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key_states, value_states = key_value
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if self.config.new_decoder_architecture:
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key_states = self._collapse_states(key_states)
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value_states = self._collapse_states(value_states)
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assert key_states.shape == value_states.shape # Both are [batch_size * num_kv_heads, seq_len, head_dim]
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key_states = key_states.permute(0, 2, 1)
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return (key_states, value_states)
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def _expand_states(self, state: torch.Tensor) -> torch.Tensor:
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batch_size_x_num_kv_heads, seq_len, head_dim = state.shape
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batch_size = batch_size_x_num_kv_heads // self.config.num_kv_heads
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state = state.view(batch_size, self.config.num_kv_heads, 1, seq_len, head_dim)
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state = state.expand(-1, -1, self.config.num_key_value_groups, -1, -1) # No copy
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state = state.reshape(batch_size * self.config.num_attention_heads, seq_len, head_dim) # Involves a copy
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return state
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def _collapse_states(self, state: torch.Tensor) -> torch.Tensor:
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batch_size_x_num_attn_heads, seq_len, head_dim = state.shape
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batch_size = batch_size_x_num_attn_heads // self.config.num_attention_heads
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state = state.view(batch_size, self.config.num_kv_heads, self.config.num_key_value_groups, seq_len, head_dim)
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state = state[:, :, 0]
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state = state.view(batch_size * self.config.num_kv_heads, seq_len, head_dim)
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return state
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class UnoptimizedWrappedLlamaBlock(LlamaDecoderLayer):
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def forward(
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self,
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hidden_states: torch.Tensor,
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*args,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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layer_past: Optional[Tuple[torch.Tensor]] = None,
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use_cache: bool = False,
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**kwargs,
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) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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batch_size, seq_length, _ = hidden_states.shape
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seq_length_with_past = seq_length
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past_key_values_length = 0
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past_key_value = layer_past
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if past_key_value is not None:
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past_key_values_length = past_key_value[0].shape[2]
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seq_length_with_past = seq_length_with_past + past_key_values_length
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past_key_value = self._reorder_cache_from_bloom_to_llama(past_key_value, batch_size, past_key_values_length)
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elif use_cache:
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past_key_value = DynamicCache()
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if position_ids is None:
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device = hidden_states.device
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position_ids = torch.arange(
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past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
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)
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position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
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else:
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position_ids = position_ids.view(-1, seq_length).long()
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# embed positions
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if attention_mask is None:
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attention_mask = torch.ones(
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(batch_size, seq_length_with_past), dtype=torch.bool, device=hidden_states.device
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)
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attention_mask = _prepare_4d_causal_attention_mask(
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attention_mask, (batch_size, seq_length), hidden_states, past_key_values_length
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)
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outputs = super().forward(
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hidden_states,
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*args,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_value=past_key_value,
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use_cache=use_cache,
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**kwargs,
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)
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if use_cache:
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present_key_value = outputs[-1]
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present_key_value = self._reorder_cache_from_llama_to_bloom(
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present_key_value, batch_size, seq_length_with_past
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)
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outputs = outputs[:-1] + (present_key_value,)
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return outputs
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def _reorder_cache_from_bloom_to_llama(
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self, key_value: Tuple[torch.Tensor], batch_size: int, seq_length: int
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) -> DynamicCache:
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key_states, value_states = key_value
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key_states = key_states.permute(0, 2, 1)
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key_states = key_states.view(
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batch_size, self.self_attn.num_key_value_heads, seq_length, self.self_attn.head_dim
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)
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value_states = value_states.view(*key_states.shape)
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past_key_values = ((key_states, value_states),)
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return DynamicCache.from_legacy_cache(past_key_values)
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def _reorder_cache_from_llama_to_bloom(
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self, key_value: DynamicCache, batch_size: int, seq_length: int
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) -> Tuple[torch.Tensor]:
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key_states, value_states = key_value.to_legacy_cache()[0]
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value_states = value_states.view(
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batch_size * self.self_attn.num_key_value_heads, seq_length, self.self_attn.head_dim
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)
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key_states = key_states.view(*value_states.shape)
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key_states = key_states.permute(0, 2, 1)
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return (key_states, value_states)
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@pytest.mark.parametrize("device", ["cpu", "cuda:0"])
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@pytest.mark.forked
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def test_optimized_block(device):
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if device == "cuda:0" and not torch.cuda.is_available():
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pytest.skip("CUDA tests can be run only in CUDA-enabled setups")
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config = AutoDistributedConfig.from_pretrained(MODEL_NAME)
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tensor_parallel_devices = (device,)
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dtype = torch.bfloat16
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quant_type = QuantType.NONE
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block_idx = 1
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block = get_model_block(config, layer_idx=block_idx).to(dtype)
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block = convert_block(block, block_idx, config, tensor_parallel_devices, device, quant_type=quant_type, freeze=True)
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if config.model_type == "falcon":
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unopt_block = UnoptimizedWrappedFalconBlock(config).to(dtype)
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elif config.model_type == "llama":
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unopt_block = UnoptimizedWrappedLlamaBlock(config, layer_idx=0).to(dtype)
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else:
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pytest.skip(f"This test is not applicable to {config.model_type} models")
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unopt_block = convert_block(
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unopt_block, block_idx, config, tensor_parallel_devices, device, quant_type=quant_type, freeze=True
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)
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unopt_block.load_state_dict(block.state_dict())
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cache = unopt_cache = None
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with torch.inference_mode():
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for length in [10, 1, 1, 1]:
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dummy_input = torch.randn(1, length, config.hidden_size, device=device, dtype=dtype)
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block_output, cache = block(dummy_input, layer_past=cache, use_cache=True)
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unopt_block_output, unopt_cache = unopt_block(dummy_input, layer_past=unopt_cache, use_cache=True)
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assert torch.allclose(block_output, unopt_block_output, atol=1e-6, rtol=0), length
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assert torch.allclose(cache[0], unopt_cache[0], atol=1e-6, rtol=0), length
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assert torch.allclose(cache[1], unopt_cache[1], atol=1e-6, rtol=0), length
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