remove falcon support and related UT (#12656)
This commit is contained in:
Yishuo Wang
GitHub
parent
fae73eee79
commit
ea65e4fecc
7 changed files with 2 additions and 1002 deletions
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@ -1492,44 +1492,6 @@ def _optimize_post(model):
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module.BloomAttention,
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bloom_attention_forward
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)
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elif "falcon" in model.config.model_type or "RefinedWeb" in model.config.model_type:
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if model.config.architectures is not None:
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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if "RWForCausalLM" in model.config.architectures:
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if model.config.hidden_size == 4544:
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# falcon-7b need to check performance drop after kv cache support.
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# from ipex_llm.transformers.models.falcon import rw_attention_forward_7b
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# convert_forward(model,
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# module.Attention,
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# rw_attention_forward_7b
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# )
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pass
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else:
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# falcon-40b
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from ipex_llm.transformers.models.falcon import rw_attention_forward_40b
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convert_forward(model,
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module.Attention,
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rw_attention_forward_40b
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)
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elif "FalconForCausalLM" in model.config.architectures:
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if model.config.hidden_size != 4544:
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# falcon-180b and new falcon-40b
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if version.parse(trans_version) >= version.parse("4.36.0"):
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# transformers version >= 4.36.0
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from ipex_llm.transformers.models.falcon import \
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falcon_attention_forward_4_36
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convert_forward(model,
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module.FalconAttention,
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falcon_attention_forward_4_36
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)
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else:
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from ipex_llm.transformers.models.falcon import falcon_attention_forward
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convert_forward(model,
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module.FalconAttention,
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falcon_attention_forward
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)
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elif model.config.model_type == "baichuan":
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modeling_module_name = model.__class__.__module__
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module = importlib.import_module(modeling_module_name)
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@ -1,829 +0,0 @@
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#
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# Copyright 2016 The BigDL Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# Some parts of this file is adapted from
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# https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/falcon/modeling_falcon.py
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# which is licensed under Apache License 2.0:
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#
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# Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""PyTorch Falcon model."""
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import math
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from typing import Optional, Tuple
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import torch
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from torch.nn import functional as F
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from ipex_llm.utils.common import invalidInputError
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from ipex_llm.transformers.models.utils import init_kv_cache, extend_kv_cache, append_kv_cache
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import warnings
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import os
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KV_CACHE_ALLOC_BLOCK_LENGTH = int(os.environ.get("KV_CACHE_ALLOC_BLOCK_LENGTH", 256))
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# Copied from transformers.models.llama.modeling_llama.rotate_half
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def rotate_half(x):
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"""Rotates half the hidden dims of the input."""
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x1 = x[..., : x.shape[-1] // 2]
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x2 = x[..., x.shape[-1] // 2:]
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return torch.cat((-x2, x1), dim=-1)
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# Copied from transformers.models.llama.modeling_llama.apply_rotary_pos_emb
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def apply_rotary_pos_emb(q, k, cos, sin, position_ids, unsqueeze_dim=1):
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"""Applies Rotary Position Embedding to the query and key tensors.
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Args:
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q (`torch.Tensor`): The query tensor.
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k (`torch.Tensor`): The key tensor.
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cos (`torch.Tensor`): The cosine part of the rotary embedding.
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sin (`torch.Tensor`): The sine part of the rotary embedding.
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position_ids (`torch.Tensor`):
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The position indices of the tokens corresponding to the query and key tensors. For
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example, this can be used to pass offsetted position ids when working with a KV-cache.
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unsqueeze_dim (`int`, *optional*, defaults to 1):
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The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze
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cos[position_ids] and sin[position_ids] so that they can be properly broadcasted to the
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dimensions of q and k. For example, note that cos[position_ids] and sin[position_ids]
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have the shape [batch_size, seq_len, head_dim]. Then, if q and k have the shape
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[batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes
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cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k.
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Similarly, if q and k have the shape [batch_size, seq_len, heads, head_dim],
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then set unsqueeze_dim=2.
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Returns: `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary
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Position Embedding.
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"""
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cos = cos[position_ids].unsqueeze(unsqueeze_dim)
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sin = sin[position_ids].unsqueeze(unsqueeze_dim)
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q_embed = (q * cos) + (rotate_half(q) * sin)
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k_embed = (k * cos) + (rotate_half(k) * sin)
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return q_embed, k_embed
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def rw_attention_forward_7b(
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self,
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hidden_states: torch.Tensor,
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alibi: torch.Tensor,
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attention_mask: torch.Tensor,
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layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
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head_mask: Optional[torch.Tensor]=None,
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use_cache: bool=False,
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output_attentions: bool=False,
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):
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fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
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# 3 x [batch_size, seq_length, num_heads, head_dim]
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(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
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batch_size, q_length, _, _ = query_layer.shape
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query_layer = query_layer.transpose(1, 2).reshape(
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batch_size * self.num_heads,
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q_length,
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self.head_dim
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)
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key_layer = key_layer.transpose(1, 2).reshape(
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batch_size * self.num_kv,
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q_length,
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self.head_dim,
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)
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value_layer = value_layer.transpose(1, 2).reshape(
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batch_size * self.num_kv,
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q_length,
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self.head_dim
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)
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# query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
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_, seq_len, _ = query_layer.shape
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if layer_past is not None:
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_, seq_len_past, _ = layer_past[0].shape
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seq_len = seq_len + seq_len_past
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query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, seq_len)
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_, kv_length, _ = key_layer.shape
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if layer_past is not None:
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kv_length += layer_past[0].shape[-2]
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query_layer = query_layer.view(batch_size, self.num_heads, q_length, self.head_dim)
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key_layer = key_layer.view(batch_size, self.num_kv, q_length, self.head_dim)
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value_layer = value_layer.view(batch_size, self.num_kv, q_length, self.head_dim)
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device = hidden_states.device
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if layer_past is not None:
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# reuse k, v, self_attention
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cache_k = layer_past[0].view(batch_size, self.num_kv, -1, self.head_dim)
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cache_v = layer_past[1].view(batch_size, self.num_kv, -1, self.head_dim)
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if cache_k.stride()[1] < kv_length * cache_k.size(3):
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# allocate new
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new_cache_k, new_cache_v = extend_kv_cache(
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batch_size,
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self.num_kv,
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self.head_dim,
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cache_k.size(2),
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kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH,
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dtype=cache_k.dtype,
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device=device
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)
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new_cache_k[:] = cache_k
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new_cache_v[:] = cache_v
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cache_k = new_cache_k
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cache_v = new_cache_v
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key_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
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elif use_cache:
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max_cache_length = kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH
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new_key_states, new_value_states = init_kv_cache(
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batch_size,
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self.num_kv,
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self.head_dim,
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kv_length,
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max_cache_length,
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dtype=key_layer.dtype,
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device=device
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)
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new_key_states[:] = key_layer
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new_value_states[:] = value_layer
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key_layer = new_key_states
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value_layer = new_value_states
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query_layer = query_layer.view(batch_size*self.num_heads, -1, self.head_dim)
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key_layer = key_layer.view(batch_size*self.num_kv, -1, self.head_dim)
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value_layer = value_layer.view(batch_size*self.num_kv, -1, self.head_dim)
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_, kv_length, _ = key_layer.shape
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if use_cache is True:
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present = (key_layer, value_layer)
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else:
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present = None
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if alibi is None:
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query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
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key_layer_ = key_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
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value_layer_ = value_layer.reshape(batch_size, self.num_kv, -1, self.head_dim)
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# attn_output = F.scaled_dot_product_attention(
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# query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
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# )
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if layer_past is not None:
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L = query_layer_.shape[-2]
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S = key_layer_.shape[-2]
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attn_mask = torch.ones(L, S, dtype=torch.bool, device=query_layer_.device)
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attn_output = F.scaled_dot_product_attention(
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query_layer_, key_layer_, value_layer_, attn_mask, 0.0, is_causal=False
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)
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else:
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attn_output = F.scaled_dot_product_attention(
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query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
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)
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x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
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x = x.permute(0, 2, 1, 3)
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attn_output = x.reshape(batch_size, q_length, self.num_heads * self.head_dim)
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output_tensor = self.dense(attn_output)
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outputs = (output_tensor, present)
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if output_attentions:
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invalidInputError(False,
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f"'output_attentions' are not supported yet")
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return outputs
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else:
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attention_mask_float = (attention_mask * 1.0) \
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.masked_fill(attention_mask, -1e9).to(torch.bfloat16)
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matmul_result = query_layer @ key_layer.transpose(-1, -2)
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# change view to [batch_size, num_heads, q_length, kv_length]
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attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
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# cast attention scores to fp32,
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# compute scaled softmax and cast back to initial dtype
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# - [batch_size, num_heads, q_length, kv_length]
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input_dtype = attention_scores.dtype
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# `float16` has a minimum value of -65504.0,
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# whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
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if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
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attention_scores = attention_scores.to(torch.float32)
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# attn_weights = torch. \
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# masked_fill(attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
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attention_probs = F.softmax(
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(attention_scores + alibi) * self.inv_norm_factor + attention_mask_float,
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dim=-1,
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dtype=hidden_states.dtype,
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)
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# [batch_size, num_heads, q_length, kv_length]
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attention_probs = self.attention_dropout(attention_probs)
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if head_mask is not None:
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attention_probs = attention_probs * head_mask
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# change view [batch_size x num_heads, q_length, kv_length]
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attention_probs_reshaped = attention_probs.view(
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batch_size * self.num_heads,
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q_length,
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kv_length
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)
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# matmul: [batch_size * num_heads, q_length, head_dim]
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context_layer = attention_probs_reshaped @ value_layer
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# change view [batch_size, num_heads, q_length, head_dim]
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context_layer = self._merge_heads(context_layer)
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output_tensor = self.dense(context_layer)
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outputs = (output_tensor, present)
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if output_attentions:
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outputs += (attention_probs,)
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return outputs
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def rw_attention_forward_40b(
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self,
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hidden_states: torch.Tensor,
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alibi: torch.Tensor,
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attention_mask: torch.Tensor,
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layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
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head_mask: Optional[torch.Tensor]=None,
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use_cache: bool=False,
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output_attentions: bool=False,
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):
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# [batch_size, seq_length, 3 x hidden_size]
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fused_qkv = self.query_key_value(hidden_states)
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# 3 x [batch_size, seq_length, num_heads, head_dim]
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(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
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batch_size, q_length, _, _ = query_layer.shape
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query_layer = query_layer.transpose(1, 2).reshape(
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batch_size * self.num_heads,
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q_length,
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self.head_dim
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)
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key_layer = key_layer.transpose(1, 2).reshape(
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batch_size * self.num_heads,
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q_length,
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self.head_dim,
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)
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value_layer = value_layer.transpose(1, 2).reshape(
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batch_size * self.num_heads,
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q_length,
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self.head_dim
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)
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# query_layer, key_layer = self.maybe_rotary(query_layer, key_layer)
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_, seq_len, _ = query_layer.shape
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if layer_past is not None:
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_, seq_len_past, _ = layer_past[0].shape
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seq_len = seq_len + seq_len_past
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query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, seq_len)
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_, kv_length, _ = key_layer.shape
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if layer_past is not None:
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kv_length += layer_past[0].shape[-2]
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query_layer = query_layer.view(batch_size, self.num_heads, q_length, self.head_dim)
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key_layer = key_layer.view(batch_size, self.num_heads, q_length, self.head_dim)
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value_layer = value_layer.view(batch_size, self.num_heads, q_length, self.head_dim)
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device = hidden_states.device
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if layer_past is not None:
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# reuse k, v, self_attention
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cache_k = layer_past[0].view(batch_size, self.num_heads, -1, self.head_dim)
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cache_v = layer_past[1].view(batch_size, self.num_heads, -1, self.head_dim)
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if cache_k.stride()[1] < kv_length * cache_k.size(3):
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# allocate new
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new_cache_k, new_cache_v = extend_kv_cache(
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batch_size,
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self.num_heads,
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self.head_dim,
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cache_k.size(2),
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kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH,
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dtype=cache_k.dtype,
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device=device
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)
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new_cache_k[:] = cache_k
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new_cache_v[:] = cache_v
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cache_k = new_cache_k
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cache_v = new_cache_v
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key_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
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elif use_cache:
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max_cache_length = kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH
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new_key_states, new_value_states = init_kv_cache(
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batch_size,
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self.num_heads,
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self.head_dim,
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kv_length,
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max_cache_length,
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dtype=key_layer.dtype,
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device=device
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)
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new_key_states[:] = key_layer
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new_value_states[:] = value_layer
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key_layer = new_key_states
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value_layer = new_value_states
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query_layer = query_layer.view(batch_size*self.num_heads, -1, self.head_dim)
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key_layer = key_layer.view(batch_size*self.num_heads, -1, self.head_dim)
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value_layer = value_layer.view(batch_size*self.num_heads, -1, self.head_dim)
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_, kv_length, _ = key_layer.shape
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if use_cache is True:
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present = (key_layer, value_layer)
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else:
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present = None
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if alibi is None:
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query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
|
||||
key_layer_ = key_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
|
||||
value_layer_ = value_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
|
||||
|
||||
# attn_output = F.scaled_dot_product_attention(
|
||||
# query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
|
||||
# )
|
||||
if present is not None:
|
||||
L = query_layer_.shape[-2]
|
||||
S = key_layer_.shape[-2]
|
||||
attn_mask = torch.ones(L, S, dtype=torch.bool, device=query_layer_.device)
|
||||
attn_output = F.scaled_dot_product_attention(
|
||||
query_layer_, key_layer_, value_layer_, attn_mask, 0.0, is_causal=False
|
||||
)
|
||||
else:
|
||||
attn_output = F.scaled_dot_product_attention(
|
||||
query_layer_, key_layer_, value_layer_, None, 0.0, is_causal=True
|
||||
)
|
||||
|
||||
x = attn_output.view(batch_size, self.num_heads, q_length, self.head_dim)
|
||||
x = x.permute(0, 2, 1, 3)
|
||||
attn_output = x.reshape(batch_size, q_length, self.num_heads * self.head_dim)
|
||||
|
||||
output_tensor = self.dense(attn_output)
|
||||
|
||||
outputs = (output_tensor, present)
|
||||
if output_attentions:
|
||||
invalidInputError(False,
|
||||
f"'output_attentions' are not supported yet")
|
||||
return outputs
|
||||
else:
|
||||
attention_mask_float = (attention_mask * 1.0) \
|
||||
.masked_fill(attention_mask, -1e9).to(torch.bfloat16)
|
||||
matmul_result = query_layer @ key_layer.transpose(-1, -2)
|
||||
|
||||
# change view to [batch_size, num_heads, q_length, kv_length]
|
||||
attention_scores = matmul_result.view(batch_size, self.num_heads, q_length, kv_length)
|
||||
|
||||
# cast attention scores to fp32,
|
||||
# compute scaled softmax and cast back to initial dtype
|
||||
# - [batch_size, num_heads, q_length, kv_length]
|
||||
input_dtype = attention_scores.dtype
|
||||
# `float16` has a minimum value of -65504.0,
|
||||
# whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
|
||||
if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
|
||||
attention_scores = attention_scores.to(torch.float32)
|
||||
# attn_weights = torch \
|
||||
# .masked_fill(
|
||||
# attention_scores, attention_mask, torch.finfo(attention_scores.dtype).min)
|
||||
attention_probs = F.softmax(
|
||||
(attention_scores + alibi.view(batch_size, self.num_heads, 1, -1))
|
||||
* self.inv_norm_factor + attention_mask_float,
|
||||
dim=-1,
|
||||
dtype=hidden_states.dtype,
|
||||
)
|
||||
# [batch_size, num_heads, q_length, kv_length]
|
||||
attention_probs = self.attention_dropout(attention_probs)
|
||||
|
||||
if head_mask is not None:
|
||||
attention_probs = attention_probs * head_mask
|
||||
|
||||
# change view [batch_size x num_heads, q_length, kv_length]
|
||||
attention_probs_reshaped = attention_probs.view(
|
||||
batch_size * self.num_heads,
|
||||
q_length,
|
||||
kv_length
|
||||
)
|
||||
|
||||
# matmul: [batch_size * num_heads, q_length, head_dim]
|
||||
context_layer = attention_probs_reshaped @ value_layer
|
||||
|
||||
# change view [batch_size, num_heads, q_length, head_dim]
|
||||
context_layer = self._merge_heads(context_layer)
|
||||
|
||||
output_tensor = self.dense(context_layer)
|
||||
|
||||
outputs = (output_tensor, present)
|
||||
if output_attentions:
|
||||
outputs += (attention_probs,)
|
||||
return outputs
|
||||
|
||||
|
||||
def falcon_attention_forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
alibi: Optional[torch.Tensor],
|
||||
attention_mask: torch.Tensor,
|
||||
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
|
||||
head_mask: Optional[torch.Tensor]=None,
|
||||
use_cache: bool=False,
|
||||
output_attentions: bool=False,
|
||||
):
|
||||
# [batch_size, seq_length, 3 x hidden_size]
|
||||
fused_qkv = self.query_key_value(hidden_states)
|
||||
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
|
||||
# 3 x [batch_size, seq_length, num_heads, head_dim]
|
||||
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
|
||||
|
||||
batch_size, query_length, _, _ = query_layer.shape
|
||||
|
||||
query_layer = query_layer.transpose(1, 2).reshape(
|
||||
batch_size * self.num_heads,
|
||||
query_length,
|
||||
self.head_dim
|
||||
)
|
||||
key_layer = key_layer.transpose(1, 2).reshape(
|
||||
batch_size * num_kv_heads,
|
||||
query_length,
|
||||
self.head_dim,
|
||||
)
|
||||
value_layer = value_layer.transpose(1, 2).reshape(
|
||||
batch_size * num_kv_heads,
|
||||
query_length,
|
||||
self.head_dim
|
||||
)
|
||||
|
||||
past_kv_length = 0 if layer_past is None else layer_past[0].shape[1]
|
||||
query_layer, key_layer = self.maybe_rotary(query_layer, key_layer, past_kv_length)
|
||||
|
||||
_, kv_length, _ = key_layer.shape
|
||||
if layer_past is not None:
|
||||
kv_length += layer_past[0].shape[-2]
|
||||
query_layer = query_layer.view(batch_size, self.num_heads, query_length, self.head_dim)
|
||||
key_layer = key_layer.view(batch_size, num_kv_heads, query_length, self.head_dim)
|
||||
value_layer = value_layer.view(batch_size, num_kv_heads, query_length, self.head_dim)
|
||||
device = hidden_states.device
|
||||
if layer_past is not None:
|
||||
# reuse k, v, self_attention
|
||||
cache_k = layer_past[0].view(batch_size, num_kv_heads, -1, self.head_dim)
|
||||
cache_v = layer_past[1].view(batch_size, num_kv_heads, -1, self.head_dim)
|
||||
if cache_k.stride()[1] < kv_length * cache_k.size(3):
|
||||
# allocate new
|
||||
new_cache_k, new_cache_v = extend_kv_cache(
|
||||
batch_size,
|
||||
num_kv_heads,
|
||||
self.head_dim,
|
||||
cache_k.size(2),
|
||||
kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
||||
dtype=cache_k.dtype,
|
||||
device=device
|
||||
)
|
||||
new_cache_k[:] = cache_k
|
||||
new_cache_v[:] = cache_v
|
||||
cache_k = new_cache_k
|
||||
cache_v = new_cache_v
|
||||
|
||||
key_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
|
||||
|
||||
elif use_cache:
|
||||
max_cache_length = kv_length + KV_CACHE_ALLOC_BLOCK_LENGTH
|
||||
new_key_states, new_value_states = init_kv_cache(
|
||||
batch_size,
|
||||
num_kv_heads,
|
||||
self.head_dim,
|
||||
kv_length,
|
||||
max_cache_length,
|
||||
dtype=key_layer.dtype,
|
||||
device=device
|
||||
)
|
||||
new_key_states[:] = key_layer
|
||||
new_value_states[:] = value_layer
|
||||
key_layer = new_key_states
|
||||
value_layer = new_value_states
|
||||
|
||||
query_layer = query_layer.view(batch_size * self.num_heads, -1, self.head_dim)
|
||||
key_layer = key_layer.view(batch_size * num_kv_heads, -1, self.head_dim)
|
||||
value_layer = value_layer.view(batch_size * num_kv_heads, -1, self.head_dim)
|
||||
_, kv_length, _ = key_layer.shape
|
||||
if use_cache:
|
||||
present = (key_layer, value_layer)
|
||||
else:
|
||||
present = None
|
||||
|
||||
attention_mask_float = (attention_mask * 1.0) \
|
||||
.masked_fill(attention_mask, float("-1e9")).to(query_layer.dtype)
|
||||
|
||||
query_layer_ = query_layer.reshape(batch_size, self.num_heads, -1, self.head_dim)
|
||||
key_layer_ = key_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
|
||||
value_layer_ = value_layer.reshape(batch_size, num_kv_heads, -1, self.head_dim)
|
||||
|
||||
if alibi is None:
|
||||
if output_attentions:
|
||||
# F.scaled_dot_product_attention doesn't return the attention weights, so we have
|
||||
# to do it by hand if we want them
|
||||
attention_scores = query_layer_ @ key_layer_.transpose(-1, -2)
|
||||
attention_scores /= math.sqrt(self.head_dim)
|
||||
|
||||
attention_scores = F.softmax(
|
||||
attention_scores + attention_mask_float, dim=-1, dtype=hidden_states.dtype
|
||||
)
|
||||
attn_output = attention_scores @ value_layer_
|
||||
else:
|
||||
attn_output = F.scaled_dot_product_attention(
|
||||
query_layer_,
|
||||
key_layer_,
|
||||
value_layer_,
|
||||
attention_mask_float,
|
||||
0.0,
|
||||
is_causal=False
|
||||
)
|
||||
attention_scores = None
|
||||
|
||||
attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
|
||||
attn_output = attn_output.permute(0, 2, 1, 3)
|
||||
attn_output = attn_output.reshape(
|
||||
batch_size,
|
||||
query_length,
|
||||
self.num_heads * self.head_dim
|
||||
)
|
||||
|
||||
output_tensor = self.dense(attn_output)
|
||||
|
||||
if output_attentions:
|
||||
return output_tensor, present, attention_scores
|
||||
else:
|
||||
return output_tensor, present
|
||||
|
||||
else:
|
||||
matmul_result = query_layer_ @ key_layer_.transpose(-1, -2)
|
||||
|
||||
# change view to [batch_size, num_heads, q_length, kv_length]
|
||||
attention_scores = matmul_result.view(
|
||||
batch_size,
|
||||
self.num_heads,
|
||||
query_length,
|
||||
kv_length
|
||||
)
|
||||
|
||||
# cast attention scores to fp32,
|
||||
# compute scaled softmax and cast back to initial dtype
|
||||
# - [batch_size, num_heads, q_length, kv_length]
|
||||
input_dtype = attention_scores.dtype
|
||||
# `float16` has a minimum value of -65504.0,
|
||||
# whereas `bfloat16` and `float32` have a minimum value of `-3.4e+38`
|
||||
if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
|
||||
attention_scores = attention_scores.to(torch.float32)
|
||||
# Matt (HF) note: We could possibly use F.scaled_dot_product_attention here too, by
|
||||
# adding (alibi * self.inv_norm_factor) to attention_mask_float.
|
||||
# I think this would be mathematically
|
||||
# equivalent and more performant, but there might be a numerical difference.
|
||||
# If you're reading this
|
||||
# and you'd like to experiment and maybe file a PR, feel free!
|
||||
attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
|
||||
attention_logits *= self.inv_norm_factor
|
||||
attention_probs = F.softmax(attention_logits + attention_mask_float, dim=-1,
|
||||
dtype=hidden_states.dtype)
|
||||
# [batch_size, num_heads, q_length, kv_length]
|
||||
attention_probs = self.attention_dropout(attention_probs)
|
||||
|
||||
if head_mask is not None:
|
||||
attention_probs = attention_probs * head_mask
|
||||
|
||||
# change view [batch_size, num_heads, q_length, kv_length]
|
||||
attention_probs_reshaped = attention_probs.view(
|
||||
batch_size,
|
||||
self.num_heads,
|
||||
query_length,
|
||||
kv_length
|
||||
)
|
||||
|
||||
# matmul: [batch_size * num_heads, q_length, head_dim]
|
||||
context_layer = (attention_probs_reshaped @ value_layer_).flatten(0, 1)
|
||||
|
||||
# change view [batch_size, q_length, num_heads * head_dim]
|
||||
context_layer = self._merge_heads(context_layer)
|
||||
|
||||
output_tensor = self.dense(context_layer)
|
||||
|
||||
if output_attentions:
|
||||
return output_tensor, present, attention_probs
|
||||
else:
|
||||
return output_tensor, present
|
||||
|
||||
|
||||
def falcon_attention_forward_4_36(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
alibi: Optional[torch.Tensor],
|
||||
attention_mask: torch.Tensor,
|
||||
position_ids: Optional[torch.LongTensor]=None,
|
||||
layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]]=None,
|
||||
head_mask: Optional[torch.Tensor]=None,
|
||||
use_cache: bool=False,
|
||||
output_attentions: bool=False,
|
||||
**kwargs,
|
||||
):
|
||||
""" based on transformers==4.36.0
|
||||
https://github.com/huggingface/transformers/blob/v4.36.0/src/transformers/models/falcon/modeling_falcon.py
|
||||
"""
|
||||
if "padding_mask" in kwargs:
|
||||
warnings.warn(
|
||||
"Passing `padding_mask` is deprecated and will be removed in v4.37. \
|
||||
Please make sure use `attention_mask` instead.`"
|
||||
)
|
||||
|
||||
fused_qkv = self.query_key_value(hidden_states) # [batch_size, seq_length, 3 x hidden_size]
|
||||
num_kv_heads = self.num_heads if self.new_decoder_architecture else self.num_kv_heads
|
||||
# 3 x [batch_size, seq_length, num_heads, head_dim]
|
||||
(query_layer, key_layer, value_layer) = self._split_heads(fused_qkv)
|
||||
|
||||
batch_size, query_length, _, _ = query_layer.shape
|
||||
|
||||
query_layer = query_layer.transpose(1, 2).reshape(
|
||||
batch_size, self.num_heads, query_length, self.head_dim)
|
||||
key_layer = key_layer.transpose(1, 2).reshape(
|
||||
batch_size, num_kv_heads, query_length, self.head_dim)
|
||||
value_layer = value_layer.transpose(1, 2).reshape(
|
||||
batch_size, num_kv_heads, query_length, self.head_dim)
|
||||
|
||||
kv_seq_len = key_layer.shape[-2]
|
||||
device = hidden_states.device
|
||||
|
||||
if layer_past is not None:
|
||||
kv_seq_len += layer_past[0].shape[-2]
|
||||
|
||||
if alibi is None:
|
||||
cos, sin = self.rotary_emb(value_layer, seq_len=kv_seq_len)
|
||||
query_layer, key_layer = apply_rotary_pos_emb(
|
||||
query_layer, key_layer, cos, sin, position_ids)
|
||||
|
||||
if layer_past is not None:
|
||||
# reuse k, v, self_attention
|
||||
cache_k = layer_past[0].view(batch_size, self.num_heads, -1, self.head_dim)
|
||||
cache_v = layer_past[1].view(batch_size, self.num_heads, -1, self.head_dim)
|
||||
if cache_k.stride()[1] <= cache_k.size(2) * cache_k.size(3):
|
||||
# allocate new
|
||||
new_cache_k, new_cache_v = extend_kv_cache(
|
||||
batch_size,
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
cache_k.size(2),
|
||||
kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH,
|
||||
dtype=cache_k.dtype,
|
||||
device=device
|
||||
)
|
||||
new_cache_k[:] = cache_k
|
||||
new_cache_v[:] = cache_v
|
||||
cache_k = new_cache_k
|
||||
cache_v = new_cache_v
|
||||
|
||||
key_layer, value_layer = append_kv_cache(cache_k, cache_v, key_layer, value_layer)
|
||||
|
||||
elif use_cache:
|
||||
max_cache_length = kv_seq_len + KV_CACHE_ALLOC_BLOCK_LENGTH
|
||||
new_key_states, new_value_states = init_kv_cache(
|
||||
batch_size,
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
kv_seq_len,
|
||||
max_cache_length,
|
||||
dtype=key_layer.dtype,
|
||||
device=device
|
||||
)
|
||||
new_key_states[:] = key_layer
|
||||
new_value_states[:] = value_layer
|
||||
key_layer = new_key_states
|
||||
value_layer = new_value_states
|
||||
|
||||
query_layer = query_layer.view(batch_size, self.num_heads, -1, self.head_dim)
|
||||
key_layer = key_layer.view(batch_size, self.num_heads, -1, self.head_dim)
|
||||
value_layer = value_layer.view(batch_size, self.num_heads, -1, self.head_dim)
|
||||
|
||||
kv_length = key_layer.shape[-2]
|
||||
if use_cache:
|
||||
present = (key_layer, value_layer)
|
||||
else:
|
||||
present = None
|
||||
|
||||
# SDPA with memory-efficient backend is currently (torch==2.1.2)
|
||||
# bugged with non-contiguous inputs with custom attn_mask,
|
||||
# Reference: https://github.com/pytorch/pytorch/issues/112577.
|
||||
if query_layer.device.type == "cuda" and attention_mask is not None:
|
||||
query_layer = query_layer.contiguous()
|
||||
key_layer = key_layer.contiguous()
|
||||
value_layer = value_layer.contiguous()
|
||||
|
||||
if alibi is None:
|
||||
if self._use_sdpa and not output_attentions:
|
||||
attn_output = F.scaled_dot_product_attention(
|
||||
query_layer,
|
||||
key_layer,
|
||||
value_layer,
|
||||
attention_mask,
|
||||
0.0,
|
||||
# The query_length > 1 is necessary to match with
|
||||
# AttentionMaskConverter.to_causal_4d that does not create a causal mask in case
|
||||
# query_length == 1.
|
||||
is_causal=self.is_causal and attention_mask is None and query_length > 1,
|
||||
)
|
||||
attention_scores = None
|
||||
else:
|
||||
attention_scores = query_layer @ key_layer.transpose(-1, -2)
|
||||
attention_scores /= math.sqrt(self.head_dim)
|
||||
|
||||
attention_scores = F.softmax(
|
||||
attention_scores + attention_mask, dim=-1, dtype=hidden_states.dtype)
|
||||
# It is unclear why neither dropout nor head_mask is applied here
|
||||
# (while it is with alibi).
|
||||
attn_output = attention_scores @ value_layer
|
||||
|
||||
attn_output = attn_output.view(batch_size, self.num_heads, query_length, self.head_dim)
|
||||
attn_output = attn_output.permute(0, 2, 1, 3)
|
||||
attn_output = attn_output.reshape(batch_size, query_length, self.num_heads * self.head_dim)
|
||||
|
||||
attn_output = self.dense(attn_output)
|
||||
|
||||
if output_attentions:
|
||||
return attn_output, present, attention_scores
|
||||
else:
|
||||
return attn_output, present
|
||||
|
||||
else:
|
||||
if self._use_sdpa and not output_attentions and head_mask is None:
|
||||
attn_output = F.scaled_dot_product_attention(
|
||||
query_layer,
|
||||
key_layer,
|
||||
value_layer,
|
||||
attn_mask=attention_mask,
|
||||
dropout_p=self.attention_dropout.p if self.training else 0.0,
|
||||
is_causal=self.is_causal and attention_mask is None and query_length > 1,
|
||||
)
|
||||
attn_output = attn_output.transpose(1, 2)
|
||||
attn_output = attn_output.reshape(
|
||||
batch_size, query_length, self.num_heads * self.head_dim)
|
||||
|
||||
attn_output = self.dense(attn_output)
|
||||
else:
|
||||
matmul_result = query_layer @ key_layer.transpose(-1, -2)
|
||||
|
||||
# change view to [batch_size, num_heads, q_length, kv_length]
|
||||
attention_scores = matmul_result.view(
|
||||
batch_size, self.num_heads, query_length, kv_length)
|
||||
|
||||
# cast attention scores to fp32, compute scaled softmax and cast back to initial dtype -
|
||||
# [batch_size, num_heads, q_length, kv_length]
|
||||
input_dtype = attention_scores.dtype
|
||||
# `float16` has a minimum value of -65504.0, whereas `bfloat16` and `float32` have a
|
||||
# minimum value of `-3.4e+38`
|
||||
if input_dtype == torch.float16 or input_dtype == torch.bfloat16:
|
||||
attention_scores = attention_scores.to(torch.float32)
|
||||
|
||||
attention_logits = attention_scores + alibi.view(batch_size, self.num_heads, 1, -1)
|
||||
attention_logits *= self.inv_norm_factor
|
||||
attention_probs = F.softmax(
|
||||
attention_logits + attention_mask, dim=-1, dtype=hidden_states.dtype)
|
||||
# [batch_size, num_heads, q_length, kv_length]
|
||||
attention_probs = self.attention_dropout(attention_probs)
|
||||
|
||||
if head_mask is not None:
|
||||
attention_probs = attention_probs * head_mask
|
||||
|
||||
# change view [batch_size, num_heads, q_length, kv_length]
|
||||
attention_probs_reshaped = attention_probs.view(
|
||||
batch_size, self.num_heads, query_length, kv_length)
|
||||
|
||||
# matmul: [batch_size * num_heads, q_length, head_dim]
|
||||
attn_output = (attention_probs_reshaped @ value_layer).flatten(0, 1)
|
||||
|
||||
# change view [batch_size, q_length, num_heads * head_dim]
|
||||
attn_output = self._merge_heads(attn_output)
|
||||
|
||||
attn_output = self.dense(attn_output)
|
||||
|
||||
if output_attentions:
|
||||
return attn_output, present, attention_probs
|
||||
else:
|
||||
return attn_output, present
|
||||
|
|
@ -32,7 +32,6 @@ print(f'Running on {device}')
|
|||
@pytest.mark.parametrize('Model, Tokenizer, model_path',[
|
||||
(AutoModelForCausalLM, LlamaTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH')),
|
||||
(AutoModel, AutoTokenizer, os.environ.get('CHATGLM2_6B_ORIGIN_PATH')),
|
||||
(AutoModelForCausalLM, AutoTokenizer, os.environ.get('FALCON_7B_ORIGIN_PATH')),
|
||||
(AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
|
||||
# (AutoModelForCausalLM, AutoTokenizer, os.environ.get('MISTRAL_7B_INSTRUCT_V0_1_ORIGIN_PATH')),
|
||||
# (AutoModelForCausalLM, AutoTokenizer, os.environ.get('BAICHUAN2_7B_ORIGIN_PATH')),
|
||||
|
|
@ -67,7 +66,7 @@ def test_load_low_bit_completion(Model, Tokenizer, model_path, prompt, answer):
|
|||
load_in_4bit=True,
|
||||
optimize_model=True,
|
||||
trust_remote_code=True)
|
||||
|
||||
|
||||
with tempfile.TemporaryDirectory() as tempdir:
|
||||
model.save_low_bit(tempdir)
|
||||
loaded_model = Model.load_low_bit(tempdir,
|
||||
|
|
|
|||
|
|
@ -30,7 +30,6 @@ PROMPT = "Once upon a time, there existed a little girl who liked to have advent
|
|||
TEST_MODEL_LIST = [
|
||||
("MPT-7B", AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
|
||||
("Llama2-7B", AutoModelForCausalLM, LlamaTokenizer, os.environ.get('LLAMA2_7B_ORIGIN_PATH')),
|
||||
("Falcon-7B", AutoModelForCausalLM, AutoTokenizer, os.environ.get('FALCON_7B_ORIGIN_PATH')),
|
||||
("ChatGLM2-6B", AutoModel, AutoTokenizer, os.environ.get('CHATGLM2_6B_ORIGIN_PATH')),
|
||||
("Mistral-7B-Instruct-v0.1", AutoModelForCausalLM, AutoTokenizer, os.environ.get('MISTRAL_7B_INSTRUCT_V0_1_ORIGIN_PATH')),
|
||||
("Baichuan2-7B-Chat", AutoModelForCausalLM, AutoTokenizer, os.environ.get('BAICHUAN2_7B_ORIGIN_PATH')),
|
||||
|
|
@ -128,8 +127,6 @@ class Test_Optimize_Gpu_Model:
|
|||
self.MPT_7B_gpu_model(Name, Model, Tokenizer, model_path)
|
||||
elif Name == "Llama2-7B":
|
||||
self.Llama2_7B_gpu_model(Name, Model, Tokenizer, model_path)
|
||||
elif Name == "Falcon-7B":
|
||||
self.Falcon_7B_gpu_model(Name, Model, Tokenizer, model_path)
|
||||
elif Name == "ChatGLM2-6B":
|
||||
self.Chatglm2_gpu_model(Name, Model, Tokenizer, model_path)
|
||||
elif Name == "Mistral-7B-Instruct-v0.1":
|
||||
|
|
@ -154,13 +151,6 @@ class Test_Optimize_Gpu_Model:
|
|||
lower_bound = 2e-1
|
||||
self.run_optimize_gpu_model(Name, Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
|
||||
|
||||
def Falcon_7B_gpu_model(self, Name, Model, Tokenizer, model_path):
|
||||
# currently only compare the output of the last self-attention layer.
|
||||
layer_norm = "transformer.h.31.input_layernorm"
|
||||
self_attn = "transformer.h.31.self_attention"
|
||||
lower_bound = 0
|
||||
self.run_optimize_gpu_model(Name, Model, Tokenizer, model_path, self_attn, layer_norm, lower_bound)
|
||||
|
||||
def Chatglm2_gpu_model(self, Name, Model, Tokenizer, model_path):
|
||||
# currently only need to compare the output of one self-attention layer.
|
||||
layer_norm = "transformer.encoder.layers.27.input_layernorm"
|
||||
|
|
|
|||
|
|
@ -29,7 +29,6 @@ print(f'Running on {device}')
|
|||
PROMPT = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
|
||||
TEST_MODEL_LIST = [
|
||||
("MPT-7B", AutoModelForCausalLM, AutoTokenizer, os.environ.get('MPT_7B_ORIGIN_PATH')),
|
||||
("Falcon-7B", AutoModelForCausalLM, AutoTokenizer, os.environ.get('FALCON_7B_ORIGIN_PATH')),
|
||||
]
|
||||
|
||||
|
||||
|
|
@ -60,7 +59,7 @@ def test_optimize_model(Name, Model, Tokenizer, model_path):
|
|||
tol = 1e-03
|
||||
num_false = torch.isclose(logits_optimized_model, logits_base_model, rtol=tol, atol=tol)\
|
||||
.flatten().tolist().count(False)
|
||||
|
||||
|
||||
percent_false = num_false / logits_optimized_model.numel()
|
||||
torch.xpu.empty_cache()
|
||||
del model
|
||||
|
|
|
|||
|
|
@ -1,117 +0,0 @@
|
|||
#
|
||||
# Copyright 2016 The BigDL Authors.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
|
||||
import os
|
||||
import pytest
|
||||
import gc
|
||||
|
||||
import torch
|
||||
from ipex_llm.transformers import AutoModelForCausalLM, AutoModel
|
||||
from transformers import LlamaTokenizer, AutoTokenizer
|
||||
|
||||
device = os.environ['DEVICE']
|
||||
print(f'Running on {device}')
|
||||
|
||||
PROMPT = "Once upon a time, there existed a little girl who liked to have adventures. She wanted to go to places and meet new people, and have fun"
|
||||
TEST_MODEL_LIST = [
|
||||
("Falcon-7B", AutoModelForCausalLM, AutoTokenizer, os.environ.get('FALCON_7B_ORIGIN_PATH'))
|
||||
]
|
||||
|
||||
class Test_Optimize_Gpu_Model:
|
||||
def setup_method(self):
|
||||
self.layer_outputs = []
|
||||
self.pre_layer_outputs = []
|
||||
|
||||
def run_optimize_gpu_model(self, Name, Model, Tokenizer, model_path, LayerNorm_layer, layer_before_LayerNorm, lower_bound):
|
||||
with torch.inference_mode():
|
||||
def pre_forward_hook(module, input, output, layer_name):
|
||||
self.pre_layer_outputs.append(output)
|
||||
|
||||
def forward_hook(module, input, output, layer_name):
|
||||
self.layer_outputs.append(output)
|
||||
|
||||
tokenizer = Tokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||||
input_ids = tokenizer.encode(PROMPT, return_tensors="pt").to(device)
|
||||
|
||||
model = Model.from_pretrained(model_path,
|
||||
load_in_4bit=True,
|
||||
optimize_model=False,
|
||||
trust_remote_code=True)
|
||||
model = model.to(device)
|
||||
for layer_name, layer_module in model.named_modules():
|
||||
if layer_name == layer_before_LayerNorm:
|
||||
layer_module.register_forward_hook(
|
||||
lambda module, input, output, layer_name=layer_name: pre_forward_hook(module, input,
|
||||
output, layer_name))
|
||||
if layer_name == LayerNorm_layer:
|
||||
layer_module.register_forward_hook(
|
||||
lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
|
||||
output, layer_name))
|
||||
logits_base_model = (model(input_ids)).logits
|
||||
# the list `layer_output` has only one element.
|
||||
layer_tensor = self.layer_outputs.pop()
|
||||
model.to('cpu')
|
||||
|
||||
opt_model = Model.from_pretrained(model_path,
|
||||
load_in_4bit=True,
|
||||
optimize_model=True,
|
||||
trust_remote_code=True)
|
||||
opt_model = opt_model.to(device)
|
||||
|
||||
|
||||
def replace_forward_hook(module, input, output, layer_name):
|
||||
output = self.pre_layer_outputs[0]
|
||||
return output
|
||||
|
||||
for layer_name, layer_module in opt_model.named_modules():
|
||||
if layer_name == layer_before_LayerNorm:
|
||||
layer_module.register_forward_hook(
|
||||
lambda module, input, output, layer_name=layer_name: replace_forward_hook(module, input,
|
||||
output, layer_name))
|
||||
if layer_name == LayerNorm_layer:
|
||||
layer_module.register_forward_hook(
|
||||
lambda module, input, output, layer_name=layer_name: forward_hook(module, input,
|
||||
output, layer_name))
|
||||
logits_optimized_model = (opt_model(input_ids)).logits
|
||||
# the list `layer_output` has only one element.
|
||||
opt_layer_tensor = self.layer_outputs[0]
|
||||
opt_model.to('cpu')
|
||||
|
||||
|
||||
LayerNorm_output_diff = []
|
||||
for i, (t1, t2) in enumerate(zip(layer_tensor, opt_layer_tensor)):
|
||||
LayerNorm_output_diff.append(t1 - t2)
|
||||
|
||||
max_diff_tensor = [torch.max(item).item() for item in LayerNorm_output_diff]
|
||||
print(max_diff_tensor)
|
||||
torch.xpu.empty_cache()
|
||||
del model
|
||||
del opt_model
|
||||
gc.collect()
|
||||
assert all(max_diff <= lower_bound for max_diff in max_diff_tensor)
|
||||
|
||||
@pytest.mark.parametrize('Name, Model, Tokenizer, model_path',TEST_MODEL_LIST)
|
||||
def test_dynamic_functions(self, Name, Model, Tokenizer, model_path):
|
||||
if Name == "Falcon-7B":
|
||||
self.Falcon_7B_gpu_model(Name, Model, Tokenizer, model_path)
|
||||
|
||||
|
||||
def Falcon_7B_gpu_model(self, Name, Model, Tokenizer, model_path):
|
||||
# currently only compare the output of the last LayerNorm layer.
|
||||
layer_before_LayerNorm = "transformer.h.30"
|
||||
LayerNorm_layer = "transformer.h.31.input_layernorm"
|
||||
lower_bound = 1e-5
|
||||
self.run_optimize_gpu_model(Name, Model, Tokenizer, model_path, LayerNorm_layer, layer_before_LayerNorm, lower_bound)
|
||||
|
|
@ -29,14 +29,10 @@ start=$(date "+%s")
|
|||
source ${ANALYTICS_ZOO_ROOT}/python/llm/test/run-llm-check-function.sh
|
||||
|
||||
pytest_check_error pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api.py -v -s
|
||||
pytest_check_error pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_layernorm.py -v -s
|
||||
|
||||
export BIGDL_LLM_XMX_DISABLED=1
|
||||
pytest_check_error pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_final_logits.py -v -s
|
||||
pytest_check_error pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_attention.py -v -s
|
||||
pytest_check_error pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_mlp.py -v -s
|
||||
pytest_check_error pytest ${LLM_INFERENCE_TEST_DIR}/test_transformers_api_RMSNorm.py -v -s
|
||||
unset BIGDL_LLM_XMX_DISABLED
|
||||
|
||||
now=$(date "+%s")
|
||||
time=$((now-start))
|
||||
|
|
|
|||
Loading…
Reference in a new issue