fix llama related import (#12611)

python/llm/src/ipex_llm/transformers/models/cohere.py

@@ -45,7 +45,7 @@ import torch.nn.functional as F
 import torch.nn as nn
 import torch.utils.checkpoint
 from typing import Optional, Tuple, List
-from ipex_llm.transformers.models.llama import repeat_kv
+from ipex_llm.transformers.models.utils import repeat_kv
 from ipex_llm.transformers.models.utils import extend_kv_cache, append_kv_cache
 from transformers.models.cohere.modeling_cohere import apply_rotary_pos_emb
 from ipex_llm.transformers.models.utils import is_enough_kv_cache_room_4_36

python/llm/src/ipex_llm/transformers/models/decilm.py

@@ -34,8 +34,8 @@
 import torch
 from typing import Optional, Tuple
 import torch.nn.functional as F
+from ipex_llm.transformers.models.utils import repeat_kv
 from ipex_llm.transformers.models.utils import apply_rotary_pos_emb
-from ipex_llm.transformers.models.llama import repeat_kv
 from ipex_llm.transformers.models.utils import should_use_fuse_rope
 from ipex_llm.transformers.models.utils import update_past_key_value
 from ipex_llm.utils.common import invalidInputError

python/llm/test/inference_gpu/test_layer_fast_rope.py

@@ -1,124 +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.
-#
-#
-# This file is adapted from 
-# https://github.com/Dao-AILab/flash-attention/blob/main/tests/layers/test_rotary.py
-#
-# Copyright (c) 2023, Tri Dao.
-#
-
-import os
-import pytest
-import torch
-import intel_extension_for_pytorch as ipex
-import torch.nn.functional as F
-from einops import rearrange
-from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding
-from transformers.models.llama.modeling_llama import (
-    apply_rotary_pos_emb as apply_rotary_pos_emb_llama,
-)
-from ipex_llm.transformers.layers.rope_embedding import apply_fast_rope_embedding
-
-device = os.environ['DEVICE']
-print(f'Running on {device}')
-if 'xpu' not in device:
-    print(f"The layer.fast_rope test should running on xpu")
-
-# llama-style rotary embedding
-@pytest.mark.parametrize("seqlen_offset", [0, 711])
-@pytest.mark.parametrize("rotary_emb_fraction", [0.5, 1.0])
-def test_rotary(rotary_emb_fraction, seqlen_offset):
-    device = "xpu"
-    dtype = torch.float16
-    rtol, atol = (1e-3, 5e-3)
-    # set seed
-    torch.random.manual_seed(0)
-    batch_size = 8
-    seqlen_total = 2048
-    seqlen = seqlen_total - seqlen_offset
-    seqlen_offset = torch.tensor([[seqlen_offset]], device=device)
-    nheads = 32
-    headdim = 128
-    rotary_dim = int(headdim * rotary_emb_fraction)
-    qkv = torch.randn(
-        batch_size, seqlen, 3, nheads, headdim, device=device, dtype=dtype,
-        requires_grad=True
-    )
-    rotary_llama = LlamaRotaryEmbedding(rotary_dim, seqlen_total, device=device)
-    # Doesn't matter what tensor we pass in, rotary_llama only uses the device
-    # of the tensor
-    cos_llama, sin_llama = rotary_llama(qkv, seq_len=seqlen_total)
-    cos_llama, sin_llama = cos_llama.to(dtype=dtype), sin_llama.to(dtype=dtype)
-    q_pt = (
-        rearrange(qkv[:, :, 0, :, :rotary_dim], "b s h d -> b h s d")
-        .detach()
-        .clone()
-        .requires_grad_(True)
-    )
-    k_pt = (
-        rearrange(qkv[:, :, 1, :, :rotary_dim], "b s h d -> b h s d")
-        .detach()
-        .clone()
-        .requires_grad_(True)
-    )
-    q_pt_fast = (
-        rearrange(qkv[:, :, 0, :, :rotary_dim], "b s h d -> b h s d")
-        .detach()
-        .clone()
-        .requires_grad_(True)
-    )
-    k_pt_fast = (
-        rearrange(qkv[:, :, 1, :, :rotary_dim], "b s h d -> b h s d")
-        .detach()
-        .clone()
-        .requires_grad_(True)
-    )
-    q_llama, k_llama = apply_rotary_pos_emb_llama(q_pt, k_pt, cos_llama,
-                                                  sin_llama, position_ids=seqlen_offset)
-    q_fast, k_fast = apply_fast_rope_embedding(q_pt_fast, k_pt_fast,
-                                               position_ids=seqlen_offset,
-                                               model_family="llama")
-    assert torch.allclose(
-        rearrange(q_llama, "b h s d -> b s h d"),
-        rearrange(q_fast, "b h s d -> b s h d"), rtol=rtol, atol=atol
-    )
-    assert torch.allclose(
-        rearrange(k_llama, "b h s d -> b s h d"),
-        rearrange(k_fast, "b h s d -> b s h d"), rtol=rtol, atol=atol
-    )
-
-    g = torch.randn_like(q_fast)
-    q_fast.backward(g)
-    k_fast.backward(g)
-    q_llama.backward(g)
-    k_llama.backward(g)
-
-    assert torch.allclose(
-        q_pt.grad,
-        q_pt_fast.grad,
-        rtol=rtol,
-        atol=atol,
-    )
-
-    assert torch.allclose(
-        k_pt.grad,
-        k_pt_fast.grad,
-        rtol=rtol,
-        atol=atol,
-    )
-
-if __name__ == "__main__":
-    pytest.main([__file__])

python/llm/test/run-llm-inference-tests-gpu.sh

@@ -43,14 +43,3 @@ time=$((now-start))
 
 echo "Bigdl-llm gpu inference tests finished"
 echo "Time used:$time seconds"
-
-echo "# Start testing layers.fast_rope_embedding"
-start=$(date "+%s")
-
-pytest_check_error pytest ${LLM_INFERENCE_TEST_DIR}/test_layer_fast_rope.py -v -s
-
-now=$(date "+%s")
-time=$((now-start))
-
-echo "Bigdl-llm gpu layers.fast_rope_embedding tests finished"
-echo "Time used:$time seconds"