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[NPU] Optimize Qwen2 lm_head to use INT4 (#12072)
* temp save * update * fix * fix * Split lm_head into 7 parts & remove int8 for lm_head when sym_int4 * Simlify and add condition to code * Small fix * refactor some code * fix style * fix style * fix style * fix * fix * temp sav e * refactor * fix style * further refactor * simplify code * meet code review * fix style --------- Co-authored-by: Yuwen Hu <[email protected]>
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python/llm/src/ipex_llm/transformers/npu_models/lm_head.py
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| # | ||
| # 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. | ||
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| import torch | ||
| from torch import nn | ||
| import numpy as np | ||
| from intel_npu_acceleration_library.backend import NNFactory | ||
| from intel_npu_acceleration_library.backend.bindings import lib as backend_lib | ||
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| class LMHeadLinear(NNFactory): | ||
| """Quantized Linear class for sliced lm_head, computing a matrix matrix multiplication | ||
| with weights prefetching.""" | ||
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| def __init__( | ||
| self, | ||
| inC: int, | ||
| outC: int, | ||
| batch: int, | ||
| split_num: int = 2, | ||
| profile: bool = False, | ||
| device: str = "NPU", | ||
| dtype: np.dtype = np.int8, | ||
| ): | ||
| """Initialize the LMHeadLinear class. | ||
| Args: | ||
| inC (int): input channels | ||
| outC (int): output channels | ||
| batch (int): batch | ||
| split_num (int): split in_features of lm_head to how many parts | ||
| profile (bool): Enable/Disable profiling. Defaults to False. | ||
| device (str): Target device, default to "NPU". | ||
| dtype (np.dtype): weights datatype. Defaults to np.int8. | ||
| """ | ||
| super().__init__(profile, device) | ||
| self.inC, self.outC = inC, outC | ||
| self.batch = batch | ||
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| input = self.parameter((self.batch, self.inC)) | ||
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| self.split_num = split_num | ||
| split_size = self.inC // split_num // 2 * 2 | ||
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| for i in range(self.split_num): | ||
| start_idx = i * split_size | ||
| end_idx = (i + 1) * split_size if i < self.split_num - 1 else self.inC | ||
| input_slice = self.slice(input, begin=[0, start_idx], | ||
| end=[self.batch, end_idx]) | ||
| linear_slice = self.linear(input_slice, outC, split_size, bias=False, wt_dtype=dtype) | ||
| if i == 0: | ||
| res = linear_slice | ||
| else: | ||
| res += linear_slice | ||
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| print("start compiling lm_head") | ||
| self.compile() | ||
| print("end compiling lm_head") | ||
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| def run( | ||
| self, X: np.ndarray | ||
| ) -> np.ndarray: | ||
| """Run the layer: $X * (W * S)^T$ . | ||
| Args: | ||
| X (np.ndarray): activation | ||
| Raises: | ||
| RuntimeError: Input, weights or scale shape mismatch | ||
| Returns: | ||
| np.ndarray: result | ||
| """ | ||
| self.prefetchWeights(1, verify_size=False) | ||
| self.set_input_tensor(X, 0) | ||
| self.elapsed = backend_lib.run(self._mm) | ||
| if len(self.out) == 1: | ||
| return self.out[0] | ||
| return self.out | ||
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| class SlicedLMHead(nn.Module): | ||
| def __init__(self, weight, bias, split_num): | ||
| super().__init__() | ||
| self.split_num = split_num | ||
| self.outC, self.inC = weight.shape | ||
| split_size = weight.size(1) // split_num // 2 * 2 | ||
| self.lm_heads = nn.Sequential() | ||
| for i in range(split_num): | ||
| new_linear = torch.nn.Linear(0, 0, bias=False) | ||
| start_idx = i * split_size | ||
| end_idx = (i + 1) * split_size if i < split_num - 1 else weight.size(1) | ||
| new_weight = torch.nn.Parameter(weight[:, start_idx:end_idx], | ||
| requires_grad=False) | ||
| new_linear.weight = new_weight | ||
| new_linear.in_features = new_weight.size(1) | ||
| new_linear.out_features = new_weight.size(0) | ||
| self.lm_heads.append(new_linear) | ||
| self.bias = bias | ||
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| def forward(self, hidden_states): | ||
| if hidden_states.size(0) * hidden_states.size(1) == 1: | ||
| original_shape = hidden_states.shape | ||
| x_2d = hidden_states.view(-1, hidden_states.shape[-1]) | ||
| target_shape = tuple(list(original_shape[:-1]) + [self.outC]) | ||
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| out = self.fused_lm_head.run(x_2d.numpy()) | ||
| logits = torch.from_numpy(out) | ||
| logits = logits.view(target_shape) | ||
| else: | ||
| split_size = hidden_states.size(-1) // self.split_num // 2 * 2 | ||
| logits = None | ||
| for i in range(self.split_num): | ||
| start_idx = i * split_size | ||
| end_idx = (i + 1) * split_size if i < self.split_num - 1 else self.inC | ||
| hidden_states_slice = hidden_states[:, :, start_idx:end_idx] | ||
| logits_slice = self.lm_heads[i](hidden_states_slice) | ||
| if logits is None: | ||
| logits = logits_slice | ||
| else: | ||
| logits += logits_slice | ||
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| if self.bias is None: | ||
| return logits | ||
| return logits + self.bias | ||
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| def get_weight_dtype(self): | ||
| return self.lm_heads[0].weight.dtype | ||
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| def get_fused_lm_head(self): | ||
| np_dtype = np.uint8 if self.get_weight_dtype() == torch.uint8 else np.int8 | ||
| self.fused_lm_head = LMHeadLinear(self.inC, self.outC, 1, self.split_num, | ||
| False, "NPU", dtype=np_dtype) | ||
| fused_lm_head_weights = [(self.lm_heads[i].weight.data.numpy(), | ||
| self.lm_heads[i].scale.data.numpy()) | ||
| for i in range(self.split_num)] | ||
| self.fused_lm_head.setWeights(1, self.lm_heads[0].op_id, | ||
| *fused_lm_head_weights) |