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[LLM] apply allreduce and bias to training in LowBitLinear (#9395)

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Heyang Sun 2023-11-09 14:35:54 +08:00 committed by GitHub
parent 40cead6b5b
commit df8e4d7889
1 changed files with 8 additions and 8 deletions
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python/llm/src/bigdl/llm/transformers/low_bit_linear.py
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@ -463,7 +463,7 @@ class LowBitLinear(nn.Linear):
if self.training and x.requires_grad: if self.training and x.requires_grad:
result = MatMulLowBitCPU.apply(x, self.weight) result = MatMulLowBitCPU.apply(x, self.weight)
else: else:
# Step 1. convert if necessary, and compute a linear result # convert if necessary, and compute a linear result
if IS_SERVER and (not IS_SPR) and \ if IS_SERVER and (not IS_SPR) and \
self.qtype == SYM_INT4 and x_2d.shape[0] >= TORCH_LINEAR_THRESHOLD: self.qtype == SYM_INT4 and x_2d.shape[0] >= TORCH_LINEAR_THRESHOLD:
x0_fp32 = ggml_int4_convert_fp32(x0, self.weight_shape, self.weight_length) x0_fp32 = ggml_int4_convert_fp32(x0, self.weight_shape, self.weight_length)
@ -473,7 +473,7 @@ class LowBitLinear(nn.Linear):
result = ggml_matmul_src1_x_src0_t(x0, x_2d, self.weight_shape, self.qtype) result = ggml_matmul_src1_x_src0_t(x0, x_2d, self.weight_shape, self.qtype)
new_shape = x_shape[:-1] + (self.out_len,) new_shape = x_shape[:-1] + (self.out_len,)
result = result.view(new_shape) result = result.view(new_shape)
# Step 2. allreduce to combine partial results and add bias if necessary # allreduce to combine partial results and add bias if necessary
if self.mp_group is not None: if self.mp_group is not None:
# deepspeed distibuted mode # deepspeed distibuted mode
from deepspeed import comm as dist from deepspeed import comm as dist