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[LLM]Fix Arc falcon abnormal output issue (#9096)

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* update

* update

* fix error & style

* fix style

* update train

* to input_seq_size
This commit is contained in:
Yina Chen 2023-10-09 15:09:37 +08:00 committed by GitHub
parent 548e4dd5fe
commit 4c4f8d1663
1 changed files with 8 additions and 6 deletions
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14
python/llm/src/bigdl/llm/transformers/low_bit_linear.py
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@ -288,10 +288,10 @@ def ggml_matmul_src1_x_src0_t(src0: torch.Tensor,
class MatMulLowBit(torch.autograd.Function): class MatMulLowBit(torch.autograd.Function):
@staticmethod @staticmethod
def forward(ctx, A, weight): def forward(ctx, A, weight, input_seq_size):
ctx.is_empty = False ctx.is_empty = False
import linear_q4_0 import linear_q4_0
result = linear_q4_0.forward_new(A, weight.data, weight.qtype) result = linear_q4_0.forward_new(A, weight.data, weight.qtype, input_seq_size)
if any(ctx.needs_input_grad[:2]): if any(ctx.needs_input_grad[:2]):
ctx.tensors = (A, weight) ctx.tensors = (A, weight)
else: else:
@ -304,14 +304,14 @@ class MatMulLowBit(torch.autograd.Function):
if ctx.is_empty: if ctx.is_empty:
bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias) bias_grad = None if ctx.bias is None else torch.zeros_like(ctx.bias)
return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
req_gradA, _ = ctx.needs_input_grad req_gradA, _, _ = ctx.needs_input_grad
A, weight = ctx.tensors A, weight = ctx.tensors
grad_A, grad_weight = None, None grad_A, grad_weight = None, None
if req_gradA: if req_gradA:
dequant_weight = linear_q4_0.dequant(A, weight.data, weight.qtype) dequant_weight = linear_q4_0.dequant(A, weight.data, weight.qtype)
grad_A = torch.matmul(grad_output, dequant_weight.reshape(weight._shape)) grad_A = torch.matmul(grad_output, dequant_weight.reshape(weight._shape))
return grad_A, grad_weight return grad_A, grad_weight, None
class LowBitLinear(nn.Linear): class LowBitLinear(nn.Linear):
@ -353,10 +353,12 @@ class LowBitLinear(nn.Linear):
# disable the conversion when training # disable the conversion when training
if self.conver_to_half and x_2d.shape[0] > 1 and x_2d.dtype == torch.float32: if self.conver_to_half and x_2d.shape[0] > 1 and x_2d.dtype == torch.float32:
x_2d = x_2d.half() x_2d = x_2d.half()
input_seq_size = x_shape[1]
if self.training and x_2d.requires_grad: if self.training and x_2d.requires_grad:
result = MatMulLowBit.apply(x_2d, self.weight) result = MatMulLowBit.apply(x_2d, self.weight, input_seq_size)
else: else:
result = linear_q4_0.forward_new(x_2d, self.weight.data, self.weight.qtype) result = linear_q4_0.forward_new(x_2d, self.weight.data, self.weight.qtype,
input_seq_size)
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)
if self.bias is not None: if self.bias is not None: