Added matmul_mixed.
This commit is contained in:
Tim Dettmers
parent
7b764d3569
commit
c5c38ca19c
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@ -10,7 +10,8 @@ from .autograd._functions import (
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matmul,
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matmul_cublas,
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mm_cublas,
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matmul_fp8
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matmul_fp8,
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matmul_mixed
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)
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from .cextension import COMPILED_WITH_CUDA
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from .nn import modules
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@ -461,6 +461,190 @@ class MatMulFP8(torch.autograd.Function):
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return grad_A, grad_B, None, None, None
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class MatMul8bitMixed(torch.autograd.Function):
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@staticmethod
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def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState()):
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# default to pytorch behavior if inputs are empty
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ctx.is_empty = False
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if prod(A.shape) == 0:
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ctx.is_empty = True
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ctx.A = A
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ctx.B = B
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ctx.bias = bias
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if A.shape[-1] == B.shape[0]:
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return torch.empty(A.shape[:-1]+B.shape[1:], dtype=A.dtype, device=A.device)
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else:
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return torch.empty(A.shape[:-1]+B.shape[:1], dtype=A.dtype, device=A.device)
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# 1. Quantize A
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# 2. Quantize B
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# 3. Matmul
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# 4. Mixed-precision decomposition matmul
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# 5. Save state
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formatB = state.formatB
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input_shape = A.shape
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if state.outlier_pool is None:
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state.outlier_pool = GlobalOutlierPooler.get_instance()
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# Cast A to fp16
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if A.dtype != torch.float16:
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warnings.warn(f"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization")
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# 1. Quantize A
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if len(A.shape) == 3:
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A = A.view(-1, A.shape[-1]).contiguous()
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CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(
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A.to(torch.float16), threshold=state.threshold
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)
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if state.threshold > 0.0 and coo_tensorA is not None:
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if state.has_fp16_weights:
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idx = torch.unique(coo_tensorA.colidx).long()
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CA[:, idx] = 0
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CAt[:, idx] = 0
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subA = A[:, idx]
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state.subB = B[:, idx].t().contiguous()
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state.idx = idx
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else:
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if state.CxB is None:
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# B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions
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# we also need to convert it to the turing/ampere format
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state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
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else:
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if not state.has_fp16_weights and state.CxB is None:
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state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
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subA = None
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# 2. Quantize B
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if state.has_fp16_weights:
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has_grad = True if (getattr(B, "grad", None) is not None) else False
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is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1)
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if is_transposed:
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B = B.contiguous()
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if (state.is_training and not has_grad) or state.CxB is None:
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state.reset_grads()
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(
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CB,
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state.CBt,
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state.SCB,
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state.SCBt,
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coo_tensorB,
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) = F.double_quant(B.to(torch.float16))
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state.CxB, state.SB = F.transform(CB, to_order=formatB)
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else:
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has_grad = False
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if coo_tensorA is not None and not state.has_fp16_weights:
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# extract outliers
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outlier_idx = torch.unique(coo_tensorA.colidx)
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state.idx = outlier_idx
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# state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
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# if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
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# # do not use pool for 2nd FFN layer
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# state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
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# else:
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# state.idx = outlier_idx
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outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
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state.subB = (
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(outliers * state.SCB.view(-1, 1) / 127.0)
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.t()
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.contiguous()
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.to(A.dtype)
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)
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CA[:, state.idx.long()] = 0
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CAt[:, state.idx.long()] = 0
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subA = A[:, state.idx.long()]
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shapeB = state.SB[0]
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if len(input_shape) == 3:
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output_shape = (input_shape[0], input_shape[1], shapeB[0])
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else:
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output_shape = (input_shape[0], shapeB[0])
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# 3. Matmul
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C32A, SA = F.transform(CA, "col32")
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out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
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# we apply the fused bias here
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if bias is None or bias.dtype == torch.float16:
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output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias)
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output = output.to(A.dtype)
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else: # apply bias separately
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output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=None)
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output = output.to(A.dtype).add_(bias)
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# 4. Mixed-precision decomposition matmul
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if coo_tensorA is not None and subA is not None:
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output += torch.matmul(subA, state.subB)
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# 5. Save state
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ctx.state = state
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ctx.formatB = formatB
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ctx.grad_shape = input_shape
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ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype
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if any(ctx.needs_input_grad[:2]):
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ctx.tensors = (CAt, subA, A)
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ctx.tensor_states = (SCAt, state.idx)
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else:
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ctx.tensors = [None, None, None]
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ctx.tensor_states = (None, None)
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ctx.save_for_backward(None, None)
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clone_func = torch.clone if len(output_shape) == 3 else lambda x : x
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return clone_func(output.view(output_shape))
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@staticmethod
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def backward(ctx, grad_output):
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if ctx.is_empty:
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bias_grad = (None if ctx.bias is None else torch.zeros_like(ctx.bias))
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return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
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req_gradA, req_gradB, _, req_gradBias, _ = ctx.needs_input_grad
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CAt, subA, A = ctx.tensors
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SCAt, idx = ctx.tensor_states
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formatB = ctx.formatB
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state = ctx.state
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grad_A = grad_B = grad_bias = None
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if req_gradBias:
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# compute grad_bias first before changing grad_output dtype
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grad_bias = grad_output.sum(0, dtype=ctx.dtype_bias)
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# Cast grad_output to fp16
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if len(grad_output.shape) == 3:
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grad_output = grad_output.reshape(
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-1, grad_output.shape[-1]
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).contiguous()
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Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output.to(torch.float16))
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if req_gradB:
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grad_B = torch.matmul(grad_output.t(), A)
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if req_gradA:
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if state.CBt is not None:
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C32grad, Sgrad = F.transform(Cgrad, "col32")
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if state.CxBt is None:
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state.CxBt, state.SBt = F.transform(
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state.CBt, to_order=formatB, transpose=True
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)
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gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt)
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grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape).to(ctx.dtype_A)
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elif state.CB is not None:
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CB = state.CB.to(ctx.dtype_A, copy=True).mul_(state.SCB.unsqueeze(1).mul(1. / 127.0))
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grad_A = torch.matmul(grad_output, CB).view(ctx.grad_shape).to(ctx.dtype_A)
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else:
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raise Exception('State must contain either CBt or CB matrix for backward')
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return grad_A, grad_B, None, grad_bias, None
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def matmul(
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A: tensor,
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B: tensor,
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@ -479,7 +663,7 @@ def matmul_fp8(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tens
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return MatMulFP8.apply(A, B, out, fw_code, bw_code)
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def matmul(
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def matmul_mixed(
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A: tensor,
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B: tensor,
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out: tensor = None,
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@ -490,4 +674,4 @@ def matmul(
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state = state or MatmulLtState()
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if threshold > 0.0:
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state.threshold = threshold
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return MatMul8bitLt.apply(A, B, out, bias, state)
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return MatMul8bitMixed.apply(A, B, out, bias, state)
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@ -239,7 +239,7 @@ dim4 = torch.randint(32, 96, size=(n,)).tolist()
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dim2.append(0)
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decomp = [0.0, 6.0]
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funcs = [(torch.matmul, bnb.matmul)]
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funcs = [(torch.matmul, bnb.matmul_mixed)]
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str_funcs = ["matmul"]
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req_grad = [(False, False), (True, False), (True, True), (False, True)]
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req_grad = list(product([True, False], repeat=3))
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