Added fp8 simulation layer.
This commit is contained in:
Tim Dettmers
parent
c9f505064e
commit
6bdb6c351e
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@ -10,6 +10,7 @@ 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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)
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from .cextension import COMPILED_WITH_CUDA
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from .nn import modules
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@ -390,6 +390,98 @@ class MatMul8bitLt(torch.autograd.Function):
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return grad_A, grad_B, None, grad_bias, None
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class MatMulFP8(torch.autograd.Function):
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# forward is the same, but we added the fallback for pre-turing GPUs
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# backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
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@staticmethod
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def forward(ctx, A, B, out=None, bias=None, fw_code=None, bw_code=None):
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# default of 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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B_shape = state[1]
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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. Dequantize
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# 2. MatmulnN
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cA, state = F.quantize_blockwise(A, code=fw_code, blocksize=1024)
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fp8A = F.dequantize_blockwise(cA, state)
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cB, state = F.quantize_blockwise(B, code=fw_code, blocksize=1024)
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fp8B = F.dequantize_blockwise(cB, state)
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output = torch.nn.functional.linear(fp8A, fp8B)
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# 3. Save state
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ctx.bw_code = bw_code
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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 = (fp8A, fp8B)
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else:
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ctx.tensors = (None, None)
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return output
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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_gradBias, _= ctx.needs_input_grad
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fp8A, B = ctx.tensors
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state = ctx.state
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grad_A, grad_B, grad_bias = None, None, None
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cgrad_out, state = F.quantize_blockwise(grad_ouput, code=ctx.bw_code, blocksize=1024)
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fp8out = F.dequantize_blockwise(cgrad_out, state)
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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 = fp8out.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(-1, grad_output.shape[-1]).contiguous()
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# not supported by PyTorch. TODO: create work-around
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#if req_gradB: grad_B = torch.matmul(grad_output.t(), A)
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if req_gradA: grad_A = torch.matmul(fp8out, B.t())
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if req_gradB: grad_B = torch.matmul(fp8A.t(), fp8out)
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return grad_A, grad_B, None, grad_bias, None, None
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def matmul(
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A: tensor,
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B: tensor,
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out: tensor = None,
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state: MatmulLtState = None,
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threshold=0.0,
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bias=None
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):
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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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def matmul_fp8(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None, bias=None):
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assert quant_state is not None
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return MatMulFP8.apply(A, B, out, bias, fw_code, bw_code)
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def matmul(
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A: tensor,
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@ -343,3 +343,19 @@ class Linear8bitLt(nn.Linear):
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del self.state.CxB
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return out
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class LinearFP8(nn.Linear):
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def __init__(self, input_features, output_features, bias=True):
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super().__init__(input_features, output_features, bias)
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self.bw_code = None
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self.fw_code = None
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def forward(self, x: torch.Tensor):
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if self.fw_code is None:
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self.bw_code = F.create_fp8_map(True, 5, 2, 8).to(x.device)
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self.fw_code = F.create_fp8_map(True, 4, 3, 8).to(x.device)
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out = bnb.matmul_fp8(x, self.weight.t(), bias=self.bias, fw_code=self.fw_code, code=self.bw_code)
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return out
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@ -429,3 +429,103 @@ def test_matmullt(
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if req_grad[2]:
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torch.testing.assert_allclose(gradBias1, gradBias2)
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n = 1
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k = 3
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dim1 = torch.randint(16, 64, size=(n,)).tolist()
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dim2 = torch.randint(32, 96, size=(n,)).tolist()
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dim3 = torch.randint(32, 96, size=(n,)).tolist()
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dim4 = torch.randint(32, 96, size=(n,)).tolist()
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dim2.append(0)
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funcs = [(torch.matmul, bnb.matmul_fp8)]
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str_funcs = ["matmul"]
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req_grad = list(product([True, False], repeat=3))
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req_grad_str = []
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for c in req_grad:
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strval = ''
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for v in c:
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if v == True: strval += 'T'
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else: strval += 'F'
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req_grad_str.append(strval)
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transpose = [(False, True), (False, False)]
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str_transpose = ["NT", "NN"]
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dtype = [torch.float16, torch.float32]
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has_fp16_weights = [True, False]
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has_bias = [True, False]
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values = list(product(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, has_bias))
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str_values = list(product(dim1, dim2, dim3, dim4, str_funcs, dtype, req_grad_str, str_transpose, has_bias))
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names = ["dim1_{}_dim2_{}_dim3_{}_dim4_{}_func_{}_dtype_{}_requires_grad_{}_transpose_{}_has_bias_{}".format(*vals) for vals in str_values]
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
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@pytest.mark.parametrize( "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, has_bias", values, ids=names)
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def test_matmul_fp8( dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, has_bias):
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dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
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dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
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if has_bias == False:
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req_grad = list(req_grad)
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req_grad[2] = False
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for i in range(k):
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# normal multiply
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if funcs[0] in [torch.mm, torch.matmul]:
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A = torch.randn(size=dimA, device="cuda", requires_grad=req_grad[0], dtype=dtype)
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B = torch.randn(size=dimB, device="cuda", requires_grad=req_grad[1], dtype=dtype)
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target = torch.randn(size=(dim2, dim4), device="cuda", requires_grad=req_grad[1], dtype=dtype)
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bias = None
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bias2 = None
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if has_bias:
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bias = torch.randn(dim4, device='cuda', dtype=dtype, requires_grad=req_grad[2])
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bias2 = bias.clone()
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torch.nn.init.xavier_uniform_(B)
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B2, quant_state = bnb.functional.quantize_fp8(B)
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if not transpose[0] and transpose[1]:
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out_torch = funcs[0](A, B.t())
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out_bnb = funcs[1](A, B2.t(), quant_state, bias=bias2)
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elif not transpose[0] and not transpose[1]:
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out_torch = funcs[0](A, B)
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out_bnb = funcs[1](A, B2, quant_state, bias=bias2)
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if has_bias:
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out_torch += bias
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assert out_bnb.dtype == A.dtype, f"bnb matmullt received {A.dtype} but returned {out_bnb.dtype}"
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n = out_bnb.numel()
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err = torch.abs(out_bnb - out_torch).float().mean().item()
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if n > 0:
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assert err < 0.115
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if any(req_grad):
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out_bnb.data.copy_(out_torch)
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torch.cuda.synchronize()
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loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
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loss_bnb.backward()
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gradA1 = A.grad
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gradB1 = B.grad
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A.grad = None
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B.grad = None
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if has_bias:
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gradBias1 = bias.grad
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bias.grad = None
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loss_torch = torch.nn.functional.mse_loss( out_torch, target ).mean()
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loss_torch.backward()
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gradA2 = A.grad
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gradB2 = B.grad
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A.grad = None
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B.grad = None
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if has_bias:
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gradBias2 = bias.grad
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bias.grad = None
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if req_grad[0]:
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torch.testing.assert_allclose( gradA1, gradA2, atol=0.015, rtol=0.1)
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if req_grad[2]:
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torch.testing.assert_allclose(gradBias1, gradBias2)
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