[Kernel/Model] Migrate mamba_ssm and causal_conv1d kernels to vLLM (#7651)
This commit is contained in:
Mor Zusman
324
tests/kernels/test_mamba_ssm.py
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324
tests/kernels/test_mamba_ssm.py
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import pytest
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import torch
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import torch.nn.functional as F
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from einops import rearrange, repeat
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from vllm.model_executor.layers.mamba.ops.mamba_ssm import (
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selective_scan_fn, selective_state_update)
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def selective_state_update_ref(state,
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x,
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dt,
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A,
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B,
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C,
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D=None,
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z=None,
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dt_bias=None,
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dt_softplus=False):
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"""
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Argument:
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state: (batch, dim, dstate) or (batch, nheads, dim, dstate)
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x: (batch, dim) or (batch, nheads, dim)
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dt: (batch, dim) or (batch, nheads, dim)
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A: (dim, dstate) or (nheads, dim, dstate)
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B: (batch, dstate) or (batch, ngroups, dstate)
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C: (batch, dstate) or (batch, ngroups, dstate)
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D: (dim,) or (nheads, dim)
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z: (batch, dim) or (batch, nheads, dim)
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dt_bias: (dim,) or (nheads, dim)
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Return:
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out: (batch, dim) or (batch, nheads, dim)
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"""
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has_heads = state.dim() > 3
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if state.dim() == 3:
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state = state.unsqueeze(1)
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if x.dim() == 2:
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x = x.unsqueeze(1)
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if dt.dim() == 2:
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dt = dt.unsqueeze(1)
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if A.dim() == 2:
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A = A.unsqueeze(0)
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if B.dim() == 2:
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B = B.unsqueeze(1)
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if C.dim() == 2:
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C = C.unsqueeze(1)
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if D is not None and D.dim() == 1:
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D = D.unsqueeze(0)
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if z is not None and z.dim() == 2:
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z = z.unsqueeze(1)
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if dt_bias is not None and dt_bias.dim() == 1:
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dt_bias = dt_bias.unsqueeze(0)
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batch, nheads, dim, dstate = state.shape
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assert x.shape == (batch, nheads, dim)
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assert dt.shape == x.shape
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assert A.shape == (nheads, dim, dstate)
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ngroups = B.shape[1]
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assert nheads % ngroups == 0, "nheads must be divisible by ngroups"
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assert B.shape == (batch, ngroups, dstate)
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assert C.shape == B.shape
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if D is not None:
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assert D.shape == (nheads, dim)
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if z is not None:
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assert z.shape == x.shape
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if dt_bias is not None:
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assert dt_bias.shape == (nheads, dim)
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dt = dt + dt_bias
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dt = F.softplus(dt) if dt_softplus else dt
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dA = torch.exp(rearrange(dt, "b h d -> b h d 1") *
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A) # (batch, nheads, dim, dstate)
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B = repeat(B, "b g n -> b (g h) n",
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h=nheads // ngroups) # (batch, nheads, dstate)
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C = repeat(C, "b g n -> b (g h) n",
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h=nheads // ngroups) # (batch, nheads, dstate)
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dB = rearrange(dt, "b h d -> b h d 1") * rearrange(
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B, "b h n -> b h 1 n") # (batch, nheads, dim, dstate)
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state.copy_(state * dA +
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dB * rearrange(x, "b h d -> b h d 1")) # (batch, dim, dstate
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out = torch.einsum("bhdn,bhn->bhd", state.to(C.dtype), C)
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if D is not None:
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out += (x * D).to(out.dtype)
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out = (out if z is None else out * F.silu(z)).to(x.dtype)
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if not has_heads:
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out = out.squeeze(1)
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return out
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def selective_scan_ref(u,
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delta,
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A,
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B,
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C,
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D=None,
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z=None,
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delta_bias=None,
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delta_softplus=False,
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return_last_state=False,
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position_indices=None,
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prev_state=None):
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"""
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u: r(B D L)
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delta: r(B D L)
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A: c(D N) or r(D N)
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B: c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L)
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C: c(D N) or r(B N L) or r(B N 2L) or r(B G N L) or (B G N L)
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D: r(D)
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z: r(B D L)
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delta_bias: r(D), fp32
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prev_state: r(B D N), fp32
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out: r(B D L)
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last_state (optional): r(B D dstate) or c(B D dstate)
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"""
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dtype_in = u.dtype
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u = u.float()
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delta = delta.float()
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if delta_bias is not None:
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delta = delta + delta_bias[..., None].float()
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if delta_softplus:
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delta = F.softplus(delta)
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batch, dim, dstate = u.shape[0], A.shape[0], A.shape[1]
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is_variable_B = B.dim() >= 3
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is_variable_C = C.dim() >= 3
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B = B.float()
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C = C.float()
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x = A.new_zeros((batch, dim, dstate)) if prev_state is None else prev_state
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ys = []
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deltaA = torch.exp(torch.einsum('bdl,dn->bdln', delta, A))
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if not is_variable_B:
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deltaB_u = torch.einsum('bdl,dn,bdl->bdln', delta, B, u)
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else:
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if B.dim() == 3:
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deltaB_u = torch.einsum('bdl,bnl,bdl->bdln', delta, B, u)
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else:
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B = repeat(B, "B G N L -> B (G H) N L", H=dim // B.shape[1])
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deltaB_u = torch.einsum('bdl,bdnl,bdl->bdln', delta, B, u)
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if is_variable_C and C.dim() == 4:
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C = repeat(C, "B G N L -> B (G H) N L", H=dim // C.shape[1])
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last_state = None
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for i in range(u.shape[2]):
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if position_indices is not None and position_indices[0, i] == 0:
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x = deltaB_u[:, :, i]
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else:
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x = deltaA[:, :, i] * x + deltaB_u[:, :, i]
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if not is_variable_C:
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y = torch.einsum('bdn,dn->bd', x, C)
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else:
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if C.dim() == 3:
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y = torch.einsum('bdn,bn->bd', x, C[:, :, i])
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else:
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y = torch.einsum('bdn,bdn->bd', x, C[:, :, :, i])
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if i == u.shape[2] - 1:
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last_state = x
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ys.append(y)
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y = torch.stack(ys, dim=2) # (batch dim L)
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out = y if D is None else y + u * rearrange(D, "d -> d 1")
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if z is not None:
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out = out * F.silu(z)
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out = out.to(dtype=dtype_in)
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return out if not return_last_state else (out, last_state)
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@pytest.mark.parametrize('wtype', [torch.float32])
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@pytest.mark.parametrize('itype', [torch.float32])
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@pytest.mark.parametrize('seqlen', [128, 256, 512, 1024, 2048, 4096])
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@pytest.mark.parametrize("return_last_state", [True])
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@pytest.mark.parametrize('has_delta_bias', [True])
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@pytest.mark.parametrize('delta_softplus', [True])
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@pytest.mark.parametrize('has_z', [True])
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@pytest.mark.parametrize('has_D', [True])
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@pytest.mark.parametrize("varBC_groups", [1, 2])
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@pytest.mark.parametrize("is_variable_C", [True])
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@pytest.mark.parametrize("is_variable_B", [True])
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@pytest.mark.parametrize("scan_chunks", [1, 2, 3])
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def test_selective_scan(is_variable_B, is_variable_C, varBC_groups, has_D,
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has_z, has_delta_bias, delta_softplus,
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return_last_state, seqlen, itype, wtype, scan_chunks):
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if varBC_groups > 1 and (not is_variable_B or not is_variable_C):
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pytest.skip() # This config is not applicable
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device = 'cuda'
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rtol, atol = (6e-4, 2e-3) if itype == torch.float32 else (3e-3, 5e-3)
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if itype == torch.bfloat16:
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rtol, atol = 3e-2, 5e-2
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rtolw, atolw = (1e-3, 1e-3)
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if has_z: # If we have z, the errors on the weights seem higher
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rtolw = max(rtolw, rtol)
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atolw = max(atolw, atol)
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# set seed
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torch.random.manual_seed(0)
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batch_size = 2
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dim = 4
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dstate = 8
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A = (-0.5 * torch.rand(dim, dstate, device=device, dtype=wtype))
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if not is_variable_B:
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B_shape = [dim, dstate]
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elif varBC_groups == 1:
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B_shape = [batch_size, dstate, seqlen]
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else:
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B_shape = [batch_size, varBC_groups, dstate, seqlen]
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B = torch.randn(B_shape,
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device=device,
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dtype=wtype if not is_variable_B else itype)
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if not is_variable_C:
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C_shape = [dim, dstate]
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elif varBC_groups == 1:
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C_shape = [batch_size, dstate, seqlen]
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else:
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C_shape = [batch_size, varBC_groups, dstate, seqlen]
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C = torch.randn(C_shape,
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device=device,
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dtype=wtype if not is_variable_C else itype)
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D = torch.randn(dim, device=device, dtype=torch.float32) if has_D else None
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z = torch.randn(batch_size, dim, seqlen, device=device,
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dtype=itype) if has_z else None
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delta_bias = (0.5 * torch.rand(dim, device=device, dtype=torch.float32)
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) if has_delta_bias else None
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u = torch.randn(batch_size, dim, seqlen, device=device, dtype=itype)
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delta = (0.5 *
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torch.rand(batch_size, dim, seqlen, device=device, dtype=itype))
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state = None
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state_ref = None
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out = None
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out_ref = None
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outs = []
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for c in range(scan_chunks):
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chunked_prompt_len = seqlen // scan_chunks
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chunk_start = chunked_prompt_len * c
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chunk_end = chunked_prompt_len * (c + 1)
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if c == scan_chunks - 1:
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chunk_end = seqlen
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_B = B
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if is_variable_B:
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_B = B[..., chunk_start:chunk_end]
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_C = C
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if is_variable_B:
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_C = C[..., chunk_start:chunk_end]
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_z = z
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if has_z:
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assert z is not None
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_z = z[..., chunk_start:chunk_end]
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out, *rest = selective_scan_fn(u[..., chunk_start:chunk_end],
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delta[..., chunk_start:chunk_end],
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A,
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_B,
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_C,
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D,
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z=_z,
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delta_bias=delta_bias,
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delta_softplus=delta_softplus,
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return_last_state=return_last_state,
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prev_state=state if c > 0 else None)
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outs.append(out)
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if return_last_state:
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state = rest[0]
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if len(outs) > 1:
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out = torch.cat(outs, dim=-1)
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out_ref, *rest = selective_scan_ref(u,
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delta,
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A,
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B,
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C,
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D,
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z=z,
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delta_bias=delta_bias,
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delta_softplus=delta_softplus,
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return_last_state=return_last_state)
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if return_last_state:
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state_ref = rest[0]
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assert out is not None and out_ref is not None
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assert torch.allclose(out, out_ref, rtol=rtol, atol=atol)
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if return_last_state:
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assert state is not None and state_ref is not None
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assert torch.allclose(state, state_ref, rtol=rtol, atol=atol)
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@pytest.mark.parametrize("itype",
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[torch.float32, torch.float16, torch.bfloat16])
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@pytest.mark.parametrize("has_z", [False, True])
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@pytest.mark.parametrize("dstate", [16, 32, 64])
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@pytest.mark.parametrize("dim", [2048, 2048 + 16, 4096])
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def test_selective_state_update(dim, dstate, has_z, itype):
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device = "cuda"
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rtol, atol = (3e-4, 1e-3) if itype == torch.float32 else (5e-3, 1e-2)
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if itype == torch.bfloat16:
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rtol, atol = 1e-2, 5e-2
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if torch.version.hip:
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atol *= 2
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# set seed
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torch.random.manual_seed(0)
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batch_size = 1
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state = torch.randn(batch_size, dim, dstate, dtype=itype, device=device)
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x = torch.randn(batch_size, dim, device=device, dtype=itype)
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dt = torch.randn(batch_size, dim, device=device, dtype=itype)
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dt_bias = torch.rand(dim, device=device) - 4.0
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A = -torch.rand(dim, dstate, device=device) - 1.0
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B = torch.randn(batch_size, dstate, device=device)
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C = torch.randn(batch_size, dstate, device=device)
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D = torch.randn(dim, device=device)
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z = torch.randn_like(x) if has_z else None
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state_ref = state.detach().clone()
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out = selective_state_update(state,
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x,
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dt,
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A,
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B,
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C,
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D=D,
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z=z,
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dt_bias=dt_bias,
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dt_softplus=True)
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out_ref = selective_state_update_ref(state_ref,
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x,
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dt,
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A,
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B,
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C,
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D=D,
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z=z,
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dt_bias=dt_bias,
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dt_softplus=True)
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assert torch.allclose(state, state_ref, rtol=rtol, atol=atol)
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assert torch.allclose(out, out_ref, rtol=rtol, atol=atol)
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