fix: own fp8 linear backward via autograd Function
- backward used to read the global fp8 flag at loss.backward() time, so calling it outside fp8_autocast silently fell back to bf16 mm (953 ms cublas per step, 49.9% of the model step) - _LinearFp8(torch.autograd.Function) now owns the fwd/bwd pair: forward captures fmt/recipe/meta on ctx inside the autocast region, backward reads only ctx (scales from the meta rings, masks from ctx.needs_input_grad), so backward is fp8 wherever it runs - register the aten::linear impl on AutogradCUDA (replaces torch's generated linear formula that calls aten::linear_backward into the bf16 fallback) and keep the CUDA key for inference_mode - drop the aten::linear_backward override and fp8_linear_backward (dead paths) - regression test asserts the fp8 backward fires outside the autocast region and grads match the bf16 reference by direction/norm (E5M2 noise) - model step (0.67B, CE loss, batch 4x1024): backward GEMMs 953 -> 618 ms (1.54x), full step ~1.2x
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+53
-63
@@ -15,9 +15,11 @@ Usage::
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with fp8_autocast(enabled=True, fp8_format="hybrid"):
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logits = model(input_ids)
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loss.backward()
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loss.backward() # fp8 backward runs wherever it is called: the
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# forward captures the fmt/recipe/meta on the autograd node
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Importing this module registers the aten::linear CUDA implementation.
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Importing this module registers the aten::linear CUDA and AutogradCUDA
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implementations.
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Format defaults follow the ecosystem consensus: E4M3 for the forward pass,
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E5M2 for the backward (gradient) pass ("hybrid"); every operand's scale is a
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@@ -235,7 +237,7 @@ def fp8_autocast(
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with fp8_autocast(enabled=True, fp8_format="hybrid"):
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logits = model(input_ids) # aten::linear -> fp8 path
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loss.backward()
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loss.backward() # fp8 backward; state was captured at forward time
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Args:
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enabled: toggle fp8 dispatch for aten::linear.
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@@ -298,29 +300,48 @@ def fp8_linear_forward(x: torch.Tensor, w: torch.Tensor, bias=None):
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return out
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def fp8_linear_backward(g: torch.Tensor, x: torch.Tensor, w: torch.Tensor, masks):
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"""Scaled fp8 linear backward (called from aten::linear_backward).
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class _LinearFp8(torch.autograd.Function):
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"""The fp8 linear forward/backward pair (standard Function style).
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The gradient is quantized to the backward format (E5M2 in hybrid mode)
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and the dX / dW GEMMs share that single quantization.
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The forward runs inside the ``fp8_autocast`` region and captures the
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active fmt/recipe/meta on ``ctx``; the backward reads only the captured
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state, so ``loss.backward()`` may run after the context exits. The
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gradient is quantized once (E5M2 in hybrid mode) and the dX / dW GEMMs
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share that quantization; output masks come from ``needs_input_grad``.
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"""
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state = fp8_state()
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fmt = state.fp8_format.bwd()
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meta = state.get_weight_meta(w)
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if isinstance(state.recipe, DynamicScaling):
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sg = _dynamic_scale(g, state.recipe, fmt)
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sw = _dynamic_scale(w, state.recipe, fmt)
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sx = _dynamic_scale(x, state.recipe, fmt)
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else:
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if not meta.g_init:
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meta.init_g(g, fmt)
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sg, sw, sx = meta.g_scale, meta.w_scale, meta.x_scale
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grad_x, grad_w, grad_b, amax_g = linear_backward_fp8(
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g, x, w, masks, sg, sw, sx, fmt
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)
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if not isinstance(state.recipe, DynamicScaling):
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meta.update_g(amax_g, fmt)
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return grad_x, grad_w, grad_b
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@staticmethod
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def forward(ctx, x, w, bias):
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out = fp8_linear_forward(x, w, bias)
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state = fp8_state()
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ctx.save_for_backward(x, w)
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ctx.fmt_bwd = state.fp8_format.bwd()
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ctx.recipe = state.recipe
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ctx.meta = state.get_weight_meta(w)
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ctx.is_dynamic = isinstance(state.recipe, DynamicScaling)
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return out
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@staticmethod
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@torch.autograd.function.once_differentiable
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def backward(ctx, g):
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x, w = ctx.saved_tensors
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fmt = ctx.fmt_bwd
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if ctx.is_dynamic:
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sg = _dynamic_scale(g, ctx.recipe, fmt)
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sw = _dynamic_scale(w, ctx.recipe, fmt)
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sx = _dynamic_scale(x, ctx.recipe, fmt)
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else:
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meta = ctx.meta
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if not meta.g_init:
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meta.init_g(g, fmt)
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sg, sw, sx = meta.g_scale, meta.w_scale, meta.x_scale
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masks = list(ctx.needs_input_grad)
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grad_x, grad_w, grad_b, amax_g = linear_backward_fp8(
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g, x, w, masks, sg, sw, sx, fmt
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)
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if not ctx.is_dynamic:
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ctx.meta.update_g(amax_g, fmt)
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return grad_x, grad_w, grad_b if masks[2] else None
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# ---------------------------------------------------------------------------
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@@ -356,7 +377,7 @@ def _linear_cuda_impl(x: torch.Tensor, w: torch.Tensor, bias=None):
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and w.dtype == torch.bfloat16
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and _fp8_supported(x, w)
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):
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return fp8_linear_forward(x, w, bias)
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return _LinearFp8.apply(x, w, bias)
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return torch.ops.aten.linear.default.redispatch(
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torch._C.DispatchKeySet(torch._C.DispatchKey.CompositeImplicitAutograd),
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x,
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@@ -365,43 +386,12 @@ def _linear_cuda_impl(x: torch.Tensor, w: torch.Tensor, bias=None):
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)
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def _linear_backward_cuda_impl(input_tensor, grad_output, weight, output_mask):
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# Backward dim contract: grad_output is [..., N], weight is [N, K], so
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# the contraction check is grad_output.size(-1) == weight.size(0) (not the
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# forward's x.size(-1) == w.size(1) — that would silently skip fp8 for
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# every non-square layer).
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if (
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fp8_linear_enabled()
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and weight.dtype == torch.bfloat16
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and grad_output.dim() >= 2
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and weight.dim() == 2
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and grad_output.size(-1) == weight.size(0)
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and input_tensor.dim() >= 2
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and input_tensor.size(-1) == weight.size(1)
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):
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return fp8_linear_backward(grad_output, input_tensor, weight, list(output_mask))
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compute_dtype = weight.dtype
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grad = grad_output.to(compute_dtype)
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grad_2d = grad.reshape(-1, weight.size(0))
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input_2d = input_tensor.reshape(-1, input_tensor.size(-1)).to(compute_dtype)
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# Unneeded grads come back full-shape-but-uninitialized (mirroring the
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# fp8 binding), so reshape_as can never hit an empty tensor.
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grad_input = (
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torch.mm(grad_2d, weight).reshape_as(input_tensor)
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if output_mask[0]
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else torch.empty_like(input_tensor)
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)
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grad_weight = (
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torch.mm(grad_2d.t(), input_2d) if output_mask[1] else torch.empty_like(weight)
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)
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grad_bias = (
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grad.sum(dim=0)
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if output_mask[2]
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else torch.empty(0, device=grad.device, dtype=grad.dtype)
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)
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return grad_input, grad_weight, grad_bias
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_lib = Library("aten", "IMPL", "CUDA")
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_lib.impl("linear", _linear_cuda_impl)
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_lib.impl("linear_backward", _linear_backward_cuda_impl)
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# Also replace torch's generated linear autograd formula (which would call
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# aten::linear_backward after the fp8_autocast region exits). The fp8
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# backward is owned by _LinearFp8 with its state captured at forward time,
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# so loss.backward() works wherever it is called; the same CUDA registration
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# still covers inference_mode, where autograd keys are skipped entirely.
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_lib_autograd = Library("aten", "IMPL", "AutogradCUDA")
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_lib_autograd.impl("linear", _linear_cuda_impl)
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