[ Misc ] fbgemm checkpoints

[robertgshaw2-neuralmagic]

SUMMARY:

• support fbgemm checkpoints from https://github.com/huggingface/transformers/pull/32047/files using our existing cutlass kernels
• note: updated with latest state that removes activation_scale_ub from the state dict and instead uses the config.json

from vllm import LLM
model = LLM("nm-testing/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform") 

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[robertgshaw2-neuralmagic]

/ready

[mgoin]

Nice work keeping it clean, LGTM

[vlasenkoalexey]

I don't think that apply_fp8_linear would work here. Using fbgemm it would look like:

        xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row(
            x, num_tokens, self.activation_scale_ub
        )
        y = torch.ops.fbgemm.f8f8bf16_rowwise(
            xq, layer.weight, x_scale, layer.weight_scale, use_fast_accum=True
        )

Particularly input_scale=None is most likely wrong, here is a reference implementation for quantize_fp8_per_row

def fp8_row_quantize_ref(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    # Quantize an input tensor and return the fp8 tensor and its inverse scale.
    x_row_max = torch.max(torch.abs(x), dim=1).values
    max_scaling_factor = E4M3_MAX_POS * 512.0  # Match kernel logics
    scale = torch.Tensor(E4M3_MAX_POS / x_row_max).clamp(max=max_scaling_factor)
    xq = (x * scale.unsqueeze(1)).to(fp8_e4m3)
    return xq, scale.reciprocal().to(torch.float32)

[robertgshaw2-neuralmagic]

yup, we are updating the quant kernel right now to use the activation_scale_ub

[robertgshaw2-neuralmagic]

@varun-sundar-rabindranath

[robertgshaw2-neuralmagic]

in combo with https://github.com/vllm-project/vllm/pull/6547/files

which enables per token scales

[vlasenkoalexey]

Besides both torch._scaled_mm and ops.scaled_fp8_quant expect scale to be scalar

[robertgshaw2-neuralmagic]

#6547 extends ops.scaled_fp8_quant to accepts per token scales (vector of scales)

[robertgshaw2-neuralmagic]

torch._scaled_mm will not be used.

cutlass_scaled_mm accepts per channel weights and per token activations

[robertgshaw2-neuralmagic]

I pulled in #6547 to this PR so you can see

[robertgshaw2-neuralmagic]

Adds #6593 adds the ub

[robertgshaw2-neuralmagic]

Okay, this PR currently now has the

• scale_ub
• uses dynamic per token activation scales

[mgoin]

LGTM!