[Kernel Slimming] Migrate NVFP4 kernels to JIT (#19437)
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@@ -199,13 +199,6 @@ void gelu_quick(at::Tensor& out, const at::Tensor& input);
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* From csrc/gemm
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*/
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torch::Tensor awq_dequantize(torch::Tensor qweight, torch::Tensor scales, torch::Tensor qzeros);
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void cutlass_scaled_fp4_mm(
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torch::Tensor& D,
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torch::Tensor const& A,
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torch::Tensor const& B,
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torch::Tensor const& A_sf,
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torch::Tensor const& B_sf,
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torch::Tensor const& alpha);
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torch::Tensor int8_scaled_mm(
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const torch::Tensor& mat_a,
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const torch::Tensor& mat_b,
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@@ -226,8 +219,6 @@ torch::Tensor fp8_blockwise_scaled_mm(
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const torch::Tensor& scales_a,
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const torch::Tensor& scales_b,
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const torch::Dtype& out_dtype);
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void scaled_fp4_quant(
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torch::Tensor& output, torch::Tensor const& input, torch::Tensor& output_scale, torch::Tensor const& input_scale);
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void sgl_per_token_group_quant_8bit(
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at::Tensor input,
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at::Tensor output_q,
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@@ -380,35 +371,6 @@ void fused_qk_norm_rope(
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double attention_factor,
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int64_t rotary_dim);
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void cutlass_fp4_group_mm(
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torch::Tensor& output,
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const torch::Tensor& a,
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const torch::Tensor& b,
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const torch::Tensor& a_blockscale,
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const torch::Tensor& b_blockscales,
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const torch::Tensor& alphas,
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const torch::Tensor& ab_strides,
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const torch::Tensor& c_strides,
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const torch::Tensor& problem_sizes,
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const torch::Tensor& expert_offsets,
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const torch::Tensor& sf_offsets);
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void scaled_fp4_experts_quant(
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torch::Tensor& output,
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torch::Tensor& output_scale,
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torch::Tensor const& input,
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torch::Tensor const& input_global_scale,
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torch::Tensor const& input_offset_by_experts,
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torch::Tensor const& output_scale_offset_by_experts);
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void silu_and_mul_scaled_fp4_experts_quant(
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torch::Tensor& output,
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torch::Tensor& output_scale,
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torch::Tensor const& input,
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torch::Tensor const& input_global_scale,
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torch::Tensor const& mask,
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bool use_silu_and_mul);
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/*
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* From csrc/moe/cutlass_moe/w4a8
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*/
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