[Kernel] Add Fused Layernorm + Dynamic-Per-Token Quant Kernels #6763

varun-sundar-rabindranath · 2024-07-24T23:43:48Z

Add Fused Layernorm + Dynamic-Per-Token Quant kernels.

Numbers:
Take away: The fused kernels out perform the unfused counterparts. It is important to note that we don't yet know how much performance gain is actually coming from the fusion (the fused kernels have vectorization optimizations that are not necessarily in their unfused counterparts).

GPU : A6000 x 1
Command: python3 benchmarks/fused_kernels/layernorm_rms_benchmarks.py

num-tokens x hidden-size x add-residual x input-dtype	unfused_int8_impl (us)	unfused_fp8_impl (us)	fused_int8_impl (us)	fused_fp8_impl (us)
N 1 x D 1024 x R True x DT torch.bfloat16	22.1	25.4	17.3	17.3
N 1 x D 1024 x R True x DT torch.float32	22.5	25.5	17	16.7
N 1 x D 1024 x R False x DT torch.bfloat16	25	28	16.8	16.9
N 1 x D 1024 x R False x DT torch.float32	24.8	27.8	17.1	17.1
N 1 x D 2048 x R True x DT torch.bfloat16	22.5	25.6	17.1	17.4
N 1 x D 2048 x R True x DT torch.float32	22.5	25.6	17.1	17.2
N 1 x D 2048 x R False x DT torch.bfloat16	24.7	27.8	17.2	17
N 1 x D 2048 x R False x DT torch.float32	25.1	28	16.8	16.9
N 1 x D 3072 x R True x DT torch.bfloat16	22.4	25.4	17.2	17.3
N 1 x D 3072 x R True x DT torch.float32	22.3	25.5	16.9	17.2
N 1 x D 3072 x R False x DT torch.bfloat16	25	27.9	17	17.2
N 1 x D 3072 x R False x DT torch.float32	25.1	27.9	17	17
N 1 x D 4096 x R True x DT torch.bfloat16	22.5	25.5	17	17.4
N 1 x D 4096 x R True x DT torch.float32	22.4	25.7	17.3	17.3
N 1 x D 4096 x R False x DT torch.bfloat16	25.2	28.1	16.9	17.1
N 1 x D 4096 x R False x DT torch.float32	24.9	28.2	17	17.2
N 1 x D 5120 x R True x DT torch.bfloat16	22.5	25.5	17.3	17.4
N 1 x D 5120 x R True x DT torch.float32	22.5	25.6	17.2	17.1
N 1 x D 5120 x R False x DT torch.bfloat16	25	28.1	17.3	17.3
N 1 x D 5120 x R False x DT torch.float32	25.2	28.2	17.2	17.4
N 1 x D 6144 x R True x DT torch.bfloat16	22.4	25.7	17.4	17.3
N 1 x D 6144 x R True x DT torch.float32	22.7	25.8	17.2	17.3
N 1 x D 6144 x R False x DT torch.bfloat16	25.3	28.1	17.1	17.4
N 1 x D 6144 x R False x DT torch.float32	26.5	28.1	16.4	16.5
N 1 x D 7168 x R True x DT torch.bfloat16	22.6	25.6	16.5	16.5
N 1 x D 7168 x R True x DT torch.float32	22.6	25.4	16.5	16.5
N 1 x D 7168 x R False x DT torch.bfloat16	25.2	27.9	16.5	16.6
N 1 x D 7168 x R False x DT torch.float32	25.2	28.2	16.5	16.7
N 16 x D 1024 x R True x DT torch.bfloat16	23.5	25.3	17	17.1
N 16 x D 1024 x R True x DT torch.float32	23.7	25.3	17	17.2
N 16 x D 1024 x R False x DT torch.bfloat16	26.3	27.7	17	17
N 16 x D 1024 x R False x DT torch.float32	26.2	27.9	17.1	17.1
N 16 x D 2048 x R True x DT torch.bfloat16	23.5	25.6	17.1	17.1
N 16 x D 2048 x R True x DT torch.float32	23.9	25.5	16.9	17.1
N 16 x D 2048 x R False x DT torch.bfloat16	26.3	27.9	17.1	17.1
N 16 x D 2048 x R False x DT torch.float32	26.5	27.7	17	16.9
N 16 x D 3072 x R True x DT torch.bfloat16	23.7	25.4	17	17.1
N 16 x D 3072 x R True x DT torch.float32	23.7	25.9	17.1	17.1
N 16 x D 3072 x R False x DT torch.bfloat16	26.3	28	17.1	17.1
N 16 x D 3072 x R False x DT torch.float32	26.4	28	16.8	17.1
N 16 x D 4096 x R True x DT torch.bfloat16	23.6	25.2	17.1	17.1
N 16 x D 4096 x R True x DT torch.float32	23.7	25.5	17	17
N 16 x D 4096 x R False x DT torch.bfloat16	26.1	27.7	16.9	16.9
N 16 x D 4096 x R False x DT torch.float32	26.4	28	17.1	17.1
N 16 x D 5120 x R True x DT torch.bfloat16	23.4	25.3	17.4	17.1
N 16 x D 5120 x R True x DT torch.float32	23.6	25.4	17.1	17
N 16 x D 5120 x R False x DT torch.bfloat16	26.4	28.2	17.1	17
N 16 x D 5120 x R False x DT torch.float32	26.3	28	17.1	17.1
N 16 x D 6144 x R True x DT torch.bfloat16	23.9	25.4	17.2	17.1
N 16 x D 6144 x R True x DT torch.float32	24.2	25.4	17	17.2
N 16 x D 6144 x R False x DT torch.bfloat16	26.4	27.9	17.1	17.4
N 16 x D 6144 x R False x DT torch.float32	26.6	27.9	17	17.1
N 16 x D 7168 x R True x DT torch.bfloat16	23.9	25.4	17	17.3
N 16 x D 7168 x R True x DT torch.float32	23.6	25.2	16.9	17.1
N 16 x D 7168 x R False x DT torch.bfloat16	26.3	27.9	17.1	17.1
N 16 x D 7168 x R False x DT torch.float32	26.4	27.9	16.9	16.9
N 32 x D 1024 x R True x DT torch.bfloat16	23.6	25.4	17	17.3
N 32 x D 1024 x R True x DT torch.float32	23.5	25.6	17.3	17.2
N 32 x D 1024 x R False x DT torch.bfloat16	26.5	28.1	17.1	17.2
N 32 x D 1024 x R False x DT torch.float32	26.5	28.1	17.1	17.3
N 32 x D 2048 x R True x DT torch.bfloat16	23.9	25.4	17.3	17.4
N 32 x D 2048 x R True x DT torch.float32	23.7	25.4	17.2	17.3
N 32 x D 2048 x R False x DT torch.bfloat16	26.5	28	17.3	17.2
N 32 x D 2048 x R False x DT torch.float32	26.4	28.2	17.1	17.2
N 32 x D 3072 x R True x DT torch.bfloat16	24.1	25.6	17.4	17.4
N 32 x D 3072 x R True x DT torch.float32	24.1	25.5	17.3	17.2
N 32 x D 3072 x R False x DT torch.bfloat16	26.5	28.1	17.2	17.2
N 32 x D 3072 x R False x DT torch.float32	26.5	27.8	17.3	17.2
N 32 x D 4096 x R True x DT torch.bfloat16	24	25.5	17	17.4
N 32 x D 4096 x R True x DT torch.float32	23.8	25.5	17.1	17.2
N 32 x D 4096 x R False x DT torch.bfloat16	26.4	27.8	16.9	17.1
N 32 x D 4096 x R False x DT torch.float32	26.5	28.1	16.9	17.1
N 32 x D 5120 x R True x DT torch.bfloat16	23.9	25.4	17.1	17.2
N 32 x D 5120 x R True x DT torch.float32	23.6	25.5	17.1	17
N 32 x D 5120 x R False x DT torch.bfloat16	26.3	28	17.1	17
N 32 x D 5120 x R False x DT torch.float32	26.4	28.2	17.2	17.1
N 32 x D 6144 x R True x DT torch.bfloat16	23.7	25.3	17.1	17.3
N 32 x D 6144 x R True x DT torch.float32	23.9	25.4	17	17.2
N 32 x D 6144 x R False x DT torch.bfloat16	26.6	28.2	17	17
N 32 x D 6144 x R False x DT torch.float32	26.5	28	17.2	17.2
N 32 x D 7168 x R True x DT torch.bfloat16	23.9	25.5	17.1	17.2
N 32 x D 7168 x R True x DT torch.float32	23.8	25.4	17	17.5
N 32 x D 7168 x R False x DT torch.bfloat16	26.4	28.2	17	17.3
N 32 x D 7168 x R False x DT torch.float32	26.3	28.1	17.1	17.2
N 64 x D 1024 x R True x DT torch.bfloat16	23.9	25.5	17.2	17.3
N 64 x D 1024 x R True x DT torch.float32	24	25.4	17.1	17.2
N 64 x D 1024 x R False x DT torch.bfloat16	26.4	27.9	17.1	17.2
N 64 x D 1024 x R False x DT torch.float32	26.5	28	17	17.1
N 64 x D 2048 x R True x DT torch.bfloat16	24.1	25.6	17.3	17.2
N 64 x D 2048 x R True x DT torch.float32	24	25.7	17.3	17.2
N 64 x D 2048 x R False x DT torch.bfloat16	26.4	28.1	17	17.2
N 64 x D 2048 x R False x DT torch.float32	26	28.1	17.1	17.3
N 64 x D 3072 x R True x DT torch.bfloat16	24	25.7	17.2	17.4
N 64 x D 3072 x R True x DT torch.float32	23.5	25.5	17.1	17.4
N 64 x D 3072 x R False x DT torch.bfloat16	26.4	27.9	17.2	17.2
N 64 x D 3072 x R False x DT torch.float32	26.7	28.1	17.2	17
N 64 x D 4096 x R True x DT torch.bfloat16	23.9	25.4	17.1	17.3
N 64 x D 4096 x R True x DT torch.float32	23.7	25.2	17.2	17.4
N 64 x D 4096 x R False x DT torch.bfloat16	26.4	27.9	17.2	17.2
N 64 x D 4096 x R False x DT torch.float32	26.4	28.2	17	17.2
N 64 x D 5120 x R True x DT torch.bfloat16	24	25.5	17.1	17.4
N 64 x D 5120 x R True x DT torch.float32	23.9	25.4	16.9	17
N 64 x D 5120 x R False x DT torch.bfloat16	26.3	27.9	17	16.9
N 64 x D 5120 x R False x DT torch.float32	26.2	27.8	17	17.1
N 64 x D 6144 x R True x DT torch.bfloat16	23.9	25.6	17	17.1
N 64 x D 6144 x R True x DT torch.float32	23.7	25.7	16.9	16.9
N 64 x D 6144 x R False x DT torch.bfloat16	26	28.1	17	17
N 64 x D 6144 x R False x DT torch.float32	25.9	28	16.9	16.9
N 64 x D 7168 x R True x DT torch.bfloat16	23.5	25.5	17	16.9
N 64 x D 7168 x R True x DT torch.float32	23.7	25.5	16.9	16.9
N 64 x D 7168 x R False x DT torch.bfloat16	26.1	27.9	17	17.3
N 64 x D 7168 x R False x DT torch.float32	26.6	28.1	17.1	17.4
N 128 x D 1024 x R True x DT torch.bfloat16	24	25.4	16.9	17.3
N 128 x D 1024 x R True x DT torch.float32	24.1	25.4	16.9	17.1
N 128 x D 1024 x R False x DT torch.bfloat16	26.1	28	16.8	17.1
N 128 x D 1024 x R False x DT torch.float32	26.2	27.9	16.9	17
N 128 x D 2048 x R True x DT torch.bfloat16	23.8	25.7	17	17.2
N 128 x D 2048 x R True x DT torch.float32	23.7	25.5	17.1	17.1
N 128 x D 2048 x R False x DT torch.bfloat16	26.4	28.1	17.2	17.1
N 128 x D 2048 x R False x DT torch.float32	26	27.9	16.9	17.3
N 128 x D 3072 x R True x DT torch.bfloat16	23.5	25.5	17	17
N 128 x D 3072 x R True x DT torch.float32	23.6	25.6	17.1	17.2
N 128 x D 3072 x R False x DT torch.bfloat16	26.3	28	17.2	17.2
N 128 x D 3072 x R False x DT torch.float32	26.3	27.9	17.2	17.2
N 128 x D 4096 x R True x DT torch.bfloat16	23.8	25.5	17.1	17.1
N 128 x D 4096 x R True x DT torch.float32	23.9	25.6	17	17.2
N 128 x D 4096 x R False x DT torch.bfloat16	26.4	27.8	17.3	17.4
N 128 x D 4096 x R False x DT torch.float32	26.4	27.9	17.2	17.3
N 128 x D 5120 x R True x DT torch.bfloat16	23.6	25.6	17.2	17.1
N 128 x D 5120 x R True x DT torch.float32	24.1	25.7	17.2	17.3
N 128 x D 5120 x R False x DT torch.bfloat16	26.3	27.9	17.1	17.1
N 128 x D 5120 x R False x DT torch.float32	26.3	28	16.9	17.1
N 128 x D 6144 x R True x DT torch.bfloat16	24.1	25.5	17.2	17.2
N 128 x D 6144 x R True x DT torch.float32	24.1	28.5	17.2	17.3
N 128 x D 6144 x R False x DT torch.bfloat16	26.2	28	17.3	17.3
N 128 x D 6144 x R False x DT torch.float32	26.4	28.1	17.1	17.2
N 128 x D 7168 x R True x DT torch.bfloat16	24.1	25.9	17.3	17.5
N 128 x D 7168 x R True x DT torch.float32	27.8	34.8	20.3	21.4
N 128 x D 7168 x R False x DT torch.bfloat16	26.5	28.2	16.9	17.1
N 128 x D 7168 x R False x DT torch.float32	26.2	28.6	17.1	16.9
N 256 x D 1024 x R True x DT torch.bfloat16	23.6	25.6	17	17.1
N 256 x D 1024 x R True x DT torch.float32	23.6	25.7	17	17.2
N 256 x D 1024 x R False x DT torch.bfloat16	26.4	28.2	17	17.2
N 256 x D 1024 x R False x DT torch.float32	26.5	28	17	17
N 256 x D 2048 x R True x DT torch.bfloat16	23.6	25.2	17	17
N 256 x D 2048 x R True x DT torch.float32	23.7	25.6	17.1	17.2
N 256 x D 2048 x R False x DT torch.bfloat16	26.5	28.2	17	16.9
N 256 x D 2048 x R False x DT torch.float32	26.4	28.1	17	17.2
N 256 x D 3072 x R True x DT torch.bfloat16	24.3	25.9	17.3	17.3
N 256 x D 3072 x R True x DT torch.float32	24.3	31.2	17.3	17.3
N 256 x D 3072 x R False x DT torch.bfloat16	26.3	28.2	17.2	17.1
N 256 x D 3072 x R False x DT torch.float32	26.7	28.4	17.2	17.2
N 256 x D 4096 x R True x DT torch.bfloat16	23.7	25.6	17.4	17.5
N 256 x D 4096 x R True x DT torch.float32	32.6	41.9	23	24.4
N 256 x D 4096 x R False x DT torch.bfloat16	26.5	28.2	17.3	17.3
N 256 x D 4096 x R False x DT torch.float32	26.9	36	17.1	17.1
N 256 x D 5120 x R True x DT torch.bfloat16	23.8	27	16.8	18
N 256 x D 5120 x R True x DT torch.float32	45.4	53.6	32.3	35.8
N 256 x D 5120 x R False x DT torch.bfloat16	26.5	30.2	17.3	17.2
N 256 x D 5120 x R False x DT torch.float32	32.9	41.8	17.2	17
N 256 x D 6144 x R True x DT torch.bfloat16	25.7	34.5	20.3	26.4
N 256 x D 6144 x R True x DT torch.float32	58.4	67.9	36.8	39.2
N 256 x D 6144 x R False x DT torch.bfloat16	28.5	36.8	17	17
N 256 x D 6144 x R False x DT torch.float32	41	50.2	18.2	20.5
N 256 x D 7168 x R True x DT torch.bfloat16	31	39.7	23.7	27.4
N 256 x D 7168 x R True x DT torch.float32	69.7	80.9	42.7	44.7
N 256 x D 7168 x R False x DT torch.bfloat16	32.2	41.4	17.2	17.1
N 256 x D 7168 x R False x DT torch.float32	48.9	59.6	19.7	22.2
N 512 x D 1024 x R True x DT torch.bfloat16	23.5	25.7	17.4	17.1
N 512 x D 1024 x R True x DT torch.float32	23.8	25.5	17.1	17.2
N 512 x D 1024 x R False x DT torch.bfloat16	26.7	28.1	17.2	17
N 512 x D 1024 x R False x DT torch.float32	26.6	28.5	17	17.4
N 512 x D 2048 x R True x DT torch.bfloat16	23.9	26.6	17.3	17.4
N 512 x D 2048 x R True x DT torch.float32	37.2	49.4	24.6	25.2
N 512 x D 2048 x R False x DT torch.bfloat16	26.4	31.9	17.1	17.3
N 512 x D 2048 x R False x DT torch.float32	29	40.8	17.2	17.2
N 512 x D 3072 x R True x DT torch.bfloat16	29.3	41	21.7	24.5
N 512 x D 3072 x R True x DT torch.float32	59.6	73.7	35.4	35.5
N 512 x D 3072 x R False x DT torch.bfloat16	30.5	41.9	17.2	18.1
N 512 x D 3072 x R False x DT torch.float32	40.2	51.9	18.9	21
N 512 x D 4096 x R True x DT torch.bfloat16	40	52.3	26.4	29.1
N 512 x D 4096 x R True x DT torch.float32	82.9	99.4	45.8	46.1
N 512 x D 4096 x R False x DT torch.bfloat16	40.2	50.7	18.4	21.6
N 512 x D 4096 x R False x DT torch.float32	52.9	69.2	21.7	24.5
N 512 x D 5120 x R True x DT torch.bfloat16	53.6	64.3	36	43.4
N 512 x D 5120 x R True x DT torch.float32	106	126.5	57.2	64.7
N 512 x D 5120 x R False x DT torch.bfloat16	49.3	59.4	24.1	29
N 512 x D 5120 x R False x DT torch.float32	65.3	83.8	28.5	32.8
N 512 x D 6144 x R True x DT torch.bfloat16	65.7	78.2	38.8	44.9
N 512 x D 6144 x R True x DT torch.float32	127.4	151	67.3	71.8
N 512 x D 6144 x R False x DT torch.bfloat16	57.6	68.8	25.7	31.1
N 512 x D 6144 x R False x DT torch.float32	75.3	98.5	30.4	34.7
N 512 x D 7168 x R True x DT torch.bfloat16	79.7	93.3	43.6	48.9
N 512 x D 7168 x R True x DT torch.float32	149.5	176.6	77.5	80.4
N 512 x D 7168 x R False x DT torch.bfloat16	66	79.1	27.4	33.3
N 512 x D 7168 x R False x DT torch.float32	88.2	113.7	33.6	37.5

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github-actions · 2024-07-24T23:44:02Z

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varun-sundar-rabindranath · 2024-07-24T23:47:14Z

~~Marked it draft for now. Will open it up after some performance profiling.~~ Done.

varun-sundar-rabindranath · 2024-07-25T20:11:26Z

/ready

tlrmchlsmth

Left some in-line comments. I see some potential issues with using int for large activations that will need to be resolved. Along those lines, I thing being explicit about using int32_t instead of int will improve things.

csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu

tlrmchlsmth · 2024-07-31T21:09:04Z

csrc/quantization/fused_kernels/layernorm_utils.cuh

+__device__ void compute_rms(float* rms, scalar_t const* __restrict__ input,
+                            int const hidden_size, float const epsilon,
+                            scalar_t const* __restrict__ residual = nullptr) {
+  int const token_offset = blockIdx.x * hidden_size;


this should be an int64_t

Thanks for catching this. Updated the ints to int32_t / int64_t 👍

csrc/quantization/fused_kernels/layernorm_utils.cuh

tlrmchlsmth · 2024-07-31T21:11:43Z

csrc/quantization/fused_kernels/quant_conversions.cuh

+template <typename scalar_t, typename quant_type_t, bool is_scale_inverted>
+__device__ void scaled_quant_conversion(quant_type_t* __restrict__ output,
+                                        scalar_t const* __restrict__ input,
+                                        float const scale, int const tid,
+                                        int const num_elements,
+                                        int const step) {
+  for (int i = tid; i < num_elements; i += step) {
+    output[i] = ScaledQuant<quant_type_t, is_scale_inverted>(input[i], scale);
+  }
+}
+
+namespace vectorized {
+
+// Vectorized version of scaled_quant_conversion
+template <typename scalar_t, typename quant_type_t, bool is_scale_inverted>
+__device__ void scaled_quant_conversion(quant_type_t* __restrict__ out,
+                                        scalar_t const* __restrict__ input,
+                                        float const scale, int const tid,
+                                        int const num_elems, int const step) {


Are these functions used anywhere?

No. it isn't yet. I plan to use them in the static-per-tensor case. On second thought, let me remove it from here and introduce it in static-per-tensor PR.

github-actions · 2024-11-01T02:05:43Z

This pull request has been automatically marked as stale because it has not had any activity within 90 days. It will be automatically closed if no further activity occurs within 30 days. Leave a comment if you feel this pull request should remain open. Thank you!

mergify · 2024-11-26T05:50:56Z

This pull request has merge conflicts that must be resolved before it can be
merged. Please rebase the PR, @varun-sundar-rabindranath.

https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/syncing-a-fork

ProExpertProg · 2024-12-13T23:52:13Z

@varun-sundar-rabindranath close in favor of #10906 ?

varun-sundar-rabindranath · 2024-12-14T00:00:16Z

close in favor of #10906 . Thanks @ProExpertProg

varun-sundar-rabindranath marked this pull request as draft July 24, 2024 23:43

varun-sundar-rabindranath marked this pull request as ready for review July 25, 2024 15:31

github-actions bot added the ready ONLY add when PR is ready to merge/full CI is needed label Jul 25, 2024

Varun Sundar Rabindranath added 21 commits July 26, 2024 21:32

Fused rms-norm + quant kernels

b6d1966

remove int8 from entity names

a2b6243

fixup dynamic per token quant dispatch

b85f6a9

wip

0798edb

fix compile errors

98e95e6

format

6baabd0

passing tests

bb948bf

remove clutter

3a72a2b

format

10aedae

refactor

a1e2f96

remove unused file

dac7c77

cruft

85a75ef

remove unused code

48de6d0

format

7262016

remove cruft

bb69a06

Add fused benchmarks

5130ab3

format

03c374a

pickle bench results

6a21468

format

3d9b474

fix label and sublabel

68e694c

relax tolerance

c505155

varun-sundar-rabindranath force-pushed the varun/fused-rms-quant-dyn-per-token branch from 3642313 to c505155 Compare July 26, 2024 21:33

varun-sundar-rabindranath changed the title ~~[ Kernel ] Add Fused Layernorm + Dynamic-Per-Token Quant Kernels~~ [Kernel] Add Fused Layernorm + Dynamic-Per-Token Quant Kernels Jul 26, 2024

tlrmchlsmth reviewed Jul 31, 2024

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varun-sundar-rabindranath mentioned this pull request Jul 31, 2024

[Kernel] Add Fused Layernorm + Asymmetric int8 Quant #7010

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Varun Sundar Rabindranath added 3 commits August 1, 2024 14:20

fix integer bits

6465084

remove unused vectorized quant conversion code

6b984d6

remove cruft

a270cda

varun-sundar-rabindranath requested a review from tlrmchlsmth August 1, 2024 14:38

github-actions bot added the stale Over 90 days of inactivity label Nov 1, 2024

mergify bot added the ci/build label Nov 1, 2024

github-actions bot added unstale Recieved activity after being labelled stale and removed stale Over 90 days of inactivity labels Nov 4, 2024

simon-mo requested a review from WoosukKwon as a code owner November 26, 2024 05:49

mergify bot added the needs-rebase label Nov 26, 2024

varun-sundar-rabindranath closed this Dec 14, 2024

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[Kernel] Add Fused Layernorm + Dynamic-Per-Token Quant Kernels #6763

[Kernel] Add Fused Layernorm + Dynamic-Per-Token Quant Kernels #6763

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