mirror of https://github.com/hpcaitech/ColossalAI
73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
import torch
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try:
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import triton
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import triton.language as tl
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HAS_TRITON = True
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except ImportError:
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HAS_TRITON = False
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print("please install triton from https://github.com/openai/triton")
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if HAS_TRITON:
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'''
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this kernel function is modified from
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https://github.com/ModelTC/lightllm/blob/main/lightllm/models/llama/triton_kernel/rmsnorm.py
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'''
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@triton.jit
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def _rms_norm_fwd_fused(
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X, # pointer to the input
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Y, # pointer to the output
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W, # pointer to the weights
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stride, # how much to increase the pointer when moving by 1 row
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N, # number of columns in X
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eps, # epsilon to avoid division by zero
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BLOCK_SIZE: tl.constexpr,
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):
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# Map the program id to the row of X and Y it should compute.
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row = tl.program_id(0)
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Y += row * stride
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X += row * stride
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# Compute variance
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_var = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
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for off in range(0, N, BLOCK_SIZE):
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cols = off + tl.arange(0, BLOCK_SIZE)
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x = tl.load(X + cols, mask=cols < N, other=0.).to(tl.float32)
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_var += x * x
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var = tl.sum(_var, axis=0) / N
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rstd = 1 / tl.sqrt(var + eps)
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# Normalize and apply linear transformation
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for off in range(0, N, BLOCK_SIZE):
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cols = off + tl.arange(0, BLOCK_SIZE)
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mask = cols < N
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w = tl.load(W + cols, mask=mask).to(tl.float32)
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x = tl.load(X + cols, mask=mask, other=0.).to(tl.float32)
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x_hat = x * rstd
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y = x_hat * w
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# Write output
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tl.store(Y + cols, y.to(tl.float16), mask=mask)
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def rmsnorm_forward(x, weight, eps):
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# allocate output
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y = torch.empty_like(x)
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# reshape input data into 2D tensor
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x_arg = x.view(-1, x.shape[-1])
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M, N = x_arg.shape
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# Less than 64KB per feature: enqueue fused kernel
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MAX_FUSED_SIZE = 65536 // x.element_size()
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BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(N))
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# print("BLOCK_SIZE:", BLOCK_SIZE)
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if N > BLOCK_SIZE:
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raise RuntimeError("This layer norm doesn't support feature dim >= 64KB.")
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# heuristics for number of warps
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num_warps = min(max(BLOCK_SIZE // 256, 1), 8)
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# print(BLOCK_SIZE, num_warps, "block_size, numwarps")
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BLOCK_SIZE = 128 * 2 * 2 * 2 * 2 * 2 * 2 * 2
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num_warps = 8
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# enqueue kernel
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_rms_norm_fwd_fused[(M,)](x_arg, y, weight,
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x_arg.stride(0), N, eps,
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BLOCK_SIZE=BLOCK_SIZE, num_warps=num_warps)
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return y
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