137 lines
5.5 KiB
Python
137 lines
5.5 KiB
Python
import triton
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import triton.language as tl
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@triton.jit
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def _fc1_bias_gelu_kernel(
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X_ptr, W_ptr, B_ptr, Y_ptr,
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M: tl.constexpr, N: tl.constexpr, K: tl.constexpr,
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stride_xm, stride_xk,
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stride_wn, stride_wk, # W is [N, K]
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stride_ym, stride_yn,
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BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
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GROUP_SIZE_M: tl.constexpr
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):
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"""Kernel for computing the matmul C = A x B.
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A has shape (M, K), B has shape (K, N) and C has shape (M, N)
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"""
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# -----------------------------------------------------------
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# Map program ids `pid` to the block of C it should compute.
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# This is done in a grouped ordering to promote L2 data reuse.
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# See above `L2 Cache Optimizations` section for details.
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pid = tl.program_id(axis=0)
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num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
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num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
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num_pid_in_group = GROUP_SIZE_M * num_pid_n
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group_id = pid // num_pid_in_group
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first_pid_m = group_id * GROUP_SIZE_M
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group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
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pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m)
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pid_n = (pid % num_pid_in_group) // group_size_m
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# -----------------------------------------------------------
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# Add some integer bound assumptions.
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# This helps to guide integer analysis in the backend to optimize
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# load/store offset address calculation
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tl.assume(pid_m >= 0)
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tl.assume(pid_n >= 0)
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tl.assume(stride_xm > 0)
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tl.assume(stride_xk > 0)
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tl.assume(stride_wn > 0)
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tl.assume(stride_wk > 0)
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tl.assume(stride_ym > 0)
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tl.assume(stride_yn > 0)
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# ----------------------------------------------------------
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# Create pointers for the first blocks of A and B.
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# We will advance this pointer as we move in the K direction
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# and accumulate
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# `X_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
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# `w_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
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# See above `Pointer Arithmetic` section for details
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offs_xm = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
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offs_wn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
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offs_k = tl.arange(0, BLOCK_SIZE_K)
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X_ptrs = X_ptr + (offs_xm[:, None] * stride_xm + offs_k[None, :] * stride_xk)
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W_ptrs = W_ptr + (offs_k[:, None] * stride_wk + offs_wn[None, :] * stride_wn)
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# -----------------------------------------------------------
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# Iterate to compute a block of the C matrix.
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# We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
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# of fp32 values for higher accuracy.
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# `accumulator` will be converted back to fp16 after the loop.
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
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# Load the next block of A and B, generate a mask by checking the K dimension.
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# If it is out of bounds, set it to 0.
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a = tl.load(X_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
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b = tl.load(W_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
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# We accumulate along the K dimension.
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accumulator = tl.dot(a, b, accumulator)
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# Advance the ptrs to the next K block.
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X_ptrs += BLOCK_SIZE_K * stride_xk
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W_ptrs += BLOCK_SIZE_K * stride_wk
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# Bias add (broadcast over M)
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b = tl.load(B_ptr + offs_wn, mask=offs_wn < N, other=0.0).to(tl.float32)
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accumulator = accumulator + b[None, :]
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# GELU(tanh) epilogue (fp32 compute)
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# You can fuse arbitrary activation functions here
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# while the accumulator is still in FP32!
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c0 = 0.7978845608028654
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c1 = 0.044715
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y = c0 * (accumulator + c1 * accumulator * accumulator * accumulator)
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# exp_2y = tl.exp(2.0 * y)
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# tanh_y = (exp_2y - 1.0) / (exp_2y + 1.0)
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ay = tl.abs(y)
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e = tl.exp(-2.0 * ay)
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t = (1.0 - e) / (1.0 + e)
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t = tl.where(y >= 0, t, -t)
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accumulator = 0.5 * accumulator * (1.0 + t)
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y = accumulator.to(tl.float16)
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# -----------------------------------------------------------
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# Write back the block of the output matrix C with masks.
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offs_ym = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_yn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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Y_ptrs = Y_ptr + stride_ym * offs_ym[:, None] + stride_yn * offs_yn[None, :]
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Y_mask = (offs_ym[:, None] < M) & (offs_yn[None, :] < N)
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tl.store(Y_ptrs, y, mask=Y_mask)
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def fc1_bias_gelu_triton(x, w, b,
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BLOCK_SIZE_M=128, BLOCK_SIZE_N=128, BLOCK_SIZE_K=32, GROUP_SIZE_M=8, num_warps=8):
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"""
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x: [M, K]
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w: [N, K] (PyTorch Linear weight layout)
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b: [N]
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returns y: [M, N]
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"""
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import torch
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assert x.is_cuda and w.is_cuda and b.is_cuda
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M, K = x.shape
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N, K2 = w.shape
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assert K == K2 and b.shape[0] == N
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y = torch.empty((M, N), device=x.device, dtype=x.dtype)
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grid = (triton.cdiv(M, BLOCK_SIZE_M) * triton.cdiv(N, BLOCK_SIZE_N),)
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_fc1_bias_gelu_kernel[grid](
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x, w, b, y,
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M=M, N=N, K=K,
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stride_xm=x.stride(0), stride_xk=x.stride(1),
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stride_wn=w.stride(0), stride_wk=w.stride(1),
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stride_ym=y.stride(0), stride_yn=y.stride(1),
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BLOCK_SIZE_M=BLOCK_SIZE_M, BLOCK_SIZE_N=BLOCK_SIZE_N, BLOCK_SIZE_K=BLOCK_SIZE_K,
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GROUP_SIZE_M=GROUP_SIZE_M,
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num_warps=num_warps
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)
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return y
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import functools
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from torch import nn
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def path_linear_forward(self, hidden_state):
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return self.linear_fc2(fc1_bias_gelu_triton(hidden_state, self.linear_fc1.weight, self.linear_fc1.bias)) |