# Adapted from AutoGPTQ auto_gptq: https://github.com/PanQiWei/AutoGPTQ import torch import triton import triton.language as tl from .custom_autotune import autotune, matmul248_kernel_config_pruner @triton.jit def tanh(x): # Tanh is just a scaled sigmoid return 2 * tl.sigmoid(2 * x) - 1 @triton.jit def cosh(x): exp_x = tl.exp(x) return (exp_x + 1.0 / exp_x) * 0.5 # a Triton implementation of the most used activations # See for instance http://arxiv.org/abs/1606.08415 for an overview # ReLU @triton.jit def relu(x): """ ReLU_ activation function .. _ReLU: https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html """ return tl.where(x >= 0, x, 0.0) @triton.jit def squared_relu(x): """ Squared ReLU activation, as proposed in the Primer_ paper. .. _Primer: https://arxiv.org/abs/2109.08668 """ x_sq = x * x return tl.where(x > 0.0, x_sq, 0.0) @triton.jit def star_relu(x): """ Star ReLU activation, as proposed in the "MetaFormer Baselines for Vision"_ paper. .. _ "MetaFormer Baselines for Vision": https://arxiv.org/pdf/2210.13452.pdf """ x_sq = x * x return 0.8944 * tl.where(x > 0.0, x_sq, 0.0) - 0.4472 # Leaky ReLU @triton.jit def leaky_relu(x): """ LeakyReLU_ activation .. _LeakyReLU: https://pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html """ return tl.where(x >= 0.0, x, 0.01 * x) @triton.jit def gelu(x): """ GeLU_ activation - Gaussian error linear unit .. _GeLU: https://arxiv.org/pdf/1606.08415.pdf """ return 0.5 * x * (1 + tanh(_kAlpha * (x + 0.044715 * x * x * x))) @triton.jit def smelu(x): """ SmeLU_ activation - Smooth ReLU with beta=2.0 .. _SmeLU: https://arxiv.org/pdf/2202.06499.pdf """ beta = 2.0 relu = tl.where(x >= beta, x, 0.0) return tl.where(tl.abs(x) <= beta, (x + beta) * (x + beta) / (4.0 * beta), relu) @triton.jit def silu(x): return x * tl.sigmoid(x) @autotune( configs=[ triton.Config( {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=4, num_warps=4 ), triton.Config( {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=4, num_warps=4 ), triton.Config( {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=4, num_warps=4 ), triton.Config( {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=4, num_warps=4 ), triton.Config( {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=4, num_warps=4 ), triton.Config( {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=2, num_warps=8 ), triton.Config( {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 64, "GROUP_SIZE_M": 8}, num_stages=3, num_warps=8 ), triton.Config( {"BLOCK_SIZE_M": 32, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 128, "GROUP_SIZE_M": 8}, num_stages=2, num_warps=4 ), ], key=["M", "N", "K"], nearest_power_of_two=True, prune_configs_by={ "early_config_prune": matmul248_kernel_config_pruner, "perf_model": None, "top_k": None, }, ) @triton.jit def cai_gptq_matmul_248_kernel( a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, bias_ptr, residual_ptr, M, N, K, bits, maxq, gptq_group_size, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales, stride_zeros, QKV_FUSED: tl.constexpr, ADD_BIAS: tl.constexpr, ADD_RESIDUAL: tl.constexpr, ACT_TYPE: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr, ): """ Compute the matrix multiplication C = A x B. A is of shape (M, K) float16 B is of shape (K//8, N) int32 C is of shape (M, N) float16 scales is of shape (G, N) float16 zeros is of shape (G, N) float16 """ infearure_per_bits = 32 // bits pid = tl.program_id(axis=0) NK = K num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) num_pid_k = tl.cdiv(NK, BLOCK_SIZE_K) qkv_offset = pid // (num_pid_m * num_pid_n) pid = pid % (num_pid_m * num_pid_n) num_pid_in_group = GROUP_SIZE_M * num_pid_n group_id = pid // num_pid_in_group first_pid_m = group_id * GROUP_SIZE_M group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) pid_m = first_pid_m + (pid % group_size_m) pid_n = (pid % num_pid_in_group) // group_size_m offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) offs_k = tl.arange(0, BLOCK_SIZE_K) # offs_bk = offs_k + qkv_offset * NK a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_K) a_mask = offs_am[:, None] < M # b_ptrs is set up such that it repeats elements along the K axis 8 times b_ptrs = ( b_ptr + qkv_offset * N * NK // infearure_per_bits + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn) ) # (BLOCK_SIZE_K, BLOCK_SIZE_N) # g_ptrs = g_ptr + offs_k # shifter is used to extract the N bits of each element in the 32-bit word from B scales_ptrs = scales_ptr + qkv_offset * NK * N // gptq_group_size + offs_bn[None, :] zeros_ptrs = ( zeros_ptr + qkv_offset * NK * N // gptq_group_size // infearure_per_bits + (offs_bn[None, :] // infearure_per_bits) ) shifter = (offs_k % infearure_per_bits) * bits zeros_shifter = (offs_bn % infearure_per_bits) * bits accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) g_idx_base = tl.arange(0, BLOCK_SIZE_K) g_idx_base = g_idx_base // gptq_group_size g_idx = g_idx_base # tl.device_print("gidx, ", g_idx) scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros = (zeros >> zeros_shifter[None, :]) & maxq zeros = zeros + 1 for k in range(0, num_pid_k): # g_idx = tl.load(g_ptrs) # if (k + 1) * BLOCK_SIZE_K > currend_group_end: scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros = (zeros >> zeros_shifter[None, :]) & maxq zeros = zeros + 1 # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop a = tl.load(a_ptrs, mask=a_mask, other=0.0) # (BLOCK_SIZE_M, BLOCK_SIZE_K) b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated # Now we need to unpack b (which is N-bit values) into 32-bit values b = (b >> shifter[:, None]) & maxq # Extract the N-bit values b = (b - zeros).to(tl.float16) * scales # Scale and shift accumulator += tl.dot(a, b) a_ptrs += BLOCK_SIZE_K b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk g_idx = g_idx_base + ((k + 1) * BLOCK_SIZE_K) // gptq_group_size # if (k + 2) * BLOCK_SIZE_K > currend_group_end: c_ptrs = c_ptr + qkv_offset * M * N + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :] c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N) if ADD_BIAS: bias_mask = offs_bn < N offs_bn += qkv_offset * N bias_ptrs = bias_ptr + stride_cn * offs_bn bias = tl.load(bias_ptrs, mask=bias_mask, other=0.0) # (BLOCK_SIZE_M, BLOCK_SIZE_K) accumulator += bias[None, :] if ACT_TYPE == 1: accumulator = relu(accumulator) elif ACT_TYPE == 2: accumulator = gelu(accumulator) elif ACT_TYPE == 3: accumulator = silu(accumulator) if ADD_RESIDUAL: residual_ptrs = residual_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :] res = tl.load(residual_ptrs, mask=c_mask, other=0.0) accumulator += res tl.store(c_ptrs, accumulator, mask=c_mask) @autotune( configs=[ triton.Config( {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 256, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=4, num_warps=4 ), triton.Config( {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=4, num_warps=4 ), triton.Config( {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=4, num_warps=4 ), triton.Config( {"BLOCK_SIZE_M": 128, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=4, num_warps=4 ), triton.Config( {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=4, num_warps=4 ), triton.Config( {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 128, "BLOCK_SIZE_K": 32, "GROUP_SIZE_M": 8}, num_stages=2, num_warps=8 ), triton.Config( {"BLOCK_SIZE_M": 64, "BLOCK_SIZE_N": 64, "BLOCK_SIZE_K": 64, "GROUP_SIZE_M": 8}, num_stages=3, num_warps=8 ), triton.Config( {"BLOCK_SIZE_M": 32, "BLOCK_SIZE_N": 32, "BLOCK_SIZE_K": 128, "GROUP_SIZE_M": 8}, num_stages=2, num_warps=4 ), ], key=["M", "N", "K"], nearest_power_of_two=True, prune_configs_by={ "early_config_prune": matmul248_kernel_config_pruner, "perf_model": None, "top_k": None, }, ) @triton.jit def cai_gptq_idx_matmul_248_kernel( a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, idx_ptr, bias_ptr, residual_ptr, M, N, K, bits, maxq, gptq_group_size, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales, stride_zeros, QKV_FUSED: tl.constexpr, ADD_BIAS: tl.constexpr, ADD_RESIDUAL: tl.constexpr, ACT_TYPE: tl.constexpr, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr, ): """ Compute the matrix multiplication C = A x B. A is of shape (M, K) float16 B is of shape (K//8, N) int32 C is of shape (M, N) float16 scales is of shape (G, N) float16 zeros is of shape (G, N) float16 """ infearure_per_bits = 32 // bits pid = tl.program_id(axis=0) NK = K # if QKV_FUSED: # NK = K//3 # else: # NK = K # NK = K num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) num_pid_k = tl.cdiv(NK, BLOCK_SIZE_K) qkv_offset = pid // (num_pid_m * num_pid_n) pid = pid % (num_pid_m * num_pid_n) num_pid_in_group = GROUP_SIZE_M * num_pid_n group_id = pid // num_pid_in_group first_pid_m = group_id * GROUP_SIZE_M group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) pid_m = first_pid_m + (pid % group_size_m) pid_n = (pid % num_pid_in_group) // group_size_m offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) offs_k = tl.arange(0, BLOCK_SIZE_K) # offs_bk = offs_k + qkv_offset * NK a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_K) a_mask = offs_am[:, None] < M # b_ptrs is set up such that it repeats elements along the K axis 8 times b_ptrs = ( b_ptr + qkv_offset * N * NK // infearure_per_bits + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn) ) # (BLOCK_SIZE_K, BLOCK_SIZE_N) # g_ptrs = g_ptr + offs_k # shifter is used to extract the N bits of each element in the 32-bit word from B scales_ptrs = scales_ptr + qkv_offset * NK * N // gptq_group_size + offs_bn[None, :] zeros_ptrs = ( zeros_ptr + qkv_offset * NK * N // gptq_group_size // infearure_per_bits + (offs_bn[None, :] // infearure_per_bits) ) shifter = (offs_k % infearure_per_bits) * bits zeros_shifter = (offs_bn % infearure_per_bits) * bits accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32) g_ptrs = idx_ptr + offs_k g_idx = tl.load(g_ptrs) # tl.device_print("gidx, ", g_idx) zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits) scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) for k in range(0, num_pid_k): g_idx = tl.load(g_ptrs) scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) zeros = (zeros >> zeros_shifter[None, :]) & maxq zeros = zeros + 1 # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop a = tl.load(a_ptrs, mask=a_mask, other=0.0) # (BLOCK_SIZE_M, BLOCK_SIZE_K) b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated # Now we need to unpack b (which is N-bit values) into 32-bit values b = (b >> shifter[:, None]) & maxq # Extract the N-bit values b = (b - zeros).to(tl.float16) * scales # Scale and shift accumulator += tl.dot(a, b) a_ptrs += BLOCK_SIZE_K b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk g_ptrs += BLOCK_SIZE_K c_ptrs = c_ptr + qkv_offset * M * N + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :] c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N) if ADD_BIAS: bias_mask = offs_bn < N offs_bn += qkv_offset * N bias_ptrs = bias_ptr + stride_cn * offs_bn bias = tl.load(bias_ptrs, mask=bias_mask, other=0.0) # (BLOCK_SIZE_M, BLOCK_SIZE_K) accumulator += bias[None, :] if ACT_TYPE == 1: accumulator = relu(accumulator) elif ACT_TYPE == 2: accumulator = gelu(accumulator) elif ACT_TYPE == 3: accumulator = silu(accumulator) if ADD_RESIDUAL: residual_ptrs = residual_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :] res = tl.load(residual_ptrs, mask=c_mask, other=0.0) accumulator += res tl.store(c_ptrs, accumulator, mask=c_mask) def gptq_fused_linear_triton( input, qweight, scales, qzeros, bias, residual, bits, maxq, gptq_group_size, qkv_fused, add_bias, add_residual, g_idx=None, act_type=0, ): # print("gptq fused ", qkv_fused, add_bias, add_residual) assert input.is_cuda, "input is not in cuda" assert qweight.is_cuda, "qweight is not in cuda" assert scales.is_cuda, "scales is not in cuda" assert qzeros.is_cuda, "qzeros is not in cuda" with torch.cuda.device(input.device): if qkv_fused: grid = lambda META: ( triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"]) * triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]) * 3, ) output = torch.empty((input.shape[0] * 3, qweight.shape[1]), device=input.device, dtype=torch.float16) else: grid = lambda META: ( triton.cdiv(input.shape[0], META["BLOCK_SIZE_M"]) * triton.cdiv(qweight.shape[1], META["BLOCK_SIZE_N"]), ) output = torch.empty((input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16) # print("dtype, ", qweight.dtype, output.dtype, scales.dtype, qzeros.dtype, bias.dtype, residual.dtype) if g_idx is None: cai_gptq_matmul_248_kernel[grid]( input, qweight, output, scales, qzeros, bias, residual, input.shape[0], qweight.shape[1], input.shape[1], bits, maxq, gptq_group_size, input.stride(0), input.stride(1), qweight.stride(0), qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0), QKV_FUSED=qkv_fused, ADD_BIAS=add_bias, ADD_RESIDUAL=add_residual, ACT_TYPE=act_type, ) else: cai_gptq_idx_matmul_248_kernel[grid]( input, qweight, output, scales, qzeros, g_idx, bias, residual, input.shape[0], qweight.shape[1], input.shape[1], bits, maxq, gptq_group_size, input.stride(0), input.stride(1), qweight.stride(0), qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0), QKV_FUSED=qkv_fused, ADD_BIAS=add_bias, ADD_RESIDUAL=add_residual, ACT_TYPE=act_type, ) if qkv_fused: return output.view(3, input.shape[0], qweight.shape[1]) else: return output