diff --git a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py index 17f590270..b94400528 100644 --- a/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py +++ b/server/text_generation_server/models/custom_modeling/flash_llama_modeling.py @@ -175,6 +175,7 @@ class FastLinear(nn.Linear): return tensor.contiguous() if isinstance(self, TensorParallelRowLinear): + raise ValueError("This is currently not functionning") get_slice = get_row_slice elif isinstance(self, TensorParallelColumnLinear): get_slice = get_col_slice @@ -203,6 +204,7 @@ class FastLinear(nn.Linear): torch.testing.assert_close(f.get_tensor(f"{query_name}.q_proj.g_idx"), f.get_tensor(f"{query_name}.v_proj.g_idx")) self.qlinear.g_idx[:] = f.get_tensor(f"{query_name}.q_proj.g_idx") + elif name == "self_attn.o_proj": self.qlinear.qweight[:] = get_slice(f, f"model.layers.{layer}.self_attn.o_proj.qweight") self.qlinear.qzeros[:] = get_slice(f, f"model.layers.{layer}.self_attn.o_proj.qzeros") @@ -231,6 +233,11 @@ class FastLinear(nn.Linear): self.qlinear.g_idx[:] = get_slice(f, f"model.layers.{layer}.mlp.down_proj.g_idx") else: raise ValueError("Not handled") + print(layer, name) + if name == 'self_attn.query_key_value': + out = self.qlinear(torch.zeros((6, self.in_features)).cuda().half()) + if name == "self_attn.o_proj": + out = self.qlinear(torch.zeros((6, self.in_features)).cuda().half()) # Delete reference to data self.weight = None diff --git a/server/text_generation_server/quant_linear.py b/server/text_generation_server/quant_linear.py deleted file mode 100644 index be6ec37fc..000000000 --- a/server/text_generation_server/quant_linear.py +++ /dev/null @@ -1,423 +0,0 @@ -import math -import numpy as np -import torch -import torch.nn as nn -from torch.cuda.amp import custom_bwd, custom_fwd - -try: - import triton - import triton.language as tl - from . import custom_autotune - - # code based https://github.com/fpgaminer/GPTQ-triton - @custom_autotune.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': custom_autotune.matmul248_kernel_config_pruner, - 'perf_model': None, - 'top_k': None, - }, - ) - @triton.jit - def matmul_248_kernel(a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, g_ptr, M, N, K, bits, maxq, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales, stride_zeros, - 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 - g_ptr is of shape (K) int32 - """ - infearure_per_bits = 32 // bits - - pid = tl.program_id(axis=0) - num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) - num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) - num_pid_k = tl.cdiv(K, BLOCK_SIZE_K) - 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) - 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 + ((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 + offs_bn[None, :] - zeros_ptrs = zeros_ptr + (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) - - for k in range(0, num_pid_k): - g_idx = tl.load(g_ptrs) - - # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop - 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) - - a = tl.load(a_ptrs, mask=a_mask, other=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) * 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 + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :] - c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N) - tl.store(c_ptrs, accumulator, mask=c_mask) - - @custom_autotune.autotune(configs=[ - triton.Config({ - 'BLOCK_SIZE_M': 64, - 'BLOCK_SIZE_N': 32, - 'BLOCK_SIZE_K': 256, - 'GROUP_SIZE_M': 8 - }, num_stages=4, num_warps=4), - triton.Config({ - 'BLOCK_SIZE_M': 128, - 'BLOCK_SIZE_N': 32, - 'BLOCK_SIZE_K': 128, - 'GROUP_SIZE_M': 8 - }, num_stages=4, num_warps=4), - triton.Config({ - 'BLOCK_SIZE_M': 64, - 'BLOCK_SIZE_N': 32, - 'BLOCK_SIZE_K': 128, - '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': 32, - 'BLOCK_SIZE_K': 64, - 'GROUP_SIZE_M': 8 - }, num_stages=4, num_warps=4), - triton.Config({ - 'BLOCK_SIZE_M': 64, - 'BLOCK_SIZE_N': 32, - 'BLOCK_SIZE_K': 128, - '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': 128, - 'BLOCK_SIZE_K': 32, - 'GROUP_SIZE_M': 8 - }, num_stages=2, num_warps=4), - ], - key=['M', 'N', 'K'], - nearest_power_of_two=True) - @triton.jit - def transpose_matmul_248_kernel(a_ptr, b_ptr, c_ptr, scales_ptr, zeros_ptr, g_ptr, M, N, K, bits, maxq, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, stride_scales, - stride_zeros, 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, N) float16 - B is of shape (K//8, N) int32 - C is of shape (M, K) float16 - scales is of shape (G, N) float16 - zeros is of shape (G, N) float16 - g_ptr is of shape (K) int32 - """ - infearure_per_bits = 32 // bits - - pid = tl.program_id(axis=0) - num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) - num_pid_k = tl.cdiv(K, BLOCK_SIZE_K) - num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) - num_pid_in_group = GROUP_SIZE_M * num_pid_k - 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_k = (pid % num_pid_in_group) // group_size_m - - offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) - offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K) - offs_n = tl.arange(0, BLOCK_SIZE_N) - a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_N) - 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 + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N) - g_ptrs = g_ptr + offs_bk - g_idx = tl.load(g_ptrs) - - # shifter is used to extract the N bits of each element in the 32-bit word from B - scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales - zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros - - shifter = (offs_bk % infearure_per_bits) * bits - zeros_shifter = (offs_n % infearure_per_bits) * bits - accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32) - - for n in range(0, num_pid_n): - # Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop - scales = tl.load(scales_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) - zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,) - - zeros = (zeros >> zeros_shifter[None, :]) & maxq - zeros = (zeros + 1) - - a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_N) - 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) * scales # Scale and shift - b = tl.trans(b) - - accumulator += tl.dot(a, b) - a_ptrs += BLOCK_SIZE_N - b_ptrs += BLOCK_SIZE_N - scales_ptrs += BLOCK_SIZE_N - zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits) - - c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :] - c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K) - tl.store(c_ptrs, accumulator, mask=c_mask) -except: - print('trioton not installed.') - - -def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq): - with torch.cuda.device(input.device): - output = torch.empty((input.shape[0], qweight.shape[1]), device=input.device, dtype=torch.float16) - grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']), ) - matmul_248_kernel[grid](input, qweight, output, scales, qzeros, g_idx, input.shape[0], qweight.shape[1], input.shape[1], bits, maxq, input.stride(0), input.stride(1), qweight.stride(0), - qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0)) - return output - - -def transpose_matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq): - with torch.cuda.device(input.device): - output_dim = (qweight.shape[0] * 32) // bits - output = torch.empty((input.shape[0], output_dim), device=input.device, dtype=torch.float16) - grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_dim, META['BLOCK_SIZE_K']), ) - transpose_matmul_248_kernel[grid](input, qweight, output, scales, qzeros, g_idx, input.shape[0], qweight.shape[1], output_dim, bits, maxq, input.stride(0), input.stride(1), qweight.stride(0), - qweight.stride(1), output.stride(0), output.stride(1), scales.stride(0), qzeros.stride(0)) - return output - - -class QuantLinearFunction(torch.autograd.Function): - - @staticmethod - @custom_fwd(cast_inputs=torch.float16) - def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq): - output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq) - ctx.save_for_backward(qweight, scales, qzeros, g_idx) - ctx.bits, ctx.maxq = bits, maxq - return output - - @staticmethod - @custom_bwd - def backward(ctx, grad_output): - qweight, scales, qzeros, g_idx = ctx.saved_tensors - bits, maxq = ctx.bits, ctx.maxq - grad_input = None - - if ctx.needs_input_grad[0]: - grad_input = transpose_matmul248(grad_output, qweight, scales, qzeros, g_idx, bits, maxq) - return grad_input, None, None, None, None, None, None - - -class QuantLinear(nn.Module): - - def __init__(self, bits, groupsize, infeatures, outfeatures, bias): - super().__init__() - if bits not in [2, 4, 8]: - raise NotImplementedError("Only 2,4,8 bits are supported.") - self.infeatures = infeatures - self.outfeatures = outfeatures - self.bits = bits - self.maxq = 2**self.bits - 1 - self.groupsize = groupsize if groupsize != -1 else infeatures - - self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32)) - self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32)) - self.register_buffer('scales', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16)) - self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32)) - if bias: - self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16)) - else: - self.bias = None - - def pack(self, linear, scales, zeros, g_idx=None): - self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx - - scales = scales.t().contiguous() - zeros = zeros.t().contiguous() - scale_zeros = zeros * scales - self.scales = scales.clone().half() - if linear.bias is not None: - self.bias = linear.bias.clone().half() - - intweight = [] - for idx in range(self.infeatures): - intweight.append(torch.round((linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]]) / self.scales[self.g_idx[idx]]).to(torch.int)[:, None]) - intweight = torch.cat(intweight, dim=1) - intweight = intweight.t().contiguous() - intweight = intweight.numpy().astype(np.uint32) - qweight = np.zeros((intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32) - i = 0 - row = 0 - while row < qweight.shape[0]: - if self.bits in [2, 4, 8]: - for j in range(i, i + (32 // self.bits)): - qweight[row] |= intweight[j] << (self.bits * (j - i)) - i += 32 // self.bits - row += 1 - else: - raise NotImplementedError("Only 2,4,8 bits are supported.") - - qweight = qweight.astype(np.int32) - self.qweight = torch.from_numpy(qweight) - - zeros -= 1 - zeros = zeros.numpy().astype(np.uint32) - qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32) - i = 0 - col = 0 - while col < qzeros.shape[1]: - if self.bits in [2, 4, 8]: - for j in range(i, i + (32 // self.bits)): - qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i)) - i += 32 // self.bits - col += 1 - else: - raise NotImplementedError("Only 2,4,8 bits are supported.") - - qzeros = qzeros.astype(np.int32) - self.qzeros = torch.from_numpy(qzeros) - - def forward(self, x): - out_shape = x.shape[:-1] + (self.outfeatures, ) - out = QuantLinearFunction.apply(x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, self.g_idx, self.bits, self.maxq) - out = out + self.bias if self.bias is not None else out - return out.reshape(out_shape) - - -def make_quant_linear(module, names, bits, groupsize, name=''): - if isinstance(module, QuantLinear): - return - for attr in dir(module): - tmp = getattr(module, attr) - name1 = name + '.' + attr if name != '' else attr - if name1 in names: - delattr(module, attr) - setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None)) - for name1, child in module.named_children(): - make_quant_linear(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1) - - -def autotune_warmup_linear(model, transpose=False): - """ - Pre-tunes the quantized kernel - """ - from tqdm import tqdm - - kn_values = {} - - for _, m in model.named_modules(): - if not isinstance(m, QuantLinear): - continue - - k = m.infeatures - n = m.outfeatures - - if (k, n) not in kn_values: - kn_values[(k, n)] = (m.qweight.cuda(), m.scales.cuda(), m.qzeros.cuda(), m.g_idx.cuda(), m.bits, m.maxq) - - print(f'Found {len(kn_values)} unique KN Linear values.') - - print('Warming up autotune cache ...') - with torch.no_grad(): - for m in tqdm(range(0, 12)): - m = 2**m # [1, 2048] - for (k, n), (qweight, scales, qzeros, g_idx, bits, maxq) in kn_values.items(): - a = torch.randn(m, k, dtype=torch.float16, device='cuda') - matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq) - if transpose: - a = torch.randn(m, n, dtype=torch.float16, device='cuda') - transpose_matmul248(a, qweight, scales, qzeros, g_idx, bits, maxq) - del kn_values