3ecf2c76ad
Co-authored-by: mingfeima <mingfei.ma@intel.com> Co-authored-by: jianan-gu <jianan.gu@intel.com>
657 lines
20 KiB
Python
657 lines
20 KiB
Python
import itertools
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import math
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import torch
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import torch.nn.functional as F
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precision = {
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torch.bfloat16: 1e-2,
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torch.float16: 1e-3,
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torch.float32: 1e-5,
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}
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BLOCK_N, BLOCK_K = 64, 128
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factor_for_scale = 1e-3
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fp8_max, fp8_min = 400, -400
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def parametrize(**params):
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def decorator(func):
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def wrapper(self):
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for combo in itertools.product(*params.values()):
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kwargs = dict(zip(params.keys(), combo))
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with self.subTest(**kwargs):
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func(self, **kwargs)
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return wrapper
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return decorator
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def SiluAndMul(x: torch.Tensor) -> torch.Tensor:
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d = x.shape[-1] // 2
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return F.silu(x[..., :d]) * x[..., d:]
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def GeluAndMul(x: torch.Tensor, approximate="tanh") -> torch.Tensor:
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d = x.shape[-1] // 2
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return F.gelu(x[..., :d], approximate=approximate) * x[..., d:]
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def per_token_quant_int8(x):
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x = x.float()
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absmax = x.abs().max(dim=-1).values
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absmax = absmax.clamp_min(1e-10).unsqueeze(-1)
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scale_x = absmax / 127
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x_q = x.mul(127 / absmax)
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x_q = torch.round(x_q).to(torch.int8)
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return x_q, scale_x
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def convert_weight(weight, scale_block_size, A_dtype):
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N, K = weight.size()
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fp8_max = 448.0
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scale_block_size_N, scale_block_size_K = scale_block_size # (128, 128)
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pad_N = (scale_block_size_N - (N % scale_block_size_N)) % scale_block_size_N
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pad_K = (scale_block_size_K - (K % scale_block_size_K)) % scale_block_size_K
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if pad_N > 0 or pad_K > 0:
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weight = torch.nn.functional.pad(weight, (0, pad_K, 0, pad_N))
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weight_blocks = weight.view(
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math.ceil(N / scale_block_size_N),
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scale_block_size_N,
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math.ceil(K / scale_block_size_K),
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scale_block_size_K,
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) # (8, 128, 8, 128)
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weight_blocks = weight_blocks.permute(0, 2, 1, 3).contiguous() # (8, 8, 128, 128)
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# Step 2: compute per-block max abs values → scale
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abs_max = weight_blocks.abs().amax(dim=(-2, -1), keepdim=True) # (8, 8, 1, 1)
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scales = abs_max / fp8_max
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scales = torch.where(
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scales == 0, torch.ones_like(scales), scales
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) # avoid division by zero
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q_fp8 = (weight_blocks / scales).to(torch.float8_e4m3fn)
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q_fp8_reshape = q_fp8.permute(0, 2, 1, 3).contiguous()
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if pad_N > 0 or pad_K > 0:
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q_fp8_reshape = q_fp8_reshape.view(N + pad_N, K + pad_K)
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q_fp8_reshape = q_fp8_reshape[:N, :K].contiguous()
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else:
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q_fp8_reshape = q_fp8_reshape.view(N, K)
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dq_weight = q_fp8.float() * scales
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dq_weight = dq_weight.permute(0, 2, 1, 3).contiguous() # (8, 128, 8, 128)
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if pad_N > 0 or pad_K > 0:
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w_dq = dq_weight.view(N + pad_N, K + pad_K).to(A_dtype)
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w_dq = w_dq[:N, :K].contiguous()
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else:
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w_dq = dq_weight.view(N, K).to(A_dtype)
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scales = scales.view(
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math.ceil(N / scale_block_size_N), math.ceil(K / scale_block_size_K)
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)
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return q_fp8_reshape, scales, w_dq
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def native_w8a8_per_token_matmul(A, B, As, Bs, bias, output_dtype=torch.bfloat16):
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"""Matrix multiplication function that supports per-token input quantization and per-column weight quantization"""
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A = A.to(torch.float32)
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B = B.to(torch.float32)
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assert A.shape[-1] == B.shape[-1], "Dimension mismatch"
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assert B.ndim == 2 and B.is_contiguous(), "B must be a 2D contiguous tensor"
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# Reshape input
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M = A.numel() // A.shape[-1]
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B = B.t() # Transpose weight matrix
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N, K = B.shape
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origin_C_shape = A.shape[:-1] + (K,)
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A = A.reshape(M, N)
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# As is per-token [M, 1], Bs is per-column [1, K]
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C = torch.matmul(A, B) # [M, K]
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C = As * C * Bs.view(1, -1) # Broadcast per-column scale
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if bias is not None:
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C.add_(bias.view(1, -1))
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return C.reshape(origin_C_shape).to(output_dtype)
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def torch_naive_moe(a, w1, w2, b, routed_scaling_factor, output_dtype=torch.bfloat16):
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a = a.to(torch.float32)
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w1 = w1.to(torch.float32)
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w2 = w2.to(torch.float32)
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b = b.to(torch.float32) if b is not None else None
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ic1 = torch.matmul(a, w1.transpose(0, 1))
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ic2 = SiluAndMul(ic1)
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ic3 = torch.matmul(ic2, w2.transpose(0, 1))
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out = ic3 if b is None else ic3 + b * routed_scaling_factor
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return out.to(output_dtype)
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def torch_w8a8_per_column_moe(
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a, w1_q, w2_q, w1_s, w2_s, b, routed_scaling_factor, output_dtype=torch.bfloat16
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):
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a = a.to(torch.float32)
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b = b.to(torch.float32) if b is not None else None
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# Perform per-token quantization
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a_q, a_s = per_token_quant_int8(a)
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ic1 = native_w8a8_per_token_matmul(
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a_q, w1_q, a_s, w1_s, bias=None, output_dtype=torch.float32
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)
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ic2 = SiluAndMul(ic1)
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a1_q, a1_s = per_token_quant_int8(ic2)
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ic3 = native_w8a8_per_token_matmul(
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a1_q, w2_q, a1_s, w2_s, bias=None, output_dtype=torch.float32
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)
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out = ic3 if b is None else ic3 + b * routed_scaling_factor
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return out.to(output_dtype)
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def scaled_weight(weight, scales):
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E, N, K = weight.shape
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pad_N = (BLOCK_N - (N % BLOCK_N)) % BLOCK_N
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pad_K = (BLOCK_K - (K % BLOCK_K)) % BLOCK_K
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if pad_N > 0 or pad_K > 0:
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weight = torch.nn.functional.pad(weight, (0, pad_K, 0, pad_N))
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weight_block = (
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weight.view(E, math.ceil(N / BLOCK_N), BLOCK_N, math.ceil(K / BLOCK_K), BLOCK_K)
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.permute(0, 1, 3, 2, 4)
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.float()
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.contiguous()
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)
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weight_scaled = (
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(
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weight_block
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* scales.view(E, math.ceil(N / BLOCK_N), math.ceil(K / BLOCK_K), 1, 1)
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)
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.permute(0, 1, 3, 2, 4)
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.contiguous()
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)
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if pad_N > 0 or pad_K > 0:
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weight_scaled = weight_scaled.view(E, N + pad_N, K + pad_K)
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weight_scaled = weight_scaled[..., :N, :K].contiguous()
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else:
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weight_scaled = weight_scaled.view(E, N, K)
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return weight_scaled
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def torch_naive_fused_moe(a, w1, w2, score, topk, renormalize):
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B, D = a.shape
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a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
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out = torch.zeros(B * topk, w2.shape[1], dtype=a.dtype, device=a.device)
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score = torch.softmax(score, dim=-1, dtype=torch.float32)
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topk_weight, topk_ids = torch.topk(score, topk)
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if renormalize:
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topk_weight = topk_weight / topk_weight.sum(dim=-1, keepdim=True)
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topk_weight = topk_weight.view(-1)
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topk_ids = topk_ids.view(-1)
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for i in range(w1.shape[0]):
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mask = topk_ids == i
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if mask.sum():
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out[mask] = SiluAndMul(a[mask] @ w1[i].transpose(0, 1)) @ w2[i].transpose(
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0, 1
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)
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return (
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out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype)
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).sum(dim=1)
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def moe_gptoss_act(x, alpha: float = 1.702, limit: float = 7.0):
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x_glu, x_linear = x[..., ::2], x[..., 1::2]
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# Clamp the input values
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x_glu = x_glu.clamp(min=None, max=limit)
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x_linear = x_linear.clamp(min=-limit, max=limit)
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out_glu = x_glu * torch.sigmoid(alpha * x_glu)
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# Note we add an extra bias of 1 to the linear layer
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return out_glu * (x_linear + 1.0)
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def torch_naive_gptoss_fused_moe(
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x,
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w1,
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w2,
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w1_bias,
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w2_bias,
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topk_weights,
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topk_ids,
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activation_alpha,
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swiglu_limit,
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len_experts,
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) -> torch.Tensor:
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# Ref code from https://huggingface.co/deepseek-ai/DeepSeek-V2/blob/e0828e3cc0a03408724b80c3cc92c8e072db8d01/modeling_deepseek.py#L589
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cnts = topk_ids.new_zeros((topk_ids.shape[0], len_experts))
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cnts.scatter_(1, topk_ids.to(torch.int64), 1)
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tokens_per_expert = cnts.sum(dim=0)
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idxs = topk_ids.view(-1).argsort()
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sorted_tokens = x[idxs // topk_ids.shape[1]]
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tokens_per_expert = tokens_per_expert.cpu().numpy()
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outputs = []
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start_idx = 0
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for i, num_tokens in enumerate(tokens_per_expert):
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end_idx = start_idx + num_tokens
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if num_tokens == 0:
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continue
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tokens_for_this_expert = sorted_tokens[start_idx:end_idx]
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layer_w13_weight = w1[i]
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layer_w13_weight_bias = w1_bias[i]
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layer_w2_weight_bias = w2_bias[i]
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layer_w2_weight = w2[i]
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gate_up = F.linear(
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tokens_for_this_expert,
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layer_w13_weight,
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bias=layer_w13_weight_bias.to(torch.bfloat16),
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)
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gate_up = moe_gptoss_act(gate_up, activation_alpha, swiglu_limit)
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expert_out = F.linear(
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gate_up, layer_w2_weight, bias=layer_w2_weight_bias.to(torch.bfloat16)
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)
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outputs.append(expert_out)
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start_idx = end_idx
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outs = torch.cat(outputs, dim=0) if len(outputs) else sorted_tokens.new_empty(0)
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new_x = torch.empty_like(outs)
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new_x[idxs] = outs
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final_out = (
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new_x.view(*topk_ids.shape, -1)
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.type(topk_weights.dtype)
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.mul_(topk_weights.unsqueeze(dim=-1))
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.sum(dim=1)
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.type(new_x.dtype)
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)
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return final_out
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def torch_naive_fused_moe_gptoss(
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a,
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w1,
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w2,
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w1_bias,
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w2_bias,
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topk_weight,
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topk_ids,
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renormalize,
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activation_alpha,
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swiglu_limit,
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len_experts,
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):
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if renormalize:
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topk_weight = topk_weight / topk_weight.sum(dim=-1, keepdim=True)
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return torch_naive_gptoss_fused_moe(
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a,
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w1,
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w2,
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w1_bias,
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w2_bias,
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topk_weight,
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topk_ids,
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activation_alpha,
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swiglu_limit,
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len_experts,
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)
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def torch_w8a8_per_column_fused_moe(a, w1, w2, w1_s, w2_s, topk_weight, topk_ids, topk):
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"""This function performs fused moe with per-column int8 quantization using native torch."""
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B, D = a.shape
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# Perform per-token quantization
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a_q, a_s = per_token_quant_int8(a)
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# Repeat tokens to match topk
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a_q = a_q.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D)
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# Also repeat the scale
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a_s = a_s.view(B, -1, 1).repeat(1, topk, 1).reshape(-1, 1) # [B*topk, 1]
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out = torch.zeros(B * topk, w2.shape[1], dtype=torch.float32, device=a.device)
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# Calculate routing
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topk_weight = topk_weight.view(-1)
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topk_ids = topk_ids.view(-1)
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# Process each expert
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for i in range(w1.shape[0]):
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mask = topk_ids == i
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if mask.sum():
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# First MLP layer: note that a_s is now per-token
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inter_out = native_w8a8_per_token_matmul(
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a_q[mask],
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w1[i],
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a_s[mask],
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w1_s[i],
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bias=None,
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output_dtype=torch.float32,
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)
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# Activation function
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act_out = SiluAndMul(inter_out)
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# Quantize activation output with per-token
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act_out_q, act_out_s = per_token_quant_int8(act_out)
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# Second MLP layer
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out[mask] = native_w8a8_per_token_matmul(
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act_out_q,
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w2[i],
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act_out_s,
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w2_s[i],
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bias=None,
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output_dtype=torch.float32,
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)
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# Apply routing weights and sum
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return (
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(out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype))
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.sum(dim=1)
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.to(a.dtype)
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)
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def native_fp8_fused_moe(a, w1, w2, topk_weight, topk_ids, topk):
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B, D = a.shape
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a = a.view(B, -1, D).repeat(1, topk, 1).reshape(-1, D).float()
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out = torch.zeros(B * topk, w2.shape[1], dtype=torch.float32, device=a.device)
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# Calculate routing
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topk_weight = topk_weight.view(-1)
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topk_ids = topk_ids.view(-1)
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for i in range(w1.shape[0]):
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mask = topk_ids == i
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if mask.sum():
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ic0 = torch.matmul(a[mask], w1[i].transpose(0, 1))
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ic1 = SiluAndMul(ic0)
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out[mask] = torch.matmul(ic1, w2[i].transpose(0, 1))
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return (
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(out.view(B, -1, w2.shape[1]) * topk_weight.view(B, -1, 1).to(out.dtype))
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.sum(dim=1)
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.to(a.dtype)
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)
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# https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/modelopt/torch/quantization/qtensor/mxfp4_tensor.py
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class MXFP4QuantizeUtil:
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E2M1_max = 6.0
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E2M1_values = [0, 0.5, 1, 1.5, 2, 3, 4, 6]
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E2M1_bounds = torch.tensor([0.25, 0.75, 1.25, 1.75, 2.5, 3.5, 5])
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block_size = 32
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@classmethod
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def quantize(cls, input: torch.Tensor) -> tuple:
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"""Converting a tensor to a quantized format based on MXFP4 quantization. Only E4M3 is supported.
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Args:
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input (torch.Tensor): The input tensor to be quantized.
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"""
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def cast_fp4(x):
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sign = torch.sign(x)
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sign_bit = (2 - sign) // 2
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ord_ = torch.sum(
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(x.abs().unsqueeze(-1) - cls.E2M1_bounds.to(x.device)) > 0, dim=-1
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)
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fp4_val = (sign_bit * 0b1000 + ord_).to(torch.uint8)
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return fp4_val
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def fuse_uint4_to_uint8(x):
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# If the last dimension is odd, pad with zeros
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# If this behavior is not desired, please modify the code accordingly
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left_side = x[..., 0::2] # Even indices (0, 2, 4...)
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right_side = x[..., 1::2] # Odd indices (1, 3, 5...)
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new_data = (
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right_side.clone() << 4
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) # Put odd indices (higher addresses) in high bits
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new_data[
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..., : left_side.shape[-1]
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] += left_side # Put even indices in low bits
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return new_data
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original_shape = input.shape
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original_dtype = input.dtype
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input = input.view(-1, cls.block_size)
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# get scales
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input_amax = input.abs().max(dim=-1, keepdim=True).values
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descale = input_amax / cls.E2M1_max
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min_value = torch.tensor(-127.0, device=descale.device)
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e8m0_scale = torch.ceil(torch.maximum(torch.log2(descale), min_value))
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input = (input / torch.exp2(e8m0_scale)).view(original_shape)
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input_q = cast_fp4(input)
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input_q = fuse_uint4_to_uint8(input_q)
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e8m0_scale = (e8m0_scale + 127).to(torch.uint8)
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return input_q, e8m0_scale
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|
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@classmethod
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def dequantize(cls, quantized_data, dtype: torch.dtype, scale):
|
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"""Dequantze MXFP4 packed tensor to a target dtype."""
|
|
|
|
def unfuse_uint8_to_uint4(x):
|
|
"""Unfuse uint8 values back to uint4 values.
|
|
This is the inverse operation of fuse_uint4_to_uint8.
|
|
"""
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|
# Extract the lower 4 bits (even indices)
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|
left_side = x & 0x0F
|
|
|
|
# Extract the upper 4 bits (odd indices)
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|
right_side = (x >> 4) & 0x0F
|
|
|
|
# Create a new tensor with alternating values
|
|
shape = list(x.shape)
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|
shape[-1] = shape[-1] * 2
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|
result = torch.zeros(shape, dtype=torch.uint8, device=x.device)
|
|
|
|
# Fill in the values - even indices get low bits, odd indices get high bits
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|
result[..., 0::2] = left_side # Even indices from low bits
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|
result[..., 1::2] = right_side # Odd indices from high bits
|
|
|
|
return result
|
|
|
|
e8m0_scale = scale
|
|
|
|
# Unfuse the uint8 values back to uint4
|
|
x_unfused = unfuse_uint8_to_uint4(quantized_data)
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|
# print("@@@ x_unfused: ", x_unfused)
|
|
# Extract sign and magnitude
|
|
sign = 1 - 2 * ((x_unfused & 0b1000) >> 3).to(
|
|
torch.float32
|
|
) # Extract sign bit and convert to +1/-1
|
|
magnitude = x_unfused & 0b0111 # Extract magnitude bits
|
|
magnitude = magnitude.to(torch.long)
|
|
|
|
# Create a tensor with the E2M1 values
|
|
values = torch.tensor(cls.E2M1_values, device=quantized_data.device)
|
|
|
|
# Use gather to index the values tensor properly
|
|
# We need to reshape magnitude to match the dimensions we want to gather along
|
|
original_shape = magnitude.shape
|
|
x_float = values[magnitude.reshape(-1)].reshape(original_shape)
|
|
|
|
# Apply sign and scale
|
|
x_float = sign.float() * x_float
|
|
|
|
# Reshape to apply block-wise scaling
|
|
x_float = x_float.reshape(-1, cls.block_size)
|
|
|
|
# Apply the E8M0 scale
|
|
scale_factor = torch.exp2(e8m0_scale.float() - 127)
|
|
scale_factor = scale_factor.reshape(-1, 1) # Reshape for proper broadcasting
|
|
|
|
# Apply scaling and reshape back to original shape
|
|
x_float = x_float * scale_factor
|
|
|
|
# Reshape back to the original shape
|
|
return x_float.reshape(original_shape).to(dtype)
|
|
|
|
|
|
def make_non_contiguous(x: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
Make a tensor non-contiguous by slicing it via last dimension.
|
|
"""
|
|
last_dim = x.shape[-1]
|
|
return x[..., : last_dim // 2] if x.is_contiguous() else x
|
|
|
|
|
|
def awq_reverse_reorder_int_tensor(int_tensor, bits: int):
|
|
assert bits == 4
|
|
|
|
int_tensor = int_tensor.T.contiguous()
|
|
compress_ratio = 32 // bits
|
|
assert int_tensor.shape[-1] % compress_ratio == 0
|
|
|
|
order_map = [0, 2, 4, 6, 1, 3, 5, 7]
|
|
order_tensor = torch.tensor(
|
|
order_map, dtype=torch.int32, device=int_tensor.device
|
|
).reshape(1, -1)
|
|
order_tensor = order_tensor.repeat(int_tensor.shape[1] // compress_ratio, 1)
|
|
order_tensor = order_tensor + torch.arange(
|
|
0,
|
|
int_tensor.shape[1],
|
|
compress_ratio,
|
|
dtype=torch.int32,
|
|
device=int_tensor.device,
|
|
).reshape(-1, 1)
|
|
order_tensor = order_tensor.reshape(-1)
|
|
|
|
reverse_order_tensor = torch.arange(order_tensor.shape[0])[order_tensor]
|
|
reverse_order_tensor = reverse_order_tensor[order_tensor]
|
|
int_tensor = int_tensor[:, reverse_order_tensor]
|
|
return int_tensor
|
|
|
|
|
|
def unpack_and_dequant_awq(
|
|
awq_qweight: torch.Tensor,
|
|
awq_qzeros: torch.Tensor,
|
|
awq_scales: torch.Tensor,
|
|
bits: int,
|
|
group_size: int,
|
|
):
|
|
"""
|
|
Args:
|
|
awq_qweight (`torch.LongTensor`):
|
|
Expected shape: (in_features, out_features // (32 // bits))
|
|
awq_qzeros (`torch.LongTensor`):
|
|
Expected shape: (in_features // group_size, out_features // (32 // bits))
|
|
awq_scales (`torch.LongTensor`):
|
|
Expected shape: (in_features // group_size, out_features)
|
|
|
|
Returns:
|
|
fp16_weight (`torch.LongTensor`):
|
|
With shape (in_features, out_features).
|
|
zeros (`torch.LongTensor`):
|
|
With shape (in_features // group_size, out_features).
|
|
"""
|
|
assert bits == 4
|
|
|
|
qzeros = awq_qzeros
|
|
qweight = awq_qweight
|
|
qweight = qweight.T.contiguous()
|
|
|
|
scales = awq_scales
|
|
scales = scales.reshape(-1, 1, scales.shape[-1])
|
|
|
|
infeatures = awq_qweight.shape[0]
|
|
|
|
wf = torch.tensor(
|
|
list(range(0, 32, bits)), dtype=torch.int32, device=qzeros.device
|
|
).unsqueeze(0)
|
|
zeros = torch.bitwise_right_shift(torch.unsqueeze(qzeros, 2), wf.unsqueeze(0)).to(
|
|
torch.int16 if bits == 8 else torch.int8
|
|
)
|
|
|
|
torch.bitwise_and(zeros, (2**bits) - 1, out=zeros)
|
|
|
|
zeros = zeros.reshape(-1, 1, zeros.shape[1] * zeros.shape[2])
|
|
|
|
weight = torch.bitwise_right_shift(
|
|
torch.unsqueeze(qweight, 1), wf.unsqueeze(-1)
|
|
).to(torch.int16 if bits == 8 else torch.int8)
|
|
torch.bitwise_and(weight, (2**bits) - 1, out=weight)
|
|
weight = weight.reshape(-1, group_size, weight.shape[2])
|
|
|
|
weight = weight.view(-1, weight.shape[-1])
|
|
zeros = zeros.view(-1, zeros.shape[-1])
|
|
|
|
zeros = zeros.T.contiguous()
|
|
zeros = awq_reverse_reorder_int_tensor(zeros, bits)
|
|
weight = awq_reverse_reorder_int_tensor(weight, bits)
|
|
|
|
# Dequantize weights.
|
|
scales = awq_scales
|
|
zeros = zeros.contiguous()
|
|
scale_zeros = zeros * scales
|
|
|
|
g_idx = torch.tensor(
|
|
[i // group_size for i in range(infeatures)], dtype=torch.int32
|
|
)
|
|
scale_mat = scales[g_idx]
|
|
scale_zeros_mat = scale_zeros[g_idx].to(torch.bfloat16)
|
|
|
|
qdq_weight_T = weight * scale_mat - scale_zeros_mat.to(torch.bfloat16)
|
|
|
|
fp16_weight = qdq_weight_T.T
|
|
|
|
return fp16_weight, zeros
|
|
|
|
|
|
def unpack_4bit_to_32bit_signed(qweight, qzeros):
|
|
# Unpack 4-bit values and interpret them as signed integers
|
|
unpacked_weights = torch.zeros(
|
|
(qweight.shape[0] * 8, qweight.shape[1]),
|
|
dtype=torch.int8,
|
|
device=qweight.device,
|
|
requires_grad=False,
|
|
)
|
|
unpacked_zeros = torch.zeros(
|
|
(qzeros.shape[0], qzeros.shape[1] * 8),
|
|
dtype=torch.int8,
|
|
device=qzeros.device,
|
|
requires_grad=False,
|
|
)
|
|
|
|
for row in range(unpacked_weights.shape[0]):
|
|
i = row % 8
|
|
unpacked_weights[row, :] = (qweight[row // 8, :] >> (4 * i)) & 0xF
|
|
|
|
for col in range(unpacked_zeros.shape[1]):
|
|
i = col % 8
|
|
unpacked_zeros[:, col] = (qzeros[:, col // 8] >> (4 * i)) & 0xF
|
|
|
|
return unpacked_weights, unpacked_zeros + 1
|
|
|
|
|
|
def unpack_and_dequant_gptq(qweight, qzeros, scales):
|
|
unpacked_qweight, unpacked_qzeros = unpack_4bit_to_32bit_signed(qweight, qzeros)
|
|
group_size = unpacked_qweight.shape[0] // scales.shape[0]
|
|
scales = scales.repeat_interleave(group_size, dim=0)
|
|
unpacked_qzeros = unpacked_qzeros.repeat_interleave(group_size, dim=0)
|
|
unpacked_qweight = (unpacked_qweight - unpacked_qzeros) * scales
|
|
|
|
return unpacked_qweight.T
|